Plotting the solar cycle index¶

How to plot the current and possible next solar cycle.

```import datetime
import matplotlib.pyplot as plt

import sunpy.timeseries as ts
from sunpy.data.sample import NOAAINDICES_TIMESERIES, NOAAPREDICT_TIMESERIES
```

For this example we will use the SunPy sample data. This code snippet grabs the most current NOAA solar cycle data as a `TimeSeries`.

```noaa = ts.TimeSeries(NOAAINDICES_TIMESERIES, source='noaaindices')
noaa_predict = ts.TimeSeries(NOAAPREDICT_TIMESERIES, source='noaapredictindices')
```

Next, we grab a new copy of the data and shift it forward 11.5 years to simulate the next solar cycle. We will also truncate the data to ensure that we only plot what is necessary.

```noaa2 = ts.TimeSeries(NOAAINDICES_TIMESERIES, source='noaaindices')
noaa2.data = noaa2.data.shift(1, freq=datetime.timedelta(days=365 * 11.5))
noaa2 = noaa2.truncate('2020/04/01', '2026/01/01')
```

Finally, we plot both `noaa` and `noaa2` together, with an arbitrary range for the strength of the next solar cycle.

```plt.plot(noaa.data.index, noaa.data['sunspot RI'], label='Sunspot Number')
plt.plot(noaa_predict.data.index, noaa_predict.data['sunspot'],
color='grey', label='Near-term Prediction')
plt.fill_between(noaa_predict.data.index, noaa_predict.data['sunspot low'],
noaa_predict.data['sunspot high'], alpha=0.3, color='grey')
plt.fill_between(noaa2.data.index, noaa2.data['sunspot RI smooth']*0.8,
noaa2.data['sunspot RI smooth']*1.2, alpha=0.3, color='grey',
label='Next Cycle Predict')
plt.plot(noaa2.data.index, noaa2.data['sunspot RI smooth'], color='grey')
plt.ylim(0)
plt.text('2011-01-01', 120, 'Cycle 24')
plt.text('2024-01-01', 120, 'Cycle 25')
plt.ylabel('Sunspot Number')
plt.xlabel('Year')
plt.legend()
plt.show()
```

Total running time of the script: ( 0 minutes 0.000 seconds)

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