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.. _timeseries-data-access:
Accessing Data in Time Series
*****************************
.. |time_attr| replace:: :attr:`~astropy.timeseries.TimeSeries.time`
.. |time_bin_start| replace:: :attr:`~astropy.timeseries.BinnedTimeSeries.time_bin_start`
.. |time_bin_center| replace:: :attr:`~astropy.timeseries.BinnedTimeSeries.time_bin_center`
.. |time_bin_end| replace:: :attr:`~astropy.timeseries.BinnedTimeSeries.time_bin_end`
.. |time_bin_size| replace:: :attr:`~astropy.timeseries.BinnedTimeSeries.time_bin_size`
Accessing Data
==============
.. EXAMPLE START: Accessing Data in Time Series
For the examples in this page, we will consider a sampled time series
with two data columns — ``flux`` and ``temp``::
>>> from astropy import units as u
>>> from astropy.timeseries import TimeSeries
>>> ts = TimeSeries(time_start='2016-03-22T12:30:31',
... time_delta=3 * u.s,
... data={'flux': [1., 4., 5., 3., 2.] * u.Jy,
... 'temp': [40., 41., 39., 24., 20.] * u.K},
... names=('flux', 'temp'))
As for |Table|, columns can be accessed by name::
>>> ts['flux'] # doctest: +FLOAT_CMP
<Quantity [ 1., 4., 5., 3., 2.] Jy>
>>> ts['time']
<Time object: scale='utc' format='isot' value=['2016-03-22T12:30:31.000' '2016-03-22T12:30:34.000'
'2016-03-22T12:30:37.000' '2016-03-22T12:30:40.000'
'2016-03-22T12:30:43.000']>
And rows can be accessed by index::
>>> ts[0]
<Row index=0>
time flux temp
Jy K
Time float64 float64
----------------------- ------- -------
2016-03-22T12:30:31.000 1.0 40.0
Accessing individual values can then be done either by accessing a column and
then a row, or vice versa::
>>> ts[0]['flux'] # doctest: +FLOAT_CMP
<Quantity 1. Jy>
>>> ts['temp'][2] # doctest: +FLOAT_CMP
<Quantity 39. K>
.. EXAMPLE END
.. _timeseries-accessing-times:
Accessing Times
===============
.. duplicate example from index.rst
For |TimeSeries|, the ``time`` column can be accessed using the regular column
access notation, as shown in `Accessing Data`_, but it can also be accessed
more conveniently using the |time_attr| attribute::
>>> ts.time
<Time object: scale='utc' format='isot' value=['2016-03-22T12:30:31.000' '2016-03-22T12:30:34.000'
'2016-03-22T12:30:37.000' '2016-03-22T12:30:40.000'
'2016-03-22T12:30:43.000']>
.. EXAMPLE START: Accessing the Time Column in BinnedTimeSeries
For |BinnedTimeSeries|, we provide three attributes: |time_bin_start|,
|time_bin_center|, and |time_bin_end|::
>>> from astropy.timeseries import BinnedTimeSeries
>>> bts = BinnedTimeSeries(time_bin_start='2016-03-22T12:30:31',
... time_bin_size=3 * u.s, n_bins=5)
>>> bts.time_bin_start
<Time object: scale='utc' format='isot' value=['2016-03-22T12:30:31.000' '2016-03-22T12:30:34.000'
'2016-03-22T12:30:37.000' '2016-03-22T12:30:40.000'
'2016-03-22T12:30:43.000']>
>>> bts.time_bin_center
<Time object: scale='utc' format='isot' value=['2016-03-22T12:30:32.500' '2016-03-22T12:30:35.500'
'2016-03-22T12:30:38.500' '2016-03-22T12:30:41.500'
'2016-03-22T12:30:44.500']>
>>> bts.time_bin_end
<Time object: scale='utc' format='isot' value=['2016-03-22T12:30:34.000' '2016-03-22T12:30:37.000'
'2016-03-22T12:30:40.000' '2016-03-22T12:30:43.000'
'2016-03-22T12:30:46.000']>
In addition, the |time_bin_size| attribute can be used to access the bin sizes::
>>> bts.time_bin_size # doctest: +SKIP
<Quantity [3., 3., 3., 3., 3.] s>
Note that only |time_bin_start| and |time_bin_size| are available as actual
columns, and |time_bin_center| and |time_bin_end| are computed on the fly.
.. EXAMPLE END
See :ref:`timeseries-times` for more information about changing between
different representations of time.
Extracting a Subset of Columns
==============================
.. EXAMPLE START: Extracting a Subset of Columns in TimeSeries
We can create a new time series with just the ``flux`` column by doing::
>>> ts['time', 'flux']
<TimeSeries length=5>
time flux
Jy
Time float64
----------------------- -------
2016-03-22T12:30:31.000 1.0
2016-03-22T12:30:34.000 4.0
2016-03-22T12:30:37.000 5.0
2016-03-22T12:30:40.000 3.0
2016-03-22T12:30:43.000 2.0
Note that the new columns will be copies (not views) of the original columns.
We can also create a plain |QTable| by extracting just the ``flux`` and
``temp`` columns::
>>> ts['flux', 'temp']
<QTable length=5>
flux temp
Jy K
float64 float64
------- -------
1.0 40.0
4.0 41.0
5.0 39.0
3.0 24.0
2.0 20.0
.. EXAMPLE END
Extracting a Subset of Rows
===========================
.. EXAMPLE START: Extracting a Subset of Rows in TimeSeries
|TimeSeries| objects can be sliced by rows, using the same syntax as for |Time|,
for example::
>>> ts[0:2]
<TimeSeries length=2>
time flux temp
Jy K
Time float64 float64
----------------------- ------- -------
2016-03-22T12:30:31.000 1.0 40.0
2016-03-22T12:30:34.000 4.0 41.0
|TimeSeries| objects are also automatically indexed using the functionality
described in :ref:`table-indexing`. This provides the ability to access rows and
a subset of rows using the :attr:`~astropy.timeseries.TimeSeries.loc` and
:attr:`~astropy.timeseries.TimeSeries.iloc` attributes.
.. EXAMPLE END
.. EXAMPLE START: Slicing TimeSeries by Time
The :attr:`~astropy.timeseries.TimeSeries.loc` attribute can be used to slice
|TimeSeries| objects by time. For example, the following can be used to extract
all entries for a given timestamp::
>>> from astropy.time import Time
>>> ts.loc[Time('2016-03-22T12:30:31.000')] # doctest: +SKIP
<Row index=0>
time flux temp
Jy K
Time float64 float64
----------------------- ------- -------
2016-03-22T12:30:31.000 1.0 40.0
Or within a time range::
>>> ts.loc['2016-03-22T12:30:30':'2016-03-22T12:30:41']
<TimeSeries length=4>
time flux temp
Jy K
Time float64 float64
----------------------- ------- -------
2016-03-22T12:30:31.000 1.0 40.0
2016-03-22T12:30:34.000 4.0 41.0
2016-03-22T12:30:37.000 5.0 39.0
2016-03-22T12:30:40.000 3.0 24.0
.. EXAMPLE END
Note that in this case we did not specify |Time| — this is not needed if the
string is an ISO 8601 time string. As for the |QTable| and |Table| class ``loc``
attribute, in order to be consistent with `pandas
<https://pandas.pydata.org/>`_, the last item in the ``loc`` range is inclusive.
Also note that the result will always be sorted by time. Similarly, the
:attr:`~astropy.timeseries.TimeSeries.iloc` attribute can be used to fetch
rows from the time series *sorted by time*, so for example, the first two
entries (by time) can be accessed with::
>>> ts.iloc[0:2]
<TimeSeries length=2>
time flux temp
Jy K
Time float64 float64
----------------------- ------- -------
2016-03-22T12:30:31.000 1.0 40.0
2016-03-22T12:30:34.000 4.0 41.0
|
