def testaddAFNI(self):
post_dict = {
'name': "test map",
'cognitive_paradigm_cogatlas': 'trm_4f24126c22011',
'modality':'fMRI-BOLD',
'map_type': 'T',
'collection':self.coll.pk,
}
testpath = os.path.abspath(os.path.dirname(__file__))
fname = os.path.join(testpath,'test_data/statmaps/saccade.I_C.MNI.nii.gz')
nii = nb.load(fname)
self.assertTrue(detect_4D(nii))
self.assertTrue(len(split_4D_to_3D(nii)) > 0)
file_dict = {'file': SimpleUploadedFile(fname, open(fname).read())}
form = StatisticMapForm(post_dict, file_dict)
self.assertTrue(form.is_valid())
form.save()
self.assertEqual(StatisticMap.objects.filter(collection=self.coll.pk).count(), 2)
def populate_afni(image):
try:
orig_name = image.name
tmpdir = tempfile.mkdtemp()
bricks = split_4D_to_3D(image.file.path, tmp_dir=tmpdir)
for label, brick in bricks:
brick_fname = os.path.split(brick)[-1]
mfile = memory_uploadfile(brick, brick_fname, image.file)
brick_img = Image(name='%s - %s' % (orig_name, label), file=mfile)
for field in ['collection', 'description', 'map_type', 'tags']:
setattr(brick_img, field, getattr(image, field))
if image.tags.exists():
brick_img.save() # generate PK before copying tags
for tag in image.tags.all():
tagobj = ValueTaggedItem(content_object=brick_img, tag=tag)
tagobj.save()
brick_img.save()
finally:
print 'converted afni4d %s to %s sub-brick images.' % (orig_name,
len(bricks))
shutil.rmtree(tmpdir)
os.remove(image.file.path)
image.delete()
def testaddAFNI(self):
post_dict = {
'name': "test map",
'cognitive_paradigm_cogatlas': 'trm_4f24126c22011',
'modality': 'fMRI-BOLD',
'map_type': 'T',
'number_of_subjects': 10,
'analysis_level': 'G',
'collection': self.coll.pk,
'target_template_image': 'GenericMNI',
}
testpath = os.path.abspath(os.path.dirname(__file__))
fname = os.path.join(testpath,
'test_data/statmaps/saccade.I_C.MNI.nii.gz')
nii = nb.load(fname)
self.assertTrue(detect_4D(nii))
self.assertTrue(len(split_4D_to_3D(nii)) > 0)
file_dict = {'file': SimpleUploadedFile(fname, open(fname).read())}
form = StatisticMapForm(post_dict, file_dict)
self.assertTrue(form.is_valid())
form.save()
self.assertEqual(
StatisticMap.objects.filter(collection=self.coll.pk).count(), 2)
def populate_afni(image):
try:
orig_name = image.name
tmpdir = tempfile.mkdtemp()
bricks = split_4D_to_3D(image.file.path,tmp_dir=tmpdir)
for label,brick in bricks:
brick_fname = os.path.split(brick)[-1]
mfile = memory_uploadfile(brick, brick_fname, image.file)
brick_img = Image(name='%s - %s' % (orig_name, label), file=mfile)
for field in ['collection','description','map_type','tags']:
setattr(brick_img, field, getattr(image,field))
if image.tags.exists():
brick_img.save() # generate PK before copying tags
for tag in image.tags.all():
tagobj = ValueTaggedItem(content_object=brick_img,tag=tag)
tagobj.save()
brick_img.save()
finally:
print 'converted afni4d %s to %s sub-brick images.' % (orig_name,len(bricks))
shutil.rmtree(tmpdir)
os.remove(image.file.path)
image.delete()
def setUp(self):
print "Preparing to test image comparison..."
self.tmpdir = tempfile.mkdtemp()
app_path = os.path.abspath(os.path.dirname(__file__))
self.u1 = User.objects.create(username='******')
self.comparisonCollection1 = Collection(name='comparisonCollection1', owner=self.u1,
DOI='10.3389/fninf.2015.00008')
self.comparisonCollection1.save()
self.comparisonCollection2 = Collection(name='comparisonCollection2', owner=self.u1,
DOI='10.3389/fninf.2015.00009')
self.comparisonCollection2.save()
self.comparisonCollection3 = Collection(name='comparisonCollection3', owner=self.u1,
DOI='10.3389/fninf.2015.00010')
self.comparisonCollection3.save()
self.comparisonCollection4 = Collection(name='comparisonCollection4', owner=self.u1,
DOI='10.3389/fninf.2015.00011')
self.comparisonCollection4.save()
self.comparisonCollection5 = Collection(name='comparisonCollection5', owner=self.u1,
DOI='10.3389/fninf.2015.00012')
self.comparisonCollection5.save()
image1 = save_statmap_form(image_path=os.path.join(app_path,'test_data/api/VentralFrontal_thr75_summaryimage_2mm.nii.gz'),
collection=self.comparisonCollection1,
image_name = "image1",
ignore_file_warning=True)
self.pk1 = image1.id
# Image 2 is equivalent to 1, so pearson should be 1.0
image2 = save_statmap_form(image_path=os.path.join(app_path,'test_data/api/VentralFrontal_thr75_summaryimage_2mm.nii.gz'),
collection=self.comparisonCollection2,
image_name = "image1_copy",
ignore_file_warning=True)
self.pk1_copy = image2.id
# "Bricks" images
bricks = split_4D_to_3D(nibabel.load(os.path.join(app_path,'test_data/TTatlas.nii.gz')),tmp_dir=self.tmpdir)
image3 = save_statmap_form(image_path=bricks[0][1],collection=self.comparisonCollection3,image_name="image2",ignore_file_warning=True)
self.pk2 = image3.id
image4 = save_statmap_form(image_path=bricks[1][1],collection=self.comparisonCollection4,image_name="image3",ignore_file_warning=True)
self.pk3 = image4.id
# This last image is a statmap with NaNs to test that transformation doesn't eliminate them
image_nan = save_statmap_form(image_path=os.path.join(app_path,'test_data/statmaps/motor_lips_nan.nii.gz'),
collection=self.comparisonCollection5,
image_name = "image_nan",
ignore_file_warning=True)
self.pknan = image_nan.id
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")
self.pearson_metric = Similarity.objects.filter(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")
def testAfni4DSlicing(self):
test_afni = detect_4D(nibabel.load(self.afni_file))
test_non_afni = detect_4D(nibabel.load(self.nii_file))
bricks = split_4D_to_3D(nibabel.load(self.afni_file), tmp_dir=self.tmpdir)
# check detection of 4D is correct
self.assertTrue(test_afni)
self.assertFalse(test_non_afni)
# check for 2 sub bricks
self.assertEquals(len(bricks), 2)
# check that brick labels match afni 3dinfo binary output
self.assertEquals(bricks[0][0], "uu3[0]")
self.assertEquals(bricks[1][0], "uu5[0]")
# check that sliced niftis exist at output location
self.assertTrue(os.path.exists(bricks[0][1]))
self.assertTrue(os.path.exists(bricks[1][1]))
def testAfni4DSlicing(self):
test_afni = detect_4D(nibabel.load(self.afni_file))
test_non_afni = detect_4D(nibabel.load(self.nii_file))
bricks = split_4D_to_3D(nibabel.load(self.afni_file),tmp_dir=self.tmpdir)
# check detection of 4D is correct
self.assertTrue(test_afni)
self.assertFalse(test_non_afni)
# check for 2 sub bricks
self.assertEquals(len(bricks),2)
# check that brick labels match afni 3dinfo binary output
self.assertEquals(bricks[0][0],'uu3[0]')
self.assertEquals(bricks[1][0],'uu5[0]')
# check that sliced niftis exist at output location
self.assertTrue(os.path.exists(bricks[0][1]))
self.assertTrue(os.path.exists(bricks[1][1]))
def setUp(self):
print "Preparing to test image comparison..."
self.tmpdir = tempfile.mkdtemp()
app_path = os.path.abspath(os.path.dirname(__file__))
self.u1 = User.objects.create(username='******')
self.comparisonCollection1 = Collection(name='comparisonCollection1',
owner=self.u1,
DOI='10.3389/fninf.2015.00008')
self.comparisonCollection1.save()
self.comparisonCollection2 = Collection(name='comparisonCollection2',
owner=self.u1,
DOI='10.3389/fninf.2015.00009')
self.comparisonCollection2.save()
self.comparisonCollection3 = Collection(name='comparisonCollection3',
owner=self.u1,
DOI='10.3389/fninf.2015.00010')
self.comparisonCollection3.save()
self.comparisonCollection4 = Collection(name='comparisonCollection4',
owner=self.u1,
DOI='10.3389/fninf.2015.00011')
self.comparisonCollection4.save()
self.comparisonCollection5 = Collection(name='comparisonCollection5',
owner=self.u1,
DOI='10.3389/fninf.2015.00012')
self.comparisonCollection5.save()
image1 = save_statmap_form(image_path=os.path.join(
app_path,
'test_data/api/VentralFrontal_thr75_summaryimage_2mm.nii.gz'),
collection=self.comparisonCollection1,
image_name="image1",
ignore_file_warning=True)
self.pk1 = image1.id
# Image 2 is equivalent to 1, so pearson should be 1.0
image2 = save_statmap_form(image_path=os.path.join(
app_path,
'test_data/api/VentralFrontal_thr75_summaryimage_2mm.nii.gz'),
collection=self.comparisonCollection2,
image_name="image1_copy",
ignore_file_warning=True)
self.pk1_copy = image2.id
# "Bricks" images
bricks = split_4D_to_3D(nibabel.load(
os.path.join(app_path, 'test_data/TTatlas.nii.gz')),
tmp_dir=self.tmpdir)
image3 = save_statmap_form(image_path=bricks[0][1],
collection=self.comparisonCollection3,
image_name="image2",
ignore_file_warning=True)
self.pk2 = image3.id
image4 = save_statmap_form(image_path=bricks[1][1],
collection=self.comparisonCollection4,
image_name="image3",
ignore_file_warning=True)
self.pk3 = image4.id
# This last image is a statmap with NaNs to test that transformation doesn't eliminate them
image_nan = save_statmap_form(image_path=os.path.join(
app_path, 'test_data/statmaps/motor_lips_nan.nii.gz'),
collection=self.comparisonCollection5,
image_name="image_nan",
ignore_file_warning=True)
self.pknan = image_nan.id
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")
self.pearson_metric = Similarity.objects.filter(
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")
del cleaned_data["hdr_file"]
return cleaned_data
# check if it is really nifti
try:
# print file_map
if "header" in file_map:
nii = nb.Nifti1Pair.from_file_map(file_map)
else:
nii = nb.Nifti1Image.from_file_map(file_map)
except Exception as e:
raise
# detect AFNI 4D files and prepare 3D slices
if nii is not None and detect_4D(nii):
self.afni_subbricks = split_4D_to_3D(nii, tmp_dir=tmp_dir)
else:
squeezable_dimensions = len([a for a in nii.shape if a not in [0, 1]])
if squeezable_dimensions != 3:
self._errors["file"] = self.error_class(
["4D files are not supported.\n "
"If it's multiple maps in one "
"file please split them and "
"upload separately"])
del cleaned_data["file"]
return cleaned_data
# convert to nii.gz if needed
if (ext.lower() != ".nii.gz"
or squeezable_dimensions < len(nii.shape)):
def clean_and_validate(self, cleaned_data):
file = cleaned_data.get('file')
if file:
# check extension of the data file
_, fname, ext = split_filename(file.name)
if not ext.lower() in [".nii.gz", ".nii", ".img"]:
self._errors["file"] = self.error_class(
["Doesn't have proper extension"])
del cleaned_data["file"]
return cleaned_data
# prepare file to loading into memory
file.open()
fileobj = file.file
if file.name.lower().endswith(".gz"):
fileobj = GzipFile(filename=file.name,
mode='rb',
fileobj=fileobj)
file_map = {'image': nb.FileHolder(file.name, fileobj)}
try:
tmp_dir = tempfile.mkdtemp()
if ext.lower() == ".img":
hdr_file = cleaned_data.get('hdr_file')
if hdr_file:
# check extension of the hdr file
_, _, hdr_ext = split_filename(hdr_file.name)
if not hdr_ext.lower() in [".hdr"]:
self._errors["hdr_file"] = self.error_class(
["Doesn't have proper extension"])
del cleaned_data["hdr_file"]
return cleaned_data
else:
hdr_file.open()
file_map["header"] = nb.FileHolder(
hdr_file.name, hdr_file.file)
else:
self._errors["hdr_file"] = self.error_class(
[".img file requires .hdr file"])
del cleaned_data["hdr_file"]
return cleaned_data
# check if it is really nifti
try:
print file_map
if "header" in file_map:
nii = nb.Nifti1Pair.from_file_map(file_map)
else:
nii = nb.Nifti1Image.from_file_map(file_map)
except Exception as e:
raise
# detect AFNI 4D files and prepare 3D slices
if nii is not None and detect_4D(nii):
self.afni_subbricks = split_4D_to_3D(nii, tmp_dir=tmp_dir)
else:
squeezable_dimensions = len(
filter(lambda a: a not in [0, 1], nii.shape))
if squeezable_dimensions != 3:
self._errors["file"] = self.error_class([
"4D files are not supported.\n "
"If it's multiple maps in one "
"file please split them and "
"upload separately"
])
del cleaned_data["file"]
return cleaned_data
# convert to nii.gz if needed
if (ext.lower() != ".nii.gz"
or squeezable_dimensions < len(nii.shape)):
# convert pseudo 4D to 3D
if squeezable_dimensions < len(nii.shape):
new_data = np.squeeze(nii.get_data())
nii = nb.Nifti1Image(new_data, nii.get_affine(),
nii.get_header())
# Papaya does not handle float64, but by converting
# files we loose precision
# if nii.get_data_dtype() == np.float64:
# ii.set_data_dtype(np.float32)
new_name = fname + ".nii.gz"
nii_tmp = os.path.join(tmp_dir, new_name)
nb.save(nii, nii_tmp)
print "updating file in cleaned_data"
cleaned_data['file'] = memory_uploadfile(
nii_tmp, new_name, cleaned_data['file'])
finally:
try:
if self.afni_subbricks:
# keep temp dir for AFNI slicing
self.afni_tmp = tmp_dir
else:
print "removing %s" % tmp_dir
shutil.rmtree(tmp_dir)
except OSError as exc:
if exc.errno != 2: # code 2 - no such file or directory
raise # re-raise exception
elif not getattr(self, 'partial', False):
# Skip validation error if this is a partial update from the API
raise ValidationError("Couldn't read uploaded file")
return cleaned_data
def clean_and_validate(self, cleaned_data):
file = cleaned_data.get('file')
if file:
# check extension of the data file
_, fname, ext = split_filename(file.name)
if not ext.lower() in [".nii.gz", ".nii", ".img"]:
self._errors["file"] = self.error_class(
["Doesn't have proper extension"]
)
del cleaned_data["file"]
return cleaned_data
# prepare file to loading into memory
file.open()
fileobj = file.file
if file.name.lower().endswith(".gz"):
fileobj = GzipFile(filename=file.name, mode='rb',
fileobj=fileobj)
file_map = {'image': nb.FileHolder(file.name, fileobj)}
try:
tmp_dir = tempfile.mkdtemp()
if ext.lower() == ".img":
hdr_file = cleaned_data.get('hdr_file')
if hdr_file:
# check extension of the hdr file
_, _, hdr_ext = split_filename(hdr_file.name)
if not hdr_ext.lower() in [".hdr"]:
self._errors["hdr_file"] = self.error_class(
["Doesn't have proper extension"])
del cleaned_data["hdr_file"]
return cleaned_data
else:
hdr_file.open()
file_map["header"] = nb.FileHolder(hdr_file.name,
hdr_file.file)
else:
self._errors["hdr_file"] = self.error_class(
[".img file requires .hdr file"]
)
del cleaned_data["hdr_file"]
return cleaned_data
# check if it is really nifti
try:
print file_map
if "header" in file_map:
nii = nb.Nifti1Pair.from_file_map(file_map)
else:
nii = nb.Nifti1Image.from_file_map(file_map)
except Exception as e:
raise
# detect AFNI 4D files and prepare 3D slices
if nii is not None and detect_4D(nii):
self.afni_subbricks = split_4D_to_3D(nii, tmp_dir=tmp_dir)
else:
squeezable_dimensions = len(
filter(lambda a: a not in [0, 1], nii.shape)
)
if squeezable_dimensions != 3:
self._errors["file"] = self.error_class(
["4D files are not supported.\n "
"If it's multiple maps in one "
"file please split them and "
"upload separately"])
del cleaned_data["file"]
return cleaned_data
# convert to nii.gz if needed
if (ext.lower() != ".nii.gz"
or squeezable_dimensions < len(nii.shape)):
# convert pseudo 4D to 3D
if squeezable_dimensions < len(nii.shape):
new_data = np.squeeze(nii.get_data())
nii = nb.Nifti1Image(new_data, nii.get_affine(),
nii.get_header())
# Papaya does not handle float64, but by converting
# files we loose precision
# if nii.get_data_dtype() == np.float64:
# ii.set_data_dtype(np.float32)
new_name = fname + ".nii.gz"
nii_tmp = os.path.join(tmp_dir, new_name)
nb.save(nii, nii_tmp)
print "updating file in cleaned_data"
cleaned_data['file'] = memory_uploadfile(
nii_tmp, new_name, cleaned_data['file']
)
finally:
try:
if self.afni_subbricks:
# keep temp dir for AFNI slicing
self.afni_tmp = tmp_dir
else:
print "removing %s"%tmp_dir
shutil.rmtree(tmp_dir)
except OSError as exc:
if exc.errno != 2: # code 2 - no such file or directory
raise # re-raise exception
elif not getattr(self, 'partial', False):
# Skip validation error if this is a partial update from the API
raise ValidationError("Couldn't read uploaded file")
return cleaned_data
del cleaned_data["hdr_file"]
return cleaned_data
# check if it is really nifti
try:
# print file_map
if "header" in file_map:
nii = nb.Nifti1Pair.from_file_map(file_map)
else:
nii = nb.Nifti1Image.from_file_map(file_map)
except Exception as e:
raise
# detect AFNI 4D files and prepare 3D slices
if nii is not None and detect_4D(nii):
self.afni_subbricks = split_4D_to_3D(nii, tmp_dir=tmp_dir)
else:
squeezable_dimensions = len([a for a in nii.shape if a not in [0, 1]])
if squeezable_dimensions != 3:
self._errors["file"] = self.error_class(
["4D files are not supported.\n "
"If it's multiple maps in one "
"file please split them and "
"upload separately"])
del cleaned_data["file"]
return cleaned_data
# convert to nii.gz if needed
if (ext.lower() != ".nii.gz"
or squeezable_dimensions < len(nii.shape)):
def upload_folder(request, collection_cid):
collection = get_collection(collection_cid,request)
allowed_extensions = ['.nii', '.img', '.nii.gz']
niftiFiles = []
if request.method == 'POST':
print request.POST
print request.FILES
form = UploadFileForm(request.POST, request.FILES)
if form.is_valid():
tmp_directory = tempfile.mkdtemp()
print tmp_directory
try:
# Save archive (.zip or .tar.gz) to disk
if "file" in request.FILES:
archive_name = request.FILES['file'].name
if fnmatch(archive_name,'*.nidm.zip'):
form = populate_nidm_results(request,collection)
if not form:
messages.warning(request, "Invalid NIDM-Results file.")
return HttpResponseRedirect(collection.get_absolute_url())
_, archive_ext = os.path.splitext(archive_name)
if archive_ext == '.zip':
compressed = zipfile.ZipFile(request.FILES['file'])
elif archive_ext == '.gz':
django_file = request.FILES['file']
django_file.open()
compressed = tarfile.TarFile(fileobj=gzip.GzipFile(fileobj=django_file.file, mode='r'), mode='r')
else:
raise Exception("Unsupported archive type %s."%archive_name)
compressed.extractall(path=tmp_directory)
elif "file_input[]" in request.FILES:
for f, path in zip(request.FILES.getlist(
"file_input[]"), request.POST.getlist("paths[]")):
if fnmatch(f.name,'*.nidm.zip'):
request.FILES['file'] = f
populate_nidm_results(request,collection)
continue
new_path, _ = os.path.split(os.path.join(tmp_directory, path))
mkdir_p(new_path)
filename = os.path.join(new_path,f.name)
tmp_file = open(filename, 'w')
tmp_file.write(f.read())
tmp_file.close()
else:
raise Exception("Unable to find uploaded files.")
atlases = {}
for root, subdirs, filenames in os.walk(tmp_directory):
if detect_feat_directory(root):
populate_feat_directory(request,collection,root)
del(subdirs)
filenames = []
# .gfeat parent dir under cope*.feat should not be added as statmaps
# this may be affected by future nidm-results_fsl parsing changes
if root.endswith('.gfeat'):
filenames = []
filenames = [f for f in filenames if not f[0] == '.']
for fname in sorted(filenames):
name, ext = splitext_nii_gz(fname)
nii_path = os.path.join(root, fname)
if ext == '.xml':
print "found xml"
dom = minidom.parse(os.path.join(root, fname))
for atlas in dom.getElementsByTagName("summaryimagefile"):
print "found atlas"
path, base = os.path.split(atlas.lastChild.nodeValue)
nifti_name = os.path.join(path, base)
atlases[str(os.path.join(root,
nifti_name[1:]))] = os.path.join(root, fname)
if ext in allowed_extensions:
nii = nib.load(nii_path)
if detect_4D(nii):
niftiFiles.extend(split_4D_to_3D(nii))
else:
niftiFiles.append((fname,nii_path))
for label,fpath in niftiFiles:
# Read nifti file information
nii = nib.load(fpath)
if len(nii.get_shape()) > 3 and nii.get_shape()[3] > 1:
messages.warning(request, "Skipping %s - not a 3D file."%label)
continue
hdr = nii.get_header()
raw_hdr = hdr.structarr
# SPM only !!!
# Check if filename corresponds to a T-map
Tregexp = re.compile('spmT.*')
# Fregexp = re.compile('spmF.*')
if Tregexp.search(fpath) is not None:
map_type = StatisticMap.T
else:
# Check if filename corresponds to a F-map
if Tregexp.search(fpath) is not None:
map_type = StatisticMap.F
else:
map_type = StatisticMap.OTHER
path, name, ext = split_filename(fpath)
dname = name + ".nii.gz"
spaced_name = name.replace('_',' ').replace('-',' ')
if ext.lower() != ".nii.gz":
new_file_tmp_dir = tempfile.mkdtemp()
new_file_tmp = os.path.join(new_file_tmp_dir, name) + '.nii.gz'
nib.save(nii, new_file_tmp)
f = ContentFile(open(new_file_tmp).read(), name=dname)
shutil.rmtree(new_file_tmp_dir)
label += " (old ext: %s)" % ext
else:
f = ContentFile(open(fpath).read(), name=dname)
collection = get_collection(collection_cid,request)
if os.path.join(path, name) in atlases:
new_image = Atlas(name=spaced_name,
description=raw_hdr['descrip'], collection=collection)
new_image.label_description_file = ContentFile(
open(atlases[os.path.join(path,name)]).read(),
name=name + ".xml")
else:
new_image = StatisticMap(name=spaced_name,
description=raw_hdr['descrip'] or label, collection=collection)
new_image.map_type = map_type
new_image.file = f
new_image.save()
except:
error = traceback.format_exc().splitlines()[-1]
msg = "An error occurred with this upload: {}".format(error)
messages.warning(request, msg)
return HttpResponseRedirect(collection.get_absolute_url())
finally:
shutil.rmtree(tmp_directory)
if not niftiFiles:
messages.warning(request, "No NIFTI files (.nii, .nii.gz, .img/.hdr) found in the upload.")
return HttpResponseRedirect(collection.get_absolute_url())
else:
form = UploadFileForm()
return render_to_response("statmaps/upload_folder.html",
{'form': form}, RequestContext(request))
