def __init__(self, *dfargs, **dfkwargs):
### Pop default DataFrame keywords before initializing###
self.name=dfkwargs.pop('name', 'TimeSpectra')
###Spectral index-related keywords
specunit=dfkwargs.pop('specunit', None)
###Intensity data-related stuff
iunit=dfkwargs.pop('iunit', None)
###Time index-related keywords (note, the are only used if a DatetimeIndex is not passed in)
freq=dfkwargs.pop('freq', None)
start=dfkwargs.pop('start', None)
stop= dfkwargs.pop('stop', None)
periods=dfkwargs.pop('periods',None)
timeunit=dfkwargs.pop('timeunit',None) #Not the same as freq, but inferred from it
baseline=dfkwargs.pop('baseline', None)
if stop and periods:
raise AttributeError('TimeSpectra cannot be initialized with both periods and stop; please choose one or the other.')
df=DataFrame(*dfargs, **dfkwargs)
### If user passes non datetime index to columns, make sure they didn't accidetnally pass SpecIndex by mistake.
if not isinstance(df.columns, DatetimeIndex):
try:
if df.columns._kind == 'spectral':
raise IOError("SpecIndex must be passed as index, not columns.") ### Can't be an attribute error or next won't be raised
### df.columns has no attribute _kind, meaning it is likely a normal pandas index
except AttributeError:
self._interval=True
self.start=start
self.stop=stop
self.freq=freq
self.timeunit=timeunit
### Take Datetime info and use to recreate the array
else:
self._interval=False
self.start=df.columns[0]
self.stop=df.columns[-1]
self.freq=df.columns.freq
### ADD TRANSLATION FOR FREQ--> basetimeuint
# self.timeunit=get_time_from_freq(df.columns.freq)
### Have to do it here instead of defaulting on instantiation.
df._tkind='temporal'
### Assign spectral intensity related stuff but
### DONT CALL _set_itype function
iunit=_valid_iunit(iunit)
self._itype=iunit
self.df=df
### This has to be done AFTER self.df has been set
self._baseline=self._baseline_valid(baseline)#SHOULD DEFAULT TO NONE SO USER CAN PASS NORMALIZED DATA WITHOUT REF
###Set Index as spectral variables
self.specunit = specunit #This will automatically convert to a spectral index
