def subtract_base():
"""
"""
subtracted_traces = []
for i in range(stf.get_size_channel()):
stf.set_trace(i)
subtracted_traces.append(stf.get_trace() - stf.get_base())
stf.new_window_list(subtracted_traces)
return
def reverse():
"""
Reverse the order of all traces
"""
reversed_traces = []
n = stf.get_size_channel()
for i in range(n):
reversed_traces.append(stf.get_trace(n - 1 - i))
stf.new_window_list(reversed_traces)
return
def sloping_base(trace=-1, method='scale'):
"""
Correct for linear sloping baseline in the displayed trace of the active channel.
Useful for approximate correction of photobleaching during short periods of imaging.
Available methods are 'scale' or 'subtract'.
"""
# Get trace and trace attributes
selected_trace = stf.get_trace(trace)
fit_start = stf.get_base_start()
fit_end = stf.get_base_end()
# Linear fit to baseline region
fit = np.polyfit(np.arange(fit_start, fit_end, 1, int),
selected_trace[fit_start:fit_end], 1)
# Correct trace for sloping baseline
l = stf.get_size_trace(trace)
t = np.arange(0, l, 1, np.double)
if method == 'subtract':
corrected_trace = selected_trace - t * fit[0]
elif method == 'scale':
corrected_trace = selected_trace * fit[1] / (t * fit[0] + fit[1])
return stf.new_window_list([corrected_trace])
def blankstim():
"""
Blank values between fit cursors in all traces in the active channel.
Typically used to blank stimulus artifacts.
"""
fit_start = stf.get_fit_start()
fit_end = stf.get_fit_end()
blanked_traces = []
for i in range(stf.get_size_channel()):
tmp = stf.get_trace(i)
tmp[fit_start:fit_end] = np.nan
blanked_traces.append(tmp)
stf.new_window_list(blanked_traces)
return
def median_filter(n):
"""
Perform median smoothing filter on the selected traces.
Computationally this is achieved by a central simple moving
median over a sliding window of n points.
The function uses reflect (or bounce) end corrections
"""
# Check that at least one trace was selected
if not stf.get_selected_indices():
raise IndexError('No traces were selected')
# Check that the number of points in the sliding window is odd
n = int(n)
if n % 2 != 1:
raise ValueError('The filter rank must be an odd integer')
elif n <= 1:
raise ValueError('The filter rank must > 1')
# Apply smoothing filter
filtered_traces = []
for i in stf.get_selected_indices():
l = stf.get_size_trace(i)
padded_trace = np.pad(stf.get_trace(i), (n - 1) / 2, 'reflect')
filtered_traces.append(
[np.median(padded_trace[j:n + j]) for j in range(l)])
print "Window width was %g ms" % (stf.get_sampling_interval() * (n - 1))
return stf.new_window_list(filtered_traces)
def normalize():
"""
Normalize to the peak amplitude of the selected trace and
scale all other traces in the currently active channel by
the same factor.
Ensure that you subtract the baseline before normalizing
"""
# Find index of the selected trace
idx = stf.get_selected_indices()
if len(idx) > 1:
raise ValueError('More than one trace was selected')
elif len(idx) < 1:
raise ValueError('Select one trace to subtract from the others')
# Measure peak amplitude in the selected trace
stf.set_trace(idx[0])
refval = np.abs(stf.get_peak())
# Apply normalization
scaled_traces = [
stf.get_trace(i) / refval for i in range(stf.get_size_channel())
]
return stf.new_window_list(scaled_traces)
def risealign():
"""
Shift the selected traces in the currently active channel to align to the rise.
"""
# Measure peak indices in the selected traces
rtidx = []
for i in stf.get_selected_indices():
stf.set_trace(i)
rtidx.append(stf.rtlow_index())
# Find the earliest peak
rtref = min(rtidx)
# Align the traces
j = 0
shifted_traces = []
for i in stf.get_selected_indices():
stf.set_trace(i)
shift = int(round(rtref - rtidx[j]))
shifted_traces.append(np.roll(stf.get_trace(), shift))
j += 1
return stf.new_window_list(shifted_traces)
def peakalign():
"""
Shift the selected traces in the currently active channel to align the peaks.
"""
# Measure peak indices in the selected traces
pidx = []
for i in stf.get_selected_indices():
stf.set_trace(i)
pidx.append(stf.peak_index())
# Find the earliest peak
pref = min(pidx)
# Align the traces
j = 0
shifted_traces = []
for i in stf.get_selected_indices():
stf.set_trace(i)
shift = int(pref - pidx[j])
shifted_traces.append(np.roll(stf.get_trace(), shift))
j += 1
return stf.new_window_list(shifted_traces)
def peakscale():
"""
Scale the selected traces in the currently active channel to their mean peak amplitude.
"""
# Measure baseline in selected traces
base = []
for i in stf.get_selected_indices():
stf.set_trace(i)
base.append(stf.get_base())
# Subtract baseline from selected traces
stf.subtract_base()
# Measure peak amplitudes in baseline-subtracted traces
stf.select_all()
peak = []
for i in stf.get_selected_indices():
stf.set_trace(i)
peak.append(stf.get_peak())
# Calculate scale factor to make peak equal to the mean peak amplitude
scale_factor = peak / np.mean(peak)
# Scale the traces and apply offset equal to the mean baseline
scaled_traces = [
stf.get_trace(i) / scale_factor[i] + np.mean(base)
for i in stf.get_selected_indices()
]
# Close window of baseline-subtracted traces
stf.close_this()
return stf.new_window_list(scaled_traces)
def hpfilter(n):
"""
Perform median smoothing filter on the active trace.
Computationally this is achieved by a central simple moving
median over a sliding window of n points. The function then
subtracts the smoothed trace from the original trace.
The function uses reflect (or bounce) end corrections
"""
# Check that the number of points in the sliding window is odd
n = int(n)
if n % 2 != 1:
raise ValueError('The filter rank must be an odd integer')
elif n <= 1:
raise ValueError('The filter rank must > 1')
# Apply smoothing filter
filtered_trace = []
l = stf.get_size_trace()
padded_trace = np.pad(stf.get_trace(), (n - 1) / 2, 'reflect')
filtered_trace.append([np.median(padded_trace[j:n + j]) for j in range(l)])
print "Window width was %g ms" % (stf.get_sampling_interval() * (n - 1))
# Apply subtraction
subtracted_trace = stf.get_trace() - np.array(filtered_trace)
return stf.new_window_list(subtracted_trace)
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 upsample_flex():
"""
Upsample to sampling interval of 1 ms using cubic spline interpolation
"""
old_time = [
i * stf.get_sampling_interval() for i in range(stf.get_size_trace())
]
new_time = range(
int(np.fix((stf.get_size_trace() - 1) * stf.get_sampling_interval())))
new_traces = []
for i in range(stf.get_size_channel()):
f = interpolate.interp1d(old_time, stf.get_trace(i), 'cubic')
new_traces.append(f(new_time))
stf.new_window_list(new_traces)
stf.set_sampling_interval(1)
return
def yoffset(value):
"""
Apply a common offset to all traces in the currently active channel.
"""
offset_traces = [
stf.get_trace(i) + value for i in range(stf.get_size_channel())
]
return stf.new_window_list(offset_traces)
def remove_artifacts_from_sweeps(artifact_start_time, artifact_end_time):
sampling_interval = stf.get_sampling_interval()
artifact_start = int(artifact_start_time / sampling_interval)
artifact_end = int(artifact_end_time / sampling_interval)
continuous_trace = []
output_artifacts_removed = []
for sweep in range(stf.get_size_channel()):
sweep_trace_before_artifact = stf.get_trace(sweep)[0:artifact_start]
sweep_trace_after_artifact = stf.get_trace(sweep)[artifact_end:]
sweep_trace = np.append(sweep_trace_before_artifact,
sweep_trace_after_artifact)
output_artifacts_removed.append(sweep_trace)
continuous_trace.extend(sweep_trace)
stf.new_window_list(output_artifacts_removed)
return (continuous_trace)
def interpstim():
"""
Interpolate values between fit cursors in all traces in the active channel.
Typically used to remove stimulus artifacts.
"""
x = np.array(
[i * stf.get_sampling_interval() for i in range(stf.get_size_trace())])
fit_start = int(stf.get_fit_start())
fit_end = int(stf.get_fit_end())
interp_traces = []
for i in range(stf.get_size_channel()):
tmp = stf.get_trace(i)
tmp[fit_start:fit_end] = np.interp(x[fit_start:fit_end],
[x[fit_start], x[fit_end]],
[tmp[fit_start], tmp[fit_end]])
interp_traces.append(tmp)
stf.new_window_list(interp_traces)
return
def analyze_iv(pulses, trace_start=0, factor=1.0):
"""Creates an IV for the currently active channel.
Keyword arguments:
pulses -- Number of pulses for the IV.
trace_start -- ZERO-BASED index of the first trace to be
used for the IV. Note that this is one less
than what is diplayed in the drop-down box.
factor -- Multiply result with an optional factor, typically
from some external scaling.
Returns:
True upon success, False otherwise.
"""
if (stf.check_doc() == False):
print("Couldn\'t find an open file; aborting now.")
return False
if (pulses < 1):
print("Number of pulses has to be greater or equal 1.")
return False
# create an empty array (will contain random numbers)
channel = list()
for m in range(pulses):
# A temporary array to calculate the average:
set = np.empty((int(
(stf.get_size_channel() - m - 1 - trace_start) / pulses) + 1,
stf.get_size_trace(trace_start + m)))
n_set = 0
for n in range(trace_start + m, stf.get_size_channel(), pulses):
# Add this trace to set:
set[n_set, :] = stf.get_trace(n)
n_set = n_set + 1
# calculate average and create a new section from it, multiply:
channel.append(np.average(set, 0) * factor)
stf.new_window_list(channel)
return True
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 multiscale_traces(multiplier_list):
"""
Scale each trace to the respective multiplier in the list argument
"""
if len(multiplier_list) != stf.get_size_channel():
raise ValueError('The number of multipliers and traces are not equal')
scaled_traces = [
stf.get_trace(i) * multiplier_list[i]
for i in range(stf.get_size_channel())
]
return stf.new_window_list(scaled_traces)
def mean_every_Nth(N):
"""
Perform mean of the first and every Nth trace
"""
m = stf.get_size_channel() / (N - 1)
if np.fix(m) != m:
raise ValueError('The number of traces is not divisible by N')
# loop index calculations: [[i*n+j for j in range(n)] for i in range(m)]
binned_traces = [[
stf.get_trace((i + 1) + j * (N - 1) - 1) for j in range(m)
] for i in range(N - 1)]
mean_traces = [np.mean(binned_traces[i], 0) for i in range(N - 1)]
return stf.new_window_list(mean_traces)
def rmeantraces(binwidth):
"""
Perform running mean of all traces in the active channel.
The number of traces averaged is defined by binwidth.
"""
n = binwidth
N = stf.get_size_channel()
m = N / n
if np.fix(m) != m:
raise ValueError('The number of traces is not divisible by n')
# loop index calculations: [[i*n+j for j in range(n)] for i in range(m)]
binned_traces = [[stf.get_trace(i * n + j) for j in range(n)]
for i in range(m)]
mean_traces = [np.mean(binned_traces[i], 0) for i in range(m)]
return stf.new_window_list(mean_traces)
def subtract_trace():
"""
Subtract the selected trace from all traces in the currently active channel
"""
# Find index of the selected trace to subtract from all the other traces
idx = stf.get_selected_indices()
if len(idx) > 1:
raise ValueError('More than one trace was selected')
elif len(idx) < 1:
raise ValueError('Select one trace to subtract from the others')
# Apply subtraction
subtracted_traces = [
stf.get_trace(i) - stf.get_trace(idx[0])
for i in range(stf.get_size_channel())
]
return stf.new_window_list(subtracted_traces)
def cut_sweeps(start, delta, sequence=None):
"""
Cuts a sequence of traces and present
them in a new window.
Arguments:
start -- starting point (in ms) to cut.
delta -- time interval (in ms) to cut
sequence -- list of indices to be cut. If None, every trace in the
channel will be cut.
Returns:
A new window with the traced cut.
Examples:
cut_sweeps(200,300) cut the traces between t=200 ms and t=500 ms
within the whole channel.
cut_sweeps(200,300,range(30,60)) the same as above, but only between
traces 30 and 60.
cut_sweeps(200,300,stf.get_selected_indices()) cut between 200 ms and 500 ms only in the selected traces.
"""
# select every trace in the channel if not selection is given in sequence
if sequence is None:
sequence = range(stf.get_size_channel())
# transform time into sampling points
dt = stf.get_sampling_interval()
pstart = int(round(start / dt))
pdelta = int(round(delta / dt))
# creates a destination python list
dlist = [stf.get_trace(i)[pstart:(pstart + pdelta)] for i in sequence]
return stf.new_window_list(dlist)
def cut_sweeps(start, delta, sequence=None):
"""
Cuts a sequence of traces and present
them in a new window.
Arguments:
start -- starting point (in ms) to cut.
delta -- time interval (in ms) to cut
sequence -- list of indices to be cut. If None, every trace in the
channel will be cut.
Returns:
A new window with the traced cut.
Examples:
cut_sweeps(200,300) cut the traces between t=200 ms and t=500 ms
within the whole channel.
cut_sweeps(200,300,range(30,60)) the same as above, but only between
traces 30 and 60.
cut_sweeps(200,300,stf.get_selected_indices()) cut between 200 ms and 500 ms only in the selected traces.
"""
# select every trace in the channel if not selection is given in sequence
if sequence is None:
sequence = range(stf.get_size_channel())
# transform time into sampling points
dt = stf.get_sampling_interval()
pstart = int( round(start/dt) )
pdelta = int( round(delta/dt) )
# creates a destination python list
dlist = [ stf.get_trace(i)[pstart:(pstart+pdelta)] for i in sequence ]
return stf.new_window_list(dlist)
def rscomp(V_hold=-60,
V_reversal=0,
R_s_final=2,
V_step=-5,
step_start=10,
step_duration=20):
"""
Function for offline series resistance compensation on current trace in the active channel
This script is my adaptation of Erwin Neher's Igor functions:
SeriesresistanceComp
These were freely available in in Proc02_Apr4Web.ipf from Erwin Neher's webpage:
http://www3.mpibpc.mpg.de/groups/neher/index.php?page=software
(last accessed: 01 July 2014)
Instead of setting the fraction compensation, this function asks for the desired
uncompensated series resistance. Whole-cell properties are calcuated using the wcp
function.
rsomp replaces current traces by their series-compensated version;
the value at i is replaced by the average at i and i+1
R_s is in ohms, C_m in Farads, fraction is the fraction to be compensated
if R_s was 5 MOhm in the experiment and if it was 50% hardware compensated
then R_s = 2.5e6 has to be entered and f=1 for a complete overall compensation
The routine, similarly to that published by Traynelis J. Neurosc. Meth. 86:25,
compensates the frequency response, assuming a single R_s*C_m - time constant
(at constant V-hold) and a three component equivalent circuit for the pipette cell
assembly with R_s, C_m, R_m
Theory: V_h = R_s*I+U_m;
I_r is membrane resistive current,
I is total current
I_r = I-I_c = I-C_m*dU_m/dt = I+C_m*R_s*dI/dt (because R_s*I+U_m = const.)
G_m=I_r/ U_m = (I+C_m*R_s*dI/dt)/ (V_h-R_s*I)
For complete correction (fraction = 1) : I_corr = V_h*(I+C_m*R_s*dI/dt)/ (V_h-R_s*I)
"""
# Get Whole cell properties
wcp_stats = wcp(V_step, step_start, step_duration)
# sampInt is sampling interval in seconds
sampInt = stf.get_sampling_interval() * 1e-3
# R_c is the cell membrane resistance in ohms
R_c = wcp_stats["Cell resistance (Mohm)"] * 1e+6
# R_s is the (initial) series resistance in ohms
R_s = wcp_stats["Series resistance (Mohm)"] * 1e+6
# C_m is the cell capacitance in farads
C_m = wcp_stats["Cell capacitance (pF)"] * 1e-12
# R_s_final is the final (uncompensated series resistance in ohms
R_s_final *= 1e+6
# fraction is the amount of compensation
fraction = 1 - (R_s_final / R_s)
# Convert unit of holding and reversal potentials to volts
V_hold *= 1e-3
V_reversal *= 1e-3
# Calculate voltage difference
voltage = V_hold - V_reversal
# Calculate cell time constants
#tau = R_s * C_m
tau = (R_s * R_c * C_m) / (R_s + R_c) # more accurate
tau_corr = tau * (1 - fraction)
# Assign current recording trace to I_wave and convert to amps
I_wave = stf.get_trace() * 1e-12
# First point: (we have to calculate this separately, because we need the value at i-1 below)
denominator = voltage - R_s * fraction * I_wave[0]
if denominator != 0:
I_wave[0] = I_wave[0] * (voltage / denominator)
for i in range(stf.get_size_trace() - 2):
i += 1
# this is the loop doing the correction for all other points
# first calculate R_m for zero series resistance under the assumptions
# that U_m + U_Rs = const = voltage
current = (I_wave[i + 1] + I_wave[i]) / 2 # The in between(mean) value
derivative = (I_wave[i + 1] - I_wave[i]) / sampInt
denominator = current + tau * derivative
if denominator != 0:
R_m = (voltage -
R_s * current) / denominator # calculate the true R_m
# Now calculate current for new series resitance
denominator = (R_m + (1 - fraction) * R_s) * (1 + tau_corr / sampInt)
if denominator != 0:
I_wave[i] = tau_corr / (
tau_corr + sampInt) * I_wave[i - 1] + voltage / denominator
else:
I_wave[i] = I_wave[i - 1] # old value
# Convert units of I_wave to pA
I_wave *= 1e+12
# Print information about the compensation performed
print "Percentage series resistance compensation was %.1f%%" % (fraction *
100)
print "Residual (uncompensated) series resistance is %.2f Mohm" % (
R_s_final * 1e-6)
return stf.new_window_list([I_wave])
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
def EPSPtrains(latency=200,
numStim=4,
intvlList=[1, 0.8, 0.6, 0.4, 0.2, 0.1, 0.08, 0.06, 0.04, 0.02]):
# Initialize
numTrains = len(intvlList) # Number of trains
intvlArray = np.array(intvlList) * 1000 # Units in ms
si = stf.get_sampling_interval() # Units in ms
# Background subtraction
traceBaselines = []
subtractedTraces = []
k = 1e-4
x = [i * stf.get_sampling_interval() for i in range(stf.get_size_trace())]
for i in range(numTrains):
stf.set_trace(i)
z = x
y = stf.get_trace()
traceBaselines.append(y)
ridx = []
if intvlArray[i] > 500:
for j in range(numStim):
ridx += range(
int(round(((intvlArray[i] * j) + latency - 1) / si)),
int(round(
((intvlArray[i] * (j + 1)) + latency - 1) / si)) - 1)
else:
ridx += range(
int(round((latency - 1) / si)),
int(
round(((intvlArray[i] *
(numStim - 1)) + latency + 500) / si)) - 1)
ridx += range(int(round(4999 / si)), int(round(5199 / si)))
z = np.delete(z, ridx, 0)
y = np.delete(y, ridx, 0)
yi = np.interp(x, z, y)
yf = signal.symiirorder1(yi, (k**2), 1 - k)
traceBaselines.append(yf)
subtractedTraces.append(stf.get_trace() - yf)
stf.new_window_list(traceBaselines)
stf.new_window_list(subtractedTraces)
# Measure depolarization
# Initialize variables
a = []
b = []
# Set baseline start and end cursors
stf.set_base_start(np.round(
(latency - 50) / si)) # Average during 50 ms period before stimulus
stf.set_base_end(np.round(latency / si))
# Set fit start cursor
stf.set_fit_start(np.round(latency / si))
stf.set_fit_end(
np.round(((intvlArray[1] * (numStim - 1)) + latency + 1000) /
si)) # Include a 1 second window after last stimulus
# Start AUC calculations
for i in range(numTrains):
stf.set_trace(i)
stf.measure()
b.append(stf.get_base())
n = int(stf.get_fit_end() + 1 - stf.get_fit_start())
x = np.array([k * stf.get_sampling_interval() for k in range(n)])
y = stf.get_trace()[int(stf.get_fit_start()):int(stf.get_fit_end() +
1)]
a.append(np.trapz(y - b[i], x)) # Units in V.s
return a
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 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 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
