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 save_statmap_form(image_path,collection,ignore_file_warning=False,image_name=None):
if image_name == None:
if isinstance(image_path,list):
image_name = image_path[0]
else:
image_name = image_path
post_dict = {
'name': image_name,
'cognitive_paradigm_cogatlas': 'trm_4f24126c22011',
'modality': 'fMRI-BOLD',
'map_type': 'T',
'collection': collection.pk,
'ignore_file_warning': ignore_file_warning
}
# If image path is a list, we have img/hdr
if isinstance(image_path,list):
file_dict = {'file': SimpleUploadedFile(image_path[0], open(image_path[0]).read()),
'hdr_file': SimpleUploadedFile(image_path[1], open(image_path[1]).read())}
else:
file_dict = {'file': SimpleUploadedFile(image_path, open(image_path).read())}
form = StatisticMapForm(post_dict, file_dict)
# this is necessary to split 4D volumes
form.is_valid()
return form.save()
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 save_statmap_form(image_path,collection,ignore_file_warning=False,image_name=None):
if image_name == None:
if isinstance(image_path,list):
image_name = image_path[0]
else:
image_name = image_path
post_dict = {
'name': image_name,
'cognitive_paradigm_cogatlas': 'trm_4f24126c22011',
'modality': 'fMRI-BOLD',
'map_type': 'T',
'target_template_image': 'GenericMNI',
'collection': collection.pk,
'ignore_file_warning': ignore_file_warning
}
# If image path is a list, we have img/hdr
if isinstance(image_path,list):
file_dict = {'file': SimpleUploadedFile(image_path[0], open(image_path[0]).read()),
'hdr_file': SimpleUploadedFile(image_path[1], open(image_path[1]).read())}
else:
file_dict = {'file': SimpleUploadedFile(image_path, open(image_path).read())}
form = StatisticMapForm(post_dict, file_dict)
# this is necessary to split 4D volumes
form.is_valid()
return form.save()
def save_statmap_form(image_path, collection, ignore_file_warning=False, image_name=None):
if image_name == None:
if isinstance(image_path, list):
image_name = image_path[0]
else:
image_name = image_path
post_dict = {
"name": image_name,
"cognitive_paradigm_cogatlas": "trm_4f24126c22011",
"modality": "fMRI-BOLD",
"map_type": "T",
"collection": collection.pk,
"ignore_file_warning": ignore_file_warning,
}
# If image path is a list, we have img/hdr
if isinstance(image_path, list):
file_dict = {
"file": SimpleUploadedFile(image_path[0], open(image_path[0]).read()),
"hdr_file": SimpleUploadedFile(image_path[1], open(image_path[1]).read()),
}
else:
file_dict = {"file": SimpleUploadedFile(image_path, open(image_path).read())}
form = StatisticMapForm(post_dict, file_dict)
return form.save()
def add_image(request, collection_cid):
collection = get_collection(collection_cid,request)
image = StatisticMap(collection=collection)
if request.method == "POST":
form = StatisticMapForm(request.POST, request.FILES, instance=image)
if form.is_valid():
image = form.save()
return HttpResponseRedirect(image.get_absolute_url())
else:
form = StatisticMapForm(instance=image)
context = {"form": form}
return render(request, "statmaps/add_image.html.haml", context)
def testaddImgHdr(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_img = os.path.join(testpath,
'test_data/statmaps/box_0b_vs_1b.img')
fname_hdr = os.path.join(testpath,
'test_data/statmaps/box_0b_vs_1b.hdr')
file_dict = {
'file': SimpleUploadedFile(fname_img,
open(fname_img).read()),
'hdr_file': SimpleUploadedFile(fname_hdr,
open(fname_hdr).read())
}
form = StatisticMapForm(post_dict, file_dict)
self.assertFalse(form.is_valid())
self.assertTrue("thresholded" in form.errors["file"][0])
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,
'ignore_file_warning': True,
'target_template_image': 'GenericMNI',
}
testpath = os.path.abspath(os.path.dirname(__file__))
fname_img = os.path.join(testpath,
'test_data/statmaps/box_0b_vs_1b.img')
fname_hdr = os.path.join(testpath,
'test_data/statmaps/box_0b_vs_1b.hdr')
file_dict = {
'file': SimpleUploadedFile(fname_img,
open(fname_img).read()),
'hdr_file': SimpleUploadedFile(fname_hdr,
open(fname_hdr).read())
}
form = StatisticMapForm(post_dict, file_dict)
self.assertTrue(form.is_valid())
form.save()
self.assertEqual(
StatisticMap.objects.filter(collection=self.coll.pk)[0].name,
"test map")
def testaddNiiGz(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/motor_lips.nii.gz')
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)[0].name, "test map")
def testaddNiiGz(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/motor_lips.nii.gz")
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)[0].name, "test map")
def testaddNiiGz(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/motor_lips.nii.gz')
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)[0].name,
"test map")
def add_image(request, collection_cid):
collection = get_collection(collection_cid, request)
image = StatisticMap(collection=collection)
if request.method == "POST":
form = StatisticMapForm(request.POST, request.FILES, instance=image)
if form.is_valid():
image = form.save()
return HttpResponseRedirect(image.get_absolute_url())
else:
form = StatisticMapForm(instance=image)
context = {"form": form}
return render(request, "statmaps/add_image.html.haml", context)
def edit_images(request, collection_cid):
collection = get_collection(collection_cid, request)
if not owner_or_contrib(request, collection):
return HttpResponseForbidden()
if request.method == "POST":
formset = CollectionFormSet(request.POST,
request.FILES,
instance=collection)
for n, form in enumerate(formset):
# hack: check fields to determine polymorphic type
if form.instance.polymorphic_ctype is None:
atlas_f = 'image_set-{0}-label_description_file'.format(n)
has_atlas = [v for v in form.files if v == atlas_f]
if has_atlas:
use_model = Atlas
use_form = AtlasForm
else:
use_model = StatisticMap
use_form = StatisticMapForm
form.instance = use_model(collection=collection)
form.base_fields = use_form.base_fields
form.fields = use_form.base_fields
if formset.is_valid():
formset.save()
return HttpResponseRedirect(collection.get_absolute_url())
else:
formset = CollectionFormSet(instance=collection)
blank_statmap = StatisticMapForm(instance=StatisticMap(
collection=collection))
blank_atlas = AtlasForm(instance=Atlas(collection=collection))
upload_form = UploadFileForm()
context = {
"formset": formset,
"blank_statmap": blank_statmap,
"blank_atlas": blank_atlas,
"upload_form": upload_form
}
return render(request, "statmaps/edit_images.html", context)
def testaddImgHdr(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_img = os.path.join(testpath,'test_data/statmaps/box_0b_vs_1b.img')
fname_hdr = os.path.join(testpath,'test_data/statmaps/box_0b_vs_1b.hdr')
file_dict = {'file': SimpleUploadedFile(fname_img, open(fname_img).read()),
'hdr_file': SimpleUploadedFile(fname_hdr, open(fname_hdr).read())}
form = StatisticMapForm(post_dict, file_dict)
self.assertFalse(form.is_valid())
self.assertTrue("thresholded" in form.errors["file"][0])
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,
'ignore_file_warning': True,
'target_template_image': 'GenericMNI',
}
testpath = os.path.abspath(os.path.dirname(__file__))
fname_img = os.path.join(testpath,'test_data/statmaps/box_0b_vs_1b.img')
fname_hdr = os.path.join(testpath,'test_data/statmaps/box_0b_vs_1b.hdr')
file_dict = {'file': SimpleUploadedFile(fname_img, open(fname_img).read()),
'hdr_file': SimpleUploadedFile(fname_hdr, open(fname_hdr).read())}
form = StatisticMapForm(post_dict, file_dict)
self.assertTrue(form.is_valid())
form.save()
self.assertEqual(StatisticMap.objects.filter(collection=self.coll.pk)[0].name, "test map")
def testaddImgHdr(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_img = os.path.join(testpath, "test_data/statmaps/box_0b_vs_1b.img")
fname_hdr = os.path.join(testpath, "test_data/statmaps/box_0b_vs_1b.hdr")
file_dict = {
"file": SimpleUploadedFile(fname_img, open(fname_img).read()),
"hdr_file": SimpleUploadedFile(fname_hdr, open(fname_hdr).read()),
}
form = StatisticMapForm(post_dict, file_dict)
self.assertFalse(form.is_valid())
self.assertTrue("thresholded" in form.errors["file"][0])
post_dict = {
"name": "test map",
"cognitive_paradigm_cogatlas": "trm_4f24126c22011",
"modality": "fMRI-BOLD",
"map_type": "T",
"collection": self.coll.pk,
"ignore_file_warning": True,
}
testpath = os.path.abspath(os.path.dirname(__file__))
fname_img = os.path.join(testpath, "test_data/statmaps/box_0b_vs_1b.img")
fname_hdr = os.path.join(testpath, "test_data/statmaps/box_0b_vs_1b.hdr")
file_dict = {
"file": SimpleUploadedFile(fname_img, open(fname_img).read()),
"hdr_file": SimpleUploadedFile(fname_hdr, open(fname_hdr).read()),
}
form = StatisticMapForm(post_dict, file_dict)
self.assertTrue(form.is_valid())
form.save()
self.assertEqual(StatisticMap.objects.filter(collection=self.coll.pk)[0].name, "test map")
def crawl_anima():
import neurovault.apps.statmaps.models as models
from neurovault.apps.statmaps.forms import StatisticMapForm, CollectionForm
username = "******"
email = "*****@*****.**"
try:
anima_user = models.User.objects.create_user(username, email)
anima_user.save()
except IntegrityError:
anima_user = models.User.objects.get(username=username, email=email)
url = "http://anima.fz-juelich.de/api/studies"
response = urllib.urlopen(url)
datasets = json.loads(response.read())
# results = tarfile.open(mode="r:gz", fileobj=StringIO(response.content))
# for member in results.getmembers():
# f = results.extractfile(member)
# if member.name.endswith(".study"):
for url in datasets:
response = requests.get(url)
content = response.content.replace("PubMed ID", "PubMedID")
xml_obj = e.fromstring(content)
version = xml_obj.find(".").find(
".//Element[@name='Version']").text.strip()
study_description = xml_obj.find(
".//Element[@name='Description']").text.strip()
study_description += " This dataset was automatically imported from the ANIMA <http://anima.fz-juelich.de/> database. Version: %s" % version
study_name = xml_obj.find(".").attrib['name']
tags = xml_obj.find(".//Element[@name='Keywords']").text.strip().split(
";")
tags.append("ANIMA")
doi = xml_obj.find(".//Element[@name='DOI']")
pubmedid = xml_obj.find(".//Element[@name='PubMedID']")
post_dict = {
'name':
study_name,
'description':
study_description,
'full_dataset_url':
"http://anima.fz-juelich.de/studies/" +
os.path.split(url)[1].replace(".study", "")
}
if doi != None:
post_dict['DOI'] = doi.text.strip()
elif pubmedid != None:
pubmedid = pubmedid.text.strip()
url = "http://www.ncbi.nlm.nih.gov/pmc/utils/idconv/v1.0/?ids=%s&format=json" % pubmedid
response = urllib.urlopen(url)
parsed = json.loads(response.read())
post_dict['DOI'] = parsed['records'][0]['doi']
try:
col = models.Collection.objects.get(DOI=post_dict['DOI'])
except models.Collection.DoesNotExist:
col = None
if col and not col.description.endswith(version):
col.DOI = None
old_version = re.search(r"Version: (?P<version>\w)",
col.description).group("version")
col.name = study_name + " (version %s - deprecated)" % old_version
col.save()
if not col or not col.description.endswith(version):
collection = models.Collection(owner=anima_user)
form = CollectionForm(post_dict, instance=collection)
form.is_valid()
collection = form.save()
arch_response = requests.get(
url.replace("library",
"library/archives").replace(".study", ".tar.gz"))
arch_results = tarfile.open(mode="r:gz",
fileobj=StringIO(
arch_response.content))
for study_element in xml_obj.findall(
".//StudyElement[@type='VolumeFile']"):
image_name = study_element.attrib['name'].strip()
image_filename = study_element.attrib['file']
try:
image_fileobject = arch_results.extractfile(
xml_obj.find(".").attrib['directory'] + "/" +
image_filename)
except KeyError:
image_fileobject = arch_results.extractfile(
xml_obj.find(".").attrib['directory'] + "/" +
xml_obj.find(".").attrib['directory'] + "/" +
image_filename)
map_type = models.BaseStatisticMap.OTHER
quantity_dict = {
"Mask": models.BaseStatisticMap.M,
"F-statistic": models.BaseStatisticMap.F,
"T-statistic": models.BaseStatisticMap.T,
"Z-statistic": models.BaseStatisticMap.Z,
"Beta": models.BaseStatisticMap.U
}
quantity = study_element.find(
"./Metadata/Element[@name='Quantity']")
if quantity != None:
quantity = quantity.text.strip()
if quantity in quantity_dict.keys():
map_type = quantity_dict[quantity]
post_dict = {
'name': image_name,
'modality': models.StatisticMap.fMRI_BOLD,
'map_type': map_type,
'analysis_level': models.BaseStatisticMap.M,
'collection': collection.pk,
'ignore_file_warning': True,
'cognitive_paradigm_cogatlas': 'None',
'tags': ", ".join(tags)
}
image_description = study_element.find(
"./Metadata/Element[@name='Caption']").text
if image_description:
post_dict["description"] = image_description.strip()
file_dict = {
'file':
SimpleUploadedFile(image_filename, image_fileobject.read())
}
form = StatisticMapForm(post_dict, file_dict)
form.is_valid()
form.save()
print quantity
if quantity in quantity_dict.keys():
map_type = quantity_dict[quantity]
post_dict = {
'name': image_name,
'modality': StatisticMap.fMRI_BOLD,
'map_type': map_type,
'analysis_level': BaseStatisticMap.M,
'collection': collection.pk,
'ignore_file_warning': True,
'cognitive_paradigm_cogatlas': 'None',
'tags': ", ".join(tags)
}
image_description = study_element.find(
"./Metadata/Element[@name='Caption']").text
if image_description:
post_dict["description"] = image_description.strip()
file_dict = {
'file': SimpleUploadedFile(image_filename,
image_fileobject.read())
}
form = StatisticMapForm(post_dict, file_dict)
form.is_valid()
print post_dict
print form.errors.keys()
print form.errors
form.save()
