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Create Custom TimeSeries#

Sometimes you will have data that you want to transform into a TimeSeries. You can use the factory to create a GenericTimeSeries from a variety of data sources currently including pandas.DataFrame and astropy.table.Table.

Creating a TimeSeries from a Pandas DataFrame#

A TimeSeries object must be supplied with some data when it is created. The data can either be in your current Python session, in a local file, or in a remote file. Let’s create some data and pass it into a TimeSeries object:

>>> import numpy as np

>>> intensity = np.sin(np.arange(0, 12 * np.pi, ((12 * np.pi) / (24*60))))

This creates a basic numpy array of values representing a sine wave. We can use this array along with a suitable time storing object (such as Astropy time or a list of datetime objects) to make a Pandas DataFrame. A suitable list of times must contain the same number of values as the data, this can be created using:

>>> import datetime

>>> base = datetime.datetime.today()
>>> times = [base - datetime.timedelta(minutes=x) for x in range(24*60, 0, -1)]

The Pandas DataFrame will use the dates list as the index:

>>> from pandas import DataFrame

>>> data = DataFrame(intensity, index=times, columns=['intensity'])

This DataFrame can then be used to construct a TimeSeries:

>>> import astropy.units as u

>>> import sunpy.timeseries as ts

>>> header = {'key': 'value'}
>>> units = {'intensity': u.W/u.m**2}
>>> ts_custom = ts.TimeSeries(data, header, units)

Creating Custom TimeSeries from an Astropy Table#

A Pandas DataFrame is the underlying object used to store the data within a TimeSeries, so the above example is the most lightweight to create a custom TimeSeries, but being scientific data it will often be more convenient to use an Astropy Table to create a TimeSeries. An advantage of this method is it allows you to include metadata and Astropy Quantity values, which are both supported in tables, without additional arguments. For example:

>>> from astropy.time import Time
>>> from astropy.table import Table

>>> base = datetime.datetime.today()
>>> times = [base - datetime.timedelta(minutes=x) for x in range(24*60, 0, -1)]
>>> intensity = u.Quantity(np.sin(np.arange(0, 12 * np.pi, ((12 * np.pi) / (24*60)))), u.W/u.m**2)
>>> tbl_meta = {'t_key':'t_value'}
>>> table = Table([times, intensity], names=['time', 'intensity'], meta=tbl_meta)
>>> table.add_index('time')
>>> ts_table = ts.TimeSeries(table)

Note that due to the properties of the Time object, this will be a mixin column which since it is a single object, limits the versatility of the Table a little. For more on mixin columns see the Astropy docs. The units will be taken from the table quantities for each column, the metadata will simply be the table.meta dictionary. You can also explicitly add metadata and units, these will be added to the relevant dictionaries using the dictionary update method, with the explicit user-given values taking precedence:

>>> from collections import OrderedDict

>>> from sunpy.util.metadata import MetaDict

>>> meta = MetaDict({'key':'value'})
>>> units = OrderedDict([('intensity', u.W/u.m**2)])
>>> ts_table = ts.TimeSeries(table, meta, units)
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