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Python module for analyzing electrophysiological data

Project description

pyibt

pyibt is a Python module that extends the functionality of the ECCELES Electrophysiology Suite with a simple and intuitive API for the analysis and visualization of electrophysiology data in Python.

ECCELES is an online electrophysiology acquisition program developed for the IGOR Pro data analysis platform (WaveMetrics) that allows for conducting and and analyzing highly customizable and flexible electrophysiological experiments. ELLECES however requires a very specific computing environment to run correctly, requires proprietary software (Igor Pro), and has a limited ability to implement automated and repetitive analyses.

pyibt was developed to read the custom binary files generated by ECCELES (ibt files) and provide a simple API to interact with the data stored with them. At its core, pyibt provides an easy access to ibt data to facilitate custom analysis in Python. However, pyibt also includes a set of native methods to quickly and easily plot and analyze ephys data. Please see the examples below to learn how to get started with pyibt.

Loading data

from pyibt.read_ibt import Read_IBT
ibt = Read_IBT('demo.ibt')

Accessing file information

print('ibt file name:', ibt.name)
print('Number of sweeps:', len(ibt.sweeps))
ibt file name: demo
Number of sweeps: 28

Accessing sweep data

sweep = ibt.sweeps[0]
print('Recording mode:', sweep.rec_mode)
print('Sweep data:', sweep.data)
print('Recording mode:', sweep.y_label)
print('Sweep time:', sweep.time)
print('Recording mode:', sweep.x_label)
print('Sweep command:', sweep.command)
Recording mode: current clamp
Sweep data: [-63.18666667 -62.98666667 -62.89333333 ... -62.98666667 -62.98666667 -63.18666667]
Recording mode: Membrane Potential (mV)
Sweep time: [0.0000e+00 2.0000e-05 4.0000e-05 ... 9.9994e-01 9.9996e-01 9.9998e-01]
Recording mode: Time (seconds)
Sweep command: [0. 0. 0. ... 0. 0. 0.]

Quick plot functions

fig = plt.figure(figsize=(8, 7))
ax1 = fig.add_subplot(211)
ax1 = ibt.plot_sweep(sweep_num=16, ax=ax1)

ax2 = fig.add_subplot(212)
ax2 = ibt.plot_command(sweep_num=16, ax=ax2)

docs/example_plots/single_sweep.png

fig = plt.figure(figsize=(5, 5))
ax=ibt.plot_sweep_phase_plane(sweep_num=16)
ax.set_xlim(-50, 60)

docs/examples/example_plots/phase_plane.png

fig=plt.figure(figsize=(8,5))
ax=ibt.plot_all_sweeps()

docs/examples/example_plots/all_sweeps.png

Get Creative

sweeps = ibt.sweeps[9:25]
cm = plt.get_cmap("winter")
colors = [cm(i/len(sweeps)) for i, x in enumerate(sweeps)]

plt.figure(figsize=(12, 8))
for i, sweep in enumerate(sweeps):
    num_pnts = int(0.5/sweep.dx)
    x = sweep.time[:num_pnts] + 0.02 * i
    y = sweep.data[:num_pnts] + 10 * i
    plt.plot(x, y, color=colors[i], alpha=0.5)
plt.gca().axis('off')

docs/examples/example_plots/fancy_FI_curve.png

Automatic detection of action potentials

command = 4
sweeps = ibt.sweeps[9:25]

num_APs = []
current = []
for sweep  in sweeps:
    num_APs.append(len(sweep.spike_times_during_command(command)))
    current.append(sweep.commands[4]['value'])

fig=plt.figure(figsize=(8, 5))
plt.plot(current, num_APs, '-o', markersize=10)
plt.ylabel('Number of Action potentials')
plt.xlabel('Applied current (pA)')
plt.savefig('example_plots/FI_curve.png')

docs/examples/example_plots/FI_curve.png

import numpy as np

sweep = ibt.sweeps[25]
spikes = sweep.spike_properties()

spike_num = [x for x in range(len(spikes))]
spike_height = [spike['height'] for spike in spikes]
spike_width = [spike['half_width'] for spike in spikes]
spike_thresh = [spike['thresh'] for spike in spikes]
spike_dVdt = [spike['peak_dVdt'] for spike in spikes]

fig = plt.figure(figsize=(12, 8))

ax1 = fig.add_subplot(231)
ax1.set_ylabel('Membrane Potential (mV)')
ax1.set_xlabel('Time (ms)')

ax4 = fig.add_subplot(234)
ax4.set_ylabel('dVdt (V/s')
ax4.set_xlabel('Membrane Potential (mV)')

cm=plt.cm.magma
colors = [cm(i/len(sweeps)) for i, x in enumerate(sweeps)]
for i, spike in enumerate(spikes):
    ax1.plot(spike['time'] * 1000, spike['Vm'], color=colors[i])

    dVdt = np.gradient(spike['Vm'], spike['time'])/1000
    ax4.plot(spike['Vm'], dVdt, color=colors[i])

ax2 = fig.add_subplot(232)
ax2.set_ylabel('AP half width (ms)')
ax2.set_xlabel('AP number')
ax2.set_xticks(spike_num)
ax2.plot(spike_num,spike_width, '-o', markersize=8, color='tab:blue')

ax3 = fig.add_subplot(233, sharex=ax2)
ax3.set_ylabel('AP Height (mV)')
ax3.set_xlabel('AP number')
ax3.plot(spike_num,spike_height, '-o', markersize=8, color='tab:orange')

ax2 = fig.add_subplot(235, sharex=ax2)
ax2.set_ylabel('Threshold (mV)')
ax2.set_xlabel('AP number')
ax2.plot(spike_num,spike_thresh, '-o', markersize=8, color='tab:green')

ax3 = fig.add_subplot(236, sharex=ax2)
ax3.set_ylabel('dVdt (V/s)')
ax3.set_xlabel('AP number')
ax3.plot(spike_num,spike_dVdt, '-o', markersize=8, color='tab:red')

docs/examples/example_plots/AP_char.png

Details of the ibt file structure can be found here

pyibt was inspired by the pyABF module written by Scott Harden for Axon Binary Format (ABF) files.

Author

Perry Spratt
PhD Candidate, Bender Lab
University of California, San Francisco
perrywespratt@gmail.com

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