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 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 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 plot_traces(plotwindow=None, ichannel=0, vchannel=1):
"""
Show traces in a figure
Parameters
----------
plotwindow : (float, float), optional
Plot window (in ms from beginning of trace)
None for whole trace. Default: None
ichannel : int, optional
current channel number. Default: 0
vchannel : int, optional
voltage channel number. Default: 1
"""
import stf
if not stf.check_doc():
return None
nchannels = stf.get_size_recording()
if nchannels < 2:
sys.stderr.write(
"Function requires 2 channels (0: current; 1: voltage)\n")
return
dt = stf.get_sampling_interval()
fig = stf.mpl_panel(figsize=(12, 8)).fig
fig.clear()
gs = gridspec.GridSpec(4, 1)
ax_currents = stfio_plot.StandardAxis(
fig, gs[:3, 0], hasx=False, hasy=False)
ax_voltages = stfio_plot.StandardAxis(
fig, gs[3:, 0], hasx=False, hasy=False, sharex=ax_currents)
if plotwindow is not None:
istart = int(plotwindow[0]/dt)
istop = int(plotwindow[1]/dt)
else:
istart = 0
istop = None
for ntrace in range(stf.get_size_channel()):
stf.set_trace(ntrace)
stf.set_channel(ichannel)
trace = stf.get_trace()[istart:istop]
ax_currents.plot(np.arange(len(trace))*dt, trace)
# Measure pulse amplitude
stf.set_channel(vchannel)
trace = stf.get_trace()[istart:istop]
ax_voltages.plot(np.arange(len(trace))*dt, trace)
# Reset active channel
stf.set_channel(ichannel)
stfio_plot.plot_scalebars(
ax_currents, xunits=stf.get_xunits(), yunits=stf.get_yunits(channel=0))
stfio_plot.plot_scalebars(
ax_voltages, xunits=stf.get_xunits(), yunits=stf.get_yunits(channel=1))
def find_sample_points_of_detected_events(whole_trace_file,
extracted_events_file, sweep_num):
"""takes the window of detected events from stimfit and, for each events, runs through the full trace to pull out time (in samples) of event
"""
#open and load trace from whole file
stf.file_open(whole_trace_file)
stf.set_trace(sweep_num)
whole_trace = stf.get_trace()
sampling_interval = stf.get_sampling_interval()
#open extracted events file
stf.file_open(extracted_events_file)
time_points = []
for trace in range(stf.get_size_channel()):
stf.set_trace(trace)
trace_to_search = stf.get_trace(trace)
# run find trace with updated search index
# start at sample = 0 for first run through
if len(time_points) == 0:
sample_start = 0
else:
sample_start = int(time_points[len(time_points) - 1] /
sampling_interval)
output_index = sub_func_find_trace(trace_to_search, whole_trace,
sample_start)
time_point = output_index * sampling_interval
time_points.append(time_point)
return (time_points)
def jjm_count(start, delta, threshold=0, up=True, trace=None, mark=True): """ Counts the number of events (e.g action potentials (AP)) in the current trace. Arguments: start -- starting time (in ms) to look for events. delta -- time interval (in ms) to look for events. threshold -- (optional) detection threshold (default = 0). up -- (optional) True (default) will look for upward events, False downwards. trace -- (optional) zero-based index of the trace in the current channel, if None, the current trace is selected. mark -- (optional) if True (default), set a mark at the point of threshold crossing Returns: An integer with the number of events. Examples: count_events(500,1000) returns the number of events found between t=500 ms and t=1500 ms above 0 in the current trace and shows a stf marker. count_events(500,1000,0,False,-10,i) returns the number of events found below -10 in the trace i and shows the corresponding stf markers. """ # sets the current trace or the one given in trace. if trace is None: sweep = stf.get_trace_index() else: if type(trace) !=int: print "trace argument admits only integers" return False sweep = trace # set the trace described in sweep stf.set_trace(sweep) # transform time into sampling points dt = stf.get_sampling_interval() pstart = int( round(start/dt) ) pdelta = int( round(delta/dt) ) # select the section of interest within the trace selection = stf.get_trace()[pstart:(pstart+pdelta)] # algorithm to detect events EventCounter,i = 0,0 # set counter and index to zero # list of sample points sample_points = [] # choose comparator according to direction: if up: comp = lambda a, b: a > b else: comp = lambda a, b: a < b # run the loop while i<len(selection): if comp(selection[i],threshold): EventCounter +=1 if mark: sample_point = pstart+i; sample_points.append(sample_point); stf.set_marker(pstart+i, selection[i]) while i<len(selection) and comp(selection[i],threshold): i+=1 # skip values if index in bounds AND until the value is below/above threshold again else: i+=1 time_points = [sample_point*dt for sample_point in sample_points]; return (EventCounter, sample_points, time_points)
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 yvalue(origin, interval):
stf.set_fit_start(origin, True)
stf.set_fit_end(origin + interval, True)
stf.measure()
x = int(stf.get_fit_end(False))
y = []
for i in range(stf.get_size_channel()):
stf.set_trace(i)
y.append(stf.get_trace(i)[x])
return y
def analyze_file(baseline_start, baseline_end, cap_trans_start, cap_trans_end,
amplitude, EPSC1_s, EPSC1_e, EPSC2_s, EPSC2_e, sweep_start,
sweep_end):
"""inputs: (baseline_start, baseline_end, cap_trans_start, cap_trans_end, amplitude, EPSC1_s, EPSC1_e, EPSC2_s, EPSC2_e, sweep_start, sweep_end)
output: numpy array where 1st column is capacitance transient amplitude, 2nd is series resistance, 3rd is 1st EPSC, 4th is 2nd EPCSC
also writes output to .csv file"""
num_sweeps = stf.get_size_channel()
print('there are')
print(num_sweeps)
print('sweeps in recording')
print('analyzing sweeps')
print(sweep_start)
print('to')
print(sweep_end)
sweeps_to_analyze = sweep_end - sweep_start
#create array for results
data_array = np.zeros((sweeps_to_analyze + 1, 4))
y = 0
for x in range(sweep_start - 1, sweep_end):
#moves to next trace
stf.set_trace(x)
[cap_trans_amplitude,
series_resistance] = jjm_resistance(baseline_start, baseline_end,
cap_trans_start, cap_trans_end,
amplitude)
data_array[y][0] = cap_trans_amplitude
data_array[y][1] = series_resistance
EPSC_1 = jjm_peak(baseline_start, baseline_end, EPSC1_s, EPSC1_e)
data_array[y][2] = EPSC_1
EPSC_2 = jjm_peak(baseline_start, baseline_end, EPSC2_s, EPSC2_e)
data_array[y][3] = EPSC_2
pp_40 = float(float(EPSC_2) / float(EPSC_1))
y += 1
#print first few entries to check accuracy
print(data_array[:3])
#make csv file with data
file_name = stf.get_filename()
#expt = file_name[-12:].rstrip('.abf');
np.savetxt(file_name + '_stimfitanalysis.csv',
data_array,
delimiter=',',
newline='\n')
return (data_array)
def get_time_points(self):
#need to get specified sweep of the whole trace to an array
stf.file_open(self.whole_trace_file)
stf.set_trace(self.sweep_number)
trace_array = stf.get_trace()
#runs program to find sample indicies of selected EPSCs and converts to times
stf.file_open(self.event_file)
event_samples_list = find_sample_points_of_detected_events(trace_array)
sampling_interval = stf.get_sampling_interval()
event_times_list = [(sample * sampling_interval)
for sample in event_samples_list]
return (event_times_list)
def get_amplitude(base, peak, delta, trace=None):
""" Calculates the amplitude deviation (peak-base) in units of the Y-axis
Arguments:
base -- Starting point (in ms) of the baseline cursor.
peak -- Starting point (in ms) of the peak cursor.
delta -- Time interval to calculate baseline/find the peak.
trace -- Zero-based index of the trace to be processed, if None then current
trace is computed.
Returns:
A float with the variation of the amplitude. False if
Example:
get_amplitude(980,1005,10,i) returns the variation of the Y unit of the
trace i between
peak value (10050+10) msec and baseline (980+10) msec
"""
# sets the current trace or the one given in trace
if trace is None:
sweep = stf.get_trace_index()
else:
if type(trace) != int:
print('trace argument admits only intergers')
return False
sweep = trace
# set base cursors:
if not(stf.set_base_start(base, True)):
return False # out-of range
if not(stf.set_base_end(base+delta, True)):
return False
# set peak cursors:
if not(stf.set_peak_start(peak, True)):
return False # out-of range
if not(stf.set_peak_end(peak+delta, True)):
return False
# update measurements
stf.set_trace(sweep)
amplitude = stf.get_peak()-stf.get_base()
return amplitude
def get_amplitude(base, peak, delta, trace=None):
""" Calculates the amplitude deviation (peak-base) in units of the Y-axis
Arguments:
base -- Starting point (in ms) of the baseline cursor.
peak -- Starting point (in ms) of the peak cursor.
delta -- Time interval to calculate baseline/find the peak.
trace -- Zero-based index of the trace to be processed, if None then current
trace is computed.
Returns:
A float with the variation of the amplitude. False if
Example:
get_amplitude(980,1005,10,i) returns the variation of the Y unit of the
trace i between
peak value (10050+10) msec and baseline (980+10) msec
"""
# sets the current trace or the one given in trace
if trace is None:
sweep = stf.get_trace_index()
else:
if type(trace) != int:
print('trace argument admits only intergers')
return False
sweep = trace
# set base cursors:
if not (stf.set_base_start(base, True)):
return False # out-of range
if not (stf.set_base_end(base + delta, True)):
return False
# set peak cursors:
if not (stf.set_peak_start(peak, True)):
return False # out-of range
if not (stf.set_peak_end(peak + delta, True)):
return False
# update measurements
stf.set_trace(sweep)
amplitude = stf.get_peak() - stf.get_base()
return amplitude
def return_base_for_file(start_sweep, end_sweep):
#dict_to_return = {}
baselines = []
for sweep in range(start_sweep, end_sweep):
stf.set_trace(sweep)
stf.set_base_start(100, is_time=True)
stf.set_base_end(125, is_time=True)
baselines.append(stf.get_base())
file_baseline = np.mean(baselines)
#dict_to_return[stf.get_filename()] = file_baseline
#df_out = pd.DataFrame(dict_to_return)
#file_name = stf.get_filename()
#df_out.to_excel('/Users/johnmarshall/Documents/Analysis/eCB_paper/'+str(file_name)+'holding_current.xlsx')
return (file_baseline)
def batch_integration():
"""
Perform batch integration between the decay/fit cursors of all traces
in the active window
"""
n = int(stf.get_fit_end() + 1 - stf.get_fit_start())
x = [i * stf.get_sampling_interval() for i in range(n)]
dictlist = []
for i in range(stf.get_size_channel()):
stf.set_trace(i)
y = stf.get_trace()[int(stf.get_fit_start()):int(stf.get_fit_end() +
1)]
auc = np.trapz(y - stf.get_base(), x)
dictlist += [("%i" % (i + 1), auc)]
retval = dict(dictlist)
stf.show_table(retval, "Area Under Curve")
stf.set_trace(0)
return
def get_amplitude_select_NMDA(amplithresh):
stf.unselect_all()
stf.set_peak_direction('both')
# total number of traces
traces = stf.get_size_channel()
selectedtraces, i = 0, 0
while i < traces:
stf.set_trace(i)
amplitude = stf.get_peak() - stf.get_base()
if amplitude < amplithresh and amplitude > 0:
# print(i)
stf.select_trace(i)
i += 1
selectedtraces += 1
else:
i += 1
return selectedtraces
def compile_amplitudes_in_trace():
# for each trace in file run find_baseline_amplitudes
output_array = np.array(['baseline', 'peak', 'peak_from_baseline'])
for trace in range(stf.get_size_channel()):
stf.set_trace(trace)
fba_output = find_baseline_amplitude(10)
output_array = np.vstack([output_array, fba_output])
output_df = pd.DataFrame(output_array[1:],
columns=output_array[0],
dtype=float)
output_df.to_excel(str(stf.get_filename()[-40:-3]) + '.xlsx')
return (output_df)
def fit_experiment(params, pulse_length, function_to_fit):
num_sweeps = stf.get_size_channel()
stf.set_channel(0)
stf.set_trace(0)
#jjm_analysis.set_params(params);
#stf.measure();
#this is in samples
#peak_index = stf.peak_index();
#stf.set_fit_start(peak_index, is_time=False);
#fit_start_time = peak_index*stf.get_sampling_interval();
#stf.set_fit_end(fit_start_time+pulse_length-(10*stf.get_sampling_interval()), is_time=True);
#fit_func = stf.leastsq(function_to_fit);
#fit_func['Baseline(pA)']=stf.get_base();
#fit_df = pd.DataFrame(fit_func, index=[0]);
fits = []
traces = []
for x in range(0, num_sweeps):
stf.set_trace(x)
jjm_analysis.set_params(params)
stf.measure()
#this is in samples
peak_index = stf.peak_index()
stf.set_fit_start(peak_index, is_time=False)
fit_start_time = peak_index * stf.get_sampling_interval()
stf.set_fit_end(fit_start_time + pulse_length -
(10 * stf.get_sampling_interval()),
is_time=True)
sweep_fit = stf.leastsq(function_to_fit)
sweep_fit['Baseline(pA)'] = stf.get_base()
fits.append(sweep_fit)
traces.append(x)
fit_df = pd.DataFrame(fits)
return (fit_df)
def scan_through_train(start_params, train_increment, num_stims, train_trace):
"""scans through a tran of length "num_stims" in time increments of "train_increment", saves peak
amplitudes to an array peak_values (1st output) and sweep segments of peak regions for viewing are in peak_arrays"""
stf.set_trace(train_trace)
baseline_s = start_params[0]
baseline_e = start_params[1]
params_ = start_params
len_trace_in_samples = len(stf.get_trace(train_trace))
peak_values = np.zeros(num_stims)
len_peak_region_in_samples = round(
(start_params[3] - start_params[2]) / stf.get_sampling_interval())
peak_arrays = np.zeros((num_stims, (len_peak_region_in_samples)))
stim_count = 1
while stim_count <= num_stims:
peak_start = params_[2]
peak_end = params_[3]
print(peak_start, peak_end)
peak = jjm_peak(baseline_s, baseline_e, peak_start, peak_end)
print(peak)
peak_values[stim_count - 1] = peak
peak_region_slice = slice_peak_region(params_, train_trace)
peak_arrays[stim_count - 1] = peak_region_slice
params_ = increment_peak(params_, True, train_increment)
stim_count += 1
return (peak_values, peak_arrays)
def return_holding_current(selected_wb, raw_wb):
wb_ = xlrd.open_workbook(selected_wb)
sheets = [
str(sheet.name) for sheet in wb_.sheets()
if 'normalized' in str(sheet.name)
]
sheets_to_load_from_raw = [
sheet.strip('normalized') + 'iled_files' for sheet in sheets
]
wb_raw = xlrd.open_workbook(raw_wb)
raw_sheets = [
str(sheet.name) for sheet in wb_raw.sheets()
if 'compiled_files' in str(sheet.name)
]
baseline_files = []
exp_files = []
for raw_sheet in raw_sheets:
try:
df = pd.read_excel(wb_raw,
engine='xlrd',
sheetname=str(raw_sheet),
index_col=[0, 1])
files_in_experiment = df.index.levels[0].values[:2]
baseline_files.append(files_in_experiment[0])
exp_files.append(files_in_experiment[1])
except:
pass
print(baseline_files)
baseline_files_to_load = []
exp_files_to_load = []
rootsearchdir = '/Users/johnmarshall/Documents/Analysis/RecordingData/'
for fname, efname in zip(baseline_files, exp_files):
try:
if 'TSeries' in fname:
dir_data = search_dir_iterative_for_extension_tupleoutput(
rootsearchdir, str(fname + 'vrecd_loaded.csv'))
else:
dir_data = search_dir_iterative_for_extension_tupleoutput(
rootsearchdir, fname)
baseline_files_to_load.append(dir_data[1][1] + '/' +
dir_data[1][0])
dir_data = search_dir_iterative_for_extension_tupleoutput(
rootsearchdir, efname)
exp_files_to_load.append(dir_data[1][1] + '/' + dir_data[1][0])
except IndexError:
print('could not find:', fname)
pass
print(baseline_files_to_load)
dict_to_return = {}
holding_current_time_series = {}
for f, ef in zip(baseline_files_to_load, exp_files_to_load):
currents = []
print(f)
if f.endswith('.abf'):
stf.file_open(f)
else:
if 'TSeries' in f:
sweeps_compiled_from_pv_tseries = pv.import_t_series_episodic(
f.strip('vrecd_loaded.csv'))
pv.plot_episodic_array(sweeps_compiled_from_pv_tseries)
baselines = []
for sweep in range((stf.get_size_channel() - 30),
stf.get_size_channel()):
stf.set_trace(sweep)
stf.set_base_start(100, is_time=True)
stf.set_base_end(100, is_time=True)
baselines.append(stf.get_base())
file_baseline = np.mean(baselines)
currents.append(file_baseline)
holding_current_time_series[f] = baselines
stf.file_open(ef)
baselines = []
for sweep in range((stf.get_size_channel() - 15),
stf.get_size_channel()):
stf.set_trace(sweep)
stf.set_base_start(100, is_time=True)
stf.set_base_end(100, is_time=True)
baselines.append(stf.get_base())
file_baseline = np.mean(baselines)
currents.append(file_baseline)
dict_to_return[f] = currents
df_out = pd.DataFrame(dict_to_return)
time_series_df = pd.DataFrame(holding_current_time_series)
df_out.to_excel(
'/Users/johnmarshall/Documents/Analysis/eCB_paper/holding_current.xlsx'
)
time_series_df.to_excel(
'/Users/johnmarshall/Documents/Analysis/eCB_paper/holding_current_time_series.xlsx'
)
return (df_out)
def count_events(start, delta, threshold=0, up=True, trace=None, mark=True):
"""
Counts the number of events (e.g action potentials (AP)) in the current trace.
Arguments:
start -- starting time (in ms) to look for events.
delta -- time interval (in ms) to look for events.
threshold -- (optional) detection threshold (default = 0).
up -- (optional) True (default) will look for upward events,
False downwards.
trace -- (optional) zero-based index of the trace in the current
channel, if None, the current trace is selected.
mark -- (optional) if True (default), set a mark at the point
of threshold crossing
Returns:
An integer with the number of events.
Examples:
count_events(500,1000) returns the number of events found between t=500
ms and t=1500 ms above 0 in the current trace and shows a stf
marker.
count_events(500,1000,0,False,-10,i) returns the number of events found
below -10 in the trace i and shows the corresponding stf markers.
"""
# sets the current trace or the one given in trace.
if trace is None:
sweep = stf.get_trace_index()
else:
if type(trace) !=int:
print('trace argument admits only integers')
return False
sweep = trace
# set the trace described in sweep
stf.set_trace(sweep)
# transform time into sampling points
dt = stf.get_sampling_interval()
pstart = int( round(start/dt) )
pdelta = int( round(delta/dt) )
# select the section of interest within the trace
selection = stf.get_trace()[pstart:(pstart+pdelta)]
# algorithm to detect events
event_counter, i = 0, 0 # set counter and index to zero
# choose comparator according to direction:
if up:
comp = lambda a, b: a > b
else:
comp = lambda a, b: a < b
# run the loop
while i < len(selection):
if comp(selection[i], threshold):
event_counter += 1
if mark:
stf.set_marker(pstart+i, selection[i])
while i < len(selection) and comp(selection[i], threshold):
i += 1 # skip until value is below/above threshold
else:
i += 1
return event_counter
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 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 glu_iv(pulses=13, subtract_base=True):
"""Calculates an iv from a repeated series of fast application and
voltage pulses.
Keyword arguments:
pulses -- Number of pulses for the iv.
subtract_base -- If True (default), baseline will be subtracted.
Returns:
True if successful.
"""
# Some ugly definitions for the time being
# Cursors are in ms here.
gFitEnd = 330.6 # fit end cursor is variable
gFSelect = 0 # Monoexp
gDictSize = stf.leastsq_param_size(
gFSelect) + 2 # Parameters, chisqr, peak value
gBaseStart = 220.5 # Start and end of the baseline before the control pulse, in ms
gBaseEnd = 223.55
gPeakStart = 223.55 # Start and end of the peak cursors for the control pulse, in ms
gPeakEnd = 253.55
if (gDictSize < 0):
print('Couldn\'t retrieve function id=%d, aborting now.' % gFSelect)
return False
if (not (stf.check_doc())):
print('Couldn\'t find an open file; aborting now.')
return False
# analyse iv, subtract baseline if requested:
ivtools.analyze_iv(pulses)
if (subtract_base == True):
if (not (stf.set_base_start(gBaseStart, True))): return False
if (not (stf.set_base_end(gBaseEnd, True))): return False
stf.measure()
stf.select_all()
stf.subtract_base()
# set cursors:
if (not (stf.set_peak_start(gPeakStart, True))): return False
if (not (stf.set_peak_end(gPeakEnd, True))): return False
if (not (stf.set_base_start(gBaseStart, True))): return False
if (not (stf.set_base_end(gBaseEnd, True))): return False
if (not (stf.set_fit_end(gFitEnd, True))): return False
if (not (stf.set_peak_mean(3))): return False
if (not (stf.set_peak_direction("both"))): return False
# A list for dictionary keys and values:
dict_keys = []
dict_values = np.empty((gDictSize, stf.get_size_channel()))
firstpass = True
for n in range(0, stf.get_size_channel()):
if (stf.set_trace(n) == False):
print('Couldn\'t set a new trace; aborting now.')
return False
print('Analyzing trace %d of %d' % (n + 1, stf.get_size_channel()))
# set the fit window cursors:
if (not (stf.set_fit_start(stf.peak_index()))): return False
# Least-squares fitting:
p_dict = stf.leastsq(gFSelect)
if (p_dict == 0):
print('Couldn\'t perform a fit; aborting now.')
return False
# Create an empty list:
tempdict_entry = []
row = 0
for k, v in p_dict.iteritems():
if (firstpass == True):
dict_keys.append(k)
dict_values[row][n] = v
row = row + 1
if (firstpass):
dict_keys.append("Peak amplitude")
dict_values[row][n] = stf.get_peak() - stf.get_base()
firstpass = False
retDict = dict()
# Create the dictionary for the table:
entry = 0
for elem in dict_keys:
retDict[elem] = dict_values[entry].tolist()
entry = entry + 1
return stf.show_table_dictlist(retDict)
def iv(peakwindow=None, basewindow=None, pulsewindow=None,
erev=None, peakmode="both", ichannel=0, vchannel=1,
exclude=None):
"""
Compute and plot an IV curve for currents
Parameters
----------
peakwindow : (float, float), optional
Window for peak measurement (time in ms from beginning of sweep)
None for current cursor settings. Default: None
basewindow : (float, float), optional
Window for baseline measurement (time in ms from beginning of sweep)
None for current cursor settings. Default: None
pulsewindow : (float, float), optional
Window for voltage pulse measurement (time in ms from beginning of sweep)
None for current cursor settings. Default: None
erev : float, optional
End of v clamp pulse in ms or None to determine automatically.
Default: None
peakmode : string, optional
Peak direction - one of "up", "down", "both" or "mean". Default: "up"
ichannel : int, optional
current channel number. Default: 0
vchannel : int, optional
voltage channel number. Default: 1
exclude : list of ints, optional
List of trace indices to be excluded from the analysis. Default: None
Returns
-------
v_commands : numpy.ndarray
Command voltages
ipeaks : numpy.ndarray
Peak currents
gpeaks : numpy.ndarray
Peak normalized conductances
g_fit : numpy.ndarray
Half-maximal voltage and slope of best-fit Boltzmann function
"""
import stf
if not stf.check_doc():
return None
nchannels = stf.get_size_recording()
if nchannels < 2:
sys.stderr.write(
"Function requires 2 channels (0: current; 1: voltage)\n")
return
dt = stf.get_sampling_interval()
olddirection = stf.get_peak_direction()
v_commands = []
ipeaks = []
if basewindow is not None:
stf.base.cursor_time = basewindow
fig = stf.mpl_panel(figsize=(12, 8)).fig
fig.clear()
gs = gridspec.GridSpec(4, 8)
ax_currents = stfio_plot.StandardAxis(
fig, gs[:3, :4], hasx=False, hasy=False)
ax_voltages = stfio_plot.StandardAxis(
fig, gs[3:, :4], hasx=False, hasy=False, sharex=ax_currents)
for ntrace in range(stf.get_size_channel()):
if exclude is not None:
if ntrace in exclude:
continue
stf.set_trace(ntrace)
stf.set_channel(ichannel)
trace = stf.get_trace()
ax_currents.plot(np.arange(len(trace))*dt, trace)
# Measure only downward peaks (inward currents)
if peakmode is "mean":
stf.set_peak_direction("up")
stf.set_peak_mean(-1)
else:
stf.set_peak_direction(peakmode)
# Set peak computation to single sampling point
stf.set_peak_mean(1)
if peakwindow is not None:
stf.peak.cursor_time = peakwindow
stf.measure()
if basewindow is not None:
ipeaks.append(stf.peak.value-stf.base.value)
else:
ipeaks.append(stf.peak.value)
# Measure pulse amplitude
stf.set_channel(vchannel)
trace = stf.get_trace()
ax_voltages.plot(np.arange(len(trace))*dt, trace)
stf.set_peak_direction("up")
stf.set_peak_mean(-1)
if pulsewindow is not None:
stf.peak.cursor_time = pulsewindow
stf.measure()
v_commands.append(stf.peak.value)
stfio_plot.plot_scalebars(
ax_currents, xunits=stf.get_xunits(), yunits=stf.get_yunits(channel=0))
stfio_plot.plot_scalebars(
ax_voltages, xunits=stf.get_xunits(), yunits=stf.get_yunits(channel=1))
v_commands = np.array(v_commands)
ipeaks = np.array(ipeaks)
if erev is None:
# Find first zero crossing in ipeaks:
for npulse in range(ipeaks.shape[0]-1):
if np.sign(ipeaks[npulse]) != np.sign(ipeaks[npulse+1]):
# linear interpolation
m1 = (ipeaks[npulse+1]-ipeaks[npulse]) / (
v_commands[npulse+1]-v_commands[npulse])
c1 = ipeaks[npulse] - m1*v_commands[npulse]
erev = -c1/m1
break
if erev is None:
sys.stderr.write(
"Could not determine reversal potential. Aborting now\n")
return None
# Reset peak computation to single sampling point
stf.set_peak_mean(1)
stf.set_peak_direction(olddirection)
# Reset active channel
stf.set_channel(ichannel)
# Compute conductances:
gpeaks, g_fit = gv(ipeaks, v_commands, erev)
ax_ipeaks = plot_iv(
ipeaks, v_commands, stf.get_yunits(channel=ichannel),
stf.get_yunits(channel=1), fig, 222)
ax_ipeaks.set_title("Peak current")
ax_gpeaks = plot_gv(
gpeaks, v_commands, stf.get_yunits(channel=vchannel),
g_fit, fig, 224)
ax_gpeaks.set_title("Peak conductance")
stf.show_table_dictlist({
"Voltage ({0})".format(
stf.get_yunits(channel=vchannel)): v_commands.tolist(),
"Peak current ({0})".format(
stf.get_yunits(channel=ichannel)): ipeaks.tolist(),
"Peak conductance (g/g_max)": gpeaks.tolist(),
})
return v_commands, ipeaks, gpeaks, g_fit
def find_AP_peak_ADP_trace(*argv):
""" count number of APs, find ADPs and thesholds in indicated trace with current injection/gradually increasing steps
inputs: (time (msec) to start search, length of search region, starting current value,
current delta between traces, threshold value, deflection direction ('up'/'down'), mark traces (True/False))"""
##if times are input, use those, otherwise use peak cursor settings
#TO DO: optional change to threshold_values and deflection_direction
if len(argv) > 0:
trace_selection = argv[0]
threshold_value = float(argv[1])
deflection_direction = argv[2]
mark_option = argv[3]
start_msec = float(argv[4])
delta_msec = float(argv[5])
else:
trace_selection = stf.get_trace_index()
threshold_value = 0
deflection_direction = 'up'
mark_option = True
start_msec = float(stf.get_peak_start(True))
delta_msec = float(stf.get_peak_end(True) - start_msec)
stf.set_trace(trace_selection)
##gets AP counts and sample points in current trace
if deflection_direction == 'up':
direction_input = True
else:
direction_input = False
##count function will return number of APs in trace and sample points for subsequent functions
trace_count, trace_sample_points_absolute = jjm_count(
start_msec,
delta_msec,
threshold=threshold_value,
up=direction_input,
trace=trace_selection,
mark=mark_option)
##finds afterdepolarizations--minimums between peaks
trace_ADP_values, trace_ADP_indicies = find_ADPs(
trace_sample_points_absolute)
trace_si = stf.get_sampling_interval()
trace_ADP_times = [sample * trace_si for sample in trace_ADP_indicies]
trace_AP_values, trace_AP_indicies = find_ADPs(
trace_sample_points_absolute)
trace_si = stf.get_sampling_interval()
trace_ADP_times = [sample * trace_si for sample in trace_AP_indicies]
trace_thresholds_indicies = find_thresholds(stf.get_trace(trace_selection),
trace_si, trace_ADP_indicies)
trace_threshold_values = [
stf.get_trace(trace_selection)[index]
for index in trace_thresholds_indicies
]
trace_threshold_times = [
sample * trace_si for sample in trace_thresholds_indicies
]
for sample, mv in zip(trace_thresholds_indicies, trace_threshold_values):
stf.set_marker(sample, mv)
for x in range(len(trace_threshold_values)):
if trace_threshold_values[
x] > threshold_value or trace_threshold_values[
x] < trace_ADP_values[x]:
trace_threshold_values[x] = 'NaN'
#arrays for output
ADP_out_array = np.transpose(np.array([trace_ADP_times, trace_ADP_values]))
threshold_out_array = np.transpose(
np.array([trace_threshold_times, trace_threshold_values]))
out_array = np.hstack([ADP_out_array, threshold_out_array])
df_out = pd.DataFrame(
out_array,
columns=['ADP time', 'ADP (mV)', 'threshold time', 'threshold (mV)'])
return (trace_count, df_out)
def timeconstants(fitwindow, pulsewindow, ichannel=0, vchannel=1):
"""
Compute and plot decay time constants
Parameters
----------
fitwindow : (float, float), optional
Window for fitting time constant (time in ms from beginning of sweep)
None for current cursor settings. Default: None
pulsewindow : (float, float), optional
Window for voltage pulse measurement (time in ms from beginning of sweep)
None for current cursor settings. Default: None
ichannel : int, optional
current channel number. Default: 0
vchannel : int, optional
voltage channel number. Default: 1
Returns
-------
v_commands : numpy.ndarray
Command voltages
taus : numpy.ndarray
Time constants
"""
import stf
if not stf.check_doc():
return None
nchannels = stf.get_size_recording()
if nchannels < 2:
sys.stderr.write(
"Function requires 2 channels (0: current; 1: voltage)\n")
return
dt = stf.get_sampling_interval()
v_commands = []
taus = []
fig = stf.mpl_panel(figsize=(12, 8)).fig
fig.clear()
gs = gridspec.GridSpec(4, 8)
ax_currents = stfio_plot.StandardAxis(fig,
gs[:3, :4],
hasx=False,
hasy=False)
ax_voltages = stfio_plot.StandardAxis(fig,
gs[3:, :4],
hasx=False,
hasy=False,
sharex=ax_currents)
for ntrace in range(stf.get_size_channel()):
stf.set_trace(ntrace)
stf.set_channel(ichannel)
trace = stf.get_trace()
ax_currents.plot(np.arange(len(trace)) * dt, trace)
if fitwindow is not None:
stf.fit.cursor_time = fitwindow
res = stf.leastsq(0, False)
taus.append(res['Tau_0'])
# Measure pulse amplitude
stf.set_channel(vchannel)
trace = stf.get_trace()
ax_voltages.plot(np.arange(len(trace)) * dt, trace)
stf.set_peak_direction("up")
stf.set_peak_mean(-1)
if pulsewindow is not None:
stf.peak.cursor_time = pulsewindow
stf.measure()
v_commands.append(stf.peak.value)
stfio_plot.plot_scalebars(ax_currents,
xunits=stf.get_xunits(),
yunits=stf.get_yunits(channel=ichannel))
stfio_plot.plot_scalebars(ax_voltages,
xunits=stf.get_xunits(),
yunits=stf.get_yunits(channel=vchannel))
v_commands = np.array(v_commands)
taus = np.array(taus)
ax_taus = plot_iv(taus, v_commands, "ms", stf.get_yunits(channel=vchannel),
fig, 122)
# Reset peak computation to single sampling point
stf.set_peak_mean(1)
# Reset active channel
stf.set_channel(ichannel)
# Compute conductances:
stf.show_table_dictlist({
"Voltage ({0})".format(stf.get_yunits(channel=vchannel)):
v_commands.tolist(),
"Taus (ms)":
taus.tolist(),
})
return v_commands, taus
def glu_iv( pulses = 13, subtract_base=True ):
"""Calculates an iv from a repeated series of fast application and
voltage pulses.
Keyword arguments:
pulses -- Number of pulses for the iv.
subtract_base -- If True (default), baseline will be subtracted.
Returns:
True if successful.
"""
# Some ugly definitions for the time being
# Cursors are in ms here.
gFitEnd = 330.6 # fit end cursor is variable
gFSelect = 0 # Monoexp
gDictSize = stf.leastsq_param_size( gFSelect ) + 2 # Parameters, chisqr, peak value
gBaseStart = 220.5 # Start and end of the baseline before the control pulse, in ms
gBaseEnd = 223.55
gPeakStart = 223.55 # Start and end of the peak cursors for the control pulse, in ms
gPeakEnd = 253.55
if ( gDictSize < 0 ):
print('Couldn\'t retrieve function id=%d, aborting now.'%gFSelect)
return False
if ( not(stf.check_doc()) ):
print('Couldn\'t find an open file; aborting now.')
return False
# analyse iv, subtract baseline if requested:
ivtools.analyze_iv( pulses )
if ( subtract_base == True ):
if ( not(stf.set_base_start( gBaseStart, True )) ): return False
if ( not(stf.set_base_end( gBaseEnd, True )) ): return False
stf.measure()
stf.select_all()
stf.subtract_base()
# set cursors:
if ( not(stf.set_peak_start( gPeakStart, True )) ): return False
if ( not(stf.set_peak_end( gPeakEnd, True )) ): return False
if ( not(stf.set_base_start( gBaseStart, True )) ): return False
if ( not(stf.set_base_end( gBaseEnd, True )) ): return False
if ( not(stf.set_fit_end( gFitEnd, True )) ): return False
if ( not(stf.set_peak_mean( 3 )) ): return False
if ( not(stf.set_peak_direction( "both" )) ): return False
# A list for dictionary keys and values:
dict_keys = []
dict_values = np.empty( (gDictSize, stf.get_size_channel()) )
firstpass = True
for n in range( 0, stf.get_size_channel() ):
if ( stf.set_trace( n ) == False ):
print('Couldn\'t set a new trace; aborting now.')
return False
print('Analyzing trace %d of %d'%( n+1, stf.get_size_channel() ) )
# set the fit window cursors:
if ( not(stf.set_fit_start( stf.peak_index() )) ): return False
# Least-squares fitting:
p_dict = stf.leastsq( gFSelect )
if ( p_dict == 0 ):
print('Couldn\'t perform a fit; aborting now.')
return False
# Create an empty list:
tempdict_entry = []
row = 0
for k, v in p_dict.iteritems():
if ( firstpass == True ):
dict_keys.append( k )
dict_values[row][n] = v
row = row+1
if ( firstpass ):
dict_keys.append( "Peak amplitude" )
dict_values[row][n] = stf.get_peak()-stf.get_base()
firstpass = False
retDict = dict()
# Create the dictionary for the table:
entry = 0
for elem in dict_keys:
retDict[ elem ] = dict_values[entry].tolist()
entry = entry+1
return stf.show_table_dictlist( retDict )
def iv(peakwindow=None,
basewindow=None,
pulsewindow=None,
erev=None,
peakmode="both",
ichannel=0,
vchannel=1,
exclude=None):
"""
Compute and plot an IV curve for currents
Parameters
----------
peakwindow : (float, float), optional
Window for peak measurement (time in ms from beginning of sweep)
None for current cursor settings. Default: None
basewindow : (float, float), optional
Window for baseline measurement (time in ms from beginning of sweep)
None for current cursor settings. Default: None
pulsewindow : (float, float), optional
Window for voltage pulse measurement (time in ms from beginning of sweep)
None for current cursor settings. Default: None
erev : float, optional
End of v clamp pulse in ms or None to determine automatically.
Default: None
peakmode : string, optional
Peak direction - one of "up", "down", "both" or "mean". Default: "up"
ichannel : int, optional
current channel number. Default: 0
vchannel : int, optional
voltage channel number. Default: 1
exclude : list of ints, optional
List of trace indices to be excluded from the analysis. Default: None
Returns
-------
v_commands : numpy.ndarray
Command voltages
ipeaks : numpy.ndarray
Peak currents
gpeaks : numpy.ndarray
Peak normalized conductances
g_fit : numpy.ndarray
Half-maximal voltage and slope of best-fit Boltzmann function
"""
import stf
if not stf.check_doc():
return None
nchannels = stf.get_size_recording()
if nchannels < 2:
sys.stderr.write(
"Function requires 2 channels (0: current; 1: voltage)\n")
return
dt = stf.get_sampling_interval()
olddirection = stf.get_peak_direction()
v_commands = []
ipeaks = []
if basewindow is not None:
stf.base.cursor_time = basewindow
fig = stf.mpl_panel(figsize=(12, 8)).fig
fig.clear()
gs = gridspec.GridSpec(4, 8)
ax_currents = stfio_plot.StandardAxis(fig,
gs[:3, :4],
hasx=False,
hasy=False)
ax_voltages = stfio_plot.StandardAxis(fig,
gs[3:, :4],
hasx=False,
hasy=False,
sharex=ax_currents)
for ntrace in range(stf.get_size_channel()):
if exclude is not None:
if ntrace in exclude:
continue
stf.set_trace(ntrace)
stf.set_channel(ichannel)
trace = stf.get_trace()
ax_currents.plot(np.arange(len(trace)) * dt, trace)
# Measure only downward peaks (inward currents)
if peakmode is "mean":
stf.set_peak_direction("up")
stf.set_peak_mean(-1)
else:
stf.set_peak_direction(peakmode)
# Set peak computation to single sampling point
stf.set_peak_mean(1)
if peakwindow is not None:
stf.peak.cursor_time = peakwindow
stf.measure()
if basewindow is not None:
ipeaks.append(stf.peak.value - stf.base.value)
else:
ipeaks.append(stf.peak.value)
# Measure pulse amplitude
stf.set_channel(vchannel)
trace = stf.get_trace()
ax_voltages.plot(np.arange(len(trace)) * dt, trace)
stf.set_peak_direction("up")
stf.set_peak_mean(-1)
if pulsewindow is not None:
stf.peak.cursor_time = pulsewindow
stf.measure()
v_commands.append(stf.peak.value)
stfio_plot.plot_scalebars(ax_currents,
xunits=stf.get_xunits(),
yunits=stf.get_yunits(channel=0))
stfio_plot.plot_scalebars(ax_voltages,
xunits=stf.get_xunits(),
yunits=stf.get_yunits(channel=1))
v_commands = np.array(v_commands)
ipeaks = np.array(ipeaks)
if erev is None:
# Find first zero crossing in ipeaks:
for npulse in range(ipeaks.shape[0] - 1):
if np.sign(ipeaks[npulse]) != np.sign(ipeaks[npulse + 1]):
# linear interpolation
m1 = (ipeaks[npulse + 1] - ipeaks[npulse]) / (
v_commands[npulse + 1] - v_commands[npulse])
c1 = ipeaks[npulse] - m1 * v_commands[npulse]
erev = -c1 / m1
break
if erev is None:
sys.stderr.write(
"Could not determine reversal potential. Aborting now\n")
return None
# Reset peak computation to single sampling point
stf.set_peak_mean(1)
stf.set_peak_direction(olddirection)
# Reset active channel
stf.set_channel(ichannel)
# Compute conductances:
gpeaks, g_fit = gv(ipeaks, v_commands, erev)
ax_ipeaks = plot_iv(ipeaks, v_commands, stf.get_yunits(channel=ichannel),
stf.get_yunits(channel=1), fig, 222)
ax_ipeaks.set_title("Peak current")
ax_gpeaks = plot_gv(gpeaks, v_commands, stf.get_yunits(channel=vchannel),
g_fit, fig, 224)
ax_gpeaks.set_title("Peak conductance")
stf.show_table_dictlist({
"Voltage ({0})".format(stf.get_yunits(channel=vchannel)):
v_commands.tolist(),
"Peak current ({0})".format(stf.get_yunits(channel=ichannel)):
ipeaks.tolist(),
"Peak conductance (g/g_max)":
gpeaks.tolist(),
})
return v_commands, ipeaks, gpeaks, g_fit
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 plot_traces(plotwindow=None, ichannel=0, vchannel=1):
"""
Show traces in a figure
Parameters
----------
plotwindow : (float, float), optional
Plot window (in ms from beginning of trace)
None for whole trace. Default: None
ichannel : int, optional
current channel number. Default: 0
vchannel : int, optional
voltage channel number. Default: 1
"""
import stf
if not stf.check_doc():
return None
nchannels = stf.get_size_recording()
if nchannels < 2:
sys.stderr.write(
"Function requires 2 channels (0: current; 1: voltage)\n")
return
dt = stf.get_sampling_interval()
fig = stf.mpl_panel(figsize=(12, 8)).fig
fig.clear()
gs = gridspec.GridSpec(4, 1)
ax_currents = stfio_plot.StandardAxis(fig,
gs[:3, 0],
hasx=False,
hasy=False)
ax_voltages = stfio_plot.StandardAxis(fig,
gs[3:, 0],
hasx=False,
hasy=False,
sharex=ax_currents)
if plotwindow is not None:
istart = int(plotwindow[0] / dt)
istop = int(plotwindow[1] / dt)
else:
istart = 0
istop = None
for ntrace in range(stf.get_size_channel()):
stf.set_trace(ntrace)
stf.set_channel(ichannel)
trace = stf.get_trace()[istart:istop]
ax_currents.plot(np.arange(len(trace)) * dt, trace)
# Measure pulse amplitude
stf.set_channel(vchannel)
trace = stf.get_trace()[istart:istop]
ax_voltages.plot(np.arange(len(trace)) * dt, trace)
# Reset active channel
stf.set_channel(ichannel)
stfio_plot.plot_scalebars(ax_currents,
xunits=stf.get_xunits(),
yunits=stf.get_yunits(channel=0))
stfio_plot.plot_scalebars(ax_voltages,
xunits=stf.get_xunits(),
yunits=stf.get_yunits(channel=1))
def timeconstants(fitwindow, pulsewindow, ichannel=0, vchannel=1):
"""
Compute and plot decay time constants
Parameters
----------
fitwindow : (float, float), optional
Window for fitting time constant (time in ms from beginning of sweep)
None for current cursor settings. Default: None
pulsewindow : (float, float), optional
Window for voltage pulse measurement (time in ms from beginning of sweep)
None for current cursor settings. Default: None
ichannel : int, optional
current channel number. Default: 0
vchannel : int, optional
voltage channel number. Default: 1
Returns
-------
v_commands : numpy.ndarray
Command voltages
taus : numpy.ndarray
Time constants
"""
import stf
if not stf.check_doc():
return None
nchannels = stf.get_size_recording()
if nchannels < 2:
sys.stderr.write(
"Function requires 2 channels (0: current; 1: voltage)\n")
return
dt = stf.get_sampling_interval()
v_commands = []
taus = []
fig = stf.mpl_panel(figsize=(12, 8)).fig
fig.clear()
gs = gridspec.GridSpec(4, 8)
ax_currents = stfio_plot.StandardAxis(
fig, gs[:3, :4], hasx=False, hasy=False)
ax_voltages = stfio_plot.StandardAxis(
fig, gs[3:, :4], hasx=False, hasy=False, sharex=ax_currents)
for ntrace in range(stf.get_size_channel()):
stf.set_trace(ntrace)
stf.set_channel(ichannel)
trace = stf.get_trace()
ax_currents.plot(np.arange(len(trace))*dt, trace)
if fitwindow is not None:
stf.fit.cursor_time = fitwindow
res = stf.leastsq(0, False)
taus.append(res['Tau_0'])
# Measure pulse amplitude
stf.set_channel(vchannel)
trace = stf.get_trace()
ax_voltages.plot(np.arange(len(trace))*dt, trace)
stf.set_peak_direction("up")
stf.set_peak_mean(-1)
if pulsewindow is not None:
stf.peak.cursor_time = pulsewindow
stf.measure()
v_commands.append(stf.peak.value)
stfio_plot.plot_scalebars(
ax_currents, xunits=stf.get_xunits(),
yunits=stf.get_yunits(channel=ichannel))
stfio_plot.plot_scalebars(
ax_voltages, xunits=stf.get_xunits(),
yunits=stf.get_yunits(channel=vchannel))
v_commands = np.array(v_commands)
taus = np.array(taus)
ax_taus = plot_iv(
taus, v_commands, "ms",
stf.get_yunits(channel=vchannel), fig, 122)
# Reset peak computation to single sampling point
stf.set_peak_mean(1)
# Reset active channel
stf.set_channel(ichannel)
# Compute conductances:
stf.show_table_dictlist({
"Voltage ({0})".format(
stf.get_yunits(channel=vchannel)): v_commands.tolist(),
"Taus (ms)": taus.tolist(),
})
return v_commands, taus
