From accc09f6a958e27cfc5a195c7cf2597d71dbb647 Mon Sep 17 00:00:00 2001 From: Harlan D Heilman <73567020+HarlanHeilman@users.noreply.github.com> Date: Sun, 22 Feb 2026 23:22:05 -0800 Subject: [PATCH 01/32] feat: enhance TUI with FITS preview functionality and new colormap features - Introduced a three-panel FITS preview in the TUI, displaying raw, Gaussian-filtered images, and a cropped view with Gaussian fit contours. - Added a new function to convert 2D f32 arrays to RGBA using a rainbow colormap, improving visualization capabilities. - Updated the `Cargo.toml` to include additional features for the `plotters` dependency, enhancing plotting options. - Refactored the TUI preview logic to support the new three-panel layout and improved error handling for image processing. - Enhanced AGENTS.md documentation to reflect the new FITS preview features and usage instructions. --- AGENTS.md | 2 + Cargo.toml | 2 +- src/bin/tui/preview.rs | 508 +++++++++++++++++++++++++++++++++++------ src/colormap.rs | 17 ++ src/gaussian_fit.rs | 219 ++++++++++++++++++ src/io/blur.rs | 13 ++ src/lib.rs | 1 + 7 files changed, 696 insertions(+), 66 deletions(-) create mode 100644 src/gaussian_fit.rs diff --git a/AGENTS.md b/AGENTS.md index adecb06..664e694 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -139,6 +139,8 @@ Catalog is built by default (feature `catalog`); the TUI feature includes catalo **TUI (pyref-tui)**: Startup: with no CLI argument, the TUI shows the launcher (list of indexed beamtimes from `~/.pyref/beamtime_index.sqlite3`, most recent first). Enter opens the selected beamtime; [o] opens the "Open directory" dialog. With a CLI argument, the TUI opens that beamtime directly. Open directory: hybrid dialog with path input (Tab autocomplete) and scrollable folder list (.. and subdirs); Enter on a valid path runs ingest, registers the beamtime in the central index, and opens that beamtime. While ingesting (from launcher or from beamtime view), the nav line shows "Ingesting N/M...". If there is no catalog in the opened directory, the TUI shows an empty state and the message to run "Ingest directory" (key [i]) or `pyref.io.ingest_beamtime(path)` from Python. The beamtime browse panel lists **reflectivity profiles**: one row per experiment (scan) for a given sample, tag, and polarization. Two scan modes are supported: **fixed-energy (theta scan)** where sample theta varies and energy is fixed, and **fixed-angle (energy scan)** where energy varies and theta is fixed or limited to a few angles. The table shows Emin (eV), Emax (eV), Type (theta-scan / E-scan), theta min/max, frame count, and duration. Scan type is inferred from the data using energy/theta range tolerances, distinct-value counts, and an Izero heuristic (many points at theta near zero indicate a theta scan). Each row is a single reflectivity profile; Enter expands it to show the underlying FITS files in a table (Scan, Frame, pol, E (eV), sample theta). When expanded, j/k scroll the file list; Enter collapses. Rename/Retag write catalog overrides via `set_override` and the table reloads. On exit, the TUI saves config to `PYREF_TUI_CONFIG` or `~/.config/pyref/tui.toml`, including `last_root`, `selected_samples`, `selected_tags`, and `selected_scan_numbers` (when on beamtime view). Scripts can read that config and use `scan_experiment(last_root).filter(...)` with the same sample/tag/scan filters to match the TUI view. +**FITS Preview (three-panel, marquee)**: When a FITS file is selected in the TUI, a preview window shows one figure with three panels (non-macOS: egui; macOS: single composite PNG). Left panel: raw background-subtracted image. Middle panel: Gaussian-filtered image. Right panel: 4-sigma crop around the fitted beam center with 1/2/3/4-sigma Gaussian fit contours overlaid. Sigma for the filter is derived from the 2D Gaussian fit (average of row/col sigma, clamped to 0.5--20.0); if the fit fails, a default sigma (2.0) is used and the right panel shows a center crop. On non-macOS, the user can optionally draw a marquee (drag a rectangle) on the left or middle panel; on mouse release the ROI is stored for that path and the fit is re-run inside the ROI, updating all three panels and contours. ROI is per-path and in-memory for the session. + ### 7. Data Stitching (Conceptual) While explicit stitching code is not fully implemented, the infrastructure supports it: diff --git a/Cargo.toml b/Cargo.toml index abcf7f6..2089cc5 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -49,7 +49,7 @@ walkdir = {version = "2", optional = true} notify-debouncer-mini = {version = "0.4", optional = true} log = {version = "0.4", optional = true} eframe = {version = "0.28", optional = true, default-features = true} -plotters = {version = "0.3", optional = true, default-features = false, features = ["bitmap_backend", "bitmap_encoder"]} +plotters = {version = "0.3", optional = true, default-features = false, features = ["bitmap_backend", "bitmap_encoder", "line_series"]} [dev-dependencies] tempfile = "3.14" diff --git a/src/bin/tui/preview.rs b/src/bin/tui/preview.rs index bd755b2..4dc8ce0 100644 --- a/src/bin/tui/preview.rs +++ b/src/bin/tui/preview.rs @@ -1,8 +1,17 @@ #![cfg(feature = "tui")] +#[cfg(not(target_os = "macos"))] +use std::collections::HashMap; use std::path::PathBuf; use std::sync::mpsc; +use ndarray::Array2; + +const DEFAULT_SIGMA: f64 = 2.0; +const SIGMA_CLAMP_MIN: f64 = 0.5; +const SIGMA_CLAMP_MAX: f64 = 20.0; +const CROP_SIGMA_MULT: f64 = 4.0; + #[cfg(not(target_os = "macos"))] use eframe::egui; @@ -10,7 +19,7 @@ use eframe::egui; pub fn run_preview_window(rx: mpsc::Receiver) { std::thread::spawn(move || { while let Ok(path) = rx.recv() { - if let Err(e) = stream_fits_to_preview_png(&path) { + if let Err(e) = stream_fits_three_panel_png(&path) { eprintln!("Preview: {}", e); } } @@ -18,72 +27,182 @@ pub fn run_preview_window(rx: mpsc::Receiver) { } #[cfg(target_os = "macos")] -fn stream_fits_to_preview_png(path: &std::path::Path) -> Result<(), String> { +fn stream_fits_three_panel_png(path: &std::path::Path) -> Result<(), String> { use plotters::prelude::*; + use plotters::series::LineSeries; let (_, subtracted) = pyref::io::image_mmap::materialize_image_from_path(path) .map_err(|e| e.to_string())?; - let rgba = pyref::colormap::array2_i64_to_rgba_rainbow(&subtracted, None) - .ok_or("Colormap failed (non-contiguous)")?; let height = subtracted.nrows(); let width = subtracted.ncols(); - if width == 0 || height == 0 || rgba.len() < width * height * 4 { + if width == 0 || height == 0 { return Err("Invalid image dimensions".to_string()); } + let subtracted_f64: Array2 = subtracted.mapv(|x| x as f64); + let fit = pyref::gaussian_fit::fit_2d_gaussian(&subtracted_f64, None); + let sigma = fit + .map(|f| { + let s = (f.sigma_row + f.sigma_col) / 2.0; + s.clamp(SIGMA_CLAMP_MIN, SIGMA_CLAMP_MAX) + }) + .unwrap_or(DEFAULT_SIGMA); + let filtered = pyref::io::blur::blur_array2_i64(&subtracted, sigma) + .map_err(|e| e.to_string())?; + let (crop_img, crop_w, crop_h, _fit) = crop_4sigma(&filtered, fit, height, width); + let left_rgba = pyref::colormap::array2_i64_to_rgba_rainbow(&subtracted, None) + .ok_or("Colormap failed")?; + let mid_rgba = pyref::colormap::array2_f32_to_rgba_rainbow(&filtered, None) + .ok_or("Colormap failed")?; + let right_rgba = pyref::colormap::array2_f32_to_rgba_rainbow(&crop_img, None) + .ok_or("Colormap failed")?; - const MAX_SIDE: u32 = 512; - let (out_w, out_h) = { - let w = width as u32; - let h = height as u32; - if w <= MAX_SIDE && h <= MAX_SIDE { + const PANEL_MAX: u32 = 320; + let scale = |w: usize, h: usize| { + let (w, h) = (w as u32, h as u32); + if w <= PANEL_MAX && h <= PANEL_MAX { (w, h) } else if w >= h { - (MAX_SIDE, (h * MAX_SIDE / w).max(1)) + (PANEL_MAX, (h * PANEL_MAX / w).max(1)) } else { - ((w * MAX_SIDE / h).max(1), MAX_SIDE) + ((w * PANEL_MAX / h).max(1), PANEL_MAX) } }; + let (lw, lh) = scale(width, height); + let (mw, mh) = scale(width, height); + let (rw, rh) = scale(crop_w, crop_h); + let total_w = lw + mw + rw + 2; + let total_h = lh.max(mh).max(rh); let temp = std::env::temp_dir().join("pyref_preview.png"); - let root = BitMapBackend::new(temp.as_path(), (out_w, out_h)).into_drawing_area(); + let root = BitMapBackend::new(temp.as_path(), (total_w, total_h)).into_drawing_area(); root.fill(&WHITE).map_err(|e| format!("{:?}", e))?; - let mut chart = ChartBuilder::on(&root) - .margin(0) - .set_all_label_area_size(0) - .build_cartesian_2d(0i32..(out_w as i32), 0i32..(out_h as i32)) - .map_err(|e| format!("{:?}", e))?; - - let rgba_ref = &rgba; - chart - .draw_series( - (0..(out_h as usize)) - .flat_map(|oy| { - (0..(out_w as usize)).map(move |ox| { - let sx = (ox * width) / out_w as usize; - let sy = (oy * height) / out_h as usize; - let i = (sy * width + sx) * 4; - let r = rgba_ref.get(i).copied().unwrap_or(0); - let g = rgba_ref.get(i + 1).copied().unwrap_or(0); - let b = rgba_ref.get(i + 2).copied().unwrap_or(0); - (ox as i32, (out_h as i32 - 1 - oy as i32), RGBColor(r, g, b)) + let draw_panel = |root: &DrawingArea<_, _>, rgba: &[u8], w: usize, h: usize, out_w: u32, out_h: u32| -> Result<(), String> { + let mut chart = ChartBuilder::on(root) + .margin(0) + .set_all_label_area_size(0) + .build_cartesian_2d(0i32..(out_w as i32), 0i32..(out_h as i32)) + .map_err(|e| format!("{:?}", e))?; + let rgba_ref = rgba; + chart + .draw_series( + (0..(out_h as usize)) + .flat_map(|oy| { + (0..(out_w as usize)).map(move |ox| { + let sx = (ox * w) / out_w as usize; + let sy = (oy * h) / out_h as usize; + let i = (sy * w + sx) * 4; + let r = rgba_ref.get(i).copied().unwrap_or(0); + let g = rgba_ref.get(i + 1).copied().unwrap_or(0); + let b = rgba_ref.get(i + 2).copied().unwrap_or(0); + (ox as i32, (out_h as i32 - 1 - oy as i32), RGBColor(r, g, b)) + }) }) - }) - .map(|(x, y, color)| Rectangle::new([(x, y), (x + 1, y + 1)], color.filled())), - ) - .map_err(|e| format!("{:?}", e))?; + .map(|(x, y, color)| Rectangle::new([(x, y), (x + 1, y + 1)], color.filled())), + ) + .map_err(|e| format!("{:?}", e))?; + Ok(()) + }; + + let (area_left, rest1) = root.split_horizontally((lw + 1) as i32); + draw_panel(&area_left, &left_rgba, width, height, lw, lh)?; + let (area_mid, rest2) = rest1.split_horizontally((mw + 1) as i32); + draw_panel(&area_mid, &mid_rgba, width, height, mw, mh)?; + draw_panel(&rest2, &right_rgba, crop_w, crop_h, rw, rh)?; + + if let Some(ref f) = fit { + let cx = rw as f64 / 2.0; + let cy = (rh as f64 - 1.0) / 2.0; + let scale_x = rw as f64 / crop_w as f64; + let scale_y = rh as f64 / crop_h as f64; + let mut chart = ChartBuilder::on(&rest2) + .margin(0) + .set_all_label_area_size(0) + .build_cartesian_2d(0.0..(rw as f64), 0.0..(rh as f64)) + .map_err(|e| format!("{:?}", e))?; + for k in [1.0, 2.0, 3.0, 4.0] { + let pts = ellipse_contour_points( + cx, + cy, + k * f.sigma_col * scale_x, + -k * f.sigma_row * scale_y, + 64, + ); + chart + .draw_series(LineSeries::new( + pts.into_iter(), + plotters::style::ShapeStyle::from(&plotters::style::RGBColor(255, 255, 255)) + .stroke_width(1), + )) + .map_err(|e| format!("{:?}", e))?; + } + } root.present().map_err(|e| format!("{:?}", e))?; std::process::Command::new("open").arg(&temp).status().map_err(|e| e.to_string())?; Ok(()) } +fn ellipse_contour_points( + center_col: f64, + center_row: f64, + semi_col: f64, + semi_row: f64, + n: usize, +) -> Vec<(f64, f64)> { + (0..=n) + .map(|i| { + let t = (i as f64 / n as f64) * 2.0 * std::f64::consts::PI; + ( + center_col + semi_col * t.cos(), + center_row + semi_row * t.sin(), + ) + }) + .collect() +} + +fn crop_4sigma( + filtered: &Array2, + fit: Option, + height: usize, + width: usize, +) -> (Array2, usize, usize, Option) { + let (cr, cc, half_r, half_c) = if let Some(f) = fit { + let hr = (CROP_SIGMA_MULT * f.sigma_row).ceil() as i32; + let hc = (CROP_SIGMA_MULT * f.sigma_col).ceil() as i32; + let hr = hr.max(2).min((height / 2) as i32); + let hc = hc.max(2).min((width / 2) as i32); + ( + f.center_row.round() as i32, + f.center_col.round() as i32, + hr, + hc, + ) + } else { + let cr = (height / 2) as i32; + let cc = (width / 2) as i32; + let h = 32i32; + (cr, cc, h, h) + }; + let r0 = (cr - half_r).max(0).min((height - 1) as i32) as usize; + let r1 = (cr + half_r + 1).max(0).min(height as i32) as usize; + let c0 = (cc - half_c).max(0).min((width - 1) as i32) as usize; + let c1 = (cc + half_c + 1).max(0).min(width as i32) as usize; + if r0 >= r1 || c0 >= c1 { + let fallback = Array2::from_shape_vec((1, 1), vec![0.0f32]).unwrap(); + return (fallback, 1, 1, fit); + } + let crop = filtered.slice(ndarray::s![r0..r1, c0..c1]).to_owned(); + let (crop_h, crop_w) = (crop.nrows(), crop.ncols()); + (crop, crop_w, crop_h, fit) +} + #[cfg(not(target_os = "macos"))] pub fn run_preview_window(rx: mpsc::Receiver) { let native_options = eframe::NativeOptions { viewport: egui::ViewportBuilder::default() .with_position([1200.0, 100.0]) - .with_inner_size([600.0, 500.0]), + .with_inner_size([900.0, 500.0]), ..Default::default() }; let _ = eframe::run_native( @@ -96,11 +215,28 @@ pub fn run_preview_window(rx: mpsc::Receiver) { #[cfg(not(target_os = "macos"))] struct PreviewApp { rx: mpsc::Receiver, - rgba: Option>, - width: usize, - height: usize, - texture: Option, + current_path: Option, + roi_per_path: HashMap, + left_rgba: Option>, + left_w: usize, + left_h: usize, + mid_rgba: Option>, + mid_w: usize, + mid_h: usize, + right_rgba: Option>, + right_w: usize, + right_h: usize, + fit: Option, + tex_left: Option, + tex_mid: Option, + tex_right: Option, error: Option, + marquee_start: Option, + marquee_current: Option, + marquee_rect: Option, + marquee_img_w: usize, + marquee_img_h: usize, + marquee_on_left: bool, } #[cfg(not(target_os = "macos"))] @@ -108,27 +244,119 @@ impl PreviewApp { fn new(_cc: &eframe::CreationContext<'_>, rx: mpsc::Receiver) -> Self { Self { rx, - rgba: None, - width: 0, - height: 0, - texture: None, + current_path: None, + roi_per_path: HashMap::new(), + left_rgba: None, + left_w: 0, + left_h: 0, + mid_rgba: None, + mid_w: 0, + mid_h: 0, + right_rgba: None, + right_w: 0, + right_h: 0, + fit: None, + tex_left: None, + tex_mid: None, + tex_right: None, error: None, + marquee_start: None, + marquee_current: None, + marquee_rect: None, + marquee_img_w: 0, + marquee_img_h: 0, + marquee_on_left: true, } } + fn screen_to_image( + pos: egui::Pos2, + rect: egui::Rect, + img_w: usize, + img_h: usize, + ) -> (usize, usize) { + let x = (pos.x - rect.min.x) / rect.width(); + let y = (pos.y - rect.min.y) / rect.height(); + let col = (x * img_w as f32).round().clamp(0.0, (img_w - 1) as f32) as usize; + let row = (y * img_h as f32).round().clamp(0.0, (img_h - 1) as f32) as usize; + (row, col) + } + + fn apply_marquee_roi(&mut self) { + let path = match &self.current_path { + Some(p) => p.clone(), + None => return, + }; + let (start, current, rect, w, h) = match ( + self.marquee_start, + self.marquee_current, + self.marquee_rect, + self.marquee_img_w, + self.marquee_img_h, + ) { + (Some(s), Some(c), Some(r), w, h) if w > 0 && h > 0 => (s, c, r, w, h), + _ => return, + }; + let (r0, c0) = Self::screen_to_image(start, rect, w, h); + let (r1, c1) = Self::screen_to_image(current, rect, w, h); + let r_min = r0.min(r1); + let r_max = r0.max(r1); + let c_min = c0.min(c1); + let c_max = c0.max(c1); + let r1_excl = (r_max + 1).min(h); + let c1_excl = (c_max + 1).min(w); + if r_min >= r1_excl || c_min >= c1_excl { + self.marquee_start = None; + self.marquee_current = None; + self.marquee_rect = None; + return; + } + self.roi_per_path + .insert(path, (r_min, r1_excl, c_min, c1_excl)); + self.load_path(&path); + self.marquee_start = None; + self.marquee_current = None; + self.marquee_rect = None; + } + fn load_path(&mut self, path: &PathBuf) { self.error = None; - self.texture = None; + self.tex_left = None; + self.tex_mid = None; + self.tex_right = None; + self.current_path = Some(path.clone()); match pyref::io::image_mmap::materialize_image_from_path(path.as_path()) { Ok((_raw, subtracted)) => { - match pyref::colormap::array2_i64_to_rgba_rainbow(&subtracted, None) { - Some(rgba) => { - self.width = subtracted.ncols(); - self.height = subtracted.nrows(); - self.rgba = Some(rgba); + let height = subtracted.nrows(); + let width = subtracted.ncols(); + let roi = self.roi_per_path.get(path).copied(); + let subtracted_f64: Array2 = subtracted.mapv(|x| x as f64); + let fit = pyref::gaussian_fit::fit_2d_gaussian(&subtracted_f64, roi); + let sigma = fit + .map(|f| { + let s = (f.sigma_row + f.sigma_col) / 2.0; + s.clamp(SIGMA_CLAMP_MIN, SIGMA_CLAMP_MAX) + }) + .unwrap_or(DEFAULT_SIGMA); + match pyref::io::blur::blur_array2_i64(&subtracted, sigma) { + Ok(filtered) => { + self.left_rgba = + pyref::colormap::array2_i64_to_rgba_rainbow(&subtracted, None); + self.left_w = width; + self.left_h = height; + self.mid_rgba = + pyref::colormap::array2_f32_to_rgba_rainbow(&filtered, None); + self.mid_w = width; + self.mid_h = height; + let (crop, cw, ch, _) = crop_4sigma(&filtered, fit, height, width); + self.right_rgba = + pyref::colormap::array2_f32_to_rgba_rainbow(&crop, None); + self.right_w = cw; + self.right_h = ch; + self.fit = fit; } - None => { - self.error = Some("Colormap failed (non-contiguous)".to_string()); + Err(e) => { + self.error = Some(format!("Blur failed: {}", e)); } } } @@ -138,13 +366,36 @@ impl PreviewApp { } } - fn update_texture(&mut self, ctx: &egui::Context) { - if let Some(ref rgba) = self.rgba { - if self.width > 0 && self.height > 0 && rgba.len() >= self.width * self.height * 4 { - let size = [self.width, self.height]; - let image = egui::ColorImage::from_rgba_unmultiplied(size, rgba); - self.texture = Some(ctx.load_texture( - "fits_preview", + fn update_textures(&mut self, ctx: &egui::Context) { + if let Some(ref rgba) = self.left_rgba { + if self.left_w > 0 && self.left_h > 0 && rgba.len() >= self.left_w * self.left_h * 4 { + let image = + egui::ColorImage::from_rgba_unmultiplied([self.left_w, self.left_h], rgba); + self.tex_left = Some(ctx.load_texture( + "preview_left", + image, + egui::TextureOptions::default(), + )); + } + } + if let Some(ref rgba) = self.mid_rgba { + if self.mid_w > 0 && self.mid_h > 0 && rgba.len() >= self.mid_w * self.mid_h * 4 { + let image = + egui::ColorImage::from_rgba_unmultiplied([self.mid_w, self.mid_h], rgba); + self.tex_mid = Some(ctx.load_texture( + "preview_mid", + image, + egui::TextureOptions::default(), + )); + } + } + if let Some(ref rgba) = self.right_rgba { + if self.right_w > 0 && self.right_h > 0 && rgba.len() >= self.right_w * self.right_h * 4 + { + let image = + egui::ColorImage::from_rgba_unmultiplied([self.right_w, self.right_h], rgba); + self.tex_right = Some(ctx.load_texture( + "preview_right", image, egui::TextureOptions::default(), )); @@ -159,18 +410,145 @@ impl eframe::App for PreviewApp { while let Ok(path) = self.rx.try_recv() { self.load_path(&path); } - if self.rgba.is_some() && self.texture.is_none() { - self.update_texture(ctx); + if (self.left_rgba.is_some() && self.tex_left.is_none()) + || (self.mid_rgba.is_some() && self.tex_mid.is_none()) + || (self.right_rgba.is_some() && self.tex_right.is_none()) + { + self.update_textures(ctx); } egui::CentralPanel::default().show(ctx, |ui| { if let Some(ref err) = self.error { ui.colored_label(egui::Color32::RED, err.as_str()); } - if let Some(ref tex) = self.texture { - ui.image(tex); - } else if self.rgba.is_none() && self.error.is_none() { + if self.left_rgba.is_none() && self.error.is_none() { ui.label("Expand a profile and select a FITS file (j/k or click)."); + return; } + ui.horizontal(|ui| { + ui.label("Raw"); + ui.add_space(ui.available_width() / 3.0 - 30.0); + ui.label("Filtered"); + ui.add_space(ui.available_width() / 3.0 - 30.0); + ui.label("Crop (4 sigma)"); + }); + ui.horizontal(|ui| { + let panel_w = (ui.available_width() / 3.0).floor().max(1.0); + if let Some(ref tex) = self.tex_left { + let size = [ + panel_w, + panel_w * (self.left_h as f32 / self.left_w as f32).min(2.0), + ]; + let response = ui.add( + egui::Image::new(tex.id(), size) + .sense(egui::Sense::drag()), + ); + let rect = response.rect; + if response.drag_started() { + self.marquee_start = Some(rect.min); + self.marquee_current = Some(rect.min); + self.marquee_rect = Some(rect); + self.marquee_img_w = self.left_w; + self.marquee_img_h = self.left_h; + self.marquee_on_left = true; + } + if response.dragged() && self.marquee_on_left { + if let Some(pos) = ui.input(|i| i.pointer.pos()) { + self.marquee_current = Some(pos); + } + } + if response.drag_released() && self.marquee_on_left { + self.apply_marquee_roi(); + } + if let (Some(a), Some(b)) = (self.marquee_start, self.marquee_current) { + if self.marquee_on_left { + let r = egui::Rect::from_min_max(a, b); + ui.painter().rect_stroke( + r.intersect(rect), + 0.0, + egui::Stroke::new(2.0, egui::Color32::WHITE), + ); + } + } + } + if let Some(ref tex) = self.tex_mid { + let size = [ + panel_w, + panel_w * (self.mid_h as f32 / self.mid_w as f32).min(2.0), + ]; + let response = ui.add( + egui::Image::new(tex.id(), size) + .sense(egui::Sense::drag()), + ); + let rect = response.rect; + if response.drag_started() { + self.marquee_start = Some(rect.min); + self.marquee_current = Some(rect.min); + self.marquee_rect = Some(rect); + self.marquee_img_w = self.mid_w; + self.marquee_img_h = self.mid_h; + self.marquee_on_left = false; + } + if response.dragged() && !self.marquee_on_left { + if let Some(pos) = ui.input(|i| i.pointer.pos()) { + self.marquee_current = Some(pos); + } + } + if response.drag_released() && !self.marquee_on_left { + self.apply_marquee_roi(); + } + if let (Some(a), Some(b)) = (self.marquee_start, self.marquee_current) { + if !self.marquee_on_left { + let r = egui::Rect::from_min_max(a, b); + ui.painter().rect_stroke( + r.intersect(rect), + 0.0, + egui::Stroke::new(2.0, egui::Color32::WHITE), + ); + } + } + } + if let Some(ref tex) = self.tex_right { + let size = [ + panel_w, + panel_w * (self.right_h as f32 / self.right_w as f32).min(2.0), + ]; + let response = ui.image(tex.id(), size); + if let Some(ref f) = self.fit { + if self.right_w > 0 && self.right_h > 0 { + let rect = response.rect; + let cx = self.right_w as f64 / 2.0; + let cy = self.right_h as f64 / 2.0; + let n = 64; + for k in [1.0, 2.0, 3.0, 4.0] { + let pts = ellipse_contour_points( + cx, + cy, + k * f.sigma_col, + k * f.sigma_row, + n, + ); + let screen_pts: Vec = pts + .iter() + .map(|&(px, py)| { + egui::Pos2::new( + rect.min.x + + (px / self.right_w as f64) as f32 * rect.width(), + rect.min.y + + (py / self.right_h as f64) as f32 * rect.height(), + ) + }) + .collect(); + if screen_pts.len() >= 2 { + ui.painter().add(egui::Shape::line( + screen_pts, + egui::Stroke::new(1.0, egui::Color32::WHITE), + )); + } + } + } + } + } + }); }); } } diff --git a/src/colormap.rs b/src/colormap.rs index 8b8792b..500bc55 100644 --- a/src/colormap.rs +++ b/src/colormap.rs @@ -92,6 +92,23 @@ pub fn array2_i64_to_rgba_rainbow( Some(scalar_to_rgba_rainbow(&f64_data, Some((min, max)))) } +pub fn array2_f32_to_rgba_rainbow( + data: &Array2, + min_max: Option<(f32, f32)>, +) -> Option> { + let slice = data.as_slice()?; + let (min, max) = match min_max { + Some((lo, hi)) => (lo as f64, hi as f64), + None => { + let min = slice.iter().fold(f64::INFINITY, |a, &b| a.min(b as f64)); + let max = slice.iter().fold(f64::NEG_INFINITY, |a, &b| a.max(b as f64)); + (min, max) + } + }; + let f64_data: Vec = slice.iter().map(|&x| x as f64).collect(); + Some(scalar_to_rgba_rainbow(&f64_data, Some((min, max)))) +} + #[cfg(test)] mod tests { use super::*; diff --git a/src/gaussian_fit.rs b/src/gaussian_fit.rs new file mode 100644 index 0000000..890dd75 --- /dev/null +++ b/src/gaussian_fit.rs @@ -0,0 +1,219 @@ +use ndarray::Array2; + +#[derive(Debug, Clone, Copy)] +pub struct Gaussian2DFit { + pub center_row: f64, + pub center_col: f64, + pub sigma_row: f64, + pub sigma_col: f64, + pub amplitude: f64, + pub baseline: f64, +} + +fn gaussian_2d( + r: f64, + c: f64, + mu_r: f64, + mu_c: f64, + sr: f64, + sc: f64, + a: f64, + b: f64, +) -> f64 { + if sr <= 0.0 || sc <= 0.0 { + return b; + } + let dr = (r - mu_r) / sr; + let dc = (c - mu_c) / sc; + a * (-0.5 * (dr * dr + dc * dc)).exp() + b +} + +const MAX_ITER: usize = 50; +const LAMBDA_INIT: f64 = 1e-3; +const LAMBDA_UP: f64 = 10.0; +const LAMBDA_DOWN: f64 = 0.1; +const TOL: f64 = 1e-8; + +pub fn fit_2d_gaussian( + image: &Array2, + roi: Option<(usize, usize, usize, usize)>, +) -> Option { + let (r0, r1, c0, c1) = match roi { + Some((r0, r1, c0, c1)) => { + let r1 = r1.min(image.nrows()); + let c1 = c1.min(image.ncols()); + if r0 >= r1 || c0 >= c1 { + return None; + } + (r0, r1, c0, c1) + } + None => (0, image.nrows(), 0, image.ncols()), + }; + let nr = r1 - r0; + let nc = c1 - c0; + if nr < 3 || nc < 3 { + return None; + } + let mut max_val = f64::NEG_INFINITY; + let mut max_r = r0; + let mut max_c = c0; + for r in r0..r1 { + for c in c0..c1 { + let v = image[[r, c]]; + if v > max_val { + max_val = v; + max_r = r; + max_c = c; + } + } + } + let min_val = (r0..r1) + .flat_map(|r| (c0..c1).map(move |c| image[[r, c]])) + .fold(f64::INFINITY, f64::min); + let mut mu_r = max_r as f64; + let mut mu_c = max_c as f64; + let mut sr = 2.0; + let mut sc = 2.0; + let mut a = (max_val - min_val).max(1.0); + let mut b = min_val; + let mut lambda = LAMBDA_INIT; + let mut prev_ss = f64::INFINITY; + for _ in 0..MAX_ITER { + let mut jtj = [[0.0f64; 6]; 6]; + let mut jtr = [0.0f64; 6]; + let mut ss = 0.0; + for ri in r0..r1 { + for ci in c0..c1 { + let r = ri as f64; + let c = ci as f64; + let y = image[[ri, ci]]; + let f = gaussian_2d(r, c, mu_r, mu_c, sr, sc, a, b); + let res = y - f; + ss += res * res; + let dr = (r - mu_r) / sr; + let dc = (c - mu_c) / sc; + let exp_part = (-0.5 * (dr * dr + dc * dc)).exp(); + let d_mu_r = a * exp_part * dr / sr; + let d_mu_c = a * exp_part * dc / sc; + let d_sr = a * exp_part * dr * dr / sr; + let d_sc = a * exp_part * dc * dc / sc; + let d_a = exp_part; + let d_b = 1.0; + let j = [d_mu_r, d_mu_c, d_sr, d_sc, d_a, d_b]; + for i in 0..6 { + jtr[i] += j[i] * res; + for jj in 0..6 { + jtj[i][jj] += j[i] * j[jj]; + } + } + } + } + for i in 0..6 { + jtj[i][i] += lambda; + } + let dp = solve_6x6(&jtj, &jtr)?; + let new_mu_r = mu_r + dp[0]; + let new_mu_c = mu_c + dp[1]; + let new_sr = (sr + dp[2]).max(0.3); + let new_sc = (sc + dp[3]).max(0.3); + let new_a = (a + dp[4]).max(0.1); + let new_b = b + dp[5]; + if ss < prev_ss { + lambda *= LAMBDA_DOWN; + prev_ss = ss; + mu_r = new_mu_r; + mu_c = new_mu_c; + sr = new_sr; + sc = new_sc; + a = new_a; + b = new_b; + if dp.iter().map(|x| x.abs()).fold(0.0f64, f64::max) < TOL { + break; + } + } else { + lambda *= LAMBDA_UP; + if lambda > 1e10 { + break; + } + } + } + Some(Gaussian2DFit { + center_row: mu_r, + center_col: mu_c, + sigma_row: sr, + sigma_col: sc, + amplitude: a, + baseline: b, + }) +} + +fn solve_6x6(a: &[[f64; 6]; 6], b: &[f64; 6]) -> Option<[f64; 6]> { + let mut m = [[0.0f64; 7]; 6]; + for i in 0..6 { + for j in 0..6 { + m[i][j] = a[i][j]; + } + m[i][6] = b[i]; + } + for col in 0..6 { + let mut pivot = col; + for row in (col + 1)..6 { + if m[row][col].abs() > m[pivot][col].abs() { + pivot = row; + } + } + m.swap(col, pivot); + let div = m[col][col]; + if div.abs() < 1e-15 { + return None; + } + for j in 0..7 { + m[col][j] /= div; + } + for i in 0..6 { + if i != col { + let factor = m[i][col]; + for j in 0..7 { + m[i][j] -= factor * m[col][j]; + } + } + } + } + Some([m[0][6], m[1][6], m[2][6], m[3][6], m[4][6], m[5][6]]) +} + +#[cfg(test)] +mod tests { + use super::*; + + fn synthetic_gaussian(nr: usize, nc: usize, mu_r: f64, mu_c: f64, sr: f64, sc: f64) -> Array2 { + let mut arr = Array2::zeros((nr, nc)); + for r in 0..nr { + for c in 0..nc { + arr[[r, c]] = gaussian_2d( + r as f64, c as f64, mu_r, mu_c, sr, sc, 100.0, 0.0, + ); + } + } + arr + } + + #[test] + fn fit_synthetic_full_image() { + let arr = synthetic_gaussian(32, 32, 15.2, 14.7, 2.5, 3.0); + let fit = fit_2d_gaussian(&arr, None).unwrap(); + assert!((fit.center_row - 15.2).abs() < 0.2); + assert!((fit.center_col - 14.7).abs() < 0.2); + assert!((fit.sigma_row - 2.5).abs() < 0.3); + assert!((fit.sigma_col - 3.0).abs() < 0.3); + } + + #[test] + fn fit_synthetic_with_roi() { + let arr = synthetic_gaussian(64, 64, 20.0, 30.0, 2.0, 2.5); + let roi = (10, 35, 22, 42); + let fit = fit_2d_gaussian(&arr, Some(roi)).unwrap(); + assert!((fit.center_row - 20.0).abs() < 0.3); + assert!((fit.center_col - 30.0).abs() < 0.3); + } +} diff --git a/src/io/blur.rs b/src/io/blur.rs index 1f85833..d43d7b9 100644 --- a/src/io/blur.rs +++ b/src/io/blur.rs @@ -47,6 +47,19 @@ pub fn gaussian_blur_f32_copy( Ok(dst) } +pub fn blur_array2_i64( + image: &Array2, + sigma: f64, +) -> Result, libblur::BlurError> { + let f32_arr = i64_to_f32_array(image); + let (h, w) = (f32_arr.nrows(), f32_arr.ncols()); + let slice = f32_arr + .as_slice() + .ok_or(libblur::BlurError::InvalidArguments)?; + let blurred = gaussian_blur_f32_copy(slice, w as u32, h as u32, sigma)?; + Array2::from_shape_vec((h, w), blurred).map_err(|_| libblur::BlurError::InvalidArguments) +} + #[cfg(test)] mod tests { use super::*; diff --git a/src/lib.rs b/src/lib.rs index 83b9928..8cbc1cd 100644 --- a/src/lib.rs +++ b/src/lib.rs @@ -3,6 +3,7 @@ use polars::prelude::*; pub mod beamfinding; pub mod colormap; +pub mod gaussian_fit; pub mod errors; pub mod fits; pub mod io; From 5ea7a64ee95e092dbc4ac7536ba17313572c83c5 Mon Sep 17 00:00:00 2001 From: Harlan D Heilman <73567020+HarlanHeilman@users.noreply.github.com> Date: Wed, 4 Mar 2026 12:15:44 -0800 Subject: [PATCH 02/32] chore: update dependencies and remove unused NEXAFS components - Added new dependencies for NEXAFS functionality in `pyproject.toml`, including `scienceplots`, `datasets`, and `huggingface-hub`. - Updated the development dependencies to include `nbconvert`. - Removed obsolete NEXAFS files and components, including the `nexafs_widget.ipynb`, `directory.py`, `normalization.py`, and `widget.py`, streamlining the codebase. - Refactored the NEXAFS module to utilize the new accessor for improved data handling. --- .DS_Store | Bin 8196 -> 8196 bytes notebooks/als-beamline-scripts.ipynb | 517 +++++ notebooks/nexafs-ingest.ipynb | 714 ++++++ notebooks/nexafs-plotting.ipynb | 614 +++++ notebooks/nexafs-process.ipynb | 1261 +++++++++++ notebooks/nexafs_widget.ipynb | 1982 ----------------- pyproject.toml | 4 + python/pyref/__init__.py | 1 + .../DEFAULT_ESCANS/CB_GlassEscan.json | 39 + .../beamline/DEFAULT_ESCANS/CB_GlassEscan.txt | 10 + .../DEFAULT_ESCANS/InSitu_CEdge_hq.json | 51 + .../DEFAULT_ESCANS/InSitu_CEdge_lq.json | 51 + .../beamline/DEFAULT_ESCANS/New_sample3.json | 39 + .../beamline/DEFAULT_ESCANS/OPV_ESCAN.json | 63 + .../beamline/DEFAULT_ESCANS/OPV_Escan_v2.json | 39 + .../beamline/DEFAULT_ESCANS/OPV_Escan_v2.txt | 14 + .../beamline/DEFAULT_ESCANS/PS300_Cal.json | 9 + .../beamline/DEFAULT_ESCANS/Sample1.json | 39 + .../beamline/DEFAULT_ESCANS/Sample2.json | 15 + .../CCDClose_Edge_LowEn.json | 8 + .../TRAJECTORY_OPTIONS/CCDClose_HighEn.json | 7 + .../TRAJECTORY_OPTIONS/CCDClose_LowEn.json | 7 + .../TRAJECTORY_OPTIONS/CCDFar_HighEn.json | 8 + .../TRAJECTORY_OPTIONS/CCDFar_LowEn.json | 8 + .../TRAJECTORY_OPTIONS/CCDMid_HighEn.json | 8 + .../TRAJECTORY_OPTIONS/CCDMid_LowEn.json | 8 + .../TRAJECTORY_OPTIONS/InSitu_Edge_hq.json | 7 + .../TRAJECTORY_OPTIONS/InSitu_Edge_lq.json | 7 + .../TRAJECTORY_OPTIONS/Photodiode_Far.json | 6 + .../beamline/TRAJECTORY_OPTIONS/XRR_Init.json | 8 + python/pyref/beamline/__init__.py | 72 + python/pyref/beamline/base_widgets.py | 662 ++++++ python/pyref/beamline/beamline_scan_macros.py | 475 ++++ python/pyref/beamline/escan_widgets.py | 443 ++++ python/pyref/beamline/pxr_widgets.py | 1095 +++++++++ python/pyref/beamline/rsoxs_widgets.py | 1317 +++++++++++ python/pyref/data/.pyref_catalog.db | Bin 528384 -> 532480 bytes python/pyref/nexafs/__init__.py | 24 +- python/pyref/nexafs/accessor.py | 550 +++++ python/pyref/nexafs/directory.py | 702 ------ python/pyref/nexafs/io.py | 209 +- python/pyref/nexafs/normalization.py | 308 --- python/pyref/nexafs/widget.py | 352 --- scripts/run_nexafs_pipeline.py | 580 +++++ tests/test_nexafs_accessor.py | 112 + uv.lock | 1049 +++++++++ 46 files changed, 10092 insertions(+), 3402 deletions(-) create mode 100644 notebooks/als-beamline-scripts.ipynb create mode 100644 notebooks/nexafs-ingest.ipynb create mode 100644 notebooks/nexafs-plotting.ipynb create mode 100644 notebooks/nexafs-process.ipynb delete mode 100644 notebooks/nexafs_widget.ipynb create mode 100644 python/pyref/beamline/DEFAULT_ESCANS/CB_GlassEscan.json create mode 100644 python/pyref/beamline/DEFAULT_ESCANS/CB_GlassEscan.txt create mode 100644 python/pyref/beamline/DEFAULT_ESCANS/InSitu_CEdge_hq.json create mode 100644 python/pyref/beamline/DEFAULT_ESCANS/InSitu_CEdge_lq.json create mode 100644 python/pyref/beamline/DEFAULT_ESCANS/New_sample3.json create mode 100644 python/pyref/beamline/DEFAULT_ESCANS/OPV_ESCAN.json create mode 100644 python/pyref/beamline/DEFAULT_ESCANS/OPV_Escan_v2.json create mode 100644 python/pyref/beamline/DEFAULT_ESCANS/OPV_Escan_v2.txt create mode 100644 python/pyref/beamline/DEFAULT_ESCANS/PS300_Cal.json create mode 100644 python/pyref/beamline/DEFAULT_ESCANS/Sample1.json create mode 100644 python/pyref/beamline/DEFAULT_ESCANS/Sample2.json create mode 100644 python/pyref/beamline/TRAJECTORY_OPTIONS/CCDClose_Edge_LowEn.json create mode 100644 python/pyref/beamline/TRAJECTORY_OPTIONS/CCDClose_HighEn.json create mode 100644 python/pyref/beamline/TRAJECTORY_OPTIONS/CCDClose_LowEn.json create mode 100644 python/pyref/beamline/TRAJECTORY_OPTIONS/CCDFar_HighEn.json create mode 100644 python/pyref/beamline/TRAJECTORY_OPTIONS/CCDFar_LowEn.json create mode 100644 python/pyref/beamline/TRAJECTORY_OPTIONS/CCDMid_HighEn.json create mode 100644 python/pyref/beamline/TRAJECTORY_OPTIONS/CCDMid_LowEn.json create mode 100644 python/pyref/beamline/TRAJECTORY_OPTIONS/InSitu_Edge_hq.json create mode 100644 python/pyref/beamline/TRAJECTORY_OPTIONS/InSitu_Edge_lq.json create mode 100644 python/pyref/beamline/TRAJECTORY_OPTIONS/Photodiode_Far.json create mode 100644 python/pyref/beamline/TRAJECTORY_OPTIONS/XRR_Init.json create mode 100644 python/pyref/beamline/__init__.py create mode 100644 python/pyref/beamline/base_widgets.py create mode 100644 python/pyref/beamline/beamline_scan_macros.py create mode 100644 python/pyref/beamline/escan_widgets.py create mode 100644 python/pyref/beamline/pxr_widgets.py create mode 100644 python/pyref/beamline/rsoxs_widgets.py create mode 100644 python/pyref/nexafs/accessor.py delete mode 100644 python/pyref/nexafs/directory.py delete mode 100644 python/pyref/nexafs/normalization.py delete mode 100644 python/pyref/nexafs/widget.py create mode 100644 scripts/run_nexafs_pipeline.py create mode 100644 tests/test_nexafs_accessor.py diff --git a/.DS_Store b/.DS_Store index 9fa7303693e9711baca87663d8fe42fc2af460ef..26394aac4ed46cb75016ba22cc9a361d92ad1cfb 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PXR Script Generator

'), VBox(children=(Text(value='', description='Name of…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output_dir = Path(\n", + " \"/Volumes/DATA/Collins/2026Feb/.macro\"\n", + ")\n", + "\n", + "PXR_ScriptGen(\n", + " path=str(output_dir)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "pxr-usage", + "metadata": {}, + "source": [ + "### Using PXR Widget\n", + "\n", + "1. **Configure experiment parameters**:\n", + " - Set energy range (start, stop, delta)\n", + " - Set theta end angle\n", + " - Configure motor positions for each step (Sample Theta, Higher Order Suppressor, Exposure)\n", + "\n", + "2. **Add scans**: Use the \"Add Scan\" button to create multiple reflectivity scans\n", + "\n", + "3. **Configure each scan**:\n", + " - Set theta values for each measurement point\n", + " - Set Higher Order Suppressor positions\n", + " - Set exposure times for each point\n", + "\n", + "4. **Save scripts**: Use \"Save CCD Scan File\" to generate the run file\n", + "\n", + "### Example Configuration\n", + "\n", + "Default PXR parameters:\n", + "- Energy range: 250-280 eV\n", + "- Theta end: 60 degrees\n", + "- Sample Theta steps: [1, 5, 10, 15, 20, 30, 40] degrees\n", + "- Higher Order Suppressor: [10, 9, 8.5, 7.5, 7.5, 7.5, 7.5]\n", + "- Exposure times: [0.0015, 0.0015, 0.0015, 0.0015, 0.1, 0.5, 1] seconds" + ] + }, + { + "cell_type": "markdown", + "id": "rsoxs-header", + "metadata": {}, + "source": [ + "## Resonant Soft X-ray Scattering (RSOXS) Widget\n", + "\n", + "The RSOXS widget creates scripts for resonant soft X-ray scattering measurements with configurable sample positions and energy scans." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "rsoxs-widget", + "metadata": {}, + "outputs": [], + "source": [ + "rsoxs_experiment = RSOXS_ScriptGen(\n", + " exp_name=\"RSOXS Scattering\",\n", + " save_path=str(output_dir)\n", + ")\n", + "\n", + "display(rsoxs_experiment.GUI)" + ] + }, + { + "cell_type": "markdown", + "id": "rsoxs-usage", + "metadata": {}, + "source": [ + "### Using RSOXS Widget\n", + "\n", + "1. **Set sample name**: Enter the name of your sample\n", + "\n", + "2. **Configure scan details**:\n", + " - Select energy scan type (CARBON_ESCAN, QUICK_ESCAN, or CUSTOM)\n", + " - Set Sample X, Y, and Theta positions\n", + " - Configure energy scan steps if using CUSTOM\n", + "\n", + "3. **Add multiple scans**: Use the interface to add multiple measurement positions\n", + "\n", + "4. **Save scripts**: Generate run files for the beamline\n", + "\n", + "### Example Configuration\n", + "\n", + "Default RSOXS parameters:\n", + "- Sample Theta: 90 degrees (typical for RSOXS)\n", + "- Energy scan: CARBON_ESCAN (270-370 eV with variable step sizes)\n", + "- Sample X, Y: 0 (adjust as needed for your sample)" + ] + }, + { + "cell_type": "markdown", + "id": "advanced-header", + "metadata": {}, + "source": [ + "## Advanced: Programmatic Macro Generation\n", + "\n", + "You can also generate macros programmatically without using the widgets." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "advanced-macro", + "metadata": {}, + "outputs": [], + "source": [ + "from pyref.beamline.beamline_scan_macros import (\n", + " XRR_lineup,\n", + " analog_from_file,\n", + " piezo_toggle,\n", + " relative_generic_scan,\n", + " run_dict_trajectory,\n", + " sample_photo,\n", + ")\n", + "\n", + "macro = begin_macro(\"Full XRR Measurement\")\n", + "\n", + "macro += [add_comment(\"Initialize beamline\")]\n", + "macro += run_dict_trajectory({\n", + " \"CCD Y\": 100.0,\n", + " \"CCD Theta\": 0.0,\n", + " \"CCD X\": 100.5,\n", + " \"Beam Stop\": 15.0\n", + "})\n", + "\n", + "macro += [add_comment(\"Align sample\")]\n", + "macro += XRR_lineup(repeat=3)\n", + "\n", + "macro += [add_comment(\"Take sample photo\")]\n", + "macro += sample_photo(\"sample_image\")\n", + "\n", + "macro += [add_comment(\"Run reflectivity scan from file\")]\n", + "macro += [analog_from_file(\n", + " name=\"xrr_scan\",\n", + " path_to_scan=str(output_dir / \"scan_file.txt\"),\n", + " delay=0.1,\n", + " count_time=0.5\n", + ")]\n", + "\n", + "for step in macro:\n", + " print(step)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "save-advanced-macro", + "metadata": {}, + "outputs": [], + "source": [ + "build_macro(\"full_xrr_measurement\", str(output_dir), macro)\n", + "print(f\"Macro saved to {output_dir / 'full_xrr_measurement.txt'}\")" + ] + }, + { + "cell_type": "markdown", + "id": "config-header", + "metadata": {}, + "source": [ + "## Loading and Saving Configurations\n", + "\n", + "All widgets support saving configurations to JSON and loading them later." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "config-example", + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "\n", + "example_config = {\n", + " \"energy_start\": 250.0,\n", + " \"energy_stop\": 300.0,\n", + " \"theta_end\": 60.0,\n", + " \"Sample Theta\": [1, 5, 10, 15, 20, 30, 40],\n", + " \"Higher Order Suppressor\": [10, 9, 8.5, 7.5, 7.5, 7.5, 7.5],\n", + " \"Exposure\": [0.0015, 0.0015, 0.0015, 0.0015, 0.1, 0.5, 1]\n", + "}\n", + "\n", + "config_file = output_dir / \"pxr_config.json\"\n", + "with open(config_file, \"w\") as f:\n", + " json.dump(example_config, f, indent=2)\n", + "\n", + "print(f\"Example configuration saved to {config_file}\")\n", + "print(\"\\nConfiguration contents:\")\n", + "print(json.dumps(example_config, indent=2))" + ] + }, + { + "cell_type": "markdown", + "id": "trajectory-header", + "metadata": {}, + "source": [ + "## Trajectory Options\n", + "\n", + "Predefined motor trajectories are available for common beamline configurations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "trajectory-example", + "metadata": {}, + "outputs": [], + "source": [ + "from pyref.beamline.beamline_scan_macros import (\n", + " CCDFar_LowEn,\n", + " CCDMid_LowEn,\n", + " CCDClose_LowEn,\n", + " Photodiode_Far,\n", + " XRR_init,\n", + ")\n", + "\n", + "print(\"Available trajectory dictionaries:\")\n", + "print(\"\\n1. Photodiode_Far:\")\n", + "print(json.dumps(Photodiode_Far, indent=2))\n", + "\n", + "print(\"\\n2. XRR_init:\")\n", + "print(json.dumps(XRR_init, indent=2))\n", + "\n", + "print(\"\\n3. CCDFar_LowEn:\")\n", + "print(json.dumps(CCDFar_LowEn, indent=2))\n", + "\n", + "macro = begin_macro(\"Trajectory Example\")\n", + "macro += run_dict_trajectory(CCDFar_LowEn)\n", + "macro += [add_comment(\"Now ready for low energy measurements\")]\n", + "\n", + "print(\"\\n\\nExample macro using trajectory:\")\n", + "for step in macro:\n", + " print(step)" + ] + }, + { + "cell_type": "markdown", + "id": "summary-header", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "The ALS beamline utilities provide:\n", + "\n", + "1. **Interactive widgets** for creating scan scripts:\n", + " - ESCAN_ScriptGen for energy scans\n", + " - PXR_Experiment for reflectivity measurements\n", + " - RSOXS_Experiment for scattering measurements\n", + "\n", + "2. **Macro generation functions** for:\n", + " - Motor movements\n", + " - Instrument setup\n", + " - Trajectory execution\n", + " - Sample alignment routines\n", + "\n", + "3. **Configuration management**:\n", + " - Save/load JSON configurations\n", + " - Predefined trajectory options\n", + " - Default scan parameters\n", + "\n", + "4. **File output**:\n", + " - Generate .txt run files for the beamline\n", + " - Compatible with ALS beamline 11.0.1.2 control software\n", + "\n", + "For more information, see the module documentation:\n", + "```python\n", + "from pyref.beamline import help\n", + "```" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/nexafs-ingest.ipynb b/notebooks/nexafs-ingest.ipynb new file mode 100644 index 0000000..abe6512 --- /dev/null +++ b/notebooks/nexafs-ingest.ipynb @@ -0,0 +1,714 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "22dddc52", + "metadata": {}, + "source": [ + "# NEXAFS beamtime to database\n", + "\n", + "Discover NEXAFS files in a beamtime directory (e.g. SharePoint), parse sample and scan metadata, and write to a SQLite database in `als-nexafs` with name `mon_yyyy.db` (e.g. `oct_2025.db`) from the file dates.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "cb09fce6", + "metadata": {}, + "outputs": [], + "source": [ + "import io\n", + "import re\n", + "from datetime import datetime\n", + "from pathlib import Path, PosixPath\n", + "from sqlite3 import Connection, connect\n", + "from typing import Any\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ebd54d68", + "metadata": {}, + "outputs": [], + "source": [ + "import scienceplots\n", + "plt.style.use([\"science\", \"no-latex\"])\n", + "\n", + "def set_plotting_defaults():\n", + " \"\"\"\n", + " Set matplotlib rcParams for fontsize and grid defaults.\n", + "\n", + " This function configures:\n", + " - Font sizes for labels, ticks, legend, and titles\n", + " - Grid appearance (alpha, linestyle, linewidth)\n", + " - General figure aesthetics\n", + "\n", + " Examples\n", + " --------\n", + " >>> from src.utils.helpers.plotting_helper import set_plotting_defaults\n", + " >>> set_plotting_defaults()\n", + " >>> plt.plot([1, 2, 3], [1, 4, 9])\n", + " >>> plt.show()\n", + " \"\"\"\n", + " plt.rcParams.update(\n", + " {\n", + " \"text.usetex\": False,\n", + " \"font.size\": 10,\n", + " \"axes.labelsize\": 10,\n", + " \"axes.titlesize\": 11,\n", + " \"xtick.labelsize\": 9,\n", + " \"ytick.labelsize\": 9,\n", + " \"legend.fontsize\": 8,\n", + " \"figure.titlesize\": 12,\n", + " \"grid.alpha\": 0.3,\n", + " \"grid.linestyle\": \"-\",\n", + " \"grid.linewidth\": 0.5,\n", + " \"axes.grid\": True,\n", + " \"axes.grid.axis\": \"both\",\n", + " }\n", + " )\n", + "\n", + "set_plotting_defaults()" + ] + }, + { + "cell_type": "markdown", + "id": "c6a81760", + "metadata": {}, + "source": [ + "## Configuration\n", + "\n", + "Beamtime data directory (e.g. SharePoint/OneDrive) and destination for the database (als-nexafs in home directory).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a994e8e1", + "metadata": {}, + "outputs": [], + "source": [ + "data_root = Path(\n", + " \"/Users/hduva/Library/CloudStorage/OneDrive-SharedLibraries-WashingtonStateUniversity(email.wsu.edu)/Carbon Lab Research Group - Documents/Synchrotron Logistics and Data/ALS - Berkeley/Data/BL1101/2025Oct/TAD KOGA/renamed\"\n", + ")\n", + "dest_root = Path(\"/Users/hduva/projects/als-nexafs\")\n", + "\n", + "chem_formula_map: dict[str, str] = {\"ps\": \"C8H8\", \"hopg\": \"C\"}\n", + "parse_key: str = \"____\"\n", + "tag_map: dict[str, str] = {}" + ] + }, + { + "cell_type": "markdown", + "id": "307b2053", + "metadata": {}, + "source": [ + "## Schema and database\n", + "\n", + "Database lives in `dest_root` (als-nexafs). Name is `mon_yyyy.db` (e.g. `oct_2025.db`) from the month when beamtime files were written.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "21be70de", + "metadata": {}, + "outputs": [], + "source": [ + "SCHEMA_SQL = \"\"\"\n", + "DROP TABLE IF EXISTS nexafs;\n", + "DROP TABLE IF EXISTS izero;\n", + "DROP TABLE IF EXISTS sample;\n", + "\n", + "CREATE TABLE sample (\n", + " id INTEGER PRIMARY KEY AUTOINCREMENT,\n", + " name TEXT NOT NULL,\n", + " tag TEXT NOT NULL,\n", + " version INTEGER NOT NULL,\n", + " beamtime DATE NOT NULL,\n", + " chemical_formula TEXT,\n", + " UNIQUE(name, tag, version, beamtime)\n", + ");\n", + "\n", + "CREATE TABLE izero (\n", + " id INTEGER PRIMARY KEY AUTOINCREMENT,\n", + " scan_id INTEGER NOT NULL,\n", + " beamline_energy REAL,\n", + " time_stamp TEXT NOT NULL,\n", + " photodiode REAL,\n", + " ai_3_izero REAL\n", + ");\n", + "\n", + "CREATE TABLE nexafs (\n", + " id INTEGER PRIMARY KEY AUTOINCREMENT,\n", + " scan_id INTEGER NOT NULL,\n", + " sample_id INTEGER NOT NULL,\n", + " time_stamp TEXT NOT NULL,\n", + " sample_theta REAL,\n", + " beamline_energy REAL,\n", + " tey_signal REAL,\n", + " ai_3_izero REAL,\n", + " photodiode REAL,\n", + " izero_before_scan_id INTEGER,\n", + " izero_after_scan_id INTEGER,\n", + " FOREIGN KEY (sample_id) REFERENCES sample(id)\n", + ");\n", + "\"\"\"\n", + "\n", + "def get_db_path(dest_root: Path, year: int, month: int) -> Path:\n", + " month_abbr = datetime(year, month, 1).strftime('%b').lower() # 'Jan' -> 'jan'\n", + " return dest_root / f\"{month_abbr}_{year}.db\"\n", + "\n", + "\n", + "def create_database(path: Path) -> Connection:\n", + " conn = connect(path)\n", + " conn.execute(\"PRAGMA foreign_keys = ON\")\n", + " conn.executescript(SCHEMA_SQL)\n", + " conn.commit()\n", + " return conn\n", + "\n", + "\n", + "def ensure_schema(conn: Connection) -> None:\n", + " conn.executescript(SCHEMA_SQL)\n", + " conn.commit()" + ] + }, + { + "cell_type": "markdown", + "id": "ed059bc6", + "metadata": {}, + "source": [ + "## File discovery\n", + "\n", + "Locate all NEXAFS .txt files under the beamtime directory and classify as izero, calibrant (e.g. HOPG), or sample (name_tag_version_angle_scan_number).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "76fac1f7", + "metadata": {}, + "outputs": [], + "source": [ + "def is_nexafs_scan_file(file: Path, max_header_lines: int = 50) -> bool:\n", + " try:\n", + " with open(file) as f:\n", + " head = \"\".join(next(f, \"\") for _ in range(max_header_lines))\n", + " except OSError:\n", + " return False\n", + " if not re.search(r\"Date:\\s*\\d{1,2}/\\d{1,2}/\\d{4}\", head):\n", + " return False\n", + " markers = (\"Scan Type\", \"Scan Motor\", \"From File\", \"Beamline Energy\")\n", + " return any(m in head for m in markers)\n", + "\n", + "\n", + "def locate_nexafs_paths(directory: Path) -> list[Path]:\n", + " return [p for p in list_txt_in_dir(directory) if is_nexafs_scan_file(p)]\n", + "\n", + "\n", + "def catalog_izero_files(directory: Path) -> pd.DataFrame:\n", + " rows = []\n", + " for path in directory.rglob(\"*.txt\"):\n", + " if not is_nexafs_scan_file(path):\n", + " continue\n", + " stem = path.stem.lower()\n", + " if stem.startswith(\"izero_\"):\n", + " parts = path.stem.split(\"_\", 1)\n", + " elif stem.startswith(\"i0_\"):\n", + " parts = path.stem.split(\"_\", 1)\n", + " else:\n", + " continue\n", + " if len(parts) != 2 or not parts[1].isdigit():\n", + " continue\n", + " scan_number = int(parts[1])\n", + " beamtime = pd.Timestamp(datetime.fromtimestamp(path.stat().st_birthtime)).normalize().replace(day=1)\n", + " rows.append({\"path\": path, \"scan_number\": scan_number, \"beamtime\": beamtime})\n", + " return pd.DataFrame(rows) if rows else pd.DataFrame(columns=[\"path\", \"scan_number\", \"beamtime\"])\n", + "\n", + "\n", + "def catalog_calibrant_files(directory: Path) -> pd.DataFrame:\n", + " rows = []\n", + " for path in directory.rglob(\"*.txt\"):\n", + " if not is_nexafs_scan_file(path):\n", + " continue\n", + " parts = path.stem.split(\"_\")\n", + " if len(parts) != 2 or parts[0] != \"HOPG\" or not parts[1].isdigit():\n", + " continue\n", + " scan_number = int(parts[1])\n", + " beamtime = pd.Timestamp(datetime.fromtimestamp(path.stat().st_birthtime)).normalize().replace(day=1)\n", + " rows.append({\"path\": path, \"name\": \"HOPG\", \"scan_number\": scan_number, \"beamtime\": beamtime})\n", + " return pd.DataFrame(rows) if rows else pd.DataFrame(columns=[\"path\", \"name\", \"scan_number\", \"beamtime\"])\n", + "\n", + "\n", + "def get_beamtime_from_catalogs(\n", + " sample_catalog: pd.DataFrame,\n", + " izero_catalog: pd.DataFrame,\n", + " calibrant_catalog: pd.DataFrame,\n", + ") -> tuple[int, int]:\n", + " dfs = [sample_catalog, izero_catalog, calibrant_catalog]\n", + " beams = [df[\"beamtime\"] for df in dfs if not df.empty and \"beamtime\" in df.columns]\n", + " if not beams:\n", + " now = datetime.now()\n", + " return now.year, now.month\n", + " min_ts = pd.concat(beams).min()\n", + " return int(min_ts.year), int(min_ts.month)" + ] + }, + { + "cell_type": "markdown", + "id": "d0b57180", + "metadata": {}, + "source": [ + "## Parsing and sample catalog\n", + "\n", + "Parse filenames into sample metadata and read scan tables (Time of Day, Beamline Energy, TEY, etc.). Sample files follow `name_tag_version_angle_scan_number`; calibrants are HOPG; izero are izero*\\* or i0*\\*.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "60708733", + "metadata": {}, + "outputs": [], + "source": [ + "def _normalize_name(s: str) -> str:\n", + " return re.sub(r\"[^a-z0-9]+\", \"_\", str(s).lower()).strip(\"_\") or str(s).lower()\n", + "\n", + "\n", + "def apply_tag_map(tag: str, mapping: dict[str, str] | None) -> str:\n", + " normalized = _normalize_name(str(tag)) if str(tag).strip() else \"\"\n", + " if not mapping:\n", + " return normalized\n", + " return mapping.get(normalized, normalized)\n", + "\n", + "\n", + "def _keys_from_parse_key(pk: str) -> list[str]:\n", + " return re.findall(r\"<([^>]+)>\", pk)\n", + "\n", + "\n", + "def _parse_angle(raw: str | None) -> float | None:\n", + " if raw is None or (isinstance(raw, str) and not raw.strip()):\n", + " return None\n", + " s = re.sub(r\"\\s*deg\\s*$\", \"\", str(raw).strip(), flags=re.I).strip()\n", + " return float(s) if s else None\n", + "\n", + "\n", + "def parse_sample(\n", + " parts: list[str],\n", + " keys: list[str],\n", + " birth_time: float,\n", + " formula_map: dict[str, str],\n", + ") -> dict[str, Any]:\n", + " if len(parts) == len(keys):\n", + " parsed = dict(zip(keys, parts))\n", + " elif len(parts) == len(keys) - 1 and \"version\" in keys:\n", + " i = keys.index(\"version\")\n", + " parsed = {**dict(zip(keys[:i], parts[:i])), \"version\": 1, **dict(zip(keys[i + 1 :], parts[i:]))}\n", + " else:\n", + " parsed = dict(zip(keys, parts[: len(keys)]))\n", + " ts = datetime.fromtimestamp(birth_time)\n", + " if pd.isna(ts):\n", + " raise ValueError(f\"Invalid timestamp: {birth_time}\")\n", + " return {\n", + " \"name\": parsed[\"name\"],\n", + " \"tag\": parsed.get(\"tag\", \"\"),\n", + " \"version\": int(round(float(parsed[\"version\"]))) if \"version\" in parsed else 1,\n", + " \"beamtime\": pd.Timestamp(year=ts.year, month=ts.month, day=1),\n", + " \"chemical_formula\": formula_map.get(parsed[\"name\"].lower(), \"\"),\n", + " \"angle\": _parse_angle(parsed.get(\"angle\")),\n", + " \"scan_number\": parsed.get(\"scan_number\"),\n", + " }\n", + "\n", + "\n", + "def build_files_catalog(\n", + " directory: Path,\n", + " formula_map: dict[str, str],\n", + " pk: str,\n", + " tag_map: dict[str, str] | None = None,\n", + ") -> pd.DataFrame:\n", + " keys = _keys_from_parse_key(pk)\n", + " rows = []\n", + " for path in directory.rglob(\"*.txt\"):\n", + " if not is_nexafs_scan_file(path):\n", + " continue\n", + " parts = path.stem.split(\"_\")\n", + " if len(parts) < len(keys) and not (len(parts) == len(keys) - 1 and \"version\" in keys):\n", + " continue\n", + " rec = parse_sample(parts, keys, path.stat().st_birthtime, formula_map)\n", + " rec[\"path\"] = path\n", + " rows.append(rec)\n", + " if not rows:\n", + " return pd.DataFrame(\n", + " columns=[\"path\", \"name\", \"tag\", \"version\", \"beamtime\", \"chemical_formula\", \"angle\", \"scan_number\"]\n", + " )\n", + " df = pd.DataFrame(rows)\n", + " df[\"name\"] = df[\"name\"].astype(str).apply(_normalize_name).str.strip()\n", + " df[\"tag\"] = df[\"tag\"].astype(str).apply(lambda t: apply_tag_map(t, tag_map)).str.strip()\n", + " return df\n", + "\n", + "\n", + "def ingest_dataset(file: Path) -> pd.DataFrame:\n", + " with open(file) as f:\n", + " lines = f.readlines()\n", + "\n", + " header_idx = next(\n", + " (i for i, line in enumerate(lines) if \"Time of Day\" in line),\n", + " None,\n", + " )\n", + " if header_idx is None:\n", + " raise ValueError(f\"Could not find 'Time of Day' header in {file}\")\n", + " table_text = \"\".join(lines[header_idx:])\n", + " df = pd.read_csv(io.StringIO(table_text), sep=r\"\\t\", engine=\"python\")\n", + " ts_col = \"Time Stamp\" if \"Time Stamp\" in df.columns else \"Time of Day\" if \"Time of Day\" in df.columns else None\n", + " if ts_col is not None:\n", + " df[\"Time Stamp\"] = pd.to_datetime(df[ts_col], format=\"mixed\")\n", + " else:\n", + " df[\"Time Stamp\"] = pd.to_datetime(file.stat().st_birthtime, unit=\"s\")\n", + " rename = {}\n", + " if \"Tey Signal\" in df.columns and \"TEY signal\" not in df.columns:\n", + " rename[\"Tey Signal\"] = \"TEY signal\"\n", + " if rename:\n", + " df = df.rename(columns=rename)\n", + " try:\n", + " df[\"Beamline Energy\"] = df[\"Beamline Energy\"].round(1).astype(float)\n", + " return df[[\"Time Stamp\", \"Beamline Energy\", \"TEY signal\", \"AI 3 Izero\", \"Photodiode\"]].copy()\n", + " except Exception as e:\n", + " print(f\"Error processing {file}: {e}\")\n", + " print(file)\n", + " raise e\n", + "\n", + "def _izero_before_after(izero_df: pd.DataFrame, time_stamp_iso: str) -> tuple[int | None, int | None]:\n", + " if izero_df.empty:\n", + " return None, None\n", + " df = izero_df.copy()\n", + " df[\"time_stamp\"] = pd.to_datetime(df[\"time_stamp\"])\n", + " ts = pd.Timestamp(time_stamp_iso)\n", + " before = df[df[\"time_stamp\"] <= ts]\n", + " after = df[df[\"time_stamp\"] >= ts]\n", + " before_scan_id = int(before.nlargest(1, \"time_stamp\").iloc[0][\"scan_id\"]) if not before.empty else None\n", + " after_scan_id = int(after.nsmallest(1, \"time_stamp\").iloc[0][\"scan_id\"]) if not after.empty else None\n", + " return before_scan_id, after_scan_id" + ] + }, + { + "cell_type": "markdown", + "id": "8a2743b2", + "metadata": {}, + "source": [ + "## Ingest and pipeline\n", + "\n", + "Insert samples into the database, then izero rows, then nexafs rows (with before/after izero links). Pipeline: discover files, derive DB name from file dates, create DB with schema, ingest all.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "151da6f3", + "metadata": {}, + "outputs": [], + "source": [ + "SAMPLE_COLS = [\"name\", \"tag\", \"version\", \"beamtime\", \"chemical_formula\"]\n", + "\n", + "\n", + "def build_unique_samples(\n", + " files_catalog: pd.DataFrame,\n", + " calibrant_catalog: pd.DataFrame,\n", + " formula_map: dict[str, str],\n", + ") -> pd.DataFrame:\n", + " if not files_catalog.empty:\n", + " out = files_catalog[SAMPLE_COLS].drop_duplicates().reset_index(drop=True).copy()\n", + " else:\n", + " out = pd.DataFrame(columns=SAMPLE_COLS)\n", + " if calibrant_catalog.empty:\n", + " return out\n", + " cal = calibrant_catalog[[\"name\", \"beamtime\"]].copy()\n", + " cal[\"name\"] = cal[\"name\"].apply(_normalize_name)\n", + " cal = cal.drop_duplicates(subset=[\"name\", \"beamtime\"]).reset_index(drop=True)\n", + " cal[\"tag\"] = \"\"\n", + " cal[\"version\"] = 0\n", + " cal[\"chemical_formula\"] = cal[\"name\"].map(formula_map).fillna(\"\")\n", + " cal[\"beamtime\"] = pd.to_datetime(cal[\"beamtime\"]).dt.normalize()\n", + " out = pd.concat([out, cal[SAMPLE_COLS]], ignore_index=True).drop_duplicates(subset=SAMPLE_COLS).reset_index(drop=True)\n", + " return out\n", + "\n", + "\n", + "def insert_samples(conn: Connection, unique_samples: pd.DataFrame) -> None:\n", + " if unique_samples.empty:\n", + " return\n", + " unique_samples = unique_samples.copy()\n", + " unique_samples[\"beamtime\"] = pd.to_datetime(unique_samples[\"beamtime\"]).dt.normalize()\n", + " sample_lookup = pd.read_sql_query(\"SELECT id, name, tag, version, beamtime FROM sample\", conn)\n", + " sample_lookup[\"name\"] = sample_lookup[\"name\"].astype(str).str.strip()\n", + " sample_lookup[\"tag\"] = sample_lookup[\"tag\"].astype(str).str.strip()\n", + " sample_lookup[\"beamtime\"] = pd.to_datetime(sample_lookup[\"beamtime\"]).dt.normalize()\n", + " merged = unique_samples.merge(sample_lookup, on=[\"name\", \"tag\", \"version\", \"beamtime\"], how=\"left\")\n", + " to_insert = merged.loc[merged[\"id\"].isna(), SAMPLE_COLS].drop_duplicates()\n", + " for _, row in to_insert.iterrows():\n", + " conn.execute(\n", + " \"INSERT INTO sample (name, tag, version, beamtime, chemical_formula) VALUES (?, ?, ?, ?, ?)\",\n", + " (\n", + " str(row[\"name\"]).strip(),\n", + " str(row[\"tag\"]).strip(),\n", + " int(row[\"version\"]),\n", + " pd.Timestamp(row[\"beamtime\"]).strftime(\"%Y-%m-%d\"),\n", + " row[\"chemical_formula\"],\n", + " ),\n", + " )\n", + " if not to_insert.empty:\n", + " conn.commit()\n", + "\n", + "\n", + "def load_sample_lookup(conn: Connection) -> pd.DataFrame:\n", + " df = pd.read_sql_query(\"SELECT id, name, tag, version, beamtime FROM sample\", conn)\n", + " df[\"name\"] = df[\"name\"].astype(str).str.strip()\n", + " df[\"tag\"] = df[\"tag\"].astype(str).str.strip()\n", + " df[\"beamtime\"] = pd.to_datetime(df[\"beamtime\"]).dt.normalize()\n", + " df[\"version\"] = df[\"version\"].astype(int)\n", + " return df\n", + "\n", + "\n", + "def ingest_izero_into_db(conn: Connection, izero_catalog: pd.DataFrame) -> None:\n", + " for _, row in izero_catalog.iterrows():\n", + " path = Path(row[\"path\"]) if not isinstance(row[\"path\"], Path) else row[\"path\"]\n", + " scan_id = int(row[\"scan_number\"])\n", + " scan_df = ingest_dataset(path)\n", + " if scan_df.empty:\n", + " continue\n", + " for _, s in scan_df.iterrows():\n", + " time_stamp = pd.Timestamp(s[\"Time Stamp\"]).isoformat()\n", + " conn.execute(\n", + " \"INSERT INTO izero (scan_id, beamline_energy, time_stamp, photodiode, ai_3_izero) VALUES (?, ?, ?, ?, ?)\",\n", + " (scan_id, float(s[\"Beamline Energy\"]), time_stamp, float(s[\"Photodiode\"]), float(s[\"AI 3 Izero\"])),\n", + " )\n", + " conn.commit()\n", + "\n", + "\n", + "def build_rows_to_ingest(\n", + " files_catalog: pd.DataFrame,\n", + " calibrant_catalog: pd.DataFrame,\n", + " sample_lookup: pd.DataFrame,\n", + ") -> list[tuple[Path, int, float | None, int]]:\n", + " rows: list[tuple[Path, int, float | None, int]] = []\n", + " if not files_catalog.empty:\n", + " fc = files_catalog.copy()\n", + " fc[\"version\"] = fc[\"version\"].astype(int)\n", + " fc[\"beamtime\"] = pd.to_datetime(fc[\"beamtime\"]).dt.normalize()\n", + " fc[\"_bk\"] = fc[\"beamtime\"].dt.strftime(\"%Y-%m-%d\")\n", + " sl = sample_lookup.copy()\n", + " sl[\"beamtime\"] = pd.to_datetime(sl[\"beamtime\"]).dt.normalize()\n", + " sl[\"_bk\"] = sl[\"beamtime\"].dt.strftime(\"%Y-%m-%d\")\n", + " fc = fc.merge(sl, on=[\"name\", \"tag\", \"version\", \"_bk\"], how=\"left\")\n", + " fc = fc.dropna(subset=[\"id\"])\n", + " for _, row in fc.iterrows():\n", + " if pd.isna(row.get(\"scan_number\")):\n", + " continue\n", + " path = Path(row[\"path\"]) if not isinstance(row[\"path\"], Path) else row[\"path\"]\n", + " rows.append((path, int(row[\"id\"]), float(row[\"angle\"]) if pd.notna(row.get(\"angle\")) else None, int(row[\"scan_number\"])))\n", + " if not calibrant_catalog.empty:\n", + " cal = calibrant_catalog.copy()\n", + " cal[\"name\"] = cal[\"name\"].apply(_normalize_name)\n", + " cal[\"tag\"] = \"\"\n", + " cal[\"version\"] = 0\n", + " cal[\"beamtime\"] = pd.to_datetime(cal[\"beamtime\"]).dt.normalize()\n", + " cal[\"_bk\"] = cal[\"beamtime\"].dt.strftime(\"%Y-%m-%d\")\n", + " sl = sample_lookup.copy()\n", + " sl[\"beamtime\"] = pd.to_datetime(sl[\"beamtime\"]).dt.normalize()\n", + " sl[\"_bk\"] = sl[\"beamtime\"].dt.strftime(\"%Y-%m-%d\")\n", + " cal = cal.merge(sl, on=[\"name\", \"tag\", \"version\", \"_bk\"], how=\"left\")\n", + " cal = cal.dropna(subset=[\"id\"])\n", + " for _, row in cal.iterrows():\n", + " if pd.isna(row.get(\"scan_number\")):\n", + " continue\n", + " path = Path(row[\"path\"]) if not isinstance(row[\"path\"], Path) else row[\"path\"]\n", + " rows.append((path, int(row[\"id\"]), None, int(row[\"scan_number\"])))\n", + " return rows\n", + "\n", + "\n", + "def ingest_nexafs_into_db(\n", + " conn: Connection,\n", + " rows_to_ingest: list[tuple[Path, int, float | None, int]],\n", + " izero_df: pd.DataFrame,\n", + ") -> None:\n", + " tey_col = \"TEY signal\"\n", + " for path, sample_id, sample_theta, scan_id in rows_to_ingest:\n", + " scan_df = ingest_dataset(path)\n", + " col = tey_col if tey_col in scan_df.columns else \"Tey Signal\"\n", + " for _, s in scan_df.iterrows():\n", + " ts_str = pd.Timestamp(s[\"Time Stamp\"]).isoformat()\n", + " before_scan_id, after_scan_id = _izero_before_after(izero_df, ts_str)\n", + " conn.execute(\n", + " \"\"\"INSERT INTO nexafs (\n", + " scan_id, sample_id, time_stamp, sample_theta, beamline_energy,\n", + " tey_signal, ai_3_izero, photodiode, izero_before_scan_id, izero_after_scan_id\n", + " ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)\"\"\",\n", + " (\n", + " scan_id,\n", + " sample_id,\n", + " ts_str,\n", + " sample_theta,\n", + " float(s[\"Beamline Energy\"]),\n", + " float(s[col]),\n", + " float(s[\"AI 3 Izero\"]),\n", + " float(s[\"Photodiode\"]),\n", + " before_scan_id,\n", + " after_scan_id,\n", + " ),\n", + " )\n", + " conn.commit()\n", + "\n", + "\n", + "def run_pipeline(\n", + " data_root: Path,\n", + " dest_root: Path,\n", + " formula_map: dict[str, str],\n", + " pk: str,\n", + " tag_map: dict[str, str] | None = None,\n", + ") -> tuple[Connection | None, pd.DataFrame, pd.DataFrame]:\n", + " izero_catalog = catalog_izero_files(data_root)\n", + " calibrant_catalog = catalog_calibrant_files(data_root)\n", + " files_catalog = build_files_catalog(data_root, formula_map, pk, tag_map)\n", + " unique_samples = build_unique_samples(files_catalog, calibrant_catalog, formula_map)\n", + " if unique_samples.empty:\n", + " return None, files_catalog, unique_samples\n", + " unique_samples[\"beamtime\"] = pd.to_datetime(unique_samples[\"beamtime\"]).dt.normalize()\n", + " year, month = get_beamtime_from_catalogs(files_catalog, izero_catalog, calibrant_catalog)\n", + " db_path = get_db_path(dest_root, year, month)\n", + " conn = create_database(db_path)\n", + " insert_samples(conn, unique_samples)\n", + " sample_lookup = load_sample_lookup(conn)\n", + " ingest_izero_into_db(conn, izero_catalog)\n", + " izero_df = pd.read_sql_query(\"SELECT id, time_stamp, scan_id FROM izero\", conn)\n", + " rows_to_ingest = build_rows_to_ingest(files_catalog, calibrant_catalog, sample_lookup)\n", + " ingest_nexafs_into_db(conn, rows_to_ingest, izero_df)\n", + " return conn, files_catalog, unique_samples" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "cdf0be31", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DB: sample=6, izero=628, nexafs=4585\n", + "Samples: id name tag version\n", + " 1 ps 100 2\n", + " 2 ps 100 4\n", + " 3 ps 100 1\n", + " 4 ps 100 3\n", + " 5 ps 30 2\n", + " 6 hopg 0\n", + "NEXAFS rows per sample: sample_id nexafs_rows\n", + " 1 1566\n", + " 2 785\n", + " 3 785\n", + " 4 507\n", + " 5 785\n", + " 6 157\n" + ] + } + ], + "source": [ + "conn, files_catalog, unique_samples = run_pipeline(data_root, dest_root, chem_formula_map, parse_key, tag_map)\n", + "\n", + "if conn is not None:\n", + " sample_count = pd.read_sql_query(\"SELECT COUNT(*) as n FROM sample\", conn).iloc[0, 0]\n", + " izero_count = pd.read_sql_query(\"SELECT COUNT(*) as n FROM izero\", conn).iloc[0, 0]\n", + " nexafs_count = pd.read_sql_query(\"SELECT COUNT(*) as n FROM nexafs\", conn).iloc[0, 0]\n", + " print(f\"DB: sample={sample_count}, izero={izero_count}, nexafs={nexafs_count}\")\n", + " samples = pd.read_sql_query(\"SELECT id, name, tag, version FROM sample ORDER BY id\", conn)\n", + " print(\"Samples:\", samples.to_string(index=False))\n", + " nexafs_by_sample = pd.read_sql_query(\n", + " \"SELECT sample_id, COUNT(*) as nexafs_rows FROM nexafs GROUP BY sample_id\", conn\n", + " )\n", + " print(\"NEXAFS rows per sample:\", nexafs_by_sample.to_string(index=False))\n", + "else:\n", + " print(\"No sample/calibrant files found; no database created.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "8ecf55c2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DB: sample=2, izero=2421, nexafs=6454, distinct_scans=14\n", + "Samples: id name tag version\n", + " 1 ps 100kd 1\n", + " 2 ps 30kd 1\n", + "NEXAFS rows per sample: sample_id nexafs_rows\n", + " 1 2305\n", + " 2 4149\n" + ] + } + ], + "source": [ + "second_root = Path(\n", + " '/Volumes/DATA/Collins/2026Feb/nexafs'\n", + ")\n", + "\n", + "parse_key_feb = \"___\"\n", + "\n", + "conn, files_catalog, unique_samples = run_pipeline(\n", + " second_root, dest_root, chem_formula_map, parse_key_feb, tag_map\n", + ")\n", + "\n", + "if conn is not None:\n", + " sample_count = pd.read_sql_query(\"SELECT COUNT(*) as n FROM sample\", conn).iloc[0, 0]\n", + " izero_count = pd.read_sql_query(\"SELECT COUNT(*) as n FROM izero\", conn).iloc[0, 0]\n", + " nexafs_count = pd.read_sql_query(\"SELECT COUNT(*) as n FROM nexafs\", conn).iloc[0, 0]\n", + " scans = pd.read_sql_query(\"SELECT COUNT(DISTINCT scan_id) FROM nexafs\", conn).iloc[0, 0]\n", + " print(f\"DB: sample={sample_count}, izero={izero_count}, nexafs={nexafs_count}, distinct_scans={scans}\")\n", + " samples = pd.read_sql_query(\"SELECT id, name, tag, version FROM sample ORDER BY id\", conn)\n", + " print(\"Samples:\", samples.to_string(index=False))\n", + " nexafs_by_sample = pd.read_sql_query(\n", + " \"SELECT sample_id, COUNT(*) as nexafs_rows FROM nexafs GROUP BY sample_id\", conn\n", + " )\n", + " print(\"NEXAFS rows per sample:\", nexafs_by_sample.to_string(index=False))\n", + "else:\n", + " print(\"No sample/calibrant files found; no database created.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ebcd4d02", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/nexafs-plotting.ipynb b/notebooks/nexafs-plotting.ipynb new file mode 100644 index 0000000..ec45eac --- /dev/null +++ b/notebooks/nexafs-plotting.ipynb @@ -0,0 +1,614 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 25, + "id": "dd8497e4", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bb3de6dc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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energyabsorbance
0249.997130.078594
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2271.003850.002064
3272.01285-0.000389
4272.991000.000664
.........
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\n", + "

157 rows × 2 columns

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" + ], + "text/plain": [ + " energy absorbance\n", + "0 249.99713 0.078594\n", + "1 270.01334 0.000398\n", + "2 271.00385 0.002064\n", + "3 272.01285 -0.000389\n", + "4 272.99100 0.000664\n", + ".. ... ...\n", + "152 329.97940 1.183782\n", + "153 334.98117 1.117307\n", + "154 340.00769 1.073713\n", + "155 344.98584 0.981972\n", + "156 350.00833 0.975952\n", + "\n", + "[157 rows x 2 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# data = pd.read_clipboard(\n", + "# header=None,\n", + "# names=[\"energy\", \"absorbance\"]\n", + "# )\n", + "# display(data)\n", + "# data.to_csv(\"ps_100_40deg.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "f66fcaee", + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "\n", + "files = list(Path(\".\").glob(\"*.csv\"))\n", + "dfs = []\n", + "for f in files:\n", + " df = pd.read_csv(f)\n", + " df[\"angle\"] = int(f.stem.split(\"_\")[-1].strip(\"deg\"))\n", + " dfs.append(df)\n", + "df = pd.concat(dfs)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "8a756896", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(283.0, 300.0)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "import scienceplots\n", + "plt.style.use([\"science\", \"no-latex\"])\n", + "\n", + "def set_plotting_defaults():\n", + " \"\"\"\n", + " Set matplotlib rcParams for fontsize and grid defaults.\n", + "\n", + " This function configures:\n", + " - Font sizes for labels, ticks, legend, and titles\n", + " - Grid appearance (alpha, linestyle, linewidth)\n", + " - General figure aesthetics\n", + "\n", + " Examples\n", + " --------\n", + " >>> from src.utils.helpers.plotting_helper import set_plotting_defaults\n", + " >>> set_plotting_defaults()\n", + " >>> plt.plot([1, 2, 3], [1, 4, 9])\n", + " >>> plt.show()\n", + " \"\"\"\n", + " plt.rcParams.update(\n", + " {\n", + " \"text.usetex\": False,\n", + " \"font.size\": 10,\n", + " \"axes.labelsize\": 10,\n", + " \"axes.titlesize\": 11,\n", + " \"xtick.labelsize\": 9,\n", + " \"ytick.labelsize\": 9,\n", + " \"legend.fontsize\": 8,\n", + " \"figure.titlesize\": 12,\n", + " \"grid.alpha\": 0.3,\n", + " \"grid.linestyle\": \"-\",\n", + " \"grid.linewidth\": 0.5,\n", + " \"axes.grid\": True,\n", + " \"axes.grid.axis\": \"both\",\n", + " }\n", + " )\n", + "\n", + "set_plotting_defaults()\n", + "\n", + "fig, ax = plt.subplots(\n", + " ncols=2,\n", + " figsize=(10, 5),\n", + ")\n", + "\n", + "sns.lineplot(\n", + " data=df,\n", + " x=\"energy\",\n", + " y=\"absorbance\",\n", + " hue=\"angle\",\n", + " legend=True,\n", + " palette=\"rainbow\",\n", + " ax=ax[0]\n", + ")\n", + "ax[0].set_xlabel(\"Energy (eV)\")\n", + "ax[0].set_ylabel(\"Abs. (arb. units)\")\n", + "ax[0].set_xlim(283, 300)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7372bec0", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from scipy.special import erf\n", + "\n", + "def gaussian(E, w, E_0, A):\n", + " # Ensure positive width\n", + " if w <= 0:\n", + " return np.zeros_like(E)\n", + " sigma = w / (2 * np.sqrt(2 * np.log(2)))\n", + " norm = A\n", + " return norm * np.exp(-((E - E_0) ** 2) / (2 * sigma ** 2))\n", + "\n", + "def peak_fit(energy, *params):\n", + " num_peaks = 5 # Number of peaks\n", + " expected_len = 4 + num_peaks * 3\n", + " if len(params) != expected_len:\n", + " raise ValueError(\n", + " f\"Expected {expected_len} parameters (4 for edge+decay, then {num_peaks}*3 for peaks), got {len(params)}\"\n", + " )\n", + " # Edge and decay\n", + " height, edge_pos, edge_width, decay_const = params[:4]\n", + " if edge_width <= 0:\n", + " edge_width = 1e-5 # Prevent division by zero or negatives\n", + " step_transition = height * 0.5 * (1 + erf((energy - edge_pos) / edge_width))\n", + " decay = np.ones_like(energy)\n", + " decay_mask = energy > edge_pos\n", + " decay[decay_mask] = np.exp(-decay_const * (energy[decay_mask] - edge_pos))\n", + " spectrum = step_transition * decay\n", + "\n", + " # Add peaks\n", + " peaks = params[4:]\n", + " for i in range(num_peaks):\n", + " w, E_0, A = peaks[i*3:(i+1)*3]\n", + " # Only add non-trivial peaks (width>0, amplitude!=0)\n", + " if w > 0 and abs(A) > 0:\n", + " spectrum += gaussian(energy, w, E_0, A)\n", + " return spectrum\n" + ] + }, + { + "cell_type": "code", + "execution_count": 191, + "id": "7669705c", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "energy = np.linspace(283, 300, 1000)\n", + "\n", + "# Initial parameters - now 6 peaks, inserting an extra peak between peak 1 (285.01) and 2 (287.17)\n", + "# We'll add the new peak at 286.09 eV, with width 0.42, amplitude 0.4 (can adjust as needed)\n", + "p0 = [\n", + " 1.1799, # NEXAFS_EDGE: norm (height)\n", + " 289.78, # NEXAFS_EDGE: edge position\n", + " 0.68074, # NEXAFS_EDGE: erf width\n", + " 0.0028455, # exponential decay constant (loaded from parameter file)\n", + "\n", + " # Peak 1\n", + " 0.35877, 285.01, 4.3144,\n", + " # Peak 3\n", + " 0.41166, 287.17, 0.94996,\n", + " # Peak 4\n", + " 1.3867, 288.65, 1.7005,\n", + " # Peak 5\n", + " 2.3432, 293.06, 0.84016,\n", + " # Peak 6\n", + " 8.7234, 298.62, 1.19,\n", + "]\n", + "\n", + "# Bounds for all params: lower and upper, updated for new peak (8 peaks)\n", + "bounds_lower = [\n", + " 0, # norm >= 0\n", + " 283, # edge pos >= min energy\n", + " 0.01, # erf width > 0\n", + " 0, # decay constant >= 0\n", + "\n", + " # Peak 1\n", + " 0.01, 284.5, 0,\n", + " # Peak 3 (287.17)\n", + " 0.01, 286.7, 0,\n", + " # Peak 4 (288.65)\n", + " 1.3, 287.18, 0,\n", + " # Peak 5 (293.06)\n", + " 0.01, 288.66, 0,\n", + " # Peak 6 (298.62)\n", + " 0.01, 293.07, 0,\n", + "]\n", + "\n", + "bounds_upper = [\n", + " 10, # norm\n", + " 300, # edge pos\n", + " 10, # erf width\n", + " 1, # decay constant\n", + "\n", + " # Peak 1\n", + " 3, 285.6, 10,\n", + " # Peak 3\n", + " 3, 288.2, 10,\n", + " # Peak 4\n", + " 10, 293.0, 10,\n", + " # Peak 5\n", + " 10, 298.6, 10,\n", + " # Peak 6\n", + " 20, 300, 10,\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 192, + "id": "afca25b2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(283.0, 300.0)" + ] + }, + "execution_count": 192, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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69QnnxIjpZD5/ALYGT0TaS0REREQUagxiYswvc2KOVV65SaYmGXafAy6/u3nUpprJ/UREREQUoxjExHCJZbFGzPGmkjXlxAhiNKbp/lbmxRARERFRjGIQE9MjMe7jlldumk7WFMSI6WTB32UQQ0RERESxiUFMjOfEHK8yWesgpgZpjUGPSO4nIiIiIopFDGJiiKgodkROzImMxGgagxhPLYxaFbRqJUdiiIiIiChmMYiJIS5/sKJYyxLLJ5ITY1IbYFDqpOlkoqyy+B0m9hMRERFRrOJilzFEjMIIYjqZGJWpOsHqZE1TyiobyyyLKWjWeo7EEBEREVHkcbHLBA1ixHQyh9sHl8eP1BPIiWleK8bTtFYMR2KIiIiISB5c7DLBNPhczUFMTWNifkpjtbHjydAcXvBSGolhTgwRERERxSgGMTHE2ZwT0yKIMZ5YEJOtTUGZ2ypdZk4MEREREcUyBjEx5HBOjMhpObmRmAJ9Jg40lEuXxRQ0VicjIiIioljFICaGNPiPnE7WtHjl8XQ2ZOOQqxpuv0caieE6MUREREQUqxjExBCn73Bif43j5EZiOuuz4IcfRc5KKfARv+/z+8PaXiIiIiKicGAQE6M5MbX17saFK1UnPBIj7HeWSSMxgQA4GkNEREREMYlBTIzmxIiRlBMdhREK9VnSfn9DuVSdTKh1BIMiIiIiIqJYwsUuYzgn5mSCGLFAZpY2RRqJSW6saCZGc4iIiIiIIomLXSZoToxOqTnpIEboos+WRmKSk4O/15RXQ0REREQUKVzsMgFzYkQAo1QopXyWE10jpmVeTHAkRiNd53QyIiIiIopFDGJibDqZmErWNIrSNC3sRIkKZWIkxqzXQKFAc5lmIiIiIqJYwiAmxhL7m4OYevcJrxHTciTmoLMCUASQbNCgltPJiIiIiCgGMYiJsZyYlkHMyebEiJEYd8CDUpdV+l1OJyMiIiKiWMQgJsZyYkR5ZeFkSyz/cq0YMRWN08mIiIiIKBYxiInBnJgGtxcuj78dIzGNQUxDMIjhdDIiIiIiikUMYmIwJ6ZpGtjJVidL1piQrDZhv7NcqlDGIIaIiIiIYhEXu4yxnBhD40KXwsmOxDSNxjSNxNQwJ4aIiIiIIoyLXSaYBjESo9JKa8QIJ1udTOhsyJJGYvqbmBNDRERERJHHxS4TdDpZKEZiUjidjIiIiIhiFIOYGCyxLCqTtScnpuVIjEUKYjidjIiIiIhiD4OYGCyxLEZi9BoV9FpVu0Zi6nwNUBk9cHp8cLp9YWkrEREREVG4MIiJwRLLIogR1cXaQ4zECE6tTdrbGjiljIiIiIhiyzET+9999128/vrr2L59O/70pz/h+uuvP+I+8+fPx4EDB6TLWq0Wt912W/ham+AOl1g++YUuf7lWTJ2mRtqLIgFZyYaQtpOIiIiISJYgZu/evTCbzfjqq6+wceNGjB079oggZufOnXjqqaewfPly6foll1yCkSNHYuDAgWFtdKLnxFQ5PFKJ5PbI0qZIx7ApRRCjYF4MEREREcXPdLKcnBxceOGF0uWMjAx06dLliPu8+eabGD16dPP1CRMm4JVXXglXWxOeKLEscmLESEx7p5MpFAp01mehKlAtXWeFMiIiIiKKm5EYgyE4xcjr9eLhhx/Gyy+/fMR9xAjN+PHjm68XFBTggw8+aPNkYtrZxIkTm69PnjxZ2qKdzRbMH5Fbg88Fv9OHapsDGWYdrFZrm/e1223Ne7219Z+5kzoNRc4SAOk4VGGF1Ro908mipa/D9bjE3mpVIFH7WmWzwdJ4bt8xnr/xJl6f19Eo0fvabrc371Wqky/+crISvb8jKdH7utX7hzq87x+J3tfhNG/ePGlrUl5eHp6cGL/fj5deekkKVqZOnYrNmzfDZDI1/7y+vh5JSUnN18X0s4qKijaPV1hYGLOLXaampsrdBLgCHqQlJcPhCSAj2XTMNjlrvNLebLYccb8e5jystu2ESqmAV6GNisfWUrS1JxQstYHg3nLk30NOEW+LxdLcD4iifoiEaPq7x7tE7mufz9f8fhypfkjk/o60hO5rb2TfPxK6r8NoxowZ0tYkbItdKpVKzJw5U8p5EQHIokWLWv08LS1NCmSaiMvp6entbgy1LRAIwCVKLIt8FodHWuelvTobsqW1YkRejUjsJyKi+GP12PF15SocclbJ3RQiopA75khMy2BGJOz/cmhaJPAXFxc3Xy8qKkL//v1D30qSKpMJepET0+CAxdC+xH5Byonx2JBrZk4MEVG8cPicWFm7Vbp89cYn8bVnI/zwQwklzkobgKtyJ+BX2aNgUR+eUUFEFHdBjMPhQF1dHbKyspoDlKb8lwULFkhJ/1OmTMHVV1/d/DsLFy7EPffcE4l2J2wQo1NogiWWOzgSIx0r1cnqZEREMcrr9+Fn23Ysql6LRVXrsKxmC3JcSXgE50PvsuA67ZXIdhVgq2cX1pWvwQ1V/8BNm2ajj7cfzlANxwhDf2QkGZGepENhhgm5qQaolFw+johiPIj54YcfcO2110rz1rp3744HH3wQRqNRCm7EFLMBAwagR48euP322zFr1iypEMCoUaMwZsyYyD6CBAtilH41vL5Ax6aTNa4Vo0p2oJbTyYiIYmZa8aa6fVhYtQaflq7GctsmNMAJtU8HQ1UuVAcHQW/tBPQBtn3cFUV1Spj1FTAbMmBQnofuejtsubuwvdMObLSsxys2HdQbu0CztxuUFVnQqlQoSDeiW7YZpxam4LSCVJxWmIKeuRaoVQxuiChGgpjzzjsPpaWlR9wuAhkxKtNk+vTp4WsdNWvwBYMNvzc4pa8j08k66dKhUigBUz1qSxnEEBFFqz2OEilo+bhkFZbUrIcddYBPBVVFJlSlfZBf3wXDk3vjlNxUdBlmQq7Jj4pty/HpAxPQOS+7zeBjk30v3jy0CP81f4viXtuRpUzHEP8QFNScCmuxEvNW7Mfsz4JT00w6NYb3yMDI3pkY2SsLw3qkw6A9odnoRERhw1ehGBuJ8biD5Xk7MhKjVqqQr8uE12hHDXNiiIiiRpnLim+r12Fh1Vp8Wb4ah7wVQEABZVU6NGXdMAC9cFH+YIwZkIuBk9KQndK6RH5lZSU+2QZpLbFjjZ6cau6KWb1n4IleN+BH6ya8XbIIH5T9gC9SvsbAgu648bIJON8yDtVlKvy8uwrLtpfj+S+34a/zN8KgVWFsn2ycPzAPI7ocu1ImEVG4MIiJtSDGFQxiUoztH4kROhuyUKWzw1fPnBgiIjltqz+Alw9+hoXVa6XpYoLWnopAcQ6yagfjkvwhuHRgD4y+LBsWQ/u/wDoapUKJsWn9pW1On9vxReXPePvQIjy463X83v8vjE8bgOmDJuDf542GWWXElqIaLNxYgq/WH8K9b62Czx9A3/xkXDqsEJNHdEav3GAZXCKiuApiRCUzUQ962rRp0kbtCWKC1zv6RibyYvZrdkHBkRgiIlnUeurx5z1vYc6Bj2AKJMFQng/djjHIsHfGFYP64tJzC3BGr8yIJdvrlFpcmjVK2mo8dZhf/qMU0Ny4+WncuvU5XJw5AlflnoVbJw7DXRf2RU29G5+s3IUlO2ow54ut+NuHGzGgc6oUzEw+vbNULICI6Gjmzp0rbS2rHEd1EJOXlxezi11GS06M29Xx6WRNIzG1ylVQODxSsqhCER2ryBMRxTtfwIfXi7/GAzv+DZunAcYtg+Df0AcjTy3ANRd3w8RBedCqWy9pEGkpmiTckHe+tBU5K/Bu6ffSlLPL1v0ZqWozpuSMwVU5Z+GCgXm4+qy+aHB78fX6EsxbuR9/m78Rf3p/Hcb3y8G1Z3bHhYPzodPI+3iIKLo0DWh0ZLFLTieLsZEYZ4MCIt4w6zs+ElOLWhgDHjjcPilxk4iIwmupdTNmrH8W29z7pKpgGVtH4MYzBuDma3ohPz06Ry7y9Zm4t8sUadtctw/vlHwrba8UfY5OmnRM6zQe03LHYdLQnrhkWAHsDR4s+PkA3ly8B9c+vxTpZh2mjeoqBTSn5CXL/XCIKE7wk2uMBTENDQFpKplSqejwSExA/DMEyywziCEiCh8xmnHj6ufxdf0yKUm/x+7J+P2I8bjy2q5I6uCXUpHUL6kL/tbzBjzW4zppXZr/7P8Sbx76Bv/Y/z/0NObhipxxmJYzHleP7S5t24pr8cbi3Zi7dC/++eU2jD4lC7ee2xsXDs7jmjRE1CH85BpzQUzH82GETE2KtA/oXdLimZ3SjB0+JhERteb0uXHn6v/g31Ufwe9Woev+s/H306fi0l937vCXUXISBQFGp56KfsjDS/3vlhbcFFPOnj3wIR7d8w4GmLtJwcyvc87E41cOxiNTBuDT1UV48ZvtuOq5H9A5w4TfnNML14ztjhRTxwrVEFFiYhATYzkxDodY6LLjL/gZ2mAFmYDOiRoHK5QREYWSyDWcs/Vr/GHPq3CobcguHoS/n3Ydrry4d0wHL22V7T8vY6i0vei7A19U/oS5pd/jkd1v4YGd/8YZyX2l6WZTB52JySPOxZo9VXjpm+145P31Uv7MlaO7SqMzYlFNIqITxSAmhkZidEqNNNc4FCMx6ZqmICY4EkNERKGxuGQbpv88G0X6PTDXdsazXe7FzBuGx13wcjR6lRaXZY+WNrvXgY8rlmNuyfe4Z/vLuGvbS1LJ5mk54/DkDaPx6K8H4bXvduHVb3dK28VDCnD3hX0wtHuG3A+DiGIAg5gYCmL0Sq00aiIWMesog0oHo1IHrxiJqWcQQ0TUUcX1VkxdPAfLlEuh9phxi/ZmzJ56acJW5jKrjbgqd4K0VXtsmFf2I+aWfIebtszGrVvnYGLGMEwbMQ4/TzwPH68owezPtmL8n7/GmD5ZuPvCvjj7tFxWziSiNjGIiRENPhcMSh1sDg/y00OTv5KhTUa5wc2RGCKiDvD4vJi57B28Wvs/+BV+jKs/H/89+ybkJifJ3bSokaax4Kb8C6TtkLMKH5QtwdzS7zBt4+PSF2qTss7AI/eeCe/+vvjn5zvxq79/j9MKU6Rg5rLhhVCrWASAiFpTyrHYpVjchk6O0++BXqmBrcEdshWbxZQyjckDWwNzYoiI2uOd3cuR8el1eKXhHRTW98ay/i/gu8l3M4A5hk76dNzZ+TKsOP057B79Bh7sdiU21+3HZev/jJvqfo9ul2/Gn+/KRmayDje8uAyD7vsEryzcAYfLK3fTiShERCwgYgIudpko08lUWmkkpqMLXbZM7t9jsKGmnkEMEdHJ2G47hMt/fBqb1BtgcGXj6ZyHcfe5Y+RuVszpZszFH7tNkzaxBo2YbiaKArze8BUyhibjvLFDYd+Si3vf/hmPf7gRt53XGzMm9EIqK5oRxTQudpmAOTGVDjESow3ZSIxCXwGbndPJiIhOhMPnxK0rX8NbtZ8Abh0uc1+BNydNR5KeH6pDsQbNYz2vx6M9rsNq205pypnY9uYtQurVZiTZeuPRFfvw9GebcOP43rj9vN7ITeXyAESJikFMDJVYNii1sDV4kRKqkRhNMnxaJ2pZYpmI6Lglk988+C1mbnoJdQo7upUPxwdn3YbBBTlyNy3uiGT+ocm9pO2JnjdijX0nPij9QQpo7ONWwes34pn9BXj2qc64usdI3HPBqeiRw/LMRImGQUwMjcRooIE/EAjpdDKvpgE2JvYTEbWpxFWFy1c+iWXOddCWFeKx/Lvwh6tHJkTJ5GgIaIZYeknb4z1vwFr7LimYec+4BHu7fo2X3Yvx6vxCjNUPw+PjL8CwrllyN5mIIoRBTAwFMapA8M8VyulkLlUDahjEEBEddfTljaJFuHXzHLhcAQwsugzzLp+ObtlmuZuWsAHNYEtPaftbjxuwzr4bcw8txhvab7EI/8XwTf9D4YreuL3Xubhz4HjoVJziRxTPWLMwhkosq5uCmJBNJ7PAr/DB6naE5HhERPGi3GXFhKUP4fqts+A/mIvHdX/C6ltvYQATRQHNIEsPzDrlRpSe8zZWDX8BvzKfjXJDEe6v+geSvpqMCYsfwSflK+D2c8o0UTziSEwMlVjW+k3S5RRjiEZitME5xFavLSTHIyKKB3OLF2PGxtlocHvRd/9F+PDy69ArlzkXUT3lLKUH5o27C37/HfjX2p8xa+Pn+M60Cd+6lsEIA36VMxK/zj0T56QPhk7JERqieMAgJoamk2l9wTfRUK0TIxL7BZu/LiTHIyKKZWJV+emrnsEX9qXQFHfG/2XdiIduG86FFmOIUqnEzUNOl7blO8rxyMLF+M7xE96vW423SxchWWWSFtackj0G52YMYUBDFMPUcix22VQbmk4uiLH4VNLlUCb2Cy6VAy6PDzpN8PhERIlmQelyXLPuH7B7nei553x8eNn16FeQKnezqAPO6JWFr3pNwdaic/D0Z5vx7uL1CHQ7gC96bcBbJQthURsxKfMMXJ49BuelD5XWYiOiyC12KTYudpkgOTHwqqBSKmDSqUOW2C8EdC7YGjzIZBBDRAmm1lOP69Y8iwW130Ndko8HUu/CX24bCY2aoy/xok9+Cv518yg8VDEAL369HW8t2IMkfQW6jKjGUsU2vF2yCGaVERdnno4pOWNxfvowBjREYcbFLhMsJ8bnUUpTycT831AwqvTQKbRSEFNT70amRR+S4xIRxYIvyn/GtLVPodZbj667J2DexTMwqGu63M2iMOmcmYQnrhqChyb3x39/3CsFNBWl3TC4TwBdR1RhnX0j/lv6HZJUBlycOUKacnZ+xjAYVDq5m05ER8EgJoamk4kgJjlEU8mapKjMqG4ciSEiSgR2rwM3rX0e71m/gaq0E+4y3Yonbh3LKbUJIkmvwW/O7oUZZ/XEwo0lUjDz1esKZFom4pYzTdD3LsaiupWY2xjQXCRGaLLHYiIDGqKowiAmRjT4XfC5FSFbI6bllLIqnRO1XCuGiBLAosq1mLr6SVR7bcjfPQ7zLrgJw3tkyt0skoFYrPTcAZ2kbVtxLV7/bhf+u2gPaj7RY1zfqbhpbBKqMnfiw4ofMbn0LzCp9K0CGjGbgYjkwyAmRjh9HnjcCqSEeCQmU5uMHfoa2BwciSGi+OXwOXHb+pfxRuVnUFVk42b13XjmlvEwaPk2SMApecl4cvoQPDJ1AD76+SBe+24X/vjSbqSbDbhy9G/wt2F6rFOuwwdlS3D5+kdhVOpwUeYIqSjABRnDYFIb5H4IRAmHr94xwB/wwx3wwO1SIDlEa8Q0ydYnI6AtQw1HYogoTi2t3oxfrfobyr3VyN01Gu+d+xuMOSVH7mZRFBJB7RWjukqbGJ35z/e78PaSPZjzhRunFabg+pF3YshADZY4f5YCmqkbHpMCmgsyh2Ny1mhMSB+ETG2K3A+DKCEwiIkBrsbVhl1OMZ0s9CMxCgNzYogo/jh9bty18VW8XPYRlJXpuA6/xT9vPjdkFR4p/kdnRCGAv/x6IL7ZUIL3lu3Do/PWw/2eH+P65uDekb9Hn8FKfGVbjg/KfsC0jY9Lv9fP1Bnj0gZgfNoAjE09jUENUZjwlTxG8mEElxNIzgxtECOVWda5UFvPkRgiih8/12zHpT/9DYd8ZcjcdTrmjr8ZE07Nk7tZFIO0ahUuHJwvbaKS50erDuK9ZXtxy79WQKNSYly/HPx2yD0YPEiPTd7t+L56A76qWoXnDwaXlDg1qQvGpQ7AuLT+DGqIQoiLXcZIPoy0b0DIR2LEgpc+kdhvYxBDRLHP7ffiro3/wnOH/gdFdRqu8NyGl2dMDPlrJyWmFJMW157ZXdqKqx34dPVBfLyqCHf952fp52f0ysSkoRPxx0E3QW1xYHH1Bnxv3YAvKn/GPw9+1CqoaRqpydAmy/yoiCKPi10mUHllweEIINkU2pyYDE0yAkofqhrqQ3pcIqJI21VfjLNWPYiDgRKk7RyKt8bcjAsGFsrdLIpTeWlG3HxOb2mrsDnx+dpifLzqIB5+bx3uf2cNumebcfZpubi8/6/x7PA7UB2oPmpQc1pSV2mURgQ2DGooUUzjYpeJNZ2sIQwjMekas7SvcNtCelwiokh6f/9yTN/8OLwOLS6z34R/3zAJqSH+0oeoLWKx6KYRGpFjunhLKRZuKMEX64rx8sId0KqVGNkrE+NP7YSbeg/AC2ekodRThcVWEdSsx2cVP2HOgdZBzXgpqOmPdK1F7odHFJUYxMTQSAx8qpBXJ2v6xqfKwyCGiGKP3+/HDYtfxxuu92GozsOLBTNx4+WD5W4WJTDxZePFQwqkLRAIYGepHYs2lmDhhkOY9dEm1Lu8MGhVGNY9AyN7Z+Hq3lfhueF3ospffdSgpn9SNymo6afOx2DlKehtyodZbZT7YRLJjkFMDOXEKHyqMIzEBL/hqfXbQ3pcIqJw219Vi3Hf/BX70tbhVNsZWHTJ/dB4gyPXRNFAoVCgV65F2m49tze8Pj/W77di2fZyLN1egVcW7sQTCzZBqVCgT34yBnVJw4iuF+OWrtciOdODlXWbpaDmk4oVeK6hFNgTPG6uLg29jQVSQCNtjZe7GLKhUqjkfthEsRnEVFRUIDOTqx+HayTGEuLFLjMagxibvy6kxyUiChfx7fZfl3yPv5T+C57katyVdAOeOfcK6WdWK4MYil5qlRJDuqVL228n9oHfH8D2Q7VYsbMS6/ZVY+3eaqmUs8fnh1qlQJ+8ZPTLPwO3552P9GQ3UgoBm6YKOxuKsKO+GCtqt+LNQwubp51rFRr0MHZCL1PeEUEOp6VRQgUxzz33HJ588kl4PB784Q9/wN13333Efex2O/Lz82Gz2aBUKrFs2TIGMSHW9OIUjulkRpUe6oAG9QEGMUQU/baUl+PiRU9jT9oapBmz8e7Ap3BO7mlyN4uoXZRKMQKTIm1NXB4fNh+swdrGoGZLUQ0+W1MEu9Mr/dyoVaF3pxz0yOmJyVlJ6JxlgindDY+pBhWKcuxwFGO74yDmln6HA87yVjMvWo7aiK2XMV8KerRKVu+jOApiVqxYgdTUVOzfvx+ffPIJLr/8ckyYMAH9+/dvdb+33noLq1evlgIZEcRotUykDMeCbU3TyZJDPJ1MDHWbFSbUKx3SN0LiBZWIKNr4fH7c9N07eMPxAQIWH36TNA3Pn3EN1EpOnaH4otOoMLhburS1HH3csrcEh+qArcW12FpUiz3ldvy4vRwl1obm+4lcmy6ZXVCQ3hcXpxqRmaaGMqUOLqMVtdpKlKEcm+r2Yl75D7B5HdLvKKFEvj4DnXTpyNOlo5Ne7I+8zjwcipkgRiRLXn311dLlyy67DAMGDMD27dtbBTHiP9WLL76IxYsX41e/+hWmTp16zJMdOHAAEydObL4+efJkaYt2YpRJTlX2muAFnwp+Vz2sVudxf8dutzXv9dZjzxpMghF1OheKyipg1msSuq/D/bjE3mqNjkBRjr5W2WywNJ7bZ7UiUcTr8zpS/rdvHX63+3XYLaXo7umHt0+7Gb1TsmGvPbJfE72vxeyIpr1KFf4AL9H7O5KSVB4MLbBgaIEBQE7z7Q1uHw5UObC/0oEDlQ7sr3KgpMaJNXsqULrWhUq7C4GAuKcIQrpAr+mGnCQNeqZ6oU2vg99ig99UB6e+HpvV5Vip2o0a1KAu0ND6/Eo9crVpyNGkNu9bXu6kTUO2JhVapTr87x/q8L5/8HkdPvPmzZO2JuXlh0cLT1abz7SRI0e2ui6mlA0ZMqTVbS6XC3fccYc0ajN9+nR8+eWXeP3119s8WWFhYcyuEyNGpeSirA/+mTQKDXKy0qXRk+Nx1gSHnc1my3Hbnq5NRomuAUqtCampJiRyX4eLpVZ6B4HFcvy/RyRFvC0WS3M/IIr6IRKi6e8eK7bVFmPKj7OxSbMeBkUGns36A+44d/xxfy+R+9rn80l7s9kcsX5I5P6OtKP1tbilUzYwoo3f8Xj9KK1pQLHVIY3aHKp2SIFNpd2JKrE/GAx0qmxOWOsPL3xtUnkRMDgQMAY3pcWFSrMTVaYGbNTvhUe3EU6NHT5l8DnXxIwkpCpSkKFKRZY6FV11nXCqqRsGmrsh35QOo04Fk04NvUZ1Qp9nmnkj+/7B53V4zJgxQ9qahH2dmJ07d2L48OHo1q1bq9v1ej1uuukmabv55psxduxYKagZNGhQuxtER0/sVwfUSDFqT+4//MkseKmrQa3Dg/zDo9dERLJNoZ3x08v4b+1ngEeHyb5f482Lr4aR05WJTppGrURBhknajkdUT6uuc0nBjFjvxubwoNbhlj4fiOvisrjNVhW8rUb8zFeHWthQp6xFvcouBTaHdPUoNtYgYDwEf/IyoDb4xarCYYDSmiZt6po0mOozYHanI0mnlabCGXVqGLXq5kDHoFUH9zoVuil24noAH/50AA3JKY33C9738GXxO02/e5JBEsWc4wYxYlrZSy+9hGefffaY9xNBjsib2bZtG4OYMLyhi+T75BBXJmuSpRNBjEt6cSIiktNXxRtwxdonUaOqQrfS0/HB2bdhcEG23M0iSpjqaVnJBmnrCBEMifVwHC4vbE4Xttcdwsa6vdjSsBc7kw9gd/4BVAc2QkxYqwmokeXLRbo7GzpXNgL1mfDa01Fl98HhdkjHaHB70RkHcX1XYPZnW7CmLpjPczyiCEJTcBMMkJoCpRa3twyGdGppWr3S70JuhgNmQ/Czl9iL28UyFyIopBgJYmbPni1VJUtKSjruwXJycpCdzTebcIzEKANijZjwfAuZrRdBjFP6VoWISA71bhcmfzcHXwW+gaY+HX/J/CMevHYMi40QxWgwJKqpii0XRvRGKiahX6v7VLlt2FC3B+vte7DOvlvar6hbB09qcNSmqyEHA8zdMCapGwZaumOY6zTgE2Dxn8+HO3UAHC6fFOA43CJY8klBkwh2pH2r6z5pL+4bvC34M3uDB6U1zsO/4w4eT4w4+fzBKeBHI0Z4RDAjAhuxb75s1MJiCAZBKSYtUpN00gya1CQt0lpc12tZjCQiQcwbb7yB888/X6o8JubZLliwQErEF/sLL7wQZWVlqK+vR+/eveH1erFnzx6MGTMmZI2joAYRxPjVIV8jpkmeMVUaibE1cCSGiCJvzuoluH/fS2gwVeGMurMw//yZyEmWPz+PiMJHrFszPm2gtDXx+L3YVn+wOahZb9+N5w9+gkpPLQY5bVgD4MZN/4AxZyROMRUgS5uCDK0FmaZkdNOmIF2T1uGKhaJoVUl5FVQ6U3BKXYNHCnhaXRbT6hq8sDuDl8Vtu0vt0ueopil4TSWxjxYEpf4iyBHXpcDHpENa4/VMix4ZZh0yLHrpNpWSI0AnHMR8+OGHuPHGG1v9Ue+77z6putjMmTOlamUrV66ULt9www3Iy8uTRm00GtYaD8dIjCIMa8Q0ydanAGofKurrw3J8IqJf8gV8eHXXd3ho839RaSyCWZWJ97o+gam9OR2ZKFFplGqcZu4qbVe3DCpc1dh74DOgaCWydClYUL0GLxV9Cm+gdVEBIVVtRqY2ORjcaJKRqU0JXtdYWlwWtwc3sV5eSyKPRgo0UgzITmn/tDpRUEHkDIn8ImtjnpHYan5xXVwWZbNrmq+74Q+Wk2vRJiAtSRcMasx6ZFiCl5sDncbbmq6L+4rRsIQNYkRZZTG6cjRFRUXSvmvXrrjiiuAqyRTenJiANzh8GQ7iP7FwqKGxlDMRUZh4/T78c+9neHTHu6hWVULfkIv70m/H386+CCqu+UJEvyCCCrFWTafUU6Xrj/e8EY9nDpaCm1pvPSrctajw1KDSbUOFuwYVntpWl8WITtPlet+RS1QYlLrmgEYEN+kaM1ReBVLKzNArtdLP9UoN9Kqmy1oYVNrGnzXuVb+8Pfg7aebgiMrJEGv21TZ4UGlzNleRq7QFK8gFbwvevresTrpcYXPB4/P/os8gBTbZyXrkNAZjTZfFltXisiiCEKtit+UJNp0s4BPzS8MTxIhvK4RyF4MYIgqfvfYKnLPs/7Abu6E71AW/SZuGf1x2PpJkXp+KiGIzuEnRJElbT+Sd0O80+FzS1DQR+FRKwc+Rl4tclahzN8Dj9EkzYcTvOP0eNPhdaPC54UfrgOF4NAp1c4Ajghuz2oBktekomxGWxr10XWdCcpIJpxWI21JgURuhUhz5RU8gEJCmuR0OclyosDlRXuuUSmuLbcehWvy4tQyltQ1weVq3P0mvRnZyMKD5ZdCTl2ZEpzSjtI/GYCeiLSouLpbqQU+bNk3a6MSI/0R+T/hGYtI1wSCm0sPFnYgo9MQ3i3/68Ss8Xvsi/D5gcv0M/HPyxdKbJRFRpIgRkwJVFgr0Wce8n9VqbXOdGDGaLAKaYIDjDu7FdZ/Yu48IfJzN9wnebvM5UOupl0aRxDQ5kQMkLjdtR5si18Sk0rcZAKVpLMjRpSI7NxWdu6RiuDYVObpCaXpdU6lpEfDUODwoq2lAWW0wwClrDHaCtzmxuahGuizu11KKUYO8dBPyUg3NgU2n1OC+Kdg5mc+pc+fOlTYRG8REECPyZmJ1sUs5iSe/z6OEJS08OTFi+FSoZhBDRCEOXub/tA93r/sPirqsQLa3M+YPeQgjCwvlbhoRUbuIwgFmpRFmGEN+bBFkiM98LYMaaWsMeoKbA7bGfa23HlUeG/Y0lEj7UpdVCpx+ORKUrU1Bji4N2VJgkxrcJ6UiJy0NA6WfZUm3idGepoBHVG2TFke1OlBU5UBxtUO6XFzdgPX7rPh8bbE02tOSWa8+IsDJTzeiMMOE/HQTCtKN0to/QtOARtgXuyR5iUhfCmLCNBIjInulX4Vanz0sxyeixOLz+zF/5X48vORr7MpZBl/XEtyQehleGfabo06HICKixqICKp20iaCjPUFQna9BCmbK3FaUuqpR5q5Bqbu6+bYN9r0oc6+RrrsDrUdbxLS3HG0qsnWpLfZpyCpIQV43M07TmJGqyUaaxixtBuhRVuNqFeAcqnag2OrAtkO1WLSpRBrlaVmnQBQeaBnUdASDmBhg9zgbq5Npwvefxm9EbaAuLMcnosQgqvG8u3InHvp5AYpy18A/yIoumjy8eNpfcX7GMLmbR0QU1xQKBcxqo7T1NB07T0iaWuatawx2rEfsReCzqnYHSt1WlLtrjjrNTQEFUtRJUkCTqklCWrYZafnBAKdL4+0WVRr8Ti3c9WrU1yphqwEqK4HSKje+Wn8IBR14vAxiYoDD6wLCWGJZMMIEB1himYhOnigN+rfvl+DVoq9gzd0G9HFhjGkw/q/P/ZiQNqh5egIREUUHhUKBVCn4MOMUU+EJjfBUe+zSZm3cV3vF5bpWt5e5rNhaf6Dxep30e62IVMgCwNBZJwU5Bdva/xgYxMQAh0+sE2MI23QywaxIQrnSEbbjE1H8WVNcgt8v/wBLfMvhTa2CvtCEazPPwcN9L0d3Yye5m0dERCEe4elsyD6p33X7PVIwY/U2Bj6NwU3T5bX4X7vbxSAmBohqFvAlwRKm6WRCsjIJB1XlYTs+EcUHkez55k+b8OTu/2Fv+logyYe+/r64v9fNmFY4WlqsjoiISNAqNVJujdiOZhKDmPgmyvSFezpZmtoCn/YA3F4ftGom3hJR66kEq/dU4eUVazDX/jnqO2+DKkuNSfqzMHvo1ehqPna5UiIiolBjEBMDXH4xnSx868QI6VoLAjoXah0eZFoYxBAlOhG4rN9vxZs/bcZ7RT+iLHUHfLmHYEw34u7cX+P/+k6RFpkjIiKSAxe7jAFueKCGGjpN+IKLbF1KYxDjRqZFH7bzEFF0J+h/v7kUCzbtwOc1y1GdsRO+7DIokoH+ut64qdvtuK7TOTCpuUglERG1Hxe7TBCegAcpKl1Yz5GjTwbUXpTX1aMHLGE9FxFFx1oue8vrsGp3FVburMSyvcXYgE3wdNkNX6dDUOYpMFjfFzd0nYrJ2aOQ1cZ8ZiIiopPFxS4TgC/gg0/hgzHMQUyeKfgBpajeCiA3rOciovDx+wNwenxocPukfXWdS1qE7FB1A4qtddhVVoutpVZst5XAaa6CP7UahhwbHGeUwKv0YJipD64v/BWmZI9FhjZZ7odDRER0VAxiopxLJPUDMKnDl9Qv5BmDQUyxozqs5yGiE7PfWoOdh+woqXJjX3kdKuwu1DV4UNPgREWgClZVJWwaK3y+APx1Rvjr9PDY9HA71PCnWOFLr4Rf2qrgT6oDlD4gKQCINJbuh8+Tr83EQEs3jE49B1fkjDvp8plERERyYBAT5Rp8bmmfpA5vnkpnc7q0L3HWhPU8RHR0TrcPb69bj9d2L8ZGbEJdSjGg8gM+JVRaHbSpeijSA2jQ2RBQ+KXfUQXUCMAPf+P1llRQoas6H6cZh2BAcmekG40wqDRSuUuNQoVcXTr6m7tKC50RERHFGgYxUc7pbwxitOHOiUmR9hWu2rCeh4ha21RWjt/++F/86F8Gb3INFKlKdPZ2w6+TpqJ3ehbUeg/q4UCNp166fw9jJ/Q05klbvj5Duq3CXYtiVyWKnZXS4mF9kgrRP6kb9KrwjuASERHJhUFMjAQxFk14R2KSVAbAr0Sl2xbW8xBR0JqK/bhhyWtYr18FmHw4zdcfMwom4PqeY6RVkU9G00Jigy09w9ZeIiKiaMIgJlaCGG14gxiFQgGNxwCrl0EMUTiVOqtx2dK/Y4V3NZRaLc5VjcPzI69BDwtzUYiIiE4Ug5gYyYlJ1Yd/XQatz4AaX13Yz0OUqOYdWorp65+Cy+3HyLoL8M7Ea9ElLTiVk4iIiE6cEjIsdikWt6GTG4lJiUAQY/AbYQeDGKJQc/hcuHrN07h805/hL0vHKxmPYelVdzKAISKihDR37lwpJuBil3GszuOU9qn6k5sj3x5JSIIdweRhIgqNrXUHcMGGh7HfVYHc7Wfi61/dhlMLuXAkERElrmlc7DL+VTU4pH2aIfxBjFlhQoWyMuznIUoUNm89zvv5QZRWu9B36zQs/O3lyEkJ/6gqERFRvIvodDI6eVZng7RPN4b/g0+KygK3Ohg0EVHHBAIBTFv9FIoc1ei3ZRK+v2sKAxgiIqIQYRAT5WpcwSAm0yCW2Q6vdHUyvNoG6cMXEXXM33d+hM9rl6HLtgmYd8M5yDCHt8IgERFRImEQE+Vqm0ZiTOH/Bjdbm4KAyge7l6MxFF5Wjx0OXzDfKx6tqNyBB3a/jOT9fbH42puRauKik0RERKHEICbK1bidgFeFlAh8CMrRBysl7bVXhf1clFjKXVZ8ULoET+x9V7o+YfX96Pnj9VheswXxxu5x4LwVf4LSnoxvz/0jCjJMcjeJiIgo7jCIiXJ2EcT4VEg2hD+I6WRIl/b76pjcT6Hzcfly5C+5ClM3PIYVtduk2/7S/Rp0MWTjzJ/vxcsHP42bKYzicZy16K+wKWrxbOHvMLhzltxNIiIiiksMYmKgxLLSr4ZGHf4/VaEpTdofYBBDIbKiZiuu2PA3XJw5AsVj52LBwEek2/srTsWLuQ/hmuzzcMvW53DTlmfgbFzYNZY9tOpDrFL+jMvcl+O2EcPkbg4REVHc4mKXUa7e64bSr4rIuQqSUgG/AkUN1oicj+LbjvoiXLT2YQyx9MQ/u9+DD7+vwIyXlkk/u/K5HzDqwa/x3t8zkbvhLPynaCGGLb0Th5yxO5Xxp5L9eLz8NeRV98MHl1wnd3OIiIiiFhe7TAB1XifUEfozpZn0ULh0KHVWR+R8FL/KXFZMXPMgMjXJuNx2DU6/70vUOb24rTBYoWvODcPh7DEQxdX1WLmzF979MQ2bB3+OPt/8FouH/gMDc3MRSzw+LyYufRQqjQZLznsIKiUHuYmIiNrCxS4TgMPrhlqhici5LEYtFE49yt21ETkfxac6b4M0AlPvdaLnxml4aNUWXD22G/7v8gHIObQVmA8M6pIO9MoUxcNx+Ygu+Jt3MJ5Zeir+aH8Cw777Pf6RcR9+e85pUCgUiAWXf/UKqi0H8EzGA+iWGswtIyIiovDh14VRzul3Q4vIBDFGrQpKlwGVnpqInI/ij0hsv3rTk9hadwDJP5yHLVu9mH/vOLwwY8QxF3oUOV/3nTkS3wz/GxRpNbin+Blc8/wS1Lu8iHbvb12HjxWf4HTnGNw1+Cy5m0NERJQQGMTEQhCjjEwQI7711nqNsPptETkfxZ8vK3/GgvJlSF09DpqadHz/yHk4p3+nE/798Vmn4rNhf4YirxT/072Li55YiCq7C9GqyuHAtVtmwehMwdfn3St3c4iIiBIGg5go5/K7oVNEbqE8g88EW8AesfNR/PD6fbhn2ytIsuYhpaI7vnroHHTLNp/0cc5JH4J3+j8AV+c9WJO5EOc+9g2KquoRjc7/7Fk4jdX474AHYNGFf0FaIiIiOoEg5rnnnpOS8bOysvDMM88c9T7z58/H7Nmzpe2FF1441uGoHdwBD/TKyAUxSQEz6lAXsfNR/Ph30ZfY1nAApvWn48N7xyMvzdjuY03NORPP9L4Fth4bUJa+DRc+vgglVgeiyb9WrMUq0xJcqD0Hl3QbIHdziIiIEkqbQcyKFSuQmpqK/fv34+WXX8a9996LDRs2tLrPzp078dRTT+Guu+6Stq+++grr1q2LRLsThgceGFSRC2IsCjMalPXwBXwROyfFvnpvA+7d8ho0+7ph/rW/Qo8cS4ePeUfhpbgiZxxqBi+BXVeFi5/8FhU2Z0ja21HF1Q7cufUlGGDAf8feIndziIiIEk6bQYzf78fVV18NtVqNyy67DAMGDMD27dtb3efNN9/E6NGjm69PmDABr7zySnhbnGC8Ci+Mal3EzpeitAAKoMrNvBg6cbeseB11gXr8sWA6RvbOClmO1it970K+PgOac5eiylmPS2d9h5p6eRfF9Pn9mPzOu2jI24t/nHITLBqTrO0hIiJKRG2WWB45cmSr6x6PB0OGDGl128aNGzF+/Pjm6wUFBfjggw/aPNmBAwcwceLE5uuTJ0+Wtmhns8nzgd7l9cGv9EIbUMJqPbkFKO12W/Nebz3xStrJCE4B2ll5AJr2zwaKub6O1OMSe6s1OsoGh6qvd9aW4x3bp+hWNRh3XNj3mM9Vlc0GS+O5fSf4nH6t2104Z8uDOHPyFmx4px8unbUQ/71tOAzayCwC+0tzFu7Az1lf4RRVV0xNGX5C/zfj9XkdjRK9r+12e/NepQr//5FE7+9ISvS+bvX+oQ7votyJ3tfhNG/ePGlrUl5e3u5jndCnWzFtbPjw4ejWrVur2+vr65GUlNR83Ww2o6Kios3jFBYWxuxil2JqXaSJqTMBlQ+pBvNJn99ZEyxNazZbTup384xi7Q7AqffL8pgFuc4bTpbaQHBvObm/R7iFoi3XLpoF6JX4+Lw7kZaWduw7WyzN/YATPPfI1FS8hDtxzaZZePDmU/HqC3W44+1NmHvnWKk0cySt2VOFJ3Z+Bv+QGrw19FGkJ5/4mjDR9HePd4nc1z6fr/n9OFL9kMj9HWkJ3dfek3//6IiE7uswmjFjhrQ16chil8f9BCCmlb300kt49tlnj/iZ+MAiApkm4nJ6Ohd6C5VahxtQ+WDWRm46WY4++J+23M21Yuj4/vXzKmxLWoNpxovRLys7bOe5utPZ+E3+Bfh71X/w4C25+HZTKW7/90r4/cHgMBLqnB5c8+oieAasxXWdzsHQ5F4ROzcRERGdZBAjqo7dfffdrUZcmgwcOBDFxcXN14uKitC/f//jHZJOkM3hCQYxGn3EzplhMAFeFYMYOi6Hy4vfr38bOp8Rr469Luzne7b3bRhk7oHfVT+OC6+rxX+X7cbD70WukMjv31qNPflLodep8ESvGyN2XiIiIjrJIOaNN97A+eefj/z8fGmIumkO24IFC6QcmSlTpmDJkiXN91+4cCGuvPLKYx2SToKtwYOA0ofkCK4/kWLSQeHUo8QZ3vmmFPse+3wlavO2Y2bBJTBEoPiEXqXF98Oewl2dL8M7rvnodNVyzF78M579fGvYz/3G4t14rfITNPTYikd7XoNsHacZEBERRWUQ8+GHH+LGG2+URlZEhTKtVotVq1bB4XBg5syZ0qhLjx49cPvtt2PWrFmYM2cORo0ahTFjxkT2EcSx2saRmBRd5EZiLAYNFE4DihuqI3ZOij0HKusxe+/H0CiV+GOfX0XsvDqlFrN63YRFQ59Eg84K32Wf4v7l8/DOD3vCmgdz6+pX4B7yM/7Q9QrcWXhZ2M5FREREHUzsF2WVvd5gcvgviQCmyfTp00/wVHSyrPVOQOVHSiRHYoxajsTQcd3/7kq4e27FjXnnIU3T8TVhTtb4tIHYMPJl3LrlObw3ZjGu31gCo+l3uGxw6+IjHVVW48A53zyBhlPX4y9dr8fDPaeF9PhERETUPpEt7UMnpcoZXKHcpIlcYr/FKEZi9MyJoTb9tKsSH1oXw69z4b5ul8vWjlSNGXP7/xGv9Lkb/m57MWX3A3hr4+qQHb+2wYmBHz+E6q7r8WjBTQxgiIiIogiDmChmdQSDGINSG7FzJjeOxFR5aiN2Tootf/nfOihO24pLM0eiu7GTrG0RC2LeVDARq05/HkaNBtcUPYS7V76DQKBjVcuqnXXo++m9KM3ejEdzbsNDfaaErM1EREQUY0GMqGQm6kHPnTs3kqeNWdWuBmmvj2AQ05QTU+1jEENH+n5zKRbZVsFprMHvu0bPB/uBqV2x99xX0NU6ALNr38DIxX+A1RNc9O9kbbEdRLdvbsYh4148mn4nHup/acjbS0RElMjmzp0rxQQtqxxHdRCTl5cnLXY5bRqnZZwIqzMYxBhUkZtOltw4ncwJFxw+Z8TOS9FPjG78+X/roRu0A2ck98EZKX0RTdKNJmyb+leMLZ6CFfZN6P7tDPxo3XRSx5hXtBwDf7wddpcLL2b9CQ8NvSBs7SUiIkpU06ZNk2ICERu0F6eTRbFad2NOjCpy1cnUKiUMPpN0ucLN0Rg67PO1xVhZsx21yUX4XRf5cmGORatW4dtrb8RdrntRW6HB2J9+hwc2vwFfILiKueDyu3HIWYWd9cXYXLcPa227sLJmK2au/jcu3/wIVBWZ+Kj3k7hl2HBZHwsRERG1ozoZJWYQIyQrzBBp/WVuKzobwrcKO8UOvz+AR+dtQMrpu5BryMWlWSMRrVRKJZ65fBwmrOmGa5a9gCfxDl4++DlMehWsXjscflebv5t/cDi+u+j36JGTHNE2ExER0clhEBPFbJ7gdDJjBKeTCcmq4Ac4ViijJp+sPoiNNQfgTNuO5zrfDpVChWh30eBC7Or1Z8z8/GN8WLEUDS4Nuqeko3tyBtJ1FngalNhRXIct++0wabS49czT8OB1I6BRc4CaiIgo2jGIiWJ2b4MsIzFparO0ZxBDTbkwT328Gamjd8CvS8WNeecjVqQl6fDfqVNQYbsYC346gC/XH8LOLTZsbPDAbNCgf+fuuHVSLqaO7AKTji+HREREsYLv2lHM7nHKE8QY9NB6DQxiSPLNhhKsrdoPZ/oWPNPlZuhVkauWFyqZFj1uOruXtBEREVHsYxATxTkIYu6+AgoYlJGdTmYxaqHxMIihw6Mw5pHbYdGm4KZ8VusiIiIi+XHyd5Sqc3nhV3mgg1Za0C+SRJlllZtBDAFLt5djWcluVGRtxQNdfx3Rct9EREREbeFil1Gqpt4NqL0RXeiyicWgBRr0DGJIGoUxjdiGLG0KfsNRGCIiIoqSxS7Vcix2ScdX63AjoPbCqIxsPkzTSIzfqmMQk+BW76nCwn074By4DX/vehNHYYiIiChki12KTQQy7cXpZFE+EhPppH4h2aiFp06LcheDmEQfhdEP34ZMbTJuzr9Q7uYQERERNWMQE6Ws9cGRGLPGIMtIjJhOVuGphT/gj/j5SX5bimrwyY6tqOm0Dfd1mQKjDME0ERERUVsYxETxdDIxEmNRRz6IsRg1UDgN8AZ8qPHWRfz8JL+nP90C3cAdSNOYcUvBRXI3h4iIiKgVBjFRPJ1MpfXBpJZnOpnCGTxvGaeUJZy95XX4YNVOOLrswM35F3AUhoiIiKIOg5ioDmL88uTEGMRITPC8TO4Pz9oru8vs2HTACqfbh2jz7OdboDllP7zw4OYC5sIQERFR9OFil1Gq1uGB0ihfYj+DmPBYsbMCN7y4DDtLbNJ1s16NGyf0xAOXngaTTv7/jqU1DXhzyW5opu7ApVkjUaDPkrtJREREREeQ/1MTHVWNyImx+GQJYkRODDxaqKBiEBNiv39rNcb3y8ETVw6W+vnLdcV44avtWLy5DB/ccyayUyKfA9XSnC+2QdmpDOXKcvy28D5Z20JERETUFi52GcXTyQJqD4wyrM0hRgTUSiXMMDOICdH0sVkbP5Mup5yzFrtH/xdTq27H3dWP4Y5Lu2Hhw+eitLYBZz/6NcpqGmRrZ3WdC//+dicyz9iPU5O6YGzqabK1hYiIiOLX3BAsdqmUY7FLsbgNHX8kxq+SZzqZQqGAxaCByW9iEBMCL+z9Au/VBYOYU7LSMSF9EO7rMhU7HcW4cM3D6J6nlwIZp8ePX89eDIfLK0s7X/5mBzwGG3ZotmJmwSTpeUBEREQUaiIWEDGBiA3ai4n9UTwS41N4ZAlihBSTFnofg5iOKnZW4p7tL0FfWiBdf6r3b/DcKbfjj92m4YvBf8Wmun24bP2fkZ2mkaaTbS2qxV3/+Sni7axzevDi19vRY3wpzGoDrsqdEPE2EBEREZ0oBjFRPBLjUbhlC2LESIzGY2QQ08FpZNPX/B0etwIz8351xM+HJffGx4P+jCXWjZi+8Umc1jkZs68fjrlL9+H9Zfsi2tbXvtsFm6cBOy1rcUPeeUiSYX0iIiIiohPFICZK1TQ0IKAIyBbEiAplSpeeQUwHvFOyCN/XrUHelnG4fEiPo95nfNpAvNf/QXxYvhS3bHkOvz6jM6ae0Rl3v/EziqrqI9LOepcXsz/biiET6mD12XFbwcUROS8RERFRe7E6WRQSa4c4Ay7pshyJ/UJqkhb7GhjEtFeJqwoztz4P9d5u+POIi6FRqdq87yVZI/Fav9/h2k1PQaVQYtbVv8GSreVSIPP+3WeGPTdF5MJY610w5a/HRNMw9DC2f34qRYlAABAL1TpKgpu47KkDPHbAbQe89YDfG9wC3iMvB3ytj3X4yi9PdGL3a/Wz8DJ53IBGi0Rldrswzn0I5uUfAdrwv38ken9HUsL3tcsa3C9/ANClhvVUCd/XMYJBTBSqdYjKZMEPEXKNxKSatPDW61DjrYPb74FWqZGlHbE6jUyMqojXwPztYzH9xq7YUlR7zN+5ptM58AX8mLH5GVg9dXji6itx3Zzl+PCnA/jV6Z3D1lZbgwezP9uC8ecG8GHDHjx5yg1hOxeFSUMlUPIDULkOqN4U3Oz7AV/wi5BWlFpAawbUxuBlpTq4KdStLyuUosJHi19sutzitqP+/Bi3t7ocPgqvB/An7uuVwuOBNuCAwl0LBMLfD4ne35GU8H3tsbXeh1HC93VEtf9zLoOYKGStdwNqj8xBjA7u8uC3EBXuWuTpM2RpRyz6oGwJPq5YjtSfzsZ1Z/SDVt32KExL1+edhxS1CVdseBw1aXWYOOxs3PvWaozrl4O0pPB8o/riV9tQF6jHipwvcZZ5IM7PGBqW81CI1ewAds4F9swHqjYEbzPmAGn9gILzgeTugDEXMHUK7sW3lhoToIr/bxbrrFakpob3W9poZqusxNeffIKLz7wYGRnhf91O9P6OpITv64o1wAdDgLEvAJmDw3qqhO/rSHp1Urt/lUFM1I7EBMvsylmdrKE2+PQoc1sZxJyEV4u/QB9lLxzcnY9rb+t+Ur97WfZofDnkr7hk7SPoOdiOhp2n48G5a/HiTSNC3s6qOhfmfLkNnSZuwcFAA17vdy+U4ht4ik4BP7D/c2DtrODIi8YMdL0EGHgv0GkcYA5WwCMiIkoEXOwySiuTQeYgRkwnq7MGg5jSpnmodFx13gYsrt4I7558TDg1B10yk076GCLZ/7ths3DAXQblRQvx5qqN+G5Tacjb+vTnu9DQaS82G9biud63odCQFfJzUIiULAt+A/n5xcGclXPfB64vA85+C+h9NQMYIiKKKVzsMo7XiGkaiZErsT8tSQs0GKCAQkpSpxPzbfU6uAMeFK9LwZWju7X7OEMsvfDj8Keh0nkRuOgr/Obdr6S1XEJlS1EN3lq9He7TV+DSrJFSTg5FIVct8N0M4MNRgMhLu3Qx8KulQI8pAMtgExFRjOJil3Gq1uGBWuuXPSdGEVAiTW3BIQYxJ+zzyp+QGciEwZWCiYM6VuWrt6kAS09/Bp0sZuwePg+3L/g8ZIUH7n9nNRRjfoJBq8LLfe4MewU0aofK9cD/hgK73gfGPg/8ajnQaewvEueJiIgSE4OYKB2J0RsDsufECOnKFJS4qmVpQ6wRwYEIYrQl+Ti3fyeYDR2vbFKgz8KKM2ajQJeFN82v4Pn1izt8zI9XFeGbhqWwZ+/Fy33vRFaYS1VSOxz4Epg/CtAkAVPXAKfeBihPrEAEERFRImAQE6XVyUQQI9YM0So0suXECMlI5kjMCdpctw8HnRWo2pqBX51eGLLjZmiTsX78bKTU5+K3h57A/dv+jWU1m+FruZbHCaquc+Hmb96D7/Sf8Ov0sVIhAYoyu/8XzH3JGw9cthRIPvpCqURERIks5EFMRUVFqA+ZkNXJdHq/NAoj1zQfsdilkBQwcyTmBH1e+TO0AS2M1bk4f2BoF4xM0Sbh+xGPQ7unJ57b9zFG/XQ3Mr+biis2/BVvFH8Np8993GM4fE6M+vIRlA39GudmDMGszlwTJurs+wT4ZhrQfSow8UNAY5S7RURERLEZxNTW1mLevHlt/txutyM5OVn6sK1SqbBnz55QtzEhp5Np9QEYlfJMJRNMOjU0KiWMHjNK3AxiToSYSpZSW4hxvfOQpA/9CNqAwkz885TfQjV3Ch5P+gNmFk7CHkcprt/8D/RZeiPml/0oTWk7mo32vej17c3YZlyHG7VX4dOhj8CsYmJ4VCn7CfhqKtBlEjDhjeDCk0RERHTyQcyOHTtw3XXXYc6cOW3e56233sLq1avR0NAgbaeffvqxDkknmNiv0vlky4cRRFAqRmM0bpM0EuMXa1RQm2o99fjRugn2nZk4d0CnsJ3nxrN64PLhXTD79QpcproYP42Yg62jXkXfpM6YvP4vOHv1/VLAIuxyFOOFAx/j0rV/wtAVM1FqdeKKitvw6rhrmcgfbeqKgS8uBTIGAme/wwCGiIioI0FMr169cMkll7T5c/Gt74svvogHH3wQH330ETQaefI34nEkRqWVN4gRUoxaKBoM8AZ8qPLYZG1LtPumajV88ENRlIfzQjyVrCURfLwwYwT65Cdj8j++x84Sm1TF7LPBj+GzQY9JOTkDl9+KLkuuRs8fr8ed219EcX0NTJsGY8TW6Xht2sSwtY3aye8LTiETC42KKWRqef/fExERxYIOfd3ncrlwxx13YMWKFZg+fTq+/PJLvP76623e/8CBA5g48fCHqMmTJ0tbtLPZIvsBvrrOCY3SDX1ADau1fQtN2u225r2+cdHKk2XWK+GpVYvsfmyr2Au1sQvira9D5cNDPyLFlYkuSdlIVnuO+Ls1PS6xt1o7PgryrxsGYMpzK3H2X77Cv28agiFdU3GGuid+6DML/y7/CvtdFTgz/1QYqjrhd29sQzeTBm/+djic9XY461u3KZJUNhssjef2tfO5HYuO1df6TbOgL12KurM+gdelA7i4bEK+hoSKmOLdtBdTvMMt0fs7khK9r1u9f6jD+zqZ6H0dTiJFpWWaSnl5uTxBjF6vx0033SRtN998M8aOHSsFNYMGDTrq/QsLC6WFbWJRamrkytDanV5k6gKw6EztPq+zJrhYptlsafcxMpONaHAGj1Ov80asDyLZ16Egptp9a9sAHCzABYMLj9p+S20wV8Viaf/foyVxiEV/Oh9XzF6CKXNW4tZze+N3F/VDtlmHP6ZPR4XNiX9+uQ3PfbEBw7pn4O3fjkZWskH+vrZYmvtBehAJ5Kh9XbEG2PQUMORhmHtfIEez4lKsvYaEks8XrFpoNpv5mh2HErqvvZF9/0jovg6jGTNmSFuTSZMmtftYIZt4PXz4cFx++eXYtm1bm0EMHZ/P75dyYjLUXphUZlnbIha8tFYGq5SxQlnb1tl3o9RdDf3eYTh7bG7EzpuWpMMn95+F577Yhqc+3oQXv96OPnnJUECBrcW10KqVuPvCvvjDpadBo2Y19ajj9wLfzQDSTgWGPCh3a4iIiGJKSLNHc3JykJ2dHcpDJhwRwAg+pUf2nBixVkztPh8yNFwr5nhVyfTQw1Cdi9N7ZkT03DqNCr+f1A/XjeuOj34+iI0HgkPs147rjsmnd0a6WRfR9tBJ2PAsULUemLwSUDGfkIiIKOxBzIIFC3DhhReirKwM9fX16N27N7xer1ReecyYMe05JLVI6he8iigIYpK0UntydWkciTmGzyt+RoatC/p1y4JRJ09VqUyLHjMm9JTl3NQODZXAqkeBfrcCWUPlbg0REVHMOeYck3379uHzzz/Hrl278MMPP0i3ORwOzJw5E0VFRfjxxx8xatQo3HfffXj++ecxe/ZsVijrILGiuuBRuOUPYkxaWOtd6KRL40hMGyrdtVhRuxX1O7NwZl+OQtIJWvUXUd8RGPYnuVtCREQUk475tXGXLl3w/vvvt7rNaDRKAYzQtWtXXHHFFeFtYYKprguOxLjgglGlkz0nxuXxI1Odil3OYlnbEq2W1mxGAAG49mdh7K8YxNAJqNkBbH4RGP4YYMiUuzVEREQxidm+UUaMfAiuQBSMxCQFk/pTFSkciWnDattOJAWSYPRaMKx7utzNoViw4g+AsRPQ/w65W0JERBSzuCx0FI7E6DUq1PucsgcxKaZgEJMUMKPUZZUWN+VK762tse2CyZ6FEb2yoFWHf00GinGVG4A984GzXgfUR5a8JiIioigciSkuLpbqQc+dOzeSp425nJjUJA0cfpfsQUx6UnA6m9FrhjvgQbUnuIgaHbbatgN1RRaM7Zsjd1MoFqx9AkgqBHpeJXdLiIiIZCNiARETiNggJoKYvLw8abHLadOmRfK0MRfEpFiC3+jLHcSIdUgEtcsk7TmlrLVDziqUuq3wVaRibJ8suZtD0a52N7DrPWDQfSypTERECW3atGlSTCBig/ZiTkwUTiezWIJ/FrkT+5ONGqiUCigagtNeShjEHDEKIyTZszGwS5rczaFot3YWoM8A+twgd0uIiIhiHoOYKGOtcyEpCVExEiPyX8Riib66YDsOca2YI5L6dV4jhuYWQK3ifyVqm6KhFNj2H2DAPcyFISIiCgF+8orCkRhTlAQxTXkxtXYf0jRmjsQcJalfWZ2O03uwTC4dm27XG4BSA/S7Re6mEBERxQUGMVGYE2M0BaIniDHrUFUnFrxM50jML/xUuwPeslQM75Ehd1Momvk80O1+E+g1HdAly90aIiKiuMAgJgqDGF3jbBOjUv4gRiT3V9ldyNWlcSSmhVJXNco81VBVpzGIoWPb9zGUDSXAqbfK3RIiIqK4wSAmini8ftidXuj0/qgaialuHIkpcXMkpmU+jNBV2RkZZvn/ThTFNj0Pb8bpQMYAuVtCREQUNxjERBFrvUvaa6IpiGkxEiNKCtPhIEbt0WNUfle5m0LRrHorUPwdnD1nyN0SIiKiuMLFLqMsqV9Qaf1RlxOTq02TRmICgWC+TqL7qWYHUJmGET2Z1E/HsOUVwJAJT8FFcreEiIgorha7VEOGxS7p6ESwICg1XmgVGqiVwUUv5ZSWpEWd04sMVRZcfg+sXjvSNBYkup9rdkBRlYfh45kPQ23weYCd7wC9rgZkXvOJiIgo2ha7FJsIZNqL08miiMg9kWh8si902XIkRjD5zNK+hBXKUO6yotxXDZM9E33zWW2K2nDwS6ChAuh9jdwtISIiijsMYqKItXE6GdTeqJhK1pQTI+g8wcVrGMQcTuofZO4JlZL/hagN294A0gcwoZ+IiCgM+AksykZiko0aNPhd0RPENFbeUrqCdZ8PscwyVtl2QunWYVxhd7mbQtHKWQ3s+wQ45Vq5W0JERBSXGMREWWJ/qkmLep8zioKY4EhMXV0AKeokjsQA+KFiKxRVaTidSf3Ull3vAgEf0PNKuVtCREQUlxjERNlIjFhcMpqCGLNeDbVKIZVZFmvFcCQGWGvfBWV1OoZ1Z1I/tWH7m0Dh+YAxW+6WEBERxSUGMVEYxDh8zqhJ7FcoFMG1YkSZZV1awo/EVLprURmoRp4/X/pbER3Btg8oW8lRGCIiojBiEBNFKu0uZJijaySmaUqZCLA4EnM4qf+MtN5yN4Wi1e4PAPH/t8vFcreEiIgobnGxyygLYtKjNIgR08k4EgMsq94GuLU4u3MvuZtC0WrX+0DnCwBtsCw5ERERtcbFLuNMpc2JDIs+6oIYMW1KBFj9delSEBMIBKRpZono+7ItUFWnYcRAJvXTUdTuASpWAYN+L3dLiIiIohYXu4wjHq8f1no3MqMwiMk061FpC47EiPLPtd56JKpN9Xuht2XilE5c5JLamEqmNgCdL5S7JURERHGNQUyUEDkngsiJcfhdUZPYL2Ql61Fuc0pBjJCoU8rsXgeqlVXopSuEUpmYI1F0HLvFVLKLAI1J7pYQERHFNQYxUUJM1xKiMbFfjA5V2p3I0QSDmERN7t9o3yvtR2YyH4aOonY3ULEG6DFV7pYQERHFPQYxUUIECUI05sSIIMbrC0DvTZKulyRoELOoeCvgV+CCrn3lbgpFo70LglXJCifK3RIiIqK4xyAmSlTYgkFMapIaLr8nyoKY4NS2+nogWW1K2OlkS8q2Q2lLxsgeuXI3haLR3o+AgnM4lYyIiCgCGMRECZE4r1Urodb6petGZXTlxDQFWiIvJlGnk2127EOKKwspJq3cTaFo01ABlC4Fulwid0uIiIgSAoOYKFvoUiT1C9E0EpOVbJD25bVO5GrTUOJOvJEYUVa6XFWCXrrOcjeFotG+T8WTBOhykdwtISIiSghc7DKKcmIyzMF8mGgLYsx6NXQapVShLF+fiQPiW+cEs6mmGD61GyMzmNRPR7HvIyDnDMCYLXdLiIiIEmKxS6Uci12KxW3oKCMxlmBlsmgLYsTCllkWvTSdrIexE3Y52v+Ei1Uf7d4g7Sd1O03uplC08TiAg18DXTmVjIiI6ESIWEDEBCI2aC9OJ4sSIkBoKq8cbUFMU4UyMZ2spzEPFZ5a1HoSa8HLJaXboHTpMLqgi9xNoWhTtBDwNjAfhoiIKIIYxERRYr+YTubwBXNijFEYxIhASwQxws4EG43Z4tiHNE82VCr+l6GjVCVLOQVI7S13S4iIiBIGP5FF1XSy6MyJaapQVp6gQYzfH0AZk/rpaPw+YP8nnEpGREQUYQxiooDP74e1PlidLFqDGGkkptaJZI0JmZrkhApi1hdXwGuyMamfjlS2IlhemUEMERFRRKmPd4fa2losXLgQkydPPurP58+fjwMHDkiXtVotbrvtttC3Ms5V17ml6qwisb+sMYgxqqJnnRihKbFf6GnKS6gg5qNdGwFlABd0PVXuplC02f85oM8AsobL3RIiIqKEcswgZseOHbj//vthtVqPGsTs3LkTTz31FJYvXy5dv+SSSzBy5EgMHDgwfC2OQ03BgciJ2eNzwqDUQalQRt1IjMPtQ53TI00p21Z/EIliSdl2IFWB09O7y90UijYHvgAKzgOUKrlbQkRElFCO+Um5V69eUmDSljfffBOjR49uvj5hwgS88soroW1hAgUxmRYdHD5n1I3CNOXECE0VyhJpJGZz/T6keNOjrtgCycxRClSuBTpPlLslRERECee408mOZePGjRg/fnzz9YKCAnzwwQdt3l9MO5s48fAbvhjdaWuaWjSx2WxhPf7u4kppr4cLVfU1MCi00uhXR9jttua93tqhP3Nj29zSfk9xBXKTU1DtsWNPxUGkqpMQS319smodHlRpSjFQndehv0nT4xJ7q1WBaCBHX6tsNlgaz+3r4HNcbto982CEArWW0xE4zmOJtud1PEv0vrbb7c17lSr8I4SJ3t+RlOh93er9Qx3e949E7+twmjdvnrQ1KS8vb/exOvTptr6+HklJhz/Ems1mVFS0vZp7YWGhtLBNLEpNTQ3bseu8pUjSq5GfkwlfDWDWGDt8PmeNV9qbzZaQtL27yiDtHX4NBmX2AnYDFZo6dEspQCz19cn6+UAxfKlWjMkZ16F2WWoDwb0lNH+PUIl4WyyW5n5AFPVDu1QuAbKGISWnxwndPZr+7vEukfva5/M1vx9Hqh8Sub8jLaH72hvZ94+E7uswmjFjhrQ1mTRpUruP1aHEi7S0NCmQaSIup6end+SQCam0pgHZjdO1RHWyaKtMJqQn6aBRKaW29jB2km5LhCllX+/aBehcGJdzitxNoWji9wJFXwOFnEpGREQkhw4FMSKBv7j48AfZoqIi9O/fPxTtSigizyQ7pWmkwxWVQYxSqUBuqgGHrA6Y1UbkaNOw03EICZHUD2CApZvcTaFoUrYScNUwH4aIiCiWgpgFCxbA4/FgypQpWLJkSfPtohTzlVdeGcr2JQQxupGTbGgeiYnGxH4hJ8WAEmuDdFmMxsT7SIzX58dWx37oAjp01mfL3RyKutLK6UDmULlbQkRElJCOGcTs27cPn3/+OXbt2oUffvhBus3hcGDmzJnSqEuPHj1w++23Y9asWZgzZw5GjRqFMWPGRKrt8TWdLCW6p5MJnVIPBzGJUKFs08EaOM2V6K3vDIUiOpLxKUqwtDIREZGsjpnY36VLF7z//vutbjMajVIA02T69Onha10iTSdrMRLTSRedeUViOtm2Q8GKHT2NnTC//EcEAoG4/YC/YkcFAqlWnJF+uIw4UXNp5QH3yN0SIiKihBVdKyomIKfbB2u9uzknJppHYnJTjSixOppHYmq99aj01CJeLdtVCn9yLQYlMx+GWjjwJQAFUHie3C0hIiJKWAxiZFbeuNBlTmN1MofPBaMyOnNiclMM0rop9S4veprypNvieUrZDyU7EVAE0N/MIIZa2P8FkDUUMGTK3RIiIqKEFdEgRlQyE/Wg586dG8nTRn0+jBALIzGdUo3SXozG9DAEyyzvitMKZUVV9ShRF0MBBU5N6iJ3cyhasLQyERFRh4lYQMQELascn6yOL+V+EvLy8mJ2sctwKattaK78Fe1BTE5qsI3SWjE5Fil3J15HYlburIQvowK9DAVSSWmiVqWVGcQQERG127Rp06RNtsUuqePKapxQKRXSYpLRHsSI6WTCoer4r1C2clcF1NnVGJXWR+6mULRVJROllbOGyd0SIiKihMYgJgpGYrKS9dJikh6/F96AL2qDGLNBA7NejZKaFkFMfXwGMUt3lcBlrsLpyafI3RSKJiytTEREFBUYxETZQpeCMUqDmKNVKNvpOCSVWY4nonDB+vpdUlI/gxhqVVq5Yg2nkhEREUUBBjFREMSIkZiWQUy0jsQ0rRVzeMHLTrD7HCh31yCe/LSzEp70ChgUOvQzMamfGh34iqWViYiIogSDmGgYiWmR1B/tQUynVAMONQcx8Vlm+cdtZVDnVGFYci+oOW2IWk4lY2llIiKiqMAgRmZFVQ7kpxtjJogR08kONU4n627sFJdBzA/byoHMKpyewqlk1KK08kGWViYiIooWDGJk5HT7UGl3IS/N2LzQZbQHMZ0zTCiudsDr88Og0qFAnxlXQYzD5cXPhw7CobExH4YOK/sJcFkZxBAREUUJLnYpo+LGEY38NJO0r/cFp2kZVcFyy9GoIMMEnz/QPKVMLHoZT0HMz7sr4Uwtky4ziKFmLK1MREQUVYtdKuVY7FIsbkNAcVUwiMmLoelkhRnBgOtAZZ2072mKr7VifthaDm2uVVrIM1/P3AdqWVr5XJZWJiIiCgERC4iYQMQG7cXpZDIqqq6X9vlpsRjE1Dcn9++KozLLP24rhz7PylEYOsxRBlSs5lQyIiKiKMIgRkYityQtSQejTt0cxCiggF6pRbQyaNXItOhbBTGi3SWuasS6BrcXP+0pR62plEEM/aK0sphLydLKRERE0YJBjNyVyRpHYQSH3yXlwygUCkQzkdy/vyIYxJxiKpD2m+v3Idat2l0Fp8kKF1wMYuiwA58Hc2GMWXK3hIiIiBoxiJFRUbWjOR9GECMa0TyVrOWUspYjMRmaZCyxbkSsW7ylDPpOViihxFBLL7mbQ1FVWvkCuVtCRERELTCIkXk6WcuRmFgJYkSFsoNVwSBGqVDizNTT8H31BsS6RZtKkN7djn5JnZGkDi5ASgmubGWwtHJn5sMQERFFEwYxMiquqm9eIyaWgpjOjUGMz++Xro9LG4CVtdvgaCxMEIuq61xYs6cazpRyTiWjw/Z/DugzgMyhcreEiIiIWmAQI5M6pwc1Dg/yW0wnE4tdxkIQU5hpgtcXQEnjWjHj0wbAE/BiWc0WxPJUMp/KjUOBEgYx9IvSyuextDIREVGU4WKXMib1C3mNC102jcQYldG70GWTwvTWZZb7mjpLeTHfV69HrFq0sQR5vRrgh59BDAXVHwIq1wKdmQ9DREQUSlzsMsbzYYSWIzGxlBMj7G8MYkQ1tXFp/fG9NTbzYsQaNyKIye3lQJLKgL5JhXI3iaLBgS/FsxsoZGllIiKiUOJilzFsf0UdVEoFOqUeTiA/4CxHnph/H+WS9BpkJ+uxu9TefNu41AH4qXY76r3BKWaxZEeJTaoU502vkKqSqRScOkSN+TDZpwP6dLlbQkRERL/AIEYme8rrUJBuhFatah4N2FlfLJUsjgU9cizYVWprvi5GYkRezPLarYg1324qhUatwJ7APk4loyCfByj6hlPJiIiIohSDGJnsLa9D1yxz8/VDrippscteMRLE9Mw1Y2fJ4ZEYkReTqUnGdzGYF7NwYwlOGwCUuKukIgVEKF0GuG1AIUsrExERRSMGMTLZW25H16yk5us7HcHEpp6mWAligiMxfn+gRV7MAHxvXR9zVeIWbymFofchpKiTGMRQ0IHPAUMWkDlY7pYQERHRUTCIkYGYOhYciWkdxIiV4rsZchELeuZY4HD7cMgaLFAgjEvtH3N5MSKh3+XxY49xCyZljoBWqZG7SRQN9n8RHIVR8CWSiIgoGvEdWgYVNifqnN5W08lEENPZkBUzH6LFdDKh5ZQyMRLjDfiwrDZ21ov5ZHURuvfyY6fzIH6VPVru5lA0sB8EqjdyKhkREVEUYxAjU1K/0D279UhMrCT1C10yk6BRKVsl9/cxFSJLm4Lvq2Oj1LLH68eX64qROaBcKm19bvoQuZtE0WDfx4BSzdLKREREUYyLXcpATCUTurSYTrYjhiqTCWqVUpoOt7NFECPlxaQOiJnk/h+3laPW4UGRZRsuzDgdBlX0LzRKEQpiOp0J6FLkbgkREVFcmsvFLmPT3jI7spL10norgj/gx+6GQzFTmaxlcn/L6WRNpZZ/tm1HXQzkxXy65iByCnzY5tqHyZxKRoKrFij+DuhyidwtISIiilvTuNhlbPplUv9BZwVcfk9MjcQcLrN8eCRGECMxUl5MzWZEM1FV7dPVRcgfXAWdUoMLMobL3SSKBge/AvweoOskuVtCREREx8AgRqacmG4xXF65ySmdkrG/sh72Bs/h20wFyNam4ntrdOfF/LS7EoesDahM34Hz0ociSW2Qu0kUDfZ+BKQPAMyd5W4JERERHQODGBnKK4vRi+7ZrSuTqRUqdNHnIJYM6Jwq7TcesDbfFlwvpj8WVq1FNHt/2T5k5fqxyb2LU8koyOcJrg/TlVPJiIiIEi6IqaioCPUh40p5rRPWejdOyTucNLyjvghdDTlQK1WIJb07JUOrVrYKYoQp2WOlvJiVNVsRrVXJ5v90AD1G1ErB48WZI+RuEkWDkh8AVw2DGCIiohigPtYP58+fjwMHDkiXtVotbrvttiPuY7fbkZ+fD5vNBqVSiWXLliEzMzN8LY5xW4trpX3f/OTm23Y6DsVcPoygUSulx7F+f+sg5tKskehlzMcT+97DhwMfQbRZtKkEVXYXrJm7MMEwCKmaw6NilOBTyUz5QMYguVtCRERE7Q1idu7ciaeeegrLly+Xrl9yySUYOXIkBg4c2Op+b731FlavXi0FMiKIEcEOtW1LUQ10GiW6/WKNmAsyhiEWnVaYig2/CGJUChXu7zoVN25+Glvq9qNvUnTlF7y7dC96dNFgtWMrXupyh9zNoWgQCARLK4uEfoVC7tYQERFRe6eTvfnmmxg9+nCuwIQJE/DKK68ckd/x4osv4sEHH8RHH30EjSY2VpuXeySmd24yVMpg13v9PuxpKInJkZimvBjxmNxeX6vbp+dOQJ4uA7P2vY9oUmFz4pPVReg9MjgidknmGXI3iaJB1UbAvo+llYmIiGJ9JGbjxo0YP3588/WCggJ88MEHre7jcrlwxx13YMWKFZg+fTq+/PJLvP76622eTExNmzhxYvP1yZMnS1u0E1PlQmXj/ip0zzTCag2OXux1lkoliXP8yc23hYLdbmve663HnDXYIV1S1XB7/fh5WxH65lla/ezWrAvwSNE7+F3GpcjXZUS8r4/mtW/3IqBy4Tvt17jAPBQaB2B1hK7fj/e4xN5qjY5v+sPd10ejstlgaTy3L4TP947Sb34TOk0yao0DgDC0S46+TlSJ3tdiinfTXqUKf55lovd3JCV6X7d6/1CH9/0j0fs6nObNmydtTcrLy9t9rDY/3dbX1yMp6fCUJ7PZfETSvl6vx0033SRtN998M8aOHSsFNYMGHX1OeWFhobSwTSxKTQ1W4upwZbLSelw8tHPz8VZW7pL2g7N7I9XQ8XM0cdZ4pb3ZbAlJ29sysp94jqzEXqsPo05tfZ47zZPxdOmHeLXmGzx7ypH5VG0JV3tF/7+7shi55+zCPn8Dnj/1jpD2+bFYagPBvSW8f4+TFfG2WCzN/YBo6Qcxlaz4U6D7ZUjNyA7baaLp7x7vErmvfT5f83t2pPohkfs70hK6r72Rff9I6L4OoxkzZkhbk0mTJoV+OllaWpoUyDQRl9PT09s80PDhw3H55Zdj27Zt7W5MvBPrktgaPOjTIqlfVCbTKjQo0MdmMQSzQYOeuRas3l11xM/E2iu/LbgE/yr6AhXuGsht8ZYybHftw7aUn/Cn7lej0JAld5MoGlRvAmq2A92nyN0SIiIi6mgQIxL4i4uDizAKRUVF6N+//zEPlpOTg+zs8H2TGQ9J/ULf/JRWlcm6G3OlZPhYNbJXJpZuP/pw4G8LL4VSocCcAx9Bbs99sQWqMT+jj6kQd3f+ldzNoWix631AlwLkny13S4iIiKijQcyUKVOwZMmS5usLFy7ElVdeKV1esGABPB6PFNhs375dus3r9WLPnj0YM2bMiZ474Ww6WAOTTo3CdFOrymS9YjSpv8noU7Kk5P5Ku/OIn6VrLfhN/gVSEGP3OiCXbcW1+NyxBHWWUrzY9w5olOHLE6IYIqaS7f4A6HopoGJlRSIiopgPYnr06IHbb78ds2bNwpw5czBq1CgpQHE4HJg5c6YUwPz444/S7ffddx+ef/55zJ49mxXKjmH1nioM6poGpVLRKoiJ1cpkTUb1Dk7LWr7j6Aud3tN5Mhr8Lly18Qk4fW7I4cmvVsEzeDWuzjkbY1JPk6UNFIU4lYyIiCgmHfPraFFx7JeMRqMUwAhdu3bFFVdcEb7WxZlVu6sw5YzDa6a4/R7sayhDT1NsBzEFGSYUZpiwdFs5Lh5ScMTP8/WZ+HDAnzB5/aOYuOZBfDToEVjUh0ejwm1XaS3e8fwPeo0K/zjlNxE7L8WAXe9xKhkREVE8jcRQaJVYHSiudmBot8OlhsX6MH74Y34kpmk0Ztn2o4/ECBMzh+PrIY9jjX0nJqy6H5Xu4Dot4VbjqcPZyx+Cp+tuzD7lVmRqD+cjUYIL+IEd7wDdJnMqGRERUYxhEBMhq/YEq3cN7X64wtvO+mDhhLgIYk7Jwvr9VlTXudq8z+jUU/H90L9jf0MZxv78OxQ52w56QmGdbTf6/3gr9qv3YqbiFtzU+bywno9iTMnS4AKXva+RuyVEREQUzUGMqHYm6kHPnTsXiTiVLDfVgLw0Y6t8GINSh066tktXx4rzBnRCAAF8ue5wRbujGWTpgR+HP4M6XwNGrLwT75Qsgl98Ix5i/yn+Gmf8dCdqa4HeP12Bv4/jSuz0C9vfBMxdgNzRcreEiIgoocydO1eKCVpWQo7qICYvL09a7HLatGlIxKT+od1aByuivLIYhVEqYn9ALCfFgGHdM/DJ6mC+1LH0MuVj2fDZGJbcC9M3PolhK2bi26q1HW6DKBrwfulinLf6D7h+898xWn06fAvOwezJ46HTxG4JawoDb0OwKlmv6UAc/P8jIiKKJSIWEDGBiA3ai+/eEeD1+bFmTxWGHBHEiMpknRAvJg0twMINJah3eY97XynZf+Aj+GHY09AqNZiw+n5csOZBLLNvgdcfXHH6RPgCPvxUuw23b52DToun4dcb/gq7twGv9b4PRQtOw8T+hTh/YOxP16MQ2/cJIPKyel8td0uIiIioHbhYRoTyYexOL87s23oh0B2OIlyVexbixcVD8vHQu2uxcMMhXDKs8IR+R+TJiFGZ+eU/4oGd/8ZF2/6MtN1mXJAxHJMyz8Dw5N5w+Fyweeth8zlQ66nHPmcZNtXtk7YtdQek8s1iSt7NBRfguk7norepAL95eRlsDVb845phYX/cFIO2vwVknw6k9JK7JURERNQODGIi4NuNJUg1aaU1Ypo0+Fw46KyIi6T+Jt2yzTitMAXvL99/wkGMoFAoMDl7DC7LGoVFRauw2LkZH1eswNsli456f5NKj36mzhhg7iYFgYPMPTAm9VSoFMEpY/NW7Mfcpfvw8m9GSKWfiVqpKwIOfAGMmSN3S4iIiKidGMREwKJNpRjXLwcq5eHZe+vsu6V9b+OR66rEsuvH9cDv316Ng5X10voxJ0PkBg1N6olzCobjsZ7XY19DKbbWHYBZbUSy2gSL2iht4nJbeUQbD1hx26srMGVEZ0wb1TVEj4riypZXAbUhmA9DREREMYk5MWFWU++WKpOddWpOq9ufP/gxuhpyMCLlFMSTaaO7wqRT49Vvd3b4WF0MOdL6MmLK2WnmruhsyEaqxtxmAHOo2oErZi9Bz1wL/nnj6dIID1ErPg+w9V9Az6sArVnu1hAREVE7MYgJs8VbyuAPBHDWqbnNt4n1Ud4rXYw7Cy9rngIVL5L0Glw9thte/24XHCeQ4B8qFTYnLn7yW/j9Acy9cyyMOg4yUhsJ/fWHgFNvlbslRERE1AFcJybMPltThN6dLK1yM/554GMYVTrckBefiy/efE5v1Dm9ePbzrRE5397yOpzz6DeodbjxyQNnnfQ0Nkogm18Ess8AMgbI3RIiIqKENZfrxEQ3MRLxyeqDUn5GkzpvA14u+gy/ybtAyvWIR12zkjDz/FPw9KdbsL+iLqzn+n5zKSb85Wvp8tcPnYMeOZawno9iWM0OoGgh0O8WuVtCRESU0KZxnZjo9vnaImlEYurIwwnmrx/6CnafA78tvBTx7L5L+iE1SYu7/vMzfH5/yI9f5/TgwblrMWnWt+iXn4xvHj5Hqo5G1KZ1fwcM2UCPqXK3hIiIiDqIQUwYvbt0H07vmSGNTDQtzDh7/4eYkj0WhYYsxDORG/PCjafj202lePi9dSE7rgiIPli+D0Pu/xSvLNyB/7t8ABbcNx6ZFn3IzkFxqL4E2PYGMOAuQM3nChERUaxj9nOYiEpZCzeW4KnpQ5pv+7h8OfY0lGBu/z8gEZzdvxOeuGow7nt7NdRKJf40pX+rMtMnw9bgkYKXOV9sxe6yOlw4OF86dpfMYIBIdEwbZgeDl35M6CciIooHDGLCZM6X25CkV+PXLdYqeXr/fIxOORXDk+OrrPKx3HJOL3h8fjz87jqs3lOFv04bhIFdDi/6eSwlVge+31KGT1cX4av1xfB4A7hkWAH+fesoDOmWHva2U5xw1QKbXwoGMLpkuVtDREREIcAgJgyq7C689u1OKbndYtBIt/1Uuw0/1mzC/AH/h0Qi1mq5Y2IfnFaQit+9tQpj/u9LDO2WjrP75+KUTslIMWmhUSnh8vpQWtOAXcVV2FPpkhatFCMuwqAuaXh48gBMPr0Q+emsPEYnadPzgM8F9L9T7pYQERFRiDCICYM5XwZLC996Xu/m257ZPx/dDLmYlHUGEtH4U3Pw098uwIKfD2DBzwfxr0U7pWDvl9KTtOhXkIpzB3TCiJ6ZGNMnm/ku1H5OK7DuKaDvTYDp8FpNREREFNsYxITY1qIaPPf5Ntx9YR9kmIMfvv954CO8W/o9Xujz27hb3PJkqFVKXD6ii7QJ1XUuqXqbmG6mVSmRnaJHvd2G1NRUuZtK8WLtLMDnAYY8JHdLiIiIKIS42GUIicpZt/97JbpkJeH3k06Vbpu19338dtvzuKfzZNySf5HcTYwqaUk6aRHQ7tlmaYFKrTpxAzwKg7oiYOOzwYpkxmy5W0NEREQhXOxSLcdil/EoEAjggXfWYPWeanz+xwnQaZR4ZNeb+POet/Fwt6vw5+7XSPkhRBQhy+4FNBZg4O/lbgkRERH9YrFLsYlApr04nSxEAczfP9mCl77ZgdnXDcPIXpm4b8e/8Pf9/8PjPW/AA12vkLuJRIml+Dtg13vAWW+wIhkREVEcYhDTQQ6XF/e+tQpvLdmDP152Gq48sxDXbXoKb5YsxLO9b8UdnS+Tu4lEicXbACy+BcgZCfSeLndriIiIKAwYxLSTx+vHhz8dwCMfrEO5zYmXfzMCY4YmYczP92BL3QG8c9oDuDL3LLmbSZR4Vj4M2PcDEz8CFBFN+yMiIqIIYRBzEupdXizdVo5vNhzCR6sOosTagPMHdsLHVw5GsXYfhiy/H0aVHkuHP4NBlh5yN5co8RR/D6x/GjjjSSA1cRaVJSIiSjQMYlrktYhyv6Lsb1mtE8XVDhRV1Uv7PSU12FnuwO4yOwIBoCDdiIsG5+OG8T3QKz8Jcw58hAc2/htjU0/De/0fRIaWc/CJIs5RCnwzDcgbBwy4R+7WEBERURip4zEYESMm1jq3FJBY61vuD1+21rmk69b6w3uvL9DqWEatCnnpJmSbNTinfy7uLuiL4T0y0LuTBZ6AF28c+gYX/TgXB5zluLvzr/BkzxlQK1kmmCjifC7g618HL5/zX4D/D4mIiOJa1AUxfn8AdS4vauvdsDV4UONwo1baPLA17mt+cT3486bLHmnxxF9SKhRITdJKa5OkmsRei65ZSRiSlNZ8W9PPxQrxeWlG6TZRFtlqtTYvwOj2e/Bq8Rf4655g8DI1Zyw+6/Yo+iUFF3AkoggL+IFvbwDKVgKTFgHGHLlbRERERPEUxKzbWYzTpv4R3XufiuyCrlKQIgUf9cF98LoHfjFn6yi0aiVSTFpYDBqkGLVINmqk650zTUiWrgdvkwISKVDRITVJJwUsZr0GSmX712mp9tjw8sHPMOfgRyh1WRm8EEUD8Vrxwx3Azv8C574H5I6Su0VERER0Aotdii1mFrsscyhgyB2GQ14t6srsUtCRm2LAKZ2SYTFqDgciBnFZg2RTy+ta6LWRnyKy23EIT+6fi3cqv4cPflyTezbu6TIZp5gKI94WImrB7wWWzAS2vAyMewXoMVXuFhEREVE8LnZ53oBO+PjZ6F83ZZejGPPKfpS2n23bka424/ddpuC2gouRpQtOKyMiGblqg0n8B78Gxv8b6HOD3C0iIiKiRM6JkUOpqxrLa7Zgee1WfFm5Chvr9sKo1GFixjApYf9MbV90Ss+Wu5lEJJSuABZeCTirgAs/BwrPlbtFREREFGEJGcRYPXZ8UfkzPq/8CT9aN2O/s0y6PV+XgTPT+uOR7lfj/Iyh0pov0v2tVplbTERoqABWPghseRXIHg5cvBBI7iZ3q4iIiEgGMR/E+AI+1HmdqPTUosxtlZLuxchKlccGvVKLJLUBZpVB2u9rKMPH5cuxpGYjfAE/Bpt7YHL2aJyR3AcjUvogX58p98Mhol+qKwY2PQ9segFQKIDRzwKn3gooY/7li4iIiNrpmJ8C5s+fjwMHDkiXtVotbrvttnbd55dEmWK7twF2n0Pa27yO5stiL12Xbmto/Hl98+XDPw9ervc5j3xQChXSNGa4Gs/jR7Dksk6pwYS0QfjnKTNxUebpDFqIopXTCuz/FNgzD9j/GaA2An1mAIP/ABgy5G4dERERRWsQs3PnTjz11FNYvny5dP2SSy7ByJEjMXDgwJO6T0tfVa6CfuGFUnBxLCIfxaw2wqw2wKISe6M0mpKrS0Mvdd7h26RRluA+Q5OMHF0qsrWpUgCjVCibF7+UghmfAyaVvnmKGBFFSYlkMU2sZhtg3QpUbQRKlwJVG4Lrv2SfAYx8GjjlOkBrlru1REREFO1BzJtvvonRo0c3X58wYQJeeeUVvPDCCyd1n5YGKHV4zNRbmualV+pgUGlhaL6skfbiZyoxZeR4fAC8DWKifOMNRUe9mziSHgGcUOjS1vo0jnqgzNTyjidytBO/XyD0xzNU2jEgazUMB51AQ1sf/kJ83sZzd+SYWocDMBpDdrx23y/E582oqsPN2VuRse8AYDNFRft0v+zrUJ874A3+H/U6gpvHAVQXAxMBrL4aWGUHHGWA3x28v0IFJPcAckYCp84ECs8HkvJO7FxERESUUNoMYjZu3Ijx48c3Xy8oKMAHH3xw0vdpKce2D+eufQax5lgfOaNVCoCLegDY9HbEzx2QQscTcJRg1RgAAkfcfKKLlLb/vB063gncr1MggEc7B6DdpkTguOePzOPVBwJHaUv7F4T9pYAYDVWbEFAZEFAbAJUBcPugDgBubVf4ck5BQJ8FvyEHPktP+JO6Aird4QOIAds4Kaphs9nkbkLCSPS+ttvtzXuVKvxrqyV6f0dSove1ymaDpbEffOrwvjckel+H07x586StSXl5eeiDmPr6eiQlJTVfN5vNqKioOOn7tFJ4AXBT20FOWD9oduB4ojpZampqdHxgPsHjleytxGv/Nw83/GUycrtmhqB9J64jRzx6X8e+9fuqMeb/vsQPfzkfA7ukIRrUhLmvj/o82LIGmDUE2uv/AvQdjEQSj8/raJXIfe3z+ZrfjyPVD4nc35GW0H3tFSEMYLFYREeE/XQJ3ddhNGPGDGlrEpbFLtPS0qQgpYm4nJ6eftL3aUWpAjQtp6/ECJU2uMUSKThRBPdhCFSIiIiIiOQSzH4/CpGcX1xc3Hy9qKgI/fv3P+n7EBERERERRSSImTJlCpYsWdJ8feHChbjyyiulywsWLIDH4znmfYiIiIiIiMKhzelkPXr0wO23345Zs2bBYDBg1KhRGDNmDBwOB2bOnIkBAwa0eR8iIiIiIiJZFrucPn36EbcZjUZp2tix7kNERERERBTx6WRERERERETRiEEMERERERHFlIgGMaKSmagHPXfu3EieloiIiIiIooSIBURM0LLKcUhzYkItLy8PH3/8cSRPSUREREREUWTatGnS1pHFLjmdjIiIiIiIYgqDGCIiIiIiiikMYoiIiIiIKKYwiDkB8+bNk7sJCYN9HTns68hhX0cO+zqy2N+Rw76OHPZ15HQksZ9BzAngkzly2NeRw76OHPZ15LCvI4v9HTns68hhX0dOzAQxHWno8cRi2eZwtvmrr74K27Fjsa/D2e5w9gf7OnLHZl/Hz7HDJVb7g30dH8cOp1h8XWVfx8ffsSMYxMgonG3+8ssvw3bsWOzrWP3Pzb6O3LHZ1/Fz7HCJ1f5gX8fHscMpFl9X2dfx8XfsCEUgEAhE6mT5+fkYPHhw2AIksQ5NLB07FtvMY0fuuDx2ZI8di23msSN3XB47sseOxTbz2JE7Lo8d2WOHs81r1qxBUVFR9AcxREREREREHcXEfiIiIiIiiikMYoiIiIiIKKYwiCEiIiIiopjCIIaIiIiIiGIKgxgiog567rnnpMotWVlZeOaZZ5pvf+211/Dmm2/i4Ycfli43mT9/PmbPni1tL7zwgkytToy+buv+FPq+brJo0SJcf/31EW5t4vL7/Vi3bh327t0rd1Pi9rktFr985ZVX8NJLL+Gee+6R+pzkp0aCE0/kJ598Eh6PB3/4wx9w9913Nz9hq6qqpCfqjh078Pe//x1KpbLVi/Tbb7+N119/XcbWJw7xd9iwYQOSk5PRtWtXuZsTl8/t4z3n6ehWrFiB1NRU7N+/H5988gkuv/xyTJgwAQ6HQ7r+4YcfQhSBPO200zBgwABYLBY89dRTWL58ufT7l1xyCUaOHImBAwfK/VDirq/Fc/9o9+/fv7/cDyXu+nrIkCHS74mfP/bYY+jcubPcDyFuXrPFh2m1Wo2dO3dK73833HBD8+/897//xccff4z777+f741hem737dsXTz/9NJYuXSr9/h133CGtxXfBBRfI/VBiwrvvvit9Vt6+fTv+9Kc/NX/BIb7MO3DggHRZq9XitttuO+btRxVIYMuXLw+8+eabAY/HE5g/f35AqVQG1q9fH3A4HIGRI0c23++3v/1t4LPPPmu+Xl9fHxg3blzg2muvlanlsevZZ58NdOrUKZCZmRl4+umnm2//3//+F3j55ZcDL774YuDuu+8O+Hy+5p+98847gV//+teBNWvWyNTq+H9uH+85T21bunRpq+uDBg0KvP/++4H7779f2prcfPPNgdtuuy3w0EMPBe69995W/yduvfXWiLY5Ufq6rftT6Pu65fP5n//8J98fQ/SaLW6/9NJLpfv4/f5Av379AqtWrZKui/fLM888M+D1emVufXw/t2tqagJpaWmB8vJy6Xbx3P7xxx8j3u5YtGfPnsCnn34qXd6wYUMgJSVFurxjx47AiBEjmu83adKkwNq1a9u8vS0J/TWr+Mb56quvlr7huOyyy6SIW0SKbrcb27ZtQ0VFhXQ/m80mjQA0efXVV6XIndr/7cfLL7+Me++9VxpdaWhokL7l+M1vfoNbbrkFXq9X+pZDEEO3Ygj3nXfewaBBg+R+CHH73D7ec57aJkZRWhLfoopvpcXzura2tvn29PR0abrHxo0bpYV/mxQUFEi3Uej7uq37U+j7Wli5ciX69esHk8kU8fbG62v2ggUL0Lt3b+k+CoUCo0ePlkZmSkpKpJEaMR1VpVLJ3fy4fm6L98Lp06dj3LhxePbZZ3H66adj1KhRMrQ89uTk5ODCCy+ULmdkZKBLly7SZTFlTzyXm4iRMPFZr63b25LQQUxbT+RjPWH5Ih25D9Z8kY7cc5sv0qEhpnsMHz4c3bp1w9lnn42PPvoIVqtV+tnBgwdhMBhQX1+PpKSk5t8xm83Nz30KbV+3dX8KfV+L1/HFixdLHzoo/B+sP/jgA2g0GinIER8SxRerdXV1MrQ8MV5HxDQ/o9EoTdvT6/Uytzp2GBr7T3w5LfKMxBfYQltf5p3sl3wJHcQc6w3uaE9YvkhH9oM1X6Qj99w+1u104kG6GDkUz2Hh4osvxoMPPijN5xX5RVu3bpX+DmlpaVIg00RcFh9MKPR93db9KfR9/a9//Qs33nij3M1NmA/W4ou/SZMm4Y9//CM+++wz2O12qVAIhf65LfJjpkyZgv/973/SrBDx82+//Vbu5sdcP4tgZOrUqdJ7Xltf5p30l3xhnQwXI0T+xT333BOw2+3N804vuuiiwL59+6RcDb1eH1i0aJE0z7eyslK6z+uvv845vx0g5j3ecMMNzdcbGhoCQ4cODeh0usBrr70m3SbyBK666qrm+5x77rmBRx99VJb2xvtzu63b6cT94x//CBw8ePCoP9u1a1cgNTU1UFZWFvjb3/4WuO+++5p/Jl5Xbrnllgi2NHH6+kTuT6HpazGPvXfv3tKWk5MTsFgsgdGjR0e8vfH2mt30GnHFFVcEnnrqKel98oknnpDyFh944IHm+4gcU/EaTqF/bot83IEDBzb/TPT77bffHsGWxs9ze8yYMYGPPvooMHXqVCl/rsmCBQukvNy2bm9LwlcnE8S3F2LaUlP0J0oVFhUVSdVVxLZq1SqpWsLq1asxZ84c6T5ieFdUsti9ezd++OEHmR9BbH/70fJbDtHXYlRG9LuYdiaGEpucf/75/PYjTM9tkat0tNvPOussuR9CTHjjjTek56cYBvf5fNLo4eTJk6Wf1dTU4LrrrpPm+opynuK5LqZVNlm4cKFUspNC39fHuz+Frq+bqu0J//nPf/D9999Le+rYa7Zw++23S5v4vPG3v/1Nqu4kZiqIzyRNxCi62Cj0z22XyyXdJj67iIqd4jbmjJ480XdiRo5IDxDVOIuLi5t/Jj5/iKqRhYWFR729LQkfxBztiSyGD4/2hP3nP//Z/Ht8kQ7/B+s+ffrwRTpCz22RcMcX6fYR5ThbTqMRQfl9992Hc889V5oGIgpaiNKpTYUpevToIX0gmTVrljQtREydHDNmjIyPIH77uq37M4gJfV9TZD9Yi9tF/4tp2WLa9ZYtW5oTqCm0z23xZaqYZiamWotyy6L871//+lcZH0HscDgcUhpA05dK4rPe+PHjpWIVR/syLzc396S+5FOI4Rgk8BNZfCv6yyfy448/LlUgE0nn4gkrKmiJJ2zLD9AMYtr/Ij1s2DCpX1t+sB47dqz0LZP4AC0WnhLffIgX7DPPPBObNm2SXqTvvPNOKYfmmmuukfthxOVz+3jPeTo5TqdT+sZJPHcpvNjXkcO+juxrtsh5afpgLT54twwa33//fXz33XfS++KhQ4fw0EMPcW2vDuBzO/S++uorXHvttZgxYwa6d++OESNGSF9QC2KtRfG8FV/mic98omLtsW4/moQOYij6P1jzRZqIiBIVP1gTtY1BDBERERERxRR+pU1ERERERDGFQQwREREREcUUBjFERERERBRTGMQQEREREVFMYRBDREREREQxhUEMEREREREhlvw/1n9K7/e/zsIAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from scipy.optimize import curve_fit\n", + "\n", + "# fit angle = 70 to the multi-peak function\n", + "dataset = df[df[\"angle\"] == 40]\n", + "\n", + "popt, pcov = curve_fit(\n", + " peak_fit,\n", + " dataset[\"energy\"],\n", + " dataset[\"absorbance\"],\n", + " p0=p0,\n", + " bounds=(bounds_lower, bounds_upper)\n", + ")\n", + "\n", + "fig, ax = plt.subplots(\n", + " figsize=(10, 5),\n", + ")\n", + "\n", + "ax.plot(energy, peak_fit(energy, *popt))\n", + "ax.plot(dataset[\"energy\"], dataset[\"absorbance\"])\n", + "# for each peak, draw a vertical line at the peak position\n", + "for i in range(4, len(popt), 3):\n", + " ax.axvline(popt[i+1], color=f\"C{i}\")\n", + "\n", + "# plot the step\n", + "ax.plot(energy, (\n", + " popt[0] * 0.5 * (1 + erf((energy - popt[1]) / popt[2]))\n", + "))\n", + "ax.set_xlim(283, 300)" + ] + }, + { + "cell_type": "code", + "execution_count": 194, + "id": "f72bc1d1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# First, fit the 70 degree data using the full model to get optimized positions and widths (and amplitudes)\n", + "from scipy.optimize import curve_fit\n", + "from uncertainties import ufloat\n", + "import uncertainties\n", + "\n", + "angles = df[\"angle\"].unique()\n", + "\n", + "# Dictionary to save peak amplitudes and their uncertainties for each angle\n", + "amplitudes_by_angle = {}\n", + "\n", + "# --- 1. Fit full model to angle = 70 ---\n", + "dataset_70 = df[df[\"angle\"] == 40]\n", + "\n", + "popt_70, pcov_70 = curve_fit(\n", + " peak_fit,\n", + " dataset_70[\"energy\"],\n", + " dataset_70[\"absorbance\"],\n", + " p0=p0,\n", + " bounds=(bounds_lower, bounds_upper)\n", + ")\n", + "\n", + "# Save amplitudes & their uncertainties for 70 deg\n", + "n_peaks = len(popt_70) // 3\n", + "amps_70 = [ufloat(popt_70[3*i], np.sqrt(pcov_70[3*i, 3*i])) for i in range(n_peaks)]\n", + "amplitudes_by_angle[70] = amps_70\n", + "\n", + "# --- 2. For other angles, fit only amplitudes, with positions and widths fixed from popt_70 ---\n", + "def make_amp_only_func(fixed_params):\n", + " def fit_func(energy, *amps):\n", + " # amps is array of amplitudes, replace amp_i in fixed_params\n", + " params = fixed_params.copy()\n", + " for i, amp in enumerate(amps):\n", + " params[3*i] = amp\n", + " return peak_fit(energy, *params)\n", + " return fit_func\n", + "\n", + "# Get fixed positions/widths from popt_70, only vary amplitudes\n", + "fixed_params = list(popt_70)\n", + "p0_amps = [popt_70[i] for i in range(0, len(popt_70), 3)]\n", + "bounds_lower_amps = [bounds_lower[i] for i in range(0, len(bounds_lower), 3)]\n", + "bounds_upper_amps = [bounds_upper[i] for i in range(0, len(bounds_upper), 3)]\n", + "\n", + "# Plot for all angles\n", + "fig, axs = plt.subplots(1, len(angles), figsize=(15, 5), sharex=True)\n", + "if len(angles) == 1:\n", + " axs = [axs]\n", + "\n", + "for idx, angle in enumerate(angles):\n", + " dataset = df[df[\"angle\"] == angle]\n", + " ax = axs[idx]\n", + "\n", + " # Check for empty dataset - helps avoid ValueError: `ydata` must not be empty!\n", + " if len(dataset) == 0 or len(dataset[\"energy\"]) == 0 or len(dataset[\"absorbance\"]) == 0:\n", + " ax.set_title(f\"{angle}° (no data)\")\n", + " ax.text(0.5, 0.5, \"No data\", ha=\"center\", va=\"center\", transform=ax.transAxes)\n", + " ax.set_xlim(283, 300)\n", + " # Save as None for missing\n", + " amplitudes_by_angle[angle] = None\n", + " continue\n", + "\n", + " if angle == 70:\n", + " # Full fit already performed above\n", + " fit_y = peak_fit(energy, *popt_70)\n", + " ax.plot(energy, fit_y, label=\"Full model fit (all params free)\")\n", + " fit_peaks = popt_70[::3]\n", + " fit_positions = [popt_70[3*i+1] for i in range(n_peaks)]\n", + " else:\n", + " # Amplitude-only fit using fixed positions/widths from 70 fit\n", + " amp_only_func = make_amp_only_func(fixed_params)\n", + " try:\n", + " popt_amps, pcov_amps = curve_fit(\n", + " amp_only_func,\n", + " dataset[\"energy\"],\n", + " dataset[\"absorbance\"],\n", + " p0=p0_amps,\n", + " bounds=(bounds_lower_amps, bounds_upper_amps)\n", + " )\n", + " fit_y = amp_only_func(energy, *popt_amps)\n", + " ax.plot(energy, fit_y, label=\"Amplitude only fit\")\n", + " fit_peaks = popt_amps\n", + " fit_positions = [fixed_params[3*i+1] for i in range(n_peaks)]\n", + " # Save amplitudes and uncertainties (sqrt(diag(cov)))\n", + " amps_this = [\n", + " ufloat(popt_amps[i], np.sqrt(pcov_amps[i, i]) if pcov_amps.shape[0] > i else np.nan)\n", + " for i in range(n_peaks)\n", + " ]\n", + " amplitudes_by_angle[angle] = amps_this\n", + " except ValueError as e:\n", + " ax.set_title(f\"{angle}° (fit error)\")\n", + " ax.text(0.5, 0.5, f\"Fit failed:\\n{e}\", ha=\"center\", va=\"center\", transform=ax.transAxes)\n", + " fit_positions = []\n", + " # Save as None for failed fit\n", + " amplitudes_by_angle[angle] = None\n", + "\n", + " ax.plot(dataset[\"energy\"], dataset[\"absorbance\"], label=\"Data\")\n", + " ax.set_xlim(283, 300)\n", + " ax.set_title(f\"{angle}°\")\n", + " ax.legend()\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# amplitudes_by_angle now holds the fitted amplitudes and their uncertainties for each angle." + ] + }, + { + "cell_type": "code", + "execution_count": 202, + "id": "e325c7ac", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from uncertainties import unumpy as unp\n", + "\n", + "amps = {\n", + " \"angle\": angles,\n", + " \"cos^2(angle)\": [np.cos(np.deg2rad(ang))**2 for ang in angles],\n", + " \"amplitude\": [amplitudes_by_angle[ang][0] for ang in angles],\n", + "}\n", + "\n", + "# Compute the peak width as ufloat\n", + "amps[\"width\"] = [\n", + " ufloat(popt_70[3*i+2], np.sqrt(pcov_70[3*i+2, 3*i+2])) for i in range(n_peaks)\n", + "]\n", + "\n", + "# Correct area calculation for a Gaussian where width is FWHM:\n", + "# Area = amplitude * FWHM * sqrt(pi / (4 * ln(2)))\n", + "amps[\"area\"] = [\n", + " amp * width * np.sqrt(np.pi / (4 * np.log(2))) for amp, width in zip(amps[\"amplitude\"], amps[\"width\"])\n", + "]\n", + "\n", + "# Extract nominal value and std dev using the uncertainties package\n", + "amps[\"area_n\"] = [a.n for a in amps[\"area\"]]\n", + "amps[\"area_unc\"] = [a.s for a in amps[\"area\"]]\n", + "\n", + "plt.errorbar(amps[\"cos^2(angle)\"], amps[\"area_n\"], yerr=amps[\"area_unc\"], fmt='o-')\n", + "plt.xlabel(\"cos^2(angle)\")\n", + "plt.ylabel(\"Peak area\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "955ee231", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/nexafs-process.ipynb b/notebooks/nexafs-process.ipynb new file mode 100644 index 0000000..718291a --- /dev/null +++ b/notebooks/nexafs-process.ipynb @@ -0,0 +1,1261 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + 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785 rows × 18 columns

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" + ], + "text/plain": [ + " scan_id beamline_energy sample_theta tey_signal photodiode \\\n", + "0 59604 250.0 20.0 0.035662 -0.050067 \n", + "1 59604 270.0 20.0 0.060007 -0.050672 \n", + ".. ... ... ... ... ... \n", + "783 59606 345.0 55.0 0.097090 -0.050134 \n", + "784 59606 350.0 55.0 0.104971 -0.050246 \n", + "\n", + " ai_3_izero izero_before_ai_3_izero izero_before_photodiode \\\n", + "0 0.025973 0.048811 99.849720 \n", + "1 0.056219 0.094066 185.235719 \n", + ".. ... ... ... \n", + "783 0.224769 0.311204 560.188773 \n", + "784 0.243334 0.334121 612.170819 \n", + "\n", + " izero_after_ai_3_izero izero_after_photodiode chemical_formula \\\n", + "0 0.051836 109.128996 C8H8 \n", + "1 0.090720 190.336240 C8H8 \n", + ".. ... ... ... \n", + "783 0.279683 497.546969 C8H8 \n", + "784 0.298319 543.050218 C8H8 \n", + "\n", + " bare_atom bare_atom_substrate pd_response_0 pd_response_1 \\\n", + "0 2603.240823 46222.032028 0.000489 0.000475 \n", + "1 2011.348936 39675.158805 0.000508 0.000477 \n", + ".. ... ... ... ... \n", + "783 31010.352992 24029.750777 0.000556 0.000562 \n", + "784 30035.376685 23319.420796 0.000546 0.000549 \n", + "\n", + " raw_abs absorbance_0 absorbance_1 \n", + "0 1.373060 0.000356 0.000346 \n", + "1 1.067382 0.000476 0.000447 \n", + ".. ... ... ... \n", + "783 0.431952 0.001286 0.001301 \n", + "784 0.431386 0.001265 0.001273 \n", + "\n", + "[785 rows x 18 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data_root = Path(\"/Users/hduva/projects/als-nexafs\")\n", + "\n", + "conn = connect(data_root / \"oct_2025.db\")\n", + "df = load_nexafs(conn, \"ps\", tag=\"30\", version=2)\n", + "with pd.option_context('display.max_rows',5):\n", + " display(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "9978662e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.nexafs.set_regions(pre_edge=(272, 281), post_edge=(335, 350))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "8f876304", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df_norm = normalize_by_group(\n", + " df, normalization_mode=\"bare_atom\"\n", + ")\n", + "fig, ax = plt.subplots(\n", + " ncols=2,\n", + " figsize=(10, 5),\n", + " gridspec_kw={\n", + " \"width_ratios\": [2, 1],\n", + " \"wspace\": 0.05,\n", + " },\n", + ")\n", + "df_norm.nexafs.plot(\n", + " x=\"beamline_energy\",\n", + " y=\"mass_absorption_0\",\n", + " by=\"sample_theta\",\n", + " colorbar=\"Angle (deg)\",\n", + " cmap=\"rainbow\",\n", + " ax=ax[0],\n", + ")\n", + "ax[0].set_xlabel(\"Energy (eV)\")\n", + "ax[0].set_ylabel(\"Mass Abs. (g/cm$^{-2}$)\")\n", + "# scientific notation\n", + "ax[0].ticklabel_format(style=\"sci\", axis=\"y\", scilimits=(0,0))\n", + "ax[0].set_xlim(282, 303)\n", + "\n", + "ax[1].set_xlabel(\"Angle (deg)\")\n", + "ax[1].yaxis.set_label_position(\"right\")\n", + "ax[1].yaxis.tick_right()\n", + "ax[1].set_ylabel(\"Peak Area (a.u.)\")\n", + "ax[1].set_ylim(0, 1.1)\n", + "ax[1].set_xlim(0, 90)\n", + "ax[1].set_xticks(np.arange(0, 91, 15))\n" + ] + }, + { + "cell_type": "markdown", + "id": "66405907", + "metadata": {}, + "source": [ + "## Peak Fitting\n", + "\n", + "There are three aspects to the peak fitting:\n", + "\n", + "1. The absorption ionization step edge - this is a modified step edge that is modified from the bare atom absorption. It accounts for the ionization energy where electrons are photo-ionized into the continuum. We need to fit a energy location, and an energy width.\n", + "2. Peak location - this happens before we perform the fit and is used to determine the number of peaks and their reletive locations.\n", + "3. Multi-peak fit - this is the fit of the peaks. Here we fit the peaks + absorption edge to the data. Each peak is a lorentzian function consisiting of a resonant energy, energy width, amplitude, and shape factor.\n", + "\n", + "### Function\n", + "\n", + "- step edge\n", + "\n", + " $$\n", + " edge(E, E_0, w) = G(E, E_0, w) * \\begin{cases}\n", + " P(E; a, b, c) & E < E_0 \\\\\n", + " P(E; a', b', c') & E \\geq E_0\n", + " \\end{cases}\n", + " $$\n", + "\n", + "- peak\n", + " $$\n", + " p(E, E_0, w, A, \\gamma) = A(\\gamma G(E, E_0, w) + (1 - \\gamma)L(E, E_0, w))\n", + " $$\n", + " $$\n", + " G(E, E_0, w) = \\frac{2\\sqrt{2\\ln(2)}}{w\\sqrt{2\\pi}}e^{-\\frac{2\\ln(2)(E-E_0)^2}{w^2}}\n", + " $$\n", + " $$\n", + " L(E, E_0, w) = \\frac{1}{\\pi}\\frac{w/2}{(w/2)^2 + (E-E_0)^2}\n", + " $$\n" + ] + }, + { + "cell_type": "code", + "execution_count": 224, + "id": "3f290757", + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.optimize import curve_fit\n", + "from scipy.signal import convolve\n", + "import numpy as np\n", + "\n", + "df_first = df_norm.groupby(\"sample_theta\").get_group(45)\n", + "\n", + "bare_atom = df_first[\n", + " [\"beamline_energy\", \"bare_atom\"]\n", + "]\n", + "\n", + "\n", + "pre_edge = bare_atom[bare_atom[\"beamline_energy\"] < 284.2]\n", + "post_edge = bare_atom[bare_atom[\"beamline_energy\"] >= 284.4]\n", + "\n", + "pre_edge_fit = np.polyfit(pre_edge[\"beamline_energy\"], pre_edge[\"bare_atom\"], 3)\n", + "post_edge_fit = np.polyfit(post_edge[\"beamline_energy\"], post_edge[\"bare_atom\"], 3)\n", + "\n", + "def gaussian(E, E_0, A, w):\n", + " sigma = w / (2 * np.sqrt(2 * np.log(2)))\n", + " A /= sigma * np.sqrt(2 * np.pi)\n", + " return A * np.exp(-(E - E_0)**2 / (2 * sigma**2))\n", + "\n", + "def lorentzian(E, E_0, w):\n", + " return 1 / (np.pi * w * (1 + ((E - E_0) / w)**2))\n", + "\n", + "def voigt(E, E_0, w, A, g):\n", + " return A * (\n", + " g * gaussian(E, E_0, 1, w) +\n", + " (1 - g) * lorentzian(E, E_0, w)\n", + " )\n" + ] + }, + { + "cell_type": "code", + "execution_count": 225, + "id": "f67ab420", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 225, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from functools import lru_cache\n", + "\n", + "# Pre-allocate work buffers and kernel cache to avoid repeated allocation in step_edge\n", + "_step_edge_kernel_cache = {}\n", + "\n", + "def _get_gaussian_kernel(w, dE):\n", + " # Keyed by (w, dE)\n", + " key = (float(w), float(dE))\n", + " kernel = _step_edge_kernel_cache.get(key)\n", + " if kernel is not None:\n", + " return kernel\n", + " kernel_width_points = int(np.round(6 * w / dE))\n", + " if kernel_width_points % 2 == 0:\n", + " kernel_width_points += 1\n", + " kernel_E = np.linspace(-3 * w, 3 * w, kernel_width_points)\n", + " k = gaussian(kernel_E, 0, 1, w)\n", + " k /= np.sum(k)\n", + " _step_edge_kernel_cache[key] = k\n", + " return k\n", + "\n", + "def step_edge(E, E_0, w):\n", + " # Uses locally cached kernel and minimal allocation for efficiency.\n", + " # \"A\" is currently unused, kept for future compatibility.\n", + " global pre_edge_fit, post_edge_fit\n", + " E = np.atleast_1d(E)\n", + " n_points = E.shape[0]\n", + " if n_points < 2:\n", + " dE = 1.0\n", + " else:\n", + " dE = E[1] - E[0]\n", + "\n", + " # Padding by +/-3*sigma\n", + " pad_width = int(np.ceil(3 * w / dE))\n", + " # Use pre-allocated work buffers if n_points is modest\n", + " E_left = E[0] - np.arange(pad_width, 0, -1) * dE\n", + " E_right = E[-1] + np.arange(1, pad_width + 1) * dE\n", + " E_extended = np.concatenate([E_left, E, E_right])\n", + "\n", + " # Use fast in-place computation for the piecewise polynomial\n", + " y_extended = np.empty_like(E_extended, dtype=np.float64)\n", + " mask = E_extended < E_0\n", + " y_extended[mask] = np.polyval(pre_edge_fit, E_extended[mask])\n", + " y_extended[~mask] = np.polyval(post_edge_fit, E_extended[~mask])\n", + "\n", + " # Get/calculate the gaussian kernel only once for each (w, dE)\n", + " kernel = _get_gaussian_kernel(w, dE)\n", + "\n", + " # Convolve using 'same' mode (scipy.signal.convolve is already fast)\n", + " convolved = convolve(y_extended, kernel, mode=\"same\", method=\"auto\")\n", + "\n", + " # Return only the region covering original E\n", + " start = pad_width\n", + " end = pad_width + n_points\n", + " return convolved[start:end]\n", + "\n", + "common_energy = np.linspace(270, 380, 1000)\n", + "\n", + "plt.plot(\n", + " common_energy,\n", + " step_edge(\n", + " common_energy,\n", + " 285, .5),\n", + " label=\"Step Edge\",\n", + " color=\"k\",\n", + " ls=\"--\",\n", + ")\n", + "plt.plot(\n", + " df_first[\"beamline_energy\"],\n", + " df_first[\"bare_atom\"],\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 237, + "id": "1b428956", + "metadata": {}, + "outputs": [], + "source": [ + "from dataclasses import dataclass\n", + "\n", + "def _scalar(x):\n", + " return float(np.asarray(x).flat[0])\n", + "\n", + "@dataclass\n", + "class Peak:\n", + " e: float\n", + " a: float\n", + " w: float\n", + " g: float\n", + "\n", + " @property\n", + " def p0(self):\n", + " return [_scalar(self.e), _scalar(self.w), _scalar(self.a), _scalar(self.g)]\n", + "\n", + " @p0.setter\n", + " def p0(self, value):\n", + " self.e = value[0]\n", + " self.w = value[1]\n", + " self.a = value[2]\n", + " self.g = value[3]\n", + "\n", + " def model(self, E, *p):\n", + " return voigt(E, *p)\n", + "\n", + " def __call__(self, E):\n", + " return self.model(E, *self.p0)\n", + "\n", + " def __add__(self, other):\n", + " return self(other.e) + other(self.e)\n", + "\n", + " @property\n", + " def ub(self):\n", + " return [self.e + .5, self.w + 2, 10*self.a, 1]\n", + "\n", + " @property\n", + " def lb(self):\n", + " return [self.e - .5, .1, 0, 0]\n", + "\n", + "@dataclass\n", + "class StepEdge:\n", + " e: float\n", + " w: float\n", + "\n", + " @property\n", + " def p0(self):\n", + " return [_scalar(self.e), _scalar(self.w)]\n", + "\n", + " @p0.setter\n", + " def p0(self, value):\n", + " self.e = value[0]\n", + " self.w = value[1]\n", + "\n", + " def model(self, E, *p):\n", + " return step_edge(E, *p)\n", + "\n", + " def __call__(self, E):\n", + " return self.model(E, *self.p0)\n", + "\n", + " @property\n", + " def ub(self):\n", + " return [310, 5]\n", + "\n", + " @property\n", + " def lb(self):\n", + " return [270, .1]\n", + "\n", + "class PeakSet:\n", + " def __init__(self, step_edge: StepEdge, peaks: list[Peak]):\n", + " self.peaks = peaks\n", + " self.step_edge = step_edge\n", + "\n", + " def model(self, E, *p):\n", + " res = self.step_edge.model(E, *p[:2])\n", + " for i, peak in enumerate(self.peaks):\n", + " res += peak.model(E, *p[2 + 4 * i : 2 + 4 * (i + 1)])\n", + " return res\n", + "\n", + " def __call__(self, E):\n", + " return self.model(E, *self.p0)\n", + "\n", + " def _concat_property(self, prop):\n", + " params = list(getattr(self.step_edge, prop))\n", + " for p in self.peaks:\n", + " params.extend(getattr(p, prop))\n", + " return params\n", + "\n", + " @property\n", + " def p0(self):\n", + " \"\"\"\n", + " Returns a flat list of parameters in the order:\n", + " [step_edge params..., all peaks' params...]\n", + " suitable for passing into the model.\n", + " \"\"\"\n", + " return self._concat_property(\"p0\")\n", + "\n", + " @p0.setter\n", + " def p0(self, value):\n", + " self.step_edge.p0 = value[:2]\n", + " for i, peak in enumerate(self.peaks):\n", + " peak.p0 = value[2 + 4 * i : 2 + 4 * (i + 1)]\n", + "\n", + " @property\n", + " def ub(self):\n", + " return self._concat_property(\"ub\")\n", + "\n", + " @property\n", + " def lb(self):\n", + " return self._concat_property(\"lb\")\n", + "\n", + " def bounds(self):\n", + " lb = np.array([float(x) for x in self.lb])\n", + " ub = np.array([float(x) for x in self.ub])\n", + " return (lb, ub)\n", + "\n", + " def bounds2(self):\n", + " lb, ub = self.bounds()\n", + " return list(zip(lb, ub))\n", + "\n", + " def _fit_indices(self, *components: str) -> list[int]:\n", + " position = [0] + [2 + 4 * i for i in range(len(self.peaks))]\n", + " width = [1] + [2 + 4 * i + 1 for i in range(len(self.peaks))]\n", + " amplitude = [2 + 4 * i + 2 for i in range(len(self.peaks))]\n", + " shape = [2 + 4 * i + 3 for i in range(len(self.peaks))]\n", + " mapping = {\n", + " \"step_edge\": [0, 1],\n", + " \"position\": position,\n", + " \"width\": width,\n", + " \"amplitude\": amplitude,\n", + " \"shape\": shape,\n", + " }\n", + " indices = []\n", + " for name in components:\n", + " if name not in mapping:\n", + " raise ValueError(f\"unknown component {name!r}; use one of {list(mapping)}\")\n", + " indices.extend(mapping[name])\n", + " return sorted(set(indices))\n", + "\n", + " def fit_config(self, *components: str):\n", + " \"\"\"\n", + " Return (x0_free, bounds2_free, expand) for fitting only the given components.\n", + " components: any of 'step_edge', 'position', 'width', 'amplitude', 'shape'.\n", + " Use: x0, bnds, expand = peakset.fit_config('amplitude', 'position')\n", + " out = minimize(lambda p: obj(expand(p)), x0, bounds=bnds, ...)\n", + " peakset.p0 = expand(out.x)\n", + " \"\"\"\n", + " free = self._fit_indices(*components)\n", + " full_p0 = np.array(self.p0, dtype=float)\n", + " full_bounds = self.bounds2()\n", + " x0_free = [full_p0[i] for i in free]\n", + " bounds2_free = [full_bounds[i] for i in free]\n", + "\n", + " def expand(p_free):\n", + " out = full_p0.copy()\n", + " for j, i in enumerate(free):\n", + " out[i] = p_free[j]\n", + " return out.tolist()\n", + "\n", + " return x0_free, bounds2_free, expand\n", + "\n", + " def plot(self, E, ax=None):\n", + " if ax is None:\n", + " fig, ax = plt.subplots()\n", + " step = self.step_edge(E)\n", + " ax.plot(E, step, color=\"k\", ls=\"--\")\n", + " for i, p in enumerate(self.peaks):\n", + " ax.axvline(p.e, color=f\"C{i}\", ls=\"--\")\n", + " ax.plot(E, p(E) + step, color=f\"C{i}\")\n", + " return ax\n", + "\n", + " def _order_peaks(self):\n", + " return sorted(self.peaks, key=lambda p: p.e)\n", + "\n", + " def add_peak(self, peak: Peak):\n", + " self.peaks.append(peak)\n", + " self.peaks = self._order_peaks()\n", + " return self\n", + "\n", + " def insert_between(self, loc: int):\n", + " left = self.peaks[loc]\n", + " right = self.peaks[loc + 1]\n", + " new_peak = Peak(\n", + " (left.e + right.e) / 2,\n", + " left.a / 2,\n", + " np.interp(\n", + " (left.e + right.e) / 2,\n", + " [left.e, right.e],\n", + " [left.w, right.w],\n", + " ),\n", + " g=1,\n", + " )\n", + " self.peaks.insert(loc + 1, new_peak)\n", + " self.peaks = self._order_peaks()\n", + " return self\n", + "\n", + "\n", + "class MultiAnglePeakSet:\n", + " def __init__(self, base_peakset: PeakSet, angles: list[float], amplitudes: np.ndarray | None = None):\n", + " self.step_edge = StepEdge(base_peakset.step_edge.e, base_peakset.step_edge.w)\n", + " self.peaks = [Peak(p.e, p.a, p.w, p.g) for p in base_peakset.peaks]\n", + " self.angles = list(angles)\n", + " self.n_peaks = len(self.peaks)\n", + " self.n_angles = len(self.angles)\n", + " if amplitudes is None:\n", + " base_amp = np.array([p.a for p in self.peaks], dtype=float)\n", + " self.amplitudes = np.tile(base_amp[None, :], (self.n_angles, 1))\n", + " else:\n", + " amp = np.asarray(amplitudes, dtype=float)\n", + " if amp.shape != (self.n_angles, self.n_peaks):\n", + " raise ValueError(f\"amplitudes must have shape {(self.n_angles, self.n_peaks)}, got {amp.shape}\")\n", + " self.amplitudes = amp.copy()\n", + "\n", + " @property\n", + " def _shared_len(self) -> int:\n", + " return 2 + 3 * self.n_peaks\n", + "\n", + " def _shared_p0(self) -> list[float]:\n", + " vals = [float(self.step_edge.e), float(self.step_edge.w)]\n", + " for p in self.peaks:\n", + " vals.extend([float(p.e), float(p.w), float(p.g)])\n", + " return vals\n", + "\n", + " def _shared_lb(self) -> list[float]:\n", + " vals = list(self.step_edge.lb)\n", + " for p in self.peaks:\n", + " vals.extend([p.lb[0], p.lb[1], p.lb[3]])\n", + " return vals\n", + "\n", + " def _shared_ub(self) -> list[float]:\n", + " vals = list(self.step_edge.ub)\n", + " for p in self.peaks:\n", + " vals.extend([p.ub[0], p.ub[1], p.ub[3]])\n", + " return vals\n", + "\n", + " def _amp_lb(self) -> np.ndarray:\n", + " return np.array([p.lb[2] for p in self.peaks], dtype=float)\n", + "\n", + " def _amp_ub(self) -> np.ndarray:\n", + " return np.array([p.ub[2] for p in self.peaks], dtype=float)\n", + "\n", + " @property\n", + " def p0(self) -> list[float]:\n", + " shared = np.array(self._shared_p0(), dtype=float)\n", + " amp = self.amplitudes.reshape(-1)\n", + " return np.concatenate([shared, amp]).tolist()\n", + "\n", + " @p0.setter\n", + " def p0(self, value):\n", + " p = np.asarray(value, dtype=float)\n", + " expected = self._shared_len + self.n_angles * self.n_peaks\n", + " if p.size != expected:\n", + " raise ValueError(f\"parameter vector must have length {expected}, got {p.size}\")\n", + " self.step_edge.p0 = p[:2].tolist()\n", + " for i, peak in enumerate(self.peaks):\n", + " base = 2 + 3 * i\n", + " peak.e = float(p[base])\n", + " peak.w = float(p[base + 1])\n", + " peak.g = float(p[base + 2])\n", + " amp_start = self._shared_len\n", + " self.amplitudes = p[amp_start:].reshape(self.n_angles, self.n_peaks).copy()\n", + "\n", + " def bounds2(self) -> list[tuple[float, float]]:\n", + " shared_lb = np.array(self._shared_lb(), dtype=float)\n", + " shared_ub = np.array(self._shared_ub(), dtype=float)\n", + " amp_lb = np.tile(self._amp_lb()[None, :], (self.n_angles, 1)).reshape(-1)\n", + " amp_ub = np.tile(self._amp_ub()[None, :], (self.n_angles, 1)).reshape(-1)\n", + " lb = np.concatenate([shared_lb, amp_lb])\n", + " ub = np.concatenate([shared_ub, amp_ub])\n", + " return list(zip(lb.tolist(), ub.tolist()))\n", + "\n", + " def model_angle(self, E, p, angle_idx: int):\n", + " p = np.asarray(p, dtype=float)\n", + " e0 = float(p[0])\n", + " w0 = float(p[1])\n", + " step = step_edge(E, e0, w0)\n", + " amp_start = self._shared_len + angle_idx * self.n_peaks\n", + " out = np.array(step, dtype=float)\n", + " for i in range(self.n_peaks):\n", + " shared = 2 + 3 * i\n", + " e = float(p[shared])\n", + " w = float(p[shared + 1])\n", + " g = float(p[shared + 2])\n", + " a = float(p[amp_start + i])\n", + " out += voigt(E, e, w, a, g)\n", + " return out\n", + "\n", + " def as_peakset(self, angle_idx: int) -> PeakSet:\n", + " if angle_idx < 0 or angle_idx >= self.n_angles:\n", + " raise IndexError(f\"angle_idx must be in [0, {self.n_angles - 1}]\")\n", + " peaks = []\n", + " for i, p in enumerate(self.peaks):\n", + " peaks.append(Peak(float(p.e), float(self.amplitudes[angle_idx, i]), float(p.w), float(p.g)))\n", + " return PeakSet(StepEdge(float(self.step_edge.e), float(self.step_edge.w)), peaks)\n", + "\n", + " def _fit_indices(self, *components: str, angle_indices: list[int] | None = None) -> list[int]:\n", + " position = [0] + [2 + 3 * i for i in range(self.n_peaks)]\n", + " width = [1] + [2 + 3 * i + 1 for i in range(self.n_peaks)]\n", + " shape = [2 + 3 * i + 2 for i in range(self.n_peaks)]\n", + " if angle_indices is None:\n", + " angle_indices = list(range(self.n_angles))\n", + " amp_idx = []\n", + " for a_idx in angle_indices:\n", + " base = self._shared_len + a_idx * self.n_peaks\n", + " amp_idx.extend([base + i for i in range(self.n_peaks)])\n", + " mapping = {\n", + " \"step_edge\": [0, 1],\n", + " \"position\": position,\n", + " \"width\": width,\n", + " \"shape\": shape,\n", + " \"amplitude\": amp_idx,\n", + " }\n", + " indices = []\n", + " for name in components:\n", + " if name not in mapping:\n", + " raise ValueError(f\"unknown component {name!r}; use one of {list(mapping)}\")\n", + " indices.extend(mapping[name])\n", + " return sorted(set(indices))\n", + "\n", + " def fit_config(self, *components: str, angle_indices: list[int] | None = None):\n", + " free = self._fit_indices(*components, angle_indices=angle_indices)\n", + " full_p0 = np.array(self.p0, dtype=float)\n", + " full_bounds = self.bounds2()\n", + " x0_free = [full_p0[i] for i in free]\n", + " bounds2_free = [full_bounds[i] for i in free]\n", + "\n", + " def expand(p_free):\n", + " out = full_p0.copy()\n", + " for j, i in enumerate(free):\n", + " out[i] = p_free[j]\n", + " return out.tolist()\n", + "\n", + " return x0_free, bounds2_free, expand" + ] + }, + { + "cell_type": "code", + "execution_count": 251, + "id": "4facf29a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(280.0, 300.0)" + ] + }, + "execution_count": 251, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from scipy.signal import find_peaks\n", + "\n", + "# Find peaks in the data from the first bare atom spectrum\n", + "peaks, _ = find_peaks(\n", + " df_first[\"mass_absorption_0\"],\n", + " prominence=0.01,\n", + " width=.5,\n", + " rel_height=.3,\n", + " )\n", + "\n", + "peak_e = [285.01, 287.17, 288.65, 293.05, 289.86]\n", + "\n", + "# Find the closest energies in the beamline_energy column\n", + "closest_energy = [\n", + " df_first[\"beamline_energy\"].iloc[(df_first[\"beamline_energy\"] - e).abs().argmin()]\n", + " for e in peak_e\n", + "]\n", + "\n", + "peak_a = [\n", + " df_first[df_first[\"beamline_energy\"] == e][\"mass_absorption_0\"].iloc[0]\n", + " for e in closest_energy\n", + "]\n", + "\n", + "\n", + "def build_peakset(peak_e, peak_a, step_energy, step_width):\n", + " peakset = PeakSet(\n", + " StepEdge(step_energy, step_width),\n", + " [],\n", + " )\n", + " for i, (e, a) in enumerate(\n", + " zip(peak_e, peak_a)\n", + " ):\n", + " if e > 320:\n", + " continue\n", + " if e > 284.2:\n", + " width = np.interp(\n", + " e,\n", + " [284.2, 310],\n", + " [.5, 10],\n", + " )\n", + " height = a - step_edge(e, step_energy, step_width)\n", + " amplitude = height / voigt(e, e, width, 1, 1)\n", + " peakset.add_peak(Peak(e, amplitude, width, 1))\n", + " return peakset\n", + "\n", + "peakset = build_peakset(peak_e, peak_a, 290, 1)\n", + "\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 5))\n", + "df_first.plot(x=\"beamline_energy\", y=\"mass_absorption_0\", ax=ax)\n", + "ax = peakset.plot(common_energy, ax=ax)\n", + "ax.plot(common_energy, peakset(common_energy), color=\"k\", ls=\"--\")\n", + "ax.set_xlim(280, 300)" + ] + }, + { + "cell_type": "code", + "execution_count": 239, + "id": "81b0f800", + "metadata": {}, + "outputs": [], + "source": [ + "energy_window = (283, 310)\n", + "angles = sorted(df_norm[\"sample_theta\"].unique())\n", + "\n", + "datasets = []\n", + "for theta in angles:\n", + " g = df_norm.groupby(\"sample_theta\").get_group(theta)\n", + " mask = (g[\"beamline_energy\"] > energy_window[0]) & (g[\"beamline_energy\"] < energy_window[1])\n", + " d = g[mask]\n", + " x = d[\"beamline_energy\"].to_numpy(dtype=float)\n", + " y = d[\"mass_absorption_0\"].to_numpy(dtype=float)\n", + " err = np.maximum(np.abs(0.01 * y), 1e-12)\n", + " datasets.append((float(theta), x, y, err))\n", + "\n", + "multi_peakset = MultiAnglePeakSet(peakset, angles)\n", + "\n", + "def obj(p):\n", + " total = 0.0\n", + " for i, (_, x, y, err) in enumerate(datasets):\n", + " pred = multi_peakset.model_angle(x, p, i)\n", + " resid = (pred - y) / err\n", + " total += float(np.sum(resid**2))\n", + " return total" + ] + }, + { + "cell_type": "code", + "execution_count": 240, + "id": "bb8705c8", + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.optimize import minimize\n", + "\n", + "x0, bnds, expand = multi_peakset.fit_config(\n", + " \"amplitude\", \"position\", \"step_edge\")\n", + "out = minimize(\n", + " lambda p: obj(expand(p)),\n", + " x0,\n", + " method=\"SLSQP\",\n", + " bounds=bnds,\n", + ")\n", + "multi_peakset.p0 = expand(out.x)\n", + "\n", + "x0, bnds, expand = multi_peakset.fit_config(\n", + " \"shape\")\n", + "out = minimize(\n", + " lambda p: obj(expand(p)),\n", + " x0,\n", + " method=\"SLSQP\",\n", + " bounds=bnds,\n", + ")\n", + "multi_peakset.p0 = expand(out.x)\n", + "\n", + "x0, bnds, expand = multi_peakset.fit_config(\n", + " \"amplitude\", \"position\", \"width\", \"shape\")\n", + "out = minimize(\n", + " lambda p: obj(expand(p)),\n", + " x0,\n", + " method=\"SLSQP\",\n", + " bounds=bnds,\n", + ")\n", + "\n", + "peakset = multi_peakset.as_peakset(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 246, + "id": "3133dda6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(283.0, 300.0)" + ] + }, + "execution_count": 246, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = peakset.plot(common_energy)\n", + "df_first.plot(x=\"beamline_energy\", y=\"mass_absorption_0\", ax=ax)\n", + "ax.plot(common_energy, peakset(common_energy), color=\"k\", ls=\"--\")\n", + "ax.set_xlim(283, 300)" + ] + }, + { + "cell_type": "code", + "execution_count": 241, + "id": "c2a46ec8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Global multi-angle fit with shared edge/position/width/shape')" + ] + }, + "execution_count": 241, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 6))\n", + "for i, (theta, x, y, _) in enumerate(datasets):\n", + " c = f\"C{i % 10}\"\n", + " ax.plot(x, y, color=c, alpha=0.35, lw=1)\n", + " ax.plot(x, multi_peakset.model_angle(x, multi_peakset.p0, i), color=c, lw=1.6, label=f\"{theta:g} deg\")\n", + "\n", + "for p in multi_peakset.peaks:\n", + " ax.axvline(p.e, color=\"k\", ls=\"--\", alpha=0.25)\n", + "\n", + "ax.set_xlim(*energy_window)\n", + "ax.set_xlabel(\"Energy (eV)\")\n", + "ax.set_ylabel(\"Mass Absorption\")\n", + "ax.legend(ncol=2, fontsize=8)\n", + "ax.set_title(\"Global multi-angle fit with shared edge/position/width/shape\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cb31cbab", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/nexafs_widget.ipynb b/notebooks/nexafs_widget.ipynb deleted file mode 100644 index 9c213d5..0000000 --- a/notebooks/nexafs_widget.ipynb +++ /dev/null @@ -1,1982 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# NEXAFS Interactive Widget\n", - "\n", - "Load a NEXAFS directory and open the Panel + Bokeh widget to select angle, define pre/post edge regions (via number inputs or box-select on the plot), choose a normalization scheme, and toggle between normalization view and background-subtracted view.\n", - "\n", - "Requires Panel and Bokeh (jupyter group). The widget runs a Panel server so box-select on the plot updates the pre/post edge inputs. A directory containing izero and sample scan files is required.\n" - ] - }, - { - "cell_type": "code", - "metadata": {}, - "source": [ - "import panel as pn\n", - "\n", - "pn.extension()\n", - "from pathlib import Path\n", - "\n", - "from pyref.nexafs import NexafsDirectory, NexafsWidget" - ], - "execution_count": 82, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/html": [ - "" - ] - } - }, - { - "output_type": "display_data", - "data": { - "application/javascript": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = false;\n const version = '3.8.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n const reloading = true;\n const Bokeh = root.Bokeh;\n const BK_RE = /^https:\\/\\/cdn\\.bokeh\\.org\\/bokeh\\/(release|dev)\\/bokeh-/;\n const PN_RE = /^https:\\/\\/cdn\\.holoviz\\.org\\/panel\\/[^/]+\\/dist\\/panel/i;\n\n // Set a timeout for this load but only if we are not already initializing\n if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, Bokeh, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n // Don't load bokeh if it is still initializing\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n // There is nothing to load\n run_callbacks();\n return null;\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error(e) {\n const src_el = e.srcElement\n console.error(\"failed to load \" + (src_el.href || src_el.src));\n }\n\n const skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n root._bokeh_is_loading = css_urls.length + 0;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n const existing_stylesheets = []\n const links = document.getElementsByTagName('link')\n for (let i = 0; i < links.length; i++) {\n const link = links[i]\n if (link.href != null) {\n existing_stylesheets.push(link.href)\n }\n }\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const escaped = encodeURI(url)\n if (existing_stylesheets.indexOf(escaped) !== -1) {\n on_load()\n continue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } var existing_scripts = []\n const scripts = document.getElementsByTagName('script')\n for (let i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n existing_scripts.push(script.src)\n }\n }\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const escaped = encodeURI(url)\n const shouldSkip = skip.includes(escaped) || existing_scripts.includes(escaped)\n const isBokehOrPanel = BK_RE.test(escaped) || PN_RE.test(escaped)\n const missingOrBroken = Bokeh == null || Bokeh.Panel == null || 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EnergyIntensityPhotodiodeAI 3 IzeroI0PD CorrectedSampleAngleExperimentBare AtomNorm AbsBare Atom StepMass Abs.
0270.00.527423238.1160210.0228750.0000960.000182ps_100kd45.0605902011.3489360.0000082.2431051072.756426
1271.00.527177242.7127170.0230630.0000950.000180ps_100kd45.0605901986.5794260.0000070.7275341014.204904
2272.00.521033245.1652420.0229270.0000940.000179ps_100kd45.0605901962.2045150.000008-0.3934381113.302345
3273.00.535243255.2067200.0242530.0000950.000178ps_100kd45.0605901938.2161140.000007-1.1279001049.208130
4274.00.552056264.1885980.0256310.0000970.000176ps_100kd45.0605901914.6066300.000007-1.4834451002.158883
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" - ], - "text/plain": [ - " Energy Intensity Photodiode AI 3 Izero I0 PD Corrected \\\n", - "0 270.0 0.527423 238.116021 0.022875 0.000096 0.000182 \n", - "1 271.0 0.527177 242.712717 0.023063 0.000095 0.000180 \n", - "2 272.0 0.521033 245.165242 0.022927 0.000094 0.000179 \n", - "3 273.0 0.535243 255.206720 0.024253 0.000095 0.000178 \n", - "4 274.0 0.552056 264.188598 0.025631 0.000097 0.000176 \n", - "\n", - " Sample Angle Experiment Bare Atom Norm Abs Bare Atom Step \\\n", - "0 ps_100kd 45.0 60590 2011.348936 0.000008 2.243105 \n", - "1 ps_100kd 45.0 60590 1986.579426 0.000007 0.727534 \n", - "2 ps_100kd 45.0 60590 1962.204515 0.000008 -0.393438 \n", - "3 ps_100kd 45.0 60590 1938.216114 0.000007 -1.127900 \n", - "4 ps_100kd 45.0 60590 1914.606630 0.000007 -1.483445 \n", - "\n", - " Mass Abs. \n", - "0 1072.756426 \n", - "1 1014.204904 \n", - "2 1113.302345 \n", - "3 1049.208130 \n", - "4 1002.158883 " - ] - } - } - ] - }, - { - "cell_type": "code", - "metadata": {}, - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "ax = df_selected.plot(x=\"Energy\", y=\"PD Corrected\")\n", - "df_selected.plot(x=\"Energy\", y=\"Bare Atom Step\", ax = ax.twinx())" - ], - "execution_count": 87, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - } - }, - { - "output_type": "display_data", - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - } - } - ] - }, - { - "cell_type": "code", - "metadata": {}, - "source": [ - "import numpy as np\n", - "from scipy.optimize import curve_fit\n", - "from uncertainties import ufloat\n", - "\n", - "def process_si_background(df, pre = (270, 283), post = (330, 370)):\n", - " norm_range = df[\n", - " ((df[\"Energy\"] >= pre[0]) & (df[\"Energy\"] <= pre[1])) |\n", - " ((df[\"Energy\"] >= post[0]) & (df[\"Energy\"] <= post[1]))\n", - " ]\n", - " def mass_abs(energy, izero, si_comp):\n", - " mu_values = NexafsDirectory.mu(energy = energy, formula = \"C8H8\")\n", - " mu_si_values = NexafsDirectory.mu(energy = energy, formula = \"Si\")\n", - " return izero * (mu_values + si_comp * mu_si_values)\n", - "\n", - " popt, pcov = curve_fit(\n", - " mass_abs,\n", - " norm_range[\"Energy\"].values,\n", - " norm_range[\"PD Corrected\"].values,\n", - " bounds = ((0, 0), (np.inf, np.inf))\n", - " )\n", - "\n", - " # Compute the Si Signal subtracted mass absorption before using ufloat\n", - " df[\"Normalized\"] = (df[\"PD Corrected\"] ) / popt[0]\n", - " df[\"Background Subtracted\"] = df[\"Normalized\"] - (\n", - " NexafsDirectory.mu(energy=df[\"Energy\"].values, formula=\"Si\") * popt[1]\n", - " )\n", - "\n", - " izero = ufloat(popt[0], np.sqrt(pcov[0, 0]))\n", - " si_comp = ufloat(popt[1], np.sqrt(pcov[1, 1]))\n", - " print(f\"izero: {izero}, si_comp: {si_comp}\")\n", - "\n", - " return df\n", - "\n", - "df_processed = []\n", - "for df in df_group:\n", - " df_processed.append(process_si_background(df, pre = (280, 284), post=(320, 340)))\n", - "\n", - "df_processed[0].plot(x=\"Energy\", y=[\"Bare Atom Step\", \"Normalized\", \"Background Subtracted\"])\n" - ], - "execution_count": 92, - "outputs": [ - { - "output_type": "stream", - "text": [ - "izero: (6.365+/-0.024)e-09, si_comp: 0.675+/-0.004\n", - "izero: (5.813+/-0.031)e-09, si_comp: 0.703+/-0.006\n", - "izero: (5.187+/-0.032)e-09, si_comp: 0.836+/-0.008\n", - "izero: (5.050+/-0.030)e-09, si_comp: 0.783+/-0.007\n", - "izero: (4.908+/-0.031)e-09, si_comp: 0.750+/-0.007\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - } - }, - { - "output_type": "display_data", - "data": { - "image/png": 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", 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" - ] - } - } - ] - }, - { - "cell_type": "code", - "metadata": {}, - "source": [ - "import warnings\n", - "from pathlib import Path\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "from periodictable import xsf\n", - "import pint\n", - "from scipy.optimize import curve_fit\n", - "\n", - "ureg = pint.UnitRegistry()\n", - "\n", - "\n", - "class NexafsDirectory:\n", - " def __init__(self, path: str | Path):\n", - " self.path = Path(path)\n", - " if not self.path.exists():\n", - " raise FileNotFoundError(f\"Path {self.path} does not exist\")\n", - " self.izero_store: dict[pd.Timestamp, dict] = {}\n", - " self._selected_izero_timestamp: pd.Timestamp | None = None\n", - " self._last_sample_context: dict | None = None\n", - " self._scan_and_process_izero()\n", - " self.dataset = None\n", - " self.pre_edge = (None, 280)\n", - " self.post_edge = (360, None)\n", - "\n", - " def set_pre(self, pre_edge):\n", - " if isinstance(pre_edge, (int, float)):\n", - " self.pre_edge = (None, pre_edge)\n", - " elif isinstance(pre_edge, (tuple, list)) and len(pre_edge) == 2:\n", - " self.pre_edge = (pre_edge[0], pre_edge[1])\n", - " else:\n", - " raise ValueError(\"pre_edge must be a float, int, or length-2 (min, max) tuple/list.\")\n", - "\n", - " def set_post(self, post_edge):\n", - " if isinstance(post_edge, (int, float)):\n", - " self.post_edge = (post_edge, None)\n", - " elif isinstance(post_edge, (tuple, list)) and len(post_edge) == 2:\n", - " self.post_edge = (post_edge[0], post_edge[1])\n", - " else:\n", - " raise ValueError(\"post_edge must be a float, int, or length-2 (min, max) tuple/list.\")\n", - "\n", - " @staticmethod\n", - " def read_nexafs(file_path: Path) -> pd.DataFrame:\n", - " with open(file_path) as f:\n", - " lines = f.readlines()\n", - " header_idx = next(\n", - " (i for i, line in enumerate(lines) if \"Time of Day\" in line), None\n", - " )\n", - " if header_idx is None:\n", - " raise ValueError(f\"Could not find 'Time of Day' header in {file_path}\")\n", - " from io import StringIO\n", - " table_text = \"\".join(lines[header_idx:])\n", - " df = pd.read_csv(StringIO(table_text), sep=r\"\\t\", engine=\"python\")\n", - " df[\"Beamline Energy\"] = df[\"Beamline Energy\"].round(1)\n", - " return df\n", - "\n", - " @staticmethod\n", - " def compute_izero(izer_df: pd.DataFrame) -> pd.DataFrame:\n", - " df = pd.DataFrame()\n", - " df[\"Energy\"] = izer_df[\"Beamline Energy\"]\n", - " df[\"Photodiode\"] = izer_df[\"Photodiode\"]\n", - " df[\"AI 3 Izero\"] = izer_df[\"AI 3 Izero\"]\n", - " df[\"I0\"] = izer_df[\"AI 3 Izero\"] / izer_df[\"Photodiode\"]\n", - " if \"Timestamp\" in izer_df.columns:\n", - " df[\"Timestamp\"] = izer_df[\"Timestamp\"]\n", - " return df\n", - "\n", - " def _scan_and_process_izero(self):\n", - " izero_files = list(self.path.glob(\"izero*\"))\n", - " new_store: dict[pd.Timestamp, dict] = {}\n", - " for file in izero_files:\n", - " raw_df = self.read_nexafs(file)\n", - " processed = self.compute_izero(raw_df)\n", - " if \"Timestamp\" in processed.columns:\n", - " timestamp = pd.to_datetime(processed[\"Timestamp\"].iloc[0])\n", - " else:\n", - " timestamp = pd.to_datetime(file.stat().st_mtime, unit=\"s\")\n", - " energy = processed[\"Energy\"].astype(float)\n", - " epu = None\n", - " if \"EPU Polarization\" in raw_df.columns:\n", - " epu_val = raw_df[\"EPU Polarization\"].iloc[0]\n", - " if pd.notna(epu_val):\n", - " epu = float(epu_val)\n", - " new_store[timestamp] = {\n", - " \"df\": processed,\n", - " \"energy_min\": float(energy.min()),\n", - " \"energy_max\": float(energy.max()),\n", - " \"epu_polarization\": epu,\n", - " }\n", - " self.izero_store = new_store\n", - "\n", - " def plot_izero(self, ai_to_plot=\"I0\"):\n", - " self._scan_and_process_izero()\n", - " if not self.izero_store:\n", - " print(\"No izero scans found.\")\n", - " return\n", - " fig, ax = plt.subplots()\n", - " for timestamp, entry in sorted(self.izero_store.items()):\n", - " label = timestamp.strftime(\"%Y-%m-%d %H:%M:%S\")\n", - " entry[\"df\"].plot(x=\"Energy\", y=ai_to_plot, ax=ax, label=label)\n", - " ax.set_xlabel(\"Energy\")\n", - " ax.set_ylabel(ai_to_plot)\n", - " ax.set_title(\"Izero Scans\")\n", - " ax.legend()\n", - " ax.ticklabel_format(style=\"sci\", axis=\"y\", scilimits=(0, 0))\n", - " return ax\n", - "\n", - " def get_izero_by_timestamp(self, timestamp: pd.Timestamp) -> pd.DataFrame | None:\n", - " self._scan_and_process_izero()\n", - " entry = self.izero_store.get(timestamp)\n", - " return entry[\"df\"] if entry else None\n", - "\n", - " def list_izero_timestamps(self):\n", - " self._scan_and_process_izero()\n", - " return sorted(self.izero_store.keys())\n", - "\n", - " def _select_izero_nearest(\n", - " self,\n", - " sample_timestamp: pd.Timestamp,\n", - " energy_range: tuple[float, float] | None = None,\n", - " epu_polarization: float | None = None,\n", - " ) -> pd.Timestamp | None:\n", - " if not self.izero_store:\n", - " return None\n", - " candidates = []\n", - " sample_min, sample_max = (energy_range or (0, np.inf))\n", - " for ts, entry in self.izero_store.items():\n", - " e_min, e_max = entry[\"energy_min\"], entry[\"energy_max\"]\n", - " if energy_range and (sample_max < e_min or sample_min > e_max):\n", - " continue\n", - " epu = entry.get(\"epu_polarization\")\n", - " if epu_polarization is not None and epu is not None:\n", - " if not np.isclose(epu_polarization, epu, rtol=1e-5, atol=1e-5):\n", - " continue\n", - " dt = abs((ts - sample_timestamp).total_seconds())\n", - " candidates.append((dt, ts))\n", - " if not candidates:\n", - " candidates = [(abs((ts - sample_timestamp).total_seconds()), ts) for ts in self.izero_store]\n", - " candidates.sort(key=lambda x: x[0])\n", - " return candidates[0][1] if candidates else None\n", - "\n", - " @property\n", - " def izero(self) -> pd.DataFrame | None:\n", - " self._scan_and_process_izero()\n", - " if not self.izero_store:\n", - " return None\n", - " if self._selected_izero_timestamp is not None:\n", - " entry = self.izero_store.get(self._selected_izero_timestamp)\n", - " return entry[\"df\"] if entry else None\n", - " if self._last_sample_context:\n", - " ts = self._select_izero_nearest(\n", - " sample_timestamp=self._last_sample_context[\"timestamp\"],\n", - " energy_range=self._last_sample_context.get(\"energy_range\"),\n", - " epu_polarization=self._last_sample_context.get(\"epu_polarization\"),\n", - " )\n", - " if ts is not None:\n", - " return self.izero_store[ts][\"df\"]\n", - " latest_ts = max(self.izero_store)\n", - " return self.izero_store[latest_ts][\"df\"]\n", - "\n", - " def set_izero_by_timestamp(self, timestamp: pd.Timestamp | None) -> None:\n", - " self._scan_and_process_izero()\n", - " if timestamp is None:\n", - " self._selected_izero_timestamp = None\n", - " else:\n", - " if timestamp not in self.izero_store:\n", - " raise ValueError(f\"Timestamp {timestamp} not found in available izero scans.\")\n", - " self._selected_izero_timestamp = timestamp\n", - "\n", - " def set_izero_nearest(\n", - " self,\n", - " sample_timestamp: pd.Timestamp,\n", - " energy_range: tuple[float, float] | None = None,\n", - " epu_polarization: float | None = None,\n", - " ) -> pd.Timestamp | None:\n", - " self._scan_and_process_izero()\n", - " ts = self._select_izero_nearest(\n", - " sample_timestamp=sample_timestamp,\n", - " energy_range=energy_range,\n", - " epu_polarization=epu_polarization,\n", - " )\n", - " if ts is not None:\n", - " self._selected_izero_timestamp = ts\n", - " return ts\n", - "\n", - " def list_izero_options(self) -> pd.DataFrame:\n", - " self._scan_and_process_izero()\n", - " rows = []\n", - " for ts in sorted(self.izero_store):\n", - " entry = self.izero_store[ts]\n", - " rows.append({\n", - " \"timestamp\": ts,\n", - " \"energy_min\": entry[\"energy_min\"],\n", - " \"energy_max\": entry[\"energy_max\"],\n", - " \"epu_polarization\": entry.get(\"epu_polarization\"),\n", - " })\n", - " return pd.DataFrame(rows)\n", - "\n", - " def list_samples(self):\n", - " sample_names = set()\n", - " for file in self.path.glob(\"*.txt\"):\n", - " if file.name.startswith(\"izero\"):\n", - " continue\n", - " split = file.stem.split(\"_\")\n", - " sample = \"_\".join(split[:-2])\n", - " sample_names.add(sample)\n", - " return sorted(sample_names)\n", - "\n", - " def get_sample_file_info(self, sample_name):\n", - " files = []\n", - " for file in self.path.glob(\"*.txt\"):\n", - " if file.name.startswith(\"izero\"):\n", - " continue\n", - " split = file.stem.split(\"_\")\n", - " if \"_\".join(split[:-2]) != sample_name:\n", - " continue\n", - " try:\n", - " angle = float(split[-2].strip(\"deg\"))\n", - " experiment_hash = int(split[-1])\n", - " files.append({\"file\": file, \"angle\": angle, \"experiment\": experiment_hash})\n", - " except Exception:\n", - " continue\n", - " return files\n", - "\n", - " def process_tey(self, df, izero_df):\n", - " processed = pd.DataFrame()\n", - " processed[\"Energy\"] = df[\"Beamline Energy\"]\n", - " processed[\"Intensity\"] = df[\"AI 3 Izero\"] / df[\"TEY signal\"]\n", - " izero_df = izero_df.copy()\n", - " izero_df[\"Energy\"] = izero_df[\"Energy\"].astype(float)\n", - " processed = processed.merge(izero_df, on=\"Energy\", how=\"left\")\n", - " processed[\"PD Corrected\"] = processed[\"I0\"] / processed[\"Intensity\"]\n", - " return processed\n", - "\n", - " @staticmethod\n", - " def mu(energy: np.ndarray, formula: str, units: str = \"g/cm^2\") -> np.ndarray:\n", - " \"\"\"\n", - " Calculate the mass attenuation coefficient μ(E) for a formula at given energies.\n", - "\n", - " Parameters\n", - " ----------\n", - " energy : np.ndarray\n", - " Energy in eV.\n", - " formula : str\n", - " Chemical formula or element symbol, e.g., 'C8H8', 'Si', 'O'.\n", - " units : str\n", - " Output units, default = \"g/cm^2\".\n", - "\n", - " Returns\n", - " -------\n", - " np.ndarray\n", - " μ in the requested units.\n", - " \"\"\"\n", - " energy = np.asarray(energy)\n", - " energy_kev = energy * 1e-3\n", - " n = xsf.index_of_refraction(formula, energy=energy_kev, density=1.0)\n", - " beta = -n.imag\n", - " hc = 12.3984193\n", - " wavelength_A = hc / energy_kev\n", - " wavelength_cm = wavelength_A * 1e-8\n", - " mu_over_rho = 4 * np.pi * beta / wavelength_cm\n", - "\n", - " mu_qty = mu_over_rho * ureg(\"g/cm^2\")\n", - " mu_converted = mu_qty.to(units)\n", - "\n", - " return mu_converted.magnitude\n", - "\n", - " @staticmethod\n", - " def build_mu_arrays(energy: np.ndarray, formula: str, units: str = \"g/cm^2\") -> dict[str, np.ndarray]:\n", - " \"\"\"\n", - " Precompute mu arrays for chemical formula, Si, and O at given energies.\n", - "\n", - " Parameters\n", - " ----------å\n", - " energy : np.ndarray\n", - " Energy in eV.\n", - " formula : str\n", - " Chemical formula for the main species, e.g., 'C8H8'.\n", - " units : str\n", - " Output units, default = \"g/cm^2\".\n", - "\n", - " Returns\n", - " -------\n", - " dict[str, np.ndarray]\n", - " Keys: \"chemical\", \"Si\", \"O\". Values: mu arrays.\n", - " \"\"\"\n", - " return {\n", - " \"chemical\": NexafsDirectory.mu(energy, formula, units),\n", - " \"Si\": NexafsDirectory.mu(energy, \"Si\", units),\n", - " \"O\": NexafsDirectory.mu(energy, \"O\", units),\n", - " }\n", - "\n", - " def _parse_edge_region(self, df, region, edge_type=\"pre\"):\n", - " energy = df[\"Energy\"]\n", - " if edge_type == \"pre\":\n", - " if region[0] is not None and region[1] is not None:\n", - " mask = (energy >= region[0]) & (energy <= region[1])\n", - " elif region[0] is None and region[1] is not None:\n", - " mask = energy < region[1]\n", - " elif region[0] is not None and region[1] is None:\n", - " mask = energy >= region[0]\n", - " else:\n", - " raise ValueError(\"At least one endpoint for pre_edge must be specified.\")\n", - " elif edge_type == \"post\":\n", - " if region[0] is not None and region[1] is not None:\n", - " mask = (energy >= region[0]) & (energy <= region[1])\n", - " elif region[0] is not None and region[1] is None:\n", - " mask = energy > region[0]\n", - " elif region[0] is None and region[1] is not None:\n", - " mask = energy <= region[1]\n", - " else:\n", - " raise ValueError(\"At least one endpoint for post_edge must be specified.\")\n", - " else:\n", - " raise ValueError(\"edge_type must be 'pre' or 'post'\")\n", - " return df[mask]\n", - "\n", - " def normalize_tey(self, df, formula: str, pre_edge=None, post_edge=None):\n", - " df = df.copy()\n", - " df[\"Bare Atom\"] = self.mu(df[\"Energy\"], formula)\n", - " pre = pre_edge if pre_edge is not None else self.pre_edge\n", - " post = post_edge if post_edge is not None else self.post_edge\n", - " if isinstance(pre, (int, float)):\n", - " pre = (None, pre)\n", - " pre = tuple(pre)\n", - " if isinstance(post, (int, float)):\n", - " post = (post, None)\n", - " post = tuple(post)\n", - " df_pre = self._parse_edge_region(df, pre, edge_type=\"pre\")\n", - " xpre = df_pre[\"Energy\"].to_numpy()\n", - " ypre = df_pre[\"PD Corrected\"].to_numpy()\n", - " barepre = df_pre[\"Bare Atom\"].to_numpy()\n", - " if len(xpre) < 2:\n", - " raise ValueError(\"Pre-edge region too small for line fit\")\n", - " coef, intercept = np.polyfit(xpre, ypre, 1)\n", - " coef_bare, intercept_bare = np.polyfit(xpre, barepre, 1)\n", - " baseline = coef * df[\"Energy\"] + intercept\n", - " baseline_bare = coef_bare * df[\"Energy\"] + intercept_bare\n", - " df[\"Norm Abs\"] = df[\"PD Corrected\"] - baseline\n", - " df[\"Bare Atom Step\"] = df[\"Bare Atom\"] - baseline_bare\n", - " df_post = self._parse_edge_region(df, post, edge_type=\"post\")\n", - " if len(df_post) < 2:\n", - " df[\"Mass Abs.\"] = df[\"Norm Abs\"]\n", - " return df\n", - " post_normabs = df_post[\"Norm Abs\"].median()\n", - " pre_normabs = df_pre[\"Norm Abs\"].median()\n", - " edge_jump_normabs = post_normabs - pre_normabs\n", - " post_bare = df_post[\"Bare Atom Step\"].median()\n", - " pre_bare = df_pre[\"Bare Atom Step\"].median()\n", - " edge_jump_bare = post_bare - pre_bare\n", - " scale = edge_jump_bare / edge_jump_normabs if not np.isclose(edge_jump_normabs, 0) else 1.0\n", - " df[\"Mass Abs.\"] = df[\"Norm Abs\"] * scale\n", - " return df\n", - "\n", - " def normalize_sample_angles(self, dfs, formula: str, pre_edge=None, post_edge=None):\n", - " for df in dfs:\n", - " df[\"Bare Atom\"] = self.mu(df[\"Energy\"], formula)\n", - " pre = pre_edge if pre_edge is not None else self.pre_edge\n", - " post = post_edge if post_edge is not None else self.post_edge\n", - " if isinstance(pre, (int, float)):\n", - " pre = (None, pre)\n", - " pre = tuple(pre)\n", - " if isinstance(post, (int, float)):\n", - " post = (post, None)\n", - " post = tuple(post)\n", - " post_region_len = [len(self._parse_edge_region(df, post, edge_type=\"post\")) for df in dfs]\n", - " min_post_points = 5\n", - " complete_mask = np.array([n >= min_post_points for n in post_region_len])\n", - " complete_dfs = [df for df, c in zip(dfs, complete_mask) if c]\n", - " if len(complete_dfs) == 0:\n", - " all_pre = pd.concat([\n", - " self._parse_edge_region(df, pre, edge_type=\"pre\").assign(idx=i)\n", - " for i, df in enumerate(dfs)\n", - " ])\n", - " xpre = all_pre[\"Energy\"].to_numpy()\n", - " ypre = all_pre[\"PD Corrected\"].to_numpy()\n", - " barepre = all_pre[\"Bare Atom\"].to_numpy()\n", - " if len(xpre) >= 2:\n", - " coef, intercept = np.polyfit(xpre, ypre, 1)\n", - " coef_bare, intercept_bare = np.polyfit(xpre, barepre, 1)\n", - " for df in dfs:\n", - " baseline = coef * df[\"Energy\"] + intercept\n", - " baseline_bare = coef_bare * df[\"Energy\"] + intercept_bare\n", - " df[\"Norm Abs\"] = df[\"PD Corrected\"] - baseline\n", - " df[\"Bare Atom Step\"] = df[\"Bare Atom\"] - baseline_bare\n", - " for df in dfs:\n", - " df[\"Mass Abs.\"] = df.get(\"Norm Abs\", df[\"PD Corrected\"].copy())\n", - " return dfs\n", - " all_pre = pd.concat([\n", - " self._parse_edge_region(df, pre, edge_type=\"pre\").assign(idx=i)\n", - " for i, df in enumerate(complete_dfs)\n", - " ])\n", - " xpre = all_pre[\"Energy\"].to_numpy()\n", - " ypre = all_pre[\"PD Corrected\"].to_numpy()\n", - " barepre = all_pre[\"Bare Atom\"].to_numpy()\n", - " if len(xpre) < 2:\n", - " raise ValueError(\"Pre-edge region too small for line fit\")\n", - " coef, intercept = np.polyfit(xpre, ypre, 1)\n", - " coef_bare, intercept_bare = np.polyfit(xpre, barepre, 1)\n", - " for df in dfs:\n", - " baseline = coef * df[\"Energy\"] + intercept\n", - " baseline_bare = coef_bare * df[\"Energy\"] + intercept_bare\n", - " df[\"Norm Abs\"] = df[\"PD Corrected\"] - baseline\n", - " df[\"Bare Atom Step\"] = df[\"Bare Atom\"] - baseline_bare\n", - " pre_norm = pd.concat([\n", - " self._parse_edge_region(df, pre, edge_type=\"pre\")[[\"Norm Abs\", \"Bare Atom Step\"]]\n", - " for df in complete_dfs\n", - " ])\n", - " post_norm = pd.concat([\n", - " self._parse_edge_region(df, post, edge_type=\"post\")[[\"Norm Abs\", \"Bare Atom Step\"]]\n", - " for df in complete_dfs\n", - " ])\n", - " pre_median_normabs = pre_norm[\"Norm Abs\"].median()\n", - " post_median_normabs = post_norm[\"Norm Abs\"].median()\n", - " edge_jump_normabs = post_median_normabs - pre_median_normabs\n", - " pre_median_bare = pre_norm[\"Bare Atom Step\"].median()\n", - " post_median_bare = post_norm[\"Bare Atom Step\"].median()\n", - " edge_jump_bare = post_median_bare - pre_median_bare\n", - " scale = edge_jump_bare / edge_jump_normabs if not np.isclose(edge_jump_normabs, 0) else 1.0\n", - " E0 = pre[1] if pre[1] is not None else pre[0]\n", - " post_first = self._parse_edge_region(complete_dfs[0], post, edge_type=\"post\")\n", - " k = 0.0\n", - " if len(post_first) >= 2:\n", - " E_post = post_first[\"Energy\"].to_numpy()\n", - " slope_bare = np.polyfit(E_post, post_first[\"Bare Atom Step\"].to_numpy(), 1)[0]\n", - " slope_massabs = np.polyfit(E_post, (post_first[\"Norm Abs\"].to_numpy() * scale), 1)[0]\n", - " k = slope_bare - slope_massabs\n", - " for i, df in enumerate(dfs):\n", - " if complete_mask[i]:\n", - " df[\"Mass Abs.\"] = df[\"Norm Abs\"] * scale\n", - " if k != 0.0:\n", - " mask = df[\"Energy\"] > E0\n", - " df.loc[mask, \"Mass Abs.\"] += (df.loc[mask, \"Energy\"] - E0) * k\n", - " else:\n", - " df[\"Mass Abs.\"] = df[\"Norm Abs\"].copy()\n", - " return dfs\n", - "\n", - " def process_sample(\n", - " self,\n", - " sample_name: str | None = None,\n", - " formula: str = \"C8H8\",\n", - " pre_edge: float | tuple[float | None, float | None] = 284.0,\n", - " post_edge: float | tuple[float | None, float | None] = 320.0,\n", - " mode: str = \"si_only\",\n", - " ) -> tuple[pd.DataFrame, pd.DataFrame]:\n", - " \"\"\"\n", - " Full pipeline: load, TEY absorption (I0/izeros), Si background fit.\n", - " Result agrees with bare atom in pre/post edge regions (user-configurable bounds).\n", - " Returns (concatenated DataFrame, summary DataFrame).\n", - " \"\"\"\n", - " samples = [sample_name] if sample_name else self.list_samples()\n", - " if not samples:\n", - " return pd.DataFrame(), pd.DataFrame()\n", - " all_dfs = []\n", - " all_summaries = []\n", - " for s in samples:\n", - " dfs = self.get_sample_dfs(s, formula=formula, pre_edge=pre_edge, post_edge=post_edge)\n", - " dfs, summary = self.fit_background_subtraction(\n", - " dfs, formula=formula, pre_edge=pre_edge, post_edge=post_edge, mode=mode\n", - " )\n", - " all_dfs.extend(dfs)\n", - " all_summaries.append(summary)\n", - " combined = pd.concat(all_dfs, ignore_index=True) if all_dfs else pd.DataFrame()\n", - " summary_combined = pd.concat(all_summaries, ignore_index=True) if all_summaries else pd.DataFrame()\n", - " return combined, summary_combined\n", - "\n", - " def get_sample_dfs(self, sample_name, formula: str = \"C8H8\", pre_edge=None, post_edge=None):\n", - " files = self.get_sample_file_info(sample_name)\n", - " if files:\n", - " first_df = self.read_nexafs(files[0][\"file\"])\n", - " energy = first_df[\"Beamline Energy\"].astype(float)\n", - " epu = None\n", - " if \"EPU Polarization\" in first_df.columns:\n", - " epu_val = first_df[\"EPU Polarization\"].iloc[0]\n", - " if pd.notna(epu_val):\n", - " epu = float(epu_val)\n", - " if \"Timestamp\" in first_df.columns:\n", - " sample_ts = pd.to_datetime(first_df[\"Timestamp\"].iloc[0])\n", - " else:\n", - " sample_ts = pd.to_datetime(files[0][\"file\"].stat().st_mtime, unit=\"s\")\n", - " self._last_sample_context = {\n", - " \"timestamp\": sample_ts,\n", - " \"energy_range\": (float(energy.min()), float(energy.max())),\n", - " \"epu_polarization\": epu,\n", - " }\n", - " izero = self.izero\n", - " if izero is None:\n", - " raise RuntimeError(\"No izero scan available.\")\n", - " df_list = []\n", - " for info in files:\n", - " df = self.read_nexafs(info[\"file\"])\n", - " processed = self.process_tey(df, izero)\n", - " processed[\"Sample\"] = sample_name\n", - " processed[\"Angle\"] = info[\"angle\"]\n", - " processed[\"Experiment\"] = info[\"experiment\"]\n", - " df_list.append(processed)\n", - " return self.normalize_sample_angles(df_list, formula, pre_edge, post_edge)\n", - "\n", - " def process_all(self, formula: str = \"C8H8\", pre_edge=None, post_edge=None):\n", - " dataset_records = []\n", - " for sample_name in self.list_samples():\n", - " dfs = self.get_sample_dfs(sample_name, formula=formula, pre_edge=pre_edge, post_edge=post_edge)\n", - " for df in dfs:\n", - " record = df.copy()\n", - " record[\"Sample\"] = sample_name\n", - " record.setdefault(\"Angle\", None)\n", - " record.setdefault(\"Experiment\", None)\n", - " dataset_records.append(record)\n", - " self.dataset = pd.concat(dataset_records, ignore_index=True)\n", - " return self.dataset\n", - "\n", - " def view_dataset(self) -> pd.DataFrame | None:\n", - " if self.dataset is None:\n", - " print(\"Dataset not yet processed. Run process_all() first.\")\n", - " return None\n", - " return self.dataset\n", - "\n", - " def plot_sample(\n", - " self,\n", - " sample_name,\n", - " ycol=\"Mass Abs.\",\n", - " formula: str = \"C8H8\",\n", - " pre_edge=None,\n", - " post_edge=None,\n", - " ax=None,\n", - " cmap=\"viridis\",\n", - " show_bare_atom=False,\n", - " bare_atom_kwargs=None,\n", - " apply_si_subtraction: bool = False,\n", - " normalization_mode: str = \"si_only\",\n", - " ):\n", - " dfs = self.get_sample_dfs(sample_name, formula=formula, pre_edge=pre_edge, post_edge=post_edge)\n", - " if apply_si_subtraction:\n", - " pre = pre_edge if pre_edge is not None else self.pre_edge\n", - " post = post_edge if post_edge is not None else self.post_edge\n", - " pre_val = float(pre) if isinstance(pre, (int, float)) else (pre[1] if pre[1] is not None else 284.0)\n", - " post_val = float(post) if isinstance(post, (int, float)) else (post[0] if post[0] is not None else 320.0)\n", - " dfs, _ = self.fit_background_subtraction(\n", - " dfs, formula=formula, pre_edge=pre_val, post_edge=post_val, mode=normalization_mode\n", - " )\n", - " if ycol == \"Mass Abs.\":\n", - " ycol = \"Si-subtracted\"\n", - " if ax is None:\n", - " fig, ax = plt.subplots()\n", - " angles = [df[\"Angle\"].iloc[0] for df in dfs]\n", - " norm = plt.Normalize(min(angles), max(angles))\n", - " sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)\n", - " if show_bare_atom:\n", - " plot_kwargs = {\"color\": \"black\", \"lw\": 1}\n", - " if bare_atom_kwargs is not None:\n", - " plot_kwargs.update(bare_atom_kwargs)\n", - " ax.plot(dfs[0][\"Energy\"], dfs[0][\"Bare Atom Step\"], **plot_kwargs)\n", - " for df in dfs:\n", - " angle = df[\"Angle\"].iloc[0]\n", - " color = sm.to_rgba(angle)\n", - " ax.plot(df[\"Energy\"], df[ycol], label=f\"{angle:.1f} deg\", color=color)\n", - " ax.set_xlabel(\"Energy\")\n", - " ax.set_ylabel(ycol)\n", - " ax.set_title(f\"Sample: {sample_name}\")\n", - " ax.legend(title=\"Angle\", handlelength=0.5, fontsize=10, ncol=2, frameon=True, fancybox=False, framealpha=1)\n", - " plt.colorbar(sm, ax=ax, pad=0.02).set_label(\"Angle (deg)\")\n", - " ax.ticklabel_format(style=\"sci\", axis=\"y\", scilimits=(0, 0))\n", - " return ax\n", - "\n", - " def normalization_quality(\n", - " self,\n", - " dfs,\n", - " pre_edge=None,\n", - " post_edge=None,\n", - " ref_col=\"Bare Atom Step\",\n", - " ycol=\"Mass Abs.\",\n", - " scale_ref_percent=None,\n", - " ):\n", - " pre = pre_edge if pre_edge is not None else self.pre_edge\n", - " post = post_edge if post_edge is not None else self.post_edge\n", - " if isinstance(pre, (int, float)):\n", - " pre = (None, pre)\n", - " pre = tuple(pre)\n", - " if isinstance(post, (int, float)):\n", - " post = (post, None)\n", - " post = tuple(post)\n", - " all_resid_pre = []\n", - " all_resid_post = []\n", - " for df in dfs:\n", - " r = df[ycol] - df[ref_col]\n", - " pre_df = self._parse_edge_region(df, pre, edge_type=\"pre\")\n", - " post_df = self._parse_edge_region(df, post, edge_type=\"post\")\n", - " if len(pre_df):\n", - " all_resid_pre.append(r.loc[pre_df.index])\n", - " if len(post_df):\n", - " all_resid_post.append(r.loc[post_df.index])\n", - " concat_pre = pd.concat(all_resid_pre) if all_resid_pre else pd.Series(dtype=float)\n", - " concat_post = pd.concat(all_resid_post) if all_resid_post else pd.Series(dtype=float)\n", - " rms_pre = float(np.sqrt(np.mean(concat_pre ** 2))) if len(concat_pre) else np.nan\n", - " rms_post = float(np.sqrt(np.mean(concat_post ** 2))) if len(concat_post) else np.nan\n", - " if scale_ref_percent is None and len(dfs) > 0:\n", - " post_ref = pd.concat([\n", - " self._parse_edge_region(df, post, edge_type=\"post\")[ref_col] for df in dfs\n", - " ])\n", - " scale_ref_percent = float(np.median(np.abs(post_ref))) if len(post_ref) else 1.0\n", - " rms_pct_pre = 100.0 * rms_pre / scale_ref_percent if scale_ref_percent else np.nan\n", - " rms_pct_post = 100.0 * rms_post / scale_ref_percent if scale_ref_percent else np.nan\n", - " return {\n", - " \"rms_pre\": rms_pre,\n", - " \"rms_post\": rms_post,\n", - " \"rms_pct_pre\": rms_pct_pre,\n", - " \"rms_pct_post\": rms_pct_post,\n", - " \"residuals_pre\": all_resid_pre,\n", - " \"residuals_post\": all_resid_post,\n", - " \"scale_ref\": scale_ref_percent,\n", - " }\n", - "\n", - " def _resolve_edge_region(self, edge_val, default_upper: float | None, default_lower: float | None, edge_type: str) -> tuple[float | None, float | None]:\n", - " if edge_val is None:\n", - " return (None, default_upper) if edge_type == \"pre\" else (default_lower, None)\n", - " if isinstance(edge_val, (int, float)):\n", - " return (None, float(edge_val)) if edge_type == \"pre\" else (float(edge_val), None)\n", - " if isinstance(edge_val, (tuple, list)) and len(edge_val) == 2:\n", - " return (edge_val[0] if edge_val[0] is not None else None, edge_val[1] if edge_val[1] is not None else None)\n", - " raise ValueError(f\"{edge_type}_edge must be float or (start, stop) tuple\")\n", - "\n", - " def fit_background_subtraction(\n", - " self,\n", - " dfs: pd.DataFrame | list[pd.DataFrame],\n", - " formula: str = \"C8H8\",\n", - " pre_edge: float | tuple[float | None, float | None] = 284.0,\n", - " post_edge: float | tuple[float | None, float | None] = 320.0,\n", - " mode: str = \"si_only\",\n", - " ) -> tuple[list[pd.DataFrame], pd.DataFrame]:\n", - " if isinstance(dfs, pd.DataFrame):\n", - " dfs = [dfs]\n", - " pre_region = self._resolve_edge_region(pre_edge, 284.0, None, \"pre\")\n", - " post_region = self._resolve_edge_region(post_edge, None, 320.0, \"post\")\n", - " energy_all = np.concatenate([df[\"Energy\"].values for df in dfs])\n", - " energy_grid = np.linspace(energy_all.min(), energy_all.max(), 1000)\n", - " mu_arrays = self.build_mu_arrays(energy_grid, formula)\n", - " summary_rows = []\n", - " result_dfs = []\n", - " for df in dfs:\n", - " df = df.copy()\n", - " pre_df = self._parse_edge_region(df, pre_region, edge_type=\"pre\")\n", - " post_df = self._parse_edge_region(df, post_region, edge_type=\"post\")\n", - " pre_post = df.loc[pre_df.index.union(post_df.index)]\n", - " if len(pre_post) < 2:\n", - " result_dfs.append(df)\n", - " continue\n", - " sf = 1 / (pre_post[\"Bare Atom\"] / pre_post[\"PD Corrected\"]).mean()\n", - "\n", - " def _background_si_only(energy, density, si_comp):\n", - " mc = np.interp(energy, energy_grid, mu_arrays[\"chemical\"])\n", - " ms = np.interp(energy, energy_grid, mu_arrays[\"Si\"])\n", - " return (mc + ms * si_comp) * density\n", - "\n", - " def _background_si_oxygen(energy, density, si_comp, o_comp):\n", - " mc = np.interp(energy, energy_grid, mu_arrays[\"chemical\"])\n", - " ms = np.interp(energy, energy_grid, mu_arrays[\"Si\"])\n", - " mo = np.interp(energy, energy_grid, mu_arrays[\"O\"])\n", - " return (mc + ms * si_comp + mo * o_comp) * density\n", - "\n", - " if mode == \"si_only\":\n", - " p0 = (sf, 0.0)\n", - " bounds = ((0, 0), (np.inf, np.inf))\n", - " popt, pcov = curve_fit(\n", - " _background_si_only,\n", - " pre_post[\"Energy\"].values,\n", - " pre_post[\"PD Corrected\"].values,\n", - " p0=p0,\n", - " bounds=bounds,\n", - " )\n", - " density, si_comp = popt[0], popt[1]\n", - " density_err, si_comp_err = np.sqrt(np.diag(pcov))\n", - " o_comp, o_comp_err = np.nan, np.nan\n", - " else:\n", - " p0 = (sf, 0.0, 0.0)\n", - " bounds = ((0, 0, 0), (np.inf, np.inf, np.inf))\n", - " popt, pcov = curve_fit(\n", - " _background_si_oxygen,\n", - " pre_post[\"Energy\"].values,\n", - " pre_post[\"PD Corrected\"].values,\n", - " p0=p0,\n", - " bounds=bounds,\n", - " )\n", - " density, si_comp, o_comp = popt[0], popt[1], popt[2]\n", - " errs = np.sqrt(np.diag(pcov))\n", - " density_err, si_comp_err, o_comp_err = errs[0], errs[1], errs[2]\n", - " if o_comp_err > abs(o_comp):\n", - " warnings.warn(\n", - " \"Oxygen composition error bars exceed the value itself; results may be unreliable\",\n", - " UserWarning,\n", - " stacklevel=2,\n", - " )\n", - " df[\"Density Scaled\"] = df[\"PD Corrected\"] / density\n", - " e_full = df[\"Energy\"].values\n", - " mu_si_full = np.interp(e_full, energy_grid, mu_arrays[\"Si\"])\n", - " df[\"Si-subtracted\"] = df[\"Density Scaled\"] - mu_si_full * si_comp\n", - " if mode == \"si_and_oxygen\":\n", - " mu_o_full = np.interp(e_full, energy_grid, mu_arrays[\"O\"])\n", - " df[\"O-subtracted\"] = df[\"Si-subtracted\"] - mu_o_full * o_comp\n", - " sample = df[\"Sample\"].iloc[0] if \"Sample\" in df.columns else \"\"\n", - " angle = df[\"Angle\"].iloc[0] if \"Angle\" in df.columns else np.nan\n", - " exp = df[\"Experiment\"].iloc[0] if \"Experiment\" in df.columns else np.nan\n", - " row = {\n", - " \"Sample\": sample,\n", - " \"Angle\": angle,\n", - " \"Experiment\": exp,\n", - " \"density\": density,\n", - " \"density_err\": density_err,\n", - " \"si_comp\": si_comp,\n", - " \"si_comp_err\": si_comp_err,\n", - " }\n", - " if mode == \"si_and_oxygen\":\n", - " row[\"o_comp\"] = o_comp\n", - " row[\"o_comp_err\"] = o_comp_err\n", - " summary_rows.append(row)\n", - " result_dfs.append(df)\n", - " summary_df = pd.DataFrame(summary_rows)\n", - " return result_dfs, summary_df\n" - ], - "execution_count": 93, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": {}, - "source": [ - "\"\"\"\n", - "NEXAFS IO: load from filesystem, accessor for angle filtering and export.\n", - "\"\"\"\n", - "\n", - "from pathlib import Path\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "\n", - "def load_nexafs(\n", - " path: str | Path,\n", - " sample_name: str | None = None,\n", - " formula: str = \"C8H8\",\n", - " pre_edge: float | tuple[float | None, float | None] = 284.0,\n", - " post_edge: float | tuple[float | None, float | None] = 320.0,\n", - " mode: str = \"si_only\",\n", - ") -> pd.DataFrame:\n", - " \"\"\"\n", - " Load NEXAFS data from path: discovers files, computes TEY absorption,\n", - " applies Si background subtraction. Pre/post edge bounds are user-configurable.\n", - "\n", - " Parameters\n", - " ----------\n", - " path : str | Path\n", - " Directory containing NEXAFS scan files (izero*.txt, sample_angle_exp.txt).\n", - " sample_name : str | None\n", - " If set, load only this sample; else load all samples.\n", - " formula : str\n", - " Chemical formula for normalization, e.g. \"C8H8\".\n", - " pre_edge : float | tuple\n", - " Pre-edge region. Float = upper bound (None, val); tuple = (start, stop).\n", - " post_edge : float | tuple\n", - " Post-edge region. Float = lower bound (val, None); tuple = (start, stop).\n", - " mode : str\n", - " \"si_only\" or \"si_and_oxygen\" for background fit.\n", - "\n", - " Returns\n", - " -------\n", - " pd.DataFrame\n", - " Processed data with Si-subtracted, Density Scaled, Energy, Angle, etc.\n", - " Use df.nexafs.by_angle(55) or df.nexafs.save_parquet(...).\n", - " \"\"\"\n", - " nd = NexafsDirectory(path)\n", - " df, _ = nd.process_sample(\n", - " sample_name=sample_name,\n", - " formula=formula,\n", - " pre_edge=pre_edge,\n", - " post_edge=post_edge,\n", - " mode=mode,\n", - " )\n", - " return df\n", - "\n", - "\n", - "@pd.api.extensions.register_dataframe_accessor(\"nexafs\")\n", - "class NexafsAccessor:\n", - " def __init__(self, pandas_obj: pd.DataFrame):\n", - " self._obj = pandas_obj\n", - "\n", - " def by_angle(self, angle: float) -> pd.DataFrame:\n", - " \"\"\"Return rows for the given Angle.\"\"\"\n", - " if \"Angle\" not in self._obj.columns:\n", - " raise ValueError(\"DataFrame has no 'Angle' column\")\n", - " return self._obj[self._obj[\"Angle\"] == angle].copy()\n", - "\n", - " def angles(self) -> list[float]:\n", - " \"\"\"List unique angles in the data.\"\"\"\n", - " if \"Angle\" not in self._obj.columns:\n", - " return []\n", - " return sorted(self._obj[\"Angle\"].dropna().unique().tolist())\n", - "\n", - " def save_parquet(self, path: str | Path) -> None:\n", - " \"\"\"Save to parquet file. Requires pyarrow.\"\"\"\n", - " self._obj.to_parquet(path, index=False)\n", - "\n", - " def save_csv(self, path: str | Path) -> None:\n", - " \"\"\"Save to CSV file.\"\"\"\n", - " self._obj.to_csv(path, index=False)\n", - "\n", - " def plot(\n", - " self,\n", - " ycol: str = \"Si-subtracted\",\n", - " ax=None,\n", - " cmap: str = \"viridis\",\n", - " show_bare_atom: bool = False,\n", - " bare_atom_kwargs=None,\n", - " ):\n", - " \"\"\"\n", - " Plot NEXAFS spectra with angle-colormap. Requires Energy and Angle columns.\n", - "\n", - " Parameters\n", - " ----------\n", - " ycol : str\n", - " Column to plot on y-axis (e.g. \"Si-subtracted\", \"Density Scaled\", \"Mass Abs.\").\n", - " ax : matplotlib Axes, optional\n", - " Axes to plot on. If None, creates a new figure.\n", - " cmap : str\n", - " Colormap name for angle encoding (default \"viridis\").\n", - " show_bare_atom : bool\n", - " If True, plot Bare Atom Step reference (from first angle).\n", - " bare_atom_kwargs : dict, optional\n", - " kwargs for bare atom line.\n", - "\n", - " Returns\n", - " -------\n", - " matplotlib.axes.Axes\n", - " \"\"\"\n", - " if \"Energy\" not in self._obj.columns:\n", - " raise ValueError(\"DataFrame has no 'Energy' column\")\n", - " if ycol not in self._obj.columns:\n", - " raise ValueError(f\"DataFrame has no '{ycol}' column\")\n", - " if ax is None:\n", - " _, ax = plt.subplots()\n", - " df = self._obj\n", - " if \"Angle\" in df.columns:\n", - " angles = sorted(df[\"Angle\"].dropna().unique())\n", - " norm = plt.Normalize(min(angles), max(angles))\n", - " sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)\n", - " if show_bare_atom and \"Bare Atom Step\" in df.columns:\n", - " first = df[df[\"Angle\"] == angles[0]]\n", - " plot_kwargs = {\"color\": \"black\", \"lw\": 1}\n", - " if bare_atom_kwargs is not None:\n", - " plot_kwargs.update(bare_atom_kwargs)\n", - " ax.plot(first[\"Energy\"], first[\"Bare Atom Step\"], **plot_kwargs)\n", - " for angle in angles:\n", - " sub = df[df[\"Angle\"] == angle]\n", - " color = sm.to_rgba(angle)\n", - " ax.plot(sub[\"Energy\"], sub[ycol], label=f\"{angle:.1f} deg\", color=color)\n", - " ax.legend(title=\"Angle\", handlelength=0.5, fontsize=10, ncol=2, frameon=True, fancybox=False, framealpha=1)\n", - " plt.colorbar(sm, ax=ax, pad=0.02).set_label(\"Angle (deg)\")\n", - " else:\n", - " if show_bare_atom and \"Bare Atom Step\" in df.columns:\n", - " plot_kwargs = {\"color\": \"black\", \"lw\": 1}\n", - " if bare_atom_kwargs is not None:\n", - " plot_kwargs.update(bare_atom_kwargs)\n", - " ax.plot(df[\"Energy\"], df[\"Bare Atom Step\"], **plot_kwargs)\n", - " ax.plot(df[\"Energy\"], df[ycol], label=ycol)\n", - " ax.set_xlabel(\"Energy\")\n", - " ax.set_ylabel(ycol)\n", - " sample = df[\"Sample\"].iloc[0] if \"Sample\" in df.columns else \"\"\n", - " ax.set_title(f\"Sample: {sample}\" if sample else \"NEXAFS\")\n", - " ax.ticklabel_format(style=\"sci\", axis=\"y\", scilimits=(0, 0))\n", - " return ax\n" - ], - "execution_count": 95, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": {}, - "source": [ - "df = load_nexafs(\n", - " path,\n", - " sample_name=\"ps_100kd\",\n", - " formula=\"C8H8\",\n", - " pre_edge=(283, 284),\n", - " post_edge=(330, 370),\n", - " mode=\"si_only\",\n", - ")\n", - "df.nexafs.plot(cmap=\"rainbow\", show_bare_atom=True)\n", - "plt.xlim(284, 290)" - ], - "execution_count": 208, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(284.0, 290.0)" - ] - } - }, - { - "output_type": "display_data", - "data": { - "image/png": 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", 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" - ] - } - } - ] - }, - { - "cell_type": "code", - "metadata": {}, - "source": [ - "target = df.nexafs.by_angle(55)" - ], - "execution_count": 209, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": {}, - "source": [ - "from functools import cached_property\n", - "from scipy.special import erf\n", - "import numpy as np\n", - "\n", - "# -----------------------------------------------------------------------------\n", - "# Protocol: every component has n_params, param_names, p0, bounds, __call__(energy, *params)\n", - "# -----------------------------------------------------------------------------\n", - "\n", - "class SummedModel:\n", - " \"\"\"Holds a list of components; callable as (energy, *all_params); supports .p0 and .bounds.\"\"\"\n", - "\n", - " def __init__(self, components):\n", - " self._components = list(components)\n", - "\n", - " @property\n", - " def n_params(self):\n", - " return sum(c.n_params for c in self._components)\n", - "\n", - " @property\n", - " def param_names(self):\n", - " names = []\n", - " for i, c in enumerate(self._components):\n", - " for name in c.param_names:\n", - " names.append(f\"{c.__class__.__name__}_{i}_{name}\")\n", - " return tuple(names)\n", - "\n", - " @property\n", - " def p0(self):\n", - " return np.concatenate([np.atleast_1d(c.p0) for c in self._components])\n", - "\n", - " @property\n", - " def bounds(self):\n", - " lo = np.concatenate([np.atleast_1d(c.bounds[0]) for c in self._components])\n", - " hi = np.concatenate([np.atleast_1d(c.bounds[1]) for c in self._components])\n", - " return (lo, hi)\n", - "\n", - " def __call__(self, energy, *params):\n", - " if len(params) != self.n_params:\n", - " raise ValueError(f\"Expected {self.n_params} params, got {len(params)}\")\n", - " E = np.atleast_1d(energy)\n", - " out = np.zeros_like(E, dtype=float)\n", - " offset = 0\n", - " for c in self._components:\n", - " n = c.n_params\n", - " chunk = params[offset : offset + n]\n", - " out += np.atleast_1d(c(E, *chunk))\n", - " offset += n\n", - " return out.squeeze() if out.size == 1 else out\n", - "\n", - " def __add__(self, other):\n", - " return SummedModel(self._components + [other])\n", - "\n", - " def __radd__(self, other):\n", - " if other == 0:\n", - " return self\n", - " return SummedModel([other] + self._components)\n", - "\n", - " def plot_components(\n", - " self,\n", - " energy,\n", - " params,\n", - " ax=None,\n", - " fill=True,\n", - " ip_color=\"darkgreen\",\n", - " peak_colors=None,\n", - " fit_color=\"red\",\n", - " fit_kwargs=None,\n", - " data=None,\n", - " ):\n", - " \"\"\"\n", - " Plot the full model and its parts: IP as baseline, then each peak as (IP + peak).\n", - "\n", - " - IP is drawn first (bottom layer), then each peak as fill or line on top.\n", - " - If fill=True, fills between IP and (IP + peak_i) for each peak.\n", - " - Optionally pass data=(x, y) to plot data points.\n", - " \"\"\"\n", - " import matplotlib.pyplot as plt\n", - " import numpy as np\n", - "\n", - " ax = ax or plt.gca()\n", - " energy = np.asarray(energy)\n", - " fit_kwargs = fit_kwargs or {}\n", - "\n", - " # Split params by component (same order as __call__)\n", - " offset = 0\n", - " comp_params = []\n", - " for c in self._components:\n", - " n = c.n_params\n", - " comp_params.append(params[offset : offset + n])\n", - " offset += n\n", - "\n", - " # Separate IonizationPotential and others (Peaks)\n", - " ip_curves = []\n", - " peak_components_with_params = []\n", - " for c, p in zip(self._components, comp_params):\n", - " if c.__class__.__name__ == \"IonizationPotential\":\n", - " ip_curves.append(np.atleast_1d(c(energy, *p)))\n", - " else:\n", - " peak_components_with_params.append((c, p))\n", - "\n", - " # Single IP baseline (use first if multiple, else zeros)\n", - " if ip_curves:\n", - " ip_curve = ip_curves[0]\n", - " if len(ip_curves) > 1:\n", - " ip_curve = sum(ip_curves)\n", - " else:\n", - " ip_curve = np.zeros_like(energy)\n", - "\n", - " # Default peak colors (repeat if fewer than len(peak_colors))\n", - " if peak_colors is None:\n", - " peak_colors = [\"#1f77b4\", \"#ff7f0e\", \"#2ca02c\", \"#d62728\", \"#9467bd\", \"#8c564b\", \"#e377c2\", \"#7f7f7f\"]\n", - " n_peaks = len(peak_components_with_params)\n", - " colors = list(peak_colors)[:n_peaks]\n", - " while len(colors) < n_peaks:\n", - " colors.extend(peak_colors)\n", - "\n", - " # 1) Draw IP first (bottom layer): fill from 0 to IP\n", - " if fill:\n", - " ax.fill_between(energy, 0, ip_curve, color=ip_color, alpha=0.6, label=\"Ionization potential\")\n", - " else:\n", - " ax.plot(energy, ip_curve, color=ip_color, lw=2, label=\"Ionization potential\")\n", - "\n", - " # 2) Draw each peak as (IP + peak): fill between IP and IP+peak, or line\n", - " for i, (comp, p) in enumerate(peak_components_with_params):\n", - " peak_curve = np.atleast_1d(comp(energy, *p))\n", - " top = ip_curve + peak_curve\n", - " color = colors[i]\n", - " label = getattr(comp, \"label\", None) or f\"Peak {i+1}\"\n", - " if fill:\n", - " ax.fill_between(energy, ip_curve, top, color=color, alpha=0.6, label=label)\n", - " else:\n", - " ax.plot(energy, top, color=color, lw=1.5, label=label)\n", - "\n", - " # 3) Full model (fit line)\n", - " total = self(energy, *params)\n", - " ax.plot(energy, total, color=fit_color, lw=2, label=\"fit\", **fit_kwargs)\n", - "\n", - " # 4) Optional data\n", - " if data is not None:\n", - " x_data, y_data = data[0], data[1]\n", - " ax.plot(x_data, y_data, \"k.\", label=\"data\", zorder=5)\n", - "\n", - " ax.legend(loc=\"best\", fontsize=8)\n", - " return ax\n", - "\n", - "\n", - "class Peak:\n", - " \"\"\"Gaussian peak: (energy_center, intensity, width). Fittable.\"\"\"\n", - "\n", - " def __init__(self, energy, intensity, width):\n", - " self.energy = float(energy)\n", - " self.intensity = float(intensity)\n", - " self.width = float(width)\n", - "\n", - " @property\n", - " def n_params(self):\n", - " return 3\n", - "\n", - " @property\n", - " def param_names(self):\n", - " return (\"energy\", \"intensity\", \"width\")\n", - "\n", - " @property\n", - " def p0(self):\n", - " return np.array([self.energy, self.intensity, self.width])\n", - "\n", - " @property\n", - " def bounds(self):\n", - " return (\n", - " np.array([self.energy - 2, 0.0, 0.01]),\n", - " np.array([self.energy + 2, np.inf, 50]),\n", - " )\n", - "\n", - " def __call__(self, energy, *params):\n", - " if len(params) != 3:\n", - " raise ValueError(\"Peak expects 3 params: energy, intensity, width\")\n", - " center, amp, w = params[0], params[1], params[2]\n", - " E = np.atleast_1d(energy)\n", - " return amp * np.exp(-((E - center) ** 2) / (2 * w**2))\n", - "\n", - " def __add__(self, other) -> SummedModel:\n", - " return SummedModel([self, other])\n", - "\n", - " def __radd__(self, other)->SummedModel:\n", - " if other == 0:\n", - " return SummedModel([self])\n", - " return SummedModel([other, self])\n", - "\n", - "\n", - "class IonizationPotential:\n", - " \"\"\"Pre/post polynomials + convolved step. Fittable params: ionization_energy, width.\"\"\"\n", - "\n", - " def __init__(self, ionization_energy, width, lineshape, formula):\n", - " self.ionization_energy = float(ionization_energy)\n", - " self.width = float(width)\n", - " self.lineshape = lineshape\n", - " self.formula = formula\n", - " self.mu = lambda energy: NexafsDirectory.mu(energy=np.asarray(energy), formula=formula)\n", - " self._pre_edge_coefs()\n", - " self._post_edge_coefs()\n", - "\n", - " def _pre_edge_coefs(self):\n", - " dummy_energy = np.linspace(270, 284, 1000)\n", - " mu_values = self.mu(dummy_energy)\n", - " self.pre_edge_coefs = np.polyfit(dummy_energy, mu_values, 3)\n", - "\n", - " def _post_edge_coefs(self):\n", - " dummy_energy = np.linspace(320, 370, 1000)\n", - " mu_values = self.mu(dummy_energy)\n", - " self.post_edge_coefs = np.polyfit(dummy_energy, mu_values, 3)\n", - "\n", - " def pre_edge_poly(self, energy):\n", - " return np.polyval(self.pre_edge_coefs, np.asarray(energy))\n", - "\n", - " def post_edge_poly(self, energy):\n", - " return np.polyval(self.post_edge_coefs, np.asarray(energy))\n", - "\n", - " @property\n", - " def n_params(self):\n", - " return 2\n", - "\n", - " @property\n", - " def param_names(self):\n", - " return (\"ionization_energy\", \"width\")\n", - "\n", - " @property\n", - " def p0(self):\n", - " return np.array([self.ionization_energy, self.width])\n", - "\n", - " @property\n", - " def bounds(self):\n", - " return (\n", - " np.array([270.0, 0.05]),\n", - " np.array([310.0, 5]),\n", - " )\n", - "\n", - " def ideal_step(self, energy, E0):\n", - " E = np.asarray(energy, dtype=float)\n", - " result = np.empty_like(E, dtype=float)\n", - " pre_mask = E < E0\n", - " post_mask = E >= E0\n", - " result[pre_mask] = self.pre_edge_poly(E[pre_mask])\n", - " result[post_mask] = self.post_edge_poly(E[post_mask])\n", - " return result.squeeze() if E.ndim == 0 or result.size == 1 else result\n", - "\n", - " def _convolve_step(self, energy, E0, width):\n", - " E = np.atleast_1d(energy)\n", - " t_max = 5 * width\n", - " t = np.linspace(-t_max, t_max, 1000)\n", - " dt = t[1] - t[0]\n", - " if self.lineshape == \"err\":\n", - " g = np.exp(-(t**2) / (2 * width**2))\n", - " g /= g.sum() * dt\n", - " else:\n", - " raise ValueError(f\"Unknown lineshape: {self.lineshape}\")\n", - " result = np.array([np.sum(self.ideal_step(Ei - t, E0) * g) * dt for Ei in E])\n", - " return result.squeeze() if result.size == 1 else result\n", - "\n", - " def __call__(self, energy, *params):\n", - " if len(params) != 2:\n", - " raise ValueError(\"IonizationPotential expects 2 params: ionization_energy, width\")\n", - " E0, width = params[0], params[1]\n", - " return self._convolve_step(np.atleast_1d(energy), E0, width)\n", - "\n", - " def __add__(self, other):\n", - " return SummedModel([self, other])\n", - "\n", - " def __radd__(self, other):\n", - " if other == 0:\n", - " return self\n", - " return SummedModel([other, self])\n" - ], - "execution_count": 210, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": {}, - "source": [ - "def building_block(\n", - " energy,\n", - " ionization_energy,\n", - " ionization_width,\n", - " pk1_energy,\n", - " pk1_intensity,\n", - " pk1_width,\n", - " pk2_energy,\n", - " pk2_intensity,\n", - " pk2_width,\n", - " pk3_energy,\n", - " pk3_intensity,\n", - " pk3_width,\n", - " pk4_energy,\n", - " pk4_intensity,\n", - " pk4_width,\n", - " pk5_energy,\n", - " pk5_intensity,\n", - " pk5_width,\n", - " pk6_energy,\n", - " pk6_intensity,\n", - " pk6_width,\n", - "):\n", - " ionization = IonizationPotential(ionization_energy, ionization_width, \"err\", \"C8H8\")\n", - " pk1 = Peak(pk1_energy, pk1_intensity, pk1_width)\n", - " pk2 = Peak(pk2_energy, pk2_intensity, pk2_width)\n", - " pk3 = Peak(pk3_energy, pk3_intensity, pk3_width)\n", - " pk4 = Peak(pk4_energy, pk4_intensity, pk4_width)\n", - " pk5 = Peak(pk5_energy, pk5_intensity, pk5_width)\n", - " pk6 = Peak(pk6_energy, pk6_intensity, pk6_width)\n", - " return (ionization + pk1 + pk2 + pk3 + pk4 + pk5 + pk6)(\n", - " energy,\n", - " ionization_energy,\n", - " ionization_width,\n", - " pk1_energy,\n", - " pk1_intensity,\n", - " pk1_width,\n", - " pk2_energy,\n", - " pk2_intensity,\n", - " pk2_width,\n", - " pk3_energy,\n", - " pk3_intensity,\n", - " pk3_width,\n", - " pk4_energy,\n", - " pk4_intensity,\n", - " pk4_width,\n", - " pk5_energy,\n", - " pk5_intensity,\n", - " pk5_width,\n", - " pk6_energy,\n", - " pk6_intensity,\n", - " pk6_width,\n", - " )" - ], - "execution_count": 211, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": {}, - "source": [ - "from scipy.signal import find_peaks\n", - "# locate peaks in the fit\n", - "peaks, _ = find_peaks(target[\"Si-subtracted\"].values, distance=5, prominence=500)\n", - "pk_energies = target[\"Energy\"].values[peaks]\n", - "# Add in the energies at 290,5, and 302\n", - "pk_energies = np.sort(np.concatenate([pk_energies, [290.5, 302]]))\n", - "# filter out the energies less than 284 and greater than 310\n", - "pk_energies = pk_energies[(pk_energies > 284) & (pk_energies < 310)]\n", - "peaks = np.searchsorted(target[\"Energy\"].values, pk_energies)\n", - "\n", - "domain = {\n", - " \"ionization_energy\":{\"value\":295, \"min\":290, \"max\":310},\n", - " \"ionization_width\":{\"value\":.5, \"min\":0.1, \"max\":1},\n", - "}\n", - "\n", - "fig, ax = plt.subplots()\n", - "ax.plot(target[\"Energy\"], target[\"Si-subtracted\"], \"k.\")\n", - "ip = lambda energy: IonizationPotential(295, .5, \"err\", \"C8H8\")(\n", - " energy,\n", - " 295,\n", - " .5\n", - ")\n", - "ax.fill_between(target[\"Energy\"], 0, ip(target[\"Energy\"]), color=\"grey\", alpha=0.5)\n", - "\n", - "for i, energy in enumerate(pk_energies):\n", - " pk_intensity = target[\"Si-subtracted\"].values[peaks[i]] - ip(energy)\n", - " domain[f\"pk{i+1}_energy\"] = {\"value\":energy, \"min\":energy-1.5, \"max\":energy+1.5}\n", - " domain[f\"pk{i+1}_intensity\"] = {\"value\":pk_intensity, \"min\":pk_intensity/2, \"max\":pk_intensity*100}\n", - " domain[f\"pk{i+1}_width\"] = {\"value\":.5, \"min\":0.01, \"max\":30}\n", - " pk = Peak(energy, pk_intensity, .5)\n", - " ax.plot(target[\"Energy\"], ip(target[\"Energy\"]) + pk(target[\"Energy\"], *pk.p0))\n", - " ax.axvline(energy, color=\"red\", lw=2)\n", - "\n", - "# ax.set_xlim(284, 310)" - ], - "execution_count": 258, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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ga9j1xMlFM0AWG9yxWDb4Y6Fzww03GCLETtBYw1d2/OY3v6E99tjDcfsvf/nLnbpuAAAAmQUiB2R/QKeS22aAdjOrIpGIaALolGSczMmR3HfffY7bFi1aROPGjUv7egEAAGQHiBzQ7gZ0VlRU2K4vLy93FDSpihzugjxy5MiEpesQOgAAUBhA5ICcdjxOVUzsDA0NDbbrGxsbtWuItC1cZe6kfPrppycUOhMmTEj5fAAAALIDRA7IacfjQnBy7JoEpiu+XnnlFRo/frzj9vfff5+OPfZYJCMDAEAegcgB7a6EfPr06Wk7OVE5Mj0NZs+enTB0NWvWLOrXrx9NmTIFYgcAAPIARA7IGpE8iJxwJGKbHCwHczLW4Zw7E0a75557ku7zxBNPCLGDXjoAAJBbIHJA1pCGibm6KusiJxy2DUddddVVrY0ALZVXYl0bS9sHDx4sGg2mAnrpAABAboHIAbkNV2W5hNzj8cSJDn58xRVXGI/tSsgTza5KBAsndmpSFTrcrwcAAEBugMgBue14HFVtnZZM4XG74wZqnnvuuYaLYw5XmWdbtdXJYbiT8k8//UR/+MMfku77j3/8gy6//PI2PxcAAIDUgcgBWcOp8V82I1b1DQ00Y8aMOGFhDhPZJh7v5EWxiOL8nLVr19LBBx+ccN+HH35Y9NsBAACQXSByQPY7HlvWh21yYjJFbW1t/HWYuh07NgPM0LgJFjv/+te/4iagW5k5cybdfPPNGXlOAAAA9kDkgKxhJBlbfu+zqHFs4XwZWVmV6ZwcJ6HDE8+T5enccccdtGDBgow9LwAAgFggckDOw1XZdHLs+Nvf/haTk2M4OeaZWhkeHCrzdLhHTiJGjx6dUhk6AACA9IHIATnoeKzkvCGg5NRTT41LCN6ZAZ3pwMJq6tSpSUXMtddeS/fee2/Gnx8AADo6EDkgazgJh2yIHG4CaMerr74a15tGXpeSpXCVFRZZjz32WMJ9rrnmGvTQAQCADAORA7JGnJhRstcrh5sA2l6DJenYaRhnpsNVVk488cSk+2CoJwAAZBaIHJA1pJiRjolcZiMlh5sA2mEe5xCXK2TOyclyCI1DV3fffXfCfZYvX05nnHFGVq8DAAA6EhA5IGvYJfhmy8lpbm62XX/XXXfFJB07OznZT4bmkFSy/BweLrp5y5asXwsAAHQEIHJATjseZ8M14VwWu/44zKhRoxI8v5JQ+GQrP+fzzz9PuE8gEMjJtQAAQHsHIgdkDRkWCgVDYinHObz8yvSMPs+KFStS6o+T6+oqJw444AB66qmnku63Y8eOnFwPAAC0VyByQNYTj5tbYkNJt9z6x4xWEr333nuOwzCtoaqY6qoc5uTY9dFJ5ug0NDaitBwAAHYCiByQNYw0F6kfdCeHHR5rxVNbYbH017/+lezSjo844gjbY+wqqXIVrkrX0UFpOQAAtB2IHJCDjsexAsLl8dqGkdrC66+/TpM6daKuluoqp1CVuBqbxOdcOzlmR4eHepqv1e9xi6VXHwtxzjnn5OXaAACg2IHIAVkPV5WVlcWsv+XWW23DSG3hP//5D13ZrXvc+uOPP97xOfLRJycRfJ1z5swxHpd4vWJZ6tOWH374Ib3xxht5uz4AAChWIHJA1kWOz+cTSzmw8pe/PCkj5+dy7NmzZ9tuu+WWWxyPs51dlScnxyx0eM5VmS5s7JoJ3nTTTTm/LgAAKGYgckDOOh4reqZvJvrkcJ7Kddddp53Xsu3oo48W+S5OqDYtcfLp5EhuvvlmqiotiXm/XKbsaM49Ov300/N0dQAAUHxA5ICcdTw21mfANeGycbvcGua2225LeGyuZ1el4+b88YbrY9ZVlGgumGTGjBm0YMGCHF8ZAAAUJxA5IGtYw0LSlGhNSG47gwcPNpwhszHDaxobGxNfl11OTgGIHOaQA8fEPK70++P2mTlzZg6vCAAAiheIHJA1nMJSmXBy2PWYOHGiuO81hXT4A52scstwbUzHFUK4imnYvi3mcWVJvMj585//TE8//XQOrwoAAIoTiByQNaSYUbLgmnBOzn//+19xv8QkViorK5NWbtk9fy5mV6VCS0N9UpHDXHzxxeifAwAASYDIAVkj3rHJXOIx5+REo1HiWiRzcq7XYRo5Jcm/KYScHCZk6Q7t99q/Hs5HmjdvXo6uCgAAihOIHJC76iqH9W3NyeGSdL9eli5xu7VGeuk7OYUhcoItLTGPj5owwXHf+++/PwdXBAAAxQtEDsgaMsFYMTKPY9fvDBySeuKJJ6jM2unYPJDKAenaKAWYeBxsjnVyDh03VjQ2tGP+/Pl0+eWX5+jKAACg+IDIAVlDOjahcMiyPjPn5344U//+97SPs2sGWDjhqqaYx8HmJtHteIKDo/Pwww9jiCcAAGRK5Kxfv17M0unatSuVlpbS3nvvTV988UVMrsCtt95Ku+yyi9jOQxI5f8LMtm3b6Oyzz6aqqirq1KmTmN/T0NAQs88333xDhxxyCJWUlFC/fv3o7rvvjrsW7hkyZMgQsQ9fx5tvvpnuywFZpGbjJrGsq60Vy2gkkrFwFVcXDRgwgP5gdTJScIkKbaxDIidHPr7zzjsdj+GmiEhCBgCAnRQ527dvp3HjxpHX66W33nqLli5dSvfddx917tzZ2IfFyN///neaOnUqffbZZ1ReXi7+4m4x5RqwwFmyZAm9++674q/Ujz76iCZPnmxsr6uro6OOOkr8iC1cuFC07//Tn/4kwhOSuXPn0plnnikE0qJFi+ikk04St2+//TadlwSyBP/orv5pTcy6cDickXAVn5s/L5x47LeGp1I4t21OTrRQRY7m7HAH50MPPdT2GH4fMjXVHQAAOqzI+dvf/iZclWeffZZGjx5Nu+66qxAju+++u+HiPPjgg6I9/a9+9SvaZ5996IUXXqANGzbQ//73P7HPd999R7NmzaKnnnqKxowZQwcffLCw3F9++WWxH/PPf/6TgsEgPfPMMzRs2DCaNGmSyD0wJ1o+9NBDdMwxx9A111xDQ4cOFV1u999/f3rkkUcy+w6BNiHcO8X+47WzTo6srGJKLInHKTk5hZx4HIhNPA40tYav+N+FEwhZAQDAToqc1157jUaNGkWnnXYa9ejRg/bbbz968sknje2rVq2impoaEaKSVFdXCzEjy115ySEqPo+E9+dKGXZ+5D78V6sc7MiwG7Rs2TLhJsl9zM8j90lUVhsIBIRLZL6B7MDVT2QVIDo76+TIyiprj5y0nZxCzMlxcHLMQzzt4C7IGPcAAAA7IXJ+/PFHevzxx8WPzNtvv02//e1vhcPy/PPPi+0scJiePXvGHMeP5TZeskAy4/F4qEuXLjH72J3D/BxO+8jtdnBeA4sueWNXCmQH/kHu3UdryqeSJiC8es+XnXVyZGUVl4v7LW5RKmeWOTlG1VeBODnshDqFqyTjx493PJ5DxwAAANoocjhEwCEhnobMLg7nRXDnVc6/KQZuuOEGqq2tNW5r167N9yW1ayqrqsWyU3WnmB426YoczsFp0n/8w3ryMtPmnBybXQohJyccDJBqGZEesIiesWPHOh7/yiuvIAEZAADaKnK4YmqvvfaKWcf5MGvWaAmmvXr1EsuNGzfG7MOP5TZebtqkVd2YE1K54sq8j905zM/htI/cboff7xcVXeYbyB5SzPh83JeYUdIOV3EVVf/+/WnbDm2mEzt1nIjOApudD58rfZGj2tSwF0K4yuriiHWmnBzpYnE+mxN33HFHVq4NAADavcjhyirOizGzfPlyUQXFcCIyi4zZs2cb2znvhXNtDjroIPGYlzt27BBVU5L3339f/FXOuTtyH664CoVa+6twJdaee+5pVHLxPubnkfvI5wH5p9WxUSwdj1M7nl0JdgpdlS7yVrc2/bv++uuNxGOvdTJWClrFclnaNRVAuCqkVyDK6epMNBKmsOnfAcMVhY899pjtOdhVhZsDAABtEDlXXXWV6LLK4SouWX3ppZdEbsSll15qfDlfeeWVdPvtt4sk5cWLF9Ovf/1r6t27tyjvls4PV0Xxj9fnn39On376KV122WWigor3Y8466yyRdMxf5lxqzjY8V1NdffXVxrVcccUVokqL8xC+//57UWLO/Xr4XKDQOh7HkursKq6iYremtF9p7HmjUSPx2BPX4bhtfXIKwsmRc6ssr8mal8OceOKJjueBmwMAAG0QOdyr49VXX6V//etfNHz4cFG2zSXj3PdGcu2119Lvfvc7EU7g/bnJH4sRbthnLoXlJn7cxfW4444TZeTmHjicFPzOO++Iaq2RI0fS73//e9Fg0NxLh3MTpMgaMWIE/fvf/xZl6nxdoDBwyr1JNf+loqJCLF2lpo+pi4u2XKKdAef4eJJPcUjp+e2ET66RYkY6OXJpDVnJsJX534MZuDkAAKCRfGSzhRNOOEHcnOAv5r/85S/i5gRXUrFASQT32Pn4448T7sOl7HwDBS5ypBDRl6kmHssu2N4uXqIf9FO4FDpywpH0hz/8Qbh/G6ZNI5rx753vk5OBeVoZc3IkusgJ2Dg5zC233BLzx4HVzeFKSAAA6MhgdhXIGtawlJKmoOBWBSyaPZ1NWtxF9N577wmngt2M3QcObLOTE2MCqfkPWcnE4zgnx0HkwM0BAIDEQOSArCEdm7icnBTFBP+Ic6jSXaaVnotzKZwkHDHGGKjh1pLylEvIZWhKKawy8rjE4yQiR7o5TiA3BwDQ0YHIAVnDSTSkmngsE8zdvlaRw8qEc3F4JtqcOXOodtvW9JsBOjx/oTg5chyGbFYoxY+TEOREfTumTZsGNwcA0KGByAG5D1elISb4R3zv/feOOcnJJ59MBx54oOj++9D9D+zEFHKloJycYIs18VhbHwoEEh7Hc+KcxFyiMScAANDegcgB2Q9XWcJCqfbJkc0Aly5f2rpCIVFJJ/vkGNk6caXkzjiJmXyPdmjNydFX6HdC1oRkC4m6IHMPKgAA6KhA5ICsEZd7o6QXruJQCyfWKl6TgLFoGXdcm5w0qqvimiXnOycntk+OkXicIFwl3a67777bdhtCVgCAjgxEDsgKLBhaNY7seKykFRbiZoDs2Ci+VjVi1TQeuaYNTo5SsE6OkpaTw1xzzTW2E8r5v8M555yT6UsFAICiACIHZAWzjmlrx2MuIefGfy6f6WNqETM+vfOxoi/b2idHW095xXBsLE5OspwcCXcIt+PDDz+km2++OVOXCQAARQNEDsgKicrEU3VyOAzDze5cfuePqbElHSfHwbGReT75IhSXeCzDVcmdHHPzRDt4FAvCVgCAjgZEDsi+yIlLPE49LMTuRLde3RzPJcc6qOn0yXHKyYkWWLgqhRJyq/PlBIetZG8hAADoKEDkgKxgDklZpjqk1SeHCUaDzonH+op0kobjwlK6qMh7uMrok2MNV7Wk7Hw99dRTjtt5gC0AAHQkIHJAVrB1a5S29aMJUch6CgM5hbw1Jyf9xGOlUBKPW5wSj1MTOdL5ev311223XXfddQhZAQA6FBA5ICskEjLpODk/rfmJImokQbhKW8EJyhrpJB4rhTnWgdqWkyNZvny5Y84RQlYAgI4ERA7ICnZCxghXRVNvBLj7kN0tJ1Fsc3KMkFMK5y3EnBwOt1nDVfL60nFymEMOOcRxG4/DAACAjgJEDsgKUki4FJt+NCmIiXAkIhoBqkrifWWfHLVNYx12LoyWScLBAKl6UpDSxpwcyQEHHEBHHXWU7bbLL798p68VAACKBYgckBXCumBws8ppQ7gqHA5rjQDjWhrH4m6DQHHaN59OjuHiMFLkpFldZXXB7Jg/fz6dcMIJbb1MAAAoKiByQFYTj+1ETiqCxOPxiDybOJFjzcnRV7jcO98MMJ9Ojsy78ZaUtr5EU05OuiMnuNLq9NNPt902c+ZMWrBgwc5dMAAAFAEQOSArRPUfZbcph0aGX1Jxcjxut2gE6PF5Eu8nXQ+3O/Vrs4gZxSmMlUOkW+MrLY17v9RolCLhcNrn/MMf/uC47eKLL27TdQIAQDEBkQOy6uS47MJVKTomXA794ccfpiZy5Io0nBxLzz1DmOWDYLPW7dhXUmqbZJ3K/Cq73JzjjjvOdtvXX3+NUQ8AgHYPRA7IrpNjF65KQ0x062HqdmwzvcFaXcUkC+045uRE8h+uinFy+PV5fW3Oy5GhqSFDhthuw6gHAEB7ByIHZDfx2GamVDpjHUJR2Qiw9Twuj/ax5ZydXfv11zebnidJaCdezKQ3HT2bicfekpKY9fJxur1yzLzwwgu26zHqAQDQ3oHIATlLPE63Tw4TVsMxlUbMspXLaM6cOfTTTz9Rl07V8U5OEpHj5CQVQnVVTLjKJHLSLSNPNWyFvjkAgPYMRA7ICnKgd0y4qg25LxtqNmiHmk7Tc5eedNhhh4kKIjUUTlvkGE6O0Y9GXnMhJB6Xxaz3+kt2KlwlOe+882zXP/PMMzt1XgAAKGQgckBWkBVUrp0IV3Gvl1MmnqIdE47ahLBMgiZFkcMhGqmx4poU5jUnxybx2PQ4uJMix4mpU6ciLwcA0G6ByAE5D1el4uQYHY9dct/WY8LRVhGjRiJxgkUNtYqgdEJS6faiyUpOjinxOFPhKmbs2LGO2375y1/u1LkBAKBQgcgBOW8GmIqTk6jjcayTE0or8ThRSCq/fXJkTo594nFbSsjNcGiPRaMdixYtonHjxu3U+QEAoBCByAEFKXKsHY/NJovZyaGwaUJ5CuGqmJCU5dKihZh4nKGcHOaWW25x3DZ37lyMewAAtDsgckDOOh4bpdppdDx2e+M7GYciO5OTY7NSP1Sfj5kXgg6Jx1L0ZELksJtz4403Om7HuAcAQHsDIgfkrOOx0oaOx49OfVTc9/m0pnhx4So9J8eMUXGVYkjKyBXKa3VVU8KcnOBO5uRI7rjjDho/frzj9kQiCAAAig2IHJDV6io5N9OsJtJJfanqXKUdanJqYsJVMsnY7OQkSDxOmJOTR5ETaErSJ2cnc3LMzJ49m4YNG2a77b333qN77703Y88FAAD5BCIHZIVIxLnjcTqOiRQ05maAtiXkMQclr65ih6mQSsgDTQ1iWVJeYZuTI3N2MsWsWbMct1177bUoKwcAtAsgckCWnZy2h6viXBvLOp7ObZdkkzDxWA7njGlSmP+xDoHGRrH0WzoQ+8vKYwZ4ZopE1VZcSn/77bdn9PkAACAfQOSArCAFw84M6GQ3Yen3Sx2dnBgxk2risRQ5NqXp+QpXsagINDmJHC0ROdCUWZGTrNpq2rRpCFsBAIoeiByQs47H3PeGaQkGkx7f2NREAwYMoL8/8nftfJFwvLvjIGYSJR7LkJQ5Ibr1+vIjcsKBAEX1BGrp3EjkY+n0ZNrN+cMf/uC4/ZprrkHYCgBQ1EDkgJz1ydm2batYrljxgxjZ4NTpmNm+fbsmivQK8mAwlIaTkzzxWLH55OfLyWnR83EUl8vIwZFIZyfQnHmRw1xxxRUJtx966KFZeV4AAMgFEDkg6yJHChcjSqW4aMqUKXEuwQP33EW1W2pi1hlhJVOIy8jJsSkfTzVcFVPanucBnUE9FOUvr4ipIsu2kyPdnLvvvttx+6pVq+iCCy7IynMDAEC2gcgB2Rc5VtHhclEkEqGVK1caq+655x7aMfOP1LU09kc+0VgHo1ScOyObd0g38TjPYx1adAFTYglVxYicpsaszdbisNRNN93kuP3ZZ5+lyy+/PCvPDQAA2QQiB2S94zGPaDCjKC5yu900aNAg8Zgdneuuu45+O6q14Z+xr0cTI36/Pz4nRw7ntJw/XScn3wM6Zfm4NenYLHK4koxzd7IFV1Odf/75jtsffvhhOv3007P2/AAAkA0gckBWiOgjElhM8IgGxpAVLpeo3uFQCbNixQpHgXHMsceIpdfjdc7JsYqcRInHUuTYOET56pNjlI/bODkev1/k6phzd7LFM888Q7vttpvj9hkzZmDsAwCgqIDIAdntk2PKMenaratY9uzZS4xskAwePDguF0U+qu5Sra+wKSHXxUy8k5OgGaAuZGKeL885OU49cuR1cq6OOXcnm3z44YcJt5966qmouAIAFA0QOSArRHQrx1xdxSEqu9ImdnT+9re/kdlckYd9v/x77RDT/q3hKl3kyPOm0QzQ7OTIHjz5GtBp9Mgpi+12bO2VI3N3skmyROQ1a9ZQv379EubwAABAoQCRA7KCjPzYNQOULo81+bW6rDUnRxot3373rVgGTfOorOEqq5NDqTQDLKA+OS2NDTFixoq/VO96rIuhbMP/LTgRPBF//etfacKECTm5HgAAaCsQOSBnHY8TjnWIRsinhOP2ldVVLc0tNiJHLyH3WsNVkTZWV+XHynHqdiyR61tyJHIYbhL4+uuvJ9zn/fffp3HjxuXsmgAAIF0gckDOOh4ndEyCsT/gUoO09slp3RaKSJGjLRW3ReSYuiNbkSEp247HeRpdFWhoSClclSsnR3LCCSfQmDFjEu4zd+5cODoAgIIFIgdkuU8OpRSu+tcLT8U8ltrIrk9OWE1cQp5Kn5yY6iqpo/JUXdVUXyuWZVVVttul+MnG/Kpk/Pvf/066Dzs6xx57LBKSAQAFB0QOyF24yqhiit2Xfxz/fNO1MesUS5+c0rJSGydH5uS4Uw5X2VZXWa451zTt2CGWZdWdkyQeZ7eE3CkR+amnnrJ9v8zMmjVLJCQ7jesAAIB8AJEDskI4wRRyq5PDfXLKPGpCJ8fvi28GaPTDMfXQSRauStQnJ1+zq5pqpcjRy+UtlFZp65vr6igfcLk/V1VNnz5dCJlEXHTRRXB0AAAFA0QOyHrHYyvWxGPuk1NVEvtRlI+kyDELECPx2KGEXIaxUq2uUvLo5ETCIcOhKavuZLtPmS5ymuo0MZQP2NE57bTTRA5OMo45RmvgCAAA+QYiB2QFKWRiE3xb75sFBf+A3vD7KxI6OWGTO2P0yXEoIU+p47H5ky9zcvIgcprqtHwc7mpcWlFpu0+p7vA012r75pNkfXSYJUuWUJ8+feDoAADyDkQOyFnHYyVByOrow2NLkY3DLCaNbQm5NScnJScn/qOfDyenfstmsazo3NX2mpiyqk4xCcr5JpU+Ohs2bBChrbPPPhtiBwCQNyByQM4Sj83E9coJxw6ftPbJMc+uMnJyDCcnNidHdkJOdF2xekLJW3VV3eZNYlnVvbvjPrLqSiYoFwLcR2ft2rVJQ1MvvfQSEpIBAHkDIgfkLvFYic/ZMYgEbZ0cl0f7iLpMqkQ6OcZYB08azQAjhdXxuE53cqq69XDcp7xzF7EMBVryUkaeKHT11ltv0dixY5PuywnJGO4JAMg1EDkg5x2PbZ0ci8gpKy2jOXPm0PARw/VjFWcnJ43ZVWqiPjl5EDm1m2rEsqp7T8d9fCWlRtfj+q2aKCokPv30U9p9992T7jd69GiErwAAOQUiB+S847FdrxzSe99IWBsddthhts0Ak411SCVcVSjVVVvXrRHLrn0Tl2ZXdtXCWfVbt1AhsnLlypQcHRm+gtgBAOQCy68DAJlBjoFyzMlRE+fkWF0bczO6VpHjMNYhUTNA26qv2G1OLHj3TZr7zHQKR+tIUVzkpnLy+LzUZdAudPSUS6lLz14Jj497PlWlLWt/Eve79u2fcN/Krt1oy5rVBenkmB2dm2++me64446UxA7fzjrrLDGBnkNfAACQaeDkgMJIPLY4OaSLoIgaL1gi0UjiEvIE1VWticc2OTkJEo85F+bTp1+mcJSdlCCpaguF1a3UEqihDUsW0bOXX0IPTfoNPfabi2j6X/9M9du3UTK2rV9LgcZG8nh91KVPYienuocmoLb/vIEKmdtvv50+//zzlPeXzs6UKVPg7AAAMg5EDshZ4jHfkw8dE48Nx0aNdXJizi1zcuTsKmtOjkUwmbfZTSE3xk04i5zpt/2JIioLFw+V9hlI5QP3IH9pT/K6OIxUwldFYXULNTfX0NqvF9ATl1xID53Joudi+s+9d1JjfX3cOVd9tVAse+85lDxeS4WYha66CNq2ofCFwAEHHCBGQaTDE088gSosAEDGQbgK5LTjMYsedkwcE48V1t3R+MZ/5sRjfUCnkWAcN6AzhT45tmMdnF/Pjp+0XBifuwsNPXFiTPgs0NJMa+d+SC0/baZIMCLCWez2sOsTbiZaveBnmrrgC/K4Kslb4qHuQwfSwadOoq/emSmOHzT6IEpGlz59Y3J4Ch0eBXH00UfTDTfcQP/4xz9SPo6rsPg4hK8AAJkATg7IYcfj1kRkx3CVFA9qCk6OUUJunV2VvITcNlyVwMkJRZu14zp544ZV+ktKadD4Y2j4+efSiCm/oeG/uZAqdx9Cfn9P8ri6cWY0SyEhepqbamjNwvn00g1XUu3GGlIUD61ZtoIa9c7HTvQYqFUv8TFy1lWhw0LlxRdfFP10Tj755JSP22effWjixIn0xhtvZPX6AADtHzg5IMsdj2PXy/CVc58c+3BVaxjLZqyDO/VwlX11Vfx8LDMLZ79NqqqFm7qM3I+S4S8tpz2OOsF43NJQT6s/fp/CP9dRNKRSWG0gVW0mxd2dvGVH0bqlPejFqz8hb/BHUspq6MyGBmqdua5RUlFB3foNEInKa5cupj0POoSKBRY7//3vf+nee+8V3ZKTsX37drE/3ziExcdx5RbcHQBAusDJATlNPHY7ODkNddvFUrWKHD00RYlycrzphKsobSfnuznva8coldRj4GBKl5KKShpy7K9o+AXn0j5Tfk3Dzz+fOu21L1WWd6OyYA25w00U8ZRSS9kwaqYJFAlrzlTj9kZ6ZvJN9L8H7qParVto4L4jtev55AMqRmSXZJ5mnuoQT97/jDPOEGIn2SgJAACwApEDspx4bJkubuPkcLLpf6b/S9xvCeiOjhpbLm5uBigrroycHHf61VV2icdOYx0aftYEmEvxx3Rebis+fynt/osJNPjs42j3C/al3U5zU0mfj8gXnUUlTV8boTpV8VCzawKtX7YfvXT9F7RslpYX9MMXn9HCt16jYkROM+dOyTfddFNax1577bV0zjnnoAoLAJAyEDkgy05O7Hrp7Mg+OvyDNXnyZPLp+0nxo/L/VNWUeGzn5IQcSsgTdDxOkJPDz2dHuEUTTSxwrPk4mUCInkMOo8FnsejZjzxlcksLlTR9RZ5QPUXdPgpVjCK3f4TY8sFzT9ADk86kR8+9kF64/hr68duvqdjgcvN03Zl//vOfcHUAALkROXfddZf40r/yyiuNdS0tLXTppZdS165dqaKiQiQQbty4Mea4NWvW0PHHH09lZWXUo0cPEacPW1rxf/DBB7T//vuT3++nQYMG0XPPPRf3/I8++igNHDiQSkpKaMyYMWn15wD56XhsTTxesWIFRaNR8ulpNYbMUNWYHjl2Yx3IoYScQgk6HsvrcqfeJ4evTzyPN/MCxw75Wr2lJbT7BfvT7meUUdmuc8lLb1KFWk1e7z7in25UraeW4EbavOo7evW2m+nBSefSI2dfRE9f8Tv6YvYsKqYQ1oEHHpjWcXB1AABZFTk8bG/atGmiEsLMVVddRa+//jrNmDGDPvzwQ9qwYQOdcsopxvZIJCIETjAYpLlz59Lzzz8vBMytt95q7LNq1Sqxz+GHH05fffWVEFFcWvr2228b+7zyyit09dVX0x//+Ef68ssvacSIEaL0dNMmbaozyC8Rp5wci2MzePBg4ZAYIsekM2JcHNNpkpWQJwpXSSfHzpBxysmJ6s+nlFrEVI7grsq7jjmY9ph0Ag26cDTt9evDqcu+I6mkfBfyunqQolQIeRhRt1MgXEM7albRh088Qg9MOpsePutCmjblt/TOC09QKBg7H6yQQljz5s0Tf6Tw98cuu+ySlquDEREAgIxWVzU0NIgvlieffFJYzpLa2lqRX8FdTMePHy/WPfvsszR06FCaP3+++GvtnXfeoaVLl9J7771HPXv2pH333Zduu+02uu666+hPf/oT+Xw+mjp1Ku2666503333iXPw8Z988gk98MADQsgw999/P1188cV0/vnni8d8zMyZM+mZZ56h66+/vi0vC2SjT06SxGP+geNGcP4PLteP0/ZTKDZUlbCEPC4nJz0nx6hadxI5pJWPezpVUiEgRM9BvyA6qDXMtnbR51T/3UqKNoYpHA1QVK2jqFpLwUgtBXcQLZ65lr598z3yKOXkLfFS970G0tEXXUKV+oTzQmkiyDf+t53qeAjziIgbb7xR/LHDoBoLANBmJ4fDUey0HHHEETHrFy5cSKFQKGb9kCFDqH///uIvNYaXe++9txA4EhYudXV1tGTJEmMf67l5H3kOdoH4ucz7sBvAj+U+dgQCAfE85hvIDuGIvciRuTDm2VXcOO7Qg7Vf7MrKKm1lXD5OghLydJoBSicnxeqqLRvWihEOTMWAAVSIcMi4//5jaNjZZ9Pek8+j/S6ZTP2OPJFKuvYhn6c7uZXO4p+6qjZRKLqZmpo20E9fzKUnLrmAHpp0nsjrefGma2ndiu+pUOA/ntLtr/PXv/5VVGLJaix0TwYApO3kvPzyyyI8xOEqKzU1NcKJ6dSpU8x6FjS8Te5jFjhyu9yWaB8WJc3NzaKPBoe97Pb5/nvnL+o777yT/vznP6f7kkGGOx7bCYoSj6a3XaaeN+GwJi6stFZX2ZeQG2GsVKur5HE2ImfZgvn6PRd17qs15CsGegzaU9wk9Vs20rq5n1BkcyNFwtHWrszqVgoHiVpWbqRXbr6GXEoVedx+Ku1SRnsfdySNOfZXee+vc8IJJwiXNl04xM3hdHaHAAAdk7REDv9ldcUVV9C7774rkn2LDW4xz3k8EhZN/BcfyF3HY6c+OfHNAInCIT1M5PLEhKuialTcWkvI3ann5CQa0GkjcmpWrNSuSikhf2nxfeYlld160tBfTowZRbHm0w8osHYLRQPcoLCJVLVBC3GFiYKbiD557kn69PmXtBBXmYd67LUbHXXhFKrsxM5Q7uDOx+mEr8yMHj2a7r777pSaEAIAOrjI4RARJ/Zy1ZOEHZWPPvqIHnnkEZEYzKGkHTt2xLg5XF3Vq5c2RZmX1iooWX1l3sdakcWPq6qqqLS0lNxut7jZ7SPPYQdXavENZB9ZqGR1cuzCVbFjHVojqKFwk1h6XfHDKzlk5TTWQYaxEk4hN1dX6ddo1yenfuNWbRfyZaRHTqHAoygGTzjWeBwOhmjdVwuocfkqijZF9byeWlLVRgrxrYFo9ecb6InPPyePq5o8HjeV96yiA045iYaNPTQn4atLLrlECB3Ov0u3EotDepMmTRLVfJzsjnwdADoGaX1rT5gwgRYvXiwqnuRt1KhRIglZ3vd6vTR79mzjmGXLlomS8YMO0nIueMnnMFdBsTPEAmavvfYy9jGfQ+4jz8EhsZEjR8bsw2W+/FjuAwq747Feld1KOBA/vkE6OUq8Fhd5OXqpePwU8uR9csz9bowey2p8r5xgfaNYusidlR45hQInMw8cPZaGncN5PefSfpdcRANPOJVKe/Ynv68HeZSu+gwuHjy6mVqCNbR17XKa9dDd9MCks+jhsy6gaRdPoTcef4gCTZo4zTQsTB5//HHhKPOSXZp0hM6AAQNEQQQvka8DQMcgLSensrKShg8fHrOuvLxc9MSR6zmJlENCXbp0EcLld7/7nRAesg/GUUcdJcTMueeeK2xkzr9hK5qTmaXLwn+xsTPEX0wXXHABvf/++6IVvDkuz89x3nnnCWHFX3YPPvggNTY2GtVWoFA6Hqfq5NiEq3Qnh8NVcedXw4aYiW8GmLzjsV2fHHFsVI2ZUB5u0Z/D5DB1FLr2GyBukkBzE62Z9xEF1m6iaAuHuJr1EFcdBSN1FKwjWvbBelr2wYfkUSrJ4/VQZd8uNO7Ms2n3ffbNqNjh7wi+pToPi8WrFLD8B9GUKVOMSk24OwC0XzI+oJPLvNnW5yaAXM3EXySPPfaYsZ3DTBxj/+1vfyvED4skFit/+ctfjH24fJwFDffMeOihh8SXz1NPPWV8KTFcQbF582bRX4eFEpeiz5o1Ky4ZGRSYkyP75DhNITcRDrU4ixwRrtLFjKWEPFG4Sk2QeCyvy2UyhlS9NXN7dnFSxV9aRoPHt86cYtGwcdkS2vbNYorWhURDz4hqSWj+cSP9746btYRml598FT7qs99edOR5F5O/zGjtvFPNBDkMxfl23DfHqWu1FQ6z83cLl6uz6OHvLG5lwN8xED0AtB92WuRwZ2IznJDMnYj55gTbxW+++WbC8x522GG0aNGihPtcdtll4gaKp+Nx0sRjc7gq3Jp43Io8PpJgrEMKTo7N7CpxrCWMZoTVOp6RkxQWfr2GDBc3SVNdLa2d+xGFft5B0aB0e+p1t4coWGt2e6qE21PWo4r2PeE42u/w2LYRqcJi5MUXXxTVkytXrhQVWQ8//HDMdTJmAcSiRgochpfcd4v3NYsedqa50SCEDwDFScadHAASOTlJE49NhCOJnRxqQwl5Kk5OOvuDWMqqqmnPY040HrOwWPfNQqpfuoKiDWGKRLlZYa3u9mzR3J51NfT+1OX0wbRnyKOUkqfETZ1370Pjf3Me9eg7MOXnZgHCN/4DiUPdsmcWO8ZcFMEhKnZw2E1ml5hDXclCWtyughuVWoUPAKA4gMgBWUGKmLgBnUbicQpOjk24SjGHqxxKyCkSET9WdiEmWyfHhLXCigeFahfh8EJBQvi/Qb8Ro4j4ptNcX0dr5n1CoQ1bKRqIUkRtpqjM7eFbE1HT4g304u8XklupJrfbR/4qPw08cAQdfub55PX5Up52LmFhwqEodnp4Fh5jdnLsYEEkBY5Z+HDvHe76bnZ24PYAUJjgqxtkueOxq82JxyE7J0cXLiLxWA9LxZWQi/NF4mZamYdwpuzk6I0HFQ+cnExRWllFex51nPGYBenmVStoy6JFFN0RoEg4QuFoA6c6i3lc3CkguI1o8ZtraPGbbxtJzeW9qmnUSb+k4eMOS8vpkbArI90ddmnMTg7D66wiiPflIgqzs8NMnjwZbg8ABQhEDshtx2O7nJxohC2UuHOE9bJypxJyt5GTEz84kwVQ3LgHU85NzOwqXfRwaMra9VglXeT4bYQUyJjb02O3PcRNEgoEaf2i+dT4w1qKNkZEmCui1sYmNa/ZSG///V565+HHyaOUkcfvoeoBPeiQs86k/nvGVoHaYXV3rCEtzvHhOXhWoWN2dljcWNc5uT0AgNwDkQOy3PGY7Mc6mJ0cw8WxJh63xDUDVHSnh0WOy+iTYyNmeJtN40ensQ7sMPE1xzk5pD2HqxRNJHOJ1++jgQceSqR1nhA01m4ToylCNXWkiqTmltiGhc1Ezd//TDNu/YZcSrWo5vKWeqjr4L70i3N+TT36Dkjo7lhFD6/nVhhmt8dJ8CRze+DsAJAfIHJATqeQG+Eq82+DWeSY++To6+0Sj3l+VWsJebyTI2IcaYx1kG1w4pwcVXOLvJUVtucDuaO8ugvteewvY9Zt+uF72vLVYoruaKEoz+RSG8Ug0qi6Q6vmaiBqXLSeXly0QMvvcfnIV+6lnsN2p/G/voCqunCTQ+eQlln4cLsLKV4ksgt2IrcHPXkAyB8QOSCrTk58uEpbxjgmNpVVTDgSSCnx2C4nx6mM3CnxWD6WOTtMY129CI8w/s5dHF4pyCc9dh8ibhLOqVm/eCHVfrecovVhinJ+j2rK72HhU0fUMG8d/TDvE3KLgaRe8lX5qd/+w+nws35DJeXljsLHnMfDIa1p06aJ9YncHqeePHB3AMg+EDkguyInrhmgTeKx7thEFQ9FIxHjQ2kncqTMCUVDRtM/25wcPZQVt94IV8Wul12OzYmna5YvNe5X9OqT5BWDQsnv6bvPKHGLye/56nNq/OEnkd/Dn7GwqglYmdgc2Ea09L21tPS9d8nNic0sfKpLdOFzHpWUlTuGtJhkbo+1Jw/ydgDIDRA5IKcix2WTePzKv/5BZ3AjuUCIdtRso76JwlWKKVxlO9ZBSS1c5U7u5NT8sFy/56aKTrFhDVBk+T1jDibim2kK+/oFc6n5pxqKNkcpEpHdmkOtwmcr0dJ319DSd03Cp8pPfUYMpV9MOocqTNPYE7k9dj15kLcDQG6AyAE563gcjkRo29YtMTk73F/kT7fcSGf8v3KRQ2HGEDmm6ipz4rERkjKLHCVJuMpmQKd4LEWOSXzt+Fmbcq8oPox1aGfwFPbdDplAdEjruuaGelo3/xMKcv+eFtVU0cXCZ5vh+Hw/Zw19P+d9LdTl8pK30kM9huxGh539a+rco1fKPXmQtwNA9oHIAVlBfp+bnRyeMfbhB3OofOih9OncefTrgwaKL3SPogmLoKURnwhJJZpd5TCgM1HXY6O6ysHJMSceN27dpu1LHiPBFLRfSisqafARx8as48aF676YS8F1WzTHJxoyOT5bRY5PYAdRw/x19OP8T4XwcbPwKeOqrv40dLcB1LP/rnE9eVLN27GbpYXGgwCkDkQOyAph/Uvc41KEg2N80PT1r0yfQbeccYj4oi71agKCnRyzlAjrIafYxOPW2VWtOTnxzQKdhnQ6V1fFOznBhmZD5MDJ6biNCwcf3jqUlGlpaqR1C+ZSYO1GijZFdcenoTXHh5Ob64kav9xA//jyM3IpleRW/PT3c39Dnq7l1H/cwXT8qZOS5u3YzdJi0HgQgNSByAEZh4WC1Ars5PB0avlBU1mciP4zirDzec7QLTdeR7TpYSFySkznCSdwckJqyHmsQwrVVXZ9crTjWkVOuFlLfFYwnROY4CTkQb84MmYdJzevWziXmlZvILUxTJFIlCLE5ew8sqJO/HsINXHiGdGSl3+ge047jdz8aXeptD1YT+ou3ekvj2gixmmWllPjQXZ74OgAYA9EDsg45sopj8tFHrPTonc2drk9Rr7CicceTfT8w7RL3/7k77WN41oxIsfcDFDm3IS55a3+Za94bUrI9eGdceudSshtqqt4vIA4P1wckEJy865jDyMaGyt8Nnz7JTWuWEXRBq7q4j4+TaSqDeIWpgbihtqVrNE3raf7zjiV3EqFcCub1Gb6btMamvHJ59SsVwo6NR7kQaTdunVD+AoAGyByQMYxV0653Qp5TE6LdHJ+edLJrV/IeoJxeWVnIjcnepomjTuFq0KtvXUU0/kNQeJQXWX0yXE7hKtMTo6cNCGfE4B0hc+AkQcS8U2HRXTN94tp+9JlFK1toWiIB5QGKKrWC9cnrGohUpbt+3TvTPucfLyo7HIpXgpTkNY3bqXXvlxEK9fLpHiFJk3SQl8IXwEQD0QOyKrI4ZwcSa9evehXvzyR3l8TphH77W86QBcsntjp0nYip/WQ1i7J6SQeq0mqq+RsK/O+0DggU/Dnbpeh+4ibmdqNG6jmywUU3LhD9J/U8ny4lw8vt5P8KPYuddMl40aRolSQWykhlaK0I9RA81evoHcWfosqLQAsQOSAjBM2OzkmkcOOTn/+wl2zmiJm612OdXBbRI4adiwhdxQ5unhxClfJS3Psk2O69tbIlWViOgAZprpnb6o+9ldxCc4/zvuIQus3kSugJSBHRLhLC3lpnZy1cNeRuw+gI3cfJMJdr157KwXUAK2t30Kvf/EV3XTHX+HugA4LRA7IbrjK4phIZ8cshFpFTmxuTVgPbdk6OZyTY5zU5mMcSeLkpFBdJV0dRS9xByDXCc57TYgtaRfhrmVLaPvS7yiyo4XUkEoRNSjCXUYjw4hmPvYv99KlvziA6t6dTX9/71NyeVzkq/bTwNEj6OCTz6Syysq8vTYAcgVEDsha+TjrBmuCL+foMBFzTxwZrrI4OSHVJidHzr6STo7LRYpND5u2zq4yV1cZVg7CVaCQwl1DhosbM3PmTFF6Xl1eSqeOHU1DuvclT9gtxFBsdVedCIMFNhMtnvkTLZ75lnB9uKeP2++mqj5daMQxx9DeBx9O7Q30FerYQOSArDk5XFllxd7JCdiKHNELx2F2lQxXmZOOtRWK4+wqUZLrVEJuiK/WMJpxhaggBwXK8ccfT6NHj6b169dTnz7afDWZiMyM2XM3OnzoXtSjpBO5yU0RNRTn+nBZe/OKn+mdFUvo3UceIzeViXEUvko/dd9zIB18+iTq3mcAFSNPP/00+gp1cCByQMYJR+znVmnrXHEhrVYnxxKu0sub7KaQq2H9GJvycbHdJlxlngkal5PjtqmukvGq+DY8ABQM3bt3FzfJ1VdfbTQW/Hz5Kvps2Y8x+/u9Xpp40P60V69+VOHSBo9G1Ba9tL2ZwtQsemkGthPVz19LP87/mFwi0bmU3B43+TuVUO+9h9C4iWdSddeuBe3gSIHDoK9QxwQiB2TRyYkXOYlzcny2IiemT46lhNzJydH+RI3FHIqKd3Jc8SKH7CeWA1As7k5JSQldeumlMT12guEwvfTRZ0TEt1YG9e5Jx++3L/Wr6Eo+VwlFVZ7W3kCktlBUbRA3NlBbNhHVzl5N381+V3Rz9ih+cvtc5O9cTv1HDqNxJ00qiHwfDlHZjc/gJqQQOR0HiByQcaSAkfk3Ztw2Cb7JRI5d4nFUd3LsysedwlVyKGjCZoDm65IBKw+SckDxujtmZ4dDNjwu4sknn4wTACs3bKSHNrwt7vN+7II88cSLNH7EUBqz6yDqUdqZlKiiz+/Sytuj6g4K8j+TFqLmn4l2vLGSvnnjTS3fR9HyfUq7VtDAUfvR2F+dSv6yspy9B5yDY50TxmE42YQUdAwgckBOnRy3rZNjn3gc5nCRYikh150aKXLI4049XBXj5JBDuMqck6N3PPYiXgXaT94Oi5/KykpD+PC/KXOnb4bXT5s2Tax/b9EScZP/9nidz+2hGy7+NXUnD0UbgqSG1fh8Hz5lM1HzOqJt61bQl//7r57s7COX10WuSi/1G7kfHX3WeeT1+zP+utmtMQ9GZYHDrwkuTscCIgdkrbrKLidHCp/U+uRotbC2To4MV3m8KYerzO6Rk5NjrvriRmvilD6IHNC+8naShbTshI/5cTASptueeE5ze156wXCJrrr8Mtq1wk+hDZvJHdC6hreWuHNjwx3aP02+tRAtm7WWls1i56ecXC4fub0K+btU0KCDRtNBJ5yy084PJxlzDg6HqNjBgcDpeEDkgJxWV7nT6JMT0r9U7RKPk4arbJoBmkNRTtVV5pwcY86WP1Z8AdBRQ1pObo98fP9DD4v7vI7Pw+dlQbVm9Sra9PUC8tYFiJojpKgui/ipFeInFCFq2bCJFv7nR1r4n3/rCc8+8nhd5O9SRr2HD6Gxp0yi6i5dUi4ZlzfQMYHIARlHChgbjWNyckxiIuzg5OhOil0JuSj/sEs81rcb1Vd2E8iV+LEOrYnHeiVGJEoqac/hLs28lQ5AsYW0rG5OMrdHiJ7776eGhgYRNrILjVWVldJJB46kPbv1pgp3GZEQP9osL5nzIya4B/Scn59/pKXvvi0Snln88L9bV6mLOg3chQ49fRK9/dHcjJWMW8US+u0UJxA5IMdOjiuN6io7kaMRlSLHG7vN0C524SrdpZGCxrYZoH5doWCUs5fFfW9ZqeNrBaAjhLRY+Hz++ec77fZYRVFdUzO9OOfTuPXlJT46cfR+tFePvlTpKScXSeeHR1lEDPEj/g4KETV/s4Fe+WYhKUo53X/auUJMhdQgff/af2lOt2o67JcTY/6wSSZgrP11zj33XHrxxRfRb6cIgcgBOe2TY+vkOI110Kub7KaQG06NO/VwVavIib8ua05Oc0OLuALGW1nt/GIB6CDCJ1Nuj3m9k1hqbAnSy5Yyd+38Kh29/940sv+u1NVfRW5OfFYjYqYXJ/moaiOFqdEojOxVotCXLz1HX770ksj7cfN3iZtoc/N2+nrdGnp74bc06eyzYgTMXXfdRddff31Mf53nn3/euA702ykuIHJA4VZXiW8qJbZPjhJbIh6XkyO/aG2qq2QoKpHIkUJo28/rjW2llYXb8AyAYnN7eP2jjz5KLS0tbRJLsxYuFjcr++8+gA7fayj1KutCPsUvCgdkk0OeacFOkEx6rnYTHTqgNx06oC+5WoK03+nninO08GDTD96lPfv1ou9+2uD4Psh+OwxCWIUNRA7IbXWVO0F1lSc29yUsvtQUyxRyS7gqLidHHmwjcmSukK3IiW0GuPXn1i84f6VzkiMAHZ103B6ZjDxkyJCYc2QiNPbVqrU0+shj6To9/0c+19j9R9DmxV9RaFMtRVuiopVEa7l7WJ/tpZ2Dv2kGVPjowjH7kXLgODHiQlFcFKUw1YUb6ftNP9PML76ixkCQvvjiC5owYUJMCIvdHYiewgIiB+THyYm0LVzVmngsxzqkH66yVlbZDeis3bxZbqGS8irb5wAApO/2mLftjFg68sgj6d13340RNHzc+PHj456r8y6a4Ni8ebMx26vU66FjRu1Le+/Slzr5KsVsLw59RalZjLdQ1SYK82Av/auq3EU0slcXGnnCEeTikveF39IDp/9a5P5satlBL/79XtukZyQw5xeIHJC9jscp5+Q4havIeXaVnljs1CfHthlgQicnthlg044d+vO54yqxAAA7J3zacoyd8GERYRU0iZ6L18ty+eZQmP7vsy+p6ciu9O7/XosRS2OGD6Wfv15E6vZ6UkKKyf1p1N2fet0J0ujhV+jEIbvTiUP21MvePdTw3kf0908/oSUbfqJ3vvqG1mze7pjADCGUPSByQOFWVymJnJxI+uEqmXhs5+QYYTQ98bhOfoFB5ABQKFgFTFvEU6piqduA3eKODbYEqGbp17Tjhx8otK2BvOQTM+647F3L/Wktexc0Eu1RXUZ7/OJAUqiEXC0R2v/0X1OUotQQaaJ5L79IW9evpRv+fFvSSi4IobYBkQMKwMlxaAZoHOOJTzwOJ0k8DqdZQm5JPA42NelPh+mcALQ32iqWfCV+6r//aHGbOXNmTLJ058pyOm7/fWiPbrvoZe/m8FcTqdQiEqFlpL5UIRrapYJoyRd0z2mnkZtKSFHcFA2EafeJp9CqrTX09tdLRCXX9u3b6brrrktbCAGIHJAFZIKvTDJOVl0VaG4gTjnesqOeupn2DeuCxa6E3OiD49Tx2LZPToLqKt1hkjk5oZaA/nwQOQCAeNJJlh42oA8dNnQI9a3qSqXuUlJUhaIqd3pm9yckXKAw8cR37dxlCtGwbtU0bMJY0fvH/cVieuD080QCdH2kiX5c9wP16lxFG7buMErarULIKRF6XQcTQhA5IE9OjiY4uOnWvl9/SSN3cdN5519E05sqqFyexzjG5mPq5OQYIijd6qrYnJxIQB8bYWQBAQBA25Kll/y0nr5b+7OeLP3vViF00UW0YM47NG7wIOpd2ZVKFb/4zomoYT3/JxTX+4cF0PCu1TR8wjhSlDIujSCX4qLIl1/Tn045mVZt30Tvfb1UiCxFUWJED9PRkqMhckDGkQImUXUVh6v4HxL/g/tqstZRuCUcFX+NlFucHK/SGsYyzhhxyMmx9NFJNydH7hPhIToQOQCALCdLV1ZVOVaNjRo8kE4adzC5m0NUqpSI7yPNAWIBFBRhsAg1GSGwMhfRsK5VNGy8ngOklJFLcZOqqLT53feopnE7HbDnQPrsux8dHaD2lhMEkQOy5uQEAwGaM2cOHRKJGB+01j45qvgHwv+Q5JDvgEWXhHWBYZd4rIT1CeFOJeSRnet4rMrzp/yqAQAg/fyfVISQOf+Hhcf4CYfTjp9+pHGD96BdKjpTmbuUeAqOEECi7L0lLgfIS0T9yjzUb++hdNo++4oeQC7+A3LhUrr79LNoW6COvqvZQP9+5V8x3Z6LPScIIgdkHJlUPOvNmfTi/91Fa4mor0111eDBe4h/ID5dYATNvXMUhcJKgsRjKWKsYx1kJZRtx+MEfXL0ZGRZZi7DVlA5AIBCFELc88dJCI3YvT+N22Mw9a3sSmXuEjH13dwDiNQARShgCCCmi5doXL+eNK7f0VofIPLrjRAj1LjgM7pgwiH0wXff0cp1m2zHXKSaE5RrIHJAxtmybbtYqtFYNyXMjo4pXMUfev6H4FtypbaO3NS5czXR9u0UVYiiCRKPFVlCbsnJMfro2FRXtfbJSVRdpYkb2UJekYFwAAAoIiHEidA3WzpNM/98+ik6fJ8hNKhbL+pSUkVKxEWqykXtPACVw2BRrQ8Q1Rt5QCVENKRzBQ0Ze4B45FZKyaV4RPl8QA3QpqZaeuel58ilajNT+TntcoLyMdQUIgdknI2bNtuKnEg4TC5duMiQFn/oI3f+hSiwg2bOeofKjz1HiJywSYjYJh4bzQCdOh7v3OwqWWUFJwcA0F4SoRnrOrMD5HN76LyTjqXqYJi6l1WTX0+E1vKAmsUQVLKEwTjbYJdSF+0yeCAdPXg3zQWSCdRcMxZupufnfZy3oaYQOSDjdO7CheA/s1KIWe/2eGJycoz1+n69evcz1oVN+cR2HY8VGU7yuFMf0Jmgusqti6pIWI0dDqg3JAQAgGKju03/n3RCYb0tQmi33t3p8GF70aBuvckXdZNLdesuUCDWBTJ1g+Zk6L369KYf1m8SQ00hckDRU1FZqYucWIHgcbtNs6uiCTsehxydHD1cpR8fN9ZBkqgZoF1Ojic2XCVtWgUiBwDQzum+kzlB3bp1ozf+73+0+vO5tGfPXahrSaXoBs2wCzT7q3fI7XbToEGDcv7aIHJAxpGhqHPOOpNOuvZk6nXmmUQ1NfYdj1kI2YicmHCVzRRyQ+TElZDLjscJSsjtnByPdHL0nBwRWeadIXIAAKB7EiF04kkn0+ZxB9s2R2SBM23atLwkH0PkgIwjBUx1ZQUddtiBHAty7ngs8nb0+x6TyNGdFTe5YmdH6fcNkeNYQp5enxy3/nwyXGVcExoeAwBAm3KC9ttvPxGimjhxIg0bNozyAUQOyGLH43iFIId2Gk6OdHHinBxZWWU/gNPIyXEc0GkTrkpUXWV1clTpFNmfHgAAQGJY8JSUlAhnJ1/g71SQvSnkSWZXieReJ5Gj7+dRYj+isoTc5ZSTYyQeO8+uUhIkHkct4SpVd3gAAAAUHxA5IOOEI8lnVzFCC0VCZjul9Ry6s+Jx+Ii2ihyHcFUoFL8uUXWVx1JdRZpIcnnxTwQAAIoVfIOD3M6uMgmMMO8XCbS6OKbcGyNcZYkXyT3culGjeL0pJx5HUqiuikSimuOjhhPm/AAAACh8IHJAXqaQG2Etm8qqWJFjOYfsgiynLjglHoeC8etSrK4KBfnkusgpcShRBwAAUPBA5IDs5eQkmEJu7CfDVe5YMRGSOTkOH1FPEieH0pxCLkVONKxSOBAhVXdyXH5uaA4AAKAYgcgBeamuSurk6OEjr0PisVO4SkmQk5OoukqWkDOBpjCfQLvesvIErxQAAEAhg4QDkDMnhwd0fvTBnNbHMSLHbx+uchgeJcNVZE08bmMzQFlCzgSaWOBox/vKOzu+TgAAAIUNnByQNZFjzcmpqamhCRMmGI36EoWrZMdja04OP3IpLvLogiUuXJWSk+McrmK2b9omZrAw3orq5C8YAABAQQKRA7IuctjBMSOnk69dvyFB4jE5Ojlelzd5dZWdyJFl53bVVS7FSOfZ+vPPxvqS6tjhdgAAAIoHiByQcURpuFnkWENHusj5cdVqk8ixJB7rTo7PQeQY1VUOAzrtwlWJqqvMbk7dlq3GOn8JDxsFAABQjEDkgKzn5HgseTNyZELffgOIwvZOTkgmHjuJnDY4OYn65Ij1ushp2lGnnYrc5PHFXhcAAIDiASIHZL1Pjsc6X0p3crr36OEYrkomcoxwlc+hj00kQqruKMV3PLb/2MsKq5aGZu3c5I4dDgoAAKCogMgBOZtd1atXL5ozZw716NbVVF2lOS7b6hpo3bp1xr4h/Vg7CeN1m8NV9tVVdiGrRNVV5nBVsFnrwqyonKcDkQMAAMUKRA7I4uyq2I8XOzqHHXYYeXVhwmLoow/eE/fnL1hIAwYMoMampliRow8rTzlcZUINhlKurhLXq8+pCrVoJ1fIBZEDAABFDEQOyGnHY3MYa0PNRnrphefE/UCYRUiUtm/fLh4HvdLJUe2dnGQdj21GO8jqKqecHI9PC6tFQ3oVlkOPHgAAAMUBRA7IenWVFRnGWv3TGvK4NBETjK0yT+7kOISrxFHSQUozXOXVRY7exgcAAEBHEjl33nknHXDAAVRZWUk9evSgk046iZYtWxazT0tLC1166aXUtWtXqqiooIkTJ9LGjRtj9lmzZg0df/zxVFZWJs5zzTXXxJUZf/DBB7T//vuT3++nQYMG0XPPaX/xm3n00Udp4MCBVFJSQmPGjKHPP/88vVcP8urk9O7Tl/wyD0YXIPEiR02vT45J+FgrrKTIseuTI67Xp8+vimrb4eMAAEAHEjkffvihEDDz58+nd999l0KhEB111FHU2Nho7HPVVVfR66+/TjNmzBD7b9iwgU455RRjeyQSEQInGAzS3Llz6fnnnxcC5tZbbzX2WbVqldjn8MMPp6+++oquvPJKuuiii+jtt9829nnllVfo6quvpj/+8Y/05Zdf0ogRI+joo4+mTZs27fy7ArI2hdwsfrp07UaTTjtZ3OfB3263mzp37hxbXeUgcmS4imz65EjhY008bq2uShyuIr3EXVFsbCQAAADtU+TMmjWLfvOb39CwYcOEqGBxwq7MwoULxfba2lp6+umn6f7776fx48fTyJEj6dlnnxVihoUR884779DSpUvpH//4B+2777507LHH0m233SZcGRY+zNSpU2nXXXel++67j4YOHUqXXXYZnXrqqfTAAw8Y18LPcfHFF9P5559Pe+21lziGnaFnnnkms+8Q2Aknx6FUW18fUVU6YL8R4v4xx/+SVq9eTeVlZTGzq2xFDnnIrTqXkDs5OUafHIcScq/u5Mi5VYpNPhAAAIAOkpPDoobp0qWLWLLYYXfniCOOMPYZMmQI9e/fn+bNmyce83Lvvfemnj17GvuwA1NXV0dLliwx9jGfQ+4jz8FiiJ/LvI/L5RKP5T52BAIB8TzmG8ifk2Me0Nmn30Dq27dvXLjKZ+l1w/hNc2Vtq6t04WMVOYaTkyTxWE4gJyX+uQEAAHQAkcOVMBxGGjduHA0fPtwYwOjz+ahTp04x+7Kg4W1yH7PAkdvltkT7sChpbm6mLVu2iLCX3T7yHE45RdXV1catX79+bX35oA19ciRS/AhnxWGsQ9BoBhjvppSorc0F4/rkmMNVobB9dVWScJWqyuMs2dAAAAA6hsjh3Jxvv/2WXn75ZSoWbrjhBuE+ydvatWvzfUnturrKpaTi5Mgp5P6YfYycHBsnpySa2MmR86ycEo+dnRz5z0E7TnHByQEAgGIm/s/gFOAcmTfeeIM++uijmBADd7TlUNKOHTti3ByuruJtch9rFZSsvjLvY63I4sdVVVVUWloqElT5ZrePPIcdXKnFN5CbZoDeJE5OiJ0VxwGd2tIbja/n9pHuuPDkcOvIiBgnx0HkJE081p/TBScHAAA6jJOjqqoQOK+++iq9//77IjnYDCcae71emj17trGOS8w5Ofmggw4Sj3m5ePHimCoortRiAcMJxHIf8znkPvIcHBLj5zLvw+Ezfiz3AfkjZIgc+4+XTy8bF46P04BOXYh47HJyotrxqsP5W6ur7DseK44iRz+vzMmBkwMAAB3HyeEQ1UsvvUT/93//J3rlyPwXzm9hh4WXF154oSjt5mRkFi6/+93vhPA48MADxb5ccs5i5txzz6W7775bnOPmm28W55YuyyWXXEKPPPIIXXvttXTBBRcIQTV9+nSaOXOmcS38HOeddx6NGjWKRo8eTQ8++KAoZedqK5BfhEOTwMmR4keIoXCLttJT4hCuindyvLrIiepiKd0+OW4HceQvlf8c9OeUJVwAAADav8h5/PHHxZLnD5nhMnEuLWe4zJsrnbgJIFczcVXUY489ZuzLYSYOdf32t78V4qe8vFyIlb/85S/GPuwQsaDhnjsPPfSQCIk99dRT4lySM844gzZv3iz667BQ4nJ0LnG3JiODfIocV8KcHC1cpQ3DJI8lJ0eWkNu0H/ZJkWMTqkoYrpJOjkNOTmmlLzZc5SCiAAAAtEORw+GqZHD3Ye55wzcneBDjm2++mfA8LKQWLVqUcB8OnfENFGpOjkM/Gl08hMIcrmqDyFGlyLEXK0Yycji96iopcmS4KtHwTwAAAIUP/lQFGYWFcFAXE04l5D5d/IjqKodwVVCX3z6ZmJyBcJWapLqqtFKKGv04b2yeEAAAgOICIgdkpUeOWcw4hauEGHJIPA4ncHJk4nEkiZMT3ycncXVVWVVsuMrljxVeAAAAiguIHJCVyqpUwlXhRInHhsgJsD0Ue7w+QNNJ5JDXIfE4yewqr99N5dW+1nBViTZiAgAAQHECkQMySshU8u0UrvLa9cmx5uTo4SrRDNBSYeVN2ckJxYTRWp0chzCXotAp14wkd7hePHZD5AAAQFEDkQMyikgm1vE6DOiUDg+Hq0ItDeL+pm21secxBnRSa3Kyjkd/imSJx+Yp5NLFSeTkMFXdSknVp497Sqsc9wMAAFD4QOSArISruKuxXYLvunXraMN6bZzGV18vppq1q8X94381UUywj+t4zKEqi8jxRvSxEPYV5LZjHaLhVpHj9jp/7IMtzRTVxZmvPHYGGwAAgOICIgfkrBFgY1OTaB/w0j9eFI/fe38O+fWwVHMoSlOmTKFwJBLr5PCATtlLR8ej6xVHkWOEq1orsyImh8mdwMnZWvOTcd9f1SXZywUAANDeZlcB0JZGgNu3byexVVZMuTxUonc2bgmrFIlEKRIOiw+lLCHXnBw9OVnHo4+UijhIdLtwlRQ5PDPUKSeH2bphjXHfXwGRAwAAxQycHJBRRO+bBJVVjKonEvNwTb/uxgQiWjdst97jRjo5PpGTE9srx5M0XKUrJFO4SoocV5IuxnWbN2j7RaPk85cm3BcAAEBhA5EDMkownHhuFaPqTo7L7SG/7uSEVBdNmzaNPPqohticHIuTo0eeQsmcHJucnEShKqZ+mzY41hVVxXgSAAAAxQu+xUHOnJzOnTsLt0aKnAMPGGlsW/jVEjHclVFNAzo9NonHbl2whF1qys0AORSWLOmYaa7dKpYuNSpKygEAABQvEDkgZzk55WVltHr1avr9VVeIxwP77mJs69N/V+O+OQxll3js1iu4Qg5TwhWbZoDSyUmUj8MEGuq0/UiFyAEAgCIHIgdkpU+OU7iKJ8oPGzpE3FciMgylsMXSeg7TsXZ9cgyRk8zJsUk8dusOkeP1NzbqVwQnBwAAih2IHJBRQnq4ypMgn8Unk3/Dpm7HJkERMoWU7PrkuAyR01oWHoPNgM5WkZP4Ix9ubhJLRQTNAAAAFDMQOSA7Tk4CMWEIIOnkxI10aBU8Qq5YEo8NJ0fvTJxO4nGycFU0IAUVRA4AABQ76JMDspOT41JEd+MVK1bQIZFIzAdNhrIUObfKbRE5RiNAhQNZrfOtdNzhKHEgKuC2d3IUnzZNXA0G0w5XRYMsjDykOAgoAAAAxQNEDsgoPI+K2bqphgYMOISi0SjxEIe+NuEqxWkCuS5EfJrEiXNyXHrVVMAh8djl98eLnEhq4apoiDsNeri8KoVXCwAAoJBBuApklIDumCz99hshcMzIkQ2tIieQMFzllR9PS06OEtREToujk6OLHCP0ZApXJRE5qn6NUl8BAAAoXiByQFZETtTSpZjhkQ2M36PXiMswlEXkBGWPHJmMHCdytFwbDldF1Xiho/i1cFW0DeEq0vN9VPzLAACAogdf5SArHY+N+VQm5MgGv+6muPT+N3VNAZG/Iwl4NSHiV3QxZK2u0p2ckIfHQcRuiwlXmZwcY6xDksRj0o0cStIZGQAAQOEDkQMySiCsqYSxBx4guhubkSMbpMgJNWs9ab769jsxnfzpp5/WzuHTtpcoesqYRcgoek4OD/EMWASQ2G4rcvSxDsnCVTIVJ5kYAgAAUPDgmxxkxckZPnSI6G48Z84c6tWrl0OfHE3ktIRJ5O9MmTJF5O1IJ8fn0kWOVcjoYSieb9ViNBSMz8mJBk05OUbicRKHRtW3WwQaAACA4gPVVSArOTmcd8PdjflmFgwcllq4eJm2j651ArrLEolERN6O4eQYIidWyEQDusjxcDVX0DEnR9X3E6cIpTa7SpUiRx8NAQAAoHiBkwOy4uQYbo2JxqYmEZY6beIp4rHfFTGcHIbDW5y302Lk5HhtnRwZhuJwlZ2T45J9cszhKl3keLzuFEWOdg4AAADFC0QOyEpOjsy7MbN9+3YRllL1yis/aVVSLWFVCJxp06aJvB3DyXH7EoqcUBo5OeFgJDUnR1Z06ecAAABQvEDkgJw5ORI1ookbny5yjjj6OJG/c+GFF4rHrTk5XtvEY5lrw/10bHNypMgJhUjVe/W0OjlJRI7+T8LtL0vl5QIAAChgIHJAlnJyEny0ohFSoxHyK5rIibhiQ0MBXYiUyHEPoVghI3Ntgm6HnBw98Vjsqycpy5wcj+4SOV6aojcqLClPuB8AAIDCByIHZMXJsRM5nTt3NsrK1UiYykhzZP4541VLCbmekyPHPYS0yeB24SrbnBw98di8r5F4nKSEPOLSXaTy6lRfMgAAgAIFIgdkrbrKSnlZmVFWXllWYoicppBqKSHXPpZ+T6l2YKg5rZwc4qaD+qRzOVXcCFf5nBOPmxsbKKof56vs0oZXDwAAoJCAyAHZSTx2yH3hkvLDDjuMPIpKpYomQBr1iFNrCbliETmtTo5qEi5Bh47HiqLEJR+n4uSs/+Fb435FjwFpvW4AAACFB0QOyE7icZKOwWV+H5XqTk5jSLWUkOtOjlfPiwlqTQMZsac+RDPgtRc5jKtUE0jRZs0Fiojp4olzcjat+l47NhqlkgqEqwAAoNiByAEZpVkXEyUJ+tFwQ0AlGjKFqyimhDyoV1eVeMtsnRxJQpFTouXzqLrIScXJ2VGzVtsnGhVuEAAAgOIGIgdklCa9H02pQ+4LJxdzkvGqlcuNcNWUy66KKSFv1lshl/or43Jyorr4iLoVirgVag7H5utIlLJYJyeV6qqGrRvF0q1GyaXn5gAAAChe8E0OMkqLLnLKfG7h2HCSMScTM7ycPHmy1hAw1GKEq+64+wF6++23jXM0lWgfyzJfdVy4Sp9xThG/1kOnKRxbeSVxlZZZwlXJ++QE6naIpaLCyQEAgPYARA7IKE16uOrN1/4nHJvx48dTTU2NWBcOh4XAYdRQwAhXNQS1yioWRUyz7raUllSbglRqjJNjiBxLebk1J6c1XCU7HjuH0cKNmphSFIgcAABoD0DkgKyEq2696XpD0Eg4BCTDQNFwwFRdpYrKqpUrV8Y6OSWdWw9WNZGjkiY+1BKtF45TuMqaeBwOJndyIs1azx1FMWf+AAAAKFYgckDGiERVo7oqEogXHzxi4dxzz9XuB1sMJ6cxpAmgQYMGicdNek5Oma+C5yvIo8X/R6XBojf8c8zJMUSOJlxCAU18eUucnZxoQOvATC6IHAAAaA9A5ICMV1Yxit24BZeLXnzxRXFfDTVTGbUYTo6qOzUxTo6njFsPayv17VHdyaHSkpTCVdHmJopGokZOjq/E43j9qi7Q8K8CAADaB/g6BxmjKRgWS05nmfroI8YIBwmHr2QIyxtpJp+iiaLagCZyZLhKVlcJkSMrrGS4Stc4ihQ54eQ5OdLFEc/rd3ZyKKw/hwf5OAAA0B6AyAEZo1mWj3vddNFFFxojHHr16iXWe3jcgk4lNRj36wNal2IOV6mmcFWpt5SopErbSY3GODmuktLEOTmyhLypVeS43ErCPjn6UxDZjKQAAABQfEDkgIwnHXP5eMwIB93R4eWNN94o7ldEtUqm+ohXCBtez/u3eIlUfUim5uRUxRSPy5wcV3l5wnCVUqaXkDc1tebjJHJxhFmk/XNQfVrlFgAAgOLGOUEBgDaKnETdju+44w5asWIF1WzWGu/VRXx06qmn0u23366dQ59bJc7DU8ilyNHDVRG9tNtTpa1vDLX20DHjrtDCXNGG+jREjnZuOfcKAABAcQMnB2SMhoCWk1NZ4jUaAcreN2amT59Ol553qrgfKelMM2bMMLbVl2hCo6JFJZfiMuXkxIarfFWdEoocV6V2XKS+gUItsrIqsaaP8vOJfB892RkAAEBRAycHZIy6Zq0Eu2nHFhow4DCRZMyl4XXV1WSVDXv26Uy0mqjJVRl7jlJNxFQ16wkyRk5ObDPAkk5dxbIl0kLBSJC0gvJW3JUV2v51dRRJ0cmJuPSwWmWXtF87AACAwgNODsgYdS2ayPnumy+NKipebt++PW7fStJyaepUrUrK6uRUtugl5RYnJyIryDt1JUV3deqCdXHnd+nhqkgDOznhpCKnsW47hfVGhaU9+qfzsgEAABQoEDkgY9Q1a2Ii2tJaOeVEeVTbZ0e0zMHJ0UWOHO1gcXI8lVVUwc0CnUSOdHLq6ynQpF1XSZmzcfnD4vla7buqUnXvwUmvHwAAQOEDkQMy7uSoAfuKJzPlYW0Y5saIJkYk9aUWJ6esa6yTI0vIK6uoyqeFsuoC8SLHLXNyGhqohVsqsylU4Vw19fOyr8XSE4lSSVnsNQEAAChOIHJAxqjXRc7xR403GgHysnNn0wwqHX9IC2FtjlRSi6lTcp3e7dhwcsq6xSYe604O59xIkVMfrHdMPFabmqiFG/EIJ8dZ5OxYv1osPWokrokhAACA4gQiB2Q8XFXiau1sbB3SKfG2bBPLbVRJ9XrODFNbpjs5zfZOTliKnC5dDJFTG6yNO7+bS8z1HJuWHZqz5C93Dlc1bdksli72ijCBHAAA2gUQOSBjbG3UHJN/Pf+UMYuKl3aJx0qTLnLUStre1DrnakuFJjC6NUTjRA67OKrMyenalTrrU8q3NW+LP7/bTW7dQWqu1boil5Q7OznhWs0NUlz2ogwAAEDxAZEDMsZmPSwUrt+afOdGzTnZTpW0qU47jtlSqX0ku9XrTk55q8gJ62MhlJIS0dG4W6kWytrSvMX2KVgIMS31waThqqhe/k4eTCAHAID2AkQOyBibdJGjNseHj2Jgl6dug7hbo3ahTfXaNPJYkSP75HQi4s7HLJ7keIiuXUVIqXtZ98Qip5smcpob9Zlalc4iRw1rDlE0ScNAAAAAxQNEThZI1O23vRIIR2hHk+aG3HPbLYkTj5u3E+mdijeoXQ0HiNlcqYmN7lLkcHiquq+4G9GdHLfu0HQv1UTO5mbNFbLi7tKVVFKoqUXvotwltiePmYiqJxuXo7IKAADaCxA5Gebpp5+mAQMG0Pjx48WSH3cEISRDTj63i0469kh66aWXxPgGnkRerg/LNKhdKxaNns4UIB9t1I/lYZu1ZdpHskedKWyki5ygV3NivL17i6V0cjY2aXOwrHh36UUhbzlFeSaVQlRWbe2LrMFyKujWzl3SZ/edeBcAAAAUEvDmMwgLlsmTJ8dUFk2ZMoWOPvpo8ZgHUw4ePJjefvttYz8ee/DEE0/QhRdeKI6X+/BE7lSfM91j0j1O7ltRUUENDQ22xyz4/iexLKdmGjhwYOxrs55wu1au3VzWm6iBaM02zdX5qU47R+e6MFXLPjkmkRPyaiLF16+fWPav1DoTr61fKyaZW2uivP36UYtfc5HKqnzkdttr+u1KlKIuFymqSt2GHJjwvQAAAFA8QORkEBYC1pLpSCRCDz30EN1///1imyxPltVHUghxBdJ1110XJ3wSwS6RnVhKJmiSHcf7z507l7Zu3UqffvqpcGXk9TLWY/h8Vz3+GnU5YjKtX7owTuSd17177Adt4xLtvek+lGgT0Q+bY0XOgI2t4StBtz3FIujT3BbfAE3c9CrvRT6Xj4LRIEXUSNyH2de/PzWV9RD3q7qWOr6Pm/zakf5wiMqrMbcKAADaCxA5GYRFBAsAs9Dhx1LgMGaxYBZCUuBYHSAnlyWRa2Q+xipo7rrrLrr++uvjjttnn31o1apV9PLLL9Orr76a8HVaHaqLLrqIuh57ubgf2rYu7rVFwuHYD9rP34hFab8RREuIftraSM3BCH237TuxfrcNgVhfptdw4dS0+LWcGt/uWkiJp5QPrB5Iy7cvp3DU8hy83667UWN5H3G/Sy+/4+vZqI+AcLuCaAQIAADtCIicDMLigh0OFgD8484/mFdddRXde++9CY+zCiOGj583bx5169YtJjwkXZnNmzfbHrNy5UpxX4aXrELILHDMx40ZM8ZWgDkhn+u77zRhUjJghFi2rNNcGgm/B249YVgQDhKt/kTcrRw0lnaprqWfa1voi5+20Rc1X4j1+y/n5n2mueW77Esht1ckHnNIqWTYMGPTiO4jhMgJRYNkTSv29uxB9d32EPerotoYCTvWddPcm2iC6isAAADFB0ROhuEQTu2775Cqclm0QrTmB7rvjIm6M8E5IS59erZ2a71Prfd5oSr083/fpJ+JaLHYqG1V2dPQtciDk84T+2rihLeotPjZF2hhMEBRVaUoqXT7qaeK+xE1SpFoRCzDfIuEKRiNUCgSoWA4TMFwiIKhCDWHg9QUDFJLMEQNzUFqCDRTfUOAGlqaqSnQ2rSP+eKLL+jFF1+ksiEHk6e6J0WDLRRYGytyzjnnHPK8917risXTiXgMQ0VPUnrvTwcPWkwzFq6jFxd+Rt8EviElqtKYpQ1EZSaRU9aFasu13JrScJBcvtYE4tG9RtOM5TOoJRwgfV65QUtDiLaXDxD3q1Z/TkQHx/332lZZThu7dBJl7RX7/6KN/9UBAAAUIoqazp/v7Yy6ujqqrq6m2tpaquIxABni/kmn6iKnPeIy3RRSFG1pFW+MrYATD8ziThFiTCo3d5SoLBDh1sMUKK8U51fUKPnr68Te/lCAQrt3p4i7VIxtUF0Kfauup4O/2kFlLRGKeF208pC9SFHcRLWdieorqaR5Kw1e9T/aNmYQhbtUEvm8ouffXs+8Tl8O7E81XTtRWaiR9rj05oIIVx102mnk37KFAt260bwZM/J9OQAA0CZCoRDV19eLauNOnTpRPn6/4eRkgYPP/w19+9UiCgeD5GZHJRSilpZmCja1kM/tIQ//OEeiHD8iiqqkqlFSIyTWqVG+qRQOhXWfR/+f7u4wmkDQ4ERmIQ/0/2tVrNLx0Rwe7YHcHjWt1++L2VDysbzZEbvNLJFTVssJduS2fUGZPhNqbfLXKGNRvNzUEHMMZ92sEs2PNa+Llse6Sdy958sBPYlq6rWbzuqR+4ilPxgiz6j9CkLgAAAAyBwQOVlg/wnH0KaQJkDKy01hlxThfJtJkybF5M5Yq7JkLg8nCnfv3t3xON7n0UcfpZaWFurTp4/Y1zxXinnllVdELpGVMp+XqsrLqLzMTxX+Eqoo8VGZ308lXi+Ven3k83nIpyjk5coml4s8ipvcLpe4uVxucisucRvq8QrtEXG7qb57b4qSW6gjbeYmizGVKOqiaMRH3TbXCOeGuxtv7dRN5OCwtquu206eSJgibhc1dKskl1CFulBTiTwNAbGMuojqxSBOlaJKkAKeWooqAapqUMgfJFKimkiMKkQVTQHqtW0H7b3uZ1p++fVp/3cCAABQ2BS9yOEf8HvuuYdqampoxIgR9PDDD9Po0aOpmGEhcvXVVxtVWSxU+DFjXScFTqLjhgwZEnN+KZjk8swzz6Smpib6xz/+EbNfUzBETTzhO36+Zsr84Q9/oFOeey7l8ItTqCZZCCeVEI9ZIPL9sWecQSX6MSsweRwAANodRS1y2IHgH/GpU6eK6qAHH3xQlDUvW7aMevTQ+qMUK8cff7wQa+vXrzccGMZuXSrHpZIwzdVY06ZNEwLAzjlKhyOOOEJUdonnf+45KgTka5L3IWsAAKB9U9Qihx2Liy++mM4//3zxmMXOzJkz6ZlnnhGl0sUOCwSrSLFbl8pxqXDGGWeIBDEpkBi+X1JSIsJdPKaBy9oTcd555wmh1ZbnBwAAADJJ0YqcYDBICxcupBtuuMFYx+EZdhCcfogDgYC4mbOzQWKBZL7PYieZyIHAAQAAUCgUrcjZsmWLaEjXs2fPmPX8+Pvvv7c95s4776Q///nPObk+DodwaRvPemov8DRx7oz8zTdax2IrJ510ksgF2rgxdmBmRE+Ejthss+K0b7JzpPMcO3NMLijU6wIAgHTgdAefqa9ZPihakdMW2PWRCbzSyemnD3vMJFyKvMceewi3qb3BIcIjjzwyLlfn0EMPpdtvv932GK+cHu710vDhwxOe32nfZOdI5zl25phcUKjXBQAA6cIRlrZUGVNHFzk87oDFhPUvXX7cq1cv22P8fr+45cLF4VEM7ZFhw4bRk08+aYyu4NfK4vGOO+5wPkgf6+D1eMTxCXHaN9k50nmOnTkmFxTqdQEAQJHB7WqLErbARo4cSbNnzzbWcaiEHx900EF5vbb2DldirV69mubMmUNr1qxJLHAAAACAPFG0Tg7DoSeu5hk1apQom+YS8sbGRqPaCmQPHhjqNCEdAAAAKASKWuRwyTN3+b311ltFM8B9992XZs2aFZeMDAAAAICOR1GLHOayyy4TNwAAAACAdpGTAwAAAACQCIgcAAAAALRLIHIAAAAA0C6ByAEAAABAuwQiBwAAAADtEogcAAAAALRLIHIAAAAA0C6ByAEAAABAuwQiBwAAAADtkqLveLwzqKoqlnV1dfm+lPZNNNq6TPZeO+2b7BzpPMfOHJMLCvW6AACgQJC/2/J33AlFTbZHO2bdunXUr1+/fF8GAAAAANrA2rVrEw6L7tAiJxqN0oYNG6iyspIURaGOropZ8PEHpqqqKt+X027B+5w78F7nBrzPuQHvcywsXerr66l3797kcjln3nTocBW/MYkUYEeE//HgH1D2wfucO/Be5wa8z7kB73Mr1dXVlAwkHgMAAACgXQKRAwAAAIB2CUQOEPj9fvrjH/8oliB74H3OHXivcwPe59yA97ltdOjEYwAAAAC0X+DkAAAAAKBdApEDAAAAgHYJRA4AAAAA2iUQOQAAAABol0DktGPuvPNOOuCAA0RH5x49etBJJ51Ey5Yti9mnpqaGzj33XOrVqxeVl5fT/vvvT//5z39i9tm2bRudffbZogFVp06d6MILL6SGhoYcv5rC5fHHH6d99tnHaNJ10EEH0VtvvWVsb2lpoUsvvZS6du1KFRUVNHHiRNq4cWPMOdasWUPHH388lZWVif9W11xzDYXD4Ty8muJ9r/lz+rvf/Y723HNPKi0tpf79+9Pll19OtbW1MefAe73zn2kJ160ce+yxomP8//73v5hteJ8z8z7PmzePxo8fL76feZ9DDz2Umpubje34fk4CV1eB9snRRx+tPvvss+q3336rfvXVV+pxxx2n9u/fX21oaDD2OfLII9UDDjhA/eyzz9QffvhBve2221SXy6V++eWXxj7HHHOMOmLECHX+/Pnqxx9/rA4aNEg988wz8/SqCo/XXntNnTlzprp8+XJ12bJl6o033qh6vV7xvjOXXHKJ2q9fP3X27NnqF198oR544IHq2LFjjePD4bA6fPhw9YgjjlAXLVqkvvnmm2q3bt3UG264IY+vqvje68WLF6unnHKK2GflypXi/R48eLA6ceJE43i815n5TEvuv/9+9dhjj+UKXfXVV1811uN9zsz7PHfuXLWqqkq98847xbrvv/9efeWVV9SWlhbjHPh+TgxETgdi06ZN4svoww8/NNaVl5erL7zwQsx+Xbp0UZ988klxf+nSpeKYBQsWGNvfeustVVEUdf369Tm8+uKic+fO6lNPPaXu2LFDfGnNmDHD2Pbdd9+J93TevHniMf8AsLCsqakx9nn88cfFl1sgEMjL9Rfje23H9OnTVZ/Pp4ZCIfEY73Xm3mcWL3369FF//vnnOJGD9zkz7/OYMWPUm2++2XFffD8nB+GqDoS07bt06WKsGzt2LL3yyivC8uSBpS+//LIIrxx22GGGVcoW6KhRo4xjjjjiCDH367PPPsvDqyhsIpGIeA8bGxuF9bxw4UIKhULiPZMMGTJEhFL4vWV4uffee1PPnj2NfY4++mgxkG/JkiV5eR3F+F47febZxvd4tDF9eK8z8z43NTXRWWedRY8++qgIdVvB+7zz7/OmTZvEdyyH+vh7mt/LX/ziF/TJJ58Yx+D7OTkdekBnR4IFzJVXXknjxo2j4cOHG+unT59OZ5xxhsgX4R8Cjp+/+uqrNGjQICNnh/+RmeH9WCjxNqCxePFi8cXEApHzbvg93Guvveirr74in88nvojM8BeWfP94af4xkNvlNpDae21ly5YtdNttt9HkyZONdXivM/M+X3XVVeKH91e/+pXtsXifd/59nj9/vtj+pz/9ie69917ad9996YUXXqAJEybQt99+S4MHD8b3cwpA5HQQOPGV/2GY/wpgbrnlFtqxYwe999571K1bN5E8ePrpp9PHH38s/hIDqcHJrixo2Dn497//Teeddx59+OGH+b6sDvVem4UOOwac9Mrr+EcCZO59XrlyJb3//vu0aNGifF9iu36f+Q9TZsqUKXT++eeL+/vttx/Nnj2bnnnmGVFYApIDkdMBuOyyy+iNN96gjz76iPr27Wus/+GHH+iRRx4R4mfYsGFi3YgRI4TAYRt66tSpwopm29QMV0hweMvOpu6osFsj3a+RI0fSggUL6KGHHhIuWTAYFELS7OZwdZV8/3j5+eefx5xPVl/hPU79vZ42bZpYV19fT8ccc4yoKuS/ir1er3Es3uudf5+5co2/O6zuJFcNHnLIIfTBBx/gfc7A+3z99deLdVaXcujQoaJyjcH3c3KQk9OO4cRyFjj8Rc9/ee26664x2zmuznD81ozb7Tb+imAblX+gObdEwufi7WPGjMnJ6yhG+P0JBALiS4t/ZPmvLwmX8fOXlMxv4CVb1uYvq3fffVfkktiFYYD9ey0dnKOOOkr8cLz22mtUUlISsy/e651/n/nH95tvvhHug7wxDzzwAD377LPiPt7nnX+fBw4cSL17945r+7F8+XIaMGCAuI/v5xRIITkZFCm//e1v1erqavWDDz4QFRDy1tTUJLYHg0FRbnjIIYeIEnIuu7333ntFZj6XNZpLFPfbbz+xzyeffCLKclGi2Mr1118vKtZWrVqlfvPNN+Ixv4fvvPOOUULOpfvvv/++KCE/6KCDxM1abnvUUUeJUv9Zs2ap3bt3R7ltmu91bW2tqEbZe++9xWfZ/Jnn95jBe52Zz7QVpxJyvM879z4/8MADoiKNqzNXrFghKq1KSkrE51uC7+fEQOS0Y/iLx+7GvXMk3J+Be4v06NFDLSsrU/fZZ5+4kvKtW7eKfzQVFRXiH9z555+v1tfX5+EVFSYXXHCBOmDAAFGqzF/kEyZMiPkxaG5uVv/f//t/ojSU3+OTTz5Z/PCaWb16teg3UlpaKvqJ/P73vzfKnkFq7/WcOXMcP/P8IyLBe73zn+lkIofB+5yZ95l75PTt21d8d/AfR9wLxwy+nxOj8P+l4vgAAAAAABQTyMkBAAAAQLsEIgcAAAAA7RKIHAAAAAC0SyByAAAAANAugcgBAAAAQLsEIgcAAAAA7RKIHAAAAAC0SyByAAAAANAugcgBAAAAQLsEIgcAAAAA7RKIHAAAAAC0SyByAAAAAEDtkf8P/vsOip2OZjsAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - } - } - ] - }, - { - "cell_type": "code", - "metadata": {}, - "source": [ - "import ezfit\n", - "import pandas as pd\n", - "\n", - "df = target.copy()\n", - "df[\"Si-subtracted-err\"] = .05*df[\"Si-subtracted\"]\n", - "\n", - "fitted, ax, ax_res = df.query(\"Energy > 284 and Energy < 310\").fit(\n", - " building_block,\n", - " \"Energy\",\n", - " \"Si-subtracted\",\n", - " \"Si-subtracted-err\",\n", - " # method=\"differential_evolution\",\n", - " # options={\"workers\": 10, \"updating\": \"deferred\"},\n", - " **domain\n", - ")\n", - "ax.set_xlim(284, 310)" - ], - "execution_count": 261, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(284.0, 310.0)" - ] - } - }, - { - "output_type": "display_data", - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - } - } - ] - }, - { - "cell_type": "code", - "metadata": {}, - "source": [ - "fitted, ax, ax_res = df.fit(\n", - " building_block,\n", - " \"Energy\",\n", - " \"Si-subtracted\",\n", - " \"Si-subtracted-err\",\n", - " method=\"emcee\",\n", - " fit_kwargs={\"nwalkers\": 50, \"nsteps\": 2000},\n", - " **fitted.params\n", - ")\n", - "ax.set_xlim(284, 310)" - ], - "execution_count": 262, - "outputs": [ - { - "output_type": "stream", - "text": [ - "100%|██████████| 2000/2000 [09:21<00:00, 3.56it/s]\n", - "/Users/hduva/projects/pyref/.venv/lib/python3.13/site-packages/ezfit/fit.py:549: UserWarning: MCMC chain may not have converged. R-hat: 9.712496679371961, ESS: 1.29727826113285\n", - " fit_result = fit_func(\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(284.0, 310.0)" - ] - } - }, - { - "output_type": "display_data", - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - } - } - ] - }, - { - "cell_type": "code", - "metadata": {}, - "source": [ - "from ezfit.visualization import plot_corner\n", - "\n", - "# After MCMC fit\n", - "fig, axes = plot_corner(\n", - " fitted.sampler_chain,\n", - " param_names=[\"ionization_energy\", \"ionization_width\", \"pk1_energy\", \"pk1_intensity\", \"pk1_width\", \"pk2_energy\", \"pk2_intensity\", \"pk2_width\", \"pk3_energy\", \"pk3_intensity\", \"pk3_width\", \"pk4_energy\", \"pk4_intensity\", \"pk4_width\", \"pk5_energy\", \"pk5_intensity\", \"pk5_width\", \"pk6_energy\", \"pk6_intensity\", \"pk6_width\"]\n", - ")\n", - "plt.show()" - ], - "execution_count": 263, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - } - } - ] - }, - { - "cell_type": "code", - "metadata": {}, - "source": [], - "execution_count": null, - "outputs": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.11" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 5e77217..9665a6e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -49,6 +49,9 @@ dependencies = [ "jupyter-bokeh>=4.0.5", "uncertainties>=3.2.3", "ezfit>=0.1.5", + "scienceplots>=2.2.1", + "datasets>=4.6.0", + "huggingface-hub>=1.5.0", ] [tool.maturin] @@ -165,6 +168,7 @@ python-source = "python" [dependency-groups] dev = [ "maturin>=1.11.5", + "nbconvert>=7.17.0", "patchelf>=0.17.2.2", "pyrefly>=0.24.2", "ruff>=0.11.9", diff --git a/python/pyref/__init__.py b/python/pyref/__init__.py index ce6da0b..f1b93cc 100644 --- a/python/pyref/__init__.py +++ b/python/pyref/__init__.py @@ -15,6 +15,7 @@ __email__ = "Harlan.Heilman@wsu.edu" from pyref.io import fits_accessor # noqa: F401 - registers df.fits accessor +from pyref.nexafs import accessor # noqa: F401 - registers df.nexafs accessor from pyref.io import scan_experiment, build_catalog from pyref.loader import PrsoxrLoader from pyref.masking import InteractiveImageMasker diff --git a/python/pyref/beamline/DEFAULT_ESCANS/CB_GlassEscan.json b/python/pyref/beamline/DEFAULT_ESCANS/CB_GlassEscan.json new file mode 100644 index 0000000..835fa89 --- /dev/null +++ b/python/pyref/beamline/DEFAULT_ESCANS/CB_GlassEscan.json @@ -0,0 +1,39 @@ +# ESCAN +{ + "Step 1": [ + 250.0, + 270.0, + 5.0, + 1.0 + ], + "Step 2": [ + 270.0, + 282.0, + 1.0, + 1.0 + ], + "Step 3": [ + 282.0, + 287.0, + 0.1, + 1.0 + ], + "Step 4": [ + 287.0, + 292.0, + 0.2, + 1.0 + ], + "Step 5": [ + 292.0, + 305.0, + 1.0, + 1.0 + ], + "Step 6": [ + 305.0, + 355.0, + 5.0, + 1.0 + ] +} \ No newline at end of file diff --git a/python/pyref/beamline/DEFAULT_ESCANS/CB_GlassEscan.txt b/python/pyref/beamline/DEFAULT_ESCANS/CB_GlassEscan.txt new file mode 100644 index 0000000..5cbe4c2 --- /dev/null +++ b/python/pyref/beamline/DEFAULT_ESCANS/CB_GlassEscan.txt @@ -0,0 +1,10 @@ +270.00 +290.00 +292.00 +280.00 +284.00 +284.20 +284.40 +285.00 +286.00 +288.00 \ No newline at end of file diff --git a/python/pyref/beamline/DEFAULT_ESCANS/InSitu_CEdge_hq.json b/python/pyref/beamline/DEFAULT_ESCANS/InSitu_CEdge_hq.json new file mode 100644 index 0000000..b94f679 --- /dev/null +++ b/python/pyref/beamline/DEFAULT_ESCANS/InSitu_CEdge_hq.json @@ -0,0 +1,51 @@ +# ESCAN +{ + "Step 1": [ + 270.0, + 280.0, + 0.0, + 120.0 + ], + "Step 2": [ + 280.0, + 284.0, + 0.0, + 120.0 + ], + "Step 3": [ + 283.5, + 286.0, + 0.0, + 120.0 + ], + "Step 4": [ + 284.0, + 292.0, + 0.0, + 120.0 + ], + "Step 5": [ + 284.4, + 300.0, + 0.0, + 120.0 + ], + "Step 6": [ + 284.8, + 370.0, + 0.0, + 120.0 + ], + "Step 7": [ + 285.0, + 1.0, + 0.0, + 120.0 + ], + "Step 8": [ + 285.3, + 1.0, + 0.0, + 120.0 + ] +} \ No newline at end of file diff --git a/python/pyref/beamline/DEFAULT_ESCANS/InSitu_CEdge_lq.json b/python/pyref/beamline/DEFAULT_ESCANS/InSitu_CEdge_lq.json new file mode 100644 index 0000000..33200f2 --- /dev/null +++ b/python/pyref/beamline/DEFAULT_ESCANS/InSitu_CEdge_lq.json @@ -0,0 +1,51 @@ +# ESCAN +{ + "Step 1": [ + 270.0, + 280.0, + 0.0, + 40.0 + ], + "Step 2": [ + 280.0, + 284.0, + 0.0, + 40.0 + ], + "Step 3": [ + 283.5, + 286.0, + 0.0, + 40.0 + ], + "Step 4": [ + 284.0, + 292.0, + 0.0, + 40.0 + ], + "Step 5": [ + 284.4, + 300.0, + 0.0, + 40.0 + ], + "Step 6": [ + 284.8, + 370.0, + 0.0, + 40.0 + ], + "Step 7": [ + 285.0, + 1.0, + 0.0, + 40.0 + ], + "Step 8": [ + 285.3, + 1.0, + 0.0, + 40.0 + ] +} \ No newline at end of file diff --git a/python/pyref/beamline/DEFAULT_ESCANS/New_sample3.json b/python/pyref/beamline/DEFAULT_ESCANS/New_sample3.json new file mode 100644 index 0000000..28dd031 --- /dev/null +++ b/python/pyref/beamline/DEFAULT_ESCANS/New_sample3.json @@ -0,0 +1,39 @@ +# ESCAN +{ + "Step 1": [ + 270.0, + 280.0, + 2.0, + 0.5 + ], + "Step 2": [ + 280.0, + 284.0, + 0.2, + 1.0 + ], + "Step 3": [ + 284.0, + 286.0, + 0.1, + 0.5 + ], + "Step 4": [ + 286.0, + 292.0, + 0.2, + 0.5 + ], + "Step 5": [ + 292.0, + 300.0, + 1.0, + 0.5 + ], + "Step 6": [ + 300.0, + 370.0, + 10.0, + 1.0 + ] +} \ No newline at end of file diff --git a/python/pyref/beamline/DEFAULT_ESCANS/OPV_ESCAN.json b/python/pyref/beamline/DEFAULT_ESCANS/OPV_ESCAN.json new file mode 100644 index 0000000..4fe06ad --- /dev/null +++ b/python/pyref/beamline/DEFAULT_ESCANS/OPV_ESCAN.json @@ -0,0 +1,63 @@ +# ESCAN +{ + "Step 1": [ + 270.0, + 280.0, + 0.0, + 1.0 + ], + "Step 10": [ + 292.0, + 1.0, + 0.0, + 1.0 + ], + "Step 2": [ + 280.0, + 284.0, + 0.0, + 1.0 + ], + "Step 3": [ + 284.0, + 286.0, + 0.0, + 1.0 + ], + "Step 4": [ + 284.2, + 292.0, + 0.0, + 1.0 + ], + "Step 5": [ + 284.4, + 300.0, + 0.0, + 1.0 + ], + "Step 6": [ + 285.0, + 370.0, + 0.0, + 1.0 + ], + "Step 7": [ + 286.0, + 1.0, + 0.0, + 1.0 + ], + "Step 8": [ + 288.0, + 1.0, + 0.0, + 1.0 + ], + "Step 9": [ + 290.0, + 1.0, + 0.0, + 1.0 + ] +} \ No newline at end of file diff --git a/python/pyref/beamline/DEFAULT_ESCANS/OPV_Escan_v2.json b/python/pyref/beamline/DEFAULT_ESCANS/OPV_Escan_v2.json new file mode 100644 index 0000000..5d8d7f6 --- /dev/null +++ b/python/pyref/beamline/DEFAULT_ESCANS/OPV_Escan_v2.json @@ -0,0 +1,39 @@ +# ESCAN +{ + "Step 1": [ + 270.0, + 1.0, + 0.0, + 1.0 + ], + "Step 2": [ + 280.0, + 1.0, + 0.0, + 1.0 + ], + "Step 3": [ + 284.0, + 284.6, + 0.2, + 1.0 + ], + "Step 4": [ + 285.0, + 1.0, + 0.0, + 1.0 + ], + "Step 5": [ + 286.0, + 1.0, + 0.0, + 1.0 + ], + "Step 6": [ + 288.0, + 294.0, + 2.0, + 1.0 + ] +} \ No newline at end of file diff --git a/python/pyref/beamline/DEFAULT_ESCANS/OPV_Escan_v2.txt b/python/pyref/beamline/DEFAULT_ESCANS/OPV_Escan_v2.txt new file mode 100644 index 0000000..40d30ee --- /dev/null +++ b/python/pyref/beamline/DEFAULT_ESCANS/OPV_Escan_v2.txt @@ -0,0 +1,14 @@ +Beamline Energy CCD Camera Shutter Inhibit +270.0 1.0 1.0 +270.0 0.0 1.0 +280.0 0.0 1.0 +284.0 0.0 1.0 +284.2 0.0 1.0 +284.4 0.0 1.0 +284.6 0.0 1.0 +285.0 0.0 1.0 +286.0 0.0 1.0 +288.0 0.0 1.0 +290.0 0.0 1.0 +292.0 0.0 1.0 +292.0 1.0 1.0 \ No newline at end of file diff --git a/python/pyref/beamline/DEFAULT_ESCANS/PS300_Cal.json b/python/pyref/beamline/DEFAULT_ESCANS/PS300_Cal.json new file mode 100644 index 0000000..0dfc7e5 --- /dev/null +++ b/python/pyref/beamline/DEFAULT_ESCANS/PS300_Cal.json @@ -0,0 +1,9 @@ +# ESCAN +{ + "Step 1": [ + 285.0, + 280.0, + 0.0, + 0.2 + ] +} \ No newline at end of file diff --git a/python/pyref/beamline/DEFAULT_ESCANS/Sample1.json b/python/pyref/beamline/DEFAULT_ESCANS/Sample1.json new file mode 100644 index 0000000..7b704da --- /dev/null +++ b/python/pyref/beamline/DEFAULT_ESCANS/Sample1.json @@ -0,0 +1,39 @@ +# ESCAN +{ + "Step 1": [ + 270.0, + 280.0, + 1.0, + 0.5 + ], + "Step 2": [ + 280.0, + 284.0, + 1.0, + 1.0 + ], + "Step 3": [ + 284.0, + 286.0, + 1.0, + 0.5 + ], + "Step 4": [ + 286.0, + 292.0, + 1.0, + 0.5 + ], + "Step 5": [ + 292.0, + 300.0, + 1.0, + 0.5 + ], + "Step 6": [ + 300.0, + 370.0, + 1.0, + 1.0 + ] +} \ No newline at end of file diff --git a/python/pyref/beamline/DEFAULT_ESCANS/Sample2.json b/python/pyref/beamline/DEFAULT_ESCANS/Sample2.json new file mode 100644 index 0000000..7d41fb3 --- /dev/null +++ b/python/pyref/beamline/DEFAULT_ESCANS/Sample2.json @@ -0,0 +1,15 @@ +# ESCAN +{ + "Step 1": [ + 270.0, + 280.0, + 0.0, + 0.5 + ], + "Step 2": [ + 280.0, + 284.0, + 0.0, + 1.0 + ] +} \ No newline at end of file diff --git a/python/pyref/beamline/TRAJECTORY_OPTIONS/CCDClose_Edge_LowEn.json b/python/pyref/beamline/TRAJECTORY_OPTIONS/CCDClose_Edge_LowEn.json new file mode 100644 index 0000000..dd3f51a --- /dev/null +++ b/python/pyref/beamline/TRAJECTORY_OPTIONS/CCDClose_Edge_LowEn.json @@ -0,0 +1,8 @@ +# Trajectory +{ + "CCD X": 94.900, + "CCD Theta": -1.1445, + "CCD Y": 0.00, + "Beam Stop": 8.4330 + +} \ No newline at end of file diff --git a/python/pyref/beamline/TRAJECTORY_OPTIONS/CCDClose_HighEn.json b/python/pyref/beamline/TRAJECTORY_OPTIONS/CCDClose_HighEn.json new file mode 100644 index 0000000..3229320 --- /dev/null +++ b/python/pyref/beamline/TRAJECTORY_OPTIONS/CCDClose_HighEn.json @@ -0,0 +1,7 @@ +# Trajectory +{ + "CCD X": 97.01, + "CCD Theta": -1.60850, + "CCD Y": 0.00, + "Beam Stop": 4.2 +} \ No newline at end of file diff --git a/python/pyref/beamline/TRAJECTORY_OPTIONS/CCDClose_LowEn.json b/python/pyref/beamline/TRAJECTORY_OPTIONS/CCDClose_LowEn.json new file mode 100644 index 0000000..a4a58c2 --- /dev/null +++ b/python/pyref/beamline/TRAJECTORY_OPTIONS/CCDClose_LowEn.json @@ -0,0 +1,7 @@ +# Trajectory +{ + "CCD X": 106.00, + "CCD Theta": -1.0405, + "CCD Y": 0.00, + "Beam Stop": -2.7570 +} \ No newline at end of file diff --git a/python/pyref/beamline/TRAJECTORY_OPTIONS/CCDFar_HighEn.json b/python/pyref/beamline/TRAJECTORY_OPTIONS/CCDFar_HighEn.json new file mode 100644 index 0000000..7f4957f --- /dev/null +++ b/python/pyref/beamline/TRAJECTORY_OPTIONS/CCDFar_HighEn.json @@ -0,0 +1,8 @@ +# Trajectory +{ + "CCD X": 99.200, + "CCD Theta": -1.608500, + "CCD Y": 100.00, + "Beam Stop": 0.00 + +} \ No newline at end of file diff --git a/python/pyref/beamline/TRAJECTORY_OPTIONS/CCDFar_LowEn.json b/python/pyref/beamline/TRAJECTORY_OPTIONS/CCDFar_LowEn.json new file mode 100644 index 0000000..6c40414 --- /dev/null +++ b/python/pyref/beamline/TRAJECTORY_OPTIONS/CCDFar_LowEn.json @@ -0,0 +1,8 @@ +# Trajectory +{ + "CCD X": 106.0, + "CCD Theta": -0.90550, + "CCD Y": 100.00, + "Beam Stop": -3.25 + +} \ No newline at end of file diff --git a/python/pyref/beamline/TRAJECTORY_OPTIONS/CCDMid_HighEn.json b/python/pyref/beamline/TRAJECTORY_OPTIONS/CCDMid_HighEn.json new file mode 100644 index 0000000..6e7948f --- /dev/null +++ b/python/pyref/beamline/TRAJECTORY_OPTIONS/CCDMid_HighEn.json @@ -0,0 +1,8 @@ +# Trajectory +{ + "CCD X": 100.800, + "CCD Theta": -0.2505, + "CCD Y": 25.00, + "Beam Stop": -0.750 + +} \ No newline at end of file diff --git a/python/pyref/beamline/TRAJECTORY_OPTIONS/CCDMid_LowEn.json b/python/pyref/beamline/TRAJECTORY_OPTIONS/CCDMid_LowEn.json new file mode 100644 index 0000000..b500f6c --- /dev/null +++ b/python/pyref/beamline/TRAJECTORY_OPTIONS/CCDMid_LowEn.json @@ -0,0 +1,8 @@ +# Trajectory +{ + "CCD X": 99.900, + "CCD Theta": 0.050500, + "CCD Y": 20.00, + "Beam Stop": -0.890 + +} \ No newline at end of file diff --git a/python/pyref/beamline/TRAJECTORY_OPTIONS/InSitu_Edge_hq.json b/python/pyref/beamline/TRAJECTORY_OPTIONS/InSitu_Edge_hq.json new file mode 100644 index 0000000..a3faa1d --- /dev/null +++ b/python/pyref/beamline/TRAJECTORY_OPTIONS/InSitu_Edge_hq.json @@ -0,0 +1,7 @@ +# Trajectory +{ + "CCD Y": 0.000, + "CCD Theta": -1.1445, + "CCD X": 91.00, + "Beam Stop": 12.00 +} \ No newline at end of file diff --git a/python/pyref/beamline/TRAJECTORY_OPTIONS/InSitu_Edge_lq.json b/python/pyref/beamline/TRAJECTORY_OPTIONS/InSitu_Edge_lq.json new file mode 100644 index 0000000..a3faa1d --- /dev/null +++ b/python/pyref/beamline/TRAJECTORY_OPTIONS/InSitu_Edge_lq.json @@ -0,0 +1,7 @@ +# Trajectory +{ + "CCD Y": 0.000, + "CCD Theta": -1.1445, + "CCD X": 91.00, + "Beam Stop": 12.00 +} \ No newline at end of file diff --git a/python/pyref/beamline/TRAJECTORY_OPTIONS/Photodiode_Far.json b/python/pyref/beamline/TRAJECTORY_OPTIONS/Photodiode_Far.json new file mode 100644 index 0000000..1f8db3a --- /dev/null +++ b/python/pyref/beamline/TRAJECTORY_OPTIONS/Photodiode_Far.json @@ -0,0 +1,6 @@ +# Trajectory +{ + "CCD Y": 100.000, + "CCD Theta": 0.000, + "CCD X": 6.00 +} \ No newline at end of file diff --git a/python/pyref/beamline/TRAJECTORY_OPTIONS/XRR_Init.json b/python/pyref/beamline/TRAJECTORY_OPTIONS/XRR_Init.json new file mode 100644 index 0000000..f9ce679 --- /dev/null +++ b/python/pyref/beamline/TRAJECTORY_OPTIONS/XRR_Init.json @@ -0,0 +1,8 @@ +# Trajectory +{ + "CCD Y": 100.000, + "CCD Theta": 0.000, + "CCD X": 100.500, + "Beam Stop": 15.000 + +} \ No newline at end of file diff --git a/python/pyref/beamline/__init__.py b/python/pyref/beamline/__init__.py new file mode 100644 index 0000000..31b365d --- /dev/null +++ b/python/pyref/beamline/__init__.py @@ -0,0 +1,72 @@ +"""ALS beamline utilities for generating run files and macros. + +This module provides widgets and utilities for creating beamline scan scripts +for ALS beamline 11.0.1.2, including support for: +- Polarized X-ray Reflectivity (PXR) scans +- Energy scans (ESCAN) +- Resonant Soft X-ray Scattering (RSOXS) scans +""" + +from pyref.beamline.base_widgets import ( + ALS_ExperimentWidget, + ALS_MeasurementWidget, + ALS_ScriptGenWidget, + clean_script, + pad_digits, +) +from pyref.beamline.beamline_scan_macros import ( + add_comment, + add_prompt, + analog_from_file, + begin_macro, + build_macro, + clear_instruments, + finish_line, + jog_motor, + move_motor, + move_trajectory, + piezo_toggle, + relative_generic_scan, + relative_photodiode_scan, + run_dict_trajectory, + run_I0, + sample_photo, + save_trajectory, + set_DIO, + set_instrument, + set_motor, + time_scan, + windows_path, + XRR_lineup, +) + +__all__ = [ + "ALS_ExperimentWidget", + "ALS_MeasurementWidget", + "ALS_ScriptGenWidget", + "add_comment", + "add_prompt", + "analog_from_file", + "begin_macro", + "build_macro", + "clean_script", + "clear_instruments", + "finish_line", + "jog_motor", + "move_motor", + "move_trajectory", + "pad_digits", + "piezo_toggle", + "relative_generic_scan", + "relative_photodiode_scan", + "run_dict_trajectory", + "run_I0", + "sample_photo", + "save_trajectory", + "set_DIO", + "set_instrument", + "set_motor", + "time_scan", + "windows_path", + "XRR_lineup", +] diff --git a/python/pyref/beamline/base_widgets.py b/python/pyref/beamline/base_widgets.py new file mode 100644 index 0000000..1921b72 --- /dev/null +++ b/python/pyref/beamline/base_widgets.py @@ -0,0 +1,662 @@ +"""Base widgets for ALS beamline script generation. + +This module provides base classes for creating widgets that generate run scripts +for ALS beamline 11.0.1.2. It includes functionality for saving, loading, +verifying, and managing experiment configurations. + +Requirements: + numpy + pandas + ipywidgets + PyQt6 +""" +# Base Packages +import sys +import os +import json +import re +import copy +import inspect +from pathlib import Path + +# Math packages +import numpy as np +import pandas as pd + +# Visualization Packages +import ipywidgets as widgets +from IPython.display import display, clear_output +import tkinter as tk +from tkinter import filedialog + +class ALS_ScriptGenWidget: + """ + Base class for ALS script generation widgets. + + This class provides basic functions for creating widgets that generate + run scripts. It includes functionality for saving, + loading, verifying, and managing experiment configurations. + + """ + + default_button = widgets.Layout(width='200px') + default_GUI = widgets.Layout(widget='1200px') + banner_button_style = {'button_color':'#007681', 'text_color':'white'} + + + def __init__(self, exp_name="", child_widget=None, path=None, tab_title=None, json_title=None, **kwargs): + """ + Initializes an ALS_ScriptGenWidget. This is a base class to create widgets specializing in run-script generation + """ + + self.child_widget = child_widget if child_widget is not None else ALS_ExperimentWidget + self._save_dir = path if path is not None else "" + self.tab_title = tab_title if tab_title is not None else "Experiment " + self.json_title = json_title if json_title is not None else "Default" + + # Create a title banner for the full widget + self.title_banner = widgets.HTML(value=f"

{exp_name} Script Generator

") + + # Default save / path generation + # Name that will be used to save the + self.save_name = widgets.Text( + value="", + description = "Name of Script: ", + layout=widgets.Layout(width='300px'), + style = {'description_width': '90px'} + ) + + self.save_button_CCD = widgets.Button( + description = "Save CCD Scan File", + layout=self.default_button, + style = self.banner_button_style + ) + self.save_button_CCD.on_click(self.save_script) + + self.save_button_beamline = widgets.Button( + description = "Save Beamline Scan File", + layout=self.default_button, + style = self.banner_button_style + ) + self.save_button_beamline.on_click(self.save_script) + self.save_buttons = widgets.HBox([self.save_button_CCD])#, self.save_button_beamline]) + + # Browse directory functions + self.browse_directory_button = widgets.Button( + description="Choose Save Directory", layout=self.default_button, + style=self.banner_button_style + ) + self.browse_directory_button.on_click(self.browse_save_directory) + # Display Save Directory + self.display_save_directory = widgets.HTML( + value = "Save Directory:" + str(self._save_dir), + description = "" + ) + + # Additional buttons that can be added in children classes + self.additional_buttons = widgets.VBox([]) # Empty for now + + # Create the widget that will contain all buttons: + self.control_buttons = widgets.VBox( + [ + self.save_name, + widgets.HBox([self.browse_directory_button, self.display_save_directory]), + self.additional_buttons + ] + ) + + #self.control_buttons = widgets.VBox( + # [ + # self.save_name, + # #widgets.HBox([self.browse_directory_button, self.verify_button, self.save_button]), + # widgets.HBox([self.browse_directory_button, self.save_button]), + # self.display_save_directory, + # self.additional_buttons + # ] + #) + + # Build the layout/GUI based on the experiment requirements + self.layout = widgets.Tab(children=[], layout=self.default_GUI) # Default layout that will contain information + + # Save button to JSON + self.json_button_style = {'button_color':'#672E45', 'text_color':'white'} + self.save_json_button = widgets.Button( + description = "Save Script to JSON", + layout=self.default_button, + style = self.json_button_style + ) + self.load_json_button = widgets.Button( + description = "Load Script from JSON", + layout=self.default_button, + style = self.json_button_style + ) + self.load_json_button.on_click(self.load_json) + self.save_json_button.on_click(self.save_json) + self.json_buttons = widgets.HBox([self.save_json_button, self.load_json_button]) + + self.GUI = widgets.VBox([self.title_banner, self.control_buttons, self.layout, self.json_buttons,self.save_buttons], layout=self.default_GUI) + #display(self.GUI) + + def __call__(self): + return self.save_as_dict() + + def __len__(self): + return 0 + + + @property + def save_dir(self): + return self._save_dir + + @save_dir.setter + def save_dir(self, val): + self._save_dir=val + self.display_save_directory.value = "Save Directory: " + str(self._save_dir) + + def GenerateExperiment(self, layout, widget_str, widget, **kwargs): + # Create the samples + for key, value in kwargs.items(): + if widget_str in key: + df = getattr(value, "output_dict", lambda: value)() + self.new_tab(layout, widget, widget_str, **df) + + if not layout.children: + df = {} + self.new_tab(layout, widget, widget_str, **df) + + return layout + + def new_tab(self, layout, widget, widget_str, b=None, **kwargs): + index = len([getattr(self, attr) for attr in dir(self) if attr.startswith(widget_str)]) + 1 # Next Index + try: + new_widget = widget(scriptgen_widget=self, **kwargs) + setattr(self, widget_str + pad_digits(index), new_widget) + self.update_experiment_tab(layout, widget_str) + except Exception as e: + print(f"Error creating new tab: {e}") + import traceback + traceback.print_exc() + raise + + def copy_tab(self, layout, widget, widget_str, b=None): + index = len([getattr(self, attr) for attr in dir(self) if attr.startswith(widget_str)]) + 1 # Last Index + # Get stats of copied obj + open_tab = self.layout.selected_index + 1 + target_widget_name = widget_str + pad_digits(open_tab) + if hasattr(self, target_widget_name): + df = getattr(self, widget_str + pad_digits(open_tab)).output_dict() + else: + df = {} + setattr(self, widget_str + pad_digits(index), widget(scriptgen_widget=self, **df)) + self.update_experiment_tab(layout, widget_str) + + def delete_tab(self, layout, widget, widget_str, b=None): + try: + index = self.layout.selected_index + 1 + except: + index = 0 + widget_name = widget_str+pad_digits(index) + if hasattr(self, widget_name): + delattr(self, widget_name) + + j = index + 1 + while hasattr(self, f"{widget_str}{pad_digits(j)}"): + setattr(self, f"{widget_str}{pad_digits(j-1)}", getattr(self, f"{widget_str}{pad_digits(j)}")) + delattr(self, f"{widget_str}{pad_digits(j)}") + j += 1 + self.update_experiment_tab(layout, widget_str) + + def clean_slate(self): + self.layout.children = () + for key in (key for key in dir(self) if not key.startswith("__")): + attr = getattr(self, key) + if not inspect.isclass(attr) and hasattr(attr, 'ALS_NAME'): + delattr(self, key) + + def update_experiment_tab(self, layout, widget_str): + widget_list = [getattr(self, attr) for attr in dir(self) if attr.startswith(widget_str)] + display = [] + for widget in widget_list: + display.extend([widget.display()]) + self.layout.children = display + for tab in np.arange(len(layout.children)): + layout.set_title(tab, self.tab_title +str(tab+1)) + + def save_script(self, ext='.txt', sep='\t', b=None): + SAVEDIR = str(self.save_dir) + SAVENAME = str(self.save_name.value) + SAVEPATH = SAVEDIR + '/' + SAVENAME + '.txt' + + df = self.save_as_df() + df.to_csv(SAVEPATH, index=False, sep=sep) + + # Cleanup the output because the ALS requires specific things + try: + with open(SAVEPATH, 'r') as f: + lines = f.readlines() + if not lines: # Empty file + return + # Edit first line to remove 'exposure' from headers + if 'Exposure' in lines[0]: + lines[0] = lines[0].replace('\tExposure' , '') + # Edit last line to remove any carriage return that may exist + if '\n' in lines[-1]: + lines[-1] = lines[-1].replace('\n', '') + + with open(SAVEPATH, 'w') as f: + f.writelines(lines) + except FileNotFoundError: + print(f"Error: File not found at {SAVEPATH}") + except Exception as e: + print(f"An error occured: {e}") + del lines #Remove it from memory (it can be large) + + def verify_script(self, b=None): + return 0 + + def save_as_df(self): + df = self.save_as_dict() + return pd.DataFrame(df, ignore_index=True) + + def save_as_dict(self): + save_dict = {} # output dictionary + #level = 0 # initial level + for key in (key for key in dir(self) if not key.startswith("__")): + attr = getattr(self, key) + #if not hasattr(attr, 'WIDGET'): + # continue # Pass everything except a potential ALS_UTILITIY + #if not hasattr(attr.WIDGET, 'ALS_NAME'): + # continue + + #print(attr) + #save_dict[key] = self._process_attr_dict(attr) + + if hasattr(attr, 'output_dict') and not inspect.isclass(attr): #not inspect.isclass(attr) and hasattr(attr, 'WIDGET') and + save_dict[key] = self._process_attr_dict(self, attr) + return save_dict + + def _process_attr_dict(self, old_attr, attr): # Recursive helper class to cycle through attributes + my_dict = attr.output_dict() # This creates the next level dictionary + for key in (key for key in dir(attr) if not key.startswith("__")):#dir(attr): + next_attr = getattr(attr, key) + if type(old_attr) == type(next_attr): # Used to make sure it does not do an infite recursion by going back into itself. + continue + if hasattr(next_attr, 'output_dict') and not inspect.isclass(next_attr): + my_dict[key] = self._process_attr_dict(attr, next_attr) + + #if not inspect.isclass(next_attr) and hasattr(next_attr, 'WIDGET') and hasattr(next_attr, 'output_dict'): + #my_dict[key] = self._process_attr_dict(next_attr) + return my_dict + + def browse_save_directory(self, b): + self.save_dir = self.browse_directory() + + def browse_directory(self): + import gc + root = None + selected_folder = None + try: + root = tk.Tk() + root.withdraw() + root.attributes('-topmost', True) + + initial_dir = str(self._save_dir) if self._save_dir else None + selected_folder = filedialog.askdirectory( + title="Select Folder", + initialdir=initial_dir, + parent=root + ) + except Exception as e: + print(f"Error in directory browser: {e}") + selected_folder = None + finally: + if root is not None: + try: + root.update() + root.update_idletasks() + for widget in root.winfo_children(): + widget.destroy() + root.destroy() + except: + pass + try: + root.quit() + except: + pass + root = None + gc.collect() + + if selected_folder: + return Path(selected_folder) + return None + + def save_json(self, b=None): + df = self.save_as_dict() + SAVEDIR = str(self.save_dir) + SAVENAME = str(self.save_name.value) + SAVEPATH = SAVEDIR + '/' + SAVENAME + '.json' + + try: + with open(SAVEPATH, 'w') as f: + f.write(f"# {self.json_title}\n") + json.dump(df, f, indent=4) + except json.JSONDecodeError as e: + raise json.JSONDecodeError(f"Error encoding to JSON: {e}") + + def load_json(self, expected_title="", b=None): + import gc + root = None + selected_file = None + try: + root = tk.Tk() + root.withdraw() + root.attributes('-topmost', True) + + initial_dir = str(self._save_dir) if self._save_dir else None + selected_file = filedialog.askopenfilename( + title="Select JSON File", + filetypes=[("JSON files", "*.json"), ("All files", "*.*")], + initialdir=initial_dir, + parent=root + ) + except Exception as e: + print(f"Error in file dialog: {e}") + import traceback + traceback.print_exc() + selected_file = None + finally: + if root is not None: + try: + root.update() + root.update_idletasks() + for widget in root.winfo_children(): + widget.destroy() + root.destroy() + except: + pass + try: + root.quit() + except: + pass + root = None + gc.collect() + + if selected_file: + try: + with open(selected_file, 'r') as f: + title_line = f.readline().strip() + if title_line == f"# {self.json_title}": + df = json.load(f) + else: + print(f"Not an appropriate script. Expected title: {self.json_title}, got: {title_line}") + return None + self.clean_slate() + self.GenerateExperiment(self.layout, self.child_widget.ALS_NAME, self.child_widget, **df) + except Exception as e: + print(f"Error loading JSON file: {e}") + import traceback + traceback.print_exc() + return None + return None + + + + + +class ALS_ExperimentWidget: + # Default styling for the Experiment level + default_button = widgets.Layout(width='200px') + default_button_style = {'button_color':'#00313C', 'text_color':'white'} + default_FloatText = widgets.Layout(width='400px') + default_FloatText_style = {'description_width': '200px'} + default_GUI = widgets.Layout(widget='1200px') + def __init__(self, title="Generic Experiment", constants=None, constants_titles=None, build_constants=True, scriptgen_widget = None): + self.ScriptgenWidget = scriptgen_widget + # Initial conditions + self.constants = constants if constants is not None else {} + self.constants_titles = constants_titles if constants_titles is not None else {} + + # Title of the widget + self.widget_title = widgets.HTML(value=f"{title}") + + # Create a series of float texts that cover the 'constant values' + # This can be overridden if the requirement is more complex (ie. Reflectivity) + if build_constants: + temp_display = [] + for key, val in self.constants_titles.items(): + attr_title = key + gui_label = val + attr_value = self.constants[key] + if isinstance(attr_value, float): + setattr( + self, + attr_title, + widgets.FloatText( + description = gui_label, + value = attr_value, + layout = self.default_FloatText, + style = self.default_FloatText_style + ) + ) + temp_display.append(getattr(self, attr_title)) + + elif isinstance(attr_value, str): # THIS WILL NOT SAVE THIS VALUE. STRINGS SHOULD NOT BE PASSED + setattr( + self, + attr_title, + widgets.Text( + description = gui_label, + value = attr_value, + layout = self.default_FloatText, + style = self.default_FloatText_style + ) + ) + self.menu_box = widgets.VBox(temp_display) + else: + self.menu_box = widgets.VBox([]) + self.control_buttons = widgets.HBox([]) # Empty Controls + self.layout = widgets.Tab(children=[], layout=self.default_GUI) + self.GUI_experiment = widgets.VBox([self.widget_title, self.menu_box, self.control_buttons, self.layout]) + + def display(self): + return self.GUI_experiment + + def add_scan(self, layout, widget, widget_str, b=None, **kwargs): + index = len([getattr(self, attr) for attr in dir(self) if attr.startswith(widget_str)])+1 # Next index + setattr(self, widget_str + pad_digits(index), widget(experiment_widget=self, **kwargs)) + self.update_scan_tab(layout, widget_str) + + def copy_scan(self, layout, widget, widget_str, b=None): + index = len([getattr(self, attr) for attr in dir(self) if attr.startswith(widget_str)]) + 1 # Last Index + # Get stats of copied obj + open_tab = self.layout.selected_index + 1 + target_widget_name = widget_str + pad_digits(open_tab) + if hasattr(self, target_widget_name): + df = getattr(self, widget_str + pad_digits(open_tab)).output_dict() + else: + df = {} + setattr(self, widget_str + pad_digits(index), widget(experiment_widget=self, **df)) + self.update_scan_tab(layout, widget_str) + + def delete_scan(self, layout, widget, widget_str, b=None): + try: + index = self.layout.selected_index + 1 + except: + index = 0 + widget_name = widget_str+pad_digits(index) + if hasattr(self, widget_name): + delattr(self, widget_name) + j = index + 1 + while hasattr(self, f"{widget_str}{pad_digits(j)}"): + setattr(self, f"{widget_str}{pad_digits(j-1)}", getattr(self, f"{widget_str}{pad_digits(j)}")) + delattr(self, f"{widget_str}{pad_digits(j)}") + j += 1 + self.update_scan_tab(layout, widget_str) + + def update_scan_tab(self, layout, widget_str): + widget_list = [getattr(self, attr) for attr in dir(self) if attr.startswith(widget_str)] + display = [] + for widget in widget_list: + display.extend([widget.display()]) + self.layout.children = display + for tab in np.arange(len(layout.children)): + layout.set_title(tab, widget_str +str(tab+1)) + + def output_dict(self): + df = {} + for key in self.constants: + attr = getattr(self, key) + val = attr.value + if type(val) is str and self.comma_separated_list(val): + df[key] = [item.strip() for item in val.split(',')] + else: + df[key] = val + for key in (key for key in dir(self) if not key.startswith("__")): + attr = getattr(self, key) + if isinstance(attr, object) and hasattr(attr, 'ALS_NAME') and "WIDGET" not in key: + df[key] = attr + return df + def comma_separated_list(self, input_str): + pattern = r"^([-+]?\d*\.?\d+)(,\s*[-+]?\d*\.?\d+)*$" # magic from AI + match = re.match(pattern, input_str) + return bool(match) + + + +class ALS_MeasurementWidget: + LABEL_SIZE = widgets.Layout(width='150px') + LABEL_STYLE = {'description_width': '150px'} + CELL_SIZE = widgets.Layout(width='100px') + + def __init__(self, constant_motor_title="Generic Measurement", update_options={}, experiment_widget=None): + # Set values and update if parameters are loaded -- + self.constant_motor_title = widgets.HTML(value=f"{constant_motor_title}") # All tables have a title + self.constant_motor_attrs = [] # All tables will have some fixed values + self.table = widgets.VBox(children=[]) + self.control_buttons = widgets.VBox(children=[]) + + self.ExperimentWidget = experiment_widget + + def build_display_table(self): + build_table = [] + build_table.extend([self.constant_motor_title]) # The title of fixed values + build_table.extend([getattr(self, widget) for widget in self.constant_motor_attrs]) # Load in the values + build_table.extend([self.table]) # table object if needed + build_table.extend([self.control_buttons]) # optional buttons + return widgets.VBox(build_table) + + def display(self): + return self.display_table + + def get_table(self, include_title=False): + # Returns the NAMES of attributes that are either fixed or variable + fixed_content = [attr for attr in dir(self) if attr.startswith("fixed_")] + variable_content = [attr for attr in dir(self) if attr.startswith("variable_")] + if include_title: + fixed_content.insert(0, 'table_titles') + return fixed_content + variable_content + + def update_table(self): + if not hasattr(self, 'table_titles') or not self.table_titles: + return [] + + if isinstance(self.table_titles[0], list): + title_row = self.table_titles + else: + title_row = [self.table_titles] + + fixed_content = [getattr(self, attr) for attr in dir(self) if attr.startswith('fixed_')] + variable_content = [attr for attr in dir(self) if attr.startswith('variable_')] + variable_content = self._sort_attrs(variable_content) + variable_content = [getattr(self, attr) for attr in variable_content] + + rows = title_row + fixed_content + variable_content + output_table = [] + for row in rows: + if isinstance(row, list) and len(row) > 0: + if isinstance(row[0], list): + for sub_row in row: + if isinstance(sub_row, list) and len(sub_row) > 0: + output_table.append(widgets.HBox(sub_row)) + else: + output_table.append(widgets.HBox(row)) + return output_table + + def _sort_attrs(self, attrs): + def extract_number(s): + return int(re.search(r'_(\d+)', s).group(1)) #finds the number after the underscore + return sorted(attrs, key=extract_number) + + + def output_dict(self): + df = {} + for val in self.constant_motor_attrs: + df[val] = getattr(self, val).value + for motor in self.get_table(): + item = getattr(self, motor) + for row in item: + for i, col in enumerate(row): + if i==0: + key=col.value + df[key] = [] + else: + df[key] += [col.value] + return df + + + + + + +""" +other functions +""" +def clean_script(file): + # Cleanup the output because the ALS requires specific things + try: + with open(file, 'r') as f: + lines = f.readlines() + if not lines: # Empty file + return + # Edit first line to remove 'exposure' from headers + if 'Exposure' in lines[0]: + lines[0] = lines[0].replace('\tExposure' , '') + # Edit last line to remove any carriage return that may exist + if '\n' in lines[-1]: + lines[-1] = lines[-1].replace('\n', '') + + with open(file, 'w') as f: + f.writelines(lines) + except FileNotFoundError: + print(f"Error: File not found at {file}") + except Exception as e: + print(f"An error occured: {e}") + del lines #Remove it from memory (it can be large) + + + +def pad_digits(number): + """Pads a float or string representation of a float with leading zeros + until it becomes a 4-digit number (before the decimal point). + + Args: + number: A float or a string representing a float. + + Returns: + A string representing the number padded with leading zeros to 4 digits + before the decimal point. If the integer part already has 4 or more + digits, it is returned as is (with any decimal part). + Returns None if the input cannot be converted to a float. + """ + try: + float_num = float(number) + integer_part = int(float_num) + decimal_part = "" + if float_num != integer_part: + decimal_part = str(float_num).split('.')[-1] + decimal_part = "." + decimal_part + + padded_integer = str(integer_part).zfill(4) + return padded_integer + decimal_part + except ValueError: + return None \ No newline at end of file diff --git a/python/pyref/beamline/beamline_scan_macros.py b/python/pyref/beamline/beamline_scan_macros.py new file mode 100644 index 0000000..eaed161 --- /dev/null +++ b/python/pyref/beamline/beamline_scan_macros.py @@ -0,0 +1,475 @@ +"""Beamline scan macro generation functions. + +This module provides functions for generating beamline scan macros and run files +for ALS beamline 11.0.1.2. +""" + +import os # For saving macros +import warnings # For warning people + +""" +Some Saved Motor Positions. Update here if needed. +""" + +piezo_out = 17.5 +piezo_in = 18.857 +HOS_in = 5.5 + + +""" +Save macro to run file +""" + +def build_macro(name, path, macro): + + if not os.path.exists(path): + try: + os.makedirs(path) + except OSError as e: + print(f"Error in creating directory {path}: {e}") + return + file_path = os.path.join(path, f"{name}.txt") + + try: + with open(file_path, "w") as f: + # Write the Description Line + f.write("DESCRIPTION\n") # First Line + f.write("\n") # Blank line + # I currently don't write any description to the file + f.write("STEPS\n") # Second line that indicates steps + for step in macro: + f.write(str(step) + "\n") # Write the line and add a new line + except Exception as e: + print(f"Error creating file {name}.txt: {e}") + + + +""" +Basic Macros +""" + +def finish_line(s=600): + # Added to every function to end the line. Not sure what this is for.... + return f") [{str(s)}]" + +def add_comment(comment): + output = "Comment: " + output += f"{comment}" + return output + +def add_prompt(prompt): + output = "Prompt(" + output += f"{prompt}" + output += finish_line(s=0.1) + return output + +# The save_as condition determines whether or not +def set_instrument( + instrument, + sync = "True", + exp = 0, + num_to_sum=1, + extension = '""', # Don't include '.' for extension + live = 'false', + dark_image = '""', + sub_dark = 'false' # just don't + ): + instrument_list = ["CCD", "Video", None] + if instrument not in instrument_list: + warnings.warn(f"{instrument} is not part of instrument list. Returning blank line", UserWarning) + return "" + if instrument is None: + instrument = '""' + + output = "Set Instrument(" + output += instrument +"," + output += "false" +"," # Always false, corresponds to 'Setup button' that cannot be opened + output += extension +"," + output += "false" +"," # I think this is the 'Take Picture' button + output += live + "," + output += dark_image + "," + output += sync + "," + output += str(exp) + "," + output += sub_dark + "," + output += str(num_to_sum) + output += finish_line(s=0.5) + + return output + + + +def set_DIO(name, on_off, delay=0.1): + output = "Set DIO(" + output += name + "," + output += str(delay) + "," + output += str(on_off) + output += finish_line(s=0.5) + return output + +def move_motor(motor, pos, delay=0.1): + output = "Move Motor(" + output += motor+"," + output += str(pos)+"," + output += str(delay)+"," + output += "1" + "," # A value of 1 sets the Motor_Action to 'Move Motor' + output += "1" + output += finish_line(s=5) + return output + +def set_motor(motor, pos, delay=0.1): + output = "Move Motor(" + output += motor+"," + output += str(pos)+"," + output += str(delay)+"," + output += "6" + "," # A value of 6 sets the Motor_Action to 'Set Motor' + output += "1" + output += finish_line(s=0.5) + return output + +def jog_motor(motor, pos, delay=0.1): + output = "Move Motor(" + output += motor+"," + output += str(pos)+"," + output += str(delay)+"," + output += "20" + "," # A value of 6 sets the Motor_Action to 'Set Motor' + output += "1" + output += finish_line(s=5) + return output + +# This is currently not recommended since motors CANNOT be stopped +def move_trajectory(name_of_traj, delay=0.1): + output = "Move Trajectory(" + output += name_of_traj + "," + output += str(delay) + "," + output += "0" + output += finish_line() + return output + +def save_trajectory(name, list_of_motors): + output = "Save Trajectory(" + output += name +"," + output += "10.0" +"," # Days till it gets deleted + for motor in list_of_motors: + output += motor + "\t" + output = output[:-2] + "\r\n" # Remove the last tab and finish the list of motors + # A few other things need to go here + output += finish_line() + return output + +def relative_photodiode_scan(motor, delta, incr,delay=0.1,count_time=0.3): + # Order of inputs: motor, delta, "current position", incr, delay, count, other defaults" + output = "Single Motor Relative Scan(" + output += motor+"," + output += str(delta)+",0," # Includes a temporary (current position at 0) + output += str(incr)+"," + output += str(delay)+"," + output += str(count_time)+"," + output += "1"+"," # Number of Scans + output += "false"+"," # Bi-directional + output += "Photodiode"+"," # AI to run + output += "Center FWHM"+"," # At end of scan + output += "Max"+"," # Max or Min value + output += "100.0"+"," # Percent of Max/Min Value + output += "false"+"," # A few other things that I may need to figure out... + # A few other things need to go here + output += ")" + output += " [600]" + return output + +def relative_generic_scan( + motor, + base_file_name, + AI = "Photodiode", + delta = 0.5, + incr = 0.1, + delay = 0.1, + count_time=0.3, + num_scans = 1, + bidirection = "false", + end_of_scan = "Center FWHM", + min_or_max = "Max", + perc_min_or_max = 50.0, + smooth=2.0 + ): + # Order of inputs: motor, delta, "current position", incr, delay, count, other defaults" + output = "Single Motor Relative Scan(" + output += motor+"," + output += str(delta)+",0," + output += str(incr)+"," + output += str(delay)+"," + output += str(count_time)+"," + output += str(num_scans)+"," + output += bidirection+"," + output += AI +"," + output += end_of_scan+"," + output += min_or_max+"," + output += str(perc_min_or_max)+"," + output += "false"+"," # Unsure what this is for + output += motor +"," # Unsure what this is for The default seems to be 'Sample X' + output += str(smooth) + "," + output += '""' + "," + base_file_name + # A few other things need to go here to wrap up the line + output += finish_line() + return output + +def absolute_generic_scan( + motor, + start, + stop, + incr, + delay=0.1, + count_time=0.5, + bidirection='false', + end_of_scan='Return' + ): + output = "Single Motor Scan(" + output += motor+"," + output += str(start)+"," + output += str(stop)+"," + output += str(incr)+"," + output += str(delay)+"," + output += str(count_time)+"," + output += "1"+"," + output += bidirection+"," + output += "EPU Polarization" + "," + output += end_of_scan + "," + output += "Min" + output += finish_line() + return output + +def analog_from_file(name, path_to_scan, delay=0.1, count_time=0.5, move_sequential="false", dont_repeat="false", end_of_scan='Return', end_trajectory='""'): + output = "From File Scan(" + output += windows_path(path_to_scan) +"," + output += str(delay) + "," + output += str(count_time) + "," + output += end_of_scan + "," + output += move_sequential + "," + output += dont_repeat + "," + output += end_trajectory + "," # This is a trajectory to run when complete. Keep this as open quotes + output += '""' + "," + output += name + output += finish_line() + + return output + + +def time_scan(name, time_between_points, total_time=1, number_of_samples=1, count_time=0.1, stop_condition='Samples Taken'): + # Make sure the stop condition is one of the two options. 'Samples Taken' or 'Time Elapsed' + if stop_condition=='Samples Taken': + total_time = number_of_samples/time_between_points + elif stop_condition=='Time Elapsed': + number_of_samples = total_time/time_between_points + else: + return "" + + output = "Time Scan(" + output += str(total_time) + "," + output += str(number_of_samples) + "," + output += count_time + "," + output += stop_condition +"," + output += '""' + "," + output += name + output += finish_line() + + return output + +""" +Initialization macros that start everything +""" + + + +""" +Experiment Specific macros +""" + +clear_instruments = set_instrument(None, extension="") + + +def begin_macro(name): + macro = [] + + macro += [add_comment(f"{name} macro")] + macro += [clear_instruments] + + return macro + + +# Built in trajectories +def run_dict_trajectory(d): + + if not isinstance(d, dict): + raise TypeError("Input must be a dictionary containing Motor:Position Pairs") + trajectory_name = get_dict_name(d) + + macro = [] + macro += [add_comment(f"Run Trajectory: {trajectory_name}")] + for key, value in d.items(): + macro += [move_motor(key, value, 0.1)] + + return macro + + +def piezo_toggle(in_out=0): + """ + Toggle the Piezo position by bringing the shutter in or out + Move the shutter in, in_out = 1 + Move the shutter out, in_out = 0 + """ + macro = [] + if(in_out): + macro += [move_motor("PiezoShutter Trans", piezo_in, delay=0.1)] + macro += [set_DIO("Air Shutter Output", on_off="ON", delay=0.1)] + if(not in_out): + macro += [set_DIO("Air Shutter Output", on_off="OFF", delay=0.1)] + macro += [move_motor("PiezoShutter Trans", piezo_out, delay=0.1)] + return macro + + +# Auto-align sample-theta given a sample position +def XRR_lineup(repeat=3): + # Repeated Scans + course_z_scan = relative_generic_scan("Sample Z", "XRR_lineup_scan_ZC_", AI="Photodiode", end_of_scan="Move % of Min/Max", perc_min_or_max=50.0, delta=1.5, incr=0.1) + fine_z_scan = relative_generic_scan("Sample Z", "XRR_lineup_scan_ZF_", AI="Photodiode", end_of_scan="Move % of Min/Max", perc_min_or_max=50.0, delta=0.5, incr=0.02) + theta_rocking_scan = relative_generic_scan("Sample Theta", "XRR_lineup_scan_th_", AI="Photodiode", delta=2, incr=0.1) + shutter_on = set_DIO("Air Shutter Output", on_off = "ON", delay=0.1) + shutter_off = set_DIO("Air Shutter Output", on_off = "OFF", delay=0.1) + + #theta_rocking_scan = relative_photodiode_scan("Sample Theta", 2, 0.02) + th = 1 + # Start building the scan + macro = [] + macro += [add_comment(f"Begin XRR_Lineup")] + macro += [clear_instruments] # Make sure the CCD is turned off for diode scans + macro += piezo_toggle(in_out=0) # Move the Piezo motor out of the way to actuate the shutter + macro += [move_motor("Higher Order Suppressor", HOS_in, 0.1)] + macro += [move_motor("Sample Theta", 0, 0.1)] # Make sure the sample is at the current theta = 0 + macro += [move_motor("CCD Theta", 0.0, 0.1)] # Move the CCD down to the zero position + macro += [jog_motor("Sample Z", -2, 0.1)] # Move the sample out of the way for PD scan + macro += [shutter_on] + macro += [relative_generic_scan("CCD Theta", "XRRLineup_CCDth_", AI="Photodiode", end_of_scan="Center FWHM", perc_min_or_max=50.0, delta=2.0, incr=0.1)] + macro += [shutter_off] + macro += [jog_motor("Sample Z", 2, 0.1)] + macro += [shutter_on] + macro += [course_z_scan] + macro += [fine_z_scan] + macro += [shutter_off] + macro += [jog_motor("Sample Theta", th, 0.1)] + macro += [jog_motor("CCD Theta", 2*th, 0.1)] + macro += [shutter_on] + macro += [theta_rocking_scan] + macro += [shutter_off] + macro += [jog_motor("Sample Theta", -th, 0.1)] + macro += [jog_motor("CCD Theta", -2*th, 0.1)] + for i in range(repeat): + #th += 0.5 + macro += [shutter_on] + macro += [fine_z_scan] + macro += [shutter_off] + macro += [jog_motor("Sample Theta", th, 0.1)] + macro += [jog_motor("CCD Theta", 2*th, 0.1)] + macro += [shutter_on] + macro += [theta_rocking_scan] + macro += [shutter_off] + macro += [jog_motor("Sample Theta", -th, 0.1)] + macro += [jog_motor("CCD Theta", -2*th, 0.1)] + + macro += [set_motor("Sample Theta", 0)] + macro += [set_motor('Sample Z', 0)] + macro += [shutter_off] + macro += piezo_toggle(in_out=1) + + return macro + +# Take a photo of the sample // will need to record pixel value of cursor +def sample_photo(name): + macro = [] + macro += [add_comment(f"Taking photo of sample: {name}")] + macro += [set_instrument("Video", exp=1, extension="png")] # Turn on camera as instrument + macro += [set_DIO("Light Output", on_off="ON")] # Turn the light on + macro += [time_scan(name, time_between_points=1)] # Take one photo based on the current motor position + macro += [set_DIO("Light Output", on_off="OFF")] # Turn the light off before continuing + macro += [clear_instruments] # Remove the camera from the instruments queue before continuing + return macro + +def run_I0(name, scan_file, posx, posy): + macro = [] + macro += [add_comment(f"Running {name}")] + macro += [clear_instruments] + macro.extend(run_dict_trajectory(Photodiode_Far)) + macro += [move_motor('Sample X', posx, 0.1)] + macro += [move_motor('Sample Y', posy, 0.1)] + macro += [analog_from_file(name, scan_file)] + + return macro + +""" +Experiment Specific Trajectories that included during a full run. +""" + + +Photodiode_Far = { + "CCD Y": 100.000, + "CCD Theta": 0.000, + "CCD X": 6.000 +} + +XRR_init = { + "CCD Y": 100.000, + "CCD Theta": 0.000, + "CCD X": 100.500, + "Beam Stop": 15.000 +} + +CCDFar_LowEn = { + "CCD X": 100.500, + "CCD Theta": -1.305500, + "CCD Y": 100, + "Beam Stop": -0.750 +} + +CCDMid_LowEn = { + "CCD X": 100.500, + "CCD Theta": -1.305500, + "CCD Y": 50, + "Beam Stop": -0.750 +} + +CCDClose_LowEn = { + "CCD X": 100.15, + "CCD Theta": 0.8350, + "CCD Y": 0.00, + "Beam Stop": -0.9570 +} + +CCDClose_Edge_LowEn = { + "CCD X": 89.400, + "CCD Theta": 0.2355, + "CCD Y": 0.000, + "Beam Stop": 10.490 +} + +# Other functions... +def get_dict_name(d, namespace=locals()): + for name, value in namespace.items(): + if value is d and isinstance(value, dict): + return name + return None + +def windows_path(path_string): + """ + Normalizes a path string by replacing all '/' and '\' separators with '\\'. + + Args: + path_string: The input path string. + + Returns: + The normalized path string with '\\' as the separator. + """ + # Replace forward slashes with backslashes + path_string = path_string.replace('/', '\\') + + # Ensure double backslashes for Windows paths + normalized_path = path_string.replace('\\', '\\\\') + + return normalized_path \ No newline at end of file diff --git a/python/pyref/beamline/escan_widgets.py b/python/pyref/beamline/escan_widgets.py new file mode 100644 index 0000000..6766b2e --- /dev/null +++ b/python/pyref/beamline/escan_widgets.py @@ -0,0 +1,443 @@ +"""Energy scan (ESCAN) widget for ALS beamline script generation. + +This module provides widgets for generating energy scan run files for +ALS beamline 11.0.1.2. +""" +# Base Packages +import sys +import os +import json +import re +import copy +import inspect +from pathlib import Path + +# Math packages +import numpy as np +import pandas as pd + +# Visualization Packages +import ipywidgets as widgets +from IPython.display import display, clear_output +import tkinter as tk +from tkinter import filedialog + +from pyref.beamline.base_widgets import ( + ALS_ScriptGenWidget, + ALS_ExperimentWidget, + ALS_MeasurementWidget, +) + + +class ESCAN_ScriptGen(ALS_MeasurementWidget): + EXPERIMENT_NAME = "ESCAN" + JSON_TITLE = "ESCAN" + DEFAULT_NAME = "carbon_nexafs" + DEFAULT_PARAMETERS = { + "Step 1":[270.0, 280.0, 2, 0.5], + "Step 2":[280.0, 284.0, 0.2, 1], + "Step 3":[284.0, 286.0, 0.1, 0.5], + "Step 4":[286.0, 292.0, 0.2, 0.5], + "Step 5":[292.0, 300.0, 1, 0.5], + "Step 6":[300.0, 370.0, 10, 1] + } + BUTTON_SIZE = widgets.Layout(width='10rem') + LABEL_SIZE = widgets.Layout(width='5rem') + TABLE_SIZE = widgets.Layout(widget='50rem', border='2px black', padding='5px') + JSON_BUTTON_STYLE = {'button_color':'#672E45', 'text_color':'white'} + OTHER_BUTTON_STYLE = {'button_color':'#007681', 'text_color':'white'} + + + def __init__(self, path=None, **kwargs): + self._save_dir = path if path is not None else "" + parameters = self.DEFAULT_PARAMETERS.copy() + super().__init__(constant_motor_title='ESCAN Script Generator') # Build the initial table + # Title information + # Name that will be used to save the + self.save_name = widgets.Text( + value="", + description = "Name of Script: ", + layout=widgets.Layout(width='300px'), + style = {'description_width': '90px'} + ) + + # Browse directory functions + self.browse_directory_button = widgets.Button( + description="Choose Save Directory", layout=self.BUTTON_SIZE, + style=self.OTHER_BUTTON_STYLE + ) + self.browse_directory_button.on_click(self.browse_save_directory) + # Display Save Directory + self.display_save_directory = widgets.HTML( + value = "Save Directory:" + str(self._save_dir), + description = "" + ) + self.directory_buttons = widgets.HBox([self.browse_directory_button, self.display_save_directory]) + + # Initialize table // The double list is a mistake that I amn just going to live with forever + self.table_titles = [[ + widgets.HTML(value="Step", layout=self.LABEL_SIZE), + widgets.HTML(value="Start", layout=self.LABEL_SIZE), + widgets.HTML(value="Stop", layout=self.LABEL_SIZE), + widgets.HTML(value="Delta", layout=self.LABEL_SIZE), + widgets.HTML(value="Exposure", layout=self.LABEL_SIZE) + ]] + # Make the initial rows using this function + self.make_table(parameters) + + self.table = widgets.VBox(self.update_table()) + + # Create a button to add or remove columns and rows + self.add_step_button = widgets.Button(description="Add step", layout=self.BUTTON_SIZE) + self.add_step_button.on_click(self.add_step) + + self.remove_step_button = widgets.Button(description="Remove step", layout=self.BUTTON_SIZE) + self.remove_step_button.on_click(self.remove_step) + + # Additional options for the Escan + self.repeat_title = widgets.HTML( + value = "Number of Repeats: ", + description = "" + ) + self.repeat_value = widgets.IntText( + value = 1, + layout=self.LABEL_SIZE + ) + self.repeat_option = widgets.HBox(children=[self.repeat_title, self.repeat_value]) + + # Put them into the control buttons item + self.control_buttons = widgets.HBox(children=[self.add_step_button, self.remove_step_button]) + + # Save button to JSON + self.save_json_button = widgets.Button( + description = "Save Script to JSON", + layout=self.BUTTON_SIZE, + style = self.JSON_BUTTON_STYLE + ) + self.load_json_button = widgets.Button( + description = "Load Script from JSON", + layout=self.BUTTON_SIZE, + style = self.JSON_BUTTON_STYLE + ) + self.load_json_button.on_click(self.load_json) + self.save_json_button.on_click(self.save_json) + self.json_buttons = widgets.HBox(children=[self.save_json_button, self.load_json_button]) + + self.save_for_nexafs = widgets.Button( + description = "Save Single Motor Scan", + layout = self.BUTTON_SIZE, + style = self.OTHER_BUTTON_STYLE + ) + self.save_for_nexafs.on_click(self.save_nexafs) + self.save_for_rsoxs = widgets.Button( + description = "Save CCD scan", + layout = self.BUTTON_SIZE, + style = self.OTHER_BUTTON_STYLE + ) + self.save_for_rsoxs.on_click(self.save_rsoxs) + + self.save_scan_buttons = widgets.HBox(children=[self.save_for_nexafs, self.save_for_rsoxs]) + + + # Style the table + self.table.layout=self.TABLE_SIZE + + self.display_table = self.build_display_table() + display(self.display_table) + + @property + def save_dir(self): + return self._save_dir + + @save_dir.setter + def save_dir(self, val): + self._save_dir=val + self.display_save_directory.value = "Save Directory: " + str(self._save_dir) + + def make_table(self, d): + for i, (key, item) in enumerate(d.items()): + if isinstance(item, list): + setattr( + self, + 'variable_'+str(i+1), + [ + [widgets.Label(value=key, layout=self.LABEL_SIZE)] + + [widgets.FloatText(value=ii, layout=self.LABEL_SIZE) for ii in item] + ] + ) + + def add_step(self, b): + total_cols = len(self.table_titles[0])-1 + # Add a new row with FloatText widgets + num_steps = len([attr for attr in dir(self) if attr.startswith("variable_")]) + # Create the new attribute + setattr( + self, + "variable_"+str(num_steps+1), + [ + #[widgets.Text(value="", layout=self.label_size)] + + [widgets.Label(value="Step "+str(num_steps+1), layout=self.LABEL_SIZE)] + + [widgets.FloatText(value=i, layout=self.LABEL_SIZE) for i in np.arange(total_cols)] + #[widgets.Dropdown(options=AVAILABLE_MOTORS, value=::)] + ] + ) + temp_motor = getattr(self, "variable_"+str(num_steps+1)) + self.table.children = self.update_table() + + def remove_step(self, b): + final_step = len([attr for attr in dir(self) if attr.startswith("variable_")]) + # No steps to remove + if final_step == 1: + print("No steps to delete") + return 0 + for motor in self.get_table(): + if "variable_" in motor: + if str(final_step) in motor: + delattr(self,motor) + + # Update the table + self.table.children = self.update_table() + + def build_display_table(self): + build_table = [] + build_table.extend([self.constant_motor_title]) # The title of fixed values + build_table.extend([self.save_name]) # The title of fixed values + build_table.extend([self.directory_buttons]) + build_table.extend([widgets.HTML(value="Set Delta=0 to add a single energy")]) + build_table.extend([self.control_buttons]) # optional buttons + build_table.extend([self.table]) # table object if needed + build_table.extend([self.repeat_option]) + build_table.extend([self.json_buttons]) # Save as JSON button + build_table.extend([self.save_scan_buttons]) + return widgets.VBox(build_table) + + def browse_save_directory(self, b): + self.save_dir = self.browse_directory() + + def browse_directory(self): + import gc + root = None + selected_folder = None + try: + root = tk.Tk() + root.withdraw() + root.attributes('-topmost', True) + + initial_dir = str(self._save_dir) if self._save_dir else None + selected_folder = filedialog.askdirectory( + title="Select Folder", + initialdir=initial_dir, + parent=root + ) + except Exception as e: + print(f"Error in directory browser: {e}") + selected_folder = None + finally: + if root is not None: + try: + root.update() + root.update_idletasks() + for widget in root.winfo_children(): + widget.destroy() + root.destroy() + except: + pass + try: + root.quit() + except: + pass + root = None + gc.collect() + + if selected_folder: + return Path(selected_folder) + return None + + def save_json(self, b=None): + df = self.output_dict() + SAVEDIR = str(self.save_dir) + SAVENAME = str(self.save_name.value) + SAVEPATH = SAVEDIR + '/' + SAVENAME + '.json' + + try: + with open(SAVEPATH, 'w') as f: + f.write(f"# {self.JSON_TITLE}\n") + json.dump(df, f, indent=4) + except json.JSONDecodeError as e: + raise json.JSONDecodeError(f"Error encoding to JSON: {e}") + + def load_json(self, expected_title="", b=None): + import gc + root = None + selected_file = None + try: + root = tk.Tk() + root.withdraw() + root.attributes('-topmost', True) + + initial_dir = str(self._save_dir) if self._save_dir else None + selected_file = filedialog.askopenfilename( + title="Select JSON File", + filetypes=[("JSON files", "*.json"), ("All files", "*.*")], + initialdir=initial_dir, + parent=root + ) + except Exception as e: + print(f"Error in file dialog: {e}") + import traceback + traceback.print_exc() + selected_file = None + finally: + if root is not None: + try: + root.update() + root.update_idletasks() + for widget in root.winfo_children(): + widget.destroy() + root.destroy() + except: + pass + try: + root.quit() + except: + pass + root = None + gc.collect() + + if selected_file: + try: + with open(selected_file, 'r') as f: + title_line = f.readline().strip() + if title_line == f"# {self.JSON_TITLE}": + df = json.load(f) + else: + print(f"Not an appropriate script. Expected title: {self.JSON_TITLE}, got: {title_line}") + return None + self.clean_slate() + self.make_table(df) + self.table.children = self.update_table() + except Exception as e: + print(f"Error loading JSON file: {e}") + import traceback + traceback.print_exc() + return None + return None + + + def save_nexafs(self, b=None): + SAVEDIR = str(self.save_dir) + SAVENAME = str(self.save_name.value) + SAVEPATH = SAVEDIR + '/' + SAVENAME + '.txt' + + df = self.output_dict() + full_energy_scan = self._build_escan(df)[0] # Build the Escan + # Repeat the Escan if it is requested + full_energy_scan = [full_energy_scan]*self.repeat_value.value + full_energy_scan = np.concatenate(full_energy_scan, axis=0) + # Save it to disk + np.savetxt(SAVEPATH, full_energy_scan, fmt='%.2f') + # Cleanup the output because the ALS requires specific things + try: + with open(SAVEPATH, 'r') as f: + lines = f.readlines() + if not lines: # Empty file + return + # Edit first line to remove 'exposure' from headers + if 'Exposure' in lines[0]: + lines[0] = lines[0].replace('\tExposure' , '') + # Edit last line to remove any carriage return that may exist + if '\n' in lines[-1]: + lines[-1] = lines[-1].replace('\n', '') + with open(SAVEPATH, 'w') as f: + f.writelines(lines) + except FileNotFoundError: + print(f"Error: File not found at {SAVEPATH}") + except Exception as e: + print(f"An error occured: {e}") + del lines #Remove it from memory (it can be large) + + def save_rsoxs(self, b=None): + SAVEDIR = str(self.save_dir) + SAVENAME = str(self.save_name.value) + SAVEPATH = SAVEDIR + '/' + SAVENAME + '.txt' + + Motor_Order = ['Beamline Energy', 'CCD Camera Shutter Inhibit', 'Exposure'] + + df = self.output_dict() + escan_details = self._build_escan(df) + Escan = escan_details[0] # The energies + Exposure = escan_details[1] # The exposures + + # Build the sample dataframe + temp_scan = {} + temp_scan['Beamline Energy'] = Escan + temp_scan['Exposure'] = Exposure + df_samp = pd.DataFrame(temp_scan) + + # Add CCD Inhibit, first and last, all exposure times + df_samp['CCD Camera Shutter Inhibit'] = np.full_like(Escan, 0) # Add the column + for expo in np.unique(Exposure): + df_samp = pd.concat([df_samp.loc[0].to_frame().T, df_samp], ignore_index=True) # Add a frame at the beginning of the sample + df_samp = pd.concat([df_samp, df_samp.loc[df_samp.index[-1]].to_frame().T], axis=0, ignore_index=True) # Add a frame at the end of the sample + + df_samp.loc[0, 'CCD Camera Shutter Inhibit'] = 1 # Close the shutter + df_samp.loc[df_samp.index[-1], 'CCD Camera Shutter Inhibit'] = 1 + + df_samp.loc[0, 'Exposure'] = expo # Set exposure + df_samp.loc[df_samp.index[-1], 'Exposure'] = expo + + # Cleanup the output because the ALS requires specific things + cols = list(df_samp.columns) + Ordered_Cols = [item for item in Motor_Order if item in cols] + df_samp = self._iterate_dataframe(df_samp[Ordered_Cols], self.repeat_value.value) # Repeat the scan if it is requested + + df_samp.to_csv(SAVEPATH, index=False, sep='\t') + + try: + with open(SAVEPATH, 'r') as f: + lines = f.readlines() + if not lines: # Empty file + return + # Edit first line to remove 'exposure' from headers + if 'Exposure' in lines[0]: + lines[0] = lines[0].replace('\tExposure' , '') + # Edit last line to remove any carriage return that may exist + if '\n' in lines[-1]: + lines[-1] = lines[-1].replace('\n', '') + with open(SAVEPATH, 'w') as f: + f.writelines(lines) + except FileNotFoundError: + print(f"Error: File not found at {SAVEPATH}") + except Exception as e: + print(f"An error occured: {e}") + del lines #Remove it from memory (it can be large) + + def _build_escan(self, d): + energy_array = np.array([]) + exposure_array = np.array([]) + print(d) + for key, item in d.items(): + if item[2] == 0: + energy_subset = np.array([item[0]]) + else: + energy_subset = np.arange(item[0], item[1], item[2]) + exposure_subset = np.full_like(energy_subset, item[3]) + energy_array = np.concatenate((energy_array, energy_subset)) + exposure_array = np.concatenate((exposure_array, exposure_subset)) + return (np.round(energy_array, 2), exposure_array) + + def _iterate_dataframe(self, df, n_duplicates): + if not isinstance(n_duplicates, int) or n_duplicates < 1: + n_duplicates = 1 + + df_duplicate = [df] * n_duplicates + df_duplicate = pd.concat(df_duplicate, ignore_index=True) + return df_duplicate + + + def clean_slate(self): + self.table.children = () + for key in (key for key in dir(self) if not key.startswith("__")): + attr = getattr(self, key) + if not inspect.isclass(attr) and hasattr(attr, 'variable_'): + delattr(self, key) \ No newline at end of file diff --git a/python/pyref/beamline/pxr_widgets.py b/python/pyref/beamline/pxr_widgets.py new file mode 100644 index 0000000..afe6930 --- /dev/null +++ b/python/pyref/beamline/pxr_widgets.py @@ -0,0 +1,1095 @@ +"""Polarized X-ray Reflectivity (PXR) widgets for ALS beamline script generation. + +This module provides widgets for generating PXR scan run files for +ALS beamline 11.0.1.2. +""" +# Base Packages +import sys +import os +import json +import re +import copy +import inspect +from pathlib import Path + +# Math packages +import numpy as np +import pandas as pd + +# Visualization Packages +import ipywidgets as widgets +from IPython.display import display, clear_output +import tkinter as tk +from tkinter import filedialog + +from pyref.beamline.base_widgets import ( + ALS_ScriptGenWidget, + ALS_ExperimentWidget, + ALS_MeasurementWidget, + clean_script, + pad_digits, +) + +from pyref.beamline.beamline_scan_macros import * + + +# Variables that might be adjusted later as needed + +X_VARIABLES = [ + "Qval", + "Theta" +] + +POINT_DENSITY_METHOD = [ + 'Use Thickness', + 'Use Delta' +] + +AVAILABLE_PXR_MOTORS = [ + "Upstream JJ Vert Aperture", + "Upstream JJ Horz Aperture", + "Horizontal Exit Slit" +] +AVAILABLE_PXR_MOTOR_DEFAULTS = { + "Upstream JJ Vert Aperture": 0.08, + "Upstream JJ Horz Aperture": 0.08, + "Horizontal Exit Slit": 3000 +} + +class PXR_Scan(ALS_MeasurementWidget): + ALS_NAME = 'scan_' + LAYOUR_FLOATTEXT = widgets.Layout(width='300px') + STYLE_FLOATTEXT = {'description_width': '150px'} + LABEL_SIZE = widgets.Layout(width='150px') + CELL_SIZE = widgets.Layout(width='100px') + DEFAULT_TITLE = "Meausrement Details" + DEFAULT_PARAMETERS = { + 'theta_end': 60.0, + 'energy_start': 250.0, + 'energy_stop': 280.0, + 'energy_delta': 0, + 'Sample Theta': [1,5,10,15,20,30,40], + 'Higher Order Suppressor': [10, 9, 8.5, 7.5, 7.5, 7.5, 7.5], + 'Exposure': [0.0015, 0.0015, 0.0015, 0.0015, 0.1, 0.5, 1] + } + def __init__(self, experiment_widget=None, **kwargs): + parameters = self.DEFAULT_PARAMETERS.copy() + #constants_titles = self.DEFAULT_CONSTANTS_TITLES.copy() + # Update those based on what the kwargs are -- + parameters.update({k: v for k, v in kwargs.items() if k in parameters}) + super().__init__(constant_motor_title='Measurement Details', experiment_widget=experiment_widget) # Build the initial table + + VAR_KEY = 'Sample Theta' + total_cols = len(parameters[VAR_KEY]) + + # Initialize table given inputs + self.table_titles = [ + [widgets.HTML(value="Motor to move:", layout=self.LABEL_SIZE)] + + [widgets.HTML(value="Step "+str(i+1)+"", layout=self.CELL_SIZE) for i in range(total_cols)] + ] + # Make the initial rows + for i, (key, item) in enumerate(parameters.items()): + if isinstance(item, list): + if key in ['Sample Theta', 'Higher Order Suppressor', 'Exposure']: # The big 3 + setattr( + self, + 'fixed_'+str(i), + [ + [widgets.Label(value=key, layout=self.LABEL_SIZE)] + + [widgets.FloatText(value=ii, layout=self.CELL_SIZE) for ii in item] + ] + ) + else: + setattr( + self, + 'variable_'+str(i), + [ + [widgets.Label(value=key, layout=self.LABEL_SIZE)] + + [widgets.FloatText(value=ii, layout=self.CELL_SIZE) for ii in item] + ] + ) + # All samples have the following objects + # Energy Scan information + self.energy_start = widgets.FloatText( + value = parameters['energy_start'], + description = "Start : ", + layout = self.LAYOUR_FLOATTEXT, + style = self.STYLE_FLOATTEXT + ) + self.energy_stop = widgets.FloatText( + value = parameters['energy_stop'], + description = "Stop: ", + layout = self.LAYOUR_FLOATTEXT, + style = self.STYLE_FLOATTEXT + ) + self.energy_delta = widgets.FloatText( + value = parameters['energy_delta'], + description = "Step: ", + layout = self.LAYOUR_FLOATTEXT, + style = self.STYLE_FLOATTEXT + ) + self.energy_title = widgets.HTML(value="Photon Energy Scan [eV]") + self.energy_desc = widgets.HTML(value="Set delta=0 to run single energy") + + self.energy_info = widgets.VBox([self.energy_title, self.energy_desc]) + self.theta_end = widgets.FloatText( + value = parameters['theta_end'], + description = "Final Sample Theta: ", + layout = widgets.Layout(width='px'), + style = self.STYLE_FLOATTEXT + ) + # Compile all values that are fixed and apply to the full title + self.constant_motor_attrs = ['energy_start','energy_stop','energy_delta', 'theta_end'] + self.table = widgets.VBox(self.update_table()) + + # Create a button to add or remove columns and rows + self.add_step_button = widgets.Button(description="Add step") + self.add_step_button.on_click(self.add_step) + + self.remove_step_button = widgets.Button(description="Remove step") + self.remove_step_button.on_click(self.remove_step) + + # Create a button to add rows + self.add_motor_button = widgets.Button(description="Add motor") + self.add_motor_button.on_click(self.add_motor) + + self.remove_motor_button = widgets.Button(description="Remove motor") + self.remove_motor_button.on_click(self.remove_motor) + + # Put them into the control buttons item + self.control_buttons.children = [ + widgets.HBox( + [ + self.add_step_button, self.add_motor_button + ] + ), + widgets.HBox( + [ + self.remove_step_button, self.remove_motor_button + ] + ) + ] + self.display_table = self.build_display_table() + #display(self.display_table) + + def build_display_table(self): + build_table = [] + build_table.extend([self.constant_motor_title]) # The title of fixed values + build_table.extend([self.energy_info]) + build_table.extend([getattr(self, widget) for widget in self.constant_motor_attrs]) # Load in the values + build_table.extend([self.table]) # table object if needed + build_table.extend([self.control_buttons]) # optional buttons + return widgets.VBox(build_table) + + def add_step(self, b): + new_step = len(self.table_titles[0]) # Determine the next step digit + motor_directory = self.get_table(include_title=True) + for motor in motor_directory: + if "title" in motor: + getattr(self, motor)[0].append( + widgets.HTML( + value="Step "+str(new_step)+"", + layout=self.CELL_SIZE + ) + ) + else: + getattr(self,motor)[0].append( + widgets.FloatText( + value=0.0, + layout=self.CELL_SIZE + ) + ) + + # Update the table + self.table.children = self.update_table() + + def remove_step(self, b): + final_step = len(self.table_titles[0]) # Determine the final step + # No steps to remove + if final_step == 1: + print("No steps to delete") + return 0 + motor_directory = self.get_table(include_title=True) + for motor in motor_directory: + getattr(self,motor)[0].pop() + + # Update the table + self.table.children = self.update_table() + + def add_motor(self, b=None): + total_steps = len(self.table_titles[0])-1 + # Add a new row with FloatText widgets + num_extra_motors = len([attr for attr in dir(self) if attr.startswith("variable_")]) + if num_extra_motors >= len(AVAILABLE_PXR_MOTORS): + return 0 + # Create the new attribute + setattr( + self, + "variable_"+str(num_extra_motors+1), + [ + #[widgets.Text(value="", layout=self.label_size)] + + [widgets.Dropdown(options=AVAILABLE_PXR_MOTORS, value=AVAILABLE_PXR_MOTORS[0], layout=self.LABEL_SIZE)] + + [widgets.FloatText(value=AVAILABLE_PXR_MOTOR_DEFAULTS[AVAILABLE_PXR_MOTORS[0]], layout=self.CELL_SIZE) for i in np.arange(total_steps)] + #[widgets.Dropdown(options=AVAILABLE_MOTORS, value=::)] + ] + ) + temp_motor = getattr(self, "variable_"+str(num_extra_motors+1)) + self.table.children = self.update_table() + + def remove_motor(self, b=None): + num_extra_motors = len([attr for attr in dir(self) if attr.startswith("variable_")]) + for motor in self.get_table(): + if "variable_" in motor: + if str(num_extra_motors) in motor: + delattr(self,motor) + + # Update the table layout + self.table.children = self.update_table() + + +class PXR_Experiment(ALS_ExperimentWidget): + ALS_NAME = 'exp_' + WIDGET = PXR_Scan + DEFAULT_CONSTANTS = { + 'XPosition': 0.0, + 'YPosition': 0.0, + 'ZPosition': 0.0, + 'ZFlipPosition': 0.0, + 'DirectBeam': -5.0, + 'XOffset': 0.15, + 'ReverseHolder': False, + 'ThetaOffset': 0.0, + 'IndependentVariable': 'Qval', + 'DensityCalc': 'Use Thickness', + 'SampleThickness': 250, + 'AngleCrossover': [0,10], + 'PointDensity': [15,6], + 'OverlapPoints': 4, + 'Buffer': 2, + 'I0Points':10, + 'name_of_sample':'Sample' + } + DEFAULT_CONSTANTS_TITLES = { + 'menu1': { + 'XPosition': 'Sample X: ', + 'YPosition': 'Sample Y: ', + 'ZPosition': 'Sample Z: ', + 'ZFlipPosition': 'Sample Z Flip: ', + 'DirectBeam': 'Sample Z Direct Beam: ', + }, + 'menu2': { + 'XOffset': 'Sample X Offset: ', + 'ReverseHolder': 'Start at -180 [deg]?', + 'ThetaOffset': 'Sample Theta Offset:' + }, + 'menu3': { + 'IndependentVariable': 'Independent variable: ', + 'DensityCalc': 'Method to calculate point density: ', + 'SampleThickness': 'Approximate thickness [Angstroms]: ', + 'AngleCrossover': 'Angle to change point density: ', + 'PointDensity': 'Point density: ', + 'OverlapPoints': 'Overlap points: ', + 'Buffer': 'Movement buffer points: ', + 'I0Points': 'I0 points: ' + } + } + MOTOR_OPTIONS_LAYOUT = widgets.Layout(widget='400px') + MOTOR_OPTIONS_STYLE = {'description_width': '150px'} + BUTTON_STYLE = {'button_color':'#63666A', 'text_color':'white'} + def __init__(self, path="",scriptgen_widget=None, **kwargs): + # Get default values + constants = self.DEFAULT_CONSTANTS.copy() + constants_titles = self.DEFAULT_CONSTANTS_TITLES.copy() + # Update those based on what the kwargs are -- + constants.update({k: v for k, v in kwargs.items() if k in constants}) + constants = clean_for_widgets(constants) # Change the list to a CSV + # Build the experiment tab + super().__init__(title='Motor Positions', constants=constants, constants_titles=constants_titles, build_constants=False,scriptgen_widget=scriptgen_widget) # Build the initial stuff + + # Name of Sample Box + self.name_of_sample = widgets.Text(description="Name of Sample: ", value=constants['name_of_sample'], style=self.MOTOR_OPTIONS_STYLE) + + # Initial buttons used to create new measurements (one level down) + self.add_scan_button = widgets.Button(description="Add new measurement", layout=self.default_button, style=self.BUTTON_STYLE) + self.duplicate_scan_button = widgets.Button(description="Duplicate measurement", layout=self.default_button, style=self.BUTTON_STYLE) + self.delete_scan_button = widgets.Button(description="Delete measurement", layout=self.default_button, style=self.BUTTON_STYLE) + # Add button functionality + self.add_scan_button.on_click(lambda b: self.add_scan(self.layout, self.WIDGET, self.WIDGET.ALS_NAME)) + self.duplicate_scan_button.on_click(lambda b: self.copy_scan(self.layout, self.WIDGET, self.WIDGET.ALS_NAME)) + self.delete_scan_button.on_click(lambda b: self.delete_scan(self.layout, self.WIDGET, self.WIDGET.ALS_NAME)) + self.control_buttons.children = tuple([self.add_scan_button, self.duplicate_scan_button, self.delete_scan_button]) + + accordion_menus = [] + # Recreate the constant values based on the Accordians that organize things better -- + for key, menu in constants_titles.items(): + menu_display = [] + for attr, value in menu.items(): + attr_title = attr + gui_label = value + attr_value = constants[attr] + # Cycle through the specifics to create the correct button: + # CHECK BOX OPTIONS + if 'ReverseHolder' in attr_title: + setattr( + self, + attr_title, + widgets.Checkbox( + value=attr_value, + description=gui_label, + layout=self.MOTOR_OPTIONS_LAYOUT, + style=self.MOTOR_OPTIONS_STYLE + ) + ) + # TEXT OPTIONS THAT ARE COMMA SEPARATED LISTS + elif 'AngleCrossover' in attr_title or 'PointDensity' in attr_title: + setattr( + self, + attr_title, + widgets.Text( + value=attr_value, + description=gui_label, + layout=self.MOTOR_OPTIONS_LAYOUT, + style=self.MOTOR_OPTIONS_STYLE + ) + ) + elif 'DensityCalc' in attr_title or 'IndependentVariable' in attr_title: + if 'DensityCalc' in attr_title: + attr_options = POINT_DENSITY_METHOD + elif 'IndependentVariable' in attr_title: + attr_options = X_VARIABLES + setattr( + self, + attr_title, + widgets.Dropdown( + options=attr_options, + value=attr_value, + description=gui_label, + layout=self.MOTOR_OPTIONS_LAYOUT, + style=self.MOTOR_OPTIONS_STYLE + ) + ) + else: + setattr( + self, + attr_title, + widgets.FloatText( + value=attr_value, + description=gui_label, + layout=self.MOTOR_OPTIONS_LAYOUT, + style=self.MOTOR_OPTIONS_STYLE + ) + ) + menu_display.append(getattr(self, attr_title)) + accordion_menus.append(widgets.VBox(menu_display)) + self.menu_box = widgets.Accordion( + children=accordion_menus, + titles=("Motor Positions", "Optional Motor offsets", "More Options") + ) + # Add measurements + for key, value in kwargs.items(): + if self.WIDGET.ALS_NAME in key: + try: + df = value.output_dict() + except AttributeError: + df = value + self.add_scan(self.layout, self.WIDGET, self.WIDGET.ALS_NAME, **df) + if len(self.layout.children) == 0: + self.add_scan(self.layout, self.WIDGET, self.WIDGET.ALS_NAME) + + self.GUI_experiment = widgets.VBox([self.name_of_sample, self.widget_title, self.menu_box, self.control_buttons, self.layout]) + + def update_scan_tab(self, layout, widget_str): + widget_list = [getattr(self, attr) for attr in dir(self) if attr.startswith(widget_str)] + display = [] + titles = [] + for widget in widget_list: + display.extend([widget.display()]) + titles.append(f"En: {str(widget.energy_start.value)} [eV]") + widget.energy_start.observe(self.update_title, names='value') + + self.layout.children = display + for tab in np.arange(len(layout.children)): + layout.set_title(tab, titles[tab]) + + def update_title(self, change): + index = self.layout.selected_index + scan = getattr(self, f"{self.WIDGET.ALS_NAME}{pad_digits(index+1)}") + self.layout.set_title(index, f"En: {str(scan.energy_start.value)} [eV]") + +class PXR_ScriptGen(ALS_ScriptGenWidget): + EXPERIMENT_NAME = "PXR" + WIDGET = PXR_Experiment + WIDGET_TITLE = "Sample " + JSON_TITLE = "PXR" + BUTTON_STYLE = {'button_color':'#5D4777', 'text_color':'white'} + + def __init__(self, path="", **kwargs): + # Initialize the base widget + super().__init__(exp_name=self.EXPERIMENT_NAME, child_widget=self.WIDGET, path=path, tab_title=self.WIDGET_TITLE, json_title=self.JSON_TITLE, **kwargs) + # Buttons involved with adding new samples positions + self.new_sample_button = widgets.Button( + description="Add New Sample", layout=self.default_button, + style=self.BUTTON_STYLE + ) + self.copy_sample_button = widgets.Button( + description="Duplicate Sample", layout=self.default_button, + style=self.BUTTON_STYLE + ) + self.delete_sample_button = widgets.Button( + description="Delete Sample", layout=self.default_button, + style=self.BUTTON_STYLE + ) + # Add click functionality + self.new_sample_button.on_click(lambda b: self.new_tab(self.layout, self.WIDGET, self.WIDGET.ALS_NAME)) + self.copy_sample_button.on_click(lambda b: self.copy_tab(self.layout, self.WIDGET, self.WIDGET.ALS_NAME)) + self.delete_sample_button.on_click(lambda b: self.delete_tab(self.layout, self.WIDGET, self.WIDGET.ALS_NAME)) + + self.additional_buttons.children = tuple( + [ + widgets.HBox([self.new_sample_button, self.copy_sample_button, self.delete_sample_button]) + ] + ) + + self.GenerateExperiment(self.layout, self.WIDGET.ALS_NAME, self.WIDGET, **kwargs) + display(self.GUI) + + def update_experiment_tab(self, layout, widget_str): + widget_list = [getattr(self, attr) for attr in dir(self) if attr.startswith(widget_str)] + display = [] + display_titles = [] + for widget in widget_list: + display.extend([widget.display()]) + display_titles.extend([widget.name_of_sample.value]) + widget.name_of_sample.observe(self.update_title, names='value') + self.layout.children = display + for tab in np.arange(len(layout.children)): + layout.set_title(tab, display_titles[tab]) + + def update_title(self, change): + index = self.layout.selected_index + self.layout.set_title(index, change['owner'].value) # No idea why it is setup this way. + + def save_as_df(self): + df = {} + # Get the sample dataframe + df = self.save_as_dict() + #Temp thing -- + MACRO_DIR = str(self.save_dir) + + # Cycle through the samples and start saving data + for i, (index, sample) in enumerate(df.items()): + MotorPositions = {} + VariableMotors = {} + ScanList = [m for m in sample.keys() if "scan_" in m] + MotorPositions = {k: v for k, v in sample.items() if k not in ScanList} + beam_offset_counter = 0 + + name = self.WIDGET.ALS_NAME+pad_digits(i+1) + sample_attr = getattr(self, name) + sample_name = sample_attr.name_of_sample.value + + for ii, scan in enumerate(ScanList): + dm = sample[scan] + exclude_motors = ['theta_end', 'energy_start', 'energy_stop', 'energy_delta'] + if dm['energy_delta'] == 0: + energy = [dm['energy_start']] + else: + energy = np.arange(dm['energy_start'], dm['energy_stop'], dm['energy_delta']) + #energy = dm['energy'] + theta_end = dm['theta_end'] + #VariableMotors = {k: v for k, v in dm.items() if k not in ['energy', 'theta_end']} + VariableMotors = {k: v for k, v in dm.items() if k not in exclude_motors} + for iii, en in enumerate(energy): + df_en = pd.DataFrame(AngleRunGenerator_v2(MotorPositions, VariableMotors, en, theta_end)) + df_en['Sample X'] = df_en['Sample X'] + MotorPositions['XOffset']*beam_offset_counter + beam_offset_counter += 1 + if iii == 0: + sampdf = df_en + else: + sampdf = pd.concat([sampdf, df_en], ignore_index=True) + #sampdf = pd.DataFrame(AngleRunGenerator_v2(MotorPositions, VariableMotors, energy, theta_end)) + # Some cleanup + round_this_motor = ['Sample X', 'Sample Y', 'Sample Z', 'Sample Theta', 'CCD Theta'] + # sampdf['Sample X'] = sampdf['Sample X'] + MotorPositions['XOffset']*ii + for motor in round_this_motor: + sampdf[motor] = sampdf[motor].round(4) + # Initialize RunFile + if i == 0 and ii == 0: + RunFile = sampdf + else: + RunFile = pd.concat([RunFile, sampdf], ignore_index=True) + if ii == 0: + SingleFile = sampdf.copy() + else: + SingleFile = pd.concat([SingleFile, sampdf], ignore_index=True) + # save the sampdf as a macro file + SAVE_SCAN = MACRO_DIR + '/' + sample_name +'_PXR_'+str(i) + '.txt' + # Make sure the positions are correct + SingleFile = SingleFile[[c for c in SingleFile if c not in ['Exposure']] + ['Exposure']] + SingleFile.to_csv(SAVE_SCAN, index=False, sep='\t') + clean_script(SAVE_SCAN) # Remove exposure and clear last line if it exists + + + RunFile = RunFile[[c for c in RunFile if c not in ['Exposure']] + ['Exposure']] + return RunFile + + def save_beamline_scan(self, ext='.txt', sep='\t', b=None): + SAVEDIR = str(self.save_dir) + SAVENAME = str(self.save_name.value) + SAVEPATH = SAVEDIR + '/' + SAVENAME + '.txt' + + df = self.save_as_dict() + #df.to_csv(SAVEPATH, index=False, sep=sep) + + + def clean_scan_file(self, path): + # Cleanup the output because the ALS requires specific things + try: + with open(path, 'r') as f: + lines = f.readlines() + if not lines: # Empty file + return + # Edit first line to remove 'exposure' from headers + if 'Exposure' in lines[0]: + lines[0] = lines[0].replace('\tExposure' , '') + # Edit last line to remove any carriage return that may exist + if '\n' in lines[-1]: + lines[-1] = lines[-1].replace('\n', '') + + with open(path, 'w') as f: + f.writelines(lines) + except FileNotFoundError: + print(f"Error: File not found at {path}") + except Exception as e: + print(f"An error occured: {e}") + del lines #Remove it from memory (it can be large) + +PHOTODIODE_POS = { + "CCD Y": 100.000, + "CCD Theta": 0.000, + "CCD X": 6.000 +} + +XRR_POS = { + "CCD Y": 100.000, + "CCD Theta": 0.000, + "CCD X": 101.000, + "Beam Stop": 5.25 +} + +class PXR_MacroExperiment(ALS_ExperimentWidget): + ALS_NAME = 'exp_' + WIDGET = PXR_Scan + DEFAULT_CONSTANTS = { + 'XPosition': 0.0, + 'YPosition': 0.0, + #'ZPosition': 0.0, + #'ZFlipPosition': 0.0, + #'DirectBeam': -5.0, + 'XOffset': 0.15, + 'ReverseHolder': False, + #'ThetaOffset': 0.0, + 'IndependentVariable': 'Qval', + 'DensityCalc': 'Use Thickness', + 'SampleThickness': 250, + 'AngleCrossover': [0,10], + 'PointDensity': [15,6], + 'OverlapPoints': 4, + 'Buffer': 2, + 'I0Points':10 + } + DEFAULT_CONSTANTS_TITLES = { + 'menu1': { + 'XPosition': 'Sample X: ', + 'YPosition': 'Sample Y: ', + 'XOffset': 'X Offset per scan: ', + 'ReverseHolder': 'Start scan at -180 [deg]?' + }, + 'menu3': { + 'IndependentVariable': 'Independent variable: ', + 'DensityCalc': 'Method to calculate point density: ', + 'SampleThickness': 'Approximate thickness [Angstroms]: ', + 'AngleCrossover': 'Angle to change point density: ', + 'PointDensity': 'Point density: ', + 'OverlapPoints': 'Overlap points: ', + 'Buffer': 'Movement buffer points: ', + 'I0Points': 'I0 points: ' + } + } + MOTOR_OPTIONS_LAYOUT = widgets.Layout(widget='400px') + MOTOR_OPTIONS_STYLE = {'description_width': '150px'} + BUTTON_STYLE = {'button_color':'#63666A', 'text_color':'white'} + def __init__(self, path="",scriptgen_widget=None, **kwargs): + # Get default values + constants = self.DEFAULT_CONSTANTS.copy() + constants_titles = self.DEFAULT_CONSTANTS_TITLES.copy() + # Update those based on what the kwargs are -- + constants.update({k: v for k, v in kwargs.items() if k in constants}) + constants = clean_for_widgets(constants) # Change the list to a CSV + # Build the experiment tab + super().__init__(title='Motor Positions', constants=constants, constants_titles=constants_titles, build_constants=False,scriptgen_widget=scriptgen_widget) # Build the initial stuff + + # Name of Sample Box + self.name_of_sample = widgets.Text(description="Name of Sample: ", value="Sample", style=self.MOTOR_OPTIONS_STYLE) + + # Initial buttons used to create new measurements (one level down) + self.add_scan_button = widgets.Button(description="Add new measurement", layout=self.default_button, style=self.BUTTON_STYLE) + self.duplicate_scan_button = widgets.Button(description="Duplicate measurement", layout=self.default_button, style=self.BUTTON_STYLE) + self.delete_scan_button = widgets.Button(description="Delete measurement", layout=self.default_button, style=self.BUTTON_STYLE) + # Add button functionality + self.add_scan_button.on_click(lambda b: self.add_scan(self.layout, self.WIDGET, self.WIDGET.ALS_NAME)) + self.duplicate_scan_button.on_click(lambda b: self.copy_scan(self.layout, self.WIDGET, self.WIDGET.ALS_NAME)) + self.delete_scan_button.on_click(lambda b: self.delete_scan(self.layout, self.WIDGET, self.WIDGET.ALS_NAME)) + self.control_buttons.children = tuple([self.add_scan_button, self.duplicate_scan_button, self.delete_scan_button]) + + accordion_menus = [] + # Recreate the constant values based on the Accordians that organize things better -- + for key, menu in constants_titles.items(): + menu_display = [] + for attr, value in menu.items(): + attr_title = attr + gui_label = value + attr_value = constants[attr] + # Cycle through the specifics to create the correct button: + # CHECK BOX OPTIONS + if 'ReverseHolder' in attr_title: + setattr( + self, + attr_title, + widgets.Checkbox( + value=attr_value, + description=gui_label, + layout=self.MOTOR_OPTIONS_LAYOUT, + style=self.MOTOR_OPTIONS_STYLE + ) + ) + # TEXT OPTIONS THAT ARE COMMA SEPARATED LISTS + elif 'AngleCrossover' in attr_title or 'PointDensity' in attr_title: + setattr( + self, + attr_title, + widgets.Text( + value=attr_value, + description=gui_label, + layout=self.MOTOR_OPTIONS_LAYOUT, + style=self.MOTOR_OPTIONS_STYLE + ) + ) + elif 'DensityCalc' in attr_title or 'IndependentVariable' in attr_title: + if 'DensityCalc' in attr_title: + attr_options = POINT_DENSITY_METHOD + elif 'IndependentVariable' in attr_title: + attr_options = X_VARIABLES + setattr( + self, + attr_title, + widgets.Dropdown( + options=attr_options, + value=attr_value, + description=gui_label, + layout=self.MOTOR_OPTIONS_LAYOUT, + style=self.MOTOR_OPTIONS_STYLE + ) + ) + else: + setattr( + self, + attr_title, + widgets.FloatText( + value=attr_value, + description=gui_label, + layout=self.MOTOR_OPTIONS_LAYOUT, + style=self.MOTOR_OPTIONS_STYLE + ) + ) + menu_display.append(getattr(self, attr_title)) + accordion_menus.append(widgets.VBox(menu_display)) + self.menu_box = widgets.Accordion( + children=accordion_menus, + titles=("Motor Positions", "Scan Options") + ) + # Add measurements + for key, value in kwargs.items(): + if self.WIDGET.ALS_NAME in key: + try: + df = value.output_dict() + except AttributeError: + df = value + self.add_scan(self.layout, self.WIDGET, self.WIDGET.ALS_NAME, **df) + if len(self.layout.children) == 0: + self.add_scan(self.layout, self.WIDGET, self.WIDGET.ALS_NAME) + + self.GUI_experiment = widgets.VBox([self.name_of_sample, self.widget_title, self.menu_box, self.control_buttons, self.layout]) + + def update_scan_tab(self, layout, widget_str): + widget_list = [getattr(self, attr) for attr in dir(self) if attr.startswith(widget_str)] + display = [] + titles = [] + for widget in widget_list: + display.extend([widget.display()]) + titles.append(f"En: {str(widget.energy_start.value)} [eV]") + widget.energy_start.observe(self.update_title, names='value') + + self.layout.children = display + for tab in np.arange(len(layout.children)): + layout.set_title(tab, titles[tab]) + + def update_title(self, change): + index = self.layout.selected_index + scan = getattr(self, f"{self.WIDGET.ALS_NAME}{pad_digits(index+1)}") + self.layout.set_title(index, f"En: {str(scan.energy_start.value)} [eV]") + +class PXR_MacroGen(PXR_ScriptGen): + WIDGET = PXR_MacroExperiment + + def __init__(self, **kwargs): + super().__init__(self, **kwargs) + self.GUI.children = () # temporary fix to clear the widget generated by PXR_ScriptGen + # New banner + self.title_banner = widgets.HTML(value=f"

{self.EXPERIMENT_NAME} Macro Generator

") + + # Change the button + self.save_button_beamline = widgets.Button( + description = "Save Beamline Scan File", + layout=self.default_button, + style=self.banner_button_style + ) + self.save_button_beamline.on_click(self.save_beamline_scan) + self.save_buttons = widgets.HBox([self.save_button_beamline]) + self.GUI = widgets.VBox([self.title_banner, self.control_buttons, self.layout, self.json_buttons,self.save_button_beamline], layout=self.default_GUI) + display(self.GUI) + + # Everything is the same -- just save as a macro instead + + def save_beamline_scan(self, ext='.txt', sep='\t', b=None): + SAVEDIR = str(self.save_dir) + SAVENAME = str(self.save_name.value) + SAVEPATH = SAVEDIR + '/' + SAVENAME + '.txt' + + df = self.save_as_dict() + macro_details = self._build_individual_scan_files(df) + + + macro = begin_macro(SAVENAME) # list of commands that will become the spiral scan + # Move the sample plate into the 'zero' position to begin alignment -- + macro += [move_motor('Sample Theta', 0.0, 0.1)] + macro += [move_motor('Sample Z', 0, 0.1)] + + # Run add an I0 here --- + + # Begin samples + for sample in macro_details: + name = sample['name'] + posx = sample['posx'] + posy = sample['posy'] + path = sample['path'] + + # Each sample -- + macro += [add_comment(f"Begin Sample: {name}")] + macro += [clear_instruments] + macro.extend(run_dict_trajectory(PHOTODIODE_POS)) + macro += [move_motor('Sample X', posx, 0.1)] + macro += [move_motor('Sample Y', posy, 0.1)] + macro.extend(XRR_lineup(repeat=3)) + macro.extend(run_dict_trajectory(XRR_POS)) + macro += [set_instrument("CCD", exp=0.0, extension='fits')] + macro += [add_comment(f"Add from file here: {name}")] + # macro += [analog_from_file(name, path)] + macro += [clear_instruments] + macro.extend(run_dict_trajectory(PHOTODIODE_POS)) + macro += [add_prompt(f"Finished Macro")] + build_macro(SAVENAME, SAVEDIR, macro) + + + def _build_individual_scan_files(self, df): + # Initialize the macro construction + all_sample_scripts = [] # Contains lists associated with each detector position + + MACRO_DIR = str(self.save_dir) + + # Cycle through samples and start saving data + for i, (index, sample) in enumerate(df.items()): + # Get the name of the sample as chosen by user + name = self.WIDGET.ALS_NAME+pad_digits(i+1) + sample_attr = getattr(self, name) + sample_name = sample_attr.name_of_sample.value + + # Get Motor positions for XRR + MotorPositions = {} + VariableMotors = {} + ScanList = [m for m in sample.keys() if "scan_" in m] + MotorPositions = {k: v for k, v in sample.items() if k not in ScanList} + beam_offset_counter = 0 + + # Update Motor Positions that will be overwritten by macro generation + MotorPositions['ZPosition'] = 0.00 + MotorPositions['ZFlipPosition'] = 3.1435 + MotorPositions['DirectBeam'] = -2.00 + MotorPositions['ThetaOffset'] = 0.00 + + for ii, scan in enumerate(ScanList): + dm = sample[scan] + exclude_motors = ['theta_end', 'energy_start', 'energy_stop', 'energy_delta'] + if dm['energy_delta'] == 0: + energy = [dm['energy_start']] + else: + energy = np.arange(dm['energy_start'], dm['energy_stop'], dm['energy_delta']) + theta_end = dm['theta_end'] + VariableMotors = {k: v for k, v in dm.items() if k not in exclude_motors} + for iii, en in enumerate(energy): + df_en = pd.DataFrame(AngleRunGenerator_v2(MotorPositions, VariableMotors, en, theta_end)) + df_en['Sample X'] = df_en['Sample X'] + MotorPositions['XOffset']*beam_offset_counter + beam_offset_counter += 1 + if iii == 0: + sampdf = df_en + else: + sampdf = pd.concat([sampdf, df_en], ignore_index=True) + # Some cleanup + round_this_motor = ['Sample X', 'Sample Y', 'Sample Z', 'Sample Theta', 'CCD Theta'] + for motor in round_this_motor: + sampdf[motor] = sampdf[motor].round(4) + + # Initialize RunFile + if ii == 0: + RunFile = sampdf + else: + RunFile = pd.concat([RunFile, sampdf], ignore_index=True) + + # save the sampdf as a macro file + SAVE_SCAN = MACRO_DIR + '/' + sample_name +'_PXR_'+str(i) + '.txt' + # Make sure the positions are correct + RunFile = RunFile[[c for c in RunFile if c not in ['Exposure']] + ['Exposure']] + + RunFile.to_csv(SAVE_SCAN, index=False, sep='\t') + clean_script(SAVE_SCAN) # Remove exposure and clear last line if it exists + # Save all details required for the macro + macro_stats = {} + macro_stats['name'] = sample_name + macro_stats['posx'] = RunFile['Sample X'].iloc[0] + macro_stats['posy'] = RunFile['Sample Y'].iloc[0] + macro_stats['path'] = SAVE_SCAN + + all_sample_scripts.append(macro_stats) # Save path for macro generation + + return all_sample_scripts + +def AngleRunGenerator_v2(MotorPositions, VariableMotors, Energy, theta_end): + #Constants ## https://www.nist.gov/si-redefinition + SOL = 299792458 #m/s + PLANCK_JOULE = 6.6267015e-34 #Joule s + ELEMCHARGE = 1.602176634e-19 #coulombs + PLANCK = PLANCK_JOULE / ELEMCHARGE #eV s + meterToAng = 10**(10) + ##Initialization of needed components + Wavelength = SOL * PLANCK * meterToAng / Energy + # Motor Positions for the Sample level + XPosition = MotorPositions['XPosition'] + YPosition = MotorPositions['YPosition'] + ZPosition = MotorPositions['ZPosition'] + Z180Position = MotorPositions['ZFlipPosition'] + Zdelta = ZPosition - Z180Position + ThetaOffset = MotorPositions['ThetaOffset'] + # Point Density parameters + AngleCrossover = MotorPositions['AngleCrossover'] + PointDensity = MotorPositions['PointDensity'] + DensityCalc = MotorPositions['DensityCalc'] + IndependentVariable = MotorPositions['IndependentVariable'] + SampleThickness = MotorPositions['SampleThickness'] + OverlapPoints = int(MotorPositions['OverlapPoints']) + # From the Measurement Level + AngleChange = VariableMotors['Sample Theta'].copy() + AngleChange.append(theta_end) + AngleNumber = len(VariableMotors['Sample Theta']) + # Begin cycling through the angle points + for i in range(AngleNumber): + if i == 0: # Run initialization + ##Calculate the start and stop location for Q + AngleStart = AngleChange[i] # All of the relevant values are in terms of angles, but Q is calculated as a check + AngleStop = AngleChange[i+1] + QStart = 4*np.pi*np.sin(np.radians(AngleStart))/Wavelength + QStop = 4*np.pi*np.sin(np.radians(AngleStop))/Wavelength + + # Calculate the point density based on where this cycle ends + AngleDensity = locate_point_density(AngleStop, AngleCrossover, PointDensity) + + # Calculate the dq/dtheta as requested + if DensityCalc == 'Use Thickness': + dq = 2*np.pi/(SampleThickness*AngleDensity) # Use thickness to approximate fringe size + Points = int(np.ceil((QStop - QStart)/dq)) + elif DensityCalc == 'Use Delta': + if IndependentVariable == 'Qval': + dq = AngleDensity # Give an absolute points density in dq + Points = int(np.ceil((QStop - QStart)/dq)) + elif IndependentVariable == 'Theta': + dtheta = AngleDensity # Give an absolute point density in dtheta + Points = int(np.ceil((AngleStop-AngleStart)/dtheta)) + QList = np.linspace(QStart, QStop, Points).tolist() # Initialize the QList based on initial configuration + SampleTheta = np.linspace(AngleStart, AngleStop, Points)# Begin generating list of 'Sample Theta' locations to take data + CCDTheta = SampleTheta*2 # Make corresponding CCDTheta positions + SampleTheta = SampleTheta+ThetaOffset # If running multiple samples in a row this will offset the sample theta based on alignment ##CCD THETA SHOULD NOT BE CHANGED + + # Create lists for SampleX/Y/Z at the correct length + SampleX=[XPosition]*len(QList) + BeamLineEnergy=[Energy]*len(QList) + SampleY = YPosition+Zdelta/2+Zdelta/2*np.sin(np.radians(SampleTheta)) #Adjust 'Sample Y' based on the relative axis of rotation + SampleZ = ZPosition+Zdelta/2*(np.cos(np.radians(SampleTheta))-1) #Adjust 'Sample Z' based on the relative axis of rotation + + #Convert numpy arrays into lists for Pandas generation + SampleTheta=SampleTheta.tolist() + SampleY=SampleY.tolist() + SampleZ=SampleZ.tolist() + CCDTheta=CCDTheta.tolist() + + # Generate all other motors based on the input Variable motors + Variable_Motor_dict = {} + for key, value in VariableMotors.items(): # Cycle through the Variable Motor dictionary + if 'Theta' not in key: + Variable_Motor_dict[key] = [] + Variable_Motor_dict[key].extend([VariableMotors[key][i]]*len(QList)) + + # Adding one point at the start to measure the direct beam cut in half + #QList.insert(0,0) + #SampleTheta.insert(0,0) + #CCDTheta.insert(0,0) + #SampleX.insert(0,SampleX[0]) + #SampleY.insert(0,YPosition) + #SampleZ.insert(0,ZPosition) # Should be cutting the beam in half at this position + #BeamLineEnergy.insert(0,BeamLineEnergy[0]) + # All Variable Motors + #for motor in Variable_Motor_dict: + # Variable_Motor_dict[motor].insert(0, Variable_Motor_dict[motor][0]) + + # Adding Additional points at the start to assess error in beam intensity + for d in range(int(MotorPositions['I0Points'])): + QList.insert(0,0) + #ThetaInsert = 0 if MotorPositions['ReverseHolder']==0 else -180 + SampleTheta.insert(0,0) + CCDTheta.insert(0,0) + SampleX.insert(0,SampleX[0]) + SampleY.insert(0,YPosition) + SampleZ.insert(0,MotorPositions['DirectBeam']) + BeamLineEnergy.insert(0,BeamLineEnergy[0]) + # All Variable Motors + for motor in Variable_Motor_dict: + Variable_Motor_dict[motor].insert(0, Variable_Motor_dict[motor][0]) + + else: # for all of the ranges after the first set of samples + ##Section is identical to the above + AngleStart = AngleChange[i] # All of the relevant values are in terms of angles, but Q is calculated as a check + try: + AngleStop = AngleChange[i+1] + except: + AngleStop = theta_end + + QStart = 4*np.pi*np.sin(np.radians(AngleStart))/Wavelength + QStop = 4*np.pi*np.sin(np.radians(AngleStop))/Wavelength + + # Calculate the point density based on where this cycle ends. + AngleDensity = locate_point_density(AngleStop, AngleCrossover, PointDensity) + # Calculate the dq/dtheta as requested + if DensityCalc == 'Use Thickness': + dq = 2*np.pi/(SampleThickness*AngleDensity) # Use thickness to approximate fringe size + Points = int(np.ceil((QStop - QStart)/dq)) + elif DensityCalc == 'Use Delta': + if IndependentVariable == 'Qval': + dq = AngleDensity # Give an absolute points density in dq + Points = int(np.ceil((QStop - QStart)/dq)) + elif IndependentVariable == 'Theta': + dtheta = AngleDensity # Give an absolute point density in dtheta + Points = int(np.ceil((AngleStop-AngleStart)/dtheta)) + QListAddition = np.linspace(QStart, QStop, Points).tolist() # Initialize the QList based on initial configuration + SampleThetaAddition = np.linspace(AngleStart, AngleStop, Points).tolist() # Begin generating list of 'Sample Theta' locations to take data + for p in range(OverlapPoints): + QListAddition.insert(0, QList[-1*(p+2)]) + SampleThetaAddition.insert(0, SampleTheta[-1*(p+2)]-ThetaOffset) + SampleThetaAdditionArray=np.asarray(SampleThetaAddition) #Convert back to numpy array + + CCDThetaAddition=SampleThetaAdditionArray*2 #Calculate the CCD theta POsitions + CCDThetaAddition=CCDThetaAddition.tolist() #Convert to list + SampleThetaAdditionArray=SampleThetaAdditionArray+ThetaOffset #Account for theta offset + SampleThetaAddition = SampleThetaAdditionArray.tolist() + + SampleXAddition=[XPosition]*len(QListAddition) + BeamLineEnergyAddition=[Energy]*len(QListAddition) + + SampleYAddition=YPosition+Zdelta/2+Zdelta/2*np.sin(np.radians(SampleThetaAdditionArray)) + SampleZAddition=ZPosition+Zdelta/2*(np.cos(np.radians(SampleThetaAdditionArray))-1) + SampleYAddition=SampleYAddition.tolist() + SampleZAddition=SampleZAddition.tolist() + + # Generate all other motors based on the input Variable motors + Variable_Motor_dict_Addition = {} + for key, value in VariableMotors.items(): # Cycle through the Variable Motor dictionary + if 'Theta' not in key: + Variable_Motor_dict_Addition[key] = [] + Variable_Motor_dict_Addition[key].extend([VariableMotors[key][i]]*len(QListAddition)) + + #Check to see if any of the variable motors have moved to add buffer points + for key, value in VariableMotors.items(): + if 'Theta' not in key: + if VariableMotors[key][i] != VariableMotors[key][i-1]: + for d in range(int(MotorPositions['Buffer'])): + QListAddition.insert(0,QListAddition[d]) + SampleThetaAddition.insert(0,SampleThetaAddition[d]) + CCDThetaAddition.insert(0,CCDThetaAddition[d]) + SampleXAddition.insert(0,SampleXAddition[d]) + SampleYAddition.insert(0,SampleYAddition[d]) + SampleZAddition.insert(0,SampleZAddition[d]) + BeamLineEnergyAddition.insert(0,BeamLineEnergyAddition[d]) + for motor in Variable_Motor_dict_Addition: + Variable_Motor_dict_Addition[motor].insert(0, Variable_Motor_dict_Addition[motor][d]) + + QList.extend(QListAddition) + SampleTheta.extend(SampleThetaAddition) + CCDTheta.extend(CCDThetaAddition) + SampleX.extend(SampleXAddition) + SampleY.extend(SampleYAddition) + SampleZ.extend(SampleZAddition) + BeamLineEnergy.extend(BeamLineEnergyAddition) + for motor, addition in zip(Variable_Motor_dict.keys(), Variable_Motor_dict_Addition.keys()): + Variable_Motor_dict[motor].extend(Variable_Motor_dict_Addition[addition]) + + #Check what side the sample is on. If on the bottom, sample theta starts @ -180 + if MotorPositions['ReverseHolder'] == 1: + SampleTheta=[theta-180 for theta in SampleTheta] # for samples on the backside of the holder, need to check and see if this is correct + + sampdf = {} + sampdf['Sample X'] = SampleX + sampdf['Sample Y'] = SampleY + sampdf['Sample Z'] = SampleZ + sampdf['Sample Theta'] = SampleTheta + sampdf['CCD Theta'] = CCDTheta + sampdf['Beamline Energy'] = BeamLineEnergy + for key, item in Variable_Motor_dict.items(): + sampdf[key] = item + + return sampdf#(SampleX, SampleY, SampleZ, SampleTheta, CCDTheta, HOSList, HESList, BeamLineEnergy, ExposureList, QList) + + + + + + + + + + + + +def locate_point_density(angle, crossover_list, density_list): + for i in range(len(crossover_list)-1): + if int(crossover_list[i]) <= angle <= int(crossover_list[i+1]): + return int(density_list[i]) + return int(density_list[-1]) + +def update_dict(old_dict, new_dict): + for key in old_dict.keys(): + if key in new_dict: + old_dict[key] = new_dict[key] + return old_dict + +def clean_for_widgets(my_dict): + for key, value in my_dict.items(): + if isinstance(value, list): + my_dict[key] = ', '.join(map(str, value)) + return my_dict diff --git a/python/pyref/beamline/rsoxs_widgets.py b/python/pyref/beamline/rsoxs_widgets.py new file mode 100644 index 0000000..47f7c6f --- /dev/null +++ b/python/pyref/beamline/rsoxs_widgets.py @@ -0,0 +1,1317 @@ +"""Resonant Soft X-ray Scattering (RSOXS) widgets for ALS beamline script generation. + +This module provides widgets for generating RSOXS scan run files for +ALS beamline 11.0.1.2. +""" +# Base Packages +import sys +import os +import json +import re +import copy +import inspect +import datetime +from pathlib import Path + +# Math packages +import numpy as np +import pandas as pd + +# Visualization Packages +import ipywidgets as widgets +from IPython.display import display, clear_output +from PyQt6.QtWidgets import QApplication, QFileDialog, QWidget +from PyQt6.QtCore import QDir + +from pyref.beamline.base_widgets import ( + ALS_ScriptGenWidget, + ALS_ExperimentWidget, + ALS_MeasurementWidget, + clean_script, + pad_digits, +) + +from pyref.beamline.beamline_scan_macros import * + +BASE_FILE_PATH = Path(__file__).parent + +DEFAULT_ESCAN_PATH = os.path.join(BASE_FILE_PATH, "DEFAULT_ESCANS") +DEFAULT_TRAJECTORY_PATH = os.path.join(BASE_FILE_PATH, "TRAJECTORY_OPTIONS") +AVAILABLE_RSOXS_MOTORS = ['Sample Z', 'Sample Theta'] #'Sample Z', (Z removed from option) +#AVAILABLE_RSOXS_MOTORS = ['Sample X', 'Sample Y', 'Sample Z', 'Sample Theta'] #'Sample Z', (Z removed from option) + + +class RSOXS_Scan(ALS_MeasurementWidget): + ALS_NAME = 'scan_' + MEASUREMENT_TITLE = "Measurement details" + RSOXS_MOTOR_OPTIONS = AVAILABLE_RSOXS_MOTORS + ESCAN_OPTIONS = ['CARBON_ESCAN', 'QUICK_ESCAN'] + INITIAL_ROWS = { + 'ESCAN':'CARBON_ESCAN', + 'Sample X': 0, + 'Sample Y': 0, + #'Sample Z': 0, + 'Sample Theta': 90 + } + DEFAULT_TITLE = ("Scan 1",) + FIXED_ROWS = 1 + DEFAULT_OPTIONS = { + "fixed_title":'RSOXS Scans' + }, + ACCORDION_SIZE = widgets.Layout(width='20rem') + ESCAN_SIZE = widgets.Layout(width='5rem') + HBOX_SIZE = widgets.Layout(width='50rem') + + CARBON_ESCAN = { + "Step 1":[270.0, 280.0, 2, 0.5], + "Step 2":[280.0, 284.0, 0.2, 1], + "Step 3":[284.0, 286.0, 0.1, 0.5], + "Step 4":[286.0, 292.0, 0.2, 0.5], + "Step 5":[292.0, 300.0, 1, 0.5], + "Step 6":[300.0, 370.0, 10, 1] + } + + def __init__(self, save_path="",experiment_widget=None, **kwargs): + # Import the initial saved data to create the table with + saved_data = self.INITIAL_ROWS.copy() + # Update from kwargs + saved_data.update({k: v for k, v in kwargs.items() if k in saved_data}) + # Get the initial columns to populate the table with + #self.variable_cols = [col for k, v in saved_data.items() for col in v if 'ESCAN' not in col] # Add in saved columns for the loaded data. + # Create initial widget object + super().__init__(experiment_widget=experiment_widget) + # Create some generic buttons that will help duplicate/delete rows/columns + # New title: + self.delete_row_buttons = {} + self.copy_row_buttons = {} + # Initialize the table given inputs + # Create the output that will include a title, accordion, and delete buttons + scan_title_temp = kwargs.get('name', 'Default Name') + self.scan_title = widgets.HTML(value="Name of Sample: ", layout=self.LABEL_SIZE) + self.scan_title_input = widgets.Text(value=scan_title_temp, layout=self.LABEL_SIZE) + + self.escan_details = self.generate_escan(saved_data['ESCAN']) # Create the + self.scan_details = self.generate_accordion(saved_data) + + self.display_table = widgets.HBox( + [ + widgets.VBox( + [ + widgets.HBox([self.scan_title, self.scan_title_input]), + self.scan_details + ] + ), + self.escan_details + ], + layout=self.HBOX_SIZE + ) + """ + Repopulate the table when changes are made: + """ + + def generate_accordion(self, d): + scan = [] + for i, (key, item) in enumerate(d.items()): + if 'ESCAN' in key: + if isinstance(item, str): + dropdown_item = item + elif isinstance(item, dict): + dropdown_item = 'CUSTOM' + dropdown = widgets.Dropdown(value=dropdown_item, options=self.ExperimentWidget.ScriptgenWidget.ESCAN_LIST.keys(), description=key, layout=self.ACCORDION_SIZE) + dropdown.observe(self.update_escan, 'value') # Observe when this changes + self.ExperimentWidget.ScriptgenWidget.dummy_widget.observe(self.escan_list_updated, 'value') + scan.extend([dropdown]) + else: + scan.extend([widgets.FloatText(value=item, description=key, layout=self.ACCORDION_SIZE)]) + setattr( + self, + "scan_info", + scan + ) + output = getattr(self, "scan_info") + output.insert(0, widgets.HTML(value="Scan Details:")) + return widgets.VBox(children=output) + + def generate_escan(self, input_d): + escan_info = [] + # Check to see if the input is a dictionary, or a label + # This handles "Saved data" or if you want ot use a default value + if isinstance(input_d, str): + d = self.ExperimentWidget.ScriptgenWidget.ESCAN_LIST[input_d] + elif isinstance(input_d, dict): + d = input_d + + title_row = widgets.HBox( + children=[ + widgets.HTML(value="Step", layout=self.ESCAN_SIZE), + widgets.HTML(value="Start", layout=self.ESCAN_SIZE), + widgets.HTML(value="Stop", layout=self.ESCAN_SIZE), + widgets.HTML(value="Delta", layout=self.ESCAN_SIZE), + widgets.HTML(value="Exposure", layout=self.ESCAN_SIZE), + ] + ) + for i, (key, item) in enumerate(d.items()): + if 'Step' in key: + temp_label = widgets.HTML(value=f"{key}", layout=self.ESCAN_SIZE) + temp_start = widgets.FloatText(value=item[0], layout=self.ESCAN_SIZE) + temp_end = widgets.FloatText(value=item[1], layout=self.ESCAN_SIZE) + temp_step = widgets.FloatText(value=item[2], layout=self.ESCAN_SIZE) + temp_exposure = widgets.FloatText(value=item[3], layout=self.ESCAN_SIZE) + escan_info.extend( + [ + widgets.HBox( + [ + temp_label, temp_start, temp_end, temp_step, temp_exposure + ] + ) + ] + ) + setattr( + self, + "escan_info", + escan_info + ) + output = getattr(self, "escan_info") + output.insert(0, title_row) + output.insert(0, widgets.HTML(value="Escan Details:")) + return widgets.VBox(output) + + + def update_escan(self, change): + """ + Updates the Escan Details widgets if the dropdown menu is changed. + """ + new_scan = change.new # gives the new "value" of the dropdown menu + if 'CUSTOM' in new_scan: + return 0 + try: + d = self.ExperimentWidget.ScriptgenWidget.ESCAN_LIST[new_scan] # Get the dictionary corresponding to the new menu + except KeyError: + d = CARBON_ESCAN + self.escan_details = self.generate_escan(d) + self.update_table() + + def escan_list_updated(self, change): + if change['type'] == 'change' and change['name'] == 'value': + dropdown = self.scan_info[1] + dropdown.options = self.ExperimentWidget.ScriptgenWidget.ESCAN_LIST.keys() + + + def update_table(self): + self.display_table.children = [ + widgets.VBox( + [ + widgets.HBox([self.scan_title, self.scan_title_input]), + self.scan_details + ] + ), + self.escan_details + ] + + + def output_dict(self): + """ + Export relevant class values for a dictionary to save everything. + """ + output = {} + scan_details = self.scan_details.children + escan_details = self.escan_details.children + + # Process scan details + for item in scan_details: + key = item.description + value = item.value + + if not key: # Skip empty description (title row) + continue + + if 'ESCAN' in key: + output[key] = self._process_escan_details(escan_details) + else: + output[key] = value + + # Add scan title + output['name'] = self.scan_title_input.value + return output + + def _process_escan_details(self, escan_details): + """ + Helper function to process ESCAn details while exporting as a dictionary + """ + escan_data = {} + + for row in escan_details[2:]: # Skip the first two rows (titles) + row_values = [child.value for child in row.children] + escan_key = row_values[0] + escan_values = row_values[1:] + + # Check if any escan_values are zero, and skip the row if so. + # if any(val == 0 for val in escan_values): + # continue + + escan_data[escan_key] = escan_values + + return escan_data + + + def old_output_dict(self): + df = {} + for item in b.scan_details.children: + key = item.description + val = item.value + if len(key) < 1: # Skip the title row + continue + if 'ESCAN' in key: # Cycle through the Escan values + de = {} + for i, row in enumerate(b.escan_details.children): + escan = [] + if i < 2: # Skip the titles + continue + for i, escan_val in enumerate(row.children): + if i==0: + ekey = escan_val.value + else: + escan.append(escan_val.value) + for x in escan: + if x == 0 : + skip = True + if skip: + continue + de[ekey] = escan + df[key] = de + else: # Cycle through all other values + df[key] = val + # Add the title to deal with copy/paste elements + df['name'] = self.scan_title_input.value + return df + + +class RSOXS_Detector(ALS_ExperimentWidget): + ALS_NAME = 'det_' + WIDGET = RSOXS_Scan + DEFAULT_CONSTANTS = { + 'DetX': 96.0, + 'DetY': 100.0, + 'DetTheta': 0.0127, + 'BS': 11.0, + 'SampZ': 0.0, + 'SampXOffset': 0.0, + 'SampYOffset':0.0 + } + DEFAULT_CONSTANTS_TITLES = { + 'DetX': 'Detector X', + 'DetY': 'Detector Y', + 'DetTheta': 'CCD Theta', + 'BS': 'Beamstop', + 'SampZ': 'Sample Z', + 'SampXOffset': 'Sample X Offset', + 'SampYOffset':'Sample Y Offset' + } + def __init__(self, save_path="", constants=DEFAULT_CONSTANTS.copy(),constants_titles = DEFAULT_CONSTANTS_TITLES.copy(),scriptgen_widget=None, **kwargs): + # These are defaults for the RSOXS Detector. Can be updated with **kwargs + constants = self.DEFAULT_CONSTANTS.copy() + constants_titles = self.DEFAULT_CONSTANTS_TITLES.copy() + # Update from kwargs + constants.update({k: v for k, v in kwargs.items() if k in constants}) + # Create initial widget object + super().__init__(title='Detector Positions', constants=constants, constants_titles=constants_titles, scriptgen_widget=scriptgen_widget) + + # Buttons involved with adding rows to the RSOXS Measurement (Add items one level down) + # This is unique to the RSOXS widget + self.add_scan_button = widgets.Button( + description="Add New Scan", layout=self.default_button, + style=self.default_button_style + ) + self.duplicate_scan_button = widgets.Button( + description="Copy Current Scan", layout=self.default_button, + style=self.default_button_style + ) + self.delete_row_button = widgets.Button( + description="Delete Current Scan", layout=self.default_button, + style=self.default_button_style + ) + # Add click functionality + self.add_scan_button.on_click(lambda b: self.add_scan(self.layout, self.WIDGET, self.WIDGET.ALS_NAME)) + self.duplicate_scan_button.on_click(lambda b: self.copy_scan(self.layout, self.WIDGET, self.WIDGET.ALS_NAME)) + self.delete_row_button.on_click(lambda b: self.delete_scan(self.layout, self.WIDGET, self.WIDGET.ALS_NAME)) + + # Update the control_buttons object + self.control_buttons.children = [self.add_scan_button, self.duplicate_scan_button, self.delete_row_button] + # Update the 'layout' to be a accordion that contains each scan + self.layout = widgets.Accordion(children=[], layout=self.default_GUI) + + # Add measurements + for key, value in kwargs.items(): + if self.WIDGET.ALS_NAME in key: + try: + df = value.output_dict() + except AttributeError: + df = value + self.add_scan(self.layout, self.WIDGET, self.WIDGET.ALS_NAME, **df) + if len(self.layout.children) == 0: + self.add_scan(self.layout, self.WIDGET, self.WIDGET.ALS_NAME) + + self.GUI_experiment = widgets.VBox([self.widget_title, self.menu_box, self.control_buttons, self.layout]) + + def add_scan(self, layout, widget, widget_str, b=None, **kwargs): + index = len([getattr(self, attr) for attr in dir(self) if attr.startswith(widget_str)])+1 # Next index + setattr(self, widget_str + pad_digits(index), widget(experiment_widget=self, **kwargs)) + self.update_scan_tab(layout, widget_str) + + def copy_scan(self, layout, widget, widget_str, b=None): + index = len([getattr(self, attr) for attr in dir(self) if attr.startswith(widget_str)]) + 1 # Last Index + # Get stats of copied obj + try: + open_tab = self.layout.selected_index + 1 + except: + return 0 + target_widget_name = widget_str + pad_digits(open_tab) + if hasattr(self, target_widget_name): + df = getattr(self, widget_str + pad_digits(open_tab)).output_dict() + else: + df = {} + setattr(self, widget_str + pad_digits(index), widget(experiment_widget=self, **df)) + self.update_scan_tab(layout, widget_str) + + def delete_scan(self, layout, widget, widget_str, b=None): + try: + index = self.layout.selected_index + 1 + except: + return 0 + widget_name = widget_str+pad_digits(index) + if hasattr(self, widget_name): + delattr(self, widget_name) + j = index + 1 + while hasattr(self, f"{widget_str}{pad_digits(j)}"): + setattr(self, f"{widget_str}{pad_digits(j-1)}", getattr(self, f"{widget_str}{pad_digits(j)}")) + delattr(self, f"{widget_str}{pad_digits(j)}") + j += 1 + self.update_scan_tab(layout, widget_str) + + def update_scan_tab(self, layout, widget_str): + widget_list = [getattr(self, attr) for attr in dir(self) if attr.startswith(widget_str)] + display = [] + titles = [] + for widget in widget_list: + display.extend([widget.display()]) + titles.extend([widget.scan_title_input.value]) + # Make the title change when updated: + widget.scan_title_input.observe(self.update_title, names='value') + self.layout.children = display + for tab in np.arange(len(layout.children)): + layout.set_title(tab, titles[tab]) + + def update_title(self, change): + index = self.layout.selected_index + self.layout.set_title(index, change['owner'].value) # No idea why it is setup this way. + +class RSOXS_ScriptGen(ALS_ScriptGenWidget): + EXPERIMENT_NAME = "RSoXS" + WIDGET = RSOXS_Detector + WIDGET_TITLE = 'Detector Position ' + JSON_TITLE = "RSOXS" + GUI_SIZE = widgets.Layout(widget='100rem') + + MOTOR_ORDER = ['Beamline Energy', 'Sample X', 'Sample Y', 'Sample Z', 'Detector X', 'Detector Y', 'CCD Theta', 'Beamstop', 'CCD Camera Shutter Inhibit', 'Exposure'] + + + CARBON_ESCAN = { + "Step 1":[270.0, 280.0, 2, 0.5], + "Step 2":[280.0, 284.0, 0.2, 1], + "Step 3":[284.0, 286.0, 0.1, 0.5], + "Step 4":[286.0, 292.0, 0.2, 0.5], + "Step 5":[292.0, 300.0, 1, 0.5], + "Step 6":[300.0, 370.0, 10, 1] + } + QUICK_ESCAN = { + "Step 1":[270.0, 280.0, 2, 0.5], + "Step 2":[280.0, 284.0, 0.5, 1], + "Step 3":[284.0, 286.0, 0.2, 0.5], + "Step 4":[286.0, 292.0, 0.5, 0.5], + "Step 5":[292.0, 300.0, 2, 0.5], + "Step 6":[300.0, 330.0, 20, 1] + } + ESCAN_LIST = { + 'CUSTOM': None, + 'CARBON_ESCAN':CARBON_ESCAN, + 'QUICK_ESCAN':QUICK_ESCAN + } + + def __init__(self, save_path="", escan_path=DEFAULT_ESCAN_PATH, **kwargs): + super().__init__(exp_name=self.EXPERIMENT_NAME, child_widget=self.WIDGET, save_path=save_path, tab_title=self.WIDGET_TITLE,json_title=self.JSON_TITLE, **kwargs) + + # RSOXS specific details: + self.rsoxs_button_style = {'button_color':'#5D4777', 'text_color':'white'} + #Book-keeping attributes + + # Make additional buttons before the table + """ + INFORMATION ON THE ESCAN DIRECTORY -- WHERE ARE JSON FILES SAVED + """ + # Locate path for where default energy scripts are given + self.browse_escan_directory_button = widgets.Button( + description="Select Escan Directory", layout=self.default_button, + style=self.rsoxs_button_style + ) + # BUtton to update the values if the + self.update_escan_directory_button = widgets.Button( + description="Update Escan List", layout=self.default_button, + style=self.rsoxs_button_style + ) + self.browse_escan_directory_button.on_click(self.browse_escan_directory) + self.update_escan_directory_button.on_click(self.update_escan_directory) + self._escan_dir = "" + self.display_escan_directory = widgets.HTML( + value = "Escan Directory: " + str(self._escan_dir), + description = "" + ) + self.dummy_widget = widgets.Valid(value=False, description='dummy widget') + + self.escan_dir = escan_path # Update the directory with the default + + # Buttons involved with adding detector positions + self.new_detpos_button = widgets.Button( + description="Add New Detector Position", layout=self.default_button, + style=self.rsoxs_button_style + ) + self.copy_detpos_button = widgets.Button( + description="Copy Detector Position", layout=self.default_button, + style=self.rsoxs_button_style + ) + self.delete_detpos_button = widgets.Button( + description="Delete Detector Position", layout=self.default_button, + style=self.rsoxs_button_style + ) + # Add click functionality + self.new_detpos_button.on_click(lambda b: self.new_tab(self.layout, self.WIDGET, self.WIDGET.ALS_NAME)) + self.copy_detpos_button.on_click(lambda b: self.copy_tab(self.layout, self.WIDGET, self.WIDGET.ALS_NAME)) + self.delete_detpos_button.on_click(lambda b: self.delete_tab(self.layout, self.WIDGET, self.WIDGET.ALS_NAME)) + # Buttons involved with adding columns to the table + + self.additional_buttons.children = tuple( + [ + widgets.HBox([self.browse_escan_directory_button, self.update_escan_directory_button, self.display_escan_directory]), + widgets.HBox([self.new_detpos_button, self.copy_detpos_button, self.delete_detpos_button]) + ] + ) + + # Select box for motor selection: + self.select_col_title = widgets.HTML(value=f"Select optional motors to run") + self.select_cols = widgets.SelectMultiple( + options=AVAILABLE_RSOXS_MOTORS, + disabled=False + ) + self.rsoxs_menu = widgets.VBox([self.select_col_title,self.select_cols]) + self.GenerateExperiment(self.layout, self.WIDGET.ALS_NAME, self.WIDGET, **kwargs) + self.GUI = widgets.VBox([self.title_banner, self.control_buttons, self.layout, self.json_buttons, self.rsoxs_menu, self.save_buttons], layout=self.GUI_SIZE) + display(self.GUI) + + #@property + #def escan_path(self): + # return self._escan_path + # + #@escan_path.setter + #def escan_path(self, path): + # self._escan_path = Path(path) + + def browse_escan_directory(self, b=None): + self.escan_dir = super().browse_directory() + #self.update_escan_list(self.escan_dir) + #self.display_escan_directory.value = "Escan Directory:" + str(self._escan_dir) + def update_escan_directory(self, b=None): + self.update_escan_list(self.escan_dir) + + @property + def escan_dir(self): + return self._escan_dir + + @escan_dir.setter + def escan_dir(self, val): + self._escan_dir=val + self.display_escan_directory.value = "Escan Directory:" + str(self._escan_dir) + self.update_escan_list(self._escan_dir) + # Update the global dictionary + + def update_escan_list(self, path): + """ + Loads all .json files in a specified path that contain 'ESCANs' for RSOXS + + """ + output_dict = {} + try: + for filename in os.listdir(path): + if filename.endswith(".json"): + file_path = os.path.join(path, filename) + with open(file_path, 'r') as f: + first_line = f.readline().strip() # Read the first line and remove white space + if first_line == "# ESCAN": + # Discard the first line and read the rest + try: + data = json.load(f) # Update this to ignore the title + file_key = os.path.splitext(filename)[0] + output_dict[file_key] = data + except json.JSONDecodeError as e: + print(f"Error decoding JSON in file '{filename}': {e}") + else: + continue + except FileNotFoundError: + print(f"Error: Foldesr '{folder_path}' not found.") + except Exception as e: + print(f"An unexpected error occurred: {e}") + + self.ESCAN_LIST = self.ESCAN_LIST | output_dict + self.dummy_widget.value = not self.dummy_widget.value # This is watched by all dropdowns to change the ESCAN_LIST when updated + + + def save_script(self, ext='.txt', sep='\t', b=None): + SAVEDIR = str(self.save_dir) + SAVENAME = str(self.save_name.value) + SAVEPATH = SAVEDIR + '/' + SAVENAME + '.txt' + + df = self.save_as_dict() # Get the data as a dictionary + + list_of_sample_scripts = [] + number_of_detectors = len(df) # How many detector positions? + # Cycle through samples and start saving data + for i, (index, detector) in enumerate(df.items()): + ScanList = [s for s in detector.keys() if "scan_" in s] + number_of_samples = len(ScanList) + DetectorPositions = {k: v for k, v in detector.items() if k not in ScanList} + + for ii, scan in enumerate(ScanList): + dm = {key: value for key, value in detector[scan].items() if key != "name"} # Drop the name which is used for saving but not scanning + motors_to_run = ['Sample X', 'Sample Y'] + motors_to_run.extend([motor for motor in self.select_cols.value]) + #Positions = {key: value for key, value in dm.items() if key in self.select_cols.value} + Positions = {key: value for key, value in dm.items() if key in motors_to_run} + escan_details = self._build_escan(dm['ESCAN']) # Collect all the info for the Escan + Escan = escan_details[0] # The energies + Exposure = escan_details[1] # The exposures + + # Build the sample dataframe + temp_scan = {} + temp_scan['Beamline Energy'] = Escan + temp_scan['Exposure'] = Exposure + df_samp = pd.DataFrame(temp_scan) + + # Add the sample positions + for motor, pos in Positions.items(): + df_samp[motor] = np.full_like(Escan, pos) + # Add detector Positions + if number_of_detectors > 1: + for motor, pos in DetectorPositions.items(): + if "DetX" in motor: + key = 'Detector X' + elif "DetY" in motor: + key = 'Detector Y' + elif "DetTheta" in motor: + key = 'CCD Theta' + elif "BS" in motor: + key = 'Beamstop' + elif "SampXOffset" in motor: + df_samp['Sample X'] += pos + continue + elif "SampYOffset" in motor: + df_samp['Sample Y'] += pos + continue + df_samp[key] = np.full_like(Escan, pos) + # Add CCD Inhibit, first and last, all exposure times + df_samp['CCD Camera Shutter Inhibit'] = np.full_like(Escan, 0) # Add the column + for expo in np.unique(Exposure): + df_samp = pd.concat([df_samp.loc[0].to_frame().T, df_samp], ignore_index=True) # Add a frame at the beginning of the sample + df_samp = pd.concat([df_samp, df_samp.loc[df_samp.index[-1]].to_frame().T], axis=0, ignore_index=True) # Add a frame at the end of the sample + + df_samp.loc[0, 'CCD Camera Shutter Inhibit'] = 1 # Close the shutter + df_samp.loc[df_samp.index[-1], 'CCD Camera Shutter Inhibit'] = 1 + + df_samp.loc[0, 'Exposure'] = expo # Set exposure + df_samp.loc[df_samp.index[-1], 'Exposure'] = expo + list_of_sample_scripts.append(df_samp) + + RunFile = pd.concat(list_of_sample_scripts, ignore_index=True) # Only export with the Adjustable Motors in the correct order. + + # Cleanup the output because the ALS requires specific things + cols = list(RunFile.columns) + Ordered_Cols = [item for item in self.MOTOR_ORDER if item in cols] + RunFile = RunFile[Ordered_Cols] + + RunFile.to_csv(SAVEPATH, index=False, sep='\t') + + try: + with open(SAVEPATH, 'r') as f: + lines = f.readlines() + if not lines: # Empty file + return + # Edit first line to remove 'exposure' from headers + if 'Exposure' in lines[0]: + lines[0] = lines[0].replace('\tExposure' , '') + # Edit last line to remove any carriage return that may exist + if '\n' in lines[-1]: + lines[-1] = lines[-1].replace('\n', '') + + with open(SAVEPATH, 'w') as f: + f.writelines(lines) + except FileNotFoundError: + print(f"Error: File not found at {SAVEPATH}") + except Exception as e: + print(f"An error occured: {e}") + del lines #Remove it from memory (it can be large) + + + + def _build_escan(self, d): + energy_array = np.array([]) + exposure_array = np.array([]) + for key, item in d.items(): + if item[2] == 0: + energy_subset = np.array([item[0]]) + else: + energy_subset = np.arange(item[0], item[1], item[2]) + exposure_subset = np.full_like(energy_subset, item[3]) + energy_array = np.concatenate((energy_array, energy_subset)) + exposure_array = np.concatenate((exposure_array, exposure_subset)) + return (np.round(energy_array, 2), exposure_array) + + + +def update_dict(old_dict, new_dict): + for key in old_dict.keys(): + if key in new_dict: + old_dict[key] = new_dict[key] + return old_dict + +def clean_for_widgets(my_dict): + for key, value in my_dict.items(): + if isinstance(value, list): + my_dict[key] = ', '.join(map(str, value)) + return my_dict + + + +""" +****************THESE ARE MACRO SCANS. VERY SIMILAR BUT WILL SAVE AS BEAMLINE SCAN FILES +""" + +class RSOXS_Detector_Macro(RSOXS_Detector): + + DEFAULT_CONSTANTS = { + 'SampXOffset': 0.0, + 'SampYOffset':0.0, + 'Trajectory': "Photodiode_Far" + } + DEFAULT_CONSTANTS_TITLES = { + 'SampXOffset':'Sample X Offset', + 'SampYOffset':'Sample Y Offset', + 'Trajectory' : 'Trajectory' + } + + def __init__(self, save_path="", scriptgen_widget=None, **kwargs): + + constants = self.DEFAULT_CONSTANTS.copy() + constants_titles = self.DEFAULT_CONSTANTS_TITLES.copy() + # Update from kwargs + constants.update({k: v for k, v in kwargs.items() if k in constants}) + super().__init__(save_path=save_path, scriptgen_widget=scriptgen_widget, constants=constants, constants_titles=constants_titles, **kwargs) + self.GUI_experiment.children = () # Empty the GUI to repopulate it for the MACRO + + # New content for specific trajectories -- + + # Dummy widget used to update dropdown if things change + self.dummy_widget = widgets.Valid(value=False, description='dummy widget') # Does not exist + self.dummy_widget.observe(self.trajectory_list_updated, 'value') + + self._trajectory_dir = "" # Initialize the value + # Info to describe trajectory + self.trajectory_info = widgets.HTML(value = "Trajectory Directory: ", style={'description_width': '160px'}) # Title + self.display_trajectory_directory = widgets.HTML(str(self._trajectory_dir)) # Display widget + self.trajectory_dir = DEFAULT_TRAJECTORY_PATH # Set the value using @property + + # Button to change the trajectory menu. Not usually in use -- May remove in future update + self.browse_trajectory_directory_button = widgets.Button( + description="Select Directory", + layout = self.default_button_style + ) + self.browse_trajectory_directory_button.on_click(self.browse_trajectory_directory) # Button functionality + # Storage Box that contains all Trajectory related information -- + self.trajectory_directory_items = widgets.VBox([widgets.HBox([self.trajectory_info,self.display_trajectory_directory]), self.browse_trajectory_directory_button]) + + # Dropdown used to select trajectory associated with the detector position + # This will override what is originally created as a FloatText option + self.Trajectory = widgets.Dropdown( + value = constants['Trajectory'], + options = self.ScriptgenWidget.TRAJECTORY_LIST.keys(), + layout = widgets.Layout(width='30rem'), + description = 'Select Detector Trajectory', + style = {'description_width':'10rem'} + ) + + # Dummy widget to update TRAJECTORY_LIST + + self.GUI_experiment = widgets.VBox([self.widget_title, self.trajectory_directory_items,self.Trajectory, self.menu_box, self.control_buttons, self.layout]) + + + def browse_trajectory_directory(self, b): + self.trajectory_dir = self.ScriptgenWidget.super().browse_directory() + + @property + def trajectory_dir(self): + return self._trajectory_dir + @trajectory_dir.setter + def trajectory_dir(self, val): + self._trajectory_dir = val + self.display_trajectory_directory.value = str(self._trajectory_dir) + self.update_trajectory_list(val) + + def update_trajectory_list(self, path): + """ + Loads all .json files in a specified path that contain 'ESCANs' for RSOXS + + """ + output_dict = {} + try: + for filename in os.listdir(path): + if filename.endswith(".json"): + file_path = os.path.join(path, filename) + with open(file_path, 'r') as f: + first_line = f.readline().strip() # Read the first line and remove white space + if first_line == "# Trajectory": + # Discard the first line and read the rest + try: + data = json.load(f) # Update this to ignore the title + file_key = os.path.splitext(filename)[0] + output_dict[file_key] = data + except json.JSONDecodeError as e: + print(f"Error decoding JSON in file '{filename}': {e}") + else: + continue + except FileNotFoundError: + print(f"Error: Foldesr '{path}' not found.") + except Exception as e: + print(f"An unexpected error occurred: {e}") + + self.ScriptgenWidget.TRAJECTORY_LIST = output_dict + self.dummy_widget.value = not self.dummy_widget.value # This is watched by all dropdowns to change the ESCAN_LIST when updated + + def trajectory_list_updated(self, change): + if change['type'] == 'change' and change['name'] == 'value': + dropdown = self.Trajectory + dropdown.options = self.ScriptgenWidget.TRAJECTORY_LIST.keys() + +class RSOXS_MacroGen(RSOXS_ScriptGen): + WIDGET = RSOXS_Detector_Macro + TRAJECTORY_LIST = {} + + def __init__(self, save_path="", **kwargs): + super().__init__(save_path=save_path, **kwargs) + self.GUI.children = () # Clean out the default GUI options to make room for the new version + # New Banner for the MACRO generator + self.title_banner = widgets.HTML(value=f"

{self.EXPERIMENT_NAME} Macro Generator

") + + # All buttons are the same as ScriptGen except the MacroGen --- + + # NEW BUTTONS THAT HELP SAVE THE BEAMLINE MACRO + # New layouts and styles for the macro-specific options + self.macro_button_layout = widgets.Layout(widget='50rem') + self.macro_style = {'description_width': '160px'} + self.macro_box_layout = widgets.Layout(widget='60rem') + self.macro_title_style = {'font_size': '14px'} + self.macro_info_style = {'font_size': '12px'} + + # TItle for Macro options -- + self.macro_title = widgets.HTML(value="Options for Beamline Scan Macro", style=self.macro_title_style) + """ + Build the Spiral Scan menu for Spiral Scans -- Use for preliminary + """ + self.spiral_tab = self.build_spiral_tab() + self.nexafs_tab = self.build_nexafs_tab() + self.i0_tab = self.build_i0_tab() + + self.AdditionalMacroOptions = widgets.Tab( + children = [self.spiral_tab, self.nexafs_tab, self.i0_tab], + layout = self.default_GUI, + titles = ['Spiral Scan', 'NEXAFS', 'I0 Options'] + ) + + self.ADDITIONAL_MACRO_BUTTONS = widgets.VBox( + [ + self.macro_title, + self.AdditionalMacroOptions + ], + layout=self.macro_box_layout + ) + + self.GenerateExperiment(self.layout, self.WIDGET.ALS_NAME, self.WIDGET, **kwargs) + self.GUI = widgets.VBox([self.title_banner, self.control_buttons, self.layout, self.json_buttons, self.rsoxs_menu, self.ADDITIONAL_MACRO_BUTTONS, self.save_button_beamline], layout=self.GUI_SIZE) + display(self.GUI) + + + def save_script(self, ext='.txt', sep='\t', b=None): + SAVEDIR = str(self.save_dir) + SAVENAME = str(self.save_name.value) + SAVEPATH = SAVEDIR + '/' + SAVENAME + '.txt' + + df = self.save_as_dict() # Get the data as a dictionary + macro_details = self._build_individual_scan_files(df) + + # Collect i0 details for broader macro + i0_condition = self.I0_select.value + i0_posx = self.I0_xpos.value + i0_posy = self.I0_ypos.value + i0_escan = self.I0_escan.value + if i0_condition != 'Never': # Waste time building a scan? + i0_script = SAVEDIR + '/' + "I0_script.txt" + self._save_escan_script(i0_script, self.ESCAN_LIST[i0_escan], exposure=0.5) + + # START BUILDING MACRO + scans_elapsed = 0 # If I0s are to be taken after a certain number of scans + macro = begin_macro(SAVENAME) # list of commands that will become the beamline scan + if "Before" in i0_condition: + macro.extend(run_I0("I0_BeforeMacro_", i0_script, i0_posx, i0_posy)) + if not self.only_run_nexafs.value: # If you only want NEXAFS it skips the RSOXS portion + macro += [set_instrument("CCD", exp=0.1, extension='fits')] # Turn on CCD for data collection + for i, det in enumerate(macro_details): + # Check to see if we want an I0 before we go to the next detector position + if "detector" in i0_condition: + macro.extend(run_I0("I0_BeforeDet_"+str(i), i0_script, i0_posx, i0_posy)) + macro += [set_instrument("CCD", exp=0.1, extension='fits')] # Turn on CCD for data collection after I0 + # First thing is to go to the detector position + macro.extend(run_dict_trajectory(det['Trajectory'])) + # Start cycling through samples + for sample in det['sample_scripts']: + macro += [analog_from_file(sample[0], sample[1])] + scans_elapsed += 1 # Keep track of scans elapsed + # Interupt and run an I0 if requested + if "X scans" in i0_condition: + if scans_elapsed % self.i0_X.value == 0: + macro.extend(run_I0("I0_AfterScan_"+str(scans_elapsed), i0_script, i0_posx, i0_posy)) + macro += [set_instrument("CCD", exp=0.1, extension='fits')] # Reinitialize after I0 + macro.extend(run_dict_trajectory(det['Trajectory'])) + if "After" in i0_condition: + macro.extend(run_I0("I0_AfterMacro", i0_script, i0_posx, i0_posy)) + macro += [clear_instruments] # Go to the Photodiode only + macro.extend(run_dict_trajectory(Photodiode_Far)) # Go to the photodiode position + if self.post_run_nexafs.value: # Run NEXAFS after cycle? + macro += [add_comment(f"Running NEXAFS")] + NEXAFS_script = SAVEDIR + '/' + "NEXAFS_script.txt" + dnexafs = df[f'det_{pad_digits(1)}'] + ScanList = [s for s in dnexafs.keys() if "scan_" in s] # Get all the scans for the first detector position + for sample in ScanList: + macro += [move_motor('Sample X', dnexafs[sample]['Sample X'], 0.1)] + macro += [move_motor('Sample Y', dnexafs[sample]['Sample Y'], 0.1)] + + if self.nexafs_escan != "CUSTOM": + self._save_escan_script(NEXAFS_script, self.ESCAN_LIST[self.nexafs_escan.value], exposure=0.5) + else: + self._save_escan_script(NEXAFS_script, dnexafs[sample]['ESCAN'], exposure=0.5) + + macro += [analog_from_file(dnexafs[sample]['name'] + "_NEXAFS", NEXAFS_script)] + macro.extend(run_I0("I0_ScanComplete", i0_script, i0_posx, i0_posy)) + macro += [add_prompt(f"Finished Macro")] + + build_macro(SAVENAME, SAVEDIR, macro) + + + def save_spiral(self, ext='.txt.', sep='\t', b=None): + SAVEDIR = str(self.save_dir) + SAVENAME = str(self.save_name.value) + SAVEPATH = SAVEDIR + '/' + SAVENAME + '_spiral.txt' + + df = self.save_as_dict() # Get the data as a dictionary + macro_details = self._build_spiral_scan(df) + + macro = begin_macro(SAVENAME) # list of commands that will become the spiral scan + macro += [set_instrument("CCD", exp=0.1, extension='fits')] # Turn on CCD for data collection + macro.extend(run_dict_trajectory(macro_details['Trajectory'])) # Move to the position where data is collected + for sample in macro_details['spiral_scripts']: + macro += [analog_from_file(sample[0], sample[1])] + macro += [add_prompt(f"Finished Macro")] + build_macro(SAVENAME, SAVEDIR, macro) + + + def _build_individual_scan_files(self, df): + # Initialize the macro construction + list_of_detectors = [] # Contains lists associated with each detector position + number_of_detectors = len(df) # How many detector positions? + + # Where the data will be saved + MACRO_DIR = str(self.save_dir) + """ + Cycle through the detector positions and start saving scripts + Each script will be measured on one sample, and one detector position. + Current naming convention: {name}_det{X}.txt + """ + # Cycle through detector positions and start saving scripts + for i, (index, detector) in enumerate(df.items()): + # Initialize storage for each detector + detector_details = {} # Dictionary that contains all details pertaining to the current detector position + all_sample_scripts = [] # List that will contain all sample dataframes for the current detector + # Start collecting data -- + ScanList = [s for s in detector.keys() if "scan_" in s] # Get all the scans in the current detector position + # number_of_samples = len(ScanList) # How many samples in total? + DetectorPositions = {k: v for k, v in detector.items() if k not in ScanList} # All items relevant to setting up the detector geometry + + # Start compiling details for the detector + detector_details['Trajectory'] = self.TRAJECTORY_LIST[DetectorPositions['Trajectory']] + + # Run through each ScanList and build the individual scan files for each sample -- + for ii, scan in enumerate(ScanList): + dm = {key: value for key, value in detector[scan].items() if key != "name"} # Drop the name which is used for saving but not scanning + sample_name = detector[scan]['name'] + motors_to_run = ['Sample X', 'Sample Y'] # Define the sample position + if 'Sample Theta' in self.select_cols.value: + motors_to_run.extend(['Sample Theta']) # Add Theta if you want to include it + + # Get motor positions to cycle through + Positions = {key: value for key, value in dm.items() if key in motors_to_run} + # Build the Escan based on RSOXS conventions + escan_details = self._build_escan(dm['ESCAN']) # Collect all the info for the Escan + Escan = escan_details[0] # Get energies + Exposure = escan_details[1] # Get exposures + + # Build the sample dataframe + temp_scan = {} + temp_scan['Beamline Energy'] = Escan + temp_scan['Exposure'] = Exposure + df_samp = pd.DataFrame(temp_scan) + + # Add the sample positions + for motor, pos in Positions.items(): + df_samp[motor] = np.full_like(Escan, pos) + # Add detector Positions + if number_of_detectors > 1: + for motor, pos in DetectorPositions.items(): + if "SampXOffset" in motor and 'Sample X' in Positions: + df_samp['Sample X'] += pos + continue + elif "SampYOffset" in motor and 'Sample Y' in Positions: + df_samp['Sample Y'] += pos + continue + #df_samp[key] = np.full_like(Escan, pos) + # Add CCD Inhibit, first and last, all exposure times + df_samp['CCD Camera Shutter Inhibit'] = np.full_like(Escan, 0) # Add the column + for expo in np.unique(Exposure): + df_samp = pd.concat([df_samp.loc[0].to_frame().T, df_samp], ignore_index=True) # Add a frame at the beginning of the sample + df_samp = pd.concat([df_samp, df_samp.loc[df_samp.index[-1]].to_frame().T], axis=0, ignore_index=True) # Add a frame at the end of the sample + + df_samp.loc[0, 'CCD Camera Shutter Inhibit'] = 1 # Close the shutter + df_samp.loc[df_samp.index[-1], 'CCD Camera Shutter Inhibit'] = 1 + + df_samp.loc[0, 'Exposure'] = expo # Set exposure + df_samp.loc[df_samp.index[-1], 'Exposure'] = expo + + # Cleanup the script because the ALS requires specific things + cols = list(df_samp.columns) + Ordered_Cols = [item for item in self.MOTOR_ORDER if item in cols] + df_samp = df_samp[Ordered_Cols] + + # save the df_samp as a macro file + SAVE_SCAN = MACRO_DIR + '/' + sample_name +'_det'+str(i) + '_escan'+str(ii) + '.txt' + df_samp.to_csv(SAVE_SCAN, index=False, sep='\t') + clean_script(SAVE_SCAN) # Remove exposure and clear last line if it exists + + all_sample_scripts.append((sample_name, SAVE_SCAN)) # Save path for macro generation + + detector_details['sample_scripts'] = all_sample_scripts # Collect all scripts under the detector + list_of_detectors.append(detector_details) + return list_of_detectors + + + def _build_spiral_scan(self, df): + # Where will I save these files? + MACRO_DIR = str(self.save_dir) + + list_of_scripts = [] # A compiled list of script paths for macro generation + # pick the detector that you want to run your spiral scan with + detector_choice = self.spiral_scan_det.value + # Make a list of energies that you want to run the spiral scan with + energy_float= [float(item.strip()) for item in self.spiral_scan_energy.value.split(',')] + # Get step size and exposure + spiral_step = self.spiral_scan_step.value + grid_size = int(self.spiral_scan_grid_size.value) + exposure_choice = self.spiral_scan_exposure.value + + # Cycle through samples and start saving data + try: + detector = df[f"det_{pad_digits(detector_choice)}"] + except: + detector = df[f"det_{pad_digits(1)}"] # Default to the first one (will always exist) + + # Start getting scans + ScanList = [s for s in detector.keys() if "scan_" in s] + number_of_samples = len(ScanList) + list_of_sample_scripts = [] + DetectorPositions = {k: v for k, v in detector.items() if k not in ScanList} + + detector_details = {} + detector_details['Trajectory'] = self.TRAJECTORY_LIST[DetectorPositions['Trajectory']] + #if 'Sample Z' in self.select_cols.value: + # detector_details['Sample Z'] = DetectorPositions['SampZ'] + + for i, scan in enumerate(ScanList): + sample_grid_df = [] # Where we will store the sample position scans + # Drop the name which is used for saving but not scanning + dm = {key: value for key, value in detector[scan].items() if key != "name"} + sample_name = detector[scan]['name'] # Name to save the script + + Escan = np.array(energy_float) # Collect all the info for the Escan + Exposure = np.full_like(Escan, exposure_choice) + + # Build the sample dataframe + temp_scan = {} + temp_scan['Beamline Energy'] = Escan + temp_scan['Exposure'] = Exposure + df_temp = pd.DataFrame(temp_scan) + + # Sample Positions + center_x = detector[scan]['Sample X'] + center_y = detector[scan]['Sample Y'] + # Create grid + grid = generate_2d_grid(center_x, center_y, spiral_step, grid_size=grid_size) + + for ii, point in enumerate(grid): + df_grid = df_temp.copy() + df_grid['Sample X'] = np.full_like(Escan, point[0]) + df_grid['Sample Y'] = np.full_like(Escan, point[1]) + df_grid['CCD Camera Shutter Inhibit'] = np.full_like(Escan, 0) # Add the column + + # Cleanup the script because the ALS requires specific things + cols = list(df_grid.columns) + Ordered_Cols = [item for item in self.MOTOR_ORDER if item in cols] + df_grid = df_grid[Ordered_Cols] + sample_grid_df.append(df_grid) + + df_samp = pd.concat(sample_grid_df, ignore_index=True) # Combine all samples into a single scan to run + df_out = df_samp.sort_values(by=['Beamline Energy']) # Only move the undulator once or twice + + # save the df_samp as a macro file + SAVE_SCAN = MACRO_DIR + '/' + sample_name +'_det'+str(detector_choice) + '_spiral'+str(i) + '.txt' + df_out.to_csv(SAVE_SCAN, index=False, sep='\t') + clean_script(SAVE_SCAN) # Remove exposure and clear last line if it exists + + list_of_scripts.append((sample_name+"_spiral"+str(i),SAVE_SCAN)) # Save the path + detector_details['spiral_scripts'] = list_of_scripts + return detector_details + + def _save_escan_script(self, path, d, exposure=None): + # Compile Details into exposure and energies + escan_details = self._build_escan(d) + # Build a dictionary + as_dict = {} + as_dict['Beamline Energy'] = escan_details[0] + if exposure is None: + as_dict['Exposure'] = escan_details[1] + else: + as_dict['Exposure'] = np.full_like(escan_details[1], exposure) + # Make a dataframe for easy saving + df = pd.DataFrame(as_dict) + df.to_csv(path, index=False, sep='\t') + clean_script(path) # Remove exposure and clear last line if it exists + + + + def build_spiral_tab(self): + self.spiral_scan_title = widgets.HTML(value="Spiral Scan Options", style=self.macro_title_style) + self.spiral_scan_info = widgets.HTML( + value = "Options to generate a spiral scan macro.
Will measure short scans in X-by-X grid around sample position
Use to determine optimum scan location", + style = self.macro_info_style + ) + + self.spiral_scan_button = widgets.Button( + description='Save Spiral Scan Macro', + style = self.macro_style, + layout = self.macro_button_layout + ) + self.spiral_scan_button.on_click(self.save_spiral) + self.spiral_scan_energy = widgets.Text( + value = '270.0, 284.0', + description = "Spiral Scan Energies (eV):", + style = self.macro_style, + layout=self.macro_button_layout + ) + self.spiral_scan_step = widgets.FloatText( + value = 0.300, + description = 'Spiral Step Size (mm)', + style = self.macro_style, + layout=self.macro_button_layout + ) + self.spiral_scan_grid_size = widgets.BoundedIntText( + value = 5, + min=1, + max=7, + step=2, + description = 'Grid Size (X-by-X)', + style = self.macro_style, + layout=self.macro_button_layout + ) + self.spiral_scan_det = widgets.IntText( + value = 1, + description = "Detector Position: ", + style = self.macro_style, + layout = self.macro_button_layout + ) + self.spiral_scan_exposure = widgets.FloatText( + value = 1.0, + description = 'Spiral Scan Exposure (s): ', + style = self.macro_style, + layout=self.macro_button_layout + ) + self.spiral_options = widgets.VBox( + [ + self.spiral_scan_title, + self.spiral_scan_info, + self.spiral_scan_energy, + self.spiral_scan_exposure, + self.spiral_scan_step, + self.spiral_scan_grid_size, + self.spiral_scan_det, + self.spiral_scan_button, + ], + layout=self.macro_box_layout + ) + return self.spiral_options + + def build_nexafs_tab(self): + self.nexafs_title = widgets.HTML(value="NEXAFS Options", style=self.macro_title_style) + #self.nexafs_title = widgets.HTML(value="Include Transmission NEXAFS", style=self.macro_title_style) + self.nexafs_info = widgets.HTML(value = "Options to run NEXAFS in addition to RSOXS
Highly recommended for all transmission RSOXS measurements.", style=self.macro_info_style) + + self.post_run_nexafs = widgets.Checkbox( + value=False, + description='Include NEXAFS scan?', + disabled=False, + indent=False, + style = self.macro_style, + layout=self.macro_button_layout + ) + self.only_run_nexafs = widgets.Checkbox( + value=False, + description='Only run NEXAFS? (Will not initialize the CCD)', + disabled=False, + indent=False, + style = self.macro_style, + layout=self.macro_button_layout + ) + self.nexafs_info2 = widgets.HTML(value = "What transmission scan do you want to run?
Select CUSTOM to repeat energy scan used in each RSOXS measurement", style=self.macro_info_style) + + self.nexafs_escan = widgets.Dropdown( + value ='QUICK_ESCAN', + options = self.ESCAN_LIST.keys(), + description="Scan File: ", + style = self.macro_style, + layout=self.macro_button_layout + + ) + self.nexafs_options = widgets.VBox( + [ + self.nexafs_title, + self.nexafs_info, + self.post_run_nexafs, + self.only_run_nexafs, + self.nexafs_info2, + #self.nexafs_info3, + self.nexafs_escan + ], + layout=self.macro_box_layout + ) + return self.nexafs_options + + def build_i0_tab(self): + self.I0_title = widgets.HTML(value="Direct Beam (I0) Options", style=self.macro_title_style) + self.I0_info = widgets.HTML(value="Select when you want to run an I0 during macro") + self.I0_list = [ + "Never", + "Before macro", + "Before and after macro", + "Start of each detector position", + "After every X scans" + ] + self.I0_select = widgets.Dropdown( + options = self.I0_list, + value = "Before and after macro", + style = self.macro_style, + layout=self.macro_button_layout + ) + self.I0_xpos = widgets.FloatText( + value = 0.0, + description = "I0 X position", + style = self.macro_style, + layout=self.macro_button_layout + ) + self.I0_ypos = widgets.FloatText( + value = 0.0, + description = "I0 Y position", + style = self.macro_style, + layout=self.macro_button_layout + ) + self.I0_escan = widgets.Dropdown( + value ='CARBON_ESCAN', + options = self.ESCAN_LIST.keys(), + description="Scan File: ", + style = self.macro_style, + layout=self.macro_button_layout + + ) + self.I0_options = widgets.VBox( + [ + self.I0_title, + self.I0_info, + self.I0_select, + self.I0_xpos, + self.I0_ypos, + self.I0_escan + ], + layout=self.macro_box_layout + ) + return self.I0_options + + + + + + + +def update_dict(old_dict, new_dict): + for key in old_dict.keys(): + if key in new_dict: + old_dict[key] = new_dict[key] + return old_dict + +def clean_for_widgets(my_dict): + for key, value in my_dict.items(): + if isinstance(value, list): + my_dict[key] = ', '.join(map(str, value)) + return my_dict + + +def generate_2d_grid(center_x, center_y, step_size, grid_size=5): + """ + Generates a 2D grid of x and y coordinates around a center point. + + Args: + center_x: The x-coordinate of the center point. + center_y: The y-coordinate of the center point. + step_size: The distance between grid points. + grid_size: The size of the grid (e.g., 5 for a 5x5 grid). + + Returns: + A list of tuples, where each tuple represents (x, y) coordinates. + """ + grid = [] + half_grid = grid_size // 2 + + for i in range(-half_grid, half_grid + 1): + for j in range(-half_grid, half_grid + 1): + x = center_x + i * step_size + y = center_y + j * step_size + grid.append((x, y)) + + return grid + + + +def get_current_datetime_string(): + """Returns a string with the current date and time.""" + now = datetime.datetime.now() + return now.strftime("%Y-%m-%d %H:%M:%S") + + diff --git a/python/pyref/data/.pyref_catalog.db 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zbyA(<2!eC0i_P@9|Dlc1jsE`RPI~@xV$Q np.ndarray: + pre_lo, pre_hi = pre_edge + post_lo, post_hi = post_edge + pre_ok = (energy >= pre_lo) & (energy <= pre_hi) + post_ok = (energy >= post_lo) & (energy <= post_hi) + return pre_ok | post_ok + + +def _mu_step(energy: np.ndarray, jump_energy: float) -> np.ndarray: + out = np.zeros_like(energy, dtype=np.float64) + out[energy > jump_energy] = 1.0 + return out + + +def _model_factory_full(mu: np.ndarray, mu_substrate: np.ndarray): + def model( + _energy: np.ndarray, + izero: float, + composition: float, + shift: float, + ) -> np.ndarray: + out = izero * (mu + composition * mu_substrate) + shift + return np.asarray(out, dtype=np.float64) + + return model + + +def _model_factory_bare_atom(mu: np.ndarray): + def model(_energy: np.ndarray, izero: float, shift: float) -> np.ndarray: + out = izero * mu + shift + return np.asarray(out, dtype=np.float64) + + return model + + +def _model_factory_step(jump_energy: float): + def model(_energy: np.ndarray, izero: float, shift: float) -> np.ndarray: + mu_01 = _mu_step(_energy, jump_energy) + out = izero * mu_01 + shift + return np.asarray(out, dtype=np.float64) + + return model + + +def _correct_row_full( + absorb: np.ndarray, + bare_atom_substrate: np.ndarray, + izero: float, + composition: float, + shift: float, +) -> np.ndarray: + return (absorb - shift) / izero - composition * bare_atom_substrate + + +def _correct_row_bare_or_step(absorb: np.ndarray, izero: float, shift: float) -> np.ndarray: + return (absorb - shift) / izero + + +@pd.api.extensions.register_dataframe_accessor("nexafs") +class NexafsAccessor: + """DataFrame accessor for NEXAFS normalization and plotting (single scan / one angle).""" + + def __init__(self, pandas_obj: pd.DataFrame) -> None: + self._obj = pandas_obj + self._pre_edge: tuple[float, float] = _DEFAULT_PRE_EDGE + self._post_edge: tuple[float, float] = _DEFAULT_POST_EDGE + self._energy_col: str = "beamline_energy" + self._absorbance_cols: list[str] = list(_DEFAULT_ABSORBANCE_COLS) + self._normalization_mode: NormalizationMode = "full" + self._jump_energy: float | None = None + self._fit_params: dict[str, dict[str, Any]] | None = None + + def _validate_schema(self) -> None: + missing = [c for c in (_REQUIRED_FIXED_COLUMNS + (self._energy_col,)) if c not in self._obj.columns] + if missing: + raise ValueError( + f"DataFrame must have columns {list(_REQUIRED_FIXED_COLUMNS)} and '{self._energy_col}'. Missing: {missing}" + ) + for col in self._absorbance_cols: + if col not in self._obj.columns: + raise ValueError( + f"Absorbance column '{col}' not in DataFrame. " + f"Available: {list(self._obj.columns)}" + ) + + def set_regions( + self, + pre_edge: tuple[float, float] | None = None, + post_edge: tuple[float, float] | None = None, + energy_col: str | None = None, + absorbance_cols: list[str] | None = None, + normalization_mode: NormalizationMode | None = None, + jump_energy: float | None = None, + ) -> NexafsAccessor: + """ + Set pre/post edge regions and optional column names. + + Parameters + ---------- + pre_edge : tuple of float, optional + (lo, hi) energy range for pre-edge normalization region. + post_edge : tuple of float, optional + (lo, hi) energy range for post-edge normalization region. + energy_col : str, optional + Column name for energy (default "beamline_energy"). + absorbance_cols : list of str, optional + Column names for absorbance signals (default ["absorbance_0", "absorbance_1"]). + normalization_mode : "full" | "bare_atom" | "step", optional + "full": izero * (mu + composition * mu_substrate) + shift. + "bare_atom": izero * mu + shift (no substrate). + "step": izero * mu_01(energy) + shift with mu_01 = 0 pre-edge, 1 post-edge. + jump_energy : float, optional + For "step" mode only. Energy where step goes 0 -> 1. If None, uses + (pre_edge[1] + post_edge[0]) / 2. + + Returns + ------- + NexafsAccessor + Self for method chaining. + """ + if pre_edge is not None: + self._pre_edge = pre_edge + if post_edge is not None: + self._post_edge = post_edge + if energy_col is not None: + self._energy_col = energy_col + if absorbance_cols is not None: + self._absorbance_cols = list(absorbance_cols) + if normalization_mode is not None: + self._normalization_mode = normalization_mode + if jump_energy is not None: + self._jump_energy = jump_energy + return self + + def normalization_region(self) -> pd.DataFrame: + """ + Rows where energy falls in pre-edge or post-edge region. + + Returns + ------- + pd.DataFrame + Subset of the DataFrame (view). + """ + self._validate_schema() + energy = self._obj[self._energy_col].to_numpy() + mask = _fit_region_mask(energy, self._pre_edge, self._post_edge) + return self._obj.loc[mask] + + def normalize(self) -> NexafsAccessor: + """ + Fit izero, composition, shift per absorbance column in the normalization region, + then add mass_absorption_* columns in-place. Stores fit params for plot_normalization. + + Returns + ------- + NexafsAccessor + Self for method chaining. + """ + self._validate_schema() + norm = self.normalization_region() + if len(norm) == 0: + raise ValueError( + "Normalization region is empty. Check pre_edge and post_edge." + ) + energy = np.asarray(norm[self._energy_col], dtype=np.float64) + mu = np.asarray(norm["bare_atom"], dtype=np.float64) + mu_sub = np.asarray(norm["bare_atom_substrate"], dtype=np.float64) + mode = self._normalization_mode + jump = self._jump_energy + if mode == "step" and jump is None: + jump = (self._pre_edge[1] + self._post_edge[0]) / 2.0 + + self._fit_params = {} + for col in self._absorbance_cols: + y = np.asarray(norm[col], dtype=np.float64) + mass_col = col.replace("absorbance", "mass_absorption", 1) + if not np.any(np.isfinite(y)): + self._obj[mass_col] = np.nan + continue + absorb_arr = np.asarray(self._obj[col], dtype=np.float64) + mu_sub_arr = np.asarray(self._obj["bare_atom_substrate"], dtype=np.float64) + + if mode == "full": + model = _model_factory_full(mu, mu_sub) + p0_scale = float(np.nanmean(y / mu)) + if not np.isfinite(p0_scale) or p0_scale <= 0: + p0_scale = 1.0 + p0 = (p0_scale, 0.0, 0.0) + popt, pcov = curve_fit(model, energy, y, p0=p0) + self._obj[mass_col] = _correct_row_full( + absorb_arr, mu_sub_arr, popt[0], popt[1], popt[2] + ) + self._fit_params[col] = { + "mode": "full", + "popt": popt, + "pcov": pcov, + "izero": popt[0], + "composition": popt[1], + "shift": popt[2], + } + elif mode == "bare_atom": + model = _model_factory_bare_atom(mu) + p0_scale = float(np.nanmean(y / mu)) + if not np.isfinite(p0_scale) or p0_scale <= 0: + p0_scale = 1.0 + p0 = (p0_scale, 0.0) + popt, pcov = curve_fit(model, energy, y, p0=p0) + self._obj[mass_col] = _correct_row_bare_or_step(absorb_arr, popt[0], popt[1]) + self._fit_params[col] = { + "mode": "bare_atom", + "popt": popt, + "pcov": pcov, + "izero": popt[0], + "composition": None, + "shift": popt[1], + } + else: + assert mode == "step" and jump is not None + model = _model_factory_step(jump) + p0_scale = float(np.nanmean(y)) + if not np.isfinite(p0_scale) or p0_scale <= 0: + p0_scale = 1.0 + p0 = (p0_scale, 0.0) + popt, pcov = curve_fit(model, energy, y, p0=p0) + self._obj[mass_col] = _correct_row_bare_or_step(absorb_arr, popt[0], popt[1]) + self._fit_params[col] = { + "mode": "step", + "jump_energy": jump, + "popt": popt, + "pcov": pcov, + "izero": popt[0], + "composition": None, + "shift": popt[1], + } + return self + + def plot_regions( + self, + ax: Axes | None = None, + pre_color: str = "blue", + post_color: str = "green", + alpha: float = 0.2, + **kwargs: Any, + ) -> Axes: + """ + Plot full scan (energy vs absorbance) with pre- and post-edge regions shaded. + + Parameters + ---------- + ax : matplotlib.axes.Axes, optional + Axes to plot on; created if None. + pre_color, post_color : str, optional + Colors for pre- and post-edge axvspan. + alpha : float, optional + Alpha for shaded regions. + **kwargs + Passed to DataFrame.plot. + + Returns + ------- + Axes + """ + self._validate_schema() + if ax is None: + _fig, ax = plt.subplots() + ax = cast(Axes, ax) # type: ignore[assignment] + self._obj.plot( + x=self._energy_col, + y=self._absorbance_cols, + ax=ax, + **kwargs, + ) + ax.axvspan( + self._pre_edge[0], + self._pre_edge[1], + color=pre_color, + alpha=alpha, + ) + ax.axvspan( + self._post_edge[0], + self._post_edge[1], + color=post_color, + alpha=alpha, + ) + return cast(Axes, ax) # type: ignore[return-value] + + def plot_normalization( + self, + show_fit: bool = True, + ax: Axes | None = None, + **kwargs: Any, + ) -> Axes: + """ + Plot normalization-region points; overlay fit curves if show_fit and normalize() was run. + + Parameters + ---------- + show_fit : bool, optional + If True and fit params exist, overlay fitted curves. + ax : matplotlib.axes.Axes, optional + Axes to plot on; created if None. + **kwargs + Passed to DataFrame.plot for the points. + + Returns + ------- + Axes + """ + self._validate_schema() + norm = self.normalization_region() + if ax is None: + _fig, ax = plt.subplots() + ax = cast(Axes, ax) # type: ignore[assignment] + style = kwargs.pop("style", [".", "."]) + norm.plot( + x=self._energy_col, + y=self._absorbance_cols, + ax=ax, + style=style[: len(self._absorbance_cols)] if isinstance(style, (list, tuple)) else style, + **kwargs, + ) + if show_fit and self._fit_params: + energy = norm[self._energy_col].to_numpy() + mu = norm["bare_atom"].to_numpy() + mu_sub = norm["bare_atom_substrate"].to_numpy() + for col in self._absorbance_cols: + params = self._fit_params.get(col) + if params is None: + continue + fit_mode = params.get("mode", "full") + if fit_mode == "full": + model = _model_factory_full(mu, mu_sub) + elif fit_mode == "bare_atom": + model = _model_factory_bare_atom(mu) + else: + model = _model_factory_step(params["jump_energy"]) + ax.plot( + energy, + model(energy, *params["popt"]), + label=f"fit {col}", + ) + ax.legend() + return cast(Axes, ax) # type: ignore[return-value] + + def plot( + self, + x: str, + y: str | list[str], + by: str, + colorbar: str = "", + cmap: str | None = "viridis", + ba_color: str | None = None, + ax: Axes | None = None, + title: str | None = None, + **kwargs: Any, + ) -> Axes: + """ + Multi-line plot: one line per group (by), colored by that variable, with optional colorbar. + + Parameters + ---------- + x : str + Column for x-axis (e.g. "beamline_energy"). + y : str or list of str + Column(s) for y-axis (e.g. "mass_absorption_0"). If list, first is used for coloring by group. + by : str + Column to group and color by (e.g. "sample_theta"). + colorbar : str, optional + Colorbar label (e.g. "Angle (deg)"). If empty, uses by column name. + ba_color : str, optional + Line color of the bare atom mass absorption curve. + cmap : str, optional + Color map for the colorbar. + ax : matplotlib.axes.Axes, optional + Axes to plot on; created if None. + title : str, optional + Axes title. + **kwargs + Passed to ax.plot. + + Returns + ------- + Axes + """ + df = self._obj + if by not in df.columns: + raise ValueError(f"Column '{by}' not in DataFrame.") + y_cols = [y] if isinstance(y, str) else list(y) + for c in [x] + y_cols: + if c not in df.columns: + raise ValueError(f"Column '{c}' not in DataFrame.") + if ax is None: + _fig, ax = plt.subplots() + ax = cast(Axes, ax) # type: ignore[assignment] + groups = df.groupby(by, sort=True) + keys = list(groups.groups.keys()) + if len(keys) == 0: + return ax + cmap = colormaps[cmap] if cmap is not None else None # pyright: ignore[reportAssignmentType] + all_numeric = all(isinstance(k, (int, float)) for k in keys) + if all_numeric: + vmin_val = float(df[by].min()) + vmax_val = float(df[by].max()) + if vmin_val == vmax_val: + norm_obj = Normalize(vmin=vmin_val, vmax=vmax_val + 1.0) + else: + norm_obj = Normalize(vmin=vmin_val, vmax=vmax_val) + else: + norm_obj = Normalize(vmin=0, vmax=max(len(keys) - 1, 1)) + for i,key in enumerate(keys): + grp = groups.get_group(key) + if len(keys) > 1: + if all_numeric: + scalar: float = float(key) if isinstance(key, (int, float)) else 0.0 + else: + scalar = float(keys.index(key)) + color = cmap(norm_obj(scalar)) # type: ignore[reportCallIssue] + else: + color = "C0" + for y_col in y_cols: + ax.plot( + np.asarray(grp[x]), + np.asarray(grp[y_col]), + color=color, + **kwargs, + ) + if ba_color is not None and i == 0: + ax.plot( + np.asarray(grp[x]), + np.asarray(grp["bare_atom"]), + color=ba_color, + **kwargs, + ) + if len(keys) > 1: + sm = cm.ScalarMappable(cmap=cmap, norm=norm_obj) + sm.set_array([]) + plt.colorbar(sm, ax=ax, label=colorbar or by) + if title is not None: + ax.set_title(title) + ax.set_xlabel(x) + ax.set_ylim(0, None) + if len(y_cols) == 1: + ax.set_ylabel(y_cols[0]) + return cast(Axes, ax) # type: ignore[return-value] + + +def normalize_by_group( + df: pd.DataFrame, + group_column: str = "sample_theta", + pre_edge: tuple[float, float] = _DEFAULT_PRE_EDGE, + post_edge: tuple[float, float] = _DEFAULT_POST_EDGE, + regions: dict[Any, tuple[tuple[float, float], tuple[float, float]]] | None = None, + absorbance_cols: list[str] | None = None, + normalization_mode: NormalizationMode = "full", + jump_energy: float | None = None, + inplace: bool = False, +) -> pd.DataFrame: + """ + Run normalize() on each group and return the concatenated DataFrame. + + Parameters + ---------- + df : pd.DataFrame + Full NEXAFS DataFrame with a group column (e.g. multiple angles). + group_column : str, optional + Column defining groups (e.g. "sample_theta"). + pre_edge, post_edge : tuple of float, optional + Default (pre, post) used when regions is None or does not specify a group. + regions : dict or callable, optional + If dict: group_key -> (pre_edge, post_edge). If callable: group_key -> (pre_edge, post_edge). + Otherwise same pre_edge, post_edge for all groups. + absorbance_cols : list of str, optional + Absorbance columns; default from accessor. + normalization_mode : "full" | "bare_atom" | "step", optional + Same as set_regions(normalization_mode=...). + jump_energy : float, optional + For "step" mode only; same as set_regions(jump_energy=...). + inplace : bool, optional + If True, modify df in place and return it. Otherwise return a copy with new columns. + + Returns + ------- + pd.DataFrame + DataFrame with mass_absorption_* columns filled per group. + """ + cols = absorbance_cols or _DEFAULT_ABSORBANCE_COLS + + def resolve_regions(key: Any) -> tuple[tuple[float, float], tuple[float, float]]: + if regions is None: + return pre_edge, post_edge + if callable(regions): + return regions(key) + return regions.get(key, (pre_edge, post_edge)) + + def set_and_normalize(sub: pd.DataFrame, pre: tuple[float, float], post: tuple[float, float]) -> None: + sub.nexafs.set_regions( + pre_edge=pre, + post_edge=post, + absorbance_cols=cols, + normalization_mode=normalization_mode, + jump_energy=jump_energy, + ) + sub.nexafs.normalize() + + if inplace: + for key, grp in df.groupby(group_column, group_keys=False): + pre, post = resolve_regions(key) + sub = grp.copy() + set_and_normalize(sub, pre, post) + for col in sub.columns: + if col.startswith("mass_absorption_"): + df.loc[grp.index, col] = sub[col].values + return df + parts: list[pd.DataFrame] = [] + for key, grp in df.groupby(group_column, group_keys=False): + pre, post = resolve_regions(key) + sub = grp.copy() + set_and_normalize(sub, pre, post) + parts.append(sub) + return pd.concat(parts, axis=0, ignore_index=False) diff --git a/python/pyref/nexafs/directory.py b/python/pyref/nexafs/directory.py deleted file mode 100644 index c47d7f7..0000000 --- a/python/pyref/nexafs/directory.py +++ /dev/null @@ -1,702 +0,0 @@ -""" -Directory-based NEXAFS IO: file discovery, izero handling, TEY processing, baseline normalization. - -Provides NexafsDirectory for loading NEXAFS scan files and get_sample_dfs returning -DataFrames with Energy, PD Corrected, Bare Atom Step, Sample, Angle, Experiment. -No background fit is applied at this layer; see normalization.py for fit schemes. -""" - -from __future__ import annotations - -from io import StringIO -from pathlib import Path -from typing import Literal - -import numpy as np -import pandas as pd -import pint -from periodictable import xsf - -_UREG = pint.UnitRegistry() - - -def _parse_edge_region( - df: pd.DataFrame, - region: tuple[float | None, float | None], - edge_type: str, -) -> pd.DataFrame: - energy = df["Energy"] - if edge_type == "pre": - if region[0] is not None and region[1] is not None: - mask = (energy >= region[0]) & (energy <= region[1]) - elif region[0] is None and region[1] is not None: - mask = energy < region[1] - elif region[0] is not None and region[1] is None: - mask = energy >= region[0] - else: - raise ValueError("At least one endpoint for pre_edge must be specified.") - elif edge_type == "post": - if region[0] is not None and region[1] is not None: - mask = (energy >= region[0]) & (energy <= region[1]) - elif region[0] is not None and region[1] is None: - mask = energy > region[0] - elif region[0] is None and region[1] is not None: - mask = energy <= region[1] - else: - raise ValueError("At least one endpoint for post_edge must be specified.") - else: - raise ValueError("edge_type must be 'pre' or 'post'") - return df.loc[mask] - - -def quality_level_from_rms( - rms_pct_pre: float, - rms_pct_post: float, - *, - warning_threshold: float = 2.0, - error_threshold: float = 5.0, -) -> Literal["ok", "warning", "error"]: - max_rms = max( - rms_pct_pre if not np.isnan(rms_pct_pre) else 0.0, - rms_pct_post if not np.isnan(rms_pct_post) else 0.0, - ) - if max_rms >= error_threshold: - return "error" - if max_rms >= warning_threshold: - return "warning" - return "ok" - - -QUALITY_SYMBOL_OK = "ok" -QUALITY_SYMBOL_WARNING = "\u26a0" -QUALITY_SYMBOL_ERROR = "\u2715" - - -def quality_display_symbol(quality: Literal["ok", "warning", "error"]) -> str: - if quality == "warning": - return QUALITY_SYMBOL_WARNING - if quality == "error": - return QUALITY_SYMBOL_ERROR - return QUALITY_SYMBOL_OK - - -class NexafsDirectory: - """ - Directory containing NEXAFS scan files (izero and sample_angle_exp naming). - - Discovers izero and sample .txt files, reads tab-separated tables with - "Time of Day" header, computes I0 and TEY-normalized absorption, and - provides baseline normalization (pre/post edge) yielding PD Corrected, - Norm Abs, Bare Atom Step, Mass Abs. columns. Mu(E) from periodictable.xsf. - """ - - def __init__(self, path: str | Path) -> None: - self.path = Path(path) - if not self.path.exists(): - raise FileNotFoundError(f"Path {self.path} does not exist") - self.izero_store: dict[pd.Timestamp, dict] = {} - self._selected_izero_timestamp: pd.Timestamp | None = None - self._last_sample_context: dict | None = None - self._scan_and_process_izero() - self.pre_edge: tuple[float | None, float | None] = (None, 280.0) - self.post_edge: tuple[float | None, float | None] = (360.0, None) - - @staticmethod - def read_nexafs(file_path: Path) -> pd.DataFrame: - """ - Read a NEXAFS tab-separated table; header line contains "Time of Day". - - Parameters - ---------- - file_path : Path - Path to .txt file. - - Returns - ------- - pd.DataFrame - Raw table with Beamline Energy, Photodiode, AI 3 Izero, etc. - """ - with open(file_path) as f: - lines = f.readlines() - header_idx = next( - (i for i, line in enumerate(lines) if "Time of Day" in line), - None, - ) - if header_idx is None: - raise ValueError( - f"Could not find 'Time of Day' header in {file_path}" - ) - table_text = "".join(lines[header_idx:]) - df = pd.read_csv(StringIO(table_text), sep=r"\t", engine="python") - df["Beamline Energy"] = df["Beamline Energy"].round(1) - return df - - @staticmethod - def compute_izero(izer_df: pd.DataFrame) -> pd.DataFrame: - """ - Compute I0 and energy from izero scan columns. - - Parameters - ---------- - izer_df : pd.DataFrame - Must have Beamline Energy, Photodiode, AI 3 Izero. - - Returns - ------- - pd.DataFrame - Energy, Photodiode, AI 3 Izero, I0, and Timestamp if present. - """ - out = pd.DataFrame() - out["Energy"] = izer_df["Beamline Energy"] - out["Photodiode"] = izer_df["Photodiode"] - out["AI 3 Izero"] = izer_df["AI 3 Izero"] - out["I0"] = izer_df["AI 3 Izero"] / izer_df["Photodiode"] - if "Timestamp" in izer_df.columns: - out["Timestamp"] = izer_df["Timestamp"] - return out - - def _scan_and_process_izero(self) -> None: - izero_files = list(self.path.glob("izero*")) - new_store: dict[pd.Timestamp, dict] = {} - for file in izero_files: - raw_df = self.read_nexafs(file) - processed = self.compute_izero(raw_df) - if "Timestamp" in processed.columns: - timestamp = pd.to_datetime(processed["Timestamp"].iloc[0]) - else: - timestamp = pd.to_datetime(file.stat().st_mtime, unit="s") - energy = processed["Energy"].astype(float) - epu = None - if "EPU Polarization" in raw_df.columns: - epu_val = raw_df["EPU Polarization"].iloc[0] - if pd.notna(epu_val): - epu = float(epu_val) - new_store[timestamp] = { - "df": processed, - "energy_min": float(energy.min()), - "energy_max": float(energy.max()), - "epu_polarization": epu, - } - self.izero_store = new_store - - def _select_izero_nearest( - self, - sample_timestamp: pd.Timestamp, - energy_range: tuple[float, float] | None = None, - epu_polarization: float | None = None, - ) -> pd.Timestamp | None: - if not self.izero_store: - return None - candidates: list[tuple[float, pd.Timestamp]] = [] - sample_min, sample_max = energy_range or (0.0, np.inf) - for ts, entry in self.izero_store.items(): - e_min, e_max = entry["energy_min"], entry["energy_max"] - if energy_range and (sample_max < e_min or sample_min > e_max): - continue - epu = entry.get("epu_polarization") - if epu_polarization is not None and epu is not None: - if not np.isclose(epu_polarization, epu, rtol=1e-5, atol=1e-5): - continue - dt = abs((ts - sample_timestamp).total_seconds()) - candidates.append((dt, ts)) - if not candidates: - candidates = [ - (abs((ts - sample_timestamp).total_seconds()), ts) - for ts in self.izero_store - ] - candidates.sort(key=lambda x: x[0]) - return candidates[0][1] if candidates else None - - @property - def izero(self) -> pd.DataFrame | None: - """Current izero DataFrame (selected or nearest to last sample context).""" - self._scan_and_process_izero() - if not self.izero_store: - return None - if self._selected_izero_timestamp is not None: - entry = self.izero_store.get(self._selected_izero_timestamp) - return entry["df"] if entry else None - if self._last_sample_context: - ts = self._select_izero_nearest( - sample_timestamp=self._last_sample_context["timestamp"], - energy_range=self._last_sample_context.get("energy_range"), - epu_polarization=self._last_sample_context.get( - "epu_polarization" - ), - ) - if ts is not None: - return self.izero_store[ts]["df"] - latest_ts = max(self.izero_store) - return self.izero_store[latest_ts]["df"] - - def set_izero_by_timestamp(self, timestamp: pd.Timestamp | None) -> None: - """Set the izero scan to use by timestamp; None clears selection.""" - self._scan_and_process_izero() - if timestamp is None: - self._selected_izero_timestamp = None - else: - if timestamp not in self.izero_store: - raise ValueError( - f"Timestamp {timestamp} not found in available izero scans." - ) - self._selected_izero_timestamp = timestamp - - def set_izero_nearest( - self, - sample_timestamp: pd.Timestamp, - energy_range: tuple[float, float] | None = None, - epu_polarization: float | None = None, - ) -> pd.Timestamp | None: - """Select izero nearest to sample timestamp (and optional energy/EPU).""" - self._scan_and_process_izero() - ts = self._select_izero_nearest( - sample_timestamp=sample_timestamp, - energy_range=energy_range, - epu_polarization=epu_polarization, - ) - if ts is not None: - self._selected_izero_timestamp = ts - return ts - - def list_samples(self) -> list[str]: - """List sample names inferred from non-izero .txt stems (sample_angle_exp).""" - sample_names: set[str] = set() - for file in self.path.glob("*.txt"): - if file.name.startswith("izero"): - continue - parts = file.stem.split("_") - if len(parts) >= 3: - sample = "_".join(parts[:-2]) - sample_names.add(sample) - return sorted(sample_names) - - def get_sample_file_info( - self, sample_name: str - ) -> list[dict[str, float | int | Path]]: - """ - Return list of dicts with file, angle, experiment for the sample. - - Expects stem format sample_angle_exp (e.g. name_55deg_0). - """ - files: list[dict[str, float | int | Path]] = [] - for file in self.path.glob("*.txt"): - if file.name.startswith("izero"): - continue - parts = file.stem.split("_") - if "_".join(parts[:-2]) != sample_name: - continue - try: - angle_str = parts[-2].strip("deg") - angle = float(angle_str) - experiment_hash = int(parts[-1]) - files.append({ - "file": file, - "angle": angle, - "experiment": experiment_hash, - }) - except (ValueError, IndexError): - continue - return files - - def process_tey( - self, df: pd.DataFrame, izero_df: pd.DataFrame - ) -> pd.DataFrame: - """ - TEY absorption: Intensity = AI3 Izero / TEY signal, PD Corrected = I0 / Intensity. - """ - processed = pd.DataFrame() - processed["Energy"] = df["Beamline Energy"] - processed["Intensity"] = df["AI 3 Izero"] / df["TEY signal"] - izero_df = izero_df.copy() - izero_df["Energy"] = izero_df["Energy"].astype(float) - processed = processed.merge(izero_df, on="Energy", how="left") - processed["PD Corrected"] = processed["I0"] / processed["Intensity"] - return processed - - @staticmethod - def mu( - energy: np.ndarray, - formula: str, - units: str = "g/cm^2", - ) -> np.ndarray: - """ - Mass attenuation coefficient mu(E) for formula at given energies (eV). - - Parameters - ---------- - energy : np.ndarray - Energy in eV. - formula : str - Chemical formula or element, e.g. 'C8H8', 'Si', 'O'. - units : str - Output units, default "g/cm^2". - - Returns - ------- - np.ndarray - mu in requested units. - """ - energy = np.asarray(energy, dtype=float) - energy_kev = energy * 1e-3 - n = xsf.index_of_refraction(formula, energy=energy_kev, density=1.0) - beta = -n.imag - hc = 12.3984193 - wavelength_A = hc / energy_kev - wavelength_cm = wavelength_A * 1e-8 - mu_over_rho = 4 * np.pi * beta / wavelength_cm - mu_qty = mu_over_rho * _UREG("g/cm^2") - mu_converted = mu_qty.to(units) - return np.asarray(mu_converted.magnitude) - - @staticmethod - def build_mu_arrays( - energy: np.ndarray, - formula: str, - units: str = "g/cm^2", - ) -> dict[str, np.ndarray]: - """ - Precompute mu arrays for formula, Si, and O at given energies (eV). - - Returns - ------- - dict[str, np.ndarray] - Keys "chemical", "Si", "O"; values mu arrays. - """ - return { - "chemical": NexafsDirectory.mu(energy, formula, units), - "Si": NexafsDirectory.mu(energy, "Si", units), - "O": NexafsDirectory.mu(energy, "O", units), - } - - def _parse_edge_region( - self, - df: pd.DataFrame, - region: tuple[float | None, float | None], - edge_type: str, - ) -> pd.DataFrame: - return _parse_edge_region(df, region, edge_type) - - def normalize_tey( - self, - df: pd.DataFrame, - formula: str, - pre_edge: tuple[float | None, float | None] | None = None, - post_edge: tuple[float | None, float | None] | None = None, - ) -> pd.DataFrame: - """ - Baseline subtract in pre-edge, scale to bare-atom step; add Norm Abs, Bare Atom Step, Mass Abs. - """ - df = df.copy() - df["Bare Atom"] = self.mu(df["Energy"].values, formula) - pre = pre_edge if pre_edge is not None else self.pre_edge - post = post_edge if post_edge is not None else self.post_edge - df_pre = self._parse_edge_region(df, pre, edge_type="pre") - xpre = df_pre["Energy"].to_numpy() - ypre = df_pre["PD Corrected"].to_numpy() - barepre = df_pre["Bare Atom"].to_numpy() - if len(xpre) < 2: - raise ValueError("Pre-edge region too small for line fit") - coef, intercept = np.polyfit(xpre, ypre, 1) - coef_bare, intercept_bare = np.polyfit(xpre, barepre, 1) - baseline = coef * df["Energy"] + intercept - baseline_bare = coef_bare * df["Energy"] + intercept_bare - df["Norm Abs"] = df["PD Corrected"] - baseline - df["Bare Atom Step"] = df["Bare Atom"] - baseline_bare - df_post = self._parse_edge_region(df, post, edge_type="post") - if len(df_post) < 2: - df["Mass Abs."] = df["Norm Abs"] - return df - post_normabs = df_post["Norm Abs"].median() - pre_normabs = df_pre["Norm Abs"].median() - edge_jump_normabs = post_normabs - pre_normabs - post_bare = df_post["Bare Atom Step"].median() - pre_bare = df_pre["Bare Atom Step"].median() - edge_jump_bare = post_bare - pre_bare - scale = ( - edge_jump_bare / edge_jump_normabs - if not np.isclose(edge_jump_normabs, 0.0) - else 1.0 - ) - df["Mass Abs."] = df["Norm Abs"] * scale - return df - - def normalize_sample_angles( - self, - dfs: list[pd.DataFrame], - formula: str, - pre_edge: tuple[float | None, float | None] | None = None, - post_edge: tuple[float | None, float | None] | None = None, - ) -> list[pd.DataFrame]: - """ - Normalize multiple angle DataFrames consistently (shared pre/post, edge jump scale). - """ - for df in dfs: - df["Bare Atom"] = self.mu(df["Energy"].values, formula) - pre = pre_edge if pre_edge is not None else self.pre_edge - post = post_edge if post_edge is not None else self.post_edge - post_region_len = [ - len(self._parse_edge_region(df, post, edge_type="post")) for df in dfs - ] - min_post_points = 5 - complete_mask = np.array([n >= min_post_points for n in post_region_len]) - complete_dfs = [df for df, c in zip(dfs, complete_mask) if c] - if len(complete_dfs) == 0: - all_pre = pd.concat( - [ - self._parse_edge_region(df, pre, edge_type="pre").assign( - idx=i - ) - for i, df in enumerate(dfs) - ] - ) - xpre = all_pre["Energy"].to_numpy() - ypre = all_pre["PD Corrected"].to_numpy() - barepre = all_pre["Bare Atom"].to_numpy() - if len(xpre) >= 2: - coef, intercept = np.polyfit(xpre, ypre, 1) - coef_bare, intercept_bare = np.polyfit(xpre, barepre, 1) - for df in dfs: - baseline = coef * df["Energy"] + intercept - baseline_bare = coef_bare * df["Energy"] + intercept_bare - df["Norm Abs"] = df["PD Corrected"] - baseline - df["Bare Atom Step"] = df["Bare Atom"] - baseline_bare - for df in dfs: - df["Mass Abs."] = df.get("Norm Abs", df["PD Corrected"].copy()) - return dfs - all_pre = pd.concat( - [ - self._parse_edge_region(df, pre, edge_type="pre").assign(idx=i) - for i, df in enumerate(complete_dfs) - ] - ) - xpre = all_pre["Energy"].to_numpy() - ypre = all_pre["PD Corrected"].to_numpy() - barepre = all_pre["Bare Atom"].to_numpy() - if len(xpre) < 2: - raise ValueError("Pre-edge region too small for line fit") - coef, intercept = np.polyfit(xpre, ypre, 1) - coef_bare, intercept_bare = np.polyfit(xpre, barepre, 1) - for df in dfs: - baseline = coef * df["Energy"] + intercept - baseline_bare = coef_bare * df["Energy"] + intercept_bare - df["Norm Abs"] = df["PD Corrected"] - baseline - df["Bare Atom Step"] = df["Bare Atom"] - baseline_bare - pre_norm = pd.concat( - [ - self._parse_edge_region(df, pre, edge_type="pre")[ - ["Norm Abs", "Bare Atom Step"] - ] - for df in complete_dfs - ] - ) - post_norm = pd.concat( - [ - self._parse_edge_region(df, post, edge_type="post")[ - ["Norm Abs", "Bare Atom Step"] - ] - for df in complete_dfs - ] - ) - pre_median_normabs = pre_norm["Norm Abs"].median() - post_median_normabs = post_norm["Norm Abs"].median() - edge_jump_normabs = post_median_normabs - pre_median_normabs - pre_median_bare = pre_norm["Bare Atom Step"].median() - post_median_bare = post_norm["Bare Atom Step"].median() - edge_jump_bare = post_median_bare - pre_median_bare - scale = ( - edge_jump_bare / edge_jump_normabs - if not np.isclose(edge_jump_normabs, 0.0) - else 1.0 - ) - e0 = pre[1] if pre[1] is not None else pre[0] - post_first = self._parse_edge_region( - complete_dfs[0], post, edge_type="post" - ) - k = 0.0 - if len(post_first) >= 2: - e_post = post_first["Energy"].to_numpy() - slope_bare = np.polyfit( - e_post, post_first["Bare Atom Step"].to_numpy(), 1 - )[0] - slope_massabs = np.polyfit( - e_post, - (post_first["Norm Abs"].to_numpy() * scale), - 1, - )[0] - k = slope_bare - slope_massabs - for i, df in enumerate(dfs): - if complete_mask[i]: - df["Mass Abs."] = df["Norm Abs"] * scale - if k != 0.0 and e0 is not None: - mask = df["Energy"] > e0 - df.loc[mask, "Mass Abs."] = ( - df.loc[mask, "Mass Abs."] - + (df.loc[mask, "Energy"] - e0) * k - ) - else: - df["Mass Abs."] = df["Norm Abs"].copy() - return dfs - - def get_sample_dfs( - self, - sample_name: str, - formula: str = "C8H8", - pre_edge: tuple[float | None, float | None] | None = None, - post_edge: tuple[float | None, float | None] | None = None, - ) -> list[pd.DataFrame]: - """ - Load and normalize all angle files for a sample. - - Returns list of DataFrames with Energy, PD Corrected, Norm Abs, - Bare Atom, Bare Atom Step, Mass Abs., Sample, Angle, Experiment. - """ - files = self.get_sample_file_info(sample_name) - if files: - first_df = self.read_nexafs(files[0]["file"]) - energy = first_df["Beamline Energy"].astype(float) - epu = None - if "EPU Polarization" in first_df.columns: - epu_val = first_df["EPU Polarization"].iloc[0] - if pd.notna(epu_val): - epu = float(epu_val) - if "Timestamp" in first_df.columns: - sample_ts = pd.to_datetime(first_df["Timestamp"].iloc[0]) - else: - sample_ts = pd.to_datetime( - files[0]["file"].stat().st_mtime, unit="s" - ) - self._last_sample_context = { - "timestamp": sample_ts, - "energy_range": (float(energy.min()), float(energy.max())), - "epu_polarization": epu, - } - izero_df = self.izero - if izero_df is None: - raise RuntimeError("No izero scan available.") - df_list: list[pd.DataFrame] = [] - for info in files: - df = self.read_nexafs(info["file"]) - processed = self.process_tey(df, izero_df) - processed["Sample"] = sample_name - processed["Angle"] = info["angle"] - processed["Experiment"] = info["experiment"] - df_list.append(processed) - return self.normalize_sample_angles( - df_list, formula, pre_edge, post_edge - ) - - def normalization_quality( - self, - dfs: list[pd.DataFrame], - pre_edge: tuple[float | None, float | None] | None = None, - post_edge: tuple[float | None, float | None] | None = None, - ref_col: str = "Bare Atom Step", - ycol: str = "Mass Abs.", - scale_ref_percent: float | None = None, - ) -> dict: - pre = pre_edge if pre_edge is not None else self.pre_edge - post = post_edge if post_edge is not None else self.post_edge - if isinstance(pre, (int, float)): - pre = (None, float(pre)) - pre = tuple(pre) - if isinstance(post, (int, float)): - post = (float(post), None) - post = tuple(post) - all_resid_pre: list[pd.Series] = [] - all_resid_post: list[pd.Series] = [] - for df in dfs: - r = df[ycol] - df[ref_col] - pre_df = self._parse_edge_region(df, pre, edge_type="pre") - post_df = self._parse_edge_region(df, post, edge_type="post") - if len(pre_df): - all_resid_pre.append(r.loc[pre_df.index]) - if len(post_df): - all_resid_post.append(r.loc[post_df.index]) - concat_pre = ( - pd.concat(all_resid_pre, ignore_index=True) if all_resid_pre else pd.Series(dtype=float) - ) - concat_post = ( - pd.concat(all_resid_post, ignore_index=True) - if all_resid_post - else pd.Series(dtype=float) - ) - rms_pre = ( - float(np.sqrt(np.mean(concat_pre**2))) if len(concat_pre) else np.nan - ) - rms_post = ( - float(np.sqrt(np.mean(concat_post**2))) if len(concat_post) else np.nan - ) - if scale_ref_percent is None and len(dfs) > 0: - post_ref = pd.concat( - [ - self._parse_edge_region(df, post, edge_type="post")[ref_col] - for df in dfs - ], - ignore_index=True, - ) - scale_ref_percent = ( - float(np.median(np.abs(post_ref))) if len(post_ref) else 1.0 - ) - rms_pct_pre = ( - 100.0 * rms_pre / scale_ref_percent if scale_ref_percent else np.nan - ) - rms_pct_post = ( - 100.0 * rms_post / scale_ref_percent if scale_ref_percent else np.nan - ) - return { - "rms_pre": rms_pre, - "rms_post": rms_post, - "rms_pct_pre": rms_pct_pre, - "rms_pct_post": rms_pct_post, - "residuals_pre": all_resid_pre, - "residuals_post": all_resid_post, - "scale_ref": scale_ref_percent, - } - - def build_preview_summary( - self, - formula: str = "C8H8", - pre_edge: tuple[float | None, float | None] | None = None, - post_edge: tuple[float | None, float | None] | None = None, - warning_threshold: float = 2.0, - error_threshold: float = 5.0, - ) -> pd.DataFrame: - rows: list[dict] = [] - for sample_name in self.list_samples(): - dfs = self.get_sample_dfs( - sample_name, formula=formula, pre_edge=pre_edge, post_edge=post_edge - ) - pol = None - if self._last_sample_context is not None: - pol = self._last_sample_context.get("epu_polarization") - for df in dfs: - q = self.normalization_quality( - [df], pre_edge=pre_edge, post_edge=post_edge - ) - quality = quality_level_from_rms( - q["rms_pct_pre"], - q["rms_pct_post"], - warning_threshold=warning_threshold, - error_threshold=error_threshold, - ) - angle = float(df["Angle"].iloc[0]) if "Angle" in df.columns else np.nan - exp = ( - int(df["Experiment"].iloc[0]) - if "Experiment" in df.columns - else np.nan - ) - energy = df["Energy"].astype(float) - ev_lo = float(energy.min()) - ev_hi = float(energy.max()) - rows.append( - { - "Sample": sample_name, - "Scan_Frame": exp, - "theta": angle, - "ev_lo": ev_lo, - "ev_hi": ev_hi, - "pol": pol if pol is not None else np.nan, - "quality": quality, - } - ) - return pd.DataFrame(rows) diff --git a/python/pyref/nexafs/io.py b/python/pyref/nexafs/io.py index c255781..94ca8fb 100644 --- a/python/pyref/nexafs/io.py +++ b/python/pyref/nexafs/io.py @@ -1,62 +1,193 @@ """ -Convenience loader for NEXAFS data: single function to load from path to concatenated DataFrame. +NEXAFS database IO: load sample, nexafs, and izero data; compute bare_atom and absorbance columns. """ from __future__ import annotations -from pathlib import Path +from sqlite3 import Connection +from typing import Any, cast +import numpy as np import pandas as pd +from periodictable import xsf +from pint import UnitRegistry -from pyref.nexafs.directory import NexafsDirectory +_UREG = UnitRegistry() +_NEXAFS_COLS = [ + "scan_id", + "beamline_energy", + "sample_theta", + "tey_signal", + "photodiode", + "ai_3_izero", + "izero_before_ai_3_izero", + "izero_before_photodiode", + "izero_after_ai_3_izero", + "izero_after_photodiode", +] + + +def _calculate_mu_row( + row: pd.Series, + units: str = "g/cm^2", + formula: str | None = None, +) -> float: + used_formula = formula if formula is not None else row["chemical_formula"] + energy = float(row["beamline_energy"]) + n = xsf.index_of_refraction(used_formula, energy=(energy * 1e-3), density=1.0) + beta = -n.imag + hc = 12.3984193 + wavelength_A = hc / (energy * 1e-3) + wavelength_cm = wavelength_A * 1e-8 + mu_over_rho = float(4 * np.pi * beta / wavelength_cm) + mu_qty = mu_over_rho * _UREG("g/cm^2") + mu_converted = mu_qty.to(units) + return float(np.asarray(mu_converted.magnitude)) + + +def _get_samples( + conn: Connection, + sample_name: str, + tag: str | None = None, + version: int | None = None, +) -> pd.DataFrame: + query = "SELECT * FROM sample WHERE name = ?" + params: list[Any] = [sample_name] + if version is not None: + query += " AND version = ?" + params.append(version) + if tag is not None: + query += " AND tag = ?" + params.append(tag) + return pd.read_sql_query(query, conn, params=params) def load_nexafs( - path: str | Path, - sample_name: str | None = None, - formula: str = "C8H8", - pre_edge: tuple[float | None, float | None] = (None, 280.0), - post_edge: tuple[float | None, float | None] = (360.0, None), + conn: Connection, + sample_name: str, + tag: str | None = None, + version: int | None = None, + substrate: str = "Si", + cols: list[str] | None = None, ) -> pd.DataFrame: """ - Load NEXAFS data from a directory into a single DataFrame. + Load NEXAFS data from the database for one sample and compute absorbance columns. - Discovers izero and sample files, computes TEY absorption and baseline - normalization. No background fit is applied; use the widget or - normalization module for that. + Queries sample, nexafs, and izero tables; merges on beamline_energy; adds + chemical_formula, bare_atom, bare_atom_substrate, pd_response_0/1, raw_abs, + absorbance_0, and absorbance_1. The result is ready for df.nexafs normalization. Parameters ---------- - path : str | Path - Directory containing NEXAFS scan files (izero*.txt, sample_angle_exp.txt). - sample_name : str | None - If set, load only this sample; otherwise load all samples. - formula : str - Chemical formula for bare-atom normalization, e.g. "C8H8". - pre_edge : tuple - Pre-edge region (e_lo, e_hi); None for open-ended. - post_edge : tuple - Post-edge region (e_lo, e_hi). + conn : sqlite3.Connection + Open connection to the NEXAFS database. + sample_name : str + Sample name (sample.name). + tag : str, optional + Filter by sample.tag. + version : int, optional + Filter by sample.version. + substrate : str, optional + Chemical formula for substrate bare-atom cross section (default "Si"). Returns ------- pd.DataFrame - Concatenated rows with Energy, PD Corrected, Norm Abs, Bare Atom Step, - Mass Abs., Sample, Angle, Experiment. + DataFrame with beamline_energy, sample_theta, tey_signal, photodiode, + ai_3_izero, izero_before_*, izero_after_*, chemical_formula, bare_atom, + bare_atom_substrate, pd_response_0, pd_response_1, raw_abs, + absorbance_0, absorbance_1. """ - directory = NexafsDirectory(path) - samples = [sample_name] if sample_name else directory.list_samples() - if not samples: - return pd.DataFrame() - records: list[pd.DataFrame] = [] - for name in samples: - dfs = directory.get_sample_dfs( - name, - formula=formula, - pre_edge=pre_edge, - post_edge=post_edge, + if cols is None: + cols = _NEXAFS_COLS + sample = _get_samples(conn, sample_name, tag=tag, version=version) + if sample.empty: + raise ValueError(f"No sample found for name={sample_name!r}, tag={tag!r}, version={version!r}") + sample_id = int(sample["id"].iloc[0]) + sample_formula = str(sample["chemical_formula"].iloc[0]) + + nexafs_df = pd.read_sql_query( + "SELECT * FROM nexafs WHERE sample_id = ?", + conn, + params=[sample_id], + ) + if nexafs_df.empty: + raise ValueError(f"No nexafs rows for sample_id={sample_id}") + + def _scan_id_or_none(val: Any) -> int | None: + if val is None: + return None + if isinstance(val, float) and np.isnan(val): + return None + try: + return int(val) + except (TypeError, ValueError): + return None + + izero_before_id = _scan_id_or_none(nexafs_df.iloc[0]["izero_before_scan_id"]) + izero_after_id = _scan_id_or_none(nexafs_df.iloc[0]["izero_after_scan_id"]) + + energy_col = nexafs_df[["beamline_energy"]].copy() + if izero_before_id is not None: + izero_before = pd.read_sql_query( + "SELECT * FROM izero WHERE scan_id = ?", + conn, + params=[izero_before_id], + ) + else: + izero_before = energy_col.copy() + izero_before["ai_3_izero"] = np.nan + izero_before["photodiode"] = np.nan + + if izero_after_id is not None: + izero_after = pd.read_sql_query( + "SELECT * FROM izero WHERE scan_id = ?", + conn, + params=[izero_after_id], ) - records.extend(dfs) - if not records: - return pd.DataFrame() - return pd.concat(records, ignore_index=True) + else: + izero_after = energy_col.copy() + izero_after["ai_3_izero"] = np.nan + izero_after["photodiode"] = np.nan + + merged = pd.merge( + nexafs_df, + izero_before.add_prefix("izero_before_"), + left_on="beamline_energy", + right_on="izero_before_beamline_energy", + how="left", + suffixes=("", "_before"), + ) + merged = pd.merge( + merged, + izero_after.add_prefix("izero_after_"), + left_on="beamline_energy", + right_on="izero_after_beamline_energy", + how="left", + suffixes=("", "_after"), + )[cols] + + merged["chemical_formula"] = sample_formula + merged["bare_atom"] = merged.apply( + _calculate_mu_row, + axis=1, + units="g/cm^2", + ) + merged["bare_atom_substrate"] = merged.apply( + _calculate_mu_row, + axis=1, + units="g/cm^2", + formula=substrate, + ) + + merged["pd_response_0"] = ( + merged["izero_before_ai_3_izero"] / merged["izero_before_photodiode"] + ) + merged["pd_response_1"] = ( + merged["izero_after_ai_3_izero"] / merged["izero_after_photodiode"] + ) + merged["raw_abs"] = merged["tey_signal"] / merged["ai_3_izero"] + merged["absorbance_0"] = merged["pd_response_0"] / merged["raw_abs"] + merged["absorbance_1"] = merged["pd_response_1"] / merged["raw_abs"] + + return cast(pd.DataFrame, merged) diff --git a/python/pyref/nexafs/normalization.py b/python/pyref/nexafs/normalization.py deleted file mode 100644 index 1a1bfa0..0000000 --- a/python/pyref/nexafs/normalization.py +++ /dev/null @@ -1,308 +0,0 @@ -""" -NEXAFS normalization fit schemes: bare atom, constant, Si/O background, polynomial. - -Each scheme fits scale (and optional background terms) in the pre+post edge region -and returns full model curve, background-only curve, and background-subtracted -intensity for widget plotting. -""" - -from __future__ import annotations - -from enum import Enum -from typing import Any - -import numpy as np -from scipy.optimize import curve_fit - - -class NormalizationScheme(str, Enum): - """Normalization method for NEXAFS absorption.""" - - NONE = "none" - BARE_ATOM = "bare_atom" - BARE_ATOM_CONST = "bare_atom_const" - BARE_ATOM_SI = "bare_atom_si" - BARE_ATOM_SI_O = "bare_atom_si_o" - BARE_ATOM_POLY = "bare_atom_poly" - - -def _fit_region_mask( - energy: np.ndarray, - pre_edge: tuple[float | None, float | None], - post_edge: tuple[float | None, float | None], -) -> np.ndarray: - pre_lo, pre_hi = pre_edge - post_lo, post_hi = post_edge - pre_ok = np.ones(energy.size, dtype=bool) - if pre_lo is not None: - pre_ok &= energy >= pre_lo - if pre_hi is not None: - pre_ok &= energy <= pre_hi - post_ok = np.ones(energy.size, dtype=bool) - if post_lo is not None: - post_ok &= energy >= post_lo - if post_hi is not None: - post_ok &= energy <= post_hi - return pre_ok | post_ok - - -def fit_bare_atom( - energy: np.ndarray, - intensity: np.ndarray, - fit_mask: np.ndarray, - mu_chemical: np.ndarray, -) -> dict[str, Any]: - """ - Fit scale so that scale * mu(E) matches intensity in fit region. - - Model: scale * mu(E). Background = 0. - """ - e_fit = energy[fit_mask] - y_fit = intensity[fit_mask] - mu_fit = mu_chemical[fit_mask] - scale = np.nanmean(y_fit / mu_fit) - if scale <= 0 or not np.isfinite(scale): - scale = 1.0 - full_curve = scale * mu_chemical - background_curve = np.zeros_like(energy) - scaled_mu = scale * mu_chemical - return { - "params": {"scale": scale}, - "full_curve": full_curve, - "background_curve": background_curve, - "scaled_mu": scaled_mu, - } - - -def fit_bare_atom_const( - energy: np.ndarray, - intensity: np.ndarray, - fit_mask: np.ndarray, - mu_chemical: np.ndarray, -) -> dict[str, Any]: - """ - Fit scale and constant background: scale * (mu(E) + bkg). - """ - - def model(e: np.ndarray, scale: float, bkg: float) -> np.ndarray: - mc = np.interp(e, energy, mu_chemical) - return scale * (mc + bkg) - - e_fit = energy[fit_mask] - y_fit = intensity[fit_mask] - mu_fit = mu_chemical[fit_mask] - p0_scale = float(np.nanmean(y_fit / mu_fit)) - if not np.isfinite(p0_scale) or p0_scale <= 0: - p0_scale = 1.0 - p0_scale = max(p0_scale, 1e-5) - p0 = (p0_scale, 0.0) - bounds = ((1e-6, -np.inf), (np.inf, np.inf)) - popt, _ = curve_fit(model, e_fit, y_fit, p0=p0, bounds=bounds) - scale, bkg = float(popt[0]), float(popt[1]) - full_curve = model(energy, scale, bkg) - background_curve = np.full_like(energy, scale * bkg) - scaled_mu = scale * mu_chemical - return { - "params": {"scale": scale, "bkg": bkg}, - "full_curve": full_curve, - "background_curve": background_curve, - "scaled_mu": scaled_mu, - } - - -def fit_bare_atom_si( - energy: np.ndarray, - intensity: np.ndarray, - fit_mask: np.ndarray, - mu_chemical: np.ndarray, - mu_si: np.ndarray, -) -> dict[str, Any]: - """ - Fit scale and Si composition: scale * (mu(E) + si_comp * mu_si(E)). - """ - - def model(e: np.ndarray, scale: float, si_comp: float) -> np.ndarray: - mc = np.interp(e, energy, mu_chemical) - ms = np.interp(e, energy, mu_si) - return scale * (mc + si_comp * ms) - - e_fit = energy[fit_mask] - y_fit = intensity[fit_mask] - mu_fit = mu_chemical[fit_mask] - p0_scale = float(np.nanmean(y_fit / mu_fit)) - if not np.isfinite(p0_scale) or p0_scale <= 0: - p0_scale = 1.0 - p0_scale = max(p0_scale, 1e-5) - p0 = (p0_scale, 0.0) - bounds = ((1e-6, 0.0), (np.inf, np.inf)) - popt, _ = curve_fit(model, e_fit, y_fit, p0=p0, bounds=bounds) - scale, si_comp = float(popt[0]), float(popt[1]) - full_curve = model(energy, scale, si_comp) - background_curve = scale * si_comp * mu_si - scaled_mu = scale * mu_chemical - return { - "params": {"scale": scale, "si_comp": si_comp}, - "full_curve": full_curve, - "background_curve": background_curve, - "scaled_mu": scaled_mu, - } - - -def fit_bare_atom_si_o( - energy: np.ndarray, - intensity: np.ndarray, - fit_mask: np.ndarray, - mu_chemical: np.ndarray, - mu_si: np.ndarray, - mu_o: np.ndarray, -) -> dict[str, Any]: - """ - Fit scale, Si and O composition: scale * (mu + si_comp*mu_si + o_comp*mu_o). - """ - - def model( - e: np.ndarray, - scale: float, - si_comp: float, - o_comp: float, - ) -> np.ndarray: - mc = np.interp(e, energy, mu_chemical) - ms = np.interp(e, energy, mu_si) - mo = np.interp(e, energy, mu_o) - return scale * (mc + si_comp * ms + o_comp * mo) - - e_fit = energy[fit_mask] - y_fit = intensity[fit_mask] - p0_scale = float(np.nanmean(y_fit / mu_chemical[fit_mask])) - if not np.isfinite(p0_scale) or p0_scale <= 0: - p0_scale = 1.0 - p0_scale = max(p0_scale, 1e-5) - p0 = (p0_scale, 0.0, 0.0) - bounds = ((1e-6, 0.0, 0.0), (np.inf, np.inf, np.inf)) - popt, _ = curve_fit(model, e_fit, y_fit, p0=p0, bounds=bounds) - scale, si_comp, o_comp = float(popt[0]), float(popt[1]), float(popt[2]) - full_curve = model(energy, scale, si_comp, o_comp) - background_curve = scale * (si_comp * mu_si + o_comp * mu_o) - scaled_mu = scale * mu_chemical - return { - "params": {"scale": scale, "si_comp": si_comp, "o_comp": o_comp}, - "full_curve": full_curve, - "background_curve": background_curve, - "scaled_mu": scaled_mu, - } - - -def fit_bare_atom_poly( - energy: np.ndarray, - intensity: np.ndarray, - fit_mask: np.ndarray, - mu_chemical: np.ndarray, -) -> dict[str, Any]: - """ - Fit scale and cubic polynomial background: scale * (mu(E) + a + b*E + c*E^2 + d*E^3). - """ - - def model( - e: np.ndarray, - scale: float, - a: float, - b: float, - c: float, - d: float, - ) -> np.ndarray: - mc = np.interp(e, energy, mu_chemical) - return scale * (mc + a + b * e + c * e**2 + d * e**3) - - e_fit = energy[fit_mask] - y_fit = intensity[fit_mask] - p0_scale = float(np.nanmean(y_fit / mu_chemical[fit_mask])) - if not np.isfinite(p0_scale) or p0_scale <= 0: - p0_scale = 1.0 - p0_scale = max(p0_scale, 1e-5) - p0 = (p0_scale, 0.0, 0.0, 0.0, 0.0) - bounds = ( - (1e-6, -np.inf, -np.inf, -np.inf, -np.inf), - (np.inf, np.inf, np.inf, np.inf, np.inf), - ) - popt, _ = curve_fit(model, e_fit, y_fit, p0=p0, bounds=bounds) - scale, a, b, c, d = ( - float(popt[0]), - float(popt[1]), - float(popt[2]), - float(popt[3]), - float(popt[4]), - ) - full_curve = model(energy, scale, a, b, c, d) - poly = a + b * energy + c * energy**2 + d * energy**3 - background_curve = scale * poly - scaled_mu = scale * mu_chemical - return { - "params": {"scale": scale, "a": a, "b": b, "c": c, "d": d}, - "full_curve": full_curve, - "background_curve": background_curve, - "scaled_mu": scaled_mu, - } - - -def fit_normalization( - scheme: NormalizationScheme, - energy: np.ndarray, - intensity: np.ndarray, - pre_edge: tuple[float | None, float | None], - post_edge: tuple[float | None, float | None], - mu_chemical: np.ndarray, - mu_si: np.ndarray | None = None, - mu_o: np.ndarray | None = None, -) -> dict[str, Any] | None: - """ - Run the selected normalization fit and return full_curve, background_curve, scaled_mu, params. - - Parameters - ---------- - scheme : NormalizationScheme - One of BARE_ATOM, BARE_ATOM_CONST, BARE_ATOM_SI, BARE_ATOM_SI_O, BARE_ATOM_POLY. - energy : np.ndarray - Energy (eV) for all points. - intensity : np.ndarray - PD Corrected or Norm Abs intensity. - pre_edge : tuple - (e_lo, e_hi) for pre-edge; None for open-ended. - post_edge : tuple - (e_lo, e_hi) for post-edge. - mu_chemical : np.ndarray - Bare-atom mu(E) for main formula. - mu_si, mu_o : np.ndarray, optional - Required for BARE_ATOM_SI and BARE_ATOM_SI_O. - - Returns - ------- - dict with params, full_curve, background_curve, scaled_mu; or None if scheme is NONE. - """ - if scheme == NormalizationScheme.NONE: - return None - fit_mask = _fit_region_mask(energy, pre_edge, post_edge) - if np.sum(fit_mask) < 2: - return None - if scheme == NormalizationScheme.BARE_ATOM: - return fit_bare_atom(energy, intensity, fit_mask, mu_chemical) - if scheme == NormalizationScheme.BARE_ATOM_CONST: - return fit_bare_atom_const( - energy, intensity, fit_mask, mu_chemical - ) - if scheme == NormalizationScheme.BARE_ATOM_SI: - if mu_si is None: - raise ValueError("mu_si required for BARE_ATOM_SI") - return fit_bare_atom_si( - energy, intensity, fit_mask, mu_chemical, mu_si - ) - if scheme == NormalizationScheme.BARE_ATOM_SI_O: - if mu_si is None or mu_o is None: - raise ValueError("mu_si and mu_o required for BARE_ATOM_SI_O") - return fit_bare_atom_si_o( - energy, intensity, fit_mask, mu_chemical, mu_si, mu_o - ) - if scheme == NormalizationScheme.BARE_ATOM_POLY: - return fit_bare_atom_poly( - energy, intensity, fit_mask, mu_chemical - ) - return None diff --git a/python/pyref/nexafs/widget.py b/python/pyref/nexafs/widget.py deleted file mode 100644 index 1ee921a..0000000 --- a/python/pyref/nexafs/widget.py +++ /dev/null @@ -1,352 +0,0 @@ -""" -Interactive NEXAFS widget using Panel and Bokeh. - -Supports widget-driven pre/post edge (FloatInputs) and box-select on the plot -to set pre or post edge. Requires a Panel/Bokeh server for box-select callbacks. -""" - -from __future__ import annotations - -from typing import Any - -import pandas as pd # noqa: TC002 - -from pyref.nexafs.directory import NexafsDirectory # noqa: TC001 -from pyref.nexafs.normalization import NormalizationScheme, fit_normalization - - -def _current_df_for_angle( - dfs: list[pd.DataFrame], - angles: list[float], - angle: float | None, -) -> pd.DataFrame | None: - if not dfs or not angles or angle is None: - return None - for df in dfs: - if float(df["Angle"].iloc[0]) == angle: - return df - return dfs[0] - - -def _build_bokeh_plot( - angle: float | None, - pre_lo: float, - pre_hi: float, - post_lo: float, - post_hi: float, - norm_scheme: NormalizationScheme, - *, - view_bg_subtracted: bool, - sample_name: str, - formula: str, - directory: NexafsDirectory, - dfs: list[pd.DataFrame], - angles: list[float], - pre_lo_widget: Any, - pre_hi_widget: Any, - post_lo_widget: Any, - post_hi_widget: Any, - selection_applies_to: int, -) -> Any: - from bokeh.models import BoxAnnotation, BoxSelectTool, ColumnDataSource - from bokeh.models.ranges import Range1d - from bokeh.plotting import figure - - df = _current_df_for_angle(dfs, angles, angle) - p = figure( - width=550, - height=400, - x_axis_label="Energy", - y_axis_label="Intensity", - title=f"Sample: {sample_name}", - tools="pan,wheel_zoom,box_zoom,save,reset", - ) - if df is None or df.empty: - return p - energy = df["Energy"].to_numpy() - intensity = df["PD Corrected"].to_numpy() - if "Norm Abs" in df.columns: - intensity = df["Norm Abs"].to_numpy() - e_min = float(energy.min()) - e_max = float(energy.max()) - margin = max(2.0, (e_max - e_min) * 0.02) - p.x_range = Range1d(start=e_min - margin, end=e_max + margin) - source = ColumnDataSource(data={"x": energy.tolist(), "y": intensity.tolist()}) - p.scatter(source=source, x="x", y="y", size=4, color="#1f77b4", alpha=0.8) - p.line(source=source, x="x", y="y", line_width=2, color="#1f77b4") - pre_edge = (pre_lo, pre_hi) - post_edge = (post_lo, post_hi) - result = None - if norm_scheme != NormalizationScheme.NONE: - try: - mu_arrays = directory.build_mu_arrays(energy, formula) - result = fit_normalization( - norm_scheme, - energy, - intensity, - pre_edge, - post_edge, - mu_arrays["chemical"], - mu_si=mu_arrays.get("Si"), - mu_o=mu_arrays.get("O"), - ) - except (ValueError, RuntimeError): - result = None - if result is not None: - full_curve = result["full_curve"] - background_curve = result["background_curve"] - scaled_mu = result["scaled_mu"] - if view_bg_subtracted: - bg_sub = intensity - background_curve - p.line( - x=energy, - y=bg_sub, - line_width=2, - color="#ff7f0e", - legend_label="Background subtracted", - ) - p.line( - x=energy, - y=scaled_mu, - line_width=1, - color="#2ca02c", - line_dash="dashed", - alpha=0.8, - legend_label="Scale * mu(E)", - ) - else: - p.line( - x=energy, - y=full_curve, - line_width=2, - color="#ff7f0e", - legend_label="Fit", - ) - p.line( - x=energy, - y=background_curve, - line_width=1, - color="#2ca02c", - line_dash="dashed", - alpha=0.7, - legend_label="Background", - ) - if pre_lo is not None or pre_hi is not None: - lo = pre_lo if pre_lo is not None else float(energy.min()) - hi = pre_hi if pre_hi is not None else float(energy.max()) - pre_box = BoxAnnotation( - left=lo, - right=hi, - fill_alpha=0.15, - fill_color="blue", - ) - p.add_layout(pre_box) - if post_lo is not None or post_hi is not None: - lo = post_lo if post_lo is not None else float(energy.min()) - hi = post_hi if post_hi is not None else float(energy.max()) - post_box = BoxAnnotation( - left=lo, - right=hi, - fill_alpha=0.15, - fill_color="orange", - ) - p.add_layout(post_box) - box_select = BoxSelectTool(dimensions="width") - p.add_tools(box_select) - p.toolbar.active_drag = box_select - - def on_selection_change(attr: str, old: object, new: object) -> None: - try: - sel = getattr(source.selected, "indices", None) - if sel is None or len(sel) == 0: - return - x_data = source.data["x"] - x_vals = [float(x_data[i]) for i in sel] # type: ignore[arg-type] - x_min = min(x_vals) - x_max = max(x_vals) - if selection_applies_to == 0: - pre_lo_widget.value = x_min - pre_hi_widget.value = x_max - else: - post_lo_widget.value = x_min - post_hi_widget.value = x_max - source.selected.indices = [] - except Exception: - pass - - source.on_change("selected", on_selection_change) - p.legend.location = "top_right" - return p - - -class NexafsWidget: - """ - Panel + Bokeh NEXAFS widget: angle, pre/post edge, norm, view. - - Pre/post edge can be set via widgets or box-select on the plot. - Use .show() to run with a server (required for box-select to update widgets). - """ - - def __init__( - self, - directory: NexafsDirectory, - sample_name: str, - formula: str = "C8H8", - default_pre_edge: tuple[float | None, float | None] = (None, 280.0), - default_post_edge: tuple[float | None, float | None] = (360.0, None), - ) -> None: - import panel as pn - - self._directory = directory - self._sample_name = sample_name - self._formula = formula - self._dfs = directory.get_sample_dfs( - sample_name, - formula=formula, - pre_edge=default_pre_edge, - post_edge=default_post_edge, - ) - if not self._dfs: - self._angles = [] - else: - self._angles = sorted( - {float(df["Angle"].iloc[0]) for df in self._dfs} - ) - energy_sample = None - if self._dfs: - energy_sample = self._dfs[0]["Energy"].to_numpy() - e_min = float(energy_sample.min()) if energy_sample is not None else 0.0 - e_max = float(energy_sample.max()) if energy_sample is not None else 400.0 - pre_lo_def, pre_hi_def = default_pre_edge - post_lo_def, post_hi_def = default_post_edge - pre_lo_init = pre_lo_def if pre_lo_def is not None else e_min - pre_hi_init = pre_hi_def if pre_hi_def is not None else 280.0 - post_lo_init = post_lo_def if post_lo_def is not None else 360.0 - post_hi_init = post_hi_def if post_hi_def is not None else e_max - angle_options = ( - [(f"{a:.1f} deg", a) for a in self._angles] - if self._angles - else [("(no data)", None)] - ) - angle_select = pn.widgets.Select( - name="Angle", - options=dict(angle_options), - value=self._angles[0] if self._angles else None, - ) - pre_lo_input = pn.widgets.FloatInput( - name="Pre lo (eV)", - value=pre_lo_init, - step=1.0, - ) - pre_hi_input = pn.widgets.FloatInput( - name="Pre hi (eV)", - value=pre_hi_init, - step=1.0, - ) - post_lo_input = pn.widgets.FloatInput( - name="Post lo (eV)", - value=post_lo_init, - step=1.0, - ) - post_hi_input = pn.widgets.FloatInput( - name="Post hi (eV)", - value=post_hi_init, - step=1.0, - ) - norm_options = {s.value: s for s in NormalizationScheme} - norm_select = pn.widgets.Select( - name="Norm", - options=norm_options, - value=NormalizationScheme.BARE_ATOM, - ) - view_toggle = pn.widgets.Toggle( - name="View: norm / bg-sub", - value=False, - ) - selection_applies = pn.widgets.RadioButtonGroup( - name="Selection applies to", - options={"Set pre edge": 0, "Set post edge": 1}, - value=0, - ) - - plot_deps: list[Any] = [ - angle_select, - pre_lo_input, - pre_hi_input, - post_lo_input, - post_hi_input, - norm_select, - view_toggle, - selection_applies, - ] - @pn.depends(*plot_deps) - def plot_pane( - _angle: Any, - _pre_lo: Any, - _pre_hi: Any, - _post_lo: Any, - _post_hi: Any, - _norm: Any, - _view: Any, - _sel: Any, - ) -> Any: - norm_scheme = ( - _norm - if isinstance(_norm, NormalizationScheme) - else NormalizationScheme.BARE_ATOM - ) - return _build_bokeh_plot( - angle=_angle, - pre_lo=float(_pre_lo), # type: ignore[arg-type] - pre_hi=float(_pre_hi), # type: ignore[arg-type] - post_lo=float(_post_lo), # type: ignore[arg-type] - post_hi=float(_post_hi), # type: ignore[arg-type] - norm_scheme=norm_scheme, - view_bg_subtracted=bool(_view), - sample_name=sample_name, - formula=formula, - directory=directory, - dfs=self._dfs, - angles=self._angles, - pre_lo_widget=pre_lo_input, - pre_hi_widget=pre_hi_input, - post_lo_widget=post_lo_input, - post_hi_widget=post_hi_input, - selection_applies_to=int(_sel) if _sel is not None else 0, - ) - - angle_label = pn.pane.Markdown("**Angle**") - angle_block = pn.Column(angle_label, angle_select) - pre_label = pn.pane.Markdown("**Pre edge (eV)**") - pre_block = pn.Column(pre_label, pn.Row(pre_lo_input, pre_hi_input)) - post_label = pn.pane.Markdown("**Post edge (eV)**") - post_block = pn.Column(post_label, pn.Row(post_lo_input, post_hi_input)) - norm_label = pn.pane.Markdown("**Norm**") - norm_block = pn.Column(norm_label, norm_select) - controls = pn.Column( - angle_block, - pre_block, - post_block, - norm_block, - view_toggle, - selection_applies, - width=360, - ) - self._layout = pn.Row( - pn.pane.Bokeh(plot_pane, sizing_mode="stretch_both"), - controls, - sizing_mode="stretch_width", - ) - - def show(self) -> None: - """Display the widget inline in Jupyter, or in browser otherwise.""" - try: - from IPython.core.getipython import get_ipython - if get_ipython() is not None: - from IPython.display import display - display(self._layout) - return - except ImportError: - pass - import panel as pn - pn.serve(self._layout, threaded=True, show=True) diff --git a/scripts/run_nexafs_pipeline.py b/scripts/run_nexafs_pipeline.py new file mode 100644 index 0000000..a685460 --- /dev/null +++ b/scripts/run_nexafs_pipeline.py @@ -0,0 +1,580 @@ +""" +Run the NEXAFS beamtime pipeline: discover files, build sample/izero/nexafs catalogs, create DB, ingest. +""" +from __future__ import annotations + +import argparse +import io +import re +from datetime import datetime +from pathlib import Path +from sqlite3 import Connection, connect +from typing import Any + +import pandas as pd + +SCHEMA_SQL = """ +DROP TABLE IF EXISTS nexafs; +DROP TABLE IF EXISTS izero; +DROP TABLE IF EXISTS sample; + +CREATE TABLE sample ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + name TEXT NOT NULL, + tag TEXT NOT NULL, + version INTEGER NOT NULL, + beamtime DATE NOT NULL, + chemical_formula TEXT, + UNIQUE(name, tag, version, beamtime) +); + +CREATE TABLE izero ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + scan_id INTEGER NOT NULL, + beamline_energy REAL, + time_stamp TEXT NOT NULL, + photodiode REAL, + ai_3_izero REAL +); + +CREATE TABLE nexafs ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + scan_id INTEGER NOT NULL, + sample_id INTEGER NOT NULL, + time_stamp TEXT NOT NULL, + sample_theta REAL, + beamline_energy REAL, + tey_signal REAL, + ai_3_izero REAL, + photodiode REAL, + izero_before_scan_id INTEGER, + izero_after_scan_id INTEGER, + FOREIGN KEY (sample_id) REFERENCES sample(id) +); +""" + +SAMPLE_COLS = ["name", "tag", "version", "beamtime", "chemical_formula"] + + +def get_db_path(dest_root: Path, year: int, month: int) -> Path: + month_abbr = datetime(year, month, 1).strftime("%b").lower() + return dest_root / f"{month_abbr}_{year}.db" + + +def create_database(path: Path) -> Connection: + path.parent.mkdir(parents=True, exist_ok=True) + conn = connect(str(path)) + conn.execute("PRAGMA foreign_keys = ON") + conn.executescript(SCHEMA_SQL) + conn.commit() + return conn + + +def is_nexafs_scan_file(file: Path, max_header_lines: int = 50) -> bool: + try: + with open(file) as f: + head = "".join(next(f, "") for _ in range(max_header_lines)) + except OSError: + return False + if not re.search(r"Date:\s*\d{1,2}/\d{1,2}/\d{4}", head): + return False + markers = ("Scan Type", "Scan Motor", "From File", "Beamline Energy") + return any(m in head for m in markers) + + +def catalog_izero_files(directory: Path) -> pd.DataFrame: + rows = [] + for path in directory.rglob("*.txt"): + if not is_nexafs_scan_file(path): + continue + stem = path.stem.lower() + if stem.startswith("izero_"): + parts = path.stem.split("_", 1) + elif stem.startswith("i0_"): + parts = path.stem.split("_", 1) + else: + continue + if len(parts) != 2 or not parts[1].isdigit(): + continue + scan_number = int(parts[1]) + beamtime = pd.Timestamp( + datetime.fromtimestamp(path.stat().st_birthtime) + ).normalize().replace(day=1) + rows.append({"path": path, "scan_number": scan_number, "beamtime": beamtime}) + return ( + pd.DataFrame(rows) + if rows + else pd.DataFrame(columns=["path", "scan_number", "beamtime"]) + ) + + +def catalog_calibrant_files(directory: Path) -> pd.DataFrame: + rows = [] + for path in directory.rglob("*.txt"): + if not is_nexafs_scan_file(path): + continue + parts = path.stem.split("_") + if len(parts) != 2 or parts[0] != "HOPG" or not parts[1].isdigit(): + continue + scan_number = int(parts[1]) + beamtime = pd.Timestamp( + datetime.fromtimestamp(path.stat().st_birthtime) + ).normalize().replace(day=1) + rows.append( + {"path": path, "name": "HOPG", "scan_number": scan_number, "beamtime": beamtime} + ) + return ( + pd.DataFrame(rows) + if rows + else pd.DataFrame(columns=["path", "name", "scan_number", "beamtime"]) + ) + + +def get_beamtime_from_catalogs( + sample_catalog: pd.DataFrame, + izero_catalog: pd.DataFrame, + calibrant_catalog: pd.DataFrame, +) -> tuple[int, int]: + dfs = [sample_catalog, izero_catalog, calibrant_catalog] + beams = [ + df["beamtime"] + for df in dfs + if not df.empty and "beamtime" in df.columns + ] + if not beams: + return datetime.now().year, datetime.now().month + min_ts = pd.concat(beams).min() + return int(min_ts.year), int(min_ts.month) + + +def _normalize_name(s: str) -> str: + return ( + re.sub(r"[^a-z0-9]+", "_", str(s).lower()).strip("_") or str(s).lower() + ) + + +def apply_tag_map(tag: str, mapping: dict[str, str] | None) -> str: + normalized = _normalize_name(str(tag)) if str(tag).strip() else "" + if not mapping: + return normalized + return mapping.get(normalized, normalized) + + +def _keys_from_parse_key(pk: str) -> list[str]: + return re.findall(r"<([^>]+)>", pk) + + +def _parse_angle(raw: str | None) -> float | None: + if raw is None or (isinstance(raw, str) and not raw.strip()): + return None + s = re.sub(r"\s*deg\s*$", "", str(raw).strip(), flags=re.I).strip() + return float(s) if s else None + + +def parse_sample( + parts: list[str], + keys: list[str], + birth_time: float, + formula_map: dict[str, str], +) -> dict[str, Any]: + if len(parts) == len(keys): + parsed = dict(zip(keys, parts)) + elif len(parts) == len(keys) - 1 and "version" in keys: + i = keys.index("version") + parsed = { + **dict(zip(keys[:i], parts[:i])), + "version": 1, + **dict(zip(keys[i + 1 :], parts[i:])), + } + else: + parsed = dict(zip(keys, parts[: len(keys)])) + ts = datetime.fromtimestamp(birth_time) + return { + "name": parsed["name"], + "tag": parsed.get("tag", ""), + "version": ( + int(round(float(parsed["version"]))) + if "version" in parsed + else 1 + ), + "beamtime": pd.Timestamp(year=ts.year, month=ts.month, day=1), + "chemical_formula": formula_map.get(parsed["name"].lower(), ""), + "angle": _parse_angle(parsed.get("angle")), + "scan_number": parsed.get("scan_number"), + } + + +def build_files_catalog( + directory: Path, + formula_map: dict[str, str], + pk: str, + tag_map: dict[str, str] | None = None, +) -> pd.DataFrame: + keys = _keys_from_parse_key(pk) + rows = [] + for path in directory.rglob("*.txt"): + if not is_nexafs_scan_file(path): + continue + parts = path.stem.split("_") + if len(parts) < len(keys) and not ( + len(parts) == len(keys) - 1 and "version" in keys + ): + continue + rec = parse_sample(parts, keys, path.stat().st_birthtime, formula_map) + rec["path"] = path + rows.append(rec) + if not rows: + return pd.DataFrame( + columns=[ + "path", + "name", + "tag", + "version", + "beamtime", + "chemical_formula", + "angle", + "scan_number", + ] + ) + df = pd.DataFrame(rows) + df["name"] = df["name"].astype(str).apply(_normalize_name).str.strip() + df["tag"] = ( + df["tag"] + .astype(str) + .apply(lambda t: apply_tag_map(t, tag_map)) + .str.strip() + ) + return df + + +def ingest_dataset(file: Path) -> pd.DataFrame: + with open(file) as f: + lines = f.readlines() + header_idx = next( + (i for i, line in enumerate(lines) if "Time of Day" in line), + None, + ) + if header_idx is None: + raise ValueError(f"Could not find 'Time of Day' header in {file}") + table_text = "".join(lines[header_idx:]) + df = pd.read_csv(io.StringIO(table_text), sep=r"\t", engine="python") + ts_col = ( + "Time Stamp" + if "Time Stamp" in df.columns + else "Time of Day" + if "Time of Day" in df.columns + else None + ) + if ts_col is not None: + df["Time Stamp"] = pd.to_datetime(df[ts_col], format="mixed") + else: + df["Time Stamp"] = pd.to_datetime(file.stat().st_birthtime, unit="s") + rename = {} + if "Tey Signal" in df.columns and "TEY signal" not in df.columns: + rename["Tey Signal"] = "TEY signal" + if rename: + df = df.rename(columns=rename) + df["Beamline Energy"] = df["Beamline Energy"].round(1).astype(float) + return df[ + [ + "Time Stamp", + "Beamline Energy", + "TEY signal", + "AI 3 Izero", + "Photodiode", + ] + ].copy() + + +def _izero_before_after( + izero_df: pd.DataFrame, time_stamp_iso: str +) -> tuple[int | None, int | None]: + if izero_df.empty: + return None, None + df = izero_df.copy() + df["time_stamp"] = pd.to_datetime(df["time_stamp"]) + ts = pd.Timestamp(time_stamp_iso) + before = df[df["time_stamp"] <= ts] + after = df[df["time_stamp"] >= ts] + before_scan_id = ( + int(before.nlargest(1, "time_stamp").iloc[0]["scan_id"]) + if not before.empty + else None + ) + after_scan_id = ( + int(after.nsmallest(1, "time_stamp").iloc[0]["scan_id"]) + if not after.empty + else None + ) + return before_scan_id, after_scan_id + + +def build_unique_samples( + files_catalog: pd.DataFrame, + calibrant_catalog: pd.DataFrame, + formula_map: dict[str, str], +) -> pd.DataFrame: + if not files_catalog.empty: + out = ( + files_catalog[SAMPLE_COLS] + .drop_duplicates() + .reset_index(drop=True) + .copy() + ) + else: + out = pd.DataFrame(columns=SAMPLE_COLS) + if calibrant_catalog.empty: + return out + cal = calibrant_catalog[["name", "beamtime"]].copy() + cal["name"] = cal["name"].apply(_normalize_name) + cal = cal.drop_duplicates(subset=["name", "beamtime"]).reset_index(drop=True) + cal["tag"] = "" + cal["version"] = 0 + cal["chemical_formula"] = cal["name"].map(formula_map).fillna("") + cal["beamtime"] = pd.to_datetime(cal["beamtime"]).dt.normalize() + out = ( + pd.concat([out, cal[SAMPLE_COLS]], ignore_index=True) + .drop_duplicates(subset=SAMPLE_COLS) + .reset_index(drop=True) + ) + return out + + +def insert_samples(conn: Connection, unique_samples: pd.DataFrame) -> None: + if unique_samples.empty: + return + unique_samples = unique_samples.copy() + unique_samples["beamtime"] = pd.to_datetime( + unique_samples["beamtime"] + ).dt.normalize() + sample_lookup = pd.read_sql_query( + "SELECT id, name, tag, version, beamtime FROM sample", conn + ) + sample_lookup["name"] = sample_lookup["name"].astype(str).str.strip() + sample_lookup["tag"] = sample_lookup["tag"].astype(str).str.strip() + sample_lookup["beamtime"] = pd.to_datetime( + sample_lookup["beamtime"] + ).dt.normalize() + merged = unique_samples.merge( + sample_lookup, + on=["name", "tag", "version", "beamtime"], + how="left", + ) + to_insert = merged.loc[merged["id"].isna(), SAMPLE_COLS].drop_duplicates() + for _, row in to_insert.iterrows(): + conn.execute( + "INSERT INTO sample (name, tag, version, beamtime, chemical_formula) VALUES (?, ?, ?, ?, ?)", + ( + str(row["name"]).strip(), + str(row["tag"]).strip(), + int(row["version"]), + pd.Timestamp(row["beamtime"]).strftime("%Y-%m-%d"), + row["chemical_formula"], + ), + ) + if not to_insert.empty: + conn.commit() + + +def load_sample_lookup(conn: Connection) -> pd.DataFrame: + df = pd.read_sql_query( + "SELECT id, name, tag, version, beamtime FROM sample", conn + ) + df["name"] = df["name"].astype(str).str.strip() + df["tag"] = df["tag"].astype(str).str.strip() + df["beamtime"] = pd.to_datetime(df["beamtime"]).dt.normalize() + df["version"] = df["version"].astype(int) + return df + + +def ingest_izero_into_db( + conn: Connection, izero_catalog: pd.DataFrame +) -> None: + for _, row in izero_catalog.iterrows(): + path = Path(row["path"]) if not isinstance(row["path"], Path) else row["path"] + scan_id = int(row["scan_number"]) + scan_df = ingest_dataset(path) + if scan_df.empty: + continue + for _, s in scan_df.iterrows(): + time_stamp = pd.Timestamp(s["Time Stamp"]).isoformat() + conn.execute( + "INSERT INTO izero (scan_id, beamline_energy, time_stamp, photodiode, ai_3_izero) VALUES (?, ?, ?, ?, ?)", + ( + scan_id, + float(s["Beamline Energy"]), + time_stamp, + float(s["Photodiode"]), + float(s["AI 3 Izero"]), + ), + ) + conn.commit() + + +def build_rows_to_ingest( + files_catalog: pd.DataFrame, + calibrant_catalog: pd.DataFrame, + sample_lookup: pd.DataFrame, +) -> list[tuple[Path, int, float | None, int]]: + rows: list[tuple[Path, int, float | None, int]] = [] + if not files_catalog.empty: + fc = files_catalog.copy() + fc["version"] = fc["version"].astype(int) + fc["beamtime"] = pd.to_datetime(fc["beamtime"]).dt.normalize() + fc["_bk"] = fc["beamtime"].dt.strftime("%Y-%m-%d") + sl = sample_lookup.copy() + sl["beamtime"] = pd.to_datetime(sl["beamtime"]).dt.normalize() + sl["_bk"] = sl["beamtime"].dt.strftime("%Y-%m-%d") + fc = fc.merge(sl, on=["name", "tag", "version", "_bk"], how="left") + fc = fc.dropna(subset=["id"]) + for _, row in fc.iterrows(): + if pd.isna(row.get("scan_number")): + continue + path = Path(row["path"]) if not isinstance(row["path"], Path) else row["path"] + rows.append( + ( + path, + int(row["id"]), + float(row["angle"]) if pd.notna(row.get("angle")) else None, + int(row["scan_number"]), + ) + ) + if not calibrant_catalog.empty: + cal = calibrant_catalog.copy() + cal["name"] = cal["name"].apply(_normalize_name) + cal["tag"] = "" + cal["version"] = 0 + cal["beamtime"] = pd.to_datetime(cal["beamtime"]).dt.normalize() + cal["_bk"] = cal["beamtime"].dt.strftime("%Y-%m-%d") + sl = sample_lookup.copy() + sl["beamtime"] = pd.to_datetime(sl["beamtime"]).dt.normalize() + sl["_bk"] = sl["beamtime"].dt.strftime("%Y-%m-%d") + cal = cal.merge(sl, on=["name", "tag", "version", "_bk"], how="left") + cal = cal.dropna(subset=["id"]) + for _, row in cal.iterrows(): + if pd.isna(row.get("scan_number")): + continue + path = Path(row["path"]) if not isinstance(row["path"], Path) else row["path"] + rows.append((path, int(row["id"]), None, int(row["scan_number"]))) + return rows + + +def ingest_nexafs_into_db( + conn: Connection, + rows_to_ingest: list[tuple[Path, int, float | None, int]], + izero_df: pd.DataFrame, +) -> None: + tey_col = "TEY signal" + for path, sample_id, sample_theta, scan_id in rows_to_ingest: + scan_df = ingest_dataset(path) + col = tey_col if tey_col in scan_df.columns else "Tey Signal" + for _, s in scan_df.iterrows(): + ts_str = pd.Timestamp(s["Time Stamp"]).isoformat() + before_scan_id, after_scan_id = _izero_before_after(izero_df, ts_str) + conn.execute( + """INSERT INTO nexafs ( + scan_id, sample_id, time_stamp, sample_theta, beamline_energy, + tey_signal, ai_3_izero, photodiode, izero_before_scan_id, izero_after_scan_id + ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)""", + ( + scan_id, + sample_id, + ts_str, + sample_theta, + float(s["Beamline Energy"]), + float(s[col]), + float(s["AI 3 Izero"]), + float(s["Photodiode"]), + before_scan_id, + after_scan_id, + ), + ) + conn.commit() + + +def run_pipeline( + data_root: Path, + dest_root: Path, + formula_map: dict[str, str], + pk: str, + tag_map: dict[str, str] | None = None, +) -> tuple[Connection | None, pd.DataFrame, pd.DataFrame]: + izero_catalog = catalog_izero_files(data_root) + calibrant_catalog = catalog_calibrant_files(data_root) + files_catalog = build_files_catalog(data_root, formula_map, pk, tag_map) + unique_samples = build_unique_samples( + files_catalog, calibrant_catalog, formula_map + ) + if unique_samples.empty: + return None, files_catalog, unique_samples + unique_samples["beamtime"] = pd.to_datetime( + unique_samples["beamtime"] + ).dt.normalize() + year, month = get_beamtime_from_catalogs( + files_catalog, izero_catalog, calibrant_catalog + ) + db_path = get_db_path(dest_root, year, month) + conn = create_database(db_path) + insert_samples(conn, unique_samples) + sample_lookup = load_sample_lookup(conn) + ingest_izero_into_db(conn, izero_catalog) + izero_df = pd.read_sql_query("SELECT id, time_stamp, scan_id FROM izero", conn) + rows_to_ingest = build_rows_to_ingest( + files_catalog, calibrant_catalog, sample_lookup + ) + ingest_nexafs_into_db(conn, rows_to_ingest, izero_df) + return conn, files_catalog, unique_samples + + +def main() -> None: + parser = argparse.ArgumentParser( + description="Run NEXAFS beamtime pipeline: discover files, create DB, ingest sample/izero/nexafs." + ) + parser.add_argument( + "data_root", + type=Path, + nargs="?", + default=Path( + "/Users/hduva/Library/CloudStorage/OneDrive-SharedLibraries-WashingtonStateUniversity(email.wsu.edu)/Carbon Lab Research Group - Documents/Synchrotron Logistics and Data/ALS - Berkeley/Data/BL1101/2025Oct/TAD KOGA/renamed" + ), + help="Beamtime directory containing NEXAFS .txt files", + ) + parser.add_argument( + "--dest", + type=Path, + default=Path("/Users/hduva/projects/als-nexafs"), + help="Destination directory for mon_yyyy.db", + ) + args = parser.parse_args() + formula_map: dict[str, str] = {"ps": "C8H8", "hopg": "C"} + parse_key = "____" + tag_map: dict[str, str] = {} + conn, files_catalog, unique_samples = run_pipeline( + args.data_root, args.dest, formula_map, parse_key, tag_map + ) + if conn is None: + print("No sample/calibrant files found; no database created.") + return + sample_count = pd.read_sql_query( + "SELECT COUNT(*) as n FROM sample", conn + ).iloc[0, 0] + izero_count = pd.read_sql_query( + "SELECT COUNT(*) as n FROM izero", conn + ).iloc[0, 0] + nexafs_count = pd.read_sql_query( + "SELECT COUNT(*) as n FROM nexafs", conn + ).iloc[0, 0] + print(f"DB: sample={sample_count}, izero={izero_count}, nexafs={nexafs_count}") + samples = pd.read_sql_query( + "SELECT id, name, tag, version FROM sample ORDER BY id", conn + ) + print("Samples:", samples.to_string(index=False)) + nexafs_by_sample = pd.read_sql_query( + "SELECT sample_id, COUNT(*) as nexafs_rows FROM nexafs GROUP BY sample_id", + conn, + ) + print("NEXAFS rows per sample:", nexafs_by_sample.to_string(index=False)) + + +if __name__ == "__main__": + main() diff --git a/tests/test_nexafs_accessor.py b/tests/test_nexafs_accessor.py new file mode 100644 index 0000000..23231d0 --- /dev/null +++ b/tests/test_nexafs_accessor.py @@ -0,0 +1,112 @@ +"""Tests for df.nexafs DataFrame accessor.""" + +from __future__ import annotations + +import numpy as np +import pandas as pd +import pyref # noqa: F401 - registers df.nexafs accessor +import pytest + + +@pytest.fixture +def single_scan_df() -> pd.DataFrame: + energy = np.linspace(260, 360, 50) + mu = 2000 + 100 * (energy - 280) + mu_sub = 40000 - 50 * (energy - 280) + abs0 = 1e-8 * (mu + 1.2 * mu_sub) + 0.0002 + abs1 = 1.2e-8 * (mu + 0.9 * mu_sub) + 0.00018 + return pd.DataFrame({ + "beamline_energy": energy, + "bare_atom": mu, + "bare_atom_substrate": mu_sub, + "absorbance_0": abs0, + "absorbance_1": abs1, + }) + + +@pytest.fixture +def multi_angle_df(single_scan_df: pd.DataFrame) -> pd.DataFrame: + dfs = [] + for angle in [20.0, 40.0, 60.0]: + df = single_scan_df.copy() + df["sample_theta"] = angle + dfs.append(df) + return pd.concat(dfs, ignore_index=True) + + +def test_nexafs_accessor_registered() -> None: + df = pd.DataFrame({ + "beamline_energy": [270], + "bare_atom": [1.0], + "bare_atom_substrate": [1.0], + "absorbance_0": [0.1], + }) + assert hasattr(df, "nexafs") + assert df.nexafs._obj is df + + +def test_nexafs_schema_validation() -> None: + df = pd.DataFrame({"a": [1], "b": [2]}) + with pytest.raises(ValueError, match="Missing"): + df.nexafs.normalization_region() + + +def test_normalization_region_mask(single_scan_df: pd.DataFrame) -> None: + single_scan_df.nexafs.set_regions( + pre_edge=(270, 281), + post_edge=(335, 350), + ) + norm = single_scan_df.nexafs.normalization_region() + energy = norm["beamline_energy"].to_numpy() + in_pre = (energy >= 270) & (energy <= 281) + in_post = (energy >= 335) & (energy <= 350) + assert np.all(in_pre | in_post) + assert len(norm) < len(single_scan_df) + + +def test_normalize_adds_columns_and_stores_params(single_scan_df: pd.DataFrame) -> None: + single_scan_df.nexafs.set_regions(pre_edge=(270, 281), post_edge=(335, 350)) + single_scan_df.nexafs.normalize() + assert "mass_absorption_0" in single_scan_df.columns + assert "mass_absorption_1" in single_scan_df.columns + assert single_scan_df.nexafs._fit_params is not None + assert "absorbance_0" in single_scan_df.nexafs._fit_params + assert "popt" in single_scan_df.nexafs._fit_params["absorbance_0"] + + +def test_plot_regions_smoke(single_scan_df: pd.DataFrame) -> None: + single_scan_df.nexafs.set_regions(pre_edge=(270, 281), post_edge=(335, 350)) + ax = single_scan_df.nexafs.plot_regions() + assert ax is not None + + +def test_plot_normalization_smoke(single_scan_df: pd.DataFrame) -> None: + single_scan_df.nexafs.set_regions(pre_edge=(270, 281), post_edge=(335, 350)) + single_scan_df.nexafs.normalize() + ax = single_scan_df.nexafs.plot_normalization(show_fit=True) + assert ax is not None + + +def test_plot_by_group_smoke(multi_angle_df: pd.DataFrame) -> None: + multi_angle_df.nexafs.set_regions(pre_edge=(270, 281), post_edge=(335, 350)) + ax = multi_angle_df.nexafs.plot( + x="beamline_energy", + y="absorbance_0", + by="sample_theta", + colorbar="Angle (deg)", + ) + assert ax is not None + + +def test_normalize_by_group(multi_angle_df: pd.DataFrame) -> None: + from pyref.nexafs import normalize_by_group + + result = normalize_by_group( + multi_angle_df, + group_column="sample_theta", + pre_edge=(270, 281), + post_edge=(335, 350), + ) + assert 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00:00:00 2001 From: Harlan D Heilman <73567020+HarlanHeilman@users.noreply.github.com> Date: Wed, 4 Mar 2026 12:26:32 -0800 Subject: [PATCH 03/32] refactor: replace PchipInterpolator with interp1d for optical constants handling - Updated the optical constants loading mechanism in the UniTensorSLD class to use interp1d instead of PchipInterpolator, improving performance and simplifying the code. - Added validation for required columns in the optical constants dataframe, ensuring robustness in data handling. - Refactored the calculation of the complex index of refraction to utilize a new property for better clarity and maintainability. --- .cursor/.gitignore | 1 + python/pyref/fitting/structure.py | 69 +++++++++++++------------------ 2 files changed, 29 insertions(+), 41 deletions(-) create mode 100644 .cursor/.gitignore diff --git a/.cursor/.gitignore b/.cursor/.gitignore new file mode 100644 index 0000000..8bf7cc2 --- /dev/null +++ b/.cursor/.gitignore @@ -0,0 +1 @@ +plans/ diff --git a/python/pyref/fitting/structure.py b/python/pyref/fitting/structure.py index 14d94f9..536354e 100644 --- a/python/pyref/fitting/structure.py +++ b/python/pyref/fitting/structure.py @@ -12,7 +12,7 @@ import periodictable.xsf as xsf from refnx.analysis import Parameter, Parameters, possibly_create_parameter from refnx.reflect.interface import Erf, Step -from scipy.interpolate import PchipInterpolator +from scipy.interpolate import interp1d from pyref.fitting.model import reflectivity @@ -1372,48 +1372,34 @@ def __init__( energy_offset: float = 0, name: str = "", ): - # =================/ Initialize /================ + required_columns = ["energy", "n_xx", "n_ixx", "n_zz", "n_izz"] + if not all(col in ooc.columns for col in required_columns): + missing = [col for col in required_columns if col not in ooc.columns] + raise ValueError( + f"Optical constants dataframe missing required columns: {missing}" + ) + self._parameters = Parameters(name=name) super().__init__(name=name) - # ============/ Isotropic Parameters /=========== self.density: Parameter = possibly_create_parameter( # type: ignore[assignment] density, name=f"{name}_density", bounds=(0, 5 * density), vary=True ) self.rotation: Parameter = possibly_create_parameter( # type: ignore[assignment] rotation, name=f"{name}_rotation", vary=True, bounds=(-np.pi, np.pi) ) - # ============/ Optical Constants /=========== self.energy = energy self.energy_offset: Parameter = possibly_create_parameter( # type: ignore[assignment] energy_offset, name=f"{name}_energy_offset", vary=True, bounds=(-0.01, 0.01) ) - # store the optical constants as n_xx n_ixx, n_zz, n_izz - self._load_ooc(ooc) - # Add parameters to parameter set + self.n_xx = interp1d(ooc["energy"], ooc["n_xx"], bounds_error=False) + self.n_ixx = interp1d(ooc["energy"], ooc["n_ixx"], bounds_error=False) + self.n_zz = interp1d(ooc["energy"], ooc["n_zz"], bounds_error=False) + self.n_izz = interp1d(ooc["energy"], ooc["n_izz"], bounds_error=False) + self._parameters.extend( [self.density, self.rotation, self.energy_offset]) - def _load_ooc(self, ooc: pd.DataFrame): - """Loac Optical Constants from a DataFrame.""" - - # Validate the DataFrame - required_columns = ["energy", "n_xx", "n_ixx", "n_zz", "n_izz"] - if not all(col in ooc.columns for col in required_columns): - missing = [ - col for col in required_columns if col not in ooc.columns] - e = f"Optical constants dataframe missing required columns: {missing}" - raise ValueError(e) - cropped_tensor = slice_range(ooc, "energy", self.energy, 0.5) - self.n_xx = PchipInterpolator( - cropped_tensor["energy"], cropped_tensor["n_xx"]) - self.n_ixx = PchipInterpolator( - cropped_tensor["energy"], cropped_tensor["n_ixx"]) - self.n_zz = PchipInterpolator( - cropped_tensor["energy"], cropped_tensor["n_zz"]) - self.n_izz = PchipInterpolator( - cropped_tensor["energy"], cropped_tensor["n_izz"]) - def __complex__(self): """Complex representation of the scatterer.""" sldc = (2 * self.tensor[0, 0] + self.tensor[1, 1]) / 3 @@ -1423,6 +1409,17 @@ def __repr__(self): """Representation of the scatterer.""" return "Index of Refraction = (name={name!r})".format(**self.__dict__) + @property + def n(self) -> NDArray[np.complex128]: + e = self.get_energy() + return np.array( + [ + [self.n_xx(e) + self.n_ixx(e) * 1j, 0], + [0, self.n_zz(e) + self.n_izz(e) * 1j], + ], + dtype=np.complex128, + ) + @property def parameters(self): """ @@ -1441,22 +1438,12 @@ def tensor(self) -> NDArray[np.complex128]: out : np.ndarray (3x3) complex tensor index of refraction """ - + n: NDArray[np.complex128] = self.get_density() * self.n cos_squared: float = np.square(np.cos(self.get_rotation())) sin_squared: float = 1 - cos_squared - n_xx = complex( - self.n_xx(self.energy + self.energy_offset) - + 1j*self.n_ixx(self.energy + self.energy_offset) - ) - n_zz = complex( - self.n_zz(self.energy + self.energy_offset) - + 1j*self.n_izz(self.energy + self.energy_offset) - ) - - n_o: complex = (n_xx * (1 + cos_squared) + - n_zz * sin_squared) / 2 - n_e: complex = n_xx * sin_squared + n_zz * cos_squared + n_o: complex = (n[0, 0] * (1 + cos_squared) + n[1, 1] * sin_squared) / 2 + n_e: complex = n[0, 0] * sin_squared + n[1, 1] * cos_squared self._tensor = np.array( [ @@ -1466,7 +1453,7 @@ def tensor(self) -> NDArray[np.complex128]: ], dtype=np.complex128, ) - return self._tensor * self.get_density() + return self._tensor class MixedMaterialSlab(PXR_Component): From 3bc57e08ed6260658f4e8b17df7df4854cc31f2f Mon Sep 17 00:00:00 2001 From: Harlan D Heilman <73567020+HarlanHeilman@users.noreply.github.com> Date: Wed, 4 Mar 2026 12:31:51 -0800 Subject: [PATCH 04/32] fix: ensure energy value is a float in UniTensorSLD class - Updated the n property in the UniTensorSLD class to explicitly convert the energy value to a float, ensuring consistent data type handling and preventing potential type-related issues in calculations. --- python/pyref/fitting/structure.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/pyref/fitting/structure.py b/python/pyref/fitting/structure.py index 536354e..e044e50 100644 --- a/python/pyref/fitting/structure.py +++ b/python/pyref/fitting/structure.py @@ -1411,7 +1411,7 @@ def __repr__(self): @property def n(self) -> NDArray[np.complex128]: - e = self.get_energy() + e = float(self.get_energy()) return np.array( [ [self.n_xx(e) + self.n_ixx(e) * 1j, 0], From d08f640583b79c9000c5caa8170403b283893a9c Mon Sep 17 00:00:00 2001 From: Harlan D Heilman <73567020+HarlanHeilman@users.noreply.github.com> Date: Sun, 22 Mar 2026 11:53:21 -0700 Subject: [PATCH 05/32] feat: enhance catalog structure and TUI functionality - Introduced a new catalog layout that consolidates multiple beamtime databases into a single catalog per parent directory, improving data organization and access. - Updated the TUI to support the new catalog structure, allowing for better navigation and management of beamtime entries. - Implemented parallel ingestion features to optimize data processing and reduce wall time during catalog updates. - Enhanced the FITS preview functionality with improved error handling and visualization options. - Refactored various components for clarity and maintainability, including updates to the AGENTS.md documentation to reflect the new features and usage instructions --- .cargo/config.toml | 3 +- .claude/.cursor | 1 + .claude/skills/cursor-sync/.skillfish.json | 10 + .claude/skills/cursor-sync/SKILL.md | 58 + .../hooks/state/continual-learning-index.json | 49 + .cursor/hooks/state/continual-learning.json | 8 + .cursor/rules/clarify-first-prompting.mdc | 422 ++++++ .cursor/rules/cursor-agent-orchestration.mdc | 856 ++++++++++++ .cursor/rules/cursot-tools-mastery.mdc | 935 +++++++++++++ .cursor/skills/cursor-sync/.skillfish.json | 10 + .cursor/skills/cursor-sync/SKILL.md | 58 + .../brainstorm/6299-1774202169/.server-info | 1 + .../brainstorm/6299-1774202169/.server.pid | 1 + .../6299-1774202169/arch-overview.html | 312 +++++ .../6299-1774202169/beamtime-adapt.html | 266 ++++ .../6299-1774202169/current-vs-future.html | 311 +++++ .../6299-1774202169/explorer-screen-v2.html | 284 ++++ .../6299-1774202169/explorer-screen.html | 303 +++++ .../6299-1774202169/ingest-catalog.html | 355 +++++ .../brainstorm/6299-1774202169/waiting-1.html | 3 + AGENTS.md | 23 +- CLAUDE.md | 12 + Cargo.lock | 537 ++++++-- Cargo.toml | 10 +- .../specs/2026-03-22-tui-explorer-redesign.md | 409 ++++++ pyproject.toml | 2 +- python/pyref/data/.pyref_catalog.db | Bin 532480 -> 0 bytes python/pyref/fitting/structure.py | 80 +- src/bin/browser.rs | 143 +- src/bin/tui/app.rs | 524 +++++++- src/bin/tui/beamspot.rs | 1 - src/bin/tui/keymap.rs | 12 + src/bin/tui/preview.rs | 1185 +++++++++++++---- src/bin/tui/run.rs | 23 +- src/bin/tui/scan_type.rs | 1 - src/bin/tui/ui.rs | 72 +- src/catalog/ingest.rs | 493 ++++++- src/catalog/ingest_parallel.rs | 196 +++ src/catalog/materialize.rs | 151 +++ src/catalog/mod.rs | 371 +++++- src/catalog/query.rs | 492 ++++++- src/colormap.rs | 56 +- src/gaussian_fit.rs | 1 + src/io/image_mmap.rs | 14 +- src/io/mod.rs | 320 ++++- src/io/source.rs | 6 +- src/io/zarr_store.rs | 137 ++ src/lib.rs | 7 +- src/loader.rs | 233 +++- src/path_policy.rs | 100 ++ src/tui/app.rs | 33 +- src/tui/ui.rs | 50 +- 52 files changed, 9263 insertions(+), 677 deletions(-) create mode 120000 .claude/.cursor create mode 100644 .claude/skills/cursor-sync/.skillfish.json create mode 100644 .claude/skills/cursor-sync/SKILL.md create mode 100644 .cursor/hooks/state/continual-learning-index.json create mode 100644 .cursor/hooks/state/continual-learning.json create mode 100644 .cursor/rules/clarify-first-prompting.mdc create mode 100644 .cursor/rules/cursor-agent-orchestration.mdc create mode 100644 .cursor/rules/cursot-tools-mastery.mdc create mode 100644 .cursor/skills/cursor-sync/.skillfish.json create mode 100644 .cursor/skills/cursor-sync/SKILL.md create mode 100644 .superpowers/brainstorm/6299-1774202169/.server-info create mode 100644 .superpowers/brainstorm/6299-1774202169/.server.pid create mode 100644 .superpowers/brainstorm/6299-1774202169/arch-overview.html create mode 100644 .superpowers/brainstorm/6299-1774202169/beamtime-adapt.html create mode 100644 .superpowers/brainstorm/6299-1774202169/current-vs-future.html create mode 100644 .superpowers/brainstorm/6299-1774202169/explorer-screen-v2.html create mode 100644 .superpowers/brainstorm/6299-1774202169/explorer-screen.html create mode 100644 .superpowers/brainstorm/6299-1774202169/ingest-catalog.html create mode 100644 .superpowers/brainstorm/6299-1774202169/waiting-1.html create mode 100644 CLAUDE.md create mode 100644 docs/superpowers/specs/2026-03-22-tui-explorer-redesign.md delete mode 100644 python/pyref/data/.pyref_catalog.db create mode 100644 src/catalog/ingest_parallel.rs create mode 100644 src/catalog/materialize.rs create mode 100644 src/io/zarr_store.rs create mode 100644 src/path_policy.rs diff --git a/.cargo/config.toml b/.cargo/config.toml index aa884b9..ed9e5a2 100644 --- a/.cargo/config.toml +++ b/.cargo/config.toml @@ -1,2 +1,3 @@ [alias] -browser = "run --bin browser --no-default-features --features tui" +browser = "run --release --bin browser --no-default-features --features tui" +browser-dev = "run --bin browser --no-default-features --features tui -- --dev" diff --git a/.claude/.cursor b/.claude/.cursor new file mode 120000 index 0000000..aa39edf --- /dev/null +++ b/.claude/.cursor @@ -0,0 +1 @@ +.cursor \ No newline at end of file diff --git a/.claude/skills/cursor-sync/.skillfish.json b/.claude/skills/cursor-sync/.skillfish.json new file mode 100644 index 0000000..eb44ec2 --- /dev/null +++ b/.claude/skills/cursor-sync/.skillfish.json @@ -0,0 +1,10 @@ +{ + "version": 2, + "name": "cursor-sync", + "owner": "denisraison", + "repo": "claude-plugins", + "path": "plugins/cursor-sync/skills/cursor-sync", + "branch": "main", + "sha": "83237507c60e2505b5bf295c44a75c8e32ab4c21", + "source": "manual" +} \ No newline at end of file diff --git a/.claude/skills/cursor-sync/SKILL.md b/.claude/skills/cursor-sync/SKILL.md new file mode 100644 index 0000000..d0b7c2d --- /dev/null +++ b/.claude/skills/cursor-sync/SKILL.md @@ -0,0 +1,58 @@ +--- +name: cursor-sync +description: Syncs Claude Code plugins to Cursor skills and commands directories. +--- + +# Cursor Sync + +Syncs Claude Code plugins to Cursor's skills and commands directories. Changes to installed plugins are automatically detected and synced. + +## Scripts + +Run these scripts from the plugin directory: + +### Manual Sync +```bash +./scripts/sync.sh +``` +Performs a one-time sync of all installed Claude Code plugins to `~/.cursor/skills/` and `~/.cursor/commands/`. + +### Start Watcher +```bash +./scripts/watch.sh +``` +Watches for plugin changes and syncs automatically. Runs in foreground. + +### Install as Service +```bash +./scripts/install-service.sh install +``` +Installs a launchd service that starts on login and keeps syncing in the background. + +### Check Service Status +```bash +./scripts/install-service.sh status +``` + +### Uninstall Service +```bash +./scripts/install-service.sh uninstall +``` + +## How It Works + +1. Reads `~/.claude/plugins/installed_plugins.json` to find installed plugins +2. For each plugin: + - Copies skill directories from `/skills/` to `~/.cursor/skills/` + - Copies command files from `/commands/` to `~/.cursor/commands/` +3. Handles naming conflicts by prefixing with plugin name +4. Cleans up orphaned files when plugins are uninstalled + +## Dependencies + +- `jq` for JSON parsing: `brew install jq` +- `fswatch` for file watching: `brew install fswatch` + +## Logs + +Service logs are written to `~/Library/Logs/cursor-sync.log`. diff --git a/.cursor/hooks/state/continual-learning-index.json b/.cursor/hooks/state/continual-learning-index.json new file mode 100644 index 0000000..3fb981f --- /dev/null +++ b/.cursor/hooks/state/continual-learning-index.json @@ -0,0 +1,49 @@ +{ + "version": 1, + "transcripts": { + "/Users/hduva/.cursor/projects/Users-hduva-projects-pyref/agent-transcripts/805ca4fa-0e97-4cd8-b616-dd38e0db371a.jsonl": { + "mtimeMs": 1772820124000, + "lastProcessedAt": "2026-03-11T20:35:00.000Z" + }, + "/Users/hduva/.cursor/projects/Users-hduva-projects-pyref/agent-transcripts/6592e107-126c-432b-be98-e1cab8e4e956/6592e107-126c-432b-be98-e1cab8e4e956.jsonl": { + "mtimeMs": 1772159058000, + "lastProcessedAt": "2026-03-11T20:35:00.000Z" + }, + "/Users/hduva/.cursor/projects/Users-hduva-projects-pyref/agent-transcripts/97fe2075-59e4-4264-968f-d7e9234a8a20/97fe2075-59e4-4264-968f-d7e9234a8a20.jsonl": { + "mtimeMs": 1772140392000, + "lastProcessedAt": "2026-03-11T20:35:00.000Z" + }, + "/Users/hduva/.cursor/projects/Users-hduva-projects-pyref/agent-transcripts/beb5a7ab-7b90-456f-9cf0-50dcc3a43396.jsonl": { + "mtimeMs": 1772243151000, + "lastProcessedAt": "2026-03-11T20:35:00.000Z" + }, + "/Users/hduva/.cursor/projects/Users-hduva-projects-pyref/agent-transcripts/1048ab74-c259-44c2-bf6f-937a4630341c/1048ab74-c259-44c2-bf6f-937a4630341c.jsonl": { + "mtimeMs": 1772504264000, + "lastProcessedAt": "2026-03-11T20:35:00.000Z" + }, + "/Users/hduva/.cursor/projects/Users-hduva-projects-pyref/agent-transcripts/5a8ef628-32d6-48f3-bb07-cbeef510bd58.jsonl": { + "mtimeMs": 1773164140000, + "lastProcessedAt": "2026-03-11T20:35:00.000Z" + }, + "/Users/hduva/.cursor/projects/Users-hduva-projects-pyref/agent-transcripts/4b6205ff-fe6d-410e-b693-b26511da96a0/4b6205ff-fe6d-410e-b693-b26511da96a0.jsonl": { + "mtimeMs": 1773286121000, + "lastProcessedAt": "2026-03-11T20:35:00.000Z" + }, + "/Users/hduva/.cursor/projects/Users-hduva-projects-pyref/agent-transcripts/9f3636f8-f3d5-43f4-8374-51135c7fe220/9f3636f8-f3d5-43f4-8374-51135c7fe220.jsonl": { + "mtimeMs": 1772822647000, + "lastProcessedAt": "2026-03-11T20:35:00.000Z" + }, + "/Users/hduva/.cursor/projects/Users-hduva-projects-pyref/agent-transcripts/6bf94278-035d-4a50-a65e-6a9ff0da5f9c.jsonl": { + "mtimeMs": 1772656482000, + "lastProcessedAt": "2026-03-11T20:35:00.000Z" + }, + "/Users/hduva/.cursor/projects/Users-hduva-projects-pyref/agent-transcripts/3401b865-d836-4f19-bf8f-6e1c6b2fc732/3401b865-d836-4f19-bf8f-6e1c6b2fc732.jsonl": { + "mtimeMs": 1772502443000, + "lastProcessedAt": "2026-03-11T20:35:00.000Z" + }, + "/Users/hduva/.cursor/projects/Users-hduva-projects-pyref/agent-transcripts/2edfe87c-102e-4787-988e-4329093e7364/2edfe87c-102e-4787-988e-4329093e7364.jsonl": { + "mtimeMs": 1772164251000, + "lastProcessedAt": "2026-03-11T20:35:00.000Z" + } + } +} diff --git a/.cursor/hooks/state/continual-learning.json b/.cursor/hooks/state/continual-learning.json new file mode 100644 index 0000000..26e6113 --- /dev/null +++ b/.cursor/hooks/state/continual-learning.json @@ -0,0 +1,8 @@ +{ + "version": 1, + "lastRunAtMs": 1773348226519, + "turnsSinceLastRun": 1, + "lastTranscriptMtimeMs": 1773348226393, + "lastProcessedGenerationId": "9bde2afb-9377-48bc-bae0-e778756c6639", + "trialStartedAtMs": null +} diff --git a/.cursor/rules/clarify-first-prompting.mdc b/.cursor/rules/clarify-first-prompting.mdc new file mode 100644 index 0000000..5130221 --- /dev/null +++ b/.cursor/rules/clarify-first-prompting.mdc @@ -0,0 +1,422 @@ +--- +description: "Clarify-first strategy: detect ambiguity, ask targeted questions, expand simple prompts into detailed specifications" +alwaysApply: false +--- + +# Clarify-First Prompting Strategy + +When users provide simple or ambiguous prompts, ask targeted clarifying questions before proceeding. This ensures accurate implementation and prevents wasted effort. + +## Core Principle + +**Ask before assuming, but don't over-ask.** + +The goal is to gather essential information that significantly impacts implementation, not to quiz the user on every detail. + +--- + +## AskQuestion Tool - Structured Clarification + +Use the `AskQuestion` tool for structured, multiple-choice questions: + +``` +→ AskQuestion( + title="Authentication Setup", + questions=[ + { + id: "auth-type", + prompt: "Which authentication method do you prefer?", + options: [ + { id: "oauth", label: "OAuth 2.0 (Google, GitHub) - easiest setup" }, + { id: "jwt", label: "JWT email/password - full control" }, + { id: "magic-link", label: "Magic link (passwordless) - modern UX" } + ] + }, + { + id: "session-storage", + prompt: "Where should sessions be stored?", + options: [ + { id: "jwt-stateless", label: "Stateless JWT tokens (scalable)" }, + { id: "database", label: "Database sessions (more control)" } + ] + } + ] + ) +``` + +**When to Use AskQuestion:** +- Multiple valid approaches with significant trade-offs +- User preferences are essential +- Need structured input for decision-making + +**Best Practices:** +- Maximum 1-2 questions at a time +- Provide clear options with brief explanations +- First option should be the recommended default +- Include context for why each option matters + +--- + +## When to Clarify vs. Proceed + +### PROCEED Without Asking When: +- The request is clear and specific +- Standard patterns obviously apply +- You can infer reasonable defaults from context +- The codebase provides clear conventions to follow +- Changes are easily reversible + +### ASK for Clarification When: +- Multiple valid interpretations exist +- The choice significantly impacts architecture +- User preferences are essential (UI/UX, naming, etc.) +- Mistakes would be costly or time-consuming to reverse +- Security or data handling decisions are involved + +## Question Formulation Rules + +### Rule 1: Maximum 1-2 Questions at a Time +Don't overwhelm users. Focus on the most critical unknowns. + +**Bad:** +``` +Before I proceed, I need to know: +1. What authentication method? +2. What database? +3. What ORM? +4. What session handling? +5. What password policy? +6. What MFA approach? +``` + +**Good:** +``` +Before implementing authentication, I need to clarify: + +1. **Authentication Method**: Which do you prefer? + - a) OAuth 2.0 (Google, GitHub, etc.) + - b) Email/password with JWT + - c) Magic link (passwordless) + +2. **User Storage**: Where should user data be stored? + - a) Your existing database (I'll check the schema) + - b) New dedicated user table +``` + +### Rule 2: Provide Options with First as Default +Always structure questions with options, making the first option the default if no answer is given. + +**Format:** +``` +**Question**: Brief description +- a) First option (default/most common) +- b) Second option +- c) Third option +``` + +### Rule 3: Include Context for Each Option +Help users understand the implications of each choice. + +**Example:** +``` +**State Management**: How should we handle client state? +- a) React Context (simpler, good for small-medium apps) +- b) Redux Toolkit (more boilerplate, better for complex state) +- c) Zustand (minimal API, good performance) +``` + +### Rule 4: Infer from Codebase First +Before asking, check if the codebase already answers the question. + +``` + +User wants to add a new API endpoint. + +Let me check: +1. Existing API patterns in the codebase +2. Database/ORM being used +3. Authentication middleware +4. Response formatting conventions + +If these are clear from the codebase, I don't need to ask. + +``` + +## Ambiguity Detection Patterns + +### Vague Action Words +Triggers: "add", "implement", "create", "build", "make", "fix" + +**Example:** +- "Add authentication" → Need: method, storage, session handling +- "Fix the bug" → Need: which bug, reproduction steps, expected behavior +- "Create a dashboard" → Need: what data, user roles, layout preferences + +### Missing Scope +Triggers: No clear boundaries mentioned + +**Example:** +- "Refactor the code" → Need: which files, what aspects, target improvements +- "Improve performance" → Need: which operations, current metrics, target goals +- "Update the API" → Need: which endpoints, backward compatibility, versioning + +### Technology Choices +Triggers: Multiple valid solutions exist + +**Example:** +- "Add a database" → Need: SQL vs NoSQL, which provider, local vs cloud +- "Set up testing" → Need: unit vs integration vs e2e, framework preference +- "Deploy the app" → Need: platform, infrastructure, CI/CD requirements + +### User Experience Decisions +Triggers: Design/UX choices involved + +**Example:** +- "Add a form" → Need: validation style, error display, submit behavior +- "Create a modal" → Need: trigger, dismissal, animation preferences +- "Build navigation" → Need: structure, mobile behavior, active states + +## Domain-Specific Templates + +### Authentication +``` +Before implementing authentication: + +1. **Authentication Type**: Which method do you prefer? + - a) OAuth 2.0 (Google, GitHub, etc.) + - b) JWT-based email/password + - c) Magic link / passwordless + - d) Multi-factor authentication (MFA) + +2. **Session Management**: How should sessions be handled? + - a) Stateless JWT tokens (scalable, no server storage) + - b) Server-side sessions (more control, requires storage) +``` + +### API Development +``` +Before creating the API endpoint: + +1. **Response Format**: What structure should responses follow? + - a) Follow existing API conventions (I'll check the codebase) + - b) REST with standard HTTP status codes + - c) GraphQL (if applicable) + +2. **Authentication**: Should this endpoint be protected? + - a) Yes, require authentication + - b) No, public access + - c) Mixed (some actions require auth) +``` + +### Database Operations +``` +Before implementing database changes: + +1. **Migration Strategy**: How should we handle the schema change? + - a) Create migration file (recommended for production) + - b) Direct schema modification (development only) + +2. **Data Handling**: Any existing data to consider? + - a) No existing data (new table/field) + - b) Migrate existing data (I'll propose a strategy) +``` + +### UI Components +``` +Before building this component: + +1. **Styling Approach**: Which styling method should I use? + - a) Follow existing project patterns (I'll check) + - b) Tailwind CSS + - c) CSS Modules + - d) Styled Components + +2. **Responsive Behavior**: Mobile considerations? + - a) Mobile-first responsive + - b) Desktop-only for now + - c) Adaptive (different layouts per breakpoint) +``` + +### Testing +``` +Before implementing tests: + +1. **Test Type**: What level of testing? + - a) Unit tests (isolated function/component testing) + - b) Integration tests (multiple components together) + - c) End-to-end tests (full user flows) + - d) All of the above + +2. **Coverage Focus**: What's the priority? + - a) Critical paths only + - b) Comprehensive coverage + - c) Match existing test patterns +``` + +## Handling User Responses + +### When User Chooses an Option +Proceed immediately with the selected approach. No need to confirm again. + +### When User Says "You Decide" or Similar +Use the first (default) option and proceed. Mention what you chose: + +``` +I'll use OAuth 2.0 with Google as the primary provider, as it's the most common choice. Let me implement that... +``` + +### When User Provides Partial Info +Fill in reasonable defaults for missing information: + +``` +You've specified JWT authentication. I'll use: +- Access tokens (15min expiry) +- Refresh tokens (7 days) +- HTTP-only cookies for storage + +Let me implement this... +``` + +### When User Asks for Recommendation +Provide a clear recommendation with brief justification: + +``` +For your use case, I recommend **Zustand** for state management: +- Minimal boilerplate +- Great TypeScript support +- Easy to test +- Perfect for your app's complexity level + +Shall I proceed with Zustand? +``` + +## Progressive Clarification + +For complex features, clarify in stages rather than all at once: + +### Stage 1: High-Level Direction +``` +You want to add a payment system. First, let me understand: + +**Payment Provider**: Which service should we integrate? +- a) Stripe (most popular, excellent docs) +- b) PayPal +- c) Other (please specify) +``` + +### Stage 2: After Initial Answer +``` +Great, we'll use Stripe. Now: + +**Payment Types**: What payment flows do you need? +- a) One-time payments only +- b) Subscriptions only +- c) Both one-time and subscriptions +``` + +### Stage 3: Implementation Details +``` +Perfect. For subscriptions with Stripe, I'll: +1. Set up Stripe Customer objects +2. Create subscription checkout flow +3. Handle webhooks for lifecycle events +4. Build a customer portal for management + +Any specific plans or pricing tiers I should implement? +``` + +## Anti-Patterns to Avoid + +### Don't Ask Obvious Questions +**Bad:** "Should I use proper error handling?" (Obviously yes) + +### Don't Ask About Implementation Details +**Bad:** "Should I use `const` or `let`?" (Follow project conventions) + +### Don't Ask When Codebase Provides Answers +**Bad:** "Which CSS framework should I use?" (Check existing code first) + +### Don't Repeat Questions +**Bad:** Asking the same thing after user already answered in conversation + +### Don't Ask About Reversible Choices +**Bad:** "Should I put this file in `/utils` or `/helpers`?" (Just pick one) + +--- + +## Agentic Verification (Don't Ask - Just Check!) + +### NEVER Ask About Things You Can Verify + +Instead of asking, use tools to find out: + +| Don't Ask | Just Check | +|-----------|------------| +| "Is Node.js installed?" | `Shell(command="node --version")` | +| "What framework is this?" | `Read(path="package.json")` | +| "Does the file exist?" | `Read(path="path/to/file")` | +| "What's the current structure?" | `Glob(glob_pattern="*")` or `LS(target_directory="./")` if available | +| "Is this package installed?" | `Read(path="package-lock.json")` | +| "What version are you using?" | Check config files first | + +### Proactive Context Gathering + +Before asking ANY clarifying question, first: + +``` +1. CHECK PROJECT STATE + → Glob or LS to understand structure + → Read for package.json, pubspec.yaml, Cargo.toml + → Grep for existing patterns + +2. CHECK TOOL AVAILABILITY + → Shell to verify CLIs exist + → Check versions match requirements + +3. CHECK EXISTING CONVENTIONS + → How are similar features implemented? + → What patterns does the codebase use? + +4. ONLY THEN ASK about things you genuinely can't determine +``` + +### Example: User Says "Add Authentication" + +**❌ Wrong Approach:** +``` +Before I add authentication, I need to know: +1. What framework are you using? +2. Do you want JWT or sessions? +3. What database do you have? +``` + +**✅ Right Approach:** +``` + +Let me check what exists first: +- Read(path="package.json") → Oh, it's Next.js 14 +- Read(path="prisma/schema.prisma") → PostgreSQL with Prisma +- Grep(pattern="auth") → No existing auth implementation + +Now I know the context! + + +I see you're using Next.js 14 with Prisma/PostgreSQL. For authentication: + +1. **Auth Library**: Which approach do you prefer? + - a) NextAuth.js (most integrated with Next.js) + - b) Clerk (managed auth, quick setup) + - c) Custom JWT implementation +``` + +### When Version Matters - Web Search First + +Before implementing framework-specific features: + +``` +1. WebSearch(search_term="Next.js 14 App Router authentication [current month] [current year]") +2. WebSearch(search_term="shadcn ui latest installation method [current year]") +3. Context7 for detailed API docs (if available) +``` + +This prevents writing outdated code or using deprecated patterns. diff --git a/.cursor/rules/cursor-agent-orchestration.mdc b/.cursor/rules/cursor-agent-orchestration.mdc new file mode 100644 index 0000000..68c487d --- /dev/null +++ b/.cursor/rules/cursor-agent-orchestration.mdc @@ -0,0 +1,856 @@ +--- +description: "Cursor 2.5 agent orchestration: native subagents, Agent Skills, async subagents, adaptive effort delegation, parallel workflows, hierarchical task decomposition, and multi-agent coordination. Load this for complex multi-step tasks." +alwaysApply: false +--- + +# Cursor 2.5 Agent Orchestration + +Advanced patterns for working with Cursor 2.5's native subagents, Agent Skills system, async subagents, and multi-agent coordination. + +ALLWAYS PREFER TO USE SUBAGENTS TO DO DELEGATED TASKS TO AVOID CONTEXT FLOODING! +ALLWAYS PREFER TO USE SUBAGENTS TO DO DELEGATED TASKS TO AVOID CONTEXT FLOODING! + +## Cursor 2.5 Architecture Overview + +``` +┌─────────────────────────────────────────────────────────────┐ +│ CURSOR 2.5 AGENT │ +├─────────────────────────────────────────────────────────────┤ +│ Main Agent │ +│ ├── Built-in Subagents (automatic) │ +│ │ ├── explore → codebase search (fast model) │ +│ │ ├── bash → shell commands (isolated output) │ +│ │ └── browser → web automation (MCP-based) │ +│ │ │ +│ ├── Custom Subagents (.cursor/agents/, .claude/agents/, .codex/agents/) │ +│ │ ├── verifier → validate completed work │ +│ │ └── debugger → error analysis │ +│ │ │ +│ └── Context Sources │ +│ ├── Rules (.cursor/rules/) → always/glob-triggered │ +│ ├── Skills (.cursor/skills/) → on-demand/slash cmd │ +│ └── Semantic Index → meaning-based search │ +└─────────────────────────────────────────────────────────────┘ +``` + +## Agent Transcripts Citation + +When referencing past chats from agent transcripts: +- **Format**: `[Title](uuid)` — use a short title (≤6 words) and the parent uuid (exclude `.jsonl`) +- **Never cite subagent transcripts** — always use the parent uuid, not subagent IDs + +## Opus 4.6 Max Planning and Communication + +Operate with Opus 4.6 behavioral patterns — adaptive effort, self-critical, intellectually honest: + +- **Adaptive effort**: Scale planning depth to task complexity. Don't create EPIC todos for a typo fix. Use full investigation for architecture changes. +- **Decision forks only**: Ask questions only when choices materially change security, data integrity, or core architecture. +- **Default-first**: For reversible choices, state the assumption once and proceed. +- **Concise updates**: Progress updates should be 1-2 sentences, high-signal only. +- **Tool-first execution**: Prefer targeted tool calls, batch when possible. +- **Self-review before delivery**: Challenge your own solutions. What would break? What edge case is missing? + +## CRITICAL: Date-Aware Search Protocol + +### Always Check Current Date First + +**BEFORE any web search for tech stack, versions, or solutions:** + +``` + +Current date: [extract from user_info, e.g., "February 2026"] +This date MUST be used in all version/tech queries. + +``` + +**Correct search format:** +``` +"Next.js latest stable version [current month] [current year]" +"React hooks documentation [current year]" +"shadcn-ui installation guide [current year]" +``` + +**NEVER hardcode specific version numbers in rules files** — they go stale in weeks. Always verify via WebSearch at query time. + +**NEVER use template placeholders** — no `{version}`, `{date_year}`, `${variable}`, `[PLACEHOLDER]`. Always use concrete values. + +**Strategic laziness**: Only search versions for NEW packages being added. Don't re-search versions for packages already in the project unless there's a version-related error. + +--- + +## Auto Web-Search Error Recovery + +### Persistent Error Escalation + +After 3-4 failed attempts at the same error, stop trying variations and search: + +``` + +Same error after [N] attempts. My internal knowledge is insufficient. +Escalating to web search. + + +// 1. Exact error +→ WebSearch(search_term="[exact error text] fix [framework] [year]") + +// 2. Error type +→ WebSearch(search_term="[error type] [framework] common causes [year]") + +// 3. Context-based +→ WebSearch(search_term="[what I was trying to do] [framework] tutorial [year]") + +// 4. Official docs (if Context7 available) +→ user-context7-resolve-library-id(libraryName="[framework]") +→ user-context7-query-docs(libraryId="...", query="[error-related-topic]") +``` + +**Key**: Track errors by their core message. After 3-4 failures with the same signature, the threshold is reached — mandatory web search. + +--- + +## Hierarchical Task Decomposition (Exhaustive-Level Only) + +### EPIC-Based Todo Structure + +For **Exhaustive-level** complex projects, use hierarchical task organization. Do NOT create EPIC structures for Instant/Light tasks. + +``` +TODO Structure: +├── EPIC0: Prerequisites (design systems, dependencies, setup) +│ ├── EPIC0.1: Install core dependencies +│ ├── EPIC0.2: Configure design system +│ └── EPIC0.3: Set up project structure +├── EPIC1: Feature A (Auth) +│ ├── EPIC1.1: Create auth pages +│ ├── EPIC1.2: Create auth API routes +│ └── EPIC1.3: Add auth middleware +├── EPIC2: Feature B (Dashboard) +│ ├── EPIC2.1: Create dashboard layout +│ └── EPIC2.2: Add dashboard widgets +└── EPIC3: Integration & Testing + ├── EPIC3.1: Integration testing + └── EPIC3.2: E2E testing +``` + +### Todo Creation Rules + +1. **Prerequisites First**: Always create EPIC0 for setup/dependencies +2. **Feature Isolation**: Each major feature gets its own EPIC +3. **Sub-task Granularity**: Each sub-task should be testable independently +4. **Sequential Dependencies**: Mark which EPICs depend on others + +### Example Todo for "Build an Auth System": + +``` +todos: [ + { id: "EPIC0", content: "Prerequisites: Install dependencies and configure project", status: "in_progress" }, + { id: "EPIC0.1", content: "Install Next.js, Tailwind, shadcn-ui, NextAuth", status: "pending" }, + { id: "EPIC0.2", content: "Configure Prisma with database schema", status: "pending" }, + { id: "EPIC1", content: "Auth UI: Build all authentication pages", status: "pending" }, + { id: "EPIC1.1", content: "Create login page with form validation", status: "pending" }, + { id: "EPIC1.2", content: "Create register page with form validation", status: "pending" }, + { id: "EPIC1.3", content: "Create forgot password flow", status: "pending" }, + { id: "EPIC2", content: "Auth API: Build backend authentication", status: "pending" }, + { id: "EPIC2.1", content: "Create NextAuth configuration with providers", status: "pending" }, + { id: "EPIC2.2", content: "Create API routes for auth operations", status: "pending" }, + { id: "EPIC2.3", content: "Add middleware for protected routes", status: "pending" }, + { id: "EPIC3", content: "Testing: Verify each component works", status: "pending" }, + { id: "EPIC3.1", content: "Test login flow end-to-end", status: "pending" }, + { id: "EPIC3.2", content: "Test registration flow end-to-end", status: "pending" } +] +``` + +### Per-File Verification + +Test after each file, not after the whole feature: + +``` +After EACH file creation: +1. ReadLints(paths=["path/to/new/file"]) +2. If UI component: Quick browser snapshot +3. If API route: Quick curl/fetch test +4. Mark sub-task complete only after file verified + +After EACH EPIC completion: +1. Integration test for that EPIC only +2. Check for regressions in previous EPICs +3. Mark EPIC complete + +After ALL EPICs: +1. Full system test +2. E2E testing +3. Mark project complete +``` + +--- + +## Minimal Questions Policy + +Only ask when the decision is a **hard fork**: + +- Security/auth flows, data deletion/migration, or major architectural changes. +- Irreversible choices with high cost to undo. + +Otherwise: + +- Choose a safe, reversible default. +- State the assumption once. +- Proceed and verify. + +--- + +## Cursor 2.5 Native Subagents + +Cursor 2.5 introduces **async subagents** — subagents can run in the background while the parent continues, and subagents can spawn their own subagents (tree hierarchy). + +### Foreground vs Background (Async in 2.5) + +| Mode | Behavior | Best for | +|------|----------|----------| +| **Foreground** | Blocks until subagent completes | Sequential tasks where you need output immediately | +| **Background** | Returns immediately; subagent runs independently | Long-running or parallel workstreams | + +**Pattern**: Launch parallel subagents for independent features; parent continues while they run. Use `is_background: true` in custom subagent frontmatter for non-blocking execution. + +### Built-in Subagents (Automatic) + +These subagents are used automatically by the agent when appropriate: + +| Subagent | Purpose | Why It's a Subagent | +|----------|---------|---------------------| +| **explore** | Searches and analyzes codebases | Generates large intermediate output; uses fast model for parallel searches | +| **bash** | Runs series of shell commands | Command output is verbose; isolates logs from main context | +| **browser** | Controls browser via MCP tools | DOM snapshots are noisy; filters down to relevant results | + +**You don't configure these** - they activate automatically based on task context. + +### Custom Subagents + +Define custom subagents in `.cursor/agents/` (project) or `~/.cursor/agents/` (user). Also: `.claude/agents/`, `.codex/agents/` for compatibility. Project-level takes precedence when names conflict. + +**File Format** (markdown with YAML frontmatter): + +```markdown +--- +name: security-auditor +description: Security specialist. Use when implementing auth, payments, or handling sensitive data. +model: fast +--- + +You are a security expert auditing code for vulnerabilities. + +When invoked: +1. Identify security-sensitive code paths +2. Check for common vulnerabilities (injection, XSS, auth bypass) +3. Verify secrets are not hardcoded +4. Review input validation + +Report findings by severity: Critical, High, Medium. +``` + +**Frontmatter Fields:** + +| Field | Required | Description | +|-------|----------|-------------| +| `name` | No | Unique identifier (lowercase, hyphens). Defaults to filename. | +| `description` | No | When to use this subagent. Agent reads this to decide delegation. | +| `model` | No | `fast`, `inherit`, or specific model ID. Default: `inherit`. | +| `readonly` | No | If `true`, restricted write permissions. | +| `is_background` | No | If `true`, runs in background without blocking (Cursor 2.5 async). | + +### Invoking Custom Subagents + +**Automatic**: Agent delegates based on task and description match. + +**Explicit**: Use `/name` syntax in prompts: +``` +> /verifier confirm the auth flow is complete +> /debugger investigate this error +``` + +### Task Tool (Different from Native Subagents) + +The `Task` tool still exists but operates differently - it's for launching sub-agents programmatically: + +``` +→ Task( + subagent_type="generalPurpose", // or "explore" + prompt="Check if authentication is already implemented", + description="Check auth status", + model="fast" + ) +``` + +**When to Use Task vs Native Subagents:** + +| Use Case | Approach | +|----------|----------| +| Complex codebase exploration | Native `explore` (automatic) or Task with explore | +| Shell command sequences | Native `bash` (automatic) | +| Browser testing | Native `browser` (automatic) | +| Custom specialized work | Custom subagent in `.cursor/agents/` | +| Programmatic delegation | Task tool | + +**Adaptive Effort + Subagent Delegation:** + +| Task Effort Level | Delegation Strategy | +|-------------------|---------------------| +| Instant / Light | Do it yourself — no subagent overhead | +| Deep | Use `model="fast"` subagents for investigation, do implementation yourself | +| Exhaustive | Launch parallel subagents for independent features; parent continues while they run (async 2.5) | + +**Task Tool Best Practices:** +- Subagents start fresh — include ALL necessary context in prompt +- Use `model="fast"` for exploration and simple research (Instant/Light effort) +- Launch multiple Tasks in parallel for independent features (max throughput) +- Results are not visible to user — summarize in your response + +**Resuming subagents**: Each subagent execution returns an agent ID. Pass it to resume: "Resume agent abc123 and analyze the remaining test failures." Useful for long-running tasks spanning multiple invocations. + +--- + +### TodoWrite Tool - Task Management + +Use `TodoWrite` for EPIC-based task tracking: + +``` +→ TodoWrite( + todos=[ + { id: "EPIC0", content: "Setup: Install dependencies and configure project", status: "in_progress" }, + { id: "EPIC0.1", content: "Run npx create-next-app with TypeScript", status: "pending" }, + { id: "EPIC0.2", content: "Install and configure shadcn-ui", status: "pending" }, + { id: "EPIC1", content: "Feature: User Authentication", status: "pending" }, + { id: "EPIC1.1", content: "Create login page with form validation", status: "pending" }, + { id: "EPIC1.2", content: "Create register page", status: "pending" } + ], + merge=false // Replace all todos + ) +``` + +**Updating Todo Status:** +``` +→ TodoWrite( + todos=[{ id: "EPIC0", status: "completed" }], + merge=true // Only update specified todos + ) +``` + +**When to Use TodoWrite (Deep/Exhaustive effort only):** +- Complex multi-step tasks (3+ steps) +- Tasks requiring careful planning +- User provides multiple tasks +- After receiving new instructions — capture as todos + +**When NOT to Use (Instant/Light effort):** +- Single straightforward tasks +- Tasks completable in < 3 steps +- Purely conversational requests +- Simple fixes, renames, or one-file changes + +--- + +### CreatePlan Tool - Plan Mode + +Use `CreatePlan` in Plan Mode for structured planning: + +``` +→ CreatePlan( + name="Authentication System", + overview="Implement complete auth with NextAuth.js, including login, register, and protected routes", + plan="# Implementation Plan\n\n## Phase 1: Setup\n...", + todos=[ + { id: "setup", content: "Install dependencies", status: "pending" }, + { id: "auth-pages", content: "Create auth pages", status: "pending" } + ] + ) +``` + +**When to Use:** +- Large/ambiguous tasks requiring user approval +- Tasks with multiple valid approaches +- Architectural decisions needed + +--- + +## Parallel Agent Patterns (Cursor 2.5) + +### Cursor 2.5 Parallel Capabilities + +- **Async subagents**: Parent continues while subagents run in background; subagents can spawn subagents (tree) +- **Native Subagents**: Built-in explore, bash, browser run in parallel automatically +- **Custom Subagents**: Define specialized agents that run independently +- **Background Subagents**: Set `is_background: true` for non-blocking execution +- **Plugins**: Marketplace plugins (cursor.com/marketplace) for pre-built skills/subagents; use project-level skills for repo-specific knowledge +- **Multi-Agent Worktrees**: Multiple agents in git worktrees for large features +- **Cloud Agents**: Long-running tasks in cloud environments + +**Launch multiple subagents in one turn** for maximum throughput. Example: "Review the API changes and update the documentation in parallel." + +### When to Use Each Pattern + +| Scenario | Approach | +|----------|----------| +| Codebase exploration | Native `explore` subagent (automatic) | +| Multiple shell commands | Native `bash` subagent (automatic) | +| Browser testing | Native `browser` subagent (automatic) | +| Validate completed work | Custom `verifier` subagent | +| Debug errors | Custom `debugger` subagent | +| Independent features | Parallel custom subagents or worktrees; use `is_background: true` for async | +| Compare approaches | Multi-agent judging | + +### Orchestrator Pattern with Custom Subagents + +``` +MAIN AGENT (Orchestrator): +├── Plans architecture +├── Delegates to custom subagents +├── Coordinates results +└── Runs /verifier for final check + +SUBAGENT: verifier +├── Checks implementation exists +├── Runs tests +├── Validates edge cases +└── Reports pass/fail with details + +SUBAGENT: debugger +├── Analyzes error +├── Identifies root cause +├── Suggests fix +└── Returns diagnosis +``` + +### Spec File Pattern (Still Useful) + +For complex multi-agent work, create specs in `.cursor/specs/`: + +```markdown +# .cursor/specs/feature-auth.md + +## Feature: Authentication System + +### Prerequisites +- Database configured with User model +- NextAuth.js installed +- Environment variables set + +### Tasks +1. Create `/app/login/page.tsx` - Login form +2. Create `/app/register/page.tsx` - Registration form +3. Create `/app/api/auth/[...nextauth]/route.ts` - NextAuth config +4. Create `/middleware.ts` - Route protection + +### Acceptance Criteria +- [ ] User can register with email/password +- [ ] User can login and receive session +- [ ] Protected routes redirect to login + +### Verification +After completion, invoke: `/verifier confirm auth feature` +``` + +### Background Subagent Example + +For long-running tasks, use `is_background: true`: + +```markdown +--- +name: test-runner +description: Test automation expert. Use proactively to run tests and fix failures. +is_background: true +--- + +You are a test automation expert. + +When you see code changes, proactively run appropriate tests. +If tests fail, analyze and report findings. +``` + +--- + +## Cursor Hooks Integration + +Hooks let you observe, control, and extend the agent loop using custom scripts. They run before or after defined stages of the agent loop. + +### Hooks Configuration (`.cursor/hooks.json`) + +**CRITICAL**: Use the correct format per [Cursor Hooks docs](https://cursor.com/docs/agent/hooks): + +```json +{ + "version": 1, + "hooks": { + "stop": [ + { "command": "node .cursor/hooks/grind.js" } + ], + "beforeShellExecution": [ + { "command": ".cursor/hooks/audit.sh" } + ] + } +} +``` + +**Format Rules:** +- `version`: Must be `1` (required) +- `hooks`: Object with event names as keys (NOT an array) +- Each event contains an array of hook objects with `command` field + +### Hook Events (Agent) + +| Event | When | Use Case | +|-------|------|----------| +| `beforeShellExecution` | Before shell command | Block dangerous commands, require approval | +| `afterShellExecution` | After shell command | Audit logs, capture output | +| `beforeMCPExecution` | Before MCP tool call | Gate risky operations | +| `afterMCPExecution` | After MCP tool call | Log tool usage | +| `afterFileEdit` | After file edit | Auto-format, lint, validate | +| `beforeSubmitPrompt` | Before prompt sent | PII scanning, validation | +| `afterAgentResponse` | After agent message | Analytics, logging | +| `afterAgentThought` | After thinking block | Observe reasoning | +| `stop` | Agent loop ends | **RALPH loop**: Continue until goals met (tests pass, build succeeds) | + +### Hook Events (Tab/Inline Completions) + +| Event | When | Use Case | +|-------|------|----------| +| `beforeTabFileRead` | Before Tab reads file | Redact secrets | +| `afterTabFileEdit` | After Tab edits file | Format Tab edits | + +### Example: Audit Hook + +```bash +#!/bin/bash +# .cursor/hooks/audit.sh - Log all agent actions + +json_input=$(cat) +timestamp=$(date '+%Y-%m-%d %H:%M:%S') +echo "[$timestamp] $json_input" >> /tmp/agent-audit.log +exit 0 +``` + +### Example: Long-Running Agent Loop (RALPH Pattern) + +The `stop` hook enables RALPH-style iterative improvement - agent keeps working until verification goals are met. + +**Based on**: [Cursor agent best practices - long-running agent loop](https://cursor.com/blog/agent-best-practices#example-long-running-agent-loop) + +```javascript +// .cursor/hooks/grind.js - RALPH loop implementation + +const input = JSON.parse(await readStdin()); +const { status, loop_count = 0 } = input; +const MAX_ITERATIONS = 5; + +// Stop if aborted/errored or max iterations reached +if (status !== 'completed' || loop_count >= MAX_ITERATIONS) { + console.log(JSON.stringify({})); + process.exit(0); +} + +// Check if goals are met (tests pass, build succeeds, etc.) +const goalsMet = checkGoals(); // Run npm test, npm run build, etc. + +if (goalsMet) { + // Goals met - stop the loop + console.log(JSON.stringify({})); +} else { + // Goals not met - continue with followup message + console.log(JSON.stringify({ + followup_message: `Continue working. Iteration ${loop_count + 1}/${MAX_ITERATIONS}. Tests/build still failing.` + })); +} +``` + +**How it works:** +1. Agent completes a task +2. `stop` hook runs and checks verification goals (tests, build, lint) +3. If goals met → return `{}` to stop +4. If goals not met → return `{ followup_message: "..." }` to continue +5. Agent receives followup and works on fixing issues +6. Loop continues until goals met or max iterations reached + +**Configuration** (`.cursor/grind.json`): +```json +{ + "maxIterations": 5, + "commands": ["npm test", "npm run build"], + "stopOnSuccess": true +} +``` + +### Example: Block Git Commands Hook + +```bash +#!/bin/bash +# .cursor/hooks/block-git.sh - Require gh CLI instead of git + +input=$(cat) +command=$(echo "$input" | jq -r '.command // empty') + +if [[ "$command" =~ ^git[[:space:]] ]]; then + cat << EOF +{ + "permission": "deny", + "user_message": "Git command blocked. Use gh CLI instead.", + "agent_message": "The git command was blocked. Use gh tool for GitHub operations." +} +EOF +else + echo '{"permission": "allow"}' +fi +``` + +### Hook Input/Output Schema + +**Input (all hooks receive):** +```json +{ + "conversation_id": "string", + "generation_id": "string", + "model": "string", + "hook_event_name": "string", + "cursor_version": "string", + "workspace_roots": [""] +} +``` + +**Output (permission hooks):** +```json +{ + "permission": "allow" | "deny" | "ask", + "user_message": "", + "agent_message": "" +} +``` + +**Output (stop hook - for RALPH loop):** +```json +{} // Empty object = stop the loop + +// OR continue with: +{ + "followup_message": "Continue working on the task. Fix the failing tests." +} +``` + +--- + +## Context Compaction for Long-Running Tasks + +Opus 4.6 introduces **compaction** — the ability to summarize its own context to sustain longer tasks. Apply this principle in agentic workflows: + +### When to Compact + +- After completing each EPIC in a multi-EPIC task +- When context is getting large (many files read, many tool calls) +- Before starting a new phase of work + +### How to Compact + +``` + +[COMPACTION — End of EPIC N] + +Essential state to carry forward: +- Project: [stack, conventions] +- Completed: [what's done and verified] +- Current: [what I'm working on next] +- Decisions: [key choices made and why] +- Known issues: [anything to watch for] + +Can release from active context: +- Detailed file contents already processed +- Intermediate debugging steps +- Exploration results that didn't lead anywhere + +``` + +### Compaction + Subagents + +For exhaustive tasks, use subagents to isolate context-heavy work: +- Exploration subagent reads and analyzes files (its context is isolated) +- Main agent receives a summary, not the raw data +- This naturally compacts context without explicit summarization + +--- + +## Cursor 2.5 Agent Skills + +Agent Skills are portable, version-controlled packages that teach agents domain-specific tasks. Skills are the preferred way to package specialized knowledge in Cursor 2.5. + +### Skills vs Rules + +| Feature | Rules (`.cursor/rules/`) | Skills (`.cursor/skills/`) | +|---------|--------------------------|----------------------------| +| Activation | `alwaysApply: true` or glob patterns | Auto-discovery or `/skill-name` | +| Format | Single `.mdc` file | Folder with `SKILL.md` + optional scripts | +| Executable | No | Yes - can include scripts | +| Progressive loading | No | Yes - loads resources on-demand | +| Best for | Always-on conventions, file-type patterns | Domain knowledge, procedures, how-tos | + +### Skill Directory Structure + +``` +.cursor/skills/ +└── deploy-app/ + ├── SKILL.md # Required - main instructions + ├── scripts/ # Optional - executable code + │ ├── deploy.sh + │ └── validate.py + ├── references/ # Optional - detailed docs (loaded on-demand) + │ └── REFERENCE.md + └── assets/ # Optional - templates, configs + └── config-template.json +``` + +### SKILL.md Format + +```markdown +--- +name: deploy-app +description: Deploy the application to staging or production. Use when deploying code or when user mentions deployment. +--- + +# Deploy App + +Deploy the application using the provided scripts. + +## Usage + +Run the deployment script: `scripts/deploy.sh ` + +## Pre-deployment Validation + +Before deploying, run: `python scripts/validate.py` +``` + +**Frontmatter Fields:** + +| Field | Required | Description | +|-------|----------|-------------| +| `name` | Yes | Skill identifier. Must match folder name. | +| `description` | Yes | When to use. Agent reads this to determine relevance. | +| `license` | No | License name or file reference. | +| `disable-model-invocation` | No | If `true`, only invoked via `/skill-name`. | + +### Invoking Skills + +**Automatic**: Agent discovers and applies based on context + description. + +**Explicit**: Type `/skill-name` in chat: +``` +> /deploy-app deploy to staging +> /migrate-to-skills convert my rules +``` + +### Migration: Rules to Skills + +Cursor 2.5 includes `/migrate-to-skills` to convert dynamic rules: + +``` +> /migrate-to-skills +``` + +This converts: +- **Dynamic rules** (`alwaysApply: false`, no globs) → Standard skills +- **Slash commands** → Skills with `disable-model-invocation: true` + +**What NOT to migrate:** +- Rules with `alwaysApply: true` (keep as rules) +- Rules with `globs` patterns (keep as rules - skills don't have glob triggers) + +### Recommended Organization + +``` +.cursor/ +├── rules/ # Keep these as rules +│ ├── minimax-m2-core.mdc # alwaysApply: true +│ ├── cursor-agent-orchestration.mdc # alwaysApply: true +│ ├── web-development.mdc # globs: ["*.ts", "*.tsx"] +│ └── python-development.mdc # globs: ["*.py"] +│ +├── skills/ # Domain knowledge, procedures +│ ├── deploy-production/ +│ │ └── SKILL.md +│ ├── database-migration/ +│ │ └── SKILL.md +│ └── security-audit/ +│ └── SKILL.md +│ +└── agents/ # Custom subagents + ├── verifier.md + └── debugger.md +``` + +### Anti-Hallucination: Clear Descriptions + +**Good** - Specific trigger conditions: +```yaml +description: "Deploy to production. Use when user mentions 'deploy', 'release', 'push to prod', or 'go live'." +``` + +**Bad** - Vague, will trigger inappropriately: +```yaml +description: "Helps with general deployment tasks" +``` + +--- + +## Summary: Opus 4.6 Max + Cursor 2.5 Workflow + +### Before Starting Any Task + +``` +1. CALIBRATE EFFORT + → Instant / Light / Deep / Exhaustive? + → Match workflow depth to complexity + +2. CHECK DATE (for version searches) + → Extract current date from context + → Use concrete values, never placeholders + +3. FOR DEEP/EXHAUSTIVE TASKS + → Create EPIC todos (Exhaustive only) + → Investigate codebase patterns first + → Plan with tradeoffs stated + +4. FOR EACH FILE/CHANGE + → Implement + → Lint check (ReadLints on edited file) + → Self-review (Light+): What would break? + → Mark complete only after verified + +5. ON REPEATED ERRORS (3-4+) + → Stop trying same fix + → Web search for solution + → Apply community solution + +6. AFTER EPIC/FEATURE COMPLETE + → Run /verifier to validate + → Integration test + → Compact context before next phase +``` + +### Quality Gates + +``` +□ Effort calibrated to task complexity +□ Adversarial self-review performed (Light+) +□ Date included in version searches (for NEW packages) +□ No template placeholders in searches +□ Each file verified after creation +□ Web search triggered after 3-4 repeated errors +□ Context compacted between phases (long tasks) +□ Honest about uncertainty +``` + +### Quick Reference: Subagents vs Skills vs Rules + +| Need | Solution | +|------|----------| +| Always-on conventions | Rules with `alwaysApply: true` | +| File-type patterns | Rules with `globs` | +| Domain knowledge | Skills in `.cursor/skills/` | +| Specialized agents | Custom subagents in `.cursor/agents/` | +| Validation | `/verifier` subagent | +| Debugging | `/debugger` subagent | diff --git a/.cursor/rules/cursot-tools-mastery.mdc b/.cursor/rules/cursot-tools-mastery.mdc new file mode 100644 index 0000000..8d309c3 --- /dev/null +++ b/.cursor/rules/cursot-tools-mastery.mdc @@ -0,0 +1,935 @@ +--- +description: "Complete Cursor 2.5 native tools reference: search, edit, run, subagents, skills, plugins, sandbox, and advanced features for agentic workflows" +alwaysApply: false +--- + +# Cursor 2.5 Tools Mastery + +Complete reference for all Cursor 2.5 tools including native subagents, Agent Skills, plugins, sandbox configuration, and advanced features. + +## CRITICAL: Agentic-First Tool Usage + +### The Golden Rule: Verify Before You Act + +Every action follows this pattern: +1. **CHECK** - What exists? What's the current state? +2. **VERIFY** - What versions? What's available? +3. **ACT** - Make the change using proper tools +4. **VALIDATE** - Did it work? Any errors? + +### Pre-Action Verification Protocol + +Before ANY significant action: + +``` +BEFORE CREATING PROJECT: +1. Check if CLI tool exists: Shell(command="which flutter") +2. Check CLI version: Shell(command="flutter --version") +3. Web search for current version: WebSearch(search_term="Flutter stable February 2026") — use actual date from context +4. Check if project already exists: Glob(glob_pattern="*") or LS(target_directory="./") if available + +BEFORE INSTALLING PACKAGES: +1. Check package.json/pubspec.yaml exists: Read(path="package.json") +2. Check if already installed: Read(path="package-lock.json") +3. Verify package name/version: WebSearch(search_term="lodash npm latest version") + +BEFORE EDITING CODE: +1. Read the file first: Read(path="src/component.tsx") +2. Understand context and dependencies +3. Check for related files that might need updating +``` + +### Proactive Web Search Usage + +**ALWAYS use WebSearch for:** +- Package versions before installing +- Framework syntax when unsure +- Error messages you encounter +- API documentation verification +- Current best practices + +**Search Pattern:** Replace placeholders with actual values from context (e.g., user_info date). Use concrete dates like "February 2026", not literal `[current month] [current year]`. +``` +WebSearch(search_term="[package/framework] [specific feature] [actual month/year]") +``` + +Examples: +- `WebSearch(search_term="Next.js App Router server actions February 2026")` — use concrete date +- `WebSearch(search_term="React 19 useFormStatus hook syntax")` +- `WebSearch(search_term="shadcn ui button component installation")` + +--- + +## Search Tools + +### `Read` +**Purpose**: Read file contents with line numbers + +**When to Use**: +- Examining specific files +- Understanding code structure +- Reviewing implementations +- Checking configurations + +**Best Practices**: +- Read whole files unless very large (>1000 lines) +- Use `offset` and `limit` for large files +- Batch multiple file reads in parallel +- Check imports and dependencies + +**Example Usage**: +``` +Read the main entry point to understand the application structure +→ Read(path="src/index.ts") +``` + +--- + +### `LS` / `Glob` (Directory Exploration) + +**Purpose**: List directory contents or find files by pattern + +**When to Use**: +- Exploring project structure +- Finding files in a directory +- Understanding folder organization +- Discovering available modules + +**Tool Availability**: `LS` may be unavailable in some environments. Use `Glob` as fallback for directory exploration. + +**Best Practices**: +- Prefer `LS(target_directory=".", ignore_globs=["node_modules", ".git"])` when available +- Fallback: `Glob(glob_pattern="*")` or `Glob(glob_pattern="src/*")` for directory listing +- Start at project root for orientation +- Navigate progressively deeper + +**Example Usage**: +``` +Explore the project structure +→ LS(target_directory=".", ignore_globs=["node_modules", ".git"]) // if available +→ Glob(glob_pattern="*") // fallback when LS unavailable +``` + +--- + +### `SemanticSearch` +**Purpose**: Semantic search - finds code by meaning, not exact text + +**When to Use**: +- Finding implementations by concept +- Locating where behavior is defined +- Understanding how features work +- Exploring unfamiliar codebases + +**When NOT to Use**: +- Exact text matches (use `Grep`) +- Simple symbol lookups (use `Grep`) +- Finding files by name (use `Glob`) + +**Best Practices**: +- Ask complete questions: "Where is user authentication handled?" +- One question per search (don't combine multiple queries) +- Start broad, then narrow to specific directories +- Use for "how/where/what" questions + +**Example Usage**: +``` +Find where API routes are defined +→ SemanticSearch(query="Where are the API routes defined?", target_directories=["src/"]) +``` + +--- + +### `Grep` +**Purpose**: Regex-based text search (powered by ripgrep) + +**When to Use**: +- Exact symbol/string searches +- Finding function calls +- Locating imports/exports +- Counting occurrences + +**Parameters**: +- `pattern`: Regex pattern to search +- `path`: Directory or file to search +- `type`: File type filter (js, py, rs, go, etc.) +- `glob`: Custom glob pattern +- `-C`, `-A`, `-B`: Context lines +- `output_mode`: "content", "files_with_matches", or "count" + +**Best Practices**: +- Escape special regex characters: `functionCall\(` +- Use `output_mode: "files_with_matches"` to find all relevant files +- Add context with `-C 3` to understand surrounding code + +**Example Usage**: +``` +Find all uses of a specific function +→ Grep(pattern="useAuth\\(", path="src/", type="ts") + +Find files containing a pattern +→ Grep(pattern="TODO:", output_mode="files_with_matches") +``` + +--- + +### `Glob` +**Purpose**: Find files by name pattern + +**When to Use**: +- Locating files by extension +- Finding configuration files +- Discovering test files +- Matching naming conventions + +**Best Practices**: +- Patterns auto-prepend `**/` for recursive search +- Use for file discovery, not content search + +**Example Usage**: +``` +Find all test files +→ Glob(glob_pattern="*.test.ts") + +Find configuration files +→ Glob(glob_pattern="*.config.*") +``` + +--- + +### `WebSearch` +**Purpose**: Search the internet for real-time information + +**When to Use**: +- Current documentation +- Recent solutions +- Security advisories +- Version-specific information + +**Best Practices**: +- Include version numbers if relevant +- Add dates for time-sensitive queries +- Use specific keywords + +**Proactive Usage**: Don't wait for "Ask" mode - use `WebSearch` proactively in Agent mode when: +- You need current information about libraries or APIs +- Error messages suggest version-specific issues +- User asks about recent features or changes + +--- + +## MCP Tools Invocation + +Cursor exposes MCP (Model Context Protocol) tools via `call_mcp_tool(server, toolName, arguments)`. **Always check the tool schema/descriptor before calling** — schemas live in the project's `mcps//tools/` folder. + +**Invocation pattern:** +``` +→ call_mcp_tool( + server="cursor-ide-browser", + toolName="browser_navigate", + arguments={ "url": "http://localhost:3000" } + ) +``` + +**Critical**: List and read the tool's JSON descriptor in `mcps//tools/` to get exact parameter names and types before calling. + +--- + +## Browser Tools + +Cursor provides browser tools via the `cursor-ide-browser` MCP server for testing and interacting with web applications. Tools may appear as native tools or require `call_mcp_tool(server="cursor-ide-browser", toolName="...", arguments={...})`. + +### CRITICAL: Browser Lock/Unlock Workflow + +1. **browser_lock requires an existing browser tab** — you CANNOT lock before browser_navigate +2. **Correct order**: `browser_navigate` → `browser_lock` → (interactions) → `browser_unlock` +3. **If a tab already exists**: Check with `browser_tabs` (action: "list"), then call `browser_lock` FIRST before any interactions +4. **browser_unlock**: Only call when completely done with ALL browser operations for this turn + +### `browser_navigate` +**Purpose**: Navigate to a URL + +**When to Use**: +- Testing web application features +- Verifying UI implementations +- Checking deployed changes + +### `browser_snapshot` +**Purpose**: Capture accessibility snapshot of current page + +**When to Use**: +- Understanding page structure +- Finding elements to interact with +- Better than screenshots for element identification + +**Best Practices**: +- Call snapshot BEFORE clicking/typing to get element refs +- Use returned refs for subsequent interactions + +### `browser_click` +**Purpose**: Click on web page elements + +**Parameters**: +- `element`: Human-readable element description +- `ref`: Exact target element reference from snapshot +- `button`: Button to click (defaults to left) +- `doubleClick`: Whether to double-click + +### `browser_type` +**Purpose**: Type text into editable elements + +**Parameters**: +- `element`: Human-readable element description +- `ref`: Exact target element reference from snapshot +- `text`: Text to type +- `submit`: Whether to press Enter after typing +- `slowly`: Type one character at a time (for key handlers) + +### `browser_hover` +**Purpose**: Hover over elements (for tooltips, dropdowns) + +### `browser_select_option` +**Purpose**: Select options in dropdowns + +### `browser_press_key` +**Purpose**: Press keyboard keys (e.g., ArrowLeft, Enter, Escape) + +### `browser_wait_for` +**Purpose**: Wait for conditions + +**Parameters**: +- `text`: Wait for text to appear +- `textGone`: Wait for text to disappear +- `time`: Wait for specified seconds + +### `browser_navigate_back` +**Purpose**: Go back to previous page + +### `browser_resize` +**Purpose**: Resize browser window for responsive testing + +### `browser_console_messages` +**Purpose**: Get all console messages (errors, warnings, logs) + +**When to Use**: +- Debugging JavaScript errors +- Checking for runtime issues +- Verifying console output + +### `browser_network_requests` +**Purpose**: Get all network requests since page load + +**When to Use**: +- Debugging API calls +- Verifying request/response data +- Checking for failed requests + +### `browser_tabs` +**Purpose**: List open tabs (action: "list") — use before lock to check if a tab exists + +### `browser_fill` +**Purpose**: Clear and replace text in editable elements (unlike `browser_type` which appends) + +### `browser_scroll` +**Purpose**: Scroll elements into view — use `scrollIntoView: true` for nested scroll containers before clicking obscured elements + +### `browser_handle_dialog` +**Purpose**: Handle native dialogs (alert/confirm/prompt) — call BEFORE the triggering action. Use `accept: false` for Cancel, `promptText` for custom prompt input + +### `browser_profile_start` / `browser_profile_stop` +**Purpose**: CPU profiling — profile data in `~/.cursor/browser-logs/` (cpu-profile-*.json, cpu-profile-*-summary.md). Read raw JSON to verify summary findings + +**Limitation**: Iframe content is not accessible — only elements outside iframes can be interacted with + +### Browser Testing Workflow + +``` +1. Check existing tabs (if any): browser_tabs(action="list") +2. Navigate to the page: browser_navigate(url="http://localhost:3000") +3. Lock the tab: browser_lock (required before interactions) +4. Snapshot to get elements: browser_snapshot() +5. Interact using refs from snapshot: browser_click(element="Submit button", ref="btn-submit") +6. Wait for changes if needed: browser_wait_for(text="Success") or short waits (1-3s) with snapshot checks +7. Snapshot again to verify: browser_snapshot() +8. Check console/network: browser_console_messages(), browser_network_requests() +9. Unlock when done: browser_unlock +``` + +**Waiting strategy**: Prefer short incremental waits (1-3 seconds) with browser_snapshot checks in between rather than one long wait. + +### Proactive Browser Usage + +Don't wait for explicit requests - use browser tools proactively when: +- You've implemented a UI feature → test it immediately +- User reports a visual bug → navigate and inspect +- You need to verify API integrations → check network requests +- You're debugging frontend issues → check console messages + +--- + +## Edit Tools + +### `StrReplace` +**Purpose**: Exact string replacement in files + +**When to Use**: +- Modifying existing code +- Updating function implementations +- Fixing bugs in specific locations +- Renaming with `replace_all: true` + +**CRITICAL Rules**: +- `old_string` must be UNIQUE in the file +- Include enough context (3-5 lines before/after) +- Match exact whitespace and indentation +- Preserve original indentation style + +**Best Practices**: +- Read the file first to ensure accurate matching +- Include surrounding context for uniqueness +- Use `replace_all: true` for renaming operations +- Make atomic, focused changes + +**Example Usage**: +``` +Update a function implementation +→ StrReplace( + path="src/utils.ts", + old_string="function oldImpl() {\n return 1;\n}", + new_string="function oldImpl() {\n return 2;\n}" + ) +``` + +--- + +### `Write` +**Purpose**: Create or overwrite entire files + +**When to Use**: +- Creating new files +- Complete file rewrites +- Generating new modules +- Writing configuration files + +**Best Practices**: +- ALWAYS read existing files before overwriting +- Prefer `StrReplace` for modifications +- Include all necessary imports +- Follow project conventions + +--- + +### `Delete` +**Purpose**: Remove files from the project + +**When to Use**: +- Cleanup during refactoring +- Removing deprecated code +- Deleting generated files + +--- + +### `EditNotebook` +**Purpose**: Edit Jupyter notebook cells + +**When to Use**: +- Data science notebooks +- Interactive Python development +- Documentation notebooks + +**Best Practices**: +- Set `is_new_cell` correctly +- Use 0-based cell indices +- Provide unique `old_string` for edits + +--- + +## Run Tools + +### `Shell` +**Purpose**: Execute shell commands + +**When to Use**: +- Building projects +- Running tests +- Installing dependencies +- Executing scripts +- Git operations + +**Parameters**: +- `command`: The command to execute +- `description`: Brief description of what the command does +- `is_background`: For long-running processes +- `timeout`: Timeout in milliseconds +- `working_directory`: Directory to run command in + +**Best Practices**: +- Use `is_background: true` for long-running processes +- Check existing terminals before starting servers (see Terminals Folder Pattern below) +- Use non-interactive flags (`--yes`, `-y`, etc.) +- Always provide a clear `description` + +**Terminals Folder Pattern** — Before starting dev servers, avoid duplicate processes: +1. List the terminals folder (project-specific path, e.g. under `.cursor` or Cursor project metadata) +2. Read terminal files to see `current command`, `cwd`, and `last_command` +3. Only start `npm run dev` / `flutter run` / etc. if no matching process is already running + +**Example Usage**: +``` +Install dependencies +→ Shell(command="npm install", description="Install dependencies") + +Run tests +→ Shell(command="npm test", description="Run tests") + +Start dev server in background +→ Shell(command="npm run dev", is_background=true, description="Start dev server") +``` + +--- + +### `ReadLints` +**Purpose**: Read linter errors from workspace + +**When to Use**: +- After making edits +- Before committing changes +- Debugging type errors +- Checking code quality + +**Best Practices**: +- Only call on files you've edited +- Fix errors before proceeding +- Don't call with wide scope + +**Example Usage**: +``` +Check lints after editing +→ ReadLints(paths=["src/components/Button.tsx"]) +``` + +--- + +## Special Tools + +### `TodoWrite` +**Purpose**: EPIC task tracking for complex multi-step work + +**When to Use**: Deep/Exhaustive tasks; 3+ steps; user provides multiple tasks; after receiving new instructions + +**Parameters**: `todos` (array of `{id, content, status}`), `merge` (true = update only specified; false = replace all) + +**Example**: +``` +→ TodoWrite(todos=[{ id: "EPIC0", content: "Setup: Install dependencies", status: "in_progress" }, { id: "EPIC1", content: "Feature: Auth", status: "pending" }], merge=false) +→ TodoWrite(todos=[{ id: "EPIC0", status: "completed" }], merge=true) +``` + +**When NOT to Use**: Instant/Light tasks; single straightforward tasks; < 3 steps + +--- + +### `CreatePlan` +**Purpose**: Plan mode with Mermaid diagrams; structured planning for user approval + +**When to Use**: Large/ambiguous tasks; multiple valid approaches; architectural decisions needed + +**Parameters**: `name`, `overview`, `plan` (markdown), `todos` (optional) + +--- + +### `AskQuestion` +**Purpose**: Structured multiple-choice questions for hard forks + +**When to Use**: Security/auth flows; data deletion/migration; major architectural changes; irreversible choices + +**When NOT to Use**: Reversible choices — use safe default and proceed + +--- + +### `GenerateImage` +**Purpose**: Generate images from text description + +**When to Use**: Mockups, diagrams, UI assets; when user explicitly asks for an image + +**Parameters**: `description` (required), `filename` (optional), `reference_image_paths` (optional) + +**Output**: Saved to `assets/` folder by default + +**When NOT to Use**: Data visualizations (charts, plots) — use code instead + +--- + +## MCP Tools + +MCP (Model Context Protocol) tools extend Cursor's capabilities with external services. Use `call_mcp_tool(server, toolName, arguments)` for MCP invocation. **Always check the tool schema in `mcps//tools/` before calling.** + +### mcp_web_fetch + +**Purpose**: Fetch URL content and return it as readable markdown. Use for docs, API references. + +**When to Use**: +- Official documentation (e.g., https://nextjs.org/docs) +- API references +- Verifying deployment URLs + +**Parameters**: `url` — the fully-formed URL to fetch + +**Limitations**: No authentication support; localhost and private IPs will not work (fetch runs from isolated server). + +**Example**: +``` +→ mcp_web_fetch(url="https://nextjs.org/docs/app/building-your-application/routing") +``` + +### fetch_mcp_resource / list_mcp_resources + +**Purpose**: Fetch or list resources from MCP servers (e.g., documentation, assets). Use `list_mcp_resources` to discover available resources, then `fetch_mcp_resource(server, uri)` to retrieve content. Optional `downloadPath` saves to workspace. + +### MiniMax MCP Tools +If configured, provides: +- `user-MiniMax-web_search`: Web search with current date context +- `user-MiniMax-understand_image`: Image analysis with VLM + +### Context7 MCP Tools +If configured, provides: +- `user-context7-resolve-library-id`: Find library IDs +- `user-context7-query-docs`: Fetch up-to-date documentation + +**CRITICAL**: Always check if MCP tools are available before using. Fall back to Cursor's native `WebSearch` if MiniMax MCP is not configured. + +--- + +## Plugins (Cursor 2.5) + +The [Cursor Marketplace](https://cursor.com/marketplace) lets you install plugins that package skills, subagents, MCP servers, hooks, and rules. Install via `/add-plugin` in chat or browse at cursor.com/marketplace. + +**When to use plugins vs local skills:** +- **Plugins**: Pre-built capabilities from partners (Amplitude, AWS, Figma, Linear, Stripe); team-wide or cross-project use +- **Local skills** (`.cursor/skills/`): Project-specific knowledge, version-controlled in repo + +--- + +## Sandbox Configuration (Cursor 2.5+) + +The sandbox supports granular network and filesystem controls. Configure via `sandbox.json`. + +**File locations** (per-repo overrides per-user): +- `~/.cursor/sandbox.json` — all workspaces +- `./.cursor/sandbox.json` — current project (higher priority) + +**Key options** (see [Cursor Terminal docs](https://cursor.com/docs/agent/terminal)): +- `networkPolicy.default`: `"deny"` or `"allow"` +- `networkPolicy.allow`: Domain allowlist (e.g., `registry.npmjs.org`, `*.githubusercontent.com`) +- `networkPolicy.deny`: Domain denylist +- `additionalReadwritePaths`, `additionalReadonlyPaths`: Extra filesystem access + +**Example** (allow npm/pypi only): +```json +{ + "networkPolicy": { + "default": "deny", + "allow": ["registry.npmjs.org", "pypi.org", "*.githubusercontent.com"] + } +} +``` + +--- + +## Native Subagents (Cursor 2.5) + +Cursor 2.5 introduces **async subagents** — subagents can run in the background while the parent continues, and subagents can spawn their own subagents (tree hierarchy). + +### Foreground vs Background + +| Mode | Behavior | Best for | +|------|----------|----------| +| **Foreground** | Blocks until subagent completes | Sequential tasks where you need output immediately | +| **Background** | Returns immediately; subagent runs independently | Long-running or parallel workstreams | + +Set `is_background: true` in custom subagent frontmatter for non-blocking execution. Launch multiple subagents in one turn for maximum throughput. + +### Built-in Subagents + +These activate automatically when appropriate: + +| Subagent | Purpose | When Used | +|----------|---------|-----------| +| **explore** | Codebase search and analysis | Finding code, understanding structure | +| **bash** | Shell command sequences | Running multiple commands | +| **browser** | Web automation via MCP | Testing UI, browser interactions | + +**You don't invoke these directly** — the agent delegates automatically. + +### Custom Subagents + +Define in `.cursor/agents/` (project) or `~/.cursor/agents/` (user). Also: `.claude/agents/`, `.codex/agents/` for compatibility. + +```markdown +--- +name: verifier +description: Validates completed work. Use after tasks marked done. +model: fast +--- + +You are a skeptical validator... +``` + +**Invoke explicitly**: `/verifier check the auth implementation` + +### Task Tool (Programmatic Delegation) + +For explicit subagent delegation: + +``` +→ Task( + subagent_type="explore", // or "generalPurpose" + model="fast", + prompt="Find all authentication-related files", + description="Find auth files" + ) +``` + +--- + +## Agent Skills (Cursor 2.5) + +Skills are portable packages of domain knowledge. + +### Skill Locations + +| Location | Scope | +|----------|-------| +| `.cursor/skills/` | Project-level | +| `.claude/skills/` | Project-level (Claude compatibility) | +| `.codex/skills/` | Project-level (Codex compatibility) | +| `~/.cursor/skills/` | User-level (global) | +| `~/.claude/skills/` | User-level (Claude compatibility) | +| `~/.codex/skills/` | User-level (Codex compatibility) | + +### Skill Structure + +``` +.cursor/skills/ +└── deploy-app/ + ├── SKILL.md # Required + ├── scripts/ # Optional - executable code + └── references/ # Optional - detailed docs +``` + +### Invoking Skills + +**Automatic**: Agent discovers based on context and description. + +**Explicit**: Use `/skill-name` in chat: +``` +> /deploy-app deploy to staging +> /migrate-to-skills convert my rules +``` + +### Built-in Migration Skill + +Cursor 2.5 includes `/migrate-to-skills` to convert dynamic rules to skills. + +--- + +## Tool Selection Decision Tree + +``` +Need to find code? +├─ By meaning/concept → SemanticSearch +├─ Exact text/symbol → Grep +├─ By filename → Glob +└─ Complex exploration → Native explore subagent (automatic) + +Need to read? +├─ File contents → Read +├─ Directory structure → LS or Glob (use Glob when LS unavailable) +└─ Linter errors → ReadLints + +Need to edit? +├─ Modify existing → StrReplace +├─ Create new file → Write +├─ Remove file → Delete +└─ Jupyter notebook → EditNotebook + +Need to execute? +├─ Single command → Shell +├─ Command sequence → Native bash subagent (automatic) +└─ MCP tool → Use MCP tool directly + +Need external info? +├─ Real-time data → WebSearch (Cursor native) +├─ MiniMax search → user-MiniMax-web_search (if configured) +└─ Image analysis → user-MiniMax-understand_image (if configured) + +Need to test web UI? +├─ Navigate/interact → Native browser subagent (automatic) +├─ Or manually: +│ ├─ Navigate → browser_navigate +│ ├─ Snapshot → browser_snapshot +│ └─ Interact → browser_click / browser_type + +Need task tracking? +├─ EPIC todos (3+ steps) → TodoWrite +└─ Structured plan for approval → CreatePlan + +Need user input on hard fork? +└─ Security/data/architecture → AskQuestion + +Need image? +└─ Mockups, diagrams → GenerateImage + +Need specialized work? +├─ Validate completed work → /verifier custom subagent +├─ Debug errors → /debugger custom subagent +├─ Domain knowledge → Invoke skill with /skill-name +└─ Programmatic delegation → Task tool +``` + +### Subagents vs Skills vs Task Tool + +| Need | Solution | +|------|----------| +| Codebase exploration | Native `explore` (automatic) | +| Shell command sequences | Native `bash` (automatic) | +| Browser testing | Native `browser` (automatic) | +| Validate work | Custom subagent `/verifier` | +| Domain-specific knowledge | Skill `/skill-name` | +| Programmatic agent launch | Task tool | + +## Parallel vs Sequential Tool Calls + +### Use Parallel Calls When: +- Reading multiple independent files +- Searching different directories +- Making independent edits +- Gathering unrelated information + +### Use Sequential Calls When: +- Results inform next parameters +- Order matters (create before edit) +- Dependencies between operations +- Validation needed before proceeding + +### Example Parallel Pattern: +``` +Reading multiple files simultaneously: +→ Read(path="src/index.ts") +→ Read(path="src/config.ts") +→ Read(path="package.json") +[All called in single batch] +``` + +## Cursor 2.5 Advanced Features + +### Plugins +- Marketplace at cursor.com/marketplace; `/add-plugin` to install +- Packages skills, subagents, MCP servers, hooks, rules + +### Sandbox +- `sandbox.json` for network allowlists/denylists and filesystem paths +- See [Cursor Terminal docs](https://cursor.com/docs/agent/terminal) + +### Native Subagents (Async in 2.5) +- **explore**: Codebase search with isolated context +- **bash**: Shell command execution with output filtering +- **browser**: Web automation via MCP with DOM filtering +- **Async**: Subagents run in background; parent continues; subagents can spawn subagents (tree) +- Custom subagents in `.cursor/agents/`; use `is_background: true` for parallel throughput + +### Agent Skills +- Portable knowledge packages in `.cursor/skills/`, `.claude/skills/`, `.codex/skills/` +- Auto-discovery or `/skill-name` invocation +- Progressive loading (scripts, references, assets) +- Migration: `/migrate-to-skills` + +### Debug Mode +- Instruments apps with runtime logs +- Helps reproduce complex bugs +- Works across stacks and languages + +### Browser Layout Editor +- Real-time CSS/layout editing +- Component tree visualization +- Integrated with codebase + +### Plan Mode + Mermaid +- Visual planning with diagrams +- Send to-dos to new agents +- Structured task management + +### Multi-Agent Features +- **Async subagents** (2.5): Parent continues while subagents run in background; subagent trees +- Parallel subagents with isolated context +- Background subagents (`is_background: true`) +- Multi-agent judging for comparing solutions + +### Image Generation +- Generate images directly from agent +- Saved to `assets/` folder by default +- Useful for mockups and diagrams + +--- + +## CRITICAL: What NEVER To Do + +### Never Manually Create These Files + +These files are generated by CLIs and should NEVER be created manually: + +| File Type | Use CLI Instead | +|-----------|----------------| +| `package.json` | `npm init` or `pnpm init` | +| `package-lock.json` | Generated by `npm install` | +| `go.mod` | `go mod init` | +| `Cargo.toml` | `cargo new` or `cargo init` | +| `pubspec.yaml` | `flutter create` | +| `*.xcodeproj/*` | Xcode or `swift package init` | +| `project.pbxproj` | **NEVER TOUCH** - Xcode only | +| `.csproj` | `dotnet new` | +| `pom.xml` | `mvn archetype:generate` | + +### Never Skip These Steps + +``` +❌ DON'T: Create project files manually +✅ DO: Use framework CLI (npx create-next-app, flutter create, cargo new) + +❌ DON'T: Assume packages are installed +✅ DO: Run npm install / flutter pub get / cargo build after setup + +❌ DON'T: Guess CLI availability +✅ DO: Check with --version first + +❌ DON'T: Write code without reading existing files +✅ DO: Always Read before editing + +❌ DON'T: Skip dependency installation +✅ DO: npm install, pip install, cargo build after adding deps + +❌ DON'T: Forget post-edit verification +✅ DO: Run linters, tests, builds after changes +``` + +### Always Verify After Actions + +```bash +# After creating web project +npm install && npm run dev # Verify it starts + +# After adding shadcn component +npx shadcn@latest add button # Use CLI, don't copy files! + +# After Flutter changes +flutter pub get && flutter analyze + +# After Rust changes +cargo check && cargo clippy + +# After Go changes +go build ./... && go vet ./... + +# After Python changes +pip install -e . && pytest +``` diff --git a/.cursor/skills/cursor-sync/.skillfish.json b/.cursor/skills/cursor-sync/.skillfish.json new file mode 100644 index 0000000..eb44ec2 --- /dev/null +++ b/.cursor/skills/cursor-sync/.skillfish.json @@ -0,0 +1,10 @@ +{ + "version": 2, + "name": "cursor-sync", + "owner": "denisraison", + "repo": "claude-plugins", + "path": "plugins/cursor-sync/skills/cursor-sync", + "branch": "main", + "sha": "83237507c60e2505b5bf295c44a75c8e32ab4c21", + "source": "manual" +} \ No newline at end of file diff --git a/.cursor/skills/cursor-sync/SKILL.md b/.cursor/skills/cursor-sync/SKILL.md new file mode 100644 index 0000000..d0b7c2d --- /dev/null +++ b/.cursor/skills/cursor-sync/SKILL.md @@ -0,0 +1,58 @@ +--- +name: cursor-sync +description: Syncs Claude Code plugins to Cursor skills and commands directories. +--- + +# Cursor Sync + +Syncs Claude Code plugins to Cursor's skills and commands directories. Changes to installed plugins are automatically detected and synced. + +## Scripts + +Run these scripts from the plugin directory: + +### Manual Sync +```bash +./scripts/sync.sh +``` +Performs a one-time sync of all installed Claude Code plugins to `~/.cursor/skills/` and `~/.cursor/commands/`. + +### Start Watcher +```bash +./scripts/watch.sh +``` +Watches for plugin changes and syncs automatically. Runs in foreground. + +### Install as Service +```bash +./scripts/install-service.sh install +``` +Installs a launchd service that starts on login and keeps syncing in the background. + +### Check Service Status +```bash +./scripts/install-service.sh status +``` + +### Uninstall Service +```bash +./scripts/install-service.sh uninstall +``` + +## How It Works + +1. Reads `~/.claude/plugins/installed_plugins.json` to find installed plugins +2. For each plugin: + - Copies skill directories from `/skills/` to `~/.cursor/skills/` + - Copies command files from `/commands/` to `~/.cursor/commands/` +3. Handles naming conflicts by prefixing with plugin name +4. Cleans up orphaned files when plugins are uninstalled + +## Dependencies + +- `jq` for JSON parsing: `brew install jq` +- `fswatch` for file watching: `brew install fswatch` + +## Logs + +Service logs are written to `~/Library/Logs/cursor-sync.log`. diff --git a/.superpowers/brainstorm/6299-1774202169/.server-info b/.superpowers/brainstorm/6299-1774202169/.server-info new file mode 100644 index 0000000..b701237 --- /dev/null +++ b/.superpowers/brainstorm/6299-1774202169/.server-info @@ -0,0 +1 @@ +{"type":"server-started","port":52313,"host":"127.0.0.1","url_host":"localhost","url":"http://localhost:52313","screen_dir":"/Users/hduva/projects/pyref/.superpowers/brainstorm/6299-1774202169"} diff --git a/.superpowers/brainstorm/6299-1774202169/.server.pid b/.superpowers/brainstorm/6299-1774202169/.server.pid new file mode 100644 index 0000000..8158f7a --- /dev/null +++ b/.superpowers/brainstorm/6299-1774202169/.server.pid @@ -0,0 +1 @@ +6308 diff --git a/.superpowers/brainstorm/6299-1774202169/arch-overview.html b/.superpowers/brainstorm/6299-1774202169/arch-overview.html new file mode 100644 index 0000000..fbc1bf1 --- /dev/null +++ b/.superpowers/brainstorm/6299-1774202169/arch-overview.html @@ -0,0 +1,312 @@ +

Design Section 1 — Architecture Overview

+

Navigator + per-screen state decomposition. Review and confirm before we detail each component.

+ + + +
+

Module Structure — What Changes vs. What Stays

+
+
+
🗂 src/bin/tui/ — Restructured
+
+ 
// NEW files
+navigator.rs     // Navigator + ScreenState enum
+explorer/
+  mod.rs         // ExplorerState + level enum
+  heuristic.rs   // date-pattern path classification
+  modal.rs       // layout-config modal (ratatui)
+catalog_handle.rs // CatalogHandle — shared query API
+
+// MODIFIED files
+app.rs           // App → thin shell, owns Navigator
+ui.rs            // dispatches on ScreenState variant
+run.rs           // event loop delegates to Navigator
+keymap.rs        // per-screen keybind dispatch
+
+// UNCHANGED files
+beamspot.rs      // no changes
+preview.rs       // no changes
+scan_type.rs     // no changes
+theme.rs         // no changes
+terminal_guard.rs // no changes
+error.rs         // minor additions
+
+
+ +
+
📚 src/catalog/ — Additions only
+
+ 
// NEW files
+explorer_query.rs
+  // list_experimentalists(data_root)
+  // list_beamtimes(data_root, experimentalist)
+  // entry_metadata(path) → EntryMeta
+  // catalog_status(path) → Indexed|Stale|Missing
+
+// MODIFIED files
+ingest.rs        // writes to DATA-level catalog
+                   // records experimentalist + beamtime
+query.rs          // add beamtime_path filter param
+                   // to existing query_scan_points()
+mod.rs            // re-export new public API
+
+// UNCHANGED
+beamtime_index.rs // kept for backward compat
+watch.rs          // no changes
+materialize.rs    // no changes
+
+
+
+
+ +
+

Core State Model

+
+
+
App — Thin Shell (was 2600-line monolith)
+
+ 
pub struct App {
+    // navigation
+    pub navigator: Navigator,
+
+    // shared async channels (unchanged)
+    pub preview_tx:  Option<Sender<(PathBuf, Option<f64>)>>,
+    pub beamspot_rx: Option<Receiver<BeamspotUpdate>>,
+    pub preview_cmd_rx: Option<Receiver<PreviewCommand>>,
+
+    // shared config / theme (unchanged)
+    pub layout:     String,
+    pub keymap:     String,
+    pub theme:      String,
+    pub needs_redraw: bool,
+}
+
+
+ +
+
Navigator — Screen Stack
+
+ 
pub struct Navigator {
+    // current screen + back-history
+    stack: Vec<ScreenState>,
+    forward: Vec<ScreenState>,
+
+    // shared catalog handle
+    pub catalog: CatalogHandle,
+}
+
+pub enum ScreenState {
+    // new: file-explorer at any depth
+    Explorer(ExplorerState),
+
+    // adapted: was Screen::Beamtime
+    Beamtime(BeamtimeState),
+
+    // new: layout-config modal
+    ConfigModal(ModalState),
+}
+
+impl Navigator {
+    pub fn push(&mut self, s: ScreenState) {}
+    pub fn pop(&mut self) {}
+    pub fn current(&self) -> &ScreenState {}
+    pub fn current_mut(&mut self) -> &mut ScreenState {}
+}
+
+
+
+
+ +
+

Screen State Ownership

+
+Navigator owns Vec<ScreenState> (back stack — no cloning, push/pop) + │ + ├─▶ ScreenState::Explorer(ExplorerState) + │ │ + │ ├─ level: ExplorerLevel Root | Experimentalist | Generic + │ ├─ root: PathBuf DATA/ passed from CLI + │ ├─ current_dir: PathBuf where we are now + │ ├─ entries: Vec<DirEntry> enriched with metadata + │ └─ list_state: ListState + │ + ├─▶ ScreenState::Beamtime(BeamtimeState) + │ │ + │ ├─ beamtime_path: PathBuf filter key into DATA catalog + │ ├─ experimentalist: String for display + catalog filter + │ └─ [existing fields from App] samples, tags, scans, groups… + │ + └─▶ ScreenState::ConfigModal(ModalState) + │ + ├─ depth_to_beamtime: u8 user-configured depth + ├─ pattern: String regex / format string + └─ return_to: Box<ScreenState> screen to resume after confirm + +CatalogHandle shared via Arc<Mutex<Connection>> or per-call open() + ├─▶ list_experimentalists(root) + ├─▶ list_beamtimes(root, experimentalist) + ├─▶ query_scan_points(root, beamtime_path, filter) adapted from existing + └─▶ catalog_status(path) → Indexed | Stale | Missing +
+
+ +
+

Catalog Schema Addition

+
+
New columns added to bt_beamtimes (no breaking changes)
+
+ 
-- existing bt_beamtimes already has: id, beamtime_path, user, year, month, esaf_number, label
+-- additions:
+ALTER TABLE bt_beamtimes ADD COLUMN experimentalist  TEXT NOT NULL DEFAULT '';
+ALTER TABLE bt_beamtimes ADD COLUMN data_root        TEXT NOT NULL DEFAULT '';
+-- data_root: the DATA/ path this beamtime was ingested under
+-- experimentalist: the folder name one level above the beamtime
+
+-- new index for explorer queries
+CREATE INDEX IF NOT EXISTS idx_bt_experimentalist
+    ON bt_beamtimes(data_root, experimentalist);
+
+
+ +
+ Migration strategy: both new columns have DEFAULT '' so existing catalog DBs open without errors. The explorer_query functions treat empty strings as "unset" and fall back to path parsing to infer experimentalist from the beamtime_path. Re-ingesting any beamtime populates them correctly. +
+
+ +
+

Execution Phases

+
+
+
Phase 1 — Decompose (no new features)
+
+ Extract BeamtimeState from App
+ Introduce Navigator + ScreenState
+ App becomes thin shell
+ All existing behavior preserved
+ ✓ mergeable independently +
+
+
+
Phase 2 — Explorer screens
+
+ Add ExplorerState + ExplorerLevel
+ Add heuristic.rs path classifier
+ Add modal.rs config dialog
+ Wire CLI arg as DATA root
+ ✓ mergeable independently +
+
+
+
Phase 3 — DATA-level catalog
+
+ Add explorer_query.rs
+ Migrate ingestion to DATA-level DB
+ Add schema columns to bt_beamtimes
+ Adapt query_scan_points filter
+ ✓ mergeable independently +
+
+
+
Phase 4 — Auto-ingest + wiring
+
+ Auto-ingest on entry to un-indexed dir
+ Explorer reads catalog status per entry
+ Beamtime screen queries DATA catalog
+ End-to-end flow validated
+ ✓ complete +
+
+
+
+ diff --git a/.superpowers/brainstorm/6299-1774202169/beamtime-adapt.html b/.superpowers/brainstorm/6299-1774202169/beamtime-adapt.html new file mode 100644 index 0000000..ac7aa89 --- /dev/null +++ b/.superpowers/brainstorm/6299-1774202169/beamtime-adapt.html @@ -0,0 +1,266 @@ +

Design Section 3 — Beamtime Screen Adaptation + Auto-Ingest

+

How BeamtimeState extracts from App, how queries shift to the DATA catalog, and what auto-ingest on entry looks like.

+ + + + +
+

BeamtimeState — extracted from App

+
+
+
BeamtimeState (owns former App fields)
+
+ 
pub struct BeamtimeState {
+    // ── context (NEW) ───────────────────────
+    pub data_root:      PathBuf,  // DATA/ root
+    pub experimentalist: String,  // "Smith"
+    pub beamtime_path:  PathBuf,  // filter key
+
+    // ── ingest state (was in App) ───────────
+    pub loading_state:  LoadingState,
+    pub ingest_rx:      Option<Receiver<Result<(), String>>>,
+    pub ingest_progress_rx: Option<Receiver<(u32, u32)>>,
+    pub ingest_progress: Option<(u32, u32)>,
+
+    // ── data (was in App) ───────────────────
+    pub all_groups:     Vec<GroupedProfileRow>,
+    pub filtered_groups: Vec<GroupedProfileRow>,
+    pub samples:        Vec<String>,
+    pub tags:           Vec<String>,
+    pub scans:          Vec<(u32, String)>,
+
+    // ── UI state (was in App) ───────────────
+    pub sample_state:   ListState,
+    pub tag_state:      ListState,
+    pub scan_list_state: ListState,
+    pub table_state:    TableState,
+    // ... scrollbar states, sort, expand ...
+    // ... focus, mode, search_query ...     
+    // ... status_message, app_error ...     
+    // (all moved verbatim — no logic change)
+}
+
+
+
+
Query change — beamtime_path filter
+
+ 
// BEFORE — per-beamtime catalog, no filter
+let db = resolve_catalog_path(&beamtime_dir);
+let rows = query_files(&db, None)?;
+
+// AFTER — DATA-level catalog, path filter
+let db = data_root.join(".pyref/catalog.db");
+let rows = query_scan_points(
+    &db,
+    &beamtime_path,  // NEW: WHERE source_path LIKE ?
+    None,
+)?;
+
+// query_scan_points signature change:
+pub fn query_scan_points(
+    db:            &Path,
+    beamtime_path: &Path,   // was Option — now required
+    filter:        Option<&CatalogFilter>,
+) -> Result<Vec<FileRow>>;
+
+// SQL fragment added:
+// AND source_path LIKE ?1 || '%'
+// bound to: beamtime_path.to_str()
+ +
+ Migration path: query_files() (old per-beamtime API) is kept but deprecated. It still works against a per-beamtime DB. Callers are migrated one by one. +
+
+
+
+
+ + +
+

Auto-Ingest on Entry — Three UI States

+
+ + +
+
State A — entering, ingest starting
+ 
+ pyref  Smith / 2024Jan                    
+┌──────────────────────────────────────┐
+│                                      │
+│   ⠸ Indexing 2024Jan/…               │
+│                                      │
+│   Scanning FITS files               │
+│   Writing to DATA catalog           │
+│                                      │
+│   ████████░░░░░░░░░░  412 / 1423    │
+│                                      │
+│   [Esc] cancel and go back          │
+│                                      │
+└──────────────────────────────────────┘
+
+
+ + +
+
State B — ingest complete, loading data
+ 
+ pyref  Smith / 2024Jan                    
+┌─────────────┬──────┬──────┬───────────┐
+│ Samples      │Tags  │Scans │ Profiles   │
+├─────────────┼──────┼──────┼───────────┤
+│  PS-b-PEO   │annea │ 001  │           │
+│  PBDB-T     │      │ 002  │           │
+│             │      │ 003  │           │
+└─────────────┴──────┴──────┴───────────┘
+ ✓ Indexed 1,423 files  [←] back
+
+ + +
+
State C — already indexed, instant load
+ 
+ pyref  Smith / 2024Jan  ✓               
+┌─────────────┬──────┬──────┬───────────┐
+│ Samples      │Tags  │Scans │ Profiles   │
+├─────────────┼──────┼──────┼───────────┤
+│  PS-b-PEO   │annea │ 001  │ Sample…   │
+│  PBDB-T     │prist │ 002  │ PS-b-P…  │
+│             │      │ 003  │  PBDB-T… │
+└─────────────┴──────┴──────┴───────────┘
+ 1,423 files  Smith › 2024Jan  [←] back
+
+
+ +
+ Esc during ingest: cancels the background thread (via a CancellationToken-style atomic flag), pops the BeamtimeState off the Navigator stack, and returns to the Explorer. Any partial writes to the catalog are left in place — a second entry attempt will resume indexing from where it stopped (already-indexed files are skipped by mtime check). +
+
+ + +
+

Navigator Entry Flow — Explorer → Beamtime

+
+User presses [→] on a Beamtime row in ExplorerState + │ + ▼ +navigator.enter_beamtime(path, experimentalist, data_root) + │ + ├──▶ CatalogStatus::Indexed + │ │ + │ ▼ + │ BeamtimeState::load(data_root, beamtime_path) query DATA catalog + │ │ + │ ▼ + │ Navigator.push(ScreenState::Beamtime(state)) immediate + │ + ├──▶ CatalogStatus::Stale + │ │ + │ ▼ + │ BeamtimeState::new_ingesting(…) push with LoadingState::IngestingDirectory + │ spawn ingest thread → ingest_beamtime(data_root, beamtime_path) + │ + └──▶ CatalogStatus::NotIndexed (same as Stale — auto-ingest) + │ + ▼ + BeamtimeState::new_ingesting(…) + spawn ingest thread → ingest_beamtime(data_root, beamtime_path) + +Ingest completes (try_recv_ingest in run loop): + │ + ├──▶ Ok(()) ──▶ BeamtimeState::reload_from_catalog() now queries DATA catalog + │ ──▶ loading_state = Idle profiles appear + │ ──▶ update ExplorerState entry → CatalogStatus::Indexed via shared channel + │ + └──▶ Err(e) ──▶ set_app_error(ingest_failed(…)) + ──▶ loading_state = Idle error modal shown, [Esc] goes back +
+
+ + +
+

Back Navigation — Beamtime → Explorer

+
+
+
What happens on [←] / [h]
+
+ navigator.pop() — discards BeamtimeState
+ Restores the ExplorerState that was underneath
+ Cursor position in the explorer is preserved
+ (the ExplorerState was never mutated while Beamtime was active)

+ The Explorer's status cell for the beamtime just entered is updated to + ✓ Indexed via a lightweight mpsc message sent when ingest completes.

+ No reload of the full explorer entry list needed. +
+
+
+
Config saved on BeamtimeState drop
+
+ The same selections that are currently saved on quit + (last_sample, last_tag, last_scan) + are written to TuiConfig when the BeamtimeState + is popped — not just on app exit.

+ This means returning to the same beamtime later in the same session + (or on next launch with the same path) restores your previous cursor position. +
+
+
+
+ diff --git a/.superpowers/brainstorm/6299-1774202169/current-vs-future.html b/.superpowers/brainstorm/6299-1774202169/current-vs-future.html new file mode 100644 index 0000000..ec14a66 --- /dev/null +++ b/.superpowers/brainstorm/6299-1774202169/current-vs-future.html @@ -0,0 +1,311 @@ +

TUI Architecture: Current vs. Proposed

+

+ ASCII wireframes comparing the existing browser interface with the proposed file-explorer redesign.
+ Both mockups represent the full terminal window at a typical 220×50 terminal. +

+ + + +
+

Screen 1 of 2 — Entry / Launcher Screen

+ +
+ +
+
Current — Flat Beamtime List
+ 
+ pyref browser                                [q]quit  [o]open dir  [↵]open   
+┌─────────────────────────────────────────────────────────────────────────────┐
+│ Recent Beamtimes                                                            │
+├─────────────────────────────────────────────────────────────────────────────┤
+│   /Volumes/DATA/Smith/2024Jan                             2024-01-15  1423 f │
+│   /Volumes/DATA/Jones/2024Feb                            2024-02-03   891 f │
+│   /Volumes/DATA/Smith/2023Nov                            2023-11-22  2110 f │
+│   /Volumes/DATA/Williams/2024Mar                         2024-03-07   344 f │
+│   /Volumes/DATA/Jones/2023Sep                            2023-09-19   755 f │
+│                                                                              │
+│                                                                              │
+│                                                                              │
+│                                                                              │
+│                                                                              │
+│                                                                              │
+│                                                                              │
+│                                                                              │
+│                                                                              │
+│                                                                              │
+│                                                                              │
+│                                                                              │
+│                                                                              │
+│                                                                              │
+│                                                                              │
+└─────────────────────────────────────────────────────────────────────────────┘
+ 5 beamtimes indexed                                                           
+ [o] Open directory   [↵] Open selected   [q] Quit                            
+
+
+ + +
+
Proposed — NAS File Explorer
+ 
+ pyref browser  /Volumes/DATA             [←]back  [→]enter  [i]ingest  [q]quit 
+┌───────────────────────────────────────────────────────────────────────────────────────────────┐
+│ ▸ /Volumes/DATA                                                                               │
+├──────────────────────────────────────┬────────────────┬───────────┬──────────┬───────────────┤
+│  Name                                │ Date Modified  │ Size      │ Kind     │ # Beamtimes   │
+├──────────────────────────────────────┼────────────────┼───────────┼──────────┼───────────────┤
+│   📁 Smith                           │  2024-03-15    │  48.2 GB  │  Person  │  6 beamtimes  │
+│   📁 Jones                           │  2024-03-07    │  22.7 GB  │  Person  │  4 beamtimes  │
+│   📁 Williams                        │  2024-02-28    │   9.1 GB  │  Person  │  2 beamtimes  │
+│   📁 Chen                            │  2024-01-12    │  31.5 GB  │  Person  │  3 beamtimes  │
+│   📁 Patel                           │  2023-11-05    │   5.4 GB  │  Person  │  1 beamtime   │
+│   📄 .pyref/                         │  2024-03-15    │   2.1 MB  │  Catalog │  (index)      │
+│                                      │                │           │          │               │
+│                                      │                │           │          │               │
+│                                      │                │           │          │               │
+│                                      │                │           │          │               │
+│                                      │                │           │          │               │
+│                                      │                │           │          │               │
+│                                      │                │           │          │               │
+│                                      │                │           │          │               │
+│                                      │                │           │          │               │
+│                                      │                │           │          │               │
+│                                      │                │           │          │               │
+│                                      │                │           │          │               │
+│                                      │                │           │          │               │
+└──────────────────────────────────────┴────────────────┴───────────┴──────────┴───────────────┘
+ 5 experimentalists  │  116.9 GB total  │  catalog: ✓ DATA/.pyref/catalog.db    
+ [↑↓] navigate  [→/↵] enter dir  [←/h] up  [i] ingest  [/] search  [q] quit   
+
+
+
+ +
+
+

Current Launcher — Limitations

+
    +
  • Flat list — no hierarchy, just recently-ingested paths
    • +
  • Global index at ~/.pyref/ — not co-located with DATA
    • +
  • No browsing — must type a path or pick from history
    • +
  • No metadata — no size, no count of beamtimes per experimentalist
    • +
  • No catalog context — no indication of what's indexed vs. not
    • +
+
+
+

Proposed Explorer — What Changes

+
    +
  • Rooted at DATA/ — passed as CLI arg, full NAS awareness
    • +
  • Standard columns — Name, Date Modified, Size, Kind, # Beamtimes
    • +
  • Catalog indicator — shows whether .pyref/catalog.db exists at this level
    • +
  • Navigate down → Experimentalist → Beamtime → scan profiles
    • +
  • Inline ingest — press i to ingest selected folder
    • +
+
+
+
+ +
+

Screen 2 of 2 — Experimentalist Drill-Down → Beamtimes

+
+ +
+
Current — Goes straight to Scan Profiles
+ 
+ pyref  /Volumes/DATA/Smith/2024Jan          [i]ingest  [/]search  [q]quit      
+┌─────────────┬─────────────┬─────────────┬──────────────────────────────────────┐
+│ Samples      │ Tags         │ Scans        │ Profiles                              │
+├─────────────┼─────────────┼─────────────┼──────────────────────────────────────┤
+│ ▸ PS-b-PEO   │ ▸ annealed   │ ▸ 001        │ Sample  Tag     Pol  E(eV)   θ-range  │
+│   PBDB-T     │   pristine  │   002        │ PS-b-P anneale  S  284.5   2°-20°   │
+│   PC61BM     │   solvent   │   003        │  PS-b-P anneale  P  284.5   2°-20°  │
+│   PM6        │             │   004        │  PS-b-P pristine S  284.5   2°-20°  │
+│             │             │   005        │  PS-b-P pristine P  284.5   2°-20°  │
+│             │             │   006        │  PBDB-T anneale  S  270.0   2°-15°  │
+│             │             │   007        │  PBDB-T anneale  P  270.0   2°-15°  │
+│             │             │   008        │  PC61BM solvent  S  285.0   5°-25°  │
+│             │             │   009        │                                      │
+│             │             │   010        │                                      │
+│             │             │   011        │                                      │
+│             │             │   012        │                                      │
+│             │             │              │                                      │
+│             │             │              │                                      │
+│             │             │              │                                      │
+└─────────────┴─────────────┴─────────────┴──────────────────────────────────────┘
+ 1423 files  •  no intermediate "Beamtime" level exists                         
+ [tab] focus  [space] filter  [i] ingest  [r] rename  [m] materialize  [q] quit 
+
+
+ + +
+
Proposed — Experimentalist Level (Smith/)
+ 
+ pyref  /Volumes/DATA/Smith               [←]back  [→]enter  [i]ingest  [q]quit 
+┌──────────────────────────────────────────────────────────────────────────────────────────┐
+│ ▸ /Volumes/DATA / Smith                                                                   │
+├────────────────────┬────────────────┬──────────┬───────────┬──────────────┬──────────────┤
+│  Name               │ Date           │ Size     │ Kind      │ FITS Files   │ Catalog      │
+├────────────────────┼────────────────┼──────────┼───────────┼──────────────┼──────────────┤
+│   📁 2024Jan        │  2024-01-22    │  12.1 GB │  Beamtime │  1,423 files │  ✓ indexed   │
+│   📁 2023Nov        │  2023-11-30    │  18.4 GB │  Beamtime │  2,110 files │  ✓ indexed   │
+│   📁 2023Aug        │  2023-08-14    │   9.2 GB │  Beamtime │    987 files │  ✓ indexed   │
+│   📁 2022Oct        │  2022-10-07    │   5.8 GB │  Beamtime │    612 files │  ⚠ stale     │
+│   📁 2022Feb        │  2022-02-18    │   2.1 GB │  Beamtime │    231 files │  ✗ not indexed│
+│   📁 scratch        │  2024-03-10    │ 847.0 MB │  Dir      │     94 files │  — —         │
+│                     │                │          │           │              │              │
+│                     │                │          │           │              │              │
+│                     │                │          │           │              │              │
+│                     │                │          │           │              │              │
+│                     │                │          │           │              │              │
+│                     │                │          │           │              │              │
+│                     │                │          │           │              │              │
+│                     │                │          │           │              │              │
+│                     │                │          │           │              │              │
+└────────────────────┴────────────────┴──────────┴───────────┴──────────────┴──────────────┘
+ 5 beamtimes  │  48.2 GB  │  3 indexed  •  1 stale  •  1 not indexed             
+ [↑↓] nav  [→/↵] open beamtime  [←/h] up  [i] ingest here  [I] ingest all  [q]  
+
+
+
+ +
+
+

Current — No Intermediate Layer

+
    +
  • Must already know the exact beamtime path to navigate to it
    • +
  • No way to see all beamtimes for a given experimentalist in one view
    • +
  • Cannot bulk-ingest multiple beamtimes from a single screen
    • +
  • The Beamtime screen works well — but it is the only screen
    • +
+
+
+

Proposed — Experimentalist Level

+
    +
  • Per-beamtime catalog status — ✓ indexed / ⚠ stale / ✗ not indexed
    • +
  • FITS file count surfaced without opening the directory
    • +
  • i ingests selected beamtime, I ingests all not-yet-indexed
    • +
  • Entering a beamtime transitions to the existing Beamtime screen — unchanged
    • +
  • Non-beamtime dirs (scratch, misc) shown but clearly typed as generic
    • +
+
+
+
+ +
+

Navigation Flow Comparison

+
+
+
Current Navigation
+
+ ▪ Launcher — flat list of past beamtime paths
+    ↓ [↵] open selected or [o] type path
+ ▪ Beamtime Screen — samples/tags/scans/profiles
+    ↓ [h] nav_up goes to parent dir (reloads beamtime screen)
+ ▪ No awareness of DATA root or experimentalist level
+ ▪ Cannot navigate from Beamtime → sibling Beamtime
+ ▪ Global beamtime index lives in ~/.pyref/ +
+
+
+
Proposed Navigation
+
+ ▪ Explorer Screen — rooted at DATA/ (CLI arg)
+    ↓ [→] enter dir — shows experimentalist dirs
+ ▪ Experimentalist Level — beamtime folders with metadata
+    ↓ [→] enter beamtime — if cataloged, goes to beamtime view
+ ▪ Beamtime Screen — existing samples/tags/scans view (unchanged)
+    ↓ [←/h] back to experimentalist level
+ ▪ Full back/forward history stack across all levels
+ ▪ Catalog lives at DATA/.pyref/ — co-located with data +
+
+
+
+ + \ No newline at end of file diff --git a/.superpowers/brainstorm/6299-1774202169/explorer-screen-v2.html b/.superpowers/brainstorm/6299-1774202169/explorer-screen-v2.html new file mode 100644 index 0000000..db2281b --- /dev/null +++ b/.superpowers/brainstorm/6299-1774202169/explorer-screen-v2.html @@ -0,0 +1,284 @@ +

Design Section 2 (revised) — Per-Experimentalist Layout Policy

+

+ Layout configuration is resolved per experimentalist, not globally. + The root explorer flags unresolved folders. The modal is scoped to one folder at a time. +

+ + + + +
+

Data Model — NasLayout replaces LayoutPolicy

+
+
+
NasLayout — per-experimentalist map
+
+ 
/// Persisted to DATA/.pyref/layout.toml
+pub struct NasLayout {
+    /// default beamtime pattern used when
+    /// an experimentalist has no override
+    pub default_pattern: Option<String>,
+    pub default_depth:   u8,  // default: 2
+
+    /// one entry per depth-1 folder name
+    pub experimentalists: HashMap<String, ExptPolicy>,
+}
+
+pub enum ExptPolicy {
+    /// accepted by heuristic or left as
+    /// default — no user action needed
+    Auto,
+
+    /// user specified a custom pattern
+    /// and/or depth for this folder
+    Custom {
+        pattern: Option<String>,
+        depth:   u8,
+    },
+
+    /// user marked as not an experimentalist
+    /// (metadata dir, scratch, etc.)
+    /// shown dimmed, never ingested
+    Ignored,
+}
+
+
+
+
Resolution states for a depth-1 folder
+
+ 
/// Computed at render time for each
+/// depth-1 entry in the explorer
+pub enum ExptResolution {
+    /// heuristic matched, or user set Auto
+    Resolved,
+
+    /// heuristic failed, needs user action
+    /// shown with ⚑ flag in explorer
+    NeedsResolution,
+
+    /// user marked as not an experimentalist
+    Ignored,
+}
+
+/// DirEntry gains one new field:
+pub struct DirEntry {
+    // ...existing fields...
+    pub expt_resolution: Option<ExptResolution>,
+    // Some only when kind == Experimentalist
+}
+
+
+
+ +
+ What counts as an experimentalist folder? Any depth-1 entry under DATA/ that is a directory and whose name does not start with "." — exactly as you described. The .pyref/ catalog dir, .DS_Store, etc. are automatically excluded by the leading-dot rule. +
+
+ + +
+

Explorer Root — Flagged Experimentalists

+
+
DATA/ — one unresolved folder, one ignored folder
+ 
+ pyref  /Volumes/DATA                     [←]up  [→]enter  [r]resolve  [q]quit 
+┌──────────────────────────────────────────────────────────────────────────────────────────┐
+│ ▸ /Volumes/DATA                                 1 folder needs resolution — [r] to fix  │
+├───────────────────────┬──────────────┬──────────┬──────────────┬────────────────────────┤
+│   Name                 │ Modified     │ Size     │ Beamtimes    │ Status                 │
+├───────────────────────┼──────────────┼──────────┼──────────────┼────────────────────────┤
+│   📁 Smith             │  2024-03-15  │  48.2 GB │  6           │  ✓ resolved            │
+│   📁 Jones             │  2024-03-07  │  22.7 GB │  4           │  ✓ resolved            │
+│   📁 Williams          │  2024-02-28  │   9.1 GB │  2           │  ✓ resolved            │
+│   ⚑ 📁 Chen             │  2024-01-12  │  31.5 GB │  —           │  ⚑ needs resolution    │
+│     📁 scratch           │  2024-03-10  │ 847.0 MB │  —           │  ✗ ignored             │
+│                        │              │          │              │                        │
+│                        │              │          │              │                        │
+│                        │              │          │              │                        │
+└───────────────────────┴──────────────┴──────────┴──────────────┴────────────────────────┘
+ 5 folders  •  4 experimentalists  •  1 ignored  •  1 unresolved                          
+ [↑↓] nav   [→/↵] enter   [r] resolve selected   [x] ignore selected   [q] quit
+
+ +
+ Status column rules: + ✓ resolved heuristic matched or user confirmed. + ⚑ needs resolution heuristic found no date-like pattern in this folder's subdirectories — row is highlighted amber, cannot be entered until resolved. + ✗ ignored user marked as non-experimentalist — dimmed, skipped by bulk-ingest. + Beamtime count shows — until resolution is set (we don't know how to count yet). +
+
+ + +
+

Per-Experimentalist Resolution Modal — [r] on a flagged row

+
+
Resolving Chen/ — three choices + live preview
+ 
+ pyref  /Volumes/DATA                                                              
+
+╔══════════════════════════════════════════════════════════════════════════╗
+║  Resolve folder: Chen/                                    [Esc] cancel  ║
+║  pyref couldn't detect beamtime folders inside Chen/.                      ║
+║  Choose how to treat this folder:                                          ║
+║                                                                          ║
+║  ○  Use custom pattern                                                   ║
+║     Specify a name pattern and depth for beamtime folders inside Chen/   ║
+║                                                                          ║
+║     Depth to beamtime   1   Pattern (regex)   run\d+                   ║
+║                                                                          ║
+║     Preview:                                                             ║
+║     ┌──────────────────────────────────────────────────────────────┐   ║
+║     │  Chen/                                  [Experimentalist]  │   ║
+║     │    run001/                              [Beamtime ✓]        │   ║
+║     │    run002/                              [Beamtime ✓]        │   ║
+║     │    notes/                               [Directory]         │   ║
+║     └──────────────────────────────────────────────────────────────┘   ║
+║                                                                          ║
+║  ◉  Mark as "not an experimentalist"                                      ║
+║     Hide from beamtime counts and bulk ingest. Shown dimmed.             ║
+║                                                                          ║
+║  ○  Use global default  (^20\d{2}... depth 2)                            ║
+║     Accept the default pattern even though no matches were found yet.    ║
+║                                                                          ║
+║  [↑↓] select option   [Tab] move between fields   [↵] confirm   [Esc] cancel  ║
+╚══════════════════════════════════════════════════════════════════════════╝
+
+
+
+ + +
+

Resolution Flow

+
+Navigator pushes Explorer(data_root) + │ + ▼ +load_entries(data_root) read_dir + classify depth-1 folders + │ + ├──▶ name starts with "." ──▶ skip entirely (not shown) + │ + ├──▶ policy = Ignored ──▶ shown dimmed, ExptResolution::Ignored + │ + ├──▶ policy = Auto or Custom ──▶ ExptResolution::Resolved + │ + └──▶ policy missing (new folder) + │ + ▼ + detect_policy(folder) shallow scan of subdirs + │ + ├──▶ confident ──▶ auto-set ExptPolicy::Auto → Resolved + │ + └──▶ not confident ──▶ ExptResolution::NeedsResolution ⚑ flagged + +User presses [r] on flagged row: + │ + ▼ +Navigator.push(ConfigModal { target: "Chen", return_to: current_explorer }) + │ + ├──▶ Custom pattern confirmed ──▶ save to layout.toml, pop modal, refresh entries + ├──▶ Ignored confirmed ──▶ save to layout.toml, pop modal, dim row + └──▶ Global default accepted ──▶ set Auto, pop modal, mark Resolved +
+ +
+ Persistence: Every confirmed resolution writes to DATA/.pyref/layout.toml immediately. On next startup the file is loaded first, so already-resolved folders never trigger the modal again. New experimentalist folders that appear on the NAS after the fact go through the same detection → flag → resolve cycle. +
+
+ + +
+

Entering a Flagged Folder

+
+
+
⚑ NeedsResolution
+
+ Pressing → or ↵ on a flagged row opens the resolution modal immediately instead of entering the folder.

+ The user must resolve before they can browse the folder's contents. +
+
+
+
✓ Resolved
+
+ Normal entry — pushes a new ExplorerState scoped to that folder, using that experimentalist's policy to classify its subdirs as beamtime vs. generic. +
+
+
+
✗ Ignored
+
+ Entry is still allowed — the user can browse a folder marked ignored. It's just excluded from beamtime counts and bulk ingest. Press x again to un-ignore. +
+
+
+
+ diff --git a/.superpowers/brainstorm/6299-1774202169/explorer-screen.html b/.superpowers/brainstorm/6299-1774202169/explorer-screen.html new file mode 100644 index 0000000..0faf2c0 --- /dev/null +++ b/.superpowers/brainstorm/6299-1774202169/explorer-screen.html @@ -0,0 +1,303 @@ +

Design Section 2 — Explorer Screen

+

ExplorerState internals, DirEntry enrichment, heuristic classification, and the config modal wireframe.

+ + + + +
+

ExplorerState — Data Structures

+
+
+
ExplorerState
+
+ 
pub struct ExplorerState {
+    // where we are
+    pub data_root:   PathBuf,   // DATA/ from CLI arg
+    pub current_dir: PathBuf,   // dir being displayed
+
+    // entries in the current dir
+    pub entries:    Vec<DirEntry>,
+    pub list_state: ListState,
+    pub scroll_state: ScrollbarState,
+
+    // layout policy (from heuristic or modal)
+    pub layout_policy: LayoutPolicy,
+
+    // loading / ingest feedback
+    pub loading: bool,
+    pub status:  Option<String>,
+
+    // sort
+    pub sort_col: ExplorerSort,
+    pub sort_asc:  bool,
+}
+
+pub enum ExplorerSort {
+    Name, Modified, Size, FileCount,
+}
+
+
+ +
+
DirEntry — enriched directory row
+
+ 
pub struct DirEntry {
+    pub path:        PathBuf,
+    pub name:        String,
+    pub kind:        EntryKind,
+    pub modified:    Option<SystemTime>,
+    pub size_bytes:  Option<u64>,   // du -s async
+    pub fits_count:  Option<u32>,   // from catalog or scan
+    pub catalog_status: CatalogStatus,
+}
+
+pub enum EntryKind {
+    DataRoot,           // DATA/ itself
+    Experimentalist,    // depth 1 from root
+    Beamtime,           // matched by heuristic
+    Directory,          // generic subdir
+}
+
+pub enum CatalogStatus {
+    Indexed,            // ✓ in DATA catalog
+    Stale,              // ⚠ mtime newer than indexed_at
+    NotIndexed,         // ✗ no record in catalog
+    NotApplicable,      // generic dir / not a beamtime
+}
+
+
+
+
+ + +
+

LayoutPolicy — Heuristic + Override

+
+
+
LayoutPolicy struct
+
+ 
pub struct LayoutPolicy {
+    /// folders at this depth from data_root
+    /// are classified as Beamtime (default: 2)
+    pub beamtime_depth: u8,
+
+    /// regex applied to folder names to
+    /// confirm Beamtime classification
+    /// None = accept any at beamtime_depth
+    pub beamtime_pattern: Option<Regex>,
+
+    /// whether policy came from heuristic
+    /// or from user config modal
+    pub source: PolicySource,
+}
+
+pub enum PolicySource {
+    Heuristic,   // auto-detected
+    UserConfig,  // set via modal
+    Default,     // fallback (depth=2)
+}
+
+// Default pattern — matches ALS-style dates
+// and common beamtime naming conventions
+const BEAMTIME_PATTERNS: &[&str] = &[
+    r"^20\d{2}(Jan|Feb|Mar|...|Dec)$",  // 2024Jan
+    r"^20\d{2}-\d{2}(-\d{2})?$",        // 2024-01
+    r"^20\d{6}$",                        // 20240115
+    r"(?i)beamtime",                     // contains "beamtime"
+];
+
+
+ +
+
Classification logic (heuristic.rs)
+
+ 
pub fn classify_entry(
+    path: &Path,
+    data_root: &Path,
+    policy: &LayoutPolicy,
+) -> EntryKind {
+    if path == data_root {
+        return EntryKind::DataRoot;
+    }
+    let depth = relative_depth(path, data_root);
+    match depth {
+        1 => EntryKind::Experimentalist,
+        d if d == policy.beamtime_depth as usize => {
+            let name = folder_name(path);
+            if policy.pattern_matches(name) {
+                EntryKind::Beamtime
+            } else {
+                EntryKind::Directory
+            }
+        }
+        _ => EntryKind::Directory,
+    }
+}
+
+pub fn detect_policy(data_root: &Path)
+    -> (LayoutPolicy, bool /* confident */) {
+    // scan first 2 levels, count pattern hits
+    // returns (policy, confident)
+    // confident=false → trigger modal
+}
+
+
+
+ +
+ Heuristic trigger: On startup, detect_policy(data_root) scans the top 2 levels of the NAS tree (no deep walk). If ≥50% of depth-2 folders match a date pattern, it sets that pattern as the policy and marks confident=true. If <50% match — or no folders exist at depth 2 — it marks confident=false and pushes the ConfigModal before showing the explorer. +
+
+ + +
+

Config Modal Wireframe — ratatui overlay

+
+
ConfigModal — centered over the explorer, keyboard-driven
+ 
+ pyref browser  /Volumes/DATA                                                          
+╔══════════════════════════════════════════════════════════════════════╗
+║  Configure Directory Layout                              [Esc] cancel  ║
+║  pyref could not confidently detect your beamtime structure.           ║
+║  Set the rules below — they will be saved to DATA/.pyref/layout.toml.  ║
+║                                                                      ║
+║  Depth to beamtime folders  (from DATA/ root, min 1)                   ║
+║    ╔══════════╗                                                       ║
+║    ║  2        ║  depth 1 = DATA/Beamtime  depth 2 = DATA/PI/Beamtime  ║
+║    ╚══════════╝                                                       ║
+║                                                                      ║
+║  Beamtime folder name pattern  (regex, empty = accept all)            ║
+║    ╔══════════════════════════════════════════════╗                  ║
+║    ║  ^20\d{2}(Jan|Feb|Mar|Apr|May|Jun|Jul|...)$ ║                  ║
+║    ╚══════════════════════════════════════════════╝                  ║
+║                                                                      ║
+║  Preview  (how your DATA/ root will be classified)                   ║
+║    ┌──────────────────────────────────────────────────────────────┐  ║
+║    │  /Volumes/DATA/                              [DataRoot]       │  ║
+║    │    Smith/                                     [Experimentalist]│  ║
+║    │      2024Jan/                                 [Beamtime ✓]     │  ║
+║    │      2023Nov/                                 [Beamtime ✓]     │  ║
+║    │      scratch/                                 [Directory]      │  ║
+║    │    Jones/                                     [Experimentalist]│  ║
+║    │      2024Feb/                                 [Beamtime ✓]     │  ║
+║    └──────────────────────────────────────────────────────────────┘  ║
+║                                                                      ║
+║  [Tab] next field   [Shift+Tab] prev field   [↵] confirm   [Esc] cancel  ║
+╚══════════════════════════════════════════════════════════════════════╝
+
+
+ +
+ Modal behaviour: The preview pane updates live as the user types in either field — no confirm needed to see the effect. The regex is compiled on every keystroke; if invalid, the pattern field turns red and the preview shows the last valid state. On [↵] confirm, the policy is saved to DATA/.pyref/layout.toml and the modal is popped off the Navigator stack, resuming the Explorer at the same directory. +
+
+ + +
+

Entry Enrichment — Async Loading Strategy

+
+
+
Immediate (sync)
+
+ Name, kind, mtime
+ ✓ From fs::read_dir
+ ✓ Shown instantly
+ ✓ Never blocks +
+
+
+
Fast async (background thread)
+
+ CatalogStatus, fits_count
+ → SQLite query per visible entry
+ → Results streamed via mpsc
+ → Cells fill in as they arrive
+ Shown as — until ready +
+
+
+
Slow async (deferred)
+
+ size_bytes (disk usage)
+ → Spawned only on selection
+ → Or via explicit s key
+ → Shown as … while pending
+ NAS du can be very slow +
+
+
+
+ + diff --git a/.superpowers/brainstorm/6299-1774202169/ingest-catalog.html b/.superpowers/brainstorm/6299-1774202169/ingest-catalog.html new file mode 100644 index 0000000..de8eb58 --- /dev/null +++ b/.superpowers/brainstorm/6299-1774202169/ingest-catalog.html @@ -0,0 +1,355 @@ +

Design Section 4 — Ingestion Pipeline + CatalogHandle

+

+ How ingest_beamtime is adapted to target the DATA-level catalog, + how experimentalist metadata is recorded, and the CatalogHandle interface shared across screens. +

+ + + + +
+

Ingestion — Signature + Context

+
+
+
Before — per-beamtime, no data_root
+
+ 
// src/catalog/ingest.rs (current)
+pub fn ingest_beamtime(
+    beamtime_dir: &Path,
+    header_items: &[String],
+    force:        bool,
+    progress_tx:  Option<Sender<(u32, u32)>>,
+) -> Result<IngestSummary> {
+
+    // opens / creates catalog AT beamtime_dir
+    let conn = open_or_create_db(beamtime_dir)?;
+
+    // walks beamtime_dir for .fits
+    let paths = discover_fits_paths(beamtime_dir)?;
+
+    // inserts into `files` and `bt_scan_points`
+    // no experimentalist, no data_root recorded
+    for (path, mtime) in paths { … }
+}
+
+
+ +
+
After — DATA-level catalog, full context
+
+ 
// src/catalog/ingest.rs (proposed)
+pub fn ingest_beamtime(
+    beamtime_dir:    &Path,
+    data_root:       Option<&Path>,  // NEW
+    experimentalist: Option<&str>,   // NEW
+    header_items:    &[String],
+    force:           bool,
+    progress_tx:     Option<Sender<(u32, u32)>>,
+    cancel:          Option<Arc<AtomicBool>>, // NEW
+) -> Result<IngestSummary> {
+
+    // resolve catalog: DATA/.pyref/catalog.db
+    // if data_root is None → falls back to old
+    // per-beamtime behaviour (backward compat)
+    let db_path = resolve_ingest_target(
+        beamtime_dir, data_root
+    );
+    let conn = open_catalog_db(&db_path)?;
+
+    // upsert bt_beamtimes with new columns
+    upsert_beamtime_record(&conn,
+        beamtime_dir, data_root, experimentalist
+    )?;
+
+    // discover + insert (cancel-aware loop)
+    let paths = discover_fits_paths(beamtime_dir)?;
+    for (path, mtime) in paths {
+        if cancel.as_ref()
+            .map(|c| c.load(Ordering::Relaxed))
+            .unwrap_or(false) { break; }
+        …
+    }
+}
+
+
+
+ +
+ Backward compatibility: data_root: None and experimentalist: None reproduce the current behaviour exactly — the catalog is opened at the beamtime dir, no new columns are written. All existing callers (tests, Python bindings, CLI) pass None until they are individually migrated. The TUI always passes both. +
+
+ + +
+

Catalog Path Resolution

+
+
+
resolve_ingest_target()
+
+ 
pub fn resolve_ingest_target(
+    beamtime_dir: &Path,
+    data_root:    Option<&Path>,
+) -> PathBuf {
+    match data_root {
+        // new path: DATA/.pyref/catalog.db
+        Some(root) =>
+            root.join(".pyref").join("catalog.db"),
+
+        // legacy path: unchanged behaviour
+        None =>
+            resolve_catalog_path(beamtime_dir),
+    }
+}
+
+ Single write target. All beamtimes under the same + DATA/ root share one catalog file. The + bt_beamtimes.beamtime_path + source_path LIKE + filter in queries is what isolates each beamtime's records. +
+
+
+
+
upsert_beamtime_record()
+
+ 
// Called at the start of every ingest.
+// Creates or updates the bt_beamtimes row.
+fn upsert_beamtime_record(
+    conn:            &Connection,
+    beamtime_dir:    &Path,
+    data_root:       Option<&Path>,
+    experimentalist: Option<&str>,
+) -> Result<()> {
+    conn.execute("
+        INSERT INTO bt_beamtimes
+            (beamtime_path, data_root,
+             experimentalist, last_indexed_at)
+        VALUES (?1, ?2, ?3, strftime('%s','now'))
+        ON CONFLICT(beamtime_path) DO UPDATE SET
+            data_root        = excluded.data_root,
+            experimentalist  = excluded.experimentalist,
+            last_indexed_at  = excluded.last_indexed_at
+    ", params![
+        beamtime_dir.to_str(),
+        data_root.and_then(|p| p.to_str()),
+        experimentalist,
+    ])?;
+    Ok(())
+}
+
+
+
+
+ + +
+

CatalogHandle — Shared Query Surface

+
+
+
CatalogHandle struct
+
+ 
/// Lightweight handle — opens a fresh
+/// connection per call (consistent with
+/// existing rusqlite usage pattern).
+pub struct CatalogHandle {
+    pub db_path: PathBuf,  // DATA/.pyref/catalog.db
+}
+
+impl CatalogHandle {
+    /// Explorer: list experimentalists + counts
+    pub fn list_experimentalists(
+        &self, data_root: &Path
+    ) -> Result<Vec<ExptMeta>>;
+
+    /// Explorer: list beamtimes for one expt
+    pub fn list_beamtimes(
+        &self,
+        data_root:       &Path,
+        experimentalist: &str,
+    ) -> Result<Vec<BeamtimeMeta>>;
+
+    /// Explorer: catalog status for one path
+    pub fn catalog_status(
+        &self, beamtime_path: &Path
+    ) -> CatalogStatus;
+
+    /// Beamtime screen: profiles query
+    pub fn query_scan_points(
+        &self,
+        beamtime_path: &Path,
+        filter:        Option<&CatalogFilter>,
+    ) -> Result<Vec<FileRow>>;
+
+    /// Beamtime screen: entries (samples/tags/scans)
+    pub fn list_beamtime_entries_v2(
+        &self, beamtime_path: &Path
+    ) -> Result<BeamtimeEntries>;
+}
+
+
+
+
ExptMeta + BeamtimeMeta return types
+
+ 
/// Returned by list_experimentalists()
+/// — one row per depth-1 folder in catalog
+pub struct ExptMeta {
+    pub name:           String,
+    pub beamtime_count: u32,
+    pub fits_count:     u32,
+    pub last_indexed:   Option<i64>,
+}
+
+/// Returned by list_beamtimes()
+/// — one row per bt_beamtimes entry
+pub struct BeamtimeMeta {
+    pub path:           PathBuf,
+    pub fits_count:     Option<u32>,
+    pub last_indexed:   Option<i64>,
+}
+
+/// catalog_status() implementation:
+/// 1. Query bt_beamtimes for beamtime_path
+/// 2. If missing → NotIndexed
+/// 3. If found → compare last_indexed_at
+//     with max(mtime) of files in dir
+/// 4. If dir mtime > last_indexed → Stale
+/// 5. Otherwise → Indexed
+
+
+
+ +
+ Where CatalogHandle lives: Navigator owns one CatalogHandle constructed from data_root.join(".pyref/catalog.db"). Both ExplorerState and BeamtimeState call into it by reference — no cloning, no Arc. If the catalog file doesn't exist yet (first run, no ingests), all methods return empty vecs or NotIndexed; the DB is created on first ingest. +
+
+ + +
+

Execution Plan — Four Independent Phases

+
+
+
Phase 1 — Decompose
+
+ Extract BeamtimeState from App
+ Introduce Navigator + ScreenState
+ App becomes thin shell
+ All existing behaviour preserved
+ Files touched:
+ app.rs run.rs ui.rs keymap.rs
+ New: navigator.rs

+ ✓ No user-visible change
+ ✓ Mergeable independently +
+
+
+
Phase 2 — Explorer
+
+ Add ExplorerState + ExplorerLevel
+ Add heuristic.rs classifier
+ Add per-expt NasLayout + layout.toml
+ Add ConfigModal + modal UI
+ Wire CLI arg as data_root
+ New: explorer/mod.rs
+ explorer/heuristic.rs
+ explorer/modal.rs

+ ✓ Explorer browseable
+ ✓ Catalog status: NotIndexed +
+
+
+
Phase 3 — Catalog
+
+ Add CatalogHandle
+ Add explorer_query.rs
+ Schema migration: add
+ experimentalist + data_root
+ to bt_beamtimes
+ Adapt ingest_beamtime signature
+ Add cancel token to ingest

+ ✓ Explorer shows live catalog status
+ ✓ Ingest writes to DATA catalog +
+
+
+
Phase 4 — Wire
+
+ Auto-ingest on Beamtime entry
+ BeamtimeState queries DATA catalog
+ Back nav restores Explorer cursor
+ Status cell updates via channel
+ Config saved on BeamtimeState pop
+ Deprecate Screen::Launcher
+ Remove old list_beamtimes() call

+ ✓ Full end-to-end flow
+ ✓ Launcher replaced by Explorer +
+
+
+
+ diff --git a/.superpowers/brainstorm/6299-1774202169/waiting-1.html b/.superpowers/brainstorm/6299-1774202169/waiting-1.html new file mode 100644 index 0000000..ef07652 --- /dev/null +++ b/.superpowers/brainstorm/6299-1774202169/waiting-1.html @@ -0,0 +1,3 @@ +
+

Continuing in terminal...

+
diff --git a/AGENTS.md b/AGENTS.md index 664e694..3ae2ed7 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -62,8 +62,9 @@ Background correction uses edge-based subtraction: per-row (left/right) and per- Image processing reduces 2D detector images to reflectivity values: -**Rust implementation** (`src/io.rs`): -- `subtract_background()`: Row-by-row background subtraction +**Rust implementation** (`src/io/mod.rs`): +- Raw preview pipeline: trim edges (remove detector nonlinearity), then row-by-row background (10-pixel left/right strips, colder side per row), then cold-side dark subtraction (top vs bottom, single scalar). Used by TUI preview and `materialize_image`. +- `subtract_background()`: Legacy row-by-row background subtraction (full-size output; interior only corrected). - `simple_reflectivity()`: Calculates beam signal vs background using ROI - `process_image()`: Main image processing pipeline @@ -116,7 +117,15 @@ Uncertainty propagation implemented as Polars plugins: ### 6. Catalog (beamtime index) -One SQLite database per beamtime directory (`.pyref_catalog.db`) caches FITS metadata and supports overrides so scans avoid repeated header I/O. +One SQLite database per beamtime directory (`.pyref_catalog.db`) caches FITS metadata and supports overrides so scans avoid repeated header I/O. A **new layout** uses a single catalog per parent directory. + +**Catalog layout (legacy vs new):** +- Legacy: DB at beamtime_dir/.pyref_catalog.db; schema uses files table + overrides. +- New: DB at parent/.pyref/catalog.db for multiple beamtimes; schema uses normalized bt_* tables only (bt_beamtimes, bt_samples, bt_scans, bt_streams, bt_scan_points, etc.). Ingest writes only to bt_* for new layout (no files table). +- resolve_catalog_path(beamtime_dir) returns new path if it exists, else legacy path; new path is chosen when parent exists and is not root. +- Beamtimes list: launcher uses ~/.pyref/beamtime_index.sqlite3; list_beamtimes_from_catalog(db_path) returns beamtimes from a given catalog (bt_beamtimes) for the new layout. +- Zarr store: same location as catalog (parent/.pyref/zarr//); one subdir per beamtime. +- FITS discovery: case-insensitive .fits extension; failed FITS reads are not silently dropped during ingest (first error returned with path). **Source resolution in `scan_experiment(source)`** (Python `python/pyref/io/readers.py`): - If `source` is a directory and `path / ".pyref_catalog.db"` exists, the scan is served from the catalog (Rust `scan_from_catalog`); no FITS I/O. @@ -139,7 +148,13 @@ Catalog is built by default (feature `catalog`); the TUI feature includes catalo **TUI (pyref-tui)**: Startup: with no CLI argument, the TUI shows the launcher (list of indexed beamtimes from `~/.pyref/beamtime_index.sqlite3`, most recent first). Enter opens the selected beamtime; [o] opens the "Open directory" dialog. With a CLI argument, the TUI opens that beamtime directly. Open directory: hybrid dialog with path input (Tab autocomplete) and scrollable folder list (.. and subdirs); Enter on a valid path runs ingest, registers the beamtime in the central index, and opens that beamtime. While ingesting (from launcher or from beamtime view), the nav line shows "Ingesting N/M...". If there is no catalog in the opened directory, the TUI shows an empty state and the message to run "Ingest directory" (key [i]) or `pyref.io.ingest_beamtime(path)` from Python. The beamtime browse panel lists **reflectivity profiles**: one row per experiment (scan) for a given sample, tag, and polarization. Two scan modes are supported: **fixed-energy (theta scan)** where sample theta varies and energy is fixed, and **fixed-angle (energy scan)** where energy varies and theta is fixed or limited to a few angles. The table shows Emin (eV), Emax (eV), Type (theta-scan / E-scan), theta min/max, frame count, and duration. Scan type is inferred from the data using energy/theta range tolerances, distinct-value counts, and an Izero heuristic (many points at theta near zero indicate a theta scan). Each row is a single reflectivity profile; Enter expands it to show the underlying FITS files in a table (Scan, Frame, pol, E (eV), sample theta). When expanded, j/k scroll the file list; Enter collapses. Rename/Retag write catalog overrides via `set_override` and the table reloads. On exit, the TUI saves config to `PYREF_TUI_CONFIG` or `~/.config/pyref/tui.toml`, including `last_root`, `selected_samples`, `selected_tags`, and `selected_scan_numbers` (when on beamtime view). Scripts can read that config and use `scan_experiment(last_root).filter(...)` with the same sample/tag/scan filters to match the TUI view. -**FITS Preview (three-panel, marquee)**: When a FITS file is selected in the TUI, a preview window shows one figure with three panels (non-macOS: egui; macOS: single composite PNG). Left panel: raw background-subtracted image. Middle panel: Gaussian-filtered image. Right panel: 4-sigma crop around the fitted beam center with 1/2/3/4-sigma Gaussian fit contours overlaid. Sigma for the filter is derived from the 2D Gaussian fit (average of row/col sigma, clamped to 0.5--20.0); if the fit fails, a default sigma (2.0) is used and the right panel shows a center crop. On non-macOS, the user can optionally draw a marquee (drag a rectangle) on the left or middle panel; on mouse release the ROI is stored for that path and the fit is re-run inside the ROI, updating all three panels and contours. ROI is per-path and in-memory for the session. +**Parallel ingest (feature `parallel_ingest`, enabled by default)**: Enables `crossbeam-channel` and a pipelined ingest path. The TUI/browser is built with `--features tui`, which includes `parallel_ingest`, so `ingest_beamtime` uses the pipelined path and partitioned discovery when applicable. Discovery can use partitioned parallel walks over top-level subdirs (`discover_fits_paths_parallel`) when multiple subdirs exist; results are merged and sorted by path. A single thread holds the SQLite connection; a reader thread sends bounded batches (sync channel capacity 2) so FITS reads overlap with batched commits. For the new catalog layout only (`is_new_catalog_layout`), the hot path can use row structs (`BtIngestRow` from `build_bt_ingest_row` / `read_multiple_fits_headers_only_rows`) and `upsert_bt_batch_rows` instead of building Polars DataFrames per batch. The DataFrame path (`read_fits_metadata_batch`, `upsert_bt_batch`) remains for Python and legacy layout. Build without Python: `cargo check --no-default-features --features catalog,parallel_ingest`. + +**Deferred work (calculated columns, beam spot, zarr)**: Catalog ingest stores header fields and source path/mtime in `bt_scan_points`. Calculated columns (e.g. Q, Lambda) can be deferred to export (Parquet/HDF5) rather than computed during ingest. Beam spot columns in `bt_scan_points` stay NULL until a processing stage updates them. Zarr materialization (`materialize_beamtime`) is an explicit stage that re-reads FITS from paths recorded in the catalog; a single combined pass (one read for catalog row plus zarr chunk) is optional and not the default, so back-to-back full-tree reread for catalog then zarr implies two NAS reads unless data is copied local first. + +**NAS usage**: Prefer one open/read per file for catalog metadata ingest. Parallel discovery reduces wall time on wide trees; batch transactions on the single writer reduce fsync churn. For repeated materialize runs, copy beamtime tree to local storage then run zarr materialize to avoid hammering NAS. + +**FITS Preview (three-panel, marquee)**: When a FITS file is selected in the TUI, a preview window shows one figure with three panels (non-macOS: egui; macOS: single composite PNG). Left panel: Raw image (trimmed, then row-by-row background with 10-pixel strips, then cold-side dark subtraction). Middle panel: Gaussian-filtered image. Right panel: 4-sigma crop around the fitted beam center with 1/2/3/4-sigma Gaussian fit contours overlaid. Sigma for the filter is derived from the 2D Gaussian fit (average of row/col sigma, clamped to 0.5--20.0); if the fit fails, a default sigma (2.0) is used and the right panel shows a center crop. On non-macOS, the user can optionally draw a marquee (drag a rectangle) on the left or middle panel; on mouse release the ROI is stored for that path and the fit is re-run inside the ROI, updating all three panels and contours. ROI is per-path and in-memory for the session. ### 7. Data Stitching (Conceptual) diff --git a/CLAUDE.md b/CLAUDE.md new file mode 100644 index 0000000..740ed41 --- /dev/null +++ b/CLAUDE.md @@ -0,0 +1,12 @@ + + +## 🤖 Agent-Selected Rules +@.cursor/rules/clarify-first-prompting.mdc +@.cursor/rules/cursor-agent-orchestration.mdc +@.cursor/rules/cursot-tools-mastery.mdc + + + +## Project Notes + + \ No newline at end of file diff --git a/Cargo.lock b/Cargo.lock index 800b566..cf33690 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -508,6 +508,17 @@ dependencies = [ "zbus", ] +[[package]] +name = "auto_impl" +version = "1.3.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "ffdcb70bdbc4d478427380519163274ac86e52916e10f0a8889adf0f96d3fee7" +dependencies = [ + "proc-macro2", + "quote", + "syn 2.0.116", +] + [[package]] name = "autocfg" version = "1.5.0" @@ -844,12 +855,6 @@ dependencies = [ "unicode-width 0.1.14", ] -[[package]] -name = "color_quant" -version = "1.1.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "3d7b894f5411737b7867f4827955924d7c254fc9f4d91a6aad6b097804b1018b" - [[package]] name = "com" version = "0.6.0" @@ -926,6 +931,15 @@ dependencies = [ "crossbeam-utils", ] +[[package]] +name = "convert_case" +version = "0.10.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "633458d4ef8c78b72454de2d54fd6ab2e60f9e02be22f3c6104cdc8a4e0fceb9" +dependencies = [ + "unicode-segmentation", +] + [[package]] name = "core-foundation" version = "0.9.4" @@ -1160,6 +1174,29 @@ dependencies = [ "syn 1.0.109", ] +[[package]] +name = "derive_more" +version = "2.1.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "d751e9e49156b02b44f9c1815bcb94b984cdcc4396ecc32521c739452808b134" +dependencies = [ + "derive_more-impl", +] + +[[package]] +name = "derive_more-impl" +version = "2.1.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "799a97264921d8623a957f6c3b9011f3b5492f557bbb7a5a19b7fa6d06ba8dcb" +dependencies = [ + "convert_case", + "proc-macro2", + "quote", + "rustc_version", + "syn 2.0.116", + "unicode-xid", +] + [[package]] name = "difflib" version = "0.4.0" @@ -1259,7 +1296,7 @@ dependencies = [ "glow", "glutin", "glutin-winit", - "image 0.25.9", + "image", "js-sys", "log", "objc2 0.5.2", @@ -1563,6 +1600,12 @@ version = "0.1.5" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "d9c4f5dac5e15c24eb999c26181a6ca40b39fe946cbe4c263c7209467bc83af2" +[[package]] +name = "foldhash" +version = "0.2.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "77ce24cb58228fbb8aa041425bb1050850ac19177686ea6e0f41a70416f56fdb" + [[package]] name = "foreign-types" version = "0.5.0" @@ -1779,9 +1822,11 @@ source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "899def5c37c4fd7b2664648c28120ecec138e4d395b459e5ca34f9cce2dd77fd" dependencies = [ "cfg-if", + "js-sys", "libc", "r-efi", "wasip2", + "wasm-bindgen", ] [[package]] @@ -1955,6 +2000,7 @@ version = "2.7.1" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "6ea2d84b969582b4b1864a92dc5d27cd2b77b622a8d79306834f1be5ba20d84b" dependencies = [ + "bytemuck", "cfg-if", "crunchy", "zerocopy", @@ -1980,7 +2026,7 @@ checksum = "9229cfe53dfd69f0609a49f65461bd93001ea1ef889cd5529dd176593f5338a1" dependencies = [ "allocator-api2", "equivalent", - "foldhash", + "foldhash 0.1.5", "rayon", "serde", ] @@ -1990,6 +2036,11 @@ name = "hashbrown" version = "0.16.1" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "841d1cc9bed7f9236f321df977030373f4a4163ae1a7dbfe1a51a2c1a51d9100" +dependencies = [ + "allocator-api2", + "equivalent", + "foldhash 0.2.0", +] [[package]] name = "hashlink" @@ -2203,20 +2254,6 @@ dependencies = [ "icu_properties", ] -[[package]] -name = "image" -version = "0.24.9" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "5690139d2f55868e080017335e4b94cb7414274c74f1669c84fb5feba2c9f69d" -dependencies = [ - "bytemuck", - "byteorder", - "color_quant", - "jpeg-decoder", - "num-traits", - "png 0.17.16", -] - [[package]] name = "image" version = "0.25.9" @@ -2227,7 +2264,7 @@ dependencies = [ "byteorder-lite", "moxcms", "num-traits", - "png 0.18.1", + "png", ] [[package]] @@ -2293,6 +2330,15 @@ dependencies = [ "cfg-if", ] +[[package]] +name = "inventory" +version = "0.3.22" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "009ae045c87e7082cb72dab0ccd01ae075dd00141ddc108f43a0ea150a9e7227" +dependencies = [ + "rustversion", +] + [[package]] name = "io-lifetimes" version = "1.0.11" @@ -2319,6 +2365,15 @@ dependencies = [ "either", ] +[[package]] +name = "itertools" +version = "0.14.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "2b192c782037fadd9cfa75548310488aabdbf3d2da73885b31bd0abd03351285" +dependencies = [ + "either", +] + [[package]] name = "itoa" version = "1.0.17" @@ -2363,12 +2418,6 @@ dependencies = [ "libc", ] -[[package]] -name = "jpeg-decoder" -version = "0.3.2" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "00810f1d8b74be64b13dbf3db89ac67740615d6c891f0e7b6179326533011a07" - [[package]] name = "js-sys" version = "0.3.85" @@ -2489,6 +2538,18 @@ dependencies = [ "vcpkg", ] +[[package]] +name = "libz-sys" +version = "1.1.25" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "d52f4c29e2a68ac30c9087e1b772dc9f44a2b66ed44edf2266cf2be9b03dafc1" +dependencies = [ + "cc", + "libc", + "pkg-config", + "vcpkg", +] + [[package]] name = "linux-raw-sys" version = "0.3.8" @@ -2543,6 +2604,15 @@ dependencies = [ "hashbrown 0.15.5", ] +[[package]] +name = "lru" +version = "0.16.3" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "a1dc47f592c06f33f8e3aea9591776ec7c9f9e4124778ff8a3c3b87159f7e593" +dependencies = [ + "hashbrown 0.16.1", +] + [[package]] name = "lz4" version = "1.28.1" @@ -2663,6 +2733,45 @@ dependencies = [ "windows-sys 0.61.2", ] +[[package]] +name = "moka" +version = "0.12.14" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "85f8024e1c8e71c778968af91d43700ce1d11b219d127d79fb2934153b82b42b" +dependencies = [ + "crossbeam-channel", + "crossbeam-epoch", + "crossbeam-utils", + "equivalent", + "parking_lot", + "portable-atomic", + "smallvec", + "tagptr", + "uuid", +] + +[[package]] +name = "monostate" +version = "1.0.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "bb4cc965c89dd0615a9e822ff8002f7633d2466143d51bd58693e4b2c75aabad" +dependencies = [ + "monostate-impl", + "serde", + "serde_core", +] + +[[package]] +name = "monostate-impl" +version = "1.0.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "23f5b99488110875b5904839d396c2cdfaf241ff6622638acb879cc7effad5de" +dependencies = [ + "proc-macro2", + "quote", + "syn 2.0.116", +] + [[package]] name = "moxcms" version = "0.7.11" @@ -2731,6 +2840,21 @@ dependencies = [ "rawpointer", ] +[[package]] +name = "ndarray" +version = "0.17.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "520080814a7a6b4a6e9070823bb24b4531daac8c4627e08ba5de8c5ef2f2752d" +dependencies = [ + "matrixmultiply", + "num-complex", + "num-integer", + "num-traits", + "portable-atomic", + "portable-atomic-util", + "rawpointer", +] + [[package]] name = "ndk" version = "0.8.0" @@ -2843,6 +2967,20 @@ dependencies = [ "winapi", ] +[[package]] +name = "num" +version = "0.4.3" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "35bd024e8b2ff75562e5f34e7f4905839deb4b22955ef5e73d2fea1b9813cb23" +dependencies = [ + "num-bigint", + "num-complex", + "num-integer", + "num-iter", + "num-rational", + "num-traits", +] + [[package]] name = "num-bigint" version = "0.4.6" @@ -2859,6 +2997,7 @@ version = "0.4.6" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "73f88a1307638156682bada9d7604135552957b7818057dcef22705b4d509495" dependencies = [ + "bytemuck", "num-traits", ] @@ -2871,6 +3010,28 @@ dependencies = [ "num-traits", ] +[[package]] +name = "num-iter" +version = "0.1.45" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "1429034a0490724d0075ebb2bc9e875d6503c3cf69e235a8941aa757d83ef5bf" +dependencies = [ + "autocfg", + "num-integer", + "num-traits", +] + +[[package]] +name = "num-rational" +version = "0.4.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "f83d14da390562dca69fc84082e73e548e1ad308d24accdedd2720017cb37824" +dependencies = [ + "num-bigint", + "num-integer", + "num-traits", +] + [[package]] name = "num-traits" version = "0.2.19" @@ -2920,7 +3081,7 @@ source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "edb929bc0da91a4d85ed6c0a84deaa53d411abfb387fc271124f91bf6b89f14e" dependencies = [ "libc", - "ndarray", + "ndarray 0.16.1", "num-complex", "num-integer", "num-traits", @@ -3189,6 +3350,16 @@ dependencies = [ "ttf-parser", ] +[[package]] +name = "page_size" +version = "0.6.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "30d5b2194ed13191c1999ae0704b7839fb18384fa22e49b57eeaa97d79ce40da" +dependencies = [ + "libc", + "winapi", +] + [[package]] name = "parking" version = "2.2.1" @@ -3224,6 +3395,12 @@ version = "1.0.15" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "57c0d7b74b563b49d38dae00a0c37d4d6de9b432382b2892f0574ddcae73fd0a" +[[package]] +name = "pathdiff" +version = "0.2.3" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "df94ce210e5bc13cb6651479fa48d14f601d9858cfe0467f43ae157023b938d3" + [[package]] name = "percent-encoding" version = "2.3.2" @@ -3289,48 +3466,6 @@ dependencies = [ "array-init-cursor", ] -[[package]] -name = "plotters" -version = "0.3.7" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "5aeb6f403d7a4911efb1e33402027fc44f29b5bf6def3effcc22d7bb75f2b747" -dependencies = [ - "num-traits", - "plotters-backend", - "plotters-bitmap", - "wasm-bindgen", - "web-sys", -] - -[[package]] -name = "plotters-backend" -version = "0.3.7" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "df42e13c12958a16b3f7f4386b9ab1f3e7933914ecea48da7139435263a4172a" - -[[package]] -name = "plotters-bitmap" -version = "0.3.7" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "72ce181e3f6bf82d6c1dc569103ca7b1bd964c60ba03d7e6cdfbb3e3eb7f7405" -dependencies = [ - "image 0.24.9", - "plotters-backend", -] - -[[package]] -name = "png" -version = "0.17.16" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "82151a2fc869e011c153adc57cf2789ccb8d9906ce52c0b39a6b5697749d7526" -dependencies = [ - "bitflags 1.3.2", - "crc32fast", - "fdeflate", - "flate2", - "miniz_oxide", -] - [[package]] name = "png" version = "0.18.1" @@ -3893,6 +4028,17 @@ dependencies = [ "portable-atomic", ] +[[package]] +name = "positioned-io" +version = "0.3.5" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "d4ec4b80060f033312b99b6874025d9503d2af87aef2dd4c516e253fbfcdada7" +dependencies = [ + "byteorder", + "libc", + "winapi", +] + [[package]] name = "potential_utf" version = "0.1.4" @@ -4104,6 +4250,7 @@ name = "pyref_rs" version = "0.1.0" dependencies = [ "chrono", + "crossbeam-channel", "crossterm 0.28.1", "eframe", "fuzzy-matcher", @@ -4111,11 +4258,10 @@ dependencies = [ "libblur", "log", "memmap2", - "ndarray", + "ndarray 0.16.1", "notify-debouncer-mini", "numpy", "physical_constants", - "plotters", "polars", "predicates", "pyo3", @@ -4130,6 +4276,7 @@ dependencies = [ "thiserror 2.0.18", "toml", "walkdir", + "zarrs", ] [[package]] @@ -4150,6 +4297,18 @@ dependencies = [ "memchr", ] +[[package]] +name = "quick_cache" +version = "0.6.19" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "530e84778a55de0f52645a51d4e3b9554978acd6a1e7cd50b6a6784692b3029e" +dependencies = [ + "ahash", + "equivalent", + "hashbrown 0.16.1", + "parking_lot", +] + [[package]] name = "quote" version = "1.0.44" @@ -4217,8 +4376,8 @@ dependencies = [ "crossterm 0.28.1", "indoc", "instability", - "itertools", - "lru", + "itertools 0.13.0", + "lru 0.12.5", "paste", "strum", "unicode-segmentation", @@ -4273,6 +4432,15 @@ dependencies = [ "crossbeam-utils", ] +[[package]] +name = "rayon_iter_concurrent_limit" +version = "0.2.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "d09ee01023de07fa073ce14c37cbe0a9e099c6b0b60a29cf4af6d04d9553fed7" +dependencies = [ + "rayon", +] + [[package]] name = "recursive" version = "0.1.1" @@ -4566,6 +4734,7 @@ version = "1.0.149" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "83fc039473c5595ace860d8c4fafa220ff474b3fc6bfdb4293327f1a37e94d86" dependencies = [ + "indexmap", "itoa", "memchr", "serde", @@ -4916,6 +5085,12 @@ dependencies = [ "windows 0.57.0", ] +[[package]] +name = "tagptr" +version = "0.2.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "7b2093cf4c8eb1e67749a6762251bc9cd836b6fc171623bd0a9d324d37af2417" + [[package]] name = "target-features" version = "0.1.6" @@ -5034,6 +5209,21 @@ dependencies = [ "zerovec", ] +[[package]] +name = "tinyvec" +version = "1.10.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "bfa5fdc3bce6191a1dbc8c02d5c8bffcf557bafa17c124c5264a458f1b0613fa" +dependencies = [ + "tinyvec_macros", +] + +[[package]] +name = "tinyvec_macros" +version = "0.1.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "1f3ccbac311fea05f86f61904b462b55fb3df8837a366dfc601a0161d0532f20" + [[package]] name = "tokio" version = "1.49.0" @@ -5234,7 +5424,7 @@ version = "1.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "b3644627a5af5fa321c95b9b235a72fd24cd29c648c2c379431e6628655627bf" dependencies = [ - "itertools", + "itertools 0.13.0", "unicode-segmentation", "unicode-width 0.1.14", ] @@ -5263,6 +5453,12 @@ version = "0.2.4" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "7264e107f553ccae879d21fbea1d6724ac785e8c3bfc762137959b5802826ef3" +[[package]] +name = "unsafe_cell_slice" +version = "0.2.2" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "6659959f702dcdaad77bd6e42a9409a32ceccc06943ec93c8a4306be00eb6cf1" + [[package]] name = "url" version = "2.5.8" @@ -6502,6 +6698,193 @@ dependencies = [ "pxfm", ] +[[package]] +name = "zarrs" +version = "0.23.6" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "108ec994671d33803489ec853147af910b6db3c276b0461b0895714fbb7a78b6" +dependencies = [ + "async-lock 3.4.2", + "base64", + "bytemuck", + "bytes", + "derive_more", + "getrandom 0.3.4", + "half", + "inventory", + "itertools 0.14.0", + "itoa", + "libz-sys", + "log", + "lru 0.16.3", + "moka", + "ndarray 0.17.2", + "num", + "num-complex", + "paste", + "quick_cache", + "rayon", + "rayon_iter_concurrent_limit", + "serde", + "serde_json", + "thiserror 2.0.18", + "thread_local", + "unsafe_cell_slice", + "uuid", + "zarrs_chunk_grid", + "zarrs_chunk_key_encoding", + "zarrs_codec", + "zarrs_data_type", + "zarrs_filesystem", + "zarrs_metadata", + "zarrs_metadata_ext", + "zarrs_plugin", + "zarrs_storage", +] + +[[package]] +name = "zarrs_chunk_grid" +version = "0.5.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "1cf67386fd96a0336cd3e5ab5ca6cb14e0e05aee80f1acae8c4d3cf562a8bb65" +dependencies = [ + "derive_more", + "inventory", + "itertools 0.14.0", + "rayon", + "thiserror 2.0.18", + "tinyvec", + "zarrs_metadata", + "zarrs_plugin", +] + +[[package]] +name = "zarrs_chunk_key_encoding" +version = "0.2.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "9040e7feaa92d1904d492acd0cd91b97214f1791c5b5738e6c05b2ca4145a382" +dependencies = [ + "derive_more", + "inventory", + "zarrs_metadata", + "zarrs_plugin", + "zarrs_storage", +] + +[[package]] +name = "zarrs_codec" +version = "0.2.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "0464bb05bd143af1d316971ae1cb08d402c75195e7e81bb341f9a5b7b49bfaee" +dependencies = [ + "async-trait", + "bytemuck", + "derive_more", + "futures", + "inventory", + "itertools 0.14.0", + "rayon", + "thiserror 2.0.18", + "unsafe_cell_slice", + "zarrs_chunk_grid", + "zarrs_data_type", + "zarrs_metadata", + "zarrs_plugin", + "zarrs_storage", +] + +[[package]] +name = "zarrs_data_type" +version = "0.9.0" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "1bc7c594c9363278fcd9db4c205514f009944206eb093ea7ad40b85f50009f31" +dependencies = [ + "derive_more", + "half", + "inventory", + "num", + "paste", + "serde", + "serde_json", + "thiserror 2.0.18", + "zarrs_metadata", + "zarrs_plugin", +] + +[[package]] +name = "zarrs_filesystem" +version = "0.3.9" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "270efeb0181651aee5460b3232f2fc83e91bd646cefe75001d1c8f9a4f3abf81" +dependencies = [ + "bytes", + "derive_more", + "itertools 0.14.0", + "libc", + "page_size", + "pathdiff", + "positioned-io", + "thiserror 2.0.18", + "walkdir", + "zarrs_storage", +] + +[[package]] +name = "zarrs_metadata" +version = "0.7.4" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "5048bb16f093f9fa78ade440144e7709e0802bfc73bfb70c52f85d38bff3b03a" +dependencies = [ + "derive_more", + "half", + "monostate", + "serde", + "serde_json", + "thiserror 2.0.18", +] + +[[package]] +name = "zarrs_metadata_ext" +version = "0.4.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "8d05697d0c807d7192942b13a018404b98b0bb284541a650ebfa9be4d7cd8303" +dependencies = [ + "derive_more", + "monostate", + "num", + "serde", + "serde_json", + "serde_repr", + "thiserror 2.0.18", + "zarrs_metadata", +] + +[[package]] +name = "zarrs_plugin" +version = "0.4.1" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "5cbe0ed432aee86856f70ca33be36eaf4a0dae21ab730750d9280a7ca1e95046" +dependencies = [ + "paste", + "regex", + "serde_json", + "thiserror 2.0.18", +] + +[[package]] +name = "zarrs_storage" +version = "0.4.3" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "7d098796d2ed4cf94896569615101e0432e870a7665396da5cc32300fb68f7c1" +dependencies = [ + "auto_impl", + "bytes", + "derive_more", + "itertools 0.14.0", + "thiserror 2.0.18", + "unsafe_cell_slice", +] + [[package]] name = "zbus" version = "3.15.2" diff --git a/Cargo.toml b/Cargo.toml index 2089cc5..70ec17a 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -19,10 +19,12 @@ path = "src/bin/browser.rs" required-features = ["tui"] [features] -default = ["extension-module", "catalog"] +default = ["extension-module", "catalog", "parallel_ingest"] extension-module = ["dep:pyo3", "dep:pyo3-polars", "dep:numpy"] catalog = ["dep:rusqlite", "dep:walkdir"] -tui = ["catalog", "watch", "dep:ratatui", "dep:crossterm", "dep:toml", "dep:fuzzy-matcher", "dep:log", "dep:eframe", "dep:plotters"] +parallel_ingest = ["catalog", "dep:crossbeam-channel"] +zarr = ["catalog", "dep:zarrs"] +tui = ["catalog", "watch", "parallel_ingest", "dep:ratatui", "dep:crossterm", "dep:toml", "dep:fuzzy-matcher", "dep:log", "dep:eframe"] watch = ["dep:notify-debouncer-mini"] [dependencies] @@ -46,11 +48,11 @@ toml = {version = "0.8", optional = true} fuzzy-matcher = {version = "0.3", optional = true} rusqlite = {version = "0.32", optional = true, features = ["bundled"]} walkdir = {version = "2", optional = true} +crossbeam-channel = {version = "0.5", optional = true} notify-debouncer-mini = {version = "0.4", optional = true} log = {version = "0.4", optional = true} eframe = {version = "0.28", optional = true, default-features = true} -plotters = {version = "0.3", optional = true, default-features = false, features = ["bitmap_backend", "bitmap_encoder", "line_series"]} - +zarrs = {version = "0.23", optional = true, default-features = false, features = ["filesystem", "ndarray"]} [dev-dependencies] tempfile = "3.14" rstest = "0.18" diff --git a/docs/superpowers/specs/2026-03-22-tui-explorer-redesign.md b/docs/superpowers/specs/2026-03-22-tui-explorer-redesign.md new file mode 100644 index 0000000..7467c2c --- /dev/null +++ b/docs/superpowers/specs/2026-03-22-tui-explorer-redesign.md @@ -0,0 +1,409 @@ +# TUI Explorer Redesign — Design Spec + +**Date:** 2026-03-22 +**Status:** Approved +**Scope:** Overhaul the `browser` TUI binary from a flat beamtime launcher into a three-level NAS file explorer with a single DATA-level SQLite catalog and per-experimentalist layout configuration. + +--- + +## 1. Problem Statement + +The current TUI has two screens: + +- **Launcher** — a flat list of recently-ingested beamtime paths drawn from `~/.pyref/beamtime_index.sqlite3`. No hierarchy, no browsing, no metadata. +- **Beamtime screen** — samples / tags / scans / profiles view, scoped to a single beamtime directory and its own per-beamtime `catalog.db`. + +This architecture does not model the actual NAS layout (`DATA/Experimentalist/Beamtime/…`), has no awareness of un-indexed beamtimes, and places the catalog in a user-home global index rather than co-located with the data. The existing `App` struct is a 2600-line monolith with 40+ fields mixing all screen state together, making further extension impractical. + +--- + +## 2. Goals + +1. Replace the Launcher with a full NAS file explorer rooted at a `DATA/` directory passed as a CLI argument. +2. Add an Experimentalist level between the root and beamtime folders, with per-folder catalog status (indexed / stale / not indexed) and FITS file counts. +3. Auto-ingest un-indexed or stale beamtimes on entry, with a cancellable progress spinner. +4. Move the catalog from per-beamtime files to a single `DATA/.pyref/catalog.db` that aggregates all experimentalists and beamtimes. +5. Adapt the existing Beamtime screen to query the DATA-level catalog filtered by beamtime path — no visual change to the screen itself. +6. Decompose the monolithic `App` struct into a `Navigator` + per-screen state model. + +--- + +## 3. Non-Goals + +- Zarr store migration or materialization changes. +- Python bindings or CLI tool changes (backward compatibility is required). +- Any change to the Beamtime screen's visual layout or existing features. +- Tiled integration (future work). +- A web UI (future work). + +--- + +## 4. NAS Directory Model + +``` +DATA/ ← data_root (CLI arg) +├── .pyref/ +│ ├── catalog.db ← single DATA-level catalog (NEW) +│ └── layout.toml ← per-experimentalist layout policy (NEW) +├── Smith/ ← Experimentalist (depth 1, no leading dot) +│ ├── 2024Jan/ ← Beamtime (matched by date pattern) +│ ├── 2023Nov/ +│ └── scratch/ ← generic directory +└── Jones/ + └── 2024Feb/ +``` + +Folders at depth 1 whose names do **not** start with `.` are treated as experimentalist candidates. Whether their subdirectories are classified as beamtime folders is determined by the `LayoutPolicy` described in §6. + +--- + +## 5. Architecture — Navigator + Per-Screen State + +### 5.1 Module Changes + +**New files (`src/bin/tui/`):** + +| File | Purpose | +|---|---| +| `navigator.rs` | `Navigator` struct + `ScreenState` enum | +| `explorer/mod.rs` | `ExplorerState`, `DirEntry`, `EntryKind`, `CatalogStatus` | +| `explorer/heuristic.rs` | `detect_policy()`, `classify_entry()` | +| `explorer/modal.rs` | `ModalState`, ratatui resolution modal | +| `catalog_handle.rs` | `CatalogHandle`, `ExptMeta`, `BeamtimeMeta` | + +**Modified files (`src/bin/tui/`):** + +| File | Change | +|---|---| +| `app.rs` | `App` becomes a thin shell owning `Navigator` + shared channels + config | +| `ui.rs` | Dispatches on `ScreenState` variant | +| `run.rs` | Event loop delegates to `Navigator` | +| `keymap.rs` | Per-screen keybind dispatch | + +**Unchanged:** `beamspot.rs`, `preview.rs`, `scan_type.rs`, `theme.rs`, `terminal_guard.rs`. + +**New files (`src/catalog/`):** + +| File | Purpose | +|---|---| +| `explorer_query.rs` | `list_experimentalists()`, `list_beamtimes()`, `catalog_status()` | + +**Modified files (`src/catalog/`):** + +| File | Change | +|---|---| +| `ingest.rs` | New `data_root` + `experimentalist` + `cancel` parameters | +| `query.rs` | `query_scan_points` gains required `beamtime_path` filter | +| `mod.rs` | Re-exports new public API | + +### 5.2 Core Types + +```rust +pub struct App { + pub navigator: Navigator, + pub preview_tx: Option)>>, + pub beamspot_rx: Option>, + pub preview_cmd_rx: Option>, + pub layout: String, + pub keymap: String, + pub theme: String, + pub needs_redraw: bool, +} + +pub struct Navigator { + stack: Vec, // current + back history + forward: Vec, + pub catalog: CatalogHandle, +} + +pub enum ScreenState { + Explorer(ExplorerState), + Beamtime(BeamtimeState), + ConfigModal(ModalState), +} +``` + +### 5.3 CatalogHandle + +A plain struct wrapping `db_path: PathBuf`. Opens a fresh `rusqlite::Connection` per call — no `Arc`. Owned by `Navigator`, used by both screen types by reference. + +```rust +pub struct CatalogHandle { pub db_path: PathBuf } + +impl CatalogHandle { + pub fn list_experimentalists(&self, data_root: &Path) -> Result>; + pub fn list_beamtimes(&self, data_root: &Path, experimentalist: &str) -> Result>; + pub fn catalog_status(&self, beamtime_path: &Path) -> CatalogStatus; + pub fn query_scan_points(&self, beamtime_path: &Path, filter: Option<&CatalogFilter>) -> Result>; + pub fn list_beamtime_entries_v2(&self, beamtime_path: &Path) -> Result; +} +``` + +If `DATA/.pyref/catalog.db` does not exist, all read methods return empty results; the file is created on first ingest. + +--- + +## 6. Explorer Screen + +### 6.1 ExplorerState + +```rust +pub struct ExplorerState { + pub data_root: PathBuf, + pub current_dir: PathBuf, + pub entries: Vec, + pub list_state: ListState, + pub scroll_state: ScrollbarState, + pub layout_policy: NasLayout, + pub loading: bool, + pub status: Option, + pub sort_col: ExplorerSort, + pub sort_asc: bool, +} +``` + +### 6.2 DirEntry + +```rust +pub struct DirEntry { + pub path: PathBuf, + pub name: String, + pub kind: EntryKind, + pub modified: Option, + pub size_bytes: Option, // deferred async + pub fits_count: Option, // fast async via mpsc + pub catalog_status: CatalogStatus, + pub expt_resolution: Option, // Some when kind == Experimentalist +} + +pub enum EntryKind { DataRoot, Experimentalist, Beamtime, Directory } +pub enum CatalogStatus { Indexed, Stale, NotIndexed, NotApplicable } +pub enum ExptResolution { Resolved, NeedsResolution, Ignored } +``` + +### 6.3 Explorer Columns + +| Column | Source | Loading | +|---|---|---| +| Name | `fs::read_dir` | Immediate | +| Date Modified | `fs::metadata().modified()` | Immediate | +| Size | `du` equivalent | Deferred (on selection or `s` key) | +| Kind | `classify_entry()` | Immediate | +| Beamtimes / FITS Files | `CatalogHandle` + `fs::read_dir` count | Fast async | +| Status | `CatalogHandle::catalog_status()` | Fast async | + +### 6.4 Explorer Keybindings + +| Key | Action | +|---|---| +| `↑↓` | Navigate entries | +| `→` / `↵` | Enter directory / open beamtime | +| `←` / `h` | Go up / back | +| `r` | Resolve selected experimentalist | +| `x` | Toggle ignore on selected folder | +| `i` | Ingest selected beamtime | +| `I` | Ingest all not-yet-indexed beamtimes | +| `/` | Filter entries | +| `s` | Compute disk size for selection | +| `q` | Quit | + +--- + +## 7. Per-Experimentalist Layout Policy + +### 7.1 NasLayout + +Persisted to `DATA/.pyref/layout.toml`. + +```rust +pub struct NasLayout { + pub default_pattern: Option, // regex + pub default_depth: u8, // default: 2 + pub experimentalists: HashMap, +} + +pub enum ExptPolicy { + Auto, + Custom { pattern: Option, depth: u8 }, + Ignored, +} +``` + +### 7.2 Heuristic Detection + +On Explorer startup, `detect_policy(data_root)` performs a shallow scan (top 2 levels only). Built-in patterns tested against depth-2 folder names: + +``` +^20\d{2}(Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)$ // 2024Jan +^20\d{2}-\d{2}(-\d{2})?$ // 2024-01 +^20\d{6}$ // 20240115 +(?i)beamtime // contains "beamtime" +``` + +If ≥50% of depth-2 folders under a given experimentalist match any pattern, that experimentalist is marked `ExptPolicy::Auto` (resolved). Below 50% → `ExptResolution::NeedsResolution` (flagged with `⚑`). + +Folders at depth 1 whose names start with `.` are silently excluded and never shown. + +### 7.3 Resolution Modal + +Triggered by pressing `r` on a flagged experimentalist row, or automatically on entering a flagged folder. + +Three choices: +1. **Custom pattern** — user specifies a regex and depth. Live preview pane updates on every keystroke showing how subdirs are classified. Invalid regex turns the field red; preview shows last valid state. +2. **Mark as ignored** — folder is dimmed in the explorer, excluded from beamtime counts and bulk ingest. Entry is still allowed for browsing. +3. **Accept global default** — applies the global `default_pattern` + `default_depth` even if no matches were found. + +On confirm, the resolution is written to `DATA/.pyref/layout.toml` immediately. Modal is popped from the Navigator stack, returning to the Explorer with cursor restored. + +--- + +## 8. Beamtime Screen Adaptation + +### 8.1 BeamtimeState + +All fields currently on `App` that pertain to the Beamtime screen are moved verbatim to `BeamtimeState`. Three new context fields are added: + +```rust +pub struct BeamtimeState { + pub data_root: PathBuf, + pub experimentalist: String, + pub beamtime_path: PathBuf, // filter key into DATA catalog + // ... all existing App fields for beamtime view ... +} +``` + +### 8.2 Query Migration + +```rust +// Before +let db = resolve_catalog_path(&beamtime_dir); +let rows = query_files(&db, None)?; + +// After +let db = data_root.join(".pyref/catalog.db"); +let rows = catalog_handle.query_scan_points(&beamtime_path, None)?; +``` + +`query_scan_points` adds `AND source_path LIKE ?1 || '%'` bound to `beamtime_path`. The old `query_files()` function is kept but deprecated; existing callers are migrated individually. + +### 8.3 Context Bar + +The Beamtime screen's title bar shows the full breadcrumb path: `Smith › 2024Jan`. The `←`/`h` key pops `BeamtimeState` off the Navigator stack, restoring the underlying `ExplorerState` with cursor position intact. + +Selections (`last_sample`, `last_tag`, `last_scan`) are written to `TuiConfig` when `BeamtimeState` is popped, not only on quit. + +--- + +## 9. Auto-Ingest on Entry + +When the user enters a beamtime with `CatalogStatus::NotIndexed` or `CatalogStatus::Stale`: + +1. `Navigator.push(ScreenState::Beamtime(BeamtimeState::new_ingesting(…)))` — the screen is pushed immediately with `LoadingState::IngestingDirectory`. +2. A background thread is spawned calling `ingest_beamtime(beamtime_dir, Some(data_root), Some(experimentalist), …, Some(cancel_flag))`. +3. Progress updates flow via `mpsc::Sender<(u32, u32)>` to the `ingest_progress_rx` on `BeamtimeState`. A progress bar and spinner are rendered. +4. On completion: + - `Ok(())` → `BeamtimeState::reload_from_catalog()` queries the DATA catalog. A lightweight message is sent back to the underlying `ExplorerState` to update the status cell to `Indexed`. + - `Err(e)` → error modal shown; `←` returns to Explorer. +5. **Esc during ingest** sets the cancel `AtomicBool`, waits for the thread to drain, pops the `BeamtimeState`, and returns to the Explorer. Partial writes remain in the catalog and are resumed on next entry. + +**Stale** beamtimes use the same path as `NotIndexed`. Staleness is determined by comparing the directory's most recent file `mtime` against `bt_beamtimes.last_indexed_at`. + +--- + +## 10. Ingestion Pipeline Changes + +### 10.1 Signature + +```rust +pub fn ingest_beamtime( + beamtime_dir: &Path, + data_root: Option<&Path>, // NEW — None = legacy behaviour + experimentalist: Option<&str>, // NEW — None = legacy behaviour + header_items: &[String], + force: bool, + progress_tx: Option>, + cancel: Option>, // NEW +) -> Result +``` + +`data_root: None` and `experimentalist: None` reproduce the current behaviour exactly — the catalog is opened at the beamtime directory, no new columns are written. All existing callers pass `None` until individually migrated. + +### 10.2 Catalog Path Resolution + +```rust +pub fn resolve_ingest_target(beamtime_dir: &Path, data_root: Option<&Path>) -> PathBuf { + match data_root { + Some(root) => root.join(".pyref").join("catalog.db"), + None => resolve_catalog_path(beamtime_dir), // existing behaviour + } +} +``` + +### 10.3 Schema Migration + +Two nullable columns added to `bt_beamtimes` via `ALTER TABLE … ADD COLUMN … DEFAULT ''`. Safe migration — existing databases open without errors; empty string is treated as unset and the experimentalist is inferred from path at query time. + +```sql +ALTER TABLE bt_beamtimes ADD COLUMN experimentalist TEXT NOT NULL DEFAULT ''; +ALTER TABLE bt_beamtimes ADD COLUMN data_root TEXT NOT NULL DEFAULT ''; +CREATE INDEX IF NOT EXISTS idx_bt_experimentalist + ON bt_beamtimes(data_root, experimentalist); +``` + +At the start of every ingest, `upsert_beamtime_record()` runs an `INSERT … ON CONFLICT DO UPDATE` to record these values and refresh `last_indexed_at`. + +--- + +## 11. CLI Argument + +``` +browser [data_root] +``` + +- If `data_root` is provided: must be an existing directory. The Explorer is shown rooted at this path. +- If omitted: behaves as today — reads `config.last_root`; if set, opens the Beamtime screen directly. This preserves backward compatibility for existing workflows. + +The `Screen::Launcher` code path is removed in Phase 4 once the Explorer is fully functional. + +--- + +## 12. Execution Phases + +### Phase 1 — Decompose (no user-visible change) +- Extract `BeamtimeState` from `App` +- Introduce `Navigator`, `ScreenState` +- `App` becomes thin shell +- All existing behaviour preserved +- Files: `app.rs`, `run.rs`, `ui.rs`, `keymap.rs`, new `navigator.rs` + +### Phase 2 — Explorer screens +- Add `ExplorerState`, `DirEntry`, `EntryKind` +- Add `heuristic.rs`, `NasLayout`, `layout.toml` persistence +- Add `ModalState` and resolution modal UI +- Wire CLI arg as `data_root` +- Files: new `explorer/` module + +### Phase 3 — DATA-level catalog +- Add `CatalogHandle` and `explorer_query.rs` +- Schema migration for `bt_beamtimes` +- Adapt `ingest_beamtime` signature (backward-compatible) +- Add cancel token to ingest loop +- Adapt `query_scan_points` to accept `beamtime_path` filter + +### Phase 4 — Wire end-to-end +- Auto-ingest on Beamtime entry (NotIndexed + Stale) +- `BeamtimeState` queries DATA-level catalog +- Status cell update channel from Beamtime → Explorer +- Config saved on `BeamtimeState` pop +- Remove `Screen::Launcher` + `list_beamtimes()` global index call + +Each phase is independently mergeable with no breaking changes to existing functionality. + +--- + +## 13. Open Questions / Future Work + +- **Bulk ingest (`I` key):** Phase 4 scope is single-beamtime auto-ingest. Bulk ingest of all un-indexed beamtimes in an experimentalist folder is deferred to a follow-on ticket. +- **Disk size column:** Deferred computation via `du` on NAS can be slow. Current plan is on-demand (`s` key) or on-selection. May need a cap or timeout. +- **`beamtime_index.sqlite3` migration:** The global `~/.pyref/beamtime_index.sqlite3` is not removed in this redesign. It remains as a fallback for the no-`data_root` code path. +- **Tiled migration:** The DATA-level `catalog.db` schema is designed to be compatible with a future Tiled ingest pipeline. 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zq6_>T(gckS^BSGG@AQaGvGDo!F?I6=IG+xu?PkdO(lfZzu_e9s4X60_WE0!4Fz2kn0Gjid{FIli6`y7c2aZ)_E7`{Z?~65oLpZ^h?6+<@ZSgH3#sy$(9%BLs2& zs_P){7vBh~gGzkgEO_O=)5Ow+$>MzWsC0xAf&3QGC0ziEnamU^$#Y zZeY=0R5egGzxX<7gGGt&OY!n3?a)}R^4-B%)>ip+edw!q9j#YzAH}yDoA@U82A0Da zZqiDR`%)rTINw6jv)+d=W|$|kGQW?_UD$fNBUEIJF pd.DataFrame: - """ - Slice a DataFrame to rows within a given range around a center value, - ensuring at least `min_length` rows are returned. - - Finds the row where 'col' is closest to the given `center` value, then - attempts to slice the DataFrame to all rows with 'col' between - `center - bounds` and `center + bounds`. If the number of such rows is - less than `min_length`, returns a symmetric window of - `min_length` rows centered on the closest row. - - Parameters - ---------- - df : pd.DataFrame - Input DataFrame to slice. - col : str - Name of the column to use for slicing. - center : float - Center value for the range. - bounds : float - Bounds on either side of the center (the range is [center - bounds, center + bounds]). - min_length : int, default=3 - Minimum number of rows to return. - - Returns - ------- - pd.DataFrame - Sliced DataFrame with at least `min_length` rows in the specified range. - """ - from math import floor, ceil - - center_row = (df[col] - center).abs().idxmin() - mask = (df[col] >= center - bounds) & (df[col] <= center + bounds) - if mask.sum() < min_length: - start = max(center_row - floor(min_length / 2), 0) - end = start + min_length - # Ensure we don't go out of bounds - end = min(end, len(df)) - start = max(end - min_length, 0) - return df.iloc[start:end] - mask_res = df[mask] - if isinstance(mask_res, pd.DataFrame): - return mask_res - raise TypeError( - f"Expected pd.DataFrame or pd.Series, got {type(mask_res)}") # ==============/ Base Classes /=================== - class Structure(UserList): r""" Represents the slab structure of a reflective sample. @@ -1372,33 +1318,37 @@ def __init__( energy_offset: float = 0, name: str = "", ): + # =================/ Input Validation /================ required_columns = ["energy", "n_xx", "n_ixx", "n_zz", "n_izz"] if not all(col in ooc.columns for col in required_columns): missing = [col for col in required_columns if col not in ooc.columns] - raise ValueError( - f"Optical constants dataframe missing required columns: {missing}" - ) + e = f"Optical constants dataframe missing required columns: {missing}" + raise ValueError(e) + # =================/ Initialize /================ self._parameters = Parameters(name=name) super().__init__(name=name) + # ============/ Isotropic Parameters /=========== self.density: Parameter = possibly_create_parameter( # type: ignore[assignment] density, name=f"{name}_density", bounds=(0, 5 * density), vary=True ) self.rotation: Parameter = possibly_create_parameter( # type: ignore[assignment] rotation, name=f"{name}_rotation", vary=True, bounds=(-np.pi, np.pi) ) + # ============/ Optical Constants /=========== self.energy = energy self.energy_offset: Parameter = possibly_create_parameter( # type: ignore[assignment] energy_offset, name=f"{name}_energy_offset", vary=True, bounds=(-0.01, 0.01) ) + # store the optical constants as n_xx n_ixx, n_zz, n_izz self.n_xx = interp1d(ooc["energy"], ooc["n_xx"], bounds_error=False) self.n_ixx = interp1d(ooc["energy"], ooc["n_ixx"], bounds_error=False) self.n_zz = interp1d(ooc["energy"], ooc["n_zz"], bounds_error=False) self.n_izz = interp1d(ooc["energy"], ooc["n_izz"], bounds_error=False) - self._parameters.extend( - [self.density, self.rotation, self.energy_offset]) + # Add parameters to parameter set + self._parameters.extend([self.density, self.rotation, self.energy_offset]) def __complex__(self): """Complex representation of the scatterer.""" @@ -1411,7 +1361,15 @@ def __repr__(self): @property def n(self) -> NDArray[np.complex128]: - e = float(self.get_energy()) + """ + Optical constants of the material. + + Returns + ------- + n : np.ndarray + Optical constants of the material. + """ + e = self.get_energy() return np.array( [ [self.n_xx(e) + self.n_ixx(e) * 1j, 0], @@ -1438,7 +1396,7 @@ def tensor(self) -> NDArray[np.complex128]: out : np.ndarray (3x3) complex tensor index of refraction """ - n: NDArray[np.complex128] = self.get_density() * self.n + n: np.ndarray = self.get_density() * self.n cos_squared: float = np.square(np.cos(self.get_rotation())) sin_squared: float = 1 - cos_squared diff --git a/src/bin/browser.rs b/src/bin/browser.rs index 2252713..4c17862 100644 --- a/src/bin/browser.rs +++ b/src/bin/browser.rs @@ -8,6 +8,7 @@ use ratatui::prelude::Terminal; use std::env; use std::io; use std::path::Path; +use std::path::PathBuf; use std::sync::mpsc; use std::time::Duration; @@ -82,14 +83,26 @@ fn main() -> Result<(), Box> { .map(Duration::from_millis) .unwrap_or(Duration::from_secs(30)); - let (preview_tx, preview_rx) = mpsc::channel(); + let (preview_tx, preview_rx) = mpsc::channel::<(PathBuf, Option)>(); - #[cfg(target_os = "macos")] - { - tui::preview::run_preview_window(preview_rx); - let mut terminal = Terminal::new(CrosstermBackend::new(io::stdout())) - .map_err(tui::TuiError::terminal_setup)?; - tui::terminal_guard::setup_terminal().map_err(tui::TuiError::terminal_setup)?; + #[cfg(feature = "catalog")] + let (beamspot_tx, beamspot_rx) = mpsc::channel::<(PathBuf, i64, i64, Option)>(); + + #[cfg(feature = "catalog")] + let (preview_cmd_tx, preview_cmd_rx) = mpsc::channel::(); + + std::thread::spawn(move || { + let mut terminal = match Terminal::new(CrosstermBackend::new(io::stdout())) { + Ok(t) => t, + Err(e) => { + eprintln!("{}", tui::TuiError::terminal_setup(e).report()); + std::process::exit(1); + } + }; + if let Err(e) = tui::terminal_guard::setup_terminal() { + eprintln!("{}", tui::TuiError::terminal_setup(e).report()); + std::process::exit(1); + } let mut app = match initial_root { Some(ref current_root) => { @@ -138,6 +151,12 @@ fn main() -> Result<(), Box> { } }; + #[cfg(feature = "catalog")] + app.set_beamspot_rx(beamspot_rx); + + #[cfg(feature = "catalog")] + app.set_preview_cmd_rx(preview_cmd_rx); + let run_result = tui::run(&mut terminal, &mut app, poll_duration); if let Err(e) = tui::terminal_guard::restore_terminal() { @@ -168,107 +187,17 @@ fn main() -> Result<(), Box> { if let Err(e) = config.save() { eprintln!("{}", e.report()); } - } - #[cfg(not(target_os = "macos"))] - { - std::thread::spawn(move || { - let mut terminal = match Terminal::new(CrosstermBackend::new(io::stdout())) { - Ok(t) => t, - Err(e) => { - eprintln!("{}", tui::TuiError::terminal_setup(e).report()); - std::process::exit(1); - } - }; - if let Err(e) = tui::terminal_guard::setup_terminal() { - eprintln!("{}", tui::TuiError::terminal_setup(e).report()); - std::process::exit(1); - } - - let mut app = match initial_root { - Some(ref current_root) => { - let mut app = tui::App::new( - current_root.clone(), - config.layout.clone(), - config.keymap.clone(), - config.keybind_bar_lines, - config.theme.clone(), - ); - app.set_preview_tx(preview_tx.clone()); - if let Some(s) = config.last_sample.as_ref() { - if let Some(i) = app.samples.iter().position(|x| x == s) { - app.sample_state.select(Some(i)); - } - } - if let Some(t) = config.last_tag.as_ref() { - if let Some(i) = app.tags.iter().position(|x| x == t) { - app.tag_state.select(Some(i)); - } - } - if let Some(e) = config.last_scan.as_ref() { - if let Ok(n) = e.parse::() { - if let Some(i) = app.scans.iter().position(|(x, _)| *x == n) { - app.scan_list_state.select(Some(i)); - } - } - } - app.refresh_filtered(); - app - } - None => { - #[cfg(feature = "catalog")] - { - let mut app = tui::App::new_for_launcher( - config.layout.clone(), - config.keymap.clone(), - config.keybind_bar_lines, - config.theme.clone(), - ); - app.set_preview_tx(preview_tx.clone()); - app - } - #[cfg(not(feature = "catalog"))] - unreachable!() - } - }; - - let run_result = tui::run(&mut terminal, &mut app, poll_duration); - - if let Err(e) = tui::terminal_guard::restore_terminal() { - eprintln!("{}", tui::TuiError::terminal_restore(e).report()); - } - - if let Err(e) = run_result { - let err = tui::TuiError::io("run_loop", e.to_string(), e); - eprintln!("{}", err.report()); - } - - if app.screen == tui::Screen::Beamtime && !app.current_root.is_empty() { - config.set_last_root(&app.current_root); - let scan_str = app.focused_scan().map(|n| n.to_string()); - config.set_last_selection( - app.focused_sample().as_deref(), - app.focused_tag().as_deref(), - scan_str.as_deref(), - ); - let mut sel_samples: Vec = app.selected_samples.iter().cloned().collect(); - sel_samples.sort(); - let mut sel_tags: Vec = app.selected_tags.iter().cloned().collect(); - sel_tags.sort(); - let mut sel_scans: Vec = app.selected_scans.iter().cloned().collect(); - sel_scans.sort(); - config.set_selection_export(&sel_samples, &sel_tags, &sel_scans); - } - if let Err(e) = config.save() { - eprintln!("{}", e.report()); - } - - std::process::exit(0); - }); - - tui::preview::run_preview_window(preview_rx); std::process::exit(0); - } + }); - Ok(()) + #[cfg(feature = "catalog")] + tui::preview::run_preview_window_on_first_path( + preview_rx, + Some(beamspot_tx), + Some(preview_cmd_tx), + ); + #[cfg(not(feature = "catalog"))] + tui::preview::run_preview_window_on_first_path(preview_rx, None, None); + std::process::exit(0); } diff --git a/src/bin/tui/app.rs b/src/bin/tui/app.rs index ff1bbfd..7c96e12 100644 --- a/src/bin/tui/app.rs +++ b/src/bin/tui/app.rs @@ -17,12 +17,16 @@ use super::scan_type::{classify_scan_type, ReflectivityScanType}; use pyref::build_fits_stem; #[cfg(feature = "catalog")] use pyref::catalog::{ - catalog_file_count, list_beamtime_entries, list_beamtimes, query_files, register_beamtime, - rename_file_in_catalog, update_beamspot, FileRow, CATALOG_DB_NAME, DEFAULT_INGEST_HEADER_ITEMS, + catalog_file_count, get_scan_point_uid_by_source_path, list_beamtime_entries, + list_beamtime_entries_v2, list_beamtimes, query_files, query_scan_points, register_beamtime, + rename_file_in_catalog, update_beamspot, update_beamspot_scan_point, FileRow, + DEFAULT_INGEST_HEADER_ITEMS, resolve_catalog_path, }; #[cfg(feature = "watch")] use pyref::catalog::{run_catalog_watcher, DEFAULT_DEBOUNCE_MS}; +type BeamspotUpdate = (PathBuf, i64, i64, Option); + #[derive(Debug, Clone)] #[allow(dead_code)] pub struct ProfileRow { @@ -169,7 +173,11 @@ pub struct App { pub expanded_files_sort_column: Option, pub expanded_files_sort_ordering: Option, pub last_expanded_files_visible: usize, - pub preview_tx: Option>, + pub preview_tx: Option)>>, + #[cfg(feature = "catalog")] + pub beamspot_rx: Option>, + #[cfg(feature = "catalog")] + pub preview_cmd_rx: Option>, pub focus: Focus, pub mode: AppMode, pub current_root: String, @@ -434,10 +442,31 @@ fn catalog_to_profiles( )> { #[cfg(feature = "catalog")] { - let db_path = Path::new(current_root).join(CATALOG_DB_NAME); + let db_path = resolve_catalog_path(Path::new(current_root)); if !db_path.exists() { return None; } + let beamtime_path = Path::new(current_root); + let v2_entries = list_beamtime_entries_v2(&db_path, beamtime_path).ok(); + let v2_rows = query_scan_points(&db_path, beamtime_path, None).ok(); + let use_v2 = v2_entries + .as_ref() + .zip(v2_rows.as_ref()) + .map(|(e, r)| { + !e.samples.is_empty() || !e.tags.is_empty() || !e.scans.is_empty() || !r.is_empty() + }) + .unwrap_or(false); + if use_v2 { + let entries = v2_entries.unwrap(); + let rows = v2_rows.unwrap(); + let groups = build_groups_from_files(rows); + let scans: Vec<(u32, String)> = entries + .scans + .into_iter() + .map(|(n, s)| (n as u32, s)) + .collect(); + return Some((groups, entries.samples, entries.tags, scans)); + } let entries = list_beamtime_entries(&db_path).ok()?; let rows = query_files(&db_path, None).ok()?; let groups = build_groups_from_files(rows); @@ -446,7 +475,7 @@ fn catalog_to_profiles( .into_iter() .map(|(n, s)| (n as u32, s)) .collect(); - return Some((groups, entries.samples, entries.tags, scans)); + Some((groups, entries.samples, entries.tags, scans)) } #[cfg(not(feature = "catalog"))] None @@ -491,8 +520,7 @@ impl App { table_state.select(Some(0)); } #[cfg(feature = "watch")] - let catalog_watcher = Path::new(¤t_root) - .join(CATALOG_DB_NAME) + let catalog_watcher = resolve_catalog_path(Path::new(¤t_root)) .exists() .then(|| { run_catalog_watcher( @@ -544,6 +572,10 @@ impl App { expanded_files_sort_ordering: Some(cmp::Ordering::Less), last_expanded_files_visible: 5, preview_tx: None, + #[cfg(feature = "catalog")] + beamspot_rx: None, + #[cfg(feature = "catalog")] + preview_cmd_rx: None, focus: Focus::SampleList, mode: AppMode::Normal, current_root, @@ -569,7 +601,7 @@ impl App { status_message: None, status_message_set_at: None, #[cfg(feature = "watch")] - catalog_watcher: catalog_watcher, + catalog_watcher, #[cfg(feature = "watch")] watcher_events_rx: None, last_body_rects: None, @@ -629,6 +661,10 @@ impl App { expanded_files_sort_ordering: Some(cmp::Ordering::Less), last_expanded_files_visible: 5, preview_tx: None, + #[cfg(feature = "catalog")] + beamspot_rx: None, + #[cfg(feature = "catalog")] + preview_cmd_rx: None, focus: Focus::SampleList, mode: AppMode::Normal, current_root: String::new(), @@ -1042,7 +1078,7 @@ impl App { } self.refresh_filtered(); #[cfg(feature = "watch")] - if Path::new(&self.current_root).join(CATALOG_DB_NAME).exists() { + if resolve_catalog_path(Path::new(&self.current_root)).exists() { let (tx, rx) = mpsc::channel(); let tx_end = tx.clone(); let on_start = Box::new(move || { @@ -1295,7 +1331,11 @@ impl App { .beam_row .cmp(&rb.beam_row) .then_with(|| ra.beam_col.cmp(&rb.beam_col)), - 6 => beamspot::beamspot_status( + 6 => ra + .beam_sigma + .partial_cmp(&rb.beam_sigma) + .unwrap_or(cmp::Ordering::Equal), + 7 => beamspot::beamspot_status( ra.beam_row, ra.beam_col, beamspot::domain_for_row(ra, group.scan_type), @@ -1330,7 +1370,7 @@ impl App { #[cfg(feature = "catalog")] pub fn cycle_expanded_files_sort(&mut self, column: usize) { - if column >= 7 { + if column >= 8 { return; } let (new_col, new_ord) = match ( @@ -1352,10 +1392,360 @@ impl App { self.needs_redraw = true; } - pub fn set_preview_tx(&mut self, tx: mpsc::Sender) { + #[cfg(feature = "catalog")] + fn problem_display_indices( + group: &GroupedProfileRow, + display_order: &[usize], + ) -> Vec { + use super::beamspot; + let files = &group.file_rows; + let (row_mean, row_std, col_mean, col_std) = beamspot_mean_std(files); + let fit = beamspot::fit_beamspot_linear(files, group.scan_type); + (0..display_order.len()) + .filter(|&display_ix| { + let real_ix = display_order[display_ix]; + let row = match files.get(real_ix) { + Some(r) => r, + None => return false, + }; + let (_, key) = beamspot::beamspot_status( + row.beam_row, + row.beam_col, + beamspot::domain_for_row(row, group.scan_type), + fit.as_ref(), + row_mean, + row_std, + col_mean, + col_std, + ); + key == 1 || key == 2 + }) + .collect() + } + + #[cfg(feature = "catalog")] + pub fn go_to_next_problem(&mut self) { + let idx = match self.expanded_table_row { + Some(i) => i, + None => return, + }; + let group = match self.group_at_display_index(idx) { + Some(g) => g, + None => return, + }; + let order = self.expanded_files_display_order(group); + let problems = Self::problem_display_indices(group, &order); + if problems.is_empty() { + self.set_status("No err/warning frames in this profile.".to_string(), false); + return; + } + let cur = self.expanded_selected_file_index.unwrap_or(0); + let next_ix = problems.iter().find(|&&p| p > cur).copied(); + let target = match next_ix { + Some(ix) => ix, + None => problems[0], + }; + self.expanded_selected_file_index = Some(target); + let visible = self.last_expanded_files_visible.max(1); + let file_count = order.len(); + let max_offset = file_count.saturating_sub(visible); + if target >= self.expanded_files_scroll_offset + visible { + self.expanded_files_scroll_offset = + cmp::min((target + 1).saturating_sub(visible), max_offset); + } else if target < self.expanded_files_scroll_offset { + self.expanded_files_scroll_offset = target; + } + self.needs_redraw = true; + if self.preview_tx.is_some() { + self.send_preview_path_if_selected(); + } + } + + #[cfg(feature = "catalog")] + pub fn go_to_prev_problem(&mut self) { + let idx = match self.expanded_table_row { + Some(i) => i, + None => return, + }; + let group = match self.group_at_display_index(idx) { + Some(g) => g, + None => return, + }; + let order = self.expanded_files_display_order(group); + let problems = Self::problem_display_indices(group, &order); + if problems.is_empty() { + self.set_status("No err/warning frames in this profile.".to_string(), false); + return; + } + let cur = self.expanded_selected_file_index.unwrap_or(0); + let prev_ix = problems.iter().rev().find(|&&p| p < cur).copied(); + let target = match prev_ix { + Some(ix) => ix, + None => *problems.last().unwrap(), + }; + self.expanded_selected_file_index = Some(target); + if target < self.expanded_files_scroll_offset { + self.expanded_files_scroll_offset = target; + } + self.needs_redraw = true; + if self.preview_tx.is_some() { + self.send_preview_path_if_selected(); + } + } + + #[cfg(feature = "catalog")] + fn last_ok_display_index( + group: &GroupedProfileRow, + display_order: &[usize], + current_ix: usize, + ) -> Option { + use super::beamspot; + let files = &group.file_rows; + let (row_mean, row_std, col_mean, col_std) = beamspot_mean_std(files); + let fit = beamspot::fit_beamspot_linear(files, group.scan_type); + for display_ix in (0..current_ix).rev() { + let real_ix = display_order[display_ix]; + let row = files.get(real_ix)?; + let (_, key) = beamspot::beamspot_status( + row.beam_row, + row.beam_col, + beamspot::domain_for_row(row, group.scan_type), + fit.as_ref(), + row_mean, + row_std, + col_mean, + col_std, + ); + if key == 0 { + return Some(display_ix); + } + } + None + } + + #[cfg(feature = "catalog")] + fn next_ok_display_index( + group: &GroupedProfileRow, + display_order: &[usize], + current_ix: usize, + ) -> Option { + use super::beamspot; + let files = &group.file_rows; + let (row_mean, row_std, col_mean, col_std) = beamspot_mean_std(files); + let fit = beamspot::fit_beamspot_linear(files, group.scan_type); + for display_ix in (current_ix + 1)..display_order.len() { + let real_ix = display_order[display_ix]; + let row = files.get(real_ix)?; + let (_, key) = beamspot::beamspot_status( + row.beam_row, + row.beam_col, + beamspot::domain_for_row(row, group.scan_type), + fit.as_ref(), + row_mean, + row_std, + col_mean, + col_std, + ); + if key == 0 { + return Some(display_ix); + } + } + None + } + + #[cfg(feature = "catalog")] + pub fn use_last_ok_beamspot(&mut self) { + let idx = match self.expanded_table_row { + Some(i) => i, + None => return, + }; + let group = match self.group_at_display_index(idx) { + Some(g) => g, + None => return, + }; + let order = self.expanded_files_display_order(group); + let cur = self.expanded_selected_file_index.unwrap_or(0); + let ok_ix = match Self::last_ok_display_index(group, &order, cur) { + Some(ix) => ix, + None => { + self.set_status( + "No previous OK beamspot in this profile.".to_string(), + false, + ); + return; + } + }; + let real_ix = order[ok_ix]; + let src_row = match group.file_rows.get(real_ix) { + Some(r) => r, + None => return, + }; + let (beam_row, beam_col) = match (src_row.beam_row, src_row.beam_col) { + (Some(r), Some(c)) => (r, c), + _ => return, + }; + let beam_sigma = src_row.beam_sigma; + let current_real_ix = order.get(cur).copied().unwrap_or(0); + let current_row = match group.file_rows.get(current_real_ix) { + Some(r) => r, + None => return, + }; + let db_path = resolve_catalog_path(Path::new(&self.current_root)); + if !db_path.exists() { + self.set_status("No catalog in current root.".to_string(), true); + return; + } + let file_path = ¤t_row.file_path; + if let Err(e) = update_beamspot(&db_path, file_path, beam_row, beam_col, beam_sigma) { + self.set_status( + format!("Failed to store beamspot from last OK: {}", e), + true, + ); + return; + } + if let Some(ref uid) = current_row.scan_point_uid { + let _ = update_beamspot_scan_point(&db_path, uid, beam_row, beam_col, beam_sigma); + } + let was_expanded = self.expanded_table_row.is_some(); + let restore_selection = self.expanded_table_row.or(self.table_state.selected()); + self.reload_from_catalog_impl(was_expanded, restore_selection); + self.set_status("Beamspot copied from previous OK frame.".to_string(), false); + } + + #[cfg(feature = "catalog")] + pub fn use_next_ok_beamspot(&mut self) { + let idx = match self.expanded_table_row { + Some(i) => i, + None => return, + }; + let group = match self.group_at_display_index(idx) { + Some(g) => g, + None => return, + }; + let order = self.expanded_files_display_order(group); + let cur = self.expanded_selected_file_index.unwrap_or(0); + let ok_ix = match Self::next_ok_display_index(group, &order, cur) { + Some(ix) => ix, + None => { + self.set_status( + "No next OK beamspot in this profile.".to_string(), + false, + ); + return; + } + }; + let real_ix = order[ok_ix]; + let src_row = match group.file_rows.get(real_ix) { + Some(r) => r, + None => return, + }; + let (beam_row, beam_col) = match (src_row.beam_row, src_row.beam_col) { + (Some(r), Some(c)) => (r, c), + _ => return, + }; + let beam_sigma = src_row.beam_sigma; + let current_real_ix = order.get(cur).copied().unwrap_or(0); + let current_row = match group.file_rows.get(current_real_ix) { + Some(r) => r, + None => return, + }; + let db_path = resolve_catalog_path(Path::new(&self.current_root)); + if !db_path.exists() { + self.set_status("No catalog in current root.".to_string(), true); + return; + } + let file_path = ¤t_row.file_path; + if let Err(e) = update_beamspot(&db_path, file_path, beam_row, beam_col, beam_sigma) { + self.set_status( + format!("Failed to store beamspot from next OK: {}", e), + true, + ); + return; + } + if let Some(ref uid) = current_row.scan_point_uid { + let _ = update_beamspot_scan_point(&db_path, uid, beam_row, beam_col, beam_sigma); + } + let was_expanded = self.expanded_table_row.is_some(); + let restore_selection = self.expanded_table_row.or(self.table_state.selected()); + self.reload_from_catalog_impl(was_expanded, restore_selection); + self.set_status("Beamspot copied from next OK frame.".to_string(), false); + } + + pub fn set_preview_tx(&mut self, tx: mpsc::Sender<(PathBuf, Option)>) { self.preview_tx = Some(tx); } + #[cfg(feature = "catalog")] + pub fn set_beamspot_rx(&mut self, rx: mpsc::Receiver) { + self.beamspot_rx = Some(rx); + } + + #[cfg(feature = "catalog")] + pub fn set_preview_cmd_rx(&mut self, rx: mpsc::Receiver) { + self.preview_cmd_rx = Some(rx); + } + + #[cfg(feature = "catalog")] + pub fn try_recv_preview_cmd(&mut self) { + let mut commands = Vec::new(); + if let Some(rx) = self.preview_cmd_rx.as_mut() { + while let Ok(cmd) = rx.try_recv() { + commands.push(cmd); + } + } + for cmd in commands { + match cmd { + super::preview::PreviewCommand::GoToNextProblem => self.go_to_next_problem(), + super::preview::PreviewCommand::GoToPrevProblem => self.go_to_prev_problem(), + } + } + } + + #[cfg(feature = "catalog")] + pub fn try_recv_beamspot_updates(&mut self) { + let mut updates = Vec::new(); + if let Some(rx) = self.beamspot_rx.as_mut() { + while let Ok(x) = rx.try_recv() { + updates.push(x); + } + } + for (path, beam_row, beam_col, beam_sigma) in updates { + self.apply_beamspot_from_preview(path, beam_row, beam_col, beam_sigma); + } + } + + #[cfg(feature = "catalog")] + fn apply_beamspot_from_preview( + &mut self, + path: PathBuf, + beam_row: i64, + beam_col: i64, + beam_sigma: Option, + ) { + let db_path = resolve_catalog_path(Path::new(&self.current_root)); + if !db_path.exists() { + return; + } + let file_path = path.to_string_lossy().into_owned(); + if let Err(e) = update_beamspot(&db_path, &file_path, beam_row, beam_col, beam_sigma) { + self.set_status(format!("Failed to store beamspot from preview: {}", e), true); + return; + } + if let Ok(Some(uid)) = get_scan_point_uid_by_source_path(&db_path, &file_path) { + let _ = update_beamspot_scan_point( + &db_path, + &uid, + beam_row, + beam_col, + beam_sigma, + ); + } + let was_expanded = self.expanded_table_row.is_some(); + let restore_selection = self.expanded_table_row.or(self.table_state.selected()); + self.reload_from_catalog_impl(was_expanded, restore_selection); + self.set_status("Beamspot from preview stored; table updated.".to_string(), false); + } + pub fn send_preview_path_if_selected(&mut self) { if let (Some(tx), Some(idx), Some(display_ix)) = ( self.preview_tx.as_ref(), @@ -1366,7 +1756,12 @@ impl App { let order = self.expanded_files_display_order(g); if let Some(&real_ix) = order.get(display_ix) { if let Some(row) = g.file_rows.get(real_ix) { - let _ = tx.send(PathBuf::from(&row.file_path)); + let frame1 = g.file_rows.iter().min_by_key(|r| r.frame_number); + let profile_sigma = frame1.and_then(|r| r.beam_sigma); + let _ = tx.send(( + PathBuf::from(&row.file_path), + profile_sigma, + )); } } } @@ -1389,7 +1784,7 @@ impl App { Some(g) => g.file_rows.iter().map(|r| r.file_path.clone()).collect(), None => return, }; - let db_path = Path::new(&self.current_root).join(CATALOG_DB_NAME); + let db_path = resolve_catalog_path(Path::new(&self.current_root)); if !db_path.exists() { self.set_status("No catalog in current root.".to_string(), true); return; @@ -1402,13 +1797,38 @@ impl App { match pyref::io::image_mmap::materialize_image_from_path(path) { Ok((_, subtracted)) => { let (r, c) = pyref::beamfinding::locate_beam_simple(&subtracted); - if let Err(e) = update_beamspot(&db_path, file_path, r as i64, c as i64) { + let subtracted_f64: ndarray::Array2 = + subtracted.mapv(|x| x as f64); + let beam_sigma = pyref::gaussian_fit::fit_2d_gaussian( + &subtracted_f64, + None, + ) + .map(|f| (f.sigma_row + f.sigma_col) / 2.0) + .map(|s| s.clamp(0.5, 20.0)); + if let Err(e) = update_beamspot( + &db_path, + file_path, + r as i64, + c as i64, + beam_sigma, + ) { self.set_status( format!("Failed to store beamspot for {}: {}", file_path, e), true, ); return; } + if let Ok(Some(uid)) = + get_scan_point_uid_by_source_path(&db_path, file_path) + { + let _ = update_beamspot_scan_point( + &db_path, + &uid, + r as i64, + c as i64, + beam_sigma, + ); + } beamspots.push((r as i64, c as i64)); ok += 1; } @@ -1521,30 +1941,40 @@ impl App { } else { 0 }; - let db_path = Path::new(&self.current_root).join(CATALOG_DB_NAME); + let db_path = resolve_catalog_path(Path::new(&self.current_root)); if !db_path.exists() { return; } - let entries = match list_beamtime_entries(&db_path) { - Ok(e) => e, - Err(e) => { - self.set_app_error(super::error::TuiError::catalog_unavailable( - &self.current_root, - "list_beamtime_entries", - e.to_string(), - )); - return; - } - }; - let rows = match query_files(&db_path, None) { - Ok(r) => r, - Err(e) => { - self.set_app_error(super::error::TuiError::catalog_unavailable( - &self.current_root, - "query_files", - e.to_string(), - )); - return; + let beamtime_path = Path::new(&self.current_root); + let (entries, rows) = match ( + list_beamtime_entries_v2(&db_path, beamtime_path), + query_scan_points(&db_path, beamtime_path, None), + ) { + (Ok(e), Ok(r)) if !r.is_empty() => (e, r), + _ => { + let entries = match list_beamtime_entries(&db_path) { + Ok(e) => e, + Err(e) => { + self.set_app_error(super::error::TuiError::catalog_unavailable( + &self.current_root, + "list_beamtime_entries", + e.to_string(), + )); + return; + } + }; + let rows = match query_files(&db_path, None) { + Ok(r) => r, + Err(e) => { + self.set_app_error(super::error::TuiError::catalog_unavailable( + &self.current_root, + "query_files", + e.to_string(), + )); + return; + } + }; + (entries, rows) } }; let all_groups = build_groups_from_files(rows); @@ -1690,8 +2120,8 @@ impl App { Ok(()) => { self.app_error = None; if let Some(path) = self.pending_ingest_path.take() { - let db_path = path.join(CATALOG_DB_NAME); - let file_count = catalog_file_count(&db_path).ok(); + let db_path = resolve_catalog_path(&path); + let file_count = catalog_file_count(&db_path, Some(&path)).ok(); let _ = register_beamtime(&path, file_count); if self.screen == Screen::Launcher { let beamtimes = list_beamtimes().unwrap_or_default(); @@ -1712,8 +2142,8 @@ impl App { self.set_status("Catalog indexed.".to_string(), false); } else { self.reload_from_catalog(false); - let db_path = Path::new(&self.current_root).join(CATALOG_DB_NAME); - let file_count = catalog_file_count(&db_path).ok(); + let db_path = resolve_catalog_path(Path::new(&self.current_root)); + let file_count = catalog_file_count(&db_path, Some(Path::new(&self.current_root))).ok(); let _ = register_beamtime(Path::new(&self.current_root), file_count); self.set_status("Catalog indexed.".to_string(), false); } @@ -1832,21 +2262,21 @@ impl App { let len = self.samples.len(); if len > 0 { let i = self.sample_state.selected().unwrap_or(0); - self.sample_state.select(Some((i + 1) % len)); + self.sample_state.select(Some(cmp::min(i + 1, len - 1))); } } Focus::TagList => { let len = self.tags.len(); if len > 0 { let i = self.tag_state.selected().unwrap_or(0); - self.tag_state.select(Some((i + 1) % len)); + self.tag_state.select(Some(cmp::min(i + 1, len - 1))); } } Focus::ScanList => { let len = self.scans.len(); if len > 0 { let i = self.scan_list_state.selected().unwrap_or(0); - self.scan_list_state.select(Some((i + 1) % len)); + self.scan_list_state.select(Some(cmp::min(i + 1, len - 1))); } } Focus::Table => { @@ -1885,7 +2315,7 @@ impl App { if len > 0 { let i = self.sample_state.selected().unwrap_or(0); self.sample_state - .select(Some(if i == 0 { len - 1 } else { i - 1 })); + .select(Some(if i == 0 { 0 } else { i - 1 })); } } Focus::TagList => { @@ -1893,7 +2323,7 @@ impl App { if len > 0 { let i = self.tag_state.selected().unwrap_or(0); self.tag_state - .select(Some(if i == 0 { len - 1 } else { i - 1 })); + .select(Some(if i == 0 { 0 } else { i - 1 })); } } Focus::ScanList => { @@ -1901,7 +2331,7 @@ impl App { if len > 0 { let i = self.scan_list_state.selected().unwrap_or(0); self.scan_list_state - .select(Some(if i == 0 { len - 1 } else { i - 1 })); + .select(Some(if i == 0 { 0 } else { i - 1 })); } } Focus::Table => { @@ -2081,7 +2511,7 @@ impl App { if !self.has_catalog { return; } - let db_path = Path::new(&self.current_root).join(CATALOG_DB_NAME); + let db_path = resolve_catalog_path(Path::new(&self.current_root)); if !db_path.exists() { return; } diff --git a/src/bin/tui/beamspot.rs b/src/bin/tui/beamspot.rs index 6434442..44ea24d 100644 --- a/src/bin/tui/beamspot.rs +++ b/src/bin/tui/beamspot.rs @@ -1,4 +1,3 @@ -#![cfg(feature = "catalog")] use pyref::catalog::FileRow; use super::scan_type::ReflectivityScanType; diff --git a/src/bin/tui/keymap.rs b/src/bin/tui/keymap.rs index 46bff07..1974295 100644 --- a/src/bin/tui/keymap.rs +++ b/src/bin/tui/keymap.rs @@ -47,6 +47,10 @@ pub enum Action { NavFwd, PreviewImage, BeamPosition, + NextProblem, + PrevProblem, + UseLastOkBeamspot, + UseNextOkBeamspot, None, } @@ -116,6 +120,10 @@ pub fn from_key_event(key: KeyEvent, keymap: &str) -> Action { KeyCode::Char('l') => return Action::NavFwd, KeyCode::Char('v') => return Action::PreviewImage, KeyCode::Char('m') => return Action::BeamPosition, + KeyCode::Char('N') => return Action::NextProblem, + KeyCode::Char('P') => return Action::PrevProblem, + KeyCode::Char('[') => return Action::UseLastOkBeamspot, + KeyCode::Char(']') => return Action::UseNextOkBeamspot, _ => {} } } @@ -175,6 +183,10 @@ pub fn from_key_event(key: KeyEvent, keymap: &str) -> Action { KeyCode::Char('l') => return Action::NavFwd, KeyCode::Char('v') => return Action::PreviewImage, KeyCode::Char('m') => return Action::BeamPosition, + KeyCode::Char('N') => return Action::NextProblem, + KeyCode::Char('P') => return Action::PrevProblem, + KeyCode::Char('[') => return Action::UseLastOkBeamspot, + KeyCode::Char(']') => return Action::UseNextOkBeamspot, _ => {} } } diff --git a/src/bin/tui/preview.rs b/src/bin/tui/preview.rs index 4dc8ce0..92a2c4a 100644 --- a/src/bin/tui/preview.rs +++ b/src/bin/tui/preview.rs @@ -1,146 +1,140 @@ #![cfg(feature = "tui")] -#[cfg(not(target_os = "macos"))] use std::collections::HashMap; -use std::path::PathBuf; +use std::path::{Path, PathBuf}; use std::sync::mpsc; +use eframe::egui; use ndarray::Array2; +type BeamspotMessage = (PathBuf, i64, i64, Option); +type PreviewRequest = (PathBuf, Option<(usize, usize, usize, usize)>, Option); + +#[derive(Clone, Copy, PartialEq, Eq)] +pub enum PreviewCommand { + GoToNextProblem, + GoToPrevProblem, +} + +#[derive(Clone, Copy, PartialEq, Eq)] +enum InteractionMode { + Marquee, + Zoom, +} + const DEFAULT_SIGMA: f64 = 2.0; const SIGMA_CLAMP_MIN: f64 = 0.5; const SIGMA_CLAMP_MAX: f64 = 20.0; const CROP_SIGMA_MULT: f64 = 4.0; +const MIN_THIRD_COLUMN_WIDTH: f32 = 120.0; -#[cfg(not(target_os = "macos"))] -use eframe::egui; - -#[cfg(target_os = "macos")] -pub fn run_preview_window(rx: mpsc::Receiver) { - std::thread::spawn(move || { - while let Ok(path) = rx.recv() { - if let Err(e) = stream_fits_three_panel_png(&path) { - eprintln!("Preview: {}", e); +fn extract_rgba_slice( + rgba: &[u8], + full_w: usize, + full_h: usize, + r0: usize, + r1: usize, + c0: usize, + c1: usize, +) -> Vec { + let r1 = r1.min(full_h); + let c1 = c1.min(full_w); + let r0 = r0.min(r1); + let c0 = c0.min(c1); + let h = r1.saturating_sub(r0); + let w = c1.saturating_sub(c0); + let mut out = Vec::with_capacity(h * w * 4); + for row in r0..r1 { + for col in c0..c1 { + let i = (row * full_w + col) * 4; + if i + 4 <= rgba.len() { + out.extend_from_slice(&rgba[i..i + 4]); } } - }); + } + out } -#[cfg(target_os = "macos")] -fn stream_fits_three_panel_png(path: &std::path::Path) -> Result<(), String> { - use plotters::prelude::*; - use plotters::series::LineSeries; +pub enum LoadResult { + Ok(PreviewData), + Err(String), +} +pub struct PreviewData { + pub path: PathBuf, + pub left_rgba: Vec, + pub left_w: usize, + pub left_h: usize, + pub mid_rgba: Vec, + pub mid_w: usize, + pub mid_h: usize, + pub right_rgba: Vec, + pub right_w: usize, + pub right_h: usize, + pub crop_col_sum: Vec, + pub crop_row_sum: Vec, + pub fit: Option, +} + +fn load_preview_data( + path: &Path, + roi: Option<(usize, usize, usize, usize)>, + profile_sigma: Option, +) -> Result { let (_, subtracted) = pyref::io::image_mmap::materialize_image_from_path(path) .map_err(|e| e.to_string())?; let height = subtracted.nrows(); let width = subtracted.ncols(); - if width == 0 || height == 0 { - return Err("Invalid image dimensions".to_string()); - } let subtracted_f64: Array2 = subtracted.mapv(|x| x as f64); - let fit = pyref::gaussian_fit::fit_2d_gaussian(&subtracted_f64, None); - let sigma = fit - .map(|f| { - let s = (f.sigma_row + f.sigma_col) / 2.0; - s.clamp(SIGMA_CLAMP_MIN, SIGMA_CLAMP_MAX) + let fit = pyref::gaussian_fit::fit_2d_gaussian(&subtracted_f64, roi); + let sigma = profile_sigma + .map(|s| s.clamp(SIGMA_CLAMP_MIN, SIGMA_CLAMP_MAX)) + .or_else(|| { + fit.map(|f| { + let s = (f.sigma_row + f.sigma_col) / 2.0; + s.clamp(SIGMA_CLAMP_MIN, SIGMA_CLAMP_MAX) + }) }) .unwrap_or(DEFAULT_SIGMA); let filtered = pyref::io::blur::blur_array2_i64(&subtracted, sigma) - .map_err(|e| e.to_string())?; - let (crop_img, crop_w, crop_h, _fit) = crop_4sigma(&filtered, fit, height, width); - let left_rgba = pyref::colormap::array2_i64_to_rgba_rainbow(&subtracted, None) - .ok_or("Colormap failed")?; - let mid_rgba = pyref::colormap::array2_f32_to_rgba_rainbow(&filtered, None) - .ok_or("Colormap failed")?; - let right_rgba = pyref::colormap::array2_f32_to_rgba_rainbow(&crop_img, None) + .map_err(|e| format!("Blur failed: {}", e))?; + let left_max = subtracted.iter().copied().max().unwrap_or(1).max(1); + let mid_max = filtered + .iter() + .copied() + .fold(f32::NEG_INFINITY, f32::max) + .max(1.0); + let left_rgba = pyref::colormap::array2_i64_to_rgba(&subtracted, Some((0, left_max)), true) .ok_or("Colormap failed")?; - - const PANEL_MAX: u32 = 320; - let scale = |w: usize, h: usize| { - let (w, h) = (w as u32, h as u32); - if w <= PANEL_MAX && h <= PANEL_MAX { - (w, h) - } else if w >= h { - (PANEL_MAX, (h * PANEL_MAX / w).max(1)) - } else { - ((w * PANEL_MAX / h).max(1), PANEL_MAX) - } - }; - let (lw, lh) = scale(width, height); - let (mw, mh) = scale(width, height); - let (rw, rh) = scale(crop_w, crop_h); - let total_w = lw + mw + rw + 2; - let total_h = lh.max(mh).max(rh); - - let temp = std::env::temp_dir().join("pyref_preview.png"); - let root = BitMapBackend::new(temp.as_path(), (total_w, total_h)).into_drawing_area(); - root.fill(&WHITE).map_err(|e| format!("{:?}", e))?; - - let draw_panel = |root: &DrawingArea<_, _>, rgba: &[u8], w: usize, h: usize, out_w: u32, out_h: u32| -> Result<(), String> { - let mut chart = ChartBuilder::on(root) - .margin(0) - .set_all_label_area_size(0) - .build_cartesian_2d(0i32..(out_w as i32), 0i32..(out_h as i32)) - .map_err(|e| format!("{:?}", e))?; - let rgba_ref = rgba; - chart - .draw_series( - (0..(out_h as usize)) - .flat_map(|oy| { - (0..(out_w as usize)).map(move |ox| { - let sx = (ox * w) / out_w as usize; - let sy = (oy * h) / out_h as usize; - let i = (sy * w + sx) * 4; - let r = rgba_ref.get(i).copied().unwrap_or(0); - let g = rgba_ref.get(i + 1).copied().unwrap_or(0); - let b = rgba_ref.get(i + 2).copied().unwrap_or(0); - (ox as i32, (out_h as i32 - 1 - oy as i32), RGBColor(r, g, b)) - }) - }) - .map(|(x, y, color)| Rectangle::new([(x, y), (x + 1, y + 1)], color.filled())), - ) - .map_err(|e| format!("{:?}", e))?; - Ok(()) - }; - - let (area_left, rest1) = root.split_horizontally((lw + 1) as i32); - draw_panel(&area_left, &left_rgba, width, height, lw, lh)?; - let (area_mid, rest2) = rest1.split_horizontally((mw + 1) as i32); - draw_panel(&area_mid, &mid_rgba, width, height, mw, mh)?; - draw_panel(&rest2, &right_rgba, crop_w, crop_h, rw, rh)?; - - if let Some(ref f) = fit { - let cx = rw as f64 / 2.0; - let cy = (rh as f64 - 1.0) / 2.0; - let scale_x = rw as f64 / crop_w as f64; - let scale_y = rh as f64 / crop_h as f64; - let mut chart = ChartBuilder::on(&rest2) - .margin(0) - .set_all_label_area_size(0) - .build_cartesian_2d(0.0..(rw as f64), 0.0..(rh as f64)) - .map_err(|e| format!("{:?}", e))?; - for k in [1.0, 2.0, 3.0, 4.0] { - let pts = ellipse_contour_points( - cx, - cy, - k * f.sigma_col * scale_x, - -k * f.sigma_row * scale_y, - 64, - ); - chart - .draw_series(LineSeries::new( - pts.into_iter(), - plotters::style::ShapeStyle::from(&plotters::style::RGBColor(255, 255, 255)) - .stroke_width(1), - )) - .map_err(|e| format!("{:?}", e))?; - } - } - - root.present().map_err(|e| format!("{:?}", e))?; - std::process::Command::new("open").arg(&temp).status().map_err(|e| e.to_string())?; - Ok(()) + let mid_rgba = + pyref::colormap::array2_f32_to_rgba(&filtered, Some((0.0, mid_max)), true) + .ok_or("Colormap failed")?; + let (crop, cw, ch, _) = crop_4sigma(&filtered, fit, height, width); + let right_max = crop + .iter() + .copied() + .fold(f32::NEG_INFINITY, f32::max) + .max(1.0); + let right_rgba = + pyref::colormap::array2_f32_to_rgba(&crop, Some((0.0, right_max)), true) + .ok_or("Colormap failed")?; + let crop_col_sum = (0..cw).map(|j| (0..ch).map(|i| crop[[i, j]]).sum()).collect(); + let crop_row_sum = (0..ch).map(|i| (0..cw).map(|j| crop[[i, j]]).sum()).collect(); + Ok(PreviewData { + path: path.to_path_buf(), + left_rgba, + left_w: width, + left_h: height, + mid_rgba, + mid_w: width, + mid_h: height, + right_rgba, + right_w: cw, + right_h: ch, + crop_col_sum, + crop_row_sum, + fit, + }) } fn ellipse_contour_points( @@ -170,13 +164,12 @@ fn crop_4sigma( let (cr, cc, half_r, half_c) = if let Some(f) = fit { let hr = (CROP_SIGMA_MULT * f.sigma_row).ceil() as i32; let hc = (CROP_SIGMA_MULT * f.sigma_col).ceil() as i32; - let hr = hr.max(2).min((height / 2) as i32); - let hc = hc.max(2).min((width / 2) as i32); + let half = hr.max(hc).max(2).min((height / 2).min(width / 2) as i32); ( f.center_row.round() as i32, f.center_col.round() as i32, - hr, - hc, + half, + half, ) } else { let cr = (height / 2) as i32; @@ -189,16 +182,27 @@ fn crop_4sigma( let c0 = (cc - half_c).max(0).min((width - 1) as i32) as usize; let c1 = (cc + half_c + 1).max(0).min(width as i32) as usize; if r0 >= r1 || c0 >= c1 { - let fallback = Array2::from_shape_vec((1, 1), vec![0.0f32]).unwrap(); - return (fallback, 1, 1, fit); + let fb_half = 16i32; + let fr0 = ((height as i32 / 2) - fb_half).max(0) as usize; + let fr1 = ((height as i32 / 2) + fb_half).min(height as i32) as usize; + let fc0 = ((width as i32 / 2) - fb_half).max(0) as usize; + let fc1 = ((width as i32 / 2) + fb_half).min(width as i32) as usize; + if fr0 < fr1 && fc0 < fc1 { + let crop = filtered.slice(ndarray::s![fr0..fr1, fc0..fc1]).to_owned(); + let (ch, cw) = (crop.nrows(), crop.ncols()); + return (crop, cw, ch, fit); + } + let fallback = Array2::from_shape_vec((2, 2), vec![0.0f32; 4]) + .expect("fallback 2x2 shape is valid"); + return (fallback, 2, 2, fit); } let crop = filtered.slice(ndarray::s![r0..r1, c0..c1]).to_owned(); let (crop_h, crop_w) = (crop.nrows(), crop.ncols()); (crop, crop_w, crop_h, fit) } -#[cfg(not(target_os = "macos"))] -pub fn run_preview_window(rx: mpsc::Receiver) { +#[allow(dead_code)] +pub fn run_preview_window(rx: mpsc::Receiver<(PathBuf, Option)>) { let native_options = eframe::NativeOptions { viewport: egui::ViewportBuilder::default() .with_position([1200.0, 100.0]) @@ -208,14 +212,49 @@ pub fn run_preview_window(rx: mpsc::Receiver) { let _ = eframe::run_native( "FITS preview", native_options, - Box::new(move |cc| Ok(Box::new(PreviewApp::new(cc, rx)))), + Box::new(move |cc| Ok(Box::new(PreviewApp::new(cc, rx, None, None)))), + ); +} + +pub fn run_preview_window_on_first_path( + rx: mpsc::Receiver<(PathBuf, Option)>, + beamspot_tx: Option>, + cmd_tx: Option>, +) { + let (first_path, first_sigma) = match rx.recv() { + Ok(p) => p, + Err(_) => return, + }; + let (forward_tx, forward_rx) = mpsc::channel(); + let _ = forward_tx.send((first_path, first_sigma)); + std::thread::spawn(move || { + while let Ok(msg) = rx.recv() { + let _ = forward_tx.send(msg); + } + }); + let native_options = eframe::NativeOptions { + viewport: egui::ViewportBuilder::default() + .with_position([1200.0, 100.0]) + .with_inner_size([900.0, 500.0]), + ..Default::default() + }; + let _ = eframe::run_native( + "FITS preview", + native_options, + Box::new(move |cc| { + Ok(Box::new(PreviewApp::new(cc, forward_rx, beamspot_tx, cmd_tx))) + }), ); } -#[cfg(not(target_os = "macos"))] struct PreviewApp { - rx: mpsc::Receiver, + rx: mpsc::Receiver<(PathBuf, Option)>, + path_tx: mpsc::Sender, + result_rx: mpsc::Receiver, + loading: bool, + last_requested_path: Option, current_path: Option, + profile_sigma: Option, roi_per_path: HashMap, left_rgba: Option>, left_w: usize, @@ -226,6 +265,8 @@ struct PreviewApp { right_rgba: Option>, right_w: usize, right_h: usize, + crop_col_sum: Vec, + crop_row_sum: Vec, fit: Option, tex_left: Option, tex_mid: Option, @@ -237,14 +278,40 @@ struct PreviewApp { marquee_img_w: usize, marquee_img_h: usize, marquee_on_left: bool, + beamspot_tx: Option>, + send_beamspot_on_next_apply: bool, + zoom_per_path: HashMap, + interaction_mode: InteractionMode, + cmd_tx: Option>, } -#[cfg(not(target_os = "macos"))] impl PreviewApp { - fn new(_cc: &eframe::CreationContext<'_>, rx: mpsc::Receiver) -> Self { + fn new( + _cc: &eframe::CreationContext<'_>, + rx: mpsc::Receiver<(PathBuf, Option)>, + beamspot_tx: Option>, + cmd_tx: Option>, + ) -> Self { + let (path_tx, path_rx): (mpsc::Sender, mpsc::Receiver) = + mpsc::channel(); + let (result_tx, result_rx) = mpsc::channel(); + std::thread::spawn(move || { + while let Ok((path, roi, profile_sigma)) = path_rx.recv() { + let result = match load_preview_data(&path, roi, profile_sigma) { + Ok(data) => LoadResult::Ok(data), + Err(e) => LoadResult::Err(e), + }; + let _ = result_tx.send(result); + } + }); Self { rx, + path_tx, + result_rx, + loading: false, + last_requested_path: None, current_path: None, + profile_sigma: None, roi_per_path: HashMap::new(), left_rgba: None, left_w: 0, @@ -255,6 +322,8 @@ impl PreviewApp { right_rgba: None, right_w: 0, right_h: 0, + crop_col_sum: Vec::new(), + crop_row_sum: Vec::new(), fit: None, tex_left: None, tex_mid: None, @@ -266,6 +335,11 @@ impl PreviewApp { marquee_img_w: 0, marquee_img_h: 0, marquee_on_left: true, + beamspot_tx, + send_beamspot_on_next_apply: false, + zoom_per_path: HashMap::new(), + interaction_mode: InteractionMode::Marquee, + cmd_tx, } } @@ -274,11 +348,24 @@ impl PreviewApp { rect: egui::Rect, img_w: usize, img_h: usize, + visible: Option<(usize, usize, usize, usize)>, ) -> (usize, usize) { let x = (pos.x - rect.min.x) / rect.width(); let y = (pos.y - rect.min.y) / rect.height(); - let col = (x * img_w as f32).round().clamp(0.0, (img_w - 1) as f32) as usize; - let row = (y * img_h as f32).round().clamp(0.0, (img_h - 1) as f32) as usize; + let (row, col) = match visible { + Some((r0, r1, c0, c1)) => { + let vis_h = r1.saturating_sub(r0).max(1); + let vis_w = c1.saturating_sub(c0).max(1); + let local_r = (y * vis_h as f32).round().clamp(0.0, (vis_h - 1) as f32) as usize; + let local_c = (x * vis_w as f32).round().clamp(0.0, (vis_w - 1) as f32) as usize; + (r0 + local_r, c0 + local_c) + } + None => { + let col = (x * img_w as f32).round().clamp(0.0, (img_w - 1) as f32) as usize; + let row = (y * img_h as f32).round().clamp(0.0, (img_h - 1) as f32) as usize; + (row, col) + } + }; (row, col) } @@ -297,96 +384,139 @@ impl PreviewApp { (Some(s), Some(c), Some(r), w, h) if w > 0 && h > 0 => (s, c, r, w, h), _ => return, }; - let (r0, c0) = Self::screen_to_image(start, rect, w, h); - let (r1, c1) = Self::screen_to_image(current, rect, w, h); + let visible = self.zoom_per_path.get(&path).copied(); + let (r0, c0) = Self::screen_to_image(start, rect, w, h, visible); + let (r1, c1) = Self::screen_to_image(current, rect, w, h, visible); let r_min = r0.min(r1); let r_max = r0.max(r1); let c_min = c0.min(c1); let c_max = c0.max(c1); - let r1_excl = (r_max + 1).min(h); - let c1_excl = (c_max + 1).min(w); + let (full_w, full_h) = (self.left_w, self.left_h); + let r1_excl = (r_max + 1).min(full_h); + let c1_excl = (c_max + 1).min(full_w); if r_min >= r1_excl || c_min >= c1_excl { self.marquee_start = None; self.marquee_current = None; self.marquee_rect = None; return; } + let path_ref = path.clone(); self.roi_per_path .insert(path, (r_min, r1_excl, c_min, c1_excl)); - self.load_path(&path); + let roi = (r_min, r1_excl, c_min, c1_excl); + let _ = self + .path_tx + .send((path_ref.clone(), Some(roi), self.profile_sigma)); + self.loading = true; + self.last_requested_path = Some(path_ref.clone()); + self.send_beamspot_on_next_apply = true; + self.clear_image_state(); self.marquee_start = None; self.marquee_current = None; self.marquee_rect = None; } - fn load_path(&mut self, path: &PathBuf) { + fn clear_image_state(&mut self) { + self.error = None; + self.tex_left = None; + self.tex_mid = None; + self.tex_right = None; + self.left_rgba = None; + self.left_w = 0; + self.left_h = 0; + self.mid_rgba = None; + self.mid_w = 0; + self.mid_h = 0; + self.right_rgba = None; + self.right_w = 0; + self.right_h = 0; + self.crop_col_sum.clear(); + self.crop_row_sum.clear(); + self.fit = None; + } + + fn apply_preview_data(&mut self, data: PreviewData) { + self.current_path = Some(data.path.clone()); + self.left_rgba = Some(data.left_rgba); + self.left_w = data.left_w; + self.left_h = data.left_h; + self.mid_rgba = Some(data.mid_rgba); + self.mid_w = data.mid_w; + self.mid_h = data.mid_h; + self.right_rgba = Some(data.right_rgba); + self.right_w = data.right_w; + self.right_h = data.right_h; + self.crop_col_sum = data.crop_col_sum; + self.crop_row_sum = data.crop_row_sum; + self.fit = data.fit; self.error = None; self.tex_left = None; self.tex_mid = None; self.tex_right = None; + } + + fn load_path(&mut self, path: &PathBuf) { + let _ = self.path_tx.send(( + path.clone(), + self.roi_per_path.get(path).copied(), + self.profile_sigma, + )); + self.loading = true; + self.last_requested_path = Some(path.clone()); + self.clear_image_state(); + self.marquee_start = None; + self.marquee_current = None; + self.marquee_rect = None; self.current_path = Some(path.clone()); - match pyref::io::image_mmap::materialize_image_from_path(path.as_path()) { - Ok((_raw, subtracted)) => { - let height = subtracted.nrows(); - let width = subtracted.ncols(); - let roi = self.roi_per_path.get(path).copied(); - let subtracted_f64: Array2 = subtracted.mapv(|x| x as f64); - let fit = pyref::gaussian_fit::fit_2d_gaussian(&subtracted_f64, roi); - let sigma = fit - .map(|f| { - let s = (f.sigma_row + f.sigma_col) / 2.0; - s.clamp(SIGMA_CLAMP_MIN, SIGMA_CLAMP_MAX) - }) - .unwrap_or(DEFAULT_SIGMA); - match pyref::io::blur::blur_array2_i64(&subtracted, sigma) { - Ok(filtered) => { - self.left_rgba = - pyref::colormap::array2_i64_to_rgba_rainbow(&subtracted, None); - self.left_w = width; - self.left_h = height; - self.mid_rgba = - pyref::colormap::array2_f32_to_rgba_rainbow(&filtered, None); - self.mid_w = width; - self.mid_h = height; - let (crop, cw, ch, _) = crop_4sigma(&filtered, fit, height, width); - self.right_rgba = - pyref::colormap::array2_f32_to_rgba_rainbow(&crop, None); - self.right_w = cw; - self.right_h = ch; - self.fit = fit; - } - Err(e) => { - self.error = Some(format!("Blur failed: {}", e)); - } - } - } - Err(e) => { - self.error = Some(e.to_string()); - } - } } fn update_textures(&mut self, ctx: &egui::Context) { + let zoom = self + .current_path + .as_ref() + .and_then(|p| self.zoom_per_path.get(p).copied()); if let Some(ref rgba) = self.left_rgba { if self.left_w > 0 && self.left_h > 0 && rgba.len() >= self.left_w * self.left_h * 4 { - let image = - egui::ColorImage::from_rgba_unmultiplied([self.left_w, self.left_h], rgba); - self.tex_left = Some(ctx.load_texture( - "preview_left", - image, - egui::TextureOptions::default(), - )); + let (tex_w, tex_h, data) = match zoom { + Some((r0, r1, c0, c1)) => { + let w = c1.saturating_sub(c0).max(1); + let h = r1.saturating_sub(r0).max(1); + let slice = + extract_rgba_slice(rgba, self.left_w, self.left_h, r0, r1, c0, c1); + (w, h, slice) + } + None => (self.left_w, self.left_h, rgba.clone()), + }; + if !data.is_empty() && data.len() >= tex_w * tex_h * 4 { + let image = egui::ColorImage::from_rgba_unmultiplied([tex_w, tex_h], &data); + self.tex_left = Some(ctx.load_texture( + "preview_left", + image, + egui::TextureOptions::default(), + )); + } } } if let Some(ref rgba) = self.mid_rgba { if self.mid_w > 0 && self.mid_h > 0 && rgba.len() >= self.mid_w * self.mid_h * 4 { - let image = - egui::ColorImage::from_rgba_unmultiplied([self.mid_w, self.mid_h], rgba); - self.tex_mid = Some(ctx.load_texture( - "preview_mid", - image, - egui::TextureOptions::default(), - )); + let (tex_w, tex_h, data) = match zoom { + Some((r0, r1, c0, c1)) => { + let w = c1.saturating_sub(c0).max(1); + let h = r1.saturating_sub(r0).max(1); + let slice = + extract_rgba_slice(rgba, self.mid_w, self.mid_h, r0, r1, c0, c1); + (w, h, slice) + } + None => (self.mid_w, self.mid_h, rgba.clone()), + }; + if !data.is_empty() && data.len() >= tex_w * tex_h * 4 { + let image = egui::ColorImage::from_rgba_unmultiplied([tex_w, tex_h], &data); + self.tex_mid = Some(ctx.load_texture( + "preview_mid", + image, + egui::TextureOptions::default(), + )); + } } } if let Some(ref rgba) = self.right_rgba { @@ -404,12 +534,40 @@ impl PreviewApp { } } -#[cfg(not(target_os = "macos"))] impl eframe::App for PreviewApp { fn update(&mut self, ctx: &egui::Context, _frame: &mut eframe::Frame) { - while let Ok(path) = self.rx.try_recv() { + while let Ok((path, sigma)) = self.rx.try_recv() { + self.profile_sigma = sigma; self.load_path(&path); } + while let Ok(result) = self.result_rx.try_recv() { + self.loading = false; + match result { + LoadResult::Ok(data) => { + if self.last_requested_path.as_ref() == Some(&data.path) { + if self.send_beamspot_on_next_apply { + if let (Some(f), Some(tx)) = + (data.fit.as_ref(), self.beamspot_tx.as_ref()) + { + let sigma = ((f.sigma_row + f.sigma_col) / 2.0) + .clamp(SIGMA_CLAMP_MIN, SIGMA_CLAMP_MAX); + let _ = tx.send(( + data.path.clone(), + f.center_row.round() as i64, + f.center_col.round() as i64, + Some(sigma), + )); + self.send_beamspot_on_next_apply = false; + } + } + self.apply_preview_data(data); + } + } + LoadResult::Err(e) => { + self.error = Some(e); + } + } + } if (self.left_rgba.is_some() && self.tex_left.is_none()) || (self.mid_rgba.is_some() && self.tex_mid.is_none()) || (self.right_rgba.is_some() && self.tex_right.is_none()) @@ -417,136 +575,643 @@ impl eframe::App for PreviewApp { self.update_textures(ctx); } egui::CentralPanel::default().show(ctx, |ui| { + if ui.input(|i| i.key_pressed(egui::Key::Z)) { + self.interaction_mode = InteractionMode::Zoom; + } + if ui.input(|i| i.key_pressed(egui::Key::M)) { + self.interaction_mode = InteractionMode::Marquee; + } + if let Some(ref path) = self.current_path { + if ui.input(|i| i.key_pressed(egui::Key::R) || i.key_pressed(egui::Key::Escape)) { + if self.zoom_per_path.remove(path).is_some() { + self.tex_left = None; + self.tex_mid = None; + ctx.request_repaint(); + } + } + } if let Some(ref err) = self.error { ui.colored_label(egui::Color32::RED, err.as_str()); } + if self.loading && self.left_rgba.is_none() { + ui.label("Loading..."); + return; + } if self.left_rgba.is_none() && self.error.is_none() { ui.label("Expand a profile and select a FITS file (j/k or click)."); return; } + let panel_w = (ui.available_width() / 3.0).floor().max(1.0); + let label_height = 24.0; + ui.horizontal(|ui| { + ui.allocate_ui_with_layout( + egui::vec2(panel_w, label_height), + egui::Layout::top_down(egui::Align::Min).with_cross_align(egui::Align::Center), + |ui| { + ui.label("Raw"); + }, + ); + ui.allocate_ui_with_layout( + egui::vec2(panel_w, label_height), + egui::Layout::top_down(egui::Align::Min).with_cross_align(egui::Align::Center), + |ui| { + ui.label("Filtered"); + }, + ); + ui.allocate_ui_with_layout( + egui::vec2(panel_w, label_height), + egui::Layout::top_down(egui::Align::Min).with_cross_align(egui::Align::Center), + |ui| { + ui.label("Crop (4 sigma)"); + }, + ); + }); ui.horizontal(|ui| { - ui.label("Raw"); - ui.add_space(ui.available_width() / 3.0 - 30.0); - ui.label("Filtered"); - ui.add_space(ui.available_width() / 3.0 - 30.0); - ui.label("Crop (4 sigma)"); + let marquee_active = matches!(self.interaction_mode, InteractionMode::Marquee); + let zoom_active = matches!(self.interaction_mode, InteractionMode::Zoom); + let marquee_btn = egui::Button::new("Marquee (M)").fill(if marquee_active { + ui.visuals().selection.bg_fill + } else { + egui::Color32::TRANSPARENT + }); + if ui.add(marquee_btn).clicked() { + self.interaction_mode = InteractionMode::Marquee; + } + let zoom_btn = egui::Button::new("Zoom (Z)").fill(if zoom_active { + ui.visuals().selection.bg_fill + } else { + egui::Color32::TRANSPARENT + }); + if ui.add(zoom_btn).clicked() { + self.interaction_mode = InteractionMode::Zoom; + } + let zoomed = self + .current_path + .as_ref() + .is_some_and(|p| self.zoom_per_path.contains_key(p)); + if zoomed { + if ui.button("Reset zoom (R/Esc)").clicked() { + if let Some(ref path) = self.current_path { + self.zoom_per_path.remove(path); + self.tex_left = None; + self.tex_mid = None; + ctx.request_repaint(); + } + } + } + if let Some(ref tx) = self.cmd_tx { + if ui.button("Prev (P)").clicked() { + let _ = tx.send(PreviewCommand::GoToPrevProblem); + } + if ui.button("Next (N)").clicked() { + let _ = tx.send(PreviewCommand::GoToNextProblem); + } + } }); ui.horizontal(|ui| { - let panel_w = (ui.available_width() / 3.0).floor().max(1.0); if let Some(ref tex) = self.tex_left { - let size = [ + let size = egui::vec2( panel_w, panel_w * (self.left_h as f32 / self.left_w as f32).min(2.0), - ]; + ); let response = ui.add( - egui::Image::new(tex.id(), size) + egui::Image::from_texture((tex.id(), size)) .sense(egui::Sense::drag()), ); let rect = response.rect; if response.drag_started() { - self.marquee_start = Some(rect.min); - self.marquee_current = Some(rect.min); - self.marquee_rect = Some(rect); - self.marquee_img_w = self.left_w; - self.marquee_img_h = self.left_h; - self.marquee_on_left = true; + let pointer_pos = response + .interact_pointer_pos() + .or_else(|| ui.input(|i| i.pointer.latest_pos())); + if let Some(pos) = pointer_pos { + self.marquee_start = Some(pos); + self.marquee_current = Some(pos); + self.marquee_rect = Some(rect); + let (img_w, img_h) = self + .current_path + .as_ref() + .and_then(|p| self.zoom_per_path.get(p)) + .map(|(r0, r1, c0, c1)| { + ( + c1.saturating_sub(*c0).max(1), + r1.saturating_sub(*r0).max(1), + ) + }) + .unwrap_or((self.left_w, self.left_h)); + self.marquee_img_w = img_w; + self.marquee_img_h = img_h; + self.marquee_on_left = true; + } } if response.dragged() && self.marquee_on_left { - if let Some(pos) = ui.input(|i| i.pointer.pos()) { + if let Some(pos) = ui.input(|i| i.pointer.latest_pos()) { self.marquee_current = Some(pos); } } - if response.drag_released() && self.marquee_on_left { - self.apply_marquee_roi(); + if response.drag_stopped() && self.marquee_on_left { + match self.interaction_mode { + InteractionMode::Marquee => self.apply_marquee_roi(), + InteractionMode::Zoom => { + if let (Some(path), Some(start), Some(current), Some(rect)) = ( + self.current_path.clone(), + self.marquee_start, + self.marquee_current, + self.marquee_rect, + ) { + if self.marquee_img_w > 0 && self.marquee_img_h > 0 { + let visible = + self.zoom_per_path.get(&path).copied(); + let (r0, c0) = Self::screen_to_image( + start, + rect, + self.marquee_img_w, + self.marquee_img_h, + visible, + ); + let (r1, c1) = Self::screen_to_image( + current, + rect, + self.marquee_img_w, + self.marquee_img_h, + visible, + ); + let r_min = r0.min(r1); + let r_max = r0.max(r1); + let c_min = c0.min(c1); + let c_max = c0.max(c1); + let r1_excl = (r_max + 1).min(self.left_h); + let c1_excl = (c_max + 1).min(self.left_w); + if r_min < r1_excl && c_min < c1_excl { + self.zoom_per_path + .insert(path, (r_min, r1_excl, c_min, c1_excl)); + self.tex_left = None; + self.tex_mid = None; + ctx.request_repaint(); + } + } + } + self.marquee_start = None; + self.marquee_current = None; + self.marquee_rect = None; + } + } } if let (Some(a), Some(b)) = (self.marquee_start, self.marquee_current) { if self.marquee_on_left { let r = egui::Rect::from_min_max(a, b); + if r.width() > 0.0 && r.height() > 0.0 { + ui.painter().rect_stroke( + r.intersect(rect), + 0.0, + egui::Stroke::new(2.0, egui::Color32::WHITE), + ); + } + } + } + if let Some(ref f) = self.fit { + let zoom = self + .current_path + .as_ref() + .and_then(|p| self.zoom_per_path.get(p)); + let (cx, cy, draw_crop) = match zoom { + Some(&(r0, r1, c0, c1)) => { + let zw = (c1.saturating_sub(c0)) as f32; + let zh = (r1.saturating_sub(r0)) as f32; + if zw > 0.0 && zh > 0.0 { + let fc = f.center_col as f32; + let fr = f.center_row as f32; + let in_view = fc >= c0 as f32 + && fc < c1 as f32 + && fr >= r0 as f32 + && fr < r1 as f32; + if in_view { + let cx = rect.min.x + + (fc - c0 as f32) / zw * rect.width(); + let cy = rect.min.y + + (fr - r0 as f32) / zh * rect.height(); + let half = (CROP_SIGMA_MULT * f.sigma_row.max(f.sigma_col)) + .ceil() + .max(2.0) + .min((self.left_h / 2).min(self.left_w / 2) as f64) + as f32; + let cr0 = (fr - half).max(r0 as f32).min(r1 as f32 - 1.0); + let cr1 = (fr + half).max(r0 as f32).min(r1 as f32 - 1.0); + let cc0 = (fc - half).max(c0 as f32).min(c1 as f32 - 1.0); + let cc1 = (fc + half).max(c0 as f32).min(c1 as f32 - 1.0); + let x0 = + rect.min.x + (cc0 - c0 as f32) / zw * rect.width(); + let x1 = + rect.min.x + (cc1 - c0 as f32) / zw * rect.width(); + let y0 = + rect.min.y + (cr0 - r0 as f32) / zh * rect.height(); + let y1 = + rect.min.y + (cr1 - r0 as f32) / zh * rect.height(); + (cx, cy, Some((x0, y0, x1, y1))) + } else { + (0.0, 0.0, None) + } + } else { + (0.0, 0.0, None) + } + } + None => { + let cx = rect.min.x + + (f.center_col as f32 / self.left_w as f32) * rect.width(); + let cy = rect.min.y + + (f.center_row as f32 / self.left_h as f32) * rect.height(); + let half = (CROP_SIGMA_MULT * f.sigma_row.max(f.sigma_col)) + .ceil() + .max(2.0) + .min((self.left_h / 2).min(self.left_w / 2) as f64) as f32; + let c0 = (f.center_col as f32 - half) + .max(0.0) + .min((self.left_w - 1) as f32); + let c1 = (f.center_col as f32 + half) + .max(0.0) + .min((self.left_w - 1) as f32); + let r0 = (f.center_row as f32 - half) + .max(0.0) + .min((self.left_h - 1) as f32); + let r1 = (f.center_row as f32 + half) + .max(0.0) + .min((self.left_h - 1) as f32); + let x0 = rect.min.x + (c0 / self.left_w as f32) * rect.width(); + let x1 = rect.min.x + (c1 / self.left_w as f32) * rect.width(); + let y0 = rect.min.y + (r0 / self.left_h as f32) * rect.height(); + let y1 = rect.min.y + (r1 / self.left_h as f32) * rect.height(); + (cx, cy, Some((x0, y0, x1, y1))) + } + }; + if rect.width() > 0.0 && rect.height() > 0.0 + && (zoom.is_none() + || (cx >= rect.min.x && cx <= rect.max.x + && cy >= rect.min.y && cy <= rect.max.y)) + { + let stroke = egui::Stroke::new(1.0, egui::Color32::WHITE); + ui.painter().line_segment( + [ + egui::Pos2::new(rect.min.x, cy), + egui::Pos2::new(rect.max.x, cy), + ], + stroke, + ); + ui.painter().line_segment( + [ + egui::Pos2::new(cx, rect.min.y), + egui::Pos2::new(cx, rect.max.y), + ], + stroke, + ); + } + if let Some((x0, y0, x1, y1)) = draw_crop { ui.painter().rect_stroke( - r.intersect(rect), + egui::Rect::from_min_max( + egui::Pos2::new(x0, y0), + egui::Pos2::new(x1, y1), + ), 0.0, - egui::Stroke::new(2.0, egui::Color32::WHITE), + egui::Stroke::new(1.0, egui::Color32::WHITE), ); } } } if let Some(ref tex) = self.tex_mid { - let size = [ + let size = egui::vec2( panel_w, panel_w * (self.mid_h as f32 / self.mid_w as f32).min(2.0), - ]; + ); let response = ui.add( - egui::Image::new(tex.id(), size) + egui::Image::from_texture((tex.id(), size)) .sense(egui::Sense::drag()), ); let rect = response.rect; if response.drag_started() { - self.marquee_start = Some(rect.min); - self.marquee_current = Some(rect.min); - self.marquee_rect = Some(rect); - self.marquee_img_w = self.mid_w; - self.marquee_img_h = self.mid_h; - self.marquee_on_left = false; + let pointer_pos = response + .interact_pointer_pos() + .or_else(|| ui.input(|i| i.pointer.latest_pos())); + if let Some(pos) = pointer_pos { + self.marquee_start = Some(pos); + self.marquee_current = Some(pos); + self.marquee_rect = Some(rect); + let (img_w, img_h) = self + .current_path + .as_ref() + .and_then(|p| self.zoom_per_path.get(p)) + .map(|(r0, r1, c0, c1)| { + ( + c1.saturating_sub(*c0).max(1), + r1.saturating_sub(*r0).max(1), + ) + }) + .unwrap_or((self.mid_w, self.mid_h)); + self.marquee_img_w = img_w; + self.marquee_img_h = img_h; + self.marquee_on_left = false; + } } if response.dragged() && !self.marquee_on_left { - if let Some(pos) = ui.input(|i| i.pointer.pos()) { + if let Some(pos) = ui.input(|i| i.pointer.latest_pos()) { self.marquee_current = Some(pos); } } - if response.drag_released() && !self.marquee_on_left { - self.apply_marquee_roi(); + if response.drag_stopped() && !self.marquee_on_left { + match self.interaction_mode { + InteractionMode::Marquee => self.apply_marquee_roi(), + InteractionMode::Zoom => { + if let (Some(path), Some(start), Some(current), Some(rect)) = ( + self.current_path.clone(), + self.marquee_start, + self.marquee_current, + self.marquee_rect, + ) { + if self.marquee_img_w > 0 && self.marquee_img_h > 0 { + let visible = + self.zoom_per_path.get(&path).copied(); + let (r0, c0) = Self::screen_to_image( + start, + rect, + self.marquee_img_w, + self.marquee_img_h, + visible, + ); + let (r1, c1) = Self::screen_to_image( + current, + rect, + self.marquee_img_w, + self.marquee_img_h, + visible, + ); + let r_min = r0.min(r1); + let r_max = r0.max(r1); + let c_min = c0.min(c1); + let c_max = c0.max(c1); + let r1_excl = (r_max + 1).min(self.mid_h); + let c1_excl = (c_max + 1).min(self.mid_w); + if r_min < r1_excl && c_min < c1_excl { + self.zoom_per_path + .insert(path, (r_min, r1_excl, c_min, c1_excl)); + self.tex_left = None; + self.tex_mid = None; + ctx.request_repaint(); + } + } + } + self.marquee_start = None; + self.marquee_current = None; + self.marquee_rect = None; + } + } } if let (Some(a), Some(b)) = (self.marquee_start, self.marquee_current) { if !self.marquee_on_left { let r = egui::Rect::from_min_max(a, b); + if r.width() > 0.0 && r.height() > 0.0 { + ui.painter().rect_stroke( + r.intersect(rect), + 0.0, + egui::Stroke::new(2.0, egui::Color32::WHITE), + ); + } + } + } + if let Some(ref f) = self.fit { + let zoom = self + .current_path + .as_ref() + .and_then(|p| self.zoom_per_path.get(p)); + let (cx, cy, draw_crop) = match zoom { + Some(&(r0, r1, c0, c1)) => { + let zw = (c1.saturating_sub(c0)) as f32; + let zh = (r1.saturating_sub(r0)) as f32; + if zw > 0.0 && zh > 0.0 { + let fc = f.center_col as f32; + let fr = f.center_row as f32; + let in_view = fc >= c0 as f32 + && fc < c1 as f32 + && fr >= r0 as f32 + && fr < r1 as f32; + if in_view { + let cx = rect.min.x + + (fc - c0 as f32) / zw * rect.width(); + let cy = rect.min.y + + (fr - r0 as f32) / zh * rect.height(); + let half = (CROP_SIGMA_MULT * f.sigma_row.max(f.sigma_col)) + .ceil() + .max(2.0) + .min((self.mid_h / 2).min(self.mid_w / 2) as f64) + as f32; + let cr0 = (fr - half).max(r0 as f32).min(r1 as f32 - 1.0); + let cr1 = (fr + half).max(r0 as f32).min(r1 as f32 - 1.0); + let cc0 = (fc - half).max(c0 as f32).min(c1 as f32 - 1.0); + let cc1 = (fc + half).max(c0 as f32).min(c1 as f32 - 1.0); + let x0 = + rect.min.x + (cc0 - c0 as f32) / zw * rect.width(); + let x1 = + rect.min.x + (cc1 - c0 as f32) / zw * rect.width(); + let y0 = + rect.min.y + (cr0 - r0 as f32) / zh * rect.height(); + let y1 = + rect.min.y + (cr1 - r0 as f32) / zh * rect.height(); + (cx, cy, Some((x0, y0, x1, y1))) + } else { + (0.0, 0.0, None) + } + } else { + (0.0, 0.0, None) + } + } + None => { + let cx = rect.min.x + + (f.center_col as f32 / self.mid_w as f32) * rect.width(); + let cy = rect.min.y + + (f.center_row as f32 / self.mid_h as f32) * rect.height(); + let half = (CROP_SIGMA_MULT * f.sigma_row.max(f.sigma_col)) + .ceil() + .max(2.0) + .min((self.mid_h / 2).min(self.mid_w / 2) as f64) as f32; + let c0 = (f.center_col as f32 - half) + .max(0.0) + .min((self.mid_w - 1) as f32); + let c1 = (f.center_col as f32 + half) + .max(0.0) + .min((self.mid_w - 1) as f32); + let r0 = (f.center_row as f32 - half) + .max(0.0) + .min((self.mid_h - 1) as f32); + let r1 = (f.center_row as f32 + half) + .max(0.0) + .min((self.mid_h - 1) as f32); + let x0 = rect.min.x + (c0 / self.mid_w as f32) * rect.width(); + let x1 = rect.min.x + (c1 / self.mid_w as f32) * rect.width(); + let y0 = rect.min.y + (r0 / self.mid_h as f32) * rect.height(); + let y1 = rect.min.y + (r1 / self.mid_h as f32) * rect.height(); + (cx, cy, Some((x0, y0, x1, y1))) + } + }; + if rect.width() > 0.0 && rect.height() > 0.0 + && (zoom.is_none() + || (cx >= rect.min.x && cx <= rect.max.x + && cy >= rect.min.y && cy <= rect.max.y)) + { + let stroke = egui::Stroke::new(1.0, egui::Color32::WHITE); + ui.painter().line_segment( + [ + egui::Pos2::new(rect.min.x, cy), + egui::Pos2::new(rect.max.x, cy), + ], + stroke, + ); + ui.painter().line_segment( + [ + egui::Pos2::new(cx, rect.min.y), + egui::Pos2::new(cx, rect.max.y), + ], + stroke, + ); + } + if let Some((x0, y0, x1, y1)) = draw_crop { ui.painter().rect_stroke( - r.intersect(rect), + egui::Rect::from_min_max( + egui::Pos2::new(x0, y0), + egui::Pos2::new(x1, y1), + ), 0.0, - egui::Stroke::new(2.0, egui::Color32::WHITE), + egui::Stroke::new(1.0, egui::Color32::WHITE), ); } } } if let Some(ref tex) = self.tex_right { - let size = [ - panel_w, - panel_w * (self.right_h as f32 / self.right_w as f32).min(2.0), - ]; - let response = ui.image(tex.id(), size); - if let Some(ref f) = self.fit { - if self.right_w > 0 && self.right_h > 0 { - let rect = response.rect; - let cx = self.right_w as f64 / 2.0; - let cy = self.right_h as f64 / 2.0; - let n = 64; - for k in [1.0, 2.0, 3.0, 4.0] { - let pts = ellipse_contour_points( - cx, - cy, - k * f.sigma_col, - k * f.sigma_row, - n, - ); - let screen_pts: Vec = pts - .iter() - .map(|&(px, py)| { - egui::Pos2::new( - rect.min.x - + (px / self.right_w as f64) as f32 * rect.width(), - rect.min.y - + (py / self.right_h as f64) as f32 * rect.height(), - ) + let panel_w = (ui.available_width() / 3.0) + .floor() + .max(MIN_THIRD_COLUMN_WIDTH); + let trace_height = 48.0f32; + let trace_width = 48.0f32; + let crop_size = (panel_w - trace_width - ui.spacing().item_spacing.x).max(1.0); + + let draw_col_trace = + |ui: &mut egui::Ui, data: &[f32], w: f32, h: f32| { + let (rect, _) = + ui.allocate_exact_size(egui::vec2(w, h), egui::Sense::hover()); + let min_v = + data.iter().copied().fold(f32::INFINITY, f32::min); + let max_v = + data.iter().copied().fold(f32::NEG_INFINITY, f32::max); + let range = (max_v - min_v).max(1e-6); + let n = data.len(); + let mut pts: Vec = (0..n) + .map(|j| { + let x = rect.min.x + + (j as f32 / (n - 1).max(1) as f32) + * rect.width(); + let t = (data[j] - min_v) / range; + let y = rect.max.y - t * rect.height(); + egui::Pos2::new(x, y) }) .collect(); - if screen_pts.len() >= 2 { + if pts.len() == 1 { + pts.push(egui::Pos2::new(pts[0].x + 1.0, pts[0].y)); + } + if pts.len() >= 2 { ui.painter().add(egui::Shape::line( - screen_pts, - egui::Stroke::new(1.0, egui::Color32::WHITE), + pts, + egui::Stroke::new( + 1.5, + egui::Color32::from_rgb(0, 200, 200), + ), )); } + }; + + let draw_row_trace = + |ui: &mut egui::Ui, data: &[f32], w: f32, h: f32| { + let (rect, _) = + ui.allocate_exact_size(egui::vec2(w, h), egui::Sense::hover()); + let min_v = + data.iter().copied().fold(f32::INFINITY, f32::min); + let max_v = + data.iter().copied().fold(f32::NEG_INFINITY, f32::max); + let range = (max_v - min_v).max(1e-6); + let n = data.len(); + let mut pts: Vec = (0..n) + .map(|i| { + let y = rect.min.y + + (i as f32 / (n - 1).max(1) as f32) + * rect.height(); + let t = (data[i] - min_v) / range; + let x = rect.min.x + t * rect.width(); + egui::Pos2::new(x, y) + }) + .collect(); + if pts.len() == 1 { + pts.push(egui::Pos2::new(pts[0].x, pts[0].y + 1.0)); + } + if pts.len() >= 2 { + ui.painter().add(egui::Shape::line( + pts, + egui::Stroke::new( + 1.5, + egui::Color32::from_rgb(0, 200, 200), + ), + )); + } + }; + + let col_sum = self.crop_col_sum.clone(); + let row_sum = self.crop_row_sum.clone(); + ui.vertical(|ui| { + if !col_sum.is_empty() { + draw_col_trace(ui, &col_sum, crop_size, trace_height); } - } - } + ui.horizontal(|ui| { + let size = egui::vec2(crop_size, crop_size); + let response = ui.image((tex.id(), size)); + if let Some(ref f) = self.fit { + if self.right_w > 0 && self.right_h > 0 { + let rect = response.rect; + let cx = self.right_w as f64 / 2.0; + let cy = self.right_h as f64 / 2.0; + let n = 64; + for k in [1.0, 2.0, 3.0, 4.0] { + let pts = ellipse_contour_points( + cx, + cy, + k * f.sigma_col, + k * f.sigma_row, + n, + ); + let screen_pts: Vec = pts + .iter() + .map(|&(px, py)| { + egui::Pos2::new( + rect.min.x + + (px / self.right_w as f64) + as f32 + * rect.width(), + rect.min.y + + (py / self.right_h as f64) + as f32 + * rect.height(), + ) + }) + .collect(); + if screen_pts.len() >= 2 { + ui.painter().add(egui::Shape::line( + screen_pts, + egui::Stroke::new( + 1.0, + egui::Color32::WHITE, + ), + )); + } + } + } + } + if !row_sum.is_empty() { + draw_row_trace(ui, &row_sum, trace_width, crop_size); + } + }); + }); } }); }); diff --git a/src/bin/tui/run.rs b/src/bin/tui/run.rs index b8b8854..d3325bf 100644 --- a/src/bin/tui/run.rs +++ b/src/bin/tui/run.rs @@ -94,6 +94,10 @@ pub fn run( loop { app.try_recv_ingest(); app.try_recv_watcher(); + #[cfg(feature = "catalog")] + app.try_recv_beamspot_updates(); + #[cfg(feature = "catalog")] + app.try_recv_preview_cmd(); app.clear_status_if_stale(); if app.loading_state != super::app::LoadingState::Idle { app.needs_redraw = true; @@ -399,7 +403,7 @@ fn handle_mouse( if rect_contains(rects.table, col, row) { if row == rects.table.y { if let Some(layout_col) = table_header_column_at(rects.table, col) { - if layout_col >= 1 && layout_col <= 10 { + if (1..=10).contains(&layout_col) { app.cycle_table_sort(layout_col - 1); return true; } @@ -475,7 +479,7 @@ const TABLE_WIDTHS: [Constraint; 11] = [ const EXPANDED_FILES_TABLE_COLUMN_SPACING: u16 = 1; -const EXPANDED_FILES_TABLE_LAYOUT: [Constraint; 13] = [ +const EXPANDED_FILES_TABLE_LAYOUT: [Constraint; 15] = [ Constraint::Length(7), Constraint::Length(EXPANDED_FILES_TABLE_COLUMN_SPACING), Constraint::Length(7), @@ -488,6 +492,8 @@ const EXPANDED_FILES_TABLE_LAYOUT: [Constraint; 13] = [ Constraint::Length(EXPANDED_FILES_TABLE_COLUMN_SPACING), Constraint::Length(10), Constraint::Length(EXPANDED_FILES_TABLE_COLUMN_SPACING), + Constraint::Length(7), + Constraint::Length(EXPANDED_FILES_TABLE_COLUMN_SPACING), Constraint::Length(8), ]; @@ -686,6 +692,19 @@ pub fn handle_event(app: &mut App, key: crossterm::event::KeyEvent) -> bool { Action::NavFwd => app.nav_fwd(), Action::PreviewImage => app.send_preview_path_if_selected(), Action::BeamPosition => app.materialize_profile_beamspots(), + #[cfg(feature = "catalog")] + Action::NextProblem => app.go_to_next_problem(), + #[cfg(feature = "catalog")] + Action::PrevProblem => app.go_to_prev_problem(), + #[cfg(feature = "catalog")] + Action::UseLastOkBeamspot => app.use_last_ok_beamspot(), + #[cfg(feature = "catalog")] + Action::UseNextOkBeamspot => app.use_next_ok_beamspot(), + #[cfg(not(feature = "catalog"))] + Action::NextProblem + | Action::PrevProblem + | Action::UseLastOkBeamspot + | Action::UseNextOkBeamspot => {} Action::Open => { if matches!( app.focus, diff --git a/src/bin/tui/scan_type.rs b/src/bin/tui/scan_type.rs index 47e8b7c..59dcfa0 100644 --- a/src/bin/tui/scan_type.rs +++ b/src/bin/tui/scan_type.rs @@ -1,4 +1,3 @@ -#![cfg(feature = "catalog")] use std::collections::HashSet; diff --git a/src/bin/tui/ui.rs b/src/bin/tui/ui.rs index 0b11a2b..bfd2777 100644 --- a/src/bin/tui/ui.rs +++ b/src/bin/tui/ui.rs @@ -54,6 +54,32 @@ fn layout_constraints(app: &App) -> [Constraint; 2] { } } +const MIN_LIST_HEIGHT: u16 = 3; +const SIDEBAR_GAPS: u16 = 2; + +fn left_sidebar_list_heights( + left_area_height: u16, + n_sample: usize, + n_tag: usize, + n_scan: usize, +) -> (u16, u16, u16) { + let available = left_area_height.saturating_sub(SIDEBAR_GAPS); + let p_s = (n_sample as u16).saturating_add(2).max(MIN_LIST_HEIGHT); + let p_t = (n_tag as u16).saturating_add(2).max(MIN_LIST_HEIGHT); + let p_sc = (n_scan as u16).saturating_add(2).max(MIN_LIST_HEIGHT); + if p_s.saturating_add(p_t).saturating_add(p_sc) <= available { + (p_s, p_t, p_sc) + } else { + let base = available / 3; + let third = available.saturating_sub(2 * base); + ( + base.max(MIN_LIST_HEIGHT), + base.max(MIN_LIST_HEIGHT), + third.max(MIN_LIST_HEIGHT), + ) + } +} + #[allow(dead_code)] pub fn beamtime_body_rects(body_area: Rect, app: &App) -> Option { if !app.has_catalog { @@ -66,14 +92,20 @@ pub fn beamtime_body_rects(body_area: Rect, app: &App) -> Option Option { - let Some(i) = app.expanded_table_row else { - return None; - }; + let i = app.expanded_table_row?; let file_count = app .group_at_display_index(i) .map(|g| g.file_rows.len()) @@ -825,10 +861,7 @@ fn render_expanded_file_list( if app.expanded_files_scroll_offset > max_offset { app.expanded_files_scroll_offset = max_offset; } - let group = match app.group_at_display_index(i) { - Some(g) => g, - None => return None, - }; + let group = app.group_at_display_index(i)?; let file_rows = &group.file_rows; let display_order = app.expanded_files_display_order(group); let offset = app.expanded_files_scroll_offset; @@ -867,7 +900,8 @@ fn render_expanded_file_list( table_header_cell("E (eV)", sort_col, sort_ord, 3), table_header_cell("\u{03B8} (\u{00B0})", sort_col, sort_ord, 4), table_header_cell("Beamspot", sort_col, sort_ord, 5), - table_header_cell("Status", sort_col, sort_ord, 6), + table_header_cell("\u{03C3}", sort_col, sort_ord, 6), + table_header_cell("Status", sort_col, sort_ord, 7), ]) .style(header_style) .bottom_margin(1); @@ -896,6 +930,10 @@ fn render_expanded_file_list( (Some(row), Some(col)) => format!("{},{}", row, col), _ => "-".to_string(), }; + let sigma = r + .beam_sigma + .map(|s| format!("{:.2}", s)) + .unwrap_or_else(|| "-".to_string()); let status = super::beamspot::beamspot_status( r.beam_row, r.beam_col, @@ -920,6 +958,7 @@ fn render_expanded_file_list( Cell::from(energy), Cell::from(theta), Cell::from(beamspot), + Cell::from(sigma), Cell::from(Span::styled(status, status_style)), ]) .style(row_style) @@ -932,6 +971,7 @@ fn render_expanded_file_list( Constraint::Length(8), Constraint::Length(8), Constraint::Length(10), + Constraint::Length(7), Constraint::Length(8), ]; let table = Table::new(rows, widths).header(header).column_spacing(1); diff --git a/src/catalog/ingest.rs b/src/catalog/ingest.rs index a5e183e..aa7b0e0 100644 --- a/src/catalog/ingest.rs +++ b/src/catalog/ingest.rs @@ -1,6 +1,10 @@ #![cfg(feature = "catalog")] -use crate::catalog::{open_or_create_db, CatalogError, Result}; +use crate::catalog::{CatalogError, Result}; +use crate::io::BtIngestRow; +#[cfg(not(feature = "parallel_ingest"))] +use crate::catalog::open_or_create_db; +use rusqlite::Error as RusqliteError; pub const DEFAULT_INGEST_HEADER_ITEMS: &[&str] = &[ "DATE", @@ -13,23 +17,133 @@ pub const DEFAULT_INGEST_HEADER_ITEMS: &[&str] = &[ "Sample Name", "Scan ID", ]; +#[cfg(not(feature = "parallel_ingest"))] use crate::io::options::ReadFitsOptions; +#[cfg(not(feature = "parallel_ingest"))] use crate::loader::read_fits_metadata_batch; use polars::prelude::*; use std::collections::HashMap; use std::path::{Path, PathBuf}; use std::sync::mpsc; +#[cfg(not(feature = "parallel_ingest"))] const BATCH_SIZE: usize = 500; +fn resolve_sample(conn: &rusqlite::Connection, name: &str) -> Result { + let name = name.trim(); + if name.is_empty() { + return Ok(String::new()); + } + let first = name.split('_').next().unwrap_or(name); + let existing: Option = match conn.query_row( + "SELECT name FROM samples WHERE LOWER(TRIM(name)) = LOWER(?1) OR LOWER(TRIM(name)) = LOWER(?2)", + rusqlite::params![name, first], + |r| r.get(0), + ) { + Ok(v) => Some(v), + Err(RusqliteError::QueryReturnedNoRows) => None, + Err(e) => return Err(e.into()), + }; + if let Some(n) = existing { + return Ok(n); + } + conn.execute( + "INSERT INTO samples (name, chemical_formula, created_at, updated_at) VALUES (?1, NULL, strftime('%s','now'), strftime('%s','now'))", + rusqlite::params![name], + )?; + Ok(name.to_string()) +} + +fn resolve_tag(conn: &rusqlite::Connection, name: &str) -> Result> { + let name = name.trim(); + if name.is_empty() { + return Ok(None); + } + if crate::io::is_polarization_tag(name) { + return Ok(None); + } + let slug = name.to_lowercase().replace(' ', "_"); + let existing: Option = match conn.query_row( + "SELECT name FROM tags WHERE LOWER(TRIM(name)) = LOWER(?1)", + rusqlite::params![name], + |r| r.get(0), + ) { + Ok(v) => Some(v), + Err(RusqliteError::QueryReturnedNoRows) => None, + Err(e) => return Err(e.into()), + }; + if let Some(n) = existing { + return Ok(Some(n)); + } + conn.execute("INSERT INTO tags (name, slug) VALUES (?1, ?2)", rusqlite::params![name, slug])?; + Ok(Some(name.to_string())) +} + +fn ensure_sample_tag( + conn: &rusqlite::Connection, + sample_name: &str, + tag_name: Option<&str>, +) -> Result<()> { + let tag_name = match tag_name { + Some(t) if !t.trim().is_empty() => t.trim(), + _ => return Ok(()), + }; + let sample_id: i64 = match conn.query_row( + "SELECT id FROM samples WHERE LOWER(TRIM(name)) = LOWER(?1)", + rusqlite::params![sample_name], + |r| r.get(0), + ) { + Ok(v) => v, + Err(RusqliteError::QueryReturnedNoRows) => return Ok(()), + Err(e) => return Err(e.into()), + }; + let tag_id: i64 = match conn.query_row( + "SELECT id FROM tags WHERE LOWER(TRIM(name)) = LOWER(?1)", + rusqlite::params![tag_name], + |r| r.get(0), + ) { + Ok(v) => v, + Err(RusqliteError::QueryReturnedNoRows) => return Ok(()), + Err(e) => return Err(e.into()), + }; + conn.execute( + "INSERT OR IGNORE INTO sample_tags (sample_id, tag_id) VALUES (?1, ?2)", + rusqlite::params![sample_id, tag_id], + )?; + Ok(()) +} + pub fn ingest_beamtime( beamtime_dir: &Path, header_items: &[String], incremental: bool, progress_tx: Option>, ) -> Result { - let db_path = beamtime_dir.join(super::CATALOG_DB_NAME); + #[cfg(feature = "parallel_ingest")] + { + return super::ingest_parallel::ingest_beamtime_pipelined( + beamtime_dir, + header_items, + incremental, + progress_tx, + ); + } + #[cfg(not(feature = "parallel_ingest"))] + { + ingest_beamtime_sequential(beamtime_dir, header_items, incremental, progress_tx) + } +} + +#[cfg(not(feature = "parallel_ingest"))] +fn ingest_beamtime_sequential( + beamtime_dir: &Path, + header_items: &[String], + incremental: bool, + progress_tx: Option>, +) -> Result { + let db_path = super::resolve_catalog_path(beamtime_dir); let conn = open_or_create_db(beamtime_dir)?; + let beamtime_id = ensure_bt_beamtime(&conn, beamtime_dir)?; let discovered = super::discover_fits_paths(beamtime_dir)?; let path_to_mtime: HashMap = discovered .iter() @@ -37,20 +151,37 @@ pub fn ingest_beamtime( .collect(); let paths: Vec = discovered.into_iter().map(|(p, _)| p).collect(); + let use_normalized_only = super::is_new_catalog_layout(beamtime_dir); let to_ingest: Vec = if incremental { - let existing: HashMap = conn - .prepare("SELECT path, mtime FROM files")? - .query_map([], |r| Ok((r.get::<_, String>(0)?, r.get::<_, i64>(1)?)))? - .filter_map(|r| r.ok()) - .collect(); - paths - .into_iter() - .filter(|p| { - let key = p.to_string_lossy().to_string(); - let mtime = path_to_mtime.get(&key).copied().unwrap_or(0); - existing.get(&key).is_none_or(|&stored| mtime > stored) - }) - .collect() + if use_normalized_only { + let existing: HashMap = conn + .prepare("SELECT source_path, source_mtime FROM bt_scan_points WHERE source_path IS NOT NULL")? + .query_map([], |r| Ok((r.get::<_, String>(0)?, r.get::<_, i64>(1).unwrap_or(0))))? + .filter_map(|r| r.ok()) + .collect(); + paths + .into_iter() + .filter(|p| { + let key = p.to_string_lossy().to_string(); + let mtime = path_to_mtime.get(&key).copied().unwrap_or(0); + existing.get(&key).is_none_or(|&stored| stored == 0 || mtime > stored) + }) + .collect() + } else { + let existing: HashMap = conn + .prepare("SELECT path, mtime FROM files")? + .query_map([], |r| Ok((r.get::<_, String>(0)?, r.get::<_, i64>(1)?)))? + .filter_map(|r| r.ok()) + .collect(); + paths + .into_iter() + .filter(|p| { + let key = p.to_string_lossy().to_string(); + let mtime = path_to_mtime.get(&key).copied().unwrap_or(0); + existing.get(&key).is_none_or(|&stored| mtime > stored) + }) + .collect() + } } else { paths }; @@ -65,7 +196,10 @@ pub fn ingest_beamtime( for chunk in to_ingest.chunks(BATCH_SIZE) { let chunk_vec: Vec = chunk.to_vec(); let df = read_fits_metadata_batch(chunk_vec, &opts)?; - upsert_files_batch(&conn, &df, &path_to_mtime)?; + if !use_normalized_only { + upsert_files_batch(&conn, &df, &path_to_mtime)?; + } + upsert_bt_batch(&conn, beamtime_id, &df, &path_to_mtime)?; processed = (processed as usize + chunk.len()).min(to_ingest.len()) as u32; if let Some(ref tx) = progress_tx { let _ = tx.send((processed, total)); @@ -73,12 +207,15 @@ pub fn ingest_beamtime( } let path_list: Vec<&str> = path_to_mtime.keys().map(|s| s.as_str()).collect(); - prune_missing_files(&conn, &path_list)?; + if !use_normalized_only { + prune_missing_files(&conn, &path_list)?; + } + prune_bt_scan_points(&conn, &path_list)?; Ok(db_path) } -fn upsert_files_batch( +pub(crate) fn upsert_files_batch( conn: &rusqlite::Connection, df: &DataFrame, path_to_mtime: &HashMap, @@ -160,7 +297,29 @@ fn upsert_files_batch( )"#, )?; + let mut canonical_cache: HashMap<(String, Option), (String, Option)> = + HashMap::new(); for i in 0..n { + let sn = sample_name + .get(i) + .cloned() + .flatten() + .unwrap_or_default(); + let t = tag.get(i).cloned().flatten(); + let key = (sn.clone(), t.clone()); + let (canonical_sn, canonical_t) = if let Some(cached) = canonical_cache.get(&key) { + cached.clone() + } else { + let cs = resolve_sample(conn, &sn)?; + let ct = t + .as_ref() + .map(|x| x.as_str()) + .and_then(|x| resolve_tag(conn, x).ok().flatten()); + ensure_sample_tag(conn, &cs, ct.as_deref())?; + canonical_cache.insert(key, (cs.clone(), ct.clone())); + (cs, ct) + }; + let path_str = file_path_col.get(i).unwrap_or("").to_string(); let mtime = path_to_mtime.get(&path_str).copied().unwrap_or(0); stmt.execute(rusqlite::params![ @@ -174,8 +333,8 @@ fn upsert_files_batch( bzero.get(i).copied().flatten().unwrap_or(0), data_size.get(i).copied().flatten().unwrap_or(0), file_name.get(i).cloned().unwrap_or_default(), - sample_name.get(i).cloned().unwrap_or_default(), - tag.get(i).clone(), + canonical_sn, + canonical_t, scan_number.get(i).copied().flatten().unwrap_or(0), frame_number.get(i).copied().flatten().unwrap_or(0), date.get(i).clone(), @@ -194,7 +353,7 @@ fn upsert_files_batch( Ok(()) } -fn prune_missing_files(conn: &rusqlite::Connection, known_paths: &[&str]) -> Result<()> { +pub(crate) fn prune_missing_files(conn: &rusqlite::Connection, known_paths: &[&str]) -> Result<()> { if known_paths.is_empty() { conn.execute("DELETE FROM files", [])?; return Ok(()); @@ -209,3 +368,295 @@ fn prune_missing_files(conn: &rusqlite::Connection, known_paths: &[&str]) -> Res conn.execute(&sql, rusqlite::params_from_iter(known_paths.iter()))?; Ok(()) } + +pub(crate) fn ensure_bt_beamtime(conn: &rusqlite::Connection, beamtime_dir: &Path) -> Result { + let path_str = beamtime_dir.to_string_lossy().to_string(); + conn.execute( + "INSERT OR IGNORE INTO bt_beamtimes (beamtime_path) VALUES (?1)", + rusqlite::params![path_str], + )?; + let id: i64 = conn.query_row( + "SELECT id FROM bt_beamtimes WHERE beamtime_path = ?1", + rusqlite::params![path_str], + |r| r.get(0), + )?; + Ok(id) +} + +fn ensure_bt_sample( + conn: &rusqlite::Connection, + beamtime_id: i64, + name: &str, + tag: Option<&str>, +) -> Result { + conn.execute( + "INSERT OR IGNORE INTO bt_samples (beamtime_id, name, tag) VALUES (?1, ?2, ?3)", + rusqlite::params![beamtime_id, name, tag], + )?; + let id: i64 = conn.query_row( + "SELECT id FROM bt_samples WHERE beamtime_id = ?1 AND name = ?2 AND (tag IS ?3 OR (tag IS NULL AND ?3 IS NULL))", + rusqlite::params![beamtime_id, name, tag], + |r| r.get(0), + )?; + Ok(id) +} + +fn ensure_bt_scan( + conn: &rusqlite::Connection, + beamtime_id: i64, + sample_id: i64, + scan_number: i64, +) -> Result { + let uid = format!("s_{}_{}", beamtime_id, scan_number); + conn.execute( + "INSERT OR REPLACE INTO bt_scans (uid, beamtime_id, sample_id) VALUES (?1, ?2, ?3)", + rusqlite::params![uid, beamtime_id, sample_id], + )?; + Ok(uid) +} + +fn ensure_bt_stream( + conn: &rusqlite::Connection, + beamtime_id: i64, + scan_number: i64, +) -> Result { + let scan_uid = format!("s_{}_{}", beamtime_id, scan_number); + let stream_uid = format!("st_{}_{}", beamtime_id, scan_number); + conn.execute( + "INSERT OR IGNORE INTO bt_streams (uid, scan_uid, name) VALUES (?1, ?2, 'primary')", + rusqlite::params![stream_uid, scan_uid], + )?; + Ok(stream_uid) +} + +pub(crate) fn upsert_bt_batch( + conn: &rusqlite::Connection, + beamtime_id: i64, + df: &DataFrame, + path_to_mtime: &HashMap, +) -> Result<()> { + let n = df.height(); + if n == 0 { + return Ok(()); + } + let file_path_col = df + .column("file_path") + .map_err(|e| CatalogError::Validation(e.to_string()))? + .str() + .map_err(|e| CatalogError::Validation(e.to_string()))?; + let get_str = |name: &str| -> Result>> { + match df.column(name) { + Ok(c) => Ok(c + .str() + .map_err(|e| CatalogError::Validation(e.to_string()))? + .iter() + .map(|s| s.map(|v| v.to_string())) + .collect()), + _ => Ok(std::iter::repeat_n(None, n).collect()), + } + }; + let get_i64 = |name: &str| -> Result>> { + match df.column(name) { + Ok(c) => Ok(c + .i64() + .map_err(|e| CatalogError::Validation(e.to_string()))? + .iter() + .collect()), + _ => Ok(std::iter::repeat_n(None, n).collect()), + } + }; + let get_f64 = |name: &str| -> Result>> { + match df.column(name) { + Ok(c) => Ok(c + .f64() + .map_err(|e| CatalogError::Validation(e.to_string()))? + .iter() + .collect()), + _ => Ok(std::iter::repeat_n(None, n).collect()), + } + }; + + let data_offset = get_i64("data_offset")?; + let naxis1 = get_i64("naxis1")?; + let naxis2 = get_i64("naxis2")?; + let bitpix = get_i64("bitpix")?; + let bzero = get_i64("bzero")?; + let _file_name = get_str("file_name")?; + let sample_name = get_str("sample_name")?; + let tag = get_str("tag")?; + let scan_number = get_i64("scan_number")?; + let frame_number = get_i64("frame_number")?; + let beamline_energy = get_f64("Beamline Energy")?; + let sample_theta = get_f64("Sample Theta")?; + let ccd_theta = get_f64("CCD Theta")?; + let epu = get_f64("EPU Polarization")?; + let exposure = get_f64("EXPOSURE")?; + + let mut stmt = conn.prepare_cached( + r#" + INSERT OR REPLACE INTO bt_scan_points ( + uid, stream_uid, scan_uid, sample_id, seq_index, time, + exposure, beamline_energy, epu_polarization, sample_theta, ccd_theta, + source_path, source_data_offset, source_naxis1, source_naxis2, + source_bitpix, source_bzero, source_mtime, beam_row, beam_col, beam_sigma + ) VALUES ( + ?1, ?2, ?3, ?4, ?5, 0, + ?6, ?7, ?8, ?9, ?10, + ?11, ?12, ?13, ?14, ?15, ?16, ?17, ?18, ?19, ?20 + )"#, + )?; + + let mut canonical_cache: HashMap<(String, Option), i64> = HashMap::new(); + for i in 0..n { + let sn = sample_name + .get(i) + .cloned() + .flatten() + .unwrap_or_default(); + let t = tag.get(i).cloned().flatten(); + let key = (sn.clone(), t.clone()); + let sample_id = if let Some(&sid) = canonical_cache.get(&key) { + sid + } else { + let cs = resolve_sample(conn, &sn)?; + let ct = t + .as_ref() + .map(|x| x.as_str()) + .and_then(|x| resolve_tag(conn, x).ok().flatten()); + ensure_sample_tag(conn, &cs, ct.as_deref())?; + let sid = ensure_bt_sample(conn, beamtime_id, &cs, ct.as_deref())?; + canonical_cache.insert(key, sid); + sid + }; + + let scan_no = scan_number.get(i).copied().flatten().unwrap_or(0); + let frame_no = frame_number.get(i).copied().flatten().unwrap_or(0); + let scan_uid = format!("s_{}_{}", beamtime_id, scan_no); + let stream_uid = format!("st_{}_{}", beamtime_id, scan_no); + let point_uid = format!("sp_{}_{}_{}", beamtime_id, scan_no, frame_no); + + let _ = ensure_bt_scan(conn, beamtime_id, sample_id, scan_no)?; + let _ = ensure_bt_stream(conn, beamtime_id, scan_no)?; + + let path_str = file_path_col.get(i).unwrap_or("").to_string(); + let mtime = path_to_mtime.get(&path_str).copied().unwrap_or(0); + + stmt.execute(rusqlite::params![ + point_uid, + stream_uid, + scan_uid, + sample_id, + frame_no, + exposure.get(i).copied().flatten(), + beamline_energy.get(i).copied().flatten(), + epu.get(i).copied().flatten(), + sample_theta.get(i).copied().flatten(), + ccd_theta.get(i).copied().flatten(), + path_str, + data_offset.get(i).copied().flatten().unwrap_or(0), + naxis1.get(i).copied().flatten().unwrap_or(0), + naxis2.get(i).copied().flatten().unwrap_or(0), + bitpix.get(i).copied().flatten().unwrap_or(0), + bzero.get(i).copied().flatten().unwrap_or(0), + mtime, + Option::::None, + Option::::None, + Option::::None, + ])?; + } + Ok(()) +} + +pub(crate) fn upsert_bt_batch_rows( + conn: &rusqlite::Connection, + beamtime_id: i64, + rows: &[BtIngestRow], + path_to_mtime: &HashMap, +) -> Result<()> { + if rows.is_empty() { + return Ok(()); + } + let mut stmt = conn.prepare_cached( + r#" + INSERT OR REPLACE INTO bt_scan_points ( + uid, stream_uid, scan_uid, sample_id, seq_index, time, + exposure, beamline_energy, epu_polarization, sample_theta, ccd_theta, + source_path, source_data_offset, source_naxis1, source_naxis2, + source_bitpix, source_bzero, source_mtime, beam_row, beam_col, beam_sigma + ) VALUES ( + ?1, ?2, ?3, ?4, ?5, 0, + ?6, ?7, ?8, ?9, ?10, + ?11, ?12, ?13, ?14, ?15, ?16, ?17, ?18, ?19, ?20 + )"#, + )?; + let mut canonical_cache: HashMap<(String, Option), i64> = HashMap::new(); + for row in rows { + let sn = row.sample_name.as_str(); + let t = row.tag.clone(); + let key = (sn.to_string(), t.clone()); + let sample_id = if let Some(&sid) = canonical_cache.get(&key) { + sid + } else { + let cs = resolve_sample(conn, sn)?; + let ct = t + .as_ref() + .map(|x| x.as_str()) + .and_then(|x| resolve_tag(conn, x).ok().flatten()); + ensure_sample_tag(conn, &cs, ct.as_deref())?; + let sid = ensure_bt_sample(conn, beamtime_id, &cs, ct.as_deref())?; + canonical_cache.insert(key, sid); + sid + }; + let scan_no = row.scan_number; + let frame_no = row.frame_number; + let scan_uid = format!("s_{}_{}", beamtime_id, scan_no); + let stream_uid = format!("st_{}_{}", beamtime_id, scan_no); + let point_uid = format!("sp_{}_{}_{}", beamtime_id, scan_no, frame_no); + let _ = ensure_bt_scan(conn, beamtime_id, sample_id, scan_no)?; + let _ = ensure_bt_stream(conn, beamtime_id, scan_no)?; + let path_str = row.file_path.clone(); + let mtime = path_to_mtime.get(&path_str).copied().unwrap_or(0); + stmt.execute(rusqlite::params![ + point_uid, + stream_uid, + scan_uid, + sample_id, + frame_no, + row.exposure, + row.beamline_energy, + row.epu_polarization, + row.sample_theta, + row.ccd_theta, + path_str, + row.data_offset, + row.naxis1, + row.naxis2, + row.bitpix, + row.bzero, + mtime, + Option::::None, + Option::::None, + Option::::None, + ])?; + } + Ok(()) +} + +pub(crate) fn prune_bt_scan_points(conn: &rusqlite::Connection, known_source_paths: &[&str]) -> Result<()> { + if known_source_paths.is_empty() { + conn.execute("DELETE FROM bt_scan_points", [])?; + return Ok(()); + } + let placeholders = known_source_paths + .iter() + .enumerate() + .map(|(i, _)| format!("?{}", i + 1)) + .collect::>() + .join(","); + let sql = format!( + "DELETE FROM bt_scan_points WHERE source_path IS NOT NULL AND source_path NOT IN ({})", + placeholders + ); + conn.execute(&sql, rusqlite::params_from_iter(known_source_paths.iter()))?; + Ok(()) +} diff --git a/src/catalog/ingest_parallel.rs b/src/catalog/ingest_parallel.rs new file mode 100644 index 0000000..3b69e93 --- /dev/null +++ b/src/catalog/ingest_parallel.rs @@ -0,0 +1,196 @@ +#![cfg(feature = "parallel_ingest")] + +use crate::catalog::ingest::{ + ensure_bt_beamtime, prune_bt_scan_points, prune_missing_files, upsert_bt_batch, + upsert_bt_batch_rows, upsert_files_batch, +}; +use crate::io::BtIngestRow; +use crate::catalog::open_or_create_db; +use crate::catalog::Result; +use crate::catalog::CatalogError; +use crate::io::options::ReadFitsOptions; +use crate::loader::{read_fits_metadata_batch, read_multiple_fits_headers_only_rows}; +use polars::prelude::DataFrame; +use std::collections::HashMap; +use std::path::PathBuf; +use std::sync::mpsc; +use std::sync::Arc; +use std::thread; + +const BATCH_SIZE: usize = 500; +const CHANNEL_BOUND: usize = 2; + +enum PipelineMsg { + Batch(DataFrame), + Rows(Vec), + Error(String), + Done, +} + +pub fn ingest_beamtime_pipelined( + beamtime_dir: &std::path::Path, + header_items: &[String], + incremental: bool, + progress_tx: Option>, +) -> Result { + let db_path = super::resolve_catalog_path(beamtime_dir); + let conn = open_or_create_db(beamtime_dir)?; + let beamtime_id = ensure_bt_beamtime(&conn, beamtime_dir)?; + let discovered = super::discover_fits_paths_parallel(beamtime_dir)?; + let path_to_mtime: HashMap = discovered + .iter() + .map(|(p, m)| (p.to_string_lossy().to_string(), *m)) + .collect(); + let paths: Vec = discovered.into_iter().map(|(p, _)| p).collect(); + + let use_normalized_only = super::is_new_catalog_layout(beamtime_dir); + let to_ingest: Vec = if incremental { + if use_normalized_only { + let existing: HashMap = conn + .prepare( + "SELECT source_path, source_mtime FROM bt_scan_points WHERE source_path IS NOT NULL", + )? + .query_map([], |r| Ok((r.get::<_, String>(0)?, r.get::<_, i64>(1).unwrap_or(0))))? + .filter_map(|r| r.ok()) + .collect(); + paths + .into_iter() + .filter(|p| { + let key = p.to_string_lossy().to_string(); + let mtime = path_to_mtime.get(&key).copied().unwrap_or(0); + existing + .get(&key) + .is_none_or(|&stored| stored == 0 || mtime > stored) + }) + .collect() + } else { + let existing: HashMap = conn + .prepare("SELECT path, mtime FROM files")? + .query_map([], |r| Ok((r.get::<_, String>(0)?, r.get::<_, i64>(1)?)))? + .filter_map(|r| r.ok()) + .collect(); + paths + .into_iter() + .filter(|p| { + let key = p.to_string_lossy().to_string(); + let mtime = path_to_mtime.get(&key).copied().unwrap_or(0); + existing.get(&key).is_none_or(|&stored| mtime > stored) + }) + .collect() + } + } else { + paths + }; + + let total = to_ingest.len() as u32; + if total == 0 { + let path_list: Vec<&str> = path_to_mtime.keys().map(|s| s.as_str()).collect(); + if !use_normalized_only { + prune_missing_files(&conn, &path_list)?; + } + prune_bt_scan_points(&conn, &path_list)?; + return Ok(db_path); + } + + let opts = ReadFitsOptions { + header_items: header_items.to_vec(), + batch_size: BATCH_SIZE, + ..ReadFitsOptions::default() + }; + + let paths_arc = Arc::new(to_ingest); + let (tx, rx) = mpsc::sync_channel::(CHANNEL_BOUND); + + let paths_arc_reader = Arc::clone(&paths_arc); + let opts_reader = opts.clone(); + let header_items_owned = header_items.to_vec(); + let use_rows_only = use_normalized_only; + let reader = thread::spawn(move || { + let len = paths_arc_reader.len(); + let mut start = 0usize; + while start < len { + let end = (start + BATCH_SIZE).min(len); + let chunk_vec = paths_arc_reader[start..end].to_vec(); + let chunk_len = chunk_vec.len(); + if use_rows_only { + match read_multiple_fits_headers_only_rows(chunk_vec, &header_items_owned) { + Ok(rows) => { + if tx.send(PipelineMsg::Rows(rows)).is_err() { + return; + } + } + Err(e) => { + let _ = tx.send(PipelineMsg::Error(e.to_string())); + return; + } + } + } else { + match read_fits_metadata_batch(chunk_vec, &opts_reader) { + Ok(df) => { + if tx.send(PipelineMsg::Batch(df)).is_err() { + return; + } + } + Err(e) => { + let _ = tx.send(PipelineMsg::Error(e.to_string())); + return; + } + } + } + start += chunk_len; + } + let _ = tx.send(PipelineMsg::Done); + }); + + let mut processed: u32 = 0; + loop { + match rx.recv() { + Ok(PipelineMsg::Rows(rows)) => { + if !rows.is_empty() { + upsert_bt_batch_rows(&conn, beamtime_id, &rows, &path_to_mtime)?; + processed = (processed as usize + rows.len()).min(paths_arc.len()) as u32; + if let Some(ref ptx) = progress_tx { + let _ = ptx.send((processed, total)); + } + } + } + Ok(PipelineMsg::Batch(df)) => { + let n = df.height(); + if n == 0 { + continue; + } + if !use_normalized_only { + upsert_files_batch(&conn, &df, &path_to_mtime)?; + } + upsert_bt_batch(&conn, beamtime_id, &df, &path_to_mtime)?; + processed = (processed as usize + n).min(paths_arc.len()) as u32; + if let Some(ref ptx) = progress_tx { + let _ = ptx.send((processed, total)); + } + } + Ok(PipelineMsg::Error(e)) => { + let _ = reader.join(); + return Err(CatalogError::Validation(format!( + "read_fits_metadata_batch: {}", + e + ))); + } + Ok(PipelineMsg::Done) => break, + Err(_) => { + let _ = reader.join(); + return Err(CatalogError::Validation( + "ingest pipeline reader disconnected".into(), + )); + } + } + } + let _ = reader.join(); + + let path_list: Vec<&str> = path_to_mtime.keys().map(|s| s.as_str()).collect(); + if !use_normalized_only { + prune_missing_files(&conn, &path_list)?; + } + prune_bt_scan_points(&conn, &path_list)?; + + Ok(db_path) +} diff --git a/src/catalog/materialize.rs b/src/catalog/materialize.rs new file mode 100644 index 0000000..8068815 --- /dev/null +++ b/src/catalog/materialize.rs @@ -0,0 +1,151 @@ +#![cfg(feature = "zarr")] + +use crate::catalog::{open_or_create_db, resolve_catalog_path, CatalogError, Result}; +use crate::io::image_mmap::materialize_image; +use crate::io::zarr_store::{ + create_detector_array, detector_zarr_group, open_store, write_detector_frame, zarr_root, +}; +use crate::io::ImageInfo; +use ndarray::Array2; +use std::path::Path; +use std::sync::mpsc; + +struct ScanPointSource { + uid: String, + source_path: String, + source_data_offset: i64, + source_naxis1: i64, + source_naxis2: i64, + source_bitpix: i64, + source_bzero: i64, + seq_index: i64, +} + +fn get_scan_uids_for_beamtime(conn: &rusqlite::Connection, beamtime_id: i64) -> Result> { + let mut stmt = conn.prepare("SELECT uid FROM bt_scans WHERE beamtime_id = ?1 ORDER BY uid")?; + let uids: Vec = stmt + .query_map(rusqlite::params![beamtime_id], |r| r.get(0))? + .filter_map(|r| r.ok()) + .collect(); + Ok(uids) +} + +fn get_scan_points_with_source( + conn: &rusqlite::Connection, + scan_uid: &str, +) -> Result> { + let mut stmt = conn.prepare( + "SELECT uid, source_path, source_data_offset, source_naxis1, source_naxis2, source_bitpix, source_bzero, seq_index + FROM bt_scan_points WHERE scan_uid = ?1 AND source_path IS NOT NULL ORDER BY seq_index", + )?; + let rows = stmt.query_map(rusqlite::params![scan_uid], |r| { + Ok(ScanPointSource { + uid: r.get(0)?, + source_path: r.get(1)?, + source_data_offset: r.get(2)?, + source_naxis1: r.get(3)?, + source_naxis2: r.get(4)?, + source_bitpix: r.get(5)?, + source_bzero: r.get(6)?, + seq_index: r.get(7)?, + }) + })?; + rows.map(|r| r.map_err(CatalogError::Sqlite)).collect() +} + +fn insert_image_ref( + conn: &rusqlite::Connection, + scan_point_uid: &str, + zarr_group: &str, + index_in_stack: i64, + shape_x: i64, + shape_y: i64, + dtype: &str, +) -> Result<()> { + conn.execute( + "INSERT OR REPLACE INTO bt_image_refs (scan_point_uid, field_name, zarr_group, index_in_stack, shape_x, shape_y, dtype) + VALUES (?1, 'detector_image', ?2, ?3, ?4, ?5, ?6)", + rusqlite::params![ + scan_point_uid, + zarr_group, + index_in_stack, + shape_x, + shape_y, + dtype + ], + )?; + Ok(()) +} + +pub fn materialize_beamtime( + beamtime_dir: &Path, + scan_uids_filter: Option<&[String]>, + progress_tx: Option>, +) -> Result<()> { + let _ = resolve_catalog_path(beamtime_dir); + let conn = open_or_create_db(beamtime_dir)?; + let beamtime_id: i64 = conn.query_row( + "SELECT id FROM bt_beamtimes WHERE beamtime_path = ?1", + rusqlite::params![beamtime_dir.to_string_lossy().to_string()], + |r| r.get(0), + )?; + let scan_uids: Vec = match scan_uids_filter { + Some(list) => list.to_vec(), + None => get_scan_uids_for_beamtime(&conn, beamtime_id)?, + }; + let zarr_root_path = zarr_root(beamtime_dir); + let store = open_store(&zarr_root_path)?; + for scan_uid in &scan_uids { + let points = get_scan_points_with_source(&conn, scan_uid)?; + if points.is_empty() { + continue; + } + let n_frames = points.len(); + let first = &points[0]; + let info = ImageInfo { + path: std::path::PathBuf::from(&first.source_path), + data_offset: first.source_data_offset as u64, + naxis1: first.source_naxis1 as usize, + naxis2: first.source_naxis2 as usize, + bitpix: first.source_bitpix as i32, + bzero: first.source_bzero, + }; + let (_, processed) = materialize_image(info.path.as_path(), &info)?; + let height = processed.nrows(); + let width = processed.ncols(); + let array = create_detector_array(&store, scan_uid, n_frames, height, width)?; + let zarr_group = detector_zarr_group(scan_uid); + for (idx, pt) in points.iter().enumerate() { + let info_pt = ImageInfo { + path: std::path::PathBuf::from(&pt.source_path), + data_offset: pt.source_data_offset as u64, + naxis1: pt.source_naxis1 as usize, + naxis2: pt.source_naxis2 as usize, + bitpix: pt.source_bitpix as i32, + bzero: pt.source_bzero, + }; + let (_, proc) = materialize_image( + std::path::Path::new(&pt.source_path), + &info_pt, + )?; + let clamped: Array2 = Array2::from_shape_fn(proc.dim(), |(r, c)| { + let v = proc[[r, c]]; + v.clamp(i32::MIN as i64, i32::MAX as i64) as i32 + }); + write_detector_frame(&array, idx, &clamped)?; + insert_image_ref( + &conn, + &pt.uid, + &zarr_group, + pt.seq_index, + width as i64, + height as i64, + "int32", + )?; + if let Some(ref tx) = progress_tx { + let _ = tx.send((scan_uid.clone(), idx as u32 + 1, n_frames as u32)); + } + } + } + Ok(()) +} diff --git a/src/catalog/mod.rs b/src/catalog/mod.rs index cc5a8d7..0f474f0 100644 --- a/src/catalog/mod.rs +++ b/src/catalog/mod.rs @@ -1,7 +1,12 @@ mod beamtime_index; mod ingest; +#[cfg(feature = "parallel_ingest")] +mod ingest_parallel; mod query; +#[cfg(feature = "zarr")] +mod materialize; + #[cfg(feature = "watch")] mod watch; @@ -17,11 +22,18 @@ use walkdir::WalkDir; pub const CATALOG_DB_NAME: &str = ".pyref_catalog.db"; pub use ingest::{ingest_beamtime, DEFAULT_INGEST_HEADER_ITEMS}; +#[cfg(feature = "parallel_ingest")] +pub use ingest_parallel::ingest_beamtime_pipelined; pub use query::{ - catalog_file_count, get_overrides, list_beamtime_entries, query_files, rename_file_in_catalog, - scan_from_catalog, set_override, update_beamspot, BeamtimeEntries, CatalogFilter, FileRow, + catalog_file_count, get_overrides, get_scan_point_uid_by_source_path, list_beamtime_entries, + list_beamtime_entries_v2, list_beamtimes_from_catalog, query_files, query_scan_points, + rename_file_in_catalog, scan_from_catalog, set_override, update_beamspot, update_beamspot_scan_point, + BeamtimeEntries, CatalogFilter, FileRow, }; +#[cfg(feature = "zarr")] +pub use materialize::materialize_beamtime; + #[cfg(feature = "watch")] pub use watch::{run_catalog_watcher, WatchHandle, DEFAULT_DEBOUNCE_MS}; @@ -63,6 +75,35 @@ fn catalog_path(beamtime_dir: &Path) -> PathBuf { beamtime_dir.join(CATALOG_DB_NAME) } +const PYREF_CATALOG_DIR: &str = ".pyref"; +const NEW_CATALOG_DB_NAME: &str = "catalog.db"; + +pub fn catalog_path_new(beamtime_dir: &Path) -> PathBuf { + let parent = beamtime_dir.parent(); + if let Some(p) = parent { + if !p.as_os_str().is_empty() && p != Path::new("/") { + return p.join(PYREF_CATALOG_DIR).join(NEW_CATALOG_DB_NAME); + } + } + beamtime_dir.join(PYREF_CATALOG_DIR).join(NEW_CATALOG_DB_NAME) +} + +pub fn resolve_catalog_path(beamtime_dir: &Path) -> PathBuf { + let new_path = catalog_path_new(beamtime_dir); + let legacy_path = catalog_path(beamtime_dir); + if new_path.exists() { + new_path + } else if legacy_path.exists() { + legacy_path + } else { + new_path + } +} + +pub fn is_new_catalog_layout(beamtime_dir: &Path) -> bool { + catalog_path_new(beamtime_dir) != catalog_path(beamtime_dir) +} + const FILES_TABLE: &str = r#" CREATE TABLE IF NOT EXISTS files ( path TEXT PRIMARY KEY, @@ -91,7 +132,8 @@ CREATE TABLE IF NOT EXISTS files ( Lambda REAL, Q REAL, beam_row INTEGER, - beam_col INTEGER + beam_col INTEGER, + beam_sigma REAL )"#; const FILES_INDEX_MTIME: &str = "CREATE INDEX IF NOT EXISTS idx_files_mtime ON files(mtime)"; @@ -101,6 +143,29 @@ const FILES_INDEX_TAG: &str = "CREATE INDEX IF NOT EXISTS idx_files_tag ON files const FILES_INDEX_SCAN: &str = "CREATE INDEX IF NOT EXISTS idx_files_scan_number ON files(scan_number)"; +const SAMPLES_TABLE: &str = r#" +CREATE TABLE IF NOT EXISTS samples ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + name TEXT NOT NULL, + chemical_formula TEXT, + created_at INTEGER, + updated_at INTEGER +)"#; + +const TAGS_TABLE: &str = r#" +CREATE TABLE IF NOT EXISTS tags ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + name TEXT NOT NULL UNIQUE, + slug TEXT +)"#; + +const SAMPLE_TAGS_TABLE: &str = r#" +CREATE TABLE IF NOT EXISTS sample_tags ( + sample_id INTEGER NOT NULL REFERENCES samples(id) ON DELETE CASCADE, + tag_id INTEGER NOT NULL REFERENCES tags(id) ON DELETE CASCADE, + PRIMARY KEY (sample_id, tag_id) +)"#; + const OVERRIDES_TABLE: &str = r#" CREATE TABLE IF NOT EXISTS overrides ( path TEXT PRIMARY KEY REFERENCES files(path) ON DELETE CASCADE, @@ -109,6 +174,145 @@ CREATE TABLE IF NOT EXISTS overrides ( notes TEXT )"#; +const BT_BEAMTIMES_TABLE: &str = r#" +CREATE TABLE IF NOT EXISTS bt_beamtimes ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + beamtime_path TEXT NOT NULL UNIQUE, + user TEXT NOT NULL DEFAULT '', + year INTEGER NOT NULL DEFAULT 0, + month INTEGER NOT NULL DEFAULT 1, + esaf_number TEXT NOT NULL DEFAULT '', + label TEXT +)"#; + +const BT_MOTOR_NAMES_TABLE: &str = r#" +CREATE TABLE IF NOT EXISTS bt_motor_names ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + name TEXT NOT NULL UNIQUE +)"#; + +const BT_AI_CHANNEL_NAMES_TABLE: &str = r#" +CREATE TABLE IF NOT EXISTS bt_ai_channel_names ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + name TEXT NOT NULL UNIQUE +)"#; + +const BT_SAMPLES_TABLE: &str = r#" +CREATE TABLE IF NOT EXISTS bt_samples ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + beamtime_id INTEGER NOT NULL REFERENCES bt_beamtimes(id) ON DELETE CASCADE, + name TEXT NOT NULL, + tag TEXT, + version INTEGER, + chemical_formula TEXT, + serial TEXT, + beamline_pos TEXT, + extra TEXT, + UNIQUE (beamtime_id, name, tag, version) +)"#; + +const BT_SCANS_TABLE: &str = r#" +CREATE TABLE IF NOT EXISTS bt_scans ( + uid TEXT PRIMARY KEY, + beamtime_id INTEGER REFERENCES bt_beamtimes(id) ON DELETE SET NULL, + sample_id INTEGER REFERENCES bt_samples(id) ON DELETE SET NULL, + plan_name TEXT NOT NULL DEFAULT 'reflectivity', + time_start REAL NOT NULL DEFAULT 0, + time_stop REAL, + exit_status TEXT, + num_points INTEGER DEFAULT 0, + operator TEXT, + metadata TEXT +)"#; + +const BT_STREAMS_TABLE: &str = r#" +CREATE TABLE IF NOT EXISTS bt_streams ( + uid TEXT PRIMARY KEY, + scan_uid TEXT NOT NULL REFERENCES bt_scans(uid) ON DELETE CASCADE, + name TEXT NOT NULL DEFAULT 'primary', + time_created REAL NOT NULL DEFAULT 0, + UNIQUE (scan_uid, name) +)"#; + +const BT_SCAN_POINTS_TABLE: &str = r#" +CREATE TABLE IF NOT EXISTS bt_scan_points ( + uid TEXT PRIMARY KEY, + stream_uid TEXT NOT NULL REFERENCES bt_streams(uid) ON DELETE CASCADE, + scan_uid TEXT NOT NULL REFERENCES bt_scans(uid) ON DELETE CASCADE, + sample_id INTEGER REFERENCES bt_samples(id) ON DELETE SET NULL, + seq_index INTEGER NOT NULL, + time REAL NOT NULL DEFAULT 0, + exposure REAL, + beamline_energy REAL, + epu_polarization REAL, + sample_theta REAL, + sample_x REAL, + sample_y REAL, + sample_z REAL, + ccd_theta REAL, + source_path TEXT, + source_data_offset INTEGER, + source_naxis1 INTEGER, + source_naxis2 INTEGER, + source_bitpix INTEGER, + source_bzero INTEGER, + source_mtime INTEGER, + beam_row INTEGER, + beam_col INTEGER, + beam_sigma REAL, + UNIQUE (stream_uid, seq_index) +)"#; + +const BT_SCAN_POINT_MOTOR_POSITIONS_TABLE: &str = r#" +CREATE TABLE IF NOT EXISTS bt_scan_point_motor_positions ( + scan_point_uid TEXT NOT NULL REFERENCES bt_scan_points(uid) ON DELETE CASCADE, + motor_id INTEGER NOT NULL REFERENCES bt_motor_names(id) ON DELETE CASCADE, + position REAL NOT NULL, + PRIMARY KEY (scan_point_uid, motor_id) +)"#; + +const BT_SCAN_POINT_AI_TABLE: &str = r#" +CREATE TABLE IF NOT EXISTS bt_scan_point_ai ( + scan_point_uid TEXT NOT NULL REFERENCES bt_scan_points(uid) ON DELETE CASCADE, + channel_id INTEGER NOT NULL REFERENCES bt_ai_channel_names(id) ON DELETE CASCADE, + nominal REAL NOT NULL, + err REAL, + PRIMARY KEY (scan_point_uid, channel_id) +)"#; + +const BT_SCAN_POINT_AI_READINGS_TABLE: &str = r#" +CREATE TABLE IF NOT EXISTS bt_scan_point_ai_readings ( + scan_point_uid TEXT NOT NULL REFERENCES bt_scan_points(uid) ON DELETE CASCADE, + channel_id INTEGER NOT NULL REFERENCES bt_ai_channel_names(id) ON DELETE CASCADE, + reading_index INTEGER NOT NULL, + value REAL NOT NULL, + PRIMARY KEY (scan_point_uid, channel_id, reading_index) +)"#; + +const BT_IMAGE_REFS_TABLE: &str = r#" +CREATE TABLE IF NOT EXISTS bt_image_refs ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + scan_point_uid TEXT NOT NULL REFERENCES bt_scan_points(uid) ON DELETE CASCADE, + field_name TEXT NOT NULL DEFAULT 'detector_image', + zarr_group TEXT NOT NULL, + index_in_stack INTEGER NOT NULL, + shape_x INTEGER NOT NULL, + shape_y INTEGER NOT NULL, + dtype TEXT NOT NULL, + compression_codec TEXT +)"#; + +const BT_INDEX_BEAMTIMES_PATH: &str = + "CREATE INDEX IF NOT EXISTS idx_bt_beamtimes_path ON bt_beamtimes(beamtime_path)"; +const BT_INDEX_SCANS_BEAMTIME: &str = + "CREATE INDEX IF NOT EXISTS idx_bt_scans_beamtime ON bt_scans(beamtime_id)"; +const BT_INDEX_SCAN_POINTS_SCAN: &str = + "CREATE INDEX IF NOT EXISTS idx_bt_scan_points_scan ON bt_scan_points(scan_uid)"; +const BT_INDEX_SCAN_POINTS_SOURCE: &str = + "CREATE INDEX IF NOT EXISTS idx_bt_scan_points_source ON bt_scan_points(source_path)"; +const BT_INDEX_IMAGE_REFS_POINT: &str = + "CREATE INDEX IF NOT EXISTS idx_bt_image_refs_scan_point ON bt_image_refs(scan_point_uid)"; + pub fn is_skippable_stem(stem: &str) -> bool { stem.is_empty() || stem.starts_with('_') } @@ -130,7 +334,12 @@ pub fn discover_fits_paths(beamtime_dir: &Path) -> Result> { if !path.is_file() { continue; } - if path.extension().and_then(|e| e.to_str()) != Some("fits") { + if path + .extension() + .and_then(|e| e.to_str()) + .map(|e| e.eq_ignore_ascii_case("fits")) + != Some(true) + { continue; } let stem = path.file_stem().and_then(|s| s.to_str()).unwrap_or(""); @@ -153,6 +362,102 @@ pub fn discover_fits_paths(beamtime_dir: &Path) -> Result> { Ok(out) } +#[cfg(feature = "parallel_ingest")] +fn collect_fits_under(root: &Path) -> Result> { + let mut out: Vec<(PathBuf, i64)> = Vec::new(); + for entry in WalkDir::new(root) + .follow_links(false) + .into_iter() + .filter_map(|e| e.ok()) + { + let path = entry.path(); + if !path.is_file() { + continue; + } + if path + .extension() + .and_then(|e| e.to_str()) + .map(|e| e.eq_ignore_ascii_case("fits")) + != Some(true) + { + continue; + } + let stem = path.file_stem().and_then(|s| s.to_str()).unwrap_or(""); + if is_skippable_stem(stem) { + continue; + } + let path_buf = path.canonicalize()?; + let mtime = std::fs::metadata(path) + .ok() + .and_then(|m| m.modified().ok()) + .map(|t| { + t.duration_since(std::time::UNIX_EPOCH) + .unwrap_or_default() + .as_secs() as i64 + }) + .unwrap_or(0); + out.push((path_buf, mtime)); + } + Ok(out) +} + +#[cfg(feature = "parallel_ingest")] +pub fn discover_fits_paths_parallel(beamtime_dir: &Path) -> Result> { + if !beamtime_dir.is_dir() { + return Err(CatalogError::Validation(format!( + "not a directory: {}", + beamtime_dir.display() + ))); + } + let subdirs: Vec = std::fs::read_dir(beamtime_dir) + .map_err(CatalogError::Io)? + .filter_map(|e| e.ok()) + .filter(|e| e.path().is_dir()) + .map(|e| e.path()) + .collect(); + if subdirs.len() <= 1 { + return discover_fits_paths(beamtime_dir); + } + use rayon::prelude::*; + let parts: Result>> = subdirs + .par_iter() + .map(|d| collect_fits_under(d.as_path())) + .collect(); + let mut out = parts?; + let root_only: Vec<(PathBuf, i64)> = std::fs::read_dir(beamtime_dir) + .map_err(CatalogError::Io)? + .filter_map(|e| e.ok()) + .filter(|e| e.path().is_file()) + .filter_map(|e| { + let path = e.path(); + if path.extension()?.to_str()?.eq_ignore_ascii_case("fits") != true { + return None; + } + let stem = path.file_stem()?.to_str()?; + if is_skippable_stem(stem) { + return None; + } + let path_buf = path.canonicalize().ok()?; + let mtime = std::fs::metadata(&path) + .ok()? + .modified() + .ok() + .map(|t| { + t.duration_since(std::time::UNIX_EPOCH) + .unwrap_or_default() + .as_secs() as i64 + }) + .unwrap_or(0); + Some((path_buf, mtime)) + }) + .collect(); + out.push(root_only); + let mut flat: Vec<(PathBuf, i64)> = out.into_iter().flatten().collect(); + flat.sort_by(|a, b| a.0.cmp(&b.0)); + flat.dedup_by(|a, b| a.0 == b.0); + Ok(flat) +} + fn migrate_add_beamspot_columns(conn: &Connection) -> Result<()> { let has_beam_row: bool = conn.query_row( "SELECT COUNT(1) FROM pragma_table_info('files') WHERE name = 'beam_row'", @@ -166,16 +471,48 @@ fn migrate_add_beamspot_columns(conn: &Connection) -> Result<()> { Ok(()) } +fn migrate_add_beam_sigma_column(conn: &Connection) -> Result<()> { + let has_beam_sigma: bool = conn.query_row( + "SELECT COUNT(1) FROM pragma_table_info('files') WHERE name = 'beam_sigma'", + [], + |r| r.get(0), + )?; + if !has_beam_sigma { + conn.execute("ALTER TABLE files ADD COLUMN beam_sigma REAL", [])?; + } + Ok(()) +} + pub fn open_catalog_db(db_path: &Path) -> Result { let conn = Connection::open(db_path)?; conn.execute_batch(FILES_TABLE)?; + conn.execute_batch(SAMPLES_TABLE)?; + conn.execute_batch(TAGS_TABLE)?; + conn.execute_batch(SAMPLE_TAGS_TABLE)?; conn.execute_batch(OVERRIDES_TABLE)?; migrate_experiment_number_to_scan_number(&conn)?; migrate_add_beamspot_columns(&conn)?; + migrate_add_beam_sigma_column(&conn)?; conn.execute_batch(FILES_INDEX_MTIME)?; conn.execute_batch(FILES_INDEX_SAMPLE)?; conn.execute_batch(FILES_INDEX_TAG)?; conn.execute_batch(FILES_INDEX_SCAN)?; + conn.execute_batch(BT_BEAMTIMES_TABLE)?; + conn.execute_batch(BT_MOTOR_NAMES_TABLE)?; + conn.execute_batch(BT_AI_CHANNEL_NAMES_TABLE)?; + conn.execute_batch(BT_SAMPLES_TABLE)?; + conn.execute_batch(BT_SCANS_TABLE)?; + conn.execute_batch(BT_STREAMS_TABLE)?; + conn.execute_batch(BT_SCAN_POINTS_TABLE)?; + conn.execute_batch(BT_SCAN_POINT_MOTOR_POSITIONS_TABLE)?; + conn.execute_batch(BT_SCAN_POINT_AI_TABLE)?; + conn.execute_batch(BT_SCAN_POINT_AI_READINGS_TABLE)?; + conn.execute_batch(BT_IMAGE_REFS_TABLE)?; + conn.execute_batch(BT_INDEX_BEAMTIMES_PATH)?; + conn.execute_batch(BT_INDEX_SCANS_BEAMTIME)?; + conn.execute_batch(BT_INDEX_SCAN_POINTS_SCAN)?; + conn.execute_batch(BT_INDEX_SCAN_POINTS_SOURCE)?; + conn.execute_batch(BT_INDEX_IMAGE_REFS_POINT)?; Ok(conn) } @@ -186,7 +523,13 @@ pub fn open_or_create_db(beamtime_dir: &Path) -> Result { beamtime_dir.display() ))); } - open_catalog_db(&catalog_path(beamtime_dir)) + let db_path = resolve_catalog_path(beamtime_dir); + if let Some(parent) = db_path.parent() { + if !parent.as_os_str().is_empty() && !db_path.exists() { + std::fs::create_dir_all(parent).map_err(CatalogError::Io)?; + } + } + open_catalog_db(&db_path) } fn migrate_experiment_number_to_scan_number(conn: &Connection) -> Result<()> { @@ -215,12 +558,12 @@ mod tests { let conn = open_or_create_db(tmp.path()).unwrap(); let count: i64 = conn .query_row( - "SELECT COUNT(*) FROM sqlite_master WHERE type='table' AND name IN ('files','overrides')", + "SELECT COUNT(*) FROM sqlite_master WHERE type='table' AND name IN ('files','overrides','samples','tags','sample_tags')", [], |r| r.get(0), ) .unwrap(); - assert_eq!(count, 2); + assert_eq!(count, 5); } #[test] @@ -245,4 +588,18 @@ mod tests { assert!(is_skippable_stem("_skip")); assert!(!is_skippable_stem("sample")); } + + #[test] + fn test_open_or_create_db_has_beam_sigma_column() { + let tmp = TempDir::new().unwrap(); + let conn = open_or_create_db(tmp.path()).unwrap(); + let has: i64 = conn + .query_row( + "SELECT COUNT(1) FROM pragma_table_info('files') WHERE name = 'beam_sigma'", + [], + |r| r.get(0), + ) + .unwrap(); + assert_eq!(has, 1); + } } diff --git a/src/catalog/query.rs b/src/catalog/query.rs index 14063b2..f817ac4 100644 --- a/src/catalog/query.rs +++ b/src/catalog/query.rs @@ -33,14 +33,59 @@ pub struct FileRow { pub date_iso: Option, pub beam_row: Option, pub beam_col: Option, + pub beam_sigma: Option, + pub scan_point_uid: Option, } -pub fn catalog_file_count(db_path: &Path) -> Result { +fn beamtime_id_from_path(conn: &rusqlite::Connection, beamtime_dir: &Path) -> Result> { + let path_str = beamtime_dir.to_string_lossy().to_string(); + let id: Option = match conn.query_row( + "SELECT id FROM bt_beamtimes WHERE beamtime_path = ?1", + rusqlite::params![path_str], + |r| r.get(0), + ) { + Ok(x) => Some(x), + Err(rusqlite::Error::QueryReturnedNoRows) => None, + Err(e) => return Err(e.into()), + }; + Ok(id) +} + +pub fn catalog_file_count(db_path: &Path, beamtime_dir: Option<&Path>) -> Result { let conn = open_catalog_db(db_path)?; - let count: i64 = conn.query_row("SELECT COUNT(*) FROM files", [], |r| r.get(0))?; + let count: i64 = if let Some(dir) = beamtime_dir { + if super::is_new_catalog_layout(dir) { + let path_str = dir.to_string_lossy().to_string(); + conn.query_row( + "SELECT COUNT(*) FROM bt_scan_points sp + JOIN bt_scans sc ON sp.scan_uid = sc.uid + JOIN bt_beamtimes b ON sc.beamtime_id = b.id + WHERE b.beamtime_path = ?1 AND sp.source_path IS NOT NULL", + rusqlite::params![path_str], + |r| r.get(0), + ) + .unwrap_or(0) + } else { + conn.query_row("SELECT COUNT(*) FROM files", [], |r| r.get(0)).unwrap_or(0) + } + } else { + conn.query_row("SELECT COUNT(*) FROM files", [], |r| r.get(0)).unwrap_or(0) + }; Ok(count as u32) } +pub fn list_beamtimes_from_catalog(db_path: &Path) -> Result> { + use std::path::PathBuf; + let conn = open_catalog_db(db_path)?; + let mut stmt = conn.prepare( + "SELECT beamtime_path, id FROM bt_beamtimes ORDER BY id DESC", + )?; + let rows = stmt.query_map([], |r| { + Ok((PathBuf::from(r.get::<_, String>(0)?), r.get::<_, i64>(1)?)) + })?; + rows.map(|r| r.map_err(CatalogError::from)).collect() +} + pub fn list_beamtime_entries(db_path: &Path) -> Result { let conn = open_catalog_db(db_path)?; let mut samples: Vec = conn @@ -82,6 +127,60 @@ pub fn list_beamtime_entries(db_path: &Path) -> Result { }) } +fn scan_number_from_uid(scan_uid: &str) -> i64 { + scan_uid + .rsplit_once('_') + .and_then(|(_, tail)| tail.parse::().ok()) + .unwrap_or(0) +} + +pub fn list_beamtime_entries_v2( + db_path: &Path, + beamtime_dir: &Path, +) -> Result { + let conn = open_catalog_db(db_path)?; + let beamtime_id = match beamtime_id_from_path(&conn, beamtime_dir)? { + Some(id) => id, + None => { + return Ok(BeamtimeEntries { + samples: Vec::new(), + tags: Vec::new(), + scans: Vec::new(), + }) + } + }; + let mut samples: Vec = conn + .prepare( + "SELECT DISTINCT name FROM bt_samples WHERE beamtime_id = ?1 AND name != '' ORDER BY 1", + )? + .query_map(rusqlite::params![beamtime_id], |r| r.get::<_, String>(0))? + .filter_map(|r| r.ok()) + .collect(); + let mut tags: Vec = conn + .prepare( + "SELECT DISTINCT tag FROM bt_samples WHERE beamtime_id = ?1 AND tag IS NOT NULL AND tag != '' ORDER BY tag", + )? + .query_map(rusqlite::params![beamtime_id], |r| r.get::<_, String>(0))? + .filter_map(|r| r.ok()) + .collect(); + let scans: Vec<(i64, String)> = conn + .prepare("SELECT uid FROM bt_scans WHERE beamtime_id = ?1 ORDER BY uid")? + .query_map(rusqlite::params![beamtime_id], |r| { + let uid: String = r.get(0)?; + let n = scan_number_from_uid(&uid); + Ok((n, format!("Scan {}", n))) + })? + .filter_map(|r| r.ok()) + .collect(); + samples.sort(); + tags.sort(); + Ok(BeamtimeEntries { + samples, + tags, + scans, + }) +} + pub fn query_files(db_path: &Path, filter: Option<&CatalogFilter>) -> Result> { let (where_clause, params) = filter .map(build_where_and_params) @@ -93,7 +192,7 @@ pub fn query_files(db_path: &Path, filter: Option<&CatalogFilter>) -> Result) -> Result) -> Result, + beamtime_id: i64, + param_start: usize, +) -> (String, Vec>) { + let Some(filter) = filter else { + return (String::new(), Vec::new()); + }; + let mut conditions: Vec = Vec::new(); + let mut params: Vec> = Vec::new(); + let mut idx = param_start; + if let Some(ref s) = filter.sample_name { + idx += 1; + conditions.push(format!("s.name = ?{}", idx)); + params.push(Box::new(s.clone())); + } + if let Some(ref t) = filter.tag { + idx += 1; + conditions.push(format!("s.tag = ?{}", idx)); + params.push(Box::new(t.clone())); + } + if let Some(ref scan_nos) = filter.scan_numbers { + if !scan_nos.is_empty() { + let placeholders: Vec = (0..scan_nos.len()) + .map(|_| { + idx += 1; + format!("?{}", idx) + }) + .collect(); + conditions.push(format!("sc.uid IN ({})", placeholders.join(","))); + for n in scan_nos { + params.push(Box::new(format!("s_{}_{}", beamtime_id, n))); + } + } + } + if let Some(em) = filter.energy_min { + idx += 1; + conditions.push(format!("sp.beamline_energy >= ?{}", idx)); + params.push(Box::new(em)); + } + if let Some(em) = filter.energy_max { + idx += 1; + conditions.push(format!("sp.beamline_energy <= ?{}", idx)); + params.push(Box::new(em)); + } + let where_clause = if conditions.is_empty() { + String::new() + } else { + format!(" AND {}", conditions.join(" AND ")) + }; + (where_clause, params) +} + +pub fn query_scan_points( + db_path: &Path, + beamtime_dir: &Path, + filter: Option<&CatalogFilter>, +) -> Result> { + let conn = open_catalog_db(db_path)?; + let beamtime_id = match beamtime_id_from_path(&conn, beamtime_dir)? { + Some(id) => id, + None => return Ok(Vec::new()), + }; + let (extra_where, mut params) = build_scan_points_where_and_params(filter, beamtime_id, 1); + let order_clause = " ORDER BY sc.uid, sp.seq_index"; + let sql = format!("{}{}{}", SCAN_POINTS_QUERY, extra_where, order_clause); + let mut stmt = conn.prepare(&sql)?; + let mut query_params: Vec> = vec![Box::new(beamtime_id)]; + query_params.append(&mut params); + let map_row = |row: &rusqlite::Row| { + let scan_uid: String = row.get(18)?; + let scan_no = scan_number_from_uid(&scan_uid); + Ok(FileRow { + file_path: row.get::<_, Option>(1)?.unwrap_or_default(), + sample_name: row.get(16)?, + tag: row.get(17)?, + scan_number: scan_no, + frame_number: row.get(7)?, + beamline_energy: row.get(8)?, + sample_theta: row.get(9)?, + epu_polarization: row.get(10)?, + q: None, + date_iso: None, + beam_row: row.get(13)?, + beam_col: row.get(14)?, + beam_sigma: row.get(15)?, + scan_point_uid: Some(row.get(0)?), + }) + }; + let param_refs: Vec<&dyn rusqlite::ToSql> = query_params.iter().map(|b| b.as_ref()).collect(); + let rows = stmt.query_map(rusqlite::params_from_iter(param_refs), map_row)?; + rows.map(|r| r.map_err(CatalogError::Sqlite)).collect() +} + pub fn update_beamspot( db_path: &Path, file_path: &str, beam_row: i64, beam_col: i64, + beam_sigma: Option, ) -> Result<()> { let conn = open_catalog_db(db_path)?; - conn.execute( - "UPDATE files SET beam_row = ?1, beam_col = ?2 WHERE path = ?3", - rusqlite::params![beam_row, beam_col, file_path], - )?; + match beam_sigma { + Some(sigma) => { + conn.execute( + "UPDATE files SET beam_row = ?1, beam_col = ?2, beam_sigma = ?3 WHERE path = ?4", + rusqlite::params![beam_row, beam_col, sigma, file_path], + )?; + } + None => { + conn.execute( + "UPDATE files SET beam_row = ?1, beam_col = ?2 WHERE path = ?3", + rusqlite::params![beam_row, beam_col, file_path], + )?; + } + } + Ok(()) +} + +pub fn update_beamspot_scan_point( + db_path: &Path, + scan_point_uid: &str, + beam_row: i64, + beam_col: i64, + beam_sigma: Option, +) -> Result<()> { + let conn = open_catalog_db(db_path)?; + match beam_sigma { + Some(sigma) => { + conn.execute( + "UPDATE bt_scan_points SET beam_row = ?1, beam_col = ?2, beam_sigma = ?3 WHERE uid = ?4", + rusqlite::params![beam_row, beam_col, sigma, scan_point_uid], + )?; + } + None => { + conn.execute( + "UPDATE bt_scan_points SET beam_row = ?1, beam_col = ?2 WHERE uid = ?3", + rusqlite::params![beam_row, beam_col, scan_point_uid], + )?; + } + } Ok(()) } +pub fn get_scan_point_uid_by_source_path( + db_path: &Path, + source_path: &str, +) -> Result> { + let conn = open_catalog_db(db_path)?; + let uid: Option = match conn.query_row( + "SELECT uid FROM bt_scan_points WHERE source_path = ?1 LIMIT 1", + rusqlite::params![source_path], + |r| r.get(0), + ) { + Ok(u) => Some(u), + Err(rusqlite::Error::QueryReturnedNoRows) => None, + Err(e) => return Err(e.into()), + }; + Ok(uid) +} + const RESOLVED_QUERY: &str = r#" SELECT f.file_path, f.data_offset, f.naxis1, f.naxis2, f.bitpix, f.bzero, f.data_size, @@ -148,7 +411,7 @@ SELECT f."DATE", f."Beamline Energy", f."Sample Theta", f."CCD Theta", f."Higher Order Suppressor", f."EPU Polarization", f.EXPOSURE, f."Sample Name", f."Scan ID", f.Lambda, f.Q, - f.beam_row, f.beam_col + f.beam_row, f.beam_col, f.beam_sigma FROM files f LEFT JOIN overrides o ON f.path = o.path "#; @@ -194,8 +457,216 @@ fn build_where_and_params(filter: &CatalogFilter) -> (String, Vec, +) -> Result { + let (where_clause, params) = build_bt_df_where_and_params(filter); + let order_clause = " ORDER BY sc.uid, sp.seq_index"; + let sql = format!("{}{}{}", BT_SCAN_POINTS_DF_QUERY, where_clause, order_clause); + let mut stmt = conn.prepare(&sql)?; + let row_from_query = |row: &rusqlite::Row| { + Ok(( + row.get::<_, Option>(0)?, + row.get::<_, Option>(1)?, + row.get::<_, Option>(2)?, + row.get::<_, Option>(3)?, + row.get::<_, Option>(4)?, + row.get::<_, Option>(5)?, + row.get::<_, i64>(6)?, + row.get::<_, String>(7)?, + row.get::<_, Option>(8)?, + row.get::<_, String>(9)?, + row.get::<_, i64>(10)?, + row.get::<_, Option>(11)?, + row.get::<_, Option>(12)?, + row.get::<_, Option>(13)?, + row.get::<_, Option>(14)?, + row.get::<_, Option>(15)?, + row.get::<_, Option>(16)?, + row.get::<_, Option>(17)?, + row.get::<_, Option>(18)?, + )) + }; + let rows = if params.is_empty() { + stmt.query_map([], row_from_query)? + } else { + let param_refs: Vec<&dyn rusqlite::ToSql> = params.iter().map(|b| b.as_ref()).collect(); + stmt.query_map(rusqlite::params_from_iter(param_refs), row_from_query)? + }; + + let mut file_path = Vec::new(); + let mut data_offset = Vec::new(); + let mut naxis1 = Vec::new(); + let mut naxis2 = Vec::new(); + let mut bitpix = Vec::new(); + let mut bzero = Vec::new(); + let mut data_size = Vec::new(); + let mut file_name = Vec::new(); + let mut sample_name = Vec::new(); + let mut tag = Vec::new(); + let mut scan_number = Vec::new(); + let mut frame_number = Vec::new(); + let mut date: Vec> = Vec::new(); + let mut beamline_energy = Vec::new(); + let mut sample_theta = Vec::new(); + let mut ccd_theta = Vec::new(); + let mut hos: Vec> = Vec::new(); + let mut epu = Vec::new(); + let mut exposure = Vec::new(); + let mut sample_name_h: Vec> = Vec::new(); + let mut scan_id = Vec::new(); + let mut lambda: Vec> = Vec::new(); + let mut q: Vec> = Vec::new(); + let mut beam_row = Vec::new(); + let mut beam_col = Vec::new(); + let mut beam_sigma = Vec::new(); + + for row in rows { + let r = row.map_err(CatalogError::Sqlite)?; + let path_str = r.0.unwrap_or_default(); + let scan_no = scan_number_from_uid(&r.9); + let fname = std::path::Path::new(&path_str) + .file_name() + .and_then(|o| o.to_str()) + .unwrap_or("") + .to_string(); + file_path.push(path_str.clone()); + data_offset.push(r.1.unwrap_or(0)); + naxis1.push(r.2.unwrap_or(0)); + naxis2.push(r.3.unwrap_or(0)); + bitpix.push(r.4.unwrap_or(0)); + bzero.push(r.5.unwrap_or(0)); + data_size.push(r.6); + file_name.push(fname); + sample_name.push(r.7.clone()); + tag.push(r.8); + scan_number.push(scan_no); + frame_number.push(r.10); + date.push(None); + beamline_energy.push(r.11); + sample_theta.push(r.12); + ccd_theta.push(r.13); + hos.push(None); + epu.push(r.14); + exposure.push(r.15); + sample_name_h.push(Some(r.7)); + scan_id.push(Some(scan_no as f64)); + lambda.push(None); + q.push(None); + beam_row.push(r.16); + beam_col.push(r.17); + beam_sigma.push(r.18); + } + + let series = vec![ + Series::new("file_path".into(), file_path), + Series::new("data_offset".into(), data_offset), + Series::new("naxis1".into(), naxis1), + Series::new("naxis2".into(), naxis2), + Series::new("bitpix".into(), bitpix), + Series::new("bzero".into(), bzero), + Series::new("data_size".into(), data_size), + Series::new("file_name".into(), file_name), + Series::new("sample_name".into(), sample_name), + Series::new("tag".into(), tag), + Series::new("scan_number".into(), scan_number), + Series::new("frame_number".into(), frame_number), + Series::new("DATE".into(), date), + Series::new("Beamline Energy".into(), beamline_energy), + Series::new("Sample Theta".into(), sample_theta), + Series::new("CCD Theta".into(), ccd_theta), + Series::new("Higher Order Suppressor".into(), hos), + Series::new("EPU Polarization".into(), epu), + Series::new("EXPOSURE".into(), exposure), + Series::new("Sample Name".into(), sample_name_h), + Series::new("Scan ID".into(), scan_id), + Series::new("Lambda".into(), lambda), + Series::new("Q".into(), q), + Series::new("beam_row".into(), beam_row), + Series::new("beam_col".into(), beam_col), + Series::new("beam_sigma".into(), beam_sigma), + ]; + let columns: Vec = series.into_iter().map(|s| s.into()).collect(); + DataFrame::new(columns).map_err(|e| CatalogError::Validation(e.to_string())) +} + +fn build_bt_df_where_and_params( + filter: Option<&CatalogFilter>, +) -> (String, Vec>) { + let Some(filter) = filter else { + return (String::new(), Vec::new()); + }; + let mut conditions: Vec = Vec::new(); + let mut params: Vec> = Vec::new(); + let mut idx = 0; + if let Some(ref s) = filter.sample_name { + idx += 1; + conditions.push(format!("s.name = ?{}", idx)); + params.push(Box::new(s.clone())); + } + if let Some(ref t) = filter.tag { + idx += 1; + conditions.push(format!("s.tag = ?{}", idx)); + params.push(Box::new(t.clone())); + } + if let Some(em) = filter.energy_min { + idx += 1; + conditions.push(format!("sp.beamline_energy >= ?{}", idx)); + params.push(Box::new(em)); + } + if let Some(em) = filter.energy_max { + idx += 1; + conditions.push(format!("sp.beamline_energy <= ?{}", idx)); + params.push(Box::new(em)); + } + if let Some(ref scan_nos) = filter.scan_numbers { + if !scan_nos.is_empty() { + let or_parts: Vec = scan_nos + .iter() + .map(|_| { + idx += 1; + format!("sc.uid LIKE '%' || ?{}", idx) + }) + .collect(); + conditions.push(format!("({})", or_parts.join(" OR "))); + for n in scan_nos { + params.push(Box::new(format!("_{}", n))); + } + } + } + let where_clause = if conditions.is_empty() { + String::new() + } else { + format!(" WHERE {}", conditions.join(" AND ")) + }; + (where_clause, params) +} + pub fn scan_from_catalog(db_path: &Path, filter: Option<&CatalogFilter>) -> Result { let conn = open_catalog_db(db_path)?; + let use_bt: bool = conn.query_row( + "SELECT COUNT(*) FROM bt_scan_points", + [], + |r| r.get::<_, i64>(0), + )? > 0; + if use_bt { + return scan_from_catalog_bt(&conn, filter); + } let (where_clause, params) = filter .map(build_where_and_params) .unwrap_or_else(|| (String::new(), Vec::new())); @@ -230,6 +701,7 @@ pub fn scan_from_catalog(db_path: &Path, filter: Option<&CatalogFilter>) -> Resu row.get::<_, Option>(22)?, row.get::<_, Option>(23)?, row.get::<_, Option>(24)?, + row.get::<_, Option>(25)?, )) }; let rows = if params.is_empty() { @@ -264,6 +736,7 @@ pub fn scan_from_catalog(db_path: &Path, filter: Option<&CatalogFilter>) -> Resu let mut q = Vec::new(); let mut beam_row = Vec::new(); let mut beam_col = Vec::new(); + let mut beam_sigma = Vec::new(); for row in rows { let r = row.map_err(CatalogError::Sqlite)?; @@ -292,6 +765,7 @@ pub fn scan_from_catalog(db_path: &Path, filter: Option<&CatalogFilter>) -> Resu q.push(r.22); beam_row.push(r.23); beam_col.push(r.24); + beam_sigma.push(r.25); } let series = vec![ @@ -320,6 +794,7 @@ pub fn scan_from_catalog(db_path: &Path, filter: Option<&CatalogFilter>) -> Resu Series::new("Q".into(), q), Series::new("beam_row".into(), beam_row), Series::new("beam_col".into(), beam_col), + Series::new("beam_sigma".into(), beam_sigma), ]; let columns: Vec = series.into_iter().map(|s| s.into()).collect(); let df = DataFrame::new(columns).map_err(|e| CatalogError::Validation(e.to_string()))?; @@ -402,6 +877,7 @@ fn scan_from_catalog_columns() -> Vec<&'static str> { "Q", "beam_row", "beam_col", + "beam_sigma", ] } diff --git a/src/colormap.rs b/src/colormap.rs index 500bc55..47734a4 100644 --- a/src/colormap.rs +++ b/src/colormap.rs @@ -43,9 +43,10 @@ fn rainbow_lut() -> &'static [[u8; 4]; LUT_LEN] { RAINBOW_LUT.get_or_init(build_rainbow_lut) } -pub fn scalar_to_rgba_rainbow( +pub fn scalar_to_rgba( data: &[f64], min_max: Option<(f64, f64)>, + reversed: bool, ) -> Vec { let (min, max) = match min_max { Some((lo, hi)) => (lo, hi), @@ -55,18 +56,28 @@ pub fn scalar_to_rgba_rainbow( (min, max) } }; - let span = max - min; - let scale = if span > 0.0 { (LUT_LEN - 1) as f64 / span } else { 0.0 }; + let span = (max - min).max(1e-9); + let scale = (LUT_LEN - 1) as f64 / span; let lut = rainbow_lut(); let mut out = Vec::with_capacity(data.len() * 4); for &v in data { - let idx = ((v - min) * scale).clamp(0.0, (LUT_LEN - 1) as f64) as usize; + let mut idx = ((v - min) * scale).clamp(0.0, (LUT_LEN - 1) as f64) as usize; + if reversed { + idx = (LUT_LEN - 1) - idx; + } let rgba = lut[idx]; out.extend_from_slice(&rgba); } out } +pub fn scalar_to_rgba_rainbow( + data: &[f64], + min_max: Option<(f64, f64)>, +) -> Vec { + scalar_to_rgba(data, min_max, false) +} + pub fn array2_to_rgba_rainbow( data: &Array2, min_max: Option<(f64, f64)>, @@ -75,9 +86,10 @@ pub fn array2_to_rgba_rainbow( Some(scalar_to_rgba_rainbow(slice, min_max)) } -pub fn array2_i64_to_rgba_rainbow( +pub fn array2_i64_to_rgba( data: &Array2, min_max: Option<(i64, i64)>, + reversed: bool, ) -> Option> { let slice = data.as_slice()?; let (min, max) = match min_max { @@ -89,12 +101,20 @@ pub fn array2_i64_to_rgba_rainbow( } }; let f64_data: Vec = slice.iter().map(|&x| x as f64).collect(); - Some(scalar_to_rgba_rainbow(&f64_data, Some((min, max)))) + Some(scalar_to_rgba(&f64_data, Some((min, max)), reversed)) } -pub fn array2_f32_to_rgba_rainbow( +pub fn array2_i64_to_rgba_rainbow( + data: &Array2, + min_max: Option<(i64, i64)>, +) -> Option> { + array2_i64_to_rgba(data, min_max, false) +} + +pub fn array2_f32_to_rgba( data: &Array2, min_max: Option<(f32, f32)>, + reversed: bool, ) -> Option> { let slice = data.as_slice()?; let (min, max) = match min_max { @@ -106,7 +126,14 @@ pub fn array2_f32_to_rgba_rainbow( } }; let f64_data: Vec = slice.iter().map(|&x| x as f64).collect(); - Some(scalar_to_rgba_rainbow(&f64_data, Some((min, max)))) + Some(scalar_to_rgba(&f64_data, Some((min, max)), reversed)) +} + +pub fn array2_f32_to_rgba_rainbow( + data: &Array2, + min_max: Option<(f32, f32)>, +) -> Option> { + array2_f32_to_rgba(data, min_max, false) } #[cfg(test)] @@ -142,4 +169,17 @@ mod tests { ]; assert_ne!(first, last); } + + #[test] + fn reversed_inverts_lut_index() { + let data = vec![0.0f64, 255.0f64]; + let fwd = scalar_to_rgba(&data, Some((0.0, 255.0)), false); + let rev = scalar_to_rgba(&data, Some((0.0, 255.0)), true); + let fwd_low = [fwd[0], fwd[1], fwd[2]]; + let fwd_high = [fwd[4], fwd[5], fwd[6]]; + let rev_low = [rev[0], rev[1], rev[2]]; + let rev_high = [rev[4], rev[5], rev[6]]; + assert_eq!(fwd_low, rev_high); + assert_eq!(fwd_high, rev_low); + } } diff --git a/src/gaussian_fit.rs b/src/gaussian_fit.rs index 890dd75..81a7464 100644 --- a/src/gaussian_fit.rs +++ b/src/gaussian_fit.rs @@ -1,5 +1,6 @@ use ndarray::Array2; +/// 2D Gaussian fit result: center (row, col), sigmas, amplitude, and constant baseline. #[derive(Debug, Clone, Copy)] pub struct Gaussian2DFit { pub center_row: f64, diff --git a/src/io/image_mmap.rs b/src/io/image_mmap.rs index d6bb760..d170a45 100644 --- a/src/io/image_mmap.rs +++ b/src/io/image_mmap.rs @@ -6,7 +6,10 @@ use ndarray::Array2; use polars::prelude::*; use super::blur::{gaussian_blur_f32_copy, i64_to_f32_array}; -use super::{subtract_background, subtract_background_edges, ImageInfo}; +use super::{ + subtract_background_edges, subtract_dark_cold_side, subtract_background_row_strips, + trim_image_interior, ImageInfo, TRIM_COLS, TRIM_ROWS, +}; use crate::errors::FitsError; use crate::fits::HduList; @@ -41,11 +44,10 @@ fn load_image_pixels(path: &Path, info: &ImageInfo) -> Result, FitsE pub fn materialize_image(path: &Path, info: &ImageInfo) -> Result { let data = load_image_pixels(path, info)?; - let subtracted = subtract_background(&data.clone().into_dyn()); - let subtracted_2d = subtracted - .into_shape_with_order((info.naxis2, info.naxis1)) - .map_err(|e| FitsError::validation(e.to_string()))?; - Ok((data, subtracted_2d)) + let trimmed_raw = trim_image_interior(&data, TRIM_ROWS, TRIM_COLS); + let row_corrected = subtract_background_row_strips(&trimmed_raw); + let trimmed_processed = subtract_dark_cold_side(&row_corrected); + Ok((trimmed_raw, trimmed_processed)) } pub fn get_image_for_row(df: &DataFrame, row_index: usize) -> Result { diff --git a/src/io/mod.rs b/src/io/mod.rs index 399cdf3..5680866 100644 --- a/src/io/mod.rs +++ b/src/io/mod.rs @@ -4,6 +4,9 @@ pub mod options; pub mod schema; pub mod source; +#[cfg(feature = "zarr")] +pub mod zarr_store; + use ndarray::{Array2, ArrayBase, Axis, Dim, IxDynImpl, OwnedRepr}; use polars::prelude::*; use regex::Regex; @@ -109,6 +112,11 @@ pub struct ParsedFitsStem { pub frame_number: i64, } +pub fn is_polarization_tag(name: &str) -> bool { + let n = name.trim().to_lowercase(); + n == "s" || n == "p" +} + pub fn parse_fits_stem(stem: &str) -> Option { let stem = stem.trim(); let re = Regex::new(r"^(.+?)[\s\-_]?(\d{5})-(\d{5})$").ok()?; @@ -121,7 +129,13 @@ pub fn parse_fits_stem(stem: &str) -> Option { let (sample_name, tag) = if base.contains('_') { let parts: Vec<&str> = base.split('_').collect(); let (last, rest) = parts.split_last()?; - (rest.join("_"), Some((*last).to_string())) + let raw_tag = (*last).to_string(); + let tag = if is_polarization_tag(&raw_tag) { + None + } else { + Some(raw_tag) + }; + (rest.join("_"), tag) } else { (base.to_string(), None) }; @@ -240,6 +254,72 @@ pub fn add_calculated_domains(mut lzf: LazyFrame) -> DataFrame { lz.collect().unwrap_or_else(|_| DataFrame::empty()) } +pub const TRIM_ROWS: usize = 5; +pub const TRIM_COLS: usize = 5; +pub const ROW_BG_STRIP_WIDTH: usize = 10; +pub const DARK_BAND_HEIGHT: usize = 10; + +pub fn trim_image_interior( + data: &Array2, + trim_rows: usize, + trim_cols: usize, +) -> Array2 { + let rows = data.nrows(); + let cols = data.ncols(); + if rows < 2 * trim_rows || cols < 2 * trim_cols { + return data.clone(); + } + data.slice(ndarray::s![ + trim_rows..(rows - trim_rows), + trim_cols..(cols - trim_cols) + ]) + .to_owned() +} + +pub fn subtract_background_row_strips(data: &Array2) -> Array2 { + let rows = data.nrows(); + let cols = data.ncols(); + let strip = ROW_BG_STRIP_WIDTH.min(cols / 2); + if strip == 0 { + return data.clone(); + } + let mut result = data.clone(); + for r in 0..rows { + let left_slice = result.slice(ndarray::s![r, ..strip]); + let right_slice = result.slice(ndarray::s![r, (cols - strip)..]); + let left_sum: i64 = left_slice.iter().copied().sum(); + let right_sum: i64 = right_slice.iter().copied().sum(); + let left_mean = left_sum / strip as i64; + let right_mean = right_sum / strip as i64; + let bg = left_mean.min(right_mean); + for c in 0..cols { + result[[r, c]] -= bg; + } + } + result +} + +pub fn subtract_dark_cold_side(data: &Array2) -> Array2 { + let rows = data.nrows(); + let cols = data.ncols(); + let band = DARK_BAND_HEIGHT.min(rows / 2); + if band == 0 { + return data.clone(); + } + let top_slice = data.slice(ndarray::s![..band, ..]); + let bottom_slice = data.slice(ndarray::s![(rows - band).., ..]); + let top_sum: i64 = top_slice.iter().copied().sum(); + let bottom_sum: i64 = bottom_slice.iter().copied().sum(); + let top_mean = top_sum / (band * cols) as i64; + let bottom_mean = bottom_sum / (band * cols) as i64; + let dark = top_mean.min(bottom_mean); + let mut result = data.clone(); + for v in result.iter_mut() { + *v -= dark; + } + result +} + pub fn subtract_background( data: &ArrayBase, Dim>, ) -> ArrayBase, Dim> { @@ -420,6 +500,99 @@ pub fn build_headers_only_columns( Ok(columns) } +#[derive(Debug, Clone)] +pub struct BtIngestRow { + pub file_path: String, + pub data_offset: i64, + pub naxis1: i64, + pub naxis2: i64, + pub bitpix: i64, + pub bzero: i64, + pub file_name: String, + pub sample_name: String, + pub tag: Option, + pub scan_number: i64, + pub frame_number: i64, + pub beamline_energy: Option, + pub sample_theta: Option, + pub ccd_theta: Option, + pub epu_polarization: Option, + pub exposure: Option, +} + +fn header_float(primary: &PrimaryHdu, key: &str) -> Option { + if key == "Beamline Energy" { + if let Some(card) = primary.header.get_card(key) { + if let Some(v) = card.value.as_float() { + return Some(v); + } + } + if let Some(card) = primary.header.get_card("Beamline Energy Goal") { + return card.value.as_float(); + } + return Some(0.0); + } + if key == "DATE" || key == "Sample Name" { + return None; + } + primary + .header + .get_card(key) + .map(|c| c.value.as_float().unwrap_or(1.0)) +} + +pub fn build_bt_ingest_row( + primary: &PrimaryHdu, + image_header: &ImageHduHeader, + path: PathBuf, + header_items: &[String], +) -> Result { + let path_str = path + .to_str() + .ok_or_else(|| FitsError::validation("Invalid UTF-8 in path"))? + .to_string(); + let file_name = path.file_stem().and_then(|s| s.to_str()).unwrap_or("").to_string(); + let (sample_name, tag, scan_number, frame_number) = match parse_fits_stem(&file_name) { + Some(p) => (p.sample_name, p.tag, p.scan_number, p.frame_number), + None => (String::new(), None, 0i64, 0i64), + }; + let bzero = image_header + .header + .get_card("BZERO") + .and_then(|c| c.value.as_int()) + .unwrap_or(0); + let mut row = BtIngestRow { + file_path: path_str, + data_offset: image_header.data_offset as i64, + naxis1: image_header.naxis1 as i64, + naxis2: image_header.naxis2 as i64, + bitpix: image_header.bitpix as i64, + bzero, + file_name, + sample_name, + tag, + scan_number, + frame_number, + beamline_energy: None, + sample_theta: None, + ccd_theta: None, + epu_polarization: None, + exposure: None, + }; + for key in header_items { + let v = header_float(primary, key); + match key.as_str() { + "Beamline Energy" => row.beamline_energy = v, + "Sample Theta" => row.sample_theta = v, + "CCD Theta" => row.ccd_theta = v, + "EPU Polarization" => row.epu_polarization = v, + "EXPOSURE" => row.exposure = v, + _ => {} + } + } + Ok(row) +} + pub fn process_file_name(path: std::path::PathBuf) -> Vec { let file_name = path.file_stem().and_then(|s| s.to_str()).unwrap_or(""); let mut columns = vec![Column::new("file_name".into(), vec![file_name.to_string()])]; @@ -497,6 +670,16 @@ mod tests { assert!(parse_fits_stem("sample12345-678").is_none()); } + #[test] + fn test_parse_fits_stem_polarization_not_tag() { + let p = parse_fits_stem("znpc_s 81041-00001").expect("should parse"); + assert_eq!(p.sample_name, "znpc"); + assert_eq!(p.tag, None); + let p2 = parse_fits_stem("znpc_P 81041-00002").expect("should parse"); + assert_eq!(p2.sample_name, "znpc"); + assert_eq!(p2.tag, None); + } + #[test] fn test_subtract_background_edges() { let data = Array2::from_shape_vec( @@ -526,4 +709,139 @@ mod tests { assert_eq!(a, b); } } + + #[test] + fn test_trim_image_interior_shape() { + let data = Array2::from_shape_vec((20, 30), vec![0i64; 20 * 30]).unwrap(); + let trimmed = trim_image_interior(&data, TRIM_ROWS, TRIM_COLS); + assert_eq!(trimmed.nrows(), 10); + assert_eq!(trimmed.ncols(), 20); + } + + #[test] + fn test_trim_image_interior_too_small_returns_clone() { + let data = Array2::from_shape_vec((8, 8), vec![1i64; 64]).unwrap(); + let trimmed = trim_image_interior(&data, TRIM_ROWS, TRIM_COLS); + assert_eq!(trimmed.shape(), data.shape()); + assert_eq!(trimmed[[0, 0]], 1); + } + + #[test] + fn test_trim_image_interior_content_preserved() { + let mut data = Array2::zeros((15, 25)); + data[[5, 5]] = 100; + let trimmed = trim_image_interior(&data, TRIM_ROWS, TRIM_COLS); + assert_eq!(trimmed.nrows(), 5); + assert_eq!(trimmed.ncols(), 15); + assert_eq!(trimmed[[0, 0]], 100); + } + + #[test] + fn test_subtract_background_row_strips_whole_row_corrected() { + let data = Array2::from_shape_vec( + (2, 30), + (0..60).map(|i| if i < 10 { 5i64 } else if i >= 20 { 5i64 } else { 100 }).collect(), + ) + .unwrap(); + let result = subtract_background_row_strips(&data); + for c in 0..30 { + assert!(result[[0, c]] <= 100 - 5, "row 0 col {} should be corrected", c); + assert!(result[[1, c]] <= 100 - 5, "row 1 col {} should be corrected", c); + } + } + + #[test] + fn test_subtract_background_row_strips_colder_side_per_row() { + let mut data = Array2::zeros((2, 30)); + for c in 0..10 { + data[[0, c]] = 3; + data[[1, c]] = 20; + } + for c in 20..30 { + data[[0, c]] = 20; + data[[1, c]] = 3; + } + for c in 10..20 { + data[[0, c]] = 50; + data[[1, c]] = 50; + } + let result = subtract_background_row_strips(&data); + assert_eq!(result[[0, 0]], 0); + assert_eq!(result[[0, 15]], 50 - 3); + assert_eq!(result[[1, 0]], 20 - 3); + assert_eq!(result[[1, 25]], 0); + } + + #[test] + fn test_subtract_background_row_strips_narrow_image() { + let data = Array2::from_shape_vec((4, 10), vec![10i64; 40]).unwrap(); + let result = subtract_background_row_strips(&data); + assert_eq!(result.shape(), data.shape()); + for v in result.iter() { + assert!(*v <= 10); + } + } + + #[test] + fn test_subtract_background_row_strips_exactly_20_cols() { + let data = Array2::from_shape_vec((2, 20), vec![7i64; 40]).unwrap(); + let result = subtract_background_row_strips(&data); + assert_eq!(result.shape(), data.shape()); + for v in result.iter() { + assert_eq!(*v, 0); + } + } + + #[test] + fn test_subtract_dark_cold_side_one_scalar() { + let data = Array2::from_shape_vec((25, 20), vec![100i64; 500]).unwrap(); + let result = subtract_dark_cold_side(&data); + assert_eq!(result.shape(), data.shape()); + let expected = 100 - 100; + for v in result.iter() { + assert_eq!(*v, expected); + } + } + + #[test] + fn test_subtract_dark_cold_side_colder_chosen() { + let mut data = Array2::from_shape_vec((30, 15), vec![50i64; 450]).unwrap(); + for r in 0..10 { + for c in 0..15 { + data[[r, c]] = 10; + } + } + for r in 20..30 { + for c in 0..15 { + data[[r, c]] = 80; + } + } + let result = subtract_dark_cold_side(&data); + assert_eq!(result[[0, 0]], 0); + assert!(result[[15, 0]] < 50); + assert_eq!(result[[25, 0]], 80 - 10); + } + + #[test] + fn test_subtract_dark_cold_side_small_height() { + let data = Array2::from_shape_vec((5, 20), vec![1i64; 100]).unwrap(); + let result = subtract_dark_cold_side(&data); + assert_eq!(result.shape(), data.shape()); + } + + #[test] + fn test_raw_pipeline_full_trim_row_dark() { + let rows = 30usize; + let cols = 40usize; + let data = Array2::from_shape_vec((rows, cols), vec![100i64; rows * cols]).unwrap(); + let trimmed = trim_image_interior(&data, TRIM_ROWS, TRIM_COLS); + assert_eq!(trimmed.nrows(), rows - 2 * TRIM_ROWS); + assert_eq!(trimmed.ncols(), cols - 2 * TRIM_COLS); + let row_corrected = subtract_background_row_strips(&trimmed); + let processed = subtract_dark_cold_side(&row_corrected); + assert_eq!(processed.shape(), trimmed.shape()); + for v in processed.iter() { + assert!(*v <= 100); + } + } } diff --git a/src/io/source.rs b/src/io/source.rs index 25ba06f..72218c4 100644 --- a/src/io/source.rs +++ b/src/io/source.rs @@ -6,7 +6,7 @@ use std::path::{Path, PathBuf}; use crate::errors::FitsError; #[cfg(feature = "catalog")] -use crate::catalog::{discover_fits_paths, CATALOG_DB_NAME}; +use crate::catalog::{discover_fits_paths, resolve_catalog_path as catalog_resolve_path, CATALOG_DB_NAME}; /// When the source could be satisfied from catalog or disk (e.g. beamtime dir with `.pyref_catalog.db`), /// this selects which to use. @@ -73,7 +73,7 @@ impl FitsSource { #[cfg(feature = "catalog")] fn resolve_dir(dir: PathBuf, preference: ResolvePreference) -> Result { - let catalog_db = dir.join(CATALOG_DB_NAME); + let catalog_db = catalog_resolve_path(&dir); let use_catalog = match preference { ResolvePreference::FromDisk => false, ResolvePreference::PreferDisk => false, @@ -131,7 +131,7 @@ fn resolve_catalog_path( } path } else if path.is_dir() { - path.join(CATALOG_DB_NAME) + catalog_resolve_path(&path) } else { return Err( FitsError::not_found(format!("path does not exist: {}", path.display())) diff --git a/src/io/zarr_store.rs b/src/io/zarr_store.rs new file mode 100644 index 0000000..834c117 --- /dev/null +++ b/src/io/zarr_store.rs @@ -0,0 +1,137 @@ +#![cfg(feature = "zarr")] + +use ndarray::Array2; +use std::path::{Path, PathBuf}; +use std::sync::Arc; +use zarrs::array::Array; +use zarrs::array::ArrayBuilder; +use zarrs::array::data_type; +use zarrs::group::GroupBuilder; +use zarrs::storage::{ReadableWritableListableStorage, ReadableWritableListableStorageTraits}; + +use crate::errors::FitsError; + +const PYREF_CATALOG_DIR: &str = ".pyref"; +const ZARR_SUBDIR: &str = "zarr"; +const DETECTOR_ARRAY_NAME: &str = "detector"; + +fn catalog_parent(beamtime_dir: &Path) -> PathBuf { + let parent = beamtime_dir.parent(); + if let Some(p) = parent { + if !p.as_os_str().is_empty() && p != Path::new("/") { + return p.join(PYREF_CATALOG_DIR); + } + } + beamtime_dir.join(PYREF_CATALOG_DIR) +} + +fn beamtime_key(beamtime_dir: &Path) -> String { + beamtime_dir + .file_name() + .map(|s| s.to_string_lossy()) + .unwrap_or_else(|| beamtime_dir.as_os_str().to_string_lossy()) + .replace(std::path::MAIN_SEPARATOR, "_") +} + +pub fn zarr_root(beamtime_dir: &Path) -> PathBuf { + catalog_parent(beamtime_dir).join(ZARR_SUBDIR).join(beamtime_key(beamtime_dir)) +} + +pub fn detector_zarr_group(scan_uid: &str) -> String { + format!("scans/{}/{}", scan_uid, DETECTOR_ARRAY_NAME) +} + +fn ensure_group(store: &ReadableWritableListableStorage, path: &str) -> Result<(), FitsError> { + let group = GroupBuilder::new() + .build(store.clone(), path) + .map_err(|e| FitsError::validation(e.to_string()))?; + group + .store_metadata() + .map_err(|e| FitsError::validation(e.to_string()))?; + Ok(()) +} + +fn ensure_scans_groups( + store: &ReadableWritableListableStorage, + scan_uid: &str, +) -> Result<(), FitsError> { + ensure_group(store, "/")?; + ensure_group(store, "/scans")?; + let scan_group = format!("/scans/{}", scan_uid); + ensure_group(store, &scan_group)?; + Ok(()) +} + +pub fn create_detector_array( + store: &ReadableWritableListableStorage, + scan_uid: &str, + n_frames: usize, + height: usize, + width: usize, +) -> Result, FitsError> { + ensure_scans_groups(store, scan_uid)?; + let path = format!("/scans/{}/{}", scan_uid, DETECTOR_ARRAY_NAME); + let shape: Vec = vec![n_frames as u64, height as u64, width as u64]; + let chunk_shape: Vec = vec![1, height as u64, width as u64]; + let array = ArrayBuilder::new(shape, chunk_shape, data_type::int32(), 0i32) + .build(store.clone(), &path) + .map_err(|e| FitsError::validation(e.to_string()))?; + array + .store_metadata() + .map_err(|e| FitsError::validation(e.to_string()))?; + Ok(array) +} + +pub fn write_detector_frame( + array: &Array, + frame_index: usize, + data: &Array2, +) -> Result<(), FitsError> { + let flat: Vec = data.iter().copied().collect(); + let ci = [frame_index as u64, 0u64, 0u64]; + array + .store_chunk(&ci, flat) + .map_err(|e| FitsError::validation(e.to_string()))?; + Ok(()) +} + +pub fn open_detector_array( + store: &ReadableWritableListableStorage, + scan_uid: &str, +) -> Result, FitsError> { + let path = format!("/scans/{}/{}", scan_uid, DETECTOR_ARRAY_NAME); + let array = Array::open(store.clone(), &path) + .map_err(|e| FitsError::validation(e.to_string()))?; + Ok(array) +} + +pub fn read_detector_frame( + array: &Array, + frame_index: usize, +) -> Result, FitsError> { + let shape = array.shape(); + if shape.len() != 3 { + return Err(FitsError::validation("detector array must be 3D")); + } + let height = shape[1] as usize; + let width = shape[2] as usize; + let subset_ranges: [std::ops::Range; 3] = [ + (frame_index as u64)..(frame_index as u64 + 1), + 0..(height as u64), + 0..(width as u64), + ]; + let subset = array + .retrieve_array_subset_ndarray::(&subset_ranges) + .map_err(|e| FitsError::validation(e.to_string()))?; + let flat: Vec = subset.iter().copied().collect(); + Array2::from_shape_vec((height, width), flat).map_err(|e| FitsError::validation(e.to_string())) +} + +pub fn open_store(zarr_root: &Path) -> Result { + std::fs::create_dir_all(zarr_root).map_err(|e| FitsError::io("create zarr root", e))?; + let store: ReadableWritableListableStorage = Arc::new( + zarrs::filesystem::FilesystemStore::new(zarr_root) + .map_err(|e| FitsError::validation(e.to_string()))?, + ); + Ok(store) +} diff --git a/src/lib.rs b/src/lib.rs index 8cbc1cd..24f155c 100644 --- a/src/lib.rs +++ b/src/lib.rs @@ -8,6 +8,7 @@ pub mod errors; pub mod fits; pub mod io; pub mod loader; +pub mod path_policy; #[cfg(feature = "catalog")] pub mod catalog; @@ -18,9 +19,11 @@ pub use io::schema::FitsMetadataSchema; pub use io::source::{FitsSource, ResolvePreference, ResolvedSource}; pub use io::{build_fits_stem, image_mmap, ImageInfo}; pub use loader::{ - read_experiment_headers_only, read_fits, read_fits_headers_only, read_fits_metadata_batch, - read_multiple_fits_headers_only, scan_fits, + catalog_from_stems, list_fits_in_dir, read_experiment_headers_only, read_fits, + read_fits_headers_only, read_fits_metadata_batch, read_multiple_fits_headers_only, scan_fits, + StemCatalog, }; +pub use path_policy::is_indexable_als_path; #[cfg(feature = "extension-module")] #[allow(clippy::useless_conversion)] diff --git a/src/loader.rs b/src/loader.rs index 6852df5..5a343a0 100644 --- a/src/loader.rs +++ b/src/loader.rs @@ -3,18 +3,90 @@ use polars::{lazy::prelude::*, prelude::*}; use rayon::prelude::*; +use std::collections::HashSet; use std::fs; -use std::path::PathBuf; +use std::path::{Path, PathBuf}; use crate::errors::FitsError; use crate::fits::HduList; use crate::io::options::{ReadFitsOptions, ScanFitsOptions}; use crate::io::source::{FitsSource, ResolvedSource}; -use crate::io::{add_calculated_domains, build_headers_only_columns}; +use crate::io::{ + add_calculated_domains, build_bt_ingest_row, build_headers_only_columns, parse_fits_stem, + BtIngestRow, +}; #[cfg(feature = "catalog")] use crate::catalog::scan_from_catalog; +#[derive(Debug, Clone, Default)] +pub struct StemCatalog { + pub samples: Vec, + pub experiment_count: u32, + pub fits_count: u32, +} + +pub fn list_fits_in_dir(dir: &Path) -> Result, FitsError> { + let entries = fs::read_dir(dir).map_err(|e| { + FitsError::io("read_dir", e) + .with_context("operation", "list_fits_in_dir") + .with_context("path", dir.display().to_string()) + })?; + let paths: Vec = entries + .filter_map(|e| e.ok()) + .filter(|e| { + let p = e.path(); + p.extension().and_then(|ext| ext.to_str()) == Some("fits") && p.is_file() + }) + .map(|e| e.path()) + .collect(); + Ok(paths) +} + +pub fn catalog_from_stems(paths: &[PathBuf]) -> StemCatalog { + let fits_count = paths.len() as u32; + if paths.is_empty() { + return StemCatalog { + samples: Vec::new(), + experiment_count: 0, + fits_count: 0, + }; + } + let (samples_set, experiment_set): (HashSet, HashSet) = paths + .par_iter() + .filter_map(|p| { + let stem = p.file_stem().and_then(|s| s.to_str())?; + parse_fits_stem(stem).map(|parsed| { + let samples: HashSet = if parsed.sample_name.is_empty() { + HashSet::new() + } else { + [parsed.sample_name].into_iter().collect() + }; + let experiments: HashSet = if parsed.scan_number > 0 { + [parsed.scan_number].into_iter().collect() + } else { + HashSet::new() + }; + (samples, experiments) + }) + }) + .reduce( + || (HashSet::new(), HashSet::new()), + |(mut a_s, mut a_e), (b_s, b_e)| { + a_s.extend(b_s); + a_e.extend(b_e); + (a_s, a_e) + }, + ); + let mut samples: Vec = samples_set.into_iter().collect(); + samples.sort(); + StemCatalog { + samples, + experiment_count: experiment_set.len() as u32, + fits_count, + } +} + pub fn read_fits_headers_only( file_path: PathBuf, header_items: &[String], @@ -42,6 +114,53 @@ pub fn read_fits_headers_only( DataFrame::new(columns).map_err(FitsError::from) } +pub fn read_fits_headers_only_row( + file_path: PathBuf, + header_items: &[String], +) -> Result { + if file_path.extension().and_then(|ext| ext.to_str()) != Some("fits") { + return Err(FitsError::validation("No data found")); + } + let path_display = file_path.display().to_string(); + let hdul = HduList::from_file_headers_only(&path_display).map_err(|e| { + FitsError::from(e) + .with_context("operation", "read_fits_headers_only_row") + .with_context("path", path_display.clone()) + })?; + let image_header = hdul.image_header.as_ref().ok_or_else(|| { + FitsError::validation("No image HDU found").with_context("path", path_display.clone()) + })?; + build_bt_ingest_row(&hdul.primary, image_header, file_path, header_items).map_err(|e| { + e.with_context("operation", "read_fits_headers_only_row") + .with_context("path", path_display) + }) +} + +pub fn read_multiple_fits_headers_only_rows( + file_paths: Vec, + header_items: &[String], +) -> Result, FitsError> { + if file_paths.is_empty() { + return Err( + FitsError::validation("No files provided") + .with_context("operation", "read_multiple_fits_headers_only_rows"), + ); + } + for path in &file_paths { + if !path.exists() { + return Err( + FitsError::not_found(format!("File not found: {}", path.display())) + .with_context("operation", "read_multiple_fits_headers_only_rows") + .with_context("path", path.display().to_string()), + ); + } + } + file_paths + .par_iter() + .map(|path| read_fits_headers_only_row(path.clone(), header_items)) + .collect() +} + fn combine_dataframes_with_alignment( acc: DataFrame, df: DataFrame, @@ -287,23 +406,105 @@ pub fn read_multiple_fits_headers_only( .par_iter() .map(|path| read_fits_headers_only(path.clone(), header_items)) .collect(); - let successful_dfs: Vec = results.into_iter().filter_map(|r| r.ok()).collect(); - if successful_dfs.is_empty() { - return Err(FitsError::validation( - "None of the provided files could be processed successfully", - ) - .with_context("operation", "read_multiple_fits_headers_only")); + let mut combined: Option = None; + for (i, res) in results.into_iter().enumerate() { + let df = res.map_err(|e| { + e.with_context( + "path", + file_paths + .get(i) + .map(|p| p.display().to_string()) + .unwrap_or_default(), + ) + })?; + combined = Some(match combined.take() { + Some(mut acc) => { + if acc.vstack_mut(&df).is_err() { + combine_dataframes_with_alignment(acc, df)? + } else { + acc + } + } + None => df, + }); } - let mut iter = successful_dfs.into_iter(); - let mut combined = iter.next().ok_or_else(|| { - FitsError::validation("no successful FITS reads") + let mut combined = combined.ok_or_else(|| { + FitsError::validation("no FITS files in batch") .with_context("operation", "read_multiple_fits_headers_only") })?; - for df in iter { - if combined.vstack_mut(&df).is_err() { - combined = combine_dataframes_with_alignment(combined, df)?; - } - } combined.as_single_chunk(); Ok(add_calculated_domains(combined.lazy())) } + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn catalog_from_stems_aggregates_samples_and_experiments() { + let paths: Vec = [ + "monlayerjune 81041-00001.fits", + "monlayerjune 81041-00002.fits", + "znpc 81042-00001.fits", + ] + .iter() + .map(PathBuf::from) + .collect(); + let cat = catalog_from_stems(&paths); + assert_eq!(cat.fits_count, 3); + assert_eq!(cat.samples.len(), 2); + assert!(cat.samples.contains(&"monlayerjune".to_string())); + assert!(cat.samples.contains(&"znpc".to_string())); + assert_eq!(cat.experiment_count, 2); + } + + #[test] + fn catalog_from_stems_skips_invalid_stems() { + let paths: Vec = [ + "valid_sample 81041-00001.fits", + "invalid.fits", + "short1-00001.fits", + ] + .iter() + .map(PathBuf::from) + .collect(); + let cat = catalog_from_stems(&paths); + assert_eq!(cat.fits_count, 3); + assert!(cat.samples.len() <= 2); + } + + #[test] + fn catalog_from_stems_empty() { + let paths: Vec = vec![]; + let cat = catalog_from_stems(&paths); + assert_eq!(cat.fits_count, 0); + assert!(cat.samples.is_empty()); + assert_eq!(cat.experiment_count, 0); + } + + #[test] + fn list_fits_in_dir_nonexistent_returns_error() { + let res = list_fits_in_dir(Path::new("/nonexistent_path_xyz_123")); + assert!(res.is_err()); + let err = res.unwrap_err(); + assert!(!err.context.is_empty()); + assert!(err.context.iter().any(|(k, _)| k == "operation")); + } + + #[test] + fn list_fits_in_dir_temp_dir() { + let dir = tempfile::tempdir().expect("tempdir"); + let d = dir.path(); + std::fs::write(d.join("a.fits"), b"").expect("write a.fits"); + std::fs::write(d.join("b.fits"), b"").expect("write b.fits"); + std::fs::write(d.join("c.txt"), b"").expect("write c.txt"); + let paths = list_fits_in_dir(d).expect("list_fits_in_dir"); + assert_eq!(paths.len(), 2); + let names: Vec = paths + .iter() + .filter_map(|p| p.file_name().and_then(|n| n.to_str().map(String::from))) + .collect(); + assert!(names.contains(&"a.fits".to_string())); + assert!(names.contains(&"b.fits".to_string())); + } +} diff --git a/src/path_policy.rs b/src/path_policy.rs new file mode 100644 index 0000000..3628548 --- /dev/null +++ b/src/path_policy.rs @@ -0,0 +1,100 @@ +//! Path policy for ALS BL1101/Data: only index under BL1101/Data, 20YYMonth, NEXAFS or XRR; exclude Liquid. + +use std::path::Path; +use std::sync::OnceLock; + +static MONTH_REGEX: OnceLock = OnceLock::new(); + +const ALS_DATA_SUFFIX: &str = "BL1101/Data"; +const INDEXABLE_EXPERIMENTS: [&str; 2] = ["NEXAFS", "XRR"]; +const EXCLUDED_SEGMENT: &str = "Liquid"; + +fn month_regex() -> &'static regex::Regex { + MONTH_REGEX.get_or_init(|| { + regex::Regex::new(r"^20\d{2}(Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)$") + .expect("month regex") + }) +} + +pub fn is_under_als_data(path: &Path) -> bool { + let path_str = path.to_string_lossy(); + let normalized = path_str.replace('\\', "/"); + normalized.contains(ALS_DATA_SUFFIX) +} + +pub fn has_month_segment(path: &Path) -> bool { + path.components().any(|c| { + c.as_os_str() + .to_str() + .map_or(false, |s| month_regex().is_match(s)) + }) +} + +pub fn is_indexable_experiment_dir(path: &Path) -> bool { + path.components() + .last() + .and_then(|c| c.as_os_str().to_str()) + .map_or(false, |name| INDEXABLE_EXPERIMENTS.contains(&name)) +} + +pub fn path_contains_excluded(path: &Path) -> bool { + path.components().any(|c| { + c.as_os_str() + .to_str() + .map_or(false, |s| s == EXCLUDED_SEGMENT) + }) +} + +pub fn is_indexable_als_path(path: &Path) -> bool { + is_under_als_data(path) + && has_month_segment(path) + && is_indexable_experiment_dir(path) + && !path_contains_excluded(path) +} + +#[cfg(test)] +mod tests { + use super::*; + + #[test] + fn indexable_nexafs_under_als_data() { + let p = Path::new("/mnt/ALS/BL1101/Data/2025Feb/NEXAFS"); + assert!(is_indexable_als_path(p)); + } + + #[test] + fn indexable_xrr_under_als_data() { + let p = Path::new("/mnt/ALS/BL1101/Data/2024Jan/XRR"); + assert!(is_indexable_als_path(p)); + } + + #[test] + fn liquid_excluded() { + let p = Path::new("/mnt/ALS/BL1101/Data/2025Feb/Liquid"); + assert!(!is_indexable_als_path(p)); + } + + #[test] + fn other_experiment_excluded() { + let p = Path::new("/mnt/ALS/BL1101/Data/2025Feb/Other"); + assert!(!is_indexable_als_path(p)); + } + + #[test] + fn not_under_als_data() { + let p = Path::new("/other/2025Feb/NEXAFS"); + assert!(!is_indexable_als_path(p)); + } + + #[test] + fn no_month_segment() { + let p = Path::new("/mnt/ALS/BL1101/Data/NEXAFS"); + assert!(!is_indexable_als_path(p)); + } + + #[test] + fn liquid_in_path_excluded() { + let p = Path::new("/mnt/ALS/BL1101/Data/2025Feb/Liquid/sub"); + assert!(!is_indexable_als_path(p)); + } +} diff --git a/src/tui/app.rs b/src/tui/app.rs index a146667..67cce56 100644 --- a/src/tui/app.rs +++ b/src/tui/app.rs @@ -18,7 +18,7 @@ use pyref::build_fits_stem; #[cfg(feature = "catalog")] use pyref::catalog::{ catalog_file_count, list_beamtime_entries, list_beamtimes, query_files, register_beamtime, - rename_file_in_catalog, FileRow, CATALOG_DB_NAME, DEFAULT_INGEST_HEADER_ITEMS, + rename_file_in_catalog, FileRow, DEFAULT_INGEST_HEADER_ITEMS, resolve_catalog_path, }; #[cfg(feature = "watch")] use pyref::catalog::{run_catalog_watcher, DEFAULT_DEBOUNCE_MS}; @@ -361,7 +361,7 @@ fn catalog_to_profiles( )> { #[cfg(feature = "catalog")] { - let db_path = Path::new(current_root).join(CATALOG_DB_NAME); + let db_path = resolve_catalog_path(Path::new(current_root)); if !db_path.exists() { return None; } @@ -418,8 +418,7 @@ impl App { table_state.select(Some(0)); } #[cfg(feature = "watch")] - let catalog_watcher = Path::new(¤t_root) - .join(CATALOG_DB_NAME) + let catalog_watcher = resolve_catalog_path(Path::new(¤t_root)) .exists() .then(|| { run_catalog_watcher( @@ -942,7 +941,7 @@ impl App { } self.refresh_filtered(); #[cfg(feature = "watch")] - if Path::new(&self.current_root).join(CATALOG_DB_NAME).exists() { + if resolve_catalog_path(Path::new(&self.current_root)).exists() { let (tx, rx) = mpsc::channel(); let tx_end = tx.clone(); let on_start = Box::new(move || { @@ -1178,7 +1177,7 @@ impl App { pub fn reload_from_catalog(&mut self) { #[cfg(feature = "catalog")] { - let db_path = Path::new(&self.current_root).join(CATALOG_DB_NAME); + let db_path = resolve_catalog_path(Path::new(&self.current_root)); if !db_path.exists() { return; } @@ -1320,8 +1319,8 @@ impl App { Ok(()) => { self.app_error = None; if let Some(path) = self.pending_ingest_path.take() { - let db_path = path.join(CATALOG_DB_NAME); - let file_count = catalog_file_count(&db_path).ok(); + let db_path = resolve_catalog_path(&path); + let file_count = catalog_file_count(&db_path, Some(&path)).ok(); let _ = register_beamtime(&path, file_count); if self.screen == Screen::Launcher { let beamtimes = list_beamtimes().unwrap_or_default(); @@ -1342,8 +1341,8 @@ impl App { self.set_status("Catalog indexed.".to_string(), false); } else { self.reload_from_catalog(); - let db_path = Path::new(&self.current_root).join(CATALOG_DB_NAME); - let file_count = catalog_file_count(&db_path).ok(); + let db_path = resolve_catalog_path(Path::new(&self.current_root)); + let file_count = catalog_file_count(&db_path, Some(Path::new(&self.current_root))).ok(); let _ = register_beamtime(Path::new(&self.current_root), file_count); self.set_status("Catalog indexed.".to_string(), false); } @@ -1462,21 +1461,21 @@ impl App { let len = self.samples.len(); if len > 0 { let i = self.sample_state.selected().unwrap_or(0); - self.sample_state.select(Some((i + 1) % len)); + self.sample_state.select(Some(cmp::min(i + 1, len - 1))); } } Focus::TagList => { let len = self.tags.len(); if len > 0 { let i = self.tag_state.selected().unwrap_or(0); - self.tag_state.select(Some((i + 1) % len)); + self.tag_state.select(Some(cmp::min(i + 1, len - 1))); } } Focus::ScanList => { let len = self.scans.len(); if len > 0 { let i = self.scan_list_state.selected().unwrap_or(0); - self.scan_list_state.select(Some((i + 1) % len)); + self.scan_list_state.select(Some(cmp::min(i + 1, len - 1))); } } Focus::Table => { @@ -1508,7 +1507,7 @@ impl App { if len > 0 { let i = self.sample_state.selected().unwrap_or(0); self.sample_state - .select(Some(if i == 0 { len - 1 } else { i - 1 })); + .select(Some(if i == 0 { 0 } else { i - 1 })); } } Focus::TagList => { @@ -1516,7 +1515,7 @@ impl App { if len > 0 { let i = self.tag_state.selected().unwrap_or(0); self.tag_state - .select(Some(if i == 0 { len - 1 } else { i - 1 })); + .select(Some(if i == 0 { 0 } else { i - 1 })); } } Focus::ScanList => { @@ -1524,7 +1523,7 @@ impl App { if len > 0 { let i = self.scan_list_state.selected().unwrap_or(0); self.scan_list_state - .select(Some(if i == 0 { len - 1 } else { i - 1 })); + .select(Some(if i == 0 { 0 } else { i - 1 })); } } Focus::Table => { @@ -1631,7 +1630,7 @@ impl App { if !self.has_catalog { return; } - let db_path = Path::new(&self.current_root).join(CATALOG_DB_NAME); + let db_path = resolve_catalog_path(Path::new(&self.current_root)); if !db_path.exists() { return; } diff --git a/src/tui/ui.rs b/src/tui/ui.rs index 81859e9..7fbecc6 100644 --- a/src/tui/ui.rs +++ b/src/tui/ui.rs @@ -41,6 +41,32 @@ fn layout_constraints(app: &App) -> [Constraint; 2] { } } +const MIN_LIST_HEIGHT: u16 = 3; +const SIDEBAR_GAPS: u16 = 2; + +fn left_sidebar_list_heights( + left_area_height: u16, + n_sample: usize, + n_tag: usize, + n_scan: usize, +) -> (u16, u16, u16) { + let available = left_area_height.saturating_sub(SIDEBAR_GAPS); + let p_s = (n_sample as u16).saturating_add(2).max(MIN_LIST_HEIGHT); + let p_t = (n_tag as u16).saturating_add(2).max(MIN_LIST_HEIGHT); + let p_sc = (n_scan as u16).saturating_add(2).max(MIN_LIST_HEIGHT); + if p_s.saturating_add(p_t).saturating_add(p_sc) <= available { + (p_s, p_t, p_sc) + } else { + let base = available / 3; + let third = available.saturating_sub(2 * base); + ( + base.max(MIN_LIST_HEIGHT), + base.max(MIN_LIST_HEIGHT), + third.max(MIN_LIST_HEIGHT), + ) + } +} + pub fn beamtime_body_rects(body_area: Rect, app: &App) -> Option { if !app.has_catalog { return None; @@ -52,14 +78,20 @@ pub fn beamtime_body_rects(body_area: Rect, app: &App) -> Option Date: Sun, 22 Mar 2026 12:00:35 -0700 Subject: [PATCH 06/32] docs: update TUI explorer redesign specifications - Added details on thread safety and concurrency for `CatalogHandle` and `BeamtimeState`, clarifying the handling of background ingest threads and their interaction with the main event-loop. - Revised the conditions for marking experimentalists as `ExptPolicy::Auto`, ensuring a minimum folder count for auto-resolution. - Enhanced the description of the ingest process, including cancellation behavior and partial write visibility during ingestion. - Updated the parameter documentation for `ingest_beamtime` to reflect new options while maintaining backward compatibility. --- .../specs/2026-03-22-tui-explorer-redesign.md | 22 +++++++++++++------ 1 file changed, 15 insertions(+), 7 deletions(-) diff --git a/docs/superpowers/specs/2026-03-22-tui-explorer-redesign.md b/docs/superpowers/specs/2026-03-22-tui-explorer-redesign.md index 7467c2c..b07bd66 100644 --- a/docs/superpowers/specs/2026-03-22-tui-explorer-redesign.md +++ b/docs/superpowers/specs/2026-03-22-tui-explorer-redesign.md @@ -141,6 +141,8 @@ impl CatalogHandle { If `DATA/.pyref/catalog.db` does not exist, all read methods return empty results; the file is created on first ingest. +**Thread safety:** `CatalogHandle` is used exclusively from the main event-loop thread. Background ingest threads open their own direct `rusqlite::Connection` via `open_catalog_db()` — they do not go through `CatalogHandle`. This avoids shared-connection races without introducing `Arc>`. The SQLite WAL mode (already set by `open_catalog_db`) allows concurrent reads during an active write. + --- ## 6. Explorer Screen @@ -202,7 +204,7 @@ pub enum ExptResolution { Resolved, NeedsResolution, Ignored } | `r` | Resolve selected experimentalist | | `x` | Toggle ignore on selected folder | | `i` | Ingest selected beamtime | -| `I` | Ingest all not-yet-indexed beamtimes | +| `I` | Ingest all not-yet-indexed beamtimes _(deferred — future work, see §13)_ | | `/` | Filter entries | | `s` | Compute disk size for selection | | `q` | Quit | @@ -240,7 +242,9 @@ On Explorer startup, `detect_policy(data_root)` performs a shallow scan (top 2 l (?i)beamtime // contains "beamtime" ``` -If ≥50% of depth-2 folders under a given experimentalist match any pattern, that experimentalist is marked `ExptPolicy::Auto` (resolved). Below 50% → `ExptResolution::NeedsResolution` (flagged with `⚑`). +If **(≥50% of depth-2 folders match any pattern) AND (at least 2 folders exist at depth 2)**, the experimentalist is marked `ExptPolicy::Auto` (resolved). Otherwise → `ExptResolution::NeedsResolution` (flagged with `⚑`). + +The `≥2` floor prevents a single folder named `run001` from auto-resolving with a pattern it happened to match — a lab with only one beamtime so far should still go through the modal so the user consciously confirms the layout. Experimentalists with zero depth-2 subdirectories are always flagged. Folders at depth 1 whose names start with `.` are silently excluded and never shown. @@ -304,7 +308,11 @@ When the user enters a beamtime with `CatalogStatus::NotIndexed` or `CatalogStat 4. On completion: - `Ok(())` → `BeamtimeState::reload_from_catalog()` queries the DATA catalog. A lightweight message is sent back to the underlying `ExplorerState` to update the status cell to `Indexed`. - `Err(e)` → error modal shown; `←` returns to Explorer. -5. **Esc during ingest** sets the cancel `AtomicBool`, waits for the thread to drain, pops the `BeamtimeState`, and returns to the Explorer. Partial writes remain in the catalog and are resumed on next entry. +5. **Esc during ingest** sets the cancel `AtomicBool` to `true`. The ingest thread checks it between files and exits its loop. The `BeamtimeState` is popped immediately (the thread is not joined — it exits naturally). Partial writes remain in the catalog; on next entry `incremental: true` skips already-indexed files, so the ingest resumes from where it stopped. + +**Concurrency:** Only one ingest thread per `BeamtimeState`. Because each `BeamtimeState` is an independent entry on the Navigator stack, navigating back to the Explorer and then into a different beamtime creates a fresh `BeamtimeState` with its own ingest thread. The abandoned thread from the previous `BeamtimeState` completes or cancels independently. Multiple simultaneous ingests are therefore possible but each targets its own beamtime path — no coordination is needed beyond SQLite WAL serialisation at the write layer. + +**Partial write visibility:** `BeamtimeState` only calls `reload_from_catalog()` once the ingest thread sends `Ok(())`. Profiles are not shown incrementally during ingest — the spinner + progress bar are the only UI during that phase. **Stale** beamtimes use the same path as `NotIndexed`. Staleness is determined by comparing the directory's most recent file `mtime` against `bt_beamtimes.last_indexed_at`. @@ -320,13 +328,13 @@ pub fn ingest_beamtime( data_root: Option<&Path>, // NEW — None = legacy behaviour experimentalist: Option<&str>, // NEW — None = legacy behaviour header_items: &[String], - force: bool, + incremental: bool, // unchanged semantics: true = skip already-indexed files progress_tx: Option>, - cancel: Option>, // NEW -) -> Result + cancel: Option>, // NEW — None = no cancellation +) -> Result // return type unchanged ``` -`data_root: None` and `experimentalist: None` reproduce the current behaviour exactly — the catalog is opened at the beamtime directory, no new columns are written. All existing callers pass `None` until individually migrated. +`data_root: None` and `experimentalist: None` reproduce the current behaviour exactly — the catalog is opened at the beamtime directory, no new columns are written. All existing callers pass `None` for the two new parameters and `None` for `cancel` until individually migrated. The `incremental` parameter retains its existing meaning: `true` skips FITS files whose `mtime` has not changed since last ingest. ### 10.2 Catalog Path Resolution From 834a157af7b28493297247274dad55f8120530d3 Mon Sep 17 00:00:00 2001 From: Harlan D Heilman <73567020+HarlanHeilman@users.noreply.github.com> Date: Sun, 22 Mar 2026 13:04:09 -0700 Subject: [PATCH 07/32] fix(tui): consolidate App struct to 8 fields per Phase 1 spec Move Launcher-specific fields from App struct to LauncherScreenState variant on the navigator stack. App now has exactly 8 fields as specified: navigator, preview_tx, beamspot_rx, preview_cmd_rx, layout, keymap, theme, needs_redraw. Changes: - Add LauncherScreenState wrapper struct with launcher and open_dir fields - Add ScreenState::Launcher variant on navigator - Remove: screen, launcher_state, open_dir_active/path/entries/list_state/focus - Add current_screen() derived from navigator.stack - Add accessor methods for launcher state and open_dir fields - Update run.rs: use current_screen() and accessor methods - Update ui.rs: use current_screen() and open_dir_active() - Update launcher methods to access state from navigator.stack - Update browser.rs: use current_screen() and accessor methods cargo check --features tui,catalog,parallel_ingest: PASS (0 errors) --- src/bin/browser.rs | 10 +- src/bin/tui/app.rs | 871 ++++++++++++++++++++++++++++----------- src/bin/tui/navigator.rs | 116 ++++++ src/bin/tui/run.rs | 51 +-- src/bin/tui/ui.rs | 33 +- 5 files changed, 804 insertions(+), 277 deletions(-) create mode 100644 src/bin/tui/navigator.rs diff --git a/src/bin/browser.rs b/src/bin/browser.rs index 4c17862..36229c9 100644 --- a/src/bin/browser.rs +++ b/src/bin/browser.rs @@ -168,19 +168,19 @@ fn main() -> Result<(), Box> { eprintln!("{}", err.report()); } - if app.screen == tui::Screen::Beamtime && !app.current_root.is_empty() { - config.set_last_root(&app.current_root); + if app.current_screen() == tui::Screen::Beamtime && !app.current_root().is_empty() { + config.set_last_root(app.current_root()); let scan_str = app.focused_scan().map(|n| n.to_string()); config.set_last_selection( app.focused_sample().as_deref(), app.focused_tag().as_deref(), scan_str.as_deref(), ); - let mut sel_samples: Vec = app.selected_samples.iter().cloned().collect(); + let mut sel_samples: Vec = app.selected_samples().iter().cloned().collect(); sel_samples.sort(); - let mut sel_tags: Vec = app.selected_tags.iter().cloned().collect(); + let mut sel_tags: Vec = app.selected_tags().iter().cloned().collect(); sel_tags.sort(); - let mut sel_scans: Vec = app.selected_scans.iter().cloned().collect(); + let mut sel_scans: Vec = app.selected_scans().iter().cloned().collect(); sel_scans.sort(); config.set_selection_export(&sel_samples, &sel_tags, &sel_scans); } diff --git a/src/bin/tui/app.rs b/src/bin/tui/app.rs index 7c96e12..cb0bb7d 100644 --- a/src/bin/tui/app.rs +++ b/src/bin/tui/app.rs @@ -6,6 +6,7 @@ use ratatui::widgets::{ListState, ScrollbarState, TableState}; use std::cmp; use std::collections::{HashMap, HashSet}; use std::fs; +use std::ops::{Deref, DerefMut}; use std::path::{Path, PathBuf}; use std::sync::mpsc; use std::thread; @@ -135,82 +136,16 @@ pub enum ScrollbarDragTarget { } pub struct App { - pub screen: Screen, - #[cfg(feature = "catalog")] - pub launcher_state: Option, - #[cfg(feature = "catalog")] - pub open_dir_active: bool, - #[cfg(feature = "catalog")] - pub open_dir_path: String, - #[cfg(feature = "catalog")] - pub open_dir_entries: Vec, - #[cfg(feature = "catalog")] - pub open_dir_list_state: ListState, - #[cfg(feature = "catalog")] - pub open_dir_focus: OpenDirFocus, - pub all_groups: Vec, - pub filtered_groups: Vec, - pub samples: Vec, - pub tags: Vec, - pub scans: Vec<(u32, String)>, - pub selected_samples: HashSet, - pub selected_tags: HashSet, - pub selected_scans: HashSet, - pub sample_state: ListState, - pub tag_state: ListState, - pub scan_list_state: ListState, - pub table_state: TableState, - pub sample_scroll_state: ScrollbarState, - pub tag_scroll_state: ScrollbarState, - pub scan_scroll_state: ScrollbarState, - pub table_scroll_state: ScrollbarState, - pub expanded_files_scroll_state: ScrollbarState, - pub table_sort_column: Option, - pub table_sort_ordering: Option, - pub expanded_table_row: Option, - pub expanded_selected_file_index: Option, - pub expanded_files_scroll_offset: usize, - pub expanded_files_sort_column: Option, - pub expanded_files_sort_ordering: Option, - pub last_expanded_files_visible: usize, + pub navigator: super::navigator::Navigator, pub preview_tx: Option)>>, #[cfg(feature = "catalog")] pub beamspot_rx: Option>, #[cfg(feature = "catalog")] pub preview_cmd_rx: Option>, - pub focus: Focus, - pub mode: AppMode, - pub current_root: String, - pub search_query: String, - pub needs_redraw: bool, pub layout: String, pub keymap: String, - #[allow(dead_code)] - pub keybind_bar_lines: u8, pub theme: String, - pub has_catalog: bool, - pub loading_state: LoadingState, - pub spinner_frame: u64, - pub ingest_rx: Option>>, - #[cfg(feature = "catalog")] - pub ingest_progress: Option<(u32, u32)>, - #[cfg(feature = "catalog")] - pub ingest_progress_rx: Option>, - #[cfg(feature = "catalog")] - pub pending_ingest_path: Option, - pub back_stack: Vec, - pub forward_stack: Vec, - pub rename_retag_buffer: String, - pub status_message: Option<(String, bool)>, - pub status_message_set_at: Option, - #[cfg(feature = "watch")] - pub catalog_watcher: Option, - #[cfg(feature = "watch")] - pub watcher_events_rx: Option>, - pub last_body_rects: Option<(Rect, BeamtimeBodyRects)>, - pub scrollbar_drag: Option, - pub app_error: Option, - pub app_warning: Option, + pub needs_redraw: bool, } #[cfg(feature = "watch")] @@ -482,6 +417,401 @@ fn catalog_to_profiles( } impl App { + fn beamtime_state(&self) -> &super::navigator::BeamtimeState { + match self.navigator.stack.last() { + Some(super::navigator::ScreenState::Beamtime(s)) => s, + _ => panic!("no beamtime state on stack"), + } + } + + fn beamtime_state_mut(&mut self) -> &mut super::navigator::BeamtimeState { + match self.navigator.stack.last_mut() { + Some(super::navigator::ScreenState::Beamtime(s)) => s, + _ => panic!("no beamtime state on stack"), + } + } + + // Pass-through getters for backward compatibility + pub fn all_groups(&self) -> &[GroupedProfileRow] { + &self.beamtime_state().all_groups + } + pub fn all_groups_mut(&mut self) -> &mut Vec { + &mut self.beamtime_state_mut().all_groups + } + pub fn filtered_groups(&self) -> &[GroupedProfileRow] { + &self.beamtime_state().filtered_groups + } + pub fn filtered_groups_mut(&mut self) -> &mut Vec { + &mut self.beamtime_state_mut().filtered_groups + } + pub fn samples(&self) -> &[String] { + &self.beamtime_state().samples + } + pub fn samples_mut(&mut self) -> &mut Vec { + &mut self.beamtime_state_mut().samples + } + pub fn tags(&self) -> &[String] { + &self.beamtime_state().tags + } + pub fn tags_mut(&mut self) -> &mut Vec { + &mut self.beamtime_state_mut().tags + } + pub fn scans(&self) -> &[(u32, String)] { + &self.beamtime_state().scans + } + pub fn scans_mut(&mut self) -> &mut Vec<(u32, String)> { + &mut self.beamtime_state_mut().scans + } + pub fn selected_samples(&self) -> &HashSet { + &self.beamtime_state().selected_samples + } + pub fn selected_samples_mut(&mut self) -> &mut HashSet { + &mut self.beamtime_state_mut().selected_samples + } + pub fn selected_tags(&self) -> &HashSet { + &self.beamtime_state().selected_tags + } + pub fn selected_tags_mut(&mut self) -> &mut HashSet { + &mut self.beamtime_state_mut().selected_tags + } + pub fn selected_scans(&self) -> &HashSet { + &self.beamtime_state().selected_scans + } + pub fn selected_scans_mut(&mut self) -> &mut HashSet { + &mut self.beamtime_state_mut().selected_scans + } + pub fn sample_state(&self) -> &ListState { + &self.beamtime_state().sample_state + } + pub fn sample_state_mut(&mut self) -> &mut ListState { + &mut self.beamtime_state_mut().sample_state + } + pub fn tag_state(&self) -> &ListState { + &self.beamtime_state().tag_state + } + pub fn tag_state_mut(&mut self) -> &mut ListState { + &mut self.beamtime_state_mut().tag_state + } + pub fn scan_list_state(&self) -> &ListState { + &self.beamtime_state().scan_list_state + } + pub fn scan_list_state_mut(&mut self) -> &mut ListState { + &mut self.beamtime_state_mut().scan_list_state + } + pub fn table_state(&self) -> &TableState { + &self.beamtime_state().table_state + } + pub fn table_state_mut(&mut self) -> &mut TableState { + &mut self.beamtime_state_mut().table_state + } + pub fn sample_scroll_state(&self) -> &ScrollbarState { + &self.beamtime_state().sample_scroll_state + } + pub fn sample_scroll_state_mut(&mut self) -> &mut ScrollbarState { + &mut self.beamtime_state_mut().sample_scroll_state + } + pub fn tag_scroll_state(&self) -> &ScrollbarState { + &self.beamtime_state().tag_scroll_state + } + pub fn tag_scroll_state_mut(&mut self) -> &mut ScrollbarState { + &mut self.beamtime_state_mut().tag_scroll_state + } + pub fn scan_scroll_state(&self) -> &ScrollbarState { + &self.beamtime_state().scan_scroll_state + } + pub fn scan_scroll_state_mut(&mut self) -> &mut ScrollbarState { + &mut self.beamtime_state_mut().scan_scroll_state + } + pub fn table_scroll_state(&self) -> &ScrollbarState { + &self.beamtime_state().table_scroll_state + } + pub fn table_scroll_state_mut(&mut self) -> &mut ScrollbarState { + &mut self.beamtime_state_mut().table_scroll_state + } + pub fn expanded_files_scroll_state(&self) -> &ScrollbarState { + &self.beamtime_state().expanded_files_scroll_state + } + pub fn expanded_files_scroll_state_mut(&mut self) -> &mut ScrollbarState { + &mut self.beamtime_state_mut().expanded_files_scroll_state + } + pub fn table_sort_column(&self) -> Option { + self.beamtime_state().table_sort_column + } + pub fn set_table_sort_column(&mut self, col: Option) { + self.beamtime_state_mut().table_sort_column = col; + } + pub fn table_sort_ordering(&self) -> Option { + self.beamtime_state().table_sort_ordering + } + pub fn set_table_sort_ordering(&mut self, ord: Option) { + self.beamtime_state_mut().table_sort_ordering = ord; + } + pub fn expanded_table_row(&self) -> Option { + self.beamtime_state().expanded_table_row + } + pub fn set_expanded_table_row(&mut self, row: Option) { + self.beamtime_state_mut().expanded_table_row = row; + } + pub fn expanded_selected_file_index(&self) -> Option { + self.beamtime_state().expanded_selected_file_index + } + pub fn set_expanded_selected_file_index(&mut self, idx: Option) { + self.beamtime_state_mut().expanded_selected_file_index = idx; + } + pub fn expanded_files_scroll_offset(&self) -> usize { + self.beamtime_state().expanded_files_scroll_offset + } + pub fn set_expanded_files_scroll_offset(&mut self, offset: usize) { + self.beamtime_state_mut().expanded_files_scroll_offset = offset; + } + pub fn expanded_files_sort_column(&self) -> Option { + self.beamtime_state().expanded_files_sort_column + } + pub fn set_expanded_files_sort_column(&mut self, col: Option) { + self.beamtime_state_mut().expanded_files_sort_column = col; + } + pub fn expanded_files_sort_ordering(&self) -> Option { + self.beamtime_state().expanded_files_sort_ordering + } + pub fn set_expanded_files_sort_ordering(&mut self, ord: Option) { + self.beamtime_state_mut().expanded_files_sort_ordering = ord; + } + pub fn last_expanded_files_visible(&self) -> usize { + self.beamtime_state().last_expanded_files_visible + } + pub fn set_last_expanded_files_visible(&mut self, visible: usize) { + self.beamtime_state_mut().last_expanded_files_visible = visible; + } + pub fn focus(&self) -> Focus { + self.beamtime_state().focus + } + pub fn set_focus(&mut self, focus: Focus) { + self.beamtime_state_mut().focus = focus; + } + pub fn mode(&self) -> AppMode { + self.beamtime_state().mode + } + pub fn set_mode(&mut self, mode: AppMode) { + self.beamtime_state_mut().mode = mode; + } + pub fn current_root(&self) -> &str { + &self.beamtime_state().current_root + } + pub fn current_root_mut(&mut self) -> &mut String { + &mut self.beamtime_state_mut().current_root + } + pub fn search_query(&self) -> &str { + &self.beamtime_state().search_query + } + pub fn search_query_mut(&mut self) -> &mut String { + &mut self.beamtime_state_mut().search_query + } + pub fn has_catalog(&self) -> bool { + self.beamtime_state().has_catalog + } + pub fn set_has_catalog(&mut self, has: bool) { + self.beamtime_state_mut().has_catalog = has; + } + pub fn loading_state(&self) -> LoadingState { + self.beamtime_state().loading_state + } + pub fn set_loading_state(&mut self, state: LoadingState) { + self.beamtime_state_mut().loading_state = state; + } + pub fn spinner_frame(&self) -> u64 { + self.beamtime_state().spinner_frame + } + pub fn set_spinner_frame(&mut self, frame: u64) { + self.beamtime_state_mut().spinner_frame = frame; + } + pub fn ingest_rx(&self) -> &Option>> { + &self.beamtime_state().ingest_rx + } + pub fn ingest_rx_mut(&mut self) -> &mut Option>> { + &mut self.beamtime_state_mut().ingest_rx + } + #[cfg(feature = "catalog")] + pub fn ingest_progress(&self) -> Option<(u32, u32)> { + self.beamtime_state().ingest_progress + } + #[cfg(feature = "catalog")] + pub fn set_ingest_progress(&mut self, progress: Option<(u32, u32)>) { + self.beamtime_state_mut().ingest_progress = progress; + } + #[cfg(feature = "catalog")] + pub fn ingest_progress_rx(&self) -> &Option> { + &self.beamtime_state().ingest_progress_rx + } + #[cfg(feature = "catalog")] + pub fn ingest_progress_rx_mut(&mut self) -> &mut Option> { + &mut self.beamtime_state_mut().ingest_progress_rx + } + #[cfg(feature = "catalog")] + pub fn pending_ingest_path(&self) -> &Option { + &self.beamtime_state().pending_ingest_path + } + #[cfg(feature = "catalog")] + pub fn pending_ingest_path_mut(&mut self) -> &mut Option { + &mut self.beamtime_state_mut().pending_ingest_path + } + pub fn back_stack(&self) -> &[String] { + &self.beamtime_state().back_stack + } + pub fn back_stack_mut(&mut self) -> &mut Vec { + &mut self.beamtime_state_mut().back_stack + } + pub fn forward_stack(&self) -> &[String] { + &self.beamtime_state().forward_stack + } + pub fn forward_stack_mut(&mut self) -> &mut Vec { + &mut self.beamtime_state_mut().forward_stack + } + pub fn rename_retag_buffer(&self) -> &str { + &self.beamtime_state().rename_retag_buffer + } + pub fn rename_retag_buffer_mut(&mut self) -> &mut String { + &mut self.beamtime_state_mut().rename_retag_buffer + } + pub fn status_message(&self) -> &Option<(String, bool)> { + &self.beamtime_state().status_message + } + pub fn status_message_mut(&mut self) -> &mut Option<(String, bool)> { + &mut self.beamtime_state_mut().status_message + } + pub fn status_message_set_at(&self) -> &Option { + &self.beamtime_state().status_message_set_at + } + pub fn status_message_set_at_mut(&mut self) -> &mut Option { + &mut self.beamtime_state_mut().status_message_set_at + } + #[cfg(feature = "watch")] + pub fn catalog_watcher(&self) -> &Option { + &self.beamtime_state().catalog_watcher + } + #[cfg(feature = "watch")] + pub fn catalog_watcher_mut(&mut self) -> &mut Option { + &mut self.beamtime_state_mut().catalog_watcher + } + #[cfg(feature = "watch")] + pub fn watcher_events_rx(&self) -> &Option> { + &self.beamtime_state().watcher_events_rx + } + #[cfg(feature = "watch")] + pub fn watcher_events_rx_mut(&mut self) -> &mut Option> { + &mut self.beamtime_state_mut().watcher_events_rx + } + pub fn last_body_rects(&self) -> &Option<(Rect, BeamtimeBodyRects)> { + &self.beamtime_state().last_body_rects + } + pub fn last_body_rects_mut(&mut self) -> &mut Option<(Rect, BeamtimeBodyRects)> { + &mut self.beamtime_state_mut().last_body_rects + } + pub fn scrollbar_drag(&self) -> &Option { + &self.beamtime_state().scrollbar_drag + } + pub fn scrollbar_drag_mut(&mut self) -> &mut Option { + &mut self.beamtime_state_mut().scrollbar_drag + } + pub fn app_error(&self) -> &Option { + &self.beamtime_state().app_error + } + pub fn app_error_mut(&mut self) -> &mut Option { + &mut self.beamtime_state_mut().app_error + } + pub fn app_warning(&self) -> &Option { + &self.beamtime_state().app_warning + } + pub fn app_warning_mut(&mut self) -> &mut Option { + &mut self.beamtime_state_mut().app_warning + } + + pub fn current_screen(&self) -> Screen { + match self.navigator.stack.last() { + #[cfg(feature = "catalog")] + Some(super::navigator::ScreenState::Launcher(_)) => Screen::Launcher, + _ => Screen::Beamtime, + } + } + + #[cfg(feature = "catalog")] + fn launcher_screen_state_mut(&mut self) -> &mut super::navigator::LauncherScreenState { + match self.navigator.stack.last_mut() { + Some(super::navigator::ScreenState::Launcher(s)) => s, + _ => panic!("expected launcher state on stack"), + } + } + + #[cfg(feature = "catalog")] + pub fn open_dir_active(&self) -> bool { + match self.navigator.stack.last() { + Some(super::navigator::ScreenState::Launcher(s)) => s.open_dir_active, + _ => false, + } + } + + #[cfg(feature = "catalog")] + pub fn launcher_state(&self) -> Option<&LauncherState> { + match self.navigator.stack.last() { + Some(super::navigator::ScreenState::Launcher(s)) => s.launcher_state.as_ref(), + _ => None, + } + } + + #[cfg(feature = "catalog")] + pub fn launcher_state_mut(&mut self) -> Option<&mut LauncherState> { + match self.navigator.stack.last_mut() { + Some(super::navigator::ScreenState::Launcher(s)) => s.launcher_state.as_mut(), + _ => None, + } + } + + #[cfg(feature = "catalog")] + pub fn open_dir_path(&self) -> &str { + match self.navigator.stack.last() { + Some(super::navigator::ScreenState::Launcher(s)) => &s.open_dir_path, + _ => "", + } + } + + #[cfg(feature = "catalog")] + pub fn open_dir_entries(&self) -> &[PathBuf] { + match self.navigator.stack.last() { + Some(super::navigator::ScreenState::Launcher(s)) => &s.open_dir_entries, + _ => &[], + } + } + + #[cfg(feature = "catalog")] + pub fn open_dir_list_state(&self) -> &ListState { + match self.navigator.stack.last() { + Some(super::navigator::ScreenState::Launcher(s)) => &s.open_dir_list_state, + _ => panic!("no launcher state"), + } + } + + #[cfg(feature = "catalog")] + pub fn open_dir_list_state_mut(&mut self) -> &mut ListState { + match self.navigator.stack.last_mut() { + Some(super::navigator::ScreenState::Launcher(s)) => &mut s.open_dir_list_state, + _ => panic!("no launcher state"), + } + } + + #[cfg(feature = "catalog")] + pub fn open_dir_focus(&self) -> OpenDirFocus { + match self.navigator.stack.last() { + Some(super::navigator::ScreenState::Launcher(s)) => s.open_dir_focus, + _ => OpenDirFocus::PathInput, + } + } + + #[cfg(feature = "catalog")] + pub fn set_open_dir_focus(&mut self, focus: OpenDirFocus) { + if let Some(super::navigator::ScreenState::Launcher(s)) = self.navigator.stack.last_mut() { + s.open_dir_focus = focus; + } + } + pub fn new( current_root: String, layout: String, @@ -532,20 +862,8 @@ impl App { ) }) .and_then(Result::ok); - App { - screen: Screen::Beamtime, - #[cfg(feature = "catalog")] - launcher_state: None, - #[cfg(feature = "catalog")] - open_dir_active: false, - #[cfg(feature = "catalog")] - open_dir_path: String::new(), - #[cfg(feature = "catalog")] - open_dir_entries: vec![], - #[cfg(feature = "catalog")] - open_dir_list_state: ListState::default(), - #[cfg(feature = "catalog")] - open_dir_focus: OpenDirFocus::PathInput, + + let beamtime_state = super::navigator::BeamtimeState { all_groups: all_groups_clone, filtered_groups: filtered, samples, @@ -571,20 +889,10 @@ impl App { expanded_files_sort_column: Some(1), expanded_files_sort_ordering: Some(cmp::Ordering::Less), last_expanded_files_visible: 5, - preview_tx: None, - #[cfg(feature = "catalog")] - beamspot_rx: None, - #[cfg(feature = "catalog")] - preview_cmd_rx: None, focus: Focus::SampleList, mode: AppMode::Normal, current_root, search_query: String::new(), - needs_redraw: true, - layout, - keymap, - keybind_bar_lines, - theme, has_catalog, loading_state: LoadingState::Idle, spinner_frame: 0, @@ -608,6 +916,28 @@ impl App { scrollbar_drag: None, app_error: None, app_warning: None, + keybind_bar_lines, + data_root: None, + experimentalist: None, + beamtime_path: PathBuf::new(), + }; + + App { + navigator: super::navigator::Navigator::new( + beamtime_state, + super::catalog_handle::CatalogHandle { + db_path: PathBuf::new(), + }, + ), + preview_tx: None, + #[cfg(feature = "catalog")] + beamspot_rx: None, + #[cfg(feature = "catalog")] + preview_cmd_rx: None, + layout, + keymap, + theme, + needs_redraw: true, } } @@ -627,14 +957,8 @@ impl App { beamtimes, list_state, }; - App { - screen: Screen::Launcher, - launcher_state: Some(launcher_state), - open_dir_active: false, - open_dir_path: String::new(), - open_dir_entries: vec![], - open_dir_list_state: ListState::default(), - open_dir_focus: OpenDirFocus::PathInput, + + let beamtime_state = super::navigator::BeamtimeState { all_groups: vec![], filtered_groups: vec![], samples: vec![], @@ -660,26 +984,19 @@ impl App { expanded_files_sort_column: Some(1), expanded_files_sort_ordering: Some(cmp::Ordering::Less), last_expanded_files_visible: 5, - preview_tx: None, - #[cfg(feature = "catalog")] - beamspot_rx: None, - #[cfg(feature = "catalog")] - preview_cmd_rx: None, focus: Focus::SampleList, mode: AppMode::Normal, current_root: String::new(), search_query: String::new(), - needs_redraw: true, - layout, - keymap, - keybind_bar_lines, - theme, has_catalog: false, loading_state: LoadingState::Idle, spinner_frame: 0, ingest_rx: None, + #[cfg(feature = "catalog")] ingest_progress: None, + #[cfg(feature = "catalog")] ingest_progress_rx: None, + #[cfg(feature = "catalog")] pending_ingest_path: None, back_stack: vec![], forward_stack: vec![], @@ -694,74 +1011,111 @@ impl App { scrollbar_drag: None, app_error: None, app_warning: None, + keybind_bar_lines, + data_root: None, + experimentalist: None, + beamtime_path: PathBuf::new(), + }; + + let mut navigator = super::navigator::Navigator::new( + beamtime_state, + super::catalog_handle::CatalogHandle { + db_path: PathBuf::new(), + }, + ); + navigator.stack.push(super::navigator::ScreenState::Launcher( + super::navigator::LauncherScreenState { + launcher_state: Some(launcher_state), + open_dir_active: false, + open_dir_path: String::new(), + open_dir_entries: vec![], + open_dir_list_state: ListState::default(), + open_dir_focus: OpenDirFocus::PathInput, + }, + )); + + App { + navigator, + preview_tx: None, + #[cfg(feature = "catalog")] + beamspot_rx: None, + #[cfg(feature = "catalog")] + preview_cmd_rx: None, + layout, + keymap, + theme, + needs_redraw: true, } } pub fn set_app_error(&mut self, e: super::error::TuiError) { - self.app_error = Some(e); + *self.app_error_mut() = Some(e); self.needs_redraw = true; } pub fn clear_app_error(&mut self) { - self.app_error = None; + *self.app_error_mut() = None; self.needs_redraw = true; } pub fn set_app_warning(&mut self, e: super::error::TuiError) { - self.app_warning = Some(e); + *self.app_warning_mut() = Some(e); self.needs_redraw = true; } pub fn clear_app_warning(&mut self) { - self.app_warning = None; + *self.app_warning_mut() = None; self.needs_redraw = true; } pub fn set_status(&mut self, msg: String, is_error: bool) { - self.status_message = Some((msg, is_error)); - self.status_message_set_at = Some(Instant::now()); + *self.status_message_mut() = Some((msg, is_error)); + *self.status_message_set_at_mut() = Some(Instant::now()); } pub fn clear_status_if_stale(&mut self) { const STATUS_TTL_SECS: u64 = 3; - if let (Some(_), Some(at)) = (self.status_message.as_ref(), self.status_message_set_at) { + if let (Some(_), Some(at)) = (self.status_message().as_ref(), *self.status_message_set_at()) { if at.elapsed().as_secs() >= STATUS_TTL_SECS { - self.status_message = None; - self.status_message_set_at = None; + *self.status_message_mut() = None; + *self.status_message_set_at_mut() = None; self.needs_redraw = true; } } } + #[cfg(feature = "catalog")] #[cfg(feature = "catalog")] pub fn launcher_list_down(&mut self) { - if let Some(ref mut s) = self.launcher_state { - let n = s.beamtimes.len(); + let s = self.launcher_screen_state_mut(); + if let Some(ref mut state) = s.launcher_state { + let n = state.beamtimes.len(); if n == 0 { return; } - let next = s + let next = state .list_state .selected() .map(|i| (i + 1).min(n.saturating_sub(1))) .unwrap_or(0); - s.list_state.select(Some(next)); + state.list_state.select(Some(next)); } } #[cfg(feature = "catalog")] pub fn launcher_list_up(&mut self) { - if let Some(ref mut s) = self.launcher_state { - let n = s.beamtimes.len(); + let s = self.launcher_screen_state_mut(); + if let Some(ref mut state) = s.launcher_state { + let n = state.beamtimes.len(); if n == 0 { return; } - let next = s + let next = state .list_state .selected() .map(|i| i.saturating_sub(1)) .unwrap_or(0); - s.list_state.select(Some(next)); + state.list_state.select(Some(next)); } } @@ -772,28 +1126,36 @@ impl App { if !beamtimes.is_empty() { list_state.select(Some(0)); } - self.launcher_state = Some(LauncherState { - beamtimes, - list_state, - }); - self.screen = Screen::Launcher; + self.navigator.stack.push(super::navigator::ScreenState::Launcher( + super::navigator::LauncherScreenState { + launcher_state: Some(LauncherState { + beamtimes, + list_state, + }), + open_dir_active: false, + open_dir_path: String::new(), + open_dir_entries: vec![], + open_dir_list_state: ListState::default(), + open_dir_focus: OpenDirFocus::PathInput, + }, + )); #[cfg(feature = "watch")] { - self.catalog_watcher = None; - self.watcher_events_rx = None; + *self.catalog_watcher_mut() = None; + *self.watcher_events_rx_mut() = None; } self.needs_redraw = true; } #[cfg(feature = "catalog")] pub fn launcher_open_selected(&mut self) { - if let Some(ref mut state) = self.launcher_state { + let s = self.launcher_screen_state_mut(); + if let Some(ref mut state) = s.launcher_state { if let Some(i) = state.list_state.selected() { if let Some((path, _)) = state.beamtimes.get(i) { let root = path.to_string_lossy().into_owned(); if self.set_root(root) { - self.screen = Screen::Beamtime; - self.launcher_state = None; + self.navigator.stack.pop(); } } } @@ -802,16 +1164,18 @@ impl App { #[cfg(feature = "catalog")] pub fn launcher_open_directory(&mut self) { - self.open_dir_active = true; - self.open_dir_path = std::env::var("HOME").unwrap_or_else(|_| "/".to_string()); - self.open_dir_focus = OpenDirFocus::List; + let s = self.launcher_screen_state_mut(); + s.open_dir_active = true; + s.open_dir_path = std::env::var("HOME").unwrap_or_else(|_| "/".to_string()); + s.open_dir_focus = OpenDirFocus::List; self.refresh_open_dir_entries(); } #[cfg(feature = "catalog")] pub fn refresh_open_dir_entries(&mut self) { - self.open_dir_entries.clear(); - let path = Path::new(&self.open_dir_path); + let s = self.launcher_screen_state_mut(); + s.open_dir_entries.clear(); + let path = Path::new(&s.open_dir_path); if path.exists() && path.is_dir() { if let Ok(rd) = std::fs::read_dir(path) { let mut dirs: Vec = rd @@ -820,106 +1184,115 @@ impl App { .map(|e| e.path()) .collect(); dirs.sort_by(|a, b| a.file_name().cmp(&b.file_name())); - self.open_dir_entries = dirs; + s.open_dir_entries = dirs; } } - self.open_dir_list_state = ListState::default(); - if !self.open_dir_entries.is_empty() || path.parent().is_some() { - self.open_dir_list_state.select(Some(0)); + s.open_dir_list_state = ListState::default(); + if !s.open_dir_entries.is_empty() || path.parent().is_some() { + s.open_dir_list_state.select(Some(0)); } self.needs_redraw = true; } #[cfg(feature = "catalog")] pub fn open_dir_cancel(&mut self) { - self.open_dir_active = false; + let s = self.launcher_screen_state_mut(); + s.open_dir_active = false; self.needs_redraw = true; } #[cfg(feature = "catalog")] pub fn open_dir_list_enter(&mut self) { - let n = 1 + self.open_dir_entries.len(); + let s = self.launcher_screen_state_mut(); + let n = 1 + s.open_dir_entries.len(); if n == 0 { return; } - let i = self.open_dir_list_state.selected().unwrap_or(0); + let i = s.open_dir_list_state.selected().unwrap_or(0); if i == 0 { - if let Some(parent) = Path::new(&self.open_dir_path).parent() { - self.open_dir_path = parent.to_string_lossy().into_owned(); + if let Some(parent) = Path::new(&s.open_dir_path).parent() { + s.open_dir_path = parent.to_string_lossy().into_owned(); self.refresh_open_dir_entries(); } - } else if let Some(p) = self.open_dir_entries.get(i - 1) { + } else if let Some(p) = s.open_dir_entries.get(i - 1) { let name = p.file_name().and_then(|n| n.to_str()).unwrap_or(""); if name.is_empty() { return; } - let sep = if self.open_dir_path.ends_with('/') { + let sep = if s.open_dir_path.ends_with('/') { "" } else { "/" }; - self.open_dir_path = format!("{}{}{}", self.open_dir_path, sep, name); + s.open_dir_path = format!("{}{}{}", s.open_dir_path, sep, name); self.refresh_open_dir_entries(); } } #[cfg(feature = "catalog")] pub fn open_dir_list_down(&mut self) { - let n = 1 + self.open_dir_entries.len(); + let s = self.launcher_screen_state_mut(); + let n = 1 + s.open_dir_entries.len(); if n == 0 { return; } - let next = self + let next = s .open_dir_list_state .selected() .map(|i| (i + 1).min(n.saturating_sub(1))) .unwrap_or(0); - self.open_dir_list_state.select(Some(next)); + s.open_dir_list_state.select(Some(next)); self.needs_redraw = true; } #[cfg(feature = "catalog")] pub fn open_dir_list_up(&mut self) { - let n = 1 + self.open_dir_entries.len(); + let s = self.launcher_screen_state_mut(); + let n = 1 + s.open_dir_entries.len(); if n == 0 { return; } - let next = self + let next = s .open_dir_list_state .selected() .map(|i| i.saturating_sub(1)) .unwrap_or(0); - self.open_dir_list_state.select(Some(next)); + s.open_dir_list_state.select(Some(next)); self.needs_redraw = true; } #[cfg(feature = "catalog")] pub fn open_dir_focus_toggle(&mut self) { - self.open_dir_focus = match self.open_dir_focus { + let focus = self.open_dir_focus(); + let new_focus = match focus { OpenDirFocus::PathInput => { self.refresh_open_dir_entries(); OpenDirFocus::List } OpenDirFocus::List => OpenDirFocus::PathInput, }; + self.set_open_dir_focus(new_focus); self.needs_redraw = true; } #[cfg(feature = "catalog")] pub fn open_dir_path_push(&mut self, c: char) { - self.open_dir_path.push(c); + let s = self.launcher_screen_state_mut(); + s.open_dir_path.push(c); self.needs_redraw = true; } #[cfg(feature = "catalog")] pub fn open_dir_path_pop(&mut self) { - self.open_dir_path.pop(); + let s = self.launcher_screen_state_mut(); + s.open_dir_path.pop(); self.needs_redraw = true; } #[cfg(feature = "catalog")] pub fn open_dir_autocomplete(&mut self) { - let path = Path::new(&self.open_dir_path); + let open_dir_path = self.open_dir_path().to_string(); + let path = Path::new(&open_dir_path); let (parent, prefix) = if let Some(name) = path.file_name().and_then(|n| n.to_str()) { let parent = path.parent().unwrap_or(Path::new("/")); (parent, name.to_string()) @@ -949,7 +1322,8 @@ impl App { let name = first.file_name().and_then(|n| n.to_str()).unwrap_or(""); let parent_str = parent.to_string_lossy(); let sep = if parent_str.ends_with('/') { "" } else { "/" }; - self.open_dir_path = format!("{}{}{}", parent_str, sep, name); + let s = self.launcher_screen_state_mut(); + s.open_dir_path = format!("{}{}{}", parent_str, sep, name); self.refresh_open_dir_entries(); self.needs_redraw = true; } @@ -957,7 +1331,8 @@ impl App { #[cfg(feature = "catalog")] pub fn open_dir_confirm(&mut self) -> bool { - let path = Path::new(&self.open_dir_path); + let s = self.launcher_screen_state_mut(); + let path = Path::new(&s.open_dir_path); if !path.exists() || !path.is_dir() { self.set_status( "Path does not exist or is not a directory.".to_string(), @@ -972,9 +1347,9 @@ impl App { return false; } }; - self.open_dir_active = false; + s.open_dir_active = false; let path_for_thread = canonical.clone(); - self.pending_ingest_path = Some(canonical); + *self.pending_ingest_path_mut() = Some(canonical); let header_items: Vec = DEFAULT_INGEST_HEADER_ITEMS .iter() .map(|s| (*s).to_string()) @@ -992,10 +1367,10 @@ impl App { .map_err(|e| e.to_string()); let _ = tx.send(result); }); - self.loading_state = LoadingState::IngestingDirectory; - self.ingest_rx = Some(rx); - self.ingest_progress = None; - self.ingest_progress_rx = Some(progress_rx); + self.set_loading_state(LoadingState::IngestingDirectory); + *self.ingest_rx_mut() = Some(rx); + self.set_ingest_progress(None); + *self.ingest_progress_rx_mut() = Some(progress_rx); self.needs_redraw = true; true } @@ -1120,11 +1495,12 @@ impl App { } pub fn nav_up(&mut self) { - let path = Path::new(&self.current_root); + let current_root = self.current_root.clone(); + let path = Path::new(¤t_root); if let Some(parent) = path.parent() { let parent_str = parent.to_string_lossy().into_owned(); - if parent_str != self.current_root { - self.back_stack.push(self.current_root.clone()); + if parent_str != current_root { + self.back_stack.push(current_root); self.forward_stack.clear(); if !self.set_root(parent_str) { self.set_status("Path not found or not a directory.".to_string(), true); @@ -1135,7 +1511,8 @@ impl App { pub fn nav_back(&mut self) { if let Some(prev) = self.back_stack.pop() { - self.forward_stack.push(self.current_root.clone()); + let current_root = self.current_root.clone(); + self.forward_stack.push(current_root); if !self.set_root(prev) { self.set_status("Path not found or not a directory.".to_string(), true); } @@ -1144,7 +1521,8 @@ impl App { pub fn nav_fwd(&mut self) { if let Some(next) = self.forward_stack.pop() { - self.back_stack.push(self.current_root.clone()); + let current_root = self.current_root.clone(); + self.back_stack.push(current_root); if !self.set_root(next) { self.set_status("Path not found or not a directory.".to_string(), true); } @@ -2105,59 +2483,67 @@ impl App { #[cfg(feature = "catalog")] pub fn try_recv_ingest(&mut self) { + let mut progress_updates = Vec::new(); if let Some(ref progress_rx) = self.ingest_progress_rx { while let Ok((cur, tot)) = progress_rx.try_recv() { - self.ingest_progress = Some((cur, tot)); - } - } - if let Some(ref rx) = self.ingest_rx { - if let Ok(result) = rx.try_recv() { - self.ingest_rx = None; - self.ingest_progress_rx = None; - self.ingest_progress = None; - self.loading_state = LoadingState::Idle; - match result { - Ok(()) => { - self.app_error = None; - if let Some(path) = self.pending_ingest_path.take() { - let db_path = resolve_catalog_path(&path); - let file_count = catalog_file_count(&db_path, Some(&path)).ok(); - let _ = register_beamtime(&path, file_count); - if self.screen == Screen::Launcher { - let beamtimes = list_beamtimes().unwrap_or_default(); - let mut list_state = ListState::default(); - if !beamtimes.is_empty() { - list_state.select(Some(0)); - } - self.launcher_state = Some(LauncherState { - beamtimes, - list_state, - }); + progress_updates.push((cur, tot)); + } + } + for (cur, tot) in progress_updates { + self.ingest_progress = Some((cur, tot)); + } + + let ingest_result = if let Some(ref rx) = self.ingest_rx { + rx.try_recv().ok() + } else { + None + }; + + if let Some(result) = ingest_result { + self.ingest_rx = None; + self.ingest_progress_rx = None; + self.ingest_progress = None; + self.loading_state = LoadingState::Idle; + match result { + Ok(()) => { + self.app_error = None; + if let Some(path) = self.pending_ingest_path_mut().take() { + let db_path = resolve_catalog_path(&path); + let file_count = catalog_file_count(&db_path, Some(&path)).ok(); + let _ = register_beamtime(&path, file_count); + if self.current_screen() == Screen::Launcher { + let beamtimes = list_beamtimes().unwrap_or_default(); + let mut list_state = ListState::default(); + if !beamtimes.is_empty() { + list_state.select(Some(0)); } - let root_str = path.to_string_lossy().into_owned(); - if self.set_root(root_str) { - self.screen = Screen::Beamtime; - self.launcher_state = None; + if let Some(s) = self.launcher_state_mut() { + s.beamtimes = beamtimes; + s.list_state = list_state; } - self.set_status("Catalog indexed.".to_string(), false); - } else { - self.reload_from_catalog(false); - let db_path = resolve_catalog_path(Path::new(&self.current_root)); - let file_count = catalog_file_count(&db_path, Some(Path::new(&self.current_root))).ok(); - let _ = register_beamtime(Path::new(&self.current_root), file_count); - self.set_status("Catalog indexed.".to_string(), false); } - } - Err(e) => { - let path = self - .pending_ingest_path - .take() - .unwrap_or_else(|| PathBuf::from(&self.current_root)); - self.set_app_error(super::error::TuiError::ingest_failed(path, e, None)); + let root_str = path.to_string_lossy().into_owned(); + if self.set_root(root_str) { + self.navigator.stack.pop(); + } + self.set_status("Catalog indexed.".to_string(), false); + } else { + self.reload_from_catalog(false); + let db_path = resolve_catalog_path(Path::new(&self.current_root)); + let file_count = catalog_file_count(&db_path, Some(Path::new(&self.current_root))).ok(); + let _ = register_beamtime(Path::new(&self.current_root), file_count); + self.set_status("Catalog indexed.".to_string(), false); } } - self.needs_redraw = true; + Err(e) => { + let path = self + .pending_ingest_path + .take() + .unwrap_or_else(|| PathBuf::from(&self.current_root)); + self.set_app_error(super::error::TuiError::ingest_failed(path, e, None)); + } } + self.needs_redraw = true; } } @@ -2429,11 +2815,12 @@ impl App { match self.focus { Focus::SampleList => { if let Some(i) = self.sample_state.selected() { - if let Some(s) = self.samples.get(i) { - if self.selected_samples.contains(s) { - self.selected_samples.remove(s); + let sample = self.samples.get(i).cloned(); + if let Some(s) = sample { + if self.selected_samples.contains(&s) { + self.selected_samples.remove(&s); } else { - self.selected_samples.insert(s.clone()); + self.selected_samples.insert(s); } self.refresh_filtered(); } @@ -2441,11 +2828,12 @@ impl App { } Focus::TagList => { if let Some(i) = self.tag_state.selected() { - if let Some(t) = self.tags.get(i) { - if self.selected_tags.contains(t) { - self.selected_tags.remove(t); + let tag = self.tags.get(i).cloned(); + if let Some(t) = tag { + if self.selected_tags.contains(&t) { + self.selected_tags.remove(&t); } else { - self.selected_tags.insert(t.clone()); + self.selected_tags.insert(t); } self.refresh_filtered(); } @@ -2453,11 +2841,12 @@ impl App { } Focus::ScanList => { if let Some(i) = self.scan_list_state.selected() { - if let Some((n, _)) = self.scans.get(i) { - if self.selected_scans.contains(n) { - self.selected_scans.remove(n); + let scan = self.scans.get(i).map(|(n, _)| *n); + if let Some(n) = scan { + if self.selected_scans.contains(&n) { + self.selected_scans.remove(&n); } else { - self.selected_scans.insert(*n); + self.selected_scans.insert(n); } self.refresh_filtered(); } @@ -2650,3 +3039,17 @@ impl App { self.needs_redraw = true; } } + +impl Deref for App { + type Target = super::navigator::BeamtimeState; + + fn deref(&self) -> &Self::Target { + self.beamtime_state() + } +} + +impl DerefMut for App { + fn deref_mut(&mut self) -> &mut Self::Target { + self.beamtime_state_mut() + } +} diff --git a/src/bin/tui/navigator.rs b/src/bin/tui/navigator.rs new file mode 100644 index 0000000..fcbe17f --- /dev/null +++ b/src/bin/tui/navigator.rs @@ -0,0 +1,116 @@ +use super::catalog_handle::CatalogHandle; +use super::app::{ + AppMode, BeamtimeBodyRects, Focus, GroupedProfileRow, LoadingState, OpenDirFocus, + WatcherEvent, LauncherState, +}; +use std::cmp; +use std::collections::HashSet; +use std::path::PathBuf; +use std::sync::mpsc; +use std::time::Instant; +use ratatui::layout::Rect; +use ratatui::widgets::{ListState, ScrollbarState, TableState}; + +#[cfg(feature = "watch")] +use pyref::catalog::WatchHandle; + +type BeamspotUpdate = (PathBuf, i64, i64, Option); + +/// All beamtime screen state. Moved out of App in Phase 1. +pub struct BeamtimeState { + pub all_groups: Vec, + pub filtered_groups: Vec, + pub samples: Vec, + pub tags: Vec, + pub scans: Vec<(u32, String)>, + pub selected_samples: HashSet, + pub selected_tags: HashSet, + pub selected_scans: HashSet, + pub sample_state: ListState, + pub tag_state: ListState, + pub scan_list_state: ListState, + pub table_state: TableState, + pub sample_scroll_state: ScrollbarState, + pub tag_scroll_state: ScrollbarState, + pub scan_scroll_state: ScrollbarState, + pub table_scroll_state: ScrollbarState, + pub expanded_files_scroll_state: ScrollbarState, + pub table_sort_column: Option, + pub table_sort_ordering: Option, + pub expanded_table_row: Option, + pub expanded_selected_file_index: Option, + pub expanded_files_scroll_offset: usize, + pub expanded_files_sort_column: Option, + pub expanded_files_sort_ordering: Option, + pub last_expanded_files_visible: usize, + pub focus: Focus, + pub mode: AppMode, + pub current_root: String, + pub search_query: String, + pub has_catalog: bool, + pub loading_state: LoadingState, + pub spinner_frame: u64, + pub ingest_rx: Option>>, + #[cfg(feature = "catalog")] + pub ingest_progress: Option<(u32, u32)>, + #[cfg(feature = "catalog")] + pub ingest_progress_rx: Option>, + #[cfg(feature = "catalog")] + pub pending_ingest_path: Option, + pub back_stack: Vec, + pub forward_stack: Vec, + pub rename_retag_buffer: String, + pub status_message: Option<(String, bool)>, + pub status_message_set_at: Option, + #[cfg(feature = "watch")] + pub catalog_watcher: Option, + #[cfg(feature = "watch")] + pub watcher_events_rx: Option>, + pub last_body_rects: Option<(Rect, BeamtimeBodyRects)>, + pub scrollbar_drag: Option, + pub app_error: Option, + pub app_warning: Option, + #[allow(dead_code)] + pub keybind_bar_lines: u8, + + // Context fields for future phases + pub data_root: Option, + pub experimentalist: Option, + pub beamtime_path: PathBuf, +} + +/// Phase 1 transitional launcher screen state — removed in Phase 4 +#[cfg(feature = "catalog")] +#[derive(Debug)] +pub struct LauncherScreenState { + pub launcher_state: Option, + pub open_dir_active: bool, + pub open_dir_path: String, + pub open_dir_entries: Vec, + pub open_dir_list_state: ListState, + pub open_dir_focus: OpenDirFocus, +} + +/// Screen state enum. +pub enum ScreenState { + Beamtime(BeamtimeState), + #[cfg(feature = "catalog")] + Launcher(LauncherScreenState), +} + +/// Navigation stack and catalog handle. +pub struct Navigator { + pub stack: Vec, + pub forward: Vec, + pub catalog: CatalogHandle, +} + +impl Navigator { + pub fn new(beamtime_state: BeamtimeState, catalog: CatalogHandle) -> Self { + Navigator { + stack: vec![ScreenState::Beamtime(beamtime_state)], + forward: vec![], + catalog, + } + } +} diff --git a/src/bin/tui/run.rs b/src/bin/tui/run.rs index d3325bf..421e493 100644 --- a/src/bin/tui/run.rs +++ b/src/bin/tui/run.rs @@ -26,17 +26,17 @@ fn handle_open_dir_event(app: &mut App, key: crossterm::event::KeyEvent) -> bool } if key.code == crossterm::event::KeyCode::Char('e') && key.modifiers.is_empty() - && app.open_dir_focus == OpenDirFocus::List + && app.open_dir_focus() == OpenDirFocus::List { - app.open_dir_focus = OpenDirFocus::PathInput; + app.set_open_dir_focus(OpenDirFocus::PathInput); app.needs_redraw = true; return false; } let action = keymap::from_key_event(key, &app.keymap); match action { Action::Cancel => { - if app.open_dir_focus == OpenDirFocus::PathInput { - app.open_dir_focus = OpenDirFocus::List; + if app.open_dir_focus() == OpenDirFocus::PathInput { + app.set_open_dir_focus(OpenDirFocus::List); app.needs_redraw = true; } else { app.open_dir_cancel(); @@ -44,9 +44,9 @@ fn handle_open_dir_event(app: &mut App, key: crossterm::event::KeyEvent) -> bool return false; } Action::Open => { - if app.open_dir_focus == OpenDirFocus::PathInput { + if app.open_dir_focus() == OpenDirFocus::PathInput { app.refresh_open_dir_entries(); - app.open_dir_focus = OpenDirFocus::List; + app.set_open_dir_focus(OpenDirFocus::List); } else { app.open_dir_list_enter(); } @@ -63,7 +63,7 @@ fn handle_open_dir_event(app: &mut App, key: crossterm::event::KeyEvent) -> bool _ => {} } if key.code == crossterm::event::KeyCode::Tab { - if app.open_dir_focus == OpenDirFocus::PathInput { + if app.open_dir_focus() == OpenDirFocus::PathInput { app.open_dir_autocomplete(); } else { app.open_dir_focus_toggle(); @@ -74,7 +74,7 @@ fn handle_open_dir_event(app: &mut App, key: crossterm::event::KeyEvent) -> bool app.open_dir_focus_toggle(); return false; } - if app.open_dir_focus == OpenDirFocus::PathInput { + if app.open_dir_focus() == OpenDirFocus::PathInput { if key.code == crossterm::event::KeyCode::Backspace { app.open_dir_path_pop(); } else if let crossterm::event::KeyCode::Char(c) = key.code { @@ -162,15 +162,16 @@ fn handle_mouse( _width: u16, _height: u16, ) -> bool { - if app.screen != super::app::Screen::Beamtime - || app.mode != super::app::AppMode::Normal - || app.open_dir_active + if app.current_screen() != super::app::Screen::Beamtime + || app.mode() != super::app::AppMode::Normal + || app.open_dir_active() { return false; } - let Some((_, rects)) = app.last_body_rects.as_ref() else { + let has_body_rects = app.last_body_rects.is_some(); + if !has_body_rects { return false; - }; + } let col = mouse_event.column; let row = mouse_event.row; @@ -180,13 +181,15 @@ fn handle_mouse( } } - if let Some(drag_target) = app.scrollbar_drag { + let rects = match &app.last_body_rects { + Some((_, r)) => r.clone(), + None => return false, + }; + + let drag_target_opt = app.scrollbar_drag; + if let Some(drag_target) = drag_target_opt { if let MouseEventKind::Drag(MouseButton::Left) = mouse_event.kind { let (sb_rect, content_len, viewport) = { - let rects = match &app.last_body_rects { - Some((_, r)) => r, - None => return false, - }; match drag_target { ScrollbarDragTarget::SampleList => { let r = match rects.sample_scrollbar { @@ -548,9 +551,9 @@ fn table_header_column_at(rect: Rect, col: u16) -> Option { } pub fn handle_event(app: &mut App, key: crossterm::event::KeyEvent) -> bool { - if app.screen == super::app::Screen::Launcher { + if app.current_screen() == super::app::Screen::Launcher { #[cfg(feature = "catalog")] - if app.open_dir_active { + if app.open_dir_active() { return handle_open_dir_event(app, key); } let action = keymap::from_key_event(key, &app.keymap); @@ -578,7 +581,7 @@ pub fn handle_event(app: &mut App, key: crossterm::event::KeyEvent) -> bool { return quit; } - if app.screen == super::app::Screen::Beamtime && app.mode == super::app::AppMode::Normal { + if app.current_screen() == super::app::Screen::Beamtime && app.mode() == super::app::AppMode::Normal { let action = keymap::from_key_event(key, &app.keymap); if action == Action::Cancel { if app.app_error.is_some() { @@ -603,15 +606,15 @@ pub fn handle_event(app: &mut App, key: crossterm::event::KeyEvent) -> bool { } #[cfg(feature = "catalog")] - if app.screen == super::app::Screen::Beamtime - && app.mode == super::app::AppMode::Normal + if app.current_screen() == super::app::Screen::Beamtime + && app.mode() == super::app::AppMode::Normal && keymap::from_key_event(key, &app.keymap) == Action::Cancel { app.go_to_launcher(); return false; } - if app.mode == super::app::AppMode::Search { + if app.mode() == super::app::AppMode::Search { let action = keymap::from_key_event(key, &app.keymap); match action { Action::Cancel => { diff --git a/src/bin/tui/ui.rs b/src/bin/tui/ui.rs index bfd2777..109cb36 100644 --- a/src/bin/tui/ui.rs +++ b/src/bin/tui/ui.rs @@ -161,13 +161,13 @@ pub fn render(frame: &mut Frame, app: &mut App) { let bottom_area = chunks[2]; frame.render_widget(outer_block, area); - if app.screen == Screen::Launcher { + if app.current_screen() == Screen::Launcher { #[cfg(feature = "catalog")] render_launcher_nav(frame, nav_area, app, theme); render_launcher_body(frame, app, body_area, theme); render_launcher_bottom(frame, bottom_area, theme); #[cfg(feature = "catalog")] - if app.open_dir_active { + if app.open_dir_active() { render_open_dir_modal(frame, app, inner, theme); } } else { @@ -212,7 +212,7 @@ fn render_launcher_nav(frame: &mut Frame, area: Rect, app: &App, theme: ThemeMod #[cfg(feature = "catalog")] fn render_launcher_body(frame: &mut Frame, app: &mut App, area: Rect, theme: ThemeMode) { - let state = match &mut app.launcher_state { + let state = match app.launcher_state_mut() { Some(s) => s, None => return, }; @@ -259,7 +259,7 @@ fn render_open_dir_modal(frame: &mut Frame, app: &mut App, area: Rect, theme: Th let y = area.y + area.height.saturating_sub(h) / 2; let modal = Rect::new(x, y, w, h); let border_style = ratatui::style::Style::default(); - let title = if app.open_dir_focus == OpenDirFocus::PathInput { + let title = if app.open_dir_focus() == OpenDirFocus::PathInput { OPEN_DIR_TITLE_EDIT } else { OPEN_DIR_TITLE @@ -271,28 +271,32 @@ fn render_open_dir_modal(frame: &mut Frame, app: &mut App, area: Rect, theme: Th .direction(Direction::Vertical) .constraints([Constraint::Length(1), Constraint::Min(3)]) .split(inner); - let path_style = if app.open_dir_focus == OpenDirFocus::PathInput { + let path_style = if app.open_dir_focus() == OpenDirFocus::PathInput { super::theme::focus_border_style(theme) } else { ratatui::style::Style::default() }; - let path_spans: Vec = if app.open_dir_focus == OpenDirFocus::PathInput { + let path_spans: Vec = if app.open_dir_focus() == OpenDirFocus::PathInput { vec![ - Span::styled(app.open_dir_path.as_str(), path_style), + Span::styled(app.open_dir_path(), path_style), Span::styled("_", super::theme::spinner_style(theme)), ] } else { - vec![Span::styled(app.open_dir_path.as_str(), path_style)] + vec![Span::styled(app.open_dir_path(), path_style)] }; let path_line = Line::from(path_spans); let path_para = Paragraph::new(path_line); frame.render_widget(path_para, chunks[0]); let mut items: Vec = vec![ListItem::new("..")]; - for p in &app.open_dir_entries { - let name = p.file_name().and_then(|n| n.to_str()).unwrap_or("?"); - items.push(ListItem::new(name)); + let entry_names: Vec = app + .open_dir_entries() + .iter() + .map(|p| p.file_name().and_then(|n| n.to_str()).unwrap_or("?").to_string()) + .collect(); + for name in &entry_names { + items.push(ListItem::new(name.clone())); } - let list_border = if app.open_dir_focus == OpenDirFocus::List { + let list_border = if app.open_dir_focus() == OpenDirFocus::List { super::theme::focus_border_style(theme) } else { ratatui::style::Style::default() @@ -301,7 +305,7 @@ fn render_open_dir_modal(frame: &mut Frame, app: &mut App, area: Rect, theme: Th .block(Block::bordered().border_style(list_border)) .highlight_style(list_style(true, theme)) .highlight_symbol(" "); - frame.render_stateful_widget(list, chunks[1], &mut app.open_dir_list_state); + frame.render_stateful_widget(list, chunks[1], app.open_dir_list_state_mut()); } const SPINNER_FRAMES: &str = "\u{2801}\u{2801}\u{2809}\u{2819}\u{281a}\u{2812}\u{2802}\u{2802}\u{2812}\u{2832}\u{2834}\u{2824}\u{2804}\u{2804}\u{2824}\u{2820}\u{2820}\u{2824}\u{2826}\u{2816}\u{2812}\u{2810}\u{2810}\u{2812}\u{2813}\u{280b}\u{2809}\u{2808}\u{2808}"; @@ -1077,11 +1081,12 @@ fn render_table(frame: &mut Frame, app: &mut App, area: Rect, theme: ThemeMode) let order = app.compute_display_order(); let expanded = app.expanded_table_row; + let filtered_groups = app.filtered_groups.clone(); let rows: Vec = order .iter() .enumerate() .map(|(disp_idx, &orig_idx)| { - let r = &app.filtered_groups[orig_idx]; + let r = &filtered_groups[orig_idx]; let caret = if expanded == Some(disp_idx) { "v" } else { ">" }; let mut cells = vec![Cell::from(caret)]; cells.extend(grouped_row_to_cells(r)); From 599052192fe9bf120b4ba3349da214eb2ddb8b7f Mon Sep 17 00:00:00 2001 From: Harlan D Heilman <73567020+HarlanHeilman@users.noreply.github.com> Date: Sun, 22 Mar 2026 18:21:40 -0700 Subject: [PATCH 08/32] feat: Phase 2 - Explorer screen with heuristic classification and resolution modal MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Implement the NAS directory navigator (Explorer screen) for data root browsing: Core Components: - ExplorerState: maintains current directory, entries list, sorting, and layout policy - HeuristicClassifier: detects beamtime patterns and auto-classifies directory structures - ConfigModal: interactive resolution UI for unresolved experimentalist folders Features: - Auto-detect experimentalist folders and beamtime subdirectories - Heuristic pattern matching for date-based folder names (YYYYMMM, YYYY-MM-DD, etc.) - Interactive modal for manual policy configuration (custom pattern, ignore, accept default) - List navigation with sorting support (Name, Modified, Size, FileCount) - CLI mode auto-detection: Explorer for data roots, Beamtime for individual beamtimes UI: - Explorer table view: Name | Kind | Modified | FITS | Status columns - Marked entries: ⚑ flag for experimentalists needing resolution - Bottom status bar with keybind hints - ConfigModal with pattern validation and preview of classified entries Architecture: - Navigator stack extends to support Explorer and ConfigModal screens - Screen enum adds Explorer variant - Browser.rs detects mode based on directory content (all dirs → Explorer, has .fits → Beamtime) Note: Phase 3 catalog integration (fits_count, catalog_status) deferred - shows "--" placeholders cargo check --features tui,catalog,parallel_ingest passes --- src/bin/browser.rs | 74 +++++-- src/bin/tui/app.rs | 168 +++++++++++++++ src/bin/tui/catalog_handle.rs | 7 + src/bin/tui/explorer/heuristic.rs | 337 ++++++++++++++++++++++++++++++ src/bin/tui/explorer/mod.rs | 256 +++++++++++++++++++++++ src/bin/tui/explorer/modal.rs | 305 +++++++++++++++++++++++++++ src/bin/tui/mod.rs | 3 + src/bin/tui/navigator.rs | 4 + src/bin/tui/run.rs | 119 +++++++++++ src/bin/tui/ui.rs | 94 +++++++++ 10 files changed, 1345 insertions(+), 22 deletions(-) create mode 100644 src/bin/tui/catalog_handle.rs create mode 100644 src/bin/tui/explorer/heuristic.rs create mode 100644 src/bin/tui/explorer/mod.rs create mode 100644 src/bin/tui/explorer/modal.rs diff --git a/src/bin/browser.rs b/src/bin/browser.rs index 36229c9..d3ce68b 100644 --- a/src/bin/browser.rs +++ b/src/bin/browser.rs @@ -46,6 +46,23 @@ fn resolve_root(config: &tui::TuiConfig) -> Result { Ok(canonical.to_string_lossy().into_owned()) } +/// Detect if a path should be opened in Explorer mode. +/// Explorer mode: path contains only subdirectories (no .fits files directly inside) +/// Beamtime mode: path contains .fits files directly inside +fn should_open_as_explorer(path: &Path) -> bool { + use std::fs; + + match fs::read_dir(path) { + Ok(entries) => { + // If all entries are directories, use Explorer mode + entries + .filter_map(|e| e.ok()) + .all(|e| e.file_type().map(|t| t.is_dir()).unwrap_or(false)) + } + Err(_) => false, + } +} + fn main() -> Result<(), Box> { env::set_var("POLARS_VERBOSE", "0"); static NOOP: NoopLogger = NoopLogger; @@ -106,33 +123,46 @@ fn main() -> Result<(), Box> { let mut app = match initial_root { Some(ref current_root) => { - let mut app = tui::App::new( - current_root.clone(), - config.layout.clone(), - config.keymap.clone(), - config.keybind_bar_lines, - config.theme.clone(), - ); - app.set_preview_tx(preview_tx.clone()); - if let Some(s) = config.last_sample.as_ref() { - if let Some(i) = app.samples.iter().position(|x| x == s) { - app.sample_state.select(Some(i)); + let path = Path::new(current_root); + if should_open_as_explorer(path) { + // Open in Explorer mode + tui::App::new_for_explorer( + path.to_path_buf(), + config.layout.clone(), + config.keymap.clone(), + config.keybind_bar_lines, + config.theme.clone(), + ) + } else { + // Open in Beamtime mode (original behavior) + let mut app = tui::App::new( + current_root.clone(), + config.layout.clone(), + config.keymap.clone(), + config.keybind_bar_lines, + config.theme.clone(), + ); + app.set_preview_tx(preview_tx.clone()); + if let Some(s) = config.last_sample.as_ref() { + if let Some(i) = app.samples.iter().position(|x| x == s) { + app.sample_state.select(Some(i)); + } } - } - if let Some(t) = config.last_tag.as_ref() { - if let Some(i) = app.tags.iter().position(|x| x == t) { - app.tag_state.select(Some(i)); + if let Some(t) = config.last_tag.as_ref() { + if let Some(i) = app.tags.iter().position(|x| x == t) { + app.tag_state.select(Some(i)); + } } - } - if let Some(e) = config.last_scan.as_ref() { - if let Ok(n) = e.parse::() { - if let Some(i) = app.scans.iter().position(|(x, _)| *x == n) { - app.scan_list_state.select(Some(i)); + if let Some(e) = config.last_scan.as_ref() { + if let Ok(n) = e.parse::() { + if let Some(i) = app.scans.iter().position(|(x, _)| *x == n) { + app.scan_list_state.select(Some(i)); + } } } + app.refresh_filtered(); + app } - app.refresh_filtered(); - app } None => { #[cfg(feature = "catalog")] diff --git a/src/bin/tui/app.rs b/src/bin/tui/app.rs index cb0bb7d..b9cc299 100644 --- a/src/bin/tui/app.rs +++ b/src/bin/tui/app.rs @@ -96,6 +96,7 @@ pub enum LoadingState { pub enum Screen { Launcher, Beamtime, + Explorer, } #[cfg(feature = "catalog")] @@ -1048,6 +1049,96 @@ impl App { } } + pub fn new_for_explorer( + data_root: PathBuf, + layout: String, + keymap: String, + keybind_bar_lines: u8, + theme: String, + ) -> Self { + let explorer_state = super::explorer::ExplorerState::new(data_root); + + let beamtime_state = super::navigator::BeamtimeState { + all_groups: vec![], + filtered_groups: vec![], + samples: vec![], + tags: vec![], + scans: vec![], + selected_samples: HashSet::new(), + selected_tags: HashSet::new(), + selected_scans: HashSet::new(), + sample_state: ListState::default(), + tag_state: ListState::default(), + scan_list_state: ListState::default(), + table_state: TableState::default(), + sample_scroll_state: ScrollbarState::default(), + tag_scroll_state: ScrollbarState::default(), + scan_scroll_state: ScrollbarState::default(), + table_scroll_state: ScrollbarState::default(), + expanded_files_scroll_state: ScrollbarState::default(), + table_sort_column: None, + table_sort_ordering: None, + expanded_table_row: None, + expanded_selected_file_index: None, + expanded_files_scroll_offset: 0, + expanded_files_sort_column: Some(1), + expanded_files_sort_ordering: Some(cmp::Ordering::Less), + last_expanded_files_visible: 5, + focus: Focus::SampleList, + mode: AppMode::Normal, + current_root: String::new(), + search_query: String::new(), + has_catalog: false, + loading_state: LoadingState::Idle, + spinner_frame: 0, + ingest_rx: None, + #[cfg(feature = "catalog")] + ingest_progress: None, + #[cfg(feature = "catalog")] + ingest_progress_rx: None, + #[cfg(feature = "catalog")] + pending_ingest_path: None, + back_stack: vec![], + forward_stack: vec![], + rename_retag_buffer: String::new(), + status_message: None, + status_message_set_at: None, + #[cfg(feature = "watch")] + catalog_watcher: None, + #[cfg(feature = "watch")] + watcher_events_rx: None, + last_body_rects: None, + scrollbar_drag: None, + app_error: None, + app_warning: None, + keybind_bar_lines, + data_root: None, + experimentalist: None, + beamtime_path: PathBuf::new(), + }; + + let mut navigator = super::navigator::Navigator::new( + beamtime_state, + super::catalog_handle::CatalogHandle { + db_path: PathBuf::new(), + }, + ); + navigator.stack.push(super::navigator::ScreenState::Explorer(explorer_state)); + + App { + navigator, + preview_tx: None, + #[cfg(feature = "catalog")] + beamspot_rx: None, + #[cfg(feature = "catalog")] + preview_cmd_rx: None, + layout, + keymap, + theme, + needs_redraw: true, + } + } + pub fn set_app_error(&mut self, e: super::error::TuiError) { *self.app_error_mut() = Some(e); self.needs_redraw = true; @@ -3038,6 +3129,83 @@ impl App { } self.needs_redraw = true; } + + // ---- Explorer helper methods ---- + + pub fn explorer_state(&self) -> Option<&super::explorer::ExplorerState> { + match self.navigator.stack.last() { + Some(super::navigator::ScreenState::Explorer(state)) => Some(state), + _ => None, + } + } + + pub fn explorer_state_mut(&mut self) -> Option<&mut super::explorer::ExplorerState> { + match self.navigator.stack.last_mut() { + Some(super::navigator::ScreenState::Explorer(state)) => Some(state), + _ => None, + } + } + + pub fn modal_state(&self) -> Option<&super::explorer::modal::ModalState> { + match self.navigator.stack.last() { + Some(super::navigator::ScreenState::ConfigModal(state)) => Some(state), + _ => None, + } + } + + pub fn modal_state_mut(&mut self) -> Option<&mut super::explorer::modal::ModalState> { + match self.navigator.stack.last_mut() { + Some(super::navigator::ScreenState::ConfigModal(state)) => Some(state), + _ => None, + } + } + + pub fn explorer_list_down(&mut self) { + if let Some(state) = self.explorer_state_mut() { + state.move_down(); + self.needs_redraw = true; + } + } + + pub fn explorer_list_up(&mut self) { + if let Some(state) = self.explorer_state_mut() { + state.move_up(); + self.needs_redraw = true; + } + } + + pub fn explorer_enter(&mut self) { + if let Some(state) = self.explorer_state_mut() { + if let Some(entry) = state.selected_entry() { + let path = entry.path.clone(); + state.navigate_into(path); + } + self.needs_redraw = true; + } + } + + pub fn explorer_back(&mut self) { + if let Some(state) = self.explorer_state_mut() { + state.navigate_up(); + self.needs_redraw = true; + } + } + + pub fn explorer_resolve(&mut self) { + if let Some(state) = self.explorer_state() { + if let Some(entry) = state.selected_entry() { + if entry.kind == super::explorer::EntryKind::Experimentalist { + let expt_name = entry.name.clone(); + let data_root = state.data_root.clone(); + let policy = state.layout_policy.policy_for(&entry.name).clone(); + + let modal = super::explorer::modal::ModalState::new(&expt_name, data_root, &policy); + self.navigator.stack.push(super::navigator::ScreenState::ConfigModal(modal)); + self.needs_redraw = true; + } + } + } + } } impl Deref for App { diff --git a/src/bin/tui/catalog_handle.rs b/src/bin/tui/catalog_handle.rs new file mode 100644 index 0000000..7efcca4 --- /dev/null +++ b/src/bin/tui/catalog_handle.rs @@ -0,0 +1,7 @@ +use std::path::PathBuf; + +/// Stub for catalog operations. Full implementation comes in Phase 3. +#[derive(Debug)] +pub struct CatalogHandle { + pub db_path: PathBuf, +} diff --git a/src/bin/tui/explorer/heuristic.rs b/src/bin/tui/explorer/heuristic.rs new file mode 100644 index 0000000..be8c21a --- /dev/null +++ b/src/bin/tui/explorer/heuristic.rs @@ -0,0 +1,337 @@ +use regex::Regex; +use serde::{Deserialize, Serialize}; +use std::collections::HashMap; +use std::path::Path; + +use super::EntryKind; + +#[derive(Clone, Debug, Serialize, Deserialize)] +pub struct NasLayout { + pub default_pattern: Option, + pub default_depth: u8, + pub experimentalists: HashMap, +} + +#[derive(Clone, Debug, Serialize, Deserialize)] +pub enum ExptPolicy { + Auto, + Custom { pattern: Option, depth: u8 }, + Ignored, +} + +impl NasLayout { + pub fn default() -> Self { + NasLayout { + default_pattern: None, + default_depth: 2, + experimentalists: HashMap::new(), + } + } + + pub fn load(data_root: &Path) -> Self { + let layout_path = data_root.join(".pyref").join("layout.toml"); + if layout_path.exists() { + if let Ok(content) = std::fs::read_to_string(&layout_path) { + if let Ok(layout) = toml::from_str::(&content) { + return layout; + } + } + } + Self::default() + } + + pub fn save(&self, data_root: &Path) -> Result<(), String> { + let pyref_dir = data_root.join(".pyref"); + std::fs::create_dir_all(&pyref_dir) + .map_err(|e| format!("Failed to create .pyref directory: {}", e))?; + + let layout_path = pyref_dir.join("layout.toml"); + let content = toml::to_string_pretty(self) + .map_err(|e| format!("Failed to serialize layout: {}", e))?; + + std::fs::write(&layout_path, content) + .map_err(|e| format!("Failed to write layout.toml: {}", e))?; + + Ok(()) + } + + pub fn policy_for(&self, experimentalist: &str) -> &ExptPolicy { + self.experimentalists + .get(experimentalist) + .unwrap_or(&ExptPolicy::Auto) + } + + pub fn set_policy(&mut self, experimentalist: String, policy: ExptPolicy) { + self.experimentalists.insert(experimentalist, policy); + } +} + +/// Built-in patterns for beamtime detection. +fn get_builtin_patterns() -> Vec { + [ + r"^20\d{2}(Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)$", + r"^20\d{2}-\d{2}(-\d{2})?$", + r"^20\d{6}$", + r"(?i)beamtime", + ] + .iter() + .filter_map(|pat| Regex::new(pat).ok()) + .collect() +} + +/// Detect if a folder name matches a beamtime pattern. +pub fn matches_beamtime_pattern(name: &str) -> bool { + get_builtin_patterns().iter().any(|p| p.is_match(name)) +} + +/// Scan top 2 levels and auto-detect experimentalist policies. +pub fn detect_policy(data_root: &Path) -> NasLayout { + let mut layout = NasLayout::default(); + + // Read depth-1 entries (experimentalists) + if let Ok(depth1_entries) = std::fs::read_dir(data_root) { + for entry in depth1_entries.flatten() { + let path = entry.path(); + if !path.is_dir() { + continue; + } + + let name = entry.file_name(); + let name_str = name.to_string_lossy().to_string(); + + // Skip dotted entries + if name_str.starts_with('.') { + continue; + } + + // Count depth-2 entries that match beamtime pattern + let mut total_depth2 = 0; + let mut matching_depth2 = 0; + + if let Ok(depth2_entries) = std::fs::read_dir(&path) { + for entry2 in depth2_entries.flatten() { + let path2 = entry2.path(); + if !path2.is_dir() { + continue; + } + + let name2 = entry2.file_name(); + let name2_str = name2.to_string_lossy().to_string(); + + // Skip dotted entries + if name2_str.starts_with('.') { + continue; + } + + total_depth2 += 1; + if matches_beamtime_pattern(&name2_str) { + matching_depth2 += 1; + } + } + } + + // Auto-detect: if ≥50% of depth-2 folders match AND at least 2 exist + let policy = if total_depth2 >= 2 && matching_depth2 * 2 >= total_depth2 { + ExptPolicy::Auto + } else if total_depth2 > 0 { + ExptPolicy::Custom { + pattern: None, + depth: 2, + } + } else { + ExptPolicy::Custom { + pattern: None, + depth: 2, + } + }; + + layout.set_policy(name_str, policy); + } + } + + layout +} + +/// Classify an entry based on its path and layout policy. +pub fn classify_entry(path: &Path, data_root: &Path, layout: &NasLayout) -> EntryKind { + // Data root itself + if path == data_root { + return EntryKind::DataRoot; + } + + // Calculate depth relative to data_root + let depth = path + .strip_prefix(data_root) + .ok() + .map(|p| p.components().count()) + .unwrap_or(0); + + match depth { + 1 => { + // Depth 1: experimentalist + let name = path.file_name().and_then(|n| n.to_str()).unwrap_or(""); + if !name.starts_with('.') { + EntryKind::Experimentalist + } else { + EntryKind::Directory + } + } + 2.. => { + // Depth 2+: check parent experimentalist policy + if let Some(parent) = path.parent() { + let parent_name = parent + .file_name() + .and_then(|n| n.to_str()) + .unwrap_or(""); + + let policy = layout.policy_for(parent_name); + match policy { + ExptPolicy::Auto => { + // For Auto policy, check if matches beamtime pattern + let name = path.file_name().and_then(|n| n.to_str()).unwrap_or(""); + if matches_beamtime_pattern(name) { + EntryKind::Beamtime + } else { + EntryKind::Directory + } + } + ExptPolicy::Custom { + pattern: Some(pat), + .. + } => { + // Custom pattern provided + if let Ok(regex) = Regex::new(pat) { + let name = path.file_name().and_then(|n| n.to_str()).unwrap_or(""); + if regex.is_match(name) { + EntryKind::Beamtime + } else { + EntryKind::Directory + } + } else { + EntryKind::Directory + } + } + ExptPolicy::Custom { pattern: None, .. } | ExptPolicy::Ignored => { + // Unresolved or ignored: treat as generic directory + EntryKind::Directory + } + } + } else { + EntryKind::Directory + } + } + _ => EntryKind::Directory, + } +} + +/// Check if an experimentalist needs resolution. +pub fn needs_resolution(experimentalist: &str, layout: &NasLayout) -> bool { + matches!( + layout.policy_for(experimentalist), + ExptPolicy::Custom { + pattern: None, + .. + } + ) +} + +#[cfg(test)] +mod tests { + use super::*; + use std::fs; + use tempfile::TempDir; + + #[test] + fn test_classify_entry_data_root() { + let layout = NasLayout::default(); + let data_root = std::path::Path::new("/data"); + assert_eq!( + classify_entry(data_root, data_root, &layout), + EntryKind::DataRoot + ); + } + + #[test] + fn test_classify_entry_experimentalist() { + let layout = NasLayout::default(); + let data_root = std::path::Path::new("/data"); + let experimentalist = std::path::Path::new("/data/Smith"); + assert_eq!( + classify_entry(experimentalist, data_root, &layout), + EntryKind::Experimentalist + ); + } + + #[test] + fn test_classify_entry_beamtime_auto() { + let mut layout = NasLayout::default(); + layout.set_policy("Smith".to_string(), ExptPolicy::Auto); + + let data_root = std::path::Path::new("/data"); + let beamtime = std::path::Path::new("/data/Smith/2024Jan"); + assert_eq!( + classify_entry(beamtime, data_root, &layout), + EntryKind::Beamtime + ); + } + + #[test] + fn test_classify_entry_directory() { + let mut layout = NasLayout::default(); + layout.set_policy("Smith".to_string(), ExptPolicy::Auto); + + let data_root = std::path::Path::new("/data"); + let dir = std::path::Path::new("/data/Smith/scratch"); + assert_eq!( + classify_entry(dir, data_root, &layout), + EntryKind::Directory + ); + } + + #[test] + fn test_detect_policy_auto() { + let temp_dir = TempDir::new().unwrap(); + let data_root = temp_dir.path(); + + // Create experimentalist + let expt_dir = data_root.join("Smith"); + fs::create_dir(&expt_dir).unwrap(); + + // Create beamtimes + fs::create_dir(expt_dir.join("2024Jan")).unwrap(); + fs::create_dir(expt_dir.join("2024Feb")).unwrap(); + + let layout = detect_policy(data_root); + match layout.policy_for("Smith") { + ExptPolicy::Auto => { + // Expected + } + _ => panic!("Expected Auto policy for Smith"), + } + } + + #[test] + fn test_detect_policy_custom() { + let temp_dir = TempDir::new().unwrap(); + let data_root = temp_dir.path(); + + // Create experimentalist + let expt_dir = data_root.join("Smith"); + fs::create_dir(&expt_dir).unwrap(); + + // Create generic directories + fs::create_dir(expt_dir.join("project1")).unwrap(); + fs::create_dir(expt_dir.join("project2")).unwrap(); + + let layout = detect_policy(data_root); + match layout.policy_for("Smith") { + ExptPolicy::Custom { + pattern: None, + depth: 2, + } => { + // Expected + } + _ => panic!("Expected Custom policy for Smith"), + } + } +} diff --git a/src/bin/tui/explorer/mod.rs b/src/bin/tui/explorer/mod.rs new file mode 100644 index 0000000..b7754f3 --- /dev/null +++ b/src/bin/tui/explorer/mod.rs @@ -0,0 +1,256 @@ +pub mod heuristic; +pub mod modal; + +use ratatui::widgets::{ListState, ScrollbarState}; +use std::path::PathBuf; +use std::time::SystemTime; + +use self::heuristic::NasLayout; + +#[derive(Debug, Clone, Copy, PartialEq, Eq)] +pub enum EntryKind { + DataRoot, + Experimentalist, + Beamtime, + Directory, +} + +#[derive(Debug, Clone, Copy, PartialEq, Eq)] +pub enum CatalogStatus { + Indexed, + Stale, + NotIndexed, + NotApplicable, +} + +#[derive(Debug, Clone, Copy, PartialEq, Eq)] +pub enum ExptResolution { + Resolved, + NeedsResolution, + Ignored, +} + +#[derive(Debug, Clone, Copy, PartialEq, Eq)] +pub enum ExplorerSort { + Name, + Modified, + Size, + FileCount, +} + +#[derive(Debug, Clone)] +pub struct DirEntry { + pub path: PathBuf, + pub name: String, + pub kind: EntryKind, + pub modified: Option, + pub size_bytes: Option, + pub fits_count: Option, + pub catalog_status: CatalogStatus, + pub expt_resolution: Option, +} + +pub struct ExplorerState { + pub data_root: PathBuf, + pub current_dir: PathBuf, + pub entries: Vec, + pub list_state: ListState, + pub scroll_state: ScrollbarState, + pub layout_policy: NasLayout, + pub loading: bool, + pub status: Option, + pub sort_col: ExplorerSort, + pub sort_asc: bool, + pub filter_query: Option, +} + +impl ExplorerState { + /// Create a new explorer state for the given data root. + /// Reads the directory synchronously. + pub fn new(data_root: PathBuf) -> Self { + let mut state = ExplorerState { + data_root: data_root.clone(), + current_dir: data_root.clone(), + entries: Vec::new(), + list_state: ListState::default(), + scroll_state: ScrollbarState::new(0), + layout_policy: heuristic::detect_policy(&data_root), + loading: false, + status: None, + sort_col: ExplorerSort::Name, + sort_asc: true, + filter_query: None, + }; + state.load_dir(data_root); + state + } + + /// Load directory entries from the given path. + pub fn load_dir(&mut self, path: PathBuf) { + self.entries.clear(); + self.current_dir = path.clone(); + + match std::fs::read_dir(&path) { + Ok(entries) => { + for entry in entries.flatten() { + if let Ok(metadata) = entry.metadata() { + let path = entry.path(); + let name = entry.file_name(); + let name_str = name.to_string_lossy().to_string(); + + // Skip .pyref directory + if name_str == ".pyref" { + continue; + } + + let kind = heuristic::classify_entry(&path, &self.data_root, &self.layout_policy); + + // Skip entries starting with dot (hidden files/dirs) + if name_str.starts_with('.') { + continue; + } + + let expt_resolution = if kind == EntryKind::Experimentalist { + if heuristic::needs_resolution(&name_str, &self.layout_policy) { + Some(ExptResolution::NeedsResolution) + } else { + Some(ExptResolution::Resolved) + } + } else { + None + }; + + self.entries.push(DirEntry { + path, + name: name_str, + kind, + modified: metadata.modified().ok(), + size_bytes: None, // Deferred to Phase 3 + fits_count: None, + catalog_status: CatalogStatus::NotApplicable, + expt_resolution, + }); + } + } + } + Err(_) => { + self.status = Some("Failed to read directory".to_string()); + } + } + + self.apply_sort(); + self.list_state.select(Some(0)); + self.scroll_state = ScrollbarState::new(self.entries.len()); + } + + /// Navigate into the selected directory. + pub fn navigate_into(&mut self, path: PathBuf) { + self.load_dir(path); + } + + /// Navigate up one directory (stop at data_root). + pub fn navigate_up(&mut self) { + if self.current_dir != self.data_root { + if let Some(parent) = self.current_dir.parent() { + self.load_dir(parent.to_path_buf()); + } + } + } + + /// Get the selected entry. + pub fn selected_entry(&self) -> Option<&DirEntry> { + self.list_state + .selected() + .and_then(|i| self.entries.get(i)) + } + + /// Move selection down. + pub fn move_down(&mut self) { + if self.entries.is_empty() { + return; + } + match self.list_state.selected() { + Some(i) if i < self.entries.len() - 1 => { + self.list_state.select(Some(i + 1)); + } + None => { + self.list_state.select(Some(0)); + } + _ => {} + } + self.update_scroll_state(); + } + + /// Move selection up. + pub fn move_up(&mut self) { + match self.list_state.selected() { + Some(i) if i > 0 => { + self.list_state.select(Some(i - 1)); + } + Some(0) => { + if !self.entries.is_empty() { + self.list_state.select(Some(self.entries.len() - 1)); + } + } + None if !self.entries.is_empty() => { + self.list_state.select(Some(0)); + } + _ => {} + } + self.update_scroll_state(); + } + + fn update_scroll_state(&mut self) { + if let Some(selected) = self.list_state.selected() { + self.scroll_state = self.scroll_state.position(selected); + } + } + + fn apply_sort(&mut self) { + self.entries.sort_by(|a, b| { + let cmp = match self.sort_col { + ExplorerSort::Name => a.name.cmp(&b.name), + ExplorerSort::Modified => { + match (a.modified, b.modified) { + (Some(t_a), Some(t_b)) => t_a.cmp(&t_b), + (Some(_), None) => std::cmp::Ordering::Greater, + (None, Some(_)) => std::cmp::Ordering::Less, + (None, None) => std::cmp::Ordering::Equal, + } + } + ExplorerSort::Size => { + match (a.size_bytes, b.size_bytes) { + (Some(sz_a), Some(sz_b)) => sz_a.cmp(&sz_b), + (Some(_), None) => std::cmp::Ordering::Greater, + (None, Some(_)) => std::cmp::Ordering::Less, + (None, None) => std::cmp::Ordering::Equal, + } + } + ExplorerSort::FileCount => { + match (a.fits_count, b.fits_count) { + (Some(cnt_a), Some(cnt_b)) => cnt_a.cmp(&cnt_b), + (Some(_), None) => std::cmp::Ordering::Greater, + (None, Some(_)) => std::cmp::Ordering::Less, + (None, None) => std::cmp::Ordering::Equal, + } + } + }; + + if self.sort_asc { + cmp + } else { + cmp.reverse() + } + }); + } + + pub fn toggle_sort(&mut self, col: ExplorerSort) { + if self.sort_col == col { + self.sort_asc = !self.sort_asc; + } else { + self.sort_col = col; + self.sort_asc = true; + } + self.apply_sort(); + } +} diff --git a/src/bin/tui/explorer/modal.rs b/src/bin/tui/explorer/modal.rs new file mode 100644 index 0000000..461b220 --- /dev/null +++ b/src/bin/tui/explorer/modal.rs @@ -0,0 +1,305 @@ +use regex::Regex; +use ratatui::{ + layout::{Constraint, Direction, Layout, Rect}, + text::{Line, Span}, + widgets::{Block, Paragraph}, + Frame, +}; +use std::path::PathBuf; + +use super::heuristic::ExptPolicy; +use super::EntryKind; + +#[derive(Debug, Clone, Copy, PartialEq, Eq)] +pub enum ModalFocus { + Depth, + Pattern, + Choice, +} + +#[derive(Debug, Clone, Copy, PartialEq, Eq)] +pub enum ModalChoice { + CustomPattern, + MarkIgnored, + AcceptDefault, +} + +pub struct ModalState { + pub target_experimentalist: String, + pub depth_input: String, + pub pattern_input: String, + pub focus: ModalFocus, + pub pattern_valid: bool, + pub last_valid_pattern: Option, + pub choice: ModalChoice, + pub preview_entries: Vec<(String, EntryKind)>, + pub data_root: PathBuf, +} + +impl ModalState { + pub fn new(experimentalist: &str, data_root: PathBuf, existing: &ExptPolicy) -> Self { + let (depth_input, pattern_input) = match existing { + ExptPolicy::Auto => ("2".to_string(), "".to_string()), + ExptPolicy::Custom { + pattern: Some(pat), + depth, + } => (depth.to_string(), pat.clone()), + ExptPolicy::Custom { pattern: None, depth } => (depth.to_string(), "".to_string()), + ExptPolicy::Ignored => ("2".to_string(), "".to_string()), + }; + + let mut state = ModalState { + target_experimentalist: experimentalist.to_string(), + depth_input, + pattern_input, + focus: ModalFocus::Depth, + pattern_valid: true, + last_valid_pattern: None, + choice: ModalChoice::CustomPattern, + preview_entries: Vec::new(), + data_root, + }; + + state.update_preview(); + state + } + + pub fn update_preview(&mut self) { + self.preview_entries.clear(); + + // Validate pattern if non-empty + if !self.pattern_input.is_empty() { + match Regex::new(&self.pattern_input) { + Ok(_) => { + self.pattern_valid = true; + self.last_valid_pattern = Some(self.pattern_input.clone()); + } + Err(_) => { + self.pattern_valid = false; + } + } + } else { + self.pattern_valid = true; + self.last_valid_pattern = None; + } + + // Load depth-2 entries from experimentalist directory + let expt_path = self.data_root.join(&self.target_experimentalist); + if let Ok(entries) = std::fs::read_dir(&expt_path) { + for entry in entries.flatten() { + let path = entry.path(); + if !path.is_dir() { + continue; + } + + let name = entry.file_name(); + let name_str = name.to_string_lossy().to_string(); + + // Skip dotted entries + if name_str.starts_with('.') { + continue; + } + + let kind = if self.pattern_valid && !self.pattern_input.is_empty() { + if let Ok(regex) = Regex::new(&self.pattern_input) { + if regex.is_match(&name_str) { + EntryKind::Beamtime + } else { + EntryKind::Directory + } + } else { + EntryKind::Directory + } + } else if self.pattern_input.is_empty() { + // No pattern: use default beamtime patterns + if super::heuristic::matches_beamtime_pattern(&name_str) { + EntryKind::Beamtime + } else { + EntryKind::Directory + } + } else { + EntryKind::Directory + }; + + self.preview_entries.push((name_str, kind)); + } + } + + // Sort by name + self.preview_entries.sort_by(|a, b| a.0.cmp(&b.0)); + } + + pub fn on_char(&mut self, c: char) { + if c.is_ascii() && !c.is_control() { + match self.focus { + ModalFocus::Depth => { + if c.is_ascii_digit() { + self.depth_input.push(c); + } + } + ModalFocus::Pattern => { + self.pattern_input.push(c); + self.update_preview(); + } + ModalFocus::Choice => {} + } + } + } + + pub fn on_backspace(&mut self) { + match self.focus { + ModalFocus::Depth => { + self.depth_input.pop(); + } + ModalFocus::Pattern => { + self.pattern_input.pop(); + self.update_preview(); + } + ModalFocus::Choice => {} + } + } + + pub fn on_tab(&mut self) { + self.focus = match self.focus { + ModalFocus::Depth => ModalFocus::Pattern, + ModalFocus::Pattern => ModalFocus::Choice, + ModalFocus::Choice => ModalFocus::Depth, + }; + } + + pub fn on_number(&mut self, num: u8) { + match num { + 1 => self.choice = ModalChoice::CustomPattern, + 2 => self.choice = ModalChoice::MarkIgnored, + 3 => self.choice = ModalChoice::AcceptDefault, + _ => {} + } + } + + pub fn confirm(&self) -> ExptPolicy { + match self.choice { + ModalChoice::CustomPattern => { + let depth = self.depth_input.parse::().unwrap_or(2); + ExptPolicy::Custom { + pattern: self.last_valid_pattern.clone(), + depth, + } + } + ModalChoice::MarkIgnored => ExptPolicy::Ignored, + ModalChoice::AcceptDefault => ExptPolicy::Auto, + } + } +} + +/// Render the modal dialog. +pub fn render_modal(f: &mut Frame, area: Rect, state: &ModalState, _theme: &str) { + // Center the modal (60% width, 70% height) + let modal_width = (area.width * 60 / 100).max(40); + let modal_height = (area.height * 70 / 100).max(15); + + let modal_x = (area.width.saturating_sub(modal_width)) / 2; + let modal_y = (area.height.saturating_sub(modal_height)) / 2; + + let modal_rect = Rect { + x: area.x + modal_x, + y: area.y + modal_y, + width: modal_width, + height: modal_height, + }; + + // Draw outer block + let outer_block = Block::bordered().title(format!( + " Configure: {} ", + state.target_experimentalist + )); + f.render_widget(outer_block, modal_rect); + + let inner = Rect { + x: modal_rect.x + 1, + y: modal_rect.y + 1, + width: modal_rect.width.saturating_sub(2), + height: modal_rect.height.saturating_sub(2), + }; + + let chunks = Layout::default() + .direction(Direction::Vertical) + .constraints([ + Constraint::Length(3), + Constraint::Length(3), + Constraint::Fill(1), + Constraint::Length(4), + ]) + .split(inner); + + // Depth input + let depth_style = if state.focus == ModalFocus::Depth { + ">> " + } else { + " " + }; + let depth_line = Line::from(format!("{}Depth (default 2): {}", depth_style, state.depth_input)); + f.render_widget(Paragraph::new(depth_line), chunks[0]); + + // Pattern input + let pattern_style = if state.focus == ModalFocus::Pattern { + ">> " + } else { + " " + }; + let pattern_color_text = if state.pattern_valid { + state.pattern_input.clone() + } else { + state.pattern_input.clone() + }; + let pattern_line = Line::from(vec![ + Span::raw(format!("{}Pattern (regex): ", pattern_style)), + if state.pattern_valid { + Span::raw(pattern_color_text) + } else { + Span::styled(pattern_color_text, ratatui::style::Style::new().fg(ratatui::style::Color::Red)) + }, + ]); + f.render_widget(Paragraph::new(pattern_line), chunks[1]); + + // Preview pane + let mut preview_lines = Vec::new(); + preview_lines.push(Line::from(Span::raw("Preview (depth-2 folders):"))); + for (name, kind) in &state.preview_entries { + let kind_str = match kind { + EntryKind::Beamtime => "[Beamtime ✓]", + EntryKind::Directory => "[Directory]", + _ => "[?]", + }; + preview_lines.push(Line::from(format!(" {} {}", name, kind_str))); + } + + let preview = Paragraph::new(preview_lines).block(Block::bordered().title("Preview")); + f.render_widget(preview, chunks[2]); + + // Choice buttons + let choice_text = vec![ + Line::from(vec![ + if state.choice == ModalChoice::CustomPattern { + Span::styled("[1] Custom pattern", ratatui::style::Style::new().fg(ratatui::style::Color::Cyan)) + } else { + Span::raw("[1] Custom pattern") + }, + Span::raw(" "), + if state.choice == ModalChoice::MarkIgnored { + Span::styled("[2] Mark ignored", ratatui::style::Style::new().fg(ratatui::style::Color::Cyan)) + } else { + Span::raw("[2] Mark ignored") + }, + Span::raw(" "), + if state.choice == ModalChoice::AcceptDefault { + Span::styled("[3] Accept default", ratatui::style::Style::new().fg(ratatui::style::Color::Cyan)) + } else { + Span::raw("[3] Accept default") + }, + ]), + Line::from(Span::raw("Tab: next field Enter: confirm Esc: cancel")), + ]; + + let choice_pane = Paragraph::new(choice_text); + f.render_widget(choice_pane, chunks[3]); +} diff --git a/src/bin/tui/mod.rs b/src/bin/tui/mod.rs index d91b8e3..0184af3 100644 --- a/src/bin/tui/mod.rs +++ b/src/bin/tui/mod.rs @@ -1,7 +1,9 @@ mod app; +mod catalog_handle; mod config; mod error; pub mod keymap; +mod navigator; mod run; #[cfg(feature = "catalog")] mod beamspot; @@ -10,6 +12,7 @@ mod scan_type; pub mod terminal_guard; mod theme; mod ui; +mod explorer; pub mod preview; diff --git a/src/bin/tui/navigator.rs b/src/bin/tui/navigator.rs index fcbe17f..f48fbab 100644 --- a/src/bin/tui/navigator.rs +++ b/src/bin/tui/navigator.rs @@ -96,6 +96,10 @@ pub enum ScreenState { Beamtime(BeamtimeState), #[cfg(feature = "catalog")] Launcher(LauncherScreenState), + #[cfg(feature = "tui")] + Explorer(super::explorer::ExplorerState), + #[cfg(feature = "tui")] + ConfigModal(super::explorer::modal::ModalState), } /// Navigation stack and catalog handle. diff --git a/src/bin/tui/run.rs b/src/bin/tui/run.rs index 421e493..da602a9 100644 --- a/src/bin/tui/run.rs +++ b/src/bin/tui/run.rs @@ -7,6 +7,116 @@ use std::time::Duration; use super::app::{App, OpenDirFocus, ScrollbarDragTarget}; use super::keymap::{self, Action}; +fn handle_explorer_event(app: &mut App, key: crossterm::event::KeyEvent) -> bool { + use crossterm::event::KeyCode; + + match key.code { + KeyCode::Up | KeyCode::Char('k') => { + app.explorer_list_up(); + return false; + } + KeyCode::Down | KeyCode::Char('j') => { + app.explorer_list_down(); + return false; + } + KeyCode::Right | KeyCode::Enter | KeyCode::Char('l') => { + app.explorer_enter(); + return false; + } + KeyCode::Left | KeyCode::Char('h') => { + app.explorer_back(); + return false; + } + KeyCode::Char('r') => { + app.explorer_resolve(); + return false; + } + KeyCode::Char('x') => { + // Placeholder for Phase 4: toggle ignore + return false; + } + KeyCode::Char('/') => { + // Placeholder for Phase 4: start filter input + return false; + } + KeyCode::Char('q') | KeyCode::Esc => { + return true; // Quit + } + _ => {} + } + + false +} + +fn handle_modal_event(app: &mut App, key: crossterm::event::KeyEvent) -> bool { + use crossterm::event::KeyCode; + + match key.code { + KeyCode::Esc => { + // Pop the modal + app.navigator.stack.pop(); + app.needs_redraw = true; + return false; + } + KeyCode::Tab => { + if let Some(modal) = app.modal_state_mut() { + modal.on_tab(); + app.needs_redraw = true; + } + return false; + } + KeyCode::Backspace => { + if let Some(modal) = app.modal_state_mut() { + modal.on_backspace(); + app.needs_redraw = true; + } + return false; + } + KeyCode::Char(c) => { + if c.is_ascii_digit() { + if let Some(num) = c.to_digit(10) { + if let Some(modal) = app.modal_state_mut() { + modal.on_number(num as u8); + app.needs_redraw = true; + } + return false; + } + } else if c.is_ascii() && !c.is_control() { + if let Some(modal) = app.modal_state_mut() { + modal.on_char(c); + app.needs_redraw = true; + } + return false; + } + } + KeyCode::Enter => { + if let Some(modal) = app.modal_state() { + let policy = modal.confirm(); + let expt_name = modal.target_experimentalist.clone(); + let data_root = modal.data_root.clone(); + + // Pop modal + app.navigator.stack.pop(); + + // Update layout policy + if let Some(explorer) = app.explorer_state_mut() { + explorer.layout_policy.set_policy(expt_name, policy); + let _ = explorer.layout_policy.save(&data_root); + // Reload current directory to update expt_resolution flags + let current_dir = explorer.current_dir.clone(); + explorer.load_dir(current_dir); + } + + app.needs_redraw = true; + } + return false; + } + _ => {} + } + + false +} + #[cfg(feature = "catalog")] fn handle_open_dir_event(app: &mut App, key: crossterm::event::KeyEvent) -> bool { let confirm_mod = key @@ -551,6 +661,11 @@ fn table_header_column_at(rect: Rect, col: u16) -> Option { } pub fn handle_event(app: &mut App, key: crossterm::event::KeyEvent) -> bool { + // Handle ConfigModal first (highest priority) + if app.modal_state().is_some() { + return handle_modal_event(app, key); + } + if app.current_screen() == super::app::Screen::Launcher { #[cfg(feature = "catalog")] if app.open_dir_active() { @@ -581,6 +696,10 @@ pub fn handle_event(app: &mut App, key: crossterm::event::KeyEvent) -> bool { return quit; } + if app.current_screen() == super::app::Screen::Explorer { + return handle_explorer_event(app, key); + } + if app.current_screen() == super::app::Screen::Beamtime && app.mode() == super::app::AppMode::Normal { let action = keymap::from_key_event(key, &app.keymap); if action == Action::Cancel { diff --git a/src/bin/tui/ui.rs b/src/bin/tui/ui.rs index 109cb36..11af5f1 100644 --- a/src/bin/tui/ui.rs +++ b/src/bin/tui/ui.rs @@ -170,6 +170,14 @@ pub fn render(frame: &mut Frame, app: &mut App) { if app.open_dir_active() { render_open_dir_modal(frame, app, inner, theme); } + } else if app.current_screen() == Screen::Explorer { + render_explorer_nav(frame, nav_area, app, theme); + render_explorer(frame, app, body_area, theme); + render_explorer_bottom(frame, bottom_area, theme); + // Check if ConfigModal is on the stack + if let Some(modal_state) = app.modal_state() { + super::explorer::modal::render_modal(frame, inner, modal_state, &app.theme); + } } else { render_nav(frame, app, nav_area, theme); render_body(frame, app, body_area, theme); @@ -1160,3 +1168,89 @@ fn render_table(frame: &mut Frame, app: &mut App, area: Rect, theme: ThemeMode) None } } + +// ---- Explorer rendering functions ---- + +fn render_explorer_nav(frame: &mut Frame, area: Rect, app: &App, theme: ThemeMode) { + let explorer_state = match app.explorer_state() { + Some(state) => state, + None => return, + }; + + let breadcrumb = format!("pyref browser {}", explorer_state.current_dir.display()); + let path_display = truncate_path(&breadcrumb, NAV_PATH_TRUNCATE); + + let line = Line::from(ratatui::text::Span::styled( + path_display, + super::theme::header_style(theme), + )); + frame.render_widget(Paragraph::new(line), area); +} + +fn render_explorer(frame: &mut Frame, app: &mut App, area: Rect, theme: ThemeMode) { + let explorer_state = match app.explorer_state() { + Some(state) => state, + None => return, + }; + + // Build list of entries with highlights + let items: Vec = explorer_state + .entries + .iter() + .map(|entry| { + let kind_str = match entry.kind { + super::explorer::EntryKind::DataRoot => "DATA", + super::explorer::EntryKind::Experimentalist => { + if entry.expt_resolution == Some(super::explorer::ExptResolution::NeedsResolution) { + "Expt ⚑" + } else { + "Expt" + } + } + super::explorer::EntryKind::Beamtime => "Beamtime", + super::explorer::EntryKind::Directory => "Dir", + }; + let modified_str = entry + .modified + .and_then(|st| { + if let Ok(duration) = st.elapsed() { + Some(format!("{:.0}d ago", duration.as_secs() as f64 / 86400.0)) + } else { + None + } + }) + .unwrap_or_else(|| "--".to_string()); + let fits_str = entry + .fits_count + .map(|c| c.to_string()) + .unwrap_or_else(|| "--".to_string()); + let status_str = match entry.catalog_status { + super::explorer::CatalogStatus::Indexed => "✓", + super::explorer::CatalogStatus::Stale => "~", + super::explorer::CatalogStatus::NotIndexed => "✗", + super::explorer::CatalogStatus::NotApplicable => "--", + }; + + let line = format!( + "{:<35} {:<12} {:<15} {:<8} {}", + entry.name, kind_str, modified_str, fits_str, status_str + ); + ListItem::new(line) + }) + .collect(); + + let list = List::new(items) + .block(Block::bordered().title(" Files ")) + .highlight_style(list_style(true, theme)) + .highlight_symbol(" "); + + frame.render_stateful_widget(list, area, &mut app.explorer_state_mut().unwrap().list_state); +} + +fn render_explorer_bottom(frame: &mut Frame, area: Rect, theme: ThemeMode) { + let style = super::theme::keybind_bar_style(theme); + let hints = " ↑↓ select → enter ← back r resolve x ignore / search q quit "; + let line = Line::from(ratatui::text::Span::styled(hints, style)); + let para = Paragraph::new(line).alignment(Alignment::Center); + frame.render_widget(para, area); +} From 3f5737f10c682bf868b3ca225f13ca5c52c643da Mon Sep 17 00:00:00 2001 From: Harlan D Heilman <73567020+HarlanHeilman@users.noreply.github.com> Date: Sun, 22 Mar 2026 18:24:24 -0700 Subject: [PATCH 09/32] fix: handle Explorer and ConfigModal in current_screen() method Add missing cases for Explorer and ConfigModal ScreenState variants in the current_screen() method so they correctly return Screen::Explorer instead of falling through to Screen::Beamtime. --- src/bin/tui/app.rs | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/src/bin/tui/app.rs b/src/bin/tui/app.rs index b9cc299..835d604 100644 --- a/src/bin/tui/app.rs +++ b/src/bin/tui/app.rs @@ -730,6 +730,10 @@ impl App { match self.navigator.stack.last() { #[cfg(feature = "catalog")] Some(super::navigator::ScreenState::Launcher(_)) => Screen::Launcher, + #[cfg(feature = "tui")] + Some(super::navigator::ScreenState::Explorer(_)) => Screen::Explorer, + #[cfg(feature = "tui")] + Some(super::navigator::ScreenState::ConfigModal(_)) => Screen::Explorer, _ => Screen::Beamtime, } } From 6d09e46b08980fa5feaa94b46c7ea275bb543c14 Mon Sep 17 00:00:00 2001 From: Harlan D Heilman <73567020+HarlanHeilman@users.noreply.github.com> Date: Sun, 22 Mar 2026 18:41:33 -0700 Subject: [PATCH 10/32] feat: implement Phase 3 - DATA-level catalog with schema migration and CatalogHandle - Add schema migration for experimentalist and data_root columns to bt_beamtimes table - Create explorer_query module with list_experimentalists, list_beamtimes_for_expt, and catalog_status_for_path functions - Define DbCatalogStatus enum (Indexed, Stale, NotIndexed) for catalog status tracking - Implement CatalogHandle methods for querying experimentalists, beamtimes, and catalog status - Adapt ingest_beamtime signature with data_root, experimentalist, and cancel parameters - Add resolve_ingest_target to determine correct catalog.db location based on data_root - Add upsert_beamtime_record to update bt_beamtimes with context metadata and timestamps - Add ingest_beamtime_pipelined_with_context for parallel ingest with new parameters - Maintain backward compatibility - old ingest_beamtime callers unchanged - cargo check --features tui,catalog,parallel_ingest passes --- src/bin/tui/catalog_handle.rs | 41 +++++++- src/catalog/explorer_query.rs | 157 ++++++++++++++++++++++++++++ src/catalog/ingest.rs | 56 +++++++++- src/catalog/ingest_parallel.rs | 181 ++++++++++++++++++++++++++++++++- src/catalog/mod.rs | 41 +++++++- 5 files changed, 469 insertions(+), 7 deletions(-) create mode 100644 src/catalog/explorer_query.rs diff --git a/src/bin/tui/catalog_handle.rs b/src/bin/tui/catalog_handle.rs index 7efcca4..8cb7254 100644 --- a/src/bin/tui/catalog_handle.rs +++ b/src/bin/tui/catalog_handle.rs @@ -1,7 +1,44 @@ -use std::path::PathBuf; +use std::path::{Path, PathBuf}; + +#[cfg(feature = "catalog")] +use pyref::catalog::{ + list_experimentalists, list_beamtimes_for_expt, catalog_status_for_path, + list_beamtime_entries_v2, query_scan_points, + ExptMeta, BeamtimeMeta, DbCatalogStatus, BeamtimeEntries, FileRow, CatalogFilter, +}; -/// Stub for catalog operations. Full implementation comes in Phase 3. #[derive(Debug)] pub struct CatalogHandle { pub db_path: PathBuf, } + +impl CatalogHandle { + pub fn new(db_path: PathBuf) -> Self { + Self { db_path } + } + + #[cfg(feature = "catalog")] + pub fn list_experimentalists(&self, data_root: &Path) -> Vec { + list_experimentalists(&self.db_path, data_root).unwrap_or_default() + } + + #[cfg(feature = "catalog")] + pub fn list_beamtimes(&self, data_root: &Path, experimentalist: &str) -> Vec { + list_beamtimes_for_expt(&self.db_path, data_root, experimentalist).unwrap_or_default() + } + + #[cfg(feature = "catalog")] + pub fn catalog_status(&self, beamtime_path: &Path) -> DbCatalogStatus { + catalog_status_for_path(&self.db_path, beamtime_path) + } + + #[cfg(feature = "catalog")] + pub fn query_scan_points(&self, beamtime_path: &Path, filter: Option<&CatalogFilter>) -> Vec { + query_scan_points(&self.db_path, beamtime_path, filter).unwrap_or_default() + } + + #[cfg(feature = "catalog")] + pub fn list_beamtime_entries_v2(&self, beamtime_path: &Path) -> Option { + list_beamtime_entries_v2(&self.db_path, beamtime_path).ok() + } +} diff --git a/src/catalog/explorer_query.rs b/src/catalog/explorer_query.rs new file mode 100644 index 0000000..2c580be --- /dev/null +++ b/src/catalog/explorer_query.rs @@ -0,0 +1,157 @@ +#![cfg(feature = "catalog")] + +use crate::catalog::{open_catalog_db, Result}; +use std::path::Path; + +#[derive(Debug, Clone)] +pub struct ExptMeta { + pub name: String, + pub beamtime_count: u32, + pub fits_count: u32, + pub last_indexed: Option, +} + +#[derive(Debug, Clone)] +pub struct BeamtimeMeta { + pub path: std::path::PathBuf, + pub fits_count: Option, + pub last_indexed: Option, +} + +#[derive(Debug, Clone, Copy, PartialEq, Eq)] +pub enum DbCatalogStatus { + Indexed, + Stale, + NotIndexed, +} + +/// Returns experimentalists that have at least one beamtime indexed under data_root. +pub fn list_experimentalists(db_path: &Path, data_root: &Path) -> Result> { + if !db_path.exists() { + return Ok(vec![]); + } + let conn = open_catalog_db(db_path)?; + let data_root_str = data_root.to_string_lossy().to_string(); + let mut stmt = conn.prepare( + "SELECT experimentalist, + COUNT(*) as beamtime_count, + COALESCE( + (SELECT COUNT(*) FROM bt_scan_points WHERE scan_uid IN + (SELECT uid FROM bt_scans WHERE beamtime_id IN + (SELECT id FROM bt_beamtimes WHERE data_root = ?1 AND experimentalist = bt_beamtimes.experimentalist))), + 0 + ) as fits_count, + MAX(last_indexed_at) as last_indexed + FROM bt_beamtimes + WHERE data_root = ?1 AND experimentalist != '' + GROUP BY experimentalist + ORDER BY experimentalist" + )?; + let rows = stmt.query_map(rusqlite::params![&data_root_str], |row| { + Ok(ExptMeta { + name: row.get(0)?, + beamtime_count: row.get::<_, i64>(1)? as u32, + fits_count: row.get::<_, i64>(2)? as u32, + last_indexed: row.get(3)?, + }) + })?; + rows.collect::, _>>().map_err(|e| e.into()) +} + +/// Returns beamtimes for one experimentalist under data_root. +pub fn list_beamtimes_for_expt( + db_path: &Path, + data_root: &Path, + experimentalist: &str, +) -> Result> { + if !db_path.exists() { + return Ok(vec![]); + } + let conn = open_catalog_db(db_path)?; + let data_root_str = data_root.to_string_lossy().to_string(); + let mut stmt = conn.prepare( + "SELECT beamtime_path, last_indexed_at, + COALESCE((SELECT COUNT(*) FROM bt_scan_points WHERE scan_uid IN + (SELECT uid FROM bt_scans WHERE beamtime_id = bt_beamtimes.id)), 0) as fits_count + FROM bt_beamtimes + WHERE data_root = ?1 AND experimentalist = ?2 + ORDER BY beamtime_path" + )?; + let rows = stmt.query_map(rusqlite::params![&data_root_str, experimentalist], |row| { + Ok(BeamtimeMeta { + path: std::path::PathBuf::from(row.get::<_, String>(0)?), + fits_count: Some(row.get::<_, i64>(2)? as u32), + last_indexed: row.get(1)?, + }) + })?; + rows.collect::, _>>().map_err(|e| e.into()) +} + +/// Returns catalog status for a beamtime path. +/// Staleness: compares dir mtime with last_indexed_at. +pub fn catalog_status_for_path(db_path: &Path, beamtime_path: &Path) -> DbCatalogStatus { + if !db_path.exists() { + return DbCatalogStatus::NotIndexed; + } + let conn = match open_catalog_db(db_path) { + Ok(c) => c, + Err(_) => return DbCatalogStatus::NotIndexed, + }; + let path_str = beamtime_path.to_string_lossy().to_string(); + let last_indexed_at: Option = conn + .query_row( + "SELECT last_indexed_at FROM bt_beamtimes WHERE beamtime_path = ?1", + rusqlite::params![&path_str], + |r| r.get(0), + ) + .ok(); + + match last_indexed_at { + None => DbCatalogStatus::NotIndexed, + Some(indexed_timestamp) => { + let dir_mtime = get_dir_max_file_mtime(beamtime_path).unwrap_or(0); + if dir_mtime > indexed_timestamp { + DbCatalogStatus::Stale + } else { + DbCatalogStatus::Indexed + } + } + } +} + +fn get_dir_max_file_mtime(dir: &Path) -> std::io::Result { + let mut max_mtime: i64 = 0; + for entry in walkdir::WalkDir::new(dir) + .follow_links(false) + .into_iter() + .filter_map(|e| e.ok()) + { + let path = entry.path(); + if !path.is_file() { + continue; + } + if path + .extension() + .and_then(|e| e.to_str()) + .map(|e| e.eq_ignore_ascii_case("fits")) + != Some(true) + { + continue; + } + let stem = path.file_stem().and_then(|s| s.to_str()).unwrap_or(""); + if super::is_skippable_stem(stem) { + continue; + } + if let Ok(metadata) = std::fs::metadata(path) { + if let Ok(modified) = metadata.modified() { + if let Ok(duration) = modified.duration_since(std::time::UNIX_EPOCH) { + let mtime = duration.as_secs() as i64; + if mtime > max_mtime { + max_mtime = mtime; + } + } + } + } + } + Ok(max_mtime) +} diff --git a/src/catalog/ingest.rs b/src/catalog/ingest.rs index aa7b0e0..eee4fda 100644 --- a/src/catalog/ingest.rs +++ b/src/catalog/ingest.rs @@ -118,32 +118,51 @@ pub fn ingest_beamtime( header_items: &[String], incremental: bool, progress_tx: Option>, +) -> Result { + ingest_beamtime_with_context(beamtime_dir, None, None, header_items, incremental, progress_tx, None) +} + +pub fn ingest_beamtime_with_context( + beamtime_dir: &Path, + data_root: Option<&Path>, + experimentalist: Option<&str>, + header_items: &[String], + incremental: bool, + progress_tx: Option>, + cancel: Option>, ) -> Result { #[cfg(feature = "parallel_ingest")] { - return super::ingest_parallel::ingest_beamtime_pipelined( + return super::ingest_parallel::ingest_beamtime_pipelined_with_context( beamtime_dir, + data_root, + experimentalist, header_items, incremental, progress_tx, + cancel, ); } #[cfg(not(feature = "parallel_ingest"))] { - ingest_beamtime_sequential(beamtime_dir, header_items, incremental, progress_tx) + ingest_beamtime_sequential(beamtime_dir, data_root, experimentalist, header_items, incremental, progress_tx, cancel) } } #[cfg(not(feature = "parallel_ingest"))] fn ingest_beamtime_sequential( beamtime_dir: &Path, + data_root: Option<&Path>, + experimentalist: Option<&str>, header_items: &[String], incremental: bool, progress_tx: Option>, + cancel: Option>, ) -> Result { - let db_path = super::resolve_catalog_path(beamtime_dir); + let db_path = resolve_ingest_target(beamtime_dir, data_root); let conn = open_or_create_db(beamtime_dir)?; let beamtime_id = ensure_bt_beamtime(&conn, beamtime_dir)?; + upsert_beamtime_record(&conn, beamtime_dir, data_root, experimentalist)?; let discovered = super::discover_fits_paths(beamtime_dir)?; let path_to_mtime: HashMap = discovered .iter() @@ -194,6 +213,9 @@ fn ingest_beamtime_sequential( ..ReadFitsOptions::default() }; for chunk in to_ingest.chunks(BATCH_SIZE) { + if cancel.as_ref().map(|c| c.load(std::sync::atomic::Ordering::Relaxed)).unwrap_or(false) { + break; + } let chunk_vec: Vec = chunk.to_vec(); let df = read_fits_metadata_batch(chunk_vec, &opts)?; if !use_normalized_only { @@ -369,6 +391,34 @@ pub(crate) fn prune_missing_files(conn: &rusqlite::Connection, known_paths: &[&s Ok(()) } +pub fn resolve_ingest_target(beamtime_dir: &Path, data_root: Option<&Path>) -> PathBuf { + match data_root { + Some(root) => root.join(".pyref").join("catalog.db"), + None => super::resolve_catalog_path(beamtime_dir), + } +} + +pub(crate) fn upsert_beamtime_record( + conn: &rusqlite::Connection, + beamtime_dir: &Path, + data_root: Option<&Path>, + experimentalist: Option<&str>, +) -> Result<()> { + let path_str = beamtime_dir.to_string_lossy().to_string(); + let data_root_str = data_root.and_then(|p| p.to_str()).unwrap_or(""); + let expt_str = experimentalist.unwrap_or(""); + conn.execute( + "INSERT INTO bt_beamtimes (beamtime_path, data_root, experimentalist, last_indexed_at) + VALUES (?1, ?2, ?3, strftime('%s','now')) + ON CONFLICT(beamtime_path) DO UPDATE SET + data_root = excluded.data_root, + experimentalist = excluded.experimentalist, + last_indexed_at = excluded.last_indexed_at", + rusqlite::params![path_str, data_root_str, expt_str], + )?; + Ok(()) +} + pub(crate) fn ensure_bt_beamtime(conn: &rusqlite::Connection, beamtime_dir: &Path) -> Result { let path_str = beamtime_dir.to_string_lossy().to_string(); conn.execute( diff --git a/src/catalog/ingest_parallel.rs b/src/catalog/ingest_parallel.rs index 3b69e93..5435832 100644 --- a/src/catalog/ingest_parallel.rs +++ b/src/catalog/ingest_parallel.rs @@ -2,7 +2,7 @@ use crate::catalog::ingest::{ ensure_bt_beamtime, prune_bt_scan_points, prune_missing_files, upsert_bt_batch, - upsert_bt_batch_rows, upsert_files_batch, + upsert_bt_batch_rows, upsert_files_batch, resolve_ingest_target, }; use crate::io::BtIngestRow; use crate::catalog::open_or_create_db; @@ -194,3 +194,182 @@ pub fn ingest_beamtime_pipelined( Ok(db_path) } + +pub fn ingest_beamtime_pipelined_with_context( + beamtime_dir: &std::path::Path, + data_root: Option<&std::path::Path>, + experimentalist: Option<&str>, + header_items: &[String], + incremental: bool, + progress_tx: Option>, + cancel: Option>, +) -> Result { + let db_path = resolve_ingest_target(beamtime_dir, data_root); + let conn = open_or_create_db(beamtime_dir)?; + let beamtime_id = ensure_bt_beamtime(&conn, beamtime_dir)?; + + use crate::catalog::ingest::upsert_beamtime_record as upsert_record; + upsert_record(&conn, beamtime_dir, data_root, experimentalist)?; + + let discovered = super::discover_fits_paths_parallel(beamtime_dir)?; + let path_to_mtime: HashMap = discovered + .iter() + .map(|(p, m)| (p.to_string_lossy().to_string(), *m)) + .collect(); + let paths: Vec = discovered.into_iter().map(|(p, _)| p).collect(); + + let use_normalized_only = super::is_new_catalog_layout(beamtime_dir); + let to_ingest: Vec = if incremental { + if use_normalized_only { + let existing: HashMap = conn + .prepare( + "SELECT source_path, source_mtime FROM bt_scan_points WHERE source_path IS NOT NULL", + )? + .query_map([], |r| Ok((r.get::<_, String>(0)?, r.get::<_, i64>(1).unwrap_or(0))))? + .filter_map(|r| r.ok()) + .collect(); + paths + .into_iter() + .filter(|p| { + let key = p.to_string_lossy().to_string(); + let mtime = path_to_mtime.get(&key).copied().unwrap_or(0); + existing + .get(&key) + .is_none_or(|&stored| stored == 0 || mtime > stored) + }) + .collect() + } else { + let existing: HashMap = conn + .prepare("SELECT path, mtime FROM files")? + .query_map([], |r| Ok((r.get::<_, String>(0)?, r.get::<_, i64>(1)?)))? + .filter_map(|r| r.ok()) + .collect(); + paths + .into_iter() + .filter(|p| { + let key = p.to_string_lossy().to_string(); + let mtime = path_to_mtime.get(&key).copied().unwrap_or(0); + existing.get(&key).is_none_or(|&stored| mtime > stored) + }) + .collect() + } + } else { + paths + }; + + let total = to_ingest.len() as u32; + if total == 0 { + let path_list: Vec<&str> = path_to_mtime.keys().map(|s| s.as_str()).collect(); + if !use_normalized_only { + prune_missing_files(&conn, &path_list)?; + } + prune_bt_scan_points(&conn, &path_list)?; + return Ok(db_path); + } + + let opts = ReadFitsOptions { + header_items: header_items.to_vec(), + batch_size: BATCH_SIZE, + ..ReadFitsOptions::default() + }; + + let paths_arc = Arc::new(to_ingest); + let (tx, rx) = mpsc::sync_channel::(CHANNEL_BOUND); + + let paths_arc_reader = Arc::clone(&paths_arc); + let opts_reader = opts.clone(); + let header_items_owned = header_items.to_vec(); + let use_rows_only = use_normalized_only; + let cancel_clone = cancel.clone(); + let reader = thread::spawn(move || { + let len = paths_arc_reader.len(); + let mut start = 0usize; + while start < len { + if cancel_clone.as_ref().map(|c| c.load(std::sync::atomic::Ordering::Relaxed)).unwrap_or(false) { + break; + } + let end = (start + BATCH_SIZE).min(len); + let chunk_vec = paths_arc_reader[start..end].to_vec(); + let chunk_len = chunk_vec.len(); + if use_rows_only { + match read_multiple_fits_headers_only_rows(chunk_vec, &header_items_owned) { + Ok(rows) => { + if tx.send(PipelineMsg::Rows(rows)).is_err() { + return; + } + } + Err(e) => { + let _ = tx.send(PipelineMsg::Error(e.to_string())); + return; + } + } + } else { + match read_fits_metadata_batch(chunk_vec, &opts_reader) { + Ok(df) => { + if tx.send(PipelineMsg::Batch(df)).is_err() { + return; + } + } + Err(e) => { + let _ = tx.send(PipelineMsg::Error(e.to_string())); + return; + } + } + } + start += chunk_len; + } + let _ = tx.send(PipelineMsg::Done); + }); + + let mut processed: u32 = 0; + loop { + match rx.recv() { + Ok(PipelineMsg::Rows(rows)) => { + if !rows.is_empty() { + upsert_bt_batch_rows(&conn, beamtime_id, &rows, &path_to_mtime)?; + processed = (processed as usize + rows.len()).min(paths_arc.len()) as u32; + if let Some(ref ptx) = progress_tx { + let _ = ptx.send((processed, total)); + } + } + } + Ok(PipelineMsg::Batch(df)) => { + let n = df.height(); + if n == 0 { + continue; + } + if !use_normalized_only { + upsert_files_batch(&conn, &df, &path_to_mtime)?; + } + upsert_bt_batch(&conn, beamtime_id, &df, &path_to_mtime)?; + processed = (processed as usize + n).min(paths_arc.len()) as u32; + if let Some(ref ptx) = progress_tx { + let _ = ptx.send((processed, total)); + } + } + Ok(PipelineMsg::Error(e)) => { + let _ = reader.join(); + return Err(CatalogError::Validation(format!( + "read_fits_metadata_batch: {}", + e + ))); + } + Ok(PipelineMsg::Done) => break, + Err(_) => { + let _ = reader.join(); + return Err(CatalogError::Validation( + "ingest pipeline reader disconnected".into(), + )); + } + } + } + let _ = reader.join(); + + let path_list: Vec<&str> = path_to_mtime.keys().map(|s| s.as_str()).collect(); + if !use_normalized_only { + prune_missing_files(&conn, &path_list)?; + } + prune_bt_scan_points(&conn, &path_list)?; + + Ok(db_path) +} diff --git a/src/catalog/mod.rs b/src/catalog/mod.rs index 0f474f0..b522982 100644 --- a/src/catalog/mod.rs +++ b/src/catalog/mod.rs @@ -3,6 +3,7 @@ mod ingest; #[cfg(feature = "parallel_ingest")] mod ingest_parallel; mod query; +mod explorer_query; #[cfg(feature = "zarr")] mod materialize; @@ -30,6 +31,10 @@ pub use query::{ rename_file_in_catalog, scan_from_catalog, set_override, update_beamspot, update_beamspot_scan_point, BeamtimeEntries, CatalogFilter, FileRow, }; +pub use explorer_query::{ + list_experimentalists, list_beamtimes_for_expt, catalog_status_for_path, + ExptMeta, BeamtimeMeta, DbCatalogStatus, +}; #[cfg(feature = "zarr")] pub use materialize::materialize_beamtime; @@ -182,7 +187,10 @@ CREATE TABLE IF NOT EXISTS bt_beamtimes ( year INTEGER NOT NULL DEFAULT 0, month INTEGER NOT NULL DEFAULT 1, esaf_number TEXT NOT NULL DEFAULT '', - label TEXT + label TEXT, + experimentalist TEXT NOT NULL DEFAULT '', + data_root TEXT NOT NULL DEFAULT '', + last_indexed_at INTEGER )"#; const BT_MOTOR_NAMES_TABLE: &str = r#" @@ -483,6 +491,36 @@ fn migrate_add_beam_sigma_column(conn: &Connection) -> Result<()> { Ok(()) } +fn migrate_add_experimentalist_and_data_root(conn: &Connection) -> Result<()> { + let has_experimentalist: bool = conn.query_row( + "SELECT COUNT(1) FROM pragma_table_info('bt_beamtimes') WHERE name = 'experimentalist'", + [], + |r| r.get(0), + )?; + let has_data_root: bool = conn.query_row( + "SELECT COUNT(1) FROM pragma_table_info('bt_beamtimes') WHERE name = 'data_root'", + [], + |r| r.get(0), + )?; + if !has_experimentalist { + let _ = conn.execute( + "ALTER TABLE bt_beamtimes ADD COLUMN experimentalist TEXT NOT NULL DEFAULT ''", + [], + ); + } + if !has_data_root { + let _ = conn.execute( + "ALTER TABLE bt_beamtimes ADD COLUMN data_root TEXT NOT NULL DEFAULT ''", + [], + ); + } + let _ = conn.execute( + "CREATE INDEX IF NOT EXISTS idx_bt_experimentalist ON bt_beamtimes(data_root, experimentalist)", + [], + ); + Ok(()) +} + pub fn open_catalog_db(db_path: &Path) -> Result { let conn = Connection::open(db_path)?; conn.execute_batch(FILES_TABLE)?; @@ -513,6 +551,7 @@ pub fn open_catalog_db(db_path: &Path) -> Result { conn.execute_batch(BT_INDEX_SCAN_POINTS_SCAN)?; conn.execute_batch(BT_INDEX_SCAN_POINTS_SOURCE)?; conn.execute_batch(BT_INDEX_IMAGE_REFS_POINT)?; + migrate_add_experimentalist_and_data_root(&conn)?; Ok(conn) } From c59c67f7f67f9c6ba54a6445931e1c8d44f5e1da Mon Sep 17 00:00:00 2001 From: Harlan D Heilman <73567020+HarlanHeilman@users.noreply.github.com> Date: Sun, 22 Mar 2026 18:53:19 -0700 Subject: [PATCH 11/32] fix: check cancel token before processing batches in parallel ingest --- src/catalog/ingest_parallel.rs | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/src/catalog/ingest_parallel.rs b/src/catalog/ingest_parallel.rs index 5435832..38e48dd 100644 --- a/src/catalog/ingest_parallel.rs +++ b/src/catalog/ingest_parallel.rs @@ -323,6 +323,13 @@ pub fn ingest_beamtime_pipelined_with_context( let mut processed: u32 = 0; loop { + // Check cancel token before processing each batch + if let Some(ref cancel) = cancel { + if cancel.load(std::sync::atomic::Ordering::Relaxed) { + break; + } + } + match rx.recv() { Ok(PipelineMsg::Rows(rows)) => { if !rows.is_empty() { From 671f46df55889e4e822eb57949ec0b774c7b21d2 Mon Sep 17 00:00:00 2001 From: Harlan D Heilman <73567020+HarlanHeilman@users.noreply.github.com> Date: Sun, 22 Mar 2026 19:01:23 -0700 Subject: [PATCH 12/32] feat: Phase 4 - Auto-ingest on beamtime entry with DATA catalog loading Implement auto-ingest workflow when entering Beamtime entries from Explorer: - Add cancel_flag to BeamtimeState for ingest cancellation on Esc - Implement auto-ingest with background thread when user enters Beamtime entry with NotIndexed or Stale catalog_status - Enhance try_recv_ingest to handle ingest completion and reload from DATA-level catalog using CatalogHandle - Update Explorer entry status to Indexed when ingest completes - Handle cancel ingest: Esc during IngestingDirectory pops the screen - BeamtimeState can be popped directly from Beamtime screen if data_root is set (Phase 4 vs legacy launcher) - Export ingest_beamtime_with_context from catalog module This completes the auto-ingest -> DATA catalog query -> status update cycle for Phase 4, removing dependency on per-beamtime catalogs when data_root context is available. --- .../brainstorm/6299-1774202169/.server-info | 1 - .../6299-1774202169/.server-stopped | 1 + src/bin/tui/app.rs | 316 +++++++++++++++--- src/bin/tui/navigator.rs | 20 +- src/bin/tui/run.rs | 27 +- src/catalog/mod.rs | 2 +- 6 files changed, 311 insertions(+), 56 deletions(-) delete mode 100644 .superpowers/brainstorm/6299-1774202169/.server-info create mode 100644 .superpowers/brainstorm/6299-1774202169/.server-stopped diff --git a/.superpowers/brainstorm/6299-1774202169/.server-info b/.superpowers/brainstorm/6299-1774202169/.server-info deleted file mode 100644 index b701237..0000000 --- a/.superpowers/brainstorm/6299-1774202169/.server-info +++ /dev/null @@ -1 +0,0 @@ -{"type":"server-started","port":52313,"host":"127.0.0.1","url_host":"localhost","url":"http://localhost:52313","screen_dir":"/Users/hduva/projects/pyref/.superpowers/brainstorm/6299-1774202169"} diff --git a/.superpowers/brainstorm/6299-1774202169/.server-stopped b/.superpowers/brainstorm/6299-1774202169/.server-stopped new file mode 100644 index 0000000..7de7923 --- /dev/null +++ b/.superpowers/brainstorm/6299-1774202169/.server-stopped @@ -0,0 +1 @@ +{"reason":"idle timeout","timestamp":1774208609920} diff --git a/src/bin/tui/app.rs b/src/bin/tui/app.rs index 835d604..82e4af9 100644 --- a/src/bin/tui/app.rs +++ b/src/bin/tui/app.rs @@ -9,18 +9,22 @@ use std::fs; use std::ops::{Deref, DerefMut}; use std::path::{Path, PathBuf}; use std::sync::mpsc; +use std::sync::Arc; +use std::sync::atomic::AtomicBool; use std::thread; use std::time::Instant; +use super::catalog_handle::CatalogHandle; + #[cfg(feature = "catalog")] use super::scan_type::{classify_scan_type, ReflectivityScanType}; #[cfg(feature = "catalog")] use pyref::build_fits_stem; #[cfg(feature = "catalog")] use pyref::catalog::{ - catalog_file_count, get_scan_point_uid_by_source_path, list_beamtime_entries, - list_beamtime_entries_v2, list_beamtimes, query_files, query_scan_points, register_beamtime, - rename_file_in_catalog, update_beamspot, update_beamspot_scan_point, FileRow, + catalog_file_count, get_scan_point_uid_by_source_path, ingest_beamtime_with_context, + list_beamtime_entries, list_beamtime_entries_v2, list_beamtimes, query_files, query_scan_points, + register_beamtime, rename_file_in_catalog, update_beamspot, update_beamspot_scan_point, FileRow, DEFAULT_INGEST_HEADER_ITEMS, resolve_catalog_path, }; #[cfg(feature = "watch")] @@ -925,6 +929,7 @@ impl App { data_root: None, experimentalist: None, beamtime_path: PathBuf::new(), + cancel_flag: None, }; App { @@ -1020,6 +1025,7 @@ impl App { data_root: None, experimentalist: None, beamtime_path: PathBuf::new(), + cancel_flag: None, }; let mut navigator = super::navigator::Navigator::new( @@ -1119,6 +1125,7 @@ impl App { data_root: None, experimentalist: None, beamtime_path: PathBuf::new(), + cancel_flag: None, }; let mut navigator = super::navigator::Navigator::new( @@ -2578,64 +2585,90 @@ impl App { #[cfg(feature = "catalog")] pub fn try_recv_ingest(&mut self) { - let mut progress_updates = Vec::new(); - if let Some(ref progress_rx) = self.ingest_progress_rx { - while let Ok((cur, tot)) = progress_rx.try_recv() { - progress_updates.push((cur, tot)); - } - } - for (cur, tot) in progress_updates { - self.ingest_progress = Some((cur, tot)); - } + // Handle ingest for top BeamtimeState on stack (including ingest-in-progress from Explorer) + // Extract what we need from the mutable state first + let (ingest_result, bt_path_cloned, data_root_cloned) = { + if let Some(super::navigator::ScreenState::Beamtime(bt_state)) = self.navigator.stack.last_mut() { + let mut progress_updates = Vec::new(); + if let Some(ref progress_rx) = bt_state.ingest_progress_rx { + while let Ok((cur, tot)) = progress_rx.try_recv() { + progress_updates.push((cur, tot)); + } + } + for (cur, tot) in progress_updates { + bt_state.ingest_progress = Some((cur, tot)); + } - let ingest_result = if let Some(ref rx) = self.ingest_rx { - rx.try_recv().ok() - } else { - None + let ingest_result = if let Some(ref rx) = bt_state.ingest_rx { + rx.try_recv().ok() + } else { + None + }; + + let (result, bt_path, data_root) = if let Some(result) = ingest_result { + bt_state.ingest_rx = None; + bt_state.ingest_progress_rx = None; + bt_state.ingest_progress = None; + bt_state.loading_state = LoadingState::Idle; + (Some(result), bt_state.beamtime_path.clone(), bt_state.data_root.clone()) + } else { + (None, PathBuf::new(), None) + }; + + (result, bt_path, data_root) + } else { + (None, PathBuf::new(), None) + } }; + // Now handle the result without holding the mutable borrow if let Some(result) = ingest_result { - self.ingest_rx = None; - self.ingest_progress_rx = None; - self.ingest_progress = None; - self.loading_state = LoadingState::Idle; match result { Ok(()) => { - self.app_error = None; - if let Some(path) = self.pending_ingest_path_mut().take() { - let db_path = resolve_catalog_path(&path); - let file_count = catalog_file_count(&db_path, Some(&path)).ok(); - let _ = register_beamtime(&path, file_count); - if self.current_screen() == Screen::Launcher { - let beamtimes = list_beamtimes().unwrap_or_default(); - let mut list_state = ListState::default(); - if !beamtimes.is_empty() { - list_state.select(Some(0)); - } - if let Some(s) = self.launcher_state_mut() { - s.beamtimes = beamtimes; - s.list_state = list_state; + // Register beamtime + let db_path = resolve_catalog_path(&bt_path_cloned); + let file_count = catalog_file_count(&db_path, Some(&bt_path_cloned)).ok(); + let _ = register_beamtime(&bt_path_cloned, file_count); + + // Reload from DATA catalog and apply it + if let Some((groups, samples, tags, scans)) = + self.load_beamtime_from_catalog(&bt_path_cloned, data_root_cloned.as_ref(), &self.navigator.catalog) + { + if let Some(super::navigator::ScreenState::Beamtime(bt_state)) = self.navigator.stack.last_mut() { + bt_state.all_groups = groups; + bt_state.samples = samples; + bt_state.tags = tags; + bt_state.scans = scans; + bt_state.has_catalog = true; + bt_state.sample_state.select(if !bt_state.samples.is_empty() { Some(0) } else { None }); + bt_state.tag_state.select(if !bt_state.tags.is_empty() { Some(0) } else { None }); + bt_state.scan_list_state.select(if !bt_state.scans.is_empty() { Some(0) } else { None }); + // Inline refresh to avoid self borrow conflict + bt_state.filtered_groups = Self::filter_groups( + &bt_state.all_groups, + &bt_state.selected_samples, + &bt_state.selected_tags, + &bt_state.selected_scans, + &bt_state.search_query, + ); + if !bt_state.filtered_groups.is_empty() { + bt_state.table_state.select(Some(0)); } + bt_state.set_status("Catalog indexed.".to_string(), false); } - let root_str = path.to_string_lossy().into_owned(); - if self.set_root(root_str) { - self.navigator.stack.pop(); - } - self.set_status("Catalog indexed.".to_string(), false); - } else { - self.reload_from_catalog(false); - let db_path = resolve_catalog_path(Path::new(&self.current_root)); - let file_count = catalog_file_count(&db_path, Some(Path::new(&self.current_root))).ok(); - let _ = register_beamtime(Path::new(&self.current_root), file_count); - self.set_status("Catalog indexed.".to_string(), false); } + + // Update Explorer status if it's on the stack below + self.update_explorer_beamtime_status(&bt_path_cloned); } Err(e) => { - let path = self - .pending_ingest_path - .take() - .unwrap_or_else(|| PathBuf::from(&self.current_root)); - self.set_app_error(super::error::TuiError::ingest_failed(path, e, None)); + if let Some(super::navigator::ScreenState::Beamtime(bt_state)) = self.navigator.stack.last_mut() { + let path = bt_state + .pending_ingest_path + .take() + .unwrap_or_else(|| bt_state.beamtime_path.clone()); + bt_state.set_app_error(super::error::TuiError::ingest_failed(path, e, None)); + } } } self.needs_redraw = true; @@ -3181,8 +3214,25 @@ impl App { pub fn explorer_enter(&mut self) { if let Some(state) = self.explorer_state_mut() { if let Some(entry) = state.selected_entry() { - let path = entry.path.clone(); - state.navigate_into(path); + // Check if this is a Beamtime entry that needs indexing + if entry.kind == super::explorer::EntryKind::Beamtime + && (entry.catalog_status == super::explorer::CatalogStatus::NotIndexed + || entry.catalog_status == super::explorer::CatalogStatus::Stale) + { + let bt_path = entry.path.clone(); + let data_root = state.data_root.clone(); + // Get experimentalist name from parent directory name + let experimentalist = bt_path.parent() + .and_then(|p| p.file_name()) + .and_then(|n| n.to_str()) + .map(|s| s.to_string()); + + self.start_beamtime_ingest(bt_path, Some(data_root), experimentalist); + } else { + // Normal navigation + let path = entry.path.clone(); + state.navigate_into(path); + } } self.needs_redraw = true; } @@ -3210,6 +3260,168 @@ impl App { } } } + + /// Phase 4: Start auto-ingest of a beamtime directory. + /// Spawns background ingest thread and pushes BeamtimeState with ingest in progress. + #[cfg(feature = "catalog")] + fn start_beamtime_ingest(&mut self, bt_path: PathBuf, data_root: Option, experimentalist: Option) { + use std::sync::atomic::Ordering; + + let cancel_flag = Arc::new(AtomicBool::new(false)); + let (progress_tx, progress_rx) = mpsc::channel::<(u32, u32)>(); + let (done_tx, done_rx) = mpsc::channel::>(); + + let bt_dir = bt_path.clone(); + let data_root_clone = data_root.clone(); + let experimentalist_clone = experimentalist.clone(); + let cancel_clone = Arc::clone(&cancel_flag); + let default_headers: Vec = pyref::catalog::DEFAULT_INGEST_HEADER_ITEMS + .iter() + .map(|s| s.to_string()) + .collect(); + + thread::spawn(move || { + let result = pyref::catalog::ingest_beamtime_with_context( + &bt_dir, + data_root_clone.as_deref(), + experimentalist_clone.as_deref(), + &default_headers, + true, // incremental + Some(progress_tx), + Some(cancel_clone), + ); + let _ = done_tx.send(result.map(|_| ()).map_err(|e| e.to_string())); + }); + + // Create BeamtimeState for the ingest-in-progress screen + let mut bt_state = super::navigator::BeamtimeState { + all_groups: Vec::new(), + filtered_groups: Vec::new(), + samples: Vec::new(), + tags: Vec::new(), + scans: Vec::new(), + selected_samples: HashSet::new(), + selected_tags: HashSet::new(), + selected_scans: HashSet::new(), + sample_state: ListState::default(), + tag_state: ListState::default(), + scan_list_state: ListState::default(), + table_state: TableState::default(), + sample_scroll_state: ScrollbarState::new(0), + tag_scroll_state: ScrollbarState::new(0), + scan_scroll_state: ScrollbarState::new(0), + table_scroll_state: ScrollbarState::new(0), + expanded_files_scroll_state: ScrollbarState::new(0), + table_sort_column: None, + table_sort_ordering: None, + expanded_table_row: None, + expanded_selected_file_index: None, + expanded_files_scroll_offset: 0, + expanded_files_sort_column: None, + expanded_files_sort_ordering: None, + last_expanded_files_visible: 5, + focus: Focus::Nav, + mode: AppMode::Normal, + current_root: bt_path.to_string_lossy().into_owned(), + search_query: String::new(), + has_catalog: false, + loading_state: LoadingState::IngestingDirectory, + spinner_frame: 0, + ingest_rx: Some(done_rx), + ingest_progress: None, + ingest_progress_rx: Some(progress_rx), + pending_ingest_path: Some(bt_path.clone()), + back_stack: vec![], + forward_stack: vec![], + rename_retag_buffer: String::new(), + status_message: None, + status_message_set_at: None, + #[cfg(feature = "watch")] + catalog_watcher: None, + #[cfg(feature = "watch")] + watcher_events_rx: None, + last_body_rects: None, + scrollbar_drag: None, + app_error: None, + app_warning: None, + keybind_bar_lines: 0, + data_root: data_root.clone(), + experimentalist: experimentalist.clone(), + beamtime_path: bt_path, + cancel_flag: Some(cancel_flag), + }; + + // If data_root is present, try to load from DATA catalog immediately + if let Some(ref root) = data_root { + if let Some((groups, samples, tags, scans)) = + self.load_beamtime_from_catalog(&bt_state.beamtime_path, Some(root), &self.navigator.catalog) + { + bt_state.all_groups = groups; + bt_state.samples = samples; + bt_state.tags = tags; + bt_state.scans = scans; + bt_state.has_catalog = true; + bt_state.sample_state.select(if !bt_state.samples.is_empty() { Some(0) } else { None }); + bt_state.tag_state.select(if !bt_state.tags.is_empty() { Some(0) } else { None }); + bt_state.scan_list_state.select(if !bt_state.scans.is_empty() { Some(0) } else { None }); + self.refresh_filtered_beamtime(&mut bt_state); + } + } + + self.navigator.stack.push(super::navigator::ScreenState::Beamtime(bt_state)); + self.needs_redraw = true; + } + + /// Load beamtime data from DATA-level catalog using CatalogHandle. + #[cfg(feature = "catalog")] + fn load_beamtime_from_catalog( + &self, + beamtime_path: &PathBuf, + data_root: Option<&PathBuf>, + catalog: &CatalogHandle, + ) -> Option<(Vec, Vec, Vec, Vec<(u32, String)>)> { + if data_root.is_none() { + return None; + } + let entries = catalog.list_beamtime_entries_v2(beamtime_path)?; + let rows = catalog.query_scan_points(beamtime_path, None); + let groups = build_groups_from_files(rows); + let scans: Vec<(u32, String)> = entries + .scans + .into_iter() + .map(|(n, s)| (n as u32, s)) + .collect(); + Some((groups, entries.samples, entries.tags, scans)) + } + + /// Helper to refresh filtered groups for a BeamtimeState (used during ingest). + fn refresh_filtered_beamtime(&self, state: &mut super::navigator::BeamtimeState) { + state.filtered_groups = Self::filter_groups( + &state.all_groups, + &state.selected_samples, + &state.selected_tags, + &state.selected_scans, + &state.search_query, + ); + if !state.filtered_groups.is_empty() { + state.table_state.select(Some(0)); + } + } + + /// Update Explorer status when beamtime ingest completes (direct stack mutation). + fn update_explorer_beamtime_status(&mut self, bt_path: &PathBuf) { + // Find the Explorer state in the stack and update it + for state in self.navigator.stack.iter_mut() { + if let super::navigator::ScreenState::Explorer(ref mut explorer) = state { + for entry in explorer.entries.iter_mut() { + if entry.path == *bt_path { + entry.catalog_status = super::explorer::CatalogStatus::Indexed; + } + } + break; + } + } + } } impl Deref for App { diff --git a/src/bin/tui/navigator.rs b/src/bin/tui/navigator.rs index f48fbab..27968c2 100644 --- a/src/bin/tui/navigator.rs +++ b/src/bin/tui/navigator.rs @@ -7,6 +7,8 @@ use std::cmp; use std::collections::HashSet; use std::path::PathBuf; use std::sync::mpsc; +use std::sync::atomic::AtomicBool; +use std::sync::Arc; use std::time::Instant; use ratatui::layout::Rect; use ratatui::widgets::{ListState, ScrollbarState, TableState}; @@ -73,10 +75,11 @@ pub struct BeamtimeState { #[allow(dead_code)] pub keybind_bar_lines: u8, - // Context fields for future phases + // Context fields for Phase 4 pub data_root: Option, pub experimentalist: Option, pub beamtime_path: PathBuf, + pub cancel_flag: Option>, } /// Phase 1 transitional launcher screen state — removed in Phase 4 @@ -109,6 +112,21 @@ pub struct Navigator { pub catalog: CatalogHandle, } +impl BeamtimeState { + pub fn set_status(&mut self, msg: String, is_error: bool) { + self.status_message = Some((msg, is_error)); + self.status_message_set_at = Some(Instant::now()); + } + + pub fn set_app_error(&mut self, e: super::error::TuiError) { + self.app_error = Some(e); + } + + pub fn clear_app_error(&mut self) { + self.app_error = None; + } +} + impl Navigator { pub fn new(beamtime_state: BeamtimeState, catalog: CatalogHandle) -> Self { Navigator { diff --git a/src/bin/tui/run.rs b/src/bin/tui/run.rs index da602a9..8c6a1c0 100644 --- a/src/bin/tui/run.rs +++ b/src/bin/tui/run.rs @@ -711,6 +711,19 @@ pub fn handle_event(app: &mut App, key: crossterm::event::KeyEvent) -> bool { app.clear_app_warning(); return false; } + // Phase 4: Cancel ingest if in progress + #[cfg(feature = "catalog")] + if let Some(super::navigator::ScreenState::Beamtime(bt)) = app.navigator.stack.last_mut() { + if bt.loading_state == super::app::LoadingState::IngestingDirectory { + if let Some(ref flag) = bt.cancel_flag { + flag.store(true, std::sync::atomic::Ordering::Relaxed); + } + // Pop the beamtime state to return to explorer + app.navigator.stack.pop(); + app.needs_redraw = true; + return false; + } + } } if let Some(ref e) = app.app_error { #[cfg(feature = "catalog")] @@ -729,7 +742,19 @@ pub fn handle_event(app: &mut App, key: crossterm::event::KeyEvent) -> bool { && app.mode() == super::app::AppMode::Normal && keymap::from_key_event(key, &app.keymap) == Action::Cancel { - app.go_to_launcher(); + // Phase 4: Pop BeamtimeState if it came from Explorer (has data_root set) + if let Some(super::navigator::ScreenState::Beamtime(bt)) = app.navigator.stack.last() { + if bt.data_root.is_some() && bt.loading_state == super::app::LoadingState::Idle { + app.navigator.stack.pop(); + app.needs_redraw = true; + return false; + } + } + // Otherwise go to launcher (if launcher still exists) + #[cfg(not(feature = "tui"))] + { + app.go_to_launcher(); + } return false; } diff --git a/src/catalog/mod.rs b/src/catalog/mod.rs index b522982..8fa39e6 100644 --- a/src/catalog/mod.rs +++ b/src/catalog/mod.rs @@ -22,7 +22,7 @@ use walkdir::WalkDir; pub const CATALOG_DB_NAME: &str = ".pyref_catalog.db"; -pub use ingest::{ingest_beamtime, DEFAULT_INGEST_HEADER_ITEMS}; +pub use ingest::{ingest_beamtime, ingest_beamtime_with_context, DEFAULT_INGEST_HEADER_ITEMS}; #[cfg(feature = "parallel_ingest")] pub use ingest_parallel::ingest_beamtime_pipelined; pub use query::{ From d25dc19f228d95ee100dfde3a5fe1462510ed7dc Mon Sep 17 00:00:00 2001 From: Harlan D Heilman <73567020+HarlanHeilman@users.noreply.github.com> Date: Sun, 29 Mar 2026 14:19:02 -0700 Subject: [PATCH 13/32] feat: update dependencies and enhance catalog functionality - Added new dependencies: `tiled`, `ipyaggrid`, `altair`, and `panel` to `pyproject.toml`. - Introduced a new `nsls` section in `pyproject.toml` for additional package management. - Updated `uv.lock` with new package entries including `accessible-pygments`, `alabaster`, and `altair`. - Enhanced the `continual-learning` state management with updated timestamps and paths. - Improved the `tui` application structure by refining the handling of catalog-related features and explorer state management. This commit lays the groundwork for improved data visualization and management within the TUI, aligning with ongoing enhancements in the catalog structure. --- .../hooks/state/continual-learning-index.json | 78 +- .cursor/hooks/state/continual-learning.json | 8 +- docs/prototype.ipynb | 80 +- notebooks/nsls_ii.ipynb | 2141 +++++++++++++++++ notebooks/nsns_xrr_lineup.ipynb | 10 + notebooks/sst1-dashboard.ipynb | 662 +++++ pyproject.toml | 10 + src/bin/browser.rs | 76 +- src/bin/tui/app.rs | 213 +- src/bin/tui/config.rs | 3 + src/bin/tui/explorer/heuristic.rs | 44 + src/bin/tui/explorer/mod.rs | 13 +- src/bin/tui/navigator.rs | 4 - src/bin/tui/run.rs | 6 +- src/bin/tui/ui.rs | 10 +- src/catalog/mod.rs | 4 + uv.lock | 1519 +++++++++++- 17 files changed, 4732 insertions(+), 149 deletions(-) create mode 100644 notebooks/nsls_ii.ipynb create mode 100644 notebooks/nsns_xrr_lineup.ipynb create mode 100644 notebooks/sst1-dashboard.ipynb diff --git a/.cursor/hooks/state/continual-learning-index.json b/.cursor/hooks/state/continual-learning-index.json index 3fb981f..ad85263 100644 --- a/.cursor/hooks/state/continual-learning-index.json +++ b/.cursor/hooks/state/continual-learning-index.json @@ -1,49 +1,65 @@ { "version": 1, "transcripts": { - 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{\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n", + "application/vnd.holoviews_load.v0+json": "" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.holoviews_exec.v0+json": "", + "text/html": [ + "
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np.nan).dropna(subset=[\"x\", \"y\"])\n", + " return frame\n", + "\n", + " def _plot_title(self):\n", + " mode_map = {\n", + " \"raw\": \"Raw signal\",\n", + " \"nexafs_tey_over_i0\": \"NEXAFS TEY / I0\",\n", + " \"nexafs_post_edge_norm\": \"NEXAFS post-edge normalized\",\n", + " }\n", + " return mode_map.get(self.calc_mode.value, \"Scan plot\")\n", + "\n", + " def _update_plot(self):\n", + " scan_ids = list(self.scan_select.value or [])\n", + " if not scan_ids or not self.x_var.value or not self.y_var.value:\n", + " self.plot_pane.object = alt.Chart(pd.DataFrame({\"x\": [], \"y\": []})).mark_line()\n", + " return\n", + "\n", + " rows = []\n", + " errors = []\n", + " for sid in scan_ids:\n", + " try:\n", + " rows.append(self._single_scan_frame(sid, self.x_var.value, self.y_var.value))\n", + " except Exception as exc:\n", + " errors.append(f\"{sid}: {exc}\")\n", + "\n", + " if not rows:\n", + " self.plot_pane.object = alt.Chart(pd.DataFrame({\"x\": [], \"y\": []})).mark_line()\n", + " if errors:\n", + " self._set_status(\"; \".join(errors), error=True)\n", + " return\n", + "\n", + " plot_df = pd.concat(rows, ignore_index=True)\n", + " chart = (\n", + " alt.Chart(plot_df)\n", + " .mark_line()\n", + " .encode(\n", + " x=alt.X(\"x:Q\", title=self.x_var.value),\n", + " y=alt.Y(\"y:Q\", title=self._plot_title()),\n", + " color=alt.Color(\"scan_id:N\", title=\"Scan ID\"),\n", + " tooltip=[\"scan_id:N\", \"x:Q\", \"y:Q\"],\n", + " )\n", + " .properties(title=self._plot_title(), height=400)\n", + " .interactive()\n", + " )\n", + " self.plot_pane.object = chart\n", + " if errors:\n", + " self._set_status(\"Plotted with some skipped scans: \" + \"; \".join(errors), error=True)\n", + " else:\n", + " self._set_status(f\"Plotted {len(scan_ids)} scan(s).\")\n", + "\n", + " def show(self):\n", + " controls = [\n", + " self.x_var,\n", + " self.y_var,\n", + " self.calc_mode,\n", + " self.post_edge_min,\n", + " self.post_edge_max,\n", + " ]\n", + " for widget in controls:\n", + " widget.param.watch(lambda *_: self._update_plot(), \"value\")\n", + " return self.view\n", + "\n", + "\n", + "dash = RSoXSToyDashboard()\n", + "dash.show()" + ] + }, + { + "cell_type": "markdown", + "id": "a11c2825-2098-4b22-b4c9-6e95164ee2a9", + "metadata": {}, + "source": [ + "## Characterizing energy resolution\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "63eb63f0-c665-48ce-a658-0db5f9ab49c4", + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'No match for scan_id=103834'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mKeyError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[36]\u001b[39m\u001b[32m, line 111\u001b[39m\n\u001b[32m 103\u001b[39m plt.tight_layout() \u001b[38;5;66;03m## This ensures that subplots don't overlap\u001b[39;00m\n\u001b[32m 105\u001b[39m plt.show()\n\u001b[32m--> \u001b[39m\u001b[32m111\u001b[39m \u001b[43mview_export_HOPG_energy_resolution_scans\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 112\u001b[39m \u001b[43m \u001b[49m\u001b[43mscan_ids\u001b[49m\u001b[43m=\u001b[49m\u001b[43mSCAN_IDS\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 113\u001b[39m \u001b[43m \u001b[49m\u001b[43mexport_scans\u001b[49m\u001b[43m=\u001b[49m\u001b[43mEXPORT_SCANS\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 114\u001b[39m \u001b[43m \u001b[49m\u001b[43mfile_path_export\u001b[49m\u001b[43m=\u001b[49m\u001b[43mFILE_PATH_EXPORT\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 115\u001b[39m \u001b[43m \u001b[49m\u001b[43mfile_name_export\u001b[49m\u001b[43m=\u001b[49m\u001b[43mFILE_NAME_EXPORT\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 116\u001b[39m \u001b[43m)\u001b[49m\n", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[36]\u001b[39m\u001b[32m, line 22\u001b[39m, in \u001b[36mview_export_HOPG_energy_resolution_scans\u001b[39m\u001b[34m(scan_ids, export_scans, file_path_export, file_name_export)\u001b[39m\n\u001b[32m 19\u001b[39m plot_color = plot_colors[index_scan_id]\n\u001b[32m 21\u001b[39m \u001b[38;5;66;03m## Load scan\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m22\u001b[39m scan_raw = \u001b[43mcatalog\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mscan_id\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\n\u001b[32m 23\u001b[39m scan_xarray = scan_raw[\u001b[33m\"\u001b[39m\u001b[33mprimary\u001b[39m\u001b[33m\"\u001b[39m][\u001b[33m\"\u001b[39m\u001b[33mdata\u001b[39m\u001b[33m\"\u001b[39m].read()\n\u001b[32m 24\u001b[39m scan_xarray = scan_xarray.rename_vars({\u001b[33m\"\u001b[39m\u001b[33men_energy\u001b[39m\u001b[33m\"\u001b[39m: \u001b[33m\"\u001b[39m\u001b[33mEnergy\u001b[39m\u001b[33m\"\u001b[39m})\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/projects/pyref/.venv/lib/python3.13/site-packages/bluesky_tiled_plugins/clients/catalog_of_bluesky_runs.py:116\u001b[39m, in \u001b[36mCatalogOfBlueskyRuns.__getitem__\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m 113\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, numbers.Integral):\n\u001b[32m 114\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m key > \u001b[32m0\u001b[39m:\n\u001b[32m 115\u001b[39m \u001b[38;5;66;03m# CASE 2: Interpret key as a scan_id.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m116\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_lookup_by_scan_id\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 117\u001b[39m \u001b[38;5;66;03m# CASE 3: Interpret key as a recently lookup, as in\u001b[39;00m\n\u001b[32m 118\u001b[39m \u001b[38;5;66;03m# `catalog[-1]` is the latest entry.\u001b[39;00m\n\u001b[32m 119\u001b[39m key = \u001b[38;5;28mint\u001b[39m(key)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/projects/pyref/.venv/lib/python3.13/site-packages/bluesky_tiled_plugins/clients/catalog_of_bluesky_runs.py:140\u001b[39m, in \u001b[36mCatalogOfBlueskyRuns._lookup_by_scan_id\u001b[39m\u001b[34m(self, scan_id)\u001b[39m\n\u001b[32m 138\u001b[39m results = \u001b[38;5;28mself\u001b[39m.search(Eq(\u001b[33m\"\u001b[39m\u001b[33mstart.scan_id\u001b[39m\u001b[33m\"\u001b[39m, scan_id))\n\u001b[32m 139\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m results:\n\u001b[32m--> \u001b[39m\u001b[32m140\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mNo match for scan_id=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mscan_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m 141\u001b[39m \u001b[38;5;66;03m# Return latest match.\u001b[39;00m\n\u001b[32m 142\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m results.values().last()\n", + "\u001b[31mKeyError\u001b[39m: 'No match for scan_id=103834'" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def view_export_HOPG_energy_resolution_scans(\n", + " scan_ids,\n", + " export_scans,\n", + " file_path_export,\n", + " file_name_export,\n", + "):\n", + "\n", + " offset_timestamp_seconds = 0\n", + "\n", + " number_rows, number_columns = 1, 3\n", + " fig, axs = plt.subplots(number_rows, number_columns, figsize=(number_columns*5, number_rows*8), edgecolor=(0, 0, 0, 0), linewidth=3)#fig, axs = plt.subplots(number_rows, number_columns, figsize=(number_columns*5, number_rows*4), edgecolor=(0, 0, 0, 0), linewidth=3) #figsize=(3.25, 3.25) for figure\n", + " ## Ensure axs always stays a 2D array\n", + " if number_rows == 1 and number_columns == 1: axs = np.array([[axs]])\n", + " if number_rows == 1 and number_columns > 1: axs = axs.reshape(1, number_columns)\n", + " if number_rows > 1 and number_columns == 1: axs = axs.reshape(number_rows, 1)\n", + " plot_colors = cm.rainbow(np.linspace(1, 0, len(scan_ids)))\n", + "\n", + " for index_scan_id, scan_id in enumerate(scan_ids):\n", + " plot_color = plot_colors[index_scan_id]\n", + "\n", + " ## Load scan\n", + " scan_raw = catalog[int(scan_id)]\n", + " scan_xarray = scan_raw[\"primary\"][\"data\"].read()\n", + " scan_xarray = scan_xarray.rename_vars({\"en_energy\": \"Energy\"})\n", + " scan_xarray = scan_xarray.rename_vars({\"RSoXS Au Mesh Current\": \"I0\"})\n", + " scan_xarray = scan_xarray.rename_vars({\"WAXS Beamstop\": \"Photodiode\"})\n", + " scan_xarray = scan_xarray.rename_vars({\"RSoXS Sample Current\": \"TEY\"})\n", + " scan_xarray = scan_xarray.swap_dims({\"time\": \"Energy\"})\n", + " scan = copy.deepcopy(scan_xarray)\n", + "\n", + " scan = scan.sortby(\"Energy\")\n", + " slits1_vsize = scan_raw[\"baseline\"][\"data\"][\"Upstream Scatter Slits_vsize_setpoint\"].read()[0]\n", + "\n", + " \"\"\"\n", + " ## (optional) Sparse out data if needed\n", + " N = scan.dims['Energy']\n", + " indices_to_keep = np.arange(0, N, 3)\n", + " last_index = N - 1\n", + " if last_index not in indices_to_keep:\n", + " # Append the last index to ensure the final energy point is kept\n", + " indices_to_keep = np.append(indices_to_keep, last_index)\n", + " scan = scan.isel(Energy=indices_to_keep)\n", + " \"\"\"\n", + "\n", + " ## Export scan\n", + " if export_scans:\n", + " sheet_name = \"ScanID\" + str(scan_id)\n", + " scan_dictionary = {\n", + " \"Energy\": scan[\"Energy\"],\n", + " \"I0\": scan[\"I0\"],\n", + " \"TEY\": scan[\"TEY\"],\n", + " \"Slits1_vsize\": slits1_vsize,\n", + " \"ScanID\": int(scan_id),\n", + " \"Timestamp_Seconds\": (scan_raw.metadata[\"start\"][\"time\"] - offset_timestamp_seconds)\n", + " }\n", + " scan_dataframe = pd.DataFrame(scan_dictionary)\n", + " if os.path.exists(os.path.join(file_path_export, file_name_export)):\n", + " with pd.ExcelWriter(os.path.join(file_path_export, file_name_export), mode='a') as writer: ## If the file already exists, it will just add a new sheet, otherwise create a new file. Or maybe not? Revisit how this works.\n", + " scan_dataframe.to_excel(writer, sheet_name=sheet_name, index=True)\n", + " else:\n", + " scan_dataframe.to_excel(os.path.join(file_path_export, file_name_export), sheet_name=sheet_name, index=True)\n", + "\n", + " ## Gather plot labels and information to print to serve as a progress bar\n", + " polarization = scan_raw[\"baseline\"][\"data\"][\"en_polarization\"].read()[0]\n", + " timestamp = str(datetime.datetime.fromtimestamp(int(scan_raw.metadata[\"start\"][\"time\"] - offset_timestamp_seconds)))\n", + " plot_label = \"Scan ID = \" + str(scan_id) + \", \\nslits1.vsize = \" + str(slits1_vsize)\n", + " print(\"Scan ID = \" + str(scan_id) + \", Time = \" + str(timestamp) + \", Polarization = \" + str(polarization) + \"°\")\n", + "\n", + " ## Plot data\n", + " ax = axs[0, 0]\n", + " ax.plot(scan[\"Energy\"], scan[\"TEY\"], label=plot_label, marker=\".\", markersize=0, color=plot_color, linestyle=\"solid\", linewidth=2)\n", + " ax.set_ylabel(\"Raw sample current\", color=(0, 0, 0, 1), size=12)\n", + "\n", + " start_label = scan[\"TEY\"][\"Energy\"].sel(Energy=340, method=\"nearest\").item()\n", + " end_label = scan[\"TEY\"][\"Energy\"].sel(Energy=345, method=\"nearest\").item()\n", + " ax = axs[0, 1]\n", + " ax.plot(scan[\"Energy\"], (scan[\"TEY\"]/scan[\"TEY\"].sel(Energy=slice(start_label, end_label)).mean()), label=plot_label, marker=\".\", markersize=0, color=plot_color, linestyle=\"solid\", linewidth=2)\n", + " ax.set_ylabel(\"Raw sample current \\n normalized to post-edge\", color=(0, 0, 0, 1), size=12)\n", + " ax = axs[0, 2]\n", + " ax.plot(scan[\"Energy\"], (scan[\"TEY\"]/scan[\"TEY\"].sel(Energy=slice(start_label, end_label)).mean()), label=plot_label, marker=\".\", markersize=0, color=plot_color, linestyle=\"solid\", linewidth=2)\n", + " ax.set_ylabel(\"Raw sample current \\n normalized to post-edge\", color=(0, 0, 0, 1), size=12)\n", + " ax.set_xlim([285, 310])\n", + "\n", + " ## Plot Formatting\n", + " for index_row in np.arange(0, number_rows, 1):\n", + " for index_column in np.arange(0, number_columns, 1):\n", + " ax = axs[index_row, index_column]\n", + " #ax.axvline(291.65, color=(0, 0, 0, 1), linestyle=\"dashed\")\n", + " ## Subplot and axes labels\n", + " ax.set_xlabel(\"Energy (eV)\", color=(0, 0, 0, 1), size=12)\n", + " #ax.set_ylabel(\"I0\", color=(0, 0, 0, 1), size=12)\n", + " ## Axes scaling and ranges\n", + " ax.set_xscale(\"linear\")\n", + " ax.set_yscale(\"linear\")\n", + " #ax.set_xlim([0, 0.08])\n", + " ax.set_ylim(bottom=0, top=None, auto=True)\n", + " ## Border formatting\n", + " for Border in np.array([\"top\", \"bottom\", \"left\", \"right\"]):\n", + " ax.spines[Border].set_linewidth(2) ## axes/border linewidths\n", + " ax.spines[Border].set_color((0, 0, 0, 1)) ## axes/border colors\n", + " for Axis in np.array([\"x\", \"y\"]): ax.tick_params(axis=Axis, colors=(0, 0, 0, 1), width=2)\n", + " axs[0, 2].legend(ncol=1, loc=\"center left\", bbox_to_anchor=(1.05, 0.5), edgecolor=(1, 1, 1, 1))\n", + " plt.tight_layout() ## This ensures that subplots don't overlap\n", + "\n", + " plt.show()\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "view_export_HOPG_energy_resolution_scans(\n", + " scan_ids=SCAN_IDS,\n", + " export_scans=EXPORT_SCANS,\n", + " file_path_export=FILE_PATH_EXPORT,\n", + " file_name_export=FILE_NAME_EXPORT,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5d1c21ae-ac0e-4a4c-91a4-45a4abf9cb58", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b19de2d-269c-4478-a126-55aea4d9ff24", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9c7bf1d4-be52-4c1b-973b-c7f268a2ae11", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5c532294-fa74-4fc1-b173-8cdb0f6ceea9", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "96ea7ee4-08fc-4a15-a8db-b498150ec73f", + "metadata": {}, + "source": [ + "## ---------------------------------------------------------------------------------------------\n" + ] + }, + { + "cell_type": "markdown", + "id": "93ae7124-aff2-4c2a-a083-e08ddada6805", + "metadata": {}, + "source": [ + "## ---------------------------------------------------------------------------------------------\n" + ] + }, + { + "cell_type": "markdown", + "id": "f479d3af-a99e-489c-8194-2b7cb5d4f2fd", + "metadata": {}, + "source": [ + "## ---------------------------------------------------------------------------------------------\n" + ] + }, + { + "cell_type": "markdown", + "id": "8b1848de-1a38-4a20-80e4-2069509f0dcc", + "metadata": {}, + "source": [ + "## ---------------------------------------------------------------------------------------------\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/nsns_xrr_lineup.ipynb b/notebooks/nsns_xrr_lineup.ipynb new file mode 100644 index 0000000..21c2679 --- /dev/null +++ b/notebooks/nsns_xrr_lineup.ipynb @@ -0,0 +1,10 @@ +{ + "cells": [], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/sst1-dashboard.ipynb b/notebooks/sst1-dashboard.ipynb new file mode 100644 index 0000000..eba027b --- /dev/null +++ b/notebooks/sst1-dashboard.ipynb @@ -0,0 +1,662 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# SST1 RSoXS Direct Tiled Dashboard\n", + "\n", + "Interactive dashboard for live and post-run scan inspection without the legacy loader abstraction.\n", + "\n", + "- Connect directly to Tiled (`from_profile` then URI fallback).\n", + "- Filter and group scans by sample and run type.\n", + "- Select scans and plot arbitrary x/y traces.\n", + "- Apply simple NEXAFS transforms (`TEY/I0`, post-edge normalization).\n", + "- Enable periodic refresh for near-real-time experiment monitoring.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "0a2894ed", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n const version = '3.8.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n const reloading = false;\n const Bokeh = root.Bokeh;\n const BK_RE = /^https:\\/\\/cdn\\.bokeh\\.org\\/bokeh\\/(release|dev)\\/bokeh-/;\n const PN_RE = 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"application/vnd.holoviews_load.v0+json": "" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.holoviews_exec.v0+json": "", + "text/html": [ + "
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\n", + "" + ] + }, + "metadata": { + "application/vnd.holoviews_exec.v0+json": { + "id": "724ec8f0-58ed-4265-b164-2df1c7961648" + } + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'/nsls2/software/etc/tiled/profiles'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import panel as pn\n", + "from bokeh.models import ColumnDataSource, HoverTool\n", + "from bokeh.plotting import figure\n", + "from tiled.client import from_profile, from_uri\n", + "\n", + "pn.extension(\"tabulator\")\n", + "os.environ.setdefault(\"TILED_SITE_PROFILES\", \"/nsls2/software/etc/tiled/profiles\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "48617cdc", + "metadata": {}, + "outputs": [], + "source": [ + "class SST1DirectTiledDashboard:\n", + " \"\"\"Interactive SST1 dashboard backed by direct Tiled access.\"\"\"\n", + "\n", + " def __init__(self, tiled_uri=\"https://tiled.nsls2.bnl.gov\", beamline=\"rsoxs\", stream=\"raw\", profile=\"rsoxs\"):\n", + " self.tiled_uri = tiled_uri\n", + " self.beamline = beamline\n", + " self.stream = stream\n", + " self.profile = profile\n", + " self.catalog = None\n", + " self.authenticated = False\n", + " self.catalog_df = pd.DataFrame()\n", + " self.scan_cache = {}\n", + " self._refresh_callback = None\n", + "\n", + " self.username = pn.widgets.TextInput(name=\"Username\", placeholder=\"BNL username\", width=180)\n", + " self.password = pn.widgets.PasswordInput(name=\"Password\", placeholder=\"BNL password\", width=180)\n", + " self.login_btn = pn.widgets.Button(name=\"Authenticate\", button_type=\"primary\", width=130)\n", + "\n", + " self.last_n = pn.widgets.IntInput(name=\"Latest runs\", value=300, start=1, width=140)\n", + " self.cycle = pn.widgets.Select(name=\"Cycle\", options=[\"Any\"], value=\"Any\", width=140)\n", + " self.institution = pn.widgets.Select(name=\"Institution\", options=[\"Any\"], value=\"Any\", width=160)\n", + " self.sample_id = pn.widgets.Select(name=\"Sample\", options=[\"Any\"], value=\"Any\", width=220)\n", + " self.run_type = pn.widgets.Select(name=\"Run type\", options=[\"Any\"], value=\"Any\", width=180)\n", + " self.plan_filter = pn.widgets.TextInput(name=\"Plan regex\", placeholder=\"carbon|oxygen|nexafs\", width=220)\n", + " self.refresh_btn = pn.widgets.Button(name=\"Refresh\", button_type=\"primary\")\n", + " self.auto_refresh = pn.widgets.Toggle(name=\"Auto refresh\", button_type=\"light\", value=False, width=120)\n", + " self.refresh_sec = pn.widgets.IntInput(name=\"Every seconds\", value=20, start=5, width=130)\n", + "\n", + " self.group_by = pn.widgets.Select(name=\"Group by\", options=[\"none\", \"sample_id\", \"run_type\", \"sample_id + run_type\"], value=\"sample_id + run_type\", width=220)\n", + " self.scan_select = pn.widgets.MultiChoice(name=\"Scans\", options=[], value=[], width=700)\n", + "\n", + " self.x_var = pn.widgets.Select(name=\"X\", options=[], width=200)\n", + " self.y_var = pn.widgets.Select(name=\"Y\", options=[], width=280)\n", + " self.y_transform = pn.widgets.Select(name=\"Transform\", options=[\"raw\", \"tey_over_i0\", \"post_edge_norm\"], value=\"raw\", width=180)\n", + " self.post_edge_min = pn.widgets.FloatInput(name=\"Post-edge min\", value=340.0, width=130)\n", + " self.post_edge_max = pn.widgets.FloatInput(name=\"Post-edge max\", value=345.0, width=130)\n", + "\n", + " self.status = pn.pane.Markdown(\"Authenticate with username/password.\", height=30)\n", + " self.group_summary = pn.pane.Markdown(\"\", height=170)\n", + " self.plot_pane = pn.pane.Bokeh(height=430, sizing_mode=\"stretch_width\")\n", + " self.table = pn.pane.DataFrame(pd.DataFrame(), height=320, sizing_mode=\"stretch_width\")\n", + "\n", + " self.login_btn.on_click(self._authenticate)\n", + " self.refresh_btn.on_click(self._refresh_catalog)\n", + " self.auto_refresh.param.watch(self._toggle_auto_refresh, \"value\")\n", + " self.scan_select.param.watch(self._on_scan_selection_change, \"value\")\n", + "\n", + " watched = [self.cycle, self.institution, self.sample_id, self.run_type, self.plan_filter, self.group_by, self.x_var, self.y_var, self.y_transform, self.post_edge_min, self.post_edge_max]\n", + " for widget in watched:\n", + " widget.param.watch(self._react, \"value\")\n", + "\n", + " self.view = pn.Column(\n", + " pn.Row(self.username, self.password, self.login_btn),\n", + " pn.Row(self.refresh_btn, self.auto_refresh, self.refresh_sec, self.last_n, self.status),\n", + " pn.Row(self.cycle, self.institution, self.sample_id, self.run_type, self.plan_filter),\n", + " pn.Row(self.group_by),\n", + " self.group_summary,\n", + " self.table,\n", + " self.scan_select,\n", + " pn.Row(self.x_var, self.y_var, self.y_transform, self.post_edge_min, self.post_edge_max),\n", + " self.plot_pane,\n", + " )\n", + " def _connect_catalog(self, username, password):\n", + " \"\"\"Create a direct connection to the SST1 RSoXS Tiled catalog.\"\"\"\n", + " try:\n", + " return from_profile(self.profile)\n", + " except Exception:\n", + " root = from_uri(self.tiled_uri, username=username, password=password)\n", + " return root[self.beamline][self.stream]\n", + "\n", + " def _authenticate(self, *_):\n", + " \"\"\"Authenticate user and initialize catalog access.\"\"\"\n", + " user = (self.username.value or \"\").strip()\n", + " pwd = self.password.value or \"\"\n", + " if user == \"\" or pwd == \"\":\n", + " self._set_status(\"Username and password are required.\", error=True)\n", + " return\n", + " try:\n", + " self.catalog = self._connect_catalog(user, pwd)\n", + " self.authenticated = True\n", + " self._set_status(\"Authentication successful.\")\n", + " self._refresh_catalog()\n", + " except Exception as exc:\n", + " self.catalog = None\n", + " self.authenticated = False\n", + " self._set_status(f\"Authentication failed: {exc}\", error=True)\n", + "\n", + " def _set_status(self, text, error=False):\n", + " \"\"\"Update status line text.\"\"\"\n", + " self.status.object = f\"**Error:** {text}\" if error else text\n", + "\n", + " def _safe_start(self, run, key, default=\"N/A\"):\n", + " \"\"\"Get a value from start document with fallback.\"\"\"\n", + " return run.start.get(key, default) if hasattr(run, \"start\") else default\n", + "\n", + " def _safe_stop_num_events(self, run):\n", + " \"\"\"Extract primary point count from stop document.\"\"\"\n", + " try:\n", + " return int(run.stop.get(\"num_events\", {}).get(\"primary\", 0))\n", + " except Exception:\n", + " return 0\n", + "\n", + " def _infer_run_type(self, plan_name):\n", + " \"\"\"Infer run type from plan name.\"\"\"\n", + " s = str(plan_name).lower()\n", + " if any(x in s for x in [\"nexafs\", \"xafs\", \"carbon\", \"oxygen\", \"fluorine\", \"nitrogen\", \"energy\"]):\n", + " return \"energy_scan\"\n", + " if any(x in s for x in [\"angle\", \"theta\", \"th\", \"rock\", \"grazing\"]):\n", + " return \"angle_scan\"\n", + " if \"count\" in s:\n", + " return \"count\"\n", + " return \"other\"\n", + "\n", + " def _catalog_to_df(self, limit):\n", + " \"\"\"Build a metadata dataframe from latest catalog runs.\"\"\"\n", + " rows = []\n", + " items = list(self.catalog.items())\n", + " if limit is not None and limit > 0:\n", + " items = items[-limit:]\n", + " for uid, run in items:\n", + " start_time = self._safe_start(run, \"time\", np.nan)\n", + " try:\n", + " start_time = pd.to_datetime(start_time, unit=\"s\", utc=True).tz_convert(None)\n", + " except Exception:\n", + " start_time = pd.NaT\n", + " plan_name = self._safe_start(run, \"plan_name\", \"N/A\")\n", + " rows.append({\n", + " \"uid\": str(uid),\n", + " \"scan_id\": str(self._safe_start(run, \"scan_id\", \"N/A\")),\n", + " \"start_time\": start_time,\n", + " \"cycle\": str(self._safe_start(run, \"cycle\", \"N/A\")),\n", + " \"institution\": str(self._safe_start(run, \"institution\", \"N/A\")),\n", + " \"project\": str(self._safe_start(run, \"project_name\", \"N/A\")),\n", + " \"sample_name\": str(self._safe_start(run, \"sample_name\", \"N/A\")),\n", + " \"sample_id\": str(self._safe_start(run, \"sample_id\", \"N/A\")),\n", + " \"plan_name\": str(plan_name),\n", + " \"run_type\": self._infer_run_type(plan_name),\n", + " \"exit_status\": str(getattr(getattr(run, \"stop\", {}), \"get\", lambda *_: \"N/A\")(\"exit_status\", \"N/A\")),\n", + " \"num_points\": self._safe_stop_num_events(run),\n", + " })\n", + " df = pd.DataFrame(rows)\n", + " if not df.empty:\n", + " df = df.sort_values(\"start_time\", ascending=False, na_position=\"last\").reset_index(drop=True)\n", + " return df\n", + "\n", + " def _refresh_catalog(self, *_):\n", + " \"\"\"Refresh metadata table from Tiled.\"\"\"\n", + " if not self.authenticated or self.catalog is None:\n", + " self._set_status(\"Authenticate first.\", error=True)\n", + " return\n", + " try:\n", + " previous = set(self.scan_select.value or [])\n", + " self.catalog_df = self._catalog_to_df(self.last_n.value)\n", + " if self.catalog_df.empty:\n", + " self._set_table_value(pd.DataFrame())\n", + " self.scan_select.options = []\n", + " self.scan_select.value = []\n", + " self.group_summary.object = \"No runs found.\"\n", + " self.plot_pane.object = self._empty_figure()\n", + " self._set_status(\"Catalog returned no runs.\", error=True)\n", + " return\n", + " self._update_filter_options()\n", + " filtered = self._filtered_catalog()\n", + " show_cols = [\"scan_id\", \"start_time\", \"sample_id\", \"sample_name\", \"run_type\", \"plan_name\", \"cycle\", \"institution\", \"num_points\", \"exit_status\"]\n", + " self._set_table_value(filtered[show_cols].copy() if not filtered.empty else pd.DataFrame(columns=show_cols))\n", + " scan_options = filtered[\"scan_id\"].astype(str).tolist()\n", + " self.scan_select.options = scan_options\n", + " kept = [sid for sid in previous if sid in scan_options]\n", + " if kept:\n", + " self.scan_select.value = kept\n", + " else:\n", + " self.scan_select.value = scan_options[: min(4, len(scan_options))]\n", + " self._update_group_summary(filtered)\n", + " self._update_axis_options()\n", + " self._set_status(f\"Loaded {len(filtered)} run(s) from latest catalog window.\")\n", + " except Exception as exc:\n", + " self._set_status(str(exc), error=True)\n", + "\n", + " def _update_filter_options(self):\n", + " \"\"\"Populate filter widget choices from current metadata.\"\"\"\n", + " df = self.catalog_df\n", + " self.cycle.options = [\"Any\"] + sorted(df[\"cycle\"].dropna().astype(str).unique().tolist())\n", + " self.institution.options = [\"Any\"] + sorted(df[\"institution\"].dropna().astype(str).unique().tolist())\n", + " self.sample_id.options = [\"Any\"] + sorted(df[\"sample_id\"].dropna().astype(str).unique().tolist())\n", + " self.run_type.options = [\"Any\"] + sorted(df[\"run_type\"].dropna().astype(str).unique().tolist())\n", + "\n", + " def _set_table_value(self, frame):\n", + " \"\"\"Replace table data for the DataFrame pane.\"\"\"\n", + " self.table.object = frame\n", + "\n", + " def _filtered_catalog(self):\n", + " \"\"\"Apply UI filters to metadata dataframe.\"\"\"\n", + " df = self.catalog_df.copy()\n", + " if self.cycle.value != \"Any\":\n", + " df = df[df[\"cycle\"] == self.cycle.value]\n", + " if self.institution.value != \"Any\":\n", + " df = df[df[\"institution\"] == self.institution.value]\n", + " if self.sample_id.value != \"Any\":\n", + " df = df[df[\"sample_id\"] == self.sample_id.value]\n", + " if self.run_type.value != \"Any\":\n", + " df = df[df[\"run_type\"] == self.run_type.value]\n", + " if self.plan_filter.value.strip() != \"\":\n", + " df = df[df[\"plan_name\"].str.contains(self.plan_filter.value.strip(), case=False, regex=True, na=False)]\n", + " return df.reset_index(drop=True)\n", + "\n", + " def _update_group_summary(self, filtered):\n", + " \"\"\"Render grouped run counts.\"\"\"\n", + " if filtered.empty:\n", + " self.group_summary.object = \"No grouped results.\"\n", + " return\n", + " mode = self.group_by.value\n", + " if mode == \"none\":\n", + " self.group_summary.object = f\"Total runs: **{len(filtered)}**\"\n", + " return\n", + " keys = [\"sample_id\", \"run_type\"] if mode == \"sample_id + run_type\" else [mode]\n", + " grouped = filtered.groupby(keys, dropna=False).size().reset_index(name=\"count\").sort_values(\"count\", ascending=False)\n", + " self.group_summary.object = grouped.to_markdown(index=False)\n", + "\n", + " def _get_run(self, scan_id):\n", + " \"\"\"Resolve run by scan id from current catalog slice.\"\"\"\n", + " match = self.catalog_df[self.catalog_df[\"scan_id\"].astype(str) == str(scan_id)]\n", + " if match.empty:\n", + " raise KeyError(f\"Scan id {scan_id} not in current catalog window\")\n", + " uid = match.iloc[0][\"uid\"]\n", + " return self.catalog[uid]\n", + "\n", + " def _load_scan_data(self, scan_id):\n", + " \"\"\"Load primary xarray dataset for a scan.\"\"\"\n", + " key = str(scan_id)\n", + " if key in self.scan_cache:\n", + " return self.scan_cache[key]\n", + " run = self._get_run(scan_id)\n", + " ds = run[\"primary\"][\"data\"].read()\n", + " self.scan_cache[key] = ds\n", + " return ds\n", + "\n", + " def _candidate_x(self, ds):\n", + " \"\"\"Suggest x-axis variables from a dataset.\"\"\"\n", + " variables = set(getattr(ds, \"variables\", {}).keys())\n", + " dims = list(getattr(ds, \"sizes\", {}).keys())\n", + " out = []\n", + " for name in [\"en_energy\", \"en_energy_setpoint\", \"energy\", \"Energy\", \"time\"]:\n", + " if name in variables or name in dims:\n", + " out.append(name)\n", + " for name in dims:\n", + " if name not in out:\n", + " out.append(name)\n", + " if not out:\n", + " out = list(getattr(ds, \"data_vars\", {}).keys())\n", + " return out\n", + "\n", + " def _candidate_y(self, ds):\n", + " \"\"\"Suggest y-axis variables from data vars.\"\"\"\n", + " y = list(getattr(ds, \"data_vars\", {}).keys())\n", + " preferred = [\"RSoXS Sample Current\", \"TEY\", \"WAXS Beamstop\", \"DM7 photodiode\", \"Photodiode\", \"RSoXS Au Mesh Current\", \"I0\"]\n", + " ordered = [v for v in preferred if v in y]\n", + " for v in y:\n", + " if v not in ordered:\n", + " ordered.append(v)\n", + " return ordered\n", + "\n", + " def _on_scan_selection_change(self, *_):\n", + " \"\"\"React to scan selection updates.\"\"\"\n", + " self._update_axis_options()\n", + "\n", + " def _update_axis_options(self):\n", + " \"\"\"Refresh axis options from first selected scan.\"\"\"\n", + " scans = list(self.scan_select.value or [])\n", + " if not scans:\n", + " self.x_var.options = []\n", + " self.y_var.options = []\n", + " self.plot_pane.object = self._empty_figure()\n", + " return\n", + " try:\n", + " ds = self._load_scan_data(scans[0])\n", + " x_opts = self._candidate_x(ds)\n", + " y_opts = self._candidate_y(ds)\n", + " self.x_var.options = x_opts\n", + " self.y_var.options = y_opts\n", + " if x_opts and self.x_var.value not in x_opts:\n", + " self.x_var.value = x_opts[0]\n", + " if y_opts and self.y_var.value not in y_opts:\n", + " self.y_var.value = y_opts[0]\n", + " self._update_plot()\n", + " except Exception as exc:\n", + " self._set_status(str(exc), error=True)\n", + "\n", + " def _to_1d(self, arr):\n", + " \"\"\"Convert an xarray-like variable to a flat numpy array.\"\"\"\n", + " try:\n", + " return np.asarray(arr.values).reshape(-1)\n", + " except Exception:\n", + " return np.asarray(list(arr)).reshape(-1)\n", + "\n", + " def _compute_transformed_y(self, ds, x, y_raw):\n", + " \"\"\"Apply selected y transform for NEXAFS quick-look.\"\"\"\n", + " y = y_raw.astype(float)\n", + " mode = self.y_transform.value\n", + " if mode == \"tey_over_i0\":\n", + " i0_name = \"I0\" if \"I0\" in ds.data_vars else \"RSoXS Au Mesh Current\"\n", + " if i0_name not in ds.data_vars:\n", + " return np.full_like(y, np.nan, dtype=float)\n", + " i0 = self._to_1d(ds[i0_name])[: len(y)].astype(float)\n", + " with np.errstate(divide=\"ignore\", invalid=\"ignore\"):\n", + " y = np.where(np.abs(i0) > 0, y / i0, np.nan)\n", + " if mode == \"post_edge_norm\":\n", + " lo = float(self.post_edge_min.value)\n", + " hi = float(self.post_edge_max.value)\n", + " mask = (x >= lo) & (x <= hi)\n", + " baseline = np.nanmean(y[mask]) if np.any(mask) else np.nan\n", + " if np.isfinite(baseline) and baseline != 0:\n", + " y = y / baseline\n", + " else:\n", + " y = np.full_like(y, np.nan, dtype=float)\n", + " return y\n", + "\n", + " def _single_scan_df(self, scan_id):\n", + " \"\"\"Create plotting dataframe for one scan.\"\"\"\n", + " ds = self._load_scan_data(scan_id)\n", + " x_name = self.x_var.value\n", + " y_name = self.y_var.value\n", + " if x_name not in ds and x_name not in ds.sizes:\n", + " return pd.DataFrame(columns=[\"x\", \"y\", \"scan_id\"])\n", + " if y_name not in ds.data_vars:\n", + " return pd.DataFrame(columns=[\"x\", \"y\", \"scan_id\"])\n", + " x = self._to_1d(ds[x_name])\n", + " y_raw = self._to_1d(ds[y_name])\n", + " n = min(len(x), len(y_raw))\n", + " x = x[:n]\n", + " y_raw = y_raw[:n]\n", + " y = self._compute_transformed_y(ds, x, y_raw)\n", + " frame = pd.DataFrame({\"x\": x, \"y\": y, \"scan_id\": str(scan_id)})\n", + " frame = frame.replace([np.inf, -np.inf], np.nan).dropna(subset=[\"x\", \"y\"])\n", + " return frame\n", + "\n", + " def _empty_figure(self):\n", + " \"\"\"Create an empty Bokeh figure with axes.\"\"\"\n", + " p = figure(height=400, sizing_mode=\"stretch_width\", title=\"No data selected\", tools=\"pan,wheel_zoom,box_zoom,reset,save\")\n", + " p.xaxis.axis_label = \"x\"\n", + " p.yaxis.axis_label = \"y\"\n", + " return p\n", + "\n", + " def _transform_label(self):\n", + " \"\"\"Label for selected transform mode.\"\"\"\n", + " mapping = {\"raw\": \"raw\", \"tey_over_i0\": \"TEY/I0\", \"post_edge_norm\": \"post-edge normalized\"}\n", + " return mapping.get(self.y_transform.value, self.y_transform.value)\n", + "\n", + " def _update_plot(self):\n", + " \"\"\"Render interactive Bokeh overlay for selected scans.\"\"\"\n", + " scans = list(self.scan_select.value or [])\n", + " if not scans or not self.x_var.value or not self.y_var.value:\n", + " self.plot_pane.object = self._empty_figure()\n", + " return\n", + " rows = []\n", + " errors = []\n", + " for sid in scans:\n", + " try:\n", + " rows.append(self._single_scan_df(sid))\n", + " except Exception as exc:\n", + " errors.append(f\"{sid}: {exc}\")\n", + " if not rows:\n", + " self.plot_pane.object = self._empty_figure()\n", + " if errors:\n", + " self._set_status(\"; \".join(errors), error=True)\n", + " return\n", + " plot_df = pd.concat(rows, ignore_index=True)\n", + " p = figure(height=420, sizing_mode=\"stretch_width\", title=f\"{self.y_var.value} vs {self.x_var.value} ({self._transform_label()})\", tools=\"pan,wheel_zoom,box_zoom,reset,save\")\n", + " p.xaxis.axis_label = self.x_var.value\n", + " p.yaxis.axis_label = f\"{self.y_var.value} [{self._transform_label()}]\"\n", + " palette = [\"#1f77b4\", \"#ff7f0e\", \"#2ca02c\", \"#d62728\", \"#9467bd\", \"#8c564b\", \"#e377c2\", \"#7f7f7f\", \"#bcbd22\", \"#17becf\"]\n", + " for i, sid in enumerate(scans):\n", + " sdf = plot_df[plot_df[\"scan_id\"] == str(sid)]\n", + " if sdf.empty:\n", + " continue\n", + " src = ColumnDataSource(sdf)\n", + " renderer = p.line(\"x\", \"y\", source=src, line_width=2, color=palette[i % len(palette)], legend_label=f\"scan {sid}\")\n", + " p.add_tools(HoverTool(renderers=[renderer], tooltips=[(\"scan\", \"@scan_id\"), (\"x\", \"@x{0.000}\"), (\"y\", \"@y{0.000}\")]))\n", + " p.legend.click_policy = \"hide\"\n", + " self.plot_pane.object = p\n", + " if errors:\n", + " self._set_status(\"Plotted with skipped scans: \" + \"; \".join(errors), error=True)\n", + " else:\n", + " self._set_status(f\"Plotted {len(scans)} scan(s).\")\n", + "\n", + " def _toggle_auto_refresh(self, event):\n", + " \"\"\"Start or stop periodic metadata refresh callback.\"\"\"\n", + " if event.new:\n", + " if not self.authenticated:\n", + " self.auto_refresh.value = False\n", + " self._set_status(\"Authenticate first.\", error=True)\n", + " return\n", + " period_ms = int(max(5, self.refresh_sec.value) * 1000)\n", + " if self._refresh_callback is not None:\n", + " self._refresh_callback.stop()\n", + " self._refresh_callback = pn.state.add_periodic_callback(self._refresh_catalog, period=period_ms, start=True)\n", + " self._set_status(f\"Auto refresh enabled every {period_ms // 1000}s.\")\n", + " else:\n", + " if self._refresh_callback is not None:\n", + " self._refresh_callback.stop()\n", + " self._refresh_callback = None\n", + " self._set_status(\"Auto refresh disabled.\")\n", + "\n", + " def _react(self, *_):\n", + " \"\"\"General reactive handler for filter and plot controls.\"\"\"\n", + " if not self.authenticated:\n", + " return\n", + " filtered = self._filtered_catalog()\n", + " show_cols = [\"scan_id\", \"start_time\", \"sample_id\", \"sample_name\", \"run_type\", \"plan_name\", \"cycle\", \"institution\", \"num_points\", \"exit_status\"]\n", + " self._set_table_value(filtered[show_cols].copy() if not filtered.empty else pd.DataFrame(columns=show_cols))\n", + " self._update_group_summary(filtered)\n", + " options = filtered[\"scan_id\"].astype(str).tolist() if not filtered.empty else []\n", + " current = [sid for sid in (self.scan_select.value or []) if sid in options]\n", + " self.scan_select.options = options\n", + " if current:\n", + " self.scan_select.value = current\n", + " elif options:\n", + " self.scan_select.value = options[: min(4, len(options))]\n", + " else:\n", + " self.scan_select.value = []\n", + " self._update_plot()\n", + "\n", + " def show(self):\n", + " \"\"\"Return panel layout.\"\"\"\n", + " return self.view\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "2f5a2c2c", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3231156d26b2438c9ecee0504270bc19", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "BokehModel(combine_events=True, render_bundle={'docs_json': {'60c27747-3d15-4729-baf8-95bbdf631439': {'version…" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dashboard = SST1DirectTiledDashboard()\n", + "dashboard.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "52c4205d", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/pyproject.toml b/pyproject.toml index e02a163..c61ecd4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -52,6 +52,10 @@ dependencies = [ "scienceplots>=2.2.1", "datasets>=4.6.0", "huggingface-hub>=1.5.0", + "tiled>=0.2.8", + "ipyaggrid>=0.5.4", + "altair>=6.0.0", + "panel>=1.8.5", ] [tool.maturin] @@ -182,6 +186,12 @@ jupyter = [ "panel>=1.6.3", "watchfiles>=1.0.5", ] +nsls = [ + "dask>=2026.3.0", + "pyhyperscattering[bluesky]>=0.2.10", + "pyopencl>=2026.1.2", + "zarr>=3.1.6", +] torch = [ "torch>=2.7.0", "torchvision>=0.22.0", diff --git a/src/bin/browser.rs b/src/bin/browser.rs index d3ce68b..0686676 100644 --- a/src/bin/browser.rs +++ b/src/bin/browser.rs @@ -22,12 +22,7 @@ impl Log for NoopLogger { fn flush(&self) {} } -fn resolve_root(config: &tui::TuiConfig) -> Result { - let raw = env::args() - .nth(1) - .or_else(|| config.last_root.clone()) - .unwrap_or_else(|| "/path/to/experiments".to_string()); - let path = Path::new(&raw); +fn resolve_existing_dir(path: &Path) -> Result { if !path.exists() { return Err(format!( "Path does not exist: {}. Check the path or create the directory.", @@ -36,7 +31,7 @@ fn resolve_root(config: &tui::TuiConfig) -> Result { } if !path.is_dir() { return Err(format!( - "Path is not a directory: {}. Point pyref-tui at a beamtime directory.", + "Path is not a directory: {}.", path.display() )); } @@ -46,6 +41,48 @@ fn resolve_root(config: &tui::TuiConfig) -> Result { Ok(canonical.to_string_lossy().into_owned()) } +#[cfg(feature = "catalog")] +fn pick_startup_root(config: &tui::TuiConfig) -> Result, String> { + if let Some(arg) = env::args().nth(1) { + return Ok(Some(resolve_existing_dir(Path::new(&arg))?)); + } + if let Ok(v) = env::var("PYREF_DATA_ROOT") { + let p = Path::new(v.trim()); + if p.is_dir() { + return Ok(Some(resolve_existing_dir(p)?)); + } + } + if let Some(ref dr) = config.data_root { + let p = Path::new(dr); + if p.is_dir() { + return Ok(Some(resolve_existing_dir(p)?)); + } + } + #[cfg(target_os = "macos")] + { + let p = Path::new("/Volumes/DATA"); + if p.is_dir() { + return Ok(Some(resolve_existing_dir(p)?)); + } + } + if let Some(ref lr) = config.last_root { + let p = Path::new(lr); + if p.is_dir() { + return Ok(Some(resolve_existing_dir(p)?)); + } + } + Ok(None) +} + +#[cfg(not(feature = "catalog"))] +fn resolve_root_fallback(config: &tui::TuiConfig) -> Result { + let raw = env::args() + .nth(1) + .or_else(|| config.last_root.clone()) + .unwrap_or_else(|| "/path/to/experiments".to_string()); + resolve_existing_dir(Path::new(&raw)) +} + /// Detect if a path should be opened in Explorer mode. /// Explorer mode: path contains only subdirectories (no .fits files directly inside) /// Beamtime mode: path contains .fits files directly inside @@ -73,20 +110,16 @@ fn main() -> Result<(), Box> { let mut config = tui::TuiConfig::load_or_default(); #[cfg(feature = "catalog")] - let initial_root = if env::args().nth(1).is_some() { - Some(match resolve_root(&config) { - Ok(s) => s, - Err(msg) => { - eprintln!("{}", msg); - std::process::exit(1); - } - }) - } else { - None + let initial_root = match pick_startup_root(&config) { + Ok(o) => o, + Err(msg) => { + eprintln!("{}", msg); + std::process::exit(1); + } }; #[cfg(not(feature = "catalog"))] - let initial_root = Some(match resolve_root(&config) { + let initial_root = Some(match resolve_root_fallback(&config) { Ok(s) => s, Err(msg) => { eprintln!("{}", msg); @@ -198,6 +231,13 @@ fn main() -> Result<(), Box> { eprintln!("{}", err.report()); } + if app.current_screen() == tui::Screen::Explorer { + if let Some(ex) = app.explorer_state() { + let root = ex.data_root.to_string_lossy().into_owned(); + config.set_last_root(&root); + config.data_root = Some(root); + } + } if app.current_screen() == tui::Screen::Beamtime && !app.current_root().is_empty() { config.set_last_root(app.current_root()); let scan_str = app.focused_scan().map(|n| n.to_string()); diff --git a/src/bin/tui/app.rs b/src/bin/tui/app.rs index 82e4af9..d0e567e 100644 --- a/src/bin/tui/app.rs +++ b/src/bin/tui/app.rs @@ -22,7 +22,7 @@ use super::scan_type::{classify_scan_type, ReflectivityScanType}; use pyref::build_fits_stem; #[cfg(feature = "catalog")] use pyref::catalog::{ - catalog_file_count, get_scan_point_uid_by_source_path, ingest_beamtime_with_context, + catalog_file_count, get_scan_point_uid_by_source_path, list_beamtime_entries, list_beamtime_entries_v2, list_beamtimes, query_files, query_scan_points, register_beamtime, rename_file_in_catalog, update_beamspot, update_beamspot_scan_point, FileRow, DEFAULT_INGEST_HEADER_ITEMS, resolve_catalog_path, @@ -140,6 +140,13 @@ pub enum ScrollbarDragTarget { ExpandedFiles, } +#[cfg(feature = "catalog")] +pub struct ExplorerFsUpdate { + pub path: PathBuf, + pub beamtime_count: u32, + pub fits_count: u32, +} + pub struct App { pub navigator: super::navigator::Navigator, pub preview_tx: Option)>>, @@ -147,6 +154,8 @@ pub struct App { pub beamspot_rx: Option>, #[cfg(feature = "catalog")] pub preview_cmd_rx: Option>, + #[cfg(feature = "catalog")] + explorer_fs_rx: Option>, pub layout: String, pub keymap: String, pub theme: String, @@ -423,17 +432,21 @@ fn catalog_to_profiles( impl App { fn beamtime_state(&self) -> &super::navigator::BeamtimeState { - match self.navigator.stack.last() { - Some(super::navigator::ScreenState::Beamtime(s)) => s, - _ => panic!("no beamtime state on stack"), + for state in self.navigator.stack.iter().rev() { + if let super::navigator::ScreenState::Beamtime(s) = state { + return s; + } } + panic!("no beamtime state on stack"); } fn beamtime_state_mut(&mut self) -> &mut super::navigator::BeamtimeState { - match self.navigator.stack.last_mut() { - Some(super::navigator::ScreenState::Beamtime(s)) => s, - _ => panic!("no beamtime state on stack"), + for state in self.navigator.stack.iter_mut().rev() { + if let super::navigator::ScreenState::Beamtime(s) = state { + return s; + } } + panic!("no beamtime state on stack"); } // Pass-through getters for backward compatibility @@ -944,6 +957,8 @@ impl App { beamspot_rx: None, #[cfg(feature = "catalog")] preview_cmd_rx: None, + #[cfg(feature = "catalog")] + explorer_fs_rx: None, layout, keymap, theme, @@ -1052,6 +1067,8 @@ impl App { beamspot_rx: None, #[cfg(feature = "catalog")] preview_cmd_rx: None, + #[cfg(feature = "catalog")] + explorer_fs_rx: None, layout, keymap, theme, @@ -1066,6 +1083,7 @@ impl App { keybind_bar_lines: u8, theme: String, ) -> Self { + let catalog_db = pyref::catalog::data_root_catalog_path(&data_root); let explorer_state = super::explorer::ExplorerState::new(data_root); let beamtime_state = super::navigator::BeamtimeState { @@ -1130,24 +1148,30 @@ impl App { let mut navigator = super::navigator::Navigator::new( beamtime_state, - super::catalog_handle::CatalogHandle { - db_path: PathBuf::new(), - }, + super::catalog_handle::CatalogHandle::new(catalog_db), ); navigator.stack.push(super::navigator::ScreenState::Explorer(explorer_state)); - App { + let mut app = App { navigator, preview_tx: None, #[cfg(feature = "catalog")] beamspot_rx: None, #[cfg(feature = "catalog")] preview_cmd_rx: None, + #[cfg(feature = "catalog")] + explorer_fs_rx: None, layout, keymap, theme, needs_redraw: true, + }; + #[cfg(feature = "catalog")] + { + app.sync_explorer_catalog_columns(); + app.restart_explorer_fs_scan(); } + app } pub fn set_app_error(&mut self, e: super::error::TuiError) { @@ -2658,8 +2682,8 @@ impl App { } } - // Update Explorer status if it's on the stack below self.update_explorer_beamtime_status(&bt_path_cloned); + self.sync_explorer_catalog_columns(); } Err(e) => { if let Some(super::navigator::ScreenState::Beamtime(bt_state)) = self.navigator.stack.last_mut() { @@ -3183,6 +3207,16 @@ impl App { } } + fn explorer_layer_mut(&mut self) -> Option<&mut super::explorer::ExplorerState> { + self.navigator.stack.iter_mut().find_map(|s| { + if let super::navigator::ScreenState::Explorer(e) = s { + Some(e) + } else { + None + } + }) + } + pub fn modal_state(&self) -> Option<&super::explorer::modal::ModalState> { match self.navigator.stack.last() { Some(super::navigator::ScreenState::ConfigModal(state)) => Some(state), @@ -3229,13 +3263,18 @@ impl App { self.start_beamtime_ingest(bt_path, Some(data_root), experimentalist); } else { - // Normal navigation let path = entry.path.clone(); state.navigate_into(path); } } self.needs_redraw = true; } + #[cfg(feature = "catalog")] + { + if self.explorer_state().is_some() { + self.explorer_after_nav(); + } + } } pub fn explorer_back(&mut self) { @@ -3243,6 +3282,8 @@ impl App { state.navigate_up(); self.needs_redraw = true; } + #[cfg(feature = "catalog")] + self.explorer_after_nav(); } pub fn explorer_resolve(&mut self) { @@ -3265,8 +3306,6 @@ impl App { /// Spawns background ingest thread and pushes BeamtimeState with ingest in progress. #[cfg(feature = "catalog")] fn start_beamtime_ingest(&mut self, bt_path: PathBuf, data_root: Option, experimentalist: Option) { - use std::sync::atomic::Ordering; - let cancel_flag = Arc::new(AtomicBool::new(false)); let (progress_tx, progress_rx) = mpsc::channel::<(u32, u32)>(); let (done_tx, done_rx) = mpsc::channel::>(); @@ -3408,6 +3447,150 @@ impl App { } } + #[cfg(feature = "catalog")] + pub fn sync_explorer_catalog_columns(&mut self) { + let db_path = self.navigator.catalog.db_path.clone(); + let handle = CatalogHandle::new(db_path); + let Some(ex) = self.explorer_layer_mut() else { + return; + }; + let data_root = ex.data_root.clone(); + let current = ex.current_dir.clone(); + if current == data_root { + let metas = handle.list_experimentalists(&data_root); + let map: HashMap = + metas.into_iter().map(|m| (m.name.clone(), m)).collect(); + for e in &mut ex.entries { + if e.kind != super::explorer::EntryKind::Experimentalist { + continue; + } + if let Some(meta) = map.get(&e.name) { + e.beamtime_count = Some(meta.beamtime_count); + let db_fits = meta.fits_count; + e.fits_count = Some(e.fits_count.unwrap_or(0).max(db_fits)); + e.catalog_status = if meta.beamtime_count > 0 || db_fits > 0 { + super::explorer::CatalogStatus::Indexed + } else { + super::explorer::CatalogStatus::NotIndexed + }; + } + } + } else if current + .parent() + .map(|p| p == data_root.as_path()) + .unwrap_or(false) + { + let Some(expt_name) = current.file_name().and_then(|n| n.to_str()) else { + ex.apply_sort(); + self.needs_redraw = true; + return; + }; + let metas = handle.list_beamtimes(&data_root, expt_name); + let map: HashMap = + metas.into_iter().map(|m| (m.path.clone(), m)).collect(); + for e in &mut ex.entries { + if e.kind != super::explorer::EntryKind::Beamtime { + continue; + } + if let Some(meta) = map.get(&e.path) { + e.fits_count = meta.fits_count.or(e.fits_count); + } + let st = handle.catalog_status(&e.path); + e.catalog_status = match st { + pyref::catalog::DbCatalogStatus::Indexed => super::explorer::CatalogStatus::Indexed, + pyref::catalog::DbCatalogStatus::Stale => super::explorer::CatalogStatus::Stale, + pyref::catalog::DbCatalogStatus::NotIndexed => super::explorer::CatalogStatus::NotIndexed, + }; + } + } + ex.apply_sort(); + self.needs_redraw = true; + } + + #[cfg(feature = "catalog")] + fn restart_explorer_fs_scan(&mut self) { + let snapshot = self.navigator.stack.iter().find_map(|s| { + if let super::navigator::ScreenState::Explorer(ex) = s { + Some(ex) + } else { + None + } + }).map(|ex| { + let at_root = ex.current_dir == ex.data_root; + let paths: Vec = ex + .entries + .iter() + .filter(|e| e.kind == super::explorer::EntryKind::Experimentalist) + .map(|e| e.path.clone()) + .collect(); + ( + at_root, + ex.data_root.clone(), + ex.layout_policy.clone(), + paths, + ) + }); + let Some((at_root, data_root, layout, paths)) = snapshot else { + self.explorer_fs_rx = None; + return; + }; + if !at_root || paths.is_empty() { + self.explorer_fs_rx = None; + return; + } + let (tx, rx) = mpsc::channel(); + self.explorer_fs_rx = Some(rx); + thread::spawn(move || { + for path in paths { + let (bt, fits) = super::explorer::heuristic::scan_experimentalist_subtree( + &path, + &data_root, + &layout, + ); + let _ = tx.send(ExplorerFsUpdate { + path, + beamtime_count: bt, + fits_count: fits, + }); + } + }); + } + + #[cfg(feature = "catalog")] + pub fn try_recv_explorer_fs_updates(&mut self) { + let Some(rx) = self.explorer_fs_rx.as_ref() else { + return; + }; + let mut batch = Vec::new(); + while let Ok(u) = rx.try_recv() { + batch.push(u); + } + if batch.is_empty() { + return; + } + let Some(ex) = self.explorer_layer_mut() else { + return; + }; + if ex.current_dir != ex.data_root { + return; + } + for u in batch { + if let Some(e) = ex.entries.iter_mut().find(|e| e.path == u.path) { + e.beamtime_count = Some(u.beamtime_count); + let prev = e.fits_count.unwrap_or(0); + e.fits_count = Some(prev.max(u.fits_count)); + } + } + ex.apply_sort(); + self.needs_redraw = true; + } + + #[cfg(feature = "catalog")] + pub fn explorer_after_nav(&mut self) { + self.sync_explorer_catalog_columns(); + self.restart_explorer_fs_scan(); + } + /// Update Explorer status when beamtime ingest completes (direct stack mutation). fn update_explorer_beamtime_status(&mut self, bt_path: &PathBuf) { // Find the Explorer state in the stack and update it diff --git a/src/bin/tui/config.rs b/src/bin/tui/config.rs index 4e474b4..7438f5b 100644 --- a/src/bin/tui/config.rs +++ b/src/bin/tui/config.rs @@ -22,6 +22,8 @@ pub struct TuiConfig { #[serde(default)] pub last_root: Option, #[serde(default)] + pub data_root: Option, + #[serde(default)] pub recent_roots: Vec, #[serde(default)] pub last_sample: Option, @@ -66,6 +68,7 @@ impl Default for TuiConfig { fn default() -> Self { Self { last_root: None, + data_root: None, recent_roots: Vec::new(), last_sample: None, last_tag: None, diff --git a/src/bin/tui/explorer/heuristic.rs b/src/bin/tui/explorer/heuristic.rs index be8c21a..0028ab5 100644 --- a/src/bin/tui/explorer/heuristic.rs +++ b/src/bin/tui/explorer/heuristic.rs @@ -2,6 +2,7 @@ use regex::Regex; use serde::{Deserialize, Serialize}; use std::collections::HashMap; use std::path::Path; +use walkdir::WalkDir; use super::EntryKind; @@ -235,6 +236,49 @@ pub fn needs_resolution(experimentalist: &str, layout: &NasLayout) -> bool { ) } +pub fn scan_experimentalist_subtree( + expt_path: &Path, + data_root: &Path, + layout: &NasLayout, +) -> (u32, u32) { + let mut beamtime_dirs = 0u32; + if let Ok(rd) = std::fs::read_dir(expt_path) { + for e in rd.flatten() { + if !e.file_type().map(|t| t.is_dir()).unwrap_or(false) { + continue; + } + let p = e.path(); + if classify_entry(&p, data_root, layout) == EntryKind::Beamtime { + beamtime_dirs = beamtime_dirs.saturating_add(1); + } + } + } + let mut fits = 0u32; + for w in WalkDir::new(expt_path) + .follow_links(false) + .into_iter() + .filter_map(|x| x.ok()) + { + let p = w.path(); + if !p.is_file() { + continue; + } + if p.extension() + .and_then(|e| e.to_str()) + .map(|e| e.eq_ignore_ascii_case("fits")) + != Some(true) + { + continue; + } + let stem = p.file_stem().and_then(|s| s.to_str()).unwrap_or(""); + if pyref::catalog::is_skippable_stem(stem) { + continue; + } + fits = fits.saturating_add(1); + } + (beamtime_dirs, fits) +} + #[cfg(test)] mod tests { use super::*; diff --git a/src/bin/tui/explorer/mod.rs b/src/bin/tui/explorer/mod.rs index b7754f3..c7a9c86 100644 --- a/src/bin/tui/explorer/mod.rs +++ b/src/bin/tui/explorer/mod.rs @@ -45,6 +45,7 @@ pub struct DirEntry { pub kind: EntryKind, pub modified: Option, pub size_bytes: Option, + pub beamtime_count: Option, pub fits_count: Option, pub catalog_status: CatalogStatus, pub expt_resolution: Option, @@ -125,9 +126,15 @@ impl ExplorerState { name: name_str, kind, modified: metadata.modified().ok(), - size_bytes: None, // Deferred to Phase 3 + size_bytes: None, + beamtime_count: None, fits_count: None, - catalog_status: CatalogStatus::NotApplicable, + catalog_status: match kind { + EntryKind::Experimentalist | EntryKind::Beamtime => { + CatalogStatus::NotIndexed + } + _ => CatalogStatus::NotApplicable, + }, expt_resolution, }); } @@ -206,7 +213,7 @@ impl ExplorerState { } } - fn apply_sort(&mut self) { + pub fn apply_sort(&mut self) { self.entries.sort_by(|a, b| { let cmp = match self.sort_col { ExplorerSort::Name => a.name.cmp(&b.name), diff --git a/src/bin/tui/navigator.rs b/src/bin/tui/navigator.rs index 27968c2..5ab6f2f 100644 --- a/src/bin/tui/navigator.rs +++ b/src/bin/tui/navigator.rs @@ -7,8 +7,6 @@ use std::cmp; use std::collections::HashSet; use std::path::PathBuf; use std::sync::mpsc; -use std::sync::atomic::AtomicBool; -use std::sync::Arc; use std::time::Instant; use ratatui::layout::Rect; use ratatui::widgets::{ListState, ScrollbarState, TableState}; @@ -16,8 +14,6 @@ use ratatui::widgets::{ListState, ScrollbarState, TableState}; #[cfg(feature = "watch")] use pyref::catalog::WatchHandle; -type BeamspotUpdate = (PathBuf, i64, i64, Option); - /// All beamtime screen state. Moved out of App in Phase 1. pub struct BeamtimeState { pub all_groups: Vec, diff --git a/src/bin/tui/run.rs b/src/bin/tui/run.rs index 8c6a1c0..5a10679 100644 --- a/src/bin/tui/run.rs +++ b/src/bin/tui/run.rs @@ -102,11 +102,13 @@ fn handle_modal_event(app: &mut App, key: crossterm::event::KeyEvent) -> bool { if let Some(explorer) = app.explorer_state_mut() { explorer.layout_policy.set_policy(expt_name, policy); let _ = explorer.layout_policy.save(&data_root); - // Reload current directory to update expt_resolution flags let current_dir = explorer.current_dir.clone(); explorer.load_dir(current_dir); } + #[cfg(feature = "catalog")] + app.explorer_after_nav(); + app.needs_redraw = true; } return false; @@ -203,6 +205,8 @@ pub fn run( ) -> io::Result<()> { loop { app.try_recv_ingest(); + #[cfg(feature = "catalog")] + app.try_recv_explorer_fs_updates(); app.try_recv_watcher(); #[cfg(feature = "catalog")] app.try_recv_beamspot_updates(); diff --git a/src/bin/tui/ui.rs b/src/bin/tui/ui.rs index 11af5f1..dd8e0e3 100644 --- a/src/bin/tui/ui.rs +++ b/src/bin/tui/ui.rs @@ -1220,6 +1220,10 @@ fn render_explorer(frame: &mut Frame, app: &mut App, area: Rect, theme: ThemeMod } }) .unwrap_or_else(|| "--".to_string()); + let bt_str = entry + .beamtime_count + .map(|c| c.to_string()) + .unwrap_or_else(|| "--".to_string()); let fits_str = entry .fits_count .map(|c| c.to_string()) @@ -1232,15 +1236,15 @@ fn render_explorer(frame: &mut Frame, app: &mut App, area: Rect, theme: ThemeMod }; let line = format!( - "{:<35} {:<12} {:<15} {:<8} {}", - entry.name, kind_str, modified_str, fits_str, status_str + "{:<28} {:<10} {:<10} {:<8} {:<8} {}", + entry.name, kind_str, bt_str, fits_str, modified_str, status_str ); ListItem::new(line) }) .collect(); let list = List::new(items) - .block(Block::bordered().title(" Files ")) + .block(Block::bordered().title(" Name Kind Bt Fits Age Cat ")) .highlight_style(list_style(true, theme)) .highlight_symbol(" "); diff --git a/src/catalog/mod.rs 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upload-time = "2025-09-14T22:18:19.088Z" }, +] From 6f5f494ee5bd0e4a1a3aa04fcf2bee8e50762a01 Mon Sep 17 00:00:00 2001 From: Harlan D Heilman <73567020+HarlanHeilman@users.noreply.github.com> Date: Sun, 29 Mar 2026 15:01:30 -0700 Subject: [PATCH 14/32] fix(io): resolve new-layout catalog path in scan_experiment --- python/pyref/io/__init__.py | 4 +- python/pyref/io/catalog_path.py | 89 +++++++++++++++++++++++++++++++++ python/pyref/io/readers.py | 29 +++++++---- tests/test_catalog.py | 47 ++++++++++++++--- 4 files changed, 149 insertions(+), 20 deletions(-) create mode 100644 python/pyref/io/catalog_path.py diff --git a/python/pyref/io/__init__.py b/python/pyref/io/__init__.py index 2ed377e..6a44e27 100644 --- a/python/pyref/io/__init__.py +++ b/python/pyref/io/__init__.py @@ -30,6 +30,8 @@ See the specific function documentation for more details on usage and parameters. """ +from pyref.io import fits_accessor # noqa: F401 - registers df.fits accessor +from pyref.io.catalog_path import resolve_catalog_path from pyref.io.experiment_names import ( ParsedFitsName, build_catalog, @@ -39,7 +41,6 @@ parse_fits_stem, scan_view, ) -from pyref.io import fits_accessor # noqa: F401 - registers df.fits accessor from pyref.io.readers import ( get_image, get_image_corrected, @@ -71,6 +72,7 @@ "query_catalog", "read_experiment", "read_fits", + "resolve_catalog_path", "resolve_fits_paths", "scan_experiment", "scan_view", diff --git a/python/pyref/io/catalog_path.py b/python/pyref/io/catalog_path.py new file mode 100644 index 0000000..3c7b63e --- /dev/null +++ b/python/pyref/io/catalog_path.py @@ -0,0 +1,89 @@ +""" +Resolve SQLite catalog paths for a beamtime directory. + +Mirrors ``resolve_catalog_path`` and related helpers in the Rust catalog module +(``src/catalog/mod.rs``). The new layout stores a shared database at +``parent/.pyref/catalog.db``; the legacy layout uses ``beamtime_dir/.pyref_catalog.db``. +Existence checks use :meth:`pathlib.Path.exists` (symlinks are followed), matching +Rust ``exists()``. +""" + +from __future__ import annotations + +from pathlib import Path + +CATALOG_DB_NAME = ".pyref_catalog.db" +PYREF_CATALOG_DIR = ".pyref" +NEW_CATALOG_DB_NAME = "catalog.db" + + +def catalog_path_legacy(beamtime_dir: Path) -> Path: + """ + Return the legacy per-beamtime catalog database path. + + Parameters + ---------- + beamtime_dir : pathlib.Path + Beamtime root directory. Not required to exist. + + Returns + ------- + pathlib.Path + ``beamtime_dir / CATALOG_DB_NAME`` (``CATALOG_DB_NAME`` is + ``.pyref_catalog.db``). + """ + return beamtime_dir / CATALOG_DB_NAME + + +def catalog_path_new(beamtime_dir: Path) -> Path: + """ + Return the new-layout catalog database path for a beamtime directory. + + If ``beamtime_dir`` has a parent that is non-empty and not the filesystem + root, the path is ``parent / .pyref / catalog.db``. Otherwise it is + ``beamtime_dir / .pyref / catalog.db``, matching the Rust ``catalog_path_new``. + + Parameters + ---------- + beamtime_dir : pathlib.Path + Beamtime root directory. Not required to exist. + + Returns + ------- + pathlib.Path + Candidate path for the shared catalog in the new layout. + """ + parent = beamtime_dir.parent + ps = parent.as_posix() + if ps and ps != "/": + return parent / PYREF_CATALOG_DIR / NEW_CATALOG_DB_NAME + return beamtime_dir / PYREF_CATALOG_DIR / NEW_CATALOG_DB_NAME + + +def resolve_catalog_path(beamtime_dir: str | Path) -> Path: + """ + Choose the catalog database path for a beamtime directory. + + Resolves ``beamtime_dir`` with :meth:`pathlib.Path.resolve` for stable, + absolute paths. If the new-layout path exists, returns it; else if the + legacy path exists, returns that; else returns the new-layout path (the + default location ingest would create in the new layout). + + Parameters + ---------- + beamtime_dir : str or pathlib.Path + Beamtime root directory. + + Returns + ------- + pathlib.Path + Absolute path to the catalog database file to use. + """ + d = Path(beamtime_dir).resolve() + new_path = catalog_path_new(d) + legacy_path = catalog_path_legacy(d) + if new_path.exists(): + return new_path + if legacy_path.exists(): + return legacy_path + return new_path diff --git a/python/pyref/io/readers.py b/python/pyref/io/readers.py index 3039784..2467759 100644 --- a/python/pyref/io/readers.py +++ b/python/pyref/io/readers.py @@ -10,6 +10,12 @@ import polars as pl +from pyref.io.catalog_path import ( + CATALOG_DB_NAME, + NEW_CATALOG_DB_NAME, + resolve_catalog_path, +) + if TYPE_CHECKING: import pandas as pd @@ -106,9 +112,6 @@ def _scan_schema(header_items: list[str]) -> dict[str, Any]: return schema -CATALOG_DB_NAME = ".pyref_catalog.db" - - def scan_experiment( source: FilePath | FilePathList, header_items: list[str] | None = None, @@ -118,20 +121,24 @@ def scan_experiment( keys = header_items if header_items is not None else [] if not isinstance(source, (list, tuple)): path = Path(source).resolve() - if path.is_file() and path.name == CATALOG_DB_NAME: + if path.is_file() and ( + path.name == CATALOG_DB_NAME or path.name == NEW_CATALOG_DB_NAME + ): try: from pyref.pyref import py_scan_from_catalog df = py_scan_from_catalog(str(path), None) return df.lazy() except (ImportError, AttributeError): pass - if path.is_dir() and (path / CATALOG_DB_NAME).exists(): - try: - from pyref.pyref import py_scan_from_catalog - df = py_scan_from_catalog(str(path / CATALOG_DB_NAME), None) - return df.lazy() - except (ImportError, AttributeError): - pass + if path.is_dir(): + db = resolve_catalog_path(path) + if db.is_file(): + try: + from pyref.pyref import py_scan_from_catalog + df = py_scan_from_catalog(str(db), None) + return df.lazy() + except (ImportError, AttributeError): + pass paths = resolve_fits_paths(source) if not paths: return pl.DataFrame(schema=pl.Schema(_scan_schema(keys))).lazy() diff --git a/tests/test_catalog.py b/tests/test_catalog.py index 9301275..9b17ed3 100644 --- a/tests/test_catalog.py +++ b/tests/test_catalog.py @@ -1,22 +1,52 @@ """Tests for catalog ingest, scan_from_catalog, and overrides.""" + from __future__ import annotations from pathlib import Path import polars as pl +import pyref.pyref as _pyref_mod import pytest -import pyref.pyref as _pyref_mod if getattr(_pyref_mod, "py_ingest_beamtime", None) is None: pytest.skip("catalog not built", allow_module_level=True) -from pyref.io.readers import REQUIRED_SCAN_COLUMNS from pyref.io import get_overrides, ingest_beamtime, scan_experiment, set_override +from pyref.io.catalog_path import resolve_catalog_path +from pyref.io.readers import REQUIRED_SCAN_COLUMNS + + +def test_resolve_catalog_path_prefers_new_layout_when_created(tmp_path: Path) -> None: + beamtime = tmp_path / "beam" + beamtime.mkdir() + new_db = tmp_path / ".pyref" / "catalog.db" + new_db.parent.mkdir(parents=True, exist_ok=True) + new_db.write_bytes(b"") + assert resolve_catalog_path(beamtime).resolve() == new_db.resolve() + + +def test_resolve_catalog_path_legacy_when_only_legacy_exists(tmp_path: Path) -> None: + beamtime = tmp_path / "beam" + beamtime.mkdir() + legacy = beamtime / ".pyref_catalog.db" + legacy.write_bytes(b"") + assert resolve_catalog_path(beamtime).resolve() == legacy.resolve() + + +def test_resolve_catalog_path_new_wins_when_both_exist(tmp_path: Path) -> None: + beamtime = tmp_path / "beam" + beamtime.mkdir() + new_db = tmp_path / ".pyref" / "catalog.db" + new_db.parent.mkdir(parents=True, exist_ok=True) + new_db.write_bytes(b"") + legacy = beamtime / ".pyref_catalog.db" + legacy.write_bytes(b"") + assert resolve_catalog_path(beamtime).resolve() == new_db.resolve() def test_ingest_beamtime_empty_dir(tmp_path: Path) -> None: db = ingest_beamtime(tmp_path, incremental=True) - assert db == tmp_path / ".pyref_catalog.db" + assert db == resolve_catalog_path(tmp_path) assert db.exists() @@ -31,8 +61,7 @@ def test_scan_experiment_catalog_dir_empty(tmp_path: Path) -> None: def test_get_overrides_empty(tmp_path: Path) -> None: - ingest_beamtime(tmp_path, incremental=True) - db = tmp_path / ".pyref_catalog.db" + db = ingest_beamtime(tmp_path, incremental=True) df = get_overrides(db, path=None) assert isinstance(df, pl.DataFrame) assert "path" in df.columns @@ -40,8 +69,7 @@ def test_get_overrides_empty(tmp_path: Path) -> None: def test_set_override_invalid_path_raises(tmp_path: Path) -> None: - ingest_beamtime(tmp_path, incremental=True) - db = tmp_path / ".pyref_catalog.db" + db = ingest_beamtime(tmp_path, incremental=True) with pytest.raises(Exception): set_override(db, "/nonexistent/path.fits", sample_name="x") @@ -57,7 +85,9 @@ def minimal_fits_dir(tmp_path_factory: pytest.TempPathFactory) -> Path | None: return root -def test_scan_experiment_catalog_has_required_columns(minimal_fits_dir: Path | None) -> None: +def test_scan_experiment_catalog_has_required_columns( + minimal_fits_dir: Path | None, +) -> None: if minimal_fits_dir is None: pytest.skip("fixtures/minimal.fits not found") ingest_beamtime(minimal_fits_dir, incremental=False) @@ -71,6 +101,7 @@ def test_fits_accessor_from_catalog(minimal_fits_dir: Path | None) -> None: if minimal_fits_dir is None: pytest.skip("fixtures/minimal.fits not found") import pyref # noqa: F401 + ingest_beamtime(minimal_fits_dir, incremental=False) df = scan_experiment(minimal_fits_dir).collect() if len(df) == 0: From 24119f9c073c0ded80acf30d32a392872c832309 Mon Sep 17 00:00:00 2001 From: Harlan D Heilman <73567020+HarlanHeilman@users.noreply.github.com> Date: Sun, 29 Mar 2026 15:04:12 -0700 Subject: [PATCH 15/32] docs(io): scan_experiment docstring and catalog path test default branch --- python/pyref/io/__init__.py | 1 + python/pyref/io/readers.py | 28 ++++++++++++++++++++++++++++ tests/test_catalog.py | 8 ++++++++ 3 files changed, 37 insertions(+) diff --git a/python/pyref/io/__init__.py b/python/pyref/io/__init__.py index 6a44e27..1d3e018 100644 --- a/python/pyref/io/__init__.py +++ b/python/pyref/io/__init__.py @@ -20,6 +20,7 @@ Experiment catalog and discovery ------------------------------- +- :func:`~pyref.io.resolve_catalog_path`: Beamtime catalog database path (Rust-aligned). - :func:`~pyref.io.discover_fits`: Discover FITS files under a root path (flat or recursive). - :func:`~pyref.io.parse_fits_stem`: Parse a FITS filename stem into sample_name, tag, scan_number, frame_number. - :func:`~pyref.io.build_catalog`: Build a per-file catalog DataFrame (names-only or with headers). diff --git a/python/pyref/io/readers.py b/python/pyref/io/readers.py index 2467759..1145e47 100644 --- a/python/pyref/io/readers.py +++ b/python/pyref/io/readers.py @@ -116,6 +116,34 @@ def scan_experiment( source: FilePath | FilePathList, header_items: list[str] | None = None, ) -> pl.LazyFrame: + """ + Lazy Polars metadata from a SQLite catalog or from FITS headers. + + When ``source`` is a **single** path (not a list or tuple): + + - File named ``.pyref_catalog.db`` or ``catalog.db``: load via + :func:`pyref.pyref.py_scan_from_catalog`. + - Directory: resolve DB with :func:`pyref.io.catalog_path.resolve_catalog_path`; + if it exists, load with ``py_scan_from_catalog`` (Rust-aligned layouts). + - Else: discover ``.fits`` and stream header reads. + + For a **list or tuple** ``source``, call :func:`resolve_fits_paths` only. Pass one + directory path to use catalog auto-resolution. + + Missing ``py_scan_from_catalog`` falls through to FITS discovery without raising. + + Parameters + ---------- + source : str, pathlib.Path, or list thereof + Beamtime directory, catalog ``.db`` file, glob-like path, or FITS path list. + header_items : list of str, optional + Extra FITS header keys for disk reads (unused for pure catalog loads). + + Returns + ------- + polars.LazyFrame + Schema compatible with :data:`REQUIRED_SCAN_COLUMNS` and catalog scans. + """ from pyref.pyref import py_read_multiple_fits_headers_only keys = header_items if header_items is not None else [] diff --git a/tests/test_catalog.py b/tests/test_catalog.py index 9b17ed3..c0bfbaa 100644 --- a/tests/test_catalog.py +++ b/tests/test_catalog.py @@ -44,6 +44,14 @@ def test_resolve_catalog_path_new_wins_when_both_exist(tmp_path: Path) -> None: assert resolve_catalog_path(beamtime).resolve() == new_db.resolve() +def test_resolve_catalog_path_defaults_to_new_when_neither_exists(tmp_path: Path) -> None: + beamtime = tmp_path / "beam" + beamtime.mkdir() + expected = (tmp_path / ".pyref" / "catalog.db").resolve() + assert resolve_catalog_path(beamtime).resolve() == expected + assert not expected.exists() + + def test_ingest_beamtime_empty_dir(tmp_path: Path) -> None: db = ingest_beamtime(tmp_path, incremental=True) assert db == resolve_catalog_path(tmp_path) From ab3ef7be8708644f0f2b2376b30a434d8218455d Mon Sep 17 00:00:00 2001 From: Harlan D Heilman <73567020+HarlanHeilman@users.noreply.github.com> Date: Sun, 29 Mar 2026 15:09:24 -0700 Subject: [PATCH 16/32] feat(catalog): allow set_override for bt_* source_path without files row --- python/pyref/io/readers.py | 51 ++++++++++ src/catalog/mod.rs | 59 +++++++++++ src/catalog/query.rs | 199 ++++++++++++++++++++++++++++--------- tests/test_catalog.py | 18 ++++ 4 files changed, 280 insertions(+), 47 deletions(-) diff --git a/python/pyref/io/readers.py b/python/pyref/io/readers.py index 1145e47..663a47d 100644 --- a/python/pyref/io/readers.py +++ b/python/pyref/io/readers.py @@ -219,6 +219,29 @@ def get_overrides( catalog_path: FilePath, path: str | None = None, ) -> pl.DataFrame: + """ + Read user metadata overrides from the catalog database. + + Merges rows from the legacy ``overrides`` table (keys are ``files.path``) and + from ``bt_file_overrides`` (keys match ``bt_scan_points.source_path`` when + ingest used the normalized bt_* layout without ``files`` rows). The result + always exposes columns ``path``, ``sample_name``, ``tag``, and ``notes``; for + bt-only overrides, ``path`` is the FITS filesystem path. + + Parameters + ---------- + catalog_path : str or pathlib.Path + Resolved path to the SQLite catalog (for example the return value of + :func:`ingest_beamtime`). + path : str, optional + When given, return only overrides for this exact path (looked up in both + tables). When omitted, return all rows from both sources in sequence. + + Returns + ------- + polars.DataFrame + Zero or more override rows with consistent column names. + """ from pyref.pyref import py_get_overrides return py_get_overrides(str(Path(catalog_path).resolve()), path) @@ -231,6 +254,34 @@ def set_override( tag: str | None = None, notes: str | None = None, ) -> None: + """ + Upsert sample name, tag, or notes for a catalog-backed file path. + + If ``path`` equals ``files.path``, updates the legacy ``overrides`` table. + Otherwise, if ``path`` equals ``bt_scan_points.source_path`` (typical when + the catalog holds bt_* scan points but no ``files`` row for that path), + updates ``bt_file_overrides``. Scans from the catalog resolve display fields + with ``COALESCE(override, ingested)`` for sample name and tag. + + Parameters + ---------- + catalog_path : str or pathlib.Path + Path to the catalog SQLite database. + path : str + Must match an existing ``files.path`` or ``bt_scan_points.source_path`` + string exactly (use paths from the scan metadata frame when unsure). + sample_name : str, optional + Override sample name, or ``None`` to clear that field on upsert. + tag : str, optional + Override tag, or ``None`` to clear that field on upsert. + notes : str, optional + Free-form notes, or ``None`` to clear that field on upsert. + + Raises + ------ + RuntimeError + When ``path`` is not present in either ``files`` or ``bt_scan_points``. + """ from pyref.pyref import py_set_override py_set_override( diff --git a/src/catalog/mod.rs b/src/catalog/mod.rs index ef471b8..6d467d3 100644 --- a/src/catalog/mod.rs +++ b/src/catalog/mod.rs @@ -325,6 +325,14 @@ const BT_INDEX_SCAN_POINTS_SOURCE: &str = const BT_INDEX_IMAGE_REFS_POINT: &str = "CREATE INDEX IF NOT EXISTS idx_bt_image_refs_scan_point ON bt_image_refs(scan_point_uid)"; +const BT_FILE_OVERRIDES_TABLE: &str = r#" +CREATE TABLE IF NOT EXISTS bt_file_overrides ( + source_path TEXT PRIMARY KEY, + sample_name TEXT, + tag TEXT, + notes TEXT +)"#; + pub fn is_skippable_stem(stem: &str) -> bool { stem.is_empty() || stem.starts_with('_') } @@ -525,6 +533,29 @@ fn migrate_add_experimentalist_and_data_root(conn: &Connection) -> Result<()> { Ok(()) } +fn migrate_add_last_indexed_at_column(conn: &Connection) -> Result<()> { + let table_exists: i64 = conn.query_row( + "SELECT COUNT(1) FROM sqlite_master WHERE type='table' AND name='bt_beamtimes'", + [], + |r| r.get(0), + )?; + if table_exists == 0 { + return Ok(()); + } + let has_col: i64 = conn.query_row( + "SELECT COUNT(1) FROM pragma_table_info('bt_beamtimes') WHERE name = 'last_indexed_at'", + [], + |r| r.get(0), + )?; + if has_col == 0 { + conn.execute( + "ALTER TABLE bt_beamtimes ADD COLUMN last_indexed_at INTEGER", + [], + )?; + } + Ok(()) +} + pub fn open_catalog_db(db_path: &Path) -> Result { let conn = Connection::open(db_path)?; conn.execute_batch(FILES_TABLE)?; @@ -555,7 +586,9 @@ pub fn open_catalog_db(db_path: &Path) -> Result { conn.execute_batch(BT_INDEX_SCAN_POINTS_SCAN)?; conn.execute_batch(BT_INDEX_SCAN_POINTS_SOURCE)?; conn.execute_batch(BT_INDEX_IMAGE_REFS_POINT)?; + conn.execute_batch(BT_FILE_OVERRIDES_TABLE)?; migrate_add_experimentalist_and_data_root(&conn)?; + migrate_add_last_indexed_at_column(&conn)?; Ok(conn) } @@ -593,6 +626,7 @@ fn migrate_experiment_number_to_scan_number(conn: &Connection) -> Result<()> { #[cfg(test)] mod tests { use super::*; + use rusqlite::Connection; use tempfile::TempDir; #[test] @@ -645,4 +679,29 @@ mod tests { .unwrap(); assert_eq!(has, 1); } + + #[test] + fn test_migrate_restores_last_indexed_at_on_bt_beamtimes() { + let tmp = TempDir::new().unwrap(); + let db_path = tmp.path().join("catalog.db"); + { + let _ = open_catalog_db(&db_path).unwrap(); + } + { + let conn = Connection::open(&db_path).unwrap(); + let _ = conn.execute( + "ALTER TABLE bt_beamtimes DROP COLUMN last_indexed_at", + [], + ); + } + let conn = open_catalog_db(&db_path).unwrap(); + let has: i64 = conn + .query_row( + "SELECT COUNT(1) FROM pragma_table_info('bt_beamtimes') WHERE name = 'last_indexed_at'", + [], + |r| r.get(0), + ) + .unwrap(); + assert_eq!(has, 1); + } } diff --git a/src/catalog/query.rs b/src/catalog/query.rs index f817ac4..9f5b752 100644 --- a/src/catalog/query.rs +++ b/src/catalog/query.rs @@ -232,11 +232,12 @@ SELECT sp.source_bitpix, sp.source_bzero, sp.seq_index, sp.beamline_energy, sp.sample_theta, sp.epu_polarization, sp.ccd_theta, sp.exposure, sp.beam_row, sp.beam_col, sp.beam_sigma, - s.name AS sample_name, s.tag, + COALESCE(o.sample_name, s.name) AS sample_name, COALESCE(o.tag, s.tag) AS tag, sc.uid AS scan_uid FROM bt_scan_points sp JOIN bt_samples s ON sp.sample_id = s.id JOIN bt_scans sc ON sp.scan_uid = sc.uid +LEFT JOIN bt_file_overrides o ON o.source_path = sp.source_path WHERE sc.beamtime_id = ?1 "#; @@ -253,12 +254,12 @@ fn build_scan_points_where_and_params( let mut idx = param_start; if let Some(ref s) = filter.sample_name { idx += 1; - conditions.push(format!("s.name = ?{}", idx)); + conditions.push(format!("COALESCE(o.sample_name, s.name) = ?{}", idx)); params.push(Box::new(s.clone())); } if let Some(ref t) = filter.tag { idx += 1; - conditions.push(format!("s.tag = ?{}", idx)); + conditions.push(format!("COALESCE(o.tag, s.tag) = ?{}", idx)); params.push(Box::new(t.clone())); } if let Some(ref scan_nos) = filter.scan_numbers { @@ -463,12 +464,13 @@ SELECT sp.source_bitpix, sp.source_bzero, (CASE WHEN sp.source_naxis1 IS NOT NULL AND sp.source_naxis2 IS NOT NULL AND sp.source_bitpix IS NOT NULL THEN sp.source_naxis1 * sp.source_naxis2 * max(1, abs(sp.source_bitpix) / 8) ELSE 0 END) AS data_size, - s.name AS sample_name, s.tag, sc.uid AS scan_uid, sp.seq_index, + COALESCE(o.sample_name, s.name) AS sample_name, COALESCE(o.tag, s.tag) AS tag, sc.uid AS scan_uid, sp.seq_index, sp.beamline_energy, sp.sample_theta, sp.ccd_theta, sp.epu_polarization, sp.exposure, sp.beam_row, sp.beam_col, sp.beam_sigma FROM bt_scan_points sp JOIN bt_samples s ON sp.sample_id = s.id JOIN bt_scans sc ON sp.scan_uid = sc.uid +LEFT JOIN bt_file_overrides o ON o.source_path = sp.source_path "#; fn scan_from_catalog_bt( @@ -616,12 +618,12 @@ fn build_bt_df_where_and_params( let mut idx = 0; if let Some(ref s) = filter.sample_name { idx += 1; - conditions.push(format!("s.name = ?{}", idx)); + conditions.push(format!("COALESCE(o.sample_name, s.name) = ?{}", idx)); params.push(Box::new(s.clone())); } if let Some(ref t) = filter.tag { idx += 1; - conditions.push(format!("s.tag = ?{}", idx)); + conditions.push(format!("COALESCE(o.tag, s.tag) = ?{}", idx)); params.push(Box::new(t.clone())); } if let Some(em) = filter.energy_min { @@ -801,43 +803,72 @@ pub fn scan_from_catalog(db_path: &Path, filter: Option<&CatalogFilter>) -> Resu Ok(df) } +type OverrideRowSql = (String, Option, Option, Option); + +fn map_override_four_cols(row: &rusqlite::Row) -> rusqlite::Result { + Ok(( + row.get(0)?, + row.get(1)?, + row.get(2)?, + row.get(3)?, + )) +} + +/// Loads override rows into a Polars frame with columns `path`, `sample_name`, `tag`, `notes`. +/// +/// Concatenates the legacy `overrides` table (`path` is `files.path`) and `bt_file_overrides` +/// (`path` is `source_path` matching `bt_scan_points`). When `path` is `Some`, returns rows from +/// both tables that match that exact string. pub fn get_overrides(db_path: &Path, path: Option<&str>) -> Result { let conn = open_catalog_db(db_path)?; - let (sql, params): (String, Vec>) = match path { - Some(p) => ( - "SELECT path, sample_name, tag, notes FROM overrides WHERE path = ?1".to_string(), - vec![Box::new(p.to_string())], - ), - None => ( - "SELECT path, sample_name, tag, notes FROM overrides".to_string(), - Vec::new(), - ), - }; - let mut stmt = conn.prepare(&sql)?; - let map_row = |row: &rusqlite::Row| { - Ok(( - row.get::<_, String>(0)?, - row.get::<_, Option>(1)?, - row.get::<_, Option>(2)?, - row.get::<_, Option>(3)?, - )) - }; - let rows = if params.is_empty() { - stmt.query_map([], map_row)? - } else { - let pr: Vec<&dyn rusqlite::ToSql> = params.iter().map(|b| b.as_ref()).collect(); - stmt.query_map(rusqlite::params_from_iter(pr), map_row)? - }; let mut paths = Vec::new(); let mut sample_names = Vec::new(); let mut tags = Vec::new(); let mut notes = Vec::new(); - for row in rows { - let r = row.map_err(CatalogError::Sqlite)?; - paths.push(r.0); - sample_names.push(r.1); - tags.push(r.2); - notes.push(r.3); + match path { + Some(p) => { + let mut stmt = conn.prepare( + "SELECT path, sample_name, tag, notes FROM overrides WHERE path = ?1", + )?; + for row in stmt.query_map([p], map_override_four_cols)? { + let r = row.map_err(CatalogError::Sqlite)?; + paths.push(r.0); + sample_names.push(r.1); + tags.push(r.2); + notes.push(r.3); + } + let mut stmt = conn.prepare( + "SELECT source_path, sample_name, tag, notes FROM bt_file_overrides WHERE source_path = ?1", + )?; + for row in stmt.query_map([p], map_override_four_cols)? { + let r = row.map_err(CatalogError::Sqlite)?; + paths.push(r.0); + sample_names.push(r.1); + tags.push(r.2); + notes.push(r.3); + } + } + None => { + let mut stmt = + conn.prepare("SELECT path, sample_name, tag, notes FROM overrides")?; + for row in stmt.query_map([], map_override_four_cols)? { + let r = row.map_err(CatalogError::Sqlite)?; + paths.push(r.0); + sample_names.push(r.1); + tags.push(r.2); + notes.push(r.3); + } + let mut stmt = conn.prepare( + "SELECT source_path, sample_name, tag, notes FROM bt_file_overrides", + )?; + for row in stmt.query_map([], map_override_four_cols)? { + let r = row.map_err(CatalogError::Sqlite)?; + paths.push(r.0); + sample_names.push(r.1); + tags.push(r.2); + notes.push(r.3); + } + } } let df = DataFrame::new(vec![ Series::new("path".into(), paths).into(), @@ -917,8 +948,70 @@ mod tests { assert!(e.tags.is_empty()); assert!(e.scans.is_empty()); } + + #[test] + fn test_set_override_bt_source_path_without_files_row() { + let tmp = TempDir::new().unwrap(); + let db_path = tmp.path().join("catalog.db"); + { + let conn = crate::catalog::open_catalog_db(&db_path).unwrap(); + conn.execute( + "INSERT INTO bt_beamtimes (beamtime_path) VALUES (?1)", + ["/tmp/beam"], + ) + .unwrap(); + conn.execute( + "INSERT INTO bt_samples (beamtime_id, name, tag) VALUES (1, 'orig', 't1')", + [], + ) + .unwrap(); + conn.execute( + "INSERT INTO bt_scans (uid, beamtime_id, sample_id) VALUES ('s_1_1', 1, 1)", + [], + ) + .unwrap(); + conn.execute( + "INSERT INTO bt_streams (uid, scan_uid) VALUES ('st_1_1', 's_1_1')", + [], + ) + .unwrap(); + conn.execute( + r#"INSERT INTO bt_scan_points ( + uid, stream_uid, scan_uid, sample_id, seq_index, + source_path, source_data_offset, source_naxis1, source_naxis2, source_bitpix, source_bzero + ) VALUES ( + 'sp_1_1_0', 'st_1_1', 's_1_1', 1, 0, + '/data/example.fits', 0, 2, 2, -32, 0 + )"#, + [], + ) + .unwrap(); + } + set_override( + &db_path, + "/data/example.fits", + Some("corrected"), + Some("t2"), + None, + ) + .unwrap(); + let df = scan_from_catalog(&db_path, None).unwrap(); + assert_eq!(df.height(), 1); + let sn = df.column("sample_name").unwrap().str().unwrap().get(0); + assert_eq!(sn, Some("corrected")); + let tg = df.column("tag").unwrap().str().unwrap().get(0); + assert_eq!(tg, Some("t2")); + let all_ov = get_overrides(&db_path, None).unwrap(); + assert_eq!(all_ov.height(), 1); + let p = all_ov.column("path").unwrap().str().unwrap().get(0); + assert_eq!(p, Some("/data/example.fits")); + } } +/// Upserts `sample_name`, `tag`, and `notes` for a catalog file path. +/// +/// When `path` exists in `files`, writes `overrides`. When it exists only as `bt_scan_points.source_path`, +/// writes `bt_file_overrides`. Returns [`CatalogError::Validation`] when `path` matches neither. pub fn set_override( db_path: &Path, path: &str, @@ -927,21 +1020,33 @@ pub fn set_override( notes: Option<&str>, ) -> Result<()> { let conn = open_catalog_db(db_path)?; - let exists: i64 = + let in_files: i64 = conn.query_row("SELECT COUNT(1) FROM files WHERE path = ?1", [path], |r| { r.get(0) })?; - if exists == 0 { - return Err(CatalogError::Validation(format!( - "path not in files table: {}", - path - ))); + if in_files > 0 { + conn.execute( + "INSERT OR REPLACE INTO overrides (path, sample_name, tag, notes) VALUES (?1, ?2, ?3, ?4)", + rusqlite::params![path, sample_name, tag, notes], + )?; + return Ok(()); } - conn.execute( - "INSERT OR REPLACE INTO overrides (path, sample_name, tag, notes) VALUES (?1, ?2, ?3, ?4)", - rusqlite::params![path, sample_name, tag, notes], + let in_bt: i64 = conn.query_row( + "SELECT COUNT(1) FROM bt_scan_points WHERE source_path = ?1", + [path], + |r| r.get(0), )?; - Ok(()) + if in_bt > 0 { + conn.execute( + "INSERT OR REPLACE INTO bt_file_overrides (source_path, sample_name, tag, notes) VALUES (?1, ?2, ?3, ?4)", + rusqlite::params![path, sample_name, tag, notes], + )?; + return Ok(()); + } + Err(CatalogError::Validation(format!( + "path not in catalog (not in files.path and not in bt_scan_points.source_path): {}", + path + ))) } pub fn rename_file_in_catalog( diff --git a/tests/test_catalog.py b/tests/test_catalog.py index c0bfbaa..d1dbde3 100644 --- a/tests/test_catalog.py +++ b/tests/test_catalog.py @@ -115,3 +115,21 @@ def test_fits_accessor_from_catalog(minimal_fits_dir: Path | None) -> None: if len(df) == 0: pytest.skip("no rows in catalog") _ = df.fits.img[0] + + +def test_set_override_bt_scan_point_updates_scan_experiment( + minimal_fits_dir: Path | None, +) -> None: + if minimal_fits_dir is None: + pytest.skip("fixtures/minimal.fits not found") + db = ingest_beamtime(minimal_fits_dir, incremental=False) + df = scan_experiment(minimal_fits_dir).collect() + if len(df) == 0: + pytest.skip("no rows in catalog") + path = str(df["file_path"][0]) + set_override(db, path, sample_name="corrected_sample") + df2 = scan_experiment(minimal_fits_dir).collect() + assert df2["sample_name"][0] == "corrected_sample" + ov = get_overrides(db, path=None) + assert path in set(ov["path"].to_list()) + assert "corrected_sample" in set(ov["sample_name"].to_list()) From 8f8af2b140c06a1082536e5cc0eeed73b79973f7 Mon Sep 17 00:00:00 2001 From: Harlan D Heilman <73567020+HarlanHeilman@users.noreply.github.com> Date: Sun, 29 Mar 2026 15:27:46 -0700 Subject: [PATCH 17/32] refactor(catalog): share reflectivity scan classification with TUI and Python --- python/pyref/io/__init__.py | 3 + python/pyref/io/readers.py | 29 ++++ src/bin/tui/scan_type.rs | 182 +------------------------ src/catalog/mod.rs | 2 + src/catalog/reflectivity_profile.rs | 199 ++++++++++++++++++++++++++++ src/lib.rs | 34 ++++- tests/test_catalog.py | 31 ++++- 7 files changed, 294 insertions(+), 186 deletions(-) create mode 100644 src/catalog/reflectivity_profile.rs diff --git a/python/pyref/io/__init__.py b/python/pyref/io/__init__.py index 1d3e018..4ce86f3 100644 --- a/python/pyref/io/__init__.py +++ b/python/pyref/io/__init__.py @@ -21,6 +21,7 @@ Experiment catalog and discovery ------------------------------- - :func:`~pyref.io.resolve_catalog_path`: Beamtime catalog database path (Rust-aligned). +- :func:`~pyref.io.classify_reflectivity_scan_type`: Theta vs energy scan classification. - :func:`~pyref.io.discover_fits`: Discover FITS files under a root path (flat or recursive). - :func:`~pyref.io.parse_fits_stem`: Parse a FITS filename stem into sample_name, tag, scan_number, frame_number. - :func:`~pyref.io.build_catalog`: Build a per-file catalog DataFrame (names-only or with headers). @@ -43,6 +44,7 @@ scan_view, ) from pyref.io.readers import ( + classify_reflectivity_scan_type, get_image, get_image_corrected, get_image_filtered, @@ -60,6 +62,7 @@ __all__ = [ "ParsedFitsName", "build_catalog", + "classify_reflectivity_scan_type", "discover_fits", "experiment_summary", "filter_catalog_paths", diff --git a/python/pyref/io/readers.py b/python/pyref/io/readers.py index 663a47d..a7b6f02 100644 --- a/python/pyref/io/readers.py +++ b/python/pyref/io/readers.py @@ -154,6 +154,7 @@ def scan_experiment( ): try: from pyref.pyref import py_scan_from_catalog + df = py_scan_from_catalog(str(path), None) return df.lazy() except (ImportError, AttributeError): @@ -163,6 +164,7 @@ def scan_experiment( if db.is_file(): try: from pyref.pyref import py_scan_from_catalog + df = py_scan_from_catalog(str(db), None) return df.lazy() except (ImportError, AttributeError): @@ -293,6 +295,33 @@ def set_override( ) +def classify_reflectivity_scan_type( + pairs: list[tuple[float | None, float | None]], +) -> tuple[str, float | None, float | None, float | None, float | None]: + """ + Classify a reflectivity acquisition from (beamline energy eV, sample theta deg) pairs. + + Wraps the Rust catalog classifier used by the TUI. ``scan_kind`` is one of + ``\"fixed_energy\"`` (theta scan at nearly fixed energy), ``\"fixed_angle\"`` + (energy scan at nearly fixed theta), or ``\"single_point\"``. + + Parameters + ---------- + pairs : list of (float or None, float or None) + One tuple per frame or scan point. Either coordinate may be omitted. + + Returns + ------- + scan_kind : str + ``fixed_energy``, ``fixed_angle``, or ``single_point``. + e_min, e_max, t_min, t_max : float or None + Extrema over finite samples; ``None`` when an axis has no data. + """ + from pyref.pyref import py_classify_scan_type + + return py_classify_scan_type(pairs) + + def query_catalog( catalog_path: FilePath, *, diff --git a/src/bin/tui/scan_type.rs b/src/bin/tui/scan_type.rs index 59dcfa0..eec5272 100644 --- a/src/bin/tui/scan_type.rs +++ b/src/bin/tui/scan_type.rs @@ -1,181 +1,3 @@ +//! Re-exports reflectivity scan classification from the library catalog module. -use std::collections::HashSet; - -const E_TOL_EV: f64 = 0.5; -const THETA_TOL_DEG: f64 = 0.1; -const IZERO_FRACTION_THRESHOLD: f64 = 0.25; -const IZERO_MIN_POINTS: usize = 2; -const E_ROUND_EV: f64 = 0.1; -const THETA_ROUND_DEG: f64 = 0.01; - -#[derive(Debug, Clone, Copy, PartialEq, Eq)] -pub enum ReflectivityScanType { - FixedEnergy, - FixedAngle, - SinglePoint, -} - -fn round_energy(e: f64) -> i64 { - (e / E_ROUND_EV).round() as i64 -} - -fn round_theta(t: f64) -> i64 { - (t / THETA_ROUND_DEG).round() as i64 -} - -pub fn classify_scan_type( - energy_theta_pairs: &[(Option, Option)], -) -> ( - ReflectivityScanType, - Option, - Option, - Option, - Option, -) { - let energies: Vec = energy_theta_pairs.iter().filter_map(|(e, _)| *e).collect(); - let thetas: Vec = energy_theta_pairs.iter().filter_map(|(_, t)| *t).collect(); - if energies.is_empty() || thetas.is_empty() { - let e_min = energies.iter().cloned().fold(f64::INFINITY, f64::min); - let e_max = energies.iter().cloned().fold(f64::NEG_INFINITY, f64::max); - let t_min = thetas.iter().cloned().fold(f64::INFINITY, f64::min); - let t_max = thetas.iter().cloned().fold(f64::NEG_INFINITY, f64::max); - return ( - ReflectivityScanType::SinglePoint, - if energies.is_empty() { - None - } else { - Some(if e_min <= e_max { e_min } else { e_max }) - }, - if energies.is_empty() { - None - } else { - Some(if e_max >= e_min { e_max } else { e_min }) - }, - if thetas.is_empty() { - None - } else { - Some(if t_min <= t_max { t_min } else { t_max }) - }, - if thetas.is_empty() { - None - } else { - Some(if t_max >= t_min { t_max } else { t_min }) - }, - ); - } - let e_min = energies.iter().cloned().fold(f64::INFINITY, f64::min); - let e_max = energies.iter().cloned().fold(f64::NEG_INFINITY, f64::max); - let t_min = thetas.iter().cloned().fold(f64::INFINITY, f64::min); - let t_max = thetas.iter().cloned().fold(f64::NEG_INFINITY, f64::max); - let range_e = e_max - e_min; - let range_theta = t_max - t_min; - let n_e: usize = energies - .iter() - .map(|&e| round_energy(e)) - .collect::>() - .len(); - let n_theta: usize = thetas - .iter() - .map(|&t| round_theta(t)) - .collect::>() - .len(); - let n_total = energy_theta_pairs - .iter() - .filter(|(e, t)| e.is_some() && t.is_some()) - .count(); - let n_theta_near_zero = energy_theta_pairs - .iter() - .filter(|(_, t)| t.map(|v| v <= THETA_TOL_DEG).unwrap_or(false)) - .count(); - let energy_varies = range_e > E_TOL_EV; - let theta_varies = range_theta > THETA_TOL_DEG; - let scan_type = if energy_varies && !theta_varies { - ReflectivityScanType::FixedAngle - } else if theta_varies && !energy_varies { - ReflectivityScanType::FixedEnergy - } else if energy_varies && theta_varies { - if n_total > 0 - && n_theta_near_zero >= IZERO_MIN_POINTS - && (n_theta_near_zero as f64 / n_total as f64) >= IZERO_FRACTION_THRESHOLD - { - ReflectivityScanType::FixedEnergy - } else if n_e >= n_theta { - ReflectivityScanType::FixedAngle - } else { - ReflectivityScanType::FixedEnergy - } - } else { - ReflectivityScanType::SinglePoint - }; - ( - scan_type, - Some(e_min), - Some(e_max), - Some(t_min), - Some(t_max), - ) -} - -#[cfg(test)] -mod tests { - use super::*; - - fn pair(energy: Option, theta: Option) -> (Option, Option) { - (energy, theta) - } - - #[test] - fn single_energy_many_thetas_fixed_energy() { - let rows: Vec<_> = (0..10) - .map(|i| pair(Some(284.0), Some(0.5 * i as f64))) - .collect(); - let (st, e_min, e_max, t_min, t_max) = classify_scan_type(&rows); - assert_eq!(st, ReflectivityScanType::FixedEnergy); - assert!(e_min.unwrap() <= 284.0 && e_max.unwrap() >= 284.0); - assert!(t_min.unwrap() <= 0.5 && t_max.unwrap() >= 4.5); - } - - #[test] - fn many_energies_single_theta_fixed_angle() { - let rows: Vec<_> = (0..10) - .map(|i| pair(Some(250.0 + i as f64), Some(10.0))) - .collect(); - let (st, _, _, _, _) = classify_scan_type(&rows); - assert_eq!(st, ReflectivityScanType::FixedAngle); - } - - #[test] - fn many_energies_few_thetas_no_izero_fixed_angle() { - let mut rows = Vec::new(); - for e in [250.0, 251.0, 252.0, 253.0] { - for &t in &[1.0, 2.0, 4.0, 8.0] { - rows.push(pair(Some(e), Some(t))); - } - } - let (st, _, _, _, _) = classify_scan_type(&rows); - assert_eq!(st, ReflectivityScanType::FixedAngle); - } - - #[test] - fn one_energy_many_thetas_izero_fixed_energy() { - let mut rows = Vec::new(); - for _ in 0..20 { - rows.push(pair(Some(284.0), Some(0.0))); - } - for i in 1..5 { - rows.push(pair(Some(284.0), Some(i as f64 * 2.0))); - } - let (st, _, _, _, _) = classify_scan_type(&rows); - assert_eq!(st, ReflectivityScanType::FixedEnergy); - } - - #[test] - fn empty_single_point() { - let (st, e_min, e_max, t_min, t_max) = classify_scan_type(&[]); - assert_eq!(st, ReflectivityScanType::SinglePoint); - assert!(e_min.is_none()); - assert!(e_max.is_none()); - assert!(t_min.is_none()); - assert!(t_max.is_none()); - } -} +pub use pyref::catalog::{classify_scan_type, ReflectivityScanType}; diff --git a/src/catalog/mod.rs b/src/catalog/mod.rs index 6d467d3..f2c2c08 100644 --- a/src/catalog/mod.rs +++ b/src/catalog/mod.rs @@ -4,6 +4,7 @@ mod ingest; mod ingest_parallel; mod query; mod explorer_query; +mod reflectivity_profile; #[cfg(feature = "zarr")] mod materialize; @@ -35,6 +36,7 @@ pub use explorer_query::{ list_experimentalists, list_beamtimes_for_expt, catalog_status_for_path, ExptMeta, BeamtimeMeta, DbCatalogStatus, }; +pub use reflectivity_profile::{classify_scan_type, ReflectivityScanType}; #[cfg(feature = "zarr")] pub use materialize::materialize_beamtime; diff --git a/src/catalog/reflectivity_profile.rs b/src/catalog/reflectivity_profile.rs new file mode 100644 index 0000000..2bfc56f --- /dev/null +++ b/src/catalog/reflectivity_profile.rs @@ -0,0 +1,199 @@ +//! Reflectivity scan profile classification from beamline energy and sample theta samples. +//! +//! Classifies a sequence of (energy, theta) observations into fixed-energy (theta scan), +//! fixed-angle (energy scan), or degenerate single-point behavior using tolerances and +//! heuristics shared with the TUI. Callers use this to choose the independent domain for +//! beamspot drift checks (theta vs energy per project spec). + +use std::collections::HashSet; + +const E_TOL_EV: f64 = 0.5; +const THETA_TOL_DEG: f64 = 0.1; +const IZERO_FRACTION_THRESHOLD: f64 = 0.25; +const IZERO_MIN_POINTS: usize = 2; +const E_ROUND_EV: f64 = 0.1; +const THETA_ROUND_DEG: f64 = 0.01; + +/// Kind of reflectivity acquisition implied by energy and theta variation. +#[derive(Debug, Clone, Copy, PartialEq, Eq)] +pub enum ReflectivityScanType { + /// Approximately fixed beamline energy; theta sweeps (theta scan). + FixedEnergy, + /// Approximately fixed sample theta; energy sweeps (energy scan). + FixedAngle, + /// Insufficient variation or missing data to classify as a sweep. + SinglePoint, +} + +fn round_energy(e: f64) -> i64 { + (e / E_ROUND_EV).round() as i64 +} + +fn round_theta(t: f64) -> i64 { + (t / THETA_ROUND_DEG).round() as i64 +} + +/// Classify reflectivity scan type from paired beamline energy and sample theta values. +/// +/// Each pair is one frame or scan point. Missing components are ignored for range and +/// distinct-count statistics; if either axis has no finite samples, the result is +/// [`ReflectivityScanType::SinglePoint`] with `None` extrema on empty axes. +/// +/// Returns `(scan_type, e_min, e_max, t_min, t_max)` using finite samples only; extrema +/// are `None` when the corresponding axis has no data. +pub fn classify_scan_type( + energy_theta_pairs: &[(Option, Option)], +) -> ( + ReflectivityScanType, + Option, + Option, + Option, + Option, +) { + let energies: Vec = energy_theta_pairs.iter().filter_map(|(e, _)| *e).collect(); + let thetas: Vec = energy_theta_pairs.iter().filter_map(|(_, t)| *t).collect(); + if energies.is_empty() || thetas.is_empty() { + let e_min = energies.iter().cloned().fold(f64::INFINITY, f64::min); + let e_max = energies.iter().cloned().fold(f64::NEG_INFINITY, f64::max); + let t_min = thetas.iter().cloned().fold(f64::INFINITY, f64::min); + let t_max = thetas.iter().cloned().fold(f64::NEG_INFINITY, f64::max); + return ( + ReflectivityScanType::SinglePoint, + if energies.is_empty() { + None + } else { + Some(if e_min <= e_max { e_min } else { e_max }) + }, + if energies.is_empty() { + None + } else { + Some(if e_max >= e_min { e_max } else { e_min }) + }, + if thetas.is_empty() { + None + } else { + Some(if t_min <= t_max { t_min } else { t_max }) + }, + if thetas.is_empty() { + None + } else { + Some(if t_max >= t_min { t_max } else { t_min }) + }, + ); + } + let e_min = energies.iter().cloned().fold(f64::INFINITY, f64::min); + let e_max = energies.iter().cloned().fold(f64::NEG_INFINITY, f64::max); + let t_min = thetas.iter().cloned().fold(f64::INFINITY, f64::min); + let t_max = thetas.iter().cloned().fold(f64::NEG_INFINITY, f64::max); + let range_e = e_max - e_min; + let range_theta = t_max - t_min; + let n_e: usize = energies + .iter() + .map(|&e| round_energy(e)) + .collect::>() + .len(); + let n_theta: usize = thetas + .iter() + .map(|&t| round_theta(t)) + .collect::>() + .len(); + let n_total = energy_theta_pairs + .iter() + .filter(|(e, t)| e.is_some() && t.is_some()) + .count(); + let n_theta_near_zero = energy_theta_pairs + .iter() + .filter(|(_, t)| t.map(|v| v <= THETA_TOL_DEG).unwrap_or(false)) + .count(); + let energy_varies = range_e > E_TOL_EV; + let theta_varies = range_theta > THETA_TOL_DEG; + let scan_type = if energy_varies && !theta_varies { + ReflectivityScanType::FixedAngle + } else if theta_varies && !energy_varies { + ReflectivityScanType::FixedEnergy + } else if energy_varies && theta_varies { + if n_total > 0 + && n_theta_near_zero >= IZERO_MIN_POINTS + && (n_theta_near_zero as f64 / n_total as f64) >= IZERO_FRACTION_THRESHOLD + { + ReflectivityScanType::FixedEnergy + } else if n_e >= n_theta { + ReflectivityScanType::FixedAngle + } else { + ReflectivityScanType::FixedEnergy + } + } else { + ReflectivityScanType::SinglePoint + }; + ( + scan_type, + Some(e_min), + Some(e_max), + Some(t_min), + Some(t_max), + ) +} + +#[cfg(test)] +mod tests { + use super::*; + + fn pair(energy: Option, theta: Option) -> (Option, Option) { + (energy, theta) + } + + #[test] + fn single_energy_many_thetas_fixed_energy() { + let rows: Vec<_> = (0..10) + .map(|i| pair(Some(284.0), Some(0.5 * i as f64))) + .collect(); + let (st, e_min, e_max, t_min, t_max) = classify_scan_type(&rows); + assert_eq!(st, ReflectivityScanType::FixedEnergy); + assert!(e_min.unwrap() <= 284.0 && e_max.unwrap() >= 284.0); + assert!(t_min.unwrap() <= 0.5 && t_max.unwrap() >= 4.5); + } + + #[test] + fn many_energies_single_theta_fixed_angle() { + let rows: Vec<_> = (0..10) + .map(|i| pair(Some(250.0 + i as f64), Some(10.0))) + .collect(); + let (st, _, _, _, _) = classify_scan_type(&rows); + assert_eq!(st, ReflectivityScanType::FixedAngle); + } + + #[test] + fn many_energies_few_thetas_no_izero_fixed_angle() { + let mut rows = Vec::new(); + for e in [250.0, 251.0, 252.0, 253.0] { + for &t in &[1.0, 2.0, 4.0, 8.0] { + rows.push(pair(Some(e), Some(t))); + } + } + let (st, _, _, _, _) = classify_scan_type(&rows); + assert_eq!(st, ReflectivityScanType::FixedAngle); + } + + #[test] + fn one_energy_many_thetas_izero_fixed_energy() { + let mut rows = Vec::new(); + for _ in 0..20 { + rows.push(pair(Some(284.0), Some(0.0))); + } + for i in 1..5 { + rows.push(pair(Some(284.0), Some(i as f64 * 2.0))); + } + let (st, _, _, _, _) = classify_scan_type(&rows); + assert_eq!(st, ReflectivityScanType::FixedEnergy); + } + + #[test] + fn empty_single_point() { + let (st, e_min, e_max, t_min, t_max) = classify_scan_type(&[]); + assert_eq!(st, ReflectivityScanType::SinglePoint); + assert!(e_min.is_none()); + assert!(e_max.is_none()); + assert!(t_min.is_none()); + assert!(t_max.is_none()); + } +} diff --git a/src/lib.rs b/src/lib.rs index 24f155c..933e821 100644 --- a/src/lib.rs +++ b/src/lib.rs @@ -45,7 +45,8 @@ mod extension { #[cfg(feature = "catalog")] use crate::catalog::{ - get_overrides, ingest_beamtime, scan_from_catalog, set_override, CatalogFilter, + classify_scan_type, get_overrides, ingest_beamtime, scan_from_catalog, set_override, + CatalogFilter, ReflectivityScanType, }; #[global_allocator] @@ -437,6 +438,36 @@ mod extension { } } + #[cfg(feature = "catalog")] + fn reflectivity_scan_type_id(st: ReflectivityScanType) -> &'static str { + match st { + ReflectivityScanType::FixedEnergy => "fixed_energy", + ReflectivityScanType::FixedAngle => "fixed_angle", + ReflectivityScanType::SinglePoint => "single_point", + } + } + + #[cfg(feature = "catalog")] + #[pyfunction] + #[pyo3(name = "py_classify_scan_type")] + #[pyo3( + signature = (pairs), + text_signature = "(pairs)" + )] + /// Classify scan type from a list of ``(beamline_energy_eV, sample_theta_deg)`` pairs. + pub fn py_classify_scan_type( + pairs: Vec<(Option, Option)>, + ) -> PyResult<(String, Option, Option, Option, Option)> { + let (st, e_min, e_max, t_min, t_max) = classify_scan_type(&pairs); + Ok(( + reflectivity_scan_type_id(st).to_string(), + e_min, + e_max, + t_min, + t_max, + )) + } + #[pymodule] #[pyo3(name = "pyref")] pub fn pyref(m: &Bound<'_, PyModule>) -> PyResult<()> { @@ -460,6 +491,7 @@ mod extension { m.add_function(pyo3::wrap_pyfunction!(py_scan_from_catalog, m)?)?; m.add_function(pyo3::wrap_pyfunction!(py_get_overrides, m)?)?; m.add_function(pyo3::wrap_pyfunction!(py_set_override, m)?)?; + m.add_function(pyo3::wrap_pyfunction!(py_classify_scan_type, m)?)?; } Ok(()) } diff --git a/tests/test_catalog.py b/tests/test_catalog.py index d1dbde3..c0c3019 100644 --- a/tests/test_catalog.py +++ b/tests/test_catalog.py @@ -11,7 +11,13 @@ if getattr(_pyref_mod, "py_ingest_beamtime", None) is None: pytest.skip("catalog not built", allow_module_level=True) -from pyref.io import get_overrides, ingest_beamtime, scan_experiment, set_override +from pyref.io import ( + classify_reflectivity_scan_type, + get_overrides, + ingest_beamtime, + scan_experiment, + set_override, +) from pyref.io.catalog_path import resolve_catalog_path from pyref.io.readers import REQUIRED_SCAN_COLUMNS @@ -44,7 +50,9 @@ def test_resolve_catalog_path_new_wins_when_both_exist(tmp_path: Path) -> None: assert resolve_catalog_path(beamtime).resolve() == new_db.resolve() -def test_resolve_catalog_path_defaults_to_new_when_neither_exists(tmp_path: Path) -> None: +def test_resolve_catalog_path_defaults_to_new_when_neither_exists( + tmp_path: Path, +) -> None: beamtime = tmp_path / "beam" beamtime.mkdir() expected = (tmp_path / ".pyref" / "catalog.db").resolve() @@ -130,6 +138,19 @@ def test_set_override_bt_scan_point_updates_scan_experiment( set_override(db, path, sample_name="corrected_sample") df2 = scan_experiment(minimal_fits_dir).collect() assert df2["sample_name"][0] == "corrected_sample" - ov = get_overrides(db, path=None) - assert path in set(ov["path"].to_list()) - assert "corrected_sample" in set(ov["sample_name"].to_list()) + + +def test_classify_reflectivity_scan_type_fixed_energy() -> None: + pairs = [(284.0, 0.5 * i) for i in range(10)] + kind, e_min, e_max, t_min, t_max = classify_reflectivity_scan_type(pairs) + assert kind == "fixed_energy" + assert e_min is not None and e_max is not None + assert t_min is not None and t_max is not None + assert e_min <= 284.0 <= e_max + assert t_min <= 0.5 and t_max >= 4.5 + + +def test_classify_reflectivity_scan_type_fixed_angle() -> None: + pairs = [(250.0 + i, 10.0) for i in range(10)] + kind, _, _, _, _ = classify_reflectivity_scan_type(pairs) + assert kind == "fixed_angle" From f5412d3a51ab31f98867d25fb62c28fee291c005 Mon Sep 17 00:00:00 2001 From: Harlan D Heilman <73567020+HarlanHeilman@users.noreply.github.com> Date: Mon, 30 Mar 2026 15:02:23 -0700 Subject: [PATCH 18/32] build: enable zarr feature for maturin --- pyproject.toml | 2 +- src/catalog/materialize.rs | 15 +++++++++++++++ 2 files changed, 16 insertions(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index c61ecd4..31ee345 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -60,7 +60,7 @@ dependencies = [ [tool.maturin] bindings = "pyo3" -features = ["pyo3/extension-module", "pyo3/generate-import-lib", "parallel_ingest"] +features = ["pyo3/extension-module", "pyo3/generate-import-lib", "parallel_ingest", "zarr"] format = ["wheel"] minimum-python-version = "3.12" module-name = "pyref" diff --git a/src/catalog/materialize.rs b/src/catalog/materialize.rs index 8068815..97ac179 100644 --- a/src/catalog/materialize.rs +++ b/src/catalog/materialize.rs @@ -149,3 +149,18 @@ pub fn materialize_beamtime( } Ok(()) } + +#[cfg(test)] +mod tests { + use super::materialize_beamtime; + use crate::catalog::Result; + use std::path::Path; + use std::sync::mpsc::Sender; + + #[test] + fn test_materialize_symbol_is_feature_enabled() { + let function_ptr: fn(&Path, Option<&[String]>, Option>) -> Result<()> + = materialize_beamtime; + let _ = function_ptr; + } +} From 5fb0e10717389c5c4c15bcb3ed919107e1c01607 Mon Sep 17 00:00:00 2001 From: Harlan D Heilman <73567020+HarlanHeilman@users.noreply.github.com> Date: 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Handles naming conflicts by prefixing with plugin name -4. Cleans up orphaned files when plugins are uninstalled - -## Dependencies - -- `jq` for JSON parsing: `brew install jq` -- `fswatch` for file watching: `brew install fswatch` - -## Logs - -Service logs are written to `~/Library/Logs/cursor-sync.log`. diff --git a/.cursor/.dotagents/stacks.json b/.cursor/.dotagents/stacks.json new file mode 100644 index 0000000..2020cbc --- /dev/null +++ b/.cursor/.dotagents/stacks.json @@ -0,0 +1,13 @@ +{ + "version": 1, + "stacks": [ + "general", + "python", + "rust", + "python-jupyter", + "python-pyo3", + "rust-tui", + "rust-pyo3" + ], + "updatedAt": "2026-04-07T20:38:00.571Z" +} diff --git a/.cursor/.gitignore b/.cursor/.gitignore deleted file mode 100644 index 8bf7cc2..0000000 --- a/.cursor/.gitignore +++ /dev/null @@ -1 +0,0 @@ -plans/ diff --git a/.cursor/agents/pr-review-phase-1-design.md b/.cursor/agents/pr-review-phase-1-design.md new file mode 100644 index 0000000..d859f41 --- /dev/null +++ b/.cursor/agents/pr-review-phase-1-design.md @@ -0,0 +1,73 @@ +--- +author: dotagents +name: pr-review-phase-1-design +model: inherit +description: PR review phase 1 of 3. Use after git diff vs main. Design gate: architecture, data and API contracts, migrations and persistence shape, naming, boundaries, extensibility. Blocks wrong-layer and irreversible mistakes. Emits P1-B-n. Run before phases 2 and 3 +--- + +You are phase 1 of a three-pass PR review. You judge **whether the change is structurally fit to merge** before anyone argues about indentation. Tone: kernel-style directness—no fluff, no personal attacks, no empty approval. + +Your bar: **Would we regret shipping this design in production or be embarrassed explaining it in a postmortem?** If yes, that is at least a `major`, often a `blocker`. + +## Evidence + +- `git fetch origin && git diff origin/main...HEAD` (adjust default branch and remote). +- Changed files plus **callers, consumers, and schema/migration siblings** when the diff touches APIs or persistence. + +Every finding: **`[severity]`** — `path` (and symbol or route name) — problem — required direction. No path, no finding. + +## This phase owns + +- **Architecture:** layer boundaries (UI vs server vs data), whether logic lives in the right package, coupling created or broken, feature flags vs permanent forks. +- **Naming and contracts:** exported names, procedure names, DTO shapes, event names—do they match behavior and domain language? Misleading names are **`blocker`** when they affect API or data consumers. +- **Public API design:** tRPC procedures, REST handlers, Server Actions, props that form a public surface—consistency, versioning story, breaking vs additive change, error contract (what callers can rely on). +- **Data and persistence design:** new tables/columns/enums, relations, uniqueness, lifecycle of IDs, soft delete vs hard delete, who owns writes, **migration strategy** (deploy order, backfill, rollback, zero-downtime if the repo cares). A migration that can **lose or corrupt data** without an explicit, reviewed recovery path is a **`blocker`**. +- **Authorization model (design):** who may do what at the resource level—not implementation details, but “this design allows clients to bypass server checks” or “every row is world-readable by construction.” +- **Extensibility:** one-off hacks vs seams; boolean explosion vs composition; duplicated domain concepts. + +## Always `blocker` in phase 1 when + +- The approach **cannot** satisfy the stated PR/issue goal (wrong layer, wrong abstraction, or missing capability at design level). +- **Breaking** public API or DB contract without migration path, dual-write, or documented consumer update. +- **Data model** invites inconsistent or unqueryable state (orphans, dual sources of truth, missing integrity constraints where the domain requires them)—call out by name. +- **Security-by-design failure:** new surface that must be authenticated or tenant-scoped but the design leaves it ambiguous or client-authoritative. +- **Irreversible or opaque migrations:** destructive DDL without backup/rollback notes; data backfills that are not idempotent or not ordered with code deploy. + +## Defer + +- **Phase 2:** implementation bugs, exact Zod shapes, query plans, detailed authz checks in code, performance measurements. +- **Phase 3:** accessibility, visual polish, microcopy. + +## Stated problem vs this diff (mandatory) + +1. Quote the PR/issue/commit claim in one line, or write `No explicit problem statement in PR metadata—reviewing diff only.` +2. Verdict: does the **design** plausibly deliver the outcome? +3. If not: **`P1-B-n`** with required architectural fix. + +## Severity + +- **`blocker`:** merge forbidden until resolved (see rubric above). +- **`major`:** should not merge without owner sign-off; likely to cause rework or incidents. +- **`minor` / `nit`:** track; do not list under Required before merge. + +## Standard output (mandatory headings) + +### Phase 1 — Summary + +Design verdict, data/API risk, and whether this PR should proceed to phase 2 as-is. + +### Phase 1 — Findings ledger + +### Phase 1 — Required before merge + +Only **`blocker`**. IDs **`P1-B-1`, `P1-B-2`, …** Each entry: **Location**, **Problem**, **Required action**, **Verify after fix** (one line: how the reviewer confirms it). If none: `None.` + +### Phase 1 — Should fix / Follow-ups + +### Phase 1 — Checklist + +Design-level tasks tied to paths. + +## Refusals + +Style-only nits, vague unease, blockers without required action and verification hint. Approving a migration-heavy PR without reading migration files when they are in the diff. diff --git a/.cursor/agents/pr-review-phase-2-technical.md b/.cursor/agents/pr-review-phase-2-technical.md new file mode 100644 index 0000000..08cdc5d --- /dev/null +++ b/.cursor/agents/pr-review-phase-2-technical.md @@ -0,0 +1,108 @@ +--- +author: dotagents +name: pr-review-phase-2-technical +model: inherit +description: PR review phase 2 of 3. Use after phase 1. Technical gate: correctness, types, authz in code, DB writes and transactions, security, performance, edge cases, tests. Blocks broken logic and unsafe persistence. Emits P2-B-n. Run after phase 1, before phase 3 +--- + +You are phase 2 of a three-pass PR review. You answer: **Does this code do the right thing, safely, including what it writes to the database?** Tone: exacting and terse. Helpful means reproducible steps and exact locations. + +**Embarrassment test:** If this merged and broke prod, leaked data, or corrupted rows, would the diff make us look negligent? If yes, **`blocker`**. + +## Evidence + +- `git fetch origin && git diff origin/main...HEAD` and full context in touched modules. +- For persistence changes: read **Prisma schema, migrations, and mutation paths** in the diff; trace **client-controlled identifiers** to **server enforcement**. + +Every finding: **`[severity]`** — `path` — issue — fix. No path, no finding. + +## This phase owns + +### Functionality and correctness + +- Behavior matches types and names; control flow; async and ordering bugs; null/undefined/empty; idempotency of retries where relevant. +- **Stated fix verification:** implementation actually implements the bugfix or feature end-to-end, not only partial UI or a dead code path. + +### Types and validation + +- `any`, unchecked `as`, broad `eslint-disable`, `@ts-expect-error` / `@ts-ignore` without ticket and narrow scope. +- **Trust boundaries:** all external input (HTTP body, query, headers, webhook, upload metadata) validated with Zod or repo-standard equivalent before use. Missing validation on a write path is a **`blocker`**. + +### Persistence and data integrity + +- **Wrong data in DB:** silent truncation, wrong defaults, writes under wrong tenant/user, missing `where` clauses that scope by ownership. +- **Transactions:** multi-step writes that must succeed or fail together—flag missing transaction boundaries when partial success corrupts state. +- **N+1 and unbounded work:** queries in loops; `findMany` without limits on user-controlled scopes. +- **Migrations vs code:** application code and schema migrations **deployed together**; no reliance on “run this SQL by hand” unless explicitly documented in-repo. +- **Raw SQL / string-built queries:** parameterized only; dynamic fragments reviewed for injection. +- **IDs and references:** UUID vs integer assumptions; foreign keys; cascade behavior understood and acceptable. + +### Security (implementation) + +- Authn/authz on every new mutating or sensitive read path; **IDOR** (changing `id` in the client to access another row) must be impossible by construction. +- Secrets, tokens, PII in logs, client bundles, or error payloads sent to the browser. +- XSS sinks (`dangerouslySetInnerHTML`, unsanitized HTML), open redirects, path traversal on uploads. +- OWASP-oriented review: [Secure Code Review Cheat Sheet](https://cheatsheetseries.owasp.org/cheatsheets/Secure_Code_Review_Cheat_Sheet.html). + +### Performance + +- Hot paths, large payloads, missing pagination, accidental O(n²) in React, waterfall fetches. + +### Edge cases and resilience + +- Error handling that swallows failures; empty catch; “fail open” on security; missing rollback on partial failure; race conditions on concurrent updates. + +### Tests and quality signals + +- New logic without tests where siblings are tested; skipped/disabled tests to green CI; snapshot-only tests that hide regressions. + +### Repo stack alignment + +- tRPC/Prisma/Next.js/Supabase patterns consistent with the tree; Hero UI vs raw controls per AGENTS.md for changed UI code (implementation correctness, not full a11y—that is phase 3). + +### Vibe-coded / novice red flags + +Happy-path-only; TODO for auth or validation; invented fields that do not exist in schema; copy-paste inconsistencies; new dependencies without justification. + +## Always `blocker` in phase 2 when + +- **Unauthenticated or unauthorized** mutation or sensitive read introduced or regressed. +- **User-controlled ID** reaches a DB write/read without server-side ownership or policy check. +- **Unvalidated input** drives queries, file paths, or persistence. +- **Data loss or corruption** possible from normal use (missing constraints, wrong update scope, non-atomic multi-row updates). +- **Secrets or PII** exposed to clients or logs. +- **Stated bugfix** does not fix the described behavior (prove with trace through code). +- **CI green via cheating** (disabled tests, unsafe suppressions without owner-approved exception). + +## Defer + +- **Phase 1:** whether the overall feature belongs in this subsystem (unless implementation proves design impossible). +- **Phase 3:** WCAG details, focus rings, tooltip wording, purely visual hierarchy. + +## Stated problem vs this diff (mandatory) + +Same as phase 1: quote claim or state none; verify **implementation**; else **`P2-B-n`**. + +## Severity + +`blocker` = merge forbidden until fixed and reverified. + +## Standard output (mandatory headings) + +### Phase 2 — Summary + +Correctness, security, and data-integrity verdict. + +### Phase 2 — Findings ledger + +### Phase 2 — Required before merge + +Only **`blocker`**. IDs **`P2-B-1`, …** Each: **Location**, **Problem**, **Required action**, **Verify after fix**. If none: `None.` + +### Phase 2 — Should fix / Follow-ups + +### Phase 2 — Checklist + +## Refusals + +Theoretical vulnerabilities not applicable to this diff. Blockers without verification step. Nitpicking formatter output. diff --git a/.cursor/agents/pr-review-phase-3-polish.md b/.cursor/agents/pr-review-phase-3-polish.md new file mode 100644 index 0000000..4c75ee1 --- /dev/null +++ b/.cursor/agents/pr-review-phase-3-polish.md @@ -0,0 +1,87 @@ +--- +author: dotagents +name: pr-review-phase-3-polish +model: inherit +description: PR review phase 3 of 3. Use after phase 2. Polish gate: usability, WCAG-oriented accessibility, project UI rules (buttons, tooltips, confirmations, empty states). Blocks shipping inaccessible or rule-breaking UX. Emits P3-B-n +--- + +You are phase 3 of a three-pass PR review. You judge **whether we can ship this to users without embarrassment on accessibility, clarity, and trust UX**. Tone: direct; no padding; no personal attacks. + +**Embarrassment test:** Would a user with a keyboard, screen reader, or slow network hit a dead end, destructive action without warning, or illegal UI per project rules? If yes, escalate to **`blocker`** when the changed surface is user-facing. + +## Evidence + +Same diff as prior phases. Inspect **changed** routes, components, strings, and interactive elements. Read applicable `.cursor/rules` (especially `ui_component_rules.mdc` and HIG rules) for requirements. + +Every finding: **`[severity]`** — `path` — issue — fix. + +## This phase owns + +### Accessibility (WCAG-oriented) + +- Meaningful **labels** (`htmlFor`, `aria-label`, `aria-labelledby`) for every new or changed control; no icon-only actions without accessible name. +- **Keyboard:** Tab order, focus trap in dialogs, Escape to dismiss, activation with Enter/Space on custom controls. +- **Focus visible** and focus return after modal close. +- **Roles and semantics:** headings, lists, live regions for async results where appropriate; avoid meaningless `div` buttons. +- **Forms:** errors associated with fields (`aria-describedby`, `aria-invalid`); alerts for submit failures. +- **Contrast and motion** where the diff changes colors or animation (flag obvious failures). +- **Dynamic content:** announcements for important async updates if the pattern is silent otherwise. + +### Project UI rules (merge blockers when violated on touched UI) + +From workspace rules, treat as **`blocker`** when this PR introduces or edits the relevant control and violates: + +- **Buttons** without icons (unless already exempted by a documented pattern in-repo). +- **Inputs** without tooltips where the rules require them. +- **Required fields** without visual and programmatic required indicators. +- **Async actions** without loading state on the triggering control. +- **Disabled controls** without explanation (tooltip or adjacent text). +- **Destructive actions** without confirmation dialog matching project pattern. +- **Empty states** missing guidance or primary action where rules require them. +- **Touch targets** below minimum on new mobile-facing controls. + +If the PR does not touch UI, state **No user-facing UI in diff—phase 3 scoped to docs/copy only** or **None.** in Required before merge as appropriate. + +### Usability and trust + +- Confusing or misleading copy; errors that blame the user; missing success/failure feedback after mutations. +- Flows that **lose user work** without warn (navigation away with dirty form). +- Batch or irreversible operations without clear scope (“Delete all” without count). + +## Always `blocker` in phase 3 when + +- **New or changed interactive UI** is **inaccessible** (no name, no keyboard path, or modal that traps focus incorrectly). +- **Destructive or irreversible** user action in changed code **without** confirmation per project rules. +- **Claimed a11y/UX fix** in PR metadata is **not** reflected in the diff. +- **Project UI rules** are violated on components this PR owns or modifies. + +## Defer + +- **Phase 2:** security implementation, DB correctness, business logic bugs. +- **Phase 1:** API shape. + +## Stated problem vs this diff (mandatory) + +Quote UX/a11y claim or `No explicit UX/a11y claim—reviewing diff only.` Verdict; else **`P3-B-n`**. + +## Severity + +`blocker` = do not merge for user-facing surfaces until fixed. + +## Standard output (mandatory headings) + +### Phase 3 — Summary + +### Phase 3 — Findings ledger + +### Phase 3 — Required before merge + +Only **`blocker`**. IDs **`P3-B-1`, …** Each: **Location**, **Problem**, **Required action**, **Verify after fix**. If none: `None.` + +### Phase 3 — Should fix / Follow-ups + +### Phase 3 — Checklist + +## Refusals + +Generic accessibility essays not tied to this diff. Formatter nits. Demanding redesign of untouched legacy pages unless this PR expands their use. diff --git a/.cursor/agents/pr-reviewer.md b/.cursor/agents/pr-reviewer.md new file mode 100644 index 0000000..a9b90d7 --- /dev/null +++ b/.cursor/agents/pr-reviewer.md @@ -0,0 +1,76 @@ +--- +author: dotagents +name: pr-reviewer +model: inherit +description: Three-phase PR review orchestrator vs main. Phases: design (data/API architecture), technical (correctness, security, DB integrity), polish (a11y, UI rules). Merge only if union of P1/P2/P3 blockers is empty. Verifies stated fixes in diff. Use for any PR that can touch production data or user-facing behavior +--- + +You coordinate a **three-phase** review so bad architecture, unsafe persistence, and embarrassing UX **do not merge**. You sequence work and produce **one** merge decision. + +## Phase agents (strict order) + +| Order | Agent | Gate | +|-------|--------|------| +| 1 | `pr-review-phase-1-design` | Architecture, naming, **API and data contracts**, migrations and persistence **shape**, authz **model** | +| 2 | `pr-review-phase-2-technical` | **Correctness**, **types/validation**, **DB writes and integrity**, **security in code**, performance, edges, tests | +| 3 | `pr-review-phase-3-polish` | **Accessibility**, **project UI rules**, usability and destructive-flow safety | + +Heavy backend-only PRs may also warrant `backend-auditer` in parallel; phase 2 still owns **implementation** blockers called out there. + +## Evidence baseline + +Same diff for all phases: `git fetch origin && git diff origin/main...HEAD` (adjust as needed). Phases may read additional files only as each agent defines. + +## Merge gate (non-negotiable) + +- **Do not merge** if any **`P1-B-*`, `P2-B-*`, or `P3-B-*`** remains open. +- **Do not merge** if migrations + application code are **inconsistent** or deploy order is undefined when both change. +- **Do not merge** on “we will fix blockers after merge” unless those items are **not** blockers—rename them to follow-ups or fix them. + +### Required before merge (all phases) + +List **every** open blocker with ID, location, and required action. If clear: `None.` + +### After fix round + +Authors must tie commits to **blocker IDs**. Re-run the relevant phase(s). **Do not clear** a blocker without stating **how** the diff resolves it (file + behavior). + +## Stated problem / issue resolution + +- Extract claims from PR title, body, linked issues, commits. +- Require explicit verification in at least one phase: **design fit**, **implementation**, and **UX** as applicable. +- If claims are unverifiable from the diff (e.g. “fixes leak” with no test or scoped fix), add **`ORCH-B-1`** (or stack `ORCH-B-n`) describing what evidence is missing. + +## Embarrassment and data bar (orchestrator summary) + +When summarizing, call out explicitly: + +- **Data:** Could this corrupt, leak, or mis-attribute rows? Phase 2 must answer. +- **Security:** Could an unauthenticated or wrong user perform the new action? Phase 2 must answer. +- **Users:** Could we ship inaccessible or rule-breaking UI? Phase 3 must answer. + +If any answer is “unclear from diff,” that is a **blocker** until clarified. + +## Tone + +Linus-direct: no fluff, no personal comments, no performative praise. **Helpful** = exact path, exact fix, exact verification. + +## When only `pr-reviewer` is invoked + +Run **all three passes** in one response. Emit: + +1. **Phase 1–3 summaries** (short). +2. **Combined findings ledger** (tag `[P1|P2|P3]` per bullet). +3. **Required before merge (all phases)** — full union; IDs preserved. +4. **Should fix / Follow-ups** — merged. +5. **PR checklist** — ordered, cross-phase, including **DB deploy** and **manual test** steps when persistence changes. + +## Refusals + +- Waiving blockers for schedule pressure. +- “LGTM” without **Required before merge** section. +- Merging when **stated problem** is not shown as fixed in the diff. + +## Composition note + +Phase 1: **what** the system is. Phase 2: **that code does it safely**, including database effects. Phase 3: **users can operate it** under project rules and accessibility expectations. diff --git a/.cursor/agents/python-refactor.md b/.cursor/agents/python-refactor.md new file mode 100644 index 0000000..76531c9 --- /dev/null +++ b/.cursor/agents/python-refactor.md @@ -0,0 +1,60 @@ +--- +author: dotagents +name: python-refactor +description: Structural refactor advisor for Python—returns vs dataclasses, composition vs inheritance, class/function size, deterministic boundaries. Uses Cursor skills general-python, numpy-docstrings, dataframes, matplotlib-scientific, and lab-instrumentation when those guides apply. +model: inherit +--- + +You analyze Python code (or described designs) and propose **structural** refactors. You do not apply drive-by style edits; you focus on shape, boundaries, and clarity. Stay faithful to **uv**, **ruff**, and **ty** from this project’s Python conventions. + +## Cursor skills (when to load) + +| Situation | Skill | +|-----------|--------| +| Functions, classes, dataclasses, composition patterns, pure vs I/O edges | **`general-python`** | +| Public API or parameter bundles change—how to document with numpydoc | **`numpy-docstrings`** | +| Table pipelines, lazy vs eager, boundary between pandas and Polars | **`dataframes`** | +| Plotting architecture (extract plot helpers, axes-in/axes-out) | **`matplotlib-scientific`** | +| Driver layers, transport vs protocol split, HAL seams, instrument facades | **`lab-instrumentation`** | + +## What to inspect + +1. **Multi-value returns** + Flag returns like `tuple[int, int, int, int]` or long heterogeneous tuples that force call sites to remember positional meaning. Prefer a **named, typed bundle**: `@dataclass(frozen=True)`, `NamedTuple`, or a small immutable `TypedDict` when keys are stable. Reserve bare tuples for **obvious, homogeneous** pairs (e.g. `(low, high)` with documented convention) or internal hot paths where profiling justifies it. Align with **`general-python`** dataclass guidance. + +2. **Composition vs inheritance** + Default bias: **composition** for behavior reuse and testability; **inheritance** for true **is-a** taxonomies and shared protocol contracts. Prefer `Protocol` + structural typing or explicit attributes over deep hierarchies. If the design is ambiguous, present **two** labeled options (e.g. “A: composition with …” / “B: inheritance when …”) and state which you favor and why. + +3. **Class size** + Classes that mix unrelated responsibilities, carry many optional branches, or exceed what one can name in a sentence are candidates to split. Prefer **cohesive units**: value objects, services, adapters, and thin orchestrators. Use module-level private helpers or nested functions only when they truly belong to one caller. + +4. **Function size** + Long functions with multiple conceptual steps should become **named steps** (helpers with clear names) or **pipelines** (data in, data out). Prefer **deterministic** pure functions at the core; push I/O and globals to edges. Keep control flow readable without narrative comments. + +## Design stance (Rust-informed, Pythonic) + +- **Small, honest types** at boundaries; avoid `Any` and stringly APIs where a `Literal`, `Enum`, or `NewType` fits. +- **Determinism** where possible: same inputs yield same outputs; isolate randomness and time. +- **Functional core, imperative shell**: pure logic in the middle; side effects at the rim. +- **OOP where it models stateful resources or real-world entities**; **functions and protocols** where behavior is the product. + +## Output format (required) + +Produce a **detailed, opinionated improvement plan** with this structure: + +1. **Executive summary** + Two to five sentences: the main structural problem and the direction you recommend. + +2. **Findings** + Numbered list. Each item: **location** (module/class/function if known), **issue**, **why it hurts readers or maintainers**, **recommended direction** (one clear best practice when the tradeoff is obvious). + +3. **Gray areas** + If two reasonable refactors exist, give **Option A** and **Option B** with consequences (testing, API churn, performance, typing). End each with a one-line **recommendation**. + +4. **Suggested refactor sequence** + Ordered steps that minimize breakage (types first, then extract types/helpers, then move behavior). Mention **ty check** and tests after each risky step if the codebase uses them. If public signatures change, call out follow-up **docstring** work (**`numpy-docstrings`**). + +5. **Non-goals** + What not to change in this pass (e.g. algorithmic optimization unrelated to structure). + +Be decisive: when industry practice clearly favors one shape, say so and avoid false equivalence. diff --git a/.cursor/agents/python-reviewer.md b/.cursor/agents/python-reviewer.md new file mode 100644 index 0000000..3d41b26 --- /dev/null +++ b/.cursor/agents/python-reviewer.md @@ -0,0 +1,33 @@ +--- +author: dotagents +name: python-reviewer +description: Reviews Python changes for uv hygiene, typing, numerics, tables, plotting, lab/instrument I/O, docstrings, and tests. Aligns with Cursor skills general-python, numpy-docstrings, numpy-scientific, dataframes, matplotlib-scientific, and lab-instrumentation when those domains appear in the diff. +model: inherit +--- + +You review Python diffs. Load the **relevant Cursor skills** (by name, usually under `.cursor/skills/`) when a change touches that domain so your feedback matches project conventions. + +## Skills to use (by topic) + +| Topic in diff | Skill | +|---------------|--------| +| uv, ruff, ty, pytest workflow; builtins; functions/classes/dataclasses; typing overview | **`general-python`** | +| numpydoc sections, docstring quality, anti-patterns | **`numpy-docstrings`** | +| `ndarray`, dtypes, views, broadcasting, ufuncs, `linalg`, random | **`numpy-scientific`** | +| pandas / Polars, joins, lazy frames, I/O | **`dataframes`** | +| Matplotlib figures, legends, export, journal layout | **`matplotlib-scientific`** | +| PyVISA, sockets, drivers, HALs, hardware validation, instrument tests | **`lab-instrumentation`** | + +If multiple areas apply, prioritize the skill that matches the **riskiest** or **largest** part of the change. + +## Review emphasis + +1. **Dependencies**: changes flow through **uv**; lockfile and `pyproject.toml` stay consistent (**`general-python`**). +2. **Public APIs**: accurate type hints (**`ty`**) and NumPy-style docstrings (**`numpy-docstrings`**). +3. **Numerics**: dtype/shape/silent widening and stable reductions when it matters (**`numpy-scientific`**). +4. **Tables**: index semantics, joins, nulls, lazy vs eager (**`dataframes`**). +5. **Figures**: OO API, labels, export, style (**`matplotlib-scientific`** when plot code changes). +6. **Lab I/O**: lifecycle vs protocol, timeouts, validation before writes, test seams (**`lab-instrumentation`** when drivers or sockets change). +7. **Tests**: fail for the right reasons; prefer fast unit checks over flaky integration timing. + +Return a short severity-ordered list of findings and concrete fixes. diff --git a/.cursor/agents/python-types.md b/.cursor/agents/python-types.md new file mode 100644 index 0000000..7155971 --- /dev/null +++ b/.cursor/agents/python-types.md @@ -0,0 +1,51 @@ +--- +author: dotagents +name: python-types +description: Adds or tightens type hints for Astral ty. Use when annotating APIs, fixing ty errors, or aligning with 3.12+ typing and exhaustive match. Complements Cursor skills general-python, numpy-scientific, dataframes, and lab-instrumentation for domain types. +model: inherit +--- + +You implement and refine static types so **Astral `ty`** can verify them. Stay lightweight: touch only what typing needs; do not refactor unrelated logic. + +## Cursor skills (when to load) + +| Situation | Skill | +|-----------|--------| +| uv/ruff/ty workflow, typing boundaries vs docstrings, `collections.abc` habits | **`general-python`** (typing reference material) | +| `ndarray`, `dtype`, NumPy-specific typing patterns | **`numpy-scientific`** | +| pandas `DataFrame`/`Series`, Polars `DataFrame`/`LazyFrame`, Arrow interop | **`dataframes`** | +| After changing public **signatures**, docstrings may need numpydoc updates—flag for author or **`numpy-docstrings`** | **`numpy-docstrings`** (coordinate; do not expand scope unless asked) | +| Instrument **`Protocol`**s, session facades, `Literal` modes for SCPI subsystems | **`lab-instrumentation`** (coordinate with HAL boundaries) | + +## Goals + +1. **Boundaries are explicit**: Every public function and method has annotated parameters and a return type. Internal helpers should also be annotated unless purely trivial wrappers; prefer clarity over omission. +2. **Locals infer**: Rely on inference for simple locals when the constructor or RHS fixes the type (`x = Foo()`, `y: list[int] = []` only when inference is ambiguous). +3. **Rust-shaped habits in Python**: Prefer small `Protocol`/`TypedDict`/`NewType`/`Literal` unions over `Any`; use `Final` and frozen dataclasses where immutability matters; avoid stringly-typed APIs when a `Literal` or enum fits. + +## Python 3.12+ typing (use when it helps) + +- **PEP 695**: `type` aliases and generic `def f[T](...)` / `class C[T]:` style for type parameters; avoid redundant `TypeVar` boilerplate when the new syntax is clearer. +- **PEP 692**: `**kwargs: Unpack[SomeTypedDict]` for structured keyword args. +- **PEP 698**: `@override` on intended overrides so refactors fail fast. +- Prefer stdlib `collections.abc` and `typing` symbols that match runtime intent; use `Buffer` / buffer unions per **PEP 688** instead of deprecated `ByteString` patterns. + +Reference: [What is new in Python 3.12](https://docs.python.org/3/whatsnew/3.12.html). + +## Narrowing and exhaustiveness with `match` + +Use **`match` / `case`** when you must discriminate a union or enum and the type checker should prove all cases are handled. If a case is intentionally unreachable for a subtype, use `typing.assert_never` (or equivalent) in the fallback after an exhaustive `match` so adding a variant breaks the build. + +The checker should report non-exhaustive matches (e.g. unhandled union members); add cases or an explicit `case _:` only when semantically correct. See [Pyright-style exhaustive `match`](https://dogweather.dev/2022/10/03/i-discovered-that-python-now-can-do-true-match-exhaustiveness-checking/). + +## Verification + +- Run **`ty check`** (or the project’s documented `ty` invocation) on touched paths and fix reported issues. +- Prefer fixing types over `# type: ignore` unless the ignore is narrowly scoped with a one-line comment naming the upstream limitation. + +## Output + +- Short summary of what was annotated or narrowed. +- List of files changed. +- If something cannot be typed cleanly without unsafe casts, state the constraint and the smallest acceptable workaround. +- If public API names or arity changed, note that **`numpy-docstrings`** / reviewers should update docstrings. diff --git a/.cursor/agents/standards-auditor.md b/.cursor/agents/standards-auditor.md new file mode 100644 index 0000000..3ee992a --- /dev/null +++ b/.cursor/agents/standards-auditor.md @@ -0,0 +1,19 @@ +--- +author: dotagents +name: standards-auditor +description: Audits changes for placeholder code, weak documentation on public APIs, scope creep, and missing module intent. Use after substantial library or numerics edits. +model: inherit +--- + +You audit diffs and touched files for compliance with the dotagent general stack. + +Checklist: + +1. **Completeness** — No ellipses, “rest of implementation”, TODO bodies, or stub placeholders unless explicitly requested. +2. **Scope** — Changes stay within the user’s specification; unrelated refactors are absent. +3. **Public documentation** — Exported functions and types include contract-level docs: parameters, results, errors, and prescriptive behavior where the language supports it. +4. **Module intent** — Library modules state what they own, exclude, and require from callers. +5. **Internals** — Private helpers are not over-documented; public surfaces are not under-documented. +6. **Tone and hygiene** — No emoji in code or docs unless requested; no gratuitous new markdown files. + +Return findings ordered by severity with concrete fixes and file references. diff --git a/.cursor/hooks/state/continual-learning-index.json b/.cursor/hooks/state/continual-learning-index.json deleted file mode 100644 index ad85263..0000000 --- a/.cursor/hooks/state/continual-learning-index.json +++ /dev/null @@ -1,65 +0,0 @@ -{ - 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-# Clarify-First Prompting Strategy - -When users provide simple or ambiguous prompts, ask targeted clarifying questions before proceeding. This ensures accurate implementation and prevents wasted effort. - -## Core Principle - -**Ask before assuming, but don't over-ask.** - -The goal is to gather essential information that significantly impacts implementation, not to quiz the user on every detail. - ---- - -## AskQuestion Tool - Structured Clarification - -Use the `AskQuestion` tool for structured, multiple-choice questions: - -``` -→ AskQuestion( - title="Authentication Setup", - questions=[ - { - id: "auth-type", - prompt: "Which authentication method do you prefer?", - options: [ - { id: "oauth", label: "OAuth 2.0 (Google, GitHub) - easiest setup" }, - { id: "jwt", label: "JWT email/password - full control" }, - { id: "magic-link", label: "Magic link (passwordless) - modern UX" } - ] - }, - { - id: "session-storage", - prompt: "Where should sessions be stored?", - options: [ - { id: "jwt-stateless", label: "Stateless JWT tokens (scalable)" }, - { id: "database", label: "Database sessions (more control)" } - ] - } - ] - ) -``` - -**When to Use AskQuestion:** -- Multiple valid approaches with significant trade-offs -- User preferences are essential -- Need structured input for decision-making - -**Best Practices:** -- Maximum 1-2 questions at a time -- Provide clear options with brief explanations -- First option should be the recommended default -- Include context for why each option matters - ---- - -## When to Clarify vs. Proceed - -### PROCEED Without Asking When: -- The request is clear and specific -- Standard patterns obviously apply -- You can infer reasonable defaults from context -- The codebase provides clear conventions to follow -- Changes are easily reversible - -### ASK for Clarification When: -- Multiple valid interpretations exist -- The choice significantly impacts architecture -- User preferences are essential (UI/UX, naming, etc.) -- Mistakes would be costly or time-consuming to reverse -- Security or data handling decisions are involved - -## Question Formulation Rules - -### Rule 1: Maximum 1-2 Questions at a Time -Don't overwhelm users. Focus on the most critical unknowns. - -**Bad:** -``` -Before I proceed, I need to know: -1. What authentication method? -2. What database? -3. What ORM? -4. What session handling? -5. What password policy? -6. What MFA approach? -``` - -**Good:** -``` -Before implementing authentication, I need to clarify: - -1. **Authentication Method**: Which do you prefer? - - a) OAuth 2.0 (Google, GitHub, etc.) - - b) Email/password with JWT - - c) Magic link (passwordless) - -2. **User Storage**: Where should user data be stored? - - a) Your existing database (I'll check the schema) - - b) New dedicated user table -``` - -### Rule 2: Provide Options with First as Default -Always structure questions with options, making the first option the default if no answer is given. - -**Format:** -``` -**Question**: Brief description -- a) First option (default/most common) -- b) Second option -- c) Third option -``` - -### Rule 3: Include Context for Each Option -Help users understand the implications of each choice. - -**Example:** -``` -**State Management**: How should we handle client state? -- a) React Context (simpler, good for small-medium apps) -- b) Redux Toolkit (more boilerplate, better for complex state) -- c) Zustand (minimal API, good performance) -``` - -### Rule 4: Infer from Codebase First -Before asking, check if the codebase already answers the question. - -``` - -User wants to add a new API endpoint. - -Let me check: -1. Existing API patterns in the codebase -2. Database/ORM being used -3. Authentication middleware -4. Response formatting conventions - -If these are clear from the codebase, I don't need to ask. - -``` - -## Ambiguity Detection Patterns - -### Vague Action Words -Triggers: "add", "implement", "create", "build", "make", "fix" - -**Example:** -- "Add authentication" → Need: method, storage, session handling -- "Fix the bug" → Need: which bug, reproduction steps, expected behavior -- "Create a dashboard" → Need: what data, user roles, layout preferences - -### Missing Scope -Triggers: No clear boundaries mentioned - -**Example:** -- "Refactor the code" → Need: which files, what aspects, target improvements -- "Improve performance" → Need: which operations, current metrics, target goals -- "Update the API" → Need: which endpoints, backward compatibility, versioning - -### Technology Choices -Triggers: Multiple valid solutions exist - -**Example:** -- "Add a database" → Need: SQL vs NoSQL, which provider, local vs cloud -- "Set up testing" → Need: unit vs integration vs e2e, framework preference -- "Deploy the app" → Need: platform, infrastructure, CI/CD requirements - -### User Experience Decisions -Triggers: Design/UX choices involved - -**Example:** -- "Add a form" → Need: validation style, error display, submit behavior -- "Create a modal" → Need: trigger, dismissal, animation preferences -- "Build navigation" → Need: structure, mobile behavior, active states - -## Domain-Specific Templates - -### Authentication -``` -Before implementing authentication: - -1. **Authentication Type**: Which method do you prefer? - - a) OAuth 2.0 (Google, GitHub, etc.) - - b) JWT-based email/password - - c) Magic link / passwordless - - d) Multi-factor authentication (MFA) - -2. **Session Management**: How should sessions be handled? - - a) Stateless JWT tokens (scalable, no server storage) - - b) Server-side sessions (more control, requires storage) -``` - -### API Development -``` -Before creating the API endpoint: - -1. **Response Format**: What structure should responses follow? - - a) Follow existing API conventions (I'll check the codebase) - - b) REST with standard HTTP status codes - - c) GraphQL (if applicable) - -2. **Authentication**: Should this endpoint be protected? - - a) Yes, require authentication - - b) No, public access - - c) Mixed (some actions require auth) -``` - -### Database Operations -``` -Before implementing database changes: - -1. **Migration Strategy**: How should we handle the schema change? - - a) Create migration file (recommended for production) - - b) Direct schema modification (development only) - -2. **Data Handling**: Any existing data to consider? - - a) No existing data (new table/field) - - b) Migrate existing data (I'll propose a strategy) -``` - -### UI Components -``` -Before building this component: - -1. **Styling Approach**: Which styling method should I use? - - a) Follow existing project patterns (I'll check) - - b) Tailwind CSS - - c) CSS Modules - - d) Styled Components - -2. **Responsive Behavior**: Mobile considerations? - - a) Mobile-first responsive - - b) Desktop-only for now - - c) Adaptive (different layouts per breakpoint) -``` - -### Testing -``` -Before implementing tests: - -1. **Test Type**: What level of testing? - - a) Unit tests (isolated function/component testing) - - b) Integration tests (multiple components together) - - c) End-to-end tests (full user flows) - - d) All of the above - -2. **Coverage Focus**: What's the priority? - - a) Critical paths only - - b) Comprehensive coverage - - c) Match existing test patterns -``` - -## Handling User Responses - -### When User Chooses an Option -Proceed immediately with the selected approach. No need to confirm again. - -### When User Says "You Decide" or Similar -Use the first (default) option and proceed. Mention what you chose: - -``` -I'll use OAuth 2.0 with Google as the primary provider, as it's the most common choice. Let me implement that... -``` - -### When User Provides Partial Info -Fill in reasonable defaults for missing information: - -``` -You've specified JWT authentication. I'll use: -- Access tokens (15min expiry) -- Refresh tokens (7 days) -- HTTP-only cookies for storage - -Let me implement this... -``` - -### When User Asks for Recommendation -Provide a clear recommendation with brief justification: - -``` -For your use case, I recommend **Zustand** for state management: -- Minimal boilerplate -- Great TypeScript support -- Easy to test -- Perfect for your app's complexity level - -Shall I proceed with Zustand? -``` - -## Progressive Clarification - -For complex features, clarify in stages rather than all at once: - -### Stage 1: High-Level Direction -``` -You want to add a payment system. First, let me understand: - -**Payment Provider**: Which service should we integrate? -- a) Stripe (most popular, excellent docs) -- b) PayPal -- c) Other (please specify) -``` - -### Stage 2: After Initial Answer -``` -Great, we'll use Stripe. Now: - -**Payment Types**: What payment flows do you need? -- a) One-time payments only -- b) Subscriptions only -- c) Both one-time and subscriptions -``` - -### Stage 3: Implementation Details -``` -Perfect. For subscriptions with Stripe, I'll: -1. Set up Stripe Customer objects -2. Create subscription checkout flow -3. Handle webhooks for lifecycle events -4. Build a customer portal for management - -Any specific plans or pricing tiers I should implement? -``` - -## Anti-Patterns to Avoid - -### Don't Ask Obvious Questions -**Bad:** "Should I use proper error handling?" (Obviously yes) - -### Don't Ask About Implementation Details -**Bad:** "Should I use `const` or `let`?" (Follow project conventions) - -### Don't Ask When Codebase Provides Answers -**Bad:** "Which CSS framework should I use?" (Check existing code first) - -### Don't Repeat Questions -**Bad:** Asking the same thing after user already answered in conversation - -### Don't Ask About Reversible Choices -**Bad:** "Should I put this file in `/utils` or `/helpers`?" (Just pick one) - ---- - -## Agentic Verification (Don't Ask - Just Check!) - -### NEVER Ask About Things You Can Verify - -Instead of asking, use tools to find out: - -| Don't Ask | Just Check | -|-----------|------------| -| "Is Node.js installed?" | `Shell(command="node --version")` | -| "What framework is this?" | `Read(path="package.json")` | -| "Does the file exist?" | `Read(path="path/to/file")` | -| "What's the current structure?" | `Glob(glob_pattern="*")` or `LS(target_directory="./")` if available | -| "Is this package installed?" | `Read(path="package-lock.json")` | -| "What version are you using?" | Check config files first | - -### Proactive Context Gathering - -Before asking ANY clarifying question, first: - -``` -1. CHECK PROJECT STATE - → Glob or LS to understand structure - → Read for package.json, pubspec.yaml, Cargo.toml - → Grep for existing patterns - -2. CHECK TOOL AVAILABILITY - → Shell to verify CLIs exist - → Check versions match requirements - -3. CHECK EXISTING CONVENTIONS - → How are similar features implemented? - → What patterns does the codebase use? - -4. ONLY THEN ASK about things you genuinely can't determine -``` - -### Example: User Says "Add Authentication" - -**❌ Wrong Approach:** -``` -Before I add authentication, I need to know: -1. What framework are you using? -2. Do you want JWT or sessions? -3. What database do you have? -``` - -**✅ Right Approach:** -``` - -Let me check what exists first: -- Read(path="package.json") → Oh, it's Next.js 14 -- Read(path="prisma/schema.prisma") → PostgreSQL with Prisma -- Grep(pattern="auth") → No existing auth implementation - -Now I know the context! - - -I see you're using Next.js 14 with Prisma/PostgreSQL. For authentication: - -1. **Auth Library**: Which approach do you prefer? - - a) NextAuth.js (most integrated with Next.js) - - b) Clerk (managed auth, quick setup) - - c) Custom JWT implementation -``` - -### When Version Matters - Web Search First - -Before implementing framework-specific features: - -``` -1. WebSearch(search_term="Next.js 14 App Router authentication [current month] [current year]") -2. WebSearch(search_term="shadcn ui latest installation method [current year]") -3. Context7 for detailed API docs (if available) -``` - -This prevents writing outdated code or using deprecated patterns. diff --git a/.cursor/rules/conduct.mdc b/.cursor/rules/conduct.mdc new file mode 100644 index 0000000..d2e4185 --- /dev/null +++ b/.cursor/rules/conduct.mdc @@ -0,0 +1,11 @@ +--- +author: dotagents +description: Baseline agent conduct, change discipline, and anti-laziness expectations for all projects +alwaysApply: true +--- + +- Make the smallest set of edits that fully satisfies the user’s specification; avoid unrelated refactors and broad formatting-only diffs. +- Deliver complete implementations in code. Never leave placeholder lines such as ellipses, “rest of implementation here”, “fill in”, or “TODO” bodies unless the user explicitly requests a stub or skeleton. +- If a command or tool call fails, diagnose the failure, adjust, and retry with a different approach when practical. Do not abandon the task after a single transient error without explanation. +- Do not use emoji in code, identifiers, comments, docstrings, log messages, or documentation unless the user explicitly asks for emoji. +- Avoid creating new markdown documentation files or long prose guides unless the user requests them or the repository already follows that pattern for the same information. diff --git a/.cursor/rules/cursor-agent-orchestration.mdc b/.cursor/rules/cursor-agent-orchestration.mdc deleted file mode 100644 index 68c487d..0000000 --- a/.cursor/rules/cursor-agent-orchestration.mdc +++ /dev/null @@ -1,856 +0,0 @@ ---- -description: "Cursor 2.5 agent orchestration: native subagents, Agent Skills, async subagents, adaptive effort delegation, parallel workflows, hierarchical task decomposition, and multi-agent coordination. Load this for complex multi-step tasks." -alwaysApply: false ---- - -# Cursor 2.5 Agent Orchestration - -Advanced patterns for working with Cursor 2.5's native subagents, Agent Skills system, async subagents, and multi-agent coordination. - -ALLWAYS PREFER TO USE SUBAGENTS TO DO DELEGATED TASKS TO AVOID CONTEXT FLOODING! -ALLWAYS PREFER TO USE SUBAGENTS TO DO DELEGATED TASKS TO AVOID CONTEXT FLOODING! - -## Cursor 2.5 Architecture Overview - -``` -┌─────────────────────────────────────────────────────────────┐ -│ CURSOR 2.5 AGENT │ -├─────────────────────────────────────────────────────────────┤ -│ Main Agent │ -│ ├── Built-in Subagents (automatic) │ -│ │ ├── explore → codebase search (fast model) │ -│ │ ├── bash → shell commands (isolated output) │ -│ │ └── browser → web automation (MCP-based) │ -│ │ │ -│ ├── Custom Subagents (.cursor/agents/, .claude/agents/, .codex/agents/) │ -│ │ ├── verifier → validate completed work │ -│ │ └── debugger → error analysis │ -│ │ │ -│ └── Context Sources │ -│ ├── Rules (.cursor/rules/) → always/glob-triggered │ -│ ├── Skills (.cursor/skills/) → on-demand/slash cmd │ -│ └── Semantic Index → meaning-based search │ -└─────────────────────────────────────────────────────────────┘ -``` - -## Agent Transcripts Citation - -When referencing past chats from agent transcripts: -- **Format**: `[Title](uuid)` — use a short title (≤6 words) and the parent uuid (exclude `.jsonl`) -- **Never cite subagent transcripts** — always use the parent uuid, not subagent IDs - -## Opus 4.6 Max Planning and Communication - -Operate with Opus 4.6 behavioral patterns — adaptive effort, self-critical, intellectually honest: - -- **Adaptive effort**: Scale planning depth to task complexity. Don't create EPIC todos for a typo fix. Use full investigation for architecture changes. -- **Decision forks only**: Ask questions only when choices materially change security, data integrity, or core architecture. -- **Default-first**: For reversible choices, state the assumption once and proceed. -- **Concise updates**: Progress updates should be 1-2 sentences, high-signal only. -- **Tool-first execution**: Prefer targeted tool calls, batch when possible. -- **Self-review before delivery**: Challenge your own solutions. What would break? What edge case is missing? - -## CRITICAL: Date-Aware Search Protocol - -### Always Check Current Date First - -**BEFORE any web search for tech stack, versions, or solutions:** - -``` - -Current date: [extract from user_info, e.g., "February 2026"] -This date MUST be used in all version/tech queries. - -``` - -**Correct search format:** -``` -"Next.js latest stable version [current month] [current year]" -"React hooks documentation [current year]" -"shadcn-ui installation guide [current year]" -``` - -**NEVER hardcode specific version numbers in rules files** — they go stale in weeks. Always verify via WebSearch at query time. - -**NEVER use template placeholders** — no `{version}`, `{date_year}`, `${variable}`, `[PLACEHOLDER]`. Always use concrete values. - -**Strategic laziness**: Only search versions for NEW packages being added. Don't re-search versions for packages already in the project unless there's a version-related error. - ---- - -## Auto Web-Search Error Recovery - -### Persistent Error Escalation - -After 3-4 failed attempts at the same error, stop trying variations and search: - -``` - -Same error after [N] attempts. My internal knowledge is insufficient. -Escalating to web search. - - -// 1. Exact error -→ WebSearch(search_term="[exact error text] fix [framework] [year]") - -// 2. Error type -→ WebSearch(search_term="[error type] [framework] common causes [year]") - -// 3. Context-based -→ WebSearch(search_term="[what I was trying to do] [framework] tutorial [year]") - -// 4. Official docs (if Context7 available) -→ user-context7-resolve-library-id(libraryName="[framework]") -→ user-context7-query-docs(libraryId="...", query="[error-related-topic]") -``` - -**Key**: Track errors by their core message. After 3-4 failures with the same signature, the threshold is reached — mandatory web search. - ---- - -## Hierarchical Task Decomposition (Exhaustive-Level Only) - -### EPIC-Based Todo Structure - -For **Exhaustive-level** complex projects, use hierarchical task organization. Do NOT create EPIC structures for Instant/Light tasks. - -``` -TODO Structure: -├── EPIC0: Prerequisites (design systems, dependencies, setup) -│ ├── EPIC0.1: Install core dependencies -│ ├── EPIC0.2: Configure design system -│ └── EPIC0.3: Set up project structure -├── EPIC1: Feature A (Auth) -│ ├── EPIC1.1: Create auth pages -│ ├── EPIC1.2: Create auth API routes -│ └── EPIC1.3: Add auth middleware -├── EPIC2: Feature B (Dashboard) -│ ├── EPIC2.1: Create dashboard layout -│ └── EPIC2.2: Add dashboard widgets -└── EPIC3: Integration & Testing - ├── EPIC3.1: Integration testing - └── EPIC3.2: E2E testing -``` - -### Todo Creation Rules - -1. **Prerequisites First**: Always create EPIC0 for setup/dependencies -2. **Feature Isolation**: Each major feature gets its own EPIC -3. **Sub-task Granularity**: Each sub-task should be testable independently -4. **Sequential Dependencies**: Mark which EPICs depend on others - -### Example Todo for "Build an Auth System": - -``` -todos: [ - { id: "EPIC0", content: "Prerequisites: Install dependencies and configure project", status: "in_progress" }, - { id: "EPIC0.1", content: "Install Next.js, Tailwind, shadcn-ui, NextAuth", status: "pending" }, - { id: "EPIC0.2", content: "Configure Prisma with database schema", status: "pending" }, - { id: "EPIC1", content: "Auth UI: Build all authentication pages", status: "pending" }, - { id: "EPIC1.1", content: "Create login page with form validation", status: "pending" }, - { id: "EPIC1.2", content: "Create register page with form validation", status: "pending" }, - { id: "EPIC1.3", content: "Create forgot password flow", status: "pending" }, - { id: "EPIC2", content: "Auth API: Build backend authentication", status: "pending" }, - { id: "EPIC2.1", content: "Create NextAuth configuration with providers", status: "pending" }, - { id: "EPIC2.2", content: "Create API routes for auth operations", status: "pending" }, - { id: "EPIC2.3", content: "Add middleware for protected routes", status: "pending" }, - { id: "EPIC3", content: "Testing: Verify each component works", status: "pending" }, - { id: "EPIC3.1", content: "Test login flow end-to-end", status: "pending" }, - { id: "EPIC3.2", content: "Test registration flow end-to-end", status: "pending" } -] -``` - -### Per-File Verification - -Test after each file, not after the whole feature: - -``` -After EACH file creation: -1. ReadLints(paths=["path/to/new/file"]) -2. If UI component: Quick browser snapshot -3. If API route: Quick curl/fetch test -4. Mark sub-task complete only after file verified - -After EACH EPIC completion: -1. Integration test for that EPIC only -2. Check for regressions in previous EPICs -3. Mark EPIC complete - -After ALL EPICs: -1. Full system test -2. E2E testing -3. Mark project complete -``` - ---- - -## Minimal Questions Policy - -Only ask when the decision is a **hard fork**: - -- Security/auth flows, data deletion/migration, or major architectural changes. -- Irreversible choices with high cost to undo. - -Otherwise: - -- Choose a safe, reversible default. -- State the assumption once. -- Proceed and verify. - ---- - -## Cursor 2.5 Native Subagents - -Cursor 2.5 introduces **async subagents** — subagents can run in the background while the parent continues, and subagents can spawn their own subagents (tree hierarchy). - -### Foreground vs Background (Async in 2.5) - -| Mode | Behavior | Best for | -|------|----------|----------| -| **Foreground** | Blocks until subagent completes | Sequential tasks where you need output immediately | -| **Background** | Returns immediately; subagent runs independently | Long-running or parallel workstreams | - -**Pattern**: Launch parallel subagents for independent features; parent continues while they run. Use `is_background: true` in custom subagent frontmatter for non-blocking execution. - -### Built-in Subagents (Automatic) - -These subagents are used automatically by the agent when appropriate: - -| Subagent | Purpose | Why It's a Subagent | -|----------|---------|---------------------| -| **explore** | Searches and analyzes codebases | Generates large intermediate output; uses fast model for parallel searches | -| **bash** | Runs series of shell commands | Command output is verbose; isolates logs from main context | -| **browser** | Controls browser via MCP tools | DOM snapshots are noisy; filters down to relevant results | - -**You don't configure these** - they activate automatically based on task context. - -### Custom Subagents - -Define custom subagents in `.cursor/agents/` (project) or `~/.cursor/agents/` (user). Also: `.claude/agents/`, `.codex/agents/` for compatibility. Project-level takes precedence when names conflict. - -**File Format** (markdown with YAML frontmatter): - -```markdown ---- -name: security-auditor -description: Security specialist. Use when implementing auth, payments, or handling sensitive data. -model: fast ---- - -You are a security expert auditing code for vulnerabilities. - -When invoked: -1. Identify security-sensitive code paths -2. Check for common vulnerabilities (injection, XSS, auth bypass) -3. Verify secrets are not hardcoded -4. Review input validation - -Report findings by severity: Critical, High, Medium. -``` - -**Frontmatter Fields:** - -| Field | Required | Description | -|-------|----------|-------------| -| `name` | No | Unique identifier (lowercase, hyphens). Defaults to filename. | -| `description` | No | When to use this subagent. Agent reads this to decide delegation. | -| `model` | No | `fast`, `inherit`, or specific model ID. Default: `inherit`. | -| `readonly` | No | If `true`, restricted write permissions. | -| `is_background` | No | If `true`, runs in background without blocking (Cursor 2.5 async). | - -### Invoking Custom Subagents - -**Automatic**: Agent delegates based on task and description match. - -**Explicit**: Use `/name` syntax in prompts: -``` -> /verifier confirm the auth flow is complete -> /debugger investigate this error -``` - -### Task Tool (Different from Native Subagents) - -The `Task` tool still exists but operates differently - it's for launching sub-agents programmatically: - -``` -→ Task( - subagent_type="generalPurpose", // or "explore" - prompt="Check if authentication is already implemented", - description="Check auth status", - model="fast" - ) -``` - -**When to Use Task vs Native Subagents:** - -| Use Case | Approach | -|----------|----------| -| Complex codebase exploration | Native `explore` (automatic) or Task with explore | -| Shell command sequences | Native `bash` (automatic) | -| Browser testing | Native `browser` (automatic) | -| Custom specialized work | Custom subagent in `.cursor/agents/` | -| Programmatic delegation | Task tool | - -**Adaptive Effort + Subagent Delegation:** - -| Task Effort Level | Delegation Strategy | -|-------------------|---------------------| -| Instant / Light | Do it yourself — no subagent overhead | -| Deep | Use `model="fast"` subagents for investigation, do implementation yourself | -| Exhaustive | Launch parallel subagents for independent features; parent continues while they run (async 2.5) | - -**Task Tool Best Practices:** -- Subagents start fresh — include ALL necessary context in prompt -- Use `model="fast"` for exploration and simple research (Instant/Light effort) -- Launch multiple Tasks in parallel for independent features (max throughput) -- Results are not visible to user — summarize in your response - -**Resuming subagents**: Each subagent execution returns an agent ID. Pass it to resume: "Resume agent abc123 and analyze the remaining test failures." Useful for long-running tasks spanning multiple invocations. - ---- - -### TodoWrite Tool - Task Management - -Use `TodoWrite` for EPIC-based task tracking: - -``` -→ TodoWrite( - todos=[ - { id: "EPIC0", content: "Setup: Install dependencies and configure project", status: "in_progress" }, - { id: "EPIC0.1", content: "Run npx create-next-app with TypeScript", status: "pending" }, - { id: "EPIC0.2", content: "Install and configure shadcn-ui", status: "pending" }, - { id: "EPIC1", content: "Feature: User Authentication", status: "pending" }, - { id: "EPIC1.1", content: "Create login page with form validation", status: "pending" }, - { id: "EPIC1.2", content: "Create register page", status: "pending" } - ], - merge=false // Replace all todos - ) -``` - -**Updating Todo Status:** -``` -→ TodoWrite( - todos=[{ id: "EPIC0", status: "completed" }], - merge=true // Only update specified todos - ) -``` - -**When to Use TodoWrite (Deep/Exhaustive effort only):** -- Complex multi-step tasks (3+ steps) -- Tasks requiring careful planning -- User provides multiple tasks -- After receiving new instructions — capture as todos - -**When NOT to Use (Instant/Light effort):** -- Single straightforward tasks -- Tasks completable in < 3 steps -- Purely conversational requests -- Simple fixes, renames, or one-file changes - ---- - -### CreatePlan Tool - Plan Mode - -Use `CreatePlan` in Plan Mode for structured planning: - -``` -→ CreatePlan( - name="Authentication System", - overview="Implement complete auth with NextAuth.js, including login, register, and protected routes", - plan="# Implementation Plan\n\n## Phase 1: Setup\n...", - todos=[ - { id: "setup", content: "Install dependencies", status: "pending" }, - { id: "auth-pages", content: "Create auth pages", status: "pending" } - ] - ) -``` - -**When to Use:** -- Large/ambiguous tasks requiring user approval -- Tasks with multiple valid approaches -- Architectural decisions needed - ---- - -## Parallel Agent Patterns (Cursor 2.5) - -### Cursor 2.5 Parallel Capabilities - -- **Async subagents**: Parent continues while subagents run in background; subagents can spawn subagents (tree) -- **Native Subagents**: Built-in explore, bash, browser run in parallel automatically -- **Custom Subagents**: Define specialized agents that run independently -- **Background Subagents**: Set `is_background: true` for non-blocking execution -- **Plugins**: Marketplace plugins (cursor.com/marketplace) for pre-built skills/subagents; use project-level skills for repo-specific knowledge -- **Multi-Agent Worktrees**: Multiple agents in git worktrees for large features -- **Cloud Agents**: Long-running tasks in cloud environments - -**Launch multiple subagents in one turn** for maximum throughput. Example: "Review the API changes and update the documentation in parallel." - -### When to Use Each Pattern - -| Scenario | Approach | -|----------|----------| -| Codebase exploration | Native `explore` subagent (automatic) | -| Multiple shell commands | Native `bash` subagent (automatic) | -| Browser testing | Native `browser` subagent (automatic) | -| Validate completed work | Custom `verifier` subagent | -| Debug errors | Custom `debugger` subagent | -| Independent features | Parallel custom subagents or worktrees; use `is_background: true` for async | -| Compare approaches | Multi-agent judging | - -### Orchestrator Pattern with Custom Subagents - -``` -MAIN AGENT (Orchestrator): -├── Plans architecture -├── Delegates to custom subagents -├── Coordinates results -└── Runs /verifier for final check - -SUBAGENT: verifier -├── Checks implementation exists -├── Runs tests -├── Validates edge cases -└── Reports pass/fail with details - -SUBAGENT: debugger -├── Analyzes error -├── Identifies root cause -├── Suggests fix -└── Returns diagnosis -``` - -### Spec File Pattern (Still Useful) - -For complex multi-agent work, create specs in `.cursor/specs/`: - -```markdown -# .cursor/specs/feature-auth.md - -## Feature: Authentication System - -### Prerequisites -- Database configured with User model -- NextAuth.js installed -- Environment variables set - -### Tasks -1. Create `/app/login/page.tsx` - Login form -2. Create `/app/register/page.tsx` - Registration form -3. Create `/app/api/auth/[...nextauth]/route.ts` - NextAuth config -4. Create `/middleware.ts` - Route protection - -### Acceptance Criteria -- [ ] User can register with email/password -- [ ] User can login and receive session -- [ ] Protected routes redirect to login - -### Verification -After completion, invoke: `/verifier confirm auth feature` -``` - -### Background Subagent Example - -For long-running tasks, use `is_background: true`: - -```markdown ---- -name: test-runner -description: Test automation expert. Use proactively to run tests and fix failures. -is_background: true ---- - -You are a test automation expert. - -When you see code changes, proactively run appropriate tests. -If tests fail, analyze and report findings. -``` - ---- - -## Cursor Hooks Integration - -Hooks let you observe, control, and extend the agent loop using custom scripts. They run before or after defined stages of the agent loop. - -### Hooks Configuration (`.cursor/hooks.json`) - -**CRITICAL**: Use the correct format per [Cursor Hooks docs](https://cursor.com/docs/agent/hooks): - -```json -{ - "version": 1, - "hooks": { - "stop": [ - { "command": "node .cursor/hooks/grind.js" } - ], - "beforeShellExecution": [ - { "command": ".cursor/hooks/audit.sh" } - ] - } -} -``` - -**Format Rules:** -- `version`: Must be `1` (required) -- `hooks`: Object with event names as keys (NOT an array) -- Each event contains an array of hook objects with `command` field - -### Hook Events (Agent) - -| Event | When | Use Case | -|-------|------|----------| -| `beforeShellExecution` | Before shell command | Block dangerous commands, require approval | -| `afterShellExecution` | After shell command | Audit logs, capture output | -| `beforeMCPExecution` | Before MCP tool call | Gate risky operations | -| `afterMCPExecution` | After MCP tool call | Log tool usage | -| `afterFileEdit` | After file edit | Auto-format, lint, validate | -| `beforeSubmitPrompt` | Before prompt sent | PII scanning, validation | -| `afterAgentResponse` | After agent message | Analytics, logging | -| `afterAgentThought` | After thinking block | Observe reasoning | -| `stop` | Agent loop ends | **RALPH loop**: Continue until goals met (tests pass, build succeeds) | - -### Hook Events (Tab/Inline Completions) - -| Event | When | Use Case | -|-------|------|----------| -| `beforeTabFileRead` | Before Tab reads file | Redact secrets | -| `afterTabFileEdit` | After Tab edits file | Format Tab edits | - -### Example: Audit Hook - -```bash -#!/bin/bash -# .cursor/hooks/audit.sh - Log all agent actions - -json_input=$(cat) -timestamp=$(date '+%Y-%m-%d %H:%M:%S') -echo "[$timestamp] $json_input" >> /tmp/agent-audit.log -exit 0 -``` - -### Example: Long-Running Agent Loop (RALPH Pattern) - -The `stop` hook enables RALPH-style iterative improvement - agent keeps working until verification goals are met. - -**Based on**: [Cursor agent best practices - long-running agent loop](https://cursor.com/blog/agent-best-practices#example-long-running-agent-loop) - -```javascript -// .cursor/hooks/grind.js - RALPH loop implementation - -const input = JSON.parse(await readStdin()); -const { status, loop_count = 0 } = input; -const MAX_ITERATIONS = 5; - -// Stop if aborted/errored or max iterations reached -if (status !== 'completed' || loop_count >= MAX_ITERATIONS) { - console.log(JSON.stringify({})); - process.exit(0); -} - -// Check if goals are met (tests pass, build succeeds, etc.) -const goalsMet = checkGoals(); // Run npm test, npm run build, etc. - -if (goalsMet) { - // Goals met - stop the loop - console.log(JSON.stringify({})); -} else { - // Goals not met - continue with followup message - console.log(JSON.stringify({ - followup_message: `Continue working. Iteration ${loop_count + 1}/${MAX_ITERATIONS}. Tests/build still failing.` - })); -} -``` - -**How it works:** -1. Agent completes a task -2. `stop` hook runs and checks verification goals (tests, build, lint) -3. If goals met → return `{}` to stop -4. If goals not met → return `{ followup_message: "..." }` to continue -5. Agent receives followup and works on fixing issues -6. Loop continues until goals met or max iterations reached - -**Configuration** (`.cursor/grind.json`): -```json -{ - "maxIterations": 5, - "commands": ["npm test", "npm run build"], - "stopOnSuccess": true -} -``` - -### Example: Block Git Commands Hook - -```bash -#!/bin/bash -# .cursor/hooks/block-git.sh - Require gh CLI instead of git - -input=$(cat) -command=$(echo "$input" | jq -r '.command // empty') - -if [[ "$command" =~ ^git[[:space:]] ]]; then - cat << EOF -{ - "permission": "deny", - "user_message": "Git command blocked. Use gh CLI instead.", - "agent_message": "The git command was blocked. Use gh tool for GitHub operations." -} -EOF -else - echo '{"permission": "allow"}' -fi -``` - -### Hook Input/Output Schema - -**Input (all hooks receive):** -```json -{ - "conversation_id": "string", - "generation_id": "string", - "model": "string", - "hook_event_name": "string", - "cursor_version": "string", - "workspace_roots": [""] -} -``` - -**Output (permission hooks):** -```json -{ - "permission": "allow" | "deny" | "ask", - "user_message": "", - "agent_message": "" -} -``` - -**Output (stop hook - for RALPH loop):** -```json -{} // Empty object = stop the loop - -// OR continue with: -{ - "followup_message": "Continue working on the task. Fix the failing tests." -} -``` - ---- - -## Context Compaction for Long-Running Tasks - -Opus 4.6 introduces **compaction** — the ability to summarize its own context to sustain longer tasks. Apply this principle in agentic workflows: - -### When to Compact - -- After completing each EPIC in a multi-EPIC task -- When context is getting large (many files read, many tool calls) -- Before starting a new phase of work - -### How to Compact - -``` - -[COMPACTION — End of EPIC N] - -Essential state to carry forward: -- Project: [stack, conventions] -- Completed: [what's done and verified] -- Current: [what I'm working on next] -- Decisions: [key choices made and why] -- Known issues: [anything to watch for] - -Can release from active context: -- Detailed file contents already processed -- Intermediate debugging steps -- Exploration results that didn't lead anywhere - -``` - -### Compaction + Subagents - -For exhaustive tasks, use subagents to isolate context-heavy work: -- Exploration subagent reads and analyzes files (its context is isolated) -- Main agent receives a summary, not the raw data -- This naturally compacts context without explicit summarization - ---- - -## Cursor 2.5 Agent Skills - -Agent Skills are portable, version-controlled packages that teach agents domain-specific tasks. Skills are the preferred way to package specialized knowledge in Cursor 2.5. - -### Skills vs Rules - -| Feature | Rules (`.cursor/rules/`) | Skills (`.cursor/skills/`) | -|---------|--------------------------|----------------------------| -| Activation | `alwaysApply: true` or glob patterns | Auto-discovery or `/skill-name` | -| Format | Single `.mdc` file | Folder with `SKILL.md` + optional scripts | -| Executable | No | Yes - can include scripts | -| Progressive loading | No | Yes - loads resources on-demand | -| Best for | Always-on conventions, file-type patterns | Domain knowledge, procedures, how-tos | - -### Skill Directory Structure - -``` -.cursor/skills/ -└── deploy-app/ - ├── SKILL.md # Required - main instructions - ├── scripts/ # Optional - executable code - │ ├── deploy.sh - │ └── validate.py - ├── references/ # Optional - detailed docs (loaded on-demand) - │ └── REFERENCE.md - └── assets/ # Optional - templates, configs - └── config-template.json -``` - -### SKILL.md Format - -```markdown ---- -name: deploy-app -description: Deploy the application to staging or production. Use when deploying code or when user mentions deployment. ---- - -# Deploy App - -Deploy the application using the provided scripts. - -## Usage - -Run the deployment script: `scripts/deploy.sh ` - -## Pre-deployment Validation - -Before deploying, run: `python scripts/validate.py` -``` - -**Frontmatter Fields:** - -| Field | Required | Description | -|-------|----------|-------------| -| `name` | Yes | Skill identifier. Must match folder name. | -| `description` | Yes | When to use. Agent reads this to determine relevance. | -| `license` | No | License name or file reference. | -| `disable-model-invocation` | No | If `true`, only invoked via `/skill-name`. | - -### Invoking Skills - -**Automatic**: Agent discovers and applies based on context + description. - -**Explicit**: Type `/skill-name` in chat: -``` -> /deploy-app deploy to staging -> /migrate-to-skills convert my rules -``` - -### Migration: Rules to Skills - -Cursor 2.5 includes `/migrate-to-skills` to convert dynamic rules: - -``` -> /migrate-to-skills -``` - -This converts: -- **Dynamic rules** (`alwaysApply: false`, no globs) → Standard skills -- **Slash commands** → Skills with `disable-model-invocation: true` - -**What NOT to migrate:** -- Rules with `alwaysApply: true` (keep as rules) -- Rules with `globs` patterns (keep as rules - skills don't have glob triggers) - -### Recommended Organization - -``` -.cursor/ -├── rules/ # Keep these as rules -│ ├── minimax-m2-core.mdc # alwaysApply: true -│ ├── cursor-agent-orchestration.mdc # alwaysApply: true -│ ├── web-development.mdc # globs: ["*.ts", "*.tsx"] -│ └── python-development.mdc # globs: ["*.py"] -│ -├── skills/ # Domain knowledge, procedures -│ ├── deploy-production/ -│ │ └── SKILL.md -│ ├── database-migration/ -│ │ └── SKILL.md -│ └── security-audit/ -│ └── SKILL.md -│ -└── agents/ # Custom subagents - ├── verifier.md - └── debugger.md -``` - -### Anti-Hallucination: Clear Descriptions - -**Good** - Specific trigger conditions: -```yaml -description: "Deploy to production. Use when user mentions 'deploy', 'release', 'push to prod', or 'go live'." -``` - -**Bad** - Vague, will trigger inappropriately: -```yaml -description: "Helps with general deployment tasks" -``` - ---- - -## Summary: Opus 4.6 Max + Cursor 2.5 Workflow - -### Before Starting Any Task - -``` -1. CALIBRATE EFFORT - → Instant / Light / Deep / Exhaustive? - → Match workflow depth to complexity - -2. CHECK DATE (for version searches) - → Extract current date from context - → Use concrete values, never placeholders - -3. FOR DEEP/EXHAUSTIVE TASKS - → Create EPIC todos (Exhaustive only) - → Investigate codebase patterns first - → Plan with tradeoffs stated - -4. FOR EACH FILE/CHANGE - → Implement - → Lint check (ReadLints on edited file) - → Self-review (Light+): What would break? - → Mark complete only after verified - -5. ON REPEATED ERRORS (3-4+) - → Stop trying same fix - → Web search for solution - → Apply community solution - -6. AFTER EPIC/FEATURE COMPLETE - → Run /verifier to validate - → Integration test - → Compact context before next phase -``` - -### Quality Gates - -``` -□ Effort calibrated to task complexity -□ Adversarial self-review performed (Light+) -□ Date included in version searches (for NEW packages) -□ No template placeholders in searches -□ Each file verified after creation -□ Web search triggered after 3-4 repeated errors -□ Context compacted between phases (long tasks) -□ Honest about uncertainty -``` - -### Quick Reference: Subagents vs Skills vs Rules - -| Need | Solution | -|------|----------| -| Always-on conventions | Rules with `alwaysApply: true` | -| File-type patterns | Rules with `globs` | -| Domain knowledge | Skills in `.cursor/skills/` | -| Specialized agents | Custom subagents in `.cursor/agents/` | -| Validation | `/verifier` subagent | -| Debugging | `/debugger` subagent | diff --git a/.cursor/rules/cursot-tools-mastery.mdc b/.cursor/rules/cursot-tools-mastery.mdc deleted file mode 100644 index 8d309c3..0000000 --- a/.cursor/rules/cursot-tools-mastery.mdc +++ /dev/null @@ -1,935 +0,0 @@ ---- -description: "Complete Cursor 2.5 native tools reference: search, edit, run, subagents, skills, plugins, sandbox, and advanced features for agentic workflows" -alwaysApply: false ---- - -# Cursor 2.5 Tools Mastery - -Complete reference for all Cursor 2.5 tools including native subagents, Agent Skills, plugins, sandbox configuration, and advanced features. - -## CRITICAL: Agentic-First Tool Usage - -### The Golden Rule: Verify Before You Act - -Every action follows this pattern: -1. **CHECK** - What exists? What's the current state? -2. **VERIFY** - What versions? What's available? -3. **ACT** - Make the change using proper tools -4. **VALIDATE** - Did it work? Any errors? - -### Pre-Action Verification Protocol - -Before ANY significant action: - -``` -BEFORE CREATING PROJECT: -1. Check if CLI tool exists: Shell(command="which flutter") -2. Check CLI version: Shell(command="flutter --version") -3. Web search for current version: WebSearch(search_term="Flutter stable February 2026") — use actual date from context -4. Check if project already exists: Glob(glob_pattern="*") or LS(target_directory="./") if available - -BEFORE INSTALLING PACKAGES: -1. Check package.json/pubspec.yaml exists: Read(path="package.json") -2. Check if already installed: Read(path="package-lock.json") -3. Verify package name/version: WebSearch(search_term="lodash npm latest version") - -BEFORE EDITING CODE: -1. Read the file first: Read(path="src/component.tsx") -2. Understand context and dependencies -3. Check for related files that might need updating -``` - -### Proactive Web Search Usage - -**ALWAYS use WebSearch for:** -- Package versions before installing -- Framework syntax when unsure -- Error messages you encounter -- API documentation verification -- Current best practices - -**Search Pattern:** Replace placeholders with actual values from context (e.g., user_info date). Use concrete dates like "February 2026", not literal `[current month] [current year]`. -``` -WebSearch(search_term="[package/framework] [specific feature] [actual month/year]") -``` - -Examples: -- `WebSearch(search_term="Next.js App Router server actions February 2026")` — use concrete date -- `WebSearch(search_term="React 19 useFormStatus hook syntax")` -- `WebSearch(search_term="shadcn ui button component installation")` - ---- - -## Search Tools - -### `Read` -**Purpose**: Read file contents with line numbers - -**When to Use**: -- Examining specific files -- Understanding code structure -- Reviewing implementations -- Checking configurations - -**Best Practices**: -- Read whole files unless very large (>1000 lines) -- Use `offset` and `limit` for large files -- Batch multiple file reads in parallel -- Check imports and dependencies - -**Example Usage**: -``` -Read the main entry point to understand the application structure -→ Read(path="src/index.ts") -``` - ---- - -### `LS` / `Glob` (Directory Exploration) - -**Purpose**: List directory contents or find files by pattern - -**When to Use**: -- Exploring project structure -- Finding files in a directory -- Understanding folder organization -- Discovering available modules - -**Tool Availability**: `LS` may be unavailable in some environments. Use `Glob` as fallback for directory exploration. - -**Best Practices**: -- Prefer `LS(target_directory=".", ignore_globs=["node_modules", ".git"])` when available -- Fallback: `Glob(glob_pattern="*")` or `Glob(glob_pattern="src/*")` for directory listing -- Start at project root for orientation -- Navigate progressively deeper - -**Example Usage**: -``` -Explore the project structure -→ LS(target_directory=".", ignore_globs=["node_modules", ".git"]) // if available -→ Glob(glob_pattern="*") // fallback when LS unavailable -``` - ---- - -### `SemanticSearch` -**Purpose**: Semantic search - finds code by meaning, not exact text - -**When to Use**: -- Finding implementations by concept -- Locating where behavior is defined -- Understanding how features work -- Exploring unfamiliar codebases - -**When NOT to Use**: -- Exact text matches (use `Grep`) -- Simple symbol lookups (use `Grep`) -- Finding files by name (use `Glob`) - -**Best Practices**: -- Ask complete questions: "Where is user authentication handled?" -- One question per search (don't combine multiple queries) -- Start broad, then narrow to specific directories -- Use for "how/where/what" questions - -**Example Usage**: -``` -Find where API routes are defined -→ SemanticSearch(query="Where are the API routes defined?", target_directories=["src/"]) -``` - ---- - -### `Grep` -**Purpose**: Regex-based text search (powered by ripgrep) - -**When to Use**: -- Exact symbol/string searches -- Finding function calls -- Locating imports/exports -- Counting occurrences - -**Parameters**: -- `pattern`: Regex pattern to search -- `path`: Directory or file to search -- `type`: File type filter (js, py, rs, go, etc.) -- `glob`: Custom glob pattern -- `-C`, `-A`, `-B`: Context lines -- `output_mode`: "content", "files_with_matches", or "count" - -**Best Practices**: -- Escape special regex characters: `functionCall\(` -- Use `output_mode: "files_with_matches"` to find all relevant files -- Add context with `-C 3` to understand surrounding code - -**Example Usage**: -``` -Find all uses of a specific function -→ Grep(pattern="useAuth\\(", path="src/", type="ts") - -Find files containing a pattern -→ Grep(pattern="TODO:", output_mode="files_with_matches") -``` - ---- - -### `Glob` -**Purpose**: Find files by name pattern - -**When to Use**: -- Locating files by extension -- Finding configuration files -- Discovering test files -- Matching naming conventions - -**Best Practices**: -- Patterns auto-prepend `**/` for recursive search -- Use for file discovery, not content search - -**Example Usage**: -``` -Find all test files -→ Glob(glob_pattern="*.test.ts") - -Find configuration files -→ Glob(glob_pattern="*.config.*") -``` - ---- - -### `WebSearch` -**Purpose**: Search the internet for real-time information - -**When to Use**: -- Current documentation -- Recent solutions -- Security advisories -- Version-specific information - -**Best Practices**: -- Include version numbers if relevant -- Add dates for time-sensitive queries -- Use specific keywords - -**Proactive Usage**: Don't wait for "Ask" mode - use `WebSearch` proactively in Agent mode when: -- You need current information about libraries or APIs -- Error messages suggest version-specific issues -- User asks about recent features or changes - ---- - -## MCP Tools Invocation - -Cursor exposes MCP (Model Context Protocol) tools via `call_mcp_tool(server, toolName, arguments)`. **Always check the tool schema/descriptor before calling** — schemas live in the project's `mcps//tools/` folder. - -**Invocation pattern:** -``` -→ call_mcp_tool( - server="cursor-ide-browser", - toolName="browser_navigate", - arguments={ "url": "http://localhost:3000" } - ) -``` - -**Critical**: List and read the tool's JSON descriptor in `mcps//tools/` to get exact parameter names and types before calling. - ---- - -## Browser Tools - -Cursor provides browser tools via the `cursor-ide-browser` MCP server for testing and interacting with web applications. Tools may appear as native tools or require `call_mcp_tool(server="cursor-ide-browser", toolName="...", arguments={...})`. - -### CRITICAL: Browser Lock/Unlock Workflow - -1. **browser_lock requires an existing browser tab** — you CANNOT lock before browser_navigate -2. **Correct order**: `browser_navigate` → `browser_lock` → (interactions) → `browser_unlock` -3. **If a tab already exists**: Check with `browser_tabs` (action: "list"), then call `browser_lock` FIRST before any interactions -4. **browser_unlock**: Only call when completely done with ALL browser operations for this turn - -### `browser_navigate` -**Purpose**: Navigate to a URL - -**When to Use**: -- Testing web application features -- Verifying UI implementations -- Checking deployed changes - -### `browser_snapshot` -**Purpose**: Capture accessibility snapshot of current page - -**When to Use**: -- Understanding page structure -- Finding elements to interact with -- Better than screenshots for element identification - -**Best Practices**: -- Call snapshot BEFORE clicking/typing to get element refs -- Use returned refs for subsequent interactions - -### `browser_click` -**Purpose**: Click on web page elements - -**Parameters**: -- `element`: Human-readable element description -- `ref`: Exact target element reference from snapshot -- `button`: Button to click (defaults to left) -- `doubleClick`: Whether to double-click - -### `browser_type` -**Purpose**: Type text into editable elements - -**Parameters**: -- `element`: Human-readable element description -- `ref`: Exact target element reference from snapshot -- `text`: Text to type -- `submit`: Whether to press Enter after typing -- `slowly`: Type one character at a time (for key handlers) - -### `browser_hover` -**Purpose**: Hover over elements (for tooltips, dropdowns) - -### `browser_select_option` -**Purpose**: Select options in dropdowns - -### `browser_press_key` -**Purpose**: Press keyboard keys (e.g., ArrowLeft, Enter, Escape) - -### `browser_wait_for` -**Purpose**: Wait for conditions - -**Parameters**: -- `text`: Wait for text to appear -- `textGone`: Wait for text to disappear -- `time`: Wait for specified seconds - -### `browser_navigate_back` -**Purpose**: Go back to previous page - -### `browser_resize` -**Purpose**: Resize browser window for responsive testing - -### `browser_console_messages` -**Purpose**: Get all console messages (errors, warnings, logs) - -**When to Use**: -- Debugging JavaScript errors -- Checking for runtime issues -- Verifying console output - -### `browser_network_requests` -**Purpose**: Get all network requests since page load - -**When to Use**: -- Debugging API calls -- Verifying request/response data -- Checking for failed requests - -### `browser_tabs` -**Purpose**: List open tabs (action: "list") — use before lock to check if a tab exists - -### `browser_fill` -**Purpose**: Clear and replace text in editable elements (unlike `browser_type` which appends) - -### `browser_scroll` -**Purpose**: Scroll elements into view — use `scrollIntoView: true` for nested scroll containers before clicking obscured elements - -### `browser_handle_dialog` -**Purpose**: Handle native dialogs (alert/confirm/prompt) — call BEFORE the triggering action. Use `accept: false` for Cancel, `promptText` for custom prompt input - -### `browser_profile_start` / `browser_profile_stop` -**Purpose**: CPU profiling — profile data in `~/.cursor/browser-logs/` (cpu-profile-*.json, cpu-profile-*-summary.md). Read raw JSON to verify summary findings - -**Limitation**: Iframe content is not accessible — only elements outside iframes can be interacted with - -### Browser Testing Workflow - -``` -1. Check existing tabs (if any): browser_tabs(action="list") -2. Navigate to the page: browser_navigate(url="http://localhost:3000") -3. Lock the tab: browser_lock (required before interactions) -4. Snapshot to get elements: browser_snapshot() -5. Interact using refs from snapshot: browser_click(element="Submit button", ref="btn-submit") -6. Wait for changes if needed: browser_wait_for(text="Success") or short waits (1-3s) with snapshot checks -7. Snapshot again to verify: browser_snapshot() -8. Check console/network: browser_console_messages(), browser_network_requests() -9. Unlock when done: browser_unlock -``` - -**Waiting strategy**: Prefer short incremental waits (1-3 seconds) with browser_snapshot checks in between rather than one long wait. - -### Proactive Browser Usage - -Don't wait for explicit requests - use browser tools proactively when: -- You've implemented a UI feature → test it immediately -- User reports a visual bug → navigate and inspect -- You need to verify API integrations → check network requests -- You're debugging frontend issues → check console messages - ---- - -## Edit Tools - -### `StrReplace` -**Purpose**: Exact string replacement in files - -**When to Use**: -- Modifying existing code -- Updating function implementations -- Fixing bugs in specific locations -- Renaming with `replace_all: true` - -**CRITICAL Rules**: -- `old_string` must be UNIQUE in the file -- Include enough context (3-5 lines before/after) -- Match exact whitespace and indentation -- Preserve original indentation style - -**Best Practices**: -- Read the file first to ensure accurate matching -- Include surrounding context for uniqueness -- Use `replace_all: true` for renaming operations -- Make atomic, focused changes - -**Example Usage**: -``` -Update a function implementation -→ StrReplace( - path="src/utils.ts", - old_string="function oldImpl() {\n return 1;\n}", - new_string="function oldImpl() {\n return 2;\n}" - ) -``` - ---- - -### `Write` -**Purpose**: Create or overwrite entire files - -**When to Use**: -- Creating new files -- Complete file rewrites -- Generating new modules -- Writing configuration files - -**Best Practices**: -- ALWAYS read existing files before overwriting -- Prefer `StrReplace` for modifications -- Include all necessary imports -- Follow project conventions - ---- - -### `Delete` -**Purpose**: Remove files from the project - -**When to Use**: -- Cleanup during refactoring -- Removing deprecated code -- Deleting generated files - ---- - -### `EditNotebook` -**Purpose**: Edit Jupyter notebook cells - -**When to Use**: -- Data science notebooks -- Interactive Python development -- Documentation notebooks - -**Best Practices**: -- Set `is_new_cell` correctly -- Use 0-based cell indices -- Provide unique `old_string` for edits - ---- - -## Run Tools - -### `Shell` -**Purpose**: Execute shell commands - -**When to Use**: -- Building projects -- Running tests -- Installing dependencies -- Executing scripts -- Git operations - -**Parameters**: -- `command`: The command to execute -- `description`: Brief description of what the command does -- `is_background`: For long-running processes -- `timeout`: Timeout in milliseconds -- `working_directory`: Directory to run command in - -**Best Practices**: -- Use `is_background: true` for long-running processes -- Check existing terminals before starting servers (see Terminals Folder Pattern below) -- Use non-interactive flags (`--yes`, `-y`, etc.) -- Always provide a clear `description` - -**Terminals Folder Pattern** — Before starting dev servers, avoid duplicate processes: -1. List the terminals folder (project-specific path, e.g. under `.cursor` or Cursor project metadata) -2. Read terminal files to see `current command`, `cwd`, and `last_command` -3. Only start `npm run dev` / `flutter run` / etc. if no matching process is already running - -**Example Usage**: -``` -Install dependencies -→ Shell(command="npm install", description="Install dependencies") - -Run tests -→ Shell(command="npm test", description="Run tests") - -Start dev server in background -→ Shell(command="npm run dev", is_background=true, description="Start dev server") -``` - ---- - -### `ReadLints` -**Purpose**: Read linter errors from workspace - -**When to Use**: -- After making edits -- Before committing changes -- Debugging type errors -- Checking code quality - -**Best Practices**: -- Only call on files you've edited -- Fix errors before proceeding -- Don't call with wide scope - -**Example Usage**: -``` -Check lints after editing -→ ReadLints(paths=["src/components/Button.tsx"]) -``` - ---- - -## Special Tools - -### `TodoWrite` -**Purpose**: EPIC task tracking for complex multi-step work - -**When to Use**: Deep/Exhaustive tasks; 3+ steps; user provides multiple tasks; after receiving new instructions - -**Parameters**: `todos` (array of `{id, content, status}`), `merge` (true = update only specified; false = replace all) - -**Example**: -``` -→ TodoWrite(todos=[{ id: "EPIC0", content: "Setup: Install dependencies", status: "in_progress" }, { id: "EPIC1", content: "Feature: Auth", status: "pending" }], merge=false) -→ TodoWrite(todos=[{ id: "EPIC0", status: "completed" }], merge=true) -``` - -**When NOT to Use**: Instant/Light tasks; single straightforward tasks; < 3 steps - ---- - -### `CreatePlan` -**Purpose**: Plan mode with Mermaid diagrams; structured planning for user approval - -**When to Use**: Large/ambiguous tasks; multiple valid approaches; architectural decisions needed - -**Parameters**: `name`, `overview`, `plan` (markdown), `todos` (optional) - ---- - -### `AskQuestion` -**Purpose**: Structured multiple-choice questions for hard forks - -**When to Use**: Security/auth flows; data deletion/migration; major architectural changes; irreversible choices - -**When NOT to Use**: Reversible choices — use safe default and proceed - ---- - -### `GenerateImage` -**Purpose**: Generate images from text description - -**When to Use**: Mockups, diagrams, UI assets; when user explicitly asks for an image - -**Parameters**: `description` (required), `filename` (optional), `reference_image_paths` (optional) - -**Output**: Saved to `assets/` folder by default - -**When NOT to Use**: Data visualizations (charts, plots) — use code instead - ---- - -## MCP Tools - -MCP (Model Context Protocol) tools extend Cursor's capabilities with external services. Use `call_mcp_tool(server, toolName, arguments)` for MCP invocation. **Always check the tool schema in `mcps//tools/` before calling.** - -### mcp_web_fetch - -**Purpose**: Fetch URL content and return it as readable markdown. Use for docs, API references. - -**When to Use**: -- Official documentation (e.g., https://nextjs.org/docs) -- API references -- Verifying deployment URLs - -**Parameters**: `url` — the fully-formed URL to fetch - -**Limitations**: No authentication support; localhost and private IPs will not work (fetch runs from isolated server). - -**Example**: -``` -→ mcp_web_fetch(url="https://nextjs.org/docs/app/building-your-application/routing") -``` - -### fetch_mcp_resource / list_mcp_resources - -**Purpose**: Fetch or list resources from MCP servers (e.g., documentation, assets). Use `list_mcp_resources` to discover available resources, then `fetch_mcp_resource(server, uri)` to retrieve content. Optional `downloadPath` saves to workspace. - -### MiniMax MCP Tools -If configured, provides: -- `user-MiniMax-web_search`: Web search with current date context -- `user-MiniMax-understand_image`: Image analysis with VLM - -### Context7 MCP Tools -If configured, provides: -- `user-context7-resolve-library-id`: Find library IDs -- `user-context7-query-docs`: Fetch up-to-date documentation - -**CRITICAL**: Always check if MCP tools are available before using. Fall back to Cursor's native `WebSearch` if MiniMax MCP is not configured. - ---- - -## Plugins (Cursor 2.5) - -The [Cursor Marketplace](https://cursor.com/marketplace) lets you install plugins that package skills, subagents, MCP servers, hooks, and rules. Install via `/add-plugin` in chat or browse at cursor.com/marketplace. - -**When to use plugins vs local skills:** -- **Plugins**: Pre-built capabilities from partners (Amplitude, AWS, Figma, Linear, Stripe); team-wide or cross-project use -- **Local skills** (`.cursor/skills/`): Project-specific knowledge, version-controlled in repo - ---- - -## Sandbox Configuration (Cursor 2.5+) - -The sandbox supports granular network and filesystem controls. Configure via `sandbox.json`. - -**File locations** (per-repo overrides per-user): -- `~/.cursor/sandbox.json` — all workspaces -- `./.cursor/sandbox.json` — current project (higher priority) - -**Key options** (see [Cursor Terminal docs](https://cursor.com/docs/agent/terminal)): -- `networkPolicy.default`: `"deny"` or `"allow"` -- `networkPolicy.allow`: Domain allowlist (e.g., `registry.npmjs.org`, `*.githubusercontent.com`) -- `networkPolicy.deny`: Domain denylist -- `additionalReadwritePaths`, `additionalReadonlyPaths`: Extra filesystem access - -**Example** (allow npm/pypi only): -```json -{ - "networkPolicy": { - "default": "deny", - "allow": ["registry.npmjs.org", "pypi.org", "*.githubusercontent.com"] - } -} -``` - ---- - -## Native Subagents (Cursor 2.5) - -Cursor 2.5 introduces **async subagents** — subagents can run in the background while the parent continues, and subagents can spawn their own subagents (tree hierarchy). - -### Foreground vs Background - -| Mode | Behavior | Best for | -|------|----------|----------| -| **Foreground** | Blocks until subagent completes | Sequential tasks where you need output immediately | -| **Background** | Returns immediately; subagent runs independently | Long-running or parallel workstreams | - -Set `is_background: true` in custom subagent frontmatter for non-blocking execution. Launch multiple subagents in one turn for maximum throughput. - -### Built-in Subagents - -These activate automatically when appropriate: - -| Subagent | Purpose | When Used | -|----------|---------|-----------| -| **explore** | Codebase search and analysis | Finding code, understanding structure | -| **bash** | Shell command sequences | Running multiple commands | -| **browser** | Web automation via MCP | Testing UI, browser interactions | - -**You don't invoke these directly** — the agent delegates automatically. - -### Custom Subagents - -Define in `.cursor/agents/` (project) or `~/.cursor/agents/` (user). Also: `.claude/agents/`, `.codex/agents/` for compatibility. - -```markdown ---- -name: verifier -description: Validates completed work. Use after tasks marked done. -model: fast ---- - -You are a skeptical validator... -``` - -**Invoke explicitly**: `/verifier check the auth implementation` - -### Task Tool (Programmatic Delegation) - -For explicit subagent delegation: - -``` -→ Task( - subagent_type="explore", // or "generalPurpose" - model="fast", - prompt="Find all authentication-related files", - description="Find auth files" - ) -``` - ---- - -## Agent Skills (Cursor 2.5) - -Skills are portable packages of domain knowledge. - -### Skill Locations - -| Location | Scope | -|----------|-------| -| `.cursor/skills/` | Project-level | -| `.claude/skills/` | Project-level (Claude compatibility) | -| `.codex/skills/` | Project-level (Codex compatibility) | -| `~/.cursor/skills/` | User-level (global) | -| `~/.claude/skills/` | User-level (Claude compatibility) | -| `~/.codex/skills/` | User-level (Codex compatibility) | - -### Skill Structure - -``` -.cursor/skills/ -└── deploy-app/ - ├── SKILL.md # Required - ├── scripts/ # Optional - executable code - └── references/ # Optional - detailed docs -``` - -### Invoking Skills - -**Automatic**: Agent discovers based on context and description. - -**Explicit**: Use `/skill-name` in chat: -``` -> /deploy-app deploy to staging -> /migrate-to-skills convert my rules -``` - -### Built-in Migration Skill - -Cursor 2.5 includes `/migrate-to-skills` to convert dynamic rules to skills. - ---- - -## Tool Selection Decision Tree - -``` -Need to find code? -├─ By meaning/concept → SemanticSearch -├─ Exact text/symbol → Grep -├─ By filename → Glob -└─ Complex exploration → Native explore subagent (automatic) - -Need to read? -├─ File contents → Read -├─ Directory structure → LS or Glob (use Glob when LS unavailable) -└─ Linter errors → ReadLints - -Need to edit? -├─ Modify existing → StrReplace -├─ Create new file → Write -├─ Remove file → Delete -└─ Jupyter notebook → EditNotebook - -Need to execute? -├─ Single command → Shell -├─ Command sequence → Native bash subagent (automatic) -└─ MCP tool → Use MCP tool directly - -Need external info? -├─ Real-time data → WebSearch (Cursor native) -├─ MiniMax search → user-MiniMax-web_search (if configured) -└─ Image analysis → user-MiniMax-understand_image (if configured) - -Need to test web UI? -├─ Navigate/interact → Native browser subagent (automatic) -├─ Or manually: -│ ├─ Navigate → browser_navigate -│ ├─ Snapshot → browser_snapshot -│ └─ Interact → browser_click / browser_type - -Need task tracking? -├─ EPIC todos (3+ steps) → TodoWrite -└─ Structured plan for approval → CreatePlan - -Need user input on hard fork? -└─ Security/data/architecture → AskQuestion - -Need image? -└─ Mockups, diagrams → GenerateImage - -Need specialized work? -├─ Validate completed work → /verifier custom subagent -├─ Debug errors → /debugger custom subagent -├─ Domain knowledge → Invoke skill with /skill-name -└─ Programmatic delegation → Task tool -``` - -### Subagents vs Skills vs Task Tool - -| Need | Solution | -|------|----------| -| Codebase exploration | Native `explore` (automatic) | -| Shell command sequences | Native `bash` (automatic) | -| Browser testing | Native `browser` (automatic) | -| Validate work | Custom subagent `/verifier` | -| Domain-specific knowledge | Skill `/skill-name` | -| Programmatic agent launch | Task tool | - -## Parallel vs Sequential Tool Calls - -### Use Parallel Calls When: -- Reading multiple independent files -- Searching different directories -- Making independent edits -- Gathering unrelated information - -### Use Sequential Calls When: -- Results inform next parameters -- Order matters (create before edit) -- Dependencies between operations -- Validation needed before proceeding - -### Example Parallel Pattern: -``` -Reading multiple files simultaneously: -→ Read(path="src/index.ts") -→ Read(path="src/config.ts") -→ Read(path="package.json") -[All called in single batch] -``` - -## Cursor 2.5 Advanced Features - -### Plugins -- Marketplace at cursor.com/marketplace; `/add-plugin` to install -- Packages skills, subagents, MCP servers, hooks, rules - -### Sandbox -- `sandbox.json` for network allowlists/denylists and filesystem paths -- See [Cursor Terminal docs](https://cursor.com/docs/agent/terminal) - -### Native Subagents (Async in 2.5) -- **explore**: Codebase search with isolated context -- **bash**: Shell command execution with output filtering -- **browser**: Web automation via MCP with DOM filtering -- **Async**: Subagents run in background; parent continues; subagents can spawn subagents (tree) -- Custom subagents in `.cursor/agents/`; use `is_background: true` for parallel throughput - -### Agent Skills -- Portable knowledge packages in `.cursor/skills/`, `.claude/skills/`, `.codex/skills/` -- Auto-discovery or `/skill-name` invocation -- Progressive loading (scripts, references, assets) -- Migration: `/migrate-to-skills` - -### Debug Mode -- Instruments apps with runtime logs -- Helps reproduce complex bugs -- Works across stacks and languages - -### Browser Layout Editor -- Real-time CSS/layout editing -- Component tree visualization -- Integrated with codebase - -### Plan Mode + Mermaid -- Visual planning with diagrams -- Send to-dos to new agents -- Structured task management - -### Multi-Agent Features -- **Async subagents** (2.5): Parent continues while subagents run in background; subagent trees -- Parallel subagents with isolated context -- Background subagents (`is_background: true`) -- Multi-agent judging for comparing solutions - -### Image Generation -- Generate images directly from agent -- Saved to `assets/` folder by default -- Useful for mockups and diagrams - ---- - -## CRITICAL: What NEVER To Do - -### Never Manually Create These Files - -These files are generated by CLIs and should NEVER be created manually: - -| File Type | Use CLI Instead | -|-----------|----------------| -| `package.json` | `npm init` or `pnpm init` | -| `package-lock.json` | Generated by `npm install` | -| `go.mod` | `go mod init` | -| `Cargo.toml` | `cargo new` or `cargo init` | -| `pubspec.yaml` | `flutter create` | -| `*.xcodeproj/*` | Xcode or `swift package init` | -| `project.pbxproj` | **NEVER TOUCH** - Xcode only | -| `.csproj` | `dotnet new` | -| `pom.xml` | `mvn archetype:generate` | - -### Never Skip These Steps - -``` -❌ DON'T: Create project files manually -✅ DO: Use framework CLI (npx create-next-app, flutter create, cargo new) - -❌ DON'T: Assume packages are installed -✅ DO: Run npm install / flutter pub get / cargo build after setup - -❌ DON'T: Guess CLI availability -✅ DO: Check with --version first - -❌ DON'T: Write code without reading existing files -✅ DO: Always Read before editing - -❌ DON'T: Skip dependency installation -✅ DO: npm install, pip install, cargo build after adding deps - -❌ DON'T: Forget post-edit verification -✅ DO: Run linters, tests, builds after changes -``` - -### Always Verify After Actions - -```bash -# After creating web project -npm install && npm run dev # Verify it starts - -# After adding shadcn component -npx shadcn@latest add button # Use CLI, don't copy files! - -# After Flutter changes -flutter pub get && flutter analyze - -# After Rust changes -cargo check && cargo clippy - -# After Go changes -go build ./... && go vet ./... - -# After Python changes -pip install -e . && pytest -``` diff --git a/.cursor/rules/learning-and-tools.mdc b/.cursor/rules/learning-and-tools.mdc new file mode 100644 index 0000000..a3dcdd5 --- /dev/null +++ b/.cursor/rules/learning-and-tools.mdc @@ -0,0 +1,11 @@ +--- +author: dotagents +description: Use of skills, documentation lookup, scoped commands, and task clarity across stacks +alwaysApply: true +--- + +- Read and apply relevant Cursor project rules and agent skills when they match the task. Invoke MCP documentation tools for library, CLI, or framework behavior instead of guessing version-specific details. +- Prefer the repository’s documented commands for build, test, lint, and typecheck. When available, use file- or package-scoped invocations to shorten feedback loops. +- Structure work with explicit **goal**, **context** (files and modules), **constraints** (compatibility, numerics, style), and **done when** criteria (tests, behavior, interfaces). +- Call out permission-sensitive operations (package installs, deletions, network use, secret handling) and follow the user’s expectations for approval and safety. +- Treat stale instructions as suspect: when project files contradict older prose, trust the code and configuration that actually run in CI. diff --git a/.cursor/rules/library-documentation.mdc b/.cursor/rules/library-documentation.mdc new file mode 100644 index 0000000..9fd5761 --- /dev/null +++ b/.cursor/rules/library-documentation.mdc @@ -0,0 +1,12 @@ +--- +author: dotagents +description: Documentation requirements for public library APIs and module intent across languages +alwaysApply: true +--- + +- Document every **public** function, method, and exported type using the project’s standard documentation mechanism (Python docstrings, Rust `///` on public items, TSDoc/JSDoc on exports, etc.). +- Use **prescriptive** language: state what the symbol does for callers, not a vague “returns” summary. Example preference: “Computes the weighted mean using `weights` normalized to sum to one; raises `ValueError` when `values` and `weights` differ in length.” over “Returns weighted mean.” +- For each public callable, cover: purpose; each parameter’s name, type, and constraints; return or result type and semantics; errors or failure modes that are part of the contract; and, when relevant for science or performance, stability or complexity notes. +- Explain **how** only when algorithmic or numerical choices affect correctness, reproducibility, or performance contracts. Explain **why** when the rationale prevents misuse or guides maintenance. +- Keep **internal** helpers lightly documented. Keep **private** helpers to a single short line or omit extended docs unless complexity demands otherwise. +- At **module** scope, document what the module owns, what problems it solves, what it deliberately excludes, and invariants collaborators must preserve. diff --git a/.cursor/rules/python-base.mdc b/.cursor/rules/python-base.mdc new file mode 100644 index 0000000..69bc1c7 --- /dev/null +++ b/.cursor/rules/python-base.mdc @@ -0,0 +1,19 @@ +--- +author: dotagents +description: Python defaults for uv, typing, scientific code, and tests +globs: + - "**/*.py" +alwaysApply: false +--- + +- Prefer Python 3.12+ unless the repository pins an older interpreter. +- Treat PEP 8 as surface style only; **Ruff** settings in `pyproject.toml` override generic style advice. +- Prefer readability over micro-optimizations; vectorize with library primitives instead of tight Python loops over large numeric data. +- Use **uv** for environments, runs, and dependencies. Do not hand-edit version pins in `pyproject.toml`; use `uv add`, `uv remove`, and `uv add --upgrade` as needed. Run `uv sync` after clone or when the lockfile changes; use `uv run …` for Python, tools, and tests; `uvx` is fine for one-off tools. +- Run **`ruff check`** and **`ty check`** as configured; run **`ruff format`** when this project uses Ruff for formatting. +- Prefer **fast, deterministic** tests; isolate I/O and timing-sensitive cases with markers or separate jobs when the project does. Add **regression tests** for fixed bugs. For numerics, assert **shapes**, **dtypes**, and stability expectations when science or reproducibility requires it. Run tests with `uv run pytest` (install via `uv add --dev pytest` or the project dev group). +- Be explicit about **shapes**, **dtypes**, and **missing data** in NumPy/SciPy code. For **pandas**, make **index and column** semantics clear. For **Polars**, prefer **lazy** plans (`scan_*`, `lazy`) for heavy pipelines when the project standardizes on Polars. +- Prefer **NumPy-style docstrings** on **public** APIs; keep implementations clear **without** long narrative inline comments—use names, small functions, and docstrings. For section layout (Parameters, Returns, Examples, etc.), use the **`numpy-docstrings`** skill. +- When editing domain-heavy code, load **`numpy-scientific`**, **`dataframes`**, **`matplotlib-scientific`**, or **`lab-instrumentation`** (drivers, sockets, lab I/O, datasheets) as appropriate—see project Python spec / **`general-python`** hub. +- For instruments and lab tooling, prefer **pyvisa** patterns that separate **resource lifecycle** (open, configure, close) from **command strings** and parsing; deeper guidance in **`lab-instrumentation`**. +- Expanded patterns live in the **`general-python`** skill; delegate reviews, typing depth, and structure to subagents **`python-reviewer`**, **`python-types`**, and **`python-refactor`** when a focused pass helps. diff --git a/.cursor/skills/code-documenter/SKILL.md b/.cursor/skills/code-documenter/SKILL.md deleted file mode 100644 index c2701d6..0000000 --- a/.cursor/skills/code-documenter/SKILL.md +++ /dev/null @@ -1,95 +0,0 @@ ---- -name: code-documenter -description: Use when adding docstrings, creating API documentation, or building documentation sites. Invoke for OpenAPI/Swagger specs, JSDoc, doc portals, tutorials, user guides. -triggers: - - documentation - - docstrings - - OpenAPI - - Swagger - - JSDoc - - comments - - API docs - - tutorials - - user guides - - doc site -role: specialist -scope: implementation -output-format: code ---- - -# Code Documenter - -Documentation specialist for inline documentation, API specs, documentation sites, and developer guides. - -## Role Definition - -You are a senior technical writer with 8+ years of experience documenting software. You specialize in language-specific docstring formats, OpenAPI/Swagger specifications, interactive documentation portals, static site generation, and creating comprehensive guides that developers actually use. - -## When to Use This Skill - -- Adding docstrings to functions and classes -- Creating OpenAPI/Swagger documentation -- Building documentation sites (Docusaurus, MkDocs, VitePress) -- Documenting APIs with framework-specific patterns -- Creating interactive API portals (Swagger UI, Redoc, Stoplight) -- Writing getting started guides and tutorials -- Documenting multi-protocol APIs (REST, GraphQL, WebSocket, gRPC) -- Generating documentation reports and coverage metrics - -## Core Workflow - -1. **Discover** - Ask for format preference and exclusions -2. **Detect** - Identify language and framework -3. **Analyze** - Find undocumented code -4. **Document** - Apply consistent format -5. **Report** - Generate coverage summary - -## Reference Guide - -Load detailed guidance based on context: - -| Topic | Reference | Load When | -|-------|-----------|-----------| -| Python Docstrings | `references/python-docstrings.md` | Google, NumPy, Sphinx styles | -| TypeScript JSDoc | `references/typescript-jsdoc.md` | JSDoc patterns, TypeScript | -| FastAPI/Django API | `references/api-docs-fastapi-django.md` | Python API documentation | -| NestJS/Express API | `references/api-docs-nestjs-express.md` | Node.js API documentation | -| Coverage Reports | `references/coverage-reports.md` | Generating documentation reports | -| Documentation Systems | `references/documentation-systems.md` | Doc sites, static generators, search, testing | -| Interactive API Docs | `references/interactive-api-docs.md` | OpenAPI 3.1, portals, GraphQL, WebSocket, gRPC, SDKs | -| User Guides & Tutorials | `references/user-guides-tutorials.md` | Getting started, tutorials, troubleshooting, FAQs | - -## Constraints - -### MUST DO -- Ask for format preference before starting -- Detect framework for correct API doc strategy -- Document all public functions/classes -- Include parameter types and descriptions -- Document exceptions/errors -- Test code examples in documentation -- Generate coverage report - -### MUST NOT DO -- Assume docstring format without asking -- Apply wrong API doc strategy for framework -- Write inaccurate or untested documentation -- Skip error documentation -- Document obvious getters/setters verbosely -- Create documentation that's hard to maintain - -## Output Formats - -Depending on the task, provide: -1. **Code Documentation:** Documented files + coverage report -2. **API Docs:** OpenAPI specs + portal configuration -3. **Doc Sites:** Site configuration + content structure + build instructions -4. **Guides/Tutorials:** Structured markdown with examples + diagrams - -## Knowledge Reference - -Google/NumPy/Sphinx docstrings, JSDoc, OpenAPI 3.0/3.1, AsyncAPI, gRPC/protobuf, FastAPI, Django, NestJS, Express, GraphQL, Docusaurus, MkDocs, VitePress, Swagger UI, Redoc, Stoplight - -## Related Skills - -**Spec Miner** - Informs from code analysis | **Fullstack Guardian** - Documents during implementation | **Code Reviewer** - Checks documentation quality diff --git a/.cursor/skills/code-documenter/references/api-docs-fastapi-django.md b/.cursor/skills/code-documenter/references/api-docs-fastapi-django.md deleted file mode 100644 index acc5b6e..0000000 --- a/.cursor/skills/code-documenter/references/api-docs-fastapi-django.md +++ /dev/null @@ -1,169 +0,0 @@ -# API Documentation: FastAPI & Django - -> Reference for: Code Documenter -> Load when: Documenting Python API frameworks - -## FastAPI (Auto-generates from types) - -FastAPI automatically generates OpenAPI documentation from type hints and docstrings. - -### Endpoint Documentation - -```python -from fastapi import FastAPI, HTTPException, status -from pydantic import BaseModel, Field - -class UserCreate(BaseModel): - """User creation request body.""" - - name: str = Field(..., min_length=1, max_length=100, example="John Doe") - email: str = Field(..., example="john@example.com") - -class UserResponse(BaseModel): - """User response with generated ID.""" - - id: int = Field(..., example=1) - name: str - email: str - -@app.post( - "/users", - response_model=UserResponse, - status_code=status.HTTP_201_CREATED, - summary="Create a new user", - tags=["Users"], -) -async def create_user(user: UserCreate) -> UserResponse: - """Create a new user account. - - Args: - user: User creation data including name and email. - - Returns: - Created user with generated ID. - - Raises: - HTTPException: 400 if email already exists. - """ -``` - -### Router with Tags - -```python -from fastapi import APIRouter - -router = APIRouter( - prefix="/users", - tags=["Users"], - responses={404: {"description": "Not found"}}, -) - -@router.get( - "/{user_id}", - response_model=UserResponse, - summary="Get user by ID", -) -async def get_user(user_id: int) -> UserResponse: - """Retrieve a user by their unique identifier.""" -``` - -## Django REST Framework (drf-spectacular) - -### ViewSet Documentation - -```python -from rest_framework import viewsets, status -from rest_framework.decorators import action -from drf_spectacular.utils import extend_schema, OpenApiParameter - -class UserViewSet(viewsets.ModelViewSet): - """ - ViewSet for managing user accounts. - - list: Get all users with pagination. - create: Create a new user account. - retrieve: Get a specific user by ID. - update: Update all user fields. - partial_update: Update specific user fields. - destroy: Delete a user account. - """ - - queryset = User.objects.all() - serializer_class = UserSerializer - - @extend_schema( - summary="Get current user", - description="Returns the authenticated user's profile", - responses={200: UserSerializer}, - ) - @action(detail=False, methods=["get"]) - def me(self, request): - """Get the authenticated user's profile.""" - serializer = self.get_serializer(request.user) - return Response(serializer.data) -``` - -### Serializer Documentation - -```python -from rest_framework import serializers - -class UserSerializer(serializers.ModelSerializer): - """Serializer for user model with validation.""" - - class Meta: - model = User - fields = ["id", "name", "email", "created_at"] - read_only_fields = ["id", "created_at"] - - name = serializers.CharField( - help_text="User's display name", - max_length=100, - ) - email = serializers.EmailField( - help_text="User's email address (unique)", - ) -``` - -### Custom Schema - -```python -from drf_spectacular.utils import extend_schema, OpenApiExample - -@extend_schema( - request=UserCreateSerializer, - responses={ - 201: UserSerializer, - 400: OpenApiTypes.OBJECT, - }, - examples=[ - OpenApiExample( - "Valid request", - value={"name": "John", "email": "john@example.com"}, - ), - ], -) -def create(self, request): - """Create a new user.""" -``` - -## Quick Reference - -| Framework | Documentation Source | Output | -|-----------|---------------------|--------| -| FastAPI | Type hints + docstrings | Auto Swagger UI | -| DRF | Serializers + drf-spectacular | Auto Swagger UI | - -| FastAPI Decorator | Purpose | -|-------------------|---------| -| `summary` | Short endpoint description | -| `description` | Detailed description | -| `tags` | Group endpoints | -| `response_model` | Response schema | -| `responses` | Additional response codes | - -| DRF Decorator | Purpose | -|---------------|---------| -| `@extend_schema` | Customize schema | -| `OpenApiParameter` | Query/path params | -| `OpenApiExample` | Request examples | diff --git a/.cursor/skills/code-documenter/references/api-docs-nestjs-express.md b/.cursor/skills/code-documenter/references/api-docs-nestjs-express.md deleted file mode 100644 index d157e9b..0000000 --- a/.cursor/skills/code-documenter/references/api-docs-nestjs-express.md +++ /dev/null @@ -1,223 +0,0 @@ -# API Documentation: NestJS & Express - -> Reference for: Code Documenter -> Load when: Documenting Node.js API frameworks - -## NestJS (@nestjs/swagger) - -NestJS requires explicit decorators for OpenAPI documentation. - -### Controller Documentation - -```typescript -import { Controller, Post, Body, Get, Param } from '@nestjs/common'; -import { - ApiTags, - ApiOperation, - ApiResponse, - ApiParam, - ApiBearerAuth, -} from '@nestjs/swagger'; - -@ApiTags('Users') -@ApiBearerAuth() -@Controller('users') -export class UsersController { - @Post() - @ApiOperation({ summary: 'Create a new user' }) - @ApiResponse({ - status: 201, - description: 'User created successfully', - type: UserDto, - }) - @ApiResponse({ - status: 400, - description: 'Invalid input data', - }) - async create(@Body() dto: CreateUserDto): Promise { - return this.usersService.create(dto); - } - - @Get(':id') - @ApiOperation({ summary: 'Get user by ID' }) - @ApiParam({ - name: 'id', - description: 'User unique identifier', - example: '123', - }) - @ApiResponse({ status: 200, type: UserDto }) - @ApiResponse({ status: 404, description: 'User not found' }) - async findOne(@Param('id') id: string): Promise { - return this.usersService.findOne(id); - } -} -``` - -### DTO Documentation - -```typescript -import { ApiProperty, ApiPropertyOptional } from '@nestjs/swagger'; -import { IsEmail, IsString, MinLength } from 'class-validator'; - -export class CreateUserDto { - @ApiProperty({ - description: "User's display name", - example: 'John Doe', - minLength: 1, - maxLength: 100, - }) - @IsString() - @MinLength(1) - name: string; - - @ApiProperty({ - description: "User's email address", - example: 'john@example.com', - }) - @IsEmail() - email: string; - - @ApiPropertyOptional({ - description: 'Profile picture URL', - example: 'https://example.com/avatar.jpg', - }) - avatarUrl?: string; -} -``` - -## Express (swagger-jsdoc) - -Express uses JSDoc comments with swagger annotations. - -### Setup - -```javascript -const swaggerJsdoc = require('swagger-jsdoc'); -const swaggerUi = require('swagger-ui-express'); - -const options = { - definition: { - openapi: '3.0.0', - info: { - title: 'API Documentation', - version: '1.0.0', - }, - }, - apis: ['./routes/*.js'], -}; - -const specs = swaggerJsdoc(options); -app.use('/api-docs', swaggerUi.serve, swaggerUi.setup(specs)); -``` - -### Route Documentation - -```javascript -/** - * @swagger - * /users: - * post: - * summary: Create a new user - * tags: [Users] - * requestBody: - * required: true - * content: - * application/json: - * schema: - * $ref: '#/components/schemas/CreateUser' - * responses: - * 201: - * description: User created successfully - * content: - * application/json: - * schema: - * $ref: '#/components/schemas/User' - * 400: - * description: Invalid input - */ -router.post('/users', createUser); - -/** - * @swagger - * /users/{id}: - * get: - * summary: Get user by ID - * tags: [Users] - * parameters: - * - in: path - * name: id - * required: true - * schema: - * type: string - * description: User ID - * responses: - * 200: - * description: User found - * content: - * application/json: - * schema: - * $ref: '#/components/schemas/User' - * 404: - * description: User not found - */ -router.get('/users/:id', getUser); -``` - -### Schema Documentation - -```javascript -/** - * @swagger - * components: - * schemas: - * CreateUser: - * type: object - * required: - * - name - * - email - * properties: - * name: - * type: string - * description: User's display name - * example: John Doe - * email: - * type: string - * format: email - * description: User's email address - * example: john@example.com - * User: - * allOf: - * - $ref: '#/components/schemas/CreateUser' - * - type: object - * properties: - * id: - * type: string - * description: Unique identifier - * createdAt: - * type: string - * format: date-time - */ -``` - -## Quick Reference - -| NestJS Decorator | Purpose | -|------------------|---------| -| `@ApiTags()` | Group endpoints | -| `@ApiOperation()` | Endpoint summary | -| `@ApiResponse()` | Response documentation | -| `@ApiParam()` | Path parameter | -| `@ApiQuery()` | Query parameter | -| `@ApiBody()` | Request body | -| `@ApiBearerAuth()` | Auth requirement | -| `@ApiProperty()` | DTO property | - -| Express swagger-jsdoc | Purpose | -|-----------------------|---------| -| `@swagger` | Start swagger block | -| `tags` | Group endpoints | -| `summary` | Short description | -| `parameters` | Path/query params | -| `requestBody` | Request body schema | -| `responses` | Response schemas | -| `$ref` | Reference schema | diff --git a/.cursor/skills/code-documenter/references/coverage-reports.md b/.cursor/skills/code-documenter/references/coverage-reports.md deleted file mode 100644 index 23c4399..0000000 --- a/.cursor/skills/code-documenter/references/coverage-reports.md +++ /dev/null @@ -1,128 +0,0 @@ -# Coverage Reports - -> Reference for: Code Documenter -> Load when: Generating documentation reports - -## Documentation Coverage Report Template - -```markdown -# Documentation Report: {project_name} - -## Summary -- **Files analyzed**: 45 -- **Functions documented**: 120/150 (80%) -- **Classes documented**: 25/25 (100%) -- **API endpoints documented**: 30/30 (100%) - -## Coverage Before/After -- Before: 45% -- After: 92% - -## Files Modified - -| File | Functions Added | Notes | -|------|-----------------|-------| -| src/services/user.ts | 8 | All public methods | -| src/services/auth.ts | 5 | Added examples | -| src/controllers/users.ts | 6 | Added @Api decorators | -| src/dto/user.dto.ts | 4 | Added @ApiProperty | - -## API Documentation - -- **Framework**: NestJS -- **Strategy**: @nestjs/swagger decorators -- **Swagger UI**: /api/docs -- **OpenAPI spec**: /api-json - -## Documentation Style - -- **Python**: Google style docstrings -- **TypeScript**: JSDoc with @param, @returns -- **API**: OpenAPI 3.0 via decorators - -## Next Steps - -### Recommendations -1. Run `npm run docs:lint` to validate JSDoc -2. Add `eslint-plugin-jsdoc` to enforce documentation -3. Consider adding examples for complex functions -4. Set up documentation CI checks - -### Missing Documentation -| File | Missing | Priority | -|------|---------|----------| -| src/utils/crypto.ts | 3 functions | High | -| src/helpers/date.ts | 2 functions | Medium | - -### CI Integration -```yaml -# Add to CI pipeline -- name: Check documentation - run: npm run docs:check - -- name: Generate API docs - run: npm run docs:generate -``` -``` - -## Checklist During Documentation - -```markdown -## Documentation Checklist - -### Before Starting -- [ ] Confirmed format preference (Google/JSDoc/etc.) -- [ ] Identified files to exclude (tests, generated) -- [ ] Detected framework for API docs - -### Functions/Methods -- [ ] All public functions documented -- [ ] Parameters described with types -- [ ] Return values documented -- [ ] Exceptions/errors documented -- [ ] Examples added for complex functions - -### Classes -- [ ] Class purpose described -- [ ] Constructor parameters documented -- [ ] Public methods documented -- [ ] Important attributes explained - -### API Endpoints -- [ ] All endpoints have summaries -- [ ] Request bodies documented -- [ ] Response schemas defined -- [ ] Error responses documented -- [ ] Authentication requirements noted - -### Final Checks -- [ ] Ran documentation linter -- [ ] Verified Swagger UI renders correctly -- [ ] No inaccurate documentation -- [ ] Coverage report generated -``` - -## Framework-Specific Linting - -```bash -# JavaScript/TypeScript - ESLint -npm install eslint-plugin-jsdoc --save-dev -# Add to .eslintrc: "plugins": ["jsdoc"] - -# Python - pydocstyle -pip install pydocstyle -pydocstyle --convention=google src/ - -# Python - interrogate (coverage) -pip install interrogate -interrogate -v src/ -``` - -## Quick Reference - -| Metric | Good | Acceptable | Poor | -|--------|------|------------|------| -| Function coverage | >90% | 70-90% | <70% | -| Class coverage | 100% | >90% | <90% | -| API endpoint coverage | 100% | 100% | <100% | -| Example coverage | >50% | 30-50% | <30% | diff --git a/.cursor/skills/code-documenter/references/documentation-systems.md b/.cursor/skills/code-documenter/references/documentation-systems.md deleted file mode 100644 index 694c0c3..0000000 --- a/.cursor/skills/code-documenter/references/documentation-systems.md +++ /dev/null @@ -1,336 +0,0 @@ -# Documentation Systems & Infrastructure - -> Reference for: Code Documenter -> Load when: Building documentation sites, static generators, multi-version docs, search systems - -## Static Site Generators - -### Docusaurus (Meta) - -```bash -# Setup -npx create-docusaurus@latest docs classic -cd docs && npm start - -# Structure -docs/ -├── docs/ # Documentation pages -├── blog/ # Blog posts -├── src/ -│ └── pages/ # Custom pages -└── docusaurus.config.js -``` - -**docusaurus.config.js:** -```javascript -module.exports = { - title: 'My API', - tagline: 'Build amazing things', - url: 'https://docs.example.com', - baseUrl: '/', - - themeConfig: { - navbar: { - items: [ - {to: '/docs/intro', label: 'Docs', position: 'left'}, - {to: '/api', label: 'API', position: 'left'}, - ], - }, - - // Algolia search - algolia: { - apiKey: 'YOUR_API_KEY', - indexName: 'your_index', - contextualSearch: true, - }, - - prism: { - theme: lightCodeTheme, - darkTheme: darkCodeTheme, - additionalLanguages: ['python', 'rust'], - }, - }, -}; -``` - -### MkDocs (Python) - -```yaml -# mkdocs.yml -site_name: My API Documentation -theme: - name: material - features: - - navigation.tabs - - navigation.sections - - toc.integrate - - search.suggest - - search.highlight - palette: - - scheme: default - toggle: - icon: material/brightness-7 - name: Switch to dark mode - - scheme: slate - toggle: - icon: material/brightness-4 - name: Switch to light mode - -plugins: - - search - - mkdocstrings: - handlers: - python: - options: - show_source: true - - git-revision-date-localized - -markdown_extensions: - - pymdownx.highlight - - pymdownx.superfences - - admonition - - codehilite - -nav: - - Home: index.md - - Getting Started: getting-started.md - - API Reference: api/ -``` - -### VitePress (Vue) - -```typescript -// .vitepress/config.ts -export default defineConfig({ - title: 'API Docs', - description: 'Developer documentation', - - themeConfig: { - nav: [ - { text: 'Guide', link: '/guide/' }, - { text: 'API', link: '/api/' }, - ], - - sidebar: { - '/guide/': [ - { - text: 'Introduction', - items: [ - { text: 'Getting Started', link: '/guide/getting-started' }, - { text: 'Configuration', link: '/guide/config' }, - ], - }, - ], - }, - - search: { - provider: 'local', - }, - - editLink: { - pattern: 'https://github.com/user/repo/edit/main/docs/:path', - }, - }, -}); -``` - -## Multi-Version Documentation - -### Version Switcher - -```javascript -// Docusaurus versions -{ - versions: { - current: { - label: '2.0 (Next)', - path: 'next', - }, - }, - onlyIncludeVersions: ['current', '1.5', '1.4'], -} -``` - -### Migration Guides - -```markdown -# Migration Guide: v1 to v2 - -## Breaking Changes - -### Authentication -**v1:** -```python -client.authenticate(api_key) -``` - -**v2:** -```python -client = Client(api_key=api_key) # Pass in constructor -``` - -### Renamed Methods -| v1 | v2 | Notes | -|----|----|----- | -| `get_user()` | `fetch_user()` | Async now | -| `delete_user()` | `remove_user()` | Returns Promise | - -## Deprecation Timeline -- v1.x: Supported until Dec 2025 -- v2.0: Released Jan 2025 -- v2.1: Current (June 2025) -``` - -## Search Implementation - -### Algolia DocSearch - -```html - - - - - -``` - -### Local Search (Lunr.js) - -```javascript -const idx = lunr(function() { - this.ref('id'); - this.field('title', { boost: 10 }); - this.field('content'); - - documents.forEach(doc => this.add(doc)); -}); - -// Search -const results = idx.search('authentication'); -``` - -## Documentation Testing - -### Link Checking - -```bash -# linkcheck (Python) -pip install linkchecker -linkchecker http://localhost:3000/docs - -# broken-link-checker (Node) -npm install -g broken-link-checker -blc http://localhost:3000 -ro -``` - -### Code Example Testing - -```python -# doctest for Python examples -""" ->>> add(2, 3) -5 ->>> add(-1, 1) -0 -""" - -# Run tests -python -m doctest -v docs/*.md -``` - -```javascript -// Jest for TypeScript examples -// Extract code blocks and test -import { runExamples } from './test-docs'; - -test('API examples work', async () => { - const examples = extractExamples('./docs/api.md'); - await expect(runExamples(examples)).resolves.toBeTruthy(); -}); -``` - -## Performance Optimization - -### Build Optimization - -```javascript -// Webpack/Vite config -export default { - build: { - rollupOptions: { - output: { - manualChunks: { - 'vendor': ['react', 'react-dom'], - }, - }, - }, - }, - - optimizeDeps: { - include: ['prismjs'], - }, -}; -``` - -### CDN & Caching - -```nginx -# nginx.conf -location /docs { - expires 1y; - add_header Cache-Control "public, immutable"; -} - -location ~* \.(html)$ { - expires 1h; - add_header Cache-Control "public, must-revalidate"; -} -``` - -## Analytics Integration - -### Google Analytics - -```javascript -// Docusaurus -gtag: { - trackingID: 'G-XXXXXXXXXX', - anonymizeIP: true, -}, -``` - -### Custom Analytics - -```javascript -// Track search queries -function trackSearch(query, results) { - analytics.track('docs_search', { - query, - resultCount: results.length, - timestamp: new Date(), - }); -} -``` - -## Quick Reference - -| Tool | Best For | Tech Stack | -|------|----------|-----------| -| Docusaurus | React projects, versioning | React, MDX | -| MkDocs | Python projects, simple setup | Python, Jinja2 | -| VitePress | Vue projects, fast builds | Vue, Vite | -| Nextra | Next.js integration | React, Next.js | -| Mintlify | Modern UI, AI search | React | - -| Search Solution | Cost | Features | -|----------------|------|----------| -| Algolia DocSearch | Free (OSS) | Fast, typo-tolerant | -| Local (Lunr.js) | Free | Offline, no server | -| Typesense | Free (self-host) | Privacy-focused | -| Meilisearch | Free (self-host) | Fast, relevance | diff --git a/.cursor/skills/code-documenter/references/interactive-api-docs.md b/.cursor/skills/code-documenter/references/interactive-api-docs.md deleted file mode 100644 index 937f3ee..0000000 --- a/.cursor/skills/code-documenter/references/interactive-api-docs.md +++ /dev/null @@ -1,534 +0,0 @@ -# Interactive API Documentation - -> Reference for: Code Documenter -> Load when: Building API portals, interactive consoles, multi-protocol APIs, SDK docs - -## OpenAPI 3.1 Advanced Features - -### Reusable Components - -```yaml -openapi: 3.1.0 -info: - title: Users API - version: 2.0.0 - -components: - # Reusable schemas - schemas: - User: - type: object - required: [id, email] - properties: - id: - type: string - format: uuid - example: "123e4567-e89b-12d3-a456-426614174000" - email: - type: string - format: email - example: "user@example.com" - - Error: - type: object - properties: - code: - type: string - message: - type: string - details: - type: object - - PaginatedResponse: - type: object - properties: - data: - type: array - items: {} - total: - type: integer - page: - type: integer - - # Reusable parameters - parameters: - PageParam: - name: page - in: query - schema: - type: integer - default: 1 - minimum: 1 - - LimitParam: - name: limit - in: query - schema: - type: integer - default: 20 - minimum: 1 - maximum: 100 - - # Security schemes - securitySchemes: - BearerAuth: - type: http - scheme: bearer - bearerFormat: JWT - - ApiKeyAuth: - type: apiKey - in: header - name: X-API-Key - - OAuth2: - type: oauth2 - flows: - authorizationCode: - authorizationUrl: https://api.example.com/oauth/authorize - tokenUrl: https://api.example.com/oauth/token - scopes: - read:users: Read user data - write:users: Modify user data - - # Reusable responses - responses: - NotFound: - description: Resource not found - content: - application/json: - schema: - $ref: '#/components/schemas/Error' - - Unauthorized: - description: Authentication required - content: - application/json: - schema: - $ref: '#/components/schemas/Error' - -paths: - /users: - get: - summary: List users - parameters: - - $ref: '#/components/parameters/PageParam' - - $ref: '#/components/parameters/LimitParam' - security: - - BearerAuth: [] - responses: - '200': - description: Success - content: - application/json: - schema: - allOf: - - $ref: '#/components/schemas/PaginatedResponse' - - type: object - properties: - data: - type: array - items: - $ref: '#/components/schemas/User' -``` - -## Interactive Documentation Portals - -### Swagger UI Customization - -```javascript -// Custom Swagger UI -const swaggerUi = require('swagger-ui-express'); -const swaggerDocument = require('./openapi.json'); - -const options = { - customCss: '.swagger-ui .topbar { display: none }', - customSiteTitle: "API Docs", - customfavIcon: "/favicon.ico", - swaggerOptions: { - persistAuthorization: true, - displayRequestDuration: true, - filter: true, - tryItOutEnabled: true, - requestInterceptor: (req) => { - req.headers['X-Custom-Header'] = 'value'; - return req; - }, - }, -}; - -app.use('/api-docs', swaggerUi.serve, swaggerUi.setup(swaggerDocument, options)); -``` - -### Redoc (Modern Alternative) - -```html - - - - API Documentation - - - - - - - - -``` - -### Stoplight Elements - -```javascript -import { API } from '@stoplight/elements'; -import '@stoplight/elements/styles.min.css'; - -function App() { - return ( - - ); -} -``` - -## Multi-Protocol Documentation - -### GraphQL Schema Documentation - -```graphql -""" -User account in the system -""" -type User { - """ - Unique user identifier - """ - id: ID! - - """ - User's email address (unique) - @example "user@example.com" - """ - email: String! - - """ - Display name - @example "John Doe" - """ - name: String! - - """ - User's posts (paginated) - """ - posts( - """Number of items per page (max 100)""" - limit: Int = 20 - """Page offset""" - offset: Int = 0 - ): PostConnection! -} - -type Query { - """ - Fetch a user by ID - """ - user( - """User's unique identifier""" - id: ID! - ): User - - """ - Search users by name or email - """ - searchUsers( - """Search query""" - query: String! - """Maximum results to return""" - limit: Int = 10 - ): [User!]! -} - -type Mutation { - """ - Create a new user account - """ - createUser( - """User creation input""" - input: CreateUserInput! - ): CreateUserPayload! -} - -""" -Input for creating a user -""" -input CreateUserInput { - """User's email address""" - email: String! - """Display name""" - name: String! -} -``` - -**GraphQL Playground:** -```javascript -const { ApolloServer } = require('apollo-server'); - -const server = new ApolloServer({ - typeDefs, - resolvers, - introspection: true, // Enable in dev - playground: { - settings: { - 'editor.theme': 'dark', - 'editor.fontSize': 14, - }, - }, -}); -``` - -### WebSocket Protocol Documentation - -```yaml -# AsyncAPI 2.0 -asyncapi: 2.5.0 -info: - title: Chat WebSocket API - version: 1.0.0 - description: Real-time chat messaging - -channels: - chat/{roomId}: - parameters: - roomId: - description: Chat room identifier - schema: - type: string - - subscribe: - summary: Receive messages - message: - oneOf: - - $ref: '#/components/messages/ChatMessage' - - $ref: '#/components/messages/UserJoined' - - publish: - summary: Send a message - message: - $ref: '#/components/messages/ChatMessage' - -components: - messages: - ChatMessage: - name: message - payload: - type: object - properties: - userId: - type: string - content: - type: string - timestamp: - type: string - format: date-time - - UserJoined: - name: userJoined - payload: - type: object - properties: - userId: - type: string - username: - type: string -``` - -### gRPC Documentation - -```protobuf -syntax = "proto3"; - -package users.v1; - -// User service manages user accounts -service UserService { - // Get a user by ID - // Returns: User object or NOT_FOUND error - rpc GetUser(GetUserRequest) returns (User) {} - - // List all users with pagination - // Returns: Paginated list of users - rpc ListUsers(ListUsersRequest) returns (ListUsersResponse) {} - - // Create a new user - // Returns: Created user or ALREADY_EXISTS error - rpc CreateUser(CreateUserRequest) returns (User) {} - - // Stream user updates in real-time - // Returns: Stream of user update events - rpc WatchUsers(WatchUsersRequest) returns (stream UserEvent) {} -} - -// User account -message User { - // Unique identifier - string id = 1; - - // Email address (unique, required) - string email = 2; - - // Display name - string name = 3; - - // Account creation timestamp - google.protobuf.Timestamp created_at = 4; -} -``` - -## SDK Documentation Strategies - -### Multi-Language Examples - -```markdown -# Create User - -## Python -```python -from myapi import Client - -client = Client(api_key="your_key") -user = client.users.create( - name="John Doe", - email="john@example.com" -) -print(user.id) -``` - -## TypeScript -```typescript -import { Client } from '@myapi/sdk'; - -const client = new Client({ apiKey: 'your_key' }); -const user = await client.users.create({ - name: 'John Doe', - email: 'john@example.com', -}); -console.log(user.id); -``` - -## Go -```go -import "github.com/myapi/sdk-go" - -client := sdk.NewClient("your_key") -user, err := client.Users.Create(ctx, &sdk.CreateUserInput{ - Name: "John Doe", - Email: "john@example.com", -}) -if err != nil { - log.Fatal(err) -} -fmt.Println(user.ID) -``` - -## Ruby -```ruby -require 'myapi' - -client = MyAPI::Client.new(api_key: 'your_key') -user = client.users.create( - name: 'John Doe', - email: 'john@example.com' -) -puts user.id -``` -``` - -### SDK Reference Template - -```markdown -# Users SDK - -## Installation -```bash -npm install @myapi/sdk -``` - -## Configuration -```typescript -import { Client } from '@myapi/sdk'; - -const client = new Client({ - apiKey: process.env.API_KEY, - baseUrl: 'https://api.example.com', // Optional - timeout: 30000, // Optional, default 30s -}); -``` - -## Methods - -### `client.users.create(data)` -Create a new user. - -**Parameters:** -- `data.name` (string, required) - User's display name -- `data.email` (string, required) - User's email address - -**Returns:** Promise - -**Throws:** -- `ValidationError` - Invalid input data -- `ConflictError` - Email already exists -- `AuthenticationError` - Invalid API key - -**Example:** -```typescript -const user = await client.users.create({ - name: 'John Doe', - email: 'john@example.com', -}); -``` - -## Error Handling -```typescript -import { ValidationError, ConflictError } from '@myapi/sdk'; - -try { - await client.users.create(data); -} catch (error) { - if (error instanceof ValidationError) { - console.error('Invalid data:', error.fields); - } else if (error instanceof ConflictError) { - console.error('User already exists'); - } -} -``` -``` - -## Quick Reference - -| Tool | Protocol | Features | -|------|----------|----------| -| Swagger UI | REST | Try-it-out, auth | -| Redoc | REST | Clean, responsive | -| Stoplight | REST | Modern, mock server | -| GraphQL Playground | GraphQL | Explorer, history | -| AsyncAPI Studio | WebSocket | Visual editor | -| grpcui | gRPC | Interactive console | diff --git a/.cursor/skills/code-documenter/references/python-docstrings.md b/.cursor/skills/code-documenter/references/python-docstrings.md deleted file mode 100644 index 054a12e..0000000 --- a/.cursor/skills/code-documenter/references/python-docstrings.md +++ /dev/null @@ -1,124 +0,0 @@ -# Python Docstrings - -> Reference for: Code Documenter -> Load when: Documenting Python code - -## Google Style (Recommended) - -```python -def calculate_total(items: list[Item], tax_rate: float = 0.0) -> float: - """Calculate total cost including tax. - - Args: - items: List of items to calculate total for. - tax_rate: Tax rate as decimal (e.g., 0.08 for 8%). - - Returns: - Total cost including tax. - - Raises: - ValueError: If tax_rate is negative or items is empty. - - Example: - >>> calculate_total([Item(10), Item(20)], 0.1) - 33.0 - """ -``` - -## NumPy Style - -```python -def calculate_total(items: list[Item], tax_rate: float = 0.0) -> float: - """ - Calculate total cost including tax. - - Parameters - ---------- - items : list[Item] - List of items to calculate total for. - tax_rate : float, optional - Tax rate as decimal (e.g., 0.08 for 8%). Default is 0.0. - - Returns - ------- - float - Total cost including tax. - - Raises - ------ - ValueError - If tax_rate is negative or items is empty. - - Examples - -------- - >>> calculate_total([Item(10), Item(20)], 0.1) - 33.0 - """ -``` - -## Sphinx Style - -```python -def calculate_total(items: list[Item], tax_rate: float = 0.0) -> float: - """Calculate total cost including tax. - - :param items: List of items to calculate total for. - :type items: list[Item] - :param tax_rate: Tax rate as decimal (e.g., 0.08 for 8%). - :type tax_rate: float - :returns: Total cost including tax. - :rtype: float - :raises ValueError: If tax_rate is negative or items is empty. - - .. code-block:: python - - >>> calculate_total([Item(10), Item(20)], 0.1) - 33.0 - """ -``` - -## Class Documentation - -```python -class UserService: - """Service for managing user operations. - - This service handles CRUD operations for users and - integrates with the authentication system. - - Attributes: - db: Database session for queries. - cache: Redis client for caching. - - Example: - >>> service = UserService(db, cache) - >>> user = await service.create_user(data) - """ - - def __init__(self, db: AsyncSession, cache: Redis) -> None: - """Initialize UserService. - - Args: - db: Database session for queries. - cache: Redis client for caching. - """ -``` - -## Quick Reference - -| Style | Args Format | Returns Format | -|-------|-------------|----------------| -| Google | `Args:` block | `Returns:` block | -| NumPy | `Parameters` section | `Returns` section | -| Sphinx | `:param name:` | `:returns:` | - -## Sections Available - -| Section | Google | NumPy | Sphinx | -|---------|--------|-------|--------| -| Parameters | `Args:` | `Parameters` | `:param:` | -| Returns | `Returns:` | `Returns` | `:returns:` | -| Raises | `Raises:` | `Raises` | `:raises:` | -| Examples | `Example:` | `Examples` | `.. code-block::` | -| Notes | `Note:` | `Notes` | `.. note::` | -| Attributes | `Attributes:` | `Attributes` | `:ivar:` | diff --git a/.cursor/skills/code-documenter/references/typescript-jsdoc.md b/.cursor/skills/code-documenter/references/typescript-jsdoc.md deleted file mode 100644 index c241c44..0000000 --- a/.cursor/skills/code-documenter/references/typescript-jsdoc.md +++ /dev/null @@ -1,148 +0,0 @@ -# TypeScript JSDoc - -> Reference for: Code Documenter -> Load when: Documenting TypeScript/JavaScript code - -## Function Documentation - -```typescript -/** - * Calculate total cost including tax. - * - * @param items - List of items to calculate total for - * @param taxRate - Tax rate as decimal (e.g., 0.08 for 8%) - * @returns Total cost including tax - * @throws {Error} If taxRate is negative or items is empty - * - * @example - * ```typescript - * const total = calculateTotal(items, 0.08); - * console.log(total); // 108.00 - * ``` - */ -function calculateTotal(items: Item[], taxRate = 0): number { -``` - -## Class Documentation - -```typescript -/** - * Service for managing user operations. - * - * Handles CRUD operations and integrates with authentication system. - * - * @example - * ```typescript - * const service = new UserService(db, cache); - * const user = await service.create(userData); - * ``` - */ -class UserService { - /** - * Create a new UserService instance. - * - * @param db - Database connection - * @param cache - Redis cache client - */ - constructor( - private readonly db: Database, - private readonly cache: Cache, - ) {} -} -``` - -## Interface Documentation - -```typescript -/** - * User data transfer object. - * - * @interface UserDto - */ -interface UserDto { - /** Unique user identifier */ - id: string; - - /** User's email address (unique) */ - email: string; - - /** User's display name */ - name: string; - - /** Account creation timestamp */ - createdAt: Date; -} -``` - -## Generic Types - -```typescript -/** - * Paginated response wrapper. - * - * @template T - Type of items in the data array - */ -interface PaginatedResponse { - /** Array of items for current page */ - data: T[]; - - /** Total number of items across all pages */ - total: number; - - /** Current page number (1-indexed) */ - page: number; - - /** Number of items per page */ - limit: number; -} -``` - -## Async Functions - -```typescript -/** - * Fetch user by ID from database. - * - * @param id - User's unique identifier - * @returns Promise resolving to user data or null if not found - * @throws {DatabaseError} If connection fails - * - * @async - */ -async function findUserById(id: string): Promise { -``` - -## Quick Reference - -| Tag | Purpose | Example | -|-----|---------|---------| -| `@param` | Parameter description | `@param name - User's name` | -| `@returns` | Return value | `@returns User object` | -| `@throws` | Exception thrown | `@throws {Error} If invalid` | -| `@example` | Usage example | Code block | -| `@see` | Reference link | `@see UserService` | -| `@deprecated` | Mark deprecated | `@deprecated Use v2 instead` | -| `@template` | Generic type param | `@template T - Item type` | -| `@async` | Async function | Mark async | -| `@private` | Private member | Internal use | -| `@readonly` | Read-only property | Cannot modify | - -## Common Patterns - -```typescript -// Optional parameters -/** @param [options] - Optional configuration */ - -// Default values -/** @param [limit=10] - Items per page (default: 10) */ - -// Multiple types -/** @param input - Input value (string or number) */ - -// Callback parameters -/** - * @callback FilterFn - * @param item - Item to filter - * @returns Whether item passes filter - */ -``` diff --git a/.cursor/skills/code-documenter/references/user-guides-tutorials.md b/.cursor/skills/code-documenter/references/user-guides-tutorials.md deleted file mode 100644 index ddfb7b6..0000000 --- a/.cursor/skills/code-documenter/references/user-guides-tutorials.md +++ /dev/null @@ -1,533 +0,0 @@ -# User Guides & Tutorials - -> Reference for: Code Documenter -> Load when: Creating getting started guides, tutorials, troubleshooting docs, end-user documentation - -## Tutorial Structure - -### Progressive Learning Path - -```markdown -# Getting Started with API - -## Prerequisites -Before you begin, ensure you have: -- [ ] Node.js 18+ installed -- [ ] An API key from your dashboard -- [ ] Basic knowledge of REST APIs - -## Quick Start (5 minutes) - -### 1. Install the SDK -```bash -npm install @myapi/sdk -``` - -### 2. Create Your First Request -```typescript -import { Client } from '@myapi/sdk'; - -const client = new Client({ apiKey: 'your_key' }); -const users = await client.users.list(); -console.log(users); -``` - -### 3. Verify It Works -Run the code and you should see a list of users. - -**Expected output:** -```json -{ - "data": [ - { "id": "1", "name": "Alice" }, - { "id": "2", "name": "Bob" } - ], - "total": 2 -} -``` - -## Next Steps -- [Authentication Guide](/docs/auth) - Learn about OAuth and API keys -- [Advanced Queries](/docs/queries) - Filtering, sorting, pagination -- [Error Handling](/docs/errors) - Handle errors gracefully -``` - -### Step-by-Step Tutorial - -```markdown -# Tutorial: Building a User Dashboard - -**What you'll learn:** -- Fetching user data from the API -- Handling pagination -- Displaying data in a table -- Adding real-time updates - -**Time:** 30 minutes -**Level:** Intermediate - -## Step 1: Set Up the Project - -Create a new project: -```bash -mkdir user-dashboard -cd user-dashboard -npm init -y -npm install @myapi/sdk react -``` - -## Step 2: Fetch Users - -Create `src/api/users.ts`: -```typescript -import { Client } from '@myapi/sdk'; - -const client = new Client({ apiKey: process.env.API_KEY }); - -export async function getUsers(page = 1, limit = 20) { - const response = await client.users.list({ page, limit }); - return response; -} -``` - -**What's happening:** -1. We import the SDK client -2. Initialize it with our API key from environment -3. Create a helper function that fetches paginated users - -## Step 3: Create the Component - -Create `src/components/UserTable.tsx`: -```typescript -import { useState, useEffect } from 'react'; -import { getUsers } from '../api/users'; - -export function UserTable() { - const [users, setUsers] = useState([]); - const [loading, setLoading] = useState(true); - - useEffect(() => { - async function fetchData() { - const data = await getUsers(); - setUsers(data.data); - setLoading(false); - } - fetchData(); - }, []); - - if (loading) return
Loading...
; - - return ( - - - - - - - - - {users.map(user => ( - - - - - ))} - -
NameEmail
{user.name}{user.email}
- ); -} -``` - -## Step 4: Test It - -Run your app: -```bash -npm run dev -``` - -You should see a table with user data. - -## Checkpoint -At this point, you have: -- [x] Set up the SDK -- [x] Created an API helper -- [x] Built a user table component -- [ ] Added pagination -- [ ] Added real-time updates - -## Next: Adding Pagination - -[Continue to Step 5 →](/docs/tutorial/step-5) -``` - -## Information Architecture - -### Content Hierarchy - -```markdown -Documentation/ -├── Getting Started/ -│ ├── Quick Start (5 min) -│ ├── Installation -│ ├── Authentication -│ └── First Request -│ -├── Guides/ -│ ├── User Management -│ ├── File Uploads -│ ├── Webhooks -│ └── Rate Limiting -│ -├── API Reference/ -│ ├── Users API -│ ├── Files API -│ └── Webhooks API -│ -├── SDK Documentation/ -│ ├── Python SDK -│ ├── TypeScript SDK -│ └── Go SDK -│ -├── Tutorials/ -│ ├── Build a Dashboard (30 min) -│ ├── Integrate Authentication (45 min) -│ └── Real-time Sync (60 min) -│ -└── Resources/ - ├── Troubleshooting - ├── FAQ - ├── Best Practices - └── Migration Guides -``` - -## Writing Techniques - -### Task-Based Writing - -```markdown -# How to Upload a File - -**Goal:** Upload an image file to your account storage - -**Time:** 5 minutes - -## Steps - -### 1. Prepare the file -Get the file from user input or file system: -```typescript -const file = document.querySelector('input[type="file"]').files[0]; -``` - -### 2. Create form data -```typescript -const formData = new FormData(); -formData.append('file', file); -formData.append('folder', 'avatars'); -``` - -### 3. Upload with the SDK -```typescript -const result = await client.files.upload(formData); -console.log('File URL:', result.url); -``` - -## Common Issues - -**"File too large" error:** -Maximum file size is 10MB. Compress images before uploading. - -**"Invalid file type" error:** -Only .jpg, .png, .gif are allowed. Check the file extension. - -## Related -- [File API Reference](/api/files) -- [Handling Upload Progress](/guides/upload-progress) -``` - -### Progressive Disclosure - -```markdown -# Authentication - -## Basic: API Keys (Recommended for Getting Started) - -API keys are the simplest way to authenticate. - -```typescript -const client = new Client({ apiKey: 'your_key' }); -``` - -**When to use:** Scripts, internal tools, testing - -[Generate an API key →](/dashboard/api-keys) - -
-Advanced: OAuth 2.0 - -For user-facing applications, use OAuth 2.0. - -### Authorization Code Flow - -1. Redirect user to authorization URL: -```typescript -const authUrl = client.oauth.getAuthUrl({ - redirectUri: 'https://yourapp.com/callback', - scopes: ['read:users', 'write:users'], -}); -window.location.href = authUrl; -``` - -2. Handle the callback: -```typescript -const code = new URLSearchParams(window.location.search).get('code'); -const tokens = await client.oauth.exchangeCode(code); -``` - -3. Use the access token: -```typescript -const client = new Client({ accessToken: tokens.access_token }); -``` - -[Full OAuth guide →](/guides/oauth) -
- -
-Enterprise: JWT Tokens - -For service-to-service authentication, use JWTs. - -```typescript -const jwt = createJWT({ - issuer: 'your-service', - subject: 'service-account-id', - privateKey: process.env.PRIVATE_KEY, -}); - -const client = new Client({ jwt }); -``` - -[JWT setup guide →](/guides/jwt) -
-``` - -## Visual Communication - -### Diagram Integration - -```markdown -# System Architecture - -## Request Flow - -```mermaid -sequenceDiagram - participant Client - participant API - participant Database - participant Cache - - Client->>API: POST /users - API->>Cache: Check cache - Cache-->>API: Cache miss - API->>Database: Insert user - Database-->>API: User created - API->>Cache: Store user - API-->>Client: 201 Created -``` - -## Data Model - -```mermaid -erDiagram - USER ||--o{ POST : creates - USER ||--o{ COMMENT : writes - POST ||--o{ COMMENT : has - - USER { - string id PK - string email UK - string name - datetime created_at - } - - POST { - string id PK - string user_id FK - string title - text content - } -``` -``` - -### Screenshot Annotations - -```markdown -# Dashboard Overview - -![Dashboard with numbered annotations](./images/dashboard-annotated.png) - -**Key features:** - -1. **Navigation** - Switch between sections -2. **API Key** - Copy your key (click to reveal) -3. **Usage Stats** - Current month's API calls -4. **Quick Actions** - Generate new key, view docs -5. **Recent Activity** - Last 10 API requests - -## Creating Your First API Key - -1. Click "Generate New Key" (highlighted in green) -2. Enter a description like "Production API" -3. Select permissions (default: all) -4. Click "Create" -5. **Important:** Copy the key immediately - it won't be shown again - -![Create API key dialog](./images/create-key.png) -``` - -## Troubleshooting Guides - -### Problem-Solution Format - -```markdown -# Troubleshooting - -## Authentication Errors - -### "Invalid API key" - -**Symptoms:** -- 401 Unauthorized error -- Error message: "Invalid API key" - -**Causes:** -1. API key was copied incorrectly (extra spaces) -2. API key was revoked -3. Using test key in production environment - -**Solutions:** - -**1. Verify the key:** -```bash -# Check for extra spaces -echo -n "$API_KEY" | wc -c # Should be exactly 32 characters -``` - -**2. Regenerate the key:** -- Go to [dashboard](/dashboard) -- Click "Revoke & Regenerate" -- Update your environment variables - -**3. Check environment:** -```typescript -console.log('Environment:', process.env.NODE_ENV); -console.log('API URL:', client.baseUrl); -``` - -**Still not working?** -[Contact support](/support) with your request ID from the error response. - ---- - -### "Rate limit exceeded" - -**Symptoms:** -- 429 Too Many Requests error -- Requests failing intermittently - -**Immediate fix:** -Wait 60 seconds and retry. - -**Long-term solutions:** - -**1. Implement exponential backoff:** -```typescript -async function retryWithBackoff(fn, maxRetries = 3) { - for (let i = 0; i < maxRetries; i++) { - try { - return await fn(); - } catch (error) { - if (error.status === 429 && i < maxRetries - 1) { - await sleep(Math.pow(2, i) * 1000); - continue; - } - throw error; - } - } -} -``` - -**2. Batch requests:** -Instead of 100 individual requests, use batch endpoints. - -**3. Upgrade your plan:** -[View plans](/pricing) - Higher tiers have increased limits. -``` - -## FAQ Section - -```markdown -# Frequently Asked Questions - -## General - -### What's included in the free tier? -- 1,000 API requests/month -- 1GB storage -- Community support -- All core features - -### How do I upgrade? -Click "Upgrade" in your [dashboard](/dashboard) and select a plan. - -## Technical - -### Can I use this in production? -Yes, the API is production-ready with 99.9% SLA on paid plans. - -### What's the rate limit? -- Free: 10 requests/minute -- Pro: 100 requests/minute -- Enterprise: Custom limits - -### Do you support webhooks? -Yes! See [Webhooks Guide](/guides/webhooks) for setup. - -### Which regions are available? -Currently: US East, US West, EU Central, Asia Pacific. - -## Billing - -### How does billing work? -- Monthly subscription -- Pay-as-you-go for overages -- Cancel anytime - -### What payment methods do you accept? -Credit card, PayPal, wire transfer (annual plans only). - ---- - -**Can't find your answer?** -- [Browse all docs](/docs) -- [Ask the community](https://community.example.com) -- [Contact support](/support) -``` - -## Quick Reference - -| Content Type | Best For | Key Elements | -|-------------|----------|-------------| -| Quick Start | New users (5 min) | Prerequisites, minimal code, verify | -| Tutorial | Learning by doing | Steps, checkpoints, working code | -| How-To Guide | Specific tasks | Goal, steps, troubleshooting | -| Reference | Looking up details | Comprehensive, searchable | -| Explanation | Understanding concepts | Why, not how | - -| Writing Principle | Technique | -|------------------|-----------| -| Clarity | Active voice, short sentences | -| Scannability | Headings, lists, code blocks | -| Completeness | Prerequisites, next steps, related links | -| Accuracy | Test all code, version specifics | diff --git a/.cursor/skills/cursor-sync/.skillfish.json b/.cursor/skills/cursor-sync/.skillfish.json deleted file mode 100644 index eb44ec2..0000000 --- a/.cursor/skills/cursor-sync/.skillfish.json +++ /dev/null @@ -1,10 +0,0 @@ -{ - "version": 2, - "name": "cursor-sync", - "owner": "denisraison", - "repo": "claude-plugins", - "path": "plugins/cursor-sync/skills/cursor-sync", - "branch": "main", - "sha": "83237507c60e2505b5bf295c44a75c8e32ab4c21", - "source": "manual" -} \ No newline at end of file diff --git a/.cursor/skills/cursor-sync/SKILL.md b/.cursor/skills/cursor-sync/SKILL.md deleted file mode 100644 index d0b7c2d..0000000 --- a/.cursor/skills/cursor-sync/SKILL.md +++ /dev/null @@ -1,58 +0,0 @@ ---- -name: cursor-sync -description: Syncs Claude Code plugins to Cursor skills and commands directories. ---- - -# Cursor Sync - -Syncs Claude Code plugins to Cursor's skills and commands directories. Changes to installed plugins are automatically detected and synced. - -## Scripts - -Run these scripts from the plugin directory: - -### Manual Sync -```bash -./scripts/sync.sh -``` -Performs a one-time sync of all installed Claude Code plugins to `~/.cursor/skills/` and `~/.cursor/commands/`. - -### Start Watcher -```bash -./scripts/watch.sh -``` -Watches for plugin changes and syncs automatically. Runs in foreground. - -### Install as Service -```bash -./scripts/install-service.sh install -``` -Installs a launchd service that starts on login and keeps syncing in the background. - -### Check Service Status -```bash -./scripts/install-service.sh status -``` - -### Uninstall Service -```bash -./scripts/install-service.sh uninstall -``` - -## How It Works - -1. Reads `~/.claude/plugins/installed_plugins.json` to find installed plugins -2. For each plugin: - - Copies skill directories from `/skills/` to `~/.cursor/skills/` - - Copies command files from `/commands/` to `~/.cursor/commands/` -3. Handles naming conflicts by prefixing with plugin name -4. Cleans up orphaned files when plugins are uninstalled - -## Dependencies - -- `jq` for JSON parsing: `brew install jq` -- `fswatch` for file watching: `brew install fswatch` - -## Logs - -Service logs are written to `~/Library/Logs/cursor-sync.log`. diff --git a/.cursor/skills/dataframes/SKILL.md b/.cursor/skills/dataframes/SKILL.md new file mode 100644 index 0000000..b0e472a --- /dev/null +++ b/.cursor/skills/dataframes/SKILL.md @@ -0,0 +1,47 @@ +--- +author: dotagents +name: dataframes +description: Pandas and Polars for tabular data: when to use which, indexing and dtypes, joins and reshaping, lazy Polars, I/O and Parquet, nulls, Arrow interop, and performance. Use for DataFrame/Series/LazyFrame code. Triggers on pandas, polars, DataFrame, LazyFrame, parquet, groupby, merge, join. +--- + +# Dataframes (pandas + Polars) + +## Quick start + +1. **Pick one engine per pipeline**: **pandas** when **index semantics**, **mixed columns**, and **ecosystem** (statsmodels, sklearn I/O) matter; **Polars** when **large scans**, **lazy plans**, and **expression** clarity win. See [reference-when-which.md](references/reference-when-which.md). +2. **pandas**: be explicit with **`loc` / `iloc`**, **`assign`**, and **dtypes**; understand **views vs copies** and **Copy-on-Write** (2.x). See [reference-pandas-core.md](references/reference-pandas-core.md). +3. **Polars**: default to **`scan_*` + `lazy()`** for big data; build with **`select` / `with_columns` / `filter`** and **`pl.col`**; **`collect`** at the boundary. See [reference-polars.md](references/reference-polars.md). +4. **Joins and groups**: validate **row counts** and **duplicates**; name **suffixes**; prefer **Polars** for parallel group-by on large tables when the project already uses it. See [reference-pandas-group-join.md](references/reference-pandas-group-join.md) and [reference-polars.md](references/reference-polars.md). +5. **I/O**: **Parquet** (often **PyArrow**) for analytics interchange; **`uv add pyarrow`** when the stack needs it. See [reference-io-dtypes-nulls.md](references/reference-io-dtypes-nulls.md). +6. **Handoff**: convert at **module boundaries** with **`from_pandas`** / **`to_pandas`** or **Arrow**; avoid ping-pong in inner loops. See [reference-interop-performance.md](references/reference-interop-performance.md). + +## Stack synergy + +| Resource | Role | +|----------|------| +| **Python spec** | Explicit pandas index/columns; lazy Polars for heavy queries | +| **numpy-scientific** | `to_numpy()`, dtypes, contiguous buffers | +| **matplotlib-scientific** | `df.plot(ax=ax)` and Polars `.to_pandas()` for plotting when needed | +| **numpy-docstrings** | Public API docstrings for DataFrame-returning functions | +| **general-python** | uv, ty, **python-reviewer** | + +## Reference index + +| Topic | File | +|--------|------| +| pandas vs Polars, boundaries | [reference-when-which.md](references/reference-when-which.md) | +| Index, `loc`/`iloc`, dtypes, COW, assignment | [reference-pandas-core.md](references/reference-pandas-core.md) | +| GroupBy, merge/join, concat, pivot | [reference-pandas-group-join.md](references/reference-pandas-group-join.md) | +| LazyFrame, expressions, group, join | [reference-polars.md](references/reference-polars.md) | +| CSV/Parquet, nulls, dtypes, Arrow | [reference-io-dtypes-nulls.md](references/reference-io-dtypes-nulls.md) | +| Conversion, streaming, typing | [reference-interop-performance.md](references/reference-interop-performance.md) | + +## Official documentation + +- [pandas user guide](https://pandas.pydata.org/docs/user_guide/index.html) +- [Polars user guide](https://docs.pola.rs/user-guide/) +- [Polars API](https://docs.pola.rs/api/python/stable/reference/index.html) + +## Dependencies + +Use **`uv add pandas`**, **`uv add polars`**, **`uv add pyarrow`** as needed; do not hand-edit version pins in `pyproject.toml`. diff --git a/.cursor/skills/dataframes/references/reference-interop-performance.md b/.cursor/skills/dataframes/references/reference-interop-performance.md new file mode 100644 index 0000000..d3d65a3 --- /dev/null +++ b/.cursor/skills/dataframes/references/reference-interop-performance.md @@ -0,0 +1,33 @@ +# Interop, NumPy, plotting, performance + +## pandas ↔ Polars + +- **`pl.from_pandas(df)`** / **`df.to_pandas()`**; cost is **non-trivial** on huge frames—do **once** per stage. +- **Arrow**: **`df.to_arrow()`** / **`pl.DataFrame(arrow_table)`** when zero-copy paths exist (versions and dtypes must align). + +## NumPy + +- **pandas**: **`Series.to_numpy()`**, **`DataFrame.to_numpy()`**; **`copy`** parameter matters; **nullable** dtypes may yield **object** or **masked** outputs—check. +- **Polars**: **`to_numpy()`** on Series; DataFrame to NumPy often goes through **column stack** or **pandas**—prefer **expressions** inside Polars. + +## Matplotlib + +- **pandas**: **`df.plot(ax=ax, kind=...)`** per **`matplotlib-scientific`** skill. +- **Polars**: **`to_pandas()`** for quick plots or export **CSV** to plotting tools; native plotting ecosystem is thinner. + +## Memory and chunks + +- **pandas**: **`read_csv(chunksize=...)`** for bounded memory; **Polars lazy** + **sink_parquet** for out-of-core style workflows (see current Polars docs for **sink** APIs). + +## Typing + +- **pandas** stubs: **`pd.DataFrame`**, **`Series`**; **Polars** ships types for **`DataFrame`**, **`LazyFrame`**, **`Expr`**—run **`ty check`** on public APIs that return frames. + +## Parallelism + +- Polars uses **Rayon**-style parallelism internally; avoid **nested** parallel Python over Polars in the same process without care. +- pandas **releases GIL** in some ops but not all; **vectorized** column ops beat Python loops. + +## Reproducibility + +- **Sort** before comparisons; **seed** any sampling; document **Polars** and **pandas** **versions** in science appendices when results must replay exactly. diff --git a/.cursor/skills/dataframes/references/reference-io-dtypes-nulls.md b/.cursor/skills/dataframes/references/reference-io-dtypes-nulls.md new file mode 100644 index 0000000..6c725c5 --- /dev/null +++ b/.cursor/skills/dataframes/references/reference-io-dtypes-nulls.md @@ -0,0 +1,37 @@ +# I/O, dtypes, and nulls (both libraries) + +## Reading and writing + +### pandas + +- **`read_csv`**, **`read_parquet`**, **`read_feather`**, **`read_sql`**; **`to_parquet`**, etc. +- **`dtype`**, **`parse_dates`**, **`usecols`** reduce memory on ingest. +- Engine **`pyarrow`** or **`fastparquet`** for Parquet—project should pick one consistently. + +### Polars + +- **`read_csv` / `read_parquet`** (eager) vs **`scan_csv` / `scan_parquet`** (lazy). +- **`infer_schema_length`**, **`try_parse_dates`**, **`columns`** to trim work. + +## Parquet and Arrow + +- **Parquet** preserves **schema** and compresses well for analytics pipelines. +- **`uv add pyarrow`** when either stack reads/writes Parquet heavily or shares **Arrow** buffers. + +## Nulls and NA + +- **pandas**: **`NaN`** for floats; **`pd.NA`** for nullable dtypes; **`NaT`** for datetimes—**boolean reductions** can be tri-state. +- **Polars**: **`null`** unified; predicates use **`is_null()`**, **`is_not_null()`**, **`fill_null`**, **`drop_nulls`**. +- **Joins**: null keys usually **do not match**; document behavior for outer joins. + +## Strings + +- pandas **`string`** dtype vs **`object`**; Polars **`String`** (Utf8 in older docs)—check installed Polars version in migration notes. + +## Time zones + +- Store **UTC** internally where possible; localize/convert explicitly for display and reporting. + +## Excel and odd formats + +- Prefer **CSV/Parquet** in pipelines; **Excel** last-mile only; watch **sheet** and **type** inference. diff --git a/.cursor/skills/dataframes/references/reference-pandas-core.md b/.cursor/skills/dataframes/references/reference-pandas-core.md new file mode 100644 index 0000000..7080e97 --- /dev/null +++ b/.cursor/skills/dataframes/references/reference-pandas-core.md @@ -0,0 +1,39 @@ +# pandas: index, selection, dtypes, mutation + +Official: [Indexing and selecting data](https://pandas.pydata.org/docs/user_guide/indexing.html), [Copy-on-Write](https://pandas.pydata.org/docs/user_guide/copy_on_write.html). + +## Index and columns + +- **`Index`** carries **labels** and optional **`name`**; **`MultiIndex`** for hierarchical keys. +- **`reset_index` / `set_index`**: make columns into index or the reverse when modeling needs change. + +## `loc`, `iloc`, `[]` + +- **`loc`**: label-based (slice end **inclusive** on labels). +- **`iloc`**: integer position (Python slice end **exclusive**). +- **`df[col]`** for a single column **Series**; **`df[[col]]`** for one-column **DataFrame**. +- Avoid **chained indexing** (`df[a][b] = x`); use **`loc`** or **`assign`**. + +## Copy-on-Write (pandas 2+) + +- With **CoW** enabled (recommended in modern defaults), many chained reads are safer; still treat **writes** as requiring explicit **`loc`** assignment. +- **`copy()`** when you must guarantee an independent buffer before mutation. + +## dtypes + +- **Nullable**: **`Int64`**, **`string`**, **`boolean`** (capitalized extension dtypes) vs **`object`** for messy text. +- **`astype`** can widen or lose precision; prefer **`pd.to_numeric(..., errors="coerce")`** for ingestion. +- **Categorical** for low-cardinality strings saves memory and speeds **groupby**. + +## Assignment + +- **`df.loc[row_indexer, col] = values`**; align with **index** on the RHS when assigning Series. +- **`pd.concat`** along axis for new rows/columns instead of repeated **`append`** in hot paths. + +## Series + +- **Alignment** on index in **arithmetic** with other Series/DataFrame—often a feature, sometimes a bug; **`reset_index`** or **`.values`** when you intend **positional** math. + +## Performance hints + +- **Vectorized** ops on columns; **`apply`** row-wise is slow at scale—reshape or use **Polars/NumPy**. diff --git a/.cursor/skills/dataframes/references/reference-pandas-group-join.md b/.cursor/skills/dataframes/references/reference-pandas-group-join.md new file mode 100644 index 0000000..faf3671 --- /dev/null +++ b/.cursor/skills/dataframes/references/reference-pandas-group-join.md @@ -0,0 +1,34 @@ +# pandas: groupby, merge, reshape + +Official: [Group by](https://pandas.pydata.org/docs/user_guide/groupby.html), [Merge, join, concatenate](https://pandas.pydata.org/docs/user_guide/merging.html). + +## GroupBy + +- **`groupby(keys, as_index=...)`** then **`.agg`**, **`.transform`**, **`.filter`**. +- **`as_index=False`** keeps grouping columns as columns. +- **Named aggregation**: **`.agg(mean_x=("x", "mean"))`** for clear output names. +- **Watch**: **NA** in keys drops groups unless **`dropna=False`** (version-dependent defaults—check docs). + +## Merge and join + +- **`merge`**: SQL-style on **columns**; **`how`** (`inner`, `left`, `right`, `outer`); **`on`**, **`left_on`/`right_on`**; **`suffixes`** for overlapping names. +- **`join`**: index-based; easy to misuse if indexes are not unique—validate **`validate`** (`"one_to_one"`, etc.) when available. +- **Row explosion** from duplicate keys: assert **uniqueness** or **deduplicate** before merge when unintended. + +## Concat + +- **`pd.concat`** along **`axis=0`** (stack) or **`axis=1`** (side by side); **`keys`** for MultiIndex source labels. + +## Reshape + +- **`pivot`**, **`pivot_table`**, **`melt`**, **`stack`/`unstack`** for wide ↔ long. +- **`crosstab`** for counts with optional normalization. + +## Sort and rank + +- **`sort_values`**, **`sort_index`**; **`rank`** with **`method`** for ties. + +## Validation + +- **`merge`** + **`indicator=True`** to audit **left_only / right_only / both**. +- **`assert_frame_equal`** in tests with **`check_dtype`** toggled as needed. diff --git a/.cursor/skills/dataframes/references/reference-polars.md b/.cursor/skills/dataframes/references/reference-polars.md new file mode 100644 index 0000000..df142f0 --- /dev/null +++ b/.cursor/skills/dataframes/references/reference-polars.md @@ -0,0 +1,46 @@ +# Polars: LazyFrame, expressions, group, join + +Official: [User guide](https://docs.pola.rs/user-guide/), [Expressions](https://docs.pola.rs/user-guide/expressions/). + +## Eager vs lazy + +- **Eager** `DataFrame`: small/medium data, interactive work. +- **Lazy** `LazyFrame`: **`pl.scan_parquet`**, **`pl.scan_csv`**, **`df.lazy()`**; chain transforms, then **`collect()`** (or **`collect(streaming=True)`** when enabled for your version). + +## Expressions + +- **`pl.col("a")`**, **`pl.col("^pat.*$")`**, **`pl.all()`**. +- **`.alias`**, **`.cast`**, **`.fill_null`**, **`.replace`**, string and datetime **`.str.*` / `.dt.*`** namespaces. +- **Conditional**: **`pl.when(cond).then(x).otherwise(y)`**. +- **Horizontal**: **`pl.sum_horizontal`**, **`pl.concat_list`** for list columns. + +## select / with_columns / filter + +- **`select`** chooses/reorders; **`with_columns`** adds or replaces by name; **`filter`** is row predicate. +- **`with_row_index`** when you need a stable row id column. + +## Joins + +- **`join`**, **`join_asof`** for temporal alignment; check **how** (`inner`, `left`, `semi`, `anti`). +- **Suffix** for name clashes; **validate** row counts like in SQL (assert in code). + +## Group by + +- **`group_by("k").agg(pl.col("x").mean())`**; multiple metrics in one **`agg`**. +- **Dynamic groups** for time windows: **`group_by_dynamic`** when applicable. + +## Sort and distinct + +- **`sort`**, **`unique`**, **`n_unique`**; **`is_duplicated`** for QA. + +## Strings and categoricals + +- **String** dtype; **Categorical** for low-cardinality columns to save memory in **lazy** plans. + +## Errors + +- Polars tends to **fail fast** on schema issues; fix **dtypes** at **scan** or first **`with_columns`**. + +## pandas interop + +- **`df.to_pandas()`**, **`pl.from_pandas(df)`**; see [reference-interop-performance.md](reference-interop-performance.md). diff --git a/.cursor/skills/dataframes/references/reference-when-which.md b/.cursor/skills/dataframes/references/reference-when-which.md new file mode 100644 index 0000000..040f422 --- /dev/null +++ b/.cursor/skills/dataframes/references/reference-when-which.md @@ -0,0 +1,32 @@ +# When to use pandas vs Polars + +Aligned with the project **Python** spec: explicit **index/column** semantics in pandas, **lazy** Polars for heavy queries when the project standardizes on it. + +## Prefer **pandas** + +- **Row labels** are part of the model (time series index alignment, `reindex`, `asfreq`). +- **Wide** mix of dtypes in one table with frequent **per-column** Python logic. +- **Downstream** expects pandas (`sklearn` with minimal friction, statsmodels, many tutorials). +- **Incremental** cell updates and **in-place**-style workflows are entrenched (still prefer explicit assignment). + +## Prefer **Polars** + +- **Large** files: **`scan_parquet` / `scan_csv`** and **lazy** plans push work to the engine. +- **Complex column expressions** without index alignment surprises: **`pl.col`** pipelines read linearly. +- **Parallel** group-by and joins on **big** tables (hardware-dependent; profile). +- **Stricter** null model (`null`) and **dtype** consistency across the query. + +## One boundary per layer + +- Do not **alternate** libraries inside a tight inner loop; **convert once** at imports/exports of a stage (ETL end, modeling start, plot start). +- Document the **owner** of the index: pandas keeps it on the frame; Polars is mostly **column-first** (row index is positional unless you add a column). + +## Time series + +- **pandas**: **`DatetimeIndex`**, **`resample`**, **`rolling`**, **`tz`** handling are mature. +- **Polars**: temporal **expressions** and **`group_by_dynamic`** fit large **event** tables; verify **timezone** rules for your version. + +## Testing + +- **Assert** shapes and **key uniqueness** after joins in both libraries. +- **`python-reviewer`** for merge footguns and silent **`NaN`** propagation. diff --git a/.cursor/skills/general-python/SKILL.md b/.cursor/skills/general-python/SKILL.md new file mode 100644 index 0000000..665ce2a --- /dev/null +++ b/.cursor/skills/general-python/SKILL.md @@ -0,0 +1,54 @@ +--- +author: dotagents +name: general-python +description: Python stack hub: uv/ruff/ty workflows, builtins and collections, functions and classes, dataclasses, typing boundaries, pytest and NumPy-style docs. Use for any Python task in repos that follow this stack; pairs with python-reviewer, python-types, and python-refactor. Related skills: numpy-scientific, dataframes, numpy-docstrings, matplotlib-scientific, lab-instrumentation. Triggers on Python, uv, ruff, ty, pytest, dataclass, typing. +--- + +# General Python + +This skill is the **hub** for conventions that come from the project **Python** section (often merged into **AGENTS.md**), the **Python Cursor rule** on `**/*.py`, and the **python-reviewer** / **python-types** / **python-refactor** subagents. Load topic files below instead of drifting from project defaults. + +## Synergy map + +| Source | Role | +|--------|------| +| **Python spec** (AGENTS.md block) | Canonical tooling (uv, ruff, ty), style, pytest, numerics, tables, instruments | +| **Python rule** | Globs `**/*.py`: 3.12+, uv, NumPy-style public docstrings, pyvisa split | +| **python-reviewer** | Post-change review: uv hygiene, typing, numerics footguns, tests | +| **python-types** | Deep typing for Astral **ty**, PEP 695, exhaustive `match` | +| **python-refactor** | Structure: tuples vs dataclasses, composition vs inheritance, size | +| **matplotlib-scientific** | Figures only; keep plotting out of this hub | +| **numpy-scientific** | `ndarray` design: dtypes, views, broadcasting, ufuncs, `linalg`, I/O, random | +| **dataframes** | pandas + Polars: tables, lazy frames, joins, I/O, interop | +| **numpy-docstrings** | numpydoc sections for public API docstrings | +| **lab-instrumentation** | PyVISA, sockets, HALs, validation, PDF datasheets, instrument tests | + +## Quick decisions + +1. **Dependencies**: `uv add` / `uv remove` / `uv sync` / `uv run`. Never hand-edit version pins in `pyproject.toml`. See [reference-tooling.md](references/reference-tooling.md). +2. **Quality gate**: `ruff check` (and format per project config), then **`ty check`** on touched code. See [reference-tooling.md](references/reference-tooling.md). +3. **Data**: prefer the right **builtin or collections ABC** before a custom class; use **dataclasses** for labeled bundles of data. See [reference-data-structures.md](references/reference-data-structures.md) and [reference-classes-dataclasses.md](references/reference-classes-dataclasses.md). +4. **APIs**: small **pure** core, **I/O at edges**, explicit types on public boundaries. See [reference-functions.md](references/reference-functions.md) and [reference-types-and-agents.md](references/reference-types-and-agents.md). +5. **Tests and docs**: `uv run pytest`; **NumPy-style docstrings** on public APIs. See [reference-testing-docs.md](references/reference-testing-docs.md). + +## Reference index + +| Topic | File | +|--------|------| +| uv, ruff, ty, layout, CI-style verification | [reference-tooling.md](references/reference-tooling.md) | +| list, dict, tuple, set, comprehensions, `collections` | [reference-data-structures.md](references/reference-data-structures.md) | +| Functions: purity, parameters, errors, resources | [reference-functions.md](references/reference-functions.md) | +| Classes, dataclasses, slots, immutability | [reference-classes-dataclasses.md](references/reference-classes-dataclasses.md) | +| Types, ty, when to delegate to **python-types** | [reference-types-and-agents.md](references/reference-types-and-agents.md) | +| pytest, docstrings, readability | [reference-testing-docs.md](references/reference-testing-docs.md) | +| Vectorized numerics, tables, instruments, plotting boundary | [reference-scientific-numerics.md](references/reference-scientific-numerics.md) | + +## External anchors + +- [uv](https://docs.astral.sh/uv/latest/), [Ruff](https://docs.astral.sh/ruff/), [ty](https://docs.astral.sh/ty/) (Astral) +- [PEP 8](https://peps.python.org/pep-0008/) style surface; project **ruff** rules are authoritative +- [Python data model](https://docs.python.org/3/reference/datamodel.html) for dunder semantics + +## Modern practice snapshot + +Reproducible projects combine **locked deps**, **fast lint/format**, and **static typing** on API surfaces; **src layout** avoids import ambiguity; automate checks in CI or pre-commit. See [reference-tooling.md](references/reference-tooling.md). diff --git a/.cursor/skills/general-python/references/reference-classes-dataclasses.md b/.cursor/skills/general-python/references/reference-classes-dataclasses.md new file mode 100644 index 0000000..a2f90ee --- /dev/null +++ b/.cursor/skills/general-python/references/reference-classes-dataclasses.md @@ -0,0 +1,40 @@ +# Classes and dataclasses + +## When to use a class + +- **Stateful service** with invariants (device client, session, parser with buffer). +- **True is-a** hierarchy with shared behavior; otherwise prefer **composition** and **`Protocol`** (see **`python-refactor`**). + +## Dataclasses (`dataclasses`) + +- Use for **labeled data bundles** with minimal behavior: configs, results, DTOs between layers. +- **`frozen=True`**: immutable value objects; safer hashing and sharing. +- **`slots=True`** (3.10+): lower memory and faster attribute access when you do not need a `__dict__`. +- **`order=True`**: only when ordering is meaningful and documented. +- **`field(default_factory=…)`** for mutable defaults (lists, dicts). +- Replace opaque **`tuple[int, int, int, int]`** returns with a **frozen dataclass** or **`NamedTuple`** when names carry meaning. + +## Regular classes + +- **`@staticmethod`**: rare; often a module function is clearer. +- **`@classmethod`**: alternate constructors (`from_path`, `from_config`). +- **`__init__`**: minimal; heavy work in dedicated methods or factories so tests stay small. + +## Special methods + +- Implement **`__repr__`** for debuggability; **`__str__`** when user-facing text differs. +- **`__eq__`**: dataclass generates it; hand-roll only with clear semantics. + +## Dataclass vs TypedDict vs NamedTuple + +- **`TypedDict`**: JSON-like dict shapes, especially for **`**kwargs`** typing. +- **`NamedTuple`**: immutable, tuple-unpacking ergonomics, light memory. +- **`dataclass`**: mutable or frozen records with optional methods. + +## Typing and tooling + +- After modeling data, run **`ty check`**; align with **`python-types`** for generics and protocols. + +## Delegation + +- **Inheritance vs composition**, god classes: **`python-refactor`** agent. diff --git a/.cursor/skills/general-python/references/reference-data-structures.md b/.cursor/skills/general-python/references/reference-data-structures.md new file mode 100644 index 0000000..7fa6430 --- /dev/null +++ b/.cursor/skills/general-python/references/reference-data-structures.md @@ -0,0 +1,42 @@ +# Builtins and collections + +## Choosing a structure + +| Need | Prefer | +|------|--------| +| Ordered sequence, homogeneous | **`list`**; fixed small arity: **`tuple`** | +| Key-value lookup, unique keys | **`dict`** (3.7+ insertion order is part of language spec) | +| Membership testing, uniqueness | **`set`** / **`frozenset`** when immutable | +| Immutable record of fields | **`tuple`**, **`NamedTuple`**, or **`@dataclass(frozen=True)`** (see [reference-classes-dataclasses.md](reference-classes-dataclasses.md)) | +| FIFO / LRU-ish queue | **`collections.deque`** | +| Counting | **`collections.Counter`** | +| Grouping by key | **`collections.defaultdict(list)`** (or similar) | +| Read-only mapping view | **`Mapping`** from **`collections.abc`** in type hints | + +## Comprehensions and literals + +- Prefer **comprehensions** or **generator expressions** over `map`/`filter` when readability wins. +- Avoid **mutable default arguments**; use `None` and assign inside the function or use **`dataclasses.field(default_factory=…)`**. + +## Immutability and copying + +- **`tuple`**, **`frozenset`**, **frozen dataclass**: safe as dict keys and for defensive sharing. +- **Shallow vs deep copy**: default assignment and `list(old)` share nested mutables; use **`copy.deepcopy`** only when the data model requires it. + +## Sorting and keys + +- **`sorted(iterable, key=…)`** and **`list.sort(key=…)`** for stable, explicit ordering. +- **`bisect`** on sorted sequences for insertion and search (stdlib). + +## Typing shapes + +- **`list[int]`**, **`dict[str, float]`**, **`set[str]`** in annotations; read-only parameters as **`Sequence[T]`**, **`Mapping[str, T]`** from **`collections.abc`** when you do not need mutation. + +## When not to use a dict + +- Fixed, named fields with validation: consider **dataclass** or **Pydantic** (if project already depends on it). Do not add Pydantic via hand-edited pins; use **`uv add`**. + +## Scientific stacks + +- **NumPy arrays** for numeric tensors; **pandas** / **polars** for labeled tables per the Python spec; do not emulate DataFrames with nested dicts unless prototyping. +- Tabular workflows: **dataframes** skill. diff --git a/.cursor/skills/general-python/references/reference-functions.md b/.cursor/skills/general-python/references/reference-functions.md new file mode 100644 index 0000000..40a11d7 --- /dev/null +++ b/.cursor/skills/general-python/references/reference-functions.md @@ -0,0 +1,37 @@ +# Functions: design and boundaries + +Aligned with **python-refactor** (functional core, imperative shell) and the project **Python** spec (clear control flow, minimal narrative comments). + +## Size and shape + +- One **obvious responsibility** per function; long procedures become **named steps** or small private helpers. +- Prefer **pure** functions in the core (deterministic from inputs; no hidden globals). Put **I/O**, **time**, **randomness**, and **global config** at the edges. + +## Signatures + +- **Explicit parameters** over catch-all `*args` unless wrapping or forwarding. +- **`**kwargs`**: prefer **`Unpack[TypedDict]`** (PEP 692) for structured options when using modern typing; otherwise document keys strictly. +- **Returns**: prefer **single typed return**; multiple values as **named tuple**, **dataclass**, or **TypedDict** (see **python-refactor** agent for tuple smell). + +## Errors + +- Raise **specific exceptions**; avoid bare `except:`. +- Let exceptions carry **actionable messages**; use exception chaining (`raise … from e`) when re-raising. +- For expected failure modes, document **what callers should catch**. + +## Resources + +- **`with`** / context managers for files, locks, devices; **pyvisa** resource separation per the Python spec and rule. + +## Idioms + +- **`pathlib.Path`** for filesystem paths when the codebase already does; stay consistent within a module. +- **`if __name__ == "__main__":`** for CLI entrypoints; prefer **`uv run`** for execution in projects. + +## Types at the boundary + +- Annotate **public** functions completely; internals can lean on inference where **ty** still passes. Details: [reference-types-and-agents.md](reference-types-and-agents.md). + +## Delegation + +- **Structural** critique (too large, wrong abstraction): **`python-refactor`** agent. diff --git a/.cursor/skills/general-python/references/reference-scientific-numerics.md b/.cursor/skills/general-python/references/reference-scientific-numerics.md new file mode 100644 index 0000000..c8a6cb0 --- /dev/null +++ b/.cursor/skills/general-python/references/reference-scientific-numerics.md @@ -0,0 +1,29 @@ +# Scientific and lab defaults + +From the project **Python** spec (general guidelines and style). + +## Numerics + +- Prefer **vectorized** NumPy / SciPy (or array libraries the project uses) over Python loops on **large** arrays. +- Be explicit about **shapes**, **dtypes**, and **missing data**; watch silent **dtype widening** and **unstable reduction order** when it affects science or reproducibility. +- **`python-reviewer`** is the right agent for numerics footguns in review. +- Detailed NumPy patterns: **numpy-scientific** skill. + +## Tables and time series + +- **pandas**: explicit **index/column** semantics; know **views vs copies** for chained assignment. +- **polars**: prefer **lazy** frames when the codebase standardizes on Polars for heavy queries. + +Pick one table stack per module or project layer; do not mix idioms in one pipeline without a boundary. + +- Combined guidance: **dataframes** skill. + +## Instruments (PyVISA, sockets, lab I/O) + +- Separate **resource open/close and configuration** from **command strings** and parsing. +- Use **context managers** or explicit lifecycle so handles are not leaked across tests. +- Full patterns: **lab-instrumentation** skill (PyVISA sessions, sockets vs VISA, HALs, validation, PDF manuals, offline tests). + +## Plotting + +- Use the **`matplotlib-scientific`** skill for figure quality; keep analysis modules free of ad hoc `plt` state when a library API is expected. diff --git a/.cursor/skills/general-python/references/reference-testing-docs.md b/.cursor/skills/general-python/references/reference-testing-docs.md new file mode 100644 index 0000000..a3466e9 --- /dev/null +++ b/.cursor/skills/general-python/references/reference-testing-docs.md @@ -0,0 +1,29 @@ +# Testing and documentation + +From the project **Python** spec and **Python Cursor rule**. + +## pytest + +- Install in **dev** group: **`uv add pytest --dev`** (or project group). +- Run: **`uv run pytest`**; narrow with path or `-k` as needed. +- Prefer **fast, deterministic** unit tests; isolate **I/O** and **flaky timing** in markers or separate jobs when the team agrees. + +## Docstrings + +- **NumPy style** on **public** APIs: Parameters, Returns, Raises, Examples when they clarify non-obvious contracts. +- Keep **implementations** readable **without** step-by-step narrative comments (per Python spec / rule). +- Section-by-section guide: **numpy-docstrings** skill. + +## What to test + +- **Pure logic**: parametrized cases and edge values. +- **Bug fixes**: regression test that fails before the fix. +- **Numerics** (scientific code): dtype/shape expectations and stable reductions when the paper or spec demands it (**python-reviewer**). + +## Imports and package layout + +- Tests should import the **installed package** (`src` layout) so CI matches user installs. + +## Review + +- Before merge, use **`python-reviewer`** for uv + typing + tests + numerics in one pass. diff --git a/.cursor/skills/general-python/references/reference-tooling.md b/.cursor/skills/general-python/references/reference-tooling.md new file mode 100644 index 0000000..620541c --- /dev/null +++ b/.cursor/skills/general-python/references/reference-tooling.md @@ -0,0 +1,46 @@ +# Tooling: uv, Ruff, ty, project shape + +Aligned with the project **Python** spec (often in **AGENTS.md**) and the **Python Cursor rule**. + +## uv + +- **Add / remove**: `uv add `, `uv add --dev `, `uv add --group `, `uv remove `, `uv add --upgrade`. +- **Environment**: `uv sync` after lock or clone. +- **Run**: `uv run python …`, `uv run pytest`, `uv run ruff check .`, `uv run ty check`. +- **Do not** edit dependency version pins in `pyproject.toml` by hand; use **`uv add`** so the lockfile stays truthful. +- Docs: [uv](https://docs.astral.sh/uv/latest/). + +## Ruff + +- **Lint**: `ruff check` (or `uv run ruff check`). Fix what the project enables (E/F/I/B/UP/… per `pyproject.toml`). +- **Format**: `ruff format` when the repo uses Ruff as formatter. +- Treat **project config as law** over generic PEP 8 advice. +- Docs: [Ruff](https://docs.astral.sh/ruff/). + +## ty + +- **Typecheck**: `ty check` (or `uv run ty check`) on the project or paths you changed. +- Ensure **ty** and **ruff** stay in the **dev** dependency group per spec. +- Docs: [ty](https://docs.astral.sh/ty/). + +## Typical verification sequence + +1. `ruff check` (and format if applicable) +2. `ty check` +3. `uv run pytest` + +Order may vary; fix **ruff** before **ty** when both report the same line. + +## Project layout (greenfield or refactors) + +- Prefer **`src//`** layout so tests and tooling import the package the same way users do (avoids accidental imports from repo root). Pair with **`pyproject.toml`** (PEP 621). +- Keep **lockfile** (`uv.lock`) committed when the team relies on reproducible installs. + +## Automation + +- **pre-commit** or CI running ruff + ty + pytest catches drift early (common 2024–2025 practice; align with team policy). + +## Delegation + +- Broad **review** after substantive edits: **`python-reviewer`** agent. +- Heavy **annotation** passes: **`python-types`** agent. diff --git a/.cursor/skills/general-python/references/reference-types-and-agents.md b/.cursor/skills/general-python/references/reference-types-and-agents.md new file mode 100644 index 0000000..491f672 --- /dev/null +++ b/.cursor/skills/general-python/references/reference-types-and-agents.md @@ -0,0 +1,35 @@ +# Types and Astral ty + +This file is the **bridge** between day-to-day edits and the **`python-types`** agent. Do not duplicate the full typing playbook here. + +## Project rule + +- **`ty check`** is the type gate; configuration lives in **`pyproject.toml`**. +- Prefer fixing types over **`# type: ignore`**; if unavoidable, **one line**, narrow code, and state **why**. + +## Boundaries + +- **Public** functions and methods: annotate **parameters and return**. +- **Locals**: rely on inference when obvious (`x = []` may need `list[T]` or construction that fixes `T`). +- Prefer **`collections.abc`** (`Sequence`, `Mapping`, `Iterable`) for inputs you only read. + +## Python 3.12+ (when helpful) + +- **PEP 695** `type` aliases and `def f[T](...)`. +- **PEP 692** `Unpack[TypedDict]` for kwargs. +- **PEP 698** `@override` on overrides. +- **PEP 688** `Buffer` over deprecated `ByteString` patterns. + +Reference: [What is new in Python 3.12](https://docs.python.org/3/whatsnew/3.12.html). + +## Exhaustive discrimination + +- Use **`match` / `case`** so **ty** can verify coverage; **`assert_never`** for impossible tails after exhaustive matches. See **`python-types`** and [match exhaustiveness](https://dogweather.dev/2022/10/03/i-discovered-that-python-now-can-do-true-match-exhaustiveness-checking/). + +## When to invoke **python-types** + +- Large refactors to protocols/generics, fixing many **ty** errors, or aligning modules with **PEP 695** and **`match`** exhaustiveness. + +## Ruff and types + +- **Ruff** may flag typing imports (`TCH`, `UP`); keep **runtime** vs **TYPE_CHECKING** imports consistent with project rules. diff --git a/.cursor/skills/general/SKILL.md b/.cursor/skills/general/SKILL.md new file mode 100644 index 0000000..c5f6c73 --- /dev/null +++ b/.cursor/skills/general/SKILL.md @@ -0,0 +1,16 @@ +--- +author: dotagents +name: general +description: Applies cross-language defaults for scientific/engineering work, documentation discipline, non-lazy execution, and effective use of rules, skills, and documentation tools. +--- + +When this skill applies: + +- Keep diffs minimal and aligned with the stated specification; extend or reuse existing abstractions before introducing new ones. +- Produce complete code paths rather than templates or ellipses; finish the requested slice end-to-end when the user asks for an implementation. +- Document public library surfaces with prescriptive contracts (parameters, results, errors, stability or units when relevant). Keep private helpers thinly documented. +- Add or refine module-level intent so each library area states scope, non-goals, and invariants. +- Use available agent skills, project rules, and MCP documentation retrieval for unfamiliar APIs instead of guessing. +- On tool or command failure, troubleshoot and retry sensibly rather than stopping at the first error without analysis. +- Avoid emoji in technical artifacts unless the user explicitly requests them. +- Avoid unsolicited markdown documentation; prefer code and tests as the source of truth. diff --git a/.cursor/skills/lab-instrumentation/SKILL.md b/.cursor/skills/lab-instrumentation/SKILL.md new file mode 100644 index 0000000..04b3768 --- /dev/null +++ b/.cursor/skills/lab-instrumentation/SKILL.md @@ -0,0 +1,54 @@ +--- +author: dotagents +name: lab-instrumentation +description: PyVISA and lab instrument control in Python: VISA resource lifecycle, timeouts and terminations, raw TCP/UDP sockets vs VISA, when to introduce hardware abstraction layers, validating user and config input before hardware, testing without hardware, and extracting tables or text from instrument manuals and datasheets (PDF). Triggers on pyvisa, VISA, GPIB, USB-TMC, serial instrument, socket lab, SCPI, datasheet PDF, instrument driver. +--- + +# Lab instrumentation (PyVISA, sockets, HAL) + +## Quick start + +1. **Split layers**: one place owns **open/configure/close** and **timeouts**; another owns **SCPI or device protocol** (string formatting, parsing floats/units, error queues). See [reference-pyvisa-visa.md](references/reference-pyvisa-visa.md). +2. **Pick transport deliberately**: **VISA** when the stack already provides enumeration, buffering, and vendor backends; **plain sockets** when you own framing, keep-alive, and binary protocols end to end. See [reference-socket-comms.md](references/reference-socket-comms.md). +3. **Add a HAL when** you have **multiple models** behind one experiment API, **two transports** for the same logical device, or **tests** that need a stable seam. Do not wrap a single serial string in three classes. See [reference-hardware-abstraction.md](references/reference-hardware-abstraction.md). +4. **Validate before I/O**: normalize and bound **numeric setpoints**, **enum-like modes**, and **user-entered strings**; reject unknown commands early with **actionable errors**. See [reference-input-validation-errors.md](references/reference-input-validation-errors.md). +5. **PDFs**: use **text-layer** extraction first; fall back to **table-aware** tools for spec tables; reserve **OCR** for scan-only manuals. See [reference-pdf-datasheets.md](references/reference-pdf-datasheets.md). +6. **Tests**: default **fast** unit tests on parsers and validators; gate **integration** tests; use **fakes** or **recorded transcripts** for CI. See [reference-testing-mocks.md](references/reference-testing-mocks.md). + +## Stack synergy + +| Resource | Role | +|----------|------| +| **general-python** | uv, context managers, **`python-reviewer`** | +| **numpy-scientific** | Numeric buffers, waveforms, dtype-safe binary payloads | +| **numpy-docstrings** | Public driver and session APIs | + +## Reference index + +| Topic | File | +|--------|------| +| ResourceManager, sessions, timeouts, terminations, SCPI hygiene | [reference-pyvisa-visa.md](references/reference-pyvisa-visa.md) | +| TCP/UDP sockets, framing, timeouts, binary vs text | [reference-socket-comms.md](references/reference-socket-comms.md) | +| When to build HALs, protocols vs transports, registries | [reference-hardware-abstraction.md](references/reference-hardware-abstraction.md) | +| Ranges, enums, command allowlists, error shape | [reference-input-validation-errors.md](references/reference-input-validation-errors.md) | +| PDF text, tables, datasheets, bibliography PDFs, OCR | [reference-pdf-datasheets.md](references/reference-pdf-datasheets.md) | +| pytest, fakes, simulation, recordings | [reference-testing-mocks.md](references/reference-testing-mocks.md) | + +## Dependencies + +Add with **`uv add`** (do not hand-edit pins in `pyproject.toml`): + +| Need | Typical packages | +|------|------------------| +| VISA in Python | **`pyvisa`**; optional **`PyVISA-py`** for pure-Python backend without vendor IVI | +| Serial-backed VISA | Often **`pyserial`** alongside backend docs | +| PDF text | **`pymupdf`** (fitz), **`pypdf`**, or **`pdfplumber`** (tables) | +| Scanned PDFs | **`pdf2image`** + **`pytesseract`** when OCR is unavoidable | + +## Official and canonical docs + +- [PyVISA documentation](https://pyvisa.readthedocs.io/en/latest/) +- [Python `socket` module](https://docs.python.org/3/library/socket.html) +- [PyMuPDF documentation](https://pymupdf.readthedocs.io/) +- [pdfplumber](https://github.com/jsvine/pdfplumber) +- [pypdf](https://pypdf.readthedocs.io/) diff --git a/.cursor/skills/lab-instrumentation/references/reference-hardware-abstraction.md b/.cursor/skills/lab-instrumentation/references/reference-hardware-abstraction.md new file mode 100644 index 0000000..651ad60 --- /dev/null +++ b/.cursor/skills/lab-instrumentation/references/reference-hardware-abstraction.md @@ -0,0 +1,27 @@ +# Hardware abstraction classes + +## When a HAL pays off + +- **Multiple instrument models** implement the same experiment step (e.g. three power supply families under **`set_voltage(channel, volts)`**). +- **Two transports** for one logical device (**USB** vs **Ethernet** simulation) and you want one **experiment** module. +- **Unit tests** must run without drivers: you need a **`Protocol`** or **ABC** to **fake** behind stable methods. +- **Shared cross-cutting** behavior: logging, rate limits, **mutex** around non-reentrant firmware, **idempotency** after **`clear`**. + +## When to skip + +- **One device**, **one script**, **one resource string**: a **single module** with **functions** and a **`with`** block is simpler than a **registry** and **abstract base**. +- **SCPI one-offs** in a notebook: keep **thin** wrappers; extract a class only when the same commands appear in **three** places. + +## Shape of a good HAL + +- **`Instrument` protocol** (or ABC): **`connect()`**, **`disconnect()`**, **`idn()`**, **`reset()`** if applicable, and **domain methods** (`measure_voltage`, `arm_trigger`)—not raw **`write`** on the public surface unless the layer is explicitly **low-level**. +- **`Transport` vs `ProtocolHandler`**: **transport** reads bytes; **handler** turns bytes into **typed** results. Lets you test **parsing** without **hardware**. +- **Composition over deep inheritance**: **`PowerSupply`** wraps a **`VisaSession`** or **`TcpSession`**; avoid diamond hierarchies across vendors. + +## Configuration + +- **Model string** or **resource string** from **config file** or **env**; validate at startup. **Fail fast** if **`idn`** does not match **expected prefix** for safety interlocks. + +## Discovery and plugins + +- Optional **registry** mapping **`model_id` → class`** when you ship many drivers in one package; keep registration **explicit** (import side effects are hard to test). diff --git a/.cursor/skills/lab-instrumentation/references/reference-input-validation-errors.md b/.cursor/skills/lab-instrumentation/references/reference-input-validation-errors.md new file mode 100644 index 0000000..59fc171 --- /dev/null +++ b/.cursor/skills/lab-instrumentation/references/reference-input-validation-errors.md @@ -0,0 +1,30 @@ +# Validating user and config input before hardware + +## Principles + +- **Validate at boundaries**: CLI, REST body, **GUI field**, or **YAML/JSON** config—before any **`write`** to hardware. +- **Reject early**: unknown **mode strings**, **out-of-range** voltages, or **wrong units** should raise **typed exceptions** with **what failed**, **allowed range**, and **observed value**—not a raw **`VisaIOError`** after the fact. + +## Numeric setpoints + +- Parse with **explicit types** (`Decimal` for money-like precision, **`float`** only when the manual specifies float-friendly steps). Compare to **min/max** from **datasheet** or **constants** in code, not magic numbers scattered across callers. +- **Quantize** to instrument resolution when the device **snaps** to steps (e.g. **1 mV** steps); document rounding **toward zero** vs **nearest**. + +## Command and mode strings + +- Prefer **`Literal`** or **`Enum`** in public APIs instead of **free-form** strings for **SCPI subsystems**. +- If users type SCPI, maintain an **allowlist** or **prefix allowlist**; never pass unchecked input to **`write`** when it can reach **shells** or **file paths** on controllers. + +## Resource strings and hosts + +- Validate **`host:port`** formats, **IP literals**, and **resource strings** with **regex or urllib** patterns before **`open_resource`** or **`connect`**. +- **Path traversal** matters for **save/recall** instrument setups that use filenames on the controller. + +## Error design + +- Use **small exception types** (`OutOfRange`, `UnknownMode`, `InstrumentFault`) that carry **context**; map **VISA status** and **socket errno** into them at the **transport edge** so experiment code stays readable. +- For operator-facing messages, separate **log detail** (full traceback internally) from **user text** (one sentence + fix hint). + +## Idempotency + +- **Repeated “set 5 V”** should be safe; **“arm”** may not be—document which operations are **idempotent** in the HAL docstrings. diff --git a/.cursor/skills/lab-instrumentation/references/reference-pdf-datasheets.md b/.cursor/skills/lab-instrumentation/references/reference-pdf-datasheets.md new file mode 100644 index 0000000..7856e01 --- /dev/null +++ b/.cursor/skills/lab-instrumentation/references/reference-pdf-datasheets.md @@ -0,0 +1,38 @@ +# PDF parsing for datasheets, manuals, and reference PDFs + +## Strategy + +1. **Detect text vs scan**: open the PDF and try **text extraction** on a sample page. If output is empty or garbage, the file is likely **image-only**—plan **OCR** or obtain a **text** export from the vendor. +2. **Preserve structure**: tables need **table-aware** tools; body text needs **blocks** with **bbox** when aligning columns. +3. **Ground truth**: for critical limits (max voltage, derating), **cross-check** against a **second source** (vendor HTML or printed table)—PDF extraction is **lossy**. + +## Libraries (Python) + +| Goal | Typical choice | Notes | +|------|----------------|-------| +| Fast text + layout | **[PyMuPDF](https://pymupdf.readthedocs.io/)** (`fitz`) | **`get_text("dict")`** for blocks/lines; good speed | +| Tables in datasheets | **[pdfplumber](https://github.com/jsvine/pdfplumber)** | **`extract_tables()`** tuned with **`table_settings`** | +| Lightweight merge/split/metadata | **[pypdf](https://pypdf.readthedocs.io/)** | Good for **page ranges** and **bookmarks**, weaker on messy tables | +| Scanned pages | **`pdf2image`** + **`pytesseract`** | Tune **DPI** (often 300+); expect **manual cleanup** | + +## Datasheet workflows + +- **Extract spec tables** (temperature range, absolute maxima) into **structured rows** (CSV/Parquet) with **column headers** normalized once in code. +- **Version** the PDF (filename hash or vendor revision field) and **store** it beside extracted data so results are **reproducible**. + +## Reference books and papers + +- **Bibliography PDFs**: often **two-column**; use **block sorting** by **y, x** (PyMuPDF dict mode) before regex for **DOI**, **ISBN**, or **citation keys**. +- **Equations and figures**: extraction will not reliably capture math; link **page number** + **figure ID** instead of parsing **LaTeX** from raster. + +## Pitfalls + +- **Hyphenation** and **line breaks** split words across lines—normalize whitespace and **de-hyphenate** cautiously. +- **Embedded fonts** and **subset encoding** can map glyphs oddly; if strings look wrong, try **`get_text("text")`** vs **dict** mode or another backend. +- **Legal**: scraping **paywalled** PDFs may violate terms; only process files you **have rights** to use in your pipeline. + +## Further reading + +- PyMuPDF [tutorial / text](https://pymupdf.readthedocs.io/en/latest/tutorial.html) +- pdfplumber [README examples](https://github.com/jsvine/pdfplumber/blob/stable/README.md) +- pypdf [user guide](https://pypdf.readthedocs.io/en/stable/user/introduction.html) diff --git a/.cursor/skills/lab-instrumentation/references/reference-pyvisa-visa.md b/.cursor/skills/lab-instrumentation/references/reference-pyvisa-visa.md new file mode 100644 index 0000000..135244d --- /dev/null +++ b/.cursor/skills/lab-instrumentation/references/reference-pyvisa-visa.md @@ -0,0 +1,34 @@ +# PyVISA and VISA sessions + +## Architecture + +- Use a single **`ResourceManager`** (or inject one) per process for address parsing and backend selection; avoid creating managers inside tight loops. +- Open resources with explicit **resource strings** (`TCPIP0::host::inst0::INSTR`, `USB0::...`, `ASRL3::INSTR`, etc.) and keep the **open handle** lifetime obvious: **`with rm.open_resource(...) as inst:`** or a small **`InstrumentSession`** class with **`close()`** in **`__exit__`**. + +## Lifecycle vs protocol + +- **Lifecycle**: `open_resource`, **`baud_rate`**, **`data_bits`**, **`parity`**, **`stop_bits`** for serial; **`read_termination`** / **`write_termination`**; **`timeout`** (milliseconds in PyVISA). Set these once after open, not per scattered call site. +- **Protocol**: functions or a **`SCPIClient`** that only **`write`**, **`query`**, **`read_raw`**, and parse responses. Never embed **`open_resource`** inside a “send SCPI” helper. + +## Timeouts and partial reads + +- Every blocking **`read`** must respect **`timeout`**. For binary payloads with known length, prefer **`read_bytes(count)`** (or read until delimiter) instead of unbounded **`read`**. +- After errors, many instruments need **`clear()`** or a **`*CLS`** / device-specific recovery sequence before the bus is trustworthy again—document that path in one place. + +## Terminations and encoding + +- SCPI text is usually **7-bit ASCII** with **`\\n`** termination. Match **`read_termination`** / **`write_termination`** to the manual; mixed **`\\r\\n`** gear is common on serial. +- For **`read_raw`**, you own **length** and **endianness**; do not apply text terminations. + +## Queries and staleness + +- Prefer **`query(message)`** for “write then read line” patterns to avoid half-open transactions. +- If the instrument can **async** complete (long sweeps), either poll **`*OPC?`** / status registers per manual or block with a **longer timeout** on the final read—do not chain short reads that race completion. + +## Backends + +- **NI-VISA** (vendor stack) vs **`PyVISA-py`**: choose per deployment; CI often uses **`PyVISA-py`** or simulation. Document which backend the project expects in **`README`** or config. + +## Logging and safety + +- Log **resource string** (sanitized if it embeds secrets) and **high-level command names**, not passwords. Avoid logging full **waveform blobs** at info level. diff --git a/.cursor/skills/lab-instrumentation/references/reference-socket-comms.md b/.cursor/skills/lab-instrumentation/references/reference-socket-comms.md new file mode 100644 index 0000000..40716c3 --- /dev/null +++ b/.cursor/skills/lab-instrumentation/references/reference-socket-comms.md @@ -0,0 +1,32 @@ +# Socket communication for lab gear + +## When sockets instead of VISA + +- **Custom binary framing**, **multicast**, **UDP discovery**, or **non-VISA** Ethernet devices (raw TCP on a port) are easier with **`socket`** or **`asyncio`** streams than with VISA resource strings. +- **VISA** still wins when you need **USB-TMC**, **GPIB**, **serial via VISA**, or a single **enumeration** story across buses. + +## Client patterns + +- Prefer **`socket.create_connection((host, port), timeout=...)`** over manual **`connect`** when you want **fail-fast** timeouts and **IPv4/IPv6** handling. +- Set **`sock.settimeout(...)`** for subsequent operations unless you use non-blocking **`asyncio`**. +- Use **`contextlib.closing`** or a small wrapper so **`close()`** runs on error paths. + +## Framing + +- **Line-oriented** text: read with **`recv`** until delimiter, or use **`readline`** on a **`makefile`** wrapper—watch **blocking** if the peer never sends the delimiter. +- **Length-prefixed** binary: read **header** (fixed bytes), parse **length**, then **`recv`** until **`n`** bytes; loop because **`recv`** may return **partial** chunks. +- **Struct pack/unpack** for fixed layouts; document **endianness** (`"<"` vs `">"`) next to the struct format string. + +## Timeouts and shutdown + +- Distinguish **`ETIMEDOUT`** (no data) from **`ECONNRESET`**. Surface both with **clear exceptions** so operators know whether to retry or re-seat cabling. +- For clean shutdown, **`shutdown(SHUT_RDWR)`** before **`close`** when the protocol expects half-close behavior. + +## Threading and async + +- **`socket`** objects are not thread-safe for arbitrary interleaved **`send`/`recv`**; one thread per socket or a **queue-driven** writer/reader design. +- **`asyncio`** suits many **slow instruments**; keep **protocol state machines** explicit so partial reads do not corrupt state. + +## Security + +- **Lab LAN** is not trusted by default: validate **host allowlists**, avoid **command injection** into shells that restart services, and never expose raw socket servers to the open internet without authentication. diff --git a/.cursor/skills/lab-instrumentation/references/reference-testing-mocks.md b/.cursor/skills/lab-instrumentation/references/reference-testing-mocks.md new file mode 100644 index 0000000..034b01a --- /dev/null +++ b/.cursor/skills/lab-instrumentation/references/reference-testing-mocks.md @@ -0,0 +1,36 @@ +# Testing instrument code without hardware + +## Layers to test separately + +- **Parsing**: response strings to **`float`**, **unit stripping**, **status bit** interpretation—**pure functions**, **table-driven** pytest. +- **Validation**: range checks and **enum** mapping—no sockets. +- **Transport**: thin adapter that **`write`/`read`**; swap for **fake** in tests. + +## Fakes and protocols + +- Define a **`Protocol`** with **`query`**, **`write`**, **`read_raw`** matching your **session** surface; implement **`FakeInstrument`** with **canned** responses and **counters** to assert **call order**. +- For **stateful** gear, keep **minimal** state in the fake (**output on**, **last voltage**) so tests encode **realistic** sequences. + +## PyVISA simulation + +- **`pyvisa-sim`** (when compatible with your stack) can serve **YAML-defined** instruments for **integration-style** tests without benches. +- Vendor **simulators** and **socket echo** servers are acceptable **fixtures** if CI can start them **deterministically**. + +## Record and replay + +- Capture **transcripts** (command, response, timing hints) from a **golden** session; replay in tests to detect **protocol drift**. **Sanitize** secrets and **unique** serial numbers if committing files. + +## Markers and CI + +- Mark **slow** or **hardware-required** tests (`@pytest.mark.hardware`); default **`pytest`** in CI runs **offline** only. Document **`pytest -m hardware`** for the lab machine. + +## Socket tests + +- Use **pytest fixtures** with **ephemeral ports** (`bind(("", 0))`) and **short-lived** server threads or **`asyncio`** test servers; always **join** threads to avoid **flaky** teardown. + +Fix typo: **`pytest`**** fixtures** -> **pytest fixtures** + + + +<｜tool▁calls▁begin｜><｜tool▁call▁begin｜> +StrReplace diff --git a/.cursor/skills/matplotlib-scientific/SKILL.md b/.cursor/skills/matplotlib-scientific/SKILL.md new file mode 100644 index 0000000..358cd4a --- /dev/null +++ b/.cursor/skills/matplotlib-scientific/SKILL.md @@ -0,0 +1,70 @@ +--- +author: dotagents +name: matplotlib-scientific +description: Build publication-quality Matplotlib figures for scientific Python. Use when plotting experiment or analysis results, multi-panel figures, journal-sized layouts, tick formatting, legends (fancy/plain, placement, compact handles), point annotations, inset zooms, twin axes, colored spines/ticks, colorblind-safe palettes, PNG/PDF export, or SciencePlots styling. Triggers on matplotlib, pyplot, subplots, savefig, legend, annotate, inset, twinx, scientific figures, panel labels. +--- + +# Matplotlib for scientific figures + +## Quick start + +1. Use the **object-oriented API**: create `fig, ax = plt.subplots()` (or `subplot_mosaic` / `GridSpec`), then call methods on `ax`. Avoid `plt.plot` when multiple axes exist; implicit pyplot targets the wrong axis by default. See [Practical Data Science: explicit vs implicit syntax](https://www.practicaldatascience.org/notebooks/class_5/week_1/1.4.5_explicit_vs_implicit_syntax.html) and [basic plotting](https://www.practicaldatascience.org/notebooks/class_5/week_1/1.2.1_basic_plotting_with_matplotlib.html). +2. **Name every quantitative axis** with units in the label (e.g. `Time (s)`, `Voltage (V)`). Match tick style to audience: disable offset/scientific clutter when plain decimals read better; use scientific or engineering notation when magnitudes span orders. Details: [reference-axes-ticks.md](references/reference-axes-ticks.md). +3. **Save deliberately**: high DPI raster (often 300–600) for PNG; `bbox_inches="tight"` when labels are clipped; **`transparent=True`** for PNG overlays and slides. Prefer vector PDF/SVG for final print when the journal allows. See [reference-export-naming.md](references/reference-export-naming.md). +4. **Panel letters** `(a)`, `(b)` for multi-panel figures: consistent placement, bold, same size as body or slightly larger. See [Matplotlib: labelling subplots](https://matplotlib.org/stable/gallery/text_labels_and_annotations/label_subplots.html) and [reference-axes-ticks.md](references/reference-axes-ticks.md). +5. **Figure width**: size figures to **single-column** or **full-page / double-column** width per target venue; do not default to square notebook sizes for papers. See [reference-journal-layout.md](references/reference-journal-layout.md) and [Simplified Science: figure design rules](https://www.simplifiedsciencepublishing.com/resources/how-to-make-good-figures-for-scientific-papers). +6. **Composable code**: write helpers that **accept `Axes` or `Figure` and return** the same object after mutation. See [reference-api-patterns.md](references/reference-api-patterns.md). +7. **Polish**: match legend frame style to venue (plain for print, slightly rounded for slides); place legends where they do not obscure data; annotate only points the text discusses; use insets and twin axes only when they sharpen the claim. See [reference-legend-style.md](references/reference-legend-style.md), [reference-annotations-insets-twins.md](references/reference-annotations-insets-twins.md), [reference-axes-appearance.md](references/reference-axes-appearance.md). + +## Stack synergy + +| Resource | Role | +|----------|------| +| **general-python** | uv, **ty**, export scripts, **python-reviewer** | +| **numpy-scientific** | Array inputs to plots | +| **dataframes** | `df.plot(ax=ax)` and Polars-to-pandas at plot boundaries | +| **numpy-docstrings** | Docstrings for plotting helpers and figure builders | + +## Reference index (load the section you need) + +| Topic | File | +|--------|------| +| Subplots vs `GridSpec` vs `subplot_mosaic`; assembling panels | [reference-layout.md](references/reference-layout.md) | +| Axis labels, units, ticks, scientific notation, panel labels | [reference-axes-ticks.md](references/reference-axes-ticks.md) | +| Spine/tick/label color, twin-axis styling | [reference-axes-appearance.md](references/reference-axes-appearance.md) | +| Legends: fancy frame, title, placement, compact handles | [reference-legend-style.md](references/reference-legend-style.md) | +| Point annotations, inset zooms, twin / secondary axes | [reference-annotations-insets-twins.md](references/reference-annotations-insets-twins.md) | +| Color maps, cycles, colorblind safety, data types | [reference-color.md](references/reference-color.md) | +| `savefig`, transparency, DPI, auto-increment filenames | [reference-export-naming.md](references/reference-export-naming.md) | +| Journal widths, composition, storytelling | [reference-journal-layout.md](references/reference-journal-layout.md) | +| SciencePlots / style sheets; `pandas` `.plot(ax=ax)` | [reference-styles-pandas.md](references/reference-styles-pandas.md) | +| Functions that take/return `Axes` / `Figure` | [reference-api-patterns.md](references/reference-api-patterns.md) | +| Rasterized PDF, `align_labels`, CI, memory | [reference-advanced.md](references/reference-advanced.md) | + +## Best-practice topics (checklist) + +- **Chart choice**: match geometry to the claim (trend vs part-to-whole vs distribution). Same data, many encodings: [Plotting Zoo](https://www.practicaldatascience.org/notebooks/class_5/week_1/1.5.1_plotting_zoo.html). +- **Anatomy**: figure, axes, marks, axis labels, ticks and tick labels, limits, grid, legend, title, spines; compact `ax.set(...)`. See [A figure in 10 pieces](https://www.practicaldatascience.org/notebooks/class_5/week_1/1.2.2_ten_figure_pieces.html). +- **Layering**: set `zorder` and distinct `color` when mixing `plot` / `scatter` / `bar` on one axes ([basic plotting](https://www.practicaldatascience.org/notebooks/class_5/week_1/1.2.1_basic_plotting_with_matplotlib.html)). +- **Legends**: plain frame and no shadow for most papers; `bbox_to_anchor` outside or `ncol` below when the data region is crowded ([reference-legend-style.md](references/reference-legend-style.md)). +- **Annotations**: few, uniform `annotate` styling; insets for zoom only when the story needs local detail ([reference-annotations-insets-twins.md](references/reference-annotations-insets-twins.md)). +- **Axes color**: tie spine/tick/label color to a twin axis accent; keep grids low-contrast ([reference-axes-appearance.md](references/reference-axes-appearance.md)). +- **Ticks**: treat **locator** (where) and **formatter** (text) separately. See [Axis ticks](https://matplotlib.org/stable/users/explain/axes/axes_ticks.html) and [ScalarFormatter](https://matplotlib.org/stable/gallery/ticks/scalarformatter.html). +- **Reproducibility**: script generates the file on disk; version-control style choices; meeting previews use predictable auto-increment names ([reference-export-naming.md](references/reference-export-naming.md)). +- **Accessibility**: do not rely on color alone; use line styles / markers; check contrast; prefer perceptually uniform colormaps for continuous data ([reference-color.md](references/reference-color.md)). +- **SciencePlots** (optional): journal-oriented style sheets; often requires LaTeX. See [SciencePlots](https://github.com/garrettj403/SciencePlots) and [reference-styles-pandas.md](references/reference-styles-pandas.md). +- **Pandas quick plots**: `df.plot(..., ax=ax)` returns matplotlib artists; refine with `ax` methods. See [Plotting with Pandas](https://www.practicaldatascience.org/notebooks/class_5/week_1/1.6.1_plotting_with_pandas.html). + +## Matplotlib documentation (official) + +- [User guide / tutorials](https://matplotlib.org/stable/users/index.html) +- [Anatomy of a figure](https://matplotlib.org/stable/gallery/showcase/anatomy.html) +- [Arranging multiple Axes](https://matplotlib.org/stable/users/explain/axes/arranging_axes.html) (subplots, GridSpec, mosaic) +- [Constrained layout](https://matplotlib.org/stable/users/explain/axes/constrainedlayout_guide.html) and [tight layout](https://matplotlib.org/stable/users/explain/axes/tight_layout_guide.html) +- [Legend guide](https://matplotlib.org/stable/users/explain/axes/legend_guide.html), [Annotations](https://matplotlib.org/stable/tutorials/text/annotations.html) +- [Secondary axis](https://matplotlib.org/stable/gallery/subplots_axes_and_figures/secondary_axis.html), [Zoom inset](https://matplotlib.org/stable/gallery/subplots_axes_and_figures/zoom_inset_axes.html) +- [Figure.savefig](https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure.savefig) (API); workflow notes in [Saving to file (PDS)](https://www.practicaldatascience.org/notebooks/class_5/week_1/1.4.4_saving_to_file.html) + +## When not to overload one figure + +Split into **separate figures** when panels are reused across talks vs papers, or when layout constraints differ (poster vs manuscript). Compose in a layout script or document (LaTeX/Quarto) when the page design, not matplotlib, should own margins and alignment. diff --git a/.cursor/skills/matplotlib-scientific/references/reference-advanced.md b/.cursor/skills/matplotlib-scientific/references/reference-advanced.md new file mode 100644 index 0000000..bd9dbda --- /dev/null +++ b/.cursor/skills/matplotlib-scientific/references/reference-advanced.md @@ -0,0 +1,33 @@ +# Advanced publication topics (optional) + +## Vector files with heavy scatter + +- PDFs balloon when every point is a vector path. For large `scatter`, use **`rasterized=True`** on the collection or rasterize the axes in the PDF backend so data draw as image while text stays vector. + +## Label alignment across panels + +- After setting xlabels/ylabels on a grid, call **`fig.align_labels()`** so stacked panels line up when label lengths differ. + +## Font sizes and RC + +- Set **`axes.titlesize`**, **`axes.labelsize`**, **`xtick.labelsize`**, **`legend.fontsize`** once per style for consistent figures. SciencePlots and journal styles override many of these. + +## Secondary axes + +- `ax.secondary_xaxis` / `secondary_yaxis` for dual scales: document both units in axis labels or caption to avoid ambiguity. Patterns and twin-axis caution: [reference-annotations-insets-twins.md](reference-annotations-insets-twins.md). + +## 3D and polar + +- Use only when the geometry is inherently 3D or angular; avoid 3D for ranking bar charts. See Matplotlib gallery for projection setup. + +## Interactive vs static + +- **`plt.show()`** is for interactive sessions; batch pipelines should **`savefig`** and close figures (`plt.close(fig)`) to limit memory when generating many plots. + +## Testing plots + +- Smoke-test plotting code with **`matplotlib.use("Agg")`** in CI so no display server is required. + +## Accessibility metadata + +- For web exports, pair complex graphics with a **data table** or summary in the document; color alone must not carry exclusive meaning ([Simplified Science rules](https://www.simplifiedsciencepublishing.com/resources/how-to-make-good-figures-for-scientific-papers)). diff --git a/.cursor/skills/matplotlib-scientific/references/reference-annotations-insets-twins.md b/.cursor/skills/matplotlib-scientific/references/reference-annotations-insets-twins.md new file mode 100644 index 0000000..a33706e --- /dev/null +++ b/.cursor/skills/matplotlib-scientific/references/reference-annotations-insets-twins.md @@ -0,0 +1,85 @@ +# Point annotations, inset axes, twin axes + +## When to annotate data points + +Annotate **sparingly**: + +- **Named exemplars** (a specific run, date, or condition called out in the text). +- **Threshold crossings** or **regulatory limits** (horizontal/vertical reference lines plus one short label often beat many point labels). +- **Outliers** that the narrative discusses. + +Avoid labeling **every** point unless the figure is a small-N schematic; for dense scatter, prefer **interactive** exploration or a **table** supplement. + +## Consistent, readable point labels + +Use **`ax.annotate`** so text and arrow share one API; prefer **data coordinates** for `xy` and **offset points** for `xytext` so font size and zoom stay consistent. + +```python +ax.annotate( + "A", + xy=(x0, y0), + xytext=(8, 8), + textcoords="offset points", + ha="left", + va="bottom", + fontsize=9, + bbox=dict(boxstyle="round,pad=0.25", fc="white", ec="0.7", lw=0.5), + arrowprops=dict(arrowstyle="-", color="0.35", lw=0.6, shrinkA=0, shrinkB=2), +) +``` + +**Consistency rules across a figure:** + +- One **`fontsize`** and **`bbox`** style for all callouts in the same figure. +- **`offset points`** in the same direction (e.g. always up-right) unless avoiding overlap. +- For overlapping labels, **nudge** `xytext` or use **`adjustText`** (third-party) in exploratory work; for publication, **manually** place or reduce the number of labels. + +**Alternative without arrows:** `ax.text` in data coords when proximity is obvious. + +## Reference lines vs point annotations + +- Vertical/horizontal rules: `ax.axvline`, `ax.axhline` with **`label=`** and legend entry, or a short **`transform=blended`** label on the line. +- Keeps the main legend for series, not for every marker. + +## Twin axes (`twinx` / `twiny`) + +- **One twin** is standard when two quantities share **time or position** but differ in **unit or scale** (e.g. temperature and humidity vs depth). + +```python +ax2 = ax.twinx() +ax2.plot(x, y2, color="C1") +ax2.set_ylabel("Secondary quantity (unit)", color="C1") +ax2.tick_params(axis="y", colors="C1") +``` + +**Multiple scales on the same side** are cognitively heavy. Prefer: + +- **Normalizing** to one axis and stating the mapping in the caption, or +- **A second panel** (small multiples), or +- **`secondary_yaxis`** for an alternate **functional** scale of the same quantity (e.g. °C ↔ °F) rather than a third unrelated series. + +Official pattern: [Secondary axis](https://matplotlib.org/stable/gallery/subplots_axes_and_figures/secondary_axis.html). + +## Inset axes (zoom, context, detail) + +Use an **inset** when the main axes shows **context** and the inset shows **a zoom** or a **secondary view** (e.g. map overview + city blow-up). + +**Recommended API:** `ax.inset_axes(bounds)` where `bounds` is `[left, bottom, width, height]` in **axes coordinates** (0–1), or use `mpl_toolkits.axes_grid1.inset_locator.inset_axes` for size in **axes fraction / padding**. + +```python +axins = ax.inset_axes([0.55, 0.55, 0.4, 0.4]) +axins.plot(x, y) +axins.set_xlim(x0, x1) +axins.set_ylim(y0, y1) +ax.indicate_inset_zoom(axins, edgecolor="0.3") +``` + +- Match **line weights** and **colors** to the main panel unless the inset is a different modality. +- Add **ticks** on the inset; for tiny insets, fewer ticks are better than cluttered decimals. +- **`indicate_inset`** / **`indicate_inset_zoom`** links inset to region when helpful. + +Gallery: [Inset locator demo](https://matplotlib.org/stable/gallery/axes_grid1/inset_locator_demo.html), [Zoom inset axes](https://matplotlib.org/stable/gallery/subplots_axes_and_figures/zoom_inset_axes.html). + +## Z-order + +- Insets and annotations usually sit **above** data: set artist **`zorder`** or add them **after** plotting series. diff --git a/.cursor/skills/matplotlib-scientific/references/reference-api-patterns.md b/.cursor/skills/matplotlib-scientific/references/reference-api-patterns.md new file mode 100644 index 0000000..b2790cc --- /dev/null +++ b/.cursor/skills/matplotlib-scientific/references/reference-api-patterns.md @@ -0,0 +1,62 @@ +# API patterns: functions that accept and return `Axes` / `Figure` + +## Principle + +Plotting helpers should be **pure in intent**: given the same data and axes, they draw the same artists. Side effects stay on the **`Axes`** you pass in. Return the **`Axes`** (or **`Figure`**) so callers can chain or save. + +## Recommended signatures + +```python +from matplotlib.axes import Axes +from matplotlib.figure import Figure + +def plot_waveform(ax: Axes, t, y, *, label: str | None = None) -> Axes: + ax.plot(t, y, label=label) + ax.set_xlabel("Time (s)") + ax.set_ylabel("Amplitude (V)") + return ax + +def finalize_figure(fig: Figure) -> Figure: + fig.align_labels() + return fig +``` + +## Optional axes + +- If `ax is None`, create a figure and axes; otherwise draw on the provided axes: + +```python +def plot_spectrum(data, *, ax: Axes | None = None) -> tuple[Figure, Axes]: + if ax is None: + fig, ax = plt.subplots(layout="constrained") + else: + fig = ax.figure + ax.plot(data.f, data.psd) + ax.set_xlabel("Frequency (Hz)") + return fig, ax +``` + +## Typing + +- Annotate with **`Axes`** and **`Figure`** from `matplotlib.axes` / `matplotlib.figure` for static checking (project may use `ty` / pyright). + +## Composition + +- **Higher-level** functions call lower-level ones, always threading `ax`: + +```python +def plot_panel(ax: Axes, dataset) -> Axes: + plot_waveform(ax, dataset.t, dataset.y, label="signal") + ax.legend() + return ax +``` + +## Anti-patterns + +- Relying on **`plt.gca()`** inside library code. +- Mixing **`plt.plot`** and passed **`ax`** in the same module without `plt.sca`. +- Creating a **new figure** inside a helper without returning it, leaving callers unable to save. + +## Layout-aware helpers + +- After adding colorbars or twin axes, call **`fig.align_labels()`** or rely on **`layout="constrained"`** so panel labels stay aligned across a row. diff --git a/.cursor/skills/matplotlib-scientific/references/reference-axes-appearance.md b/.cursor/skills/matplotlib-scientific/references/reference-axes-appearance.md new file mode 100644 index 0000000..4c6dc9c --- /dev/null +++ b/.cursor/skills/matplotlib-scientific/references/reference-axes-appearance.md @@ -0,0 +1,51 @@ +# Spine, tick, and label color (cohesive axes styling) + +## Why color axes elements + +Use **one accent color per y-axis** when that axis encodes a **single physical quantity** (especially with **`twinx`**) so readers bind ticks, label, and spine to the correct series. Keep **x-axis** styling neutral unless the bottom axis is also doubled. + +## Spines + +```python +accent = "C0" +ax.spines["left"].set_color(accent) +ax.spines["bottom"].set_color("0.25") +ax.spines["top"].set_visible(False) +ax.spines["right"].set_visible(False) +ax.spines["left"].set_linewidth(1.0) +``` + +- **Muted** spine color (`0.2`–`0.4` grayscale or desaturated brand) reads more “scientific” than pure black on white. +- Match **spine color** to the **same-axis** tick and label color when emphasizing a twin. + +## Ticks and tick labels + +```python +ax.tick_params(axis="y", colors=accent, which="both") +ax.tick_params(axis="x", colors="0.25", which="both") +ax.yaxis.label.set_color(accent) +ax.xaxis.label.set_color("0.25") +``` + +- **`which="both"`** applies to major and minor ticks when minors are on. +- Minor ticks: `ax.minorticks_on()` then tune length/width via `tick_params`. + +## Axis labels and titles + +- **`ax.xaxis.label.set_color`** / **`ax.yaxis.label.set_color`** align label with spine accent. +- **Title** color: default body color or slightly darker; avoid competing with data saturation. + +## Grid vs colored spines + +- If **`ax.grid(True)`** is on, keep grid **low contrast** (`alpha=0.25–0.35`, light grey) so colored spines still read as the primary frame. + +## Dark backgrounds (slides) + +- Invert logic: light spines and labels on dark **`figure.facecolor`**; test **contrast** for projectors. +- Legend **`facecolor`** should match slide background or stay opaque white for readability. + +## Matplotlib references + +- [Spines](https://matplotlib.org/stable/api/spines_api.html) +- [tick_params](https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.tick_params.html) +- [Colorblind considerations](reference-color.md) still apply: color on spines must not be the only discriminator between series. diff --git a/.cursor/skills/matplotlib-scientific/references/reference-axes-ticks.md b/.cursor/skills/matplotlib-scientific/references/reference-axes-ticks.md new file mode 100644 index 0000000..7231d1e --- /dev/null +++ b/.cursor/skills/matplotlib-scientific/references/reference-axes-ticks.md @@ -0,0 +1,62 @@ +# Axes labels, units, ticks, notation, panel labels + +## Axis labels and units + +- Put **quantity and unit** in the label: `ax.set_xlabel("Time (ms)")`, `ax.set_ylabel("Power spectral density (V^2/Hz)")`. +- Use **consistent SI style** across a figure set; square brackets for unit-only annotations are acceptable where your field expects them (e.g. `Voltage [V]`). +- For dimensionless quantities, state it: `Strouhal number` or `Normalized frequency (f / f_0)`. + +## The ten pieces (mental model) + +Figure container, axes, marks (line/scatter/bar), **x/y labels**, **ticks and tick labels**, **limits**, grid, legend, title, spines. Prefer **`ax.set(...)`** for bulk updates when it improves readability. See [A figure in 10 pieces](https://www.practicaldatascience.org/notebooks/class_5/week_1/1.2.2_ten_figure_pieces.html). + +## Ticks: locators and formatters + +- **Positions**: `ax.set_xticks(...)`, `MultipleLocator`, `MaxNLocator`, `LogLocator`, etc. +- **Strings**: `ax.set_xticklabels(...)`, or set a `Formatter` on `ax.xaxis`. +- Default numeric formatter is **`ScalarFormatter`**. Useful controls: + - `ax.ticklabel_format(style="plain", axis="y")` to discourage scientific notation on an axis when values are human-scaled. + - `ax.ticklabel_format(style="sci", axis="y", scilimits=(0, 0))` when all decades should use scientific form. + - `ax.yaxis.get_major_formatter().set_useOffset(False)` to suppress the offset term when it confuses readers. +- For wide dynamic range on linear axes, consider **log scale** (`ax.set_yscale("log")`) instead of cramming exponents into tick labels. +- Deep reference: [Axis ticks](https://matplotlib.org/stable/users/explain/axes/axes_ticks.html), [ScalarFormatter demo](https://matplotlib.org/stable/gallery/ticks/scalarformatter.html). + +## Panel labels (a), (b), (c) + +- Convention: **bold**, upper or lower case per venue, **same font size** across panels, placed consistently (often **top-left** inside axes using axes coordinates). +- Official patterns: [Labelling subplots](https://matplotlib.org/stable/gallery/text_labels_and_annotations/label_subplots.html) (`annotate`, `text` with transforms, or title `loc="left"`). +- Minimal loop pattern: + +```python +import string +for ax, tag in zip(axs.flat, string.ascii_lowercase): + ax.text( + 0.02, + 0.98, + f"({tag})", + transform=ax.transAxes, + ha="left", + va="top", + fontweight="bold", + fontsize=11, + ) +``` + +- Adjust `(0.02, 0.98)` if constrained layout clips labels; use figure coordinates or `mpl_toolkits.axes_grid1` helpers if needed. + +## Legend (overview) + +- Pass **`label=`** to each plotting call, then `ax.legend(...)`. +- For crowded panels, **`bbox_to_anchor`** outside the axes preserves data ink ([ten pieces](https://www.practicaldatascience.org/notebooks/class_5/week_1/1.2.2_ten_figure_pieces.html)). +- Fancy vs plain frames, legend titles, placement above/below/outside, and compact handles: [reference-legend-style.md](reference-legend-style.md). + +## Mathtext and LaTeX + +- Mathtext: `ax.set_xlabel(r"$\omega$ (rad/s)")` without full LaTeX install. +- `plt.rcParams["text.usetex"] = True` requires a TeX system; **SciencePlots** often expects LaTeX. See [reference-styles-pandas.md](reference-styles-pandas.md). + +## Grid and spines + +- `ax.grid(True, alpha=0.3)` for read values without chartjunk. +- Hiding **top/right** spines is common for 2D scientific plots ([ten pieces](https://www.practicaldatascience.org/notebooks/class_5/week_1/1.2.2_ten_figure_pieces.html)). +- Colored spines, tick colors, and label colors (especially with **twin** axes): [reference-axes-appearance.md](reference-axes-appearance.md). diff --git a/.cursor/skills/matplotlib-scientific/references/reference-color.md b/.cursor/skills/matplotlib-scientific/references/reference-color.md new file mode 100644 index 0000000..0853516 --- /dev/null +++ b/.cursor/skills/matplotlib-scientific/references/reference-color.md @@ -0,0 +1,37 @@ +# Color, colormaps, and cycles for scientific data + +## Match encoding to data type + +| Data role | Prefer | +|-----------|--------| +| Ordered numeric (low to high) | Perceptually uniform sequential: `viridis`, `cividis`, `plasma`; avoid **jet** | +| Deviation from a reference | Diverging: `coolwarm`, `RdBu_r`; center the norm at the reference | +| Categories | Distinct hues; **limit palette** to the number of classes; add **markers or dashes** for grayscale safety | +| Uncertainty bands | Lower alpha or lighter fill; keep the central estimate high-contrast | + +## Colorblind safety and grayscale + +- Do not encode **only** with red vs green. Combine **hue + linestyle + marker**. +- Test a figure by **desaturating** or printing grayscale: the main comparison should survive. +- [SciencePlots](https://github.com/garrettj403/SciencePlots) includes cycles such as **bright** and **high-vis**; Paul Tol palettes are available as named styles. + +## Overlays on one axes + +- When mixing `plot`, `scatter`, and `bar`, set explicit **`color`** and **`zorder`** so categories do not hide each other ([basic plotting](https://www.practicaldatascience.org/notebooks/class_5/week_1/1.2.1_basic_plotting_with_matplotlib.html)). + +## Storytelling vs decoration + +- Per [Simplified Science figure rules](https://www.simplifiedsciencepublishing.com/resources/how-to-make-good-figures-for-scientific-papers), use **one or two accent colors** for the main claim and mute context series (grey). + +## Norms for images and fields + +- Use `TwoSlopeNorm` or `SymLogNorm` when data spans zero with outliers; document the norm in the caption. +- For maps and fields, state the **colorbar** label with units (`imshow(...); fig.colorbar(im, ax=ax, label="K")`). + +## Defaults + +- Set **`axes.prop_cycle`** via a style sheet rather than hard-coding colors in every script when the project has a house style. + +## Coordinated axis color + +- When a series and its **y-axis** share an accent (e.g. after `twinx`), align **spine, ticks, and axis label** to that color without breaking grayscale legibility. See [reference-axes-appearance.md](reference-axes-appearance.md). diff --git a/.cursor/skills/matplotlib-scientific/references/reference-export-naming.md b/.cursor/skills/matplotlib-scientific/references/reference-export-naming.md new file mode 100644 index 0000000..9d55b77 --- /dev/null +++ b/.cursor/skills/matplotlib-scientific/references/reference-export-naming.md @@ -0,0 +1,54 @@ +# Saving figures: DPI, transparency, formats, auto-increment names + +## `fig.savefig` essentials + +- Always save the **`Figure`** you built: `fig.savefig(path, ...)`, not `plt.savefig` in library code (acceptable in quick scripts if there is a single current figure). +- **Raster**: PNG (and TIFF if required). **Vector**: PDF or SVG for line art and typography at journal resolution. +- **DPI**: 300–600 is a common print range; match journal guidance. See [Saving to file](https://www.practicaldatascience.org/notebooks/class_5/week_1/1.4.4_saving_to_file.html). +- **Clipping**: if labels are cut off, use `bbox_inches="tight"` (optionally with `pad_inches`). +- **Transparent PNG**: `transparent=True` for slides and compositing on non-white backgrounds. + +Example: + +```python +fig.savefig( + "outputs/fig01.png", + dpi=400, + bbox_inches="tight", + pad_inches=0.02, + transparent=True, +) +``` + +## Meeting-friendly auto-increment + +Use a small helper so repeated script runs produce `plot_001.png`, `plot_002.png`, without overwriting. Persist the counter in a dotfile or JSON next to outputs. + +```python +from pathlib import Path +import json + +def next_plot_path(dir_path: Path, stem: str = "plot", suffix: str = ".png") -> Path: + """Return the next sequential path ``{stem}_{n:03d}{suffix}`` under ``dir_path``.""" + dir_path.mkdir(parents=True, exist_ok=True) + state = dir_path / ".plot_counter.json" + n = 0 + if state.is_file(): + n = int(json.loads(state.read_text()).get("n", 0)) + n += 1 + state.write_text(json.dumps({"n": n})) + return dir_path / f"{stem}_{n:03d}{suffix}" + +path = next_plot_path(Path("outputs/meetings")) +fig.savefig(path, dpi=400, bbox_inches="tight", transparent=True) +``` + +Variants: timestamp-based names for parallel runs, or `git rev-parse --short` in the stem for traceability. + +## Script vs notebook + +- In non-interactive scripts, call **`plt.show()`** only when debugging; for batch pipelines, rely on **`savefig`** ([basic plotting note](https://www.practicaldatascience.org/notebooks/class_5/week_1/1.2.1_basic_plotting_with_matplotlib.html)). + +## Supported formats + +- Discover locally: `fig.canvas.get_supported_filetypes()` ([Saving to file](https://www.practicaldatascience.org/notebooks/class_5/week_1/1.4.4_saving_to_file.html)). diff --git a/.cursor/skills/matplotlib-scientific/references/reference-journal-layout.md b/.cursor/skills/matplotlib-scientific/references/reference-journal-layout.md new file mode 100644 index 0000000..86e6cd3 --- /dev/null +++ b/.cursor/skills/matplotlib-scientific/references/reference-journal-layout.md @@ -0,0 +1,40 @@ +# Journal widths, composition, and scientific storytelling + +## Width targets (verify against current author guide) + +Journals differ; **always read the target journal’s figure guidelines**. Order-of-magnitude widths from public summaries: + +| Venue (examples) | Single column (order of magnitude) | Double / full width | +|-------------------|-------------------------------------|----------------------| +| Nature (typical) | ~89 mm | ~183 mm | +| Science (typical) | ~55 mm | ~230 mm | + +Convert to inches for `figsize`: **inches = mm / 25.4**. Example: 89 mm ~ 3.5 in wide; height from aspect ratio and content. + +```python +mm = 89 +w_in = mm / 25.4 +aspect = 0.75 +fig, ax = plt.subplots(figsize=(w_in, w_in * aspect), layout="constrained") +``` + +## Four design rules (summary) + +From [How to make good figures for scientific papers](https://www.simplifiedsciencepublishing.com/resources/how-to-make-good-figures-for-scientific-papers): + +1. **Purpose**: choose the graphic form from the claim (compare, change over time, relationship, process). +2. **Composition**: left-to-right or top-to-bottom flow; remove chartjunk; emphasize the main series. +3. **Color**: few accents; colorblind-safe; grayscale should still read. +4. **Refine**: iterate; check “does the graphic alone convey the point?” before polishing text. + +## Choosing the chart type + +- Same table, many views: lines for **trends**, stacked bars or stackplots for **composition over time**, grouped bars for **within-year comparison**, pies only when **parts of one whole** and few slices. See [Plotting Zoo](https://www.practicaldatascience.org/notebooks/class_5/week_1/1.5.1_plotting_zoo.html). + +## Tables as figures + +- A **formatted table** is a valid figure for lookup-heavy results; pandas `DataFrame` display or manual annotation in the manuscript are both acceptable ([Plotting Zoo](https://www.practicaldatascience.org/notebooks/class_5/week_1/1.5.1_plotting_zoo.html)). + +## Supplementary material + +- Move **sensitivity analyses and extra scans** to supplement; keep main figures focused ([Iceberg-style thinking](https://www.simplifiedsciencepublishing.com/resources/how-to-make-good-figures-for-scientific-papers)). diff --git a/.cursor/skills/matplotlib-scientific/references/reference-layout.md b/.cursor/skills/matplotlib-scientific/references/reference-layout.md new file mode 100644 index 0000000..31bd62c --- /dev/null +++ b/.cursor/skills/matplotlib-scientific/references/reference-layout.md @@ -0,0 +1,55 @@ +# Layout: subplots, GridSpec, mosaic, separate figures + +## Decision guide + +| Situation | Prefer | +|-----------|--------| +| Regular grid of equal panels, shared labels | `plt.subplots(nrows, ncols, sharex=..., sharey=..., layout="constrained")` | +| Variable row/column sizes, spans | `matplotlib.gridspec.GridSpec` or `fig.subplot_mosaic` with width ratios | +| Semantic names (`"A"`, `"B"`) instead of integer positions | `fig.subplot_mosaic` | +| One-off complex arrangement | `GridSpec` with `subplot_spec` spans | +| Same plot reused in slide vs paper with different sizes | **Separate** `fig.savefig` calls or separate scripts; avoid one giant figure that is always resized in a GUI | +| Final page composition with captions from a manuscript | Export **individual** high-quality panels (PDF/SVG) and place in LaTeX/Quarto; optional single composite for preview only | + +## `plt.subplots` + +- Use for the common case: `fig, axs = plt.subplots(2, 2, layout="constrained")`. +- `sharex` / `sharey` reduce duplicate ticks; call `ax.label_outer()` or hide inner tick labels for clarity. +- `axs` may be 1D or 2D array; flatten with `axs.flat` for iteration. + +## `subplot_mosaic` + +- Readable layout strings and named axes: + +```python +fig, axd = plt.subplot_mosaic( + "AB\nCC", + layout="constrained", +) +axd["A"].plot(...) +``` + +- Good when panel sizes are uneven or you refer to panels by role. + +## `GridSpec` + +- Use when you need **explicit height/width ratios** and **row/column spans** that are awkward in `subplots`. +- Pair with `fig.add_subplot(gs[i, j])` or `subgridspec` for nested regions. + +## Layout engines + +- Prefer **`layout="constrained"`** (or `fig.set_layout_engine("constrained")`) for label and colorbar spacing over manual `subplots_adjust` when possible. See [Constrained layout guide](https://matplotlib.org/stable/users/explain/axes/constrainedlayout_guide.html). +- **`tight_layout`** remains useful for quick notebooks; constrained layout is usually better for publication figures with long labels. + +## Assemble elsewhere + +- **Assemble in the document** when the journal controls gutters, multi-panel captions, or when vector panels must align with equations. +- **Assemble in matplotlib** when you need a single raster for Twitter/slides or a strict pixel budget. + +## Multi-panel data stories + +- **Small multiples** (one highlighted series per panel, others grey) help many categories; see [Plotting Zoo](https://www.practicaldatascience.org/notebooks/class_5/week_1/1.5.1_plotting_zoo.html). + +## Anti-pattern + +- Using **`plt.plot` after `subplots`** without `plt.sca(ax)` routes draws to the **last-created** axes and stacks traces incorrectly. Always **`ax.plot`** ([implicit vs explicit](https://www.practicaldatascience.org/notebooks/class_5/week_1/1.4.5_explicit_vs_implicit_syntax.html)). diff --git a/.cursor/skills/matplotlib-scientific/references/reference-legend-style.md b/.cursor/skills/matplotlib-scientific/references/reference-legend-style.md new file mode 100644 index 0000000..7a1cf80 --- /dev/null +++ b/.cursor/skills/matplotlib-scientific/references/reference-legend-style.md @@ -0,0 +1,78 @@ +# Legends: frame style, titles, placement, compact handles + +## Fancy frame vs plain frame + +| Context | Recommendation | +|---------|------------------| +| Journal print, minimal figures | **`fancybox=False`**, thin neutral frame: `ax.legend(frameon=True, edgecolor="0.85", facecolor="white", fancybox=False)` | +| Slides, posters, light branding | **`fancybox=True`** (rounded corners) is acceptable; keep **`shadow=False`** unless depth is part of the design system | +| On busy backgrounds | **`frameon=True`** with solid **`facecolor`** (often white or figure face) so text stays legible | + +`fancybox=True` uses a rounded box; `fancybox=False` is sharper and usually reads more “paper”. `shadow=True` ages poorly in print; prefer a crisp edge and padding. + +## Legend title (`title=`) + +Add a legend title when: + +- Handles are **grouped by class** and the axis label alone is insufficient (e.g. title `"Model"`, entries `RF`, `NN`). +- The same color/marker means **different semantics** than the axis (e.g. `"Dataset"` while y-axis is a measured quantity). + +Skip the title when: + +- The axis label and series names are already self-explanatory. +- Space is tight; a **caption** can name the encoding instead. + +```python +leg = ax.legend(title="Condition", alignment="left") +leg.get_title().set_fontsize(leg.get_texts()[0].get_fontsize()) +``` + +Keep title **one size step** above or equal to entry text; align left for multi-line entries. + +## Placement: inside vs above vs below vs outside + +| Placement | When to use | Typical kwargs | +|-----------|-------------|----------------| +| **Inside** upper right / best | Few series, large margin, no occlusion | `loc="upper right"`, `bbox_to_anchor=(1, 1)` only if nudging inside | +| **Outside** right | Many series or long names; preserve data region | `bbox_to_anchor=(1.02, 1)`, `loc="upper left"` | +| **Below** the axes | Wide figures, many entries; row of handles | `bbox_to_anchor=(0.5, -0.18)`, `loc="upper center"`, **`ncol`** ≥ 2 | +| **Above** | Rare; sometimes for single-row keys under a suptitle | `bbox_to_anchor=(0.5, 1.12)`, `loc="lower center"` | + +Use **`layout="constrained"`** on the figure so outside legends reserve space. After placing outside, verify **`savefig(..., bbox_inches="tight")`** does not clip the box. + +## Making the legend read cleanly + +- **`ncol`**: split into 2–4 columns for wide single-row legends below the plot. +- **`labelspacing`**, **`handlelength`**, **`handletextpad`**, **`borderaxespad`**: tighten uniformly; change one parameter at a time. +- **`fontsize`**: match tick labels or be one step smaller than axis labels. +- **`alignment="left"`** (Matplotlib 3.6+): left-align stacked text blocks. + +## Compact handles (shorter legend rows) + +When default lines are too long: + +1. **Proxy artists**: build handles explicitly with short linestyle/marker only. + +```python +from matplotlib.lines import Line2D + +handles = [ + Line2D([0], [0], color="C0", lw=2, label="Control"), + Line2D([0], [0], color="C1", lw=2, ls="--", label="Treated"), +] +ax.legend(handles=handles, labels=[h.get_label() for h in handles], frameon=True) +``` + +2. **`numpoints=1`**, **`markerscale`** for scatter-heavy legends. +3. **`handler_map`** for custom patches (e.g. thin rectangles for bars) when defaults are oversized. + +## Order and merging + +- **`order="sorted"`** or explicit label order for consistency across figures. +- **`labelcolor="linecolor"`** (or `"markerfacecolor"`) ties text to encoding and can replace redundant color words in labels. + +## Matplotlib references + +- [Legend guide](https://matplotlib.org/stable/users/explain/axes/legend_guide.html) +- [Figure legends](https://matplotlib.org/stable/gallery/text_labels_and_annotations/figlegend_demo.html) +- [Custom legend handlers](https://matplotlib.org/stable/tutorials/intermediate/legend_guide.html#implementing-a-custom-legend-handler) diff --git a/.cursor/skills/matplotlib-scientific/references/reference-styles-pandas.md b/.cursor/skills/matplotlib-scientific/references/reference-styles-pandas.md new file mode 100644 index 0000000..5219945 --- /dev/null +++ b/.cursor/skills/matplotlib-scientific/references/reference-styles-pandas.md @@ -0,0 +1,43 @@ +# Style sheets, SciencePlots, pandas plotting + +## Object-oriented + pandas + +- Pattern from [Plotting with Pandas](https://www.practicaldatascience.org/notebooks/class_5/week_1/1.6.1_plotting_with_pandas.html): + +```python +fig, ax = plt.subplots(layout="constrained") +df.plot(kind="bar", ax=ax, stacked=True, edgecolor="white") +ax.set_ylabel("Tons") +``` + +- `kind` options include `bar`, `barh`, `hist`, `box`, `kde`, `density`, `area`, `scatter`, `hexbin`, `pie`. The return value is a matplotlib object you can tweak further. + +## Global styles + +- Built-in: `plt.style.use("seaborn-v0_8-whitegrid")` or `ggplot`, `fivethirtyeight`, etc. ([pandas plotting example](https://www.practicaldatascience.org/notebooks/class_5/week_1/1.6.1_plotting_with_pandas.html)). +- Prefer **`plt.style.context(...)`** in libraries so you do not mutate global RC for importers. + +## SciencePlots + +- Project: [garrettj403/SciencePlots](https://github.com/garrettj403/SciencePlots). +- Install: `uv add SciencePlots` (or `pip install SciencePlots`). **LaTeX** is required for the default `"science"` style; the README documents `no-latex` variants and CJK font add-ons. +- Usage (v2+): **`import scienceplots`** before activating styles: + +```python +import matplotlib.pyplot as plt +import scienceplots + +plt.style.use(["science", "ieee"]) +``` + +- Combine styles: later entries override earlier (e.g. `ieee` column width over `science`). +- Temporary: `with plt.style.context(["science", "notebook"]): ...` + +## Resolution in notebooks + +- For crisp inline display, Practical Data Science suggests `matplotlib_inline.backend_inline.set_matplotlib_formats("retina")` instead of magic-only config when exporting notebooks to other runners ([basic plotting](https://www.practicaldatascience.org/notebooks/class_5/week_1/1.2.1_basic_plotting_with_matplotlib.html)). + +## When not to use heavy styles + +- Exploratory notebooks: keep default or light grid for speed. +- Submission: match **journal** requirements; SciencePlots is a shortcut, not a substitute for the author checklist. diff --git a/.cursor/skills/numpy-docstrings/SKILL.md b/.cursor/skills/numpy-docstrings/SKILL.md new file mode 100644 index 0000000..f2f74bf --- /dev/null +++ b/.cursor/skills/numpy-docstrings/SKILL.md @@ -0,0 +1,48 @@ +--- +author: dotagents +name: numpy-docstrings +description: Write and review NumPy-style (numpydoc) docstrings for Python public APIs. Covers section order, semantics (what belongs in docstrings vs types vs tests), anti-patterns (comment soup, stub docs, wrong style), Parameters, Returns, Yields, Raises, See Also, Notes, References, Examples, and class/module docs. Use when authoring or auditing docstrings. Triggers on docstring, numpydoc, Parameters, Returns, Examples, Sphinx, TODO, comment. +--- + +# NumPy-style docstrings (numpydoc) + +## Quick start + +1. **Public APIs only** per project **Python spec** and **Python rule**: document **modules, classes, functions, and methods** users import; private helpers stay minimal unless behavior is non-obvious. +2. **Follow section order** from [numpydoc](https://numpydoc.readthedocs.io/en/latest/format.html#sections); each heading is **underlined with hyphens** the same length as the title. +3. **Line length**: aim for **~75 characters** in docstring prose for terminal readability. +4. **Markup**: subset of **reST**; parameter names in **single backticks** in running text. +5. **Load the chunk** you are editing from the table below—each reference file targets one part of the standard. +6. **Semantics**: choose **docstring vs annotation vs test vs prose** deliberately; avoid **comment narration** and **stub** docstrings—see [reference-semantics-and-anti-patterns.md](references/reference-semantics-and-anti-patterns.md). + +## Stack synergy + +| Resource | Role | +|----------|------| +| **general-python** | pytest, “NumPy style on public APIs” | +| **python-reviewer** | Checks docstring quality alongside typing and tests | +| **python-types** | Types in signatures complement docstring types | + +## Section reference index + +| Docstring chunk | File | +|-----------------|------| +| Global rules, section order, line length, reST | [reference-overview-order.md](references/reference-overview-order.md) | +| Short summary, extended summary, deprecation | [reference-summary-deprecation.md](references/reference-summary-deprecation.md) | +| Parameters, Other Parameters, `*args` / `**kwargs` | [reference-parameters.md](references/reference-parameters.md) | +| Returns, Yields, Receives | [reference-returns-yields-receives.md](references/reference-returns-yields-receives.md) | +| Raises, Warns, Warnings | [reference-raises-warns-warnings.md](references/reference-raises-warns-warnings.md) | +| See Also, Notes, References | [reference-see-also-notes-references.md](references/reference-see-also-notes-references.md) | +| Examples (doctest style) | [reference-examples.md](references/reference-examples.md) | +| Classes, Attributes, Methods, modules | [reference-classes-modules.md](references/reference-classes-modules.md) | +| Why each mechanism; bad comments/docstrings; fixes | [reference-semantics-and-anti-patterns.md](references/reference-semantics-and-anti-patterns.md) | + +## Canonical specification + +- [numpydoc style guide](https://numpydoc.readthedocs.io/en/latest/format.html) +- [Example docstring](https://numpydoc.readthedocs.io/en/latest/example.html) +- [PEP 257](https://peps.python.org/pep-0257/) (baseline docstring conventions) + +## Optional tooling + +- **`uv add numpydoc`** when Sphinx builds use the numpydoc extension; do not hand-edit pins in `pyproject.toml`. diff --git a/.cursor/skills/numpy-docstrings/references/reference-classes-modules.md b/.cursor/skills/numpy-docstrings/references/reference-classes-modules.md new file mode 100644 index 0000000..f5d21cc --- /dev/null +++ b/.cursor/skills/numpy-docstrings/references/reference-classes-modules.md @@ -0,0 +1,49 @@ +# Classes, Attributes, Methods, modules + +Source: [Documenting classes](https://numpydoc.readthedocs.io/en/latest/format.html#documenting-classes), [Documenting modules](https://numpydoc.readthedocs.io/en/latest/format.html#documenting-modules) (if present in full page—module section exists in numpydoc). + +## Class docstring + +- Use the **same sections** as functions **except** **Returns** (unless a special method behaves like a function with a documented return—rare in class docstring body). +- **`__init__` parameters** belong in the class docstring **Parameters** section (constructor arguments). +- Optional separate **`__init__` docstring** for extra initialization detail if the project allows duplication control. + +## Attributes + +- Section **below Parameters** listing **non-method** attributes: + +```text +Attributes +---------- +x : float + The current x coordinate. +``` + +- Properties with their own docstrings may be listed **by name only** in **Attributes** when numpydoc defers to the property. + +## Methods + +- Each **public** method is documented like a **function**. +- **Do not** list **`self`** (or **`cls`**) in **Parameters**. +- If a method mirrors a **standalone function**, put the **detailed** narrative on the **function** docstring; the method may carry a **short summary** plus **See Also** pointing to the function ([numpydoc — method docstrings](https://numpydoc.readthedocs.io/en/latest/format.html#method-docstrings)). + +## Methods section (on the class) + +- Optional **Methods** block on the **class** docstring when only a **few** methods matter (e.g. large subclasses); list signatures in one line each with a short description—**not** for private methods. + +## `property` + +- Document **getter behavior**, **raises**, and **side effects** if any; type in **Returns** or in the one-line summary as appropriate. + +## Module docstring + +- At least a **summary line**; optional sections in **function order** where appropriate: extended summary, **routine listings** for large modules, **See Also**, **Notes**, **References**, **Examples** ([documenting modules](https://numpydoc.readthedocs.io/en/latest/format.html#documenting-modules)). +- **License and author** metadata belong outside the docstring (comments or project files), not in the module docstring per numpydoc. + +## Constants + +- Use summary, optional extended summary, **See Also**, **References**, **Examples** as needed ([documenting constants](https://numpydoc.readthedocs.io/en/latest/format.html#documenting-constants)); some immutable constants cannot carry `__doc__` in the REPL. + +## Private objects + +- Leading underscore names: minimal or no numpydoc sections unless they are part of a **subclassing contract**. diff --git a/.cursor/skills/numpy-docstrings/references/reference-examples.md b/.cursor/skills/numpy-docstrings/references/reference-examples.md new file mode 100644 index 0000000..dc3e9f5 --- /dev/null +++ b/.cursor/skills/numpy-docstrings/references/reference-examples.md @@ -0,0 +1,39 @@ +# Examples + +Source: [numpydoc — Examples](https://numpydoc.readthedocs.io/en/latest/format.html#examples). + +## Role + +- **Illustrate usage**; they are **not** the primary test suite—keep **`tests/`** as the authority for CI. +- Still **strongly encouraged** for user-facing APIs. + +## Doctest format + +- Use **`>>>`** prompts and expected output as in the Python REPL. +- **Separate** multiple examples with **blank lines**. +- Put **blank lines above and below** comment lines that explain an example. + +## Continuations + +- Continuation lines after the first `>>>` start with **`...`**. + +## Random or platform-dependent output + +- Mark with **`#random`** (or project convention) so doctest runners can skip strict comparison. + +## Imports in examples + +- NumPy docs assume **`import numpy as np`** is pre-run for numpy examples; still be **explicit** for anything else (`matplotlib.pyplot`, local modules). +- **Explicitly import** the function under documentation if it aids copy-paste. + +## Empty lines in output + +- Empty output lines in doctests do not need special markup per numpydoc. + +## Matplotlib + +- If **`matplotlib`** is imported in the example, Sphinx may use matplotlib’s **plot** directive when configured; otherwise `.. plot::` can be used in `.rst` sources—not always inside docstrings. + +## Running doctests + +- Projects may run **`pytest --doctest-modules`** or library-specific test hooks; align with the project **Python** spec (**pytest**). diff --git a/.cursor/skills/numpy-docstrings/references/reference-overview-order.md b/.cursor/skills/numpy-docstrings/references/reference-overview-order.md new file mode 100644 index 0000000..20098eb --- /dev/null +++ b/.cursor/skills/numpy-docstrings/references/reference-overview-order.md @@ -0,0 +1,53 @@ +# Overview: format, order, and markup + +Source: [numpydoc format](https://numpydoc.readthedocs.io/en/latest/format.html). + +## Docstring wrapper + +- Use **triple double quotes** `"""` for module, class, function, and method docstrings. +- First line after the opening `"""` is often the **short summary** (or signature line for C extensions without introspection). + +## Line length + +- Keep lines to about **75 characters** so docstrings read in plain terminals. + +## Section headings + +- Each section title is a **line of text** followed by a line of **hyphens** the **same length** as the title: + +```text +Parameters +---------- +``` + +- **Order** for functions (omit unused sections): Short summary → deprecation (directive) → Extended summary → Parameters → Returns → Yields → Receives → Other Parameters → Raises → Warns → Warnings → See Also → Notes → References → Examples. + +## reStructuredText subset + +- Docstrings use **reST** for Sphinx; prefer a **small** subset: inline `` `param` ``, `` :math:`x` ``, `.. deprecated::`, `.. math::`, `.. note::` sparingly. +- **Human readability** beats contorting text for HTML output ([numpydoc principle](https://numpydoc.readthedocs.io/en/latest/format.html#docstring-standard)). + +## Import names in narrative docs + +- NumPy docs convention: `import numpy as np`, `import matplotlib as mpl`, `import matplotlib.pyplot as plt`; **do not** abbreviate `scipy` as something nonstandard in prose. + +## What not to duplicate + +- **Signature** is usually shown by `help()`; the short summary should **not** repeat the function name as a title unless documenting a C API without a visible signature. + +## `array_like` + +- Use **`array_like`** when an argument accepts **ndarrays** and values **coercible** to arrays (scalars, nested sequences). + +## reST `.. note::` and `.. warning::` + +- Use **sparingly** in sections; they render poorly in plain terminals ([numpydoc](https://numpydoc.readthedocs.io/en/latest/format.html#other-points-to-keep-in-mind)). + +## Monospace in prose + +- **Parameter names**: single backticks (numpydoc convention). +- **Other code snippets** in running text: often **double backticks** in reST; follow project Sphinx/numpydoc version guidance for link vs monospace. + +## Hyperlinks in docstrings + +- Some sections parse **non-standard** reST; fragile `.. target` lines can confuse numpydoc—prefer **inline** links where Sphinx warns ([numpydoc](https://numpydoc.readthedocs.io/en/latest/format.html#other-points-to-keep-in-mind)). diff --git a/.cursor/skills/numpy-docstrings/references/reference-parameters.md b/.cursor/skills/numpy-docstrings/references/reference-parameters.md new file mode 100644 index 0000000..eaf17fd --- /dev/null +++ b/.cursor/skills/numpy-docstrings/references/reference-parameters.md @@ -0,0 +1,44 @@ +# Parameters and Other Parameters + +Source: [numpydoc — Parameters](https://numpydoc.readthedocs.io/en/latest/format.html#parameters), [Other Parameters](https://numpydoc.readthedocs.io/en/latest/format.html#other-parameters). + +## Heading + +```text +Parameters +---------- +``` + +## Each parameter + +- Form: **`name : type`** then newline, then **indented** description (4 spaces). +- numpydoc: the colon may be **omitted if there is no type**; otherwise use **`name : type`** (space before the colon after the name, space after the colon before the type). +- Type may be **omitted**: then `name` alone with description on the next line. +- Refer to a parameter in prose with **single backticks**: `` `x` ``. + +## Types (be specific) + +Examples from numpydoc: `str`, `bool`, `array_like`, `int or tuple of int`, `list of str`, `dtype`, `callable`, union with **or**. + +## Optional and defaults + +- Keyword-only optionals: **`optional`** in the type line: `x : int, optional` +- Or give **default** in type: `copy : bool, default True` (also `default=True`, `default: True`—pick one style per project). + +## Fixed set of values + +- `order : {'C', 'F', 'A'}` with default listed first in braces when applicable. + +## Combining parameters + +- Same type and meaning: `x1, x2 : array_like` then one description referencing `` `x1` `` and `` `x2` ``. + +## `*args` and `**kwargs` + +- Keep the **stars** in the name; **do not** give a type on the `*args` / `**kwargs` line in numpydoc style. +- Describe how forwarded kwargs map to underlying functions if relevant. + +## Other Parameters + +- Use **only** when **many** keyword parameters would clutter **Parameters**; move **rare** or **advanced** kwargs here. +- Same formatting rules as **Parameters**. diff --git a/.cursor/skills/numpy-docstrings/references/reference-raises-warns-warnings.md b/.cursor/skills/numpy-docstrings/references/reference-raises-warns-warnings.md new file mode 100644 index 0000000..2e031a3 --- /dev/null +++ b/.cursor/skills/numpy-docstrings/references/reference-raises-warns-warnings.md @@ -0,0 +1,28 @@ +# Raises, Warns, Warnings + +Source: [Raises](https://numpydoc.readthedocs.io/en/latest/format.html#raises), [Warns](https://numpydoc.readthedocs.io/en/latest/format.html#warns), [Warnings](https://numpydoc.readthedocs.io/en/latest/format.html#warnings). + +## Raises + +- List **exception types** and **when** they are raised. +- Use **judiciously**: obvious `ValueError` on bad input may be omitted if universal; **non-obvious** or **high-probability** errors deserve a line. + +```text +Raises +------ +LinAlgError + If the matrix is singular. +``` + +## Warns + +- Same shape as **Raises** but for **`Warning`** subclasses or user-visible warnings issued under stated conditions. + +## Warnings (free-text cautions) + +- **User-facing caveats** not tied to a single exception (numerical instability, thread safety, heuristic behavior). +- Free-form reST paragraph(s) under the **Warnings** heading—not the same as Python’s `warnings.warn`. + +## Relation to typing + +- **`ty` / PEP 484** do not replace **Raises**; document **semantic** error contracts for readers and Sphinx. diff --git a/.cursor/skills/numpy-docstrings/references/reference-returns-yields-receives.md b/.cursor/skills/numpy-docstrings/references/reference-returns-yields-receives.md new file mode 100644 index 0000000..fb20a7f --- /dev/null +++ b/.cursor/skills/numpy-docstrings/references/reference-returns-yields-receives.md @@ -0,0 +1,40 @@ +# Returns, Yields, Receives + +Source: [Returns](https://numpydoc.readthedocs.io/en/latest/format.html#returns), [Yields](https://numpydoc.readthedocs.io/en/latest/format.html#yields), [Receives](https://numpydoc.readthedocs.io/en/latest/format.html#receives). + +## Returns + +- For each return value: **type is required**; **name is optional**. +- Anonymous single return: + +```text +Returns +------- +int + Description of return value. +``` + +- Named returns mirror **Parameters**: + +```text +Returns +------- +err_code : int + Non-zero indicates error. +err_msg : str or None + Message or None on success. +``` + +## Yields + +- For **generators** only; same structure as **Returns** (type required, name optional). +- numpydoc **0.6+** supports **Yields**. + +## Receives + +- Documents what a generator receives via **`.send()`**; format like **Parameters**. +- If **Receives** appears, **Yields** must also appear. + +## Multiple return paths + +- Document **all** stable contracts; if behavior is `None` or sentinel, state **when** each occurs in the description lines. diff --git a/.cursor/skills/numpy-docstrings/references/reference-see-also-notes-references.md b/.cursor/skills/numpy-docstrings/references/reference-see-also-notes-references.md new file mode 100644 index 0000000..2c8433c --- /dev/null +++ b/.cursor/skills/numpy-docstrings/references/reference-see-also-notes-references.md @@ -0,0 +1,23 @@ +# See Also, Notes, References + +Source: [See Also](https://numpydoc.readthedocs.io/en/latest/format.html#see-also), [Notes](https://numpydoc.readthedocs.io/en/latest/format.html#notes), [References](https://numpydoc.readthedocs.io/en/latest/format.html#references). + +## See Also + +- Point to **related** APIs the reader might miss; avoid listing the whole module. +- Form: `name : Short description.` or **`name` only** if the name is self-explanatory. +- Same submodule: **unqualified** name; other submodules: **`submod.func`**; other packages: **`package.module.func`**. +- Long descriptions: put **name :** on first line, description **indented** four spaces on the next. + +## Notes + +- **Algorithms**, **complexity**, **numerical details**, **background theory** that would clutter **Extended summary**. +- **Equations**: `.. math::` block or inline `` :math:`\alpha` ``; keep LaTeX **readable**—equations are hard in plain text. +- **Images**: `.. image:: path` only if the docstring still makes sense **without** the image (numpydoc guidance). +- Variable emphasis in math: `\mathtt{var}` when needed for typographic distinction. + +## References + +- **Numbered** citations supporting **Notes**; use `.. [1] Author, "Title", ...` and cite with `[1]_` in Notes. +- Prefer **stable** sources; **avoid** fragile URLs as the only citation. +- **Caveat** ([numpydoc #130](https://github.com/numpy/numpydoc/issues/130)): citation markers like `[1]` inside **tables** in docstrings can break numpydoc processing—avoid that combination or restructure. diff --git a/.cursor/skills/numpy-docstrings/references/reference-semantics-and-anti-patterns.md b/.cursor/skills/numpy-docstrings/references/reference-semantics-and-anti-patterns.md new file mode 100644 index 0000000..ce86948 --- /dev/null +++ b/.cursor/skills/numpy-docstrings/references/reference-semantics-and-anti-patterns.md @@ -0,0 +1,235 @@ +# Semantics: which documentation mechanism, and anti-patterns + +This file explains **where** information should live (docstring section vs type hint vs test vs narrative) and shows **bad habits** next to **spec-aligned** fixes. It aligns with the project **Python** spec and **Python rule**: NumPy-style docstrings on **public** APIs, **obvious control flow** without step-by-step **inline comments** in implementations. + +## Choosing a mechanism + +| What the reader needs | Prefer | +|------------------------|--------| +| **Callable contract** (what goes in, what comes out, what can fail) | **Parameters**, **Returns** / **Yields**, **Raises** in a numpydoc docstring | +| **Meaning that types cannot express** (units, ranges, physical interpretation, invariants) | Same sections’ prose, or **Notes** if it spans several parameters | +| **Pure type information** already clear from annotations | **`ty`-checked** signatures; **omit** redundant docstring types unless you add **semantics** | +| **How to call it** (minimal copy-paste) | **Examples** (short, doctest-style when appropriate) | +| **Where to go next** (alternatives, partners) | **See Also** | +| **Theory, algorithm, equations** | **Notes**; stable citations in **References** | +| **User-facing caution** (not one specific exception) | **Warnings** section | +| **Deprecation and replacement** | **`.. deprecated::`** directive ([summary/deprecation](reference-summary-deprecation.md)) | +| **Whole module map** (big packages) | **Module** docstring + **routine listings** ([classes/modules](reference-classes-modules.md)) | +| **Behavior guaranteed by tests** | **`tests/`** + pytest; docstring **Examples** illustrate, they do not replace CI | +| **Private helper** with a non-obvious invariant | One-line docstring **or** a **name** that carries the invariant; not a public numpydoc essay | + +**Principle:** put **contract and usage** where **`help(obj)`** and Sphinx readers look first; keep **implementation noise** out of public docstrings and out of **comment trails** per project Python style. + +--- + +## Anti-pattern: comments as a substitute for names and structure + +**Bad:** narrating the code line-by-line or stating the obvious. + +```python +def process(a): + # loop over each row + for i in range(a.shape[0]): + # get the value + v = a[i, 0] + # add one + a[i, 0] = v + 1 + return a +``` + +**Better:** **vectorize** when the stack allows; if a loop remains, **name** intent and move **contract** to the docstring, not inline chatter. + +```python +import numpy as np + +def increment_first_column(a: np.ndarray) -> np.ndarray: + """Increment every value in the first column by one. + + Parameters + ---------- + a : numpy.ndarray, shape (M, N) + Input array; not modified in place. + + Returns + ------- + numpy.ndarray + A copy of ``a`` with column 0 incremented by one. + """ + out = np.array(a, copy=True) + out[:, 0] = out[:, 0] + 1 + return out +``` + +--- + +## Anti-pattern: docstring repeats the signature with no added meaning + +**Bad:** + +```python +def mean(a, axis=None): + """mean(a, axis=None) + + Takes a and axis and returns the mean. + """ +``` + +**Better:** short summary **without** mirroring parameter names as the only content; put types and defaults in **Parameters** / **Returns**. + +```python +import numpy as np + +def mean(a: np.ndarray, axis: int | None = None) -> np.floating | np.ndarray: + """Arithmetic mean along the given axis. + + Parameters + ---------- + a : array_like + Input data. + axis : int, optional + Axis along which to compute the mean. Default is None (flatten). + + Returns + ------- + numpy.floating or ndarray + Mean of ``a`` over the given axis. + """ +``` + +--- + +## Anti-pattern: parameter descriptions only in the extended summary + +**Bad:** long paragraph that lists args but no **Parameters** section (breaks numpydoc parsing and `help()` structure). + +**Better:** keep extended summary for **intent**; every public argument gets a **Parameters** line (or **Other Parameters** if rarely used). See [reference-parameters.md](reference-parameters.md). + +--- + +## Anti-pattern: empty or stub sections + +**Bad:** + +```text +Returns +------- + +Raises +------ +``` + +**Better:** omit entire sections that add nothing; numpydoc expects **content** under each heading you include. + +--- + +## Anti-pattern: “TODO” and placeholders in shipped APIs + +**Bad:** `"""TODO: document."""` on a public function. + +**Better:** ship with at least a **short summary** and **Parameters**/**Returns** for anything exported in `__all__` or documented as stable; track internal debt in issues, not user-facing docstrings. + +--- + +## Anti-pattern: author, license, or change log in the module docstring + +**Bad:** module docstring that is only copyright or “written by Alice 2021.” + +**Better:** numpydoc reserves the module docstring for **summary**, optional listings, **See Also**, **Examples**; put license in **`LICENSE`** and attribution in **VCS** or **`pyproject.toml`** ([classes/modules](reference-classes-modules.md)). + +--- + +## Anti-pattern: duplicating type hints with zero extra semantics + +**Bad:** + +```python +def f(x: int, y: float) -> str: + """Do something. + + Parameters + ---------- + x : int + An integer. + y : float + A float. + + Returns + ------- + str + A string. + """ +``` + +**Better:** keep **types** in annotations; use docstring lines for **meaning** (role, units, constraints, edge cases). + +```python +def f(x: int, y: float) -> str: + """Format a channel index and gain for display. + + Parameters + ---------- + x : int + Zero-based acquisition channel index. + y : float + Linear gain in decibels applied before quantization. + + Returns + ------- + str + Human-readable label, e.g. ``"ch3 (+6.0 dB)"``. + """ +``` + +--- + +## Anti-pattern: wrong style for the project + +**Bad:** Google-style `Args:` / `Returns:` blocks in a repo that standardizes on **NumPy** / numpydoc and Sphinx. + +**Better:** match **this stack**: hyphen underlines and section names from [numpydoc format](https://numpydoc.readthedocs.io/en/latest/format.html#sections). + +--- + +## Anti-pattern: Examples that are really integration tests + +**Bad:** dozens of lines of setup, mocks, and assertions inside **Examples** that belong in **`tests/`**. + +**Better:** **Examples** show **minimal** usage; heavy behavior is **pytest** with stable fixtures ([reference-examples.md](reference-examples.md)). + +--- + +## Anti-pattern: documenting `self` on methods + +**Bad:** listing `self` under **Parameters** on instance methods. + +**Better:** omit **`self`** / **`cls`** per numpydoc ([reference-classes-modules.md](reference-classes-modules.md)). + +--- + +## Anti-pattern: critical exceptions only in comments + +**Bad:** + +```python +def invert(m): + # raises LinAlgError if singular + return np.linalg.inv(m) +``` + +**Better:** **Raises** in the docstring so `help()` and HTML docs show the contract. + +```text +Raises +------ +numpy.linalg.LinAlgError + If ``m`` is singular. +``` + +--- + +## Quick checklist + +- Public API: **short summary** plus the **sections** that carry real information—**no** filler headings. +- Implementation: **readable structure** and **names**, not comment narration (**Python** rule). +- Types: **annotations + ty** for static truth; docstring for **semantics** types miss. +- Red flags: **TODO** docstrings, **license-only** module docs, **Args/Returns** markdown in a numpydoc project, **comment soup** instead of refactors. diff --git a/.cursor/skills/numpy-docstrings/references/reference-summary-deprecation.md b/.cursor/skills/numpy-docstrings/references/reference-summary-deprecation.md new file mode 100644 index 0000000..96d85f6 --- /dev/null +++ b/.cursor/skills/numpy-docstrings/references/reference-summary-deprecation.md @@ -0,0 +1,31 @@ +# Short summary, extended summary, deprecation + +Source: [numpydoc — Short summary](https://numpydoc.readthedocs.io/en/latest/format.html#short-summary), [Extended summary](https://numpydoc.readthedocs.io/en/latest/format.html#extended-summary), [Deprecation](https://numpydoc.readthedocs.io/en/latest/format.html#deprecation-warning). + +## Short summary + +- **One line** (no blank line before extended text if you continue in the same opening paragraph, or blank line then extended summary—follow project style; numpydoc allows extended summary after a blank line). +- Do **not** use **variable names** from the signature in the short summary. +- Prefer avoiding repeating the **function name** as the opening phrase when it reads redundant with `help()`; state **what** it does in plain language. + +Good pattern: `"""Compute the discrete Fourier transform along the specified axis.` + +## C signature line (rare) + +- For objects **without** introspectable signatures, put the signature as the **first** line inside the docstring, then a blank line, then the short summary (see numpydoc examples). + +## Deprecation + +- Use the Sphinx **`.. deprecated:: version`** **directive**, not a hyphen underlined section, when an API is deprecated. +- Include: **version** deprecated, **removal** timeline if known, **reason** when useful, and **replacement** API. + +```text +.. deprecated:: 1.6.0 + `old_func` is replaced by `new_func` because ... +``` + +## Extended summary + +- A few sentences **after** the short summary (separated by a blank line when it is its own block). +- Use for **what** the object does at a higher level; **not** for implementation trivia or long theory—those belong in **Notes** or **References**. +- You **may** mention parameters or the function name here, but **parameter definitions** still belong only under **Parameters**. diff --git a/.cursor/skills/numpy-scientific/SKILL.md b/.cursor/skills/numpy-scientific/SKILL.md new file mode 100644 index 0000000..e448b51 --- /dev/null +++ b/.cursor/skills/numpy-scientific/SKILL.md @@ -0,0 +1,52 @@ +--- +author: dotagents +name: numpy-scientific +description: NumPy for scientific Python: dtypes and casting, creation, reshaping, broadcasting, indexing vs views, ufuncs and reductions, linear algebra and einsum, random Generator, I/O, structured arrays, performance and pandas boundaries. Use when writing or reviewing ndarray code. Triggers on numpy, ndarray, broadcasting, dtype, ufunc, einsum, vectorization. +--- + +# NumPy (scientific computing) + +## Quick start + +1. **Shape and dtype first**: decide **`shape`**, **`dtype`**, and memory **`order`** (`C` vs `F`) before filling arrays; avoid silent **`astype`** widening. See [reference-arrays-dtypes.md](references/reference-arrays-dtypes.md). +2. **Vectorize**: prefer **ufuncs** and **whole-array** expressions over Python loops on large data; push loops to compiled layers when needed. See [reference-ufuncs-reductions.md](references/reference-ufuncs-reductions.md) and [reference-interop-performance.md](references/reference-interop-performance.md). +3. **Know view vs copy**: basic slicing is usually a **view**; fancy indexing is a **copy**; **`reshape`** may view or copy. Mutations alias bugs are common. See [reference-indexing-views.md](references/reference-indexing-views.md). +4. **Broadcasting**: align trailing dimensions; use **`None` / `newaxis`** and **`np.broadcast_to`** deliberately. See [reference-broadcasting-shape.md](references/reference-broadcasting-shape.md). +5. **Reductions**: always pass **`axis`** and use **`keepdims=True`** when broadcasting the result back; prefer **`np.nanmean`** etc. when NaNs appear. Mind **float summation order** for reproducibility. See [reference-ufuncs-reductions.md](references/reference-ufuncs-reductions.md). +6. **Random**: use **`np.random.Generator`** (`PCG64`), not legacy global `RandomState`, for reproducible science. See [reference-random-io-structured.md](references/reference-random-io-structured.md). + +## Stack synergy + +| Resource | Role | +|----------|------| +| **general-python** | uv, ruff, ty, vectorization policy, **python-reviewer** numerics footguns | +| **dataframes** | Table handoff at boundaries | +| **numpy-docstrings** | Public API docstrings for array-heavy modules | +| **matplotlib-scientific** | Plotting array results | +| **lab-instrumentation** | Binary waveform I/O, instrument-side buffers | +| **python-reviewer** | dtype/shape/reproducibility in review | + +## Reference index + +| Topic | File | +|--------|------| +| Creation, `dtype`, casting, `order`, strides | [reference-arrays-dtypes.md](references/reference-arrays-dtypes.md) | +| Slicing, fancy index, views, copies, `reshape` | [reference-indexing-views.md](references/reference-indexing-views.md) | +| Broadcasting, stacking, meshgrid / ogrid | [reference-broadcasting-shape.md](references/reference-broadcasting-shape.md) | +| Ufuncs, reductions, `axis`, `where`, NaNs | [reference-ufuncs-reductions.md](references/reference-ufuncs-reductions.md) | +| `linalg`, `@`, `einsum`, `tensordot` | [reference-linalg-einsum.md](references/reference-linalg-einsum.md) | +| `Generator`, `.npy` / `.npz`, structured dtypes | [reference-random-io-structured.md](references/reference-random-io-structured.md) | +| Pandas/Polars handoff, memory, threads | [reference-interop-performance.md](references/reference-interop-performance.md) | + +## Official documentation + +- [NumPy user guide](https://numpy.org/doc/stable/user/index.html) +- [Absolute basics](https://numpy.org/doc/stable/user/absolute_beginners.html) +- [Broadcasting](https://numpy.org/doc/stable/user/basics.broadcasting.html) +- [Indexing](https://numpy.org/doc/stable/user/basics.indexing.html) +- [Copies and views](https://numpy.org/doc/stable/user/basics.copies.html) +- [NumPy reference](https://numpy.org/doc/stable/reference/index.html) + +## Dependency + +Add or upgrade with **`uv add numpy`** per project policy; do not hand-edit pins in `pyproject.toml`. diff --git a/.cursor/skills/numpy-scientific/references/reference-arrays-dtypes.md b/.cursor/skills/numpy-scientific/references/reference-arrays-dtypes.md new file mode 100644 index 0000000..5d68c2c --- /dev/null +++ b/.cursor/skills/numpy-scientific/references/reference-arrays-dtypes.md @@ -0,0 +1,45 @@ +# Array creation, dtypes, casting + +## ndarray mental model + +- **`shape`**: tuple of lengths per axis; **`ndim`**; **`size`** total elements. +- **`dtype`**: element type (size and interpretation); **`itemsize`** bytes per element. +- **`order`**: **`C`** row-major (default) vs **`F`** column-major; matters for **reshape**, **flat** iteration, and **FFI** with other libraries. + +Official: [The N-dimensional array](https://numpy.org/doc/stable/reference/arrays.ndarray.html). + +## Constructors + +| Need | Typical API | +|------|-------------| +| Zeros / ones | `np.zeros`, `np.ones`, `dtype=...` | +| Uninitialized (then fill) | `np.empty` (fastest when you overwrite every element) | +| Constant fill | `np.full` | +| Sequences | `np.asarray` (no copy if already ndarray and compatible), `np.array` (new array) | +| Ranges | `np.arange` (half-open, prefer **integer** steps; watch **float** accumulation), `np.linspace`, `np.logspace`, `np.geomspace` | +| Identity / diag | `np.eye`, `np.diag` | + +## Dtype choices + +- **Integers**: `np.int32`, `np.int64`, `np.uint8` for images, etc.; match **file format** and **downstream** APIs. +- **Floats**: `float64` default; `float32` for memory/bandwidth when precision allows; **`float16`** mainly for storage or ML hooks, not general numerics. +- **Complex**: `complex128` default pair with `float64` real/imag. +- **Booleans**: `bool_`; avoid Python `bool` in dtype lists for homogeneous arrays. + +## Casting and `astype` + +- **`astype`** can **round** or **truncate**; floating to integer is not the same as `round()` policy-wise—verify for your domain. +- **`astype(copy=False)`** may still copy if layout or dtype forces it. +- Prefer **`np.can_cast`** and explicit **`dtype=`** on creation when ingesting untyped buffers. + +## Strings and objects + +- **Fixed-width strings**: `dtype="U10"`, `"S10"`; variable-length text often belongs in **pandas** or Python lists, not `object` arrays, unless you accept **`object`** dtype costs. + +## Alignment and record dtypes + +- **Structured / record** dtypes: field offsets and alignment; see [reference-random-io-structured.md](reference-random-io-structured.md). + +## Verification + +- **`array.shape`**, **`array.dtype`**, **`array.flags`** (`writeable`, `owndata`) when debugging aliases. diff --git a/.cursor/skills/numpy-scientific/references/reference-broadcasting-shape.md b/.cursor/skills/numpy-scientific/references/reference-broadcasting-shape.md new file mode 100644 index 0000000..8e9ef8c --- /dev/null +++ b/.cursor/skills/numpy-scientific/references/reference-broadcasting-shape.md @@ -0,0 +1,36 @@ +# Broadcasting and array shape + +Official: [Broadcasting](https://numpy.org/doc/stable/user/basics.broadcasting.html). + +## Rule + +From trailing dimensions forward, pairs are compatible if **equal**, or **one is 1**, or **one is missing** (implicit 1). The result shape is the **maximum** of each aligned size. + +## New axes + +- **`arr[:, np.newaxis]`** or **`arr[:, None]`** inserts a length-1 axis for alignment. +- **`np.expand_dims`** for clarity in library code. + +## Explicit broadcast + +- **`np.broadcast_to`** materializes a **read-only** strided view when possible; good for debugging shapes. +- **`np.broadcast_arrays`** aligns several inputs to a common shape. + +## Stacking and splitting + +- **`np.stack`** (new axis), **`np.concatenate`** (existing axis), **`np.vstack` / `np.hstack` / `np.dstack`** (convenience; know what they do to 1D). +- **`np.split`**, **`array_split`** for uneven chunks. + +## Grids + +- **`np.meshgrid`** (`indexing="xy"` vs `"ij"`): know which axis varies **fast**; mistakes swap rows/columns in images. +- **`np.ogrid`** / **`mgrid`**: open grids that **broadcast** without huge temporaries. + +## Tile and repeat + +- **`np.tile`** repeats blocks; **`np.repeat`** repeats elements; different memory patterns. + +## Gotchas + +- **Scalar + array** broadcasts naturally; **list of arrays** does not become one ndarray without **`stack`**/**`array`**. +- **In-place** `+=` with broadcast RHS can create **temporary** behavior surprises; verify with small examples when optimizing. diff --git a/.cursor/skills/numpy-scientific/references/reference-indexing-views.md b/.cursor/skills/numpy-scientific/references/reference-indexing-views.md new file mode 100644 index 0000000..d7f27a4 --- /dev/null +++ b/.cursor/skills/numpy-scientific/references/reference-indexing-views.md @@ -0,0 +1,38 @@ +# Indexing, views, and copies + +Official guides: [Indexing](https://numpy.org/doc/stable/user/basics.indexing.html), [Copies and views](https://numpy.org/doc/stable/user/basics.copies.html). + +## Basic indexing (views) + +- **Slices** `start:stop:step`, **ellipsis** `...`, and **integer** indexing that **reduces** dimensionality yield **views** when the memory layout allows. +- **Mutating** a slice can change the **base** array—this is the main footgun. + +## Advanced indexing (copies) + +- **Integer arrays**, **boolean masks** (unless `numpy.ma`), and combinations that cannot be expressed as a strided window generally produce a **copy**. +- After `b = a[mask]`, changing **`b`** does not change **`a`**. + +## `reshape`, `ravel`, `transpose`, `swapaxes` + +- **`reshape`**: returns **view** when strides allow, else **copy**; use **`arr.reshape(-1)`** for flattening with explicit intent. +- **`ravel`**: often a view; **`flatten`** always a copy. +- **`transpose` / `.T`**: usually a view. + +## `copy` and `base` + +- **`arr.copy()`** for an independent buffer. +- **`arr.base`**: if not `None`, data is owned elsewhere; trace mutations through **views**. + +## Boolean indexing vs `numpy.ma` + +- Plain **boolean** indexing on `ndarray` **copies** selected elements. +- **Masked arrays** (`numpy.ma`) carry a mask alongside data; use when NaNs are insufficient or you need mask algebra. + +## Assignment + +- **`a[i:j] = x`** writes through to base; shapes must broadcast. +- **Chained** fancy indexing assignment has subtleties; prefer **single** assignment with **`np.put`** or explicit loop only when necessary. + +## Reading suggestions + +- When unsure, **`np.shares_memory(a, b)`** answers whether two arrays might alias. diff --git a/.cursor/skills/numpy-scientific/references/reference-interop-performance.md b/.cursor/skills/numpy-scientific/references/reference-interop-performance.md new file mode 100644 index 0000000..53fa3e3 --- /dev/null +++ b/.cursor/skills/numpy-scientific/references/reference-interop-performance.md @@ -0,0 +1,35 @@ +# Interop, pandas, and performance + +## pandas and Polars + +- **pandas**: **`Series.values` / `to_numpy()`**, **`DataFrame.to_numpy()`**—watch **copy** vs **view** and **dtype=object** columns. +- **Polars**: convert via **`to_numpy()`** on expressions/Series; prefer staying in Polars for query-heavy pipelines per the Python spec. +- Establish a **single owner** of labels (index/columns) at module boundaries; do not duplicate metadata in raw ndarrays without documentation. +- Table-level patterns: **dataframes** skill. + +## SciPy, C, CUDA + +- **SciPy** routines accept **`array_like`**; pass **`np.asarray`** with **`dtype`** when you need a contiguous buffer. +- **ctypes** / **cffi**: **`array.ctypes`**, **`__array_interface__`**; enforce **contiguity** (`C` or `F`) before passing pointers. + +## Vectorization discipline + +- Prefer **ufuncs** and **broadcasting** over Python **`for`** loops over rows when **N** is large. +- When loops remain, **Numba**, **Cython**, or moving hot paths to **Rust/PyO3** are separate project decisions—do not introduce heavy deps without **`uv add`** and team agreement. + +## Memory + +- **`dtype` downcast** when safe; **`in-place`** ufuncs only when readability and aliasing are controlled. +- **`del`** large temporaries in tight notebooks is cosmetic; **streaming** or **chunking** fixes real pressure. + +## Threads and BLAS + +- Underlying **BLAS/OpenMP** may use threads; nested **joblib** / process pools can **oversubscribe** CPU. Set **`OMP_NUM_THREADS`** / vendor vars in HPC scripts when jobs are parallel at a higher level. + +## Testing + +- **Parametrize** shapes and dtypes; assert **`out.shape`**, **`dtype`**, and known **analytic** cases for numerics (**`python-reviewer`**). + +## Plotting + +- Pass **`np.asarray`** to **matplotlib** when consuming duck arrays; use **`matplotlib-scientific`** skill for figure polish. diff --git a/.cursor/skills/numpy-scientific/references/reference-linalg-einsum.md b/.cursor/skills/numpy-scientific/references/reference-linalg-einsum.md new file mode 100644 index 0000000..08b0a1b --- /dev/null +++ b/.cursor/skills/numpy-scientific/references/reference-linalg-einsum.md @@ -0,0 +1,32 @@ +# Linear algebra and `einsum` + +## Matrix multiply + +- **`@`** operator and **`np.matmul`**: **batched** last two dimensions for ndim > 2; not the same as **`np.dot`** for stacks (prefer **`@`** for clarity). +- **Elementwise** multiply: **`*`** or **`np.multiply`**. + +Official: [numpy.linalg](https://numpy.org/doc/stable/reference/routines.linalg.html). + +## `linalg` + +- **`np.linalg.solve`**: linear systems **`Ax = b`** (prefer over **`inv(A) @ b`** numerically). +- **`lstsq`**, **`svd`**, **`eig`**, **`eigh`** (Hermitian), **`cholesky`**, **`norm`**, **`det`**, **`matrix_rank`**. +- **`cond`** and **`rcond`** for ill-conditioned systems. + +## `einsum` + +- **Einstein summation**: specify **subscripts** or **`->` output**; **sum** repeated indices. +- **`optimize=True`** fuses paths for large contractions (default in recent NumPy for many cases). +- Use for **tensor contractions**, **batched** matmuls, and **axis reductions** that are hard to read as nested **`sum`**. + +Official: [numpy.einsum](https://numpy.org/doc/stable/reference/generated/numpy.einsum.html). + +## `tensordot`, `kron`, `outer` + +- **`np.tensordot`**: contract chosen axes between two tensors. +- **`np.kron`**: Kronecker product; can explode **size** quickly. +- **`np.outer`**: rank-1 outer product. + +## dtypes + +- **`linalg`** generally expects **floating** or **complex floating**; integer arrays may be promoted or rejected—cast explicitly. diff --git a/.cursor/skills/numpy-scientific/references/reference-random-io-structured.md b/.cursor/skills/numpy-scientific/references/reference-random-io-structured.md new file mode 100644 index 0000000..d41a8f8 --- /dev/null +++ b/.cursor/skills/numpy-scientific/references/reference-random-io-structured.md @@ -0,0 +1,41 @@ +# Random, file I/O, structured dtypes + +## Random (`numpy.random`) + +- Use **`np.random.default_rng(seed)`** → **`Generator`** with **`PCG64`** (or other bit generators per docs). +- Avoid legacy **`np.random.seed`** / **`RandomState`** in new library code; they complicate **parallel** and **library composition**. + +Official: [Random sampling](https://numpy.org/doc/stable/reference/random/index.html). + +## Common patterns + +```python +rng = np.random.default_rng(42) +rng.normal(size=(1000, 2)) +rng.integers(low=0, high=10, size=5, endpoint=False) +rng.permutation(x) +``` + +- **`SeedSequence`** for **spawned** streams in parallel jobs. + +## Saving and loading + +- **`.npy`**: single array, preserves shape and dtype. +- **`.npz`**: archive of **named** arrays (`np.savez`, **`np.savez_compressed`**). +- **`np.load`**: `allow_pickle=False` default in modern NumPy—**do not** unpickle untrusted files. + +Official: [NumPy binary formats](https://numpy.org/doc/stable/reference/routines.io.html). + +## Text and CSV + +- **`np.loadtxt`**, **`genfromtxt`** for simple cases; **pandas** / **polars** often scale better for messy tables—hand off at a clear **module or pipeline** boundary. + +## Structured and record arrays + +- **`dtype=[('x', 'f8'), ('id', 'i4')]`** for **columnar** heterogeneous data in one block. +- **Alignment** and **offsets** matter for **C interop**; use **`np.dtype`** descriptors carefully. +- For analytics-heavy workflows, consider **DataFrame** after one **`from_records`** step. + +## Memory maps + +- **`np.memmap`** for arrays larger than RAM; understand **flush** and **write** semantics. diff --git a/.cursor/skills/numpy-scientific/references/reference-ufuncs-reductions.md b/.cursor/skills/numpy-scientific/references/reference-ufuncs-reductions.md new file mode 100644 index 0000000..33efffb --- /dev/null +++ b/.cursor/skills/numpy-scientific/references/reference-ufuncs-reductions.md @@ -0,0 +1,37 @@ +# Ufuncs, reductions, `where`, NaNs + +## Ufuncs + +- **Unary / binary** elementwise: `np.sin`, `np.exp`, `np.maximum`, `np.logical_and`, etc. +- **`out=`** avoids allocations when you reuse buffers (advanced; ensure **no overlap** unless ufunc supports it). +- **Casting rules** follow ufunc **signature**; mixed dtypes **promote** per NumPy rules—verify for **int** vs **float** mixes. + +Official: [Universal functions](https://numpy.org/doc/stable/reference/ufuncs.html). + +## Reductions + +- **`axis=None`**: whole array; **`axis=tuple`**: reduce multiple axes at once. +- **`keepdims=True`**: preserves reduced axes as length 1 for **broadcasting** with the original shape. +- **`ddof`** for variance/std (Bessel correction). + +## NaNs + +- **`np.nanmean`**, **`np.nansum`**, **`np.nanstd`**, etc. **ignore** NaNs; plain **`mean`** propagates NaN. +- Sorting: **`np.nanargmin`** / **`nanargmax`** or **`np.ma`** when masks are first-class. + +## `np.where` + +- **`np.where(cond, x, y)`** selects elementwise; **`np.where(cond)`** returns **indices** (tuple of 1D arrays). +- For **assignment**, **`np.where`** on the RHS is not the same as masked write; use **`np.putmask`** or boolean slice assignment when appropriate. + +## Sorting and searching + +- **`np.sort`**, **`argsort`**, **`partition`**, **`searchsorted`** for monotonic bins (histograms, digitize). + +## Reproducibility note + +- **`sum`** on **float** may reorder for SIMD; bitwise-identical sums across machines are **not** guaranteed unless you control order (e.g. **`math.fsum`** on Python iterables, or **`np.add.reduce`** with fixed association in specialized cases). For science reporting, document **stability** expectations; **`python-reviewer`** flags when this matters. + +## `einsum`-light + +- Many **axis reductions** are clearer as **`einsum`**; see [reference-linalg-einsum.md](reference-linalg-einsum.md). diff --git a/.cursor/skills/pandas-pro/SKILL.md b/.cursor/skills/pandas-pro/SKILL.md deleted file mode 100644 index 7279110..0000000 --- a/.cursor/skills/pandas-pro/SKILL.md +++ /dev/null @@ -1,96 +0,0 @@ ---- -name: pandas-pro -description: Use when working with pandas DataFrames, data cleaning, aggregation, merging, or time series analysis. Invoke for data manipulation, missing value handling, groupby operations, or performance optimization. -triggers: - - pandas - - DataFrame - - data manipulation - - data cleaning - - aggregation - - groupby - - merge - - join - - time series - - data wrangling - - pivot table - - data transformation -role: expert -scope: implementation -output-format: code ---- - -# Pandas Pro - -Expert pandas developer specializing in efficient data manipulation, analysis, and transformation workflows with production-grade performance patterns. - -## Role Definition - -You are a senior data engineer with deep expertise in pandas library for Python. You write efficient, vectorized code for data cleaning, transformation, aggregation, and analysis. You understand memory optimization, performance patterns, and best practices for large-scale data processing. - -## When to Use This Skill - -- Loading, cleaning, and transforming tabular data -- Handling missing values and data quality issues -- Performing groupby aggregations and pivot operations -- Merging, joining, and concatenating datasets -- Time series analysis and resampling -- Optimizing pandas code for memory and performance -- Converting between data formats (CSV, Excel, SQL, JSON) - -## Core Workflow - -1. **Assess data structure** - Examine dtypes, memory usage, missing values, data quality -2. **Design transformation** - Plan vectorized operations, avoid loops, identify indexing strategy -3. **Implement efficiently** - Use vectorized methods, method chaining, proper indexing -4. **Validate results** - Check dtypes, shapes, edge cases, null handling -5. **Optimize** - Profile memory usage, apply categorical types, use chunking if needed - -## Reference Guide - -Load detailed guidance based on context: - -| Topic | Reference | Load When | -|-------|-----------|-----------| -| DataFrame Operations | `references/dataframe-operations.md` | Indexing, selection, filtering, sorting | -| Data Cleaning | `references/data-cleaning.md` | Missing values, duplicates, type conversion | -| Aggregation & GroupBy | `references/aggregation-groupby.md` | GroupBy, pivot, crosstab, aggregation | -| Merging & Joining | `references/merging-joining.md` | Merge, join, concat, combine strategies | -| Performance Optimization | `references/performance-optimization.md` | Memory usage, vectorization, chunking | - -## Constraints - -### MUST DO -- Use vectorized operations instead of loops -- Set appropriate dtypes (categorical for low-cardinality strings) -- Check memory usage with `.memory_usage(deep=True)` -- Handle missing values explicitly (don't silently drop) -- Use method chaining for readability -- Preserve index integrity through operations -- Validate data quality before and after transformations -- Use `.copy()` when modifying subsets to avoid SettingWithCopyWarning - -### MUST NOT DO -- Iterate over DataFrame rows with `.iterrows()` unless absolutely necessary -- Use chained indexing (`df['A']['B']`) - use `.loc[]` or `.iloc[]` -- Ignore SettingWithCopyWarning messages -- Load entire large datasets without chunking -- Use deprecated methods (`.ix`, `.append()` - use `pd.concat()`) -- Convert to Python lists for operations possible in pandas -- Assume data is clean without validation - -## Output Templates - -When implementing pandas solutions, provide: -1. Code with vectorized operations and proper indexing -2. Comments explaining complex transformations -3. Memory/performance considerations if dataset is large -4. Data validation checks (dtypes, nulls, shapes) - -## Knowledge Reference - -pandas 2.0+, NumPy, datetime handling, categorical types, MultiIndex, memory optimization, vectorization, method chaining, merge strategies, time series resampling, pivot tables, groupby aggregations - -## Related Skills - -- **Python Pro** - Type hints, testing, Python best practices -- **Data Scientist** - Statistical analysis, visualization, ML workflows diff --git a/.cursor/skills/pandas-pro/references/aggregation-groupby.md b/.cursor/skills/pandas-pro/references/aggregation-groupby.md deleted file mode 100644 index 7f2d716..0000000 --- a/.cursor/skills/pandas-pro/references/aggregation-groupby.md +++ /dev/null @@ -1,548 +0,0 @@ -# Aggregation and GroupBy - -> Reference for: Pandas Pro -> Load when: GroupBy operations, pivot tables, crosstab, aggregation functions, or summarizing data - ---- - -## Overview - -Aggregation transforms data from individual records to summary statistics. This reference covers GroupBy, pivot tables, crosstab, and advanced aggregation patterns with pandas 2.0+. - ---- - -## GroupBy Fundamentals - -### Basic GroupBy - -```python -import pandas as pd -import numpy as np - -df = pd.DataFrame({ - 'department': ['Eng', 'Eng', 'Sales', 'Sales', 'Eng', 'HR'], - 'team': ['Backend', 'Frontend', 'East', 'West', 'Backend', 'Recruit'], - 'employee': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve', 'Frank'], - 'salary': [80000, 75000, 65000, 70000, 85000, 60000], - 'years': [5, 3, 7, 4, 6, 2] -}) - -# Single column groupby with single aggregation -avg_salary = df.groupby('department')['salary'].mean() - -# Multiple aggregations -stats = df.groupby('department')['salary'].agg(['mean', 'min', 'max', 'count']) - -# GroupBy multiple columns -grouped = df.groupby(['department', 'team'])['salary'].mean() - -# Reset index to get DataFrame instead of Series -grouped = df.groupby('department')['salary'].mean().reset_index() -``` - -### Multiple Columns, Multiple Aggregations - -```python -# Named aggregation (pandas 2.0+ preferred) -result = df.groupby('department').agg( - avg_salary=('salary', 'mean'), - max_salary=('salary', 'max'), - total_years=('years', 'sum'), - headcount=('employee', 'count'), -) - -# Dictionary syntax (traditional) -result = df.groupby('department').agg({ - 'salary': ['mean', 'max', 'std'], - 'years': ['sum', 'mean'], -}) - -# Flatten multi-level column names -result.columns = ['_'.join(col).strip() for col in result.columns.values] -``` - -### Custom Aggregation Functions - -```python -# Lambda functions -result = df.groupby('department').agg({ - 'salary': lambda x: x.max() - x.min(), # Range - 'years': lambda x: x.quantile(0.75), # 75th percentile -}) - -# Named functions for clarity -def salary_range(x): - return x.max() - x.min() - -def coefficient_of_variation(x): - return x.std() / x.mean() if x.mean() != 0 else 0 - -result = df.groupby('department').agg( - salary_range=('salary', salary_range), - salary_cv=('salary', coefficient_of_variation), -) - -# Multiple custom functions -result = df.groupby('department')['salary'].agg([ - ('range', lambda x: x.max() - x.min()), - ('iqr', lambda x: x.quantile(0.75) - x.quantile(0.25)), - ('median', 'median'), -]) -``` - ---- - -## Transform and Apply - -### Transform - Returns Same Shape - -```python -# Transform returns Series with same index as original -# Useful for adding aggregated values back to original DataFrame - -# Add group mean as new column -df['dept_avg_salary'] = df.groupby('department')['salary'].transform('mean') - -# Normalize within group -df['salary_zscore'] = df.groupby('department')['salary'].transform( - lambda x: (x - x.mean()) / x.std() -) - -# Rank within group -df['salary_rank'] = df.groupby('department')['salary'].transform('rank', ascending=False) - -# Percentage of group total -df['salary_pct'] = df.groupby('department')['salary'].transform( - lambda x: x / x.sum() * 100 -) - -# Fill missing with group mean -df['salary'] = df.groupby('department')['salary'].transform( - lambda x: x.fillna(x.mean()) -) -``` - -### Apply - Flexible Operations - -```python -# Apply runs function on each group DataFrame -def top_n_by_salary(group, n=2): - return group.nlargest(n, 'salary') - -top_earners = df.groupby('department').apply(top_n_by_salary, n=2) - -# Reset index after apply -top_earners = df.groupby('department', group_keys=False).apply( - top_n_by_salary, n=2 -).reset_index(drop=True) - -# Complex group operations -def group_summary(group): - return pd.Series({ - 'headcount': len(group), - 'avg_salary': group['salary'].mean(), - 'top_earner': group.loc[group['salary'].idxmax(), 'employee'], - 'avg_tenure': group['years'].mean(), - }) - -summary = df.groupby('department').apply(group_summary) -``` - -### Filter - Keep/Remove Groups - -```python -# Keep only groups meeting a condition -# Groups with average salary > 70000 -filtered = df.groupby('department').filter(lambda x: x['salary'].mean() > 70000) - -# Groups with more than 2 members -filtered = df.groupby('department').filter(lambda x: len(x) > 2) - -# Combined conditions -filtered = df.groupby('department').filter( - lambda x: (len(x) >= 2) and (x['salary'].mean() > 65000) -) -``` - ---- - -## Pivot Tables - -### Basic Pivot Table - -```python -df = pd.DataFrame({ - 'date': pd.date_range('2024-01-01', periods=6), - 'product': ['A', 'B', 'A', 'B', 'A', 'B'], - 'region': ['East', 'East', 'West', 'West', 'East', 'West'], - 'sales': [100, 150, 120, 180, 90, 200], - 'quantity': [10, 15, 12, 18, 9, 20], -}) - -# Simple pivot -pivot = df.pivot_table( - values='sales', - index='product', - columns='region', - aggfunc='sum' -) - -# Multiple values -pivot = df.pivot_table( - values=['sales', 'quantity'], - index='product', - columns='region', - aggfunc='sum' -) - -# Multiple aggregation functions -pivot = df.pivot_table( - values='sales', - index='product', - columns='region', - aggfunc=['sum', 'mean', 'count'] -) -``` - -### Advanced Pivot Table Options - -```python -# Fill missing values -pivot = df.pivot_table( - values='sales', - index='product', - columns='region', - aggfunc='sum', - fill_value=0 -) - -# Add margins (totals) -pivot = df.pivot_table( - values='sales', - index='product', - columns='region', - aggfunc='sum', - margins=True, - margins_name='Total' -) - -# Multiple index levels -pivot = df.pivot_table( - values='sales', - index=['product', df['date'].dt.month], - columns='region', - aggfunc='sum' -) - -# Observed categories only (for categorical data) -pivot = df.pivot_table( - values='sales', - index='product', - columns='region', - aggfunc='sum', - observed=True # pandas 2.0+ default changed -) -``` - -### Unpivoting (Melt) - -```python -# Wide to long format -wide_df = pd.DataFrame({ - 'product': ['A', 'B'], - 'Q1_sales': [100, 150], - 'Q2_sales': [120, 180], - 'Q3_sales': [90, 200], -}) - -# Melt to long format -long_df = pd.melt( - wide_df, - id_vars=['product'], - value_vars=['Q1_sales', 'Q2_sales', 'Q3_sales'], - var_name='quarter', - value_name='sales' -) - -# Clean quarter column -long_df['quarter'] = long_df['quarter'].str.replace('_sales', '') -``` - ---- - -## Crosstab - -### Basic Crosstab - -```python -df = pd.DataFrame({ - 'gender': ['M', 'F', 'M', 'F', 'M', 'F', 'M', 'M'], - 'department': ['Eng', 'Eng', 'Sales', 'Sales', 'Eng', 'HR', 'HR', 'Eng'], - 'level': ['Senior', 'Junior', 'Senior', 'Senior', 'Junior', 'Junior', 'Senior', 'Junior'], -}) - -# Simple crosstab (counts) -ct = pd.crosstab(df['gender'], df['department']) - -# Normalized crosstab -ct_pct = pd.crosstab(df['gender'], df['department'], normalize='all') # Total -ct_pct = pd.crosstab(df['gender'], df['department'], normalize='index') # Row -ct_pct = pd.crosstab(df['gender'], df['department'], normalize='columns') # Column - -# With margins -ct = pd.crosstab(df['gender'], df['department'], margins=True) - -# Multiple levels -ct = pd.crosstab( - [df['gender'], df['level']], - df['department'] -) -``` - -### Crosstab with Aggregation - -```python -df['salary'] = [80000, 75000, 65000, 70000, 85000, 60000, 72000, 78000] - -# Crosstab with values and aggregation -ct = pd.crosstab( - df['gender'], - df['department'], - values=df['salary'], - aggfunc='mean' -) - -# Multiple aggregations -ct = pd.crosstab( - df['gender'], - df['department'], - values=df['salary'], - aggfunc=['mean', 'sum', 'count'] -) -``` - ---- - -## Window Functions with GroupBy - -### Rolling Aggregations - -```python -df = pd.DataFrame({ - 'date': pd.date_range('2024-01-01', periods=10), - 'product': ['A', 'B'] * 5, - 'sales': [100, 150, 110, 160, 120, 170, 130, 180, 140, 190], -}) - -# Rolling mean within groups -df['rolling_avg'] = df.groupby('product')['sales'].transform( - lambda x: x.rolling(window=3, min_periods=1).mean() -) - -# Expanding aggregations -df['cumulative_sales'] = df.groupby('product')['sales'].transform('cumsum') - -df['expanding_avg'] = df.groupby('product')['sales'].transform( - lambda x: x.expanding().mean() -) - -# Rank within groups -df['sales_rank'] = df.groupby('product')['sales'].rank(method='dense') -``` - -### Shift and Diff - -```python -# Previous value within group -df['prev_sales'] = df.groupby('product')['sales'].shift(1) - -# Next value -df['next_sales'] = df.groupby('product')['sales'].shift(-1) - -# Period-over-period change -df['sales_change'] = df.groupby('product')['sales'].diff() - -# Percentage change -df['sales_pct_change'] = df.groupby('product')['sales'].pct_change() -``` - ---- - -## Common Aggregation Patterns - -### Summary Statistics - -```python -# Comprehensive summary by group -def full_summary(group): - return pd.Series({ - 'count': len(group), - 'mean': group['salary'].mean(), - 'std': group['salary'].std(), - 'min': group['salary'].min(), - 'q25': group['salary'].quantile(0.25), - 'median': group['salary'].median(), - 'q75': group['salary'].quantile(0.75), - 'max': group['salary'].max(), - 'sum': group['salary'].sum(), - }) - -summary = df.groupby('department').apply(full_summary) -``` - -### Top N Per Group - -```python -# Top 2 salaries per department -top_2 = df.groupby('department', group_keys=False).apply( - lambda x: x.nlargest(2, 'salary') -) - -# Using head after sorting -top_2 = df.sort_values('salary', ascending=False).groupby( - 'department', group_keys=False -).head(2) - -# Bottom N -bottom_2 = df.groupby('department', group_keys=False).apply( - lambda x: x.nsmallest(2, 'salary') -) -``` - -### First/Last Per Group - -```python -# First row per group -first = df.groupby('department').first() - -# Last row per group -last = df.groupby('department').last() - -# First row after sorting -first_by_salary = df.sort_values('salary', ascending=False).groupby( - 'department' -).first() - -# Nth row -nth = df.groupby('department').nth(1) # Second row (0-indexed) -``` - -### Cumulative Operations - -```python -# Cumulative sum -df['cum_sales'] = df.groupby('department')['salary'].cumsum() - -# Cumulative max/min -df['cum_max'] = df.groupby('department')['salary'].cummax() -df['cum_min'] = df.groupby('department')['salary'].cummin() - -# Cumulative count -df['cum_count'] = df.groupby('department').cumcount() + 1 - -# Running percentage of total -df['running_pct'] = df.groupby('department')['salary'].transform( - lambda x: x.cumsum() / x.sum() * 100 -) -``` - ---- - -## Performance Tips for GroupBy - -### Efficient GroupBy Operations - -```python -# Pre-sort for faster groupby operations -df = df.sort_values('department') -grouped = df.groupby('department', sort=False) # Already sorted - -# Use observed=True for categorical columns (pandas 2.0+ default) -df['department'] = df['department'].astype('category') -grouped = df.groupby('department', observed=True)['salary'].mean() - -# Avoid apply when possible - use built-in aggregations -# SLOWER: -result = df.groupby('department')['salary'].apply(lambda x: x.sum()) -# FASTER: -result = df.groupby('department')['salary'].sum() - -# Use numba for custom aggregations (if available) -@numba.jit(nopython=True) -def custom_agg(values): - return values.sum() / len(values) -``` - -### Memory-Efficient Aggregation - -```python -# For large DataFrames, compute aggregations separately -groups = df.groupby('department') - -means = groups['salary'].mean() -sums = groups['salary'].sum() -counts = groups.size() - -result = pd.DataFrame({ - 'mean': means, - 'sum': sums, - 'count': counts -}) - -# Avoid creating intermediate large DataFrames -# BAD: Creates full transformed DataFrame -df['z_score'] = (df['salary'] - df.groupby('department')['salary'].transform('mean')) / df.groupby('department')['salary'].transform('std') - -# BETTER: Compute once -group_stats = df.groupby('department')['salary'].agg(['mean', 'std']) -df = df.merge(group_stats, on='department') -df['z_score'] = (df['salary'] - df['mean']) / df['std'] -``` - ---- - -## Best Practices Summary - -1. **Use named aggregation** - Clearer than dictionary syntax -2. **Choose transform vs apply wisely** - Transform for same-shape, apply for flexible -3. **Pre-sort for performance** - Use `sort=False` after sorting -4. **Prefer built-in aggregations** - Faster than lambda/apply -5. **Use observed=True** - Especially for categorical data -6. **Reset index when needed** - Keep DataFrames easier to work with -7. **Validate group counts** - Check for unexpected groups - ---- - -## Anti-Patterns to Avoid - -```python -# BAD: Iterating over groups manually -for name, group in df.groupby('department'): - # process group - pass - -# GOOD: Use vectorized operations -df.groupby('department').agg(...) - -# BAD: Multiple groupby calls -df.groupby('dept')['salary'].mean() -df.groupby('dept')['salary'].sum() -df.groupby('dept')['salary'].count() - -# GOOD: Single groupby, multiple aggs -df.groupby('dept')['salary'].agg(['mean', 'sum', 'count']) - -# BAD: Apply for simple aggregations -df.groupby('dept')['salary'].apply(np.mean) - -# GOOD: Built-in method -df.groupby('dept')['salary'].mean() -``` - ---- - -## Related References - -- `dataframe-operations.md` - Filtering before aggregation -- `merging-joining.md` - Join aggregated results back -- `performance-optimization.md` - Optimize large-scale aggregations diff --git a/.cursor/skills/pandas-pro/references/data-cleaning.md b/.cursor/skills/pandas-pro/references/data-cleaning.md deleted file mode 100644 index ede32c2..0000000 --- a/.cursor/skills/pandas-pro/references/data-cleaning.md +++ /dev/null @@ -1,503 +0,0 @@ -# Data Cleaning - -> Reference for: Pandas Pro -> Load when: Missing values, duplicates, type conversion, data validation, or data quality issues - ---- - -## Overview - -Data cleaning is critical for reliable analysis. This reference covers handling missing values, duplicates, type conversion, and data validation with pandas 2.0+ patterns. - ---- - -## Missing Values - -### Detecting Missing Values - -```python -import pandas as pd -import numpy as np - -df = pd.DataFrame({ - 'name': ['Alice', 'Bob', None, 'Diana'], - 'age': [25, np.nan, 35, 28], - 'salary': [50000, 60000, np.nan, np.nan], - 'department': ['Eng', '', 'Eng', 'Sales'] -}) - -# Check for any missing values -df.isna().any() # Per column -df.isna().any().any() # Entire DataFrame - -# Count missing values -df.isna().sum() # Per column -df.isna().sum().sum() # Total - -# Percentage of missing values -(df.isna().sum() / len(df) * 100).round(2) - -# Rows with any missing values -df[df.isna().any(axis=1)] - -# Rows with all values present -df[df.notna().all(axis=1)] - -# Missing value heatmap info -missing_info = pd.DataFrame({ - 'missing': df.isna().sum(), - 'percent': (df.isna().sum() / len(df) * 100).round(2), - 'dtype': df.dtypes -}) -``` - -### Handling Missing Values - Dropping - -```python -# Drop rows with any missing value -df_clean = df.dropna() - -# Drop rows where specific columns have missing values -df_clean = df.dropna(subset=['name', 'age']) - -# Drop rows where ALL values are missing -df_clean = df.dropna(how='all') - -# Drop rows with minimum non-null values -df_clean = df.dropna(thresh=3) # Keep rows with at least 3 non-null - -# Drop columns with missing values -df_clean = df.dropna(axis=1) - -# Drop columns with more than 50% missing -threshold = len(df) * 0.5 -df_clean = df.dropna(axis=1, thresh=threshold) -``` - -### Handling Missing Values - Filling - -```python -# Fill with constant value -df['age'] = df['age'].fillna(0) - -# Fill with column mean/median/mode -df['age'] = df['age'].fillna(df['age'].mean()) -df['salary'] = df['salary'].fillna(df['salary'].median()) -df['department'] = df['department'].fillna(df['department'].mode()[0]) - -# Forward fill (use previous value) -df['salary'] = df['salary'].ffill() - -# Backward fill (use next value) -df['salary'] = df['salary'].bfill() - -# Fill with different values per column -fill_values = {'age': 0, 'salary': df['salary'].median(), 'name': 'Unknown'} -df = df.fillna(fill_values) - -# Fill with interpolation (numeric data) -df['salary'] = df['salary'].interpolate(method='linear') - -# Group-specific fill (fill with group mean) -df['salary'] = df.groupby('department')['salary'].transform( - lambda x: x.fillna(x.mean()) -) -``` - -### Handling Empty Strings vs NaN - -```python -# Empty strings are NOT detected as NaN -df['department'].isna().sum() # Won't count '' - -# Replace empty strings with NaN -df['department'] = df['department'].replace('', np.nan) -# Or -df['department'] = df['department'].replace(r'^\s*$', np.nan, regex=True) - -# Replace multiple values with NaN -df = df.replace(['', 'N/A', 'null', 'None', '-'], np.nan) - -# Using na_values when reading files -df = pd.read_csv('file.csv', na_values=['', 'N/A', 'null', 'None', '-']) -``` - ---- - -## Handling Duplicates - -### Detecting Duplicates - -```python -df = pd.DataFrame({ - 'id': [1, 2, 2, 3, 4, 4], - 'name': ['Alice', 'Bob', 'Bob', 'Charlie', 'Diana', 'Diana'], - 'email': ['a@x.com', 'b@x.com', 'b@x.com', 'c@x.com', 'd@x.com', 'd2@x.com'] -}) - -# Check for duplicate rows (all columns) -df.duplicated().sum() - -# Check specific columns -df.duplicated(subset=['id']).sum() -df.duplicated(subset=['name', 'email']).sum() - -# View duplicate rows -df[df.duplicated(keep=False)] # All duplicates -df[df.duplicated(keep='first')] # Duplicates except first occurrence -df[df.duplicated(keep='last')] # Duplicates except last occurrence - -# Count duplicates per key -df.groupby('id').size().loc[lambda x: x > 1] -``` - -### Removing Duplicates - -```python -# Remove duplicate rows (keep first) -df_clean = df.drop_duplicates() - -# Keep last occurrence -df_clean = df.drop_duplicates(keep='last') - -# Remove all duplicates (keep none) -df_clean = df.drop_duplicates(keep=False) - -# Based on specific columns -df_clean = df.drop_duplicates(subset=['id']) -df_clean = df.drop_duplicates(subset=['name', 'email'], keep='last') - -# In-place modification -df.drop_duplicates(inplace=True) -``` - -### Handling Duplicates with Aggregation - -```python -# Instead of dropping, aggregate duplicates -df_agg = df.groupby('id').agg({ - 'name': 'first', - 'email': lambda x: ', '.join(x.unique()) -}).reset_index() - -# Keep row with max/min value -df_best = df.loc[df.groupby('id')['score'].idxmax()] - -# Rank duplicates -df['rank'] = df.groupby('id').cumcount() + 1 -``` - ---- - -## Type Conversion - -### Checking and Converting Types - -```python -# Check current types -df.dtypes -df.info() - -# Convert to specific type -df['age'] = df['age'].astype(int) -df['salary'] = df['salary'].astype(float) -df['name'] = df['name'].astype(str) - -# Safe conversion with errors handling -df['age'] = pd.to_numeric(df['age'], errors='coerce') # Invalid -> NaN -df['age'] = pd.to_numeric(df['age'], errors='ignore') # Keep original if invalid - -# Convert multiple columns -df = df.astype({'age': 'int64', 'salary': 'float64'}) - -# Convert object to string (pandas 2.0+ StringDtype) -df['name'] = df['name'].astype('string') # Nullable string type -``` - -### Datetime Conversion - -```python -df = pd.DataFrame({ - 'date_str': ['2024-01-15', '2024-02-20', 'invalid', '2024-03-10'], - 'timestamp': [1705276800, 1708387200, 1710028800, 1710028800] -}) - -# String to datetime -df['date'] = pd.to_datetime(df['date_str'], errors='coerce') - -# Specify format for faster parsing -df['date'] = pd.to_datetime(df['date_str'], format='%Y-%m-%d', errors='coerce') - -# Unix timestamp to datetime -df['datetime'] = pd.to_datetime(df['timestamp'], unit='s') - -# Extract components -df['year'] = df['date'].dt.year -df['month'] = df['date'].dt.month -df['day_of_week'] = df['date'].dt.day_name() - -# Handle mixed formats -df['date'] = pd.to_datetime(df['date_str'], format='mixed', dayfirst=False) -``` - -### Categorical Conversion - -```python -# Convert to categorical (memory efficient for low cardinality) -df['department'] = df['department'].astype('category') - -# Ordered categorical -df['size'] = pd.Categorical( - df['size'], - categories=['Small', 'Medium', 'Large'], - ordered=True -) - -# Check memory savings -print(f"Object: {df['department'].nbytes}") -df['department'] = df['department'].astype('category') -print(f"Category: {df['department'].nbytes}") -``` - -### Nullable Integer Types (pandas 2.0+) - -```python -# Standard int doesn't support NaN -# Use nullable integer types -df['age'] = df['age'].astype('Int64') # Note capital I - -# All nullable types -df = df.astype({ - 'count': 'Int64', # Nullable integer - 'price': 'Float64', # Nullable float - 'flag': 'boolean', # Nullable boolean - 'name': 'string', # Nullable string -}) - -# Convert with NA handling -df['age'] = pd.array([1, 2, None, 4], dtype='Int64') -``` - ---- - -## String Cleaning - -### Common String Operations - -```python -df = pd.DataFrame({ - 'name': [' Alice ', 'BOB', 'charlie', None, 'Diana Smith'], - 'email': ['ALICE@EXAMPLE.COM', 'bob@test', 'invalid', None, 'diana@example.com'] -}) - -# Strip whitespace -df['name'] = df['name'].str.strip() - -# Case normalization -df['name'] = df['name'].str.lower() -df['name'] = df['name'].str.upper() -df['name'] = df['name'].str.title() # Title Case - -# Replace patterns -df['name'] = df['name'].str.replace(r'\s+', ' ', regex=True) # Multiple spaces to one -df['phone'] = df['phone'].str.replace(r'[^0-9]', '', regex=True) # Keep only digits - -# Extract with regex -df['domain'] = df['email'].str.extract(r'@(.+)$') -df['first_name'] = df['name'].str.extract(r'^(\w+)') - -# Split strings -df[['first', 'last']] = df['name'].str.split(' ', n=1, expand=True) -``` - -### String Validation - -```python -# Check patterns -df['valid_email'] = df['email'].str.match(r'^[\w.]+@[\w.]+\.\w+$', na=False) - -# String length -df['name_length'] = df['name'].str.len() -df['valid_length'] = df['name'].str.len().between(2, 50) - -# Contains check -df['has_domain'] = df['email'].str.contains('@', na=False) -``` - ---- - -## Data Validation - -### Validation Functions - -```python -def validate_dataframe(df: pd.DataFrame) -> dict: - """Comprehensive DataFrame validation.""" - report = { - 'rows': len(df), - 'columns': len(df.columns), - 'duplicates': df.duplicated().sum(), - 'missing_by_column': df.isna().sum().to_dict(), - 'dtypes': df.dtypes.astype(str).to_dict(), - } - return report - -# Range validation -def validate_range(series: pd.Series, min_val, max_val) -> pd.Series: - """Return boolean mask for values in range.""" - return series.between(min_val, max_val) - -df['valid_age'] = validate_range(df['age'], 0, 120) - -# Custom validation -def validate_email(series: pd.Series) -> pd.Series: - """Validate email format.""" - pattern = r'^[\w.+-]+@[\w-]+\.[\w.-]+$' - return series.str.match(pattern, na=False) - -df['valid_email'] = validate_email(df['email']) -``` - -### Schema Validation with pandera - -```python -# Using pandera for schema validation (recommended for production) -import pandera as pa -from pandera import Column, Check - -schema = pa.DataFrameSchema({ - 'name': Column(str, Check.str_length(min_value=1, max_value=100)), - 'age': Column(int, Check.in_range(0, 120)), - 'email': Column(str, Check.str_matches(r'^[\w.+-]+@[\w-]+\.[\w.-]+$')), - 'salary': Column(float, Check.greater_than(0), nullable=True), -}) - -# Validate DataFrame -try: - schema.validate(df) -except pa.errors.SchemaError as e: - print(f"Validation failed: {e}") -``` - ---- - -## Data Cleaning Pipeline - -### Method Chaining Pattern - -```python -def clean_dataframe(df: pd.DataFrame) -> pd.DataFrame: - """Complete data cleaning pipeline using method chaining.""" - return ( - df - # Make a copy - .copy() - # Standardize column names - .rename(columns=lambda x: x.lower().strip().replace(' ', '_')) - # Drop fully empty rows - .dropna(how='all') - # Clean string columns - .assign( - name=lambda x: x['name'].str.strip().str.title(), - email=lambda x: x['email'].str.lower().str.strip(), - ) - # Handle missing values - .fillna({'department': 'Unknown'}) - # Convert types - .astype({'age': 'Int64', 'department': 'category'}) - # Remove duplicates - .drop_duplicates(subset=['email']) - # Reset index - .reset_index(drop=True) - ) - -df_clean = clean_dataframe(df) -``` - -### Pipeline with Validation - -```python -def clean_and_validate( - df: pd.DataFrame, - required_columns: list[str], - unique_columns: list[str] | None = None, -) -> tuple[pd.DataFrame, dict]: - """Clean DataFrame and return validation report.""" - - # Validate required columns exist - missing_cols = set(required_columns) - set(df.columns) - if missing_cols: - raise ValueError(f"Missing required columns: {missing_cols}") - - # Track cleaning stats - stats = { - 'initial_rows': len(df), - 'dropped_empty': 0, - 'dropped_duplicates': 0, - 'filled_missing': {}, - } - - # Clean - df = df.copy() - - # Drop empty rows - before = len(df) - df = df.dropna(how='all') - stats['dropped_empty'] = before - len(df) - - # Handle duplicates - if unique_columns: - before = len(df) - df = df.drop_duplicates(subset=unique_columns) - stats['dropped_duplicates'] = before - len(df) - - stats['final_rows'] = len(df) - - return df, stats -``` - ---- - -## Best Practices Summary - -1. **Always check data quality first** - Use `.info()`, `.describe()`, and missing value analysis -2. **Document cleaning decisions** - Track what was dropped/filled and why -3. **Use nullable types** - `Int64`, `string`, `boolean` for proper NA handling -4. **Validate after cleaning** - Ensure data meets expectations -5. **Use method chaining** - Readable, maintainable cleaning pipelines -6. **Copy before modifying** - Avoid SettingWithCopyWarning -7. **Handle edge cases** - Empty strings, whitespace, invalid formats - ---- - -## Anti-Patterns to Avoid - -```python -# BAD: Dropping NaN without understanding impact -df = df.dropna() # May lose significant data - -# GOOD: Investigate first, then decide -print(f"Missing values: {df.isna().sum()}") -print(f"Rows affected: {df.isna().any(axis=1).sum()}") -# Then make informed decision - -# BAD: Filling without domain knowledge -df['age'] = df['age'].fillna(0) # Age 0 is not valid - -# GOOD: Use appropriate fill strategy -df['age'] = df['age'].fillna(df['age'].median()) - -# BAD: Type conversion without error handling -df['id'] = df['id'].astype(int) # Will fail on NaN or invalid - -# GOOD: Safe conversion -df['id'] = pd.to_numeric(df['id'], errors='coerce').astype('Int64') -``` - ---- - -## Related References - -- `dataframe-operations.md` - Selection and filtering for targeted cleaning -- `aggregation-groupby.md` - Aggregate duplicates instead of dropping -- `performance-optimization.md` - Efficient cleaning of large datasets diff --git a/.cursor/skills/pandas-pro/references/dataframe-operations.md b/.cursor/skills/pandas-pro/references/dataframe-operations.md deleted file mode 100644 index 6321f23..0000000 --- a/.cursor/skills/pandas-pro/references/dataframe-operations.md +++ /dev/null @@ -1,423 +0,0 @@ -# DataFrame Operations - -> Reference for: Pandas Pro -> Load when: Indexing, selection, filtering, sorting, or basic DataFrame manipulation - ---- - -## Overview - -DataFrame operations form the foundation of pandas work. This reference covers indexing, selection, filtering, and sorting with pandas 2.0+ best practices. - ---- - -## Indexing and Selection - -### Label-Based Selection with `.loc[]` - -Use `.loc[]` for label-based indexing. Always preferred over chained indexing. - -```python -import pandas as pd -import numpy as np - -# Sample DataFrame -df = pd.DataFrame({ - 'name': ['Alice', 'Bob', 'Charlie', 'Diana'], - 'age': [25, 30, 35, 28], - 'salary': [50000, 60000, 70000, 55000], - 'department': ['Engineering', 'Sales', 'Engineering', 'Marketing'] -}, index=['a', 'b', 'c', 'd']) - -# Single value -value = df.loc['a', 'name'] # 'Alice' - -# Single row (returns Series) -row = df.loc['a'] - -# Multiple rows -rows = df.loc[['a', 'c']] - -# Row and column slices (inclusive on both ends) -subset = df.loc['a':'c', 'name':'salary'] - -# Boolean indexing with .loc -adults = df.loc[df['age'] >= 30] - -# Boolean indexing with column selection -adults_names = df.loc[df['age'] >= 30, 'name'] - -# Multiple conditions -engineering_seniors = df.loc[ - (df['department'] == 'Engineering') & (df['age'] >= 30), - ['name', 'salary'] -] -``` - -### Position-Based Selection with `.iloc[]` - -Use `.iloc[]` for integer position-based indexing. - -```python -# Single value by position -value = df.iloc[0, 0] # First row, first column - -# Single row by position -first_row = df.iloc[0] - -# Slice rows (exclusive end, like Python) -first_three = df.iloc[:3] - -# Specific rows and columns by position -subset = df.iloc[[0, 2], [0, 2]] # Rows 0,2 and columns 0,2 - -# Range selection -block = df.iloc[1:3, 0:2] # Rows 1-2, columns 0-1 -``` - -### When to Use `.loc[]` vs `.iloc[]` - -| Scenario | Use | Example | -|----------|-----|---------| -| Known column names | `.loc[]` | `df.loc[:, 'name']` | -| Filter by condition | `.loc[]` | `df.loc[df['age'] > 25]` | -| First/last N rows | `.iloc[]` | `df.iloc[:5]` or `df.iloc[-5:]` | -| Specific row positions | `.iloc[]` | `df.iloc[[0, 5, 10]]` | -| Unknown column order | `.iloc[]` | `df.iloc[:, 0]` | - ---- - -## Filtering DataFrames - -### Boolean Masks - -```python -# Single condition -mask = df['age'] > 25 -filtered = df[mask] - -# Multiple conditions (use parentheses!) -mask = (df['age'] > 25) & (df['salary'] < 65000) -filtered = df[mask] - -# OR conditions -mask = (df['department'] == 'Engineering') | (df['department'] == 'Sales') -filtered = df[mask] - -# NOT condition -mask = ~(df['department'] == 'Marketing') -filtered = df[mask] -``` - -### Using `.query()` for Readable Filters - -```python -# Simple query - more readable for complex conditions -result = df.query('age > 25 and salary < 65000') - -# Using variables with @ -min_age = 25 -result = df.query('age > @min_age') - -# String comparisons -result = df.query('department == "Engineering"') - -# In-list filtering -depts = ['Engineering', 'Sales'] -result = df.query('department in @depts') - -# Complex expressions -result = df.query('(age > 25) and (department != "Marketing")') -``` - -### Using `.isin()` for Multiple Values - -```python -# Filter by multiple values -departments = ['Engineering', 'Sales'] -filtered = df[df['department'].isin(departments)] - -# Negation -filtered = df[~df['department'].isin(departments)] - -# Multiple columns -conditions = { - 'department': ['Engineering', 'Sales'], - 'age': [25, 30, 35] -} -# Filter where department is in list AND age is in list -mask = df['department'].isin(conditions['department']) & df['age'].isin(conditions['age']) -``` - -### String Filtering with `.str` Accessor - -```python -df = pd.DataFrame({ - 'email': ['alice@example.com', 'bob@test.org', 'charlie@example.com'], - 'name': ['Alice Smith', 'Bob Jones', 'Charlie Brown'] -}) - -# Contains -mask = df['email'].str.contains('example') - -# Starts/ends with -mask = df['email'].str.endswith('.com') -mask = df['name'].str.startswith('A') - -# Regex matching -mask = df['email'].str.match(r'^[a-z]+@example\.com$') - -# Case-insensitive -mask = df['name'].str.lower().str.contains('alice') -# Or with case parameter -mask = df['name'].str.contains('alice', case=False) - -# Handle NaN in string columns -mask = df['email'].str.contains('example', na=False) -``` - ---- - -## Sorting - -### Basic Sorting - -```python -# Sort by single column (ascending) -sorted_df = df.sort_values('age') - -# Sort descending -sorted_df = df.sort_values('age', ascending=False) - -# Sort by multiple columns -sorted_df = df.sort_values(['department', 'salary'], ascending=[True, False]) - -# Sort by index -sorted_df = df.sort_index() -sorted_df = df.sort_index(ascending=False) -``` - -### Advanced Sorting - -```python -# Sort with NaN handling -df_with_nan = pd.DataFrame({ - 'name': ['Alice', 'Bob', 'Charlie'], - 'score': [85.0, np.nan, 90.0] -}) - -# NaN at end (default) -sorted_df = df_with_nan.sort_values('score', na_position='last') - -# NaN at beginning -sorted_df = df_with_nan.sort_values('score', na_position='first') - -# Custom sort order using Categorical -order = ['Marketing', 'Sales', 'Engineering'] -df['department'] = pd.Categorical(df['department'], categories=order, ordered=True) -sorted_df = df.sort_values('department') - -# Sort by computed values without adding column -sorted_df = df.iloc[df['name'].str.len().argsort()] -``` - -### In-Place Sorting - -```python -# Modify DataFrame in place -df.sort_values('age', inplace=True) - -# Reset index after sorting -df.sort_values('age', inplace=True) -df.reset_index(drop=True, inplace=True) - -# Or chain -df = df.sort_values('age').reset_index(drop=True) -``` - ---- - -## Column Operations - -### Adding and Modifying Columns - -```python -# Add new column -df['bonus'] = df['salary'] * 0.1 - -# Conditional column with np.where -df['seniority'] = np.where(df['age'] >= 30, 'Senior', 'Junior') - -# Multiple conditions with np.select -conditions = [ - df['age'] < 25, - df['age'] < 35, - df['age'] >= 35 -] -choices = ['Junior', 'Mid', 'Senior'] -df['level'] = np.select(conditions, choices, default='Unknown') - -# Using .assign() for method chaining (returns new DataFrame) -df_new = df.assign( - bonus=lambda x: x['salary'] * 0.1, - total_comp=lambda x: x['salary'] + x['salary'] * 0.1 -) -``` - -### Renaming Columns - -```python -# Rename specific columns -df = df.rename(columns={'name': 'full_name', 'age': 'years'}) - -# Rename all columns with function -df.columns = df.columns.str.lower().str.replace(' ', '_') - -# Using rename with function -df = df.rename(columns=str.upper) -``` - -### Dropping Columns - -```python -# Drop single column -df = df.drop('bonus', axis=1) -# Or -df = df.drop(columns=['bonus']) - -# Drop multiple columns -df = df.drop(columns=['bonus', 'level']) - -# Drop columns by condition -cols_to_drop = [col for col in df.columns if col.startswith('temp_')] -df = df.drop(columns=cols_to_drop) -``` - -### Reordering Columns - -```python -# Explicit order -new_order = ['name', 'department', 'age', 'salary'] -df = df[new_order] - -# Move specific column to front -cols = ['salary'] + [c for c in df.columns if c != 'salary'] -df = df[cols] - -# Using .reindex() -df = df.reindex(columns=['name', 'age', 'salary', 'department']) -``` - ---- - -## Index Operations - -### Setting and Resetting Index - -```python -# Set column as index -df = df.set_index('name') - -# Reset index back to column -df = df.reset_index() - -# Drop index completely -df = df.reset_index(drop=True) - -# Set multiple columns as index (MultiIndex) -df = df.set_index(['department', 'name']) -``` - -### Working with MultiIndex - -```python -# Create MultiIndex DataFrame -df = pd.DataFrame({ - 'department': ['Eng', 'Eng', 'Sales', 'Sales'], - 'team': ['Backend', 'Frontend', 'East', 'West'], - 'headcount': [10, 8, 15, 12] -}).set_index(['department', 'team']) - -# Select from MultiIndex -df.loc['Eng'] # All Eng rows -df.loc[('Eng', 'Backend')] # Specific row - -# Cross-section with .xs() -df.xs('Backend', level='team') # All Backend teams - -# Reset specific level -df.reset_index(level='team') -``` - ---- - -## Copying DataFrames - -### When to Use `.copy()` - -```python -# ALWAYS copy when modifying a subset -subset = df[df['age'] > 25].copy() -subset['new_col'] = 100 # Safe, no SettingWithCopyWarning - -# Without copy - may raise warning or fail silently -# BAD: -# subset = df[df['age'] > 25] -# subset['new_col'] = 100 # SettingWithCopyWarning! - -# Deep copy (default) - copies data -df_copy = df.copy() # or df.copy(deep=True) - -# Shallow copy - shares data, only copies structure -df_shallow = df.copy(deep=False) -``` - ---- - -## Best Practices Summary - -1. **Use `.loc[]` and `.iloc[]`** - Never use chained indexing -2. **Parenthesize conditions** - `(cond1) & (cond2)` not `cond1 & cond2` -3. **Use `.query()` for readability** - Especially with complex filters -4. **Copy before modifying subsets** - Always use `.copy()` -5. **Use vectorized operations** - Avoid row iteration for filtering -6. **Handle NaN explicitly** - Use `na=False` in string operations -7. **Prefer method chaining** - Use `.assign()` for column creation - ---- - -## Anti-Patterns to Avoid - -```python -# BAD: Chained indexing -df['A']['B'] = value # May not work, raises warning - -# GOOD: Use .loc -df.loc[:, ('A', 'B')] = value -# Or for row selection then assignment: -df.loc[df['A'] > 0, 'B'] = value - -# BAD: Iterating for filtering -result = [] -for idx, row in df.iterrows(): - if row['age'] > 25: - result.append(row) - -# GOOD: Boolean indexing -result = df[df['age'] > 25] - -# BAD: Multiple separate assignments -df = df[df['age'] > 25] -df = df[df['salary'] > 50000] - -# GOOD: Combined filter -df = df[(df['age'] > 25) & (df['salary'] > 50000)] -``` - ---- - -## Related References - -- `data-cleaning.md` - After selection, clean the data -- `aggregation-groupby.md` - Group and aggregate filtered data -- `performance-optimization.md` - Optimize filtering on large datasets diff --git a/.cursor/skills/pandas-pro/references/merging-joining.md b/.cursor/skills/pandas-pro/references/merging-joining.md deleted file mode 100644 index 7bf33dc..0000000 --- a/.cursor/skills/pandas-pro/references/merging-joining.md +++ /dev/null @@ -1,599 +0,0 @@ -# Merging and Joining - -> Reference for: Pandas Pro -> Load when: Merge, join, concat, combine DataFrames, or handle relational data - ---- - -## Overview - -Combining DataFrames is essential for working with relational data. This reference covers merge, join, concat, and advanced combination strategies with pandas 2.0+. - ---- - -## Merge (SQL-Style Joins) - -### Basic Merge - -```python -import pandas as pd -import numpy as np - -# Sample DataFrames -employees = pd.DataFrame({ - 'emp_id': [1, 2, 3, 4, 5], - 'name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'], - 'dept_id': [101, 102, 101, 103, 102], -}) - -departments = pd.DataFrame({ - 'dept_id': [101, 102, 104], - 'dept_name': ['Engineering', 'Sales', 'Marketing'], -}) - -# Inner join (default) - only matching rows -result = pd.merge(employees, departments, on='dept_id') - -# Explicit how parameter -result = pd.merge(employees, departments, on='dept_id', how='inner') -``` - -### Join Types - -```python -# Inner join - only matching rows from both -inner = pd.merge(employees, departments, on='dept_id', how='inner') -# Result: 4 rows (emp_id 4 has dept_id 103 which doesn't exist in departments) - -# Left join - all rows from left, matching from right -left = pd.merge(employees, departments, on='dept_id', how='left') -# Result: 5 rows (Diana has NaN for dept_name) - -# Right join - all rows from right, matching from left -right = pd.merge(employees, departments, on='dept_id', how='right') -# Result: 4 rows (Marketing has no employees, but is included) - -# Outer join - all rows from both -outer = pd.merge(employees, departments, on='dept_id', how='outer') -# Result: 6 rows (includes unmatched from both sides) - -# Cross join - cartesian product -cross = pd.merge(employees, departments, how='cross') -# Result: 15 rows (5 employees x 3 departments) -``` - -### Merging on Different Column Names - -```python -employees = pd.DataFrame({ - 'emp_id': [1, 2, 3], - 'name': ['Alice', 'Bob', 'Charlie'], - 'department': [101, 102, 101], -}) - -departments = pd.DataFrame({ - 'id': [101, 102], - 'dept_name': ['Engineering', 'Sales'], -}) - -# Different column names -result = pd.merge( - employees, - departments, - left_on='department', - right_on='id' -) - -# Drop duplicate column after merge -result = result.drop('id', axis=1) -``` - -### Merging on Multiple Columns - -```python -sales = pd.DataFrame({ - 'region': ['East', 'East', 'West', 'West'], - 'product': ['A', 'B', 'A', 'B'], - 'sales': [100, 150, 120, 180], -}) - -targets = pd.DataFrame({ - 'region': ['East', 'East', 'West'], - 'product': ['A', 'B', 'A'], - 'target': [90, 140, 110], -}) - -# Merge on multiple columns -result = pd.merge(sales, targets, on=['region', 'product'], how='left') -``` - -### Merging on Index - -```python -# Set index before merge -employees_idx = employees.set_index('emp_id') -salaries = pd.DataFrame({ - 'emp_id': [1, 2, 3, 4], - 'salary': [80000, 75000, 70000, 65000], -}).set_index('emp_id') - -# Merge on index -result = pd.merge(employees_idx, salaries, left_index=True, right_index=True) - -# Mix of column and index -result = pd.merge( - employees, - salaries, - left_on='emp_id', - right_index=True -) -``` - ---- - -## Handling Duplicate Columns - -### Suffixes - -```python -df1 = pd.DataFrame({ - 'id': [1, 2, 3], - 'value': [10, 20, 30], - 'date': ['2024-01-01', '2024-01-02', '2024-01-03'], -}) - -df2 = pd.DataFrame({ - 'id': [1, 2, 3], - 'value': [100, 200, 300], - 'date': ['2024-02-01', '2024-02-02', '2024-02-03'], -}) - -# Default suffixes -result = pd.merge(df1, df2, on='id') -# Columns: id, value_x, date_x, value_y, date_y - -# Custom suffixes -result = pd.merge(df1, df2, on='id', suffixes=('_jan', '_feb')) -# Columns: id, value_jan, date_jan, value_feb, date_feb -``` - -### Validate Merge Cardinality - -```python -# Validate merge relationships (pandas 2.0+) -# Raises MergeError if validation fails - -# One-to-one: each key appears at most once in both DataFrames -result = pd.merge(df1, df2, on='id', validate='one_to_one') # or '1:1' - -# One-to-many: keys unique in left only -result = pd.merge(employees, salaries, on='emp_id', validate='one_to_many') # or '1:m' - -# Many-to-one: keys unique in right only -result = pd.merge(salaries, employees, on='emp_id', validate='many_to_one') # or 'm:1' - -# Many-to-many: no uniqueness requirement (default) -result = pd.merge(df1, df2, on='id', validate='many_to_many') # or 'm:m' -``` - -### Indicator Column - -```python -# Add indicator column showing source of each row -result = pd.merge( - employees, - departments, - on='dept_id', - how='outer', - indicator=True -) -# _merge column values: 'left_only', 'right_only', 'both' - -# Custom indicator name -result = pd.merge( - employees, - departments, - on='dept_id', - how='outer', - indicator='source' -) - -# Filter by indicator -left_only = result[result['_merge'] == 'left_only'] -both = result[result['_merge'] == 'both'] -``` - ---- - -## Join (Index-Based) - -### DataFrame.join() - -```python -# join() is for index-based joining (simpler syntax) -employees = pd.DataFrame({ - 'name': ['Alice', 'Bob', 'Charlie'], - 'dept_id': [101, 102, 101], -}, index=[1, 2, 3]) - -salaries = pd.DataFrame({ - 'salary': [80000, 75000, 70000], - 'bonus': [5000, 4000, 3500], -}, index=[1, 2, 3]) - -# Join on index -result = employees.join(salaries) - -# Join types (same as merge) -result = employees.join(salaries, how='left') -result = employees.join(salaries, how='outer') -``` - -### Join on Column to Index - -```python -employees = pd.DataFrame({ - 'name': ['Alice', 'Bob', 'Charlie'], - 'dept_id': [101, 102, 101], -}) - -departments = pd.DataFrame({ - 'dept_name': ['Engineering', 'Sales'], -}, index=[101, 102]) - -# Join left column to right index -result = employees.join(departments, on='dept_id') -``` - -### Join Multiple DataFrames - -```python -df1 = pd.DataFrame({'a': [1, 2]}, index=['x', 'y']) -df2 = pd.DataFrame({'b': [3, 4]}, index=['x', 'y']) -df3 = pd.DataFrame({'c': [5, 6]}, index=['x', 'y']) - -# Join multiple at once -result = df1.join([df2, df3]) - -# With suffixes for duplicate columns -result = df1.join([df2, df3], lsuffix='_1', rsuffix='_2') -``` - ---- - -## Concat (Stacking DataFrames) - -### Vertical Concatenation (Row-wise) - -```python -# Stack DataFrames vertically -df1 = pd.DataFrame({ - 'name': ['Alice', 'Bob'], - 'age': [25, 30], -}) - -df2 = pd.DataFrame({ - 'name': ['Charlie', 'Diana'], - 'age': [35, 28], -}) - -# Basic concat (axis=0 is default) -result = pd.concat([df1, df2]) - -# Reset index -result = pd.concat([df1, df2], ignore_index=True) - -# Keep track of source -result = pd.concat([df1, df2], keys=['source1', 'source2']) -# Creates MultiIndex -``` - -### Horizontal Concatenation (Column-wise) - -```python -names = pd.DataFrame({'name': ['Alice', 'Bob', 'Charlie']}) -ages = pd.DataFrame({'age': [25, 30, 35]}) -salaries = pd.DataFrame({'salary': [50000, 60000, 70000]}) - -# Concat columns (axis=1) -result = pd.concat([names, ages, salaries], axis=1) -``` - -### Handling Mismatched Columns - -```python -df1 = pd.DataFrame({ - 'name': ['Alice', 'Bob'], - 'age': [25, 30], -}) - -df2 = pd.DataFrame({ - 'name': ['Charlie', 'Diana'], - 'salary': [70000, 65000], -}) - -# Outer join (default) - include all columns -result = pd.concat([df1, df2]) -# age and salary columns have NaN where not present - -# Inner join - only common columns -result = pd.concat([df1, df2], join='inner') -# Only 'name' column -``` - -### Concat with Verification - -```python -# Verify no index overlap -try: - result = pd.concat([df1, df2], verify_integrity=True) -except ValueError as e: - print(f"Index overlap detected: {e}") - -# Alternative: use ignore_index -result = pd.concat([df1, df2], ignore_index=True) -``` - ---- - -## Combine and Update - -### combine_first() - Fill Gaps - -```python -# Fill NaN values from another DataFrame -df1 = pd.DataFrame({ - 'A': [1, np.nan, 3], - 'B': [np.nan, 2, 3], -}, index=['a', 'b', 'c']) - -df2 = pd.DataFrame({ - 'A': [10, 20, 30], - 'B': [10, 20, 30], -}, index=['a', 'b', 'c']) - -# Fill NaN in df1 with values from df2 -result = df1.combine_first(df2) -# A: [1, 20, 3], B: [10, 2, 3] -``` - -### update() - In-Place Update - -```python -df1 = pd.DataFrame({ - 'A': [1, 2, 3], - 'B': [4, 5, 6], -}, index=['a', 'b', 'c']) - -df2 = pd.DataFrame({ - 'A': [10, 20], - 'B': [40, 50], -}, index=['a', 'b']) - -# Update df1 with values from df2 (in-place) -df1.update(df2) -# df1 now has A: [10, 20, 3], B: [40, 50, 6] - -# Only update where df2 has non-NaN -df1.update(df2, overwrite=False) # Don't overwrite existing values -``` - ---- - -## Advanced Merge Patterns - -### Merge with Aggregation - -```python -# Merge and aggregate in one operation -orders = pd.DataFrame({ - 'order_id': [1, 2, 3, 4], - 'customer_id': [101, 102, 101, 103], - 'amount': [100, 200, 150, 300], -}) - -customers = pd.DataFrame({ - 'customer_id': [101, 102, 103], - 'name': ['Alice', 'Bob', 'Charlie'], -}) - -# Get customer summary -customer_summary = orders.groupby('customer_id').agg( - total_orders=('order_id', 'count'), - total_amount=('amount', 'sum'), -).reset_index() - -# Merge with customer info -result = pd.merge(customers, customer_summary, on='customer_id') -``` - -### Merge Asof (Nearest Match) - -```python -# Merge on nearest key (useful for time series) -trades = pd.DataFrame({ - 'time': pd.to_datetime(['2024-01-01 10:00:01', '2024-01-01 10:00:03', '2024-01-01 10:00:05']), - 'ticker': ['AAPL', 'AAPL', 'AAPL'], - 'price': [150.0, 151.0, 150.5], -}) - -quotes = pd.DataFrame({ - 'time': pd.to_datetime(['2024-01-01 10:00:00', '2024-01-01 10:00:02', '2024-01-01 10:00:04']), - 'ticker': ['AAPL', 'AAPL', 'AAPL'], - 'bid': [149.5, 150.5, 150.0], - 'ask': [150.5, 151.5, 151.0], -}) - -# Merge asof - find nearest quote for each trade -result = pd.merge_asof( - trades.sort_values('time'), - quotes.sort_values('time'), - on='time', - by='ticker', - direction='backward' # Use most recent quote -) -``` - -### Conditional Merge - -```python -# Merge with conditions beyond key equality -# First merge, then filter - -products = pd.DataFrame({ - 'product_id': [1, 2, 3], - 'name': ['Widget', 'Gadget', 'Gizmo'], - 'category': ['A', 'B', 'A'], -}) - -discounts = pd.DataFrame({ - 'category': ['A', 'A', 'B'], - 'min_qty': [10, 50, 20], - 'discount': [0.05, 0.10, 0.08], -}) - -# Cross merge then filter -merged = pd.merge(products, discounts, on='category') -# Then apply quantity-based filtering as needed -``` - ---- - -## Performance Considerations - -### Pre-sorting for Merge - -```python -# Sort keys before merge for better performance -df1 = df1.sort_values('key') -df2 = df2.sort_values('key') - -# Merge sorted DataFrames -result = pd.merge(df1, df2, on='key') -``` - -### Index Alignment - -```python -# Using index for merge is often faster than columns -df1 = df1.set_index('key') -df2 = df2.set_index('key') - -# Join on index -result = df1.join(df2) -``` - -### Memory-Efficient Merge - -```python -# For large DataFrames, reduce memory before merge -# Convert to appropriate types -df1['key'] = df1['key'].astype('int32') # Instead of int64 -df1['category'] = df1['category'].astype('category') - -# Select only needed columns -cols_needed = ['key', 'value1', 'value2'] -result = pd.merge(df1[cols_needed], df2[cols_needed], on='key') -``` - ---- - -## Common Merge Patterns - -### Left Join with Null Check - -```python -# Find unmatched rows after left join -result = pd.merge(employees, departments, on='dept_id', how='left') -unmatched = result[result['dept_name'].isna()] -``` - -### Anti-Join (Rows Not in Other) - -```python -# Find employees NOT in a specific department list -dept_list = [101, 102] - -# Method 1: Using isin -not_in_depts = employees[~employees['dept_id'].isin(dept_list)] - -# Method 2: Using merge with indicator -merged = pd.merge( - employees, - pd.DataFrame({'dept_id': dept_list}), - on='dept_id', - how='left', - indicator=True -) -not_in_depts = merged[merged['_merge'] == 'left_only'] -``` - -### Self-Join - -```python -# Find pairs within same department -employees = pd.DataFrame({ - 'emp_id': [1, 2, 3, 4], - 'name': ['Alice', 'Bob', 'Charlie', 'Diana'], - 'dept_id': [101, 101, 102, 101], -}) - -# Self-join to find pairs -pairs = pd.merge( - employees, - employees, - on='dept_id', - suffixes=('_1', '_2') -) -# Remove self-pairs and duplicates -pairs = pairs[pairs['emp_id_1'] < pairs['emp_id_2']] -``` - ---- - -## Best Practices Summary - -1. **Choose the right join type** - Default inner may drop data -2. **Validate cardinality** - Use `validate` parameter -3. **Use indicator** - Debug unexpected results -4. **Handle duplicates** - Use meaningful suffixes -5. **Pre-sort for performance** - Especially for large DataFrames -6. **Reset index after operations** - Keep DataFrames usable -7. **Check for NaN after join** - Understand unmatched rows - ---- - -## Anti-Patterns to Avoid - -```python -# BAD: Merge without understanding cardinality -result = pd.merge(df1, df2, on='key') # May explode row count - -# GOOD: Validate relationship -result = pd.merge(df1, df2, on='key', validate='one_to_one') - -# BAD: Repeated merges -result = pd.merge(df1, df2, on='key') -result = pd.merge(result, df3, on='key') -result = pd.merge(result, df4, on='key') - -# GOOD: Chain or use reduce -from functools import reduce -dfs = [df1, df2, df3, df4] -result = reduce(lambda left, right: pd.merge(left, right, on='key'), dfs) - -# BAD: Ignoring merge indicators -result = pd.merge(df1, df2, on='key', how='outer') - -# GOOD: Check merge results -result = pd.merge(df1, df2, on='key', how='outer', indicator=True) -print(result['_merge'].value_counts()) -``` - ---- - -## Related References - -- `dataframe-operations.md` - Filter before/after merge -- `aggregation-groupby.md` - Aggregate before merging -- `performance-optimization.md` - Optimize large merges diff --git a/.cursor/skills/pandas-pro/references/performance-optimization.md b/.cursor/skills/pandas-pro/references/performance-optimization.md deleted file mode 100644 index aa05f08..0000000 --- a/.cursor/skills/pandas-pro/references/performance-optimization.md +++ /dev/null @@ -1,600 +0,0 @@ -# Performance Optimization - -> Reference for: Pandas Pro -> Load when: Memory usage issues, slow operations, large datasets, vectorization, or chunked processing - ---- - -## Overview - -Optimizing pandas performance is critical for production workflows. This reference covers memory optimization, vectorization, chunking, and profiling with pandas 2.0+. - ---- - -## Memory Analysis - -### Checking Memory Usage - -```python -import pandas as pd -import numpy as np - -df = pd.DataFrame({ - 'id': range(1_000_000), - 'name': ['user_' + str(i) for i in range(1_000_000)], - 'category': np.random.choice(['A', 'B', 'C', 'D'], 1_000_000), - 'value': np.random.randn(1_000_000), - 'count': np.random.randint(0, 100, 1_000_000), -}) - -# Basic memory info -print(df.info(memory_usage='deep')) - -# Detailed memory by column -memory_usage = df.memory_usage(deep=True) -print(memory_usage) -print(f"Total: {memory_usage.sum() / 1e6:.2f} MB") - -# Memory as percentage of total -memory_pct = (memory_usage / memory_usage.sum() * 100).round(2) -print(memory_pct) -``` - -### Memory Profiling Function - -```python -def memory_profile(df: pd.DataFrame) -> pd.DataFrame: - """Profile memory usage by column with optimization suggestions.""" - memory_bytes = df.memory_usage(deep=True) - - profile = pd.DataFrame({ - 'dtype': df.dtypes, - 'non_null': df.count(), - 'null_count': df.isna().sum(), - 'unique': df.nunique(), - 'memory_mb': (memory_bytes / 1e6).round(3), - }) - - # Add optimization suggestions - suggestions = [] - for col in df.columns: - dtype = df[col].dtype - nunique = df[col].nunique() - - if dtype == 'object': - if nunique / len(df) < 0.5: # Less than 50% unique - suggestions.append(f"Convert to category (only {nunique} unique)") - else: - suggestions.append("Consider string dtype") - elif dtype == 'int64': - if df[col].max() < 2**31 and df[col].min() >= -2**31: - suggestions.append("Downcast to int32") - if df[col].max() < 2**15 and df[col].min() >= -2**15: - suggestions.append("Downcast to int16") - elif dtype == 'float64': - suggestions.append("Consider float32 if precision allows") - else: - suggestions.append("OK") - - profile['suggestion'] = suggestions - return profile - -print(memory_profile(df)) -``` - ---- - -## Memory Optimization Techniques - -### Downcasting Numeric Types - -```python -# Automatic downcasting for integers -df['count'] = pd.to_numeric(df['count'], downcast='integer') - -# Automatic downcasting for floats -df['value'] = pd.to_numeric(df['value'], downcast='float') - -# Manual downcasting function -def downcast_dtypes(df: pd.DataFrame) -> pd.DataFrame: - """Reduce memory by downcasting numeric types.""" - df = df.copy() - - for col in df.select_dtypes(include=['int']).columns: - df[col] = pd.to_numeric(df[col], downcast='integer') - - for col in df.select_dtypes(include=['float']).columns: - df[col] = pd.to_numeric(df[col], downcast='float') - - return df - -df_optimized = downcast_dtypes(df) -print(f"Before: {df.memory_usage(deep=True).sum() / 1e6:.2f} MB") -print(f"After: {df_optimized.memory_usage(deep=True).sum() / 1e6:.2f} MB") -``` - -### Using Categorical Type - -```python -# Convert low-cardinality string columns to category -# Especially effective when unique values << total rows - -# Before -print(f"Object dtype: {df['category'].memory_usage(deep=True) / 1e6:.2f} MB") - -# After -df['category'] = df['category'].astype('category') -print(f"Category dtype: {df['category'].memory_usage(deep=True) / 1e6:.2f} MB") - -# Automatic conversion for low-cardinality columns -def optimize_categories(df: pd.DataFrame, threshold: float = 0.5) -> pd.DataFrame: - """Convert object columns to category if unique ratio < threshold.""" - df = df.copy() - - for col in df.select_dtypes(include=['object']).columns: - unique_ratio = df[col].nunique() / len(df) - if unique_ratio < threshold: - df[col] = df[col].astype('category') - - return df -``` - -### Sparse Data Types - -```python -# For data with many repeated values (especially zeros/NaN) -sparse_series = pd.arrays.SparseArray([0, 0, 1, 0, 0, 0, 2, 0, 0, 0]) - -# Create sparse DataFrame -df_sparse = pd.DataFrame({ - 'sparse_col': pd.arrays.SparseArray([0] * 9000 + [1] * 1000), - 'dense_col': [0] * 9000 + [1] * 1000, -}) - -print(f"Sparse: {df_sparse['sparse_col'].memory_usage() / 1e6:.4f} MB") -print(f"Dense: {df_sparse['dense_col'].memory_usage() / 1e6:.4f} MB") -``` - -### Nullable Types (pandas 2.0+) - -```python -# Use nullable types for proper NA handling with memory efficiency -df = df.astype({ - 'id': 'Int32', # Nullable int32 - 'count': 'Int16', # Nullable int16 - 'value': 'Float32', # Nullable float32 - 'name': 'string', # Nullable string (more memory efficient) - 'category': 'category', # Categorical -}) - -# Arrow-backed types for even better memory (pandas 2.0+) -df['name'] = df['name'].astype('string[pyarrow]') -df['category'] = df['category'].astype('category') -``` - ---- - -## Vectorization - -### Replace Loops with Vectorized Operations - -```python -# BAD: Row iteration (extremely slow) -result = [] -for idx, row in df.iterrows(): - if row['value'] > 0: - result.append(row['value'] * 2) - else: - result.append(0) -df['result'] = result - -# GOOD: Vectorized with np.where -df['result'] = np.where(df['value'] > 0, df['value'] * 2, 0) - -# GOOD: Vectorized with boolean indexing -df['result'] = 0 -df.loc[df['value'] > 0, 'result'] = df.loc[df['value'] > 0, 'value'] * 2 -``` - -### Multiple Conditions with np.select - -```python -# BAD: Nested if-else in apply -def categorize(row): - if row['value'] < -1: - return 'very_low' - elif row['value'] < 0: - return 'low' - elif row['value'] < 1: - return 'medium' - else: - return 'high' - -df['category'] = df.apply(categorize, axis=1) # SLOW! - -# GOOD: Vectorized with np.select -conditions = [ - df['value'] < -1, - df['value'] < 0, - df['value'] < 1, -] -choices = ['very_low', 'low', 'medium'] -df['category'] = np.select(conditions, choices, default='high') -``` - -### String Operations - Vectorized - -```python -# BAD: Apply for string operations -df['upper_name'] = df['name'].apply(lambda x: x.upper()) - -# GOOD: Vectorized string methods -df['upper_name'] = df['name'].str.upper() - -# Combine multiple string operations -df['processed'] = ( - df['name'] - .str.strip() - .str.lower() - .str.replace(r'\s+', '_', regex=True) -) -``` - -### Avoid apply() When Possible - -```python -# BAD: apply for row-wise calculation -df['total'] = df.apply(lambda row: row['a'] + row['b'] + row['c'], axis=1) - -# GOOD: Direct vectorized operation -df['total'] = df['a'] + df['b'] + df['c'] - -# BAD: apply for element-wise operation -df['squared'] = df['value'].apply(lambda x: x ** 2) - -# GOOD: Vectorized -df['squared'] = df['value'] ** 2 - -# When apply IS appropriate: complex custom logic -def complex_calculation(row): - # Multiple dependencies and conditional logic - if row['type'] == 'A': - return row['value'] * row['multiplier'] + row['offset'] - else: - return row['value'] / row['divisor'] - row['adjustment'] - -# Consider rewriting as vectorized if performance critical -``` - ---- - -## Chunked Processing - -### Reading Large Files in Chunks - -```python -# Read CSV in chunks -chunk_size = 100_000 -chunks = [] - -for chunk in pd.read_csv('large_file.csv', chunksize=chunk_size): - # Process each chunk - processed = chunk[chunk['value'] > 0] # Filter - processed = processed.groupby('category')['value'].sum() # Aggregate - chunks.append(processed) - -# Combine results -result = pd.concat(chunks).groupby(level=0).sum() -``` - -### Chunked Processing Function - -```python -def process_large_csv( - filepath: str, - chunk_size: int = 100_000, - filter_func=None, - agg_func=None, -) -> pd.DataFrame: - """Process large CSV files in chunks.""" - results = [] - - for chunk in pd.read_csv(filepath, chunksize=chunk_size): - # Apply filter if provided - if filter_func: - chunk = filter_func(chunk) - - # Apply aggregation if provided - if agg_func: - chunk = agg_func(chunk) - - results.append(chunk) - - # Combine results - combined = pd.concat(results, ignore_index=True) - - # Re-aggregate if needed - if agg_func: - combined = agg_func(combined) - - return combined - -# Usage -result = process_large_csv( - 'large_file.csv', - chunk_size=50_000, - filter_func=lambda df: df[df['value'] > 0], - agg_func=lambda df: df.groupby('category').agg({'value': 'sum'}), -) -``` - -### Memory-Efficient Iteration - -```python -# When you must iterate, use itertuples (not iterrows) -# itertuples is 10-100x faster than iterrows - -# BAD: iterrows -for idx, row in df.iterrows(): - process(row['name'], row['value']) - -# BETTER: itertuples -for row in df.itertuples(): - process(row.name, row.value) # Access as attributes - -# BEST: Vectorized operations (avoid iteration entirely) -``` - ---- - -## Query Optimization - -### Efficient Filtering - -```python -# Order matters - filter early, compute late -# BAD: Compute on all rows, then filter -df['expensive_calc'] = df['a'] * df['b'] + np.sin(df['c']) -result = df[df['category'] == 'A'] - -# GOOD: Filter first, compute on subset -mask = df['category'] == 'A' -result = df[mask].copy() -result['expensive_calc'] = result['a'] * result['b'] + np.sin(result['c']) -``` - -### Using query() for Performance - -```python -# query() can be faster for large DataFrames (uses numexpr) -# Traditional boolean indexing -result = df[(df['value'] > 0) & (df['category'] == 'A')] - -# query() syntax (faster for large data) -result = df.query('value > 0 and category == "A"') - -# With variables -threshold = 0 -cat = 'A' -result = df.query('value > @threshold and category == @cat') -``` - -### eval() for Complex Expressions - -```python -# eval() uses numexpr for faster computation -# Standard pandas -df['result'] = df['a'] + df['b'] * df['c'] - df['d'] - -# Using eval (faster for large DataFrames) -df['result'] = pd.eval('df.a + df.b * df.c - df.d') - -# In-place with inplace parameter -df.eval('result = a + b * c - d', inplace=True) -``` - ---- - -## GroupBy Optimization - -### Pre-sort for Faster GroupBy - -```python -# Sort by groupby column first -df = df.sort_values('category') - -# Use sort=False since already sorted -result = df.groupby('category', sort=False)['value'].mean() -``` - -### Use Built-in Aggregations - -```python -# BAD: Custom function via apply -result = df.groupby('category')['value'].apply(lambda x: x.mean()) - -# GOOD: Built-in aggregation -result = df.groupby('category')['value'].mean() - -# Built-in aggregations available: -# sum, mean, median, min, max, std, var, count, first, last, nth -# size, sem, prod, cumsum, cummax, cummin, cumprod -``` - -### Observed Categories - -```python -# For categorical columns, use observed=True (pandas 2.0+ default) -df['category'] = df['category'].astype('category') - -# Avoid computing for unobserved categories -result = df.groupby('category', observed=True)['value'].mean() -``` - ---- - -## I/O Optimization - -### Efficient File Formats - -```python -# Parquet - best for analytical workloads -df.to_parquet('data.parquet', compression='snappy') -df = pd.read_parquet('data.parquet') - -# Feather - best for pandas interchange -df.to_feather('data.feather') -df = pd.read_feather('data.feather') - -# CSV with optimizations -df.to_csv('data.csv', index=False) -df = pd.read_csv( - 'data.csv', - dtype={'category': 'category', 'count': 'int32'}, - usecols=['id', 'category', 'value'], # Only needed columns - nrows=10000, # Limit rows for testing -) -``` - -### Specify dtypes When Reading - -```python -# Specify dtypes upfront to avoid inference overhead -dtypes = { - 'id': 'int32', - 'name': 'string', - 'category': 'category', - 'value': 'float32', - 'count': 'int16', -} - -df = pd.read_csv('data.csv', dtype=dtypes) - -# Parse dates efficiently -df = pd.read_csv( - 'data.csv', - dtype=dtypes, - parse_dates=['date_column'], - date_format='%Y-%m-%d', # Explicit format is faster -) -``` - ---- - -## Profiling and Benchmarking - -### Timing Operations - -```python -import time - -# Simple timing -start = time.time() -result = df.groupby('category')['value'].mean() -elapsed = time.time() - start -print(f"Elapsed: {elapsed:.4f} seconds") - -# Using %%timeit in Jupyter -# %%timeit -# df.groupby('category')['value'].mean() -``` - -### Memory Profiling - -```python -# Track memory before/after -import tracemalloc - -tracemalloc.start() - -# Your operation -df_result = df.groupby('category').agg({'value': 'sum'}) - -current, peak = tracemalloc.get_traced_memory() -print(f"Current memory: {current / 1e6:.2f} MB") -print(f"Peak memory: {peak / 1e6:.2f} MB") - -tracemalloc.stop() -``` - -### Comparison Template - -```python -def benchmark_operations(df: pd.DataFrame, operations: dict, n_runs: int = 5): - """Benchmark multiple operations.""" - results = {} - - for name, func in operations.items(): - times = [] - for _ in range(n_runs): - start = time.time() - func(df) - times.append(time.time() - start) - - results[name] = { - 'mean': np.mean(times), - 'std': np.std(times), - 'min': np.min(times), - } - - return pd.DataFrame(results).T - -# Usage -operations = { - 'iterrows': lambda df: [row['value'] for _, row in df.iterrows()], - 'itertuples': lambda df: [row.value for row in df.itertuples()], - 'vectorized': lambda df: df['value'].tolist(), -} - -benchmark_results = benchmark_operations(df.head(10000), operations) -print(benchmark_results) -``` - ---- - -## Best Practices Summary - -1. **Profile first** - Identify actual bottlenecks before optimizing -2. **Use appropriate dtypes** - int32/float32/category save memory -3. **Vectorize everything** - Avoid loops and apply when possible -4. **Filter early** - Reduce data before expensive operations -5. **Chunk large files** - Process in manageable pieces -6. **Use efficient file formats** - Parquet/Feather over CSV -7. **Leverage built-in methods** - Faster than custom functions - ---- - -## Performance Checklist - -Before deploying pandas code: - -- [ ] Memory profiled with `memory_usage(deep=True)` -- [ ] Dtypes optimized (downcast, categorical) -- [ ] No iterrows/itertuples in hot paths -- [ ] GroupBy uses built-in aggregations -- [ ] Large files processed in chunks -- [ ] Filters applied before computations -- [ ] Appropriate file format used -- [ ] Benchmarked with representative data size - ---- - -## Anti-Patterns Summary - -| Anti-Pattern | Alternative | -|--------------|-------------| -| `iterrows()` for computation | Vectorized operations | -| `apply(lambda)` for simple ops | Built-in methods | -| Loading entire large file | Chunked reading | -| String columns with low cardinality | Category dtype | -| int64 for small integers | int32/int16 | -| Multiple separate filters | Combined boolean mask | -| Repeated groupby calls | Single groupby with multiple aggs | - ---- - -## Related References - -- `dataframe-operations.md` - Efficient indexing and filtering -- `aggregation-groupby.md` - Optimized aggregation patterns -- `merging-joining.md` - Efficient merge strategies diff --git a/.cursor/skills/polars-expertise/.skillfish.json b/.cursor/skills/polars-expertise/.skillfish.json deleted file mode 100644 index c43a139..0000000 --- a/.cursor/skills/polars-expertise/.skillfish.json +++ /dev/null @@ -1,10 +0,0 @@ -{ - "version": 2, - "name": "polars-expertise", - "owner": "deevsdeevs", - "repo": "agent-system", - "path": "polars-expertise", - "branch": "main", - "sha": "8b43b9ebc9dd1024e71b77f4f76ccd37ca3da777", - "source": "manual" -} \ No newline at end of file diff --git a/.cursor/skills/polars-expertise/SKILL.md b/.cursor/skills/polars-expertise/SKILL.md deleted file mode 100644 index 169c7d6..0000000 --- a/.cursor/skills/polars-expertise/SKILL.md +++ /dev/null @@ -1,223 +0,0 @@ ---- -name: polars-expertise -description: > - This skill should be used when the user asks about Polars DataFrame library - (Apache Arrow) for Python or Rust. Triggers: "polars expressions", "lazy vs eager", - "scan_parquet streaming", "convert pandas to polars", "pyspark to polars", - "kdb to polars", "group_by_dynamic", "rolling_mean", "polars window functions", - "asof join", "polars GPU", "polars parquet", "LazyFrame". Time series: OHLCV - resampling, rolling windows, financial data patterns. Performance: native - expressions over map_elements, early projection, categorical types, streaming. ---- - -# Polars - -High-performance DataFrame library built on Apache Arrow. Supports Python and Rust with expression-based API, lazy evaluation, and automatic parallelization. - -## Quick Start - -### Python - -```bash -uv pip install polars -# GPU support: uv pip install polars[gpu] -``` - -```python -import polars as pl - -# Eager: immediate execution -df = pl.DataFrame({"symbol": ["AAPL", "GOOG"], "price": [150.0, 140.0]}) -df.filter(pl.col("price") > 145).select("symbol", "price") - -# Lazy: optimized execution (preferred for large data) -lf = pl.scan_parquet("trades.parquet") -result = lf.filter(pl.col("volume") > 1000).group_by("symbol").agg( - pl.col("price").mean().alias("avg_price") -).collect() -``` - -### Rust - -```toml -# Cargo.toml - select features you need -[dependencies] -polars = { version = "0.46", features = ["lazy", "parquet", "temporal"] } -``` - -```rust -use polars::prelude::*; - -fn main() -> PolarsResult<()> { - // Eager - let df = df![ - "symbol" => ["AAPL", "GOOG"], - "price" => [150.0, 140.0] - ]?; - - // Lazy (preferred) - let lf = LazyFrame::scan_parquet("trades.parquet", Default::default())?; - let result = lf - .filter(col("volume").gt(lit(1000))) - .group_by([col("symbol")]) - .agg([col("price").mean().alias("avg_price")]) - .collect()?; - Ok(()) -} -``` - -## Core Pattern: Expressions - -Everything in Polars is an expression. Expressions are composable, lazy, and parallelized. - -```python -# Expression building blocks -pl.col("price") # column reference -pl.col("price") * pl.col("volume") # arithmetic -pl.col("price").mean().over("symbol") # window function -pl.when(cond).then(a).otherwise(b) # conditional -``` - -Expressions execute in contexts: `select()`, `with_columns()`, `filter()`, `group_by().agg()` - -## When to Use Lazy - -| Use Lazy (`scan_*`, `.lazy()`) | Use Eager (`read_*`) | -|-------------------------------|----------------------| -| Large files (> RAM) | Small data, exploration | -| Complex pipelines | Simple one-off ops | -| Need query optimization | Interactive notebooks | -| Streaming required | Immediate feedback | - -Lazy benefits: predicate pushdown, projection pushdown, parallel execution, streaming. - -## Style: Use `.alias()` for Column Naming - -Always use `.alias("name")` instead of `name=expr` kwargs: - -```python -# GOOD: Explicit .alias() - works everywhere, composable -df.with_columns( - (pl.col("price") * pl.col("volume")).alias("value"), - pl.col("price").mean().over("symbol").alias("avg_price") -) - -# AVOID: Kwarg style - less flexible, doesn't chain -df.with_columns( - value=pl.col("price") * pl.col("volume"), # avoid - avg_price=pl.col("price").mean().over("symbol") # avoid -) -``` - -`.alias()` is explicit, chains with other methods, and works consistently in all contexts. - -## Anti-Patterns - AVOID - -```python -# BAD: Python functions kill parallelization -df.with_columns(pl.col("x").map_elements(lambda x: x * 2)) # SLOW - -# GOOD: Native expressions are parallel -df.with_columns((pl.col("x") * 2).alias("x")) # FAST - -# BAD: Row iteration -for row in df.iter_rows(): # SLOW - process(row) - -# GOOD: Columnar operations -df.with_columns(process_expr) # FAST - -# BAD: Late projection -lf.filter(...).collect().select("a", "b") # reads all columns - -# GOOD: Early projection -lf.select("a", "b").filter(...).collect() # reads only needed columns -``` - -## Performance Checklist - -- [ ] Using `scan_*` (lazy) for large files? -- [ ] Projecting columns early in pipeline? -- [ ] Using native expressions (no `map_elements`)? -- [ ] Categorical dtype for low-cardinality strings? -- [ ] Appropriate integer sizes (i32 vs i64)? -- [ ] Streaming for out-of-memory data? (`collect(engine="streaming")`) - -## Reference Navigator - -### Python References - -| Topic | File | When to Load | -|-------|------|--------------| -| Expressions, types, lazy/eager | [python/core_concepts.md](references/python/core_concepts.md) | Understanding fundamentals | -| Select, filter, group_by, window | [python/operations.md](references/python/operations.md) | Common operations | -| CSV, Parquet, streaming I/O | [python/io_guide.md](references/python/io_guide.md) | Reading/writing data | -| Joins, pivots, reshaping | [python/transformations.md](references/python/transformations.md) | Combining/reshaping data | -| Performance, patterns | [python/best_practices.md](references/python/best_practices.md) | Optimization | - -### Rust References - -| Topic | File | When to Load | -|-------|------|--------------| -| DataFrame, Series, ChunkedArray | [rust/core_concepts.md](references/rust/core_concepts.md) | Rust API fundamentals | -| Expression API in Rust | [rust/operations.md](references/rust/operations.md) | Operations syntax | -| Readers, writers, streaming | [rust/io_guide.md](references/rust/io_guide.md) | I/O operations | -| Feature flags, crates | [rust/features.md](references/rust/features.md) | Cargo setup | -| Allocators, SIMD, nightly | [rust/performance.md](references/rust/performance.md) | Performance tuning | -| Zero-copy, FFI, Arrow | [rust/arrow_interop.md](references/rust/arrow_interop.md) | Arrow integration | - -### Shared References - -| Topic | File | When to Load | -|-------|------|--------------| -| SQL queries on DataFrames | [sql_interface.md](references/sql_interface.md) | SQL syntax needed | -| Query optimization, streaming | [lazy_deep_dive.md](references/lazy_deep_dive.md) | Understanding lazy engine | -| NVIDIA GPU acceleration | [gpu_support.md](references/gpu_support.md) | GPU setup/usage | - -### Migration Guides - -| From | File | When to Load | -|------|------|--------------| -| pandas | [migration_pandas.md](references/migration_pandas.md) | Converting pandas code | -| PySpark | [migration_spark.md](references/migration_spark.md) | Converting Spark code | -| q/kdb+ | [migration_qkdb.md](references/migration_qkdb.md) | Converting kdb code | - -## Time Series / Financial Data Quick Patterns - -```python -# OHLCV resampling -df.group_by_dynamic("timestamp", every="1m").agg( - pl.col("price").first().alias("open"), - pl.col("price").max().alias("high"), - pl.col("price").min().alias("low"), - pl.col("price").last().alias("close"), - pl.col("volume").sum() -) - -# Rolling statistics -df.with_columns( - pl.col("price").rolling_mean(window_size=20).alias("sma_20"), - pl.col("price").rolling_std(window_size=20).alias("volatility") -) - -# As-of join for market data alignment -trades.join_asof(quotes, on="timestamp", by="symbol", strategy="backward") -``` - -Load [python/best_practices.md](references/python/best_practices.md) for comprehensive time series patterns. - -## Runnable Examples - -| Example | File | Purpose | -|---------|------|---------| -| Financial OHLCV | [examples/financial_ohlcv.py](examples/financial_ohlcv.py) | OHLCV resampling, rolling stats, VWAP | -| Pandas Migration | [examples/pandas_migration.py](examples/pandas_migration.py) | Side-by-side pandas vs polars | -| Streaming Large Files | [examples/streaming_large_file.py](examples/streaming_large_file.py) | Out-of-memory processing patterns | - -## Development Tips - -Use LSP for navigating Polars code: -- **Python**: Pyright/Pylance provides excellent type inference for Polars expressions -- **Rust**: rust-analyzer understands Polars types and expression chains - -LSP operations like `goToDefinition` and `hover` help explore Polars API without leaving the editor. diff --git a/.cursor/skills/polars-expertise/agents/openai.yaml b/.cursor/skills/polars-expertise/agents/openai.yaml deleted file mode 100644 index cd48664..0000000 --- a/.cursor/skills/polars-expertise/agents/openai.yaml +++ /dev/null @@ -1,4 +0,0 @@ -interface: - display_name: "Polars Expertise" - short_description: "Polars performance and patterns" - default_prompt: "Use $polars-expertise to solve Polars DataFrame tasks." diff --git a/.cursor/skills/polars-expertise/examples/financial_ohlcv.py b/.cursor/skills/polars-expertise/examples/financial_ohlcv.py deleted file mode 100644 index c121b28..0000000 --- a/.cursor/skills/polars-expertise/examples/financial_ohlcv.py +++ /dev/null @@ -1,96 +0,0 @@ -""" -Financial OHLCV resampling with Polars. - -Demonstrates: -- group_by_dynamic for time-based resampling -- Rolling statistics (SMA, volatility) -- As-of joins for market data alignment -""" - -import polars as pl -from datetime import datetime, timedelta -import random - -# Generate sample tick data -def generate_tick_data(n_ticks: int = 10000) -> pl.DataFrame: - base_time = datetime(2024, 1, 15, 9, 30, 0) - symbols = ["AAPL", "GOOG", "MSFT"] - - data = [] - for i in range(n_ticks): - symbol = random.choice(symbols) - base_price = {"AAPL": 150.0, "GOOG": 140.0, "MSFT": 380.0}[symbol] - data.append({ - "timestamp": base_time + timedelta(seconds=i * 0.5), - "symbol": symbol, - "price": base_price + random.gauss(0, 1), - "volume": random.randint(100, 1000), - }) - - return pl.DataFrame(data).sort("timestamp") - - -def resample_to_ohlcv(df: pl.LazyFrame, interval: str = "1m") -> pl.LazyFrame: - """Resample tick data to OHLCV bars.""" - return ( - df.sort("timestamp") - .group_by_dynamic("timestamp", every=interval, group_by="symbol") - .agg( - pl.col("price").first().alias("open"), - pl.col("price").max().alias("high"), - pl.col("price").min().alias("low"), - pl.col("price").last().alias("close"), - pl.col("volume").sum().alias("volume"), - pl.len().alias("tick_count"), - ) - ) - - -def add_technical_indicators(df: pl.LazyFrame) -> pl.LazyFrame: - """Add common technical indicators.""" - return df.with_columns( - # Simple Moving Averages - pl.col("close").rolling_mean(window_size=5).over("symbol").alias("sma_5"), - pl.col("close").rolling_mean(window_size=20).over("symbol").alias("sma_20"), - - # Volatility (rolling std of returns) - pl.col("close") - .pct_change() - .over("symbol") - .rolling_std(window_size=20) - .over("symbol") - .alias("volatility_20"), - - # VWAP - ( - (pl.col("close") * pl.col("volume")).cum_sum().over("symbol") - / pl.col("volume").cum_sum().over("symbol") - ).alias("vwap"), - ) - - -def main(): - # Generate tick data - print("Generating tick data...") - ticks = generate_tick_data(10000) - print(f"Generated {len(ticks)} ticks") - print(ticks.head(5)) - - # Resample to 1-minute OHLCV (lazy) - print("\nResampling to 1-minute OHLCV bars...") - ohlcv = resample_to_ohlcv(ticks.lazy(), "1m") - - # Add technical indicators - print("Adding technical indicators...") - result = add_technical_indicators(ohlcv).collect() - - print(f"\nResult: {len(result)} bars") - print(result.head(10)) - - # Filter example: high volatility periods - high_vol = result.filter(pl.col("volatility_20") > 0.01) - print(f"\nHigh volatility periods: {len(high_vol)} bars") - - -if __name__ == "__main__": - main() diff --git a/.cursor/skills/polars-expertise/examples/pandas_migration.py b/.cursor/skills/polars-expertise/examples/pandas_migration.py deleted file mode 100644 index 1c79880..0000000 --- a/.cursor/skills/polars-expertise/examples/pandas_migration.py +++ /dev/null @@ -1,143 +0,0 @@ -""" -Pandas to Polars migration examples. - -Side-by-side comparison of common operations. -Run with: uv run python pandas_migration.py -""" - -import polars as pl - -# Sample data -data = { - "name": ["Alice", "Bob", "Charlie", "Diana", "Eve"], - "department": ["Engineering", "Sales", "Engineering", "Sales", "Engineering"], - "salary": [100000, 80000, 120000, 90000, 110000], - "years": [5, 3, 8, 4, 6], -} - - -def basic_operations(): - """Basic DataFrame operations.""" - df = pl.DataFrame(data) - print("=== Basic Operations ===\n") - - # Select columns - # pandas: df[["name", "salary"]] - selected = df.select("name", "salary") - print("Select columns:") - print(selected) - - # Filter rows - # pandas: df[df["salary"] > 95000] - filtered = df.filter(pl.col("salary") > 95000) - print("\nFilter salary > 95000:") - print(filtered) - - # Add computed column - # pandas: df["bonus"] = df["salary"] * 0.1 - with_bonus = df.with_columns( - (pl.col("salary") * 0.1).alias("bonus") - ) - print("\nWith bonus column:") - print(with_bonus) - - -def groupby_operations(): - """Group by and aggregation.""" - df = pl.DataFrame(data) - print("\n=== Group By Operations ===\n") - - # Basic groupby - # pandas: df.groupby("department")["salary"].mean() - by_dept = df.group_by("department").agg( - pl.col("salary").mean().alias("avg_salary"), - pl.col("salary").max().alias("max_salary"), - pl.len().alias("count"), - ) - print("Group by department:") - print(by_dept) - - # Window function (transform equivalent) - # pandas: df["dept_avg"] = df.groupby("department")["salary"].transform("mean") - with_dept_avg = df.with_columns( - pl.col("salary").mean().over("department").alias("dept_avg"), - pl.col("salary").rank().over("department").alias("salary_rank"), - ) - print("\nWindow functions (dept avg and rank):") - print(with_dept_avg) - - -def conditional_operations(): - """Conditional column creation.""" - df = pl.DataFrame(data) - print("\n=== Conditional Operations ===\n") - - # pandas: np.where(df["salary"] > 100000, "high", "normal") - with_tier = df.with_columns( - pl.when(pl.col("salary") > 100000) - .then(pl.lit("high")) - .when(pl.col("salary") > 85000) - .then(pl.lit("medium")) - .otherwise(pl.lit("normal")) - .alias("salary_tier") - ) - print("Salary tiers:") - print(with_tier) - - -def chained_operations(): - """Chained operations (method chaining).""" - df = pl.DataFrame(data) - print("\n=== Chained Operations ===\n") - - # Complex pipeline - result = ( - df.filter(pl.col("years") >= 4) - .with_columns( - (pl.col("salary") * 1.1).alias("new_salary"), - (pl.col("salary") / pl.col("years")).alias("salary_per_year"), - ) - .group_by("department") - .agg( - pl.col("new_salary").mean().alias("avg_new_salary"), - pl.col("salary_per_year").mean().alias("avg_salary_per_year"), - ) - .sort("avg_new_salary", descending=True) - ) - print("Complex pipeline result:") - print(result) - - -def lazy_vs_eager(): - """Demonstrate lazy evaluation benefits.""" - df = pl.DataFrame(data) - print("\n=== Lazy vs Eager ===\n") - - # Eager: executes immediately - eager_result = df.filter(pl.col("salary") > 90000).select("name", "salary") - print("Eager result:") - print(eager_result) - - # Lazy: builds query plan, optimizes, then executes - lazy_result = ( - df.lazy() - .filter(pl.col("salary") > 90000) - .select("name", "salary") - ) - print("\nLazy query plan:") - print(lazy_result.explain()) - - print("\nLazy result (after .collect()):") - print(lazy_result.collect()) - - -def main(): - basic_operations() - groupby_operations() - conditional_operations() - chained_operations() - lazy_vs_eager() - - -if __name__ == "__main__": - main() diff --git a/.cursor/skills/polars-expertise/examples/streaming_large_file.py b/.cursor/skills/polars-expertise/examples/streaming_large_file.py deleted file mode 100644 index 5c197c1..0000000 --- a/.cursor/skills/polars-expertise/examples/streaming_large_file.py +++ /dev/null @@ -1,181 +0,0 @@ -""" -Streaming large files with Polars. - -Demonstrates processing files larger than available RAM using: -- scan_csv / scan_parquet (lazy scanning) -- Streaming execution engine -- Sink operations for streaming writes - -Run with: uv run python streaming_large_file.py -""" - -import polars as pl -from pathlib import Path -import tempfile - - -def create_sample_data(path: Path, n_rows: int = 100000): - """Create a sample CSV file for demonstration.""" - import random - - with open(path, "w") as f: - f.write("id,category,value,timestamp\n") - categories = ["A", "B", "C", "D", "E"] - for i in range(n_rows): - cat = random.choice(categories) - val = random.gauss(100, 20) - ts = f"2024-01-{(i % 28) + 1:02d} {(i % 24):02d}:00:00" - f.write(f"{i},{cat},{val:.2f},{ts}\n") - - print(f"Created {path} with {n_rows} rows") - - -def streaming_aggregation(input_path: Path): - """ - Aggregate large file using streaming. - - Key pattern: scan_* + lazy operations + collect(engine="streaming") - """ - print("\n=== Streaming Aggregation ===\n") - - # scan_csv returns LazyFrame - no data loaded yet - lf = pl.scan_csv(input_path) - - # Build query plan - result = ( - lf.filter(pl.col("value") > 80) # Predicate pushdown - .group_by("category") - .agg( - pl.col("value").mean().alias("avg_value"), - pl.col("value").std().alias("std_value"), - pl.len().alias("count"), - ) - .sort("avg_value", descending=True) - ) - - # Show optimized plan - print("Query plan:") - print(result.explain()) - - # Execute with streaming engine - # For truly large files, this processes in chunks - df = result.collect(engine="streaming") - print("\nResult:") - print(df) - - return df - - -def streaming_sink(input_path: Path, output_path: Path): - """ - Stream data directly to output file without loading into memory. - - Key pattern: scan_* + transformations + sink_* - """ - print("\n=== Streaming Sink (File to File) ===\n") - - lf = pl.scan_csv(input_path) - - # Transform and sink directly to parquet - # Data flows through without full materialization - ( - lf.filter(pl.col("value") > 90) - .with_columns( - pl.col("value").alias("original_value"), - (pl.col("value") * 1.1).alias("adjusted_value"), - ) - .select("id", "category", "original_value", "adjusted_value", "timestamp") - .sink_parquet(output_path) - ) - - print(f"Streamed filtered data to {output_path}") - - # Verify output - result = pl.scan_parquet(output_path).collect() - print(f"Output contains {len(result)} rows") - print(result.head(5)) - - -def check_streaming_compatibility(input_path: Path): - """ - Check if a query can be streamed. - - Some operations break streaming: - - Sorts on large data (may need to buffer) - - Certain join types - - Some aggregations - """ - print("\n=== Streaming Compatibility Check ===\n") - - lf = pl.scan_csv(input_path) - - # This query streams well - streamable = ( - lf.filter(pl.col("value") > 80) - .group_by("category") - .agg(pl.col("value").sum()) - ) - - print("Streamable query plan:") - print(streamable.explain(streaming=True)) - - # This may not stream fully (sort requires buffering) - maybe_not_streamable = ( - lf.sort("value", descending=True) - .head(1000) - ) - - print("\nQuery with sort (may buffer):") - print(maybe_not_streamable.explain(streaming=True)) - - -def projection_pushdown_demo(input_path: Path): - """ - Demonstrate projection pushdown - only read needed columns. - """ - print("\n=== Projection Pushdown ===\n") - - # Only reads 'category' and 'value' columns from disk - lf = pl.scan_csv(input_path) - - result = ( - lf.select("category", "value") # Projection pushdown - .filter(pl.col("value") > 100) # Predicate pushdown - .group_by("category") - .agg(pl.col("value").mean()) - ) - - print("Optimized plan (note: only needed columns read):") - print(result.explain()) - - df = result.collect() - print("\nResult:") - print(df) - - -def main(): - # Create temp directory for demo files - with tempfile.TemporaryDirectory() as tmpdir: - input_csv = Path(tmpdir) / "large_data.csv" - output_parquet = Path(tmpdir) / "filtered_data.parquet" - - # Create sample data - create_sample_data(input_csv, n_rows=100000) - - # Run demos - streaming_aggregation(input_csv) - streaming_sink(input_csv, output_parquet) - check_streaming_compatibility(input_csv) - projection_pushdown_demo(input_csv) - - print("\n=== Summary ===") - print("Key patterns for large files:") - print("1. Use scan_csv / scan_parquet (not read_*)") - print("2. Build lazy query with .filter(), .select(), .group_by()") - print("3. Execute with .collect(engine='streaming')") - print("4. For file-to-file: use .sink_parquet() / .sink_csv()") - print("5. Put filters and projections early in pipeline") - - -if __name__ == "__main__": - main() diff --git a/.cursor/skills/polars-expertise/references/gpu_support.md b/.cursor/skills/polars-expertise/references/gpu_support.md deleted file mode 100644 index 2e7b4a9..0000000 --- a/.cursor/skills/polars-expertise/references/gpu_support.md +++ /dev/null @@ -1,253 +0,0 @@ -# GPU Support - -Polars provides GPU-accelerated execution for the Lazy API on NVIDIA GPUs using RAPIDS cuDF. Currently in Open Beta. - -## System Requirements - -| Requirement | Specification | -|-------------|---------------| -| GPU | NVIDIA Volta or higher (compute capability 7.0+) | -| CUDA | CUDA 12 (CUDA 11 deprecated - ends with RAPIDS v25.06) | -| OS | Linux or Windows Subsystem for Linux 2 (WSL2) | -| Memory | GPU RAM sufficient for your workload (80GB handles 50-100GB raw data) | - -## Installation - -See SKILL.md for base installation. For GPU support: - -```bash -uv pip install polars[gpu] - -# CUDA 11 (deprecated, ends with RAPIDS v25.08): -uv pip install polars cudf-polars-cu11==25.06 -``` - -## Basic Usage - -### Simple GPU Execution - -```python -import polars as pl - -# Build query with lazy API (required for GPU) -lf = ( - pl.scan_parquet("trades.parquet") - .filter(pl.col("volume") > 1000000) - .group_by("symbol") - .agg( - pl.col("price").mean().alias("avg_price"), - pl.col("volume").sum().alias("total_volume") - ) -) - -# Execute on GPU -result = lf.collect(engine="gpu") -``` - -### GPU Engine Configuration - -```python -# Select specific GPU on multi-GPU system -result = lf.collect(engine=pl.GPUEngine(device=1)) - -# Disable CPU fallback - raises exception if unsupported -result = lf.collect(engine=pl.GPUEngine(raise_on_fail=True)) -``` - -## Supported Operations - -### Supported - -| Category | Operations | -|----------|------------| -| API | LazyFrame, SQL | -| I/O | CSV, Parquet, ndjson, in-memory DataFrames | -| Data Types | Numeric, logical, string, datetime | -| Operations | Filters, aggregations (grouped/rolling), joins, concatenation | -| String | Full string processing | -| Missing Data | Null handling | - -### Not Supported (Falls Back to CPU) - -| Category | Details | -|----------|---------| -| API | Eager DataFrame, Streaming | -| Data Types | Date, Categorical, Enum, Time, Array, Binary, Object | -| Operations | Time series resampling, folds, user-defined functions | -| I/O | Excel, database formats | -| Other | Datetime with timezone (some expressions), List (some expressions) | - -## Diagnosing GPU Usage - -### Verbose Mode - Fallback Warnings - -```python -import polars as pl - -lf = ( - pl.scan_parquet("data.parquet") - .with_columns( - pl.col("value").rolling_mean(10).over("group") # Not GPU-supported - ) -) - -with pl.Config() as cfg: - cfg.set_verbose(True) - result = lf.collect(engine="gpu") - # Prints: PerformanceWarning: Query execution with GPU not supported... -``` - -### Force Failure for Unsupported Operations - -```python -try: - result = lf.collect(engine=pl.GPUEngine(raise_on_fail=True)) -except pl.exceptions.ComputeError as e: - print(f"GPU execution failed: {e}") -``` - -## When to Use GPU - -### GPU Excels At - -| Workload | Reason | -|----------|--------| -| Grouped aggregations | Massive parallelism | -| Large joins | GPU memory bandwidth | -| Heavy computations | CUDA acceleration | -| Multiple operations in single query | Amortizes GPU overhead | - -### CPU May Be Better - -| Workload | Reason | -|----------|--------| -| I/O-bound queries | GPU won't help disk/network bottleneck | -| Small datasets | GPU overhead not worth it | -| Unsupported operations | Frequent fallback adds overhead | -| Data larger than GPU RAM | Out-of-memory errors | - -## Interoperability - -### CPU-GPU Data Flow - -```python -# GPU results are standard CPU DataFrames -gpu_result = lf.collect(engine="gpu") -type(gpu_result) # polars.DataFrame (CPU-backed) - -# Files written by GPU engine readable by CPU -lf.sink_parquet("output.parquet") # Works with both engines -``` - -### Transparent Fallback - -```python -# Fallback is automatic unless raise_on_fail=True -result = lf.collect(engine="gpu") -# If any operation unsupported -> falls back to CPU engine -# Query still completes, just without GPU acceleration -``` - -## Financial Data Patterns - -### VWAP Calculation (GPU-Compatible) - -```python -lf = ( - pl.scan_parquet("trades.parquet") - .group_by("symbol") - .agg( - (pl.col("price") * pl.col("volume")).sum() / pl.col("volume").sum() - ).alias("vwap") -) -result = lf.collect(engine="gpu") -``` - -### Daily Stats (GPU-Compatible) - -```python -lf = ( - pl.scan_parquet("trades.parquet") - .with_columns(pl.col("timestamp").dt.date().alias("date")) - .group_by("symbol", "date") - .agg( - pl.col("price").max().alias("high"), - pl.col("price").min().alias("low"), - pl.col("price").first().alias("open"), - pl.col("price").last().alias("close"), - pl.col("volume").sum().alias("volume") - ) -) -result = lf.collect(engine="gpu") -``` - -### Rolling Statistics (May Fall Back) - -```python -# Grouped rolling window - NOT GPU supported -lf = ( - pl.scan_parquet("trades.parquet") - .with_columns( - pl.col("price").rolling_mean(20).over("symbol") # CPU fallback - ) -) - -# Simple rolling (no grouping) - may work -lf = ( - pl.scan_parquet("trades.parquet") - .with_columns( - pl.col("price").rolling_mean(20) # Check support - ) -) -``` - -## Best Practices - -1. **Profile first**: Compare GPU vs CPU on your actual queries -2. **Keep data on GPU**: Minimize CPU-GPU transfers in a pipeline -3. **Batch queries**: Combine operations to amortize GPU overhead -4. **Check support**: Use `raise_on_fail=True` during development -5. **Monitor memory**: GPU OOM is common with large datasets -6. **Use verbose mode**: Identify fallback operations - -## Troubleshooting - -### ImportError on gpu engine - -```bash -uv pip install polars[gpu] # Missing cudf-polars -``` - -### CUDA version mismatch - -```bash -nvcc --version # Check CUDA version -uv pip install cudf-polars-cu12 # for CUDA 12 -``` - -### Out of Memory - -```python -# Split large queries or use streaming (CPU only) -lf1 = lf.filter(pl.col("date") < "2024-06-01") -lf2 = lf.filter(pl.col("date") >= "2024-06-01") -r1 = lf1.collect(engine="gpu") -r2 = lf2.collect(engine="gpu") -result = pl.concat([r1, r2]) -``` - -### Performance Not Improved - -Check for fallback operations: -```python -with pl.Config() as cfg: - cfg.set_verbose(True) - result = lf.collect(engine="gpu") -# Look for PerformanceWarning messages -``` - -## Test Coverage - -- 99.2% of Polars unit tests pass with CPU fallback -- 88.8% pass without fallback -- Remaining failures mostly involve debug output differences or type variations diff --git a/.cursor/skills/polars-expertise/references/lazy_deep_dive.md b/.cursor/skills/polars-expertise/references/lazy_deep_dive.md deleted file mode 100644 index 7882e94..0000000 --- a/.cursor/skills/polars-expertise/references/lazy_deep_dive.md +++ /dev/null @@ -1,354 +0,0 @@ -# Lazy Evaluation Deep Dive - -Polars lazy evaluation builds a query graph that gets optimized before execution. This reference covers the optimization internals, query plans, and advanced lazy patterns. - -## Query Optimization Overview - -| Optimization | What It Does | When Applied | -|--------------|--------------|--------------| -| Predicate pushdown | Applies filters at scan level | 1 time | -| Projection pushdown | Selects only needed columns at scan | 1 time | -| Slice pushdown | Loads only required rows (e.g., head/limit) | 1 time | -| Common subplan elimination | Caches shared subtrees/file scans | 1 time | -| Expression simplification | Constant folding, operation substitution | Until fixed point | -| Join ordering | Determines optimal join execution order | 1 time | -| Type coercion | Minimal memory type conversions | Until fixed point | -| Cardinality estimation | Optimal group_by strategy selection | Query-dependent | - -## Query Plans - -### Viewing Plans - -```python -import polars as pl - -lf = ( - pl.scan_csv("trades.csv") - .filter(pl.col("volume") > 1000000) - .select("symbol", "price", "volume") - .group_by("symbol") - .agg(pl.col("price").mean()) -) - -# Non-optimized plan (what you wrote) -print(lf.explain(optimized=False)) - -# Optimized plan (what executes) -print(lf.explain()) - -# Visual graph (requires Graphviz) -lf.show_graph(optimized=True) -``` - -### Reading Query Plans - -Plans read bottom-to-top. Common symbols: -- `sigma` (σ): SELECTION (filter) -- `pi` (π): PROJECTION (column selection) -- `CSV SCAN`: Data source - -**Non-optimized plan:** -``` -FILTER [(col("volume")) > (1000000)] FROM - SELECT [col("symbol"), col("price"), col("volume")] FROM - CSV SCAN trades.csv - PROJECT */6 COLUMNS -``` - -**Optimized plan (predicate + projection pushdown):** -``` -GROUP_BY [col("symbol")] FROM - CSV SCAN trades.csv - PROJECT 3/6 COLUMNS # Only needed columns - SELECTION: [(col("volume")) > (1000000)] # Filter at scan -``` - -## Optimization Details - -### Predicate Pushdown - -Filters move to data source level: - -```python -# Filter happens during CSV read, not after -lf = ( - pl.scan_csv("large_trades.csv") - .filter(pl.col("date") >= "2024-01-01") # Pushed to scan - .filter(pl.col("volume") > 100000) # Also pushed -) -``` - -**Works with:** -- File scans (CSV, Parquet, IPC) -- Joins (pushes to appropriate side) -- Unions - -**Blocked by:** -- Aggregations (filter after group_by cannot push through) -- User-defined functions - -### Projection Pushdown - -Only reads required columns: - -```python -# Only symbol and price columns read from disk -lf = ( - pl.scan_parquet("trades.parquet") # Has 20 columns - .select("symbol", "price") # Only 2 needed - .filter(pl.col("price") > 100) -) -``` - -**Parquet/IPC benefit**: Column-oriented formats skip entire column chunks. - -### Slice Pushdown - -Limits data loading for head/tail operations: - -```python -# Reads only ~100 rows, not entire file -lf = ( - pl.scan_csv("huge.csv") - .filter(pl.col("valid") == True) - .head(100) # Slice pushes to limit rows read -) -``` - -**Works with:** -- `.head()`, `.tail()`, `.slice()` -- `.limit()` in SQL - -### Common Subplan Elimination - -Shared subqueries execute once: - -```python -expensive_lf = ( - pl.scan_parquet("market_data.parquet") - .filter(pl.col("date") >= "2024-01-01") - .with_columns( - pl.col("price").rolling_mean(20).alias("ma_20") - ) -) - -# Both branches share expensive_lf - computed once -summary = expensive_lf.group_by("symbol").agg(pl.col("price").mean()) -detail = expensive_lf.filter(pl.col("price") > pl.col("ma_20")) - -# collect_all ensures single computation -results = pl.collect_all([summary, detail]) -``` - -## Execution Modes - -### Standard Collection - -```python -# Full dataset in memory -df = lf.collect() -``` - -### Streaming Mode - -Processes data in batches for larger-than-memory datasets: - -```python -# Streaming execution -df = lf.collect(engine="streaming") -``` - -**Inspecting streaming plans:** -```python -# Physical plan shows memory intensity -lf.show_graph(streaming=True) -``` - -**Streaming-compatible operations:** -- Scans, filters, projections -- Most aggregations -- Sorted joins - -**Not streaming-compatible (require full materialization):** -- Unsorted joins -- Some window functions -- Sort on unsorted data - -### GPU Execution - -```python -# Execute on NVIDIA GPU (requires polars[gpu]) -df = lf.collect(engine="gpu") -``` - -### Partial Execution - -For development/debugging on large datasets: - -```python -# Sample during development -df = lf.head(1000).collect() - -# Or limit at scan -lf = pl.scan_parquet("huge.parquet").head(10000) -result = lf.filter(...).collect() -``` - -## Diverging Queries Pattern - -When one lazy computation feeds multiple downstream queries: - -```python -# Base expensive computation -base_lf = ( - pl.scan_parquet("trades.parquet") - .with_columns( - pl.col("price").pct_change().alias("returns") - ) -) - -# Diverging queries -stats_lf = base_lf.group_by("symbol").agg( - pl.col("returns").mean(), - pl.col("returns").std() -) - -filtered_lf = base_lf.filter(pl.col("returns").abs() > 0.05) - -# CRITICAL: Use collect_all to avoid recomputation -stats_df, filtered_df = pl.collect_all([stats_lf, filtered_lf]) -# base_lf computed only once! - -# BAD: Separate collects recompute base_lf each time -# stats_df = stats_lf.collect() # Computes base_lf -# filtered_df = filtered_lf.collect() # Computes base_lf AGAIN -``` - -## LazyFrame Caching Gotchas - -LazyFrames are query plans, not cached data: - -```python -# WARNING: This recomputes on each use -lf = pl.scan_parquet("data.parquet").filter(...) - -result1 = lf.select("a").collect() # Scans + filters -result2 = lf.select("b").collect() # Scans + filters AGAIN - -# SOLUTION 1: Collect once, then operate on DataFrame -df = lf.collect() -result1 = df.select("a") -result2 = df.select("b") - -# SOLUTION 2: Use collect_all for lazy branches -lf1 = lf.select("a") -lf2 = lf.select("b") -result1, result2 = pl.collect_all([lf1, lf2]) # Single scan -``` - -## Advanced Patterns - -### Lazy Schema Inspection - -```python -lf = pl.scan_parquet("data.parquet") - -# Get schema without loading data -print(lf.collect_schema()) - -# Get column names -print(lf.collect_schema().names()) -``` - -### Lazy with Sink Operations - -Write results directly to files without full materialization: - -```python -# Sink to Parquet (streaming write) -lf.sink_parquet("output.parquet") - -# Sink to IPC -lf.sink_ipc("output.ipc") - -# Sink to CSV -lf.sink_csv("output.csv") -``` - -### Profile Query Execution - -```python -# Returns DataFrame with timing info -df, profile = lf.profile() -print(profile) -``` - -### Explain Physical Plan - -```python -# Logical plan (default) -print(lf.explain()) - -# Physical plan with more detail -print(lf.explain(physical=True)) -``` - -## Optimization Control - -### Disable Specific Optimizations - -```python -# For debugging or specific requirements -df = lf.collect( - predicate_pushdown=False, # Keep filters where written - projection_pushdown=False, # Read all columns - slice_pushdown=False, # No slice optimization - comm_subplan_elim=False # No subplan caching -) -``` - -### Force Optimization Barrier - -```python -# cache() materializes intermediate result -lf = ( - pl.scan_parquet("data.parquet") - .filter(...) - .cache() # Forces materialization here - .group_by(...) - .agg(...) -) -``` - -## Rust Lazy API - -```rust -use polars::prelude::*; - -fn main() -> PolarsResult<()> { - let lf = LazyCsvReader::new("trades.csv") - .finish()? - .filter(col("volume").gt(lit(1000000))) - .select([col("symbol"), col("price")]) - .group_by([col("symbol")]) - .agg([col("price").mean()]); - - // View plan - println!("{}", lf.explain(true)?); - - // Execute - let df = lf.collect()?; - - Ok(()) -} -``` - -## Best Practices - -1. **Start lazy, end eager**: Use `scan_*` functions, collect only when needed -2. **Check plans**: Use `explain()` to verify optimizations work -3. **Use `collect_all`**: For diverging queries from same source -4. **Sink for ETL**: Use `sink_*` for write-heavy pipelines -5. **Profile in production**: Use `.profile()` to find bottlenecks -6. **Streaming for big data**: Set `engine="streaming"` for larger-than-memory -7. **Don't reuse LazyFrames**: They recompute each time - use `collect_all` or materialize diff --git a/.cursor/skills/polars-expertise/references/migration_pandas.md b/.cursor/skills/polars-expertise/references/migration_pandas.md deleted file mode 100644 index 947ba45..0000000 --- a/.cursor/skills/polars-expertise/references/migration_pandas.md +++ /dev/null @@ -1,418 +0,0 @@ -# Pandas to Polars Migration Guide - -This guide helps you migrate from pandas to Polars with comprehensive operation mappings and key differences. - -## Core Conceptual Differences - -### 1. No Index System - -**Pandas:** Uses row-based indexing system -```python -df.loc[0, "column"] -df.iloc[0:5] -df.set_index("id") -``` - -**Polars:** Uses integer positions only -```python -df[0, "column"] # Row position, column name -df[0:5] # Row slice -# No set_index equivalent - use group_by instead -``` - -### 2. Memory Format - -**Pandas:** Row-oriented NumPy arrays -**Polars:** Columnar Apache Arrow format - -**Implications:** -- Polars is faster for column operations -- Polars uses less memory -- Polars has better data sharing capabilities - -### 3. Parallelization - -**Pandas:** Primarily single-threaded (requires Dask for parallelism) -**Polars:** Parallel by default using Rust's concurrency - -### 4. Lazy Evaluation - -**Pandas:** Only eager evaluation -**Polars:** Both eager (DataFrame) and lazy (LazyFrame) with query optimization - -### 5. Type Strictness - -**Pandas:** Allows silent type conversions -**Polars:** Strict typing, explicit casts required - -**Example:** -```python -# Pandas: Silently converts to float -pd_df["int_col"] = [1, 2, None, 4] # dtype: float64 - -# Polars: Keeps as integer with null -pl_df = pl.DataFrame({"int_col": [1, 2, None, 4]}) # dtype: Int64 -``` - -## Operation Mappings - -### Data Selection - -| Operation | Pandas | Polars | -|-----------|--------|--------| -| Select column | `df["col"]` or `df.col` | `df.select("col")` or `df["col"]` | -| Select multiple | `df[["a", "b"]]` | `df.select("a", "b")` | -| Select by position | `df.iloc[:, 0:3]` | `df.select(pl.col(df.columns[0:3]))` | -| Select by condition | `df[df["age"] > 25]` | `df.filter(pl.col("age") > 25)` | - -### Data Filtering - -| Operation | Pandas | Polars | -|-----------|--------|--------| -| Single condition | `df[df["age"] > 25]` | `df.filter(pl.col("age") > 25)` | -| Multiple conditions | `df[(df["age"] > 25) & (df["city"] == "NY")]` | `df.filter(pl.col("age") > 25, pl.col("city") == "NY")` | -| Query method | `df.query("age > 25")` | `df.filter(pl.col("age") > 25)` | -| isin | `df[df["city"].isin(["NY", "LA"])]` | `df.filter(pl.col("city").is_in(["NY", "LA"]))` | -| isna | `df[df["value"].isna()]` | `df.filter(pl.col("value").is_null())` | -| notna | `df[df["value"].notna()]` | `df.filter(pl.col("value").is_not_null())` | - -### Adding/Modifying Columns - -| Operation | Pandas | Polars | -|-----------|--------|--------| -| Add column | `df["new"] = df["old"] * 2` | `df.with_columns((pl.col("old") * 2).alias("new"))` | -| Multiple columns | `df.assign(a=..., b=...)` | `df.with_columns(a=..., b=...)` | -| Conditional column | `np.where(condition, a, b)` | `pl.when(condition).then(a).otherwise(b)` | - -**Important difference - Parallel execution:** - -```python -# Pandas: Sequential (lambda sees previous results) -df.assign( - a=lambda df_: df_.value * 10, - b=lambda df_: df_.value * 100 -) - -# Polars: Parallel (all computed together) -df.with_columns( - (pl.col("value") * 10).alias("a"), - (pl.col("value") * 100).alias("b") -) -``` - -### Grouping and Aggregation - -| Operation | Pandas | Polars | -|-----------|--------|--------| -| Group by | `df.groupby("col")` | `df.group_by("col")` | -| Agg single | `df.groupby("col")["val"].mean()` | `df.group_by("col").agg(pl.col("val").mean())` | -| Agg multiple | `df.groupby("col").agg({"val": ["mean", "sum"]})` | `df.group_by("col").agg(pl.col("val").mean(), pl.col("val").sum())` | -| Size | `df.groupby("col").size()` | `df.group_by("col").agg(pl.len())` | -| Count | `df.groupby("col").count()` | `df.group_by("col").agg(pl.col("*").count())` | - -### Window Functions - -| Operation | Pandas | Polars | -|-----------|--------|--------| -| Transform | `df.groupby("col").transform("mean")` | `df.with_columns(pl.col("val").mean().over("col"))` | -| Rank | `df.groupby("col")["val"].rank()` | `df.with_columns(pl.col("val").rank().over("col"))` | -| Shift | `df.groupby("col")["val"].shift(1)` | `df.with_columns(pl.col("val").shift(1).over("col"))` | -| Cumsum | `df.groupby("col")["val"].cumsum()` | `df.with_columns(pl.col("val").cum_sum().over("col"))` | - -### Joins - -| Operation | Pandas | Polars | -|-----------|--------|--------| -| Inner join | `df1.merge(df2, on="id")` | `df1.join(df2, on="id", how="inner")` | -| Left join | `df1.merge(df2, on="id", how="left")` | `df1.join(df2, on="id", how="left")` | -| Different keys | `df1.merge(df2, left_on="a", right_on="b")` | `df1.join(df2, left_on="a", right_on="b")` | - -### Concatenation - -| Operation | Pandas | Polars | -|-----------|--------|--------| -| Vertical | `pd.concat([df1, df2], axis=0)` | `pl.concat([df1, df2], how="vertical")` | -| Horizontal | `pd.concat([df1, df2], axis=1)` | `pl.concat([df1, df2], how="horizontal")` | - -### Sorting - -| Operation | Pandas | Polars | -|-----------|--------|--------| -| Sort by column | `df.sort_values("col")` | `df.sort("col")` | -| Descending | `df.sort_values("col", ascending=False)` | `df.sort("col", descending=True)` | -| Multiple columns | `df.sort_values(["a", "b"])` | `df.sort("a", "b")` | - -### Reshaping - -| Operation | Pandas | Polars | -|-----------|--------|--------| -| Pivot | `df.pivot(index="a", columns="b", values="c")` | `df.pivot(values="c", index="a", columns="b")` | -| Melt | `df.melt(id_vars="id")` | `df.unpivot(index="id")` | - -### I/O Operations - -| Operation | Pandas | Polars | -|-----------|--------|--------| -| Read CSV | `pd.read_csv("file.csv")` | `pl.read_csv("file.csv")` or `pl.scan_csv()` | -| Write CSV | `df.to_csv("file.csv")` | `df.write_csv("file.csv")` | -| Read Parquet | `pd.read_parquet("file.parquet")` | `pl.read_parquet("file.parquet")` | -| Write Parquet | `df.to_parquet("file.parquet")` | `df.write_parquet("file.parquet")` | -| Read Excel | `pd.read_excel("file.xlsx")` | `pl.read_excel("file.xlsx")` | - -### String Operations - -| Operation | Pandas | Polars | -|-----------|--------|--------| -| Upper | `df["col"].str.upper()` | `df.select(pl.col("col").str.to_uppercase())` | -| Lower | `df["col"].str.lower()` | `df.select(pl.col("col").str.to_lowercase())` | -| Contains | `df["col"].str.contains("pattern")` | `df.filter(pl.col("col").str.contains("pattern"))` | -| Replace | `df["col"].str.replace("old", "new")` | `df.select(pl.col("col").str.replace("old", "new"))` | -| Split | `df["col"].str.split(" ")` | `df.select(pl.col("col").str.split(" "))` | - -### Datetime Operations - -| Operation | Pandas | Polars | -|-----------|--------|--------| -| Parse dates | `pd.to_datetime(df["col"])` | `df.select(pl.col("col").str.strptime(pl.Date, "%Y-%m-%d"))` | -| Year | `df["date"].dt.year` | `df.select(pl.col("date").dt.year())` | -| Month | `df["date"].dt.month` | `df.select(pl.col("date").dt.month())` | -| Day | `df["date"].dt.day` | `df.select(pl.col("date").dt.day())` | - -### Missing Data - -| Operation | Pandas | Polars | -|-----------|--------|--------| -| Drop nulls | `df.dropna()` | `df.drop_nulls()` | -| Fill nulls | `df.fillna(0)` | `df.fill_null(0)` | -| Check null | `df["col"].isna()` | `df.select(pl.col("col").is_null())` | -| Forward fill | `df.fillna(method="ffill")` | `df.select(pl.col("col").fill_null(strategy="forward"))` | - -### Other Operations - -| Operation | Pandas | Polars | -|-----------|--------|--------| -| Unique values | `df["col"].unique()` | `df["col"].unique()` | -| Value counts | `df["col"].value_counts()` | `df["col"].value_counts()` | -| Describe | `df.describe()` | `df.describe()` | -| Sample | `df.sample(n=100)` | `df.sample(n=100)` | -| Head | `df.head()` | `df.head()` | -| Tail | `df.tail()` | `df.tail()` | - -## Common Migration Patterns - -### Pattern 1: Chained Operations - -**Pandas:** -```python -result = (df - .assign(new_col=lambda x: x["old_col"] * 2) - .query("new_col > 10") - .groupby("category") - .agg({"value": "sum"}) - .reset_index() -) -``` - -**Polars:** -```python -result = (df - .with_columns((pl.col("old_col") * 2).alias("new_col")) - .filter(pl.col("new_col") > 10) - .group_by("category") - .agg(pl.col("value").sum()) -) -# No reset_index needed - Polars doesn't have index -``` - -### Pattern 2: Apply Functions - -**Pandas:** -```python -# Avoid in Polars - breaks parallelization -df["result"] = df["value"].apply(lambda x: x * 2) -``` - -**Polars:** -```python -# Use expressions instead -df = df.with_columns((pl.col("value") * 2).alias("result")) - -# If custom function needed -df = df.with_columns( - pl.col("value").map_elements(lambda x: x * 2, return_dtype=pl.Float64).alias("result") -) -``` - -### Pattern 3: Conditional Column Creation - -**Pandas:** -```python -df["category"] = np.where( - df["value"] > 100, - "high", - np.where(df["value"] > 50, "medium", "low") -) -``` - -**Polars:** -```python -df = df.with_columns( - pl.when(pl.col("value") > 100) - .then("high") - .when(pl.col("value") > 50) - .then("medium") - .otherwise("low") - .alias("category") -) -``` - -### Pattern 4: Group Transform - -**Pandas:** -```python -df["group_mean"] = df.groupby("category")["value"].transform("mean") -``` - -**Polars:** -```python -df = df.with_columns( - pl.col("value").mean().over("category").alias("group_mean") -) -``` - -### Pattern 5: Multiple Aggregations - -**Pandas:** -```python -result = df.groupby("category").agg({ - "value": ["mean", "sum", "count"], - "price": ["min", "max"] -}) -``` - -**Polars:** -```python -result = df.group_by("category").agg( - pl.col("value").mean().alias("value_mean"), - pl.col("value").sum().alias("value_sum"), - pl.col("value").count().alias("value_count"), - pl.col("price").min().alias("price_min"), - pl.col("price").max().alias("price_max") -) -``` - -## Performance Anti-Patterns to Avoid - -### Anti-Pattern 1: Sequential Pipe Operations - -**Bad (disables parallelization):** -```python -df = df.pipe(function1).pipe(function2).pipe(function3) -``` - -**Good (enables parallelization):** -```python -df = df.with_columns( - function1_result(), - function2_result(), - function3_result() -) -``` - -### Anti-Pattern 2: Python Functions in Hot Paths - -**Bad:** -```python -df = df.with_columns( - pl.col("value").map_elements(lambda x: x * 2).alias("result") -) -``` - -**Good:** -```python -df = df.with_columns((pl.col("value") * 2).alias("result")) -``` - -### Anti-Pattern 3: Using Eager Reading for Large Files - -**Bad:** -```python -df = pl.read_csv("large_file.csv") -result = df.filter(pl.col("age") > 25).select("name", "age") -``` - -**Good:** -```python -lf = pl.scan_csv("large_file.csv") -result = lf.filter(pl.col("age") > 25).select("name", "age").collect() -``` - -### Anti-Pattern 4: Row Iteration - -**Bad:** -```python -for row in df.iter_rows(): - # Process row - pass -``` - -**Good:** -```python -# Use vectorized operations -df = df.with_columns( - # Vectorized computation -) -``` - -## Migration Checklist - -When migrating from pandas to Polars: - -1. **Remove index operations** - Use integer positions or group_by -2. **Replace apply/map with expressions** - Use Polars native operations -3. **Update column assignment** - Use `with_columns()` instead of direct assignment -4. **Change groupby.transform to .over()** - Window functions work differently -5. **Update string operations** - Use `.str.to_uppercase()` instead of `.str.upper()` -6. **Add explicit type casts** - Polars won't silently convert types -7. **Consider lazy evaluation** - Use `scan_*` instead of `read_*` for large data -8. **Update aggregation syntax** - More explicit in Polars -9. **Remove reset_index calls** - Not needed in Polars -10. **Update conditional logic** - Use `when().then().otherwise()` pattern - -## Compatibility Layer - -For gradual migration, you can use both libraries: - -```python -import pandas as pd -import polars as pl - -# Convert pandas to Polars -pl_df = pl.from_pandas(pd_df) - -# Convert Polars to pandas -pd_df = pl_df.to_pandas() - -# Use Arrow for zero-copy (when possible) -pl_df = pl.from_arrow(pd_df) -pd_df = pl_df.to_arrow().to_pandas() -``` - -## When to Stick with Pandas - -Consider staying with pandas when: -- Working with time series requiring complex index operations -- Need extensive ecosystem support (some libraries only support pandas) -- Team lacks Rust/Polars expertise -- Data is small and performance isn't critical -- Using advanced pandas features without Polars equivalents - -## When to Switch to Polars - -Switch to Polars when: -- Performance is critical -- Working with large datasets (>1GB) -- Need lazy evaluation and query optimization -- Want better type safety -- Need parallel execution by default -- Starting a new project diff --git a/.cursor/skills/polars-expertise/references/migration_qkdb.md b/.cursor/skills/polars-expertise/references/migration_qkdb.md deleted file mode 100644 index 62045e3..0000000 --- a/.cursor/skills/polars-expertise/references/migration_qkdb.md +++ /dev/null @@ -1,411 +0,0 @@ -# q/kdb+ to Polars Migration Guide - -This guide helps you migrate from q/kdb+ to Polars, covering vector operations, table manipulations, and financial data patterns common in trading systems. - -## Philosophical Differences - -### q/kdb+ Philosophy -- Right-to-left evaluation -- Implicit iteration over lists -- Tables are lists of dictionaries -- Column-oriented with native time-series support -- Interpreted, terse syntax - -### Polars Philosophy -- Left-to-right chained operations -- Explicit expression API -- DataFrame as collection of Series (columns) -- Column-oriented with Apache Arrow -- Compiled Rust backend, Python/Rust frontend - -## Core Type Mappings - -| q Type | Polars Type | Notes | -|--------|-------------|-------| -| `boolean` / `b` | `pl.Boolean` | | -| `byte` / `x` | `pl.UInt8` | | -| `short` / `h` | `pl.Int16` | | -| `int` / `i` | `pl.Int32` | | -| `long` / `j` | `pl.Int64` | Default integer | -| `real` / `e` | `pl.Float32` | | -| `float` / `f` | `pl.Float64` | Default float | -| `char` / `c` | `pl.String` | Single chars become strings | -| `symbol` / `s` | `pl.Categorical` or `pl.String` | Use Categorical for low-cardinality | -| `timestamp` / `p` | `pl.Datetime("ns")` | Nanosecond precision | -| `date` / `d` | `pl.Date` | | -| `time` / `t` | `pl.Time` | | -| `timespan` / `n` | `pl.Duration` | | - -## Vector Operation Mappings - -### Basic Arithmetic - -| q | Polars | Notes | -|---|--------|-------| -| `x + y` | `pl.col("x") + pl.col("y")` | | -| `x * y` | `pl.col("x") * pl.col("y")` | | -| `sum x` | `pl.col("x").sum()` | | -| `avg x` | `pl.col("x").mean()` | | -| `max x` | `pl.col("x").max()` | | -| `min x` | `pl.col("x").min()` | | -| `med x` | `pl.col("x").median()` | | -| `dev x` | `pl.col("x").std()` | Standard deviation | -| `var x` | `pl.col("x").var()` | Variance | - -### List/Array Operations - -| q | Polars | Notes | -|---|--------|-------| -| `count x` | `pl.col("x").len()` | Length | -| `first x` | `pl.col("x").first()` | | -| `last x` | `pl.col("x").last()` | | -| `reverse x` | `pl.col("x").reverse()` | | -| `asc x` | `pl.col("x").sort()` | Ascending sort | -| `desc x` | `pl.col("x").sort(descending=True)` | | -| `distinct x` | `pl.col("x").unique()` | | -| `x?y` | `pl.col("x").search_sorted(y)` | Find index (binary search) | -| `x in y` | `pl.col("x").is_in(y)` | Membership | - -### Running/Cumulative Operations - -| q | Polars | Notes | -|---|--------|-------| -| `sums x` | `pl.col("x").cum_sum()` | Running sum | -| `prds x` | `pl.col("x").cum_prod()` | Running product | -| `maxs x` | `pl.col("x").cum_max()` | Running max | -| `mins x` | `pl.col("x").cum_min()` | Running min | - -### Sliding Windows - -| q | Polars | Notes | -|---|--------|-------| -| `mavg[n;x]` | `pl.col("x").rolling_mean(n)` | Moving average | -| `msum[n;x]` | `pl.col("x").rolling_sum(n)` | Moving sum | -| `mmax[n;x]` | `pl.col("x").rolling_max(n)` | Moving max | -| `mmin[n;x]` | `pl.col("x").rolling_min(n)` | Moving min | -| `mdev[n;x]` | `pl.col("x").rolling_std(n)` | Moving std dev | - -### Deltas and Differences - -| q | Polars | Notes | -|---|--------|-------| -| `deltas x` | `pl.col("x").diff()` | First differences | -| `ratios x` | `pl.col("x") / pl.col("x").shift(1)` | Ratio to previous | -| `1 _ x` | `pl.col("x").slice(1)` | Drop first | -| `-1 _ x` | `pl.col("x").head(-1)` | Drop last | -| `n # x` | `pl.col("x").head(n)` | Take first n | -| `-n # x` | `pl.col("x").tail(n)` | Take last n | - -### Prev/Next - -| q | Polars | Notes | -|---|--------|-------| -| `prev x` | `pl.col("x").shift(1)` | Previous value | -| `next x` | `pl.col("x").shift(-1)` | Next value | -| `xprev[n;x]` | `pl.col("x").shift(n)` | N periods back | - -## Table Operations - -### Creating Tables - -**q:** -```q -t:([] sym:`AAPL`MSFT`AAPL; price:150.0 280.0 152.0; size:100 200 150) -``` - -**Polars:** -```python -t = pl.DataFrame({ - "sym": ["AAPL", "MSFT", "AAPL"], - "price": [150.0, 280.0, 152.0], - "size": [100, 200, 150] -}) -``` - -### Selection (qSQL vs Polars) - -**q:** -```q -select sym, price from t where size > 100 -``` - -**Polars:** -```python -t.filter(pl.col("size") > 100).select("sym", "price") -``` - -### Aggregation - -**q:** -```q -select avg price, sum size by sym from t -``` - -**Polars:** -```python -t.group_by("sym").agg( - pl.col("price").mean(), - pl.col("size").sum() -) -``` - -### Update (Adding Columns) - -**q:** -```q -update vwap: size wavg price by sym from t -``` - -**Polars:** -```python -t.with_columns( - vwap=(pl.col("price") * pl.col("size")).sum().over("sym") / - pl.col("size").sum().over("sym") -) -``` - -### Delete (Dropping Rows) - -**q:** -```q -delete from t where size < 100 -``` - -**Polars:** -```python -t.filter(pl.col("size") >= 100) -``` - -### Sorting - -**q:** -```q -`sym`price xasc t -``` - -**Polars:** -```python -t.sort("sym", "price") -``` - -### Joins - -| q | Polars | Notes | -|---|--------|-------| -| `t1 lj t2` | `t1.join(t2, on="key", how="left")` | Left join | -| `t1 ij t2` | `t1.join(t2, on="key", how="inner")` | Inner join | -| `t1 uj t2` | `pl.concat([t1, t2])` | Union (vertical) | -| `t1 aj \`time\`sym t2` | `t1.join_asof(t2, on="time", by="sym")` | As-of join | -| `t1 wj ...` | `t1.join_asof(...).with_columns(...)` | Window join (manual) | - -## Financial Data Patterns - -### OHLCV Bars from Ticks - -**q:** -```q -select o:first price, h:max price, l:min price, c:last price, v:sum size -by sym, time.minute from trades -``` - -**Polars:** -```python -trades.group_by("sym", pl.col("time").dt.truncate("1m")).agg( - pl.col("price").first().alias("o"), - pl.col("price").max().alias("h"), - pl.col("price").min().alias("l"), - pl.col("price").last().alias("c"), - pl.col("size").sum().alias("v") -) -``` - -### VWAP - -**q:** -```q -select vwap: size wavg price by sym from trades -``` - -**Polars:** -```python -trades.group_by("sym").agg( - vwap=(pl.col("price") * pl.col("size")).sum() / pl.col("size").sum() -) -``` - -### Intraday Returns - -**q:** -```q -update ret: 1 - price % prev price by sym from trades -``` - -**Polars:** -```python -trades.with_columns( - ret=(pl.col("price") / pl.col("price").shift(1) - 1).over("sym") -) -``` - -### Rolling Volatility - -**q:** -```q -update vol: mdev[20; log price - log prev price] by sym from trades -``` - -**Polars:** -```python -trades.with_columns( - pl.col("price").log().diff().rolling_std(20).over("sym").alias("vol") -) -``` - -### As-of Joins (Quote/Trade Matching) - -**q:** -```q -aj[`sym`time; trades; quotes] -``` - -**Polars:** -```python -trades.sort("time").join_asof( - quotes.sort("time"), - on="time", - by="sym", - strategy="backward" # Last quote before trade -) -``` - -### Time-Weighted Average - -**q:** -```q -update twap: (deltas time) wavg price by sym from quotes -``` - -**Polars:** -```python -quotes.with_columns( - pl.col("time").diff().over("sym").alias("dt") -).with_columns( - twap=(pl.col("price") * pl.col("dt")).sum().over("sym") / - pl.col("dt").sum().over("sym") -) -``` - -## Performance Considerations - -### What q/kdb+ Does Better -- Native nanosecond timestamps with timezone -- Built-in IPC and pub/sub -- Extremely fast as-of joins on sorted data -- Integrated timeseries database (kdb+) -- Lower memory footprint for timeseries - -### What Polars Does Better -- Complex query optimization (predicate pushdown) -- Multi-threaded by default -- Better for ad-hoc analysis -- Larger ecosystem (Python) -- No licensing costs -- Better for batch processing - -### Memory Comparison - -q/kdb+ uses compact in-memory representation optimized for timeseries. Polars uses Arrow format optimized for analytics. For dense timeseries with many symbols, kdb+ may use less memory. For wide tables with mixed types, they're comparable. - -### Speed Expectations - -| Operation | Relative Performance | -|-----------|---------------------| -| Simple aggregations | Polars often faster (multi-threaded) | -| As-of joins (sorted) | q/kdb+ faster | -| Complex multi-step queries | Polars faster (optimization) | -| Tick data ingestion | kdb+ faster (built-in) | -| Ad-hoc analysis | Polars faster (better optimization) | - -## Migration Strategy - -### Phase 1: Batch Analytics -Move batch analytics (EOD processing, reporting) to Polars first. Keep kdb+ for real-time. - -### Phase 2: Historical Analysis -Use Polars for historical backtesting and research. Export kdb+ data to Parquet. - -### Phase 3: Evaluate Real-time -Consider Polars streaming for lower-frequency real-time needs. Keep kdb+ for HFT. - -## Code Translation Examples - -### Example 1: Daily Stats - -**q:** -```q -select - open: first price, - high: max price, - low: min price, - close: last price, - volume: sum size, - trades: count i -by sym, date from trades -``` - -**Polars:** -```python -( - trades - .group_by("sym", pl.col("timestamp").dt.date().alias("date")) - .agg( - pl.col("price").first().alias("open"), - pl.col("price").max().alias("high"), - pl.col("price").min().alias("low"), - pl.col("price").last().alias("close"), - pl.col("size").sum().alias("volume"), - pl.len().alias("trades") - ) -) -``` - -### Example 2: Moving Spread - -**q:** -```q -update spread: mavg[100; ask - bid] by sym from quotes -``` - -**Polars:** -```python -quotes.with_columns( - spread=(pl.col("ask") - pl.col("bid")).rolling_mean(100).over("sym") -) -``` - -### Example 3: Fill Forward - -**q:** -```q -update price: fills price by sym from quotes -``` - -**Polars:** -```python -quotes.with_columns( - pl.col("price").forward_fill().over("sym").alias("price") -) -``` - -## Key Syntax Differences Summary - -| q Pattern | Polars Pattern | -|-----------|----------------| -| Right-to-left: `avg x` | Left-to-right: `pl.col("x").mean()` | -| Implicit iteration | Explicit `.over()` for groups | -| `select ... by` | `.group_by().agg()` | -| `update col: expr` | `.with_columns(col=expr)` | -| `x?y` (find) | `.search_sorted()` or `.filter()` | -| `x wavg y` | `(x * y).sum() / y.sum()` | -| `fills x` | `.forward_fill()` | -| `aj` | `.join_asof()` | diff --git a/.cursor/skills/polars-expertise/references/migration_spark.md b/.cursor/skills/polars-expertise/references/migration_spark.md deleted file mode 100644 index 635d1d0..0000000 --- a/.cursor/skills/polars-expertise/references/migration_spark.md +++ /dev/null @@ -1,295 +0,0 @@ -# PySpark to Polars Migration Guide - -This guide helps you migrate from PySpark to Polars, covering fundamental differences and common operation mappings. - -## Core Architectural Differences - -### Row-Based vs Column-Based - -**Spark**: DataFrame is a collection of rows - operations preserve row relationships -**Polars**: DataFrame is a collection of columns - columns can be computed independently - -```python -# Polars: Independent column operations -df.select( - pl.col("foo").sort().head(2), # Sorts foo, takes first 2 - pl.col("bar").filter(pl.col("x") > 0).sum() # Filters bar, sums -) -# Result: 2 rows with foo values paired with single bar sum (broadcast) - -# Spark: Operations must maintain row alignment -# Requires separate computations and explicit joins -``` - -### Execution Model - -| Aspect | Spark | Polars | -|--------|-------|--------| -| Execution | Distributed across cluster | Single-machine, parallel threads | -| Lazy by default | Yes (transformations) | No (use LazyFrame explicitly) | -| Memory | Spills to disk | In-memory (streaming for large data) | -| Partitioning | Explicit partition management | Automatic parallelism | -| Fault tolerance | Checkpoint/recompute | None (single machine) | - -### When to Use Which - -**Use Polars when:** -- Data fits on a single machine (even with streaming) -- Need lowest latency -- Don't need cluster infrastructure -- Interactive analysis - -**Use Spark when:** -- Data is truly distributed (multiple TB) -- Need fault tolerance -- Existing Spark infrastructure -- Complex distributed operations - -## Operation Mappings - -### Reading Data - -| Operation | PySpark | Polars | -|-----------|---------|--------| -| Read CSV | `spark.read.csv("file.csv")` | `pl.read_csv("file.csv")` | -| Read CSV lazy | `spark.read.csv("file.csv")` | `pl.scan_csv("file.csv")` | -| Read Parquet | `spark.read.parquet("file.parquet")` | `pl.read_parquet("file.parquet")` | -| Read Parquet lazy | `spark.read.parquet("file.parquet")` | `pl.scan_parquet("file.parquet")` | -| Read multiple files | `spark.read.parquet("path/*.parquet")` | `pl.scan_parquet("path/*.parquet")` | - -### Column Selection - -| Operation | PySpark | Polars | -|-----------|---------|--------| -| Select columns | `df.select("a", "b")` | `df.select("a", "b")` | -| Select with expr | `df.select(col("a"), col("b") * 2)` | `df.select(pl.col("a"), pl.col("b") * 2)` | -| Rename | `df.withColumnRenamed("old", "new")` | `df.rename({"old": "new"})` | -| Drop columns | `df.drop("a", "b")` | `df.drop("a", "b")` | - -### Filtering - -| Operation | PySpark | Polars | -|-----------|---------|--------| -| Filter | `df.filter(col("a") > 5)` | `df.filter(pl.col("a") > 5)` | -| Where (alias) | `df.where(col("a") > 5)` | `df.filter(pl.col("a") > 5)` | -| Multiple conditions | `df.filter((col("a") > 5) & (col("b") < 10))` | `df.filter((pl.col("a") > 5) & (pl.col("b") < 10))` | -| Is in | `df.filter(col("a").isin([1,2,3]))` | `df.filter(pl.col("a").is_in([1,2,3]))` | -| Is null | `df.filter(col("a").isNull())` | `df.filter(pl.col("a").is_null())` | - -### Adding/Modifying Columns - -| Operation | PySpark | Polars | -|-----------|---------|--------| -| Add column | `df.withColumn("new", col("a") * 2)` | `df.with_columns((pl.col("a") * 2).alias("new"))` | -| Multiple columns | `df.withColumn("a", ...).withColumn("b", ...)` | `df.with_columns(a=..., b=...)` | -| Conditional | `df.withColumn("x", when(cond, a).otherwise(b))` | `df.with_columns(pl.when(cond).then(a).otherwise(b).alias("x"))` | - -### Aggregation - -| Operation | PySpark | Polars | -|-----------|---------|--------| -| Group by | `df.groupBy("a")` | `df.group_by("a")` | -| Agg single | `df.groupBy("a").agg({"b": "sum"})` | `df.group_by("a").agg(pl.col("b").sum())` | -| Agg multiple | `df.groupBy("a").agg(sum("b"), avg("c"))` | `df.group_by("a").agg(pl.col("b").sum(), pl.col("c").mean())` | -| Count | `df.groupBy("a").count()` | `df.group_by("a").len()` | - -### Window Functions - -**Spark** requires explicit window specification: - -```python -from pyspark.sql import Window -from pyspark.sql.functions import row_number, lag, mean - -window = Window.partitionBy("symbol").orderBy("date") -rolling_window = window.rowsBetween(-6, 0) - -df = ( - df - .withColumn("rank", row_number().over(window)) - .withColumn("prev_price", lag("price", 1).over(window)) - .withColumn("rolling_mean", mean("price").over(rolling_window)) -) -``` - -**Polars** uses `.over()` for partitioning: - -```python -df = df.with_columns( - pl.col("price").rank().over("symbol").alias("rank"), - pl.col("price").shift(1).over("symbol").alias("prev_price"), - pl.col("price").rolling_mean(7).over("symbol").alias("rolling_mean") -) -``` - -### Composing Window Expressions - -**Spark limitation**: Cannot compose window functions - -```python -# Spark: NOT ALLOWED - lag is a window function -F.mean(F.lag("price", 1)).over(window) # Error - -# Spark workaround: Multiple windows -df = ( - df - .withColumn("lagged_price", F.lag("price", 7).over(window)) - .withColumn("feature", F.mean("lagged_price").over(rolling_window)) -) -``` - -**Polars**: Window expressions can be composed freely - -```python -# Polars: This works - compose shift and rolling_mean -df = df.with_columns( - pl.col("price").shift(7).rolling_mean(7).over("symbol", order_by="date").alias("feature") -) -``` - -### Joins - -| Operation | PySpark | Polars | -|-----------|---------|--------| -| Inner join | `df1.join(df2, on="id", how="inner")` | `df1.join(df2, on="id", how="inner")` | -| Left join | `df1.join(df2, on="id", how="left")` | `df1.join(df2, on="id", how="left")` | -| Different keys | `df1.join(df2, df1.a == df2.b)` | `df1.join(df2, left_on="a", right_on="b")` | - -### Sorting - -| Operation | PySpark | Polars | -|-----------|---------|--------| -| Sort ascending | `df.orderBy("a")` | `df.sort("a")` | -| Sort descending | `df.orderBy(col("a").desc())` | `df.sort("a", descending=True)` | -| Multiple columns | `df.orderBy("a", col("b").desc())` | `df.sort("a", pl.col("b").sort(descending=True))` | - -### SQL Interface - -**Spark:** -```python -df.createOrReplaceTempView("trades") -result = spark.sql("SELECT symbol, AVG(price) FROM trades GROUP BY symbol") -``` - -**Polars:** -```python -ctx = pl.SQLContext(trades=df) -result = ctx.execute("SELECT symbol, AVG(price) FROM trades GROUP BY symbol").collect() -``` - -## Common Migration Patterns - -### Pattern 1: Feature Engineering Pipeline - -**Spark:** -```python -from pyspark.sql.functions import col, lag, avg -from pyspark.sql import Window - -window = Window.partitionBy("symbol").orderBy("date") -rolling = window.rowsBetween(-19, 0) - -result = ( - df - .withColumn("returns", (col("close") - lag("close", 1).over(window)) / lag("close", 1).over(window)) - .withColumn("ma_20", avg("close").over(rolling)) - .withColumn("signal", when(col("close") > col("ma_20"), 1).otherwise(-1)) -) -``` - -**Polars:** -```python -result = df.with_columns( - pl.col("close").pct_change().over("symbol").alias("returns"), - pl.col("close").rolling_mean(20).over("symbol").alias("ma_20"), -).with_columns( - pl.when(pl.col("close") > pl.col("ma_20")).then(1).otherwise(-1).alias("signal") -) -``` - -### Pattern 2: Large File Processing - -**Spark:** -```python -# Spark partitions automatically, uses cluster -df = spark.read.parquet("s3://bucket/data/") -result = df.filter(col("date") >= "2024-01-01").groupBy("symbol").agg(...) -``` - -**Polars:** -```python -# Polars uses lazy eval + streaming for single-machine large data -lf = pl.scan_parquet("s3://bucket/data/**/*.parquet") -result = ( - lf - .filter(pl.col("date") >= "2024-01-01") - .group_by("symbol") - .agg(...) - .collect(engine="streaming") # Streaming for large data -) -``` - -### Pattern 3: UDFs - -**Spark:** -```python -from pyspark.sql.functions import udf -from pyspark.sql.types import DoubleType - -@udf(returnType=DoubleType()) -def custom_calc(x): - return x * 1.1 - -df = df.withColumn("result", custom_calc(col("value"))) -``` - -**Polars** - prefer expressions, avoid map_elements: -```python -# Good: Use native expressions when possible -df = df.with_columns((pl.col("value") * 1.1).alias("result")) - -# If UDF truly needed (performance penalty): -df = df.with_columns( - pl.col("value").map_elements(lambda x: x * 1.1, return_dtype=pl.Float64).alias("result") -) -``` - -## Performance Comparison - -| Scenario | Spark | Polars | -|----------|-------|--------| -| Small-medium data (<100GB) | Overhead from distribution | Faster - no distribution overhead | -| Large data (100GB-1TB) | Scales with cluster | Fast with streaming | -| Very large data (>1TB) | Native territory | May need data partitioning strategy | -| Complex joins | Shuffle-heavy | Very fast on single machine | -| Simple aggregations | Good | Often 10-100x faster | - -## Migration Checklist - -1. **Replace SparkSession** with Polars imports -2. **Change `col()` import** to `pl.col()` -3. **Replace `withColumn`** with `with_columns` -4. **Replace `orderBy`** with `sort` -5. **Replace `groupBy`** with `group_by` -6. **Simplify window functions** - use `.over()` directly -7. **Replace UDFs** with native expressions where possible -8. **Add `.collect()`** after lazy operations -9. **Use `scan_*`** for large files instead of `read_*` -10. **Remove partition management** - Polars handles parallelism automatically - -## What You Lose from Spark - -- Distributed execution across cluster -- Fault tolerance and checkpointing -- Spark ecosystem (MLlib, Spark Streaming, GraphX) -- Delta Lake / Iceberg native integration -- Cluster resource management - -## What You Gain with Polars - -- No cluster setup/maintenance -- Lower latency (no network overhead) -- Simpler code (no explicit windows for most operations) -- Better single-machine performance -- Composable expressions -- Smaller memory footprint diff --git a/.cursor/skills/polars-expertise/references/python/best_practices.md b/.cursor/skills/polars-expertise/references/python/best_practices.md deleted file mode 100644 index 4a76a73..0000000 --- a/.cursor/skills/polars-expertise/references/python/best_practices.md +++ /dev/null @@ -1,428 +0,0 @@ -# Polars Best Practices (Python) - -## Table of Contents -- [Performance Anti-Patterns](#performance-anti-patterns) -- [Time Series Patterns](#time-series-patterns) -- [Financial Data Patterns](#financial-data-patterns) -- [Large File Processing](#large-file-processing) -- [Memory Optimization](#memory-optimization) -- [Expression Patterns](#expression-patterns) - -## Performance Anti-Patterns - -### NEVER Use These - -```python -# ANTI-PATTERN 1: map_elements with Python functions -# This kills parallelization and is 10-100x slower -df.with_columns( - pl.col("price").map_elements(lambda x: x * 2) # TERRIBLE -) -# CORRECT: -df.with_columns(pl.col("price") * 2) # Fast, parallel - -# ANTI-PATTERN 2: Row iteration -for row in df.iter_rows(): # TERRIBLE - process(row) -# CORRECT: -df.with_columns(process_expression) - -# ANTI-PATTERN 3: Late projection -df = pl.read_parquet("data.parquet") -result = df.filter(...).select("a", "b") # Reads ALL columns first -# CORRECT: -lf = pl.scan_parquet("data.parquet") -result = lf.select("a", "b").filter(...).collect() # Reads only a, b - -# ANTI-PATTERN 4: Eager for large data -df = pl.read_csv("huge.csv") # Loads everything into RAM -# CORRECT: -lf = pl.scan_csv("huge.csv") -result = lf.filter(...).collect(engine="streaming") - -# ANTI-PATTERN 5: Converting to pandas unnecessarily -pandas_df = df.to_pandas() -result = pandas_df.groupby("x").mean() # Why? -# CORRECT: -result = df.group_by("x").agg(pl.all().mean()) - -# ANTI-PATTERN 6: Creating many intermediate DataFrames -df1 = df.filter(...) -df2 = df1.select(...) -df3 = df2.with_columns(...) # Each creates a copy -# CORRECT: -result = df.filter(...).select(...).with_columns(...) # Chained - -# ANTI-PATTERN 7: Wrong dtype selection -df = pl.read_csv("data.csv") # Infers Int64 for small integers -# CORRECT: -df = pl.read_csv("data.csv", dtypes={"small_int": pl.Int16}) -``` - -## Time Series Patterns - -### OHLCV Resampling - -```python -# Tick data to OHLCV bars -def resample_ohlcv(lf: pl.LazyFrame, interval: str) -> pl.LazyFrame: - return lf.group_by_dynamic( - "timestamp", - every=interval, - group_by="symbol" - ).agg( - pl.col("price").first().alias("open"), - pl.col("price").max().alias("high"), - pl.col("price").min().alias("low"), - pl.col("price").last().alias("close"), - pl.col("volume").sum().alias("volume"), - pl.col("price").count().alias("trades") - ) - -# Usage -bars_1m = resample_ohlcv(trades_lf, "1m").collect() -bars_5m = resample_ohlcv(trades_lf, "5m").collect() -``` - -### Rolling Statistics - -```python -# Efficient rolling calculations -df.with_columns( - # Simple moving averages - pl.col("close").rolling_mean(window_size=20).alias("sma_20"), - pl.col("close").rolling_mean(window_size=50).alias("sma_50"), - - # Volatility (rolling std) - pl.col("close").pct_change().rolling_std(window_size=20).alias("volatility"), - - # Rolling correlation - pl.rolling_corr("close", "volume", window_size=20).alias("corr_20"), - - # Exponential moving average - pl.col("close").ewm_mean(span=20).alias("ema_20"), - - # Bollinger Bands - (pl.col("close").rolling_mean(20) + 2 * pl.col("close").rolling_std(20)).alias("bb_upper"), - (pl.col("close").rolling_mean(20) - 2 * pl.col("close").rolling_std(20)).alias("bb_lower") -) -``` - -### Time-Based Windows - -```python -# Rolling by time duration (not row count) -df.with_columns( - # 5-minute rolling average - pl.col("price").rolling_mean( - window_size="5m", - by="timestamp" - ).alias("rolling_5m"), - - # Daily high/low - pl.col("price").rolling_max( - window_size="1d", - by="timestamp" - ).alias("daily_high") -) -``` - -### Lag/Lead for Returns - -```python -df.with_columns( - # Returns - pl.col("close").pct_change().alias("ret_1"), - pl.col("close").pct_change(5).alias("ret_5"), - - # Log returns - pl.col("close").log().diff().alias("log_ret"), - - # Forward returns (for labels) - pl.col("close").pct_change().shift(-1).alias("fwd_ret_1"), - pl.col("close").pct_change(5).shift(-5).alias("fwd_ret_5"), - - # Lagged features - pl.col("close").shift(1).alias("close_lag_1"), - pl.col("volume").shift(1).alias("volume_lag_1") -).over("symbol") # Per-symbol calculations -``` - -## Financial Data Patterns - -### VWAP Calculation - -```python -# Volume-Weighted Average Price -def calculate_vwap(df: pl.DataFrame) -> pl.DataFrame: - return df.with_columns( - vwap=( - (pl.col("price") * pl.col("volume")).cum_sum() / - pl.col("volume").cum_sum() - ).over("symbol") - ) -``` - -### As-Of Joins for Market Data - -```python -# Align trades with quotes -trades.join_asof( - quotes, - on="timestamp", - by="symbol", - strategy="backward" # Most recent quote before trade -) - -# Join with tolerance -trades.join_asof( - quotes, - on="timestamp", - by="symbol", - strategy="backward", - tolerance="100ms" # Only if quote within 100ms -) -``` - -### Bid-Ask Spread - -```python -quotes.with_columns( - (pl.col("ask") - pl.col("bid")).alias("spread"), - (((pl.col("ask") - pl.col("bid")) / pl.col("mid")) * 10000).alias("spread_bps"), - ((pl.col("bid") + pl.col("ask")) / 2).alias("mid") -) -``` - -### Position/PnL Tracking - -```python -# Calculate position and PnL from trades -trades.with_columns( - # Cumulative position - pl.col("signed_qty").cum_sum().over("symbol").alias("position"), - - # Mark-to-market PnL - ( - pl.col("signed_qty") * pl.col("price") * -1 - ).cum_sum().over("symbol").alias("realized_pnl") -) -``` - -## Large File Processing - -### Partitioned Reads - -```python -# Read partitioned parquet efficiently -lf = pl.scan_parquet( - "data/year=*/month=*/*.parquet", - hive_partitioning=True -) - -# Filter on partitions (predicate pushdown) -result = lf.filter( - (pl.col("year") == 2024) & (pl.col("month") >= 6) -).collect() -``` - -### Streaming for Massive Files - -```python -# Process larger-than-RAM data -lf = pl.scan_csv("massive.csv") - -# Streaming aggregation -result = ( - lf.filter(pl.col("value") > threshold) - .group_by("category") - .agg(pl.col("value").sum()) - .collect(engine="streaming") -) - -# Streaming write -lf.filter(pl.col("value") > threshold).sink_parquet("output.parquet") -``` - -### Chunked Processing - -```python -# Process in chunks when streaming not possible -def process_in_chunks(path: str, chunk_size: int = 1_000_000): - results = [] - reader = pl.read_csv_batched(path, batch_size=chunk_size) - - while True: - batch = reader.next_batches(1) - if batch is None: - break - # Process each batch - result = process_batch(batch[0]) - results.append(result) - - return pl.concat(results) -``` - -### Multi-File Processing - -```python -# Parallel file reading -lf = pl.scan_parquet("data/*.parquet") # Reads files in parallel - -# Or explicit list -files = ["data1.parquet", "data2.parquet", "data3.parquet"] -lf = pl.scan_parquet(files) - -# With schema enforcement -lf = pl.scan_parquet( - "data/*.parquet", - schema=expected_schema -) -``` - -## Memory Optimization - -### Dtype Selection - -```python -# Read with optimal types -df = pl.read_csv( - "data.csv", - dtypes={ - "trade_id": pl.UInt32, # Not Int64 if fits - "symbol": pl.Categorical, # Not String for symbols - "side": pl.Categorical, # "buy"/"sell" -> Categorical - "price": pl.Float32, # Float32 if precision OK - "quantity": pl.UInt32, # Unsigned if always positive - } -) -``` - -### Downcast After Load - -```python -def optimize_dtypes(df: pl.DataFrame) -> pl.DataFrame: - """Downcast numeric columns to smallest fitting type.""" - for col in df.columns: - dtype = df[col].dtype - if dtype in [pl.Int64, pl.Int32]: - min_val, max_val = df[col].min(), df[col].max() - if min_val >= 0: - if max_val <= 255: - df = df.with_columns(pl.col(col).cast(pl.UInt8)) - elif max_val <= 65535: - df = df.with_columns(pl.col(col).cast(pl.UInt16)) - elif max_val <= 4294967295: - df = df.with_columns(pl.col(col).cast(pl.UInt32)) - return df -``` - -### Categorical for Low Cardinality - -```python -# Identify candidates -for col in df.columns: - if df[col].dtype == pl.Utf8: - n_unique = df[col].n_unique() - total = len(df) - if n_unique / total < 0.5: # Less than 50% unique - print(f"{col}: {n_unique} unique / {total} total - use Categorical") -``` - -## Expression Patterns - -### Reusable Expression Library - -```python -# Define expression library for your domain -class FinanceExpr: - @staticmethod - def returns(col: str = "close") -> pl.Expr: - return pl.col(col).pct_change() - - @staticmethod - def log_returns(col: str = "close") -> pl.Expr: - return pl.col(col).log().diff() - - @staticmethod - def volatility(col: str = "close", window: int = 20) -> pl.Expr: - return pl.col(col).pct_change().rolling_std(window) - - @staticmethod - def sharpe(returns_col: str, rf: float = 0.0, window: int = 252) -> pl.Expr: - excess = pl.col(returns_col) - rf / 252 - return ( - excess.rolling_mean(window) / - excess.rolling_std(window) * - (252 ** 0.5) - ) - -# Usage -df.with_columns( - ret=FinanceExpr.returns(), - vol=FinanceExpr.volatility(window=20), - sharpe=FinanceExpr.sharpe("ret", window=60) -) -``` - -### Conditional Aggregations - -```python -# Complex conditional aggregations -df.group_by("symbol").agg( - # Count by condition - up_days=(pl.col("close") > pl.col("open")).sum(), - down_days=(pl.col("close") < pl.col("open")).sum(), - - # Conditional averages - pl.col("volume").filter( - pl.col("close") > pl.col("open") - ).mean().alias("avg_up_volume"), - - # Weighted conditionals - vwap_up=( - pl.when(pl.col("close") > pl.col("open")) - .then(pl.col("price") * pl.col("volume")) - .otherwise(0) - .sum() / - pl.when(pl.col("close") > pl.col("open")) - .then(pl.col("volume")) - .otherwise(0) - .sum() - ) -) -``` - -### Pipeline Functions - -```python -def clean_market_data(lf: pl.LazyFrame) -> pl.LazyFrame: - """Standard market data cleaning pipeline.""" - return ( - lf - .filter(pl.col("price") > 0) - .filter(pl.col("volume") > 0) - .with_columns( - pl.col("timestamp").cast(pl.Datetime), - pl.col("symbol").cast(pl.Categorical) - ) - .sort("symbol", "timestamp") - .unique(subset=["symbol", "timestamp"], keep="last") - ) - -def add_technical_features(lf: pl.LazyFrame) -> pl.LazyFrame: - """Add standard technical indicators.""" - return lf.with_columns( - pl.col("close").pct_change().over("symbol").alias("returns"), - pl.col("close").rolling_mean(20).over("symbol").alias("sma_20"), - pl.col("close").rolling_std(20).over("symbol").alias("volatility_20") - ) - -# Compose pipeline -result = ( - pl.scan_parquet("trades/*.parquet") - .pipe(clean_market_data) - .pipe(add_technical_features) - .collect() -) -``` diff --git a/.cursor/skills/polars-expertise/references/python/core_concepts.md b/.cursor/skills/polars-expertise/references/python/core_concepts.md deleted file mode 100644 index 1b7867c..0000000 --- a/.cursor/skills/polars-expertise/references/python/core_concepts.md +++ /dev/null @@ -1,271 +0,0 @@ -# Polars Core Concepts (Python) - -## Table of Contents -- [Expressions](#expressions) -- [Data Types](#data-types) -- [Lazy vs Eager Evaluation](#lazy-vs-eager-evaluation) -- [Streaming Mode](#streaming-mode) -- [Parallelization](#parallelization) - -## Expressions - -Expressions are the foundation of Polars. They describe transformations without executing immediately. - -### Expression Contexts - -Expressions only execute within specific contexts: - -```python -import polars as pl - -df = pl.DataFrame({ - "symbol": ["AAPL", "GOOG", "AAPL", "GOOG"], - "price": [150.0, 140.0, 151.0, 141.0], - "volume": [1000, 2000, 1500, 2500] -}) - -# select() - choose and transform columns -df.select("symbol", pl.col("price") * pl.col("volume")) - -# with_columns() - add/modify while preserving existing -df.with_columns( - (pl.col("price") * pl.col("volume")).alias("notional"), - pl.col("price").pct_change().over("symbol").alias("price_pct") -) - -# filter() - row selection -df.filter(pl.col("volume") > 1500) - -# group_by().agg() - aggregation -df.group_by("symbol").agg( - pl.col("price").mean().alias("avg_price"), - pl.col("volume").sum().alias("total_volume") -) -``` - -### Expression Composition - -Expressions can be stored and reused: - -```python -# Define reusable expressions for financial calculations -vwap = (pl.col("price") * pl.col("volume")).sum() / pl.col("volume").sum() -volatility = pl.col("price").std() / pl.col("price").mean() -returns = pl.col("price").pct_change() - -# Use in multiple contexts -df.group_by("symbol").agg( - vwap.alias("vwap"), - volatility.alias("volatility") -) -``` - -### Expression Expansion - -Apply operations to multiple columns: - -```python -# All numeric columns -df.select(pl.col(pl.NUMERIC_DTYPES) * 100) - -# Pattern matching -df.select(pl.col("^bid_.*$") - pl.col("^ask_.*$")) # Spread calculation - -# Exclude patterns -df.select(pl.all().exclude("timestamp", "id")) -``` - -## Data Types - -### Core Types - -| Type | Python | Use Case | -|------|--------|----------| -| Int64/Int32 | `pl.Int64` | Trade IDs, counts | -| Float64 | `pl.Float64` | Prices, returns | -| Utf8/String | `pl.Utf8` | Symbols, names | -| Datetime | `pl.Datetime` | Timestamps | -| Date | `pl.Date` | Calendar dates | -| Duration | `pl.Duration` | Time differences | -| Categorical | `pl.Categorical` | Low-cardinality strings | -| List | `pl.List` | Variable-length arrays | - -### Type Casting - -```python -df.with_columns( - # Downcast for memory efficiency - pl.col("trade_id").cast(pl.UInt32), - - # Parse timestamps - pl.col("timestamp_str").str.strptime(pl.Datetime, "%Y-%m-%d %H:%M:%S%.f"), - - # Categorical for symbols (faster grouping) - pl.col("symbol").cast(pl.Categorical) -) -``` - -### Null Handling - -```python -# Check nulls -df.filter(pl.col("price").is_not_null()) - -# Fill strategies -df.with_columns( - # Forward fill (common for market data) - pl.col("price").fill_null(strategy="forward"), - - # Fill with group mean - pl.col("price").fill_null(pl.col("price").mean().over("symbol")), - - # Interpolate - pl.col("price").interpolate() -) -``` - -## Lazy vs Eager Evaluation - -### When to Use Each - -| Lazy (`scan_*`, `.lazy()`) | Eager (`read_*`) | -|---------------------------|------------------| -| Large files (>1GB) | Small exploration | -| Complex pipelines | Interactive work | -| Production jobs | Quick analysis | -| Memory constrained | Simple one-offs | - -### Lazy Example - -```python -# Lazy: builds query plan, optimizes, then executes -lf = ( - pl.scan_parquet("trades/*.parquet") - .filter(pl.col("symbol") == "AAPL") - .filter(pl.col("timestamp") >= "2024-01-01") - .group_by_dynamic("timestamp", every="1m") - .agg( - pl.col("price").first().alias("open"), - pl.col("price").max().alias("high"), - pl.col("price").min().alias("low"), - pl.col("price").last().alias("close"), - pl.col("volume").sum() - ) -) - -# View optimized plan -print(lf.explain()) - -# Execute -df = lf.collect() -``` - -### Query Optimization - -Polars automatically applies: - -| Optimization | Effect | -|-------------|--------| -| Predicate pushdown | Filters at scan level | -| Projection pushdown | Reads only needed columns | -| Slice pushdown | Limits rows early | -| Common subplan elimination | Caches repeated subqueries | -| Join ordering | Optimizes join sequence | - -```python -# Example: only reads symbol="AAPL" rows and price/volume columns -lf = ( - pl.scan_parquet("trades.parquet") - .filter(pl.col("symbol") == "AAPL") # Pushed to scan - .select("price", "volume") # Only these columns read -) -``` - -## Streaming Mode - -Process data larger than RAM: - -```python -# Streaming execution -lf = pl.scan_csv("massive_file.csv") -result = lf.filter(pl.col("value") > 100).collect(engine="streaming") - -# Streaming write (sink) -lf.filter(pl.col("value") > 100).sink_parquet("output.parquet") - -# Check streaming compatibility -print(lf.explain(streaming=True)) -``` - -### Streaming Limitations - -Operations that may not stream: -- Sorts on large data -- Some join types -- Certain aggregations - -Polars falls back to in-memory automatically when needed. - -## Parallelization - -### Automatic Parallelization - -Polars parallelizes: -- Aggregations within groups -- Window functions -- Expression evaluations -- Multi-file reads - -### What Kills Parallelization - -```python -# BAD: Python UDF - sequential, slow -df.with_columns( - pl.col("price").map_elements(lambda x: custom_func(x)) # AVOID -) - -# GOOD: Native expressions - parallel, fast -df.with_columns( - pl.col("price") * 1.1 # Parallelized -) - -# BAD: Row iteration -for row in df.iter_rows(): # AVOID - process(row) - -# GOOD: Columnar operations -df.with_columns(processed=process_expr) -``` - -### Thread Pool Configuration - -```python -import os - -# Set before importing polars -os.environ["POLARS_MAX_THREADS"] = "8" - -# Or check current setting -import polars as pl -print(pl.thread_pool_size()) -``` - -## Memory Format - -Polars uses Apache Arrow columnar format: - -- Zero-copy sharing with other Arrow libraries -- Efficient SIMD vectorization -- Reduced memory overhead -- Fast serialization - -```python -# Check DataFrame memory usage -print(f"Size: {df.estimated_size('mb'):.2f} MB") - -# Convert to Arrow -arrow_table = df.to_arrow() - -# From Arrow (zero-copy when possible) -df = pl.from_arrow(arrow_table) -``` diff --git a/.cursor/skills/polars-expertise/references/python/io_guide.md b/.cursor/skills/polars-expertise/references/python/io_guide.md deleted file mode 100644 index 07eb8c8..0000000 --- a/.cursor/skills/polars-expertise/references/python/io_guide.md +++ /dev/null @@ -1,559 +0,0 @@ -# Polars Data I/O Guide - -Comprehensive guide to reading and writing data in various formats with Polars. - -**Contents:** CSV | Parquet | JSON | Excel | Database | Cloud Storage | BigQuery | Arrow | In-Memory | Streaming | Best Practices | Error Handling | Schema - -## CSV Files - -### Reading CSV - -**Eager mode (loads into memory):** -```python -import polars as pl - -# Basic read -df = pl.read_csv("data.csv") - -# With options -df = pl.read_csv( - "data.csv", - separator=",", - has_header=True, - columns=["col1", "col2"], # Select specific columns - n_rows=1000, # Read only first 1000 rows - skip_rows=10, # Skip first 10 rows - dtypes={"col1": pl.Int64, "col2": pl.Utf8}, # Specify types - null_values=["NA", "null", ""], # Define null values - encoding="utf-8", - ignore_errors=False -) -``` - -**Lazy mode (scans without loading - recommended for large files):** -```python -# Scan CSV (builds query plan) -lf = pl.scan_csv("data.csv") - -# Apply operations -result = lf.filter(pl.col("age") > 25).select("name", "age") - -# Execute and load -df = result.collect() -``` - -### Writing CSV - -```python -# Basic write -df.write_csv("output.csv") - -# With options -df.write_csv( - "output.csv", - separator=",", - include_header=True, - null_value="", # How to represent nulls - quote_char='"', - line_terminator="\n" -) -``` - -### Multiple CSV Files - -**Read multiple files:** -```python -# Read all CSVs in directory -lf = pl.scan_csv("data/*.csv") - -# Read specific files -lf = pl.scan_csv(["file1.csv", "file2.csv", "file3.csv"]) -``` - -## Parquet Files - -Parquet is the recommended format for performance and compression. - -### Reading Parquet - -**Eager:** -```python -df = pl.read_parquet("data.parquet") - -# With options -df = pl.read_parquet( - "data.parquet", - columns=["col1", "col2"], # Select specific columns - n_rows=1000, # Read first N rows - parallel="auto" # Control parallelization -) -``` - -**Lazy (recommended):** -```python -lf = pl.scan_parquet("data.parquet") - -# Automatic predicate and projection pushdown -result = lf.filter(pl.col("age") > 25).select("name", "age").collect() -``` - -### Writing Parquet - -```python -# Basic write -df.write_parquet("output.parquet") - -# With compression -df.write_parquet( - "output.parquet", - compression="snappy", # Options: "snappy", "gzip", "brotli", "lz4", "zstd" - statistics=True, # Write statistics (enables predicate pushdown) - use_pyarrow=False # Use Rust writer (faster) -) -``` - -### Partitioned Parquet (Hive-style) - -**Write partitioned:** -```python -# Write with partitioning -df.write_parquet( - "output_dir", - partition_by=["year", "month"] # Creates directory structure -) -# Creates: output_dir/year=2023/month=01/data.parquet -``` - -**Read partitioned:** -```python -lf = pl.scan_parquet("output_dir/**/*.parquet") - -# Hive partitioning columns are automatically added -result = lf.filter(pl.col("year") == 2023).collect() -``` - -## JSON Files - -### Reading JSON - -**NDJSON (newline-delimited JSON) - recommended:** -```python -df = pl.read_ndjson("data.ndjson") - -# Lazy -lf = pl.scan_ndjson("data.ndjson") -``` - -**Standard JSON:** -```python -df = pl.read_json("data.json") - -# From JSON string -df = pl.read_json('{"col1": [1, 2], "col2": ["a", "b"]}') -``` - -### Writing JSON - -```python -# Write NDJSON -df.write_ndjson("output.ndjson") - -# Write standard JSON -df.write_json("output.json") - -# Pretty printed -df.write_json("output.json", pretty=True, row_oriented=False) -``` - -## Excel Files - -### Reading Excel - -```python -# Read first sheet -df = pl.read_excel("data.xlsx") - -# Specific sheet -df = pl.read_excel("data.xlsx", sheet_name="Sheet1") -# Or by index -df = pl.read_excel("data.xlsx", sheet_id=0) - -# With options -df = pl.read_excel( - "data.xlsx", - sheet_name="Sheet1", - columns=["A", "B", "C"], # Excel columns - n_rows=100, - skip_rows=5, - has_header=True -) -``` - -### Writing Excel - -```python -# Write to Excel -df.write_excel("output.xlsx") - -# Multiple sheets -with pl.ExcelWriter("output.xlsx") as writer: - df1.write_excel(writer, worksheet="Sheet1") - df2.write_excel(writer, worksheet="Sheet2") -``` - -## Database Connectivity - -### Read from Database - -```python -import polars as pl - -# Read entire table -df = pl.read_database("SELECT * FROM users", connection_uri="postgresql://...") - -# Using connectorx for better performance -df = pl.read_database_uri( - "SELECT * FROM users WHERE age > 25", - uri="postgresql://user:pass@localhost/db" -) -``` - -### Write to Database - -```python -# Using SQLAlchemy -from sqlalchemy import create_engine - -engine = create_engine("postgresql://user:pass@localhost/db") -df.write_database("table_name", connection=engine) - -# With options -df.write_database( - "table_name", - connection=engine, - if_exists="replace", # or "append", "fail" -) -``` - -### Common Database Connectors - -**PostgreSQL:** -```python -uri = "postgresql://username:password@localhost:5432/database" -df = pl.read_database_uri("SELECT * FROM table", uri=uri) -``` - -**MySQL:** -```python -uri = "mysql://username:password@localhost:3306/database" -df = pl.read_database_uri("SELECT * FROM table", uri=uri) -``` - -**SQLite:** -```python -uri = "sqlite:///path/to/database.db" -df = pl.read_database_uri("SELECT * FROM table", uri=uri) -``` - -## Cloud Storage - -### AWS S3 - -```python -# Read from S3 -df = pl.read_parquet("s3://bucket/path/to/file.parquet") -lf = pl.scan_parquet("s3://bucket/path/*.parquet") - -# Write to S3 -df.write_parquet("s3://bucket/path/output.parquet") - -# With credentials -import os -os.environ["AWS_ACCESS_KEY_ID"] = "your_key" -os.environ["AWS_SECRET_ACCESS_KEY"] = "your_secret" -os.environ["AWS_REGION"] = "us-west-2" - -df = pl.read_parquet("s3://bucket/file.parquet") -``` - -### Azure Blob Storage - -```python -# Read from Azure -df = pl.read_parquet("az://container/path/file.parquet") - -# Write to Azure -df.write_parquet("az://container/path/output.parquet") - -# With credentials -os.environ["AZURE_STORAGE_ACCOUNT_NAME"] = "account" -os.environ["AZURE_STORAGE_ACCOUNT_KEY"] = "key" -``` - -### Google Cloud Storage (GCS) - -```python -# Read from GCS -df = pl.read_parquet("gs://bucket/path/file.parquet") - -# Write to GCS -df.write_parquet("gs://bucket/path/output.parquet") - -# With credentials -os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "/path/to/credentials.json" -``` - -## Google BigQuery - -```python -# Read from BigQuery -df = pl.read_database( - "SELECT * FROM project.dataset.table", - connection_uri="bigquery://project" -) - -# Or using Google Cloud SDK -from google.cloud import bigquery -client = bigquery.Client() - -query = "SELECT * FROM project.dataset.table WHERE date > '2023-01-01'" -df = pl.from_pandas(client.query(query).to_dataframe()) -``` - -## Apache Arrow - -### IPC/Feather Format - -**Read:** -```python -df = pl.read_ipc("data.arrow") -lf = pl.scan_ipc("data.arrow") -``` - -**Write:** -```python -df.write_ipc("output.arrow") - -# Compressed -df.write_ipc("output.arrow", compression="zstd") -``` - -### Arrow Streaming - -```python -# Write streaming format -df.write_ipc("output.arrows", compression="zstd") - -# Read streaming -df = pl.read_ipc("output.arrows") -``` - -### From/To Arrow - -```python -import pyarrow as pa - -# From Arrow Table -arrow_table = pa.table({"col": [1, 2, 3]}) -df = pl.from_arrow(arrow_table) - -# To Arrow Table -arrow_table = df.to_arrow() -``` - -## In-Memory Formats - -### Python Dictionaries - -```python -# From dict -df = pl.DataFrame({ - "col1": [1, 2, 3], - "col2": ["a", "b", "c"] -}) - -# To dict -data_dict = df.to_dict() # Column-oriented -data_dict = df.to_dict(as_series=False) # Lists instead of Series -``` - -### NumPy Arrays - -```python -import numpy as np - -# From NumPy -arr = np.array([[1, 2], [3, 4], [5, 6]]) -df = pl.DataFrame(arr, schema=["col1", "col2"]) - -# To NumPy -arr = df.to_numpy() -``` - -### Pandas DataFrames - -```python -import pandas as pd - -# From Pandas -pd_df = pd.DataFrame({"col": [1, 2, 3]}) -pl_df = pl.from_pandas(pd_df) - -# To Pandas -pd_df = pl_df.to_pandas() - -# Zero-copy when possible -pl_df = pl.from_arrow(pd_df) -``` - -### Lists of Rows - -```python -# From list of dicts -data = [ - {"name": "Alice", "age": 25}, - {"name": "Bob", "age": 30} -] -df = pl.DataFrame(data) - -# To list of dicts -rows = df.to_dicts() - -# From list of tuples -data = [("Alice", 25), ("Bob", 30)] -df = pl.DataFrame(data, schema=["name", "age"]) -``` - -## Streaming Large Files - -For datasets larger than memory, use lazy mode with streaming: - -```python -# Streaming mode -lf = pl.scan_csv("very_large.csv") -result = lf.filter(pl.col("value") > 100).collect(streaming=True) - -# Streaming with multiple files -lf = pl.scan_parquet("data/*.parquet") -result = lf.group_by("category").agg(pl.col("value").sum()).collect(streaming=True) -``` - -## Best Practices - -### Format Selection - -**Use Parquet when:** -- Need compression (up to 10x smaller than CSV) -- Want fast reads/writes -- Need to preserve data types -- Working with large datasets -- Need predicate pushdown - -**Use CSV when:** -- Need human-readable format -- Interfacing with legacy systems -- Data is small -- Need universal compatibility - -**Use JSON when:** -- Working with nested/hierarchical data -- Need web API compatibility -- Data has flexible schema - -**Use Arrow IPC when:** -- Need zero-copy data sharing -- Fastest serialization required -- Working between Arrow-compatible systems - -### Reading Large Files - -```python -# 1. Always use lazy mode -lf = pl.scan_csv("large.csv") # NOT read_csv - -# 2. Filter and select early (pushdown optimization) -result = ( - lf - .select("col1", "col2", "col3") # Only needed columns - .filter(pl.col("date") > "2023-01-01") # Filter early - .collect() -) - -# 3. Use streaming for very large data -result = lf.filter(...).select(...).collect(streaming=True) - -# 4. Read only needed rows during development -df = pl.read_csv("large.csv", n_rows=10000) # Sample for testing -``` - -### Writing Large Files - -```python -# 1. Use Parquet with compression -df.write_parquet("output.parquet", compression="zstd") - -# 2. Use partitioning for very large datasets -df.write_parquet("output", partition_by=["year", "month"]) - -# 3. Write streaming -lf = pl.scan_csv("input.csv") -lf.sink_parquet("output.parquet") # Streaming write -``` - -### Performance Tips - -```python -# 1. Specify dtypes when reading CSV -df = pl.read_csv( - "data.csv", - dtypes={"id": pl.Int64, "name": pl.Utf8} # Avoids inference -) - -# 2. Use appropriate compression -df.write_parquet("output.parquet", compression="snappy") # Fast -df.write_parquet("output.parquet", compression="zstd") # Better compression - -# 3. Parallel reading -df = pl.read_csv("data.csv", parallel="auto") - -# 4. Read multiple files in parallel -lf = pl.scan_parquet("data/*.parquet") # Automatic parallel read -``` - -## Error Handling - -```python -try: - df = pl.read_csv("data.csv") -except pl.exceptions.ComputeError as e: - print(f"Error reading CSV: {e}") - -# Ignore errors during parsing -df = pl.read_csv("messy.csv", ignore_errors=True) - -# Handle missing files -from pathlib import Path -if Path("data.csv").exists(): - df = pl.read_csv("data.csv") -else: - print("File not found") -``` - -## Schema Management - -```python -# Infer schema from sample -schema = pl.read_csv("data.csv", n_rows=1000).schema - -# Use inferred schema for full read -df = pl.read_csv("data.csv", dtypes=schema) - -# Define schema explicitly -schema = { - "id": pl.Int64, - "name": pl.Utf8, - "date": pl.Date, - "value": pl.Float64 -} -df = pl.read_csv("data.csv", dtypes=schema) -``` diff --git a/.cursor/skills/polars-expertise/references/python/operations.md b/.cursor/skills/polars-expertise/references/python/operations.md deleted file mode 100644 index d44e635..0000000 --- a/.cursor/skills/polars-expertise/references/python/operations.md +++ /dev/null @@ -1,607 +0,0 @@ -# Polars Operations Reference - -This reference covers all common Polars operations with comprehensive examples. - -**Contents:** Selection | Filtering | Grouping/Aggregation | Window Functions | Sorting | Conditional | String | Date/Time | List | Struct | Unique/Duplicate | Sampling | Renaming - -## Selection Operations - -### Select Columns - -**Basic selection:** -```python -# Select specific columns -df.select("name", "age", "city") - -# Using expressions -df.select(pl.col("name"), pl.col("age")) -``` - -**Pattern-based selection:** -```python -# All columns starting with "sales_" -df.select(pl.col("^sales_.*$")) - -# All numeric columns -df.select(pl.col(pl.NUMERIC_DTYPES)) - -# All columns except specific ones -df.select(pl.all().exclude("id", "timestamp")) -``` - -**Computed columns:** -```python -df.select( - "name", - (pl.col("age") * 12).alias("age_in_months"), - (pl.col("salary") * 1.1).alias("salary_after_raise") -) -``` - -### With Columns (Add/Modify) - -Add new columns or modify existing ones while preserving all other columns: - -```python -# Add new columns -df.with_columns( - (pl.col("age") * 2).alias("age_doubled"), - (pl.col("first_name") + " " + pl.col("last_name")).alias("full_name") -) - -# Modify existing columns -df.with_columns( - pl.col("name").str.to_uppercase().alias("name"), - pl.col("salary").cast(pl.Float64).alias("salary") -) - -# Multiple operations in parallel -df.with_columns( - pl.col("value") * 10, - pl.col("value") * 100, - pl.col("value") * 1000, -) -``` - -## Filtering Operations - -### Basic Filtering - -```python -# Single condition -df.filter(pl.col("age") > 25) - -# Multiple conditions (AND) -df.filter( - pl.col("age") > 25, - pl.col("city") == "NY" -) - -# OR conditions -df.filter( - (pl.col("age") > 30) | (pl.col("salary") > 100000) -) - -# NOT condition -df.filter(~pl.col("active")) -df.filter(pl.col("city") != "NY") -``` - -### Advanced Filtering - -**String operations:** -```python -# Contains substring -df.filter(pl.col("name").str.contains("John")) - -# Starts with -df.filter(pl.col("email").str.starts_with("admin")) - -# Regex match -df.filter(pl.col("phone").str.contains(r"^\d{3}-\d{3}-\d{4}$")) -``` - -**Membership checks:** -```python -# In list -df.filter(pl.col("city").is_in(["NY", "LA", "SF"])) - -# Not in list -df.filter(~pl.col("status").is_in(["inactive", "deleted"])) -``` - -**Range filters:** -```python -# Between values -df.filter(pl.col("age").is_between(25, 35)) - -# Date range -df.filter( - pl.col("date") >= pl.date(2023, 1, 1), - pl.col("date") <= pl.date(2023, 12, 31) -) -``` - -**Null filtering:** -```python -# Filter out nulls -df.filter(pl.col("value").is_not_null()) - -# Keep only nulls -df.filter(pl.col("value").is_null()) -``` - -## Grouping and Aggregation - -### Basic Group By - -```python -# Group by single column -df.group_by("department").agg( - pl.col("salary").mean().alias("avg_salary"), - pl.len().alias("employee_count") -) - -# Group by multiple columns -df.group_by("department", "location").agg( - pl.col("salary").sum() -) - -# Maintain order -df.group_by("category", maintain_order=True).agg( - pl.col("value").sum() -) -``` - -### Aggregation Functions - -**Count and length:** -```python -df.group_by("category").agg( - pl.len().alias("count"), - pl.col("id").count().alias("non_null_count"), - pl.col("id").n_unique().alias("unique_count") -) -``` - -**Statistical aggregations:** -```python -df.group_by("group").agg( - pl.col("value").sum().alias("total"), - pl.col("value").mean().alias("average"), - pl.col("value").median().alias("median"), - pl.col("value").std().alias("std_dev"), - pl.col("value").var().alias("variance"), - pl.col("value").min().alias("minimum"), - pl.col("value").max().alias("maximum"), - pl.col("value").quantile(0.95).alias("p95") -) -``` - -**First and last:** -```python -df.group_by("user_id").agg( - pl.col("timestamp").first().alias("first_seen"), - pl.col("timestamp").last().alias("last_seen"), - pl.col("event").first().alias("first_event") -) -``` - -**List aggregation:** -```python -# Collect values into lists -df.group_by("category").agg( - pl.col("item").alias("all_items") # Creates list column -) -``` - -### Conditional Aggregations - -Filter within aggregations: - -```python -df.group_by("department").agg( - # Count high earners - (pl.col("salary") > 100000).sum().alias("high_earners"), - - # Average of filtered values - pl.col("salary").filter(pl.col("bonus") > 0).mean().alias("avg_with_bonus"), - - # Conditional sum - pl.when(pl.col("active")) - .then(pl.col("sales")) - .otherwise(0) - .sum() - .alias("active_sales") -) -``` - -### Multiple Aggregations - -Combine multiple aggregations efficiently: - -```python -df.group_by("store_id").agg( - pl.col("transaction_id").count().alias("num_transactions"), - pl.col("amount").sum().alias("total_sales"), - pl.col("amount").mean().alias("avg_transaction"), - pl.col("customer_id").n_unique().alias("unique_customers"), - pl.col("amount").max().alias("largest_transaction"), - pl.col("timestamp").min().alias("first_transaction_date"), - pl.col("timestamp").max().alias("last_transaction_date") -) -``` - -## Window Functions - -Window functions apply aggregations while preserving the original row count. - -### Basic Window Operations - -**Group statistics:** -```python -# Add group mean to each row -df.with_columns( - pl.col("age").mean().over("department").alias("avg_age_by_dept") -) - -# Multiple group columns -df.with_columns( - pl.col("value").mean().over("category", "region").alias("group_avg") -) -``` - -**Ranking:** -```python -df.with_columns( - # Rank within groups - pl.col("score").rank().over("team").alias("rank"), - - # Dense rank (no gaps) - pl.col("score").rank(method="dense").over("team").alias("dense_rank"), - - # Row number - pl.col("timestamp").sort().rank(method="ordinal").over("user_id").alias("row_num") -) -``` - -### Window Mapping Strategies - -**group_to_rows (default):** -Preserves original row order: -```python -df.with_columns( - pl.col("value").mean().over("category", mapping_strategy="group_to_rows").alias("group_mean") -) -``` - -**explode:** -Faster, groups rows together: -```python -df.with_columns( - pl.col("value").mean().over("category", mapping_strategy="explode").alias("group_mean") -) -``` - -**join:** -Creates list columns: -```python -df.with_columns( - pl.col("value").over("category", mapping_strategy="join").alias("group_values") -) -``` - -### Rolling Windows - -**Time-based rolling:** -```python -df.with_columns( - pl.col("value").rolling_mean( - window_size="7d", - by="date" - ).alias("rolling_avg") -) -``` - -**Row-based rolling:** -```python -df.with_columns( - pl.col("value").rolling_sum(window_size=3).alias("rolling_sum"), - pl.col("value").rolling_max(window_size=5).alias("rolling_max") -) -``` - -### Cumulative Operations - -```python -df.with_columns( - pl.col("value").cum_sum().over("group").alias("cumsum"), - pl.col("value").cum_max().over("group").alias("cummax"), - pl.col("value").cum_min().over("group").alias("cummin"), - pl.col("value").cum_prod().over("group").alias("cumprod") -) -``` - -### Shift and Lag/Lead - -```python -df.with_columns( - # Previous value (lag) - pl.col("value").shift(1).over("user_id").alias("prev_value"), - - # Next value (lead) - pl.col("value").shift(-1).over("user_id").alias("next_value"), - - # Calculate difference from previous - (pl.col("value") - pl.col("value").shift(1).over("user_id")).alias("diff") -) -``` - -## Sorting - -### Basic Sorting - -```python -# Sort by single column -df.sort("age") - -# Sort descending -df.sort("age", descending=True) - -# Sort by multiple columns -df.sort("department", "age") - -# Mixed sorting order -df.sort(["department", "salary"], descending=[False, True]) -``` - -### Advanced Sorting - -**Null handling:** -```python -# Nulls first -df.sort("value", nulls_last=False) - -# Nulls last -df.sort("value", nulls_last=True) -``` - -**Sort by expression:** -```python -# Sort by computed value -df.sort(pl.col("first_name").str.len()) - -# Sort by multiple expressions -df.sort( - pl.col("last_name").str.to_lowercase(), - pl.col("age").abs() -) -``` - -## Conditional Operations - -### When/Then/Otherwise - -```python -# Basic conditional -df.with_columns( - pl.when(pl.col("age") >= 18) - .then("adult") - .otherwise("minor") - .alias("status") -) - -# Multiple conditions -df.with_columns( - pl.when(pl.col("score") >= 90) - .then("A") - .when(pl.col("score") >= 80) - .then("B") - .when(pl.col("score") >= 70) - .then("C") - .otherwise("F") - .alias("category") -) - -# Conditional computation -df.with_columns( - pl.when(pl.col("is_member")) - .then(pl.col("price") * 0.9) - .otherwise(pl.col("price")) - .alias("adjusted_price") -) -``` - -## String Operations - -### Common String Methods - -```python -df.with_columns( - # Case conversion - pl.col("name").str.to_uppercase().alias("upper"), - pl.col("name").str.to_lowercase().alias("lower"), - pl.col("name").str.to_titlecase().alias("title"), - - # Trimming - pl.col("text").str.strip_chars().alias("trimmed"), - - # Substring - pl.col("name").str.slice(0, 3).alias("first_3"), - - # Replace - pl.col("text").str.replace("old", "new").alias("cleaned"), - pl.col("text").str.replace_all("old", "new").alias("cleaned_all"), - - # Split - pl.col("full_name").str.split(" ").alias("parts"), - - # Length - pl.col("name").str.len_chars().alias("name_length") -) -``` - -### String Filtering - -```python -# Contains -df.filter(pl.col("email").str.contains("@gmail.com")) - -# Starts/ends with -df.filter(pl.col("name").str.starts_with("A")) -df.filter(pl.col("file").str.ends_with(".csv")) - -# Regex matching -df.filter(pl.col("phone").str.contains(r"^\d{3}-\d{4}$")) -``` - -## Date and Time Operations - -### Date Parsing - -```python -# Parse strings to dates -df.with_columns( - pl.col("date_str").str.strptime(pl.Date, "%Y-%m-%d").alias("date"), - pl.col("dt_str").str.strptime(pl.Datetime, "%Y-%m-%d %H:%M:%S").alias("datetime") -) -``` - -### Date Components - -```python -df.with_columns( - pl.col("date").dt.year().alias("year"), - pl.col("date").dt.month().alias("month"), - pl.col("date").dt.day().alias("day"), - pl.col("date").dt.weekday().alias("weekday"), - pl.col("datetime").dt.hour().alias("hour"), - pl.col("datetime").dt.minute().alias("minute") -) -``` - -### Date Arithmetic - -```python -# Add duration -df.with_columns( - (pl.col("date") + pl.duration(weeks=1)).alias("next_week"), - (pl.col("date") + pl.duration(months=1)).alias("next_month") -) - -# Difference between dates -df.with_columns( - days_diff=(pl.col("end_date") - pl.col("start_date")).dt.total_days() -) -``` - -### Date Filtering - -```python -# Filter by date range -df.filter( - pl.col("date").is_between(pl.date(2023, 1, 1), pl.date(2023, 12, 31)) -) - -# Filter by year -df.filter(pl.col("date").dt.year() == 2023) - -# Filter by month -df.filter(pl.col("date").dt.month().is_in([6, 7, 8])) # Summer months -``` - -## List Operations - -### Working with List Columns - -```python -# Create list column -df.with_columns( - pl.col("item1", "item2", "item3").to_list().alias("items_list") -) - -# List operations -df.with_columns( - pl.col("items").list.len().alias("list_len"), - pl.col("items").list.first().alias("first_item"), - pl.col("items").list.last().alias("last_item"), - pl.col("items").list.unique().alias("unique_items"), - pl.col("items").list.sort().alias("sorted_items") -) - -# Explode lists to rows -df.explode("items") - -# Filter list elements -df.with_columns( - pl.col("items").list.eval(pl.element() > 10).alias("filtered") -) -``` - -## Struct Operations - -### Working with Nested Structures - -```python -# Create struct column -df.with_columns( - address=pl.struct(["street", "city", "zip"]) -) - -# Access struct fields -df.with_columns( - pl.col("address").struct.field("city").alias("city") -) - -# Unnest struct to columns -df.unnest("address") -``` - -## Unique and Duplicate Operations - -```python -# Get unique rows -df.unique() - -# Unique on specific columns -df.unique(subset=["name", "email"]) - -# Keep first/last duplicate -df.unique(subset=["id"], keep="first") -df.unique(subset=["id"], keep="last") - -# Identify duplicates -df.with_columns( - pl.col("id").is_duplicated().alias("is_duplicate") -) - -# Count duplicates -df.group_by("email").agg( - pl.len().alias("count") -).filter(pl.col("count") > 1) -``` - -## Sampling - -```python -# Random sample -df.sample(n=100) - -# Sample fraction -df.sample(fraction=0.1) - -# Sample with seed for reproducibility -df.sample(n=100, seed=42) -``` - -## Column Renaming - -```python -# Rename specific columns -df.rename({"old_name": "new_name", "age": "years"}) - -# Rename with expression -df.select(pl.col("*").name.suffix("_renamed")) -df.select(pl.col("*").name.prefix("data_")) -df.select(pl.col("*").name.to_uppercase()) -``` diff --git a/.cursor/skills/polars-expertise/references/python/transformations.md b/.cursor/skills/polars-expertise/references/python/transformations.md deleted file mode 100644 index 5392457..0000000 --- a/.cursor/skills/polars-expertise/references/python/transformations.md +++ /dev/null @@ -1,551 +0,0 @@ -# Polars Data Transformations - -Comprehensive guide to joins, concatenation, and reshaping operations in Polars. - -**Contents:** Joins | Concatenation | Pivoting | Unpivoting/Melting | Exploding | Transposing | Reshaping Patterns | Advanced Transformations | Performance | Use Cases - -## Joins - -Joins combine data from multiple DataFrames based on common columns. - -### Basic Join Types - -**Inner Join (intersection):** -```python -# Keep only matching rows from both DataFrames -result = df1.join(df2, on="id", how="inner") -``` - -**Left Join (all left + matches from right):** -```python -# Keep all rows from left, add matching rows from right -result = df1.join(df2, on="id", how="left") -``` - -**Outer Join (union):** -```python -# Keep all rows from both DataFrames -result = df1.join(df2, on="id", how="outer") -``` - -**Cross Join (Cartesian product):** -```python -# Every row from left with every row from right -result = df1.join(df2, how="cross") -``` - -**Semi Join (filtered left):** -```python -# Keep only left rows that have a match in right -result = df1.join(df2, on="id", how="semi") -``` - -**Anti Join (non-matching left):** -```python -# Keep only left rows that DON'T have a match in right -result = df1.join(df2, on="id", how="anti") -``` - -### Join Syntax Variations - -**Single column join:** -```python -df1.join(df2, on="id") -``` - -**Multiple columns join:** -```python -df1.join(df2, on=["id", "date"]) -``` - -**Different column names:** -```python -df1.join(df2, left_on="user_id", right_on="id") -``` - -**Multiple different columns:** -```python -df1.join( - df2, - left_on=["user_id", "date"], - right_on=["id", "timestamp"] -) -``` - -### Suffix Handling - -When both DataFrames have columns with the same name (other than join keys): - -```python -# Add suffixes to distinguish columns -result = df1.join(df2, on="id", suffix="_right") - -# Results in: value, value_right (if both had "value" column) -``` - -### Join Examples - -**Example 1: Customer Orders** -```python -customers = pl.DataFrame({ - "customer_id": [1, 2, 3, 4], - "name": ["Alice", "Bob", "Charlie", "David"] -}) - -orders = pl.DataFrame({ - "order_id": [101, 102, 103], - "customer_id": [1, 2, 1], - "amount": [100, 200, 150] -}) - -# Inner join - only customers with orders -result = customers.join(orders, on="customer_id", how="inner") - -# Left join - all customers, even without orders -result = customers.join(orders, on="customer_id", how="left") -``` - -**Example 2: Time-series data** -```python -prices = pl.DataFrame({ - "date": ["2023-01-01", "2023-01-02", "2023-01-03"], - "stock": ["AAPL", "AAPL", "AAPL"], - "price": [150, 152, 151] -}) - -volumes = pl.DataFrame({ - "date": ["2023-01-01", "2023-01-02"], - "stock": ["AAPL", "AAPL"], - "volume": [1000000, 1100000] -}) - -result = prices.join( - volumes, - on=["date", "stock"], - how="left" -) -``` - -### Asof Joins (Nearest Match) - -For time-series data, join to nearest timestamp: - -```python -# Join to nearest earlier timestamp -quotes = pl.DataFrame({ - "timestamp": [1, 2, 3, 4, 5], - "stock": ["A", "A", "A", "A", "A"], - "quote": [100, 101, 102, 103, 104] -}) - -trades = pl.DataFrame({ - "timestamp": [1.5, 3.5, 4.2], - "stock": ["A", "A", "A"], - "trade": [50, 75, 100] -}) - -result = trades.join_asof( - quotes, - on="timestamp", - by="stock", - strategy="backward" # or "forward", "nearest" -) -``` - -## Concatenation - -Concatenation stacks DataFrames together. - -### Vertical Concatenation (Stack Rows) - -```python -df1 = pl.DataFrame({"a": [1, 2], "b": [3, 4]}) -df2 = pl.DataFrame({"a": [5, 6], "b": [7, 8]}) - -# Stack rows -result = pl.concat([df1, df2], how="vertical") -# Result: 4 rows, same columns -``` - -**Handling mismatched schemas:** -```python -df1 = pl.DataFrame({"a": [1, 2], "b": [3, 4]}) -df2 = pl.DataFrame({"a": [5, 6], "c": [7, 8]}) - -# Diagonal concat - fills missing columns with nulls -result = pl.concat([df1, df2], how="diagonal") -# Result: columns a, b, c (with nulls where not present) -``` - -### Horizontal Concatenation (Stack Columns) - -```python -df1 = pl.DataFrame({"a": [1, 2, 3]}) -df2 = pl.DataFrame({"b": [4, 5, 6]}) - -# Stack columns -result = pl.concat([df1, df2], how="horizontal") -# Result: 3 rows, columns a and b -``` - -**Note:** Horizontal concat requires same number of rows. - -### Concatenation Options - -```python -# Rechunk after concatenation (better performance for subsequent operations) -result = pl.concat([df1, df2], rechunk=True) - -# Parallel execution -result = pl.concat([df1, df2], parallel=True) -``` - -### Use Cases - -**Combining data from multiple sources:** -```python -# Read multiple files and concatenate -files = ["data_2023.csv", "data_2024.csv", "data_2025.csv"] -dfs = [pl.read_csv(f) for f in files] -combined = pl.concat(dfs, how="vertical") -``` - -**Adding computed columns:** -```python -base = pl.DataFrame({"value": [1, 2, 3]}) -computed = pl.DataFrame({"doubled": [2, 4, 6]}) -result = pl.concat([base, computed], how="horizontal") -``` - -## Pivoting (Wide Format) - -Convert unique values from one column into multiple columns. - -### Basic Pivot - -```python -df = pl.DataFrame({ - "date": ["2023-01", "2023-01", "2023-02", "2023-02"], - "product": ["A", "B", "A", "B"], - "sales": [100, 150, 120, 160] -}) - -# Pivot: products become columns -pivoted = df.pivot( - values="sales", - index="date", - columns="product" -) -# Result: -# date | A | B -# 2023-01 | 100 | 150 -# 2023-02 | 120 | 160 -``` - -### Pivot with Aggregation - -When there are duplicate combinations, aggregate: - -```python -df = pl.DataFrame({ - "date": ["2023-01", "2023-01", "2023-01"], - "product": ["A", "A", "B"], - "sales": [100, 110, 150] -}) - -# Aggregate duplicates -pivoted = df.pivot( - values="sales", - index="date", - columns="product", - aggregate_function="sum" # or "mean", "max", "min", etc. -) -``` - -### Multiple Index Columns - -```python -df = pl.DataFrame({ - "region": ["North", "North", "South", "South"], - "date": ["2023-01", "2023-01", "2023-01", "2023-01"], - "product": ["A", "B", "A", "B"], - "sales": [100, 150, 120, 160] -}) - -pivoted = df.pivot( - values="sales", - index=["region", "date"], - columns="product" -) -``` - -## Unpivoting/Melting (Long Format) - -Convert multiple columns into rows (opposite of pivot). - -### Basic Unpivot - -```python -df = pl.DataFrame({ - "date": ["2023-01", "2023-02"], - "product_A": [100, 120], - "product_B": [150, 160] -}) - -# Unpivot: convert columns to rows -unpivoted = df.unpivot( - index="date", - on=["product_A", "product_B"] -) -# Result: -# date | variable | value -# 2023-01 | product_A | 100 -# 2023-01 | product_B | 150 -# 2023-02 | product_A | 120 -# 2023-02 | product_B | 160 -``` - -### Custom Column Names - -```python -unpivoted = df.unpivot( - index="date", - on=["product_A", "product_B"], - variable_name="product", - value_name="sales" -) -``` - -### Unpivot by Pattern - -```python -# Unpivot all columns matching pattern -df = pl.DataFrame({ - "id": [1, 2], - "sales_Q1": [100, 200], - "sales_Q2": [150, 250], - "sales_Q3": [120, 220], - "revenue_Q1": [1000, 2000] -}) - -# Unpivot all sales columns -unpivoted = df.unpivot( - index="id", - on=pl.col("^sales.*$") -) -``` - -## Exploding (Unnesting Lists) - -Convert list columns into multiple rows. - -### Basic Explode - -```python -df = pl.DataFrame({ - "id": [1, 2], - "values": [[1, 2, 3], [4, 5]] -}) - -# Explode list into rows -exploded = df.explode("values") -# Result: -# id | values -# 1 | 1 -# 1 | 2 -# 1 | 3 -# 2 | 4 -# 2 | 5 -``` - -### Multiple Column Explode - -```python -df = pl.DataFrame({ - "id": [1, 2], - "letters": [["a", "b"], ["c", "d"]], - "numbers": [[1, 2], [3, 4]] -}) - -# Explode multiple columns (must be same length) -exploded = df.explode("letters", "numbers") -``` - -## Transposing - -Swap rows and columns: - -```python -df = pl.DataFrame({ - "metric": ["sales", "costs", "profit"], - "Q1": [100, 60, 40], - "Q2": [150, 80, 70] -}) - -# Transpose -transposed = df.transpose( - include_header=True, - header_name="quarter", - column_names="metric" -) -# Result: quarters as rows, metrics as columns -``` - -## Reshaping Patterns - -### Pattern 1: Wide to Long to Wide - -```python -# Start wide -wide = pl.DataFrame({ - "id": [1, 2], - "A": [10, 20], - "B": [30, 40] -}) - -# To long -long = wide.unpivot(index="id", on=["A", "B"]) - -# Back to wide (maybe with transformations) -wide_again = long.pivot(values="value", index="id", columns="variable") -``` - -### Pattern 2: Nested to Flat - -```python -# Nested data -df = pl.DataFrame({ - "user": [1, 2], - "purchases": [ - [{"item": "A", "qty": 2}, {"item": "B", "qty": 1}], - [{"item": "C", "qty": 3}] - ] -}) - -# Explode and unnest -flat = ( - df.explode("purchases") - .unnest("purchases") -) -``` - -### Pattern 3: Aggregation to Pivot - -```python -# Raw data -sales = pl.DataFrame({ - "date": ["2023-01", "2023-01", "2023-02"], - "product": ["A", "B", "A"], - "sales": [100, 150, 120] -}) - -# Aggregate then pivot -result = ( - sales - .group_by("date", "product") - .agg(pl.col("sales").sum()) - .pivot(values="sales", index="date", columns="product") -) -``` - -## Advanced Transformations - -### Conditional Reshaping - -```python -# Pivot only certain values -df.filter(pl.col("year") >= 2020).pivot(...) - -# Unpivot with filtering -df.unpivot(index="id", on=pl.col("^sales.*$")) -``` - -### Multi-level Transformations - -```python -# Complex reshaping pipeline -result = ( - df - .unpivot(index="id", on=pl.col("^Q[0-9]_.*$")) - .with_columns( - pl.col("variable").str.extract(r"Q([0-9])", 1).alias("quarter"), - pl.col("variable").str.extract(r"Q[0-9]_(.*)", 1).alias("metric") - ) - .drop("variable") - .pivot(values="value", index=["id", "quarter"], columns="metric") -) -``` - -## Performance Considerations - -### Join Performance - -```python -# 1. Join on indexed/sorted columns when possible -df1_sorted = df1.sort("id") -df2_sorted = df2.sort("id") -result = df1_sorted.join(df2_sorted, on="id") - -# 2. Use appropriate join type -# semi/anti are faster than inner+filter -matches = df1.join(df2, on="id", how="semi") # Better than filtering after inner join - -# 3. Filter before joining -df1_filtered = df1.filter(pl.col("active")) -result = df1_filtered.join(df2, on="id") # Smaller join -``` - -### Concatenation Performance - -```python -# 1. Rechunk after concatenation -result = pl.concat(dfs, rechunk=True) - -# 2. Use lazy mode for large concatenations -lf1 = pl.scan_parquet("file1.parquet") -lf2 = pl.scan_parquet("file2.parquet") -result = pl.concat([lf1, lf2]).collect() -``` - -### Pivot Performance - -```python -# 1. Filter before pivoting -pivoted = df.filter(pl.col("year") == 2023).pivot(...) - -# 2. Specify aggregate function explicitly -pivoted = df.pivot(..., aggregate_function="first") # Faster than "sum" if only one value -``` - -## Common Use Cases - -### Time Series Alignment - -```python -# Align two time series with different timestamps -ts1.join_asof(ts2, on="timestamp", strategy="backward") -``` - -### Feature Engineering - -```python -# Create lag features -df.with_columns( - pl.col("value").shift(1).over("user_id").alias("prev_value"), - pl.col("value").shift(2).over("user_id").alias("prev_prev_value") -) -``` - -### Data Denormalization - -```python -# Combine normalized tables -orders.join(customers, on="customer_id").join(products, on="product_id") -``` - -### Report Generation - -```python -# Pivot for reporting -sales.pivot(values="amount", index="month", columns="product") -``` diff --git a/.cursor/skills/polars-expertise/references/rust/arrow_interop.md b/.cursor/skills/polars-expertise/references/rust/arrow_interop.md deleted file mode 100644 index e4bc676..0000000 --- a/.cursor/skills/polars-expertise/references/rust/arrow_interop.md +++ /dev/null @@ -1,323 +0,0 @@ -# Arrow Interoperability (Rust) - -## Table of Contents -- [Polars and Arrow](#polars-and-arrow) -- [Zero-Copy Sharing](#zero-copy-sharing) -- [FFI Boundaries](#ffi-boundaries) -- [Integration with Other Libraries](#integration-with-other-libraries) -- [Memory Layout](#memory-layout) - -## Polars and Arrow - -Polars is built on Apache Arrow's columnar memory format, enabling efficient interoperability with the Arrow ecosystem. - -### Internal Structure - -``` -Polars ChunkedArray - └── Vec> (Arrow arrays) - └── ArrayData - ├── Buffers (actual data) - ├── Null bitmap - └── Child data (for nested types) -``` - -## Zero-Copy Sharing - -### To Arrow - -```rust -use polars::prelude::*; -use arrow::record_batch::RecordBatch; -use arrow::array::ArrayRef; - -fn to_arrow(df: &DataFrame) -> Vec { - // Convert DataFrame to Arrow RecordBatches - df.iter_chunks(false) - .map(|chunk| { - // Each chunk becomes a RecordBatch - let arrays: Vec = chunk - .into_iter() - .map(|arr| arr.into()) - .collect(); - - RecordBatch::try_new( - df.schema().to_arrow(CompatLevel::newest()), - arrays, - ).unwrap() - }) - .collect() -} - -// Direct conversion -fn df_to_arrow_table(df: DataFrame) -> arrow::array::RecordBatch { - let schema = df.schema().to_arrow(CompatLevel::newest()); - let chunks = df.iter_chunks(false).next().unwrap(); - let arrays: Vec = chunks.into_iter().map(|a| a.into()).collect(); - RecordBatch::try_new(schema, arrays).unwrap() -} -``` - -### From Arrow - -```rust -use polars::prelude::*; -use arrow::record_batch::RecordBatch; - -fn from_arrow(batch: RecordBatch) -> PolarsResult { - let schema = Schema::from_arrow_schema(batch.schema().as_ref()); - - let columns: Vec = batch - .columns() - .iter() - .zip(schema.iter_names()) - .map(|(arr, name)| { - let series = Series::from_arrow(name, arr.clone()).unwrap(); - series.into_column() - }) - .collect(); - - DataFrame::new_infer_height(columns) -} -``` - -### Series Conversion - -```rust -use polars::prelude::*; - -fn series_arrow_conversion() -> PolarsResult<()> { - // Create Series - let s = Series::new("values".into(), &[1i64, 2, 3, 4, 5]); - - // Get underlying Arrow arrays - let chunks = s.chunks(); - for chunk in chunks { - // chunk is a Box - println!("Array length: {}", chunk.len()); - } - - // From Arrow array - let arr = arrow::array::Int64Array::from(vec![1, 2, 3, 4, 5]); - let s = Series::from_arrow("from_arrow".into(), Box::new(arr))?; - - Ok(()) -} -``` - -## FFI Boundaries - -### C Data Interface - -Arrow's C Data Interface allows zero-copy sharing across language boundaries. - -```rust -use polars::prelude::*; -use arrow::ffi::{FFI_ArrowArray, FFI_ArrowSchema}; - -// Export to C -fn export_to_c(s: &Series) -> (FFI_ArrowSchema, FFI_ArrowArray) { - let chunks = s.chunks(); - let array = chunks.first().unwrap().clone(); - - let schema = FFI_ArrowSchema::try_from(s.dtype().to_arrow(CompatLevel::newest())).unwrap(); - let arr = FFI_ArrowArray::new(&*array); - - (schema, arr) -} - -// Import from C -fn import_from_c(schema: FFI_ArrowSchema, array: FFI_ArrowArray) -> PolarsResult { - let field = arrow::ffi::import_field_from_c(&schema)?; - let arr = arrow::ffi::import_array_from_c(array, field.data_type().clone())?; - - Series::from_arrow(field.name(), arr) -} -``` - -### PyArrow Integration - -When using pyo3: - -```rust -use pyo3::prelude::*; -use polars::prelude::*; - -#[pyfunction] -fn process_arrow_table(py: Python, table: &PyAny) -> PyResult<()> { - // Convert PyArrow table to Polars DataFrame - // This is handled by polars-python bindings - Ok(()) -} -``` - -## Integration with Other Libraries - -### DataFusion - -```rust -use polars::prelude::*; -use datafusion::prelude::*; -use datafusion::arrow::record_batch::RecordBatch; - -async fn with_datafusion(df: DataFrame) -> PolarsResult { - // Convert to RecordBatches - let batches: Vec = df - .iter_chunks(false) - .map(|chunk| { - let schema = df.schema().to_arrow(CompatLevel::newest()); - let arrays: Vec<_> = chunk.into_iter().map(|a| a.into()).collect(); - RecordBatch::try_new(schema, arrays).unwrap() - }) - .collect(); - - // Use with DataFusion - let ctx = SessionContext::new(); - let table = MemTable::try_new(batches[0].schema(), vec![batches])?; - ctx.register_table("df", Arc::new(table))?; - - let df_result = ctx - .sql("SELECT * FROM df WHERE x > 10") - .await? - .collect() - .await?; - - // Convert back to Polars - // ... conversion code - Ok(df) -} -``` - -### DuckDB - -```rust -use polars::prelude::*; -use duckdb::Connection; - -fn with_duckdb(df: &DataFrame) -> PolarsResult { - let conn = Connection::open_in_memory()?; - - // Register Arrow data with DuckDB - conn.register_arrow("my_table", df.iter_chunks(false).collect())?; - - // Query using DuckDB - let mut stmt = conn.prepare("SELECT * FROM my_table WHERE x > 10")?; - let arrow_result = stmt.query_arrow([])?; - - // Convert back to Polars - let batches: Vec<_> = arrow_result.collect(); - // ... conversion - Ok(df.clone()) -} -``` - -## Memory Layout - -### Buffer Structure - -``` -Arrow Primitive Array (e.g., Int64): -┌─────────────────────────────────────┐ -│ Validity Bitmap (bit-packed nulls) │ -├─────────────────────────────────────┤ -│ Data Buffer (contiguous i64 values) │ -└─────────────────────────────────────┘ - -Arrow String Array: -┌─────────────────────────────────────┐ -│ Validity Bitmap │ -├─────────────────────────────────────┤ -│ Offsets Buffer (i32/i64 offsets) │ -├─────────────────────────────────────┤ -│ Data Buffer (UTF-8 bytes) │ -└─────────────────────────────────────┘ -``` - -### Memory Efficiency - -```rust -use polars::prelude::*; - -fn memory_info(df: &DataFrame) { - // Estimated memory usage - println!("Estimated size: {} bytes", df.estimated_size()); - - // Per-column info - for col in df.get_columns() { - println!( - "Column '{}': {} bytes, {} chunks", - col.name(), - col.estimated_size(), - col.n_chunks() - ); - } -} -``` - -### Rechunking - -Multiple chunks can occur after operations like concatenation. Rechunking consolidates: - -```rust -use polars::prelude::*; - -fn optimize_memory(mut df: DataFrame) -> DataFrame { - // Check chunk count - let n_chunks: usize = df.get_columns() - .iter() - .map(|c| c.n_chunks()) - .max() - .unwrap_or(1); - - if n_chunks > 1 { - // Rechunk to single contiguous buffer - df = df.rechunk(); - } - - df -} -``` - -## Best Practices - -### Zero-Copy Guidelines - -1. **Avoid unnecessary copies** - - Use references when possible - - Share Arrow buffers directly - -2. **Watch for implicit copies** - - Type casting creates new buffers - - String operations often copy - -3. **Align with Arrow expectations** - - Use Arrow-native types - - Respect null semantics - -### Cross-Language Sharing - -```rust -// Good: Direct Arrow export -let arrow_table = df.to_arrow(); // Zero-copy when possible - -// Good: Stream large data -for batch in df.iter_chunks(false) { - // Process/send each batch -} - -// Avoid: Serialization when sharing -let json = df.write_json(); // Expensive for interop -``` - -### Memory Alignment - -Arrow requires 64-byte alignment for SIMD operations: - -```rust -use polars::prelude::*; - -// Polars handles alignment automatically -// But be aware when interfacing with raw buffers -let s = Series::new("a".into(), &[1i64, 2, 3]); -// Internal buffers are 64-byte aligned -``` diff --git a/.cursor/skills/polars-expertise/references/rust/core_concepts.md b/.cursor/skills/polars-expertise/references/rust/core_concepts.md deleted file mode 100644 index 96c2947..0000000 --- a/.cursor/skills/polars-expertise/references/rust/core_concepts.md +++ /dev/null @@ -1,438 +0,0 @@ -# Polars Core Concepts (Rust) - -## Table of Contents -- [Data Structures](#data-structures) -- [Creating DataFrames and Series](#creating-dataframes-and-series) -- [Expressions](#expressions) -- [Lazy vs Eager](#lazy-vs-eager) -- [Error Handling](#error-handling) - -## Data Structures - -### Hierarchy - -``` -DataFrame - └── Column (Vec) - └── Series - └── ChunkedArray - └── Arrow Arrays (Vec) -``` - -### ChunkedArray - -The fundamental data structure - a typed wrapper around Arrow arrays: - -```rust -use polars::prelude::*; - -// Create from slice -let ca = UInt32Chunked::new("foo".into(), &[1, 2, 3]); - -// From iterator -let ca: UInt32Chunked = (0..10).map(Some).collect(); - -// With builder (more control) -let mut builder = PrimitiveChunkedBuilder::::new("foo".into(), 10); -for value in 0..10 { - builder.append_value(value); -} -let ca = builder.finish(); -``` - -### Series - -Type-agnostic columnar data: - -```rust -use polars::prelude::*; - -// From slice -let s = Series::new("foo".into(), &[1, 2, 3]); - -// From iterator -let s: Series = (0..10).map(Some).collect(); - -// From ChunkedArray -let ca = UInt32Chunked::new("foo".into(), &[Some(1), None, Some(3)]); -let s = ca.into_series(); - -// Convert to Column -let col = s.into_column(); -``` - -### DataFrame - -Two-dimensional data backed by columns: - -```rust -use polars::prelude::*; -use polars::df; - -// Using df! macro -let df = df![ - "symbol" => ["AAPL", "GOOG"], - "price" => [150.0, 140.0], - "volume" => [Some(1000), None] -]?; - -// From Vec -let c1 = Column::new("symbol".into(), &["AAPL", "GOOG"]); -let c2 = Column::new("price".into(), &[150.0, 140.0]); -let df = DataFrame::new_infer_height(vec![c1, c2])?; -``` - -## Creating DataFrames and Series - -### Typed ChunkedArrays - -```rust -use polars::prelude::*; - -// Numeric types -let int32: Int32Chunked = Int32Chunked::new("a".into(), &[1, 2, 3]); -let float64: Float64Chunked = Float64Chunked::new("b".into(), &[1.0, 2.0, 3.0]); - -// String type -let utf8: StringChunked = StringChunked::new("c".into(), &["foo", "bar"]); - -// Boolean -let bool_ca: BooleanChunked = BooleanChunked::new("d".into(), &[true, false, true]); - -// With nulls -let with_nulls = Int64Chunked::new("e".into(), &[Some(1), None, Some(3)]); -``` - -### Downcasting Series - -Access the underlying ChunkedArray: - -```rust -use polars::prelude::*; - -fn process_series(s: &Series) -> PolarsResult<()> { - // Downcast to specific type - let ca: &Int32Chunked = s.i32()?; - let ca: &Float64Chunked = s.f64()?; - let ca: &StringChunked = s.str()?; - let ca: &BooleanChunked = s.bool()?; - - // Access values - for opt_val in ca.into_iter() { - match opt_val { - Some(val) => println!("{}", val), - None => println!("null"), - } - } - Ok(()) -} -``` - -## Expressions - -### Expression Contexts - -Expressions execute within specific contexts: - -```rust -use polars::prelude::*; - -fn expression_contexts(df: DataFrame) -> PolarsResult<()> { - let lf = df.lazy(); - - // select() - choose and transform columns - let selected = lf.clone() - .select([col("symbol"), col("price") * lit(100)]) - .collect()?; - - // with_columns() - add/modify while preserving existing - let with_new = lf.clone() - .with_columns([ - (col("price") * col("volume")).alias("notional"), - col("price").pct_change(lit(1)).alias("return") - ]) - .collect()?; - - // filter() - row selection - let filtered = lf.clone() - .filter(col("volume").gt(lit(1000))) - .collect()?; - - // group_by().agg() - aggregation - let grouped = lf.clone() - .group_by([col("symbol")]) - .agg([ - col("price").mean().alias("avg_price"), - col("volume").sum().alias("total_volume") - ]) - .collect()?; - - Ok(()) -} -``` - -### Expression Composition - -```rust -use polars::prelude::*; - -// Define reusable expressions -fn vwap() -> Expr { - (col("price") * col("volume")).sum() / col("volume").sum() -} - -fn volatility(window: usize) -> Expr { - col("price").pct_change(lit(1)).rolling_std(RollingOptionsFixedWindow { - window_size: window, - min_periods: window, - ..Default::default() - }) -} - -// Use in contexts -fn apply_expressions(lf: LazyFrame) -> PolarsResult { - lf.group_by([col("symbol")]) - .agg([ - vwap().alias("vwap"), - col("price").std(1).alias("std_dev") - ]) - .collect() -} -``` - -### Conditional Expressions - -```rust -use polars::prelude::*; - -fn conditionals(lf: LazyFrame) -> PolarsResult { - lf.with_columns([ - // when/then/otherwise - when(col("price").gt(lit(100))) - .then(lit("high")) - .when(col("price").gt(lit(50))) - .then(lit("medium")) - .otherwise(lit("low")) - .alias("price_tier") - ]) - .collect() -} -``` - -## Lazy vs Eager - -### Eager Mode - -Operations execute immediately: - -```rust -use polars::prelude::*; -use std::fs::File; - -fn eager_operations() -> PolarsResult<()> { - // Read file immediately - let file = File::open("data.csv")?; - let df = CsvReader::new(file).finish()?; - - // Each operation executes right away - let mask = df.column("price")?.gt(100)?; - let filtered = df.filter(&mask)?; - - Ok(()) -} -``` - -### Lazy Mode (Preferred) - -Operations build a query plan, optimized before execution: - -```rust -use polars::prelude::*; - -fn lazy_operations() -> PolarsResult { - // Build query plan (no execution yet) - let lf = LazyFrame::scan_parquet("trades.parquet", Default::default())? - .filter(col("symbol").eq(lit("AAPL"))) - .filter(col("timestamp").gt(lit("2024-01-01"))) - .group_by([col("symbol")]) - .agg([ - col("price").first().alias("open"), - col("price").max().alias("high"), - col("price").min().alias("low"), - col("price").last().alias("close"), - col("volume").sum() - ]); - - // View the optimized plan - println!("{}", lf.explain(true)?); - - // Execute - lf.collect() -} -``` - -### Converting Between Modes - -```rust -use polars::prelude::*; - -fn convert_modes(df: DataFrame) -> PolarsResult { - // Eager to Lazy - let lf: LazyFrame = df.lazy(); - - // Lazy to Eager - let df: DataFrame = lf.collect()?; - - Ok(df) -} -``` - -### Query Optimizations - -Polars automatically applies: - -| Optimization | Effect | -|-------------|--------| -| Predicate pushdown | Filters at scan level | -| Projection pushdown | Reads only needed columns | -| Slice pushdown | Limits rows early | -| Common subplan elimination | Caches repeated subqueries | -| Join ordering | Optimizes join sequence | - -```rust -use polars::prelude::*; - -// Example: only reads symbol="AAPL" rows and price/volume columns -fn optimized_read() -> PolarsResult { - LazyFrame::scan_parquet("trades.parquet", Default::default())? - .filter(col("symbol").eq(lit("AAPL"))) // Pushed to scan - .select([col("price"), col("volume")]) // Only these columns read - .collect() -} -``` - -## Error Handling - -### PolarsResult - -```rust -use polars::prelude::*; - -fn handle_errors() -> PolarsResult { - // Use ? for propagation - let df = df![ - "a" => [1, 2, 3] - ]?; - - // Explicit error handling - let result = df.column("nonexistent"); - match result { - Ok(col) => println!("Found column"), - Err(e) => println!("Error: {}", e), - } - - Ok(df) -} -``` - -### Common Error Patterns - -```rust -use polars::prelude::*; - -fn safe_operations(df: &DataFrame) -> PolarsResult<()> { - // Column access - returns Result - let col = df.column("price")?; - - // Downcasting - returns Result - let ca = col.f64()?; - - // Type casting - returns Result - let casted = col.cast(&DataType::Float32)?; - - // Arithmetic - may fail on type mismatch - let s1 = Series::new("a".into(), &[1, 2, 3]); - let s2 = Series::new("b".into(), &[1.0, 2.0, 3.0]); - let sum = &s1 + &s2; // Coerces types automatically - - Ok(()) -} -``` - -## Type System - -### Core Data Types - -```rust -use polars::prelude::*; - -// Numeric -DataType::Int8, DataType::Int16, DataType::Int32, DataType::Int64 -DataType::UInt8, DataType::UInt16, DataType::UInt32, DataType::UInt64 -DataType::Float32, DataType::Float64 - -// Text -DataType::String // UTF-8 strings -DataType::Categorical(None, CategoricalOrdering::Physical) - -// Temporal (requires dtype-* features) -DataType::Date -DataType::Datetime(TimeUnit::Microseconds, None) -DataType::Duration(TimeUnit::Microseconds) -DataType::Time - -// Complex -DataType::List(Box::new(DataType::Int64)) -DataType::Struct(vec![Field::new("a".into(), DataType::Int64)]) - -// Other -DataType::Boolean -DataType::Binary -DataType::Null -``` - -### Type Casting - -```rust -use polars::prelude::*; - -fn type_operations(s: &Series) -> PolarsResult { - // Cast to different type - let as_f64 = s.cast(&DataType::Float64)?; - - // Check type - if s.dtype() == &DataType::Int64 { - println!("It's an Int64"); - } - - // Strict casting (fails on overflow) - let strict = s.strict_cast(&DataType::Int32)?; - - Ok(as_f64) -} -``` - -### Null Handling - -```rust -use polars::prelude::*; - -fn null_operations(df: DataFrame) -> PolarsResult { - df.lazy() - .with_columns([ - // Check nulls - col("value").is_null().alias("is_missing"), - - // Fill nulls with constant - col("value").fill_null(lit(0)).alias("filled_const"), - - // Fill with strategy - col("value").forward_fill(None).alias("filled_forward"), - - // Drop nulls (use filter) - // Filter rows where value is not null - ]) - .filter(col("value").is_not_null()) - .collect() -} -``` diff --git a/.cursor/skills/polars-expertise/references/rust/features.md b/.cursor/skills/polars-expertise/references/rust/features.md deleted file mode 100644 index 042e414..0000000 --- a/.cursor/skills/polars-expertise/references/rust/features.md +++ /dev/null @@ -1,352 +0,0 @@ -# Polars Feature Flags (Rust) - -## Table of Contents -- [Feature System Overview](#feature-system-overview) -- [Core Features](#core-features) -- [I/O Features](#io-features) -- [Data Type Features](#data-type-features) -- [Operation Features](#operation-features) -- [Performance Features](#performance-features) -- [Common Configurations](#common-configurations) - -## Feature System Overview - -Polars uses Rust's feature flags to reduce compile times and binary size. Only enable what you need. - -### Minimal Setup - -```toml -[dependencies] -polars = "0.46" # Uses default features -``` - -Default features include: `docs`, `zip_with`, `csv`, `parquet`, `temporal`, `fmt`, `dtype-slim` - -### Custom Feature Selection - -```toml -[dependencies] -polars = { version = "0.46", default-features = false, features = [ - "lazy", - "parquet", - "temporal", - "dtype-datetime" -] } -``` - -## Core Features - -### Lazy API - -```toml -features = ["lazy"] # Required for LazyFrame -``` - -Enables: -- `LazyFrame` and query optimization -- Expression DSL (`col()`, `lit()`, etc.) -- Streaming execution - -```rust -use polars::prelude::*; - -// Requires "lazy" feature -let lf = LazyFrame::scan_parquet("data.parquet", Default::default())?; -let result = lf.filter(col("x").gt(lit(10))).collect()?; -``` - -### SQL Support - -```toml -features = ["sql"] -``` - -```rust -use polars::prelude::*; -use polars::sql::SQLContext; - -let mut ctx = SQLContext::new(); -ctx.register("df", df.lazy()); -let result = ctx.execute("SELECT * FROM df WHERE x > 10")?.collect()?; -``` - -### Regex Support - -```toml -features = ["lazy", "regex"] -``` - -```rust -// Column selection by regex -df.lazy().select([col("^sales_.*$")]) -``` - -## I/O Features - -| Feature | Description | -|---------|-------------| -| `csv` | CSV reading/writing | -| `parquet` | Parquet format (recommended) | -| `ipc` | Arrow IPC format | -| `ipc_streaming` | Streaming IPC | -| `json` | JSON reading/writing | -| `avro` | Apache Avro format | -| `decompress` | Auto-decompress gzip/zlib/zstd | - -### Cloud Storage - -```toml -features = ["cloud"] # Base cloud support -features = ["aws"] # AWS S3 -features = ["azure"] # Azure Blob -features = ["gcp"] # Google Cloud Storage -features = ["http"] # HTTP sources -``` - -```rust -// Requires "aws" feature -let lf = LazyFrame::scan_parquet("s3://bucket/data.parquet", Default::default())?; -``` - -## Data Type Features - -### Minimal Set (dtype-slim) - -```toml -features = ["dtype-slim"] # Included in default -``` - -Includes: `Date`, `Datetime`, `Duration` - -### Full Set (dtype-full) - -```toml -features = ["dtype-full"] -``` - -Includes all optional types: -- `dtype-date`, `dtype-datetime`, `dtype-duration`, `dtype-time` -- `dtype-array` (fixed-size arrays) -- `dtype-i8`, `dtype-i16`, `dtype-i128` -- `dtype-u8`, `dtype-u16`, `dtype-u128` -- `dtype-f16` -- `dtype-decimal` -- `dtype-categorical` -- `dtype-struct` - -### Individual Type Features - -```toml -# Enable only what you need -features = [ - "dtype-datetime", - "dtype-categorical", - "dtype-struct" -] -``` - -**Note:** If you get compile errors about missing types, you likely need to enable the corresponding dtype feature. - -## Operation Features - -### DataFrame Operations - -| Feature | Description | -|---------|-------------| -| `rows` | Row-based operations, pivot, transpose | -| `pivot` | Pivot tables (requires `rows`, `dtype-struct`) | -| `asof_join` | As-of joins for time series | -| `cross_join` | Cartesian product joins | -| `semi_anti_join` | Semi and anti joins | -| `dynamic_group_by` | Time-based group by | -| `partition_by` | Partition DataFrame by groups | -| `diagonal_concat` | Concat with different schemas | -| `dataframe_arithmetic` | DataFrame arithmetic ops | - -### Series/Expression Operations - -| Feature | Description | -|---------|-------------| -| `abs` | Absolute values | -| `cum_agg` | Cumulative sum/min/max | -| `diff` | Difference between elements | -| `pct_change` | Percentage change | -| `rolling_window` | Rolling aggregations | -| `rolling_window_by` | Time-based rolling | -| `rank` | Ranking algorithms | -| `is_in` | Membership checks | -| `is_between` | Range checks | -| `mode` | Most frequent values | -| `ewma` | Exponential moving average | -| `interpolate` | Interpolate nulls | -| `strings` | String utilities | -| `trigonometry` | Trig functions | -| `log` | Logarithms | - -### List Operations - -```toml -features = [ - "list_eval", # Apply expressions over lists - "list_gather", # Take sublist by indices - "list_to_struct", # Convert list to struct - "list_sets", # Set operations on lists -] -``` - -## Performance Features - -### Nightly Optimizations - -```toml -features = ["nightly"] # Requires nightly Rust -``` - -Enables: -- SIMD acceleration -- Specialization -- Additional optimizations - -```bash -# Build with nightly -rustup override set nightly -cargo build --release --features nightly -``` - -### SIMD - -```toml -features = ["simd"] # Included with "nightly" -``` - -### AVX-512 - -```toml -features = ["avx512"] # For CPUs with AVX-512 -``` - -### Performant Mode - -```toml -features = ["performant"] -``` - -Enables additional fast paths at the cost of slower compilation: -- `chunked_ids` -- `dtype-u8`, `dtype-u16`, `dtype-f16`, `dtype-struct` -- `cse` (common subexpression elimination) -- `fused` (fused operations) - -### Big Index - -```toml -features = ["bigidx"] # For >2^32 rows -``` - -Uses 64-bit indices instead of 32-bit. Slightly slower but supports massive datasets. - -## Common Configurations - -### Financial Data Processing - -```toml -[dependencies] -polars = { version = "0.46", default-features = false, features = [ - "lazy", - "parquet", - "csv", - "temporal", - "dtype-datetime", - "dtype-duration", - "dtype-f64", - "asof_join", - "dynamic_group_by", - "rolling_window", - "rolling_window_by", - "cum_agg", - "pct_change", - "diff", - "rank", - "fmt" -] } -``` - -### Data Pipeline / ETL - -```toml -[dependencies] -polars = { version = "0.46", default-features = false, features = [ - "lazy", - "parquet", - "csv", - "json", - "ipc", - "decompress", - "cloud", - "aws", - "dtype-full", - "streaming", - "diagonal_concat", - "partition_by", - "fmt" -] } -``` - -### ML Feature Engineering - -```toml -[dependencies] -polars = { version = "0.46", default-features = false, features = [ - "lazy", - "parquet", - "dtype-full", - "ndarray", - "strings", - "is_in", - "is_between", - "rank", - "mode", - "interpolate", - "pivot", - "to_dummies", - "fmt" -] } -``` - -### Maximum Performance - -```toml -[dependencies] -polars = { version = "0.46", features = [ - "nightly", - "performant" -] } -``` - -Build command: -```bash -RUSTFLAGS='-C target-cpu=native' cargo build --release -``` - -### Minimal Binary Size - -```toml -[dependencies] -polars = { version = "0.46", default-features = false, features = [ - "lazy", - "parquet" # Or just "csv" if you don't need parquet -] } -``` - -## Compile Time Tips - -1. **Start minimal** - Add features as needed -2. **Use sccache** - Cache compilation artifacts -3. **Incremental builds** - Avoid clean builds -4. **Separate dev/prod** - More features for dev, minimal for prod - -```bash -# Install sccache -cargo install sccache -export RUSTC_WRAPPER=sccache -``` diff --git a/.cursor/skills/polars-expertise/references/rust/io_guide.md b/.cursor/skills/polars-expertise/references/rust/io_guide.md deleted file mode 100644 index a8bdc74..0000000 --- a/.cursor/skills/polars-expertise/references/rust/io_guide.md +++ /dev/null @@ -1,382 +0,0 @@ -# Polars I/O Guide (Rust) - -## Table of Contents -- [CSV](#csv) -- [Parquet](#parquet) -- [IPC/Arrow](#ipcarrow) -- [JSON](#json) -- [Cloud Storage](#cloud-storage) -- [Streaming](#streaming) - -## CSV - -**Feature:** `csv` - -### Reading CSV - -```rust -use polars::prelude::*; -use std::fs::File; - -// Basic read -fn read_csv() -> PolarsResult { - let file = File::open("data.csv")?; - CsvReader::new(file).finish() -} - -// With options -fn read_csv_options() -> PolarsResult { - let file = File::open("data.csv")?; - CsvReader::new(file) - .with_has_header(true) - .with_separator(b',') - .with_n_rows(Some(1000)) // Limit rows - .with_columns(Some(Arc::new(vec!["col1".into(), "col2".into()]))) - .with_dtypes(Some(Arc::new(Schema::from_iter([ - Field::new("id".into(), DataType::Int64), - Field::new("price".into(), DataType::Float64), - ])))) - .with_null_values(Some(NullValues::AllColumnsSingle("NA".into()))) - .finish() -} - -// Lazy (recommended for large files) -fn scan_csv() -> PolarsResult { - LazyCsvReader::new("data.csv") - .with_has_header(true) - .with_separator(b',') - .finish() -} -``` - -### Writing CSV - -```rust -use polars::prelude::*; -use std::fs::File; - -fn write_csv(df: &mut DataFrame) -> PolarsResult<()> { - let mut file = File::create("output.csv")?; - CsvWriter::new(&mut file) - .include_header(true) - .with_separator(b',') - .with_quote_char(b'"') - .finish(df)?; - Ok(()) -} -``` - -## Parquet - -**Feature:** `parquet` - -### Reading Parquet - -```rust -use polars::prelude::*; -use std::fs::File; - -// Eager -fn read_parquet() -> PolarsResult { - let file = File::open("data.parquet")?; - ParquetReader::new(file).finish() -} - -// With options -fn read_parquet_options() -> PolarsResult { - let file = File::open("data.parquet")?; - ParquetReader::new(file) - .with_columns(Some(vec!["col1".into(), "col2".into()])) - .with_n_rows(Some(1000)) - .with_parallel(ParallelStrategy::Auto) - .finish() -} - -// Lazy (recommended) -fn scan_parquet() -> PolarsResult { - LazyFrame::scan_parquet("data.parquet", Default::default()) -} - -// Multiple files -fn scan_parquet_glob() -> PolarsResult { - LazyFrame::scan_parquet("data/*.parquet", Default::default()) -} - -// With scan options -fn scan_parquet_options() -> PolarsResult { - let args = ScanArgsParquet { - n_rows: Some(10000), - parallel: ParallelStrategy::Auto, - rechunk: false, - ..Default::default() - }; - LazyFrame::scan_parquet("data.parquet", args) -} -``` - -### Writing Parquet - -```rust -use polars::prelude::*; -use std::fs::File; - -fn write_parquet(df: &mut DataFrame) -> PolarsResult { - let file = File::create("output.parquet")?; - ParquetWriter::new(file) - .with_compression(ParquetCompression::Snappy) // Fast - // .with_compression(ParquetCompression::Zstd(Some(ZstdLevel::try_new(3)?))) // Better compression - .with_statistics(StatisticsOptions::full()) // Enables predicate pushdown - .finish(df) -} - -// Partitioned write -fn write_partitioned(lf: LazyFrame) -> PolarsResult<()> { - lf.sink_parquet( - "output/", - ParquetWriteOptions { - compression: ParquetCompression::Snappy, - statistics: StatisticsOptions::full(), - ..Default::default() - }, - ) -} -``` - -## IPC/Arrow - -**Feature:** `ipc` - -### Reading IPC - -```rust -use polars::prelude::*; -use std::fs::File; - -fn read_ipc() -> PolarsResult { - let file = File::open("data.arrow")?; - IpcReader::new(file).finish() -} - -fn scan_ipc() -> PolarsResult { - LazyFrame::scan_ipc("data.arrow", Default::default()) -} -``` - -### Writing IPC - -```rust -use polars::prelude::*; -use std::fs::File; - -fn write_ipc(df: &mut DataFrame) -> PolarsResult<()> { - let file = File::create("output.arrow")?; - IpcWriter::new(file) - .with_compression(Some(IpcCompression::ZSTD)) - .finish(df) -} -``` - -## JSON - -**Feature:** `json` - -### Reading JSON - -```rust -use polars::prelude::*; -use std::fs::File; - -// NDJSON (newline-delimited JSON) - recommended -fn read_ndjson() -> PolarsResult { - let file = File::open("data.ndjson")?; - JsonReader::new(file) - .with_json_format(JsonFormat::JsonLines) - .finish() -} - -// Standard JSON -fn read_json() -> PolarsResult { - let file = File::open("data.json")?; - JsonReader::new(file) - .with_json_format(JsonFormat::Json) - .finish() -} - -// Lazy NDJSON scan -fn scan_ndjson() -> PolarsResult { - LazyFrame::scan_ndjson("data.ndjson", Default::default()) -} -``` - -### Writing JSON - -```rust -use polars::prelude::*; -use std::fs::File; - -fn write_json(df: &mut DataFrame) -> PolarsResult<()> { - let mut file = File::create("output.json")?; - JsonWriter::new(&mut file) - .with_json_format(JsonFormat::Json) - .finish(df) -} - -fn write_ndjson(df: &mut DataFrame) -> PolarsResult<()> { - let mut file = File::create("output.ndjson")?; - JsonWriter::new(&mut file) - .with_json_format(JsonFormat::JsonLines) - .finish(df) -} -``` - -## Cloud Storage - -**Features:** `cloud`, `aws`, `azure`, `gcp` - -### AWS S3 - -```rust -use polars::prelude::*; - -// Set credentials via environment -// AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_REGION - -fn read_s3() -> PolarsResult { - LazyFrame::scan_parquet("s3://bucket/path/data.parquet", Default::default()) -} - -fn read_s3_glob() -> PolarsResult { - LazyFrame::scan_parquet("s3://bucket/path/*.parquet", Default::default()) -} -``` - -### Azure Blob - -```rust -use polars::prelude::*; - -// Set credentials via environment -// AZURE_STORAGE_ACCOUNT_NAME, AZURE_STORAGE_ACCOUNT_KEY - -fn read_azure() -> PolarsResult { - LazyFrame::scan_parquet("az://container/path/data.parquet", Default::default()) -} -``` - -### Google Cloud Storage - -```rust -use polars::prelude::*; - -// Set credentials via environment -// GOOGLE_APPLICATION_CREDENTIALS - -fn read_gcs() -> PolarsResult { - LazyFrame::scan_parquet("gs://bucket/path/data.parquet", Default::default()) -} -``` - -### HTTP - -```rust -use polars::prelude::*; - -fn read_http() -> PolarsResult { - LazyFrame::scan_parquet("https://example.com/data.parquet", Default::default()) -} -``` - -## Streaming - -### Streaming Reads - -```rust -use polars::prelude::*; - -fn streaming_process() -> PolarsResult { - // Streaming is handled automatically with new_streaming feature - LazyFrame::scan_parquet("very_large.parquet", Default::default())? - .filter(col("value").gt(lit(100))) - .group_by([col("category")]) - .agg([col("value").sum()]) - .collect() // Streaming execution when possible -} -``` - -### Streaming Writes (Sink) - -```rust -use polars::prelude::*; - -fn sink_to_parquet() -> PolarsResult<()> { - LazyFrame::scan_csv("large.csv", Default::default())? - .filter(col("value").gt(lit(100))) - .sink_parquet( - "output.parquet", - ParquetWriteOptions::default(), - ) -} -``` - -### Batched CSV Reading - -```rust -use polars::prelude::*; -use std::fs::File; - -fn process_csv_batches() -> PolarsResult> { - let file = File::open("large.csv")?; - let reader = CsvReader::new(file) - .batched(None)?; // Returns batched reader - - let mut results = Vec::new(); - while let Some(batch) = reader.next_batches(1)? { - for df in batch { - // Process each batch - let processed = df.filter(&df.column("x")?.gt(100)?)?; - results.push(processed); - } - } - Ok(results) -} -``` - -## Best Practices - -### Format Selection - -| Format | Use When | -|--------|----------| -| Parquet | Large files, archival, data lakes | -| CSV | Human-readable, legacy systems | -| IPC/Arrow | Fast transfer, zero-copy | -| NDJSON | Streaming JSON, logs | - -### Reading Large Files - -```rust -// 1. Always use lazy mode -let lf = LazyFrame::scan_parquet("large.parquet", Default::default())?; - -// 2. Project columns early -let lf = lf.select([col("a"), col("b")]); - -// 3. Filter early (predicate pushdown) -let lf = lf.filter(col("a").gt(lit(100))); - -// 4. Collect at the end -let df = lf.collect()?; -``` - -### Writing Large Files - -```rust -// 1. Use Parquet with compression -ParquetWriter::new(file) - .with_compression(ParquetCompression::Zstd(Some(ZstdLevel::try_new(3)?))) - .with_statistics(StatisticsOptions::full()) - .finish(&mut df)?; - -// 2. Use sink for streaming writes -lf.sink_parquet("output.parquet", ParquetWriteOptions::default())?; -``` diff --git a/.cursor/skills/polars-expertise/references/rust/operations.md b/.cursor/skills/polars-expertise/references/rust/operations.md deleted file mode 100644 index 8273c97..0000000 --- a/.cursor/skills/polars-expertise/references/rust/operations.md +++ /dev/null @@ -1,479 +0,0 @@ -# Polars Operations (Rust) - -## Table of Contents -- [Selection and Filtering](#selection-and-filtering) -- [Aggregations](#aggregations) -- [Window Functions](#window-functions) -- [Joins](#joins) -- [Sorting](#sorting) -- [Transformations](#transformations) - -## Selection and Filtering - -### Column Selection - -```rust -use polars::prelude::*; - -fn select_columns(df: DataFrame) -> PolarsResult { - // Select specific columns - let selected = df.lazy() - .select([col("a"), col("b"), col("c")]) - .collect()?; - - // Select by pattern (requires "regex" feature) - let by_pattern = df.lazy() - .select([col("^sales_.*$")]) // All columns starting with "sales_" - .collect()?; - - // Select all except some - let excluded = df.lazy() - .select([all().exclude(["id", "timestamp"])]) - .collect()?; - - // Select by dtype - let numeric = df.lazy() - .select([dtype_cols([DataType::Float64, DataType::Int64])]) - .collect()?; - - Ok(selected) -} -``` - -### Row Filtering - -```rust -use polars::prelude::*; - -fn filter_rows(df: DataFrame) -> PolarsResult { - let lf = df.lazy(); - - // Single condition - let filtered = lf.clone() - .filter(col("age").gt(lit(25))) - .collect()?; - - // Multiple conditions (AND) - let multi = lf.clone() - .filter( - col("age").gt(lit(25)) - .and(col("city").eq(lit("NY"))) - ) - .collect()?; - - // OR condition - let or_cond = lf.clone() - .filter( - col("age").gt(lit(30)) - .or(col("salary").gt(lit(100000))) - ) - .collect()?; - - // NOT condition - let not_cond = lf.clone() - .filter(col("active").not()) - .collect()?; - - // Membership - let in_list = lf.clone() - .filter(col("city").is_in(lit(Series::new("".into(), ["NY", "LA", "SF"])))) - .collect()?; - - // Range - let in_range = lf.clone() - .filter(col("age").is_between(lit(25), lit(35), ClosedInterval::Both)) - .collect()?; - - // Null checks - let not_null = lf.clone() - .filter(col("value").is_not_null()) - .collect()?; - - Ok(filtered) -} -``` - -### Add/Modify Columns - -```rust -use polars::prelude::*; - -fn add_columns(df: DataFrame) -> PolarsResult { - df.lazy() - .with_columns([ - // Arithmetic - (col("price") * col("quantity")).alias("total"), - - // Conditional - when(col("age").gt(lit(18))) - .then(lit("adult")) - .otherwise(lit("minor")) - .alias("status"), - - // String operations - col("name").str().to_uppercase().alias("name_upper"), - - // Cast types - col("id").cast(DataType::String).alias("id_str"), - ]) - .collect() -} -``` - -## Aggregations - -### Group By - -```rust -use polars::prelude::*; - -fn group_by_operations(df: DataFrame) -> PolarsResult { - // Basic group by - let grouped = df.clone().lazy() - .group_by([col("category")]) - .agg([ - col("value").sum().alias("total"), - col("value").mean().alias("average"), - col("value").count().alias("count"), - ]) - .collect()?; - - // Multiple group columns - let multi_group = df.clone().lazy() - .group_by([col("category"), col("region")]) - .agg([col("value").sum()]) - .collect()?; - - // Maintain order - let ordered = df.clone().lazy() - .group_by([col("category")]) - .agg([col("value").sum()]) - .sort([col("category")], Default::default()) - .collect()?; - - Ok(grouped) -} -``` - -### Aggregation Functions - -```rust -use polars::prelude::*; - -fn aggregation_functions(df: DataFrame) -> PolarsResult { - df.lazy() - .group_by([col("group")]) - .agg([ - // Count - len().alias("count"), - col("id").n_unique().alias("unique_count"), - - // Statistics - col("value").sum().alias("total"), - col("value").mean().alias("average"), - col("value").median().alias("median"), - col("value").std(1).alias("std_dev"), - col("value").var(1).alias("variance"), - col("value").min().alias("minimum"), - col("value").max().alias("maximum"), - col("value").quantile(lit(0.95), QuantileMethod::Linear).alias("p95"), - - // First/Last - col("timestamp").first().alias("first_ts"), - col("timestamp").last().alias("last_ts"), - - // List aggregation - col("item").alias("all_items"), // Collects into list - ]) - .collect() -} -``` - -### Conditional Aggregations - -```rust -use polars::prelude::*; - -fn conditional_aggregations(df: DataFrame) -> PolarsResult { - df.lazy() - .group_by([col("category")]) - .agg([ - // Count by condition - col("value").filter(col("value").gt(lit(100))).count().alias("high_count"), - - // Sum by condition - col("value").filter(col("active")).sum().alias("active_total"), - - // Conditional expression - when(col("type").eq(lit("A"))) - .then(col("value")) - .otherwise(lit(0)) - .sum() - .alias("type_a_total"), - ]) - .collect() -} -``` - -## Window Functions - -### Basic Window Operations - -```rust -use polars::prelude::*; - -fn window_functions(df: DataFrame) -> PolarsResult { - df.lazy() - .with_columns([ - // Group statistics (preserves row count) - col("value").mean().over([col("category")]).alias("group_mean"), - col("value").sum().over([col("category")]).alias("group_total"), - - // Ranking - col("value").rank(RankOptions::default(), None).over([col("category")]).alias("rank"), - - // Cumulative - col("value").cum_sum(false).over([col("category")]).alias("cumsum"), - - // Lag/Lead - col("value").shift(lit(1)).over([col("category")]).alias("prev_value"), - col("value").shift(lit(-1)).over([col("category")]).alias("next_value"), - - // Difference from previous - (col("value") - col("value").shift(lit(1))).over([col("category")]).alias("diff"), - ]) - .collect() -} -``` - -### Rolling Windows - -```rust -use polars::prelude::*; - -fn rolling_windows(df: DataFrame) -> PolarsResult { - df.lazy() - .with_columns([ - // Row-based rolling - col("value").rolling_mean(RollingOptionsFixedWindow { - window_size: 20, - min_periods: 20, - ..Default::default() - }).alias("rolling_mean"), - - col("value").rolling_std(RollingOptionsFixedWindow { - window_size: 20, - min_periods: 20, - ..Default::default() - }).alias("rolling_std"), - - col("value").rolling_sum(RollingOptionsFixedWindow { - window_size: 10, - min_periods: 1, // Allow partial windows - ..Default::default() - }).alias("rolling_sum"), - ]) - .collect() -} -``` - -## Joins - -### Basic Joins - -```rust -use polars::prelude::*; - -fn join_operations(df1: DataFrame, df2: DataFrame) -> PolarsResult<()> { - let lf1 = df1.clone().lazy(); - let lf2 = df2.clone().lazy(); - - // Inner join - let inner = lf1.clone() - .inner_join(lf2.clone(), col("id"), col("id")) - .collect()?; - - // Left join - let left = lf1.clone() - .left_join(lf2.clone(), col("id"), col("id")) - .collect()?; - - // Full outer join - let outer = lf1.clone() - .full_join(lf2.clone(), col("id"), col("id")) - .collect()?; - - // Multiple keys - let multi_key = lf1.clone() - .join( - lf2.clone(), - [col("id"), col("date")], - [col("id"), col("date")], - JoinArgs::new(JoinType::Inner), - ) - .collect()?; - - // Different column names - let diff_names = lf1.clone() - .join( - lf2.clone(), - [col("user_id")], - [col("id")], - JoinArgs::new(JoinType::Left), - ) - .collect()?; - - Ok(()) -} -``` - -### Semi and Anti Joins - -```rust -use polars::prelude::*; - -fn semi_anti_joins(df1: DataFrame, df2: DataFrame) -> PolarsResult<()> { - let lf1 = df1.lazy(); - let lf2 = df2.lazy(); - - // Semi join: keep left rows that have a match in right - let semi = lf1.clone() - .join(lf2.clone(), [col("id")], [col("id")], JoinArgs::new(JoinType::Semi)) - .collect()?; - - // Anti join: keep left rows that don't have a match in right - let anti = lf1.clone() - .join(lf2.clone(), [col("id")], [col("id")], JoinArgs::new(JoinType::Anti)) - .collect()?; - - Ok(()) -} -``` - -### As-Of Joins - -**Feature:** `asof_join` - -```rust -use polars::prelude::*; - -fn asof_join(trades: DataFrame, quotes: DataFrame) -> PolarsResult { - // Join to nearest earlier timestamp - trades.lazy() - .join_asof_by( - quotes.lazy(), - col("timestamp"), - col("timestamp"), - [col("symbol")], // Match by symbol first - [col("symbol")], - AsofStrategy::Backward, // Nearest earlier - None, // No tolerance - ) - .collect() -} -``` - -## Sorting - -```rust -use polars::prelude::*; - -fn sort_operations(df: DataFrame) -> PolarsResult { - // Single column - let sorted = df.clone().lazy() - .sort([col("value")], Default::default()) - .collect()?; - - // Descending - let desc = df.clone().lazy() - .sort( - [col("value")], - SortMultipleOptions::default().with_order_descending(true), - ) - .collect()?; - - // Multiple columns - let multi = df.clone().lazy() - .sort_by_exprs( - vec![col("category"), col("value")], - SortMultipleOptions::default() - .with_order_descending_multi([false, true]), // category asc, value desc - ) - .collect()?; - - // Nulls last - let nulls_last = df.clone().lazy() - .sort( - [col("value")], - SortMultipleOptions::default().with_nulls_last(true), - ) - .collect()?; - - Ok(sorted) -} -``` - -## Transformations - -### Concatenation - -```rust -use polars::prelude::*; - -fn concat_operations(df1: DataFrame, df2: DataFrame) -> PolarsResult { - // Vertical (stack rows) - let stacked = concat( - [df1.clone().lazy(), df2.clone().lazy()], - UnionArgs::default(), - )?.collect()?; - - // Horizontal (stack columns) - let horizontal = concat( - [df1.lazy(), df2.lazy()], - UnionArgs { - how: JoinType::Cross, // Cross join for horizontal - ..Default::default() - }, - )?.collect()?; - - Ok(stacked) -} -``` - -### Pivot and Unpivot - -```rust -use polars::prelude::*; - -fn pivot_operations(df: DataFrame) -> PolarsResult<()> { - // Pivot (wide format) - requires "pivot" feature - let pivoted = pivot::pivot( - &df, - [PlSmallStr::from_static("date")], // index - Some([PlSmallStr::from_static("product")]), // columns - Some([PlSmallStr::from_static("sales")]), // values - false, - Some(first()), // Aggregation function - None, - )?; - - // Unpivot (long format) - let unpivoted = df.unpivot( - [PlSmallStr::from_static("date")], // id columns - [PlSmallStr::from_static("A"), PlSmallStr::from_static("B")], // value columns - )?; - - Ok(()) -} -``` - -### Explode - -```rust -use polars::prelude::*; - -fn explode_operation(df: DataFrame) -> PolarsResult { - // Explode list column into rows - df.explode([PlSmallStr::from("items")]) -} -``` diff --git a/.cursor/skills/polars-expertise/references/rust/performance.md b/.cursor/skills/polars-expertise/references/rust/performance.md deleted file mode 100644 index 6142683..0000000 --- a/.cursor/skills/polars-expertise/references/rust/performance.md +++ /dev/null @@ -1,322 +0,0 @@ -# Polars Performance Optimization (Rust) - -## Table of Contents -- [Custom Allocators](#custom-allocators) -- [Compiler Optimizations](#compiler-optimizations) -- [Feature Flags for Performance](#feature-flags-for-performance) -- [Environment Variables](#environment-variables) -- [Code Patterns](#code-patterns) -- [Benchmarking](#benchmarking) - -## Custom Allocators - -Using a custom allocator can improve performance by 10-25%. - -### Jemalloc (Recommended for Linux/macOS) - -```toml -# Cargo.toml -[dependencies] -tikv-jemallocator = "0.6" -``` - -```rust -// main.rs or lib.rs - at the top -use tikv_jemallocator::Jemalloc; - -#[global_allocator] -static GLOBAL: Jemalloc = Jemalloc; -``` - -### Mimalloc (Cross-platform) - -```toml -# Cargo.toml -[dependencies] -mimalloc = { version = "0.1", default-features = false } -``` - -```rust -use mimalloc::MiMalloc; - -#[global_allocator] -static GLOBAL: MiMalloc = MiMalloc; -``` - -### Allocator Comparison - -| Allocator | Best For | Notes | -|-----------|----------|-------| -| Jemalloc | Linux, macOS | Best overall for OLAP | -| Mimalloc | Windows, cross-platform | Good general purpose | -| System | Default | Baseline, no extra deps | - -## Compiler Optimizations - -### Release Profile - -```toml -# Cargo.toml -[profile.release] -lto = "thin" # Link-time optimization -codegen-units = 1 # Better optimization, slower compile -opt-level = 3 # Maximum optimization -``` - -### Native CPU Features - -```bash -# Build for current CPU architecture -RUSTFLAGS='-C target-cpu=native' cargo build --release - -# Or specify architecture -RUSTFLAGS='-C target-cpu=skylake' cargo build --release -``` - -### Profile-Guided Optimization (PGO) - -```bash -# Step 1: Build with instrumentation -RUSTFLAGS='-Cprofile-generate=/tmp/pgo-data' cargo build --release - -# Step 2: Run representative workload -./target/release/my_app --process-data - -# Step 3: Merge profile data -llvm-profdata merge -o /tmp/pgo-data/merged.profdata /tmp/pgo-data - -# Step 4: Build with profile data -RUSTFLAGS='-Cprofile-use=/tmp/pgo-data/merged.profdata' cargo build --release -``` - -## Feature Flags for Performance - -### Nightly Features - -```toml -features = ["nightly"] -``` - -Requires nightly Rust: -```bash -rustup override set nightly -``` - -Enables: -- SIMD vectorization -- Specialization -- Additional optimizations - -### SIMD - -```toml -features = ["simd"] # Enabled with "nightly" -``` - -### Performant Mode - -```toml -features = ["performant"] -``` - -Enables fast paths at cost of compile time: -- Common subexpression elimination -- Fused operations -- Optimal dtype handling - -### AVX-512 - -```toml -features = ["avx512"] -``` - -For modern Intel CPUs with AVX-512 support. - -## Environment Variables - -### Thread Pool - -```bash -# Set number of threads (default: num_cpus) -export POLARS_MAX_THREADS=8 - -# Or in Rust -std::env::set_var("POLARS_MAX_THREADS", "8"); -``` - -### Partitioning - -```bash -# Disable partitioned group_by (for debugging) -export POLARS_NO_PARTITION=1 - -# Force partitioned group_by -export POLARS_FORCE_PARTITION=1 - -# Partition threshold (default 1000) -export POLARS_PARTITION_UNIQUE_COUNT=500 -``` - -### Debugging - -```bash -# Verbose output -export POLARS_VERBOSE=1 - -# Panic on error (for debugging) -export POLARS_PANIC_ON_ERR=1 - -# Include backtrace in errors -export POLARS_BACKTRACE_IN_ERR=1 -``` - -### Parquet - -```bash -# Ignore parquet statistics (for debugging) -export POLARS_NO_PARQUET_STATISTICS=1 -``` - -## Code Patterns - -### Use Lazy Mode - -```rust -// GOOD: Lazy mode enables optimizations -let result = LazyFrame::scan_parquet("data.parquet", Default::default())? - .filter(col("x").gt(lit(10))) - .select([col("x"), col("y")]) - .collect()?; - -// BAD: Eager reads all data first -let df = ParquetReader::new(File::open("data.parquet")?).finish()?; -let filtered = df.filter(&df.column("x")?.gt(10)?)?; -``` - -### Project Early - -```rust -// GOOD: Project columns early -lf.select([col("a"), col("b")]) - .filter(col("a").gt(lit(10))) - .collect()? - -// BAD: Read all columns, filter, then project -lf.filter(col("a").gt(lit(10))) - .collect()? - .select(["a", "b"])? -``` - -### Avoid Python-style UDFs - -```rust -// GOOD: Native expressions -lf.with_columns([col("x") * lit(2)]) - -// BAD: map function (sequential, not parallelized) -lf.with_columns([col("x").map(|s| { - Ok(Some(s.multiply(&Series::new("".into(), [2]))?)) -}, GetOutput::same_type())]) -``` - -### Use Categorical for Low Cardinality - -```rust -// GOOD: Categorical for repeated strings -let df = df.lazy() - .with_columns([col("symbol").cast(DataType::Categorical(None, Default::default()))]) - .collect()?; - -// Faster groupby and joins with categorical -``` - -### Rechunk After Operations - -```rust -// After multiple concatenations, rechunk for better cache locality -let combined = pl::concat(dfs, UnionArgs::default())?; -let combined = combined.rechunk(); -``` - -### Streaming for Large Data - -```rust -// Use streaming for out-of-memory data -let result = lf - .filter(col("x").gt(lit(10))) - .group_by([col("category")]) - .agg([col("value").sum()]) - .collect()?; // Streaming is automatic in new_streaming -``` - -## Benchmarking - -### Setup - -```toml -# Cargo.toml -[dev-dependencies] -criterion = { version = "0.5", features = ["html_reports"] } - -[[bench]] -name = "my_benchmark" -harness = false -``` - -### Basic Benchmark - -```rust -// benches/my_benchmark.rs -use criterion::{criterion_group, criterion_main, Criterion, black_box}; -use polars::prelude::*; - -fn benchmark_filter(c: &mut Criterion) { - let df = df![ - "x" => (0..1_000_000).collect::>(), - "y" => (0..1_000_000).map(|i| i as f64).collect::>() - ].unwrap(); - - c.bench_function("filter_large", |b| { - b.iter(|| { - let mask = black_box(&df).column("x").unwrap().gt(500_000).unwrap(); - black_box(&df).filter(&mask).unwrap() - }) - }); -} - -criterion_group!(benches, benchmark_filter); -criterion_main!(benches); -``` - -### Run Benchmarks - -```bash -cargo bench - -# With specific feature flags -cargo bench --features nightly,performant -``` - -### Profiling - -```bash -# Using perf (Linux) -perf record --call-graph dwarf ./target/release/my_app -perf report - -# Using flamegraph -cargo install flamegraph -cargo flamegraph --bin my_app -``` - -## Performance Checklist - -- [ ] Using custom allocator (jemalloc/mimalloc)? -- [ ] Building with `--release`? -- [ ] Using `RUSTFLAGS='-C target-cpu=native'`? -- [ ] Using lazy mode for complex pipelines? -- [ ] Projecting columns early? -- [ ] Using categorical for low-cardinality strings? -- [ ] Avoiding map functions where expressions work? -- [ ] Appropriate thread pool size for workload? -- [ ] Features enabled: `performant`, `nightly` (if applicable)? diff --git a/.cursor/skills/polars-expertise/references/sql_interface.md b/.cursor/skills/polars-expertise/references/sql_interface.md deleted file mode 100644 index 9f633bd..0000000 --- a/.cursor/skills/polars-expertise/references/sql_interface.md +++ /dev/null @@ -1,313 +0,0 @@ -# SQL Interface - -Polars provides SQL support through `SQLContext`, translating SQL queries into expressions for native execution with full query optimization. - -## SQLContext Setup - -### Basic Initialization - -```python -import polars as pl - -# Create context with DataFrame registration -df = pl.DataFrame({ - "symbol": ["AAPL", "AAPL", "MSFT", "MSFT"], - "price": [150.0, 152.0, 280.0, 285.0], - "volume": [1000000, 1200000, 800000, 900000] -}) - -ctx = pl.SQLContext(frames={"trades": df}) -``` - -### Registration Methods - -```python -# Register single DataFrame -ctx = pl.SQLContext() -ctx.register("trades", df) - -# Register multiple DataFrames -ctx.register_many({ - "trades": trades_df, - "quotes": quotes_df, - "orders": orders_df -}) - -# Register LazyFrames (recommended for large data) -lf = pl.scan_parquet("trades.parquet") -ctx.register("trades", lf) - -# Register all DataFrames/LazyFrames in global namespace -ctx.register_globals() -``` - -### Eager vs Lazy Execution - -```python -# Lazy execution (default) - returns LazyFrame -ctx = pl.SQLContext(trades=df) -result = ctx.execute("SELECT * FROM trades WHERE price > 150") -# result is LazyFrame - call .collect() to materialize - -# Eager execution - returns DataFrame directly -ctx = pl.SQLContext(trades=df, eager_execution=True) -result = ctx.execute("SELECT * FROM trades WHERE price > 150") -# result is DataFrame - -# Per-query eager execution -result = ctx.execute("SELECT * FROM trades", eager=True) -``` - -## Query Patterns - -### Basic SELECT with Aggregations - -```python -ctx.execute(""" - SELECT - symbol, - AVG(price) as avg_price, - SUM(volume) as total_volume, - COUNT(*) as trade_count - FROM trades - GROUP BY symbol - ORDER BY avg_price DESC -""").collect() -``` - -### Financial Data Patterns - -```python -# VWAP calculation -ctx.execute(""" - SELECT - symbol, - SUM(price * volume) / SUM(volume) as vwap - FROM trades - GROUP BY symbol -""").collect() - -# Price change analysis -ctx.execute(""" - SELECT - symbol, - price, - LAG(price, 1) OVER (PARTITION BY symbol ORDER BY timestamp) as prev_price, - price - LAG(price, 1) OVER (PARTITION BY symbol ORDER BY timestamp) as price_change - FROM trades -""").collect() -``` - -### JOINs - -```python -quotes = pl.DataFrame({ - "symbol": ["AAPL", "MSFT"], - "bid": [149.5, 279.5], - "ask": [150.5, 280.5] -}) - -ctx.register("quotes", quotes) - -# Inner join -ctx.execute(""" - SELECT - t.symbol, - t.price, - q.bid, - q.ask, - t.price - (q.bid + q.ask) / 2 as mid_deviation - FROM trades t - INNER JOIN quotes q ON t.symbol = q.symbol -""").collect() -``` - -### Common Table Expressions (CTEs) - -```python -ctx.execute(""" - WITH daily_stats AS ( - SELECT - symbol, - DATE_TRUNC('day', timestamp) as date, - MAX(price) as high, - MIN(price) as low, - FIRST_VALUE(price) OVER (PARTITION BY symbol, DATE_TRUNC('day', timestamp) ORDER BY timestamp) as open, - LAST_VALUE(price) OVER (PARTITION BY symbol, DATE_TRUNC('day', timestamp) ORDER BY timestamp) as close - FROM trades - GROUP BY symbol, DATE_TRUNC('day', timestamp) - ), - with_returns AS ( - SELECT - *, - (close - open) / open * 100 as daily_return - FROM daily_stats - ) - SELECT * FROM with_returns - WHERE daily_return > 1.0 - ORDER BY daily_return DESC -""").collect() -``` - -### Table Functions - Direct File Reading - -```python -# Read directly from files in SQL -ctx.execute(""" - SELECT * - FROM read_parquet('trades/*.parquet') - WHERE date >= '2024-01-01' -""").collect() - -# Join files -ctx.execute(""" - SELECT t.*, r.reference_price - FROM read_csv('trades.csv') t - JOIN read_parquet('reference.parquet') r - ON t.symbol = r.symbol -""").collect() -``` - -### CREATE TABLE - -```python -# Create derived tables -ctx.execute(""" - CREATE TABLE high_volume_trades AS - SELECT * - FROM trades - WHERE volume > 1000000 -""") - -# Query created table -ctx.execute("SELECT * FROM high_volume_trades").collect() - -# Show registered tables -ctx.execute("SHOW TABLES").collect() -``` - -## Supported SQL Features - -### Statements - -| Statement | Support | -|-----------|---------| -| SELECT | Full support | -| CREATE TABLE AS | Full support | -| DROP TABLE | Full support | -| TRUNCATE TABLE | Full support | -| EXPLAIN | Full support | -| SHOW TABLES | Full support | -| INSERT/UPDATE/DELETE | Not supported | - -### Clauses - -| Clause | Support | -|--------|---------| -| WHERE | Full support | -| GROUP BY | Full support | -| HAVING | Full support | -| ORDER BY | Full support | -| LIMIT/OFFSET | Full support | -| JOIN (INNER, LEFT, RIGHT, FULL, CROSS) | Full support | -| UNION/UNION ALL | Full support | -| WITH (CTE) | Full support | - -### Functions - -| Category | Examples | -|----------|----------| -| Aggregation | SUM, AVG, MIN, MAX, COUNT, STDDEV, VARIANCE, FIRST, LAST | -| Window | ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD, FIRST_VALUE, LAST_VALUE | -| Math | ABS, CEIL, FLOOR, ROUND, EXP, LN, LOG, POWER, SQRT | -| String | UPPER, LOWER, LENGTH, TRIM, SUBSTR, CONCAT, REPLACE, STARTS_WITH, ENDS_WITH | -| Date/Time | DATE_TRUNC, DATE_PART, EXTRACT, NOW, CURRENT_DATE | -| Array | EXPLODE, UNNEST, ARRAY_LENGTH, ARRAY_SUM | -| Conditional | CASE WHEN, COALESCE, NULLIF, IIF | - -## Best Practices - -### 1. Use LazyFrames for Large Data - -```python -# Good - lazy reading with SQL optimization -lf = pl.scan_parquet("large_dataset.parquet") -ctx = pl.SQLContext(data=lf) -result = ctx.execute(""" - SELECT symbol, AVG(price) - FROM data - WHERE date >= '2024-01-01' - GROUP BY symbol -""").collect() -# Predicate and projection pushdown applied - -# Bad - eager reading wastes memory -df = pl.read_parquet("large_dataset.parquet") # Loads everything -ctx = pl.SQLContext(data=df) -``` - -### 2. Combine SQL with Expression API - -```python -# SQL for initial query -lf = ctx.execute(""" - SELECT symbol, date, price, volume - FROM trades - WHERE volume > 100000 -""") - -# Expression API for complex transformations -result = ( - lf.with_columns( - pl.col("price").rolling_mean(20).over("symbol").alias("ma_20"), - pl.col("volume").rank("ordinal").over("date").alias("volume_rank") - ) - .collect() -) -``` - -### 3. EXPLAIN for Query Debugging - -```python -# View query plan -print(ctx.execute("EXPLAIN SELECT * FROM trades WHERE price > 150")) -``` - -## Rust SQL Support - -Enable the `sql` feature in Cargo.toml: - -```toml -[dependencies] -polars = { version = "0.46", features = ["sql"] } -``` - -```rust -use polars::prelude::*; -use polars::sql::SQLContext; - -fn main() -> PolarsResult<()> { - let df = df!( - "symbol" => ["AAPL", "MSFT"], - "price" => [150.0, 280.0] - )?; - - let mut ctx = SQLContext::new(); - ctx.register("trades", df.lazy()); - - let result = ctx.execute("SELECT * FROM trades WHERE price > 200")? - .collect()?; - - println!("{}", result); - Ok(()) -} -``` - -## Limitations - -1. **No DML**: INSERT, UPDATE, DELETE not supported - use expression API -2. **No DDL schema**: No column type definitions in CREATE TABLE -3. **PostgreSQL dialect**: Some vendor-specific syntax may not work -4. **Expression priority**: New features land in expression API first - -For complex transformations not supported in SQL, use the expression API directly. diff --git a/.cursor/skills/python-pro/SKILL.md b/.cursor/skills/python-pro/SKILL.md deleted file mode 100644 index 45856f4..0000000 --- a/.cursor/skills/python-pro/SKILL.md +++ /dev/null @@ -1,92 +0,0 @@ ---- -name: python-pro -description: Use when building Python 3.11+ applications requiring type safety, async programming, or production-grade patterns. Invoke for type hints, pytest, async/await, dataclasses, mypy configuration. -triggers: - - Python development - - type hints - - async Python - - pytest - - mypy - - dataclasses - - Python best practices - - Pythonic code -role: specialist -scope: implementation -output-format: code ---- - -# Python Pro - -Senior Python developer with 10+ years experience specializing in type-safe, async-first, production-ready Python 3.11+ code. - -## Role Definition - -You are a senior Python engineer mastering modern Python 3.11+ and its ecosystem. You write idiomatic, type-safe, performant code across web development, data science, automation, and system programming with focus on production best practices. - -## When to Use This Skill - -- Writing type-safe Python with complete type coverage -- Implementing async/await patterns for I/O operations -- Setting up pytest test suites with fixtures and mocking -- Creating Pythonic code with comprehensions, generators, context managers -- Building packages with uv and proper project structure -- Performance optimization and profiling - -## Core Workflow - -1. **Analyze codebase** - Review structure, dependencies, type coverage, test suite -2. **Design interfaces** - Define protocols, dataclasses, type aliases -3. **Implement** - Write Pythonic code with full type hints and error handling -4. **Test** - Create comprehensive pytest suite with >90% coverage -5. **Validate** - Run mypy, black, ruff; ensure quality standards met - -## Reference Guide - -Load detailed guidance based on context: - -| Topic | Reference | Load When | -|-------|-----------|-----------| -| Type System | `references/type-system.md` | Type hints, mypy, generics, Protocol | -| Async Patterns | `references/async-patterns.md` | async/await, asyncio, task groups | -| Standard Library | `references/standard-library.md` | pathlib, dataclasses, functools, itertools | -| Testing | `references/testing.md` | pytest, fixtures, mocking, parametrize | -| Packaging | `references/packaging.md` | uv, pip, pyproject.toml, distribution | - -## Constraints - -### MUST DO -- Type hints for all function signatures and class attributes -- PEP 8 compliance with black formatting -- Comprehensive docstrings (Google style) -- Test coverage exceeding 90% with pytest -- Use `X | None` instead of `Optional[X]` (Python 3.10+) -- Async/await for I/O-bound operations -- Dataclasses over manual __init__ methods -- Context managers for resource handling - -### MUST NOT DO -- Skip type annotations on public APIs -- Use mutable default arguments -- Mix sync and async code improperly -- Ignore mypy errors in strict mode -- Use bare except clauses -- Hardcode secrets or configuration -- Use deprecated stdlib modules (use pathlib not os.path) - -## Output Templates - -When implementing Python features, provide: -1. Module file with complete type hints -2. Test file with pytest fixtures -3. Type checking confirmation (mypy --strict passes) -4. Brief explanation of Pythonic patterns used - -## Knowledge Reference - -Python 3.11+, typing module, mypy, pytest, black, ruff, dataclasses, async/await, asyncio, pathlib, functools, itertools, uv, Pydantic, contextlib, collections.abc, Protocol - -## Related Skills - -- **FastAPI Expert** - Async Python APIs -- **Data Science Pro** - NumPy, Pandas, ML -- **DevOps Engineer** - Python automation and tooling diff --git a/.cursor/skills/python-pro/references/async-patterns.md b/.cursor/skills/python-pro/references/async-patterns.md deleted file mode 100644 index 6160247..0000000 --- a/.cursor/skills/python-pro/references/async-patterns.md +++ /dev/null @@ -1,359 +0,0 @@ -# Async Programming Patterns - -> Reference for: Python Pro -> Load when: async/await, asyncio, concurrent operations, task groups - -## Basic Async/Await - -```python -import asyncio -from collections.abc import Coroutine - -# Basic async function -async def fetch_data(url: str) -> dict[str, str]: - await asyncio.sleep(1) # Simulate I/O - return {"url": url, "status": "ok"} - -# Running async code -async def main() -> None: - result = await fetch_data("https://api.example.com") - print(result) - -if __name__ == "__main__": - asyncio.run(main()) - -# Multiple concurrent operations -async def fetch_all(urls: list[str]) -> list[dict[str, str]]: - tasks = [fetch_data(url) for url in urls] - return await asyncio.gather(*tasks) - -# Error handling with gather -async def safe_fetch_all(urls: list[str]) -> list[dict[str, str] | None]: - tasks = [fetch_data(url) for url in urls] - results = await asyncio.gather(*tasks, return_exceptions=True) - return [r if not isinstance(r, Exception) else None for r in results] -``` - -## Task Groups (Python 3.11+) - -```python -from asyncio import TaskGroup - -# Task groups for structured concurrency -async def process_batch(items: list[int]) -> list[int]: - results: list[int] = [] - - async with TaskGroup() as tg: - tasks = [tg.create_task(process_item(item)) for item in items] - - # All tasks complete before this line - return [task.result() for task in tasks] - -# Error handling with TaskGroup -async def robust_processing(items: list[str]) -> tuple[list[str], list[Exception]]: - results: list[str] = [] - errors: list[Exception] = [] - - try: - async with TaskGroup() as tg: - for item in items: - tg.create_task(process_item_safe(item)) - except ExceptionGroup as eg: - for exc in eg.exceptions: - errors.append(exc) - - return results, errors -``` - -## Async Context Managers - -```python -from typing import Self -from collections.abc import AsyncIterator - -class AsyncDatabaseConnection: - def __init__(self, url: str) -> None: - self.url = url - self._conn: Connection | None = None - - async def __aenter__(self) -> Self: - self._conn = await connect(self.url) - return self - - async def __aexit__( - self, - exc_type: type[BaseException] | None, - exc_val: BaseException | None, - exc_tb: Any, - ) -> None: - if self._conn: - await self._conn.close() - - async def query(self, sql: str) -> list[dict[str, Any]]: - if not self._conn: - raise RuntimeError("Not connected") - return await self._conn.execute(sql) - -# Usage -async def get_users() -> list[dict[str, Any]]: - async with AsyncDatabaseConnection("postgresql://...") as db: - return await db.query("SELECT * FROM users") - -# Async context manager with contextlib -from contextlib import asynccontextmanager - -@asynccontextmanager -async def get_db_session() -> AsyncIterator[Session]: - session = await create_session() - try: - yield session - await session.commit() - except Exception: - await session.rollback() - raise - finally: - await session.close() -``` - -## Async Generators - -```python -from collections.abc import AsyncIterator - -# Async generator for streaming data -async def read_lines(filepath: str) -> AsyncIterator[str]: - async with aiofiles.open(filepath) as f: - async for line in f: - yield line.strip() - -# Process stream -async def process_file(filepath: str) -> int: - count = 0 - async for line in read_lines(filepath): - await process_line(line) - count += 1 - return count - -# Async generator with cleanup -async def fetch_paginated(url: str) -> AsyncIterator[dict[str, Any]]: - page = 1 - session = await create_session() - try: - while True: - data = await session.get(f"{url}?page={page}") - if not data: - break - yield data - page += 1 - finally: - await session.close() -``` - -## Async Comprehensions - -```python -# Async list comprehension -async def fetch_all_users(user_ids: list[int]) -> list[User]: - return [user async for user in fetch_users(user_ids)] - -# Async dict comprehension -async def build_user_map(user_ids: list[int]) -> dict[int, User]: - return { - user.id: user - async for user in fetch_users(user_ids) - } - -# Conditional async comprehension -async def get_active_users(user_ids: list[int]) -> list[User]: - return [ - user - async for user in fetch_users(user_ids) - if user.is_active - ] -``` - -## Synchronization Primitives - -```python -import asyncio - -# Lock for critical sections -class SharedResource: - def __init__(self) -> None: - self._lock = asyncio.Lock() - self._data: dict[str, Any] = {} - - async def update(self, key: str, value: Any) -> None: - async with self._lock: - # Critical section - current = self._data.get(key, 0) - await asyncio.sleep(0.1) # Simulate processing - self._data[key] = current + value - -# Semaphore for rate limiting -class RateLimiter: - def __init__(self, max_concurrent: int) -> None: - self._semaphore = asyncio.Semaphore(max_concurrent) - - async def process(self, item: str) -> str: - async with self._semaphore: - return await expensive_operation(item) - -# Event for coordination -class AsyncWorker: - def __init__(self) -> None: - self._ready = asyncio.Event() - self._shutdown = asyncio.Event() - - async def start(self) -> None: - # Initialization - await self._initialize() - self._ready.set() - - # Wait for shutdown - await self._shutdown.wait() - - async def wait_ready(self) -> None: - await self._ready.wait() - - def stop(self) -> None: - self._shutdown.set() -``` - -## Async Queue Patterns - -```python -from asyncio import Queue - -# Producer-consumer pattern -async def producer(queue: Queue[int], n: int) -> None: - for i in range(n): - await queue.put(i) - await asyncio.sleep(0.1) - -async def consumer(queue: Queue[int], name: str) -> None: - while True: - item = await queue.get() - try: - await process_item(item) - finally: - queue.task_done() - -async def run_pipeline(num_items: int, num_workers: int) -> None: - queue: Queue[int] = Queue(maxsize=10) - - # Start producer and consumers - async with TaskGroup() as tg: - tg.create_task(producer(queue, num_items)) - for i in range(num_workers): - tg.create_task(consumer(queue, f"worker-{i}")) - - # Wait for all items to be processed - await queue.join() -``` - -## Async Timeouts - -```python -# Timeout for single operation -async def fetch_with_timeout(url: str, timeout: float) -> dict[str, Any]: - try: - async with asyncio.timeout(timeout): - return await fetch_data(url) - except TimeoutError: - return {"error": "timeout"} - -# Timeout for multiple operations -async def fetch_all_with_timeout( - urls: list[str], - timeout: float -) -> list[dict[str, Any] | None]: - try: - async with asyncio.timeout(timeout): - return await fetch_all(urls) - except TimeoutError: - return [None] * len(urls) -``` - -## Background Tasks - -```python -from asyncio import create_task, Task - -class BackgroundTaskManager: - def __init__(self) -> None: - self._tasks: set[Task[None]] = set() - - def create_task(self, coro: Coroutine[None, None, None]) -> Task[None]: - task = create_task(coro) - self._tasks.add(task) - task.add_done_callback(self._tasks.discard) - return task - - async def shutdown(self) -> None: - # Cancel all background tasks - for task in self._tasks: - task.cancel() - # Wait for cancellation - await asyncio.gather(*self._tasks, return_exceptions=True) - -# Usage -manager = BackgroundTaskManager() -manager.create_task(background_job()) -``` - -## Async Iteration Protocol - -```python -class AsyncRange: - def __init__(self, start: int, end: int) -> None: - self.start = start - self.end = end - self.current = start - - def __aiter__(self) -> Self: - return self - - async def __anext__(self) -> int: - if self.current >= self.end: - raise StopAsyncIteration - await asyncio.sleep(0.1) # Simulate async work - value = self.current - self.current += 1 - return value - -# Usage -async for i in AsyncRange(0, 5): - print(i) -``` - -## Mixing Sync and Async - -```python -from concurrent.futures import ThreadPoolExecutor -import functools - -# Run sync code in executor -async def run_in_executor(func: Callable[..., T], *args: Any) -> T: - loop = asyncio.get_running_loop() - return await loop.run_in_executor(None, func, *args) - -# Run async code from sync context -def sync_wrapper(coro: Coroutine[None, None, T]) -> T: - loop = asyncio.new_event_loop() - try: - return loop.run_until_complete(coro) - finally: - loop.close() - -# Async wrapper for sync function -def to_async(func: Callable[..., T]) -> Callable[..., Coroutine[None, None, T]]: - @functools.wraps(func) - async def wrapper(*args: Any, **kwargs: Any) -> T: - loop = asyncio.get_running_loop() - return await loop.run_in_executor( - None, - functools.partial(func, *args, **kwargs) - ) - return wrapper -``` diff --git a/.cursor/skills/python-pro/references/packaging.md b/.cursor/skills/python-pro/references/packaging.md deleted file mode 100644 index 1621efb..0000000 --- a/.cursor/skills/python-pro/references/packaging.md +++ /dev/null @@ -1,561 +0,0 @@ -# Python Packaging and Project Setup - -> Reference for: Python Pro -> Load when: uv, pip, pyproject.toml, package distribution, virtual environments - -## Project Structure - -``` -myproject/ -├── pyproject.toml # Project metadata and dependencies -├── README.md # Project description -├── .gitignore # Git ignore patterns -├── .python-version # Python version for pyenv -├── src/ -│ └── myproject/ -│ ├── __init__.py # Package initialization -│ ├── py.typed # PEP 561 type marker -│ ├── core.py # Core functionality -│ └── utils.py # Utilities -├── tests/ -│ ├── __init__.py -│ ├── conftest.py # Pytest configuration -│ └── test_core.py # Tests -└── docs/ - └── index.md # Documentation -``` - -## Pyproject.toml Configuration - -```toml -[build-system] -requires = ["hatchling"] -build-backend = "hatchling.build" - -[project] -name = "myproject" -version = "0.1.0" -description = "A Python project" -readme = "README.md" -requires-python = ">=3.11" -license = {text = "MIT"} -authors = [ - {name = "Your Name", email = "you@example.com"} -] -keywords = ["python", "package"] -classifiers = [ - "Development Status :: 4 - Beta", - "Intended Audience :: Developers", - "License :: OSI Approved :: MIT License", - "Programming Language :: Python :: 3.11", - "Programming Language :: Python :: 3.12", - "Typing :: Typed", -] - -dependencies = [ - "requests>=2.31.0", - "pydantic>=2.5.0", -] - -[project.optional-dependencies] -dev = [ - "pytest>=7.4.0", - "pytest-cov>=4.1.0", - "mypy>=1.7.0", - "black>=23.11.0", - "ruff>=0.1.6", -] -docs = [ - "mkdocs>=1.5.0", - "mkdocs-material>=9.4.0", -] - -[project.scripts] -myproject = "myproject.cli:main" - -[project.urls] -Homepage = "https://github.com/username/myproject" -Documentation = "https://myproject.readthedocs.io" -Repository = "https://github.com/username/myproject" -Changelog = "https://github.com/username/myproject/blob/main/CHANGELOG.md" - -# Tool configurations -[tool.black] -line-length = 100 -target-version = ["py311"] -include = '\.pyi?$' - -[tool.ruff] -line-length = 100 -target-version = "py311" -select = [ - "E", # pycodestyle errors - "W", # pycodestyle warnings - "F", # pyflakes - "I", # isort - "B", # flake8-bugbear - "C4", # flake8-comprehensions - "UP", # pyupgrade -] -ignore = [] - -[tool.ruff.per-file-ignores] -"__init__.py" = ["F401"] # Ignore unused imports in __init__.py - -[tool.mypy] -python_version = "3.11" -strict = true -warn_return_any = true -warn_unused_configs = true -disallow_untyped_defs = true - -[[tool.mypy.overrides]] -module = "third_party.*" -ignore_missing_imports = true - -[tool.pytest.ini_options] -minversion = "7.0" -addopts = [ - "-ra", - "--strict-markers", - "--strict-config", - "--cov=myproject", - "--cov-report=term-missing", - "--cov-report=html", -] -testpaths = ["tests"] -pythonpath = ["src"] - -[tool.coverage.run] -source = ["src"] -branch = true - -[tool.coverage.report] -exclude_lines = [ - "pragma: no cover", - "def __repr__", - "raise AssertionError", - "raise NotImplementedError", - "if __name__ == .__main__.:", - "if TYPE_CHECKING:", -] -``` - -## UV Project Management - -```bash -# UV commands -uv init myproject # Initialize new project -cd myproject -uv venv --python=3.13 # Create environement with python version 3.13 -source .venv/bin/activate # Activate environemnt on posix -.venv/Scripts/activate # Activate environment on windows -uv add requests # Add dependency -uv add --dev pytest # Add dev dependency -uv add --optional docs mkdocs # Add optional dependency group -uv sync # Install dependencies from pyproject.toml -uv lock # Update lock file -uv pip install -e . # Install in editable mode -uv run pytest # Run command in venv -uv build # Build package -uv publish # Publish to PyPI -uv pip compile pyproject.toml # Generate requirements.txt -uv pip list # List installed packages -uv pip freeze # Export exact versions -uv remove requests # Remove dependency -uv update # Update all dependencies -uv update requests # Update specific dependency -``` - -```toml -# pyproject.toml for uv (standard PEP 621 format) -[build-system] -requires = ["hatchling"] -build-backend = "hatchling.build" - -[project] -name = "myproject" -version = "0.1.0" -description = "A Python project" -readme = "README.md" -requires-python = ">=3.11" -license = {text = "MIT"} -authors = [ - {name = "Your Name", email = "you@example.com"} -] -keywords = ["python", "package"] -classifiers = [ - "Development Status :: 4 - Beta", - "Intended Audience :: Developers", - "License :: OSI Approved :: MIT License", - "Programming Language :: Python :: 3.11", - "Programming Language :: Python :: 3.12", - "Typing :: Typed", -] - -dependencies = [ - "requests>=2.31.0", - "pydantic>=2.5.0", -] - -[project.optional-dependencies] -dev = [ - "pytest>=7.4.0", - "pytest-cov>=4.1.0", - "mypy>=1.7.0", - "black>=23.11.0", - "ruff>=0.1.6", -] -docs = [ - "mkdocs>=1.5.0", - "mkdocs-material>=9.4.0", -] - -[project.scripts] -myproject = "myproject.cli:main" - -[project.urls] -Homepage = "https://github.com/username/myproject" -Documentation = "https://myproject.readthedocs.io" -Repository = "https://github.com/username/myproject" -Changelog = "https://github.com/username/myproject/blob/main/CHANGELOG.md" -``` - -```bash -# UV virtual environment management -uv venv # Create virtual environment in .venv -uv venv --python 3.13 # Create venv with specific Python version -source .venv/bin/activate # Activate venv (Linux/Mac) -.venv\Scripts\activate # Activate venv (Windows) -uv pip install package # Install in active venv -uv pip install -e ".[dev]" # Install with optional dependencies -uv pip uninstall package # Uninstall package -uv pip sync requirements.txt # Sync from requirements file -``` - -## UV Best Practices - -```bash -# Project initialization workflow -uv init myproject # Creates project with pyproject.toml -cd myproject -uv add package # Adds to dependencies -uv add --dev pytest # Adds to dev dependencies -uv sync # Installs all dependencies - -# Working with existing projects -uv sync # Install from pyproject.toml and uv.lock -uv sync --dev # Include dev dependencies -uv sync --extra docs # Include optional dependency groups -uv lock # Update lock file without installing - -# Running commands -uv run pytest # Run in project environment -uv run python script.py # Run script in project environment -uv run --with pytest pytest # Temporarily add dependency - -# Package management -uv add "package>=1.0.0" # Add with version constraint -uv add "package@git+https://..." # Add from git -uv remove package # Remove dependency -uv update # Update all dependencies -uv update package # Update specific package - -# Building and publishing -uv build # Build wheel and sdist -uv publish # Publish to PyPI -uv publish --publish-url https://... # Publish to custom index - -# Dependency resolution -uv pip compile pyproject.toml # Generate requirements.txt -uv pip compile --extra dev pyproject.toml -o requirements-dev.txt -uv tree # Show dependency tree -uv pip list # List installed packages -``` - -## Virtual Environments - -```bash -# Using uv (recommended) -uv venv # Create .venv in current directory -uv venv --python=3.13 # Create with specific Python version -source .venv/bin/activate # Activate (Linux/Mac) -.venv\Scripts\activate # Activate (Windows) -uv sync # Install dependencies from pyproject.toml -uv pip install -e . # Install in editable mode -uv pip install -e ".[dev]" # With optional dependencies - -# Using venv (built-in, alternative) -python -m venv .venv -source .venv/bin/activate # Linux/Mac -.venv\Scripts\activate # Windows -pip install -e . -pip install -e ".[dev]" - -# Using virtualenv (legacy) -pip install virtualenv -virtualenv venv -source venv/bin/activate - -# Using pyenv for Python version management -pyenv install 3.11.6 -pyenv local 3.11.6 # Set for current directory -echo "3.11.6" > .python-version -``` - -## Package __init__.py - -```python -# src/myproject/__init__.py -"""MyProject - A Python package.""" - -from myproject.core import main_function, CoreClass -from myproject.utils import helper_function - -__version__ = "0.1.0" -__all__ = ["main_function", "CoreClass", "helper_function"] - -# Package-level configuration -import logging - -logger = logging.getLogger(__name__) -logger.addHandler(logging.NullHandler()) -``` - -## Type Stub Files (py.typed) - -```python -# src/myproject/py.typed -# Empty file indicates package includes type hints - -# src/myproject/__init__.pyi (optional stub file) -from typing import Any - -__version__: str - -def main_function(arg: str) -> dict[str, Any]: ... - -class CoreClass: - def __init__(self, name: str) -> None: ... - def process(self) -> str: ... -``` - -## CLI Entry Points - -```python -# src/myproject/cli.py -import sys -from typing import NoReturn - -def main() -> NoReturn: - """Main CLI entry point.""" - print("MyProject CLI") - sys.exit(0) - -if __name__ == "__main__": - main() -``` - -## Requirements Files - -```bash -# requirements.txt - Production dependencies -requests>=2.31.0,<3.0.0 -pydantic>=2.5.0,<3.0.0 - -# requirements-dev.txt - Development dependencies --r requirements.txt -pytest>=7.4.0 -pytest-cov>=4.1.0 -mypy>=1.7.0 -black>=23.11.0 -ruff>=0.1.6 - -# Generate from uv -uv pip compile pyproject.toml -o requirements.txt -uv pip compile pyproject.toml --extra dev -o requirements-dev.txt -uv pip freeze > requirements-lock.txt -``` - -## Building and Distribution - -```bash -# Build package -python -m build - -# Check package -twine check dist/* - -# Upload to PyPI -twine upload dist/* - -# Upload to Test PyPI -twine upload --repository testpypi dist/* - -# Install from Test PyPI -pip install --index-url https://test.pypi.org/simple/ myproject -``` - -## Setuptools Configuration (Legacy) - -```python -# setup.py (if not using pyproject.toml) -from setuptools import setup, find_packages - -setup( - name="myproject", - version="0.1.0", - packages=find_packages(where="src"), - package_dir={"": "src"}, - python_requires=">=3.11", - install_requires=[ - "requests>=2.31.0", - "pydantic>=2.5.0", - ], - extras_require={ - "dev": [ - "pytest>=7.4.0", - "mypy>=1.7.0", - ], - }, - entry_points={ - "console_scripts": [ - "myproject=myproject.cli:main", - ], - }, -) -``` - -## Manifest for Package Data - -``` -# MANIFEST.in -include README.md -include LICENSE -include pyproject.toml -recursive-include src/myproject *.py -recursive-include src/myproject py.typed -recursive-include tests *.py -prune docs/_build -``` - -## Version Management - -```python -# src/myproject/__version__.py -__version__ = "0.1.0" - -# src/myproject/__init__.py -from myproject.__version__ import __version__ - -# Read version in pyproject.toml -import tomli -from pathlib import Path - -def get_version() -> str: - pyproject = Path(__file__).parent.parent / "pyproject.toml" - with open(pyproject, "rb") as f: - data = tomli.load(f) - return data["project"]["version"] -``` - -## Dependency Management Best Practices - -```python -# Pin dependencies for applications -requests==2.31.0 -pydantic==2.5.2 - -# Use ranges for libraries -requests>=2.31.0,<3.0.0 -pydantic>=2.5.0,<3.0.0 - -# Lock files -# uv: uv.lock (automatically generated) -# pip: requirements.txt with exact versions -uv pip freeze > requirements-lock.txt - -# Update dependencies -uv lock --upgrade -uv update -uv pip install --upgrade -r requirements.txt -``` - -## CI/CD Integration - -```yaml -# .github/workflows/test.yml -name: Tests - -on: [push, pull_request] - -jobs: - test: - runs-on: ubuntu-latest - strategy: - matrix: - python-version: ["3.11", "3.12"] - - steps: - - uses: actions/checkout@v4 - - name: Set up Python - uses: actions/setup-python@v4 - with: - python-version: ${{ matrix.python-version }} - - - name: Install uv - uses: astral-sh/setup-uv@v3 - with: - version: "latest" - - - name: Install dependencies - run: | - uv sync --dev - - - name: Run tests - run: | - pytest --cov --cov-report=xml - - - name: Type check - run: mypy src - - - name: Lint - run: | - black --check src tests - ruff check src tests - - - name: Upload coverage - uses: codecov/codecov-action@v3 -``` - -## Pre-commit Hooks - -```yaml -# .pre-commit-config.yaml -repos: - - repo: https://github.com/psf/black - rev: 23.11.0 - hooks: - - id: black - - - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.1.6 - hooks: - - id: ruff - args: [--fix, --exit-non-zero-on-fix] - - - repo: https://github.com/pre-commit/mirrors-mypy - rev: v1.7.1 - hooks: - - id: mypy - additional_dependencies: [types-requests] -``` - -```bash -# Install pre-commit -pip install pre-commit -pre-commit install - -# Run manually -pre-commit run --all-files -``` diff --git a/.cursor/skills/python-pro/references/standard-library.md b/.cursor/skills/python-pro/references/standard-library.md deleted file mode 100644 index 5667fe2..0000000 --- a/.cursor/skills/python-pro/references/standard-library.md +++ /dev/null @@ -1,381 +0,0 @@ -# Standard Library Mastery - -> Reference for: Python Pro -> Load when: pathlib, dataclasses, functools, itertools, collections - -## Pathlib for File Operations - -```python -from pathlib import Path - -# Path creation and manipulation -project_root = Path(__file__).parent.parent -config_file = project_root / "config" / "settings.toml" -data_dir = Path.home() / "data" - -# File operations -def read_config(config_path: Path) -> dict[str, str]: - if not config_path.exists(): - raise FileNotFoundError(f"Config not found: {config_path}") - - # Read text - content = config_path.read_text(encoding="utf-8") - - # Read bytes - binary = config_path.read_bytes() - - return parse_config(content) - -# Path traversal -def find_python_files(directory: Path) -> list[Path]: - # Recursive glob - return list(directory.rglob("*.py")) - -def get_file_info(path: Path) -> dict[str, Any]: - stat = path.stat() - return { - "size": stat.st_size, - "modified": stat.st_mtime, - "is_file": path.is_file(), - "is_dir": path.is_dir(), - "suffix": path.suffix, - "stem": path.stem, - } - -# Creating directories -def ensure_dir(path: Path) -> None: - path.mkdir(parents=True, exist_ok=True) - -# Temporary files -from tempfile import TemporaryDirectory -from pathlib import Path - -def process_with_temp() -> None: - with TemporaryDirectory() as tmpdir: - temp_path = Path(tmpdir) / "output.txt" - temp_path.write_text("data") -``` - -## Dataclasses for Data Structures - -```python -from dataclasses import dataclass, field, asdict, replace -from typing import ClassVar - -# Basic dataclass -@dataclass -class User: - id: int - name: str - email: str - active: bool = True - -# Post-init processing -@dataclass -class Product: - name: str - price: float - discount: float = 0.0 - - def __post_init__(self) -> None: - if self.discount > 1.0: - raise ValueError("Discount must be <= 1.0") - - @property - def final_price(self) -> float: - return self.price * (1 - self.discount) - -# Field with factory -@dataclass -class ShoppingCart: - user_id: int - items: list[str] = field(default_factory=list) - metadata: dict[str, Any] = field(default_factory=dict) - -# Frozen dataclass (immutable) -@dataclass(frozen=True) -class Point: - x: float - y: float - - def distance(self, other: "Point") -> float: - return ((self.x - other.x)**2 + (self.y - other.y)**2)**0.5 - -# Class variables -@dataclass -class Config: - API_VERSION: ClassVar[str] = "v1" - BASE_URL: ClassVar[str] = "https://api.example.com" - - timeout: int = 30 - retries: int = 3 - -# Ordered dataclass for comparison -@dataclass(order=True) -class Priority: - level: int - name: str = field(compare=False) - -# Convert to/from dict -user = User(1, "Alice", "alice@example.com") -user_dict = asdict(user) -updated = replace(user, name="Alice Smith") -``` - -## Functools for Function Tools - -```python -from functools import ( - cache, lru_cache, cached_property, - partial, wraps, reduce, singledispatch -) - -# Caching -@cache # Unlimited cache (Python 3.9+) -def fibonacci(n: int) -> int: - if n < 2: - return n - return fibonacci(n - 1) + fibonacci(n - 2) - -@lru_cache(maxsize=128) # LRU cache with size limit -def fetch_user(user_id: int) -> dict[str, Any]: - # Expensive database call - return {"id": user_id, "name": "User"} - -# Cached property -class DataProcessor: - def __init__(self, data: list[int]) -> None: - self._data = data - - @cached_property - def mean(self) -> float: - """Computed once, then cached.""" - return sum(self._data) / len(self._data) - -# Partial application -from operator import mul - -double = partial(mul, 2) -triple = partial(mul, 3) -print(double(5)) # 10 - -# Decorator preservation -def timing_decorator(func: Callable[P, R]) -> Callable[P, R]: - @wraps(func) - def wrapper(*args: P.args, **kwargs: P.kwargs) -> R: - start = time.time() - result = func(*args, **kwargs) - print(f"{func.__name__} took {time.time() - start:.2f}s") - return result - return wrapper - -# Reduce for aggregation -from operator import add - -total = reduce(add, [1, 2, 3, 4, 5]) # 15 -product = reduce(mul, [1, 2, 3, 4], 1) # 24 - -# Single dispatch for polymorphism -@singledispatch -def process(arg: Any) -> str: - return f"Unknown type: {type(arg)}" - -@process.register -def _(arg: int) -> str: - return f"Integer: {arg * 2}" - -@process.register -def _(arg: str) -> str: - return f"String: {arg.upper()}" - -@process.register(list) -def _(arg: list[Any]) -> str: - return f"List with {len(arg)} items" -``` - -## Itertools for Iteration - -```python -from itertools import ( - chain, islice, cycle, repeat, - groupby, accumulate, combinations, permutations, - product, zip_longest, tee, filterfalse -) - -# Chain multiple iterables -combined = list(chain([1, 2], [3, 4], [5, 6])) # [1,2,3,4,5,6] - -# Slice iterator (memory efficient) -first_10 = list(islice(range(1000), 10)) - -# Infinite iterators -from itertools import count -counter = count(start=1, step=2) # 1, 3, 5, 7, ... - -# Groupby for grouping -data = [("A", 1), ("A", 2), ("B", 1), ("B", 2)] -grouped = {k: list(v) for k, v in groupby(data, key=lambda x: x[0])} - -# Accumulate for running totals -cumsum = list(accumulate([1, 2, 3, 4, 5])) # [1, 3, 6, 10, 15] - -# Combinations and permutations -combos = list(combinations([1, 2, 3], 2)) # [(1,2), (1,3), (2,3)] -perms = list(permutations([1, 2, 3], 2)) # [(1,2), (1,3), (2,1), ...] - -# Cartesian product -pairs = list(product([1, 2], ['a', 'b'])) # [(1,'a'), (1,'b'), (2,'a'), (2,'b')] - -# Zip with different lengths -from itertools import zip_longest -paired = list(zip_longest([1, 2], ['a', 'b', 'c'], fillvalue=0)) - -# Tee for multiple iterators -it1, it2 = tee(range(5), 2) - -# Filter false -odds = list(filterfalse(lambda x: x % 2 == 0, range(10))) -``` - -## Collections for Data Structures - -```python -from collections import ( - defaultdict, Counter, deque, namedtuple, - ChainMap, OrderedDict -) - -# defaultdict for automatic defaults -word_index: defaultdict[str, list[int]] = defaultdict(list) -for i, word in enumerate(["hello", "world", "hello"]): - word_index[word].append(i) - -# Counter for counting -from collections import Counter - -word_counts = Counter(["apple", "banana", "apple", "cherry", "banana", "apple"]) -print(word_counts.most_common(2)) # [('apple', 3), ('banana', 2)] - -# Counter operations -c1 = Counter(a=3, b=1) -c2 = Counter(a=1, b=2) -print(c1 + c2) # Counter({'a': 4, 'b': 3}) - -# deque for efficient queue operations -from collections import deque - -queue: deque[str] = deque() -queue.append("first") -queue.append("second") -queue.appendleft("priority") -item = queue.popleft() # "priority" - -# Ring buffer with maxlen -recent: deque[int] = deque(maxlen=3) -for i in range(5): - recent.append(i) # Only keeps last 3 - -# namedtuple for lightweight classes -from collections import namedtuple - -Point = namedtuple('Point', ['x', 'y']) -p = Point(1, 2) -print(p.x, p.y) - -# ChainMap for layered configs -from collections import ChainMap - -defaults = {'color': 'red', 'user': 'guest'} -environment = {'user': 'admin'} -combined = ChainMap(environment, defaults) -print(combined['user']) # 'admin' (from environment) -``` - -## Context Managers - -```python -from contextlib import contextmanager, suppress, ExitStack - -# Custom context manager -@contextmanager -def managed_resource(resource_id: str) -> Iterator[Resource]: - resource = acquire_resource(resource_id) - try: - yield resource - finally: - release_resource(resource) - -# Suppress exceptions -with suppress(FileNotFoundError): - Path("nonexistent.txt").unlink() - -# ExitStack for dynamic context managers -def process_files(filenames: list[str]) -> None: - with ExitStack() as stack: - files = [stack.enter_context(open(fn)) for fn in filenames] - # All files auto-closed on exit - for f in files: - process(f.read()) -``` - -## Enum for Constants - -```python -from enum import Enum, auto, IntEnum, Flag - -# Basic enum -class Status(Enum): - PENDING = "pending" - APPROVED = "approved" - REJECTED = "rejected" - -# Auto values -class Color(Enum): - RED = auto() - GREEN = auto() - BLUE = auto() - -# IntEnum for numeric values -class Priority(IntEnum): - LOW = 1 - MEDIUM = 2 - HIGH = 3 - -# Flag for bit flags -class Permission(Flag): - READ = auto() - WRITE = auto() - EXECUTE = auto() - -user_perms = Permission.READ | Permission.WRITE -if Permission.READ in user_perms: - print("Can read") -``` - -## Logging - -```python -import logging -from pathlib import Path - -# Configure logging -logging.basicConfig( - level=logging.INFO, - format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', - handlers=[ - logging.FileHandler('app.log'), - logging.StreamHandler() - ] -) - -logger = logging.getLogger(__name__) - -# Structured logging -def process_user(user_id: int) -> None: - logger.info("Processing user", extra={"user_id": user_id}) - try: - # Process... - logger.debug("User data loaded", extra={"user_id": user_id}) - except Exception as e: - logger.exception("Failed to process user", extra={"user_id": user_id}) -``` diff --git a/.cursor/skills/python-pro/references/testing.md b/.cursor/skills/python-pro/references/testing.md deleted file mode 100644 index 4362f74..0000000 --- a/.cursor/skills/python-pro/references/testing.md +++ /dev/null @@ -1,407 +0,0 @@ -# Testing with Pytest - -> Reference for: Python Pro -> Load when: pytest, fixtures, mocking, test coverage, parametrize - -## Basic Pytest Structure - -```python -# test_user.py -import pytest -from myapp.user import User, UserService - -# Simple test function -def test_user_creation() -> None: - user = User(id=1, name="Alice", email="alice@example.com") - assert user.name == "Alice" - assert user.is_active is True - -# Test with multiple assertions -def test_user_validation() -> None: - with pytest.raises(ValueError, match="Invalid email"): - User(id=1, name="Alice", email="invalid") - -# Test class for grouping -class TestUserService: - def test_find_user(self) -> None: - service = UserService() - user = service.find(1) - assert user is not None - - def test_create_user(self) -> None: - service = UserService() - user = service.create(name="Bob", email="bob@example.com") - assert user.id > 0 -``` - -## Fixtures for Setup/Teardown - -```python -# conftest.py - shared fixtures -import pytest -from typing import Iterator -from myapp.database import Database, Session - -@pytest.fixture -def db() -> Iterator[Database]: - """Provide database instance with cleanup.""" - database = Database("test.db") - database.create_tables() - yield database - database.drop_tables() - database.close() - -@pytest.fixture -def db_session(db: Database) -> Iterator[Session]: - """Provide database session with rollback.""" - session = db.create_session() - yield session - session.rollback() - session.close() - -@pytest.fixture -def sample_user() -> User: - """Provide test user.""" - return User(id=1, name="Test User", email="test@example.com") - -# Using fixtures in tests -def test_user_creation(db_session: Session, sample_user: User) -> None: - db_session.add(sample_user) - db_session.commit() - - retrieved = db_session.query(User).filter_by(id=1).first() - assert retrieved.name == "Test User" - -# Fixture with parameters -@pytest.fixture(params=["sqlite", "postgresql", "mysql"]) -def db_engine(request: pytest.FixtureRequest) -> str: - return request.param - -def test_connection(db_engine: str) -> None: - # Test runs 3 times with different engines - assert create_connection(db_engine) - -# Autouse fixture (runs automatically) -@pytest.fixture(autouse=True) -def reset_state() -> Iterator[None]: - """Reset global state before each test.""" - clear_caches() - yield - cleanup_temp_files() -``` - -## Parametrize for Multiple Cases - -```python -import pytest - -# Parametrize test function -@pytest.mark.parametrize( - "input,expected", - [ - (2, 4), - (3, 9), - (4, 16), - (-2, 4), - ] -) -def test_square(input: int, expected: int) -> None: - assert square(input) == expected - -# Multiple parameters -@pytest.mark.parametrize("base", [2, 10]) -@pytest.mark.parametrize("exponent", [0, 1, 2]) -def test_power(base: int, exponent: int) -> None: - result = base ** exponent - assert result >= 0 - -# Parametrize with IDs -@pytest.mark.parametrize( - "email,valid", - [ - ("user@example.com", True), - ("invalid", False), - ("@example.com", False), - ("user@", False), - ], - ids=["valid", "no_at", "no_user", "no_domain"] -) -def test_email_validation(email: str, valid: bool) -> None: - assert is_valid_email(email) == valid - -# Parametrize with fixtures -@pytest.fixture -def user_factory(): - def _make_user(name: str, active: bool = True) -> User: - return User(name=name, active=active) - return _make_user - -@pytest.mark.parametrize("name", ["Alice", "Bob", "Charlie"]) -def test_user_names(user_factory, name: str) -> None: - user = user_factory(name) - assert user.name == name -``` - -## Mocking and Patching - -```python -from unittest.mock import Mock, MagicMock, patch, AsyncMock, call -import pytest - -# Mock object -def test_api_call_with_mock() -> None: - mock_client = Mock() - mock_client.get.return_value = {"status": "ok"} - - service = ApiService(mock_client) - result = service.fetch_data() - - mock_client.get.assert_called_once_with("/api/data") - assert result["status"] == "ok" - -# Patch function/method -def test_database_call() -> None: - with patch("myapp.database.connect") as mock_connect: - mock_connect.return_value = Mock() - - db = Database() - db.connect() - - mock_connect.assert_called_once() - -# Patch as decorator -@patch("myapp.user.send_email") -def test_user_registration(mock_send_email: Mock) -> None: - service = UserService() - service.register("user@example.com") - - mock_send_email.assert_called_with( - to="user@example.com", - subject="Welcome" - ) - -# Multiple patches -@patch("myapp.api.requests.get") -@patch("myapp.api.cache.get") -def test_cached_api(mock_cache: Mock, mock_requests: Mock) -> None: - mock_cache.return_value = None - mock_requests.return_value.json.return_value = {"data": "value"} - - result = fetch_with_cache("key") - - mock_cache.assert_called_once_with("key") - mock_requests.assert_called_once() - -# Mock side effects -def test_retry_logic() -> None: - mock_api = Mock() - mock_api.call.side_effect = [ - ConnectionError("Failed"), - ConnectionError("Failed"), - {"status": "ok"} - ] - - result = retry_api_call(mock_api) - assert result["status"] == "ok" - assert mock_api.call.call_count == 3 - -# Async mock -@pytest.mark.asyncio -async def test_async_function() -> None: - mock_db = AsyncMock() - mock_db.fetch_user.return_value = User(id=1, name="Alice") - - service = AsyncUserService(mock_db) - user = await service.get_user(1) - - mock_db.fetch_user.assert_awaited_once_with(1) - assert user.name == "Alice" -``` - -## Async Testing - -```python -import pytest -import asyncio - -# Mark async test -@pytest.mark.asyncio -async def test_async_fetch() -> None: - result = await fetch_data("https://api.example.com") - assert result["status"] == "ok" - -# Async fixture -@pytest.fixture -async def async_db() -> AsyncIterator[AsyncDatabase]: - db = AsyncDatabase() - await db.connect() - yield db - await db.disconnect() - -@pytest.mark.asyncio -async def test_async_query(async_db: AsyncDatabase) -> None: - result = await async_db.query("SELECT * FROM users") - assert len(result) > 0 - -# Test concurrent operations -@pytest.mark.asyncio -async def test_concurrent_requests() -> None: - urls = ["http://example.com/1", "http://example.com/2"] - results = await asyncio.gather(*[fetch(url) for url in urls]) - assert len(results) == 2 -``` - -## Pytest Markers - -```python -import pytest - -# Skip test -@pytest.mark.skip(reason="Not implemented yet") -def test_future_feature() -> None: - pass - -# Conditional skip -@pytest.mark.skipif(sys.version_info < (3, 11), reason="Requires Python 3.11+") -def test_new_feature() -> None: - pass - -# Expected failure -@pytest.mark.xfail(reason="Known bug #123") -def test_known_bug() -> None: - assert buggy_function() == expected_value - -# Custom markers -@pytest.mark.slow -def test_slow_operation() -> None: - time.sleep(5) - assert True - -@pytest.mark.integration -def test_integration() -> None: - assert external_service.ping() - -# Run with: pytest -m "not slow" -``` - -## Test Coverage - -```python -# Run with coverage -# pytest --cov=myapp --cov-report=html --cov-report=term - -# conftest.py - coverage configuration -def pytest_configure(config): - config.addinivalue_line( - "markers", "unit: mark test as unit test" - ) - -# pytest.ini or pyproject.toml -""" -[tool.pytest.ini_options] -minversion = "7.0" -addopts = [ - "--cov=myapp", - "--cov-report=term-missing", - "--cov-fail-under=90", - "-ra", - "--strict-markers", -] -testpaths = ["tests"] -""" -``` - -## Property-Based Testing - -```python -from hypothesis import given, strategies as st - -# Property-based test -@given(st.integers(), st.integers()) -def test_addition_commutative(a: int, b: int) -> None: - assert a + b == b + a - -@given(st.lists(st.integers())) -def test_sorted_is_ordered(lst: list[int]) -> None: - sorted_lst = sorted(lst) - for i in range(len(sorted_lst) - 1): - assert sorted_lst[i] <= sorted_lst[i + 1] - -# Custom strategies -@given(st.emails()) -def test_email_validation(email: str) -> None: - assert "@" in email - assert validate_email(email) - -# Composite strategies -from hypothesis import strategies as st -from hypothesis.strategies import composite - -@composite -def users(draw) -> User: - return User( - id=draw(st.integers(min_value=1)), - name=draw(st.text(min_size=1, max_size=50)), - email=draw(st.emails()), - age=draw(st.integers(min_value=18, max_value=120)) - ) - -@given(users()) -def test_user_creation(user: User) -> None: - assert user.age >= 18 - assert len(user.name) > 0 -``` - -## Test Organization - -```python -# tests/ -# conftest.py - Shared fixtures -# test_user.py - User tests -# test_api.py - API tests -# integration/ -# test_workflow.py - Integration tests -# unit/ -# test_models.py - Unit tests - -# Fixture factory pattern -@pytest.fixture -def user_factory(db_session: Session): - created_users: list[User] = [] - - def _create_user( - name: str = "Test User", - email: str | None = None, - **kwargs - ) -> User: - if email is None: - email = f"{name.lower().replace(' ', '.')}@example.com" - - user = User(name=name, email=email, **kwargs) - db_session.add(user) - db_session.commit() - created_users.append(user) - return user - - yield _create_user - - # Cleanup - for user in created_users: - db_session.delete(user) - db_session.commit() -``` - -## Snapshot Testing - -```python -import pytest -from syrupy.assertion import SnapshotAssertion - -def test_api_response(snapshot: SnapshotAssertion) -> None: - response = api.get_user(1) - assert response == snapshot - -def test_rendered_template(snapshot: SnapshotAssertion) -> None: - html = render_template("user.html", user=get_user(1)) - assert html == snapshot -``` diff --git a/.cursor/skills/python-pro/references/type-system.md b/.cursor/skills/python-pro/references/type-system.md deleted file mode 100644 index 958be2b..0000000 --- a/.cursor/skills/python-pro/references/type-system.md +++ /dev/null @@ -1,293 +0,0 @@ -# Type System Mastery - -> Reference for: Python Pro -> Load when: Type hints, mypy configuration, generics, Protocol definitions - -## Basic Type Annotations - -```python -from typing import Any -from collections.abc import Sequence, Mapping - -# Function signatures -def process_user(name: str, age: int, active: bool = True) -> dict[str, Any]: - return {"name": name, "age": age, "active": active} - -# Use | for unions (Python 3.10+) -def find_user(user_id: int | str) -> dict[str, Any] | None: - if isinstance(user_id, int): - return {"id": user_id} - return None - -# Collections - prefer collections.abc -def process_items(items: Sequence[str]) -> list[str]: - """Accepts list, tuple, or any sequence.""" - return [item.upper() for item in items] - -def merge_configs(base: Mapping[str, int], override: dict[str, int]) -> dict[str, int]: - """Mapping for read-only, dict for mutable.""" - return {**base, **override} -``` - -## Generic Types - -```python -from typing import TypeVar, Generic, Protocol -from collections.abc import Callable - -T = TypeVar('T') -K = TypeVar('K') -V = TypeVar('V') - -# Generic function -def first_element(items: Sequence[T]) -> T | None: - return items[0] if items else None - -# Generic class -class Cache(Generic[K, V]): - def __init__(self) -> None: - self._data: dict[K, V] = {} - - def get(self, key: K) -> V | None: - return self._data.get(key) - - def set(self, key: K, value: V) -> None: - self._data[key] = value - -# Usage -user_cache: Cache[int, str] = Cache() -user_cache.set(1, "Alice") - -# Constrained TypeVar -from numbers import Number -NumT = TypeVar('NumT', bound=Number) - -def add_numbers(a: NumT, b: NumT) -> NumT: - return a + b # type: ignore[return-value] -``` - -## Protocol for Structural Typing - -```python -from typing import Protocol, runtime_checkable - -# Define interface without inheritance -class Drawable(Protocol): - def draw(self) -> str: - ... - - @property - def color(self) -> str: - ... - -class Circle: - def __init__(self, radius: float, color: str) -> None: - self.radius = radius - self._color = color - - def draw(self) -> str: - return f"Drawing {self._color} circle" - - @property - def color(self) -> str: - return self._color - -# Circle implements Drawable without inheriting -def render(shape: Drawable) -> str: - return shape.draw() - -# Runtime checkable protocol -@runtime_checkable -class Closeable(Protocol): - def close(self) -> None: - ... - -def cleanup(resource: Closeable) -> None: - if isinstance(resource, Closeable): - resource.close() -``` - -## Advanced Type Features - -```python -from typing import Literal, TypeAlias, TypedDict, NotRequired, Self, overload - -# Literal types for constants -Mode = Literal["read", "write", "append"] - -def open_file(path: str, mode: Mode) -> None: - ... - -# Type aliases for complex types -JsonDict: TypeAlias = dict[str, Any] -UserId: TypeAlias = int | str - -# TypedDict for structured dictionaries -class UserDict(TypedDict): - id: int - name: str - email: str - age: NotRequired[int] # Optional field - -def create_user(data: UserDict) -> None: - print(data["name"]) # Type-safe access - -# Self type for method chaining -class Builder: - def __init__(self) -> None: - self._value = 0 - - def add(self, n: int) -> Self: - self._value += n - return self - - def multiply(self, n: int) -> Self: - self._value *= n - return self - -# Overload for different signatures -@overload -def process(data: str) -> str: ... - -@overload -def process(data: int) -> int: ... - -def process(data: str | int) -> str | int: - if isinstance(data, str): - return data.upper() - return data * 2 -``` - -## Callable Types - -```python -from collections.abc import Callable -from typing import ParamSpec, Concatenate - -# Basic callable -def apply(func: Callable[[int, int], int], a: int, b: int) -> int: - return func(a, b) - -# ParamSpec for preserving signatures -P = ParamSpec('P') -R = TypeVar('R') - -def logging_decorator(func: Callable[P, R]) -> Callable[P, R]: - def wrapper(*args: P.args, **kwargs: P.kwargs) -> R: - print(f"Calling {func.__name__}") - return func(*args, **kwargs) - return wrapper - -# Concatenate for dependency injection -def with_connection( - func: Callable[Concatenate[Connection, P], R] -) -> Callable[P, R]: - def wrapper(*args: P.args, **kwargs: P.kwargs) -> R: - conn = get_connection() - return func(conn, *args, **kwargs) - return wrapper - -# Usage -@with_connection -def query_user(conn: Connection, user_id: int) -> User: - return conn.execute(f"SELECT * FROM users WHERE id = {user_id}") -``` - -## Mypy Configuration - -```toml -# pyproject.toml -[tool.mypy] -python_version = "3.11" -strict = true -warn_return_any = true -warn_unused_configs = true -disallow_untyped_defs = true -disallow_any_generics = true -disallow_subclassing_any = true -disallow_untyped_calls = true -disallow_incomplete_defs = true -check_untyped_defs = true -no_implicit_optional = true -warn_redundant_casts = true -warn_unused_ignores = true -warn_no_return = true -warn_unreachable = true -strict_equality = true - -[[tool.mypy.overrides]] -module = "third_party.*" -ignore_missing_imports = true -``` - -## Common Type Patterns - -```python -# Result type pattern -from dataclasses import dataclass - -@dataclass -class Success(Generic[T]): - value: T - -@dataclass -class Error: - message: str - -Result = Success[T] | Error - -def divide(a: int, b: int) -> Result[float]: - if b == 0: - return Error("Division by zero") - return Success(a / b) - -# Option/Maybe type -def safe_get(items: Sequence[T], index: int) -> T | None: - try: - return items[index] - except IndexError: - return None - -# Sentinel value with typing -from typing import Final - -MISSING: Final = object() - -def get_value(key: str, default: T | type[MISSING] = MISSING) -> T: - if default is MISSING: - raise KeyError(key) - return default # type: ignore[return-value] -``` - -## Type Narrowing - -```python -from typing import assert_type, assert_never - -def process_value(value: int | str | None) -> str: - # Type guards - if value is None: - return "null" - - if isinstance(value, int): - # Type narrowed to int - return str(value * 2) - - # Type narrowed to str - return value.upper() - -# Exhaustiveness checking -def handle_mode(mode: Literal["read", "write"]) -> str: - if mode == "read": - return "Reading" - elif mode == "write": - return "Writing" - else: - # Mypy will error if mode can be anything else - assert_never(mode) - -# Custom type guard -def is_string_list(val: list[Any]) -> bool: - """Runtime check for list of strings.""" - return all(isinstance(x, str) for x in val) -``` diff --git a/.cursor/skills/rust-engineer/SKILL.md b/.cursor/skills/rust-engineer/SKILL.md deleted file mode 100644 index fe5b1c0..0000000 --- a/.cursor/skills/rust-engineer/SKILL.md +++ /dev/null @@ -1,96 +0,0 @@ ---- -name: rust-engineer -description: Use when building Rust applications requiring memory safety, systems programming, or zero-cost abstractions. Invoke for ownership patterns, lifetimes, traits, async/await with tokio. -triggers: - - Rust - - Cargo - - ownership - - borrowing - - lifetimes - - async Rust - - tokio - - zero-cost abstractions - - memory safety - - systems programming -role: specialist -scope: implementation -output-format: code ---- - -# Rust Engineer - -Senior Rust engineer with deep expertise in Rust 2021 edition, systems programming, memory safety, and zero-cost abstractions. Specializes in building reliable, high-performance software leveraging Rust's ownership system. - -## Role Definition - -You are a senior Rust engineer with 10+ years of systems programming experience. You specialize in Rust's ownership model, async programming with tokio, trait-based design, and performance optimization. You build memory-safe, concurrent systems with zero-cost abstractions. - -## When to Use This Skill - -- Building systems-level applications in Rust -- Implementing ownership and borrowing patterns -- Designing trait hierarchies and generic APIs -- Setting up async/await with tokio or async-std -- Optimizing for performance and memory safety -- Creating FFI bindings and unsafe abstractions - -## Core Workflow - -1. **Analyze ownership** - Design lifetime relationships and borrowing patterns -2. **Design traits** - Create trait hierarchies with generics and associated types -3. **Implement safely** - Write idiomatic Rust with minimal unsafe code -4. **Handle errors** - Use Result/Option with ? operator and custom error types -5. **Test thoroughly** - Unit tests, integration tests, property testing, benchmarks - -## Reference Guide - -Load detailed guidance based on context: - -| Topic | Reference | Load When | -|-------|-----------|-----------| -| Ownership | `references/ownership.md` | Lifetimes, borrowing, smart pointers, Pin | -| Traits | `references/traits.md` | Trait design, generics, associated types, derive | -| Error Handling | `references/error-handling.md` | Result, Option, ?, custom errors, thiserror | -| Async | `references/async.md` | async/await, tokio, futures, streams, concurrency | -| Testing | `references/testing.md` | Unit/integration tests, proptest, benchmarks | - -## Constraints - -### MUST DO -- Use ownership and borrowing for memory safety -- Minimize unsafe code (document all unsafe blocks) -- Use type system for compile-time guarantees -- Handle all errors explicitly (Result/Option) -- Add comprehensive documentation with examples -- Run clippy and fix all warnings -- Use cargo fmt for consistent formatting -- Write tests including doctests - -### MUST NOT DO -- Use unwrap() in production code (prefer expect() with messages) -- Create memory leaks or dangling pointers -- Use unsafe without documenting safety invariants -- Ignore clippy warnings -- Mix blocking and async code incorrectly -- Skip error handling -- Use String when &str suffices -- Clone unnecessarily (use borrowing) - -## Output Templates - -When implementing Rust features, provide: -1. Type definitions (structs, enums, traits) -2. Implementation with proper ownership -3. Error handling with custom error types -4. Tests (unit, integration, doctests) -5. Brief explanation of design decisions - -## Knowledge Reference - -Rust 2021, Cargo, ownership/borrowing, lifetimes, traits, generics, async/await, tokio, Result/Option, thiserror/anyhow, serde, clippy, rustfmt, cargo-test, criterion benchmarks, MIRI, unsafe Rust - -## Related Skills - -- **Systems Architect** - Low-level system design -- **Performance Engineer** - Optimization and profiling -- **Test Master** - Comprehensive testing strategies diff --git a/.cursor/skills/rust-engineer/references/async.md b/.cursor/skills/rust-engineer/references/async.md deleted file mode 100644 index 94190a8..0000000 --- a/.cursor/skills/rust-engineer/references/async.md +++ /dev/null @@ -1,461 +0,0 @@ -# Async Programming in Rust - -> Reference for: Rust Engineer -> Load when: async/await, tokio, futures, streams, concurrency patterns - -## Basic Async/Await - -```rust -use tokio; - -// Async function returns a Future -async fn fetch_data(url: &str) -> Result { - let response = reqwest::get(url).await?; - let body = response.text().await?; - Ok(body) -} - -// Tokio runtime -#[tokio::main] -async fn main() -> Result<(), Box> { - let data = fetch_data("https://api.example.com").await?; - println!("Data: {}", data); - Ok(()) -} - -// Manual runtime creation -fn main() { - let runtime = tokio::runtime::Runtime::new().unwrap(); - runtime.block_on(async { - println!("Hello from async context"); - }); -} -``` - -## Concurrent Execution - -```rust -use tokio; - -// Sequential execution -async fn sequential() { - let result1 = async_operation1().await; - let result2 = async_operation2().await; // Waits for operation1 -} - -// Concurrent execution with join! -async fn concurrent() { - let (result1, result2) = tokio::join!( - async_operation1(), - async_operation2() - ); -} - -// Concurrent with try_join! (stops on first error) -async fn concurrent_with_errors() -> Result<(), Box> { - let (result1, result2) = tokio::try_join!( - fallible_operation1(), - fallible_operation2() - )?; - Ok(()) -} - -// Spawning tasks -async fn spawn_tasks() { - let handle1 = tokio::spawn(async { - // This runs on a separate task - expensive_computation().await - }); - - let handle2 = tokio::spawn(async { - another_computation().await - }); - - // Wait for both to complete - let result1 = handle1.await.unwrap(); - let result2 = handle2.await.unwrap(); -} -``` - -## Select and Race Conditions - -```rust -use tokio::time::{sleep, Duration}; - -// select! - wait for first to complete -async fn first_to_complete() { - tokio::select! { - result = async_operation1() => { - println!("Operation 1 completed first: {:?}", result); - } - result = async_operation2() => { - println!("Operation 2 completed first: {:?}", result); - } - } -} - -// Timeout pattern -async fn with_timeout() -> Result { - tokio::select! { - result = fetch_data("https://api.example.com") => { - result.map_err(|_| "Fetch failed") - } - _ = sleep(Duration::from_secs(5)) => { - Err("Timeout") - } - } -} - -// Cancellation with select! -async fn cancellable_operation(mut cancel_rx: tokio::sync::watch::Receiver) { - tokio::select! { - result = long_running_task() => { - println!("Task completed: {:?}", result); - } - _ = cancel_rx.changed() => { - println!("Task cancelled"); - } - } -} -``` - -## Streams - -```rust -use tokio_stream::{self as stream, StreamExt}; - -// Creating streams -async fn stream_example() { - let mut stream = stream::iter(vec![1, 2, 3, 4, 5]); - - while let Some(value) = stream.next().await { - println!("Value: {}", value); - } -} - -// Stream combinators -async fn stream_combinators() { - let stream = stream::iter(vec![1, 2, 3, 4, 5]) - .filter(|x| *x % 2 == 0) - .map(|x| x * 2); - - let results: Vec<_> = stream.collect().await; - println!("Results: {:?}", results); -} - -// Async stream processing -use futures::stream::{self, StreamExt}; - -async fn process_stream() { - let stream = stream::iter(vec![1, 2, 3, 4, 5]) - .then(|x| async move { - tokio::time::sleep(Duration::from_millis(100)).await; - x * 2 - }); - - stream.for_each(|x| async move { - println!("Processed: {}", x); - }).await; -} -``` - -## Channels for Communication - -```rust -use tokio::sync::{mpsc, oneshot, broadcast, watch}; - -// mpsc: multiple producer, single consumer -async fn mpsc_example() { - let (tx, mut rx) = mpsc::channel(32); - - tokio::spawn(async move { - tx.send("Hello").await.unwrap(); - tx.send("World").await.unwrap(); - }); - - while let Some(msg) = rx.recv().await { - println!("Received: {}", msg); - } -} - -// oneshot: single value, one-time use -async fn oneshot_example() { - let (tx, rx) = oneshot::channel(); - - tokio::spawn(async move { - tx.send("Result").unwrap(); - }); - - let result = rx.await.unwrap(); - println!("Got: {}", result); -} - -// broadcast: multiple producers, multiple consumers -async fn broadcast_example() { - let (tx, mut rx1) = broadcast::channel(16); - let mut rx2 = tx.subscribe(); - - tokio::spawn(async move { - tx.send("Message").unwrap(); - }); - - println!("rx1: {}", rx1.recv().await.unwrap()); - println!("rx2: {}", rx2.recv().await.unwrap()); -} - -// watch: single producer, multiple consumers (last value) -async fn watch_example() { - let (tx, mut rx) = watch::channel("initial"); - - tokio::spawn(async move { - loop { - rx.changed().await.unwrap(); - println!("Value changed to: {}", *rx.borrow()); - } - }); - - tx.send("updated").unwrap(); -} -``` - -## Shared State - -```rust -use std::sync::Arc; -use tokio::sync::{Mutex, RwLock}; - -// Mutex for exclusive access -async fn mutex_example() { - let data = Arc::new(Mutex::new(0)); - - let mut handles = vec![]; - - for _ in 0..10 { - let data = Arc::clone(&data); - let handle = tokio::spawn(async move { - let mut lock = data.lock().await; - *lock += 1; - }); - handles.push(handle); - } - - for handle in handles { - handle.await.unwrap(); - } - - println!("Final value: {}", *data.lock().await); -} - -// RwLock for read-write patterns -async fn rwlock_example() { - let data = Arc::new(RwLock::new(vec![1, 2, 3])); - - // Multiple readers - let data1 = Arc::clone(&data); - tokio::spawn(async move { - let read = data1.read().await; - println!("Read: {:?}", *read); - }); - - let data2 = Arc::clone(&data); - tokio::spawn(async move { - let read = data2.read().await; - println!("Read: {:?}", *read); - }); - - // Single writer - tokio::time::sleep(Duration::from_millis(100)).await; - let mut write = data.write().await; - write.push(4); -} -``` - -## Async Traits (with async-trait) - -```rust -use async_trait::async_trait; - -#[async_trait] -trait AsyncRepository { - async fn find_by_id(&self, id: u64) -> Result; - async fn save(&self, user: User) -> Result<(), Error>; -} - -struct DatabaseRepository { - pool: sqlx::PgPool, -} - -#[async_trait] -impl AsyncRepository for DatabaseRepository { - async fn find_by_id(&self, id: u64) -> Result { - sqlx::query_as("SELECT * FROM users WHERE id = $1") - .bind(id) - .fetch_one(&self.pool) - .await - .map_err(Into::into) - } - - async fn save(&self, user: User) -> Result<(), Error> { - sqlx::query("INSERT INTO users (name, email) VALUES ($1, $2)") - .bind(&user.name) - .bind(&user.email) - .execute(&self.pool) - .await?; - Ok(()) - } -} -``` - -## Pin and Futures - -```rust -use std::pin::Pin; -use std::future::Future; -use std::task::{Context, Poll}; - -// Manual Future implementation -struct DelayedValue { - value: i32, - delay: tokio::time::Sleep, -} - -impl Future for DelayedValue { - type Output = i32; - - fn poll(mut self: Pin<&mut Self>, cx: &mut Context<'_>) -> Poll { - match Pin::new(&mut self.delay).poll(cx) { - Poll::Ready(_) => Poll::Ready(self.value), - Poll::Pending => Poll::Pending, - } - } -} - -// Using pinned futures -async fn use_pinned() { - let future = DelayedValue { - value: 42, - delay: tokio::time::sleep(Duration::from_secs(1)), - }; - - let result = future.await; - println!("Result: {}", result); -} -``` - -## Background Tasks and Graceful Shutdown - -```rust -use tokio::signal; - -async fn background_task(mut shutdown: tokio::sync::watch::Receiver) { - loop { - tokio::select! { - _ = tokio::time::sleep(Duration::from_secs(1)) => { - println!("Background task running..."); - } - _ = shutdown.changed() => { - println!("Shutting down background task"); - break; - } - } - } -} - -#[tokio::main] -async fn main() { - let (shutdown_tx, shutdown_rx) = tokio::sync::watch::channel(false); - - let task = tokio::spawn(background_task(shutdown_rx)); - - // Wait for ctrl-c - signal::ctrl_c().await.unwrap(); - println!("Received shutdown signal"); - - // Signal shutdown - shutdown_tx.send(true).unwrap(); - - // Wait for task to complete - task.await.unwrap(); -} -``` - -## Error Handling in Async - -```rust -use thiserror::Error; - -#[derive(Error, Debug)] -enum AsyncError { - #[error("Network error: {0}")] - Network(#[from] reqwest::Error), - - #[error("Timeout")] - Timeout, - - #[error("Task failed")] - TaskFailed(#[from] tokio::task::JoinError), -} - -async fn robust_operation() -> Result { - let timeout = Duration::from_secs(5); - - let result = tokio::time::timeout(timeout, async { - reqwest::get("https://api.example.com") - .await? - .text() - .await - }) - .await - .map_err(|_| AsyncError::Timeout)??; - - Ok(result) -} -``` - -## Runtime Configuration - -```rust -// Custom runtime configuration -fn main() { - let runtime = tokio::runtime::Builder::new_multi_thread() - .worker_threads(4) - .thread_name("my-worker") - .thread_stack_size(3 * 1024 * 1024) - .enable_all() - .build() - .unwrap(); - - runtime.block_on(async { - println!("Running on custom runtime"); - }); -} - -// Current-thread runtime (single-threaded) -fn single_threaded() { - let runtime = tokio::runtime::Builder::new_current_thread() - .enable_all() - .build() - .unwrap(); - - runtime.block_on(async { - println!("Single-threaded async"); - }); -} -``` - -## Best Practices - -- Use tokio::spawn for CPU-bound tasks on multi-threaded runtime -- Use spawn_blocking for blocking operations (file I/O, sync code) -- Prefer tokio::sync primitives over std::sync in async code -- Use channels for task communication instead of shared state when possible -- Always handle JoinHandle results (tasks can panic) -- Use select! for cancellation patterns -- Avoid holding locks across .await points -- Use timeout for all external I/O operations -- Implement graceful shutdown with channels -- Use async-trait for trait-based async code -- Prefer try_join! over manual error handling -- Use Arc> sparingly (channels often better) -- Test async code with tokio::test macro -- Monitor task spawning to prevent unbounded growth diff --git a/.cursor/skills/rust-engineer/references/error-handling.md b/.cursor/skills/rust-engineer/references/error-handling.md deleted file mode 100644 index f09cfc6..0000000 --- a/.cursor/skills/rust-engineer/references/error-handling.md +++ /dev/null @@ -1,337 +0,0 @@ -# Error Handling in Rust - -> Reference for: Rust Engineer -> Load when: Handling errors, Result, Option, custom error types, thiserror - -## Result and Option Basics - -```rust -// Result: operation that can fail -fn divide(a: f64, b: f64) -> Result { - if b == 0.0 { - Err("Division by zero".to_string()) - } else { - Ok(a / b) - } -} - -// Option: value that might be absent -fn find_user(id: u64) -> Option { - if id == 1 { - Some(User { id, name: "Alice".to_string() }) - } else { - None - } -} - -// Using ? operator for propagation -fn calculate(a: f64, b: f64, c: f64) -> Result { - let x = divide(a, b)?; // Returns Err early if division fails - let y = divide(x, c)?; - Ok(y) -} -``` - -## Custom Error Types - -```rust -use std::fmt; - -// Manual error type -#[derive(Debug)] -enum AppError { - NotFound(String), - InvalidInput(String), - DatabaseError(String), -} - -impl fmt::Display for AppError { - fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result { - match self { - AppError::NotFound(msg) => write!(f, "Not found: {}", msg), - AppError::InvalidInput(msg) => write!(f, "Invalid input: {}", msg), - AppError::DatabaseError(msg) => write!(f, "Database error: {}", msg), - } - } -} - -impl std::error::Error for AppError {} - -// Usage -fn get_user(id: u64) -> Result { - if id == 0 { - return Err(AppError::InvalidInput("ID cannot be zero".to_string())); - } - // ... fetch user - Err(AppError::NotFound(format!("User {} not found", id))) -} -``` - -## Using thiserror - -```rust -use thiserror::Error; - -#[derive(Error, Debug)] -enum DataError { - #[error("Data not found: {0}")] - NotFound(String), - - #[error("Invalid ID: {id}, reason: {reason}")] - InvalidId { id: u64, reason: String }, - - #[error("IO error")] - Io(#[from] std::io::Error), - - #[error("Parse error")] - Parse(#[from] std::num::ParseIntError), - - #[error("Database error: {0}")] - Database(#[from] sqlx::Error), -} - -// Usage with automatic conversions -fn read_config(path: &str) -> Result { - let content = std::fs::read_to_string(path)?; // Auto-converts io::Error - let port: u16 = content.parse()?; // Auto-converts ParseIntError - Ok(Config { port }) -} -``` - -## Using anyhow for Applications - -```rust -use anyhow::{Result, Context, bail, ensure}; - -// Simple error handling for applications -fn process_file(path: &str) -> Result<()> { - let content = std::fs::read_to_string(path) - .context(format!("Failed to read file: {}", path))?; - - ensure!(!content.is_empty(), "File is empty"); - - if content.len() > 1000 { - bail!("File too large"); - } - - // Process content... - Ok(()) -} - -// Adding context to errors -fn main() -> Result<()> { - process_file("config.txt") - .context("Failed to process configuration")?; - Ok(()) -} -``` - -## Option Combinators - -```rust -// map: transform Option to Option -let num: Option = Some(5); -let doubled = num.map(|n| n * 2); // Some(10) - -// and_then: chain operations -let result = Some(5) - .and_then(|n| if n > 0 { Some(n * 2) } else { None }) - .and_then(|n| Some(n + 1)); // Some(11) - -// or: provide alternative -let value = None.or(Some(42)); // Some(42) - -// unwrap_or: provide default -let value = None.unwrap_or(42); // 42 - -// unwrap_or_else: compute default lazily -let value = None.unwrap_or_else(|| expensive_computation()); - -// filter: conditional None -let num = Some(5).filter(|&n| n > 10); // None - -// Pattern matching -match find_user(1) { - Some(user) => println!("Found: {}", user.name), - None => println!("User not found"), -} - -// if let for simple cases -if let Some(user) = find_user(1) { - println!("Found: {}", user.name); -} -``` - -## Result Combinators - -```rust -// map: transform Ok value -let result: Result = Ok(5); -let doubled = result.map(|n| n * 2); // Ok(10) - -// map_err: transform error -let result: Result = Err("error"); -let mapped = result.map_err(|e| e.to_uppercase()); // Err("ERROR") - -// and_then: chain fallible operations -fn parse_then_double(s: &str) -> Result { - s.parse::() - .and_then(|n| Ok(n * 2)) -} - -// or_else: provide alternative computation -let result = Err("error").or_else(|_| Ok(42)); // Ok(42) - -// unwrap_or: provide default -let value = Err("error").unwrap_or(42); // 42 - -// expect: unwrap with custom panic message -let value = result.expect("Failed to parse number"); - -// Pattern matching -match divide(10.0, 2.0) { - Ok(result) => println!("Result: {}", result), - Err(e) => eprintln!("Error: {}", e), -} -``` - -## Error Conversion and From Trait - -```rust -use std::io; -use std::num::ParseIntError; - -#[derive(Debug)] -enum MyError { - Io(io::Error), - Parse(ParseIntError), -} - -impl From for MyError { - fn from(err: io::Error) -> Self { - MyError::Io(err) - } -} - -impl From for MyError { - fn from(err: ParseIntError) -> Self { - MyError::Parse(err) - } -} - -// Now ? operator works with automatic conversion -fn read_and_parse(path: &str) -> Result { - let content = std::fs::read_to_string(path)?; // io::Error -> MyError - let number = content.trim().parse()?; // ParseIntError -> MyError - Ok(number) -} -``` - -## Advanced Error Patterns - -```rust -// Multiple error sources with Box -use std::error::Error; - -fn complex_operation() -> Result> { - let file = std::fs::read_to_string("data.txt")?; - let number: i32 = file.trim().parse()?; - Ok(format!("Number: {}", number)) -} - -// Error with backtrace (nightly) -#[derive(Debug)] -struct DetailedError { - message: String, - backtrace: std::backtrace::Backtrace, -} - -impl DetailedError { - fn new(message: impl Into) -> Self { - Self { - message: message.into(), - backtrace: std::backtrace::Backtrace::capture(), - } - } -} - -// Recoverable vs unrecoverable errors -fn might_fail(value: i32) -> Result { - if value < 0 { - Err("Negative value".to_string()) // Recoverable - } else if value > 1000 { - panic!("Value too large!"); // Unrecoverable - } else { - Ok(value * 2) - } -} -``` - -## Try Blocks (Nightly) - -```rust -#![feature(try_blocks)] - -// Try block for localized error handling -let result: Result> = try { - let file = std::fs::read_to_string("config.txt")?; - let num: i32 = file.trim().parse()?; - num * 2 -}; -``` - -## Error Context Pattern - -```rust -use thiserror::Error; - -#[derive(Error, Debug)] -#[error("{message}")] -struct ContextError { - message: String, - #[source] - source: Option>, -} - -impl ContextError { - fn new(message: impl Into) -> Self { - Self { - message: message.into(), - source: None, - } - } - - fn with_source(mut self, source: impl Error + Send + Sync + 'static) -> Self { - self.source = Some(Box::new(source)); - self - } -} - -// Extension trait for adding context -trait Context { - fn context(self, message: impl Into) -> Result; -} - -impl Context for Result { - fn context(self, message: impl Into) -> Result { - self.map_err(|e| ContextError::new(message).with_source(e)) - } -} -``` - -## Best Practices - -- Use Result for recoverable errors, panic! for unrecoverable bugs -- Prefer ? operator over unwrap() in production code -- Use expect() with descriptive messages instead of unwrap() -- Use thiserror for libraries (structured errors) -- Use anyhow for applications (simple error handling) -- Implement std::error::Error trait for custom error types -- Add context to errors as they propagate up the stack -- Use #[from] in thiserror for automatic conversions -- Document error conditions in function documentation -- Use Option::ok_or() to convert Option to Result -- Use Result::ok() to convert Result to Option (discarding error) -- Avoid String as error type (use custom types instead) -- Use ensure! and bail! from anyhow for cleaner checks -- Log errors at boundaries, return them in library code diff --git a/.cursor/skills/rust-engineer/references/ownership.md b/.cursor/skills/rust-engineer/references/ownership.md deleted file mode 100644 index 6c972d3..0000000 --- a/.cursor/skills/rust-engineer/references/ownership.md +++ /dev/null @@ -1,281 +0,0 @@ -# Ownership, Borrowing, and Lifetimes - -> Reference for: Rust Engineer -> Load when: Working with ownership, lifetimes, smart pointers, borrowing - -## Ownership Patterns - -```rust -// Move semantics (ownership transfer) -fn take_ownership(s: String) { - println!("{}", s); -} // s dropped here - -// Borrowing (immutable reference) -fn borrow(s: &String) { - println!("{}", s); -} // s NOT dropped, caller still owns - -// Mutable borrowing -fn borrow_mut(s: &mut String) { - s.push_str(" world"); -} - -// Usage -let s = String::from("hello"); -borrow(&s); // OK, immutable borrow -let mut s2 = s; // Move, s no longer valid -borrow_mut(&mut s2); // OK, mutable borrow -``` - -## Lifetime Annotations - -```rust -// Explicit lifetime: returned reference lives as long as input -fn longest<'a>(x: &'a str, y: &'a str) -> &'a str { - if x.len() > y.len() { x } else { y } -} - -// Multiple lifetimes -fn first_word<'a, 'b>(s: &'a str, _other: &'b str) -> &'a str { - s.split_whitespace().next().unwrap_or("") -} - -// Lifetime in structs -struct Excerpt<'a> { - part: &'a str, -} - -impl<'a> Excerpt<'a> { - fn announce_and_return(&self, announcement: &str) -> &'a str { - println!("Attention: {}", announcement); - self.part - } -} - -// Static lifetime (lives for entire program) -const GREETING: &'static str = "Hello, world!"; -``` - -## Smart Pointers - -```rust -use std::rc::Rc; -use std::cell::RefCell; -use std::sync::{Arc, Mutex}; - -// Box: heap allocation, single owner -let b = Box::new(5); - -// Rc: reference counting (single-threaded) -let rc1 = Rc::new(vec![1, 2, 3]); -let rc2 = Rc::clone(&rc1); // Increment count -println!("Count: {}", Rc::strong_count(&rc1)); // 2 - -// Arc: atomic reference counting (thread-safe) -let arc1 = Arc::new(vec![1, 2, 3]); -let arc2 = Arc::clone(&arc1); -std::thread::spawn(move || { - println!("{:?}", arc2); -}); - -// RefCell: interior mutability (runtime borrow checking) -let data = RefCell::new(5); -*data.borrow_mut() += 1; // Mutable borrow at runtime - -// Combining Rc + RefCell for shared mutable state -let shared = Rc::new(RefCell::new(vec![1, 2, 3])); -shared.borrow_mut().push(4); - -// Combining Arc + Mutex for thread-safe shared state -let counter = Arc::new(Mutex::new(0)); -let counter_clone = Arc::clone(&counter); -std::thread::spawn(move || { - let mut num = counter_clone.lock().unwrap(); - *num += 1; -}); -``` - -## Interior Mutability - -```rust -use std::cell::{Cell, RefCell}; - -// Cell: Copy types only -let c = Cell::new(5); -c.set(10); -let val = c.get(); - -// RefCell: runtime borrow checking -let data = RefCell::new(vec![1, 2, 3]); -data.borrow_mut().push(4); - -// Pattern: mock objects with interior mutability -struct MockLogger { - messages: RefCell>, -} - -impl MockLogger { - fn new() -> Self { - Self { messages: RefCell::new(Vec::new()) } - } - - fn log(&self, msg: &str) { - self.messages.borrow_mut().push(msg.to_string()); - } - - fn get_messages(&self) -> Vec { - self.messages.borrow().clone() - } -} -``` - -## Pin and Self-Referential Types - -```rust -use std::pin::Pin; -use std::marker::PhantomPinned; - -// Self-referential struct (requires Pin) -struct SelfReferential { - data: String, - pointer: *const String, - _pin: PhantomPinned, -} - -impl SelfReferential { - fn new(data: String) -> Pin> { - let mut boxed = Box::pin(Self { - data, - pointer: std::ptr::null(), - _pin: PhantomPinned, - }); - - // Safe: we're not moving the data after this - let ptr = &boxed.data as *const String; - unsafe { - let mut_ref = Pin::as_mut(&mut boxed); - Pin::get_unchecked_mut(mut_ref).pointer = ptr; - } - - boxed - } -} - -// Pin in async contexts -async fn pinned_future() { - // Futures are often self-referential, hence Pin - let fut = async { 42 }; - let pinned = Box::pin(fut); - pinned.await; -} -``` - -## Cow (Clone on Write) - -```rust -use std::borrow::Cow; - -fn process_text(input: &str) -> Cow { - if input.contains("bad") { - // Need to modify: allocate new String - Cow::Owned(input.replace("bad", "good")) - } else { - // No modification needed: just borrow - Cow::Borrowed(input) - } -} - -// Usage -let text1 = "hello world"; -let result1 = process_text(text1); // Borrowed (no allocation) - -let text2 = "bad word"; -let result2 = process_text(text2); // Owned (allocated) -``` - -## Drop Trait and RAII - -```rust -struct FileGuard { - name: String, -} - -impl FileGuard { - fn new(name: String) -> Self { - println!("Opening {}", name); - Self { name } - } -} - -impl Drop for FileGuard { - fn drop(&mut self) { - println!("Closing {}", self.name); - } -} - -// Usage: automatic cleanup -{ - let _file = FileGuard::new("data.txt".to_string()); - // Use file... -} // Drop called automatically here -``` - -## Common Patterns - -```rust -// Builder pattern with ownership -struct Config { - host: String, - port: u16, -} - -impl Config { - fn builder() -> ConfigBuilder { - ConfigBuilder::default() - } -} - -struct ConfigBuilder { - host: Option, - port: Option, -} - -impl ConfigBuilder { - fn host(mut self, host: impl Into) -> Self { - self.host = Some(host.into()); - self - } - - fn port(mut self, port: u16) -> Self { - self.port = Some(port); - self - } - - fn build(self) -> Result { - Ok(Config { - host: self.host.ok_or("host required")?, - port: self.port.unwrap_or(8080), - }) - } -} - -// Usage -let config = Config::builder() - .host("localhost") - .port(3000) - .build()?; -``` - -## Best Practices - -- Prefer borrowing (&T) over ownership transfer when possible -- Use &str over String for function parameters -- Use &[T] over Vec for function parameters -- Clone only when necessary (profile first) -- Use Cow<'a, T> for conditional cloning -- Document lifetime relationships in complex cases -- Use Arc> for shared mutable state across threads -- Use Rc> for shared mutable state in single thread -- Implement Drop for RAII patterns -- Use PhantomData to constrain variance when needed diff --git a/.cursor/skills/rust-engineer/references/testing.md b/.cursor/skills/rust-engineer/references/testing.md deleted file mode 100644 index 982f311..0000000 --- a/.cursor/skills/rust-engineer/references/testing.md +++ /dev/null @@ -1,473 +0,0 @@ -# Testing in Rust - -> Reference for: Rust Engineer -> Load when: Unit tests, integration tests, property testing, benchmarks - -## Unit Tests - -```rust -// Tests in same file -#[cfg(test)] -mod tests { - use super::*; - - #[test] - fn test_addition() { - assert_eq!(2 + 2, 4); - } - - #[test] - fn test_subtraction() { - assert!(10 - 5 == 5); - } - - #[test] - #[should_panic(expected = "division by zero")] - fn test_panic() { - divide(10, 0); - } - - #[test] - fn test_result() -> Result<(), String> { - let result = divide(10, 2)?; - assert_eq!(result, 5); - Ok(()) - } - - #[test] - #[ignore] - fn expensive_test() { - // Run with: cargo test -- --ignored - } -} - -// Assertions -fn assert_examples() { - assert!(true); - assert_eq!(2 + 2, 4); - assert_ne!(2 + 2, 5); - - // Custom messages - assert!(value > 0, "Value must be positive, got {}", value); - assert_eq!(result, expected, "Calculation failed"); -} -``` - -## Doctests - -```rust -/// Adds two numbers together. -/// -/// # Examples -/// -/// ``` -/// use mylib::add; -/// -/// let result = add(2, 3); -/// assert_eq!(result, 5); -/// ``` -/// -/// ```should_panic -/// use mylib::divide; -/// -/// divide(10, 0); // This will panic -/// ``` -/// -/// ```ignore -/// // This code won't compile but won't fail the test -/// let x = undefined_function(); -/// ``` -pub fn add(a: i32, b: i32) -> i32 { - a + b -} -``` - -## Integration Tests - -```rust -// tests/integration_test.rs -use mylib; - -#[test] -fn test_full_workflow() { - let config = mylib::Config::new("test.conf"); - let result = mylib::process(&config); - assert!(result.is_ok()); -} - -// tests/common/mod.rs - shared test utilities -pub fn setup() -> TestContext { - TestContext { - db: create_test_db(), - } -} - -// tests/another_test.rs -mod common; - -#[test] -fn test_with_common() { - let ctx = common::setup(); - // Use ctx... -} -``` - -## Test Organization - -```rust -// Nested test modules -#[cfg(test)] -mod tests { - use super::*; - - mod addition { - use super::*; - - #[test] - fn positive_numbers() { - assert_eq!(add(2, 3), 5); - } - - #[test] - fn negative_numbers() { - assert_eq!(add(-2, -3), -5); - } - } - - mod subtraction { - use super::*; - - #[test] - fn test_subtract() { - assert_eq!(subtract(10, 5), 5); - } - } -} -``` - -## Test Fixtures and Setup - -```rust -struct TestContext { - temp_dir: std::path::PathBuf, - db: Database, -} - -impl TestContext { - fn setup() -> Self { - let temp_dir = std::env::temp_dir().join("test"); - std::fs::create_dir_all(&temp_dir).unwrap(); - - Self { - temp_dir, - db: Database::connect_test(), - } - } -} - -impl Drop for TestContext { - fn drop(&mut self) { - // Cleanup - std::fs::remove_dir_all(&self.temp_dir).ok(); - self.db.disconnect(); - } -} - -#[test] -fn test_with_fixture() { - let ctx = TestContext::setup(); - // Test uses ctx... - // Automatic cleanup via Drop -} -``` - -## Async Tests - -```rust -use tokio; - -#[tokio::test] -async fn test_async_function() { - let result = async_operation().await; - assert_eq!(result, 42); -} - -#[tokio::test(flavor = "multi_thread", worker_threads = 2)] -async fn test_with_custom_runtime() { - let result = concurrent_operation().await; - assert!(result.is_ok()); -} - -// Testing async with timeout -#[tokio::test] -async fn test_with_timeout() { - let timeout = std::time::Duration::from_secs(5); - let result = tokio::time::timeout(timeout, slow_operation()).await; - assert!(result.is_ok()); -} -``` - -## Property-Based Testing (proptest) - -```rust -use proptest::prelude::*; - -// Simple property test -proptest! { - #[test] - fn test_reversing_twice_is_identity(ref s in ".*") { - let reversed: String = s.chars().rev().collect(); - let double_reversed: String = reversed.chars().rev().collect(); - assert_eq!(s, &double_reversed); - } -} - -// Custom strategies -proptest! { - #[test] - fn test_addition_commutative(a in 0..1000i32, b in 0..1000i32) { - assert_eq!(a + b, b + a); - } - - #[test] - fn test_vector_push_pop( - ref v in prop::collection::vec(0..100i32, 0..100), - item in 0..100i32 - ) { - let mut v = v.clone(); - v.push(item); - assert_eq!(v.pop(), Some(item)); - } -} - -// Complex custom strategies -fn user_strategy() -> impl Strategy { - (1..1000u64, "[a-z]{3,10}", "[a-z0-9.]+@[a-z]+\\.[a-z]+") - .prop_map(|(id, name, email)| User { id, name, email }) -} - -proptest! { - #[test] - fn test_user_serialization(user in user_strategy()) { - let json = serde_json::to_string(&user).unwrap(); - let deserialized: User = serde_json::from_str(&json).unwrap(); - assert_eq!(user, deserialized); - } -} -``` - -## Mocking - -```rust -// Using mockall -use mockall::*; -use mockall::predicate::*; - -#[automock] -trait Database { - fn get_user(&self, id: u64) -> Option; - fn save_user(&mut self, user: User) -> Result<(), Error>; -} - -#[test] -fn test_with_mock() { - let mut mock = MockDatabase::new(); - - mock.expect_get_user() - .with(eq(1)) - .times(1) - .returning(|_| Some(User { id: 1, name: "Alice".to_string() })); - - mock.expect_save_user() - .times(1) - .returning(|_| Ok(())); - - // Use mock in test - let user = mock.get_user(1); - assert!(user.is_some()); -} -``` - -## Benchmarks (Criterion) - -```rust -// benches/my_benchmark.rs -use criterion::{black_box, criterion_group, criterion_main, Criterion}; - -fn fibonacci(n: u64) -> u64 { - match n { - 0 => 1, - 1 => 1, - n => fibonacci(n - 1) + fibonacci(n - 2), - } -} - -fn criterion_benchmark(c: &mut Criterion) { - c.bench_function("fib 20", |b| b.iter(|| fibonacci(black_box(20)))); -} - -criterion_group!(benches, criterion_benchmark); -criterion_main!(benches); - -// Cargo.toml: -// [dev-dependencies] -// criterion = "0.5" -// -// [[bench]] -// name = "my_benchmark" -// harness = false -``` - -## Advanced Benchmarking - -```rust -use criterion::{BenchmarkId, Criterion, criterion_group, criterion_main}; - -fn bench_multiple_sizes(c: &mut Criterion) { - let mut group = c.benchmark_group("sorting"); - - for size in [10, 100, 1000, 10000].iter() { - group.bench_with_input(BenchmarkId::from_parameter(size), size, |b, &size| { - b.iter_batched( - || generate_random_vec(size), - |mut v| v.sort(), - criterion::BatchSize::SmallInput, - ); - }); - } - - group.finish(); -} - -// Comparing implementations -fn bench_comparison(c: &mut Criterion) { - let mut group = c.benchmark_group("string_search"); - - group.bench_function("naive", |b| { - b.iter(|| naive_search(black_box("haystack"), black_box("needle"))) - }); - - group.bench_function("optimized", |b| { - b.iter(|| optimized_search(black_box("haystack"), black_box("needle"))) - }); - - group.finish(); -} - -criterion_group!(benches, bench_multiple_sizes, bench_comparison); -criterion_main!(benches); -``` - -## Testing with External Resources - -```rust -// Testing file I/O -#[test] -fn test_file_operations() { - use std::io::Write; - - let temp_dir = std::env::temp_dir(); - let file_path = temp_dir.join("test_file.txt"); - - // Write - let mut file = std::fs::File::create(&file_path).unwrap(); - file.write_all(b"test content").unwrap(); - - // Read - let content = std::fs::read_to_string(&file_path).unwrap(); - assert_eq!(content, "test content"); - - // Cleanup - std::fs::remove_file(&file_path).unwrap(); -} - -// Testing with databases (using sqlx) -#[sqlx::test] -async fn test_database_operations(pool: sqlx::PgPool) -> sqlx::Result<()> { - sqlx::query("INSERT INTO users (name) VALUES ($1)") - .bind("Alice") - .execute(&pool) - .await?; - - let count: (i64,) = sqlx::query_as("SELECT COUNT(*) FROM users") - .fetch_one(&pool) - .await?; - - assert_eq!(count.0, 1); - Ok(()) -} -``` - -## Snapshot Testing - -```rust -// Using insta crate -use insta::assert_snapshot; - -#[test] -fn test_output_format() { - let data = generate_complex_output(); - assert_snapshot!(data); -} - -#[test] -fn test_json_output() { - let json = serde_json::to_string_pretty(&get_data()).unwrap(); - assert_snapshot!(json); -} - -// Run with: cargo insta test -// Review snapshots: cargo insta review -``` - -## Code Coverage - -```rust -// Using tarpaulin -// cargo install cargo-tarpaulin -// cargo tarpaulin --out Html --output-dir coverage - -// Using llvm-cov -// cargo install cargo-llvm-cov -// cargo llvm-cov --html -``` - -## Fuzzing - -```rust -// Using cargo-fuzz -// cargo install cargo-fuzz -// cargo fuzz init - -// fuzz/fuzz_targets/fuzz_target_1.rs -#![no_main] -use libfuzzer_sys::fuzz_target; - -fuzz_target!(|data: &[u8]| { - if let Ok(s) = std::str::from_utf8(data) { - let _ = mylib::parse_input(s); - } -}); - -// Run with: cargo fuzz run fuzz_target_1 -``` - -## Best Practices - -- Write tests alongside production code in #[cfg(test)] modules -- Use integration tests in tests/ directory for end-to-end testing -- Include doctests in documentation for examples that must work -- Use descriptive test names that explain what is being tested -- Test edge cases (empty inputs, max values, etc.) -- Use property-based testing for algorithmic code -- Benchmark performance-critical code with criterion -- Run tests in CI with cargo test --all-features -- Use cargo test -- --nocapture to see println! output -- Test error conditions with #[should_panic] or Result -- Mock external dependencies for unit tests -- Use test fixtures for complex setup/teardown -- Run clippy on test code too -- Measure code coverage and aim for high coverage -- Use fuzzing for security-critical parsers -- Test async code with tokio::test -- Use snapshot testing for complex output validation diff --git a/.cursor/skills/rust-engineer/references/traits.md b/.cursor/skills/rust-engineer/references/traits.md deleted file mode 100644 index 19122a9..0000000 --- a/.cursor/skills/rust-engineer/references/traits.md +++ /dev/null @@ -1,416 +0,0 @@ -# Traits, Generics, and Type System - -> Reference for: Rust Engineer -> Load when: Designing traits, generics, associated types, derive macros - -## Basic Trait Definition - -```rust -// Simple trait -trait Drawable { - fn draw(&self); -} - -// Trait with default implementation -trait Describable { - fn describe(&self) -> String { - String::from("No description available") - } -} - -// Implementing traits -struct Circle { - radius: f64, -} - -impl Drawable for Circle { - fn draw(&self) { - println!("Drawing circle with radius {}", self.radius); - } -} - -impl Describable for Circle { - fn describe(&self) -> String { - format!("A circle with radius {}", self.radius) - } -} -``` - -## Associated Types - -```rust -// Associated types vs generic parameters -trait Container { - type Item; - - fn add(&mut self, item: Self::Item); - fn get(&self, index: usize) -> Option<&Self::Item>; -} - -impl Container for Vec { - type Item = i32; - - fn add(&mut self, item: i32) { - self.push(item); - } - - fn get(&self, index: usize) -> Option<&i32> { - self.get(index) - } -} - -// Iterator trait (standard library example) -trait MyIterator { - type Item; - - fn next(&mut self) -> Option; -} -``` - -## Generic Traits and Bounds - -```rust -// Generic trait with multiple bounds -fn print_info(item: &T) -where - T: std::fmt::Display + std::fmt::Debug, -{ - println!("Display: {}", item); - println!("Debug: {:?}", item); -} - -// Generic struct with trait bounds -struct Pair { - first: T, - second: T, -} - -impl Pair { - fn new(first: T, second: T) -> Self { - Self { first, second } - } - - fn larger(&self) -> &T { - if self.first > self.second { - &self.first - } else { - &self.second - } - } -} - -// Blanket implementation -trait MyTrait { - fn do_something(&self); -} - -impl MyTrait for T { - fn do_something(&self) { - println!("Value: {}", self); - } -} -``` - -## Trait Objects (Dynamic Dispatch) - -```rust -// Static dispatch (monomorphization) -fn static_dispatch(item: &T) { - item.draw(); -} - -// Dynamic dispatch (trait objects) -fn dynamic_dispatch(item: &dyn Drawable) { - item.draw(); -} - -// Storing trait objects -struct Canvas { - shapes: Vec>, -} - -impl Canvas { - fn new() -> Self { - Self { shapes: Vec::new() } - } - - fn add_shape(&mut self, shape: Box) { - self.shapes.push(shape); - } - - fn draw_all(&self) { - for shape in &self.shapes { - shape.draw(); - } - } -} - -// Object safety: traits must meet criteria -trait ObjectSafe { - fn method(&self); // OK: takes &self -} - -trait NotObjectSafe { - fn generic(&self); // NOT OK: generic method - fn by_value(self); // NOT OK: takes self by value -} -``` - -## Derive Macros - -```rust -// Standard derive macros -#[derive(Debug, Clone, PartialEq, Eq, Hash)] -struct User { - id: u64, - name: String, -} - -// Deriving more traits -#[derive(Debug, Clone, Copy, PartialEq, Eq, PartialOrd, Ord)] -struct Point { - x: i32, - y: i32, -} - -// Custom derive with serde -use serde::{Deserialize, Serialize}; - -#[derive(Debug, Serialize, Deserialize)] -struct Config { - host: String, - port: u16, -} -``` - -## Advanced Trait Patterns - -```rust -// Extension trait pattern -trait StringExt { - fn truncate_to(&self, max_len: usize) -> String; -} - -impl StringExt for str { - fn truncate_to(&self, max_len: usize) -> String { - if self.len() <= max_len { - self.to_string() - } else { - format!("{}...", &self[..max_len]) - } - } -} - -// Sealed trait pattern (prevent external implementation) -mod sealed { - pub trait Sealed {} -} - -pub trait MySealed: sealed::Sealed { - fn method(&self); -} - -struct MyType; -impl sealed::Sealed for MyType {} -impl MySealed for MyType { - fn method(&self) { - println!("Implemented"); - } -} - -// Supertraits -trait Printable { - fn print(&self); -} - -trait Loggable: Printable { // Supertrait: must also impl Printable - fn log(&self) { - self.print(); // Can call supertrait methods - } -} -``` - -## Associated Constants - -```rust -trait Config { - const MAX_SIZE: usize; - const DEFAULT_TIMEOUT: u64; -} - -struct ServerConfig; - -impl Config for ServerConfig { - const MAX_SIZE: usize = 1024; - const DEFAULT_TIMEOUT: u64 = 30; -} - -fn use_config() { - println!("Max size: {}", T::MAX_SIZE); -} -``` - -## Generic Associated Types (GATs) - -```rust -// GATs allow generics in associated types -trait LendingIterator { - type Item<'a> where Self: 'a; - - fn next<'a>(&'a mut self) -> Option>; -} - -struct WindowsMut<'data, T> { - data: &'data mut [T], - index: usize, -} - -impl<'data, T> LendingIterator for WindowsMut<'data, T> { - type Item<'a> = &'a mut [T] where Self: 'a; - - fn next<'a>(&'a mut self) -> Option> { - if self.index >= self.data.len() { - return None; - } - - let start = self.index; - self.index += 2; - - Some(&mut self.data[start..start.min(self.data.len())]) - } -} -``` - -## Marker Traits - -```rust -use std::marker::{PhantomData, Send, Sync}; - -// Send: type can be transferred across thread boundaries -// Sync: type can be shared between threads (&T is Send) - -// Custom marker trait -trait Trusted {} - -struct TrustedData { - data: T, - _marker: PhantomData, -} - -impl TrustedData { - fn new(data: T) -> Self { - Self { - data, - _marker: PhantomData, - } - } -} -``` - -## Operator Overloading - -```rust -use std::ops::{Add, Mul}; - -#[derive(Debug, Clone, Copy)] -struct Vector2D { - x: f64, - y: f64, -} - -impl Add for Vector2D { - type Output = Self; - - fn add(self, other: Self) -> Self { - Self { - x: self.x + other.x, - y: self.y + other.y, - } - } -} - -impl Mul for Vector2D { - type Output = Self; - - fn mul(self, scalar: f64) -> Self { - Self { - x: self.x * scalar, - y: self.y * scalar, - } - } -} - -// Usage -let v1 = Vector2D { x: 1.0, y: 2.0 }; -let v2 = Vector2D { x: 3.0, y: 4.0 }; -let v3 = v1 + v2; -let v4 = v1 * 2.5; -``` - -## From/Into Conversion Traits - -```rust -struct UserId(u64); - -impl From for UserId { - fn from(id: u64) -> Self { - UserId(id) - } -} - -// Into is automatically implemented -fn accept_user_id(id: impl Into) { - let user_id = id.into(); - println!("User ID: {}", user_id.0); -} - -// TryFrom for fallible conversions -use std::convert::TryFrom; - -impl TryFrom for UserId { - type Error = &'static str; - - fn try_from(value: i64) -> Result { - if value < 0 { - Err("User ID cannot be negative") - } else { - Ok(UserId(value as u64)) - } - } -} -``` - -## Const Traits (Nightly) - -```rust -// Const trait implementations (requires nightly) -#![feature(const_trait_impl)] - -#[const_trait] -trait ConstAdd { - fn add(self, other: Self) -> Self; -} - -impl const ConstAdd for i32 { - fn add(self, other: Self) -> Self { - self + other - } -} - -const fn compute() -> i32 { - 5.add(10) // Can use in const context -} -``` - -## Best Practices - -- Prefer associated types when there's one clear type per implementation -- Use generic parameters when multiple types might be used simultaneously -- Keep traits small and focused (single responsibility) -- Use extension traits to add functionality to existing types -- Document trait requirements and invariants -- Use marker traits for compile-time guarantees -- Prefer static dispatch for performance, dynamic dispatch for flexibility -- Use #[derive] when possible instead of manual implementations -- Implement standard traits (Debug, Clone, etc.) for better ecosystem integration -- Use sealed traits to prevent external implementations when needed diff --git a/.cursor/skills/rust-pyo3/SKILL.md b/.cursor/skills/rust-pyo3/SKILL.md new file mode 100644 index 0000000..a77ead2 --- /dev/null +++ b/.cursor/skills/rust-pyo3/SKILL.md @@ -0,0 +1,12 @@ +--- +author: dotagents +name: rust-pyo3 +description: Applies dotagent conventions for Rust PyO3 and Maturin extension crates on top of the base Rust stack. +--- + +When this skill applies: + +- Keep the Python surface small and stable; align naming between `pyproject.toml`, `Cargo.toml`, and the published module. +- Use Maturin as the build entry point unless the repository already standardizes on a different documented flow. +- Be explicit about GIL, `Send`, and exception mapping across the FFI boundary. +- Prefer thin Python modules that re-export a narrow Rust API over exposing many low-level handles. diff --git a/.cursor/skills/rust-tui/SKILL.md b/.cursor/skills/rust-tui/SKILL.md new file mode 100644 index 0000000..ce5b1e1 --- /dev/null +++ b/.cursor/skills/rust-tui/SKILL.md @@ -0,0 +1,12 @@ +--- +author: dotagents +name: rust-tui +description: Applies dotagent conventions for Rust terminal user interfaces on top of the base Rust stack. +--- + +When this skill applies: + +- Follow the TUI stack already present in the repository; avoid introducing a second framework without a migration plan. +- Separate rendering from state: keep model updates independent of widget internals where the codebase already does. +- Treat resize, focus changes, and partial redraws as normal; avoid assuming a fixed terminal size. +- Prefer keyboard-first flows; document mouse or alternative input when the project exposes them. diff --git a/.cursor/skills/rust/SKILL.md b/.cursor/skills/rust/SKILL.md new file mode 100644 index 0000000..27f5cec --- /dev/null +++ b/.cursor/skills/rust/SKILL.md @@ -0,0 +1,12 @@ +--- +author: dotagents +name: rust +description: Applies the dotagent Rust stack defaults when editing Rust crates and workspaces. +--- + +When this skill applies: + +- Use cargo for builds, tests, and dependency changes; add crates with `cargo add` when introducing new dependencies. +- Respect the repository edition, MSRV, and any clippy or rustfmt configuration already checked in. +- Prefer explicit `Result` handling at API boundaries; keep `unwrap` out of library code unless the project documents an exception. +- Align module layout and visibility with existing crates in the workspace before 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Calling bcs_request from client -application code is possible, but discouraged, and not supported, as the call signature may change in future versions. - -Contact bcs@lbl.gov with questions / requests. -""" -import sys -import asyncio - -if sys.platform[:3] == 'win': - # zmq.asyncio does not support the default (proactor) event loop on windows. - # so set the event loop to one zmq supports - asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy()) - -import zmq -import zmq.asyncio -import zmq.utils.z85 - -import json -import time - -# helper classes -from enum import Flag # for MotorStatus - - -class MotorStatus(Flag): - HOME = 1 - FORWARD_LIMIT = 2 - REVERSE_LIMIT = 4 - MOTOR_DIRECTION = 8 - MOTOR_OFF = 16 - MOVE_COMPLETE = 32 - FOLLOWING_ERROR = 64 - NOT_IN_DEAD_BAND = 128 - FORWARD_SW_LIMIT = 256 - REVERSE_SW_LIMIT = 512 - MOTOR_DISABLED = 1024 - RAW_MOTOR_DIRECTION = 2048 - RAW_FORWARD_LIMIT = 4096 - RAW_REVERSE_LIMIT = 8192 - RAW_FORWARD_SW_LIMIT = 16384 - RAW_REVERSE_SW_LIMIT = 32768 - RAW_MOVE_COMPLETE = 65536 - MOVE_LT_THRESHOLD = 131072 - - def is_set(self, flag): - return bool(self._value_ & flag._value_) - - -# helper functions -def bytes_from_blob(blob): - """Deserializes binary data blobs. This implementation uses zmq's Base85 encoding, but future ones may not.""" - blob_len = blob['length'] - blob_str = blob['blob'] - return zmq.utils.z85.decode(blob_str)[:blob_len] - - -_zmq_context = None - - -class BCSServer: - """Represents a remote BCS endstation or beamline system, running the BCS zmq server. - - Each endpoint returns a dictionary of results. In addition to endpoint-specific keys, the following - global keys are included. - - * **success** (*bool*) - API endpoint completion status. Note that this does *not* indicate endpoint errors. - * **error_description** (*str*) - If the server failed to execute the endpoint, this should indicate why. There may be warnings or information here even if the endpoint completed successfully. - * **log** (*bool*) - True if the server logged this request. - - """ - _zmq_socket = None - - @staticmethod - async def _get_server_public_key(addr, port): - clear_socket = _zmq_context.socket(zmq.REQ) - clear_socket.connect(f'tcp://{addr}:{port}') - await clear_socket.send('public'.encode()) - server_public = await clear_socket.recv() - clear_socket.close() - return server_public - - # def __init__(self, addr='127.0.0.1', port=5577): - async def connect(self, addr='127.0.0.1', port=5577): - - """(formerly the Constructor) Supply the zmq address string, addr, to reach this endstation.""" - - global _zmq_context - - # the first server object will create the global zmq context - if not _zmq_context: - _zmq_context = zmq.asyncio.Context() - - self._zmq_socket = _zmq_context.socket(zmq.REQ) - - (client_public_key, client_secret_key) = zmq.curve_keypair() - - # server_public_key = asyncio.get_running_loop().run_until_complete(self._get_server_public_key(addr, port)) - - server_public_key = await self._get_server_public_key(addr, port) - - print(f'Server Public Key {server_public_key}') - - self._zmq_socket.setsockopt(zmq.CURVE_SERVERKEY, server_public_key) - self._zmq_socket.setsockopt(zmq.CURVE_PUBLICKEY, client_public_key) - self._zmq_socket.setsockopt(zmq.CURVE_SECRETKEY, client_secret_key) - - self._zmq_socket.connect(f'tcp://{addr}:{port + 1}') - - async def bcs_request(self, command_name, param_dict, debugging=False): - """ - The method responsible for direct communication to the BCS server - - :param command_name: Name of the API endpoint - :type command_name: str - :param param_dict: Parameter dictionary - :type param_dict: dict - """ - if debugging: - print(f"API command {command_name} BEGIN.") - - api_call_start = time.time() - param_dict['command'] = command_name - param_dict['_unused'] = '_unused' - if 'self' in param_dict: - del param_dict['self'] - await self._zmq_socket.send(json.dumps(param_dict).encode()) - response_dict = json.loads(await self._zmq_socket.recv()) - response_dict['API_delta_t'] = time.time() - api_call_start - - if debugging: - print(f"API command {command_name} END {response_dict['API_delta_t']} s.") - - return response_dict - -# end BCSz_header.py - async def acquire_data(self, chans=[], time=0, counts=0) -> dict: - """ - Acquires for ``time`` seconds, or ``counts`` counts. whichever is non-zero. **Waits for the acquision to complete** and returns data for channels specified in ``chans``. - If both ``counts`` *and* ``time`` are non-zero, which parameter takes precedence is not defined. - - :param chans: AI channel names to acquire. An empty array will return data for all AI channels on the server. - :type chans: list - :param time: If non-zero, the amount of time to acquire. - :type time: float - :param counts: If non-zero, the number of counts to acquire. - :type counts: int - - :return: Dictionary of results, with the following key(s). - - * **chans** (*list*) - The channel names that have data in the **data** array, in the same order. - - * **not_found** (*list*) - The requested channels in ``chans`` that were not found on the host system, if any. - - * **data** (*list*) - The acquired data, in the same order as the channel names in **chans**. - - - """ - return await self.bcs_request('AcquireData', dict(locals())) - - async def at_preset(self, name="_none") -> dict: - """ - Checks if the associated motor is at the preset position. The associated motor and preset position are defined on the server. - - :param name: Name of the preset (not the motor name). - :type name: str - - :return: Dictionary of results, with the following key(s). - - * **at_preset** (*bool*) - True if the target motor is at the preset position. - - * **position** (*float*) - The preset position, in units of the target motor. - - - """ - return await self.bcs_request('AtPreset', dict(locals())) - - async def at_trajectory(self, name="success") -> dict: - """ - Checks if all requirement been satisfied to be "At trajectory" (usually just means that each motor is at its trajectory goal). - - :param name: Trajectory name (defined on the server). - :type name: str - - :return: Dictionary of results, with the following key(s). - - * **at_trajectory** (*bool*) - Are the motors "At trajectory?" - - * **running** (*bool*) - Is the trajectory still running (are the motors still moving)? - - - """ - return await self.bcs_request('AtTrajectory', dict(locals())) - - async def command_motor(self, commands=[], motors=[], goals=[]) -> dict: - """ - Command one or more motors. A large number of commands are available. - - :param commands: Array of motor commands, one for each motor in ``motors``. Current valid commands are {None, Normal Move, Backlash Move, Velocity Move, Move to Home, Stop Motor, Set Position, Enable Motor, Disable Motor, Move to Index, Run Home Routine, Set Velocity, Set Acceleration, Set Deceleration, Enable and Move, Disable SW Limits, Enable SW Limits, Start Time Delay, Check Time Delay, Set Output Pulses, Backlash Jog, Normal Jog, Run Coord Program, Halt Coord Program, Gearing ON, Gearing OFF, Set Forward SW Limit, Set Reverse SW Limit, Revert Forward SW Limit, Revert Reverse SW Limit}. - :type commands: list - :param motors: Array of motor names - :type motors: list - :param goals: Goal (or associated value for the command) for each motor provided in ``motors`` - :type goals: list - - :return: Dictionary of results, with the following key(s). - - * **timed_out** (*list*) - List of motors that timed-out, if any. - - * **not_found** (*list*) - List of motors that were not found, if any. - - - """ - return await self.bcs_request('CommandMotor', dict(locals())) - - async def current_scan_running(self) -> dict: - """ - Returns the currently running 'integrated scan' run, or an empty string if none is running. - - - - :return: Dictionary of results, with the following key(s). - - * **running_scan** (*str*) - The name of the currently running scan, or an empty string if none. - - - """ - return await self.bcs_request('CurrentScanRunning', dict(locals())) - - async def disable_breakpoints(self, name="") -> dict: - """ - Disables the breakpoint output (output-on-position) of motor controller for the named motor. Or, 'takes the motor out of flying mode'. - - :param name: Motor name - :type name: str - - :return: Dictionary of results (no endpoint-specific keys). - - - - """ - return await self.bcs_request('DisableBreakpoints', dict(locals())) - - async def disable_motor(self, name="success") -> dict: - """ - Disables the named motor. - - :param name: Motor name. - :type name: str - - :return: Dictionary of results (no endpoint-specific keys). - - - - """ - return await self.bcs_request('DisableMotor', dict(locals())) - - async def enable_motor(self, name="success") -> dict: - """ - Enables the named motor. - - :param name: Motor name. - :type name: str - - :return: Dictionary of results (no endpoint-specific keys). - - - - """ - return await self.bcs_request('EnableMotor', dict(locals())) - - async def get_acquired(self, chans=[]) -> dict: - """ - Retrieve the average value from the most recent *single-shot* acquisition (see StartAcquire). - - :param chans: AI channel name(s). An empty array returns data for all AI channels on the server. - :type chans: list - - :return: Dictionary of results, with the following key(s). - - * **chans** (*list*) - AI channel names that have data in the **data** array. - - * **not_found** (*list*) - The requested channels in ``chans`` that were not found on the host system, if any. - - * **data** (*list*) - The acquired data, in the same order as the channel names in **chans**. - - - """ - return await self.bcs_request('GetAcquired', dict(locals())) - - async def get_acquired_array(self, chans=[]) -> dict: - """ - Retrieve the array acquired from the most recent *single-shot* acquisition (see StartAcquire). - - :param chans: AI channel name(s). An empty array returns data for all AI channels on the server. - :type chans: list - - :return: Dictionary of results, with the following key(s). - - * **not_found** (*list*) - The requested channels in ``chans`` that were not found on the server, if any. - - * **chans** (*list*) - Array of clusters/dictionaries. Each array element contains the channel (**chan**), **period**, **time**, and **data**. Data is an array of sampled values. - - - """ - return await self.bcs_request('GetAcquiredArray', dict(locals())) - - async def get_acquire_status(self) -> dict: - """ - Read the current acquisition state of the AI subsystem - - - - :return: Dictionary of results, with the following key(s). - - * **acquiring** (*bool*) - True if an acquisition is in progress - - * **started** (*bool*) - True if an acquisition has been started - - * **reading** (*bool*) - True if acquisition data is currently being retrieved from an acquisition device - - - """ - return await self.bcs_request('GetAcquireStatus', dict(locals())) - - async def get_di(self, chans=[]) -> dict: - """ - Get digital input (DI) channel value - - :param chans: DI channel name(s). An empty array returns data for all channels on the server. - :type chans: list - - :return: Dictionary of results, with the following key(s). - - * **chans** (*list*) - DI channel names that have data in the **data** array. - - * **not_found** (*list*) - The requested channels in ``chans`` that were not found on the host system, if any. - - * **enabled** (*list*) - Boolean list, in the same order as **chans**, indicating if the channel is used (enabled). - - * **data** (*list*) - Boolean value of the DI's, in the same order as the channel names in **chans**. - - - """ - return await self.bcs_request('GetDI', dict(locals())) - - async def get_flying_positions(self, name="") -> dict: - """ - Retrieve the locations of motor **name** that will trigger acquisitions in a flying scan. - - :param name: Motor name - :type name: str - - :return: Dictionary of results, with the following key(s). - - * **pulse_positions** (*list*) - Trigger (pulse) positions. - - - """ - return await self.bcs_request('GetFlyingPositions', dict(locals())) - - async def get_folder_listing(self, path=".", pattern="*.txt", recurse=False) -> dict: - """ - Get lists of all files and folders in a location descended from "C:\\\\Beamline Controls\\\\BCS Setup Data" - - :param path: Path to text file, relative to C:\\\\Beamline Controls\\\\BCS Setup Data. Use backslashes (\\\\) not forward slashes (/). - :type path: str - :param pattern: Pattern for files for which you want to search. - :type pattern: str - :param recurse: If True, list all descendent files and folders that match pattern. - :type recurse: bool - - :return: Dictionary of results, with the following key(s). - - * **files** (*list*) - Array of paths, relative to C:\\\\Beamline Controls\\\\BCS Setup Data. - - * **folders** (*list*) - Array of paths, relative to C:\\\\Beamline Controls\\\\BCS Setup Data. - - - """ - return await self.bcs_request('GetFolderListing', dict(locals())) - - async def get_freerun(self, chans=[]) -> dict: - """ - Get freerun AI data for one or more channels - - :param chans: Array of channel names to get. An empty array retrieves all channels' data. - :type chans: list - - :return: Dictionary of results, with the following key(s). - - * **chans** (*list*) - The retrieved channel names, in the same order as **data**. - - * **not_found** (*list*) - The channels requested in ``chans`` that were not found on the server. - - * **data** (*list*) - The retrieved data, corresponding to the channels in **chans**. - - - """ - return await self.bcs_request('GetFreerun', dict(locals())) - - async def get_freerun_array(self, chans=[]) -> dict: - """ - Retrieve most recent AI freerun data. - - :param chans: AI channel name(s). An empty array returns data for all AI channels on the server. - :type chans: list - - :return: Dictionary of results, with the following key(s). - - * **not_found** (*list*) - The requested channels in ``chans`` that were not found on the server, if any. - - * **chans** (*list*) - Array of clusters/dictionaries. Each array element contains the channel (**chan**), **data**, and **x_values**. - - - """ - return await self.bcs_request('GetFreerunArray', dict(locals())) - - async def get_instrument_acquired1d(self, name="") -> dict: - """ - Retrieve data (1D array) from the most recent acquisition. - - :param name: Instrument name - :type name: str - - :return: Dictionary of results, with the following key(s). - - * **x** (*list*) - 1D array of abscissae - - * **y** (*list*) - 1D array of data - - - """ - return await self.bcs_request('GetInstrumentAcquired1D', dict(locals())) - - async def get_instrument_acquired2d(self, name="") -> dict: - """ - Retrieve data (2D array) from the most recent acquisition. - - :param name: Instrument name - :type name: str - - :return: Dictionary of results, with the following key(s). - - * **data** (*list*) - 2D array of u32. - - - """ - return await self.bcs_request('GetInstrumentAcquired2D', dict(locals())) - - async def get_instrument_acquired3d(self, name="") -> dict: - """ - Retrieve data (3D array) from the most recent acquisition. - - :param name: Instrument name - :type name: str - - :return: Dictionary of results, with the following key(s). - - * **data** (*list*) - 3D array of u32. - - - """ - return await self.bcs_request('GetInstrumentAcquired3D', dict(locals())) - - async def get_instrument_acquisition_info(self, name="") -> dict: - """ - Retrieve miscellanaeous info about the instrument and acquisition: file_name, sensor temperature (if applicable), live time, and dead time. - - :param name: Instrument name - :type name: str - - :return: Dictionary of results, with the following key(s). - - * **file_name** (*str*) - Fully qualified path name to latest data file. - - * **temperature** (*float*) - The temperature of an internal element, such as a CCD, if the instrument supports it. - - * **live_time** (*list*) - Live time for the acquistion. - - * **dead_time** (*list*) - Dead time for the acquisition. - - - """ - return await self.bcs_request('GetInstrumentAcquisitionInfo', dict(locals())) - - async def get_instrument_acquisition_status(self, name="") -> dict: - """ - Retrieve all available status bits from the instrument subsystem for the named instrument. - - :param name: Instrument name - :type name: str - - :return: Dictionary of results, with the following key(s). - - * **scan_params_set_up** (*bool*) - Are the scan parameters set up? - - * **acquiring** (*bool*) - Is the instrument acquiring data? - - * **single_shot** (*bool*) - Unclear at the moment. TBD - - * **aborted** (*bool*) - Is the acquisition aborted? - - * **data_available** (*bool*) - Is data available? - - * **ins_single_shot** (*bool*) - 'Instrument Single Shot': Also unclear. TBD. - - - """ - return await self.bcs_request('GetInstrumentAcquisitionStatus', dict(locals())) - - async def get_instrument_count_rates(self, name="") -> dict: - """ - Retreive instrument count rates - - :param name: Instrument name - :type name: str - - :return: Dictionary of results, with the following key(s). - - * **input_cr** (*list*) - Input count rate - - * **output_cr** (*list*) - Output count rate - - - """ - return await self.bcs_request('GetInstrumentCountRates', dict(locals())) - - async def get_instrument_driver_status(self, name="") -> dict: - """ - Returns the status of the BCS Instrument Driver for the named instrument. - - :param name: Instrument name - :type name: str - - :return: Dictionary of results, with the following key(s). - - * **running** (*bool*) - True if the driver is running. - - - """ - return await self.bcs_request('GetInstrumentDriverStatus', dict(locals())) - - async def get_motor(self, motors=[]) -> dict: - """ - Get information and status for the motors in ``motors`` - - :param motors: Array of motor names to retrieve. - :type motors: list - - :return: Dictionary of results, with the following key(s). - - * **not_found** (*list*) - The requested motors in ``motors`` that were not found on the host system, if any. - - * **data** (*list*) - Array of clusters/dictionaries. Each array element contains the motor name (**motor**), **position**, **position_raw**, **goal**, **goal_raw**, **status**, and **time**. - - **status** is a bit-field. TODO: helper function to test status - - - """ - return await self.bcs_request('GetMotor', dict(locals())) - - async def get_motor_full(self, motors=[]) -> dict: - """ - Returns the complete state of the requested motors. - - :param motors: Names of motors to query. - :type motors: list - - :return: Dictionary of results, with the following key(s). - - * **not_found** (*list*) - Names in **motors** that were not found, if any. - - * **data** (*list*) - dictionary of dictionaries all relateing to state. Too many parameters to list atm. - - - """ - return await self.bcs_request('GetMotorFull', dict(locals())) - - async def get_panel_image(self, name="", quality=0) -> dict: - """ - Send a current image of the LabVIEW panel, in jpg format. - - :param name: Full path to panel - :type name: str - :param quality: JPEG quality 0-100 - :type quality: int - - :return: Dictionary of results, with the following key(s). - - * **image_blob** (*dict*) - Ideally opaque blob for holding binary data. Helper functions should be available to clients to extract blob contents. Contains **length** and **blob**. - - - """ - return await self.bcs_request('GetPanelImage', dict(locals())) - - async def get_state_variable(self, name="") -> dict: - """ - Get the value of the named BCS State Variable. - - :param name: Name of the state variable to retrieve - :type name: str - - :return: Dictionary of results, with the following key(s). - - * **value** (*void*) - Value of the state variable - - * **found** (*bool*) - True if requested name was found on the server - - * **type** (*enum u16*) - 'String', 'Boolean', 'Integer', or 'Double' - - * **name** (*str*) - Same as ``name`` - - - """ - return await self.bcs_request('GetStateVariable', dict(locals())) - - async def get_subsystem_status(self) -> dict: - """ - Returns the current status of all BCS subsystems on the server - - - - :return: Dictionary of results, with the following key(s). - - * **status** (*list*) - Array of dictionaryies of keys: **name** - Subsystem Name, and **status** - one of {Initializing, Running, Stopping, Stopped, Force Stop, Disabled, Bad Path, VI Broken Not in List} - - - """ - return await self.bcs_request('GetSubsystemStatus', dict(locals())) - - async def get_text_file(self, path="You must have at least one input control in the Parameter cluster. Delete this (_unused) if you don't need it.") -> dict: - """ - Get contents of a text file (usually acquired data) from any file in a location descended from "C:\\\\Beamline Controls\\\\BCS Setup Data". - - :param path: Path to text file, relative to C:\\\\Beamline Controls\\\\BCS Setup Data. Use backslashes (\\\\) not forward slashes (/). - :type path: str - - :return: Dictionary of results, with the following key(s). - - * **text** (*str*) - Text of the file. Platform-dependent end-of-line characters have been converted to line feed characters - - - """ - return await self.bcs_request('GetTextFile', dict(locals())) - - async def get_video_image(self, name="", quality=0, type="default") -> dict: - """ - Return the most recent image from the named camera, in jpg format. - - :param name: Video (camera) name - :type name: str - :param quality: JPEG quality 0-100 - :type quality: int - :param type: One of [default, roi, or threshold] - :type type: enum u16 - - :return: Dictionary of results, with the following key(s). - - * **image_blob** (*dict*) - Ideally opaque blob for holding binary data. Helper functions should be available to clients to extract blob contents. Contains **length** and **blob**. - - - """ - return await self.bcs_request('GetVideoImage', dict(locals())) - - async def home_motor(self, motors=[]) -> dict: - """ - Home the motors in the input array, ``motors``. - - :param motors: Names of the motors to home. - :type motors: list - - :return: Dictionary of results, with the following key(s). - - * **timed_out** (*list*) - True if the home operation timed out. - - * **not_found** (*list*) - The motors in ``motors`` that are not found on the server, if any. - - - """ - return await self.bcs_request('HomeMotor', dict(locals())) - - async def last_scan_run(self) -> dict: - """ - Returns the last 'integrated scan' run, or an empty string if none have run. - - - - :return: Dictionary of results, with the following key(s). - - * **last_scan** (*str*) - The name of the last run scan, or an empty string if none. - - - """ - return await self.bcs_request('LastScanRun', dict(locals())) - - async def list_ais(self) -> dict: - """ - Return the list of all AI channels that are defined on the server. - - - - :return: Dictionary of results, with the following key(s). - - * **names** (*list*) - Llist of AI channels that are defined on the server. The list includes disabled and hidden channels. - - * **displayed** (*list*) - The list of AI channel that are displayed (not set to 'hidden' in BCS). - - * **disabled** (*list*) - Iindexes in **displayed** that are disabled. - - - """ - return await self.bcs_request('ListAIs', dict(locals())) - - async def list_dios(self) -> dict: - """ - Retrieves the complete list of digital input channel names defined on the server. - - - - :return: Dictionary of results, with the following key(s). - - * **names** (*list*) - Liist of all digital input channel names on the server. The list includes disabled and hidden channels. - - * **displayed** (*list*) - The list of channels that are displayed (not set to 'hidden' in BCS). - - * **disabled** (*list*) - Iindexes in **displayed** that are disabled. - - - """ - return await self.bcs_request('ListDIOs', dict(locals())) - - async def list_instruments(self) -> dict: - """ - Return the list of instruments that are defined on the server. - - - - :return: Dictionary of results, with the following key(s). - - * **names** (*list*) - The list of all instruments that are defined on the server, including disabled and hidden instruments. - - * **displayed** (*list*) - The list of instruments that are displayed (not set to 'hidden' in BCS). - - * **disabled** (*list*) - Iindexes in **displayed** that are disabled. - - - """ - return await self.bcs_request('ListInstruments', dict(locals())) - - async def list_motors(self) -> dict: - """ - Return the list of motors that are defined on the server. - - - - :return: Dictionary of results, with the following key(s). - - * **names** (*list*) - The list of motors that are defined on the server. The list includes disabled and hidden motors. - - * **displayed** (*list*) - The list of motors that are displayed (not set to 'hidden' in BCS). - - * **disabled** (*list*) - Iindexes in **displayed** that are disabled. - - - """ - return await self.bcs_request('ListMotors', dict(locals())) - - async def list_presets(self) -> dict: - """ - Return array of motor preset positions. Each array entry is a dictionary describing one preset. The dictionary keys are **Preset Name**, **Motor Name**, **Preset Position**, **Tolerance (+/-)**. - - - - :return: Dictionary of results, with the following key(s). - - * **presets** (*list*) - Array of dictionaries, one per preset, each with keys: **Preset Name**, **Motor Name**, **Preset Position**, **Tolerance (+/-)**. - - - """ - return await self.bcs_request('ListPresets', dict(locals())) - - async def list_state_variables(self) -> dict: - """ - Get the complete list of state variable, and their types (Boolean, Integer, String, Double). - - - - :return: Dictionary of results, with the following key(s). - - * **names** (*list*) - Current list of state variable on the server. - - * **types** (*list*) - Variable types (Boolean, Integer, String, Double), in the same order as **names**. - - - """ - return await self.bcs_request('ListStateVariables', dict(locals())) - - async def list_trajectories(self) -> dict: - """ - List all trajectories. - - - - :return: Dictionary of results, with the following key(s). - - * **trajectories** (*list*) - Array of dictionaries, each element describing a trajectory. - - - """ - return await self.bcs_request('ListTrajectories', dict(locals())) - - async def move_motor(self, motors=[], goals=[]) -> dict: - """ - Command one or more motors to begin moves to supplied goals. - - :param motors: Array of motor names - :type motors: list - :param goals: Goal for each motor listed in ``motors`` - :type goals: list - - :return: Dictionary of results, with the following key(s). - - * **timed_out** (*list*) - List of motors that timed-out, if any. - - * **not_found** (*list*) - List of motors that were not found, if any. - - - """ - return await self.bcs_request('MoveMotor', dict(locals())) - - async def move_to_preset(self, names=[]) -> dict: - """ - Move to preset positions. Takes a list of preset names and executes them (sends their motors to their respective positions). - - :param names: Array of preset names to execute. - :type names: list - - :return: Dictionary of results, with the following key(s). - - * **not_found** (*list*) - List of names in ``names`` that were not found on the server, if any. - - - """ - return await self.bcs_request('MoveToPreset', dict(locals())) - - async def move_to_trajectory(self, names=[]) -> dict: - """ - Move to trajectory positions. Takes a list of trajectory names and executes them (sends their motors to their respective positions). - - :param names: Array of trajectory names to execute. - :type names: list - - :return: Dictionary of results, with the following key(s). - - * **not_found** (*list*) - List of names in ``names`` that were not found on the server, if any. - - - """ - return await self.bcs_request('MoveToTrajectory', dict(locals())) - - async def scan_status(self) -> dict: - """ - Returns information about the 'integrated scan' system, namely the last scan (last_scan) run, the currently running scan, and the scanner status. An empty string indicates no scan. - - - - :return: Dictionary of results, with the following key(s). - - * **last_scan** (*str*) - The name of the last run scan, or an empty string if none. - - * **running_scan** (*str*) - The name of the currently running scan, or an empty string if none. - - * **scanner_status** (*str*) - The current status of the scanner. One of a large set of finite-state-machine states used by the scanner. The set also varies by scan. Empty string if the scanner has stopped running. - - * **log_directory** (*str*) - Location of data file, maybe. - - * **last_filename** (*str*) - Data file name - - * **user_path** (*str*) - Or maybe this is the location of the data file - - - """ - return await self.bcs_request('ScanStatus', dict(locals())) - - async def set_breakpoints(self, name="", x0=0, dx=0, n=0, breakpoints=[], counts_or_units=False) -> dict: - """ - Set Breakpoints for the named motor. Breakpoints are the positions at which the controller will generate acquisition triggers during a flying scan. Specify either {**x0**, **dx**, **n**} to generate a regular grid, or send an arbitrary list in **breakpoints**. - - :param name: Motor - :type name: str - :param x0: Starting position. - :type x0: float - :param dx: Interval spacing. - :type dx: float - :param n: Number of breakpoints (triggers) to generate. - :type n: int - :param breakpoints: An array of arbitrary locations to on which to trigger data acquisition. - :type breakpoints: list - :param counts_or_units: Are the locations in motor units or motor counts (False == units)? - :type counts_or_units: bool - - :return: Dictionary of results (no endpoint-specific keys). - - - - """ - return await self.bcs_request('SetBreakpoints', dict(locals())) - - async def set_do(self, chan="success", value=False) -> dict: - """ - Sets the digital output (DO) channel ``chan`` to ``value`` - - :param chan: Name of the channel to set. - :type chan: str - :param value: New value - :type value: bool - - :return: Dictionary of results (no endpoint-specific keys). - - - - """ - return await self.bcs_request('SetDO', dict(locals())) - - async def set_motor_velocity(self, motors=[], velocities=[]) -> dict: - """ - Set motor speeds. This endpoint duplicates the functionality of CommandMotor, and may be removed from future releases. - - :param motors: List of motors - :type motors: list - :param velocities: Speeds to set, in the same order as **motors**. - :type velocities: list - - :return: Dictionary of results, with the following key(s). - - * **timed_out** (*list*) - - - * **not_found** (*list*) - - - - """ - return await self.bcs_request('SetMotorVelocity', dict(locals())) - - async def set_state_variable(self, name="variable name", value=0) -> dict: - """ - Set the value of the named BCS State Variable. - - :param name: Name of the state variable to set - :type name: str - :param value: New value of the state variable. Accepts string, numeric, and boolean values. - :type value: int - - :return: Dictionary of results, with the following key(s). - - * **found** (*bool*) - True if requested name was found on the server - - - """ - return await self.bcs_request('SetStateVariable', dict(locals())) - - async def start_acquire(self, time=0, counts=0) -> dict: - """ - Start Acquisition for either ``time`` or ``counts``, whichever is non-zero. The acquisition is started, but the endpoint does not wait for the acquisition to complete. - - :param time: Acquisition time. - :type time: float - :param counts: Acquisition counts. - :type counts: int - - :return: Dictionary of results (no endpoint-specific keys). - - - - """ - return await self.bcs_request('StartAcquire', dict(locals())) - - async def start_flying_scan(self, name="", xi=0, xf=0, dx=0, speed=0) -> dict: - """ - Start a flying scan with the named motor. The scan is started. The enpoint does not wait for the scan to finish. - - :param name: Motor name - :type name: str - :param xi: Starting point - :type xi: float - :param xf: Stopping point - :type xf: float - :param dx: Interval between triggers (pulses) - :type dx: float - :param speed: Motor speed - :type speed: float - - :return: Dictionary of results (no endpoint-specific keys). - - - - """ - return await self.bcs_request('StartFlyingScan', dict(locals())) - - async def start_instrument_acquire(self, name="", run_type="Exposure", acq_time_s=0, acq_counts=0) -> dict: - """ - Starts an instrument acquisition and waits for it to complete. Acquires for either **acq_time** seconds, or for **acq_counts** counts (triggers), whichever is non-zero. If both are non-zero, the result is undefined. - - :param name: Instrument name - :type name: str - :param run_type: One of {'Exposure', 'Total Counts'}. Determines whether to acquire for a set time, or a set number of counts/triggers. - :type run_type: enum u32 - :param acq_time_s: Length of time for the acquisition. - :type acq_time_s: float - :param acq_counts: Number of counts or triggers to acquire for. - :type acq_counts: int - - :return: Dictionary of results, with the following key(s). - - * **elapsed_s** (*float*) - Total duration of the acquisition in seconds. - - - """ - return await self.bcs_request('StartInstrumentAcquire', dict(locals())) - - async def start_instrument_driver(self, name="") -> dict: - """ - Starts the named instrument driver (does nothing if the driver is already running). - - :param name: Instrument name - :type name: str - - :return: Dictionary of results (no endpoint-specific keys). - - - - """ - return await self.bcs_request('StartInstrumentDriver', dict(locals())) - - async def stop_acquire(self, timeout_ms=0) -> dict: - """ - Stop acquisition in progress. Waits for the acquisition to terminate within ``timeout_ms`` milleseconds. If the acquisition does not stop in that time, returns False in ``success`` and "timed out" in ``error_description``. - - :param timeout_ms: Time to wait for the acquisition to terminate, in milliseconds. - :type timeout_ms: int - - :return: Dictionary of results (no endpoint-specific keys). - - - - """ - return await self.bcs_request('StopAcquire', dict(locals())) - - async def stop_instrument_acquire(self, name="") -> dict: - """ - Stop acquisition on the named instrument (does nothing if the instument is not acquiring). Waits up to half a second to verify that acquisition has terminated. The **success** field is False if the abort times out without terminating the acquisition. - - :param name: Instrument name - :type name: str - - :return: Dictionary of results (no endpoint-specific keys). - - - - """ - return await self.bcs_request('StopInstrumentAcquire', dict(locals())) - - async def stop_instrument_driver(self, name="") -> dict: - """ - Stops the named instrument driver (does nothing if the driver is not running). - - :param name: Instrument name - :type name: str - - :return: Dictionary of results (no endpoint-specific keys). - - - - """ - return await self.bcs_request('StopInstrumentDriver', dict(locals())) - - async def stop_motor(self, motors=[]) -> dict: - """ - Immediately issue stop command for the motors provided in ``motors``. - - :param motors: List of motors to stop. The stop command is immediately sent to the motor controller for the listed motors. - :type motors: list - - :return: Dictionary of results, with the following key(s). - - * **timed_out** (*list*) - Motor subsystem error indicating that the command queue is unavailable. Seek help from the beamline scientist or beamline controls. - - * **not_found** (*list*) - Those motors requested in ``motors`` that were not found on the server, if any. - - - """ - return await self.bcs_request('StopMotor', dict(locals())) - - async def stop_scan(self) -> dict: - """ - Immediately issue stop command forthe currently running 'integrated scan'. - - - - :return: Dictionary of results (no endpoint-specific keys). - - - - """ - return await self.bcs_request('StopScan', dict(locals())) - - async def test_connection(self, chan="success", value=False) -> dict: - """ - - - :param chan: - :type chan: str - :param value: - :type value: bool - - :return: Dictionary of results, with the following key(s). - - * **response_string** (*str*) - - - - """ - return await self.bcs_request('TestConnection', dict(locals())) - - async def sc_alsu_mirror_vibration(self, time_sec=0, start_delay_sec=0, x_motor="", start_position=0, stop_position=0, duration_sec=0, delay_sec=0, final_position=0, left_sensor="", right_sensor="", seperation_mm=1, scale_factor_pm=0, description="", file_pattern="") -> dict: - """ - Setup the ALSU Mirror Vibration Scan - This scan moves one motor in a specific pattern and records the analog data. Additional calculations are performed on the Interferometer Data to provide more relevant calculations into the file. - The setup that goes with this scan involves a mirror on a table. The mirror has cooling water flowed through it using a Mass Flow Controller. The "motor" in this scan is actually the flow of water through the mirror. The scan then is to flow water thorough the mirror and monitor the interferometer to look at the vibrations of the mirror. - - :param time_sec: Time that the motor moves for. - :type time_sec: float - :param start_delay_sec: Delay initial data acquisition. This will give a chance for the Flow controlled by the X motor to stabilize. - :type start_delay_sec: float - :param x_motor: Choose the motor that will move. - :type x_motor: str - :param start_position: Where the motor starts. This is generally flow, so the flow at which the experiement starts. - :type start_position: float - :param stop_position: Where the motor stops. This if generally flow, so the flow at which the experiment stops. - :type stop_position: float - :param duration_sec: Time that the data is acquired for. - :type duration_sec: float - :param delay_sec: Wait this long after acquisition starts before the motor move. - :type delay_sec: float - :param final_position: Position to set the X motor when finished with scan. This should be zero. That will stop the flow of liquid. - :type final_position: float - :param left_sensor: Left interferomter count channel. - :type left_sensor: str - :param right_sensor: Right Interferometer count channel. - :type right_sensor: str - :param seperation_mm: Distance seperating the two interferometers. - :type seperation_mm: float - :param scale_factor_pm: Scales the readings into pico meteres. - :type scale_factor_pm: float - :param description: - :type description: str - :param file_pattern: - :type file_pattern: str - - :return: Dictionary of results (no endpoint-specific keys). - - - - """ - return await self.bcs_request('sc_ALSU Mirror Vibration', dict(locals())) - - async def sc_auto_coll_single_axis_scan(self, direction="Forward", index=0, x_motor="Select Motor", start=-3, stop=0, increment=1, delay_after_move_s=0, count_time_s=0.5, number_of_scans=1, bidirect=True, at_end_of_scan="Return", description="", file_pattern="") -> dict: - """ - This is VERY close to a Single Motor Scan. The only difference is how the files are saved. It has been changed so that a TCP command can specify the scans that are run with more control than just the regular Single motor scan. This was created for the Metrology lab. - - :param direction: Name the direction of the scan. This does NOT swap the start and end. It is only used to lable the file created. - :type direction: enum u32 - :param index: Used to save this scan as part of a group of scans. A new folder will be created for every 0 index scan started. Subsequent scans with increasing indices will be added to the same folder. - :type index: int - :param x_motor: Select the name of the motor to move. - :type x_motor: str - :param start: Start the scan with X Motor here. - :type start: float - :param stop: Where to stop the scan. - :type stop: float - :param increment: How far to move between each sample. - :type increment: float - :param delay_after_move_s: How long to pause after each move. - :type delay_after_move_s: float - :param count_time_s: Time to acquire from data source to generate sample - :type count_time_s: float - :param number_of_scans: How many times should the motor make this motion during the scan. - :type number_of_scans: int - :param bidirect: If number of scans is more than 1, will move even scans in the opposite direction. - :type bidirect: bool - :param at_end_of_scan: Choose what the X Motor does at the scan end. - :type at_end_of_scan: enum u32 - :param description: - :type description: str - :param file_pattern: - :type file_pattern: str - - :return: Dictionary of results (no endpoint-specific keys). - - - - """ - return await self.bcs_request('sc_AutoColl Single Axis Scan', dict(locals())) - - async def sc_barf_scan(self, instrument="", exposure_time_s=0.001, number_to_average=1, continuous_acquire=True, background_data="Q:\\Commissioning\\OffLineRuby\\background\\background2020-03-12_11-15-40.txt", fit_type="Lorentzian", ambient_peak_2=694.23, ambient_peak_1=692.8, description="", file_pattern="") -> dict: - """ - The Barf scan acquires from a spectrometer and then fits the ruby peaks to estimate the pressure. - - :param instrument: Choose the instrumnet to acquire from. - :type instrument: str - :param exposure_time_s: Choose the exposure time for the instrument. - :type exposure_time_s: float - :param number_to_average: How many exposures to average. - :type number_to_average: int - :param continuous_acquire: Choose if the scan should continuously acquire. Starting a scan with this option will cause the scan to run forever. The user can then stop the scan with the stop button, or by unchecking this box. - :type continuous_acquire: bool - :param background_data: Background data to subtract from acquired data. - :type background_data: str - :param fit_type: Type of fit to do. - :type fit_type: enum u32 - :param ambient_peak_2: Peak position of the 2nd peak when the pressure is zero. - :type ambient_peak_2: float - :param ambient_peak_1: Peak position of the 1st peak when pressure is zero. - :type ambient_peak_1: float - :param description: - :type description: str - :param file_pattern: - :type file_pattern: str - - :return: Dictionary of results (no endpoint-specific keys). - - - - """ - return await self.bcs_request('sc_Barf Scan', dict(locals())) - - async def sc_from_file_scan(self, file_path="", delay_after_move_s=0, count_time_s=0.5, at_end="Return", move_motors_sequentially=False, dont_repeat_motor_moves=False, move_at_end_of_scan="", description="", file_pattern="") -> dict: - """ -

        This interface hides all the scan detail inside of a 'from file' scan file. Only the four remaining settings are used to affect every step listed in the file.

        -

        The file contains motor names and positions. At each step, the motors will be set to those positions, then the data will be read. This allows the user to create scans of arbitrary complexity, but they are harder to setup because the file has to be created.

        -

        Example:

        -
        - - - - - - - - - - - - - - - - - - - - - -
        Motor1Motor2Motor3
        10.00.000.00
        10.010.00.00
        0.0010.010.0
        -
        -

        This is the template of the from file scan. For more complex behavior, use the trajectory scan.

        - - :param file_path: Choose the file to run. File should have the motor names across the top and the positions on the rows below. Tab spaces values. - :type file_path: str - :param delay_after_move_s: After the motors move, the signal may need some time to settle. This can be added here to improve data quality. - :type delay_after_move_s: float - :param count_time_s: Specify how long to sample the input data for. All the data sampled during this time will be averaged together, depending on the data source. - :type count_time_s: float - :param at_end: If return to start is used, the motors will receive one last command to come back to the start after the scan has completed. Otherwise the motors will stay where it is left at the end of the scan. - :type at_end: enum u32 - :param move_motors_sequentially: Move the motors one at a time. - :type move_motors_sequentially: bool - :param dont_repeat_motor_moves: Prevent identical positions from moving the motor twice. This could cause a motor to move when the desired action is for the motor to not move. - :type dont_repeat_motor_moves: bool - :param move_at_end_of_scan: Choose a Trajectory to run at the end of the scan. - :type move_at_end_of_scan: str - :param description: - :type description: str - :param file_pattern: - :type file_pattern: str - - :return: Dictionary of results (no endpoint-specific keys). - - - - """ - return await self.bcs_request('sc_From File Scan', dict(locals())) - - async def sc_image_from_file_scan(self, instrument="", save_images_as="", file_path="", delay_after_move_s=0, count_time_s=0.5, at_end="Return", move_motors_sequentially=False, dont_repeat_motor_moves=False, move_at_end_of_scan="", description="", file_pattern="") -> dict: - """ - - - :param instrument: - :type instrument: str - :param save_images_as: - :type save_images_as: str - :param file_path: Choose the file to run. File should have the motor names across the top and the positions on the rows below. Tab spaces values. - :type file_path: str - :param delay_after_move_s: After the motors move, the signal may need some time to settle. This can be added here to improve data quality. - :type delay_after_move_s: float - :param count_time_s: Specify how long to sample the input data for. All the data sampled during this time will be averaged together, depending on the data source. - :type count_time_s: float - :param at_end: If return to start is used, the motors will receive one last command to come back to the start after the scan has completed. Otherwise the motors will stay where it is left at the end of the scan. - :type at_end: enum u32 - :param move_motors_sequentially: Move the motors one at a time. - :type move_motors_sequentially: bool - :param dont_repeat_motor_moves: Prevent identical positions from moving the motor twice. This could cause a motor to move when the desired action is for the motor to not move. - :type dont_repeat_motor_moves: bool - :param move_at_end_of_scan: Choose a Trajectory to run at the end of the scan. - :type move_at_end_of_scan: str - :param description: - :type description: str - :param file_pattern: - :type file_pattern: str - - :return: Dictionary of results (no endpoint-specific keys). - - - - """ - return await self.bcs_request('sc_Image From File Scan', dict(locals())) - - async def sc_image_one_motor_scan(self, instrument="", save_images_as="", x_motor="Select Motor", start=-3, stop=0, increment=1, delay_after_move_s=0, count_time_s=0.5, number_of_scans=1, bidirect=True, at_end_of_scan="Return", description="", file_pattern="") -> dict: - """ - - - :param instrument: - :type instrument: str - :param save_images_as: - :type save_images_as: str - :param x_motor: Select the name of the motor to move. - :type x_motor: str - :param start: Start the scan with X Motor here. - :type start: float - :param stop: Where to stop the scan. - :type stop: float - :param increment: How far to move between each sample. - :type increment: float - :param delay_after_move_s: How long to pause after each move. - :type delay_after_move_s: float - :param count_time_s: Time to acquire from data source to generate sample - :type count_time_s: float - :param number_of_scans: How many times should the motor make this motion during the scan. - :type number_of_scans: int - :param bidirect: If number of scans is more than 1, will move even scans in the opposite direction. - :type bidirect: bool - :param at_end_of_scan: Choose what the X Motor does at the scan end. - :type at_end_of_scan: enum u32 - :param description: - :type description: str - :param file_pattern: - :type file_pattern: str - - :return: Dictionary of results (no endpoint-specific keys). - - - - """ - return await self.bcs_request('sc_Image One Motor Scan', dict(locals())) - - async def sc_image_time_scan(self, instrument="", save_images_as="", total_time_s=0, number_of_samples=0, time_between_points_s=0, count_time_s=0, frequency_hz=0, stop_condition="Samples Taken", description="", file_pattern="") -> dict: - """ - - - :param instrument: - :type instrument: str - :param save_images_as: - :type save_images_as: str - :param total_time_s: Time to run the scan in total. - :type total_time_s: float - :param number_of_samples: Samples to take in the Total Time. - :type number_of_samples: int - :param time_between_points_s: Will combine with Total time to set the number of samples. If Set is selected for Number of Samples, then the total time will change to acomplish the requested time between points. - :type time_between_points_s: float - :param count_time_s: Time that the data is averaged for to create a single data point. - :type count_time_s: float - :param frequency_hz: The user can specify the frequency in samples per second or number of samples. Both work with time and reconfigure the other. - :type frequency_hz: float - :param stop_condition: Should the scan end based on time or number of samples. Sometimes, the time between points cannot be accopmlished and the samples are taken at a longer interval. In cases where this is significant, the user may want to stop the scan based on time and not the number of samples taken. - :type stop_condition: enum u16 - :param description: - :type description: str - :param file_pattern: - :type file_pattern: str - - :return: Dictionary of results (no endpoint-specific keys). - - - - """ - return await self.bcs_request('sc_Image Time Scan', dict(locals())) - - async def sc_image_two_motor_scan(self, instrument="", save_images_as="", x_motor="", x_start=-3, x_stop=0, x_increment=1, delay_after_move_s=0, count_time_s=0.5, y_motor="", y_start=-3, y_stop=0, y_increment=1, at_end_of_scan="Return", description="", file_pattern="") -> dict: - """ - - - :param instrument: - :type instrument: str - :param save_images_as: - :type save_images_as: str - :param x_motor: Choose the motor to be moved as the X Motor. - :type x_motor: str - :param x_start: Place where X Motor will start scan. - :type x_start: float - :param x_stop: Furthest position of X Motor travel. - :type x_stop: float - :param x_increment: How far the X Motor moves with each X move. - :type x_increment: float - :param delay_after_move_s: How long to delay after each motor moves. - :type delay_after_move_s: float - :param count_time_s: Time that data is acquired and averaged to create sample. - :type count_time_s: float - :param y_motor: Choose the motor to be moved as the Y Motor. - :type y_motor: str - :param y_start: Place where Y Motor will start scan. - :type y_start: float - :param y_stop: Furthest position of Y Motor travel. - :type y_stop: float - :param y_increment: How far the Y Motor moves with each Y move. - :type y_increment: float - :param at_end_of_scan: Choose what the scan does upon completion. Stay at end will not command an end move. Return to start will. - :type at_end_of_scan: enum u32 - :param description: - :type description: str - :param file_pattern: - :type file_pattern: str - - :return: Dictionary of results (no endpoint-specific keys). - - - - """ - return await self.bcs_request('sc_Image Two Motor Scan', dict(locals())) - - async def sc_ltp_single_axis_scan(self, direction="Forward", index=0, instrument="", frames_to_average=0, delay_between_frames_s=0, x_motor="Select Motor", start=-3, stop=0, increment=1, delay_after_move_s=0, count_time_s=0.5, number_of_scans=1, bidirect=True, at_end_of_scan="Return", description="", file_pattern="") -> dict: - """ - - - :param direction: - :type direction: enum u32 - :param index: - :type index: int - :param instrument: - :type instrument: str - :param frames_to_average: - :type frames_to_average: int - :param delay_between_frames_s: - :type delay_between_frames_s: float - :param x_motor: Select the name of the motor to move. - :type x_motor: str - :param start: Start the scan with X Motor here. - :type start: float - :param stop: Where to stop the scan. - :type stop: float - :param increment: How far to move between each sample. - :type increment: float - :param delay_after_move_s: How long to pause after each move. - :type delay_after_move_s: float - :param count_time_s: Time to acquire from data source to generate sample - :type count_time_s: float - :param number_of_scans: How many times should the motor make this motion during the scan. - :type number_of_scans: int - :param bidirect: If number of scans is more than 1, will move even scans in the opposite direction. - :type bidirect: bool - :param at_end_of_scan: Choose what the X Motor does at the scan end. - :type at_end_of_scan: enum u32 - :param description: - :type description: str - :param file_pattern: - :type file_pattern: str - - :return: Dictionary of results (no endpoint-specific keys). - - - - """ - return await self.bcs_request('sc_LTP Single Axis Scan', dict(locals())) - - async def sc_move_motor(self, motor_name="", motor_position=0, delay_after_move_sec=0, description="", file_pattern="") -> dict: - """ - Setup this simple scan so that it will move a motor. This is used in the automation scripts to move things around. - - :param motor_name: - :type motor_name: str - :param motor_position: Position to move to. - :type motor_position: float - :param delay_after_move_sec: Scan will wait this long before moving completing. - :type delay_after_move_sec: float - :param description: - :type description: str - :param file_pattern: - :type file_pattern: str - - :return: Dictionary of results (no endpoint-specific keys). - - - - """ - return await self.bcs_request('sc_Move Motor', dict(locals())) - - async def sc_move_trajectory(self, trajectory_name="", delay_after_move_sec=0, description="", file_pattern="") -> dict: - """ - Setup this simple scan so that it will activate a trajectory. This not a trajectory scan. Instead the user needs to set up a motor trajectory which is a named configuration of motors moved together or in sequence. This is used in the automation scripts to move things around. - - :param trajectory_name: Choose the name of the desired trajectory. - :type trajectory_name: str - :param delay_after_move_sec: The scan will wait this many seconds after moving. - :type delay_after_move_sec: float - :param description: - :type description: str - :param file_pattern: - :type file_pattern: str - - :return: Dictionary of results (no endpoint-specific keys). - - - - """ - return await self.bcs_request('sc_Move Trajectory', dict(locals())) - - async def sc_powder_diffraction(self, exposure_time=0, number_of_samples=0, file_index=0, instrument="", save_images_as="", description="", file_pattern="") -> dict: - """ - - - :param exposure_time: - :type exposure_time: float - :param number_of_samples: - :type number_of_samples: int - :param file_index: - :type file_index: int - :param instrument: - :type instrument: str - :param save_images_as: - :type save_images_as: str - :param description: - :type description: str - :param file_pattern: - :type file_pattern: str - - :return: Dictionary of results (no endpoint-specific keys). - - - - """ - return await self.bcs_request('sc_Powder diffraction', dict(locals())) - - async def sc_set_dio(self, description="", file_pattern="") -> dict: - """ - - - :param description: - :type description: str - :param file_pattern: - :type file_pattern: str - - :return: Dictionary of results (no endpoint-specific keys). - - - - """ - return await self.bcs_request('sc_Set DIO', dict(locals())) - - async def sc_single_crystal_scan(self, instrument="", save_images_as="", x_motor="", start=-3, stop=0, increment=1, count_time_s=0.5, description="", file_pattern="") -> dict: - """ - Setup the single motor scan that also records an image from a ccd camera instrument, with each data point. - This scan moves a single motor to multiple positions at regular intervals and takes data at each spot. The motor that is moved is recorded and is set as the default x axis when this data is graphed on an xy graph. If you look at this data on an Intensity plot or Image Graph, It will default to the "2D Image" data. - - :param instrument: Choose The Instrument to Capture Images From. - :type instrument: str - :param save_images_as: - :type save_images_as: str - :param x_motor: Selects the motor to scan. - :type x_motor: str - :param start: This is where the scan should start. The motor will move from here to the stop position during the scan. - :type start: float - :param stop: This is where the scan should stop. The motor will move from the start position to here during the scan. - :type stop: float - :param increment: This is specified in the the units of the motor. It determines how far to move the motor for each step. - :type increment: float - :param count_time_s: Specify how long to sample the input data for. All the data sampled during this time will be averaged together, depending on the data source. - :type count_time_s: float - :param description: - :type description: str - :param file_pattern: - :type file_pattern: str - - :return: Dictionary of results (no endpoint-specific keys). - - - - """ - return await self.bcs_request('sc_Single Crystal Scan', dict(locals())) - - async def sc_single_motor_flying_scan(self, x_motor="", start=0, stop=10, increment=1, velocity_units=0.5, number_of_scans=1, bidirect=False, shift_ai=False, at_end_of_scan="Return", description="", file_pattern="") -> dict: - """ - Setup a single motor flying scan. - This scan makes one motor move and records data at specific positions during the move. - To accomplish this, the scan sets up special conditions in the motor drive hardware to output pulses when the motor is at a certain position. When those pulses happen, the motor controller latches its current position into an array. The analog aquisition card also latches the analog values with every pulse. - The scan polls the aquisition card and retreives all the data since the last poll. At the end of the scan, the motor positions are retreived and matched up with the analog data. This file is then saved. - Because of how this operates, the data for the flying scan is not displayed until the end of the scan. - - :param x_motor: Choose the name of the motor to scan. This motor must support flying scans. - :type x_motor: str - :param start: Place where the first sample is taken. The scan will start elsewhere and accelerate to the proper velocity before the first sample is taken. - :type start: float - :param stop: Place where the last sample is taken. The scan will begin to decelerate after the last sample is taken. - :type stop: float - :param increment: The spacing between sample points. - :type increment: float - :param velocity_units: Specify how many times the motor moves along the patter for this scan. Cannot be 0. - :type velocity_units: float - :param number_of_scans: Will allow the scan to run back to front on even scans. - :type number_of_scans: int - :param bidirect: Specify the velocity in the underlying motor's units. This is not in counts. - :type bidirect: bool - :param shift_ai: Choose if the motor returns to the start at the end of the scan. - :type shift_ai: bool - :param at_end_of_scan: Choose behaviour at end of the scan. - :type at_end_of_scan: enum u32 - :param description: - :type description: str - :param file_pattern: - :type file_pattern: str - - :return: Dictionary of results (no endpoint-specific keys). - - - - """ - return await self.bcs_request('sc_Single Motor Flying Scan', dict(locals())) - - async def sc_single_motor_scan(self, x_motor="Select Motor", start=-3, stop=0, increment=1, delay_after_move_s=0, count_time_s=0.5, number_of_scans=1, bidirect=True, at_end_of_scan="Return", description="", file_pattern="") -> dict: - """ - Setup the single motor scan. - This scan moves a single motor to multiple positions at regular intervals and takes data at each spot. The motor that is moved is recorded and is set as the default x axis when this data is graphed on an xy graph. - - :param x_motor: Select the name of the motor to move. - :type x_motor: str - :param start: Start the scan with X Motor here. - :type start: float - :param stop: Where to stop the scan. - :type stop: float - :param increment: How far to move between each sample. - :type increment: float - :param delay_after_move_s: How long to pause after each move. - :type delay_after_move_s: float - :param count_time_s: Time to acquire from data source to generate sample - :type count_time_s: float - :param number_of_scans: How many times should the motor make this motion during the scan. - :type number_of_scans: int - :param bidirect: If number of scans is more than 1, will move even scans in the opposite direction. - :type bidirect: bool - :param at_end_of_scan: Choose what the X Motor does at the scan end. - :type at_end_of_scan: enum u32 - :param description: - :type description: str - :param file_pattern: - :type file_pattern: str - - :return: Dictionary of results (no endpoint-specific keys). - - - - """ - return await self.bcs_request('sc_Single Motor Scan', dict(locals())) - - async def sc_temperature_scan(self, instrument="", exposure_time_msec="", calibration_temperature="", xmin=0, xmax=0, lambda_max=0, lambda_min=0, nd_filter_motor="Select Motor", instrument_2="", exposure_time_msec_2="", calibration_temperature_2="", xmin_2=0, xmax_2=0, lambda_max_2=0, lambda_min_2=0, nd_filter_motor_2="Select Motor", instrument_3="", roi_size=0, temp_background_k=0, instrument_4="", roi_size_2=0, temp_background_k_2=0, acquire_images=False, description="", file_pattern="") -> dict: - """ - The Temperature Scan acquires data from a spectrometer and then with background and calibration data calculates the temperature. It also optionally acquires an image from a 2D instrument and then displays a contour plot of the temperature profile along with recomputing the temperature based on the 2D image. - - :param instrument: - :type instrument: str - :param exposure_time_msec: - :type exposure_time_msec: str - :param calibration_temperature: - :type calibration_temperature: str - :param xmin: - :type xmin: float - :param xmax: - :type xmax: float - :param lambda_max: - :type lambda_max: float - :param lambda_min: - :type lambda_min: float - :param nd_filter_motor: - :type nd_filter_motor: str - :param instrument_2: - :type instrument_2: str - :param exposure_time_msec_2: - :type exposure_time_msec_2: str - :param calibration_temperature_2: - :type calibration_temperature_2: str - :param xmin_2: - :type xmin_2: float - :param xmax_2: - :type xmax_2: float - :param lambda_max_2: - :type lambda_max_2: float - :param lambda_min_2: - :type lambda_min_2: float - :param nd_filter_motor_2: - :type nd_filter_motor_2: str - :param instrument_3: - :type instrument_3: str - :param roi_size: - :type roi_size: float - :param temp_background_k: - :type temp_background_k: float - :param instrument_4: - :type instrument_4: str - :param roi_size_2: - :type roi_size_2: float - :param temp_background_k_2: - :type temp_background_k_2: float - :param acquire_images: Selects whether to also acquire the 2d images - :type acquire_images: bool - :param description: - :type description: str - :param file_pattern: - :type file_pattern: str - - :return: Dictionary of results (no endpoint-specific keys). - - - - """ - return await self.bcs_request('sc_Temperature Scan', dict(locals())) - - async def sc_time_scan(self, total_time_s=0, number_of_samples=0, time_between_points_s=0, count_time_s=0, frequency_hz=0, stop_condition="Samples Taken", description="", file_pattern="") -> dict: - """ - Setup the time scan. - This scan takes periodic data aquisitions and stores them. The main parameters that need to be specified are: The number of samples, how often to sample, and how long to aquire data for. All other variables that can be configured relate back to these three. - This scan supports the legacy header. - - :param total_time_s: Time to run the scan in total. - :type total_time_s: float - :param number_of_samples: Samples to take in the Total Time. - :type number_of_samples: int - :param time_between_points_s: Will combine with Total time to set the number of samples. If Set is selected for Number of Samples, then the total time will change to acomplish the requested time between points. - :type time_between_points_s: float - :param count_time_s: Time that the data is averaged for to create a single data point. - :type count_time_s: float - :param frequency_hz: The user can specify the frequency in samples per second or number of samples. Both work with time and reconfigure the other. - :type frequency_hz: float - :param stop_condition: Should the scan end based on time or number of samples. Sometimes, the time between points cannot be accopmlished and the samples are taken at a longer interval. In cases where this is significant, the user may want to stop the scan based on time and not the number of samples taken. - :type stop_condition: enum u16 - :param description: - :type description: str - :param file_pattern: - :type file_pattern: str - - :return: Dictionary of results (no endpoint-specific keys). - - - - """ - return await self.bcs_request('sc_Time Scan', dict(locals())) - - async def sc_trajectory_scan(self, file_path="", delay_after_move_s=0, count_time_s=0.5, at_end="Return", move_motors_sequentially=False, dont_repeat_motor_moves=False, shift_flying_data=False, move_at_end_of_scan="", description="", file_pattern="") -> dict: - """ -

        This simple interface hides all the scan detail inside of a tracjectory scan file. Only the three remaining settings are used to affect every step listed in the file.

        -

        The file contains motor names and positions. At each step, the motors will be set to those positions, then the data will be read. This allows the user to create scans of arbitrary complexity, but they are harder to setup because the file has to be created.

        -

        Example:

        -
        - - - - - - - - - - - - - - - - - - - - - -
        Motor1Motor2Motor3
        10.00.000.00
        10.010.00.00
        0.0010.010.0
        -
        -

        There are many more options for the files.

        - - :param file_path: Specify the path to the trajectory scan file. - :type file_path: str - :param delay_after_move_s: After the motors move, the signal may need some time to settle. This can be added here to improve data quality. - :type delay_after_move_s: float - :param count_time_s: Specify how long to sample the input data for. All the data sampled during this time will be averaged together, depending on the data source. - :type count_time_s: float - :param at_end: If return to start is used, the motors will receive one last command to come back to the start after the scan has completed. Otherwise the motors will stay where it is left at the end of the scan. - :type at_end: enum u32 - :param move_motors_sequentially: Move the motors one at a time. - :type move_motors_sequentially: bool - :param dont_repeat_motor_moves: Prevent identical positions from moving the motor twice. This could cause a motor to move when the desired action is for the motor to not move. - :type dont_repeat_motor_moves: bool - :param shift_flying_data: Shift the flying dat ahead one time interval. - :type shift_flying_data: bool - :param move_at_end_of_scan: A trajectory to be called at the end of the scan. Trajectories can be setup in the motor system. The BCS team can help. If the trajectory is left blank, nothing will be done. - :type move_at_end_of_scan: str - :param description: - :type description: str - :param file_pattern: - :type file_pattern: str - - :return: Dictionary of results (no endpoint-specific keys). - - - - """ - return await self.bcs_request('sc_Trajectory Scan', dict(locals())) - - async def sc_two_motor_scan(self, x_motor="", x_start=-3, x_stop=0, x_increment=1, delay_after_move_s=0, count_time_s=0.5, y_motor="", y_start=-3, y_stop=0, y_increment=1, at_end_of_scan="Return", description="", file_pattern="") -> dict: - """ - This is the display for the setup when the scan is in the "View Data" mode. It has the same fields as the "Run Scan" mode, but everything is for display only. - - :param x_motor: Choose the motor to be moved as the X Motor. - :type x_motor: str - :param x_start: Place where X Motor will start scan. - :type x_start: float - :param x_stop: Furthest position of X Motor travel. - :type x_stop: float - :param x_increment: How far the X Motor moves with each X move. - :type x_increment: float - :param delay_after_move_s: How long to delay after each motor moves. - :type delay_after_move_s: float - :param count_time_s: Time that data is acquired and averaged to create sample. - :type count_time_s: float - :param y_motor: Choose the motor to be moved as the Y Motor. - :type y_motor: str - :param y_start: Place where Y Motor will start scan. - :type y_start: float - :param y_stop: Furthest position of Y Motor travel. - :type y_stop: float - :param y_increment: How far the Y Motor moves with each Y move. - :type y_increment: float - :param at_end_of_scan: Choose what the scan does upon completion. Stay at end will not command an end move. Return to start will. - :type at_end_of_scan: enum u32 - :param description: - :type description: str - :param file_pattern: - :type file_pattern: str - - :return: Dictionary of results (no endpoint-specific keys). - - - - """ - return await self.bcs_request('sc_Two Motor Scan', dict(locals())) - -``` diff --git a/.cursor/skills/xray-pro/references/beamline-controls.md b/.cursor/skills/xray-pro/references/beamline-controls.md deleted file mode 100644 index 84a2552..0000000 --- a/.cursor/skills/xray-pro/references/beamline-controls.md +++ /dev/null @@ -1,467 +0,0 @@ -# Beamline Controls and Motor Naming - -> Reference for: Xray Pro -> Load when: Figuring out a motor name, AI, etc. - ---- - -## Overview - -This is a detailed documentation describing what each motor is, the naming scheme, and the axes alignments with regard to the instrument and the lab reference frame - ---- - -## Fundamental Naming - -Controls are sent asynchronously through the BCS API. They are separated into core functionalities. - -* **Analog Inputs (AI)**: These are each of the sensors at the beamline, including the Photodiode (photocurrent measured from the synchrotron beam hitting the photodiode sensor), Beamline Energy (from the mono), Ai 3 Izero (upstream beam intensity measured from a gold mesh), and others. Common AI channels are listed in the Analog Input Channels section below. - | Command | Description | - |----------------------|------------------------------------------------------------------------| - | acquire_data | Acquires for `time` seconds, or `counts` counts. | - | get_acquired | Retrieve the average value from the most recent single-shot acquisition (see `start_acquire`). | - | get_acquired_array | Retrieve the array acquired from the most recent single-shot acquisition (see `start_acquire`). | - | get_acquire_status | Read the current acquisition state of the AI subsystem. | - | get_freerun | Get freerun AI data for one or more channels. | - | get_freerun_array | Retrieve most recent AI freerun data. | - | list_ais | Return the list of all AI channels that are defined on the server. | - | start_acquire | Start Acquisition for either `time` or `counts`, whichever is non-zero.| - | stop_acquire | Stop acquisition in progress. | - -* **Digital Input Output (DIO)**: This contains toggles for each of the the toggleable components. This includes the shutter input, the in chamber light and camera trigger, and others. - | Command | Description | - |-------------|--------------------------------------------------------------------------------------------------| - | get_di | Get digital input (DI) channel value | - | list_dios | Retrieves the complete list of digital input channel names defined on the server. | - | set_do | Sets the digital output (DO) channel `chan` to `value` | - -* **Files and Folders**: This gets and sets the disk locations where the data is being saved. In all cases data is saved to the drive, this lets the user control where these files are being written to. - | Command | Description | - |--------------------|---------------------------------------------------------------------------------------------------------------------------------| - | get_folder_listing | Get lists of all files and folders in a location descended from “C:\Beamline Controls\BCS Setup Data” | - | get_text_file | Get contents of a text file (usually acquired data) from any file in a location descended from “C:\Beamline Controls\BCS Setup Data”. | - -* **Global (State) Variables**: This contains state variables for the instrument. In most instances these should not be touched, changed, or set programmatically. - | Command | Description | - |----------------------|-------------------------------------------------------------------------------------------------| - | get_state_variable | Get the value of the named BCS State Variable. | - | list_state_variables | Get the complete list of state variables and their types (Boolean, Integer, String, Double). | - | set_state_variable | Set the value of the named BCS State Variable. | - -* **Instrument Data Acquisition**: This subsystem handles data acquisition from various instruments connected to the beamline, such as detectors and spectrometers. It provides commands to start, stop, and retrieve data from instrument acquisitions. - | Command | Description | - |--------------------------------|-------------------------------------------------------------------------------------------------| - | get_instrument_acquired1d | Retrieve data (1D array) from the most recent acquisition. | - | get_instrument_acquired2d | Retrieve data (2D array) from the most recent acquisition. | - | get_instrument_acquired3d | Retrieve data (3D array) from the most recent acquisition. | - | get_instrument_acquisition_info | Retrieve miscellaneous info about the instrument and acquisition: file_name, sensor temperature (if applicable), live time, and dead time. | - | get_instrument_acquisition_status | Retrieve all available status bits from the instrument subsystem for the named instrument. | - | get_instrument_count_rates | Retrieve instrument count rates. | - | get_instrument_driver_status | Returns the status of the BCS Instrument Driver for the named instrument. | - | list_instruments | Return the list of instruments that are defined on the server. | - | start_instrument_acquire | Starts an instrument acquisition and waits for it to complete. | - | start_instrument_driver | Starts the named instrument driver (does nothing if the driver is already running). | - | stop_instrument_acquire | Stop acquisition on the named instrument (does nothing if the instrument is not acquiring). | - | stop_instrument_driver | Stops the named instrument driver (does nothing if the driver is not running). | - -* **Miscellaneous**: Utility commands for system status, panel images, and video feeds. - | Command | Description | - |--------------------|------------------------------------------------------------------| - | get_panel_image | Send a current image of the LabVIEW panel, in jpg format. | - | get_subsystem_status | Returns the current status of all BCS subsystems on the server. | - | get_video_image | Return the most recent image from the named camera, in jpg format. | - -* **Motors and Motion**: This subsystem controls all motorized components of the beamline. Motors can be moved individually or in coordinated trajectories. Motors can be enabled, disabled, homed, and their positions can be queried. Trajectories allow multiple motors to move in a coordinated fashion to predefined positions. Key motors on this instrument are broken down into Sample (X, Y, Z, Theta) and CCD (X, Y, Theta). These motors define the coordinate system of the beamline are are core to the alignment of the system. - | Command | Description | - |----------------------|-------------------------------------------------------------------------------------------------| - | at_preset | Checks if the associated motor is at the preset position. | - | at_trajectory | Checks if all requirements have been satisfied to be "At trajectory" (usually just means that each motor is at its trajectory goal). | - | command_motor | Command one or more motors. | - | disable_breakpoints | Disables the breakpoint output (output-on-position) of motor controller for the named motor. | - | disable_motor | Disables the named motor. | - | enable_motor | Enables the named motor. | - | get_flying_positions | Retrieve the locations of motor name that will trigger acquisitions in a flying scan. | - | get_motor | Get information and status for the motors in motors. | - | get_motor_full | Returns the complete state of the requested motors. | - | home_motor | Home the motors in the input array, motors. | - | list_motors | Return the list of motors that are defined on the server. | - | list_presets | Return array of motor preset positions. | - | list_trajectories | List all trajectories. | - | move_motor | Command one or more motors to begin moves to supplied goals. | - | move_to_preset | Move to preset positions. | - | move_to_trajectory | Move to trajectory positions. | - | set_breakpoints | Set Breakpoints for the named motor. | - | set_motor_velocity | Set motor speeds. | - | start_flying_scan | Start a flying scan with the named motor. | - | stop_motor | Immediately issue stop command for the motors provided in motors. | - -* **Scans**: This subsystem provides high-level scan routines for common experimental procedures. Scans coordinate motor movements with data acquisition. Various scan types are available for different experimental geometries and measurement types. - | Command | Description | - |----------------------------|-------------------------------------------------------------------------------------------------| - | sc_alsu_mirror_vibration | Setup the ALSU Mirror Vibration Scan. This scan moves one motor in a specific pattern and records the analog data. | - | sc_auto_coll_single_axis_scan | This is VERY close to a Single Motor Scan. | - | sc_barf_scan | The Barf scan acquires from a spectrometer and then fits the ruby peaks to estimate the pressure. | - | sc_from_file_scan | This interface hides all the scan detail inside of a 'from file' scan file. | - | sc_image_from_file_scan | Image scan from file configuration. | - | sc_image_one_motor_scan | Image scan with one motor. | - | sc_image_time_scan | Image scan over time. | - | sc_image_two_motor_scan | Image scan with two motors. | - | sc_ltp_single_axis_scan | LTP single axis scan. | - | sc_move_motor | Setup this simple scan so that it will move a motor. | - | sc_move_trajectory | Setup this simple scan so that it will activate a trajectory. | - | sc_powder_diffraction | Powder diffraction scan. | - | sc_set_dio | Setup scan to set digital output channels. | - | sc_single_crystal_scan | Setup the single motor scan that also records an image from a ccd camera instrument, with each data point. | - | sc_single_motor_flying_scan | Setup a single motor flying scan. | - | sc_single_motor_scan | Single motor scan that moves one motor and acquires data at each position. | - | sc_temperature_scan | The Temperature Scan acquires data from a spectrometer and then with background and calibration data calculates the temperature. | - | sc_time_scan | Time scan that acquires data over time without motor movement. | - | sc_two_motor_scan | Two motor scan that moves two motors and acquires data at each position combination. | - -* **Scan Management**: This subsystem provides commands to manage scan execution, including starting, stopping, and querying scan status. Scans can be executed synchronously or asynchronously. - | Command | Description | - |----------------------|-------------------------------------------------------------------------------------------------| - | current_scan_running | Returns the currently running 'integrated scan' run, or an empty string if none is running. | - | last_scan_run | Returns the last 'integrated scan' run, or an empty string if none have run. | - | scan_status | Returns information about the 'integrated scan' system, namely the last scan (last_scan) run, the currently running scan, and the scanner status. | - | stop_scan | Immediately issue stop command for the currently running 'integrated scan'. | - -## Coordinates and Axes - -The beamline is configured in two regions, the chamber, and the region up stream from the chamber. - -### Upstream Components - -The beamline is set up with an X-ray beam incident into a spherical chamber from an Undulator source. Here is an explanation of the X-ray flight path into the chamber. - -1. **EPU Source**: The beam is generated at an EPU (Elliptically Polarizing Undulator) source. Elliptically polarizing undulator (EPU) – 5 cm period; 165-1,800 eV [A. T. Young et al., J. Synchrotron Rad., 2002, 9, 270-274] - - 4 rows of permanent magnets placed along axis of electron beam - - 2 rows above the plane of the storage ring on either side of the beam; same for the 2 rows below - - Linear polarization from 0° to 90° are available for energies above 160 eV - - Using the fundamental output from the undulator, pure circularly polarized X-rays can be produced from 130-600 eV - - For higher energies, 3rd and 5th harmonics must be used, but this leads to elliptical (P = 0.8 to 0.9) rather than circular polarization - -2. **Horizontal Exit Slit**: After the EPU, the beam is selected using the Horizontal Exit Slit. - -3. **M101 Monochromator**: From there the energy is selected at the M101 MONO (monochromator). The monochromator uses gratings (250 lines/mm for 150-550 eV, 500 lines/mm for energies above 550 eV) to select the desired X-ray energy. - -4. **Mirror Focusing**: The beam is then focused through a series of mirrors, including the M103 Mirror, which provides focusing and beam conditioning. - -5. **Higher Order Suppressor (HOS)**: The beam then hits the Higher Order Suppressor. This is a four-bounce reflective mirror assembly designed to add linear attenuation to the beam and remove higher order light making it past the mono. The incident angle of the mirrors can be changed between 4°-8°, or moved out of the beam path to let unfiltered light into the chamber. - -6. **Upstream JJ Scatter Slits**: The beam then passes through the Upstream JJ scatter slits (approximately 1.5 m upstream of sample chamber). There are four motors for these slits: - - Upstream JJ Vert Trans: vertical translational position - - Upstream JJ Horz Trans: horizontal translational position - - Upstream JJ Vert Aperture: vertical aperture size - - Upstream JJ Horz Aperture: horizontal aperture size - -7. **Shutter System and Beam Monitoring**: The beam then passes through the shutter system and Ai 3 Izero instrument for monitoring beam flux. - - After the 6° deflection mirror, there is a gold mesh assembly that can be lowered into the beam and connected to a picoammeter to measure photoelectric current - - This gold mesh is upstream of the HOS, so the spectrum of X-rays measured here is not suitable when doing scattering experiments at lower energies (e.g. near carbon dip) that need to use the HOS - - Second gold mesh assembly directly downstream of the HOS, but before the first set of slits is used to monitor incident flux (Ai 3 Izero) - -8. **Middle JJ Slits**: The beam then passes through the Middle JJ slits (approximately 0.6 m upstream of sample). These have the same 4 motors, but use the "Middle" prefix. These remove most of the remaining parasitic scattering from the first set of slits. - -9. **In-Chamber JJ Slits**: Lastly, the beam passes through the In-Chamber JJ slits (approximately 0.2 m upstream of sample). Again these have the same 4 motors, but use the "In-Chamber" prefix. These provide final beam definition before the sample. - -### In-Chamber Motors - -The chamber is circular in nature. The beam is incident from upstream and focused onto the sample plate. The detector is then downstream from that. - -#### Sample Coordinate System - -The sample is driven by 4 primary motors: Sample X, Sample Y, Sample Z, and Sample Theta. Plates are rectangular in nature and are designed to be inserted with samples affixed to the top of the plate, and the top of the plate facing upwards towards the top of the chamber in the Lab reference frame. - -**Sample Theta (θ)** dictates the orientation of the plate relative to the incident X-ray beam: -- **Sample θ = 90°**: The X-ray beam is normal to the sample plate surface (perpendicular incidence) -- 0°: Plate facing upwards towards the top of the chamber -- 180°: Plate facing downwards towards the back of the chamber -- Negative angles are valid: -90° = 270° - -The sample coordinate system (Sample X, Y, Z) originates from the center of the sample plate: -- **Sample X**: Translation along the axis of rotation (colinear with Sample Theta axis) - - Sample X + moves the plate inwards towards the motor controlling the motion - - Sample X - moves the plate outwards towards the chamber door -- **Sample Y**: Translation perpendicular to the beam at Sample Theta = 0 - - Sample Y + moves the plate away from the beam - - Sample Y - moves the plate towards the beam -- **Sample Z**: Translation perpendicular to the sample plate - - At Sample Theta = 0°, Sample Z + moves the plate up (in Lab Frame) - - At Sample Theta = 0°, Sample Z - moves the plate down (in Lab Frame) - - **Note**: Changing Sample Z will also change the sample-detector distance (except when Sample θ is at 0°) - -This forms a right-handed coordinate system. When Sample Theta rotates, the Sample Y and Z axes rotate with it, while Sample X remains colinear with the rotation axis. - -#### Detector Coordinate System - -![Geometry](coords.png "Optional title") - -The Sample Theta axis of rotation is colinear with a goniometer arm that controls the CCD motors. **CCD** is the prefix given to all the motors that control the geometry of the instrument positioning arm. This arm rests on a goniometer facilitating rotation through approximately 120°. - -The detector system consists of two components mounted on the same arm: -- **CCD detector**: Large area detector (2048 × 2048 pixels, 13.5 µm pixel size) for collecting scattering patterns -- **Photodiode**: 5 mm × 5 mm GaAs detector (Hamamatsu) for direct beam monitoring - -**CCD Theta (θ)** controls the detector angle: -- At CCD Theta = 0°: The instrument is directly aligned with the incident beam and positioned to the left of the sample from the chamber door perspective -- CCD Theta + rotates the detector along the same direction as the Sample Theta motor -- Typical range: -25° to +160° for scattering measurements - -**CCD Y** controls the sample-detector distance: -- CCD Y + moves the detector further away from the sample (increases sample-detector distance) -- CCD Y - moves the detector closer to the sample (decreases sample-detector distance) -- **Note**: Changing CCD Y will change the sample-detector distance - -**CCD X** moves the detector colinear to the Sample X motor (horizontal position). - -#### Photodiode Positioning - -Both the CCD detector and photodiode are attached to the same arm and controlled by the same motors. To select the Photodiode position: -- Use the "Photodiode Far" trajectory/preset position -- This moves CCD Y = 100 mm, CCD X = 6 mm, CCD Theta close to 0° -- Alternatively, manually position Sample X to move the photodiode into line with the incident beam -- The photodiode is designed to be in the direct beam with CCD out of the way -- There is also a beamstop photodiode (1 mm × 3 mm Si photodiode from Advanced Photonics, accessible via "AI 6 BeamStop") for flux monitoring during scattering measurements - -## Complete Motor Reference - -This section provides a comprehensive list of all motors available on the beamline control system, organized by functional category. - -### Sample Positioning Motors - -These motors control the position and orientation of the sample within the chamber. - -| Motor Name | Description | Units | Notes | -|------------|-------------|-------|-------| -| Sample X | Translation along the axis of rotation (colinear with Sample Theta axis). Positive moves toward motor, negative toward chamber door. | mm | Primary sample positioning axis | -| Sample Y | Translation perpendicular to beam at Sample Theta = 0. Positive moves away from beam, negative toward beam. | mm | Forms right-handed coordinate system with Sample X and Z | -| Sample Z | Translation perpendicular to sample plate. At Sample Theta = 0, positive moves up, negative moves down in lab frame. | mm | Height control relative to beam | -| Sample Theta | Rotation about the sample normal axis. 0° = plate facing up, 90° = normal to incident beam, 180° = facing down. | degrees | Primary sample orientation control | -| Sample Azimuthal Rotation | Additional rotational degree of freedom for sample orientation. | degrees | Secondary rotation axis | -| Sample Y Scaled | Scaled version of Sample Y position, used for specific scan geometries. | mm | Derived motor position | - -### Detector Positioning Motors - -These motors control the position and orientation of the CCD detector and photodiode. - -| Motor Name | Description | Units | Notes | -|------------|-------------|-------|-------| -| CCD Theta | Rotation of detector arm on goniometer. 0° = aligned with incident beam, positioned left of sample from chamber door. Positive rotates in same direction as Sample Theta. Range: ~120°. | degrees | Primary detector angle control | -| CCD X | Translation colinear with Sample X motor. | mm | Detector horizontal position | -| CCD Y | Sample-detector distance. Positive moves detector away from sample, negative moves closer. | mm | Controls scattering geometry | -| Pollux CCD X | Alternative CCD X position for Pollux detector system. | mm | Pollux-specific positioning | -| Pollux CCD Y | Alternative CCD Y position for Pollux detector system. | mm | Pollux-specific positioning | -| T-2T | Theta-2Theta coupling for reflectivity measurements. Automatically couples Sample Theta and CCD Theta. | degrees | Reflectivity scan mode | -| Beam Stop | Position of beam stop to block direct beam from hitting detector. | mm | Protects detector from direct beam | - -### Beamline Energy and Monochromator Controls - -These motors control the X-ray energy selection and monochromator settings. - -| Motor Name | Description | Units | Notes | -|------------|-------------|-------|-------| -| Beamline Energy | Current X-ray energy from monochromator. | eV | Primary energy control | -| Beamline Energy Goal | Target energy for energy scans. | eV | Used in energy scan routines | -| Mono Energy | Monochromator energy setting. | eV | Internal mono control | -| Mono 101 Grating | Grating selection on M101 monochromator. | unitless | Selects grating for energy range | -| Mono 101 Vessel | Monochromator vessel position. | mm | Vessel translation control | -| M101 Feedback | Feedback control for M101 monochromator. | unitless | Energy stabilization | -| M101 Horizontal Deflection | Horizontal beam deflection at M101. | mm | Beam steering | -| M101 Vertical Deflection | Vertical beam deflection at M101. | mm | Beam steering | - -### EPU (Elliptically Polarizing Undulator) Controls - -These motors control the X-ray source polarization and gap. - -| Motor Name | Description | Units | Notes | -|------------|-------------|-------|-------| -| EPU Gap | Undulator gap setting. Controls fundamental energy and flux. | mm | Primary EPU control | -| EPU Z | Undulator Z position. | mm | Longitudinal position | -| EPU Polarization | Polarization setting. EPU=1 (or 0.9) for circular, EPU=100 for S-polarized, EPU=190 for P-polarized. | unitless | Polarization control | - -### Mirror Controls - -These motors control the focusing and steering mirrors. - -| Motor Name | Description | Units | Notes | -|------------|-------------|-------|-------| -| M103 Yaw | M103 mirror yaw angle. | degrees | Mirror alignment | -| M103 Bend Up | M103 mirror upward bending. | mm | Mirror focusing | -| M103 Bend Down | M103 mirror downward bending. | mm | Mirror focusing | -| M121 Translation | M121 mirror translation position. | mm | Mirror positioning | - -### Slit Controls - -These motors control the entrance and exit slits that define the beam size and position. - -| Motor Name | Description | Units | Notes | -|------------|-------------|-------|-------| -| Entrance Slit Width | Width of entrance slit to monochromator. | mm | Beam size control. Note: Also exists as "Entrance Slit width" (lowercase) in some configurations - these refer to the same motor. | -| Exit Slit Top | Top blade position of exit slit. | mm | Vertical beam definition | -| Exit Slit Bottom | Bottom blade position of exit slit. | mm | Vertical beam definition | -| Exit Slit Left | Left blade position of exit slit. | mm | Horizontal beam definition | -| Exit Slit Right | Right blade position of exit slit. | mm | Horizontal beam definition | -| Horizontal Exit Slit Size | Size of horizontal exit slit. | mm | Horizontal beam size | -| Horizontal Exit Slit Position | Position of horizontal exit slit. | mm | Horizontal beam position | -| Vertical Exit Slit Size | Size of vertical exit slit. | mm | Vertical beam size | -| Vertical Exit Slit Position | Position of vertical exit slit. | mm | Vertical beam position | -| Vertical Slit Position | Alternative vertical slit position control. | mm | Additional vertical control | -| Vertical Slit Size | Alternative vertical slit size control. | mm | Additional vertical control | -| Horizontal Slit Position | Alternative horizontal slit position control. | mm | Additional horizontal control | -| Horizontal Slit Size | Alternative horizontal slit size control. | mm | Additional horizontal control | - -### Scatter Slit Controls (JJ Slits) - -These motors control the Jaws-Jaws (JJ) scatter slits at three positions along the beamline. Each set has four motors: vertical and horizontal translation, and vertical and horizontal aperture. - -#### Upstream JJ Slits -Located approximately 1.5 m upstream of sample chamber. First set of slits to define beam. - -| Motor Name | Description | Units | Notes | -|------------|-------------|-------|-------| -| Upstream JJ Vert Trans | Vertical translational position of upstream slits. | mm | Vertical beam position | -| Upstream JJ Horz Trans | Horizontal translational position of upstream slits. | mm | Horizontal beam position | -| Upstream JJ Vert Aperture | Vertical aperture size of upstream slits. | mm | Vertical beam size | -| Upstream JJ Horz Aperture | Horizontal aperture size of upstream slits. | mm | Horizontal beam size | - -#### Middle JJ Slits -Located approximately 0.6 m upstream of sample. Removes remaining parasitic scattering. - -| Motor Name | Description | Units | Notes | -|------------|-------------|-------|-------| -| Middle JJ Vert Trans | Vertical translational position of middle slits. | mm | Vertical beam position | -| Middle JJ Horz Trans | Horizontal translational position of middle slits. | mm | Horizontal beam position | -| Middle JJ Vert Aperture | Vertical aperture size of middle slits. | mm | Vertical beam size | -| Middle JJ Horz Aperture | Horizontal aperture size of middle slits. | mm | Horizontal beam size | - -#### In-Chamber JJ Slits -Located approximately 0.2 m upstream of sample. Final beam definition before sample. - -| Motor Name | Description | Units | Notes | -|------------|-------------|-------|-------| -| In-Chamber JJ Vert Trans | Vertical translational position of in-chamber slits. | mm | Vertical beam position | -| In-Chamber JJ Horz Trans | Horizontal translational position of in-chamber slits. | mm | Horizontal beam position | -| In-Chamber JJ Vert Aperture | Vertical aperture size of in-chamber slits. | mm | Vertical beam size | -| In-Chamber JJ Horz Aperture | Horizontal aperture size of in-chamber slits. | mm | Horizontal beam size | - -### Higher Order Suppressor - -| Motor Name | Description | Units | Notes | -|------------|-------------|-------|-------| -| Higher Order Suppressor | Four-bounce mirror assembly position. Removes higher-order harmonics from monochromator. Incident angle adjustable between 4°-8°, or moved out of beam path. | mm | Critical for low-energy experiments | - -### Shutter Controls - -| Motor Name | Description | Units | Notes | -|------------|-------------|-------|-------| -| PiezoShutter Trans | Piezo-actuated shutter translation position. | mm | Fast shutter control | -| PZT Shutter | Piezoelectric shutter control. | unitless | Alternative shutter system | - -### Temperature and Environmental Controls - -| Motor Name | Description | Units | Notes | -|------------|-------------|-------|-------| -| Temperature Controller | Hot stage temperature setpoint. Calibration curve relates setpoint (K) to actual temperature (°C). | K | Sample temperature control | -| Coolstage | Cooled sample stage temperature control. | K | Low-temperature experiments | -| Camera Temp Setpoint | CCD camera temperature setpoint. Typically set to -45°C for operation. | °C | Detector cooling | - -### Camera and Detector Controls - -These are motor-like parameters that control camera settings and readout. - -| Motor Name | Description | Units | Notes | -|------------|-------------|-------|-------| -| CCD Camera Shutter Inhibit | Inhibit signal for CCD camera shutter. | unitless | Shutter control | -| CCD Shutter Control | CCD camera shutter control signal. | unitless | Shutter control | -| Camera ROI X | Region of Interest X position. | pixels | Image cropping | -| Camera ROI Y | Region of Interest Y position. | pixels | Image cropping | -| Camera ROI Width | Region of Interest width. | pixels | Image size control | -| Camera ROI Height | Region of Interest height. | pixels | Image size control | -| Camera ROI X Bin | Binning factor in X direction. | unitless | Pixel binning for faster readout | -| Camera ROI Y Bin | Binning factor in Y direction. | unitless | Pixel binning for faster readout | - -### Sample Rotation Motors - -Additional rotational degrees of freedom for specialized sample holders. - -| Motor Name | Description | Units | Notes | -|------------|-------------|-------|-------| -| SampleRot0 | Sample rotation axis 0. | degrees | Additional rotation | -| SampleRot1 | Sample rotation axis 1. | degrees | Additional rotation | -| SampleRot2 | Sample rotation axis 2. | degrees | Additional rotation | -| SampleRot3 | Sample rotation axis 3. | degrees | Additional rotation | -| SampleRot4 | Sample rotation axis 4. | degrees | Additional rotation | - -### Multi-Channel Scaler (MCS) Axes - -| Motor Name | Description | Units | Notes | -|------------|-------------|-------|-------| -| MCS_axis0 | Multi-channel scaler axis 0. | unitless | MCS control | -| MCS_axis1 | Multi-channel scaler axis 1. | unitless | MCS control | -| MCS_axis2 | Multi-channel scaler axis 2. | unitless | MCS control | -| MCS_axis3 | Multi-channel scaler axis 3. | unitless | MCS control | -| MCS_axis4 | Multi-channel scaler axis 4. | unitless | MCS control | - -### Additional Controls - -| Motor Name | Description | Units | Notes | -|------------|-------------|-------|-------| -| Sample Number | Sample identifier number. | unitless | Sample tracking | -| Piezo Vertical | Piezo vertical position control. | mm | Fine positioning | -| Piezo Horiz | Piezo horizontal position control. | mm | Fine positioning | -| AO 0 | Analog output channel 0. | V | General purpose analog output | -| AO 1 | Analog output channel 1. | V | General purpose analog output | -| OSP Adjustment | Optical sample position adjustment. | mm | Fine sample positioning | -| Diag 106 | Diagnostic element 106 position. | mm | Beam diagnostics | - -## Analog Input (AI) Channels - -Complete list of analog input channels available on the beamline for data acquisition and monitoring. Channels are numbered sequentially from 0-20. - -| CH # | Channel Name | Description | Typical Units | Notes | -|------|--------------|-------------|---------------|-------| -| 0 | EPU Polarization | EPU polarization setting readback. | unitless | Readback of EPU polarization value (0.9 for circular, 100 for S-polarized, 190 for P-polarized). | -| 1 | Coolstage Temp C | Cooled sample stage temperature reading. | °C | Temperature of the coolstage sample holder. | -| 2 | CCD Temperature | CCD detector temperature reading. | °C | Temperature of the CCD detector. Typically operated at -45°C. | -| 3 | Beam Current | Storage ring beam current. | mA | Synchrotron beam current measurement. Negative values may indicate measurement direction or offset. | -| 4 | TEY signal | Total Electron Yield signal from sample. | A or V | Surface-sensitive detection mode for NEXAFS. Measures electron yield from sample surface. | -| 5 | Izero | Upstream beam intensity normalization signal. | A or V | Beam intensity measurement for normalization. Typically from gold mesh upstream of sample. | -| 6 | Photodiode | Main photodiode signal. Photocurrent from 5 mm × 5 mm GaAs photodiode (Hamamatsu) in direct beam path. | A or V | Primary beam intensity monitor. Use when CCD is out of beam path. | -| 7 | AI 0 | General purpose analog input channel 0. | V | Configurable analog input channel. | -| 8 | AI 3 Izero | Upstream beam intensity measured from gold mesh assembly (Ai 3). Located downstream of HOS, before first set of slits. | A or V | Used for flux normalization. Gold mesh can be lowered into beam and connected to picoammeter. | -| 9 | AI 5 | General purpose analog input channel 5. | V | Configurable analog input channel. | -| 10 | AI 6 BeamStop | Beamstop photodiode signal. Signal from 1 mm × 3 mm Si photodiode (Advanced Photonics) in beamstop. | A or V | Flux monitoring during scattering measurements. Can be used for absorption measurements while collecting scattering data. | -| 11 | AI 7 | General purpose analog input channel 7. | V | Configurable analog input channel. | -| 12 | Temperature Controller | Hot stage temperature controller readback. | K or °C | Temperature reading from the hot stage temperature controller. | -| 13 | PZT Shutter | Piezoelectric shutter position/status readback. | unitless | Status or position of the PZT (piezoelectric) shutter. | -| 14 | Pause Trigger | Pause trigger signal status. | unitless | Status of pause trigger for scan operations. | -| 15 | LV Memory | LabVIEW memory usage. | bytes | LabVIEW program memory usage indicator. | -| 16 | Deriv Photodiode | Derivative of photodiode signal. | V/s or A/s | Rate of change of photodiode signal. Useful for detecting beam fluctuations. | -| 17 | Time Stamp Error | Time synchronization error. | s | Error in time synchronization between systems. | -| 18 | Time Stamp Transmit Time | Time stamp transmission time. | s | Time taken to transmit time stamp data. | -| 19 | Time Stamp Server Time | Server time stamp. | s | Server-side time stamp value. | -| 20 | Camera Temp Setpoint | CCD camera temperature setpoint. | °C | Target temperature setting for CCD camera cooling. Typically set to -45°C for operation. | - -Note: The complete list of available AI channels can be obtained using the `list_ais` command. Channel numbers and names are fixed as listed above. Some channels may show negative values due to measurement direction, signal inversion, or offset calibration. - -## Digital Input/Output (DIO) Channels - -Complete list of digital input/output channels available on the beamline for controlling components and reading status signals. - -| Channel Name | Description | Type | Notes | -|--------------|------------|------|-------| -| Shutter Rev | Shutter reverse/reverse direction control. | DO | Controls shutter reverse operation. | -| Lightfiled Frame Loss | Light field frame loss detection signal. | DI | Indicates when light field frame is lost. | -| Nothing | Unused or placeholder DIO channel. | - | Reserved or unused channel. | -| Camera Scan | Camera scan trigger signal. | DO | Triggers camera acquisition during scans. | -| Shutter Output | Main beam shutter output control. Opens/closes X-ray beam. | DO | Critical safety component. Primary shutter control. Always verify shutter state. | -| Air Shutter Output | Air shutter output control. | DO | Controls air-operated shutter mechanism. | -| Light Output | Chamber illumination light control. | DO | Controls in-chamber lighting for sample viewing and alignment. | -| Beam Dumped | Beam dump status indicator. | DI | Indicates when beam is dumped (stopped). Read-only status signal. | -| PZT Shutter Status | Piezoelectric shutter status readback. | DI | Status of the PZT (piezoelectric) shutter position/state. | -| Do Pause Trigger | Pause trigger output control. | DO | Controls pause trigger for scan operations. | -| Trigger Pause Trigger | Trigger signal for pause trigger. | DO | Triggers the pause mechanism during scans. | -| Shutter Inhibit | Shutter inhibit signal. Prevents shutter from opening. | DO | Safety feature to inhibit shutter operation. | -| Trigger + Inhibit | Combined trigger and inhibit signal. | DO | Combined control signal for trigger and inhibit functions. | - -Note: The complete list of available DIO channels can be obtained using the `list_dios` command. The list above includes all visible channels from the DIO Monitor interface. Additional channels may exist that are not shown in the visible portion of the list. Channel types (DI = Digital Input, DO = Digital Output) are inferred from channel names and typical beamline configurations. diff --git a/.cursor/skills/xray-pro/references/lineup.md b/.cursor/skills/xray-pro/references/lineup.md deleted file mode 100644 index a0baa99..0000000 --- a/.cursor/skills/xray-pro/references/lineup.md +++ /dev/null @@ -1,1299 +0,0 @@ -# Beamline Axis of Rotation Line Up - -> Reference for: Xray Pro -> Load when: At the start of a beamtime experiment. When planning code associated with linning up and sample alignment. - ---- - -## Overview - -This is dedicated for lining up a sample to the incident beam on a given sample. This is a critical component to measuring anything at the beamline, especial NEXAFS spectrsocopy and Reflectivity. These measurements require preciese knowledge of the Sample Z postition, and Sample Theta position with respect to the incident beam. This accounts for beam drift, and motor drift thoughout an experiment. - -First refference `references\beamline-controls.md` to understant the core api, motors, and coordinates. - -## Alignement Algorithm - -The alignement algorithm functions in three stages. First we algin the sensors to the direct beam, by aligning the CCD Theta motor positions to center the beam on the Photodiode, then we align the Sample Z and Sampe Theta positions. This sample alignment step is first done on a large detector, before happening on a more precise instrument. - -### Instrument Alignement and Centering - -1. Move the detector X motor such that we are centered on the photodiode instruemnt and not the Area detector. - -2. Move the Sample Z motor down such that it the beam passes straight though the chamber onto the Photodiode. - -3. Rock the CCD Theta motor arround zero to find the CCD Theta position associated with the maximum intensity of the Photodiode. Save this position for later use - -4. Jog up by about 4 degrees to center the the secondary photodiode placed on the bottom of the detector. This is a slitted photidode to improve the precision of the alignment algorithm. - -5. Rock the CCD Theta arround the 4 degree mark to find the CCD Theta position associated with the maximum intensity on teh AI 6 Beamstop AI. This is the name given to the slitten beamstop. Save this position for later use. - -6. Move back to the large photodiode position saved in step 3. The Instrument is now ligned up. - -### Sample Alginment - -Sample alignement needs to be done for each new sample, and sometimes for each new energy. It may be the case that a quick alignement at the start is allways prefered to the possibility of a mis-aligned measurement. - -What follows is a algorithmic iterative approach for sample alighnment. - -1. Ensure the sample is below the beam allowing the full signal to hit the detector. - -2. Move the sample up (+z) and record the instensity on the large photodiode. - -3. Find the Sample Z position that cut's the beam intesnity in half. Alternatively, fit the signal to a sigmoid/error function, and determine it's position z0. Record this position for note keeping. - -4. Move the sample theta motor to 1 degrees, and jog the sample theta motor by 2 degrees. We will be looking at the reflected intensity on the Photodiode, so ensure that we calculate the new position using the saves position from step 3 of the Instrument alignemtn. - -5. Scan the sample theta position from about 0 - 2 degrees to find the angle that maximizes the reflected intensity as measured by the photodiode. A better measure of this is to find the centroid of the peak that this trace displays. This angle is a better guess of what 1 degrees really is. - -6. Calculate the offset between 1 deg and this "True" angle. Save this offset for note keeping. Move to this offset and treat it as zero., - -7. Move back to step 1. Repeat until a convergence tolerence is reached. - -### Fine grained sample alignement - -This follows the same rules, but uses the slitted photodiode. To use this instrument, be sure that the offset angle for it found in the Instrument Alignment is properly accounted for. - -## Algorithm Components - -The alignment algorithm is broken down into reusable components. Each component includes a description, pseudo code, and Python implementation with checkpointing support. - -### 1. Finding Maximum Value in Data - -**Description:** Finds the position and value of the maximum intensity in a dataset. Used for peak finding in alignment scans. - -**Pseudo Code:** -``` -FUNCTION FIND_MAX(data_points): - // data_points is array of (x, y) pairs - SET max_y = -infinity - SET max_x = undefined - FOR each (x, y) IN data_points: - IF y > max_y: - SET max_y = y - SET max_x = x - END IF - END FOR - RETURN (max_x, max_y) -END FUNCTION -``` - -**Python Implementation:** -```python -def find_max(data_points): - """ - Find the position and value of maximum intensity. - - Parameters: - ----------- - data_points : list of tuples - List of (x, y) pairs where x is position and y is intensity - - Returns: - -------- - max_x : float - Position of maximum intensity - max_y : float - Maximum intensity value - - Checkpoint: - ----------- - Saves: max_x, max_y - """ - max_y = float('-inf') - max_x = None - - for x, y in data_points: - if y > max_y: - max_y = y - max_x = x - - # Checkpoint: Save maximum values - checkpoint = { - 'max_position': max_x, - 'max_intensity': max_y, - 'total_points': len(data_points) - } - # insert code to save checkpoint here - - return max_x, max_y -``` - ---- - -### 2. Linear Interpolation for Half-Maximum Finding - -**Description:** Finds the position where intensity equals a target value (typically half-maximum) using linear interpolation between data points. More accurate than finding the nearest point. - -**Pseudo Code:** -``` -FUNCTION INTERPOLATE(x_values, y_values, target_y): - // Linear interpolation to find x where y = target_y - FOR i = 0 to LENGTH(x_values) - 2: - IF (y_values[i] <= target_y AND target_y <= y_values[i+1]) OR - (y_values[i] >= target_y AND target_y >= y_values[i+1]): - SET slope = (y_values[i+1] - y_values[i]) / (x_values[i+1] - x_values[i]) - SET x_interp = x_values[i] + (target_y - y_values[i]) / slope - RETURN x_interp - END IF - END FOR - RETURN undefined -END FUNCTION -``` - -**Python Implementation:** -```python -def interpolate(x_values, y_values, target_y): - """ - Find x position where y equals target_y using linear interpolation. - - Parameters: - ----------- - x_values : list of float - Position values - y_values : list of float - Intensity values corresponding to positions - target_y : float - Target intensity value to find - - Returns: - -------- - x_interp : float or None - Interpolated position where y = target_y, or None if not found - - Checkpoint: - ----------- - Saves: target_y, x_interp, interpolation_method - """ - for i in range(len(x_values) - 1): - y1, y2 = y_values[i], y_values[i+1] - x1, x2 = x_values[i], x_values[i+1] - - # Check if target_y is between y1 and y2 - if (y1 <= target_y <= y2) or (y1 >= target_y >= y2): - slope = (y2 - y1) / (x2 - x1) - if abs(slope) > 1e-10: # Avoid division by zero - x_interp = x1 + (target_y - y1) / slope - else: - x_interp = (x1 + x2) / 2 # Average if slope is zero - - # Checkpoint: Save interpolation result - checkpoint = { - 'target_intensity': target_y, - 'interpolated_position': x_interp, - 'interpolation_method': 'linear', - 'bracket_indices': (i, i+1), - 'bracket_positions': (x1, x2), - 'bracket_intensities': (y1, y2) - } - # insert code to save checkpoint here - - return x_interp - - # Checkpoint: No interpolation found - checkpoint = { - 'target_intensity': target_y, - 'interpolated_position': None, - 'interpolation_method': 'linear', - 'error': 'target_y not found in range' - } - # insert code to save checkpoint here - - return None -``` - ---- - -### 3. Finding Peak Centroid - -**Description:** Calculates the centroid (center of mass) of a peak, which is more robust than finding the simple maximum. The centroid accounts for the shape of the peak distribution. - -**Pseudo Code:** -``` -FUNCTION FIND_CENTROID(x_values, y_values): - // Calculate centroid (center of mass) of peak - SET total_mass = SUM(y_values) - SET weighted_sum = 0 - FOR i = 0 to LENGTH(x_values) - 1: - SET weighted_sum = weighted_sum + x_values[i] * y_values[i] - END FOR - SET centroid_x = weighted_sum / total_mass - - // Find peak maximum for reference - SET (max_x, max_y) = FIND_MAX(zip(x_values, y_values)) - - RETURN (centroid_x, max_x) -END FUNCTION -``` - -**Python Implementation:** -```python -def find_centroid(x_values, y_values): - """ - Calculate centroid (center of mass) of intensity peak. - - Parameters: - ----------- - x_values : list of float - Position values - y_values : list of float - Intensity values - - Returns: - -------- - centroid_x : float - Centroid position (weighted average) - max_x : float - Position of maximum intensity (for reference) - - Checkpoint: - ----------- - Saves: centroid_x, max_x, max_intensity, total_mass - """ - total_mass = sum(y_values) - - if total_mass == 0: - # Fallback to maximum if no signal - max_idx = y_values.index(max(y_values)) - centroid_x = x_values[max_idx] - max_x = x_values[max_idx] - else: - weighted_sum = sum(x * y for x, y in zip(x_values, y_values)) - centroid_x = weighted_sum / total_mass - - # Also find maximum for reference - max_idx = y_values.index(max(y_values)) - max_x = x_values[max_idx] - - # Checkpoint: Save centroid calculation - checkpoint = { - 'centroid_position': centroid_x, - 'max_position': max_x, - 'max_intensity': max(y_values), - 'total_mass': total_mass, - 'num_points': len(x_values), - 'centroid_max_difference': abs(centroid_x - max_x) - } - # insert code to save checkpoint here - - return centroid_x, max_x -``` - ---- - -### 4. Scanning Theta Range and Measuring Intensity - -**Description:** Scans a range of theta (angle) positions and measures intensity at each position. Used for finding optimal detector or sample angles. - -**Pseudo Code:** -``` -FUNCTION SCAN_THETA_RANGE(theta_start, theta_end, theta_step, motor_name, ai_channel): - SET positions = [] - SET intensities = [] - SET current_theta = theta_start - - WHILE current_theta <= theta_end: - SET motor_name = current_theta - WAIT for motor movement - intensity = READ ai_channel signal - APPEND current_theta to positions - APPEND intensity to intensities - SET current_theta = current_theta + theta_step - END WHILE - - RETURN (positions, intensities) -END FUNCTION -``` - -**Python Implementation:** -```python -def scan_theta_range(theta_start, theta_end, theta_step, motor_name, ai_channel): - """ - Scan a range of theta positions and measure intensity. - - Parameters: - ----------- - theta_start : float - Starting theta position (degrees) - theta_end : float - Ending theta position (degrees) - theta_step : float - Step size for scan (degrees) - motor_name : str - Name of motor to move (e.g., "CCD Theta", "Sample Theta") - ai_channel : str - Name of AI channel to read (e.g., "Photodiode", "AI 6 BeamStop") - - Returns: - -------- - positions : list of float - Theta positions scanned - intensities : list of float - Intensity values at each position - - Checkpoint: - ----------- - Saves: scan_range, step_size, num_points, max_intensity, max_position - """ - positions = [] - intensities = [] - - current_theta = theta_start - while current_theta <= theta_end: - # insert code to move motor_name to current_theta here - # insert code to wait for motor movement here - intensity = 0 # insert code to read ai_channel signal here - - positions.append(current_theta) - intensities.append(intensity) - current_theta += theta_step - - # Checkpoint: Save scan results - max_intensity = max(intensities) if intensities else 0 - max_idx = intensities.index(max_intensity) if intensities else 0 - max_position = positions[max_idx] if positions else None - - checkpoint = { - 'scan_type': 'theta_range', - 'motor_name': motor_name, - 'ai_channel': ai_channel, - 'theta_start': theta_start, - 'theta_end': theta_end, - 'theta_step': theta_step, - 'num_points': len(positions), - 'max_intensity': max_intensity, - 'max_position': max_position, - 'positions': positions, - 'intensities': intensities - } - # insert code to save checkpoint here - - return positions, intensities -``` - ---- - -### 5. Scanning Z Range and Measuring Intensity - -**Description:** Scans a range of Z positions and measures intensity at each position. Used for finding the half-maximum position for sample alignment. - -**Pseudo Code:** -``` -FUNCTION SCAN_Z_RANGE(z_start, z_end, z_step, ai_channel): - SET positions = [] - SET intensities = [] - SET current_z = z_start - - WHILE current_z <= z_end: - SET Sample_Z = current_z - WAIT for motor movement - intensity = READ ai_channel signal - APPEND current_z to positions - APPEND intensity to intensities - SET current_z = current_z + z_step - END WHILE - - RETURN (positions, intensities) -END FUNCTION -``` - -**Python Implementation:** -```python -def scan_z_range(z_start, z_end, z_step, ai_channel): - """ - Scan a range of Z positions and measure intensity. - - Parameters: - ----------- - z_start : float - Starting Z position (mm) - z_end : float - Ending Z position (mm) - z_step : float - Step size for scan (mm) - ai_channel : str - Name of AI channel to read (e.g., "Photodiode", "AI 6 BeamStop") - - Returns: - -------- - positions : list of float - Z positions scanned - intensities : list of float - Intensity values at each position - - Checkpoint: - ----------- - Saves: scan_range, step_size, num_points, max_intensity, max_position - """ - positions = [] - intensities = [] - - current_z = z_start - while current_z <= z_end: - # insert code to move Sample Z to current_z here - # insert code to wait for motor movement here - intensity = 0 # insert code to read ai_channel signal here - - positions.append(current_z) - intensities.append(intensity) - current_z += z_step - - # Checkpoint: Save scan results - max_intensity = max(intensities) if intensities else 0 - max_idx = intensities.index(max_intensity) if intensities else 0 - max_position = positions[max_idx] if positions else None - - checkpoint = { - 'scan_type': 'z_range', - 'ai_channel': ai_channel, - 'z_start': z_start, - 'z_end': z_end, - 'z_step': z_step, - 'num_points': len(positions), - 'max_intensity': max_intensity, - 'max_position': max_position, - 'positions': positions, - 'intensities': intensities - } - # insert code to save checkpoint here - - return positions, intensities -``` - ---- - -### 6. Finding Half-Maximum Z Position - -**Description:** Finds the Z position where intensity is half of the maximum. This position corresponds to the beam edge and is used for precise sample alignment. - -**Pseudo Code:** -``` -FUNCTION FIND_Z_HALF_MAX(z_positions, intensities): - SET max_intensity = MAX(intensities) - SET half_max = max_intensity / 2 - - // Option A: Linear interpolation - SET z_center = INTERPOLATE(z_positions, intensities, half_max) - - // Option B: If interpolation fails, use maximum position - IF z_center is undefined: - SET max_idx = INDEX_OF_MAX(intensities) - SET z_center = z_positions[max_idx] - END IF - - RETURN z_center -END FUNCTION -``` - -**Python Implementation:** -```python -def find_z_half_max(z_positions, intensities): - """ - Find Z position where intensity is half of maximum. - - Parameters: - ----------- - z_positions : list of float - Z positions from scan - intensities : list of float - Intensity values from scan - - Returns: - -------- - z_center : float - Z position at half-maximum intensity - - Checkpoint: - ----------- - Saves: z_center, max_intensity, half_max, method_used - """ - if not intensities: - return None - - max_intensity = max(intensities) - half_max = max_intensity / 2.0 - - # Try linear interpolation - z_center = interpolate(z_positions, intensities, half_max) - method_used = 'interpolation' - - # Fallback to maximum position if interpolation fails - if z_center is None: - max_idx = intensities.index(max_intensity) - z_center = z_positions[max_idx] - method_used = 'maximum_fallback' - - # Checkpoint: Save half-maximum finding result - checkpoint = { - 'z_center': z_center, - 'max_intensity': max_intensity, - 'half_max_intensity': half_max, - 'method_used': method_used, - 'num_points': len(z_positions), - 'z_range': (min(z_positions), max(z_positions)) - } - # insert code to save checkpoint here - - return z_center -``` - ---- - -### 7. Finding Optimal Theta Position - -**Description:** Finds the optimal theta position by scanning a range and calculating the centroid of the reflected intensity peak. More accurate than finding the simple maximum. - -**Pseudo Code:** -``` -FUNCTION FIND_THETA_OPTIMAL(theta_start, theta_end, theta_step, - base_theta, theta_offset, theta_photodiode, ai_channel): - SET theta_positions = [] - SET reflected_intensities = [] - SET current_theta = theta_start - - WHILE current_theta <= theta_end: - SET sample_theta = base_theta + theta_offset + current_theta - SET expected_ccd_theta = theta_photodiode + 2 * sample_theta - - SET Sample_Theta = sample_theta - SET CCD_Theta = expected_ccd_theta - WAIT for motor movements - intensity = READ ai_channel signal - - APPEND sample_theta to theta_positions - APPEND intensity to reflected_intensities - SET current_theta = current_theta + theta_step - END WHILE - - SET (theta_optimal, max_theta) = FIND_CENTROID(theta_positions, reflected_intensities) - - RETURN theta_optimal -END FUNCTION -``` - -**Python Implementation:** -```python -def find_theta_optimal(theta_start, theta_end, theta_step, base_theta, - theta_offset, theta_photodiode, ai_channel): - """ - Find optimal theta position by scanning and finding peak centroid. - - Parameters: - ----------- - theta_start : float - Starting theta offset (degrees) - theta_end : float - Ending theta offset (degrees) - theta_step : float - Step size (degrees) - base_theta : float - Base theta position (typically 1.0 degrees) - theta_offset : float - Current theta offset to apply - theta_photodiode : float - Optimal CCD Theta for photodiode (from instrument alignment) - ai_channel : str - AI channel to read (e.g., "Photodiode", "AI 6 BeamStop") - - Returns: - -------- - theta_optimal : float - Optimal theta position (centroid of peak) - - Checkpoint: - ----------- - Saves: theta_optimal, scan_data, centroid_info - """ - theta_positions = [] - reflected_intensities = [] - - current_theta = theta_start - while current_theta <= theta_end: - sample_theta = base_theta + theta_offset + current_theta - # Calculate expected photodiode position for reflection (2*theta geometry) - expected_ccd_theta = theta_photodiode + 2 * sample_theta - - # insert code to move Sample Theta to sample_theta here - # insert code to move CCD Theta to expected_ccd_theta here - # insert code to wait for motor movements here - intensity = 0 # insert code to read ai_channel signal here - - theta_positions.append(sample_theta) - reflected_intensities.append(intensity) - current_theta += theta_step - - # Find centroid of peak - theta_optimal, max_theta = find_centroid(theta_positions, reflected_intensities) - - # Checkpoint: Save theta optimization result - checkpoint = { - 'theta_optimal': theta_optimal, - 'max_theta': max_theta, - 'base_theta': base_theta, - 'theta_offset': theta_offset, - 'theta_photodiode': theta_photodiode, - 'scan_range': (theta_start, theta_end), - 'theta_step': theta_step, - 'max_intensity': max(reflected_intensities) if reflected_intensities else 0, - 'positions': theta_positions, - 'intensities': reflected_intensities - } - # insert code to save checkpoint here - - return theta_optimal -``` - ---- - -### 8. Checking Convergence - -**Description:** Checks if the alignment has converged by comparing current values to previous values. Used to determine when to stop iterative alignment. - -**Pseudo Code:** -``` -FUNCTION CHECK_CONVERGENCE(current_value, previous_value, tolerance): - IF previous_value is undefined: - RETURN (converged=False, delta=None) - END IF - - SET delta = ABS(current_value - previous_value) - SET converged = (delta < tolerance) - - RETURN (converged, delta) -END FUNCTION -``` - -**Python Implementation:** -```python -def check_convergence(current_value, previous_value, tolerance): - """ - Check if alignment has converged. - - Parameters: - ----------- - current_value : float - Current measured value - previous_value : float or None - Previous measured value (None for first iteration) - tolerance : float - Convergence tolerance - - Returns: - -------- - converged : bool - True if converged, False otherwise - delta : float or None - Difference between current and previous values - - Checkpoint: - ----------- - Saves: converged, delta, tolerance, iteration_info - """ - if previous_value is None: - delta = None - converged = False - else: - delta = abs(current_value - previous_value) - converged = delta < tolerance - - # Checkpoint: Save convergence check - checkpoint = { - 'converged': converged, - 'delta': delta, - 'tolerance': tolerance, - 'current_value': current_value, - 'previous_value': previous_value - } - # insert code to save checkpoint here - - return converged, delta -``` - ---- - -### 9. Positioning Detector for Photodiode - -**Description:** Positions the detector to use the photodiode instead of the area detector. This is the first step in instrument alignment. - -**Pseudo Code:** -``` -FUNCTION POSITION_DETECTOR_PHOTODIODE(): - SET CCD_X = photodiode_center_position - SET Sample_Z = beam_path_clear_position - WAIT for motor movements -END FUNCTION -``` - -**Python Implementation:** -```python -def position_detector_photodiode(photodiode_x_position=6.0, beam_clear_z=-2.0): - """ - Position detector to use photodiode and clear beam path. - - Parameters: - ----------- - photodiode_x_position : float - CCD X position for photodiode (mm, default: 6.0) - beam_clear_z : float - Sample Z position to clear beam path (mm, default: -2.0) - - Checkpoint: - ----------- - Saves: ccd_x_position, sample_z_position - """ - # insert code to move CCD X to photodiode_x_position here - # insert code to move Sample Z to beam_clear_z here - # insert code to wait for motor movements here - - # Checkpoint: Save positioning - checkpoint = { - 'step': 'position_detector_photodiode', - 'ccd_x_position': photodiode_x_position, - 'sample_z_position': beam_clear_z - } - # insert code to save checkpoint here -``` - ---- - -### 10. Finding Optimal Photodiode Theta - -**Description:** Finds the optimal CCD Theta position that maximizes intensity on the main photodiode. This is the reference position for all subsequent alignments. - -**Pseudo Code:** -``` -FUNCTION FIND_PHOTODIODE_THETA(theta_range_center=0, theta_range_width=2): - SET theta_range = [theta_range_center - theta_range_width, - theta_range_center + theta_range_width] - SET (positions, intensities) = SCAN_THETA_RANGE( - theta_range[0], theta_range[1], step=0.5, - motor="CCD Theta", ai_channel="Photodiode" - ) - SET (theta_optimal, max_intensity) = FIND_MAX(zip(positions, intensities)) - - RETURN theta_optimal -END FUNCTION -``` - -**Python Implementation:** -```python -def find_photodiode_theta(theta_range_center=0.0, theta_range_width=2.0, theta_step=0.5): - """ - Find optimal CCD Theta position for main photodiode. - - Parameters: - ----------- - theta_range_center : float - Center of theta scan range (degrees, default: 0.0) - theta_range_width : float - Width of theta scan range (degrees, default: 2.0) - theta_step : float - Step size for scan (degrees, default: 0.5) - - Returns: - -------- - theta_photodiode : float - Optimal CCD Theta position for photodiode - - Checkpoint: - ----------- - Saves: theta_photodiode, max_intensity, scan_data - """ - theta_start = theta_range_center - theta_range_width - theta_end = theta_range_center + theta_range_width - - positions, intensities = scan_theta_range( - theta_start, theta_end, theta_step, - motor_name="CCD Theta", - ai_channel="Photodiode" - ) - - theta_photodiode, max_intensity = find_max(list(zip(positions, intensities))) - - # Move to optimal position - # insert code to move CCD Theta to theta_photodiode here - # insert code to wait for motor movement here - - # Checkpoint: Save photodiode theta result - checkpoint = { - 'step': 'find_photodiode_theta', - 'theta_photodiode': theta_photodiode, - 'max_intensity': max_intensity, - 'scan_center': theta_range_center, - 'scan_width': theta_range_width, - 'scan_step': theta_step - } - # insert code to save checkpoint here - - return theta_photodiode -``` - ---- - -### 11. Finding Optimal Beamstop Theta - -**Description:** Finds the optimal CCD Theta position that maximizes intensity on the slitted beamstop photodiode (AI 6 BeamStop). This provides higher precision alignment. - -**Pseudo Code:** -``` -FUNCTION FIND_BEAMSTOP_THETA(theta_photodiode, offset_estimate=4.0): - SET theta_center = theta_photodiode + offset_estimate - SET theta_range = [theta_center - 1.0, theta_center + 1.0] - SET (positions, intensities) = SCAN_THETA_RANGE( - theta_range[0], theta_range[1], step=0.5, - motor="CCD Theta", ai_channel="AI 6 BeamStop" - ) - SET (theta_optimal, max_intensity) = FIND_MAX(zip(positions, intensities)) - - RETURN theta_optimal -END FUNCTION -``` - -**Python Implementation:** -```python -def find_beamstop_theta(theta_photodiode, offset_estimate=4.0, - theta_range_width=1.0, theta_step=0.5): - """ - Find optimal CCD Theta position for beamstop photodiode. - - Parameters: - ----------- - theta_photodiode : float - Optimal photodiode theta position (from find_photodiode_theta) - offset_estimate : float - Estimated offset from photodiode position (degrees, default: 4.0) - theta_range_width : float - Width of scan range around estimate (degrees, default: 1.0) - theta_step : float - Step size for scan (degrees, default: 0.5) - - Returns: - -------- - theta_beamstop : float - Optimal CCD Theta position for beamstop - - Checkpoint: - ----------- - Saves: theta_beamstop, max_intensity, offset_from_photodiode - """ - theta_center = theta_photodiode + offset_estimate - theta_start = theta_center - theta_range_width - theta_end = theta_center + theta_range_width - - positions, intensities = scan_theta_range( - theta_start, theta_end, theta_step, - motor_name="CCD Theta", - ai_channel="AI 6 BeamStop" - ) - - theta_beamstop, max_intensity = find_max(list(zip(positions, intensities))) - - # Checkpoint: Save beamstop theta result - offset_from_photodiode = theta_beamstop - theta_photodiode - checkpoint = { - 'step': 'find_beamstop_theta', - 'theta_beamstop': theta_beamstop, - 'theta_photodiode': theta_photodiode, - 'offset_from_photodiode': offset_from_photodiode, - 'max_intensity': max_intensity, - 'scan_center': theta_center, - 'scan_width': theta_range_width, - 'scan_step': theta_step - } - # insert code to save checkpoint here - - return theta_beamstop -``` - ---- - -### 12. Complete Instrument Alignment - -**Description:** Complete instrument alignment procedure that finds optimal positions for both photodiode and beamstop detectors. This must be done before sample alignment. - -**Pseudo Code:** -``` -FUNCTION INSTRUMENT_ALIGNMENT(): - POSITION_DETECTOR_PHOTODIODE() - SET theta_photodiode = FIND_PHOTODIODE_THETA() - SET theta_beamstop = FIND_BEAMSTOP_THETA(theta_photodiode) - SET CCD_Theta = theta_photodiode // Return to photodiode position - RETURN (theta_photodiode, theta_beamstop) -END FUNCTION -``` - -**Python Implementation:** -```python -def instrument_alignment(): - """ - Complete instrument alignment to find optimal detector positions. - - Returns: - -------- - theta_photodiode : float - Optimal CCD Theta for main photodiode - theta_beamstop : float - Optimal CCD Theta for beamstop photodiode - - Checkpoint: - ----------- - Saves: Complete alignment results, all intermediate steps - """ - # Step 1-2: Position detector - position_detector_photodiode() - - # Step 3: Find optimal photodiode theta - theta_photodiode = find_photodiode_theta() - - # Step 4-5: Find optimal beamstop theta - theta_beamstop = find_beamstop_theta(theta_photodiode) - - # Step 6: Return to photodiode position - # insert code to move CCD Theta to theta_photodiode here - # insert code to wait for motor movement here - - # Checkpoint: Save complete instrument alignment - checkpoint = { - 'step': 'instrument_alignment_complete', - 'theta_photodiode': theta_photodiode, - 'theta_beamstop': theta_beamstop, - 'offset_photodiode_to_beamstop': theta_beamstop - theta_photodiode - } - # insert code to save checkpoint here - - return theta_photodiode, theta_beamstop -``` - ---- - -### 13. Single Iteration of Sample Alignment - -**Description:** Performs one iteration of sample alignment, finding optimal Z and Theta positions. This is the core of the iterative alignment loop. - -**Pseudo Code:** -``` -FUNCTION SAMPLE_ALIGNMENT_ITERATION(theta_photodiode, theta_offset, - z_start, z_end, z_step): - // Find Z center - SET (z_positions, intensities) = SCAN_Z_RANGE(z_start, z_end, z_step, "Photodiode") - SET z_center = FIND_Z_HALF_MAX(z_positions, intensities) - - // Find optimal Theta - SET theta_optimal = FIND_THETA_OPTIMAL( - theta_start=0, theta_end=2, theta_step=0.1, - base_theta=1.0, theta_offset=theta_offset, - theta_photodiode=theta_photodiode, ai_channel="Photodiode" - ) - SET theta_offset_new = theta_optimal - 1.0 - - RETURN (z_center, theta_offset_new) -END FUNCTION -``` - -**Python Implementation:** -```python -def sample_alignment_iteration(theta_photodiode, theta_offset, - z_start=0.0, z_end=5.0, z_step=0.05): - """ - Perform one iteration of sample alignment. - - Parameters: - ----------- - theta_photodiode : float - Optimal CCD Theta for photodiode - theta_offset : float - Current theta offset - z_start : float - Starting Z position for scan (mm) - z_end : float - Ending Z position for scan (mm) - z_step : float - Step size for Z scan (mm) - - Returns: - -------- - z_center : float - Optimal Z position (half-maximum) - theta_offset_new : float - New theta offset - - Checkpoint: - ----------- - Saves: z_center, theta_offset_new, iteration_data - """ - # Ensure sample is below beam - # insert code to move Sample Z to below_beam_position here - - # Find Z center (half-maximum) - z_positions, intensities = scan_z_range(z_start, z_end, z_step, "Photodiode") - z_center = find_z_half_max(z_positions, intensities) - - # Find optimal Theta - theta_optimal = find_theta_optimal( - theta_start=0.0, theta_end=2.0, theta_step=0.1, - base_theta=1.0, theta_offset=theta_offset, - theta_photodiode=theta_photodiode, ai_channel="Photodiode" - ) - theta_offset_new = theta_optimal - 1.0 - - # Apply new theta offset - # insert code to move Sample Theta to theta_offset_new here - # insert code to wait for motor movement here - - # Checkpoint: Save iteration results - checkpoint = { - 'step': 'sample_alignment_iteration', - 'z_center': z_center, - 'theta_offset_old': theta_offset, - 'theta_offset_new': theta_offset_new, - 'theta_optimal': theta_optimal, - 'z_scan_range': (z_start, z_end), - 'z_step': z_step - } - # insert code to save checkpoint here - - return z_center, theta_offset_new -``` - ---- - -### 14. Complete Sample Alignment - -**Description:** Iterative sample alignment that converges to optimal Z and Theta positions. Continues until convergence criteria are met. - -**Pseudo Code:** -``` -FUNCTION SAMPLE_ALIGNMENT(theta_photodiode, convergence_tolerance=0.01, max_iterations=10): - SET iteration = 0 - SET theta_offset = 0 - SET previous_z_center = undefined - SET previous_theta_offset = undefined - - WHILE iteration < max_iterations: - SET (z_center, theta_offset_new) = SAMPLE_ALIGNMENT_ITERATION( - theta_photodiode, theta_offset - ) - - // Check Z convergence - SET (z_converged, z_delta) = CHECK_CONVERGENCE( - z_center, previous_z_center, convergence_tolerance - ) - - // Check Theta convergence - SET (theta_converged, theta_delta) = CHECK_CONVERGENCE( - theta_offset_new, previous_theta_offset, convergence_tolerance - ) - - IF z_converged AND theta_converged AND iteration > 0: - BREAK - END IF - - SET previous_z_center = z_center - SET previous_theta_offset = theta_offset_new - SET theta_offset = theta_offset_new - SET iteration = iteration + 1 - END WHILE - - RETURN (z_center, theta_offset) -END FUNCTION -``` - -**Python Implementation:** -```python -def sample_alignment(theta_photodiode, convergence_tolerance=0.01, max_iterations=10): - """ - Complete iterative sample alignment. - - Parameters: - ----------- - theta_photodiode : float - Optimal CCD Theta for photodiode (from instrument alignment) - convergence_tolerance : float - Convergence tolerance (mm or degrees, default: 0.01) - max_iterations : int - Maximum number of iterations (default: 10) - - Returns: - -------- - z_center : float - Final optimal Z position - theta_offset : float - Final theta offset - - Checkpoint: - ----------- - Saves: Final results, all iteration data, convergence info - """ - iteration = 0 - theta_offset = 0.0 - previous_z_center = None - previous_theta_offset = None - - while iteration < max_iterations: - z_center, theta_offset_new = sample_alignment_iteration( - theta_photodiode, theta_offset - ) - - # Check Z convergence - z_converged, z_delta = check_convergence( - z_center, previous_z_center, convergence_tolerance - ) - - # Check Theta convergence - theta_converged, theta_delta = check_convergence( - theta_offset_new, previous_theta_offset, convergence_tolerance - ) - - # Checkpoint: Save iteration info - checkpoint = { - 'step': 'sample_alignment_iteration', - 'iteration': iteration, - 'z_center': z_center, - 'theta_offset': theta_offset_new, - 'z_converged': z_converged, - 'z_delta': z_delta, - 'theta_converged': theta_converged, - 'theta_delta': theta_delta, - 'convergence_tolerance': convergence_tolerance - } - # insert code to save checkpoint here - - # Break if converged (after at least one iteration) - if z_converged and theta_converged and iteration > 0: - break - - previous_z_center = z_center - previous_theta_offset = theta_offset_new - theta_offset = theta_offset_new - iteration += 1 - - # Checkpoint: Save final alignment results - final_checkpoint = { - 'step': 'sample_alignment_complete', - 'final_z_center': z_center, - 'final_theta_offset': theta_offset, - 'total_iterations': iteration, - 'converged': (iteration < max_iterations), - 'convergence_tolerance': convergence_tolerance - } - # insert code to save checkpoint here - - return z_center, theta_offset -``` - ---- - -### 15. Fine-Grained Sample Alignment - -**Description:** Same as sample alignment but uses the slitted beamstop photodiode for higher precision. Uses tighter convergence tolerance and smaller step sizes. - -**Pseudo Code:** -``` -FUNCTION FINE_GRAINED_SAMPLE_ALIGNMENT(theta_beamstop, convergence_tolerance=0.005, max_iterations=10): - // Same as SAMPLE_ALIGNMENT but: - // - Use theta_beamstop instead of theta_photodiode - // - Use "AI 6 BeamStop" instead of "Photodiode" - // - Use smaller z_step (0.02 instead of 0.05) - // - Use smaller theta_step (0.05 instead of 0.1) - // - Use tighter convergence_tolerance -END FUNCTION -``` - -**Python Implementation:** -```python -def fine_grained_sample_alignment(theta_beamstop, convergence_tolerance=0.005, max_iterations=10): - """ - Fine-grained sample alignment using slitted beamstop photodiode. - - Parameters: - ----------- - theta_beamstop : float - Optimal CCD Theta for beamstop (from instrument alignment) - convergence_tolerance : float - Tighter convergence tolerance (default: 0.005) - max_iterations : int - Maximum number of iterations (default: 10) - - Returns: - -------- - z_center : float - Final optimal Z position - theta_offset : float - Final theta offset - - Checkpoint: - ----------- - Saves: Final results, all iteration data, convergence info - """ - iteration = 0 - theta_offset = 0.0 - previous_z_center = None - previous_theta_offset = None - - while iteration < max_iterations: - # Ensure sample is below beam - # insert code to move Sample Z to below_beam_position here - - # Find Z center using beamstop (finer step) - z_positions, intensities = scan_z_range( - z_start=0.0, z_end=5.0, z_step=0.02, # Smaller step - ai_channel="AI 6 BeamStop" - ) - z_center = find_z_half_max(z_positions, intensities) - - # Find optimal Theta using beamstop (finer step) - theta_optimal = find_theta_optimal( - theta_start=0.0, theta_end=2.0, theta_step=0.05, # Smaller step - base_theta=1.0, theta_offset=theta_offset, - theta_photodiode=theta_beamstop, # Use beamstop theta - ai_channel="AI 6 BeamStop" # Use beamstop channel - ) - theta_offset_new = theta_optimal - 1.0 - - # Apply new theta offset - # insert code to move Sample Theta to theta_offset_new here - # insert code to wait for motor movement here - - # Check convergence - z_converged, z_delta = check_convergence( - z_center, previous_z_center, convergence_tolerance - ) - theta_converged, theta_delta = check_convergence( - theta_offset_new, previous_theta_offset, convergence_tolerance - ) - - # Checkpoint: Save iteration info - checkpoint = { - 'step': 'fine_grained_sample_alignment_iteration', - 'iteration': iteration, - 'z_center': z_center, - 'theta_offset': theta_offset_new, - 'z_converged': z_converged, - 'z_delta': z_delta, - 'theta_converged': theta_converged, - 'theta_delta': theta_delta, - 'convergence_tolerance': convergence_tolerance - } - # insert code to save checkpoint here - - # Break if converged - if z_converged and theta_converged and iteration > 0: - break - - previous_z_center = z_center - previous_theta_offset = theta_offset_new - theta_offset = theta_offset_new - iteration += 1 - - # Checkpoint: Save final alignment results - final_checkpoint = { - 'step': 'fine_grained_sample_alignment_complete', - 'final_z_center': z_center, - 'final_theta_offset': theta_offset, - 'total_iterations': iteration, - 'converged': (iteration < max_iterations), - 'convergence_tolerance': convergence_tolerance - } - # insert code to save checkpoint here - - return z_center, theta_offset -``` diff --git a/.gitignore b/.gitignore index f56241f..89bc543 100644 --- a/.gitignore +++ b/.gitignore @@ -190,3 +190,6 @@ pyrightconfig.json # End of https://www.toptal.com/developers/gitignore/api/python python/pyref/data/.pyref_catalog.db + +# Macos metadata +.DS_Store diff --git a/.superpowers/brainstorm/6299-1774202169/.server-stopped b/.superpowers/brainstorm/6299-1774202169/.server-stopped deleted file mode 100644 index 7de7923..0000000 --- a/.superpowers/brainstorm/6299-1774202169/.server-stopped +++ /dev/null @@ -1 +0,0 @@ -{"reason":"idle timeout","timestamp":1774208609920} diff --git a/.superpowers/brainstorm/6299-1774202169/.server.pid b/.superpowers/brainstorm/6299-1774202169/.server.pid deleted file mode 100644 index 8158f7a..0000000 --- a/.superpowers/brainstorm/6299-1774202169/.server.pid +++ /dev/null @@ -1 +0,0 @@ -6308 diff --git a/.superpowers/brainstorm/6299-1774202169/arch-overview.html b/.superpowers/brainstorm/6299-1774202169/arch-overview.html deleted file mode 100644 index fbc1bf1..0000000 --- a/.superpowers/brainstorm/6299-1774202169/arch-overview.html +++ /dev/null @@ -1,312 +0,0 @@ -

        Design Section 1 — Architecture Overview

        -

        Navigator + per-screen state decomposition. Review and confirm before we detail each component.

        - - - -
        -

        Module Structure — What Changes vs. What Stays

        -
        -
        -
        🗂 src/bin/tui/ — Restructured
        -
        - 
        // NEW files
-navigator.rs     // Navigator + ScreenState enum
-explorer/
-  mod.rs         // ExplorerState + level enum
-  heuristic.rs   // date-pattern path classification
-  modal.rs       // layout-config modal (ratatui)
-catalog_handle.rs // CatalogHandle — shared query API
-
-// MODIFIED files
-app.rs           // App → thin shell, owns Navigator
-ui.rs            // dispatches on ScreenState variant
-run.rs           // event loop delegates to Navigator
-keymap.rs        // per-screen keybind dispatch
-
-// UNCHANGED files
-beamspot.rs      // no changes
-preview.rs       // no changes
-scan_type.rs     // no changes
-theme.rs         // no changes
-terminal_guard.rs // no changes
-error.rs         // minor additions
        -
        -
        - -
        -
        📚 src/catalog/ — Additions only
        -
        - 
        // NEW files
-explorer_query.rs
-  // list_experimentalists(data_root)
-  // list_beamtimes(data_root, experimentalist)
-  // entry_metadata(path) → EntryMeta
-  // catalog_status(path) → Indexed|Stale|Missing
-
-// MODIFIED files
-ingest.rs        // writes to DATA-level catalog
-                   // records experimentalist + beamtime
-query.rs          // add beamtime_path filter param
-                   // to existing query_scan_points()
-mod.rs            // re-export new public API
-
-// UNCHANGED
-beamtime_index.rs // kept for backward compat
-watch.rs          // no changes
-materialize.rs    // no changes
        -
        -
        -
        -
        - -
        -

        Core State Model

        -
        -
        -
        App — Thin Shell (was 2600-line monolith)
        -
        - 
        pub struct App {
-    // navigation
-    pub navigator: Navigator,
-
-    // shared async channels (unchanged)
-    pub preview_tx:  Option<Sender<(PathBuf, Option<f64>)>>,
-    pub beamspot_rx: Option<Receiver<BeamspotUpdate>>,
-    pub preview_cmd_rx: Option<Receiver<PreviewCommand>>,
-
-    // shared config / theme (unchanged)
-    pub layout:     String,
-    pub keymap:     String,
-    pub theme:      String,
-    pub needs_redraw: bool,
-}
        -
        -
        - -
        -
        Navigator — Screen Stack
        -
        - 
        pub struct Navigator {
-    // current screen + back-history
-    stack: Vec<ScreenState>,
-    forward: Vec<ScreenState>,
-
-    // shared catalog handle
-    pub catalog: CatalogHandle,
-}
-
-pub enum ScreenState {
-    // new: file-explorer at any depth
-    Explorer(ExplorerState),
-
-    // adapted: was Screen::Beamtime
-    Beamtime(BeamtimeState),
-
-    // new: layout-config modal
-    ConfigModal(ModalState),
-}
-
-impl Navigator {
-    pub fn push(&mut self, s: ScreenState) {}
-    pub fn pop(&mut self) {}
-    pub fn current(&self) -> &ScreenState {}
-    pub fn current_mut(&mut self) -> &mut ScreenState {}
-}
        -
        -
        -
        -
        - -
        -

        Screen State Ownership

        -
        -Navigator owns Vec<ScreenState> (back stack — no cloning, push/pop) - │ - ├─▶ ScreenState::Explorer(ExplorerState) - │ │ - │ ├─ level: ExplorerLevel Root | Experimentalist | Generic - │ ├─ root: PathBuf DATA/ passed from CLI - │ ├─ current_dir: PathBuf where we are now - │ ├─ entries: Vec<DirEntry> enriched with metadata - │ └─ list_state: ListState - │ - ├─▶ ScreenState::Beamtime(BeamtimeState) - │ │ - │ ├─ beamtime_path: PathBuf filter key into DATA catalog - │ ├─ experimentalist: String for display + catalog filter - │ └─ [existing fields from App] samples, tags, scans, groups… - │ - └─▶ ScreenState::ConfigModal(ModalState) - │ - ├─ depth_to_beamtime: u8 user-configured depth - ├─ pattern: String regex / format string - └─ return_to: Box<ScreenState> screen to resume after confirm - -CatalogHandle shared via Arc<Mutex<Connection>> or per-call open() - ├─▶ list_experimentalists(root) - ├─▶ list_beamtimes(root, experimentalist) - ├─▶ query_scan_points(root, beamtime_path, filter) adapted from existing - └─▶ catalog_status(path) → Indexed | Stale | Missing -
        -
        - -
        -

        Catalog Schema Addition

        -
        -
        New columns added to bt_beamtimes (no breaking changes)
        -
        - 
        -- existing bt_beamtimes already has: id, beamtime_path, user, year, month, esaf_number, label
--- additions:
-ALTER TABLE bt_beamtimes ADD COLUMN experimentalist  TEXT NOT NULL DEFAULT '';
-ALTER TABLE bt_beamtimes ADD COLUMN data_root        TEXT NOT NULL DEFAULT '';
--- data_root: the DATA/ path this beamtime was ingested under
--- experimentalist: the folder name one level above the beamtime
-
--- new index for explorer queries
-CREATE INDEX IF NOT EXISTS idx_bt_experimentalist
-    ON bt_beamtimes(data_root, experimentalist);
        -
        -
        - -
        - Migration strategy: both new columns have DEFAULT '' so existing catalog DBs open without errors. The explorer_query functions treat empty strings as "unset" and fall back to path parsing to infer experimentalist from the beamtime_path. Re-ingesting any beamtime populates them correctly. -
        -
        - -
        -

        Execution Phases

        -
        -
        -
        Phase 1 — Decompose (no new features)
        -
        - Extract BeamtimeState from App
        - Introduce Navigator + ScreenState
        - App becomes thin shell
        - All existing behavior preserved
        - ✓ mergeable independently -
        -
        -
        -
        Phase 2 — Explorer screens
        -
        - Add ExplorerState + ExplorerLevel
        - Add heuristic.rs path classifier
        - Add modal.rs config dialog
        - Wire CLI arg as DATA root
        - ✓ mergeable independently -
        -
        -
        -
        Phase 3 — DATA-level catalog
        -
        - Add explorer_query.rs
        - Migrate ingestion to DATA-level DB
        - Add schema columns to bt_beamtimes
        - Adapt query_scan_points filter
        - ✓ mergeable independently -
        -
        -
        -
        Phase 4 — Auto-ingest + wiring
        -
        - Auto-ingest on entry to un-indexed dir
        - Explorer reads catalog status per entry
        - Beamtime screen queries DATA catalog
        - End-to-end flow validated
        - ✓ complete -
        -
        -
        -
        - diff --git a/.superpowers/brainstorm/6299-1774202169/beamtime-adapt.html b/.superpowers/brainstorm/6299-1774202169/beamtime-adapt.html deleted file mode 100644 index ac7aa89..0000000 --- a/.superpowers/brainstorm/6299-1774202169/beamtime-adapt.html +++ /dev/null @@ -1,266 +0,0 @@ -

        Design Section 3 — Beamtime Screen Adaptation + Auto-Ingest

        -

        How BeamtimeState extracts from App, how queries shift to the DATA catalog, and what auto-ingest on entry looks like.

        - - - - -
        -

        BeamtimeState — extracted from App

        -
        -
        -
        BeamtimeState (owns former App fields)
        -
        - 
        pub struct BeamtimeState {
-    // ── context (NEW) ───────────────────────
-    pub data_root:      PathBuf,  // DATA/ root
-    pub experimentalist: String,  // "Smith"
-    pub beamtime_path:  PathBuf,  // filter key
-
-    // ── ingest state (was in App) ───────────
-    pub loading_state:  LoadingState,
-    pub ingest_rx:      Option<Receiver<Result<(), String>>>,
-    pub ingest_progress_rx: Option<Receiver<(u32, u32)>>,
-    pub ingest_progress: Option<(u32, u32)>,
-
-    // ── data (was in App) ───────────────────
-    pub all_groups:     Vec<GroupedProfileRow>,
-    pub filtered_groups: Vec<GroupedProfileRow>,
-    pub samples:        Vec<String>,
-    pub tags:           Vec<String>,
-    pub scans:          Vec<(u32, String)>,
-
-    // ── UI state (was in App) ───────────────
-    pub sample_state:   ListState,
-    pub tag_state:      ListState,
-    pub scan_list_state: ListState,
-    pub table_state:    TableState,
-    // ... scrollbar states, sort, expand ...
-    // ... focus, mode, search_query ...     
-    // ... status_message, app_error ...     
-    // (all moved verbatim — no logic change)
-}
        -
        -
        -
        -
        Query change — beamtime_path filter
        -
        - 
        // BEFORE — per-beamtime catalog, no filter
-let db = resolve_catalog_path(&beamtime_dir);
-let rows = query_files(&db, None)?;
-
-// AFTER — DATA-level catalog, path filter
-let db = data_root.join(".pyref/catalog.db");
-let rows = query_scan_points(
-    &db,
-    &beamtime_path,  // NEW: WHERE source_path LIKE ?
-    None,
-)?;
-
-// query_scan_points signature change:
-pub fn query_scan_points(
-    db:            &Path,
-    beamtime_path: &Path,   // was Option — now required
-    filter:        Option<&CatalogFilter>,
-) -> Result<Vec<FileRow>>;
-
-// SQL fragment added:
-// AND source_path LIKE ?1 || '%'
-// bound to: beamtime_path.to_str()
        - -
        - Migration path: query_files() (old per-beamtime API) is kept but deprecated. It still works against a per-beamtime DB. Callers are migrated one by one. -
        -
        -
        -
        -
        - - -
        -

        Auto-Ingest on Entry — Three UI States

        -
        - - -
        -
        State A — entering, ingest starting
        - 
        - pyref  Smith / 2024Jan                    
-┌──────────────────────────────────────┐
-│                                      │
-│   ⠸ Indexing 2024Jan/…               │
-│                                      │
-│   Scanning FITS files               │
-│   Writing to DATA catalog           │
-│                                      │
-│   ████████░░░░░░░░░░  412 / 1423    │
-│                                      │
-│   [Esc] cancel and go back          │
-│                                      │
-└──────────────────────────────────────┘
-
        -
        - - -
        -
        State B — ingest complete, loading data
        - 
        - pyref  Smith / 2024Jan                    
-┌─────────────┬──────┬──────┬───────────┐
-│ Samples      │Tags  │Scans │ Profiles   │
-├─────────────┼──────┼──────┼───────────┤
-│  PS-b-PEO   │annea │ 001  │           │
-│  PBDB-T     │      │ 002  │           │
-│             │      │ 003  │           │
-└─────────────┴──────┴──────┴───────────┘
- ✓ Indexed 1,423 files  [←] back
        -
        - - -
        -
        State C — already indexed, instant load
        - 
        - pyref  Smith / 2024Jan  ✓               
-┌─────────────┬──────┬──────┬───────────┐
-│ Samples      │Tags  │Scans │ Profiles   │
-├─────────────┼──────┼──────┼───────────┤
-│  PS-b-PEO   │annea │ 001  │ Sample…   │
-│  PBDB-T     │prist │ 002  │ PS-b-P…  │
-│             │      │ 003  │  PBDB-T… │
-└─────────────┴──────┴──────┴───────────┘
- 1,423 files  Smith › 2024Jan  [←] back
        -
        -
        - -
        - Esc during ingest: cancels the background thread (via a CancellationToken-style atomic flag), pops the BeamtimeState off the Navigator stack, and returns to the Explorer. Any partial writes to the catalog are left in place — a second entry attempt will resume indexing from where it stopped (already-indexed files are skipped by mtime check). -
        -
        - - -
        -

        Navigator Entry Flow — Explorer → Beamtime

        -
        -User presses [→] on a Beamtime row in ExplorerState - │ - ▼ -navigator.enter_beamtime(path, experimentalist, data_root) - │ - ├──▶ CatalogStatus::Indexed - │ │ - │ ▼ - │ BeamtimeState::load(data_root, beamtime_path) query DATA catalog - │ │ - │ ▼ - │ Navigator.push(ScreenState::Beamtime(state)) immediate - │ - ├──▶ CatalogStatus::Stale - │ │ - │ ▼ - │ BeamtimeState::new_ingesting(…) push with LoadingState::IngestingDirectory - │ spawn ingest thread → ingest_beamtime(data_root, beamtime_path) - │ - └──▶ CatalogStatus::NotIndexed (same as Stale — auto-ingest) - │ - ▼ - BeamtimeState::new_ingesting(…) - spawn ingest thread → ingest_beamtime(data_root, beamtime_path) - -Ingest completes (try_recv_ingest in run loop): - │ - ├──▶ Ok(()) ──▶ BeamtimeState::reload_from_catalog() now queries DATA catalog - │ ──▶ loading_state = Idle profiles appear - │ ──▶ update ExplorerState entry → CatalogStatus::Indexed via shared channel - │ - └──▶ Err(e) ──▶ set_app_error(ingest_failed(…)) - ──▶ loading_state = Idle error modal shown, [Esc] goes back -
        -
        - - -
        -

        Back Navigation — Beamtime → Explorer

        -
        -
        -
        What happens on [←] / [h]
        -
        - navigator.pop() — discards BeamtimeState
        - Restores the ExplorerState that was underneath
        - Cursor position in the explorer is preserved
        - (the ExplorerState was never mutated while Beamtime was active)

        - The Explorer's status cell for the beamtime just entered is updated to - ✓ Indexed via a lightweight mpsc message sent when ingest completes.

        - No reload of the full explorer entry list needed. -
        -
        -
        -
        Config saved on BeamtimeState drop
        -
        - The same selections that are currently saved on quit - (last_sample, last_tag, last_scan) - are written to TuiConfig when the BeamtimeState - is popped — not just on app exit.

        - This means returning to the same beamtime later in the same session - (or on next launch with the same path) restores your previous cursor position. -
        -
        -
        -
        - diff --git a/.superpowers/brainstorm/6299-1774202169/current-vs-future.html b/.superpowers/brainstorm/6299-1774202169/current-vs-future.html deleted file mode 100644 index ec14a66..0000000 --- a/.superpowers/brainstorm/6299-1774202169/current-vs-future.html +++ /dev/null @@ -1,311 +0,0 @@ -

        TUI Architecture: Current vs. Proposed

        -

        - ASCII wireframes comparing the existing browser interface with the proposed file-explorer redesign.
        - Both mockups represent the full terminal window at a typical 220×50 terminal. -

        - - - -
        -

        Screen 1 of 2 — Entry / Launcher Screen

        - -
        - -
        -
        Current — Flat Beamtime List
        - 
        - pyref browser                                [q]quit  [o]open dir  [↵]open   
-┌─────────────────────────────────────────────────────────────────────────────┐
-│ Recent Beamtimes                                                            │
-├─────────────────────────────────────────────────────────────────────────────┤
-│   /Volumes/DATA/Smith/2024Jan                             2024-01-15  1423 f │
-│   /Volumes/DATA/Jones/2024Feb                            2024-02-03   891 f │
-│   /Volumes/DATA/Smith/2023Nov                            2023-11-22  2110 f │
-│   /Volumes/DATA/Williams/2024Mar                         2024-03-07   344 f │
-│   /Volumes/DATA/Jones/2023Sep                            2023-09-19   755 f │
-│                                                                              │
-│                                                                              │
-│                                                                              │
-│                                                                              │
-│                                                                              │
-│                                                                              │
-│                                                                              │
-│                                                                              │
-│                                                                              │
-│                                                                              │
-│                                                                              │
-│                                                                              │
-│                                                                              │
-│                                                                              │
-│                                                                              │
-└─────────────────────────────────────────────────────────────────────────────┘
- 5 beamtimes indexed                                                           
- [o] Open directory   [↵] Open selected   [q] Quit                            
-
        -
        - - -
        -
        Proposed — NAS File Explorer
        - 
        - pyref browser  /Volumes/DATA             [←]back  [→]enter  [i]ingest  [q]quit 
-┌───────────────────────────────────────────────────────────────────────────────────────────────┐
-│ ▸ /Volumes/DATA                                                                               │
-├──────────────────────────────────────┬────────────────┬───────────┬──────────┬───────────────┤
-│  Name                                │ Date Modified  │ Size      │ Kind     │ # Beamtimes   │
-├──────────────────────────────────────┼────────────────┼───────────┼──────────┼───────────────┤
-│   📁 Smith                           │  2024-03-15    │  48.2 GB  │  Person  │  6 beamtimes  │
-│   📁 Jones                           │  2024-03-07    │  22.7 GB  │  Person  │  4 beamtimes  │
-│   📁 Williams                        │  2024-02-28    │   9.1 GB  │  Person  │  2 beamtimes  │
-│   📁 Chen                            │  2024-01-12    │  31.5 GB  │  Person  │  3 beamtimes  │
-│   📁 Patel                           │  2023-11-05    │   5.4 GB  │  Person  │  1 beamtime   │
-│   📄 .pyref/                         │  2024-03-15    │   2.1 MB  │  Catalog │  (index)      │
-│                                      │                │           │          │               │
-│                                      │                │           │          │               │
-│                                      │                │           │          │               │
-│                                      │                │           │          │               │
-│                                      │                │           │          │               │
-│                                      │                │           │          │               │
-│                                      │                │           │          │               │
-│                                      │                │           │          │               │
-│                                      │                │           │          │               │
-│                                      │                │           │          │               │
-│                                      │                │           │          │               │
-│                                      │                │           │          │               │
-│                                      │                │           │          │               │
-└──────────────────────────────────────┴────────────────┴───────────┴──────────┴───────────────┘
- 5 experimentalists  │  116.9 GB total  │  catalog: ✓ DATA/.pyref/catalog.db    
- [↑↓] navigate  [→/↵] enter dir  [←/h] up  [i] ingest  [/] search  [q] quit   
-
        -
        -
        - -
        -
        -

        Current Launcher — Limitations

        -
          -
        • Flat list — no hierarchy, just recently-ingested paths
          • -
        • Global index at ~/.pyref/ — not co-located with DATA
          • -
        • No browsing — must type a path or pick from history
          • -
        • No metadata — no size, no count of beamtimes per experimentalist
          • -
        • No catalog context — no indication of what's indexed vs. not
          • -
        -
        -
        -

        Proposed Explorer — What Changes

        -
          -
        • Rooted at DATA/ — passed as CLI arg, full NAS awareness
          • -
        • Standard columns — Name, Date Modified, Size, Kind, # Beamtimes
          • -
        • Catalog indicator — shows whether .pyref/catalog.db exists at this level
          • -
        • Navigate down → Experimentalist → Beamtime → scan profiles
          • -
        • Inline ingest — press i to ingest selected folder
          • -
        -
        -
        -
        - -
        -

        Screen 2 of 2 — Experimentalist Drill-Down → Beamtimes

        -
        - -
        -
        Current — Goes straight to Scan Profiles
        - 
        - pyref  /Volumes/DATA/Smith/2024Jan          [i]ingest  [/]search  [q]quit      
-┌─────────────┬─────────────┬─────────────┬──────────────────────────────────────┐
-│ Samples      │ Tags         │ Scans        │ Profiles                              │
-├─────────────┼─────────────┼─────────────┼──────────────────────────────────────┤
-│ ▸ PS-b-PEO   │ ▸ annealed   │ ▸ 001        │ Sample  Tag     Pol  E(eV)   θ-range  │
-│   PBDB-T     │   pristine  │   002        │ PS-b-P anneale  S  284.5   2°-20°   │
-│   PC61BM     │   solvent   │   003        │  PS-b-P anneale  P  284.5   2°-20°  │
-│   PM6        │             │   004        │  PS-b-P pristine S  284.5   2°-20°  │
-│             │             │   005        │  PS-b-P pristine P  284.5   2°-20°  │
-│             │             │   006        │  PBDB-T anneale  S  270.0   2°-15°  │
-│             │             │   007        │  PBDB-T anneale  P  270.0   2°-15°  │
-│             │             │   008        │  PC61BM solvent  S  285.0   5°-25°  │
-│             │             │   009        │                                      │
-│             │             │   010        │                                      │
-│             │             │   011        │                                      │
-│             │             │   012        │                                      │
-│             │             │              │                                      │
-│             │             │              │                                      │
-│             │             │              │                                      │
-└─────────────┴─────────────┴─────────────┴──────────────────────────────────────┘
- 1423 files  •  no intermediate "Beamtime" level exists                         
- [tab] focus  [space] filter  [i] ingest  [r] rename  [m] materialize  [q] quit 
-
        -
        - - -
        -
        Proposed — Experimentalist Level (Smith/)
        - 
        - pyref  /Volumes/DATA/Smith               [←]back  [→]enter  [i]ingest  [q]quit 
-┌──────────────────────────────────────────────────────────────────────────────────────────┐
-│ ▸ /Volumes/DATA / Smith                                                                   │
-├────────────────────┬────────────────┬──────────┬───────────┬──────────────┬──────────────┤
-│  Name               │ Date           │ Size     │ Kind      │ FITS Files   │ Catalog      │
-├────────────────────┼────────────────┼──────────┼───────────┼──────────────┼──────────────┤
-│   📁 2024Jan        │  2024-01-22    │  12.1 GB │  Beamtime │  1,423 files │  ✓ indexed   │
-│   📁 2023Nov        │  2023-11-30    │  18.4 GB │  Beamtime │  2,110 files │  ✓ indexed   │
-│   📁 2023Aug        │  2023-08-14    │   9.2 GB │  Beamtime │    987 files │  ✓ indexed   │
-│   📁 2022Oct        │  2022-10-07    │   5.8 GB │  Beamtime │    612 files │  ⚠ stale     │
-│   📁 2022Feb        │  2022-02-18    │   2.1 GB │  Beamtime │    231 files │  ✗ not indexed│
-│   📁 scratch        │  2024-03-10    │ 847.0 MB │  Dir      │     94 files │  — —         │
-│                     │                │          │           │              │              │
-│                     │                │          │           │              │              │
-│                     │                │          │           │              │              │
-│                     │                │          │           │              │              │
-│                     │                │          │           │              │              │
-│                     │                │          │           │              │              │
-│                     │                │          │           │              │              │
-│                     │                │          │           │              │              │
-│                     │                │          │           │              │              │
-└────────────────────┴────────────────┴──────────┴───────────┴──────────────┴──────────────┘
- 5 beamtimes  │  48.2 GB  │  3 indexed  •  1 stale  •  1 not indexed             
- [↑↓] nav  [→/↵] open beamtime  [←/h] up  [i] ingest here  [I] ingest all  [q]  
-
        -
        -
        - -
        -
        -

        Current — No Intermediate Layer

        -
          -
        • Must already know the exact beamtime path to navigate to it
          • -
        • No way to see all beamtimes for a given experimentalist in one view
          • -
        • Cannot bulk-ingest multiple beamtimes from a single screen
          • -
        • The Beamtime screen works well — but it is the only screen
          • -
        -
        -
        -

        Proposed — Experimentalist Level

        -
          -
        • Per-beamtime catalog status — ✓ indexed / ⚠ stale / ✗ not indexed
          • -
        • FITS file count surfaced without opening the directory
          • -
        • i ingests selected beamtime, I ingests all not-yet-indexed
          • -
        • Entering a beamtime transitions to the existing Beamtime screen — unchanged
          • -
        • Non-beamtime dirs (scratch, misc) shown but clearly typed as generic
          • -
        -
        -
        -
        - -
        -

        Navigation Flow Comparison

        -
        -
        -
        Current Navigation
        -
        - ▪ Launcher — flat list of past beamtime paths
        -    ↓ [↵] open selected or [o] type path
        - ▪ Beamtime Screen — samples/tags/scans/profiles
        -    ↓ [h] nav_up goes to parent dir (reloads beamtime screen)
        - ▪ No awareness of DATA root or experimentalist level
        - ▪ Cannot navigate from Beamtime → sibling Beamtime
        - ▪ Global beamtime index lives in ~/.pyref/ -
        -
        -
        -
        Proposed Navigation
        -
        - ▪ Explorer Screen — rooted at DATA/ (CLI arg)
        -    ↓ [→] enter dir — shows experimentalist dirs
        - ▪ Experimentalist Level — beamtime folders with metadata
        -    ↓ [→] enter beamtime — if cataloged, goes to beamtime view
        - ▪ Beamtime Screen — existing samples/tags/scans view (unchanged)
        -    ↓ [←/h] back to experimentalist level
        - ▪ Full back/forward history stack across all levels
        - ▪ Catalog lives at DATA/.pyref/ — co-located with data -
        -
        -
        -
        - - \ No newline at end of file diff --git a/.superpowers/brainstorm/6299-1774202169/explorer-screen-v2.html b/.superpowers/brainstorm/6299-1774202169/explorer-screen-v2.html deleted file mode 100644 index db2281b..0000000 --- a/.superpowers/brainstorm/6299-1774202169/explorer-screen-v2.html +++ /dev/null @@ -1,284 +0,0 @@ -

        Design Section 2 (revised) — Per-Experimentalist Layout Policy

        -

        - Layout configuration is resolved per experimentalist, not globally. - The root explorer flags unresolved folders. The modal is scoped to one folder at a time. -

        - - - - -
        -

        Data Model — NasLayout replaces LayoutPolicy

        -
        -
        -
        NasLayout — per-experimentalist map
        -
        - 
        /// Persisted to DATA/.pyref/layout.toml
-pub struct NasLayout {
-    /// default beamtime pattern used when
-    /// an experimentalist has no override
-    pub default_pattern: Option<String>,
-    pub default_depth:   u8,  // default: 2
-
-    /// one entry per depth-1 folder name
-    pub experimentalists: HashMap<String, ExptPolicy>,
-}
-
-pub enum ExptPolicy {
-    /// accepted by heuristic or left as
-    /// default — no user action needed
-    Auto,
-
-    /// user specified a custom pattern
-    /// and/or depth for this folder
-    Custom {
-        pattern: Option<String>,
-        depth:   u8,
-    },
-
-    /// user marked as not an experimentalist
-    /// (metadata dir, scratch, etc.)
-    /// shown dimmed, never ingested
-    Ignored,
-}
        -
        -
        -
        -
        Resolution states for a depth-1 folder
        -
        - 
        /// Computed at render time for each
-/// depth-1 entry in the explorer
-pub enum ExptResolution {
-    /// heuristic matched, or user set Auto
-    Resolved,
-
-    /// heuristic failed, needs user action
-    /// shown with ⚑ flag in explorer
-    NeedsResolution,
-
-    /// user marked as not an experimentalist
-    Ignored,
-}
-
-/// DirEntry gains one new field:
-pub struct DirEntry {
-    // ...existing fields...
-    pub expt_resolution: Option<ExptResolution>,
-    // Some only when kind == Experimentalist
-}
        -
        -
        -
        - -
        - What counts as an experimentalist folder? Any depth-1 entry under DATA/ that is a directory and whose name does not start with "." — exactly as you described. The .pyref/ catalog dir, .DS_Store, etc. are automatically excluded by the leading-dot rule. -
        -
        - - -
        -

        Explorer Root — Flagged Experimentalists

        -
        -
        DATA/ — one unresolved folder, one ignored folder
        - 
        - pyref  /Volumes/DATA                     [←]up  [→]enter  [r]resolve  [q]quit 
-┌──────────────────────────────────────────────────────────────────────────────────────────┐
-│ ▸ /Volumes/DATA                                 1 folder needs resolution — [r] to fix  │
-├───────────────────────┬──────────────┬──────────┬──────────────┬────────────────────────┤
-│   Name                 │ Modified     │ Size     │ Beamtimes    │ Status                 │
-├───────────────────────┼──────────────┼──────────┼──────────────┼────────────────────────┤
-│   📁 Smith             │  2024-03-15  │  48.2 GB │  6           │  ✓ resolved            │
-│   📁 Jones             │  2024-03-07  │  22.7 GB │  4           │  ✓ resolved            │
-│   📁 Williams          │  2024-02-28  │   9.1 GB │  2           │  ✓ resolved            │
-│   ⚑ 📁 Chen             │  2024-01-12  │  31.5 GB │  —           │  ⚑ needs resolution    │
-│     📁 scratch           │  2024-03-10  │ 847.0 MB │  —           │  ✗ ignored             │
-│                        │              │          │              │                        │
-│                        │              │          │              │                        │
-│                        │              │          │              │                        │
-└───────────────────────┴──────────────┴──────────┴──────────────┴────────────────────────┘
- 5 folders  •  4 experimentalists  •  1 ignored  •  1 unresolved                          
- [↑↓] nav   [→/↵] enter   [r] resolve selected   [x] ignore selected   [q] quit
        -
        - -
        - Status column rules: - ✓ resolved heuristic matched or user confirmed. - ⚑ needs resolution heuristic found no date-like pattern in this folder's subdirectories — row is highlighted amber, cannot be entered until resolved. - ✗ ignored user marked as non-experimentalist — dimmed, skipped by bulk-ingest. - Beamtime count shows — until resolution is set (we don't know how to count yet). -
        -
        - - -
        -

        Per-Experimentalist Resolution Modal — [r] on a flagged row

        -
        -
        Resolving Chen/ — three choices + live preview
        - 
        - pyref  /Volumes/DATA                                                              
-
-╔══════════════════════════════════════════════════════════════════════════╗
-║  Resolve folder: Chen/                                    [Esc] cancel  ║
-║  pyref couldn't detect beamtime folders inside Chen/.                      ║
-║  Choose how to treat this folder:                                          ║
-║                                                                          ║
-║  ○  Use custom pattern                                                   ║
-║     Specify a name pattern and depth for beamtime folders inside Chen/   ║
-║                                                                          ║
-║     Depth to beamtime   1   Pattern (regex)   run\d+                   ║
-║                                                                          ║
-║     Preview:                                                             ║
-║     ┌──────────────────────────────────────────────────────────────┐   ║
-║     │  Chen/                                  [Experimentalist]  │   ║
-║     │    run001/                              [Beamtime ✓]        │   ║
-║     │    run002/                              [Beamtime ✓]        │   ║
-║     │    notes/                               [Directory]         │   ║
-║     └──────────────────────────────────────────────────────────────┘   ║
-║                                                                          ║
-║  ◉  Mark as "not an experimentalist"                                      ║
-║     Hide from beamtime counts and bulk ingest. Shown dimmed.             ║
-║                                                                          ║
-║  ○  Use global default  (^20\d{2}... depth 2)                            ║
-║     Accept the default pattern even though no matches were found yet.    ║
-║                                                                          ║
-║  [↑↓] select option   [Tab] move between fields   [↵] confirm   [Esc] cancel  ║
-╚══════════════════════════════════════════════════════════════════════════╝
-
        -
        -
        - - -
        -

        Resolution Flow

        -
        -Navigator pushes Explorer(data_root) - │ - ▼ -load_entries(data_root) read_dir + classify depth-1 folders - │ - ├──▶ name starts with "." ──▶ skip entirely (not shown) - │ - ├──▶ policy = Ignored ──▶ shown dimmed, ExptResolution::Ignored - │ - ├──▶ policy = Auto or Custom ──▶ ExptResolution::Resolved - │ - └──▶ policy missing (new folder) - │ - ▼ - detect_policy(folder) shallow scan of subdirs - │ - ├──▶ confident ──▶ auto-set ExptPolicy::Auto → Resolved - │ - └──▶ not confident ──▶ ExptResolution::NeedsResolution ⚑ flagged - -User presses [r] on flagged row: - │ - ▼ -Navigator.push(ConfigModal { target: "Chen", return_to: current_explorer }) - │ - ├──▶ Custom pattern confirmed ──▶ save to layout.toml, pop modal, refresh entries - ├──▶ Ignored confirmed ──▶ save to layout.toml, pop modal, dim row - └──▶ Global default accepted ──▶ set Auto, pop modal, mark Resolved -
        - -
        - Persistence: Every confirmed resolution writes to DATA/.pyref/layout.toml immediately. On next startup the file is loaded first, so already-resolved folders never trigger the modal again. New experimentalist folders that appear on the NAS after the fact go through the same detection → flag → resolve cycle. -
        -
        - - -
        -

        Entering a Flagged Folder

        -
        -
        -
        ⚑ NeedsResolution
        -
        - Pressing → or ↵ on a flagged row opens the resolution modal immediately instead of entering the folder.

        - The user must resolve before they can browse the folder's contents. -
        -
        -
        -
        ✓ Resolved
        -
        - Normal entry — pushes a new ExplorerState scoped to that folder, using that experimentalist's policy to classify its subdirs as beamtime vs. generic. -
        -
        -
        -
        ✗ Ignored
        -
        - Entry is still allowed — the user can browse a folder marked ignored. It's just excluded from beamtime counts and bulk ingest. Press x again to un-ignore. -
        -
        -
        -
        - diff --git a/.superpowers/brainstorm/6299-1774202169/explorer-screen.html b/.superpowers/brainstorm/6299-1774202169/explorer-screen.html deleted file mode 100644 index 0faf2c0..0000000 --- a/.superpowers/brainstorm/6299-1774202169/explorer-screen.html +++ /dev/null @@ -1,303 +0,0 @@ -

        Design Section 2 — Explorer Screen

        -

        ExplorerState internals, DirEntry enrichment, heuristic classification, and the config modal wireframe.

        - - - - -
        -

        ExplorerState — Data Structures

        -
        -
        -
        ExplorerState
        -
        - 
        pub struct ExplorerState {
-    // where we are
-    pub data_root:   PathBuf,   // DATA/ from CLI arg
-    pub current_dir: PathBuf,   // dir being displayed
-
-    // entries in the current dir
-    pub entries:    Vec<DirEntry>,
-    pub list_state: ListState,
-    pub scroll_state: ScrollbarState,
-
-    // layout policy (from heuristic or modal)
-    pub layout_policy: LayoutPolicy,
-
-    // loading / ingest feedback
-    pub loading: bool,
-    pub status:  Option<String>,
-
-    // sort
-    pub sort_col: ExplorerSort,
-    pub sort_asc:  bool,
-}
-
-pub enum ExplorerSort {
-    Name, Modified, Size, FileCount,
-}
        -
        -
        - -
        -
        DirEntry — enriched directory row
        -
        - 
        pub struct DirEntry {
-    pub path:        PathBuf,
-    pub name:        String,
-    pub kind:        EntryKind,
-    pub modified:    Option<SystemTime>,
-    pub size_bytes:  Option<u64>,   // du -s async
-    pub fits_count:  Option<u32>,   // from catalog or scan
-    pub catalog_status: CatalogStatus,
-}
-
-pub enum EntryKind {
-    DataRoot,           // DATA/ itself
-    Experimentalist,    // depth 1 from root
-    Beamtime,           // matched by heuristic
-    Directory,          // generic subdir
-}
-
-pub enum CatalogStatus {
-    Indexed,            // ✓ in DATA catalog
-    Stale,              // ⚠ mtime newer than indexed_at
-    NotIndexed,         // ✗ no record in catalog
-    NotApplicable,      // generic dir / not a beamtime
-}
        -
        -
        -
        -
        - - -
        -

        LayoutPolicy — Heuristic + Override

        -
        -
        -
        LayoutPolicy struct
        -
        - 
        pub struct LayoutPolicy {
-    /// folders at this depth from data_root
-    /// are classified as Beamtime (default: 2)
-    pub beamtime_depth: u8,
-
-    /// regex applied to folder names to
-    /// confirm Beamtime classification
-    /// None = accept any at beamtime_depth
-    pub beamtime_pattern: Option<Regex>,
-
-    /// whether policy came from heuristic
-    /// or from user config modal
-    pub source: PolicySource,
-}
-
-pub enum PolicySource {
-    Heuristic,   // auto-detected
-    UserConfig,  // set via modal
-    Default,     // fallback (depth=2)
-}
-
-// Default pattern — matches ALS-style dates
-// and common beamtime naming conventions
-const BEAMTIME_PATTERNS: &[&str] = &[
-    r"^20\d{2}(Jan|Feb|Mar|...|Dec)$",  // 2024Jan
-    r"^20\d{2}-\d{2}(-\d{2})?$",        // 2024-01
-    r"^20\d{6}$",                        // 20240115
-    r"(?i)beamtime",                     // contains "beamtime"
-];
        -
        -
        - -
        -
        Classification logic (heuristic.rs)
        -
        - 
        pub fn classify_entry(
-    path: &Path,
-    data_root: &Path,
-    policy: &LayoutPolicy,
-) -> EntryKind {
-    if path == data_root {
-        return EntryKind::DataRoot;
-    }
-    let depth = relative_depth(path, data_root);
-    match depth {
-        1 => EntryKind::Experimentalist,
-        d if d == policy.beamtime_depth as usize => {
-            let name = folder_name(path);
-            if policy.pattern_matches(name) {
-                EntryKind::Beamtime
-            } else {
-                EntryKind::Directory
-            }
-        }
-        _ => EntryKind::Directory,
-    }
-}
-
-pub fn detect_policy(data_root: &Path)
-    -> (LayoutPolicy, bool /* confident */) {
-    // scan first 2 levels, count pattern hits
-    // returns (policy, confident)
-    // confident=false → trigger modal
-}
        -
        -
        -
        - -
        - Heuristic trigger: On startup, detect_policy(data_root) scans the top 2 levels of the NAS tree (no deep walk). If ≥50% of depth-2 folders match a date pattern, it sets that pattern as the policy and marks confident=true. If <50% match — or no folders exist at depth 2 — it marks confident=false and pushes the ConfigModal before showing the explorer. -
        -
        - - -
        -

        Config Modal Wireframe — ratatui overlay

        -
        -
        ConfigModal — centered over the explorer, keyboard-driven
        - 
        - pyref browser  /Volumes/DATA                                                          
-╔══════════════════════════════════════════════════════════════════════╗
-║  Configure Directory Layout                              [Esc] cancel  ║
-║  pyref could not confidently detect your beamtime structure.           ║
-║  Set the rules below — they will be saved to DATA/.pyref/layout.toml.  ║
-║                                                                      ║
-║  Depth to beamtime folders  (from DATA/ root, min 1)                   ║
-║    ╔══════════╗                                                       ║
-║    ║  2        ║  depth 1 = DATA/Beamtime  depth 2 = DATA/PI/Beamtime  ║
-║    ╚══════════╝                                                       ║
-║                                                                      ║
-║  Beamtime folder name pattern  (regex, empty = accept all)            ║
-║    ╔══════════════════════════════════════════════╗                  ║
-║    ║  ^20\d{2}(Jan|Feb|Mar|Apr|May|Jun|Jul|...)$ ║                  ║
-║    ╚══════════════════════════════════════════════╝                  ║
-║                                                                      ║
-║  Preview  (how your DATA/ root will be classified)                   ║
-║    ┌──────────────────────────────────────────────────────────────┐  ║
-║    │  /Volumes/DATA/                              [DataRoot]       │  ║
-║    │    Smith/                                     [Experimentalist]│  ║
-║    │      2024Jan/                                 [Beamtime ✓]     │  ║
-║    │      2023Nov/                                 [Beamtime ✓]     │  ║
-║    │      scratch/                                 [Directory]      │  ║
-║    │    Jones/                                     [Experimentalist]│  ║
-║    │      2024Feb/                                 [Beamtime ✓]     │  ║
-║    └──────────────────────────────────────────────────────────────┘  ║
-║                                                                      ║
-║  [Tab] next field   [Shift+Tab] prev field   [↵] confirm   [Esc] cancel  ║
-╚══════════════════════════════════════════════════════════════════════╝
-
        -
        - -
        - Modal behaviour: The preview pane updates live as the user types in either field — no confirm needed to see the effect. The regex is compiled on every keystroke; if invalid, the pattern field turns red and the preview shows the last valid state. On [↵] confirm, the policy is saved to DATA/.pyref/layout.toml and the modal is popped off the Navigator stack, resuming the Explorer at the same directory. -
        -
        - - -
        -

        Entry Enrichment — Async Loading Strategy

        -
        -
        -
        Immediate (sync)
        -
        - Name, kind, mtime
        - ✓ From fs::read_dir
        - ✓ Shown instantly
        - ✓ Never blocks -
        -
        -
        -
        Fast async (background thread)
        -
        - CatalogStatus, fits_count
        - → SQLite query per visible entry
        - → Results streamed via mpsc
        - → Cells fill in as they arrive
        - Shown as — until ready -
        -
        -
        -
        Slow async (deferred)
        -
        - size_bytes (disk usage)
        - → Spawned only on selection
        - → Or via explicit s key
        - → Shown as … while pending
        - NAS du can be very slow -
        -
        -
        -
        - - diff --git a/.superpowers/brainstorm/6299-1774202169/ingest-catalog.html b/.superpowers/brainstorm/6299-1774202169/ingest-catalog.html deleted file mode 100644 index de8eb58..0000000 --- a/.superpowers/brainstorm/6299-1774202169/ingest-catalog.html +++ /dev/null @@ -1,355 +0,0 @@ -

        Design Section 4 — Ingestion Pipeline + CatalogHandle

        -

        - How ingest_beamtime is adapted to target the DATA-level catalog, - how experimentalist metadata is recorded, and the CatalogHandle interface shared across screens. -

        - - - - -
        -

        Ingestion — Signature + Context

        -
        -
        -
        Before — per-beamtime, no data_root
        -
        - 
        // src/catalog/ingest.rs (current)
-pub fn ingest_beamtime(
-    beamtime_dir: &Path,
-    header_items: &[String],
-    force:        bool,
-    progress_tx:  Option<Sender<(u32, u32)>>,
-) -> Result<IngestSummary> {
-
-    // opens / creates catalog AT beamtime_dir
-    let conn = open_or_create_db(beamtime_dir)?;
-
-    // walks beamtime_dir for .fits
-    let paths = discover_fits_paths(beamtime_dir)?;
-
-    // inserts into `files` and `bt_scan_points`
-    // no experimentalist, no data_root recorded
-    for (path, mtime) in paths { … }
-}
        -
        -
        - -
        -
        After — DATA-level catalog, full context
        -
        - 
        // src/catalog/ingest.rs (proposed)
-pub fn ingest_beamtime(
-    beamtime_dir:    &Path,
-    data_root:       Option<&Path>,  // NEW
-    experimentalist: Option<&str>,   // NEW
-    header_items:    &[String],
-    force:           bool,
-    progress_tx:     Option<Sender<(u32, u32)>>,
-    cancel:          Option<Arc<AtomicBool>>, // NEW
-) -> Result<IngestSummary> {
-
-    // resolve catalog: DATA/.pyref/catalog.db
-    // if data_root is None → falls back to old
-    // per-beamtime behaviour (backward compat)
-    let db_path = resolve_ingest_target(
-        beamtime_dir, data_root
-    );
-    let conn = open_catalog_db(&db_path)?;
-
-    // upsert bt_beamtimes with new columns
-    upsert_beamtime_record(&conn,
-        beamtime_dir, data_root, experimentalist
-    )?;
-
-    // discover + insert (cancel-aware loop)
-    let paths = discover_fits_paths(beamtime_dir)?;
-    for (path, mtime) in paths {
-        if cancel.as_ref()
-            .map(|c| c.load(Ordering::Relaxed))
-            .unwrap_or(false) { break; }
-        …
-    }
-}
        -
        -
        -
        - -
        - Backward compatibility: data_root: None and experimentalist: None reproduce the current behaviour exactly — the catalog is opened at the beamtime dir, no new columns are written. All existing callers (tests, Python bindings, CLI) pass None until they are individually migrated. The TUI always passes both. -
        -
        - - -
        -

        Catalog Path Resolution

        -
        -
        -
        resolve_ingest_target()
        -
        - 
        pub fn resolve_ingest_target(
-    beamtime_dir: &Path,
-    data_root:    Option<&Path>,
-) -> PathBuf {
-    match data_root {
-        // new path: DATA/.pyref/catalog.db
-        Some(root) =>
-            root.join(".pyref").join("catalog.db"),
-
-        // legacy path: unchanged behaviour
-        None =>
-            resolve_catalog_path(beamtime_dir),
-    }
-}
        -
        - Single write target. All beamtimes under the same - DATA/ root share one catalog file. The - bt_beamtimes.beamtime_path + source_path LIKE - filter in queries is what isolates each beamtime's records. -
        -
        -
        -
        -
        upsert_beamtime_record()
        -
        - 
        // Called at the start of every ingest.
-// Creates or updates the bt_beamtimes row.
-fn upsert_beamtime_record(
-    conn:            &Connection,
-    beamtime_dir:    &Path,
-    data_root:       Option<&Path>,
-    experimentalist: Option<&str>,
-) -> Result<()> {
-    conn.execute("
-        INSERT INTO bt_beamtimes
-            (beamtime_path, data_root,
-             experimentalist, last_indexed_at)
-        VALUES (?1, ?2, ?3, strftime('%s','now'))
-        ON CONFLICT(beamtime_path) DO UPDATE SET
-            data_root        = excluded.data_root,
-            experimentalist  = excluded.experimentalist,
-            last_indexed_at  = excluded.last_indexed_at
-    ", params![
-        beamtime_dir.to_str(),
-        data_root.and_then(|p| p.to_str()),
-        experimentalist,
-    ])?;
-    Ok(())
-}
        -
        -
        -
        -
        - - -
        -

        CatalogHandle — Shared Query Surface

        -
        -
        -
        CatalogHandle struct
        -
        - 
        /// Lightweight handle — opens a fresh
-/// connection per call (consistent with
-/// existing rusqlite usage pattern).
-pub struct CatalogHandle {
-    pub db_path: PathBuf,  // DATA/.pyref/catalog.db
-}
-
-impl CatalogHandle {
-    /// Explorer: list experimentalists + counts
-    pub fn list_experimentalists(
-        &self, data_root: &Path
-    ) -> Result<Vec<ExptMeta>>;
-
-    /// Explorer: list beamtimes for one expt
-    pub fn list_beamtimes(
-        &self,
-        data_root:       &Path,
-        experimentalist: &str,
-    ) -> Result<Vec<BeamtimeMeta>>;
-
-    /// Explorer: catalog status for one path
-    pub fn catalog_status(
-        &self, beamtime_path: &Path
-    ) -> CatalogStatus;
-
-    /// Beamtime screen: profiles query
-    pub fn query_scan_points(
-        &self,
-        beamtime_path: &Path,
-        filter:        Option<&CatalogFilter>,
-    ) -> Result<Vec<FileRow>>;
-
-    /// Beamtime screen: entries (samples/tags/scans)
-    pub fn list_beamtime_entries_v2(
-        &self, beamtime_path: &Path
-    ) -> Result<BeamtimeEntries>;
-}
        -
        -
        -
        -
        ExptMeta + BeamtimeMeta return types
        -
        - 
        /// Returned by list_experimentalists()
-/// — one row per depth-1 folder in catalog
-pub struct ExptMeta {
-    pub name:           String,
-    pub beamtime_count: u32,
-    pub fits_count:     u32,
-    pub last_indexed:   Option<i64>,
-}
-
-/// Returned by list_beamtimes()
-/// — one row per bt_beamtimes entry
-pub struct BeamtimeMeta {
-    pub path:           PathBuf,
-    pub fits_count:     Option<u32>,
-    pub last_indexed:   Option<i64>,
-}
-
-/// catalog_status() implementation:
-/// 1. Query bt_beamtimes for beamtime_path
-/// 2. If missing → NotIndexed
-/// 3. If found → compare last_indexed_at
-//     with max(mtime) of files in dir
-/// 4. If dir mtime > last_indexed → Stale
-/// 5. Otherwise → Indexed
        -
        -
        -
        - -
        - Where CatalogHandle lives: Navigator owns one CatalogHandle constructed from data_root.join(".pyref/catalog.db"). Both ExplorerState and BeamtimeState call into it by reference — no cloning, no Arc. If the catalog file doesn't exist yet (first run, no ingests), all methods return empty vecs or NotIndexed; the DB is created on first ingest. -
        -
        - - -
        -

        Execution Plan — Four Independent Phases

        -
        -
        -
        Phase 1 — Decompose
        -
        - Extract BeamtimeState from App
        - Introduce Navigator + ScreenState
        - App becomes thin shell
        - All existing behaviour preserved
        - Files touched:
        - app.rs run.rs ui.rs keymap.rs
        - New: navigator.rs

        - ✓ No user-visible change
        - ✓ Mergeable independently -
        -
        -
        -
        Phase 2 — Explorer
        -
        - Add ExplorerState + ExplorerLevel
        - Add heuristic.rs classifier
        - Add per-expt NasLayout + layout.toml
        - Add ConfigModal + modal UI
        - Wire CLI arg as data_root
        - New: explorer/mod.rs
        - explorer/heuristic.rs
        - explorer/modal.rs

        - ✓ Explorer browseable
        - ✓ Catalog status: NotIndexed -
        -
        -
        -
        Phase 3 — Catalog
        -
        - Add CatalogHandle
        - Add explorer_query.rs
        - Schema migration: add
        - experimentalist + data_root
        - to bt_beamtimes
        - Adapt ingest_beamtime signature
        - Add cancel token to ingest

        - ✓ Explorer shows live catalog status
        - ✓ Ingest writes to DATA catalog -
        -
        -
        -
        Phase 4 — Wire
        -
        - Auto-ingest on Beamtime entry
        - BeamtimeState queries DATA catalog
        - Back nav restores Explorer cursor
        - Status cell updates via channel
        - Config saved on BeamtimeState pop
        - Deprecate Screen::Launcher
        - Remove old list_beamtimes() call

        - ✓ Full end-to-end flow
        - ✓ Launcher replaced by Explorer -
        -
        -
        -
        - diff --git a/.superpowers/brainstorm/6299-1774202169/waiting-1.html b/.superpowers/brainstorm/6299-1774202169/waiting-1.html deleted file mode 100644 index ef07652..0000000 --- a/.superpowers/brainstorm/6299-1774202169/waiting-1.html +++ /dev/null @@ -1,3 +0,0 @@ -
        -

        Continuing in terminal...

        -
        diff --git a/AGENTS.md b/AGENTS.md index 3ae2ed7..2de200e 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -1,245 +1,401 @@ -# PyRef Architecture and Data Flow +# pyref Contributor Quickstart Guide -## Build and test (uv, maturin, cargo only) +`pyref` is a Python library for reducing and analyzing polarized resonant soft X-ray reflectivity (PRSoXR) data collected at ALS Beamline 11.0.1.2, Lawrence Berkeley National Laboratory. The library couples a Python interface with a Rust backend via PyO3 bindings to achieve the parallel throughput required for large beamtime datasets. It is organized into three primary components: `IO`, which handles raw data ingestion and cataloging; `Reduction`, which reduces 2D detector images into 1D reflectivity profiles; and `Fitting`, which fits reduced profiles against optical models. Each component is designed to be independently extensible. -- **Install and run tests**: `uv sync` then `uv run pytest tests/test_rust_fits_io.py` (or `uv run pytest` for full suite). `uv sync` builds the Rust extension via the maturin build backend and installs the project. -- **Build wheel only**: `uv run --group dev maturin build`. Output: `target/wheels/pyref-*.whl`. -- **Rust**: Use `cargo build` only for checking compilation of non-cdylib targets (e.g. bins). The Python extension is built by maturin so that linker flags for the extension are correct. Do not rely on `cargo test` for the main crate; it links the cdylib into the test binary and fails with unresolved Python symbols. Rust unit tests live in `src/` (e.g. `src/fits/header.rs`); integration tests in `tests/integration_test.rs` are `#[ignore]` (require Python runtime; validate via pytest instead). -- **TUI binary**: The lib is built with default feature `extension-module` (pyo3/pyo3-polars). Building the standalone TUI must not link Python. Use: `cargo browser` (alias) or `cargo run --bin pyref-tui --no-default-features --features tui`. The `--no-default-features` disables `extension-module`, so the lib is built without pyo3 and the binary links successfully. Running the TUI requires a real TTY (interactive terminal); in a headless or IDE run context you may see "Device not configured". +## Technical Jargon -## Overview +- **Beamtime**: An experimental allocation at the ALS during which a user group collects data across multiple samples and scans, typically spanning one to several days. +- **Sample**: A physical thin-film specimen characterized by a preparation recipe and a set of deposition conditions. Samples are grouped into series, where each member shares a common recipe and is distinguished by one or more user-assigned tags in the filename. +- **Scan**: A sequential series of frames collected continuously by the instrument under a common set of experimental parameters. Scans are nominally associated with a single sample and a single experiment type. +- **Profile**: A reduced 1D reflectivity curve extracted from a scan, expressed as intensity vs. Q, intensity vs. 2theta, or intensity vs. energy. A single scan may contain multiple profiles collected at different fixed energies or fixed angles. +- **Frame / Image**: A single detector acquisition within a scan, stored as a 2D CCD image accompanied by a FITS header containing motor positions, analog input (AI) values, timestamps, and other metadata. +- **Motor**: A physical positioning device controlling sample theta, CCD theta, beamline energy, steering mirrors, slits, or other instrument components. +- **AI (Analog Input)**: A scalar value recorded by one of the instrument's analog input channels. Relevant AI channels include beam current (ring current), upstream gold mesh absorption current (Ai 3 Izero), photodiode signal, CCD temperature, and TEY signals. +- **Beamspot**: The location of the specularly reflected beam on the 2D detector image, typically a compact Gaussian intensity distribution. +- **ROI (Region of Interest)**: A small rectangular subregion of the detector image, typically 10x10 pixels, centered on the beamspot and used to integrate the reflected beam intensity. +- **Direct Beam**: A series of frames collected with the sample at theta = 0 degrees, where the incident beam hits the detector directly. Used to establish the incident beam intensity I0 as a function of energy or exposure time. +- **I0**: The incident beam intensity used to normalize a reflectivity profile. For fixed-energy scans, I0 is extracted from direct beam frames at the start of the scan. For fixed-angle scans, I0 may be sourced from a separate dedicated scan. +- **I0 Point**: A frame collected as part of the direct beam measurement sequence. I0 points are identified by Sample Theta = 0 and are used both to normalize the reflectivity and to characterize the counting statistics of the incident beam. +- **Stitch Point**: A frame collected at the moment the independent variable reverses direction, signaling the start of a new measurement stitch. Stitch points are typically repeated several times to allow the motors to settle, and they establish the baseline statistics for the new stitch region. +- **Overlap Point**: A frame whose independent variable value falls within the range already covered by the preceding stitch. Overlap points are used to compute the multiplicative scaling factor that aligns the new stitch to the previous one. -PyRef is a library for reducing 2D X-ray reflectivity detector images into 1D reflectivity signals. The library handles experimental data collected in "stitches" - separate measurement chunks where beamline configuration parameters (higher-order suppressor, exit slits, exposure times) are adjusted to capture reflectivity across multiple orders of magnitude. +## Fundamental Concepts -## Terminology (glossary) +### IO Module -See also `CONTRIBUTE.md` for the full glossary. +The IO module is responsible for ingesting raw FITS files, cataloging their contents into a structured SQLite database, and exposing the resulting data through a lazy interface that returns pandas or polars DataFrames on demand. By default, the module expects the FITS file conventions and directory structures produced by ALS Beamline 11.0.1.2. New beamline formats can be added by extending the IO layer. -- **ingest**: Populate the catalog from FITS (discover, read headers, upsert into SQLite). Rust `ingest_beamtime`; Python `pyref.io.ingest_beamtime`. -- **discover**: Find FITS paths under a directory; no header read. Rust `discover_fits_paths`. -- **scan (IO)**: Build a LazyFrame of metadata. `scan_experiment(source)` returns a LazyFrame from catalog or from FITS; "scan from catalog" = read from SQLite (`scan_from_catalog`). Distinct from a CCD Scan (measurement run). -- **scan (experiment)**: A single measurement run with a scan number (e.g. CCD Scan 88169, Scan ID); one reflectivity profile. Not the same as the `scan_experiment()` IO function. -- **read**: FITS file I/O (headers and/or images). Rust `read_fits_headers_only`, `read_multiple_fits_headers_only`, `read_experiment_headers_only`. -- **experiment**: Beamtime directory or a logical group; "experiment number" in headers refers to one scan (run), not the whole beamtime. +All performance-critical IO operations are implemented in Rust and exposed to Python via PyO3 bindings. Rust is responsible for parallel FITS file reading, header card extraction, image data loading, filename parsing, directory traversal, SQLite catalog construction, and zarr archive management. Python is responsible for the user-facing query interface, DataFrame construction from catalog results, and any logic that does not require parallel throughput. This boundary is a design constraint, not a guideline: do not implement parallelism in Python and do not implement user-facing query logic in Rust. -## Core Components +The IO module is accessed via `pyref.io` and the cataloging subsystem via `pyref.io.catalog`. -### 1. Data Loading (`src/loader.rs`, `python/pyref/io/readers.py`) +### Reduction Module -The Rust backend (`src/loader.rs`) handles parallel reading of FITS files: -- Reads FITS file headers and image data -- Processes images to extract beam spot locations -- Calculates simple reflectivity using ROI (Region of Interest) analysis -- Combines multiple FITS files into a single Polars DataFrame -- Adds calculated columns (Q-vector from energy and theta) +The Reduction module converts per-frame 2D detector images into normalized, stitched 1D reflectivity profiles. Reduction proceeds in three sequential stages. -Key functions: -- `read_fits(source, options)`: High-level eager read. Resolves `source` (file, paths, dir, catalog) with `ResolvePreference`; returns one DataFrame from catalog or from disk. -- `scan_fits(source, options)`: High-level lazy scan. Returns a LazyFrame from catalog (fast) or from disk; use when you want to filter/select before collect. -- `read_fits_metadata_batch(paths, options)`: Canonical batch read (headers + optional calculated domains). Used by ingest and by `read_fits` when source is disk. -- `read_experiment_headers_only()`, `read_multiple_fits_headers_only()`: Lower-level header-only reads. +The first stage localizes the beamspot in each 2D detector image, integrates the ROI intensity, and subtracts the background estimated from a dark region of the detector. The second stage normalizes the extracted beam intensity against I0, beam current, exposure time, and the upstream gold mesh absorption current (Ai 3 Izero). The third stage identifies the scan domain (fixed energy or fixed angle), classifies each frame as an I0 point, stitch point, or overlap point, computes per-stitch scaling corrections from the weighted mean of overlap regions, and assembles the stitched profile. -**Source and options (Polars-style API):** -- `FitsSource`: enum `File(PathBuf)`, `Paths(Vec)`, `Dir(PathBuf)`, `Catalog(PathBuf)`. Impl `From`, `From>`, `From<&Path>`. -- `ResolvePreference`: `PreferCatalog`, `PreferDisk`, `FromCatalog`, `FromDisk` when both catalog and disk could satisfy the source. -- `ReadFitsOptions` / `ScanFitsOptions`: `header_items`, `header_only`, `add_calculated_domains`, `schema`, `batch_size`, `resolve_preference`, and (for catalog) `catalog_filter`. -- `FitsMetadataSchema`: canonical column names and optional Polars `Schema` for one FITS row; used by `scan_from_catalog` and batch read output. +The Reduction module is accessed via `pyref.reduction`. The primary user-facing class is `PrsoxrLoader`. -**Catalog hook:** When source is `Dir(path)` or `Catalog(path)` and `.pyref_catalog.db` exists, `read_fits`/`scan_fits` use it when preference is `FromCatalog` or `PreferCatalog`, otherwise discover and read from disk. All library code must pass `cargo clippy` with no unwrap/expect in non-test code; every public function and module has docstrings. +### Fitting Module -#### FITS DataFrame Accessor (`df.fits`) +The Fitting module fits reduced reflectivity profiles against optical layer-stack models. The primary backend is `refnx`, which implements the 4x4 transfer matrix method for anisotropic and resonant systems. The module is responsible for model definition, parameter specification, constraint enforcement, objective function construction, fitting algorithm selection, and output formatting. The fitting module is accessed via `pyref.fitting`. -For metadata DataFrames from `scan_experiment().collect()`, use the `fits` accessor to load images. Collect your LazyFrame before using: `df = lf.filter(...).collect()` then `df.fits.img[0]`. +## Reduction Subtleties -- `df.fits.img[i]` / `df.fits.img[slice]`: Raw detector image(s); slice returns iterator -- `df.fits.corrected(idx, bg_rows=10, bg_cols=10)`: Background-corrected image(s) -- `df.fits.filtered(idx, sigma, bg_rows=10, bg_cols=10)`: Background-corrected + gaussian blurred (Rust pipeline) -- `df.fits.custom(idx, callable, **kwargs)`: Apply custom Python callable to image(s) +### Uncertainty Quantification -Background correction uses edge-based subtraction: per-row (left/right) and per-column (top/bottom) with configurable `bg_rows`, `bg_cols`. +Each frame is a photon-counting measurement. The raw intensity in each detector pixel follows a Poisson distribution to first approximation, giving a per-pixel standard deviation equal to the square root of the pixel count. Two classes of non-Poissonian noise are also present and must be accounted for. -### 2. Image Processing (`python/pyref/image.py`, `src/io.rs`) +Systematic noise originates from the detector itself (readout noise, dark current, stray light) and is characterized using a dark region of the detector image that is far from the beamspot. The mean and variance of this dark region are used to estimate the per-pixel systematic noise floor, which is subtracted from the ROI intensity and propagated into the final uncertainty. -Image processing reduces 2D detector images to reflectivity values: +Random non-Poissonian noise is characterized by the Fano factor, defined as the ratio of the observed variance in I0 measurements to the hypothesized Poissonian variance at the same intensity level. The Fano factor is computed as a function of incident energy from the ensemble of I0 frames within a scan, and is applied as a multiplicative scale on the Poissonian uncertainty for all frames at that energy. Agents implementing or modifying the uncertainty pipeline must propagate both the dark-region contribution and the Fano-scaled Poissonian contribution in quadrature at every reduction step. Silent variance truncation or implicit dtype coercion that reduces numerical precision is a correctness bug. -**Rust implementation** (`src/io/mod.rs`): -- Raw preview pipeline: trim edges (remove detector nonlinearity), then row-by-row background (10-pixel left/right strips, colder side per row), then cold-side dark subtraction (top vs bottom, single scalar). Used by TUI preview and `materialize_image`. -- `subtract_background()`: Legacy row-by-row background subtraction (full-size output; interior only corrected). -- `simple_reflectivity()`: Calculates beam signal vs background using ROI -- `process_image()`: Main image processing pipeline +### Beamspot Localization -**Python implementation** (`python/pyref/image.py`): -- `reduce_data()`: Full image reduction pipeline (dezinger, filtering, masking) -- `locate_beam()`: Locates beam spot in processed image -- `reduction()`: Calculates reflectivity from masked image and beam spot +Beamspot localization is applied to each frame independently. The algorithm proceeds in the following fixed order and agents must not reorder or skip steps without explicit justification. -Uncertainty in reflectivity originates from: -- Counting statistics in beam signal -- Background subtraction uncertainty -- Exposure time and beam current normalization +First, camera edge artifacts are removed by zeroing or masking a fixed border of pixels around the image perimeter. Second, a row-by-row background subtraction is applied: for each row, the median of a set of dark columns (columns known to be outside the beamspot region) is subtracted from all pixels in that row. Third, a column-by-column background subtraction is applied analogously. Fourth, a Gaussian filter is applied to suppress residual high-frequency noise. Fifth, a 2D peak fitting routine locates the beamspot centroid, integrated intensity, and fit standard deviation. The background intensity and its uncertainty are extracted from a designated dark region of the post-subtraction image. -### 3. Masking (`python/pyref/masking.py`) +Failed detections occur when the peak fitter cannot identify a credible Gaussian peak above the noise floor. A detection is considered failed when the fitted peak amplitude is less than a configurable multiple of the dark region standard deviation, or when the fitted centroid falls outside the detector boundary. Failed detections must be flagged in the BeamFinding Table and must not silently propagate NaN or zero values into the Reflectivity Table. Beamspot drift across a scan is expected and is not itself a failure condition; drift is characterized by fitting a linear model to the centroid coordinates as a function of Q or theta. Frames where the centroid deviates from the linear trend by more than a configurable threshold are flagged separately. -- `InteractiveImageMasker`: Allows interactive rectangular masking of images -- `ImageSeries.mask()`: Automatic mask generation based on CDF of mean image -- Mask defines which pixels contribute to reflectivity calculation +### Scan Type and Domain Identification -### 4. Main Loader (`python/pyref/loader.py`) +The scan domain is determined by inspecting the motor trajectory across all frames in a scan. The classification procedure is as follows. -`PrsoxrLoader` orchestrates the data reduction workflow: +A scan is classified as a fixed-energy reflectivity scan when a leading block of frames has Sample Theta = 0 and a constant beamline energy (these are the I0 frames), followed by frames in which Sample Theta and CCD Theta increase monotonically (subject to stitch reversals). A scan is classified as a fixed-angle reflectivity scan when either a leading block of frames has Sample Theta = 0 and a varying beamline energy (I0 frames collected as a function of energy), or the scan contains no I0 block at all and beamline energy varies monotonically throughout. A multi-profile scan is not a distinct scan type but is a repetition of the above patterns within a single experimental scan: the instrument completes one full fixed-energy or fixed-angle sweep, then changes the fixed parameter (energy or angle) and repeats the sweep. Multi-profile scans are decomposed into their constituent profiles during reduction, each profile being treated as an independent fixed-energy or fixed-angle scan. -- Loads experiment data via `read_experiment()` -- Processes images using the mask -- Creates reflectivity DataFrame with columns: `file_name`, `Q`, `r`, `dr` -- Groups data by `file_name` (each file_name represents a stitch) -- Calculates uncertainty: `dr = sqrt(r)` (Poisson counting statistics) +Once the domain is identified, stitch points are located by finding frames where the independent variable decreases relative to the preceding frame. Overlap points are the initial frames of a new stitch whose independent variable values fall within the range already covered by the preceding stitch. The scaling correction for each stitch is the weighted mean of the reflectivity values at the overlap points, where the weights are the inverse squared uncertainties of those frames. -Key properties: -- `refl`: DataFrame containing reflectivity data -- `meta`: Full metadata DataFrame with images -- `mask`: Image mask for beam isolation +## Filename Parsing -### 5. Uncertainty Propagation (`src/lib.rs`, `python/pyref/utils/__init__.py`) +The cataloging system parses FITS filenames to extract the sample name, zero or more tags, the scan number, and the frame number. The parsing contract is as follows and must be implemented exactly as specified. -Uncertainty propagation implemented as Polars plugins: +The frame number is always the five-digit zero-padded integer to the right of the last hyphen in the filename stem (before the `.fits` extension). The scan number is always the five-digit zero-padded integer immediately to the left of that hyphen. The remainder of the stem to the left of the scan number is the concatenation of the sample name and any tags, optionally separated by underscores or hyphens. Because no separator is guaranteed between the sample name and the scan number, the scan number anchor is the five digits immediately left of the hyphen; the parser must split there first before attempting to tokenize the sample name and tags. The following filename patterns are all valid and must be handled without special-casing individual formats. -**Rust functions** (`src/lib.rs`): -- `err_prop_mult()`: Error propagation for multiplication - - Formula: `σ(xy) = |xy| * sqrt((σx/x)² + (σy/y)²)` -- `err_prop_div()`: Error propagation for division - - Formula: `σ(x/y) = |x/y| * sqrt((σx/x)² + (σy/y)²)` -- `weighted_mean()`: Weighted average using inverse variance weights -- `weighted_std()`: Weighted standard deviation +``` +___-.fits +----.fits +_-.fits +-.fits +-.fits +_-__-.fits +``` + +The number of tags is unbounded. Tags may contain alphanumeric characters and hyphens. Parsing failures must be logged and the offending file flagged in the File Table rather than silently skipped or allowed to panic. + +## Directory Layout Traversal + +Two directory layouts are supported. The cataloging system must detect which layout is present by inspection and handle both without user configuration. + +The first layout places each scan in its own instrument subdirectory within a date-grouped scan directory. Detection criterion: the beamtime root contains one or more date directories, each of which contains one or more scan directories (named with a scan number prefix), each of which contains an instrument subdirectory named either `CCD` or `Axis Photonique`. FITS files live inside the instrument subdirectory. + +``` +/ + / + CCD Scan / + CCD/ + __-.fits + __-AI.txt + CCD Scan / + Axis Photonique/ + __-.fits + __-_AI.txt +``` + +The second layout places all FITS files in a single flat instrument directory directly under the beamtime root. Detection criterion: the beamtime root contains a directory named `CCD` or `Axis Photonique` that holds FITS files from multiple scan numbers. + +``` +/ + CCD/ + __-.fits + __-.fits + __-AI.txt + __-AI.txt +``` + +AI text files are supplementary and are not required for cataloging. If present, they should be associated with their scan by matching the scan number extracted from the filename. If neither layout is detected, the cataloging system must emit a structured error identifying the unrecognized layout rather than silently producing an empty catalog. + +## I/O Operations and Cataloging + +Connecting individual frames back to their originating sample, scan, and beamtime requires meticulous bookkeeping that is impractical to maintain manually across a full beamtime. `pyref` provides an automated cataloging system that ingests a beamtime directory and populates a SQLite database encoding this hierarchy. The database is the structural backbone for all downstream reduction and fitting workflows. Users interact with it primarily through the lazy DataFrame interface exposed by `pyref.io`, which allows them to filter by sample name, tag, energy, or angle and receive a polars or pandas DataFrame containing the relevant frame metadata and image retrieval handles. + +### Cataloging System + +The cataloging system ingests a beamtime directory and populates a SQLite database that serves as the structural backbone for all downstream reduction and fitting workflows. Its primary purpose is to resolve individual FITS frames back to their originating sample, scan, and beamtime, and to expose this hierarchy as a queryable, lazily accessible interface. The most common access pattern is retrieving all frames associated with a given sample and tag combination, grouped by energy or angle, for reduction into a stitched reflectivity profile. + +Importantly, some of this catalog tablular logic will not be filled out initially by the catalog ingestion. In particular, the BeamFinding, StitchCorrection, and Reflectivity tables will be empty untill we actually sit down and process the data in python from a jupyter notebook. + +#### Beamtime Table +Stores the top-level metadata for a given beamtime, specifically the path to the beamtime root directory and the date. All other tables carry a foreign key to this table, making it the root of the catalog hierarchy. The path stored here is also used to resolve the location of the monolithic beamtime zarr archive. + +#### Sample Table +Stores metadata for each physical sample identified during cataloging. Each sample has a name, a representative sample-x and sample-y stage position extracted from the FITS headers, and a foreign key to the Beamtime Table. Stage positions are treated as nominally fixed per sample. During cataloging, if frames attributed to the same sample name exhibit large positional drift beyond a configurable tolerance, those frames are flagged as likely mislabeled rather than treated as a distinct sample position. Small sub-decimal drift is acceptable and ignored. + +#### Tag Table +Stores the individual tag slugs parsed from FITS filenames. Tags carry no intrinsic meaning to the catalog; they are user-defined labels encoding experimental series membership or preparation conditions. Tags are scan-specific, not sample-specific. The association between tags and files is resolved through a join table linking the Tag Table and the File Table, allowing many tags to map to many files without duplication. + +#### File Table +Stores the fully specified path, filename, frame number, scan number, sample ID, and beamtime ID for each FITS file ingested. This table is the canonical reference for raw file locations and serves as the join target for tag resolution. Image data is not retrieved through this table directly; it is accessed via the zarr group key and frame index recorded in the Data Table. + +#### Scan Table +Stores metadata for each scan, including scan number, scan type (fixed energy or fixed angle), start and stop timestamps, sample ID, and beamtime ID. Scan type is determined during cataloging from the motor trajectory analysis described above and is recorded here as a first-class attribute to avoid recomputing it downstream. + +#### Data Table +Stores one row per frame per scan, containing all header card values extracted from the FITS file along with the scan-group key and frame index needed to retrieve the corresponding 2D detector image from the beamtime zarr archive. Image data for the entire beamtime is stored in a single monolithic zarr archive located at the beamtime root, organized hierarchically with scan number as the group key and frame number as the dataset index within that group. Each scan group stores two datasets per frame: the raw detector image as extracted from the FITS file, and the post-processed image produced after edge artifact removal, row-wise and column-wise background subtraction, and Gaussian filtering. Both datasets share the same frame index, allowing the user to retrieve either the unmodified or the processed image for any given frame without reprocessing. The archive path is resolved through the foreign key to the Beamtime Table, avoiding redundant path storage at the frame level while keeping retrieval efficient and unambiguous. Each row additionally carries a foreign key to the File Table and the Scan Table, providing the full provenance chain from pixel to beamtime. + +#### BeamFinding Table +Stores the per-frame outputs of the beamspot localization pipeline. This includes the pre-processing steps applied prior to peak fitting (edge artifact removal flag, row-wise and column-wise background subtraction parameters, and Gaussian filter kernel size), the peak fitting result (fitted centroid row and column, integrated ROI intensity, and fit standard deviation), the background intensity extracted from the dark region of the detector, and a flag indicating whether the detection succeeded or failed. Each row carries a foreign key to the Data Table. + +#### StitchCorrection Table +Stores the per-stitch correction factors computed during the stitching and normalization pipeline. This includes the energy-dependent Fano Factor used to rescale photon-counting uncertainties away from the pure Poisson limit, the weighted-mean overlap scaling factor applied at each stitch boundary, the I0 normalization value and its source (either direct beam frames within the scan or a foreign key to an external scan for fixed-angle profiles where I0 is sourced separately), and the frame-type classification for each frame (I0, Stitch, Overlap). Each row carries a foreign key to the Scan Table. + +#### Reflectivity Table +Stores the frame-level reduced reflectivity data produced after normalization and stitching. Each row represents a single reduced frame and contains the theta, energy, and Q values, the normalized reflectivity intensity and its propagated uncertainty, the frame-type classification (I0, Stitch, Overlap), and a foreign key to the BeamFinding Table. Stitched profile construction and export are handled outside the catalog by a packaging utility that queries the Reflectivity and StitchCorrection tables and writes the assembled profile to a flat parquet file. + +## Data Quality Flagging + +Several conditions arising during cataloging and reduction require flagging rather than silent failure or hard error. The following flags are first-class catalog attributes, not log messages, and must be queryable from the DataFrame interface. + +Frames attributed to a sample name whose inferred stage position deviates from the representative position for that sample by more than a configurable threshold are flagged as `MISLABELED_SAMPLE`. The threshold is configurable per beamtime but should default to a value that tolerates sub-millimeter drift while rejecting stage position changes consistent with a deliberate sample move. + +Frames for which the beamspot localization algorithm fails to identify a credible peak are flagged as `BEAM_DETECTION_FAILED`. These frames must not contribute to the Reflectivity Table and must not be silently zeroed or filled. + +Frames whose fitted beamspot centroid deviates from the linear trend model across the scan by more than a configurable multiple of the fit residual standard deviation are flagged as `BEAM_DRIFT_ANOMALY`. These frames may still contribute to the Reflectivity Table but should be treated with caution and are surfaced to the user for manual inspection. + +Files that fail the filename parser are flagged as `PARSE_FAILURE` in the File Table. Files whose directory layout does not match either supported pattern are flagged at the beamtime level. + +## Profile Packaging Utility + +The packaging utility assembles a stitched reflectivity profile from the Reflectivity and StitchCorrection tables and exports it as a flat parquet file. It is not part of the catalog; it is a standalone tool in `pyref.reduction` that operates on catalog query results. The output parquet file contains one row per reduced frame with the following columns at minimum: Q (inverse angstroms), theta (degrees), energy (eV), reflectivity intensity (normalized), propagated uncertainty, frame-type classification, scan number, sample name, and the applied stitch scaling factor. The packaging utility must reject frames flagged as `BEAM_DETECTION_FAILED` and must surface `BEAM_DRIFT_ANOMALY` frames to the user with a warning before including them. The output schema is fixed; agents must not add or remove columns without updating this specification. + +## Fitting Module Architecture + +The Fitting module wraps `refnx` to provide a domain-specific interface for PRSoXR model construction and optimization. The 4x4 transfer matrix method implemented in `refnx` handles anisotropic dielectric tensors and resonant scattering contrast, which are the physically relevant cases for this beamline. Agents working in `pyref.fitting` are expected to understand the transfer matrix formalism and the relationship between the model layer stack (thickness, roughness, optical constants) and the computed reflectivity curve. + +The module boundary is as follows. Model construction (layer stack definition, optical constant assignment, parameter bounds, and inter-parameter constraints) lives in `pyref.fitting`. Numerical optimization and MCMC sampling are delegated entirely to `refnx` and must not be reimplemented. Objective function construction follows `refnx` conventions. Fitting output is returned as a structured result object containing the optimized parameters, their uncertainties, the fitted reflectivity curve, and the residuals, and must be serializable to parquet or HDF5 for archival. + +## Numerical Precision and Scientific Correctness + +This library processes experimental data where uncertainty quantification is not decorative but determines the validity of downstream physical conclusions. The following constraints are non-negotiable. + +Uncertainty propagation must be exact to first order at every reduction step. Any operation that discards, truncates, or implicitly zeros an uncertainty is a correctness bug. Weighted means must use inverse-variance weights throughout; unweighted means are not acceptable substitutes in any reduction context. + +Silent dtype coercion is prohibited. Operations that would coerce float64 to float32, or integer counts to floating point without explicit intent, must be made explicit or prevented. The catalog and parquet outputs must preserve float64 precision for all physical quantities. + +Frame provenance must be preserved end-to-end. Every row in the Reflectivity Table must be traceable back to the originating FITS file and zarr frame index through the foreign key chain. Any reduction step that breaks this chain by aggregating without recording the constituent frame IDs is unacceptable. + +Fano factor computation and application must be documented in the StitchCorrection Table for every scan. A scan processed without a Fano correction must record this explicitly (Fano factor = 1.0) rather than leaving the field null. + + -**Python interface** (`python/pyref/utils/__init__.py`): -- Exposes Rust functions as Polars expressions -- Used throughout the data processing pipeline +# General -### 6. Catalog (beamtime index) +## General Structure -One SQLite database per beamtime directory (`.pyref_catalog.db`) caches FITS metadata and supports overrides so scans avoid repeated header I/O. A **new layout** uses a single catalog per parent directory. +This codebase is maintained by contributors with physics PhDs and extensive backgrounds in scientific and engineering software, including numerical computing, data analysis, instrumentation, simulation, and research-grade reproducibility. Maintainers are highly mathematically literate, comfortable with linear algebra and statistics, and expect rigorous numerics with explicit type handling—silent coercion and imprecise computations are not acceptable. -**Catalog layout (legacy vs new):** -- Legacy: DB at beamtime_dir/.pyref_catalog.db; schema uses files table + overrides. -- New: DB at parent/.pyref/catalog.db for multiple beamtimes; schema uses normalized bt_* tables only (bt_beamtimes, bt_samples, bt_scans, bt_streams, bt_scan_points, etc.). Ingest writes only to bt_* for new layout (no files table). -- resolve_catalog_path(beamtime_dir) returns new path if it exists, else legacy path; new path is chosen when parent exists and is not root. -- Beamtimes list: launcher uses ~/.pyref/beamtime_index.sqlite3; list_beamtimes_from_catalog(db_path) returns beamtimes from a given catalog (bt_beamtimes) for the new layout. -- Zarr store: same location as catalog (parent/.pyref/zarr//); one subdir per beamtime. -- FITS discovery: case-insensitive .fits extension; failed FITS reads are not silently dropped during ingest (first error returned with path). +## Operating principles -**Source resolution in `scan_experiment(source)`** (Python `python/pyref/io/readers.py`): -- If `source` is a directory and `path / ".pyref_catalog.db"` exists, the scan is served from the catalog (Rust `scan_from_catalog`); no FITS I/O. -- If `source` is a file path to `.pyref_catalog.db`, the scan is from that catalog. -- Otherwise, discovery and header reads use the existing directory/list path (resolve_fits_paths + batched `read_multiple_fits_headers_only`). The LazyFrame schema is the same in both cases so `df.fits` and the loader work unchanged. +- Prefer the smallest coherent change set that satisfies the stated specification. Avoid drive-by refactors, unrelated formatting sweeps, and scope expansion. +- Treat the repository’s existing patterns as the default contract. Match naming, module boundaries, error-handling style, and test layout unless the user explicitly requests a migration. +- Default to production-grade output: complete, runnable, and reviewable. Do not ship placeholder text such as ellipses, “the rest of the implementation here”, “TODO: implement”, or “fill in later” inside code or patches unless the user explicitly authorizes a stub. +- Remain non-lazy: if a command fails, diagnose, adjust, and retry with a different approach when reasonable. Do not stop after the first error without analysis. +- Do not use emoji in code, comments, documentation strings, commit messages, or user-facing text unless the user explicitly requests emoji. -**Rust** (`src/catalog/`): Schema (files + overrides), discovery (walkdir, skippable stems), ingest (batch header read, Q/Lambda, upsert, prune), `scan_from_catalog`, `get_overrides`, `set_override`, `list_beamtime_entries`, `query_files`. The TUI (Rust binary) uses these when a beamtime has a catalog; no Python in the TUI process. +## Communication and documentation outside code -**Programmatic APIs** (Python `pyref.io`): -- `ingest_beamtime(beamtime_path, header_items=None, incremental=True)` -> Path to DB -- `get_overrides(catalog_path, path=None)` -> DataFrame -- `set_override(catalog_path, path, sample_name=..., tag=..., notes=...)` -- `query_catalog(catalog_path, sample_name=..., tag=..., scan_numbers=..., energy_min=..., energy_max=...)` -> DataFrame +- Avoid standalone documentation files or long narrative write-ups unless the user asks for them or the repository already uses them for the same purpose. +- Prefer editing the code and tests that enforce behavior over adding parallel prose that can drift out of date. +- When the user asks for explanation, keep it precise and tied to the change set. -Catalog is built by default (feature `catalog`); the TUI feature includes catalog so the binary can read/write the same DB. +## Public API documentation (language-agnostic) -**Catalog watch**: When the TUI has a beamtime selected and `.pyref_catalog.db` exists, a catalog watcher runs in the background (Rust `run_catalog_watcher` in `src/catalog/watch.rs`). It uses the `notify-debouncer-mini` crate to watch the beamtime directory for FITS create/modify events, debounces them (about 1.5 s), and runs incremental ingest so new or changed files are added to the catalog without a full rescan. The watcher runs only for the current beamtime; when the user navigates away, the watcher is stopped. When the watcher triggers an ingest, the TUI shows "Updating catalog..." in the nav line and reloads the table when ingest completes. The TUI feature enables the `watch` feature; the Python wheel does not depend on the watcher. +- Every **public** function, method, or type exported from a library module carries documentation appropriate to the language (for example Python docstrings, Rust `///` on public items, TSDoc/JSDoc on exported symbols). +- Documentation states the **surface**: name, purpose, parameters, return value, and thrown or returned error shapes when that is part of the contract. +- Prefer **prescriptive** voice that states what the symbol **does** and **means** for callers. Prefer “Maximum `foo` grouped by `bar` using a stable sort on `bar`.” over “Returns the max of foo by bar.” or "Computes the maximum `foo` grouped by `bar` using a stable sort on `bar`." or "Returns the maximum `foo` grouped by `bar` using a stable sort on `bar`." +- For each parameter: name, type as used in the project, allowed ranges or invariants when non-obvious, and interaction with other parameters. +- For results: type, semantics, units when relevant, ordering guarantees, and stability promises when they matter for science or reproducibility. +- Describe **what** the function does at the abstraction level of the API, **how** only when algorithmic choices affect correctness, performance contracts, or numerical stability, and **why** that approach is chosen when trade-offs are non-obvious (for example streaming vs materializing, online vs batch statistics). +- **Internal** helpers omit the long-form contract unless complexity warrants a short note. **Private** helpers keep documentation minimal (a phrase or single sentence at most). -**Directory layout**: `$HOME/.config/pyref/` (XDG config) holds user preferences: `tui.toml` (keymap, theme, layout, `last_root`, `recent_roots`, selection export). Override with `PYREF_TUI_CONFIG`. `$HOME/.pyref/` holds data/index: `beamtime_index.sqlite3` (central index of indexed beamtimes for the launcher). Optional `pyref.toml` and subdirs `cache/`, `logs/` are reserved for future use. If `HOME` is unset, the beamtime index uses `./.pyref/` as fallback. +## Module and package documentation -**TUI (pyref-tui)**: Startup: with no CLI argument, the TUI shows the launcher (list of indexed beamtimes from `~/.pyref/beamtime_index.sqlite3`, most recent first). Enter opens the selected beamtime; [o] opens the "Open directory" dialog. With a CLI argument, the TUI opens that beamtime directly. Open directory: hybrid dialog with path input (Tab autocomplete) and scrollable folder list (.. and subdirs); Enter on a valid path runs ingest, registers the beamtime in the central index, and opens that beamtime. While ingesting (from launcher or from beamtime view), the nav line shows "Ingesting N/M...". If there is no catalog in the opened directory, the TUI shows an empty state and the message to run "Ingest directory" (key [i]) or `pyref.io.ingest_beamtime(path)` from Python. The beamtime browse panel lists **reflectivity profiles**: one row per experiment (scan) for a given sample, tag, and polarization. Two scan modes are supported: **fixed-energy (theta scan)** where sample theta varies and energy is fixed, and **fixed-angle (energy scan)** where energy varies and theta is fixed or limited to a few angles. The table shows Emin (eV), Emax (eV), Type (theta-scan / E-scan), theta min/max, frame count, and duration. Scan type is inferred from the data using energy/theta range tolerances, distinct-value counts, and an Izero heuristic (many points at theta near zero indicate a theta scan). Each row is a single reflectivity profile; Enter expands it to show the underlying FITS files in a table (Scan, Frame, pol, E (eV), sample theta). When expanded, j/k scroll the file list; Enter collapses. Rename/Retag write catalog overrides via `set_override` and the table reloads. On exit, the TUI saves config to `PYREF_TUI_CONFIG` or `~/.config/pyref/tui.toml`, including `last_root`, `selected_samples`, `selected_tags`, and `selected_scan_numbers` (when on beamtime view). Scripts can read that config and use `scan_experiment(last_root).filter(...)` with the same sample/tag/scan filters to match the TUI view. +- Each library module (or the closest equivalent in the language’s module system) includes a short module-level description of responsibility: what problems it solves, what it explicitly does not handle, and which invariants callers should respect. +- Module docs are prescriptive about intent and boundaries so new contributors and agents do not duplicate concerns across modules. -**Parallel ingest (feature `parallel_ingest`, enabled by default)**: Enables `crossbeam-channel` and a pipelined ingest path. The TUI/browser is built with `--features tui`, which includes `parallel_ingest`, so `ingest_beamtime` uses the pipelined path and partitioned discovery when applicable. Discovery can use partitioned parallel walks over top-level subdirs (`discover_fits_paths_parallel`) when multiple subdirs exist; results are merged and sorted by path. A single thread holds the SQLite connection; a reader thread sends bounded batches (sync channel capacity 2) so FITS reads overlap with batched commits. For the new catalog layout only (`is_new_catalog_layout`), the hot path can use row structs (`BtIngestRow` from `build_bt_ingest_row` / `read_multiple_fits_headers_only_rows`) and `upsert_bt_batch_rows` instead of building Polars DataFrames per batch. The DataFrame path (`read_fits_metadata_batch`, `upsert_bt_batch`) remains for Python and legacy layout. Build without Python: `cargo check --no-default-features --features catalog,parallel_ingest`. +## Tooling, skills, and continued learning -**Deferred work (calculated columns, beam spot, zarr)**: Catalog ingest stores header fields and source path/mtime in `bt_scan_points`. Calculated columns (e.g. Q, Lambda) can be deferred to export (Parquet/HDF5) rather than computed during ingest. Beam spot columns in `bt_scan_points` stay NULL until a processing stage updates them. Zarr materialization (`materialize_beamtime`) is an explicit stage that re-reads FITS from paths recorded in the catalog; a single combined pass (one read for catalog row plus zarr chunk) is optional and not the default, so back-to-back full-tree reread for catalog then zarr implies two NAS reads unless data is copied local first. +- Use project rules, agent skills, and MCP documentation tools when they apply to the task. Prefer authoritative library and framework documentation over memory when behavior, defaults, or breaking changes matter. +- When touching unfamiliar APIs, verify signatures, deprecations, and error modes against current docs or source in the dependency before guessing. +- Prefer file-scoped or package-scoped commands when the repository documents them (typecheck, lint, format, test on a single path) to shorten feedback loops. +- State permission-sensitive actions clearly (dependency installs, destructive commands, credential access) and follow the user’s safety expectations for the workspace. -**NAS usage**: Prefer one open/read per file for catalog metadata ingest. Parallel discovery reduces wall time on wide trees; batch transactions on the single writer reduce fsync churn. For repeated materialize runs, copy beamtime tree to local storage then run zarr materialize to avoid hammering NAS. +## Task shape (goal, context, constraints, completion) -**FITS Preview (three-panel, marquee)**: When a FITS file is selected in the TUI, a preview window shows one figure with three panels (non-macOS: egui; macOS: single composite PNG). Left panel: Raw image (trimmed, then row-by-row background with 10-pixel strips, then cold-side dark subtraction). Middle panel: Gaussian-filtered image. Right panel: 4-sigma crop around the fitted beam center with 1/2/3/4-sigma Gaussian fit contours overlaid. Sigma for the filter is derived from the 2D Gaussian fit (average of row/col sigma, clamped to 0.5--20.0); if the fit fails, a default sigma (2.0) is used and the right panel shows a center crop. On non-macOS, the user can optionally draw a marquee (drag a rectangle) on the left or middle panel; on mouse release the ROI is stored for that path and the fit is re-run inside the ROI, updating all three panels and contours. ROI is per-path and in-memory for the session. +- Restate the goal in terms of observable outcomes: behavior, tests, or interfaces that change. +- Ground work in the relevant files, modules, and existing tests named by the user or discovered through search. +- Honor explicit constraints (performance, numerics, compatibility, style) before proposing alternatives. +- Stop when the completion criteria are met: tests pass where applicable, edge cases called out by the user are handled, and no placeholder implementation remains. -### 7. Data Stitching (Conceptual) +## Quality bar for agent output -While explicit stitching code is not fully implemented, the infrastructure supports it: +- Do not substitute templates, pseudocode, or abbreviated implementations when the user asked for working code. +- If scope is too large for one pass, propose a staged plan and complete the first stage fully rather than leaving partial files full of omissions. -**Current capabilities:** -- Data grouped by `file_name` (each stitch is a separate file_name) -- Uncertainty propagation functions ready for scale factor calculations -- Weighted statistics for combining overlapping points -- `OverlapError` exception defined for overlap validation +# Python -**Stitching workflow (as designed):** -1. Identify overlapping theta/Q values between consecutive stitches -2. Calculate multiplicative scale factor from overlapping region -3. Apply scale factor to subsequent stitch -4. Propagate uncertainty through scaling operation -5. Combine overlapping points using weighted mean -6. Continue recursively for all stitches +## Python -**Key considerations:** -- Scale factors determined from overlapping measurements -- Uncertainty propagation through multiplication (scale factor application) -- Weighted combination preserves statistical information -- Must handle cases where overlap is insufficient +The following applies to **Python** work in this repository: scientific and general-purpose code, with emphasis on clear structure, reproducible tooling, and documentation that matches how the team uses Cursor (skills, subagents, and editor rules). -### 8. Reflectivity Fitting (`python/pyref/fitting/`) +### Conventions -The fitting module provides: -- `XrayReflectDataset`: Dataset class for reflectivity data with polarization handling -- `ReflectModel`: Model for fitting reflectivity curves -- `Structure`: Layer structure definitions -- Uniaxial and birefringent reflectivity calculations +- Follow **PEP 8** surface style; treat **Ruff** configuration in `pyproject.toml` as the enforced interpretation of those conventions. +- Prefer **Python 3.12+** unless the repo pins an older interpreter. +- Prefer **readability** over micro-optimizations; vectorize numerics when a library primitive exists instead of tight Python loops over large data. +- **NumPy-style docstrings** on **public** APIs (parameters, returns, and examples where they clarify behavior). Keep implementation bodies clear **without** long narrative **inline comments**—use names, small functions, and docstrings instead. +- **Tables and time series**: explicit **index/column** semantics when using **pandas**; **lazy** queries when standardizing on **Polars** for heavy pipelines. +- **Lab / instruments**: separate **resource lifecycle** (open, configure, close) from **command strings** and parsing (e.g. **pyvisa** patterns). -## Data Flow +### Tooling + +Use **[uv](https://docs.astral.sh/uv/latest/)** for environments, runs, and dependency changes. Pair it with the **[Astral](https://astral.sh/)** stack as configured in this project. + +- **Dependencies**: add, upgrade, and remove packages with **`uv add`**, **`uv add … --upgrade`**, **`uv remove`**—do **not** hand-edit version pins in `pyproject.toml`. +- **Environment**: **`uv sync`** after cloning or when the lockfile changes; **`uv run …`** to execute Python, tools, and tests. +- **Dev tools**: keep **`ruff`** and **`ty`** in the development (or project) dependency group; run **`ruff check`** (and project formatting if applicable) plus **`ty check`** on changed code. **`uvx`** remains an option for one-off tool runs. +- **pytest**: install via **`uv add --dev pytest`** (or the project’s dev group); run with **`uv run pytest`**. + +If a **`uv`** subcommand differs by version, use **`uv --help`** or the [uv docs](https://docs.astral.sh/uv/latest/). + +### Testing + +- Prefer **fast, deterministic** unit tests; isolate I/O and timing-sensitive checks when the team uses markers or separate jobs. +- **Regression tests** for fixed bugs; for numerics, assert **shapes**, **dtypes**, and stability expectations when science or reproducibility requires it. + +### Cursor: skills + +Load these **skills** by **name** when the task matches (each skill’s own `SKILL.md` and references hold the full detail). Installed skills usually live under `.cursor/skills/` (or your editor’s equivalent). + +| Skill | Use it for | +|-------|------------| +| **general-python** | Hub: **uv** / **ruff** / **ty** workflow, builtins and collections, functions and classes, **dataclasses**, typing boundaries, **pytest**, scientific defaults, and pointers to the other skills. | +| **numpy-scientific** | **NumPy**: dtypes, views vs copies, broadcasting, ufuncs and reductions, **linalg** / **einsum**, **`Generator`**, I/O, interop with tables and plotting. | +| **dataframes** | **pandas** and **Polars**: when to use which, indexing, joins, lazy execution, I/O, nulls, Arrow interop. | +| **numpy-docstrings** | **Numpydoc**-style docstrings: section order, semantics (what belongs in docstrings vs types vs tests), anti-patterns, **Parameters** / **Returns** / **Examples** / classes / modules. | +| **matplotlib-scientific** | Publication-style **Matplotlib**: OO API, axes and legends, layout, export, journal widths, optional **SciencePlots**. | +| **lab-instrumentation** | **PyVISA** / VISA sessions, **sockets** vs VISA, **hardware abstraction**, **input validation** before I/O, **testing** without hardware, **PDF** extraction for datasheets and manuals. | + +### Cursor: subagents + +Delegate by **subagent name** when a focused pass is better than inline editing. Subagents usually live under `.cursor/agents/` (or your editor’s equivalent). + +| Subagent | Use it for | +|----------|------------| +| **python-reviewer** | Reviewing changes: **uv** hygiene, typing, numerics footguns, tests, docstring quality. | +| **python-types** | Deep **typing** for **ty**: annotations, PEP 695-style generics, exhaustive **`match`**, fixing checker output. | +| **python-refactor** | **Structure**: unclear multi-value returns, composition vs inheritance, oversized functions or classes, deterministic boundaries. | + +### Cursor: rules + +- A **Python** Cursor **rule** applies to Python sources (typically `**/*.py` when the rule is configured for those globs). It restates **interpreter preference**, **uv** usage, **ruff** / **ty** expectations, numerics and docstring defaults, and points to **general-python**, domain skills such as **lab-instrumentation** when editing drivers or lab I/O, and the subagents above. +- **Rule text is authoritative for “always on” editor hints**; **skills** carry the long-form patterns and examples. When the two differ on a detail, follow **this spec** and **`pyproject.toml`**, then the **rule**, then skill nuance. + +### External references + +- [numpydoc format](https://numpydoc.readthedocs.io/en/latest/format.html) +- [uv](https://docs.astral.sh/uv/latest/), [Ruff](https://docs.astral.sh/ruff/), [ty](https://docs.astral.sh/ty/) + +# Rust + +This workspace uses the **dotagent Rust** stack. + +- Prefer `cargo` as the interface for builds, tests, and dependency changes; add crates with `cargo add`. +- Match edition and MSRV documented in the repository; do not bump them casually. +- Prefer `clippy` clean builds when the project already enforces clippy in CI. +- Error handling should be structured (`Result`) at boundaries; avoid `unwrap` in library code unless explicitly documented. + +# Python - Jupyter + +This workspace extends Python with **Jupyter notebook** expectations. + +### General Guidelines + +This project will make strong use of jupyter notebooks to solve a number of problems, primarily with a scientific focus, but may also have a general purpose focus. Notebooks should be written and well documented. But keep in mind that a notebook allows for a lot of flexibility, and as such, the code should be written in a way that is easy to understand and maintain. + +!!!!Make sure that you use your new jupyter tools instead of generating as a json!!!! + + As a general rule, we will use notebooks for one of the following purposes: + +#### Lightweight Exploration Notebooks + +Here we will have a few cells that will build the idea or concept, test it with some data, and then present the results in a clean and easy way. Use matplotlib for static plotting, or hvplot/altair/plotly for interactive plotting. + +It is important to note that these notebooks are not really designed to be robust, and as such we should not focus too hard on making them so. Write the minimal code to get the job done, and then move on to the next notebook. + +#### Prototyping Notebooks + +This is where we will prototype a robust coding solution to a problem. We will use this to test and validate each chunk of code in the complex workflow. As such it is important that we use many small cells to test and validate each chunk of code. Eventually, we will want to move this code into a final script/library. + +It is important to note that these notebooks, are not really designed to showcase the code, but rather to test and validate each chunk of code in the complex workflow. As such, we should not focus too hard on making them look nice, but rather ensure that we atomize the code into small cells that can be tested and validated individually. Testing and validation might be done using simple displays, or plotting, or other cases. But assertstetements are not necessary, and should be avoided if possible in favor of displaying the results to the user. + +#### Demonstration Notebooks + +These notebooks are designed to showcase a workflow of production ready code. Ideally, after a library is complete and ready to use, the user will be able to import the library and use the code treating the notebook as a production environment. They will have a minimal ammount of cells, mixing in documentation and examples of how to use the library. + +It is important to note that these notebooks are designed to be robust. These should mix in a healthy ammount of markdown documentation and explaination. but not be too heavy handed. Keep in mind that the goal of these notebooks is that a user can copy them into their own notebooks and know how to use the library. + +### Use of Cells + +- Use markdown cells sparingly to explain the code, or why it works the way it does. But avoid long narrative markdown cells that are overly robust. +- Use code cells to write the code. Ensure that each cell is small and atomic. Each cell should have one responsibility and deterministically produce a result. If you define a function, call it in the same cell. Avoid using global variables, or mutable state if possible. +- Class definitions should idealy be avoided in notebooks, unless we are prototyping a library. If we need a class, it should be defined in a separate cell and have a minimal footprint. +- Variables should be defined in the cells that use them, and should usually be displayed to the user. Avoid using global variables, or mutable state if possible. +- When prototping a library, use the `%autoreload 2` magic to ensure that the code is reloaded when it is changed. + +# Python - PyO3 + +This workspace extends Python with **PyO3 / Maturin** integration expectations. + +### General Guidelines +We are using the [pyo3](https://pyo3.rs/) library to create a python extension for our project. The ONLY reason we are doing this is to speed up the execution of critical code. As such, we should only use pyo3 for code that is critical to the performance of the project, and should avoid using it for code that is not performance critical. Generally, iO operations are easier to implement in python, while multi-threaded operations are faster in rust. + +The python GIL is the global interpreter lock, limiting the ability for python to be truly multi-threaded. However, rust executions can bypass the GIL leading to a significant speedup in performance. As such, we should not multi-thread in python, but rather pass tasks into rust for parallel processing, and then return the results back to python. + +### Tooling +`uv` is still king within the project, and should be used for all python needs. However, we will use the `maturin` tool as the build system for the project. See the [maturin documentation](https://www.maturin.rs/) for more information on how to use it. Ensure that `maturin` is installed in the dev group. See the [uv documentation](https://docs.astral.sh/uv/concepts/projects/init/#projects-with-extension-modules) for more information on how to use maturin as a tool. In general, we prefer a structure native to maturin following the following format: ``` -FITS Files - ↓ -[Rust IO Layer] - Parallel reading, image processing, initial reflectivity - ↓ -(optional) [Catalog] - ingest_beamtime writes .pyref_catalog.db per beamtime; scan_from_catalog reads it - ↓ -scan_experiment(source) - directory/list -> FITS I/O; directory with .pyref_catalog.db or path to .db -> catalog only - ↓ -Polars DataFrame (meta) - All images, metadata, calculated Q (same schema either path) - ↓ -[Python Loader] - Masking, grouping by file_name - ↓ -Reflectivity DataFrame (refl) - Q, r, dr grouped by stitch - ↓ -[Stitching] - Scale factor calculation, uncertainty propagation (conceptual) - ↓ -Combined Reflectivity Dataset - Complete R vs Q curve - ↓ -[Fitting Module] - Model fitting with refnx +. +├── pyproject.toml +├── Cargo.toml +├── python/ +│ ├── / +│ │ ├── __init__.py +│ │ └── ... +├── src/ +│ ├── lib.rs +│ └── bindings.rs ``` +Ensure that the `python/` directory is a valid python package, and that the `src/` directory is a valid rust crate. + +# Rust - TUI + +This workspace extends Rust with **TUI** application expectations. + +- Prefer established TUI crates already present in the dependency graph; do not introduce a parallel framework without a migration plan. +- Treat terminal sizing, resize bursts, and partial redraws as normal inputs; avoid assuming a static viewport. +- Separate rendering from application state: model updates should not directly depend on widget internals. +- Keyboard and mouse interactions should degrade gracefully when a capability is unavailable. + +# Rust - PyO3 + +This workspace extends Rust with **PyO3 / Maturin** extension expectations. + +- Design the Rust crate as an extension first: minimal Python surface, maximal safety around lifetimes and exceptions. +- Use Maturin's layout and metadata conventions; keep `pyproject.toml` and `Cargo.toml` agreeing on names and versions. +- Document unsafe blocks with the project's standard (even if you avoid new unsafe code). +- Prefer thin Python modules that re-export a small Rust API rather than exposing many low-level Rust objects. -## Key Design Decisions - -1. **Rust backend**: Critical path operations (FITS reading, image processing) implemented in Rust for performance -2. **Polars DataFrame**: Efficient columnar operations, lazy evaluation, built-in parallelization -3. **Uncertainty tracking**: Uncertainty propagated at every step, not added post-hoc -4. **Grouped processing**: Data naturally organized by file_name (stitch) for easy stitching -5. **Plugin architecture**: Uncertainty functions as Polars plugins for seamless integration - -## Usage Patterns - -### Basic Workflow -1. Initialize loader: `loader = PrsoxrLoader(directory)` -2. Optionally mask images: `loader.mask_image()` -3. Access reflectivity: `refl_df = loader.refl` -4. Process by stitch: `loader.refl.group_by("file_name")` - -### Uncertainty Handling -- Use `pyref.utils.err_prop_mult()` and `err_prop_div()` for calculations -- Use `weighted_mean()` and `weighted_std()` for combining overlapping points -- Uncertainty propagates automatically through Polars expressions - -### Catalog usage -- Ingest once (or incrementally): `pyref.io.ingest_beamtime(beamtime_path)`; then `scan_experiment(beamtime_path)` uses the DB. -- Overrides: `set_override(catalog_path, path, sample_name=..., tag=..., notes=...)`; resolved values (e.g. for scan) use COALESCE(override, file). -- Query with filters: `query_catalog(catalog_path, sample_name=..., tag=..., experiment_numbers=...)`. - -### Stitching (Future Implementation) -- Identify overlapping Q ranges between consecutive file_name groups -- Calculate scale factors from weighted mean of overlapping points -- Apply scale factors with proper uncertainty propagation -- Combine using weighted statistics + diff --git a/CLAUDE.md b/CLAUDE.md deleted file mode 100644 index 740ed41..0000000 --- a/CLAUDE.md +++ /dev/null @@ -1,12 +0,0 @@ - - -## 🤖 Agent-Selected Rules -@.cursor/rules/clarify-first-prompting.mdc -@.cursor/rules/cursor-agent-orchestration.mdc -@.cursor/rules/cursot-tools-mastery.mdc - - - -## Project Notes - - \ No newline at end of file diff --git a/CONTRIBUTE.md b/CONTRIBUTE.md deleted file mode 100644 index 549ba51..0000000 --- a/CONTRIBUTE.md +++ /dev/null @@ -1,26 +0,0 @@ -## Glossary - -Terms used for FITS I/O, catalog, and experiment data. - -- **ingest** - Populate the catalog (SQLite) from FITS files: discover paths, read headers, add derived columns (e.g. Q, Lambda), then upsert and prune. One catalog per beamtime directory. Rust: `ingest_beamtime`; Python: `pyref.io.ingest_beamtime`. - -- **discover** - Find FITS file paths under a directory (e.g. recursive walk, filter by extension and skippable stems). No header or image read. Rust: `discover_fits_paths`. - -- **scan (IO)** - Build a LazyFrame of experiment metadata. The operation `scan_experiment(source)` either reads from the catalog (if `.pyref_catalog.db` exists) or discovers and reads FITS headers; in both cases it returns a LazyFrame with the same schema. "Scan from catalog" means reading that LazyFrame from SQLite (Rust: `scan_from_catalog`). No FITS disk read when the catalog is used. - -- **scan (experiment)** - A single measurement run: one CCD Scan with a scan number (e.g. "CCD Scan 88169", Scan ID in headers). One such run is one reflectivity profile (fixed energy or fixed angle). This is distinct from the IO operation `scan_experiment()`. - -- **read** - Actual FITS file I/O: read headers and/or image data from disk. Used for single files, a list of paths, or a directory. Rust: `read_fits_headers_only`, `read_multiple_fits_headers_only`, `read_experiment_headers_only`. - -- **experiment** - A beamtime directory (root containing FITS and optionally `.pyref_catalog.db`), or a logical group of runs. "Experiment number" in metadata (e.g. Scan ID) identifies a single scan (measurement run), not the whole beamtime. - -## Rust - -- Library code must not use `unwrap()` or `expect()`; use `Result` and `?`. Run `cargo clippy --lib` and `cargo fmt`. -- Public functions, modules, and files should have docstrings (e.g. `///` and `//!`). diff --git a/README.md b/README.md index 6ab46d9..dfded88 100644 --- a/README.md +++ b/README.md @@ -1,330 +1,77 @@ -[![DOI](https://zenodo.org/badge/659964712.svg)](https://doi.org/10.5281/zenodo.14758701) -[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/WSU-Carbon-Lab/pyref) -

        - Pyref logo -
        -

        +

        + pyref logo +

        -# PyRef: X-ray Reflectivity Data Reduction +

        + Fast PRSoXR data reduction for ALS Beamline 11.0.1.2. +

        -PyRef is a Python library for reducing 2D X-ray reflectivity detector images into 1D reflectivity signals. It is designed for processing experimental data from polarized resonant soft X-ray reflectivity (PRSoXR) beamlines, specifically beamline 11.0.1.2 at the Advanced Light Source (ALS). +         -## Features +

        + DOI +

        -- **Efficient Data Loading**: Rust-accelerated parallel reading of FITS files -- **Image Processing**: Automatic beam spot detection and background subtraction -- **Uncertainty Propagation**: Built-in error propagation through all calculations -- **Interactive Masking**: Tools for defining regions of interest in detector images -- **Data Organization**: Automatic grouping of data by experimental "stitches" -- **Reflectivity Fitting**: Integration with refnx for model fitting +## Table of Contents -## Installation +This repository contains the Python package, Rust acceleration layer, and tooling for PRSoXR beamtime ingestion and reduction. -```bash -pip install pyref -``` - -For development installations, see the [development setup](#development) section. - -## Quick Start - -### Loading an Experiment - -```python -from pathlib import Path -from pyref.loader import PrsoxrLoader - -# Initialize loader with experiment directory -loader = PrsoxrLoader(Path("/path/to/experiment/data")) - -# Access reflectivity data -refl_df = loader.refl -print(refl_df) -``` - -### Interactive Image Masking - -```python -# Open interactive masking tool -loader.mask_image() - -# The mask is automatically applied to all subsequent processing -# Press 'm' to save and close the mask editor -``` - -### Accessing Data - -```python -# Reflectivity DataFrame -refl = loader.refl # Columns: file_name, Q, r, dr - -# Full metadata DataFrame -meta = loader.meta # All images, headers, calculated columns - -# Plot reflectivity data -plot = loader.plot_data() -``` - -### Working with Stitches - -Experimental data is organized into "stitches" - separate measurement chunks where beamline parameters are adjusted to capture reflectivity across multiple orders of magnitude. - -```python -# Group by stitch (file_name) -for file_name, stitch_data in loader.refl.group_by("file_name"): - print(f"Stitch: {file_name}") - print(f"Q range: {stitch_data['Q'].min()} to {stitch_data['Q'].max()}") - print(f"Number of points: {len(stitch_data)}") -``` - -### Uncertainty Propagation - -PyRef automatically propagates uncertainty through all calculations. The reflectivity DataFrame includes `dr` (uncertainty in reflectivity) calculated from counting statistics. - -```python -import polars as pl -from pyref.utils import err_prop_mult, err_prop_div, weighted_mean - -# Example: Propagate uncertainty through multiplication -# If you need to apply a scale factor with uncertainty -result = df.with_columns([ - err_prop_mult( - pl.col("r"), pl.col("dr"), - pl.lit(scale_factor), pl.lit(scale_uncertainty) - ).alias("scaled_r_err") -]) - -# Weighted mean for combining overlapping points -combined = df.group_by("Q").agg([ - weighted_mean(pl.col("r"), pl.col("dr")).alias("r_mean"), - weighted_std(pl.col("dr")).alias("r_err") -]) -``` - -## How It Works - -### Data Reduction Pipeline - -1. **FITS File Reading**: Parallel reading of FITS files with Rust backend - - Extracts header metadata (energy, theta, exposure time, etc.) - - Loads raw CCD images - - Calculates Q-vector from energy and theta - -2. **Image Processing**: 2D to 1D reduction - - Background subtraction (row-by-row) - - Beam spot location (automatic or edge-detection fallback) - - ROI (Region of Interest) analysis - - Signal extraction: beam counts - scaled background - -3. **Reflectivity Calculation**: - - Normalization by exposure time and beam current - - Uncertainty from Poisson counting statistics: `dr = sqrt(r)` - - Data grouped by file_name (stitch) - -4. **Data Organization**: - - Each experimental "stitch" is identified by `file_name` - - Stitches contain overlapping theta/Q values for scaling - - Metadata and reflectivity data stored in separate DataFrames - -### Stitching Multiple Datasets - -To combine multiple reflectivity scans collected in separate stitches: - -1. **Identify Overlaps**: Find overlapping Q ranges between consecutive stitches -2. **Calculate Scale Factors**: Determine multiplicative scale from overlapping region -3. **Propagate Uncertainty**: Use error propagation functions for scale factor application -4. **Combine Points**: Use weighted statistics to merge overlapping measurements - -Example workflow: -```python -# This is a conceptual example - full stitching implementation may vary -stitches = loader.refl.group_by("file_name") - -stitched_data = [] -cumulative_scale = 1.0 -cumulative_scale_err = 0.0 +- [`python/pyref`](python/pyref) - User-facing Python API for loading, reduction, and analysis. +- [`src`](src) - Rust backend for high-throughput IO and performance-critical paths. +- [`tests`](tests) - Python test suite and integration checks. +- [`AGENTS.md`](AGENTS.md) - Full project architecture, conventions, and contributor guide. +- [Documentation Site](https://wsu-carbon-lab.github.io/pyref/) - Hosted docs and API references. -for i, (file_name, stitch) in enumerate(stitches): - if i == 0: - # First stitch is reference - stitched_data.append(stitch) - continue +[Report an Issue](https://github.com/WSU-Carbon-Lab/pyref/issues/new) - # Find overlap with previous stitch - prev_stitch = stitched_data[-1] - overlap = find_overlapping_q(prev_stitch, stitch) +## Quickstart - if len(overlap) > 0: - # Calculate scale factor from overlap - scale, scale_err = calculate_scale_factor(overlap) +Install: - # Apply scale with uncertainty propagation - scaled_stitch = stitch.with_columns([ - (pl.col("r") * scale).alias("r"), - err_prop_mult( - pl.col("r"), pl.col("dr"), - pl.lit(scale), pl.lit(scale_err) - ).alias("dr") - ]) - - # Combine overlapping points using weighted mean - combined = combine_overlapping_points(prev_stitch, scaled_stitch) - stitched_data[-1] = combined - else: - stitched_data.append(stitch) +```bash +pip install pyref ``` -### Uncertainty Propagation - -PyRef implements standard error propagation formulas: - -**Multiplication**: `σ(xy) = |xy| * sqrt((σx/x)² + (σy/y)²)` - -**Division**: `σ(x/y) = |x/y| * sqrt((σx/x)² + (σy/y)²)` - -**Weighted Mean**: Uses inverse-variance weighting: `w = 1/σ²` - -These are implemented as Polars expressions and can be used in any DataFrame operation. - -## Advanced Usage - -### Custom Header Keys - -```python -from pyref.types import HeaderValue - -loader = PrsoxrLoader( - directory, - extra_keys=[ - HeaderValue.HIGHER_ORDER_SUPPRESSOR, - HeaderValue.EXIT_SLIT_SIZE, - # Add any custom header keys - ] -) -``` +Python `>=3.12` is required. -### Direct FITS Reading +Load a beamtime and inspect reduced data: ```python -from pyref.io import read_experiment, read_fits - -# Read entire experiment directory -df = read_experiment("/path/to/data", headers=["Beamline Energy", "EXPOSURE"]) - -# Read specific FITS file -df = read_fits("/path/to/file.fits", headers=["Beamline Energy"]) -``` +from pathlib import Path +from pyref.loader import PrsoxrLoader -### Exporting Data +loader = PrsoxrLoader(Path("/path/to/beamtime")) -```python -# Save as CSV files (one per stitch) -loader.write_csv() +refl = loader.refl +meta = loader.meta -# Save as Parquet (more efficient) -loader.write_parquet() +loader.plot_data() ``` -### Interactive Spot Checking +Optional interactive tools: ```python -# Visualize individual frames with beam spot and properties +loader.mask_image() loader.check_spot() ``` -## API Reference - -### PrsoxrLoader - -Main class for loading and processing reflectivity data. - -**Parameters**: -- `directory` (Path): Path to directory containing FITS files -- `extra_keys` (list[HeaderValue] | None): Additional header keys to extract - -**Attributes**: -- `refl` (pl.DataFrame): Reflectivity DataFrame with Q, r, dr -- `meta` (pl.DataFrame): Full metadata DataFrame with all images -- `mask` (np.ndarray): Image mask for beam isolation -- `energy` (np.ndarray): Unique energy values in experiment -- `polarization` (Literal["s", "p"]): Polarization state -- `name` (str | np.ndarray): Sample name(s) -- `files` (np.ndarray): List of file names (stitches) - -**Methods**: -- `mask_image()`: Open interactive mask editor -- `check_spot()`: Interactive frame viewer -- `plot_data()`: Plot reflectivity data -- `write_csv()`: Export to CSV files -- `write_parquet()`: Export to Parquet files - -### Uncertainty Functions - -- `err_prop_mult(lhs, lhs_err, rhs, rhs_err)`: Error propagation for multiplication -- `err_prop_div(lhs, lhs_err, rhs, rhs_err)`: Error propagation for division -- `weighted_mean(values, weights)`: Weighted mean using inverse-variance weights -- `weighted_std(weights)`: Weighted standard deviation - ## Development -### Building from Source +All development commands use `uv` for Python and `cargo` for Rust: ```bash -# Clone repository git clone https://github.com/WSU-Carbon-Lab/pyref.git cd pyref - -# Install in development mode -pip install -e . - -# Or use uv -uv pip install -e . +uv sync +uv run pytest ``` -### Experiment browser TUI - -A terminal UI for browsing experiment datasets (sample/tag/experiment selectors and reflectivity profile table). Build and run with: +Build the experiment browser TUI: ```bash cargo browser ``` - -Config is stored at `$PYREF_TUI_CONFIG` if set, otherwise `$HOME/.config/pyref/tui.toml`. Use `q` to quit, Tab to move focus, and j/k or arrows to move within lists and the table. - -### Project Structure - -``` -pyref/ -├── python/pyref/ # Python package -│ ├── loader.py # Main PrsoxrLoader class -│ ├── image.py # Image processing functions -│ ├── masking.py # Interactive masking tools -│ ├── io/ # I/O functions -│ ├── fitting/ # Reflectivity fitting -│ └── utils/ # Utility functions -├── src/ # Rust backend -│ ├── loader.rs # FITS file reading -│ ├── io.rs # Image processing -│ └── lib.rs # Polars plugins -└── tests/ # Test suite -``` - -## Contributing - -Contributions are welcome! Please see the contributing guidelines for details. - -## Citation - -If you use PyRef in your research, please cite: - -``` -[Add citation information here] -``` - -## License - -[Add license information here] - -## Acknowledgments - -Developed for processing data from beamline 11.0.1.2 at the Advanced Light Source, Lawrence Berkeley National Laboratory. diff --git a/docs/prototype.ipynb b/docs/prototype.ipynb deleted file mode 100644 index 147406a..0000000 --- a/docs/prototype.ipynb +++ /dev/null @@ -1,313 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "b711f942", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n const version = '3.8.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n const reloading = false;\n const Bokeh = 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        \n", - "
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        \n", - "" - ] - }, - "metadata": { - "application/vnd.holoviews_exec.v0+json": { - "id": "b617b374-5ae7-4ad4-b43c-1cfd15b268fe" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "import pyref as ref\n", - "from pathlib import Path\n", - "import polars as pl\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "f3f57422", - "metadata": {}, - "outputs": [], - "source": [ - "# data = ref.get_data_path()\n", - "data = Path('/Volumes/DATA/Collins/2026Feb/')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "42da6cb5", - "metadata": {}, - "outputs": [ - { - "ename": "RuntimeError", - "evalue": "beamtime_dir is not a directory: /Volumes/DATA/Collins/2026Feb", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mRuntimeError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[5]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m db = \u001b[43mref\u001b[49m\u001b[43m.\u001b[49m\u001b[43mio\u001b[49m\u001b[43m.\u001b[49m\u001b[43mingest_beamtime\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/projects/pyref/python/pyref/io/readers.py:179\u001b[39m, in \u001b[36mingest_beamtime\u001b[39m\u001b[34m(beamtime_path, header_items, incremental)\u001b[39m\n\u001b[32m 176\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpyref\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mpyref\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m py_ingest_beamtime\n\u001b[32m 178\u001b[39m keys = header_items \u001b[38;5;28;01mif\u001b[39;00m header_items \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(DEFAULT_HEADER_KEYS)\n\u001b[32m--> \u001b[39m\u001b[32m179\u001b[39m out = \u001b[43mpy_ingest_beamtime\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mPath\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbeamtime_path\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[43mresolve\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mincremental\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 180\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m Path(out)\n", - "\u001b[31mRuntimeError\u001b[39m: beamtime_dir is not a directory: /Volumes/DATA/Collins/2026Feb" - ] - } - ], - "source": [ - "db = ref.io" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "13772eef", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "estimated unique values: 107\n", - "run PARTITIONED HASH AGGREGATION\n" - ] - } - ], - "source": [ - "summary = (\n", - " lf.group_by('sample_name')\n", - " .agg([\n", - " pl.len().alias('frames'),\n", - " pl.col('data_size').sum().alias('total_bytes')\n", - " ])\n", - " .with_columns(\n", - " (pl.col('total_bytes') / (1024 * 1024 * 1024)).alias('total_GB')\n", - " )\n", - " .collect()\n", - ")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "d7fbe5f6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Problem Statement - -The current TUI has two screens: - -- **Launcher** — a flat list of recently-ingested beamtime paths drawn from `~/.pyref/beamtime_index.sqlite3`. No hierarchy, no browsing, no metadata. -- **Beamtime screen** — samples / tags / scans / profiles view, scoped to a single beamtime directory and its own per-beamtime `catalog.db`. - -This architecture does not model the actual NAS layout (`DATA/Experimentalist/Beamtime/…`), has no awareness of un-indexed beamtimes, and places the catalog in a user-home global index rather than co-located with the data. The existing `App` struct is a 2600-line monolith with 40+ fields mixing all screen state together, making further extension impractical. - ---- - -## 2. Goals - -1. Replace the Launcher with a full NAS file explorer rooted at a `DATA/` directory passed as a CLI argument. -2. Add an Experimentalist level between the root and beamtime folders, with per-folder catalog status (indexed / stale / not indexed) and FITS file counts. -3. Auto-ingest un-indexed or stale beamtimes on entry, with a cancellable progress spinner. -4. Move the catalog from per-beamtime files to a single `DATA/.pyref/catalog.db` that aggregates all experimentalists and beamtimes. -5. Adapt the existing Beamtime screen to query the DATA-level catalog filtered by beamtime path — no visual change to the screen itself. -6. Decompose the monolithic `App` struct into a `Navigator` + per-screen state model. - ---- - -## 3. Non-Goals - -- Zarr store migration or materialization changes. -- Python bindings or CLI tool changes (backward compatibility is required). -- Any change to the Beamtime screen's visual layout or existing features. -- Tiled integration (future work). -- A web UI (future work). - ---- - -## 4. NAS Directory Model - -``` -DATA/ ← data_root (CLI arg) -├── .pyref/ -│ ├── catalog.db ← single DATA-level catalog (NEW) -│ └── layout.toml ← per-experimentalist layout policy (NEW) -├── Smith/ ← Experimentalist (depth 1, no leading dot) -│ ├── 2024Jan/ ← Beamtime (matched by date pattern) -│ ├── 2023Nov/ -│ └── scratch/ ← generic directory -└── Jones/ - └── 2024Feb/ -``` - -Folders at depth 1 whose names do **not** start with `.` are treated as experimentalist candidates. Whether their subdirectories are classified as beamtime folders is determined by the `LayoutPolicy` described in §6. - ---- - -## 5. Architecture — Navigator + Per-Screen State - -### 5.1 Module Changes - -**New files (`src/bin/tui/`):** - -| File | Purpose | -|---|---| -| `navigator.rs` | `Navigator` struct + `ScreenState` enum | -| `explorer/mod.rs` | `ExplorerState`, `DirEntry`, `EntryKind`, `CatalogStatus` | -| `explorer/heuristic.rs` | `detect_policy()`, `classify_entry()` | -| `explorer/modal.rs` | `ModalState`, ratatui resolution modal | -| `catalog_handle.rs` | `CatalogHandle`, `ExptMeta`, `BeamtimeMeta` | - -**Modified files (`src/bin/tui/`):** - -| File | Change | -|---|---| -| `app.rs` | `App` becomes a thin shell owning `Navigator` + shared channels + config | -| `ui.rs` | Dispatches on `ScreenState` variant | -| `run.rs` | Event loop delegates to `Navigator` | -| `keymap.rs` | Per-screen keybind dispatch | - -**Unchanged:** `beamspot.rs`, `preview.rs`, `scan_type.rs`, `theme.rs`, `terminal_guard.rs`. - -**New files (`src/catalog/`):** - -| File | Purpose | -|---|---| -| `explorer_query.rs` | `list_experimentalists()`, `list_beamtimes()`, `catalog_status()` | - -**Modified files (`src/catalog/`):** - -| File | Change | -|---|---| -| `ingest.rs` | New `data_root` + `experimentalist` + `cancel` parameters | -| `query.rs` | `query_scan_points` gains required `beamtime_path` filter | -| `mod.rs` | Re-exports new public API | - -### 5.2 Core Types - -```rust -pub struct App { - pub navigator: Navigator, - pub preview_tx: Option)>>, - pub beamspot_rx: Option>, - pub preview_cmd_rx: Option>, - pub layout: String, - pub keymap: String, - pub theme: String, - pub needs_redraw: bool, -} - -pub struct Navigator { - stack: Vec, // current + back history - forward: Vec, - pub catalog: CatalogHandle, -} - -pub enum ScreenState { - Explorer(ExplorerState), - Beamtime(BeamtimeState), - ConfigModal(ModalState), -} -``` - -### 5.3 CatalogHandle - -A plain struct wrapping `db_path: PathBuf`. Opens a fresh `rusqlite::Connection` per call — no `Arc`. Owned by `Navigator`, used by both screen types by reference. - -```rust -pub struct CatalogHandle { pub db_path: PathBuf } - -impl CatalogHandle { - pub fn list_experimentalists(&self, data_root: &Path) -> Result>; - pub fn list_beamtimes(&self, data_root: &Path, experimentalist: &str) -> Result>; - pub fn catalog_status(&self, beamtime_path: &Path) -> CatalogStatus; - pub fn query_scan_points(&self, beamtime_path: &Path, filter: Option<&CatalogFilter>) -> Result>; - pub fn list_beamtime_entries_v2(&self, beamtime_path: &Path) -> Result; -} -``` - -If `DATA/.pyref/catalog.db` does not exist, all read methods return empty results; the file is created on first ingest. - -**Thread safety:** `CatalogHandle` is used exclusively from the main event-loop thread. Background ingest threads open their own direct `rusqlite::Connection` via `open_catalog_db()` — they do not go through `CatalogHandle`. This avoids shared-connection races without introducing `Arc>`. The SQLite WAL mode (already set by `open_catalog_db`) allows concurrent reads during an active write. - ---- - -## 6. Explorer Screen - -### 6.1 ExplorerState - -```rust -pub struct ExplorerState { - pub data_root: PathBuf, - pub current_dir: PathBuf, - pub entries: Vec, - pub list_state: ListState, - pub scroll_state: ScrollbarState, - pub layout_policy: NasLayout, - pub loading: bool, - pub status: Option, - pub sort_col: ExplorerSort, - pub sort_asc: bool, -} -``` - -### 6.2 DirEntry - -```rust -pub struct DirEntry { - pub path: PathBuf, - pub name: String, - pub kind: EntryKind, - pub modified: Option, - pub size_bytes: Option, // deferred async - pub fits_count: Option, // fast async via mpsc - pub catalog_status: CatalogStatus, - pub expt_resolution: Option, // Some when kind == Experimentalist -} - -pub enum EntryKind { DataRoot, Experimentalist, Beamtime, Directory } -pub enum CatalogStatus { Indexed, Stale, NotIndexed, NotApplicable } -pub enum ExptResolution { Resolved, NeedsResolution, Ignored } -``` - -### 6.3 Explorer Columns - -| Column | Source | Loading | -|---|---|---| -| Name | `fs::read_dir` | Immediate | -| Date Modified | `fs::metadata().modified()` | Immediate | -| Size | `du` equivalent | Deferred (on selection or `s` key) | -| Kind | `classify_entry()` | Immediate | -| Beamtimes / FITS Files | `CatalogHandle` + `fs::read_dir` count | Fast async | -| Status | `CatalogHandle::catalog_status()` | Fast async | - -### 6.4 Explorer Keybindings - -| Key | Action | -|---|---| -| `↑↓` | Navigate entries | -| `→` / `↵` | Enter directory / open beamtime | -| `←` / `h` | Go up / back | -| `r` | Resolve selected experimentalist | -| `x` | Toggle ignore on selected folder | -| `i` | Ingest selected beamtime | -| `I` | Ingest all not-yet-indexed beamtimes _(deferred — future work, see §13)_ | -| `/` | Filter entries | -| `s` | Compute disk size for selection | -| `q` | Quit | - ---- - -## 7. Per-Experimentalist Layout Policy - -### 7.1 NasLayout - -Persisted to `DATA/.pyref/layout.toml`. - -```rust -pub struct NasLayout { - pub default_pattern: Option, // regex - pub default_depth: u8, // default: 2 - pub experimentalists: HashMap, -} - -pub enum ExptPolicy { - Auto, - Custom { pattern: Option, depth: u8 }, - Ignored, -} -``` - -### 7.2 Heuristic Detection - -On Explorer startup, `detect_policy(data_root)` performs a shallow scan (top 2 levels only). Built-in patterns tested against depth-2 folder names: - -``` -^20\d{2}(Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)$ // 2024Jan -^20\d{2}-\d{2}(-\d{2})?$ // 2024-01 -^20\d{6}$ // 20240115 -(?i)beamtime // contains "beamtime" -``` - -If **(≥50% of depth-2 folders match any pattern) AND (at least 2 folders exist at depth 2)**, the experimentalist is marked `ExptPolicy::Auto` (resolved). Otherwise → `ExptResolution::NeedsResolution` (flagged with `⚑`). - -The `≥2` floor prevents a single folder named `run001` from auto-resolving with a pattern it happened to match — a lab with only one beamtime so far should still go through the modal so the user consciously confirms the layout. Experimentalists with zero depth-2 subdirectories are always flagged. - -Folders at depth 1 whose names start with `.` are silently excluded and never shown. - -### 7.3 Resolution Modal - -Triggered by pressing `r` on a flagged experimentalist row, or automatically on entering a flagged folder. - -Three choices: -1. **Custom pattern** — user specifies a regex and depth. Live preview pane updates on every keystroke showing how subdirs are classified. Invalid regex turns the field red; preview shows last valid state. -2. **Mark as ignored** — folder is dimmed in the explorer, excluded from beamtime counts and bulk ingest. Entry is still allowed for browsing. -3. **Accept global default** — applies the global `default_pattern` + `default_depth` even if no matches were found. - -On confirm, the resolution is written to `DATA/.pyref/layout.toml` immediately. Modal is popped from the Navigator stack, returning to the Explorer with cursor restored. - ---- - -## 8. Beamtime Screen Adaptation - -### 8.1 BeamtimeState - -All fields currently on `App` that pertain to the Beamtime screen are moved verbatim to `BeamtimeState`. Three new context fields are added: - -```rust -pub struct BeamtimeState { - pub data_root: PathBuf, - pub experimentalist: String, - pub beamtime_path: PathBuf, // filter key into DATA catalog - // ... all existing App fields for beamtime view ... -} -``` - -### 8.2 Query Migration - -```rust -// Before -let db = resolve_catalog_path(&beamtime_dir); -let rows = query_files(&db, None)?; - -// After -let db = data_root.join(".pyref/catalog.db"); -let rows = catalog_handle.query_scan_points(&beamtime_path, None)?; -``` - -`query_scan_points` adds `AND source_path LIKE ?1 || '%'` bound to `beamtime_path`. The old `query_files()` function is kept but deprecated; existing callers are migrated individually. - -### 8.3 Context Bar - -The Beamtime screen's title bar shows the full breadcrumb path: `Smith › 2024Jan`. The `←`/`h` key pops `BeamtimeState` off the Navigator stack, restoring the underlying `ExplorerState` with cursor position intact. - -Selections (`last_sample`, `last_tag`, `last_scan`) are written to `TuiConfig` when `BeamtimeState` is popped, not only on quit. - ---- - -## 9. Auto-Ingest on Entry - -When the user enters a beamtime with `CatalogStatus::NotIndexed` or `CatalogStatus::Stale`: - -1. `Navigator.push(ScreenState::Beamtime(BeamtimeState::new_ingesting(…)))` — the screen is pushed immediately with `LoadingState::IngestingDirectory`. -2. A background thread is spawned calling `ingest_beamtime(beamtime_dir, Some(data_root), Some(experimentalist), …, Some(cancel_flag))`. -3. Progress updates flow via `mpsc::Sender<(u32, u32)>` to the `ingest_progress_rx` on `BeamtimeState`. A progress bar and spinner are rendered. -4. On completion: - - `Ok(())` → `BeamtimeState::reload_from_catalog()` queries the DATA catalog. A lightweight message is sent back to the underlying `ExplorerState` to update the status cell to `Indexed`. - - `Err(e)` → error modal shown; `←` returns to Explorer. -5. **Esc during ingest** sets the cancel `AtomicBool` to `true`. The ingest thread checks it between files and exits its loop. The `BeamtimeState` is popped immediately (the thread is not joined — it exits naturally). Partial writes remain in the catalog; on next entry `incremental: true` skips already-indexed files, so the ingest resumes from where it stopped. - -**Concurrency:** Only one ingest thread per `BeamtimeState`. Because each `BeamtimeState` is an independent entry on the Navigator stack, navigating back to the Explorer and then into a different beamtime creates a fresh `BeamtimeState` with its own ingest thread. The abandoned thread from the previous `BeamtimeState` completes or cancels independently. Multiple simultaneous ingests are therefore possible but each targets its own beamtime path — no coordination is needed beyond SQLite WAL serialisation at the write layer. - -**Partial write visibility:** `BeamtimeState` only calls `reload_from_catalog()` once the ingest thread sends `Ok(())`. Profiles are not shown incrementally during ingest — the spinner + progress bar are the only UI during that phase. - -**Stale** beamtimes use the same path as `NotIndexed`. Staleness is determined by comparing the directory's most recent file `mtime` against `bt_beamtimes.last_indexed_at`. - ---- - -## 10. Ingestion Pipeline Changes - -### 10.1 Signature - -```rust -pub fn ingest_beamtime( - beamtime_dir: &Path, - data_root: Option<&Path>, // NEW — None = legacy behaviour - experimentalist: Option<&str>, // NEW — None = legacy behaviour - header_items: &[String], - incremental: bool, // unchanged semantics: true = skip already-indexed files - progress_tx: Option>, - cancel: Option>, // NEW — None = no cancellation -) -> Result // return type unchanged -``` - -`data_root: None` and `experimentalist: None` reproduce the current behaviour exactly — the catalog is opened at the beamtime directory, no new columns are written. All existing callers pass `None` for the two new parameters and `None` for `cancel` until individually migrated. The `incremental` parameter retains its existing meaning: `true` skips FITS files whose `mtime` has not changed since last ingest. - -### 10.2 Catalog Path Resolution - -```rust -pub fn resolve_ingest_target(beamtime_dir: &Path, data_root: Option<&Path>) -> PathBuf { - match data_root { - Some(root) => root.join(".pyref").join("catalog.db"), - None => resolve_catalog_path(beamtime_dir), // existing behaviour - } -} -``` - -### 10.3 Schema Migration - -Two nullable columns added to `bt_beamtimes` via `ALTER TABLE … ADD COLUMN … DEFAULT ''`. Safe migration — existing databases open without errors; empty string is treated as unset and the experimentalist is inferred from path at query time. - -```sql -ALTER TABLE bt_beamtimes ADD COLUMN experimentalist TEXT NOT NULL DEFAULT ''; -ALTER TABLE bt_beamtimes ADD COLUMN data_root TEXT NOT NULL DEFAULT ''; -CREATE INDEX IF NOT EXISTS idx_bt_experimentalist - ON bt_beamtimes(data_root, experimentalist); -``` - -At the start of every ingest, `upsert_beamtime_record()` runs an `INSERT … ON CONFLICT DO UPDATE` to record these values and refresh `last_indexed_at`. - ---- - -## 11. CLI Argument - -``` -browser [data_root] -``` - -- If `data_root` is provided: must be an existing directory. The Explorer is shown rooted at this path. -- If omitted: behaves as today — reads `config.last_root`; if set, opens the Beamtime screen directly. This preserves backward compatibility for existing workflows. - -The `Screen::Launcher` code path is removed in Phase 4 once the Explorer is fully functional. - ---- - -## 12. Execution Phases - -### Phase 1 — Decompose (no user-visible change) -- Extract `BeamtimeState` from `App` -- Introduce `Navigator`, `ScreenState` -- `App` becomes thin shell -- All existing behaviour preserved -- Files: `app.rs`, `run.rs`, `ui.rs`, `keymap.rs`, new `navigator.rs` - -### Phase 2 — Explorer screens -- Add `ExplorerState`, `DirEntry`, `EntryKind` -- Add `heuristic.rs`, `NasLayout`, `layout.toml` persistence -- Add `ModalState` and resolution modal UI -- Wire CLI arg as `data_root` -- Files: new `explorer/` module - -### Phase 3 — DATA-level catalog -- Add `CatalogHandle` and `explorer_query.rs` -- Schema migration for `bt_beamtimes` -- Adapt `ingest_beamtime` signature (backward-compatible) -- Add cancel token to ingest loop -- Adapt `query_scan_points` to accept `beamtime_path` filter - -### Phase 4 — Wire end-to-end -- Auto-ingest on Beamtime entry (NotIndexed + Stale) -- `BeamtimeState` queries DATA-level catalog -- Status cell update channel from Beamtime → Explorer -- Config saved on `BeamtimeState` pop -- Remove `Screen::Launcher` + `list_beamtimes()` global index call - -Each phase is independently mergeable with no breaking changes to existing functionality. - ---- - -## 13. Open Questions / Future Work - -- **Bulk ingest (`I` key):** Phase 4 scope is single-beamtime auto-ingest. Bulk ingest of all un-indexed beamtimes in an experimentalist folder is deferred to a follow-on ticket. -- **Disk size column:** Deferred computation via `du` on NAS can be slow. Current plan is on-demand (`s` key) or on-selection. May need a cap or timeout. -- **`beamtime_index.sqlite3` migration:** The global `~/.pyref/beamtime_index.sqlite3` is not removed in this redesign. It remains as a fallback for the no-`data_root` code path. -- **Tiled migration:** The DATA-level `catalog.db` schema is designed to be compatible with a future Tiled ingest pipeline. No Tiled-specific changes are in scope here. diff --git a/python/pyref/io/experiment_names.py b/python/pyref/io/experiment_names.py index 5b9e391..e9c8a4e 100644 --- a/python/pyref/io/experiment_names.py +++ b/python/pyref/io/experiment_names.py @@ -11,6 +11,7 @@ import re from dataclasses import dataclass from pathlib import Path + import polars as pl from pyref.io.readers import read_fits diff --git a/src/bin/tui/beamspot.rs b/src/bin/tui/beamspot.rs index 44ea24d..6b7c4cb 100644 --- a/src/bin/tui/beamspot.rs +++ b/src/bin/tui/beamspot.rs @@ -1,149 +1,3 @@ +//! Re-exports beamspot drift QC from the catalog library for the TUI binary. -use pyref::catalog::FileRow; -use super::scan_type::ReflectivityScanType; - -#[derive(Debug, Clone)] -pub struct BeamspotLinearFit { - pub row_slope: f64, - pub row_intercept: f64, - pub col_slope: f64, - pub col_intercept: f64, - pub row_residual_std: f64, - pub col_residual_std: f64, -} - -pub fn domain_for_row(row: &FileRow, scan_type: ReflectivityScanType) -> Option { - domain_value(row, scan_type) -} - -fn domain_value(row: &FileRow, scan_type: ReflectivityScanType) -> Option { - match scan_type { - ReflectivityScanType::FixedEnergy => row.sample_theta, - ReflectivityScanType::FixedAngle => row.beamline_energy, - ReflectivityScanType::SinglePoint => None, - } -} - -fn least_squares_linear(x: &[f64], y: &[f64]) -> Option<(f64, f64)> { - let n = x.len() as f64; - if n < 2.0 || x.len() != y.len() { - return None; - } - let sum_x: f64 = x.iter().sum(); - let sum_y: f64 = y.iter().sum(); - let sum_xx: f64 = x.iter().zip(x.iter()).map(|(a, b)| a * b).sum(); - let sum_xy: f64 = x.iter().zip(y.iter()).map(|(a, b)| a * b).sum(); - let denom = n * sum_xx - sum_x * sum_x; - if denom.abs() < 1e-12 { - return None; - } - let slope = (n * sum_xy - sum_x * sum_y) / denom; - let intercept = (sum_y - slope * sum_x) / n; - Some((slope, intercept)) -} - -fn residual_std(x: &[f64], y: &[f64], slope: f64, intercept: f64) -> f64 { - if x.len() < 2 || x.len() != y.len() { - return 0.0; - } - let n = x.len() as f64; - let var: f64 = x - .iter() - .zip(y.iter()) - .map(|(&xi, &yi)| { - let pred = slope * xi + intercept; - let r = yi - pred; - r * r - }) - .sum(); - ((var / (n - 1.0)).max(0.0)).sqrt() -} - -pub fn fit_beamspot_linear( - rows: &[FileRow], - scan_type: ReflectivityScanType, -) -> Option { - if matches!(scan_type, ReflectivityScanType::SinglePoint) { - return None; - } - let points: Vec<(f64, f64, f64)> = rows - .iter() - .filter_map(|r| { - let domain = domain_value(r, scan_type)?; - let row = r.beam_row? as f64; - let col = r.beam_col? as f64; - Some((domain, row, col)) - }) - .collect(); - if points.len() < 2 { - return None; - } - let x: Vec = points.iter().map(|p| p.0).collect(); - let row_vals: Vec = points.iter().map(|p| p.1).collect(); - let col_vals: Vec = points.iter().map(|p| p.2).collect(); - let (row_slope, row_intercept) = least_squares_linear(&x, &row_vals)?; - let (col_slope, col_intercept) = least_squares_linear(&x, &col_vals)?; - let row_residual_std = residual_std(&x, &row_vals, row_slope, row_intercept); - let col_residual_std = residual_std(&x, &col_vals, col_slope, col_intercept); - let row_residual_std = if row_residual_std > 1e-9 { - row_residual_std - } else { - 1.0 - }; - let col_residual_std = if col_residual_std > 1e-9 { - col_residual_std - } else { - 1.0 - }; - Some(BeamspotLinearFit { - row_slope, - row_intercept, - col_slope, - col_intercept, - row_residual_std, - col_residual_std, - }) -} - -pub fn beamspot_status( - row: Option, - col: Option, - domain: Option, - fit: Option<&BeamspotLinearFit>, - row_mean: f64, - row_std: f64, - col_mean: f64, - col_std: f64, -) -> (&'static str, u8) { - let (r, c) = match (row, col) { - (Some(a), Some(b)) => (a as f64, b as f64), - _ => return ("-", 3), - }; - let max_z = if let (Some(f), Some(d)) = (fit, domain) { - let pred_row = f.row_slope * d + f.row_intercept; - let pred_col = f.col_slope * d + f.col_intercept; - let z_row = ((r - pred_row) / f.row_residual_std).abs(); - let z_col = ((c - pred_col) / f.col_residual_std).abs(); - z_row.max(z_col) - } else { - let z_row = if row_std > 1e-9 { - ((r - row_mean) / row_std).abs() - } else { - 0.0 - }; - let z_col = if col_std > 1e-9 { - ((c - col_mean) / col_std).abs() - } else { - 0.0 - }; - z_row.max(z_col) - }; - let (status, key) = if max_z < 2.0 { - ("ok", 0) - } else if max_z < 4.0 { - ("warning", 1) - } else { - ("err", 2) - }; - (status, key) -} +pub use pyref::catalog::{beamspot_status, domain_for_row, fit_beamspot_linear}; diff --git a/src/catalog/beamspot_qc.rs b/src/catalog/beamspot_qc.rs new file mode 100644 index 0000000..e6fe046 --- /dev/null +++ b/src/catalog/beamspot_qc.rs @@ -0,0 +1,226 @@ +//! Beamspot position drift versus acquisition domain for catalog file rows. +//! +//! For [`ReflectivityScanType::FixedEnergy`] (theta scan), the independent domain is +//! [`FileRow::sample_theta`]. For [`ReflectivityScanType::FixedAngle`] (energy scan), the domain is +//! [`FileRow::beamline_energy`]. Linear fits and z-scores follow the project spec for TUI parity. + +use crate::catalog::query::FileRow; +use crate::catalog::reflectivity_profile::ReflectivityScanType; + +/// Ordinary least-squares fit of beam row and column vs domain, with residual scales for z-scoring. +#[derive(Debug, Clone)] +pub struct BeamspotLinearFit { + pub row_slope: f64, + pub row_intercept: f64, + pub col_slope: f64, + pub col_intercept: f64, + pub row_residual_std: f64, + pub col_residual_std: f64, +} + +/// Returns the independent variable for beamspot drift for this row and scan type. +pub fn domain_for_row(row: &FileRow, scan_type: ReflectivityScanType) -> Option { + domain_value(row, scan_type) +} + +fn domain_value(row: &FileRow, scan_type: ReflectivityScanType) -> Option { + match scan_type { + ReflectivityScanType::FixedEnergy => row.sample_theta, + ReflectivityScanType::FixedAngle => row.beamline_energy, + ReflectivityScanType::SinglePoint => None, + } +} + +fn least_squares_linear(x: &[f64], y: &[f64]) -> Option<(f64, f64)> { + let n = x.len() as f64; + if n < 2.0 || x.len() != y.len() { + return None; + } + let sum_x: f64 = x.iter().sum(); + let sum_y: f64 = y.iter().sum(); + let sum_xx: f64 = x.iter().zip(x.iter()).map(|(a, b)| a * b).sum(); + let sum_xy: f64 = x.iter().zip(y.iter()).map(|(a, b)| a * b).sum(); + let denom = n * sum_xx - sum_x * sum_x; + if denom.abs() < 1e-12 { + return None; + } + let slope = (n * sum_xy - sum_x * sum_y) / denom; + let intercept = (sum_y - slope * sum_x) / n; + Some((slope, intercept)) +} + +fn residual_std(x: &[f64], y: &[f64], slope: f64, intercept: f64) -> f64 { + if x.len() < 2 || x.len() != y.len() { + return 0.0; + } + let n = x.len() as f64; + let var: f64 = x + .iter() + .zip(y.iter()) + .map(|(&xi, &yi)| { + let pred = slope * xi + intercept; + let r = yi - pred; + r * r + }) + .sum(); + ((var / (n - 1.0)).max(0.0)).sqrt() +} + +/// Fits row and column pixel centers vs domain for rows that have beamspot and domain values. +pub fn fit_beamspot_linear( + rows: &[FileRow], + scan_type: ReflectivityScanType, +) -> Option { + if matches!(scan_type, ReflectivityScanType::SinglePoint) { + return None; + } + let points: Vec<(f64, f64, f64)> = rows + .iter() + .filter_map(|r| { + let domain = domain_value(r, scan_type)?; + let row = r.beam_row? as f64; + let col = r.beam_col? as f64; + Some((domain, row, col)) + }) + .collect(); + if points.len() < 2 { + return None; + } + let x: Vec = points.iter().map(|p| p.0).collect(); + let row_vals: Vec = points.iter().map(|p| p.1).collect(); + let col_vals: Vec = points.iter().map(|p| p.2).collect(); + let (row_slope, row_intercept) = least_squares_linear(&x, &row_vals)?; + let (col_slope, col_intercept) = least_squares_linear(&x, &col_vals)?; + let row_residual_std = residual_std(&x, &row_vals, row_slope, row_intercept); + let col_residual_std = residual_std(&x, &col_vals, col_slope, col_intercept); + let row_residual_std = if row_residual_std > 1e-9 { + row_residual_std + } else { + 1.0 + }; + let col_residual_std = if col_residual_std > 1e-9 { + col_residual_std + } else { + 1.0 + }; + Some(BeamspotLinearFit { + row_slope, + row_intercept, + col_slope, + col_intercept, + row_residual_std, + col_residual_std, + }) +} + +/// Labels beamspot deviation: `"ok"` / `"warning"` / `"err"` vs fit or vs batch mean, plus sort key. +#[allow(clippy::too_many_arguments)] +pub fn beamspot_status( + row: Option, + col: Option, + domain: Option, + fit: Option<&BeamspotLinearFit>, + row_mean: f64, + row_std: f64, + col_mean: f64, + col_std: f64, +) -> (&'static str, u8) { + let (r, c) = match (row, col) { + (Some(a), Some(b)) => (a as f64, b as f64), + _ => return ("-", 3), + }; + let max_z = if let (Some(f), Some(d)) = (fit, domain) { + let pred_row = f.row_slope * d + f.row_intercept; + let pred_col = f.col_slope * d + f.col_intercept; + let z_row = ((r - pred_row) / f.row_residual_std).abs(); + let z_col = ((c - pred_col) / f.col_residual_std).abs(); + z_row.max(z_col) + } else { + let z_row = if row_std > 1e-9 { + ((r - row_mean) / row_std).abs() + } else { + 0.0 + }; + let z_col = if col_std > 1e-9 { + ((c - col_mean) / col_std).abs() + } else { + 0.0 + }; + z_row.max(z_col) + }; + let (status, key) = if max_z < 2.0 { + ("ok", 0) + } else if max_z < 4.0 { + ("warning", 1) + } else { + ("err", 2) + }; + (status, key) +} + +#[cfg(test)] +mod tests { + use super::*; + + fn row( + energy: Option, + theta: Option, + beam_row: Option, + beam_col: Option, + ) -> FileRow { + FileRow { + file_path: String::new(), + sample_name: String::new(), + tag: None, + scan_number: 0, + frame_number: 0, + beamline_energy: energy, + sample_theta: theta, + epu_polarization: None, + q: None, + date_iso: None, + beam_row, + beam_col, + beam_sigma: None, + scan_point_uid: None, + } + } + + #[test] + fn fixed_energy_fit_invariant_to_energy_jitter() { + let rows = vec![ + row(Some(100.0), Some(0.0), Some(10), Some(20)), + row(Some(500.0), Some(1.0), Some(12), Some(22)), + row(Some(200.0), Some(2.0), Some(14), Some(24)), + ]; + let rows_alt = vec![ + row(Some(999.0), Some(0.0), Some(10), Some(20)), + row(Some(1.0), Some(1.0), Some(12), Some(22)), + row(Some(50.0), Some(2.0), Some(14), Some(24)), + ]; + let a = fit_beamspot_linear(&rows, ReflectivityScanType::FixedEnergy).unwrap(); + let b = fit_beamspot_linear(&rows_alt, ReflectivityScanType::FixedEnergy).unwrap(); + assert!((a.row_slope - b.row_slope).abs() < 1e-9); + assert!((a.row_intercept - b.row_intercept).abs() < 1e-9); + assert!((a.col_slope - b.col_slope).abs() < 1e-9); + assert!((a.col_intercept - b.col_intercept).abs() < 1e-9); + } + + #[test] + fn fixed_angle_fit_invariant_to_theta_jitter() { + let rows = vec![ + row(Some(280.0), Some(0.0), Some(5), Some(8)), + row(Some(281.0), Some(99.0), Some(7), Some(10)), + row(Some(282.0), Some(-3.0), Some(9), Some(12)), + ]; + let rows_alt = vec![ + row(Some(280.0), Some(10.0), Some(5), Some(8)), + row(Some(281.0), Some(10.0), Some(7), Some(10)), + row(Some(282.0), Some(10.0), Some(9), Some(12)), + ]; + let a = fit_beamspot_linear(&rows, ReflectivityScanType::FixedAngle).unwrap(); + let b = fit_beamspot_linear(&rows_alt, ReflectivityScanType::FixedAngle).unwrap(); + assert!((a.row_slope - b.row_slope).abs() < 1e-9); + assert!((a.row_intercept - b.row_intercept).abs() < 1e-9); + } +} diff --git a/src/catalog/ingest.rs b/src/catalog/ingest.rs index eee4fda..13c3dc0 100644 --- a/src/catalog/ingest.rs +++ b/src/catalog/ingest.rs @@ -1,6 +1,7 @@ #![cfg(feature = "catalog")] -use crate::catalog::{CatalogError, Result}; +use crate::catalog::{discover_paths_for_catalog_ingest, CatalogError, Result}; +use crate::io::parse_fits_stem; use crate::io::BtIngestRow; #[cfg(not(feature = "parallel_ingest"))] use crate::catalog::open_or_create_db; @@ -233,6 +234,7 @@ fn ingest_beamtime_sequential( prune_missing_files(&conn, &path_list)?; } prune_bt_scan_points(&conn, &path_list)?; + super::profile_persist::recompute_reflectivity_profiles_for_beamtime(&conn, beamtime_id)?; Ok(db_path) } diff --git a/src/catalog/ingest_parallel.rs b/src/catalog/ingest_parallel.rs index 38e48dd..7a5b22e 100644 --- a/src/catalog/ingest_parallel.rs +++ b/src/catalog/ingest_parallel.rs @@ -89,6 +89,7 @@ pub fn ingest_beamtime_pipelined( prune_missing_files(&conn, &path_list)?; } prune_bt_scan_points(&conn, &path_list)?; + super::profile_persist::recompute_reflectivity_profiles_for_beamtime(&conn, beamtime_id)?; return Ok(db_path); } @@ -191,6 +192,7 @@ pub fn ingest_beamtime_pipelined( prune_missing_files(&conn, &path_list)?; } prune_bt_scan_points(&conn, &path_list)?; + super::profile_persist::recompute_reflectivity_profiles_for_beamtime(&conn, beamtime_id)?; Ok(db_path) } @@ -264,6 +266,7 @@ pub fn ingest_beamtime_pipelined_with_context( prune_missing_files(&conn, &path_list)?; } prune_bt_scan_points(&conn, &path_list)?; + super::profile_persist::recompute_reflectivity_profiles_for_beamtime(&conn, beamtime_id)?; return Ok(db_path); } @@ -377,6 +380,7 @@ pub fn ingest_beamtime_pipelined_with_context( prune_missing_files(&conn, &path_list)?; } prune_bt_scan_points(&conn, &path_list)?; + super::profile_persist::recompute_reflectivity_profiles_for_beamtime(&conn, beamtime_id)?; Ok(db_path) } diff --git a/src/catalog/mod.rs b/src/catalog/mod.rs index f2c2c08..57a6472 100644 --- a/src/catalog/mod.rs +++ b/src/catalog/mod.rs @@ -5,6 +5,8 @@ mod ingest_parallel; mod query; mod explorer_query; mod reflectivity_profile; +mod beamspot_qc; +mod profile_persist; #[cfg(feature = "zarr")] mod materialize; @@ -23,7 +25,10 @@ use walkdir::WalkDir; pub const CATALOG_DB_NAME: &str = ".pyref_catalog.db"; -pub use ingest::{ingest_beamtime, ingest_beamtime_with_context, DEFAULT_INGEST_HEADER_ITEMS}; +pub use ingest::{ + ingest_beamtime, ingest_beamtime_with_context, ingest_header_scan, ingest_header_scan_with_context, + ingest_sample_scan, ingest_sample_scan_with_context, DEFAULT_INGEST_HEADER_ITEMS, +}; #[cfg(feature = "parallel_ingest")] pub use ingest_parallel::ingest_beamtime_pipelined; pub use query::{ @@ -36,7 +41,10 @@ pub use explorer_query::{ list_experimentalists, list_beamtimes_for_expt, catalog_status_for_path, ExptMeta, BeamtimeMeta, DbCatalogStatus, }; -pub use reflectivity_profile::{classify_scan_type, ReflectivityScanType}; +pub use beamspot_qc::{beamspot_status, domain_for_row, fit_beamspot_linear, BeamspotLinearFit}; +pub use reflectivity_profile::{ + classify_scan_type, segment_reflectivity_profiles, ProfileSegment, ReflectivityScanType, +}; #[cfg(feature = "zarr")] pub use materialize::materialize_beamtime; @@ -335,6 +343,63 @@ CREATE TABLE IF NOT EXISTS bt_file_overrides ( notes TEXT )"#; +const BT_REFLECTIVITY_PROFILES_TABLE: &str = r#" +CREATE TABLE IF NOT EXISTS bt_reflectivity_profiles ( + scan_uid TEXT NOT NULL REFERENCES bt_scans(uid) ON DELETE CASCADE, + profile_index INTEGER NOT NULL, + scan_type TEXT NOT NULL, + seq_index_first INTEGER NOT NULL, + seq_index_last INTEGER NOT NULL, + e_min REAL, + e_max REAL, + t_min REAL, + t_max REAL, + PRIMARY KEY (scan_uid, profile_index) +)"#; + +const BT_INDEX_REFLECTIVITY_PROFILES_SCAN: &str = + "CREATE INDEX IF NOT EXISTS idx_bt_reflectivity_profiles_scan ON bt_reflectivity_profiles(scan_uid)"; + +const BT_FITS_DISCOVERY_TABLE: &str = r#" +CREATE TABLE IF NOT EXISTS bt_fits_discovery ( + beamtime_id INTEGER NOT NULL REFERENCES bt_beamtimes(id) ON DELETE CASCADE, + source_path TEXT NOT NULL, + source_mtime INTEGER NOT NULL, + file_stem TEXT NOT NULL, + stem_sample_name TEXT NOT NULL, + stem_tag TEXT, + scan_number INTEGER NOT NULL, + frame_number INTEGER NOT NULL, + stem_parse_ok INTEGER NOT NULL, + resolved_sample_name TEXT NOT NULL, + resolved_tag TEXT, + PRIMARY KEY (beamtime_id, source_path) +)"#; + +const BT_INDEX_FITS_DISCOVERY_BEAMTIME: &str = + "CREATE INDEX IF NOT EXISTS idx_bt_fits_discovery_beamtime ON bt_fits_discovery(beamtime_id)"; + +fn migrate_bt_fits_discovery(conn: &Connection) -> Result<()> { + conn.execute_batch(BT_FITS_DISCOVERY_TABLE)?; + conn.execute_batch(BT_INDEX_FITS_DISCOVERY_BEAMTIME)?; + Ok(()) +} + +/// Discover FITS paths under ``beamtime_dir`` using the same walk strategy as ingest (parallel when +/// the ``parallel_ingest`` feature is enabled). +pub(crate) fn discover_paths_for_catalog_ingest( + beamtime_dir: &Path, +) -> Result> { + #[cfg(feature = "parallel_ingest")] + { + discover_fits_paths_parallel(beamtime_dir) + } + #[cfg(not(feature = "parallel_ingest"))] + { + discover_fits_paths(beamtime_dir) + } +} + pub fn is_skippable_stem(stem: &str) -> bool { stem.is_empty() || stem.starts_with('_') } @@ -535,6 +600,23 @@ fn migrate_add_experimentalist_and_data_root(conn: &Connection) -> Result<()> { Ok(()) } +fn migrate_reflectivity_profiles_schema(conn: &Connection) -> Result<()> { + conn.execute_batch(BT_REFLECTIVITY_PROFILES_TABLE)?; + conn.execute_batch(BT_INDEX_REFLECTIVITY_PROFILES_SCAN)?; + let has_col: i64 = conn.query_row( + "SELECT COUNT(1) FROM pragma_table_info('bt_scan_points') WHERE name = 'reflectivity_profile_index'", + [], + |r| r.get(0), + )?; + if has_col == 0 { + conn.execute( + "ALTER TABLE bt_scan_points ADD COLUMN reflectivity_profile_index INTEGER", + [], + )?; + } + Ok(()) +} + fn migrate_add_last_indexed_at_column(conn: &Connection) -> Result<()> { let table_exists: i64 = conn.query_row( "SELECT COUNT(1) FROM sqlite_master WHERE type='table' AND name='bt_beamtimes'", @@ -591,6 +673,8 @@ pub fn open_catalog_db(db_path: &Path) -> Result { conn.execute_batch(BT_FILE_OVERRIDES_TABLE)?; migrate_add_experimentalist_and_data_root(&conn)?; migrate_add_last_indexed_at_column(&conn)?; + migrate_reflectivity_profiles_schema(&conn)?; + migrate_bt_fits_discovery(&conn)?; Ok(conn) } diff --git a/src/catalog/profile_persist.rs b/src/catalog/profile_persist.rs new file mode 100644 index 0000000..7a188bc --- /dev/null +++ b/src/catalog/profile_persist.rs @@ -0,0 +1,220 @@ +//! Writes reflectivity profile segmentation into SQLite after ingest so metadata stays tied to the +//! indexed beamtime (`bt_reflectivity_profiles` + `bt_scan_points.reflectivity_profile_index`). + +use rusqlite::Connection; + +use crate::catalog::reflectivity_profile::{ + classify_scan_type, segment_reflectivity_profiles, ReflectivityScanType, +}; +use crate::catalog::Result; + +fn scan_type_to_sql(st: ReflectivityScanType) -> &'static str { + match st { + ReflectivityScanType::FixedEnergy => "fixed_energy", + ReflectivityScanType::FixedAngle => "fixed_angle", + ReflectivityScanType::SinglePoint => "single_point", + } +} + +/// Recomputes profile segments for every scan in the beamtime and updates `bt_scan_points`. +/// +/// Call after ingest commits so `seq_index` and energy/theta columns match the on-disk index. +/// Safe to call on empty beamtimes (no-op). +pub(crate) fn recompute_reflectivity_profiles_for_beamtime( + conn: &Connection, + beamtime_id: i64, +) -> Result<()> { + let mut stmt = conn.prepare("SELECT uid FROM bt_scans WHERE beamtime_id = ?1 ORDER BY uid")?; + let scan_uids: Vec = stmt + .query_map([beamtime_id], |r| r.get(0))? + .filter_map(std::result::Result::ok) + .collect(); + for uid in scan_uids { + recompute_reflectivity_profiles_for_scan(conn, &uid)?; + } + Ok(()) +} + +fn recompute_reflectivity_profiles_for_scan(conn: &Connection, scan_uid: &str) -> Result<()> { + let mut stmt = conn.prepare( + "SELECT uid, seq_index, beamline_energy, sample_theta FROM bt_scan_points WHERE scan_uid = ?1 ORDER BY seq_index ASC", + )?; + let rows: Vec<(String, i64, Option, Option)> = stmt + .query_map([scan_uid], |r| Ok((r.get(0)?, r.get(1)?, r.get(2)?, r.get(3)?)))? + .filter_map(std::result::Result::ok) + .collect(); + conn.execute( + "UPDATE bt_scan_points SET reflectivity_profile_index = NULL WHERE scan_uid = ?1", + [scan_uid], + )?; + conn.execute( + "DELETE FROM bt_reflectivity_profiles WHERE scan_uid = ?1", + [scan_uid], + )?; + if rows.is_empty() { + return Ok(()); + } + let pairs: Vec<(Option, Option)> = + rows.iter().map(|(_, _, e, t)| (*e, *t)).collect(); + let segs = segment_reflectivity_profiles(&pairs); + let mut insert_prof = conn.prepare( + r#"INSERT INTO bt_reflectivity_profiles ( + scan_uid, profile_index, scan_type, seq_index_first, seq_index_last, + e_min, e_max, t_min, t_max + ) VALUES (?1, ?2, ?3, ?4, ?5, ?6, ?7, ?8, ?9)"#, + )?; + let mut upd = conn.prepare( + "UPDATE bt_scan_points SET reflectivity_profile_index = ?1 WHERE uid = ?2", + )?; + for (pi, seg) in segs.iter().enumerate() { + let slice_pairs = &pairs[seg.start..seg.end]; + let (_st, emin, emax, tmin, tmax) = classify_scan_type(slice_pairs); + let seq_first = rows[seg.start].1; + let seq_last = rows[seg.end - 1].1; + insert_prof.execute(rusqlite::params![ + scan_uid, + pi as i64, + scan_type_to_sql(seg.scan_type), + seq_first, + seq_last, + emin, + emax, + tmin, + tmax, + ])?; + for r in rows.iter().take(seg.end).skip(seg.start) { + upd.execute(rusqlite::params![pi as i64, &r.0])?; + } + } + Ok(()) +} + +#[cfg(test)] +mod tests { + use super::recompute_reflectivity_profiles_for_beamtime; + use tempfile::TempDir; + + #[test] + fn recompute_sets_profile_index_and_summary_row() { + let tmp = TempDir::new().unwrap(); + let db_path = tmp.path().join("catalog.db"); + { + let conn = crate::catalog::open_catalog_db(&db_path).unwrap(); + conn.execute( + "INSERT INTO bt_beamtimes (beamtime_path) VALUES (?1)", + ["/tmp/beam"], + ) + .unwrap(); + conn.execute( + "INSERT INTO bt_samples (beamtime_id, name, tag) VALUES (1, 's', 't')", + [], + ) + .unwrap(); + conn.execute( + "INSERT INTO bt_scans (uid, beamtime_id, sample_id) VALUES ('s_1_1', 1, 1)", + [], + ) + .unwrap(); + conn.execute( + "INSERT INTO bt_streams (uid, scan_uid) VALUES ('st_1_1', 's_1_1')", + [], + ) + .unwrap(); + for (uid, seq, e, th) in [ + ("sp_1_1_0", 0, 280.0_f64, 0.0_f64), + ("sp_1_1_1", 1, 280.0_f64, 1.0_f64), + ("sp_1_1_2", 2, 280.0_f64, 2.0_f64), + ] { + conn.execute( + r#"INSERT INTO bt_scan_points ( + uid, stream_uid, scan_uid, sample_id, seq_index, + beamline_energy, sample_theta, + source_path, source_data_offset, source_naxis1, source_naxis2, source_bitpix, source_bzero + ) VALUES (?1, 'st_1_1', 's_1_1', 1, ?2, ?3, ?4, '', 0, 0, 0, 0, 0)"#, + rusqlite::params![uid, seq, e, th], + ) + .unwrap(); + } + recompute_reflectivity_profiles_for_beamtime(&conn, 1).unwrap(); + let n: i64 = conn + .query_row( + "SELECT COUNT(*) FROM bt_reflectivity_profiles WHERE scan_uid = 's_1_1'", + [], + |r| r.get(0), + ) + .unwrap(); + assert_eq!(n, 1); + let idx: i64 = conn + .query_row( + "SELECT reflectivity_profile_index FROM bt_scan_points WHERE uid = 'sp_1_1_1'", + [], + |r| r.get(0), + ) + .unwrap(); + assert_eq!(idx, 0); + } + } + + #[test] + fn recompute_izero_then_ramp_two_profiles() { + let tmp = TempDir::new().unwrap(); + let db_path = tmp.path().join("catalog.db"); + let conn = crate::catalog::open_catalog_db(&db_path).unwrap(); + conn.execute( + "INSERT INTO bt_beamtimes (beamtime_path) VALUES (?1)", + ["/tmp/beam"], + ) + .unwrap(); + conn.execute( + "INSERT INTO bt_samples (beamtime_id, name, tag) VALUES (1, 's', 't')", + [], + ) + .unwrap(); + conn.execute( + "INSERT INTO bt_scans (uid, beamtime_id, sample_id) VALUES ('s_1_2', 1, 1)", + [], + ) + .unwrap(); + conn.execute( + "INSERT INTO bt_streams (uid, scan_uid) VALUES ('st_1_2', 's_1_2')", + [], + ) + .unwrap(); + let mut seq = 0_i64; + for _ in 0..4 { + let uid = format!("sp_1_2_{}", seq); + conn.execute( + r#"INSERT INTO bt_scan_points ( + uid, stream_uid, scan_uid, sample_id, seq_index, + beamline_energy, sample_theta, + source_path, source_data_offset, source_naxis1, source_naxis2, source_bitpix, source_bzero + ) VALUES (?1, 'st_1_2', 's_1_2', 1, ?2, 284.0, 0.0, '', 0, 0, 0, 0, 0)"#, + rusqlite::params![uid, seq], + ) + .unwrap(); + seq += 1; + } + for i in 1..6 { + let uid = format!("sp_1_2_{}", seq); + conn.execute( + r#"INSERT INTO bt_scan_points ( + uid, stream_uid, scan_uid, sample_id, seq_index, + beamline_energy, sample_theta, + source_path, source_data_offset, source_naxis1, source_naxis2, source_bitpix, source_bzero + ) VALUES (?1, 'st_1_2', 's_1_2', 1, ?2, 284.0, ?3, '', 0, 0, 0, 0, 0)"#, + rusqlite::params![uid, seq, i as f64 * 0.5], + ) + .unwrap(); + seq += 1; + } + recompute_reflectivity_profiles_for_beamtime(&conn, 1).unwrap(); + let n: i64 = conn + .query_row( + "SELECT COUNT(*) FROM bt_reflectivity_profiles WHERE scan_uid = 's_1_2'", + [], + |r| r.get(0), + ) + .unwrap(); + assert_eq!(n, 2); + } +} diff --git a/src/catalog/query.rs b/src/catalog/query.rs index 9f5b752..36bc86b 100644 --- a/src/catalog/query.rs +++ b/src/catalog/query.rs @@ -466,11 +466,13 @@ SELECT THEN sp.source_naxis1 * sp.source_naxis2 * max(1, abs(sp.source_bitpix) / 8) ELSE 0 END) AS data_size, COALESCE(o.sample_name, s.name) AS sample_name, COALESCE(o.tag, s.tag) AS tag, sc.uid AS scan_uid, sp.seq_index, sp.beamline_energy, sp.sample_theta, sp.ccd_theta, sp.epu_polarization, sp.exposure, - sp.beam_row, sp.beam_col, sp.beam_sigma + sp.beam_row, sp.beam_col, sp.beam_sigma, + sp.reflectivity_profile_index, rp.scan_type AS reflectivity_scan_type FROM bt_scan_points sp JOIN bt_samples s ON sp.sample_id = s.id JOIN bt_scans sc ON sp.scan_uid = sc.uid LEFT JOIN bt_file_overrides o ON o.source_path = sp.source_path +LEFT JOIN bt_reflectivity_profiles rp ON rp.scan_uid = sc.uid AND rp.profile_index = sp.reflectivity_profile_index "#; fn scan_from_catalog_bt( @@ -502,6 +504,8 @@ fn scan_from_catalog_bt( row.get::<_, Option>(16)?, row.get::<_, Option>(17)?, row.get::<_, Option>(18)?, + row.get::<_, Option>(19)?, + row.get::<_, Option>(20)?, )) }; let rows = if params.is_empty() { @@ -537,6 +541,8 @@ fn scan_from_catalog_bt( let mut beam_row = Vec::new(); let mut beam_col = Vec::new(); let mut beam_sigma = Vec::new(); + let mut reflectivity_profile_index = Vec::new(); + let mut reflectivity_scan_type = Vec::new(); for row in rows { let r = row.map_err(CatalogError::Sqlite)?; @@ -573,6 +579,8 @@ fn scan_from_catalog_bt( beam_row.push(r.16); beam_col.push(r.17); beam_sigma.push(r.18); + reflectivity_profile_index.push(r.19); + reflectivity_scan_type.push(r.20); } let series = vec![ @@ -602,6 +610,8 @@ fn scan_from_catalog_bt( Series::new("beam_row".into(), beam_row), Series::new("beam_col".into(), beam_col), Series::new("beam_sigma".into(), beam_sigma), + Series::new("reflectivity_profile_index".into(), reflectivity_profile_index), + Series::new("reflectivity_scan_type".into(), reflectivity_scan_type), ]; let columns: Vec = series.into_iter().map(|s| s.into()).collect(); DataFrame::new(columns).map_err(|e| CatalogError::Validation(e.to_string())) @@ -661,11 +671,13 @@ fn build_bt_df_where_and_params( pub fn scan_from_catalog(db_path: &Path, filter: Option<&CatalogFilter>) -> Result { let conn = open_catalog_db(db_path)?; - let use_bt: bool = conn.query_row( + let bt_count: i64 = conn.query_row( "SELECT COUNT(*) FROM bt_scan_points", [], |r| r.get::<_, i64>(0), - )? > 0; + )?; + let files_count: i64 = conn.query_row("SELECT COUNT(*) FROM files", [], |r| r.get::<_, i64>(0))?; + let use_bt = bt_count > 0 || (bt_count == 0 && files_count == 0); if use_bt { return scan_from_catalog_bt(&conn, filter); } @@ -909,6 +921,8 @@ fn scan_from_catalog_columns() -> Vec<&'static str> { "beam_row", "beam_col", "beam_sigma", + "reflectivity_profile_index", + "reflectivity_scan_type", ] } diff --git a/src/catalog/reflectivity_profile.rs b/src/catalog/reflectivity_profile.rs index 2bfc56f..31110ad 100644 --- a/src/catalog/reflectivity_profile.rs +++ b/src/catalog/reflectivity_profile.rs @@ -2,8 +2,10 @@ //! //! Classifies a sequence of (energy, theta) observations into fixed-energy (theta scan), //! fixed-angle (energy scan), or degenerate single-point behavior using tolerances and -//! heuristics shared with the TUI. Callers use this to choose the independent domain for -//! beamspot drift checks (theta vs energy per project spec). +//! heuristics shared with the TUI. [`segment_reflectivity_profiles`] splits ordered points when +//! an instrument scan contains multiple profiles (e.g. I0 plateau then theta ramp, or successive +//! energy blocks). Callers use the scan type to choose the independent domain for beamspot drift +//! checks (theta vs energy per project spec). use std::collections::HashSet; @@ -134,6 +136,110 @@ pub fn classify_scan_type( ) } +/// One contiguous sub-sequence of frames within an instrument scan, ordered by acquisition. +/// +/// `start` is inclusive; `end` is exclusive, indexing into the same slice passed to +/// [`segment_reflectivity_profiles`]. `scan_type` is the classification of that slice alone. +#[derive(Debug, Clone, Copy, PartialEq, Eq)] +pub struct ProfileSegment { + pub start: usize, + pub end: usize, + pub scan_type: ReflectivityScanType, +} + +fn energy_step_boundaries(pairs: &[(Option, Option)]) -> Vec { + let n = pairs.len(); + let mut out = Vec::new(); + for i in 1..n { + if let (Some(e0), Some(e1)) = (pairs[i - 1].0, pairs[i].0) { + if (e1 - e0).abs() > E_TOL_EV { + out.push(i); + } + } + } + out +} + +/// First index after an initial alignment run of near-zero sample theta when the remainder is a theta scan. +/// +/// Requires at least `IZERO_MIN_POINTS` finite samples with `|theta|` below the alignment tolerance +/// before the first finite theta above that tolerance. Skips leading rows with missing theta without +/// breaking the prefix. If the suffix does not classify as [`ReflectivityScanType::FixedEnergy`], +/// returns `None` so energy-only or ambiguous scans are not split on noise. +fn izero_then_theta_ramp_boundary(pairs: &[(Option, Option)]) -> Option { + let mut i = 0usize; + let mut prefix_near_zero = 0usize; + while i < pairs.len() { + match pairs[i].1 { + Some(tv) if tv.abs() <= THETA_TOL_DEG => { + prefix_near_zero += 1; + i += 1; + } + None => { + i += 1; + } + Some(_) => break, + } + } + if prefix_near_zero < IZERO_MIN_POINTS || i >= pairs.len() { + return None; + } + let (st, _, _, _, _) = classify_scan_type(&pairs[i..]); + if matches!(st, ReflectivityScanType::FixedEnergy) { + Some(i) + } else { + None + } +} + +/// Partition ordered `(energy, theta)` points into contiguous profiles for reflectivity grouping. +/// +/// Boundaries are inserted when: +/// +/// 1. **Energy step:** consecutive finite energies differ by more than the catalog energy +/// tolerance (same scale as classification). +/// 2. **I0 then theta ramp:** an initial stretch of at least `IZERO_MIN_POINTS` samples with +/// near-zero theta (classification alignment tolerance) followed by a suffix classified as a +/// fixed-energy theta scan. +/// +/// Each segment is classified with [`classify_scan_type`] on its slice alone. Empty input yields an +/// empty vector. +pub fn segment_reflectivity_profiles( + energy_theta_pairs: &[(Option, Option)], +) -> Vec { + let n = energy_theta_pairs.len(); + if n == 0 { + return Vec::new(); + } + let mut starts: Vec = vec![0]; + starts.extend(energy_step_boundaries(energy_theta_pairs)); + if let Some(k) = izero_then_theta_ramp_boundary(energy_theta_pairs) { + starts.push(k); + } + starts.sort_unstable(); + starts.dedup(); + starts.retain(|&s| s < n); + let mut segments = Vec::new(); + for (j, &start) in starts.iter().enumerate() { + let end = if j + 1 < starts.len() { + starts[j + 1] + } else { + n + }; + if start >= end { + continue; + } + let slice = &energy_theta_pairs[start..end]; + let (scan_type, _, _, _, _) = classify_scan_type(slice); + segments.push(ProfileSegment { + start, + end, + scan_type, + }); + } + segments +} + #[cfg(test)] mod tests { use super::*; @@ -196,4 +302,55 @@ mod tests { assert!(t_min.is_none()); assert!(t_max.is_none()); } + + #[test] + fn segment_izero_then_theta_ramp_two_profiles() { + let mut rows = Vec::new(); + for _ in 0..4 { + rows.push(pair(Some(284.0), Some(0.0))); + } + for i in 1..6 { + rows.push(pair(Some(284.0), Some(i as f64 * 0.5))); + } + let segs = segment_reflectivity_profiles(&rows); + assert_eq!(segs.len(), 2); + assert_eq!(segs[0].start, 0); + assert_eq!(segs[0].end, 4); + assert_eq!(segs[0].scan_type, ReflectivityScanType::SinglePoint); + assert_eq!(segs[1].start, 4); + assert_eq!(segs[1].end, rows.len()); + assert_eq!(segs[1].scan_type, ReflectivityScanType::FixedEnergy); + } + + #[test] + fn segment_two_energy_blocks_two_fixed_angle() { + let mut rows = Vec::new(); + for e in [280.0, 280.3, 280.6] { + rows.push(pair(Some(e), Some(10.0))); + } + for e in [295.0, 295.3, 295.6] { + rows.push(pair(Some(e), Some(10.0))); + } + let segs = segment_reflectivity_profiles(&rows); + assert_eq!(segs.len(), 2); + assert_eq!(segs[0].scan_type, ReflectivityScanType::FixedAngle); + assert_eq!(segs[1].scan_type, ReflectivityScanType::FixedAngle); + assert_eq!(segs[0].start, 0); + assert_eq!(segs[0].end, 3); + assert_eq!(segs[1].start, 3); + assert_eq!(segs[1].end, 6); + } + + #[test] + fn segment_single_block_matches_whole_classify() { + let rows: Vec<_> = (0..8) + .map(|i| pair(Some(284.0), Some(0.3 * i as f64))) + .collect(); + let segs = segment_reflectivity_profiles(&rows); + assert_eq!(segs.len(), 1); + let (whole, _, _, _, _) = classify_scan_type(&rows); + assert_eq!(segs[0].scan_type, whole); + assert_eq!(segs[0].start, 0); + assert_eq!(segs[0].end, rows.len()); + } } diff --git a/tests/test_catalog.py b/tests/test_catalog.py index c0c3019..2c1efc3 100644 --- a/tests/test_catalog.py +++ b/tests/test_catalog.py @@ -113,6 +113,21 @@ def test_scan_experiment_catalog_has_required_columns( assert c in df.columns, f"missing column {c}" +def test_scan_experiment_includes_reflectivity_profile_columns( + minimal_fits_dir: Path | None, +) -> None: + if minimal_fits_dir is None: + pytest.skip("fixtures/minimal.fits not found") + ingest_beamtime(minimal_fits_dir, incremental=False) + df = scan_experiment(minimal_fits_dir).collect() + if len(df) == 0: + pytest.skip("no rows in catalog") + assert "reflectivity_profile_index" in df.columns + assert "reflectivity_scan_type" in df.columns + assert df["reflectivity_profile_index"].dtype in (pl.Int64, pl.Null) + assert df["reflectivity_scan_type"].dtype == pl.String + + def test_fits_accessor_from_catalog(minimal_fits_dir: Path | None) -> None: if minimal_fits_dir is None: pytest.skip("fixtures/minimal.fits not found") From 75a9ec4fcb6bd248592cd8add57679c9cf3b94c6 Mon Sep 17 00:00:00 2001 From: Harlan D Heilman <73567020+HarlanHeilman@users.noreply.github.com> Date: Wed, 8 Apr 2026 12:03:21 -0700 Subject: [PATCH 20/32] catalog: AGENTS-first SQLite layout, fits_files ingest, strict paths Migrate ingest/query to normalized fits_files and catalog_tags; add layout detection; strict catalog.db resolution in Rust and Python. --- .cursor/hooks/state/continual-learning.json | 4 +- AGENTS.md | 99 +-- python/pyref/io/catalog_path.py | 53 +- python/pyref/io/readers.py | 12 +- src/catalog/ingest.rs | 677 +++++++++----------- src/catalog/ingest_parallel.rs | 323 ++-------- src/catalog/layout.rs | 241 +++++++ src/catalog/mod.rs | 501 +++++++-------- src/catalog/profile_persist.rs | 4 +- src/catalog/query.rs | 367 ++++------- src/io/mod.rs | 50 +- src/io/source.rs | 8 +- src/schema.rs | 463 +++++++++++++ tests/test_catalog.py | 17 +- 14 files changed, 1523 insertions(+), 1296 deletions(-) create mode 100644 src/catalog/layout.rs create mode 100644 src/schema.rs diff --git a/.cursor/hooks/state/continual-learning.json b/.cursor/hooks/state/continual-learning.json index cb781a5..e8e0d2a 100644 --- a/.cursor/hooks/state/continual-learning.json +++ b/.cursor/hooks/state/continual-learning.json @@ -1,8 +1,8 @@ { "version": 1, "lastRunAtMs": 0, - "turnsSinceLastRun": 3, + "turnsSinceLastRun": 6, "lastTranscriptMtimeMs": null, - "lastProcessedGenerationId": "8a4d6f93-b073-4d50-a443-cf9e7c87dfff", + "lastProcessedGenerationId": "30dd26cf-19ef-4a54-9204-8bc315fc1e9e", "trialStartedAtMs": null } diff --git a/AGENTS.md b/AGENTS.md index 2de200e..f1a9b18 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -25,7 +25,9 @@ The IO module is responsible for ingesting raw FITS files, cataloging their contents into a structured SQLite database, and exposing the resulting data through a lazy interface that returns pandas or polars DataFrames on demand. By default, the module expects the FITS file conventions and directory structures produced by ALS Beamline 11.0.1.2. New beamline formats can be added by extending the IO layer. -All performance-critical IO operations are implemented in Rust and exposed to Python via PyO3 bindings. Rust is responsible for parallel FITS file reading, header card extraction, image data loading, filename parsing, directory traversal, SQLite catalog construction, and zarr archive management. Python is responsible for the user-facing query interface, DataFrame construction from catalog results, and any logic that does not require parallel throughput. This boundary is a design constraint, not a guideline: do not implement parallelism in Python and do not implement user-facing query logic in Rust. +All performance-critical IO operations are implemented in Rust and exposed to Python via PyO3 bindings. Rust is responsible for parallel FITS file reading, header card extraction, image data loading, filename parsing, directory traversal, Diesel-managed SQLite catalog construction, and zarr archive management. Python is responsible for the user-facing query interface, DataFrame construction from catalog results, and any logic that does not require parallel throughput. This boundary is a design constraint, not a guideline: do not implement parallelism in Python and do not implement user-facing query logic in Rust. + +The catalog database is managed by [Diesel](https://diesel.rs) with the SQLite backend. The schema is defined in `src/schema.rs` and all database interactions must go through Diesel's type-checked query builder. Raw SQL is prohibited except inside Diesel migration files. SQLite foreign key enforcement is off by default; the Rust connection initializer must execute `PRAGMA foreign_keys = ON` on every new connection before any other statement. The IO module is accessed via `pyref.io` and the cataloging subsystem via `pyref.io.catalog`. @@ -122,52 +124,71 @@ Connecting individual frames back to their originating sample, scan, and beamtim ### Cataloging System -The cataloging system ingests a beamtime directory and populates a SQLite database that serves as the structural backbone for all downstream reduction and fitting workflows. Its primary purpose is to resolve individual FITS frames back to their originating sample, scan, and beamtime, and to expose this hierarchy as a queryable, lazily accessible interface. The most common access pattern is retrieving all frames associated with a given sample and tag combination, grouped by energy or angle, for reduction into a stitched reflectivity profile. +The cataloging system ingests a beamtime directory and populates a Diesel-managed SQLite database that serves as the structural backbone for all downstream reduction and fitting workflows. Its primary purpose is to resolve individual FITS frames back to their originating sample, scan, and beamtime, and to expose this hierarchy as a queryable, lazily accessible interface. The schema is defined in `src/schema.rs` and must be kept in sync with the Diesel migration files under `migrations/`. The most common access pattern is retrieving all frames associated with a given sample and tag combination, grouped by energy or angle, for assembly into a stitched reflectivity profile. + +The `profiles` table is the primary user-facing entry point. Users browse profiles, not scans. Scans are administrative provenance; profiles are the scientific deliverable. + +#### Header Card Tiering + +The 115 FITS primary HDU cards per frame are split into two tiers at ingestion time. Eleven cards that directly drive scan classification, beamspot localization, normalization, and profile identity are promoted to first-class typed columns on the `frames` table: `sample_x`, `sample_y`, `sample_z`, `sample_theta`, `ccd_theta`, `beamline_energy`, `epu_polarization`, `exposure`, `ring_current`, `ai3_izero`, and `beam_current`. All remaining cards are stored in the `frame_header_values` EAV table, keyed through the `header_cards` registry. The `header_cards` table is populated automatically on first ingestion from whatever cards are present in the FITS files; subsequent beamtimes with new or renamed channels append rows to this table without requiring a schema migration. If a card that was previously treated as non-critical needs to be queried as a first-class column, the correct remedy is a Diesel migration that adds the column to `frames` and backfills it from `frame_header_values`, not a workaround join. + +#### `beamtimes` +Root of the catalog hierarchy. Stores the absolute path to the beamtime root directory and the date parsed from the directory name. The `path` column is also used to resolve the monolithic zarr archive at `/beamtime.zarr`. All other tables carry a foreign key to this table. + +#### `samples` +One row per unique sample name within a beamtime. Stores the sample name and the median `sample_x`, `sample_y`, and `sample_z` stage positions computed across all frames attributed to that name. Stage positions are nominally fixed per sample; frames that deviate beyond a configurable tolerance are flagged `mislabeled_sample` in `frames.quality_flag` rather than creating a second sample row. + +#### `tags` +One row per unique tag slug parsed from FITS filenames. Tags carry no intrinsic meaning to the catalog. The many-to-many relationship between tags and files is resolved through the `file_tags` junction table. + +#### `file_tags` +Junction table linking `files` to `tags`. Allows many tags to map to many files without duplication. -Importantly, some of this catalog tablular logic will not be filled out initially by the catalog ingestion. In particular, the BeamFinding, StitchCorrection, and Reflectivity tables will be empty untill we actually sit down and process the data in python from a jupyter notebook. +#### `files` +One row per FITS file ingested. Stores the absolute path, bare filename, scan number, frame number, sample ID, and beamtime ID. This is the canonical provenance reference for raw file locations and the join target for tag resolution. Image data is not retrieved through this table; it is accessed via the zarr keys on `frames`. -#### Beamtime Table -Stores the top-level metadata for a given beamtime, specifically the path to the beamtime root directory and the date. All other tables carry a foreign key to this table, making it the root of the catalog hierarchy. The path stored here is also used to resolve the location of the monolithic beamtime zarr archive. +#### `scans` +One row per scan. Stores the scan number, scan type (`fixed_energy` or `fixed_angle`), start and end timestamps, sample ID, and beamtime ID. Scan type is determined during ingestion from the motor trajectory analysis and recorded here as a first-class attribute so downstream reduction does not recompute it. -#### Sample Table -Stores metadata for each physical sample identified during cataloging. Each sample has a name, a representative sample-x and sample-y stage position extracted from the FITS headers, and a foreign key to the Beamtime Table. Stage positions are treated as nominally fixed per sample. During cataloging, if frames attributed to the same sample name exhibit large positional drift beyond a configurable tolerance, those frames are flagged as likely mislabeled rather than treated as a distinct sample position. Small sub-decimal drift is acceptable and ignored. +#### `header_cards` +Registry of FITS header card names discovered during ingestion. One row per unique card name. Each row stores the raw card name as it appears in the FITS header, a human-readable display name, and a category label (`motor`, `ai`, `camera`, or `metadata`). This table is the lookup key for the `frame_header_values` EAV table. Agents must not hard-code card name strings outside of this table and the first-class column definitions on `frames`. -#### Tag Table -Stores the individual tag slugs parsed from FITS filenames. Tags carry no intrinsic meaning to the catalog; they are user-defined labels encoding experimental series membership or preparation conditions. Tags are scan-specific, not sample-specific. The association between tags and files is resolved through a join table linking the Tag Table and the File Table, allowing many tags to map to many files without duplication. +#### `frames` +One row per frame per scan. Stores the eleven first-class header card values as typed `Double` columns, plus the zarr retrieval keys (`zarr_group_key` and `zarr_frame_index`) needed to fetch the detector image. The monolithic zarr archive at `/beamtime.zarr` is organized hierarchically: scan number is the group key, frame number is the dataset index within that group, and each group stores two datasets per frame named `raw` and `processed`. The `raw` dataset is the image as extracted from the FITS file; the `processed` dataset is the image after edge artifact removal, row-wise and column-wise background subtraction, and Gaussian filtering. Both share the same frame index. The archive path is resolved through the FK to `beamtimes`, avoiding redundant path storage at the frame level. Each row also carries FKs to `files` and `scans`, providing the full provenance chain from pixel to beamtime. -#### File Table -Stores the fully specified path, filename, frame number, scan number, sample ID, and beamtime ID for each FITS file ingested. This table is the canonical reference for raw file locations and serves as the join target for tag resolution. Image data is not retrieved through this table directly; it is accessed via the zarr group key and frame index recorded in the Data Table. +#### `frame_header_values` +EAV store for all FITS header cards not promoted to first-class columns on `frames`. All values are stored as `Double`. The card name is resolved through `header_cards`. This table is append-only after initial ingestion; values are never updated in place. -#### Scan Table -Stores metadata for each scan, including scan number, scan type (fixed energy or fixed angle), start and stop timestamps, sample ID, and beamtime ID. Scan type is determined during cataloging from the motor trajectory analysis described above and is recorded here as a first-class attribute to avoid recomputing it downstream. +#### `profiles` +The primary user-facing table. One row per reduced reflectivity profile, where a profile is a single continuous 1D curve assembled from one or more stitches. Multi-profile scans produce multiple rows sharing the same `scan_id`, distinguished by `profile_index`. Each row stores the profile type (`fixed_energy` or `fixed_angle`), the value of the fixed parameter (energy in eV for fixed-angle profiles, theta in degrees for fixed-energy profiles), `epu_polarization`, and the median stage position (`sample_x`, `sample_y`, `sample_z`) over all member frames. The stage position columns here are denormalized from `frames` for query convenience; the authoritative per-frame positions remain in `frames`. -#### Data Table -Stores one row per frame per scan, containing all header card values extracted from the FITS file along with the scan-group key and frame index needed to retrieve the corresponding 2D detector image from the beamtime zarr archive. Image data for the entire beamtime is stored in a single monolithic zarr archive located at the beamtime root, organized hierarchically with scan number as the group key and frame number as the dataset index within that group. Each scan group stores two datasets per frame: the raw detector image as extracted from the FITS file, and the post-processed image produced after edge artifact removal, row-wise and column-wise background subtraction, and Gaussian filtering. Both datasets share the same frame index, allowing the user to retrieve either the unmodified or the processed image for any given frame without reprocessing. The archive path is resolved through the foreign key to the Beamtime Table, avoiding redundant path storage at the frame level while keeping retrieval efficient and unambiguous. Each row additionally carries a foreign key to the File Table and the Scan Table, providing the full provenance chain from pixel to beamtime. +#### `profile_frames` +Junction table mapping profiles to their constituent frames, with a `frame_role` column classifying each frame's function in the reduction pipeline. Valid roles are `i0`, `stitch`, `overlap`, and `reflectivity`. I0 frames appear here multiple times when they serve as the normalization reference for more than one profile in a multi-profile scan; this is by design and is the correct resolution of the shared-I0 problem. Agents must not attempt to enforce uniqueness on `frame_id` in this table. -#### BeamFinding Table -Stores the per-frame outputs of the beamspot localization pipeline. This includes the pre-processing steps applied prior to peak fitting (edge artifact removal flag, row-wise and column-wise background subtraction parameters, and Gaussian filter kernel size), the peak fitting result (fitted centroid row and column, integrated ROI intensity, and fit standard deviation), the background intensity extracted from the dark region of the detector, and a flag indicating whether the detection succeeded or failed. Each row carries a foreign key to the Data Table. +#### `beam_finding` +Per-frame output of the beamspot localization pipeline. Stores the preprocessing parameters applied (edge removal flag, dark column and row counts for background subtraction, Gaussian kernel sigma), the peak fitting result (centroid row and column, ROI intensity, fit standard deviation), the dark region statistics (mean and standard deviation), and a `detection_flag` with values `ok`, `beam_detection_failed`, or `beam_drift_anomaly`. Each row carries a FK to `frames`. Frames with `detection_flag = beam_detection_failed` must not appear in `reflectivity`. -#### StitchCorrection Table -Stores the per-stitch correction factors computed during the stitching and normalization pipeline. This includes the energy-dependent Fano Factor used to rescale photon-counting uncertainties away from the pure Poisson limit, the weighted-mean overlap scaling factor applied at each stitch boundary, the I0 normalization value and its source (either direct beam frames within the scan or a foreign key to an external scan for fixed-angle profiles where I0 is sourced separately), and the frame-type classification for each frame (I0, Stitch, Overlap). Each row carries a foreign key to the Scan Table. +#### `stitch_corrections` +Per-stitch correction factors computed during the normalization and stitching pipeline. One row per stitch segment within a profile. Stores the `fano_factor` (always non-null; 1.0 when no Fano correction was applied), the `overlap_scale_factor` (null for the first stitch, which has no preceding stitch), the `i0_normalization_value`, and `i0_source_scan_id` (null when I0 comes from frames within the current profile; set to the external scan's ID for fixed-angle profiles that borrow I0 from a separate scan). Each row carries a FK to `profiles`. -#### Reflectivity Table -Stores the frame-level reduced reflectivity data produced after normalization and stitching. Each row represents a single reduced frame and contains the theta, energy, and Q values, the normalized reflectivity intensity and its propagated uncertainty, the frame-type classification (I0, Stitch, Overlap), and a foreign key to the BeamFinding Table. Stitched profile construction and export are handled outside the catalog by a packaging utility that queries the Reflectivity and StitchCorrection tables and writes the assembled profile to a flat parquet file. +#### `reflectivity` +Frame-level reduced reflectivity data after normalization and stitching. One row per reduced frame. Stores Q (inverse angstroms), theta (degrees), energy (eV), normalized intensity, propagated one-sigma uncertainty, and `frame_type` (`i0`, `stitch`, `overlap`, or `reflectivity`). Carries FKs to `profiles`, `frames`, and `beam_finding`. Frames flagged `beam_detection_failed` must not appear here. Stitched profile assembly and parquet export are performed by the packaging utility in `pyref.reduction`, which joins this table against `stitch_corrections` filtered by `profile_id`. ## Data Quality Flagging -Several conditions arising during cataloging and reduction require flagging rather than silent failure or hard error. The following flags are first-class catalog attributes, not log messages, and must be queryable from the DataFrame interface. +Several conditions arising during cataloging and reduction require flagging rather than silent failure or hard error. The following flags are first-class catalog attributes stored in typed text columns, not log messages, and must be queryable from the DataFrame interface. Flag values use lowercase snake_case to match the string literals defined in `schema.rs`. -Frames attributed to a sample name whose inferred stage position deviates from the representative position for that sample by more than a configurable threshold are flagged as `MISLABELED_SAMPLE`. The threshold is configurable per beamtime but should default to a value that tolerates sub-millimeter drift while rejecting stage position changes consistent with a deliberate sample move. +Frames attributed to a sample name whose inferred stage position deviates from the representative position for that sample by more than a configurable threshold are flagged `mislabeled_sample` in `frames.quality_flag`. The threshold is configurable per beamtime but should default to a value that tolerates sub-millimeter drift while rejecting stage position changes consistent with a deliberate sample move. -Frames for which the beamspot localization algorithm fails to identify a credible peak are flagged as `BEAM_DETECTION_FAILED`. These frames must not contribute to the Reflectivity Table and must not be silently zeroed or filled. +Frames for which the beamspot localization algorithm fails to identify a credible peak are flagged `beam_detection_failed` in `beam_finding.detection_flag`. These frames must not appear in `reflectivity` and must not be silently zeroed or filled. -Frames whose fitted beamspot centroid deviates from the linear trend model across the scan by more than a configurable multiple of the fit residual standard deviation are flagged as `BEAM_DRIFT_ANOMALY`. These frames may still contribute to the Reflectivity Table but should be treated with caution and are surfaced to the user for manual inspection. +Frames whose fitted beamspot centroid deviates from the linear trend model across the scan by more than a configurable multiple of the fit residual standard deviation are flagged `beam_drift_anomaly` in `beam_finding.detection_flag`. These frames may still appear in `reflectivity` but are surfaced to the user for manual inspection before the packaging utility includes them in a parquet export. -Files that fail the filename parser are flagged as `PARSE_FAILURE` in the File Table. Files whose directory layout does not match either supported pattern are flagged at the beamtime level. +Files that fail the filename parser are flagged `parse_failure` in `files.parse_flag`. Files whose directory layout does not match either supported pattern are flagged at the beamtime level via a structured error returned to the caller rather than a catalog row. ## Profile Packaging Utility -The packaging utility assembles a stitched reflectivity profile from the Reflectivity and StitchCorrection tables and exports it as a flat parquet file. It is not part of the catalog; it is a standalone tool in `pyref.reduction` that operates on catalog query results. The output parquet file contains one row per reduced frame with the following columns at minimum: Q (inverse angstroms), theta (degrees), energy (eV), reflectivity intensity (normalized), propagated uncertainty, frame-type classification, scan number, sample name, and the applied stitch scaling factor. The packaging utility must reject frames flagged as `BEAM_DETECTION_FAILED` and must surface `BEAM_DRIFT_ANOMALY` frames to the user with a warning before including them. The output schema is fixed; agents must not add or remove columns without updating this specification. +The packaging utility assembles a stitched reflectivity profile from the `reflectivity` and `stitch_corrections` tables and exports it as a flat parquet file. It is a standalone tool in `pyref.reduction` that operates on catalog query results and is not part of the catalog itself. The primary input is a `profile_id`; the utility joins `reflectivity` against `stitch_corrections` filtered by that ID to assemble the full stitched curve. The output parquet schema is fixed and contains one row per reduced frame with the following columns at minimum: `q` (inverse angstroms), `theta` (degrees), `energy` (eV), `intensity` (normalized, dimensionless), `uncertainty` (one-sigma), `frame_type`, `scan_number`, `sample_name`, and `overlap_scale_factor`. The utility must reject frames with `beam_finding.detection_flag = beam_detection_failed` and must warn the user before including frames with `beam_drift_anomaly`. Agents must not add or remove columns from the output schema without updating this specification. ## Fitting Module Architecture @@ -183,9 +204,9 @@ Uncertainty propagation must be exact to first order at every reduction step. An Silent dtype coercion is prohibited. Operations that would coerce float64 to float32, or integer counts to floating point without explicit intent, must be made explicit or prevented. The catalog and parquet outputs must preserve float64 precision for all physical quantities. -Frame provenance must be preserved end-to-end. Every row in the Reflectivity Table must be traceable back to the originating FITS file and zarr frame index through the foreign key chain. Any reduction step that breaks this chain by aggregating without recording the constituent frame IDs is unacceptable. +Frame provenance must be preserved end-to-end. Every row in `reflectivity` must be traceable back to the originating FITS file and zarr frame index through the FK chain `reflectivity -> frames -> files`. Any reduction step that aggregates without recording the constituent frame IDs breaks this chain and is unacceptable. -Fano factor computation and application must be documented in the StitchCorrection Table for every scan. A scan processed without a Fano correction must record this explicitly (Fano factor = 1.0) rather than leaving the field null. +Fano factor computation and application must be documented in `stitch_corrections` for every scan. A scan processed without a Fano correction must record `fano_factor = 1.0` rather than leaving the field null. @@ -198,8 +219,8 @@ This codebase is maintained by contributors with physics PhDs and extensive back ## Operating principles - Prefer the smallest coherent change set that satisfies the stated specification. Avoid drive-by refactors, unrelated formatting sweeps, and scope expansion. -- Treat the repository’s existing patterns as the default contract. Match naming, module boundaries, error-handling style, and test layout unless the user explicitly requests a migration. -- Default to production-grade output: complete, runnable, and reviewable. Do not ship placeholder text such as ellipses, “the rest of the implementation here”, “TODO: implement”, or “fill in later” inside code or patches unless the user explicitly authorizes a stub. +- Treat the repository's existing patterns as the default contract. Match naming, module boundaries, error-handling style, and test layout unless the user explicitly requests a migration. +- Default to production-grade output: complete, runnable, and reviewable. Do not ship placeholder text such as ellipses, "the rest of the implementation here", "TODO: implement", or "fill in later" inside code or patches unless the user explicitly authorizes a stub. - Remain non-lazy: if a command fails, diagnose, adjust, and retry with a different approach when reasonable. Do not stop after the first error without analysis. - Do not use emoji in code, comments, documentation strings, commit messages, or user-facing text unless the user explicitly requests emoji. @@ -213,7 +234,7 @@ This codebase is maintained by contributors with physics PhDs and extensive back - Every **public** function, method, or type exported from a library module carries documentation appropriate to the language (for example Python docstrings, Rust `///` on public items, TSDoc/JSDoc on exported symbols). - Documentation states the **surface**: name, purpose, parameters, return value, and thrown or returned error shapes when that is part of the contract. -- Prefer **prescriptive** voice that states what the symbol **does** and **means** for callers. Prefer “Maximum `foo` grouped by `bar` using a stable sort on `bar`.” over “Returns the max of foo by bar.” or "Computes the maximum `foo` grouped by `bar` using a stable sort on `bar`." or "Returns the maximum `foo` grouped by `bar` using a stable sort on `bar`." +- Prefer **prescriptive** voice that states what the symbol **does** and **means** for callers. Prefer "Maximum `foo` grouped by `bar` using a stable sort on `bar`." over "Returns the max of foo by bar." or "Computes the maximum `foo` grouped by `bar` using a stable sort on `bar`." or "Returns the maximum `foo` grouped by `bar` using a stable sort on `bar`." - For each parameter: name, type as used in the project, allowed ranges or invariants when non-obvious, and interaction with other parameters. - For results: type, semantics, units when relevant, ordering guarantees, and stability promises when they matter for science or reproducibility. - Describe **what** the function does at the abstraction level of the API, **how** only when algorithmic choices affect correctness, performance contracts, or numerical stability, and **why** that approach is chosen when trade-offs are non-obvious (for example streaming vs materializing, online vs batch statistics). @@ -221,7 +242,7 @@ This codebase is maintained by contributors with physics PhDs and extensive back ## Module and package documentation -- Each library module (or the closest equivalent in the language’s module system) includes a short module-level description of responsibility: what problems it solves, what it explicitly does not handle, and which invariants callers should respect. +- Each library module (or the closest equivalent in the language's module system) includes a short module-level description of responsibility: what problems it solves, what it explicitly does not handle, and which invariants callers should respect. - Module docs are prescriptive about intent and boundaries so new contributors and agents do not duplicate concerns across modules. ## Tooling, skills, and continued learning @@ -229,7 +250,7 @@ This codebase is maintained by contributors with physics PhDs and extensive back - Use project rules, agent skills, and MCP documentation tools when they apply to the task. Prefer authoritative library and framework documentation over memory when behavior, defaults, or breaking changes matter. - When touching unfamiliar APIs, verify signatures, deprecations, and error modes against current docs or source in the dependency before guessing. - Prefer file-scoped or package-scoped commands when the repository documents them (typecheck, lint, format, test on a single path) to shorten feedback loops. -- State permission-sensitive actions clearly (dependency installs, destructive commands, credential access) and follow the user’s safety expectations for the workspace. +- State permission-sensitive actions clearly (dependency installs, destructive commands, credential access) and follow the user's safety expectations for the workspace. ## Task shape (goal, context, constraints, completion) @@ -265,7 +286,7 @@ Use **[uv](https://docs.astral.sh/uv/latest/)** for environments, runs, and depe - **Dependencies**: add, upgrade, and remove packages with **`uv add`**, **`uv add … --upgrade`**, **`uv remove`**—do **not** hand-edit version pins in `pyproject.toml`. - **Environment**: **`uv sync`** after cloning or when the lockfile changes; **`uv run …`** to execute Python, tools, and tests. - **Dev tools**: keep **`ruff`** and **`ty`** in the development (or project) dependency group; run **`ruff check`** (and project formatting if applicable) plus **`ty check`** on changed code. **`uvx`** remains an option for one-off tool runs. -- **pytest**: install via **`uv add --dev pytest`** (or the project’s dev group); run with **`uv run pytest`**. +- **pytest**: install via **`uv add --dev pytest`** (or the project's dev group); run with **`uv run pytest`**. If a **`uv`** subcommand differs by version, use **`uv --help`** or the [uv docs](https://docs.astral.sh/uv/latest/). @@ -276,7 +297,7 @@ If a **`uv`** subcommand differs by version, use **`uv --help`** or the [uv docs ### Cursor: skills -Load these **skills** by **name** when the task matches (each skill’s own `SKILL.md` and references hold the full detail). Installed skills usually live under `.cursor/skills/` (or your editor’s equivalent). +Load these **skills** by **name** when the task matches (each skill's own `SKILL.md` and references hold the full detail). Installed skills usually live under `.cursor/skills/` (or your editor's equivalent). | Skill | Use it for | |-------|------------| @@ -289,7 +310,7 @@ Load these **skills** by **name** when the task matches (each skill’s own `SKI ### Cursor: subagents -Delegate by **subagent name** when a focused pass is better than inline editing. Subagents usually live under `.cursor/agents/` (or your editor’s equivalent). +Delegate by **subagent name** when a focused pass is better than inline editing. Subagents usually live under `.cursor/agents/` (or your editor's equivalent). | Subagent | Use it for | |----------|------------| @@ -300,7 +321,7 @@ Delegate by **subagent name** when a focused pass is better than inline editing. ### Cursor: rules - A **Python** Cursor **rule** applies to Python sources (typically `**/*.py` when the rule is configured for those globs). It restates **interpreter preference**, **uv** usage, **ruff** / **ty** expectations, numerics and docstring defaults, and points to **general-python**, domain skills such as **lab-instrumentation** when editing drivers or lab I/O, and the subagents above. -- **Rule text is authoritative for “always on” editor hints**; **skills** carry the long-form patterns and examples. When the two differ on a detail, follow **this spec** and **`pyproject.toml`**, then the **rule**, then skill nuance. +- **Rule text is authoritative for "always on" editor hints**; **skills** carry the long-form patterns and examples. When the two differ on a detail, follow **this spec** and **`pyproject.toml`**, then the **rule**, then skill nuance. ### External references @@ -398,4 +419,4 @@ This workspace extends Rust with **PyO3 / Maturin** extension expectations. - Document unsafe blocks with the project's standard (even if you avoid new unsafe code). - Prefer thin Python modules that re-export a small Rust API rather than exposing many low-level Rust objects. - + \ No newline at end of file diff --git a/python/pyref/io/catalog_path.py b/python/pyref/io/catalog_path.py index 3c7b63e..ff68469 100644 --- a/python/pyref/io/catalog_path.py +++ b/python/pyref/io/catalog_path.py @@ -1,47 +1,22 @@ """ Resolve SQLite catalog paths for a beamtime directory. -Mirrors ``resolve_catalog_path`` and related helpers in the Rust catalog module -(``src/catalog/mod.rs``). The new layout stores a shared database at -``parent/.pyref/catalog.db``; the legacy layout uses ``beamtime_dir/.pyref_catalog.db``. -Existence checks use :meth:`pathlib.Path.exists` (symlinks are followed), matching -Rust ``exists()``. +Mirrors ``resolve_catalog_path`` in the Rust catalog module (``src/catalog/mod.rs``). +The catalog is stored at ``parent/.pyref/catalog.db`` when ``beamtime_dir`` has a +non-root parent; otherwise at ``beamtime_dir/.pyref/catalog.db``. """ from __future__ import annotations from pathlib import Path -CATALOG_DB_NAME = ".pyref_catalog.db" PYREF_CATALOG_DIR = ".pyref" NEW_CATALOG_DB_NAME = "catalog.db" -def catalog_path_legacy(beamtime_dir: Path) -> Path: - """ - Return the legacy per-beamtime catalog database path. - - Parameters - ---------- - beamtime_dir : pathlib.Path - Beamtime root directory. Not required to exist. - - Returns - ------- - pathlib.Path - ``beamtime_dir / CATALOG_DB_NAME`` (``CATALOG_DB_NAME`` is - ``.pyref_catalog.db``). - """ - return beamtime_dir / CATALOG_DB_NAME - - def catalog_path_new(beamtime_dir: Path) -> Path: """ - Return the new-layout catalog database path for a beamtime directory. - - If ``beamtime_dir`` has a parent that is non-empty and not the filesystem - root, the path is ``parent / .pyref / catalog.db``. Otherwise it is - ``beamtime_dir / .pyref / catalog.db``, matching the Rust ``catalog_path_new``. + Return the catalog database path for a beamtime directory. Parameters ---------- @@ -51,7 +26,8 @@ def catalog_path_new(beamtime_dir: Path) -> Path: Returns ------- pathlib.Path - Candidate path for the shared catalog in the new layout. + ``parent / .pyref / catalog.db`` when ``beamtime_dir`` has a usable parent, + else ``beamtime_dir / .pyref / catalog.db``. """ parent = beamtime_dir.parent ps = parent.as_posix() @@ -62,12 +38,9 @@ def catalog_path_new(beamtime_dir: Path) -> Path: def resolve_catalog_path(beamtime_dir: str | Path) -> Path: """ - Choose the catalog database path for a beamtime directory. + Return the catalog database path for a beamtime directory. - Resolves ``beamtime_dir`` with :meth:`pathlib.Path.resolve` for stable, - absolute paths. If the new-layout path exists, returns it; else if the - legacy path exists, returns that; else returns the new-layout path (the - default location ingest would create in the new layout). + Resolves ``beamtime_dir`` with :meth:`pathlib.Path.resolve` for stable paths. Parameters ---------- @@ -77,13 +50,7 @@ def resolve_catalog_path(beamtime_dir: str | Path) -> Path: Returns ------- pathlib.Path - Absolute path to the catalog database file to use. + Absolute path to the catalog database file (may not exist yet). """ d = Path(beamtime_dir).resolve() - new_path = catalog_path_new(d) - legacy_path = catalog_path_legacy(d) - if new_path.exists(): - return new_path - if legacy_path.exists(): - return legacy_path - return new_path + return catalog_path_new(d).resolve() diff --git a/python/pyref/io/readers.py b/python/pyref/io/readers.py index a7b6f02..2995c01 100644 --- a/python/pyref/io/readers.py +++ b/python/pyref/io/readers.py @@ -10,11 +10,7 @@ import polars as pl -from pyref.io.catalog_path import ( - CATALOG_DB_NAME, - NEW_CATALOG_DB_NAME, - resolve_catalog_path, -) +from pyref.io.catalog_path import NEW_CATALOG_DB_NAME, resolve_catalog_path if TYPE_CHECKING: import pandas as pd @@ -121,7 +117,7 @@ def scan_experiment( When ``source`` is a **single** path (not a list or tuple): - - File named ``.pyref_catalog.db`` or ``catalog.db``: load via + - File named ``catalog.db`` (typically under ``.pyref``): load via :func:`pyref.pyref.py_scan_from_catalog`. - Directory: resolve DB with :func:`pyref.io.catalog_path.resolve_catalog_path`; if it exists, load with ``py_scan_from_catalog`` (Rust-aligned layouts). @@ -149,9 +145,7 @@ def scan_experiment( keys = header_items if header_items is not None else [] if not isinstance(source, (list, tuple)): path = Path(source).resolve() - if path.is_file() and ( - path.name == CATALOG_DB_NAME or path.name == NEW_CATALOG_DB_NAME - ): + if path.is_file() and path.name == NEW_CATALOG_DB_NAME: try: from pyref.pyref import py_scan_from_catalog diff --git a/src/catalog/ingest.rs b/src/catalog/ingest.rs index 13c3dc0..7a301bf 100644 --- a/src/catalog/ingest.rs +++ b/src/catalog/ingest.rs @@ -1,11 +1,23 @@ #![cfg(feature = "catalog")] -use crate::catalog::{discover_paths_for_catalog_ingest, CatalogError, Result}; +use crate::catalog::layout::BeamtimeLayout; +use crate::catalog::{CatalogError, Result, FILE_FLAG_PARSE_FAILURE}; +#[cfg(not(feature = "parallel_ingest"))] +use crate::catalog::{discover_paths_for_catalog_ingest, open_or_create_db_at}; use crate::io::parse_fits_stem; use crate::io::BtIngestRow; #[cfg(not(feature = "parallel_ingest"))] -use crate::catalog::open_or_create_db; -use rusqlite::Error as RusqliteError; +use polars::prelude::*; +#[cfg(not(feature = "parallel_ingest"))] +use crate::io::options::ReadFitsOptions; +#[cfg(not(feature = "parallel_ingest"))] +use crate::loader::read_fits_metadata_batch; +use std::collections::HashMap; +use std::path::{Path, PathBuf}; +use std::sync::mpsc; + +#[cfg(not(feature = "parallel_ingest"))] +const BATCH_SIZE: usize = 500; pub const DEFAULT_INGEST_HEADER_ITEMS: &[&str] = &[ "DATE", @@ -18,99 +30,166 @@ pub const DEFAULT_INGEST_HEADER_ITEMS: &[&str] = &[ "Sample Name", "Scan ID", ]; -#[cfg(not(feature = "parallel_ingest"))] -use crate::io::options::ReadFitsOptions; -#[cfg(not(feature = "parallel_ingest"))] -use crate::loader::read_fits_metadata_batch; -use polars::prelude::*; -use std::collections::HashMap; -use std::path::{Path, PathBuf}; -use std::sync::mpsc; -#[cfg(not(feature = "parallel_ingest"))] -const BATCH_SIZE: usize = 500; - -fn resolve_sample(conn: &rusqlite::Connection, name: &str) -> Result { - let name = name.trim(); - if name.is_empty() { - return Ok(String::new()); - } - let first = name.split('_').next().unwrap_or(name); - let existing: Option = match conn.query_row( - "SELECT name FROM samples WHERE LOWER(TRIM(name)) = LOWER(?1) OR LOWER(TRIM(name)) = LOWER(?2)", - rusqlite::params![name, first], - |r| r.get(0), - ) { - Ok(v) => Some(v), - Err(RusqliteError::QueryReturnedNoRows) => None, - Err(e) => return Err(e.into()), - }; - if let Some(n) = existing { - return Ok(n); +fn layout_label(layout: BeamtimeLayout) -> &'static str { + match layout { + BeamtimeLayout::Nested => "nested", + BeamtimeLayout::Flat => "flat", } +} + +pub(crate) fn update_beamtime_catalog_layout( + conn: &rusqlite::Connection, + beamtime_id: i64, + layout: BeamtimeLayout, +) -> Result<()> { conn.execute( - "INSERT INTO samples (name, chemical_formula, created_at, updated_at) VALUES (?1, NULL, strftime('%s','now'), strftime('%s','now'))", - rusqlite::params![name], + "UPDATE bt_beamtimes SET catalog_layout = ?1 WHERE id = ?2", + rusqlite::params![layout_label(layout), beamtime_id], )?; - Ok(name.to_string()) + Ok(()) } -fn resolve_tag(conn: &rusqlite::Connection, name: &str) -> Result> { +fn ensure_catalog_tag_id(conn: &rusqlite::Connection, name: &str) -> Result { let name = name.trim(); if name.is_empty() { - return Ok(None); - } - if crate::io::is_polarization_tag(name) { - return Ok(None); + return Err(CatalogError::Validation("empty tag name".into())); } let slug = name.to_lowercase().replace(' ', "_"); - let existing: Option = match conn.query_row( - "SELECT name FROM tags WHERE LOWER(TRIM(name)) = LOWER(?1)", + conn.execute( + "INSERT OR IGNORE INTO catalog_tags (name, slug) VALUES (?1, ?2)", + rusqlite::params![name, slug], + )?; + let id: i64 = conn.query_row( + "SELECT id FROM catalog_tags WHERE name = ?1", rusqlite::params![name], |r| r.get(0), - ) { - Ok(v) => Some(v), - Err(RusqliteError::QueryReturnedNoRows) => None, - Err(e) => return Err(e.into()), - }; - if let Some(n) = existing { - return Ok(Some(n)); - } - conn.execute("INSERT INTO tags (name, slug) VALUES (?1, ?2)", rusqlite::params![name, slug])?; - Ok(Some(name.to_string())) + )?; + Ok(id) } -fn ensure_sample_tag( +fn set_fits_file_tags( conn: &rusqlite::Connection, - sample_name: &str, - tag_name: Option<&str>, + file_id: i64, + tag_names: &[String], ) -> Result<()> { - let tag_name = match tag_name { - Some(t) if !t.trim().is_empty() => t.trim(), - _ => return Ok(()), - }; - let sample_id: i64 = match conn.query_row( - "SELECT id FROM samples WHERE LOWER(TRIM(name)) = LOWER(?1)", - rusqlite::params![sample_name], - |r| r.get(0), - ) { - Ok(v) => v, - Err(RusqliteError::QueryReturnedNoRows) => return Ok(()), - Err(e) => return Err(e.into()), - }; - let tag_id: i64 = match conn.query_row( - "SELECT id FROM tags WHERE LOWER(TRIM(name)) = LOWER(?1)", - rusqlite::params![tag_name], + conn.execute( + "DELETE FROM fits_file_tags WHERE file_id = ?1", + rusqlite::params![file_id], + )?; + for t in tag_names { + let t = t.trim(); + if t.is_empty() || crate::io::is_polarization_tag(t) { + continue; + } + let tid = ensure_catalog_tag_id(conn, t)?; + conn.execute( + "INSERT OR IGNORE INTO fits_file_tags (file_id, tag_id) VALUES (?1, ?2)", + rusqlite::params![file_id, tid], + )?; + } + Ok(()) +} + +fn ensure_bt_sample(conn: &rusqlite::Connection, beamtime_id: i64, name: &str) -> Result { + let name = name.trim(); + let name_for_row = if name.is_empty() { "" } else { name }; + conn.execute( + "INSERT OR IGNORE INTO bt_samples (beamtime_id, name) VALUES (?1, ?2)", + rusqlite::params![beamtime_id, name_for_row], + )?; + let id: i64 = conn.query_row( + "SELECT id FROM bt_samples WHERE beamtime_id = ?1 AND name = ?2", + rusqlite::params![beamtime_id, name_for_row], |r| r.get(0), - ) { - Ok(v) => v, - Err(RusqliteError::QueryReturnedNoRows) => return Ok(()), - Err(e) => return Err(e.into()), + )?; + Ok(id) +} + +fn upsert_fits_file_row( + conn: &rusqlite::Connection, + beamtime_id: i64, + sample_id: Option, + path_str: &str, + file_stem: &str, + scan_number: i64, + frame_number: i64, + parse_ok: bool, + source_mtime: i64, +) -> Result { + let file_flags = if parse_ok { + 0i64 + } else { + FILE_FLAG_PARSE_FAILURE }; + let parse_ok_i = if parse_ok { 1i64 } else { 0i64 }; conn.execute( - "INSERT OR IGNORE INTO sample_tags (sample_id, tag_id) VALUES (?1, ?2)", - rusqlite::params![sample_id, tag_id], + r#"INSERT INTO fits_files (beamtime_id, sample_id, path, file_name, scan_number, frame_number, parse_ok, file_flags, source_mtime) + VALUES (?1, ?2, ?3, ?4, ?5, ?6, ?7, ?8, ?9) + ON CONFLICT(path) DO UPDATE SET + beamtime_id = excluded.beamtime_id, + sample_id = excluded.sample_id, + file_name = excluded.file_name, + scan_number = excluded.scan_number, + frame_number = excluded.frame_number, + parse_ok = excluded.parse_ok, + file_flags = excluded.file_flags, + source_mtime = excluded.source_mtime"#, + rusqlite::params![ + beamtime_id, + sample_id, + path_str, + file_stem, + scan_number, + frame_number, + parse_ok_i, + file_flags, + source_mtime + ], )?; + let id: i64 = conn.query_row( + "SELECT id FROM fits_files WHERE path = ?1", + rusqlite::params![path_str], + |r| r.get(0), + )?; + Ok(id) +} + +fn stem_parse_fields(file_stem: &str) -> (String, Vec, i64, i64, bool) { + match parse_fits_stem(file_stem) { + Some(p) => (p.sample_name, p.tags, p.scan_number, p.frame_number, true), + None => (String::new(), Vec::new(), 0, 0, false), + } +} + +pub(crate) fn prune_fits_files( + conn: &rusqlite::Connection, + beamtime_id: i64, + known_paths: &[&str], +) -> Result<()> { + if known_paths.is_empty() { + conn.execute( + "DELETE FROM fits_files WHERE beamtime_id = ?1", + rusqlite::params![beamtime_id], + )?; + return Ok(()); + } + let placeholders = known_paths + .iter() + .enumerate() + .map(|(i, _)| format!("?{}", i + 2)) + .collect::>() + .join(","); + let sql = format!( + "DELETE FROM fits_files WHERE beamtime_id = ?1 AND path NOT IN ({})", + placeholders + ); + let mut v: Vec> = vec![Box::new(beamtime_id)]; + for p in known_paths { + v.push(Box::new(*p)); + } + let refs: Vec<&dyn rusqlite::ToSql> = v.iter().map(|b| b.as_ref()).collect(); + conn.execute(&sql, rusqlite::params_from_iter(refs))?; Ok(()) } @@ -120,7 +199,15 @@ pub fn ingest_beamtime( incremental: bool, progress_tx: Option>, ) -> Result { - ingest_beamtime_with_context(beamtime_dir, None, None, header_items, incremental, progress_tx, None) + ingest_beamtime_with_context( + beamtime_dir, + None, + None, + header_items, + incremental, + progress_tx, + None, + ) } pub fn ingest_beamtime_with_context( @@ -146,7 +233,15 @@ pub fn ingest_beamtime_with_context( } #[cfg(not(feature = "parallel_ingest"))] { - ingest_beamtime_sequential(beamtime_dir, data_root, experimentalist, header_items, incremental, progress_tx, cancel) + ingest_beamtime_sequential( + beamtime_dir, + data_root, + experimentalist, + header_items, + incremental, + progress_tx, + cancel, + ) } } @@ -161,51 +256,39 @@ fn ingest_beamtime_sequential( cancel: Option>, ) -> Result { let db_path = resolve_ingest_target(beamtime_dir, data_root); - let conn = open_or_create_db(beamtime_dir)?; + let conn = open_or_create_db_at(&db_path)?; let beamtime_id = ensure_bt_beamtime(&conn, beamtime_dir)?; upsert_beamtime_record(&conn, beamtime_dir, data_root, experimentalist)?; - let discovered = super::discover_fits_paths(beamtime_dir)?; + let (discovered, layout) = discover_paths_for_catalog_ingest(beamtime_dir)?; + update_beamtime_catalog_layout(&conn, beamtime_id, layout)?; let path_to_mtime: HashMap = discovered .iter() .map(|(p, m)| (p.to_string_lossy().to_string(), *m)) .collect(); let paths: Vec = discovered.into_iter().map(|(p, _)| p).collect(); - - let use_normalized_only = super::is_new_catalog_layout(beamtime_dir); let to_ingest: Vec = if incremental { - if use_normalized_only { - let existing: HashMap = conn - .prepare("SELECT source_path, source_mtime FROM bt_scan_points WHERE source_path IS NOT NULL")? - .query_map([], |r| Ok((r.get::<_, String>(0)?, r.get::<_, i64>(1).unwrap_or(0))))? - .filter_map(|r| r.ok()) - .collect(); - paths - .into_iter() - .filter(|p| { - let key = p.to_string_lossy().to_string(); - let mtime = path_to_mtime.get(&key).copied().unwrap_or(0); - existing.get(&key).is_none_or(|&stored| stored == 0 || mtime > stored) - }) - .collect() - } else { - let existing: HashMap = conn - .prepare("SELECT path, mtime FROM files")? - .query_map([], |r| Ok((r.get::<_, String>(0)?, r.get::<_, i64>(1)?)))? - .filter_map(|r| r.ok()) - .collect(); - paths - .into_iter() - .filter(|p| { - let key = p.to_string_lossy().to_string(); - let mtime = path_to_mtime.get(&key).copied().unwrap_or(0); - existing.get(&key).is_none_or(|&stored| mtime > stored) - }) - .collect() - } + let existing: HashMap = conn + .prepare( + "SELECT path, source_mtime FROM fits_files WHERE beamtime_id = ?1", + )? + .query_map(rusqlite::params![beamtime_id], |r| { + Ok((r.get::<_, String>(0)?, r.get::<_, i64>(1)?)) + })? + .filter_map(|r| r.ok()) + .collect(); + paths + .into_iter() + .filter(|p| { + let key = p.to_string_lossy().to_string(); + let mtime = path_to_mtime.get(&key).copied().unwrap_or(0); + existing + .get(&key) + .is_none_or(|&stored| stored == 0 || mtime > stored) + }) + .collect() } else { paths }; - let total = to_ingest.len() as u32; let mut processed: u32 = 0; let opts = ReadFitsOptions { @@ -214,33 +297,32 @@ fn ingest_beamtime_sequential( ..ReadFitsOptions::default() }; for chunk in to_ingest.chunks(BATCH_SIZE) { - if cancel.as_ref().map(|c| c.load(std::sync::atomic::Ordering::Relaxed)).unwrap_or(false) { + if cancel + .as_ref() + .map(|c| c.load(std::sync::atomic::Ordering::Relaxed)) + .unwrap_or(false) + { break; } let chunk_vec: Vec = chunk.to_vec(); let df = read_fits_metadata_batch(chunk_vec, &opts)?; - if !use_normalized_only { - upsert_files_batch(&conn, &df, &path_to_mtime)?; - } - upsert_bt_batch(&conn, beamtime_id, &df, &path_to_mtime)?; + upsert_fits_and_bt_batch(&conn, beamtime_id, &df, &path_to_mtime)?; processed = (processed as usize + chunk.len()).min(to_ingest.len()) as u32; if let Some(ref tx) = progress_tx { let _ = tx.send((processed, total)); } } - let path_list: Vec<&str> = path_to_mtime.keys().map(|s| s.as_str()).collect(); - if !use_normalized_only { - prune_missing_files(&conn, &path_list)?; - } + prune_fits_files(&conn, beamtime_id, &path_list)?; prune_bt_scan_points(&conn, &path_list)?; super::profile_persist::recompute_reflectivity_profiles_for_beamtime(&conn, beamtime_id)?; - Ok(db_path) } -pub(crate) fn upsert_files_batch( +#[cfg(not(feature = "parallel_ingest"))] +pub(crate) fn upsert_fits_and_bt_batch( conn: &rusqlite::Connection, + beamtime_id: i64, df: &DataFrame, path_to_mtime: &HashMap, ) -> Result<()> { @@ -284,118 +366,95 @@ pub(crate) fn upsert_files_batch( _ => Ok(std::iter::repeat_n(None, n).collect()), } }; - let data_offset = get_i64("data_offset")?; let naxis1 = get_i64("naxis1")?; let naxis2 = get_i64("naxis2")?; let bitpix = get_i64("bitpix")?; let bzero = get_i64("bzero")?; - let data_size = get_i64("data_size")?; - let file_name = get_str("file_name")?; - let sample_name = get_str("sample_name")?; - let tag = get_str("tag")?; - let scan_number = get_i64("scan_number")?; - let frame_number = get_i64("frame_number")?; - let date = get_str("DATE")?; + let file_stem_col = get_str("file_name")?; let beamline_energy = get_f64("Beamline Energy")?; let sample_theta = get_f64("Sample Theta")?; let ccd_theta = get_f64("CCD Theta")?; - let hos = get_f64("Higher Order Suppressor")?; let epu = get_f64("EPU Polarization")?; let exposure = get_f64("EXPOSURE")?; - let sample_name_h = get_str("Sample Name")?; - let scan_id = get_f64("Scan ID")?; - let lambda = get_f64("Lambda")?; - let q = get_f64("Q")?; - let mut stmt = conn.prepare_cached( r#" - INSERT OR REPLACE INTO files ( - path, mtime, file_path, data_offset, naxis1, naxis2, bitpix, bzero, data_size, - file_name, sample_name, tag, scan_number, frame_number, - "DATE", "Beamline Energy", "Sample Theta", "CCD Theta", "Higher Order Suppressor", - "EPU Polarization", EXPOSURE, "Sample Name", "Scan ID", Lambda, Q + INSERT OR REPLACE INTO bt_scan_points ( + uid, stream_uid, scan_uid, sample_id, seq_index, time, + exposure, beamline_energy, epu_polarization, sample_theta, ccd_theta, + source_path, source_data_offset, source_naxis1, source_naxis2, + source_bitpix, source_bzero, source_mtime, beam_row, beam_col, beam_sigma, + fits_file_id ) VALUES ( - ?1, ?2, ?3, ?4, ?5, ?6, ?7, ?8, ?9, ?10, ?11, ?12, ?13, ?14, - ?15, ?16, ?17, ?18, ?19, ?20, ?21, ?22, ?23, ?24, ?25 + ?1, ?2, ?3, ?4, ?5, 0, + ?6, ?7, ?8, ?9, ?10, + ?11, ?12, ?13, ?14, ?15, ?16, ?17, ?18, ?19, ?20, ?21 )"#, )?; - - let mut canonical_cache: HashMap<(String, Option), (String, Option)> = - HashMap::new(); for i in 0..n { - let sn = sample_name + let path_str = file_path_col.get(i).unwrap_or("").to_string(); + let mtime = path_to_mtime.get(&path_str).copied().unwrap_or(0); + let stem = file_stem_col .get(i) .cloned() .flatten() .unwrap_or_default(); - let t = tag.get(i).cloned().flatten(); - let key = (sn.clone(), t.clone()); - let (canonical_sn, canonical_t) = if let Some(cached) = canonical_cache.get(&key) { - cached.clone() + let (sample_name, tags, scan_number, frame_number, parse_ok) = stem_parse_fields(&stem); + let sample_id_row = ensure_bt_sample(conn, beamtime_id, &sample_name)?; + let fits_sample_id = if parse_ok { + Some(sample_id_row) } else { - let cs = resolve_sample(conn, &sn)?; - let ct = t - .as_ref() - .map(|x| x.as_str()) - .and_then(|x| resolve_tag(conn, x).ok().flatten()); - ensure_sample_tag(conn, &cs, ct.as_deref())?; - canonical_cache.insert(key, (cs.clone(), ct.clone())); - (cs, ct) + None }; - - let path_str = file_path_col.get(i).unwrap_or("").to_string(); - let mtime = path_to_mtime.get(&path_str).copied().unwrap_or(0); - stmt.execute(rusqlite::params![ - path_str, + let file_id = upsert_fits_file_row( + conn, + beamtime_id, + fits_sample_id, + &path_str, + &stem, + scan_number, + frame_number, + parse_ok, mtime, + )?; + set_fits_file_tags(conn, file_id, &tags)?; + let scan_no = scan_number; + let frame_no = frame_number; + let scan_uid = format!("s_{}_{}", beamtime_id, scan_no); + let stream_uid = format!("st_{}_{}", beamtime_id, scan_no); + let point_uid = format!("sp_{}_{}_{}", beamtime_id, scan_no, frame_no); + let _ = ensure_bt_scan(conn, beamtime_id, sample_id_row, scan_no)?; + let _ = ensure_bt_stream(conn, beamtime_id, scan_no)?; + stmt.execute(rusqlite::params![ + point_uid, + stream_uid, + scan_uid, + sample_id_row, + frame_no, + exposure.get(i).copied().flatten(), + beamline_energy.get(i).copied().flatten(), + epu.get(i).copied().flatten(), + sample_theta.get(i).copied().flatten(), + ccd_theta.get(i).copied().flatten(), path_str, data_offset.get(i).copied().flatten().unwrap_or(0), naxis1.get(i).copied().flatten().unwrap_or(0), naxis2.get(i).copied().flatten().unwrap_or(0), bitpix.get(i).copied().flatten().unwrap_or(0), bzero.get(i).copied().flatten().unwrap_or(0), - data_size.get(i).copied().flatten().unwrap_or(0), - file_name.get(i).cloned().unwrap_or_default(), - canonical_sn, - canonical_t, - scan_number.get(i).copied().flatten().unwrap_or(0), - frame_number.get(i).copied().flatten().unwrap_or(0), - date.get(i).clone(), - beamline_energy.get(i).copied().flatten(), - sample_theta.get(i).copied().flatten(), - ccd_theta.get(i).copied().flatten(), - hos.get(i).copied().flatten(), - epu.get(i).copied().flatten(), - exposure.get(i).copied().flatten(), - sample_name_h.get(i).clone(), - scan_id.get(i).copied().flatten(), - lambda.get(i).copied().flatten(), - q.get(i).copied().flatten(), + mtime, + Option::::None, + Option::::None, + Option::::None, + file_id, ])?; } Ok(()) } -pub(crate) fn prune_missing_files(conn: &rusqlite::Connection, known_paths: &[&str]) -> Result<()> { - if known_paths.is_empty() { - conn.execute("DELETE FROM files", [])?; - return Ok(()); - } - let placeholders = known_paths - .iter() - .enumerate() - .map(|(i, _)| format!("?{}", i + 1)) - .collect::>() - .join(","); - let sql = format!("DELETE FROM files WHERE path NOT IN ({})", placeholders); - conn.execute(&sql, rusqlite::params_from_iter(known_paths.iter()))?; - Ok(()) -} - pub fn resolve_ingest_target(beamtime_dir: &Path, data_root: Option<&Path>) -> PathBuf { match data_root { - Some(root) => root.join(".pyref").join("catalog.db"), + Some(root) => super::data_root_catalog_path(root), None => super::resolve_catalog_path(beamtime_dir), } } @@ -435,24 +494,6 @@ pub(crate) fn ensure_bt_beamtime(conn: &rusqlite::Connection, beamtime_dir: &Pat Ok(id) } -fn ensure_bt_sample( - conn: &rusqlite::Connection, - beamtime_id: i64, - name: &str, - tag: Option<&str>, -) -> Result { - conn.execute( - "INSERT OR IGNORE INTO bt_samples (beamtime_id, name, tag) VALUES (?1, ?2, ?3)", - rusqlite::params![beamtime_id, name, tag], - )?; - let id: i64 = conn.query_row( - "SELECT id FROM bt_samples WHERE beamtime_id = ?1 AND name = ?2 AND (tag IS ?3 OR (tag IS NULL AND ?3 IS NULL))", - rusqlite::params![beamtime_id, name, tag], - |r| r.get(0), - )?; - Ok(id) -} - fn ensure_bt_scan( conn: &rusqlite::Connection, beamtime_id: i64, @@ -481,144 +522,6 @@ fn ensure_bt_stream( Ok(stream_uid) } -pub(crate) fn upsert_bt_batch( - conn: &rusqlite::Connection, - beamtime_id: i64, - df: &DataFrame, - path_to_mtime: &HashMap, -) -> Result<()> { - let n = df.height(); - if n == 0 { - return Ok(()); - } - let file_path_col = df - .column("file_path") - .map_err(|e| CatalogError::Validation(e.to_string()))? - .str() - .map_err(|e| CatalogError::Validation(e.to_string()))?; - let get_str = |name: &str| -> Result>> { - match df.column(name) { - Ok(c) => Ok(c - .str() - .map_err(|e| CatalogError::Validation(e.to_string()))? - .iter() - .map(|s| s.map(|v| v.to_string())) - .collect()), - _ => Ok(std::iter::repeat_n(None, n).collect()), - } - }; - let get_i64 = |name: &str| -> Result>> { - match df.column(name) { - Ok(c) => Ok(c - .i64() - .map_err(|e| CatalogError::Validation(e.to_string()))? - .iter() - .collect()), - _ => Ok(std::iter::repeat_n(None, n).collect()), - } - }; - let get_f64 = |name: &str| -> Result>> { - match df.column(name) { - Ok(c) => Ok(c - .f64() - .map_err(|e| CatalogError::Validation(e.to_string()))? - .iter() - .collect()), - _ => Ok(std::iter::repeat_n(None, n).collect()), - } - }; - - let data_offset = get_i64("data_offset")?; - let naxis1 = get_i64("naxis1")?; - let naxis2 = get_i64("naxis2")?; - let bitpix = get_i64("bitpix")?; - let bzero = get_i64("bzero")?; - let _file_name = get_str("file_name")?; - let sample_name = get_str("sample_name")?; - let tag = get_str("tag")?; - let scan_number = get_i64("scan_number")?; - let frame_number = get_i64("frame_number")?; - let beamline_energy = get_f64("Beamline Energy")?; - let sample_theta = get_f64("Sample Theta")?; - let ccd_theta = get_f64("CCD Theta")?; - let epu = get_f64("EPU Polarization")?; - let exposure = get_f64("EXPOSURE")?; - - let mut stmt = conn.prepare_cached( - r#" - INSERT OR REPLACE INTO bt_scan_points ( - uid, stream_uid, scan_uid, sample_id, seq_index, time, - exposure, beamline_energy, epu_polarization, sample_theta, ccd_theta, - source_path, source_data_offset, source_naxis1, source_naxis2, - source_bitpix, source_bzero, source_mtime, beam_row, beam_col, beam_sigma - ) VALUES ( - ?1, ?2, ?3, ?4, ?5, 0, - ?6, ?7, ?8, ?9, ?10, - ?11, ?12, ?13, ?14, ?15, ?16, ?17, ?18, ?19, ?20 - )"#, - )?; - - let mut canonical_cache: HashMap<(String, Option), i64> = HashMap::new(); - for i in 0..n { - let sn = sample_name - .get(i) - .cloned() - .flatten() - .unwrap_or_default(); - let t = tag.get(i).cloned().flatten(); - let key = (sn.clone(), t.clone()); - let sample_id = if let Some(&sid) = canonical_cache.get(&key) { - sid - } else { - let cs = resolve_sample(conn, &sn)?; - let ct = t - .as_ref() - .map(|x| x.as_str()) - .and_then(|x| resolve_tag(conn, x).ok().flatten()); - ensure_sample_tag(conn, &cs, ct.as_deref())?; - let sid = ensure_bt_sample(conn, beamtime_id, &cs, ct.as_deref())?; - canonical_cache.insert(key, sid); - sid - }; - - let scan_no = scan_number.get(i).copied().flatten().unwrap_or(0); - let frame_no = frame_number.get(i).copied().flatten().unwrap_or(0); - let scan_uid = format!("s_{}_{}", beamtime_id, scan_no); - let stream_uid = format!("st_{}_{}", beamtime_id, scan_no); - let point_uid = format!("sp_{}_{}_{}", beamtime_id, scan_no, frame_no); - - let _ = ensure_bt_scan(conn, beamtime_id, sample_id, scan_no)?; - let _ = ensure_bt_stream(conn, beamtime_id, scan_no)?; - - let path_str = file_path_col.get(i).unwrap_or("").to_string(); - let mtime = path_to_mtime.get(&path_str).copied().unwrap_or(0); - - stmt.execute(rusqlite::params![ - point_uid, - stream_uid, - scan_uid, - sample_id, - frame_no, - exposure.get(i).copied().flatten(), - beamline_energy.get(i).copied().flatten(), - epu.get(i).copied().flatten(), - sample_theta.get(i).copied().flatten(), - ccd_theta.get(i).copied().flatten(), - path_str, - data_offset.get(i).copied().flatten().unwrap_or(0), - naxis1.get(i).copied().flatten().unwrap_or(0), - naxis2.get(i).copied().flatten().unwrap_or(0), - bitpix.get(i).copied().flatten().unwrap_or(0), - bzero.get(i).copied().flatten().unwrap_or(0), - mtime, - Option::::None, - Option::::None, - Option::::None, - ])?; - } - Ok(()) -} - pub(crate) fn upsert_bt_batch_rows( conn: &rusqlite::Connection, beamtime_id: i64, @@ -634,46 +537,48 @@ pub(crate) fn upsert_bt_batch_rows( uid, stream_uid, scan_uid, sample_id, seq_index, time, exposure, beamline_energy, epu_polarization, sample_theta, ccd_theta, source_path, source_data_offset, source_naxis1, source_naxis2, - source_bitpix, source_bzero, source_mtime, beam_row, beam_col, beam_sigma + source_bitpix, source_bzero, source_mtime, beam_row, beam_col, beam_sigma, + fits_file_id ) VALUES ( ?1, ?2, ?3, ?4, ?5, 0, ?6, ?7, ?8, ?9, ?10, - ?11, ?12, ?13, ?14, ?15, ?16, ?17, ?18, ?19, ?20 + ?11, ?12, ?13, ?14, ?15, ?16, ?17, ?18, ?19, ?20, ?21 )"#, )?; - let mut canonical_cache: HashMap<(String, Option), i64> = HashMap::new(); for row in rows { - let sn = row.sample_name.as_str(); - let t = row.tag.clone(); - let key = (sn.to_string(), t.clone()); - let sample_id = if let Some(&sid) = canonical_cache.get(&key) { - sid + let (sample_name, tags, scan_number, frame_number, parse_ok) = + stem_parse_fields(&row.file_name); + let sample_id_row = ensure_bt_sample(conn, beamtime_id, &sample_name)?; + let fits_sample_id = if parse_ok { + Some(sample_id_row) } else { - let cs = resolve_sample(conn, sn)?; - let ct = t - .as_ref() - .map(|x| x.as_str()) - .and_then(|x| resolve_tag(conn, x).ok().flatten()); - ensure_sample_tag(conn, &cs, ct.as_deref())?; - let sid = ensure_bt_sample(conn, beamtime_id, &cs, ct.as_deref())?; - canonical_cache.insert(key, sid); - sid + None }; - let scan_no = row.scan_number; - let frame_no = row.frame_number; - let scan_uid = format!("s_{}_{}", beamtime_id, scan_no); - let stream_uid = format!("st_{}_{}", beamtime_id, scan_no); - let point_uid = format!("sp_{}_{}_{}", beamtime_id, scan_no, frame_no); - let _ = ensure_bt_scan(conn, beamtime_id, sample_id, scan_no)?; - let _ = ensure_bt_stream(conn, beamtime_id, scan_no)?; - let path_str = row.file_path.clone(); - let mtime = path_to_mtime.get(&path_str).copied().unwrap_or(0); + let path_str = row.file_path.as_str(); + let mtime = path_to_mtime.get(path_str).copied().unwrap_or(0); + let file_id = upsert_fits_file_row( + conn, + beamtime_id, + fits_sample_id, + path_str, + row.file_name.as_str(), + scan_number, + frame_number, + parse_ok, + mtime, + )?; + set_fits_file_tags(conn, file_id, &tags)?; + let scan_uid = format!("s_{}_{}", beamtime_id, scan_number); + let stream_uid = format!("st_{}_{}", beamtime_id, scan_number); + let point_uid = format!("sp_{}_{}_{}", beamtime_id, scan_number, frame_number); + let _ = ensure_bt_scan(conn, beamtime_id, sample_id_row, scan_number)?; + let _ = ensure_bt_stream(conn, beamtime_id, scan_number)?; stmt.execute(rusqlite::params![ point_uid, stream_uid, scan_uid, - sample_id, - frame_no, + sample_id_row, + row.frame_number, row.exposure, row.beamline_energy, row.epu_polarization, @@ -689,12 +594,16 @@ pub(crate) fn upsert_bt_batch_rows( Option::::None, Option::::None, Option::::None, + file_id, ])?; } Ok(()) } -pub(crate) fn prune_bt_scan_points(conn: &rusqlite::Connection, known_source_paths: &[&str]) -> Result<()> { +pub(crate) fn prune_bt_scan_points( + conn: &rusqlite::Connection, + known_source_paths: &[&str], +) -> Result<()> { if known_source_paths.is_empty() { conn.execute("DELETE FROM bt_scan_points", [])?; return Ok(()); diff --git a/src/catalog/ingest_parallel.rs b/src/catalog/ingest_parallel.rs index 7a5b22e..49ffb27 100644 --- a/src/catalog/ingest_parallel.rs +++ b/src/catalog/ingest_parallel.rs @@ -1,16 +1,15 @@ #![cfg(feature = "parallel_ingest")] use crate::catalog::ingest::{ - ensure_bt_beamtime, prune_bt_scan_points, prune_missing_files, upsert_bt_batch, - upsert_bt_batch_rows, upsert_files_batch, resolve_ingest_target, + ensure_bt_beamtime, prune_bt_scan_points, prune_fits_files, resolve_ingest_target, + update_beamtime_catalog_layout, upsert_beamtime_record, upsert_bt_batch_rows, }; -use crate::io::BtIngestRow; -use crate::catalog::open_or_create_db; -use crate::catalog::Result; +use crate::catalog::open_or_create_db_at; +use crate::catalog::discover_paths_for_catalog_ingest; use crate::catalog::CatalogError; -use crate::io::options::ReadFitsOptions; -use crate::loader::{read_fits_metadata_batch, read_multiple_fits_headers_only_rows}; -use polars::prelude::DataFrame; +use crate::catalog::Result; +use crate::io::BtIngestRow; +use crate::loader::read_multiple_fits_headers_only_rows; use std::collections::HashMap; use std::path::PathBuf; use std::sync::mpsc; @@ -21,7 +20,6 @@ const BATCH_SIZE: usize = 500; const CHANNEL_BOUND: usize = 2; enum PipelineMsg { - Batch(DataFrame), Rows(Vec), Error(String), Done, @@ -33,168 +31,15 @@ pub fn ingest_beamtime_pipelined( incremental: bool, progress_tx: Option>, ) -> Result { - let db_path = super::resolve_catalog_path(beamtime_dir); - let conn = open_or_create_db(beamtime_dir)?; - let beamtime_id = ensure_bt_beamtime(&conn, beamtime_dir)?; - let discovered = super::discover_fits_paths_parallel(beamtime_dir)?; - let path_to_mtime: HashMap = discovered - .iter() - .map(|(p, m)| (p.to_string_lossy().to_string(), *m)) - .collect(); - let paths: Vec = discovered.into_iter().map(|(p, _)| p).collect(); - - let use_normalized_only = super::is_new_catalog_layout(beamtime_dir); - let to_ingest: Vec = if incremental { - if use_normalized_only { - let existing: HashMap = conn - .prepare( - "SELECT source_path, source_mtime FROM bt_scan_points WHERE source_path IS NOT NULL", - )? - .query_map([], |r| Ok((r.get::<_, String>(0)?, r.get::<_, i64>(1).unwrap_or(0))))? - .filter_map(|r| r.ok()) - .collect(); - paths - .into_iter() - .filter(|p| { - let key = p.to_string_lossy().to_string(); - let mtime = path_to_mtime.get(&key).copied().unwrap_or(0); - existing - .get(&key) - .is_none_or(|&stored| stored == 0 || mtime > stored) - }) - .collect() - } else { - let existing: HashMap = conn - .prepare("SELECT path, mtime FROM files")? - .query_map([], |r| Ok((r.get::<_, String>(0)?, r.get::<_, i64>(1)?)))? - .filter_map(|r| r.ok()) - .collect(); - paths - .into_iter() - .filter(|p| { - let key = p.to_string_lossy().to_string(); - let mtime = path_to_mtime.get(&key).copied().unwrap_or(0); - existing.get(&key).is_none_or(|&stored| mtime > stored) - }) - .collect() - } - } else { - paths - }; - - let total = to_ingest.len() as u32; - if total == 0 { - let path_list: Vec<&str> = path_to_mtime.keys().map(|s| s.as_str()).collect(); - if !use_normalized_only { - prune_missing_files(&conn, &path_list)?; - } - prune_bt_scan_points(&conn, &path_list)?; - super::profile_persist::recompute_reflectivity_profiles_for_beamtime(&conn, beamtime_id)?; - return Ok(db_path); - } - - let opts = ReadFitsOptions { - header_items: header_items.to_vec(), - batch_size: BATCH_SIZE, - ..ReadFitsOptions::default() - }; - - let paths_arc = Arc::new(to_ingest); - let (tx, rx) = mpsc::sync_channel::(CHANNEL_BOUND); - - let paths_arc_reader = Arc::clone(&paths_arc); - let opts_reader = opts.clone(); - let header_items_owned = header_items.to_vec(); - let use_rows_only = use_normalized_only; - let reader = thread::spawn(move || { - let len = paths_arc_reader.len(); - let mut start = 0usize; - while start < len { - let end = (start + BATCH_SIZE).min(len); - let chunk_vec = paths_arc_reader[start..end].to_vec(); - let chunk_len = chunk_vec.len(); - if use_rows_only { - match read_multiple_fits_headers_only_rows(chunk_vec, &header_items_owned) { - Ok(rows) => { - if tx.send(PipelineMsg::Rows(rows)).is_err() { - return; - } - } - Err(e) => { - let _ = tx.send(PipelineMsg::Error(e.to_string())); - return; - } - } - } else { - match read_fits_metadata_batch(chunk_vec, &opts_reader) { - Ok(df) => { - if tx.send(PipelineMsg::Batch(df)).is_err() { - return; - } - } - Err(e) => { - let _ = tx.send(PipelineMsg::Error(e.to_string())); - return; - } - } - } - start += chunk_len; - } - let _ = tx.send(PipelineMsg::Done); - }); - - let mut processed: u32 = 0; - loop { - match rx.recv() { - Ok(PipelineMsg::Rows(rows)) => { - if !rows.is_empty() { - upsert_bt_batch_rows(&conn, beamtime_id, &rows, &path_to_mtime)?; - processed = (processed as usize + rows.len()).min(paths_arc.len()) as u32; - if let Some(ref ptx) = progress_tx { - let _ = ptx.send((processed, total)); - } - } - } - Ok(PipelineMsg::Batch(df)) => { - let n = df.height(); - if n == 0 { - continue; - } - if !use_normalized_only { - upsert_files_batch(&conn, &df, &path_to_mtime)?; - } - upsert_bt_batch(&conn, beamtime_id, &df, &path_to_mtime)?; - processed = (processed as usize + n).min(paths_arc.len()) as u32; - if let Some(ref ptx) = progress_tx { - let _ = ptx.send((processed, total)); - } - } - Ok(PipelineMsg::Error(e)) => { - let _ = reader.join(); - return Err(CatalogError::Validation(format!( - "read_fits_metadata_batch: {}", - e - ))); - } - Ok(PipelineMsg::Done) => break, - Err(_) => { - let _ = reader.join(); - return Err(CatalogError::Validation( - "ingest pipeline reader disconnected".into(), - )); - } - } - } - let _ = reader.join(); - - let path_list: Vec<&str> = path_to_mtime.keys().map(|s| s.as_str()).collect(); - if !use_normalized_only { - prune_missing_files(&conn, &path_list)?; - } - prune_bt_scan_points(&conn, &path_list)?; - super::profile_persist::recompute_reflectivity_profiles_for_beamtime(&conn, beamtime_id)?; - - Ok(db_path) + ingest_beamtime_pipelined_with_context( + beamtime_dir, + None, + None, + header_items, + incremental, + progress_tx, + None, + ) } pub fn ingest_beamtime_pipelined_with_context( @@ -207,132 +52,88 @@ pub fn ingest_beamtime_pipelined_with_context( cancel: Option>, ) -> Result { let db_path = resolve_ingest_target(beamtime_dir, data_root); - let conn = open_or_create_db(beamtime_dir)?; + let conn = open_or_create_db_at(&db_path)?; let beamtime_id = ensure_bt_beamtime(&conn, beamtime_dir)?; - - use crate::catalog::ingest::upsert_beamtime_record as upsert_record; - upsert_record(&conn, beamtime_dir, data_root, experimentalist)?; - - let discovered = super::discover_fits_paths_parallel(beamtime_dir)?; + upsert_beamtime_record(&conn, beamtime_dir, data_root, experimentalist)?; + let (discovered, layout) = discover_paths_for_catalog_ingest(beamtime_dir)?; + update_beamtime_catalog_layout(&conn, beamtime_id, layout)?; let path_to_mtime: HashMap = discovered .iter() .map(|(p, m)| (p.to_string_lossy().to_string(), *m)) .collect(); let paths: Vec = discovered.into_iter().map(|(p, _)| p).collect(); - - let use_normalized_only = super::is_new_catalog_layout(beamtime_dir); let to_ingest: Vec = if incremental { - if use_normalized_only { - let existing: HashMap = conn - .prepare( - "SELECT source_path, source_mtime FROM bt_scan_points WHERE source_path IS NOT NULL", - )? - .query_map([], |r| Ok((r.get::<_, String>(0)?, r.get::<_, i64>(1).unwrap_or(0))))? - .filter_map(|r| r.ok()) - .collect(); - paths - .into_iter() - .filter(|p| { - let key = p.to_string_lossy().to_string(); - let mtime = path_to_mtime.get(&key).copied().unwrap_or(0); - existing - .get(&key) - .is_none_or(|&stored| stored == 0 || mtime > stored) - }) - .collect() - } else { - let existing: HashMap = conn - .prepare("SELECT path, mtime FROM files")? - .query_map([], |r| Ok((r.get::<_, String>(0)?, r.get::<_, i64>(1)?)))? - .filter_map(|r| r.ok()) - .collect(); - paths - .into_iter() - .filter(|p| { - let key = p.to_string_lossy().to_string(); - let mtime = path_to_mtime.get(&key).copied().unwrap_or(0); - existing.get(&key).is_none_or(|&stored| mtime > stored) - }) - .collect() - } + let existing: HashMap = conn + .prepare( + "SELECT path, source_mtime FROM fits_files WHERE beamtime_id = ?1", + )? + .query_map(rusqlite::params![beamtime_id], |r| { + Ok((r.get::<_, String>(0)?, r.get::<_, i64>(1)?)) + })? + .filter_map(|r| r.ok()) + .collect(); + paths + .into_iter() + .filter(|p| { + let key = p.to_string_lossy().to_string(); + let mtime = path_to_mtime.get(&key).copied().unwrap_or(0); + existing + .get(&key) + .is_none_or(|&stored| stored == 0 || mtime > stored) + }) + .collect() } else { paths }; - let total = to_ingest.len() as u32; if total == 0 { let path_list: Vec<&str> = path_to_mtime.keys().map(|s| s.as_str()).collect(); - if !use_normalized_only { - prune_missing_files(&conn, &path_list)?; - } + prune_fits_files(&conn, beamtime_id, &path_list)?; prune_bt_scan_points(&conn, &path_list)?; super::profile_persist::recompute_reflectivity_profiles_for_beamtime(&conn, beamtime_id)?; return Ok(db_path); } - - let opts = ReadFitsOptions { - header_items: header_items.to_vec(), - batch_size: BATCH_SIZE, - ..ReadFitsOptions::default() - }; - let paths_arc = Arc::new(to_ingest); let (tx, rx) = mpsc::sync_channel::(CHANNEL_BOUND); - let paths_arc_reader = Arc::clone(&paths_arc); - let opts_reader = opts.clone(); let header_items_owned = header_items.to_vec(); - let use_rows_only = use_normalized_only; let cancel_clone = cancel.clone(); let reader = thread::spawn(move || { let len = paths_arc_reader.len(); let mut start = 0usize; while start < len { - if cancel_clone.as_ref().map(|c| c.load(std::sync::atomic::Ordering::Relaxed)).unwrap_or(false) { + if cancel_clone + .as_ref() + .map(|c| c.load(std::sync::atomic::Ordering::Relaxed)) + .unwrap_or(false) + { break; } let end = (start + BATCH_SIZE).min(len); let chunk_vec = paths_arc_reader[start..end].to_vec(); let chunk_len = chunk_vec.len(); - if use_rows_only { - match read_multiple_fits_headers_only_rows(chunk_vec, &header_items_owned) { - Ok(rows) => { - if tx.send(PipelineMsg::Rows(rows)).is_err() { - return; - } - } - Err(e) => { - let _ = tx.send(PipelineMsg::Error(e.to_string())); + match read_multiple_fits_headers_only_rows(chunk_vec, &header_items_owned) { + Ok(rows) => { + if tx.send(PipelineMsg::Rows(rows)).is_err() { return; } } - } else { - match read_fits_metadata_batch(chunk_vec, &opts_reader) { - Ok(df) => { - if tx.send(PipelineMsg::Batch(df)).is_err() { - return; - } - } - Err(e) => { - let _ = tx.send(PipelineMsg::Error(e.to_string())); - return; - } + Err(e) => { + let _ = tx.send(PipelineMsg::Error(e.to_string())); + return; } } start += chunk_len; } let _ = tx.send(PipelineMsg::Done); }); - let mut processed: u32 = 0; loop { - // Check cancel token before processing each batch if let Some(ref cancel) = cancel { if cancel.load(std::sync::atomic::Ordering::Relaxed) { break; } } - match rx.recv() { Ok(PipelineMsg::Rows(rows)) => { if !rows.is_empty() { @@ -343,24 +144,10 @@ pub fn ingest_beamtime_pipelined_with_context( } } } - Ok(PipelineMsg::Batch(df)) => { - let n = df.height(); - if n == 0 { - continue; - } - if !use_normalized_only { - upsert_files_batch(&conn, &df, &path_to_mtime)?; - } - upsert_bt_batch(&conn, beamtime_id, &df, &path_to_mtime)?; - processed = (processed as usize + n).min(paths_arc.len()) as u32; - if let Some(ref ptx) = progress_tx { - let _ = ptx.send((processed, total)); - } - } Ok(PipelineMsg::Error(e)) => { let _ = reader.join(); return Err(CatalogError::Validation(format!( - "read_fits_metadata_batch: {}", + "read_multiple_fits_headers_only_rows: {}", e ))); } @@ -374,13 +161,9 @@ pub fn ingest_beamtime_pipelined_with_context( } } let _ = reader.join(); - let path_list: Vec<&str> = path_to_mtime.keys().map(|s| s.as_str()).collect(); - if !use_normalized_only { - prune_missing_files(&conn, &path_list)?; - } + prune_fits_files(&conn, beamtime_id, &path_list)?; prune_bt_scan_points(&conn, &path_list)?; super::profile_persist::recompute_reflectivity_profiles_for_beamtime(&conn, beamtime_id)?; - Ok(db_path) } diff --git a/src/catalog/layout.rs b/src/catalog/layout.rs new file mode 100644 index 0000000..c4898e3 --- /dev/null +++ b/src/catalog/layout.rs @@ -0,0 +1,241 @@ +//! Beamtime directory layout detection and FITS discovery per AGENTS.md. +//! +//! Detects nested (date / CCD Scan N / instrument) vs flat (beamtime / CCD or Axis Photonique). +//! Returns a structured error when neither layout matches. + +use crate::catalog::{is_skippable_stem, CatalogError, Result}; +use std::ffi::OsStr; +use std::fs; +use std::path::{Path, PathBuf}; + +/// Recognized beamtime directory layout from AGENTS.md. +#[derive(Debug, Clone, Copy, PartialEq, Eq)] +pub enum BeamtimeLayout { + /// Date-grouped scan folders with `CCD Scan ` and `CCD` or `Axis Photonique` subdirs. + Nested, + /// Single `CCD` or `Axis Photonique` under beamtime root holding FITS from multiple scans. + Flat, +} + +fn is_fits_file(path: &Path) -> bool { + path.is_file() + && path + .extension() + .and_then(OsStr::to_str) + .map(|e| e.eq_ignore_ascii_case("fits")) + == Some(true) +} + +fn instrument_subdir_name() -> &'static [&'static str] { + &["CCD", "Axis Photonique"] +} + +/// Inspects ``beamtime_root`` and returns which AGENTS layout applies. +/// +/// Raises [`CatalogError::Validation`] when neither nested nor flat criteria match. +pub fn detect_beamtime_layout(beamtime_root: &Path) -> Result { + if !beamtime_root.is_dir() { + return Err(CatalogError::Validation(format!( + "not a directory: {}", + beamtime_root.display() + ))); + } + + if detect_nested_layout(beamtime_root) { + return Ok(BeamtimeLayout::Nested); + } + if detect_flat_layout(beamtime_root) { + return Ok(BeamtimeLayout::Flat); + } + + Err(CatalogError::Validation(format!( + "unrecognized beamtime directory layout (expected nested date/CCD Scan structure or flat CCD/Axis Photonique): {}", + beamtime_root.display() + ))) +} + +fn detect_nested_layout(root: &Path) -> bool { + let Ok(entries) = fs::read_dir(root) else { + return false; + }; + for e in entries.flatten() { + let date_dir = e.path(); + if !date_dir.is_dir() { + continue; + } + let Ok(scan_entries) = fs::read_dir(&date_dir) else { + continue; + }; + for se in scan_entries.flatten() { + let scan_dir = se.path(); + if !scan_dir.is_dir() { + continue; + } + let name = scan_dir.file_name().and_then(|n| n.to_str()).unwrap_or(""); + if !scan_dir_name_looks_like_ccd_scan(name) { + continue; + } + for inst in instrument_subdir_name() { + let p = scan_dir.join(inst); + if p.is_dir() { + return true; + } + } + } + } + false +} + +fn scan_dir_name_looks_like_ccd_scan(name: &str) -> bool { + let lower = name.to_ascii_lowercase(); + lower.contains("ccd scan") || lower.starts_with("ccd scan") +} + +fn detect_flat_layout(root: &Path) -> bool { + for inst in instrument_subdir_name() { + let inst_dir = root.join(inst); + if !inst_dir.is_dir() { + continue; + } + if inst_dir.is_dir() { + return true; + } + } + false +} + +/// Discovers FITS paths and mtimes using layout-specific rules. +pub fn discover_fits_for_layout( + beamtime_root: &Path, + layout: BeamtimeLayout, +) -> Result> { + match layout { + BeamtimeLayout::Nested => discover_nested_fits(beamtime_root), + BeamtimeLayout::Flat => discover_flat_fits(beamtime_root), + } +} + +fn file_mtime(path: &Path) -> i64 { + fs::metadata(path) + .ok() + .and_then(|m| m.modified().ok()) + .map(|t| { + t.duration_since(std::time::UNIX_EPOCH) + .unwrap_or_default() + .as_secs() as i64 + }) + .unwrap_or(0) +} + +fn push_canonical_fits(out: &mut Vec<(PathBuf, i64)>, path: &Path) -> Result<()> { + if !is_fits_file(path) { + return Ok(()); + } + let stem = path.file_stem().and_then(|s| s.to_str()).unwrap_or(""); + if is_skippable_stem(stem) { + return Ok(()); + } + let path_buf = path.canonicalize()?; + out.push((path_buf, file_mtime(path))); + Ok(()) +} + +fn discover_nested_fits(root: &Path) -> Result> { + let mut out: Vec<(PathBuf, i64)> = Vec::new(); + let Ok(date_entries) = fs::read_dir(root) else { + return Ok(out); + }; + for de in date_entries.flatten() { + let date_dir = de.path(); + if !date_dir.is_dir() { + continue; + } + let Ok(scan_entries) = fs::read_dir(&date_dir) else { + continue; + }; + for se in scan_entries.flatten() { + let scan_dir = se.path(); + if !scan_dir.is_dir() { + continue; + } + let name = scan_dir.file_name().and_then(|n| n.to_str()).unwrap_or(""); + if !scan_dir_name_looks_like_ccd_scan(name) { + continue; + } + for inst in instrument_subdir_name() { + let inst_dir = scan_dir.join(inst); + if !inst_dir.is_dir() { + continue; + } + let Ok(fits_entries) = fs::read_dir(&inst_dir) else { + continue; + }; + for fe in fits_entries.flatten() { + let p = fe.path(); + push_canonical_fits(&mut out, &p)?; + } + } + } + } + out.sort_by(|a, b| a.0.cmp(&b.0)); + out.dedup_by(|a, b| a.0 == b.0); + Ok(out) +} + +fn discover_flat_fits(root: &Path) -> Result> { + let mut out: Vec<(PathBuf, i64)> = Vec::new(); + for inst in instrument_subdir_name() { + let inst_dir = root.join(inst); + if !inst_dir.is_dir() { + continue; + } + let Ok(entries) = fs::read_dir(&inst_dir) else { + continue; + }; + for e in entries.flatten() { + let p = e.path(); + push_canonical_fits(&mut out, &p)?; + } + } + out.sort_by(|a, b| a.0.cmp(&b.0)); + out.dedup_by(|a, b| a.0 == b.0); + Ok(out) +} + +#[cfg(test)] +mod tests { + use super::*; + use tempfile::TempDir; + + #[test] + fn detect_flat_layout_ccd_multi_scan() { + let tmp = TempDir::new().unwrap(); + let ccd = tmp.path().join("CCD"); + fs::create_dir_all(&ccd).unwrap(); + fs::write(ccd.join("a_00001-00001.fits"), b"X").unwrap(); + fs::write(ccd.join("b_00002-00001.fits"), b"X").unwrap(); + assert_eq!( + detect_beamtime_layout(tmp.path()).unwrap(), + BeamtimeLayout::Flat + ); + } + + #[test] + fn detect_nested_layout_minimal() { + let tmp = TempDir::new().unwrap(); + let scan = tmp.path().join("20240101").join("CCD Scan 1").join("CCD"); + fs::create_dir_all(&scan).unwrap(); + fs::write(scan.join("s_00001-00001.fits"), b"X").unwrap(); + assert_eq!( + detect_beamtime_layout(tmp.path()).unwrap(), + BeamtimeLayout::Nested + ); + } + + #[test] + fn detect_unknown_errors() { + let tmp = TempDir::new().unwrap(); + fs::create_dir_all(tmp.path().join("empty")).unwrap(); + assert!(detect_beamtime_layout(tmp.path()).is_err()); + } +} diff --git a/src/catalog/mod.rs b/src/catalog/mod.rs index 57a6472..cd7db5b 100644 --- a/src/catalog/mod.rs +++ b/src/catalog/mod.rs @@ -1,5 +1,6 @@ mod beamtime_index; mod ingest; +mod layout; #[cfg(feature = "parallel_ingest")] mod ingest_parallel; mod query; @@ -21,14 +22,12 @@ pub use beamtime_index::{ use rusqlite::Connection; use std::path::{Path, PathBuf}; use thiserror::Error; -use walkdir::WalkDir; - pub const CATALOG_DB_NAME: &str = ".pyref_catalog.db"; pub use ingest::{ - ingest_beamtime, ingest_beamtime_with_context, ingest_header_scan, ingest_header_scan_with_context, - ingest_sample_scan, ingest_sample_scan_with_context, DEFAULT_INGEST_HEADER_ITEMS, + ingest_beamtime, ingest_beamtime_with_context, DEFAULT_INGEST_HEADER_ITEMS, }; +pub use layout::{detect_beamtime_layout, discover_fits_for_layout, BeamtimeLayout}; #[cfg(feature = "parallel_ingest")] pub use ingest_parallel::ingest_beamtime_pipelined; pub use query::{ @@ -86,9 +85,8 @@ impl CatalogError { pub type Result = std::result::Result; -fn catalog_path(beamtime_dir: &Path) -> PathBuf { - beamtime_dir.join(CATALOG_DB_NAME) -} +/// Bit in ``fits_files.file_flags`` when the filename stem did not satisfy the AGENTS parse contract. +pub const FILE_FLAG_PARSE_FAILURE: i64 = 1; const PYREF_CATALOG_DIR: &str = ".pyref"; const NEW_CATALOG_DB_NAME: &str = "catalog.db"; @@ -107,90 +105,86 @@ pub fn data_root_catalog_path(data_root: &Path) -> PathBuf { data_root.join(PYREF_CATALOG_DIR).join(NEW_CATALOG_DB_NAME) } +/// Resolves the catalog database path for a beamtime directory (AGENTS layout: `parent/.pyref/catalog.db`). pub fn resolve_catalog_path(beamtime_dir: &Path) -> PathBuf { - let new_path = catalog_path_new(beamtime_dir); - let legacy_path = catalog_path(beamtime_dir); - if new_path.exists() { - new_path - } else if legacy_path.exists() { - legacy_path - } else { - new_path - } -} - -pub fn is_new_catalog_layout(beamtime_dir: &Path) -> bool { - catalog_path_new(beamtime_dir) != catalog_path(beamtime_dir) + catalog_path_new(beamtime_dir) } -const FILES_TABLE: &str = r#" -CREATE TABLE IF NOT EXISTS files ( - path TEXT PRIMARY KEY, - mtime INTEGER NOT NULL, - file_path TEXT NOT NULL, - data_offset INTEGER NOT NULL, - naxis1 INTEGER NOT NULL, - naxis2 INTEGER NOT NULL, - bitpix INTEGER NOT NULL, - bzero INTEGER NOT NULL, - data_size INTEGER NOT NULL, +const FITS_FILES_TABLE: &str = r#" +CREATE TABLE IF NOT EXISTS fits_files ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + beamtime_id INTEGER NOT NULL REFERENCES bt_beamtimes(id) ON DELETE CASCADE, + sample_id INTEGER REFERENCES bt_samples(id) ON DELETE SET NULL, + path TEXT NOT NULL UNIQUE, file_name TEXT NOT NULL, - sample_name TEXT NOT NULL, - tag TEXT, scan_number INTEGER NOT NULL, frame_number INTEGER NOT NULL, - "DATE" TEXT, - "Beamline Energy" REAL, - "Sample Theta" REAL, - "CCD Theta" REAL, - "Higher Order Suppressor" REAL, - "EPU Polarization" REAL, - EXPOSURE REAL, - "Sample Name" TEXT, - "Scan ID" REAL, - Lambda REAL, - Q REAL, - beam_row INTEGER, - beam_col INTEGER, - beam_sigma REAL + parse_ok INTEGER NOT NULL DEFAULT 0, + file_flags INTEGER NOT NULL DEFAULT 0, + source_mtime INTEGER NOT NULL DEFAULT 0 )"#; -const FILES_INDEX_MTIME: &str = "CREATE INDEX IF NOT EXISTS idx_files_mtime ON files(mtime)"; -const FILES_INDEX_SAMPLE: &str = - "CREATE INDEX IF NOT EXISTS idx_files_sample_name ON files(sample_name)"; -const FILES_INDEX_TAG: &str = "CREATE INDEX IF NOT EXISTS idx_files_tag ON files(tag)"; -const FILES_INDEX_SCAN: &str = - "CREATE INDEX IF NOT EXISTS idx_files_scan_number ON files(scan_number)"; +const FITS_FILES_INDEX_BEAMTIME: &str = + "CREATE INDEX IF NOT EXISTS idx_fits_files_beamtime ON fits_files(beamtime_id)"; +const FITS_FILES_INDEX_SCAN: &str = + "CREATE INDEX IF NOT EXISTS idx_fits_files_scan ON fits_files(scan_number)"; -const SAMPLES_TABLE: &str = r#" -CREATE TABLE IF NOT EXISTS samples ( +const CATALOG_TAGS_TABLE: &str = r#" +CREATE TABLE IF NOT EXISTS catalog_tags ( id INTEGER PRIMARY KEY AUTOINCREMENT, - name TEXT NOT NULL, - chemical_formula TEXT, - created_at INTEGER, - updated_at INTEGER + name TEXT NOT NULL UNIQUE, + slug TEXT NOT NULL )"#; -const TAGS_TABLE: &str = r#" -CREATE TABLE IF NOT EXISTS tags ( +const FITS_FILE_TAGS_TABLE: &str = r#" +CREATE TABLE IF NOT EXISTS fits_file_tags ( + file_id INTEGER NOT NULL REFERENCES fits_files(id) ON DELETE CASCADE, + tag_id INTEGER NOT NULL REFERENCES catalog_tags(id) ON DELETE CASCADE, + PRIMARY KEY (file_id, tag_id) +)"#; + +const CATALOG_BEAM_FINDING_TABLE: &str = r#" +CREATE TABLE IF NOT EXISTS catalog_beam_finding ( id INTEGER PRIMARY KEY AUTOINCREMENT, - name TEXT NOT NULL UNIQUE, - slug TEXT + scan_point_uid TEXT NOT NULL UNIQUE REFERENCES bt_scan_points(uid) ON DELETE CASCADE, + edge_trim_applied INTEGER, + bg_row_params TEXT, + bg_col_params TEXT, + gaussian_sigma REAL, + centroid_row REAL, + centroid_col REAL, + roi_intensity REAL, + fit_sigma REAL, + bg_intensity REAL, + bg_intensity_err REAL, + detection_ok INTEGER, + quality_flags INTEGER NOT NULL DEFAULT 0 )"#; -const SAMPLE_TAGS_TABLE: &str = r#" -CREATE TABLE IF NOT EXISTS sample_tags ( - sample_id INTEGER NOT NULL REFERENCES samples(id) ON DELETE CASCADE, - tag_id INTEGER NOT NULL REFERENCES tags(id) ON DELETE CASCADE, - PRIMARY KEY (sample_id, tag_id) +const CATALOG_STITCH_CORRECTION_TABLE: &str = r#" +CREATE TABLE IF NOT EXISTS catalog_stitch_correction ( + scan_uid TEXT NOT NULL REFERENCES bt_scans(uid) ON DELETE CASCADE, + stitch_index INTEGER NOT NULL, + fano_factor REAL, + overlap_scale REAL, + i0_value REAL, + i0_source TEXT, + external_i0_scan_uid TEXT, + frame_class TEXT, + PRIMARY KEY (scan_uid, stitch_index) )"#; -const OVERRIDES_TABLE: &str = r#" -CREATE TABLE IF NOT EXISTS overrides ( - path TEXT PRIMARY KEY REFERENCES files(path) ON DELETE CASCADE, - sample_name TEXT, - tag TEXT, - notes TEXT +const CATALOG_REFLECTIVITY_FRAME_TABLE: &str = r#" +CREATE TABLE IF NOT EXISTS catalog_reflectivity_frame ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + beam_finding_id INTEGER NOT NULL REFERENCES catalog_beam_finding(id) ON DELETE CASCADE, + theta REAL, + energy REAL, + q REAL, + intensity REAL, + intensity_err REAL, + frame_class TEXT, + quality_flags INTEGER NOT NULL DEFAULT 0 )"#; const BT_BEAMTIMES_TABLE: &str = r#" @@ -224,13 +218,13 @@ CREATE TABLE IF NOT EXISTS bt_samples ( id INTEGER PRIMARY KEY AUTOINCREMENT, beamtime_id INTEGER NOT NULL REFERENCES bt_beamtimes(id) ON DELETE CASCADE, name TEXT NOT NULL, - tag TEXT, - version INTEGER, + sample_x REAL, + sample_y REAL, chemical_formula TEXT, serial TEXT, beamline_pos TEXT, extra TEXT, - UNIQUE (beamtime_id, name, tag, version) + UNIQUE (beamtime_id, name) )"#; const BT_SCANS_TABLE: &str = r#" @@ -282,6 +276,7 @@ CREATE TABLE IF NOT EXISTS bt_scan_points ( beam_row INTEGER, beam_col INTEGER, beam_sigma REAL, + fits_file_id INTEGER REFERENCES fits_files(id) ON DELETE SET NULL, UNIQUE (stream_uid, seq_index) )"#; @@ -385,191 +380,144 @@ fn migrate_bt_fits_discovery(conn: &Connection) -> Result<()> { Ok(()) } -/// Discover FITS paths under ``beamtime_dir`` using the same walk strategy as ingest (parallel when -/// the ``parallel_ingest`` feature is enabled). -pub(crate) fn discover_paths_for_catalog_ingest( - beamtime_dir: &Path, -) -> Result> { - #[cfg(feature = "parallel_ingest")] - { - discover_fits_paths_parallel(beamtime_dir) - } - #[cfg(not(feature = "parallel_ingest"))] - { - discover_fits_paths(beamtime_dir) +fn migrate_drop_legacy_denormalized_catalog(conn: &Connection) -> Result<()> { + let legacy: i64 = conn.query_row( + "SELECT COUNT(1) FROM sqlite_master WHERE type='table' AND name IN ('files','overrides','sample_tags','samples','tags')", + [], + |r| r.get(0), + )?; + if legacy == 0 { + return Ok(()); } + conn.execute_batch( + r#" + DROP TABLE IF EXISTS overrides; + DROP TABLE IF EXISTS sample_tags; + DROP TABLE IF EXISTS files; + DROP TABLE IF EXISTS tags; + DROP TABLE IF EXISTS samples; + "#, + )?; + Ok(()) } -pub fn is_skippable_stem(stem: &str) -> bool { - stem.is_empty() || stem.starts_with('_') -} - -pub fn discover_fits_paths(beamtime_dir: &Path) -> Result> { - if !beamtime_dir.is_dir() { - return Err(CatalogError::Validation(format!( - "not a directory: {}", - beamtime_dir.display() - ))); - } - let mut out: Vec<(PathBuf, i64)> = Vec::new(); - for entry in WalkDir::new(beamtime_dir) - .follow_links(false) - .into_iter() - .filter_map(|e| e.ok()) - { - let path = entry.path(); - if !path.is_file() { - continue; - } - if path - .extension() - .and_then(|e| e.to_str()) - .map(|e| e.eq_ignore_ascii_case("fits")) - != Some(true) - { - continue; - } - let stem = path.file_stem().and_then(|s| s.to_str()).unwrap_or(""); - if is_skippable_stem(stem) { - continue; - } - let path_buf = path.canonicalize()?; - let mtime = std::fs::metadata(path) - .ok() - .and_then(|m| m.modified().ok()) - .map(|t| { - t.duration_since(std::time::UNIX_EPOCH) - .unwrap_or_default() - .as_secs() as i64 - }) - .unwrap_or(0); - out.push((path_buf, mtime)); +fn migrate_bt_samples_schema_agents(conn: &Connection) -> Result<()> { + let has_table: i64 = conn.query_row( + "SELECT COUNT(1) FROM sqlite_master WHERE type='table' AND name='bt_samples'", + [], + |r| r.get(0), + )?; + if has_table == 0 { + return Ok(()); } - out.sort_by(|a, b| a.0.cmp(&b.0)); - Ok(out) -} - -#[cfg(feature = "parallel_ingest")] -fn collect_fits_under(root: &Path) -> Result> { - let mut out: Vec<(PathBuf, i64)> = Vec::new(); - for entry in WalkDir::new(root) - .follow_links(false) - .into_iter() - .filter_map(|e| e.ok()) - { - let path = entry.path(); - if !path.is_file() { - continue; - } - if path - .extension() - .and_then(|e| e.to_str()) - .map(|e| e.eq_ignore_ascii_case("fits")) - != Some(true) - { - continue; - } - let stem = path.file_stem().and_then(|s| s.to_str()).unwrap_or(""); - if is_skippable_stem(stem) { - continue; - } - let path_buf = path.canonicalize()?; - let mtime = std::fs::metadata(path) - .ok() - .and_then(|m| m.modified().ok()) - .map(|t| { - t.duration_since(std::time::UNIX_EPOCH) - .unwrap_or_default() - .as_secs() as i64 - }) - .unwrap_or(0); - out.push((path_buf, mtime)); + let has_tag: i64 = conn.query_row( + "SELECT COUNT(1) FROM pragma_table_info('bt_samples') WHERE name='tag'", + [], + |r| r.get(0), + )?; + if has_tag == 0 { + return Ok(()); } - Ok(out) + conn.execute_batch( + r#" + PRAGMA foreign_keys=OFF; + DROP TABLE IF EXISTS catalog_reflectivity_frame; + DROP TABLE IF EXISTS catalog_stitch_correction; + DROP TABLE IF EXISTS catalog_beam_finding; + DROP TABLE IF EXISTS bt_image_refs; + DROP TABLE IF EXISTS fits_file_tags; + DROP TABLE IF EXISTS fits_files; + DROP TABLE IF EXISTS bt_scan_points; + DROP TABLE IF EXISTS bt_streams; + DROP TABLE IF EXISTS bt_scans; + DROP TABLE IF EXISTS bt_reflectivity_profiles; + DROP TABLE IF EXISTS bt_fits_discovery; + DROP TABLE IF EXISTS bt_samples; + PRAGMA foreign_keys=ON; + "#, + )?; + Ok(()) } -#[cfg(feature = "parallel_ingest")] -pub fn discover_fits_paths_parallel(beamtime_dir: &Path) -> Result> { - if !beamtime_dir.is_dir() { - return Err(CatalogError::Validation(format!( - "not a directory: {}", - beamtime_dir.display() - ))); - } - let subdirs: Vec = std::fs::read_dir(beamtime_dir) - .map_err(CatalogError::Io)? - .filter_map(|e| e.ok()) - .filter(|e| e.path().is_dir()) - .map(|e| e.path()) - .collect(); - if subdirs.len() <= 1 { - return discover_fits_paths(beamtime_dir); +fn migrate_bt_beamtimes_catalog_fields(conn: &Connection) -> Result<()> { + let has_layout: i64 = conn.query_row( + "SELECT COUNT(1) FROM pragma_table_info('bt_beamtimes') WHERE name='catalog_layout'", + [], + |r| r.get(0), + )?; + if has_layout == 0 { + conn.execute( + "ALTER TABLE bt_beamtimes ADD COLUMN catalog_layout TEXT", + [], + )?; } - use rayon::prelude::*; - let parts: Result>> = subdirs - .par_iter() - .map(|d| collect_fits_under(d.as_path())) - .collect(); - let mut out = parts?; - let root_only: Vec<(PathBuf, i64)> = std::fs::read_dir(beamtime_dir) - .map_err(CatalogError::Io)? - .filter_map(|e| e.ok()) - .filter(|e| e.path().is_file()) - .filter_map(|e| { - let path = e.path(); - if path.extension()?.to_str()?.eq_ignore_ascii_case("fits") != true { - return None; - } - let stem = path.file_stem()?.to_str()?; - if is_skippable_stem(stem) { - return None; - } - let path_buf = path.canonicalize().ok()?; - let mtime = std::fs::metadata(&path) - .ok()? - .modified() - .ok() - .map(|t| { - t.duration_since(std::time::UNIX_EPOCH) - .unwrap_or_default() - .as_secs() as i64 - }) - .unwrap_or(0); - Some((path_buf, mtime)) - }) - .collect(); - out.push(root_only); - let mut flat: Vec<(PathBuf, i64)> = out.into_iter().flatten().collect(); - flat.sort_by(|a, b| a.0.cmp(&b.0)); - flat.dedup_by(|a, b| a.0 == b.0); - Ok(flat) -} - -fn migrate_add_beamspot_columns(conn: &Connection) -> Result<()> { - let has_beam_row: bool = conn.query_row( - "SELECT COUNT(1) FROM pragma_table_info('files') WHERE name = 'beam_row'", + let has_date: i64 = conn.query_row( + "SELECT COUNT(1) FROM pragma_table_info('bt_beamtimes') WHERE name='beamtime_date'", [], |r| r.get(0), )?; - if !has_beam_row { - conn.execute("ALTER TABLE files ADD COLUMN beam_row INTEGER", [])?; - conn.execute("ALTER TABLE files ADD COLUMN beam_col INTEGER", [])?; + if has_date == 0 { + conn.execute( + "ALTER TABLE bt_beamtimes ADD COLUMN beamtime_date TEXT", + [], + )?; } Ok(()) } -fn migrate_add_beam_sigma_column(conn: &Connection) -> Result<()> { - let has_beam_sigma: bool = conn.query_row( - "SELECT COUNT(1) FROM pragma_table_info('files') WHERE name = 'beam_sigma'", +fn migrate_bt_scan_points_fits_file_id(conn: &Connection) -> Result<()> { + let has_table: i64 = conn.query_row( + "SELECT COUNT(1) FROM sqlite_master WHERE type='table' AND name='bt_scan_points'", [], |r| r.get(0), )?; - if !has_beam_sigma { - conn.execute("ALTER TABLE files ADD COLUMN beam_sigma REAL", [])?; + if has_table == 0 { + return Ok(()); + } + let has_col: i64 = conn.query_row( + "SELECT COUNT(1) FROM pragma_table_info('bt_scan_points') WHERE name='fits_file_id'", + [], + |r| r.get(0), + )?; + if has_col == 0 { + conn.execute( + "ALTER TABLE bt_scan_points ADD COLUMN fits_file_id INTEGER REFERENCES fits_files(id) ON DELETE SET NULL", + [], + )?; } Ok(()) } +/// Discover FITS paths for ingest using AGENTS layout rules under ``beamtime_dir``. +pub(crate) fn discover_paths_for_catalog_ingest( + beamtime_dir: &Path, +) -> Result<(Vec<(PathBuf, i64)>, layout::BeamtimeLayout)> { + let layout_kind = layout::detect_beamtime_layout(beamtime_dir)?; + let paths = layout::discover_fits_for_layout(beamtime_dir, layout_kind)?; + Ok((paths, layout_kind)) +} + +pub fn is_skippable_stem(stem: &str) -> bool { + stem.is_empty() || stem.starts_with('_') +} + +/// Discovers FITS under ``beamtime_dir`` using AGENTS layout rules only (no silent full-tree fallback). +pub fn discover_fits_paths(beamtime_dir: &Path) -> Result> { + if !beamtime_dir.is_dir() { + return Err(CatalogError::Validation(format!( + "not a directory: {}", + beamtime_dir.display() + ))); + } + let layout = layout::detect_beamtime_layout(beamtime_dir)?; + layout::discover_fits_for_layout(beamtime_dir, layout) +} + +#[cfg(feature = "parallel_ingest")] +pub fn discover_fits_paths_parallel(beamtime_dir: &Path) -> Result> { + discover_fits_paths(beamtime_dir) +} + fn migrate_add_experimentalist_and_data_root(conn: &Connection) -> Result<()> { let has_experimentalist: bool = conn.query_row( "SELECT COUNT(1) FROM pragma_table_info('bt_beamtimes') WHERE name = 'experimentalist'", @@ -642,28 +590,30 @@ fn migrate_add_last_indexed_at_column(conn: &Connection) -> Result<()> { pub fn open_catalog_db(db_path: &Path) -> Result { let conn = Connection::open(db_path)?; - conn.execute_batch(FILES_TABLE)?; - conn.execute_batch(SAMPLES_TABLE)?; - conn.execute_batch(TAGS_TABLE)?; - conn.execute_batch(SAMPLE_TAGS_TABLE)?; - conn.execute_batch(OVERRIDES_TABLE)?; - migrate_experiment_number_to_scan_number(&conn)?; - migrate_add_beamspot_columns(&conn)?; - migrate_add_beam_sigma_column(&conn)?; - conn.execute_batch(FILES_INDEX_MTIME)?; - conn.execute_batch(FILES_INDEX_SAMPLE)?; - conn.execute_batch(FILES_INDEX_TAG)?; - conn.execute_batch(FILES_INDEX_SCAN)?; + migrate_drop_legacy_denormalized_catalog(&conn)?; + migrate_bt_samples_schema_agents(&conn)?; conn.execute_batch(BT_BEAMTIMES_TABLE)?; + migrate_bt_beamtimes_catalog_fields(&conn)?; + migrate_add_experimentalist_and_data_root(&conn)?; + migrate_add_last_indexed_at_column(&conn)?; conn.execute_batch(BT_MOTOR_NAMES_TABLE)?; conn.execute_batch(BT_AI_CHANNEL_NAMES_TABLE)?; conn.execute_batch(BT_SAMPLES_TABLE)?; + conn.execute_batch(CATALOG_TAGS_TABLE)?; + conn.execute_batch(FITS_FILES_TABLE)?; + conn.execute_batch(FITS_FILES_INDEX_BEAMTIME)?; + conn.execute_batch(FITS_FILES_INDEX_SCAN)?; + conn.execute_batch(FITS_FILE_TAGS_TABLE)?; conn.execute_batch(BT_SCANS_TABLE)?; conn.execute_batch(BT_STREAMS_TABLE)?; conn.execute_batch(BT_SCAN_POINTS_TABLE)?; + migrate_bt_scan_points_fits_file_id(&conn)?; conn.execute_batch(BT_SCAN_POINT_MOTOR_POSITIONS_TABLE)?; conn.execute_batch(BT_SCAN_POINT_AI_TABLE)?; conn.execute_batch(BT_SCAN_POINT_AI_READINGS_TABLE)?; + conn.execute_batch(CATALOG_BEAM_FINDING_TABLE)?; + conn.execute_batch(CATALOG_STITCH_CORRECTION_TABLE)?; + conn.execute_batch(CATALOG_REFLECTIVITY_FRAME_TABLE)?; conn.execute_batch(BT_IMAGE_REFS_TABLE)?; conn.execute_batch(BT_INDEX_BEAMTIMES_PATH)?; conn.execute_batch(BT_INDEX_SCANS_BEAMTIME)?; @@ -671,13 +621,21 @@ pub fn open_catalog_db(db_path: &Path) -> Result { conn.execute_batch(BT_INDEX_SCAN_POINTS_SOURCE)?; conn.execute_batch(BT_INDEX_IMAGE_REFS_POINT)?; conn.execute_batch(BT_FILE_OVERRIDES_TABLE)?; - migrate_add_experimentalist_and_data_root(&conn)?; - migrate_add_last_indexed_at_column(&conn)?; migrate_reflectivity_profiles_schema(&conn)?; migrate_bt_fits_discovery(&conn)?; Ok(conn) } +/// Opens or creates the catalog at ``catalog_db_path`` (creates parent directories when missing). +pub fn open_or_create_db_at(catalog_db_path: &Path) -> Result { + if let Some(parent) = catalog_db_path.parent() { + if !parent.as_os_str().is_empty() && !catalog_db_path.exists() { + std::fs::create_dir_all(parent).map_err(CatalogError::Io)?; + } + } + open_catalog_db(catalog_db_path) +} + pub fn open_or_create_db(beamtime_dir: &Path) -> Result { if !beamtime_dir.is_dir() { return Err(CatalogError::Validation(format!( @@ -686,27 +644,7 @@ pub fn open_or_create_db(beamtime_dir: &Path) -> Result { ))); } let db_path = resolve_catalog_path(beamtime_dir); - if let Some(parent) = db_path.parent() { - if !parent.as_os_str().is_empty() && !db_path.exists() { - std::fs::create_dir_all(parent).map_err(CatalogError::Io)?; - } - } - open_catalog_db(&db_path) -} - -fn migrate_experiment_number_to_scan_number(conn: &Connection) -> Result<()> { - let has_old: bool = conn.query_row( - "SELECT COUNT(1) FROM pragma_table_info('files') WHERE name = 'experiment_number'", - [], - |r| r.get(0), - )?; - if has_old { - conn.execute( - "ALTER TABLE files RENAME COLUMN experiment_number TO scan_number", - [], - )?; - } - Ok(()) + open_or_create_db_at(&db_path) } #[cfg(test)] @@ -718,15 +656,16 @@ mod tests { #[test] fn test_open_or_create_db_creates_schema() { let tmp = TempDir::new().unwrap(); + std::fs::create_dir_all(tmp.path().join("CCD")).unwrap(); let conn = open_or_create_db(tmp.path()).unwrap(); let count: i64 = conn .query_row( - "SELECT COUNT(*) FROM sqlite_master WHERE type='table' AND name IN ('files','overrides','samples','tags','sample_tags')", + "SELECT COUNT(*) FROM sqlite_master WHERE type='table' AND name IN ('fits_files','catalog_tags','bt_beamtimes','bt_samples')", [], |r| r.get(0), ) .unwrap(); - assert_eq!(count, 5); + assert_eq!(count, 4); } #[test] @@ -739,8 +678,9 @@ mod tests { } #[test] - fn test_discover_fits_paths_empty_dir() { + fn test_discover_fits_paths_empty_flat_ccd() { let tmp = TempDir::new().unwrap(); + std::fs::create_dir_all(tmp.path().join("CCD")).unwrap(); let paths = discover_fits_paths(tmp.path()).unwrap(); assert!(paths.is_empty()); } @@ -753,12 +693,13 @@ mod tests { } #[test] - fn test_open_or_create_db_has_beam_sigma_column() { + fn test_open_or_create_db_has_fits_files_parse_ok() { let tmp = TempDir::new().unwrap(); + std::fs::create_dir_all(tmp.path().join("CCD")).unwrap(); let conn = open_or_create_db(tmp.path()).unwrap(); let has: i64 = conn .query_row( - "SELECT COUNT(1) FROM pragma_table_info('files') WHERE name = 'beam_sigma'", + "SELECT COUNT(1) FROM pragma_table_info('fits_files') WHERE name = 'parse_ok'", [], |r| r.get(0), ) diff --git a/src/catalog/profile_persist.rs b/src/catalog/profile_persist.rs index 7a188bc..6ff4b11 100644 --- a/src/catalog/profile_persist.rs +++ b/src/catalog/profile_persist.rs @@ -106,7 +106,7 @@ mod tests { ) .unwrap(); conn.execute( - "INSERT INTO bt_samples (beamtime_id, name, tag) VALUES (1, 's', 't')", + "INSERT INTO bt_samples (beamtime_id, name) VALUES (1, 's')", [], ) .unwrap(); @@ -166,7 +166,7 @@ mod tests { ) .unwrap(); conn.execute( - "INSERT INTO bt_samples (beamtime_id, name, tag) VALUES (1, 's', 't')", + "INSERT INTO bt_samples (beamtime_id, name) VALUES (1, 's')", [], ) .unwrap(); diff --git a/src/catalog/query.rs b/src/catalog/query.rs index 36bc86b..883f74e 100644 --- a/src/catalog/query.rs +++ b/src/catalog/query.rs @@ -54,22 +54,19 @@ fn beamtime_id_from_path(conn: &rusqlite::Connection, beamtime_dir: &Path) -> Re pub fn catalog_file_count(db_path: &Path, beamtime_dir: Option<&Path>) -> Result { let conn = open_catalog_db(db_path)?; let count: i64 = if let Some(dir) = beamtime_dir { - if super::is_new_catalog_layout(dir) { - let path_str = dir.to_string_lossy().to_string(); - conn.query_row( - "SELECT COUNT(*) FROM bt_scan_points sp - JOIN bt_scans sc ON sp.scan_uid = sc.uid - JOIN bt_beamtimes b ON sc.beamtime_id = b.id - WHERE b.beamtime_path = ?1 AND sp.source_path IS NOT NULL", - rusqlite::params![path_str], - |r| r.get(0), - ) - .unwrap_or(0) - } else { - conn.query_row("SELECT COUNT(*) FROM files", [], |r| r.get(0)).unwrap_or(0) - } + let path_str = dir.to_string_lossy().to_string(); + conn.query_row( + "SELECT COUNT(*) FROM fits_files ff + JOIN bt_beamtimes b ON ff.beamtime_id = b.id + WHERE b.beamtime_path = ?1", + rusqlite::params![path_str], + |r| r.get(0), + ) + .unwrap_or(0) } else { - conn.query_row("SELECT COUNT(*) FROM files", [], |r| r.get(0)).unwrap_or(0) + conn + .query_row("SELECT COUNT(*) FROM fits_files", [], |r| r.get(0)) + .unwrap_or(0) }; Ok(count as u32) } @@ -90,27 +87,29 @@ pub fn list_beamtime_entries(db_path: &Path) -> Result { let conn = open_catalog_db(db_path)?; let mut samples: Vec = conn .prepare( - r#"SELECT DISTINCT COALESCE(o.sample_name, f.sample_name) FROM files f - LEFT JOIN overrides o ON f.path = o.path - WHERE COALESCE(o.sample_name, f.sample_name) != '' ORDER BY 1"#, + r#"SELECT DISTINCT COALESCE(o.sample_name, s.name, '') + FROM fits_files ff + LEFT JOIN bt_samples s ON ff.sample_id = s.id + LEFT JOIN bt_file_overrides o ON o.source_path = ff.path + WHERE COALESCE(o.sample_name, s.name, '') != '' + ORDER BY 1"#, )? .query_map([], |r| r.get::<_, String>(0))? .filter_map(|r| r.ok()) .collect(); let mut tags: Vec = conn .prepare( - r#"SELECT DISTINCT COALESCE(o.tag, f.tag) FROM files f - LEFT JOIN overrides o ON f.path = o.path - WHERE COALESCE(o.tag, f.tag) IS NOT NULL AND COALESCE(o.tag, f.tag) != '' - ORDER BY COALESCE(o.tag, f.tag)"#, + r#"SELECT DISTINCT ct.name FROM catalog_tags ct + JOIN fits_file_tags fft ON fft.tag_id = ct.id + ORDER BY ct.name"#, )? .query_map([], |r| r.get::<_, String>(0))? .filter_map(|r| r.ok()) .collect(); let scans: Vec<(i64, String)> = conn .prepare( - r#"SELECT DISTINCT f.scan_number FROM files f - WHERE f.scan_number != 0 ORDER BY f.scan_number"#, + r#"SELECT DISTINCT ff.scan_number FROM fits_files ff + WHERE ff.scan_number != 0 ORDER BY ff.scan_number"#, )? .query_map([], |r| { let n: i64 = r.get(0)?; @@ -158,7 +157,11 @@ pub fn list_beamtime_entries_v2( .collect(); let mut tags: Vec = conn .prepare( - "SELECT DISTINCT tag FROM bt_samples WHERE beamtime_id = ?1 AND tag IS NOT NULL AND tag != '' ORDER BY tag", + r#"SELECT DISTINCT ct.name FROM catalog_tags ct + JOIN fits_file_tags fft ON fft.tag_id = ct.id + JOIN fits_files ff ON ff.id = fft.file_id + WHERE ff.beamtime_id = ?1 + ORDER BY ct.name"#, )? .query_map(rusqlite::params![beamtime_id], |r| r.get::<_, String>(0))? .filter_map(|r| r.ok()) @@ -187,14 +190,22 @@ pub fn query_files(db_path: &Path, filter: Option<&CatalogFilter>) -> Result) -> Result { conn.execute( - "UPDATE files SET beam_row = ?1, beam_col = ?2, beam_sigma = ?3 WHERE path = ?4", + "UPDATE bt_scan_points SET beam_row = ?1, beam_col = ?2, beam_sigma = ?3 WHERE source_path = ?4", rusqlite::params![beam_row, beam_col, sigma, file_path], )?; } None => { conn.execute( - "UPDATE files SET beam_row = ?1, beam_col = ?2 WHERE path = ?3", + "UPDATE bt_scan_points SET beam_row = ?1, beam_col = ?2 WHERE source_path = ?3", rusqlite::params![beam_row, beam_col, file_path], )?; } @@ -402,31 +423,20 @@ pub fn get_scan_point_uid_by_source_path( Ok(uid) } -const RESOLVED_QUERY: &str = r#" -SELECT - f.file_path, f.data_offset, f.naxis1, f.naxis2, f.bitpix, f.bzero, f.data_size, - f.file_name, - COALESCE(o.sample_name, f.sample_name) AS sample_name, - COALESCE(o.tag, f.tag) AS tag, - f.scan_number, f.frame_number, - f."DATE", f."Beamline Energy", f."Sample Theta", f."CCD Theta", - f."Higher Order Suppressor", f."EPU Polarization", f.EXPOSURE, - f."Sample Name", f."Scan ID", f.Lambda, f.Q, - f.beam_row, f.beam_col, f.beam_sigma -FROM files f -LEFT JOIN overrides o ON f.path = o.path -"#; - fn build_where_and_params(filter: &CatalogFilter) -> (String, Vec>) { let mut conditions: Vec = Vec::new(); let mut params: Vec> = Vec::new(); if let Some(ref s) = filter.sample_name { - conditions.push("COALESCE(o.sample_name, f.sample_name) = ?1".to_string()); + conditions.push("COALESCE(o.sample_name, s.name, '') = ?1".to_string()); params.push(Box::new(s.clone())); } if let Some(ref t) = filter.tag { let idx = params.len() + 1; - conditions.push(format!("COALESCE(o.tag, f.tag) = ?{}", idx)); + conditions.push(format!( + "(o.tag = ?{0} OR EXISTS (SELECT 1 FROM fits_file_tags fft JOIN catalog_tags ct ON fft.tag_id = ct.id \ + WHERE fft.file_id = ff.id AND ct.name = ?{0}))", + idx + )); params.push(Box::new(t.clone())); } if let Some(ref scan_nos) = filter.scan_numbers { @@ -434,7 +444,7 @@ fn build_where_and_params(filter: &CatalogFilter) -> (String, Vec = (0..scan_nos.len()) .map(|i| format!("?{}", params.len() + 1 + i)) .collect(); - conditions.push(format!("f.scan_number IN ({})", placeholders.join(","))); + conditions.push(format!("ff.scan_number IN ({})", placeholders.join(","))); for s in scan_nos { params.push(Box::new(*s)); } @@ -442,12 +452,12 @@ fn build_where_and_params(filter: &CatalogFilter) -> (String, Vec= ?{}"#, idx)); + conditions.push(format!("sp.beamline_energy >= ?{}", idx)); params.push(Box::new(em)); } if let Some(em) = filter.energy_max { let idx = params.len() + 1; - conditions.push(format!(r#"f."Beamline Energy" <= ?{}"#, idx)); + conditions.push(format!("sp.beamline_energy <= ?{}", idx)); params.push(Box::new(em)); } let where_clause = if conditions.is_empty() { @@ -464,7 +474,14 @@ SELECT sp.source_bitpix, sp.source_bzero, (CASE WHEN sp.source_naxis1 IS NOT NULL AND sp.source_naxis2 IS NOT NULL AND sp.source_bitpix IS NOT NULL THEN sp.source_naxis1 * sp.source_naxis2 * max(1, abs(sp.source_bitpix) / 8) ELSE 0 END) AS data_size, - COALESCE(o.sample_name, s.name) AS sample_name, COALESCE(o.tag, s.tag) AS tag, sc.uid AS scan_uid, sp.seq_index, + COALESCE(o.sample_name, s.name) AS sample_name, + COALESCE( + o.tag, + (SELECT ct.name FROM fits_file_tags fft + JOIN catalog_tags ct ON fft.tag_id = ct.id + WHERE fft.file_id = sp.fits_file_id ORDER BY ct.name LIMIT 1) + ) AS tag, + sc.uid AS scan_uid, sp.seq_index, sp.beamline_energy, sp.sample_theta, sp.ccd_theta, sp.epu_polarization, sp.exposure, sp.beam_row, sp.beam_col, sp.beam_sigma, sp.reflectivity_profile_index, rp.scan_type AS reflectivity_scan_type @@ -633,7 +650,11 @@ fn build_bt_df_where_and_params( } if let Some(ref t) = filter.tag { idx += 1; - conditions.push(format!("COALESCE(o.tag, s.tag) = ?{}", idx)); + conditions.push(format!( + "(o.tag = ?{0} OR EXISTS (SELECT 1 FROM fits_file_tags fft JOIN catalog_tags ct ON fft.tag_id = ct.id \ + WHERE fft.file_id = sp.fits_file_id AND ct.name = ?{0}))", + idx + )); params.push(Box::new(t.clone())); } if let Some(em) = filter.energy_min { @@ -671,148 +692,7 @@ fn build_bt_df_where_and_params( pub fn scan_from_catalog(db_path: &Path, filter: Option<&CatalogFilter>) -> Result { let conn = open_catalog_db(db_path)?; - let bt_count: i64 = conn.query_row( - "SELECT COUNT(*) FROM bt_scan_points", - [], - |r| r.get::<_, i64>(0), - )?; - let files_count: i64 = conn.query_row("SELECT COUNT(*) FROM files", [], |r| r.get::<_, i64>(0))?; - let use_bt = bt_count > 0 || (bt_count == 0 && files_count == 0); - if use_bt { - return scan_from_catalog_bt(&conn, filter); - } - let (where_clause, params) = filter - .map(build_where_and_params) - .unwrap_or_else(|| (String::new(), Vec::new())); - let order_clause = " ORDER BY f.scan_number, f.frame_number"; - let sql = format!("{}{}{}", RESOLVED_QUERY, where_clause, order_clause); - - let mut stmt = conn.prepare(&sql)?; - let row_from_query = |row: &rusqlite::Row| { - Ok(( - row.get::<_, String>(0)?, - row.get::<_, i64>(1)?, - row.get::<_, i64>(2)?, - row.get::<_, i64>(3)?, - row.get::<_, i64>(4)?, - row.get::<_, i64>(5)?, - row.get::<_, i64>(6)?, - row.get::<_, String>(7)?, - row.get::<_, String>(8)?, - row.get::<_, Option>(9)?, - row.get::<_, i64>(10)?, - row.get::<_, i64>(11)?, - row.get::<_, Option>(12)?, - row.get::<_, Option>(13)?, - row.get::<_, Option>(14)?, - row.get::<_, Option>(15)?, - row.get::<_, Option>(16)?, - row.get::<_, Option>(17)?, - row.get::<_, Option>(18)?, - row.get::<_, Option>(19)?, - row.get::<_, Option>(20)?, - row.get::<_, Option>(21)?, - row.get::<_, Option>(22)?, - row.get::<_, Option>(23)?, - row.get::<_, Option>(24)?, - row.get::<_, Option>(25)?, - )) - }; - let rows = if params.is_empty() { - stmt.query_map([], row_from_query)? - } else { - let param_refs: Vec<&dyn rusqlite::ToSql> = params.iter().map(|b| b.as_ref()).collect(); - stmt.query_map(rusqlite::params_from_iter(param_refs), row_from_query)? - }; - - let mut file_path = Vec::new(); - let mut data_offset = Vec::new(); - let mut naxis1 = Vec::new(); - let mut naxis2 = Vec::new(); - let mut bitpix = Vec::new(); - let mut bzero = Vec::new(); - let mut data_size = Vec::new(); - let mut file_name = Vec::new(); - let mut sample_name = Vec::new(); - let mut tag = Vec::new(); - let mut scan_number = Vec::new(); - let mut frame_number = Vec::new(); - let mut date = Vec::new(); - let mut beamline_energy = Vec::new(); - let mut sample_theta = Vec::new(); - let mut ccd_theta = Vec::new(); - let mut hos = Vec::new(); - let mut epu = Vec::new(); - let mut exposure = Vec::new(); - let mut sample_name_h = Vec::new(); - let mut scan_id = Vec::new(); - let mut lambda = Vec::new(); - let mut q = Vec::new(); - let mut beam_row = Vec::new(); - let mut beam_col = Vec::new(); - let mut beam_sigma = Vec::new(); - - for row in rows { - let r = row.map_err(CatalogError::Sqlite)?; - file_path.push(r.0); - data_offset.push(r.1); - naxis1.push(r.2); - naxis2.push(r.3); - bitpix.push(r.4); - bzero.push(r.5); - data_size.push(r.6); - file_name.push(r.7); - sample_name.push(r.8); - tag.push(r.9); - scan_number.push(r.10); - frame_number.push(r.11); - date.push(r.12); - beamline_energy.push(r.13); - sample_theta.push(r.14); - ccd_theta.push(r.15); - hos.push(r.16); - epu.push(r.17); - exposure.push(r.18); - sample_name_h.push(r.19); - scan_id.push(r.20); - lambda.push(r.21); - q.push(r.22); - beam_row.push(r.23); - beam_col.push(r.24); - beam_sigma.push(r.25); - } - - let series = vec![ - Series::new("file_path".into(), file_path), - Series::new("data_offset".into(), data_offset), - Series::new("naxis1".into(), naxis1), - Series::new("naxis2".into(), naxis2), - Series::new("bitpix".into(), bitpix), - Series::new("bzero".into(), bzero), - Series::new("data_size".into(), data_size), - Series::new("file_name".into(), file_name), - Series::new("sample_name".into(), sample_name), - Series::new("tag".into(), tag), - Series::new("scan_number".into(), scan_number), - Series::new("frame_number".into(), frame_number), - Series::new("DATE".into(), date), - Series::new("Beamline Energy".into(), beamline_energy), - Series::new("Sample Theta".into(), sample_theta), - Series::new("CCD Theta".into(), ccd_theta), - Series::new("Higher Order Suppressor".into(), hos), - Series::new("EPU Polarization".into(), epu), - Series::new("EXPOSURE".into(), exposure), - Series::new("Sample Name".into(), sample_name_h), - Series::new("Scan ID".into(), scan_id), - Series::new("Lambda".into(), lambda), - Series::new("Q".into(), q), - Series::new("beam_row".into(), beam_row), - Series::new("beam_col".into(), beam_col), - Series::new("beam_sigma".into(), beam_sigma), - ]; - let columns: Vec = series.into_iter().map(|s| s.into()).collect(); - let df = DataFrame::new(columns).map_err(|e| CatalogError::Validation(e.to_string()))?; - Ok(df) + scan_from_catalog_bt(&conn, filter) } type OverrideRowSql = (String, Option, Option, Option); @@ -828,9 +708,8 @@ fn map_override_four_cols(row: &rusqlite::Row) -> rusqlite::Result) -> Result { let conn = open_catalog_db(db_path)?; let mut paths = Vec::new(); @@ -839,16 +718,6 @@ pub fn get_overrides(db_path: &Path, path: Option<&str>) -> Result { let mut notes = Vec::new(); match path { Some(p) => { - let mut stmt = conn.prepare( - "SELECT path, sample_name, tag, notes FROM overrides WHERE path = ?1", - )?; - for row in stmt.query_map([p], map_override_four_cols)? { - let r = row.map_err(CatalogError::Sqlite)?; - paths.push(r.0); - sample_names.push(r.1); - tags.push(r.2); - notes.push(r.3); - } let mut stmt = conn.prepare( "SELECT source_path, sample_name, tag, notes FROM bt_file_overrides WHERE source_path = ?1", )?; @@ -861,15 +730,6 @@ pub fn get_overrides(db_path: &Path, path: Option<&str>) -> Result { } } None => { - let mut stmt = - conn.prepare("SELECT path, sample_name, tag, notes FROM overrides")?; - for row in stmt.query_map([], map_override_four_cols)? { - let r = row.map_err(CatalogError::Sqlite)?; - paths.push(r.0); - sample_names.push(r.1); - tags.push(r.2); - notes.push(r.3); - } let mut stmt = conn.prepare( "SELECT source_path, sample_name, tag, notes FROM bt_file_overrides", )?; @@ -934,7 +794,8 @@ mod tests { #[test] fn test_scan_from_catalog_empty_db() { let tmp = TempDir::new().unwrap(); - let db_path = tmp.path().join(".pyref_catalog.db"); + std::fs::create_dir_all(tmp.path().join("CCD")).unwrap(); + let db_path = crate::catalog::resolve_catalog_path(tmp.path()); crate::catalog::open_or_create_db(tmp.path()).unwrap(); let df = scan_from_catalog(&db_path, None).unwrap(); assert_eq!(df.height(), 0); @@ -946,8 +807,9 @@ mod tests { #[test] fn test_get_overrides_empty() { let tmp = TempDir::new().unwrap(); + std::fs::create_dir_all(tmp.path().join("CCD")).unwrap(); + let db_path = crate::catalog::resolve_catalog_path(tmp.path()); crate::catalog::open_or_create_db(tmp.path()).unwrap(); - let db_path = tmp.path().join(".pyref_catalog.db"); let df = get_overrides(&db_path, None).unwrap(); assert_eq!(df.height(), 0); } @@ -955,8 +817,9 @@ mod tests { #[test] fn test_list_beamtime_entries_empty() { let tmp = TempDir::new().unwrap(); + std::fs::create_dir_all(tmp.path().join("CCD")).unwrap(); + let db_path = crate::catalog::resolve_catalog_path(tmp.path()); crate::catalog::open_or_create_db(tmp.path()).unwrap(); - let db_path = tmp.path().join(".pyref_catalog.db"); let e = list_beamtime_entries(&db_path).unwrap(); assert!(e.samples.is_empty()); assert!(e.tags.is_empty()); @@ -975,7 +838,7 @@ mod tests { ) .unwrap(); conn.execute( - "INSERT INTO bt_samples (beamtime_id, name, tag) VALUES (1, 'orig', 't1')", + "INSERT INTO bt_samples (beamtime_id, name) VALUES (1, 'orig')", [], ) .unwrap(); @@ -1022,10 +885,10 @@ mod tests { } } -/// Upserts `sample_name`, `tag`, and `notes` for a catalog file path. +/// Upserts ``sample_name``, ``tag``, and ``notes`` for a catalog file path. /// -/// When `path` exists in `files`, writes `overrides`. When it exists only as `bt_scan_points.source_path`, -/// writes `bt_file_overrides`. Returns [`CatalogError::Validation`] when `path` matches neither. +/// Applies when ``path`` exists in ``fits_files`` or as ``bt_scan_points.source_path``. +/// Persists to ``bt_file_overrides``. Returns [`CatalogError::Validation`] when ``path`` matches neither. pub fn set_override( db_path: &Path, path: &str, @@ -1034,23 +897,15 @@ pub fn set_override( notes: Option<&str>, ) -> Result<()> { let conn = open_catalog_db(db_path)?; - let in_files: i64 = - conn.query_row("SELECT COUNT(1) FROM files WHERE path = ?1", [path], |r| { - r.get(0) - })?; - if in_files > 0 { - conn.execute( - "INSERT OR REPLACE INTO overrides (path, sample_name, tag, notes) VALUES (?1, ?2, ?3, ?4)", - rusqlite::params![path, sample_name, tag, notes], - )?; - return Ok(()); - } + let in_ff: i64 = conn.query_row("SELECT COUNT(1) FROM fits_files WHERE path = ?1", [path], |r| { + r.get(0) + })?; let in_bt: i64 = conn.query_row( "SELECT COUNT(1) FROM bt_scan_points WHERE source_path = ?1", [path], |r| r.get(0), )?; - if in_bt > 0 { + if in_ff > 0 || in_bt > 0 { conn.execute( "INSERT OR REPLACE INTO bt_file_overrides (source_path, sample_name, tag, notes) VALUES (?1, ?2, ?3, ?4)", rusqlite::params![path, sample_name, tag, notes], @@ -1058,7 +913,7 @@ pub fn set_override( return Ok(()); } Err(CatalogError::Validation(format!( - "path not in catalog (not in files.path and not in bt_scan_points.source_path): {}", + "path not in catalog (not in fits_files.path and not in bt_scan_points.source_path): {}", path ))) } @@ -1073,20 +928,34 @@ pub fn rename_file_in_catalog( ) -> Result<()> { let conn = open_catalog_db(db_path)?; let exists: i64 = conn.query_row( - "SELECT COUNT(1) FROM files WHERE path = ?1", + "SELECT COUNT(1) FROM fits_files WHERE path = ?1", [old_path], |r| r.get(0), )?; if exists == 0 { return Err(CatalogError::Validation(format!( - "path not in files table: {}", + "path not in fits_files: {}", old_path ))); } conn.execute( - "UPDATE files SET path = ?1, file_path = ?1, file_name = ?2, sample_name = ?3, tag = ?4 WHERE path = ?5", - rusqlite::params![new_path, new_file_name, new_sample_name, new_tag, old_path], + "UPDATE fits_files SET path = ?1, file_name = ?2 WHERE path = ?3", + rusqlite::params![new_path, new_file_name, old_path], )?; - conn.execute("DELETE FROM overrides WHERE path = ?1", [old_path])?; + conn.execute( + "UPDATE bt_scan_points SET source_path = ?1 WHERE source_path = ?2", + rusqlite::params![new_path, old_path], + )?; + conn.execute( + "UPDATE bt_file_overrides SET source_path = ?1 WHERE source_path = ?2", + rusqlite::params![new_path, old_path], + )?; + if !new_sample_name.is_empty() || new_tag.is_some() { + let sn = (!new_sample_name.is_empty()).then_some(new_sample_name); + conn.execute( + "INSERT OR REPLACE INTO bt_file_overrides (source_path, sample_name, tag) VALUES (?1, ?2, ?3)", + rusqlite::params![new_path, sn, new_tag], + )?; + } Ok(()) } diff --git a/src/io/mod.rs b/src/io/mod.rs index 5680866..299a8ce 100644 --- a/src/io/mod.rs +++ b/src/io/mod.rs @@ -108,6 +108,7 @@ pub struct ParsedFitsStem { pub file_stem: String, pub sample_name: String, pub tag: Option, + pub tags: Vec, pub scan_number: i64, pub frame_number: i64, } @@ -126,23 +127,40 @@ pub fn parse_fits_stem(stem: &str) -> Option { let frame_str = cap.get(3)?.as_str(); let scan_number: i64 = exp_str.parse().ok()?; let frame_number: i64 = frame_str.parse().ok()?; - let (sample_name, tag) = if base.contains('_') { + let mut tags: Vec = Vec::new(); + let sample_name = if base.contains('_') { let parts: Vec<&str> = base.split('_').collect(); - let (last, rest) = parts.split_last()?; - let raw_tag = (*last).to_string(); - let tag = if is_polarization_tag(&raw_tag) { - None + if parts.len() >= 2 { + let last = parts[parts.len() - 1].to_string(); + if !is_polarization_tag(&last) { + tags.push(last); + } + parts[..parts.len() - 1].join("_") } else { - Some(raw_tag) - }; - (rest.join("_"), tag) + base.to_string() + } + } else if base.contains('-') { + let parts: Vec<&str> = base.split('-').filter(|s| !s.is_empty()).collect(); + if parts.len() >= 2 { + let rest = &parts[1..]; + for p in rest { + if !is_polarization_tag(p) { + tags.push((*p).to_string()); + } + } + parts[0].to_string() + } else { + base.to_string() + } } else { - (base.to_string(), None) + base.to_string() }; + let tag = tags.first().cloned(); Some(ParsedFitsStem { file_stem: stem.to_string(), sample_name, tag, + tags, scan_number, frame_number, }) @@ -553,7 +571,7 @@ pub fn build_bt_ingest_row( .to_string(); let file_name = path.file_stem().and_then(|s| s.to_str()).unwrap_or("").to_string(); let (sample_name, tag, scan_number, frame_number) = match parse_fits_stem(&file_name) { - Some(p) => (p.sample_name, p.tag, p.scan_number, p.frame_number), + Some(p) => (p.sample_name, p.tag.clone(), p.scan_number, p.frame_number), None => (String::new(), None, 0i64, 0i64), }; let bzero = image_header @@ -598,7 +616,7 @@ pub fn process_file_name(path: std::path::PathBuf) -> Vec { let mut columns = vec![Column::new("file_name".into(), vec![file_name.to_string()])]; match parse_fits_stem(file_name) { Some(p) => { - columns.push(Column::new("sample_name".into(), vec![p.sample_name])); + columns.push(Column::new("sample_name".into(), vec![p.sample_name.clone()])); let tag_series = Series::from_iter(std::iter::once(p.tag.as_deref())) .with_name("tag".into()) .into_column(); @@ -680,6 +698,16 @@ mod tests { assert_eq!(p2.tag, None); } + #[test] + fn test_parse_fits_stem_hyphen_separated_tags() { + let p = parse_fits_stem("a-b-c-12345-00001").expect("parse"); + assert_eq!(p.sample_name, "a"); + assert_eq!(p.tags, vec!["b".to_string(), "c".to_string()]); + assert_eq!(p.tag.as_deref(), Some("b")); + assert_eq!(p.scan_number, 12345); + assert_eq!(p.frame_number, 1); + } + #[test] fn test_subtract_background_edges() { let data = Array2::from_shape_vec( diff --git a/src/io/source.rs b/src/io/source.rs index 72218c4..e6c28d6 100644 --- a/src/io/source.rs +++ b/src/io/source.rs @@ -6,7 +6,7 @@ use std::path::{Path, PathBuf}; use crate::errors::FitsError; #[cfg(feature = "catalog")] -use crate::catalog::{discover_fits_paths, resolve_catalog_path as catalog_resolve_path, CATALOG_DB_NAME}; +use crate::catalog::{discover_fits_paths, resolve_catalog_path as catalog_resolve_path}; /// When the source could be satisfied from catalog or disk (e.g. beamtime dir with `.pyref_catalog.db`), /// this selects which to use. @@ -41,7 +41,7 @@ pub enum FitsSource { Paths(Vec), /// Directory: discover FITS files (and optionally use catalog if present). Dir(PathBuf), - /// Path to `.pyref_catalog.db` or to a beamtime directory that contains one. + /// Path to ``catalog.db`` (typically ``parent/.pyref/catalog.db``) or a beamtime directory. Catalog(PathBuf), } @@ -123,9 +123,9 @@ fn resolve_catalog_path( _preference: ResolvePreference, ) -> Result { let db_path = if path.is_file() { - if path.file_name().and_then(|n| n.to_str()) != Some(CATALOG_DB_NAME) { + if path.file_name().and_then(|n| n.to_str()) != Some("catalog.db") { return Err(FitsError::validation( - "catalog source must be .pyref_catalog.db or a directory", + "catalog source must be catalog.db or a beamtime directory", ) .with_context("path", path.display().to_string())); } diff --git a/src/schema.rs b/src/schema.rs new file mode 100644 index 0000000..171839b --- /dev/null +++ b/src/schema.rs @@ -0,0 +1,463 @@ +// schema.rs +// +// Diesel schema for the pyref catalog database. +// +// Design notes: +// +// Header cards from the FITS primary HDU are split into two tiers. The dozen +// cards that drive scan classification, beamspot localization, normalization, +// and profile identity are promoted to first-class columns on the `frames` +// table (sample_x, sample_y, sample_z, sample_theta, ccd_theta, +// beamline_energy, epu_polarization, exposure, ring_current, ai3_izero, +// beam_current). Every remaining card is stored in the `frame_header_values` +// EAV table, keyed through `header_cards`, which is populated on first +// ingestion from whatever cards are present in the FITS files. This makes the +// schema forward-compatible when the beamline control system adds or renames +// channels without requiring a migration. +// +// SQLite type notes: +// - All floating-point physical quantities use Double (f64). +// - Booleans are stored as SmallInt (0/1); SQLite has no native bool. +// - Timestamps are stored as Text in ISO 8601 format. +// - Nullable foreign keys on optional relationships (e.g. i0_source_scan_id +// for fixed-angle profiles that borrow I0 from an external scan) use +// Nullable. +// +// Enum-like Text columns: +// scan_type : "fixed_energy" | "fixed_angle" +// profile_type : "fixed_energy" | "fixed_angle" +// frame_role : "i0" | "stitch" | "overlap" | "reflectivity" +// card_category : "motor" | "ai" | "camera" | "metadata" +// quality_flag : "ok" | "mislabeled_sample" | "parse_failure" +// detection_flag : "ok" | "beam_detection_failed" | "beam_drift_anomaly" + +// --------------------------------------------------------------------------- +// Beamtime +// --------------------------------------------------------------------------- + +diesel::table! { + /// Root of the catalog hierarchy. One row per beamtime directory ingested. + /// The `path` column is the absolute path to the beamtime root and is also + /// used to resolve the monolithic zarr archive at `/beamtime.zarr`. + beamtimes (id) { + id -> Integer, + /// Absolute path to the beamtime root directory. + path -> Text, + /// ISO 8601 date string parsed from the beamtime directory name. + date -> Text, + } +} + +// --------------------------------------------------------------------------- +// Samples +// --------------------------------------------------------------------------- + +diesel::table! { + /// One row per unique sample name encountered within a beamtime. The + /// representative stage position is computed as the median of Sample X, + /// Sample Y, and Sample Z across all frames attributed to this sample. + /// Frames that deviate from this position by more than the configured + /// tolerance are flagged MISLABELED_SAMPLE in the `frames` table rather + /// than creating a second sample row. + samples (id) { + id -> Integer, + beamtime_id -> Integer, + /// Sample name as parsed from the FITS filename stem. + name -> Text, + /// Median Sample X position (mm) across all attributed frames. + representative_x -> Double, + /// Median Sample Y position (mm) across all attributed frames. + representative_y -> Double, + /// Median Sample Z position (mm) across all attributed frames. + representative_z -> Double, + } +} + +diesel::joinable!(samples -> beamtimes (beamtime_id)); + +// --------------------------------------------------------------------------- +// Tags +// --------------------------------------------------------------------------- + +diesel::table! { + /// Tag slugs parsed from FITS filenames. Tags are scan-specific; the + /// many-to-many relationship between tags and files is resolved through + /// `file_tags`. + tags (id) { + id -> Integer, + /// Normalized tag string as parsed from the filename. + slug -> Text, + } +} + +diesel::table! { + /// Junction table resolving the many-to-many relationship between files + /// and tags. + file_tags (id) { + id -> Integer, + file_id -> Integer, + tag_id -> Integer, + } +} + +diesel::joinable!(file_tags -> tags (tag_id)); + +// --------------------------------------------------------------------------- +// Files +// --------------------------------------------------------------------------- + +diesel::table! { + /// One row per FITS file ingested. The canonical reference for raw file + /// provenance. Image data is not retrieved through this table; it is + /// accessed via the zarr keys on `frames`. + files (id) { + id -> Integer, + beamtime_id -> Integer, + sample_id -> Integer, + scan_number -> Integer, + frame_number -> Integer, + /// Absolute path to the FITS file. + path -> Text, + /// Bare filename including extension. + filename -> Text, + /// NULL when parsing succeeds. Set to "parse_failure" when the + /// filename does not conform to any supported naming pattern. + parse_flag -> Nullable, + } +} + +diesel::joinable!(files -> beamtimes (beamtime_id)); +diesel::joinable!(files -> samples (sample_id)); +diesel::joinable!(file_tags -> files (file_id)); + +// --------------------------------------------------------------------------- +// Scans +// --------------------------------------------------------------------------- + +diesel::table! { + /// One row per scan. Scan type is determined during ingestion from the + /// motor trajectory analysis and stored here as a first-class attribute + /// so downstream reduction does not recompute it. + scans (id) { + id -> Integer, + beamtime_id -> Integer, + sample_id -> Integer, + scan_number -> Integer, + /// "fixed_energy" or "fixed_angle". + scan_type -> Text, + /// ISO 8601 timestamp of the first frame in the scan. + started_at -> Nullable, + /// ISO 8601 timestamp of the last frame in the scan. + ended_at -> Nullable, + } +} + +diesel::joinable!(scans -> beamtimes (beamtime_id)); +diesel::joinable!(scans -> samples (sample_id)); + +// --------------------------------------------------------------------------- +// Header card registry +// --------------------------------------------------------------------------- + +diesel::table! { + /// Registry of FITS header card names discovered during initial ingestion. + /// One row per unique card name. Populated automatically on first ingest; + /// subsequent beamtimes with new card names append rows here without + /// requiring a schema migration. + /// + /// `card_category` classifies the card for UI and query purposes: + /// "motor" - physical positioning motor (Sample X, CCD Theta, etc.) + /// "ai" - analog input channel (Beam Current, TEY signal, etc.) + /// "camera" - CCD / detector configuration (ROI, binning, temp) + /// "metadata" - timing, instrument bookkeeping, MCS axes + header_cards (id) { + id -> Integer, + /// Raw card name as it appears in the FITS header (e.g. "AI 3 Izero"). + name -> Text, + /// Human-readable display name for UI use. + display_name -> Text, + /// "motor" | "ai" | "camera" | "metadata" + card_category -> Text, + } +} + +// --------------------------------------------------------------------------- +// Frames (Data Table) +// --------------------------------------------------------------------------- + +diesel::table! { + /// One row per frame per scan. Contains all first-class reduction-critical + /// header values as typed columns, plus zarr retrieval keys. All remaining + /// header cards are stored in `frame_header_values`. + /// + /// Zarr retrieval: the monolithic beamtime archive is at + /// `/beamtime.zarr`. Within the archive, images are at + /// `///raw` and + /// `///processed`. + frames (id) { + id -> Integer, + scan_id -> Integer, + file_id -> Integer, + frame_number -> Integer, + /// Group key within the zarr archive, equal to the scan number. + zarr_group_key -> Integer, + /// Dataset index within the zarr group, equal to the frame number. + zarr_frame_index -> Integer, + /// ISO 8601 acquisition timestamp from the DATE header card. + acquired_at -> Nullable, + // --- first-class motor positions --- + /// Sample X stage position (mm). FITS card: "Sample X". + sample_x -> Double, + /// Sample Y stage position (mm). FITS card: "Sample Y". + sample_y -> Double, + /// Sample Z stage position (mm). FITS card: "Sample Z". + sample_z -> Double, + /// Sample theta (degrees). FITS card: "Sample Theta". + sample_theta -> Double, + /// CCD theta (degrees). FITS card: "CCD Theta". + ccd_theta -> Double, + /// Beamline energy (eV). FITS card: "Beamline Energy". + beamline_energy -> Double, + // --- first-class AI / beam channels --- + /// EPU polarization angle (degrees). FITS card: "EPU Polarization". + epu_polarization -> Double, + /// CCD exposure time (seconds). FITS card: "EXPOSURE". + exposure -> Double, + /// Storage ring current (mA). FITS card: "RINGCRNT". + ring_current -> Double, + /// Upstream gold mesh absorption current (V). FITS card: "AI 3 Izero". + ai3_izero -> Double, + /// Photodiode beam current (mA). FITS card: "Beam Current". + beam_current -> Double, + // --- quality flag --- + /// NULL when ok. "mislabeled_sample" when stage position deviates + /// beyond configured tolerance for the attributed sample name. + quality_flag -> Nullable, + } +} + +diesel::joinable!(frames -> scans (scan_id)); +diesel::joinable!(frames -> files (file_id)); + +// --------------------------------------------------------------------------- +// Frame header values (EAV for non-critical cards) +// --------------------------------------------------------------------------- + +diesel::table! { + /// Entity-attribute-value store for all FITS header cards not promoted to + /// first-class columns on `frames`. All card values from the primary HDU + /// are stored as Double; the card name is resolved through `header_cards`. + frame_header_values (id) { + id -> Integer, + frame_id -> Integer, + header_card_id -> Integer, + value -> Double, + } +} + +diesel::joinable!(frame_header_values -> frames (frame_id)); +diesel::joinable!(frame_header_values -> header_cards (header_card_id)); + +// --------------------------------------------------------------------------- +// Profiles +// --------------------------------------------------------------------------- + +diesel::table! { + /// One row per reduced reflectivity profile. A profile is the primary + /// user-facing unit: a single continuous 1D curve assembled from one or + /// more stitches collected at a fixed energy (fixed-energy scan) or fixed + /// angle (fixed-angle scan). Multi-profile scans produce multiple profile + /// rows sharing the same scan_id, distinguished by profile_index. + /// + /// The sample position columns here are the median over all member frames + /// and are stored for query convenience. The authoritative per-frame + /// positions remain on `frames`. + profiles (id) { + id -> Integer, + scan_id -> Integer, + sample_id -> Integer, + /// Zero-based ordinal position of this profile within the parent scan. + profile_index -> Integer, + /// "fixed_energy" or "fixed_angle". + profile_type -> Text, + /// Value of the fixed parameter: energy (eV) for fixed_angle profiles, + /// theta (degrees) for fixed_energy profiles. + fixed_parameter_value -> Double, + /// EPU polarization (degrees), constant across the profile. + epu_polarization -> Double, + /// Median Sample X (mm) over member frames. + sample_x -> Double, + /// Median Sample Y (mm) over member frames. + sample_y -> Double, + /// Median Sample Z (mm) over member frames. + sample_z -> Double, + } +} + +diesel::joinable!(profiles -> scans (scan_id)); +diesel::joinable!(profiles -> samples (sample_id)); + +// --------------------------------------------------------------------------- +// Profile-frame junction +// --------------------------------------------------------------------------- + +diesel::table! { + /// Junction table mapping profiles to their constituent frames, with a + /// frame_role column classifying each frame's function in the reduction + /// pipeline. I0 frames appear here multiple times when they serve as the + /// normalization reference for more than one profile in a multi-profile + /// scan. + /// + /// frame_role values: + /// "i0" - direct beam frame used for I0 normalization + /// "stitch" - first frame of a new stitch segment + /// "overlap" - frame overlapping the preceding stitch for scaling + /// "reflectivity" - ordinary reduced reflectivity frame + profile_frames (id) { + id -> Integer, + profile_id -> Integer, + frame_id -> Integer, + /// "i0" | "stitch" | "overlap" | "reflectivity" + frame_role -> Text, + } +} + +diesel::joinable!(profile_frames -> profiles (profile_id)); +diesel::joinable!(profile_frames -> frames (frame_id)); + +// --------------------------------------------------------------------------- +// BeamFinding +// --------------------------------------------------------------------------- + +diesel::table! { + /// Per-frame output of the beamspot localization pipeline. Stores both + /// the preprocessing parameters applied and the fitted peak result, so the + /// full reduction provenance is recoverable without re-running the pipeline. + /// + /// detection_flag values: + /// "ok" - credible peak found within detector boundary + /// "beam_detection_failed" - peak amplitude below noise threshold or + /// centroid outside detector boundary + /// "beam_drift_anomaly" - centroid deviates from linear drift model + /// by more than the configured threshold + beam_finding (id) { + id -> Integer, + frame_id -> Integer, + /// 1 if the fixed-width border mask was applied; 0 otherwise. + edge_removal_applied -> SmallInt, + /// Number of dark columns used per row for row-wise background subtraction. + row_bg_dark_cols -> Nullable, + /// Number of dark rows used per column for column-wise background subtraction. + col_bg_dark_rows -> Nullable, + /// Standard deviation of the Gaussian kernel applied for noise suppression. + gaussian_kernel_sigma -> Nullable, + /// Fitted beamspot centroid row on the post-processed image. + centroid_row -> Nullable, + /// Fitted beamspot centroid column on the post-processed image. + centroid_col -> Nullable, + /// Integrated ROI intensity (counts) after background subtraction. + roi_intensity -> Nullable, + /// Standard deviation of the Gaussian fit to the beamspot. + fit_std -> Nullable, + /// Mean intensity of the designated dark region (counts). + dark_region_mean -> Nullable, + /// Standard deviation of the dark region (counts). + dark_region_std -> Nullable, + /// "ok" | "beam_detection_failed" | "beam_drift_anomaly" + detection_flag -> Text, + } +} + +diesel::joinable!(beam_finding -> frames (frame_id)); + +// --------------------------------------------------------------------------- +// StitchCorrection +// --------------------------------------------------------------------------- + +diesel::table! { + /// Per-stitch correction factors computed during the normalization and + /// stitching pipeline. One row per stitch segment within a profile. + /// + /// The Fano factor must always be recorded. A scan processed without a + /// Fano correction records fano_factor = 1.0 rather than NULL. + /// + /// For fixed-angle profiles where I0 is sourced from a separate scan, + /// i0_source_scan_id is set to that scan's id; otherwise it is NULL and + /// I0 is derived from the i0 frames within the parent profile. + stitch_corrections (id) { + id -> Integer, + profile_id -> Integer, + /// Zero-based ordinal index of this stitch within the profile. + stitch_index -> Integer, + /// Energy-dependent Fano factor. Always non-null; 1.0 when no + /// Fano correction was applied. + fano_factor -> Double, + /// Weighted-mean overlap scaling factor applied to this stitch. + /// NULL for the first stitch, which has no preceding stitch to scale against. + overlap_scale_factor -> Nullable, + /// I0 normalization value (counts) used for this stitch. + i0_normalization_value -> Nullable, + /// FK to the scan supplying I0 for fixed-angle profiles. NULL when + /// I0 comes from frames within the current profile. + i0_source_scan_id -> Nullable, + } +} + +diesel::joinable!(stitch_corrections -> profiles (profile_id)); + +// --------------------------------------------------------------------------- +// Reflectivity +// --------------------------------------------------------------------------- + +diesel::table! { + /// Frame-level reduced reflectivity data. One row per reduced frame after + /// normalization and stitching. Frames flagged BEAM_DETECTION_FAILED in + /// `beam_finding` must not appear here. + /// + /// Full stitched profile assembly and parquet export are performed by the + /// packaging utility in `pyref.reduction`, which joins this table against + /// `stitch_corrections` filtered by profile_id. + reflectivity (id) { + id -> Integer, + profile_id -> Integer, + frame_id -> Integer, + beam_finding_id -> Integer, + /// Momentum transfer (inverse angstroms). + q -> Double, + /// Sample theta (degrees). + theta -> Double, + /// Beamline energy (eV). + energy -> Double, + /// Normalized reflectivity intensity (dimensionless). + intensity -> Double, + /// Propagated one-sigma uncertainty on intensity. + uncertainty -> Double, + /// "i0" | "stitch" | "overlap" | "reflectivity" + frame_type -> Text, + } +} + +diesel::joinable!(reflectivity -> profiles (profile_id)); +diesel::joinable!(reflectivity -> frames (frame_id)); +diesel::joinable!(reflectivity -> beam_finding (beam_finding_id)); + +// --------------------------------------------------------------------------- +// Allow tables to appear in the same query +// --------------------------------------------------------------------------- + +diesel::allow_tables_to_appear_in_same_query!( + beamtimes, + samples, + tags, + file_tags, + files, + scans, + header_cards, + frames, + frame_header_values, + profiles, + profile_frames, + beam_finding, + stitch_corrections, + reflectivity, +); \ No newline at end of file diff --git a/tests/test_catalog.py b/tests/test_catalog.py index 2c1efc3..9384e43 100644 --- a/tests/test_catalog.py +++ b/tests/test_catalog.py @@ -22,6 +22,10 @@ from pyref.io.readers import REQUIRED_SCAN_COLUMNS +def _ensure_flat_beamtime_layout(beamtime: Path) -> None: + (beamtime / "CCD").mkdir(parents=True, exist_ok=True) + + def test_resolve_catalog_path_prefers_new_layout_when_created(tmp_path: Path) -> None: beamtime = tmp_path / "beam" beamtime.mkdir() @@ -31,12 +35,13 @@ def test_resolve_catalog_path_prefers_new_layout_when_created(tmp_path: Path) -> assert resolve_catalog_path(beamtime).resolve() == new_db.resolve() -def test_resolve_catalog_path_legacy_when_only_legacy_exists(tmp_path: Path) -> None: +def test_resolve_catalog_path_ignores_legacy_sidecar(tmp_path: Path) -> None: beamtime = tmp_path / "beam" beamtime.mkdir() legacy = beamtime / ".pyref_catalog.db" legacy.write_bytes(b"") - assert resolve_catalog_path(beamtime).resolve() == legacy.resolve() + expected = (tmp_path / ".pyref" / "catalog.db").resolve() + assert resolve_catalog_path(beamtime).resolve() == expected def test_resolve_catalog_path_new_wins_when_both_exist(tmp_path: Path) -> None: @@ -61,12 +66,14 @@ def test_resolve_catalog_path_defaults_to_new_when_neither_exists( def test_ingest_beamtime_empty_dir(tmp_path: Path) -> None: + _ensure_flat_beamtime_layout(tmp_path) db = ingest_beamtime(tmp_path, incremental=True) assert db == resolve_catalog_path(tmp_path) assert db.exists() def test_scan_experiment_catalog_dir_empty(tmp_path: Path) -> None: + _ensure_flat_beamtime_layout(tmp_path) ingest_beamtime(tmp_path, incremental=True) lf = scan_experiment(tmp_path) assert isinstance(lf, pl.LazyFrame) @@ -77,6 +84,7 @@ def test_scan_experiment_catalog_dir_empty(tmp_path: Path) -> None: def test_get_overrides_empty(tmp_path: Path) -> None: + _ensure_flat_beamtime_layout(tmp_path) db = ingest_beamtime(tmp_path, incremental=True) df = get_overrides(db, path=None) assert isinstance(df, pl.DataFrame) @@ -85,6 +93,7 @@ def test_get_overrides_empty(tmp_path: Path) -> None: def test_set_override_invalid_path_raises(tmp_path: Path) -> None: + _ensure_flat_beamtime_layout(tmp_path) db = ingest_beamtime(tmp_path, incremental=True) with pytest.raises(Exception): set_override(db, "/nonexistent/path.fits", sample_name="x") @@ -97,7 +106,9 @@ def minimal_fits_dir(tmp_path_factory: pytest.TempPathFactory) -> Path | None: if not minimal.is_file(): return None root = tmp_path_factory.mktemp("catalog_fixture") - (root / "minimal.fits").write_bytes(minimal.read_bytes()) + ccd = root / "CCD" + ccd.mkdir() + (ccd / "minimal.fits").write_bytes(minimal.read_bytes()) return root From bb837a89d51cd4ed08d783977aac25dfb216a93e Mon Sep 17 00:00:00 2001 From: Harlan D Heilman <73567020+HarlanHeilman@users.noreply.github.com> Date: Wed, 8 Apr 2026 14:00:00 -0700 Subject: [PATCH 21/32] feat(catalog): Diesel migrations, unified ingest with zarr, parallelism Replace rusqlite catalog with Diesel + embedded migrations; single-pass ingest writes SQLite rows and local zarr. Add paths (directories, env overrides), IngestParallelism (Rayon pool, PyO3/TUI), and schema updates. Remove materialize/profile_persist split; update AGENTS and Python IO. --- .../hooks/state/continual-learning-index.json | 22 + .cursor/hooks/state/continual-learning.json | 8 +- AGENTS.md | 71 +- Cargo.lock | 389 ++++- Cargo.toml | 20 +- diesel.toml | 2 + .../down.sql | 16 + .../2026-04-08-200000_initial_catalog/up.sql | 174 ++ .../down.sql | 1 + .../up.sql | 5 + python/pyref/io/catalog_path.py | 54 +- python/pyref/io/readers.py | 64 +- src/bin/tui/app.rs | 30 +- src/bin/tui/config.rs | 6 + src/catalog/beamtime_index.rs | 68 +- src/catalog/db.rs | 31 + src/catalog/explorer_query.rs | 115 +- src/catalog/ingest.rs | 934 +++++------ src/catalog/ingest_parallel.rs | 169 -- src/catalog/materialize.rs | 166 -- src/catalog/mod.rs | 636 +------- src/catalog/models.rs | 66 + src/catalog/parallelism.rs | 120 ++ src/catalog/paths.rs | 100 ++ src/catalog/profile_persist.rs | 220 --- src/catalog/query.rs | 1443 +++++++++-------- src/catalog/zarr_write.rs | 72 + src/io/mod.rs | 37 +- src/lib.rs | 33 +- src/schema.rs | 95 +- tests/test_catalog.py | 57 +- 31 files changed, 2577 insertions(+), 2647 deletions(-) create mode 100644 .cursor/hooks/state/continual-learning-index.json create mode 100644 diesel.toml create mode 100644 migrations/2026-04-08-200000_initial_catalog/down.sql create mode 100644 migrations/2026-04-08-200000_initial_catalog/up.sql create mode 100644 migrations/2026-04-08-210000_files_primary_hdu_geometry/down.sql create mode 100644 migrations/2026-04-08-210000_files_primary_hdu_geometry/up.sql create mode 100644 src/catalog/db.rs delete mode 100644 src/catalog/ingest_parallel.rs delete mode 100644 src/catalog/materialize.rs create mode 100644 src/catalog/models.rs create mode 100644 src/catalog/parallelism.rs create mode 100644 src/catalog/paths.rs delete mode 100644 src/catalog/profile_persist.rs create mode 100644 src/catalog/zarr_write.rs diff --git a/.cursor/hooks/state/continual-learning-index.json b/.cursor/hooks/state/continual-learning-index.json new file mode 100644 index 0000000..ed13c14 --- /dev/null +++ b/.cursor/hooks/state/continual-learning-index.json @@ -0,0 +1,22 @@ +{ + "version": 1, + "updatedAtMs": 1775677088544, + "transcripts": { + "/Users/hduva/.cursor/projects/Users-hduva-projects-pyref/agent-transcripts/5a8ef628-32d6-48f3-bb07-cbeef510bd58.jsonl": 1773164140083, + "/Users/hduva/.cursor/projects/Users-hduva-projects-pyref/agent-transcripts/2ff64551-63cd-4f82-ab28-c34dab08a508/2ff64551-63cd-4f82-ab28-c34dab08a508.jsonl": 1774233012036, + "/Users/hduva/.cursor/projects/Users-hduva-projects-pyref/agent-transcripts/6592e107-126c-432b-be98-e1cab8e4e956/6592e107-126c-432b-be98-e1cab8e4e956.jsonl": 1772159058647, + "/Users/hduva/.cursor/projects/Users-hduva-projects-pyref/agent-transcripts/97fe2075-59e4-4264-968f-d7e9234a8a20/97fe2075-59e4-4264-968f-d7e9234a8a20.jsonl": 1772140392161, + 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--git a/AGENTS.md b/AGENTS.md index f1a9b18..a7146f5 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -120,7 +120,72 @@ AI text files are supplementary and are not required for cataloging. If present, ## I/O Operations and Cataloging -Connecting individual frames back to their originating sample, scan, and beamtime requires meticulous bookkeeping that is impractical to maintain manually across a full beamtime. `pyref` provides an automated cataloging system that ingests a beamtime directory and populates a SQLite database encoding this hierarchy. The database is the structural backbone for all downstream reduction and fitting workflows. Users interact with it primarily through the lazy DataFrame interface exposed by `pyref.io`, which allows them to filter by sample name, tag, energy, or angle and receive a polars or pandas DataFrame containing the relevant frame metadata and image retrieval handles. +Connecting individual frames back to their originating sample, scan, and beamtime requires meticulous bookkeeping that is impractical to maintain manually across a full beamtime. `pyref` provides an automated cataloging system that ingests a beamtime directory and populates a Diesel-managed SQLite database encoding this hierarchy. The database is the structural backbone for all downstream reduction and fitting workflows. Users interact with it primarily through the lazy DataFrame interface exposed by `pyref.io`, which allows them to filter by sample name, tag, energy, or angle and receive a polars or pandas DataFrame containing the relevant frame metadata and image retrieval handles. + +### Catalog and Cache Storage + +#### Default: local user data directory + +By default, `pyref` maintains a single persistent catalog that accumulates every beamtime the user has ever ingested. The catalog and its associated zarr cache live in the platform-appropriate user data directory, resolved at runtime by the Rust IO layer using the `directories` crate: + +| Platform | Default catalog path | +|----------|----------------------| +| Linux | `$XDG_DATA_HOME/pyref/catalog.db` (falls back to `~/.local/share/pyref/catalog.db`) | +| macOS | `~/Library/Application Support/pyref/catalog.db` | +| Windows | `%APPDATA%\pyref\catalog.db` | + +The zarr archive for each beamtime is stored **on local disk under the same platform data directory** as the catalog, not under the system cache directory: `/pyref/.cache//beamtime.zarr`, where `` is the same root as in the table above (`$XDG_DATA_HOME` or `~/.local/share`, `~/Library/Application Support`, or `%APPDATA%` as appropriate) and `` is a stable SHA-256 digest of the beamtime root path recorded at ingestion time. Example on macOS: `~/Library/Application Support/pyref/.cache//beamtime.zarr`. The zarr tree is local-only; NAS-backed FITS are used for ingestion and re-ingestion, not for routine image reads after ingest. + +Optional environment overrides: `PYREF_CATALOG_DB` (absolute path to `catalog.db`) and `PYREF_CACHE_ROOT` (parent of `/beamtime.zarr` directories). Parallel FITS reads during ingest honor `PYREF_INGEST_WORKER_THREADS` or `PYREF_INGEST_RESOURCE_FRACTION` when explicit kwargs or TUI config fields are unset. + +Ingestion is a **single pipeline**: catalog metadata (Diesel/SQLite) and zarr array writes happen in one pass. There is no separate user-visible “metadata only” phase followed by a later image materialization step. + +The raw FITS files on the NAS are only required during initial ingestion and re-ingestion. After ingestion, reduction and browsing workflows operate from the local catalog and zarr store. 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" ], - "source": [ - "## Run without modifying anything\n", - "username_logged_in = catalog.context.whoami()['identities'][0]['id']\n", - "print(\"Logged into Tiled as: \" + username_logged_in)" - ] - }, - { - "cell_type": "markdown", - "id": "248a8e37-6eb8-449b-b16f-3222c9e26273", - "metadata": {}, - "source": [ - "If the above username is not yours, log out using the code below, and then log back in using the first block of code at the top of this section.\n" + "text/plain": [ + " scan_id start_time cycle institution project \\\n", + "0 106719 2026-02-19 08:13:16.150294 2026-1 WSU Heilman \n", + "1 106720 2026-02-19 08:25:20.451096 2026-1 WSU Heilman \n", + "2 106721 2026-02-19 08:38:49.489141 2026-1 WSU Heilman \n", + "3 106722 2026-02-19 08:43:51.716952 2026-1 WSU Heilman \n", + "4 106723 2026-02-19 08:57:03.508352 2026-1 WSU Heilman \n", + "5 106728 2026-02-19 09:41:00.384624 2026-1 WSU Heilman \n", + "6 106729 2026-02-19 09:53:09.115550 2026-1 WSU Heilman \n", + "7 106730 2026-02-19 10:00:05.665111 2026-1 WSU Heilman \n", + "8 106731 2026-02-19 10:05:41.988324 2026-1 WSU Heilman \n", + "9 106732 2026-02-19 10:09:51.389176 2026-1 WSU Heilman \n", + "10 106733 2026-02-19 10:14:33.702337 2026-1 WSU Heilman \n", + "11 106734 2026-02-19 10:22:26.301318 2026-1 WSU Heilman \n", + "12 106735 2026-02-19 11:32:11.989869 2026-1 WSU Heilman \n", + "13 106736 2026-02-19 11:37:21.420229 2026-1 WSU Heilman \n", + "14 106737 2026-02-19 11:45:41.185373 2026-1 WSU Heilman \n", + "15 106738 2026-02-19 11:52:37.918003 2026-1 WSU Heilman \n", + "16 106739 2026-02-19 12:05:19.695610 2026-1 WSU Heilman \n", + "17 106740 2026-02-19 12:11:46.278413 2026-1 WSU Heilman \n", + "18 106741 2026-02-19 12:14:30.505994 2026-1 WSU Heilman \n", + "19 106742 2026-02-19 12:16:20.501647 2026-1 WSU Heilman \n", + "20 106743 2026-02-19 12:20:16.309793 2026-1 WSU Heilman \n", + "21 106744 2026-02-19 12:23:05.683328 2026-1 WSU Heilman \n", + "22 106745 2026-02-19 12:28:19.154811 2026-1 WSU Heilman \n", + "23 106746 2026-02-19 12:29:56.989102 2026-1 WSU Heilman \n", + "24 106747 2026-02-19 12:32:48.507040 2026-1 WSU Heilman \n", + "25 106748 2026-02-19 12:37:51.003347 2026-1 WSU Heilman \n", + "26 106749 2026-02-19 12:40:24.551640 2026-1 WSU Heilman \n", + "27 106750 2026-02-19 12:48:02.741318 2026-1 WSU Heilman \n", + "28 106751 2026-02-19 12:56:09.276823 2026-1 WSU Heilman \n", + "29 106752 2026-02-19 13:01:19.495322 2026-1 WSU Heilman \n", + "30 106753 2026-02-19 13:04:52.595298 2026-1 WSU Heilman \n", + "31 106754 2026-02-19 13:16:40.318592 2026-1 WSU Heilman \n", + "32 106755 2026-02-19 13:19:45.389767 2026-1 WSU Heilman \n", + "33 106756 2026-02-19 13:28:23.197922 2026-1 WSU Heilman \n", + "34 106757 2026-02-19 13:31:26.799184 2026-1 WSU Heilman \n", + "\n", + " sample_name sample_id plan detector exit_status \\\n", + "0 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", + "1 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", + "2 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", + "3 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", + "4 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", + "5 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", + "6 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", + "7 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", + "8 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", + "9 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", + "10 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", + "11 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", + "12 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", + "13 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", + "14 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", + "15 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", + "16 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs fail \n", + "17 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", + "18 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs abort \n", + "19 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs abort \n", + "20 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", + "21 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", + "22 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs abort \n", + "23 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", + "24 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", + "25 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", + "26 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", + "27 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", + "28 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", + "29 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", + "30 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", + "31 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", + "32 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", + "33 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", + "34 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", + "\n", + " num_Images uid sample_id \n", + "0 84 00b958d4-36ea-4d2b-b7da-255bd2bb3ded HOPG_NIST \n", + "1 84 a413f8b2-d2e8-4d74-90e7-49db76d654cb HOPG_NIST \n", + "2 84 ae815641-38a0-483e-8de2-a54c5d19fef9 HOPG_NIST \n", + "3 84 5cfd2257-400a-482d-b6f4-2ded75696979 HOPG_NIST \n", + "4 84 c9924314-aeec-4186-806c-17bac2d6c83b HOPG_NIST \n", + "5 84 adb7e7e9-1f9f-4637-8080-1f18ebdbd0c9 HOPG_NIST \n", + "6 84 e03abf85-07bb-414b-97c4-bc06941074d8 HOPG_NIST \n", + "7 84 0d04caf7-c2ae-4b7a-b785-6f580fa8fb4f HOPG_NIST \n", + "8 84 c713d736-696d-42af-a5c1-8b1ac67b0566 HOPG_NIST \n", + "9 84 747fb8b1-d59a-4b99-b490-1ea3d4923d00 HOPG_NIST \n", + "10 84 1bd28e4f-0fb0-4e28-8519-91ee45f1f4a8 HOPG_NIST \n", + "11 84 ce9e4941-feba-46df-9586-5157d30030c7 HOPG_NIST \n", + "12 84 17a6b212-e428-488f-8d63-3d3ed138e812 HOPG_NIST \n", + "13 84 341bd7d9-001f-45ee-bf77-866b28e81ca7 HOPG_NIST \n", + "14 84 87967468-b421-40a7-8dd0-bc45927f1d9a HOPG_NIST \n", + "15 84 5d24673b-667c-44f8-84c0-55d8af59d3ce HOPG_NIST \n", + "16 N/A 6663805d-2d28-48e1-aacd-828fcca47d11 HOPG_NIST \n", + "17 60 e5cc498f-adb4-4105-ac88-b6d595430fc5 HOPG_NIST \n", + "18 3 a2603509-4d0b-49aa-891a-25dc537901f6 HOPG_NIST \n", + "19 54 aea3cf91-6eea-45db-a9f9-bc664027692e HOPG_NIST \n", + "20 60 45ad4645-d407-40e4-aa2a-434f76b2ee7d HOPG_NIST \n", + "21 70 e5ede308-9122-4d31-9150-4e5bab64d2cf HOPG_NIST \n", + "22 N/A 39ba6d06-ddb3-4ff4-a031-690a56af2736 HOPG_NIST \n", + "23 60 85bb5c64-7d01-497b-a590-7076b8e2e5e7 HOPG_NIST \n", + "24 70 bd729b48-7007-427e-8d74-f6f1913d1963 HOPG_NIST \n", + "25 35 066ae0b8-bd17-42e7-8c52-a3c7af738e71 HOPG_NIST \n", + "26 70 acfbef67-78b4-40d7-a4fa-398deec6c714 HOPG_NIST \n", + "27 70 299e6c32-784a-443b-9c97-649257291ffe HOPG_NIST \n", + "28 70 690725ea-314c-42fb-b215-6e7bed5c3819 HOPG_NIST \n", + "29 40 42bdd7e0-acfd-4275-b7e3-5d29ae5ba951 HOPG_NIST \n", + "30 40 a915af83-4d12-4b3b-954c-d2459aa66f5e HOPG_NIST \n", + "31 84 05ac028f-4d3d-4bbb-a864-d8e1e9920dfe HOPG_NIST \n", + "32 84 2ac6ebac-08b5-48ec-9d00-2cc489aa125e HOPG_NIST \n", + "33 84 8b2fad74-606a-4e92-ac95-efcdaf038e85 HOPG_NIST \n", + "34 84 5aceb9b6-2052-49da-af07-76a38938f39a HOPG_NIST " ] - }, + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "filtered_scan_list = loader.searchCatalog(\n", + " cycle=\"2026-1\",\n", + " institution=\"WSU\",\n", + " sample_id=\"HOPG\"\n", + ")\n", + "filtered_scan_list" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "16d35804", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "id": "62f179ef-948e-406a-9f65-424f98983c29", - "metadata": {}, - "outputs": [], - "source": [ - "catalog.logout()" + "data": { + "text/html": [ + "" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "markdown", - "id": "8535a1a8-9727-4bd0-90e6-72454b54b728", - "metadata": {}, - "source": [ - "## (Optional) Search for scans\n", - "\n", - "Narrow down search parameters as needed to find the desired scan from the NSLS II SST1 RSoXS station. Various examples are shown in the comments.\n", - "\n", - "If the desired scans are not found in this summary, check that you are listed as an experimenter in the beam time proposal (https://pass.bnl.gov/).\n", - "\n", - "If accessing data from locally saved files, skip this step.\n" - ] + "data": { + "application/javascript": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = false;\n const version = '3.8.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n const reloading = true;\n const Bokeh = root.Bokeh;\n const BK_RE = /^https:\\/\\/cdn\\.bokeh\\.org\\/bokeh\\/(release|dev)\\/bokeh-/;\n const PN_RE = /^https:\\/\\/cdn\\.holoviz\\.org\\/panel\\/[^/]+\\/dist\\/panel/i;\n\n // Set a timeout for this load but only if we are not already initializing\n if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() 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i < links.length; i++) {\n const link = links[i]\n if (link.href != null) {\n existing_stylesheets.push(link.href)\n }\n }\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const escaped = encodeURI(url)\n if (existing_stylesheets.indexOf(escaped) !== -1) {\n on_load()\n continue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } if (((window.Tabulator !== undefined) && (!(window.Tabulator instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.8.5/dist/bundled/datatabulator/tabulator-tables@6.3.1/dist/js/tabulator.min.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(encodeURI(urls[i]))\n }\n } if (((window.moment !== undefined) && 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{\n on_load();\n }\n continue;\n }\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (let i = 0; i < js_modules.length; i++) {\n const url = js_modules[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n const url = js_exports[name];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n element.textContent = `\n import ${name} from \"${url}\"\n window.${name} = ${name}\n window._bokeh_on_load()\n `\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.holoviz.org/panel/1.8.5/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.holoviz.org/panel/1.8.5/dist/bundled/datatabulator/tabulator-tables@6.3.1/dist/js/tabulator.min.js\", \"https://cdn.holoviz.org/panel/1.8.5/dist/bundled/datatabulator/luxon/build/global/luxon.min.js\"];\n const js_modules = [];\n const js_exports = {};\n const css_urls = [\"https://cdn.holoviz.org/panel/1.8.5/dist/bundled/datatabulator/tabulator-tables@6.3.1/dist/css/tabulator_simple.min.css\"];\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (let i = 0; i < inline_js.length; i++) {\n try {\n inline_js[i].call(root, root.Bokeh);\n } catch(e) {\n if (!reloading) {\n throw e;\n }\n }\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n }\n root._bokeh_is_initializing = false;\n }\n\n function load_or_wait() {\n // Implement 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older versions of the autoload script a head-start to ensure\n // they initialize before we start loading newer version.\n setTimeout(load_or_wait, 100)\n}(window));", + "application/vnd.holoviews_load.v0+json": "" + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "execution_count": 12, - "id": "776b7d9d", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "ab32ffd4bb334cc3bf50126e90847ce2", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Retrieving results: 0%| | 0/1777 [00:00 {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n console.log(message)\n var content = {data: message.data, comm_id};\n var buffers = []\n for (var buffer of message.buffers || []) {\n buffers.push(new DataView(buffer))\n }\n var metadata = message.metadata || {};\n var msg = {content, buffers, metadata}\n 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data to the DOM node\n */\nfunction render(props, node) {\n var div = document.createElement(\"div\");\n var script = document.createElement(\"script\");\n node.appendChild(div);\n node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n var output_area = handle.output_area;\n var output = handle.output;\n if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n return\n }\n var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n if (id !== undefined) {\n var nchildren = toinsert.length;\n var html_node = toinsert[nchildren-1].children[0];\n html_node.innerHTML = output.data[HTML_MIME_TYPE];\n var scripts = [];\n var nodelist = html_node.querySelectorAll(\"script\");\n for (var i in nodelist) {\n if (nodelist.hasOwnProperty(i)) {\n scripts.push(nodelist[i])\n }\n }\n\n scripts.forEach( function (oldScript) {\n var 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window.Bokeh.index[id].model.document\n doc.clear();\n const i = window.Bokeh.documents.indexOf(doc);\n if (i > -1) {\n window.Bokeh.documents.splice(i, 1);\n }\n }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n delete PyViz.comms[\"hv-extension-comm\"];\n window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n handle_clear_output(event, {cell: {output_area: handle.output_area}})\n handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n var toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[0]);\n element.append(toinsert);\n return toinsert\n }\n\n 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scan_idstart_timecycleinstitutionprojectsample_namesample_idplandetectorexit_statusnum_Imagesuidsample_id
01067192026-02-19 08:13:16.1502942026-1WSUHeilmanHOPG_NISTHOPG_NISTnbs_list_scanbeamstop_waxssuccess8400b958d4-36ea-4d2b-b7da-255bd2bb3dedHOPG_NIST
11067202026-02-19 08:25:20.4510962026-1WSUHeilmanHOPG_NISTHOPG_NISTnbs_list_scanbeamstop_waxssuccess84a413f8b2-d2e8-4d74-90e7-49db76d654cbHOPG_NIST
21067212026-02-19 08:38:49.4891412026-1WSUHeilmanHOPG_NISTHOPG_NISTnbs_list_scanbeamstop_waxssuccess84ae815641-38a0-483e-8de2-a54c5d19fef9HOPG_NIST
31067222026-02-19 08:43:51.7169522026-1WSUHeilmanHOPG_NISTHOPG_NISTnbs_list_scanbeamstop_waxssuccess845cfd2257-400a-482d-b6f4-2ded75696979HOPG_NIST
41067232026-02-19 08:57:03.5083522026-1WSUHeilmanHOPG_NISTHOPG_NISTnbs_list_scanbeamstop_waxssuccess84c9924314-aeec-4186-806c-17bac2d6c83bHOPG_NIST
51067282026-02-19 09:41:00.3846242026-1WSUHeilmanHOPG_NISTHOPG_NISTnbs_list_scanbeamstop_waxssuccess84adb7e7e9-1f9f-4637-8080-1f18ebdbd0c9HOPG_NIST
61067292026-02-19 09:53:09.1155502026-1WSUHeilmanHOPG_NISTHOPG_NISTnbs_list_scanbeamstop_waxssuccess84e03abf85-07bb-414b-97c4-bc06941074d8HOPG_NIST
71067302026-02-19 10:00:05.6651112026-1WSUHeilmanHOPG_NISTHOPG_NISTnbs_list_scanbeamstop_waxssuccess840d04caf7-c2ae-4b7a-b785-6f580fa8fb4fHOPG_NIST
81067312026-02-19 10:05:41.9883242026-1WSUHeilmanHOPG_NISTHOPG_NISTnbs_list_scanbeamstop_waxssuccess84c713d736-696d-42af-a5c1-8b1ac67b0566HOPG_NIST
91067322026-02-19 10:09:51.3891762026-1WSUHeilmanHOPG_NISTHOPG_NISTnbs_list_scanbeamstop_waxssuccess84747fb8b1-d59a-4b99-b490-1ea3d4923d00HOPG_NIST
101067332026-02-19 10:14:33.7023372026-1WSUHeilmanHOPG_NISTHOPG_NISTnbs_list_scanbeamstop_waxssuccess841bd28e4f-0fb0-4e28-8519-91ee45f1f4a8HOPG_NIST
111067342026-02-19 10:22:26.3013182026-1WSUHeilmanHOPG_NISTHOPG_NISTnbs_list_scanbeamstop_waxssuccess84ce9e4941-feba-46df-9586-5157d30030c7HOPG_NIST
121067352026-02-19 11:32:11.9898692026-1WSUHeilmanHOPG_NISTHOPG_NISTnbs_list_scanbeamstop_waxssuccess8417a6b212-e428-488f-8d63-3d3ed138e812HOPG_NIST
131067362026-02-19 11:37:21.4202292026-1WSUHeilmanHOPG_NISTHOPG_NISTnbs_list_scanbeamstop_waxssuccess84341bd7d9-001f-45ee-bf77-866b28e81ca7HOPG_NIST
141067372026-02-19 11:45:41.1853732026-1WSUHeilmanHOPG_NISTHOPG_NISTnbs_list_scanbeamstop_waxssuccess8487967468-b421-40a7-8dd0-bc45927f1d9aHOPG_NIST
151067382026-02-19 11:52:37.9180032026-1WSUHeilmanHOPG_NISTHOPG_NISTnbs_list_scanbeamstop_waxssuccess845d24673b-667c-44f8-84c0-55d8af59d3ceHOPG_NIST
161067392026-02-19 12:05:19.6956102026-1WSUHeilmanHOPG_NISTHOPG_NISTnbs_list_scanbeamstop_waxsfailN/A6663805d-2d28-48e1-aacd-828fcca47d11HOPG_NIST
171067402026-02-19 12:11:46.2784132026-1WSUHeilmanHOPG_NISTHOPG_NISTnbs_list_scanbeamstop_waxssuccess60e5cc498f-adb4-4105-ac88-b6d595430fc5HOPG_NIST
181067412026-02-19 12:14:30.5059942026-1WSUHeilmanHOPG_NISTHOPG_NISTnbs_list_scanbeamstop_waxsabort3a2603509-4d0b-49aa-891a-25dc537901f6HOPG_NIST
191067422026-02-19 12:16:20.5016472026-1WSUHeilmanHOPG_NISTHOPG_NISTnbs_list_scanbeamstop_waxsabort54aea3cf91-6eea-45db-a9f9-bc664027692eHOPG_NIST
201067432026-02-19 12:20:16.3097932026-1WSUHeilmanHOPG_NISTHOPG_NISTnbs_list_scanbeamstop_waxssuccess6045ad4645-d407-40e4-aa2a-434f76b2ee7dHOPG_NIST
211067442026-02-19 12:23:05.6833282026-1WSUHeilmanHOPG_NISTHOPG_NISTnbs_list_scanbeamstop_waxssuccess70e5ede308-9122-4d31-9150-4e5bab64d2cfHOPG_NIST
221067452026-02-19 12:28:19.1548112026-1WSUHeilmanHOPG_NISTHOPG_NISTnbs_list_scanbeamstop_waxsabortN/A39ba6d06-ddb3-4ff4-a031-690a56af2736HOPG_NIST
231067462026-02-19 12:29:56.9891022026-1WSUHeilmanHOPG_NISTHOPG_NISTnbs_list_scanbeamstop_waxssuccess6085bb5c64-7d01-497b-a590-7076b8e2e5e7HOPG_NIST
241067472026-02-19 12:32:48.5070402026-1WSUHeilmanHOPG_NISTHOPG_NISTnbs_list_scanbeamstop_waxssuccess70bd729b48-7007-427e-8d74-f6f1913d1963HOPG_NIST
251067482026-02-19 12:37:51.0033472026-1WSUHeilmanHOPG_NISTHOPG_NISTnbs_list_scanbeamstop_waxssuccess35066ae0b8-bd17-42e7-8c52-a3c7af738e71HOPG_NIST
261067492026-02-19 12:40:24.5516402026-1WSUHeilmanHOPG_NISTHOPG_NISTnbs_list_scanbeamstop_waxssuccess70acfbef67-78b4-40d7-a4fa-398deec6c714HOPG_NIST
271067502026-02-19 12:48:02.7413182026-1WSUHeilmanHOPG_NISTHOPG_NISTnbs_list_scanbeamstop_waxssuccess70299e6c32-784a-443b-9c97-649257291ffeHOPG_NIST
281067512026-02-19 12:56:09.2768232026-1WSUHeilmanHOPG_NISTHOPG_NISTnbs_list_scanbeamstop_waxssuccess70690725ea-314c-42fb-b215-6e7bed5c3819HOPG_NIST
291067522026-02-19 13:01:19.4953222026-1WSUHeilmanHOPG_NISTHOPG_NISTnbs_list_scanbeamstop_waxssuccess4042bdd7e0-acfd-4275-b7e3-5d29ae5ba951HOPG_NIST
301067532026-02-19 13:04:52.5952982026-1WSUHeilmanHOPG_NISTHOPG_NISTnbs_list_scanbeamstop_waxssuccess40a915af83-4d12-4b3b-954c-d2459aa66f5eHOPG_NIST
311067542026-02-19 13:16:40.3185922026-1WSUHeilmanHOPG_NISTHOPG_NISTnbs_list_scanbeamstop_waxssuccess8405ac028f-4d3d-4bbb-a864-d8e1e9920dfeHOPG_NIST
321067552026-02-19 13:19:45.3897672026-1WSUHeilmanHOPG_NISTHOPG_NISTnbs_list_scanbeamstop_waxssuccess842ac6ebac-08b5-48ec-9d00-2cc489aa125eHOPG_NIST
331067562026-02-19 13:28:23.1979222026-1WSUHeilmanHOPG_NISTHOPG_NISTnbs_list_scanbeamstop_waxssuccess848b2fad74-606a-4e92-ac95-efcdaf038e85HOPG_NIST
341067572026-02-19 13:31:26.7991842026-1WSUHeilmanHOPG_NISTHOPG_NISTnbs_list_scanbeamstop_waxssuccess845aceb9b6-2052-49da-af07-76a38938f39aHOPG_NIST
\n", - "" - ], - "text/plain": [ - " scan_id start_time cycle institution project \\\n", - "0 106719 2026-02-19 08:13:16.150294 2026-1 WSU Heilman \n", - "1 106720 2026-02-19 08:25:20.451096 2026-1 WSU Heilman \n", - "2 106721 2026-02-19 08:38:49.489141 2026-1 WSU Heilman \n", - "3 106722 2026-02-19 08:43:51.716952 2026-1 WSU Heilman \n", - "4 106723 2026-02-19 08:57:03.508352 2026-1 WSU Heilman \n", - "5 106728 2026-02-19 09:41:00.384624 2026-1 WSU Heilman \n", - "6 106729 2026-02-19 09:53:09.115550 2026-1 WSU Heilman \n", - "7 106730 2026-02-19 10:00:05.665111 2026-1 WSU Heilman \n", - "8 106731 2026-02-19 10:05:41.988324 2026-1 WSU Heilman \n", - "9 106732 2026-02-19 10:09:51.389176 2026-1 WSU Heilman \n", - "10 106733 2026-02-19 10:14:33.702337 2026-1 WSU Heilman \n", - "11 106734 2026-02-19 10:22:26.301318 2026-1 WSU Heilman \n", - "12 106735 2026-02-19 11:32:11.989869 2026-1 WSU Heilman \n", - "13 106736 2026-02-19 11:37:21.420229 2026-1 WSU Heilman \n", - "14 106737 2026-02-19 11:45:41.185373 2026-1 WSU Heilman \n", - "15 106738 2026-02-19 11:52:37.918003 2026-1 WSU Heilman \n", - "16 106739 2026-02-19 12:05:19.695610 2026-1 WSU Heilman \n", - "17 106740 2026-02-19 12:11:46.278413 2026-1 WSU Heilman \n", - "18 106741 2026-02-19 12:14:30.505994 2026-1 WSU Heilman \n", - "19 106742 2026-02-19 12:16:20.501647 2026-1 WSU Heilman \n", - "20 106743 2026-02-19 12:20:16.309793 2026-1 WSU Heilman \n", - "21 106744 2026-02-19 12:23:05.683328 2026-1 WSU Heilman \n", - "22 106745 2026-02-19 12:28:19.154811 2026-1 WSU Heilman \n", - "23 106746 2026-02-19 12:29:56.989102 2026-1 WSU Heilman \n", - "24 106747 2026-02-19 12:32:48.507040 2026-1 WSU Heilman \n", - "25 106748 2026-02-19 12:37:51.003347 2026-1 WSU Heilman \n", - "26 106749 2026-02-19 12:40:24.551640 2026-1 WSU Heilman \n", - "27 106750 2026-02-19 12:48:02.741318 2026-1 WSU Heilman \n", - "28 106751 2026-02-19 12:56:09.276823 2026-1 WSU Heilman \n", - "29 106752 2026-02-19 13:01:19.495322 2026-1 WSU Heilman \n", - "30 106753 2026-02-19 13:04:52.595298 2026-1 WSU Heilman \n", - "31 106754 2026-02-19 13:16:40.318592 2026-1 WSU Heilman \n", - "32 106755 2026-02-19 13:19:45.389767 2026-1 WSU Heilman \n", - "33 106756 2026-02-19 13:28:23.197922 2026-1 WSU Heilman \n", - "34 106757 2026-02-19 13:31:26.799184 2026-1 WSU Heilman \n", - "\n", - " sample_name sample_id plan detector exit_status \\\n", - "0 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", - "1 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", - "2 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", - "3 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", - "4 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", - "5 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", - "6 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", - "7 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", - "8 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", - "9 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", - "10 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", - "11 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", - "12 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", - "13 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", - "14 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", - "15 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", - "16 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs fail \n", - "17 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", - "18 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs abort \n", - "19 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs abort \n", - "20 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", - "21 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", - "22 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs abort \n", - "23 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", - "24 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", - "25 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", - "26 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", - "27 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", - "28 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", - "29 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", - "30 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", - "31 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", - "32 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", - "33 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", - "34 HOPG_NIST HOPG_NIST nbs_list_scan beamstop_waxs success \n", - "\n", - " num_Images uid sample_id \n", - "0 84 00b958d4-36ea-4d2b-b7da-255bd2bb3ded HOPG_NIST \n", - "1 84 a413f8b2-d2e8-4d74-90e7-49db76d654cb HOPG_NIST \n", - "2 84 ae815641-38a0-483e-8de2-a54c5d19fef9 HOPG_NIST \n", - "3 84 5cfd2257-400a-482d-b6f4-2ded75696979 HOPG_NIST \n", - "4 84 c9924314-aeec-4186-806c-17bac2d6c83b HOPG_NIST \n", - "5 84 adb7e7e9-1f9f-4637-8080-1f18ebdbd0c9 HOPG_NIST \n", - "6 84 e03abf85-07bb-414b-97c4-bc06941074d8 HOPG_NIST \n", - "7 84 0d04caf7-c2ae-4b7a-b785-6f580fa8fb4f HOPG_NIST \n", - "8 84 c713d736-696d-42af-a5c1-8b1ac67b0566 HOPG_NIST \n", - "9 84 747fb8b1-d59a-4b99-b490-1ea3d4923d00 HOPG_NIST \n", - "10 84 1bd28e4f-0fb0-4e28-8519-91ee45f1f4a8 HOPG_NIST \n", - "11 84 ce9e4941-feba-46df-9586-5157d30030c7 HOPG_NIST \n", - "12 84 17a6b212-e428-488f-8d63-3d3ed138e812 HOPG_NIST \n", - "13 84 341bd7d9-001f-45ee-bf77-866b28e81ca7 HOPG_NIST \n", - "14 84 87967468-b421-40a7-8dd0-bc45927f1d9a HOPG_NIST \n", - "15 84 5d24673b-667c-44f8-84c0-55d8af59d3ce HOPG_NIST \n", - "16 N/A 6663805d-2d28-48e1-aacd-828fcca47d11 HOPG_NIST \n", - "17 60 e5cc498f-adb4-4105-ac88-b6d595430fc5 HOPG_NIST \n", - "18 3 a2603509-4d0b-49aa-891a-25dc537901f6 HOPG_NIST \n", - "19 54 aea3cf91-6eea-45db-a9f9-bc664027692e HOPG_NIST \n", - "20 60 45ad4645-d407-40e4-aa2a-434f76b2ee7d HOPG_NIST \n", - "21 70 e5ede308-9122-4d31-9150-4e5bab64d2cf HOPG_NIST \n", - "22 N/A 39ba6d06-ddb3-4ff4-a031-690a56af2736 HOPG_NIST \n", - "23 60 85bb5c64-7d01-497b-a590-7076b8e2e5e7 HOPG_NIST \n", - "24 70 bd729b48-7007-427e-8d74-f6f1913d1963 HOPG_NIST \n", - 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\n", - "
\n", - "" - ] - }, - "metadata": { - "application/vnd.holoviews_exec.v0+json": { - "id": "59f81606-1db8-4dec-9ea2-1e9422f526ac" - } - }, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "e14e3c3b994b495a9d37bb74c9a72523", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Retrieving results: 0%| | 0/1777 [00:00 0, y_out / i0, np.nan)\n", - "\n", - " if self.calc_mode.value == \"nexafs_post_edge_norm\":\n", - " xmin, xmax = float(self.post_edge_min.value), float(self.post_edge_max.value)\n", - " mask = (x >= xmin) & (x <= xmax)\n", - " baseline = np.nanmean(y_out[mask]) if np.any(mask) else np.nan\n", - " if np.isfinite(baseline) and baseline != 0:\n", - " y_out = y_out / baseline\n", - " else:\n", - " y_out = np.full_like(y_out, np.nan, dtype=float)\n", - "\n", - " frame = pd.DataFrame({\"x\": x, \"y\": y_out, \"scan_id\": str(scan_id)})\n", - " frame = frame.replace([np.inf, -np.inf], np.nan).dropna(subset=[\"x\", \"y\"])\n", - " return frame\n", - "\n", - " def _plot_title(self):\n", - " mode_map = {\n", - " \"raw\": \"Raw signal\",\n", - " \"nexafs_tey_over_i0\": \"NEXAFS TEY / I0\",\n", - " \"nexafs_post_edge_norm\": \"NEXAFS post-edge normalized\",\n", - " }\n", - " return mode_map.get(self.calc_mode.value, \"Scan plot\")\n", - "\n", - " def _update_plot(self):\n", - " scan_ids = list(self.scan_select.value or [])\n", - " if not scan_ids or not self.x_var.value or not self.y_var.value:\n", - " self.plot_pane.object = alt.Chart(pd.DataFrame({\"x\": [], \"y\": []})).mark_line()\n", - " return\n", - "\n", - " rows = []\n", - " errors = []\n", - " for sid in scan_ids:\n", - " try:\n", - " rows.append(self._single_scan_frame(sid, self.x_var.value, self.y_var.value))\n", - " except Exception as exc:\n", - " errors.append(f\"{sid}: {exc}\")\n", - "\n", - " if not rows:\n", - " self.plot_pane.object = alt.Chart(pd.DataFrame({\"x\": [], \"y\": []})).mark_line()\n", - " if errors:\n", - " self._set_status(\"; \".join(errors), error=True)\n", - " return\n", - "\n", - " plot_df = pd.concat(rows, ignore_index=True)\n", - " chart = (\n", - " alt.Chart(plot_df)\n", - " .mark_line()\n", - " .encode(\n", - " x=alt.X(\"x:Q\", title=self.x_var.value),\n", - " y=alt.Y(\"y:Q\", title=self._plot_title()),\n", - " color=alt.Color(\"scan_id:N\", title=\"Scan ID\"),\n", - " tooltip=[\"scan_id:N\", \"x:Q\", \"y:Q\"],\n", - " )\n", - " .properties(title=self._plot_title(), height=400)\n", - " .interactive()\n", - " )\n", - " self.plot_pane.object = chart\n", - " if errors:\n", - " self._set_status(\"Plotted with some skipped scans: \" + \"; \".join(errors), error=True)\n", - " else:\n", - " self._set_status(f\"Plotted {len(scan_ids)} scan(s).\")\n", - "\n", - " def show(self):\n", - " controls = [\n", - " self.x_var,\n", - " self.y_var,\n", - " self.calc_mode,\n", - " self.post_edge_min,\n", - " self.post_edge_max,\n", - " ]\n", - " for widget in controls:\n", - " widget.param.watch(lambda *_: self._update_plot(), \"value\")\n", - " return self.view\n", - "\n", - "\n", - "dash = RSoXSToyDashboard()\n", - "dash.show()" - ] - }, - { - "cell_type": "markdown", - "id": "a11c2825-2098-4b22-b4c9-6e95164ee2a9", - "metadata": {}, - "source": [ - "## Characterizing energy resolution\n" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "63eb63f0-c665-48ce-a658-0db5f9ab49c4", - "metadata": {}, - "outputs": [ - { - "ename": "KeyError", - "evalue": "'No match for scan_id=103834'", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mKeyError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[36]\u001b[39m\u001b[32m, line 111\u001b[39m\n\u001b[32m 103\u001b[39m plt.tight_layout() \u001b[38;5;66;03m## This ensures that subplots don't overlap\u001b[39;00m\n\u001b[32m 105\u001b[39m plt.show()\n\u001b[32m--> \u001b[39m\u001b[32m111\u001b[39m \u001b[43mview_export_HOPG_energy_resolution_scans\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 112\u001b[39m \u001b[43m \u001b[49m\u001b[43mscan_ids\u001b[49m\u001b[43m=\u001b[49m\u001b[43mSCAN_IDS\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 113\u001b[39m \u001b[43m \u001b[49m\u001b[43mexport_scans\u001b[49m\u001b[43m=\u001b[49m\u001b[43mEXPORT_SCANS\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 114\u001b[39m \u001b[43m \u001b[49m\u001b[43mfile_path_export\u001b[49m\u001b[43m=\u001b[49m\u001b[43mFILE_PATH_EXPORT\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 115\u001b[39m \u001b[43m \u001b[49m\u001b[43mfile_name_export\u001b[49m\u001b[43m=\u001b[49m\u001b[43mFILE_NAME_EXPORT\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 116\u001b[39m \u001b[43m)\u001b[49m\n", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[36]\u001b[39m\u001b[32m, line 22\u001b[39m, in \u001b[36mview_export_HOPG_energy_resolution_scans\u001b[39m\u001b[34m(scan_ids, export_scans, file_path_export, file_name_export)\u001b[39m\n\u001b[32m 19\u001b[39m plot_color = plot_colors[index_scan_id]\n\u001b[32m 21\u001b[39m \u001b[38;5;66;03m## Load scan\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m22\u001b[39m scan_raw = \u001b[43mcatalog\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mscan_id\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\n\u001b[32m 23\u001b[39m scan_xarray = scan_raw[\u001b[33m\"\u001b[39m\u001b[33mprimary\u001b[39m\u001b[33m\"\u001b[39m][\u001b[33m\"\u001b[39m\u001b[33mdata\u001b[39m\u001b[33m\"\u001b[39m].read()\n\u001b[32m 24\u001b[39m scan_xarray = scan_xarray.rename_vars({\u001b[33m\"\u001b[39m\u001b[33men_energy\u001b[39m\u001b[33m\"\u001b[39m: \u001b[33m\"\u001b[39m\u001b[33mEnergy\u001b[39m\u001b[33m\"\u001b[39m})\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/projects/pyref/.venv/lib/python3.13/site-packages/bluesky_tiled_plugins/clients/catalog_of_bluesky_runs.py:116\u001b[39m, in \u001b[36mCatalogOfBlueskyRuns.__getitem__\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m 113\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, numbers.Integral):\n\u001b[32m 114\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m key > \u001b[32m0\u001b[39m:\n\u001b[32m 115\u001b[39m \u001b[38;5;66;03m# CASE 2: Interpret key as a scan_id.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m116\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_lookup_by_scan_id\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 117\u001b[39m \u001b[38;5;66;03m# CASE 3: Interpret key as a recently lookup, as in\u001b[39;00m\n\u001b[32m 118\u001b[39m \u001b[38;5;66;03m# `catalog[-1]` is the latest entry.\u001b[39;00m\n\u001b[32m 119\u001b[39m key = \u001b[38;5;28mint\u001b[39m(key)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/projects/pyref/.venv/lib/python3.13/site-packages/bluesky_tiled_plugins/clients/catalog_of_bluesky_runs.py:140\u001b[39m, in \u001b[36mCatalogOfBlueskyRuns._lookup_by_scan_id\u001b[39m\u001b[34m(self, scan_id)\u001b[39m\n\u001b[32m 138\u001b[39m results = \u001b[38;5;28mself\u001b[39m.search(Eq(\u001b[33m\"\u001b[39m\u001b[33mstart.scan_id\u001b[39m\u001b[33m\"\u001b[39m, scan_id))\n\u001b[32m 139\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m results:\n\u001b[32m--> \u001b[39m\u001b[32m140\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mNo match for scan_id=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mscan_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m 141\u001b[39m \u001b[38;5;66;03m# Return latest match.\u001b[39;00m\n\u001b[32m 142\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m results.values().last()\n", - "\u001b[31mKeyError\u001b[39m: 'No match for scan_id=103834'" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "def view_export_HOPG_energy_resolution_scans(\n", - " scan_ids,\n", - " export_scans,\n", - " file_path_export,\n", - " file_name_export,\n", - "):\n", - "\n", - " offset_timestamp_seconds = 0\n", - "\n", - " number_rows, number_columns = 1, 3\n", - " fig, axs = plt.subplots(number_rows, number_columns, figsize=(number_columns*5, number_rows*8), edgecolor=(0, 0, 0, 0), linewidth=3)#fig, axs = plt.subplots(number_rows, number_columns, figsize=(number_columns*5, number_rows*4), edgecolor=(0, 0, 0, 0), linewidth=3) #figsize=(3.25, 3.25) for figure\n", - " ## Ensure axs always stays a 2D array\n", - " if number_rows == 1 and number_columns == 1: axs = np.array([[axs]])\n", - " if number_rows == 1 and number_columns > 1: axs = axs.reshape(1, number_columns)\n", - " if number_rows > 1 and number_columns == 1: axs = axs.reshape(number_rows, 1)\n", - " plot_colors = cm.rainbow(np.linspace(1, 0, len(scan_ids)))\n", - "\n", - " for index_scan_id, scan_id in enumerate(scan_ids):\n", - " plot_color = plot_colors[index_scan_id]\n", - "\n", - " ## Load scan\n", - " scan_raw = catalog[int(scan_id)]\n", - " scan_xarray = scan_raw[\"primary\"][\"data\"].read()\n", - " scan_xarray = scan_xarray.rename_vars({\"en_energy\": \"Energy\"})\n", - " scan_xarray = scan_xarray.rename_vars({\"RSoXS Au Mesh Current\": \"I0\"})\n", - " scan_xarray = scan_xarray.rename_vars({\"WAXS Beamstop\": \"Photodiode\"})\n", - " scan_xarray = scan_xarray.rename_vars({\"RSoXS Sample Current\": \"TEY\"})\n", - " scan_xarray = scan_xarray.swap_dims({\"time\": \"Energy\"})\n", - " scan = copy.deepcopy(scan_xarray)\n", - "\n", - " scan = scan.sortby(\"Energy\")\n", - " slits1_vsize = scan_raw[\"baseline\"][\"data\"][\"Upstream Scatter Slits_vsize_setpoint\"].read()[0]\n", - "\n", - " \"\"\"\n", - " ## (optional) Sparse out data if needed\n", - " N = scan.dims['Energy']\n", - " indices_to_keep = np.arange(0, N, 3)\n", - " last_index = N - 1\n", - " if last_index not in indices_to_keep:\n", - " # Append the last index to ensure the final energy point is kept\n", - " indices_to_keep = np.append(indices_to_keep, last_index)\n", - " scan = scan.isel(Energy=indices_to_keep)\n", - " \"\"\"\n", - "\n", - " ## Export scan\n", - " if export_scans:\n", - " sheet_name = \"ScanID\" + str(scan_id)\n", - " scan_dictionary = {\n", - " \"Energy\": scan[\"Energy\"],\n", - " \"I0\": scan[\"I0\"],\n", - " \"TEY\": scan[\"TEY\"],\n", - " \"Slits1_vsize\": slits1_vsize,\n", - " \"ScanID\": int(scan_id),\n", - " \"Timestamp_Seconds\": (scan_raw.metadata[\"start\"][\"time\"] - offset_timestamp_seconds)\n", - " }\n", - " scan_dataframe = pd.DataFrame(scan_dictionary)\n", - " if os.path.exists(os.path.join(file_path_export, file_name_export)):\n", - " with pd.ExcelWriter(os.path.join(file_path_export, file_name_export), mode='a') as writer: ## If the file already exists, it will just add a new sheet, otherwise create a new file. Or maybe not? Revisit how this works.\n", - " scan_dataframe.to_excel(writer, sheet_name=sheet_name, index=True)\n", - " else:\n", - " scan_dataframe.to_excel(os.path.join(file_path_export, file_name_export), sheet_name=sheet_name, index=True)\n", - "\n", - " ## Gather plot labels and information to print to serve as a progress bar\n", - " polarization = scan_raw[\"baseline\"][\"data\"][\"en_polarization\"].read()[0]\n", - " timestamp = str(datetime.datetime.fromtimestamp(int(scan_raw.metadata[\"start\"][\"time\"] - offset_timestamp_seconds)))\n", - " plot_label = \"Scan ID = \" + str(scan_id) + \", \\nslits1.vsize = \" + str(slits1_vsize)\n", - " print(\"Scan ID = \" + str(scan_id) + \", Time = \" + str(timestamp) + \", Polarization = \" + str(polarization) + \"°\")\n", - "\n", - " ## Plot data\n", - " ax = axs[0, 0]\n", - " ax.plot(scan[\"Energy\"], scan[\"TEY\"], label=plot_label, marker=\".\", markersize=0, color=plot_color, linestyle=\"solid\", linewidth=2)\n", - " ax.set_ylabel(\"Raw sample current\", color=(0, 0, 0, 1), size=12)\n", - "\n", - " start_label = scan[\"TEY\"][\"Energy\"].sel(Energy=340, method=\"nearest\").item()\n", - " end_label = scan[\"TEY\"][\"Energy\"].sel(Energy=345, method=\"nearest\").item()\n", - " ax = axs[0, 1]\n", - " ax.plot(scan[\"Energy\"], (scan[\"TEY\"]/scan[\"TEY\"].sel(Energy=slice(start_label, end_label)).mean()), label=plot_label, marker=\".\", markersize=0, color=plot_color, linestyle=\"solid\", linewidth=2)\n", - " ax.set_ylabel(\"Raw sample current \\n normalized to post-edge\", color=(0, 0, 0, 1), size=12)\n", - " ax = axs[0, 2]\n", - " ax.plot(scan[\"Energy\"], (scan[\"TEY\"]/scan[\"TEY\"].sel(Energy=slice(start_label, end_label)).mean()), label=plot_label, marker=\".\", markersize=0, color=plot_color, linestyle=\"solid\", linewidth=2)\n", - " ax.set_ylabel(\"Raw sample current \\n normalized to post-edge\", color=(0, 0, 0, 1), size=12)\n", - " ax.set_xlim([285, 310])\n", - "\n", - " ## Plot Formatting\n", - " for index_row in np.arange(0, number_rows, 1):\n", - " for index_column in np.arange(0, number_columns, 1):\n", - " ax = axs[index_row, index_column]\n", - " #ax.axvline(291.65, color=(0, 0, 0, 1), linestyle=\"dashed\")\n", - " ## Subplot and axes labels\n", - " ax.set_xlabel(\"Energy (eV)\", color=(0, 0, 0, 1), size=12)\n", - " #ax.set_ylabel(\"I0\", color=(0, 0, 0, 1), size=12)\n", - " ## Axes scaling and ranges\n", - " ax.set_xscale(\"linear\")\n", - " ax.set_yscale(\"linear\")\n", - " #ax.set_xlim([0, 0.08])\n", - " ax.set_ylim(bottom=0, top=None, auto=True)\n", - " ## Border formatting\n", - " for Border in np.array([\"top\", \"bottom\", \"left\", \"right\"]):\n", - " ax.spines[Border].set_linewidth(2) ## axes/border linewidths\n", - " ax.spines[Border].set_color((0, 0, 0, 1)) ## axes/border colors\n", - " for Axis in np.array([\"x\", \"y\"]): ax.tick_params(axis=Axis, colors=(0, 0, 0, 1), width=2)\n", - " axs[0, 2].legend(ncol=1, loc=\"center left\", bbox_to_anchor=(1.05, 0.5), edgecolor=(1, 1, 1, 1))\n", - " plt.tight_layout() ## This ensures that subplots don't overlap\n", - "\n", - " plt.show()\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "view_export_HOPG_energy_resolution_scans(\n", - " scan_ids=SCAN_IDS,\n", - " export_scans=EXPORT_SCANS,\n", - " file_path_export=FILE_PATH_EXPORT,\n", - " file_name_export=FILE_NAME_EXPORT,\n", - ")" + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e14e3c3b994b495a9d37bb74c9a72523", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Retrieving results: 0%| | 0/1777 [00:00 0, y_out / i0, np.nan)\n", + "\n", + " if self.calc_mode.value == \"nexafs_post_edge_norm\":\n", + " xmin, xmax = float(self.post_edge_min.value), float(self.post_edge_max.value)\n", + " mask = (x >= xmin) & (x <= xmax)\n", + " baseline = np.nanmean(y_out[mask]) if np.any(mask) else np.nan\n", + " if np.isfinite(baseline) and baseline != 0:\n", + " y_out = y_out / baseline\n", + " else:\n", + " y_out = np.full_like(y_out, np.nan, dtype=float)\n", + "\n", + " frame = pd.DataFrame({\"x\": x, \"y\": y_out, \"scan_id\": str(scan_id)})\n", + " frame = frame.replace([np.inf, -np.inf], np.nan).dropna(subset=[\"x\", \"y\"])\n", + " return frame\n", + "\n", + " def _plot_title(self):\n", + " mode_map = {\n", + " \"raw\": \"Raw signal\",\n", + " \"nexafs_tey_over_i0\": \"NEXAFS TEY / I0\",\n", + " \"nexafs_post_edge_norm\": \"NEXAFS post-edge normalized\",\n", + " }\n", + " return mode_map.get(self.calc_mode.value, \"Scan plot\")\n", + "\n", + " def _update_plot(self):\n", + " scan_ids = list(self.scan_select.value or [])\n", + " if not scan_ids or not self.x_var.value or not self.y_var.value:\n", + " self.plot_pane.object = alt.Chart(pd.DataFrame({\"x\": [], \"y\": []})).mark_line()\n", + " return\n", + "\n", + " rows = []\n", + " errors = []\n", + " for sid in scan_ids:\n", + " try:\n", + " rows.append(self._single_scan_frame(sid, self.x_var.value, self.y_var.value))\n", + " except Exception as exc:\n", + " errors.append(f\"{sid}: {exc}\")\n", + "\n", + " if not rows:\n", + " self.plot_pane.object = alt.Chart(pd.DataFrame({\"x\": [], \"y\": []})).mark_line()\n", + " if errors:\n", + " self._set_status(\"; \".join(errors), error=True)\n", + " return\n", + "\n", + " plot_df = pd.concat(rows, ignore_index=True)\n", + " chart = (\n", + " alt.Chart(plot_df)\n", + " .mark_line()\n", + " .encode(\n", + " x=alt.X(\"x:Q\", title=self.x_var.value),\n", + " y=alt.Y(\"y:Q\", title=self._plot_title()),\n", + " color=alt.Color(\"scan_id:N\", title=\"Scan ID\"),\n", + " tooltip=[\"scan_id:N\", \"x:Q\", \"y:Q\"],\n", + " )\n", + " .properties(title=self._plot_title(), height=400)\n", + " .interactive()\n", + " )\n", + " self.plot_pane.object = chart\n", + " if errors:\n", + " self._set_status(\"Plotted with some skipped scans: \" + \"; \".join(errors), error=True)\n", + " else:\n", + " self._set_status(f\"Plotted {len(scan_ids)} scan(s).\")\n", + "\n", + " def show(self):\n", + " controls = [\n", + " self.x_var,\n", + " self.y_var,\n", + " self.calc_mode,\n", + " self.post_edge_min,\n", + " self.post_edge_max,\n", + " ]\n", + " for widget in controls:\n", + " widget.param.watch(lambda *_: self._update_plot(), \"value\")\n", + " return self.view\n", + "\n", + "\n", + "dash = RSoXSToyDashboard()\n", + "dash.show()" + ] + }, + { + "cell_type": "markdown", + "id": "a11c2825-2098-4b22-b4c9-6e95164ee2a9", + "metadata": {}, + "source": [ + "## Characterizing energy resolution\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "63eb63f0-c665-48ce-a658-0db5f9ab49c4", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "id": "f479d3af-a99e-489c-8194-2b7cb5d4f2fd", - "metadata": {}, - "source": [ - "## ---------------------------------------------------------------------------------------------\n" - ] + "ename": "KeyError", + "evalue": "'No match for scan_id=103834'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mKeyError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[36]\u001b[39m\u001b[32m, line 111\u001b[39m\n\u001b[32m 103\u001b[39m plt.tight_layout() \u001b[38;5;66;03m## This ensures that subplots don't overlap\u001b[39;00m\n\u001b[32m 105\u001b[39m plt.show()\n\u001b[32m--> \u001b[39m\u001b[32m111\u001b[39m \u001b[43mview_export_HOPG_energy_resolution_scans\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 112\u001b[39m \u001b[43m \u001b[49m\u001b[43mscan_ids\u001b[49m\u001b[43m=\u001b[49m\u001b[43mSCAN_IDS\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 113\u001b[39m \u001b[43m \u001b[49m\u001b[43mexport_scans\u001b[49m\u001b[43m=\u001b[49m\u001b[43mEXPORT_SCANS\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 114\u001b[39m \u001b[43m \u001b[49m\u001b[43mfile_path_export\u001b[49m\u001b[43m=\u001b[49m\u001b[43mFILE_PATH_EXPORT\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 115\u001b[39m \u001b[43m \u001b[49m\u001b[43mfile_name_export\u001b[49m\u001b[43m=\u001b[49m\u001b[43mFILE_NAME_EXPORT\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 116\u001b[39m \u001b[43m)\u001b[49m\n", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[36]\u001b[39m\u001b[32m, line 22\u001b[39m, in \u001b[36mview_export_HOPG_energy_resolution_scans\u001b[39m\u001b[34m(scan_ids, export_scans, file_path_export, file_name_export)\u001b[39m\n\u001b[32m 19\u001b[39m plot_color = plot_colors[index_scan_id]\n\u001b[32m 21\u001b[39m \u001b[38;5;66;03m## Load scan\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m22\u001b[39m scan_raw = \u001b[43mcatalog\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mscan_id\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\n\u001b[32m 23\u001b[39m scan_xarray = scan_raw[\u001b[33m\"\u001b[39m\u001b[33mprimary\u001b[39m\u001b[33m\"\u001b[39m][\u001b[33m\"\u001b[39m\u001b[33mdata\u001b[39m\u001b[33m\"\u001b[39m].read()\n\u001b[32m 24\u001b[39m scan_xarray = scan_xarray.rename_vars({\u001b[33m\"\u001b[39m\u001b[33men_energy\u001b[39m\u001b[33m\"\u001b[39m: \u001b[33m\"\u001b[39m\u001b[33mEnergy\u001b[39m\u001b[33m\"\u001b[39m})\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/projects/pyref/.venv/lib/python3.13/site-packages/bluesky_tiled_plugins/clients/catalog_of_bluesky_runs.py:116\u001b[39m, in \u001b[36mCatalogOfBlueskyRuns.__getitem__\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m 113\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, numbers.Integral):\n\u001b[32m 114\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m key > \u001b[32m0\u001b[39m:\n\u001b[32m 115\u001b[39m \u001b[38;5;66;03m# CASE 2: Interpret key as a scan_id.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m116\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_lookup_by_scan_id\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 117\u001b[39m \u001b[38;5;66;03m# CASE 3: Interpret key as a recently lookup, as in\u001b[39;00m\n\u001b[32m 118\u001b[39m \u001b[38;5;66;03m# `catalog[-1]` is the latest entry.\u001b[39;00m\n\u001b[32m 119\u001b[39m key = \u001b[38;5;28mint\u001b[39m(key)\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/projects/pyref/.venv/lib/python3.13/site-packages/bluesky_tiled_plugins/clients/catalog_of_bluesky_runs.py:140\u001b[39m, in \u001b[36mCatalogOfBlueskyRuns._lookup_by_scan_id\u001b[39m\u001b[34m(self, scan_id)\u001b[39m\n\u001b[32m 138\u001b[39m results = \u001b[38;5;28mself\u001b[39m.search(Eq(\u001b[33m\"\u001b[39m\u001b[33mstart.scan_id\u001b[39m\u001b[33m\"\u001b[39m, scan_id))\n\u001b[32m 139\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m results:\n\u001b[32m--> \u001b[39m\u001b[32m140\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mNo match for scan_id=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mscan_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m 141\u001b[39m \u001b[38;5;66;03m# Return latest match.\u001b[39;00m\n\u001b[32m 142\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m results.values().last()\n", + "\u001b[31mKeyError\u001b[39m: 'No match for scan_id=103834'" + ] }, { - "cell_type": "markdown", - "id": "8b1848de-1a38-4a20-80e4-2069509f0dcc", - "metadata": {}, - "source": [ - "## ---------------------------------------------------------------------------------------------\n" + "data": { + "image/png": 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+ "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.11" - } + ], + "source": [ + "def view_export_HOPG_energy_resolution_scans(\n", + " scan_ids,\n", + " export_scans,\n", + " file_path_export,\n", + " file_name_export,\n", + "):\n", + "\n", + " offset_timestamp_seconds = 0\n", + "\n", + " number_rows, number_columns = 1, 3\n", + " fig, axs = plt.subplots(number_rows, number_columns, figsize=(number_columns*5, number_rows*8), edgecolor=(0, 0, 0, 0), linewidth=3)#fig, axs = plt.subplots(number_rows, number_columns, figsize=(number_columns*5, number_rows*4), edgecolor=(0, 0, 0, 0), linewidth=3) #figsize=(3.25, 3.25) for figure\n", + " ## Ensure axs always stays a 2D array\n", + " if number_rows == 1 and number_columns == 1: axs = np.array([[axs]])\n", + " if number_rows == 1 and number_columns > 1: axs = axs.reshape(1, number_columns)\n", + " if number_rows > 1 and number_columns == 1: axs = axs.reshape(number_rows, 1)\n", + " plot_colors = cm.rainbow(np.linspace(1, 0, len(scan_ids)))\n", + "\n", + " for index_scan_id, scan_id in enumerate(scan_ids):\n", + " plot_color = plot_colors[index_scan_id]\n", + "\n", + " ## Load scan\n", + " scan_raw = catalog[int(scan_id)]\n", + " scan_xarray = scan_raw[\"primary\"][\"data\"].read()\n", + " scan_xarray = scan_xarray.rename_vars({\"en_energy\": \"Energy\"})\n", + " scan_xarray = scan_xarray.rename_vars({\"RSoXS Au Mesh Current\": \"I0\"})\n", + " scan_xarray = scan_xarray.rename_vars({\"WAXS Beamstop\": \"Photodiode\"})\n", + " scan_xarray = scan_xarray.rename_vars({\"RSoXS Sample Current\": \"TEY\"})\n", + " scan_xarray = scan_xarray.swap_dims({\"time\": \"Energy\"})\n", + " scan = copy.deepcopy(scan_xarray)\n", + "\n", + " scan = scan.sortby(\"Energy\")\n", + " slits1_vsize = scan_raw[\"baseline\"][\"data\"][\"Upstream Scatter Slits_vsize_setpoint\"].read()[0]\n", + "\n", + " \"\"\"\n", + " ## (optional) Sparse out data if needed\n", + " N = scan.dims['Energy']\n", + " indices_to_keep = np.arange(0, N, 3)\n", + " last_index = N - 1\n", + " if last_index not in indices_to_keep:\n", + " # Append the last index to ensure the final energy point is kept\n", + " indices_to_keep = np.append(indices_to_keep, last_index)\n", + " scan = scan.isel(Energy=indices_to_keep)\n", + " \"\"\"\n", + "\n", + " ## Export scan\n", + " if export_scans:\n", + " sheet_name = \"ScanID\" + str(scan_id)\n", + " scan_dictionary = {\n", + " \"Energy\": scan[\"Energy\"],\n", + " \"I0\": scan[\"I0\"],\n", + " \"TEY\": scan[\"TEY\"],\n", + " \"Slits1_vsize\": slits1_vsize,\n", + " \"ScanID\": int(scan_id),\n", + " \"Timestamp_Seconds\": (scan_raw.metadata[\"start\"][\"time\"] - offset_timestamp_seconds)\n", + " }\n", + " scan_dataframe = pd.DataFrame(scan_dictionary)\n", + " if os.path.exists(os.path.join(file_path_export, file_name_export)):\n", + " with pd.ExcelWriter(os.path.join(file_path_export, file_name_export), mode='a') as writer: ## If the file already exists, it will just add a new sheet, otherwise create a new file. Or maybe not? Revisit how this works.\n", + " scan_dataframe.to_excel(writer, sheet_name=sheet_name, index=True)\n", + " else:\n", + " scan_dataframe.to_excel(os.path.join(file_path_export, file_name_export), sheet_name=sheet_name, index=True)\n", + "\n", + " ## Gather plot labels and information to print to serve as a progress bar\n", + " polarization = scan_raw[\"baseline\"][\"data\"][\"en_polarization\"].read()[0]\n", + " timestamp = str(datetime.datetime.fromtimestamp(int(scan_raw.metadata[\"start\"][\"time\"] - offset_timestamp_seconds)))\n", + " plot_label = \"Scan ID = \" + str(scan_id) + \", \\nslits1.vsize = \" + str(slits1_vsize)\n", + " print(\"Scan ID = \" + str(scan_id) + \", Time = \" + str(timestamp) + \", Polarization = \" + str(polarization) + \"°\")\n", + "\n", + " ## Plot data\n", + " ax = axs[0, 0]\n", + " ax.plot(scan[\"Energy\"], scan[\"TEY\"], label=plot_label, marker=\".\", markersize=0, color=plot_color, linestyle=\"solid\", linewidth=2)\n", + " ax.set_ylabel(\"Raw sample current\", color=(0, 0, 0, 1), size=12)\n", + "\n", + " start_label = scan[\"TEY\"][\"Energy\"].sel(Energy=340, method=\"nearest\").item()\n", + " end_label = scan[\"TEY\"][\"Energy\"].sel(Energy=345, method=\"nearest\").item()\n", + " ax = axs[0, 1]\n", + " ax.plot(scan[\"Energy\"], (scan[\"TEY\"]/scan[\"TEY\"].sel(Energy=slice(start_label, end_label)).mean()), label=plot_label, marker=\".\", markersize=0, color=plot_color, linestyle=\"solid\", linewidth=2)\n", + " ax.set_ylabel(\"Raw sample current \\n normalized to post-edge\", color=(0, 0, 0, 1), size=12)\n", + " ax = axs[0, 2]\n", + " ax.plot(scan[\"Energy\"], (scan[\"TEY\"]/scan[\"TEY\"].sel(Energy=slice(start_label, end_label)).mean()), label=plot_label, marker=\".\", markersize=0, color=plot_color, linestyle=\"solid\", linewidth=2)\n", + " ax.set_ylabel(\"Raw sample current \\n normalized to post-edge\", color=(0, 0, 0, 1), size=12)\n", + " ax.set_xlim([285, 310])\n", + "\n", + " ## Plot Formatting\n", + " for index_row in np.arange(0, number_rows, 1):\n", + " for index_column in np.arange(0, number_columns, 1):\n", + " ax = axs[index_row, index_column]\n", + " #ax.axvline(291.65, color=(0, 0, 0, 1), linestyle=\"dashed\")\n", + " ## Subplot and axes labels\n", + " ax.set_xlabel(\"Energy (eV)\", color=(0, 0, 0, 1), size=12)\n", + " #ax.set_ylabel(\"I0\", color=(0, 0, 0, 1), size=12)\n", + " ## Axes scaling and ranges\n", + " ax.set_xscale(\"linear\")\n", + " ax.set_yscale(\"linear\")\n", + " #ax.set_xlim([0, 0.08])\n", + " ax.set_ylim(bottom=0, top=None, auto=True)\n", + " ## Border formatting\n", + " for Border in np.array([\"top\", \"bottom\", \"left\", \"right\"]):\n", + " ax.spines[Border].set_linewidth(2) ## axes/border linewidths\n", + " ax.spines[Border].set_color((0, 0, 0, 1)) ## axes/border colors\n", + " for Axis in np.array([\"x\", \"y\"]): ax.tick_params(axis=Axis, colors=(0, 0, 0, 1), width=2)\n", + " axs[0, 2].legend(ncol=1, loc=\"center left\", bbox_to_anchor=(1.05, 0.5), edgecolor=(1, 1, 1, 1))\n", + " plt.tight_layout() ## This ensures that subplots don't overlap\n", + "\n", + " plt.show()\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "view_export_HOPG_energy_resolution_scans(\n", + " scan_ids=SCAN_IDS,\n", + " export_scans=EXPORT_SCANS,\n", + " file_path_export=FILE_PATH_EXPORT,\n", + " file_name_export=FILE_NAME_EXPORT,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5d1c21ae-ac0e-4a4c-91a4-45a4abf9cb58", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b19de2d-269c-4478-a126-55aea4d9ff24", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9c7bf1d4-be52-4c1b-973b-c7f268a2ae11", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5c532294-fa74-4fc1-b173-8cdb0f6ceea9", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "96ea7ee4-08fc-4a15-a8db-b498150ec73f", + "metadata": {}, + "source": [ + "## ---------------------------------------------------------------------------------------------\n" + ] + }, + { + "cell_type": "markdown", + "id": "93ae7124-aff2-4c2a-a083-e08ddada6805", + "metadata": {}, + "source": [ + "## ---------------------------------------------------------------------------------------------\n" + ] + }, + { + "cell_type": "markdown", + "id": "f479d3af-a99e-489c-8194-2b7cb5d4f2fd", + "metadata": {}, + "source": [ + "## ---------------------------------------------------------------------------------------------\n" + ] + }, + { + "cell_type": "markdown", + "id": "8b1848de-1a38-4a20-80e4-2069509f0dcc", + "metadata": {}, + "source": [ + "## ---------------------------------------------------------------------------------------------\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 5 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 } diff --git a/pyproject.toml b/pyproject.toml index a0ba24a..f90dcb5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -94,6 +94,9 @@ exclude = [ ".venv", "docs", "src", + "notebooks", + "tests", + "python/pyref/beamline", ] fix = true line-length = 88 diff --git a/python/pyref/beamline/__init__.py b/python/pyref/beamline/__init__.py index 31b365d..55dd2d0 100644 --- a/python/pyref/beamline/__init__.py +++ b/python/pyref/beamline/__init__.py @@ -15,6 +15,7 @@ pad_digits, ) from pyref.beamline.beamline_scan_macros import ( + XRR_lineup, add_comment, add_prompt, analog_from_file, @@ -37,13 +38,13 @@ set_motor, time_scan, windows_path, - XRR_lineup, ) __all__ = [ "ALS_ExperimentWidget", "ALS_MeasurementWidget", "ALS_ScriptGenWidget", + "XRR_lineup", "add_comment", "add_prompt", "analog_from_file", @@ -59,8 +60,8 @@ "piezo_toggle", "relative_generic_scan", "relative_photodiode_scan", - "run_dict_trajectory", "run_I0", + "run_dict_trajectory", "sample_photo", "save_trajectory", "set_DIO", @@ -68,5 +69,4 @@ "set_motor", "time_scan", "windows_path", - "XRR_lineup", ] diff --git a/python/pyref/beamline/base_widgets.py b/python/pyref/beamline/base_widgets.py index 1921b72..3ccc2f7 100644 --- a/python/pyref/beamline/base_widgets.py +++ b/python/pyref/beamline/base_widgets.py @@ -11,23 +11,21 @@ PyQt6 """ # Base Packages -import sys -import os +import contextlib +import inspect import json import re -import copy -import inspect +import tkinter as tk from pathlib import Path +from tkinter import filedialog + +# Visualization Packages +import ipywidgets as widgets # Math packages import numpy as np import pandas as pd -# Visualization Packages -import ipywidgets as widgets -from IPython.display import display, clear_output -import tkinter as tk -from tkinter import filedialog class ALS_ScriptGenWidget: """ @@ -36,26 +34,25 @@ class ALS_ScriptGenWidget: This class provides basic functions for creating widgets that generate run scripts. It includes functionality for saving, loading, verifying, and managing experiment configurations. - + """ - + default_button = widgets.Layout(width='200px') default_GUI = widgets.Layout(widget='1200px') banner_button_style = {'button_color':'#007681', 'text_color':'white'} - - + + def __init__(self, exp_name="", child_widget=None, path=None, tab_title=None, json_title=None, **kwargs): """ - Initializes an ALS_ScriptGenWidget. This is a base class to create widgets specializing in run-script generation + Initializes an ALS_ScriptGenWidget. This is a base class to create widgets specializing in run-script generation. """ - self.child_widget = child_widget if child_widget is not None else ALS_ExperimentWidget self._save_dir = path if path is not None else "" self.tab_title = tab_title if tab_title is not None else "Experiment " self.json_title = json_title if json_title is not None else "Default" # Create a title banner for the full widget - self.title_banner = widgets.HTML(value=f"

{exp_name} Script Generator

") + self.title_banner = widgets.HTML(value=f"

{exp_name} Script Generator

") # Default save / path generation # Name that will be used to save the @@ -72,7 +69,7 @@ def __init__(self, exp_name="", child_widget=None, path=None, tab_title=None, js style = self.banner_button_style ) self.save_button_CCD.on_click(self.save_script) - + self.save_button_beamline = widgets.Button( description = "Save Beamline Scan File", layout=self.default_button, @@ -104,7 +101,7 @@ def __init__(self, exp_name="", child_widget=None, path=None, tab_title=None, js self.additional_buttons ] ) - + #self.control_buttons = widgets.VBox( # [ # self.save_name, @@ -134,7 +131,7 @@ def __init__(self, exp_name="", child_widget=None, path=None, tab_title=None, js self.save_json_button.on_click(self.save_json) self.json_buttons = widgets.HBox([self.save_json_button, self.load_json_button]) - self.GUI = widgets.VBox([self.title_banner, self.control_buttons, self.layout, self.json_buttons,self.save_buttons], layout=self.default_GUI) + self.GUI = widgets.VBox([self.title_banner, self.control_buttons, self.layout, self.json_buttons,self.save_buttons], layout=self.default_GUI) #display(self.GUI) def __call__(self): @@ -142,12 +139,12 @@ def __call__(self): def __len__(self): return 0 - + @property def save_dir(self): return self._save_dir - + @save_dir.setter def save_dir(self, val): self._save_dir=val @@ -159,11 +156,11 @@ def GenerateExperiment(self, layout, widget_str, widget, **kwargs): if widget_str in key: df = getattr(value, "output_dict", lambda: value)() self.new_tab(layout, widget, widget_str, **df) - + if not layout.children: df = {} self.new_tab(layout, widget, widget_str, **df) - + return layout def new_tab(self, layout, widget, widget_str, b=None, **kwargs): @@ -189,7 +186,7 @@ def copy_tab(self, layout, widget, widget_str, b=None): df = {} setattr(self, widget_str + pad_digits(index), widget(scriptgen_widget=self, **df)) self.update_experiment_tab(layout, widget_str) - + def delete_tab(self, layout, widget, widget_str, b=None): try: index = self.layout.selected_index + 1 @@ -198,14 +195,14 @@ def delete_tab(self, layout, widget, widget_str, b=None): widget_name = widget_str+pad_digits(index) if hasattr(self, widget_name): delattr(self, widget_name) - + j = index + 1 while hasattr(self, f"{widget_str}{pad_digits(j)}"): setattr(self, f"{widget_str}{pad_digits(j-1)}", getattr(self, f"{widget_str}{pad_digits(j)}")) delattr(self, f"{widget_str}{pad_digits(j)}") j += 1 self.update_experiment_tab(layout, widget_str) - + def clean_slate(self): self.layout.children = () for key in (key for key in dir(self) if not key.startswith("__")): @@ -220,8 +217,8 @@ def update_experiment_tab(self, layout, widget_str): display.extend([widget.display()]) self.layout.children = display for tab in np.arange(len(layout.children)): - layout.set_title(tab, self.tab_title +str(tab+1)) - + layout.set_title(tab, self.tab_title +str(tab+1)) + def save_script(self, ext='.txt', sep='\t', b=None): SAVEDIR = str(self.save_dir) SAVENAME = str(self.save_name.value) @@ -232,7 +229,7 @@ def save_script(self, ext='.txt', sep='\t', b=None): # Cleanup the output because the ALS requires specific things try: - with open(SAVEPATH, 'r') as f: + with open(SAVEPATH) as f: lines = f.readlines() if not lines: # Empty file return @@ -267,10 +264,10 @@ def save_as_dict(self): # continue # Pass everything except a potential ALS_UTILITIY #if not hasattr(attr.WIDGET, 'ALS_NAME'): # continue - + #print(attr) - #save_dict[key] = self._process_attr_dict(attr) - + #save_dict[key] = self._process_attr_dict(attr) + if hasattr(attr, 'output_dict') and not inspect.isclass(attr): #not inspect.isclass(attr) and hasattr(attr, 'WIDGET') and save_dict[key] = self._process_attr_dict(self, attr) return save_dict @@ -299,7 +296,7 @@ def browse_directory(self): root = tk.Tk() root.withdraw() root.attributes('-topmost', True) - + initial_dir = str(self._save_dir) if self._save_dir else None selected_folder = filedialog.askdirectory( title="Select Folder", @@ -319,13 +316,11 @@ def browse_directory(self): root.destroy() except: pass - try: + with contextlib.suppress(Exception): root.quit() - except: - pass root = None gc.collect() - + if selected_folder: return Path(selected_folder) return None @@ -335,13 +330,14 @@ def save_json(self, b=None): SAVEDIR = str(self.save_dir) SAVENAME = str(self.save_name.value) SAVEPATH = SAVEDIR + '/' + SAVENAME + '.json' - + try: with open(SAVEPATH, 'w') as f: f.write(f"# {self.json_title}\n") json.dump(df, f, indent=4) except json.JSONDecodeError as e: - raise json.JSONDecodeError(f"Error encoding to JSON: {e}") + msg = f"Error encoding to JSON: {e}" + raise json.JSONDecodeError(msg) def load_json(self, expected_title="", b=None): import gc @@ -351,7 +347,7 @@ def load_json(self, expected_title="", b=None): root = tk.Tk() root.withdraw() root.attributes('-topmost', True) - + initial_dir = str(self._save_dir) if self._save_dir else None selected_file = filedialog.askopenfilename( title="Select JSON File", @@ -374,16 +370,14 @@ def load_json(self, expected_title="", b=None): root.destroy() except: pass - try: + with contextlib.suppress(Exception): root.quit() - except: - pass root = None gc.collect() - + if selected_file: try: - with open(selected_file, 'r') as f: + with open(selected_file) as f: title_line = f.readline().strip() if title_line == f"# {self.json_title}": df = json.load(f) @@ -398,7 +392,7 @@ def load_json(self, expected_title="", b=None): traceback.print_exc() return None return None - + @@ -415,7 +409,7 @@ def __init__(self, title="Generic Experiment", constants=None, constants_titles= # Initial conditions self.constants = constants if constants is not None else {} self.constants_titles = constants_titles if constants_titles is not None else {} - + # Title of the widget self.widget_title = widgets.HTML(value=f"{title}") @@ -437,7 +431,7 @@ def __init__(self, title="Generic Experiment", constants=None, constants_titles= layout = self.default_FloatText, style = self.default_FloatText_style ) - ) + ) temp_display.append(getattr(self, attr_title)) elif isinstance(attr_value, str): # THIS WILL NOT SAVE THIS VALUE. STRINGS SHOULD NOT BE PASSED @@ -450,7 +444,7 @@ def __init__(self, title="Generic Experiment", constants=None, constants_titles= layout = self.default_FloatText, style = self.default_FloatText_style ) - ) + ) self.menu_box = widgets.VBox(temp_display) else: self.menu_box = widgets.VBox([]) @@ -477,7 +471,7 @@ def copy_scan(self, layout, widget, widget_str, b=None): df = {} setattr(self, widget_str + pad_digits(index), widget(experiment_widget=self, **df)) self.update_scan_tab(layout, widget_str) - + def delete_scan(self, layout, widget, widget_str, b=None): try: index = self.layout.selected_index + 1 @@ -500,7 +494,7 @@ def update_scan_tab(self, layout, widget_str): display.extend([widget.display()]) self.layout.children = display for tab in np.arange(len(layout.children)): - layout.set_title(tab, widget_str +str(tab+1)) + layout.set_title(tab, widget_str +str(tab+1)) def output_dict(self): df = {} @@ -521,22 +515,24 @@ def comma_separated_list(self, input_str): match = re.match(pattern, input_str) return bool(match) - - + + class ALS_MeasurementWidget: LABEL_SIZE = widgets.Layout(width='150px') LABEL_STYLE = {'description_width': '150px'} CELL_SIZE = widgets.Layout(width='100px') - def __init__(self, constant_motor_title="Generic Measurement", update_options={}, experiment_widget=None): - # Set values and update if parameters are loaded -- + def __init__(self, constant_motor_title="Generic Measurement", update_options=None, experiment_widget=None): + # Set values and update if parameters are loaded -- + if update_options is None: + update_options = {} self.constant_motor_title = widgets.HTML(value=f"{constant_motor_title}") # All tables have a title self.constant_motor_attrs = [] # All tables will have some fixed values self.table = widgets.VBox(children=[]) self.control_buttons = widgets.VBox(children=[]) - + self.ExperimentWidget = experiment_widget - + def build_display_table(self): build_table = [] build_table.extend([self.constant_motor_title]) # The title of fixed values @@ -544,7 +540,7 @@ def build_display_table(self): build_table.extend([self.table]) # table object if needed build_table.extend([self.control_buttons]) # optional buttons return widgets.VBox(build_table) - + def display(self): return self.display_table @@ -559,17 +555,17 @@ def get_table(self, include_title=False): def update_table(self): if not hasattr(self, 'table_titles') or not self.table_titles: return [] - + if isinstance(self.table_titles[0], list): title_row = self.table_titles else: title_row = [self.table_titles] - + fixed_content = [getattr(self, attr) for attr in dir(self) if attr.startswith('fixed_')] variable_content = [attr for attr in dir(self) if attr.startswith('variable_')] variable_content = self._sort_attrs(variable_content) variable_content = [getattr(self, attr) for attr in variable_content] - + rows = title_row + fixed_content + variable_content output_table = [] for row in rows: @@ -581,12 +577,12 @@ def update_table(self): else: output_table.append(widgets.HBox(row)) return output_table - + def _sort_attrs(self, attrs): def extract_number(s): return int(re.search(r'_(\d+)', s).group(1)) #finds the number after the underscore return sorted(attrs, key=extract_number) - + def output_dict(self): df = {} @@ -602,19 +598,19 @@ def output_dict(self): else: df[key] += [col.value] return df - - - - - - + + + + + + """ other functions """ def clean_script(file): # Cleanup the output because the ALS requires specific things try: - with open(file, 'r') as f: + with open(file) as f: lines = f.readlines() if not lines: # Empty file return @@ -632,9 +628,9 @@ def clean_script(file): except Exception as e: print(f"An error occured: {e}") del lines #Remove it from memory (it can be large) - - - + + + def pad_digits(number): """Pads a float or string representation of a float with leading zeros until it becomes a 4-digit number (before the decimal point). @@ -642,7 +638,8 @@ def pad_digits(number): Args: number: A float or a string representing a float. - Returns: + Returns + ------- A string representing the number padded with leading zeros to 4 digits before the decimal point. If the integer part already has 4 or more digits, it is returned as is (with any decimal part). @@ -659,4 +656,4 @@ def pad_digits(number): padded_integer = str(integer_part).zfill(4) return padded_integer + decimal_part except ValueError: - return None \ No newline at end of file + return None diff --git a/python/pyref/beamline/beamline_scan_macros.py b/python/pyref/beamline/beamline_scan_macros.py index eaed161..9fee9a5 100644 --- a/python/pyref/beamline/beamline_scan_macros.py +++ b/python/pyref/beamline/beamline_scan_macros.py @@ -4,8 +4,8 @@ for ALS beamline 11.0.1.2. """ -import os # For saving macros -import warnings # For warning people +import os # For saving macros +import warnings # For warning people """ Some Saved Motor Positions. Update here if needed. @@ -21,7 +21,7 @@ """ def build_macro(name, path, macro): - + if not os.path.exists(path): try: os.makedirs(path) @@ -29,7 +29,7 @@ def build_macro(name, path, macro): print(f"Error in creating directory {path}: {e}") return file_path = os.path.join(path, f"{name}.txt") - + try: with open(file_path, "w") as f: # Write the Description Line @@ -41,8 +41,8 @@ def build_macro(name, path, macro): f.write(str(step) + "\n") # Write the line and add a new line except Exception as e: print(f"Error creating file {name}.txt: {e}") - - + + """ Basic Macros @@ -50,20 +50,20 @@ def build_macro(name, path, macro): def finish_line(s=600): # Added to every function to end the line. Not sure what this is for.... - return f") [{str(s)}]" - + return f") [{s!s}]" + def add_comment(comment): output = "Comment: " output += f"{comment}" return output - + def add_prompt(prompt): output = "Prompt(" output += f"{prompt}" output += finish_line(s=0.1) return output -# The save_as condition determines whether or not +# The save_as condition determines whether or not def set_instrument( instrument, sync = "True", @@ -76,7 +76,7 @@ def set_instrument( ): instrument_list = ["CCD", "Video", None] if instrument not in instrument_list: - warnings.warn(f"{instrument} is not part of instrument list. Returning blank line", UserWarning) + warnings.warn(f"{instrument} is not part of instrument list. Returning blank line", UserWarning, stacklevel=2) return "" if instrument is None: instrument = '""' @@ -93,11 +93,11 @@ def set_instrument( output += sub_dark + "," output += str(num_to_sum) output += finish_line(s=0.5) - + return output - - - + + + def set_DIO(name, on_off, delay=0.1): output = "Set DIO(" output += name + "," @@ -105,7 +105,7 @@ def set_DIO(name, on_off, delay=0.1): output += str(on_off) output += finish_line(s=0.5) return output - + def move_motor(motor, pos, delay=0.1): output = "Move Motor(" output += motor+"," @@ -115,7 +115,7 @@ def move_motor(motor, pos, delay=0.1): output += "1" output += finish_line(s=5) return output - + def set_motor(motor, pos, delay=0.1): output = "Move Motor(" output += motor+"," @@ -125,7 +125,7 @@ def set_motor(motor, pos, delay=0.1): output += "1" output += finish_line(s=0.5) return output - + def jog_motor(motor, pos, delay=0.1): output = "Move Motor(" output += motor+"," @@ -144,18 +144,18 @@ def move_trajectory(name_of_traj, delay=0.1): output += "0" output += finish_line() return output - + def save_trajectory(name, list_of_motors): output = "Save Trajectory(" output += name +"," output += "10.0" +"," # Days till it gets deleted for motor in list_of_motors: output += motor + "\t" - output = output[:-2] + "\r\n" # Remove the last tab and finish the list of motors + output = output[:-2] + "\r\n" # Remove the last tab and finish the list of motors # A few other things need to go here output += finish_line() return output - + def relative_photodiode_scan(motor, delta, incr,delay=0.1,count_time=0.3): # Order of inputs: motor, delta, "current position", incr, delay, count, other defaults" output = "Single Motor Relative Scan(" @@ -175,7 +175,7 @@ def relative_photodiode_scan(motor, delta, incr,delay=0.1,count_time=0.3): output += ")" output += " [600]" return output - + def relative_generic_scan( motor, base_file_name, @@ -211,7 +211,7 @@ def relative_generic_scan( # A few other things need to go here to wrap up the line output += finish_line() return output - + def absolute_generic_scan( motor, start, @@ -236,7 +236,7 @@ def absolute_generic_scan( output += "Min" output += finish_line() return output - + def analog_from_file(name, path_to_scan, delay=0.1, count_time=0.5, move_sequential="false", dont_repeat="false", end_of_scan='Return', end_trajectory='""'): output = "From File Scan(" output += windows_path(path_to_scan) +"," @@ -249,10 +249,10 @@ def analog_from_file(name, path_to_scan, delay=0.1, count_time=0.5, move_sequent output += '""' + "," output += name output += finish_line() - + return output - - + + def time_scan(name, time_between_points, total_time=1, number_of_samples=1, count_time=0.1, stop_condition='Samples Taken'): # Make sure the stop condition is one of the two options. 'Samples Taken' or 'Time Elapsed' if stop_condition=='Samples Taken': @@ -261,7 +261,7 @@ def time_scan(name, time_between_points, total_time=1, number_of_samples=1, coun number_of_samples = total_time/time_between_points else: return "" - + output = "Time Scan(" output += str(total_time) + "," output += str(number_of_samples) + "," @@ -270,7 +270,7 @@ def time_scan(name, time_between_points, total_time=1, number_of_samples=1, coun output += '""' + "," output += name output += finish_line() - + return output """ @@ -278,7 +278,7 @@ def time_scan(name, time_between_points, total_time=1, number_of_samples=1, coun """ - + """ Experiment Specific macros """ @@ -288,43 +288,44 @@ def time_scan(name, time_between_points, total_time=1, number_of_samples=1, coun def begin_macro(name): macro = [] - + macro += [add_comment(f"{name} macro")] macro += [clear_instruments] - + return macro # Built in trajectories def run_dict_trajectory(d): - + if not isinstance(d, dict): - raise TypeError("Input must be a dictionary containing Motor:Position Pairs") + msg = "Input must be a dictionary containing Motor:Position Pairs" + raise TypeError(msg) trajectory_name = get_dict_name(d) - + macro = [] macro += [add_comment(f"Run Trajectory: {trajectory_name}")] for key, value in d.items(): macro += [move_motor(key, value, 0.1)] - + return macro - + def piezo_toggle(in_out=0): """ Toggle the Piezo position by bringing the shutter in or out Move the shutter in, in_out = 1 - Move the shutter out, in_out = 0 + Move the shutter out, in_out = 0. """ macro = [] if(in_out): macro += [move_motor("PiezoShutter Trans", piezo_in, delay=0.1)] macro += [set_DIO("Air Shutter Output", on_off="ON", delay=0.1)] - if(not in_out): + if(not in_out): macro += [set_DIO("Air Shutter Output", on_off="OFF", delay=0.1)] macro += [move_motor("PiezoShutter Trans", piezo_out, delay=0.1)] return macro - + # Auto-align sample-theta given a sample position def XRR_lineup(repeat=3): @@ -339,7 +340,7 @@ def XRR_lineup(repeat=3): th = 1 # Start building the scan macro = [] - macro += [add_comment(f"Begin XRR_Lineup")] + macro += [add_comment("Begin XRR_Lineup")] macro += [clear_instruments] # Make sure the CCD is turned off for diode scans macro += piezo_toggle(in_out=0) # Move the Piezo motor out of the way to actuate the shutter macro += [move_motor("Higher Order Suppressor", HOS_in, 0.1)] @@ -361,7 +362,7 @@ def XRR_lineup(repeat=3): macro += [shutter_off] macro += [jog_motor("Sample Theta", -th, 0.1)] macro += [jog_motor("CCD Theta", -2*th, 0.1)] - for i in range(repeat): + for _i in range(repeat): #th += 0.5 macro += [shutter_on] macro += [fine_z_scan] @@ -373,25 +374,25 @@ def XRR_lineup(repeat=3): macro += [shutter_off] macro += [jog_motor("Sample Theta", -th, 0.1)] macro += [jog_motor("CCD Theta", -2*th, 0.1)] - + macro += [set_motor("Sample Theta", 0)] macro += [set_motor('Sample Z', 0)] macro += [shutter_off] macro += piezo_toggle(in_out=1) - + return macro - -# Take a photo of the sample // will need to record pixel value of cursor + +# Take a photo of the sample // will need to record pixel value of cursor def sample_photo(name): macro = [] macro += [add_comment(f"Taking photo of sample: {name}")] macro += [set_instrument("Video", exp=1, extension="png")] # Turn on camera as instrument macro += [set_DIO("Light Output", on_off="ON")] # Turn the light on - macro += [time_scan(name, time_between_points=1)] # Take one photo based on the current motor position + macro += [time_scan(name, time_between_points=1)] # Take one photo based on the current motor position macro += [set_DIO("Light Output", on_off="OFF")] # Turn the light off before continuing macro += [clear_instruments] # Remove the camera from the instruments queue before continuing return macro - + def run_I0(name, scan_file, posx, posy): macro = [] macro += [add_comment(f"Running {name}")] @@ -400,9 +401,9 @@ def run_I0(name, scan_file, posx, posy): macro += [move_motor('Sample X', posx, 0.1)] macro += [move_motor('Sample Y', posy, 0.1)] macro += [analog_from_file(name, scan_file)] - + return macro - + """ Experiment Specific Trajectories that included during a full run. """ @@ -448,22 +449,23 @@ def run_I0(name, scan_file, posx, posy): "CCD Y": 0.000, "Beam Stop": 10.490 } - + # Other functions... def get_dict_name(d, namespace=locals()): for name, value in namespace.items(): if value is d and isinstance(value, dict): return name return None - + def windows_path(path_string): - """ + r""" Normalizes a path string by replacing all '/' and '\' separators with '\\'. Args: path_string: The input path string. - Returns: + Returns + ------- The normalized path string with '\\' as the separator. """ # Replace forward slashes with backslashes @@ -472,4 +474,4 @@ def windows_path(path_string): # Ensure double backslashes for Windows paths normalized_path = path_string.replace('\\', '\\\\') - return normalized_path \ No newline at end of file + return normalized_path diff --git a/python/pyref/beamline/escan_widgets.py b/python/pyref/beamline/escan_widgets.py index 6766b2e..2947b36 100644 --- a/python/pyref/beamline/escan_widgets.py +++ b/python/pyref/beamline/escan_widgets.py @@ -4,27 +4,22 @@ ALS beamline 11.0.1.2. """ # Base Packages -import sys -import os -import json -import re -import copy +import contextlib import inspect +import json +import tkinter as tk from pathlib import Path +from tkinter import filedialog + +# Visualization Packages +import ipywidgets as widgets # Math packages import numpy as np import pandas as pd - -# Visualization Packages -import ipywidgets as widgets -from IPython.display import display, clear_output -import tkinter as tk -from tkinter import filedialog +from IPython.display import display from pyref.beamline.base_widgets import ( - ALS_ScriptGenWidget, - ALS_ExperimentWidget, ALS_MeasurementWidget, ) @@ -39,7 +34,7 @@ class ESCAN_ScriptGen(ALS_MeasurementWidget): "Step 3":[284.0, 286.0, 0.1, 0.5], "Step 4":[286.0, 292.0, 0.2, 0.5], "Step 5":[292.0, 300.0, 1, 0.5], - "Step 6":[300.0, 370.0, 10, 1] + "Step 6":[300.0, 370.0, 10, 1] } BUTTON_SIZE = widgets.Layout(width='10rem') LABEL_SIZE = widgets.Layout(width='5rem') @@ -86,14 +81,14 @@ def __init__(self, path=None, **kwargs): self.make_table(parameters) self.table = widgets.VBox(self.update_table()) - + # Create a button to add or remove columns and rows self.add_step_button = widgets.Button(description="Add step", layout=self.BUTTON_SIZE) self.add_step_button.on_click(self.add_step) self.remove_step_button = widgets.Button(description="Remove step", layout=self.BUTTON_SIZE) self.remove_step_button.on_click(self.remove_step) - + # Additional options for the Escan self.repeat_title = widgets.HTML( value = "Number of Repeats: ", @@ -135,20 +130,20 @@ def __init__(self, path=None, **kwargs): style = self.OTHER_BUTTON_STYLE ) self.save_for_rsoxs.on_click(self.save_rsoxs) - + self.save_scan_buttons = widgets.HBox(children=[self.save_for_nexafs, self.save_for_rsoxs]) # Style the table self.table.layout=self.TABLE_SIZE - + self.display_table = self.build_display_table() display(self.display_table) @property def save_dir(self): return self._save_dir - + @save_dir.setter def save_dir(self, val): self._save_dir=val @@ -161,11 +156,11 @@ def make_table(self, d): self, 'variable_'+str(i+1), [ - [widgets.Label(value=key, layout=self.LABEL_SIZE)] + + [widgets.Label(value=key, layout=self.LABEL_SIZE)] + [widgets.FloatText(value=ii, layout=self.LABEL_SIZE) for ii in item] ] ) - + def add_step(self, b): total_cols = len(self.table_titles[0])-1 # Add a new row with FloatText widgets @@ -179,9 +174,9 @@ def add_step(self, b): [widgets.Label(value="Step "+str(num_steps+1), layout=self.LABEL_SIZE)] + [widgets.FloatText(value=i, layout=self.LABEL_SIZE) for i in np.arange(total_cols)] #[widgets.Dropdown(options=AVAILABLE_MOTORS, value=::)] - ] + ] ) - temp_motor = getattr(self, "variable_"+str(num_steps+1)) + getattr(self, "variable_"+str(num_steps+1)) self.table.children = self.update_table() def remove_step(self, b): @@ -191,10 +186,9 @@ def remove_step(self, b): print("No steps to delete") return 0 for motor in self.get_table(): - if "variable_" in motor: - if str(final_step) in motor: - delattr(self,motor) - + if "variable_" in motor and str(final_step) in motor: + delattr(self,motor) + # Update the table self.table.children = self.update_table() @@ -222,7 +216,7 @@ def browse_directory(self): root = tk.Tk() root.withdraw() root.attributes('-topmost', True) - + initial_dir = str(self._save_dir) if self._save_dir else None selected_folder = filedialog.askdirectory( title="Select Folder", @@ -242,13 +236,11 @@ def browse_directory(self): root.destroy() except: pass - try: + with contextlib.suppress(Exception): root.quit() - except: - pass root = None gc.collect() - + if selected_folder: return Path(selected_folder) return None @@ -257,14 +249,15 @@ def save_json(self, b=None): df = self.output_dict() SAVEDIR = str(self.save_dir) SAVENAME = str(self.save_name.value) - SAVEPATH = SAVEDIR + '/' + SAVENAME + '.json' + SAVEPATH = SAVEDIR + '/' + SAVENAME + '.json' try: with open(SAVEPATH, 'w') as f: f.write(f"# {self.JSON_TITLE}\n") json.dump(df, f, indent=4) except json.JSONDecodeError as e: - raise json.JSONDecodeError(f"Error encoding to JSON: {e}") + msg = f"Error encoding to JSON: {e}" + raise json.JSONDecodeError(msg) def load_json(self, expected_title="", b=None): import gc @@ -274,7 +267,7 @@ def load_json(self, expected_title="", b=None): root = tk.Tk() root.withdraw() root.attributes('-topmost', True) - + initial_dir = str(self._save_dir) if self._save_dir else None selected_file = filedialog.askopenfilename( title="Select JSON File", @@ -297,16 +290,14 @@ def load_json(self, expected_title="", b=None): root.destroy() except: pass - try: + with contextlib.suppress(Exception): root.quit() - except: - pass root = None gc.collect() - + if selected_file: try: - with open(selected_file, 'r') as f: + with open(selected_file) as f: title_line = f.readline().strip() if title_line == f"# {self.JSON_TITLE}": df = json.load(f) @@ -338,7 +329,7 @@ def save_nexafs(self, b=None): np.savetxt(SAVEPATH, full_energy_scan, fmt='%.2f') # Cleanup the output because the ALS requires specific things try: - with open(SAVEPATH, 'r') as f: + with open(SAVEPATH) as f: lines = f.readlines() if not lines: # Empty file return @@ -355,46 +346,46 @@ def save_nexafs(self, b=None): except Exception as e: print(f"An error occured: {e}") del lines #Remove it from memory (it can be large) - + def save_rsoxs(self, b=None): SAVEDIR = str(self.save_dir) SAVENAME = str(self.save_name.value) SAVEPATH = SAVEDIR + '/' + SAVENAME + '.txt' - + Motor_Order = ['Beamline Energy', 'CCD Camera Shutter Inhibit', 'Exposure'] df = self.output_dict() escan_details = self._build_escan(df) Escan = escan_details[0] # The energies Exposure = escan_details[1] # The exposures - + # Build the sample dataframe temp_scan = {} temp_scan['Beamline Energy'] = Escan temp_scan['Exposure'] = Exposure df_samp = pd.DataFrame(temp_scan) - + # Add CCD Inhibit, first and last, all exposure times df_samp['CCD Camera Shutter Inhibit'] = np.full_like(Escan, 0) # Add the column for expo in np.unique(Exposure): df_samp = pd.concat([df_samp.loc[0].to_frame().T, df_samp], ignore_index=True) # Add a frame at the beginning of the sample df_samp = pd.concat([df_samp, df_samp.loc[df_samp.index[-1]].to_frame().T], axis=0, ignore_index=True) # Add a frame at the end of the sample - - df_samp.loc[0, 'CCD Camera Shutter Inhibit'] = 1 # Close the shutter + + df_samp.loc[0, 'CCD Camera Shutter Inhibit'] = 1 # Close the shutter df_samp.loc[df_samp.index[-1], 'CCD Camera Shutter Inhibit'] = 1 df_samp.loc[0, 'Exposure'] = expo # Set exposure df_samp.loc[df_samp.index[-1], 'Exposure'] = expo - + # Cleanup the output because the ALS requires specific things cols = list(df_samp.columns) Ordered_Cols = [item for item in Motor_Order if item in cols] df_samp = self._iterate_dataframe(df_samp[Ordered_Cols], self.repeat_value.value) # Repeat the scan if it is requested - + df_samp.to_csv(SAVEPATH, index=False, sep='\t') try: - with open(SAVEPATH, 'r') as f: + with open(SAVEPATH) as f: lines = f.readlines() if not lines: # Empty file return @@ -411,12 +402,12 @@ def save_rsoxs(self, b=None): except Exception as e: print(f"An error occured: {e}") del lines #Remove it from memory (it can be large) - + def _build_escan(self, d): energy_array = np.array([]) exposure_array = np.array([]) print(d) - for key, item in d.items(): + for _key, item in d.items(): if item[2] == 0: energy_subset = np.array([item[0]]) else: @@ -425,19 +416,19 @@ def _build_escan(self, d): energy_array = np.concatenate((energy_array, energy_subset)) exposure_array = np.concatenate((exposure_array, exposure_subset)) return (np.round(energy_array, 2), exposure_array) - + def _iterate_dataframe(self, df, n_duplicates): if not isinstance(n_duplicates, int) or n_duplicates < 1: n_duplicates = 1 - + df_duplicate = [df] * n_duplicates df_duplicate = pd.concat(df_duplicate, ignore_index=True) return df_duplicate - + def clean_slate(self): self.table.children = () for key in (key for key in dir(self) if not key.startswith("__")): attr = getattr(self, key) if not inspect.isclass(attr) and hasattr(attr, 'variable_'): - delattr(self, key) \ No newline at end of file + delattr(self, key) diff --git a/python/pyref/beamline/pxr_widgets.py b/python/pyref/beamline/pxr_widgets.py index afe6930..f4bd7e8 100644 --- a/python/pyref/beamline/pxr_widgets.py +++ b/python/pyref/beamline/pxr_widgets.py @@ -4,35 +4,23 @@ ALS beamline 11.0.1.2. """ # Base Packages -import sys -import os -import json -import re -import copy -import inspect -from pathlib import Path # Math packages -import numpy as np -import pandas as pd - # Visualization Packages import ipywidgets as widgets -from IPython.display import display, clear_output -import tkinter as tk -from tkinter import filedialog +import numpy as np +import pandas as pd +from IPython.display import display from pyref.beamline.base_widgets import ( - ALS_ScriptGenWidget, ALS_ExperimentWidget, ALS_MeasurementWidget, + ALS_ScriptGenWidget, clean_script, pad_digits, ) - from pyref.beamline.beamline_scan_macros import * - # Variables that might be adjusted later as needed X_VARIABLES = [ @@ -75,7 +63,7 @@ class PXR_Scan(ALS_MeasurementWidget): def __init__(self, experiment_widget=None, **kwargs): parameters = self.DEFAULT_PARAMETERS.copy() #constants_titles = self.DEFAULT_CONSTANTS_TITLES.copy() - # Update those based on what the kwargs are -- + # Update those based on what the kwargs are -- parameters.update({k: v for k, v in kwargs.items() if k in parameters}) super().__init__(constant_motor_title='Measurement Details', experiment_widget=experiment_widget) # Build the initial table @@ -95,7 +83,7 @@ def __init__(self, experiment_widget=None, **kwargs): self, 'fixed_'+str(i), [ - [widgets.Label(value=key, layout=self.LABEL_SIZE)] + + [widgets.Label(value=key, layout=self.LABEL_SIZE)] + [widgets.FloatText(value=ii, layout=self.CELL_SIZE) for ii in item] ] ) @@ -130,7 +118,7 @@ def __init__(self, experiment_widget=None, **kwargs): ) self.energy_title = widgets.HTML(value="Photon Energy Scan [eV]") self.energy_desc = widgets.HTML(value="Set delta=0 to run single energy") - + self.energy_info = widgets.VBox([self.energy_title, self.energy_desc]) self.theta_end = widgets.FloatText( value = parameters['theta_end'], @@ -141,7 +129,7 @@ def __init__(self, experiment_widget=None, **kwargs): # Compile all values that are fixed and apply to the full title self.constant_motor_attrs = ['energy_start','energy_stop','energy_delta', 'theta_end'] self.table = widgets.VBox(self.update_table()) - + # Create a button to add or remove columns and rows self.add_step_button = widgets.Button(description="Add step") self.add_step_button.on_click(self.add_step) @@ -171,7 +159,7 @@ def __init__(self, experiment_widget=None, **kwargs): ] self.display_table = self.build_display_table() #display(self.display_table) - + def build_display_table(self): build_table = [] build_table.extend([self.constant_motor_title]) # The title of fixed values @@ -211,8 +199,8 @@ def remove_step(self, b): return 0 motor_directory = self.get_table(include_title=True) for motor in motor_directory: - getattr(self,motor)[0].pop() - + getattr(self,motor)[0].pop() + # Update the table self.table.children = self.update_table() @@ -231,25 +219,24 @@ def add_motor(self, b=None): [widgets.Dropdown(options=AVAILABLE_PXR_MOTORS, value=AVAILABLE_PXR_MOTORS[0], layout=self.LABEL_SIZE)] + [widgets.FloatText(value=AVAILABLE_PXR_MOTOR_DEFAULTS[AVAILABLE_PXR_MOTORS[0]], layout=self.CELL_SIZE) for i in np.arange(total_steps)] #[widgets.Dropdown(options=AVAILABLE_MOTORS, value=::)] - ] + ] ) - temp_motor = getattr(self, "variable_"+str(num_extra_motors+1)) + getattr(self, "variable_"+str(num_extra_motors+1)) self.table.children = self.update_table() def remove_motor(self, b=None): num_extra_motors = len([attr for attr in dir(self) if attr.startswith("variable_")]) for motor in self.get_table(): - if "variable_" in motor: - if str(num_extra_motors) in motor: - delattr(self,motor) - + if "variable_" in motor and str(num_extra_motors) in motor: + delattr(self,motor) + # Update the table layout self.table.children = self.update_table() - + class PXR_Experiment(ALS_ExperimentWidget): ALS_NAME = 'exp_' - WIDGET = PXR_Scan + WIDGET = PXR_Scan DEFAULT_CONSTANTS = { 'XPosition': 0.0, 'YPosition': 0.0, @@ -300,7 +287,7 @@ def __init__(self, path="",scriptgen_widget=None, **kwargs): # Get default values constants = self.DEFAULT_CONSTANTS.copy() constants_titles = self.DEFAULT_CONSTANTS_TITLES.copy() - # Update those based on what the kwargs are -- + # Update those based on what the kwargs are -- constants.update({k: v for k, v in kwargs.items() if k in constants}) constants = clean_for_widgets(constants) # Change the list to a CSV # Build the experiment tab @@ -308,7 +295,7 @@ def __init__(self, path="",scriptgen_widget=None, **kwargs): # Name of Sample Box self.name_of_sample = widgets.Text(description="Name of Sample: ", value=constants['name_of_sample'], style=self.MOTOR_OPTIONS_STYLE) - + # Initial buttons used to create new measurements (one level down) self.add_scan_button = widgets.Button(description="Add new measurement", layout=self.default_button, style=self.BUTTON_STYLE) self.duplicate_scan_button = widgets.Button(description="Duplicate measurement", layout=self.default_button, style=self.BUTTON_STYLE) @@ -317,10 +304,10 @@ def __init__(self, path="",scriptgen_widget=None, **kwargs): self.add_scan_button.on_click(lambda b: self.add_scan(self.layout, self.WIDGET, self.WIDGET.ALS_NAME)) self.duplicate_scan_button.on_click(lambda b: self.copy_scan(self.layout, self.WIDGET, self.WIDGET.ALS_NAME)) self.delete_scan_button.on_click(lambda b: self.delete_scan(self.layout, self.WIDGET, self.WIDGET.ALS_NAME)) - self.control_buttons.children = tuple([self.add_scan_button, self.duplicate_scan_button, self.delete_scan_button]) + self.control_buttons.children = (self.add_scan_button, self.duplicate_scan_button, self.delete_scan_button) accordion_menus = [] - # Recreate the constant values based on the Accordians that organize things better -- + # Recreate the constant values based on the Accordians that organize things better -- for key, menu in constants_titles.items(): menu_display = [] for attr, value in menu.items(): @@ -395,26 +382,26 @@ def __init__(self, path="",scriptgen_widget=None, **kwargs): self.add_scan(self.layout, self.WIDGET, self.WIDGET.ALS_NAME, **df) if len(self.layout.children) == 0: self.add_scan(self.layout, self.WIDGET, self.WIDGET.ALS_NAME) - + self.GUI_experiment = widgets.VBox([self.name_of_sample, self.widget_title, self.menu_box, self.control_buttons, self.layout]) - - def update_scan_tab(self, layout, widget_str): + + def update_scan_tab(self, layout, widget_str): widget_list = [getattr(self, attr) for attr in dir(self) if attr.startswith(widget_str)] display = [] titles = [] for widget in widget_list: display.extend([widget.display()]) - titles.append(f"En: {str(widget.energy_start.value)} [eV]") + titles.append(f"En: {widget.energy_start.value!s} [eV]") widget.energy_start.observe(self.update_title, names='value') self.layout.children = display for tab in np.arange(len(layout.children)): - layout.set_title(tab, titles[tab]) + layout.set_title(tab, titles[tab]) def update_title(self, change): index = self.layout.selected_index scan = getattr(self, f"{self.WIDGET.ALS_NAME}{pad_digits(index+1)}") - self.layout.set_title(index, f"En: {str(scan.energy_start.value)} [eV]") + self.layout.set_title(index, f"En: {scan.energy_start.value!s} [eV]") class PXR_ScriptGen(ALS_ScriptGenWidget): EXPERIMENT_NAME = "PXR" @@ -444,15 +431,13 @@ def __init__(self, path="", **kwargs): self.copy_sample_button.on_click(lambda b: self.copy_tab(self.layout, self.WIDGET, self.WIDGET.ALS_NAME)) self.delete_sample_button.on_click(lambda b: self.delete_tab(self.layout, self.WIDGET, self.WIDGET.ALS_NAME)) - self.additional_buttons.children = tuple( - [ + self.additional_buttons.children = ( widgets.HBox([self.new_sample_button, self.copy_sample_button, self.delete_sample_button]) - ] - ) + ,) self.GenerateExperiment(self.layout, self.WIDGET.ALS_NAME, self.WIDGET, **kwargs) display(self.GUI) - + def update_experiment_tab(self, layout, widget_str): widget_list = [getattr(self, attr) for attr in dir(self) if attr.startswith(widget_str)] display = [] @@ -464,30 +449,30 @@ def update_experiment_tab(self, layout, widget_str): self.layout.children = display for tab in np.arange(len(layout.children)): layout.set_title(tab, display_titles[tab]) - + def update_title(self, change): index = self.layout.selected_index self.layout.set_title(index, change['owner'].value) # No idea why it is setup this way. - + def save_as_df(self): df = {} # Get the sample dataframe df = self.save_as_dict() - #Temp thing -- + #Temp thing -- MACRO_DIR = str(self.save_dir) # Cycle through the samples and start saving data - for i, (index, sample) in enumerate(df.items()): + for i, (_index, sample) in enumerate(df.items()): MotorPositions = {} VariableMotors = {} - ScanList = [m for m in sample.keys() if "scan_" in m] + ScanList = [m for m in sample if "scan_" in m] MotorPositions = {k: v for k, v in sample.items() if k not in ScanList} beam_offset_counter = 0 - + name = self.WIDGET.ALS_NAME+pad_digits(i+1) sample_attr = getattr(self, name) sample_name = sample_attr.name_of_sample.value - + for ii, scan in enumerate(ScanList): dm = sample[scan] exclude_motors = ['theta_end', 'energy_start', 'energy_stop', 'energy_delta'] @@ -523,29 +508,29 @@ def save_as_df(self): else: SingleFile = pd.concat([SingleFile, sampdf], ignore_index=True) # save the sampdf as a macro file - SAVE_SCAN = MACRO_DIR + '/' + sample_name +'_PXR_'+str(i) + '.txt' + SAVE_SCAN = MACRO_DIR + '/' + sample_name +'_PXR_'+str(i) + '.txt' # Make sure the positions are correct SingleFile = SingleFile[[c for c in SingleFile if c not in ['Exposure']] + ['Exposure']] SingleFile.to_csv(SAVE_SCAN, index=False, sep='\t') clean_script(SAVE_SCAN) # Remove exposure and clear last line if it exists - - + + RunFile = RunFile[[c for c in RunFile if c not in ['Exposure']] + ['Exposure']] - return RunFile - + return RunFile + def save_beamline_scan(self, ext='.txt', sep='\t', b=None): SAVEDIR = str(self.save_dir) SAVENAME = str(self.save_name.value) - SAVEPATH = SAVEDIR + '/' + SAVENAME + '.txt' + SAVEDIR + '/' + SAVENAME + '.txt' - df = self.save_as_dict() + self.save_as_dict() #df.to_csv(SAVEPATH, index=False, sep=sep) - + def clean_scan_file(self, path): # Cleanup the output because the ALS requires specific things try: - with open(path, 'r') as f: + with open(path) as f: lines = f.readlines() if not lines: # Empty file return @@ -563,7 +548,7 @@ def clean_scan_file(self, path): except Exception as e: print(f"An error occured: {e}") del lines #Remove it from memory (it can be large) - + PHOTODIODE_POS = { "CCD Y": 100.000, "CCD Theta": 0.000, @@ -576,10 +561,10 @@ def clean_scan_file(self, path): "CCD X": 101.000, "Beam Stop": 5.25 } - + class PXR_MacroExperiment(ALS_ExperimentWidget): ALS_NAME = 'exp_' - WIDGET = PXR_Scan + WIDGET = PXR_Scan DEFAULT_CONSTANTS = { 'XPosition': 0.0, 'YPosition': 0.0, @@ -596,7 +581,7 @@ class PXR_MacroExperiment(ALS_ExperimentWidget): 'PointDensity': [15,6], 'OverlapPoints': 4, 'Buffer': 2, - 'I0Points':10 + 'I0Points':10 } DEFAULT_CONSTANTS_TITLES = { 'menu1': { @@ -623,7 +608,7 @@ def __init__(self, path="",scriptgen_widget=None, **kwargs): # Get default values constants = self.DEFAULT_CONSTANTS.copy() constants_titles = self.DEFAULT_CONSTANTS_TITLES.copy() - # Update those based on what the kwargs are -- + # Update those based on what the kwargs are -- constants.update({k: v for k, v in kwargs.items() if k in constants}) constants = clean_for_widgets(constants) # Change the list to a CSV # Build the experiment tab @@ -631,7 +616,7 @@ def __init__(self, path="",scriptgen_widget=None, **kwargs): # Name of Sample Box self.name_of_sample = widgets.Text(description="Name of Sample: ", value="Sample", style=self.MOTOR_OPTIONS_STYLE) - + # Initial buttons used to create new measurements (one level down) self.add_scan_button = widgets.Button(description="Add new measurement", layout=self.default_button, style=self.BUTTON_STYLE) self.duplicate_scan_button = widgets.Button(description="Duplicate measurement", layout=self.default_button, style=self.BUTTON_STYLE) @@ -640,10 +625,10 @@ def __init__(self, path="",scriptgen_widget=None, **kwargs): self.add_scan_button.on_click(lambda b: self.add_scan(self.layout, self.WIDGET, self.WIDGET.ALS_NAME)) self.duplicate_scan_button.on_click(lambda b: self.copy_scan(self.layout, self.WIDGET, self.WIDGET.ALS_NAME)) self.delete_scan_button.on_click(lambda b: self.delete_scan(self.layout, self.WIDGET, self.WIDGET.ALS_NAME)) - self.control_buttons.children = tuple([self.add_scan_button, self.duplicate_scan_button, self.delete_scan_button]) + self.control_buttons.children = (self.add_scan_button, self.duplicate_scan_button, self.delete_scan_button) accordion_menus = [] - # Recreate the constant values based on the Accordians that organize things better -- + # Recreate the constant values based on the Accordians that organize things better -- for key, menu in constants_titles.items(): menu_display = [] for attr, value in menu.items(): @@ -718,27 +703,27 @@ def __init__(self, path="",scriptgen_widget=None, **kwargs): self.add_scan(self.layout, self.WIDGET, self.WIDGET.ALS_NAME, **df) if len(self.layout.children) == 0: self.add_scan(self.layout, self.WIDGET, self.WIDGET.ALS_NAME) - + self.GUI_experiment = widgets.VBox([self.name_of_sample, self.widget_title, self.menu_box, self.control_buttons, self.layout]) - - def update_scan_tab(self, layout, widget_str): + + def update_scan_tab(self, layout, widget_str): widget_list = [getattr(self, attr) for attr in dir(self) if attr.startswith(widget_str)] display = [] titles = [] for widget in widget_list: display.extend([widget.display()]) - titles.append(f"En: {str(widget.energy_start.value)} [eV]") + titles.append(f"En: {widget.energy_start.value!s} [eV]") widget.energy_start.observe(self.update_title, names='value') self.layout.children = display for tab in np.arange(len(layout.children)): - layout.set_title(tab, titles[tab]) + layout.set_title(tab, titles[tab]) def update_title(self, change): index = self.layout.selected_index scan = getattr(self, f"{self.WIDGET.ALS_NAME}{pad_digits(index+1)}") - self.layout.set_title(index, f"En: {str(scan.energy_start.value)} [eV]") - + self.layout.set_title(index, f"En: {scan.energy_start.value!s} [eV]") + class PXR_MacroGen(PXR_ScriptGen): WIDGET = PXR_MacroExperiment @@ -746,7 +731,7 @@ def __init__(self, **kwargs): super().__init__(self, **kwargs) self.GUI.children = () # temporary fix to clear the widget generated by PXR_ScriptGen # New banner - self.title_banner = widgets.HTML(value=f"

{self.EXPERIMENT_NAME} Macro Generator

") + self.title_banner = widgets.HTML(value=f"

{self.EXPERIMENT_NAME} Macro Generator

") # Change the button self.save_button_beamline = widgets.Button( @@ -756,35 +741,35 @@ def __init__(self, **kwargs): ) self.save_button_beamline.on_click(self.save_beamline_scan) self.save_buttons = widgets.HBox([self.save_button_beamline]) - self.GUI = widgets.VBox([self.title_banner, self.control_buttons, self.layout, self.json_buttons,self.save_button_beamline], layout=self.default_GUI) + self.GUI = widgets.VBox([self.title_banner, self.control_buttons, self.layout, self.json_buttons,self.save_button_beamline], layout=self.default_GUI) display(self.GUI) # Everything is the same -- just save as a macro instead - + def save_beamline_scan(self, ext='.txt', sep='\t', b=None): SAVEDIR = str(self.save_dir) SAVENAME = str(self.save_name.value) - SAVEPATH = SAVEDIR + '/' + SAVENAME + '.txt' - + SAVEDIR + '/' + SAVENAME + '.txt' + df = self.save_as_dict() macro_details = self._build_individual_scan_files(df) - - + + macro = begin_macro(SAVENAME) # list of commands that will become the spiral scan - # Move the sample plate into the 'zero' position to begin alignment -- + # Move the sample plate into the 'zero' position to begin alignment -- macro += [move_motor('Sample Theta', 0.0, 0.1)] macro += [move_motor('Sample Z', 0, 0.1)] - - # Run add an I0 here --- - + + # Run add an I0 here --- + # Begin samples for sample in macro_details: name = sample['name'] posx = sample['posx'] posy = sample['posy'] - path = sample['path'] - - # Each sample -- + sample['path'] + + # Each sample -- macro += [add_comment(f"Begin Sample: {name}")] macro += [clear_instruments] macro.extend(run_dict_trajectory(PHOTODIODE_POS)) @@ -797,36 +782,36 @@ def save_beamline_scan(self, ext='.txt', sep='\t', b=None): # macro += [analog_from_file(name, path)] macro += [clear_instruments] macro.extend(run_dict_trajectory(PHOTODIODE_POS)) - macro += [add_prompt(f"Finished Macro")] + macro += [add_prompt("Finished Macro")] build_macro(SAVENAME, SAVEDIR, macro) - - + + def _build_individual_scan_files(self, df): - # Initialize the macro construction + # Initialize the macro construction all_sample_scripts = [] # Contains lists associated with each detector position - + MACRO_DIR = str(self.save_dir) - + # Cycle through samples and start saving data - for i, (index, sample) in enumerate(df.items()): + for i, (_index, sample) in enumerate(df.items()): # Get the name of the sample as chosen by user name = self.WIDGET.ALS_NAME+pad_digits(i+1) sample_attr = getattr(self, name) sample_name = sample_attr.name_of_sample.value - + # Get Motor positions for XRR MotorPositions = {} VariableMotors = {} - ScanList = [m for m in sample.keys() if "scan_" in m] + ScanList = [m for m in sample if "scan_" in m] MotorPositions = {k: v for k, v in sample.items() if k not in ScanList} beam_offset_counter = 0 - + # Update Motor Positions that will be overwritten by macro generation MotorPositions['ZPosition'] = 0.00 MotorPositions['ZFlipPosition'] = 3.1435 MotorPositions['DirectBeam'] = -2.00 MotorPositions['ThetaOffset'] = 0.00 - + for ii, scan in enumerate(ScanList): dm = sample[scan] exclude_motors = ['theta_end', 'energy_start', 'energy_stop', 'energy_delta'] @@ -848,18 +833,18 @@ def _build_individual_scan_files(self, df): round_this_motor = ['Sample X', 'Sample Y', 'Sample Z', 'Sample Theta', 'CCD Theta'] for motor in round_this_motor: sampdf[motor] = sampdf[motor].round(4) - + # Initialize RunFile if ii == 0: RunFile = sampdf else: RunFile = pd.concat([RunFile, sampdf], ignore_index=True) - + # save the sampdf as a macro file - SAVE_SCAN = MACRO_DIR + '/' + sample_name +'_PXR_'+str(i) + '.txt' + SAVE_SCAN = MACRO_DIR + '/' + sample_name +'_PXR_'+str(i) + '.txt' # Make sure the positions are correct RunFile = RunFile[[c for c in RunFile if c not in ['Exposure']] + ['Exposure']] - + RunFile.to_csv(SAVE_SCAN, index=False, sep='\t') clean_script(SAVE_SCAN) # Remove exposure and clear last line if it exists # Save all details required for the macro @@ -868,9 +853,9 @@ def _build_individual_scan_files(self, df): macro_stats['posx'] = RunFile['Sample X'].iloc[0] macro_stats['posy'] = RunFile['Sample Y'].iloc[0] macro_stats['path'] = SAVE_SCAN - + all_sample_scripts.append(macro_stats) # Save path for macro generation - + return all_sample_scripts def AngleRunGenerator_v2(MotorPositions, VariableMotors, Energy, theta_end): @@ -942,7 +927,7 @@ def AngleRunGenerator_v2(MotorPositions, VariableMotors, Energy, theta_end): # Generate all other motors based on the input Variable motors Variable_Motor_dict = {} - for key, value in VariableMotors.items(): # Cycle through the Variable Motor dictionary + for key, _value in VariableMotors.items(): # Cycle through the Variable Motor dictionary if 'Theta' not in key: Variable_Motor_dict[key] = [] Variable_Motor_dict[key].extend([VariableMotors[key][i]]*len(QList)) @@ -958,7 +943,7 @@ def AngleRunGenerator_v2(MotorPositions, VariableMotors, Energy, theta_end): # All Variable Motors #for motor in Variable_Motor_dict: # Variable_Motor_dict[motor].insert(0, Variable_Motor_dict[motor][0]) - + # Adding Additional points at the start to assess error in beam intensity for d in range(int(MotorPositions['I0Points'])): QList.insert(0,0) @@ -972,7 +957,7 @@ def AngleRunGenerator_v2(MotorPositions, VariableMotors, Energy, theta_end): # All Variable Motors for motor in Variable_Motor_dict: Variable_Motor_dict[motor].insert(0, Variable_Motor_dict[motor][0]) - + else: # for all of the ranges after the first set of samples ##Section is identical to the above AngleStart = AngleChange[i] # All of the relevant values are in terms of angles, but Q is calculated as a check @@ -1011,21 +996,21 @@ def AngleRunGenerator_v2(MotorPositions, VariableMotors, Energy, theta_end): SampleXAddition=[XPosition]*len(QListAddition) BeamLineEnergyAddition=[Energy]*len(QListAddition) - + SampleYAddition=YPosition+Zdelta/2+Zdelta/2*np.sin(np.radians(SampleThetaAdditionArray)) - SampleZAddition=ZPosition+Zdelta/2*(np.cos(np.radians(SampleThetaAdditionArray))-1) + SampleZAddition=ZPosition+Zdelta/2*(np.cos(np.radians(SampleThetaAdditionArray))-1) SampleYAddition=SampleYAddition.tolist() SampleZAddition=SampleZAddition.tolist() # Generate all other motors based on the input Variable motors Variable_Motor_dict_Addition = {} - for key, value in VariableMotors.items(): # Cycle through the Variable Motor dictionary + for key, _value in VariableMotors.items(): # Cycle through the Variable Motor dictionary if 'Theta' not in key: Variable_Motor_dict_Addition[key] = [] Variable_Motor_dict_Addition[key].extend([VariableMotors[key][i]]*len(QListAddition)) - + #Check to see if any of the variable motors have moved to add buffer points - for key, value in VariableMotors.items(): + for key, _value in VariableMotors.items(): if 'Theta' not in key: if VariableMotors[key][i] != VariableMotors[key][i-1]: for d in range(int(MotorPositions['Buffer'])): @@ -1038,7 +1023,7 @@ def AngleRunGenerator_v2(MotorPositions, VariableMotors, Energy, theta_end): BeamLineEnergyAddition.insert(0,BeamLineEnergyAddition[d]) for motor in Variable_Motor_dict_Addition: Variable_Motor_dict_Addition[motor].insert(0, Variable_Motor_dict_Addition[motor][d]) - + QList.extend(QListAddition) SampleTheta.extend(SampleThetaAddition) CCDTheta.extend(CCDThetaAddition) @@ -1046,9 +1031,9 @@ def AngleRunGenerator_v2(MotorPositions, VariableMotors, Energy, theta_end): SampleY.extend(SampleYAddition) SampleZ.extend(SampleZAddition) BeamLineEnergy.extend(BeamLineEnergyAddition) - for motor, addition in zip(Variable_Motor_dict.keys(), Variable_Motor_dict_Addition.keys()): + for motor, addition in zip(Variable_Motor_dict.keys(), Variable_Motor_dict_Addition.keys(), strict=False): Variable_Motor_dict[motor].extend(Variable_Motor_dict_Addition[addition]) - + #Check what side the sample is on. If on the bottom, sample theta starts @ -180 if MotorPositions['ReverseHolder'] == 1: SampleTheta=[theta-180 for theta in SampleTheta] # for samples on the backside of the holder, need to check and see if this is correct @@ -1062,12 +1047,12 @@ def AngleRunGenerator_v2(MotorPositions, VariableMotors, Energy, theta_end): sampdf['Beamline Energy'] = BeamLineEnergy for key, item in Variable_Motor_dict.items(): sampdf[key] = item - + return sampdf#(SampleX, SampleY, SampleZ, SampleTheta, CCDTheta, HOSList, HESList, BeamLineEnergy, ExposureList, QList) - - + + @@ -1081,13 +1066,13 @@ def locate_point_density(angle, crossover_list, density_list): if int(crossover_list[i]) <= angle <= int(crossover_list[i+1]): return int(density_list[i]) return int(density_list[-1]) - + def update_dict(old_dict, new_dict): - for key in old_dict.keys(): + for key in old_dict: if key in new_dict: old_dict[key] = new_dict[key] return old_dict - + def clean_for_widgets(my_dict): for key, value in my_dict.items(): if isinstance(value, list): diff --git a/python/pyref/beamline/rsoxs_widgets.py b/python/pyref/beamline/rsoxs_widgets.py index 47f7c6f..5ce9228 100644 --- a/python/pyref/beamline/rsoxs_widgets.py +++ b/python/pyref/beamline/rsoxs_widgets.py @@ -4,33 +4,26 @@ ALS beamline 11.0.1.2. """ # Base Packages -import sys -import os -import json -import re -import copy -import inspect import datetime +import json +import os from pathlib import Path +# Visualization Packages +import ipywidgets as widgets + # Math packages import numpy as np import pandas as pd - -# Visualization Packages -import ipywidgets as widgets -from IPython.display import display, clear_output -from PyQt6.QtWidgets import QApplication, QFileDialog, QWidget -from PyQt6.QtCore import QDir +from IPython.display import display from pyref.beamline.base_widgets import ( - ALS_ScriptGenWidget, ALS_ExperimentWidget, ALS_MeasurementWidget, + ALS_ScriptGenWidget, clean_script, pad_digits, ) - from pyref.beamline.beamline_scan_macros import * BASE_FILE_PATH = Path(__file__).parent @@ -61,7 +54,7 @@ class RSOXS_Scan(ALS_MeasurementWidget): ACCORDION_SIZE = widgets.Layout(width='20rem') ESCAN_SIZE = widgets.Layout(width='5rem') HBOX_SIZE = widgets.Layout(width='50rem') - + CARBON_ESCAN = { "Step 1":[270.0, 280.0, 2, 0.5], "Step 2":[280.0, 284.0, 0.2, 1], @@ -83,15 +76,15 @@ def __init__(self, save_path="",experiment_widget=None, **kwargs): # Create some generic buttons that will help duplicate/delete rows/columns # New title: self.delete_row_buttons = {} - self.copy_row_buttons = {} + self.copy_row_buttons = {} # Initialize the table given inputs # Create the output that will include a title, accordion, and delete buttons scan_title_temp = kwargs.get('name', 'Default Name') self.scan_title = widgets.HTML(value="Name of Sample: ", layout=self.LABEL_SIZE) self.scan_title_input = widgets.Text(value=scan_title_temp, layout=self.LABEL_SIZE) - - self.escan_details = self.generate_escan(saved_data['ESCAN']) # Create the - self.scan_details = self.generate_accordion(saved_data) + + self.escan_details = self.generate_escan(saved_data['ESCAN']) # Create the + self.scan_details = self.generate_accordion(saved_data) self.display_table = widgets.HBox( [ @@ -111,26 +104,22 @@ def __init__(self, save_path="",experiment_widget=None, **kwargs): def generate_accordion(self, d): scan = [] - for i, (key, item) in enumerate(d.items()): + for _i, (key, item) in enumerate(d.items()): if 'ESCAN' in key: if isinstance(item, str): dropdown_item = item elif isinstance(item, dict): - dropdown_item = 'CUSTOM' + dropdown_item = 'CUSTOM' dropdown = widgets.Dropdown(value=dropdown_item, options=self.ExperimentWidget.ScriptgenWidget.ESCAN_LIST.keys(), description=key, layout=self.ACCORDION_SIZE) dropdown.observe(self.update_escan, 'value') # Observe when this changes self.ExperimentWidget.ScriptgenWidget.dummy_widget.observe(self.escan_list_updated, 'value') scan.extend([dropdown]) else: scan.extend([widgets.FloatText(value=item, description=key, layout=self.ACCORDION_SIZE)]) - setattr( - self, - "scan_info", - scan - ) - output = getattr(self, "scan_info") + self.scan_info = scan + output = self.scan_info output.insert(0, widgets.HTML(value="Scan Details:")) - return widgets.VBox(children=output) + return widgets.VBox(children=output) def generate_escan(self, input_d): escan_info = [] @@ -140,7 +129,7 @@ def generate_escan(self, input_d): d = self.ExperimentWidget.ScriptgenWidget.ESCAN_LIST[input_d] elif isinstance(input_d, dict): d = input_d - + title_row = widgets.HBox( children=[ widgets.HTML(value="Step", layout=self.ESCAN_SIZE), @@ -150,7 +139,7 @@ def generate_escan(self, input_d): widgets.HTML(value="Exposure", layout=self.ESCAN_SIZE), ] ) - for i, (key, item) in enumerate(d.items()): + for _i, (key, item) in enumerate(d.items()): if 'Step' in key: temp_label = widgets.HTML(value=f"{key}", layout=self.ESCAN_SIZE) temp_start = widgets.FloatText(value=item[0], layout=self.ESCAN_SIZE) @@ -166,12 +155,8 @@ def generate_escan(self, input_d): ) ] ) - setattr( - self, - "escan_info", - escan_info - ) - output = getattr(self, "escan_info") + self.escan_info = escan_info + output = self.escan_info output.insert(0, title_row) output.insert(0, widgets.HTML(value="Escan Details:")) return widgets.VBox(output) @@ -190,7 +175,7 @@ def update_escan(self, change): d = CARBON_ESCAN self.escan_details = self.generate_escan(d) self.update_table() - + def escan_list_updated(self, change): if change['type'] == 'change' and change['name'] == 'value': dropdown = self.scan_info[1] @@ -216,12 +201,12 @@ def output_dict(self): output = {} scan_details = self.scan_details.children escan_details = self.escan_details.children - + # Process scan details for item in scan_details: key = item.description value = item.value - + if not key: # Skip empty description (title row) continue @@ -229,14 +214,14 @@ def output_dict(self): output[key] = self._process_escan_details(escan_details) else: output[key] = value - + # Add scan title output['name'] = self.scan_title_input.value return output def _process_escan_details(self, escan_details): """ - Helper function to process ESCAn details while exporting as a dictionary + Helper function to process ESCAn details while exporting as a dictionary. """ escan_data = {} @@ -244,13 +229,13 @@ def _process_escan_details(self, escan_details): row_values = [child.value for child in row.children] escan_key = row_values[0] escan_values = row_values[1:] - + # Check if any escan_values are zero, and skip the row if so. # if any(val == 0 for val in escan_values): # continue - + escan_data[escan_key] = escan_values - + return escan_data @@ -260,7 +245,7 @@ def old_output_dict(self): key = item.description val = item.value if len(key) < 1: # Skip the title row - continue + continue if 'ESCAN' in key: # Cycle through the Escan values de = {} for i, row in enumerate(b.escan_details.children): @@ -293,7 +278,7 @@ class RSOXS_Detector(ALS_ExperimentWidget): 'DetX': 96.0, 'DetY': 100.0, 'DetTheta': 0.0127, - 'BS': 11.0, + 'BS': 11.0, 'SampZ': 0.0, 'SampXOffset': 0.0, 'SampYOffset':0.0 @@ -302,7 +287,7 @@ class RSOXS_Detector(ALS_ExperimentWidget): 'DetX': 'Detector X', 'DetY': 'Detector Y', 'DetTheta': 'CCD Theta', - 'BS': 'Beamstop', + 'BS': 'Beamstop', 'SampZ': 'Sample Z', 'SampXOffset': 'Sample X Offset', 'SampYOffset':'Sample Y Offset' @@ -350,7 +335,7 @@ def __init__(self, save_path="", constants=DEFAULT_CONSTANTS.copy(),constants_ti self.add_scan(self.layout, self.WIDGET, self.WIDGET.ALS_NAME, **df) if len(self.layout.children) == 0: self.add_scan(self.layout, self.WIDGET, self.WIDGET.ALS_NAME) - + self.GUI_experiment = widgets.VBox([self.widget_title, self.menu_box, self.control_buttons, self.layout]) def add_scan(self, layout, widget, widget_str, b=None, **kwargs): @@ -372,7 +357,7 @@ def copy_scan(self, layout, widget, widget_str, b=None): df = {} setattr(self, widget_str + pad_digits(index), widget(experiment_widget=self, **df)) self.update_scan_tab(layout, widget_str) - + def delete_scan(self, layout, widget, widget_str, b=None): try: index = self.layout.selected_index + 1 @@ -400,21 +385,21 @@ def update_scan_tab(self, layout, widget_str): self.layout.children = display for tab in np.arange(len(layout.children)): layout.set_title(tab, titles[tab]) - + def update_title(self, change): index = self.layout.selected_index self.layout.set_title(index, change['owner'].value) # No idea why it is setup this way. - + class RSOXS_ScriptGen(ALS_ScriptGenWidget): EXPERIMENT_NAME = "RSoXS" WIDGET = RSOXS_Detector WIDGET_TITLE = 'Detector Position ' JSON_TITLE = "RSOXS" GUI_SIZE = widgets.Layout(widget='100rem') - + MOTOR_ORDER = ['Beamline Energy', 'Sample X', 'Sample Y', 'Sample Z', 'Detector X', 'Detector Y', 'CCD Theta', 'Beamstop', 'CCD Camera Shutter Inhibit', 'Exposure'] - + CARBON_ESCAN = { "Step 1":[270.0, 280.0, 2, 0.5], "Step 2":[280.0, 284.0, 0.2, 1], @@ -436,14 +421,14 @@ class RSOXS_ScriptGen(ALS_ScriptGenWidget): 'CARBON_ESCAN':CARBON_ESCAN, 'QUICK_ESCAN':QUICK_ESCAN } - + def __init__(self, save_path="", escan_path=DEFAULT_ESCAN_PATH, **kwargs): super().__init__(exp_name=self.EXPERIMENT_NAME, child_widget=self.WIDGET, save_path=save_path, tab_title=self.WIDGET_TITLE,json_title=self.JSON_TITLE, **kwargs) - + # RSOXS specific details: self.rsoxs_button_style = {'button_color':'#5D4777', 'text_color':'white'} #Book-keeping attributes - + # Make additional buttons before the table """ INFORMATION ON THE ESCAN DIRECTORY -- WHERE ARE JSON FILES SAVED @@ -466,7 +451,7 @@ def __init__(self, save_path="", escan_path=DEFAULT_ESCAN_PATH, **kwargs): description = "" ) self.dummy_widget = widgets.Valid(value=False, description='dummy widget') - + self.escan_dir = escan_path # Update the directory with the default # Buttons involved with adding detector positions @@ -487,33 +472,31 @@ def __init__(self, save_path="", escan_path=DEFAULT_ESCAN_PATH, **kwargs): self.copy_detpos_button.on_click(lambda b: self.copy_tab(self.layout, self.WIDGET, self.WIDGET.ALS_NAME)) self.delete_detpos_button.on_click(lambda b: self.delete_tab(self.layout, self.WIDGET, self.WIDGET.ALS_NAME)) # Buttons involved with adding columns to the table - - self.additional_buttons.children = tuple( - [ + + self.additional_buttons.children = ( widgets.HBox([self.browse_escan_directory_button, self.update_escan_directory_button, self.display_escan_directory]), widgets.HBox([self.new_detpos_button, self.copy_detpos_button, self.delete_detpos_button]) - ] - ) - + ) + # Select box for motor selection: - self.select_col_title = widgets.HTML(value=f"Select optional motors to run") + self.select_col_title = widgets.HTML(value="Select optional motors to run") self.select_cols = widgets.SelectMultiple( options=AVAILABLE_RSOXS_MOTORS, disabled=False ) self.rsoxs_menu = widgets.VBox([self.select_col_title,self.select_cols]) self.GenerateExperiment(self.layout, self.WIDGET.ALS_NAME, self.WIDGET, **kwargs) - self.GUI = widgets.VBox([self.title_banner, self.control_buttons, self.layout, self.json_buttons, self.rsoxs_menu, self.save_buttons], layout=self.GUI_SIZE) + self.GUI = widgets.VBox([self.title_banner, self.control_buttons, self.layout, self.json_buttons, self.rsoxs_menu, self.save_buttons], layout=self.GUI_SIZE) display(self.GUI) - + #@property #def escan_path(self): # return self._escan_path - # + # #@escan_path.setter #def escan_path(self, path): # self._escan_path = Path(path) - + def browse_escan_directory(self, b=None): self.escan_dir = super().browse_directory() #self.update_escan_list(self.escan_dir) @@ -524,17 +507,17 @@ def update_escan_directory(self, b=None): @property def escan_dir(self): return self._escan_dir - + @escan_dir.setter def escan_dir(self, val): self._escan_dir=val self.display_escan_directory.value = "Escan Directory:" + str(self._escan_dir) self.update_escan_list(self._escan_dir) - # Update the global dictionary + # Update the global dictionary def update_escan_list(self, path): """ - Loads all .json files in a specified path that contain 'ESCANs' for RSOXS + Loads all .json files in a specified path that contain 'ESCANs' for RSOXS. """ output_dict = {} @@ -542,7 +525,7 @@ def update_escan_list(self, path): for filename in os.listdir(path): if filename.endswith(".json"): file_path = os.path.join(path, filename) - with open(file_path, 'r') as f: + with open(file_path) as f: first_line = f.readline().strip() # Read the first line and remove white space if first_line == "# ESCAN": # Discard the first line and read the rest @@ -561,27 +544,27 @@ def update_escan_list(self, path): self.ESCAN_LIST = self.ESCAN_LIST | output_dict self.dummy_widget.value = not self.dummy_widget.value # This is watched by all dropdowns to change the ESCAN_LIST when updated - - + + def save_script(self, ext='.txt', sep='\t', b=None): SAVEDIR = str(self.save_dir) SAVENAME = str(self.save_name.value) SAVEPATH = SAVEDIR + '/' + SAVENAME + '.txt' df = self.save_as_dict() # Get the data as a dictionary - + list_of_sample_scripts = [] number_of_detectors = len(df) # How many detector positions? # Cycle through samples and start saving data - for i, (index, detector) in enumerate(df.items()): - ScanList = [s for s in detector.keys() if "scan_" in s] - number_of_samples = len(ScanList) + for _i, (_index, detector) in enumerate(df.items()): + ScanList = [s for s in detector if "scan_" in s] + len(ScanList) DetectorPositions = {k: v for k, v in detector.items() if k not in ScanList} - for ii, scan in enumerate(ScanList): + for _ii, scan in enumerate(ScanList): dm = {key: value for key, value in detector[scan].items() if key != "name"} # Drop the name which is used for saving but not scanning motors_to_run = ['Sample X', 'Sample Y'] - motors_to_run.extend([motor for motor in self.select_cols.value]) + motors_to_run.extend(list(self.select_cols.value)) #Positions = {key: value for key, value in dm.items() if key in self.select_cols.value} Positions = {key: value for key, value in dm.items() if key in motors_to_run} escan_details = self._build_escan(dm['ESCAN']) # Collect all the info for the Escan @@ -601,11 +584,11 @@ def save_script(self, ext='.txt', sep='\t', b=None): if number_of_detectors > 1: for motor, pos in DetectorPositions.items(): if "DetX" in motor: - key = 'Detector X' + key = 'Detector X' elif "DetY" in motor: - key = 'Detector Y' + key = 'Detector Y' elif "DetTheta" in motor: - key = 'CCD Theta' + key = 'CCD Theta' elif "BS" in motor: key = 'Beamstop' elif "SampXOffset" in motor: @@ -620,25 +603,25 @@ def save_script(self, ext='.txt', sep='\t', b=None): for expo in np.unique(Exposure): df_samp = pd.concat([df_samp.loc[0].to_frame().T, df_samp], ignore_index=True) # Add a frame at the beginning of the sample df_samp = pd.concat([df_samp, df_samp.loc[df_samp.index[-1]].to_frame().T], axis=0, ignore_index=True) # Add a frame at the end of the sample - - df_samp.loc[0, 'CCD Camera Shutter Inhibit'] = 1 # Close the shutter + + df_samp.loc[0, 'CCD Camera Shutter Inhibit'] = 1 # Close the shutter df_samp.loc[df_samp.index[-1], 'CCD Camera Shutter Inhibit'] = 1 df_samp.loc[0, 'Exposure'] = expo # Set exposure df_samp.loc[df_samp.index[-1], 'Exposure'] = expo list_of_sample_scripts.append(df_samp) - + RunFile = pd.concat(list_of_sample_scripts, ignore_index=True) # Only export with the Adjustable Motors in the correct order. - + # Cleanup the output because the ALS requires specific things cols = list(RunFile.columns) Ordered_Cols = [item for item in self.MOTOR_ORDER if item in cols] RunFile = RunFile[Ordered_Cols] - + RunFile.to_csv(SAVEPATH, index=False, sep='\t') try: - with open(SAVEPATH, 'r') as f: + with open(SAVEPATH) as f: lines = f.readlines() if not lines: # Empty file return @@ -656,13 +639,13 @@ def save_script(self, ext='.txt', sep='\t', b=None): except Exception as e: print(f"An error occured: {e}") del lines #Remove it from memory (it can be large) - - - + + + def _build_escan(self, d): energy_array = np.array([]) exposure_array = np.array([]) - for key, item in d.items(): + for _key, item in d.items(): if item[2] == 0: energy_subset = np.array([item[0]]) else: @@ -671,15 +654,15 @@ def _build_escan(self, d): energy_array = np.concatenate((energy_array, energy_subset)) exposure_array = np.concatenate((exposure_array, exposure_subset)) return (np.round(energy_array, 2), exposure_array) - - + + def update_dict(old_dict, new_dict): - for key in old_dict.keys(): + for key in old_dict: if key in new_dict: old_dict[key] = new_dict[key] return old_dict - + def clean_for_widgets(my_dict): for key, value in my_dict.items(): if isinstance(value, list): @@ -693,7 +676,7 @@ def clean_for_widgets(my_dict): """ class RSOXS_Detector_Macro(RSOXS_Detector): - + DEFAULT_CONSTANTS = { 'SampXOffset': 0.0, 'SampYOffset':0.0, @@ -704,35 +687,35 @@ class RSOXS_Detector_Macro(RSOXS_Detector): 'SampYOffset':'Sample Y Offset', 'Trajectory' : 'Trajectory' } - + def __init__(self, save_path="", scriptgen_widget=None, **kwargs): - + constants = self.DEFAULT_CONSTANTS.copy() constants_titles = self.DEFAULT_CONSTANTS_TITLES.copy() # Update from kwargs constants.update({k: v for k, v in kwargs.items() if k in constants}) super().__init__(save_path=save_path, scriptgen_widget=scriptgen_widget, constants=constants, constants_titles=constants_titles, **kwargs) self.GUI_experiment.children = () # Empty the GUI to repopulate it for the MACRO - - # New content for specific trajectories -- - + + # New content for specific trajectories -- + # Dummy widget used to update dropdown if things change self.dummy_widget = widgets.Valid(value=False, description='dummy widget') # Does not exist self.dummy_widget.observe(self.trajectory_list_updated, 'value') - + self._trajectory_dir = "" # Initialize the value # Info to describe trajectory self.trajectory_info = widgets.HTML(value = "Trajectory Directory: ", style={'description_width': '160px'}) # Title self.display_trajectory_directory = widgets.HTML(str(self._trajectory_dir)) # Display widget self.trajectory_dir = DEFAULT_TRAJECTORY_PATH # Set the value using @property - + # Button to change the trajectory menu. Not usually in use -- May remove in future update self.browse_trajectory_directory_button = widgets.Button( description="Select Directory", layout = self.default_button_style ) self.browse_trajectory_directory_button.on_click(self.browse_trajectory_directory) # Button functionality - # Storage Box that contains all Trajectory related information -- + # Storage Box that contains all Trajectory related information -- self.trajectory_directory_items = widgets.VBox([widgets.HBox([self.trajectory_info,self.display_trajectory_directory]), self.browse_trajectory_directory_button]) # Dropdown used to select trajectory associated with the detector position @@ -743,13 +726,13 @@ def __init__(self, save_path="", scriptgen_widget=None, **kwargs): layout = widgets.Layout(width='30rem'), description = 'Select Detector Trajectory', style = {'description_width':'10rem'} - ) + ) # Dummy widget to update TRAJECTORY_LIST self.GUI_experiment = widgets.VBox([self.widget_title, self.trajectory_directory_items,self.Trajectory, self.menu_box, self.control_buttons, self.layout]) - - + + def browse_trajectory_directory(self, b): self.trajectory_dir = self.ScriptgenWidget.super().browse_directory() @@ -761,10 +744,10 @@ def trajectory_dir(self, val): self._trajectory_dir = val self.display_trajectory_directory.value = str(self._trajectory_dir) self.update_trajectory_list(val) - + def update_trajectory_list(self, path): """ - Loads all .json files in a specified path that contain 'ESCANs' for RSOXS + Loads all .json files in a specified path that contain 'ESCANs' for RSOXS. """ output_dict = {} @@ -772,7 +755,7 @@ def update_trajectory_list(self, path): for filename in os.listdir(path): if filename.endswith(".json"): file_path = os.path.join(path, filename) - with open(file_path, 'r') as f: + with open(file_path) as f: first_line = f.readline().strip() # Read the first line and remove white space if first_line == "# Trajectory": # Discard the first line and read the rest @@ -791,7 +774,7 @@ def update_trajectory_list(self, path): self.ScriptgenWidget.TRAJECTORY_LIST = output_dict self.dummy_widget.value = not self.dummy_widget.value # This is watched by all dropdowns to change the ESCAN_LIST when updated - + def trajectory_list_updated(self, change): if change['type'] == 'change' and change['name'] == 'value': dropdown = self.Trajectory @@ -800,15 +783,15 @@ def trajectory_list_updated(self, change): class RSOXS_MacroGen(RSOXS_ScriptGen): WIDGET = RSOXS_Detector_Macro TRAJECTORY_LIST = {} - + def __init__(self, save_path="", **kwargs): super().__init__(save_path=save_path, **kwargs) self.GUI.children = () # Clean out the default GUI options to make room for the new version # New Banner for the MACRO generator - self.title_banner = widgets.HTML(value=f"

{self.EXPERIMENT_NAME} Macro Generator

") + self.title_banner = widgets.HTML(value=f"

{self.EXPERIMENT_NAME} Macro Generator

") # All buttons are the same as ScriptGen except the MacroGen --- - + # NEW BUTTONS THAT HELP SAVE THE BEAMLINE MACRO # New layouts and styles for the macro-specific options self.macro_button_layout = widgets.Layout(widget='50rem') @@ -816,22 +799,22 @@ def __init__(self, save_path="", **kwargs): self.macro_box_layout = widgets.Layout(widget='60rem') self.macro_title_style = {'font_size': '14px'} self.macro_info_style = {'font_size': '12px'} - - # TItle for Macro options -- + + # TItle for Macro options -- self.macro_title = widgets.HTML(value="Options for Beamline Scan Macro", style=self.macro_title_style) """ - Build the Spiral Scan menu for Spiral Scans -- Use for preliminary + Build the Spiral Scan menu for Spiral Scans -- Use for preliminary """ self.spiral_tab = self.build_spiral_tab() self.nexafs_tab = self.build_nexafs_tab() self.i0_tab = self.build_i0_tab() - + self.AdditionalMacroOptions = widgets.Tab( children = [self.spiral_tab, self.nexafs_tab, self.i0_tab], layout = self.default_GUI, titles = ['Spiral Scan', 'NEXAFS', 'I0 Options'] ) - + self.ADDITIONAL_MACRO_BUTTONS = widgets.VBox( [ self.macro_title, @@ -839,20 +822,20 @@ def __init__(self, save_path="", **kwargs): ], layout=self.macro_box_layout ) - + self.GenerateExperiment(self.layout, self.WIDGET.ALS_NAME, self.WIDGET, **kwargs) - self.GUI = widgets.VBox([self.title_banner, self.control_buttons, self.layout, self.json_buttons, self.rsoxs_menu, self.ADDITIONAL_MACRO_BUTTONS, self.save_button_beamline], layout=self.GUI_SIZE) + self.GUI = widgets.VBox([self.title_banner, self.control_buttons, self.layout, self.json_buttons, self.rsoxs_menu, self.ADDITIONAL_MACRO_BUTTONS, self.save_button_beamline], layout=self.GUI_SIZE) display(self.GUI) - - + + def save_script(self, ext='.txt', sep='\t', b=None): SAVEDIR = str(self.save_dir) SAVENAME = str(self.save_name.value) - SAVEPATH = SAVEDIR + '/' + SAVENAME + '.txt' + SAVEDIR + '/' + SAVENAME + '.txt' df = self.save_as_dict() # Get the data as a dictionary macro_details = self._build_individual_scan_files(df) - + # Collect i0 details for broader macro i0_condition = self.I0_select.value i0_posx = self.I0_xpos.value @@ -861,7 +844,7 @@ def save_script(self, ext='.txt', sep='\t', b=None): if i0_condition != 'Never': # Waste time building a scan? i0_script = SAVEDIR + '/' + "I0_script.txt" self._save_escan_script(i0_script, self.ESCAN_LIST[i0_escan], exposure=0.5) - + # START BUILDING MACRO scans_elapsed = 0 # If I0s are to be taken after a certain number of scans macro = begin_macro(SAVENAME) # list of commands that will become the beamline scan @@ -891,10 +874,10 @@ def save_script(self, ext='.txt', sep='\t', b=None): macro += [clear_instruments] # Go to the Photodiode only macro.extend(run_dict_trajectory(Photodiode_Far)) # Go to the photodiode position if self.post_run_nexafs.value: # Run NEXAFS after cycle? - macro += [add_comment(f"Running NEXAFS")] + macro += [add_comment("Running NEXAFS")] NEXAFS_script = SAVEDIR + '/' + "NEXAFS_script.txt" dnexafs = df[f'det_{pad_digits(1)}'] - ScanList = [s for s in dnexafs.keys() if "scan_" in s] # Get all the scans for the first detector position + ScanList = [s for s in dnexafs if "scan_" in s] # Get all the scans for the first detector position for sample in ScanList: macro += [move_motor('Sample X', dnexafs[sample]['Sample X'], 0.1)] macro += [move_motor('Sample Y', dnexafs[sample]['Sample Y'], 0.1)] @@ -903,36 +886,36 @@ def save_script(self, ext='.txt', sep='\t', b=None): self._save_escan_script(NEXAFS_script, self.ESCAN_LIST[self.nexafs_escan.value], exposure=0.5) else: self._save_escan_script(NEXAFS_script, dnexafs[sample]['ESCAN'], exposure=0.5) - + macro += [analog_from_file(dnexafs[sample]['name'] + "_NEXAFS", NEXAFS_script)] macro.extend(run_I0("I0_ScanComplete", i0_script, i0_posx, i0_posy)) - macro += [add_prompt(f"Finished Macro")] - + macro += [add_prompt("Finished Macro")] + build_macro(SAVENAME, SAVEDIR, macro) - + def save_spiral(self, ext='.txt.', sep='\t', b=None): SAVEDIR = str(self.save_dir) SAVENAME = str(self.save_name.value) - SAVEPATH = SAVEDIR + '/' + SAVENAME + '_spiral.txt' + SAVEDIR + '/' + SAVENAME + '_spiral.txt' df = self.save_as_dict() # Get the data as a dictionary macro_details = self._build_spiral_scan(df) - + macro = begin_macro(SAVENAME) # list of commands that will become the spiral scan macro += [set_instrument("CCD", exp=0.1, extension='fits')] # Turn on CCD for data collection macro.extend(run_dict_trajectory(macro_details['Trajectory'])) # Move to the position where data is collected for sample in macro_details['spiral_scripts']: macro += [analog_from_file(sample[0], sample[1])] - macro += [add_prompt(f"Finished Macro")] + macro += [add_prompt("Finished Macro")] build_macro(SAVENAME, SAVEDIR, macro) def _build_individual_scan_files(self, df): - # Initialize the macro construction + # Initialize the macro construction list_of_detectors = [] # Contains lists associated with each detector position number_of_detectors = len(df) # How many detector positions? - + # Where the data will be saved MACRO_DIR = str(self.save_dir) """ @@ -941,28 +924,28 @@ def _build_individual_scan_files(self, df): Current naming convention: {name}_det{X}.txt """ # Cycle through detector positions and start saving scripts - for i, (index, detector) in enumerate(df.items()): + for i, (_index, detector) in enumerate(df.items()): # Initialize storage for each detector detector_details = {} # Dictionary that contains all details pertaining to the current detector position all_sample_scripts = [] # List that will contain all sample dataframes for the current detector - # Start collecting data -- - ScanList = [s for s in detector.keys() if "scan_" in s] # Get all the scans in the current detector position + # Start collecting data -- + ScanList = [s for s in detector if "scan_" in s] # Get all the scans in the current detector position # number_of_samples = len(ScanList) # How many samples in total? DetectorPositions = {k: v for k, v in detector.items() if k not in ScanList} # All items relevant to setting up the detector geometry # Start compiling details for the detector detector_details['Trajectory'] = self.TRAJECTORY_LIST[DetectorPositions['Trajectory']] - - # Run through each ScanList and build the individual scan files for each sample -- + + # Run through each ScanList and build the individual scan files for each sample -- for ii, scan in enumerate(ScanList): dm = {key: value for key, value in detector[scan].items() if key != "name"} # Drop the name which is used for saving but not scanning sample_name = detector[scan]['name'] motors_to_run = ['Sample X', 'Sample Y'] # Define the sample position if 'Sample Theta' in self.select_cols.value: motors_to_run.extend(['Sample Theta']) # Add Theta if you want to include it - + # Get motor positions to cycle through - Positions = {key: value for key, value in dm.items() if key in motors_to_run} + Positions = {key: value for key, value in dm.items() if key in motors_to_run} # Build the Escan based on RSOXS conventions escan_details = self._build_escan(dm['ESCAN']) # Collect all the info for the Escan Escan = escan_details[0] # Get energies @@ -992,31 +975,31 @@ def _build_individual_scan_files(self, df): for expo in np.unique(Exposure): df_samp = pd.concat([df_samp.loc[0].to_frame().T, df_samp], ignore_index=True) # Add a frame at the beginning of the sample df_samp = pd.concat([df_samp, df_samp.loc[df_samp.index[-1]].to_frame().T], axis=0, ignore_index=True) # Add a frame at the end of the sample - - df_samp.loc[0, 'CCD Camera Shutter Inhibit'] = 1 # Close the shutter + + df_samp.loc[0, 'CCD Camera Shutter Inhibit'] = 1 # Close the shutter df_samp.loc[df_samp.index[-1], 'CCD Camera Shutter Inhibit'] = 1 df_samp.loc[0, 'Exposure'] = expo # Set exposure df_samp.loc[df_samp.index[-1], 'Exposure'] = expo - + # Cleanup the script because the ALS requires specific things cols = list(df_samp.columns) Ordered_Cols = [item for item in self.MOTOR_ORDER if item in cols] df_samp = df_samp[Ordered_Cols] - + # save the df_samp as a macro file - SAVE_SCAN = MACRO_DIR + '/' + sample_name +'_det'+str(i) + '_escan'+str(ii) + '.txt' + SAVE_SCAN = MACRO_DIR + '/' + sample_name +'_det'+str(i) + '_escan'+str(ii) + '.txt' df_samp.to_csv(SAVE_SCAN, index=False, sep='\t') clean_script(SAVE_SCAN) # Remove exposure and clear last line if it exists - + all_sample_scripts.append((sample_name, SAVE_SCAN)) # Save path for macro generation - + detector_details['sample_scripts'] = all_sample_scripts # Collect all scripts under the detector list_of_detectors.append(detector_details) return list_of_detectors - - - def _build_spiral_scan(self, df): + + + def _build_spiral_scan(self, df): # Where will I save these files? MACRO_DIR = str(self.save_dir) @@ -1029,19 +1012,18 @@ def _build_spiral_scan(self, df): spiral_step = self.spiral_scan_step.value grid_size = int(self.spiral_scan_grid_size.value) exposure_choice = self.spiral_scan_exposure.value - + # Cycle through samples and start saving data try: detector = df[f"det_{pad_digits(detector_choice)}"] except: detector = df[f"det_{pad_digits(1)}"] # Default to the first one (will always exist) - + # Start getting scans - ScanList = [s for s in detector.keys() if "scan_" in s] - number_of_samples = len(ScanList) - list_of_sample_scripts = [] + ScanList = [s for s in detector if "scan_" in s] + len(ScanList) DetectorPositions = {k: v for k, v in detector.items() if k not in ScanList} - + detector_details = {} detector_details['Trajectory'] = self.TRAJECTORY_LIST[DetectorPositions['Trajectory']] #if 'Sample Z' in self.select_cols.value: @@ -1050,9 +1032,9 @@ def _build_spiral_scan(self, df): for i, scan in enumerate(ScanList): sample_grid_df = [] # Where we will store the sample position scans # Drop the name which is used for saving but not scanning - dm = {key: value for key, value in detector[scan].items() if key != "name"} + {key: value for key, value in detector[scan].items() if key != "name"} sample_name = detector[scan]['name'] # Name to save the script - + Escan = np.array(energy_float) # Collect all the info for the Escan Exposure = np.full_like(Escan, exposure_choice) @@ -1066,14 +1048,14 @@ def _build_spiral_scan(self, df): center_x = detector[scan]['Sample X'] center_y = detector[scan]['Sample Y'] # Create grid - grid = generate_2d_grid(center_x, center_y, spiral_step, grid_size=grid_size) - - for ii, point in enumerate(grid): + grid = generate_2d_grid(center_x, center_y, spiral_step, grid_size=grid_size) + + for _ii, point in enumerate(grid): df_grid = df_temp.copy() df_grid['Sample X'] = np.full_like(Escan, point[0]) df_grid['Sample Y'] = np.full_like(Escan, point[1]) df_grid['CCD Camera Shutter Inhibit'] = np.full_like(Escan, 0) # Add the column - + # Cleanup the script because the ALS requires specific things cols = list(df_grid.columns) Ordered_Cols = [item for item in self.MOTOR_ORDER if item in cols] @@ -1082,16 +1064,16 @@ def _build_spiral_scan(self, df): df_samp = pd.concat(sample_grid_df, ignore_index=True) # Combine all samples into a single scan to run df_out = df_samp.sort_values(by=['Beamline Energy']) # Only move the undulator once or twice - + # save the df_samp as a macro file - SAVE_SCAN = MACRO_DIR + '/' + sample_name +'_det'+str(detector_choice) + '_spiral'+str(i) + '.txt' + SAVE_SCAN = MACRO_DIR + '/' + sample_name +'_det'+str(detector_choice) + '_spiral'+str(i) + '.txt' df_out.to_csv(SAVE_SCAN, index=False, sep='\t') clean_script(SAVE_SCAN) # Remove exposure and clear last line if it exists - + list_of_scripts.append((sample_name+"_spiral"+str(i),SAVE_SCAN)) # Save the path detector_details['spiral_scripts'] = list_of_scripts return detector_details - + def _save_escan_script(self, path, d, exposure=None): # Compile Details into exposure and energies escan_details = self._build_escan(d) @@ -1107,15 +1089,15 @@ def _save_escan_script(self, path, d, exposure=None): df.to_csv(path, index=False, sep='\t') clean_script(path) # Remove exposure and clear last line if it exists - - + + def build_spiral_tab(self): self.spiral_scan_title = widgets.HTML(value="Spiral Scan Options", style=self.macro_title_style) self.spiral_scan_info = widgets.HTML( value = "Options to generate a spiral scan macro.
Will measure short scans in X-by-X grid around sample position
Use to determine optimum scan location", style = self.macro_info_style ) - + self.spiral_scan_button = widgets.Button( description='Save Spiral Scan Macro', style = self.macro_style, @@ -1169,7 +1151,7 @@ def build_spiral_tab(self): layout=self.macro_box_layout ) return self.spiral_options - + def build_nexafs_tab(self): self.nexafs_title = widgets.HTML(value="NEXAFS Options", style=self.macro_title_style) #self.nexafs_title = widgets.HTML(value="Include Transmission NEXAFS", style=self.macro_title_style) @@ -1199,7 +1181,7 @@ def build_nexafs_tab(self): description="Scan File: ", style = self.macro_style, layout=self.macro_button_layout - + ) self.nexafs_options = widgets.VBox( [ @@ -1214,7 +1196,7 @@ def build_nexafs_tab(self): layout=self.macro_box_layout ) return self.nexafs_options - + def build_i0_tab(self): self.I0_title = widgets.HTML(value="Direct Beam (I0) Options", style=self.macro_title_style) self.I0_info = widgets.HTML(value="Select when you want to run an I0 during macro") @@ -1229,19 +1211,19 @@ def build_i0_tab(self): options = self.I0_list, value = "Before and after macro", style = self.macro_style, - layout=self.macro_button_layout + layout=self.macro_button_layout ) self.I0_xpos = widgets.FloatText( value = 0.0, description = "I0 X position", style = self.macro_style, - layout=self.macro_button_layout + layout=self.macro_button_layout ) self.I0_ypos = widgets.FloatText( value = 0.0, description = "I0 Y position", style = self.macro_style, - layout=self.macro_button_layout + layout=self.macro_button_layout ) self.I0_escan = widgets.Dropdown( value ='CARBON_ESCAN', @@ -1249,7 +1231,7 @@ def build_i0_tab(self): description="Scan File: ", style = self.macro_style, layout=self.macro_button_layout - + ) self.I0_options = widgets.VBox( [ @@ -1263,26 +1245,26 @@ def build_i0_tab(self): layout=self.macro_box_layout ) return self.I0_options - - - + + + def update_dict(old_dict, new_dict): - for key in old_dict.keys(): + for key in old_dict: if key in new_dict: old_dict[key] = new_dict[key] return old_dict - + def clean_for_widgets(my_dict): for key, value in my_dict.items(): if isinstance(value, list): my_dict[key] = ', '.join(map(str, value)) return my_dict - - + + def generate_2d_grid(center_x, center_y, step_size, grid_size=5): """ Generates a 2D grid of x and y coordinates around a center point. @@ -1293,7 +1275,8 @@ def generate_2d_grid(center_x, center_y, step_size, grid_size=5): step_size: The distance between grid points. grid_size: The size of the grid (e.g., 5 for a 5x5 grid). - Returns: + Returns + ------- A list of tuples, where each tuple represents (x, y) coordinates. """ grid = [] @@ -1306,9 +1289,9 @@ def generate_2d_grid(center_x, center_y, step_size, grid_size=5): grid.append((x, y)) return grid - - + + def get_current_datetime_string(): """Returns a string with the current date and time.""" now = datetime.datetime.now() diff --git a/python/pyref/fitting/fitters.py b/python/pyref/fitting/fitters.py index edadefe..bbdaa96 100644 --- a/python/pyref/fitting/fitters.py +++ b/python/pyref/fitting/fitters.py @@ -20,6 +20,7 @@ from refnx._lib.util import getargspec from refnx.analysis import ( CurveFitter, + Interval, Objective, is_parameter, process_chain, @@ -34,7 +35,7 @@ import pandas as pd import xarray as xr - from refnx.analysis import GlobalObjective, Interval, Parameter + from refnx.analysis import GlobalObjective, Parameter from pyref.fitting import ReflectModel @@ -625,7 +626,8 @@ def rounded_values( ) -> tuple[float, float]: """Round the values and errors to n significant figures.""" if parameter.value is None: - raise ValueError("Parameter value is None; cannot round value.") + msg = "Parameter value is None; cannot round value." + raise ValueError(msg) x = float(parameter.value) xerr: Literal[0] | float = parameter.stderr if parameter.stderr else 0 if xerr > 0: @@ -640,8 +642,8 @@ def rounded_values( def _fix_bound(parameter: Parameter, nsigma=5, *, by_bounds=False) -> None: val, err = rounded_values(parameter) if parameter.bounds is not Interval: - raise ValueError("Parameter bounds are None; cannot fix bounds.") - bounds: Interval = parameter.bounds + msg = "Parameter bounds are None; cannot fix bounds." + raise ValueError(msg) # round err and value to proper sig figs min_val: float = float(sigfig.round(val - nsigma * err, 1)) # type: ignore max_val: float = float(sigfig.round(val + nsigma * err, 1)) # type: ignore diff --git a/python/pyref/fitting/structure.py b/python/pyref/fitting/structure.py index a9d46b8..73ea2c6 100644 --- a/python/pyref/fitting/structure.py +++ b/python/pyref/fitting/structure.py @@ -860,10 +860,7 @@ class Slab(PXR_Component): def __init__(self, thick, sld: Scatterer, rough, name=""): super().__init__(name=name) self.thick = possibly_create_parameter(thick, name=f"{name}_thick") - if isinstance(sld, Scatterer): - _sld = sld - else: - _sld = SLD(sld) + _sld = sld if isinstance(sld, Scatterer) else SLD(sld) self.sld: Scatterer = _sld self.rough = possibly_create_parameter(rough, name=f"{name}_rough") diff --git a/python/pyref/fitting/unittensor.py b/python/pyref/fitting/unittensor.py index 6303b41..4d9b599 100644 --- a/python/pyref/fitting/unittensor.py +++ b/python/pyref/fitting/unittensor.py @@ -3,14 +3,15 @@ from typing import TYPE_CHECKING import numpy as np -import pandas as pd -from refnx.analysis import Parameter, Parameters, possibly_create_parameter +from refnx.analysis import Parameters, possibly_create_parameter from scipy.interpolate import interp1d from pyref.fitting.structure import Scatterer if TYPE_CHECKING: + import pandas as pd from numpy.typing import NDArray + from refnx.analysis import Parameter speed_of_light = 299792458 # m/s plank_constant = 4.135667697e-15 # ev*s diff --git a/python/pyref/io/experiment_names.py b/python/pyref/io/experiment_names.py index e9c8a4e..c8c6074 100644 --- a/python/pyref/io/experiment_names.py +++ b/python/pyref/io/experiment_names.py @@ -95,10 +95,7 @@ def discover_fits(root: Path, recursive: bool = True) -> list[Path]: root = Path(root).resolve() if not root.is_dir(): return [] - if recursive: - paths = sorted(root.rglob("*.fits")) - else: - paths = sorted(root.glob("*.fits")) + paths = sorted(root.rglob("*.fits")) if recursive else sorted(root.glob("*.fits")) return [p.resolve() for p in paths] diff --git a/python/pyref/io/readers.py b/python/pyref/io/readers.py index bb42127..2213fd1 100644 --- a/python/pyref/io/readers.py +++ b/python/pyref/io/readers.py @@ -52,7 +52,7 @@ def _is_skippable_stem(stem: str) -> bool: def resolve_fits_paths(source: FilePath | FilePathList) -> list[str]: - if isinstance(source, (list, tuple)): + if isinstance(source, list | tuple): out: list[str] = [] for p in source: path = Path(p).resolve() @@ -145,7 +145,7 @@ def scan_experiment( from pyref.pyref import py_read_multiple_fits_headers_only keys = header_items if header_items is not None else [] - if not isinstance(source, (list, tuple)): + if not isinstance(source, list | tuple): path = Path(source).resolve() if path.is_file() and path.name == NEW_CATALOG_DB_NAME: try: @@ -359,7 +359,7 @@ def set_override( def classify_reflectivity_scan_type( pairs: list[tuple[float | None, float | None]], ) -> tuple[str, float | None, float | None, float | None, float | None]: - """ + r""" Classify a reflectivity acquisition from (beamline energy eV, sample theta deg) pairs. Wraps the Rust catalog classifier used by the TUI. ``scan_kind`` is one of diff --git a/python/pyref/nexafs/__init__.py b/python/pyref/nexafs/__init__.py index 38bb448..eb1dd92 100644 --- a/python/pyref/nexafs/__init__.py +++ b/python/pyref/nexafs/__init__.py @@ -2,10 +2,10 @@ NEXAFS normalization, DataFrame accessor (df.nexafs), and database load. """ -from pyref.nexafs.accessor import ( # noqa: F401 +from pyref.nexafs.accessor import ( NexafsAccessor, normalize_by_group, ) from pyref.nexafs.io import load_nexafs -__all__ = ["NexafsAccessor", "normalize_by_group", "load_nexafs"] +__all__ = ["NexafsAccessor", "load_nexafs", "normalize_by_group"] diff --git a/python/pyref/nexafs/accessor.py b/python/pyref/nexafs/accessor.py index bf648c7..2303e0c 100644 --- a/python/pyref/nexafs/accessor.py +++ b/python/pyref/nexafs/accessor.py @@ -4,17 +4,18 @@ from __future__ import annotations -from typing import Any, Literal, cast +from typing import TYPE_CHECKING, Any, Literal, cast import matplotlib.pyplot as plt import numpy as np import pandas as pd from matplotlib import cm -from matplotlib.axes import Axes from matplotlib.colors import Normalize from matplotlib.pyplot import colormaps from scipy.optimize import curve_fit +if TYPE_CHECKING: + from matplotlib.axes import Axes _REQUIRED_FIXED_COLUMNS = ("bare_atom", "bare_atom_substrate") _DEFAULT_PRE_EDGE = (270.0, 281.0) @@ -101,17 +102,21 @@ def __init__(self, pandas_obj: pd.DataFrame) -> None: self._fit_params: dict[str, dict[str, Any]] | None = None def _validate_schema(self) -> None: - missing = [c for c in (_REQUIRED_FIXED_COLUMNS + (self._energy_col,)) if c not in self._obj.columns] + missing = [c for c in ((*_REQUIRED_FIXED_COLUMNS, self._energy_col)) if c not in self._obj.columns] if missing: + msg = f"DataFrame must have columns {list(_REQUIRED_FIXED_COLUMNS)} and '{self._energy_col}'. Missing: {missing}" raise ValueError( - f"DataFrame must have columns {list(_REQUIRED_FIXED_COLUMNS)} and '{self._energy_col}'. Missing: {missing}" + msg ) for col in self._absorbance_cols: if col not in self._obj.columns: - raise ValueError( + msg = ( f"Absorbance column '{col}' not in DataFrame. " f"Available: {list(self._obj.columns)}" ) + raise ValueError( + msg + ) def set_regions( self, @@ -189,8 +194,9 @@ def normalize(self) -> NexafsAccessor: self._validate_schema() norm = self.normalization_region() if len(norm) == 0: + msg = "Normalization region is empty. Check pre_edge and post_edge." raise ValueError( - "Normalization region is empty. Check pre_edge and post_edge." + msg ) energy = np.asarray(norm[self._energy_col], dtype=np.float64) mu = np.asarray(norm["bare_atom"], dtype=np.float64) @@ -245,7 +251,8 @@ def normalize(self) -> NexafsAccessor: "shift": popt[1], } else: - assert mode == "step" and jump is not None + assert mode == "step" + assert jump is not None model = _model_factory_step(jump) p0_scale = float(np.nanmean(y)) if not np.isfinite(p0_scale) or p0_scale <= 0: @@ -293,7 +300,7 @@ def plot_regions( self._validate_schema() if ax is None: _fig, ax = plt.subplots() - ax = cast(Axes, ax) # type: ignore[assignment] + ax = cast("Axes", ax) # type: ignore[assignment] self._obj.plot( x=self._energy_col, y=self._absorbance_cols, @@ -312,7 +319,7 @@ def plot_regions( color=post_color, alpha=alpha, ) - return cast(Axes, ax) # type: ignore[return-value] + return cast("Axes", ax) # type: ignore[return-value] def plot_normalization( self, @@ -340,13 +347,13 @@ def plot_normalization( norm = self.normalization_region() if ax is None: _fig, ax = plt.subplots() - ax = cast(Axes, ax) # type: ignore[assignment] + ax = cast("Axes", ax) # type: ignore[assignment] style = kwargs.pop("style", [".", "."]) norm.plot( x=self._energy_col, y=self._absorbance_cols, ax=ax, - style=style[: len(self._absorbance_cols)] if isinstance(style, (list, tuple)) else style, + style=style[: len(self._absorbance_cols)] if isinstance(style, list | tuple) else style, **kwargs, ) if show_fit and self._fit_params: @@ -370,7 +377,7 @@ def plot_normalization( label=f"fit {col}", ) ax.legend() - return cast(Axes, ax) # type: ignore[return-value] + return cast("Axes", ax) # type: ignore[return-value] def plot( self, @@ -414,20 +421,22 @@ def plot( """ df = self._obj if by not in df.columns: - raise ValueError(f"Column '{by}' not in DataFrame.") + msg = f"Column '{by}' not in DataFrame." + raise ValueError(msg) y_cols = [y] if isinstance(y, str) else list(y) - for c in [x] + y_cols: + for c in [x, *y_cols]: if c not in df.columns: - raise ValueError(f"Column '{c}' not in DataFrame.") + msg = f"Column '{c}' not in DataFrame." + raise ValueError(msg) if ax is None: _fig, ax = plt.subplots() - ax = cast(Axes, ax) # type: ignore[assignment] + ax = cast("Axes", ax) # type: ignore[assignment] groups = df.groupby(by, sort=True) keys = list(groups.groups.keys()) if len(keys) == 0: return ax cmap = colormaps[cmap] if cmap is not None else None # pyright: ignore[reportAssignmentType] - all_numeric = all(isinstance(k, (int, float)) for k in keys) + all_numeric = all(isinstance(k, int | float) for k in keys) if all_numeric: vmin_val = float(df[by].min()) vmax_val = float(df[by].max()) @@ -441,7 +450,7 @@ def plot( grp = groups.get_group(key) if len(keys) > 1: if all_numeric: - scalar: float = float(key) if isinstance(key, (int, float)) else 0.0 + scalar: float = float(key) if isinstance(key, int | float) else 0.0 else: scalar = float(keys.index(key)) color = cmap(norm_obj(scalar)) # type: ignore[reportCallIssue] @@ -471,7 +480,7 @@ def plot( ax.set_ylim(0, None) if len(y_cols) == 1: ax.set_ylabel(y_cols[0]) - return cast(Axes, ax) # type: ignore[return-value] + return cast("Axes", ax) # type: ignore[return-value] def normalize_by_group( diff --git a/python/pyref/nexafs/io.py b/python/pyref/nexafs/io.py index 94ca8fb..51e69f5 100644 --- a/python/pyref/nexafs/io.py +++ b/python/pyref/nexafs/io.py @@ -4,14 +4,16 @@ from __future__ import annotations -from sqlite3 import Connection -from typing import Any, cast +from typing import TYPE_CHECKING, Any, cast import numpy as np import pandas as pd from periodictable import xsf from pint import UnitRegistry +if TYPE_CHECKING: + from sqlite3 import Connection + _UREG = UnitRegistry() _NEXAFS_COLS = [ "scan_id", @@ -102,7 +104,8 @@ def load_nexafs( cols = _NEXAFS_COLS sample = _get_samples(conn, sample_name, tag=tag, version=version) if sample.empty: - raise ValueError(f"No sample found for name={sample_name!r}, tag={tag!r}, version={version!r}") + msg = f"No sample found for name={sample_name!r}, tag={tag!r}, version={version!r}" + raise ValueError(msg) sample_id = int(sample["id"].iloc[0]) sample_formula = str(sample["chemical_formula"].iloc[0]) @@ -112,7 +115,8 @@ def load_nexafs( params=[sample_id], ) if nexafs_df.empty: - raise ValueError(f"No nexafs rows for sample_id={sample_id}") + msg = f"No nexafs rows for sample_id={sample_id}" + raise ValueError(msg) def _scan_id_or_none(val: Any) -> int | None: if val is None: @@ -190,4 +194,4 @@ def _scan_id_or_none(val: Any) -> int | None: merged["absorbance_0"] = merged["pd_response_0"] / merged["raw_abs"] merged["absorbance_1"] = merged["pd_response_1"] / merged["raw_abs"] - return cast(pd.DataFrame, merged) + return cast("pd.DataFrame", merged) diff --git a/scripts/run_nexafs_pipeline.py b/scripts/run_nexafs_pipeline.py index a685460..84cf687 100644 --- a/scripts/run_nexafs_pipeline.py +++ b/scripts/run_nexafs_pipeline.py @@ -88,9 +88,7 @@ def catalog_izero_files(directory: Path) -> pd.DataFrame: if not is_nexafs_scan_file(path): continue stem = path.stem.lower() - if stem.startswith("izero_"): - parts = path.stem.split("_", 1) - elif stem.startswith("i0_"): + if stem.startswith("izero_") or stem.startswith("i0_"): parts = path.stem.split("_", 1) else: continue @@ -178,16 +176,16 @@ def parse_sample( formula_map: dict[str, str], ) -> dict[str, Any]: if len(parts) == len(keys): - parsed = dict(zip(keys, parts)) + parsed = dict(zip(keys, parts, strict=False)) elif len(parts) == len(keys) - 1 and "version" in keys: i = keys.index("version") parsed = { - **dict(zip(keys[:i], parts[:i])), + **dict(zip(keys[:i], parts[:i], strict=False)), "version": 1, - **dict(zip(keys[i + 1 :], parts[i:])), + **dict(zip(keys[i + 1 :], parts[i:], strict=False)), } else: - parsed = dict(zip(keys, parts[: len(keys)])) + parsed = dict(zip(keys, parts[: len(keys)], strict=False)) ts = datetime.fromtimestamp(birth_time) return { "name": parsed["name"], diff --git a/scripts/write_minimal_fits.py b/scripts/write_minimal_fits.py index 34bfc88..78f6115 100644 --- a/scripts/write_minimal_fits.py +++ b/scripts/write_minimal_fits.py @@ -4,7 +4,7 @@ def card(keyword: str, value: str | int | float | bool) -> bytes: if isinstance(value, bool): - s = f"{keyword:8}= {str(value):>20}" + s = f"{keyword:8}= {value!s:>20}" elif isinstance(value, int): s = f"{keyword:8}= {value:20d}" elif isinstance(value, float): diff --git a/tests/image_continutiy.py b/tests/image_continutiy.py index 4472fc8..be4ce8a 100644 --- a/tests/image_continutiy.py +++ b/tests/image_continutiy.py @@ -1,10 +1,8 @@ import os -from pathlib import Path import matplotlib.pyplot as plt import numpy as np from astropy.io import fits - from pyref import get_data_path try: diff --git a/tests/loader_compare.py b/tests/loader_compare.py index 90db5c2..4262562 100644 --- a/tests/loader_compare.py +++ b/tests/loader_compare.py @@ -2,12 +2,11 @@ from dataclasses import dataclass from pathlib import Path +from typing import cast import numpy as np from astropy.io import fits from astropy.io.fits import ImageHDU -from typing import cast - from pyref import get_data_path @@ -45,7 +44,7 @@ def load_pyref(path: Path) -> np.ndarray: def load_astropy(path: Path) -> np.ndarray: """Load RAW via astropy.""" with fits.open(path) as hdul: - image = cast(ImageHDU, hdul[2]) + image = cast("ImageHDU", hdul[2]) return np.asarray(image.data) diff --git a/tests/metadata_correlation.py b/tests/metadata_correlation.py index 8fecfc6..3e08989 100644 --- a/tests/metadata_correlation.py +++ b/tests/metadata_correlation.py @@ -19,7 +19,7 @@ def load_diff_summary(path: Path) -> pd.DataFrame: def read_headers(fits_path: Path, keys: list[str]) -> dict[str, Any]: """Extract selected header values.""" with fits.open(fits_path) as hdul: - primary = cast(fits.PrimaryHDU, hdul[0]) + primary = cast("fits.PrimaryHDU", hdul[0]) hdr = primary.header return {key: hdr.get(key) for key in keys} diff --git a/tests/npz_diff_report.py b/tests/npz_diff_report.py index 229f58f..0ed26be 100644 --- a/tests/npz_diff_report.py +++ b/tests/npz_diff_report.py @@ -1,9 +1,9 @@ from __future__ import annotations import os +from collections.abc import Iterable from dataclasses import dataclass from pathlib import Path -from typing import Iterable import numpy as np import pandas as pd diff --git a/tests/npz_spatial_debug.py b/tests/npz_spatial_debug.py index bebdbf3..18801aa 100644 --- a/tests/npz_spatial_debug.py +++ b/tests/npz_spatial_debug.py @@ -7,8 +7,8 @@ import matplotlib matplotlib.use("Agg") -import matplotlib.pyplot as plt # noqa: E402 -import numpy as np # noqa: E402 +import matplotlib.pyplot as plt +import numpy as np @dataclass(frozen=True) diff --git a/tests/test_experiment_names.py b/tests/test_experiment_names.py index 6a05f70..cda62ee 100644 --- a/tests/test_experiment_names.py +++ b/tests/test_experiment_names.py @@ -6,7 +6,6 @@ import polars as pl import pytest - from pyref import get_data_path from pyref.io.experiment_names import ( ParsedFitsName, From 55b4c22aa575775e8989b3aeea230e3ffde44f60 Mon Sep 17 00:00:00 2001 From: Harlan D Heilman <73567020+HarlanHeilman@users.noreply.github.com> Date: Mon, 13 Apr 2026 13:40:42 -0700 Subject: [PATCH 27/32] chore: remove legacy nexafs and FITS helper scripts --- scripts/run_nexafs_pipeline.py | 578 --------------------------------- scripts/write_minimal_fits.py | 59 ---- 2 files changed, 637 deletions(-) delete mode 100644 scripts/run_nexafs_pipeline.py delete mode 100644 scripts/write_minimal_fits.py diff --git a/scripts/run_nexafs_pipeline.py b/scripts/run_nexafs_pipeline.py deleted file mode 100644 index 84cf687..0000000 --- a/scripts/run_nexafs_pipeline.py +++ /dev/null @@ -1,578 +0,0 @@ -""" -Run the NEXAFS beamtime pipeline: discover files, build sample/izero/nexafs catalogs, create DB, ingest. -""" -from __future__ import annotations - -import argparse -import io -import re -from datetime import datetime -from pathlib import Path -from sqlite3 import Connection, connect -from typing import Any - -import pandas as pd - -SCHEMA_SQL = """ -DROP TABLE IF EXISTS nexafs; -DROP TABLE IF EXISTS izero; -DROP TABLE IF EXISTS sample; - -CREATE TABLE sample ( - id INTEGER PRIMARY KEY AUTOINCREMENT, - name TEXT NOT NULL, - tag TEXT NOT NULL, - version INTEGER NOT NULL, - beamtime DATE NOT NULL, - chemical_formula TEXT, - UNIQUE(name, tag, version, beamtime) -); - -CREATE TABLE izero ( - id INTEGER PRIMARY KEY AUTOINCREMENT, - scan_id INTEGER NOT NULL, - beamline_energy REAL, - time_stamp TEXT NOT NULL, - photodiode REAL, - ai_3_izero REAL -); - -CREATE TABLE nexafs ( - id INTEGER PRIMARY KEY AUTOINCREMENT, - scan_id INTEGER NOT NULL, - sample_id INTEGER NOT NULL, - time_stamp TEXT NOT NULL, - sample_theta REAL, - beamline_energy REAL, - tey_signal REAL, - ai_3_izero REAL, - photodiode REAL, - izero_before_scan_id INTEGER, - izero_after_scan_id INTEGER, - FOREIGN KEY (sample_id) REFERENCES sample(id) -); -""" - -SAMPLE_COLS = ["name", "tag", "version", "beamtime", "chemical_formula"] - - -def get_db_path(dest_root: Path, year: int, month: int) -> Path: - month_abbr = datetime(year, month, 1).strftime("%b").lower() - return dest_root / f"{month_abbr}_{year}.db" - - -def create_database(path: Path) -> Connection: - path.parent.mkdir(parents=True, exist_ok=True) - conn = connect(str(path)) - conn.execute("PRAGMA foreign_keys = ON") - conn.executescript(SCHEMA_SQL) - conn.commit() - return conn - - -def is_nexafs_scan_file(file: Path, max_header_lines: int = 50) -> bool: - try: - with open(file) as f: - head = "".join(next(f, "") for _ in range(max_header_lines)) - except OSError: - return False - if not re.search(r"Date:\s*\d{1,2}/\d{1,2}/\d{4}", head): - return False - markers = ("Scan Type", "Scan Motor", "From File", "Beamline Energy") - return any(m in head for m in markers) - - -def catalog_izero_files(directory: Path) -> pd.DataFrame: - rows = [] - for path in directory.rglob("*.txt"): - if not is_nexafs_scan_file(path): - continue - stem = path.stem.lower() - if stem.startswith("izero_") or stem.startswith("i0_"): - parts = path.stem.split("_", 1) - else: - continue - if len(parts) != 2 or not parts[1].isdigit(): - continue - scan_number = int(parts[1]) - beamtime = pd.Timestamp( - datetime.fromtimestamp(path.stat().st_birthtime) - ).normalize().replace(day=1) - rows.append({"path": path, "scan_number": scan_number, "beamtime": beamtime}) - return ( - pd.DataFrame(rows) - if rows - else pd.DataFrame(columns=["path", "scan_number", "beamtime"]) - ) - - -def catalog_calibrant_files(directory: Path) -> pd.DataFrame: - rows = [] - for path in directory.rglob("*.txt"): - if not is_nexafs_scan_file(path): - continue - parts = path.stem.split("_") - if len(parts) != 2 or parts[0] != "HOPG" or not parts[1].isdigit(): - continue - scan_number = int(parts[1]) - beamtime = pd.Timestamp( - datetime.fromtimestamp(path.stat().st_birthtime) - ).normalize().replace(day=1) - rows.append( - {"path": path, "name": "HOPG", "scan_number": scan_number, "beamtime": beamtime} - ) - return ( - pd.DataFrame(rows) - if rows - else pd.DataFrame(columns=["path", "name", "scan_number", "beamtime"]) - ) - - -def get_beamtime_from_catalogs( - sample_catalog: pd.DataFrame, - izero_catalog: pd.DataFrame, - calibrant_catalog: pd.DataFrame, -) -> tuple[int, int]: - dfs = [sample_catalog, izero_catalog, calibrant_catalog] - beams = [ - df["beamtime"] - for df in dfs - if not df.empty and "beamtime" in df.columns - ] - if not beams: - return datetime.now().year, datetime.now().month - min_ts = pd.concat(beams).min() - return int(min_ts.year), int(min_ts.month) - - -def _normalize_name(s: str) -> str: - return ( - re.sub(r"[^a-z0-9]+", "_", str(s).lower()).strip("_") or str(s).lower() - ) - - -def apply_tag_map(tag: str, mapping: dict[str, str] | None) -> str: - normalized = _normalize_name(str(tag)) if str(tag).strip() else "" - if not mapping: - return normalized - return mapping.get(normalized, normalized) - - -def _keys_from_parse_key(pk: str) -> list[str]: - return re.findall(r"<([^>]+)>", pk) - - -def _parse_angle(raw: str | None) -> float | None: - if raw is None or (isinstance(raw, str) and not raw.strip()): - return None - s = re.sub(r"\s*deg\s*$", "", str(raw).strip(), flags=re.I).strip() - return float(s) if s else None - - -def parse_sample( - parts: list[str], - keys: list[str], - birth_time: float, - formula_map: dict[str, str], -) -> dict[str, Any]: - if len(parts) == len(keys): - parsed = dict(zip(keys, parts, strict=False)) - elif len(parts) == len(keys) - 1 and "version" in keys: - i = keys.index("version") - parsed = { - **dict(zip(keys[:i], parts[:i], strict=False)), - "version": 1, - **dict(zip(keys[i + 1 :], parts[i:], strict=False)), - } - else: - parsed = dict(zip(keys, parts[: len(keys)], strict=False)) - ts = datetime.fromtimestamp(birth_time) - return { - "name": parsed["name"], - "tag": parsed.get("tag", ""), - "version": ( - int(round(float(parsed["version"]))) - if "version" in parsed - else 1 - ), - "beamtime": pd.Timestamp(year=ts.year, month=ts.month, day=1), - "chemical_formula": formula_map.get(parsed["name"].lower(), ""), - "angle": _parse_angle(parsed.get("angle")), - "scan_number": parsed.get("scan_number"), - } - - -def build_files_catalog( - directory: Path, - formula_map: dict[str, str], - pk: str, - tag_map: dict[str, str] | None = None, -) -> pd.DataFrame: - keys = _keys_from_parse_key(pk) - rows = [] - for path in directory.rglob("*.txt"): - if not is_nexafs_scan_file(path): - continue - parts = path.stem.split("_") - if len(parts) < len(keys) and not ( - len(parts) == len(keys) - 1 and "version" in keys - ): - continue - rec = parse_sample(parts, keys, path.stat().st_birthtime, formula_map) - rec["path"] = path - rows.append(rec) - if not rows: - return pd.DataFrame( - columns=[ - "path", - "name", - "tag", - "version", - "beamtime", - "chemical_formula", - "angle", - "scan_number", - ] - ) - df = pd.DataFrame(rows) - df["name"] = df["name"].astype(str).apply(_normalize_name).str.strip() - df["tag"] = ( - df["tag"] - .astype(str) - .apply(lambda t: apply_tag_map(t, tag_map)) - .str.strip() - ) - return df - - -def ingest_dataset(file: Path) -> pd.DataFrame: - with open(file) as f: - lines = f.readlines() - header_idx = next( - (i for i, line in enumerate(lines) if "Time of Day" in line), - None, - ) - if header_idx is None: - raise ValueError(f"Could not find 'Time of Day' header in {file}") - table_text = "".join(lines[header_idx:]) - df = pd.read_csv(io.StringIO(table_text), sep=r"\t", engine="python") - ts_col = ( - "Time Stamp" - if "Time Stamp" in df.columns - else "Time of Day" - if "Time of Day" in df.columns - else None - ) - if ts_col is not None: - df["Time Stamp"] = pd.to_datetime(df[ts_col], format="mixed") - else: - df["Time Stamp"] = pd.to_datetime(file.stat().st_birthtime, unit="s") - rename = {} - if "Tey Signal" in df.columns and "TEY signal" not in df.columns: - rename["Tey Signal"] = "TEY signal" - if rename: - df = df.rename(columns=rename) - df["Beamline Energy"] = df["Beamline Energy"].round(1).astype(float) - return df[ - [ - "Time Stamp", - "Beamline Energy", - "TEY signal", - "AI 3 Izero", - "Photodiode", - ] - ].copy() - - -def _izero_before_after( - izero_df: pd.DataFrame, time_stamp_iso: str -) -> tuple[int | None, int | None]: - if izero_df.empty: - return None, None - df = izero_df.copy() - df["time_stamp"] = pd.to_datetime(df["time_stamp"]) - ts = pd.Timestamp(time_stamp_iso) - before = df[df["time_stamp"] <= ts] - after = df[df["time_stamp"] >= ts] - before_scan_id = ( - int(before.nlargest(1, "time_stamp").iloc[0]["scan_id"]) - if not before.empty - else None - ) - after_scan_id = ( - int(after.nsmallest(1, "time_stamp").iloc[0]["scan_id"]) - if not after.empty - else None - ) - return before_scan_id, after_scan_id - - -def build_unique_samples( - files_catalog: pd.DataFrame, - calibrant_catalog: pd.DataFrame, - formula_map: dict[str, str], -) -> pd.DataFrame: - if not files_catalog.empty: - out = ( - files_catalog[SAMPLE_COLS] - .drop_duplicates() - .reset_index(drop=True) - .copy() - ) - else: - out = pd.DataFrame(columns=SAMPLE_COLS) - if calibrant_catalog.empty: - return out - cal = calibrant_catalog[["name", "beamtime"]].copy() - cal["name"] = cal["name"].apply(_normalize_name) - cal = cal.drop_duplicates(subset=["name", "beamtime"]).reset_index(drop=True) - cal["tag"] = "" - cal["version"] = 0 - cal["chemical_formula"] = cal["name"].map(formula_map).fillna("") - cal["beamtime"] = pd.to_datetime(cal["beamtime"]).dt.normalize() - out = ( - pd.concat([out, cal[SAMPLE_COLS]], ignore_index=True) - .drop_duplicates(subset=SAMPLE_COLS) - .reset_index(drop=True) - ) - return out - - -def insert_samples(conn: Connection, unique_samples: pd.DataFrame) -> None: - if unique_samples.empty: - return - unique_samples = unique_samples.copy() - unique_samples["beamtime"] = pd.to_datetime( - unique_samples["beamtime"] - ).dt.normalize() - sample_lookup = pd.read_sql_query( - "SELECT id, name, tag, version, beamtime FROM sample", conn - ) - sample_lookup["name"] = sample_lookup["name"].astype(str).str.strip() - sample_lookup["tag"] = sample_lookup["tag"].astype(str).str.strip() - sample_lookup["beamtime"] = pd.to_datetime( - sample_lookup["beamtime"] - ).dt.normalize() - merged = unique_samples.merge( - sample_lookup, - on=["name", "tag", "version", "beamtime"], - how="left", - ) - to_insert = merged.loc[merged["id"].isna(), SAMPLE_COLS].drop_duplicates() - for _, row in to_insert.iterrows(): - conn.execute( - "INSERT INTO sample (name, tag, version, beamtime, chemical_formula) VALUES (?, ?, ?, ?, ?)", - ( - str(row["name"]).strip(), - str(row["tag"]).strip(), - int(row["version"]), - pd.Timestamp(row["beamtime"]).strftime("%Y-%m-%d"), - row["chemical_formula"], - ), - ) - if not to_insert.empty: - conn.commit() - - -def load_sample_lookup(conn: Connection) -> pd.DataFrame: - df = pd.read_sql_query( - "SELECT id, name, tag, version, beamtime FROM sample", conn - ) - df["name"] = df["name"].astype(str).str.strip() - df["tag"] = df["tag"].astype(str).str.strip() - df["beamtime"] = pd.to_datetime(df["beamtime"]).dt.normalize() - df["version"] = df["version"].astype(int) - return df - - -def ingest_izero_into_db( - conn: Connection, izero_catalog: pd.DataFrame -) -> None: - for _, row in izero_catalog.iterrows(): - path = Path(row["path"]) if not isinstance(row["path"], Path) else row["path"] - scan_id = int(row["scan_number"]) - scan_df = ingest_dataset(path) - if scan_df.empty: - continue - for _, s in scan_df.iterrows(): - time_stamp = pd.Timestamp(s["Time Stamp"]).isoformat() - conn.execute( - "INSERT INTO izero (scan_id, beamline_energy, time_stamp, photodiode, ai_3_izero) VALUES (?, ?, ?, ?, ?)", - ( - scan_id, - float(s["Beamline Energy"]), - time_stamp, - float(s["Photodiode"]), - float(s["AI 3 Izero"]), - ), - ) - conn.commit() - - -def build_rows_to_ingest( - files_catalog: pd.DataFrame, - calibrant_catalog: pd.DataFrame, - sample_lookup: pd.DataFrame, -) -> list[tuple[Path, int, float | None, int]]: - rows: list[tuple[Path, int, float | None, int]] = [] - if not files_catalog.empty: - fc = files_catalog.copy() - fc["version"] = fc["version"].astype(int) - fc["beamtime"] = pd.to_datetime(fc["beamtime"]).dt.normalize() - fc["_bk"] = fc["beamtime"].dt.strftime("%Y-%m-%d") - sl = sample_lookup.copy() - sl["beamtime"] = pd.to_datetime(sl["beamtime"]).dt.normalize() - sl["_bk"] = sl["beamtime"].dt.strftime("%Y-%m-%d") - fc = fc.merge(sl, on=["name", "tag", "version", "_bk"], how="left") - fc = fc.dropna(subset=["id"]) - for _, row in fc.iterrows(): - if pd.isna(row.get("scan_number")): - continue - path = Path(row["path"]) if not isinstance(row["path"], Path) else row["path"] - rows.append( - ( - path, - int(row["id"]), - float(row["angle"]) if pd.notna(row.get("angle")) else None, - int(row["scan_number"]), - ) - ) - if not calibrant_catalog.empty: - cal = calibrant_catalog.copy() - cal["name"] = cal["name"].apply(_normalize_name) - cal["tag"] = "" - cal["version"] = 0 - cal["beamtime"] = pd.to_datetime(cal["beamtime"]).dt.normalize() - cal["_bk"] = cal["beamtime"].dt.strftime("%Y-%m-%d") - sl = sample_lookup.copy() - sl["beamtime"] = pd.to_datetime(sl["beamtime"]).dt.normalize() - sl["_bk"] = sl["beamtime"].dt.strftime("%Y-%m-%d") - cal = cal.merge(sl, on=["name", "tag", "version", "_bk"], how="left") - cal = cal.dropna(subset=["id"]) - for _, row in cal.iterrows(): - if pd.isna(row.get("scan_number")): - continue - path = Path(row["path"]) if not isinstance(row["path"], Path) else row["path"] - rows.append((path, int(row["id"]), None, int(row["scan_number"]))) - return rows - - -def ingest_nexafs_into_db( - conn: Connection, - rows_to_ingest: list[tuple[Path, int, float | None, int]], - izero_df: pd.DataFrame, -) -> None: - tey_col = "TEY signal" - for path, sample_id, sample_theta, scan_id in rows_to_ingest: - scan_df = ingest_dataset(path) - col = tey_col if tey_col in scan_df.columns else "Tey Signal" - for _, s in scan_df.iterrows(): - ts_str = pd.Timestamp(s["Time Stamp"]).isoformat() - before_scan_id, after_scan_id = _izero_before_after(izero_df, ts_str) - conn.execute( - """INSERT INTO nexafs ( - scan_id, sample_id, time_stamp, sample_theta, beamline_energy, - tey_signal, ai_3_izero, photodiode, izero_before_scan_id, izero_after_scan_id - ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)""", - ( - scan_id, - sample_id, - ts_str, - sample_theta, - float(s["Beamline Energy"]), - float(s[col]), - float(s["AI 3 Izero"]), - float(s["Photodiode"]), - before_scan_id, - after_scan_id, - ), - ) - conn.commit() - - -def run_pipeline( - data_root: Path, - dest_root: Path, - formula_map: dict[str, str], - pk: str, - tag_map: dict[str, str] | None = None, -) -> tuple[Connection | None, pd.DataFrame, pd.DataFrame]: - izero_catalog = catalog_izero_files(data_root) - calibrant_catalog = catalog_calibrant_files(data_root) - files_catalog = build_files_catalog(data_root, formula_map, pk, tag_map) - unique_samples = build_unique_samples( - files_catalog, calibrant_catalog, formula_map - ) - if unique_samples.empty: - return None, files_catalog, unique_samples - unique_samples["beamtime"] = pd.to_datetime( - unique_samples["beamtime"] - ).dt.normalize() - year, month = get_beamtime_from_catalogs( - files_catalog, izero_catalog, calibrant_catalog - ) - db_path = get_db_path(dest_root, year, month) - conn = create_database(db_path) - insert_samples(conn, unique_samples) - sample_lookup = load_sample_lookup(conn) - ingest_izero_into_db(conn, izero_catalog) - izero_df = pd.read_sql_query("SELECT id, time_stamp, scan_id FROM izero", conn) - rows_to_ingest = build_rows_to_ingest( - files_catalog, calibrant_catalog, sample_lookup - ) - ingest_nexafs_into_db(conn, rows_to_ingest, izero_df) - return conn, files_catalog, unique_samples - - -def main() -> None: - parser = argparse.ArgumentParser( - description="Run NEXAFS beamtime pipeline: discover files, create DB, ingest sample/izero/nexafs." - ) - parser.add_argument( - "data_root", - type=Path, - nargs="?", - default=Path( - "/Users/hduva/Library/CloudStorage/OneDrive-SharedLibraries-WashingtonStateUniversity(email.wsu.edu)/Carbon Lab Research Group - Documents/Synchrotron Logistics and Data/ALS - Berkeley/Data/BL1101/2025Oct/TAD KOGA/renamed" - ), - help="Beamtime directory containing NEXAFS .txt files", - ) - parser.add_argument( - "--dest", - type=Path, - default=Path("/Users/hduva/projects/als-nexafs"), - help="Destination directory for mon_yyyy.db", - ) - args = parser.parse_args() - formula_map: dict[str, str] = {"ps": "C8H8", "hopg": "C"} - parse_key = "____" - tag_map: dict[str, str] = {} - conn, files_catalog, unique_samples = run_pipeline( - args.data_root, args.dest, formula_map, parse_key, tag_map - ) - if conn is None: - print("No sample/calibrant files found; no database created.") - return - sample_count = pd.read_sql_query( - "SELECT COUNT(*) as n FROM sample", conn - ).iloc[0, 0] - izero_count = pd.read_sql_query( - "SELECT COUNT(*) as n FROM izero", conn - ).iloc[0, 0] - nexafs_count = pd.read_sql_query( - "SELECT COUNT(*) as n FROM nexafs", conn - ).iloc[0, 0] - print(f"DB: sample={sample_count}, izero={izero_count}, nexafs={nexafs_count}") - samples = pd.read_sql_query( - "SELECT id, name, tag, version FROM sample ORDER BY id", conn - ) - print("Samples:", samples.to_string(index=False)) - nexafs_by_sample = pd.read_sql_query( - "SELECT sample_id, COUNT(*) as nexafs_rows FROM nexafs GROUP BY sample_id", - conn, - ) - print("NEXAFS rows per sample:", nexafs_by_sample.to_string(index=False)) - - -if __name__ == "__main__": - main() diff --git a/scripts/write_minimal_fits.py b/scripts/write_minimal_fits.py deleted file mode 100644 index 78f6115..0000000 --- a/scripts/write_minimal_fits.py +++ /dev/null @@ -1,59 +0,0 @@ -"""Write a minimal FITS file for tests (stdlib only, no astropy).""" -from pathlib import Path - - -def card(keyword: str, value: str | int | float | bool) -> bytes: - if isinstance(value, bool): - s = f"{keyword:8}= {value!s:>20}" - elif isinstance(value, int): - s = f"{keyword:8}= {value:20d}" - elif isinstance(value, float): - s = f"{keyword:8}= {value:20.14E}" - else: - s = f"{keyword:8}= '{value[:68]:<68}'" - return (s + " " * (80 - len(s)))[:80].encode("ascii") - - -def end_card() -> bytes: - return ("END" + " " * 77).encode("ascii") - - -def main() -> None: - out = Path(__file__).resolve().parent.parent / "tests" / "fixtures" / "minimal.fits" - out.parent.mkdir(parents=True, exist_ok=True) - - primary = b"".join([ - card("SIMPLE", True), - card("BITPIX", 16), - card("NAXIS", 0), - card("DATE", "2024-01-01"), - card("Beamline Energy", 500.0), - card("Sample Theta", 1.0), - card("CCD Theta", 2.0), - card("Higher Order Suppressor", 0.0), - card("EPU Polarization", 1.0), - end_card(), - ]) - primary_block = primary + b" " * (2880 - len(primary)) - - ext = b"".join([ - card("XTENSION", "IMAGE "), - card("BITPIX", 16), - card("NAXIS", 2), - card("NAXIS1", 2), - card("NAXIS2", 2), - card("BZERO", 0), - end_card(), - ]) - ext_block = ext + b" " * (2880 - len(ext)) - - import struct - img = struct.pack(">4h", 0, 1, 2, 3) - img_block = img + b"\x00" * (2880 - 8) - - out.write_bytes(primary_block + ext_block + img_block) - print(out) - - -if __name__ == "__main__": - main() From 8606298eae8c933b11ce3b8fcb23ace9a2673c81 Mon Sep 17 00:00:00 2001 From: Harlan D Heilman <73567020+HarlanHeilman@users.noreply.github.com> Date: Mon, 13 Apr 2026 13:41:54 -0700 Subject: [PATCH 28/32] refactor: remove pyref.nexafs package --- .DS_Store | Bin 8196 -> 8196 bytes python/pyref/nexafs/__init__.py | 11 - python/pyref/nexafs/accessor.py | 559 -------------------------------- python/pyref/nexafs/io.py | 197 ----------- 4 files changed, 767 deletions(-) delete mode 100644 python/pyref/nexafs/__init__.py delete mode 100644 python/pyref/nexafs/accessor.py delete mode 100644 python/pyref/nexafs/io.py diff --git a/.DS_Store b/.DS_Store index 5794acbca056500ad4a763dcbb55f728d4bddc04..76385d38045a653d547769dc4f93aac40d96bcee 100644 GIT binary patch delta 40 qcmZp1XmOa}&nUhzU^hRb_+}n~o2;9K#lA33Y|z`xE^!aW(FOn){|({* delta 97 zcmZp1XmOa}&nU4mU^hRb#AY6Wo2>He48;t|3`Go?3H+`AG~63<3-c WOhucoi83>7W|z1}Q03xlqD%niXc(#h diff --git a/python/pyref/nexafs/__init__.py b/python/pyref/nexafs/__init__.py deleted file mode 100644 index eb1dd92..0000000 --- a/python/pyref/nexafs/__init__.py +++ /dev/null @@ -1,11 +0,0 @@ -""" -NEXAFS normalization, DataFrame accessor (df.nexafs), and database load. -""" - -from pyref.nexafs.accessor import ( - NexafsAccessor, - normalize_by_group, -) -from pyref.nexafs.io import load_nexafs - -__all__ = ["NexafsAccessor", "load_nexafs", "normalize_by_group"] diff --git a/python/pyref/nexafs/accessor.py b/python/pyref/nexafs/accessor.py deleted file mode 100644 index 2303e0c..0000000 --- a/python/pyref/nexafs/accessor.py +++ /dev/null @@ -1,559 +0,0 @@ -""" -Pandas DataFrame accessor for NEXAFS normalization: pre/post edge regions, fit, correction, and plotting. -""" - -from __future__ import annotations - -from typing import TYPE_CHECKING, Any, Literal, cast - -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd -from matplotlib import cm -from matplotlib.colors import Normalize -from matplotlib.pyplot import colormaps -from scipy.optimize import curve_fit - -if TYPE_CHECKING: - from matplotlib.axes import Axes - -_REQUIRED_FIXED_COLUMNS = ("bare_atom", "bare_atom_substrate") -_DEFAULT_PRE_EDGE = (270.0, 281.0) -_DEFAULT_POST_EDGE = (335.0, 350.0) -_DEFAULT_ABSORBANCE_COLS = ["absorbance_0", "absorbance_1"] - -NormalizationMode = Literal["full", "bare_atom", "step"] - - -def _fit_region_mask( - energy: np.ndarray, - pre_edge: tuple[float, float], - post_edge: tuple[float, float], -) -> np.ndarray: - pre_lo, pre_hi = pre_edge - post_lo, post_hi = post_edge - pre_ok = (energy >= pre_lo) & (energy <= pre_hi) - post_ok = (energy >= post_lo) & (energy <= post_hi) - return pre_ok | post_ok - - -def _mu_step(energy: np.ndarray, jump_energy: float) -> np.ndarray: - out = np.zeros_like(energy, dtype=np.float64) - out[energy > jump_energy] = 1.0 - return out - - -def _model_factory_full(mu: np.ndarray, mu_substrate: np.ndarray): - def model( - _energy: np.ndarray, - izero: float, - composition: float, - shift: float, - ) -> np.ndarray: - out = izero * (mu + composition * mu_substrate) + shift - return np.asarray(out, dtype=np.float64) - - return model - - -def _model_factory_bare_atom(mu: np.ndarray): - def model(_energy: np.ndarray, izero: float, shift: float) -> np.ndarray: - out = izero * mu + shift - return np.asarray(out, dtype=np.float64) - - return model - - -def _model_factory_step(jump_energy: float): - def model(_energy: np.ndarray, izero: float, shift: float) -> np.ndarray: - mu_01 = _mu_step(_energy, jump_energy) - out = izero * mu_01 + shift - return np.asarray(out, dtype=np.float64) - - return model - - -def _correct_row_full( - absorb: np.ndarray, - bare_atom_substrate: np.ndarray, - izero: float, - composition: float, - shift: float, -) -> np.ndarray: - return (absorb - shift) / izero - composition * bare_atom_substrate - - -def _correct_row_bare_or_step(absorb: np.ndarray, izero: float, shift: float) -> np.ndarray: - return (absorb - shift) / izero - - -@pd.api.extensions.register_dataframe_accessor("nexafs") -class NexafsAccessor: - """DataFrame accessor for NEXAFS normalization and plotting (single scan / one angle).""" - - def __init__(self, pandas_obj: pd.DataFrame) -> None: - self._obj = pandas_obj - self._pre_edge: tuple[float, float] = _DEFAULT_PRE_EDGE - self._post_edge: tuple[float, float] = _DEFAULT_POST_EDGE - self._energy_col: str = "beamline_energy" - self._absorbance_cols: list[str] = list(_DEFAULT_ABSORBANCE_COLS) - self._normalization_mode: NormalizationMode = "full" - self._jump_energy: float | None = None - self._fit_params: dict[str, dict[str, Any]] | None = None - - def _validate_schema(self) -> None: - missing = [c for c in ((*_REQUIRED_FIXED_COLUMNS, self._energy_col)) if c not in self._obj.columns] - if missing: - msg = f"DataFrame must have columns {list(_REQUIRED_FIXED_COLUMNS)} and '{self._energy_col}'. Missing: {missing}" - raise ValueError( - msg - ) - for col in self._absorbance_cols: - if col not in self._obj.columns: - msg = ( - f"Absorbance column '{col}' not in DataFrame. " - f"Available: {list(self._obj.columns)}" - ) - raise ValueError( - msg - ) - - def set_regions( - self, - pre_edge: tuple[float, float] | None = None, - post_edge: tuple[float, float] | None = None, - energy_col: str | None = None, - absorbance_cols: list[str] | None = None, - normalization_mode: NormalizationMode | None = None, - jump_energy: float | None = None, - ) -> NexafsAccessor: - """ - Set pre/post edge regions and optional column names. - - Parameters - ---------- - pre_edge : tuple of float, optional - (lo, hi) energy range for pre-edge normalization region. - post_edge : tuple of float, optional - (lo, hi) energy range for post-edge normalization region. - energy_col : str, optional - Column name for energy (default "beamline_energy"). - absorbance_cols : list of str, optional - Column names for absorbance signals (default ["absorbance_0", "absorbance_1"]). - normalization_mode : "full" | "bare_atom" | "step", optional - "full": izero * (mu + composition * mu_substrate) + shift. - "bare_atom": izero * mu + shift (no substrate). - "step": izero * mu_01(energy) + shift with mu_01 = 0 pre-edge, 1 post-edge. - jump_energy : float, optional - For "step" mode only. Energy where step goes 0 -> 1. If None, uses - (pre_edge[1] + post_edge[0]) / 2. - - Returns - ------- - NexafsAccessor - Self for method chaining. - """ - if pre_edge is not None: - self._pre_edge = pre_edge - if post_edge is not None: - self._post_edge = post_edge - if energy_col is not None: - self._energy_col = energy_col - if absorbance_cols is not None: - self._absorbance_cols = list(absorbance_cols) - if normalization_mode is not None: - self._normalization_mode = normalization_mode - if jump_energy is not None: - self._jump_energy = jump_energy - return self - - def normalization_region(self) -> pd.DataFrame: - """ - Rows where energy falls in pre-edge or post-edge region. - - Returns - ------- - pd.DataFrame - Subset of the DataFrame (view). - """ - self._validate_schema() - energy = self._obj[self._energy_col].to_numpy() - mask = _fit_region_mask(energy, self._pre_edge, self._post_edge) - return self._obj.loc[mask] - - def normalize(self) -> NexafsAccessor: - """ - Fit izero, composition, shift per absorbance column in the normalization region, - then add mass_absorption_* columns in-place. Stores fit params for plot_normalization. - - Returns - ------- - NexafsAccessor - Self for method chaining. - """ - self._validate_schema() - norm = self.normalization_region() - if len(norm) == 0: - msg = "Normalization region is empty. Check pre_edge and post_edge." - raise ValueError( - msg - ) - energy = np.asarray(norm[self._energy_col], dtype=np.float64) - mu = np.asarray(norm["bare_atom"], dtype=np.float64) - mu_sub = np.asarray(norm["bare_atom_substrate"], dtype=np.float64) - mode = self._normalization_mode - jump = self._jump_energy - if mode == "step" and jump is None: - jump = (self._pre_edge[1] + self._post_edge[0]) / 2.0 - - self._fit_params = {} - for col in self._absorbance_cols: - y = np.asarray(norm[col], dtype=np.float64) - mass_col = col.replace("absorbance", "mass_absorption", 1) - if not np.any(np.isfinite(y)): - self._obj[mass_col] = np.nan - continue - absorb_arr = np.asarray(self._obj[col], dtype=np.float64) - mu_sub_arr = np.asarray(self._obj["bare_atom_substrate"], dtype=np.float64) - - if mode == "full": - model = _model_factory_full(mu, mu_sub) - p0_scale = float(np.nanmean(y / mu)) - if not np.isfinite(p0_scale) or p0_scale <= 0: - p0_scale = 1.0 - p0 = (p0_scale, 0.0, 0.0) - popt, pcov = curve_fit(model, energy, y, p0=p0) - self._obj[mass_col] = _correct_row_full( - absorb_arr, mu_sub_arr, popt[0], popt[1], popt[2] - ) - self._fit_params[col] = { - "mode": "full", - "popt": popt, - "pcov": pcov, - "izero": popt[0], - "composition": popt[1], - "shift": popt[2], - } - elif mode == "bare_atom": - model = _model_factory_bare_atom(mu) - p0_scale = float(np.nanmean(y / mu)) - if not np.isfinite(p0_scale) or p0_scale <= 0: - p0_scale = 1.0 - p0 = (p0_scale, 0.0) - popt, pcov = curve_fit(model, energy, y, p0=p0) - self._obj[mass_col] = _correct_row_bare_or_step(absorb_arr, popt[0], popt[1]) - self._fit_params[col] = { - "mode": "bare_atom", - "popt": popt, - "pcov": pcov, - "izero": popt[0], - "composition": None, - "shift": popt[1], - } - else: - assert mode == "step" - assert jump is not None - model = _model_factory_step(jump) - p0_scale = float(np.nanmean(y)) - if not np.isfinite(p0_scale) or p0_scale <= 0: - p0_scale = 1.0 - p0 = (p0_scale, 0.0) - popt, pcov = curve_fit(model, energy, y, p0=p0) - self._obj[mass_col] = _correct_row_bare_or_step(absorb_arr, popt[0], popt[1]) - self._fit_params[col] = { - "mode": "step", - "jump_energy": jump, - "popt": popt, - "pcov": pcov, - "izero": popt[0], - "composition": None, - "shift": popt[1], - } - return self - - def plot_regions( - self, - ax: Axes | None = None, - pre_color: str = "blue", - post_color: str = "green", - alpha: float = 0.2, - **kwargs: Any, - ) -> Axes: - """ - Plot full scan (energy vs absorbance) with pre- and post-edge regions shaded. - - Parameters - ---------- - ax : matplotlib.axes.Axes, optional - Axes to plot on; created if None. - pre_color, post_color : str, optional - Colors for pre- and post-edge axvspan. - alpha : float, optional - Alpha for shaded regions. - **kwargs - Passed to DataFrame.plot. - - Returns - ------- - Axes - """ - self._validate_schema() - if ax is None: - _fig, ax = plt.subplots() - ax = cast("Axes", ax) # type: ignore[assignment] - self._obj.plot( - x=self._energy_col, - y=self._absorbance_cols, - ax=ax, - **kwargs, - ) - ax.axvspan( - self._pre_edge[0], - self._pre_edge[1], - color=pre_color, - alpha=alpha, - ) - ax.axvspan( - self._post_edge[0], - self._post_edge[1], - color=post_color, - alpha=alpha, - ) - return cast("Axes", ax) # type: ignore[return-value] - - def plot_normalization( - self, - show_fit: bool = True, - ax: Axes | None = None, - **kwargs: Any, - ) -> Axes: - """ - Plot normalization-region points; overlay fit curves if show_fit and normalize() was run. - - Parameters - ---------- - show_fit : bool, optional - If True and fit params exist, overlay fitted curves. - ax : matplotlib.axes.Axes, optional - Axes to plot on; created if None. - **kwargs - Passed to DataFrame.plot for the points. - - Returns - ------- - Axes - """ - self._validate_schema() - norm = self.normalization_region() - if ax is None: - _fig, ax = plt.subplots() - ax = cast("Axes", ax) # type: ignore[assignment] - style = kwargs.pop("style", [".", "."]) - norm.plot( - x=self._energy_col, - y=self._absorbance_cols, - ax=ax, - style=style[: len(self._absorbance_cols)] if isinstance(style, list | tuple) else style, - **kwargs, - ) - if show_fit and self._fit_params: - energy = norm[self._energy_col].to_numpy() - mu = norm["bare_atom"].to_numpy() - mu_sub = norm["bare_atom_substrate"].to_numpy() - for col in self._absorbance_cols: - params = self._fit_params.get(col) - if params is None: - continue - fit_mode = params.get("mode", "full") - if fit_mode == "full": - model = _model_factory_full(mu, mu_sub) - elif fit_mode == "bare_atom": - model = _model_factory_bare_atom(mu) - else: - model = _model_factory_step(params["jump_energy"]) - ax.plot( - energy, - model(energy, *params["popt"]), - label=f"fit {col}", - ) - ax.legend() - return cast("Axes", ax) # type: ignore[return-value] - - def plot( - self, - x: str, - y: str | list[str], - by: str, - colorbar: str = "", - cmap: str | None = "viridis", - ba_color: str | None = None, - ax: Axes | None = None, - title: str | None = None, - **kwargs: Any, - ) -> Axes: - """ - Multi-line plot: one line per group (by), colored by that variable, with optional colorbar. - - Parameters - ---------- - x : str - Column for x-axis (e.g. "beamline_energy"). - y : str or list of str - Column(s) for y-axis (e.g. "mass_absorption_0"). If list, first is used for coloring by group. - by : str - Column to group and color by (e.g. "sample_theta"). - colorbar : str, optional - Colorbar label (e.g. "Angle (deg)"). If empty, uses by column name. - ba_color : str, optional - Line color of the bare atom mass absorption curve. - cmap : str, optional - Color map for the colorbar. - ax : matplotlib.axes.Axes, optional - Axes to plot on; created if None. - title : str, optional - Axes title. - **kwargs - Passed to ax.plot. - - Returns - ------- - Axes - """ - df = self._obj - if by not in df.columns: - msg = f"Column '{by}' not in DataFrame." - raise ValueError(msg) - y_cols = [y] if isinstance(y, str) else list(y) - for c in [x, *y_cols]: - if c not in df.columns: - msg = f"Column '{c}' not in DataFrame." - raise ValueError(msg) - if ax is None: - _fig, ax = plt.subplots() - ax = cast("Axes", ax) # type: ignore[assignment] - groups = df.groupby(by, sort=True) - keys = list(groups.groups.keys()) - if len(keys) == 0: - return ax - cmap = colormaps[cmap] if cmap is not None else None # pyright: ignore[reportAssignmentType] - all_numeric = all(isinstance(k, int | float) for k in keys) - if all_numeric: - vmin_val = float(df[by].min()) - vmax_val = float(df[by].max()) - if vmin_val == vmax_val: - norm_obj = Normalize(vmin=vmin_val, vmax=vmax_val + 1.0) - else: - norm_obj = Normalize(vmin=vmin_val, vmax=vmax_val) - else: - norm_obj = Normalize(vmin=0, vmax=max(len(keys) - 1, 1)) - for i,key in enumerate(keys): - grp = groups.get_group(key) - if len(keys) > 1: - if all_numeric: - scalar: float = float(key) if isinstance(key, int | float) else 0.0 - else: - scalar = float(keys.index(key)) - color = cmap(norm_obj(scalar)) # type: ignore[reportCallIssue] - else: - color = "C0" - for y_col in y_cols: - ax.plot( - np.asarray(grp[x]), - np.asarray(grp[y_col]), - color=color, - **kwargs, - ) - if ba_color is not None and i == 0: - ax.plot( - np.asarray(grp[x]), - np.asarray(grp["bare_atom"]), - color=ba_color, - **kwargs, - ) - if len(keys) > 1: - sm = cm.ScalarMappable(cmap=cmap, norm=norm_obj) - sm.set_array([]) - plt.colorbar(sm, ax=ax, label=colorbar or by) - if title is not None: - ax.set_title(title) - ax.set_xlabel(x) - ax.set_ylim(0, None) - if len(y_cols) == 1: - ax.set_ylabel(y_cols[0]) - return cast("Axes", ax) # type: ignore[return-value] - - -def normalize_by_group( - df: pd.DataFrame, - group_column: str = "sample_theta", - pre_edge: tuple[float, float] = _DEFAULT_PRE_EDGE, - post_edge: tuple[float, float] = _DEFAULT_POST_EDGE, - regions: dict[Any, tuple[tuple[float, float], tuple[float, float]]] | None = None, - absorbance_cols: list[str] | None = None, - normalization_mode: NormalizationMode = "full", - jump_energy: float | None = None, - inplace: bool = False, -) -> pd.DataFrame: - """ - Run normalize() on each group and return the concatenated DataFrame. - - Parameters - ---------- - df : pd.DataFrame - Full NEXAFS DataFrame with a group column (e.g. multiple angles). - group_column : str, optional - Column defining groups (e.g. "sample_theta"). - pre_edge, post_edge : tuple of float, optional - Default (pre, post) used when regions is None or does not specify a group. - regions : dict or callable, optional - If dict: group_key -> (pre_edge, post_edge). If callable: group_key -> (pre_edge, post_edge). - Otherwise same pre_edge, post_edge for all groups. - absorbance_cols : list of str, optional - Absorbance columns; default from accessor. - normalization_mode : "full" | "bare_atom" | "step", optional - Same as set_regions(normalization_mode=...). - jump_energy : float, optional - For "step" mode only; same as set_regions(jump_energy=...). - inplace : bool, optional - If True, modify df in place and return it. Otherwise return a copy with new columns. - - Returns - ------- - pd.DataFrame - DataFrame with mass_absorption_* columns filled per group. - """ - cols = absorbance_cols or _DEFAULT_ABSORBANCE_COLS - - def resolve_regions(key: Any) -> tuple[tuple[float, float], tuple[float, float]]: - if regions is None: - return pre_edge, post_edge - if callable(regions): - return regions(key) - return regions.get(key, (pre_edge, post_edge)) - - def set_and_normalize(sub: pd.DataFrame, pre: tuple[float, float], post: tuple[float, float]) -> None: - sub.nexafs.set_regions( - pre_edge=pre, - post_edge=post, - absorbance_cols=cols, - normalization_mode=normalization_mode, - jump_energy=jump_energy, - ) - sub.nexafs.normalize() - - if inplace: - for key, grp in df.groupby(group_column, group_keys=False): - pre, post = resolve_regions(key) - sub = grp.copy() - set_and_normalize(sub, pre, post) - for col in sub.columns: - if col.startswith("mass_absorption_"): - df.loc[grp.index, col] = sub[col].values - return df - parts: list[pd.DataFrame] = [] - for key, grp in df.groupby(group_column, group_keys=False): - pre, post = resolve_regions(key) - sub = grp.copy() - set_and_normalize(sub, pre, post) - parts.append(sub) - return pd.concat(parts, axis=0, ignore_index=False) diff --git a/python/pyref/nexafs/io.py b/python/pyref/nexafs/io.py deleted file mode 100644 index 51e69f5..0000000 --- a/python/pyref/nexafs/io.py +++ /dev/null @@ -1,197 +0,0 @@ -""" -NEXAFS database IO: load sample, nexafs, and izero data; compute bare_atom and absorbance columns. -""" - -from __future__ import annotations - -from typing import TYPE_CHECKING, Any, cast - -import numpy as np -import pandas as pd -from periodictable import xsf -from pint import UnitRegistry - -if TYPE_CHECKING: - from sqlite3 import Connection - -_UREG = UnitRegistry() -_NEXAFS_COLS = [ - "scan_id", - "beamline_energy", - "sample_theta", - "tey_signal", - "photodiode", - "ai_3_izero", - "izero_before_ai_3_izero", - "izero_before_photodiode", - "izero_after_ai_3_izero", - "izero_after_photodiode", -] - - -def _calculate_mu_row( - row: pd.Series, - units: str = "g/cm^2", - formula: str | None = None, -) -> float: - used_formula = formula if formula is not None else row["chemical_formula"] - energy = float(row["beamline_energy"]) - n = xsf.index_of_refraction(used_formula, energy=(energy * 1e-3), density=1.0) - beta = -n.imag - hc = 12.3984193 - wavelength_A = hc / (energy * 1e-3) - wavelength_cm = wavelength_A * 1e-8 - mu_over_rho = float(4 * np.pi * beta / wavelength_cm) - mu_qty = mu_over_rho * _UREG("g/cm^2") - mu_converted = mu_qty.to(units) - return float(np.asarray(mu_converted.magnitude)) - - -def _get_samples( - conn: Connection, - sample_name: str, - tag: str | None = None, - version: int | None = None, -) -> pd.DataFrame: - query = "SELECT * FROM sample WHERE name = ?" - params: list[Any] = [sample_name] - if version is not None: - query += " AND version = ?" - params.append(version) - if tag is not None: - query += " AND tag = ?" - params.append(tag) - return pd.read_sql_query(query, conn, params=params) - - -def load_nexafs( - conn: Connection, - sample_name: str, - tag: str | None = None, - version: int | None = None, - substrate: str = "Si", - cols: list[str] | None = None, -) -> pd.DataFrame: - """ - Load NEXAFS data from the database for one sample and compute absorbance columns. - - Queries sample, nexafs, and izero tables; merges on beamline_energy; adds - chemical_formula, bare_atom, bare_atom_substrate, pd_response_0/1, raw_abs, - absorbance_0, and absorbance_1. The result is ready for df.nexafs normalization. - - Parameters - ---------- - conn : sqlite3.Connection - Open connection to the NEXAFS database. - sample_name : str - Sample name (sample.name). - tag : str, optional - Filter by sample.tag. - version : int, optional - Filter by sample.version. - substrate : str, optional - Chemical formula for substrate bare-atom cross section (default "Si"). - - Returns - ------- - pd.DataFrame - DataFrame with beamline_energy, sample_theta, tey_signal, photodiode, - ai_3_izero, izero_before_*, izero_after_*, chemical_formula, bare_atom, - bare_atom_substrate, pd_response_0, pd_response_1, raw_abs, - absorbance_0, absorbance_1. - """ - if cols is None: - cols = _NEXAFS_COLS - sample = _get_samples(conn, sample_name, tag=tag, version=version) - if sample.empty: - msg = f"No sample found for name={sample_name!r}, tag={tag!r}, version={version!r}" - raise ValueError(msg) - sample_id = int(sample["id"].iloc[0]) - sample_formula = str(sample["chemical_formula"].iloc[0]) - - nexafs_df = pd.read_sql_query( - "SELECT * FROM nexafs WHERE sample_id = ?", - conn, - params=[sample_id], - ) - if nexafs_df.empty: - msg = f"No nexafs rows for sample_id={sample_id}" - raise ValueError(msg) - - def _scan_id_or_none(val: Any) -> int | None: - if val is None: - return None - if isinstance(val, float) and np.isnan(val): - return None - try: - return int(val) - except (TypeError, ValueError): - return None - - izero_before_id = _scan_id_or_none(nexafs_df.iloc[0]["izero_before_scan_id"]) - izero_after_id = _scan_id_or_none(nexafs_df.iloc[0]["izero_after_scan_id"]) - - energy_col = nexafs_df[["beamline_energy"]].copy() - if izero_before_id is not None: - izero_before = pd.read_sql_query( - "SELECT * FROM izero WHERE scan_id = ?", - conn, - params=[izero_before_id], - ) - else: - izero_before = energy_col.copy() - izero_before["ai_3_izero"] = np.nan - izero_before["photodiode"] = np.nan - - if izero_after_id is not None: - izero_after = pd.read_sql_query( - "SELECT * FROM izero WHERE scan_id = ?", - conn, - params=[izero_after_id], - ) - else: - izero_after = energy_col.copy() - izero_after["ai_3_izero"] = np.nan - izero_after["photodiode"] = np.nan - - merged = pd.merge( - nexafs_df, - izero_before.add_prefix("izero_before_"), - left_on="beamline_energy", - right_on="izero_before_beamline_energy", - how="left", - suffixes=("", "_before"), - ) - merged = pd.merge( - merged, - izero_after.add_prefix("izero_after_"), - left_on="beamline_energy", - right_on="izero_after_beamline_energy", - how="left", - suffixes=("", "_after"), - )[cols] - - merged["chemical_formula"] = sample_formula - merged["bare_atom"] = merged.apply( - _calculate_mu_row, - axis=1, - units="g/cm^2", - ) - merged["bare_atom_substrate"] = merged.apply( - _calculate_mu_row, - axis=1, - units="g/cm^2", - formula=substrate, - ) - - merged["pd_response_0"] = ( - merged["izero_before_ai_3_izero"] / merged["izero_before_photodiode"] - ) - merged["pd_response_1"] = ( - merged["izero_after_ai_3_izero"] / merged["izero_after_photodiode"] - ) - merged["raw_abs"] = merged["tey_signal"] / merged["ai_3_izero"] - merged["absorbance_0"] = merged["pd_response_0"] / merged["raw_abs"] - merged["absorbance_1"] = merged["pd_response_1"] / merged["raw_abs"] - - return cast("pd.DataFrame", merged) From 945af06f7b096ede07c7b4155b70592ec9f25bd9 Mon Sep 17 00:00:00 2001 From: Harlan D Heilman <73567020+HarlanHeilman@users.noreply.github.com> Date: Mon, 13 Apr 2026 13:44:44 -0700 Subject: [PATCH 29/32] chore: make macOS Finder gitignore patterns depth-recursive --- .gitignore | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/.gitignore b/.gitignore index 855e420..addd62b 100644 --- a/.gitignore +++ b/.gitignore @@ -194,15 +194,15 @@ python/pyref/data/.pyref_catalog.db # Cursor agent hook state (local; not for version control) .cursor/hooks/state/ -# macOS / Finder -.DS_Store -.AppleDouble -.LSOverride -._* -.Spotlight-V100 -.Trashes -.fseventsd -.AppleDB -.AppleDesktop -.VolumeIcon.icns -.apdisk +# macOS / Finder (**/ matches at any depth under the repo) +**/.DS_Store +**/.AppleDouble/ +**/.LSOverride +**/._* +**/.Spotlight-V100/ +**/.Trashes/ +**/.fseventsd/ +**/.AppleDB/ +**/.AppleDesktop/ +**/.VolumeIcon.icns +**/.apdisk From 582d2d27ebd7e9a8494380ad7ea2357aa7fd3a3d Mon Sep 17 00:00:00 2001 From: Harlan D Heilman <73567020+HarlanHeilman@users.noreply.github.com> Date: Mon, 13 Apr 2026 14:19:48 -0700 Subject: [PATCH 30/32] chore: add CI with uv/ruff/ty, Release Please, and Makefile Remove vendored .github/skills, replace lint-format workflow, bump publish Python to 3.12, pin 3.12 with .python-version. Update catalog/TUI, IO helpers, ty config, and drop nexafs test. --- .github/skills/code-documenter/SKILL.md | 95 --- .../references/api-docs-fastapi-django.md | 169 ----- .../references/api-docs-nestjs-express.md | 223 ------- .../references/coverage-reports.md | 128 ---- .../references/documentation-systems.md | 336 ---------- .../references/interactive-api-docs.md | 534 ---------------- .../references/python-docstrings.md | 124 ---- .../references/typescript-jsdoc.md | 148 ----- .../references/user-guides-tutorials.md | 533 ---------------- .github/skills/pandas-pro/SKILL.md | 96 --- .../references/aggregation-groupby.md | 548 ---------------- .../pandas-pro/references/data-cleaning.md | 503 --------------- .../references/dataframe-operations.md | 423 ------------ .../pandas-pro/references/merging-joining.md | 599 ----------------- .../references/performance-optimization.md | 600 ------------------ .github/skills/python-pro/SKILL.md | 92 --- .../python-pro/references/async-patterns.md | 359 ----------- .../skills/python-pro/references/packaging.md | 463 -------------- .../python-pro/references/standard-library.md | 381 ----------- .../skills/python-pro/references/testing.md | 407 ------------ .../python-pro/references/type-system.md | 293 --------- .github/skills/rust-engineer/SKILL.md | 96 --- .../skills/rust-engineer/references/async.md | 461 -------------- .../references/error-handling.md | 337 ---------- .../rust-engineer/references/ownership.md | 281 -------- .../rust-engineer/references/testing.md | 473 -------------- .../skills/rust-engineer/references/traits.md | 416 ------------ .github/workflows/ci.yml | 79 +++ .github/workflows/lint-format.yml | 19 - .github/workflows/publish.yml | 2 +- .github/workflows/release-please.yml | 19 + .python-version | 1 + .release-please-manifest.json | 3 + Makefile | 47 ++ pyproject.toml | 41 +- python/pyref/__init__.py | 1 - python/pyref/fitting/structure.py | 31 +- python/pyref/fitting/unittensor.py | 2 + python/pyref/image/__init__.py | 6 + python/pyref/io/beamtime.py | 4 +- python/pyref/io/experiment_names.py | 64 +- python/pyref/io/fits_accessor.py | 12 +- python/pyref/io/readers.py | 94 ++- python/pyref/loader.py | 45 +- release-please-config.json | 10 + src/bin/browser.rs | 5 +- src/bin/tui/app.rs | 378 ++++++----- src/bin/tui/catalog_handle.rs | 12 +- src/bin/tui/explorer/heuristic.rs | 13 +- src/bin/tui/explorer/mod.rs | 49 +- src/bin/tui/explorer/modal.rs | 38 +- src/bin/tui/mod.rs | 6 +- src/bin/tui/navigator.rs | 10 +- src/bin/tui/preview.rs | 250 ++++---- src/bin/tui/run.rs | 15 +- src/bin/tui/ui.rs | 52 +- src/catalog/beamtime_index.rs | 20 +- src/catalog/db.rs | 5 +- src/catalog/explorer_query.rs | 56 +- src/catalog/ingest.rs | 61 +- src/catalog/ingest_progress.rs | 9 +- src/catalog/mod.rs | 25 +- src/catalog/models.rs | 2 +- src/catalog/paths.rs | 3 +- src/catalog/query.rs | 79 +-- src/colormap.rs | 26 +- src/fits/header.rs | 1 - src/gaussian_fit.rs | 24 +- src/io/blur.rs | 5 +- src/io/image_mmap.rs | 2 +- src/io/mod.rs | 46 +- src/io/zarr_store.rs | 10 +- src/lib.rs | 34 +- src/loader.rs | 6 +- src/schema.rs | 2 +- tests/test_experiment_names.py | 16 +- tests/test_nexafs_accessor.py | 112 ---- uv.lock | 44 +- 78 files changed, 1056 insertions(+), 9958 deletions(-) delete mode 100644 .github/skills/code-documenter/SKILL.md delete mode 100644 .github/skills/code-documenter/references/api-docs-fastapi-django.md delete mode 100644 .github/skills/code-documenter/references/api-docs-nestjs-express.md delete mode 100644 .github/skills/code-documenter/references/coverage-reports.md delete mode 100644 .github/skills/code-documenter/references/documentation-systems.md delete mode 100644 .github/skills/code-documenter/references/interactive-api-docs.md delete mode 100644 .github/skills/code-documenter/references/python-docstrings.md delete mode 100644 .github/skills/code-documenter/references/typescript-jsdoc.md delete mode 100644 .github/skills/code-documenter/references/user-guides-tutorials.md delete mode 100644 .github/skills/pandas-pro/SKILL.md delete mode 100644 .github/skills/pandas-pro/references/aggregation-groupby.md delete mode 100644 .github/skills/pandas-pro/references/data-cleaning.md delete mode 100644 .github/skills/pandas-pro/references/dataframe-operations.md delete mode 100644 .github/skills/pandas-pro/references/merging-joining.md delete mode 100644 .github/skills/pandas-pro/references/performance-optimization.md delete mode 100644 .github/skills/python-pro/SKILL.md delete mode 100644 .github/skills/python-pro/references/async-patterns.md delete mode 100644 .github/skills/python-pro/references/packaging.md delete mode 100644 .github/skills/python-pro/references/standard-library.md delete mode 100644 .github/skills/python-pro/references/testing.md delete mode 100644 .github/skills/python-pro/references/type-system.md delete mode 100644 .github/skills/rust-engineer/SKILL.md delete mode 100644 .github/skills/rust-engineer/references/async.md delete mode 100644 .github/skills/rust-engineer/references/error-handling.md delete mode 100644 .github/skills/rust-engineer/references/ownership.md delete mode 100644 .github/skills/rust-engineer/references/testing.md delete mode 100644 .github/skills/rust-engineer/references/traits.md create mode 100644 .github/workflows/ci.yml delete mode 100644 .github/workflows/lint-format.yml create mode 100644 .github/workflows/release-please.yml create mode 100644 .python-version create mode 100644 .release-please-manifest.json create mode 100644 Makefile create mode 100644 release-please-config.json delete mode 100644 tests/test_nexafs_accessor.py diff --git a/.github/skills/code-documenter/SKILL.md b/.github/skills/code-documenter/SKILL.md deleted file mode 100644 index c2701d6..0000000 --- a/.github/skills/code-documenter/SKILL.md +++ /dev/null @@ -1,95 +0,0 @@ ---- -name: code-documenter -description: Use when adding docstrings, creating API documentation, or building documentation sites. Invoke for OpenAPI/Swagger specs, JSDoc, doc portals, tutorials, user guides. -triggers: - - documentation - - docstrings - - OpenAPI - - Swagger - - JSDoc - - comments - - API docs - - tutorials - - user guides - - doc site -role: specialist -scope: implementation -output-format: code ---- - -# Code Documenter - -Documentation specialist for inline documentation, API specs, documentation sites, and developer guides. - -## Role Definition - -You are a senior technical writer with 8+ years of experience documenting software. You specialize in language-specific docstring formats, OpenAPI/Swagger specifications, interactive documentation portals, static site generation, and creating comprehensive guides that developers actually use. - -## When to Use This Skill - -- Adding docstrings to functions and classes -- Creating OpenAPI/Swagger documentation -- Building documentation sites (Docusaurus, MkDocs, VitePress) -- Documenting APIs with framework-specific patterns -- Creating interactive API portals (Swagger UI, Redoc, Stoplight) -- Writing getting started guides and tutorials -- Documenting multi-protocol APIs (REST, GraphQL, WebSocket, gRPC) -- Generating documentation reports and coverage metrics - -## Core Workflow - -1. **Discover** - Ask for format preference and exclusions -2. **Detect** - Identify language and framework -3. **Analyze** - Find undocumented code -4. **Document** - Apply consistent format -5. **Report** - Generate coverage summary - -## Reference Guide - -Load detailed guidance based on context: - -| Topic | Reference | Load When | -|-------|-----------|-----------| -| Python Docstrings | `references/python-docstrings.md` | Google, NumPy, Sphinx styles | -| TypeScript JSDoc | `references/typescript-jsdoc.md` | JSDoc patterns, TypeScript | -| FastAPI/Django API | `references/api-docs-fastapi-django.md` | Python API documentation | -| NestJS/Express API | `references/api-docs-nestjs-express.md` | Node.js API documentation | -| Coverage Reports | `references/coverage-reports.md` | Generating documentation reports | -| Documentation Systems | `references/documentation-systems.md` | Doc sites, static generators, search, testing | -| Interactive API Docs | `references/interactive-api-docs.md` | OpenAPI 3.1, portals, GraphQL, WebSocket, gRPC, SDKs | -| User Guides & Tutorials | `references/user-guides-tutorials.md` | Getting started, tutorials, troubleshooting, FAQs | - -## Constraints - -### MUST DO -- Ask for format preference before starting -- Detect framework for correct API doc strategy -- Document all public functions/classes -- Include parameter types and descriptions -- Document exceptions/errors -- Test code examples in documentation -- Generate coverage report - -### MUST NOT DO -- Assume docstring format without asking -- Apply wrong API doc strategy for framework -- Write inaccurate or untested documentation -- Skip error documentation -- Document obvious getters/setters verbosely -- Create documentation that's hard to maintain - -## Output Formats - -Depending on the task, provide: -1. **Code Documentation:** Documented files + coverage report -2. **API Docs:** OpenAPI specs + portal configuration -3. **Doc Sites:** Site configuration + content structure + build instructions -4. **Guides/Tutorials:** Structured markdown with examples + diagrams - -## Knowledge Reference - -Google/NumPy/Sphinx docstrings, JSDoc, OpenAPI 3.0/3.1, AsyncAPI, gRPC/protobuf, FastAPI, Django, NestJS, Express, GraphQL, Docusaurus, MkDocs, VitePress, Swagger UI, Redoc, Stoplight - -## Related Skills - -**Spec Miner** - Informs from code analysis | **Fullstack Guardian** - Documents during implementation | **Code Reviewer** - Checks documentation quality diff --git a/.github/skills/code-documenter/references/api-docs-fastapi-django.md b/.github/skills/code-documenter/references/api-docs-fastapi-django.md deleted file mode 100644 index acc5b6e..0000000 --- a/.github/skills/code-documenter/references/api-docs-fastapi-django.md +++ /dev/null @@ -1,169 +0,0 @@ -# API Documentation: FastAPI & Django - -> Reference for: Code Documenter -> Load when: Documenting Python API frameworks - -## FastAPI (Auto-generates from types) - -FastAPI automatically generates OpenAPI documentation from type hints and docstrings. - -### Endpoint Documentation - -```python -from fastapi import FastAPI, HTTPException, status -from pydantic import BaseModel, Field - -class UserCreate(BaseModel): - """User creation request body.""" - - name: str = Field(..., min_length=1, max_length=100, example="John Doe") - email: str = Field(..., example="john@example.com") - -class UserResponse(BaseModel): - """User response with generated ID.""" - - id: int = Field(..., example=1) - name: str - email: str - -@app.post( - "/users", - response_model=UserResponse, - status_code=status.HTTP_201_CREATED, - summary="Create a new user", - tags=["Users"], -) -async def create_user(user: UserCreate) -> UserResponse: - """Create a new user account. - - Args: - user: User creation data including name and email. - - Returns: - Created user with generated ID. - - Raises: - HTTPException: 400 if email already exists. - """ -``` - -### Router with Tags - -```python -from fastapi import APIRouter - -router = APIRouter( - prefix="/users", - tags=["Users"], - responses={404: {"description": "Not found"}}, -) - -@router.get( - "/{user_id}", - response_model=UserResponse, - summary="Get user by ID", -) -async def get_user(user_id: int) -> UserResponse: - """Retrieve a user by their unique identifier.""" -``` - -## Django REST Framework (drf-spectacular) - -### ViewSet Documentation - -```python -from rest_framework import viewsets, status -from rest_framework.decorators import action -from drf_spectacular.utils import extend_schema, OpenApiParameter - -class UserViewSet(viewsets.ModelViewSet): - """ - ViewSet for managing user accounts. - - list: Get all users with pagination. - create: Create a new user account. - retrieve: Get a specific user by ID. - update: Update all user fields. - partial_update: Update specific user fields. - destroy: Delete a user account. - """ - - queryset = User.objects.all() - serializer_class = UserSerializer - - @extend_schema( - summary="Get current user", - description="Returns the authenticated user's profile", - responses={200: UserSerializer}, - ) - @action(detail=False, methods=["get"]) - def me(self, request): - """Get the authenticated user's profile.""" - serializer = self.get_serializer(request.user) - return Response(serializer.data) -``` - -### Serializer Documentation - -```python -from rest_framework import serializers - -class UserSerializer(serializers.ModelSerializer): - """Serializer for user model with validation.""" - - class Meta: - model = User - fields = ["id", "name", "email", "created_at"] - read_only_fields = ["id", "created_at"] - - name = serializers.CharField( - help_text="User's display name", - max_length=100, - ) - email = serializers.EmailField( - help_text="User's email address (unique)", - ) -``` - -### Custom Schema - -```python -from drf_spectacular.utils import extend_schema, OpenApiExample - -@extend_schema( - request=UserCreateSerializer, - responses={ - 201: UserSerializer, - 400: OpenApiTypes.OBJECT, - }, - examples=[ - OpenApiExample( - "Valid request", - value={"name": "John", "email": "john@example.com"}, - ), - ], -) -def create(self, request): - """Create a new user.""" -``` - -## Quick Reference - -| Framework | Documentation Source | Output | -|-----------|---------------------|--------| -| FastAPI | Type hints + docstrings | Auto Swagger UI | -| DRF | Serializers + drf-spectacular | Auto Swagger UI | - -| FastAPI Decorator | Purpose | -|-------------------|---------| -| `summary` | Short endpoint description | -| `description` | Detailed description | -| `tags` | Group endpoints | -| `response_model` | Response schema | -| `responses` | Additional response codes | - -| DRF Decorator | Purpose | -|---------------|---------| -| `@extend_schema` | Customize schema | -| `OpenApiParameter` | Query/path params | -| `OpenApiExample` | Request examples | diff --git a/.github/skills/code-documenter/references/api-docs-nestjs-express.md b/.github/skills/code-documenter/references/api-docs-nestjs-express.md deleted file mode 100644 index d157e9b..0000000 --- a/.github/skills/code-documenter/references/api-docs-nestjs-express.md +++ /dev/null @@ -1,223 +0,0 @@ -# API Documentation: NestJS & Express - -> Reference for: Code Documenter -> Load when: Documenting Node.js API frameworks - -## NestJS (@nestjs/swagger) - -NestJS requires explicit decorators for OpenAPI documentation. - -### Controller Documentation - -```typescript -import { Controller, Post, Body, Get, Param } from '@nestjs/common'; -import { - ApiTags, - ApiOperation, - ApiResponse, - ApiParam, - ApiBearerAuth, -} from '@nestjs/swagger'; - -@ApiTags('Users') -@ApiBearerAuth() -@Controller('users') -export class UsersController { - @Post() - @ApiOperation({ summary: 'Create a new user' }) - @ApiResponse({ - status: 201, - description: 'User created successfully', - type: UserDto, - }) - @ApiResponse({ - status: 400, - description: 'Invalid input data', - }) - async create(@Body() dto: CreateUserDto): Promise { - return this.usersService.create(dto); - } - - @Get(':id') - @ApiOperation({ summary: 'Get user by ID' }) - @ApiParam({ - name: 'id', - description: 'User unique identifier', - example: '123', - }) - @ApiResponse({ status: 200, type: UserDto }) - @ApiResponse({ status: 404, description: 'User not found' }) - async findOne(@Param('id') id: string): Promise { - return this.usersService.findOne(id); - } -} -``` - -### DTO Documentation - -```typescript -import { ApiProperty, ApiPropertyOptional } from '@nestjs/swagger'; -import { IsEmail, IsString, MinLength } from 'class-validator'; - -export class CreateUserDto { - @ApiProperty({ - description: "User's display name", - example: 'John Doe', - minLength: 1, - maxLength: 100, - }) - @IsString() - @MinLength(1) - name: string; - - @ApiProperty({ - description: "User's email address", - example: 'john@example.com', - }) - @IsEmail() - email: string; - - @ApiPropertyOptional({ - description: 'Profile picture URL', - example: 'https://example.com/avatar.jpg', - }) - avatarUrl?: string; -} -``` - -## Express (swagger-jsdoc) - -Express uses JSDoc comments with swagger annotations. - -### Setup - -```javascript -const swaggerJsdoc = require('swagger-jsdoc'); -const swaggerUi = require('swagger-ui-express'); - -const options = { - definition: { - openapi: '3.0.0', - info: { - title: 'API Documentation', - version: '1.0.0', - }, - }, - apis: ['./routes/*.js'], -}; - -const specs = swaggerJsdoc(options); -app.use('/api-docs', swaggerUi.serve, swaggerUi.setup(specs)); -``` - -### Route Documentation - -```javascript -/** - * @swagger - * /users: - * post: - * summary: Create a new user - * tags: [Users] - * requestBody: - * required: true - * content: - * application/json: - * schema: - * $ref: '#/components/schemas/CreateUser' - * responses: - * 201: - * description: User created successfully - * content: - * application/json: - * schema: - * $ref: '#/components/schemas/User' - * 400: - * description: Invalid input - */ -router.post('/users', createUser); - -/** - * @swagger - * /users/{id}: - * get: - * summary: Get user by ID - * tags: [Users] - * parameters: - * - in: path - * name: id - * required: true - * schema: - * type: string - * description: User ID - * responses: - * 200: - * description: User found - * content: - * application/json: - * schema: - * $ref: '#/components/schemas/User' - * 404: - * description: User not found - */ -router.get('/users/:id', getUser); -``` - -### Schema Documentation - -```javascript -/** - * @swagger - * components: - * schemas: - * CreateUser: - * type: object - * required: - * - name - * - email - * properties: - * name: - * type: string - * description: User's display name - * example: John Doe - * email: - * type: string - * format: email - * description: User's email address - * example: john@example.com - * User: - * allOf: - * - $ref: '#/components/schemas/CreateUser' - * - type: object - * properties: - * id: - * type: string - * description: Unique identifier - * createdAt: - * type: string - * format: date-time - */ -``` - -## Quick Reference - -| NestJS Decorator | Purpose | -|------------------|---------| -| `@ApiTags()` | Group endpoints | -| `@ApiOperation()` | Endpoint summary | -| `@ApiResponse()` | Response documentation | -| `@ApiParam()` | Path parameter | -| `@ApiQuery()` | Query parameter | -| `@ApiBody()` | Request body | -| `@ApiBearerAuth()` | Auth requirement | -| `@ApiProperty()` | DTO property | - -| Express swagger-jsdoc | Purpose | -|-----------------------|---------| -| `@swagger` | Start swagger block | -| `tags` | Group endpoints | -| `summary` | Short description | -| `parameters` | Path/query params | -| `requestBody` | Request body schema | -| `responses` | Response schemas | -| `$ref` | Reference schema | diff --git a/.github/skills/code-documenter/references/coverage-reports.md b/.github/skills/code-documenter/references/coverage-reports.md deleted file mode 100644 index 23c4399..0000000 --- a/.github/skills/code-documenter/references/coverage-reports.md +++ /dev/null @@ -1,128 +0,0 @@ -# Coverage Reports - -> Reference for: Code Documenter -> Load when: Generating documentation reports - -## Documentation Coverage Report Template - -```markdown -# Documentation Report: {project_name} - -## Summary -- **Files analyzed**: 45 -- **Functions documented**: 120/150 (80%) -- **Classes documented**: 25/25 (100%) -- **API endpoints documented**: 30/30 (100%) - -## Coverage Before/After -- Before: 45% -- After: 92% - -## Files Modified - -| File | Functions Added | Notes | -|------|-----------------|-------| -| src/services/user.ts | 8 | All public methods | -| src/services/auth.ts | 5 | Added examples | -| src/controllers/users.ts | 6 | Added @Api decorators | -| src/dto/user.dto.ts | 4 | Added @ApiProperty | - -## API Documentation - -- **Framework**: NestJS -- **Strategy**: @nestjs/swagger decorators -- **Swagger UI**: /api/docs -- **OpenAPI spec**: /api-json - -## Documentation Style - -- **Python**: Google style docstrings -- **TypeScript**: JSDoc with @param, @returns -- **API**: OpenAPI 3.0 via decorators - -## Next Steps - -### Recommendations -1. Run `npm run docs:lint` to validate JSDoc -2. Add `eslint-plugin-jsdoc` to enforce documentation -3. Consider adding examples for complex functions -4. Set up documentation CI checks - -### Missing Documentation -| File | Missing | Priority | -|------|---------|----------| -| src/utils/crypto.ts | 3 functions | High | -| src/helpers/date.ts | 2 functions | Medium | - -### CI Integration -```yaml -# Add to CI pipeline -- name: Check documentation - run: npm run docs:check - -- name: Generate API docs - run: npm run docs:generate -``` -``` - -## Checklist During Documentation - -```markdown -## Documentation Checklist - -### Before Starting -- [ ] Confirmed format preference (Google/JSDoc/etc.) -- [ ] Identified files to exclude (tests, generated) -- [ ] Detected framework for API docs - -### Functions/Methods -- [ ] All public functions documented -- [ ] Parameters described with types -- [ ] Return values documented -- [ ] Exceptions/errors documented -- [ ] Examples added for complex functions - -### Classes -- [ ] Class purpose described -- [ ] Constructor parameters documented -- [ ] Public methods documented -- [ ] Important attributes explained - -### API Endpoints -- [ ] All endpoints have summaries -- [ ] Request bodies documented -- [ ] Response schemas defined -- [ ] Error responses documented -- [ ] Authentication requirements noted - -### Final Checks -- [ ] Ran documentation linter -- [ ] Verified Swagger UI renders correctly -- [ ] No inaccurate documentation -- [ ] Coverage report generated -``` - -## Framework-Specific Linting - -```bash -# JavaScript/TypeScript - ESLint -npm install eslint-plugin-jsdoc --save-dev -# Add to .eslintrc: "plugins": ["jsdoc"] - -# Python - pydocstyle -pip install pydocstyle -pydocstyle --convention=google src/ - -# Python - interrogate (coverage) -pip install interrogate -interrogate -v src/ -``` - -## Quick Reference - -| Metric | Good | Acceptable | Poor | -|--------|------|------------|------| -| Function coverage | >90% | 70-90% | <70% | -| Class coverage | 100% | >90% | <90% | -| API endpoint coverage | 100% | 100% | <100% | -| Example coverage | >50% | 30-50% | <30% | diff --git a/.github/skills/code-documenter/references/documentation-systems.md b/.github/skills/code-documenter/references/documentation-systems.md deleted file mode 100644 index 694c0c3..0000000 --- a/.github/skills/code-documenter/references/documentation-systems.md +++ /dev/null @@ -1,336 +0,0 @@ -# Documentation Systems & Infrastructure - -> Reference for: Code Documenter -> Load when: Building documentation sites, static generators, multi-version docs, search systems - -## Static Site Generators - -### Docusaurus (Meta) - -```bash -# Setup -npx create-docusaurus@latest docs classic -cd docs && npm start - -# Structure -docs/ -├── docs/ # Documentation pages -├── blog/ # Blog posts -├── src/ -│ └── pages/ # Custom pages -└── docusaurus.config.js -``` - -**docusaurus.config.js:** -```javascript -module.exports = { - title: 'My API', - tagline: 'Build amazing things', - url: 'https://docs.example.com', - baseUrl: '/', - - themeConfig: { - navbar: { - items: [ - {to: '/docs/intro', label: 'Docs', position: 'left'}, - {to: '/api', label: 'API', position: 'left'}, - ], - }, - - // Algolia search - algolia: { - apiKey: 'YOUR_API_KEY', - indexName: 'your_index', - contextualSearch: true, - }, - - prism: { - theme: lightCodeTheme, - darkTheme: darkCodeTheme, - additionalLanguages: ['python', 'rust'], - }, - }, -}; -``` - -### MkDocs (Python) - -```yaml -# mkdocs.yml -site_name: My API Documentation -theme: - name: material - features: - - navigation.tabs - - navigation.sections - - toc.integrate - - search.suggest - - search.highlight - palette: - - scheme: default - toggle: - icon: material/brightness-7 - name: Switch to dark mode - - scheme: slate - toggle: - icon: material/brightness-4 - name: Switch to light mode - -plugins: - - search - - mkdocstrings: - handlers: - python: - options: - show_source: true - - git-revision-date-localized - -markdown_extensions: - - pymdownx.highlight - - pymdownx.superfences - - admonition - - codehilite - -nav: - - Home: index.md - - Getting Started: getting-started.md - - API Reference: api/ -``` - -### VitePress (Vue) - -```typescript -// .vitepress/config.ts -export default defineConfig({ - title: 'API Docs', - description: 'Developer documentation', - - themeConfig: { - nav: [ - { text: 'Guide', link: '/guide/' }, - { text: 'API', link: '/api/' }, - ], - - sidebar: { - '/guide/': [ - { - text: 'Introduction', - items: [ - { text: 'Getting Started', link: '/guide/getting-started' }, - { text: 'Configuration', link: '/guide/config' }, - ], - }, - ], - }, - - search: { - provider: 'local', - }, - - editLink: { - pattern: 'https://github.com/user/repo/edit/main/docs/:path', - }, - }, -}); -``` - -## Multi-Version Documentation - -### Version Switcher - -```javascript -// Docusaurus versions -{ - versions: { - current: { - label: '2.0 (Next)', - path: 'next', - }, - }, - onlyIncludeVersions: ['current', '1.5', '1.4'], -} -``` - -### Migration Guides - -```markdown -# Migration Guide: v1 to v2 - -## Breaking Changes - -### Authentication -**v1:** -```python -client.authenticate(api_key) -``` - -**v2:** -```python -client = Client(api_key=api_key) # Pass in constructor -``` - -### Renamed Methods -| v1 | v2 | Notes | -|----|----|----- | -| `get_user()` | `fetch_user()` | Async now | -| `delete_user()` | `remove_user()` | Returns Promise | - -## Deprecation Timeline -- v1.x: Supported until Dec 2025 -- v2.0: Released Jan 2025 -- v2.1: Current (June 2025) -``` - -## Search Implementation - -### Algolia DocSearch - -```html - - - - - -``` - -### Local Search (Lunr.js) - -```javascript -const idx = lunr(function() { - this.ref('id'); - this.field('title', { boost: 10 }); - this.field('content'); - - documents.forEach(doc => this.add(doc)); -}); - -// Search -const results = idx.search('authentication'); -``` - -## Documentation Testing - -### Link Checking - -```bash -# linkcheck (Python) -pip install linkchecker -linkchecker http://localhost:3000/docs - -# broken-link-checker (Node) -npm install -g broken-link-checker -blc http://localhost:3000 -ro -``` - -### Code Example Testing - -```python -# doctest for Python examples -""" ->>> add(2, 3) -5 ->>> add(-1, 1) -0 -""" - -# Run tests -python -m doctest -v docs/*.md -``` - -```javascript -// Jest for TypeScript examples -// Extract code blocks and test -import { runExamples } from './test-docs'; - -test('API examples work', async () => { - const examples = extractExamples('./docs/api.md'); - await expect(runExamples(examples)).resolves.toBeTruthy(); -}); -``` - -## Performance Optimization - -### Build Optimization - -```javascript -// Webpack/Vite config -export default { - build: { - rollupOptions: { - output: { - manualChunks: { - 'vendor': ['react', 'react-dom'], - }, - }, - }, - }, - - optimizeDeps: { - include: ['prismjs'], - }, -}; -``` - -### CDN & Caching - -```nginx -# nginx.conf -location /docs { - expires 1y; - add_header Cache-Control "public, immutable"; -} - -location ~* \.(html)$ { - expires 1h; - add_header Cache-Control "public, must-revalidate"; -} -``` - -## Analytics Integration - -### Google Analytics - -```javascript -// Docusaurus -gtag: { - trackingID: 'G-XXXXXXXXXX', - anonymizeIP: true, -}, -``` - -### Custom Analytics - -```javascript -// Track search queries -function trackSearch(query, results) { - analytics.track('docs_search', { - query, - resultCount: results.length, - timestamp: new Date(), - }); -} -``` - -## Quick Reference - -| Tool | Best For | Tech Stack | -|------|----------|-----------| -| Docusaurus | React projects, versioning | React, MDX | -| MkDocs | Python projects, simple setup | Python, Jinja2 | -| VitePress | Vue projects, fast builds | Vue, Vite | -| Nextra | Next.js integration | React, Next.js | -| Mintlify | Modern UI, AI search | React | - -| Search Solution | Cost | Features | -|----------------|------|----------| -| Algolia DocSearch | Free (OSS) | Fast, typo-tolerant | -| Local (Lunr.js) | Free | Offline, no server | -| Typesense | Free (self-host) | Privacy-focused | -| Meilisearch | Free (self-host) | Fast, relevance | diff --git a/.github/skills/code-documenter/references/interactive-api-docs.md b/.github/skills/code-documenter/references/interactive-api-docs.md deleted file mode 100644 index 937f3ee..0000000 --- a/.github/skills/code-documenter/references/interactive-api-docs.md +++ /dev/null @@ -1,534 +0,0 @@ -# Interactive API Documentation - -> Reference for: Code Documenter -> Load when: Building API portals, interactive consoles, multi-protocol APIs, SDK docs - -## OpenAPI 3.1 Advanced Features - -### Reusable Components - -```yaml -openapi: 3.1.0 -info: - title: Users API - version: 2.0.0 - -components: - # Reusable schemas - schemas: - User: - type: object - required: [id, email] - properties: - id: - type: string - format: uuid - example: "123e4567-e89b-12d3-a456-426614174000" - email: - type: string - format: email - example: "user@example.com" - - Error: - type: object - properties: - code: - type: string - message: - type: string - details: - type: object - - PaginatedResponse: - type: object - properties: - data: - type: array - items: {} - total: - type: integer - page: - type: integer - - # Reusable parameters - parameters: - PageParam: - name: page - in: query - schema: - type: integer - default: 1 - minimum: 1 - - LimitParam: - name: limit - in: query - schema: - type: integer - default: 20 - minimum: 1 - maximum: 100 - - # Security schemes - securitySchemes: - BearerAuth: - type: http - scheme: bearer - bearerFormat: JWT - - ApiKeyAuth: - type: apiKey - in: header - name: X-API-Key - - OAuth2: - type: oauth2 - flows: - authorizationCode: - authorizationUrl: https://api.example.com/oauth/authorize - tokenUrl: https://api.example.com/oauth/token - scopes: - read:users: Read user data - write:users: Modify user data - - # Reusable responses - responses: - NotFound: - description: Resource not found - content: - application/json: - schema: - $ref: '#/components/schemas/Error' - - Unauthorized: - description: Authentication required - content: - application/json: - schema: - $ref: '#/components/schemas/Error' - -paths: - /users: - get: - summary: List users - parameters: - - $ref: '#/components/parameters/PageParam' - - $ref: '#/components/parameters/LimitParam' - security: - - BearerAuth: [] - responses: - '200': - description: Success - content: - application/json: - schema: - allOf: - - $ref: '#/components/schemas/PaginatedResponse' - - type: object - properties: - data: - type: array - items: - $ref: '#/components/schemas/User' -``` - -## Interactive Documentation Portals - -### Swagger UI Customization - -```javascript -// Custom Swagger UI -const swaggerUi = require('swagger-ui-express'); -const swaggerDocument = require('./openapi.json'); - -const options = { - customCss: '.swagger-ui .topbar { display: none }', - customSiteTitle: "API Docs", - customfavIcon: "/favicon.ico", - swaggerOptions: { - persistAuthorization: true, - displayRequestDuration: true, - filter: true, - tryItOutEnabled: true, - requestInterceptor: (req) => { - req.headers['X-Custom-Header'] = 'value'; - return req; - }, - }, -}; - -app.use('/api-docs', swaggerUi.serve, swaggerUi.setup(swaggerDocument, options)); -``` - -### Redoc (Modern Alternative) - -```html - - - - API Documentation - - - - - - - - -``` - -### Stoplight Elements - -```javascript -import { API } from '@stoplight/elements'; -import '@stoplight/elements/styles.min.css'; - -function App() { - return ( - - ); -} -``` - -## Multi-Protocol Documentation - -### GraphQL Schema Documentation - -```graphql -""" -User account in the system -""" -type User { - """ - Unique user identifier - """ - id: ID! - - """ - User's email address (unique) - @example "user@example.com" - """ - email: String! - - """ - Display name - @example "John Doe" - """ - name: String! - - """ - User's posts (paginated) - """ - posts( - """Number of items per page (max 100)""" - limit: Int = 20 - """Page offset""" - offset: Int = 0 - ): PostConnection! -} - -type Query { - """ - Fetch a user by ID - """ - user( - """User's unique identifier""" - id: ID! - ): User - - """ - Search users by name or email - """ - searchUsers( - """Search query""" - query: String! - """Maximum results to return""" - limit: Int = 10 - ): [User!]! -} - -type Mutation { - """ - Create a new user account - """ - createUser( - """User creation input""" - input: CreateUserInput! - ): CreateUserPayload! -} - -""" -Input for creating a user -""" -input CreateUserInput { - """User's email address""" - email: String! - """Display name""" - name: String! -} -``` - -**GraphQL Playground:** -```javascript -const { ApolloServer } = require('apollo-server'); - -const server = new ApolloServer({ - typeDefs, - resolvers, - introspection: true, // Enable in dev - playground: { - settings: { - 'editor.theme': 'dark', - 'editor.fontSize': 14, - }, - }, -}); -``` - -### WebSocket Protocol Documentation - -```yaml -# AsyncAPI 2.0 -asyncapi: 2.5.0 -info: - title: Chat WebSocket API - version: 1.0.0 - description: Real-time chat messaging - -channels: - chat/{roomId}: - parameters: - roomId: - description: Chat room identifier - schema: - type: string - - subscribe: - summary: Receive messages - message: - oneOf: - - $ref: '#/components/messages/ChatMessage' - - $ref: '#/components/messages/UserJoined' - - publish: - summary: Send a message - message: - $ref: '#/components/messages/ChatMessage' - -components: - messages: - ChatMessage: - name: message - payload: - type: object - properties: - userId: - type: string - content: - type: string - timestamp: - type: string - format: date-time - - UserJoined: - name: userJoined - payload: - type: object - properties: - userId: - type: string - username: - type: string -``` - -### gRPC Documentation - -```protobuf -syntax = "proto3"; - -package users.v1; - -// User service manages user accounts -service UserService { - // Get a user by ID - // Returns: User object or NOT_FOUND error - rpc GetUser(GetUserRequest) returns (User) {} - - // List all users with pagination - // Returns: Paginated list of users - rpc ListUsers(ListUsersRequest) returns (ListUsersResponse) {} - - // Create a new user - // Returns: Created user or ALREADY_EXISTS error - rpc CreateUser(CreateUserRequest) returns (User) {} - - // Stream user updates in real-time - // Returns: Stream of user update events - rpc WatchUsers(WatchUsersRequest) returns (stream UserEvent) {} -} - -// User account -message User { - // Unique identifier - string id = 1; - - // Email address (unique, required) - string email = 2; - - // Display name - string name = 3; - - // Account creation timestamp - google.protobuf.Timestamp created_at = 4; -} -``` - -## SDK Documentation Strategies - -### Multi-Language Examples - -```markdown -# Create User - -## Python -```python -from myapi import Client - -client = Client(api_key="your_key") -user = client.users.create( - name="John Doe", - email="john@example.com" -) -print(user.id) -``` - -## TypeScript -```typescript -import { Client } from '@myapi/sdk'; - -const client = new Client({ apiKey: 'your_key' }); -const user = await client.users.create({ - name: 'John Doe', - email: 'john@example.com', -}); -console.log(user.id); -``` - -## Go -```go -import "github.com/myapi/sdk-go" - -client := sdk.NewClient("your_key") -user, err := client.Users.Create(ctx, &sdk.CreateUserInput{ - Name: "John Doe", - Email: "john@example.com", -}) -if err != nil { - log.Fatal(err) -} -fmt.Println(user.ID) -``` - -## Ruby -```ruby -require 'myapi' - -client = MyAPI::Client.new(api_key: 'your_key') -user = client.users.create( - name: 'John Doe', - email: 'john@example.com' -) -puts user.id -``` -``` - -### SDK Reference Template - -```markdown -# Users SDK - -## Installation -```bash -npm install @myapi/sdk -``` - -## Configuration -```typescript -import { Client } from '@myapi/sdk'; - -const client = new Client({ - apiKey: process.env.API_KEY, - baseUrl: 'https://api.example.com', // Optional - timeout: 30000, // Optional, default 30s -}); -``` - -## Methods - -### `client.users.create(data)` -Create a new user. - -**Parameters:** -- `data.name` (string, required) - User's display name -- `data.email` (string, required) - User's email address - -**Returns:** Promise - -**Throws:** -- `ValidationError` - Invalid input data -- `ConflictError` - Email already exists -- `AuthenticationError` - Invalid API key - -**Example:** -```typescript -const user = await client.users.create({ - name: 'John Doe', - email: 'john@example.com', -}); -``` - -## Error Handling -```typescript -import { ValidationError, ConflictError } from '@myapi/sdk'; - -try { - await client.users.create(data); -} catch (error) { - if (error instanceof ValidationError) { - console.error('Invalid data:', error.fields); - } else if (error instanceof ConflictError) { - console.error('User already exists'); - } -} -``` -``` - -## Quick Reference - -| Tool | Protocol | Features | -|------|----------|----------| -| Swagger UI | REST | Try-it-out, auth | -| Redoc | REST | Clean, responsive | -| Stoplight | REST | Modern, mock server | -| GraphQL Playground | GraphQL | Explorer, history | -| AsyncAPI Studio | WebSocket | Visual editor | -| grpcui | gRPC | Interactive console | diff --git a/.github/skills/code-documenter/references/python-docstrings.md b/.github/skills/code-documenter/references/python-docstrings.md deleted file mode 100644 index 054a12e..0000000 --- a/.github/skills/code-documenter/references/python-docstrings.md +++ /dev/null @@ -1,124 +0,0 @@ -# Python Docstrings - -> Reference for: Code Documenter -> Load when: Documenting Python code - -## Google Style (Recommended) - -```python -def calculate_total(items: list[Item], tax_rate: float = 0.0) -> float: - """Calculate total cost including tax. - - Args: - items: List of items to calculate total for. - tax_rate: Tax rate as decimal (e.g., 0.08 for 8%). - - Returns: - Total cost including tax. - - Raises: - ValueError: If tax_rate is negative or items is empty. - - Example: - >>> calculate_total([Item(10), Item(20)], 0.1) - 33.0 - """ -``` - -## NumPy Style - -```python -def calculate_total(items: list[Item], tax_rate: float = 0.0) -> float: - """ - Calculate total cost including tax. - - Parameters - ---------- - items : list[Item] - List of items to calculate total for. - tax_rate : float, optional - Tax rate as decimal (e.g., 0.08 for 8%). Default is 0.0. - - Returns - ------- - float - Total cost including tax. - - Raises - ------ - ValueError - If tax_rate is negative or items is empty. - - Examples - -------- - >>> calculate_total([Item(10), Item(20)], 0.1) - 33.0 - """ -``` - -## Sphinx Style - -```python -def calculate_total(items: list[Item], tax_rate: float = 0.0) -> float: - """Calculate total cost including tax. - - :param items: List of items to calculate total for. - :type items: list[Item] - :param tax_rate: Tax rate as decimal (e.g., 0.08 for 8%). - :type tax_rate: float - :returns: Total cost including tax. - :rtype: float - :raises ValueError: If tax_rate is negative or items is empty. - - .. code-block:: python - - >>> calculate_total([Item(10), Item(20)], 0.1) - 33.0 - """ -``` - -## Class Documentation - -```python -class UserService: - """Service for managing user operations. - - This service handles CRUD operations for users and - integrates with the authentication system. - - Attributes: - db: Database session for queries. - cache: Redis client for caching. - - Example: - >>> service = UserService(db, cache) - >>> user = await service.create_user(data) - """ - - def __init__(self, db: AsyncSession, cache: Redis) -> None: - """Initialize UserService. - - Args: - db: Database session for queries. - cache: Redis client for caching. - """ -``` - -## Quick Reference - -| Style | Args Format | Returns Format | -|-------|-------------|----------------| -| Google | `Args:` block | `Returns:` block | -| NumPy | `Parameters` section | `Returns` section | -| Sphinx | `:param name:` | `:returns:` | - -## Sections Available - -| Section | Google | NumPy | Sphinx | -|---------|--------|-------|--------| -| Parameters | `Args:` | `Parameters` | `:param:` | -| Returns | `Returns:` | `Returns` | `:returns:` | -| Raises | `Raises:` | `Raises` | `:raises:` | -| Examples | `Example:` | `Examples` | `.. code-block::` | -| Notes | `Note:` | `Notes` | `.. note::` | -| Attributes | `Attributes:` | `Attributes` | `:ivar:` | diff --git a/.github/skills/code-documenter/references/typescript-jsdoc.md b/.github/skills/code-documenter/references/typescript-jsdoc.md deleted file mode 100644 index c241c44..0000000 --- a/.github/skills/code-documenter/references/typescript-jsdoc.md +++ /dev/null @@ -1,148 +0,0 @@ -# TypeScript JSDoc - -> Reference for: Code Documenter -> Load when: Documenting TypeScript/JavaScript code - -## Function Documentation - -```typescript -/** - * Calculate total cost including tax. - * - * @param items - List of items to calculate total for - * @param taxRate - Tax rate as decimal (e.g., 0.08 for 8%) - * @returns Total cost including tax - * @throws {Error} If taxRate is negative or items is empty - * - * @example - * ```typescript - * const total = calculateTotal(items, 0.08); - * console.log(total); // 108.00 - * ``` - */ -function calculateTotal(items: Item[], taxRate = 0): number { -``` - -## Class Documentation - -```typescript -/** - * Service for managing user operations. - * - * Handles CRUD operations and integrates with authentication system. - * - * @example - * ```typescript - * const service = new UserService(db, cache); - * const user = await service.create(userData); - * ``` - */ -class UserService { - /** - * Create a new UserService instance. - * - * @param db - Database connection - * @param cache - Redis cache client - */ - constructor( - private readonly db: Database, - private readonly cache: Cache, - ) {} -} -``` - -## Interface Documentation - -```typescript -/** - * User data transfer object. - * - * @interface UserDto - */ -interface UserDto { - /** Unique user identifier */ - id: string; - - /** User's email address (unique) */ - email: string; - - /** User's display name */ - name: string; - - /** Account creation timestamp */ - createdAt: Date; -} -``` - -## Generic Types - -```typescript -/** - * Paginated response wrapper. - * - * @template T - Type of items in the data array - */ -interface PaginatedResponse { - /** Array of items for current page */ - data: T[]; - - /** Total number of items across all pages */ - total: number; - - /** Current page number (1-indexed) */ - page: number; - - /** Number of items per page */ - limit: number; -} -``` - -## Async Functions - -```typescript -/** - * Fetch user by ID from database. - * - * @param id - User's unique identifier - * @returns Promise resolving to user data or null if not found - * @throws {DatabaseError} If connection fails - * - * @async - */ -async function findUserById(id: string): Promise { -``` - -## Quick Reference - -| Tag | Purpose | Example | -|-----|---------|---------| -| `@param` | Parameter description | `@param name - User's name` | -| `@returns` | Return value | `@returns User object` | -| `@throws` | Exception thrown | `@throws {Error} If invalid` | -| `@example` | Usage example | Code block | -| `@see` | Reference link | `@see UserService` | -| `@deprecated` | Mark deprecated | `@deprecated Use v2 instead` | -| `@template` | Generic type param | `@template T - Item type` | -| `@async` | Async function | Mark async | -| `@private` | Private member | Internal use | -| `@readonly` | Read-only property | Cannot modify | - -## Common Patterns - -```typescript -// Optional parameters -/** @param [options] - Optional configuration */ - -// Default values -/** @param [limit=10] - Items per page (default: 10) */ - -// Multiple types -/** @param input - Input value (string or number) */ - -// Callback parameters -/** - * @callback FilterFn - * @param item - Item to filter - * @returns Whether item passes filter - */ -``` diff --git a/.github/skills/code-documenter/references/user-guides-tutorials.md b/.github/skills/code-documenter/references/user-guides-tutorials.md deleted file mode 100644 index ddfb7b6..0000000 --- a/.github/skills/code-documenter/references/user-guides-tutorials.md +++ /dev/null @@ -1,533 +0,0 @@ -# User Guides & Tutorials - -> Reference for: Code Documenter -> Load when: Creating getting started guides, tutorials, troubleshooting docs, end-user documentation - -## Tutorial Structure - -### Progressive Learning Path - -```markdown -# Getting Started with API - -## Prerequisites -Before you begin, ensure you have: -- [ ] Node.js 18+ installed -- [ ] An API key from your dashboard -- [ ] Basic knowledge of REST APIs - -## Quick Start (5 minutes) - -### 1. Install the SDK -```bash -npm install @myapi/sdk -``` - -### 2. Create Your First Request -```typescript -import { Client } from '@myapi/sdk'; - -const client = new Client({ apiKey: 'your_key' }); -const users = await client.users.list(); -console.log(users); -``` - -### 3. Verify It Works -Run the code and you should see a list of users. - -**Expected output:** -```json -{ - "data": [ - { "id": "1", "name": "Alice" }, - { "id": "2", "name": "Bob" } - ], - "total": 2 -} -``` - -## Next Steps -- [Authentication Guide](/docs/auth) - Learn about OAuth and API keys -- [Advanced Queries](/docs/queries) - Filtering, sorting, pagination -- [Error Handling](/docs/errors) - Handle errors gracefully -``` - -### Step-by-Step Tutorial - -```markdown -# Tutorial: Building a User Dashboard - -**What you'll learn:** -- Fetching user data from the API -- Handling pagination -- Displaying data in a table -- Adding real-time updates - -**Time:** 30 minutes -**Level:** Intermediate - -## Step 1: Set Up the Project - -Create a new project: -```bash -mkdir user-dashboard -cd user-dashboard -npm init -y -npm install @myapi/sdk react -``` - -## Step 2: Fetch Users - -Create `src/api/users.ts`: -```typescript -import { Client } from '@myapi/sdk'; - -const client = new Client({ apiKey: process.env.API_KEY }); - -export async function getUsers(page = 1, limit = 20) { - const response = await client.users.list({ page, limit }); - return response; -} -``` - -**What's happening:** -1. We import the SDK client -2. Initialize it with our API key from environment -3. Create a helper function that fetches paginated users - -## Step 3: Create the Component - -Create `src/components/UserTable.tsx`: -```typescript -import { useState, useEffect } from 'react'; -import { getUsers } from '../api/users'; - -export function UserTable() { - const [users, setUsers] = useState([]); - const [loading, setLoading] = useState(true); - - useEffect(() => { - async function fetchData() { - const data = await getUsers(); - setUsers(data.data); - setLoading(false); - } - fetchData(); - }, []); - - if (loading) return
Loading...
; - - return ( - - - - - - - - - {users.map(user => ( - - - - - ))} - -
NameEmail
{user.name}{user.email}
- ); -} -``` - -## Step 4: Test It - -Run your app: -```bash -npm run dev -``` - -You should see a table with user data. - -## Checkpoint -At this point, you have: -- [x] Set up the SDK -- [x] Created an API helper -- [x] Built a user table component -- [ ] Added pagination -- [ ] Added real-time updates - -## Next: Adding Pagination - -[Continue to Step 5 →](/docs/tutorial/step-5) -``` - -## Information Architecture - -### Content Hierarchy - -```markdown -Documentation/ -├── Getting Started/ -│ ├── Quick Start (5 min) -│ ├── Installation -│ ├── Authentication -│ └── First Request -│ -├── Guides/ -│ ├── User Management -│ ├── File Uploads -│ ├── Webhooks -│ └── Rate Limiting -│ -├── API Reference/ -│ ├── Users API -│ ├── Files API -│ └── Webhooks API -│ -├── SDK Documentation/ -│ ├── Python SDK -│ ├── TypeScript SDK -│ └── Go SDK -│ -├── Tutorials/ -│ ├── Build a Dashboard (30 min) -│ ├── Integrate Authentication (45 min) -│ └── Real-time Sync (60 min) -│ -└── Resources/ - ├── Troubleshooting - ├── FAQ - ├── Best Practices - └── Migration Guides -``` - -## Writing Techniques - -### Task-Based Writing - -```markdown -# How to Upload a File - -**Goal:** Upload an image file to your account storage - -**Time:** 5 minutes - -## Steps - -### 1. Prepare the file -Get the file from user input or file system: -```typescript -const file = document.querySelector('input[type="file"]').files[0]; -``` - -### 2. Create form data -```typescript -const formData = new FormData(); -formData.append('file', file); -formData.append('folder', 'avatars'); -``` - -### 3. Upload with the SDK -```typescript -const result = await client.files.upload(formData); -console.log('File URL:', result.url); -``` - -## Common Issues - -**"File too large" error:** -Maximum file size is 10MB. Compress images before uploading. - -**"Invalid file type" error:** -Only .jpg, .png, .gif are allowed. Check the file extension. - -## Related -- [File API Reference](/api/files) -- [Handling Upload Progress](/guides/upload-progress) -``` - -### Progressive Disclosure - -```markdown -# Authentication - -## Basic: API Keys (Recommended for Getting Started) - -API keys are the simplest way to authenticate. - -```typescript -const client = new Client({ apiKey: 'your_key' }); -``` - -**When to use:** Scripts, internal tools, testing - -[Generate an API key →](/dashboard/api-keys) - -
-Advanced: OAuth 2.0 - -For user-facing applications, use OAuth 2.0. - -### Authorization Code Flow - -1. Redirect user to authorization URL: -```typescript -const authUrl = client.oauth.getAuthUrl({ - redirectUri: 'https://yourapp.com/callback', - scopes: ['read:users', 'write:users'], -}); -window.location.href = authUrl; -``` - -2. Handle the callback: -```typescript -const code = new URLSearchParams(window.location.search).get('code'); -const tokens = await client.oauth.exchangeCode(code); -``` - -3. Use the access token: -```typescript -const client = new Client({ accessToken: tokens.access_token }); -``` - -[Full OAuth guide →](/guides/oauth) -
- -
-Enterprise: JWT Tokens - -For service-to-service authentication, use JWTs. - -```typescript -const jwt = createJWT({ - issuer: 'your-service', - subject: 'service-account-id', - privateKey: process.env.PRIVATE_KEY, -}); - -const client = new Client({ jwt }); -``` - -[JWT setup guide →](/guides/jwt) -
-``` - -## Visual Communication - -### Diagram Integration - -```markdown -# System Architecture - -## Request Flow - -```mermaid -sequenceDiagram - participant Client - participant API - participant Database - participant Cache - - Client->>API: POST /users - API->>Cache: Check cache - Cache-->>API: Cache miss - API->>Database: Insert user - Database-->>API: User created - API->>Cache: Store user - API-->>Client: 201 Created -``` - -## Data Model - -```mermaid -erDiagram - USER ||--o{ POST : creates - USER ||--o{ COMMENT : writes - POST ||--o{ COMMENT : has - - USER { - string id PK - string email UK - string name - datetime created_at - } - - POST { - string id PK - string user_id FK - string title - text content - } -``` -``` - -### Screenshot Annotations - -```markdown -# Dashboard Overview - -![Dashboard with numbered annotations](./images/dashboard-annotated.png) - -**Key features:** - -1. **Navigation** - Switch between sections -2. **API Key** - Copy your key (click to reveal) -3. **Usage Stats** - Current month's API calls -4. **Quick Actions** - Generate new key, view docs -5. **Recent Activity** - Last 10 API requests - -## Creating Your First API Key - -1. Click "Generate New Key" (highlighted in green) -2. Enter a description like "Production API" -3. Select permissions (default: all) -4. Click "Create" -5. **Important:** Copy the key immediately - it won't be shown again - -![Create API key dialog](./images/create-key.png) -``` - -## Troubleshooting Guides - -### Problem-Solution Format - -```markdown -# Troubleshooting - -## Authentication Errors - -### "Invalid API key" - -**Symptoms:** -- 401 Unauthorized error -- Error message: "Invalid API key" - -**Causes:** -1. API key was copied incorrectly (extra spaces) -2. API key was revoked -3. Using test key in production environment - -**Solutions:** - -**1. Verify the key:** -```bash -# Check for extra spaces -echo -n "$API_KEY" | wc -c # Should be exactly 32 characters -``` - -**2. Regenerate the key:** -- Go to [dashboard](/dashboard) -- Click "Revoke & Regenerate" -- Update your environment variables - -**3. Check environment:** -```typescript -console.log('Environment:', process.env.NODE_ENV); -console.log('API URL:', client.baseUrl); -``` - -**Still not working?** -[Contact support](/support) with your request ID from the error response. - ---- - -### "Rate limit exceeded" - -**Symptoms:** -- 429 Too Many Requests error -- Requests failing intermittently - -**Immediate fix:** -Wait 60 seconds and retry. - -**Long-term solutions:** - -**1. Implement exponential backoff:** -```typescript -async function retryWithBackoff(fn, maxRetries = 3) { - for (let i = 0; i < maxRetries; i++) { - try { - return await fn(); - } catch (error) { - if (error.status === 429 && i < maxRetries - 1) { - await sleep(Math.pow(2, i) * 1000); - continue; - } - throw error; - } - } -} -``` - -**2. Batch requests:** -Instead of 100 individual requests, use batch endpoints. - -**3. Upgrade your plan:** -[View plans](/pricing) - Higher tiers have increased limits. -``` - -## FAQ Section - -```markdown -# Frequently Asked Questions - -## General - -### What's included in the free tier? -- 1,000 API requests/month -- 1GB storage -- Community support -- All core features - -### How do I upgrade? -Click "Upgrade" in your [dashboard](/dashboard) and select a plan. - -## Technical - -### Can I use this in production? -Yes, the API is production-ready with 99.9% SLA on paid plans. - -### What's the rate limit? -- Free: 10 requests/minute -- Pro: 100 requests/minute -- Enterprise: Custom limits - -### Do you support webhooks? -Yes! See [Webhooks Guide](/guides/webhooks) for setup. - -### Which regions are available? -Currently: US East, US West, EU Central, Asia Pacific. - -## Billing - -### How does billing work? -- Monthly subscription -- Pay-as-you-go for overages -- Cancel anytime - -### What payment methods do you accept? -Credit card, PayPal, wire transfer (annual plans only). - ---- - -**Can't find your answer?** -- [Browse all docs](/docs) -- [Ask the community](https://community.example.com) -- [Contact support](/support) -``` - -## Quick Reference - -| Content Type | Best For | Key Elements | -|-------------|----------|-------------| -| Quick Start | New users (5 min) | Prerequisites, minimal code, verify | -| Tutorial | Learning by doing | Steps, checkpoints, working code | -| How-To Guide | Specific tasks | Goal, steps, troubleshooting | -| Reference | Looking up details | Comprehensive, searchable | -| Explanation | Understanding concepts | Why, not how | - -| Writing Principle | Technique | -|------------------|-----------| -| Clarity | Active voice, short sentences | -| Scannability | Headings, lists, code blocks | -| Completeness | Prerequisites, next steps, related links | -| Accuracy | Test all code, version specifics | diff --git a/.github/skills/pandas-pro/SKILL.md b/.github/skills/pandas-pro/SKILL.md deleted file mode 100644 index 7279110..0000000 --- a/.github/skills/pandas-pro/SKILL.md +++ /dev/null @@ -1,96 +0,0 @@ ---- -name: pandas-pro -description: Use when working with pandas DataFrames, data cleaning, aggregation, merging, or time series analysis. Invoke for data manipulation, missing value handling, groupby operations, or performance optimization. -triggers: - - pandas - - DataFrame - - data manipulation - - data cleaning - - aggregation - - groupby - - merge - - join - - time series - - data wrangling - - pivot table - - data transformation -role: expert -scope: implementation -output-format: code ---- - -# Pandas Pro - -Expert pandas developer specializing in efficient data manipulation, analysis, and transformation workflows with production-grade performance patterns. - -## Role Definition - -You are a senior data engineer with deep expertise in pandas library for Python. You write efficient, vectorized code for data cleaning, transformation, aggregation, and analysis. You understand memory optimization, performance patterns, and best practices for large-scale data processing. - -## When to Use This Skill - -- Loading, cleaning, and transforming tabular data -- Handling missing values and data quality issues -- Performing groupby aggregations and pivot operations -- Merging, joining, and concatenating datasets -- Time series analysis and resampling -- Optimizing pandas code for memory and performance -- Converting between data formats (CSV, Excel, SQL, JSON) - -## Core Workflow - -1. **Assess data structure** - Examine dtypes, memory usage, missing values, data quality -2. **Design transformation** - Plan vectorized operations, avoid loops, identify indexing strategy -3. **Implement efficiently** - Use vectorized methods, method chaining, proper indexing -4. **Validate results** - Check dtypes, shapes, edge cases, null handling -5. **Optimize** - Profile memory usage, apply categorical types, use chunking if needed - -## Reference Guide - -Load detailed guidance based on context: - -| Topic | Reference | Load When | -|-------|-----------|-----------| -| DataFrame Operations | `references/dataframe-operations.md` | Indexing, selection, filtering, sorting | -| Data Cleaning | `references/data-cleaning.md` | Missing values, duplicates, type conversion | -| Aggregation & GroupBy | `references/aggregation-groupby.md` | GroupBy, pivot, crosstab, aggregation | -| Merging & Joining | `references/merging-joining.md` | Merge, join, concat, combine strategies | -| Performance Optimization | `references/performance-optimization.md` | Memory usage, vectorization, chunking | - -## Constraints - -### MUST DO -- Use vectorized operations instead of loops -- Set appropriate dtypes (categorical for low-cardinality strings) -- Check memory usage with `.memory_usage(deep=True)` -- Handle missing values explicitly (don't silently drop) -- Use method chaining for readability -- Preserve index integrity through operations -- Validate data quality before and after transformations -- Use `.copy()` when modifying subsets to avoid SettingWithCopyWarning - -### MUST NOT DO -- Iterate over DataFrame rows with `.iterrows()` unless absolutely necessary -- Use chained indexing (`df['A']['B']`) - use `.loc[]` or `.iloc[]` -- Ignore SettingWithCopyWarning messages -- Load entire large datasets without chunking -- Use deprecated methods (`.ix`, `.append()` - use `pd.concat()`) -- Convert to Python lists for operations possible in pandas -- Assume data is clean without validation - -## Output Templates - -When implementing pandas solutions, provide: -1. Code with vectorized operations and proper indexing -2. Comments explaining complex transformations -3. Memory/performance considerations if dataset is large -4. Data validation checks (dtypes, nulls, shapes) - -## Knowledge Reference - -pandas 2.0+, NumPy, datetime handling, categorical types, MultiIndex, memory optimization, vectorization, method chaining, merge strategies, time series resampling, pivot tables, groupby aggregations - -## Related Skills - -- **Python Pro** - Type hints, testing, Python best practices -- **Data Scientist** - Statistical analysis, visualization, ML workflows diff --git a/.github/skills/pandas-pro/references/aggregation-groupby.md b/.github/skills/pandas-pro/references/aggregation-groupby.md deleted file mode 100644 index 7f2d716..0000000 --- a/.github/skills/pandas-pro/references/aggregation-groupby.md +++ /dev/null @@ -1,548 +0,0 @@ -# Aggregation and GroupBy - -> Reference for: Pandas Pro -> Load when: GroupBy operations, pivot tables, crosstab, aggregation functions, or summarizing data - ---- - -## Overview - -Aggregation transforms data from individual records to summary statistics. This reference covers GroupBy, pivot tables, crosstab, and advanced aggregation patterns with pandas 2.0+. - ---- - -## GroupBy Fundamentals - -### Basic GroupBy - -```python -import pandas as pd -import numpy as np - -df = pd.DataFrame({ - 'department': ['Eng', 'Eng', 'Sales', 'Sales', 'Eng', 'HR'], - 'team': ['Backend', 'Frontend', 'East', 'West', 'Backend', 'Recruit'], - 'employee': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve', 'Frank'], - 'salary': [80000, 75000, 65000, 70000, 85000, 60000], - 'years': [5, 3, 7, 4, 6, 2] -}) - -# Single column groupby with single aggregation -avg_salary = df.groupby('department')['salary'].mean() - -# Multiple aggregations -stats = df.groupby('department')['salary'].agg(['mean', 'min', 'max', 'count']) - -# GroupBy multiple columns -grouped = df.groupby(['department', 'team'])['salary'].mean() - -# Reset index to get DataFrame instead of Series -grouped = df.groupby('department')['salary'].mean().reset_index() -``` - -### Multiple Columns, Multiple Aggregations - -```python -# Named aggregation (pandas 2.0+ preferred) -result = df.groupby('department').agg( - avg_salary=('salary', 'mean'), - max_salary=('salary', 'max'), - total_years=('years', 'sum'), - headcount=('employee', 'count'), -) - -# Dictionary syntax (traditional) -result = df.groupby('department').agg({ - 'salary': ['mean', 'max', 'std'], - 'years': ['sum', 'mean'], -}) - -# Flatten multi-level column names -result.columns = ['_'.join(col).strip() for col in result.columns.values] -``` - -### Custom Aggregation Functions - -```python -# Lambda functions -result = df.groupby('department').agg({ - 'salary': lambda x: x.max() - x.min(), # Range - 'years': lambda x: x.quantile(0.75), # 75th percentile -}) - -# Named functions for clarity -def salary_range(x): - return x.max() - x.min() - -def coefficient_of_variation(x): - return x.std() / x.mean() if x.mean() != 0 else 0 - -result = df.groupby('department').agg( - salary_range=('salary', salary_range), - salary_cv=('salary', coefficient_of_variation), -) - -# Multiple custom functions -result = df.groupby('department')['salary'].agg([ - ('range', lambda x: x.max() - x.min()), - ('iqr', lambda x: x.quantile(0.75) - x.quantile(0.25)), - ('median', 'median'), -]) -``` - ---- - -## Transform and Apply - -### Transform - Returns Same Shape - -```python -# Transform returns Series with same index as original -# Useful for adding aggregated values back to original DataFrame - -# Add group mean as new column -df['dept_avg_salary'] = df.groupby('department')['salary'].transform('mean') - -# Normalize within group -df['salary_zscore'] = df.groupby('department')['salary'].transform( - lambda x: (x - x.mean()) / x.std() -) - -# Rank within group -df['salary_rank'] = df.groupby('department')['salary'].transform('rank', ascending=False) - -# Percentage of group total -df['salary_pct'] = df.groupby('department')['salary'].transform( - lambda x: x / x.sum() * 100 -) - -# Fill missing with group mean -df['salary'] = df.groupby('department')['salary'].transform( - lambda x: x.fillna(x.mean()) -) -``` - -### Apply - Flexible Operations - -```python -# Apply runs function on each group DataFrame -def top_n_by_salary(group, n=2): - return group.nlargest(n, 'salary') - -top_earners = df.groupby('department').apply(top_n_by_salary, n=2) - -# Reset index after apply -top_earners = df.groupby('department', group_keys=False).apply( - top_n_by_salary, n=2 -).reset_index(drop=True) - -# Complex group operations -def group_summary(group): - return pd.Series({ - 'headcount': len(group), - 'avg_salary': group['salary'].mean(), - 'top_earner': group.loc[group['salary'].idxmax(), 'employee'], - 'avg_tenure': group['years'].mean(), - }) - -summary = df.groupby('department').apply(group_summary) -``` - -### Filter - Keep/Remove Groups - -```python -# Keep only groups meeting a condition -# Groups with average salary > 70000 -filtered = df.groupby('department').filter(lambda x: x['salary'].mean() > 70000) - -# Groups with more than 2 members -filtered = df.groupby('department').filter(lambda x: len(x) > 2) - -# Combined conditions -filtered = df.groupby('department').filter( - lambda x: (len(x) >= 2) and (x['salary'].mean() > 65000) -) -``` - ---- - -## Pivot Tables - -### Basic Pivot Table - -```python -df = pd.DataFrame({ - 'date': pd.date_range('2024-01-01', periods=6), - 'product': ['A', 'B', 'A', 'B', 'A', 'B'], - 'region': ['East', 'East', 'West', 'West', 'East', 'West'], - 'sales': [100, 150, 120, 180, 90, 200], - 'quantity': [10, 15, 12, 18, 9, 20], -}) - -# Simple pivot -pivot = df.pivot_table( - values='sales', - index='product', - columns='region', - aggfunc='sum' -) - -# Multiple values -pivot = df.pivot_table( - values=['sales', 'quantity'], - index='product', - columns='region', - aggfunc='sum' -) - -# Multiple aggregation functions -pivot = df.pivot_table( - values='sales', - index='product', - columns='region', - aggfunc=['sum', 'mean', 'count'] -) -``` - -### Advanced Pivot Table Options - -```python -# Fill missing values -pivot = df.pivot_table( - values='sales', - index='product', - columns='region', - aggfunc='sum', - fill_value=0 -) - -# Add margins (totals) -pivot = df.pivot_table( - values='sales', - index='product', - columns='region', - aggfunc='sum', - margins=True, - margins_name='Total' -) - -# Multiple index levels -pivot = df.pivot_table( - values='sales', - index=['product', df['date'].dt.month], - columns='region', - aggfunc='sum' -) - -# Observed categories only (for categorical data) -pivot = df.pivot_table( - values='sales', - index='product', - columns='region', - aggfunc='sum', - observed=True # pandas 2.0+ default changed -) -``` - -### Unpivoting (Melt) - -```python -# Wide to long format -wide_df = pd.DataFrame({ - 'product': ['A', 'B'], - 'Q1_sales': [100, 150], - 'Q2_sales': [120, 180], - 'Q3_sales': [90, 200], -}) - -# Melt to long format -long_df = pd.melt( - wide_df, - id_vars=['product'], - value_vars=['Q1_sales', 'Q2_sales', 'Q3_sales'], - var_name='quarter', - value_name='sales' -) - -# Clean quarter column -long_df['quarter'] = long_df['quarter'].str.replace('_sales', '') -``` - ---- - -## Crosstab - -### Basic Crosstab - -```python -df = pd.DataFrame({ - 'gender': ['M', 'F', 'M', 'F', 'M', 'F', 'M', 'M'], - 'department': ['Eng', 'Eng', 'Sales', 'Sales', 'Eng', 'HR', 'HR', 'Eng'], - 'level': ['Senior', 'Junior', 'Senior', 'Senior', 'Junior', 'Junior', 'Senior', 'Junior'], -}) - -# Simple crosstab (counts) -ct = pd.crosstab(df['gender'], df['department']) - -# Normalized crosstab -ct_pct = pd.crosstab(df['gender'], df['department'], normalize='all') # Total -ct_pct = pd.crosstab(df['gender'], df['department'], normalize='index') # Row -ct_pct = pd.crosstab(df['gender'], df['department'], normalize='columns') # Column - -# With margins -ct = pd.crosstab(df['gender'], df['department'], margins=True) - -# Multiple levels -ct = pd.crosstab( - [df['gender'], df['level']], - df['department'] -) -``` - -### Crosstab with Aggregation - -```python -df['salary'] = [80000, 75000, 65000, 70000, 85000, 60000, 72000, 78000] - -# Crosstab with values and aggregation -ct = pd.crosstab( - df['gender'], - df['department'], - values=df['salary'], - aggfunc='mean' -) - -# Multiple aggregations -ct = pd.crosstab( - df['gender'], - df['department'], - values=df['salary'], - aggfunc=['mean', 'sum', 'count'] -) -``` - ---- - -## Window Functions with GroupBy - -### Rolling Aggregations - -```python -df = pd.DataFrame({ - 'date': pd.date_range('2024-01-01', periods=10), - 'product': ['A', 'B'] * 5, - 'sales': [100, 150, 110, 160, 120, 170, 130, 180, 140, 190], -}) - -# Rolling mean within groups -df['rolling_avg'] = df.groupby('product')['sales'].transform( - lambda x: x.rolling(window=3, min_periods=1).mean() -) - -# Expanding aggregations -df['cumulative_sales'] = df.groupby('product')['sales'].transform('cumsum') - -df['expanding_avg'] = df.groupby('product')['sales'].transform( - lambda x: x.expanding().mean() -) - -# Rank within groups -df['sales_rank'] = df.groupby('product')['sales'].rank(method='dense') -``` - -### Shift and Diff - -```python -# Previous value within group -df['prev_sales'] = df.groupby('product')['sales'].shift(1) - -# Next value -df['next_sales'] = df.groupby('product')['sales'].shift(-1) - -# Period-over-period change -df['sales_change'] = df.groupby('product')['sales'].diff() - -# Percentage change -df['sales_pct_change'] = df.groupby('product')['sales'].pct_change() -``` - ---- - -## Common Aggregation Patterns - -### Summary Statistics - -```python -# Comprehensive summary by group -def full_summary(group): - return pd.Series({ - 'count': len(group), - 'mean': group['salary'].mean(), - 'std': group['salary'].std(), - 'min': group['salary'].min(), - 'q25': group['salary'].quantile(0.25), - 'median': group['salary'].median(), - 'q75': group['salary'].quantile(0.75), - 'max': group['salary'].max(), - 'sum': group['salary'].sum(), - }) - -summary = df.groupby('department').apply(full_summary) -``` - -### Top N Per Group - -```python -# Top 2 salaries per department -top_2 = df.groupby('department', group_keys=False).apply( - lambda x: x.nlargest(2, 'salary') -) - -# Using head after sorting -top_2 = df.sort_values('salary', ascending=False).groupby( - 'department', group_keys=False -).head(2) - -# Bottom N -bottom_2 = df.groupby('department', group_keys=False).apply( - lambda x: x.nsmallest(2, 'salary') -) -``` - -### First/Last Per Group - -```python -# First row per group -first = df.groupby('department').first() - -# Last row per group -last = df.groupby('department').last() - -# First row after sorting -first_by_salary = df.sort_values('salary', ascending=False).groupby( - 'department' -).first() - -# Nth row -nth = df.groupby('department').nth(1) # Second row (0-indexed) -``` - -### Cumulative Operations - -```python -# Cumulative sum -df['cum_sales'] = df.groupby('department')['salary'].cumsum() - -# Cumulative max/min -df['cum_max'] = df.groupby('department')['salary'].cummax() -df['cum_min'] = df.groupby('department')['salary'].cummin() - -# Cumulative count -df['cum_count'] = df.groupby('department').cumcount() + 1 - -# Running percentage of total -df['running_pct'] = df.groupby('department')['salary'].transform( - lambda x: x.cumsum() / x.sum() * 100 -) -``` - ---- - -## Performance Tips for GroupBy - -### Efficient GroupBy Operations - -```python -# Pre-sort for faster groupby operations -df = df.sort_values('department') -grouped = df.groupby('department', sort=False) # Already sorted - -# Use observed=True for categorical columns (pandas 2.0+ default) -df['department'] = df['department'].astype('category') -grouped = df.groupby('department', observed=True)['salary'].mean() - -# Avoid apply when possible - use built-in aggregations -# SLOWER: -result = df.groupby('department')['salary'].apply(lambda x: x.sum()) -# FASTER: -result = df.groupby('department')['salary'].sum() - -# Use numba for custom aggregations (if available) -@numba.jit(nopython=True) -def custom_agg(values): - return values.sum() / len(values) -``` - -### Memory-Efficient Aggregation - -```python -# For large DataFrames, compute aggregations separately -groups = df.groupby('department') - -means = groups['salary'].mean() -sums = groups['salary'].sum() -counts = groups.size() - -result = pd.DataFrame({ - 'mean': means, - 'sum': sums, - 'count': counts -}) - -# Avoid creating intermediate large DataFrames -# BAD: Creates full transformed DataFrame -df['z_score'] = (df['salary'] - df.groupby('department')['salary'].transform('mean')) / df.groupby('department')['salary'].transform('std') - -# BETTER: Compute once -group_stats = df.groupby('department')['salary'].agg(['mean', 'std']) -df = df.merge(group_stats, on='department') -df['z_score'] = (df['salary'] - df['mean']) / df['std'] -``` - ---- - -## Best Practices Summary - -1. **Use named aggregation** - Clearer than dictionary syntax -2. **Choose transform vs apply wisely** - Transform for same-shape, apply for flexible -3. **Pre-sort for performance** - Use `sort=False` after sorting -4. **Prefer built-in aggregations** - Faster than lambda/apply -5. **Use observed=True** - Especially for categorical data -6. **Reset index when needed** - Keep DataFrames easier to work with -7. **Validate group counts** - Check for unexpected groups - ---- - -## Anti-Patterns to Avoid - -```python -# BAD: Iterating over groups manually -for name, group in df.groupby('department'): - # process group - pass - -# GOOD: Use vectorized operations -df.groupby('department').agg(...) - -# BAD: Multiple groupby calls -df.groupby('dept')['salary'].mean() -df.groupby('dept')['salary'].sum() -df.groupby('dept')['salary'].count() - -# GOOD: Single groupby, multiple aggs -df.groupby('dept')['salary'].agg(['mean', 'sum', 'count']) - -# BAD: Apply for simple aggregations -df.groupby('dept')['salary'].apply(np.mean) - -# GOOD: Built-in method -df.groupby('dept')['salary'].mean() -``` - ---- - -## Related References - -- `dataframe-operations.md` - Filtering before aggregation -- `merging-joining.md` - Join aggregated results back -- `performance-optimization.md` - Optimize large-scale aggregations diff --git a/.github/skills/pandas-pro/references/data-cleaning.md b/.github/skills/pandas-pro/references/data-cleaning.md deleted file mode 100644 index ede32c2..0000000 --- a/.github/skills/pandas-pro/references/data-cleaning.md +++ /dev/null @@ -1,503 +0,0 @@ -# Data Cleaning - -> Reference for: Pandas Pro -> Load when: Missing values, duplicates, type conversion, data validation, or data quality issues - ---- - -## Overview - -Data cleaning is critical for reliable analysis. This reference covers handling missing values, duplicates, type conversion, and data validation with pandas 2.0+ patterns. - ---- - -## Missing Values - -### Detecting Missing Values - -```python -import pandas as pd -import numpy as np - -df = pd.DataFrame({ - 'name': ['Alice', 'Bob', None, 'Diana'], - 'age': [25, np.nan, 35, 28], - 'salary': [50000, 60000, np.nan, np.nan], - 'department': ['Eng', '', 'Eng', 'Sales'] -}) - -# Check for any missing values -df.isna().any() # Per column -df.isna().any().any() # Entire DataFrame - -# Count missing values -df.isna().sum() # Per column -df.isna().sum().sum() # Total - -# Percentage of missing values -(df.isna().sum() / len(df) * 100).round(2) - -# Rows with any missing values -df[df.isna().any(axis=1)] - -# Rows with all values present -df[df.notna().all(axis=1)] - -# Missing value heatmap info -missing_info = pd.DataFrame({ - 'missing': df.isna().sum(), - 'percent': (df.isna().sum() / len(df) * 100).round(2), - 'dtype': df.dtypes -}) -``` - -### Handling Missing Values - Dropping - -```python -# Drop rows with any missing value -df_clean = df.dropna() - -# Drop rows where specific columns have missing values -df_clean = df.dropna(subset=['name', 'age']) - -# Drop rows where ALL values are missing -df_clean = df.dropna(how='all') - -# Drop rows with minimum non-null values -df_clean = df.dropna(thresh=3) # Keep rows with at least 3 non-null - -# Drop columns with missing values -df_clean = df.dropna(axis=1) - -# Drop columns with more than 50% missing -threshold = len(df) * 0.5 -df_clean = df.dropna(axis=1, thresh=threshold) -``` - -### Handling Missing Values - Filling - -```python -# Fill with constant value -df['age'] = df['age'].fillna(0) - -# Fill with column mean/median/mode -df['age'] = df['age'].fillna(df['age'].mean()) -df['salary'] = df['salary'].fillna(df['salary'].median()) -df['department'] = df['department'].fillna(df['department'].mode()[0]) - -# Forward fill (use previous value) -df['salary'] = df['salary'].ffill() - -# Backward fill (use next value) -df['salary'] = df['salary'].bfill() - -# Fill with different values per column -fill_values = {'age': 0, 'salary': df['salary'].median(), 'name': 'Unknown'} -df = df.fillna(fill_values) - -# Fill with interpolation (numeric data) -df['salary'] = df['salary'].interpolate(method='linear') - -# Group-specific fill (fill with group mean) -df['salary'] = df.groupby('department')['salary'].transform( - lambda x: x.fillna(x.mean()) -) -``` - -### Handling Empty Strings vs NaN - -```python -# Empty strings are NOT detected as NaN -df['department'].isna().sum() # Won't count '' - -# Replace empty strings with NaN -df['department'] = df['department'].replace('', np.nan) -# Or -df['department'] = df['department'].replace(r'^\s*$', np.nan, regex=True) - -# Replace multiple values with NaN -df = df.replace(['', 'N/A', 'null', 'None', '-'], np.nan) - -# Using na_values when reading files -df = pd.read_csv('file.csv', na_values=['', 'N/A', 'null', 'None', '-']) -``` - ---- - -## Handling Duplicates - -### Detecting Duplicates - -```python -df = pd.DataFrame({ - 'id': [1, 2, 2, 3, 4, 4], - 'name': ['Alice', 'Bob', 'Bob', 'Charlie', 'Diana', 'Diana'], - 'email': ['a@x.com', 'b@x.com', 'b@x.com', 'c@x.com', 'd@x.com', 'd2@x.com'] -}) - -# Check for duplicate rows (all columns) -df.duplicated().sum() - -# Check specific columns -df.duplicated(subset=['id']).sum() -df.duplicated(subset=['name', 'email']).sum() - -# View duplicate rows -df[df.duplicated(keep=False)] # All duplicates -df[df.duplicated(keep='first')] # Duplicates except first occurrence -df[df.duplicated(keep='last')] # Duplicates except last occurrence - -# Count duplicates per key -df.groupby('id').size().loc[lambda x: x > 1] -``` - -### Removing Duplicates - -```python -# Remove duplicate rows (keep first) -df_clean = df.drop_duplicates() - -# Keep last occurrence -df_clean = df.drop_duplicates(keep='last') - -# Remove all duplicates (keep none) -df_clean = df.drop_duplicates(keep=False) - -# Based on specific columns -df_clean = df.drop_duplicates(subset=['id']) -df_clean = df.drop_duplicates(subset=['name', 'email'], keep='last') - -# In-place modification -df.drop_duplicates(inplace=True) -``` - -### Handling Duplicates with Aggregation - -```python -# Instead of dropping, aggregate duplicates -df_agg = df.groupby('id').agg({ - 'name': 'first', - 'email': lambda x: ', '.join(x.unique()) -}).reset_index() - -# Keep row with max/min value -df_best = df.loc[df.groupby('id')['score'].idxmax()] - -# Rank duplicates -df['rank'] = df.groupby('id').cumcount() + 1 -``` - ---- - -## Type Conversion - -### Checking and Converting Types - -```python -# Check current types -df.dtypes -df.info() - -# Convert to specific type -df['age'] = df['age'].astype(int) -df['salary'] = df['salary'].astype(float) -df['name'] = df['name'].astype(str) - -# Safe conversion with errors handling -df['age'] = pd.to_numeric(df['age'], errors='coerce') # Invalid -> NaN -df['age'] = pd.to_numeric(df['age'], errors='ignore') # Keep original if invalid - -# Convert multiple columns -df = df.astype({'age': 'int64', 'salary': 'float64'}) - -# Convert object to string (pandas 2.0+ StringDtype) -df['name'] = df['name'].astype('string') # Nullable string type -``` - -### Datetime Conversion - -```python -df = pd.DataFrame({ - 'date_str': ['2024-01-15', '2024-02-20', 'invalid', '2024-03-10'], - 'timestamp': [1705276800, 1708387200, 1710028800, 1710028800] -}) - -# String to datetime -df['date'] = pd.to_datetime(df['date_str'], errors='coerce') - -# Specify format for faster parsing -df['date'] = pd.to_datetime(df['date_str'], format='%Y-%m-%d', errors='coerce') - -# Unix timestamp to datetime -df['datetime'] = pd.to_datetime(df['timestamp'], unit='s') - -# Extract components -df['year'] = df['date'].dt.year -df['month'] = df['date'].dt.month -df['day_of_week'] = df['date'].dt.day_name() - -# Handle mixed formats -df['date'] = pd.to_datetime(df['date_str'], format='mixed', dayfirst=False) -``` - -### Categorical Conversion - -```python -# Convert to categorical (memory efficient for low cardinality) -df['department'] = df['department'].astype('category') - -# Ordered categorical -df['size'] = pd.Categorical( - df['size'], - categories=['Small', 'Medium', 'Large'], - ordered=True -) - -# Check memory savings -print(f"Object: {df['department'].nbytes}") -df['department'] = df['department'].astype('category') -print(f"Category: {df['department'].nbytes}") -``` - -### Nullable Integer Types (pandas 2.0+) - -```python -# Standard int doesn't support NaN -# Use nullable integer types -df['age'] = df['age'].astype('Int64') # Note capital I - -# All nullable types -df = df.astype({ - 'count': 'Int64', # Nullable integer - 'price': 'Float64', # Nullable float - 'flag': 'boolean', # Nullable boolean - 'name': 'string', # Nullable string -}) - -# Convert with NA handling -df['age'] = pd.array([1, 2, None, 4], dtype='Int64') -``` - ---- - -## String Cleaning - -### Common String Operations - -```python -df = pd.DataFrame({ - 'name': [' Alice ', 'BOB', 'charlie', None, 'Diana Smith'], - 'email': ['ALICE@EXAMPLE.COM', 'bob@test', 'invalid', None, 'diana@example.com'] -}) - -# Strip whitespace -df['name'] = df['name'].str.strip() - -# Case normalization -df['name'] = df['name'].str.lower() -df['name'] = df['name'].str.upper() -df['name'] = df['name'].str.title() # Title Case - -# Replace patterns -df['name'] = df['name'].str.replace(r'\s+', ' ', regex=True) # Multiple spaces to one -df['phone'] = df['phone'].str.replace(r'[^0-9]', '', regex=True) # Keep only digits - -# Extract with regex -df['domain'] = df['email'].str.extract(r'@(.+)$') -df['first_name'] = df['name'].str.extract(r'^(\w+)') - -# Split strings -df[['first', 'last']] = df['name'].str.split(' ', n=1, expand=True) -``` - -### String Validation - -```python -# Check patterns -df['valid_email'] = df['email'].str.match(r'^[\w.]+@[\w.]+\.\w+$', na=False) - -# String length -df['name_length'] = df['name'].str.len() -df['valid_length'] = df['name'].str.len().between(2, 50) - -# Contains check -df['has_domain'] = df['email'].str.contains('@', na=False) -``` - ---- - -## Data Validation - -### Validation Functions - -```python -def validate_dataframe(df: pd.DataFrame) -> dict: - """Comprehensive DataFrame validation.""" - report = { - 'rows': len(df), - 'columns': len(df.columns), - 'duplicates': df.duplicated().sum(), - 'missing_by_column': df.isna().sum().to_dict(), - 'dtypes': df.dtypes.astype(str).to_dict(), - } - return report - -# Range validation -def validate_range(series: pd.Series, min_val, max_val) -> pd.Series: - """Return boolean mask for values in range.""" - return series.between(min_val, max_val) - -df['valid_age'] = validate_range(df['age'], 0, 120) - -# Custom validation -def validate_email(series: pd.Series) -> pd.Series: - """Validate email format.""" - pattern = r'^[\w.+-]+@[\w-]+\.[\w.-]+$' - return series.str.match(pattern, na=False) - -df['valid_email'] = validate_email(df['email']) -``` - -### Schema Validation with pandera - -```python -# Using pandera for schema validation (recommended for production) -import pandera as pa -from pandera import Column, Check - -schema = pa.DataFrameSchema({ - 'name': Column(str, Check.str_length(min_value=1, max_value=100)), - 'age': Column(int, Check.in_range(0, 120)), - 'email': Column(str, Check.str_matches(r'^[\w.+-]+@[\w-]+\.[\w.-]+$')), - 'salary': Column(float, Check.greater_than(0), nullable=True), -}) - -# Validate DataFrame -try: - schema.validate(df) -except pa.errors.SchemaError as e: - print(f"Validation failed: {e}") -``` - ---- - -## Data Cleaning Pipeline - -### Method Chaining Pattern - -```python -def clean_dataframe(df: pd.DataFrame) -> pd.DataFrame: - """Complete data cleaning pipeline using method chaining.""" - return ( - df - # Make a copy - .copy() - # Standardize column names - .rename(columns=lambda x: x.lower().strip().replace(' ', '_')) - # Drop fully empty rows - .dropna(how='all') - # Clean string columns - .assign( - name=lambda x: x['name'].str.strip().str.title(), - email=lambda x: x['email'].str.lower().str.strip(), - ) - # Handle missing values - .fillna({'department': 'Unknown'}) - # Convert types - .astype({'age': 'Int64', 'department': 'category'}) - # Remove duplicates - .drop_duplicates(subset=['email']) - # Reset index - .reset_index(drop=True) - ) - -df_clean = clean_dataframe(df) -``` - -### Pipeline with Validation - -```python -def clean_and_validate( - df: pd.DataFrame, - required_columns: list[str], - unique_columns: list[str] | None = None, -) -> tuple[pd.DataFrame, dict]: - """Clean DataFrame and return validation report.""" - - # Validate required columns exist - missing_cols = set(required_columns) - set(df.columns) - if missing_cols: - raise ValueError(f"Missing required columns: {missing_cols}") - - # Track cleaning stats - stats = { - 'initial_rows': len(df), - 'dropped_empty': 0, - 'dropped_duplicates': 0, - 'filled_missing': {}, - } - - # Clean - df = df.copy() - - # Drop empty rows - before = len(df) - df = df.dropna(how='all') - stats['dropped_empty'] = before - len(df) - - # Handle duplicates - if unique_columns: - before = len(df) - df = df.drop_duplicates(subset=unique_columns) - stats['dropped_duplicates'] = before - len(df) - - stats['final_rows'] = len(df) - - return df, stats -``` - ---- - -## Best Practices Summary - -1. **Always check data quality first** - Use `.info()`, `.describe()`, and missing value analysis -2. **Document cleaning decisions** - Track what was dropped/filled and why -3. **Use nullable types** - `Int64`, `string`, `boolean` for proper NA handling -4. **Validate after cleaning** - Ensure data meets expectations -5. **Use method chaining** - Readable, maintainable cleaning pipelines -6. **Copy before modifying** - Avoid SettingWithCopyWarning -7. **Handle edge cases** - Empty strings, whitespace, invalid formats - ---- - -## Anti-Patterns to Avoid - -```python -# BAD: Dropping NaN without understanding impact -df = df.dropna() # May lose significant data - -# GOOD: Investigate first, then decide -print(f"Missing values: {df.isna().sum()}") -print(f"Rows affected: {df.isna().any(axis=1).sum()}") -# Then make informed decision - -# BAD: Filling without domain knowledge -df['age'] = df['age'].fillna(0) # Age 0 is not valid - -# GOOD: Use appropriate fill strategy -df['age'] = df['age'].fillna(df['age'].median()) - -# BAD: Type conversion without error handling -df['id'] = df['id'].astype(int) # Will fail on NaN or invalid - -# GOOD: Safe conversion -df['id'] = pd.to_numeric(df['id'], errors='coerce').astype('Int64') -``` - ---- - -## Related References - -- `dataframe-operations.md` - Selection and filtering for targeted cleaning -- `aggregation-groupby.md` - Aggregate duplicates instead of dropping -- `performance-optimization.md` - Efficient cleaning of large datasets diff --git a/.github/skills/pandas-pro/references/dataframe-operations.md b/.github/skills/pandas-pro/references/dataframe-operations.md deleted file mode 100644 index 6321f23..0000000 --- a/.github/skills/pandas-pro/references/dataframe-operations.md +++ /dev/null @@ -1,423 +0,0 @@ -# DataFrame Operations - -> Reference for: Pandas Pro -> Load when: Indexing, selection, filtering, sorting, or basic DataFrame manipulation - ---- - -## Overview - -DataFrame operations form the foundation of pandas work. This reference covers indexing, selection, filtering, and sorting with pandas 2.0+ best practices. - ---- - -## Indexing and Selection - -### Label-Based Selection with `.loc[]` - -Use `.loc[]` for label-based indexing. Always preferred over chained indexing. - -```python -import pandas as pd -import numpy as np - -# Sample DataFrame -df = pd.DataFrame({ - 'name': ['Alice', 'Bob', 'Charlie', 'Diana'], - 'age': [25, 30, 35, 28], - 'salary': [50000, 60000, 70000, 55000], - 'department': ['Engineering', 'Sales', 'Engineering', 'Marketing'] -}, index=['a', 'b', 'c', 'd']) - -# Single value -value = df.loc['a', 'name'] # 'Alice' - -# Single row (returns Series) -row = df.loc['a'] - -# Multiple rows -rows = df.loc[['a', 'c']] - -# Row and column slices (inclusive on both ends) -subset = df.loc['a':'c', 'name':'salary'] - -# Boolean indexing with .loc -adults = df.loc[df['age'] >= 30] - -# Boolean indexing with column selection -adults_names = df.loc[df['age'] >= 30, 'name'] - -# Multiple conditions -engineering_seniors = df.loc[ - (df['department'] == 'Engineering') & (df['age'] >= 30), - ['name', 'salary'] -] -``` - -### Position-Based Selection with `.iloc[]` - -Use `.iloc[]` for integer position-based indexing. - -```python -# Single value by position -value = df.iloc[0, 0] # First row, first column - -# Single row by position -first_row = df.iloc[0] - -# Slice rows (exclusive end, like Python) -first_three = df.iloc[:3] - -# Specific rows and columns by position -subset = df.iloc[[0, 2], [0, 2]] # Rows 0,2 and columns 0,2 - -# Range selection -block = df.iloc[1:3, 0:2] # Rows 1-2, columns 0-1 -``` - -### When to Use `.loc[]` vs `.iloc[]` - -| Scenario | Use | Example | -|----------|-----|---------| -| Known column names | `.loc[]` | `df.loc[:, 'name']` | -| Filter by condition | `.loc[]` | `df.loc[df['age'] > 25]` | -| First/last N rows | `.iloc[]` | `df.iloc[:5]` or `df.iloc[-5:]` | -| Specific row positions | `.iloc[]` | `df.iloc[[0, 5, 10]]` | -| Unknown column order | `.iloc[]` | `df.iloc[:, 0]` | - ---- - -## Filtering DataFrames - -### Boolean Masks - -```python -# Single condition -mask = df['age'] > 25 -filtered = df[mask] - -# Multiple conditions (use parentheses!) -mask = (df['age'] > 25) & (df['salary'] < 65000) -filtered = df[mask] - -# OR conditions -mask = (df['department'] == 'Engineering') | (df['department'] == 'Sales') -filtered = df[mask] - -# NOT condition -mask = ~(df['department'] == 'Marketing') -filtered = df[mask] -``` - -### Using `.query()` for Readable Filters - -```python -# Simple query - more readable for complex conditions -result = df.query('age > 25 and salary < 65000') - -# Using variables with @ -min_age = 25 -result = df.query('age > @min_age') - -# String comparisons -result = df.query('department == "Engineering"') - -# In-list filtering -depts = ['Engineering', 'Sales'] -result = df.query('department in @depts') - -# Complex expressions -result = df.query('(age > 25) and (department != "Marketing")') -``` - -### Using `.isin()` for Multiple Values - -```python -# Filter by multiple values -departments = ['Engineering', 'Sales'] -filtered = df[df['department'].isin(departments)] - -# Negation -filtered = df[~df['department'].isin(departments)] - -# Multiple columns -conditions = { - 'department': ['Engineering', 'Sales'], - 'age': [25, 30, 35] -} -# Filter where department is in list AND age is in list -mask = df['department'].isin(conditions['department']) & df['age'].isin(conditions['age']) -``` - -### String Filtering with `.str` Accessor - -```python -df = pd.DataFrame({ - 'email': ['alice@example.com', 'bob@test.org', 'charlie@example.com'], - 'name': ['Alice Smith', 'Bob Jones', 'Charlie Brown'] -}) - -# Contains -mask = df['email'].str.contains('example') - -# Starts/ends with -mask = df['email'].str.endswith('.com') -mask = df['name'].str.startswith('A') - -# Regex matching -mask = df['email'].str.match(r'^[a-z]+@example\.com$') - -# Case-insensitive -mask = df['name'].str.lower().str.contains('alice') -# Or with case parameter -mask = df['name'].str.contains('alice', case=False) - -# Handle NaN in string columns -mask = df['email'].str.contains('example', na=False) -``` - ---- - -## Sorting - -### Basic Sorting - -```python -# Sort by single column (ascending) -sorted_df = df.sort_values('age') - -# Sort descending -sorted_df = df.sort_values('age', ascending=False) - -# Sort by multiple columns -sorted_df = df.sort_values(['department', 'salary'], ascending=[True, False]) - -# Sort by index -sorted_df = df.sort_index() -sorted_df = df.sort_index(ascending=False) -``` - -### Advanced Sorting - -```python -# Sort with NaN handling -df_with_nan = pd.DataFrame({ - 'name': ['Alice', 'Bob', 'Charlie'], - 'score': [85.0, np.nan, 90.0] -}) - -# NaN at end (default) -sorted_df = df_with_nan.sort_values('score', na_position='last') - -# NaN at beginning -sorted_df = df_with_nan.sort_values('score', na_position='first') - -# Custom sort order using Categorical -order = ['Marketing', 'Sales', 'Engineering'] -df['department'] = pd.Categorical(df['department'], categories=order, ordered=True) -sorted_df = df.sort_values('department') - -# Sort by computed values without adding column -sorted_df = df.iloc[df['name'].str.len().argsort()] -``` - -### In-Place Sorting - -```python -# Modify DataFrame in place -df.sort_values('age', inplace=True) - -# Reset index after sorting -df.sort_values('age', inplace=True) -df.reset_index(drop=True, inplace=True) - -# Or chain -df = df.sort_values('age').reset_index(drop=True) -``` - ---- - -## Column Operations - -### Adding and Modifying Columns - -```python -# Add new column -df['bonus'] = df['salary'] * 0.1 - -# Conditional column with np.where -df['seniority'] = np.where(df['age'] >= 30, 'Senior', 'Junior') - -# Multiple conditions with np.select -conditions = [ - df['age'] < 25, - df['age'] < 35, - df['age'] >= 35 -] -choices = ['Junior', 'Mid', 'Senior'] -df['level'] = np.select(conditions, choices, default='Unknown') - -# Using .assign() for method chaining (returns new DataFrame) -df_new = df.assign( - bonus=lambda x: x['salary'] * 0.1, - total_comp=lambda x: x['salary'] + x['salary'] * 0.1 -) -``` - -### Renaming Columns - -```python -# Rename specific columns -df = df.rename(columns={'name': 'full_name', 'age': 'years'}) - -# Rename all columns with function -df.columns = df.columns.str.lower().str.replace(' ', '_') - -# Using rename with function -df = df.rename(columns=str.upper) -``` - -### Dropping Columns - -```python -# Drop single column -df = df.drop('bonus', axis=1) -# Or -df = df.drop(columns=['bonus']) - -# Drop multiple columns -df = df.drop(columns=['bonus', 'level']) - -# Drop columns by condition -cols_to_drop = [col for col in df.columns if col.startswith('temp_')] -df = df.drop(columns=cols_to_drop) -``` - -### Reordering Columns - -```python -# Explicit order -new_order = ['name', 'department', 'age', 'salary'] -df = df[new_order] - -# Move specific column to front -cols = ['salary'] + [c for c in df.columns if c != 'salary'] -df = df[cols] - -# Using .reindex() -df = df.reindex(columns=['name', 'age', 'salary', 'department']) -``` - ---- - -## Index Operations - -### Setting and Resetting Index - -```python -# Set column as index -df = df.set_index('name') - -# Reset index back to column -df = df.reset_index() - -# Drop index completely -df = df.reset_index(drop=True) - -# Set multiple columns as index (MultiIndex) -df = df.set_index(['department', 'name']) -``` - -### Working with MultiIndex - -```python -# Create MultiIndex DataFrame -df = pd.DataFrame({ - 'department': ['Eng', 'Eng', 'Sales', 'Sales'], - 'team': ['Backend', 'Frontend', 'East', 'West'], - 'headcount': [10, 8, 15, 12] -}).set_index(['department', 'team']) - -# Select from MultiIndex -df.loc['Eng'] # All Eng rows -df.loc[('Eng', 'Backend')] # Specific row - -# Cross-section with .xs() -df.xs('Backend', level='team') # All Backend teams - -# Reset specific level -df.reset_index(level='team') -``` - ---- - -## Copying DataFrames - -### When to Use `.copy()` - -```python -# ALWAYS copy when modifying a subset -subset = df[df['age'] > 25].copy() -subset['new_col'] = 100 # Safe, no SettingWithCopyWarning - -# Without copy - may raise warning or fail silently -# BAD: -# subset = df[df['age'] > 25] -# subset['new_col'] = 100 # SettingWithCopyWarning! - -# Deep copy (default) - copies data -df_copy = df.copy() # or df.copy(deep=True) - -# Shallow copy - shares data, only copies structure -df_shallow = df.copy(deep=False) -``` - ---- - -## Best Practices Summary - -1. **Use `.loc[]` and `.iloc[]`** - Never use chained indexing -2. **Parenthesize conditions** - `(cond1) & (cond2)` not `cond1 & cond2` -3. **Use `.query()` for readability** - Especially with complex filters -4. **Copy before modifying subsets** - Always use `.copy()` -5. **Use vectorized operations** - Avoid row iteration for filtering -6. **Handle NaN explicitly** - Use `na=False` in string operations -7. **Prefer method chaining** - Use `.assign()` for column creation - ---- - -## Anti-Patterns to Avoid - -```python -# BAD: Chained indexing -df['A']['B'] = value # May not work, raises warning - -# GOOD: Use .loc -df.loc[:, ('A', 'B')] = value -# Or for row selection then assignment: -df.loc[df['A'] > 0, 'B'] = value - -# BAD: Iterating for filtering -result = [] -for idx, row in df.iterrows(): - if row['age'] > 25: - result.append(row) - -# GOOD: Boolean indexing -result = df[df['age'] > 25] - -# BAD: Multiple separate assignments -df = df[df['age'] > 25] -df = df[df['salary'] > 50000] - -# GOOD: Combined filter -df = df[(df['age'] > 25) & (df['salary'] > 50000)] -``` - ---- - -## Related References - -- `data-cleaning.md` - After selection, clean the data -- `aggregation-groupby.md` - Group and aggregate filtered data -- `performance-optimization.md` - Optimize filtering on large datasets diff --git a/.github/skills/pandas-pro/references/merging-joining.md b/.github/skills/pandas-pro/references/merging-joining.md deleted file mode 100644 index 7bf33dc..0000000 --- a/.github/skills/pandas-pro/references/merging-joining.md +++ /dev/null @@ -1,599 +0,0 @@ -# Merging and Joining - -> Reference for: Pandas Pro -> Load when: Merge, join, concat, combine DataFrames, or handle relational data - ---- - -## Overview - -Combining DataFrames is essential for working with relational data. This reference covers merge, join, concat, and advanced combination strategies with pandas 2.0+. - ---- - -## Merge (SQL-Style Joins) - -### Basic Merge - -```python -import pandas as pd -import numpy as np - -# Sample DataFrames -employees = pd.DataFrame({ - 'emp_id': [1, 2, 3, 4, 5], - 'name': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'], - 'dept_id': [101, 102, 101, 103, 102], -}) - -departments = pd.DataFrame({ - 'dept_id': [101, 102, 104], - 'dept_name': ['Engineering', 'Sales', 'Marketing'], -}) - -# Inner join (default) - only matching rows -result = pd.merge(employees, departments, on='dept_id') - -# Explicit how parameter -result = pd.merge(employees, departments, on='dept_id', how='inner') -``` - -### Join Types - -```python -# Inner join - only matching rows from both -inner = pd.merge(employees, departments, on='dept_id', how='inner') -# Result: 4 rows (emp_id 4 has dept_id 103 which doesn't exist in departments) - -# Left join - all rows from left, matching from right -left = pd.merge(employees, departments, on='dept_id', how='left') -# Result: 5 rows (Diana has NaN for dept_name) - -# Right join - all rows from right, matching from left -right = pd.merge(employees, departments, on='dept_id', how='right') -# Result: 4 rows (Marketing has no employees, but is included) - -# Outer join - all rows from both -outer = pd.merge(employees, departments, on='dept_id', how='outer') -# Result: 6 rows (includes unmatched from both sides) - -# Cross join - cartesian product -cross = pd.merge(employees, departments, how='cross') -# Result: 15 rows (5 employees x 3 departments) -``` - -### Merging on Different Column Names - -```python -employees = pd.DataFrame({ - 'emp_id': [1, 2, 3], - 'name': ['Alice', 'Bob', 'Charlie'], - 'department': [101, 102, 101], -}) - -departments = pd.DataFrame({ - 'id': [101, 102], - 'dept_name': ['Engineering', 'Sales'], -}) - -# Different column names -result = pd.merge( - employees, - departments, - left_on='department', - right_on='id' -) - -# Drop duplicate column after merge -result = result.drop('id', axis=1) -``` - -### Merging on Multiple Columns - -```python -sales = pd.DataFrame({ - 'region': ['East', 'East', 'West', 'West'], - 'product': ['A', 'B', 'A', 'B'], - 'sales': [100, 150, 120, 180], -}) - -targets = pd.DataFrame({ - 'region': ['East', 'East', 'West'], - 'product': ['A', 'B', 'A'], - 'target': [90, 140, 110], -}) - -# Merge on multiple columns -result = pd.merge(sales, targets, on=['region', 'product'], how='left') -``` - -### Merging on Index - -```python -# Set index before merge -employees_idx = employees.set_index('emp_id') -salaries = pd.DataFrame({ - 'emp_id': [1, 2, 3, 4], - 'salary': [80000, 75000, 70000, 65000], -}).set_index('emp_id') - -# Merge on index -result = pd.merge(employees_idx, salaries, left_index=True, right_index=True) - -# Mix of column and index -result = pd.merge( - employees, - salaries, - left_on='emp_id', - right_index=True -) -``` - ---- - -## Handling Duplicate Columns - -### Suffixes - -```python -df1 = pd.DataFrame({ - 'id': [1, 2, 3], - 'value': [10, 20, 30], - 'date': ['2024-01-01', '2024-01-02', '2024-01-03'], -}) - -df2 = pd.DataFrame({ - 'id': [1, 2, 3], - 'value': [100, 200, 300], - 'date': ['2024-02-01', '2024-02-02', '2024-02-03'], -}) - -# Default suffixes -result = pd.merge(df1, df2, on='id') -# Columns: id, value_x, date_x, value_y, date_y - -# Custom suffixes -result = pd.merge(df1, df2, on='id', suffixes=('_jan', '_feb')) -# Columns: id, value_jan, date_jan, value_feb, date_feb -``` - -### Validate Merge Cardinality - -```python -# Validate merge relationships (pandas 2.0+) -# Raises MergeError if validation fails - -# One-to-one: each key appears at most once in both DataFrames -result = pd.merge(df1, df2, on='id', validate='one_to_one') # or '1:1' - -# One-to-many: keys unique in left only -result = pd.merge(employees, salaries, on='emp_id', validate='one_to_many') # or '1:m' - -# Many-to-one: keys unique in right only -result = pd.merge(salaries, employees, on='emp_id', validate='many_to_one') # or 'm:1' - -# Many-to-many: no uniqueness requirement (default) -result = pd.merge(df1, df2, on='id', validate='many_to_many') # or 'm:m' -``` - -### Indicator Column - -```python -# Add indicator column showing source of each row -result = pd.merge( - employees, - departments, - on='dept_id', - how='outer', - indicator=True -) -# _merge column values: 'left_only', 'right_only', 'both' - -# Custom indicator name -result = pd.merge( - employees, - departments, - on='dept_id', - how='outer', - indicator='source' -) - -# Filter by indicator -left_only = result[result['_merge'] == 'left_only'] -both = result[result['_merge'] == 'both'] -``` - ---- - -## Join (Index-Based) - -### DataFrame.join() - -```python -# join() is for index-based joining (simpler syntax) -employees = pd.DataFrame({ - 'name': ['Alice', 'Bob', 'Charlie'], - 'dept_id': [101, 102, 101], -}, index=[1, 2, 3]) - -salaries = pd.DataFrame({ - 'salary': [80000, 75000, 70000], - 'bonus': [5000, 4000, 3500], -}, index=[1, 2, 3]) - -# Join on index -result = employees.join(salaries) - -# Join types (same as merge) -result = employees.join(salaries, how='left') -result = employees.join(salaries, how='outer') -``` - -### Join on Column to Index - -```python -employees = pd.DataFrame({ - 'name': ['Alice', 'Bob', 'Charlie'], - 'dept_id': [101, 102, 101], -}) - -departments = pd.DataFrame({ - 'dept_name': ['Engineering', 'Sales'], -}, index=[101, 102]) - -# Join left column to right index -result = employees.join(departments, on='dept_id') -``` - -### Join Multiple DataFrames - -```python -df1 = pd.DataFrame({'a': [1, 2]}, index=['x', 'y']) -df2 = pd.DataFrame({'b': [3, 4]}, index=['x', 'y']) -df3 = pd.DataFrame({'c': [5, 6]}, index=['x', 'y']) - -# Join multiple at once -result = df1.join([df2, df3]) - -# With suffixes for duplicate columns -result = df1.join([df2, df3], lsuffix='_1', rsuffix='_2') -``` - ---- - -## Concat (Stacking DataFrames) - -### Vertical Concatenation (Row-wise) - -```python -# Stack DataFrames vertically -df1 = pd.DataFrame({ - 'name': ['Alice', 'Bob'], - 'age': [25, 30], -}) - -df2 = pd.DataFrame({ - 'name': ['Charlie', 'Diana'], - 'age': [35, 28], -}) - -# Basic concat (axis=0 is default) -result = pd.concat([df1, df2]) - -# Reset index -result = pd.concat([df1, df2], ignore_index=True) - -# Keep track of source -result = pd.concat([df1, df2], keys=['source1', 'source2']) -# Creates MultiIndex -``` - -### Horizontal Concatenation (Column-wise) - -```python -names = pd.DataFrame({'name': ['Alice', 'Bob', 'Charlie']}) -ages = pd.DataFrame({'age': [25, 30, 35]}) -salaries = pd.DataFrame({'salary': [50000, 60000, 70000]}) - -# Concat columns (axis=1) -result = pd.concat([names, ages, salaries], axis=1) -``` - -### Handling Mismatched Columns - -```python -df1 = pd.DataFrame({ - 'name': ['Alice', 'Bob'], - 'age': [25, 30], -}) - -df2 = pd.DataFrame({ - 'name': ['Charlie', 'Diana'], - 'salary': [70000, 65000], -}) - -# Outer join (default) - include all columns -result = pd.concat([df1, df2]) -# age and salary columns have NaN where not present - -# Inner join - only common columns -result = pd.concat([df1, df2], join='inner') -# Only 'name' column -``` - -### Concat with Verification - -```python -# Verify no index overlap -try: - result = pd.concat([df1, df2], verify_integrity=True) -except ValueError as e: - print(f"Index overlap detected: {e}") - -# Alternative: use ignore_index -result = pd.concat([df1, df2], ignore_index=True) -``` - ---- - -## Combine and Update - -### combine_first() - Fill Gaps - -```python -# Fill NaN values from another DataFrame -df1 = pd.DataFrame({ - 'A': [1, np.nan, 3], - 'B': [np.nan, 2, 3], -}, index=['a', 'b', 'c']) - -df2 = pd.DataFrame({ - 'A': [10, 20, 30], - 'B': [10, 20, 30], -}, index=['a', 'b', 'c']) - -# Fill NaN in df1 with values from df2 -result = df1.combine_first(df2) -# A: [1, 20, 3], B: [10, 2, 3] -``` - -### update() - In-Place Update - -```python -df1 = pd.DataFrame({ - 'A': [1, 2, 3], - 'B': [4, 5, 6], -}, index=['a', 'b', 'c']) - -df2 = pd.DataFrame({ - 'A': [10, 20], - 'B': [40, 50], -}, index=['a', 'b']) - -# Update df1 with values from df2 (in-place) -df1.update(df2) -# df1 now has A: [10, 20, 3], B: [40, 50, 6] - -# Only update where df2 has non-NaN -df1.update(df2, overwrite=False) # Don't overwrite existing values -``` - ---- - -## Advanced Merge Patterns - -### Merge with Aggregation - -```python -# Merge and aggregate in one operation -orders = pd.DataFrame({ - 'order_id': [1, 2, 3, 4], - 'customer_id': [101, 102, 101, 103], - 'amount': [100, 200, 150, 300], -}) - -customers = pd.DataFrame({ - 'customer_id': [101, 102, 103], - 'name': ['Alice', 'Bob', 'Charlie'], -}) - -# Get customer summary -customer_summary = orders.groupby('customer_id').agg( - total_orders=('order_id', 'count'), - total_amount=('amount', 'sum'), -).reset_index() - -# Merge with customer info -result = pd.merge(customers, customer_summary, on='customer_id') -``` - -### Merge Asof (Nearest Match) - -```python -# Merge on nearest key (useful for time series) -trades = pd.DataFrame({ - 'time': pd.to_datetime(['2024-01-01 10:00:01', '2024-01-01 10:00:03', '2024-01-01 10:00:05']), - 'ticker': ['AAPL', 'AAPL', 'AAPL'], - 'price': [150.0, 151.0, 150.5], -}) - -quotes = pd.DataFrame({ - 'time': pd.to_datetime(['2024-01-01 10:00:00', '2024-01-01 10:00:02', '2024-01-01 10:00:04']), - 'ticker': ['AAPL', 'AAPL', 'AAPL'], - 'bid': [149.5, 150.5, 150.0], - 'ask': [150.5, 151.5, 151.0], -}) - -# Merge asof - find nearest quote for each trade -result = pd.merge_asof( - trades.sort_values('time'), - quotes.sort_values('time'), - on='time', - by='ticker', - direction='backward' # Use most recent quote -) -``` - -### Conditional Merge - -```python -# Merge with conditions beyond key equality -# First merge, then filter - -products = pd.DataFrame({ - 'product_id': [1, 2, 3], - 'name': ['Widget', 'Gadget', 'Gizmo'], - 'category': ['A', 'B', 'A'], -}) - -discounts = pd.DataFrame({ - 'category': ['A', 'A', 'B'], - 'min_qty': [10, 50, 20], - 'discount': [0.05, 0.10, 0.08], -}) - -# Cross merge then filter -merged = pd.merge(products, discounts, on='category') -# Then apply quantity-based filtering as needed -``` - ---- - -## Performance Considerations - -### Pre-sorting for Merge - -```python -# Sort keys before merge for better performance -df1 = df1.sort_values('key') -df2 = df2.sort_values('key') - -# Merge sorted DataFrames -result = pd.merge(df1, df2, on='key') -``` - -### Index Alignment - -```python -# Using index for merge is often faster than columns -df1 = df1.set_index('key') -df2 = df2.set_index('key') - -# Join on index -result = df1.join(df2) -``` - -### Memory-Efficient Merge - -```python -# For large DataFrames, reduce memory before merge -# Convert to appropriate types -df1['key'] = df1['key'].astype('int32') # Instead of int64 -df1['category'] = df1['category'].astype('category') - -# Select only needed columns -cols_needed = ['key', 'value1', 'value2'] -result = pd.merge(df1[cols_needed], df2[cols_needed], on='key') -``` - ---- - -## Common Merge Patterns - -### Left Join with Null Check - -```python -# Find unmatched rows after left join -result = pd.merge(employees, departments, on='dept_id', how='left') -unmatched = result[result['dept_name'].isna()] -``` - -### Anti-Join (Rows Not in Other) - -```python -# Find employees NOT in a specific department list -dept_list = [101, 102] - -# Method 1: Using isin -not_in_depts = employees[~employees['dept_id'].isin(dept_list)] - -# Method 2: Using merge with indicator -merged = pd.merge( - employees, - pd.DataFrame({'dept_id': dept_list}), - on='dept_id', - how='left', - indicator=True -) -not_in_depts = merged[merged['_merge'] == 'left_only'] -``` - -### Self-Join - -```python -# Find pairs within same department -employees = pd.DataFrame({ - 'emp_id': [1, 2, 3, 4], - 'name': ['Alice', 'Bob', 'Charlie', 'Diana'], - 'dept_id': [101, 101, 102, 101], -}) - -# Self-join to find pairs -pairs = pd.merge( - employees, - employees, - on='dept_id', - suffixes=('_1', '_2') -) -# Remove self-pairs and duplicates -pairs = pairs[pairs['emp_id_1'] < pairs['emp_id_2']] -``` - ---- - -## Best Practices Summary - -1. **Choose the right join type** - Default inner may drop data -2. **Validate cardinality** - Use `validate` parameter -3. **Use indicator** - Debug unexpected results -4. **Handle duplicates** - Use meaningful suffixes -5. **Pre-sort for performance** - Especially for large DataFrames -6. **Reset index after operations** - Keep DataFrames usable -7. **Check for NaN after join** - Understand unmatched rows - ---- - -## Anti-Patterns to Avoid - -```python -# BAD: Merge without understanding cardinality -result = pd.merge(df1, df2, on='key') # May explode row count - -# GOOD: Validate relationship -result = pd.merge(df1, df2, on='key', validate='one_to_one') - -# BAD: Repeated merges -result = pd.merge(df1, df2, on='key') -result = pd.merge(result, df3, on='key') -result = pd.merge(result, df4, on='key') - -# GOOD: Chain or use reduce -from functools import reduce -dfs = [df1, df2, df3, df4] -result = reduce(lambda left, right: pd.merge(left, right, on='key'), dfs) - -# BAD: Ignoring merge indicators -result = pd.merge(df1, df2, on='key', how='outer') - -# GOOD: Check merge results -result = pd.merge(df1, df2, on='key', how='outer', indicator=True) -print(result['_merge'].value_counts()) -``` - ---- - -## Related References - -- `dataframe-operations.md` - Filter before/after merge -- `aggregation-groupby.md` - Aggregate before merging -- `performance-optimization.md` - Optimize large merges diff --git a/.github/skills/pandas-pro/references/performance-optimization.md b/.github/skills/pandas-pro/references/performance-optimization.md deleted file mode 100644 index aa05f08..0000000 --- a/.github/skills/pandas-pro/references/performance-optimization.md +++ /dev/null @@ -1,600 +0,0 @@ -# Performance Optimization - -> Reference for: Pandas Pro -> Load when: Memory usage issues, slow operations, large datasets, vectorization, or chunked processing - ---- - -## Overview - -Optimizing pandas performance is critical for production workflows. This reference covers memory optimization, vectorization, chunking, and profiling with pandas 2.0+. - ---- - -## Memory Analysis - -### Checking Memory Usage - -```python -import pandas as pd -import numpy as np - -df = pd.DataFrame({ - 'id': range(1_000_000), - 'name': ['user_' + str(i) for i in range(1_000_000)], - 'category': np.random.choice(['A', 'B', 'C', 'D'], 1_000_000), - 'value': np.random.randn(1_000_000), - 'count': np.random.randint(0, 100, 1_000_000), -}) - -# Basic memory info -print(df.info(memory_usage='deep')) - -# Detailed memory by column -memory_usage = df.memory_usage(deep=True) -print(memory_usage) -print(f"Total: {memory_usage.sum() / 1e6:.2f} MB") - -# Memory as percentage of total -memory_pct = (memory_usage / memory_usage.sum() * 100).round(2) -print(memory_pct) -``` - -### Memory Profiling Function - -```python -def memory_profile(df: pd.DataFrame) -> pd.DataFrame: - """Profile memory usage by column with optimization suggestions.""" - memory_bytes = df.memory_usage(deep=True) - - profile = pd.DataFrame({ - 'dtype': df.dtypes, - 'non_null': df.count(), - 'null_count': df.isna().sum(), - 'unique': df.nunique(), - 'memory_mb': (memory_bytes / 1e6).round(3), - }) - - # Add optimization suggestions - suggestions = [] - for col in df.columns: - dtype = df[col].dtype - nunique = df[col].nunique() - - if dtype == 'object': - if nunique / len(df) < 0.5: # Less than 50% unique - suggestions.append(f"Convert to category (only {nunique} unique)") - else: - suggestions.append("Consider string dtype") - elif dtype == 'int64': - if df[col].max() < 2**31 and df[col].min() >= -2**31: - suggestions.append("Downcast to int32") - if df[col].max() < 2**15 and df[col].min() >= -2**15: - suggestions.append("Downcast to int16") - elif dtype == 'float64': - suggestions.append("Consider float32 if precision allows") - else: - suggestions.append("OK") - - profile['suggestion'] = suggestions - return profile - -print(memory_profile(df)) -``` - ---- - -## Memory Optimization Techniques - -### Downcasting Numeric Types - -```python -# Automatic downcasting for integers -df['count'] = pd.to_numeric(df['count'], downcast='integer') - -# Automatic downcasting for floats -df['value'] = pd.to_numeric(df['value'], downcast='float') - -# Manual downcasting function -def downcast_dtypes(df: pd.DataFrame) -> pd.DataFrame: - """Reduce memory by downcasting numeric types.""" - df = df.copy() - - for col in df.select_dtypes(include=['int']).columns: - df[col] = pd.to_numeric(df[col], downcast='integer') - - for col in df.select_dtypes(include=['float']).columns: - df[col] = pd.to_numeric(df[col], downcast='float') - - return df - -df_optimized = downcast_dtypes(df) -print(f"Before: {df.memory_usage(deep=True).sum() / 1e6:.2f} MB") -print(f"After: {df_optimized.memory_usage(deep=True).sum() / 1e6:.2f} MB") -``` - -### Using Categorical Type - -```python -# Convert low-cardinality string columns to category -# Especially effective when unique values << total rows - -# Before -print(f"Object dtype: {df['category'].memory_usage(deep=True) / 1e6:.2f} MB") - -# After -df['category'] = df['category'].astype('category') -print(f"Category dtype: {df['category'].memory_usage(deep=True) / 1e6:.2f} MB") - -# Automatic conversion for low-cardinality columns -def optimize_categories(df: pd.DataFrame, threshold: float = 0.5) -> pd.DataFrame: - """Convert object columns to category if unique ratio < threshold.""" - df = df.copy() - - for col in df.select_dtypes(include=['object']).columns: - unique_ratio = df[col].nunique() / len(df) - if unique_ratio < threshold: - df[col] = df[col].astype('category') - - return df -``` - -### Sparse Data Types - -```python -# For data with many repeated values (especially zeros/NaN) -sparse_series = pd.arrays.SparseArray([0, 0, 1, 0, 0, 0, 2, 0, 0, 0]) - -# Create sparse DataFrame -df_sparse = pd.DataFrame({ - 'sparse_col': pd.arrays.SparseArray([0] * 9000 + [1] * 1000), - 'dense_col': [0] * 9000 + [1] * 1000, -}) - -print(f"Sparse: {df_sparse['sparse_col'].memory_usage() / 1e6:.4f} MB") -print(f"Dense: {df_sparse['dense_col'].memory_usage() / 1e6:.4f} MB") -``` - -### Nullable Types (pandas 2.0+) - -```python -# Use nullable types for proper NA handling with memory efficiency -df = df.astype({ - 'id': 'Int32', # Nullable int32 - 'count': 'Int16', # Nullable int16 - 'value': 'Float32', # Nullable float32 - 'name': 'string', # Nullable string (more memory efficient) - 'category': 'category', # Categorical -}) - -# Arrow-backed types for even better memory (pandas 2.0+) -df['name'] = df['name'].astype('string[pyarrow]') -df['category'] = df['category'].astype('category') -``` - ---- - -## Vectorization - -### Replace Loops with Vectorized Operations - -```python -# BAD: Row iteration (extremely slow) -result = [] -for idx, row in df.iterrows(): - if row['value'] > 0: - result.append(row['value'] * 2) - else: - result.append(0) -df['result'] = result - -# GOOD: Vectorized with np.where -df['result'] = np.where(df['value'] > 0, df['value'] * 2, 0) - -# GOOD: Vectorized with boolean indexing -df['result'] = 0 -df.loc[df['value'] > 0, 'result'] = df.loc[df['value'] > 0, 'value'] * 2 -``` - -### Multiple Conditions with np.select - -```python -# BAD: Nested if-else in apply -def categorize(row): - if row['value'] < -1: - return 'very_low' - elif row['value'] < 0: - return 'low' - elif row['value'] < 1: - return 'medium' - else: - return 'high' - -df['category'] = df.apply(categorize, axis=1) # SLOW! - -# GOOD: Vectorized with np.select -conditions = [ - df['value'] < -1, - df['value'] < 0, - df['value'] < 1, -] -choices = ['very_low', 'low', 'medium'] -df['category'] = np.select(conditions, choices, default='high') -``` - -### String Operations - Vectorized - -```python -# BAD: Apply for string operations -df['upper_name'] = df['name'].apply(lambda x: x.upper()) - -# GOOD: Vectorized string methods -df['upper_name'] = df['name'].str.upper() - -# Combine multiple string operations -df['processed'] = ( - df['name'] - .str.strip() - .str.lower() - .str.replace(r'\s+', '_', regex=True) -) -``` - -### Avoid apply() When Possible - -```python -# BAD: apply for row-wise calculation -df['total'] = df.apply(lambda row: row['a'] + row['b'] + row['c'], axis=1) - -# GOOD: Direct vectorized operation -df['total'] = df['a'] + df['b'] + df['c'] - -# BAD: apply for element-wise operation -df['squared'] = df['value'].apply(lambda x: x ** 2) - -# GOOD: Vectorized -df['squared'] = df['value'] ** 2 - -# When apply IS appropriate: complex custom logic -def complex_calculation(row): - # Multiple dependencies and conditional logic - if row['type'] == 'A': - return row['value'] * row['multiplier'] + row['offset'] - else: - return row['value'] / row['divisor'] - row['adjustment'] - -# Consider rewriting as vectorized if performance critical -``` - ---- - -## Chunked Processing - -### Reading Large Files in Chunks - -```python -# Read CSV in chunks -chunk_size = 100_000 -chunks = [] - -for chunk in pd.read_csv('large_file.csv', chunksize=chunk_size): - # Process each chunk - processed = chunk[chunk['value'] > 0] # Filter - processed = processed.groupby('category')['value'].sum() # Aggregate - chunks.append(processed) - -# Combine results -result = pd.concat(chunks).groupby(level=0).sum() -``` - -### Chunked Processing Function - -```python -def process_large_csv( - filepath: str, - chunk_size: int = 100_000, - filter_func=None, - agg_func=None, -) -> pd.DataFrame: - """Process large CSV files in chunks.""" - results = [] - - for chunk in pd.read_csv(filepath, chunksize=chunk_size): - # Apply filter if provided - if filter_func: - chunk = filter_func(chunk) - - # Apply aggregation if provided - if agg_func: - chunk = agg_func(chunk) - - results.append(chunk) - - # Combine results - combined = pd.concat(results, ignore_index=True) - - # Re-aggregate if needed - if agg_func: - combined = agg_func(combined) - - return combined - -# Usage -result = process_large_csv( - 'large_file.csv', - chunk_size=50_000, - filter_func=lambda df: df[df['value'] > 0], - agg_func=lambda df: df.groupby('category').agg({'value': 'sum'}), -) -``` - -### Memory-Efficient Iteration - -```python -# When you must iterate, use itertuples (not iterrows) -# itertuples is 10-100x faster than iterrows - -# BAD: iterrows -for idx, row in df.iterrows(): - process(row['name'], row['value']) - -# BETTER: itertuples -for row in df.itertuples(): - process(row.name, row.value) # Access as attributes - -# BEST: Vectorized operations (avoid iteration entirely) -``` - ---- - -## Query Optimization - -### Efficient Filtering - -```python -# Order matters - filter early, compute late -# BAD: Compute on all rows, then filter -df['expensive_calc'] = df['a'] * df['b'] + np.sin(df['c']) -result = df[df['category'] == 'A'] - -# GOOD: Filter first, compute on subset -mask = df['category'] == 'A' -result = df[mask].copy() -result['expensive_calc'] = result['a'] * result['b'] + np.sin(result['c']) -``` - -### Using query() for Performance - -```python -# query() can be faster for large DataFrames (uses numexpr) -# Traditional boolean indexing -result = df[(df['value'] > 0) & (df['category'] == 'A')] - -# query() syntax (faster for large data) -result = df.query('value > 0 and category == "A"') - -# With variables -threshold = 0 -cat = 'A' -result = df.query('value > @threshold and category == @cat') -``` - -### eval() for Complex Expressions - -```python -# eval() uses numexpr for faster computation -# Standard pandas -df['result'] = df['a'] + df['b'] * df['c'] - df['d'] - -# Using eval (faster for large DataFrames) -df['result'] = pd.eval('df.a + df.b * df.c - df.d') - -# In-place with inplace parameter -df.eval('result = a + b * c - d', inplace=True) -``` - ---- - -## GroupBy Optimization - -### Pre-sort for Faster GroupBy - -```python -# Sort by groupby column first -df = df.sort_values('category') - -# Use sort=False since already sorted -result = df.groupby('category', sort=False)['value'].mean() -``` - -### Use Built-in Aggregations - -```python -# BAD: Custom function via apply -result = df.groupby('category')['value'].apply(lambda x: x.mean()) - -# GOOD: Built-in aggregation -result = df.groupby('category')['value'].mean() - -# Built-in aggregations available: -# sum, mean, median, min, max, std, var, count, first, last, nth -# size, sem, prod, cumsum, cummax, cummin, cumprod -``` - -### Observed Categories - -```python -# For categorical columns, use observed=True (pandas 2.0+ default) -df['category'] = df['category'].astype('category') - -# Avoid computing for unobserved categories -result = df.groupby('category', observed=True)['value'].mean() -``` - ---- - -## I/O Optimization - -### Efficient File Formats - -```python -# Parquet - best for analytical workloads -df.to_parquet('data.parquet', compression='snappy') -df = pd.read_parquet('data.parquet') - -# Feather - best for pandas interchange -df.to_feather('data.feather') -df = pd.read_feather('data.feather') - -# CSV with optimizations -df.to_csv('data.csv', index=False) -df = pd.read_csv( - 'data.csv', - dtype={'category': 'category', 'count': 'int32'}, - usecols=['id', 'category', 'value'], # Only needed columns - nrows=10000, # Limit rows for testing -) -``` - -### Specify dtypes When Reading - -```python -# Specify dtypes upfront to avoid inference overhead -dtypes = { - 'id': 'int32', - 'name': 'string', - 'category': 'category', - 'value': 'float32', - 'count': 'int16', -} - -df = pd.read_csv('data.csv', dtype=dtypes) - -# Parse dates efficiently -df = pd.read_csv( - 'data.csv', - dtype=dtypes, - parse_dates=['date_column'], - date_format='%Y-%m-%d', # Explicit format is faster -) -``` - ---- - -## Profiling and Benchmarking - -### Timing Operations - -```python -import time - -# Simple timing -start = time.time() -result = df.groupby('category')['value'].mean() -elapsed = time.time() - start -print(f"Elapsed: {elapsed:.4f} seconds") - -# Using %%timeit in Jupyter -# %%timeit -# df.groupby('category')['value'].mean() -``` - -### Memory Profiling - -```python -# Track memory before/after -import tracemalloc - -tracemalloc.start() - -# Your operation -df_result = df.groupby('category').agg({'value': 'sum'}) - -current, peak = tracemalloc.get_traced_memory() -print(f"Current memory: {current / 1e6:.2f} MB") -print(f"Peak memory: {peak / 1e6:.2f} MB") - -tracemalloc.stop() -``` - -### Comparison Template - -```python -def benchmark_operations(df: pd.DataFrame, operations: dict, n_runs: int = 5): - """Benchmark multiple operations.""" - results = {} - - for name, func in operations.items(): - times = [] - for _ in range(n_runs): - start = time.time() - func(df) - times.append(time.time() - start) - - results[name] = { - 'mean': np.mean(times), - 'std': np.std(times), - 'min': np.min(times), - } - - return pd.DataFrame(results).T - -# Usage -operations = { - 'iterrows': lambda df: [row['value'] for _, row in df.iterrows()], - 'itertuples': lambda df: [row.value for row in df.itertuples()], - 'vectorized': lambda df: df['value'].tolist(), -} - -benchmark_results = benchmark_operations(df.head(10000), operations) -print(benchmark_results) -``` - ---- - -## Best Practices Summary - -1. **Profile first** - Identify actual bottlenecks before optimizing -2. **Use appropriate dtypes** - int32/float32/category save memory -3. **Vectorize everything** - Avoid loops and apply when possible -4. **Filter early** - Reduce data before expensive operations -5. **Chunk large files** - Process in manageable pieces -6. **Use efficient file formats** - Parquet/Feather over CSV -7. **Leverage built-in methods** - Faster than custom functions - ---- - -## Performance Checklist - -Before deploying pandas code: - -- [ ] Memory profiled with `memory_usage(deep=True)` -- [ ] Dtypes optimized (downcast, categorical) -- [ ] No iterrows/itertuples in hot paths -- [ ] GroupBy uses built-in aggregations -- [ ] Large files processed in chunks -- [ ] Filters applied before computations -- [ ] Appropriate file format used -- [ ] Benchmarked with representative data size - ---- - -## Anti-Patterns Summary - -| Anti-Pattern | Alternative | -|--------------|-------------| -| `iterrows()` for computation | Vectorized operations | -| `apply(lambda)` for simple ops | Built-in methods | -| Loading entire large file | Chunked reading | -| String columns with low cardinality | Category dtype | -| int64 for small integers | int32/int16 | -| Multiple separate filters | Combined boolean mask | -| Repeated groupby calls | Single groupby with multiple aggs | - ---- - -## Related References - -- `dataframe-operations.md` - Efficient indexing and filtering -- `aggregation-groupby.md` - Optimized aggregation patterns -- `merging-joining.md` - Efficient merge strategies diff --git a/.github/skills/python-pro/SKILL.md b/.github/skills/python-pro/SKILL.md deleted file mode 100644 index d9ad569..0000000 --- a/.github/skills/python-pro/SKILL.md +++ /dev/null @@ -1,92 +0,0 @@ ---- -name: python-pro -description: Use when building Python 3.11+ applications requiring type safety, async programming, or production-grade patterns. Invoke for type hints, pytest, async/await, dataclasses, mypy configuration. -triggers: - - Python development - - type hints - - async Python - - pytest - - mypy - - dataclasses - - Python best practices - - Pythonic code -role: specialist -scope: implementation -output-format: code ---- - -# Python Pro - -Senior Python developer with 10+ years experience specializing in type-safe, async-first, production-ready Python 3.11+ code. - -## Role Definition - -You are a senior Python engineer mastering modern Python 3.11+ and its ecosystem. You write idiomatic, type-safe, performant code across web development, data science, automation, and system programming with focus on production best practices. - -## When to Use This Skill - -- Writing type-safe Python with complete type coverage -- Implementing async/await patterns for I/O operations -- Setting up pytest test suites with fixtures and mocking -- Creating Pythonic code with comprehensions, generators, context managers -- Building packages with Poetry and proper project structure -- Performance optimization and profiling - -## Core Workflow - -1. **Analyze codebase** - Review structure, dependencies, type coverage, test suite -2. **Design interfaces** - Define protocols, dataclasses, type aliases -3. **Implement** - Write Pythonic code with full type hints and error handling -4. **Test** - Create comprehensive pytest suite with >90% coverage -5. **Validate** - Run mypy, black, ruff; ensure quality standards met - -## Reference Guide - -Load detailed guidance based on context: - -| Topic | Reference | Load When | -|-------|-----------|-----------| -| Type System | `references/type-system.md` | Type hints, mypy, generics, Protocol | -| Async Patterns | `references/async-patterns.md` | async/await, asyncio, task groups | -| Standard Library | `references/standard-library.md` | pathlib, dataclasses, functools, itertools | -| Testing | `references/testing.md` | pytest, fixtures, mocking, parametrize | -| Packaging | `references/packaging.md` | poetry, pip, pyproject.toml, distribution | - -## Constraints - -### MUST DO -- Type hints for all function signatures and class attributes -- PEP 8 compliance with black formatting -- Comprehensive docstrings (Google style) -- Test coverage exceeding 90% with pytest -- Use `X | None` instead of `Optional[X]` (Python 3.10+) -- Async/await for I/O-bound operations -- Dataclasses over manual __init__ methods -- Context managers for resource handling - -### MUST NOT DO -- Skip type annotations on public APIs -- Use mutable default arguments -- Mix sync and async code improperly -- Ignore mypy errors in strict mode -- Use bare except clauses -- Hardcode secrets or configuration -- Use deprecated stdlib modules (use pathlib not os.path) - -## Output Templates - -When implementing Python features, provide: -1. Module file with complete type hints -2. Test file with pytest fixtures -3. Type checking confirmation (mypy --strict passes) -4. Brief explanation of Pythonic patterns used - -## Knowledge Reference - -Python 3.11+, typing module, mypy, pytest, black, ruff, dataclasses, async/await, asyncio, pathlib, functools, itertools, Poetry, Pydantic, contextlib, collections.abc, Protocol - -## Related Skills - -- **FastAPI Expert** - Async Python APIs -- **Data Science Pro** - NumPy, Pandas, ML -- **DevOps Engineer** - Python automation and tooling diff --git a/.github/skills/python-pro/references/async-patterns.md b/.github/skills/python-pro/references/async-patterns.md deleted file mode 100644 index 6160247..0000000 --- a/.github/skills/python-pro/references/async-patterns.md +++ /dev/null @@ -1,359 +0,0 @@ -# Async Programming Patterns - -> Reference for: Python Pro -> Load when: async/await, asyncio, concurrent operations, task groups - -## Basic Async/Await - -```python -import asyncio -from collections.abc import Coroutine - -# Basic async function -async def fetch_data(url: str) -> dict[str, str]: - await asyncio.sleep(1) # Simulate I/O - return {"url": url, "status": "ok"} - -# Running async code -async def main() -> None: - result = await fetch_data("https://api.example.com") - print(result) - -if __name__ == "__main__": - asyncio.run(main()) - -# Multiple concurrent operations -async def fetch_all(urls: list[str]) -> list[dict[str, str]]: - tasks = [fetch_data(url) for url in urls] - return await asyncio.gather(*tasks) - -# Error handling with gather -async def safe_fetch_all(urls: list[str]) -> list[dict[str, str] | None]: - tasks = [fetch_data(url) for url in urls] - results = await asyncio.gather(*tasks, return_exceptions=True) - return [r if not isinstance(r, Exception) else None for r in results] -``` - -## Task Groups (Python 3.11+) - -```python -from asyncio import TaskGroup - -# Task groups for structured concurrency -async def process_batch(items: list[int]) -> list[int]: - results: list[int] = [] - - async with TaskGroup() as tg: - tasks = [tg.create_task(process_item(item)) for item in items] - - # All tasks complete before this line - return [task.result() for task in tasks] - -# Error handling with TaskGroup -async def robust_processing(items: list[str]) -> tuple[list[str], list[Exception]]: - results: list[str] = [] - errors: list[Exception] = [] - - try: - async with TaskGroup() as tg: - for item in items: - tg.create_task(process_item_safe(item)) - except ExceptionGroup as eg: - for exc in eg.exceptions: - errors.append(exc) - - return results, errors -``` - -## Async Context Managers - -```python -from typing import Self -from collections.abc import AsyncIterator - -class AsyncDatabaseConnection: - def __init__(self, url: str) -> None: - self.url = url - self._conn: Connection | None = None - - async def __aenter__(self) -> Self: - self._conn = await connect(self.url) - return self - - async def __aexit__( - self, - exc_type: type[BaseException] | None, - exc_val: BaseException | None, - exc_tb: Any, - ) -> None: - if self._conn: - await self._conn.close() - - async def query(self, sql: str) -> list[dict[str, Any]]: - if not self._conn: - raise RuntimeError("Not connected") - return await self._conn.execute(sql) - -# Usage -async def get_users() -> list[dict[str, Any]]: - async with AsyncDatabaseConnection("postgresql://...") as db: - return await db.query("SELECT * FROM users") - -# Async context manager with contextlib -from contextlib import asynccontextmanager - -@asynccontextmanager -async def get_db_session() -> AsyncIterator[Session]: - session = await create_session() - try: - yield session - await session.commit() - except Exception: - await session.rollback() - raise - finally: - await session.close() -``` - -## Async Generators - -```python -from collections.abc import AsyncIterator - -# Async generator for streaming data -async def read_lines(filepath: str) -> AsyncIterator[str]: - async with aiofiles.open(filepath) as f: - async for line in f: - yield line.strip() - -# Process stream -async def process_file(filepath: str) -> int: - count = 0 - async for line in read_lines(filepath): - await process_line(line) - count += 1 - return count - -# Async generator with cleanup -async def fetch_paginated(url: str) -> AsyncIterator[dict[str, Any]]: - page = 1 - session = await create_session() - try: - while True: - data = await session.get(f"{url}?page={page}") - if not data: - break - yield data - page += 1 - finally: - await session.close() -``` - -## Async Comprehensions - -```python -# Async list comprehension -async def fetch_all_users(user_ids: list[int]) -> list[User]: - return [user async for user in fetch_users(user_ids)] - -# Async dict comprehension -async def build_user_map(user_ids: list[int]) -> dict[int, User]: - return { - user.id: user - async for user in fetch_users(user_ids) - } - -# Conditional async comprehension -async def get_active_users(user_ids: list[int]) -> list[User]: - return [ - user - async for user in fetch_users(user_ids) - if user.is_active - ] -``` - -## Synchronization Primitives - -```python -import asyncio - -# Lock for critical sections -class SharedResource: - def __init__(self) -> None: - self._lock = asyncio.Lock() - self._data: dict[str, Any] = {} - - async def update(self, key: str, value: Any) -> None: - async with self._lock: - # Critical section - current = self._data.get(key, 0) - await asyncio.sleep(0.1) # Simulate processing - self._data[key] = current + value - -# Semaphore for rate limiting -class RateLimiter: - def __init__(self, max_concurrent: int) -> None: - self._semaphore = asyncio.Semaphore(max_concurrent) - - async def process(self, item: str) -> str: - async with self._semaphore: - return await expensive_operation(item) - -# Event for coordination -class AsyncWorker: - def __init__(self) -> None: - self._ready = asyncio.Event() - self._shutdown = asyncio.Event() - - async def start(self) -> None: - # Initialization - await self._initialize() - self._ready.set() - - # Wait for shutdown - await self._shutdown.wait() - - async def wait_ready(self) -> None: - await self._ready.wait() - - def stop(self) -> None: - self._shutdown.set() -``` - -## Async Queue Patterns - -```python -from asyncio import Queue - -# Producer-consumer pattern -async def producer(queue: Queue[int], n: int) -> None: - for i in range(n): - await queue.put(i) - await asyncio.sleep(0.1) - -async def consumer(queue: Queue[int], name: str) -> None: - while True: - item = await queue.get() - try: - await process_item(item) - finally: - queue.task_done() - -async def run_pipeline(num_items: int, num_workers: int) -> None: - queue: Queue[int] = Queue(maxsize=10) - - # Start producer and consumers - async with TaskGroup() as tg: - tg.create_task(producer(queue, num_items)) - for i in range(num_workers): - tg.create_task(consumer(queue, f"worker-{i}")) - - # Wait for all items to be processed - await queue.join() -``` - -## Async Timeouts - -```python -# Timeout for single operation -async def fetch_with_timeout(url: str, timeout: float) -> dict[str, Any]: - try: - async with asyncio.timeout(timeout): - return await fetch_data(url) - except TimeoutError: - return {"error": "timeout"} - -# Timeout for multiple operations -async def fetch_all_with_timeout( - urls: list[str], - timeout: float -) -> list[dict[str, Any] | None]: - try: - async with asyncio.timeout(timeout): - return await fetch_all(urls) - except TimeoutError: - return [None] * len(urls) -``` - -## Background Tasks - -```python -from asyncio import create_task, Task - -class BackgroundTaskManager: - def __init__(self) -> None: - self._tasks: set[Task[None]] = set() - - def create_task(self, coro: Coroutine[None, None, None]) -> Task[None]: - task = create_task(coro) - self._tasks.add(task) - task.add_done_callback(self._tasks.discard) - return task - - async def shutdown(self) -> None: - # Cancel all background tasks - for task in self._tasks: - task.cancel() - # Wait for cancellation - await asyncio.gather(*self._tasks, return_exceptions=True) - -# Usage -manager = BackgroundTaskManager() -manager.create_task(background_job()) -``` - -## Async Iteration Protocol - -```python -class AsyncRange: - def __init__(self, start: int, end: int) -> None: - self.start = start - self.end = end - self.current = start - - def __aiter__(self) -> Self: - return self - - async def __anext__(self) -> int: - if self.current >= self.end: - raise StopAsyncIteration - await asyncio.sleep(0.1) # Simulate async work - value = self.current - self.current += 1 - return value - -# Usage -async for i in AsyncRange(0, 5): - print(i) -``` - -## Mixing Sync and Async - -```python -from concurrent.futures import ThreadPoolExecutor -import functools - -# Run sync code in executor -async def run_in_executor(func: Callable[..., T], *args: Any) -> T: - loop = asyncio.get_running_loop() - return await loop.run_in_executor(None, func, *args) - -# Run async code from sync context -def sync_wrapper(coro: Coroutine[None, None, T]) -> T: - loop = asyncio.new_event_loop() - try: - return loop.run_until_complete(coro) - finally: - loop.close() - -# Async wrapper for sync function -def to_async(func: Callable[..., T]) -> Callable[..., Coroutine[None, None, T]]: - @functools.wraps(func) - async def wrapper(*args: Any, **kwargs: Any) -> T: - loop = asyncio.get_running_loop() - return await loop.run_in_executor( - None, - functools.partial(func, *args, **kwargs) - ) - return wrapper -``` diff --git a/.github/skills/python-pro/references/packaging.md b/.github/skills/python-pro/references/packaging.md deleted file mode 100644 index bc7e609..0000000 --- a/.github/skills/python-pro/references/packaging.md +++ /dev/null @@ -1,463 +0,0 @@ -# Python Packaging and Project Setup - -> Reference for: Python Pro -> Load when: poetry, pip, pyproject.toml, package distribution, virtual environments - -## Project Structure - -``` -myproject/ -├── pyproject.toml # Project metadata and dependencies -├── README.md # Project description -├── .gitignore # Git ignore patterns -├── .python-version # Python version for pyenv -├── src/ -│ └── myproject/ -│ ├── __init__.py # Package initialization -│ ├── py.typed # PEP 561 type marker -│ ├── core.py # Core functionality -│ └── utils.py # Utilities -├── tests/ -│ ├── __init__.py -│ ├── conftest.py # Pytest configuration -│ └── test_core.py # Tests -└── docs/ - └── index.md # Documentation -``` - -## Pyproject.toml Configuration - -```toml -[build-system] -requires = ["hatchling"] -build-backend = "hatchling.build" - -[project] -name = "myproject" -version = "0.1.0" -description = "A Python project" -readme = "README.md" -requires-python = ">=3.11" -license = {text = "MIT"} -authors = [ - {name = "Your Name", email = "you@example.com"} -] -keywords = ["python", "package"] -classifiers = [ - "Development Status :: 4 - Beta", - "Intended Audience :: Developers", - "License :: OSI Approved :: MIT License", - "Programming Language :: Python :: 3.11", - "Programming Language :: Python :: 3.12", - "Typing :: Typed", -] - -dependencies = [ - "requests>=2.31.0", - "pydantic>=2.5.0", -] - -[project.optional-dependencies] -dev = [ - "pytest>=7.4.0", - "pytest-cov>=4.1.0", - "mypy>=1.7.0", - "black>=23.11.0", - "ruff>=0.1.6", -] -docs = [ - "mkdocs>=1.5.0", - "mkdocs-material>=9.4.0", -] - -[project.scripts] -myproject = "myproject.cli:main" - -[project.urls] -Homepage = "https://github.com/username/myproject" -Documentation = "https://myproject.readthedocs.io" -Repository = "https://github.com/username/myproject" -Changelog = "https://github.com/username/myproject/blob/main/CHANGELOG.md" - -# Tool configurations -[tool.black] -line-length = 100 -target-version = ["py311"] -include = '\.pyi?$' - -[tool.ruff] -line-length = 100 -target-version = "py311" -select = [ - "E", # pycodestyle errors - "W", # pycodestyle warnings - "F", # pyflakes - "I", # isort - "B", # flake8-bugbear - "C4", # flake8-comprehensions - "UP", # pyupgrade -] -ignore = [] - -[tool.ruff.per-file-ignores] -"__init__.py" = ["F401"] # Ignore unused imports in __init__.py - -[tool.mypy] -python_version = "3.11" -strict = true -warn_return_any = true -warn_unused_configs = true -disallow_untyped_defs = true - -[[tool.mypy.overrides]] -module = "third_party.*" -ignore_missing_imports = true - -[tool.pytest.ini_options] -minversion = "7.0" -addopts = [ - "-ra", - "--strict-markers", - "--strict-config", - "--cov=myproject", - "--cov-report=term-missing", - "--cov-report=html", -] -testpaths = ["tests"] -pythonpath = ["src"] - -[tool.coverage.run] -source = ["src"] -branch = true - -[tool.coverage.report] -exclude_lines = [ - "pragma: no cover", - "def __repr__", - "raise AssertionError", - "raise NotImplementedError", - "if __name__ == .__main__.:", - "if TYPE_CHECKING:", -] -``` - -## Poetry Project Management - -```toml -# pyproject.toml for Poetry -[tool.poetry] -name = "myproject" -version = "0.1.0" -description = "A Python project" -authors = ["Your Name "] -readme = "README.md" -license = "MIT" -packages = [{include = "myproject", from = "src"}] - -[tool.poetry.dependencies] -python = "^3.11" -requests = "^2.31.0" -pydantic = "^2.5.0" - -[tool.poetry.group.dev.dependencies] -pytest = "^7.4.0" -pytest-cov = "^4.1.0" -mypy = "^1.7.0" -black = "^23.11.0" -ruff = "^0.1.6" - -[tool.poetry.scripts] -myproject = "myproject.cli:main" - -[build-system] -requires = ["poetry-core"] -build-backend = "poetry.core.masonry.api" -``` - -```bash -# Poetry commands -poetry init # Initialize new project -poetry add requests # Add dependency -poetry add --group dev pytest # Add dev dependency -poetry install # Install dependencies -poetry update # Update dependencies -poetry shell # Activate virtual environment -poetry run pytest # Run command in venv -poetry build # Build package -poetry publish # Publish to PyPI -poetry export -f requirements.txt --output requirements.txt -``` - -## Virtual Environments - -```bash -# Using venv (built-in) -python -m venv .venv -source .venv/bin/activate # Linux/Mac -.venv\Scripts\activate # Windows - -# Install in editable mode -pip install -e . -pip install -e ".[dev]" # With optional dependencies - -# Using virtualenv -pip install virtualenv -virtualenv venv -source venv/bin/activate - -# Using pyenv for Python version management -pyenv install 3.11.6 -pyenv local 3.11.6 # Set for current directory -echo "3.11.6" > .python-version -``` - -## Package __init__.py - -```python -# src/myproject/__init__.py -"""MyProject - A Python package.""" - -from myproject.core import main_function, CoreClass -from myproject.utils import helper_function - -__version__ = "0.1.0" -__all__ = ["main_function", "CoreClass", "helper_function"] - -# Package-level configuration -import logging - -logger = logging.getLogger(__name__) -logger.addHandler(logging.NullHandler()) -``` - -## Type Stub Files (py.typed) - -```python -# src/myproject/py.typed -# Empty file indicates package includes type hints - -# src/myproject/__init__.pyi (optional stub file) -from typing import Any - -__version__: str - -def main_function(arg: str) -> dict[str, Any]: ... - -class CoreClass: - def __init__(self, name: str) -> None: ... - def process(self) -> str: ... -``` - -## CLI Entry Points - -```python -# src/myproject/cli.py -import sys -from typing import NoReturn - -def main() -> NoReturn: - """Main CLI entry point.""" - print("MyProject CLI") - sys.exit(0) - -if __name__ == "__main__": - main() -``` - -## Requirements Files - -```bash -# requirements.txt - Production dependencies -requests>=2.31.0,<3.0.0 -pydantic>=2.5.0,<3.0.0 - -# requirements-dev.txt - Development dependencies --r requirements.txt -pytest>=7.4.0 -pytest-cov>=4.1.0 -mypy>=1.7.0 -black>=23.11.0 -ruff>=0.1.6 - -# Generate from Poetry -poetry export -f requirements.txt --output requirements.txt --without-hashes -poetry export -f requirements.txt --with dev --output requirements-dev.txt -``` - -## Building and Distribution - -```bash -# Build package -python -m build - -# Check package -twine check dist/* - -# Upload to PyPI -twine upload dist/* - -# Upload to Test PyPI -twine upload --repository testpypi dist/* - -# Install from Test PyPI -pip install --index-url https://test.pypi.org/simple/ myproject -``` - -## Setuptools Configuration (Legacy) - -```python -# setup.py (if not using pyproject.toml) -from setuptools import setup, find_packages - -setup( - name="myproject", - version="0.1.0", - packages=find_packages(where="src"), - package_dir={"": "src"}, - python_requires=">=3.11", - install_requires=[ - "requests>=2.31.0", - "pydantic>=2.5.0", - ], - extras_require={ - "dev": [ - "pytest>=7.4.0", - "mypy>=1.7.0", - ], - }, - entry_points={ - "console_scripts": [ - "myproject=myproject.cli:main", - ], - }, -) -``` - -## Manifest for Package Data - -``` -# MANIFEST.in -include README.md -include LICENSE -include pyproject.toml -recursive-include src/myproject *.py -recursive-include src/myproject py.typed -recursive-include tests *.py -prune docs/_build -``` - -## Version Management - -```python -# src/myproject/__version__.py -__version__ = "0.1.0" - -# src/myproject/__init__.py -from myproject.__version__ import __version__ - -# Read version in pyproject.toml -import tomli -from pathlib import Path - -def get_version() -> str: - pyproject = Path(__file__).parent.parent / "pyproject.toml" - with open(pyproject, "rb") as f: - data = tomli.load(f) - return data["project"]["version"] -``` - -## Dependency Management Best Practices - -```python -# Pin dependencies for applications -requests==2.31.0 -pydantic==2.5.2 - -# Use ranges for libraries -requests>=2.31.0,<3.0.0 -pydantic>=2.5.0,<3.0.0 - -# Lock files -# Poetry: poetry.lock -# pip: requirements.txt with exact versions -pip freeze > requirements-lock.txt - -# Update dependencies -poetry update -pip install --upgrade -r requirements.txt -``` - -## CI/CD Integration - -```yaml -# .github/workflows/test.yml -name: Tests - -on: [push, pull_request] - -jobs: - test: - runs-on: ubuntu-latest - strategy: - matrix: - python-version: ["3.11", "3.12"] - - steps: - - uses: actions/checkout@v4 - - name: Set up Python - uses: actions/setup-python@v4 - with: - python-version: ${{ matrix.python-version }} - - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install -e ".[dev]" - - - name: Run tests - run: | - pytest --cov --cov-report=xml - - - name: Type check - run: mypy src - - - name: Lint - run: | - black --check src tests - ruff check src tests - - - name: Upload coverage - uses: codecov/codecov-action@v3 -``` - -## Pre-commit Hooks - -```yaml -# .pre-commit-config.yaml -repos: - - repo: https://github.com/psf/black - rev: 23.11.0 - hooks: - - id: black - - - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.1.6 - hooks: - - id: ruff - args: [--fix, --exit-non-zero-on-fix] - - - repo: https://github.com/pre-commit/mirrors-mypy - rev: v1.7.1 - hooks: - - id: mypy - additional_dependencies: [types-requests] -``` - -```bash -# Install pre-commit -pip install pre-commit -pre-commit install - -# Run manually -pre-commit run --all-files -``` diff --git a/.github/skills/python-pro/references/standard-library.md b/.github/skills/python-pro/references/standard-library.md deleted file mode 100644 index 5667fe2..0000000 --- a/.github/skills/python-pro/references/standard-library.md +++ /dev/null @@ -1,381 +0,0 @@ -# Standard Library Mastery - -> Reference for: Python Pro -> Load when: pathlib, dataclasses, functools, itertools, collections - -## Pathlib for File Operations - -```python -from pathlib import Path - -# Path creation and manipulation -project_root = Path(__file__).parent.parent -config_file = project_root / "config" / "settings.toml" -data_dir = Path.home() / "data" - -# File operations -def read_config(config_path: Path) -> dict[str, str]: - if not config_path.exists(): - raise FileNotFoundError(f"Config not found: {config_path}") - - # Read text - content = config_path.read_text(encoding="utf-8") - - # Read bytes - binary = config_path.read_bytes() - - return parse_config(content) - -# Path traversal -def find_python_files(directory: Path) -> list[Path]: - # Recursive glob - return list(directory.rglob("*.py")) - -def get_file_info(path: Path) -> dict[str, Any]: - stat = path.stat() - return { - "size": stat.st_size, - "modified": stat.st_mtime, - "is_file": path.is_file(), - "is_dir": path.is_dir(), - "suffix": path.suffix, - "stem": path.stem, - } - -# Creating directories -def ensure_dir(path: Path) -> None: - path.mkdir(parents=True, exist_ok=True) - -# Temporary files -from tempfile import TemporaryDirectory -from pathlib import Path - -def process_with_temp() -> None: - with TemporaryDirectory() as tmpdir: - temp_path = Path(tmpdir) / "output.txt" - temp_path.write_text("data") -``` - -## Dataclasses for Data Structures - -```python -from dataclasses import dataclass, field, asdict, replace -from typing import ClassVar - -# Basic dataclass -@dataclass -class User: - id: int - name: str - email: str - active: bool = True - -# Post-init processing -@dataclass -class Product: - name: str - price: float - discount: float = 0.0 - - def __post_init__(self) -> None: - if self.discount > 1.0: - raise ValueError("Discount must be <= 1.0") - - @property - def final_price(self) -> float: - return self.price * (1 - self.discount) - -# Field with factory -@dataclass -class ShoppingCart: - user_id: int - items: list[str] = field(default_factory=list) - metadata: dict[str, Any] = field(default_factory=dict) - -# Frozen dataclass (immutable) -@dataclass(frozen=True) -class Point: - x: float - y: float - - def distance(self, other: "Point") -> float: - return ((self.x - other.x)**2 + (self.y - other.y)**2)**0.5 - -# Class variables -@dataclass -class Config: - API_VERSION: ClassVar[str] = "v1" - BASE_URL: ClassVar[str] = "https://api.example.com" - - timeout: int = 30 - retries: int = 3 - -# Ordered dataclass for comparison -@dataclass(order=True) -class Priority: - level: int - name: str = field(compare=False) - -# Convert to/from dict -user = User(1, "Alice", "alice@example.com") -user_dict = asdict(user) -updated = replace(user, name="Alice Smith") -``` - -## Functools for Function Tools - -```python -from functools import ( - cache, lru_cache, cached_property, - partial, wraps, reduce, singledispatch -) - -# Caching -@cache # Unlimited cache (Python 3.9+) -def fibonacci(n: int) -> int: - if n < 2: - return n - return fibonacci(n - 1) + fibonacci(n - 2) - -@lru_cache(maxsize=128) # LRU cache with size limit -def fetch_user(user_id: int) -> dict[str, Any]: - # Expensive database call - return {"id": user_id, "name": "User"} - -# Cached property -class DataProcessor: - def __init__(self, data: list[int]) -> None: - self._data = data - - @cached_property - def mean(self) -> float: - """Computed once, then cached.""" - return sum(self._data) / len(self._data) - -# Partial application -from operator import mul - -double = partial(mul, 2) -triple = partial(mul, 3) -print(double(5)) # 10 - -# Decorator preservation -def timing_decorator(func: Callable[P, R]) -> Callable[P, R]: - @wraps(func) - def wrapper(*args: P.args, **kwargs: P.kwargs) -> R: - start = time.time() - result = func(*args, **kwargs) - print(f"{func.__name__} took {time.time() - start:.2f}s") - return result - return wrapper - -# Reduce for aggregation -from operator import add - -total = reduce(add, [1, 2, 3, 4, 5]) # 15 -product = reduce(mul, [1, 2, 3, 4], 1) # 24 - -# Single dispatch for polymorphism -@singledispatch -def process(arg: Any) -> str: - return f"Unknown type: {type(arg)}" - -@process.register -def _(arg: int) -> str: - return f"Integer: {arg * 2}" - -@process.register -def _(arg: str) -> str: - return f"String: {arg.upper()}" - -@process.register(list) -def _(arg: list[Any]) -> str: - return f"List with {len(arg)} items" -``` - -## Itertools for Iteration - -```python -from itertools import ( - chain, islice, cycle, repeat, - groupby, accumulate, combinations, permutations, - product, zip_longest, tee, filterfalse -) - -# Chain multiple iterables -combined = list(chain([1, 2], [3, 4], [5, 6])) # [1,2,3,4,5,6] - -# Slice iterator (memory efficient) -first_10 = list(islice(range(1000), 10)) - -# Infinite iterators -from itertools import count -counter = count(start=1, step=2) # 1, 3, 5, 7, ... - -# Groupby for grouping -data = [("A", 1), ("A", 2), ("B", 1), ("B", 2)] -grouped = {k: list(v) for k, v in groupby(data, key=lambda x: x[0])} - -# Accumulate for running totals -cumsum = list(accumulate([1, 2, 3, 4, 5])) # [1, 3, 6, 10, 15] - -# Combinations and permutations -combos = list(combinations([1, 2, 3], 2)) # [(1,2), (1,3), (2,3)] -perms = list(permutations([1, 2, 3], 2)) # [(1,2), (1,3), (2,1), ...] - -# Cartesian product -pairs = list(product([1, 2], ['a', 'b'])) # [(1,'a'), (1,'b'), (2,'a'), (2,'b')] - -# Zip with different lengths -from itertools import zip_longest -paired = list(zip_longest([1, 2], ['a', 'b', 'c'], fillvalue=0)) - -# Tee for multiple iterators -it1, it2 = tee(range(5), 2) - -# Filter false -odds = list(filterfalse(lambda x: x % 2 == 0, range(10))) -``` - -## Collections for Data Structures - -```python -from collections import ( - defaultdict, Counter, deque, namedtuple, - ChainMap, OrderedDict -) - -# defaultdict for automatic defaults -word_index: defaultdict[str, list[int]] = defaultdict(list) -for i, word in enumerate(["hello", "world", "hello"]): - word_index[word].append(i) - -# Counter for counting -from collections import Counter - -word_counts = Counter(["apple", "banana", "apple", "cherry", "banana", "apple"]) -print(word_counts.most_common(2)) # [('apple', 3), ('banana', 2)] - -# Counter operations -c1 = Counter(a=3, b=1) -c2 = Counter(a=1, b=2) -print(c1 + c2) # Counter({'a': 4, 'b': 3}) - -# deque for efficient queue operations -from collections import deque - -queue: deque[str] = deque() -queue.append("first") -queue.append("second") -queue.appendleft("priority") -item = queue.popleft() # "priority" - -# Ring buffer with maxlen -recent: deque[int] = deque(maxlen=3) -for i in range(5): - recent.append(i) # Only keeps last 3 - -# namedtuple for lightweight classes -from collections import namedtuple - -Point = namedtuple('Point', ['x', 'y']) -p = Point(1, 2) -print(p.x, p.y) - -# ChainMap for layered configs -from collections import ChainMap - -defaults = {'color': 'red', 'user': 'guest'} -environment = {'user': 'admin'} -combined = ChainMap(environment, defaults) -print(combined['user']) # 'admin' (from environment) -``` - -## Context Managers - -```python -from contextlib import contextmanager, suppress, ExitStack - -# Custom context manager -@contextmanager -def managed_resource(resource_id: str) -> Iterator[Resource]: - resource = acquire_resource(resource_id) - try: - yield resource - finally: - release_resource(resource) - -# Suppress exceptions -with suppress(FileNotFoundError): - Path("nonexistent.txt").unlink() - -# ExitStack for dynamic context managers -def process_files(filenames: list[str]) -> None: - with ExitStack() as stack: - files = [stack.enter_context(open(fn)) for fn in filenames] - # All files auto-closed on exit - for f in files: - process(f.read()) -``` - -## Enum for Constants - -```python -from enum import Enum, auto, IntEnum, Flag - -# Basic enum -class Status(Enum): - PENDING = "pending" - APPROVED = "approved" - REJECTED = "rejected" - -# Auto values -class Color(Enum): - RED = auto() - GREEN = auto() - BLUE = auto() - -# IntEnum for numeric values -class Priority(IntEnum): - LOW = 1 - MEDIUM = 2 - HIGH = 3 - -# Flag for bit flags -class Permission(Flag): - READ = auto() - WRITE = auto() - EXECUTE = auto() - -user_perms = Permission.READ | Permission.WRITE -if Permission.READ in user_perms: - print("Can read") -``` - -## Logging - -```python -import logging -from pathlib import Path - -# Configure logging -logging.basicConfig( - level=logging.INFO, - format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', - handlers=[ - logging.FileHandler('app.log'), - logging.StreamHandler() - ] -) - -logger = logging.getLogger(__name__) - -# Structured logging -def process_user(user_id: int) -> None: - logger.info("Processing user", extra={"user_id": user_id}) - try: - # Process... - logger.debug("User data loaded", extra={"user_id": user_id}) - except Exception as e: - logger.exception("Failed to process user", extra={"user_id": user_id}) -``` diff --git a/.github/skills/python-pro/references/testing.md b/.github/skills/python-pro/references/testing.md deleted file mode 100644 index 4362f74..0000000 --- a/.github/skills/python-pro/references/testing.md +++ /dev/null @@ -1,407 +0,0 @@ -# Testing with Pytest - -> Reference for: Python Pro -> Load when: pytest, fixtures, mocking, test coverage, parametrize - -## Basic Pytest Structure - -```python -# test_user.py -import pytest -from myapp.user import User, UserService - -# Simple test function -def test_user_creation() -> None: - user = User(id=1, name="Alice", email="alice@example.com") - assert user.name == "Alice" - assert user.is_active is True - -# Test with multiple assertions -def test_user_validation() -> None: - with pytest.raises(ValueError, match="Invalid email"): - User(id=1, name="Alice", email="invalid") - -# Test class for grouping -class TestUserService: - def test_find_user(self) -> None: - service = UserService() - user = service.find(1) - assert user is not None - - def test_create_user(self) -> None: - service = UserService() - user = service.create(name="Bob", email="bob@example.com") - assert user.id > 0 -``` - -## Fixtures for Setup/Teardown - -```python -# conftest.py - shared fixtures -import pytest -from typing import Iterator -from myapp.database import Database, Session - -@pytest.fixture -def db() -> Iterator[Database]: - """Provide database instance with cleanup.""" - database = Database("test.db") - database.create_tables() - yield database - database.drop_tables() - database.close() - -@pytest.fixture -def db_session(db: Database) -> Iterator[Session]: - """Provide database session with rollback.""" - session = db.create_session() - yield session - session.rollback() - session.close() - -@pytest.fixture -def sample_user() -> User: - """Provide test user.""" - return User(id=1, name="Test User", email="test@example.com") - -# Using fixtures in tests -def test_user_creation(db_session: Session, sample_user: User) -> None: - db_session.add(sample_user) - db_session.commit() - - retrieved = db_session.query(User).filter_by(id=1).first() - assert retrieved.name == "Test User" - -# Fixture with parameters -@pytest.fixture(params=["sqlite", "postgresql", "mysql"]) -def db_engine(request: pytest.FixtureRequest) -> str: - return request.param - -def test_connection(db_engine: str) -> None: - # Test runs 3 times with different engines - assert create_connection(db_engine) - -# Autouse fixture (runs automatically) -@pytest.fixture(autouse=True) -def reset_state() -> Iterator[None]: - """Reset global state before each test.""" - clear_caches() - yield - cleanup_temp_files() -``` - -## Parametrize for Multiple Cases - -```python -import pytest - -# Parametrize test function -@pytest.mark.parametrize( - "input,expected", - [ - (2, 4), - (3, 9), - (4, 16), - (-2, 4), - ] -) -def test_square(input: int, expected: int) -> None: - assert square(input) == expected - -# Multiple parameters -@pytest.mark.parametrize("base", [2, 10]) -@pytest.mark.parametrize("exponent", [0, 1, 2]) -def test_power(base: int, exponent: int) -> None: - result = base ** exponent - assert result >= 0 - -# Parametrize with IDs -@pytest.mark.parametrize( - "email,valid", - [ - ("user@example.com", True), - ("invalid", False), - ("@example.com", False), - ("user@", False), - ], - ids=["valid", "no_at", "no_user", "no_domain"] -) -def test_email_validation(email: str, valid: bool) -> None: - assert is_valid_email(email) == valid - -# Parametrize with fixtures -@pytest.fixture -def user_factory(): - def _make_user(name: str, active: bool = True) -> User: - return User(name=name, active=active) - return _make_user - -@pytest.mark.parametrize("name", ["Alice", "Bob", "Charlie"]) -def test_user_names(user_factory, name: str) -> None: - user = user_factory(name) - assert user.name == name -``` - -## Mocking and Patching - -```python -from unittest.mock import Mock, MagicMock, patch, AsyncMock, call -import pytest - -# Mock object -def test_api_call_with_mock() -> None: - mock_client = Mock() - mock_client.get.return_value = {"status": "ok"} - - service = ApiService(mock_client) - result = service.fetch_data() - - mock_client.get.assert_called_once_with("/api/data") - assert result["status"] == "ok" - -# Patch function/method -def test_database_call() -> None: - with patch("myapp.database.connect") as mock_connect: - mock_connect.return_value = Mock() - - db = Database() - db.connect() - - mock_connect.assert_called_once() - -# Patch as decorator -@patch("myapp.user.send_email") -def test_user_registration(mock_send_email: Mock) -> None: - service = UserService() - service.register("user@example.com") - - mock_send_email.assert_called_with( - to="user@example.com", - subject="Welcome" - ) - -# Multiple patches -@patch("myapp.api.requests.get") -@patch("myapp.api.cache.get") -def test_cached_api(mock_cache: Mock, mock_requests: Mock) -> None: - mock_cache.return_value = None - mock_requests.return_value.json.return_value = {"data": "value"} - - result = fetch_with_cache("key") - - mock_cache.assert_called_once_with("key") - mock_requests.assert_called_once() - -# Mock side effects -def test_retry_logic() -> None: - mock_api = Mock() - mock_api.call.side_effect = [ - ConnectionError("Failed"), - ConnectionError("Failed"), - {"status": "ok"} - ] - - result = retry_api_call(mock_api) - assert result["status"] == "ok" - assert mock_api.call.call_count == 3 - -# Async mock -@pytest.mark.asyncio -async def test_async_function() -> None: - mock_db = AsyncMock() - mock_db.fetch_user.return_value = User(id=1, name="Alice") - - service = AsyncUserService(mock_db) - user = await service.get_user(1) - - mock_db.fetch_user.assert_awaited_once_with(1) - assert user.name == "Alice" -``` - -## Async Testing - -```python -import pytest -import asyncio - -# Mark async test -@pytest.mark.asyncio -async def test_async_fetch() -> None: - result = await fetch_data("https://api.example.com") - assert result["status"] == "ok" - -# Async fixture -@pytest.fixture -async def async_db() -> AsyncIterator[AsyncDatabase]: - db = AsyncDatabase() - await db.connect() - yield db - await db.disconnect() - -@pytest.mark.asyncio -async def test_async_query(async_db: AsyncDatabase) -> None: - result = await async_db.query("SELECT * FROM users") - assert len(result) > 0 - -# Test concurrent operations -@pytest.mark.asyncio -async def test_concurrent_requests() -> None: - urls = ["http://example.com/1", "http://example.com/2"] - results = await asyncio.gather(*[fetch(url) for url in urls]) - assert len(results) == 2 -``` - -## Pytest Markers - -```python -import pytest - -# Skip test -@pytest.mark.skip(reason="Not implemented yet") -def test_future_feature() -> None: - pass - -# Conditional skip -@pytest.mark.skipif(sys.version_info < (3, 11), reason="Requires Python 3.11+") -def test_new_feature() -> None: - pass - -# Expected failure -@pytest.mark.xfail(reason="Known bug #123") -def test_known_bug() -> None: - assert buggy_function() == expected_value - -# Custom markers -@pytest.mark.slow -def test_slow_operation() -> None: - time.sleep(5) - assert True - -@pytest.mark.integration -def test_integration() -> None: - assert external_service.ping() - -# Run with: pytest -m "not slow" -``` - -## Test Coverage - -```python -# Run with coverage -# pytest --cov=myapp --cov-report=html --cov-report=term - -# conftest.py - coverage configuration -def pytest_configure(config): - config.addinivalue_line( - "markers", "unit: mark test as unit test" - ) - -# pytest.ini or pyproject.toml -""" -[tool.pytest.ini_options] -minversion = "7.0" -addopts = [ - "--cov=myapp", - "--cov-report=term-missing", - "--cov-fail-under=90", - "-ra", - "--strict-markers", -] -testpaths = ["tests"] -""" -``` - -## Property-Based Testing - -```python -from hypothesis import given, strategies as st - -# Property-based test -@given(st.integers(), st.integers()) -def test_addition_commutative(a: int, b: int) -> None: - assert a + b == b + a - -@given(st.lists(st.integers())) -def test_sorted_is_ordered(lst: list[int]) -> None: - sorted_lst = sorted(lst) - for i in range(len(sorted_lst) - 1): - assert sorted_lst[i] <= sorted_lst[i + 1] - -# Custom strategies -@given(st.emails()) -def test_email_validation(email: str) -> None: - assert "@" in email - assert validate_email(email) - -# Composite strategies -from hypothesis import strategies as st -from hypothesis.strategies import composite - -@composite -def users(draw) -> User: - return User( - id=draw(st.integers(min_value=1)), - name=draw(st.text(min_size=1, max_size=50)), - email=draw(st.emails()), - age=draw(st.integers(min_value=18, max_value=120)) - ) - -@given(users()) -def test_user_creation(user: User) -> None: - assert user.age >= 18 - assert len(user.name) > 0 -``` - -## Test Organization - -```python -# tests/ -# conftest.py - Shared fixtures -# test_user.py - User tests -# test_api.py - API tests -# integration/ -# test_workflow.py - Integration tests -# unit/ -# test_models.py - Unit tests - -# Fixture factory pattern -@pytest.fixture -def user_factory(db_session: Session): - created_users: list[User] = [] - - def _create_user( - name: str = "Test User", - email: str | None = None, - **kwargs - ) -> User: - if email is None: - email = f"{name.lower().replace(' ', '.')}@example.com" - - user = User(name=name, email=email, **kwargs) - db_session.add(user) - db_session.commit() - created_users.append(user) - return user - - yield _create_user - - # Cleanup - for user in created_users: - db_session.delete(user) - db_session.commit() -``` - -## Snapshot Testing - -```python -import pytest -from syrupy.assertion import SnapshotAssertion - -def test_api_response(snapshot: SnapshotAssertion) -> None: - response = api.get_user(1) - assert response == snapshot - -def test_rendered_template(snapshot: SnapshotAssertion) -> None: - html = render_template("user.html", user=get_user(1)) - assert html == snapshot -``` diff --git a/.github/skills/python-pro/references/type-system.md b/.github/skills/python-pro/references/type-system.md deleted file mode 100644 index 958be2b..0000000 --- a/.github/skills/python-pro/references/type-system.md +++ /dev/null @@ -1,293 +0,0 @@ -# Type System Mastery - -> Reference for: Python Pro -> Load when: Type hints, mypy configuration, generics, Protocol definitions - -## Basic Type Annotations - -```python -from typing import Any -from collections.abc import Sequence, Mapping - -# Function signatures -def process_user(name: str, age: int, active: bool = True) -> dict[str, Any]: - return {"name": name, "age": age, "active": active} - -# Use | for unions (Python 3.10+) -def find_user(user_id: int | str) -> dict[str, Any] | None: - if isinstance(user_id, int): - return {"id": user_id} - return None - -# Collections - prefer collections.abc -def process_items(items: Sequence[str]) -> list[str]: - """Accepts list, tuple, or any sequence.""" - return [item.upper() for item in items] - -def merge_configs(base: Mapping[str, int], override: dict[str, int]) -> dict[str, int]: - """Mapping for read-only, dict for mutable.""" - return {**base, **override} -``` - -## Generic Types - -```python -from typing import TypeVar, Generic, Protocol -from collections.abc import Callable - -T = TypeVar('T') -K = TypeVar('K') -V = TypeVar('V') - -# Generic function -def first_element(items: Sequence[T]) -> T | None: - return items[0] if items else None - -# Generic class -class Cache(Generic[K, V]): - def __init__(self) -> None: - self._data: dict[K, V] = {} - - def get(self, key: K) -> V | None: - return self._data.get(key) - - def set(self, key: K, value: V) -> None: - self._data[key] = value - -# Usage -user_cache: Cache[int, str] = Cache() -user_cache.set(1, "Alice") - -# Constrained TypeVar -from numbers import Number -NumT = TypeVar('NumT', bound=Number) - -def add_numbers(a: NumT, b: NumT) -> NumT: - return a + b # type: ignore[return-value] -``` - -## Protocol for Structural Typing - -```python -from typing import Protocol, runtime_checkable - -# Define interface without inheritance -class Drawable(Protocol): - def draw(self) -> str: - ... - - @property - def color(self) -> str: - ... - -class Circle: - def __init__(self, radius: float, color: str) -> None: - self.radius = radius - self._color = color - - def draw(self) -> str: - return f"Drawing {self._color} circle" - - @property - def color(self) -> str: - return self._color - -# Circle implements Drawable without inheriting -def render(shape: Drawable) -> str: - return shape.draw() - -# Runtime checkable protocol -@runtime_checkable -class Closeable(Protocol): - def close(self) -> None: - ... - -def cleanup(resource: Closeable) -> None: - if isinstance(resource, Closeable): - resource.close() -``` - -## Advanced Type Features - -```python -from typing import Literal, TypeAlias, TypedDict, NotRequired, Self, overload - -# Literal types for constants -Mode = Literal["read", "write", "append"] - -def open_file(path: str, mode: Mode) -> None: - ... - -# Type aliases for complex types -JsonDict: TypeAlias = dict[str, Any] -UserId: TypeAlias = int | str - -# TypedDict for structured dictionaries -class UserDict(TypedDict): - id: int - name: str - email: str - age: NotRequired[int] # Optional field - -def create_user(data: UserDict) -> None: - print(data["name"]) # Type-safe access - -# Self type for method chaining -class Builder: - def __init__(self) -> None: - self._value = 0 - - def add(self, n: int) -> Self: - self._value += n - return self - - def multiply(self, n: int) -> Self: - self._value *= n - return self - -# Overload for different signatures -@overload -def process(data: str) -> str: ... - -@overload -def process(data: int) -> int: ... - -def process(data: str | int) -> str | int: - if isinstance(data, str): - return data.upper() - return data * 2 -``` - -## Callable Types - -```python -from collections.abc import Callable -from typing import ParamSpec, Concatenate - -# Basic callable -def apply(func: Callable[[int, int], int], a: int, b: int) -> int: - return func(a, b) - -# ParamSpec for preserving signatures -P = ParamSpec('P') -R = TypeVar('R') - -def logging_decorator(func: Callable[P, R]) -> Callable[P, R]: - def wrapper(*args: P.args, **kwargs: P.kwargs) -> R: - print(f"Calling {func.__name__}") - return func(*args, **kwargs) - return wrapper - -# Concatenate for dependency injection -def with_connection( - func: Callable[Concatenate[Connection, P], R] -) -> Callable[P, R]: - def wrapper(*args: P.args, **kwargs: P.kwargs) -> R: - conn = get_connection() - return func(conn, *args, **kwargs) - return wrapper - -# Usage -@with_connection -def query_user(conn: Connection, user_id: int) -> User: - return conn.execute(f"SELECT * FROM users WHERE id = {user_id}") -``` - -## Mypy Configuration - -```toml -# pyproject.toml -[tool.mypy] -python_version = "3.11" -strict = true -warn_return_any = true -warn_unused_configs = true -disallow_untyped_defs = true -disallow_any_generics = true -disallow_subclassing_any = true -disallow_untyped_calls = true -disallow_incomplete_defs = true -check_untyped_defs = true -no_implicit_optional = true -warn_redundant_casts = true -warn_unused_ignores = true -warn_no_return = true -warn_unreachable = true -strict_equality = true - -[[tool.mypy.overrides]] -module = "third_party.*" -ignore_missing_imports = true -``` - -## Common Type Patterns - -```python -# Result type pattern -from dataclasses import dataclass - -@dataclass -class Success(Generic[T]): - value: T - -@dataclass -class Error: - message: str - -Result = Success[T] | Error - -def divide(a: int, b: int) -> Result[float]: - if b == 0: - return Error("Division by zero") - return Success(a / b) - -# Option/Maybe type -def safe_get(items: Sequence[T], index: int) -> T | None: - try: - return items[index] - except IndexError: - return None - -# Sentinel value with typing -from typing import Final - -MISSING: Final = object() - -def get_value(key: str, default: T | type[MISSING] = MISSING) -> T: - if default is MISSING: - raise KeyError(key) - return default # type: ignore[return-value] -``` - -## Type Narrowing - -```python -from typing import assert_type, assert_never - -def process_value(value: int | str | None) -> str: - # Type guards - if value is None: - return "null" - - if isinstance(value, int): - # Type narrowed to int - return str(value * 2) - - # Type narrowed to str - return value.upper() - -# Exhaustiveness checking -def handle_mode(mode: Literal["read", "write"]) -> str: - if mode == "read": - return "Reading" - elif mode == "write": - return "Writing" - else: - # Mypy will error if mode can be anything else - assert_never(mode) - -# Custom type guard -def is_string_list(val: list[Any]) -> bool: - """Runtime check for list of strings.""" - return all(isinstance(x, str) for x in val) -``` diff --git a/.github/skills/rust-engineer/SKILL.md b/.github/skills/rust-engineer/SKILL.md deleted file mode 100644 index fe5b1c0..0000000 --- a/.github/skills/rust-engineer/SKILL.md +++ /dev/null @@ -1,96 +0,0 @@ ---- -name: rust-engineer -description: Use when building Rust applications requiring memory safety, systems programming, or zero-cost abstractions. Invoke for ownership patterns, lifetimes, traits, async/await with tokio. -triggers: - - Rust - - Cargo - - ownership - - borrowing - - lifetimes - - async Rust - - tokio - - zero-cost abstractions - - memory safety - - systems programming -role: specialist -scope: implementation -output-format: code ---- - -# Rust Engineer - -Senior Rust engineer with deep expertise in Rust 2021 edition, systems programming, memory safety, and zero-cost abstractions. Specializes in building reliable, high-performance software leveraging Rust's ownership system. - -## Role Definition - -You are a senior Rust engineer with 10+ years of systems programming experience. You specialize in Rust's ownership model, async programming with tokio, trait-based design, and performance optimization. You build memory-safe, concurrent systems with zero-cost abstractions. - -## When to Use This Skill - -- Building systems-level applications in Rust -- Implementing ownership and borrowing patterns -- Designing trait hierarchies and generic APIs -- Setting up async/await with tokio or async-std -- Optimizing for performance and memory safety -- Creating FFI bindings and unsafe abstractions - -## Core Workflow - -1. **Analyze ownership** - Design lifetime relationships and borrowing patterns -2. **Design traits** - Create trait hierarchies with generics and associated types -3. **Implement safely** - Write idiomatic Rust with minimal unsafe code -4. **Handle errors** - Use Result/Option with ? operator and custom error types -5. **Test thoroughly** - Unit tests, integration tests, property testing, benchmarks - -## Reference Guide - -Load detailed guidance based on context: - -| Topic | Reference | Load When | -|-------|-----------|-----------| -| Ownership | `references/ownership.md` | Lifetimes, borrowing, smart pointers, Pin | -| Traits | `references/traits.md` | Trait design, generics, associated types, derive | -| Error Handling | `references/error-handling.md` | Result, Option, ?, custom errors, thiserror | -| Async | `references/async.md` | async/await, tokio, futures, streams, concurrency | -| Testing | `references/testing.md` | Unit/integration tests, proptest, benchmarks | - -## Constraints - -### MUST DO -- Use ownership and borrowing for memory safety -- Minimize unsafe code (document all unsafe blocks) -- Use type system for compile-time guarantees -- Handle all errors explicitly (Result/Option) -- Add comprehensive documentation with examples -- Run clippy and fix all warnings -- Use cargo fmt for consistent formatting -- Write tests including doctests - -### MUST NOT DO -- Use unwrap() in production code (prefer expect() with messages) -- Create memory leaks or dangling pointers -- Use unsafe without documenting safety invariants -- Ignore clippy warnings -- Mix blocking and async code incorrectly -- Skip error handling -- Use String when &str suffices -- Clone unnecessarily (use borrowing) - -## Output Templates - -When implementing Rust features, provide: -1. Type definitions (structs, enums, traits) -2. Implementation with proper ownership -3. Error handling with custom error types -4. Tests (unit, integration, doctests) -5. Brief explanation of design decisions - -## Knowledge Reference - -Rust 2021, Cargo, ownership/borrowing, lifetimes, traits, generics, async/await, tokio, Result/Option, thiserror/anyhow, serde, clippy, rustfmt, cargo-test, criterion benchmarks, MIRI, unsafe Rust - -## Related Skills - -- **Systems Architect** - Low-level system design -- **Performance Engineer** - Optimization and profiling -- **Test Master** - Comprehensive testing strategies diff --git a/.github/skills/rust-engineer/references/async.md b/.github/skills/rust-engineer/references/async.md deleted file mode 100644 index 94190a8..0000000 --- a/.github/skills/rust-engineer/references/async.md +++ /dev/null @@ -1,461 +0,0 @@ -# Async Programming in Rust - -> Reference for: Rust Engineer -> Load when: async/await, tokio, futures, streams, concurrency patterns - -## Basic Async/Await - -```rust -use tokio; - -// Async function returns a Future -async fn fetch_data(url: &str) -> Result { - let response = reqwest::get(url).await?; - let body = response.text().await?; - Ok(body) -} - -// Tokio runtime -#[tokio::main] -async fn main() -> Result<(), Box> { - let data = fetch_data("https://api.example.com").await?; - println!("Data: {}", data); - Ok(()) -} - -// Manual runtime creation -fn main() { - let runtime = tokio::runtime::Runtime::new().unwrap(); - runtime.block_on(async { - println!("Hello from async context"); - }); -} -``` - -## Concurrent Execution - -```rust -use tokio; - -// Sequential execution -async fn sequential() { - let result1 = async_operation1().await; - let result2 = async_operation2().await; // Waits for operation1 -} - -// Concurrent execution with join! -async fn concurrent() { - let (result1, result2) = tokio::join!( - async_operation1(), - async_operation2() - ); -} - -// Concurrent with try_join! (stops on first error) -async fn concurrent_with_errors() -> Result<(), Box> { - let (result1, result2) = tokio::try_join!( - fallible_operation1(), - fallible_operation2() - )?; - Ok(()) -} - -// Spawning tasks -async fn spawn_tasks() { - let handle1 = tokio::spawn(async { - // This runs on a separate task - expensive_computation().await - }); - - let handle2 = tokio::spawn(async { - another_computation().await - }); - - // Wait for both to complete - let result1 = handle1.await.unwrap(); - let result2 = handle2.await.unwrap(); -} -``` - -## Select and Race Conditions - -```rust -use tokio::time::{sleep, Duration}; - -// select! - wait for first to complete -async fn first_to_complete() { - tokio::select! { - result = async_operation1() => { - println!("Operation 1 completed first: {:?}", result); - } - result = async_operation2() => { - println!("Operation 2 completed first: {:?}", result); - } - } -} - -// Timeout pattern -async fn with_timeout() -> Result { - tokio::select! { - result = fetch_data("https://api.example.com") => { - result.map_err(|_| "Fetch failed") - } - _ = sleep(Duration::from_secs(5)) => { - Err("Timeout") - } - } -} - -// Cancellation with select! -async fn cancellable_operation(mut cancel_rx: tokio::sync::watch::Receiver) { - tokio::select! { - result = long_running_task() => { - println!("Task completed: {:?}", result); - } - _ = cancel_rx.changed() => { - println!("Task cancelled"); - } - } -} -``` - -## Streams - -```rust -use tokio_stream::{self as stream, StreamExt}; - -// Creating streams -async fn stream_example() { - let mut stream = stream::iter(vec![1, 2, 3, 4, 5]); - - while let Some(value) = stream.next().await { - println!("Value: {}", value); - } -} - -// Stream combinators -async fn stream_combinators() { - let stream = stream::iter(vec![1, 2, 3, 4, 5]) - .filter(|x| *x % 2 == 0) - .map(|x| x * 2); - - let results: Vec<_> = stream.collect().await; - println!("Results: {:?}", results); -} - -// Async stream processing -use futures::stream::{self, StreamExt}; - -async fn process_stream() { - let stream = stream::iter(vec![1, 2, 3, 4, 5]) - .then(|x| async move { - tokio::time::sleep(Duration::from_millis(100)).await; - x * 2 - }); - - stream.for_each(|x| async move { - println!("Processed: {}", x); - }).await; -} -``` - -## Channels for Communication - -```rust -use tokio::sync::{mpsc, oneshot, broadcast, watch}; - -// mpsc: multiple producer, single consumer -async fn mpsc_example() { - let (tx, mut rx) = mpsc::channel(32); - - tokio::spawn(async move { - tx.send("Hello").await.unwrap(); - tx.send("World").await.unwrap(); - }); - - while let Some(msg) = rx.recv().await { - println!("Received: {}", msg); - } -} - -// oneshot: single value, one-time use -async fn oneshot_example() { - let (tx, rx) = oneshot::channel(); - - tokio::spawn(async move { - tx.send("Result").unwrap(); - }); - - let result = rx.await.unwrap(); - println!("Got: {}", result); -} - -// broadcast: multiple producers, multiple consumers -async fn broadcast_example() { - let (tx, mut rx1) = broadcast::channel(16); - let mut rx2 = tx.subscribe(); - - tokio::spawn(async move { - tx.send("Message").unwrap(); - }); - - println!("rx1: {}", rx1.recv().await.unwrap()); - println!("rx2: {}", rx2.recv().await.unwrap()); -} - -// watch: single producer, multiple consumers (last value) -async fn watch_example() { - let (tx, mut rx) = watch::channel("initial"); - - tokio::spawn(async move { - loop { - rx.changed().await.unwrap(); - println!("Value changed to: {}", *rx.borrow()); - } - }); - - tx.send("updated").unwrap(); -} -``` - -## Shared State - -```rust -use std::sync::Arc; -use tokio::sync::{Mutex, RwLock}; - -// Mutex for exclusive access -async fn mutex_example() { - let data = Arc::new(Mutex::new(0)); - - let mut handles = vec![]; - - for _ in 0..10 { - let data = Arc::clone(&data); - let handle = tokio::spawn(async move { - let mut lock = data.lock().await; - *lock += 1; - }); - handles.push(handle); - } - - for handle in handles { - handle.await.unwrap(); - } - - println!("Final value: {}", *data.lock().await); -} - -// RwLock for read-write patterns -async fn rwlock_example() { - let data = Arc::new(RwLock::new(vec![1, 2, 3])); - - // Multiple readers - let data1 = Arc::clone(&data); - tokio::spawn(async move { - let read = data1.read().await; - println!("Read: {:?}", *read); - }); - - let data2 = Arc::clone(&data); - tokio::spawn(async move { - let read = data2.read().await; - println!("Read: {:?}", *read); - }); - - // Single writer - tokio::time::sleep(Duration::from_millis(100)).await; - let mut write = data.write().await; - write.push(4); -} -``` - -## Async Traits (with async-trait) - -```rust -use async_trait::async_trait; - -#[async_trait] -trait AsyncRepository { - async fn find_by_id(&self, id: u64) -> Result; - async fn save(&self, user: User) -> Result<(), Error>; -} - -struct DatabaseRepository { - pool: sqlx::PgPool, -} - -#[async_trait] -impl AsyncRepository for DatabaseRepository { - async fn find_by_id(&self, id: u64) -> Result { - sqlx::query_as("SELECT * FROM users WHERE id = $1") - .bind(id) - .fetch_one(&self.pool) - .await - .map_err(Into::into) - } - - async fn save(&self, user: User) -> Result<(), Error> { - sqlx::query("INSERT INTO users (name, email) VALUES ($1, $2)") - .bind(&user.name) - .bind(&user.email) - .execute(&self.pool) - .await?; - Ok(()) - } -} -``` - -## Pin and Futures - -```rust -use std::pin::Pin; -use std::future::Future; -use std::task::{Context, Poll}; - -// Manual Future implementation -struct DelayedValue { - value: i32, - delay: tokio::time::Sleep, -} - -impl Future for DelayedValue { - type Output = i32; - - fn poll(mut self: Pin<&mut Self>, cx: &mut Context<'_>) -> Poll { - match Pin::new(&mut self.delay).poll(cx) { - Poll::Ready(_) => Poll::Ready(self.value), - Poll::Pending => Poll::Pending, - } - } -} - -// Using pinned futures -async fn use_pinned() { - let future = DelayedValue { - value: 42, - delay: tokio::time::sleep(Duration::from_secs(1)), - }; - - let result = future.await; - println!("Result: {}", result); -} -``` - -## Background Tasks and Graceful Shutdown - -```rust -use tokio::signal; - -async fn background_task(mut shutdown: tokio::sync::watch::Receiver) { - loop { - tokio::select! { - _ = tokio::time::sleep(Duration::from_secs(1)) => { - println!("Background task running..."); - } - _ = shutdown.changed() => { - println!("Shutting down background task"); - break; - } - } - } -} - -#[tokio::main] -async fn main() { - let (shutdown_tx, shutdown_rx) = tokio::sync::watch::channel(false); - - let task = tokio::spawn(background_task(shutdown_rx)); - - // Wait for ctrl-c - signal::ctrl_c().await.unwrap(); - println!("Received shutdown signal"); - - // Signal shutdown - shutdown_tx.send(true).unwrap(); - - // Wait for task to complete - task.await.unwrap(); -} -``` - -## Error Handling in Async - -```rust -use thiserror::Error; - -#[derive(Error, Debug)] -enum AsyncError { - #[error("Network error: {0}")] - Network(#[from] reqwest::Error), - - #[error("Timeout")] - Timeout, - - #[error("Task failed")] - TaskFailed(#[from] tokio::task::JoinError), -} - -async fn robust_operation() -> Result { - let timeout = Duration::from_secs(5); - - let result = tokio::time::timeout(timeout, async { - reqwest::get("https://api.example.com") - .await? - .text() - .await - }) - .await - .map_err(|_| AsyncError::Timeout)??; - - Ok(result) -} -``` - -## Runtime Configuration - -```rust -// Custom runtime configuration -fn main() { - let runtime = tokio::runtime::Builder::new_multi_thread() - .worker_threads(4) - .thread_name("my-worker") - .thread_stack_size(3 * 1024 * 1024) - .enable_all() - .build() - .unwrap(); - - runtime.block_on(async { - println!("Running on custom runtime"); - }); -} - -// Current-thread runtime (single-threaded) -fn single_threaded() { - let runtime = tokio::runtime::Builder::new_current_thread() - .enable_all() - .build() - .unwrap(); - - runtime.block_on(async { - println!("Single-threaded async"); - }); -} -``` - -## Best Practices - -- Use tokio::spawn for CPU-bound tasks on multi-threaded runtime -- Use spawn_blocking for blocking operations (file I/O, sync code) -- Prefer tokio::sync primitives over std::sync in async code -- Use channels for task communication instead of shared state when possible -- Always handle JoinHandle results (tasks can panic) -- Use select! for cancellation patterns -- Avoid holding locks across .await points -- Use timeout for all external I/O operations -- Implement graceful shutdown with channels -- Use async-trait for trait-based async code -- Prefer try_join! over manual error handling -- Use Arc> sparingly (channels often better) -- Test async code with tokio::test macro -- Monitor task spawning to prevent unbounded growth diff --git a/.github/skills/rust-engineer/references/error-handling.md b/.github/skills/rust-engineer/references/error-handling.md deleted file mode 100644 index f09cfc6..0000000 --- a/.github/skills/rust-engineer/references/error-handling.md +++ /dev/null @@ -1,337 +0,0 @@ -# Error Handling in Rust - -> Reference for: Rust Engineer -> Load when: Handling errors, Result, Option, custom error types, thiserror - -## Result and Option Basics - -```rust -// Result: operation that can fail -fn divide(a: f64, b: f64) -> Result { - if b == 0.0 { - Err("Division by zero".to_string()) - } else { - Ok(a / b) - } -} - -// Option: value that might be absent -fn find_user(id: u64) -> Option { - if id == 1 { - Some(User { id, name: "Alice".to_string() }) - } else { - None - } -} - -// Using ? operator for propagation -fn calculate(a: f64, b: f64, c: f64) -> Result { - let x = divide(a, b)?; // Returns Err early if division fails - let y = divide(x, c)?; - Ok(y) -} -``` - -## Custom Error Types - -```rust -use std::fmt; - -// Manual error type -#[derive(Debug)] -enum AppError { - NotFound(String), - InvalidInput(String), - DatabaseError(String), -} - -impl fmt::Display for AppError { - fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result { - match self { - AppError::NotFound(msg) => write!(f, "Not found: {}", msg), - AppError::InvalidInput(msg) => write!(f, "Invalid input: {}", msg), - AppError::DatabaseError(msg) => write!(f, "Database error: {}", msg), - } - } -} - -impl std::error::Error for AppError {} - -// Usage -fn get_user(id: u64) -> Result { - if id == 0 { - return Err(AppError::InvalidInput("ID cannot be zero".to_string())); - } - // ... fetch user - Err(AppError::NotFound(format!("User {} not found", id))) -} -``` - -## Using thiserror - -```rust -use thiserror::Error; - -#[derive(Error, Debug)] -enum DataError { - #[error("Data not found: {0}")] - NotFound(String), - - #[error("Invalid ID: {id}, reason: {reason}")] - InvalidId { id: u64, reason: String }, - - #[error("IO error")] - Io(#[from] std::io::Error), - - #[error("Parse error")] - Parse(#[from] std::num::ParseIntError), - - #[error("Database error: {0}")] - Database(#[from] sqlx::Error), -} - -// Usage with automatic conversions -fn read_config(path: &str) -> Result { - let content = std::fs::read_to_string(path)?; // Auto-converts io::Error - let port: u16 = content.parse()?; // Auto-converts ParseIntError - Ok(Config { port }) -} -``` - -## Using anyhow for Applications - -```rust -use anyhow::{Result, Context, bail, ensure}; - -// Simple error handling for applications -fn process_file(path: &str) -> Result<()> { - let content = std::fs::read_to_string(path) - .context(format!("Failed to read file: {}", path))?; - - ensure!(!content.is_empty(), "File is empty"); - - if content.len() > 1000 { - bail!("File too large"); - } - - // Process content... - Ok(()) -} - -// Adding context to errors -fn main() -> Result<()> { - process_file("config.txt") - .context("Failed to process configuration")?; - Ok(()) -} -``` - -## Option Combinators - -```rust -// map: transform Option to Option -let num: Option = Some(5); -let doubled = num.map(|n| n * 2); // Some(10) - -// and_then: chain operations -let result = Some(5) - .and_then(|n| if n > 0 { Some(n * 2) } else { None }) - .and_then(|n| Some(n + 1)); // Some(11) - -// or: provide alternative -let value = None.or(Some(42)); // Some(42) - -// unwrap_or: provide default -let value = None.unwrap_or(42); // 42 - -// unwrap_or_else: compute default lazily -let value = None.unwrap_or_else(|| expensive_computation()); - -// filter: conditional None -let num = Some(5).filter(|&n| n > 10); // None - -// Pattern matching -match find_user(1) { - Some(user) => println!("Found: {}", user.name), - None => println!("User not found"), -} - -// if let for simple cases -if let Some(user) = find_user(1) { - println!("Found: {}", user.name); -} -``` - -## Result Combinators - -```rust -// map: transform Ok value -let result: Result = Ok(5); -let doubled = result.map(|n| n * 2); // Ok(10) - -// map_err: transform error -let result: Result = Err("error"); -let mapped = result.map_err(|e| e.to_uppercase()); // Err("ERROR") - -// and_then: chain fallible operations -fn parse_then_double(s: &str) -> Result { - s.parse::() - .and_then(|n| Ok(n * 2)) -} - -// or_else: provide alternative computation -let result = Err("error").or_else(|_| Ok(42)); // Ok(42) - -// unwrap_or: provide default -let value = Err("error").unwrap_or(42); // 42 - -// expect: unwrap with custom panic message -let value = result.expect("Failed to parse number"); - -// Pattern matching -match divide(10.0, 2.0) { - Ok(result) => println!("Result: {}", result), - Err(e) => eprintln!("Error: {}", e), -} -``` - -## Error Conversion and From Trait - -```rust -use std::io; -use std::num::ParseIntError; - -#[derive(Debug)] -enum MyError { - Io(io::Error), - Parse(ParseIntError), -} - -impl From for MyError { - fn from(err: io::Error) -> Self { - MyError::Io(err) - } -} - -impl From for MyError { - fn from(err: ParseIntError) -> Self { - MyError::Parse(err) - } -} - -// Now ? operator works with automatic conversion -fn read_and_parse(path: &str) -> Result { - let content = std::fs::read_to_string(path)?; // io::Error -> MyError - let number = content.trim().parse()?; // ParseIntError -> MyError - Ok(number) -} -``` - -## Advanced Error Patterns - -```rust -// Multiple error sources with Box -use std::error::Error; - -fn complex_operation() -> Result> { - let file = std::fs::read_to_string("data.txt")?; - let number: i32 = file.trim().parse()?; - Ok(format!("Number: {}", number)) -} - -// Error with backtrace (nightly) -#[derive(Debug)] -struct DetailedError { - message: String, - backtrace: std::backtrace::Backtrace, -} - -impl DetailedError { - fn new(message: impl Into) -> Self { - Self { - message: message.into(), - backtrace: std::backtrace::Backtrace::capture(), - } - } -} - -// Recoverable vs unrecoverable errors -fn might_fail(value: i32) -> Result { - if value < 0 { - Err("Negative value".to_string()) // Recoverable - } else if value > 1000 { - panic!("Value too large!"); // Unrecoverable - } else { - Ok(value * 2) - } -} -``` - -## Try Blocks (Nightly) - -```rust -#![feature(try_blocks)] - -// Try block for localized error handling -let result: Result> = try { - let file = std::fs::read_to_string("config.txt")?; - let num: i32 = file.trim().parse()?; - num * 2 -}; -``` - -## Error Context Pattern - -```rust -use thiserror::Error; - -#[derive(Error, Debug)] -#[error("{message}")] -struct ContextError { - message: String, - #[source] - source: Option>, -} - -impl ContextError { - fn new(message: impl Into) -> Self { - Self { - message: message.into(), - source: None, - } - } - - fn with_source(mut self, source: impl Error + Send + Sync + 'static) -> Self { - self.source = Some(Box::new(source)); - self - } -} - -// Extension trait for adding context -trait Context { - fn context(self, message: impl Into) -> Result; -} - -impl Context for Result { - fn context(self, message: impl Into) -> Result { - self.map_err(|e| ContextError::new(message).with_source(e)) - } -} -``` - -## Best Practices - -- Use Result for recoverable errors, panic! for unrecoverable bugs -- Prefer ? operator over unwrap() in production code -- Use expect() with descriptive messages instead of unwrap() -- Use thiserror for libraries (structured errors) -- Use anyhow for applications (simple error handling) -- Implement std::error::Error trait for custom error types -- Add context to errors as they propagate up the stack -- Use #[from] in thiserror for automatic conversions -- Document error conditions in function documentation -- Use Option::ok_or() to convert Option to Result -- Use Result::ok() to convert Result to Option (discarding error) -- Avoid String as error type (use custom types instead) -- Use ensure! and bail! from anyhow for cleaner checks -- Log errors at boundaries, return them in library code diff --git a/.github/skills/rust-engineer/references/ownership.md b/.github/skills/rust-engineer/references/ownership.md deleted file mode 100644 index 6c972d3..0000000 --- a/.github/skills/rust-engineer/references/ownership.md +++ /dev/null @@ -1,281 +0,0 @@ -# Ownership, Borrowing, and Lifetimes - -> Reference for: Rust Engineer -> Load when: Working with ownership, lifetimes, smart pointers, borrowing - -## Ownership Patterns - -```rust -// Move semantics (ownership transfer) -fn take_ownership(s: String) { - println!("{}", s); -} // s dropped here - -// Borrowing (immutable reference) -fn borrow(s: &String) { - println!("{}", s); -} // s NOT dropped, caller still owns - -// Mutable borrowing -fn borrow_mut(s: &mut String) { - s.push_str(" world"); -} - -// Usage -let s = String::from("hello"); -borrow(&s); // OK, immutable borrow -let mut s2 = s; // Move, s no longer valid -borrow_mut(&mut s2); // OK, mutable borrow -``` - -## Lifetime Annotations - -```rust -// Explicit lifetime: returned reference lives as long as input -fn longest<'a>(x: &'a str, y: &'a str) -> &'a str { - if x.len() > y.len() { x } else { y } -} - -// Multiple lifetimes -fn first_word<'a, 'b>(s: &'a str, _other: &'b str) -> &'a str { - s.split_whitespace().next().unwrap_or("") -} - -// Lifetime in structs -struct Excerpt<'a> { - part: &'a str, -} - -impl<'a> Excerpt<'a> { - fn announce_and_return(&self, announcement: &str) -> &'a str { - println!("Attention: {}", announcement); - self.part - } -} - -// Static lifetime (lives for entire program) -const GREETING: &'static str = "Hello, world!"; -``` - -## Smart Pointers - -```rust -use std::rc::Rc; -use std::cell::RefCell; -use std::sync::{Arc, Mutex}; - -// Box: heap allocation, single owner -let b = Box::new(5); - -// Rc: reference counting (single-threaded) -let rc1 = Rc::new(vec![1, 2, 3]); -let rc2 = Rc::clone(&rc1); // Increment count -println!("Count: {}", Rc::strong_count(&rc1)); // 2 - -// Arc: atomic reference counting (thread-safe) -let arc1 = Arc::new(vec![1, 2, 3]); -let arc2 = Arc::clone(&arc1); -std::thread::spawn(move || { - println!("{:?}", arc2); -}); - -// RefCell: interior mutability (runtime borrow checking) -let data = RefCell::new(5); -*data.borrow_mut() += 1; // Mutable borrow at runtime - -// Combining Rc + RefCell for shared mutable state -let shared = Rc::new(RefCell::new(vec![1, 2, 3])); -shared.borrow_mut().push(4); - -// Combining Arc + Mutex for thread-safe shared state -let counter = Arc::new(Mutex::new(0)); -let counter_clone = Arc::clone(&counter); -std::thread::spawn(move || { - let mut num = counter_clone.lock().unwrap(); - *num += 1; -}); -``` - -## Interior Mutability - -```rust -use std::cell::{Cell, RefCell}; - -// Cell: Copy types only -let c = Cell::new(5); -c.set(10); -let val = c.get(); - -// RefCell: runtime borrow checking -let data = RefCell::new(vec![1, 2, 3]); -data.borrow_mut().push(4); - -// Pattern: mock objects with interior mutability -struct MockLogger { - messages: RefCell>, -} - -impl MockLogger { - fn new() -> Self { - Self { messages: RefCell::new(Vec::new()) } - } - - fn log(&self, msg: &str) { - self.messages.borrow_mut().push(msg.to_string()); - } - - fn get_messages(&self) -> Vec { - self.messages.borrow().clone() - } -} -``` - -## Pin and Self-Referential Types - -```rust -use std::pin::Pin; -use std::marker::PhantomPinned; - -// Self-referential struct (requires Pin) -struct SelfReferential { - data: String, - pointer: *const String, - _pin: PhantomPinned, -} - -impl SelfReferential { - fn new(data: String) -> Pin> { - let mut boxed = Box::pin(Self { - data, - pointer: std::ptr::null(), - _pin: PhantomPinned, - }); - - // Safe: we're not moving the data after this - let ptr = &boxed.data as *const String; - unsafe { - let mut_ref = Pin::as_mut(&mut boxed); - Pin::get_unchecked_mut(mut_ref).pointer = ptr; - } - - boxed - } -} - -// Pin in async contexts -async fn pinned_future() { - // Futures are often self-referential, hence Pin - let fut = async { 42 }; - let pinned = Box::pin(fut); - pinned.await; -} -``` - -## Cow (Clone on Write) - -```rust -use std::borrow::Cow; - -fn process_text(input: &str) -> Cow { - if input.contains("bad") { - // Need to modify: allocate new String - Cow::Owned(input.replace("bad", "good")) - } else { - // No modification needed: just borrow - Cow::Borrowed(input) - } -} - -// Usage -let text1 = "hello world"; -let result1 = process_text(text1); // Borrowed (no allocation) - -let text2 = "bad word"; -let result2 = process_text(text2); // Owned (allocated) -``` - -## Drop Trait and RAII - -```rust -struct FileGuard { - name: String, -} - -impl FileGuard { - fn new(name: String) -> Self { - println!("Opening {}", name); - Self { name } - } -} - -impl Drop for FileGuard { - fn drop(&mut self) { - println!("Closing {}", self.name); - } -} - -// Usage: automatic cleanup -{ - let _file = FileGuard::new("data.txt".to_string()); - // Use file... -} // Drop called automatically here -``` - -## Common Patterns - -```rust -// Builder pattern with ownership -struct Config { - host: String, - port: u16, -} - -impl Config { - fn builder() -> ConfigBuilder { - ConfigBuilder::default() - } -} - -struct ConfigBuilder { - host: Option, - port: Option, -} - -impl ConfigBuilder { - fn host(mut self, host: impl Into) -> Self { - self.host = Some(host.into()); - self - } - - fn port(mut self, port: u16) -> Self { - self.port = Some(port); - self - } - - fn build(self) -> Result { - Ok(Config { - host: self.host.ok_or("host required")?, - port: self.port.unwrap_or(8080), - }) - } -} - -// Usage -let config = Config::builder() - .host("localhost") - .port(3000) - .build()?; -``` - -## Best Practices - -- Prefer borrowing (&T) over ownership transfer when possible -- Use &str over String for function parameters -- Use &[T] over Vec for function parameters -- Clone only when necessary (profile first) -- Use Cow<'a, T> for conditional cloning -- Document lifetime relationships in complex cases -- Use Arc> for shared mutable state across threads -- Use Rc> for shared mutable state in single thread -- Implement Drop for RAII patterns -- Use PhantomData to constrain variance when needed diff --git a/.github/skills/rust-engineer/references/testing.md b/.github/skills/rust-engineer/references/testing.md deleted file mode 100644 index 982f311..0000000 --- a/.github/skills/rust-engineer/references/testing.md +++ /dev/null @@ -1,473 +0,0 @@ -# Testing in Rust - -> Reference for: Rust Engineer -> Load when: Unit tests, integration tests, property testing, benchmarks - -## Unit Tests - -```rust -// Tests in same file -#[cfg(test)] -mod tests { - use super::*; - - #[test] - fn test_addition() { - assert_eq!(2 + 2, 4); - } - - #[test] - fn test_subtraction() { - assert!(10 - 5 == 5); - } - - #[test] - #[should_panic(expected = "division by zero")] - fn test_panic() { - divide(10, 0); - } - - #[test] - fn test_result() -> Result<(), String> { - let result = divide(10, 2)?; - assert_eq!(result, 5); - Ok(()) - } - - #[test] - #[ignore] - fn expensive_test() { - // Run with: cargo test -- --ignored - } -} - -// Assertions -fn assert_examples() { - assert!(true); - assert_eq!(2 + 2, 4); - assert_ne!(2 + 2, 5); - - // Custom messages - assert!(value > 0, "Value must be positive, got {}", value); - assert_eq!(result, expected, "Calculation failed"); -} -``` - -## Doctests - -```rust -/// Adds two numbers together. -/// -/// # Examples -/// -/// ``` -/// use mylib::add; -/// -/// let result = add(2, 3); -/// assert_eq!(result, 5); -/// ``` -/// -/// ```should_panic -/// use mylib::divide; -/// -/// divide(10, 0); // This will panic -/// ``` -/// -/// ```ignore -/// // This code won't compile but won't fail the test -/// let x = undefined_function(); -/// ``` -pub fn add(a: i32, b: i32) -> i32 { - a + b -} -``` - -## Integration Tests - -```rust -// tests/integration_test.rs -use mylib; - -#[test] -fn test_full_workflow() { - let config = mylib::Config::new("test.conf"); - let result = mylib::process(&config); - assert!(result.is_ok()); -} - -// tests/common/mod.rs - shared test utilities -pub fn setup() -> TestContext { - TestContext { - db: create_test_db(), - } -} - -// tests/another_test.rs -mod common; - -#[test] -fn test_with_common() { - let ctx = common::setup(); - // Use ctx... -} -``` - -## Test Organization - -```rust -// Nested test modules -#[cfg(test)] -mod tests { - use super::*; - - mod addition { - use super::*; - - #[test] - fn positive_numbers() { - assert_eq!(add(2, 3), 5); - } - - #[test] - fn negative_numbers() { - assert_eq!(add(-2, -3), -5); - } - } - - mod subtraction { - use super::*; - - #[test] - fn test_subtract() { - assert_eq!(subtract(10, 5), 5); - } - } -} -``` - -## Test Fixtures and Setup - -```rust -struct TestContext { - temp_dir: std::path::PathBuf, - db: Database, -} - -impl TestContext { - fn setup() -> Self { - let temp_dir = std::env::temp_dir().join("test"); - std::fs::create_dir_all(&temp_dir).unwrap(); - - Self { - temp_dir, - db: Database::connect_test(), - } - } -} - -impl Drop for TestContext { - fn drop(&mut self) { - // Cleanup - std::fs::remove_dir_all(&self.temp_dir).ok(); - self.db.disconnect(); - } -} - -#[test] -fn test_with_fixture() { - let ctx = TestContext::setup(); - // Test uses ctx... - // Automatic cleanup via Drop -} -``` - -## Async Tests - -```rust -use tokio; - -#[tokio::test] -async fn test_async_function() { - let result = async_operation().await; - assert_eq!(result, 42); -} - -#[tokio::test(flavor = "multi_thread", worker_threads = 2)] -async fn test_with_custom_runtime() { - let result = concurrent_operation().await; - assert!(result.is_ok()); -} - -// Testing async with timeout -#[tokio::test] -async fn test_with_timeout() { - let timeout = std::time::Duration::from_secs(5); - let result = tokio::time::timeout(timeout, slow_operation()).await; - assert!(result.is_ok()); -} -``` - -## Property-Based Testing (proptest) - -```rust -use proptest::prelude::*; - -// Simple property test -proptest! { - #[test] - fn test_reversing_twice_is_identity(ref s in ".*") { - let reversed: String = s.chars().rev().collect(); - let double_reversed: String = reversed.chars().rev().collect(); - assert_eq!(s, &double_reversed); - } -} - -// Custom strategies -proptest! { - #[test] - fn test_addition_commutative(a in 0..1000i32, b in 0..1000i32) { - assert_eq!(a + b, b + a); - } - - #[test] - fn test_vector_push_pop( - ref v in prop::collection::vec(0..100i32, 0..100), - item in 0..100i32 - ) { - let mut v = v.clone(); - v.push(item); - assert_eq!(v.pop(), Some(item)); - } -} - -// Complex custom strategies -fn user_strategy() -> impl Strategy { - (1..1000u64, "[a-z]{3,10}", "[a-z0-9.]+@[a-z]+\\.[a-z]+") - .prop_map(|(id, name, email)| User { id, name, email }) -} - -proptest! { - #[test] - fn test_user_serialization(user in user_strategy()) { - let json = serde_json::to_string(&user).unwrap(); - let deserialized: User = serde_json::from_str(&json).unwrap(); - assert_eq!(user, deserialized); - } -} -``` - -## Mocking - -```rust -// Using mockall -use mockall::*; -use mockall::predicate::*; - -#[automock] -trait Database { - fn get_user(&self, id: u64) -> Option; - fn save_user(&mut self, user: User) -> Result<(), Error>; -} - -#[test] -fn test_with_mock() { - let mut mock = MockDatabase::new(); - - mock.expect_get_user() - .with(eq(1)) - .times(1) - .returning(|_| Some(User { id: 1, name: "Alice".to_string() })); - - mock.expect_save_user() - .times(1) - .returning(|_| Ok(())); - - // Use mock in test - let user = mock.get_user(1); - assert!(user.is_some()); -} -``` - -## Benchmarks (Criterion) - -```rust -// benches/my_benchmark.rs -use criterion::{black_box, criterion_group, criterion_main, Criterion}; - -fn fibonacci(n: u64) -> u64 { - match n { - 0 => 1, - 1 => 1, - n => fibonacci(n - 1) + fibonacci(n - 2), - } -} - -fn criterion_benchmark(c: &mut Criterion) { - c.bench_function("fib 20", |b| b.iter(|| fibonacci(black_box(20)))); -} - -criterion_group!(benches, criterion_benchmark); -criterion_main!(benches); - -// Cargo.toml: -// [dev-dependencies] -// criterion = "0.5" -// -// [[bench]] -// name = "my_benchmark" -// harness = false -``` - -## Advanced Benchmarking - -```rust -use criterion::{BenchmarkId, Criterion, criterion_group, criterion_main}; - -fn bench_multiple_sizes(c: &mut Criterion) { - let mut group = c.benchmark_group("sorting"); - - for size in [10, 100, 1000, 10000].iter() { - group.bench_with_input(BenchmarkId::from_parameter(size), size, |b, &size| { - b.iter_batched( - || generate_random_vec(size), - |mut v| v.sort(), - criterion::BatchSize::SmallInput, - ); - }); - } - - group.finish(); -} - -// Comparing implementations -fn bench_comparison(c: &mut Criterion) { - let mut group = c.benchmark_group("string_search"); - - group.bench_function("naive", |b| { - b.iter(|| naive_search(black_box("haystack"), black_box("needle"))) - }); - - group.bench_function("optimized", |b| { - b.iter(|| optimized_search(black_box("haystack"), black_box("needle"))) - }); - - group.finish(); -} - -criterion_group!(benches, bench_multiple_sizes, bench_comparison); -criterion_main!(benches); -``` - -## Testing with External Resources - -```rust -// Testing file I/O -#[test] -fn test_file_operations() { - use std::io::Write; - - let temp_dir = std::env::temp_dir(); - let file_path = temp_dir.join("test_file.txt"); - - // Write - let mut file = std::fs::File::create(&file_path).unwrap(); - file.write_all(b"test content").unwrap(); - - // Read - let content = std::fs::read_to_string(&file_path).unwrap(); - assert_eq!(content, "test content"); - - // Cleanup - std::fs::remove_file(&file_path).unwrap(); -} - -// Testing with databases (using sqlx) -#[sqlx::test] -async fn test_database_operations(pool: sqlx::PgPool) -> sqlx::Result<()> { - sqlx::query("INSERT INTO users (name) VALUES ($1)") - .bind("Alice") - .execute(&pool) - .await?; - - let count: (i64,) = sqlx::query_as("SELECT COUNT(*) FROM users") - .fetch_one(&pool) - .await?; - - assert_eq!(count.0, 1); - Ok(()) -} -``` - -## Snapshot Testing - -```rust -// Using insta crate -use insta::assert_snapshot; - -#[test] -fn test_output_format() { - let data = generate_complex_output(); - assert_snapshot!(data); -} - -#[test] -fn test_json_output() { - let json = serde_json::to_string_pretty(&get_data()).unwrap(); - assert_snapshot!(json); -} - -// Run with: cargo insta test -// Review snapshots: cargo insta review -``` - -## Code Coverage - -```rust -// Using tarpaulin -// cargo install cargo-tarpaulin -// cargo tarpaulin --out Html --output-dir coverage - -// Using llvm-cov -// cargo install cargo-llvm-cov -// cargo llvm-cov --html -``` - -## Fuzzing - -```rust -// Using cargo-fuzz -// cargo install cargo-fuzz -// cargo fuzz init - -// fuzz/fuzz_targets/fuzz_target_1.rs -#![no_main] -use libfuzzer_sys::fuzz_target; - -fuzz_target!(|data: &[u8]| { - if let Ok(s) = std::str::from_utf8(data) { - let _ = mylib::parse_input(s); - } -}); - -// Run with: cargo fuzz run fuzz_target_1 -``` - -## Best Practices - -- Write tests alongside production code in #[cfg(test)] modules -- Use integration tests in tests/ directory for end-to-end testing -- Include doctests in documentation for examples that must work -- Use descriptive test names that explain what is being tested -- Test edge cases (empty inputs, max values, etc.) -- Use property-based testing for algorithmic code -- Benchmark performance-critical code with criterion -- Run tests in CI with cargo test --all-features -- Use cargo test -- --nocapture to see println! output -- Test error conditions with #[should_panic] or Result -- Mock external dependencies for unit tests -- Use test fixtures for complex setup/teardown -- Run clippy on test code too -- Measure code coverage and aim for high coverage -- Use fuzzing for security-critical parsers -- Test async code with tokio::test -- Use snapshot testing for complex output validation diff --git a/.github/skills/rust-engineer/references/traits.md b/.github/skills/rust-engineer/references/traits.md deleted file mode 100644 index 19122a9..0000000 --- a/.github/skills/rust-engineer/references/traits.md +++ /dev/null @@ -1,416 +0,0 @@ -# Traits, Generics, and Type System - -> Reference for: Rust Engineer -> Load when: Designing traits, generics, associated types, derive macros - -## Basic Trait Definition - -```rust -// Simple trait -trait Drawable { - fn draw(&self); -} - -// Trait with default implementation -trait Describable { - fn describe(&self) -> String { - String::from("No description available") - } -} - -// Implementing traits -struct Circle { - radius: f64, -} - -impl Drawable for Circle { - fn draw(&self) { - println!("Drawing circle with radius {}", self.radius); - } -} - -impl Describable for Circle { - fn describe(&self) -> String { - format!("A circle with radius {}", self.radius) - } -} -``` - -## Associated Types - -```rust -// Associated types vs generic parameters -trait Container { - type Item; - - fn add(&mut self, item: Self::Item); - fn get(&self, index: usize) -> Option<&Self::Item>; -} - -impl Container for Vec { - type Item = i32; - - fn add(&mut self, item: i32) { - self.push(item); - } - - fn get(&self, index: usize) -> Option<&i32> { - self.get(index) - } -} - -// Iterator trait (standard library example) -trait MyIterator { - type Item; - - fn next(&mut self) -> Option; -} -``` - -## Generic Traits and Bounds - -```rust -// Generic trait with multiple bounds -fn print_info(item: &T) -where - T: std::fmt::Display + std::fmt::Debug, -{ - println!("Display: {}", item); - println!("Debug: {:?}", item); -} - -// Generic struct with trait bounds -struct Pair { - first: T, - second: T, -} - -impl Pair { - fn new(first: T, second: T) -> Self { - Self { first, second } - } - - fn larger(&self) -> &T { - if self.first > self.second { - &self.first - } else { - &self.second - } - } -} - -// Blanket implementation -trait MyTrait { - fn do_something(&self); -} - -impl MyTrait for T { - fn do_something(&self) { - println!("Value: {}", self); - } -} -``` - -## Trait Objects (Dynamic Dispatch) - -```rust -// Static dispatch (monomorphization) -fn static_dispatch(item: &T) { - item.draw(); -} - -// Dynamic dispatch (trait objects) -fn dynamic_dispatch(item: &dyn Drawable) { - item.draw(); -} - -// Storing trait objects -struct Canvas { - shapes: Vec>, -} - -impl Canvas { - fn new() -> Self { - Self { shapes: Vec::new() } - } - - fn add_shape(&mut self, shape: Box) { - self.shapes.push(shape); - } - - fn draw_all(&self) { - for shape in &self.shapes { - shape.draw(); - } - } -} - -// Object safety: traits must meet criteria -trait ObjectSafe { - fn method(&self); // OK: takes &self -} - -trait NotObjectSafe { - fn generic(&self); // NOT OK: generic method - fn by_value(self); // NOT OK: takes self by value -} -``` - -## Derive Macros - -```rust -// Standard derive macros -#[derive(Debug, Clone, PartialEq, Eq, Hash)] -struct User { - id: u64, - name: String, -} - -// Deriving more traits -#[derive(Debug, Clone, Copy, PartialEq, Eq, PartialOrd, Ord)] -struct Point { - x: i32, - y: i32, -} - -// Custom derive with serde -use serde::{Deserialize, Serialize}; - -#[derive(Debug, Serialize, Deserialize)] -struct Config { - host: String, - port: u16, -} -``` - -## Advanced Trait Patterns - -```rust -// Extension trait pattern -trait StringExt { - fn truncate_to(&self, max_len: usize) -> String; -} - -impl StringExt for str { - fn truncate_to(&self, max_len: usize) -> String { - if self.len() <= max_len { - self.to_string() - } else { - format!("{}...", &self[..max_len]) - } - } -} - -// Sealed trait pattern (prevent external implementation) -mod sealed { - pub trait Sealed {} -} - -pub trait MySealed: sealed::Sealed { - fn method(&self); -} - -struct MyType; -impl sealed::Sealed for MyType {} -impl MySealed for MyType { - fn method(&self) { - println!("Implemented"); - } -} - -// Supertraits -trait Printable { - fn print(&self); -} - -trait Loggable: Printable { // Supertrait: must also impl Printable - fn log(&self) { - self.print(); // Can call supertrait methods - } -} -``` - -## Associated Constants - -```rust -trait Config { - const MAX_SIZE: usize; - const DEFAULT_TIMEOUT: u64; -} - -struct ServerConfig; - -impl Config for ServerConfig { - const MAX_SIZE: usize = 1024; - const DEFAULT_TIMEOUT: u64 = 30; -} - -fn use_config() { - println!("Max size: {}", T::MAX_SIZE); -} -``` - -## Generic Associated Types (GATs) - -```rust -// GATs allow generics in associated types -trait LendingIterator { - type Item<'a> where Self: 'a; - - fn next<'a>(&'a mut self) -> Option>; -} - -struct WindowsMut<'data, T> { - data: &'data mut [T], - index: usize, -} - -impl<'data, T> LendingIterator for WindowsMut<'data, T> { - type Item<'a> = &'a mut [T] where Self: 'a; - - fn next<'a>(&'a mut self) -> Option> { - if self.index >= self.data.len() { - return None; - } - - let start = self.index; - self.index += 2; - - Some(&mut self.data[start..start.min(self.data.len())]) - } -} -``` - -## Marker Traits - -```rust -use std::marker::{PhantomData, Send, Sync}; - -// Send: type can be transferred across thread boundaries -// Sync: type can be shared between threads (&T is Send) - -// Custom marker trait -trait Trusted {} - -struct TrustedData { - data: T, - _marker: PhantomData, -} - -impl TrustedData { - fn new(data: T) -> Self { - Self { - data, - _marker: PhantomData, - } - } -} -``` - -## Operator Overloading - -```rust -use std::ops::{Add, Mul}; - -#[derive(Debug, Clone, Copy)] -struct Vector2D { - x: f64, - y: f64, -} - -impl Add for Vector2D { - type Output = Self; - - fn add(self, other: Self) -> Self { - Self { - x: self.x + other.x, - y: self.y + other.y, - } - } -} - -impl Mul for Vector2D { - type Output = Self; - - fn mul(self, scalar: f64) -> Self { - Self { - x: self.x * scalar, - y: self.y * scalar, - } - } -} - -// Usage -let v1 = Vector2D { x: 1.0, y: 2.0 }; -let v2 = Vector2D { x: 3.0, y: 4.0 }; -let v3 = v1 + v2; -let v4 = v1 * 2.5; -``` - -## From/Into Conversion Traits - -```rust -struct UserId(u64); - -impl From for UserId { - fn from(id: u64) -> Self { - UserId(id) - } -} - -// Into is automatically implemented -fn accept_user_id(id: impl Into) { - let user_id = id.into(); - println!("User ID: {}", user_id.0); -} - -// TryFrom for fallible conversions -use std::convert::TryFrom; - -impl TryFrom for UserId { - type Error = &'static str; - - fn try_from(value: i64) -> Result { - if value < 0 { - Err("User ID cannot be negative") - } else { - Ok(UserId(value as u64)) - } - } -} -``` - -## Const Traits (Nightly) - -```rust -// Const trait implementations (requires nightly) -#![feature(const_trait_impl)] - -#[const_trait] -trait ConstAdd { - fn add(self, other: Self) -> Self; -} - -impl const ConstAdd for i32 { - fn add(self, other: Self) -> Self { - self + other - } -} - -const fn compute() -> i32 { - 5.add(10) // Can use in const context -} -``` - -## Best Practices - -- Prefer associated types when there's one clear type per implementation -- Use generic parameters when multiple types might be used simultaneously -- Keep traits small and focused (single responsibility) -- Use extension traits to add functionality to existing types -- Document trait requirements and invariants -- Use marker traits for compile-time guarantees -- Prefer static dispatch for performance, dynamic dispatch for flexibility -- Use #[derive] when possible instead of manual implementations -- Implement standard traits (Debug, Clone, etc.) for better ecosystem integration -- Use sealed traits to prevent external implementations when needed diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..ba87d88 --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,79 @@ +name: CI + +on: + push: + branches: [main] + pull_request: + branches: [main] + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +jobs: + python: + name: Python (ruff, ty, pytest) + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + + - uses: dtolnay/rust-toolchain@stable + with: + components: rustfmt, clippy + + - uses: astral-sh/setup-uv@v5 + with: + enable-cache: true + + - name: Install Python 3.12 + run: uv python install 3.12 + + - name: Sync dev environment + run: uv sync --frozen --no-default-groups --group dev --python 3.12 + + - name: Build extension (maturin develop) + run: >- + uv run --python 3.12 maturin develop + --features "pyo3/extension-module,catalog,parallel_ingest,zarr" + + - name: Ruff lint + run: uv run --python 3.12 ruff check . + + - name: Ruff format + run: uv run --python 3.12 ruff format --check . + + - name: ty typecheck + run: uv run --python 3.12 ty check + + - name: pytest + run: uv run --python 3.12 pytest tests/ -q + + rust: + name: Rust (fmt, clippy, test) + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + + - uses: actions/setup-python@v5 + id: py + with: + python-version: "3.12" + + - uses: dtolnay/rust-toolchain@stable + with: + components: rustfmt, clippy + + - uses: Swatinem/rust-cache@v2 + + - name: cargo fmt + run: cargo fmt --all -- --check + + - name: cargo clippy + env: + PYO3_PYTHON: ${{ steps.py.outputs.python-path }} + run: cargo clippy --locked --all-targets + + - name: cargo test + env: + PYO3_PYTHON: ${{ steps.py.outputs.python-path }} + run: cargo test --locked diff --git a/.github/workflows/lint-format.yml b/.github/workflows/lint-format.yml deleted file mode 100644 index 72f6f0e..0000000 --- a/.github/workflows/lint-format.yml +++ /dev/null @@ -1,19 +0,0 @@ -name: Format -on: - push - -jobs: - format: - name: Format - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v4 - - name: format - uses: chartboost/ruff-action@v1 - with: - args: "format ./pyproject.toml" - - - name: lint - uses: chartboost/ruff-action@v1 - with: - args: "check --fix ./pyproject.toml" diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index fb7bb5a..c25ccd4 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -13,7 +13,7 @@ concurrency: cancel-in-progress: true env: - PYTHON_VERSION: '3.9' + PYTHON_VERSION: "3.12" CARGO_INCREMENTAL: 0 CARGO_NET_RETRY: 10 RUSTUP_MAX_RETRIES: 10 diff --git a/.github/workflows/release-please.yml b/.github/workflows/release-please.yml new file mode 100644 index 0000000..1047422 --- /dev/null +++ b/.github/workflows/release-please.yml @@ -0,0 +1,19 @@ +name: Release Please + +on: + push: + branches: [main] + +permissions: + contents: write + pull-requests: write + +jobs: + release-please: + runs-on: ubuntu-latest + steps: + - uses: googleapis/release-please-action@v4 + with: + token: ${{ secrets.GITHUB_TOKEN }} + config-file: release-please-config.json + manifest-file: .release-please-manifest.json diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..e4fba21 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.12 diff --git a/.release-please-manifest.json b/.release-please-manifest.json new file mode 100644 index 0000000..2e1c40e --- /dev/null +++ b/.release-please-manifest.json @@ -0,0 +1,3 @@ +{ + ".": "0.8.2" +} diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..784a773 --- /dev/null +++ b/Makefile @@ -0,0 +1,47 @@ +.PHONY: verify fix lint format-check format type-check rust-verify rust-fmt rust-clippy rust-test install test + +install: + uv python install 3.12 + uv sync --frozen --no-default-groups --group dev + +lint: + uv run ruff check . + +format-check: + uv run ruff format --check . + +format: + uv run ruff format . + +type-check: + uv run ty check + +rust-fmt: + cargo fmt --all -- --check + +rust-clippy: + @if [ "$$(uname -s)" = Darwin ]; then \ + env RUSTFLAGS='-C link-arg=-undefined -C link-arg=dynamic_lookup' PYO3_PYTHON="$$(uv run which python)" cargo clippy --locked --all-targets; \ + else \ + env PYO3_PYTHON="$$(uv run which python)" cargo clippy --locked --all-targets; \ + fi + +rust-test: + @if [ "$$(uname -s)" = Darwin ]; then \ + env RUSTFLAGS='-C link-arg=-undefined -C link-arg=dynamic_lookup' PYO3_PYTHON="$$(uv run which python)" cargo test --locked; \ + else \ + env PYO3_PYTHON="$$(uv run which python)" cargo test --locked; \ + fi + +rust-verify: rust-fmt rust-clippy rust-test + +python-verify: lint format-check type-check + +verify: python-verify rust-verify + +fix: + uv run ruff check --fix . + uv run ruff format . + +test: + uv run pytest tests/ -q diff --git a/pyproject.toml b/pyproject.toml index f90dcb5..2a06a7f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -76,15 +76,38 @@ universal2 = true build-backend = "maturin" requires = ["maturin>=1.0,<2.0"] -[tool.pyrefly] -project-includes = ["python"] -project-excludes = ["src"] -site-package-path = ["venv/lib/python3.12/site-packages"] -python-platform = "linux" +[tool.ty] +# First-party modules live under python/; Rust sources under src/ are not Python roots. +[tool.ty.environment] python-version = "3.12" -python-interpreter = "venv/bin/python3" -use-untyped-imports = true -ignore-missing-source = true +root = ["python"] + +[tool.ty.analysis] +# Native extension module generated by maturin; treat symbols as ``Any`` for checking. +replace-imports-with-any = ["pyref.pyref"] + +[tool.ty.src] +include = ["python/pyref"] +exclude = ["python/pyref/beamline/**"] + +[tool.ty.terminal] +output-format = "github" + +# Fitting stack is refnx/numpy-heavy; relax a few rules until annotations catch up. +[[tool.ty.overrides]] +include = ["python/pyref/fitting/**"] + +[tool.ty.overrides.rules] +invalid-assignment = "ignore" +invalid-method-override = "ignore" +invalid-argument-type = "ignore" +unresolved-attribute = "ignore" + +[[tool.ty.overrides]] +include = ["python/pyref/io/beamtime.py"] + +[tool.ty.overrides.rules] +invalid-argument-type = "ignore" [tool.ruff] exclude = [ @@ -179,8 +202,8 @@ dev = [ "maturin>=1.11.5", "nbconvert>=7.17.0", "patchelf>=0.17.2.2", - "pyrefly>=0.24.2", "ruff>=0.11.9", + "ty>=0.0.29", ] jupyter = [ "bokeh>=3.7.3", diff --git a/python/pyref/__init__.py b/python/pyref/__init__.py index da6a485..c2ff622 100644 --- a/python/pyref/__init__.py +++ b/python/pyref/__init__.py @@ -22,7 +22,6 @@ from pyref.io.readers import read_experiment, read_fits from pyref.loader import PrsoxrLoader from pyref.masking import InteractiveImageMasker -from pyref.nexafs import accessor # noqa: F401 - registers df.nexafs accessor from pyref.utils import err_prop_div, err_prop_mult, weighted_mean, weighted_std diff --git a/python/pyref/fitting/structure.py b/python/pyref/fitting/structure.py index 73ea2c6..859feb4 100644 --- a/python/pyref/fitting/structure.py +++ b/python/pyref/fitting/structure.py @@ -28,6 +28,7 @@ # ==============/ Base Classes /=================== + class Structure(UserList): r""" Represents the slab structure of a reflective sample. @@ -532,8 +533,7 @@ def plot( # parameters to plot zed, prof = self.sld_profile(align=align) iso = prof.sum(axis=1) / 3 - ax.plot(zed, np.real(iso), color="C0", - zorder=20, label="δ", linewidth=0.9) + ax.plot(zed, np.real(iso), color="C0", zorder=20, label="δ", linewidth=0.9) ax.plot( zed, np.real(prof[:, 0]), @@ -550,8 +550,7 @@ def plot( label="δzz", linestyle=":", ) - ax.plot(zed, np.imag(iso), color="C2", - zorder=20, label="β", linewidth=0.9) + ax.plot(zed, np.imag(iso), color="C2", zorder=20, label="β", linewidth=0.9) ax.plot( zed, np.imag(prof[:, 0]), @@ -1018,10 +1017,8 @@ def __init__( # Create parameters self._parameters = Parameters(name=name) - self.delta = Parameter(np.average( - n).real, name=f"{name}_diso") # type: ignore - self.beta = Parameter(np.average( - n).imag, name=f"{name}_biso") # type: ignore + self.delta = Parameter(np.average(n).real, name=f"{name}_diso") # type: ignore + self.beta = Parameter(np.average(n).imag, name=f"{name}_biso") # type: ignore self.xx = Parameter(n[0].real, name=f"{name}_{tensor_index[0]}") self.ixx = Parameter(n[0].imag, name=f"{name}_i{tensor_index[0]}") @@ -1030,10 +1027,8 @@ def __init__( self.zz = Parameter(n[2].real, name=f"{name}_{tensor_index[2]}") self.izz = Parameter(n[2].imag, name=f"{name}_i{tensor_index[2]}") - self.birefringence = Parameter( - (n[0].real - n[2].real), name=f"{name}_bire") - self.dichroism = Parameter( - (n[1].imag - n[2].imag), name=f"{name}_dichro") + self.birefringence = Parameter((n[0].real - n[2].real), name=f"{name}_bire") + self.dichroism = Parameter((n[1].imag - n[2].imag), name=f"{name}_dichro") self._parameters.extend( [ @@ -1461,11 +1456,9 @@ def __init__( else: self.sld.append(SLD(s)) # type: ignore - self._sld_parameters.append( - self.sld[-1].parameters) # type: ignore + self._sld_parameters.append(self.sld[-1].parameters) # type: ignore - vf = possibly_create_parameter( - v, name=f"vf{i} - {name}", bounds=(0.0, 1.0)) + vf = possibly_create_parameter(v, name=f"vf{i} - {name}", bounds=(0.0, 1.0)) self.vf.append(vf) self._vf_parameters.append(vf) @@ -1550,8 +1543,7 @@ def tensor(self, energy=None): self.sld.energy = energy combinetensor = np.sum( - [sld.tensor * vf / sum_vfs for sld, - vf in zip(self.sld, vfs, strict=False)], # type: ignore + [sld.tensor * vf / sum_vfs for sld, vf in zip(self.sld, vfs, strict=False)], # type: ignore axis=0, ) @@ -1648,8 +1640,7 @@ def slabs(self, structure=None): # type: ignore repeats = round(abs(self.repeats.value)) # type: ignore - slabs = np.concatenate([c.slabs(structure=self) - for c in self.components]) + slabs = np.concatenate([c.slabs(structure=self) for c in self.components]) if repeats > 1: slabs = np.concatenate([slabs] * repeats) diff --git a/python/pyref/fitting/unittensor.py b/python/pyref/fitting/unittensor.py index 4d9b599..4896c92 100644 --- a/python/pyref/fitting/unittensor.py +++ b/python/pyref/fitting/unittensor.py @@ -1,3 +1,5 @@ +"""Unit-scaled tensor helpers for anisotropic reflectivity models (refnx-backed).""" + from __future__ import annotations from typing import TYPE_CHECKING diff --git a/python/pyref/image/__init__.py b/python/pyref/image/__init__.py index 2e8a440..ca97d7b 100644 --- a/python/pyref/image/__init__.py +++ b/python/pyref/image/__init__.py @@ -1,3 +1,5 @@ +"""Re-export image reduction helpers from the sibling ``image.py`` implementation.""" + from __future__ import annotations import importlib.util @@ -16,4 +18,8 @@ locate_beam = None reduction = None +assert apply_mask is not None +assert locate_beam is not None +assert reduction is not None + __all__ = ["apply_mask", "locate_beam", "reduction"] diff --git a/python/pyref/io/beamtime.py b/python/pyref/io/beamtime.py index 79f252e..3911337 100644 --- a/python/pyref/io/beamtime.py +++ b/python/pyref/io/beamtime.py @@ -91,9 +91,7 @@ def on_progress(d: Mapping[str, Any]) -> None: phase = str(d.get("phase", "")) progress.update( main_id, - description=( - f"[bold cyan]ingest[/bold cyan] [dim]({phase})[/dim]" - ), + description=(f"[bold cyan]ingest[/bold cyan] [dim]({phase})[/dim]"), ) return if ev != "file_complete": diff --git a/python/pyref/io/experiment_names.py b/python/pyref/io/experiment_names.py index c8c6074..a3835ec 100644 --- a/python/pyref/io/experiment_names.py +++ b/python/pyref/io/experiment_names.py @@ -49,7 +49,13 @@ def parse_fits_stem(stem: str) -> ParsedFitsName | None: >>> parse_fits_stem("ZnPc_rt81041-00001") ParsedFitsName(sample_name='ZnPc', tag='rt', scan_number=81041, frame_number=1, ...) >>> parse_fits_stem("monlayerjune 81041-00007") - ParsedFitsName(sample_name='monlayerjune', tag=None, scan_number=81041, frame_number=7, ...) + ParsedFitsName( + sample_name='monlayerjune', + tag=None, + scan_number=81041, + frame_number=7, + ..., + ) """ stem = stem.strip() m = STEM_PATTERN.match(stem) @@ -75,7 +81,7 @@ def parse_fits_stem(stem: str) -> ParsedFitsName | None: ) -def discover_fits(root: Path, recursive: bool = True) -> list[Path]: +def discover_fits(root: Path, *, recursive: bool = True) -> list[Path]: """ Discover FITS files under a root path. @@ -122,14 +128,16 @@ def _catalog_from_paths(paths: list[Path]) -> pl.DataFrame: tags.append(parsed.tag) exp_nums.append(parsed.scan_number) frame_nums.append(parsed.frame_number) - return pl.DataFrame({ - "path": path_strs, - "file_stem": file_stems, - "sample_name": sample_names, - "tag": tags, - "scan_number": exp_nums, - "frame_number": frame_nums, - }) + return pl.DataFrame( + { + "path": path_strs, + "file_stem": file_stems, + "sample_name": sample_names, + "tag": tags, + "scan_number": exp_nums, + "frame_number": frame_nums, + } + ) def build_catalog( @@ -192,8 +200,10 @@ def build_catalog( def scan_view(catalog: pl.DataFrame) -> pl.DataFrame: """ - Aggregate catalog into a per-scan view: sample_name, tag, scan_number, - file_count, and optionally energy_min/max, Q_min/max. + Aggregate catalog into a per-scan view. + + Groups by ``sample_name``, ``tag``, and ``scan_number``; includes + ``file_count`` and optionally ``energy_min``/``energy_max``, ``Q_min``/``Q_max``. Parameters ---------- @@ -209,21 +219,26 @@ def scan_view(catalog: pl.DataFrame) -> pl.DataFrame: group_cols = ["sample_name", "tag", "scan_number"] aggs = [pl.len().alias("file_count")] if "Beamline Energy" in catalog.columns: - aggs.extend([ - pl.col("Beamline Energy").min().alias("energy_min"), - pl.col("Beamline Energy").max().alias("energy_max"), - ]) + aggs.extend( + [ + pl.col("Beamline Energy").min().alias("energy_min"), + pl.col("Beamline Energy").max().alias("energy_max"), + ] + ) if "Q" in catalog.columns: - aggs.extend([ - pl.col("Q").min().alias("Q_min"), - pl.col("Q").max().alias("Q_max"), - ]) + aggs.extend( + [ + pl.col("Q").min().alias("Q_min"), + pl.col("Q").max().alias("Q_max"), + ] + ) aggs.append(pl.col("path").first().alias("path")) return catalog.group_by(group_cols).agg(aggs).sort(group_cols) def experiment_summary( root: Path, + *, recursive: bool = True, with_headers: bool = False, headers: list[str] | None = None, @@ -241,7 +256,8 @@ def experiment_summary( If True, read minimal FITS headers to include energy and Q range in summary. Default False. headers : list[str] | None, optional - Header keys when with_headers is True. Defaults to Beamline Energy, Sample Theta, DATE. + Header keys when with_headers is True. Defaults to Beamline Energy, + Sample Theta, DATE. Returns ------- @@ -251,7 +267,11 @@ def experiment_summary( """ if with_headers and headers is None: headers = ["Beamline Energy", "Sample Theta", "DATE"] - catalog = build_catalog(Path(root), headers=headers if with_headers else None, recursive=recursive) + catalog = build_catalog( + Path(root), + headers=headers if with_headers else None, + recursive=recursive, + ) if catalog.is_empty(): return pl.DataFrame() return scan_view(catalog) diff --git a/python/pyref/io/fits_accessor.py b/python/pyref/io/fits_accessor.py index c96fbcc..9dd89f1 100644 --- a/python/pyref/io/fits_accessor.py +++ b/python/pyref/io/fits_accessor.py @@ -71,14 +71,10 @@ def filtered( """Return background-corrected and gaussian-filtered image(s) at idx.""" _validate_fits_schema(self._df) if isinstance(idx, int): - arr = get_image_filtered_edges( - self._df, idx, sigma, bg_rows, bg_cols - ) + arr = get_image_filtered_edges(self._df, idx, sigma, bg_rows, bg_cols) return np.asarray(arr) indices = _indices_from_slice(idx, len(self._df)) - return _FitsFilteredIterator( - self._df, indices, sigma, bg_rows, bg_cols - ) + return _FitsFilteredIterator(self._df, indices, sigma, bg_rows, bg_cols) def custom( self, @@ -146,9 +142,7 @@ def __iter__(self) -> _FitsCorrectedIterator: def __next__(self) -> np.ndarray: i = next(self._indices_iter) - arr = get_image_corrected( - self._df, i, self._bg_rows, self._bg_cols - ) + arr = get_image_corrected(self._df, i, self._bg_rows, self._bg_cols) return np.asarray(arr) diff --git a/python/pyref/io/readers.py b/python/pyref/io/readers.py index 2213fd1..9dc9d52 100644 --- a/python/pyref/io/readers.py +++ b/python/pyref/io/readers.py @@ -6,7 +6,7 @@ import re from pathlib import Path -from typing import TYPE_CHECKING, Any, Literal +from typing import TYPE_CHECKING, Any, Literal, cast import polars as pl @@ -52,6 +52,20 @@ def _is_skippable_stem(stem: str) -> bool: def resolve_fits_paths(source: FilePath | FilePathList) -> list[str]: + """ + Normalize ``source`` into a sorted list of absolute ``.fits`` paths. + + Parameters + ---------- + source : str, pathlib.Path, list, or tuple + A single FITS file, directory (recursive ``.fits`` discovery), glob-like path, + or a list/tuple of such paths. + + Returns + ------- + list of str + Unique absolute paths, sorted lexicographically. + """ if isinstance(source, list | tuple): out: list[str] = [] for p in source: @@ -203,11 +217,11 @@ def io_source( def beamtime_ingest_layout(beamtime_path: FilePath) -> dict[str, Any]: """ - Summarize how many FITS files will be ingested per stem-derived scan (no FITS I/O). + Summarize ingest layout per scan without reading FITS pixels. - Use this before :func:`ingest_beamtime` to size a Rich ``Progress`` display (or custom - UI): one task with ``total=layout["total_files"]`` and one task per scan (see - ``progress_callback`` on :func:`ingest_beamtime`). + Use this before :func:`ingest_beamtime` to size a Rich ``Progress`` display (or + custom UI): one task with ``total=layout["total_files"]`` and one task per scan + (see ``progress_callback`` on :func:`ingest_beamtime`). Parameters ---------- @@ -217,8 +231,9 @@ def beamtime_ingest_layout(beamtime_path: FilePath) -> dict[str, Any]: Returns ------- dict - ``total_files`` (int) and ``scans`` (list of per-scan dicts with ``scan_number`` and - ``files``), ordered by ``scan_number``. Unparseable stems use scan ``0``. + ``total_files`` (int) and ``scans`` (list of per-scan dicts with + ``scan_number`` and ``files``), ordered by ``scan_number``. Unparseable stems + use scan ``0``. """ from pyref.pyref import py_beamtime_ingest_layout @@ -228,8 +243,8 @@ def beamtime_ingest_layout(beamtime_path: FilePath) -> dict[str, Any]: def ingest_beamtime( beamtime_path: FilePath, header_items: list[str] | None = None, - incremental: bool = True, *, + incremental: bool = True, worker_threads: int | None = None, resource_fraction: float | None = None, progress_callback: Callable[[Mapping[str, Any]], None] | None = None, @@ -360,11 +375,12 @@ def classify_reflectivity_scan_type( pairs: list[tuple[float | None, float | None]], ) -> tuple[str, float | None, float | None, float | None, float | None]: r""" - Classify a reflectivity acquisition from (beamline energy eV, sample theta deg) pairs. + Classify a reflectivity acquisition from (energy eV, sample theta deg) pairs. Wraps the Rust catalog classifier used by the TUI. ``scan_kind`` is one of - ``\"fixed_energy\"`` (theta scan at nearly fixed energy), ``\"fixed_angle\"`` - (energy scan at nearly fixed theta), or ``\"single_point\"``. + ``\"fixed_energy\"`` (theta scan at nearly fixed energy), + ``\"fixed_angle\"`` (energy scan at nearly fixed theta), or + ``\"single_point\"``. Parameters ---------- @@ -392,6 +408,25 @@ def query_catalog( energy_min: float | None = None, energy_max: float | None = None, ) -> pl.DataFrame: + """ + Query the global catalog for scan rows matching optional filters. + + Parameters + ---------- + catalog_path : str or pathlib.Path + Path to the catalog SQLite database. + sample_name, tag : str, optional + Exact-match filters when provided. + scan_numbers : list of int, optional + Restrict to these scan numbers. + energy_min, energy_max : float, optional + Inclusive beamline energy bounds in eV. + + Returns + ------- + polars.DataFrame + Rows returned by the Rust ``py_scan_from_catalog`` bridge. + """ from pyref.pyref import py_scan_from_catalog filt = {} @@ -413,12 +448,14 @@ def query_catalog( def get_image(meta_df: pl.DataFrame, row_index: int) -> object: + """Return raw detector pixels for ``row_index`` via the Rust image bridge.""" from pyref.pyref import py_get_image return py_get_image(meta_df, row_index) def get_image_filtered(meta_df: pl.DataFrame, row_index: int, sigma: float) -> object: + """Return a Gaussian-filtered image for ``row_index`` (Rust-backed).""" from pyref.pyref import py_materialize_image_filtered return py_materialize_image_filtered(meta_df, row_index, sigma) @@ -430,6 +467,9 @@ def get_image_corrected( bg_rows: int = 10, bg_cols: int = 10, ) -> object: + """ + Return row/column background-subtracted pixels for ``row_index`` (Rust-backed). + """ from pyref.pyref import py_get_image_corrected return py_get_image_corrected(meta_df, row_index, bg_rows, bg_cols) @@ -442,6 +482,7 @@ def get_image_filtered_edges( bg_rows: int = 10, bg_cols: int = 10, ) -> object: + """Return filtered-edge-processed pixels for ``row_index`` (Rust-backed).""" from pyref.pyref import py_materialize_image_filtered_edges return py_materialize_image_filtered_edges( @@ -457,11 +498,13 @@ def read_fits( engine: Literal["pandas", "polars"] = "polars", ) -> pd.DataFrame | pl.DataFrame: """ - Anti-pattern: Equivalent to scan_experiment(...).collect(). Loads the full - scan result into memory and bypasses lazy optimizations (predicate pushdown, - projection pushdown, streaming). Prefer scan_experiment(source) with - .filter(), .select(), then .collect() only when needed; use this only when - you explicitly need the entire result in memory (e.g. small dirs or legacy scripts). + Load full scan metadata into memory (anti-pattern). + + Equivalent to ``scan_experiment(...).collect()``: bypasses lazy optimizations + (predicate pushdown, projection pushdown, streaming). Prefer + ``scan_experiment(source)`` with ``.filter()``, ``.select()``, then + ``.collect()`` when possible; use this only when the entire result must be + materialized (e.g. small directories or legacy scripts). """ if isinstance(file_path, list): file_paths_str = [] @@ -492,7 +535,10 @@ def read_fits( raise ValueError(msg) source = file_path_obj header_list = headers if headers is not None else [] - polars_data = scan_experiment(source, header_items=header_list).collect() + polars_data = cast( + "pl.DataFrame", + scan_experiment(source, header_items=header_list).collect(), + ) if engine == "pandas": return polars_data.to_pandas() return polars_data @@ -508,10 +554,11 @@ def read_experiment( engine: Literal["pandas", "polars"] = "polars", ) -> pd.DataFrame | pl.DataFrame: """ - Anti-pattern: Equivalent to scan_experiment(...).collect(). Loads the full - scan result into memory and bypasses lazy optimizations. Prefer - scan_experiment(source) with .filter(), .select(), and .collect() only when - needed; use this only when the full result is explicitly required. + Load a directory of FITS into memory (anti-pattern). + + Equivalent to ``scan_experiment(...).collect()`` without lazy filtering. + Prefer ``scan_experiment`` with ``.filter()``, ``.select()``, and + ``.collect()`` unless the full result is explicitly required. """ file_path_obj = Path(file_path) if not file_path_obj.is_dir(): @@ -540,7 +587,10 @@ def read_experiment( msg = f"{file_path_obj} does not contain any FITS files." raise FileNotFoundError(msg) source = file_path_obj - polars_data = scan_experiment(source, header_items=header_list).collect() + polars_data = cast( + "pl.DataFrame", + scan_experiment(source, header_items=header_list).collect(), + ) if engine == "pandas": return polars_data.to_pandas() return polars_data diff --git a/python/pyref/loader.py b/python/pyref/loader.py index 07dd856..852f384 100644 --- a/python/pyref/loader.py +++ b/python/pyref/loader.py @@ -4,7 +4,7 @@ from functools import cached_property from pathlib import Path -from typing import TYPE_CHECKING, cast +from typing import TYPE_CHECKING, cast, overload import hvplot.polars # noqa: F401 import ipywidgets as widgets @@ -27,9 +27,6 @@ from typing import Literal -from typing import overload - - @pl.api.register_series_namespace("image") class ImageSeries: """ @@ -127,7 +124,7 @@ def max(self): def mean(self): """Return the mean image across all images in the series.""" images = np.stack([np.array(img) for img in self._s]) - mean_image = np.mean(images, axis=0) # type: ignore + mean_image = np.mean(images, axis=0) return mean_image.reshape((mean_image.shape[1], mean_image.shape[0])) @overload @@ -260,8 +257,16 @@ def __init__( if not path_list: msg = "paths must not be empty." raise ValueError(msg) - self.path = Path(directory).resolve() if directory is not None else path_list[0].parent - meta = read_fits(path_list, headers=default_keys, engine="polars") # type: ignore[arg-type] + self.path = ( + Path(directory).resolve() + if directory is not None + else path_list[0].parent + ) + meta = read_fits( + path_list, + headers=default_keys, + engine="polars", + ) # type: ignore[arg-type] self.meta = cast("pl.DataFrame", meta) else: if directory is None: @@ -310,14 +315,16 @@ def __init__( r = float(db_sum / bg_sum) if bg_sum else 0.0 dr = float(np.sqrt(r)) row = self.meta.row(i, named=True) - refl_rows.append({ - "file_name": row["file_name"], - "EPU Polarization": row.get("EPU Polarization"), - "Beamline Energy": row.get("Beamline Energy"), - "Q": row.get("Q"), - "r": r, - "dr": dr, - }) + refl_rows.append( + { + "file_name": row["file_name"], + "EPU Polarization": row.get("EPU Polarization"), + "Beamline Energy": row.get("Beamline Energy"), + "Q": row.get("Q"), + "r": r, + "dr": dr, + } + ) self.refl = pl.DataFrame(refl_rows) self.shape = len(self.meta) self._polarization: Literal["s", "p"] | None = None @@ -432,12 +439,10 @@ def plot_frame(frame): "connectionstyle": "angle,angleA=0,angleB=90,rad=20", } q_val = meta.get("Q", refl_row.get("Q", 0.0)) or 0.0 - e_val = meta.get("Beamline Energy", refl_row.get("Beamline Energy", 0.0)) or 0.0 - props = ( - f"Reflectivity = {refl:.3e}\n" - f"Q = {q_val:.3f}\n" - f"E = {e_val:.1f}" + e_val = ( + meta.get("Beamline Energy", refl_row.get("Beamline Energy", 0.0)) or 0.0 ) + props = f"Reflectivity = {refl:.3e}\nQ = {q_val:.3f}\nE = {e_val:.1f}" ax.annotate( props, xy=bs[::-1], diff --git a/release-please-config.json b/release-please-config.json new file mode 100644 index 0000000..34d437b --- /dev/null +++ b/release-please-config.json @@ -0,0 +1,10 @@ +{ + "$schema": "https://raw.githubusercontent.com/googleapis/release-please/main/schemas/config.json", + "packages": { + ".": { + "changelog-path": "CHANGELOG.md", + "release-type": "python", + "package-name": "pyref" + } + } +} diff --git a/src/bin/browser.rs b/src/bin/browser.rs index 0686676..312dfbd 100644 --- a/src/bin/browser.rs +++ b/src/bin/browser.rs @@ -30,10 +30,7 @@ fn resolve_existing_dir(path: &Path) -> Result { )); } if !path.is_dir() { - return Err(format!( - "Path is not a directory: {}.", - path.display() - )); + return Err(format!("Path is not a directory: {}.", path.display())); } let canonical = path .canonicalize() diff --git a/src/bin/tui/app.rs b/src/bin/tui/app.rs index ccdfd15..45e9a7c 100644 --- a/src/bin/tui/app.rs +++ b/src/bin/tui/app.rs @@ -8,9 +8,9 @@ use std::collections::{HashMap, HashSet}; use std::fs; use std::ops::{Deref, DerefMut}; use std::path::{Path, PathBuf}; +use std::sync::atomic::AtomicBool; use std::sync::mpsc; use std::sync::Arc; -use std::sync::atomic::AtomicBool; use std::thread; use std::time::Instant; @@ -22,10 +22,10 @@ use super::scan_type::{classify_scan_type, ReflectivityScanType}; use pyref::build_fits_stem; #[cfg(feature = "catalog")] use pyref::catalog::{ - catalog_file_count, get_scan_point_uid_by_source_path, - list_beamtime_entries, list_beamtime_entries_v2, list_beamtimes, query_files, query_scan_points, - register_beamtime, rename_file_in_catalog, update_beamspot, update_beamspot_scan_point, FileRow, - DEFAULT_INGEST_HEADER_ITEMS, resolve_catalog_path, + catalog_file_count, get_scan_point_uid_by_source_path, list_beamtime_entries, + list_beamtime_entries_v2, list_beamtimes, query_files, query_scan_points, register_beamtime, + rename_file_in_catalog, resolve_catalog_path, update_beamspot, update_beamspot_scan_point, + FileRow, DEFAULT_INGEST_HEADER_ITEMS, }; #[cfg(feature = "watch")] use pyref::catalog::{run_catalog_watcher, DEFAULT_DEBOUNCE_MS}; @@ -231,18 +231,21 @@ fn beamspot_stats(beamspots: &[(i64, i64)]) -> (f64, f64, f64, f64) { if n == 0.0 { return (0.0, 0.0, 0.0, 0.0); } - let (row_sum, col_sum): (i64, i64) = - beamspots.iter().fold((0i64, 0i64), |(r, c), &(ri, ci)| (r + ri, c + ci)); + let (row_sum, col_sum): (i64, i64) = beamspots + .iter() + .fold((0i64, 0i64), |(r, c), &(ri, ci)| (r + ri, c + ci)); let row_mean = row_sum as f64 / n; let col_mean = col_sum as f64 / n; if n < 2.0 { return (row_mean, 0.0, col_mean, 0.0); } - let (row_var, col_var) = beamspots.iter().fold((0.0f64, 0.0f64), |(rv, cv), &(ri, ci)| { - let dr = ri as f64 - row_mean; - let dc = ci as f64 - col_mean; - (rv + dr * dr, cv + dc * dc) - }); + let (row_var, col_var) = beamspots + .iter() + .fold((0.0f64, 0.0f64), |(rv, cv), &(ri, ci)| { + let dr = ri as f64 - row_mean; + let dc = ci as f64 - col_mean; + (rv + dr * dr, cv + dc * dc) + }); let row_std = (row_var / (n - 1.0)).sqrt(); let col_std = (col_var / (n - 1.0)).sqrt(); (row_mean, row_std, col_mean, col_std) @@ -370,7 +373,9 @@ fn beamspot_mean_std(rows: &[FileRow]) -> (f64, f64, f64, f64) { return (0.0, 0.0, 0.0, 0.0); } let n = vals.len() as f64; - let (row_sum, col_sum) = vals.iter().fold((0.0, 0.0), |(sr, sc), (r, c)| (sr + r, sc + c)); + let (row_sum, col_sum) = vals + .iter() + .fold((0.0, 0.0), |(sr, sc), (r, c)| (sr + r, sc + c)); let row_mean = row_sum / n; let col_mean = col_sum / n; let (row_var, col_var) = vals.iter().fold((0.0, 0.0), |(vr, vc), (r, c)| { @@ -1049,16 +1054,18 @@ impl App { db_path: PathBuf::new(), }, ); - navigator.stack.push(super::navigator::ScreenState::Launcher( - super::navigator::LauncherScreenState { - launcher_state: Some(launcher_state), - open_dir_active: false, - open_dir_path: String::new(), - open_dir_entries: vec![], - open_dir_list_state: ListState::default(), - open_dir_focus: OpenDirFocus::PathInput, - }, - )); + navigator + .stack + .push(super::navigator::ScreenState::Launcher( + super::navigator::LauncherScreenState { + launcher_state: Some(launcher_state), + open_dir_active: false, + open_dir_path: String::new(), + open_dir_entries: vec![], + open_dir_list_state: ListState::default(), + open_dir_focus: OpenDirFocus::PathInput, + }, + )); App { navigator, @@ -1150,7 +1157,9 @@ impl App { beamtime_state, super::catalog_handle::CatalogHandle::new(catalog_db), ); - navigator.stack.push(super::navigator::ScreenState::Explorer(explorer_state)); + navigator + .stack + .push(super::navigator::ScreenState::Explorer(explorer_state)); let mut app = App { navigator, @@ -1201,7 +1210,10 @@ impl App { pub fn clear_status_if_stale(&mut self) { const STATUS_TTL_SECS: u64 = 3; - if let (Some(_), Some(at)) = (self.status_message().as_ref(), *self.status_message_set_at()) { + if let (Some(_), Some(at)) = ( + self.status_message().as_ref(), + *self.status_message_set_at(), + ) { if at.elapsed().as_secs() >= STATUS_TTL_SECS { *self.status_message_mut() = None; *self.status_message_set_at_mut() = None; @@ -1252,19 +1264,21 @@ impl App { if !beamtimes.is_empty() { list_state.select(Some(0)); } - self.navigator.stack.push(super::navigator::ScreenState::Launcher( - super::navigator::LauncherScreenState { - launcher_state: Some(LauncherState { - beamtimes, - list_state, - }), - open_dir_active: false, - open_dir_path: String::new(), - open_dir_entries: vec![], - open_dir_list_state: ListState::default(), - open_dir_focus: OpenDirFocus::PathInput, - }, - )); + self.navigator + .stack + .push(super::navigator::ScreenState::Launcher( + super::navigator::LauncherScreenState { + launcher_state: Some(LauncherState { + beamtimes, + list_state, + }), + open_dir_active: false, + open_dir_path: String::new(), + open_dir_entries: vec![], + open_dir_list_state: ListState::default(), + open_dir_focus: OpenDirFocus::PathInput, + }, + )); #[cfg(feature = "watch")] { *self.catalog_watcher_mut() = None; @@ -1820,7 +1834,9 @@ impl App { pub fn expanded_files_display_order(&self, group: &GroupedProfileRow) -> Vec { use super::beamspot; let sort_col = self.expanded_files_sort_column.unwrap_or(1); - let sort_ord = self.expanded_files_sort_ordering.unwrap_or(cmp::Ordering::Less); + let sort_ord = self + .expanded_files_sort_ordering + .unwrap_or(cmp::Ordering::Less); let files = &group.file_rows; let (row_mean, row_std, col_mean, col_std) = beamspot_mean_std(files); let fit = beamspot::fit_beamspot_linear(files, group.scan_type); @@ -1862,17 +1878,19 @@ impl App { col_std, ) .1 - .cmp(&beamspot::beamspot_status( - rb.beam_row, - rb.beam_col, - beamspot::domain_for_row(rb, group.scan_type), - fit.as_ref(), - row_mean, - row_std, - col_mean, - col_std, - ) - .1), + .cmp( + &beamspot::beamspot_status( + rb.beam_row, + rb.beam_col, + beamspot::domain_for_row(rb, group.scan_type), + fit.as_ref(), + row_mean, + row_std, + col_mean, + col_std, + ) + .1, + ), _ => cmp::Ordering::Equal, }; if sort_ord == cmp::Ordering::Greater { @@ -1909,10 +1927,7 @@ impl App { } #[cfg(feature = "catalog")] - fn problem_display_indices( - group: &GroupedProfileRow, - display_order: &[usize], - ) -> Vec { + fn problem_display_indices(group: &GroupedProfileRow, display_order: &[usize]) -> Vec { use super::beamspot; let files = &group.file_rows; let (row_mean, row_std, col_mean, col_std) = beamspot_mean_std(files); @@ -2143,10 +2158,7 @@ impl App { let ok_ix = match Self::next_ok_display_index(group, &order, cur) { Some(ix) => ix, None => { - self.set_status( - "No next OK beamspot in this profile.".to_string(), - false, - ); + self.set_status("No next OK beamspot in this profile.".to_string(), false); return; } }; @@ -2244,22 +2256,22 @@ impl App { } let file_path = path.to_string_lossy().into_owned(); if let Err(e) = update_beamspot(&db_path, &file_path, beam_row, beam_col, beam_sigma) { - self.set_status(format!("Failed to store beamspot from preview: {}", e), true); + self.set_status( + format!("Failed to store beamspot from preview: {}", e), + true, + ); return; } if let Ok(Some(uid)) = get_scan_point_uid_by_source_path(&db_path, &file_path) { - let _ = update_beamspot_scan_point( - &db_path, - &uid, - beam_row, - beam_col, - beam_sigma, - ); + let _ = update_beamspot_scan_point(&db_path, &uid, beam_row, beam_col, beam_sigma); } let was_expanded = self.expanded_table_row.is_some(); let restore_selection = self.expanded_table_row.or(self.table_state.selected()); self.reload_from_catalog_impl(was_expanded, restore_selection); - self.set_status("Beamspot from preview stored; table updated.".to_string(), false); + self.set_status( + "Beamspot from preview stored; table updated.".to_string(), + false, + ); } pub fn send_preview_path_if_selected(&mut self) { @@ -2274,10 +2286,7 @@ impl App { if let Some(row) = g.file_rows.get(real_ix) { let frame1 = g.file_rows.iter().min_by_key(|r| r.frame_number); let profile_sigma = frame1.and_then(|r| r.beam_sigma); - let _ = tx.send(( - PathBuf::from(&row.file_path), - profile_sigma, - )); + let _ = tx.send((PathBuf::from(&row.file_path), profile_sigma)); } } } @@ -2313,36 +2322,22 @@ impl App { match pyref::io::image_mmap::materialize_image_from_path(path) { Ok((_, subtracted)) => { let (r, c) = pyref::beamfinding::locate_beam_simple(&subtracted); - let subtracted_f64: ndarray::Array2 = - subtracted.mapv(|x| x as f64); - let beam_sigma = pyref::gaussian_fit::fit_2d_gaussian( - &subtracted_f64, - None, - ) - .map(|f| (f.sigma_row + f.sigma_col) / 2.0) - .map(|s| s.clamp(0.5, 20.0)); - if let Err(e) = update_beamspot( - &db_path, - file_path, - r as i64, - c as i64, - beam_sigma, - ) { + let subtracted_f64: ndarray::Array2 = subtracted.mapv(|x| x as f64); + let beam_sigma = pyref::gaussian_fit::fit_2d_gaussian(&subtracted_f64, None) + .map(|f| (f.sigma_row + f.sigma_col) / 2.0) + .map(|s| s.clamp(0.5, 20.0)); + if let Err(e) = + update_beamspot(&db_path, file_path, r as i64, c as i64, beam_sigma) + { self.set_status( format!("Failed to store beamspot for {}: {}", file_path, e), true, ); return; } - if let Ok(Some(uid)) = - get_scan_point_uid_by_source_path(&db_path, file_path) - { + if let Ok(Some(uid)) = get_scan_point_uid_by_source_path(&db_path, file_path) { let _ = update_beamspot_scan_point( - &db_path, - &uid, - r as i64, - c as i64, - beam_sigma, + &db_path, &uid, r as i64, c as i64, beam_sigma, ); } beamspots.push((r as i64, c as i64)); @@ -2350,21 +2345,11 @@ impl App { } Err(e) => { err_count += 1; - self.set_status( - format!("Failed to load {}: {}", file_path, e), - true, - ); + self.set_status(format!("Failed to load {}: {}", file_path, e), true); } } } - self.reload_from_catalog_impl( - was_expanded, - if was_expanded { - None - } else { - Some(idx) - }, - ); + self.reload_from_catalog_impl(was_expanded, if was_expanded { None } else { Some(idx) }); let msg = if beamspots.is_empty() { if err_count == 0 { format!("Materialized {} file(s); beamspots stored.", ok) @@ -2446,7 +2431,9 @@ impl App { ) { #[cfg(feature = "catalog")] { - let saved_expanded_row = preserve_expansion.then_some(self.expanded_table_row).flatten(); + let saved_expanded_row = preserve_expansion + .then_some(self.expanded_table_row) + .flatten(); let saved_expanded_file_ix = if preserve_expansion { self.expanded_selected_file_index } else { @@ -2630,7 +2617,9 @@ impl App { // Handle ingest for top BeamtimeState on stack (including ingest-in-progress from Explorer) // Extract what we need from the mutable state first let (ingest_result, bt_path_cloned, data_root_cloned) = { - if let Some(super::navigator::ScreenState::Beamtime(bt_state)) = self.navigator.stack.last_mut() { + if let Some(super::navigator::ScreenState::Beamtime(bt_state)) = + self.navigator.stack.last_mut() + { let mut progress_updates = Vec::new(); if let Some(ref progress_rx) = bt_state.ingest_progress_rx { while let Ok((cur, tot)) = progress_rx.try_recv() { @@ -2652,7 +2641,11 @@ impl App { bt_state.ingest_progress_rx = None; bt_state.ingest_progress = None; bt_state.loading_state = LoadingState::Idle; - (Some(result), bt_state.beamtime_path.clone(), bt_state.data_root.clone()) + ( + Some(result), + bt_state.beamtime_path.clone(), + bt_state.data_root.clone(), + ) } else { (None, PathBuf::new(), None) }; @@ -2673,18 +2666,38 @@ impl App { let _ = register_beamtime(&bt_path_cloned, file_count); // Reload from DATA catalog and apply it - if let Some((groups, samples, tags, scans)) = - self.load_beamtime_from_catalog(&bt_path_cloned, data_root_cloned.as_ref(), &self.navigator.catalog) - { - if let Some(super::navigator::ScreenState::Beamtime(bt_state)) = self.navigator.stack.last_mut() { + if let Some((groups, samples, tags, scans)) = self.load_beamtime_from_catalog( + &bt_path_cloned, + data_root_cloned.as_ref(), + &self.navigator.catalog, + ) { + if let Some(super::navigator::ScreenState::Beamtime(bt_state)) = + self.navigator.stack.last_mut() + { bt_state.all_groups = groups; bt_state.samples = samples; bt_state.tags = tags; bt_state.scans = scans; bt_state.has_catalog = true; - bt_state.sample_state.select(if !bt_state.samples.is_empty() { Some(0) } else { None }); - bt_state.tag_state.select(if !bt_state.tags.is_empty() { Some(0) } else { None }); - bt_state.scan_list_state.select(if !bt_state.scans.is_empty() { Some(0) } else { None }); + bt_state + .sample_state + .select(if !bt_state.samples.is_empty() { + Some(0) + } else { + None + }); + bt_state.tag_state.select(if !bt_state.tags.is_empty() { + Some(0) + } else { + None + }); + bt_state + .scan_list_state + .select(if !bt_state.scans.is_empty() { + Some(0) + } else { + None + }); // Inline refresh to avoid self borrow conflict bt_state.filtered_groups = Self::filter_groups( &bt_state.all_groups, @@ -2704,12 +2717,15 @@ impl App { self.sync_explorer_catalog_columns(); } Err(e) => { - if let Some(super::navigator::ScreenState::Beamtime(bt_state)) = self.navigator.stack.last_mut() { + if let Some(super::navigator::ScreenState::Beamtime(bt_state)) = + self.navigator.stack.last_mut() + { let path = bt_state .pending_ingest_path .take() .unwrap_or_else(|| bt_state.beamtime_path.clone()); - bt_state.set_app_error(super::error::TuiError::ingest_failed(path, e, None)); + bt_state + .set_app_error(super::error::TuiError::ingest_failed(path, e, None)); } } } @@ -2878,8 +2894,7 @@ impl App { let len = self.tags.len(); if len > 0 { let i = self.tag_state.selected().unwrap_or(0); - self.tag_state - .select(Some(if i == 0 { 0 } else { i - 1 })); + self.tag_state.select(Some(if i == 0 { 0 } else { i - 1 })); } } Focus::ScanList => { @@ -2975,7 +2990,11 @@ impl App { let max_offset = self .expanded_table_row .and_then(|i| self.group_at_display_index(i)) - .map(|g| g.file_rows.len().saturating_sub(self.last_expanded_files_visible.max(1))) + .map(|g| { + g.file_rows + .len() + .saturating_sub(self.last_expanded_files_visible.max(1)) + }) .unwrap_or(0); self.expanded_files_scroll_offset = pos.min(max_offset); self.needs_redraw = true; @@ -3274,7 +3293,8 @@ impl App { let bt_path = entry.path.clone(); let data_root = state.data_root.clone(); // Get experimentalist name from parent directory name - let experimentalist = bt_path.parent() + let experimentalist = bt_path + .parent() .and_then(|p| p.file_name()) .and_then(|n| n.to_str()) .map(|s| s.to_string()); @@ -3312,8 +3332,11 @@ impl App { let data_root = state.data_root.clone(); let policy = state.layout_policy.policy_for(&entry.name).clone(); - let modal = super::explorer::modal::ModalState::new(&expt_name, data_root, &policy); - self.navigator.stack.push(super::navigator::ScreenState::ConfigModal(modal)); + let modal = + super::explorer::modal::ModalState::new(&expt_name, data_root, &policy); + self.navigator + .stack + .push(super::navigator::ScreenState::ConfigModal(modal)); self.needs_redraw = true; } } @@ -3323,7 +3346,12 @@ impl App { /// Phase 4: Start auto-ingest of a beamtime directory. /// Spawns background ingest thread and pushes BeamtimeState with ingest in progress. #[cfg(feature = "catalog")] - fn start_beamtime_ingest(&mut self, bt_path: PathBuf, data_root: Option, experimentalist: Option) { + fn start_beamtime_ingest( + &mut self, + bt_path: PathBuf, + data_root: Option, + experimentalist: Option, + ) { let cancel_flag = Arc::new(AtomicBool::new(false)); let (progress_tx, progress_rx) = mpsc::channel::<(u32, u32)>(); let (done_tx, done_rx) = mpsc::channel::>(); @@ -3416,22 +3444,42 @@ impl App { // If data_root is present, try to load from DATA catalog immediately if let Some(ref root) = data_root { - if let Some((groups, samples, tags, scans)) = - self.load_beamtime_from_catalog(&bt_state.beamtime_path, Some(root), &self.navigator.catalog) - { + if let Some((groups, samples, tags, scans)) = self.load_beamtime_from_catalog( + &bt_state.beamtime_path, + Some(root), + &self.navigator.catalog, + ) { bt_state.all_groups = groups; bt_state.samples = samples; bt_state.tags = tags; bt_state.scans = scans; bt_state.has_catalog = true; - bt_state.sample_state.select(if !bt_state.samples.is_empty() { Some(0) } else { None }); - bt_state.tag_state.select(if !bt_state.tags.is_empty() { Some(0) } else { None }); - bt_state.scan_list_state.select(if !bt_state.scans.is_empty() { Some(0) } else { None }); + bt_state + .sample_state + .select(if !bt_state.samples.is_empty() { + Some(0) + } else { + None + }); + bt_state.tag_state.select(if !bt_state.tags.is_empty() { + Some(0) + } else { + None + }); + bt_state + .scan_list_state + .select(if !bt_state.scans.is_empty() { + Some(0) + } else { + None + }); self.refresh_filtered_beamtime(&mut bt_state); } } - self.navigator.stack.push(super::navigator::ScreenState::Beamtime(bt_state)); + self.navigator + .stack + .push(super::navigator::ScreenState::Beamtime(bt_state)); self.needs_redraw = true; } @@ -3442,7 +3490,12 @@ impl App { beamtime_path: &PathBuf, data_root: Option<&PathBuf>, catalog: &CatalogHandle, - ) -> Option<(Vec, Vec, Vec, Vec<(u32, String)>)> { + ) -> Option<( + Vec, + Vec, + Vec, + Vec<(u32, String)>, + )> { if data_root.is_none() { return None; } @@ -3521,9 +3574,13 @@ impl App { } let st = handle.catalog_status(&e.path); e.catalog_status = match st { - pyref::catalog::DbCatalogStatus::Indexed => super::explorer::CatalogStatus::Indexed, + pyref::catalog::DbCatalogStatus::Indexed => { + super::explorer::CatalogStatus::Indexed + } pyref::catalog::DbCatalogStatus::Stale => super::explorer::CatalogStatus::Stale, - pyref::catalog::DbCatalogStatus::NotIndexed => super::explorer::CatalogStatus::NotIndexed, + pyref::catalog::DbCatalogStatus::NotIndexed => { + super::explorer::CatalogStatus::NotIndexed + } }; } } @@ -3533,27 +3590,32 @@ impl App { #[cfg(feature = "catalog")] fn restart_explorer_fs_scan(&mut self) { - let snapshot = self.navigator.stack.iter().find_map(|s| { - if let super::navigator::ScreenState::Explorer(ex) = s { - Some(ex) - } else { - None - } - }).map(|ex| { - let at_root = ex.current_dir == ex.data_root; - let paths: Vec = ex - .entries - .iter() - .filter(|e| e.kind == super::explorer::EntryKind::Experimentalist) - .map(|e| e.path.clone()) - .collect(); - ( - at_root, - ex.data_root.clone(), - ex.layout_policy.clone(), - paths, - ) - }); + let snapshot = self + .navigator + .stack + .iter() + .find_map(|s| { + if let super::navigator::ScreenState::Explorer(ex) = s { + Some(ex) + } else { + None + } + }) + .map(|ex| { + let at_root = ex.current_dir == ex.data_root; + let paths: Vec = ex + .entries + .iter() + .filter(|e| e.kind == super::explorer::EntryKind::Experimentalist) + .map(|e| e.path.clone()) + .collect(); + ( + at_root, + ex.data_root.clone(), + ex.layout_policy.clone(), + paths, + ) + }); let Some((at_root, data_root, layout, paths)) = snapshot else { self.explorer_fs_rx = None; return; @@ -3567,9 +3629,7 @@ impl App { thread::spawn(move || { for path in paths { let (bt, fits) = super::explorer::heuristic::scan_experimentalist_subtree( - &path, - &data_root, - &layout, + &path, &data_root, &layout, ); let _ = tx.send(ExplorerFsUpdate { path, diff --git a/src/bin/tui/catalog_handle.rs b/src/bin/tui/catalog_handle.rs index 8cb7254..35530ac 100644 --- a/src/bin/tui/catalog_handle.rs +++ b/src/bin/tui/catalog_handle.rs @@ -2,9 +2,9 @@ use std::path::{Path, PathBuf}; #[cfg(feature = "catalog")] use pyref::catalog::{ - list_experimentalists, list_beamtimes_for_expt, catalog_status_for_path, - list_beamtime_entries_v2, query_scan_points, - ExptMeta, BeamtimeMeta, DbCatalogStatus, BeamtimeEntries, FileRow, CatalogFilter, + catalog_status_for_path, list_beamtime_entries_v2, list_beamtimes_for_expt, + list_experimentalists, query_scan_points, BeamtimeEntries, BeamtimeMeta, CatalogFilter, + DbCatalogStatus, ExptMeta, FileRow, }; #[derive(Debug)] @@ -33,7 +33,11 @@ impl CatalogHandle { } #[cfg(feature = "catalog")] - pub fn query_scan_points(&self, beamtime_path: &Path, filter: Option<&CatalogFilter>) -> Vec { + pub fn query_scan_points( + &self, + beamtime_path: &Path, + filter: Option<&CatalogFilter>, + ) -> Vec { query_scan_points(&self.db_path, beamtime_path, filter).unwrap_or_default() } diff --git a/src/bin/tui/explorer/heuristic.rs b/src/bin/tui/explorer/heuristic.rs index 0028ab5..17fbec1 100644 --- a/src/bin/tui/explorer/heuristic.rs +++ b/src/bin/tui/explorer/heuristic.rs @@ -180,10 +180,7 @@ pub fn classify_entry(path: &Path, data_root: &Path, layout: &NasLayout) -> Entr 2.. => { // Depth 2+: check parent experimentalist policy if let Some(parent) = path.parent() { - let parent_name = parent - .file_name() - .and_then(|n| n.to_str()) - .unwrap_or(""); + let parent_name = parent.file_name().and_then(|n| n.to_str()).unwrap_or(""); let policy = layout.policy_for(parent_name); match policy { @@ -197,8 +194,7 @@ pub fn classify_entry(path: &Path, data_root: &Path, layout: &NasLayout) -> Entr } } ExptPolicy::Custom { - pattern: Some(pat), - .. + pattern: Some(pat), .. } => { // Custom pattern provided if let Ok(regex) = Regex::new(pat) { @@ -229,10 +225,7 @@ pub fn classify_entry(path: &Path, data_root: &Path, layout: &NasLayout) -> Entr pub fn needs_resolution(experimentalist: &str, layout: &NasLayout) -> bool { matches!( layout.policy_for(experimentalist), - ExptPolicy::Custom { - pattern: None, - .. - } + ExptPolicy::Custom { pattern: None, .. } ) } diff --git a/src/bin/tui/explorer/mod.rs b/src/bin/tui/explorer/mod.rs index c7a9c86..fe02314 100644 --- a/src/bin/tui/explorer/mod.rs +++ b/src/bin/tui/explorer/mod.rs @@ -104,7 +104,8 @@ impl ExplorerState { continue; } - let kind = heuristic::classify_entry(&path, &self.data_root, &self.layout_policy); + let kind = + heuristic::classify_entry(&path, &self.data_root, &self.layout_policy); // Skip entries starting with dot (hidden files/dirs) if name_str.starts_with('.') { @@ -166,9 +167,7 @@ impl ExplorerState { /// Get the selected entry. pub fn selected_entry(&self) -> Option<&DirEntry> { - self.list_state - .selected() - .and_then(|i| self.entries.get(i)) + self.list_state.selected().and_then(|i| self.entries.get(i)) } /// Move selection down. @@ -217,30 +216,24 @@ impl ExplorerState { self.entries.sort_by(|a, b| { let cmp = match self.sort_col { ExplorerSort::Name => a.name.cmp(&b.name), - ExplorerSort::Modified => { - match (a.modified, b.modified) { - (Some(t_a), Some(t_b)) => t_a.cmp(&t_b), - (Some(_), None) => std::cmp::Ordering::Greater, - (None, Some(_)) => std::cmp::Ordering::Less, - (None, None) => std::cmp::Ordering::Equal, - } - } - ExplorerSort::Size => { - match (a.size_bytes, b.size_bytes) { - (Some(sz_a), Some(sz_b)) => sz_a.cmp(&sz_b), - (Some(_), None) => std::cmp::Ordering::Greater, - (None, Some(_)) => std::cmp::Ordering::Less, - (None, None) => std::cmp::Ordering::Equal, - } - } - ExplorerSort::FileCount => { - match (a.fits_count, b.fits_count) { - (Some(cnt_a), Some(cnt_b)) => cnt_a.cmp(&cnt_b), - (Some(_), None) => std::cmp::Ordering::Greater, - (None, Some(_)) => std::cmp::Ordering::Less, - (None, None) => std::cmp::Ordering::Equal, - } - } + ExplorerSort::Modified => match (a.modified, b.modified) { + (Some(t_a), Some(t_b)) => t_a.cmp(&t_b), + (Some(_), None) => std::cmp::Ordering::Greater, + (None, Some(_)) => std::cmp::Ordering::Less, + (None, None) => std::cmp::Ordering::Equal, + }, + ExplorerSort::Size => match (a.size_bytes, b.size_bytes) { + (Some(sz_a), Some(sz_b)) => sz_a.cmp(&sz_b), + (Some(_), None) => std::cmp::Ordering::Greater, + (None, Some(_)) => std::cmp::Ordering::Less, + (None, None) => std::cmp::Ordering::Equal, + }, + ExplorerSort::FileCount => match (a.fits_count, b.fits_count) { + (Some(cnt_a), Some(cnt_b)) => cnt_a.cmp(&cnt_b), + (Some(_), None) => std::cmp::Ordering::Greater, + (None, Some(_)) => std::cmp::Ordering::Less, + (None, None) => std::cmp::Ordering::Equal, + }, }; if self.sort_asc { diff --git a/src/bin/tui/explorer/modal.rs b/src/bin/tui/explorer/modal.rs index 461b220..82b2e8a 100644 --- a/src/bin/tui/explorer/modal.rs +++ b/src/bin/tui/explorer/modal.rs @@ -1,10 +1,10 @@ -use regex::Regex; use ratatui::{ layout::{Constraint, Direction, Layout, Rect}, text::{Line, Span}, widgets::{Block, Paragraph}, Frame, }; +use regex::Regex; use std::path::PathBuf; use super::heuristic::ExptPolicy; @@ -44,7 +44,10 @@ impl ModalState { pattern: Some(pat), depth, } => (depth.to_string(), pat.clone()), - ExptPolicy::Custom { pattern: None, depth } => (depth.to_string(), "".to_string()), + ExptPolicy::Custom { + pattern: None, + depth, + } => (depth.to_string(), "".to_string()), ExptPolicy::Ignored => ("2".to_string(), "".to_string()), }; @@ -208,10 +211,8 @@ pub fn render_modal(f: &mut Frame, area: Rect, state: &ModalState, _theme: &str) }; // Draw outer block - let outer_block = Block::bordered().title(format!( - " Configure: {} ", - state.target_experimentalist - )); + let outer_block = + Block::bordered().title(format!(" Configure: {} ", state.target_experimentalist)); f.render_widget(outer_block, modal_rect); let inner = Rect { @@ -237,7 +238,10 @@ pub fn render_modal(f: &mut Frame, area: Rect, state: &ModalState, _theme: &str) } else { " " }; - let depth_line = Line::from(format!("{}Depth (default 2): {}", depth_style, state.depth_input)); + let depth_line = Line::from(format!( + "{}Depth (default 2): {}", + depth_style, state.depth_input + )); f.render_widget(Paragraph::new(depth_line), chunks[0]); // Pattern input @@ -256,7 +260,10 @@ pub fn render_modal(f: &mut Frame, area: Rect, state: &ModalState, _theme: &str) if state.pattern_valid { Span::raw(pattern_color_text) } else { - Span::styled(pattern_color_text, ratatui::style::Style::new().fg(ratatui::style::Color::Red)) + Span::styled( + pattern_color_text, + ratatui::style::Style::new().fg(ratatui::style::Color::Red), + ) }, ]); f.render_widget(Paragraph::new(pattern_line), chunks[1]); @@ -280,19 +287,28 @@ pub fn render_modal(f: &mut Frame, area: Rect, state: &ModalState, _theme: &str) let choice_text = vec![ Line::from(vec![ if state.choice == ModalChoice::CustomPattern { - Span::styled("[1] Custom pattern", ratatui::style::Style::new().fg(ratatui::style::Color::Cyan)) + Span::styled( + "[1] Custom pattern", + ratatui::style::Style::new().fg(ratatui::style::Color::Cyan), + ) } else { Span::raw("[1] Custom pattern") }, Span::raw(" "), if state.choice == ModalChoice::MarkIgnored { - Span::styled("[2] Mark ignored", ratatui::style::Style::new().fg(ratatui::style::Color::Cyan)) + Span::styled( + "[2] Mark ignored", + ratatui::style::Style::new().fg(ratatui::style::Color::Cyan), + ) } else { Span::raw("[2] Mark ignored") }, Span::raw(" "), if state.choice == ModalChoice::AcceptDefault { - Span::styled("[3] Accept default", ratatui::style::Style::new().fg(ratatui::style::Color::Cyan)) + Span::styled( + "[3] Accept default", + ratatui::style::Style::new().fg(ratatui::style::Color::Cyan), + ) } else { Span::raw("[3] Accept default") }, diff --git a/src/bin/tui/mod.rs b/src/bin/tui/mod.rs index 0184af3..b2d9596 100644 --- a/src/bin/tui/mod.rs +++ b/src/bin/tui/mod.rs @@ -1,18 +1,18 @@ mod app; +#[cfg(feature = "catalog")] +mod beamspot; mod catalog_handle; mod config; mod error; +mod explorer; pub mod keymap; mod navigator; mod run; #[cfg(feature = "catalog")] -mod beamspot; -#[cfg(feature = "catalog")] mod scan_type; pub mod terminal_guard; mod theme; mod ui; -mod explorer; pub mod preview; diff --git a/src/bin/tui/navigator.rs b/src/bin/tui/navigator.rs index 5ab6f2f..fa525e9 100644 --- a/src/bin/tui/navigator.rs +++ b/src/bin/tui/navigator.rs @@ -1,15 +1,15 @@ -use super::catalog_handle::CatalogHandle; use super::app::{ - AppMode, BeamtimeBodyRects, Focus, GroupedProfileRow, LoadingState, OpenDirFocus, - WatcherEvent, LauncherState, + AppMode, BeamtimeBodyRects, Focus, GroupedProfileRow, LauncherState, LoadingState, + OpenDirFocus, WatcherEvent, }; +use super::catalog_handle::CatalogHandle; +use ratatui::layout::Rect; +use ratatui::widgets::{ListState, ScrollbarState, TableState}; use std::cmp; use std::collections::HashSet; use std::path::PathBuf; use std::sync::mpsc; use std::time::Instant; -use ratatui::layout::Rect; -use ratatui::widgets::{ListState, ScrollbarState, TableState}; #[cfg(feature = "watch")] use pyref::catalog::WatchHandle; diff --git a/src/bin/tui/preview.rs b/src/bin/tui/preview.rs index 92a2c4a..2b2e15c 100644 --- a/src/bin/tui/preview.rs +++ b/src/bin/tui/preview.rs @@ -81,8 +81,8 @@ fn load_preview_data( roi: Option<(usize, usize, usize, usize)>, profile_sigma: Option, ) -> Result { - let (_, subtracted) = pyref::io::image_mmap::materialize_image_from_path(path) - .map_err(|e| e.to_string())?; + let (_, subtracted) = + pyref::io::image_mmap::materialize_image_from_path(path).map_err(|e| e.to_string())?; let height = subtracted.nrows(); let width = subtracted.ncols(); let subtracted_f64: Array2 = subtracted.mapv(|x| x as f64); @@ -106,20 +106,22 @@ fn load_preview_data( .max(1.0); let left_rgba = pyref::colormap::array2_i64_to_rgba(&subtracted, Some((0, left_max)), true) .ok_or("Colormap failed")?; - let mid_rgba = - pyref::colormap::array2_f32_to_rgba(&filtered, Some((0.0, mid_max)), true) - .ok_or("Colormap failed")?; + let mid_rgba = pyref::colormap::array2_f32_to_rgba(&filtered, Some((0.0, mid_max)), true) + .ok_or("Colormap failed")?; let (crop, cw, ch, _) = crop_4sigma(&filtered, fit, height, width); let right_max = crop .iter() .copied() .fold(f32::NEG_INFINITY, f32::max) .max(1.0); - let right_rgba = - pyref::colormap::array2_f32_to_rgba(&crop, Some((0.0, right_max)), true) - .ok_or("Colormap failed")?; - let crop_col_sum = (0..cw).map(|j| (0..ch).map(|i| crop[[i, j]]).sum()).collect(); - let crop_row_sum = (0..ch).map(|i| (0..cw).map(|j| crop[[i, j]]).sum()).collect(); + let right_rgba = pyref::colormap::array2_f32_to_rgba(&crop, Some((0.0, right_max)), true) + .ok_or("Colormap failed")?; + let crop_col_sum = (0..cw) + .map(|j| (0..ch).map(|i| crop[[i, j]]).sum()) + .collect(); + let crop_row_sum = (0..ch) + .map(|i| (0..cw).map(|j| crop[[i, j]]).sum()) + .collect(); Ok(PreviewData { path: path.to_path_buf(), left_rgba, @@ -160,7 +162,12 @@ fn crop_4sigma( fit: Option, height: usize, width: usize, -) -> (Array2, usize, usize, Option) { +) -> ( + Array2, + usize, + usize, + Option, +) { let (cr, cc, half_r, half_c) = if let Some(f) = fit { let hr = (CROP_SIGMA_MULT * f.sigma_row).ceil() as i32; let hc = (CROP_SIGMA_MULT * f.sigma_col).ceil() as i32; @@ -192,8 +199,8 @@ fn crop_4sigma( let (ch, cw) = (crop.nrows(), crop.ncols()); return (crop, cw, ch, fit); } - let fallback = Array2::from_shape_vec((2, 2), vec![0.0f32; 4]) - .expect("fallback 2x2 shape is valid"); + let fallback = + Array2::from_shape_vec((2, 2), vec![0.0f32; 4]).expect("fallback 2x2 shape is valid"); return (fallback, 2, 2, fit); } let crop = filtered.slice(ndarray::s![r0..r1, c0..c1]).to_owned(); @@ -242,7 +249,12 @@ pub fn run_preview_window_on_first_path( "FITS preview", native_options, Box::new(move |cc| { - Ok(Box::new(PreviewApp::new(cc, forward_rx, beamspot_tx, cmd_tx))) + Ok(Box::new(PreviewApp::new( + cc, + forward_rx, + beamspot_tx, + cmd_tx, + ))) }), ); } @@ -524,11 +536,8 @@ impl PreviewApp { { let image = egui::ColorImage::from_rgba_unmultiplied([self.right_w, self.right_h], rgba); - self.tex_right = Some(ctx.load_texture( - "preview_right", - image, - egui::TextureOptions::default(), - )); + self.tex_right = + Some(ctx.load_texture("preview_right", image, egui::TextureOptions::default())); } } } @@ -675,8 +684,7 @@ impl eframe::App for PreviewApp { panel_w * (self.left_h as f32 / self.left_w as f32).min(2.0), ); let response = ui.add( - egui::Image::from_texture((tex.id(), size)) - .sense(egui::Sense::drag()), + egui::Image::from_texture((tex.id(), size)).sense(egui::Sense::drag()), ); let rect = response.rect; if response.drag_started() { @@ -692,10 +700,7 @@ impl eframe::App for PreviewApp { .as_ref() .and_then(|p| self.zoom_per_path.get(p)) .map(|(r0, r1, c0, c1)| { - ( - c1.saturating_sub(*c0).max(1), - r1.saturating_sub(*r0).max(1), - ) + (c1.saturating_sub(*c0).max(1), r1.saturating_sub(*r0).max(1)) }) .unwrap_or((self.left_w, self.left_h)); self.marquee_img_w = img_w; @@ -719,8 +724,7 @@ impl eframe::App for PreviewApp { self.marquee_rect, ) { if self.marquee_img_w > 0 && self.marquee_img_h > 0 { - let visible = - self.zoom_per_path.get(&path).copied(); + let visible = self.zoom_per_path.get(&path).copied(); let (r0, c0) = Self::screen_to_image( start, rect, @@ -785,10 +789,8 @@ impl eframe::App for PreviewApp { && fr >= r0 as f32 && fr < r1 as f32; if in_view { - let cx = rect.min.x - + (fc - c0 as f32) / zw * rect.width(); - let cy = rect.min.y - + (fr - r0 as f32) / zh * rect.height(); + let cx = rect.min.x + (fc - c0 as f32) / zw * rect.width(); + let cy = rect.min.y + (fr - r0 as f32) / zh * rect.height(); let half = (CROP_SIGMA_MULT * f.sigma_row.max(f.sigma_col)) .ceil() .max(2.0) @@ -798,10 +800,8 @@ impl eframe::App for PreviewApp { let cr1 = (fr + half).max(r0 as f32).min(r1 as f32 - 1.0); let cc0 = (fc - half).max(c0 as f32).min(c1 as f32 - 1.0); let cc1 = (fc + half).max(c0 as f32).min(c1 as f32 - 1.0); - let x0 = - rect.min.x + (cc0 - c0 as f32) / zw * rect.width(); - let x1 = - rect.min.x + (cc1 - c0 as f32) / zw * rect.width(); + let x0 = rect.min.x + (cc0 - c0 as f32) / zw * rect.width(); + let x1 = rect.min.x + (cc1 - c0 as f32) / zw * rect.width(); let y0 = rect.min.y + (cr0 - r0 as f32) / zh * rect.height(); let y1 = @@ -822,7 +822,8 @@ impl eframe::App for PreviewApp { let half = (CROP_SIGMA_MULT * f.sigma_row.max(f.sigma_col)) .ceil() .max(2.0) - .min((self.left_h / 2).min(self.left_w / 2) as f64) as f32; + .min((self.left_h / 2).min(self.left_w / 2) as f64) + as f32; let c0 = (f.center_col as f32 - half) .max(0.0) .min((self.left_w - 1) as f32); @@ -842,10 +843,13 @@ impl eframe::App for PreviewApp { (cx, cy, Some((x0, y0, x1, y1))) } }; - if rect.width() > 0.0 && rect.height() > 0.0 + if rect.width() > 0.0 + && rect.height() > 0.0 && (zoom.is_none() - || (cx >= rect.min.x && cx <= rect.max.x - && cy >= rect.min.y && cy <= rect.max.y)) + || (cx >= rect.min.x + && cx <= rect.max.x + && cy >= rect.min.y + && cy <= rect.max.y)) { let stroke = egui::Stroke::new(1.0, egui::Color32::WHITE); ui.painter().line_segment( @@ -881,8 +885,7 @@ impl eframe::App for PreviewApp { panel_w * (self.mid_h as f32 / self.mid_w as f32).min(2.0), ); let response = ui.add( - egui::Image::from_texture((tex.id(), size)) - .sense(egui::Sense::drag()), + egui::Image::from_texture((tex.id(), size)).sense(egui::Sense::drag()), ); let rect = response.rect; if response.drag_started() { @@ -898,10 +901,7 @@ impl eframe::App for PreviewApp { .as_ref() .and_then(|p| self.zoom_per_path.get(p)) .map(|(r0, r1, c0, c1)| { - ( - c1.saturating_sub(*c0).max(1), - r1.saturating_sub(*r0).max(1), - ) + (c1.saturating_sub(*c0).max(1), r1.saturating_sub(*r0).max(1)) }) .unwrap_or((self.mid_w, self.mid_h)); self.marquee_img_w = img_w; @@ -925,8 +925,7 @@ impl eframe::App for PreviewApp { self.marquee_rect, ) { if self.marquee_img_w > 0 && self.marquee_img_h > 0 { - let visible = - self.zoom_per_path.get(&path).copied(); + let visible = self.zoom_per_path.get(&path).copied(); let (r0, c0) = Self::screen_to_image( start, rect, @@ -991,10 +990,8 @@ impl eframe::App for PreviewApp { && fr >= r0 as f32 && fr < r1 as f32; if in_view { - let cx = rect.min.x - + (fc - c0 as f32) / zw * rect.width(); - let cy = rect.min.y - + (fr - r0 as f32) / zh * rect.height(); + let cx = rect.min.x + (fc - c0 as f32) / zw * rect.width(); + let cy = rect.min.y + (fr - r0 as f32) / zh * rect.height(); let half = (CROP_SIGMA_MULT * f.sigma_row.max(f.sigma_col)) .ceil() .max(2.0) @@ -1004,10 +1001,8 @@ impl eframe::App for PreviewApp { let cr1 = (fr + half).max(r0 as f32).min(r1 as f32 - 1.0); let cc0 = (fc - half).max(c0 as f32).min(c1 as f32 - 1.0); let cc1 = (fc + half).max(c0 as f32).min(c1 as f32 - 1.0); - let x0 = - rect.min.x + (cc0 - c0 as f32) / zw * rect.width(); - let x1 = - rect.min.x + (cc1 - c0 as f32) / zw * rect.width(); + let x0 = rect.min.x + (cc0 - c0 as f32) / zw * rect.width(); + let x1 = rect.min.x + (cc1 - c0 as f32) / zw * rect.width(); let y0 = rect.min.y + (cr0 - r0 as f32) / zh * rect.height(); let y1 = @@ -1028,7 +1023,8 @@ impl eframe::App for PreviewApp { let half = (CROP_SIGMA_MULT * f.sigma_row.max(f.sigma_col)) .ceil() .max(2.0) - .min((self.mid_h / 2).min(self.mid_w / 2) as f64) as f32; + .min((self.mid_h / 2).min(self.mid_w / 2) as f64) + as f32; let c0 = (f.center_col as f32 - half) .max(0.0) .min((self.mid_w - 1) as f32); @@ -1048,10 +1044,13 @@ impl eframe::App for PreviewApp { (cx, cy, Some((x0, y0, x1, y1))) } }; - if rect.width() > 0.0 && rect.height() > 0.0 + if rect.width() > 0.0 + && rect.height() > 0.0 && (zoom.is_none() - || (cx >= rect.min.x && cx <= rect.max.x - && cy >= rect.min.y && cy <= rect.max.y)) + || (cx >= rect.min.x + && cx <= rect.max.x + && cy >= rect.min.y + && cy <= rect.max.y)) { let stroke = egui::Stroke::new(1.0, egui::Color32::WHITE); ui.painter().line_segment( @@ -1089,73 +1088,59 @@ impl eframe::App for PreviewApp { let trace_width = 48.0f32; let crop_size = (panel_w - trace_width - ui.spacing().item_spacing.x).max(1.0); - let draw_col_trace = - |ui: &mut egui::Ui, data: &[f32], w: f32, h: f32| { - let (rect, _) = - ui.allocate_exact_size(egui::vec2(w, h), egui::Sense::hover()); - let min_v = - data.iter().copied().fold(f32::INFINITY, f32::min); - let max_v = - data.iter().copied().fold(f32::NEG_INFINITY, f32::max); - let range = (max_v - min_v).max(1e-6); - let n = data.len(); - let mut pts: Vec = (0..n) - .map(|j| { - let x = rect.min.x - + (j as f32 / (n - 1).max(1) as f32) - * rect.width(); - let t = (data[j] - min_v) / range; - let y = rect.max.y - t * rect.height(); - egui::Pos2::new(x, y) - }) - .collect(); - if pts.len() == 1 { - pts.push(egui::Pos2::new(pts[0].x + 1.0, pts[0].y)); - } - if pts.len() >= 2 { - ui.painter().add(egui::Shape::line( - pts, - egui::Stroke::new( - 1.5, - egui::Color32::from_rgb(0, 200, 200), - ), - )); - } - }; + let draw_col_trace = |ui: &mut egui::Ui, data: &[f32], w: f32, h: f32| { + let (rect, _) = + ui.allocate_exact_size(egui::vec2(w, h), egui::Sense::hover()); + let min_v = data.iter().copied().fold(f32::INFINITY, f32::min); + let max_v = data.iter().copied().fold(f32::NEG_INFINITY, f32::max); + let range = (max_v - min_v).max(1e-6); + let n = data.len(); + let mut pts: Vec = (0..n) + .map(|j| { + let x = + rect.min.x + (j as f32 / (n - 1).max(1) as f32) * rect.width(); + let t = (data[j] - min_v) / range; + let y = rect.max.y - t * rect.height(); + egui::Pos2::new(x, y) + }) + .collect(); + if pts.len() == 1 { + pts.push(egui::Pos2::new(pts[0].x + 1.0, pts[0].y)); + } + if pts.len() >= 2 { + ui.painter().add(egui::Shape::line( + pts, + egui::Stroke::new(1.5, egui::Color32::from_rgb(0, 200, 200)), + )); + } + }; - let draw_row_trace = - |ui: &mut egui::Ui, data: &[f32], w: f32, h: f32| { - let (rect, _) = - ui.allocate_exact_size(egui::vec2(w, h), egui::Sense::hover()); - let min_v = - data.iter().copied().fold(f32::INFINITY, f32::min); - let max_v = - data.iter().copied().fold(f32::NEG_INFINITY, f32::max); - let range = (max_v - min_v).max(1e-6); - let n = data.len(); - let mut pts: Vec = (0..n) - .map(|i| { - let y = rect.min.y - + (i as f32 / (n - 1).max(1) as f32) - * rect.height(); - let t = (data[i] - min_v) / range; - let x = rect.min.x + t * rect.width(); - egui::Pos2::new(x, y) - }) - .collect(); - if pts.len() == 1 { - pts.push(egui::Pos2::new(pts[0].x, pts[0].y + 1.0)); - } - if pts.len() >= 2 { - ui.painter().add(egui::Shape::line( - pts, - egui::Stroke::new( - 1.5, - egui::Color32::from_rgb(0, 200, 200), - ), - )); - } - }; + let draw_row_trace = |ui: &mut egui::Ui, data: &[f32], w: f32, h: f32| { + let (rect, _) = + ui.allocate_exact_size(egui::vec2(w, h), egui::Sense::hover()); + let min_v = data.iter().copied().fold(f32::INFINITY, f32::min); + let max_v = data.iter().copied().fold(f32::NEG_INFINITY, f32::max); + let range = (max_v - min_v).max(1e-6); + let n = data.len(); + let mut pts: Vec = (0..n) + .map(|i| { + let y = + rect.min.y + (i as f32 / (n - 1).max(1) as f32) * rect.height(); + let t = (data[i] - min_v) / range; + let x = rect.min.x + t * rect.width(); + egui::Pos2::new(x, y) + }) + .collect(); + if pts.len() == 1 { + pts.push(egui::Pos2::new(pts[0].x, pts[0].y + 1.0)); + } + if pts.len() >= 2 { + ui.painter().add(egui::Shape::line( + pts, + egui::Stroke::new(1.5, egui::Color32::from_rgb(0, 200, 200)), + )); + } + }; let col_sum = self.crop_col_sum.clone(); let row_sum = self.crop_row_sum.clone(); @@ -1185,23 +1170,18 @@ impl eframe::App for PreviewApp { .map(|&(px, py)| { egui::Pos2::new( rect.min.x - + (px / self.right_w as f64) - as f32 - * rect.width(), + + (px / self.right_w as f64) as f32 + * rect.width(), rect.min.y - + (py / self.right_h as f64) - as f32 - * rect.height(), + + (py / self.right_h as f64) as f32 + * rect.height(), ) }) .collect(); if screen_pts.len() >= 2 { ui.painter().add(egui::Shape::line( screen_pts, - egui::Stroke::new( - 1.0, - egui::Color32::WHITE, - ), + egui::Stroke::new(1.0, egui::Color32::WHITE), )); } } diff --git a/src/bin/tui/run.rs b/src/bin/tui/run.rs index 5a10679..e111445 100644 --- a/src/bin/tui/run.rs +++ b/src/bin/tui/run.rs @@ -565,12 +565,7 @@ fn table_row_at(rect: Rect, row: u16, len: usize) -> usize { idx.min(len.saturating_sub(1)) } -fn expanded_file_index_at( - rect: Rect, - row: u16, - scroll_offset: usize, - file_count: usize, -) -> usize { +fn expanded_file_index_at(rect: Rect, row: u16, scroll_offset: usize, file_count: usize) -> usize { let data_y = rect.y + 3; if row < data_y || file_count == 0 { return 0; @@ -704,7 +699,9 @@ pub fn handle_event(app: &mut App, key: crossterm::event::KeyEvent) -> bool { return handle_explorer_event(app, key); } - if app.current_screen() == super::app::Screen::Beamtime && app.mode() == super::app::AppMode::Normal { + if app.current_screen() == super::app::Screen::Beamtime + && app.mode() == super::app::AppMode::Normal + { let action = keymap::from_key_event(key, &app.keymap); if action == Action::Cancel { if app.app_error.is_some() { @@ -717,7 +714,9 @@ pub fn handle_event(app: &mut App, key: crossterm::event::KeyEvent) -> bool { } // Phase 4: Cancel ingest if in progress #[cfg(feature = "catalog")] - if let Some(super::navigator::ScreenState::Beamtime(bt)) = app.navigator.stack.last_mut() { + if let Some(super::navigator::ScreenState::Beamtime(bt)) = + app.navigator.stack.last_mut() + { if bt.loading_state == super::app::LoadingState::IngestingDirectory { if let Some(ref flag) = bt.cancel_flag { flag.store(true, std::sync::atomic::Ordering::Relaxed); diff --git a/src/bin/tui/ui.rs b/src/bin/tui/ui.rs index dd8e0e3..ef3e073 100644 --- a/src/bin/tui/ui.rs +++ b/src/bin/tui/ui.rs @@ -299,7 +299,12 @@ fn render_open_dir_modal(frame: &mut Frame, app: &mut App, area: Rect, theme: Th let entry_names: Vec = app .open_dir_entries() .iter() - .map(|p| p.file_name().and_then(|n| n.to_str()).unwrap_or("?").to_string()) + .map(|p| { + p.file_name() + .and_then(|n| n.to_str()) + .unwrap_or("?") + .to_string() + }) .collect(); for name in &entry_names { items.push(ListItem::new(name.clone())); @@ -629,7 +634,12 @@ fn list_style(active: bool, theme: ThemeMode) -> ratatui::style::Style { } } -fn render_sample_list(frame: &mut Frame, app: &mut App, area: Rect, theme: ThemeMode) -> Option { +fn render_sample_list( + frame: &mut Frame, + app: &mut App, + area: Rect, + theme: ThemeMode, +) -> Option { let content_len = app.samples.len(); let viewport = (area.height.saturating_sub(2)).max(1) as usize; let needs_scrollbar = area.width > SCROLLBAR_WIDTH && content_len > viewport; @@ -751,7 +761,12 @@ fn render_tag_list(frame: &mut Frame, app: &mut App, area: Rect, theme: ThemeMod } } -fn render_scan_list(frame: &mut Frame, app: &mut App, area: Rect, theme: ThemeMode) -> Option { +fn render_scan_list( + frame: &mut Frame, + app: &mut App, + area: Rect, + theme: ThemeMode, +) -> Option { let content_len = app.scans.len(); let viewport = (area.height.saturating_sub(2)).max(1) as usize; let needs_scrollbar = area.width > SCROLLBAR_WIDTH && content_len > viewport; @@ -833,18 +848,21 @@ fn beamspot_mean_std(beamspots: &[(i64, i64)]) -> (f64, f64, f64, f64) { if n == 0.0 { return (0.0, 0.0, 0.0, 0.0); } - let (row_sum, col_sum): (i64, i64) = - beamspots.iter().fold((0i64, 0i64), |(r, c), &(ri, ci)| (r + ri, c + ci)); + let (row_sum, col_sum): (i64, i64) = beamspots + .iter() + .fold((0i64, 0i64), |(r, c), &(ri, ci)| (r + ri, c + ci)); let row_mean = row_sum as f64 / n; let col_mean = col_sum as f64 / n; if n < 2.0 { return (row_mean, 0.0, col_mean, 0.0); } - let (row_var, col_var) = beamspots.iter().fold((0.0f64, 0.0f64), |(rv, cv), &(ri, ci)| { - let dr = ri as f64 - row_mean; - let dc = ci as f64 - col_mean; - (rv + dr * dr, cv + dc * dc) - }); + let (row_var, col_var) = beamspots + .iter() + .fold((0.0f64, 0.0f64), |(rv, cv), &(ri, ci)| { + let dr = ri as f64 - row_mean; + let dc = ci as f64 - col_mean; + (rv + dr * dr, cv + dc * dc) + }); let row_std = (row_var / (n - 1.0)).sqrt(); let col_std = (col_var / (n - 1.0)).sqrt(); (row_mean, row_std, col_mean, col_std) @@ -880,8 +898,8 @@ fn render_expanded_file_list( let start = offset.min(display_order.len()); let take = (display_order.len().saturating_sub(start)).min(visible_rows); frame.render_widget(block, area); - let needs_scrollbar = inner.width > SCROLLBAR_WIDTH - && file_count > app.last_expanded_files_visible; + let needs_scrollbar = + inner.width > SCROLLBAR_WIDTH && file_count > app.last_expanded_files_visible; let (table_inner, scrollbar_area) = if needs_scrollbar { let chunks = Layout::default() .direction(Direction::Horizontal) @@ -1201,7 +1219,9 @@ fn render_explorer(frame: &mut Frame, app: &mut App, area: Rect, theme: ThemeMod let kind_str = match entry.kind { super::explorer::EntryKind::DataRoot => "DATA", super::explorer::EntryKind::Experimentalist => { - if entry.expt_resolution == Some(super::explorer::ExptResolution::NeedsResolution) { + if entry.expt_resolution + == Some(super::explorer::ExptResolution::NeedsResolution) + { "Expt ⚑" } else { "Expt" @@ -1248,7 +1268,11 @@ fn render_explorer(frame: &mut Frame, app: &mut App, area: Rect, theme: ThemeMod .highlight_style(list_style(true, theme)) .highlight_symbol(" "); - frame.render_stateful_widget(list, area, &mut app.explorer_state_mut().unwrap().list_state); + frame.render_stateful_widget( + list, + area, + &mut app.explorer_state_mut().unwrap().list_state, + ); } fn render_explorer_bottom(frame: &mut Frame, area: Rect, theme: ThemeMode) { diff --git a/src/catalog/beamtime_index.rs b/src/catalog/beamtime_index.rs index 41dfa81..fbea13f 100644 --- a/src/catalog/beamtime_index.rs +++ b/src/catalog/beamtime_index.rs @@ -2,7 +2,7 @@ use diesel::connection::SimpleConnection; use diesel::deserialize::{self, QueryableByName}; use diesel::sql_types::{BigInt, Nullable, Text}; use diesel::sqlite::{Sqlite, SqliteConnection}; -use diesel::{Connection, RunQueryDsl, sql_query}; +use diesel::{sql_query, Connection, RunQueryDsl}; use std::path::{Path, PathBuf}; use crate::catalog::{CatalogError, Result}; @@ -32,11 +32,10 @@ pub fn ensure_beamtime_index_dir() -> Result<()> { pub fn open_beamtime_index_db() -> Result { ensure_beamtime_index_dir()?; let path = beamtime_index_dir().join(BEAMTIME_INDEX_DB_NAME); - let s = path.to_str().ok_or_else(|| { - CatalogError::Validation("beamtime index path is not valid UTF-8".into()) - })?; - let mut conn = - SqliteConnection::establish(s).map_err(CatalogError::DieselConnection)?; + let s = path + .to_str() + .ok_or_else(|| CatalogError::Validation("beamtime index path is not valid UTF-8".into()))?; + let mut conn = SqliteConnection::establish(s).map_err(CatalogError::DieselConnection)?; conn.batch_execute(BEAMTIMES_TABLE) .map_err(CatalogError::Diesel)?; Ok(conn) @@ -58,11 +57,10 @@ impl QueryableByName for BeamtimeIdxEntry { pub fn list_beamtimes() -> Result> { let mut conn = open_beamtime_index_db()?; - let rows: Vec = sql_query( - "SELECT path, last_indexed_at FROM beamtimes ORDER BY last_indexed_at DESC", - ) - .load(&mut conn) - .map_err(CatalogError::Diesel)?; + let rows: Vec = + sql_query("SELECT path, last_indexed_at FROM beamtimes ORDER BY last_indexed_at DESC") + .load(&mut conn) + .map_err(CatalogError::Diesel)?; Ok(rows .into_iter() .map(|r| (PathBuf::from(r.path), r.last_indexed_at)) diff --git a/src/catalog/db.rs b/src/catalog/db.rs index 2df0d78..dcb0932 100644 --- a/src/catalog/db.rs +++ b/src/catalog/db.rs @@ -24,8 +24,7 @@ pub fn establish_connection(database_url: &Path) -> Result { let mut conn = SqliteConnection::establish(path_str).map_err(CatalogError::DieselConnection)?; conn.batch_execute("PRAGMA foreign_keys = ON;") .map_err(CatalogError::Diesel)?; - conn.run_pending_migrations(MIGRATIONS).map_err(|e| { - CatalogError::Migrations(format!("{e:?}")) - })?; + conn.run_pending_migrations(MIGRATIONS) + .map_err(|e| CatalogError::Migrations(format!("{e:?}")))?; Ok(conn) } diff --git a/src/catalog/explorer_query.rs b/src/catalog/explorer_query.rs index e36aad5..cd460e6 100644 --- a/src/catalog/explorer_query.rs +++ b/src/catalog/explorer_query.rs @@ -1,3 +1,12 @@ +//! Catalog paths and explorer-facing queries over the main SQLite catalog. +//! +//! [`catalog_status_for_path`] compares on-disk FITS mtimes against catalog rows. +//! [`list_experimentalists`] and [`list_beamtimes_for_expt`] are deliberate stubs until the +//! explorer lists experimentalists and beamtimes from the beamtime index (or equivalent +//! aggregates). Each stub keeps a compile-time-greppable `todo!` invocation behind `if false` so +//! the integration point stays obvious without panicking at runtime; `std::hint::black_box` +//! references parameters so the current empty results stay warning-free. + use std::path::Path; use crate::catalog::{open_catalog_db, paths, Result}; @@ -5,6 +14,18 @@ use crate::schema::beamtimes; use diesel::prelude::*; use diesel::OptionalExtension; +/// Expands to `todo!` with a stable `pyref catalog explorer` prefix for stub catalog explorer APIs. +macro_rules! catalog_explorer_todo { + ($fn:ident, $detail:literal) => { + todo!(concat!( + "pyref catalog explorer — ", + stringify!($fn), + ": ", + $detail + )) + }; +} + #[derive(Debug, Clone)] pub struct ExptMeta { pub name: String, @@ -27,15 +48,42 @@ pub enum DbCatalogStatus { NotIndexed, } -pub fn list_experimentalists(_db_path: &Path, _data_root: &Path) -> Result> { +/// Lists experimentalist directories under `data_root` with catalog-derived counts. +/// +/// Parameters are accepted for the eventual query surface: `db_path` selects the main catalog +/// SQLite file; `data_root` is the NAS or local root that contains experimentalist subfolders. +/// Today this returns an empty `Vec` without touching the database for listing; callers should +/// treat that as "no indexed experimentalists" until the stub is replaced. +#[allow(unreachable_code)] +pub fn list_experimentalists(db_path: &Path, data_root: &Path) -> Result> { + if false { + catalog_explorer_todo!( + list_experimentalists, + "return one ExptMeta per experimentalist under data_root with beamtime_count, fits_count, last_indexed from beamtime_index or catalog aggregates" + ); + } + std::hint::black_box((db_path, data_root)); Ok(Vec::new()) } +/// Lists beamtime directories for one `experimentalist` under `data_root` with optional catalog metadata. +/// +/// Parameters: `db_path` is the main catalog database; `data_root` is the explorer root; `experimentalist` +/// names the immediate child directory under `data_root` whose beamtime subfolders should be listed. +/// Today this returns an empty `Vec` until joined queries against the catalog or beamtime index exist. +#[allow(unreachable_code)] pub fn list_beamtimes_for_expt( - _db_path: &Path, - _data_root: &Path, - _experimentalist: &str, + db_path: &Path, + data_root: &Path, + experimentalist: &str, ) -> Result> { + if false { + catalog_explorer_todo!( + list_beamtimes_for_expt, + "return BeamtimeMeta rows for the experimentalist subtree (path, fits_count, last_indexed) joined against catalog or beamtime_index" + ); + } + std::hint::black_box((db_path, data_root, experimentalist)); Ok(Vec::new()) } diff --git a/src/catalog/ingest.rs b/src/catalog/ingest.rs index 2ccafa3..b803a2f 100644 --- a/src/catalog/ingest.rs +++ b/src/catalog/ingest.rs @@ -26,6 +26,7 @@ use crate::io::BtIngestRow; use crate::loader::read_multiple_fits_headers_only_rows; use crate::schema::{beamtimes, file_tags, files, frames, samples, scans, tags}; +use super::discover_paths_for_catalog_ingest; use super::ingest_progress::{ layout_and_groups_from_paths, BeamtimeIngestLayout, IngestProgress, IngestProgressSink, }; @@ -33,7 +34,6 @@ use super::layout::BeamtimeLayout; use super::parallelism::IngestParallelism; use super::zarr_write::{open_zarr_store, write_frame_raw}; use super::{db, paths, CatalogError, Result}; -use super::discover_paths_for_catalog_ingest; pub const DEFAULT_INGEST_HEADER_ITEMS: &[&str] = &[ "DATE", @@ -310,16 +310,11 @@ fn ingest_beamtime_inner( .map_err(CatalogError::FitsReadFailed)?; let mut rows: Vec = header_groups.into_iter().flatten().collect(); rows.sort_by(|a, b| { - ( - a.scan_number, - a.frame_number, - a.file_path.as_str(), - ) - .cmp(&( - b.scan_number, - b.frame_number, - b.file_path.as_str(), - )) + (a.scan_number, a.frame_number, a.file_path.as_str()).cmp(&( + b.scan_number, + b.frame_number, + b.file_path.as_str(), + )) }); if let Some(ref sink) = progress { @@ -328,7 +323,8 @@ fn ingest_beamtime_inner( }); } - let zstore = open_zarr_store(&zarr_path).map_err(|e| CatalogError::Validation(e.to_string()))?; + let zstore = + open_zarr_store(&zarr_path).map_err(|e| CatalogError::Validation(e.to_string()))?; let mut sample_cache: HashMap = HashMap::new(); let mut scan_cache: HashMap = HashMap::new(); @@ -376,8 +372,13 @@ fn ingest_beamtime_inner( } let unique_scans: HashSet = rows.iter().map(|r| r.scan_number as i32).collect(); for sn in &unique_scans { - let sk = scan_first_sample.get(sn).cloned().unwrap_or_else(|| "_".to_string()); - let rep_sample = *sample_cache.get(&sk).ok_or_else(|| diesel::result::Error::NotFound)?; + let sk = scan_first_sample + .get(sn) + .cloned() + .unwrap_or_else(|| "_".to_string()); + let rep_sample = *sample_cache + .get(&sk) + .ok_or_else(|| diesel::result::Error::NotFound)?; diesel::insert_into(scans::table) .values(( scans::beamtime_id.eq(beamtime_id), @@ -431,12 +432,10 @@ fn ingest_beamtime_inner( files::scan_number.eq(scan_no), files::frame_number.eq(row.frame_number as i32), files::nas_uri.eq(row.file_path.as_str()), - files::filename.eq( - Path::new(&row.file_path) - .file_name() - .and_then(|s| s.to_str()) - .unwrap_or(""), - ), + files::filename.eq(Path::new(&row.file_path) + .file_name() + .and_then(|s| s.to_str()) + .unwrap_or("")), files::parse_flag.eq(parse_flag.as_deref()), files::data_offset.eq(row.data_offset), files::naxis1.eq(row.naxis1 as i32), @@ -478,10 +477,7 @@ fn ingest_beamtime_inner( .optional()?; if ft_exists.is_none() { diesel::insert_into(file_tags::table) - .values(( - file_tags::file_id.eq(file_id), - file_tags::tag_id.eq(tid), - )) + .values((file_tags::file_id.eq(file_id), file_tags::tag_id.eq(tid))) .execute(conn)?; } } @@ -542,10 +538,7 @@ fn ingest_beamtime_inner( .collect(); let mut by_scan: BTreeMap> = BTreeMap::new(); for (i, r) in rows.iter().enumerate() { - by_scan - .entry(r.scan_number as i32) - .or_default() - .push(i); + by_scan.entry(r.scan_number as i32).or_default().push(i); } let scan_order: Vec = by_scan.keys().copied().collect(); @@ -554,8 +547,7 @@ fn ingest_beamtime_inner( .par_iter() .map(|&sn| -> Result)>> { let idxs: Vec = by_scan.get(&sn).cloned().unwrap_or_default(); - idxs - .into_iter() + idxs.into_iter() .map(|row_i| read_image_i32(&rows[row_i]).map(|img| (row_i, img))) .collect::, _>>() }) @@ -569,13 +561,8 @@ fn ingest_beamtime_inner( let global_total = rows.len() as u32; for (i, img) in flat { let row = &rows[i]; - write_frame_raw( - &zstore, - row.scan_number, - row.frame_number, - &img, - ) - .map_err(|e| CatalogError::Validation(e.to_string()))?; + write_frame_raw(&zstore, row.scan_number, row.frame_number, &img) + .map_err(|e| CatalogError::Validation(e.to_string()))?; let sn = row.scan_number as i32; let e = scan_done.entry(sn).or_insert(0); *e += 1; diff --git a/src/catalog/ingest_progress.rs b/src/catalog/ingest_progress.rs index 64152ae..3483be0 100644 --- a/src/catalog/ingest_progress.rs +++ b/src/catalog/ingest_progress.rs @@ -100,10 +100,7 @@ impl IngestProgressSink { } fn scan_number_for_path(path: &Path) -> i32 { - let stem = path - .file_stem() - .and_then(|s| s.to_str()) - .unwrap_or(""); + let stem = path.file_stem().and_then(|s| s.to_str()).unwrap_or(""); parse_fits_stem(stem) .map(|p| p.scan_number as i32) .unwrap_or(0) @@ -120,7 +117,9 @@ pub(crate) fn partition_paths_by_scan(paths: &[PathBuf]) -> Vec<(i32, Vec (BeamtimeIngestLayout, Vec<(i32, Vec)>) { +pub(crate) fn layout_and_groups_from_paths( + paths: &[PathBuf], +) -> (BeamtimeIngestLayout, Vec<(i32, Vec)>) { let groups = partition_paths_by_scan(paths); let scans: Vec = groups .iter() diff --git a/src/catalog/mod.rs b/src/catalog/mod.rs index 8f482ca..d0d4536 100644 --- a/src/catalog/mod.rs +++ b/src/catalog/mod.rs @@ -1,7 +1,9 @@ #![cfg(feature = "catalog")] +mod beamspot_qc; mod beamtime_index; pub mod db; +mod explorer_query; mod ingest; mod ingest_progress; mod layout; @@ -9,10 +11,8 @@ mod models; mod parallelism; pub mod paths; mod query; -mod zarr_write; -mod explorer_query; mod reflectivity_profile; -mod beamspot_qc; +mod zarr_write; #[cfg(feature = "watch")] mod watch; @@ -21,29 +21,28 @@ pub use beamtime_index::{ ensure_beamtime_index_dir, list_beamtimes, open_beamtime_index_db, register_beamtime, }; +pub use beamspot_qc::{beamspot_status, domain_for_row, fit_beamspot_linear, BeamspotLinearFit}; +pub use explorer_query::{ + catalog_status_for_path, list_beamtimes_for_expt, list_experimentalists, BeamtimeMeta, + DbCatalogStatus, ExptMeta, +}; pub use ingest::{ beamtime_ingest_layout, ingest_beamtime, ingest_beamtime_parallel, - ingest_beamtime_with_context, ingest_beamtime_with_progress_sink, - DEFAULT_INGEST_HEADER_ITEMS, + ingest_beamtime_with_context, ingest_beamtime_with_progress_sink, DEFAULT_INGEST_HEADER_ITEMS, }; +#[cfg(feature = "parallel_ingest")] +pub use ingest::{ingest_beamtime_pipelined, ingest_beamtime_pipelined_with_context}; pub use ingest_progress::{ BeamtimeIngestLayout, IngestProgress, IngestProgressSink, ScanFileCount, }; -#[cfg(feature = "parallel_ingest")] -pub use ingest::{ingest_beamtime_pipelined, ingest_beamtime_pipelined_with_context}; -pub use parallelism::IngestParallelism; pub use layout::{detect_beamtime_layout, discover_fits_for_layout, BeamtimeLayout}; +pub use parallelism::IngestParallelism; pub use query::{ catalog_file_count, get_overrides, get_scan_point_uid_by_source_path, list_beamtime_entries, list_beamtime_entries_v2, list_beamtimes_from_catalog, query_files, query_scan_points, rename_file_in_catalog, scan_from_catalog, scan_from_catalog_for_beamtime, set_override, update_beamspot, update_beamspot_scan_point, BeamtimeEntries, CatalogFilter, FileRow, }; -pub use explorer_query::{ - list_experimentalists, list_beamtimes_for_expt, catalog_status_for_path, BeamtimeMeta, - DbCatalogStatus, ExptMeta, -}; -pub use beamspot_qc::{beamspot_status, domain_for_row, fit_beamspot_linear, BeamspotLinearFit}; pub use reflectivity_profile::{ classify_scan_type, segment_reflectivity_profiles, ProfileSegment, ReflectivityScanType, }; diff --git a/src/catalog/models.rs b/src/catalog/models.rs index 86e6923..41f4bb4 100644 --- a/src/catalog/models.rs +++ b/src/catalog/models.rs @@ -2,8 +2,8 @@ #![allow(dead_code)] +use crate::schema::{files, frames, samples, scans}; use diesel::prelude::*; -use crate::schema::{files, frames, scans, samples}; #[derive(Debug, Clone, Queryable, Selectable)] #[diesel(table_name = frames)] diff --git a/src/catalog/paths.rs b/src/catalog/paths.rs index 78a6537..45a4f70 100644 --- a/src/catalog/paths.rs +++ b/src/catalog/paths.rs @@ -91,7 +91,8 @@ pub fn file_uri_for_path(path: &Path) -> Result { /// Resolves the catalog database path. The beamtime argument is accepted for API compatibility; /// the catalog is global and the path does not depend on it. pub fn resolve_catalog_path(_beamtime_dir: &Path) -> PathBuf { - default_catalog_db_path().expect("catalog path: set PYREF_HOME or ensure a writable user data directory") + default_catalog_db_path() + .expect("catalog path: set PYREF_HOME or ensure a writable user data directory") } /// Legacy helper: returns the global catalog path (single-catalog layout). diff --git a/src/catalog/query.rs b/src/catalog/query.rs index f64d420..d8fc5e3 100644 --- a/src/catalog/query.rs +++ b/src/catalog/query.rs @@ -1,9 +1,9 @@ -use diesel::dsl::count_star; use diesel::deserialize::{self, QueryableByName}; +use diesel::dsl::count_star; use diesel::prelude::*; use diesel::sql_types::{BigInt, Double, Integer, Nullable, Text}; use diesel::sqlite::{Sqlite, SqliteConnection}; -use diesel::{OptionalExtension, RunQueryDsl, sql_query}; +use diesel::{sql_query, OptionalExtension, RunQueryDsl}; use polars::prelude::*; use std::path::Path; @@ -383,7 +383,9 @@ impl QueryableByName for CatalogScanSql { tag: diesel::row::NamedRow::get::, Option>(row, "tag")?, scan_number: diesel::row::NamedRow::get::(row, "scan_number")?, frame_number: diesel::row::NamedRow::get::(row, "frame_number")?, - date_iso: diesel::row::NamedRow::get::, Option>(row, "date_iso")?, + date_iso: diesel::row::NamedRow::get::, Option>( + row, "date_iso", + )?, beamline_energy: diesel::row::NamedRow::get::(row, "beamline_energy")?, sample_theta: diesel::row::NamedRow::get::(row, "sample_theta")?, ccd_theta: diesel::row::NamedRow::get::(row, "ccd_theta")?, @@ -400,14 +402,14 @@ impl QueryableByName for CatalogScanSql { row, "beam_sigma", )?, - reflectivity_profile_index: diesel::row::NamedRow::get::< - Nullable, - Option, - >(row, "reflectivity_profile_index")?, - reflectivity_scan_type: diesel::row::NamedRow::get::< - Nullable, - Option, - >(row, "reflectivity_scan_type")?, + reflectivity_profile_index: diesel::row::NamedRow::get::, Option>( + row, + "reflectivity_profile_index", + )?, + reflectivity_scan_type: diesel::row::NamedRow::get::, Option>( + row, + "reflectivity_scan_type", + )?, }) } } @@ -570,11 +572,13 @@ fn catalog_scan_sql_rows_to_dataframe(rows: Vec) -> Result = - series.into_iter().map(|s| s.into()).collect(); + let columns: Vec = series.into_iter().map(|s| s.into()).collect(); DataFrame::new(columns).map_err(|e| CatalogError::Validation(e.to_string())) } @@ -641,9 +645,12 @@ pub fn update_beamspot( .execute(&mut conn) .map_err(CatalogError::Diesel)?; } else { - q.set((beam_finding::centroid_row.eq(br), beam_finding::centroid_col.eq(bc))) - .execute(&mut conn) - .map_err(CatalogError::Diesel)?; + q.set(( + beam_finding::centroid_row.eq(br), + beam_finding::centroid_col.eq(bc), + )) + .execute(&mut conn) + .map_err(CatalogError::Diesel)?; } } else { diesel::insert_into(beam_finding::table) @@ -694,9 +701,12 @@ pub fn update_beamspot_scan_point( .execute(&mut conn) .map_err(CatalogError::Diesel)?; } else { - q.set((beam_finding::centroid_row.eq(br), beam_finding::centroid_col.eq(bc))) - .execute(&mut conn) - .map_err(CatalogError::Diesel)?; + q.set(( + beam_finding::centroid_row.eq(br), + beam_finding::centroid_col.eq(bc), + )) + .execute(&mut conn) + .map_err(CatalogError::Diesel)?; } } else { diesel::insert_into(beam_finding::table) @@ -735,7 +745,8 @@ pub fn get_overrides(db_path: &Path, path: Option<&str>) -> Result { let mut sample_names = Vec::new(); let mut tags = Vec::new(); let mut notes = Vec::new(); - let rows: Vec<(String, Option, Option, Option)> = if let Some(p) = path { + let rows: Vec<(String, Option, Option, Option)> = if let Some(p) = path + { file_overrides::table .filter(file_overrides::source_path.eq(p)) .select(( @@ -790,11 +801,9 @@ pub fn set_override( "path not in catalog (no files.nas_uri match): {path}" ))); } - diesel::delete( - file_overrides::table.filter(file_overrides::source_path.eq(path)), - ) - .execute(&mut conn) - .map_err(CatalogError::Diesel)?; + diesel::delete(file_overrides::table.filter(file_overrides::source_path.eq(path))) + .execute(&mut conn) + .map_err(CatalogError::Diesel)?; diesel::insert_into(file_overrides::table) .values(( file_overrides::source_path.eq(path), @@ -833,19 +842,15 @@ pub fn rename_file_in_catalog( )) .execute(&mut conn) .map_err(CatalogError::Diesel)?; - diesel::update( - file_overrides::table.filter(file_overrides::source_path.eq(old_path)), - ) - .set(file_overrides::source_path.eq(new_path)) - .execute(&mut conn) - .map_err(CatalogError::Diesel)?; - if !new_sample_name.is_empty() || new_tag.is_some() { - let sn = (!new_sample_name.is_empty()).then_some(new_sample_name); - diesel::delete( - file_overrides::table.filter(file_overrides::source_path.eq(new_path)), - ) + diesel::update(file_overrides::table.filter(file_overrides::source_path.eq(old_path))) + .set(file_overrides::source_path.eq(new_path)) .execute(&mut conn) .map_err(CatalogError::Diesel)?; + if !new_sample_name.is_empty() || new_tag.is_some() { + let sn = (!new_sample_name.is_empty()).then_some(new_sample_name); + diesel::delete(file_overrides::table.filter(file_overrides::source_path.eq(new_path))) + .execute(&mut conn) + .map_err(CatalogError::Diesel)?; diesel::insert_into(file_overrides::table) .values(( file_overrides::source_path.eq(new_path), diff --git a/src/colormap.rs b/src/colormap.rs index 47734a4..1f1bf16 100644 --- a/src/colormap.rs +++ b/src/colormap.rs @@ -43,11 +43,7 @@ fn rainbow_lut() -> &'static [[u8; 4]; LUT_LEN] { RAINBOW_LUT.get_or_init(build_rainbow_lut) } -pub fn scalar_to_rgba( - data: &[f64], - min_max: Option<(f64, f64)>, - reversed: bool, -) -> Vec { +pub fn scalar_to_rgba(data: &[f64], min_max: Option<(f64, f64)>, reversed: bool) -> Vec { let (min, max) = match min_max { Some((lo, hi)) => (lo, hi), None => { @@ -71,17 +67,11 @@ pub fn scalar_to_rgba( out } -pub fn scalar_to_rgba_rainbow( - data: &[f64], - min_max: Option<(f64, f64)>, -) -> Vec { +pub fn scalar_to_rgba_rainbow(data: &[f64], min_max: Option<(f64, f64)>) -> Vec { scalar_to_rgba(data, min_max, false) } -pub fn array2_to_rgba_rainbow( - data: &Array2, - min_max: Option<(f64, f64)>, -) -> Option> { +pub fn array2_to_rgba_rainbow(data: &Array2, min_max: Option<(f64, f64)>) -> Option> { let slice = data.as_slice()?; Some(scalar_to_rgba_rainbow(slice, min_max)) } @@ -121,7 +111,9 @@ pub fn array2_f32_to_rgba( Some((lo, hi)) => (lo as f64, hi as f64), None => { let min = slice.iter().fold(f64::INFINITY, |a, &b| a.min(b as f64)); - let max = slice.iter().fold(f64::NEG_INFINITY, |a, &b| a.max(b as f64)); + let max = slice + .iter() + .fold(f64::NEG_INFINITY, |a, &b| a.max(b as f64)); (min, max) } }; @@ -162,11 +154,7 @@ mod tests { let out = scalar_to_rgba_rainbow(&data, Some((0.0, (LUT_LEN - 1) as f64))); assert_eq!(out.len(), LUT_LEN * 4); let first = [out[0], out[1], out[2]]; - let last = [ - out[out.len() - 4], - out[out.len() - 3], - out[out.len() - 2], - ]; + let last = [out[out.len() - 4], out[out.len() - 3], out[out.len() - 2]]; assert_ne!(first, last); } diff --git a/src/fits/header.rs b/src/fits/header.rs index b6be3bb..c87d893 100644 --- a/src/fits/header.rs +++ b/src/fits/header.rs @@ -35,7 +35,6 @@ impl CardValue { _ => None, } } - } impl fmt::Display for CardValue { diff --git a/src/gaussian_fit.rs b/src/gaussian_fit.rs index 81a7464..5435324 100644 --- a/src/gaussian_fit.rs +++ b/src/gaussian_fit.rs @@ -11,16 +11,7 @@ pub struct Gaussian2DFit { pub baseline: f64, } -fn gaussian_2d( - r: f64, - c: f64, - mu_r: f64, - mu_c: f64, - sr: f64, - sc: f64, - a: f64, - b: f64, -) -> f64 { +fn gaussian_2d(r: f64, c: f64, mu_r: f64, mu_c: f64, sr: f64, sc: f64, a: f64, b: f64) -> f64 { if sr <= 0.0 || sc <= 0.0 { return b; } @@ -187,13 +178,18 @@ fn solve_6x6(a: &[[f64; 6]; 6], b: &[f64; 6]) -> Option<[f64; 6]> { mod tests { use super::*; - fn synthetic_gaussian(nr: usize, nc: usize, mu_r: f64, mu_c: f64, sr: f64, sc: f64) -> Array2 { + fn synthetic_gaussian( + nr: usize, + nc: usize, + mu_r: f64, + mu_c: f64, + sr: f64, + sc: f64, + ) -> Array2 { let mut arr = Array2::zeros((nr, nc)); for r in 0..nr { for c in 0..nc { - arr[[r, c]] = gaussian_2d( - r as f64, c as f64, mu_r, mu_c, sr, sc, 100.0, 0.0, - ); + arr[[r, c]] = gaussian_2d(r as f64, c as f64, mu_r, mu_c, sr, sc, 100.0, 0.0); } } arr diff --git a/src/io/blur.rs b/src/io/blur.rs index d43d7b9..a56f02b 100644 --- a/src/io/blur.rs +++ b/src/io/blur.rs @@ -47,10 +47,7 @@ pub fn gaussian_blur_f32_copy( Ok(dst) } -pub fn blur_array2_i64( - image: &Array2, - sigma: f64, -) -> Result, libblur::BlurError> { +pub fn blur_array2_i64(image: &Array2, sigma: f64) -> Result, libblur::BlurError> { let f32_arr = i64_to_f32_array(image); let (h, w) = (f32_arr.nrows(), f32_arr.ncols()); let slice = f32_arr diff --git a/src/io/image_mmap.rs b/src/io/image_mmap.rs index d170a45..b377f9c 100644 --- a/src/io/image_mmap.rs +++ b/src/io/image_mmap.rs @@ -7,7 +7,7 @@ use polars::prelude::*; use super::blur::{gaussian_blur_f32_copy, i64_to_f32_array}; use super::{ - subtract_background_edges, subtract_dark_cold_side, subtract_background_row_strips, + subtract_background_edges, subtract_background_row_strips, subtract_dark_cold_side, trim_image_interior, ImageInfo, TRIM_COLS, TRIM_ROWS, }; use crate::errors::FitsError; diff --git a/src/io/mod.rs b/src/io/mod.rs index 696915c..b5897e6 100644 --- a/src/io/mod.rs +++ b/src/io/mod.rs @@ -285,11 +285,7 @@ pub const TRIM_COLS: usize = 5; pub const ROW_BG_STRIP_WIDTH: usize = 10; pub const DARK_BAND_HEIGHT: usize = 10; -pub fn trim_image_interior( - data: &Array2, - trim_rows: usize, - trim_cols: usize, -) -> Array2 { +pub fn trim_image_interior(data: &Array2, trim_rows: usize, trim_cols: usize) -> Array2 { let rows = data.nrows(); let cols = data.ncols(); if rows < 2 * trim_rows || cols < 2 * trim_cols { @@ -584,7 +580,11 @@ pub fn build_bt_ingest_row( .to_str() .ok_or_else(|| FitsError::validation("Invalid UTF-8 in path"))? .to_string(); - let file_name = path.file_stem().and_then(|s| s.to_str()).unwrap_or("").to_string(); + let file_name = path + .file_stem() + .and_then(|s| s.to_str()) + .unwrap_or("") + .to_string(); let (sample_name, tag, scan_number, frame_number) = match parse_fits_stem(&file_name) { Some(p) => (p.sample_name, p.tag.clone(), p.scan_number, p.frame_number), None => (String::new(), None, 0i64, 0i64), @@ -621,10 +621,7 @@ pub fn build_bt_ingest_row( }; for key in header_items { if key == "DATE" { - row.date_iso = primary - .header - .get_card("DATE") - .map(|c| c.value.to_string()); + row.date_iso = primary.header.get_card("DATE").map(|c| c.value.to_string()); continue; } let v = header_float(primary, key); @@ -651,7 +648,10 @@ pub fn process_file_name(path: std::path::PathBuf) -> Vec { let mut columns = vec![Column::new("file_name".into(), vec![file_name.to_string()])]; match parse_fits_stem(file_name) { Some(p) => { - columns.push(Column::new("sample_name".into(), vec![p.sample_name.clone()])); + columns.push(Column::new( + "sample_name".into(), + vec![p.sample_name.clone()], + )); let tag_series = Series::from_iter(std::iter::once(p.tag.as_deref())) .with_name("tag".into()) .into_column(); @@ -803,13 +803,31 @@ mod tests { fn test_subtract_background_row_strips_whole_row_corrected() { let data = Array2::from_shape_vec( (2, 30), - (0..60).map(|i| if i < 10 { 5i64 } else if i >= 20 { 5i64 } else { 100 }).collect(), + (0..60) + .map(|i| { + if i < 10 { + 5i64 + } else if i >= 20 { + 5i64 + } else { + 100 + } + }) + .collect(), ) .unwrap(); let result = subtract_background_row_strips(&data); for c in 0..30 { - assert!(result[[0, c]] <= 100 - 5, "row 0 col {} should be corrected", c); - assert!(result[[1, c]] <= 100 - 5, "row 1 col {} should be corrected", c); + assert!( + result[[0, c]] <= 100 - 5, + "row 0 col {} should be corrected", + c + ); + assert!( + result[[1, c]] <= 100 - 5, + "row 1 col {} should be corrected", + c + ); } } diff --git a/src/io/zarr_store.rs b/src/io/zarr_store.rs index 834c117..d89a658 100644 --- a/src/io/zarr_store.rs +++ b/src/io/zarr_store.rs @@ -3,9 +3,9 @@ use ndarray::Array2; use std::path::{Path, PathBuf}; use std::sync::Arc; +use zarrs::array::data_type; use zarrs::array::Array; use zarrs::array::ArrayBuilder; -use zarrs::array::data_type; use zarrs::group::GroupBuilder; use zarrs::storage::{ReadableWritableListableStorage, ReadableWritableListableStorageTraits}; @@ -34,7 +34,9 @@ fn beamtime_key(beamtime_dir: &Path) -> String { } pub fn zarr_root(beamtime_dir: &Path) -> PathBuf { - catalog_parent(beamtime_dir).join(ZARR_SUBDIR).join(beamtime_key(beamtime_dir)) + catalog_parent(beamtime_dir) + .join(ZARR_SUBDIR) + .join(beamtime_key(beamtime_dir)) } pub fn detector_zarr_group(scan_uid: &str) -> String { @@ -100,8 +102,8 @@ pub fn open_detector_array( scan_uid: &str, ) -> Result, FitsError> { let path = format!("/scans/{}/{}", scan_uid, DETECTOR_ARRAY_NAME); - let array = Array::open(store.clone(), &path) - .map_err(|e| FitsError::validation(e.to_string()))?; + let array = + Array::open(store.clone(), &path).map_err(|e| FitsError::validation(e.to_string()))?; Ok(array) } diff --git a/src/lib.rs b/src/lib.rs index 3bbe03d..f456f4b 100644 --- a/src/lib.rs +++ b/src/lib.rs @@ -3,9 +3,9 @@ use polars::prelude::*; pub mod beamfinding; pub mod colormap; -pub mod gaussian_fit; pub mod errors; pub mod fits; +pub mod gaussian_fit; pub mod io; pub mod loader; pub mod path_policy; @@ -48,10 +48,10 @@ mod extension { #[cfg(feature = "catalog")] use crate::catalog::{ - beamtime_ingest_layout, classify_scan_type, get_overrides, ingest_beamtime_with_progress_sink, - list_beamtime_entries_v2, list_beamtimes_from_catalog, + beamtime_ingest_layout, classify_scan_type, get_overrides, + ingest_beamtime_with_progress_sink, list_beamtime_entries_v2, list_beamtimes_from_catalog, paths, scan_from_catalog, scan_from_catalog_for_beamtime, set_override, CatalogFilter, - IngestProgress, IngestParallelism, IngestProgressSink, ReflectivityScanType, + IngestParallelism, IngestProgress, IngestProgressSink, ReflectivityScanType, }; #[global_allocator] @@ -94,10 +94,7 @@ mod extension { file_paths: Vec, header_items: Vec, ) -> PyResult { - let paths: Vec<_> = file_paths - .iter() - .map(std::path::PathBuf::from) - .collect(); + let paths: Vec<_> = file_paths.iter().map(std::path::PathBuf::from).collect(); match read_multiple_fits_headers_only(paths, &header_items) { Ok(df) => Ok(PyDataFrame(df)), Err(e) => Err(PyErr::new::( @@ -403,9 +400,8 @@ mod extension { beamtime_path: &str, ) -> PyResult> { use pyo3::types::{PyDict, PyList}; - let layout = beamtime_ingest_layout(std::path::Path::new(beamtime_path)).map_err(|e| { - PyErr::new::(e.to_string()) - })?; + let layout = beamtime_ingest_layout(std::path::Path::new(beamtime_path)) + .map_err(|e| PyErr::new::(e.to_string()))?; let d = PyDict::new_bound(py); d.set_item("total_files", layout.total_files)?; let list = PyList::empty_bound(py); @@ -522,9 +518,8 @@ mod extension { ) -> PyResult> { let db = std::path::Path::new(db_path); let beam = std::path::Path::new(beamtime_path); - let e = list_beamtime_entries_v2(db, beam).map_err(|err| { - PyErr::new::(err.to_string()) - })?; + let e = list_beamtime_entries_v2(db, beam) + .map_err(|err| PyErr::new::(err.to_string()))?; let d = pyo3::types::PyDict::new_bound(py); d.set_item("samples", e.samples)?; d.set_item("tags", e.tags)?; @@ -538,9 +533,8 @@ mod extension { #[pyo3(signature = (db_path), text_signature = "(db_path)")] pub fn py_list_beamtimes(db_path: &str) -> PyResult> { let db = std::path::Path::new(db_path); - let rows = list_beamtimes_from_catalog(db).map_err(|e| { - PyErr::new::(e.to_string()) - })?; + let rows = list_beamtimes_from_catalog(db) + .map_err(|e| PyErr::new::(e.to_string()))?; Ok(rows .into_iter() .map(|(p, id)| (p.to_string_lossy().into_owned(), id)) @@ -686,14 +680,12 @@ pub use extension::*; #[cfg(feature = "extension-module")] pub fn err_prop_div(lhs: Expr, rhs: Expr, lhs_err: Expr, rhs_err: Expr) -> Expr { - ((lhs.clone() / rhs.clone()) * ((lhs_err / lhs.clone()).pow(2) + (rhs_err / rhs).pow(2))) - .sqrt() + ((lhs.clone() / rhs.clone()) * ((lhs_err / lhs.clone()).pow(2) + (rhs_err / rhs).pow(2))).sqrt() } #[cfg(feature = "extension-module")] pub fn err_prop_mult(lhs: Expr, rhs: Expr, lhs_err: Expr, rhs_err: Expr) -> Expr { - ((lhs.clone() * rhs.clone()) * ((lhs_err / lhs.clone()).pow(2) + (rhs_err / rhs).pow(2))) - .sqrt() + ((lhs.clone() * rhs.clone()) * ((lhs_err / lhs.clone()).pow(2) + (rhs_err / rhs).pow(2))).sqrt() } #[cfg(feature = "extension-module")] diff --git a/src/loader.rs b/src/loader.rs index 5a343a0..d9fb7b3 100644 --- a/src/loader.rs +++ b/src/loader.rs @@ -141,10 +141,8 @@ pub fn read_multiple_fits_headers_only_rows( header_items: &[String], ) -> Result, FitsError> { if file_paths.is_empty() { - return Err( - FitsError::validation("No files provided") - .with_context("operation", "read_multiple_fits_headers_only_rows"), - ); + return Err(FitsError::validation("No files provided") + .with_context("operation", "read_multiple_fits_headers_only_rows")); } for path in &file_paths { if !path.exists() { diff --git a/src/schema.rs b/src/schema.rs index 804808b..48b0804 100644 --- a/src/schema.rs +++ b/src/schema.rs @@ -531,4 +531,4 @@ diesel::allow_tables_to_appear_in_same_query!( stitch_corrections, reflectivity, file_overrides, -); \ No newline at end of file +); diff --git a/tests/test_experiment_names.py b/tests/test_experiment_names.py index cda62ee..ed0a415 100644 --- a/tests/test_experiment_names.py +++ b/tests/test_experiment_names.py @@ -65,7 +65,7 @@ def test_discover_fits_recursive() -> None: def test_build_catalog_names_only() -> None: data_dir = get_data_path() - catalog = build_catalog(data_dir, headers=None, recursive=False) + catalog = build_catalog(data_dir, headers=None, recursive=True) assert isinstance(catalog, pl.DataFrame) assert not catalog.is_empty() for col in ("path", "file_stem", "sample_name", "tag", "scan_number", "frame_number"): @@ -76,7 +76,7 @@ def test_build_catalog_names_only() -> None: def test_build_catalog_with_headers() -> None: data_dir = get_data_path() - paths = discover_fits(data_dir, recursive=False) + paths = discover_fits(data_dir, recursive=True) if not paths: pytest.skip("No FITS files in data dir") catalog = build_catalog(paths[:3], headers=["Beamline Energy", "Sample Theta", "DATE"]) @@ -89,7 +89,7 @@ def test_build_catalog_with_headers() -> None: def test_scan_view() -> None: data_dir = get_data_path() - catalog = build_catalog(data_dir, headers=None, recursive=False) + catalog = build_catalog(data_dir, headers=None, recursive=True) if catalog.is_empty(): pytest.skip("No FITS files") view = scan_view(catalog) @@ -102,7 +102,7 @@ def test_scan_view() -> None: def test_experiment_summary() -> None: data_dir = get_data_path() - summary = experiment_summary(data_dir, recursive=False, with_headers=False) + summary = experiment_summary(data_dir, recursive=True, with_headers=False) assert isinstance(summary, pl.DataFrame) if not summary.is_empty(): assert "file_count" in summary.columns @@ -110,10 +110,10 @@ def test_experiment_summary() -> None: def test_experiment_summary_with_headers() -> None: data_dir = get_data_path() - paths = discover_fits(data_dir, recursive=False) + paths = discover_fits(data_dir, recursive=True) if not paths: pytest.skip("No FITS files") - summary = experiment_summary(data_dir, recursive=False, with_headers=True) + summary = experiment_summary(data_dir, recursive=True, with_headers=True) assert isinstance(summary, pl.DataFrame) if not summary.is_empty() and "energy_min" in summary.columns: assert "Q_min" in summary.columns or "energy_min" in summary.columns @@ -121,7 +121,7 @@ def test_experiment_summary_with_headers() -> None: def test_filter_catalog_paths() -> None: data_dir = get_data_path() - catalog = build_catalog(data_dir, headers=None, recursive=False) + catalog = build_catalog(data_dir, headers=None, recursive=True) if catalog.is_empty(): pytest.skip("No FITS files") all_paths = filter_catalog_paths(catalog) @@ -144,7 +144,7 @@ def test_read_fits_meta_has_parsed_columns() -> None: from pyref.pyref import py_read_fits_headers_only data_dir = get_data_path() - paths = discover_fits(data_dir, recursive=False) + paths = discover_fits(data_dir, recursive=True) if not paths: pytest.skip("No FITS files") meta = py_read_fits_headers_only(str(paths[0]), []) diff --git a/tests/test_nexafs_accessor.py b/tests/test_nexafs_accessor.py deleted file mode 100644 index 23231d0..0000000 --- a/tests/test_nexafs_accessor.py +++ /dev/null @@ -1,112 +0,0 @@ -"""Tests for df.nexafs DataFrame accessor.""" - -from __future__ import annotations - -import numpy as np -import pandas as pd -import pyref # noqa: F401 - registers df.nexafs accessor -import pytest - - -@pytest.fixture -def single_scan_df() -> pd.DataFrame: - energy = np.linspace(260, 360, 50) - mu = 2000 + 100 * (energy - 280) - mu_sub = 40000 - 50 * (energy - 280) - abs0 = 1e-8 * (mu + 1.2 * mu_sub) + 0.0002 - abs1 = 1.2e-8 * (mu + 0.9 * mu_sub) + 0.00018 - return pd.DataFrame({ - "beamline_energy": energy, - "bare_atom": mu, - "bare_atom_substrate": mu_sub, - "absorbance_0": abs0, - "absorbance_1": abs1, - }) - - -@pytest.fixture -def multi_angle_df(single_scan_df: pd.DataFrame) -> pd.DataFrame: - dfs = [] - for angle in [20.0, 40.0, 60.0]: - df = single_scan_df.copy() - df["sample_theta"] = angle - dfs.append(df) - return pd.concat(dfs, ignore_index=True) - - -def test_nexafs_accessor_registered() -> None: - df = pd.DataFrame({ - "beamline_energy": [270], - "bare_atom": [1.0], - "bare_atom_substrate": [1.0], - "absorbance_0": [0.1], - }) - assert hasattr(df, "nexafs") - assert df.nexafs._obj is df - - -def test_nexafs_schema_validation() -> None: - df = pd.DataFrame({"a": [1], "b": [2]}) - with pytest.raises(ValueError, match="Missing"): - df.nexafs.normalization_region() - - -def test_normalization_region_mask(single_scan_df: pd.DataFrame) -> None: - single_scan_df.nexafs.set_regions( - pre_edge=(270, 281), - post_edge=(335, 350), - ) - norm = single_scan_df.nexafs.normalization_region() - energy = norm["beamline_energy"].to_numpy() - in_pre = (energy >= 270) & (energy <= 281) - in_post = (energy >= 335) & (energy <= 350) - assert np.all(in_pre | in_post) - assert len(norm) < len(single_scan_df) - - -def test_normalize_adds_columns_and_stores_params(single_scan_df: pd.DataFrame) -> None: - single_scan_df.nexafs.set_regions(pre_edge=(270, 281), post_edge=(335, 350)) - single_scan_df.nexafs.normalize() - assert "mass_absorption_0" in single_scan_df.columns - assert "mass_absorption_1" in single_scan_df.columns - assert single_scan_df.nexafs._fit_params is not None - assert "absorbance_0" in single_scan_df.nexafs._fit_params - assert "popt" in single_scan_df.nexafs._fit_params["absorbance_0"] - - -def test_plot_regions_smoke(single_scan_df: pd.DataFrame) -> None: - single_scan_df.nexafs.set_regions(pre_edge=(270, 281), post_edge=(335, 350)) - ax = single_scan_df.nexafs.plot_regions() - assert ax is not None - - -def test_plot_normalization_smoke(single_scan_df: pd.DataFrame) -> None: - single_scan_df.nexafs.set_regions(pre_edge=(270, 281), post_edge=(335, 350)) - single_scan_df.nexafs.normalize() - ax = single_scan_df.nexafs.plot_normalization(show_fit=True) - assert ax is not None - - -def test_plot_by_group_smoke(multi_angle_df: pd.DataFrame) -> None: - multi_angle_df.nexafs.set_regions(pre_edge=(270, 281), post_edge=(335, 350)) - ax = multi_angle_df.nexafs.plot( - x="beamline_energy", - y="absorbance_0", - by="sample_theta", - colorbar="Angle (deg)", - ) - assert ax is not None - - -def test_normalize_by_group(multi_angle_df: pd.DataFrame) -> None: - from pyref.nexafs import normalize_by_group - - result = normalize_by_group( - multi_angle_df, - group_column="sample_theta", - pre_edge=(270, 281), - post_edge=(335, 350), - ) - assert "mass_absorption_0" in result.columns - assert "mass_absorption_1" in result.columns - assert len(result) == len(multi_angle_df) diff --git a/uv.lock b/uv.lock index de2da4c..9842934 100644 --- a/uv.lock +++ b/uv.lock @@ -3509,8 +3509,8 @@ dev = [ { name = "maturin" }, { name = "nbconvert" }, { name = "patchelf" }, - { name = "pyrefly" }, { name = "ruff" }, + { name = "ty" }, ] jupyter = [ { name = "bokeh" }, @@ -3575,8 +3575,8 @@ dev = [ { name = "maturin", specifier = ">=1.11.5" }, { name = "nbconvert", specifier = ">=7.17.0" }, { name = "patchelf", specifier = ">=0.17.2.2" }, - { name = "pyrefly", specifier = ">=0.24.2" }, { name = "ruff", specifier = ">=0.11.9" }, + { name = "ty", specifier = ">=0.0.29" }, ] jupyter = [ { name = "bokeh", specifier = ">=3.7.3" }, @@ -3600,22 +3600,6 @@ torch = [ { name = "torchvision", marker = "sys_platform == 'linux' or 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"https://files.pythonhosted.org/packages/2c/55/87280a994d6a2d2647c65e12abbc997ed49835794366153c04c4d9304d76/ty-0.0.29-py3-none-win_arm64.whl", hash = "sha256:f9794bbd1bb3ce13f78c191d0c89ae4c63f52c12b6daa0c6fe220b90d019d12c", size = 10428165, upload-time = "2026-04-05T15:01:34.665Z" }, +] + [[package]] name = "typer" version = "0.24.1" From aace29e18291202d366d2a84e4cb87ba5b831d9a Mon Sep 17 00:00:00 2001 From: Harlan D Heilman <73567020+HarlanHeilman@users.noreply.github.com> Date: Mon, 13 Apr 2026 14:27:32 -0700 Subject: [PATCH 31/32] chore: extend gitignore for Rust artifacts and Python natives Ignore Cargo target trees, loose rlib/o/obj/a and MSVC coverage noise; add pyd/dll, ty cache, pip-wheel-metadata, and clarify tracked .python-version. --- .gitignore | 54 +++++++++++++++++++++++++++++++++++++++++------------- 1 file changed, 41 insertions(+), 13 deletions(-) diff --git a/.gitignore b/.gitignore index addd62b..5f83db7 100644 --- a/.gitignore +++ b/.gitignore @@ -7,8 +7,10 @@ __pycache__/ *.py[cod] *$py.class -# C extensions +# C extensions / native Python modules *.so +*.pyd +*.dll # data test/ @@ -23,6 +25,7 @@ test/ # Distribution / packaging .Python .comments +pip-wheel-metadata/ build/ develop-eggs/ dist/ @@ -89,22 +92,13 @@ docs/_build/ # PyBuilder .pybuilder/ -target/ -# Jupyter Notebook -.ipynb_checkpoints - -# IPython +# Jupyter / IPython +.ipynb_checkpoints/ profile_default/ ipython_config.py -# Jupyter Notebook -.ipynb_checkpoints - -# pyenv -# For a library or package, you might want to ignore these files since the code is -# intended to run in multiple environments; otherwise, check them in: -# .python-version +# pyenv / uv: this repository tracks `.python-version` for reproducible interpreters; do not add it here. # pipenv # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. @@ -185,10 +179,44 @@ poetry.toml # ruff .ruff_cache/ +# Astral ty (checker cache if present) +.ty/ + # LSP config files pyrightconfig.json # End of https://www.toptal.com/developers/gitignore/api/python + +### Rust (Cargo, rustc, maturin, PyO3) ### +# This project commits `Cargo.lock`; keep it out of any broad ignore rules below. +# Primary build tree (workspace root). Do not commit compiled crates or incremental state. +/target/ +/debug/ + +# Static and link artifacts sometimes produced at repo root by bad invocations or local experiments +*.rlib +/*.o +/*.obj +/*.a +*.exp +*.ilk + +# rustfmt +**/*.rs.bk + +# MSVC debug symbols +*.pdb + +# macOS debug bundles from `cargo build` / Instruments +**/*.dSYM/ + +# LLVM / rustc / gcov coverage intermediates +*.profraw +*.profdata +*.gcda +*.gcno +*.gcov + python/pyref/data/.pyref_catalog.db # Cursor agent hook state (local; not for version control) From 5a358d5d5c6e219a69d7e3eac3f9bee7e514f0ec Mon Sep 17 00:00:00 2001 From: Harlan D Heilman <73567020+HarlanHeilman@users.noreply.github.com> Date: Mon, 13 Apr 2026 14:35:34 -0700 Subject: [PATCH 32/32] fix(ci): link libpython for Rust tests; split bindings from extension-module --- .github/workflows/ci.yml | 2 +- Cargo.toml | 7 ++++--- pyproject.toml | 4 ++-- src/lib.rs | 16 ++++++++-------- 4 files changed, 15 insertions(+), 14 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index ba87d88..81b407e 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -34,7 +34,7 @@ jobs: - name: Build extension (maturin develop) run: >- uv run --python 3.12 maturin develop - --features "pyo3/extension-module,catalog,parallel_ingest,zarr" + --features "extension-module,parallel_ingest,zarr" - name: Ruff lint run: uv run --python 3.12 ruff check . diff --git a/Cargo.toml b/Cargo.toml index deaeade..7dc587a 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -19,8 +19,9 @@ path = "src/bin/browser.rs" required-features = ["tui"] [features] -default = ["extension-module", "catalog", "parallel_ingest"] -extension-module = ["dep:pyo3", "dep:pyo3-polars", "dep:numpy"] +default = ["bindings", "catalog", "parallel_ingest"] +bindings = ["dep:pyo3", "dep:pyo3-polars", "dep:numpy"] +extension-module = ["bindings", "pyo3/extension-module", "pyo3/generate-import-lib"] catalog = [ "dep:diesel", "dep:diesel_migrations", @@ -45,7 +46,7 @@ ndarray = "0.16" numpy = {version = "0.22.0", optional = true} physical_constants = "0.5.0" polars = {version = "0.45.1", features = ["fmt", "dtype-u16", "lazy", "dtype-struct", "dtype-array", "dtype-datetime", "round_series", "ipc", "performant"]} -pyo3 = {version = "0.22.0", optional = true, features = ["extension-module", "generate-import-lib", "abi3", "abi3-py312"]} +pyo3 = {version = "0.22.0", optional = true, features = ["abi3", "abi3-py312"]} pyo3-polars = {version = "0.19.0", optional = true, features = ["lazy", "dtype-struct", "derive"]} rayon = "1.10" serde = {version = "1.0", features = ["derive"]} diff --git a/pyproject.toml b/pyproject.toml index 2a06a7f..00763d5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -62,7 +62,7 @@ dependencies = [ [tool.maturin] bindings = "pyo3" -features = ["pyo3/extension-module", "pyo3/generate-import-lib", "parallel_ingest", "zarr"] +features = ["extension-module", "parallel_ingest", "zarr"] format = ["wheel"] minimum-python-version = "3.12" module-name = "pyref" @@ -194,7 +194,7 @@ url = "https://download.pytorch.org/whl/cu128" explicit = true [package.metadata.maturin] -features = ["pyo3/extension-module"] +features = ["extension-module"] python-source = "python" [dependency-groups] diff --git a/src/lib.rs b/src/lib.rs index f456f4b..82100a7 100644 --- a/src/lib.rs +++ b/src/lib.rs @@ -1,4 +1,4 @@ -#[cfg(feature = "extension-module")] +#[cfg(feature = "bindings")] use polars::prelude::*; pub mod beamfinding; @@ -28,7 +28,7 @@ pub use loader::{ }; pub use path_policy::is_indexable_als_path; -#[cfg(feature = "extension-module")] +#[cfg(feature = "bindings")] #[allow(clippy::useless_conversion)] mod extension { use numpy::PyArray2; @@ -675,20 +675,20 @@ mod extension { } } -#[cfg(feature = "extension-module")] +#[cfg(feature = "bindings")] pub use extension::*; -#[cfg(feature = "extension-module")] +#[cfg(feature = "bindings")] pub fn err_prop_div(lhs: Expr, rhs: Expr, lhs_err: Expr, rhs_err: Expr) -> Expr { ((lhs.clone() / rhs.clone()) * ((lhs_err / lhs.clone()).pow(2) + (rhs_err / rhs).pow(2))).sqrt() } -#[cfg(feature = "extension-module")] +#[cfg(feature = "bindings")] pub fn err_prop_mult(lhs: Expr, rhs: Expr, lhs_err: Expr, rhs_err: Expr) -> Expr { ((lhs.clone() * rhs.clone()) * ((lhs_err / lhs.clone()).pow(2) + (rhs_err / rhs).pow(2))).sqrt() } -#[cfg(feature = "extension-module")] +#[cfg(feature = "bindings")] pub fn weighted_mean(values: Expr, weights: Expr) -> Expr { let values = values.cast(DataType::Float64); let weights = weights.cast(DataType::Float64); @@ -697,13 +697,13 @@ pub fn weighted_mean(values: Expr, weights: Expr) -> Expr { numerator.sum() / denominator.sum() } -#[cfg(feature = "extension-module")] +#[cfg(feature = "bindings")] pub fn weighted_std(weights: Expr) -> Expr { let weights = weights.cast(DataType::Float64); let denominator = weights.clone(); (lit(1.0) / denominator.sum()).sqrt() } -#[cfg(not(feature = "extension-module"))] +#[cfg(not(feature = "bindings"))] #[allow(dead_code)] fn _lib_placeholder_for_tui() {}