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 pon_batch(iv_pulses, pon_pulses=8, trace_start=0, subtract_base=False, factor=1.0): """Extracts p-over-n corrected traces in FPulse-generated files, and then creates an IV for the currently active channel. Keyword arguments: iv_pulses -- Number of pulses for the IV. pon_pulses -- Number of p-over-n correction pulses. This is typically 4 (for PoN=5 in the FPulse script) or 8 (for PoN=9). 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. subtract_base -- Set to True if you want to subtract the baseline at the end. You will need to set the baseline cursors in the original file to appropriate positions if you want to do this. factor -- Multiply result with an optional factor, typically from some external scaling. Returns: True upon success, False otherwise. """ # Extract corrected pulses: if (select_pon(pon_pulses) == False): return False if (stf.new_window_selected_this() == False): return False # Create IV: if (analyze_iv(iv_pulses, trace_start, factor) == False): return False # Subtract base: if (subtract_base == True): stf.select_all() if (stf.subtract_base() == False): return False return True
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 )