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satrasatra
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sty: pep8 cleanup
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‎nipype/algorithms/rapidart.py

Lines changed: 98 additions & 67 deletions
Original file line numberDiff line numberDiff line change
@@ -36,6 +36,7 @@
3636
from .. import logging, config
3737
iflogger = logging.getLogger('interface')
3838

39+
3940
def _get_affine_matrix(params, source):
4041
"""Return affine matrix given a set of translation and rotation parameters
4142
@@ -117,7 +118,7 @@ def _calc_norm(mc, use_differences, source, brain_pts=None):
117118
if brain_pts is not None:
118119
displacement[i, :] = \
119120
np.sqrt(np.sum(np.power(np.reshape(newpos[i, :],
120-
(3, all_pts.shape[1])) - \
121+
(3, all_pts.shape[1])) -
121122
all_pts[0:3, :],
122123
2),
123124
axis=0))
@@ -135,6 +136,7 @@ def _calc_norm(mc, use_differences, source, brain_pts=None):
135136
normdata = np.sqrt(np.mean(np.power(newpos, 2), axis=1))
136137
return normdata, displacement
137138

139+
138140
def _nanmean(a, axis=None):
139141
"""Return the mean excluding items that are nan
140142
@@ -151,42 +153,56 @@ def _nanmean(a, axis=None):
151153

152154
class ArtifactDetectInputSpec(BaseInterfaceInputSpec):
153155
realigned_files = InputMultiPath(File(exists=True),
154-
desc="Names of realigned functional data files",
156+
desc="Names of realigned functional data files",
155157
mandatory=True)
156158
realignment_parameters = InputMultiPath(File(exists=True), mandatory=True,
157-
desc=("Names of realignment parameters"
158-
"corresponding to the functional data files"))
159+
desc=("Names of realignment parameters"
160+
"corresponding to the functional data files"))
159161
parameter_source = traits.Enum("SPM", "FSL", "AFNI", "NiPy",
160-
desc="Source of movement parameters", mandatory=True)
161-
use_differences = traits.ListBool([True, False], minlen=2, maxlen=2, usedefault=True,
162-
desc="Use differences between successive motion (first element)" \
163-
"and intensity paramter (second element) estimates in order" \
164-
"to determine outliers. (default is [True, False])")
165-
use_norm = traits.Bool(True, desc="Uses a composite of the motion parameters in order to determine" \
166-
"outliers. Requires ``norm_threshold`` to be set. (default is" \
167-
"True) ", usedefault=True)
168-
norm_threshold = traits.Float(desc="Threshold to use to detect motion-related outliers when" \
169-
"composite motion is being used (see ``use_norm``)", mandatory=True,
170-
xor=['rotation_threshold', 'translation_threshold'])
171-
rotation_threshold = traits.Float(desc="Threshold (in radians) to use to detect rotation-related outliers",
172-
mandatory=True, xor=['norm_threshold'])
173-
translation_threshold = traits.Float(desc="Threshold (in mm) to use to detect translation-related outliers",
174-
mandatory=True, xor=['norm_threshold'])
175-
zintensity_threshold = traits.Float(desc="Intensity Z-threshold use to detection images that deviate from the" \
176-
"mean", mandatory=True)
177-
mask_type = traits.Enum('spm_global', 'file', 'thresh', desc="Type of mask that should be used to mask the functional data." \
178-
"*spm_global* uses an spm_global like calculation to determine the" \
179-
"brain mask. *file* specifies a brain mask file (should be an image" \
180-
"file consisting of 0s and 1s). *thresh* specifies a threshold to" \
181-
"use. By default all voxels are used, unless one of these mask" \
182-
"types are defined.", mandatory=True)
183-
mask_file = File(exists=True, desc="Mask file to be used if mask_type is 'file'.")
184-
mask_threshold = traits.Float(desc="Mask threshold to be used if mask_type is 'thresh'.")
185-
intersect_mask = traits.Bool(True, desc="Intersect the masks when computed from spm_global. (default is" \
186-
"True)")
162+
desc="Source of movement parameters",
163+
mandatory=True)
164+
use_differences = traits.ListBool([True, False], minlen=2, maxlen=2,
165+
usedefault=True,
166+
desc=("Use differences between successive motion (first element)"
167+
"and intensity paramter (second element) estimates in order"
168+
"to determine outliers. (default is [True, False])"))
169+
use_norm = traits.Bool(True, requires=['norm_threshold'],
170+
desc=("Uses a composite of the motion parameters in "
171+
"order to determine outliers."),
172+
usedefault=True)
173+
norm_threshold = traits.Float(desc=("Threshold to use to detect motion-rela"
174+
"ted outliers when composite motion is "
175+
"being used"), mandatory=True,
176+
xor=['rotation_threshold',
177+
'translation_threshold'])
178+
rotation_threshold = traits.Float(mandatory=True, xor=['norm_threshold'],
179+
desc=("Threshold (in radians) to use to detect rotation-related "
180+
"outliers"))
181+
translation_threshold = traits.Float(mandatory=True, xor=['norm_threshold'],
182+
desc=("Threshold (in mm) to use to detect translation-related "
183+
"outliers"))
184+
zintensity_threshold = traits.Float(mandatory=True,
185+
desc=("Intensity Z-threshold use to detection images that deviate "
186+
"from the mean"))
187+
mask_type = traits.Enum('spm_global', 'file', 'thresh',
188+
desc=("Type of mask that should be used to mask the functional "
189+
"data. *spm_global* uses an spm_global like calculation to "
190+
"determine the brain mask. *file* specifies a brain mask "
191+
"file (should be an image file consisting of 0s and 1s). "
192+
"*thresh* specifies a threshold to use. By default all voxels"
193+
"are used, unless one of these mask types are defined."),
194+
mandatory=True)
195+
mask_file = File(exists=True,
196+
desc="Mask file to be used if mask_type is 'file'.")
197+
mask_threshold = traits.Float(desc=("Mask threshold to be used if mask_type"
198+
" is 'thresh'."))
199+
intersect_mask = traits.Bool(True,
200+
desc=("Intersect the masks when computed from "
201+
"spm_global."))
187202
save_plot = traits.Bool(True, desc="save plots containing outliers",
188203
usedefault=True)
189-
plot_type = traits.Enum('png', 'svg', 'eps', 'pdf', desc="file type of the outlier plot",
204+
plot_type = traits.Enum('png', 'svg', 'eps', 'pdf',
205+
desc="file type of the outlier plot",
190206
usedefault=True)
191207
bound_by_brainmask = traits.Bool(False, desc=("use the brain mask to "
192208
"determine bounding box"
@@ -198,16 +214,23 @@ class ArtifactDetectInputSpec(BaseInterfaceInputSpec):
198214

199215

200216
class ArtifactDetectOutputSpec(TraitedSpec):
201-
outlier_files = OutputMultiPath(File(exists=True), desc="One file for each functional run containing a list of 0-based" \
202-
"indices corresponding to outlier volumes")
203-
intensity_files = OutputMultiPath(File(exists=True), desc="One file for each functional run containing the global intensity" \
204-
"values determined from the brainmask")
205-
norm_files = OutputMultiPath(File, desc="One file for each functional run containing the composite norm")
206-
statistic_files = OutputMultiPath(File(exists=True), desc="One file for each functional run containing information about the" \
207-
"different types of artifacts and if design info is provided then" \
208-
"details of stimulus correlated motion and a listing or artifacts by" \
209-
"event type.")
210-
plot_files = OutputMultiPath(File, desc="One image file for each functional run containing the detected outliers")
217+
outlier_files = OutputMultiPath(File(exists=True),
218+
desc=("One file for each functional run containing a list of "
219+
"0-based indices corresponding to outlier volumes"))
220+
intensity_files = OutputMultiPath(File(exists=True),
221+
desc=("One file for each functional run containing the global "
222+
"intensity values determined from the brainmask"))
223+
norm_files = OutputMultiPath(File,
224+
desc=("One file for each functional run containing the composite "
225+
"norm"))
226+
statistic_files = OutputMultiPath(File(exists=True),
227+
desc=("One file for each functional run containing information "
228+
"about the different types of artifacts and if design info is"
229+
" provided then details of stimulus correlated motion and a "
230+
"listing or artifacts by event type."))
231+
plot_files = OutputMultiPath(File,
232+
desc=("One image file for each functional run containing the "
233+
"detected outliers"))
211234
mask_files = OutputMultiPath(File,
212235
desc=("One image file for each functional run containing the mask"
213236
"used for global signal calculation"))
@@ -245,7 +268,6 @@ def __init__(self, **inputs):
245268
'in release 0.8'))
246269
super(ArtifactDetect, self).__init__(**inputs)
247270

248-
249271
def _get_output_filenames(self, motionfile, output_dir):
250272
"""Generate output files based on motion filenames
251273
@@ -344,7 +366,7 @@ def _detect_outliers_core(self, imgfile, motionfile, runidx, cwd=None):
344366
affine = nim.get_affine()
345367
g = np.zeros((timepoints, 1))
346368
masktype = self.inputs.mask_type
347-
if masktype == 'spm_global': # spm_global like calculation
369+
if masktype == 'spm_global': # spm_global like calculation
348370
iflogger.debug('art: using spm global')
349371
intersect_mask = self.inputs.intersect_mask
350372
if intersect_mask:
@@ -420,7 +442,9 @@ def _detect_outliers_core(self, imgfile, motionfile, runidx, cwd=None):
420442
if displacement is not None:
421443
dmap = np.zeros((x, y, z, timepoints), dtype=np.float)
422444
for i in range(timepoints):
423-
dmap[voxel_coords[0], voxel_coords[1], voxel_coords[2], i] = displacement[i, :]
445+
dmap[voxel_coords[0],
446+
voxel_coords[1],
447+
voxel_coords[2], i] = displacement[i, :]
424448
dimg = Nifti1Image(dmap, affine)
425449
dimg.to_filename(displacementfile)
426450
else:
@@ -430,8 +454,11 @@ def _detect_outliers_core(self, imgfile, motionfile, runidx, cwd=None):
430454
axis=0)
431455
traval = mc[:, 0:3] # translation parameters (mm)
432456
rotval = mc[:, 3:6] # rotation parameters (rad)
433-
tidx = find_indices(np.sum(abs(traval) > self.inputs.translation_threshold, 1) > 0)
434-
ridx = find_indices(np.sum(abs(rotval) > self.inputs.rotation_threshold, 1) > 0)
457+
tidx = find_indices(np.sum(abs(traval) >
458+
self.inputs.translation_threshold, 1)
459+
> 0)
460+
ridx = find_indices(np.sum(abs(rotval) >
461+
self.inputs.rotation_threshold, 1) > 0)
435462

436463
outliers = np.unique(np.union1d(iidx, np.union1d(tidx, ridx)))
437464

@@ -476,25 +503,26 @@ def _detect_outliers_core(self, imgfile, motionfile, runidx, cwd=None):
476503
motion_outliers)),
477504
'motion_outliers': len(np.setdiff1d(motion_outliers, iidx)),
478505
},
479-
{'motion': [{'using differences': self.inputs.use_differences[0]},
480-
{'mean': np.mean(mc_in, axis=0).tolist(),
481-
'min': np.min(mc_in, axis=0).tolist(),
482-
'max': np.max(mc_in, axis=0).tolist(),
483-
'std': np.std(mc_in, axis=0).tolist()},
484-
]},
485-
{'intensity': [{'using differences': self.inputs.use_differences[1]},
486-
{'mean': np.mean(gz, axis=0).tolist(),
487-
'min': np.min(gz, axis=0).tolist(),
488-
'max': np.max(gz, axis=0).tolist(),
489-
'std': np.std(gz, axis=0).tolist()},
490-
]},
506+
{'motion': [{'using differences': self.inputs.use_differences[0]},
507+
{'mean': np.mean(mc_in, axis=0).tolist(),
508+
'min': np.min(mc_in, axis=0).tolist(),
509+
'max': np.max(mc_in, axis=0).tolist(),
510+
'std': np.std(mc_in, axis=0).tolist()},
511+
]},
512+
{'intensity': [{'using differences': self.inputs.use_differences[1]},
513+
{'mean': np.mean(gz, axis=0).tolist(),
514+
'min': np.min(gz, axis=0).tolist(),
515+
'max': np.max(gz, axis=0).tolist(),
516+
'std': np.std(gz, axis=0).tolist()},
517+
]},
491518
]
492519
if self.inputs.use_norm:
493-
stats.insert(3, {'motion_norm': {'mean': np.mean(normval, axis=0).tolist(),
494-
'min': np.min(normval, axis=0).tolist(),
495-
'max': np.max(normval, axis=0).tolist(),
496-
'std': np.std(normval, axis=0).tolist(),
497-
}})
520+
stats.insert(3, {'motion_norm':
521+
{'mean': np.mean(normval, axis=0).tolist(),
522+
'min': np.min(normval, axis=0).tolist(),
523+
'max': np.max(normval, axis=0).tolist(),
524+
'std': np.std(normval, axis=0).tolist(),
525+
}})
498526
save_json(statsfile, stats)
499527

500528
def _run_interface(self, runtime):
@@ -510,7 +538,8 @@ def _run_interface(self, runtime):
510538

511539
class StimCorrInputSpec(BaseInterfaceInputSpec):
512540
realignment_parameters = InputMultiPath(File(exists=True), mandatory=True,
513-
desc='Names of realignment parameters corresponding to the functional data files')
541+
desc=('Names of realignment parameters corresponding to the functional '
542+
'data files'))
514543
intensity_values = InputMultiPath(File(exists=True), mandatory=True,
515544
desc='Name of file containing intensity values')
516545
spm_mat_file = File(exists=True, mandatory=True,
@@ -561,7 +590,8 @@ def _get_output_filenames(self, motionfile, output_dir):
561590
"""
562591
(filepath, filename) = os.path.split(motionfile)
563592
(filename, ext) = os.path.splitext(filename)
564-
corrfile = os.path.join(output_dir, ''.join(('qa.', filename, '_stimcorr.txt')))
593+
corrfile = os.path.join(output_dir, ''.join(('qa.', filename,
594+
'_stimcorr.txt')))
565595
return corrfile
566596

567597
def _stimcorr_core(self, motionfile, intensityfile, designmatrix, cwd=None):
@@ -608,7 +638,8 @@ def _get_spm_submatrix(self, spmmat, sessidx, rows=None):
608638
if rows is None:
609639
rows = spmmat['SPM'][0][0].Sess[0][sessidx].row[0] - 1
610640
cols = spmmat['SPM'][0][0].Sess[0][sessidx].col[0][range(len(U))] - 1
611-
outmatrix = designmatrix.take(rows.tolist(), axis=0).take(cols.tolist(), axis=1)
641+
outmatrix = designmatrix.take(rows.tolist(), axis=0).take(cols.tolist(),
642+
axis=1)
612643
return outmatrix
613644

614645
def _run_interface(self, runtime):

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