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[WIP] Python code refactor and code styling #59

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[WIP] Python code refactor and code styling #59

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a58913b
QCRAKEN-5 - Trim whitespaces and end-of-line from status when updatin…
Feb 8, 2022
ba64a28
QCRAKEN-5 - Answer code formatting comments from MR. Refactor redunda…
Feb 14, 2022
f2d8143
QCRAKEN-5 - Run black -l 80. Add info to README for code styling and …
Feb 15, 2022
9b0f164
QCRAKEN-5 - Run black -l 100. Minor fixes
Feb 18, 2022
44903a7
QCRAKEN-5 - Extract parallelization code to avoid code repetition. Re…
Feb 21, 2022
4095cc0
QCRAKEN-5 - Fix typos, refactor stats module to avoid repetitions
Feb 28, 2022
784dba5
Transfer refactoring from Imeka. Still need to be tested
Jun 16, 2022
b994d2a
Re-add files deleted by the merge operation
Jun 16, 2022
cbaf8a4
Fix readme
Jun 16, 2022
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95 changes: 18 additions & 77 deletions dmriqcpy/analysis/stats.py
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Original file line number Diff line number Diff line change
@@ -1,9 +1,10 @@
# -*- coding: utf-8 -*-
import os

import nibabel as nib
import numpy as np
import os
import pandas as pd

from dmriqcpy.analysis.utils import get_stats_dataframes


def stats_mean_median(column_names, filenames):
Expand All @@ -26,8 +27,6 @@ def stats_mean_median(column_names, filenames):
across subjects.
"""
values = []
import time
sub_filenames = [os.path.basename(curr_subj).split('.')[0] for curr_subj in filenames]

for filename in filenames:
data = nib.load(filename).get_data()
Expand All @@ -40,24 +39,14 @@ def stats_mean_median(column_names, filenames):
mean = np.mean(data[data > 0])
median = np.median(data[data > 0])

values.append(
[mean, median])

stats_per_subjects = pd.DataFrame(values, index=sub_filenames,
columns=column_names)

stats_across_subjects = pd.DataFrame([stats_per_subjects.mean(),
stats_per_subjects.std(),
stats_per_subjects.min(),
stats_per_subjects.max()],
index=['mean', 'std', 'min', 'max'],
columns=column_names)
values.append([mean, median])

return stats_per_subjects, stats_across_subjects
return get_stats_dataframes(filenames, values, column_names)


def stats_mean_in_tissues(column_names, images, wm_images, gm_images,
csf_images):
def stats_mean_in_tissues(
column_names, images, wm_images, gm_images, csf_images
):
"""
Compute mean value in WM, GM and CSF mask.

Expand All @@ -82,7 +71,6 @@ def stats_mean_in_tissues(column_names, images, wm_images, gm_images,
DataFrame containing mean, std, min and max of mean across subjects.
"""
values = []
sub_images = [os.path.basename(curr_subj).split('.')[0] for curr_subj in images]

for i in range(len(images)):
data = nib.load(images[i]).get_data()
Expand All @@ -95,20 +83,9 @@ def stats_mean_in_tissues(column_names, images, wm_images, gm_images,
data_csf = np.mean(data[csf > 0])
data_max = np.max(data[wm > 0])

values.append(
[data_wm, data_gm, data_csf, data_max])
values.append([data_wm, data_gm, data_csf, data_max])

stats_per_subjects = pd.DataFrame(values, index=sub_images,
columns=column_names)

stats_across_subjects = pd.DataFrame([stats_per_subjects.mean(),
stats_per_subjects.std(),
stats_per_subjects.min(),
stats_per_subjects.max()],
index=['mean', 'std', 'min', 'max'],
columns=column_names)

return stats_per_subjects, stats_across_subjects
return get_stats_dataframes(images, values, column_names)


def stats_frf(column_names, filenames):
Expand All @@ -130,22 +107,12 @@ def stats_frf(column_names, filenames):
DataFrame containing mean, std, min and max of mean across subjects.
"""
values = []

for filename in filenames:
frf = np.loadtxt(filename)
values.append([frf[0], frf[1], frf[3]])

sub_filenames = [os.path.basename(curr_subj).split('.')[0] for curr_subj in filenames]
stats_per_subjects = pd.DataFrame(values,index=sub_filenames,
columns=column_names)

stats_across_subjects = pd.DataFrame([stats_per_subjects.mean(),
stats_per_subjects.std(),
stats_per_subjects.min(),
stats_per_subjects.max()],
index=['mean', 'std', 'min', 'max'],
columns=column_names)

return stats_per_subjects, stats_across_subjects
return get_stats_dataframes(filenames, values, column_names)


def stats_tractogram(column_names, tractograms):
Expand All @@ -167,24 +134,12 @@ def stats_tractogram(column_names, tractograms):
DataFrame containing mean, std, min and max of mean across subjects.
"""
values = []
sub_tractograms = [os.path.basename(curr_subj).split('.')[0] for curr_subj in tractograms]

for tractogram_file in tractograms:
tractogram = nib.streamlines.load(tractogram_file, lazy_load=True)
values.append([tractogram.header["nb_streamlines"]])

values.append(
[tractogram.header['nb_streamlines']])

stats_per_subjects = pd.DataFrame(values, index=sub_tractograms,
columns=column_names)

stats_across_subjects = pd.DataFrame([stats_per_subjects.mean(),
stats_per_subjects.std(),
stats_per_subjects.min(),
stats_per_subjects.max()],
index=['mean', 'std', 'min', 'max'],
columns=column_names)

return stats_per_subjects, stats_across_subjects
return get_stats_dataframes(tractograms, values, column_names)


def stats_mask_volume(column_names, images):
Expand All @@ -206,24 +161,10 @@ def stats_mask_volume(column_names, images):
DataFrame containing mean, std, min and max of mean across subjects.
"""
values = []
sub_images = [os.path.basename(curr_subj).split('.')[0] for curr_subj in images]

for image in images:
img = nib.load(image)
data = img.get_data()
voxel_volume = np.prod(img.header['pixdim'][1:4])
volume = np.count_nonzero(data) * voxel_volume

values.append([volume])

stats_per_subjects = pd.DataFrame(values, index=sub_images,
columns=column_names)

stats_across_subjects = pd.DataFrame([stats_per_subjects.mean(),
stats_per_subjects.std(),
stats_per_subjects.min(),
stats_per_subjects.max()],
index=['mean', 'std', 'min', 'max'],
columns=column_names)
voxel_volume = np.prod(img.header["pixdim"][1:4])
values.append([np.count_nonzero(img.get_data()) * voxel_volume])

return stats_per_subjects, stats_across_subjects
return get_stats_dataframes(images, values, column_names)
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