def test_add_DOI(self):
collection1 = Collection(name='Collection1',
owner=self.u1,
private=False)
collection1.save()
collection2 = Collection(name='Collection2',
owner=self.u1,
private=False)
collection2.save()
app_path = os.path.abspath(os.path.dirname(__file__))
image1 = save_statmap_form(image_path=os.path.join(
app_path, 'test_data/statmaps/all.nii.gz'),
collection=collection1,
image_name="image1")
image2 = save_statmap_form(image_path=os.path.join(
app_path, 'test_data/statmaps/motor_lips.nii.gz'),
collection=collection2,
image_name="image2")
comparison = Comparison.objects.filter(image1=image1, image2=image2)
self.assertEqual(len(comparison), 0)
print "without DOI: %s" % Comparison.objects.all().count()
collection = Collection.objects.get(pk=collection1.pk)
collection.DOI = '10.3389/fninf.2015.00020'
collection.save()
print "with DOI: %s" % Comparison.objects.all().count()
print collection.basecollectionitem_set.instance_of(Image).all()
comparison = Comparison.objects.filter(image1=image1, image2=image2)
self.assertEqual(len(comparison), 1)
def test_get_similar_images(self):
collection1 = Collection(name='Collection1',
owner=self.u1,
DOI='10.3389/fninf.2015.00099')
collection1.save()
collection2 = Collection(name='Collection2',
owner=self.u1,
DOI='10.3389/fninf.2015.00089')
collection2.save()
app_path = os.path.abspath(os.path.dirname(__file__))
image1 = save_statmap_form(image_path=os.path.join(
app_path, 'test_data/statmaps/all.nii.gz'),
collection=collection1,
image_name="image1")
image2 = save_statmap_form(image_path=os.path.join(
app_path, 'test_data/statmaps/all.nii.gz'),
collection=collection2,
image_name="image2")
similar_images = get_similar_images(int(image1.pk))
print "Success for this test means the pandas DataFrame shows the copy in first position with score of 1"
self.assertEqual(similar_images['image_id'][0], int(image2.pk))
self.assertEqual(similar_images['score'][0], 1)
def test_private_to_public_switch(self):
private_collection1 = Collection(name='privateCollection1',owner=self.u1, private=True,
DOI='10.3389/fninf.2015.00099')
private_collection1.save()
private_collection2 = Collection(name='privateCollection2',owner=self.u1, private=True,
DOI='10.3389/fninf.2015.00089')
private_collection2.save()
app_path = os.path.abspath(os.path.dirname(__file__))
private_image1 = save_statmap_form(image_path=os.path.join(app_path,'test_data/statmaps/all.nii.gz'),
collection=private_collection1,
image_name = "image1")
private_image2 = save_statmap_form(image_path=os.path.join(app_path,'test_data/statmaps/motor_lips.nii.gz'),
collection=private_collection2,
image_name = "image2")
comparison = Comparison.objects.filter(image1=private_image1,image2=private_image2)
self.assertEqual(len(comparison), 0)
print "before private: %s"%Comparison.objects.all().count()
private_collection1 = Collection.objects.get(pk=private_collection1.pk)
private_collection1.private = False
private_collection1.save()
private_collection2 = Collection.objects.get(pk=private_collection2.pk)
private_collection2.private = False
private_collection2.save()
print "after private: %s"%Comparison.objects.all().count()
print private_collection1.basecollectionitem_set.instance_of(Image).all()
comparison = Comparison.objects.filter(image1=private_image1,image2=private_image2)
self.assertEqual(len(comparison), 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")
def setUp(self):
self.test_path = os.path.abspath(os.path.dirname(__file__))
self.user = User.objects.create(username='******')
self.client = Client()
self.client.login(username=self.user)
self.Collection1 = Collection(name='Collection1', owner=self.user)
self.Collection1.save()
self.unorderedAtlas = Atlas(name='unorderedAtlas',
description='',
collection=self.Collection1)
# Save new atlas object, unordered
print "Adding unordered and ordered atlas..."
nii_path = os.path.join(
self.test_path,
'test_data/api/VentralFrontal_thr75_summaryimage_2mm.nii.gz')
xml_path = os.path.join(
self.test_path,
'test_data/api/unordered_VentralFrontal_thr75_summaryimage_2mm.xml'
)
self.unorderedAtlas = save_atlas_form(nii_path=nii_path,
xml_path=xml_path,
collection=self.Collection1,
name="unorderedAtlas")
# Ordered
nii_path = os.path.join(
self.test_path,
'test_data/api/VentralFrontal_thr75_summaryimage_2mm.nii.gz')
xml_path = os.path.join(
self.test_path,
'test_data/api/VentralFrontal_thr75_summaryimage_2mm.xml')
self.orderedAtlas = save_atlas_form(nii_path=nii_path,
xml_path=xml_path,
collection=self.Collection1,
name="orderedAtlas")
# Statistical Map 1 and 2
print "Adding statistical maps..."
nii_path = os.path.join(self.test_path,
'test_data/statmaps/motor_lips.nii.gz')
self.Image1 = save_statmap_form(image_path=nii_path,
collection=self.Collection1)
nii_path = os.path.join(self.test_path,
'test_data/statmaps/beta_0001.nii.gz')
self.Image2 = save_statmap_form(image_path=nii_path,
collection=self.Collection1)
# Zip file with nidm results
print "Adding nidm results..."
zip_file = os.path.join(self.test_path,
'test_data/nidm/spm_example.nidm.zip')
self.nidm = save_nidm_form(zip_file=zip_file,
collection=self.Collection1)
def handle(self, *args, **options):
down_data()
clearDB()
User.objects.all().delete()
app_path = '/code/neurovault/apps/statmaps/tests/bench'
u1 = User.objects.create(username='******')
num_files = len(os.listdir(os.path.join(app_path, 'images/')))
index_table = np.zeros(num_files)
query_table = np.zeros(num_files)
for i, file in enumerate(os.listdir(os.path.join(app_path, 'images/'))):
#print 'Adding subject ' + file
randomCollection = Collection(name='random' + file, owner=u1, DOI='10.3389/fninf.2015.00008' + str(i))
randomCollection.save()
t = Timer()
with t:
image = save_statmap_form(
image_path=os.path.join(app_path, 'images/', file),
collection=randomCollection,
image_name=file,
ignore_file_warning=True)
index_table[i] = t.interval
np.savetxt(os.path.join(app_path, 'results_index'+ str(datetime.datetime.utcnow())[:10] + '.csv'),
index_table, delimiter=",")
t = Timer()
with t:
_dummy = get_similar_images(int(image.pk))
query_table[i] = t.interval
np.savetxt(os.path.join(app_path, 'results_query' + str(datetime.datetime.utcnow())[:10] + '.csv'),
query_table, delimiter=",")
def setUp(self):
super(TestStatisticMapChange, self).setUp()
self.item = save_statmap_form(image_path=self.abs_file_path(
'test_data/statmaps/motor_lips.nii.gz'),
collection=self.coll)
self.item_url = '/api/images/%s/' % self.item.pk
def handle(self, *args, **options):
down_data()
clearDB()
app_path = '/code/neurovault/apps/statmaps/tests/bench'
u1 = User.objects.create(username='******')
num_files = len(os.listdir(os.path.join(app_path, 'images/')))
index_table = np.zeros(num_files)
query_table = np.zeros(num_files)
for i, file in enumerate(os.listdir(os.path.join(app_path, 'images/'))):
#print 'Adding subject ' + file
randomCollection = Collection(name='random' + file, owner=u1, DOI='10.3389/fninf.2015.00008' + str(i))
randomCollection.save()
t = Timer()
with t:
image = save_statmap_form(
image_path=os.path.join(app_path, 'images/', file),
collection=randomCollection,
image_name=file,
ignore_file_warning=True)
index_table[i] = t.interval
np.save(os.path.join(app_path, 'results_index'+ str(datetime.datetime.utcnow())[:10]), index_table)
factory = RequestFactory()
request = factory.get('/images/' + str(i) + '/find_similar')
t = Timer()
with t:
_dummy = find_similar(request, image.pk)
query_table[i] = t.interval
np.save(os.path.join(app_path, 'results_query' + str(datetime.datetime.utcnow())[:10]), query_table)
def createFeatures(subjects=None, resample_dim=[4, 4, 4]):
clearDB()
if os.path.isfile('/code/neurovault/apps/statmaps/tests/features'+str(subjects)+str(resample_dim)+'.npy'): #and subjects == None:
return np.load('/code/neurovault/apps/statmaps/tests/features'+str(subjects)+str(resample_dim)+'.npy').T, \
pickle.load(open('/code/neurovault/apps/statmaps/tests/dict_feat'+str(subjects)+str(resample_dim)+'.p',"rb" ))
else:
feature_dimension = get_feature_dimension(resample_dim)
features = np.empty([feature_dimension, subjects]) #4*4*4 = 28549 #16*16*16 = 450
dict_feat = {}
u1 = User.objects.create(username='******')
for i, file in enumerate(os.listdir('/code/neurovault/apps/statmaps/tests/bench/images/')):
# print 'Adding subject ' + file
print i
randomCollection = Collection(name='random' + file, owner=u1, DOI='10.3389/fninf.2015.00008' + str(i))
randomCollection.save()
image = save_statmap_form(image_path=os.path.join('/code/neurovault/apps/statmaps/tests/bench/images/', file),
collection=randomCollection, image_name=file, ignore_file_warning=True)
if not image.reduced_representation or not os.path.exists(image.reduced_representation.path):
image = save_resampled_transformation_single(image.pk, resample_dim)
features[:, i] = np.load(image.reduced_representation.file)
dict_feat[i] = int(file.split(".")[0])
if i == subjects-1:
features[np.isnan(features)] = 0
np.save('/code/neurovault/apps/statmaps/tests/features'+str(subjects)+str(resample_dim)+'.npy', features)
pickle.dump(dict_feat,open('/code/neurovault/apps/statmaps/tests/dict_feat'+str(subjects)+str(resample_dim)+'.p',"wb" ))
return features.T, dict_feat
def setUp(self):
self.test_path = os.path.abspath(os.path.dirname(__file__))
self.user = User.objects.create(username='******')
self.client = Client()
self.client.login(username=self.user)
self.Collection1 = Collection(name='Collection1', owner=self.user)
self.Collection1.save()
self.unorderedAtlas = Atlas(
name='unorderedAtlas', description='', collection=self.Collection1)
# Save new atlas object, unordered
print "Adding unordered and ordered atlas..."
nii_path = os.path.join(
self.test_path, 'test_data/api/VentralFrontal_thr75_summaryimage_2mm.nii.gz')
xml_path = os.path.join(
self.test_path, 'test_data/api/unordered_VentralFrontal_thr75_summaryimage_2mm.xml')
self.unorderedAtlas = save_atlas_form(nii_path=nii_path,
xml_path=xml_path,
collection=self.Collection1,
name="unorderedAtlas")
# Ordered
nii_path = os.path.join(
self.test_path, 'test_data/api/VentralFrontal_thr75_summaryimage_2mm.nii.gz')
xml_path = os.path.join(
self.test_path, 'test_data/api/VentralFrontal_thr75_summaryimage_2mm.xml')
self.orderedAtlas = save_atlas_form(nii_path=nii_path,
xml_path=xml_path,
collection=self.Collection1,
name="orderedAtlas")
# Statistical Map 1 and 2
print "Adding statistical maps..."
nii_path = os.path.join(
self.test_path, 'test_data/statmaps/motor_lips.nii.gz')
self.Image1 = save_statmap_form(
image_path=nii_path, collection=self.Collection1)
nii_path = os.path.join(
self.test_path, 'test_data/statmaps/beta_0001.nii.gz')
self.Image2 = save_statmap_form(
image_path=nii_path, collection=self.Collection1)
# Zip file with nidm results
print "Adding nidm results..."
zip_file = os.path.join(
self.test_path, 'test_data/nidm/spm_example.nidm.zip')
self.nidm = save_nidm_form(
zip_file=zip_file, collection=self.Collection1)
def setUp(self):
print "\n#### TESTING THRESHOLDED IMAGES IN COMPARISON\n"
self.tmpdir = tempfile.mkdtemp()
self.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()
# Image 1 is an atlas
print "Adding atlas image..."
nii_path = os.path.join(self.app_path,"test_data/api/VentralFrontal_thr75_summaryimage_2mm.nii.gz")
xml_path = os.path.join(self.app_path,"test_data/api/VentralFrontal_thr75_summaryimage_2mm.xml")
image1 = save_atlas_form(nii_path=nii_path,xml_path=xml_path,collection = self.comparisonCollection1)
self.pk1 = image1.id
# Image 2 is a statistical map
print "Adding statistical map..."
image_path = os.path.join(self.app_path,'test_data/statmaps/beta_0001.nii.gz')
image2 = save_statmap_form(image_path=image_path,collection = self.comparisonCollection2)
self.pk2 = image2.id
# Image 3 is a thresholded statistical map
print "Adding thresholded statistical map..."
image_paths = [os.path.join(self.app_path,'test_data/statmaps/box_0b_vs_1b.img'),
os.path.join(self.app_path,'test_data/statmaps/box_0b_vs_1b.hdr')]
image3 = save_statmap_form(image_path=image_paths, collection = self.comparisonCollection3,ignore_file_warning=True)
self.pk3 = image3.id
# Create similarity object
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 setUp(self):
super(TestStatisticMapChange, self).setUp()
self.item = save_statmap_form(
image_path=self.abs_data_path('statmaps/motor_lips.nii.gz'),
collection=self.coll
)
self.item_url = '/api/images/%s/' % self.item.pk
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.comparisonCollection = Collection(name='comparisonCollection',owner=self.u1)
self.comparisonCollection.save()
image1 = save_statmap_form(image_path=os.path.join(app_path,'test_data/api/VentralFrontal_thr75_summaryimage_2mm.nii.gz'),
collection=self.comparisonCollection,
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.comparisonCollection,
image_name = "image1_copy",
ignore_file_warning=True)
self.pk1_copy = image2.id
# "Bricks" images
bricks = split_afni4D_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.comparisonCollection,image_name="image2",ignore_file_warning=True)
self.pk2 = image3.id
image4 = save_statmap_form(image_path=bricks[1][1],collection=self.comparisonCollection,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.comparisonCollection,
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 test_private_to_public_switch(self):
private_collection = Collection(name='privateCollection',owner=self.u1, private=True)
private_collection.save()
app_path = os.path.abspath(os.path.dirname(__file__))
private_image1 = save_statmap_form(image_path=os.path.join(app_path,'test_data/statmaps/all.nii.gz'),
collection=private_collection,
image_name = "image1")
private_image2 = save_statmap_form(image_path=os.path.join(app_path,'test_data/statmaps/motor_lips.nii.gz'),
collection=private_collection,
image_name = "image2")
comparison = Comparison.objects.filter(image1=private_image1,image2=private_image2)
self.assertEqual(len(comparison), 0)
print "before private: %s"%Comparison.objects.all().count()
private_collection = Collection.objects.get(pk=private_collection.pk)
private_collection.private = False
private_collection.save()
print "after private: %s"%Comparison.objects.all().count()
print private_collection.image_set.all()
comparison = Comparison.objects.filter(image1=private_image1,image2=private_image2)
self.assertEqual(len(comparison), 1)
def test_get_similar_images(self):
collection1 = Collection(name='Collection1', owner=self.u1,
DOI='10.3389/fninf.2015.00099')
collection1.save()
collection2 = Collection(name='Collection2', owner=self.u1,
DOI='10.3389/fninf.2015.00089')
collection2.save()
app_path = os.path.abspath(os.path.dirname(__file__))
image1 = save_statmap_form(image_path=os.path.join(app_path, 'test_data/statmaps/all.nii.gz'),
collection=collection1,
image_name="image1")
image2 = save_statmap_form(image_path=os.path.join(app_path, 'test_data/statmaps/all.nii.gz'),
collection=collection2,
image_name="image2")
similar_images = get_similar_images(int(image1.pk))
print "Success for this test means the pandas DataFrame shows the copy in first position with score of 1"
self.assertEqual(similar_images['image_id'][0], int(image2.pk))
self.assertEqual(similar_images['score'][0], 1)
def test_private_to_public_switch(self):
private_collection1 = Collection(name='privateCollection1',
owner=self.u1,
private=True,
DOI='10.3389/fninf.2015.00099')
private_collection1.save()
private_collection2 = Collection(name='privateCollection2',
owner=self.u1,
private=True,
DOI='10.3389/fninf.2015.00089')
private_collection2.save()
app_path = os.path.abspath(os.path.dirname(__file__))
private_image1 = save_statmap_form(image_path=os.path.join(
app_path, 'test_data/statmaps/all.nii.gz'),
collection=private_collection1,
image_name="image1")
private_image2 = save_statmap_form(image_path=os.path.join(
app_path, 'test_data/statmaps/motor_lips.nii.gz'),
collection=private_collection2,
image_name="image2")
comparison = Comparison.objects.filter(image1=private_image1,
image2=private_image2)
self.assertEqual(len(comparison), 0)
print "before private: %s" % Comparison.objects.all().count()
private_collection1 = Collection.objects.get(pk=private_collection1.pk)
private_collection1.private = False
private_collection1.save()
private_collection2 = Collection.objects.get(pk=private_collection2.pk)
private_collection2.private = False
private_collection2.save()
print "after private: %s" % Comparison.objects.all().count()
print private_collection1.basecollectionitem_set.instance_of(
Image).all()
comparison = Comparison.objects.filter(image1=private_image1,
image2=private_image2)
self.assertEqual(len(comparison), 1)
def get_feature_dimension(resample_dim):
u1 = User.objects.create(username='******'+str(resample_dim))
for file in os.listdir('/code/neurovault/apps/statmaps/tests/bench/images/'):
randomCollection = Collection(name='random' + file, owner=u1, DOI='10.3389/fninf.2015.00008' + file)
randomCollection.save()
image = save_statmap_form(image_path=os.path.join('/code/neurovault/apps/statmaps/tests/bench/images/', file),
collection=randomCollection, image_name=file, ignore_file_warning=True)
if not image.reduced_representation or not os.path.exists(image.reduced_representation.path):
image = save_resampled_transformation_single(image.pk, resample_dim)
feature = np.load(image.reduced_representation.file)
dimension = feature.shape[0]
clearDB()
break
return dimension
def setUp(self):
self.user = User.objects.create_user('NeuroGuy')
self.user.save()
self.collection = Collection(owner=self.user, name="Test Collection")
self.collection.save()
self.unorderedAtlas = save_atlas_form(
nii_path=self.abs_data_path(
'api/VentralFrontal_thr75_summaryimage_2mm.nii.gz'
),
xml_path=self.abs_data_path(
'api/unordered_VentralFrontal_thr75_summaryimage_2mm.xml'
),
collection=self.collection,
name="unorderedAtlas"
)
self.image1 = save_statmap_form(
image_path=self.abs_data_path(
'statmaps/motor_lips.nii.gz'
),
collection=self.collection
)
self.image2 = save_statmap_form(
image_path=self.abs_data_path(
'statmaps/beta_0001.nii.gz'
),
collection=self.collection
)
self.nidm = save_nidm_form(
zip_file=self.abs_data_path(
'nidm/spm_example.nidm.zip'
),
collection=self.collection
)
def setUp(self):
print "\n#### TESTING THRESHOLDED IMAGES IN COMPARISON\n"
self.tmpdir = tempfile.mkdtemp()
self.app_path = os.path.abspath(os.path.dirname(__file__))
self.u1 = User.objects.create(username='******')
self.comparisonCollection = Collection(name='comparisonCollection',owner=self.u1)
self.comparisonCollection.save()
# Image 1 is an atlas
print "Adding atlas image..."
nii_path = os.path.join(self.app_path,"test_data/api/VentralFrontal_thr75_summaryimage_2mm.nii.gz")
xml_path = os.path.join(self.app_path,"test_data/api/VentralFrontal_thr75_summaryimage_2mm.xml")
image1 = save_atlas_form(nii_path=nii_path,xml_path=xml_path,collection = self.comparisonCollection)
self.pk1 = image1.id
# Image 2 is a statistical map
print "Adding statistical map..."
image_path = os.path.join(self.app_path,'test_data/statmaps/beta_0001.nii.gz')
image2 = save_statmap_form(image_path=image_path,collection = self.comparisonCollection)
self.pk2 = image2.id
# Image 3 is a thresholded statistical map
print "Adding thresholded statistical map..."
image_paths = [os.path.join(self.app_path,'test_data/statmaps/box_0b_vs_1b.img'),
os.path.join(self.app_path,'test_data/statmaps/box_0b_vs_1b.hdr')]
image3 = save_statmap_form(image_path=image_paths, collection = self.comparisonCollection,ignore_file_warning=True)
self.pk3 = image3.id
# Create similarity object
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 setUpClass(cls):
cls.test_path = os.path.abspath(os.path.dirname(__file__))
cls.user, _ = User.objects.get_or_create(username='******')
cls.client = Client()
cls.client.login(username=cls.user)
cls.Collection1 = Collection(name='Collection1', owner=cls.user)
cls.Collection1.save()
nii_path = os.path.join(
cls.test_path, cls._map)
map = save_statmap_form(
image_path=nii_path, collection=cls.Collection1)
save_resampled_transformation_single(map.pk)
response = json.loads(cls.client.get("/images/%d/gene_expression/json?mask=full" % map.pk, follow=True).content)
cls.df = pd.DataFrame(response["data"], columns=response["columns"])
def test_add_DOI(self):
collection1 = Collection(name='Collection1', owner=self.u1, private=False)
collection1.save()
collection2 = Collection(name='Collection2', owner=self.u1, private=False)
collection2.save()
app_path = os.path.abspath(os.path.dirname(__file__))
image1 = save_statmap_form(image_path=os.path.join(app_path,'test_data/statmaps/all.nii.gz'),
collection=collection1,
image_name = "image1")
image2 = save_statmap_form(image_path=os.path.join(app_path,'test_data/statmaps/motor_lips.nii.gz'),
collection=collection2,
image_name = "image2")
comparison = Comparison.objects.filter(image1=image1,image2=image2)
self.assertEqual(len(comparison), 0)
print "without DOI: %s"%Comparison.objects.all().count()
collection = Collection.objects.get(pk=collection1.pk)
collection.DOI = '10.3389/fninf.2015.00020'
collection.save()
print "with DOI: %s"%Comparison.objects.all().count()
print collection.basecollectionitem_set.instance_of(Image).all()
comparison = Comparison.objects.filter(image1=image1,image2=image2)
self.assertEqual(len(comparison), 1)
def setUpClass(cls):
cls.test_path = os.path.abspath(os.path.dirname(__file__))
cls.user, _ = User.objects.get_or_create(username='******')
cls.client = Client()
cls.client.login(username=cls.user)
cls.Collection1 = Collection(name='Collection1', owner=cls.user)
cls.Collection1.save()
nii_path = os.path.join(cls.test_path, cls._map)
map = save_statmap_form(image_path=nii_path,
collection=cls.Collection1)
save_resampled_transformation_single(map.pk)
response = json.loads(
cls.client.get("/images/%d/gene_expression/json" % map.pk,
follow=True).content)
cls.df = pd.DataFrame(response["data"], columns=response["columns"])
def test_thresholded_image_comparison(self):
# There should be no comparisons for a thresholded image
print "testing comparisons for thresholded images"
assert_equal(count_existing_comparisons(self.pk3), 0)
# There should be no comparisons for an atlas
print "testing comparisons for atlases"
assert_equal(count_existing_comparisons(self.pk1), 0)
# There should be no comparisons for statistical map because no other statistical maps
print "testing comparisons for statistical maps"
assert_equal(count_existing_comparisons(self.pk2), 0)
# Add another statistical map
image_path = os.path.join(self.app_path,
'test_data/statmaps/motor_lips.nii.gz')
image4 = save_statmap_form(image_path=image_path,
collection=self.comparisonCollection4)
self.pk4 = image4.id
# There should STILL be no comparisons for a thresholded image
print "testing comparisons for thresholded images"
assert_equal(count_existing_comparisons(self.pk3), 0)
# There should STILL be no comparisons for an of the atlas
print "testing comparisons for atlases"
assert_equal(count_existing_comparisons(self.pk1), 0)
# There should now be one comparison for each statistical map, two total
print "testing comparisons for statistical maps"
print Comparison.objects.all()
assert_equal(count_existing_comparisons(self.pk2), 1)
assert_equal(count_existing_comparisons(self.pk4), 1)
# This is the call that find_similar users to get images
comparisons = get_existing_comparisons(self.pk4)
for comp in comparisons:
pk1 = comp.image1.pk
pk2 = comp.image2.pk
im1 = Image.objects.get(pk=pk1)
im2 = Image.objects.get(pk=pk2)
assert_equal(im1.is_thresholded, False)
assert_equal(im2.is_thresholded, False)
def testDownloadCollection_NIDM_results(self):
collection = Collection(owner=self.user, name="Collection2")
collection.save()
# Upload NIMDResult zip file
zip_file = open(
os.path.join(TEST_PATH, 'test_data/nidm/auditory.nidm.zip'), 'rb')
post_dict = {
'name': 'auditory',
'description': '{0} upload test'.format('spm_auditory_v1.2.0'),
'collection': collection.pk,
}
fname = os.path.basename(
os.path.join(TEST_PATH, 'test_data/nidm/auditory.nidm.zip'))
file_dict = {'zip_file': SimpleUploadedFile(fname, zip_file.read())}
form = NIDMResultsForm(post_dict, file_dict)
form.save()
# Upload Statistic Map
image = save_statmap_form(image_path=os.path.join(
TEST_PATH, 'test_data/statmaps/all.nii.gz'),
collection=collection,
image_name="all.nii.gz")
factory = RequestFactory()
self.client.login(username=self.user)
request = factory.get('/collections/%s/download' % collection.pk,
{'format': 'img.zip'})
request.user = self.user
response = download_collection(request, str(collection.pk))
self.assertTrue(response.streaming_content)
self.assertEqual(response.status_code, 200)
self.assertEqual(response.get('Content-Disposition'),
"attachment; filename=" + collection.name + ".zip")
zf = zipfile.ZipFile(io.BytesIO(''.join(response.streaming_content)))
self.assertEqual(len(zf.filelist), 2) # 1 NIDMResult, 1 Statmap
self.assertIsNone(zf.testzip())
self.assertIn("Collection2/all.nii.gz", zf.namelist())
self.assertIn("Collection2/auditory.nidm.zip", zf.namelist())
def test_thresholded_image_comparison(self):
# There should be no comparisons for a thresholded image
print "testing comparisons for thresholded images"
assert_equal(0,count_existing_comparisons(self.pk3))
# There should be no comparisons for an atlas
print "testing comparisons for atlases"
assert_equal(0,count_existing_comparisons(self.pk1))
# There should be no comparisons for statistical map because no other statistical maps
print "testing comparisons for statistical maps"
assert_equal(0,count_existing_comparisons(self.pk2))
# Add another statistical map
image_path = os.path.join(self.app_path,'test_data/statmaps/motor_lips.nii.gz')
image4 = save_statmap_form(image_path=image_path,collection = self.comparisonCollection)
self.pk4 = image4.id
# There should STILL be no comparisons for a thresholded image
print "testing comparisons for thresholded images"
assert_equal(0,count_existing_comparisons(self.pk3))
# There should STILL be no comparisons for an of the atlas
print "testing comparisons for atlases"
assert_equal(0,count_existing_comparisons(self.pk1))
# There should now be one comparison for each statistical map, two total
print "testing comparisons for statistical maps"
print Comparison.objects.all()
assert_equal(1,count_existing_comparisons(self.pk2))
assert_equal(1,count_existing_comparisons(self.pk4))
assert_equal(1,count_existing_comparisons())
# This is the call that find_similar users to get images
comparisons = get_existing_comparisons(self.pk4)
for comp in comparisons:
pk1=comp.image1.pk
pk2=comp.image2.pk
im1 = Image.objects.get(pk=pk1)
im2 = Image.objects.get(pk=pk2)
assert_equal(im1.is_thresholded,False)
assert_equal(im2.is_thresholded,False)
def testDownloadCollection_NIDM_results(self):
collection = Collection(owner=self.user, name="Collection2")
collection.save()
# Upload NIMDResult zip file
zip_file = open(os.path.join(TEST_PATH, 'test_data/nidm/auditory.nidm.zip'), 'rb')
post_dict = {
'name': 'auditory',
'description': '{0} upload test'.format('spm_auditory_v1.2.0'),
'collection': collection.pk,
}
fname = os.path.basename(os.path.join(TEST_PATH, 'test_data/nidm/auditory.nidm.zip'))
file_dict = {
'zip_file': SimpleUploadedFile(fname, zip_file.read())}
form = NIDMResultsForm(post_dict, file_dict)
form.save()
# Upload Statistic Map
image = save_statmap_form(image_path=os.path.join(TEST_PATH, 'test_data/statmaps/all.nii.gz'),
collection=collection,
image_name="all.nii.gz")
factory = RequestFactory()
self.client.login(username=self.user)
request = factory.get('/collections/%s/download' % collection.pk, {'format': 'img.zip'})
request.user = self.user
response = download_collection(request, str(collection.pk))
self.assertTrue(response.streaming_content)
self.assertEqual(response.status_code, 200)
self.assertEqual(response.get('Content-Disposition'), "attachment; filename=" + collection.name + ".zip")
zf = zipfile.ZipFile(io.BytesIO(''.join(response.streaming_content)))
self.assertEqual(len(zf.filelist),2) # 1 NIDMResult, 1 Statmap
self.assertIsNone(zf.testzip())
self.assertIn("Collection2/all.nii.gz", zf.namelist())
self.assertIn("Collection2/auditory.nidm.zip", zf.namelist())
def handle(self, *args, **options):
clearDB()
app_path = '/code/neurovault/apps/statmaps/tests/bench'
u1 = User.objects.create(username='******')
dict_feat = {}
for i, file in enumerate(os.listdir(os.path.join(app_path, 'images/'))):
#print 'Adding subject ' + file
randomCollection = Collection(name='random' + file, owner=u1, DOI='10.3389/fninf.2015.00008' + str(i))
randomCollection.save()
t = Timer()
with t:
image = save_statmap_form(
image_path=os.path.join(app_path, 'images/', file),
collection=randomCollection,
image_name=file,
ignore_file_warning=True)
dict_feat[i] = image.pk
print i
if i == 939:
pickle.dump(dict_feat, open('/code/neurovault/apps/statmaps/tests/dict_feat_localhost.p', "wb"))
break
