19471672015

Committed 18 Nov 2025 03:32PM UTC coverage: 79.472% (+0.02%) from 79.449%

Build # 19471672015

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push

github

Committed by

AGrigis

Commit Message

doc: fix documentation.

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50.68

/brainprep/interfaces/plotting.py

##########################################################################
# NSAp - Copyright (C) CEA, 2021 - 2025
# Distributed under the terms of the CeCILL-B license, as published by
# the CEA-CNRS-INRIA. Refer to the LICENSE file or to
# http://www.cecill.info/licences/Licence_CeCILL-B_V1-en.html
# for details.
##########################################################################

"""
Plotting functions.
"""

import itertools

import matplotlib.pyplot as plt
import nibabel
import numpy as np
import pandas as pd
import seaborn as sns
from nilearn import plotting

from ..reporting import log_runtime
from ..typing import (
    Directory,
    File,
)
from ..utils import (
    coerceparams,
    outputdir,
)
from ..wrappers import pywrapper


@coerceparams
@outputdir
@log_runtime(
    bunched=False)
@pywrapper
def plot_defacing_mosaic(
        im_file: File,
        mask_file: File,
        output_dir: Directory,
        entities: dict,
        dryrun: bool = False) -> tuple[File]:
    """
    Generates a defacing mosaic image by overlaying a mask on an anatomical
    image.

    Parameters
    ----------
    im_file : File
        Path to the anatomical image.
    mask_file : File
        Path to the defacing mask.
    output_dir : Directory
        Directory where the mosaic image will be saved.
    entities : dict
        A dictionary of parsed BIDS entities including modality.
    dryrun : bool
        If True, skip actual computation and file writing. Default False.

    Returns
    -------
    mosaic_file : File
        Path to the saved mosaic image.
    """
    basename = "sub-{sub}_ses-{ses}_run-{run}_mod-T1w_deface".format(
        **entities)
    mosaic_file = output_dir / f"{basename}mosaic.png"

    if not dryrun:
        plotting.plot_roi(
            mask_file,
            bg_img=im_file,
            display_mode="z",
            cut_coords=25,
            black_bg=True,
            alpha=0.6,
            colorbar=False,
            output_file=mosaic_file
        )
        arr = plt.imread(mosaic_file)
        cut = int(arr.shape[1] / 5)
        plt.figure()
        arr = np.concatenate(
            [arr[:, idx * cut: (idx + 1) * cut] for idx in range(5)], axis=0)
        plt.imshow(arr)
        plt.axis("off")
        plt.savefig(mosaic_file)

    return (mosaic_file, )


@coerceparams
@outputdir
@log_runtime(
    bunched=False)
@pywrapper
def plot_histogram(
        table_file: File,
        col_name: str,
        output_dir: Directory,
        bar_coords: list[float] | None = None,
        dryrun: bool = False) -> tuple[File]:
    """
    Generates a histogram image with optional vertical bars.

    Parameters
    ----------
    table_file : File
        TSV table containing the data to be displayed.
    col_name : str
        Name of the column containing the histogram data.
    output_dir : Directory
        Directory where the image with the histogram will be saved.
    bar_coords: list[float] | None
        Coordianates of vertical lines to be displayed in red. Default None.
    dryrun : bool
        If True, skip actual computation and file writing. Default False.

    Returns
    -------
    histogram_file : File
        Generated image with the histogram.
    """
    histogram_file = output_dir / f"histogram_{col_name}.png"

    if not dryrun:

        data = pd.read_csv(
            histogram_file,
            sep="\t",
        )
        arr = data[col_name].astype(float)
        arr = arr[~np.isnan(arr)]
        arr = arr[~np.isinf(arr)]

        _, ax = plt.subplots()
        sns.histplot(
            arr,
            color="gray",
            alpha=0.6,
            ax=ax,
            kde=True,
            stat="density",
            label=col_name,
        )
        for x_coord in bar_coords or []:
            ax.axvline(x=x_coord, color="red")
        ax.spines["right"].set_visible(False)
        ax.spines["top"].set_visible(False)
        ax.legend()

        plt.savefig(histogram_file)

    return (histogram_file, )


@coerceparams
@outputdir
@log_runtime(
    bunched=False)
@pywrapper
def plot_brainparc(
        wm_mask_file: File,
        gm_mask_file: File,
        csf_mask_file: File,
        brain_mask_file: File,
        output_dir: Directory,
        entities: dict,
        dryrun: bool = False) -> tuple[File]:
    """

    Parameters
    ----------
    wm_mask_file : File
        Binary mask of white matter regions.
    gm_mask_file : File
        Binary mask of gray matter regions.
    csf_mask_file : File
        Binary mask of cerebrospinal fluid regions.
    brain_mask_file : File
        Binary brain mask file.
    output_dir : Directory
        FreeSurfer working directory containing all the subjects.
    entities : dict
        A dictionary of parsed BIDS entities including modality.
    dryrun : bool
        If True, skip actual computation and file writing. Default False.

    Returns
    -------
    brainparc_image_file : File
        Image of the GM mask and GM, WM, CSF tissues histograms.
    """
    basename = "sub-{sub}_ses-{ses}_run-{run}_brainparc".format(
        **entities)
    brainparc_image_file = output_dir / f"{basename}.png"

    if not dryrun:

        subject = f"run-{entities['run']}"
        anat_file = output_dir / subject / "mri" / "norm.mgz"

        fig, axs = plt.subplots(2)
        plotting.plot_roi(
            roi_img=gm_mask_file,
            bg_img=anat_file,
            alpha=0.3,
            figure=fig,
            axes=axs[0],
        )

        anat_arr = nibabel.load(anat_file).get_fdata()
        mask_arr = nibabel.load(brain_mask_file).get_fdata()
        bins = np.histogram_bin_edges(
            anat_arr[mask_arr],
            bins="auto",
        )
        palette = itertools.cycle(sns.color_palette("Set1"))
        for name, path in [("WM", wm_mask_file),
                           ("GM", gm_mask_file),
                           ("CSF", csf_mask_file)]:
            mask = nibabel.load(path).get_fdata().astype(int)
            sns.histplot(
                anat_arr[mask],
                bins=bins,
                color=next(palette),
                alpha=0.6,
                ax=axs[1],
                kde=True,
                stat="density",
                label=name,
            )
        axs[1].spines["right"].set_visible(False)
        axs[1].spines["top"].set_visible(False)
        axs[1].legend()

        plt.subplots_adjust(wspace=0, hspace=0, top=0.9, bottom=0.1)
        plt.savefig(brainparc_image_file)

    return (brainparc_image_file, )

1	##########################################################################
2	# NSAp - Copyright (C) CEA, 2021 - 2025
3	# Distributed under the terms of the CeCILL-B license, as published by
4	# the CEA-CNRS-INRIA. Refer to the LICENSE file or to
5	# http://www.cecill.info/licences/Licence_CeCILL-B_V1-en.html
6	# for details.
7	##########################################################################
8
9	"""
10	Plotting functions.
11	"""
12
13	import itertools	1✔
14
15	import matplotlib.pyplot as plt	1✔
16	import nibabel	1✔
17	import numpy as np	1✔
18	import pandas as pd	1✔
19	import seaborn as sns	1✔
20	from nilearn import plotting	1✔
21
22	from ..reporting import log_runtime	1✔
23	from ..typing import (	1✔
24	Directory,
25	File,
26	)
27	from ..utils import (	1✔
28	coerceparams,
29	outputdir,
30	)
31	from ..wrappers import pywrapper	1✔
32
33
34	@coerceparams	1✔
35	@outputdir	1✔
36	@log_runtime(	1✔
37	bunched=False)
38	@pywrapper	1✔
39	def plot_defacing_mosaic(	1✔
40	im_file: File,
41	mask_file: File,
42	output_dir: Directory,
43	entities: dict,
44	dryrun: bool = False) -> tuple[File]:
45	"""
46	Generates a defacing mosaic image by overlaying a mask on an anatomical
47	image.
48
49	Parameters
50	----------
51	im_file : File
52	Path to the anatomical image.
53	mask_file : File
54	Path to the defacing mask.
55	output_dir : Directory
56	Directory where the mosaic image will be saved.
57	entities : dict
58	A dictionary of parsed BIDS entities including modality.
59	dryrun : bool
60	If True, skip actual computation and file writing. Default False.
61
62	Returns
63	-------
64	mosaic_file : File
65	Path to the saved mosaic image.
66	"""
67	basename = "sub-{sub}_ses-{ses}_run-{run}_mod-T1w_deface".format(	1✔
68	**entities)
69	mosaic_file = output_dir / f"{basename}mosaic.png"	1✔
70
71	if not dryrun:	1✔
72	plotting.plot_roi(	×
73	mask_file,
74	bg_img=im_file,
75	display_mode="z",
76	cut_coords=25,
77	black_bg=True,
78	alpha=0.6,
79	colorbar=False,
80	output_file=mosaic_file
81	)
82	arr = plt.imread(mosaic_file)	×
83	cut = int(arr.shape[1] / 5)	×
84	plt.figure()	×
85	arr = np.concatenate(	×
86	[arr[:, idx * cut: (idx + 1) * cut] for idx in range(5)], axis=0)
87	plt.imshow(arr)	×
88	plt.axis("off")	×
89	plt.savefig(mosaic_file)	×
90
91	return (mosaic_file, )	1✔
92
93
94	@coerceparams	1✔
95	@outputdir	1✔
96	@log_runtime(	1✔
97	bunched=False)
98	@pywrapper	1✔
99	def plot_histogram(	1✔
100	table_file: File,
101	col_name: str,
102	output_dir: Directory,
103	bar_coords: list[float] \| None = None,
104	dryrun: bool = False) -> tuple[File]:
105	"""
106	Generates a histogram image with optional vertical bars.
107
108	Parameters
109	----------
110	table_file : File
111	TSV table containing the data to be displayed.
112	col_name : str
113	Name of the column containing the histogram data.
114	output_dir : Directory
115	Directory where the image with the histogram will be saved.
116	bar_coords: list[float] \| None
117	Coordianates of vertical lines to be displayed in red. Default None.
118	dryrun : bool
119	If True, skip actual computation and file writing. Default False.
120
121	Returns
122	-------
123	histogram_file : File
124	Generated image with the histogram.
125	"""
126	histogram_file = output_dir / f"histogram_{col_name}.png"	1✔
127
128	if not dryrun:	1✔
129
130	data = pd.read_csv(	×
131	histogram_file,
132	sep="\t",
133	)
134	arr = data[col_name].astype(float)	×
135	arr = arr[~np.isnan(arr)]	×
136	arr = arr[~np.isinf(arr)]	×
137
138	_, ax = plt.subplots()	×
139	sns.histplot(	×
140	arr,
141	color="gray",
142	alpha=0.6,
143	ax=ax,
144	kde=True,
145	stat="density",
146	label=col_name,
147	)
148	for x_coord in bar_coords or []:	×
149	ax.axvline(x=x_coord, color="red")	×
150	ax.spines["right"].set_visible(False)	×
151	ax.spines["top"].set_visible(False)	×
152	ax.legend()	×
153
154	plt.savefig(histogram_file)	×
155
156	return (histogram_file, )	1✔
157
158
159	@coerceparams	1✔
160	@outputdir	1✔
161	@log_runtime(	1✔
162	bunched=False)
163	@pywrapper	1✔
164	def plot_brainparc(	1✔
165	wm_mask_file: File,
166	gm_mask_file: File,
167	csf_mask_file: File,
168	brain_mask_file: File,
169	output_dir: Directory,
170	entities: dict,
171	dryrun: bool = False) -> tuple[File]:
172	"""
173
174	Parameters
175	----------
176	wm_mask_file : File
177	Binary mask of white matter regions.
178	gm_mask_file : File
179	Binary mask of gray matter regions.
180	csf_mask_file : File
181	Binary mask of cerebrospinal fluid regions.
182	brain_mask_file : File
183	Binary brain mask file.
184	output_dir : Directory
185	FreeSurfer working directory containing all the subjects.
186	entities : dict
187	A dictionary of parsed BIDS entities including modality.
188	dryrun : bool
189	If True, skip actual computation and file writing. Default False.
190
191	Returns
192	-------
193	brainparc_image_file : File
194	Image of the GM mask and GM, WM, CSF tissues histograms.
195	"""
196	basename = "sub-{sub}_ses-{ses}_run-{run}_brainparc".format(	1✔
197	**entities)
198	brainparc_image_file = output_dir / f"{basename}.png"	1✔
199
200	if not dryrun:	1✔
201
202	subject = f"run-{entities['run']}"	×
203	anat_file = output_dir / subject / "mri" / "norm.mgz"	×
204
205	fig, axs = plt.subplots(2)	×
206	plotting.plot_roi(	×
207	roi_img=gm_mask_file,
208	bg_img=anat_file,
209	alpha=0.3,
210	figure=fig,
211	axes=axs[0],
212	)
213
214	anat_arr = nibabel.load(anat_file).get_fdata()	×
215	mask_arr = nibabel.load(brain_mask_file).get_fdata()	×
216	bins = np.histogram_bin_edges(	×
217	anat_arr[mask_arr],
218	bins="auto",
219	)
220	palette = itertools.cycle(sns.color_palette("Set1"))	×
221	for name, path in [("WM", wm_mask_file),	×
222	("GM", gm_mask_file),
223	("CSF", csf_mask_file)]:
224	mask = nibabel.load(path).get_fdata().astype(int)	×
225	sns.histplot(	×
226	anat_arr[mask],
227	bins=bins,
228	color=next(palette),
229	alpha=0.6,
230	ax=axs[1],
231	kde=True,
232	stat="density",
233	label=name,
234	)
235	axs[1].spines["right"].set_visible(False)	×
236	axs[1].spines["top"].set_visible(False)	×
237	axs[1].legend()	×
238
239	plt.subplots_adjust(wspace=0, hspace=0, top=0.9, bottom=0.1)	×
240	plt.savefig(brainparc_image_file)	×
241
242	return (brainparc_image_file, )	1✔

neurospin-deepinsight / brainprep / 19471672015

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