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 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()
import os
from gzip import GzipFile
import django
import nibabel as nb
import neurovault.apps.statmaps.utils as nvutils
from neurovault.apps.statmaps.models import StatisticMap, BaseStatisticMap
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "neurovault.settings")
django.setup()
for image in StatisticMap.objects.filter(map_type=BaseStatisticMap.OTHER):
image.file.open()
gzfileobj = GzipFile(filename=image.file.name, mode='rb', fileobj=image.file.file)
nii = nb.Nifti1Image.from_file_map({'image': nb.FileHolder(image.file.name, gzfileobj)})
map_type = nvutils.infer_map_type(nii)
if map_type != BaseStatisticMap.OTHER:
print "changed type of %s to %s"%(image.get_absolute_url(), map_type)
image.map_type = map_type
image.save()
image.file.close()
def testInferMapType(self):
self.assertEquals(infer_map_type(self.roi_map), BaseStatisticMap.R)
self.assertEquals(infer_map_type(self.parcellation), BaseStatisticMap.Pa)
self.assertEquals(infer_map_type(self.brain), BaseStatisticMap.OTHER)
import os
from gzip import GzipFile
import django
import nibabel as nb
import neurovault.apps.statmaps.utils as nvutils
from neurovault.apps.statmaps.models import StatisticMap, BaseStatisticMap
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "neurovault.settings")
django.setup()
for image in StatisticMap.objects.filter(map_type=BaseStatisticMap.OTHER):
image.file.open()
gzfileobj = GzipFile(filename=image.file.name,
mode='rb',
fileobj=image.file.file)
nii = nb.Nifti1Image.from_file_map(
{'image': nb.FileHolder(image.file.name, gzfileobj)})
map_type = nvutils.infer_map_type(nii)
if map_type != BaseStatisticMap.OTHER:
print "changed type of %s to %s" % (image.get_absolute_url(), map_type)
image.map_type = map_type
image.save()
image.file.close()
def testInferMapType(self):
self.assertEquals(infer_map_type(self.roi_map), BaseStatisticMap.R)
self.assertEquals(infer_map_type(self.parcellation),
BaseStatisticMap.Pa)
self.assertEquals(infer_map_type(self.brain), BaseStatisticMap.OTHER)
