def feature_vector_input():
TEST_DATA_PATH = os.path.join(os.path.dirname(__file__), 'data')
nwb_file_name = "Pvalb-IRES-Cre;Ai14-415796.02.01.01.nwb"
nwb_file_full_path = os.path.join(TEST_DATA_PATH, nwb_file_name)
if not os.path.exists(nwb_file_full_path):
download_file(nwb_file_name, nwb_file_full_path)
data_set = AibsDataSet(nwb_file=nwb_file_full_path, ontology=ontology)
lsq_sweep_numbers = data_set.filtered_sweep_table(
clamp_mode=data_set.CURRENT_CLAMP,
stimuli=ontology.long_square_names).sweep_number.sort_values().values
lsq_sweeps = data_set.sweep_set(lsq_sweep_numbers)
lsq_start, lsq_dur, _, _, _ = stf.get_stim_characteristics(
lsq_sweeps.sweeps[0].i, lsq_sweeps.sweeps[0].t)
lsq_end = lsq_start + lsq_dur
lsq_spx, lsq_spfx = dsf.extractors_for_sweeps(lsq_sweeps,
start=lsq_start,
end=lsq_end,
**dsf.detection_parameters(
data_set.LONG_SQUARE))
lsq_an = spa.LongSquareAnalysis(lsq_spx, lsq_spfx, subthresh_min_amp=-100.)
lsq_features = lsq_an.analyze(lsq_sweeps)
return lsq_sweeps, lsq_features, lsq_start, lsq_end
def feature_vector_input():
TEST_DATA_PATH = os.path.join(os.path.dirname(__file__), 'data')
nwb_file_name = "Pvalb-IRES-Cre;Ai14-415796.02.01.01.nwb"
nwb_file_full_path = os.path.join(TEST_DATA_PATH, nwb_file_name)
if not os.path.exists(nwb_file_full_path):
download_file(nwb_file_name, nwb_file_full_path)
data_set = AibsDataSet(nwb_file=nwb_file_full_path, ontology=ontology)
lsq_sweep_numbers = [4, 5, 6, 16, 17, 18, 19, 20, 21]
lsq_sweeps = data_set.sweep_set(lsq_sweep_numbers)
lsq_sweeps.select_epoch("recording")
lsq_sweeps.align_to_start_of_epoch("experiment")
lsq_start, lsq_dur, _, _, _ = stf.get_stim_characteristics(lsq_sweeps.sweeps[0].i,
lsq_sweeps.sweeps[0].t)
lsq_end = lsq_start + lsq_dur
lsq_spx, lsq_spfx = dsf.extractors_for_sweeps(lsq_sweeps,
start=lsq_start,
end=lsq_end,
**dsf.detection_parameters(data_set.LONG_SQUARE))
lsq_an = spa.LongSquareAnalysis(lsq_spx, lsq_spfx, subthresh_min_amp=-100.)
lsq_features = lsq_an.analyze(lsq_sweeps)
return lsq_sweeps, lsq_features, lsq_start, lsq_end
import ipfx.ephys_features as ft
# download a specific experiment NWB file via AllenSDK
ct = CellTypesApi()
specimen_id = 595570553
nwb_file = "%d.nwb" % specimen_id
if not os.path.exists(nwb_file):
ct.save_ephys_data(specimen_id, nwb_file)
sweep_info = ct.get_ephys_sweeps(specimen_id)
# build a data set and find the short squares
data_set = AibsDataSet(sweep_info=sweep_info, nwb_file=nwb_file)
shsq_table = data_set.filtered_sweep_table(
stimuli=data_set.ontology.short_square_names)
shsq_sweep_set = data_set.sweep_set(shsq_table.sweep_number)
# Estimate the dv cutoff and threshold fraction (we know stimulus starts at 0.27s)
dv_cutoff, thresh_frac = ft.estimate_adjusted_detection_parameters(
shsq_sweep_set.v, shsq_sweep_set.t, 0.27, 0.271)
# Build the extractors
start = 0.27
spx = SpikeExtractor(start=start, dv_cutoff=dv_cutoff, thresh_frac=thresh_frac)
sptrx = SpikeTrainFeatureExtractor(start=start, end=None)
# Run the analysis
shsq_analysis = ShortSquareAnalysis(spx, sptrx)
results = shsq_analysis.analyze(shsq_sweep_set)
# Plot the sweeps at the lowest amplitude that evoked the most spikes
for i, swp in enumerate(shsq_sweep_set.sweeps):
from ipfx.stimulus_protocol_analysis import RampAnalysis
# download a specific experiment NWB file via AllenSDK
ct = CellTypesApi()
specimen_id = 595570553
nwb_file = "%d.nwb" % specimen_id
if not os.path.exists(nwb_file):
ct.save_ephys_data(specimen_id, nwb_file)
sweep_info = ct.get_ephys_sweeps(specimen_id)
# Build the data set and find the ramp sweeps
data_set = AibsDataSet(sweep_info=sweep_info, nwb_file=nwb_file)
ramp_table = data_set.filtered_sweep_table(
stimuli=data_set.ontology.ramp_names)
ramp_sweep_set = data_set.sweep_set(ramp_table.sweep_number)
# Build the extractors (we know stimulus starts at 0.27 s)
start = 0.27
spx = SpikeExtractor(start=start, end=None)
sptrx = SpikeTrainFeatureExtractor(start=start, end=None)
# Run the analysis
ramp_analysis = RampAnalysis(spx, sptrx)
results = ramp_analysis.analyze(ramp_sweep_set)
# Plot the sweeps and the latency to the first spike of each
sns.set_style("white")
for swp in ramp_sweep_set.sweeps:
plt.plot(swp.t, swp.v, linewidth=0.5)
sns.rugplot(results["spiking_sweeps"]["latency"].values + start)