"""
This module provides a Nobeyama Radioheliograph `~sunpy.timeseries.TimeSeries`
source.
"""
from collections import OrderedDict
import matplotlib.pyplot as plt
import numpy as np
import pandas
import astropy.units as u
from astropy.time import TimeDelta
import sunpy.io
from sunpy import config
from sunpy.time import parse_time
from sunpy.timeseries.timeseriesbase import GenericTimeSeries
from sunpy.util.metadata import MetaDict
from sunpy.visualization import peek_show
TIME_FORMAT = config.get("general", "time_format")
__all__ = ['NoRHTimeSeries']
[docs]
class NoRHTimeSeries(GenericTimeSeries):
"""
Nobeyama Radioheliograph Correlation lightcurve TimeSeries.
Nobeyama Radioheliograph (NoRH) is a radio telescope dedicated to observing the Sun.
It consists of 84 parabolic antennas with a 80 cm diameter,
sitting on lines of 490 m long in the east/west and of 220 m long in the north/south.
It observes the full solar disk at 17 GHz and 34 GHz with a temporal resolution
down to 0.1 second resolution (typically 1 second).
Its first observation was in April, 1992 and daily 8-hour observations are available starting June, 1992.
Examples
--------
>>> import sunpy.data.sample # doctest: +REMOTE_DATA
>>> import sunpy.timeseries
>>> norh = sunpy.timeseries.TimeSeries(sunpy.data.sample.NORH_TIMESERIES, source='NoRH') # doctest: +REMOTE_DATA
>>> norh.peek() # doctest: +SKIP
References
----------
* `Nobeyama Radioheliograph Homepage <https://solar.nro.nao.ac.jp/norh/>`__
* `Analysis Manual <https://solar.nro.nao.ac.jp/norh/doc/manuale/index.html>`__
* `Nobeyama Correlation Plots <https://solar.nro.nao.ac.jp/norh/html/cor_plot/>`__
"""
# Class attributes used to specify the source class of the TimeSeries
# and a URL to the mission website.
_source = 'norh'
_url = "https://solar.nro.nao.ac.jp/norh/"
def __init__(self, data, header, units, **kwargs):
super().__init__(data, header, units, **kwargs)
[docs]
def plot(self, axes=None, columns=None, **kwargs):
"""
Plot the NoRH lightcurve.
Parameters
----------
axes : `matplotlib.axes.Axes`, optional
The axes on which to plot the TimeSeries. Defaults to current axes.
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)
data_lab = str(self.meta.get('OBS-FREQ').values()).replace('[', '').replace(
']', '').replace('\'', '')
axes.plot(self.to_dataframe(), label=data_lab, **kwargs)
axes.set_yscale("log")
axes.set_ylim(1e-4, 1)
axes.set_ylabel('Correlation')
axes.legend()
self._setup_x_axis(axes)
return axes
[docs]
@peek_show
def peek(self, *, title="Nobeyama Radioheliograph", columns=None, **kwargs):
"""
Displays the NoRH lightcurve TimeSeries by calling
`~sunpy.timeseries.sources.norh.NoRHTimeSeries.plot`.
.. plot::
import sunpy.data.sample
import sunpy.timeseries
norh = sunpy.timeseries.TimeSeries(sunpy.data.sample.NORH_TIMESERIES, source='NoRH')
norh.peek()
Parameters
----------
title : `str`, optional
The title of the plot. Defaults to "Nobeyama Radioheliograph".
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.
"""
fig, ax = plt.subplots()
axes = self.plot(axes=ax, **kwargs)
axes.set_title(title)
return fig
@classmethod
def _parse_file(cls, filepath):
"""
This method parses NoRH FITS files.
Parameters
----------
filepath : `str`
The path to the file you want to parse.
"""
hdus = sunpy.io._file_tools.read_file(filepath)
return cls._parse_hdus(hdus)
@classmethod
def _parse_hdus(cls, hdulist):
"""
This method parses a NoRH `astropy.io.fits.HDUList`.
Parameters
----------
hdulist : `astropy.io.fits.HDUList`
A HDU list.
"""
header = MetaDict(OrderedDict(hdulist[0].header))
# For these NoRH files, the time series data is recorded in the primary
# HDU
data = hdulist[0].data
# No explicit time array in FITS file, so construct the time array from
# the FITS header
obs_start_time = parse_time(header['DATE-OBS'] + 'T' + header['CRVAL1'])
length = len(data)
cadence = float(header['CDELT1'])
sec_array = np.linspace(0, length - 1, int(length / cadence))
norh_time = obs_start_time + TimeDelta(sec_array*u.second)
norh_time.precision = 9
norh_time = norh_time.isot.astype('datetime64')
# Add the units data
units = OrderedDict([('Correlation Coefficient', u.dimensionless_unscaled)])
# Todo: check units used.
return pandas.DataFrame(
data, index=norh_time, columns=('Correlation Coefficient', )), header, units
[docs]
@classmethod
def is_datasource_for(cls, **kwargs):
"""
Determines if header corresponds to a Nobeyama Radioheliograph
Correlation `~sunpy.timeseries.TimeSeries`.
"""
if 'source' in kwargs.keys():
if kwargs.get('source', ''):
return kwargs.get('source', '').lower().startswith(cls._source)
if 'meta' in kwargs.keys():
return kwargs['meta'].get('ORIGIN', '').startswith('NOBEYAMA RADIO OBS')
