Merge pull request #745 from oesteban/fix/codacy-review

[FIX] Codacy issues

mriqc/bin/labeler.py

@@ -50,8 +50,7 @@ def main():
     order = range(1, len(file))
     random.shuffle(order)
     # pick a random row
-    for i in range(0, len(order)):
-        row = order[i]
+    for row in order:
         # check how many entires it has
         curEnt = num_rows(file[row])
         if curEnt <= 1:

mriqc/interfaces/__init__.py

@@ -9,6 +9,6 @@
     StructuralQC, ArtifactMask, ComputeQI2, Harmonize, RotationMask
 from .functional import FunctionalQC, Spikes
 from .bids import ReadSidecarJSON, IQMFileSink
-from .viz import PlotMosaic, PlotContours, PlotSpikes
+from .viz import PlotMosaic, PlotContours, PlotSpikes, PlotDistribution
 from .common import ConformImage, EnsureSize
 from .webapi import UploadIQMs

mriqc/interfaces/viz.py

@@ -10,15 +10,14 @@
 """ Visualization interfaces """
 from __future__ import print_function, division, absolute_import, unicode_literals
 
-import os.path as op
+from pathlib import Path
 import numpy as np
 from nipype.interfaces.base import (
     traits, TraitedSpec, File, BaseInterfaceInputSpec, isdefined,
     SimpleInterface)
 
 from io import open  # pylint: disable=W0622
-from ..utils.misc import split_ext
-from ..viz.utils import (plot_mosaic, plot_segmentation, plot_spikes)
+from ..viz.utils import (plot_mosaic, plot_segmentation, plot_spikes, plot_bg_dist)
 
 
 class PlotContoursInputSpec(BaseInterfaceInputSpec):
@@ -49,21 +48,23 @@ class PlotContours(SimpleInterface):
     output_spec = PlotContoursOutputSpec
 
     def _run_interface(self, runtime):
-        out_file = None
+        in_file_ref = Path(self.inputs.in_file)
+
         if isdefined(self.inputs.out_file):
-            out_file = self.inputs.out_file
+            in_file_ref = Path(self.inputs.out_file)
 
-        fname, _ = split_ext(self.inputs.in_file, out_file)
-        out_file = op.abspath('plot_' + fname + '_contours.svg')
-        self._results['out_file'] = out_file
+        fname = in_file_ref.name.rstrip(
+            ''.join(in_file_ref.suffixes))
+        out_file = (Path(runtime.cwd) / ('plot_%s_contours.svg' % fname)).resolve()
+        self._results['out_file'] = str(out_file)
 
         vmax = None if not isdefined(self.inputs.vmax) else self.inputs.vmax
         vmin = None if not isdefined(self.inputs.vmin) else self.inputs.vmin
 
         plot_segmentation(
             self.inputs.in_file,
             self.inputs.in_contours,
-            out_file=out_file,
+            out_file=str(out_file),
             cut_coords=self.inputs.cut_coords,
             display_mode=self.inputs.display_mode,
             levels=self.inputs.levels,
@@ -121,7 +122,7 @@ def _run_interface(self, runtime):
             bbox_mask_file=mask,
             cmap=self.inputs.cmap,
             annotate=self.inputs.annotate)
-        self._results['out_file'] = op.abspath(self.inputs.out_file)
+        self._results['out_file'] = str((Path(runtime.cwd) / self.inputs.out_file).resolve())
         return runtime
 
 
@@ -142,7 +143,7 @@ class PlotSpikes(SimpleInterface):
     output_spec = PlotSpikesOutputSpec
 
     def _run_interface(self, runtime):
-        out_file = op.abspath(self.inputs.out_file)
+        out_file = str((Path(runtime.cwd) / self.inputs.out_file).resolve())
         self._results['out_file'] = out_file
 
         spikes_list = np.loadtxt(self.inputs.in_spikes, dtype=int).tolist()
@@ -157,3 +158,25 @@ def _run_interface(self, runtime):
             self.inputs.in_file, self.inputs.in_fft, spikes_list,
             out_file=out_file)
         return runtime
+
+
+class PlotDistributionInputSpec(PlotBaseInputSpec):
+    in_file = File(exists=True, mandatory=True, desc='input distribution file')
+
+
+class PlotDistributionOutputSpec(TraitedSpec):
+    out_file = File(exists=True, desc='output svg file')
+
+
+class PlotDistribution(SimpleInterface):
+    """
+    Plot slices of a dataset with spikes
+    """
+    input_spec = PlotDistributionInputSpec
+    output_spec = PlotDistributionOutputSpec
+
+    def _run_interface(self, runtime):
+        self._results['out_file'] = plot_bg_dist(
+            Path(self.inputs.in_file),
+            cwd=Path(runtime.cwd).resolve())
+        return runtime

mriqc/utils/misc.py

@@ -6,9 +6,6 @@
 from __future__ import print_function, division, absolute_import, unicode_literals
 
 from pathlib import Path
-from os import path as op
-from glob import glob
-
 import collections
 import json
 import pandas as pd
@@ -31,18 +28,6 @@
 """
 
 
-def split_ext(in_file, out_file=None):
-    import os.path as op
-    if out_file is None:
-        fname, ext = op.splitext(op.basename(in_file))
-        if ext == '.gz':
-            fname, ext2 = op.splitext(fname)
-            ext = ext2 + ext
-        return fname, ext
-    else:
-        return split_ext(out_file)
-
-
 def reorder_csv(csv_file, out_file=None):
     """
     Put subject, session and scan in front of csv file
@@ -124,8 +109,8 @@ def generate_pred(derivatives_dir, output_dir, mod):
         return None
 
     # If some were found, generate the CSV file and group report
-    out_csv = op.join(output_dir, mod + '_predicted_qa.csv')
-    jsonfiles = glob(op.join(derivatives_dir, 'sub-*_%s.json' % mod))
+    jsonfiles = list(output_dir.glob(
+        'sub-*/**/%s/sub-*_%s.json' % (IMTYPES[mod], mod)))
     if not jsonfiles:
         return None
 
@@ -154,7 +139,9 @@ def generate_pred(derivatives_dir, output_dir, mod):
     dataframe.drop_duplicates(bdits_cols,
                               keep='last', inplace=True)
 
-    dataframe[bdits_cols + ['mriqc_pred']].to_csv(out_csv, index=False)
+    out_csv = Path(output_dir) / ('%s_predicted_qa_csv' % mod)
+    dataframe[bdits_cols + ['mriqc_pred']].to_csv(
+        str(out_csv), index=False)
     return out_csv
 
 

mriqc/viz/misc.py

@@ -204,8 +204,8 @@ def plot_raters(dataframe, ax=None, width=101, size=0.40):
 
 
 def raters_variability_plot(mdata, figsize=(22, 22), width=101, out_file=None,
-                            raters=['rater_1', 'rater_2', 'rater_3'], only_overlap=True,
-                            rater_names=['Rater 1', 'Rater 2a', 'Rater 2b']):
+                            raters=('rater_1', 'rater_2', 'rater_3'), only_overlap=True,
+                            rater_names=('Rater 1', 'Rater 2a', 'Rater 2b')):
     if only_overlap:
         mdata = mdata[np.all(~np.isnan(mdata[raters]), axis=1)]
     # Swap raters 2 and 3

mriqc/viz/utils.py

@@ -11,6 +11,7 @@
 from __future__ import print_function, division, absolute_import, unicode_literals
 
 import math
+from pathlib import Path
 import os.path as op
 import numpy as np
 import nibabel as nb
@@ -20,8 +21,6 @@
 from matplotlib.backends.backend_pdf import FigureCanvasPdf as FigureCanvas
 import seaborn as sns
 
-from builtins import zip, range, str, bytes  # pylint: disable=W0622
-
 DEFAULT_DPI = 300
 DINA4_LANDSCAPE = (11.69, 8.27)
 DINA4_PORTRAIT = (8.27, 11.69)
@@ -423,8 +422,6 @@ def _get_values_inside_a_mask(main_file, mask_file):
 
 def plot_segmentation(anat_file, segmentation, out_file,
                       **kwargs):
-    import nibabel as nb
-    import numpy as np
     from nilearn.plotting import plot_anat
 
     vmax = kwargs.get('vmax')
@@ -454,24 +451,22 @@ def plot_segmentation(anat_file, segmentation, out_file,
     return out_file
 
 
-def plot_bg_dist(in_file):
-    import os.path as op  # pylint: disable=W0621
-    import numpy as np
+def plot_bg_dist(in_file, cwd=None):
     import json
-    from io import open  # pylint: disable=W0622
-    import matplotlib.pyplot as plt
     # rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})
     # rc('text', usetex=True)
 
     # Write out figure of the fitting
-    out_file = op.abspath('background_fit.svg')
+    if cwd is None:
+        cwd = Path()
+
+    out_file = (cwd / 'background_fit.svg').resolve()
     try:
         with open(in_file, 'r') as jsonf:
             data = json.load(jsonf)
     except ValueError:
-        with open(out_file, 'w') as ofh:
-            ofh.write('<p>Background noise fitting could not be plotted.</p>')
-        return out_file
+        out_file.write_text('<p>Background noise fitting could not be plotted.</p>')
+        return str(out_file)
 
     fig = plt.figure()
     ax1 = fig.add_subplot(111)
@@ -489,15 +484,13 @@ def plot_bg_dist(in_file):
     ax1.plot(left, data['y_hat'], 'k--', linewidth=1.2)
     ax1.plot((data['x_cutoff'], data['x_cutoff']), ax1.get_ylim(), 'k--')
 
-    fig.savefig(out_file, format='svg', dpi=300)
+    fig.savefig(str(out_file), format='svg', dpi=300)
     plt.close()
-    return out_file
+    return str(out_file)
 
 
 def _get_limits(nifti_file, only_plot_noise=False):
-    from builtins import bytes, str   # pylint: disable=W0622
-
-    if isinstance(nifti_file, (str, bytes)):
+    if isinstance(nifti_file, str):
         nii = nb.as_closest_canonical(nb.load(nifti_file))
         data = nii.get_data()
     else:

mriqc/workflows/anatomical.py

@@ -31,7 +31,6 @@
 
 
 """
-import os.path as op
 from pathlib import Path
 from nipype.pipeline import engine as pe
 from nipype.interfaces import io as nio
@@ -65,9 +64,9 @@ def anat_qc_workflow(dataset, settings, mod='T1w', name='anatMRIQC'):
 
     """
     logging.getLogger('nipype.workflow').info(
-                      'Building anatomical MRIQC workflow, datasets list: %s',
-                      [str(Path(d).relative_to(settings['bids_dir']))
-                       for d in sorted(dataset)])
+        'Building anatomical MRIQC workflow, datasets list: %s',
+        [str(Path(d).relative_to(settings['bids_dir']))
+         for d in sorted(dataset)])
     # Initialize workflow
     workflow = pe.Workflow(name="%s%s" % (name, mod))
 
@@ -167,7 +166,7 @@ def spatial_normalization(settings, mod='T1w', name='SpatialNormalization',
 
     # Have some settings handy
     tpl_id = settings.get('template_id', 'mni_icbm152_nlin_asym_09c')
-    mni_template = getattr(niwgetters, 'get_{}'.format(tpl_id))()
+    mni_template = Path(getattr(niwgetters, 'get_{}'.format(tpl_id))())
 
     # Define workflow interface
     workflow = pe.Workflow(name=name)
@@ -190,8 +189,8 @@ def spatial_normalization(settings, mod='T1w', name='SpatialNormalization',
         num_threads=min(settings.get('ants_nthreads', DEFAULTS['ants_nthreads']),
                         settings.get('n_procs', 1)),
         mem_gb=3)
-    norm.inputs.reference_mask = op.join(
-        mni_template, '%dmm_brainmask.nii.gz' % int(resolution))
+    norm.inputs.reference_mask = str(
+        mni_template / ('%dmm_brainmask.nii.gz' % int(resolution)))
 
     workflow.connect([
         (inputnode, norm, [('moving_image', 'moving_image'),
@@ -255,8 +254,9 @@ def compute_iqms(settings, modality='T1w', name='ComputeIQMs'):
         dimension=3, default_value=0, interpolation='Linear',
         float=True),
         iterfield=['input_image'], name='MNItpms2t1')
-    invt.inputs.input_image = [op.join(get_mni_icbm152_nlin_asym_09c(), fname + '.nii.gz')
-                               for fname in ['1mm_tpm_csf', '1mm_tpm_gm', '1mm_tpm_wm']]
+    invt.inputs.input_image = [
+        str(Path(get_mni_icbm152_nlin_asym_09c()) / (fname + '.nii.gz'))
+        for fname in ['1mm_tpm_csf', '1mm_tpm_gm', '1mm_tpm_wm']]
 
     datasink = pe.Node(IQMFileSink(
         modality=modality, out_dir=str(settings['output_dir']),
@@ -365,11 +365,9 @@ def individual_reports(settings, name='ReportsWorkflow'):
     if not verbose:
         return workflow
 
-    from ..interfaces.viz import PlotContours
-    from ..viz.utils import plot_bg_dist
-    plot_bgdist = pe.Node(niu.Function(input_names=['in_file'], output_names=['out_file'],
-                          function=plot_bg_dist), name='PlotBackground')
+    from ..interfaces.viz import PlotContours, PlotDistribution
 
+    plot_bgdist = pe.Node(PlotDistribution(), name='PlotBackground')
     plot_segm = pe.Node(PlotContours(
         display_mode='z', levels=[.5, 1.5, 2.5], cut_coords=10,
         colors=['r', 'g', 'b']), name='PlotSegmentation')
@@ -494,7 +492,7 @@ def airmsk_wf(name='AirMaskWorkflow'):
 
     invt = pe.Node(ants.ApplyTransforms(dimension=3, default_value=0,
                                         interpolation='Linear', float=True), name='invert_xfm')
-    invt.inputs.input_image = op.join(get_mni_icbm152_nlin_asym_09c(), '1mm_headmask.nii.gz')
+    invt.inputs.input_image = str(Path(get_mni_icbm152_nlin_asym_09c()) / '1mm_headmask.nii.gz')
 
     binarize = pe.Node(niu.Function(function=_binarize), name='Binarize')
 
@@ -571,10 +569,12 @@ def _binarize(in_file, threshold=0.5, out_file=None):
 
 
 def _estimate_snr(in_file, seg_file):
+    import numpy as np
     import nibabel as nb
     from mriqc.qc.anatomical import snr
-    out_snr = snr(nb.load(in_file).get_data(), nb.load(seg_file).get_data(),
-                  fglabel='wm')
+    data = nb.load(in_file).get_data()
+    mask = nb.load(seg_file).get_data() == 2  # WM label
+    out_snr = snr(np.mean(data[mask]), data[mask].std(), mask.sum())
     return out_snr
 
 

mriqc/workflows/functional.py

@@ -24,7 +24,6 @@
 
 """
 from __future__ import print_function, division, absolute_import, unicode_literals
-import os.path as op
 from pathlib import Path
 
 from nipype.pipeline import engine as pe
@@ -720,7 +719,7 @@ def epi_mni_align(settings, name='SpatialNormalization'):
     # Warp segmentation into EPI space
     invt = pe.Node(ants.ApplyTransforms(
         float=True,
-        input_image=op.join(mni_template, '1mm_parc.nii.gz'),
+        input_image=str(Path(mni_template) / '1mm_parc.nii.gz'),
         dimension=3, default_value=0, interpolation='NearestNeighbor'),
         name='ResampleSegmentation')