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
from ipfx.ephys_extractor import SpikeExtractor, SpikeTrainFeatureExtractor
from ipfx.stimulus_protocol_analysis import ShortSquareAnalysis
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
import os
import matplotlib.pyplot as plt
# 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 long squares
data_set = AibsDataSet(sweep_info=sweep_info, nwb_file=nwb_file)
lsq_table = data_set.filtered_sweep_table(
stimuli=data_set.ontology.long_square_names)
lsq_set = data_set.sweep_set(lsq_table.sweep_number)
# build the extractors
spx = efex.SpikeExtractor(start=0.27, end=1.27)
spfx = efex.SpikeTrainFeatureExtractor(start=0.27, end=1.27)
# run the analysis and print out a few of the features
lsqa = spa.LongSquareAnalysis(spx, spfx, subthresh_min_amp=-100.0)
data = lsqa.analyze(lsq_set)
for field in [
'tau', 'v_baseline', 'input_resistance', 'vm_for_sag', 'fi_fit_slope',
'sag', 'rheobase_i'
]:
print("%s: %s" % (field, str(data[field])))
from ipfx.aibs_data_set import AibsDataSet
from ipfx.ephys_extractor import SpikeExtractor, SpikeTrainFeatureExtractor
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)
def data_for_specimen_id(specimen_id, data_source, chirp_stimulus_codes):
logging.debug("specimen_id: {}".format(specimen_id))
# Manual edit ontology to identify chirp sweeps
ontology_data = ju.read(StimulusOntology.DEFAULT_STIMULUS_ONTOLOGY_FILE)
mask = []
for od in ontology_data:
mask_val = True
for tagset in od:
for c in chirp_stimulus_codes:
if c in tagset and "code" in tagset:
mask_val = False
break
mask.append(mask_val)
ontology_data = [od for od, m in zip(ontology_data, mask) if m is True]
ontology_data.append([
["code"] + chirp_stimulus_codes,
[
"name",
"Chirp",
],
[
"core",
"Core 2"
]
])
ontology = StimulusOntology(ontology_data)
# Find or retrieve NWB file and ancillary info and construct an AibsDataSet object
if data_source == "lims":
nwb_path, h5_path = lims_nwb_information(specimen_id)
if type(nwb_path) is dict and "error" in nwb_path:
logging.warning("Problem getting NWB file for specimen {:d} from LIMS".format(specimen_id))
return nwb_path
try:
data_set = AibsDataSet(
nwb_file=nwb_path, h5_file=h5_path, ontology=ontology)
except Exception as detail:
logging.warning("Exception when loading specimen {:d} from LIMS".format(specimen_id))
logging.warning(detail)
return {"error": {"type": "dataset", "details": traceback.format_exc(limit=None)}}
elif data_source == "sdk":
nwb_path, sweep_info = sdk_nwb_information(specimen_id)
try:
data_set = AibsDataSet(
nwb_file=nwb_path, sweep_info=sweep_info, ontology=ontology)
except Exception as detail:
logging.warning("Exception when loading specimen {:d} via Allen SDK".format(specimen_id))
logging.warning(detail)
return {"error": {"type": "dataset", "details": traceback.format_exc(limit=None)}}
else:
logging.error("invalid data source specified ({})".format(data_source))
# Identify chirp sweeps
try:
iclamp_st = data_set.filtered_sweep_table(clamp_mode=data_set.CURRENT_CLAMP)
iclamp_st = data_set.filtered_sweep_table(clamp_mode=data_set.CURRENT_CLAMP, stimuli=["Chirp"])
chirp_sweep_numbers = iclamp_st["sweep_number"].sort_values().values
except Exception as detail:
logging.warning("Exception when identifying sweeps from specimen {:d}".format(specimen_id))
logging.warning(detail)
return {"error": {"type": "sweep_table", "details": traceback.format_exc(limit=1)}}
if len(chirp_sweep_numbers) == 0:
logging.info("No chirp sweeps for {:d}".format(specimen_id))
return {"error": {"type": "processing", "details:": "no available chirp sweeps"}}
try:
result = chirp.extract_chirp_feature_vector(data_set, chirp_sweep_numbers)
except Exception as detail:
logging.warning("Exception when processing specimen {:d}".format(specimen_id))
logging.warning(detail)
return {"error": {"type": "processing", "details": traceback.format_exc(limit=1)}}
return result