#finds the field potential variables by searching for the string "FP"
#puts these into a new list, FP_variables

variables = list(doc.ContinuousNames())
FP_variables = []
for x in variables:
    if x[:2] == 'FP':
        FP_variables.append(x)

#variable selection
#first deselects all, then adds only the variables cued by FP_variables

nex.DeselectAll(doc)
for i in FP_variables:
    x = nex.GetVarByName(doc, i)
    nex.Select(doc, x)

#moves into the destination directory
os.chdir(os.path.join(destination, newdir))

#modifies the analysis to te specified start and end times

nex.ModifyTemplate(doc, template, "Select Data From (sec)", str(start))
nex.ModifyTemplate(doc, template, "Select Data To (sec)", str(end))

#ensures no other interval filters are specified
nex.ModifyTemplate(doc, template, "Interval Filter", 'None')

#creates a new folder for each interval
#sets the analysis to for the correct interval
示例#2
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for i in range(n):
    # get a matching file name (recognizes indexes from 1)
    fileName = nex.GetFileName(i + 1)

    # open the file
    doc = nex.OpenDocument(fileName)

    # if file was opened successfully
    if doc > 0:

        print "Opened ", fileName

        nex.DeselectAll(doc)

        for n in range(len(continuousVarNames)):
            nex.Select(doc, doc[continuousVarNames[n]])

            # calculate spectrograms
            nex.ApplyTemplate(doc, templateName)

            # we need analysis parameters values to specify times
            startTime = float(
                nex.GetTemplateParValue(templateName, 'Start (sec)'))

            # find new var sampling rate
            shift = float(nex.GetTemplateParValue(templateName, 'Shift (sec)'))
            samplingRate = 1.0 / shift

            # get numerical results column names
            colNames = doc.GetNumResColumnNames()
            # skip first column if it contains frequency values