def ExtractEvent(): doc = nex.GetActiveDocument() #extract events #please refer to the arduino file doc["TrialEnd"] = nex.MarkerExtract(doc, "Strobed", "=242,EOF,END") doc["TrialStart"] = nex.MarkerExtract(doc, "Strobed", "=2,EOF,END") doc["Cue"] = nex.MarkerExtract(doc, "Strobed", "=6,OR,=12,EOF,END") doc["Incorrect"] = nex.MarkerExtract(doc, "Strobed", "=48,EOF,END") doc["correct"] = nex.MarkerExtract(doc, "Strobed", "=10,OR,=64,EOF,END") doc["stim"] = nex.MarkerExtract(doc, "Strobed", "=84,OR,=68,OR,=124,OR,=70,EOF,END") doc["Choice"] = nex.MarkerExtract(doc, "Strobed", "=26,OR,=4,EOF,END") #make trials #The interval include (trial start -2s) to Trial end(Choice or omission or early termination) doc["Trial"] = nex.MakeIntFromStart(doc["TrialStart"], doc["TrialEnd"], - 2, 0) #seperate trials into correct/incorrect/early/omission doc["CorrectTrial"] = nex.IntFind(doc["Trial"], doc["correct"]) doc["inCorrectTrial"] = nex.IntFind(doc["Trial"], doc["Incorrect"]) #doc["EarlyTrial"] = IntFind(doc["Trial"], doc["Early"]) #doc["OmissionTrial"] = IntFind(doc["Trial"], doc["Omission"]) #check correct algorithm def MergeFile(InputPl2path,OutputNexPath,InputUnitspath)
def Calculate():
'''
the main calculate function for Python analysis in NeuroExplorer
- unpacks parameters
- calls SyncIndex or SyncIndexPhase
- generate results for NeuroExplorer
'''
doc = nex.GetActiveDocument()
if not doc:
raise ValueError('open data file first')
inputJson = doc.GetPythonAnalysisInput()
if not inputJson:
raise ValueError('this script should be run from Python analysis only')
inputPars = json.loads(inputJson)
phaseMethod = int(inputPars['ScriptParameters']['PhaseMethod'])
binSize = float(inputPars['ScriptParameters']['Bin (Seconds)'])
numShuffles = int(inputPars['ScriptParameters']['Number of Shuffles'])
K = float(inputPars['ScriptParameters']['K threshold'])
variableNames = inputPars['Variables']
if not variableNames:
raise ValueError('no variables selected')
theVars = []
for v in variableNames:
theVars.append(doc[v['Name']])
if not theVars:
raise ValueError('no variables selected')
if len(theVars) < 2:
raise ValueError('only one variable selected')
if phaseMethod == 0:
synIndex = SyncIndex(doc, theVars, numShuffles, binSize, K)
else:
synIndex = SyncIndexPhase(doc, theVars, numShuffles, binSize, K)
globalSI = max(synIndex)
result = {}
result['XAxisLabel'] = 'Sync. Index Number'
result['YAxisLabel'] = 'Sync. Index Value'
xvalues = np.arange(1, len(synIndex) + 1, 1).tolist()
result['XValues'] = xvalues
result['YValues'] = synIndex.tolist()
graphicsCommands = []
text = 'Global Synch. Index = {:.5f}'.format(globalSI)
middleX = min(xvalues) + (max(xvalues) - min(xvalues)) / 2 - 0.5
textY = globalSI
if textY == 0:
textY = 1
graphicsCommands.append(MakeText(0, middleX, textY, text, 10))
doc.SetPythonAnalysisOutput(json.dumps(result))
doc.SetProperty('AdditionalGraphicsCommands', graphicsCommands)
def Calculate():
doc = nex.GetActiveDocument()
# get the variable info and values of script parameters
inputJson = doc.GetPythonAnalysisInput()
inputPars = json.loads(inputJson)
# get all analysis parameters (we need to get data selection options)
parsJson = doc.GetAllAnalysisParameters()
pars = json.loads(parsJson)
variable = inputPars['Variable']
if variable['Type'] != 'Continuous':
raise ValueError('This analysis can only analyze Continuous variables')
filteredVar = IntervalFilterVarIfNeeded(doc, doc[variable['Name']], pars)
v = filteredVar.ContinuousValues()
numberOfContValues = len(v)
if numberOfContValues > 100000:
raise ValueError('Too many Continuous values')
freqFrom = float(inputPars['ScriptParameters']['Show Frequency From'])
freqTo = float(inputPars['ScriptParameters']['Show Frequency To'])
samplingRate = float(variable['SamplingRate'])
spectrumStep = samplingRate / numberOfContValues
spectrum = nex.Spectrum(v)
s = sum(spectrum)
result = {}
result['XAxisLabel'] = 'Frequency (Hz)'
result['YAxisLabel'] = 'Spectrum (%)'
result['XValues'] = []
result['YValues'] = []
for i in range(len(spectrum)):
freq = i * spectrumStep
if freq >= freqFrom and freq <= freqTo:
result['XValues'].append(freq)
result['YValues'].append(100.0 * spectrum[i] / s)
if len(result['XValues']) == 0:
raise ValueError('Frequency range is too narrow: frequency step = ' +
str(spectrumStep))
doc.SetPythonAnalysisOutput(json.dumps(result))
# converted from NexScript 'C:\Users\309i7\Documents\NeuroExplorer 5\Scripts\Shifting\ExtractEvents.nsc'
from __future__ import division
import nex
import math
import sys
import json
doc = nex.GetActiveDocument()
filename = nex.GetDocTitle(doc)
print('You are extracting steady trials from this file: ' + filename)
#usually the first channel is 1st_Anti_AfterPro
#The second channel is 1st_Pro_AfterAnti
first_Anti_AfterSwitch = nex.GetVarByName(doc,
'1st_Anti_AfterPro').Timestamps()
first_Pro_AfterSwitch = nex.GetVarByName(doc, '1st_Pro_AfterAnti').Timestamps()
ProTs = nex.GetVarByName(doc, 'Pro').Timestamps()
AntiTs = nex.GetVarByName(doc, 'Anti').Timestamps()
Num_Pro_Steady = len(first_Anti_AfterSwitch)
Num_Anti_Steady = len(first_Pro_AfterSwitch)
print('Steady Trials in Pro Task has ' + str(Num_Pro_Steady) + ' blocks')
print('Steady Trials in Anti Task has ' + str(Num_Anti_Steady) + ' blocks')
#This is event based on pro and anti cue.
def GetSteadyBlock(Num_Steady, TrialTs, first_AfterSwitch, TrialType):
#Get steady block from pro/anti trials
for i in range(Num_Steady):
steady_all_ts = [ts for ts in TrialTs if ts <= first_AfterSwitch[i]]
steady_ts = steady_all_ts[-20:]
def FindPeaksAndAddColumnsAndGraphicsCommands():
doc = nex.GetActiveDocument()
deltaPercentString = doc.GetPostProcessingScriptParameter()
try:
deltaPercent = float(deltaPercentString)
except:
deltaPercent = 25
res = doc.GetAllNumericalResults()
resNames = doc.GetNumResColumnNames()
if len(resNames) < 2:
raise ValueError(
'Numerical Results table need to have at least 2 columns (x an y)')
# the first column should contain X axis values
if not resNames[0] in [
'Bin Left', 'Bin Middle', 'Bin Right', 'Frequency Value', 'Time'
]:
raise ValueError('No x data in Numerical Results')
# select only columns with real graph names
# (we may have columns with confidence, standard deviation, etc.)
sumNumRes = doc.GetAllNumResSummaryData()
# real graph names are specified in the first column
# of summary of numerical results
realGraphNames = sumNumRes[0]
graphIndexes = []
for i in range(len(resNames)):
if resNames[i] in realGraphNames:
graphIndexes.append(i)
numResults = len(graphIndexes)
# first column of numerical results contains x values
x = res[0]
xRange = max(x) - min(x)
# we may need to adjust x to draw peak center at bin center
step = x[1] - x[0]
xAdjustment = 0
if resNames[0] == 'Bin Left':
xAdjustment = step * 0.5
if resNames[0] == 'Bin Right':
xAdjustment = -step * 0.5
# prepare empty columns with peak info
columns = []
# we will report up to 5 peaks
maxNumPeaks = 5
for i in range(maxNumPeaks):
columns.append(MakeEmptyColumn('PeakValue' + str(i + 1), numResults))
columns.append(MakeEmptyColumn('PeakPosition' + str(i + 1),
numResults))
graphicsCommands = []
# for each graph, find peaks and add columns and graphical commands
for igraph in range(len(graphIndexes)):
y = res[graphIndexes[igraph]]
yRange = max(y) - min(y)
if yRange > 0:
delta = yRange * deltaPercent / 100.0
peaks = PeakDetect(x, y, delta)
for ip in range(len(peaks)):
xp = peaks[ip][0] + xAdjustment
yp = peaks[ip][1]
if ip < maxNumPeaks:
# fill extra columns
columns[ip * 2]['values'][igraph] = '%.6f' % (yp)
columns[ip * 2 + 1]['values'][igraph] = '%.6f' % (xp)
# add graphics commands
dx = xRange * 0.010
dy = yRange * 0.015
# add graphics commands to draw 'x' (red diagonal lines) at each peak
graphicsCommands.append(
MakeLine(igraph, xp - dx, yp - dy, xp + dx, yp + dy, 2.0,
'(255;0;0)'))
graphicsCommands.append(
MakeLine(igraph, xp - dx, yp + dy, xp + dx, yp - dy, 2.0,
'(255;0;0)'))
# add text command showing x value of the peak
# if you want both x and y, use
# peakText = '(%.3f,%.3f)'%(xp,yp)
peakText = '%.3f' % (xp)
graphicsCommands.append(
MakeText(igraph, xp, yp + dy, peakText + ' s', 7,
'(0;0;255)'))
doc.SetProperty('AdditionalNumResColumns', columns)
doc.SetProperty('AdditionalGraphicsCommands', graphicsCommands)
for w in range(2, len(tempVar0)): # For all waveforms
tempVar1 = tempVar0[w][:
-3] # Keep points for a single waveform
tempVar2 = str.split(
tempVar1,
', ') # Split waveform points whereever ", " exists
wavePoints = [] # To hold float type points
for q in range(len(tempVar2)): # For all waveform points
wavePoints.append(
float(tempVar2[q]) / 1000
) # Convert from string to float. Then divide by 1,000 to convert from V to mV
waves[w - 2] = [
b[w - 2][1], wavePoints
] # Append the timestamp indexing and all waveforms for neuron
waves.sort()
for q in range(len(waves)):
waves[q] = waves[q][1]
try:
doc.CreateWaveformVariable(waveName, 40000, spkTimes,
waves) # Create waveform variable
except:
continue
nexName = "%s.nex" % fileName # Create neueroexoporer file name
print(nexName)
nex.SaveDocumentAs(doc, nexName) # Save Nex file
ndoc = nex.GetActiveDocument() # Select Nex file
nex.CloseDocument(ndoc) # Close Nex file
print("All files have been converted to nex files.")