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Thu, Nov 26, 10:48

import os
import platform
import shelve
from tkinter import filedialog
import h5py
import numpy as np
import pyabf
import scipy
import scipy.signal as sig
from PyQt5 import QtGui, QtWidgets
from scipy import io
from scipy import signal
import Functions
from tkinter.filedialog import askopenfilenames
def ImportABF(datafilename):
abf = pyabf.ABF(datafilename) # shows what is available
#output={'type': 'Clampfit', 'graphene': 0, 'samplerate': abf.pointsPerSec, 'i1': -20000./65536 * abf.dataY, 'v1': abf.dataC, 'filename': datafilename}
output = {'type': 'Clampfit', 'graphene': 0, 'samplerate': abf.dataRate, 'i1':[0] * 1e-12,
'v1':[1], 'filename': datafilename}
return output
def ImportAxopatchData(datafilename):
x=np.fromfile(datafilename, np.dtype('>f4'))
f=open(datafilename, 'rb')
for i in range(0, 10):
if 'Acquisition' in a or 'Sample Rate' in a:
samplerate=int(''.join(i for i in a if i.isdigit()))/1000
if 'FEMTO preamp Bandwidth' in a:
femtoLP=int(''.join(i for i in a if i.isdigit()))
if 'I_Graphene' in a:
print('This File Has a Graphene Channel!')
end = len(x)
if graphene:
#pore current
i1 = x[250:end-3:4]
#graphene current
i2 = x[251:end-2:4]
#pore voltage
v1 = x[252:end-1:4]
#graphene voltage
v2 = x[253:end:4]
print('The femto was set to : {} Hz, if this value was correctly entered in the LabView!'.format(str(femtoLP)))
output={'FemtoLowPass': femtoLP, 'type': 'Axopatch', 'graphene': 1, 'samplerate': samplerate, 'i1': i1, 'v1': v1, 'i2': i2, 'v2': v2, 'filename': datafilename}
i1 = np.array(x[250:end-1:2])
v1 = np.array(x[251:end:2])
output={'type': 'Axopatch', 'graphene': 0, 'samplerate': samplerate, 'i1': i1, 'v1': v1, 'filename': datafilename}
return output
def ImportChimeraRaw(datafilename):
data = np.fromfile(datafilename, np.dtype('<u2'))
samplerate = np.float64(matfile['ADCSAMPLERATE'])
TIAgain = np.int32(matfile['SETUP_TIAgain'])
preADCgain = np.float64(matfile['SETUP_preADCgain'])
currentoffset = np.float64(matfile['SETUP_pAoffset'])
ADCvref = np.float64(matfile['SETUP_ADCVREF'])
ADCbits = np.int32(matfile['SETUP_ADCBITS'])
if 'blockLength' in matfile:
closedloop_gain = TIAgain * preADCgain
bitmask = (2 ** 16 - 1) - (2 ** (16 - ADCbits) - 1)
data = -ADCvref + (2 * ADCvref) * (data & bitmask) / 2 ** 16
data = (data / closedloop_gain + currentoffset)
data.shape = [data.shape[1], ]
output = {'matfilename': str(os.path.splitext(datafilename)[0]),'i1raw': data, 'v1': np.float64(matfile['SETUP_biasvoltage']), 'samplerateRaw': np.int64(samplerate), 'type': 'ChimeraRaw', 'filename': datafilename, 'graphene': 0, 'blockLength':blockLength}
return output
def ImportChimeraData(datafilename):
matfile = io.loadmat(str(os.path.splitext(datafilename)[0]))
samplerate = matfile['ADCSAMPLERATE']
if samplerate<4e6:
data = np.fromfile(datafilename, np.dtype('float64'))
buffersize = int(matfile['DisplayBuffer'])
out = Functions.Reshape1DTo2D(data, buffersize)
output = {'i1': out['i1'], 'v1': out['v1'], 'samplerate':float(samplerate), 'type': 'ChimeraNotRaw', 'filename': datafilename,'graphene': 0}
output = ImportChimeraRaw(datafilename)
return output
def OpenFile(filename = '', ChimeraLowPass = 10e3, approxImpulseResponse=False, Split=False, verbose=False):
Function used to read data. It extracts the currents and voltage signals from the file in input
by calling the import function corresponding to the file format.
filename : str
Full path to the data file.
ChimeraLowPass : float
Cutoff frequency of the digital low pass used after high bandwidth recordings.
approxImpulseResponse : bool, optional
False by default.
Split : bool, optional
False by default.
verbose : bool, optional
False by default. False by default. If True, it will display a simple figure with the shape of the signal.
Dictionary output with:
'type' : string with the type of file read
'graphene' : boolean indicating if the recording was made with a transverse current measurement (1 or True) or not (0 or False)
'i1' : numpy array of float with the currents
'v1' : numpy array of float with the voltages
'samplerate' float of sampling frequency
'filename' : string with full path to the data file
'ExperimentDuration' : float difference of file modify time and change time
'TimeFileLastModified' : float with file modify time
'TimeFileWritten' : float with inode or file change time.
if ChimeraLowPass==None:
if filename == '':
datafilename = askopenfilenames()
if verbose:
print('Loading file... ' +filename)
if datafilename[-3::] == 'dat':
isdat = 1
output = ImportAxopatchData(datafilename)
elif datafilename[-3::] == 'log':
isdat = 0
output = ImportChimeraData(datafilename)
if output['type'] is 'ChimeraRaw': # Lowpass and downsample
if verbose:
print('length: ' + str(len(output['i1raw'])))
Wn = round(2 * ChimeraLowPass / output['samplerateRaw'], 4) # [0,1] nyquist frequency
b, a = signal.bessel(4, Wn, btype='low', analog=False) # 4-th order digital filter
if approxImpulseResponse:
z, p, k = signal.tf2zpk(b, a)
eps = 1e-9
r = np.max(np.abs(p))
approx_impulse_len = int(np.ceil(np.log(eps) / np.log(r)))
Filt_sig=(signal.filtfilt(b, a, output['i1raw'], method='gust', irlen=approx_impulse_len))
Filt_sig=(signal.filtfilt(b, a, output['i1raw'], method='gust'))
ds_factor = np.ceil(output['samplerateRaw'] / (5 * ChimeraLowPass))
output['i1'] = scipy.signal.resample(Filt_sig, int(len(output['i1raw']) / ds_factor))
output['samplerate'] = output['samplerateRaw'] / ds_factor
output['v1'] = output['v1']*np.ones(len(output['i1']))
if verbose:
print('Samplerate after filtering:' + str(output['samplerate']))
print('new length: ' + str(len(output['i1'])))
if Split:
rawSplit=np.array_split(output['i1raw'], splitNr)
for raw in rawSplit:
if approxImpulseResponse:
z, p, k = signal.tf2zpk(b, a)
eps = 1e-9
r = np.max(np.abs(p))
approx_impulse_len = int(np.ceil(np.log(eps) / np.log(r)))
signalSplit=signal.filtfilt(b, a, raw, method='gust', irlen=approx_impulse_len)
signalSplit=signal.filtfilt(b, a, raw, method = 'gust')
Filt_sigSplit.append(scipy.signal.resample(signalSplit, int(len(raw) / ds_factor)))
if max(abs(output['i1']))>1e-6:
if verbose:
print('converting to SI units')
elif datafilename[-3::] == 'abf':
output = ImportABF(datafilename)
if verbose:
print('length: ' + str(len(output['i1'])))
st = os.stat(datafilename)
if platform.system() == 'Darwin':
if verbose:
print('Platform is ' + platform.system())
output['TimeFileWritten'] = st.st_birthtime
output['TimeFileLastModified'] = st.st_mtime
output['ExperimentDuration'] = st.st_mtime - st.st_birthtime
elif platform.system() == 'Windows':
if verbose:
print('Platform is Windows')
output['TimeFileWritten'] = st.st_ctime
output['TimeFileLastModified'] = st.st_mtime
output['ExperimentDuration'] = st.st_mtime - st.st_ctime
if verbose:
print('Platform is ' + platform.system() +
', might not get accurate results.')
output['TimeFileWritten'] = st.st_ctime
output['TimeFileLastModified'] = st.st_mtime
output['ExperimentDuration'] = st.st_mtime - st.st_ctime
raise Exception('Platform not detected')
return output
def SaveToHDF5(inp_file, AnalysisResults, coefficients, outdir):
file = str(os.path.split(inp_file['filename'])[1][:-4])
f = h5py.File(outdir + file + '_OriginalDB.hdf5', "w")
general = f.create_group("General")
general.create_dataset('FileName', data=inp_file['filename'])
general.create_dataset('Samplerate', data=inp_file['samplerate'])
general.create_dataset('Machine', data=inp_file['type'])
general.create_dataset('TransverseRecorded', data=inp_file['graphene'])
general.create_dataset('TimeFileWritten', data=inp_file['TimeFileWritten'])
general.create_dataset('TimeFileLastModified', data=inp_file['TimeFileLastModified'])
general.create_dataset('ExperimentDuration', data=inp_file['ExperimentDuration'])
segmentation_LP = f.create_group("LowPassSegmentation")
for k,l in AnalysisResults.items():
set1 = segmentation_LP.create_group(k)
lpset1 = set1.create_group('LowPassSettings')
for o, p in coefficients[k].items():
lpset1.create_dataset(o, data=p)
for m, l in AnalysisResults[k].items():
if m is 'AllEvents':
eventgroup = set1.create_group(m)
for i, val in enumerate(l):
eventgroup.create_dataset('{:09d}'.format(i), data=val)
elif m is 'Cusum':
eventgroup = set1.create_group(m)
for i1, val1 in enumerate(AnalysisResults[k]['Cusum']):
cusevent = eventgroup.create_group('{:09d}'.format(i1))
cusevent.create_dataset('NumberLevels', data=np.uint64(len(AnalysisResults[k]['Cusum'][i1]['levels'])))
if len(AnalysisResults[k]['Cusum'][i1]['levels']):
cusevent.create_dataset('up', data=AnalysisResults[k]['Cusum'][i1]['up'])
cusevent.create_dataset('down', data=AnalysisResults[k]['Cusum'][i1]['down'])
cusevent.create_dataset('both', data=AnalysisResults[k]['Cusum'][i1]['both'])
cusevent.create_dataset('fit', data=AnalysisResults[k]['Cusum'][i1]['fit'])
# 0: level number, 1: current, 2: length, 3: std
cusevent.create_dataset('levels_current', data=AnalysisResults[k]['Cusum'][i1]['levels'][1])
cusevent.create_dataset('levels_length', data=AnalysisResults[k]['Cusum'][i1]['levels'][2])
cusevent.create_dataset('levels_std', data=AnalysisResults[k]['Cusum'][i1]['levels'][3])
set1.create_dataset(m, data=l)
def SaveVariables(savename, **kwargs):
if os.path.isdir(savename):
#cut of .dat extension
if savename.lower().endswith('.dat'):
savename = savename[0:-4]
#Check if file already exists, otherwise popup dialog
if os.path.isfile(savename + '.dat'):
#root = tkinter.Tk()
savename = filedialog.asksaveasfile(mode='w', defaultextension=".dat")
if savename is None: # asksaveasfile return `None` if dialog closed with "cancel".
# raise IOError('File ' + savename + '.dat already exists.')
savefile = savename
#Check if directory exists
directory = os.path.dirname(savefile)
if not os.path.exists(directory):
for arg_name in kwargs:
print('saved as: ' + savefile + '.dat')
def LoadVariables(loadname, variableName):
if not isinstance(loadname, str):
raise Exception('The second argument must be a string')
#cut of .dat extension
if loadname.lower().endswith('.dat'):
loadname = loadname[0:-4]
if not os.path.isfile(loadname + '.dat'):
raise Exception('File does not exist')
shelfFile =
Variable = shelfFile[variableName]
except KeyError as e:
message = 'Key ' + variableName + ' does not exist, available Keys: \n' + "\n".join(list(shelfFile.keys()))
print('Loaded ' + variableName + 'from ' + loadname + '.dat')
return Variable
def LowPassAndResample(inpsignal, samplerate, LP, LPtoSR = 5):
Wn = np.round(2 * LP / samplerate, 4) # [0,1] nyquist frequency
b, a = signal.bessel(4, Wn, btype='low', analog=False) # 4-th order digital filter
z, p, k = signal.tf2zpk(b, a)
eps = 1e-9
r = np.max(np.abs(p))
approx_impulse_len = int(np.ceil(np.log(eps) / np.log(r)))
Filt_sig = (signal.filtfilt(b, a, inpsignal, method='gust', irlen=approx_impulse_len))
ds_factor = np.ceil(samplerate / (2 * LP))
return (scipy.signal.resample(Filt_sig, int(len(inpsignal) / ds_factor)), samplerate / ds_factor)

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