class Atlas(Image):
label_description_file = models.FileField(
upload_to=upload_img_to,
null=False,
blank=False,
storage=NiftiGzStorage(),
verbose_name='FSL compatible label description file (.xml)')
class Meta:
verbose_name_plural = "Atlases"
class StatMap(models.Model):
study = models.ForeignKey(Study)
name = models.CharField(max_length=200, null=False, blank=False)
description = models.CharField(max_length=200, blank=True)
file = models.FileField(upload_to=upload_to,
null=False,
blank=False,
storage=NiftiGzStorage())
hdr_file = models.FileField(upload_to=upload_to,
blank=True,
storage=NiftiGzStorage())
json_path = models.CharField(max_length=200, null=False, blank=True)
add_date = models.DateTimeField('date published', auto_now_add=True)
modify_date = models.DateTimeField('date modified', auto_now=True)
tags = TaggableManager(through=ValueTaggedItem, blank=True)
def __unicode__(self):
return self.name
class Meta:
unique_together = ("study", "name")
def save(self):
# Save the file before the rest of the data so we can convert it to json
if self.file and not os.path.exists(self.file.path):
self.file.save(self.file.name, self.file, save=False)
if self.hdr_file and not os.path.exists(self.hdr_file.path):
self.file.save(self.hdr_file.name, self.hdr_file, save=False)
# Convert binary image to JSON using neurosynth
# try:
if os.path.exists(self.file.path):
json_file = self.file.path + '.json'
# try:
imageutils.img_to_json(self.file.path, swap=True, save=json_file)
self.json_path = self.file.url + '.json'
# except Exception, e:
# pass
# except Exception, e:
# pass
super(StatMap, self).save()
class Image(BaseCollectionItem):
file = models.FileField(
upload_to=upload_img_to,
null=False,
blank=False,
storage=NiftiGzStorage(),
verbose_name='File with the unthresholded map (.img, .nii, .nii.gz)')
figure = models.CharField(
help_text=
"Which figure in the corresponding paper was this map displayed in?",
verbose_name="Corresponding figure",
max_length=200,
null=True,
blank=True)
thumbnail = models.FileField(
help_text="The orthogonal view thumbnail path of the nifti image",
null=True,
blank=True,
upload_to=upload_img_to,
verbose_name='Image orthogonal view thumbnail 2D bitmap',
storage=NiftiGzStorage())
reduced_representation = models.FileField(
help_text=
("Binary file with the vector of in brain values resampled to lower resolution"
),
verbose_name="Reduced representation of the image",
null=True,
blank=True,
upload_to=upload_img_to,
storage=OverwriteStorage())
data = hstore.DictionaryField(blank=True, null=True)
hstore_objects = hstore.HStoreManager()
def get_absolute_url(self):
return_args = [str(self.id)]
url_name = 'image_details'
if self.collection.private:
return_args.insert(0, str(self.collection.private_token))
url_name = 'private_image_details'
return reverse(url_name, args=return_args)
def get_thumbnail_url(self):
try:
url = self.thumbnail.url
except ValueError:
url = os.path.abspath(
os.path.join("/static", "images", "glass_brain_empty.jpg"))
return url
@classmethod
def create(cls, my_file, my_file_name, my_name, my_desc, my_collection_pk,
my_map_type):
my_collection = Collection.objects.get(pk=my_collection_pk)
# Copy the nifti file into the proper location
image = cls(description=my_desc,
name=my_name,
collection=my_collection)
f = open(my_file)
niftiFile = File(f)
image.file.save(my_file_name, niftiFile)
# If a .img file was loaded then load the correspoding .hdr file as well
_, ext = os.path.splitext(my_file_name)
print ext
if ext in ['.img']:
f = open(my_file[:-3] + "hdr")
hdrFile = File(f)
image.hdr_file.save(my_file_name[:-3] + "hdr", hdrFile)
image.map_type = my_map_type
# create JSON file for neurosynth viewer
if os.path.exists(image.file.path):
nifti_gz_file = ".".join(
image.file.path.split(".")[:-1]) + '.nii.gz'
nii = nb.load(image.file.path)
nb.save(nii, nifti_gz_file)
f = open(nifti_gz_file)
image.nifti_gz_file.save(nifti_gz_file.split(os.path.sep)[-1],
File(f),
save=False)
image.save()
return image
# Celery task to generate glass brain image on new/update
def save(self):
file_changed = False
collection_changed = False
if self.pk is not None:
old_pk = Image.objects.get(pk=self.pk)
if old_pk.file != self.file:
file_changed = True
if old_pk.collection != self.collection:
collection_changed = True
do_update = True if file_changed else False
new_image = True if self.pk is None else False
super(Image, self).save()
if (do_update or new_image
) and self.collection and self.collection.private == False:
# Generate glass brain image
generate_glassbrain_image.apply_async([self.pk])
if collection_changed:
for field_name in self._meta.get_all_field_names():
field_instance = getattr(self, field_name)
if field_instance and isinstance(field_instance, FieldFile):
old_path = field_instance.path
new_name = upload_img_to(
self,
field_instance.name.split("/")[-1])
new_name = field_instance.storage.get_available_name(
new_name)
new_path = field_instance.storage.path(new_name)
if not os.path.exists(os.path.dirname(new_path)):
os.mkdir(os.path.dirname(new_path))
shutil.copy(old_path, new_path)
field_instance.name = new_name
assert (old_path != new_path)
os.remove(old_path)
super(Image, self).save()