Welcome to Intrinsic Physiology Feature Extractor (ipfx)

ipfx is a Python package for computing intrinsic cell features from electrophysiology data. This includes:

  • action potential detection (e.g. threshold time and voltage)
  • cell quality control (e.g. resting potential stability)
  • stimulus-specific cell features (e.g. input resistance)

This software is designed for use in the Allen Institute for Brain Science electrophysiology data processing pipeline.

Want to cut to the chase? Take a look at our Gallery of Examples. Otherwise, continue on for a high-level overview.

Detect Action Potentials

To detect action potentials, first construct three 1D numpy arrays: stimulus current in pA, response voltage in mV, and timestamps in seconds. With these three arrays, you can then do the following:

from ipfx.feature_extractor import SpikeFeatureExtractor

ext = SpikeFeatureExtractor()
spikes = ext.process(t, v, i)

The spikes object is a pandas DataFrame where rows are action potentials and columns are features.

Detect Spike Train Features

Given a spike train DataFrame, you can then compute a number of other features, like adaptation and latency.

from ipfx.feature_extractor import SpikeTrainFeatureExtractor

ext = SpikeTrainFeatureExtractor()
features = ext.process(t, v, i, spikes) # re-using spikes from above

Stimulus-specific Analysis

To analyze all of the sweeps with a particular stimulus type (say, long square pulses), you’ll first need to create a SweepSet object. This is an object that groups together the stimuli and responses of a group of sweeps.

from ipfx.sweep import Sweep, SweepSet

sweep_set = SweepSet([ Sweep(t=t0, v=v0, i=i0),
                       Sweep(t=t1, v=v1, i=i1),
                       Sweep(t=t2, v=v2, i=i2) ])

Now that we have this object, we can hand it to one of the stimulus-specific analysis classes. You first need to configure a SpikeFeatureExtractor and SpikeTrainFeatureExtractor:

from ipfx.feature_extractor import SpikeFeatureExtractor, SpikeTrainFeatureExtractor
from ipfx.stimulus_protocol_analysis import LongSquareAnalysis

start, end = 1.02, 2.02 # start/end of stimulus in seconds
spx = SpikeFeatureExtractor(start=start, end=end)
spfx = SpikeTrainFeatureExtractor(start=start, end=end)

analysis = LongSquareAnalysis(spx, spfx)
results = analysis.process(sweep_set)

At this point results contains whatever features/objects the analysis instance wants to return.

Analyze a Data Set

The extract_data_set_features() function allows you to calculate all available features for a given dataset in one call. This powerful functionality relies on the EphysDataSet class, which provides a well-known interface to all of the data in an experiment. For more information on construction and using EphysDataSet, see Data Sets and Stimuli.

Analysis Pipeline

At the Allen Institute, we use ipfx in two ways:

  1. as library code imported directly into scripts and notebooks. You can see examples of this use case in the inline code samples here and in the Gallery of Examples.
  2. in an automated data processing pipeline

To support the latter use case ipfx includes a number of scripts for running sweep extraction, quality control, spike extraction, and feature extraction. To find out more, see QC and Analysis Pipeline