From f5540943d4a32836a41ada924055447d10500a42 Mon Sep 17 00:00:00 2001 From: Oscar Esteban Date: Mon, 23 Feb 2026 07:45:39 +0100 Subject: [PATCH 1/7] maint: finalize migration of visual interfaces and reports to nireports --- docs/api.rst | 2 - docs/conf.py | 12 +- docs/requirements.txt | 1 + niworkflows/_deprecated.py | 33 + niworkflows/conftest.py | 9 + niworkflows/func/tests/test_util.py | 2 +- niworkflows/func/util.py | 2 +- niworkflows/interfaces/freesurfer.py | 3 +- niworkflows/interfaces/plotting.py | 251 +--- niworkflows/interfaces/reportlets/base.py | 9 +- niworkflows/interfaces/reportlets/masks.py | 289 +---- .../interfaces/reportlets/registration.py | 313 +---- .../interfaces/reportlets/segmentation.py | 232 +--- niworkflows/interfaces/tests/test_plotting.py | 76 -- niworkflows/reports/__init__.py | 14 +- niworkflows/reports/core.py | 561 +------- niworkflows/reports/default.yml | 177 --- niworkflows/reports/report.tpl | 164 --- niworkflows/reports/tests/test_core.py | 286 ---- niworkflows/tests/test_confounds.py | 41 - .../tests/test_nireports_deprecations.py | 43 + niworkflows/tests/test_registration.py | 98 -- niworkflows/tests/test_segmentation.py | 251 ---- niworkflows/tests/test_utils.py | 93 -- niworkflows/tests/test_viz.py | 278 ---- niworkflows/viz/__init__.py | 17 +- niworkflows/viz/notebook.py | 89 +- niworkflows/viz/plots.py | 1146 +---------------- niworkflows/viz/utils.py | 731 +---------- pyproject.toml | 1 + requirements.txt | 2 + 31 files changed, 240 insertions(+), 4986 deletions(-) create mode 100644 niworkflows/_deprecated.py delete mode 100644 niworkflows/interfaces/tests/test_plotting.py delete mode 100644 niworkflows/reports/default.yml delete mode 100644 niworkflows/reports/report.tpl delete mode 100644 niworkflows/reports/tests/test_core.py create mode 100644 niworkflows/tests/test_nireports_deprecations.py delete mode 100644 niworkflows/tests/test_segmentation.py delete mode 100644 niworkflows/tests/test_utils.py delete mode 100644 niworkflows/tests/test_viz.py diff --git a/docs/api.rst b/docs/api.rst index 5921cfd6519..9853b6f9108 100644 --- a/docs/api.rst +++ b/docs/api.rst @@ -12,7 +12,5 @@ Information on specific functions, classes, and methods. api/niworkflows.engine api/niworkflows.func api/niworkflows.interfaces - api/niworkflows.reports api/niworkflows.utils - api/niworkflows.viz api/niworkflows.workflows diff --git a/docs/conf.py b/docs/conf.py index d9b08442052..83d114e521b 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -245,7 +245,17 @@ apidoc_module_dir = '../niworkflows' apidoc_output_dir = 'api' -apidoc_excluded_paths = ['conftest.py', '*/tests/*', 'tests/*', 'testing.py'] +apidoc_excluded_paths = [ + 'conftest.py', + '*/tests/*', + 'interfaces/plotting.py', + 'interfaces/reportlets/masks.py', + 'interfaces/reportlets/segmentation.py', + 'reports/*', + 'tests/*', + 'testing.py', + 'viz/*', +] apidoc_separate_modules = True apidoc_extra_args = ['--module-first', '-d 1', '-T'] diff --git a/docs/requirements.txt b/docs/requirements.txt index ad19db14f42..561945cc54d 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -10,4 +10,5 @@ sphinx <8.2 sphinxcontrib-apidoc templateflow nitransforms +nireports bidsschematools!=1.1.4 diff --git a/niworkflows/_deprecated.py b/niworkflows/_deprecated.py new file mode 100644 index 00000000000..b536ea18ad4 --- /dev/null +++ b/niworkflows/_deprecated.py @@ -0,0 +1,33 @@ +# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- +# vi: set ft=python sts=4 ts=4 sw=4 et: +"""Helpers for hard deprecations.""" + +from collections.abc import Mapping, Sequence + + +class DeprecationError(ImportError): + """Raised when a niworkflows API has moved to NiReports.""" + + +def _format_replacements(replacements): + if isinstance(replacements, str): + return [replacements] + if isinstance(replacements, Mapping): + return [f'{name} -> {target}' for name, target in replacements.items()] + if isinstance(replacements, Sequence): + return list(replacements) + + raise TypeError('replacements must be a string, mapping, or sequence') + + +def moved_to_nireports_message(module, replacements): + lines = [ + f'{module} has been removed from niworkflows and moved to nireports.', + 'Import from one of the following locations instead:', + ] + lines.extend(f' - {replacement}' for replacement in _format_replacements(replacements)) + return '\n'.join(lines) + + +def raise_moved_to_nireports(module, replacements): + raise DeprecationError(moved_to_nireports_message(module, replacements)) diff --git a/niworkflows/conftest.py b/niworkflows/conftest.py index 83452796258..84c0235fda8 100644 --- a/niworkflows/conftest.py +++ b/niworkflows/conftest.py @@ -37,6 +37,15 @@ # disable ET os.environ['NO_ET'] = '1' +# Modules intentionally raising DeprecationError are excluded from doctest collection. +collect_ignore_glob = [ + 'interfaces/plotting.py', + 'interfaces/reportlets/masks.py', + 'interfaces/reportlets/segmentation.py', + 'reports/*.py', + 'viz/*.py', +] + def find_resource_or_skip(resource): pathlike = load_resource(resource) diff --git a/niworkflows/func/tests/test_util.py b/niworkflows/func/tests/test_util.py index 093d2d3e008..0b44ddd3764 100644 --- a/niworkflows/func/tests/test_util.py +++ b/niworkflows/func/tests/test_util.py @@ -31,8 +31,8 @@ import pytest from nipype.pipeline import engine as pe from nipype.utils.filemanip import copyfile, fname_presuffix +from nireports.interfaces.reporting.masks import ROIsPlot -from ...interfaces.reportlets.masks import ROIsPlot from ...workflows.epi.refmap import init_epi_reference_wf from ..util import init_enhance_and_skullstrip_bold_wf diff --git a/niworkflows/func/util.py b/niworkflows/func/util.py index c4924f5af31..ce6c2e90e2b 100644 --- a/niworkflows/func/util.py +++ b/niworkflows/func/util.py @@ -27,6 +27,7 @@ from nipype.pipeline import engine as pe from packaging.version import Version from packaging.version import parse as parseversion +from nireports.interfaces.reporting.masks import SimpleShowMaskRPT from templateflow.api import get as get_template from .. import data @@ -41,7 +42,6 @@ FixN4BiasFieldCorrection as N4BiasFieldCorrection, ) from ..interfaces.header import CopyHeader, CopyXForm, ValidateImage -from ..interfaces.reportlets.masks import SimpleShowMaskRPT from ..utils.connections import listify from ..utils.misc import pass_dummy_scans as _pass_dummy_scans diff --git a/niworkflows/interfaces/freesurfer.py b/niworkflows/interfaces/freesurfer.py index 84901aea6a8..bbeda186e5b 100644 --- a/niworkflows/interfaces/freesurfer.py +++ b/niworkflows/interfaces/freesurfer.py @@ -41,8 +41,7 @@ from nipype.interfaces.freesurfer.preprocess import ConcatenateLTA, RobustRegister from nipype.interfaces.freesurfer.utils import LTAConvert from nipype.utils.filemanip import copyfile, filename_to_list, fname_presuffix - -from .reportlets.registration import BBRegisterRPT, MRICoregRPT +from nireports.interfaces.reporting.registration import BBRegisterRPT, MRICoregRPT class StructuralReference(fs.RobustTemplate): diff --git a/niworkflows/interfaces/plotting.py b/niworkflows/interfaces/plotting.py index 2e9b26083ac..9514f3f6f9c 100644 --- a/niworkflows/interfaces/plotting.py +++ b/niworkflows/interfaces/plotting.py @@ -1,248 +1,13 @@ # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: -# -# Copyright 2021 The NiPreps Developers -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# We support and encourage derived works from this project, please read -# about our expectations at -# -# https://www.nipreps.org/community/licensing/ -# -"""Visualization tools.""" +from .._deprecated import raise_moved_to_nireports -import nibabel as nb -import numpy as np -from nipype.interfaces.base import ( - BaseInterfaceInputSpec, - File, - SimpleInterface, - TraitedSpec, - isdefined, - traits, -) -from nipype.utils.filemanip import fname_presuffix -from niworkflows.utils.timeseries import _cifti_timeseries, _nifti_timeseries -from niworkflows.viz.plots import ( - compcor_variance_plot, - confounds_correlation_plot, - fMRIPlot, +raise_moved_to_nireports( + 'niworkflows.interfaces.plotting', + ( + 'nireports.interfaces.FMRISummary', + 'nireports.interfaces.CompCorVariancePlot', + 'nireports.interfaces.ConfoundsCorrelationPlot', + ), ) - - -class _FMRISummaryInputSpec(BaseInterfaceInputSpec): - in_func = File(exists=True, mandatory=True, desc='') - in_spikes_bg = File(exists=True, desc='') - fd = File(exists=True, desc='') - dvars = File(exists=True, desc='') - outliers = File(exists=True, desc='') - in_segm = File(exists=True, desc='') - tr = traits.Either(None, traits.Float, usedefault=True, desc='the TR') - fd_thres = traits.Float(0.2, usedefault=True, desc='') - drop_trs = traits.Int(0, usedefault=True, desc='dummy scans') - - -class _FMRISummaryOutputSpec(TraitedSpec): - out_file = File(exists=True, desc='written file path') - - -class FMRISummary(SimpleInterface): - """Prepare an fMRI summary plot for the report.""" - - input_spec = _FMRISummaryInputSpec - output_spec = _FMRISummaryOutputSpec - - def _run_interface(self, runtime): - import pandas as pd - - self._results['out_file'] = fname_presuffix( - self.inputs.in_func, - suffix='_fmriplot.svg', - use_ext=False, - newpath=runtime.cwd, - ) - - dataframe = ( - pd.DataFrame( - { - 'outliers': np.loadtxt(self.inputs.outliers, usecols=[0]).tolist(), - # Pick non-standardize dvars (col 1) - # First timepoint is NaN (difference) - 'DVARS': [np.nan] - + np.loadtxt(self.inputs.dvars, skiprows=1, usecols=[1]).tolist(), - # First timepoint is zero (reference volume) - 'FD': [0.0] + np.loadtxt(self.inputs.fd, skiprows=1, usecols=[0]).tolist(), - } - ) - if ( - isdefined(self.inputs.outliers) - and isdefined(self.inputs.dvars) - and isdefined(self.inputs.fd) - ) - else None - ) - - input_data = nb.load(self.inputs.in_func) - seg_file = self.inputs.in_segm if isdefined(self.inputs.in_segm) else None - dataset, segments = ( - _cifti_timeseries(input_data) - if isinstance(input_data, nb.Cifti2Image) - else _nifti_timeseries(input_data, seg_file) - ) - - fig = fMRIPlot( - dataset, - segments=segments, - spikes_files=( - [self.inputs.in_spikes_bg] if isdefined(self.inputs.in_spikes_bg) else None - ), - tr=(self.inputs.tr if isdefined(self.inputs.tr) else _get_tr(input_data)), - confounds=dataframe, - units={'outliers': '%', 'FD': 'mm'}, - vlines={'FD': [self.inputs.fd_thres]}, - nskip=self.inputs.drop_trs, - ).plot() - fig.savefig(self._results['out_file'], bbox_inches='tight') - return runtime - - -class _CompCorVariancePlotInputSpec(BaseInterfaceInputSpec): - metadata_files = traits.List( - File(exists=True), - mandatory=True, - desc='List of files containing component metadata', - ) - metadata_sources = traits.List( - traits.Str, - desc='List of names of decompositions ' - '(e.g., aCompCor, tCompCor) yielding ' - 'the arguments in `metadata_files`', - ) - variance_thresholds = traits.Tuple( - traits.Float(0.5), - traits.Float(0.7), - traits.Float(0.9), - usedefault=True, - desc='Levels of explained variance to include in plot', - ) - out_file = traits.Either(None, File, value=None, usedefault=True, desc='Path to save plot') - - -class _CompCorVariancePlotOutputSpec(TraitedSpec): - out_file = File(exists=True, desc='Path to saved plot') - - -class CompCorVariancePlot(SimpleInterface): - """Plot the number of components necessary to explain the specified levels of variance.""" - - input_spec = _CompCorVariancePlotInputSpec - output_spec = _CompCorVariancePlotOutputSpec - - def _run_interface(self, runtime): - if self.inputs.out_file is None: - self._results['out_file'] = fname_presuffix( - self.inputs.metadata_files[0], - suffix='_compcor.svg', - use_ext=False, - newpath=runtime.cwd, - ) - else: - self._results['out_file'] = self.inputs.out_file - compcor_variance_plot( - metadata_files=self.inputs.metadata_files, - metadata_sources=self.inputs.metadata_sources, - output_file=self._results['out_file'], - varexp_thresh=self.inputs.variance_thresholds, - ) - return runtime - - -class _ConfoundsCorrelationPlotInputSpec(BaseInterfaceInputSpec): - confounds_file = File(exists=True, mandatory=True, desc='File containing confound regressors') - out_file = traits.Either(None, File, value=None, usedefault=True, desc='Path to save plot') - reference_column = traits.Str( - 'global_signal', - usedefault=True, - desc='Column in the confound file for ' - 'which all correlation magnitudes ' - 'should be ranked and plotted', - ) - columns = traits.List(traits.Str, desc='Filter out all regressors not found in this list.') - max_dim = traits.Int( - 20, - usedefault=True, - desc='Maximum number of regressors to include in ' - 'plot. Regressors with highest magnitude of ' - 'correlation with `reference_column` will be ' - 'selected.', - ) - ignore_initial_volumes = traits.Int( - 0, - usedefault=True, - desc='Number of non-steady-state volumes at the beginning of the scan to ignore.', - ) - - -class _ConfoundsCorrelationPlotOutputSpec(TraitedSpec): - out_file = File(exists=True, desc='Path to saved plot') - - -class ConfoundsCorrelationPlot(SimpleInterface): - """Plot the correlation among confound regressors.""" - - input_spec = _ConfoundsCorrelationPlotInputSpec - output_spec = _ConfoundsCorrelationPlotOutputSpec - - def _run_interface(self, runtime): - if self.inputs.out_file is None: - self._results['out_file'] = fname_presuffix( - self.inputs.confounds_file, - suffix='_confoundCorrelation.svg', - use_ext=False, - newpath=runtime.cwd, - ) - else: - self._results['out_file'] = self.inputs.out_file - confounds_correlation_plot( - confounds_file=self.inputs.confounds_file, - columns=self.inputs.columns if isdefined(self.inputs.columns) else None, - max_dim=self.inputs.max_dim, - output_file=self._results['out_file'], - reference=self.inputs.reference_column, - ignore_initial_volumes=self.inputs.ignore_initial_volumes, - ) - return runtime - - -def _get_tr(img): - """ - Attempt to extract repetition time from NIfTI/CIFTI header - - Examples - -------- - >>> _get_tr(nb.load(Path(test_data) / - ... 'sub-ds205s03_task-functionallocalizer_run-01_bold_volreg.nii.gz')) - 2.2 - >>> _get_tr(nb.load(Path(test_data) / - ... 'sub-01_task-mixedgamblestask_run-02_space-fsLR_den-91k_bold.dtseries.nii')) - 2.0 - - """ - - try: - return img.header.matrix.get_index_map(0).series_step - except AttributeError: - return img.header.get_zooms()[-1] - raise RuntimeError('Could not extract TR - unknown data structure type') diff --git a/niworkflows/interfaces/reportlets/base.py b/niworkflows/interfaces/reportlets/base.py index e59744c1efa..b552b9edec1 100644 --- a/niworkflows/interfaces/reportlets/base.py +++ b/niworkflows/interfaces/reportlets/base.py @@ -24,9 +24,10 @@ from nipype.interfaces.base import File, traits from nipype.interfaces.mixins import reporting +from nireports.reportlets.mosaic import plot_registration, plot_segs +from nireports.reportlets.utils import compose_view, cuts_from_bbox from ... import NIWORKFLOWS_LOG -from ...viz.utils import compose_view, cuts_from_bbox class _SVGReportCapableInputSpec(reporting.ReportCapableInputSpec): @@ -59,8 +60,6 @@ def _generate_report(self): from nilearn.image import load_img, threshold_img from nilearn.masking import apply_mask, unmask - from niworkflows.viz.utils import plot_registration - NIWORKFLOWS_LOG.info('Generating visual report') fixed_image_nii = load_img(self._fixed_image) @@ -118,8 +117,6 @@ class SegmentationRC(reporting.ReportCapableInterface): """An abstract mixin to segmentation nipype interfaces.""" def _generate_report(self): - from niworkflows.viz.utils import plot_segs - compose_view( plot_segs( image_nii=self._anat_file, @@ -146,8 +143,6 @@ def _generate_report(self): from nilearn.image import load_img, threshold_img from nilearn.masking import apply_mask, unmask - from niworkflows.viz.utils import plot_registration - NIWORKFLOWS_LOG.info('Generating visual report') anat = load_img(self._anat_file) diff --git a/niworkflows/interfaces/reportlets/masks.py b/niworkflows/interfaces/reportlets/masks.py index 16673e5a823..156438a1f91 100644 --- a/niworkflows/interfaces/reportlets/masks.py +++ b/niworkflows/interfaces/reportlets/masks.py @@ -1,279 +1,16 @@ # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: -# -# Copyright 2021 The NiPreps Developers -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# We support and encourage derived works from this project, please read -# about our expectations at -# -# https://www.nipreps.org/community/licensing/ -# -"""ReportCapableInterfaces for masks tools.""" - -import os - -import nibabel as nb -import numpy as np -from nipype.algorithms import confounds -from nipype.interfaces import ants, fsl -from nipype.interfaces.base import ( - File, - InputMultiPath, - Str, - isdefined, - traits, +from ..._deprecated import raise_moved_to_nireports + + +raise_moved_to_nireports( + 'niworkflows.interfaces.reportlets.masks', + ( + 'nireports.interfaces.reporting.masks.BETRPT', + 'nireports.interfaces.reporting.masks.BrainExtractionRPT', + 'nireports.interfaces.reporting.masks.ACompCorRPT', + 'nireports.interfaces.reporting.masks.TCompCorRPT', + 'nireports.interfaces.reporting.masks.SimpleShowMaskRPT', + 'nireports.interfaces.reporting.masks.ROIsPlot', + ), ) -from nipype.interfaces.mixins import reporting - -from ... import NIWORKFLOWS_LOG -from . import base as nrb - - -class _BETInputSpecRPT(nrb._SVGReportCapableInputSpec, fsl.preprocess.BETInputSpec): - pass - - -class _BETOutputSpecRPT(reporting.ReportCapableOutputSpec, fsl.preprocess.BETOutputSpec): - pass - - -class BETRPT(nrb.SegmentationRC, fsl.BET): - input_spec = _BETInputSpecRPT - output_spec = _BETOutputSpecRPT - - def _run_interface(self, runtime): - if self.generate_report: - self.inputs.mask = True - - return super()._run_interface(runtime) - - def _post_run_hook(self, runtime): - """generates a report showing slices from each axis of an arbitrary - volume of in_file, with the resulting binary brain mask overlaid""" - - self._anat_file = self.inputs.in_file - self._mask_file = self.aggregate_outputs(runtime=runtime).mask_file - self._seg_files = [self._mask_file] - self._masked = self.inputs.mask - - NIWORKFLOWS_LOG.info( - 'Generating report for BET. file "%s", and mask file "%s"', - self._anat_file, - self._mask_file, - ) - - return super()._post_run_hook(runtime) - - -class _BrainExtractionInputSpecRPT( - nrb._SVGReportCapableInputSpec, ants.segmentation.BrainExtractionInputSpec -): - pass - - -class _BrainExtractionOutputSpecRPT( - reporting.ReportCapableOutputSpec, ants.segmentation.BrainExtractionOutputSpec -): - pass - - -class BrainExtractionRPT(nrb.SegmentationRC, ants.segmentation.BrainExtraction): - input_spec = _BrainExtractionInputSpecRPT - output_spec = _BrainExtractionOutputSpecRPT - - def _post_run_hook(self, runtime): - """generates a report showing slices from each axis""" - - brain_extraction_mask = self.aggregate_outputs(runtime=runtime).BrainExtractionMask - - if isdefined(self.inputs.keep_temporary_files) and self.inputs.keep_temporary_files == 1: - self._anat_file = self.aggregate_outputs(runtime=runtime).N4Corrected0 - else: - self._anat_file = self.inputs.anatomical_image - self._mask_file = brain_extraction_mask - self._seg_files = [brain_extraction_mask] - self._masked = False - - NIWORKFLOWS_LOG.info( - 'Generating report for ANTS BrainExtraction. file "%s", mask "%s"', - self._anat_file, - self._mask_file, - ) - - return super()._post_run_hook(runtime) - - -class _ACompCorInputSpecRPT(nrb._SVGReportCapableInputSpec, confounds.CompCorInputSpec): - pass - - -class _ACompCorOutputSpecRPT(reporting.ReportCapableOutputSpec, confounds.CompCorOutputSpec): - pass - - -class ACompCorRPT(nrb.SegmentationRC, confounds.ACompCor): - input_spec = _ACompCorInputSpecRPT - output_spec = _ACompCorOutputSpecRPT - - def _post_run_hook(self, runtime): - """generates a report showing slices from each axis""" - - if len(self.inputs.mask_files) != 1: - raise ValueError( - 'ACompCorRPT only supports a single input mask. ' - f'A list {self.inputs.mask_files} was found.' - ) - self._anat_file = self.inputs.realigned_file - self._mask_file = self.inputs.mask_files[0] - self._seg_files = self.inputs.mask_files - self._masked = False - - NIWORKFLOWS_LOG.info( - 'Generating report for aCompCor. file "%s", mask "%s"', - self.inputs.realigned_file, - self._mask_file, - ) - - return super()._post_run_hook(runtime) - - -class _TCompCorInputSpecRPT(nrb._SVGReportCapableInputSpec, confounds.TCompCorInputSpec): - pass - - -class _TCompCorOutputSpecRPT(reporting.ReportCapableOutputSpec, confounds.TCompCorOutputSpec): - pass - - -class TCompCorRPT(nrb.SegmentationRC, confounds.TCompCor): - input_spec = _TCompCorInputSpecRPT - output_spec = _TCompCorOutputSpecRPT - - def _post_run_hook(self, runtime): - """generates a report showing slices from each axis""" - - high_variance_masks = self.aggregate_outputs(runtime=runtime).high_variance_masks - - if isinstance(high_variance_masks, list): - raise ValueError( - 'TCompCorRPT only supports a single output high variance mask. ' - f'A list {high_variance_masks} was found.' - ) - self._anat_file = self.inputs.realigned_file - self._mask_file = high_variance_masks - self._seg_files = [high_variance_masks] - self._masked = False - - NIWORKFLOWS_LOG.info( - 'Generating report for tCompCor. file "%s", mask "%s"', - self.inputs.realigned_file, - self.aggregate_outputs(runtime=runtime).high_variance_masks, - ) - - return super()._post_run_hook(runtime) - - -class _SimpleShowMaskInputSpec(nrb._SVGReportCapableInputSpec): - background_file = File(exists=True, mandatory=True, desc='file before') - mask_file = File(exists=True, mandatory=True, desc='file before') - - -class SimpleShowMaskRPT(nrb.SegmentationRC, nrb.ReportingInterface): - input_spec = _SimpleShowMaskInputSpec - - def _post_run_hook(self, runtime): - self._anat_file = self.inputs.background_file - self._mask_file = self.inputs.mask_file - self._seg_files = [self.inputs.mask_file] - self._masked = True - - return super()._post_run_hook(runtime) - - -class _ROIsPlotInputSpecRPT(nrb._SVGReportCapableInputSpec): - in_file = File(exists=True, mandatory=True, desc='the volume where ROIs are defined') - in_rois = InputMultiPath( - File(exists=True), mandatory=True, desc='a list of regions to be plotted' - ) - in_mask = File(exists=True, desc='a special region, eg. the brain mask') - masked = traits.Bool(False, usedefault=True, desc='mask in_file prior plotting') - colors = traits.Either( - None, traits.List(Str), usedefault=True, desc='use specific colors for contours' - ) - levels = traits.Either( - None, - traits.List(traits.Float), - usedefault=True, - desc='pass levels to nilearn.plotting', - ) - mask_color = Str('r', usedefault=True, desc='color for mask') - - -class ROIsPlot(nrb.ReportingInterface): - input_spec = _ROIsPlotInputSpecRPT - - def _generate_report(self): - from seaborn import color_palette - - from niworkflows.viz.utils import compose_view, plot_segs - - seg_files = self.inputs.in_rois - mask_file = None if not isdefined(self.inputs.in_mask) else self.inputs.in_mask - - # Remove trait decoration and replace None with [] - levels = list(self.inputs.levels or []) - colors = list(self.inputs.colors or []) - - if len(seg_files) == 1: # in_rois is a segmentation - nsegs = len(levels) - if nsegs == 0: - levels = np.unique(np.round(nb.load(seg_files[0]).get_fdata(dtype='float32'))) - levels = (levels[levels > 0] - 0.5).tolist() - nsegs = len(levels) - - levels = [levels] - missing = nsegs - len(colors) - if missing > 0: - colors = colors + color_palette('husl', missing) - colors = [colors] - else: # in_rois is a list of masks - nsegs = len(seg_files) - levels = [[0.5]] * nsegs - missing = nsegs - len(colors) - if missing > 0: - colors = [[c] for c in colors + color_palette('husl', missing)] - - if mask_file: - seg_files.insert(0, mask_file) - if levels: - levels.insert(0, [0.5]) - colors.insert(0, [self.inputs.mask_color]) - nsegs += 1 - - self._out_report = os.path.abspath(self.inputs.out_report) - compose_view( - plot_segs( - image_nii=self.inputs.in_file, - seg_niis=seg_files, - bbox_nii=mask_file, - levels=levels, - colors=colors, - out_file=self.inputs.out_report, - masked=self.inputs.masked, - compress=self.inputs.compress_report, - ), - fg_svgs=None, - out_file=self._out_report, - ) diff --git a/niworkflows/interfaces/reportlets/registration.py b/niworkflows/interfaces/reportlets/registration.py index 91d8cc004e5..a9df8d86edc 100644 --- a/niworkflows/interfaces/reportlets/registration.py +++ b/niworkflows/interfaces/reportlets/registration.py @@ -1,42 +1,12 @@ # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: -# -# Copyright 2021 The NiPreps Developers -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# We support and encourage derived works from this project, please read -# about our expectations at -# -# https://www.nipreps.org/community/licensing/ -# -"""ReportCapableInterfaces for registration tools.""" - -import os - -from looseversion import LooseVersion -from nipype.interfaces import freesurfer as fs -from nipype.interfaces import fsl +"""Report-capable registration interfaces not yet ported to NiReports.""" + from nipype.interfaces.ants import registration, resampling -from nipype.interfaces.base import ( - File, - isdefined, - traits, -) from nipype.interfaces.mixins import reporting -from nipype.utils.filemanip import fname_presuffix from ... import NIWORKFLOWS_LOG +from ..._deprecated import DeprecationError, moved_to_nireports_message from ..fixes import ( FixHeaderApplyTransforms as ApplyTransforms, ) @@ -141,258 +111,31 @@ def _post_run_hook(self, runtime): return super()._post_run_hook(runtime) -class _ApplyTOPUPInputSpecRPT(nrb._SVGReportCapableInputSpec, fsl.epi.ApplyTOPUPInputSpec): - wm_seg = File(argstr='-wmseg %s', desc='reference white matter segmentation mask') - - -class _ApplyTOPUPOutputSpecRPT(reporting.ReportCapableOutputSpec, fsl.epi.ApplyTOPUPOutputSpec): - pass - - -class ApplyTOPUPRPT(nrb.RegistrationRC, fsl.ApplyTOPUP): - input_spec = _ApplyTOPUPInputSpecRPT - output_spec = _ApplyTOPUPOutputSpecRPT - - def _post_run_hook(self, runtime): - from nilearn.image import index_img - - self._fixed_image_label = 'after' - self._moving_image_label = 'before' - self._fixed_image = index_img(self.aggregate_outputs(runtime=runtime).out_corrected, 0) - self._moving_image = index_img(self.inputs.in_files[0], 0) - self._contour = self.inputs.wm_seg if isdefined(self.inputs.wm_seg) else None - NIWORKFLOWS_LOG.info( - 'Report - setting corrected (%s) and warped (%s) images', - self._fixed_image, - self._moving_image, - ) - - return super()._post_run_hook(runtime) - - -class _FUGUEInputSpecRPT(nrb._SVGReportCapableInputSpec, fsl.preprocess.FUGUEInputSpec): - wm_seg = File(argstr='-wmseg %s', desc='reference white matter segmentation mask') - - -class _FUGUEOutputSpecRPT(reporting.ReportCapableOutputSpec, fsl.preprocess.FUGUEOutputSpec): - pass - - -class FUGUERPT(nrb.RegistrationRC, fsl.FUGUE): - input_spec = _FUGUEInputSpecRPT - output_spec = _FUGUEOutputSpecRPT - - def _post_run_hook(self, runtime): - self._fixed_image_label = 'after' - self._moving_image_label = 'before' - self._fixed_image = self.aggregate_outputs(runtime=runtime).unwarped_file - self._moving_image = self.inputs.in_file - self._contour = self.inputs.wm_seg if isdefined(self.inputs.wm_seg) else None - NIWORKFLOWS_LOG.info( - 'Report - setting corrected (%s) and warped (%s) images', - self._fixed_image, - self._moving_image, - ) - - return super()._post_run_hook(runtime) - - -class _FLIRTInputSpecRPT(nrb._SVGReportCapableInputSpec, fsl.preprocess.FLIRTInputSpec): - pass - - -class _FLIRTOutputSpecRPT(reporting.ReportCapableOutputSpec, fsl.preprocess.FLIRTOutputSpec): - pass - - -class FLIRTRPT(nrb.RegistrationRC, fsl.FLIRT): - input_spec = _FLIRTInputSpecRPT - output_spec = _FLIRTOutputSpecRPT - - def _post_run_hook(self, runtime): - self._fixed_image = self.inputs.reference - self._moving_image = self.aggregate_outputs(runtime=runtime).out_file - self._contour = self.inputs.wm_seg if isdefined(self.inputs.wm_seg) else None - NIWORKFLOWS_LOG.info( - 'Report - setting fixed (%s) and moving (%s) images', - self._fixed_image, - self._moving_image, - ) - - return super()._post_run_hook(runtime) - - -class _ApplyXFMInputSpecRPT(nrb._SVGReportCapableInputSpec, fsl.preprocess.ApplyXFMInputSpec): - pass - - -class ApplyXFMRPT(FLIRTRPT, fsl.ApplyXFM): - input_spec = _ApplyXFMInputSpecRPT - output_spec = _FLIRTOutputSpecRPT - - -if LooseVersion('0.0.0') < fs.Info.looseversion() < LooseVersion('6.0.0'): - _BBRegisterInputSpec = fs.preprocess.BBRegisterInputSpec -else: - _BBRegisterInputSpec = fs.preprocess.BBRegisterInputSpec6 - - -class _BBRegisterInputSpecRPT(nrb._SVGReportCapableInputSpec, _BBRegisterInputSpec): - # Adds default=True, usedefault=True - out_lta_file = traits.Either( - traits.Bool, - File, - default=True, - usedefault=True, - argstr='--lta %s', - min_ver='5.2.0', - desc='write the transformation matrix in LTA format', - ) - - -class _BBRegisterOutputSpecRPT( - reporting.ReportCapableOutputSpec, fs.preprocess.BBRegisterOutputSpec -): - pass - - -class BBRegisterRPT(nrb.RegistrationRC, fs.BBRegister): - input_spec = _BBRegisterInputSpecRPT - output_spec = _BBRegisterOutputSpecRPT - - def _post_run_hook(self, runtime): - outputs = self.aggregate_outputs(runtime=runtime) - mri_dir = os.path.join(self.inputs.subjects_dir, self.inputs.subject_id, 'mri') - target_file = os.path.join(mri_dir, 'brainmask.mgz') - - # Apply transform for simplicity - mri_vol2vol = fs.ApplyVolTransform( - source_file=self.inputs.source_file, - target_file=target_file, - lta_file=outputs.out_lta_file, - interp='nearest', - ) - res = mri_vol2vol.run() - - self._fixed_image = target_file - self._moving_image = res.outputs.transformed_file - self._contour = os.path.join(mri_dir, 'ribbon.mgz') - NIWORKFLOWS_LOG.info( - 'Report - setting fixed (%s) and moving (%s) images', - self._fixed_image, - self._moving_image, - ) - - return super()._post_run_hook(runtime) - - -class _MRICoregInputSpecRPT(nrb._SVGReportCapableInputSpec, fs.registration.MRICoregInputSpec): - pass - - -class _MRICoregOutputSpecRPT( - reporting.ReportCapableOutputSpec, fs.registration.MRICoregOutputSpec -): - pass - - -class MRICoregRPT(nrb.RegistrationRC, fs.MRICoreg): - input_spec = _MRICoregInputSpecRPT - output_spec = _MRICoregOutputSpecRPT - - def _post_run_hook(self, runtime): - outputs = self.aggregate_outputs(runtime=runtime) - mri_dir = None - if isdefined(self.inputs.subject_id): - mri_dir = os.path.join(self.inputs.subjects_dir, self.inputs.subject_id, 'mri') - - if isdefined(self.inputs.reference_file): - target_file = self.inputs.reference_file - else: - target_file = os.path.join(mri_dir, 'brainmask.mgz') - - # Apply transform for simplicity - mri_vol2vol = fs.ApplyVolTransform( - source_file=self.inputs.source_file, - target_file=target_file, - lta_file=outputs.out_lta_file, - interp='nearest', - ) - res = mri_vol2vol.run() - - self._fixed_image = target_file - self._moving_image = res.outputs.transformed_file - if mri_dir is not None: - self._contour = os.path.join(mri_dir, 'ribbon.mgz') - NIWORKFLOWS_LOG.info( - 'Report - setting fixed (%s) and moving (%s) images', - self._fixed_image, - self._moving_image, - ) - - return super()._post_run_hook(runtime) - - -class _SimpleBeforeAfterInputSpecRPT(nrb._SVGReportCapableInputSpec): - before = File(exists=True, mandatory=True, desc='file before') - after = File(exists=True, mandatory=True, desc='file after') - wm_seg = File(desc='reference white matter segmentation mask') - before_label = traits.Str('before', usedefault=True) - after_label = traits.Str('after', usedefault=True) - dismiss_affine = traits.Bool(False, usedefault=True, desc='rotate image(s) to cardinal axes') - - -class SimpleBeforeAfterRPT(nrb.RegistrationRC, nrb.ReportingInterface): - input_spec = _SimpleBeforeAfterInputSpecRPT - - def _post_run_hook(self, runtime): - """there is not inner interface to run""" - self._fixed_image_label = self.inputs.after_label - self._moving_image_label = self.inputs.before_label - self._fixed_image = self.inputs.after - self._moving_image = self.inputs.before - self._contour = self.inputs.wm_seg if isdefined(self.inputs.wm_seg) else None - self._dismiss_affine = self.inputs.dismiss_affine - NIWORKFLOWS_LOG.info( - 'Report - setting before (%s) and after (%s) images', - self._fixed_image, - self._moving_image, - ) - - return super()._post_run_hook(runtime) - - -class _ResampleBeforeAfterInputSpecRPT(_SimpleBeforeAfterInputSpecRPT): - base = traits.Enum('before', 'after', usedefault=True, mandatory=True) - - -class ResampleBeforeAfterRPT(SimpleBeforeAfterRPT): - input_spec = _ResampleBeforeAfterInputSpecRPT - - def _post_run_hook(self, runtime): - from nilearn import image as nli - - self._fixed_image = self.inputs.after - self._moving_image = self.inputs.before - if self.inputs.base == 'before': - resampled_after = nli.resample_to_img(self._fixed_image, self._moving_image) - fname = fname_presuffix(self._fixed_image, suffix='_resampled', newpath=runtime.cwd) - resampled_after.to_filename(fname) - self._fixed_image = fname - else: - resampled_before = nli.resample_to_img(self._moving_image, self._fixed_image) - fname = fname_presuffix(self._moving_image, suffix='_resampled', newpath=runtime.cwd) - resampled_before.to_filename(fname) - self._moving_image = fname - self._contour = self.inputs.wm_seg if isdefined(self.inputs.wm_seg) else None - NIWORKFLOWS_LOG.info( - 'Report - setting before (%s) and after (%s) images', - self._fixed_image, - self._moving_image, +_MOVED_REPORTLETS = { + 'ApplyTOPUPRPT': 'nireports.interfaces.reporting.registration.ApplyTOPUPRPT', + 'ApplyXFMRPT': 'nireports.interfaces.reporting.registration.ApplyXFMRPT', + 'BBRegisterRPT': 'nireports.interfaces.reporting.registration.BBRegisterRPT', + 'FLIRTRPT': 'nireports.interfaces.reporting.registration.FLIRTRPT', + 'FUGUERPT': 'nireports.interfaces.reporting.registration.FUGUERPT', + 'MRICoregRPT': 'nireports.interfaces.reporting.registration.MRICoregRPT', + 'ResampleBeforeAfterRPT': 'nireports.interfaces.reporting.registration.ResampleBeforeAfterRPT', + 'SimpleBeforeAfterRPT': 'nireports.interfaces.reporting.registration.SimpleBeforeAfterRPT', +} + + +def __getattr__(name): + if name in _MOVED_REPORTLETS: + raise DeprecationError( + moved_to_nireports_message( + f'niworkflows.interfaces.reportlets.registration.{name}', + (_MOVED_REPORTLETS[name],), + ) ) + raise AttributeError(f'module {__name__!r} has no attribute {name!r}') - runtime = super()._post_run_hook(runtime) - NIWORKFLOWS_LOG.info('Successfully created report (%s)', self._out_report) - os.unlink(fname) - return runtime +__all__ = [ + 'ANTSApplyTransformsRPT', + 'ANTSRegistrationRPT', + 'SpatialNormalizationRPT', +] diff --git a/niworkflows/interfaces/reportlets/segmentation.py b/niworkflows/interfaces/reportlets/segmentation.py index 642dea987c7..2de575b8f13 100644 --- a/niworkflows/interfaces/reportlets/segmentation.py +++ b/niworkflows/interfaces/reportlets/segmentation.py @@ -1,222 +1,14 @@ # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: -# -# Copyright 2021 The NiPreps Developers -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# We support and encourage derived works from this project, please read -# about our expectations at -# -# https://www.nipreps.org/community/licensing/ -# -"""ReportCapableInterfaces for segmentation tools.""" - -import os - -from nipype.interfaces import freesurfer, fsl -from nipype.interfaces.base import File, isdefined -from nipype.interfaces.mixins import reporting - -from ... import NIWORKFLOWS_LOG -from . import base as nrb - - -class _FASTInputSpecRPT(nrb._SVGReportCapableInputSpec, fsl.preprocess.FASTInputSpec): - pass - - -class _FASTOutputSpecRPT(reporting.ReportCapableOutputSpec, fsl.preprocess.FASTOutputSpec): - pass - - -class FASTRPT(nrb.SegmentationRC, fsl.FAST): - input_spec = _FASTInputSpecRPT - output_spec = _FASTOutputSpecRPT - - def _run_interface(self, runtime): - if self.generate_report: - self.inputs.segments = True - - return super()._run_interface(runtime) - - def _post_run_hook(self, runtime): - """generates a report showing nine slices, three per axis, of an - arbitrary volume of `in_files`, with the resulting segmentation - overlaid""" - self._anat_file = self.inputs.in_files[0] - outputs = self.aggregate_outputs(runtime=runtime) - self._mask_file = outputs.tissue_class_map - # We are skipping the CSF class because with combination with others - # it only shows the skullstriping mask - self._seg_files = outputs.tissue_class_files[1:] - self._masked = False - - NIWORKFLOWS_LOG.info( - 'Generating report for FAST (in_files %s, ' - 'segmentation %s, individual tissue classes %s).', - self.inputs.in_files, - outputs.tissue_class_map, - outputs.tissue_class_files, - ) - - return super()._post_run_hook(runtime) - - -class _ReconAllInputSpecRPT( - nrb._SVGReportCapableInputSpec, freesurfer.preprocess.ReconAllInputSpec -): - pass - - -class _ReconAllOutputSpecRPT( - reporting.ReportCapableOutputSpec, freesurfer.preprocess.ReconAllOutputSpec -): - pass - - -class ReconAllRPT(nrb.SurfaceSegmentationRC, freesurfer.preprocess.ReconAll): - input_spec = _ReconAllInputSpecRPT - output_spec = _ReconAllOutputSpecRPT - - def _post_run_hook(self, runtime): - """generates a report showing nine slices, three per axis, of an - arbitrary volume of `in_files`, with the resulting segmentation - overlaid""" - outputs = self.aggregate_outputs(runtime=runtime) - self._anat_file = os.path.join( - outputs.subjects_dir, outputs.subject_id, 'mri', 'brain.mgz' - ) - self._contour = os.path.join(outputs.subjects_dir, outputs.subject_id, 'mri', 'ribbon.mgz') - self._masked = False - - NIWORKFLOWS_LOG.info('Generating report for ReconAll (subject %s)', outputs.subject_id) - - return super()._post_run_hook(runtime) - - -class _MELODICInputSpecRPT(nrb._SVGReportCapableInputSpec, fsl.model.MELODICInputSpec): - out_report = File( - 'melodic_reportlet.svg', - usedefault=True, - desc='Filename for the visual report generated by Nipype.', - ) - report_mask = File( - desc='Mask used to draw the outline on the reportlet. ' - 'If not set the mask will be derived from the data.' - ) - - -class _MELODICOutputSpecRPT(reporting.ReportCapableOutputSpec, fsl.model.MELODICOutputSpec): - pass - - -class MELODICRPT(fsl.MELODIC): - input_spec = _MELODICInputSpecRPT - output_spec = _MELODICOutputSpecRPT - _out_report = None - - def __init__(self, generate_report=False, **kwargs): - super().__init__(**kwargs) - self.generate_report = generate_report - - def _post_run_hook(self, runtime): - # Run _post_run_hook of super class - runtime = super()._post_run_hook(runtime) - # leave early if there's nothing to do - if not self.generate_report: - return runtime - - NIWORKFLOWS_LOG.info('Generating report for MELODIC.') - _melodic_dir = runtime.cwd - if isdefined(self.inputs.out_dir): - _melodic_dir = self.inputs.out_dir - self._melodic_dir = os.path.abspath(_melodic_dir) - - self._out_report = self.inputs.out_report - if not os.path.isabs(self._out_report): - self._out_report = os.path.abspath(os.path.join(runtime.cwd, self._out_report)) - - mix = os.path.join(self._melodic_dir, 'melodic_mix') - if not os.path.exists(mix): - NIWORKFLOWS_LOG.warning("MELODIC outputs not found, assuming it didn't converge.") - self._out_report = self._out_report.replace('.svg', '.html') - snippet = '

MELODIC did not converge, no output

' - with open(self._out_report, 'w') as fobj: - fobj.write(snippet) - return runtime - - self._generate_report() - return runtime - - def _list_outputs(self): - try: - outputs = super()._list_outputs() - except NotImplementedError: - outputs = {} - if self._out_report is not None: - outputs['out_report'] = self._out_report - return outputs - - def _generate_report(self): - from niworkflows.viz.utils import plot_melodic_components - - plot_melodic_components( - melodic_dir=self._melodic_dir, - in_file=self.inputs.in_files[0], - tr=self.inputs.tr_sec, - out_file=self._out_report, - compress=self.inputs.compress_report, - report_mask=self.inputs.report_mask, - ) - - -class _ICA_AROMAInputSpecRPT(nrb._SVGReportCapableInputSpec, fsl.aroma.ICA_AROMAInputSpec): - out_report = File( - 'ica_aroma_reportlet.svg', - usedefault=True, - desc='Filename for the visual report generated by Nipype.', - ) - report_mask = File( - desc='Mask used to draw the outline on the reportlet. ' - 'If not set the mask will be derived from the data.' - ) - - -class _ICA_AROMAOutputSpecRPT(reporting.ReportCapableOutputSpec, fsl.aroma.ICA_AROMAOutputSpec): - pass - - -class ICA_AROMARPT(reporting.ReportCapableInterface, fsl.ICA_AROMA): - input_spec = _ICA_AROMAInputSpecRPT - output_spec = _ICA_AROMAOutputSpecRPT - - def _generate_report(self): - from niworkflows.viz.utils import plot_melodic_components - - plot_melodic_components( - melodic_dir=self.inputs.melodic_dir, - in_file=self.inputs.in_file, - out_file=self.inputs.out_report, - compress=self.inputs.compress_report, - report_mask=self.inputs.report_mask, - noise_components_file=self._noise_components_file, - ) - - def _post_run_hook(self, runtime): - outputs = self.aggregate_outputs(runtime=runtime) - self._noise_components_file = os.path.join(outputs.out_dir, 'classified_motion_ICs.txt') - - NIWORKFLOWS_LOG.info('Generating report for ICA AROMA') - - return super()._post_run_hook(runtime) +from ..._deprecated import raise_moved_to_nireports + + +raise_moved_to_nireports( + 'niworkflows.interfaces.reportlets.segmentation', + ( + 'nireports.interfaces.reporting.segmentation.FASTRPT', + 'nireports.interfaces.reporting.segmentation.ReconAllRPT', + 'nireports.interfaces.reporting.segmentation.MELODICRPT', + 'nireports.interfaces.reporting.segmentation.ICA_AROMARPT', + ), +) diff --git a/niworkflows/interfaces/tests/test_plotting.py b/niworkflows/interfaces/tests/test_plotting.py deleted file mode 100644 index a8f61de098a..00000000000 --- a/niworkflows/interfaces/tests/test_plotting.py +++ /dev/null @@ -1,76 +0,0 @@ -# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- -# vi: set ft=python sts=4 ts=4 sw=4 et: -# -# Copyright 2022 The NiPreps Developers -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# We support and encourage derived works from this project, please read -# about our expectations at -# -# https://www.nipreps.org/community/licensing/ -# -"""Tests plotting interfaces.""" - -import os - -import nibabel as nb - -from niworkflows import viz -from niworkflows.interfaces.plotting import _get_tr -from niworkflows.utils.timeseries import _cifti_timeseries, _nifti_timeseries - -try: - from niworkflows.tests.data import load_test_data -except ImportError: - import pytest - - pytest.skip('niworkflows installed as wheel, data excluded', allow_module_level=True) - - -def test_cifti_carpetplot(): - """Exercise extraction of timeseries from CIFTI2.""" - save_artifacts = os.getenv('SAVE_CIRCLE_ARTIFACTS') - - cifti_file = load_test_data( - 'sub-01_task-mixedgamblestask_run-02_space-fsLR_den-91k_bold.dtseries.nii' - ) - data, segments = _cifti_timeseries(str(cifti_file)) - viz.plot_carpet( - data, - segments, - tr=_get_tr(nb.load(cifti_file)), - output_file=( - os.path.join(save_artifacts, 'carpetplot_cifti.svg') if save_artifacts else None - ), - drop_trs=0, - cmap='paired', - ) - - -def test_nifti_carpetplot(): - """Exercise extraction of timeseries from CIFTI2.""" - save_artifacts = os.getenv('SAVE_CIRCLE_ARTIFACTS') - - nifti_file = load_test_data('sub-ds205s03_task-functionallocalizer_run-01_bold_volreg.nii.gz') - seg_file = load_test_data('sub-ds205s03_task-functionallocalizer_run-01_bold_parc.nii.gz') - data, segments = _nifti_timeseries(str(nifti_file), str(seg_file)) - viz.plot_carpet( - data, - segments, - tr=_get_tr(nb.load(nifti_file)), - output_file=( - os.path.join(save_artifacts, 'carpetplot_nifti.svg') if save_artifacts else None - ), - drop_trs=0, - ) diff --git a/niworkflows/reports/__init__.py b/niworkflows/reports/__init__.py index 107316216bc..bd585637e11 100644 --- a/niworkflows/reports/__init__.py +++ b/niworkflows/reports/__init__.py @@ -1,6 +1,16 @@ # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: +from .._deprecated import raise_moved_to_nireports -from .core import generate_reports -__all__ = ['generate_reports'] +raise_moved_to_nireports( + 'niworkflows.reports', + ( + 'nireports.assembler.report.Report', + 'nireports.assembler.report.SubReport', + 'nireports.assembler.reportlet.Reportlet', + 'nireports.assembler.misc.Element', + 'nireports.assembler.tools.generate_reports', + 'nireports.assembler.tools.run_reports', + ), +) diff --git a/niworkflows/reports/core.py b/niworkflows/reports/core.py index 698fd67ddcd..d4e34b989ac 100644 --- a/niworkflows/reports/core.py +++ b/niworkflows/reports/core.py @@ -1,549 +1,16 @@ # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: -# -# Copyright 2021 The NiPreps Developers -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# We support and encourage derived works from this project, please read -# about our expectations at -# -# https://www.nipreps.org/community/licensing/ -# -""" -Reports builder for BIDS-Apps. - -Generalizes report generation across BIDS-Apps - -""" - -import re -from collections import defaultdict -from itertools import compress -from pathlib import Path - -import jinja2 -from bids.layout import BIDSLayout, add_config_paths -from nipype.utils.filemanip import copyfile - -from .. import data, load_resource - -# Add a new figures spec -try: - add_config_paths(figures=data.load('nipreps.json')) -except ValueError as e: - if "Configuration 'figures' already exists" != str(e): - raise - -PLURAL_SUFFIX = defaultdict('s'.format, [('echo', 'es')]) -SVG_SNIPPET = [ - """\ - -Problem loading figure {0}. If the link below works, please try \ -reloading the report in your browser. - -
- Get figure file: {0} -
-""", - """\ - - -
- Get figure file: {0} -
-""", -] - - -class Smallest: - """An object that always evaluates smaller than anything else, for sorting - - >>> Smallest() < 1 - True - >>> Smallest() < "epsilon" - True - >>> sorted([1, None, 2], key=lambda x: x if x is not None else Smallest()) - [None, 1, 2] - """ - - def __lt__(self, other): - return not isinstance(other, Smallest) - - def __eq__(self, other): - return isinstance(other, Smallest) - - def __hash__(self): - return hash('') - - def __gt__(self, other): - return False - - -class Element: - """Just a basic component of a report""" - - def __init__(self, name, title=None): - self.name = name - self.title = title - - -class Reportlet(Element): - """ - A reportlet has title, description and a list of components with either an - HTML fragment or a path to an SVG file, and possibly a caption. This is a - factory class to generate Reportlets reusing the layout from a ``Report`` - object. - - .. testsetup:: - - >>> from shutil import copytree - >>> from bids.layout import BIDSLayout - >>> source = find_resource_or_skip('data/tests/work') - >>> testdir = Path(tmpdir) - >>> _ = copytree(source, testdir / 'work') - >>> out_figs = testdir / 'out' / 'fmriprep' - >>> bl = BIDSLayout(testdir / 'work' / 'reportlets', - ... config='figures', validate=False) - - >>> bl.get(subject='01', desc='reconall') # doctest: +ELLIPSIS - [] - - >>> len(bl.get(subject='01', space='.*', regex_search=True)) - 2 - - >>> r = Reportlet(bl, out_figs, config={ - ... 'title': 'Some Title', 'bids': {'datatype': 'figures', 'desc': 'reconall'}, - ... 'description': 'Some description'}) - >>> r.name - 'datatype-figures_desc-reconall' - - >>> r.components[0][0].startswith('>> r = Reportlet(bl, out_figs, config={ - ... 'title': 'Some Title', 'bids': {'datatype': 'figures', 'desc': 'reconall'}, - ... 'description': 'Some description', 'static': False}) - >>> r.name - 'datatype-figures_desc-reconall' - - >>> r.components[0][0].startswith('>> r = Reportlet(bl, out_figs, config={ - ... 'title': 'Some Title', 'bids': {'datatype': 'figures', 'desc': 'summary'}, - ... 'description': 'Some description'}) - - >>> r.components[0][0].startswith('>> r.components[0][1] is None - True - - >>> r = Reportlet(bl, out_figs, config={ - ... 'title': 'Some Title', - ... 'bids': {'datatype': 'figures', 'space': '.*', 'regex_search': True}, - ... 'caption': 'Some description {space}'}) - >>> sorted(r.components)[0][1] - 'Some description MNI152NLin2009cAsym' - - >>> sorted(r.components)[1][1] - 'Some description MNI152NLin6Asym' - - - >>> r = Reportlet(bl, out_figs, config={ - ... 'title': 'Some Title', - ... 'bids': {'datatype': 'fmap', 'space': '.*', 'regex_search': True}, - ... 'caption': 'Some description {space}'}) - >>> r.is_empty() - True - - """ - - def __init__(self, layout, out_dir, config=None): - if not config: - raise RuntimeError('Reportlet must have a config object') - - self.name = config.get( - 'name', '_'.join(f'{k}-{v}' for k, v in sorted(config['bids'].items())) - ) - self.title = config.get('title') - self.subtitle = config.get('subtitle') - self.description = config.get('description') - - # Query the BIDS layout of reportlets - files = layout.get(**config['bids']) - - self.components = [] - for bidsfile in files: - src = Path(bidsfile.path) - ext = ''.join(src.suffixes) - desc_text = config.get('caption') - - contents = None - if ext == '.html': - contents = src.read_text().strip() - elif ext == '.svg': - entities = dict(bidsfile.entities) - if desc_text: - desc_text = desc_text.format(**entities) - - try: - html_anchor = src.relative_to(out_dir) - except ValueError: - html_anchor = src.relative_to(Path(layout.root).parent) - dst = out_dir / html_anchor - dst.parent.mkdir(parents=True, exist_ok=True) - copyfile(src, dst, copy=True, use_hardlink=True) - - contents = SVG_SNIPPET[config.get('static', True)].format(html_anchor) - - # Our current implementations of dynamic reportlets do this themselves, - # however I'll leave the code here since this is potentially something we - # will want to transfer from every figure generator to this location. - # The following code misses setting preserveAspecRatio="xMidYMid meet" - # if not is_static: - # # Remove height and width attributes from initial tag - # svglines = out_file.read_text().splitlines() - # expr = re.compile(r' (height|width)=["\'][0-9]+(\.[0-9]*)?[a-z]*["\']') - # for l, line in enumerate(svglines[:6]): - # if line.strip().startswith('>> from shutil import copytree - >>> from bids.layout import BIDSLayout - >>> source = find_resource_or_skip('data/tests/work') - >>> testdir = Path(tmpdir) - >>> _ = copytree(source, str(testdir / 'work')) - >>> out_figs = testdir / 'out' / 'fmriprep' - - >>> robj = Report(testdir / 'out', 'madeoutuuid', subject_id='01', packagename='fmriprep', - ... reportlets_dir=testdir / 'work' / 'reportlets') - >>> robj.layout.get(subject='01', desc='reconall') # doctest: +ELLIPSIS - [] - - >>> robj.generate_report() - 0 - >>> len((testdir / 'out' / 'fmriprep' / 'sub-01.html').read_text()) - 36713 - - """ - - def __init__( - self, - out_dir, - run_uuid, - config=None, - out_filename='report.html', - packagename=None, - reportlets_dir=None, - subject_id=None, - ): - self.root = Path(reportlets_dir or out_dir) - - # Initialize structuring elements - self.sections = [] - self.errors = [] - self.out_dir = Path(out_dir) - self.out_filename = out_filename - self.run_uuid = run_uuid - self.packagename = packagename - self.subject_id = subject_id - if subject_id is not None: - self.subject_id = subject_id.removeprefix('sub-') - self.out_filename = f'sub-{self.subject_id}.html' - - # Default template from niworkflows - self.template_path = load_resource('reports') / 'report.tpl' - if not self.template_path.exists(): - raise RuntimeError('Could not find report template. Corrupted installation.') - self._load_config(Path(config or load_resource('reports') / 'default.yml')) - - def _load_config(self, config): - from yaml import safe_load as load - - settings = load(config.read_text()) - self.packagename = self.packagename or settings.get('package', None) - - if self.packagename is not None: - self.root = self.root / self.packagename - self.out_dir = self.out_dir / self.packagename - - if self.subject_id is not None: - self.root = self.root / f'sub-{self.subject_id}' - - if 'template_path' in settings: - self.template_path = config.parent / settings['template_path'] - - self.index(settings['sections']) - - def init_layout(self): - self.layout = BIDSLayout(self.root, config='figures', validate=False) - - def index(self, config): - """ - Traverse the reports config definition and instantiate reportlets. - - This method also places figures in their final location. - """ - # Initialize a BIDS layout - self.init_layout() - for subrep_cfg in config: - # First determine whether we need to split by some ordering - # (ie. sessions / tasks / runs), which are separated by commas. - orderings = [s for s in subrep_cfg.get('ordering', '').strip().split(',') if s] - entities, list_combos = self._process_orderings(orderings, self.layout) - - if not list_combos: # E.g. this is an anatomical reportlet - reportlets = [ - Reportlet(self.layout, self.out_dir, config=cfg) - for cfg in subrep_cfg['reportlets'] - ] - else: - # Do not use dictionary for queries, as we need to preserve ordering - # of ordering columns. - reportlets = [] - for c in list_combos: - # do not display entities with the value None. - c_filt = list(filter(None, c)) - ent_filt = list(compress(entities, c)) - # Set a common title for this particular combination c - title = 'Reports for: {}.'.format( - ', '.join( - f'{ent_filt[i]} {c_filt[i]}' - for i in range(len(c_filt)) - ) - ) - for cfg in subrep_cfg['reportlets']: - cfg['bids'].update({entities[i]: c[i] for i in range(len(c))}) - rlet = Reportlet(self.layout, self.out_dir, config=cfg) - if not rlet.is_empty(): - rlet.title = title - title = None - reportlets.append(rlet) - - # Filter out empty reportlets - reportlets = [r for r in reportlets if not r.is_empty()] - if reportlets: - sub_report = SubReport( - subrep_cfg['name'], - isnested=bool(list_combos), - reportlets=reportlets, - title=subrep_cfg.get('title'), - ) - self.sections.append(sub_report) - - # Populate errors section - error_dir = self.out_dir / f'sub-{self.subject_id}' / 'log' / self.run_uuid - if error_dir.is_dir(): - from ..utils.misc import read_crashfile - - self.errors = [read_crashfile(str(f)) for f in error_dir.glob('crash*.*')] - - def generate_report(self): - """Once the Report has been indexed, the final HTML can be generated""" - logs_path = self.out_dir / 'logs' - - boilerplate = [] - boiler_idx = 0 - - if (logs_path / 'CITATION.html').exists(): - text = ( - re.compile(r'(.*?)', re.DOTALL | re.IGNORECASE) - .findall((logs_path / 'CITATION.html').read_text())[0] - .strip() - ) - boilerplate.append((boiler_idx, 'HTML', f'
{text}
')) - boiler_idx += 1 - - if (logs_path / 'CITATION.md').exists(): - text = (logs_path / 'CITATION.md').read_text() - boilerplate.append((boiler_idx, 'Markdown', f'
{text}
\n')) - boiler_idx += 1 - - if (logs_path / 'CITATION.tex').exists(): - text = ( - re.compile(r'\\begin{document}(.*?)\\end{document}', re.DOTALL | re.IGNORECASE) - .findall((logs_path / 'CITATION.tex').read_text())[0] - .strip() - ) - bib = data.Loader(self.packagename).readable('data/boilerplate.bib') - boilerplate.append( - ( - boiler_idx, - 'LaTeX', - f'
{text}
\n

Bibliography

\n
{bib.read_text()}
\n', - ) - ) - boiler_idx += 1 - - env = jinja2.Environment( - loader=jinja2.FileSystemLoader(searchpath=str(self.template_path.parent)), - trim_blocks=True, - lstrip_blocks=True, - autoescape=False, # noqa: S701 XXX Investigate if this is a problem in nireports. - ) - report_tpl = env.get_template(self.template_path.name) - report_render = report_tpl.render( - sections=self.sections, errors=self.errors, boilerplate=boilerplate - ) - - # Write out report - self.out_dir.mkdir(parents=True, exist_ok=True) - (self.out_dir / self.out_filename).write_text(report_render, encoding='UTF-8') - return len(self.errors) - - @staticmethod - def _process_orderings(orderings, layout): - """ - Generate relevant combinations of orderings with observed values. - - Arguments - --------- - orderings : :obj:`list` of :obj:`list` of :obj:`str` - Sections prescribing an ordering to select across sessions, acquisitions, runs, etc. - layout : :obj:`bids.layout.BIDSLayout` - The BIDS layout - - Returns - ------- - entities: :obj:`list` of :obj:`str` - The relevant orderings that had unique values - value_combos: :obj:`list` of :obj:`tuple` - Unique value combinations for the entities - - """ - # get a set of all unique entity combinations - all_value_combos = { - tuple(bids_file.get_entities().get(k, None) for k in orderings) - for bids_file in layout.get() - } - # remove the all None member if it exists - none_member = tuple(None for k in orderings) - if none_member in all_value_combos: - all_value_combos.remove(tuple(None for k in orderings)) - # see what values exist for each entity - unique_values = [ - {value[idx] for value in all_value_combos} for idx in range(len(orderings)) - ] - # if all values are None for an entity, we do not want to keep that entity - keep_idx = [val_set not in (set(), {None}) for val_set in unique_values] - # the "kept" entities - entities = list(compress(orderings, keep_idx)) - # the "kept" value combinations - value_combos = [tuple(compress(value_combo, keep_idx)) for value_combo in all_value_combos] - # sort the value combinations alphabetically from the first entity to the last entity - value_combos.sort( - key=lambda entry: tuple(value if value is not None else Smallest() for value in entry) - ) - - return entities, value_combos - - -def run_reports( - out_dir, - subject_label, - run_uuid, - config=None, - reportlets_dir=None, - packagename=None, -): - """ - Run the reports. - - .. testsetup:: - - >>> from shutil import copytree - >>> source = find_resource_or_skip('data/tests/work') - >>> testdir = Path(tmpdir) - >>> _ = copytree(source, testdir / 'work') - >>> (testdir / 'fmriprep').mkdir(parents=True, exist_ok=True) - - >>> run_reports(testdir / 'out', '01', 'madeoutuuid', packagename='fmriprep', - ... reportlets_dir=testdir / 'work' / 'reportlets') - 0 - - """ - return Report( - out_dir, - run_uuid, - config=config, - subject_id=subject_label, - packagename=packagename, - reportlets_dir=reportlets_dir, - ).generate_report() - - -def generate_reports( - subject_list, output_dir, run_uuid, config=None, work_dir=None, packagename=None -): - """Execute run_reports on a list of subjects.""" - reportlets_dir = None - if work_dir is not None: - reportlets_dir = Path(work_dir) / 'reportlets' - report_errors = [ - run_reports( - output_dir, - subject_label, - run_uuid, - config=config, - packagename=packagename, - reportlets_dir=reportlets_dir, - ) - for subject_label in subject_list - ] - - errno = sum(report_errors) - if errno: - import logging - - logger = logging.getLogger('cli') - error_list = ', '.join( - f'{subid} ({err})' - for subid, err in zip(subject_list, report_errors, strict=False) - if err - ) - logger.error( - 'Preprocessing did not finish successfully. Errors occurred while processing ' - 'data from participants: %s. Check the HTML reports for details.', - error_list, - ) - return errno +from .._deprecated import raise_moved_to_nireports + + +raise_moved_to_nireports( + 'niworkflows.reports.core', + ( + 'nireports.assembler.report.Report', + 'nireports.assembler.report.SubReport', + 'nireports.assembler.reportlet.Reportlet', + 'nireports.assembler.misc.Element', + 'nireports.assembler.tools.generate_reports', + 'nireports.assembler.tools.run_reports', + ), +) diff --git a/niworkflows/reports/default.yml b/niworkflows/reports/default.yml deleted file mode 100644 index 86bd3bc1a24..00000000000 --- a/niworkflows/reports/default.yml +++ /dev/null @@ -1,177 +0,0 @@ -package: niworkflows -sections: -- name: Summary - reportlets: - - bids: {datatype: figures, desc: summary, suffix: T1w} -- name: Anatomical - reportlets: - - bids: - datatype: figures - desc: conform - extension: [.html] - suffix: T1w - - bids: {datatype: figures, suffix: dseg} - caption: This panel shows the template T1-weighted image (if several T1w images - were found), with contours delineating the detected brain mask and brain tissue - segmentations. - subtitle: Brain mask and brain tissue segmentation of the T1w - - bids: {datatype: figures, space: .*, suffix: T1w, regex_search: True} - caption: Spatial normalization of the T1w image to the {space} template. - description: Results of nonlinear alignment of the T1w reference one or more template - space(s). Hover on the panels with the mouse pointer to transition between both - spaces. - static: false - subtitle: Spatial normalization of the anatomical T1w reference - - bids: {datatype: figures, desc: reconall, suffix: T1w} - caption: Surfaces (white and pial) reconstructed with FreeSurfer (recon-all) - overlaid on the participant's T1w template. - subtitle: Surface reconstruction - -- name: B0 field mapping - ordering: session,acquisition,reconstruction,direction,run,fmapid - reportlets: - - bids: {datatype: figures, desc: mapped, suffix: fieldmap} - caption: Inhomogeneities of the B0 field introduce (oftentimes severe) spatial distortions - along the phase-encoding direction of the image. Some scanners produce a B0 - mapping of the field, using Spiral Echo Imaging (SEI) or postprocessing a "phase-difference" - acquisition. The plot below shows an anatomical "magnitude" reference and the corresponding - fieldmap. - description: Hover over the panels with the mouse pointer to also visualize the intensity of the - field inhomogeneity in Hertz. - static: false - subtitle: "Preprocessed B0 mapping acquisition" - - bids: {datatype: figures, desc: phasediff, suffix: fieldmap} - caption: Inhomogeneities of the B0 field introduce (oftentimes severe) spatial distortions - along the phase-encoding direction of the image. A Gradient-Recalled Echo (GRE) scheme for the - mapping of the B0 inhomogeneities by subtracting the phase maps obtained at - two subsequent echoes. The plot below shows an anatomical "magnitude" reference and the corresponding - fieldmap. - description: Hover over the panels with the mouse pointer to also visualize the intensity of the - field inhomogeneity in Hertz. - static: false - subtitle: "Preprocessed mapping of phase-difference acquisition" - - bids: {datatype: figures, desc: pepolar, suffix: fieldmap} - caption: Inhomogeneities of the B0 field introduce (oftentimes severe) spatial distortions - along the phase-encoding direction of the image. Utilizing two or more images with different - phase-encoding polarities (PEPolar) or directions, it is possible to estimate the inhomogeneity - of the field. The plot below shows a reference EPI (echo-planar imaging) volume generated - using two or more EPI images with varying phase-encoding blips. - description: Hover on the panels with the mouse pointer to also visualize the intensity of the - inhomogeneity of the field in Hertz. - static: false - subtitle: "Preprocessed estimation with varying Phase-Endocing (PE) blips" - - bids: {datatype: figures, desc: anat, suffix: fieldmap} - caption: Inhomogeneities of the B0 field introduce (oftentimes severe) spatial distortions - along the phase-encoding direction of the image. Utilizing an anatomically-correct acquisition - (for instance, T1w or T2w), it is possible to estimate the inhomogeneity of the field by means of nonlinear - registration. The plot below shows a reference EPI (echo-planar imaging) volume generated - using two or more EPI images with the same PE encoding, after alignment to the anatomical scan. - description: Hover on the panels with the mouse pointer to also visualize the intensity of the - inhomogeneity of the field in Hertz. - static: false - subtitle: "Preprocessed estimation by nonlinear registration to an anatomical scan (“fieldmap-less”)" - -- name: Functional - ordering: session,task,acquisition,ceagent,reconstruction,direction,run,echo - reportlets: - - bids: {datatype: figures, desc: summary, suffix: bold} - - bids: {datatype: figures, desc: validation, suffix: bold} - - bids: {datatype: figures, desc: magnitude, suffix: bold} - caption: Results of affine coregistration between the magnitude image of the fieldmap - and the reference EPI image - static: false - subtitle: Fieldmap to EPI registration - - bids: {datatype: figures, desc: fieldmap, suffix: bold} - caption: Overlaid on the reference EPI image. - static: false - subtitle: Fieldmap - - bids: {datatype: figures, desc: sdc, suffix: bold} - caption: Results of performing susceptibility distortion correction (SDC) on the - EPI - static: false - subtitle: Susceptibility distortion correction - - bids: {datatype: figures, desc: forcedsyn, suffix: bold} - caption: The dataset contained some fieldmap information, but the argument --force-syn - was used. The higher-priority SDC method was used. Here, we show the results - of performing SyN-based SDC on the EPI for comparison. - static: false - subtitle: Experimental fieldmap-less susceptibility distortion correction - - bids: {datatype: figures, desc: flirtnobbr, suffix: bold} - caption: FSL flirt was used to generate transformations from EPI - space to T1 Space - BBR refinement rejected. Note that Nearest Neighbor interpolation - is used in the reportlets in order to highlight potential spin-history and other - artifacts, whereas final images are resampled using Lanczos interpolation. - static: false - subtitle: Alignment of functional and anatomical MRI data (volume based) - - bids: {datatype: figures, desc: coreg, suffix: bold} - caption: mri_coreg (FreeSurfer) was used to generate transformations - from EPI space to T1 Space - bbregister refinement rejected. Note - that Nearest Neighbor interpolation is used in the reportlets in order to highlight - potential spin-history and other artifacts, whereas final images are resampled - using Lanczos interpolation. - static: false - subtitle: Alignment of functional and anatomical MRI data (volume based) - - bids: {datatype: figures, desc: flirtbbr, suffix: bold} - caption: FSL flirt was used to generate transformations from EPI-space - to T1w-space - The white matter mask calculated with FSL fast (brain - tissue segmentation) was used for BBR. Note that Nearest Neighbor interpolation - is used in the reportlets in order to highlight potential spin-history and other - artifacts, whereas final images are resampled using Lanczos interpolation. - static: false - subtitle: Alignment of functional and anatomical MRI data (surface driven) - - bids: {datatype: figures, desc: bbregister, suffix: bold} - caption: bbregister was used to generate transformations from EPI-space - to T1w-space. Note that Nearest Neighbor interpolation is used in the reportlets - in order to highlight potential spin-history and other artifacts, whereas final - images are resampled using Lanczos interpolation. - static: false - subtitle: Alignment of functional and anatomical MRI data (surface driven) - - bids: {datatype: figures, desc: rois, suffix: bold} - caption: Brain mask calculated on the BOLD signal (red contour), along with the - masks used for a/tCompCor.
The aCompCor mask (magenta contour) is a conservative - CSF and white-matter mask for extracting physiological and movement confounds. -
The fCompCor mask (blue contour) contains the top 5% most variable voxels - within a heavily-eroded brain-mask. - subtitle: Brain mask and (temporal/anatomical) CompCor ROIs - - bids: - datatype: figures - desc: '[at]compcor' - extension: [.html] - suffix: bold - - bids: {datatype: figures, desc: 'compcorvar', suffix: bold} - caption: The cumulative variance explained by the first k components of the - t/aCompCor decomposition, plotted for all values of k. - The number of components that must be included in the model in order to - explain some fraction of variance in the decomposition mask can be used - as a feature selection criterion for confound regression. - subtitle: Variance explained by t/aCompCor components - - bids: {datatype: figures, desc: carpetplot, suffix: bold} - caption: Summary statistics are plotted, which may reveal trends or artifacts - in the BOLD data. Global signals calculated within the whole-brain (GS), within - the white-matter (WM) and within cerebro-spinal fluid (CSF) show the mean BOLD - signal in their corresponding masks. DVARS and FD show the standardized DVARS - and framewise-displacement measures for each time point.
A carpet plot - shows the time series for all voxels within the brain mask. Voxels are grouped - into cortical (blue), and subcortical (orange) gray matter, cerebellum (green) - and white matter and CSF (red), indicated by the color map on the left-hand - side. - subtitle: BOLD Summary - - bids: {datatype: figures, desc: 'confoundcorr', suffix: bold} - caption: | - Left: Heatmap summarizing the correlation structure among confound variables. - (Cosine bases and PCA-derived CompCor components are inherently orthogonal.) - Right: magnitude of the correlation between each confound time series and the - mean global signal. Strong correlations might be indicative of partial volume - effects and can inform decisions about feature orthogonalization prior to - confound regression. - subtitle: Correlations among nuisance regressors - - bids: {datatype: figures, desc: aroma, suffix: bold} - caption: | - Maps created with maximum intensity projection (glass brain) with a - black brain outline. Right hand side of each map: time series (top in seconds), - frequency spectrum (bottom in Hertz). Components classified as signal are plotted - in green; noise components in red. - subtitle: ICA Components classified by AROMA -- name: About - reportlets: - - bids: {datatype: figures, desc: about, suffix: T1w} diff --git a/niworkflows/reports/report.tpl b/niworkflows/reports/report.tpl deleted file mode 100644 index f0160253264..00000000000 --- a/niworkflows/reports/report.tpl +++ /dev/null @@ -1,164 +0,0 @@ - - - - - - - - - - - - - - - - - - -{% for sub_report in sections %} - {% if sub_report.reportlets %} -
-

{{ sub_report.name }}

- {% for run_report in sub_report.reportlets %} -
- {% if run_report.title %}

{{ run_report.title }}

{% endif %} - {% if run_report.subtitle %}

{{ run_report.subtitle }}

{% endif %} - {% if run_report.description %}

{{ run_report.description }}

{% endif %} - {% for elem in run_report.components %} - {% if elem[0] %} - {% if elem[1] %}

{{ elem[1] }}

{% endif %} - {{ elem[0] }} - {% endif %} - {% endfor %} -
- {% endfor %} -
- {% endif %} -{% endfor %} - -
-

Methods

- {% if boilerplate %} -

We kindly ask to report results preprocessed with this tool using the following - boilerplate.

- -
- {% for b in boilerplate %} -
{{ b[2] }}
- {% endfor %} -
- {% else %} -

Failed to generate the boilerplate

- {% endif %} -
- -
-

Errors

- {% for error in errors %} -
- Node Name: {{ error.node }}
-
- File: {{ error.file }}
- Working Directory: {{ error.node_dir }}
- Inputs:
-
    - {% for name, spec in error.inputs %} -
  • {{ name }}: {{ spec }}
    • - {% endfor %} -
- 
{{ error.traceback }}
-
-
- {% else %} -

No errors to report!

- {% endfor %} -
- - - - - diff --git a/niworkflows/reports/tests/test_core.py b/niworkflows/reports/tests/test_core.py deleted file mode 100644 index 2c8c9200f58..00000000000 --- a/niworkflows/reports/tests/test_core.py +++ /dev/null @@ -1,286 +0,0 @@ -# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- -# vi: set ft=python sts=4 ts=4 sw=4 et: -# -# Copyright 2021 The NiPreps Developers -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# We support and encourage derived works from this project, please read -# about our expectations at -# -# https://www.nipreps.org/community/licensing/ -# -"""Testing module for niworkflows.reports.core""" - -import tempfile -from itertools import product -from pathlib import Path - -import matplotlib.pyplot as plt -import pytest -from bids.layout import BIDSLayout -from bids.layout.writing import build_path -from yaml import safe_load as load - -from ... import load_resource -from ..core import Report - - -@pytest.fixture -def bids_sessions(tmpdir_factory): - f, _ = plt.subplots() - svg_dir = tmpdir_factory.mktemp('work') / 'fmriprep' - svg_dir.ensure_dir() - - pattern = ( - 'sub-{subject}[/ses-{session}]/{datatype}/' - 'sub-{subject}[_ses-{session}][_task-{task}][_acq-{acquisition}]' - '[_ce-{ceagent}][_dir-{direction}][_rec-{reconstruction}]' - '[_mod-{modality}][_run-{run}][_echo-{echo}][_space-{space}]' - '[_desc-{desc}]_{suffix}{extension<.svg>}' - ) - subjects = ['01'] - tasks = ['t1', 't2', 't3'] - runs = ['01', '02', None] - ces = ['none', 'Gd'] - descs = ['aroma', 'bbregister', 'carpetplot', 'rois'] - # create functional data for both sessions - ses1_combos = product(subjects, ['1'], tasks, [None], runs, descs) - ses2_combos = product(subjects, ['2'], tasks, ces, [None], descs) - # have no runs in the second session (ex: dmriprep test data) - # https://github.com/nipreps/dmriprep/pull/59 - all_combos = list(ses1_combos) + list(ses2_combos) - - for subject, session, task, ce, run, desc in all_combos: - entities = { - 'subject': subject, - 'session': session, - 'task': task, - 'ceagent': ce, - 'run': run, - 'desc': desc, - 'extension': '.svg', - 'suffix': 'bold', - 'datatype': 'figures', - } - bids_path = build_path(entities, pattern) - file_path = svg_dir / bids_path - file_path.ensure() - f.savefig(str(file_path)) - - # create anatomical data - anat_opts = [ - {'desc': 'brain'}, - {'desc': 'conform'}, - {'desc': 'reconall'}, - {'desc': 'rois'}, - {'suffix': 'dseg'}, - {'space': 'MNI152NLin6Asym'}, - {'space': 'MNI152NLin2009cAsym'}, - ] - anat_combos = product(subjects, anat_opts) - for subject, anat_opt in anat_combos: - anat_entities = {'subject': subject, 'datatype': 'anat', 'suffix': 't1w'} - anat_entities.update(**anat_opt) - bids_path = build_path(entities, pattern) - file_path = svg_dir / bids_path - file_path.ensure() - f.savefig(str(file_path)) - - return svg_dir.dirname - - -@pytest.fixture -def example_workdir(): - from ... import data - - workdir = data.load('tests/work') - if not workdir.is_dir(): - pytest.skip('Missing example workdir; run this test from a source repository') - return workdir - - -@pytest.fixture -def test_report1(tmp_path, example_workdir): - return Report( - tmp_path, - 'fakeuuid', - reportlets_dir=example_workdir / 'reportlets', - subject_id='01', - packagename='fmriprep', - ) - - -@pytest.fixture -def test_report2(tmp_path, bids_sessions): - return Report( - tmp_path, - 'fakeuuid', - reportlets_dir=Path(bids_sessions), - subject_id='01', - packagename='fmriprep', - ) - - -@pytest.mark.parametrize( - ('orderings', 'expected_entities', 'expected_value_combos'), - [ - ( - ['session', 'task', 'run'], - ['task', 'run'], - [ - ('faketask', None), - ('faketask2', None), - ('faketaskwithruns', 1), - ('faketaskwithruns', 2), - ('mixedgamblestask', 1), - ('mixedgamblestask', 2), - ('mixedgamblestask', 3), - ], - ), - ( - ['run', 'task', 'session'], - ['run', 'task'], - [ - (None, 'faketask'), - (None, 'faketask2'), - (1, 'faketaskwithruns'), - (1, 'mixedgamblestask'), - (2, 'faketaskwithruns'), - (2, 'mixedgamblestask'), - (3, 'mixedgamblestask'), - ], - ), - ([''], [], []), - (['session'], [], []), - ([], [], []), - (['madeupentity'], [], []), - ], -) -def test_process_orderings_small( - test_report1, orderings, expected_entities, expected_value_combos -): - report = test_report1 - report.init_layout() - entities, value_combos = report._process_orderings(orderings, report.layout) - - assert entities == expected_entities - assert expected_value_combos == value_combos - - -@pytest.mark.parametrize( - ('orderings', 'expected_entities', 'first_value_combo', 'last_value_combo'), - [ - ( - ['session', 'task', 'ceagent', 'run'], - ['session', 'task', 'ceagent', 'run'], - ('1', 't1', None, None), - ('2', 't3', 'none', None), - ), - ( - ['run', 'task', 'session'], - ['run', 'task', 'session'], - (None, 't1', '1'), - (2, 't3', '1'), - ), - ([''], [], None, None), - (['session'], ['session'], ('1',), ('2',)), - ([], [], None, None), - (['madeupentity'], [], None, None), - ], -) -def test_process_orderings_large( - test_report2, orderings, expected_entities, first_value_combo, last_value_combo -): - report = test_report2 - report.init_layout() - entities, value_combos = report._process_orderings(orderings, report.layout) - - if not value_combos: - value_combos = [None] - - assert entities == expected_entities - assert value_combos[0] == first_value_combo - assert value_combos[-1] == last_value_combo - - -@pytest.mark.parametrize( - 'ordering', - [ - ('session'), - ('task'), - ('run'), - ('session,task'), - ('session,task,run'), - ('session,task,ceagent,run'), - ('session,task,acquisition,ceagent,reconstruction,direction,run,echo'), - ('session,task,run,madeupentity'), - ], -) -def test_generated_reportlets(bids_sessions, ordering): - # make independent report - out_dir = tempfile.mkdtemp() - report = Report( - Path(out_dir), - 'fakeuuid', - reportlets_dir=Path(bids_sessions), - subject_id='01', - packagename='fmriprep', - ) - settings = load(load_resource.readable('reports/default.yml').read_text()) - # change settings to only include some missing ordering - settings['sections'][3]['ordering'] = ordering - report.index(settings['sections']) - # expected number of reportlets - expected_reportlets_num = len(report.layout.get(extension='.svg')) - # bids_session uses these entities - needed_entities = ['session', 'task', 'ceagent', 'run'] - # the last section is the most recently run - reportlets_num = len(report.sections[-1].reportlets) - # get the number of figures in the output directory - out_layout = BIDSLayout(out_dir, config='figures', validate=False) - out_figs = len(out_layout.get()) - # if ordering does not contain all the relevant entities - # then there should be fewer reportlets than expected - if all(ent in ordering for ent in needed_entities): - assert reportlets_num == expected_reportlets_num == out_figs - else: - assert reportlets_num < expected_reportlets_num == out_figs - - -@pytest.mark.parametrize( - ('subject_id', 'out_html'), - [ - ('sub-01', 'sub-01.html'), - ('sub-sub1', 'sub-sub1.html'), - ('01', 'sub-01.html'), - ('sub1', 'sub-sub1.html'), - ], -) -def test_subject_id(tmp_path, subject_id, out_html): - reports = tmp_path / 'reports' - Path( - reports - / 'fmriprep' - / (subject_id if subject_id.startswith('sub-') else f'sub-{subject_id}') - ).mkdir(parents=True) - - report = Report( - str(tmp_path), - 'myuniqueid', - reportlets_dir=reports, - subject_id=subject_id, - packagename='fmriprep', - ) - assert report.subject_id[:4] != 'sub-' - assert report.out_filename == out_html diff --git a/niworkflows/tests/test_confounds.py b/niworkflows/tests/test_confounds.py index d8f737efc4b..417671d9452 100644 --- a/niworkflows/tests/test_confounds.py +++ b/niworkflows/tests/test_confounds.py @@ -23,23 +23,12 @@ """Utilities tests""" import os -from shutil import copy import numpy as np import pandas as pd from nipype.pipeline import engine as pe from ..interfaces.confounds import ExpandModel, SpikeRegressors -from ..interfaces.plotting import CompCorVariancePlot, ConfoundsCorrelationPlot - - -def _smoke_test_report(report_interface, artifact_name): - out_report = report_interface.run().outputs.out_file - - save_artifacts = os.getenv('SAVE_CIRCLE_ARTIFACTS') - if save_artifacts: - copy(out_report, os.path.join(save_artifacts, artifact_name)) - assert os.path.isfile(out_report), f'Report "{out_report}" does not exist' def _expand_test(model_formula, datadir): @@ -161,33 +150,3 @@ def test_spikes(datadir): outliers_mc = [1, 1, 1, 1, 1] spk_data = _spikes_test(lags=lags, mincontig=mincontig, datadir=datadir) assert np.all(np.isclose(outliers_mc, spk_data['motion_outlier'])) - - -def test_CompCorVariancePlot(datadir): - """CompCor variance report test""" - metadata_file = os.path.join(datadir, 'confounds_metadata_test.tsv') - cc_rpt = CompCorVariancePlot(metadata_files=[metadata_file], metadata_sources=['aCompCor']) - _smoke_test_report(cc_rpt, 'compcor_variance.svg') - - -def test_ConfoundsCorrelationPlot(datadir): - """confounds correlation report test""" - confounds_file = os.path.join(datadir, 'confounds_test.tsv') - cc_rpt = ConfoundsCorrelationPlot( - confounds_file=confounds_file, - reference_column='a', - ignore_initial_volumes=1, - ) - _smoke_test_report(cc_rpt, 'confounds_correlation.svg') - - -def test_ConfoundsCorrelationPlotColumns(datadir): - """confounds correlation report test""" - confounds_file = os.path.join(datadir, 'confounds_test.tsv') - cc_rpt = ConfoundsCorrelationPlot( - confounds_file=confounds_file, - reference_column='a', - columns=['b', 'd', 'f'], - ignore_initial_volumes=0, - ) - _smoke_test_report(cc_rpt, 'confounds_correlation_cols.svg') diff --git a/niworkflows/tests/test_nireports_deprecations.py b/niworkflows/tests/test_nireports_deprecations.py new file mode 100644 index 00000000000..88b8dabf16f --- /dev/null +++ b/niworkflows/tests/test_nireports_deprecations.py @@ -0,0 +1,43 @@ +# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- +# vi: set ft=python sts=4 ts=4 sw=4 et: +"""Hard deprecation coverage for NiReports-migrated APIs.""" + +import importlib + +import pytest + +from niworkflows._deprecated import DeprecationError + + +@pytest.mark.parametrize( + ('module_name', 'replacement_hint'), + [ + ('niworkflows.viz', 'nireports.reportlets'), + ('niworkflows.interfaces.plotting', 'nireports.interfaces'), + ('niworkflows.reports', 'nireports.assembler'), + ('niworkflows.interfaces.reportlets.masks', 'nireports.interfaces.reporting.masks'), + ( + 'niworkflows.interfaces.reportlets.segmentation', + 'nireports.interfaces.reporting.segmentation', + ), + ], +) +def test_deprecated_modules_raise(module_name, replacement_hint): + with pytest.raises(DeprecationError, match=replacement_hint): + importlib.import_module(module_name) + + +def test_registration_migrated_classes_raise(): + registration = importlib.import_module('niworkflows.interfaces.reportlets.registration') + + with pytest.raises(DeprecationError, match='nireports.interfaces.reporting.registration'): + getattr(registration, 'BBRegisterRPT') + + +@pytest.mark.parametrize( + 'name', + ['SpatialNormalizationRPT', 'ANTSRegistrationRPT', 'ANTSApplyTransformsRPT'], +) +def test_registration_unported_classes_still_available(name): + registration = importlib.import_module('niworkflows.interfaces.reportlets.registration') + assert getattr(registration, name).__name__ == name diff --git a/niworkflows/tests/test_registration.py b/niworkflows/tests/test_registration.py index d4f3bba8912..fcf3d2e5f57 100644 --- a/niworkflows/tests/test_registration.py +++ b/niworkflows/tests/test_registration.py @@ -30,15 +30,9 @@ from nipype.pipeline import engine as pe from ..interfaces.reportlets.registration import ( - FLIRTRPT, ANTSRegistrationRPT, - ApplyXFMRPT, - BBRegisterRPT, - MRICoregRPT, - SimpleBeforeAfterRPT, SpatialNormalizationRPT, ) -from ..testing import has_freesurfer, has_fsl from .conftest import _run_interface_mock @@ -53,98 +47,6 @@ def _smoke_test_report(report_interface, artifact_name): assert os.path.isfile(out_report), 'Report does not exist' -@pytest.mark.skipif(not has_fsl, reason='No FSL') -def test_FLIRTRPT(reference, moving): - """the FLIRT report capable test""" - flirt_rpt = FLIRTRPT(generate_report=True, in_file=moving, reference=reference) - _smoke_test_report(flirt_rpt, 'testFLIRT.svg') - - -@pytest.mark.skipif(not has_freesurfer, reason='No FreeSurfer') -def test_MRICoregRPT(monkeypatch, reference, moving, nthreads, datadir): - """the MRICoreg report capable test""" - - def _agg(objekt, runtime): - outputs = objekt.output_spec() - outputs.out_lta_file = os.path.join(datadir, 'testMRICoregRPT-out_lta_file.lta') - outputs.out_report = os.path.join(runtime.cwd, objekt.inputs.out_report) - return outputs - - # Patch the _run_interface method - monkeypatch.setattr(MRICoregRPT, '_run_interface', _run_interface_mock) - monkeypatch.setattr(MRICoregRPT, 'aggregate_outputs', _agg) - - mri_coreg_rpt = MRICoregRPT( - generate_report=True, - source_file=moving, - reference_file=reference, - num_threads=nthreads, - ) - _smoke_test_report(mri_coreg_rpt, 'testMRICoreg.svg') - - -@pytest.mark.skipif(not has_fsl, reason='No FSL') -def test_ApplyXFMRPT(reference, moving): - """the ApplyXFM report capable test""" - flirt_rpt = FLIRTRPT(generate_report=False, in_file=moving, reference=reference) - - applyxfm_rpt = ApplyXFMRPT( - generate_report=True, - in_file=moving, - in_matrix_file=flirt_rpt.run().outputs.out_matrix_file, - reference=reference, - apply_xfm=True, - ) - _smoke_test_report(applyxfm_rpt, 'testApplyXFM.svg') - - -@pytest.mark.skipif(not has_fsl, reason='No FSL') -def test_SimpleBeforeAfterRPT(reference, moving): - """the SimpleBeforeAfterRPT report capable test""" - flirt_rpt = FLIRTRPT(generate_report=False, in_file=moving, reference=reference) - - ba_rpt = SimpleBeforeAfterRPT( - generate_report=True, before=reference, after=flirt_rpt.run().outputs.out_file - ) - _smoke_test_report(ba_rpt, 'test_SimpleBeforeAfterRPT.svg') - - -@pytest.mark.skipif(not has_fsl, reason='No FSL') -def test_FLIRTRPT_w_BBR(reference, reference_mask, moving): - """test FLIRTRPT with input `wm_seg` set. - For the sake of testing ONLY, `wm_seg` is set to the filename of a brain mask""" - flirt_rpt = FLIRTRPT( - generate_report=True, in_file=moving, reference=reference, wm_seg=reference_mask - ) - _smoke_test_report(flirt_rpt, 'testFLIRTRPTBBR.svg') - - -@pytest.mark.skipif(not has_freesurfer, reason='No FreeSurfer') -def test_BBRegisterRPT(monkeypatch, moving, datadir): - """the BBRegister report capable test""" - - def _agg(objekt, runtime): - outputs = objekt.output_spec() - outputs.out_lta_file = os.path.join(datadir, 'testBBRegisterRPT-out_lta_file.lta') - outputs.out_report = os.path.join(runtime.cwd, objekt.inputs.out_report) - return outputs - - # Patch the _run_interface method - monkeypatch.setattr(BBRegisterRPT, '_run_interface', _run_interface_mock) - monkeypatch.setattr(BBRegisterRPT, 'aggregate_outputs', _agg) - - subject_id = 'fsaverage' - bbregister_rpt = BBRegisterRPT( - generate_report=True, - contrast_type='t1', - init='fsl', - source_file=moving, - subject_id=subject_id, - registered_file=True, - ) - _smoke_test_report(bbregister_rpt, 'testBBRegister.svg') - - def test_SpatialNormalizationRPT(monkeypatch, moving, datadir): """the SpatialNormalizationRPT report capable test""" diff --git a/niworkflows/tests/test_segmentation.py b/niworkflows/tests/test_segmentation.py deleted file mode 100644 index 7f3174cce6d..00000000000 --- a/niworkflows/tests/test_segmentation.py +++ /dev/null @@ -1,251 +0,0 @@ -# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- -# vi: set ft=python sts=4 ts=4 sw=4 et: -# -# Copyright 2021 The NiPreps Developers -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# We support and encourage derived works from this project, please read -# about our expectations at -# -# https://www.nipreps.org/community/licensing/ -# -"""Segmentation tests""" - -import os -from shutil import copy -from tempfile import TemporaryDirectory - -import pytest -from nipype.pipeline import engine as pe -from templateflow.api import get as get_template - -from ..interfaces.reportlets.masks import ( - BETRPT, - BrainExtractionRPT, - ROIsPlot, - SimpleShowMaskRPT, -) -from ..interfaces.reportlets.segmentation import FASTRPT, ReconAllRPT -from ..testing import has_freesurfer, has_fsl -from .conftest import _run_interface_mock - - -def _smoke_test_report(report_interface, artifact_name): - with TemporaryDirectory() as tmpdir: - res = pe.Node(report_interface, name='smoke_test', base_dir=tmpdir).run() - out_report = res.outputs.out_report - - save_artifacts = os.getenv('SAVE_CIRCLE_ARTIFACTS') - if save_artifacts: - copy(out_report, os.path.join(save_artifacts, artifact_name)) - assert os.path.isfile(out_report), f'Report "{out_report}" does not exist' - - -@pytest.mark.skipif(not has_fsl, reason='No FSL') -def test_BETRPT(moving): - """the BET report capable test""" - bet_rpt = BETRPT(generate_report=True, in_file=moving) - _smoke_test_report(bet_rpt, 'testBET.svg') - - -def test_ROIsPlot(tmp_path): - """the BET report capable test""" - import nibabel as nb - import numpy as np - - im = nb.load( - str( - get_template( - 'OASIS30ANTs', - resolution=1, - desc='4', - suffix='dseg', - extension=['.nii', '.nii.gz'], - ) - ) - ) - lookup = np.zeros(5, dtype=int) - lookup[1] = 1 - lookup[2] = 4 - lookup[3] = 2 - lookup[4] = 3 - newdata = lookup[np.round(im.get_fdata()).astype(int)] - hdr = im.header.copy() - hdr.set_data_dtype('int16') - hdr['scl_slope'] = 1 - hdr['scl_inter'] = 0 - out_file = str(tmp_path / 'segments.nii.gz') - nb.Nifti1Image(newdata, im.affine, hdr).to_filename(out_file) - roi_rpt = ROIsPlot( - generate_report=True, - in_file=str(get_template('OASIS30ANTs', resolution=1, desc=None, suffix='T1w')), - in_mask=str(get_template('OASIS30ANTs', resolution=1, desc='brain', suffix='mask')), - in_rois=[out_file], - levels=[1.5, 2.5, 3.5], - colors=['gold', 'magenta', 'b'], - ) - _smoke_test_report(roi_rpt, 'testROIsPlot.svg') - - -def test_ROIsPlot2(tmp_path): - """the BET report capable test""" - import nibabel as nb - import numpy as np - - im = nb.load( - str( - get_template( - 'OASIS30ANTs', - resolution=1, - desc='4', - suffix='dseg', - extension=['.nii', '.nii.gz'], - ) - ) - ) - lookup = np.zeros(5, dtype=int) - lookup[1] = 1 - lookup[2] = 4 - lookup[3] = 2 - lookup[4] = 3 - newdata = lookup[np.round(im.get_fdata()).astype(int)] - hdr = im.header.copy() - hdr.set_data_dtype('int16') - hdr['scl_slope'] = 1 - hdr['scl_inter'] = 0 - - out_files = [] - for i in range(1, 5): - seg = np.zeros_like(newdata, dtype='uint8') - seg[(newdata > 0) & (newdata <= i)] = 1 - out_file = str(tmp_path / f'segments{i:02d}.nii.gz') - nb.Nifti1Image(seg, im.affine, hdr).to_filename(out_file) - out_files.append(out_file) - roi_rpt = ROIsPlot( - generate_report=True, - in_file=str(get_template('OASIS30ANTs', resolution=1, desc=None, suffix='T1w')), - in_mask=str(get_template('OASIS30ANTs', resolution=1, desc='brain', suffix='mask')), - in_rois=out_files, - colors=['gold', 'lightblue', 'b', 'g'], - ) - _smoke_test_report(roi_rpt, 'testROIsPlot2.svg') - - -def test_SimpleShowMaskRPT(): - """the BET report capable test""" - - msk_rpt = SimpleShowMaskRPT( - generate_report=True, - background_file=str(get_template('OASIS30ANTs', resolution=1, desc=None, suffix='T1w')), - mask_file=str( - get_template( - 'OASIS30ANTs', - resolution=1, - desc='BrainCerebellumRegistration', - suffix='mask', - ) - ), - ) - _smoke_test_report(msk_rpt, 'testSimpleMask.svg') - - -def test_BrainExtractionRPT(monkeypatch, moving, nthreads, datadir): - """test antsBrainExtraction with reports""" - - def _agg(objekt, runtime): - outputs = objekt.output_spec() - outputs.BrainExtractionMask = os.path.join( - datadir, 'testBrainExtractionRPTBrainExtractionMask.nii.gz' - ) - outputs.out_report = os.path.join(runtime.cwd, objekt.inputs.out_report) - return outputs - - # Patch the _run_interface method - monkeypatch.setattr(BrainExtractionRPT, '_run_interface', _run_interface_mock) - monkeypatch.setattr(BrainExtractionRPT, 'aggregate_outputs', _agg) - - bex_rpt = BrainExtractionRPT( - generate_report=True, - dimension=3, - use_floatingpoint_precision=1, - anatomical_image=moving, - brain_template=str(get_template('OASIS30ANTs', resolution=1, desc=None, suffix='T1w')), - brain_probability_mask=str( - get_template('OASIS30ANTs', resolution=1, label='brain', suffix='probseg') - ), - extraction_registration_mask=str( - get_template( - 'OASIS30ANTs', - resolution=1, - desc='BrainCerebellumRegistration', - suffix='mask', - ) - ), - out_prefix='testBrainExtractionRPT', - debug=True, # run faster for testing purposes - num_threads=nthreads, - ) - _smoke_test_report(bex_rpt, 'testANTSBrainExtraction.svg') - - -@pytest.mark.skipif(not has_fsl, reason='No FSL') -@pytest.mark.parametrize('segments', [True, False]) -def test_FASTRPT(monkeypatch, segments, reference, reference_mask, datadir): - """test FAST with the two options for segments""" - from nipype.interfaces.fsl.maths import ApplyMask - - def _agg(objekt, runtime): - outputs = objekt.output_spec() - outputs.out_report = os.path.join(runtime.cwd, objekt.inputs.out_report) - outputs.tissue_class_map = os.path.join(datadir, 'testFASTRPT-tissue_class_map.nii.gz') - outputs.tissue_class_files = [ - os.path.join(datadir, 'testFASTRPT-tissue_class_files0.nii.gz'), - os.path.join(datadir, 'testFASTRPT-tissue_class_files1.nii.gz'), - os.path.join(datadir, 'testFASTRPT-tissue_class_files2.nii.gz'), - ] - return outputs - - # Patch the _run_interface method - monkeypatch.setattr(FASTRPT, '_run_interface', _run_interface_mock) - monkeypatch.setattr(FASTRPT, 'aggregate_outputs', _agg) - - brain = ( - pe.Node(ApplyMask(in_file=reference, mask_file=reference_mask), name='brain') - .run() - .outputs.out_file - ) - fast_rpt = FASTRPT( - in_files=brain, - generate_report=True, - no_bias=True, - probability_maps=True, - segments=segments, - out_basename='test', - ) - _smoke_test_report(fast_rpt, 'testFAST_%ssegments.svg' % ('no' * int(not segments))) - - -@pytest.mark.skipif(not has_freesurfer, reason='No FreeSurfer') -def test_ReconAllRPT(monkeypatch): - # Patch the _run_interface method - monkeypatch.setattr(ReconAllRPT, '_run_interface', _run_interface_mock) - - rall_rpt = ReconAllRPT( - subject_id='fsaverage', - directive='all', - subjects_dir=os.getenv('SUBJECTS_DIR'), - generate_report=True, - ) - - _smoke_test_report(rall_rpt, 'testReconAll.svg') diff --git a/niworkflows/tests/test_utils.py b/niworkflows/tests/test_utils.py deleted file mode 100644 index dbfff2d59ef..00000000000 --- a/niworkflows/tests/test_utils.py +++ /dev/null @@ -1,93 +0,0 @@ -# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- -# vi: set ft=python sts=4 ts=4 sw=4 et: -# -# Copyright 2021 The NiPreps Developers -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# We support and encourage derived works from this project, please read -# about our expectations at -# -# https://www.nipreps.org/community/licensing/ -# -"""Utilities tests""" - -import os -from shutil import which - -import pytest -from nipype.pipeline import engine as pe -from templateflow.api import get as get_template - -from niworkflows.interfaces.reportlets.masks import SimpleShowMaskRPT - - -@pytest.mark.skipif( - which('svgo') is None or which('cwebp') is None, reason='svgo or cwebp missing' -) -def test_compression(tmp_path): - """the BET report capable test""" - - uncompressed = ( - pe.Node( - SimpleShowMaskRPT( - generate_report=True, - background_file=str( - get_template('OASIS30ANTs', resolution=1, desc=None, suffix='T1w') - ), - mask_file=str( - get_template( - 'OASIS30ANTs', - resolution=1, - desc='BrainCerebellumRegistration', - suffix='mask', - ) - ), - compress_report=False, - ), - name='uncompressed', - base_dir=str(tmp_path), - ) - .run() - .outputs.out_report - ) - - compressed = ( - pe.Node( - SimpleShowMaskRPT( - generate_report=True, - background_file=str( - get_template('OASIS30ANTs', resolution=1, desc=None, suffix='T1w') - ), - mask_file=str( - get_template( - 'OASIS30ANTs', - resolution=1, - desc='BrainCerebellumRegistration', - suffix='mask', - ) - ), - compress_report=True, - ), - name='compressed', - base_dir=str(tmp_path), - ) - .run() - .outputs.out_report - ) - - size = int(os.stat(uncompressed).st_size) - size_compress = int(os.stat(compressed).st_size) - assert size >= size_compress, ( - f'The uncompressed report is smaller ({size})than the compressed report ({size_compress})' - ) diff --git a/niworkflows/tests/test_viz.py b/niworkflows/tests/test_viz.py deleted file mode 100644 index 1497c2f7ae0..00000000000 --- a/niworkflows/tests/test_viz.py +++ /dev/null @@ -1,278 +0,0 @@ -# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- -# vi: set ft=python sts=4 ts=4 sw=4 et: -# -# Copyright 2021 The NiPreps Developers -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# We support and encourage derived works from this project, please read -# about our expectations at -# -# https://www.nipreps.org/community/licensing/ -# -"""Test viz module""" - -import os -from pathlib import Path - -import nibabel as nb -import numpy as np -import pandas as pd -import pytest - -from niworkflows.interfaces.plotting import _get_tr -from niworkflows.utils.timeseries import _cifti_timeseries, _nifti_timeseries -from niworkflows.viz.plots import fMRIPlot - -from .. import viz -from .generate_data import _create_dtseries_cifti - - -@pytest.mark.parametrize('tr', [None, 0.7]) -@pytest.mark.parametrize('sorting', [None, 'ward', 'linkage']) -def test_carpetplot(tr, sorting): - """Write a carpetplot""" - save_artifacts = os.getenv('SAVE_CIRCLE_ARTIFACTS') - - rng = np.random.default_rng(2010) - - viz.plot_carpet( - rng.normal(100, 20, size=(18000, 1900)), - title='carpetplot with title', - tr=tr, - output_file=( - os.path.join( - save_artifacts, - f'carpet_nosegs_{"index" if tr is None else "time"}_' - f'{"nosort" if sorting is None else sorting}.svg', - ) - if save_artifacts - else None - ), - sort_rows=sorting, - drop_trs=15, - ) - - labels = ('Ctx GM', 'Subctx GM', 'WM+CSF', 'Cereb.', 'Edge') - sizes = (200, 100, 50, 100, 50) - total_size = np.sum(sizes) - data = np.zeros((total_size, 300)) - - indexes = np.arange(total_size) - rng.shuffle(indexes) - segments = {} - start = 0 - for group, size in zip(labels, sizes, strict=False): - segments[group] = indexes[start : start + size] - data[indexes[start : start + size]] = rng.normal( - rng.standard_normal(1) * 100, rng.normal(20, 5, size=1), size=(size, 300) - ) - start += size - - viz.plot_carpet( - data, - segments, - tr=tr, - output_file=( - os.path.join( - save_artifacts, - f'carpet_random_{"index" if tr is None else "seg"}_' - f'{"nosort" if sorting is None else sorting}.svg', - ) - if save_artifacts - else None - ), - sort_rows=sorting, - ) - - data = np.zeros((total_size, 300)) - indexes = np.arange(total_size) - rng.shuffle(indexes) - segments = {} - start = 0 - for i, (group, size) in enumerate(zip(labels, sizes, strict=False)): - segments[group] = indexes[start : start + size] - data[indexes[start : start + size]] = i - start += size - - viz.plot_carpet( - data, - segments, - detrend=False, - tr=tr, - output_file=( - os.path.join( - save_artifacts, - f'carpet_const_{"index" if tr is None else "time"}_' - f'{"nosort" if sorting is None else sorting}.svg', - ) - if save_artifacts - else None - ), - sort_rows=sorting, - ) - - -@pytest.mark.parametrize( - 'input_files', - [ - ('sub-ds205s03_task-functionallocalizer_run-01_bold_volreg.nii.gz', None), - ('sub-01_task-mixedgamblestask_run-02_space-fsLR_den-91k_bold.dtseries.nii', None), - ( - 'sub-ds205s03_task-functionallocalizer_run-01_bold_volreg.nii.gz', - 'sub-ds205s03_task-functionallocalizer_run-01_bold_parc.nii.gz', - ), - ], -) -def test_fmriplot(input_files, datadir): - """Exercise the fMRIPlot class.""" - save_artifacts = os.getenv('SAVE_CIRCLE_ARTIFACTS') - rng = np.random.default_rng(2010) - - in_file = os.path.join(datadir, input_files[0]) - seg_file = os.path.join(datadir, input_files[1]) if input_files[1] is not None else None - - dtype = 'nifti' if input_files[0].endswith('volreg.nii.gz') else 'cifti' - has_seg = '_parc' if seg_file else '' - - timeseries, segments = ( - _nifti_timeseries(in_file, seg_file) if dtype == 'nifti' else _cifti_timeseries(in_file) - ) - - fig = fMRIPlot( - timeseries, - segments, - tr=_get_tr(nb.load(in_file)), - confounds=pd.DataFrame( - { - 'outliers': rng.normal(0.2, 0.2, timeseries.shape[-1] - 1), - 'DVARS': rng.normal(0.2, 0.2, timeseries.shape[-1] - 1), - 'FD': rng.normal(0.2, 0.2, timeseries.shape[-1] - 1), - } - ), - units={'FD': 'mm'}, - paired_carpet=dtype == 'cifti', - ).plot() - if save_artifacts: - fig.savefig( - os.path.join(save_artifacts, f'fmriplot_{dtype}{has_seg}.svg'), - bbox_inches='tight', - ) - - -def test_plot_melodic_components(tmp_path): - """Test plotting melodic components""" - import numpy as np - - # save the artifacts - out_dir = Path(os.getenv('SAVE_CIRCLE_ARTIFACTS', str(tmp_path))) - all_noise = str(out_dir / 'melodic_all_noise.svg') - no_noise = str(out_dir / 'melodic_no_noise.svg') - no_classified = str(out_dir / 'melodic_no_classified.svg') - - # melodic directory - melodic_dir = tmp_path / 'melodic' - melodic_dir.mkdir(exist_ok=True) - # melodic_mix - mel_mix = np.random.randint(low=-5, high=5, size=[10, 2]) - mel_mix_file = str(melodic_dir / 'melodic_mix') - np.savetxt(mel_mix_file, mel_mix, fmt='%i') - # melodic_FTmix - mel_ftmix = np.random.rand(2, 5) - mel_ftmix_file = str(melodic_dir / 'melodic_FTmix') - np.savetxt(mel_ftmix_file, mel_ftmix) - # melodic_ICstats - mel_icstats = np.random.rand(2, 2) - mel_icstats_file = str(melodic_dir / 'melodic_ICstats') - np.savetxt(mel_icstats_file, mel_icstats) - # melodic_IC - mel_ic = np.random.rand(2, 2, 2, 2) - mel_ic_file = str(melodic_dir / 'melodic_IC.nii.gz') - mel_ic_img = nb.Nifti2Image(mel_ic, np.eye(4)) - mel_ic_img.to_filename(mel_ic_file) - # noise_components - noise_comps = np.array([1, 2]) - noise_comps_file = str(tmp_path / 'noise_ics.csv') - np.savetxt(noise_comps_file, noise_comps, fmt='%i', delimiter=',') - - # create empty components file - nocomps_file = str(tmp_path / 'noise_none.csv') - open(nocomps_file, 'w').close() - - # in_file - in_fname = str(tmp_path / 'in_file.nii.gz') - voxel_ts = np.random.rand(2, 2, 2, 10) - in_file = nb.Nifti2Image(voxel_ts, np.eye(4)) - in_file.to_filename(in_fname) - # report_mask - report_fname = str(tmp_path / 'report_mask.nii.gz') - report_mask = nb.Nifti2Image(np.ones([2, 2, 2]), np.eye(4)) - report_mask.to_filename(report_fname) - - # run command with all noise components - viz.utils.plot_melodic_components( - str(melodic_dir), - in_fname, - tr=2.0, - report_mask=report_fname, - noise_components_file=noise_comps_file, - out_file=all_noise, - ) - # run command with no noise components - viz.utils.plot_melodic_components( - str(melodic_dir), - in_fname, - tr=2.0, - report_mask=report_fname, - noise_components_file=nocomps_file, - out_file=no_noise, - ) - - # run command without noise components file - viz.utils.plot_melodic_components( - str(melodic_dir), - in_fname, - tr=2.0, - report_mask=report_fname, - out_file=no_classified, - ) - - -def test_compcor_variance_plot(tmp_path, datadir): - """Test plotting CompCor variance""" - out_dir = Path(os.getenv('SAVE_CIRCLE_ARTIFACTS', str(tmp_path))) - out_file = str(out_dir / 'variance_plot_short.svg') - metadata_file = os.path.join(datadir, 'confounds_metadata_short_test.tsv') - viz.plots.compcor_variance_plot([metadata_file], output_file=out_file) - - -@pytest.fixture -def create_surface_dtseries(): - """Create a dense timeseries CIFTI-2 file with only cortex structures""" - out_file = _create_dtseries_cifti( - timepoints=10, - models=[ - ('CIFTI_STRUCTURE_CORTEX_LEFT', np.random.rand(29696, 10)), - ('CIFTI_STRUCTURE_CORTEX_RIGHT', np.random.rand(29716, 10)), - ], - ) - yield str(out_file) - out_file.unlink() - - -def test_cifti_surfaces_plot(tmp_path, create_surface_dtseries): - """Test plotting CIFTI-2 surfaces""" - os.chdir(tmp_path) - out_dir = Path(os.getenv('SAVE_CIRCLE_ARTIFACTS', str(tmp_path))) - out_file = str(out_dir / 'cifti_surfaces_plot.svg') - viz.plots.cifti_surfaces_plot(create_surface_dtseries, output_file=out_file) diff --git a/niworkflows/viz/__init__.py b/niworkflows/viz/__init__.py index 151dae80d25..bc9f8695931 100644 --- a/niworkflows/viz/__init__.py +++ b/niworkflows/viz/__init__.py @@ -1,14 +1,13 @@ # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: -import warnings +from .._deprecated import raise_moved_to_nireports -from .plots import plot_carpet -from .utils import SVGNS -msg = ( - 'Niworkflows will be deprecating visualizations in favor of a standalone library "nireports".' +raise_moved_to_nireports( + 'niworkflows.viz', + ( + 'nireports.reportlets.modality.func.plot_carpet', + 'nireports.reportlets.utils.SVGNS', + 'nireports.reportlets', + ), ) - -warnings.warn(msg, PendingDeprecationWarning, stacklevel=2) - -__all__ = ['SVGNS', 'plot_carpet'] diff --git a/niworkflows/viz/notebook.py b/niworkflows/viz/notebook.py index da31720cfe2..0ddd18cf3c1 100644 --- a/niworkflows/viz/notebook.py +++ b/niworkflows/viz/notebook.py @@ -1,88 +1,9 @@ # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: -# -# Copyright 2021 The NiPreps Developers -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# We support and encourage derived works from this project, please read -# about our expectations at -# -# https://www.nipreps.org/community/licensing/ -# -"""Visualization component for Jupyter Notebooks.""" +from .._deprecated import raise_moved_to_nireports -from pathlib import Path -import nibabel as nb -import numpy as np - -from .utils import compose_view, cuts_from_bbox, plot_registration - - -def display( - fixed_image, - moving_image, - contour=None, - cuts=None, - fixed_label='F', - moving_label='M', -): - """Plot the flickering panels to show a registration process.""" - from IPython.display import SVG - from IPython.display import display as _disp - - if isinstance(fixed_image, (str, Path)): - fixed_image = nb.load(str(fixed_image)) - if isinstance(moving_image, (str, Path)): - moving_image = nb.load(str(moving_image)) - - if cuts is None: - n_cuts = 7 - if contour is not None: - if isinstance(contour, (str, Path)): - contour = nb.load(str(contour)) - cuts = cuts_from_bbox(contour, cuts=n_cuts) - else: - hdr = fixed_image.header.copy() - hdr.set_data_dtype('uint8') - mask_nii = nb.Nifti1Image( - np.ones(fixed_image.shape, dtype='uint8'), fixed_image.affine, hdr - ) - cuts = cuts_from_bbox(mask_nii, cuts=n_cuts) - - # Call composer - _disp( - SVG( - compose_view( - plot_registration( - fixed_image, - 'fixed-image', - estimate_brightness=True, - cuts=cuts, - label=fixed_label, - contour=contour, - compress=False, - ), - plot_registration( - moving_image, - 'moving-image', - estimate_brightness=True, - cuts=cuts, - label=moving_label, - contour=contour, - compress=False, - ), - ) - ) - ) +raise_moved_to_nireports( + 'niworkflows.viz.notebook', + ('nireports.reportlets.notebook.display',), +) diff --git a/niworkflows/viz/plots.py b/niworkflows/viz/plots.py index 6b74d0e920d..2d7bf28708f 100644 --- a/niworkflows/viz/plots.py +++ b/niworkflows/viz/plots.py @@ -1,1132 +1,18 @@ # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: -# -# Copyright 2021 The NiPreps Developers -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# We support and encourage derived works from this project, please read -# about our expectations at -# -# https://www.nipreps.org/community/licensing/ -# -"""Plotting tools shared across MRIQC and fMRIPrep.""" - -import matplotlib.pyplot as plt -import nibabel as nb -import numpy as np -import pandas as pd -from matplotlib import cm, colormaps -from matplotlib import gridspec as mgs -from matplotlib.colorbar import ColorbarBase -from matplotlib.colors import Normalize - -DINA4_LANDSCAPE = (11.69, 8.27) - - -class fMRIPlot: - """Generates the fMRI Summary Plot.""" - - __slots__ = ( - 'confounds', - 'nskip', - 'paired_carpet', - 'segments', - 'sort_carpet', - 'spikes', - 'timeseries', - 'tr', - ) - - def __init__( - self, - timeseries, - segments, - confounds=None, - conf_file=None, - tr=None, - usecols=None, - units=None, - vlines=None, - spikes_files=None, - nskip=0, - sort_carpet=True, - paired_carpet=False, - ): - self.timeseries = timeseries - self.segments = segments - self.tr = tr - self.nskip = nskip - self.sort_carpet = sort_carpet - self.paired_carpet = paired_carpet - - if units is None: - units = {} - if vlines is None: - vlines = {} - self.confounds = {} - if confounds is None and conf_file: - confounds = pd.read_csv(conf_file, sep=r'[\t\s]+', usecols=usecols, index_col=False) - - if confounds is not None: - for name in confounds.columns: - self.confounds[name] = { - 'values': confounds[[name]].values.squeeze().tolist(), - 'units': units.get(name), - 'cutoff': vlines.get(name), - } - - self.spikes = [] - if spikes_files: - for sp_file in spikes_files: - self.spikes.append((np.loadtxt(sp_file), None, False)) - - def plot(self, figure=None): - """Main plotter""" - import seaborn as sns - - sns.set_style('whitegrid') - sns.set_context('paper', font_scale=0.8) - - if figure is None: - figure = plt.gcf() - - nconfounds = len(self.confounds) - nspikes = len(self.spikes) - nrows = 1 + nconfounds + nspikes - - # Create grid - grid = mgs.GridSpec( - nrows, 1, wspace=0.0, hspace=0.05, height_ratios=[1] * (nrows - 1) + [5] - ) - - grid_id = 0 - for tsz, name, iszs in self.spikes: - spikesplot(tsz, title=name, outer_gs=grid[grid_id], tr=self.tr, zscored=iszs) - grid_id += 1 - - if self.confounds: - from seaborn import color_palette - - palette = color_palette('husl', nconfounds) - - for i, (name, kwargs) in enumerate(self.confounds.items()): - tseries = kwargs.pop('values') - confoundplot(tseries, grid[grid_id], tr=self.tr, color=palette[i], name=name, **kwargs) - grid_id += 1 - - plot_carpet( - self.timeseries, - segments=self.segments, - subplot=grid[-1], - tr=self.tr, - sort_rows=self.sort_carpet, - drop_trs=self.nskip, - cmap='paired' if self.paired_carpet else None, - ) - return figure - - -def plot_carpet( - data, - segments=None, - cmap=None, - tr=None, - detrend=True, - subplot=None, - title=None, - output_file=None, - size=(900, 1200), - sort_rows='ward', - drop_trs=0, - legend=True, -): - """ - Plot an image representation of voxel intensities across time. - - This kind of plot is known as "carpet plot" or "Power plot". - See Jonathan Power Neuroimage 2017 Jul 1; 154:150-158. - - Parameters - ---------- - data : N x T :obj:`numpy.array` - The functional data to be plotted (*N* sampling locations by *T* timepoints). - segments: :obj:`dict`, optional - A mapping between segment labels (e.g., `"Left Cortex"`) and list of indexes - in the data array. - cmap : colormap - Overrides the generation of an automated colormap. - tr : float , optional - Specify the TR, if specified it uses this value. If left as None, - # of frames is plotted instead of time. - detrend : :obj:`bool`, optional - Detrend and standardize the data prior to plotting. - subplot : matplotlib subplot, optional - Subplot to plot figure on. - title : string, optional - The title displayed on the figure. - output_file : string, or None, optional - The name of an image file to export the plot to. Valid extensions - are .png, .pdf, .svg. If output_file is not None, the plot - is saved to a file, and the display is closed. - size : :obj:`tuple` - Maximum number of samples to plot (voxels, timepoints) - sort_rows : :obj:`str` or :obj:`False` or :obj:`None` - Apply a clustering algorithm to reorganize the rows of the carpet. - ``""``, ``False``, and ``None`` skip clustering sorting. - ``"linkage"`` uses linkage hierarchical clustering - :obj:`scipy.cluster.hierarchy.linkage`. - Any other value that Python evaluates to ``True`` will use the - default clustering, which is :obj:`sklearn.cluster.ward_tree`. - - """ - if segments is None: - segments = {'whole brain (voxels)': list(range(data.shape[0]))} - - if len(segments) == 1: - legend = False - - if cmap is None: - colors = colormaps['tab10'].colors - elif cmap == 'paired': - colors = list(colormaps['Paired'].colors) - colors[0], colors[1] = colors[1], colors[0] - colors[2], colors[7] = colors[7], colors[2] - - if detrend: - from nilearn.signal import clean - - if tr is not None: - tr = float(tr) - data = clean(data.T, t_r=tr, filter=False).T - - # We want all subplots to have the same dynamic range - vminmax = (np.percentile(data, 2), np.percentile(data, 98)) - - # Decimate number of time-series before clustering - n_dec = int((1.8 * data.shape[0]) // size[0]) - if n_dec > 1: - segments = { - lab: idx[::n_dec] for lab, idx in segments.items() if np.array(idx).shape >= (1,) - } - - # Cluster segments (if argument enabled) - if sort_rows: - from scipy.cluster.hierarchy import dendrogram, linkage - from sklearn.cluster import ward_tree - - for seg_label, seg_idx in segments.items(): - # In debugging cases, we might have ROIs too small to have enough rows to sort - if len(seg_idx) < 2: - continue - roi_data = data[seg_idx] - if isinstance(sort_rows, str) and sort_rows.lower() == 'linkage': - linkage_matrix = linkage( - roi_data, method='average', metric='euclidean', optimal_ordering=True - ) - else: - children, _, n_leaves, _, distances = ward_tree(roi_data, return_distance=True) - linkage_matrix = _ward_to_linkage(children, n_leaves, distances) - - dn = dendrogram(linkage_matrix, no_plot=True) - # Override the ordering of the indices in this segment - segments[seg_label] = np.array(seg_idx)[np.array(dn['leaves'])] - - # If subplot is not defined - if subplot is None: - subplot = mgs.GridSpec(1, 1)[0] - - # Length before decimation - n_trs = data.shape[-1] - drop_trs - - # Calculate time decimation factor - t_dec = max(int((1.8 * n_trs) // size[1]), 1) - data = data[:, drop_trs::t_dec] - - # Define nested GridSpec - gs = mgs.GridSpecFromSubplotSpec( - len(segments), - 1, - subplot_spec=subplot, - hspace=0.05, - height_ratios=[len(v) for v in segments.values()], - ) - - for i, indices in enumerate(segments.values()): - # Carpet plot - ax = plt.subplot(gs[i]) - - ax.imshow( - data[indices, :], - interpolation='nearest', - aspect='auto', - cmap='gray', - vmin=vminmax[0], - vmax=vminmax[1], - ) - - # Toggle the spine objects - ax.spines['top'].set_color('none') - ax.spines['top'].set_visible(False) - ax.spines['right'].set_color('none') - ax.spines['right'].set_visible(False) - - # Make colored left axis - ax.spines['left'].set_linewidth(3) - ax.spines['left'].set_color(colors[i]) - ax.spines['left'].set_capstyle('butt') - ax.spines['left'].set_position(('outward', 2)) - - # Make all subplots have same xticks - xticks = np.linspace(0, data.shape[-1], endpoint=True, num=7) - ax.set_xticks(xticks) - ax.set_yticks([]) - ax.grid(False) - - if i == (len(segments) - 1): - xlabel = 'time-points (index)' - xticklabels = (xticks * n_trs / data.shape[-1]).astype('uint32') + drop_trs - if tr is not None: - xlabel = 'time (mm:ss)' - xticklabels = [ - f'{int(t // 60):02d}:{(t % 60).round(0).astype(int):02d}' - for t in (tr * xticklabels) - ] - - ax.set_xlabel(xlabel) - ax.set_xticklabels(xticklabels) - ax.spines['bottom'].set_position(('outward', 5)) - ax.spines['bottom'].set_color('k') - ax.spines['bottom'].set_linewidth(0.8) - else: - ax.set_xticklabels([]) - ax.set_xticks([]) - ax.spines['bottom'].set_color('none') - ax.spines['bottom'].set_visible(False) - - if title and i == 0: - ax.set_title(title) - - if len(segments) == 1: - ax.set_ylabel(next(iter(segments))) - - if legend: - from matplotlib.patches import Patch - from mpl_toolkits.axes_grid1.inset_locator import inset_axes - - axlegend = inset_axes( - ax, - width='100%', - height=0.01, - loc='lower center', - borderpad=-4.1, - ) - axlegend.grid(False) - axlegend.set_xticks([]) - axlegend.set_yticks([]) - axlegend.patch.set_alpha(0.0) - for loc in ('top', 'bottom', 'left', 'right'): - axlegend.spines[loc].set_color('none') - axlegend.spines[loc].set_visible(False) - - axlegend.legend( - handles=[Patch(color=colors[i], label=label) for i, label in enumerate(segments)], - loc='upper center', - bbox_to_anchor=(0.5, 0), - shadow=False, - fancybox=False, - ncol=min(len(segments.keys()), 5), - frameon=False, - prop={'size': 8}, - ) - - if output_file is not None: - figure = plt.gcf() - figure.savefig(output_file, bbox_inches='tight') - plt.close(figure) - figure = None - return output_file - - return gs - - -def spikesplot( - ts_z, - outer_gs=None, - tr=None, - zscored=True, - spike_thresh=6.0, - title='Spike plot', - ax=None, - cmap='viridis', - hide_x=True, - nskip=0, -): - """ - A spikes plot. Thanks to Bob Dogherty (this docstring needs be improved with proper ack) - """ - - if ax is None: - ax = plt.gca() - - if outer_gs is not None: - gs = mgs.GridSpecFromSubplotSpec( - 1, 2, subplot_spec=outer_gs, width_ratios=[1, 100], wspace=0.0 - ) - ax = plt.subplot(gs[1]) - - # Define TR and number of frames - if tr is None: - tr = 1.0 - - # Load timeseries, zscored slice-wise - nslices = ts_z.shape[0] - ntsteps = ts_z.shape[1] - - # Load a colormap - my_cmap = colormaps[cmap] - norm = Normalize(vmin=0, vmax=float(nslices - 1)) - colors = [my_cmap(norm(sl)) for sl in range(nslices)] - - stem = len(np.unique(ts_z).tolist()) == 2 - # Plot one line per axial slice timeseries - for sl in range(nslices): - if not stem: - ax.plot(ts_z[sl, :], color=colors[sl], lw=0.5) - else: - markerline, stemlines, baseline = ax.stem(ts_z[sl, :]) - plt.setp(markerline, 'markerfacecolor', colors[sl]) - plt.setp(baseline, 'color', colors[sl], 'linewidth', 1) - plt.setp(stemlines, 'color', colors[sl], 'linewidth', 1) - - # Handle X, Y axes - ax.grid(False) - - # Handle X axis - last = ntsteps - 1 - ax.set_xlim(0, last) - xticks = list(range(last)[::20]) + [last] if not hide_x else [] - ax.set_xticks(xticks) - - if not hide_x: - if tr is None: - ax.set_xlabel('time (frame #)') - else: - ax.set_xlabel('time (s)') - ax.set_xticklabels([f'{t:.2f}' for t in (tr * np.array(xticks)).tolist()]) - - # Handle Y axis - ylabel = 'slice-wise noise average on background' - if zscored: - ylabel += ' (z-scored)' - zs_max = np.abs(ts_z).max() - ax.set_ylim( - ( - -(np.abs(ts_z[:, nskip:]).max()) * 1.05, - (np.abs(ts_z[:, nskip:]).max()) * 1.05, - ) - ) - - ytick_vals = np.arange(0.0, zs_max, float(np.floor(zs_max / 2.0))) - yticks = list(reversed((-1.0 * ytick_vals[ytick_vals > 0]).tolist())) + ytick_vals.tolist() - - # TODO plot min/max or mark spikes - # yticks.insert(0, ts_z.min()) - # yticks += [ts_z.max()] - for val in ytick_vals: - ax.plot((0, ntsteps - 1), (-val, -val), 'k:', alpha=0.2) - ax.plot((0, ntsteps - 1), (val, val), 'k:', alpha=0.2) - - # Plot spike threshold - if zs_max < spike_thresh: - ax.plot((0, ntsteps - 1), (-spike_thresh, -spike_thresh), 'k:') - ax.plot((0, ntsteps - 1), (spike_thresh, spike_thresh), 'k:') - else: - yticks = [ - ts_z[:, nskip:].min(), - np.median(ts_z[:, nskip:]), - ts_z[:, nskip:].max(), - ] - ax.set_ylim(0, max(yticks[-1] * 1.05, (yticks[-1] - yticks[0]) * 2.0 + yticks[-1])) - # ax.set_ylim(ts_z[:, nskip:].min() * 0.95, - # ts_z[:, nskip:].max() * 1.05) - - ax.annotate( - ylabel, - xy=(0.0, 0.7), - xycoords='axes fraction', - xytext=(0, 0), - textcoords='offset points', - va='center', - ha='left', - color='gray', - size=4, - bbox={ - 'boxstyle': 'round', - 'fc': 'w', - 'ec': 'none', - 'color': 'none', - 'lw': 0, - 'alpha': 0.8, - }, - ) - ax.set_yticks([]) - ax.set_yticklabels([]) - - # if yticks: - # # ax.set_yticks(yticks) - # # ax.set_yticklabels(['%.02f' % y for y in yticks]) - # # Plot maximum and minimum horizontal lines - # ax.plot((0, ntsteps - 1), (yticks[0], yticks[0]), 'k:') - # ax.plot((0, ntsteps - 1), (yticks[-1], yticks[-1]), 'k:') - - for side in ['top', 'right']: - ax.spines[side].set_color('none') - ax.spines[side].set_visible(False) - - if not hide_x: - ax.spines['bottom'].set_position(('outward', 10)) - ax.xaxis.set_ticks_position('bottom') - else: - ax.spines['bottom'].set_color('none') - ax.spines['bottom'].set_visible(False) - - # ax.spines["left"].set_position(('outward', 30)) - # ax.yaxis.set_ticks_position('left') - ax.spines['left'].set_visible(False) - ax.spines['left'].set_color(None) - - # labels = [label for label in ax.yaxis.get_ticklabels()] - # labels[0].set_weight('bold') - # labels[-1].set_weight('bold') - if title: - ax.set_title(title) - return ax - - -def spikesplot_cb(position, cmap='viridis', fig=None): - # Add colorbar - if fig is None: - fig = plt.gcf() - - cax = fig.add_axes(position) - cb = ColorbarBase( - cax, - cmap=colormaps[cmap], - spacing='proportional', - orientation='horizontal', - drawedges=False, - ) - cb.set_ticks([0, 0.5, 1.0]) - cb.set_ticklabels(['Inferior', '(axial slice)', 'Superior']) - cb.outline.set_linewidth(0) - cb.ax.xaxis.set_tick_params(width=0) - return cax - - -def confoundplot( - tseries, - gs_ts, - gs_dist=None, - name=None, - units=None, - tr=None, - hide_x=True, - color='b', - nskip=0, - cutoff=None, - ylims=None, -): - import seaborn as sns - - # Define TR and number of frames - notr = False - if tr is None: - notr = True - tr = 1.0 - ntsteps = len(tseries) - tseries = np.array(tseries) - - # Define nested GridSpec - gs = mgs.GridSpecFromSubplotSpec(1, 2, subplot_spec=gs_ts, width_ratios=[1, 100], wspace=0.0) - - ax_ts = plt.subplot(gs[1]) - ax_ts.grid(False) - - # Set 10 frame markers in X axis - interval = max((ntsteps // 10, ntsteps // 5, 1)) - xticks = list(range(ntsteps)[::interval]) - ax_ts.set_xticks(xticks) - - if not hide_x: - if notr: - ax_ts.set_xlabel('time (frame #)') - else: - ax_ts.set_xlabel('time (s)') - labels = tr * np.array(xticks) - ax_ts.set_xticklabels([f'{t:.2f}' for t in labels.tolist()]) - else: - ax_ts.set_xticklabels([]) - - if name is not None: - if units is not None: - name += f' [{units}]' - - ax_ts.annotate( - name, - xy=(0.0, 0.7), - xytext=(0, 0), - xycoords='axes fraction', - textcoords='offset points', - va='center', - ha='left', - color=color, - size=8, - bbox={ - 'boxstyle': 'round', - 'fc': 'w', - 'ec': 'none', - 'color': 'none', - 'lw': 0, - 'alpha': 0.8, - }, - ) - - for side in ['top', 'right']: - ax_ts.spines[side].set_color('none') - ax_ts.spines[side].set_visible(False) - - if not hide_x: - ax_ts.spines['bottom'].set_position(('outward', 20)) - ax_ts.xaxis.set_ticks_position('bottom') - else: - ax_ts.spines['bottom'].set_color('none') - ax_ts.spines['bottom'].set_visible(False) - - # ax_ts.spines["left"].set_position(('outward', 30)) - ax_ts.spines['left'].set_color('none') - ax_ts.spines['left'].set_visible(False) - # ax_ts.yaxis.set_ticks_position('left') - - ax_ts.set_yticks([]) - ax_ts.set_yticklabels([]) - - nonnan = tseries[~np.isnan(tseries)] - if nonnan.size > 0: - # Calculate Y limits - valrange = nonnan.max() - nonnan.min() - def_ylims = [nonnan.min() - 0.1 * valrange, nonnan.max() + 0.1 * valrange] - if ylims is not None: - if ylims[0] is not None: - def_ylims[0] = min([def_ylims[0], ylims[0]]) - if ylims[1] is not None: - def_ylims[1] = max([def_ylims[1], ylims[1]]) - - # Add space for plot title and mean/SD annotation - def_ylims[0] -= 0.1 * (def_ylims[1] - def_ylims[0]) - - ax_ts.set_ylim(def_ylims) - - # Annotate stats - maxv = nonnan.max() - mean = nonnan.mean() - stdv = nonnan.std() - p95 = np.percentile(nonnan, 95.0) - else: - maxv = 0 - mean = 0 - stdv = 0 - p95 = 0 - - stats_label = ( - r'max: {max:.3f}{units} $\bullet$ mean: {mean:.3f}{units} ' - r'$\bullet$ $\sigma$: {sigma:.3f}' - ).format(max=maxv, mean=mean, units=units or '', sigma=stdv) - ax_ts.annotate( - stats_label, - xy=(0.98, 0.7), - xycoords='axes fraction', - xytext=(0, 0), - textcoords='offset points', - va='center', - ha='right', - color=color, - size=4, - bbox={ - 'boxstyle': 'round', - 'fc': 'w', - 'ec': 'none', - 'color': 'none', - 'lw': 0, - 'alpha': 0.8, - }, - ) - - # Annotate percentile 95 - ax_ts.plot((0, ntsteps - 1), [p95] * 2, linewidth=0.1, color='lightgray') - ax_ts.annotate( - f'{p95:.2f}', - xy=(0, p95), - xytext=(-1, 0), - textcoords='offset points', - va='center', - ha='right', - color='lightgray', - size=3, - ) - - if cutoff is None: - cutoff = [] - - for thr in cutoff: - ax_ts.plot((0, ntsteps - 1), [thr] * 2, linewidth=0.2, color='dimgray') - - ax_ts.annotate( - f'{thr:.2f}', - xy=(0, thr), - xytext=(-1, 0), - textcoords='offset points', - va='center', - ha='right', - color='dimgray', - size=3, - ) - - ax_ts.plot(tseries, color=color, linewidth=0.8) - ax_ts.set_xlim((0, ntsteps - 1)) - - if gs_dist is not None: - ax_dist = plt.subplot(gs_dist) - sns.displot(tseries, vertical=True, ax=ax_dist) - ax_dist.set_xlabel('Timesteps') - ax_dist.set_ylim(ax_ts.get_ylim()) - ax_dist.set_yticklabels([]) - - return [ax_ts, ax_dist], gs - return ax_ts, gs - - -def compcor_variance_plot( - metadata_files, - metadata_sources=None, - output_file=None, - varexp_thresh=(0.5, 0.7, 0.9), - fig=None, -): - """ - Parameters - ---------- - metadata_files: list - List of paths to files containing component metadata. If more than one - decomposition has been performed (e.g., anatomical and temporal - CompCor decompositions), then all metadata files can be provided in - the list. However, each metadata file should have a corresponding - entry in `metadata_sources`. - metadata_sources: list or None - List of source names (e.g., ['aCompCor']) for decompositions. This - list should be of the same length as `metadata_files`. - output_file: str or None - Path where the output figure should be saved. If this is not defined, - then the plotting axes will be returned instead of the saved figure - path. - varexp_thresh: tuple - Set of variance thresholds to include in the plot (default 0.5, 0.7, - 0.9). - fig: figure or None - Existing figure on which to plot. - - Returns - ------- - ax: axes - Plotting axes. Returned only if the `output_file` parameter is None. - output_file: str - The file where the figure is saved. - """ - metadata = {} - if metadata_sources is None: - if len(metadata_files) == 1: - metadata_sources = ['CompCor'] - else: - metadata_sources = [f'Decomposition {i:d}' for i in range(len(metadata_files))] - for file, source in zip(metadata_files, metadata_sources, strict=False): - metadata[source] = pd.read_csv(str(file), sep=r'\s+') - metadata[source]['source'] = source - metadata = pd.concat(list(metadata.values())) - bbox_txt = { - 'boxstyle': 'round', - 'fc': 'white', - 'ec': 'none', - 'color': 'none', - 'linewidth': 0, - 'alpha': 0.8, - } - - decompositions = [] - data_sources = list(metadata.groupby(['source', 'mask']).groups.keys()) - for source, mask in data_sources: - if not np.isnan( - metadata.loc[(metadata['source'] == source) & (metadata['mask'] == mask)][ - 'singular_value' - ].values[0] - ): - decompositions.append((source, mask)) - - if fig is not None: - ax = [fig.add_subplot(1, len(decompositions), i + 1) for i in range(len(decompositions))] - elif len(decompositions) > 1: - fig, ax = plt.subplots(1, len(decompositions), figsize=(5 * len(decompositions), 5)) - else: - ax = [plt.axes()] - - for m, (source, mask) in enumerate(decompositions): - components = metadata[(metadata['mask'] == mask) & (metadata['source'] == source)] - if len([m for s, m in decompositions if s == source]) > 1: - title_mask = f' ({mask} mask)' - else: - title_mask = '' - fig_title = f'{source}{title_mask}' - - ax[m].plot( - np.arange(components.shape[0] + 1), - [0] + list(100 * components['cumulative_variance_explained']), - color='purple', - linewidth=2.5, - ) - ax[m].grid(False) - ax[m].set_xlabel('number of components in model') - ax[m].set_ylabel('cumulative variance explained (%)') - ax[m].set_title(fig_title) - - varexp = {} - - for i, thr in enumerate(varexp_thresh): - varexp[thr] = ( - np.atleast_1d(np.searchsorted(components['cumulative_variance_explained'], thr)) - + 1 - ) - ax[m].axhline(y=100 * thr, color='lightgrey', linewidth=0.25) - ax[m].axvline(x=varexp[thr], color=f'C{i}', linewidth=2, linestyle=':') - ax[m].text( - 0, - 100 * thr, - f'{100 * thr:.0f}', - fontsize='x-small', - bbox=bbox_txt, - ) - ax[m].text( - varexp[thr][0], - 25, - f'{varexp[thr][0]} components explain\n{100 * thr:.0f}% of variance', - rotation=90, - horizontalalignment='center', - fontsize='xx-small', - bbox=bbox_txt, - ) - - ax[m].set_yticks([]) - ax[m].set_yticklabels([]) - for label in ax[m].xaxis.get_majorticklabels(): - label.set_fontsize('x-small') - label.set_rotation('vertical') - for side in ['top', 'right', 'left']: - ax[m].spines[side].set_color('none') - ax[m].spines[side].set_visible(False) - - if output_file is not None: - figure = plt.gcf() - figure.savefig(output_file, bbox_inches='tight') - plt.close(figure) - figure = None - return output_file - return ax - - -def confounds_correlation_plot( - confounds_file, - columns=None, - figure=None, - max_dim=20, - output_file=None, - reference='global_signal', - ignore_initial_volumes=0, -): - """ - Generate a bar plot with the correlation of confounds. - - Parameters - ---------- - confounds_file: :obj:`str` - File containing all confound regressors to be included in the - correlation plot. - figure: figure or None - Existing figure on which to plot. - columns: :obj:`list` or :obj:`None`. - Select a list of columns from the dataset. - max_dim: :obj:`int` - The maximum number of regressors to be included in the output plot. - Reductions (e.g., CompCor) of high-dimensional data can yield so many - regressors that the correlation structure becomes obfuscated. This - criterion selects the ``max_dim`` regressors that have the largest - correlation magnitude with ``reference`` for inclusion in the plot. - output_file: :obj:`str` or :obj:`None` - Path where the output figure should be saved. If this is not defined, - then the plotting axes will be returned instead of the saved figure - path. - reference: :obj:`str` - ``confounds_correlation_plot`` prepares a bar plot of the correlations - of each confound regressor with a reference column. By default, this - is the global signal (so that collinearities with the global signal - can readily be assessed). - ignore_initial_volumes : :obj:`int` - Number of non-steady-state volumes at the beginning of the scan to ignore. - - Returns - ------- - axes and gridspec - Plotting axes and gridspec. Returned only if ``output_file`` is ``None``. - output_file: :obj:`str` - The file where the figure is saved. - """ - import seaborn as sns - - confounds_data = pd.read_table(confounds_file) - - if columns: - columns = dict.fromkeys(columns) # Drop duplicates - columns[reference] = None # Make sure the reference is included - confounds_data = confounds_data[list(columns)] - - confounds_data = confounds_data.loc[ - ignore_initial_volumes:, - np.logical_not(np.isclose(confounds_data.var(skipna=True), 0)), - ] - corr = confounds_data.corr() - - gscorr = corr.copy() - gscorr['index'] = gscorr.index - gscorr[reference] = np.abs(gscorr[reference]) - gs_descending = gscorr.sort_values(by=reference, ascending=False)['index'] - n_vars = corr.shape[0] - max_dim = min(n_vars, max_dim) - - gs_descending = gs_descending[:max_dim] - features = [p for p in corr.columns if p in gs_descending] - corr = corr.loc[features, features] - - # Modifying in-place is no longer supported - corr_vals = corr.to_numpy(copy=True) - np.fill_diagonal(corr_vals, 0) - corr.iloc[:, :] = corr_vals - - if figure is None: - plt.figure(figsize=(15, 5)) - gs = mgs.GridSpec(1, 21) - ax0 = plt.subplot(gs[0, :10]) - ax1 = plt.subplot(gs[0, 11:]) - - mask = np.zeros_like(corr, dtype=bool) - mask[np.triu_indices_from(mask)] = True - sns.heatmap(corr, linewidths=0.5, cmap='coolwarm', center=0, square=True, ax=ax0) - ax0.tick_params(axis='both', which='both', width=0) - - for label in ax0.xaxis.get_majorticklabels(): - label.set_fontsize('small') - for label in ax0.yaxis.get_majorticklabels(): - label.set_fontsize('small') - sns.barplot( - data=gscorr, - x='index', - y=reference, - ax=ax1, - order=gs_descending, - palette='Reds_d', - saturation=0.5, - ) - - ax1.set_xlabel('Confound time series') - ax1.set_ylabel(f'Magnitude of correlation with {reference}') - ax1.tick_params(axis='x', which='both', width=0) - ax1.tick_params(axis='y', which='both', width=5, length=5) - - for label in ax1.xaxis.get_majorticklabels(): - label.set_fontsize('small') - label.set_rotation('vertical') - for label in ax1.yaxis.get_majorticklabels(): - label.set_fontsize('small') - for side in ['top', 'right', 'left']: - ax1.spines[side].set_color('none') - ax1.spines[side].set_visible(False) - - if output_file is not None: - figure = plt.gcf() - figure.savefig(output_file, bbox_inches='tight') - plt.close(figure) - figure = None - return output_file - return [ax0, ax1], gs - - -def cifti_surfaces_plot( - in_cifti, - density='32k', - surface_type='inflated', - clip_range=(0, None), - output_file=None, - **kwargs, -): - """ - Plots a CIFTI-2 dense timeseries onto left/right mesh surfaces. - - Parameters - ---------- - in_cifti : str - CIFTI-2 dense timeseries (.dtseries.nii) - density : str - Surface density - surface_type : str - Inflation level of mesh surfaces. Supported: midthickness, inflated, veryinflated - clip_range : tuple or None - Range to clip `in_cifti` data prior to plotting. - If not None, two values must be provided as lower and upper bounds. - If values are None, no clipping is performed for that bound. - output_file: :obj:`str` or :obj:`None` - Path where the output figure should be saved. If this is not defined, - then the figure will be returned. - kwargs : dict - Keyword arguments for :obj:`nilearn.plotting.plot_surf` - - Outputs - ------- - figure : matplotlib.pyplot.figure - Surface plot figure. Returned only if ``output_file`` is ``None``. - output_file: :obj:`str` - The file where the figure is saved. - """ - from nilearn.plotting import plot_surf - - def get_surface_meshes(density, surface_type): - import templateflow.api as tf - - lh, rh = tf.get('fsLR', density=density, suffix=surface_type, extension=['.surf.gii']) - return str(lh), str(rh) - - if density != '32k': - raise NotImplementedError('Only 32k density is currently supported.') - - img = nb.cifti2.load(in_cifti) - if img.nifti_header.get_intent()[0] != 'ConnDenseSeries': - raise TypeError(f'{in_cifti} is not a dense timeseries CIFTI file') - - geo = img.header.get_index_map(1) - left_cortex, right_cortex = None, None - for bm in geo.brain_models: - if bm.brain_structure == 'CIFTI_STRUCTURE_CORTEX_LEFT': - left_cortex = bm - elif bm.brain_structure == 'CIFTI_STRUCTURE_CORTEX_RIGHT': - right_cortex = bm - - if left_cortex is None or right_cortex is None: - raise RuntimeError('CIFTI is missing cortex information') - - # calculate an average of the BOLD data, excluding the first 5 volumes - # as potential nonsteady states - data = img.dataobj[5:20].mean(axis=0) - - counts = (left_cortex.index_count, right_cortex.index_count) - if density == '32k' and counts != (29696, 29716): - raise ValueError('Cortex data is not in fsLR space') - - # medial wall needs to be added back in - lh_data = np.full(left_cortex.surface_number_of_vertices, np.nan) - rh_data = np.full(right_cortex.surface_number_of_vertices, np.nan) - lh_data[left_cortex.vertex_indices] = _concat_brain_struct_data([left_cortex], data) - rh_data[right_cortex.vertex_indices] = _concat_brain_struct_data([right_cortex], data) - - if clip_range: - lh_data = np.clip(lh_data, clip_range[0], clip_range[1], out=lh_data) - rh_data = np.clip(rh_data, clip_range[0], clip_range[1], out=rh_data) - mn, mx = clip_range - else: - mn, mx = None, None - - if mn is None: - mn = np.min(data) - if mx is None: - mx = np.max(data) - - cmap = kwargs.pop('cmap', 'YlOrRd_r') - cbar_map = cm.ScalarMappable(norm=Normalize(mn, mx), cmap=cmap) - - # Make background maps that rescale to a medium gray - lh_bg = np.zeros(lh_data.shape, 'int8') - rh_bg = np.zeros(rh_data.shape, 'int8') - lh_bg[:2] = [3, -2] - rh_bg[:2] = [3, -2] - - lh_mesh, rh_mesh = get_surface_meshes(density, surface_type) - lh_kwargs = {'surf_mesh': lh_mesh, 'surf_map': lh_data, 'bg_map': lh_bg} - rh_kwargs = {'surf_mesh': rh_mesh, 'surf_map': rh_data, 'bg_map': rh_bg} - - # Build the figure - figure = plt.figure(figsize=plt.figaspect(0.25), constrained_layout=True) - for i, view in enumerate(('lateral', 'medial')): - for j, hemi in enumerate(('left', 'right')): - title = f'{hemi.title()} - {view.title()}' - ax = figure.add_subplot(1, 4, i * 2 + j + 1, projection='3d', rasterized=True) - hemi_kwargs = (lh_kwargs, rh_kwargs)[j] - plot_surf( - hemi=hemi, - view=view, - title=title, - cmap=cmap, - vmin=mn, - vmax=mx, - axes=ax, - colorbar=False, - **hemi_kwargs, - **kwargs, - ) - # plot_surf sets this to 8, which seems a little far out, but 6 starts clipping - ax.dist = 7 - - figure.colorbar(cbar_map, shrink=0.2, ax=figure.axes, location='bottom') - - if output_file is not None: - figure.savefig(output_file, bbox_inches='tight', dpi=400) - plt.close(figure) - return output_file - - return figure - - -def _concat_brain_struct_data(structs, data): - concat_data = np.array([], dtype=data.dtype) - for struct in structs: - struct_upper_bound = struct.index_offset + struct.index_count - struct_data = data[struct.index_offset : struct_upper_bound] - concat_data = np.concatenate((concat_data, struct_data)) - return concat_data - - -def _ward_to_linkage(children, n_leaves, distances): - """Create linkage matrix from the output of Ward clustering.""" - # create the counts of samples under each node - counts = np.zeros(children.shape[0]) - n_samples = n_leaves - for i, merge in enumerate(children): - current_count = 0 - for child_idx in merge: - current_count += 1 if child_idx < n_samples else counts[child_idx - n_samples] - counts[i] = current_count - - return np.column_stack([children, distances, counts]).astype(float) +from .._deprecated import raise_moved_to_nireports + + +raise_moved_to_nireports( + 'niworkflows.viz.plots', + ( + 'nireports.reportlets.nuisance.confoundplot', + 'nireports.reportlets.nuisance.confounds_correlation_plot', + 'nireports.reportlets.nuisance.plot_carpet', + 'nireports.reportlets.nuisance.spikesplot', + 'nireports.reportlets.nuisance.spikesplot_cb', + 'nireports.reportlets.surface.cifti_surfaces_plot', + 'nireports.reportlets.xca.compcor_variance_plot', + 'nireports.reportlets.modality.func.fMRIPlot', + ), +) diff --git a/niworkflows/viz/utils.py b/niworkflows/viz/utils.py index 7694e6141f1..4efbebac0a5 100644 --- a/niworkflows/viz/utils.py +++ b/niworkflows/viz/utils.py @@ -1,714 +1,21 @@ # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: -# -# Copyright 2021 The NiPreps Developers -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# -# We support and encourage derived works from this project, please read -# about our expectations at -# -# https://www.nipreps.org/community/licensing/ -# -"""Helper tools for visualization purposes.""" - -import base64 -import re -import subprocess -from io import StringIO -from pathlib import Path -from shutil import which -from tempfile import TemporaryDirectory -from uuid import uuid4 - -import nibabel as nb -import numpy as np -from nipype.utils import filemanip - -from .. import NIWORKFLOWS_LOG -from ..utils.images import rotate_affine, rotation2canonical - -SVGNS = 'http://www.w3.org/2000/svg' - - -def robust_set_limits(data, plot_params, percentiles=(15, 99.8)): - """Set (vmax, vmin) based on percentiles of the data.""" - plot_params['vmin'] = plot_params.get('vmin', np.percentile(data, percentiles[0])) - plot_params['vmax'] = plot_params.get('vmax', np.percentile(data, percentiles[1])) - return plot_params - - -def svg_compress(image, compress='auto'): - """Generate a blob SVG from a matplotlib figure, may perform compression.""" - # Check availability of svgo and cwebp - has_compress = all((which('svgo'), which('cwebp'))) - if compress is True and not has_compress: - raise RuntimeError('Compression is required, but svgo or cwebp are not installed') - else: - compress = (compress is True or compress == 'auto') and has_compress - - # Compress the SVG file using SVGO - if compress: - cmd = 'svgo -i - -o - -q -p 3 --pretty' - try: - pout = subprocess.run( # noqa: S602 - cmd, - input=image.encode('utf-8'), - stdout=subprocess.PIPE, - shell=True, - check=True, - close_fds=True, - ).stdout - except OSError as e: - from errno import ENOENT - - if compress is True and e.errno == ENOENT: - raise e - else: - image = pout.decode('utf-8') - - # Convert all of the rasters inside the SVG file with 80% compressed WEBP - if compress: - new_lines = [] - with StringIO(image) as fp: - for line in fp: - if 'image/png' in line: - tmp_lines = [line] - while '/>' not in line: - line = fp.readline() - tmp_lines.append(line) - content = ''.join(tmp_lines).replace('\n', '').replace(', ', ',') - - left = content.split('base64,')[0] + 'base64,' - left = left.replace('image/png', 'image/webp') - right = content.split('base64,')[1] - png_b64 = right.split('"')[0] - right = '"' + '"'.join(right.split('"')[1:]) - - cmd = 'cwebp -quiet -noalpha -q 80 -o - -- -' - pout = subprocess.run( # noqa: S602 - cmd, - input=base64.b64decode(png_b64), - shell=True, - stdout=subprocess.PIPE, - check=True, - close_fds=True, - ).stdout - webpimg = base64.b64encode(pout).decode('utf-8') - new_lines.append(left + webpimg + right) - else: - new_lines.append(line) - lines = new_lines - else: - lines = image.splitlines() - - svg_start = 0 - for i, line in enumerate(lines): - if ' 0.0 - - # First, project the number of masked voxels on each axes - ijk_counts = [ - mask_data.sum(2).sum(1), # project sagittal planes to transverse (i) axis - mask_data.sum(2).sum(0), # project coronal planes to to longitudinal (j) axis - mask_data.sum(1).sum(0), # project axial planes to vertical (k) axis - ] - - # If all voxels are masked in a slice (say that happens at k=10), - # then the value for ijk_counts for the projection to k (ie. ijk_counts[2]) - # at that element of the orthogonal axes (ijk_counts[2][10]) is - # the total number of voxels in that slice (ie. Ni x Nj). - # Here we define some thresholds to consider the plane as "masked" - # The thresholds vary because of the shape of the brain - # I have manually found that for the axial view requiring 30% - # of the slice elements to be masked drops almost empty boxes - # in the mosaic of axial planes (and also addresses #281) - ijk_th = np.ceil( - [ - (mask_data.shape[1] * mask_data.shape[2]) * 0.2, # sagittal - (mask_data.shape[0] * mask_data.shape[2]) * 0.1, # coronal - (mask_data.shape[0] * mask_data.shape[1]) * 0.3, # axial - ] - ).astype(int) - - vox_coords = np.zeros((4, cuts), dtype=np.float32) - vox_coords[-1, :] = 1.0 - for ax, (c, th) in enumerate(zip(ijk_counts, ijk_th, strict=False)): - # Start with full plane if mask is seemingly empty - smin, smax = (0, mask_data.shape[ax] - 1) - - B = np.argwhere(c > th) - if B.size < cuts: # Threshold too high - B = np.argwhere(c > 0) - if B.size: - smin, smax = B.min(), B.max() - - vox_coords[ax, :] = np.linspace(smin, smax, num=cuts + 2)[1:-1] - - ras_coords = mask_nii.affine.dot(vox_coords)[:3, ...] - return {k: list(v) for k, v in zip(['x', 'y', 'z'], np.around(ras_coords, 3), strict=False)} - - -def _3d_in_file(in_file): - """if self.inputs.in_file is 3d, return it. - if 4d, pick an arbitrary volume and return that. - - if in_file is a list of files, return an arbitrary file from - the list, and an arbitrary volume from that file - """ - from nilearn import image as nlimage - - in_file = filemanip.filename_to_list(in_file)[0] - - in_file = nb.load(in_file) - - if len(in_file.shape) == 3: - return in_file - - return nlimage.index_img(in_file, 0) - - -def plot_segs( - image_nii, - seg_niis, - out_file, - bbox_nii=None, - masked=False, - colors=None, - compress='auto', - **plot_params, -): - """ - Generate a static mosaic with ROIs represented by their delimiting contour. - - Plot segmentation as contours over the image (e.g. anatomical). - seg_niis should be a list of files. mask_nii helps determine the cut - coordinates. plot_params will be passed on to nilearn plot_* functions. If - seg_niis is a list of size one, it behaves as if it was plotting the mask. - """ - from nilearn import image as nlimage - from svgutils.transform import fromstring - - plot_params = {} if plot_params is None else plot_params - - image_nii = _3d_in_file(image_nii) - canonical_r = rotation2canonical(image_nii) - image_nii = rotate_affine(image_nii, rot=canonical_r) - seg_niis = [rotate_affine(_3d_in_file(f), rot=canonical_r) for f in seg_niis] - data = image_nii.get_fdata() - - plot_params = robust_set_limits(data, plot_params) - - bbox_nii = ( - image_nii if bbox_nii is None else rotate_affine(_3d_in_file(bbox_nii), rot=canonical_r) - ) - - if masked: - bbox_nii = nlimage.threshold_img(bbox_nii, 1e-3) - - cuts = cuts_from_bbox(bbox_nii, cuts=7) - plot_params['colors'] = colors or plot_params.get('colors', None) - out_files = [] - for d in plot_params.pop('dimensions', ('z', 'x', 'y')): - plot_params['display_mode'] = d - plot_params['cut_coords'] = cuts[d] - svg = _plot_anat_with_contours(image_nii, segs=seg_niis, compress=compress, **plot_params) - # Find and replace the figure_1 id. - svg = svg.replace('figure_1', f'segmentation-{d}-{uuid4()}', 1) - out_files.append(fromstring(svg)) - - return out_files - - -def _plot_anat_with_contours(image, segs=None, compress='auto', **plot_params): - from nilearn.plotting import plot_anat - - nsegs = len(segs or []) - plot_params = plot_params or {} - # plot_params' values can be None, however they MUST NOT - # be None for colors and levels from this point on. - colors = plot_params.pop('colors', None) or [] - levels = plot_params.pop('levels', None) or [] - missing = nsegs - len(colors) - if missing > 0: # missing may be negative - from seaborn import color_palette - - colors = colors + color_palette('husl', missing) - - colors = [[c] if not isinstance(c, list) else c for c in colors] - - if not levels: - levels = [[0.5]] * nsegs - - # anatomical - display = plot_anat(image, **plot_params) - - # remove plot_anat -specific parameters - plot_params.pop('display_mode') - plot_params.pop('cut_coords') - - plot_params['linewidths'] = 0.5 - for i in reversed(range(nsegs)): - plot_params['colors'] = colors[i] - display.add_contours(segs[i], levels=levels[i], **plot_params) - - svg = extract_svg(display, compress=compress) - display.close() - return svg - - -def plot_registration( - anat_nii, - div_id, - plot_params=None, - order=('z', 'x', 'y'), - cuts=None, - estimate_brightness=False, - label=None, - contour=None, - compress='auto', - dismiss_affine=False, -): - """ - Plots the foreground and background views - Default order is: axial, coronal, sagittal - """ - from nilearn import image as nlimage - from nilearn.plotting import plot_anat - from svgutils.transform import fromstring - - plot_params = {} if plot_params is None else plot_params - - # Use default MNI cuts if none defined - if cuts is None: - raise NotImplementedError # TODO - - # nilearn 0.10.0 uses Nifti-specific methods - anat_nii = nb.Nifti1Image.from_image(anat_nii) - - out_files = [] - if estimate_brightness: - plot_params = robust_set_limits(anat_nii.get_fdata().reshape(-1), plot_params) - - # FreeSurfer ribbon.mgz - if contour: - contour = nb.Nifti1Image.from_image(contour) - - ribbon = contour is not None and np.array_equal( - np.unique(contour.get_fdata()), [0, 2, 3, 41, 42] - ) - - if ribbon: - contour_data = contour.get_fdata() % 39 - white = nlimage.new_img_like(contour, contour_data == 2) - pial = nlimage.new_img_like(contour, contour_data >= 2) - - if dismiss_affine: - canonical_r = rotation2canonical(anat_nii) - anat_nii = rotate_affine(anat_nii, rot=canonical_r) - if ribbon: - white = rotate_affine(white, rot=canonical_r) - pial = rotate_affine(pial, rot=canonical_r) - if contour: - contour = rotate_affine(contour, rot=canonical_r) - - # Plot each cut axis - for i, mode in enumerate(list(order)): - plot_params['display_mode'] = mode - plot_params['cut_coords'] = cuts[mode] - if i == 0: - plot_params['title'] = label - else: - plot_params['title'] = None - - # Generate nilearn figure - display = plot_anat(anat_nii, **plot_params) - if ribbon: - kwargs = {'levels': [0.5], 'linewidths': 0.5} - display.add_contours(white, colors='b', **kwargs) - display.add_contours(pial, colors='r', **kwargs) - elif contour is not None: - display.add_contours(contour, colors='r', levels=[0.5], linewidths=0.5) - - svg = extract_svg(display, compress=compress) - display.close() - - # Find and replace the figure_1 id. - svg = svg.replace('figure_1', f'{div_id}-{mode}-{uuid4()}', 1) - out_files.append(fromstring(svg)) - - return out_files - - -def compose_view(bg_svgs, fg_svgs, ref=0, out_file='report.svg'): - """Compose the input svgs into one standalone svg with CSS flickering animation.""" - out_file = Path(out_file).absolute() - out_file.write_text('\n'.join(_compose_view(bg_svgs, fg_svgs, ref=ref))) - return str(out_file) - - -def _compose_view(bg_svgs, fg_svgs, ref=0): - from svgutils.compose import Unit - from svgutils.transform import GroupElement, SVGFigure - - if fg_svgs is None: - fg_svgs = [] - - # Merge SVGs and get roots - svgs = bg_svgs + fg_svgs - roots = [f.getroot() for f in svgs] - - # Query the size of each - sizes = [] - for f in svgs: - viewbox = [float(v) for v in f.root.get('viewBox').split(' ')] - width = int(viewbox[2]) - height = int(viewbox[3]) - sizes.append((width, height)) - nsvgs = len(bg_svgs) - - sizes = np.array(sizes) - - # Calculate the scale to fit all widths - width = sizes[ref, 0] - scales = width / sizes[:, 0] - heights = sizes[:, 1] * scales - - # Compose the views panel: total size is the width of - # any element (used the first here) and the sum of heights - fig = SVGFigure(Unit(f'{width}px'), Unit(f'{heights[:nsvgs].sum()}px')) - - yoffset = 0 - for i, r in enumerate(roots): - r.moveto(0, yoffset, scale_x=scales[i]) - if i == (nsvgs - 1): - yoffset = 0 - else: - yoffset += heights[i] - - # Group background and foreground panels in two groups - if fg_svgs: - newroots = [ - GroupElement(roots[:nsvgs], {'class': 'background-svg'}), - GroupElement(roots[nsvgs:], {'class': 'foreground-svg'}), - ] - else: - newroots = roots - fig.append(newroots) - fig.root.attrib.pop('width', None) - fig.root.attrib.pop('height', None) - fig.root.set('preserveAspectRatio', 'xMidYMid meet') - - with TemporaryDirectory() as tmpdirname: - out_file = Path(tmpdirname) / 'tmp.svg' - fig.save(str(out_file)) - # Post processing - svg = out_file.read_text().splitlines() - - # Remove -@keyframes {0} {{ 0% {{opacity: 1;}} 100% {{ opacity: 0; }}}} -.foreground-svg {{ animation: 1s ease-in-out 0s alternate none infinite paused {0};}} -.foreground-svg:hover {{ animation-play-state: running;}} -""".format(f'flickerAnimation{uuid4()}'), - ) - - return svg - - -def transform_to_2d(data, max_axis): - """ - Projects 3d data cube along one axis using maximum intensity with - preservation of the signs. Adapted from nilearn. - """ - import numpy as np - - # get the shape of the array we are projecting to - new_shape = list(data.shape) - del new_shape[max_axis] - - # generate a 3D indexing array that points to max abs value in the - # current projection - a1, a2 = np.indices(new_shape) - inds = [a1, a2] - inds.insert(max_axis, np.abs(data).argmax(axis=max_axis)) - - # take the values where the absolute value of the projection - # is the highest - maximum_intensity_data = data[tuple(inds)] - - return np.rot90(maximum_intensity_data) - - -def plot_melodic_components( - melodic_dir, - in_file, - tr=None, - out_file='melodic_reportlet.svg', - compress='auto', - report_mask=None, - noise_components_file=None, -): - """ - Plots the spatiotemporal components extracted by FSL MELODIC - from functional MRI data. - - Parameters - ---------- - melodic_dir : str - Path pointing to the outputs of MELODIC - in_file : str - Path pointing to the reference fMRI dataset. This file - will be used to extract the TR value, if the ``tr`` argument - is not set. This file will be used to calculate a mask - if ``report_mask`` is not provided. - tr : float - Repetition time in seconds - out_file : str - Path where the resulting SVG file will be stored - compress : ``'auto'`` or bool - Whether SVG should be compressed. If ``'auto'``, compression - will be executed if dependencies are installed (SVGO) - report_mask : str - Path to a brain mask corresponding to ``in_file`` - noise_components_file : str - A CSV file listing the indexes of components classified as noise - by some manual or automated (e.g. ICA-AROMA) procedure. If a - ``noise_components_file`` is provided, then components will be - plotted with red/green colors (correspondingly to whether they - are in the file -noise components, red-, or not -signal, green-). - When all or none of the components are in the file, a warning - is printed at the top. - - """ - import os - - import nibabel as nb - import numpy as np - import pylab as plt - import seaborn as sns - from matplotlib.gridspec import GridSpec - from nilearn.image import index_img, iter_img - - sns.set_style('white') - current_palette = sns.color_palette() - in_nii = nb.load(in_file) - if not tr: - tr = in_nii.header.get_zooms()[3] - units = in_nii.header.get_xyzt_units() - if units: - if units[-1] == 'msec': - tr = tr / 1000.0 - elif units[-1] == 'usec': - tr = tr / 1000000.0 - elif units[-1] != 'sec': - NIWORKFLOWS_LOG.warning( - 'Unknown repetition time units specified - assuming seconds' - ) - else: - NIWORKFLOWS_LOG.warning('Repetition time units not specified - assuming seconds') - - try: - from nilearn.maskers import NiftiMasker - except ImportError: # nilearn < 0.9 - from nilearn.input_data import NiftiMasker - from nilearn.plotting import cm - - if not report_mask: - nifti_masker = NiftiMasker(mask_strategy='epi') - nifti_masker.fit(index_img(in_nii, range(2))) - mask_img = nifti_masker.mask_img_ - else: - mask_img = nb.load(report_mask) - - mask_sl = [transform_to_2d(mask_img.get_fdata(), j) for j in range(3)] - - timeseries = np.loadtxt(os.path.join(melodic_dir, 'melodic_mix')) - power = np.loadtxt(os.path.join(melodic_dir, 'melodic_FTmix')) - stats = np.loadtxt(os.path.join(melodic_dir, 'melodic_ICstats')) - n_components = stats.shape[0] - Fs = 1.0 / tr - Ny = Fs / 2 - f = Ny * (np.array(list(range(1, power.shape[0] + 1)))) / (power.shape[0]) - - # Set default colors - color_title = 'k' - color_time = current_palette[0] - color_power = current_palette[1] - classified_colors = None - - warning_row = 0 # Do not allocate warning row - # Only if the components file has been provided, a warning banner will - # be issued if all or none of the components were classified as noise - if noise_components_file: - noise_components = np.loadtxt(noise_components_file, dtype=int, delimiter=',', ndmin=1) - # Activate warning row if pertinent - warning_row = int(noise_components.size in (0, n_components)) - classified_colors = {True: 'r', False: 'g'} - - n_rows = int((n_components + (n_components % 2)) / 2) - fig = plt.figure(figsize=(6.5 * 1.5, (n_rows + warning_row) * 0.85)) - gs = GridSpec( - n_rows * 2 + warning_row, - 9, - width_ratios=[1, 1, 1, 4, 0.001, 1, 1, 1, 4], - height_ratios=[5] * warning_row + [1.1, 1] * n_rows, - ) - - if warning_row: - ax = fig.add_subplot(gs[0, :]) - ncomps = 'NONE of the' - if noise_components.size == n_components: - ncomps = 'ALL' - ax.annotate( - f'WARNING: {ncomps} components were classified as noise', - xy=(0.0, 0.5), - xycoords='axes fraction', - xytext=(0.01, 0.5), - textcoords='axes fraction', - size=12, - color='#ea8800', - bbox={'boxstyle': 'round', 'fc': '#f7dcb7', 'ec': '#FC990E'}, - ) - ax.axes.get_xaxis().set_visible(False) - ax.axes.get_yaxis().set_visible(False) - - titlefmt = 'C{id:d}{noise}: Tot. var. expl. {var:.2g}%'.format - ICs = nb.load(os.path.join(melodic_dir, 'melodic_IC.nii.gz')) - # Ensure 4D - if ICs.ndim == 3: - ICs = ICs.slicer[..., None] - for i, img in enumerate(iter_img(ICs)): - col = i % 2 - row = i // 2 - l_row = row * 2 + warning_row - is_noise = False - - if classified_colors: - # If a noise components list is provided, assign red/green - is_noise = (i + 1) in noise_components - color_title = color_time = color_power = classified_colors[is_noise] - - data = img.get_fdata() - for j in range(3): - ax1 = fig.add_subplot(gs[l_row : l_row + 2, j + col * 5]) - sl = transform_to_2d(data, j) - m = np.abs(sl).max() - ax1.imshow(sl, vmin=-m, vmax=+m, cmap=cm.cold_white_hot, interpolation='nearest') - ax1.contour(mask_sl[j], levels=[0.5], colors='k', linewidths=0.5) - plt.axis('off') - ax1.autoscale_view('tight') - if j == 0: - ax1.set_title( - titlefmt(id=i + 1, noise=' [noise]' * is_noise, var=stats[i, 1]), - x=0, - y=1.18, - fontsize=7, - horizontalalignment='left', - verticalalignment='top', - color=color_title, - ) - - ax2 = fig.add_subplot(gs[l_row, 3 + col * 5]) - ax3 = fig.add_subplot(gs[l_row + 1, 3 + col * 5]) - - ax2.plot( - np.arange(len(timeseries[:, i])) * tr, - timeseries[:, i], - linewidth=min(200 / len(timeseries[:, i]), 1.0), - color=color_time, - ) - ax2.set_xlim([0, len(timeseries[:, i]) * tr]) - ax2.axes.get_yaxis().set_visible(False) - ax2.autoscale_view('tight') - ax2.tick_params(axis='both', which='major', pad=0) - sns.despine(left=True, bottom=True) - for label in ax2.xaxis.get_majorticklabels(): - label.set_fontsize(6) - label.set_color(color_time) - - ax3.plot( - f[0:], - power[0:, i], - color=color_power, - linewidth=min(100 / len(power[0:, i]), 1.0), - ) - ax3.set_xlim([f[0], f.max()]) - ax3.axes.get_yaxis().set_visible(False) - ax3.autoscale_view('tight') - ax3.tick_params(axis='both', which='major', pad=0) - for label in ax3.xaxis.get_majorticklabels(): - label.set_fontsize(6) - label.set_color(color_power) - sns.despine(left=True, bottom=True) - - plt.subplots_adjust(hspace=0.5) - fig.savefig( - out_file, - dpi=300, - format='svg', - transparent=True, - bbox_inches='tight', - pad_inches=0.01, - ) - fig.clf() +from .._deprecated import raise_moved_to_nireports + + +raise_moved_to_nireports( + 'niworkflows.viz.utils', + ( + 'nireports.reportlets.utils.compose_view', + 'nireports.reportlets.utils.cuts_from_bbox', + 'nireports.reportlets.utils.extract_svg', + 'nireports.reportlets.utils.robust_set_limits', + 'nireports.reportlets.utils.svg2str', + 'nireports.reportlets.utils.svg_compress', + 'nireports.reportlets.utils.transform_to_2d', + 'nireports.reportlets.utils.SVGNS', + 'nireports.reportlets.mosaic.plot_registration', + 'nireports.reportlets.mosaic.plot_segs', + 'nireports.reportlets.xca.plot_melodic_components', + ), +) diff --git a/pyproject.toml b/pyproject.toml index 72b28b5c39a..3211e924c1e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -30,6 +30,7 @@ dependencies = [ "nibabel >= 5.2", "nilearn >= 0.11.1", "nipype >= 1.9.2", + "nireports >= 23.0.1", "nitransforms >= 24.1.1", "numpy >= 1.26", "packaging >= 23", diff --git a/requirements.txt b/requirements.txt index 1ee9819956a..12ad2da8759 100644 --- a/requirements.txt +++ b/requirements.txt @@ -97,6 +97,8 @@ nilearn==0.11.1 # via niworkflows (pyproject.toml) nipype==1.9.2 # via niworkflows (pyproject.toml) +nireports>=23.0.1 + # via niworkflows (pyproject.toml) nitransforms==24.1.1 # via niworkflows (pyproject.toml) num2words==0.5.14 From 12b56c3dce769350039211a16979354152c4ba77 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 23 Feb 2026 06:48:27 +0000 Subject: [PATCH 2/7] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- niworkflows/func/util.py | 2 +- niworkflows/interfaces/plotting.py | 1 - niworkflows/interfaces/reportlets/masks.py | 1 - niworkflows/interfaces/reportlets/segmentation.py | 1 - niworkflows/reports/__init__.py | 1 - niworkflows/reports/core.py | 1 - niworkflows/tests/test_nireports_deprecations.py | 2 +- niworkflows/tests/test_registration.py | 1 - niworkflows/viz/__init__.py | 1 - niworkflows/viz/notebook.py | 1 - niworkflows/viz/plots.py | 1 - niworkflows/viz/utils.py | 1 - 12 files changed, 2 insertions(+), 12 deletions(-) diff --git a/niworkflows/func/util.py b/niworkflows/func/util.py index ce6c2e90e2b..df8c4b862c8 100644 --- a/niworkflows/func/util.py +++ b/niworkflows/func/util.py @@ -25,9 +25,9 @@ from nipype.interfaces import afni, fsl from nipype.interfaces import utility as niu from nipype.pipeline import engine as pe +from nireports.interfaces.reporting.masks import SimpleShowMaskRPT from packaging.version import Version from packaging.version import parse as parseversion -from nireports.interfaces.reporting.masks import SimpleShowMaskRPT from templateflow.api import get as get_template from .. import data diff --git a/niworkflows/interfaces/plotting.py b/niworkflows/interfaces/plotting.py index 9514f3f6f9c..3252a74f7c3 100644 --- a/niworkflows/interfaces/plotting.py +++ b/niworkflows/interfaces/plotting.py @@ -2,7 +2,6 @@ # vi: set ft=python sts=4 ts=4 sw=4 et: from .._deprecated import raise_moved_to_nireports - raise_moved_to_nireports( 'niworkflows.interfaces.plotting', ( diff --git a/niworkflows/interfaces/reportlets/masks.py b/niworkflows/interfaces/reportlets/masks.py index 156438a1f91..be8fdff34d7 100644 --- a/niworkflows/interfaces/reportlets/masks.py +++ b/niworkflows/interfaces/reportlets/masks.py @@ -2,7 +2,6 @@ # vi: set ft=python sts=4 ts=4 sw=4 et: from ..._deprecated import raise_moved_to_nireports - raise_moved_to_nireports( 'niworkflows.interfaces.reportlets.masks', ( diff --git a/niworkflows/interfaces/reportlets/segmentation.py b/niworkflows/interfaces/reportlets/segmentation.py index 2de575b8f13..a7760773d84 100644 --- a/niworkflows/interfaces/reportlets/segmentation.py +++ b/niworkflows/interfaces/reportlets/segmentation.py @@ -2,7 +2,6 @@ # vi: set ft=python sts=4 ts=4 sw=4 et: from ..._deprecated import raise_moved_to_nireports - raise_moved_to_nireports( 'niworkflows.interfaces.reportlets.segmentation', ( diff --git a/niworkflows/reports/__init__.py b/niworkflows/reports/__init__.py index bd585637e11..edfe2628706 100644 --- a/niworkflows/reports/__init__.py +++ b/niworkflows/reports/__init__.py @@ -2,7 +2,6 @@ # vi: set ft=python sts=4 ts=4 sw=4 et: from .._deprecated import raise_moved_to_nireports - raise_moved_to_nireports( 'niworkflows.reports', ( diff --git a/niworkflows/reports/core.py b/niworkflows/reports/core.py index d4e34b989ac..c533b06f5ab 100644 --- a/niworkflows/reports/core.py +++ b/niworkflows/reports/core.py @@ -2,7 +2,6 @@ # vi: set ft=python sts=4 ts=4 sw=4 et: from .._deprecated import raise_moved_to_nireports - raise_moved_to_nireports( 'niworkflows.reports.core', ( diff --git a/niworkflows/tests/test_nireports_deprecations.py b/niworkflows/tests/test_nireports_deprecations.py index 88b8dabf16f..eca442fa7ce 100644 --- a/niworkflows/tests/test_nireports_deprecations.py +++ b/niworkflows/tests/test_nireports_deprecations.py @@ -31,7 +31,7 @@ def test_registration_migrated_classes_raise(): registration = importlib.import_module('niworkflows.interfaces.reportlets.registration') with pytest.raises(DeprecationError, match='nireports.interfaces.reporting.registration'): - getattr(registration, 'BBRegisterRPT') + registration.BBRegisterRPT @pytest.mark.parametrize( diff --git a/niworkflows/tests/test_registration.py b/niworkflows/tests/test_registration.py index fcf3d2e5f57..a99a042dd0a 100644 --- a/niworkflows/tests/test_registration.py +++ b/niworkflows/tests/test_registration.py @@ -26,7 +26,6 @@ from shutil import copy from tempfile import TemporaryDirectory -import pytest from nipype.pipeline import engine as pe from ..interfaces.reportlets.registration import ( diff --git a/niworkflows/viz/__init__.py b/niworkflows/viz/__init__.py index bc9f8695931..36e43f41bad 100644 --- a/niworkflows/viz/__init__.py +++ b/niworkflows/viz/__init__.py @@ -2,7 +2,6 @@ # vi: set ft=python sts=4 ts=4 sw=4 et: from .._deprecated import raise_moved_to_nireports - raise_moved_to_nireports( 'niworkflows.viz', ( diff --git a/niworkflows/viz/notebook.py b/niworkflows/viz/notebook.py index 0ddd18cf3c1..690ccb3bc9b 100644 --- a/niworkflows/viz/notebook.py +++ b/niworkflows/viz/notebook.py @@ -2,7 +2,6 @@ # vi: set ft=python sts=4 ts=4 sw=4 et: from .._deprecated import raise_moved_to_nireports - raise_moved_to_nireports( 'niworkflows.viz.notebook', ('nireports.reportlets.notebook.display',), diff --git a/niworkflows/viz/plots.py b/niworkflows/viz/plots.py index 2d7bf28708f..b8cdddabf90 100644 --- a/niworkflows/viz/plots.py +++ b/niworkflows/viz/plots.py @@ -2,7 +2,6 @@ # vi: set ft=python sts=4 ts=4 sw=4 et: from .._deprecated import raise_moved_to_nireports - raise_moved_to_nireports( 'niworkflows.viz.plots', ( diff --git a/niworkflows/viz/utils.py b/niworkflows/viz/utils.py index 4efbebac0a5..c20ace1e88a 100644 --- a/niworkflows/viz/utils.py +++ b/niworkflows/viz/utils.py @@ -2,7 +2,6 @@ # vi: set ft=python sts=4 ts=4 sw=4 et: from .._deprecated import raise_moved_to_nireports - raise_moved_to_nireports( 'niworkflows.viz.utils', ( From 6497f3141842cc17149737c1204c3e91b8248c56 Mon Sep 17 00:00:00 2001 From: Oscar Esteban Date: Mon, 23 Feb 2026 07:51:18 +0100 Subject: [PATCH 3/7] FIX: satisfy ruff checks in nireports deprecation test --- niworkflows/tests/test_nireports_deprecations.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/niworkflows/tests/test_nireports_deprecations.py b/niworkflows/tests/test_nireports_deprecations.py index eca442fa7ce..e98195f3e9c 100644 --- a/niworkflows/tests/test_nireports_deprecations.py +++ b/niworkflows/tests/test_nireports_deprecations.py @@ -3,6 +3,7 @@ """Hard deprecation coverage for NiReports-migrated APIs.""" import importlib +import re import pytest @@ -30,8 +31,11 @@ def test_deprecated_modules_raise(module_name, replacement_hint): def test_registration_migrated_classes_raise(): registration = importlib.import_module('niworkflows.interfaces.reportlets.registration') - with pytest.raises(DeprecationError, match='nireports.interfaces.reporting.registration'): - registration.BBRegisterRPT + with pytest.raises( + DeprecationError, + match=re.escape('nireports.interfaces.reporting.registration'), + ): + getattr(registration, 'BBRegisterRPT') @pytest.mark.parametrize( From 852c7a8e4c45f733516450d9264a3e4b053e1913 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 23 Feb 2026 06:51:50 +0000 Subject: [PATCH 4/7] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- niworkflows/tests/test_nireports_deprecations.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/niworkflows/tests/test_nireports_deprecations.py b/niworkflows/tests/test_nireports_deprecations.py index e98195f3e9c..67f1d29dc23 100644 --- a/niworkflows/tests/test_nireports_deprecations.py +++ b/niworkflows/tests/test_nireports_deprecations.py @@ -35,7 +35,7 @@ def test_registration_migrated_classes_raise(): DeprecationError, match=re.escape('nireports.interfaces.reporting.registration'), ): - getattr(registration, 'BBRegisterRPT') + registration.BBRegisterRPT @pytest.mark.parametrize( From 22965753d799b7f5391d58023b4ac3a7a832ec8d Mon Sep 17 00:00:00 2001 From: Oscar Esteban Date: Mon, 23 Feb 2026 08:24:55 +0100 Subject: [PATCH 5/7] fix(ci): require nireports reporting API and unmock matplotlib in docs --- docs/conf.py | 5 ----- pyproject.toml | 2 +- requirements.txt | 2 +- 3 files changed, 2 insertions(+), 7 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index 83d114e521b..1bc58d09e4c 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -22,11 +22,6 @@ # Could set NO_ET environment variable, but why? MOCKS = [ 'etelemetry', - 'matplotlib', - 'matplotlib.pyplot', - 'matplotlib.cm', - 'matplotlib.colors', - 'matplotlib.colorbar', ] sys.modules.update({mod: mock.Mock() for mod in MOCKS}) diff --git a/pyproject.toml b/pyproject.toml index 3211e924c1e..bfe309fd7da 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -30,7 +30,7 @@ dependencies = [ "nibabel >= 5.2", "nilearn >= 0.11.1", "nipype >= 1.9.2", - "nireports >= 23.0.1", + "nireports >= 25.3.0", "nitransforms >= 24.1.1", "numpy >= 1.26", "packaging >= 23", diff --git a/requirements.txt b/requirements.txt index 12ad2da8759..d7a9c8a870a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -97,7 +97,7 @@ nilearn==0.11.1 # via niworkflows (pyproject.toml) nipype==1.9.2 # via niworkflows (pyproject.toml) -nireports>=23.0.1 +nireports>=25.3.0 # via niworkflows (pyproject.toml) nitransforms==24.1.1 # via niworkflows (pyproject.toml) From e9a1b78c0eb2747eb061af954f798a5e74939dda Mon Sep 17 00:00:00 2001 From: Oscar Esteban Date: Mon, 23 Feb 2026 18:37:10 +0100 Subject: [PATCH 6/7] fix(tests): satisfy ruff B018 in nireports deprecation test --- niworkflows/tests/test_nireports_deprecations.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/niworkflows/tests/test_nireports_deprecations.py b/niworkflows/tests/test_nireports_deprecations.py index 67f1d29dc23..e98195f3e9c 100644 --- a/niworkflows/tests/test_nireports_deprecations.py +++ b/niworkflows/tests/test_nireports_deprecations.py @@ -35,7 +35,7 @@ def test_registration_migrated_classes_raise(): DeprecationError, match=re.escape('nireports.interfaces.reporting.registration'), ): - registration.BBRegisterRPT + getattr(registration, 'BBRegisterRPT') @pytest.mark.parametrize( From fb996c4d9d317f5d2179ebd8c50e7c738fffac59 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 23 Feb 2026 17:37:28 +0000 Subject: [PATCH 7/7] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- niworkflows/tests/test_nireports_deprecations.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/niworkflows/tests/test_nireports_deprecations.py b/niworkflows/tests/test_nireports_deprecations.py index e98195f3e9c..67f1d29dc23 100644 --- a/niworkflows/tests/test_nireports_deprecations.py +++ b/niworkflows/tests/test_nireports_deprecations.py @@ -35,7 +35,7 @@ def test_registration_migrated_classes_raise(): DeprecationError, match=re.escape('nireports.interfaces.reporting.registration'), ): - getattr(registration, 'BBRegisterRPT') + registration.BBRegisterRPT @pytest.mark.parametrize(