def __new__(cls, data, tdim, samplerate, *args, **kwargs):
# make new DimArray with timeseries attributes
ts = DimArray(data, *args, **kwargs)
# ensure that tdim is a valid dimension name:
if not (tdim in ts.dim_names):
raise ValueError(
'Provided time dimension name (tdim) is invalid!\n' +
'Provided value: ' + str(tdim) + '\nAvailable dimensions: ' +
str(ts.dim_names))
ts.tdim = tdim
# ensure that sample rate is a float:
samplerate = float(samplerate)
# ensure that sample rate is postive:
if samplerate <= 0:
raise ValueError('Samplerate must be positive! Provided value: ' +
str(samplerate))
ts.samplerate = samplerate
# convert to TimeSeries and return:
return ts.view(cls)
def __new__(cls, data, tdim, samplerate, *args, **kwargs):
# make new DimArray with timeseries attributes
ts = DimArray(data, *args, **kwargs)
# ensure that tdim is a valid dimension name:
if not(tdim in ts.dim_names):
raise ValueError(
'Provided time dimension name (tdim) is invalid!\n'+
'Provided value: '+ str(tdim)+'\nAvailable dimensions: '+
str(ts.dim_names))
ts.tdim = tdim
# ensure that sample rate is a float:
samplerate = float(samplerate)
# ensure that sample rate is postive:
if samplerate <= 0:
raise ValueError(
'Samplerate must be positive! Provided value: '+
str(samplerate))
ts.samplerate = samplerate
# convert to TimeSeries and return:
return ts.view(cls)
