sunpy.timeseries.TimeSeries = <sunpy.timeseries.timeseries_factory.TimeSeriesFactory object>#

A factory for generating solar timeseries objects.

This factory takes a variety of inputs to generate GenericTimeSeries objects.

  • *inputs – Inputs to parse for timeseries objects. See the example section for a detailed list of possible inputs.

  • source ({‘rhessi’, ‘gbmsummary’, ‘xrs’, ‘esp’, ‘norh’, ‘noaaindices’, ‘eve’, ‘noaapredictindices’, ‘lyra’}, optional) – A string to select the observational source of the data, currently necessary to define how files should be read for all instruments.

  • concatenate (bool, optional) – Defaults to False. If set, combine any resulting list of TimeSeries objects into a single TimeSeries, using successive concatenate methods.


  • sunpy.timeseries.GenericTimeSeries – If the input results in a single timeseries object that will be returned, or if concatenate=True.

  • list of GenericTimeSeries – If multiple inputs are parsed, they will be returned in a list, unless concatenate=True is set when they will be combined into a single timeseries.


>>> import sunpy.timeseries
>>> import sunpy.data.sample  
>>> my_timeseries = sunpy.timeseries.TimeSeries(sunpy.data.sample.GOES_XRS_TIMESERIES)  

The SunPy TimeSeries factory accepts a wide variety of inputs for creating timeseries

  • Preloaded tuples of (data, header) pairs or (data, header, units)

>>> my_timeseries = sunpy.timeseries.TimeSeries((data, header))   

Headers and units must be either a dict, OrderedDict or MetaDict.

  • data, header pairs, or data, header units triples, not in tuples

>>> my_timeseries = sunpy.timeseries.TimeSeries(data, header)  
>>> my_timeseries = sunpy.timeseries.TimeSeries(data, header, units)  
  • File names for files understood by the file reader and for those that are not

>>> my_timeseries = sunpy.timeseries.TimeSeries('filename.fits')   
>>> my_timeseries = sunpy.timeseries.TimeSeries('filename.fits', source='lyra')  
  • Multiple files can be combined into one TimeSeries, as long as they are the same source

>>> my_timeseries = sunpy.timeseries.TimeSeries(['goesfile1.fits', 'goesfile2.fits'],
...                                             concatenate=True)  
  • All fits files in a directory by giving a directory

>>> my_timeseries = sunpy.timeseries.TimeSeries('local_dir/sub_dir')  
  • Some regex globs

>>> my_timeseries = sunpy.timeseries.TimeSeries('eit_*.fits')  
  • URLs

>>> my_timeseries = sunpy.timeseries.TimeSeries(url)  
  • Lists of any of the above

>>> my_timeseries = sunpy.timeseries.TimeSeries(['file1.fits', 'file2.fits',
...                                              'file3.fits', 'directory1/'])  
  • Any mixture of the above not in a list

>>> my_timeseries = sunpy.timeseries.TimeSeries((data, header), data2, header2,
...                                             'file1.fits', url, 'eit_*.fits')