def datacosel(indata, cocosel = None, coords = None, outcoords = None, \
start= None,end= None,step = None, \
outstart = None, outend = None, outstep = None, \
crop = False):
''' indata: input data
'''
if (cocosel == None):
# if coordinates not given, make it from the start, end, step arguments
if (coords == None):
coords = dtrange(start, end, step)
if (outcoords == None):
if ((outstart != None) & (outend != None) ):
if (outstep != None):
outcoords = dtrange(outstart, outend, outstep)
else:
outcoords = coords[(coords >= outstart) & (coords <=outend)]
else:
outcoords = coords
cocosel = cosel(coords = coords, outcoords = outcoords)
else:
if (outcoords == None):
outcoords = cocosel
# remove NA (None) values if specified
# would be more elegant if we could simply do cosel = cosel[cosel!=None], but None inside [] does strange things
if (crop == True):
tempcosel = []
tempoutcoords = []
for icosel,ecosel in enumerate(cosel):
if (ecosel != None):
tempcosel.append(ecosel)
tempoutcoords.append(outcoords[icosel])
cocosel = array(tempcosel)
outcoords = array(tempoutcoords)
dataout = tile(nan,len(cocosel))
#dataout = indata[cosel[cosel!=None]]
#dataout[cosel==None] = nan
#dataout[cosel!=None] = indata[cosel[cosel!=None]]
for iel, el in enumerate(cocosel):
if (el != None):
dataout[iel] = indata[el]
else:
dataout[iel] = None
return(list([array(dataout), array(outcoords)]))
def avgcycle(data, timeco = None, timewarp = None, cclose=False,skipnan=False):
""" calculate averaged cycle
input:
data: input data
timeco: data coordinates
timewarp: length of the cycle
skipnan: skip nan-values
option:
cclose: add an additional point at the end identical the beginning to close the cycle
todo: add an option to count the amount of samples for each member of the cycle -> then crop in cosel should
be set to True so that we still take the mean even though there is no data available.
"""
if (timeco == None):
timeco = arange(len(data))
if (timewarp == None):
timewarp = timeco[1] - timeco[0]
iscounted = tile(False,len(data))
tempco = dtrange(timeco[0],timeco[len(timeco)-1],timewarp)
dataout = []
timecoout = []
for idata in range(len(data)):
if (iscounted[idata] == False):
tempco2 = tempco + (timeco[idata] - timeco[0])
#coordinates of data taken into account for this timestamp
tempcosel = cosel(timeco,tempco2)
# include some option to ignore the times on which there is no data
# include some option to ignore the nan data
# if some data could not be selected, a nan will be produced
# in cosel, crop is left to False, because we want to discover whenever data is not available
tdatacosel = datacosel(data,cocosel = tempcosel)[0]
# we don't want to end up with an error if None is met: we just want a 'nan' from the mean.
for edata in tdatacosel:
if (edata == None):
edata = nan
if skipnan:
tdatacosel = array(tdatacosel)
tdatacosel = tdatacosel[where(isnan(tdatacosel) == False)]
dataout.append(mean(tdatacosel))
timecoout.append(timeco[idata])
#dataout.append(mean(scisel(data,timeco,tempco2) )
#it would be much more elegant if we could simple do: iscounted[tempcosel] = True
#but None inside [] does strange things,
# (tempcosel!=None : manually crop (same as if we did cosel(...,crop=True)))
for etempcosel in tempcosel:
if (etempcosel != None): # None inside [] does strange things
iscounted[etempcosel] = True
# add an additional point to close the cycle
if (cclose == True):
timecoout.append(timeco[0]+ timewarp)
dataout.append(dataout[0])
return(list([array(dataout),array(timecoout)]))
