def save(self, *args, **kwargs):
if self.DOI is not None and self.DOI.strip() == "":
self.DOI = None
if self.private_token is not None and self.private_token.strip() == "":
self.private_token = None
if self.DOI and not self.private and not self.doi_add_date:
self.doi_add_date = datetime.now()
# run calculations when collection turns public
privacy_changed = False
DOI_changed = False
if self.pk is not None:
old_object = Collection.objects.get(pk=self.pk)
old_is_private = old_object.private
old_has_DOI = old_object.DOI is not None
privacy_changed = old_is_private != self.private
DOI_changed = old_has_DOI != (self.DOI is not None)
super(Collection, self).save(*args, **kwargs)
if (privacy_changed
and not self.private) or (DOI_changed
and self.DOI is not None):
for image in self.basecollectionitem_set.instance_of(Image).all():
if image.pk:
generate_glassbrain_image.apply_async([image.pk])
run_voxelwise_pearson_similarity.apply_async([image.pk])
def save(self, *args, **kwargs):
if self.DOI is not None and self.DOI.strip() == "":
self.DOI = None
if self.private_token is not None and self.private_token.strip() == "":
self.private_token = None
if self.DOI and not self.private and not self.doi_add_date:
self.doi_add_date = datetime.now()
# run calculations when collection turns public
privacy_changed = False
DOI_changed = False
if self.pk is not None:
old_object = Collection.objects.get(pk=self.pk)
old_is_private = old_object.private
old_has_DOI = old_object.DOI is not None
privacy_changed = old_is_private != self.private
DOI_changed = old_has_DOI != (self.DOI is not None)
super(Collection, self).save(*args, **kwargs)
if (privacy_changed and not self.private) or (DOI_changed and self.DOI is not None):
for image in self.basecollectionitem_set.instance_of(Image).all():
if image.pk:
generate_glassbrain_image.apply_async([image.pk])
run_voxelwise_pearson_similarity.apply_async([image.pk])
def save(self, *args, **kwargs):
if self.DOI is not None and self.DOI.strip() == "":
self.DOI = None
if self.private_token is not None and self.private_token.strip() == "":
self.private_token = None
if self.DOI and not self.private and not self.doi_add_date:
self.doi_add_date = datetime.now()
# run calculations when collection turns public
privacy_changed = False
if self.pk is not None:
old_object = Collection.objects.get(pk=self.pk)
old_is_private = old_object.private
privacy_changed = old_is_private != self.private
super(Collection, self).save(*args, **kwargs)
assign_perm('delete_collection', self.owner, self)
assign_perm('change_collection', self.owner, self)
for image in self.image_set.all():
assign_perm('change_image', self.owner, image)
assign_perm('delete_image', self.owner, image)
for nidmresult in self.nidmresults_set.all():
assign_perm('change_nidmresults', self.owner, nidmresult)
assign_perm('delete_nidmresults', self.owner, nidmresult)
if privacy_changed and self.private == False:
for image in self.image_set.all():
if image.pk:
generate_glassbrain_image.apply_async([image.pk])
run_voxelwise_pearson_similarity.apply_async([image.pk])
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()
def save(self):
if self.DOI is not None and self.DOI.strip() == "":
self.DOI = None
if self.private_token is not None and self.private_token.strip() == "":
self.private_token = None
# run calculations when collection turns public
privacy_changed = False
if self.pk is not None:
old_object = Collection.objects.get(pk=self.pk)
old_is_private = old_object.private
privacy_changed = old_is_private != self.private
super(Collection, self).save()
if privacy_changed and self.private == False:
for image in self.image_set.all():
if image.pk:
generate_glassbrain_image.apply_async([image.pk])
run_voxelwise_pearson_similarity.apply_async([image.pk])
django.setup()
from neurovault.apps.statmaps.models import Similarity, Comparison, Image, Collection
from neurovault.apps.statmaps.tasks import run_voxelwise_pearson_similarity
# Images should have the "transform" field after applying migrations (I think)
# First create/update the image similarity metric
pearson_metric = Similarity.objects.update_or_create(
similarity_metric="pearson product-moment correlation coefficient",
transformation="voxelwise",
metric_ontology_iri=
"http://webprotege.stanford.edu/RCS8W76v1MfdvskPLiOdPaA",
transformation_ontology_iri=
"http://webprotege.stanford.edu/R87C6eFjEftkceScn1GblDL")
# Delete all old comparisons
all_comparisons = Comparison.objects.all().delete()
# Delete all reduced representations
for img in Image.objects.all():
img.reduced_representation.delete()
# Filter down to images that are not private, not thresholded
# Now, we need to generate a "comparison" object for all files in the database
# We will use a celery task (as this will be integrated into upload workflow)
for collection in Collection.objects.filter(DOI__isnull=False):
for image in collection.basecollectionitem_set.instance_of(Image).all():
print "Calculating pearson similarity for images %s" % image
run_voxelwise_pearson_similarity.apply_async([image.pk])
import os
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "neurovault.settings")
django.setup()
from neurovault.apps.statmaps.models import Similarity, Comparison, Image, Collection
from neurovault.apps.statmaps.tasks import run_voxelwise_pearson_similarity
# Images should have the "transform" field after applying migrations (I think)
# First create/update the image similarity metric
pearson_metric = Similarity.objects.update_or_create(similarity_metric="pearson product-moment correlation coefficient",
transformation="voxelwise",
metric_ontology_iri="http://webprotege.stanford.edu/RCS8W76v1MfdvskPLiOdPaA",
transformation_ontology_iri="http://webprotege.stanford.edu/R87C6eFjEftkceScn1GblDL")
# Delete all old comparisons
all_comparisons = Comparison.objects.all().delete()
# Delete all reduced representations
for img in Image.objects.all():
img.reduced_representation.delete()
# Filter down to images that are not private, not thresholded
# Now, we need to generate a "comparison" object for all files in the database
# We will use a celery task (as this will be integrated into upload workflow)
for collection in Collection.objects.filter(DOI__isnull=False):
for image in collection.basecollectionitem_set.instance_of(Image).all():
print "Calculating pearson similarity for images %s" %image
run_voxelwise_pearson_similarity.apply_async([image.pk])