NOAAPredictIndicesTimeSeries

class sunpy.timeseries.sources.NOAAPredictIndicesTimeSeries(data, meta=None, units=None, **kwargs)

Bases: GenericTimeSeries

NOAA Solar Cycle Predicted Progression.

The predictions are updated monthly and are produced by ISES. Observed values are initially the preliminary values which are replaced with the final values as they become available.

The following predicted values are available.

• The predicted RI sunspot number is the official International Sunspot Number and is issued by the Solar Influence Data Analysis Center (SDIC) in Brussels, Belgium.

• The predicted radio flux at 10.7 cm is produced by Penticon/Ottawa and the units are in sfu.

Note

See the gallery example Plotting a solar cycle index for how to use Fido to retrieve the data file.

Examples

>>> import sunpy.timeseries
>>> noaa_url = 'https://services.swpc.noaa.gov/json/solar-cycle/predicted-solar-cycle.json'  
>>> noaa = sunpy.timeseries.TimeSeries(noaa_url, source='NOAAPredictIndices')  
>>> noaa.peek()  

References

• Solar and Geomagnetic Indices Data Archive

• Predicted solar indices

• NOAA plots of Solar Cycle Progression

• NOAA Product List

Methods Summary

is_datasource_for(**kwargs)	Determines if header corresponds to an NOAA predict indices TimeSeries.
plot([axes, columns])	Plots predicted NOAA Indices as a function of time from a pandas dataframe.

Methods Documentation

classmethod is_datasource_for(**kwargs)

Determines if header corresponds to an NOAA predict indices TimeSeries.

plot(axes=None, columns=None, **plot_args)

Plots predicted NOAA Indices as a function of time from a pandas dataframe.

Parameters:

• axes (matplotlib.axes.Axes, optional) – The axes on which to plot the TimeSeries.

• columns (list[str], optional) – Unused, but there to maintain uniformity among plot methods.

• **kwargs (dict) – Additional plot keyword arguments that are handed to plot functions.

Returns:

Axes – The plot axes.