def SBR():
"""
Calculate signal-to-baseline ratio (SBR) or delta F / F0 for
traces in the active window. The result is expressed as a %.
Useful for imaging data.
Ensure that the baseline cursors are positioned appropriately.
"""
SBR_traces = [
100 * (stf.get_trace(i) - stf.get_base()) / stf.get_base()
for i in range(stf.get_size_channel())
]
stf.new_window_list(SBR_traces)
stf.set_yunits('%')
return
def load():
# wx Widgets to create a file selection window
fname = wx.FileSelector(
"Import Ephus file",
default_extension="Ephus XSG",
default_path=".",
wildcard="Matlab Ephus xsg. text import (*.xsg)|*.xsg",
flags=wx.OPEN | wx.FILE_MUST_EXIST)
data_sio = sio.loadmat(fname, squeeze_me=True)
data = data_sio.get('data')
trace = data['ephys'].item().item()[0]
header = data_sio.get('header')
dt = header['ephys'].item().item()[0]['sampleRate'].item()
stf.new_window(trace[1:])
stf.set_sampling_interval(1000 / np.double(dt))
stf.set_xunits('ms')
stf.set_yunits('pA')
def read_heka_stf(filename):
channels, channelnames, channelunits, channeldt = read_heka(filename)
for nc, channel in enumerate(channels):
if channelunits[nc] == "V":
for ns, sweep in enumerate(channel):
channels[nc][ns] = np.array(channels[nc][ns])
channels[nc][ns] *= 1.0e3
channelunits[nc] = "mV"
if channelunits[nc] == "A":
for ns, sweep in enumerate(channel):
channels[nc][ns] = np.array(channels[nc][ns])
channels[nc][ns] *= 1.0e12
channelunits[nc] = "pA"
import stf
stf.new_window_list(channels)
for nc, name in enumerate(channelnames):
stf.set_channel_name(name, nc)
for nc, units in enumerate(channelunits):
for ns in range(stf.get_size_channel()):
stf.set_yunits(units, ns, nc)
stf.set_sampling_interval(channeldt[0] * 1e3)
def read_heka_stf(filename):
channels, channelnames, channelunits, channeldt = read_heka(filename)
for nc, channel in enumerate(channels):
if channelunits[nc]=="V":
for ns, sweep in enumerate(channel):
channels[nc][ns] = np.array(channels[nc][ns])
channels[nc][ns] *= 1.0e3
channelunits[nc]="mV"
if channelunits[nc]=="A":
for ns, sweep in enumerate(channel):
channels[nc][ns] = np.array(channels[nc][ns])
channels[nc][ns] *= 1.0e12
channelunits[nc]="pA"
import stf
stf.new_window_list(channels)
for nc, name in enumerate(channelnames):
stf.set_channel_name(name, nc)
for nc, units in enumerate(channelunits):
for ns in range(stf.get_size_channel()):
stf.set_yunits(units, ns, nc)
stf.set_sampling_interval(channeldt[0]*1e3)
def loadtxt(freq=400):
"""
Loads an ASCII file with extension *.GoR. This file contains
ratiometric fluorescent measurements (i.e Green over Red fluorescence)
saved in one column. This function opens a new Stimfit window and
sets the x-units to "ms" and y-units to "Delta G over R".
Arguments:
freq -- (float) the sampling rate (in Hz) for fluorescence acquistion.
the default value is 400 Hz
"""
fname = wx.FileSelector("Import Ca transients" ,
default_extension="Ratiometric" ,
default_path="." ,
wildcard = "Ratiometric fluorescence (*.GoR)|*.GoR" ,
flags = wx.OPEN | wx.FILE_MUST_EXIST)
stf.new_window( np.loadtxt(fname) )
stf.set_xunits('ms')
stf.set_yunits('Delta G/R')
stf.set_sampling_interval(1.0/freq*1000) # acquisition at 400 Hz
def loadtxt(freq=400):
"""
Loads an ASCII file with extension *.GoR. This file contains
ratiometric fluorescent measurements (i.e Green over Red fluorescence)
saved in one column. This function opens a new Stimfit window and
sets the x-units to "ms" and y-units to "Delta G over R".
Arguments:
freq -- (float) the sampling rate (in Hz) for fluorescence acquistion.
the default value is 400 Hz
"""
fname = wx.FileSelector("Import Ca transients",
default_extension="Ratiometric",
default_path=".",
wildcard="Ratiometric fluorescence (*.GoR)|*.GoR",
flags=wx.OPEN | wx.FILE_MUST_EXIST)
stf.new_window(np.loadtxt(fname))
stf.set_xunits('ms')
stf.set_yunits('Delta G/R')
stf.set_sampling_interval(1.0 / freq * 1000) # acquisition at 400 Hz
def loadmat():
"""
Load electrophysiology recordings from ephysIO
HDF5-based Matlab v7.3 (.mat) files
"""
# Import required modules for file IO
from Tkinter import Tk
import tkFileDialog
from gc import collect
# Use file open dialog to obtain file path
root = Tk()
opt = dict(defaultextension='.mat',
filetypes=[('MATLAB v7.3 (HDF5) file', '*.mat'),
('All files', '*.*')])
if 'loadcwd' not in globals():
global loadcwd
else:
opt['initialdir'] = loadcwd
filepath = tkFileDialog.askopenfilename(**opt)
root.withdraw()
if filepath != '':
# Move to file directory and check file version
loadcwd = filepath.rsplit('/', 1)[0]
from os import chdir
print filepath
chdir(loadcwd)
# Load data into python
import ephysIO
data = ephysIO.MATload(filepath)
# Display data in Stimfit
import stf
if data.get('xdiff') > 0:
if data.get('yunit') == "V":
stf.new_window_list(1.0e+3 * np.array(data.get('array')[1::]))
stf.set_yunits('m' + data.get('yunit'))
elif data.get('yunit') == "A":
stf.new_window_list(1.0e+12 * data.get('array')[1::])
stf.set_yunits('p' + data.get('yunit'))
else:
stf.new_window_list(data.get('array')[1::])
stf.set_yunits(data.get('yunit'))
stf.set_sampling_interval(1.0e+3 * data.get('xdiff'))
stf.set_xunits('m' + data.get('xunit'))
stf.set_trace(0)
stf.set_recording_comment('\n'.join(data['notes']))
if data['saved'] != '':
date = data['saved'][0:8]
date = tuple(map(int, (date[0:4], date[4:6], date[6:8])))
stf.set_recording_date('%s-%s-%s' % date)
time = data['saved'][9::]
time = tuple(map(int, (time[0:2], time[2:4], time[4:6])))
stf.set_recording_time('%i-%i-%i' % time)
elif data.get('xdiff') == 0:
raise ValueError("Sample interval is not constant")
else:
data = {}
collect()
return
def loadflex():
"""
Load raw traces of FlexStation data from CSV files
"""
# Import required modules
from os import chdir
import csv
import stf
import numpy as np
from Tkinter import Tk
import tkFileDialog
from gc import collect
# Use file open dialog to obtain file path
root = Tk()
opt = dict(defaultextension='.csv',
filetypes=[('Comma Separated Values file', '*.csv'),
('All files', '*.*')])
if 'loadcwd' not in globals():
global loadcwd
else:
opt['initialdir'] = loadcwd
filepath = tkFileDialog.askopenfilename(**opt)
root.withdraw()
if filepath != '':
# Move to file directory and check file version
loadcwd = filepath.rsplit('/', 1)[0]
print filepath
chdir(loadcwd)
# Load data into numpy array
with open(filepath, 'rb') as csvfile:
csvtext = csv.reader(csvfile)
data = []
for row in csvtext:
data.append(row)
data = np.array(data)
time = data.T[0][1::].astype(np.float)
sampling_interval = np.mean(np.diff(time))
comment = 'Temperature: %d degrees Centigrade' % np.mean(
data.T[1][1::].astype(np.float))
# Plot fluorescence measurements
well = data.T[2::, 0]
data = data.T[2::, 1::]
ridx = []
idx = []
for i in range(96):
if np.all(data[i] == ''):
ridx.append(i)
else:
idx.append(i)
data = np.delete(data, ridx, 0)
data[[data == '']] = 'NaN'
data[[data == ' ']] = 'NaN'
delrow = np.any(data == 'NaN', 0)
didx = []
for i in range(np.size(data, 1)):
if np.any(data[::, i] == 'NaN', 0):
didx.append(i)
time = np.delete(time, didx, 0)
data = np.delete(data, didx, 1)
data = data.astype(np.float)
stf.new_window_list(data)
# Set x-units and sampling interval
stf.set_xunits('ms')
stf.set_yunits(' ')
stf.set_sampling_interval(1000 * sampling_interval)
# Record temperature
comment += '\nTr\tWell'
for i in range(len(idx)):
comment += '\n%i\t%s' % (i + 1, well[idx[i]])
comment += '\nInitial time point: %.3g' % time[0]
print comment
stf.set_recording_comment(comment)
else:
data = {}
collect()
return
def loadacq4(channel=1):
"""
Load electrophysiology recording data from acq4 hdf5 (.ma) files.
By default the primary recording channel is loaded.
If the file is in a folder entitled 000, loadacq4 will load
the recording traces from all sibling folders (000,001,002,...)
"""
# Import required modules for file IO
from Tkinter import Tk
import tkFileDialog
from gc import collect
# Use file open dialog to obtain file path
root = Tk()
opt = dict(defaultextension='.ma',
filetypes=[('ACQ4 (HDF5) file', '*.ma'), ('All files', '*.*')])
if 'loadcwd' not in globals():
global loadcwd
else:
opt['initialdir'] = loadcwd
filepath = tkFileDialog.askopenfilename(**opt)
root.withdraw()
if filepath != '':
# Load data into python
loadcwd = filepath.rsplit('/', 1)[0]
import ephysIO
data = ephysIO.MAload(filepath, channel)
print filepath
# Display data in Stimfit
import stf
if data.get('yunit') == 'A':
stf.new_window_list(1.0e+12 * data.get('array')[1::])
stf.set_yunits('p' + data.get('yunit'))
elif data.get('yunit') == 'V':
stf.new_window_list(1.0e+3 * data.get('array')[1::])
stf.set_yunits('m' + data.get('yunit'))
stf.set_sampling_interval(1.0e+3 * data['xdiff'])
stf.set_xunits('m' + data.get('xunit'))
stf.set_trace(0)
# Import metadata into stimfit
stf.set_recording_comment('\n'.join(data['notes']))
date = data['saved'][0:8]
date = tuple(map(int, (date[0:4], date[4:6], date[6:8])))
stf.set_recording_date('%s-%s-%s' % date)
time = data['saved'][9::]
time = tuple(map(int, (time[0:2], time[2:4], time[4:6])))
stf.set_recording_time('%i-%i-%i' % time)
else:
data = {}
collect()
return
def combiRec(offset):
import os
import ephysIO
# Import required modules for file IO
from Tkinter import Tk
import tkFileDialog
from gc import collect
# Use file open dialog to obtain file path
root = Tk()
opt = dict(defaultextension='.phy',
filetypes=[('ephysIO (HDF5) file', '*.phy'),
('All files', '*.*')])
if 'loadcwd' not in globals():
global loadcwd
else:
opt['initialdir'] = loadcwd
filepath = tkFileDialog.askopenfilename(**opt)
root.withdraw()
# Set this to file name prefix (i.e. the protocol name)
filename = filepath.rsplit('/', 1)[-1] # e.g. "1.phy"
dirpath = filepath.rsplit(
'/', 1)[0] # e.g. "<path>/pair_000/dual_mixed_eEPSC_000"
protocol = (dirpath.rsplit('/',
1)[1]).rsplit('_',
1)[0] # e.g. "dual_mixed_eEPSC"
rootdir = dirpath.rsplit('/', 1)[0] # e.g. "<path>/pair_000/"
# Load data from channel 1
os.chdir(rootdir)
count = 0
allwaves = []
notes = ''
holding = []
while True:
wavename = protocol + "_" + ("000" + str(count))[-3::]
if os.path.isdir(wavename):
os.chdir(wavename)
data = ephysIO.PHYload(filename)
allwaves.append(1.0e+12 * data.get("array")[1])
notes += notes + 'Wave %d\n' % (count) + '\n'.join(
data['notes']) + '\n\n'
#print data['notes'][9][10::]
holding.append(eval(data['notes'][9][10::]))
count += 1
os.chdir("..")
else:
break
stf.new_window_list(allwaves)
stf.set_xunits('m' + data.get('xunit'))
stf.set_yunits('p' + data.get('yunit'))
stf.set_sampling_interval(1.0e+3 * data.get('xdiff'))
stf.set_recording_comment(notes)
gwaves = [stf.get_trace(i) / (holding[i] - offset) for i in range(count)]
stf.new_window_list(gwaves)
stf.set_recording_comment('Mixed AMPA/NMDA-mediated conductance')
gnmda = [stf.get_trace(i) - stf.get_trace(0) for i in range(count)]
stf.new_window_list(gnmda)
stf.set_recording_comment('NMDA-mediated conductance')
ivnmda = [stf.get_trace(i) * (holding[i] - offset) for i in range(count)]
stf.new_window_list(ivnmda)
stf.set_recording_comment('NMDA-mediated current')
return holding
