Short Square Analysis

Detect short square features

Out:

/home/docs/checkouts/readthedocs.org/user_builds/ipfx/checkouts/v1.0.0.1/docs/gallery/analysis_examples/short_square_analysis.py:29: VisibleDeprecationWarning: Function create_ephys_data_set is deprecated. Instead of using ipfx.data_set_utils.create_data_set, use ipfx.dataset.create.create_ephys_data_set
  data_set = create_data_set(sweep_info=sweep_info, nwb_file=nwb_file)
/home/docs/checkouts/readthedocs.org/user_builds/ipfx/envs/v1.0.0.1/lib/python3.7/site-packages/hdmf/spec/namespace.py:470: UserWarning: ignoring namespace 'hdmf-common' because it already exists
  warn("ignoring namespace '%s' because it already exists" % ns['name'])
/home/docs/checkouts/readthedocs.org/user_builds/ipfx/envs/v1.0.0.1/lib/python3.7/site-packages/hdmf/spec/namespace.py:470: UserWarning: ignoring namespace 'core' because it already exists
  warn("ignoring namespace '%s' because it already exists" % ns['name'])
/home/docs/checkouts/readthedocs.org/user_builds/ipfx/checkouts/v1.0.0.1/ipfx/dataset/create.py:124: VisibleDeprecationWarning: Class EphysDataSet is deprecated. Import EphysDataSet from ipfx.dataset.ephys_dataset rather than ipfx.ephys_dataset
  data=nwb_data,
/home/docs/checkouts/readthedocs.org/user_builds/ipfx/checkouts/v1.0.0.1/ipfx/stimulus_protocol_analysis.py:68: RuntimeWarning: Mean of empty slice
  output[mf] = np.nanmean(mfd)

import os
import matplotlib.pyplot as plt
from allensdk.api.queries.cell_types_api import CellTypesApi
from ipfx.data_set_utils import create_data_set
from ipfx.feature_extractor import (
    SpikeFeatureExtractor, SpikeTrainFeatureExtractor
)
from ipfx.stimulus_protocol_analysis import ShortSquareAnalysis
from ipfx.spike_features import estimate_adjusted_detection_parameters
from ipfx.epochs import get_stim_epoch

# Download and access the experimental data
ct = CellTypesApi()
nwb_file = os.path.join(
    os.path.dirname(os.getcwd()),
    "data",
    "nwb2_H17.03.008.11.03.05.nwb"
)
specimen_id = 595570553
sweep_info = ct.get_ephys_sweeps(specimen_id)
# build a data set and find the short squares
data_set = create_data_set(sweep_info=sweep_info, nwb_file=nwb_file)
shsq_table = data_set.filtered_sweep_table(
    stimuli=data_set.ontology.short_square_names
)
sweeps = data_set.sweep_set(shsq_table.sweep_number)

# find the start and end time of the stimulus
# (treating the first sweep as representative)
stim_start_index, stim_end_index = get_stim_epoch(sweeps.i[0])
stim_start_time = sweeps.t[0][stim_start_index]
stim_end_time = sweeps.t[0][stim_end_index]

# Estimate the dv cutoff and threshold fraction
dv_cutoff, thresh_frac = estimate_adjusted_detection_parameters(
    sweeps.v,
    sweeps.t,
    stim_start_time,
    stim_start_time + 0.001
)
# Build the extractors

spx = SpikeFeatureExtractor(
    start=stim_start_time, dv_cutoff=dv_cutoff, thresh_frac=thresh_frac
)
sptrx = SpikeTrainFeatureExtractor(start=stim_start_time, end=None)

# Run the analysis
shsq_analysis = ShortSquareAnalysis(spx, sptrx)
results = shsq_analysis.analyze(sweeps)

# Plot the sweeps at the lowest amplitude that evoked the most spikes
for i, swp in enumerate(sweeps.sweeps):
    if i in results["common_amp_sweeps"].index:
        plt.plot(swp.t, swp.v, linewidth=0.5, color="steelblue")

# Set the plot limits to highlight where spikes are and axis labels
plt.xlim(stim_start_time - 0.05, stim_end_time + 0.05)
plt.xlabel("Time (s)")
plt.ylabel("Membrane potential (mV)")
plt.title("Lowest amplitude spiking sweeps")

plt.show()