def get_tamplitude(self):
""" return the time a the peak in the current trace"""
#stf.peak_index() does not update cursors!!!
self.update()
return stf.peak_index()*self._dt
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 ppr_peak():
# Get function values
decay_func = stf.leastsq(0)
# Create time from fit start to next peak
x = np.arange(stf.get_fit_start(), stf.peak_index())
# Create fitted curve up until peak
trace = [(decay_func['Offset'] + decay_func['Amp_0'] * np.exp(-(ind/10.0)/decay_func['Tau_0'])) for ind, val in enumerate(x)]
# Find peak value
peak_val = stf.get_peak()-stf.get_base()
print('The measured peak is {0} pA'.format(peak_val))
# Find value of fit at peak
fit_peak = trace[-1] - stf.get_base()
print('Tau is {0} pA'.format(decay_func['Tau_0']))
print('The fitted peak is {0} pA'.format(fit_peak))
print('The baseline is {0} pA'.format(stf.get_base()))
# stf.new_window(trace)
final_peak = peak_val - fit_peak
print('The final peak is {0} pA'.format(final_peak))
return True
def get_tamplitude(self):
""" return the time a the peak in the current trace"""
#stf.peak_index() does not update cursors!!!
self.update()
return stf.peak_index() * self._dt
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 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 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 )
