withFit = 0
expname = 'FakeData'
buffer = 250
file = '/Users/migraf/Desktop/04B_FemtoIV_10mMKCl_Noise_.dat'
datafile = '/Users/migraf/Desktop/04B_FemtoIV_10mMKCl_Noise__OriginalDB.hdf5'
directory = (str(os.path.split(datafile)[0]) + os.sep + expname +
'_SavedImages')
if not os.path.exists(directory):
os.makedirs(directory)
out = uf.ImportAxopatchData(file)
f = h5py.File(datafile, 'r')
i1data = f['LowPassSegmentation/i1/']
i2data = f['LowPassSegmentation/i2/']
if common:
#Plot All Common Events
pp = PdfPages(directory + os.sep + 'SavedEventsCommon.pdf')
ind1 = np.uint64(i1data['CommonIndex'][:])
ind2 = np.uint64(i2data['CommonIndex'][:])
t = np.arange(0, len(out['i1']))
t = t / out['samplerate'] * 1e3
import scipy
import scipy.signal as sig
import os
from scipy import io
from scipy import signal
from PyQt5 import QtGui, QtWidgets
import matplotlib.pyplot as plt
from numpy import linalg as lin
import pyqtgraph as pg
import matplotlib.pyplot as plt
import matplotlib
from matplotlib.backends.backend_pdf import PdfPages
filetoload = '/Volumes/backup/2016/Michael/Axopatch/21112016/17B_10mMCis100mMtransKCl_80mer.dat'
PartToConsider = np.array([21.542, 21.566])
out = uf.ImportAxopatchData(filetoload)
partinsamples = np.int64(np.round(out['samplerate'] * PartToConsider))
i1part = out['i1'][partinsamples[0]:partinsamples[1]]
i2part = out['i2'][partinsamples[0]:partinsamples[1]]
t = self.t[partinsamples[0]:partinsamples[1]]
plt.figure(1)
plt.subplot(2, 1, 1)
plt.plot(t, i1part, 'b')
plt.title('i1 vs. i2')
plt.ylabel('Ionic Current [A]')
ax = plt.gca()
ax.set_xticklabels([])