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sty: pep8-ify
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‎nipype/algorithms/rapidart.py‎

Lines changed: 109 additions & 72 deletions
Original file line numberDiff line numberDiff line change
@@ -18,7 +18,8 @@
1818
>>> datadir = os.path.realpath(os.path.join(filepath, '../testing/data'))
1919
>>> os.chdir(datadir)
2020
"""
21-
from __future__ import print_function, division, unicode_literals, absolute_import
21+
from __future__ import (print_function, division,
22+
unicode_literals, absolute_import)
2223
from builtins import open, range, str, bytes
2324

2425
import os
@@ -160,44 +161,61 @@ def _calc_norm_affine(affines, use_differences, brain_pts=None):
160161

161162
class ArtifactDetectInputSpec(BaseInterfaceInputSpec):
162163
realigned_files = InputMultiPath(File(exists=True),
163-
desc="Names of realigned functional data files",
164+
desc=("Names of realigned functional data "
165+
"files"),
164166
mandatory=True)
165-
realignment_parameters = InputMultiPath(File(exists=True), mandatory=True,
166-
desc=("Names of realignment parameters"
167-
"corresponding to the functional data files"))
167+
realignment_parameters = InputMultiPath(File(exists=True),
168+
mandatory=True,
169+
desc=("Names of realignment "
170+
"parameters corresponding to "
171+
"the functional data files"))
168172
parameter_source = traits.Enum("SPM", "FSL", "AFNI", "NiPy", "FSFAST",
169173
desc="Source of movement parameters",
170174
mandatory=True)
171-
use_differences = traits.ListBool([True, False], minlen=2, maxlen=2,
175+
use_differences = traits.ListBool([True, False],
176+
minlen=2,
177+
maxlen=2,
172178
usedefault=True,
173-
desc=("Use differences between successive motion (first element)"
174-
"and intensity paramter (second element) estimates in order"
175-
"to determine outliers. (default is [True, False])"))
176-
use_norm = traits.Bool(requires=['norm_threshold'],
179+
desc=("Use differences between successive"
180+
" motion (first element) and "
181+
"intensity parameter (second "
182+
"element) estimates in order to "
183+
"determine outliers. "
184+
"(default is [True, False])"))
185+
use_norm = traits.Bool(True,
186+
usedefault=True,
187+
requires=['norm_threshold'],
177188
desc=("Uses a composite of the motion parameters in "
178189
"order to determine outliers."))
179-
norm_threshold = traits.Float(desc=("Threshold to use to detect motion-rela"
190+
norm_threshold = traits.Float(xor=['rotation_threshold',
191+
'translation_threshold'],
192+
mandatory=True,
193+
desc=("Threshold to use to detect motion-rela"
180194
"ted outliers when composite motion is "
181-
"being used"), mandatory=True,
182-
xor=['rotation_threshold',
183-
'translation_threshold'])
184-
rotation_threshold = traits.Float(mandatory=True, xor=['norm_threshold'],
185-
desc=("Threshold (in radians) to use to detect rotation-related "
186-
"outliers"))
187-
translation_threshold = traits.Float(mandatory=True, xor=['norm_threshold'],
188-
desc=("Threshold (in mm) to use to detect translation-related "
195+
"being used"))
196+
rotation_threshold = traits.Float(mandatory=True,
197+
xor=['norm_threshold'],
198+
desc=("Threshold (in radians) to use to "
199+
"detect rotation-related outliers"))
200+
translation_threshold = traits.Float(mandatory=True,
201+
xor=['norm_threshold'],
202+
desc=("Threshold (in mm) to use to "
203+
"detect translation-related "
189204
"outliers"))
190205
zintensity_threshold = traits.Float(mandatory=True,
191-
desc=("Intensity Z-threshold use to detection images that deviate "
206+
desc=("Intensity Z-threshold use to "
207+
"detection images that deviate "
192208
"from the mean"))
193209
mask_type = traits.Enum('spm_global', 'file', 'thresh',
194-
desc=("Type of mask that should be used to mask the functional "
195-
"data. *spm_global* uses an spm_global like calculation to "
196-
"determine the brain mask. *file* specifies a brain mask "
197-
"file (should be an image file consisting of 0s and 1s). "
198-
"*thresh* specifies a threshold to use. By default all voxels"
199-
"are used, unless one of these mask types are defined."),
200-
mandatory=True)
210+
mandatory=True,
211+
desc=("Type of mask that should be used to mask the"
212+
" functional data. *spm_global* uses an "
213+
"spm_global like calculation to determine the"
214+
" brain mask. *file* specifies a brain mask "
215+
"file (should be an image file consisting of "
216+
"0s and 1s). *thresh* specifies a threshold "
217+
"to use. By default all voxels are used,"
218+
"unless one of these mask types are defined"))
201219
mask_file = File(exists=True,
202220
desc="Mask file to be used if mask_type is 'file'.")
203221
mask_threshold = traits.Float(desc=("Mask threshold to be used if mask_type"
@@ -223,28 +241,36 @@ class ArtifactDetectInputSpec(BaseInterfaceInputSpec):
223241

224242
class ArtifactDetectOutputSpec(TraitedSpec):
225243
outlier_files = OutputMultiPath(File(exists=True),
226-
desc=("One file for each functional run containing a list of "
227-
"0-based indices corresponding to outlier volumes"))
244+
desc=("One file for each functional run "
245+
"containing a list of 0-based indices"
246+
" corresponding to outlier volumes"))
228247
intensity_files = OutputMultiPath(File(exists=True),
229-
desc=("One file for each functional run containing the global "
230-
"intensity values determined from the brainmask"))
248+
desc=("One file for each functional run "
249+
"containing the global intensity "
250+
"values determined from the "
251+
"brainmask"))
231252
norm_files = OutputMultiPath(File,
232-
desc=("One file for each functional run containing the composite "
233-
"norm"))
253+
desc=("One file for each functional run "
254+
"containing the composite norm"))
234255
statistic_files = OutputMultiPath(File(exists=True),
235-
desc=("One file for each functional run containing information "
236-
"about the different types of artifacts and if design info is"
237-
" provided then details of stimulus correlated motion and a "
238-
"listing or artifacts by event type."))
256+
desc=("One file for each functional run "
257+
"containing information about the "
258+
"different types of artifacts and "
259+
"if design info is provided then "
260+
"details of stimulus correlated "
261+
"motion and a listing or artifacts "
262+
"by event type."))
239263
plot_files = OutputMultiPath(File,
240-
desc=("One image file for each functional run containing the "
241-
"detected outliers"))
264+
desc=("One image file for each functional run "
265+
"containing the detected outliers"))
242266
mask_files = OutputMultiPath(File,
243-
desc=("One image file for each functional run containing the mask"
244-
"used for global signal calculation"))
267+
desc=("One image file for each functional run "
268+
"containing the mask used for global "
269+
"signal calculation"))
245270
displacement_files = OutputMultiPath(File,
246-
desc=("One image file for each functional run containing the voxel"
247-
"displacement timeseries"))
271+
desc=("One image file for each "
272+
"functional run containing the "
273+
"voxel displacement timeseries"))
248274

249275

250276
class ArtifactDetect(BaseInterface):
@@ -314,7 +340,7 @@ def _list_outputs(self):
314340
outputs['intensity_files'] = []
315341
outputs['statistic_files'] = []
316342
outputs['mask_files'] = []
317-
if isdefined(self.inputs.norm_threshold):
343+
if isdefined(self.inputs.use_norm) and self.inputs.use_norm:
318344
outputs['norm_files'] = []
319345
if self.inputs.bound_by_brainmask:
320346
outputs['displacement_files'] = []
@@ -328,7 +354,7 @@ def _list_outputs(self):
328354
outputs['intensity_files'].insert(i, intensityfile)
329355
outputs['statistic_files'].insert(i, statsfile)
330356
outputs['mask_files'].insert(i, maskfile)
331-
if isdefined(self.inputs.norm_threshold):
357+
if isdefined(self.inputs.use_norm) and self.inputs.use_norm:
332358
outputs['norm_files'].insert(i, normfile)
333359
if self.inputs.bound_by_brainmask:
334360
outputs['displacement_files'].insert(i, displacementfile)
@@ -434,7 +460,7 @@ def _detect_outliers_core(self, imgfile, motionfile, runidx, cwd=None):
434460
mask_img = Nifti1Image(mask.astype(np.uint8), affine)
435461
mask_img.to_filename(maskfile)
436462

437-
if isdefined(self.inputs.norm_threshold):
463+
if self.inputs.use_norm:
438464
brain_pts = None
439465
if self.inputs.bound_by_brainmask:
440466
voxel_coords = np.nonzero(mask)
@@ -477,20 +503,20 @@ def _detect_outliers_core(self, imgfile, motionfile, runidx, cwd=None):
477503
# write output to outputfile
478504
np.savetxt(artifactfile, outliers, fmt=b'%d', delimiter=' ')
479505
np.savetxt(intensityfile, g, fmt=b'%.2f', delimiter=' ')
480-
if isdefined(self.inputs.norm_threshold):
506+
if self.inputs.use_norm:
481507
np.savetxt(normfile, normval, fmt=b'%.4f', delimiter=' ')
482508

483509
if isdefined(self.inputs.save_plot) and self.inputs.save_plot:
484510
import matplotlib
485511
matplotlib.use(config.get("execution", "matplotlib_backend"))
486512
import matplotlib.pyplot as plt
487513
fig = plt.figure()
488-
if isdefined(self.inputs.norm_threshold):
514+
if isdefined(self.inputs.use_norm) and self.inputs.use_norm:
489515
plt.subplot(211)
490516
else:
491517
plt.subplot(311)
492518
self._plot_outliers_with_wave(gz, iidx, 'Intensity')
493-
if isdefined(self.inputs.norm_threshold):
519+
if isdefined(self.inputs.use_norm) and self.inputs.use_norm:
494520
plt.subplot(212)
495521
self._plot_outliers_with_wave(normval, np.union1d(tidx, ridx),
496522
'Norm (mm)')
@@ -515,20 +541,22 @@ def _detect_outliers_core(self, imgfile, motionfile, runidx, cwd=None):
515541
motion_outliers)),
516542
'motion_outliers': len(np.setdiff1d(motion_outliers, iidx)),
517543
},
518-
{'motion': [{'using differences': self.inputs.use_differences[0]},
519-
{'mean': np.mean(mc_in, axis=0).tolist(),
520-
'min': np.min(mc_in, axis=0).tolist(),
521-
'max': np.max(mc_in, axis=0).tolist(),
522-
'std': np.std(mc_in, axis=0).tolist()},
544+
{'motion': [
545+
{'using differences': self.inputs.use_differences[0]},
546+
{'mean': np.mean(mc_in, axis=0).tolist(),
547+
'min': np.min(mc_in, axis=0).tolist(),
548+
'max': np.max(mc_in, axis=0).tolist(),
549+
'std': np.std(mc_in, axis=0).tolist()},
523550
]},
524-
{'intensity': [{'using differences': self.inputs.use_differences[1]},
525-
{'mean': np.mean(gz, axis=0).tolist(),
526-
'min': np.min(gz, axis=0).tolist(),
527-
'max': np.max(gz, axis=0).tolist(),
528-
'std': np.std(gz, axis=0).tolist()},
551+
{'intensity': [
552+
{'using differences': self.inputs.use_differences[1]},
553+
{'mean': np.mean(gz, axis=0).tolist(),
554+
'min': np.min(gz, axis=0).tolist(),
555+
'max': np.max(gz, axis=0).tolist(),
556+
'std': np.std(gz, axis=0).tolist()},
529557
]},
530558
]
531-
if isdefined(self.inputs.norm_threshold):
559+
if self.inputs.use_norm:
532560
stats.insert(3, {'motion_norm':
533561
{'mean': np.mean(normval, axis=0).tolist(),
534562
'min': np.min(normval, axis=0).tolist(),
@@ -549,20 +577,27 @@ def _run_interface(self, runtime):
549577

550578

551579
class StimCorrInputSpec(BaseInterfaceInputSpec):
552-
realignment_parameters = InputMultiPath(File(exists=True), mandatory=True,
553-
desc=('Names of realignment parameters corresponding to the functional '
554-
'data files'))
555-
intensity_values = InputMultiPath(File(exists=True), mandatory=True,
556-
desc='Name of file containing intensity values')
557-
spm_mat_file = File(exists=True, mandatory=True,
558-
desc='SPM mat file (use pre-estimate SPM.mat file)')
580+
realignment_parameters = InputMultiPath(File(exists=True),
581+
mandatory=True,
582+
desc=("Names of realignment "
583+
"parameters corresponding to "
584+
"the functional data files"))
585+
intensity_values = InputMultiPath(File(exists=True),
586+
mandatory=True,
587+
desc=("Name of file containing intensity "
588+
"values"))
589+
spm_mat_file = File(exists=True,
590+
mandatory=True,
591+
desc="SPM mat file (use pre-estimate SPM.mat file)")
559592
concatenated_design = traits.Bool(mandatory=True,
560-
desc='state if the design matrix contains concatenated sessions')
593+
desc=("state if the design matrix "
594+
"contains concatenated sessions")
561595

562596

563597
class StimCorrOutputSpec(TraitedSpec):
564598
stimcorr_files = OutputMultiPath(File(exists=True),
565-
desc='List of files containing correlation values')
599+
desc=("List of files containing "
600+
"correlation values")
566601

567602

568603
class StimulusCorrelation(BaseInterface):
@@ -572,8 +607,9 @@ class StimulusCorrelation(BaseInterface):
572607
Currently this class supports an SPM generated design matrix and requires
573608
intensity parameters. This implies that one must run
574609
:ref:`ArtifactDetect <nipype.algorithms.rapidart.ArtifactDetect>`
575-
and :ref:`Level1Design <nipype.interfaces.spm.model.Level1Design>` prior to running this or
576-
provide an SPM.mat file and intensity parameters through some other means.
610+
and :ref:`Level1Design <nipype.interfaces.spm.model.Level1Design>` prior to
611+
running this or provide an SPM.mat file and intensity parameters through
612+
some other means.
577613
578614
Examples
579615
--------
@@ -649,7 +685,8 @@ def _get_spm_submatrix(self, spmmat, sessidx, rows=None):
649685
U = spmmat['SPM'][0][0].Sess[0][sessidx].U[0]
650686
if rows is None:
651687
rows = spmmat['SPM'][0][0].Sess[0][sessidx].row[0] - 1
652-
cols = spmmat['SPM'][0][0].Sess[0][sessidx].col[0][list(range(len(U)))] - 1
688+
cols = (
689+
spmmat['SPM'][0][0].Sess[0][sessidx].col[0][list(range(len(U)))]-1)
653690
outmatrix = designmatrix.take(rows.tolist(), axis=0).take(cols.tolist(),
654691
axis=1)
655692
return outmatrix

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