Source code for sunpy.timeseries.sources.noaa

"""
This module provides NOAA Solar Cycle `~sunpy.timeseries.TimeSeries` source.
"""
from pathlib import Path
from collections import OrderedDict

import numpy as np
import pandas as pd

import astropy.units as u

from sunpy.timeseries.timeseriesbase import GenericTimeSeries
from sunpy.util.decorators import deprecate_positional_args_since
from sunpy.util.metadata import MetaDict
from sunpy.visualization import peek_show

__all__ = ['NOAAIndicesTimeSeries', 'NOAAPredictIndicesTimeSeries']


[docs]class NOAAIndicesTimeSeries(GenericTimeSeries): """ NOAA Solar Cycle monthly indices. Solar activity is measured by a number of different values. The NOAA Solar Weather Prediction Center (SWPC) publishes the following indices. All of these indices are also provided as a 13-month running smoothed value. * The SWO sunspot number is issued by the NOAA Space Weather Prediction Center (SWPC) * The RI sunspot number is the official International Sunspot Number and is issued by the `Solar Influence Data Analysis Center (SDIC) <http://sidc.oma.be>`__ in Brussels, Belgium. * The ratio between the SWO and RI indices. * Radio flux at 10.7 cm is produced by `Penticon/Ottawa <https://www.ngdc.noaa.gov/stp/solar/flux.html>`__ and the units are in sfu. * The Ap Geomagnetic Index is produced by the United States Air Force (USAF). .. note:: See the gallery example :ref:`sphx_glr_generated_gallery_plotting_solar_cycle_example.py` for how to use `~sunpy.net.Fido` to retrieve the data file. Examples -------- >>> import sunpy.timeseries >>> noaa_url = "https://services.swpc.noaa.gov/json/solar-cycle/observed-solar-cycle-indices.json" >>> noaa = sunpy.timeseries.TimeSeries(noaa_url, source='NOAAIndices') # doctest: +REMOTE_DATA >>> noaa.peek() # doctest: +SKIP References ---------- * `Solar and Geomagnetic Indices Data Archive <https://www.swpc.noaa.gov/products/3-day-geomagnetic-forecast>`_ * `Recent solar indices <https://services.swpc.noaa.gov/json/solar-cycle/observed-solar-cycle-indices.json>`_ * `Indices Descriptions <ftp://ftp.swpc.noaa.gov/pub/weekly/README3>`_ * `NOAA plots of Solar Cycle Progression <https://www.swpc.noaa.gov/products/solar-cycle-progression>`_ * `NOAA Product List <https://www.swpc.noaa.gov/products-and-data>`_ """ # Class attributes used to specify the source class of the TimeSeries # and a URL to the mission website. _source = 'noaaindices' _url = "https://www.swpc.noaa.gov/products-and-data"
[docs] def plot(self, axes=None, plot_type='sunspot SWO', columns=None, **kwargs): """ Plots NOAA Indices. Parameters ---------- axes : `matplotlib.axes.Axes`, optional The axes on which to plot the TimeSeries. plot_type : {``"sunspot SWO"``, ``"sunspot RI"``, ``"sunspot compare"``, ``"radio"``, ``"geo"``}, optional The type of plot required. Defaults to ``"sunspot SWO"``. columns : list[str], optional Unused, but there to maintain uniformity among plot methods. **kwargs : `dict` Additional plot keyword arguments that are handed to `~matplotlib.axes.Axes.plot` functions. Returns ------- `~matplotlib.axes.Axes` The plot axes. """ axes, columns = self._setup_axes_columns(axes, columns) if plot_type == 'sunspot SWO': to_plot = ['sunspot SWO', 'sunspot SWO smooth'] ylabel = 'Sunspot Number' elif plot_type == 'sunspot RI': to_plot = ['sunspot RI', 'sunspot RI smooth'] ylabel = 'Sunspot Number' elif plot_type == 'sunspot compare': to_plot = ['sunspot RI', 'sunspot SWO'] ylabel = 'Sunspot Number' elif plot_type == 'radio': to_plot = ['radio flux', 'radio flux smooth'] ylabel = 'Radio Flux [sfu]' elif plot_type == 'geo': to_plot = ['geomagnetic ap', 'geomagnetic ap smooth'] ylabel = 'Geomagnetic AP Index' else: raise ValueError(f'Got unknown plot type "{type}"') to_plot = self.to_dataframe()[to_plot] to_plot.plot(ax=axes, **kwargs) axes.set_xlim(min(to_plot.dropna(how='all').index), max(to_plot.dropna(how='all').index)) axes.set_ylim(0) axes.set_ylabel(ylabel) axes.legend() self._setup_x_axis(axes) return axes
[docs] @peek_show @deprecate_positional_args_since("4.1") def peek(self, *, title="Solar Cycle Progression", plot_type='sunspot SWO', columns=None, **kwargs): """ Displays the NOAA Indices as a function of time by calling `~sunpy.timeseries.sources.noaa.NOAAPredictIndicesTimeSeries.plot`. .. plot:: import sunpy.timeseries noaa_url = "https://services.swpc.noaa.gov/json/solar-cycle/observed-solar-cycle-indices.json" noaa = sunpy.timeseries.TimeSeries(noaa_url, source='NOAAIndices') noaa.peek() Parameters ---------- title : `str`, optional The title of the plot. Defaults to "Solar Cycle Progression". plot_type : {``"sunspot SWO"``, ``"sunspot RI"``, ``"sunspot compare"``, ``"radio"``, ``"geo"``}, optional The type of plot required. Defaults to ``"sunspot SWO"``. columns : list[str], optional Unused, but there to maintain uniformity among peek methods. **kwargs : `dict` Additional plot keyword arguments that are handed to `~matplotlib.axes.Axes.plot` functions. """ axes = self.plot(plot_type=plot_type, **kwargs) axes.set_title(title) return axes.get_figure()
@classmethod def _parse_file(cls, filepath): suffix = Path(filepath).suffix if suffix == '.json': return cls._parse_json_file(filepath) else: raise ValueError(f"{Path(filepath).name} does not have a suffix of '.json'") @staticmethod def _parse_json_file(filepath): """ Parses an NOAA indices JSON file. Parameters ---------- filepath : `str` The path to the file you want to parse. """ with open(filepath) as fp: fp.seek(0) data = pd.read_json(fp.read()) rename = {'ssn': 'sunspot RI', 'smoothed_ssn': 'sunspot RI smooth', 'observed_swpc_ssn': 'sunspot SWO', 'smoothed_swpc_ssn': 'sunspot SWO smooth', 'f10.7': 'radio flux', 'smoothed_f10.7': 'radio flux smooth'} data = data.rename(columns=rename) # -1 is used as a fill value, replace with NaN data = data.replace(-1, np.nan) # Convoluted time index handling data = data.set_index('time-tag') data.index = pd.DatetimeIndex(data.index.values) # Add the units data, reported in radio flux values (sfu) originally. units = OrderedDict([('sunspot RI', u.dimensionless_unscaled), ('sunspot RI smooth', u.dimensionless_unscaled), ('sunspot SWO', u.dimensionless_unscaled), ('sunspot SWO smooth', u.dimensionless_unscaled), ('radio flux', 1e-22*u.W/(u.m**2*u.Hertz)), ('radio flux smooth', 1e-22*u.W/(u.m**2*u.Hertz))]) return data, MetaDict({'comments': ""}), units
[docs] @classmethod def is_datasource_for(cls, **kwargs): """ Determines if header corresponds to an NOAA indices timeseries. """ if kwargs.get('source', ''): return kwargs.get('source', '').lower().startswith(cls._source)
[docs]class NOAAPredictIndicesTimeSeries(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) <http://sidc.oma.be>`_ in Brussels, Belgium. * The predicted radio flux at 10.7 cm is produced by `Penticon/Ottawa <https://www.ngdc.noaa.gov/stp/solar/flux.html>`_ and the units are in sfu. .. note:: See the gallery example :ref:`sphx_glr_generated_gallery_plotting_solar_cycle_example.py` for how to use `~sunpy.net.Fido` to retrieve the data file. Examples -------- >>> import sunpy.timeseries >>> noaa_url = 'https://services.swpc.noaa.gov/json/solar-cycle/predicted-solar-cycle.json' # doctest: +REMOTE_DATA >>> noaa = sunpy.timeseries.TimeSeries(noaa_url, source='NOAAPredictIndices') # doctest: +REMOTE_DATA >>> noaa.peek() # doctest: +SKIP References ---------- * `Solar and Geomagnetic Indices Data Archive <https://www.swpc.noaa.gov/products/3-day-geomagnetic-forecast>`_ * `Predicted solar indices <https://services.swpc.noaa.gov/json/solar-cycle/predicted-solar-cycle.json>`_ * `NOAA plots of Solar Cycle Progression <https://www.swpc.noaa.gov/products/solar-cycle-progression>`_ * `NOAA Product List <https://www.swpc.noaa.gov/products-and-data>`_ """ # Class attributes used to specify the source class of the TimeSeries # and a URL to the mission website. _source = 'noaapredictindices' _url = "https://www.swpc.noaa.gov/products-and-data" _peek_title = "Solar Cycle Sunspot Number Prediction"
[docs] def plot(self, 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 `~matplotlib.axes.Axes.plot` functions. Returns ------- `~matplotlib.axes.Axes` The plot axes. """ axes, columns = self._setup_axes_columns(axes, columns) dataframe = self.to_dataframe() dataframe['sunspot'].plot(color='b', **plot_args) dataframe['sunspot high'].plot(linestyle='--', color='b', **plot_args) dataframe['sunspot low'].plot(linestyle='--', color='b', **plot_args) axes.set_ylim(0) axes.set_ylabel('Sunspot Number') axes.yaxis.grid(True, 'major') axes.xaxis.grid(True, 'major') axes.legend() self._setup_x_axis(axes) return axes
@classmethod def _parse_file(cls, filepath): suffix = Path(filepath).suffix if suffix == '.json': return cls._parse_json_file(filepath) else: raise ValueError(f"{Path(filepath).name} does not have a suffix of '.json'")
[docs] @classmethod def is_datasource_for(cls, **kwargs): """ Determines if header corresponds to an NOAA predict indices `~sunpy.timeseries.TimeSeries`. """ if kwargs.get('source', ''): return kwargs.get('source', '').lower().startswith(cls._source)
@staticmethod def _parse_json_file(filepath): """ Parses an NOAA Predict indices JSON file. Parameters ---------- filepath : `str` The path to the file you want to parse. """ with open(filepath) as fp: fp.seek(0) data = pd.read_json(fp.read()) rename = {'predicted_ssn': 'sunspot', 'high_ssn': 'sunspot high', 'low_ssn': 'sunspot low', 'predicted_f10.7': 'radio flux', 'high_f10.7': 'radio flux high', 'low_f10.7': 'radio flux low'} data = data.rename(columns=rename) # -1 is used as a fill value, replace with NaN data = data.replace(-1, np.nan) # Convoluted time index handling data = data.set_index('time-tag') data.index = pd.DatetimeIndex(data.index.values) # Add the units data, reported in radio flux values (sfu) originally. units = OrderedDict([('sunspot', u.dimensionless_unscaled), ('sunspot high', u.dimensionless_unscaled), ('sunspot low', u.dimensionless_unscaled), ('radio flux', 1e-22*u.W/(u.m**2*u.Hertz)), ('radio flux high', 1e-22*u.W/(u.m**2*u.Hertz)), ('radio flux low', 1e-22*u.W/(u.m**2*u.Hertz))]) return data, MetaDict({'comments': ""}), units