Table and EventTable objects¶Note
This document complements the upstream Astropy documentation on reading/writing Table objects, please refer to Reading and writing Table objects.
Astropy provides an excellent unified input/output system for the
Table object, and GWpy extends upon that to include common
gravitational-wave file types, as well as providing event-specific
input/output registrations for event data.
In the most general case you can read a table of data as follows:
>>> from gwpy.table import Table
>>> table = Table.read('mydata.txt')
GWpy extends the Astropy functionality with readers for the following file formats:
Each of the sub-sections below outlines how to read and write in these file formats, include the custom keyword arguments to pass to EventTable.read() and EventTable.write().
The EventTable.read() method accepts the nproc
keyword argument, allowing multi-processed reading of lists of files.
This argument can be used with any file-format, not just those defined below,
but is not backported to for use with Table.read().
LIGO_LW XML¶Additional dependencies: python-ligo-lw
The LIGO Scientific Collaboration uses a custom scheme of XML in which to
store tabular data, called LIGO_LW.
Complementing the scheme is a python library - python-ligo-lw - which
allows users to read and write all of the different types of tabular data
produced by gravitational-wave searches.
Reading and writing tables in LIGO_LW XML format is supported with
format='ligolw', tablename=<tablename>' where <tablename> can be
any of the supported LSC table names (see ligo.lw.lsctables).
When reading, the tablename keyword argument should be given to identify the table in the file, as follows:
>>> t = EventTable.read('H1-LDAS_STRAIN-968654552-10.xml.gz', tablename='sngl_burst')
The result should be something similar to this:
>>> print(t)
ifo peak_time peak_time_ns start_time ... confidence chisq chisq_dof bandwidth
--- --------- ------------ ---------- ... ---------- ----- --------- ---------
H1 968654557 783203126 968654557 ... 16.811825 0.0 512.0 256.0
H1 968654557 781250001 968654557 ... 16.816761 0.0 512.0 256.0
H1 968654557 779296876 968654557 ... 16.696106 0.0 512.0 256.0
H1 968654557 777343751 968654557 ... 16.739489 0.0 512.0 256.0
H1 968654557 775390626 968654557 ... 16.802326 0.0 512.0 256.0
H1 968654557 773437501 968654557 ... 16.30731 0.0 512.0 256.0
H1 968654557 771484376 968654557 ... 16.307253 0.0 512.0 256.0
H1 968654557 769531251 968654557 ... 16.35647 0.0 512.0 256.0
H1 968654557 767578126 968654557 ... 16.561176 0.0 512.0 256.0
H1 968654557 765625001 968654557 ... 16.393112 0.0 512.0 256.0
H1 968654557 763671876 968654557 ... 16.404041 0.0 512.0 256.0
H1 968654557 761718751 968654557 ... 16.405825 0.0 512.0 256.0
H1 968654557 759765626 968654557 ... 16.715092 0.0 512.0 256.0
H1 968654557 757812501 968654557 ... 17.512749 0.0 512.0 256.0
H1 968654557 755859376 968654557 ... 17.347675 0.0 512.0 256.0
H1 968654557 753906251 968654557 ... 17.077478 0.0 512.0 256.0
H1 968654557 751953126 968654557 ... 16.742907 0.0 512.0 256.0
... ... ... ... ... ... ... ... ...
H1 968654560 342773438 968654559 ... 11.029792 0.0 16.0 8.0
H1 968654559 280273438 968654558 ... 12.363036 0.0 16.0 8.0
H1 968654559 217773438 968654558 ... 13.985101 0.0 16.0 8.0
H1 968654559 155273438 968654558 ... 14.662391 0.0 16.0 8.0
H1 968654559 92773438 968654558 ... 15.864924 0.0 16.0 8.0
H1 968654559 30273438 968654558 ... 16.321821 0.0 16.0 8.0
H1 968654558 967773438 968654558 ... 16.975931 0.0 16.0 8.0
H1 968654558 905273438 968654558 ... 19.160393 0.0 16.0 8.0
H1 968654560 811523438 968654560 ... 11.270205 0.0 8.0 4.0
H1 968654560 686523438 968654560 ... 15.839205 0.0 8.0 4.0
H1 968654560 561523438 968654560 ... 15.944695 0.0 8.0 4.0
H1 968654560 436523438 968654559 ... 14.384432 0.0 8.0 4.0
H1 968654560 311523438 968654559 ... 14.465309 0.0 8.0 4.0
H1 968654560 186523438 968654559 ... 13.045853 0.0 8.0 4.0
H1 968654560 561523438 968654560 ... 11.636543 0.0 4.0 4.0
H1 968654560 436523438 968654560 ... 15.344837 0.0 4.0 4.0
H1 968654560 311523438 968654560 ... 11.367717 0.0 4.0 4.0
Length = 2052 rows
The tablename keyword can be omitted if there is only a single table in the file.
To restrict the columns returned in the new EventTable, use the columns keyword argument:
>>> t = EventTable.read('H1-LDAS_STRAIN-968654552-10.xml.gz', tablename='sngl_burst', columns=['peak_time', 'peak_time_ns', 'snr', 'peak_frequency'])
Many LIGO_LW table objects (as defined in glue.ligolw.lsctables) include utility functions to create new columns by combining others, e.g. to calculate the Q of a sine-Gaussian pulse from the duration and central frequency.
These ‘columns’ can be requested directly, providing the glue.ligolw.table.Table representation of the data has a get_ method for that name:
>>> t = EventTable.read('H1-LDAS_STRAIN-968654552-10.xml.gz', tablename='sngl_burst', columns=['snr', 'q', 'duration', 'central_freq'])
Note
When reading a processed column in this manner, all required input columns for a processed column must be included in the columns keyword list. To exclude these columns from the returned data, use the ligolw_columns= keyword to specify the columns required to provide the output columns:
>>> t = EventTable.read('H1-LDAS_STRAIN-968654552-10.xml.gz', tablename='sngl_burst', columns=['snr', 'q'], ligolw_columns=['snr', 'duration', 'central_freq'])
By default, the returned Table or EventTable uses the dtypes returned by the glue.ligolw library, and functions therein, which often end up as numpy.object_ arrays in the table.
To force all columns to have real numpy data types, use the use_numpy_dtypes=True keyword, which will cast (known) custom object types to a standard numpy.dtype, e.g:
>>> t = EventTable.read('H1-LDAS_STRAIN-968654552-10.xml.gz', tablename='sngl_burst', columns=['peak'], ligolw_columns=['peak_time', 'peak_time_ns'])
>>> print(type(t[0]['peak']))
<type 'lal.LIGOTimeGPS'>
>>> t = EventTable.read('H1-LDAS_STRAIN-968654552-10.xml.gz', tablename='sngl_burst', columns=['peak'], ligolw_columns=['peak_time', 'peak_time_ns'], use_numpy_dtypes=True)
>>> print(type(t[0]['peak']))
<type 'numpy.float64'>
A table can be written as follows:
>>> t.write('new-table.xml', format='ligolw', tablename='sngl_burst')
Because LIGO_LW isn’t the only scheme of XML, the format keyword is required for all Table.write() operations.
If the target file already exists, an IOError will be raised, use overwrite=True to force a new file to be written.
To write a table to an existing file, use append=True:
>>> t.write('new-table.xml', format='ligolw', tablename='sngl_burst', append=True)
To replace an existing table of the given type in an existing file, while preserving other tables, use both append=True and overwrite=True:
>>> t.write('new-table.xml', format='ligolw', tablename='sngl_burst', append=True, overwrite=True)
Note
The python-ligo-lw library reads and writes files using an updated
version of the LIGO_LW format compared to glue.ligolw used to.
GWpy should support both format versions natively when _reading_, but
when _writing_ you may need to explicitly pass the
ilwdchar_compat=True keyword in order to write using the old
format:
>>> t.write('new-table.xml', format='ligolw', tablename='sngl_burst',
... ilwdchar_compat=True)
EVENTS.txt)¶The Coherent WaveBurst analysis pipeline is used to detect generic gravitational-wave bursts, without using a signal model to restrict the analysis, and runs in both low-latency (online) and offline modes over current GWO data.
The analysis uses the ROOT framework for most data products, but also produces ASCII data in a custom format commonly written in a file called EVENTS.txt.
To read a cWB ASCII file:
>>> t = EventTable.read('EVENTS.txt', format='ascii.cwb')
See the astropy.io.ascii.read() documentation for full details on keyword arguments when reading ascii.cwb files.
To write a table using the cWB ASCII format:
>>> t.write('EVENTS.txt', format='ascii.cwb')
[the output file name is not required to be 'EVENTS.txt', this is simply the convention used in the cWB analysis.]
Additional dependencies: root_numpy
To read a ROOT tree into a Table (or EventTable), specify the relevant tree via the treename keyword argument:
>>> t = Table.read('my-data.root', treename='triggers')
If treename=None is given (default), a single tree will be read if only one exists in the file, otherwise a ValueError will be raised.
To specify the branches to read, use the branches keyword argument:
>>> t = Table.read('my-data.root', treename='triggers', branches=['time', 'frequency', 'snr'])
Any other keyword arguments will be passed directly to root_numpy.root2array().
To write a Table as a ROOT tree:
>>> t.write('new-table.root')
As with reading, the treename keyword argument can be used to specify the tree, the default is treename='tree'.
By default, an existing file with an existing tree of the given name will be appended to, to overwrite use the mode='recreate' keyword argument:
>>> t.write('new-table.root', treename='triggers', mode='recreate')
Any other keyword arguments will be passed directly to root_numpy.array2root().
PyCBC Live is a low-latency search for gravitational waves from compact binary coalescences, built from the pycbc analysis package.
This search writes files on the LIGO Data Grid (LIGO.ORG-authenticated users only) in HDF5 format, containing tables of events; each column in the table is recorded as a separate HDF5 Dataset.
To read an EventTable from a pycbc_live format HDF5 file, use the format='hdf5.pycbc_live' keyword:
>>> t = EventTable.read('H1-Live-1234567890-4.hdf', format='hdf5.pycbc_live')
To restrict the returned columns, use the columns keyword argument:
>>> t = EventTable.read('H1-Live-1234567890-4.hdf', format='hdf5.pycbc_live', columns=['end_time', 'snr', 'chisq'])
Similarly to the LIGO_LW XML format, some processed columns can be specified that are not included in the HDF5 files, but are created on-the-fly. Supported processed columns are:
mchirp
new_snr
These can be specified without having to specify any of the input columns.
Additionally, PyCBC HDF5 table Groups include extra datasets that aren’t part of the table, e.g. 'psd'.
These can be included in the returned EventTable.meta dict via the keyword extended_metadata=True (default), or excluded with extended_metadata=False).
Writing tables in PyCBC Live HDF5 format is not supported at this time.
The SNAX (Signal-based Noise Acquisition and eXtraction) analysis pipeline is a low-latency search for identifying glitches in h(t) and auxiliary channel data using glitch waveforms, operating in low-latency (online) and offline modes. This search writes files on the LIGO Data Grid (LIGO.ORG-authenticated users only) in HDF5 format containing regularly-sampled features; each channel in the table is recorded as a separate HDF5 Dataset.
To read an EventTable from a snax format HDF5 file, specify a channel and use the format='hdf5.snax' keyword:
>>> t = EventTable.read('H-GSTLAL_IDQ_FEATURES-1255853400-20.h5', 'H1:CAL-DELTAL_EXTERNAL_DQ', format='hdf5.snax')
To restrict the returned columns, use the columns keyword argument:
>>> t = EventTable.read('H-GSTLAL_IDQ_FEATURES-1255853400-20.h5', 'H1:CAL-DELTAL_EXTERNAL_DQ', format='hdf5.snax', columns=['time', 'snr'])
Writing tables in SNAX HDF5 format is not supported at this time.
Additional dependencies: LDAStools.frameCPP
The Gravitational-Wave Frame file format supports tabular data via FrEvent structures, which allow storage of arbitrary event information.
To read an EventTable from a GWF-format file, specify the filename and the name of the FrEvent structures to read:
>>> t = EventTable.read('events.gwf', 'my-event-name')
To restrict the returned columns, use the columns keyword argument:
>>> t = EventTable.read('events.gwf', 'my-event-name', columns=['time', 'amplitude'])
All FrEvent structures contain the following columns, any other columns are use-specific:
————— ————————————————————————————
Column name Description (from LIGO-T970130)
————— ————————————————————————————
time Reference time of event, as defined by the search algorithm
amplitude Continuous output amplitude returned by event
probability Likelihood estimate of event, if available (probability = -1 if cannot be estimated)
timeBefore Signal duration before reference time (seconds)
timeAfter Signal duration after reference time (seconds)
comment Descriptor of event
————— ————————————————————————————
Writing tables in GWF format is not supported at this time.
For a full list of available file formats, see the documentation for the Table.read method:
Table.read(*args, **kwargs)Read and parse a data table and return as a Table.
This function provides the Table interface to the astropy unified I/O layer. This allows easily reading a file in many supported data formats using syntax such as:
>>> from astropy.table import Table
>>> dat = Table.read('table.dat', format='ascii')
>>> events = Table.read('events.fits', format='fits')
Get help on the available readers for Table using the``help()`` method:
>>> Table.read.help() # Get help reading Table and list supported formats
>>> Table.read.help('fits') # Get detailed help on Table FITS reader
>>> Table.read.list_formats() # Print list of available formats
See also: http://docs.astropy.org/en/stable/io/unified.html
*args : tuple, optional
Positional arguments passed through to data reader. If supplied the first argument is typically the input filename.
format : str
File format specifier.
**kwargs : dict, optional
Keyword arguments passed through to data reader.
out : Table
Table corresponding to file contents
Notes
The available built-in formats are:
Format |
Read |
Write |
Auto-identify |
Deprecated |
|---|---|---|---|---|
ascii |
Yes |
Yes |
No |
|
ascii.aastex |
Yes |
Yes |
No |
|
ascii.basic |
Yes |
Yes |
No |
|
ascii.cds |
Yes |
No |
No |
|
ascii.commented_header |
Yes |
Yes |
No |
|
ascii.csv |
Yes |
Yes |
Yes |
|
ascii.cwb |
Yes |
Yes |
No |
|
ascii.daophot |
Yes |
No |
No |
|
ascii.ecsv |
Yes |
Yes |
Yes |
|
ascii.fast_basic |
Yes |
Yes |
No |
|
ascii.fast_commented_header |
Yes |
Yes |
No |
|
ascii.fast_csv |
Yes |
Yes |
No |
|
ascii.fast_no_header |
Yes |
Yes |
No |
|
ascii.fast_rdb |
Yes |
Yes |
No |
|
ascii.fast_tab |
Yes |
Yes |
No |
|
ascii.fixed_width |
Yes |
Yes |
No |
|
ascii.fixed_width_no_header |
Yes |
Yes |
No |
|
ascii.fixed_width_two_line |
Yes |
Yes |
No |
|
ascii.html |
Yes |
Yes |
Yes |
|
ascii.ipac |
Yes |
Yes |
No |
|
ascii.latex |
Yes |
Yes |
Yes |
|
ascii.no_header |
Yes |
Yes |
No |
|
ascii.omega |
Yes |
Yes |
No |
|
ascii.rdb |
Yes |
Yes |
Yes |
|
ascii.rst |
Yes |
Yes |
No |
|
ascii.sextractor |
Yes |
No |
No |
|
ascii.tab |
Yes |
Yes |
No |
|
asdf |
Yes |
Yes |
Yes |
|
fits |
Yes |
Yes |
Yes |
|
gwf |
Yes |
No |
No |
|
hdf5 |
Yes |
Yes |
Yes |
|
ligolw |
Yes |
Yes |
Yes |
|
pandas.csv |
Yes |
Yes |
No |
|
pandas.fwf |
Yes |
No |
No |
|
pandas.html |
Yes |
Yes |
No |
|
pandas.json |
Yes |
Yes |
No |
|
root |
Yes |
Yes |
Yes |
|
votable |
Yes |
Yes |
Yes |
|
aastex |
Yes |
Yes |
No |
Yes |
cds |
Yes |
No |
No |
Yes |
csv |
Yes |
Yes |
No |
Yes |
daophot |
Yes |
No |
No |
Yes |
html |
Yes |
Yes |
No |
Yes |
ipac |
Yes |
Yes |
No |
Yes |
latex |
Yes |
Yes |
No |
Yes |
rdb |
Yes |
Yes |
No |
Yes |
Deprecated format names like aastex will be removed in a future version. Use the full
name (e.g. ascii.aastex) instead.
The EventTable.read method can understand all of the above file formats (auto-identify is not inherited), and the following:
EventTable.read(source, *args, **kwargs)[source]Read data into an EventTable
*args
other positional arguments will be passed directly to the underlying reader method for the given format
format : str, optional
the format of the given source files; if not given, an attempt will be made to automatically identify the format
columns : list of str, optional
the list of column names to read
selection : str, or list of str, optional
one or more column filters with which to downselect the returned table rows as they as read, e.g.
'snr > 5'; multiple selections should be connected by ‘ && ‘, or given as alist, e.g.'snr > 5 && frequency < 1000'or['snr > 5', 'frequency < 1000']
nproc : int, optional, default: 1
number of CPUs to use for parallel reading of multiple files
verbose : bool, optional
print a progress bar showing read status, default:
False
.. note::
Keyword arguments other than those listed here may be required depending on the
format
EventTableastropy.io.registry.IORegistryError
if the
formatcannot be automatically identified
IndexError
if
sourceis an empty list
Notes
The available built-in formats are:
Format |
Read |
Write |
Auto-identify |
Deprecated |
|---|---|---|---|---|
ascii |
Yes |
Yes |
No |
|
ascii.aastex |
Yes |
Yes |
No |
|
ascii.basic |
Yes |
Yes |
No |
|
ascii.cds |
Yes |
No |
No |
|
ascii.commented_header |
Yes |
Yes |
No |
|
ascii.csv |
Yes |
Yes |
Yes |
|
ascii.cwb |
Yes |
No |
No |
|
ascii.daophot |
Yes |
No |
No |
|
ascii.ecsv |
Yes |
Yes |
Yes |
|
ascii.fast_basic |
Yes |
Yes |
No |
|
ascii.fast_commented_header |
Yes |
Yes |
No |
|
ascii.fast_csv |
Yes |
Yes |
No |
|
ascii.fast_no_header |
Yes |
Yes |
No |
|
ascii.fast_rdb |
Yes |
Yes |
No |
|
ascii.fast_tab |
Yes |
Yes |
No |
|
ascii.fixed_width |
Yes |
Yes |
No |
|
ascii.fixed_width_no_header |
Yes |
Yes |
No |
|
ascii.fixed_width_two_line |
Yes |
Yes |
No |
|
ascii.html |
Yes |
Yes |
Yes |
|
ascii.ipac |
Yes |
Yes |
No |
|
ascii.latex |
Yes |
Yes |
Yes |
|
ascii.no_header |
Yes |
Yes |
No |
|
ascii.omega |
Yes |
No |
No |
|
ascii.rdb |
Yes |
Yes |
Yes |
|
ascii.rst |
Yes |
Yes |
No |
|
ascii.sextractor |
Yes |
No |
No |
|
ascii.tab |
Yes |
Yes |
No |
|
asdf |
Yes |
Yes |
Yes |
|
fits |
Yes |
Yes |
Yes |
|
gwf |
Yes |
Yes |
Yes |
|
hdf5 |
Yes |
Yes |
Yes |
|
hdf5.pycbc_live |
Yes |
No |
Yes |
|
hdf5.snax |
Yes |
No |
No |
|
ligolw |
Yes |
Yes |
Yes |
|
pandas.csv |
Yes |
Yes |
No |
|
pandas.fwf |
Yes |
No |
No |
|
pandas.html |
Yes |
Yes |
No |
|
pandas.json |
Yes |
Yes |
No |
|
root |
Yes |
Yes |
Yes |
|
root.cwb |
Yes |
No |
No |
|
root.omicron |
Yes |
No |
No |
|
votable |
Yes |
Yes |
Yes |
|
aastex |
Yes |
Yes |
No |
Yes |
cds |
Yes |
No |
No |
Yes |
csv |
Yes |
Yes |
No |
Yes |
daophot |
Yes |
No |
No |
Yes |
html |
Yes |
Yes |
No |
Yes |
ipac |
Yes |
Yes |
No |
Yes |
latex |
Yes |
Yes |
No |
Yes |
rdb |
Yes |
Yes |
No |
Yes |
Deprecated format names like aastex will be removed in a future version. Use the full
name (e.g. ascii.aastex) instead.