Batch phase 3: sklearn transformer facades and f_rupture

CHANGELOG.md

@@ -11,6 +11,27 @@ those changes.
 
 ## [Unreleased]
 
+## [1.57.0] - 2026-07-16
+
+### Added
+
+- Batch preprocessing transformers (sklearn `BaseEstimator` / `TransformerMixin`
+  facades over the existing free functions, so a batch workflow can be a
+  pipeline): `batch.BatchScaler` (range scaling with `inverse_transform`),
+  `batch.ResampleAligner` (linear resampling to a common length),
+  `batch.DTWAligner` (iterative weighted DTW alignment, learning a reference and
+  weights and aligning new batches), and `batch.BatchFeatureExtractor` (a
+  batch-data dictionary to a batch-by-feature matrix for a PLS-to-quality model).
+- `batch.features.f_rupture` is now implemented (previously a stub): it detects
+  the first changepoint in a tag's trajectory per batch and phase using the
+  optional `ruptures` library.
+
+### Changed
+
+- The `batch` optional extra now provides `ruptures` (used by `f_rupture`)
+  instead of `openpyxl` and `scikit-image`, which were declared but imported
+  nowhere.
+
 ## [1.56.0] - 2026-07-16
 
 ### Added
@@ -2557,7 +2578,8 @@ this entry records them together.
 - Reworked the README with a sharper value proposition and a
   "Why not scikit-learn?" comparison table.
 
-[Unreleased]: https://github.com/kgdunn/process-improve/compare/v1.56.0...HEAD
+[Unreleased]: https://github.com/kgdunn/process-improve/compare/v1.57.0...HEAD
+[1.57.0]: https://github.com/kgdunn/process-improve/compare/v1.56.0...v1.57.0
 [1.56.0]: https://github.com/kgdunn/process-improve/compare/v1.55.0...v1.56.0
 [1.55.0]: https://github.com/kgdunn/process-improve/compare/v1.54.0...v1.55.0
 [1.54.0]: https://github.com/kgdunn/process-improve/compare/v1.53.0...v1.54.0

CITATION.cff

@@ -12,7 +12,7 @@ authors:
 repository-code: "https://github.com/kgdunn/process-improve"
 url: "https://kgdunn.github.io/process-improve/"
 license: MIT
-version: 1.56.0
+version: 1.57.0
 date-released: "2026-07-16"
 keywords:
   - chemometrics

docs/api/batch.rst

@@ -13,6 +13,10 @@ Batch Data Analysis
    :members:
    :show-inheritance:
 
+.. automodule:: process_improve.batch._transformers
+   :members:
+   :show-inheritance:
+
 .. automodule:: process_improve.batch._batch_plots
    :members:
    :show-inheritance:

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "process-improve"
-version = "1.56.0"
+version = "1.57.0"
 description = 'Designed Experiments; Latent Variables (PCA, PLS, multivariate methods with missing data); Process Monitoring; Batch data analysis.'
 readme = "README.md"
 license = "MIT"
@@ -61,11 +61,10 @@ expt = [
     "pyDOE3>=1.0",
 ]
 
-# Batch process data analysis: image-IO + Excel readers used by the
-# batch alignment / fixture loaders.
+# Batch process data analysis: changepoint detection for the f_rupture
+# landmark feature (process_improve.batch.features.f_rupture).
 batch = [
-    "openpyxl>=3.1.5",
-    "scikit-image>=0.25.2",
+    "ruptures>=1.1.9",
 ]
 
 # MCP server entry-point.
@@ -94,12 +93,11 @@ all = [
     "matplotlib>=3.10.8",
     "mcp>=1.0",
     "numba>=0.63.1",
-    "openpyxl>=3.1.5",
     "plotly>=6.5.2",
     "pulp>=2.8",
     "pyDOE3>=1.0",
     "ridgeplot>=0.5.0",
-    "scikit-image>=0.25.2",
+    "ruptures>=1.1.9",
     "seaborn>=0.13.2",
 ]
 

src/process_improve/_extras.py

@@ -6,7 +6,7 @@
 
     pip install 'process-improve[plotting]'    # matplotlib + plotly + seaborn + ridgeplot
     pip install 'process-improve[expt]'        # pyDOE3 (DOE / experiments helpers)
-    pip install 'process-improve[batch]'       # scikit-image + openpyxl
+    pip install 'process-improve[batch]'       # ruptures (changepoint detection)
     pip install 'process-improve[mcp]'         # mcp
     pip install 'process-improve[fast]'        # numba (JIT)
     pip install 'process-improve[ilp]'         # pulp (OMARS design generator)

src/process_improve/batch/__init__.py

@@ -7,6 +7,12 @@
     online_monitoring_plot,
     time_varying_loading_plot,
 )
+from process_improve.batch._transformers import (
+    BatchFeatureExtractor,
+    BatchScaler,
+    DTWAligner,
+    ResampleAligner,
+)
 from process_improve.batch.data_input import (
     check_valid_batch_dict,
     dict_to_melted,
@@ -50,8 +56,12 @@
 
 __all__ = [
     # Modelling and monitoring
+    "BatchFeatureExtractor",
     "BatchMonitor",
     "BatchPCA",
+    "BatchScaler",
+    "DTWAligner",
+    "ResampleAligner",
     # Preprocessing/alignment
     "batch_dtw",
     # Data input/conversion