def retrieve_nifti(peaks,retrieve,location_key):
'''retrieve_nifti
Download the image to a temporary folder if the user needs to
retrieve it. Otherwise, return file
Parameters
==========
peaks: dict
dictionary (key, is ttl_file, and value, is dictionary of {filename:fullpath}
for all brainmaps extracted from the ttl files
retrieve: boolean
if True, will download brainmaps to temporary folder first. If false, encodes
path in utf-8 for rendering in javascript
location_key: str
key to look up file name in peaks dictionary
'''
# Note: retrieve = True has not been tested!
updated_peaks = dict()
for nidm,entries in peaks.items():
if retrieve:
for e in range(len(entries)):
if location_key in entries[e]:
brainmap = entries[e][location_key]
image_ext = get_extension(brainmap)
temp_path = get_random_name()
temp_dir = tempfile.mkdtemp()
temp_image_path = "%s/%s.%s" %(temp_dir,temp_path,image_ext)
if download_file(brainmap,temp_image_path):
entries[e][location_key] = temp_image_path
updated_peaks[nidm] = entries
return updated_peaks
def retrieve_nifti(peaks,retrieve,location_key):
'''retrieve_nifti
Download the image to a temporary folder if the user needs to
retrieve it. Otherwise, return file
Parameters
==========
peaks: dict
dictionary (key, is ttl_file, and value, is dictionary of {filename:fullpath}
for all brainmaps extracted from the ttl files
retrieve: boolean
if True, will download brainmaps to temporary folder first. If false, encodes
path in utf-8 for rendering in javascript
location_key: str
key to look up file name in peaks dictionary
'''
# Note: retrieve = True has not been tested!
updated_peaks = dict()
for nidm,entries in peaks.iteritems():
if retrieve:
for e in range(len(entries)):
if location_key in entries[e]:
brainmap = entries[e][location_key]
image_ext = get_extension(brainmap)
temp_path = get_random_name()
temp_dir = tempfile.mkdtemp()
temp_image_path = "%s/%s.%s" %(temp_dir,temp_path,image_ext)
if download_file(brainmap,temp_image_path):
entries[e][location_key] = temp_image_path
updated_peaks[nidm] = entries
return updated_peaks
def generate_temp(peaks, location_key):
'''generate_temp
generate a lookup of temporary files
Parameters
==========
peaks: dict
data structure from get_coordinates_and_peaks
location_key: str
key in peaks data structure for file paths
Returns
=======
peaks: dict
(key is ttl file, equivalent to peaks, but old location_key path is replaced
with path to temporary directory
copy_list: dict
keys are current paths, values are temporary file names corresponding to
fullpath in new_nifti_files[ttl_file] dictionary. This is used to copy
images into the temporary directory with the correct names.
'''
copy_list = dict()
for nidm, entries in peaks.items():
nidm_directory = os.path.dirname(nidm)
for e in range(len(entries)):
if location_key in entries[e]:
brainmap = entries[e][location_key]
brainmap_base = os.path.basename(brainmap)
if "%s/%s" % (nidm_directory, brainmap_base) not in copy_list:
image_ext = get_extension(brainmap)
temp_path = get_random_name()
temp_image_path = "%s.%s" % (temp_path, image_ext)
copy_list["%s/%s" % (nidm_directory,
brainmap_base)] = temp_image_path
updated_peaks = dict()
for nidm, entries in peaks.items():
nidm_directory = os.path.dirname(nidm)
to_keep = []
for e in range(len(entries)):
if location_key in entries[e]:
brainmap = entries[e][location_key]
brainmap_base = os.path.basename(brainmap)
entries[e][location_key] = copy_list["%s/%s" % (nidm_directory,
brainmap_base)]
if "x" in entries[e]:
if entries[e]["x"] != "nan":
to_keep.append(e)
# Remove coordinates that are nan from the data frame
entries = [entries[e] for e in range(len(entries)) if e in to_keep]
updated_peaks[nidm] = entries
return updated_peaks, copy_list
def generate_temp(peaks,location_key):
'''generate_temp
generate a lookup of temporary files
Parameters
==========
peaks: dict
data structure from get_coordinates_and_peaks
location_key: str
key in peaks data structure for file paths
Returns
=======
peaks: dict
(key is ttl file, equivalent to peaks, but old location_key path is replaced
with path to temporary directory
copy_list: dict
keys are current paths, values are temporary file names corresponding to
fullpath in new_nifti_files[ttl_file] dictionary. This is used to copy
images into the temporary directory with the correct names.
'''
copy_list = dict()
for nidm,entries in peaks.iteritems():
nidm_directory = os.path.dirname(nidm)
for e in range(len(entries)):
if location_key in entries[e]:
brainmap = entries[e][location_key]
brainmap_base = os.path.basename(brainmap)
if "%s/%s" %(nidm_directory,brainmap_base) not in copy_list:
image_ext = get_extension(brainmap)
temp_path = get_random_name()
temp_image_path = "%s.%s" %(temp_path,image_ext)
copy_list["%s/%s" %(nidm_directory,brainmap_base)] = temp_image_path
updated_peaks = dict()
for nidm,entries in peaks.iteritems():
nidm_directory = os.path.dirname(nidm)
to_keep = []
for e in range(len(entries)):
if location_key in entries[e]:
brainmap = entries[e][location_key]
brainmap_base = os.path.basename(brainmap)
entries[e][location_key] = copy_list["%s/%s" %(nidm_directory,brainmap_base)]
if "x" in entries[e]:
if entries[e]["x"] != "nan":
to_keep.append(e)
# Remove coordinates that are nan from the data frame
entries = [entries[e] for e in range(len(entries)) if e in to_keep]
updated_peaks[nidm] = entries
return updated_peaks,copy_list
