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)