#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
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