def iterate_yfp_volumes():
"""Iterate through all the YFP volumes
Returns
-----------
vol_name_list
signal_list
"""
mouse_num_list = [2, 3, 4, 5, 6, 7]
mouse_base_fn = 'ss_stacks'
region_list = ['F000', 'F001', 'F002', 'F003']
data_dir = '/data5TB/yi_mice/'
filebase = 'ss_YFP.tif'
vol_name_list = []
signal_list = []
thresh = 0.9
for mouse_num in mouse_num_list:
for region_name in region_list:
vol_dir = os.path.join(data_dir,
str(mouse_num) + mouse_base_fn, region_name,
str(mouse_num) + filebase)
vol_name = str(mouse_num) + mouse_base_fn + '-' + region_name
vol_name_list.append(vol_name)
vol = da.imreadtiff(vol_dir)
result = measure_yfp(vol, thresh)
signal_list.append(result)
print(vol_dir, result)
return vol_name_list, signal_list
def run_synapse_detection_astro(atet_input):
"""
Run synapse detection and result evalution. The parameters need to be rethought
Parameters
-------------------
atet_input : dict
Returns
-------------------
output_dict : dict
"""
query = atet_input['query']
queryID = atet_input['queryID']
nQuery = atet_input['nQuery']
resolution = atet_input['resolution']
data_location = atet_input['data_location']
data_region_location = atet_input['data_region_location']
output_foldername = atet_input['output_foldername']
region_name = atet_input['region_name']
layer_mask_str = atet_input['mask_str']
dapi_mask_str = atet_input['dapi_mask_str']
mouse_number = atet_input['mouse_number']
# Load the data
synaptic_volumes = da.load_tiff_from_astro_query(query,
data_region_location)
# Load DAPI mask
dapi_mask_fn = os.path.join(dapi_mask_str,
str(mouse_number) + 'ss-DAPI-mask.tiff')
dapi_mask = da.imreadtiff(dapi_mask_fn)
dapi_mask = dapi_mask.astype(np.bool)
combined_mask = np.logical_not(dapi_mask)
# Mask data
synaptic_volumes = mask_synaptic_volumes(synaptic_volumes, combined_mask)
volume_um3 = get_masked_volume(synaptic_volumes, combined_mask, resolution)
print(volume_um3)
# Run Synapse Detection
print('running synapse detection')
resultvol = syn.getSynapseDetections_astro(synaptic_volumes, query)
# Save the probability map to file, if you want
outputNPYlocation = os.path.join(data_location, output_foldername,
region_name)
syn.saveresultvol(resultvol, outputNPYlocation, 'query_', queryID)
thresh = 0.9
queryresult = compute_measurements(resultvol, query, volume_um3, thresh)
output_dict = {
'queryID': queryID,
'query': query,
'queryresult': queryresult
}
return output_dict
def setup_slice(fn, z):
vol = da.imreadtiff(fn)
img = vol[:, :, z]
p2, p98 = np.percentile(img, (5, 98)) #contrast stretching
img_rescale = exposure.rescale_intensity(img, in_range=(p2, p98))
img_rgb = color.gray2rgb(img_rescale)
return img_rgb
def iterate_yfp_volumes():
"""Iterate through all the YFP volumes
Returns
-----------
vol_name_list
signal_list
"""
mouse_num_list = [2, 3, 4, 5, 6, 7]
mouse_base_fn = 'ss_stacks'
region_list = ['F000', 'F001', 'F002', 'F003']
data_dir = '/data5TB/yi_mice/'
filebase = 'ss_YFP.tif'
vol_name_list = []
signal_list = []
thresh = 0.9
num_of_dendrites = 10
output_basedir = '/data5TB/yi_mice/segmented-dendrites'
for mouse_num in mouse_num_list:
for region_name in region_list:
vol_dir = os.path.join(data_dir,
str(mouse_num) + mouse_base_fn, region_name,
str(mouse_num) + filebase)
vol_name = str(mouse_num) + mouse_base_fn + '-' + region_name
vol_name_list.append(vol_name)
vol = da.imreadtiff(vol_dir)
off_output_dir = os.path.join(output_basedir,
str(mouse_num) + mouse_base_fn,
region_name)
dir_result = os.path.isdir(off_output_dir)
if (dir_result == False):
os.makedirs(off_output_dir)
result = segment_dendrites(vol, thresh, num_of_dendrites,
off_output_dir)
print(vol_dir)
def run_blob_synapse(mouse_number, mouse_project_str, base_query_num,
channel_name):
"""
Blob Synapse Ratio. Run SACT for FXS data
Only runs on Galicia
"""
query_fn = 'queries/' + mouse_project_str + '_queries.json'
hostname = socket.gethostname()
if hostname == 'Galicia':
data_location = '/data5TB/yi_mice/' + str(mouse_number) + 'ss_stacks'
dapi_mask_str_base = '/data5TB/yi_mice/dapi-masks/' + \
str(mouse_number) + 'ss_stacks'
listOfQueries = syn.loadQueriesJSON(query_fn)
resolution = {'res_xy_nm': 100, 'res_z_nm': 70}
region_name_base = 'F00'
thresh = 0.9
mask_location_str = -1 # no mask specified
foldernames = []
measure_list = []
target_filenames = []
conjugate_filenames = []
for region_num in range(0, 4):
region_name = region_name_base + str(region_num)
data_region_location = os.path.join(data_location, region_name)
query = listOfQueries[base_query_num]
query_number = base_query_num + 12 * region_num
foldername = region_name + '-Q' + str(base_query_num)
foldernames.append(foldername)
conjugate_filenames.append('Query' + str(base_query_num))
# Load the data
synaptic_volumes = da.load_tiff_from_query(query, data_region_location)
# Load DAPI mask
dapi_mask_str = os.path.join(dapi_mask_str_base, region_name)
dapi_mask_fn = os.path.join(dapi_mask_str,
str(mouse_number) + 'ss-DAPI-mask.tiff')
dapi_mask = da.imreadtiff(dapi_mask_fn)
# Mask data
dapi_mask = dapi_mask.astype(np.bool)
combined_mask = np.logical_not(dapi_mask) # keep portions without dapi
synaptic_volumes = sa.mask_synaptic_volumes(synaptic_volumes,
combined_mask)
volume_um3 = sa.get_masked_volume(synaptic_volumes, combined_mask,
resolution)
print(volume_um3)
target_antibody_name = str(mouse_number) + channel_name
target_filenames.append(target_antibody_name)
result_location = os.path.join(
data_location, 'results_' + str(mouse_number) + 'ss_fragX',
region_name, 'query_' + str(query_number) + '.npy')
antibody_measure = run_SACT_FXS(synaptic_volumes, query, thresh,
resolution, target_antibody_name,
result_location, volume_um3)
measure_list.append(antibody_measure)
mouse_df = aa.create_df(measure_list, foldernames, target_filenames,
conjugate_filenames)
return mouse_df