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_interpolated_transform_zeros(self):
img = save_resampled_transformation_single(self.pknan,
resample_dim=[4, 4, 4])
data = numpy.load(img.reduced_representation.file)
print "Does transformation calculation maintain NaN values?: %s" % (
numpy.isnan(data).any())
assert_equal(numpy.isnan(data).any(), True)
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 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 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 test_interpolated_transform_zeros(self):
img = save_resampled_transformation_single(self.pknan, resample_dim=[4, 4, 4])
data = numpy.load(img.reduced_representation.file)
print "Does transformation calculation maintain NaN values?: %s" %(numpy.isnan(data).any())
assert_equal(numpy.isnan(data).any(),True)
def compare_images(request,pk1,pk2):
import numpy as np
image1 = get_image(pk1,None,request)
image2 = get_image(pk2,None,request)
images = [image1,image2]
# Get image: collection: [map_type] names no longer than ~125 characters
image1_custom_name = format_image_collection_names(image_name=image1.name,
collection_name=image1.collection.name,
map_type=image1.map_type,total_length=125)
image2_custom_name = format_image_collection_names(image_name=image2.name,
collection_name=image2.collection.name,
map_type=image2.map_type,total_length=125)
image_names = [image1_custom_name,image2_custom_name]
# Create custom links for the visualization
custom = {
"IMAGE_1_LINK":"/images/%s" % (image1.pk),
"IMAGE_2_LINK":"/images/%s" % (image2.pk)
}
# create reduced representation in case it's not there
if not image1.reduced_representation:
image1 = save_resampled_transformation_single(image1.id) # cannot run this async
if not image2.reduced_representation:
image2 = save_resampled_transformation_single(image1.id) # cannot run this async
# Load image vectors from npy files
image_vector1 = np.load(image1.reduced_representation.file)
image_vector2 = np.load(image2.reduced_representation.file)
# Load atlas pickle, containing vectors of atlas labels, colors, and values for same voxel dimension (4mm)
this_path = os.path.abspath(os.path.dirname(__file__))
atlas_pkl_path = os.path.join(this_path, 'static/atlas/atlas_mni_4mm.pkl')
atlas = joblib.load(atlas_pkl_path)
# Load the atlas svg, so we don't need to dynamically generate it
atlas_svg = os.path.join(this_path, 'static/atlas/atlas_mni_2mm_svg.pkl')
atlas_svg = joblib.load(atlas_svg)
# Generate html for similarity search, do not specify atlas
html_snippet, _ = scatterplot.scatterplot_compare_vector(image_vector1=image_vector1,
image_vector2=image_vector2,
image_names=image_names,
atlas_vector=atlas["atlas_vector"],
atlas_labels=atlas["atlas_labels"],
atlas_colors=atlas["atlas_colors"],
corr_type="pearson",
subsample_every=10, # subsample every 10th voxel
custom=custom,
remove_scripts="D3_MIN_JS",
width=1000)
# Add atlas svg to the image, and prepare html for rendering
html = [h.replace("[coronal]",atlas_svg) for h in html_snippet]
html = [h.strip("\n").replace("[axial]","").replace("[sagittal]","") for h in html]
context = {'html': html}
# Determine if either image is thresholded
threshold_status = np.array([image_names[i] for i in range(0,2) if images[i].is_thresholded])
if len(threshold_status) > 0:
warnings = list()
for i in range(0,len(image_names)):
warnings.append('Warning: Thresholded image: %s (%.4g%% of voxels are zeros),' %(image_names[i],images[i].perc_bad_voxels))
context["warnings"] = warnings
return render(request, 'statmaps/compare_images.html', context)