def count_aps():
"""
Shows a result table with the number of action potentials (i.e
events whose potential is above 0 mV) in selected traces. If
no trace is selected, then the current trace is analyzed.
Returns:
False if document is not open.
"""
if not stf.check_doc():
print("Open file first")
return False
if len( stf.get_selected_indices() )==0:
sel_trace = [ stf.get_trace_index()]
else:
sel_trace = stf.get_selected_indices()
mytable = dict()
for trace in sel_trace:
tstart = 0
tend = stf.get_size_trace(trace)*stf.get_sampling_interval()
threshold = 0
spikes = count_events(tstart, tend, threshold, True, trace, True)
mytable["Trace %.3d" %trace] = spikes
stf.show_table(mytable)
return True
def count_aps():
"""
Shows a result table with the number of action potentials (i.e
events whose potential is above 0 mV) in selected traces. If
no trace is selected, then the current trace is analyzed.
Returns:
False if document is not open.
"""
if not stf.check_doc():
print("Open file first")
return False
if len(stf.get_selected_indices()) == 0:
sel_trace = [stf.get_trace_index()]
else:
sel_trace = stf.get_selected_indices()
mytable = dict()
for trace in sel_trace:
tstart = 0
tend = stf.get_size_trace(trace) * stf.get_sampling_interval()
threshold = 0
spikes = count_events(tstart, tend, threshold, True, trace, True)
mytable["Trace %.3d" % trace] = spikes
stf.show_table(mytable)
return True
def monoexpfit(optimization=True, Tn=20):
"""
Fits monoexponential function with offset to data between the fit cursors
in the current trace of the active channel using a Chebyshev-Levenberg-
Marquardt hybrid algorithm. Optimization requires Scipy. Setting optimization
to False forces this function to use just the Chebyshev algorithm. The maximum
order of the Chebyshev polynomials can be set using Tn.
"""
# Get data
fit_start = stf.get_fit_start()
fit_end = stf.get_fit_end()
y = np.double(stf.get_trace()[fit_start:fit_end])
si = stf.get_sampling_interval()
l = len(y)
t = si * np.arange(0, l, 1, np.double)
# Define monoexponential function
def f(t, *p):
return p[0] + p[1] * np.exp(-t / p[2])
# Get initial values from Chebyshev transform fit
init = chebexp(1, Tn)
p0 = (init.get('Offset'), )
p0 += (init.get('Amp_0'), )
p0 += (init.get('Tau_0'), )
# Optimize (if applicable)
if optimization == True:
# Optimize fit using Levenberg-Marquardt algorithm
options = {"ftol": 2.22e-16, "xtol": 2.22e-16, "gtol": 2.22e-16}
[p, pcov] = optimize.curve_fit(f, t, y, p0, **options)
elif optimization == False:
p = list(p0)
fit = f(t, *p)
# Calculate SSE
SSE = np.sum((y - fit)**2)
# Plot fit in a new window
matrix = np.zeros((2, stf.get_size_trace())) * np.nan
matrix[0, :] = stf.get_trace()
matrix[1, fit_start:fit_end] = fit
stf.new_window_matrix(matrix)
# Create table of results
retval = [("p0_Offset", p[0])]
retval += [("p1_Amp_0", p[1])]
retval += [("p2_Tau_0", p[2])]
retval += [("SSE", SSE)]
retval += [("dSSE", 1.0 - np.sum((y - f(t, *p0))**2) / SSE)]
retval += [("Time fit begins", fit_start * si)]
retval += [("Time fit ends", fit_end * si)]
retval = dict(retval)
stf.show_table(
retval, "monoexpfit, Section #%i" % float(stf.get_trace_index() + 1))
return
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 trainpeaks():
"""
Measure a 20 Hz train of peaks starting at 260 ms into the trace
"""
pk = []
for i in range(5):
stf.set_base_start(
int(255 / stf.get_sampling_interval()) +
(50 / stf.get_sampling_interval()) * i)
stf.set_base_end(
int(259 / stf.get_sampling_interval()) +
(50 / stf.get_sampling_interval()) * i)
stf.set_peak_start(
int(260.5 / stf.get_sampling_interval()) +
(50 / stf.get_sampling_interval()) * i)
stf.set_peak_end(
int(270.5 / stf.get_sampling_interval()) +
(50 / stf.get_sampling_interval()) * i)
stf.measure()
pk.append(stf.get_peak() - stf.get_base())
# Create table of results
dictlist = [("Peak 1", pk[0])]
dictlist += [("Peak 2", pk[1])]
dictlist += [("Peak 3", pk[2])]
dictlist += [("Peak 4", pk[3])]
dictlist += [("Peak 5", pk[4])]
retval = dict(dictlist)
stf.show_table(retval,
"peaks, Section #%i" % float(stf.get_trace_index() + 1))
# Create table of results
dictlist = [("Peak 1", pk[0] / pk[0] * 100)]
dictlist += [("Peak 2", pk[1] / pk[0] * 100)]
dictlist += [("Peak 3", pk[2] / pk[0] * 100)]
dictlist += [("Peak 4", pk[3] / pk[0] * 100)]
dictlist += [("Peak 5", pk[4] / pk[0] * 100)]
retval = dict(dictlist)
stf.show_table(
retval, "norm peaks, Section #%i" % float(stf.get_trace_index() + 1))
return
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 Train10AP():
"""
An example function to perform peak measurements of a train of
evoked fluorescence signals in the active window
"""
# Setup
offset = 40
stf.set_base_start(0)
stf.set_peak_start(offset - 2)
stf.measure()
base = stf.get_base()
stf.set_peak_mean(1)
stf.set_peak_direction("up")
peak = []
# Get peak measurements
for i in range(10):
stf.set_peak_start(offset + (i * 4) - 2)
stf.set_peak_end(offset + (i * 4) + 2)
stf.measure()
peak.append(stf.get_peak())
# Plot fit in a new window
matrix = np.zeros((2, stf.get_size_trace())) * np.nan
matrix[0, :] = stf.get_trace()
for i in range(10):
matrix[1, offset + (i * 4) - 1:offset + (i * 4) + 2] = peak[i]
stf.new_window_matrix(matrix)
# Create table of results
retval = []
for i in range(10):
retval += [("Peak %d" % (i), peak[i] - base)]
retval = dict(retval)
stf.show_table(retval,
"Train10AP, Section #%i" % float(stf.get_trace_index() + 1))
return
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 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
