Contents

This notebook covers the basics of creating an event list object and carrying out various operations such as simulating time and energies, joining, storing and retrieving event lists.

Setup

Import some useful libraries.

[1]:
import numpy as np

from matplotlib import pyplot as plt
%matplotlib inline

Import some relevant stingray classes.

[2]:
from stingray import EventList, Lightcurve

Creating EventList from Photon Arrival Times

Given photon arrival times, an eventlist object can be created. Times are assumed to be seconds from a reference MJD, that can optionally be specified with the mjdref keyword and attribute.

[3]:
times = [0.5, 1.1, 2.2, 3.7]
mjdref=58000.

Create event list object by passing arrival times as argument.

[4]:
ev = EventList(times, mjdref=mjdref)
ev.time
[4]:
array([0.5, 1.1, 2.2, 3.7])

One can add all sorts of data to the EventList object, it is very flexible. In general, we suggest to stick with easily interpretable attributes, like energy or pi.

ev.energy = [0., 3., 4., 20.]

is the same as

[5]:
energy = [0., 3., 4., 20.]
ev = EventList(times, energy=energy, mjdref=mjdref)

It is always recommended to specify the good time intervals (GTIs) of the event list, as the time intervals where the instrument was fully operational. If not specified, GTIs are defined automatically as the first and the last event time.

[6]:
gti = [[0, 4]]
ev = EventList(times, gti=gti, energy=energy, mjdref=mjdref)

Roundtrip to Astropy-compatible formats

EventList has the following methods that allow an easy roundtrip to Astropy objects: to_astropy_table, to_astropy_timeseries, from_astropy_table, from_astropy_timeseries

This allows a better interoperability with the Astropy ecosystem.

In this roundtrip, a Table or Timeseries object is created, having as columns time and all other attributes of the same size (e.g. pi, energy), and the rest of the attributes (e.g. gti, mjdref) in the table’s metadata.

[7]:
table = ev.to_astropy_table()
table
[7]:
Table length=4
energytime
float64float64
0.00.5
3.01.1
4.02.2
20.03.7

When converting to Timeseries, times are transformed into astropy.time.TimeDelta objects.

[8]:
timeseries = ev.to_astropy_timeseries()
timeseries
[8]:
TimeSeries length=4
timeenergy
objectfloat64
5.787037037037037e-060.0
1.2731481481481482e-053.0
2.5462962962962965e-054.0
4.282407407407408e-0520.0
[9]:
table.meta
[9]:
OrderedDict([('dt', 0),
             ('gti', array([[0, 4]])),
             ('mjdref', 58000.0),
             ('ncounts', 4),
             ('notes', '')])

Of course, these objects can be converted back to event lists. The user should be careful in defining the proper column names and metadata so that the final object is a valid EventList

[10]:
table_ev = EventList.from_astropy_table(table)
table_ts = EventList.from_astropy_timeseries(timeseries)

[11]:
table_ev.time, table_ts.time
[11]:
(array([0.5, 1.1, 2.2, 3.7]), array([0.5, 1.1, 2.2, 3.7]))

Loading and writing EventList objects

We made it possible to save and load data in a number of different formats.

The general syntax is

ev = EventList.read(filename, format)

ev.write(filename, format)

There are three main blocks of formats that might be useful:

  1. (read-only) HEASoft-compatible formats -> read event data from HEASOFT-supported missions

  2. pickle: reading and saving EventLists from/to Python pickle objects

  3. Any format compatible with astropy.table.Table objects.

Loading an EventList from an X-ray observation in HEASoft-compatible format

Loading event data from HEASoft-supported missions in FITS format is easy. It’s sufficient to use the read method with hea or, equivalently, ogip, as format.

Beware: please use hea or ogip, not fits! It would make the roundrip to Astropy tables more complicated, as Astropy supports a generic FITS writer which is not necessarily compatible with HEASoft.

[12]:
ev = EventList.read('events.fits', 'ogip')

Times are saved to the time attribute, GTIs to the gti attribute, MJDREF to the mjdref attribute, etc.

[13]:
ev.time[:10]
[13]:
array([80000000.23635569, 80000001.47479323, 80000001.78458866,
       80000002.78943624, 80000003.42859936, 80000004.07943003,
       80000006.09310323, 80000007.18041813, 80000008.17602143,
       80000008.20403489], dtype=float128)
[14]:
ev.mjdref
[14]:
55197.00076601852
[15]:
ev.gti
[15]:
array([[80000000., 80001025.]], dtype=float128)

Roundtrip to pickle objects

It is possible to save and load eventlist objects using pickle.

[16]:
ev.write("events.p", "pickle")
ev2 = EventList.read("events.p", "pickle")

np.allclose(ev2.time, ev.time), np.allclose(ev2.gti, ev.gti)
[16]:
(True, True)

Roundtrip to Astropy-compatible formats

If the read and write methods receive a format which is not hea, ogip, or pickle, the event list is transformed into an Astropy Table object with the methods described above, and the readers and writers from the Table class are used instead. This allows to extend the save/load operations to a large number of formats, including hdf5 and enhanced CSV (ascii.ecsv).

Note that columns coming from the EVENTS (or equivalent) fits extension, those having the same length as time, when converting to astropy tables they become columns of the table. All the others, including gti, are treated as metadata.

Care should be used in selecting formats that preserve metadata. For example, simple CSV format loses all metadata, including MJDREF, GTIs etc.

[17]:
ev.write("events.hdf5", "hdf5")
ev3 = EventList.read("events.hdf5", "hdf5")
ev3.time[:10]
[17]:
array([80000000.23635569, 80000001.47479323, 80000001.78458866,
       80000002.78943624, 80000003.42859936, 80000004.07943003,
       80000006.09310323, 80000007.18041813, 80000008.17602143,
       80000008.20403489], dtype=float128)
[18]:
# Try the round trip again to verify that everything works

ev.write("events.ecsv", "ascii.ecsv")
ev4 = EventList.read("events.ecsv", "ascii.ecsv")
!cat events.ecsv
ev4.time[:10]
# %ECSV 1.0
# ---
# datatype:
# - {name: energy, datatype: float32}
# - {name: pi, datatype: float32}
# - {name: time, datatype: float128}
# meta: !!omap
# - {dt: 0}
# - gti: !numpy.ndarray
#     buffer: !!binary |
#       QUFBQUFBQ0FscGdaUURHRnBuOEFBQUFBQUFBZ0FKZVlHVUF4aGFaL0FBQT0=
#     dtype: float128
#     order: C
#     shape: !!python/tuple [1, 2]
# - {header: 'XTENSION= ''BINTABLE''           / binary table extension                         BITPIX  =                    8 / array
#     data type                                NAXIS   =                    2 / number of array dimensions                     NAXIS1  =                   12
#     / length of dimension 1                          NAXIS2  =                 1000 / length of dimension 2                          PCOUNT  =                    0
#     / number of group parameters                     GCOUNT  =                    1 / number of groups                               TFIELDS
#     =                    2 / number of table fields                         TTYPE1  = ''TIME    ''                                                            TFORM1  =
#     ''1D      ''                                                            TTYPE2  = ''PI      ''                                                            TFORM2  =
#     ''1J      ''                                                            EXTNAME = ''EVENTS  ''           / extension name                                 OBSERVER=
#     ''Edwige Bubble''                                                       TELESCOP= ''NuSTAR  ''           / Telescope (mission) name                       INSTRUME=
#     ''FPMA    ''           / Instrument name                                OBS_ID  = ''00000000001''        / Observation ID                                 TARG_ID
#     =                    0 / Target ID                                      OBJECT  = ''Fake X-1''           / Name of observed object                        RA_OBJ  =                  0.0
#     / [deg] R.A. Object                              DEC_OBJ =                  0.0 / [deg] Dec Object                               RA_NOM  =                  0.0
#     / Right Ascension used for barycenter correctionsDEC_NOM =                  0.0 / Declination used for barycenter corrections    RA_PNT  =                  0.0
#     / [deg] RA pointing                              DEC_PNT =                  0.0 / [deg] Dec pointing                             PA_PNT  =                  0.0
#     / [deg] Position angle (roll)                    EQUINOX =               2000.0 / Equinox of celestial coord system              RADECSYS=
#     ''FK5     ''           / Coordinate Reference System                    TASSIGN = ''SATELLITE''          / Time assigned by onboard
#     clock                 TIMESYS = ''TDB     ''           / All times in this file are TDB                 MJDREFI =                55197
#     / TDB time reference; Modified Julian Day (int)  MJDREFF =        0.00076601852 / TDB time reference; Modified Julian Day (frac)
#     TIMEREF = ''SOLARSYSTEM''        / Times are pathlength-corrected to barycenter   CLOCKAPP=                    F / TRUE if timestamps
#     corrected by gnd sware      TIMEUNIT= ''s       ''           / unit for time keywords                         TSTART  =           80000000.0
#     / Elapsed seconds since MJDREF at start of file  TSTOP   =           80001025.0 / Elapsed seconds since MJDREF at end of file    LIVETIME=               1025.0
#     / On-source time                                 TIMEZERO=                  0.0 / Time Zero                                      COMMENT
#     FITS (Flexible Image Transport System) format is defined in ''Astronomy aCOMMENT nd Astrophysics'', volume 376, page 359; bibcode:
#     2001A&A...376..359H    COMMENT MJDREFI+MJDREFF = epoch of Jan 1, 2010, in TT time system.              HISTORY File modified by
#     user ''meo'' with fv  on 2015-08-17T14:10:02             HISTORY File modified by user ''meo'' with fv  on 2015-08-17T14:48:52             END                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             '}
# - {instr: fpma}
# - {mission: nustar}
# - {mjdref: 55197.00076601852}
# - {ncounts: 1000}
# - {notes: ''}
# - {timeref: solarsystem}
# - {timesys: tdb}
# schema: astropy-2.0
energy pi time
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33.039997 786.0 80000001.47479322553
7.9999995 160.0 80000001.78458866477
27.84 656.0 80000002.789436236024
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[18]:
array([80000000.23635569, 80000001.47479323, 80000001.78458866,
       80000002.78943624, 80000003.42859936, 80000004.07943003,
       80000006.09310323, 80000007.18041813, 80000008.17602143,
       80000008.20403489], dtype=float128)

Transforming a Lightcurve into an EventList.

Event lists can be obtained from light curves, where the standard followed is as follows: as many events are created as the counts in the lightcurve at the time specified by time bins.

To demonstrate this, let us define a light curve.

[19]:
times = np.arange(3)
counts = np.floor(np.random.rand(3)*5)
lc = Lightcurve(times, counts, skip_checks=True, dt=1.)
[20]:
lc.time, lc.counts
[20]:
(array([0, 1, 2]), array([1., 4., 3.]))

Now, eventlist can be loaded by calling static from_lc() method.

[21]:
ev = EventList.from_lc(lc)
ev.time
[21]:
array([0, 1, 1, 1, 1, 2, 2, 2])

Simulating EventList from Lightcurve

An arguably better way is having proper random events, reproducing the initial light curve within the errors. Stingray does this by using the inverse CDF method, using the light curve as a binned probability distribution. Please note that in this case we will have to create the EventList object before (in technical terms, simulate_times is not a static method.). See simulation tutorial for more details.

[22]:
ev = EventList()
ev.simulate_times(lc)
ev.time
[22]:
array([0.60459939, 0.8644437 , 1.47100837, 1.54281243, 1.80725171,
       2.47032653])

Creating a light curve from an EventList object

After simulating event list, the original light curve can be recovered. Let’s demonstrate by creating a light curve.

[23]:
dt = 1.
times = np.arange(50)
counts = np.floor(np.random.rand(50)*50000)
lc = Lightcurve(times, counts, skip_checks=True, dt=1.)

Simulate an event list.

[24]:
ev = EventList()
ev = ev.from_lc(lc)
[25]:
ev.gti
[25]:
array([[-0.5, 49.5]])

Recover original light curve curve using to_lc() method. Here, dt defines time resolution, tstart the starting time, and tseg the total time duration.

[26]:
lc_new = ev.to_lc(dt=1)

Let us verify that this has worked properly, by comparing the input and output light curves

[27]:
plt.plot(lc.time, lc.counts,'r-', lc_new.counts, 'g-', drawstyle="steps-mid")
plt.xlabel('Times')
plt.ylabel('Counts')
[27]:
Text(0, 0.5, 'Counts')
../../_images/notebooks_EventList_EventList_Tutorial_54_1.png

… and their difference

[28]:
plt.plot(lc.time, lc.counts - lc_new.counts, 'g-', drawstyle="steps-mid")
plt.xlabel('Times')
plt.ylabel('Counts')
[28]:
Text(0, 0.5, 'Counts')
../../_images/notebooks_EventList_EventList_Tutorial_56_1.png

As can be seen from the figure above, the recovered light curve is aligned with the original light curve.

Simulating Energies

In order to simulate photon energies, a spectral distribution needs to be passed. The spectrum input is a two-dimensional array, with the energies in keV in the first dimension and the number of counts in the second. The count array will be normalized before the simulation: the raw counts do not matter, but only the ratio of the counts in each bin to the total. Again, the energies are simulated using an inverse CDF method.

[29]:
spectrum = [[1, 2, 3, 4, 5, 6],[1000, 2040, 1000, 3000, 4020, 2070]]
[30]:
ev = EventList(time=np.sort(np.random.uniform(0, 1000, 12)))
ev.simulate_energies(spectrum)
[31]:
ev.energy
[31]:
array([4.84164641, 3.62741142, 3.68169619, 4.70867585, 4.92065534,
       4.93644725, 2.26749277, 5.45959615, 3.01137686, 4.86366818,
       0.63048041, 6.26300006])

Joining EventLists

Two event lists can also be joined together. If the GTI do not overlap, the event times and GTIs are appended. Otherwise, the GTIs are crossed (i.e., only the overlapping parts are saved) and the events merged together.

[32]:
ev1 = EventList(time=[1,2,3], gti=[[0.5, 3.5]])
ev2 = EventList(time=[4,5], gti=[[3.5, 5.5]])
ev = ev1.join(ev2)
ev.time, ev.gti
[32]:
(array([1, 2, 3, 4, 5]), array([[0.5, 5.5]]))
[33]:
ev1 = EventList(time=[1,2,3], gti=[[0.5, 3.5]])
ev2 = EventList(time=[1.2, 3.3, 5.6], gti=[[0.6, 7.8]])
ev = ev1.join(ev2)
ev.time, ev.gti
[33]:
(array([1. , 1.2, 2. , 3. , 3.3, 5.6]), array([[0.6, 3.5]]))
[ ]: