"""
This module provides Proba-2 `~sunpy.timeseries.TimeSeries` source.
"""
from collections import OrderedDict
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__ = ['LYRATimeSeries']
[docs]
class LYRATimeSeries(GenericTimeSeries):
"""
Proba-2 LYRA Lightcurve TimeSeries.
LYRA (Large Yield RAdiometer) is an ultraviolet irradiance radiometer that observes the Sun in four passbands,
chosen for their relevance to solar physics and space weather.
LYRA is composed of three (redundant) units, each of them constituted of the same four channels:
* 120-123 nm Lyman-alpha channel
* 190-222 nm Herzberg continuum channel
* Aluminium filter channel (17-80 nm + a contribution below 5 nm), including He II at 30.4 nm
* Zirconium filter channel (6-20 nm + a contribution below 2 nm), rejecting He II
LYRA can take data with cadences chosen in the 100Hz to 0.1Hz interval.
PROBA2 was launched on 2 November 2009.
Examples
--------
>>> import sunpy.timeseries
>>> import sunpy.data.sample # doctest: +REMOTE_DATA
>>> lyra = sunpy.timeseries.TimeSeries(sunpy.data.sample.LYRA_LEVEL3_TIMESERIES) # doctest: +REMOTE_DATA
>>> lyra.peek() # doctest: +SKIP
References
----------
* `Proba2 SWAP Science Center <http://proba2.sidc.be/about/SWAP/>`_
* `LYRA Data Homepage <http://proba2.sidc.be/data/LYRA>`_
* `LYRA Instrument Homepage <http://proba2.sidc.be/about/LYRA>`_
"""
# Class attributes used to specify the source class of the TimeSeries
# and a URL to the mission website.
_source = 'lyra'
_url = "https://proba2.sidc.be/about/LYRA"
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def plot(self, axes=None, columns=None, names=3, **kwargs):
"""
Plots the LYRA data.
Parameters
----------
axes : array of `matplotlib.axes.Axes`, optional
The axes on which to plot the TimeSeries.
columns : list[str], optional
If provided, only plot the specified columns.
names : `int`, optional
The number of columns to plot. Defaults to 3.
**kwargs : `dict`
Additional plot keyword arguments that are handed to `~matplotlib.axes.Axes.plot` functions.
Returns
-------
array of `~matplotlib.axes.Axes`
The plot axes.
"""
axes, columns = self._setup_axes_columns(axes, columns, subplots=True)
lyranames = ({'CHANNEL1': 'Lyman alpha', 'CHANNEL2': 'Herzberg cont.',
'CHANNEL3': 'Al filter', 'CHANNEL4': 'Zr filter'},
{'CHANNEL1': '120-123nm', 'CHANNEL2': '190-222nm',
'CHANNEL3': '17-80nm + <5nm', 'CHANNEL4': '6-20nm + <2nm'})
for i, name in enumerate(columns):
axes[i].plot(self._data[columns[i]],
label=columns[i])
axes[i].legend(loc="upper right")
if names < 3:
name = lyranames[names][columns[i]]
else:
name = lyranames[0][columns[i]] + ' \n (' + lyranames[1][columns[i]] + ')'
axes[i].set_ylabel(f"{name} \n (W/m**2)", fontsize=9.5)
self._setup_x_axis(axes)
return axes
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@peek_show
def peek(self, *, title=None, columns=None, names=3, **kwargs):
"""
Displays the LYRA data by calling `~sunpy.timeseries.sources.lyra.LYRATimeSeries.plot`.
.. plot::
import sunpy.timeseries
import sunpy.data.sample
lyra = sunpy.timeseries.TimeSeries(sunpy.data.sample.LYRA_LEVEL3_TIMESERIES, source='LYRA')
lyra.peek()
Parameters
----------
title : `str`, optional
The title of the plot.
columns : list[str], optional
If provided, only plot the specified columns.
names : `int`, optional
The number of columns to plot. Defaults to 3.
**kwargs : `dict`
Additional plot keyword arguments that are handed to
:meth:`pandas.DataFrame.plot`.
"""
axes = self.plot(columns=columns, names=names, **kwargs)
if title is None:
title = "LYRA ({0:{1}})".format(self.to_dataframe().index[0], TIME_FORMAT)
axes[0].set_title(title)
return axes[0].get_figure()
@classmethod
def _parse_file(cls, filepath):
"""
Parses Lyra FITS data files to create TimeSeries.
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):
"""
Parses LYRA `astropy.io.fits.HDUList` from a FITS file.
Parameters
----------
hdulist : `astropy.io.fits.HDUList`
A HDU list.
"""
# Open file with PyFITS
fits_record = hdulist[1].data
metadata = MetaDict(OrderedDict(hdulist[0].header))
start_str = metadata.get('date-obs', metadata.get('date_obs', ''))
start = parse_time(start_str)
# First column are times. For level 2 data, the units are [s].
# For level 3 data, the units are [min]
if hdulist[1].header['TUNIT1'] == 's':
times = start + TimeDelta(fits_record.field(0)*u.second)
elif hdulist[1].header['TUNIT1'] == 'MIN':
td = [int(n) for n in fits_record.field(0)]
times = start + TimeDelta(td*u.minute)
else:
raise ValueError("Time unit in LYRA fits file not recognised. "
"Value = {}".format(hdulist[1].header['TUNIT1']))
# Rest of columns are the data
table = {}
for i, col in enumerate(fits_record.columns[1:-1]):
table[col.name] = fits_record.field(i + 1)
# Return the header and the data
times.precision = 9
data = pandas.DataFrame(table, index=times.isot.astype('datetime64'))
data.sort_index(inplace=True)
# Add the units data
units = OrderedDict([('CHANNEL1', u.W/u.m**2),
('CHANNEL2', u.W/u.m**2),
('CHANNEL3', u.W/u.m**2),
('CHANNEL4', u.W/u.m**2)])
# TODO: check: http://www.wmo-sat.info/oscar/instruments/view/733
return data, metadata, units
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@classmethod
def is_datasource_for(cls, **kwargs):
"""
Determines if the file corresponds to a LYRA LightCurve
`~sunpy.timeseries.TimeSeries`.
"""
# Check if source is explicitly assigned
if 'source' in kwargs.keys():
if kwargs.get('source', ''):
return kwargs.get('source', '').lower().startswith(cls._source)
# Check if HDU defines the source instrument
if 'meta' in kwargs.keys():
return kwargs['meta'].get('INSTRUME', '').startswith('LYRA')
