Manipulating Data

Lightcurve objects offer the first examples of data methods - these are methods that manipulate the containers themselves.

Data Methods

The set of available methods depends on the kind of data that is being stored in the DataSet, but all methods can be utilised via the DataSet’s apply() method.

To showcase this, some data will first be fetched from ZTF for Al Com - a WZ Sge cataclysmic variable:

from ATK import query

asassn_query = query("lightcurve", targets=3932951035266314496 , survey="ztf", path="example_lightcurve_4.fits.gz")
asassn_query.show(show_all=True)
asassn_query.open(time_format="original")

<Lightcurve DataSet>

.kind:      Lightcurve
.targets:   3932951035266314496 | 188.107° 14.345° (icrs, 2016-01-01T00:00:00.000, 3.0″)
.exception: False
.data:
      <ztf g-band Lightcurve>
            survey:     ztf
            band:       g
            correction: full
            search_pos: 188.107° 14.345° (icrs, 2019-01-01T00:00:00.000)
            separation: 0.088″
            mjd:        [58202.312, 58216.264, ..., 60846.259, 60853.206] d
            mag:        [19.888, 20.018, ..., 19.771, 19.994] mag
            mag_err:    [0.129, 0.141, ..., 0.118, 0.138] mag
            ra:         [188.107, 188.107, ..., 188.107, 188.107] °
            dec:        [14.345, 14.345, ..., 14.345, 14.345] °

      <ztf r-band Lightcurve>
            survey:     ztf
            band:       r
            correction: full
            search_pos: 188.107° 14.345° (icrs, 2019-01-01T00:00:00.000)
            separation: 0.043″
            mjd:        [58217.279, 58217.28, ..., 59269.348, 59269.348] d
            mag:        [20.089, 19.978, ..., 19.804, 20.122] mag
            mag_err:    [0.154, 0.145, ..., 0.103, 0.126] mag
            ra:         [188.107, 188.107, ..., 188.107, 188.107] °
            dec:        [14.345, 14.345, ..., 14.345, 14.345] °

      <ztf i-band Lightcurve>
            survey:     ztf
            band:       i
            correction: full
            search_pos: 188.107° 14.345° (icrs, 2019-01-01T00:00:00.000)
            separation: 0.077″
            mjd:        [58232.295, 58257.206, ..., 60416.388, 60450.212] d
            mag:        [20.057, 19.959, ..., 19.185, 19.328] mag
            mag_err:    [0.179, 0.171, ..., 0.107, 0.118] mag
            ra:         [188.107, 188.107, ..., 188.107, 188.107] °
            dec:        [14.345, 14.345, ..., 14.345, 14.345] °


Available Methods: .add(), .apply(), .from_target(), .merge(), .open(), .plot(), .save(), .show(), .split(), .store()

Cropping a Light Curve

The returned Lightcurve can be cropped by applying its crop() data method. This truncates all array-like attributes of the Lightcurve to match a chosen MJD range, in this case focusing on the large outburst:

cropped_data = asassn_query.apply("crop", cmin=58550, cmax=58660, inplace=False)
cropped_data.open(time_format="original")

Note

By default, apply() modifies the stored data containers in-place. To instead operate on a copy of the DataSet - thereby leaving the original unmodified - pass inplace=False to apply() (as above).

Binning a Light Curve

The Lightcurve can also be binned, combining all array-like attributes into a requested number of bins or a given bin size:

binned_data = asassn_query.apply("bin", bins=200, inplace=False)
binned_data.open(time_format="original")

import astropy.units as u

binned_data = asassn_query.apply("bin", size=10*u.day, inplace=False)
binned_data.open(time_format="original")

Sigma-Clipping a Light Curve

The Lightcurve can be sigma clipped by applying clip(), which clips all array-like attributes to eliminate data points where the light curve’s brightness is outside a given number of standard deviation from the median:

clipped_data = binned_data.apply("clip", sigma=2, inplace=False)
clipped_data.open(time_format="original")

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