Getting Started

This notebook is a general overview of how to load a raw data file, detect spikes, and plot the results.

In [1]:

%load_ext autoreload
%autoreload 2

import matplotlib.pyplot as plt

from IPython.display import display

%load_ext autoreload %autoreload 2 import matplotlib.pyplot as plt from IPython.display import display

Load raw data¶

SanPy is built with file loaders for pClamp files (using pyAbf), csv files, and matplab files.

In [2]:

import sanpy

path = '../../data/19114001.abf'
ba = sanpy.bAnalysis(path);

# print info on the file and analysis
print(ba)

import sanpy path = '../../data/19114001.abf' ba = sanpy.bAnalysis(path); # print info on the file and analysis print(ba)

WARNING sanpy.fileloaders.fileLoader_base  fileLoader_base.py getFileLoaders() line:54 -- Did not load "epochTable", no "filetype" attribute

fileLoader: file: 19114001.abf sweeps: 1 dur (Sec):60.0 spikes:0

Plot Raw Data.¶

Here we are plotting the raw data membrane potential (mV) and the first derivative (dV/dt). The first derivative is critical for accurate spike detection.

In [3]:

bp = sanpy.analysisPlot.bAnalysisPlot(ba)

bp.plotDerivAndRaw();

bp = sanpy.analysisPlot.bAnalysisPlot(ba) bp.plotDerivAndRaw();

Detect spikes¶

Spike detection requires a number of parameters. SanPy provides built in presets for common cell types including ventricular myocytes, slow and fast neurons.

Here, we grab the detection presets for a sino-atrial node cardiac myocyte named 'SA Node' and then detect spikes with spikeDetect(dDict).

dDict is a Python dictionary with keys being the detection parameters and the value is, well, the value of that parameter key. In this example we set the dvdtThreshold to 50, this was arrived at by inspecting our plot of the raw data (above).

In [4]:

dDict = sanpy.bDetection().getDetectionDict('SA Node')

dDict['dvdtThreshold'] = 50

ba.spikeDetect(dDict)
print(ba)

dDict = sanpy.bDetection().getDetectionDict('SA Node') dDict['dvdtThreshold'] = 50 ba.spikeDetect(dDict) print(ba)

 INFO sanpy.bAnalysis_  bAnalysis_.py getErrorReport() line:2247 -- Generating error report for 103 spikes

fileLoader: file: 19114001.abf sweeps: 1 dur (Sec):60.0 spikes:103

All the named preset detection parameters are available using getDetectionPresetList()

In [5]:

print(sanpy.bDetection().getDetectionPresetList())

print(sanpy.bDetection().getDetectionPresetList())

['SA Node', 'Ventricular', 'Neuron', 'Fast Neuron', 'Sub Threshold', 'Ca Spikes', 'Ca Kymograph']

Plot results¶

Once analyzed, the analysis results can be plotted over the raw data. Here we are using bAnalysisPlot to make simple plots.

In [6]:

bp = sanpy.bAnalysisPlot(ba)
ax = bp.plotSpikes(plotThreshold=True, plotPeak=True);

# zoom in on x-axis
ax.set_xlim([22,26]);

bp = sanpy.bAnalysisPlot(ba) ax = bp.plotSpikes(plotThreshold=True, plotPeak=True); # zoom in on x-axis ax.set_xlim([22,26]);

The analysis results can easily be pulled from the bAnalysis object and manipulated in any number of ways.

First, as a Pandas DataFrame.

In [7]:

df =ba.asDataFrame()
display(df.head())

df =ba.asDataFrame() display(df.head())

	spikeNumber	include	detectionType	sweep	epoch	epochLevel	sweepSpikeNumber	userType	errors	analysisDate	...	cycleLength_pnts	cycleLength_ms	diastolicDuration_ms	widths	widths_10	widths_20	widths_50	widths_80	widths_90	user_timeToPeak_ms
0	0	True	dvdt	0	0	0.0	0	0	[]	20230430	...	NaN	NaN	59.30	[{'halfHeight': 10, 'risingPnt': 5138, 'fallin...	59.05	42.15	16.30	4.00	2.10	1.45
1	1	True	dvdt	0	0	0.0	1	0	[]	20230430	...	12284.0	614.20	70.40	[{'halfHeight': 10, 'risingPnt': 17646, 'falli...	65.65	48.95	18.40	4.15	2.25	1.30
2	2	True	dvdt	0	1	0.0	2	0	[]	20230430	...	13023.0	651.15	60.95	[{'halfHeight': 10, 'risingPnt': 30479, 'falli...	64.30	45.70	14.85	4.00	2.20	1.35
3	3	True	dvdt	0	1	0.0	3	0	[]	20230430	...	14660.0	733.00	40.55	[{'halfHeight': 10, 'risingPnt': 44732, 'falli...	67.45	49.65	18.70	4.30	2.30	1.40
4	4	True	dvdt	0	1	0.0	4	0	[]	20230430	...	12455.0	622.75	90.95	[{'halfHeight': 10, 'risingPnt': 58196, 'falli...	73.95	51.00	18.30	4.80	2.40	1.80

5 rows × 61 columns

Second, as a numpy array and plotted with matplotlib.

Here we are pulling the time of AP threshold and the instantaneous frequency of all spikes.

See our documentation for all the available analysis results.

In [8]:

thresholdSec = ba.getStat('thresholdSec')
instFeq = ba.getStat('spikeFreq_hz')

import matplotlib.pyplot as plt
plt.plot(thresholdSec, instFeq, 'o')
plt.show()

thresholdSec = ba.getStat('thresholdSec') instFeq = ba.getStat('spikeFreq_hz') import matplotlib.pyplot as plt plt.plot(thresholdSec, instFeq, 'o') plt.show()

Finally, we will manually plot the raw data with an overlay of the spike peak (mV).

The bAnalysis fileLoader contains all the raw data for the file including the recording and acquisition parameters such as sampling rate.

In [9]:

# sweepX is the time of a recording
sweepX = ba.fileLoader.sweepX
# sweepY is the actual recording
sweepY = ba.fileLoader.sweepY

# plot the raw data
fig, ax = plt.subplots(1,1)

ax.plot(sweepX, sweepY, 'k');

# overlay spike peak
thresholdSec = ba.getStat('thresholdSec')
peakVal = ba.getStat('peakVal')
ax.plot(thresholdSec, peakVal, 'or');

# zoom the x-axis
ax.set_xlim([20, 30]);

# sweepX is the time of a recording sweepX = ba.fileLoader.sweepX # sweepY is the actual recording sweepY = ba.fileLoader.sweepY # plot the raw data fig, ax = plt.subplots(1,1) ax.plot(sweepX, sweepY, 'k'); # overlay spike peak thresholdSec = ba.getStat('thresholdSec') peakVal = ba.getStat('peakVal') ax.plot(thresholdSec, peakVal, 'or'); # zoom the x-axis ax.set_xlim([20, 30]);

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