def test_setDerivativeThreshold():
"""basic: Test setDerivativeThreshold"""
import efel
efel.reset()
stim_start = 500.0
stim_end = 900.0
time = efel.io.load_fragment('%s#col=1' % meanfrequency1_url)
voltage = efel.io.load_fragment('%s#col=2' % meanfrequency1_url)
trace = {}
trace['T'] = time
trace['V'] = voltage
trace['stim_start'] = [stim_start]
trace['stim_end'] = [stim_end]
features = ['AP_begin_voltage']
feature_values = \
efel.getFeatureValues(
[trace],
features)
AP_begin_voltage_orig = feature_values[0]['AP_begin_voltage'][1]
efel.setDerivativeThreshold(5)
feature_values = \
efel.getFeatureValues(
[trace],
features)
AP_begin_voltage = feature_values[0]['AP_begin_voltage'][1]
nt.assert_almost_equal(AP_begin_voltage, -51.6400489995987)
nt.assert_not_equal(AP_begin_voltage, AP_begin_voltage_orig)
def test_setDerivativeThreshold():
"""basic: Test setDerivativeThreshold"""
import efel
efel.reset()
stim_start = 500.0
stim_end = 900.0
time = efel.io.load_fragment('%s#col=1' % meanfrequency1_url)
voltage = efel.io.load_fragment('%s#col=2' % meanfrequency1_url)
trace = {}
trace['T'] = time
trace['V'] = voltage
trace['stim_start'] = [stim_start]
trace['stim_end'] = [stim_end]
features = ['AP_begin_voltage']
feature_values = \
efel.getFeatureValues(
[trace],
features)
AP_begin_voltage_orig = feature_values[0]['AP_begin_voltage'][1]
efel.setDerivativeThreshold(5)
feature_values = \
efel.getFeatureValues(
[trace],
features)
AP_begin_voltage = feature_values[0]['AP_begin_voltage'][1]
nt.assert_almost_equal(AP_begin_voltage, -51.6400489995987)
nt.assert_not_equal(AP_begin_voltage, AP_begin_voltage_orig)
def test_setDerivativeThreshold():
"""basic: Test setDerivativeThreshold"""
import efel
efel.reset()
import numpy
stim_start = 500.0
stim_end = 900.0
test_data_path = joinp(testdata_dir, 'basic', 'mean_frequency_1.txt')
data = numpy.loadtxt(test_data_path)
time = data[:, 0]
voltage = data[:, 1]
trace = {}
trace['T'] = time
trace['V'] = voltage
trace['stim_start'] = [stim_start]
trace['stim_end'] = [stim_end]
features = ['AP_begin_voltage']
feature_values = \
efel.getFeatureValues(
[trace],
features)
AP_begin_voltage_orig = feature_values[0]['AP_begin_voltage'][1]
efel.setDerivativeThreshold(5)
feature_values = \
efel.getFeatureValues(
[trace],
features)
AP_begin_voltage = feature_values[0]['AP_begin_voltage'][1]
nt.assert_almost_equal(AP_begin_voltage, -51.6400489995987)
nt.assert_not_equal(AP_begin_voltage, AP_begin_voltage_orig)
import efel
import numpy as np
import pyabf
from tkinter import filedialog
from matplotlib import pyplot as plt
#Opens a dialog box allowing the user to select the file and removes the extension from the file name
file_path = filedialog.askopenfilename()
file = file_path[:-4]
#Sets settings for derivative threshold, interpolation step, and calculation of RMP
efel.setDoubleSetting('interp_step',0.05) #Must be set to the sampling rate (20,000 Hz = 0.05)
efel.setDoubleSetting('voltage_base_start_perc',0.1) #Used for RMP calculation
efel.setDoubleSetting('voltage_base_end_perc',0.8) #Used for RMP calculation
efel.setDerivativeThreshold(15) #Normally set to 15
#Import ABF file and assign the corresponding data to Time and RawData variables
File = pyabf.ABF(file_path)
Time = (File.sweepX)*1000
RawData = File.data
#Identify the number of traces in the abf file (*Note: this is designed for a 20kHz recording lasting ~10s)
#Other differences in recording will need to change these values
RawData = np.transpose(RawData)
TraceNum = int((len(RawData) / 200000))
#Using the number of calulcated traces, split the first RawData column into equal arrays of 200000
#Then, concatenate the arrays together and segment out the first 35000 sampling points
# (the last 165000 points are irrelevant)
SortedData = np.split(RawData[:,0],TraceNum,0)
#*Files can be analyzed as single traces w/ graphs or an experiment as a whole
#*It should be used in conjunction with the HEKAv2 MATLAB script
#Install and Import these scripts
import scipy.io
import efel
import numpy as np
from tkinter import filedialog
from matplotlib import pyplot as plt
#Enable EFEL Settings
efel.setDoubleSetting(
'interp_step', 0.05) #Must be set to the sampling rate (20,000 Hz = 0.05)
efel.setDoubleSetting('voltage_base_start_perc', 0.1)
efel.setDoubleSetting('voltage_base_end_perc', 0.8)
efel.setDerivativeThreshold(10) #Value can be changed, but normally set to 10
efel.setThreshold(0)
#Create global variable
trace_results = dict()
#Opens a dialog box allowing the user to select the file and removes the extension from the file name & load in the file
file_path = filedialog.askopenfilename()
file = file_path[:-4]
mat = scipy.io.loadmat(file_path)
#Depending upon the number of cells present, split the data into a list of numpy arrays based on the number of experiments
print('There are ' + str(int(mat['CellNum'])) + ' cells in this file.')
if mat['CellNum'] == 1:
Cell1 = list(np.hsplit(mat['C1data'], int(mat['C1Expts'])))