Ctrl+K

Accessing Data in Time Series#

Accessing Data#

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']  
<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']  
<Quantity 1. Jy>

>>> ts['temp'][2]  
<Quantity 39. K>

Accessing Times#

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 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']>

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  
<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.

See Converting between Different Time Representations for more information about changing between different representations of time.

Extracting a Subset of Columns#

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

Extracting a Subset of Rows#

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 Table Indexing. This provides the ability to access rows and a subset of rows using the loc and iloc attributes.

The 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')]  
<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

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, the last item in the loc range is inclusive.

Also note that the result will always be sorted by time. Similarly, the 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
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