def clean(self, **kwargs):
cleaned_data = super(StatisticMapForm, self).clean()
django_file = cleaned_data.get("file")
cleaned_data[
"is_valid"] = True #This will be only saved if the form will validate
cleaned_data["tags"] = clean_tags(cleaned_data)
if django_file and "file" not in self._errors and "hdr_file" not in self._errors:
django_file.open()
fileobj = StringIO(django_file.read())
django_file.seek(0)
gzfileobj = GzipFile(filename=django_file.name,
mode='rb',
fileobj=fileobj)
nii = nb.Nifti1Image.from_file_map(
{'image': nb.FileHolder(django_file.name, gzfileobj)})
cleaned_data["is_thresholded"], ratio_bad = is_thresholded(nii)
cleaned_data["perc_bad_voxels"] = ratio_bad * 100.0
if cleaned_data["is_thresholded"] and not cleaned_data.get(
"ignore_file_warning") and cleaned_data.get(
"map_type") != "R":
self._errors["file"] = self.error_class([
"This map seems to be thresholded (%.4g%% of voxels are zeros). Please use an unthresholded version of the map if possible."
% (cleaned_data["perc_bad_voxels"])
])
if cleaned_data.get("hdr_file"):
self._errors["hdr_file"] = self.error_class([
"This map seems to be thresholded (%.4g%% of voxels are zeros). Please use an unthresholded version of the map if possible."
% (cleaned_data["perc_bad_voxels"])
])
self.fields[
"ignore_file_warning"].widget = forms.CheckboxInput()
else:
cleaned_data["not_mni"], cleaned_data[
"brain_coverage"], cleaned_data[
"perc_voxels_outside"] = not_in_mni(nii)
if cleaned_data["not_mni"] and not cleaned_data.get(
"ignore_file_warning") and cleaned_data.get(
"map_type") != "R":
self._errors["file"] = self.error_class([
"This map seems not to be in the MNI space (%.4g%% of meaningful voxels are outside of the brain). Please use transform your data to MNI space."
% (cleaned_data["perc_voxels_outside"])
])
if cleaned_data.get("hdr_file"):
self._errors["hdr_file"] = self.error_class([
"This map seems not to be in the MNI space (%.4g%% of meaningful voxels are outside of the brain). Please use transform your data to MNI space."
% (cleaned_data["perc_voxels_outside"])
])
self.fields[
"ignore_file_warning"].widget = forms.CheckboxInput()
if cleaned_data.get("map_type") == "R":
if "not_mni" in cleaned_data:
del cleaned_data["not_mni"]
if "is_thresholded" in cleaned_data:
del cleaned_data["is_thresholded"]
return cleaned_data
def clean(self, **kwargs):
cleaned_data = super(StatisticMapForm, self).clean()
django_file = cleaned_data.get("file")
if django_file and "file" not in self._errors and "hdr_file" not in self._errors:
django_file.open()
gzfileobj = GzipFile(filename=django_file.name, mode='rb', fileobj=django_file.file)
nii = nb.Nifti1Image.from_file_map({'image': nb.FileHolder(django_file.name, gzfileobj)})
cleaned_data["is_thresholded"], ratio_bad = is_thresholded(nii)
cleaned_data["perc_bad_voxels"] = ratio_bad*100.0
if cleaned_data["is_thresholded"] and not cleaned_data.get("ignore_file_warning"):
self._errors["file"] = self.error_class(["This map seems to be thresholded (%.4g%% of voxels are zeros). Please use an unthresholded version of the map if possible."%(cleaned_data["perc_bad_voxels"])])
if cleaned_data.get("hdr_file"):
self._errors["hdr_file"] = self.error_class(["This map seems to be thresholded (%.4g%% of voxels are zeros). Please use an unthresholded version of the map if possible."%(cleaned_data["perc_bad_voxels"])])
self.fields["ignore_file_warning"].widget = forms.CheckboxInput()
else:
cleaned_data["not_mni"], cleaned_data["brain_coverage"], cleaned_data["perc_voxels_outside"] = not_in_mni(nii)
if cleaned_data["not_mni"] and not cleaned_data.get("ignore_file_warning"):
self._errors["file"] = self.error_class(["This map seems not to be in the MNI space (%.4g%% of meaningful voxels are outside of the brain). Please use transform your data to MNI space."%(cleaned_data["perc_voxels_outside"])])
if cleaned_data.get("hdr_file"):
self._errors["hdr_file"] = self.error_class(["This map seems not to be in the MNI space (%.4g%% of meaningful voxels are outside of the brain). Please use transform your data to MNI space."%(cleaned_data["perc_voxels_outside"])])
self.fields["ignore_file_warning"].widget = forms.CheckboxInput()
return cleaned_data
def save(self):
if self.perc_bad_voxels == None and self.file:
import neurovault.apps.statmaps.utils as nvutils
self.file.open()
gzfileobj = GzipFile(filename=self.file.name,
mode='rb',
fileobj=self.file.file)
nii = nb.Nifti1Image.from_file_map(
{'image': nb.FileHolder(self.file.name, gzfileobj)})
self.is_thresholded, ratio_bad = nvutils.is_thresholded(nii)
self.perc_bad_voxels = ratio_bad * 100.0
if self.brain_coverage == None and self.file:
import neurovault.apps.statmaps.utils as nvutils
self.file.open()
gzfileobj = GzipFile(filename=self.file.name,
mode='rb',
fileobj=self.file.file)
nii = nb.Nifti1Image.from_file_map(
{'image': nb.FileHolder(self.file.name, gzfileobj)})
self.not_mni, self.brain_coverage, self.perc_voxels_outside = nvutils.not_in_mni(
nii)
if self.map_type == self.OTHER:
import neurovault.apps.statmaps.utils as nvutils
self.file.open()
gzfileobj = GzipFile(filename=self.file.name,
mode='rb',
fileobj=self.file.file)
nii = nb.Nifti1Image.from_file_map(
{'image': nb.FileHolder(self.file.name, gzfileobj)})
self.map_type = nvutils.infer_map_type(nii)
# Calculation of image reduced_representation and comparisons
file_changed = False
if self.pk is not None:
existing = Image.objects.get(pk=self.pk)
if existing.file != self.file:
file_changed = True
do_update = True if file_changed else False
new_image = True if self.pk is None else False
# If we have an update, delete old pkl and comparisons first before saving
if do_update and self.collection:
if self.reduced_representation: # not applicable for private collections
self.reduced_representation.delete()
# If more than one metric is added to NeuroVault, this must also filter based on metric
comparisons = Comparison.objects.filter(
Q(image1=self) | Q(image2=self))
if comparisons:
comparisons.delete()
super(BaseStatisticMap, self).save()
# Calculate comparisons
if do_update or new_image:
run_voxelwise_pearson_similarity.apply_async([self.pk])
self.file.close()
def clean(self, **kwargs):
cleaned_data = super(StatisticMapForm, self).clean()
django_file = cleaned_data.get("file")
cleaned_data["is_valid"] = True #This will be only saved if the form will validate
cleaned_data["tags"] = clean_tags(cleaned_data)
# print cleaned_data
if "analysis_level" in cleaned_data.keys():
if cleaned_data.get('analysis_level') == 'S':
cleaned_data['number_of_subjects'] = 1
if 'number_of_subjects' in self._errors:
del self._errors['number_of_subjects']
if "data_origin" in cleaned_data.keys() and cleaned_data["data_origin"] == "surface":
cleaned_data["is_thresholded"] = False
cleaned_data["not_mni"] = False
cleaned_data["perc_bad_voxels"] = 0
cleaned_data["brain_coverage"] = 100
elif django_file and "file" not in self._errors and "hdr_file" not in self._errors:
django_file.open()
fileobj = StringIO(django_file.read())
django_file.seek(0)
gzfileobj = GzipFile(
filename=django_file.name, mode='rb', fileobj=fileobj)
nii = nb.Nifti1Image.from_file_map(
{'image': nb.FileHolder(django_file.name, gzfileobj)})
cleaned_data["is_thresholded"], ratio_bad = is_thresholded(nii)
cleaned_data["perc_bad_voxels"] = ratio_bad*100.0
if cleaned_data["is_thresholded"] and not cleaned_data.get("ignore_file_warning") and cleaned_data.get("map_type") != "R":
self._errors["file"] = self.error_class(
["This map seems to be thresholded (%.4g%% of voxels are zeros). Please use an unthresholded version of the map if possible." % (cleaned_data["perc_bad_voxels"])])
if cleaned_data.get("hdr_file"):
self._errors["hdr_file"] = self.error_class(
["This map seems to be thresholded (%.4g%% of voxels are zeros). Please use an unthresholded version of the map if possible." % (cleaned_data["perc_bad_voxels"])])
self.fields[
"ignore_file_warning"].widget = forms.CheckboxInput()
else:
cleaned_data["not_mni"], cleaned_data["brain_coverage"], cleaned_data[
"perc_voxels_outside"] = not_in_mni(nii, target_template_image=cleaned_data["target_template_image"])
if cleaned_data["not_mni"] and not cleaned_data.get("ignore_file_warning") and cleaned_data.get(
"map_type") != "R":
self._errors["file"] = self.error_class(
["This map seems not to be in the MNI space (%.4g%% of meaningful voxels are outside of the brain). Please use transform your data to MNI space." % (cleaned_data["perc_voxels_outside"])])
if cleaned_data.get("hdr_file"):
self._errors["hdr_file"] = self.error_class(
["This map seems not to be in the MNI space (%.4g%% of meaningful voxels are outside of the brain). Please use transform your data to MNI space." % (cleaned_data["perc_voxels_outside"])])
self.fields[
"ignore_file_warning"].widget = forms.CheckboxInput()
if cleaned_data.get("map_type") == "R":
if "not_mni" in cleaned_data:
del cleaned_data["not_mni"]
if "is_thresholded" in cleaned_data:
del cleaned_data["is_thresholded"]
return cleaned_data
def testThresholded(self):
for p,t in zip(self.ratios, self.thresholded):
empty_data = np.ones(self.brain.shape)
if p != 0.0:
number_voxels = int(np.floor(p * empty_data.size))
random_idx = np.random.choice(range(empty_data.size), number_voxels, replace=False)
empty_data[np.unravel_index(random_idx, empty_data.shape)] = 0
empty_nii = nb.Nifti1Image(empty_data,affine=self.brain.get_affine(),header=self.brain.get_header())
is_thr, ratio_bad = is_thresholded(nii_obj=empty_nii)
print "Zeroed %s of values, is_thresholded returns [%s:%s]" %(p,is_thr,ratio_bad)
self.assertAlmostEqual(p, ratio_bad, delta=0.001)
self.assertEquals(t, is_thr)
def save(self):
if self.perc_bad_voxels == None and self.file:
import neurovault.apps.statmaps.utils as nvutils
self.file.open()
gzfileobj = GzipFile(filename=self.file.name, mode='rb', fileobj=self.file.file)
nii = nb.Nifti1Image.from_file_map({'image': nb.FileHolder(self.file.name, gzfileobj)})
self.is_thresholded, ratio_bad = nvutils.is_thresholded(nii)
self.perc_bad_voxels = ratio_bad*100.0
if self.brain_coverage == None and self.file:
import neurovault.apps.statmaps.utils as nvutils
self.file.open()
gzfileobj = GzipFile(filename=self.file.name, mode='rb', fileobj=self.file.file)
nii = nb.Nifti1Image.from_file_map({'image': nb.FileHolder(self.file.name, gzfileobj)})
self.not_mni, self.brain_coverage, self.perc_voxels_outside = nvutils.not_in_mni(nii)
if self.map_type == self.OTHER:
import neurovault.apps.statmaps.utils as nvutils
self.file.open()
gzfileobj = GzipFile(filename=self.file.name, mode='rb', fileobj=self.file.file)
nii = nb.Nifti1Image.from_file_map({'image': nb.FileHolder(self.file.name, gzfileobj)})
self.map_type = nvutils.infer_map_type(nii)
# Calculation of image reduced_representation and comparisons
file_changed = False
if self.pk is not None:
existing = Image.objects.get(pk=self.pk)
if existing.file != self.file:
file_changed = True
do_update = True if file_changed else False
new_image = True if self.pk is None else False
# If we have an update, delete old pkl and comparisons first before saving
if do_update and self.collection:
if self.reduced_representation: # not applicable for private collections
self.reduced_representation.delete()
# If more than one metric is added to NeuroVault, this must also filter based on metric
comparisons = Comparison.objects.filter(Q(image1=self) | Q(image2=self))
if comparisons:
comparisons.delete()
super(BaseStatisticMap, self).save()
# Calculate comparisons
if do_update or new_image:
run_voxelwise_pearson_similarity.apply_async([self.pk])
self.file.close()
def testThresholded(self):
for p, t in zip(self.ratios, self.thresholded):
empty_data = np.ones(self.brain.shape)
if p != 0.0:
number_voxels = int(np.floor(p * empty_data.size))
random_idx = np.random.choice(range(empty_data.size),
number_voxels,
replace=False)
empty_data[np.unravel_index(random_idx, empty_data.shape)] = 0
empty_nii = nb.Nifti1Image(empty_data,
affine=self.brain.get_affine(),
header=self.brain.get_header())
is_thr, ratio_bad = is_thresholded(nii_obj=empty_nii)
print "Zeroed %s of values, is_thresholded returns [%s:%s]" % (
p, is_thr, ratio_bad)
self.assertAlmostEqual(p, ratio_bad, delta=0.001)
self.assertEquals(t, is_thr)
