def main():
# Example use case of processing detections
# Load probability map
metadataFN = '/Users/anish/Documents/Connectome/SynapseAnalysis/data/M247514_Rorb_1/Site3Align2/site3_metadata.json'
metadata = syn.loadMetadata(metadataFN)
queryFN = metadata['querylocation']
# List of Queries
listOfQueries = syn.loadQueriesJSON(queryFN)
for n in range(0, len(listOfQueries)):
fn = os.path.join(metadata['datalocation'], 'resultVol')
fn = fn + str(n) + '.npy'
resultVol = np.load(fn)
print(fn)
pd.probMapToJSON(resultVol, metadata, listOfQueries[n], n)
def main():
"""
Evaluation of site3 synapse detection results
"""
# Load metadata
metadataFN = 'site3_metadata.json'
metadata = syn.loadMetadata(metadataFN)
outputJSONlocation = metadata['outputJSONlocation']
queryFN = metadata['querylocation']
evalparam = metadata['evalparam']
# List of Queries
listOfQueries = syn.loadQueriesJSON(queryFN)
listOfThresholds = []
listOfThresholds_to_text = []
listOfQueries_to_text = []
listofevals = []
thresh_list = [0.7, 0.8, 0.9]
# Evaluate each query individually
for n, query in enumerate(listOfQueries):
listOfThresholds.append(query['thresh'])
for thresh in thresh_list:
listOfThresholds_to_text.append(thresh)
query['thresh'] = thresh
print(query)
listOfQueries_to_text.append(query)
queryresult = pd.combineResultVolumes([n], [thresh], metadata,
evalparam)
listofevals.append(queryresult)
pd.printEvalToText(listofevals, listOfQueries_to_text,
listOfThresholds_to_text)
# Combine Queries
evaluation_parameters = metadata['evalparam']
pd.combineResultVolumes(list(range(0, len(listOfQueries))),
listOfThresholds, metadata, evaluation_parameters)
def main():
"""
Site3 Synaptograms
"""
metadata_fn = '/Users/anish/Documents/Connectome/SynapseAnalysis/data/M247514_Rorb_1/Site3Align2/site3_metadata_dev.json'
metadata = syn.loadMetadata(metadata_fn)
query_fn = metadata['querylocation']
listOfQueries = syn.loadQueriesJSON(query_fn)
evalargs = metadata['evalparam']
# listOfThresholds = []
# for query in listOfQueries:
# listOfThresholds.append(query['thresh'])
# listOfQueryNumbers = list(range(0, len(listOfQueries)))
listOfThresholds = [0.8, 0.7, 0.7]
listOfQueryNumbers = [0, 2, 4]
queryresult = pd.combineResultVolumes(listOfQueryNumbers, listOfThresholds,
metadata, evalargs)
data_location = metadata['datalocation']
outputpath = '/Users/anish/Documents/Connectome/Synaptome-Duke/data/collman17/Site3Align2Stacks/synaptograms/'
stack_list = [
'results', 'PSD95', 'synapsin', 'VGlut1', 'GluN1', 'GABA', 'Gephyrin',
'TdTomato'
]
text_x_offset = 0
text_y_offset = 5
win_xy = 4
win_z = 1
generateResultTiffStacks(listOfQueryNumbers, listOfThresholds,
data_location, metadata['outputNPYlocation'])
synaptogram_args = {
'win_xy': win_xy,
'win_z': win_z,
'data_location': data_location,
'stack_list': stack_list,
'text_x_offset': text_x_offset,
'text_y_offset': text_y_offset,
'outputpath': outputpath
}
# Detected synapses (True Positives)
# detected_annotations = queryresult['detected_annotations']
# synaptogram_args['outputpath'] = os.path.join(outputpath, 'true_positive_detections')
# for counter, synapse in enumerate(detected_annotations):
# synaptogram.synapseAnnoToSynaptogram(synapse, synaptogram_args)
# False negatives
missed_annotations = queryresult['missed_annotations']
synaptogram_args['outputpath'] = os.path.join(outputpath, 'false_negative')
for counter, synapse in enumerate(missed_annotations):
synaptogram.synapseAnnoToSynaptogram(synapse, synaptogram_args)
# False positive detections
false_positives = queryresult['false_positives']
synaptogram_args['outputpath'] = os.path.join(outputpath, 'false_positive')
for synapse in false_positives:
synaptogram.synapseAnnoToSynaptogram(synapse, synaptogram_args)
def main():
"""
run site 3 synapse detection
"""
# Load metadata
metadata_fn = '/Users/anish/Documents/Connectome/SynapseAnalysis/data/M247514_Rorb_1/Site3Align2/site3_metadata.json'
#metadata_fn = 'site3_metadata_TdTomato.json'
metadata = syn.loadMetadata(metadata_fn)
datalocation = metadata['datalocation']
outputNPYlocation = metadata['outputNPYlocation']
query_fn = metadata['querylocation']
# List of Queries
listOfQueries = syn.loadQueriesJSON(query_fn)
print("Number of Queries: ", len(listOfQueries))
for n in range(0, len(listOfQueries)):
query = listOfQueries[n]
print(query)
# Load the data
synapticVolumes = da.loadTiffSeriesFromQuery(query, datalocation)
# Run Synapse Detection
# Takes ~5 minutes to run
resultVol = syn.getSynapseDetections(synapticVolumes, query)
# Save the probability map to file, if you want
syn.saveresultvol(resultVol, outputNPYlocation, 'resultVol', n)
# Save the thresholded results as annotation objects
# in a json file
# pd.probMapToJSON(resultVol, metadata, query, n)
"""
Evaluation of site3 synapse detection results
"""
# Load metadata
metadataFN = 'site3_metadata.json'
metadata = syn.loadMetadata(metadataFN)
outputJSONlocation = metadata['outputJSONlocation']
queryFN = metadata['querylocation']
evalparam = metadata['evalparam']
# List of Queries
listOfQueries = syn.loadQueriesJSON(queryFN)
listOfThresholds = []
listOfThresholds_to_text = []
listOfQueries_to_text = []
listofevals = []
thresh_list = [0.7, 0.8, 0.9]
# Evaluate each query individually
for n, query in enumerate(listOfQueries):
listOfThresholds.append(query['thresh'])
for thresh in thresh_list:
listOfThresholds_to_text.append(thresh)
query['thresh'] = thresh
print(query)
listOfQueries_to_text.append(query)
queryresult = pd.combineResultVolumes([n], [thresh], metadata,
evalparam)
listofevals.append(queryresult)
pd.printEvalToText(listofevals, listOfQueries_to_text,
listOfThresholds_to_text)
# Combine Queries
evaluation_parameters = metadata['evalparam']
pd.combineResultVolumes(list(range(0, len(listOfQueries))),
listOfThresholds, metadata, evaluation_parameters)