Accessing data in time series¶
Accessing data¶
For the examples in this page, we will consider a simple sampled time series
with two data columns - flux
and temp
:
>>> from collections import OrderedDict
>>> from astropy import units as u
>>> from astropy_timeseries import TimeSeries
>>> ts = TimeSeries(time='2016-03-22T12:30:31',
... time_delta=3 * u.s,
... data={'flux': [1., 4., 5., 3., 2.],
... 'temp': [40., 41., 39., 24., 20.]},
... names=('flux', 'temp'))
As for Table
, columns can be accessed by name:
>>> ts['flux']
<Column name='flux' dtype='float64' length=5>
1.0
4.0
5.0
3.0
2.0
>>> 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
object float64 float64
----------------------- ------- -------
2016-03-22T12:30:31.000 1.0 40.0
Accessing individual values can then be done either by accessing a column then a row, or vice-versa:
>>> ts[0]['flux']
1.0
>>> ts['temp'][2]
39.0
Accessing times¶
For TimeSeries
, the time
column can be accessed using the regular column
access notation, as shown in Accessing data, but they 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
object 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
And we can also create a plain QTable
by extracting just the flux
and
temp
columns:
>>> ts['flux', 'temp']
<QTable length=5>
flux temp
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¶
Time series objects can be sliced by rows, using the same syntax as for Time
,
e.g.:
>>> ts[0:2]
<TimeSeries length=2>
time flux temp
object float64 float64
----------------------- ------- -------
2016-03-22T12:30:31.000 1.0 40.0
2016-03-22T12:30:34.000 4.0 41.0
Time series objects are also automatically indexed using the functionality
described in Table indexing. This provides the ability to access rows and
subset of rows using the loc
and
iloc
attributes.
The loc
attribute can be used to slice
the time series 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
object float64 float64
----------------------- ------- -------
2016-03-22T12:30:31.000 1.0 40.0
or within a time range:
>>> ts.loc[Time('2016-03-22T12:30:31'):Time('2016-03-22T12:30:40')]
<TimeSeries length=4>
time flux temp
object 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 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 two first
entries (by time) can be accessed with:
>>> ts.iloc[0:2]
<TimeSeries length=2>
time flux temp
object float64 float64
----------------------- ------- -------
2016-03-22T12:30:31.000 1.0 40.0
2016-03-22T12:30:34.000 4.0 41.0