def main():
# Configure readonly access to kbucket
ca.autoConfig(collection='spikeforest', key='spikeforest2-readonly')
APP = TheApp()
server = vd.VDOMRServer(APP)
server.start()
def main():
parser = argparse.ArgumentParser(description='Browse SpikeForest results')
parser.add_argument(
'--port', help='The port to listen on (for a web service). Otherwise, attempt to launch as stand-alone GUI.', required=False, default=None)
args = parser.parse_args()
# Configure readonly access to kbucket
ca.autoConfig(collection='spikeforest', key='spikeforest2-readonly')
APP = TheApp()
if args.port is not None:
vd.config_server()
server = vd.VDOMRServer(APP)
server.setPort(int(args.port))
server.start()
else:
vd.config_pyqt5()
W = APP.createSession()
vd.pyqt5_start(root=W, title='SFBrowser')
#!/usr/bin/env python
from cairio import client as ca
ca.autoConfig(collection='spikeforest',
key='spikeforest2-readwrite',
ask_password=True)
sha1_path = ca.saveFile('spyking_circus.simg')
print(sha1_path)
def setKBucketConfig(*,config=None,collection=None,key=None):
ca.autoConfig(collection=collection,key=key)
def main():
# Use this to optionally connect to a kbucket share:
ca.autoConfig(collection='spikeforest',
key='spikeforest2-readwrite',
ask_password=True,
password=os.environ.get('SPIKEFOREST_PASSWORD', None))
# Specify the compute resource (see the note above)
#compute_resource = 'ccmlin008-80'
#compute_resource_ks = 'ccmlin008-kilosort'
compute_resource = None
compute_resource_ks = None
#compute_resource = 'ccmlin000-80'
#compute_resource_ks = 'ccmlin000-kilosort'
# Use this to control whether we force the processing to re-run (by default it uses cached results)
os.environ['MLPROCESSORS_FORCE_RUN'] = 'FALSE' # FALSE or TRUE
# This is the id of the output -- for later retrieval by GUI's, etc
output_id = 'spikeforest_test3'
#group_name = 'magland_synth_test'
group_name = 'mearec_sqmea_test'
a = ca.loadObject(
key=dict(name='spikeforest_recording_group', group_name=group_name))
recordings = a['recordings']
studies = a['studies']
recordings = [recordings[0]]
# Summarize the recordings
recordings_B = sa.summarize_recordings(recordings=recordings,
compute_resource=compute_resource)
# Sorters (algs and params) are defined below
sorters = define_sorters()
# We will be assembling the sorting results here
sorting_results_A = []
for sorter in sorters:
# Sort the recordings
compute_resource0 = compute_resource
if sorter['name'] == 'KiloSort':
compute_resource0 = compute_resource_ks
sortings = sa.sort_recordings(sorter=sorter,
recordings=recordings_B,
compute_resource=compute_resource0)
# Append to results
sorting_results_A = sorting_results_A + sortings
# Summarize the sortings
sorting_results_B = sa.summarize_sortings(
sortings=sorting_results_A, compute_resource=compute_resource)
# Compare with ground truth
sorting_results_C = sa.compare_sortings_with_truth(
sortings=sorting_results_B, compute_resource=compute_resource)
# TODO: collect all the units for aggregated analysis
# Save the output
print('Saving the output')
ca.saveObject(key=dict(name='spikeforest_results', output_id=output_id),
object=dict(studies=studies,
recordings=recordings_B,
sorting_results=sorting_results_C))
def main():
ca.autoConfig(collection='spikeforest',
key='spikeforest2-readwrite',
ask_password=True,
password=os.environ.get('SPIKEFOREST_PASSWORD', None))
# Use this to optionally connect to a kbucket share:
# for downloading containers if needed
ca.setRemoteConfig(alternate_share_ids=['69432e9201d0'])
# Specify the compute resource (see the note above)
compute_resource = 'default'
#compute_resource = 'local-computer'
#compute_resource = 'ccmlin008-default'
#compute_resource_ks = 'ccmlin008-kilosort'
# Use this to control whether we force the processing to re-run (by default it uses cached results)
os.environ['MLPROCESSORS_FORCE_RUN'] = 'FALSE' # FALSE or TRUE
# This is the id of the output -- for later retrieval by GUI's, etc
output_id = 'visapy_mea'
# Grab the recordings for testing
group_name = 'visapy_mea'
a = ca.loadObject(
key=dict(name='spikeforest_recording_group', group_name=group_name))
recordings = a['recordings']
studies = a['studies']
# recordings = [recordings[0]]
# recordings = recordings[0:3]
# Summarize the recordings
recordings = sa.summarize_recordings(recordings=recordings,
compute_resource=compute_resource)
# Sorters (algs and params) are defined below
sorters = _define_sorters()
# We will be assembling the sorting results here
sorting_results = []
for sorter in sorters:
# Sort the recordings
compute_resource0 = compute_resource
if sorter['name'] == 'KiloSort':
compute_resource0 = compute_resource_ks
sortings = sa.sort_recordings(sorter=sorter,
recordings=recordings,
compute_resource=compute_resource0)
# Append to results
sorting_results = sorting_results + sortings
# Summarize the sortings
sorting_results = sa.summarize_sortings(sortings=sorting_results,
compute_resource=compute_resource)
# Compare with ground truth
sorting_results = sa.compare_sortings_with_truth(
sortings=sorting_results, compute_resource=compute_resource)
# Aggregate the results
aggregated_sorting_results = sa.aggregate_sorting_results(
studies, recordings, sorting_results)
# Save the output
print('Saving the output')
ca.saveObject(key=dict(name='spikeforest_results'),
subkey=output_id,
object=dict(studies=studies,
recordings=recordings,
sorting_results=sorting_results,
aggregated_sorting_results=ca.saveObject(
object=aggregated_sorting_results)))
for sr in aggregated_sorting_results['study_sorting_results']:
study_name = sr['study']
sorter_name = sr['sorter']
n1 = np.array(sr['num_matches'])
n2 = np.array(sr['num_false_positives'])
n3 = np.array(sr['num_false_negatives'])
accuracies = n1 / (n1 + n2 + n3)
avg_accuracy = np.mean(accuracies)
txt = 'STUDY: {}, SORTER: {}, AVG ACCURACY: {}'.format(
study_name, sorter_name, avg_accuracy)
print(txt)
def main():
# Use this to optionally connect to a kbucket share:
ca.autoConfig(collection='spikeforest',
key='spikeforest2-readwrite',
ask_password=True)
# Specify the compute resource (see the note above)
compute_resource = 'jfm-laptop'
# Use this to control whether we force the processing to re-run (by default it uses cached results)
os.environ['MLPROCESSORS_FORCE_RUN'] = 'FALSE' # FALSE or TRUE
# This is the id of the output -- for later retrieval by GUI's, etc
output_id = 'spikeforest_test1'
# Grab a couple recordings for testing
recording1 = dict(
recording_name='001_synth',
study_name='datasets_noise10_K10_C4-test',
study_set='magland_synth-test',
directory=
'kbucket://15734439d8cf/groundtruth/magland_synth/datasets_noise10_K10_C4/001_synth'
)
recording2 = dict(
recording_name='002_synth',
study_name='datasets_noise10_K10_C4-test',
study_set='magland_synth-test',
directory=
'kbucket://15734439d8cf/groundtruth/magland_synth/datasets_noise10_K10_C4/002_synth'
)
recordings = [recording1, recording2]
# Summarize the recordings
recordings_B = sa.summarize_recordings(recordings=recordings,
compute_resource=compute_resource)
# Sorters (algs and params) are defined below
sorters = define_sorters()
# We will be assembling the sorting results here
sorting_results = []
for sorter in sorters:
# Sort the recordings
sortings_A = sa.sort_recordings(sorter=sorter,
recordings=recordings_B,
compute_resource=compute_resource)
# Summarize the sortings
sortings_B = sa.summarize_sortings(sortings=sortings_A,
compute_resource=compute_resource)
# Compare with ground truth
sortings_C = sa.compare_sortings_with_truth(
sortings=sortings_B, compute_resource=compute_resource)
# Append to results
sorting_results = sorting_results + sortings_C
# TODO: collect all the units for aggregated analysis
# Save the output
print('Saving the output')
ca.saveObject(key=dict(name='spikeforest_results', output_id=output_id),
object=dict(recordings=recordings_B,
sorting_results=sorting_results))
