def setup_module(module):
# Use this so we can download containers as needed
ca.setRemoteConfig(alternate_share_ids=['69432e9201d0'])
# Check if singularity is installed
retval = os.system('singularity --version')
assert retval == 0, 'Singularity is not installed'
def test_spikeforest_analysis(tmpdir):
tmpdir = str(tmpdir)
# generate toy recordings
delete_recordings = True
num_recordings = 2
duration = 15
for num in range(1, num_recordings+1):
dirname = tmpdir+'/toy_example{}'.format(num)
if delete_recordings:
if os.path.exists(dirname):
shutil.rmtree(dirname)
if not os.path.exists(dirname):
rx, sx_true = se.example_datasets.toy_example1(
duration=duration, num_channels=4, samplerate=30000, K=10)
se.MdaRecordingExtractor.writeRecording(
recording=rx, save_path=dirname)
se.MdaSortingExtractor.writeSorting(
sorting=sx_true, save_path=dirname+'/firings_true.mda')
# Use this to optionally connect to a kbucket share:
# ca.autoConfig(collection='spikeforest',key='spikeforest2-readwrite',ask_password=True)
# for downloading containers if needed
ca.setRemoteConfig(alternate_share_ids=['69432e9201d0'])
# Specify the compute resource (see the note above)
# compute_resource = 'local-computer'
compute_resource = None
# 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_test0'
# Grab the recordings for testing
recordings = [
dict(
recording_name='toy_example{}'.format(num),
study_name='toy_examples',
directory=tmpdir+'/toy_example{}'.format(num)
)
for num in range(1, num_recordings+1)
]
studies = [
dict(
name='toy_examples',
study_set='toy_examples',
directory=os.path.abspath('.'),
description='Toy examples.'
)
]
# 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
sortings = sa.sort_recordings(
sorter=sorter,
recordings=recordings,
compute_resource=compute_resource
)
# Summarize the sortings
sortings = sa.summarize_sortings(
sortings=sortings,
compute_resource=compute_resource
)
# Compare with ground truth
sortings = sa.compare_sortings_with_truth(
sortings=sortings,
compute_resource=compute_resource
)
# Append to results
sorting_results = sorting_results+sortings
# TODO: collect all the units for aggregated analysis
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',
output_id=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)
if avg_accuracy<0.3:
if sorter_name == 'Yass':
print('Average accuracy is too low, but we are excusing Yass for now.')
else:
raise Exception('Average accuracy is too low for test----- '+txt)
def main():
# generate toy recordings
if not os.path.exists('recordings'):
os.mkdir('recordings')
delete_recordings = False
recpath = 'recordings/example1'
if os.path.exists(recpath) and (delete_recordings):
shutil.rmtree(recpath)
if not os.path.exists(recpath):
rx, sx_true = se.example_datasets.toy_example1(duration=60,
num_channels=4,
samplerate=30000,
K=10)
se.MdaRecordingExtractor.writeRecording(recording=rx,
save_path=recpath)
se.MdaSortingExtractor.writeSorting(sorting=sx_true,
save_path=recpath +
'/firings_true.mda')
# for downloading containers if needed
ca.setRemoteConfig(alternate_share_ids=['69432e9201d0'])
# Specify the compute resource
compute_resource = None
num_workers = 10
# 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 = 'toy_example_local'
# Grab the recordings for testing
recordings = [
dict(recording_name='example1',
study_name='toy_examples',
directory=os.path.abspath('recordings/example1'))
]
recordings = recordings * 10
studies = [
dict(name='toy_examples',
study_set='toy_examples',
directory=os.path.abspath('recordings'),
description='Toy examples.')
]
# 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,
num_workers=num_workers)
# 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,
num_workers=num_workers)
# 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))
def kbucketConfigLocal(write=True):
ca.setRemoteConfig(collection='',token='',share_id='',upload_token='')
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():
# generate toy recordings
delete_recordings = False
num_recordings = 1
for num in range(1, num_recordings + 1):
name = 'toy_example{}'.format(num)
if delete_recordings:
if os.path.exists(name):
shutil.rmtree(name)
if not os.path.exists(name):
rx, sx_true = se.example_datasets.toy_example1(duration=60,
num_channels=4,
samplerate=30000,
K=10)
se.MdaRecordingExtractor.writeRecording(recording=rx,
save_path=name)
se.MdaSortingExtractor.writeSorting(sorting=sx_true,
save_path=name +
'/firings_true.mda')
# Use this to optionally connect to a kbucket share:
# ca.autoConfig(collection='spikeforest',key='spikeforest2-readwrite',ask_password=True)
# for downloading containers if needed
ca.setRemoteConfig(alternate_share_ids=['69432e9201d0'])
# Specify the compute resource (see the note above)
compute_resource = None
# 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_test0'
# Grab the recordings for testing
recordings = [
dict(recording_name='toy_example{}'.format(num),
study_name='toy_examples',
directory=os.path.abspath('toy_example{}'.format(num)))
for num in range(1, num_recordings + 1)
]
studies = [
dict(name='toy_examples',
study_set='toy_examples',
directory=os.path.abspath('.'),
description='Toy examples.')
]
# 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(studies=studies,
recordings=recordings_B,
sorting_results=sorting_results))