Tabular data¶

The LIGO_LW XML format¶

The LIGO Scientific Collaboration uses a custom scheme of XML in which to store tabular data, called the LIGO_LW scheme. Complementing the scheme is a python library - glue.ligolw - which allows users to read and write all of the different types of tabular data produced by gravitational-wave searches.

The remainder of this document outlines the small number of extensions that GWpy provides for the table classes provided by glue.ligolw.

Reading LIGO_LW files¶

GWpy annotates all of the Table subclasses defined in glue.ligolw.lsctables to make reading those tables from LIGO_LW XML files a bit easier.

These annotations hook into the unified input/output scheme used for all of the other core data classes. For example, you can read a table of single-interferometer burst events as follows:

>>> from gwpy.table.lsctables import SnglBurstTable
>>> events = SnglBurstTable.read('../../gwpy/tests/data/H1-LDAS_STRAIN-968654552-10.xml.gz')

For full details, check out the read() documentation.

Plotting event triggers¶

The other annotation GWpy defines provides a simple plotting method for a number of classes.

We can extend the above example to include plotting:

>>> plot = events.plot('time', 'central_freq', color='snr', edgecolor='none', epoch=968654552)
>>> plot.set_xlim(968654552, 968654552+10)
>>> plot.set_ylabel('Frequency [Hz]')
>>> plot.set_yscale('log')
>>> plot.set_title('LIGO Hanford Observatory event triggers for GW100916')
>>> plot.add_colorbar(clim=[1, 5], label='Signal-to-noise ratio', cmap='hot_r')
>>> plot.show()

(Source code, png)

These code snippets are part of the GWpy example on plotting event triggers.

Plotting event tiles¶

Many types of event triggers define a 2-dimensional tile, for example in time and frequency. These tiles can be plotted in a similar manner to simple triggers.

>>> plot = events.plot('time', 'central_freq', 'duration', 'bandwidth', color='snr', epoch=968654552)
>>> plot.set_xlim(968654552, 968654552+10)
>>> plot.set_ylabel('Frequency [Hz]')
>>> plot.set_yscale('log')
>>> plot.set_title('LIGO Hanford Observatory event triggers for GW100916')
>>> plot.add_colorbar(clim=[1, 5], label='Signal-to-noise ratio', cmap='hot_r')
>>> plot.show()

(Source code, png)

These code snippets are part of the GWpy example on plotting events as 2-d tiles.

Table applications¶

Class reference¶

This reference includes the following class entries:

class gwpy.table.lsctables.CoincDefTable(*args)¶

Bases: glue.ligolw.table.Table

Attributes Summary

constraints
how_to_index
next_id
tableName
validcolumns

Methods Summary

from_recarray(array[, columns])	Create a new table from a numpy.recarray
get_coinc_def_id(search, search_coinc_type)	Return the coinc_def_id for the row in the table whose search string and search_coinc_type integer have the values given.
read(*args, **kwargs)	Read data into a CoincDefTable.
to_recarray([columns, on_attributeerror])	Convert this table to a structured numpy.recarray

Attributes Documentation

constraints = 'PRIMARY KEY (coinc_def_id)'¶

how_to_index = {'cd_ssct_index': ('search', 'search_coinc_type')}¶

next_id = <glue.ligolw.ilwd.coinc_definer_coinc_def_id_class object>¶

tableName = 'coinc_definer:table'¶

validcolumns = {'search': 'lstring', 'description': 'lstring', 'coinc_def_id': 'ilwd:char', 'search_coinc_type': 'int_4u'}¶

Methods Documentation

classmethod from_recarray(array, columns=None)[source]¶

Create a new table from a numpy.recarray

Parameters:	array : numpy.recarray an array of data column : list of str, optional the columns to populate, if not given, all columns present in the recarray are mapped

Notes

The columns populated in the numpy.recarray must all map exactly to valid columns of the target Table.

get_coinc_def_id(search, search_coinc_type, create_new=True, description=None)¶

Return the coinc_def_id for the row in the table whose search string and search_coinc_type integer have the values given. If a matching row is not found, the default behaviour is to create a new row and return the ID assigned to the new row. If, instead, create_new is False then KeyError is raised when a matching row is not found. The optional description parameter can be used to set the description string assigned to the new row if one is created, otherwise the new row is left with no description.

classmethod read(*args, **kwargs)¶

Read data into a CoincDefTable.

Parameters:

f : file, str, CacheEntry, list, Cache object representing one or more files. One of an open file a str pointing to a file path on disk a formatted CacheEntry representing one file a list of str file paths a formatted Cache representing many files columns : list, optional list of column name strings to read, default all. ifo : str, optional prefix of IFO to read Warning the ifo keyword argument is only applicable (but is required) when reading single-interferometer data from a multi-interferometer file filt : function, optional function by which to filter events. The callable must accept as input a row of the table event and return True/False. nproc : int, optional, default: 1 number of parallel processes with which to distribute file I/O, default: serial process. Warning The nproc keyword argument is only applicable when reading a list (or Cache) of files. contenthandler : LIGOLWContentHandler SAX content handler for parsing LIGO_LW documents. Warning The contenthandler keyword argument is only applicable when reading from LIGO_LW documents. **loadtxtkwargs : when reading from ASCII, all other keyword arguments are passed directly to numpy.loadtxt

Returns:

table : CoincDefTable CoincDefTable of data with given columns filled

Notes

The available built-in formats are:

Format	Read	Write	Auto-identify
ascii	Yes	No	Yes
coinc_definer	Yes	No	No
csv	Yes	No	No
ligolw	Yes	No	Yes

to_recarray(columns=None, on_attributeerror='raise')[source]¶

Convert this table to a structured numpy.recarray

This returned recarray is a blank data container, mapping columns in the original LIGO_LW table to fields in the output, but mapping none of the instance methods of the origin table.

Parameters:	columns : list of str, optional the columns to populate, if not given, all columns present in the table are mapped on_attributeerror : str how to handle AttributeError when accessing rows, one of ‘raise’ : raise normal exception ‘ignore’ : skip over this column ‘warn’ : print a warning instead of raising error

class gwpy.table.lsctables.CoincTable(*args)¶

Bases: glue.ligolw.table.Table

Attributes Summary

constraints
how_to_index
interncolumns
next_id
tableName
validcolumns

Methods Summary

from_recarray(array[, columns])	Create a new table from a numpy.recarray
read(*args, **kwargs)	Read data into a CoincTable.
to_recarray([columns, on_attributeerror])	Convert this table to a structured numpy.recarray

Attributes Documentation

constraints = 'PRIMARY KEY (coinc_event_id)'¶

how_to_index = {'ce_tsi_index': ('time_slide_id',), 'ce_cdi_index': ('coinc_def_id',)}¶

interncolumns = ('process_id', 'coinc_def_id', 'time_slide_id', 'instruments')¶

next_id = <glue.ligolw.ilwd.coinc_event_coinc_event_id_class object>¶

tableName = 'coinc_event:table'¶

validcolumns = {'coinc_event_id': 'ilwd:char', 'instruments': 'lstring', 'nevents': 'int_4u', 'process_id': 'ilwd:char', 'coinc_def_id': 'ilwd:char', 'time_slide_id': 'ilwd:char', 'likelihood': 'real_8'}¶

Methods Documentation

classmethod from_recarray(array, columns=None)[source]¶

Create a new table from a numpy.recarray

Parameters:	array : numpy.recarray an array of data column : list of str, optional the columns to populate, if not given, all columns present in the recarray are mapped

Notes

The columns populated in the numpy.recarray must all map exactly to valid columns of the target Table.

classmethod read(*args, **kwargs)¶

Read data into a CoincTable.

Parameters:

f : file, str, CacheEntry, list, Cache object representing one or more files. One of an open file a str pointing to a file path on disk a formatted CacheEntry representing one file a list of str file paths a formatted Cache representing many files columns : list, optional list of column name strings to read, default all. ifo : str, optional prefix of IFO to read Warning the ifo keyword argument is only applicable (but is required) when reading single-interferometer data from a multi-interferometer file filt : function, optional function by which to filter events. The callable must accept as input a row of the table event and return True/False. nproc : int, optional, default: 1 number of parallel processes with which to distribute file I/O, default: serial process. Warning The nproc keyword argument is only applicable when reading a list (or Cache) of files. contenthandler : LIGOLWContentHandler SAX content handler for parsing LIGO_LW documents. Warning The contenthandler keyword argument is only applicable when reading from LIGO_LW documents. **loadtxtkwargs : when reading from ASCII, all other keyword arguments are passed directly to numpy.loadtxt

Returns:

table : CoincTable CoincTable of data with given columns filled

Notes

The available built-in formats are:

Format	Read	Write	Auto-identify
ascii	Yes	No	Yes
coinc_event	Yes	No	No
csv	Yes	No	No
ligolw	Yes	No	Yes

to_recarray(columns=None, on_attributeerror='raise')[source]¶

Convert this table to a structured numpy.recarray

This returned recarray is a blank data container, mapping columns in the original LIGO_LW table to fields in the output, but mapping none of the instance methods of the origin table.

Parameters:	columns : list of str, optional the columns to populate, if not given, all columns present in the table are mapped on_attributeerror : str how to handle AttributeError when accessing rows, one of ‘raise’ : raise normal exception ‘ignore’ : skip over this column ‘warn’ : print a warning instead of raising error

class gwpy.table.lsctables.CoincInspiralTable(*args)¶

Bases: glue.ligolw.table.Table

Attributes Summary

how_to_index
interncolumns
tableName
validcolumns

Methods Summary

from_recarray(array[, columns])	Create a new table from a numpy.recarray
hist(column, **kwargs)	Generate a HistogramPlot of this Table.
plot(*args, **kwargs)	Generate an EventTablePlot of this Table.
read(*args, **kwargs)	Read data into a CoincInspiralTable.
to_recarray([columns, on_attributeerror])	Convert this table to a structured numpy.recarray

Attributes Documentation

how_to_index = {'ci_cei_index': ('coinc_event_id',)}¶

interncolumns = ('coinc_event_id', 'ifos')¶

tableName = 'coinc_inspiral:table'¶

validcolumns = {'false_alarm_rate': 'real_8', 'mchirp': 'real_8', 'minimum_duration': 'real_8', 'mass': 'real_8', 'end_time': 'int_4s', 'coinc_event_id': 'ilwd:char', 'snr': 'real_8', 'end_time_ns': 'int_4s', 'combined_far': 'real_8', 'ifos': 'lstring'}¶

Methods Documentation

classmethod from_recarray(array, columns=None)[source]¶

Create a new table from a numpy.recarray

Parameters:	array : numpy.recarray an array of data column : list of str, optional the columns to populate, if not given, all columns present in the recarray are mapped

Notes

The columns populated in the numpy.recarray must all map exactly to valid columns of the target Table.

hist(column, **kwargs)[source]¶

Generate a HistogramPlot of this Table.

Parameters:	column : str name of the column over which to histogram data **kwargs : any other arguments applicable to the HistogramPlot
Returns:	plot : HistogramPlot new plot displaying a histogram of this Table.

plot(*args, **kwargs)[source]¶

Generate an EventTablePlot of this Table.

Parameters:

x : str name of column defining centre point on the X-axis y : str name of column defining centre point on the Y-axis width : str, optional name of column defining width of tile height : str, optional name of column defining height of tile Note The width and height positional arguments should either both not given, in which case a scatter plot will be drawn, or both given, in which case a collections of rectangles will be drawn. color : str, optional, default:None name of column by which to color markers **kwargs : any other arguments applicable to the Plot constructor, and the Table plotter.

Returns:

plot : EventTablePlot new plot for displaying tabular data.

See also

gwpy.plotter.EventTablePlot

for more details.

classmethod read(*args, **kwargs)¶

Read data into a CoincInspiralTable.

Parameters:

f : file, str, CacheEntry, list, Cache object representing one or more files. One of an open file a str pointing to a file path on disk a formatted CacheEntry representing one file a list of str file paths a formatted Cache representing many files columns : list, optional list of column name strings to read, default all. ifo : str, optional prefix of IFO to read Warning the ifo keyword argument is only applicable (but is required) when reading single-interferometer data from a multi-interferometer file filt : function, optional function by which to filter events. The callable must accept as input a row of the table event and return True/False. nproc : int, optional, default: 1 number of parallel processes with which to distribute file I/O, default: serial process. Warning The nproc keyword argument is only applicable when reading a list (or Cache) of files. contenthandler : LIGOLWContentHandler SAX content handler for parsing LIGO_LW documents. Warning The contenthandler keyword argument is only applicable when reading from LIGO_LW documents. **loadtxtkwargs : when reading from ASCII, all other keyword arguments are passed directly to numpy.loadtxt

Returns:

table : CoincInspiralTable CoincInspiralTable of data with given columns filled

Notes

The available built-in formats are:

Format	Read	Write	Auto-identify
ascii	Yes	No	Yes
coinc_inspiral	Yes	No	No
csv	Yes	No	No
ligolw	Yes	No	Yes

to_recarray(columns=None, on_attributeerror='raise')[source]¶

Convert this table to a structured numpy.recarray

This returned recarray is a blank data container, mapping columns in the original LIGO_LW table to fields in the output, but mapping none of the instance methods of the origin table.

Parameters:	columns : list of str, optional the columns to populate, if not given, all columns present in the table are mapped on_attributeerror : str how to handle AttributeError when accessing rows, one of ‘raise’ : raise normal exception ‘ignore’ : skip over this column ‘warn’ : print a warning instead of raising error

class gwpy.table.lsctables.CoincRingdownTable(*args)¶

Bases: glue.ligolw.table.Table

Attributes Summary

how_to_index
interncolumns
tableName
validcolumns

Methods Summary

from_recarray(array[, columns])	Create a new table from a numpy.recarray
hist(column, **kwargs)	Generate a HistogramPlot of this Table.
plot(*args, **kwargs)	Generate an EventTablePlot of this Table.
read(*args, **kwargs)	Read data into a CoincRingdownTable.
to_recarray([columns, on_attributeerror])	Convert this table to a structured numpy.recarray

Attributes Documentation

how_to_index = {'cr_cei_index': ('coinc_event_id',)}¶

interncolumns = ('coinc_event_id', 'ifos')¶

tableName = 'coinc_ringdown:table'¶

validcolumns = {'snr_sq': 'real_8', 'false_alarm_rate': 'real_8', 'kappa': 'real_8', 'coinc_event_id': 'ilwd:char', 'spin': 'real_8', 'start_time': 'int_4s', 'start_time_ns': 'int_4s', 'combined_far': 'real_8', 'frequency': 'real_8', 'mass': 'real_8', 'choppedl_snr': 'real_8', 'snr': 'real_8', 'eff_coh_snr': 'real_8', 'quality': 'real_8', 'null_stat': 'real_8', 'snr_ratio': 'real_8', 'ifos': 'lstring'}¶

Methods Documentation

classmethod from_recarray(array, columns=None)[source]¶

Create a new table from a numpy.recarray

Parameters:	array : numpy.recarray an array of data column : list of str, optional the columns to populate, if not given, all columns present in the recarray are mapped

Notes

The columns populated in the numpy.recarray must all map exactly to valid columns of the target Table.

hist(column, **kwargs)[source]¶

Generate a HistogramPlot of this Table.

Parameters:	column : str name of the column over which to histogram data **kwargs : any other arguments applicable to the HistogramPlot
Returns:	plot : HistogramPlot new plot displaying a histogram of this Table.

plot(*args, **kwargs)[source]¶

Generate an EventTablePlot of this Table.

Parameters:

x : str name of column defining centre point on the X-axis y : str name of column defining centre point on the Y-axis width : str, optional name of column defining width of tile height : str, optional name of column defining height of tile Note The width and height positional arguments should either both not given, in which case a scatter plot will be drawn, or both given, in which case a collections of rectangles will be drawn. color : str, optional, default:None name of column by which to color markers **kwargs : any other arguments applicable to the Plot constructor, and the Table plotter.

Returns:

plot : EventTablePlot new plot for displaying tabular data.

See also

gwpy.plotter.EventTablePlot

for more details.

classmethod read(*args, **kwargs)¶

Read data into a CoincRingdownTable.

Parameters:

f : file, str, CacheEntry, list, Cache object representing one or more files. One of an open file a str pointing to a file path on disk a formatted CacheEntry representing one file a list of str file paths a formatted Cache representing many files columns : list, optional list of column name strings to read, default all. ifo : str, optional prefix of IFO to read Warning the ifo keyword argument is only applicable (but is required) when reading single-interferometer data from a multi-interferometer file filt : function, optional function by which to filter events. The callable must accept as input a row of the table event and return True/False. nproc : int, optional, default: 1 number of parallel processes with which to distribute file I/O, default: serial process. Warning The nproc keyword argument is only applicable when reading a list (or Cache) of files. contenthandler : LIGOLWContentHandler SAX content handler for parsing LIGO_LW documents. Warning The contenthandler keyword argument is only applicable when reading from LIGO_LW documents. **loadtxtkwargs : when reading from ASCII, all other keyword arguments are passed directly to numpy.loadtxt

Returns:

table : CoincRingdownTable CoincRingdownTable of data with given columns filled

Notes

The available built-in formats are:

Format	Read	Write	Auto-identify
ascii	Yes	No	Yes
coinc_ringdown	Yes	No	No
csv	Yes	No	No
ligolw	Yes	No	Yes

to_recarray(columns=None, on_attributeerror='raise')[source]¶

Convert this table to a structured numpy.recarray

This returned recarray is a blank data container, mapping columns in the original LIGO_LW table to fields in the output, but mapping none of the instance methods of the origin table.

Parameters:	columns : list of str, optional the columns to populate, if not given, all columns present in the table are mapped on_attributeerror : str how to handle AttributeError when accessing rows, one of ‘raise’ : raise normal exception ‘ignore’ : skip over this column ‘warn’ : print a warning instead of raising error

class gwpy.table.lsctables.ExperimentMapTable(*args)¶

Bases: glue.ligolw.table.Table

Attributes Summary

how_to_index
tableName
validcolumns

Methods Summary

from_recarray(array[, columns])	Create a new table from a numpy.recarray
get_experiment_summ_ids(coinc_event_id)	Gets all the experiment_summ_ids that map to a given coinc_event_id.
read(*args, **kwargs)	Read data into a ExperimentMapTable.
to_recarray([columns, on_attributeerror])	Convert this table to a structured numpy.recarray

Attributes Documentation

how_to_index = {'em_cei_index': ('coinc_event_id',), 'em_esi_index': ('experiment_summ_id',)}¶

tableName = 'experiment_map:table'¶

validcolumns = {'experiment_summ_id': 'ilwd:char', 'coinc_event_id': 'ilwd:char'}¶

Methods Documentation

classmethod from_recarray(array, columns=None)[source]¶

Create a new table from a numpy.recarray

Parameters:	array : numpy.recarray an array of data column : list of str, optional the columns to populate, if not given, all columns present in the recarray are mapped

Notes

The columns populated in the numpy.recarray must all map exactly to valid columns of the target Table.

get_experiment_summ_ids(coinc_event_id)¶

Gets all the experiment_summ_ids that map to a given coinc_event_id.

classmethod read(*args, **kwargs)¶

Read data into a ExperimentMapTable.

Parameters:

f : file, str, CacheEntry, list, Cache object representing one or more files. One of an open file a str pointing to a file path on disk a formatted CacheEntry representing one file a list of str file paths a formatted Cache representing many files columns : list, optional list of column name strings to read, default all. ifo : str, optional prefix of IFO to read Warning the ifo keyword argument is only applicable (but is required) when reading single-interferometer data from a multi-interferometer file filt : function, optional function by which to filter events. The callable must accept as input a row of the table event and return True/False. nproc : int, optional, default: 1 number of parallel processes with which to distribute file I/O, default: serial process. Warning The nproc keyword argument is only applicable when reading a list (or Cache) of files. contenthandler : LIGOLWContentHandler SAX content handler for parsing LIGO_LW documents. Warning The contenthandler keyword argument is only applicable when reading from LIGO_LW documents. **loadtxtkwargs : when reading from ASCII, all other keyword arguments are passed directly to numpy.loadtxt

Returns:

table : ExperimentMapTable ExperimentMapTable of data with given columns filled

Notes

The available built-in formats are:

Format	Read	Write	Auto-identify
ascii	Yes	No	Yes
csv	Yes	No	No
experiment_map	Yes	No	No
ligolw	Yes	No	Yes

to_recarray(columns=None, on_attributeerror='raise')[source]¶

Convert this table to a structured numpy.recarray

This returned recarray is a blank data container, mapping columns in the original LIGO_LW table to fields in the output, but mapping none of the instance methods of the origin table.

Parameters:	columns : list of str, optional the columns to populate, if not given, all columns present in the table are mapped on_attributeerror : str how to handle AttributeError when accessing rows, one of ‘raise’ : raise normal exception ‘ignore’ : skip over this column ‘warn’ : print a warning instead of raising error

class gwpy.table.lsctables.ExperimentSummaryTable(*args)¶

Bases: glue.ligolw.table.Table

Attributes Summary

constraints
datatypes
how_to_index
next_id
tableName
validcolumns

Methods Summary

add_nevents(experiment_summ_id, num_events)	Add num_events to the nevents column in a specific entry in the table.
as_id_dict()	Return table as a dictionary mapping experiment_id, time_slide_id, veto_def_name, and sim_proc_id (if it exists) to the expr_summ_id.
from_recarray(array[, columns])	Create a new table from a numpy.recarray
get_expr_summ_id(experiment_id, ...[, ...])	Return the expr_summ_id for the row in the table whose experiment_id, time_slide_id, veto_def_name, and datatype match the given.
read(*args, **kwargs)	Read data into a ExperimentSummaryTable.
to_recarray([columns, on_attributeerror])	Convert this table to a structured numpy.recarray
write_experiment_summ(experiment_id, ...[, ...])	Writes a single entry to the experiment_summ table.
write_non_injection_summary(experiment_id, ...)	Method for writing a new set of non-injection experiments to the experiment summary table.

Attributes Documentation

constraints = 'PRIMARY KEY (experiment_summ_id)'¶

datatypes = ['slide', 'all_data', 'playground', 'exclude_play', 'simulation']¶

how_to_index = {'es_dt_index': ('datatype',), 'es_ei_index': ('experiment_id',)}¶

next_id = <glue.ligolw.ilwd.experiment_summary_experiment_summ_id_class object>¶

tableName = 'experiment_summary:table'¶

validcolumns = {'experiment_summ_id': 'ilwd:char', 'duration': 'int_4s', 'nevents': 'int_4u', 'sim_proc_id': 'ilwd:char', 'experiment_id': 'ilwd:char', 'datatype': 'lstring', 'time_slide_id': 'ilwd:char', 'veto_def_name': 'lstring'}¶

Methods Documentation

add_nevents(experiment_summ_id, num_events, add_to_current=True)¶

Add num_events to the nevents column in a specific entry in the table. If add_to_current is set to False, will overwrite the current nevents entry in the row with num_events. Otherwise, default is to add num_events to the current value.

Note: Can subtract events by passing a negative number to num_events.

as_id_dict()¶

Return table as a dictionary mapping experiment_id, time_slide_id, veto_def_name, and sim_proc_id (if it exists) to the expr_summ_id.

classmethod from_recarray(array, columns=None)[source]¶

Create a new table from a numpy.recarray

Parameters:	array : numpy.recarray an array of data column : list of str, optional the columns to populate, if not given, all columns present in the recarray are mapped

Notes

The columns populated in the numpy.recarray must all map exactly to valid columns of the target Table.

get_expr_summ_id(experiment_id, time_slide_id, veto_def_name, datatype, sim_proc_id=None)¶

Return the expr_summ_id for the row in the table whose experiment_id, time_slide_id, veto_def_name, and datatype match the given. If sim_proc_id, will retrieve the injection run matching that sim_proc_id. If a matching row is not found, returns None.

classmethod read(*args, **kwargs)¶

Read data into a ExperimentSummaryTable.

Parameters:

f : file, str, CacheEntry, list, Cache object representing one or more files. One of an open file a str pointing to a file path on disk a formatted CacheEntry representing one file a list of str file paths a formatted Cache representing many files columns : list, optional list of column name strings to read, default all. ifo : str, optional prefix of IFO to read Warning the ifo keyword argument is only applicable (but is required) when reading single-interferometer data from a multi-interferometer file filt : function, optional function by which to filter events. The callable must accept as input a row of the table event and return True/False. nproc : int, optional, default: 1 number of parallel processes with which to distribute file I/O, default: serial process. Warning The nproc keyword argument is only applicable when reading a list (or Cache) of files. contenthandler : LIGOLWContentHandler SAX content handler for parsing LIGO_LW documents. Warning The contenthandler keyword argument is only applicable when reading from LIGO_LW documents. **loadtxtkwargs : when reading from ASCII, all other keyword arguments are passed directly to numpy.loadtxt

Returns:

table : ExperimentSummaryTable ExperimentSummaryTable of data with given columns filled

Notes

The available built-in formats are:

Format	Read	Write	Auto-identify
ascii	Yes	No	Yes
csv	Yes	No	No
experiment_summary	Yes	No	No
ligolw	Yes	No	Yes

to_recarray(columns=None, on_attributeerror='raise')[source]¶

Convert this table to a structured numpy.recarray

This returned recarray is a blank data container, mapping columns in the original LIGO_LW table to fields in the output, but mapping none of the instance methods of the origin table.

Parameters:	columns : list of str, optional the columns to populate, if not given, all columns present in the table are mapped on_attributeerror : str how to handle AttributeError when accessing rows, one of ‘raise’ : raise normal exception ‘ignore’ : skip over this column ‘warn’ : print a warning instead of raising error

write_experiment_summ(experiment_id, time_slide_id, veto_def_name, datatype, sim_proc_id=None)¶

Writes a single entry to the experiment_summ table. This can be used for either injections or non-injection experiments. However, it is recommended that this only be used for injection experiments; for non-injection experiments write_experiment_summ_set should be used to ensure that an entry gets written for every time-slide performed.

write_non_injection_summary(experiment_id, time_slide_dict, veto_def_name, write_all_data=True, write_playground=True, write_exclude_play=True, return_dict=False)¶

Method for writing a new set of non-injection experiments to the experiment summary table. This ensures that for every entry in the experiment table, an entry for every slide is added to the experiment_summ table, rather than just an entry for slides that have events in them. Default is to write a 3 rows for zero-lag: one for all_data, playground, and exclude_play. (If all of these are set to false, will only slide rows.)

Note: sim_proc_id is hard-coded to None because time-slides are not performed with injections.

@experiment_id: the experiment_id for this experiment_summary set @time_slide_dict: the time_slide table as a dictionary; used to figure out

what is zero-lag and what is slide

@veto_def_name: the name of the vetoes applied @write_all_data: if set to True, writes a zero-lag row who’s datatype column

is set to ‘all_data’

@write_playground: same, but datatype is ‘playground’ @write_exclude_play: same, but datatype is ‘exclude_play’ @return_dict: if set to true, returns an id_dict of the table

class gwpy.table.lsctables.ExperimentTable(*args)¶

Bases: glue.ligolw.table.Table

Attributes Summary

constraints
next_id
tableName
validcolumns

Methods Summary

from_recarray(array[, columns])	Create a new table from a numpy.recarray
get_expr_id(search_group, search, lars_id, ...)	Return the expr_def_id for the row in the table whose values match the givens.
get_row_from_id(experiment_id)	Returns row in matching the given experiment_id.
read(*args, **kwargs)	Read data into a ExperimentTable.
to_recarray([columns, on_attributeerror])	Convert this table to a structured numpy.recarray
write_new_expr_id(search_group, search, ...)	Creates a new def_id for the given arguments and returns it.

Attributes Documentation

constraints = 'PRIMARY KEY (experiment_id)'¶

next_id = <glue.ligolw.ilwd.experiment_experiment_id_class object>¶

tableName = 'experiment:table'¶

validcolumns = {'search': 'lstring', 'lars_id': 'lstring', 'experiment_id': 'ilwd:char', 'gps_start_time': 'int_4s', 'instruments': 'lstring', 'gps_end_time': 'int_4s', 'search_group': 'lstring', 'comments': 'lstring'}¶

Methods Documentation

classmethod from_recarray(array, columns=None)[source]¶

Create a new table from a numpy.recarray

Parameters:	array : numpy.recarray an array of data column : list of str, optional the columns to populate, if not given, all columns present in the recarray are mapped

Notes

The columns populated in the numpy.recarray must all map exactly to valid columns of the target Table.

get_expr_id(search_group, search, lars_id, instruments, gps_start_time, gps_end_time, comments=None)¶

Return the expr_def_id for the row in the table whose values match the givens. If a matching row is not found, returns None.

@search_group: string representing the search group (e.g., cbc) @serach: string representing search (e.g., inspiral) @lars_id: string representing lars_id @instruments: the instruments; must be a python set @gps_start_time: string or int representing the gps_start_time of the experiment @gps_end_time: string or int representing the gps_end_time of the experiment

get_row_from_id(experiment_id)¶

Returns row in matching the given experiment_id.

classmethod read(*args, **kwargs)¶

Read data into a ExperimentTable.

Parameters:

f : file, str, CacheEntry, list, Cache object representing one or more files. One of an open file a str pointing to a file path on disk a formatted CacheEntry representing one file a list of str file paths a formatted Cache representing many files columns : list, optional list of column name strings to read, default all. ifo : str, optional prefix of IFO to read Warning the ifo keyword argument is only applicable (but is required) when reading single-interferometer data from a multi-interferometer file filt : function, optional function by which to filter events. The callable must accept as input a row of the table event and return True/False. nproc : int, optional, default: 1 number of parallel processes with which to distribute file I/O, default: serial process. Warning The nproc keyword argument is only applicable when reading a list (or Cache) of files. contenthandler : LIGOLWContentHandler SAX content handler for parsing LIGO_LW documents. Warning The contenthandler keyword argument is only applicable when reading from LIGO_LW documents. **loadtxtkwargs : when reading from ASCII, all other keyword arguments are passed directly to numpy.loadtxt

Returns:

table : ExperimentTable ExperimentTable of data with given columns filled

Notes

The available built-in formats are:

Format	Read	Write	Auto-identify
ascii	Yes	No	Yes
csv	Yes	No	No
experiment	Yes	No	No
ligolw	Yes	No	Yes

to_recarray(columns=None, on_attributeerror='raise')[source]¶

Convert this table to a structured numpy.recarray

This returned recarray is a blank data container, mapping columns in the original LIGO_LW table to fields in the output, but mapping none of the instance methods of the origin table.

Parameters:	columns : list of str, optional the columns to populate, if not given, all columns present in the table are mapped on_attributeerror : str how to handle AttributeError when accessing rows, one of ‘raise’ : raise normal exception ‘ignore’ : skip over this column ‘warn’ : print a warning instead of raising error

write_new_expr_id(search_group, search, lars_id, instruments, gps_start_time, gps_end_time, comments=None)¶

Creates a new def_id for the given arguments and returns it. If an entry already exists with these, will just return that id.

@search_group: string representing the search group (e.g., cbc) @serach: string representing search (e.g., inspiral) @lars_id: string representing lars_id @instruments: the instruments; must be a python set @gps_start_time: string or int representing the gps_start_time of the experiment @gps_end_time: string or int representing the gps_end_time of the experiment

class gwpy.table.lsctables.FilterTable(*args)¶

Bases: glue.ligolw.table.Table

Attributes Summary

constraints
next_id
tableName
validcolumns

Methods Summary

from_recarray(array[, columns])	Create a new table from a numpy.recarray
hist(column, **kwargs)	Generate a HistogramPlot of this Table.
plot(*args, **kwargs)	Generate an EventTablePlot of this Table.
read(*args, **kwargs)	Read data into a FilterTable.
to_recarray([columns, on_attributeerror])	Convert this table to a structured numpy.recarray

Attributes Documentation

constraints = 'PRIMARY KEY (filter_id)'¶

next_id = <glue.ligolw.ilwd.filter_filter_id_class object>¶

tableName = 'filter:table'¶

validcolumns = {'comment': 'lstring', 'process_id': 'ilwd:char', 'param_set': 'int_4s', 'start_time': 'int_4s', 'filter_name': 'lstring', 'program': 'lstring', 'filter_id': 'ilwd:char'}¶

Methods Documentation

classmethod from_recarray(array, columns=None)[source]¶

Create a new table from a numpy.recarray

Parameters:	array : numpy.recarray an array of data column : list of str, optional the columns to populate, if not given, all columns present in the recarray are mapped

Notes

The columns populated in the numpy.recarray must all map exactly to valid columns of the target Table.

hist(column, **kwargs)[source]¶

Generate a HistogramPlot of this Table.

Parameters:	column : str name of the column over which to histogram data **kwargs : any other arguments applicable to the HistogramPlot
Returns:	plot : HistogramPlot new plot displaying a histogram of this Table.

plot(*args, **kwargs)[source]¶

Generate an EventTablePlot of this Table.

Parameters:

x : str name of column defining centre point on the X-axis y : str name of column defining centre point on the Y-axis width : str, optional name of column defining width of tile height : str, optional name of column defining height of tile Note The width and height positional arguments should either both not given, in which case a scatter plot will be drawn, or both given, in which case a collections of rectangles will be drawn. color : str, optional, default:None name of column by which to color markers **kwargs : any other arguments applicable to the Plot constructor, and the Table plotter.

Returns:

plot : EventTablePlot new plot for displaying tabular data.

See also

gwpy.plotter.EventTablePlot

for more details.

classmethod read(*args, **kwargs)¶

Read data into a FilterTable.

Parameters:

f : file, str, CacheEntry, list, Cache object representing one or more files. One of an open file a str pointing to a file path on disk a formatted CacheEntry representing one file a list of str file paths a formatted Cache representing many files columns : list, optional list of column name strings to read, default all. ifo : str, optional prefix of IFO to read Warning the ifo keyword argument is only applicable (but is required) when reading single-interferometer data from a multi-interferometer file filt : function, optional function by which to filter events. The callable must accept as input a row of the table event and return True/False. nproc : int, optional, default: 1 number of parallel processes with which to distribute file I/O, default: serial process. Warning The nproc keyword argument is only applicable when reading a list (or Cache) of files. contenthandler : LIGOLWContentHandler SAX content handler for parsing LIGO_LW documents. Warning The contenthandler keyword argument is only applicable when reading from LIGO_LW documents. **loadtxtkwargs : when reading from ASCII, all other keyword arguments are passed directly to numpy.loadtxt

Returns:

table : FilterTable FilterTable of data with given columns filled

Notes

The available built-in formats are:

Format	Read	Write	Auto-identify
ascii	Yes	No	Yes
csv	Yes	No	No
filter	Yes	No	No
ligolw	Yes	No	Yes

to_recarray(columns=None, on_attributeerror='raise')[source]¶

Convert this table to a structured numpy.recarray

This returned recarray is a blank data container, mapping columns in the original LIGO_LW table to fields in the output, but mapping none of the instance methods of the origin table.

Parameters:	columns : list of str, optional the columns to populate, if not given, all columns present in the table are mapped on_attributeerror : str how to handle AttributeError when accessing rows, one of ‘raise’ : raise normal exception ‘ignore’ : skip over this column ‘warn’ : print a warning instead of raising error

class gwpy.table.lsctables.GDSTriggerTable(*args)¶

Bases: glue.ligolw.table.Table

Attributes Summary

constraints
interncolumns
next_id
tableName
validcolumns

Methods Summary

from_recarray(array[, columns])	Create a new table from a numpy.recarray
hist(column, **kwargs)	Generate a HistogramPlot of this Table.
plot(*args, **kwargs)	Generate an EventTablePlot of this Table.
read(*args, **kwargs)	Read data into a GDSTriggerTable.
to_recarray([columns, on_attributeerror])	Convert this table to a structured numpy.recarray

Attributes Documentation

constraints = 'PRIMARY KEY (event_id)'¶

interncolumns = ('process_id', 'ifo', 'subtype')¶

next_id = <glue.ligolw.ilwd.gds_trigger_event_id_class object>¶

tableName = 'gds_trigger:table'¶

validcolumns = {'creator_db': 'int_4s', 'bandwidth': 'real_4', 'noise_power': 'real_4', 'duration': 'real_4', 'freq_peak': 'real_4', 'time_peak': 'real_4', 'freq_sigma': 'real_4', 'size': 'real_4', 'confidence': 'real_4', 'event_id': 'ilwd:char', 'priority': 'int_4s', 'significance': 'real_4', 'pixel_count': 'int_4s', 'start_time': 'int_4s', 'filter_id': 'ilwd:char', 'binarydata_length': 'int_4s', 'process_id': 'ilwd:char_u', 'disposition': 'int_4s', 'name': 'lstring', 'start_time_ns': 'int_4s', 'freq_average': 'real_4', 'time_average': 'real_4', 'binarydata': 'ilwd:char_u', 'subtype': 'lstring', 'frequency': 'real_4', 'time_sigma': 'real_4', 'ifo': 'lstring', 'signal_power': 'real_4'}¶

Methods Documentation

classmethod from_recarray(array, columns=None)[source]¶

Create a new table from a numpy.recarray

Parameters:	array : numpy.recarray an array of data column : list of str, optional the columns to populate, if not given, all columns present in the recarray are mapped

Notes

The columns populated in the numpy.recarray must all map exactly to valid columns of the target Table.

hist(column, **kwargs)[source]¶

Generate a HistogramPlot of this Table.

Parameters:	column : str name of the column over which to histogram data **kwargs : any other arguments applicable to the HistogramPlot
Returns:	plot : HistogramPlot new plot displaying a histogram of this Table.

plot(*args, **kwargs)[source]¶

Generate an EventTablePlot of this Table.

Parameters:

x : str name of column defining centre point on the X-axis y : str name of column defining centre point on the Y-axis width : str, optional name of column defining width of tile height : str, optional name of column defining height of tile Note The width and height positional arguments should either both not given, in which case a scatter plot will be drawn, or both given, in which case a collections of rectangles will be drawn. color : str, optional, default:None name of column by which to color markers **kwargs : any other arguments applicable to the Plot constructor, and the Table plotter.

Returns:

plot : EventTablePlot new plot for displaying tabular data.

See also

gwpy.plotter.EventTablePlot

for more details.

classmethod read(*args, **kwargs)¶

Read data into a GDSTriggerTable.

Parameters:

f : file, str, CacheEntry, list, Cache object representing one or more files. One of an open file a str pointing to a file path on disk a formatted CacheEntry representing one file a list of str file paths a formatted Cache representing many files columns : list, optional list of column name strings to read, default all. ifo : str, optional prefix of IFO to read Warning the ifo keyword argument is only applicable (but is required) when reading single-interferometer data from a multi-interferometer file filt : function, optional function by which to filter events. The callable must accept as input a row of the table event and return True/False. nproc : int, optional, default: 1 number of parallel processes with which to distribute file I/O, default: serial process. Warning The nproc keyword argument is only applicable when reading a list (or Cache) of files. contenthandler : LIGOLWContentHandler SAX content handler for parsing LIGO_LW documents. Warning The contenthandler keyword argument is only applicable when reading from LIGO_LW documents. **loadtxtkwargs : when reading from ASCII, all other keyword arguments are passed directly to numpy.loadtxt

Returns:

table : GDSTriggerTable GDSTriggerTable of data with given columns filled

Notes

The available built-in formats are:

Format	Read	Write	Auto-identify
ascii	Yes	No	Yes
csv	Yes	No	No
gds_trigger	Yes	No	No
ligolw	Yes	No	Yes

to_recarray(columns=None, on_attributeerror='raise')[source]¶

Convert this table to a structured numpy.recarray

This returned recarray is a blank data container, mapping columns in the original LIGO_LW table to fields in the output, but mapping none of the instance methods of the origin table.

Parameters:	columns : list of str, optional the columns to populate, if not given, all columns present in the table are mapped on_attributeerror : str how to handle AttributeError when accessing rows, one of ‘raise’ : raise normal exception ‘ignore’ : skip over this column ‘warn’ : print a warning instead of raising error

class gwpy.table.lsctables.MultiBurstTable(*args)¶

Bases: glue.ligolw.table.Table

Attributes Summary

how_to_index
tableName
validcolumns

Methods Summary

binned_event_rates(stride, column, bins[, ...])	Calculate an event rate TimeSeriesDict over a number of bins.
event_rate(stride[, start, end, timecolumn])	Calculate the rate TimeSeries for this Table.
from_recarray(array[, columns])	Create a new table from a numpy.recarray
hist(column, **kwargs)	Generate a HistogramPlot of this Table.
plot(*args, **kwargs)	Generate an EventTablePlot of this Table.
read(*args, **kwargs)	Read data into a MultiBurstTable.
to_recarray([columns, on_attributeerror])	Convert this table to a structured numpy.recarray

Attributes Documentation

how_to_index = {'mb_cei_index': ('coinc_event_id',)}¶

tableName = 'multi_burst:table'¶

validcolumns = {'central_freq': 'real_4', 'false_alarm_rate': 'real_4', 'confidence': 'real_4', 'creator_db': 'int_4s', 'ligo_angle_sig': 'real_4', 'coinc_event_id': 'ilwd:char', 'start_time_ns': 'int_4s', 'start_time': 'int_4s', 'ligo_axis_ra': 'real_4', 'bandwidth': 'real_4', 'process_id': 'ilwd:char', 'snr': 'real_4', 'ligo_angle': 'real_4', 'amplitude': 'real_4', 'filter_id': 'ilwd:char', 'duration': 'real_4', 'ligo_axis_dec': 'real_4', 'peak_time_ns': 'int_4s', 'peak_time': 'int_4s', 'ifos': 'lstring'}¶

Methods Documentation

binned_event_rates(stride, column, bins, operator='>=', start=None, end=None, timecolumn='time')[source]¶

Calculate an event rate TimeSeriesDict over a number of bins.

Parameters:

stride : float size (seconds) of each time bin column : str name of column by which to bin. bins : list a list of tuples marking containing bins, or a list of floats defining bin edges against which an math operation is performed for each event. operator : str, callable one of: '<', '<=', '>', '>=', '==', '!=', for a standard mathematical operation, 'in' to use the list of bins as containing bin edges, or a callable function that takes compares an event value against the bin value and returns a boolean. Note If bins is given as a list of tuples, this argument is ignored. start : float, LIGOTimeGPS, optional GPS start epoch of rate TimeSeries. end : float, LIGOTimeGPS, optional GPS end time of rate TimeSeries. This value will be rounded up to the nearest sample if needed. timecolumn : str, optional, default: time name of time-column to use when binning events

Returns:

rates : TimeSeriesDict a dict of (bin, TimeSeries) pairs describing a rate of events per second (Hz) for each of the bins.

event_rate(stride, start=None, end=None, timecolumn='time')[source]¶

Calculate the rate TimeSeries for this Table.

Parameters:	stride : float size (seconds) of each time bin start : float, LIGOTimeGPS, optional GPS start epoch of rate TimeSeries end : float, LIGOTimeGPS, optional GPS end time of rate TimeSeries. This value will be rounded up to the nearest sample if needed. timecolumn : str, optional, default: time name of time-column to use when binning events
Returns:	rate : TimeSeries a TimeSeries of events per second (Hz)

classmethod from_recarray(array, columns=None)[source]¶

Create a new table from a numpy.recarray

Parameters:	array : numpy.recarray an array of data column : list of str, optional the columns to populate, if not given, all columns present in the recarray are mapped

Notes

The columns populated in the numpy.recarray must all map exactly to valid columns of the target Table.

hist(column, **kwargs)[source]¶

Generate a HistogramPlot of this Table.

Parameters:	column : str name of the column over which to histogram data **kwargs : any other arguments applicable to the HistogramPlot
Returns:	plot : HistogramPlot new plot displaying a histogram of this Table.

plot(*args, **kwargs)[source]¶

Generate an EventTablePlot of this Table.

Parameters:

x : str name of column defining centre point on the X-axis y : str name of column defining centre point on the Y-axis width : str, optional name of column defining width of tile height : str, optional name of column defining height of tile Note The width and height positional arguments should either both not given, in which case a scatter plot will be drawn, or both given, in which case a collections of rectangles will be drawn. color : str, optional, default:None name of column by which to color markers **kwargs : any other arguments applicable to the Plot constructor, and the Table plotter.

Returns:

plot : EventTablePlot new plot for displaying tabular data.

See also

gwpy.plotter.EventTablePlot

for more details.

classmethod read(*args, **kwargs)¶

Read data into a MultiBurstTable.

Parameters:

f : file, str, CacheEntry, list, Cache object representing one or more files. One of an open file a str pointing to a file path on disk a formatted CacheEntry representing one file a list of str file paths a formatted Cache representing many files columns : list, optional list of column name strings to read, default all. ifo : str, optional prefix of IFO to read Warning the ifo keyword argument is only applicable (but is required) when reading single-interferometer data from a multi-interferometer file filt : function, optional function by which to filter events. The callable must accept as input a row of the table event and return True/False. nproc : int, optional, default: 1 number of parallel processes with which to distribute file I/O, default: serial process. Warning The nproc keyword argument is only applicable when reading a list (or Cache) of files. contenthandler : LIGOLWContentHandler SAX content handler for parsing LIGO_LW documents. Warning The contenthandler keyword argument is only applicable when reading from LIGO_LW documents. **loadtxtkwargs : when reading from ASCII, all other keyword arguments are passed directly to numpy.loadtxt

Returns:

table : MultiBurstTable MultiBurstTable of data with given columns filled

Notes

The available built-in formats are:

Format	Read	Write	Auto-identify
ascii	Yes	No	Yes
csv	Yes	No	No
ligolw	Yes	No	Yes
multi_burst	Yes	No	No

to_recarray(columns=None, on_attributeerror='raise')[source]¶

Convert this table to a structured numpy.recarray

This returned recarray is a blank data container, mapping columns in the original LIGO_LW table to fields in the output, but mapping none of the instance methods of the origin table.

Parameters:	columns : list of str, optional the columns to populate, if not given, all columns present in the table are mapped on_attributeerror : str how to handle AttributeError when accessing rows, one of ‘raise’ : raise normal exception ‘ignore’ : skip over this column ‘warn’ : print a warning instead of raising error

class gwpy.table.lsctables.MultiInspiralTable(*args)¶

Bases: glue.ligolw.table.Table

Attributes Summary

constraints
instrument_id
interncolumns
next_id
tableName
validcolumns

Methods Summary

binned_event_rates(stride, column, bins[, ...])	Calculate an event rate TimeSeriesDict over a number of bins.
event_rate(stride[, start, end, timecolumn])	Calculate the rate TimeSeries for this Table.
from_recarray(array[, columns])	Create a new table from a numpy.recarray
get_bestnr([index, nhigh, ...])	Get the BestNR statistic for each row in the table
get_coinc_chisq()	@returns the coincident chisq for each row in the table
get_coinc_snr()
get_column(column)
get_end()
get_new_snr([index, nhigh, column])
get_null_chisq()	@returns the coherent null chisq for each row in the table
get_null_snr()	Get the coherent Null SNR for each row in the table.
get_reduced_bank_chisq()	@returns the bank chisq per degree of freedom for each row in
get_reduced_chisq()	@returns the chisq per degree of freedom for each row in
get_reduced_coinc_chisq()	@returns the coincident chisq per degree of freedom for each
get_reduced_cont_chisq()	@returns the auto (continuous) chisq per degree of freedom
get_reduced_sngl_chisq(instrument)	@returns the single-detector chisq per degree of freedom for
get_sigmasq(instrument)	Get the single-detector SNR of the given instrument for each row in the table.
get_sigmasqs([instruments])	Return dictionary of single-detector sigmas for each row in the table.
get_sngl_bank_chisq(instrument)	Get the single-detector chi^2 of the given instrument for each row in the table.
get_sngl_bank_chisqs([instruments])	Get the single-detector chi^2 for each row in the table.
get_sngl_chisq(instrument)	Get the single-detector chi^2 of the given instrument for each row in the table.
get_sngl_chisqs([instruments])	Get the single-detector chi^2 for each row in the table.
get_sngl_cont_chisq(instrument)	Get the single-detector chi^2 of the given instrument for each row in the table.
get_sngl_cont_chisqs([instruments])	Get the single-detector chi^2 for each row in the table.
get_sngl_new_snr(ifo[, column, index, nhigh])
get_sngl_snr(instrument)	Get the single-detector SNR of the given instrument for each row in the table.
get_sngl_snrs([instruments])	Get the single-detector SNRs for each row in the table.
getslide(slide_num)	Return the triggers with a specific slide number.
getstat()
hist(column, **kwargs)	Generate a HistogramPlot of this Table.
plot(*args, **kwargs)	Generate an EventTablePlot of this Table.
read(*args, **kwargs)	Read data into a MultiInspiralTable.
to_recarray([columns, on_attributeerror])	Convert this table to a structured numpy.recarray
veto(seglist)
vetoed(seglist)	Return the inverse of what veto returns, i.e., return the triggers that lie within a given seglist.

Attributes Documentation

constraints = 'PRIMARY KEY (event_id)'¶

instrument_id = {'G1': 'g', 'H2': 'h2', 'H1': 'h1', 'T1': 't', 'V1': 'v', 'L1': 'l'}¶

interncolumns = ('process_id', 'ifos', 'search')¶

next_id = <glue.ligolw.ilwd.multi_inspiral_event_id_class object>¶

tableName = 'multi_inspiral:table'¶

validcolumns = {'amp_term_1': 'real_4', 'amp_term_2': 'real_4', 'amp_term_3': 'real_4', 'amp_term_4': 'real_4', 'amp_term_5': 'real_4', 'amp_term_6': 'real_4', 'amp_term_7': 'real_4', 'amp_term_8': 'real_4', 'amp_term_9': 'real_4', 'chisq_dof': 'int_4s', 't1quad_re': 'real_4', 'autoCorrCohSq': 'real_4', 'v1quad_im': 'real_4', 'sigmasq_h2': 'real_8', 'null_stat_h1h2': 'real_4', 'sigmasq_h1': 'real_8', 'bank_chisq_t': 'real_4', 'bank_chisq_v': 'real_4', 'bank_chisq_l': 'real_4', 'sngl_bank_chisq_dof': 'int_4s', 'ttotal': 'real_4', 'bank_chisq_g': 'real_4', 'snr_h1': 'real_4', 'snr_h2': 'real_4', 'h2quad_re': 'real_4', 'process_id': 'ilwd:char', 'sngl_chisq_dof': 'int_4s', 'end_time': 'int_4s', 'end_time_ns': 'int_4s', 'eff_dist_h1h2': 'real_4', 'h2quad_im': 'real_4', 'ligo_angle_sig': 'real_4', 'cont_chisq_h1': 'real_4', 'l1quad_im': 'real_4', 'ra': 'real_4', 'impulse_time_ns': 'int_4s', 'inclination': 'real_4', 't1quad_im': 'real_4', 'bank_chisq_dof': 'int_4s', 'amp_term_10': 'real_4', 'cont_chisq_g': 'real_4', 'search': 'lstring', 'cont_chisq_l': 'real_4', 'cont_chisq_v': 'real_4', 'l1quad_re': 'real_4', 'cont_chisq_t': 'real_4', 'eta': 'real_4', 'ligo_angle': 'real_4', 'bank_chisq': 'real_4', 'g1quad_re': 'real_4', 'end_time_gmst': 'real_8', 'snr_dof': 'int_4s', 'v1quad_re': 'real_4', 'cont_chisq_h2': 'real_4', 'coa_phase': 'real_4', 'ifos': 'lstring', 'coh_snr_h1h2': 'real_4', 'mchirp': 'real_4', 'tau3': 'real_4', 'chi': 'real_4', 'tau2': 'real_4', 'bank_chisq_h1': 'real_4', 'tau0': 'real_4', 'autoCorrNullSq': 'real_4', 'tau4': 'real_4', 'tau5': 'real_4', 'h1quad_im': 'real_4', 'impulse_time': 'int_4s', 'crossCorrCohSq': 'real_4', 'mass1': 'real_4', 'ampMetricEigenVal2': 'real_8', 'ampMetricEigenVal1': 'real_8', 'distance': 'real_4', 'g1quad_im': 'real_4', 'kappa': 'real_4', 'crossCorrNullSq': 'real_4', 'h1quad_re': 'real_4', 'sngl_cont_chisq_dof': 'int_4s', 'amplitude': 'real_4', 'cohSnrSqLocal': 'real_4', 'dec': 'real_4', 'eff_dist_l': 'real_4', 'chisq_h1': 'real_4', 'chisq_h2': 'real_4', 'eff_dist_g': 'real_4', 'bank_chisq_h2': 'real_4', 'chisq': 'real_4', 'eff_dist_v': 'real_4', 'eff_dist_t': 'real_4', 'sigmasq_t': 'real_8', 'sigmasq_v': 'real_8', 'null_statistic': 'real_4', 'snr_l': 'real_4', 'event_id': 'ilwd:char', 'snr_g': 'real_4', 'polarization': 'real_4', 'cont_chisq': 'real_4', 'sigmasq_g': 'real_8', 'null_stat_degen': 'real_4', 'sigmasq_l': 'real_8', 'time_slide_id': 'ilwd:char', 'snr_v': 'real_4', 'snr_t': 'real_4', 'cont_chisq_dof': 'int_4s', 'trace_snr': 'real_4', 'snr': 'real_4', 'eff_dist_h1': 'real_4', 'eff_dist_h2': 'real_4', 'chisq_t': 'real_4', 'chisq_v': 'real_4', 'mass2': 'real_4', 'chisq_g': 'real_4', 'chisq_l': 'real_4'}¶

Methods Documentation

binned_event_rates(stride, column, bins, operator='>=', start=None, end=None, timecolumn='time')[source]¶

Calculate an event rate TimeSeriesDict over a number of bins.

Parameters:

stride : float size (seconds) of each time bin column : str name of column by which to bin. bins : list a list of tuples marking containing bins, or a list of floats defining bin edges against which an math operation is performed for each event. operator : str, callable one of: '<', '<=', '>', '>=', '==', '!=', for a standard mathematical operation, 'in' to use the list of bins as containing bin edges, or a callable function that takes compares an event value against the bin value and returns a boolean. Note If bins is given as a list of tuples, this argument is ignored. start : float, LIGOTimeGPS, optional GPS start epoch of rate TimeSeries. end : float, LIGOTimeGPS, optional GPS end time of rate TimeSeries. This value will be rounded up to the nearest sample if needed. timecolumn : str, optional, default: time name of time-column to use when binning events

Returns:

rates : TimeSeriesDict a dict of (bin, TimeSeries) pairs describing a rate of events per second (Hz) for each of the bins.

event_rate(stride, start=None, end=None, timecolumn='time')[source]¶

Calculate the rate TimeSeries for this Table.

Parameters:	stride : float size (seconds) of each time bin start : float, LIGOTimeGPS, optional GPS start epoch of rate TimeSeries end : float, LIGOTimeGPS, optional GPS end time of rate TimeSeries. This value will be rounded up to the nearest sample if needed. timecolumn : str, optional, default: time name of time-column to use when binning events
Returns:	rate : TimeSeries a TimeSeries of events per second (Hz)

classmethod from_recarray(array, columns=None)[source]¶

Create a new table from a numpy.recarray

Parameters:	array : numpy.recarray an array of data column : list of str, optional the columns to populate, if not given, all columns present in the recarray are mapped

Notes

The columns populated in the numpy.recarray must all map exactly to valid columns of the target Table.

get_bestnr(index=4.0, nhigh=3.0, null_snr_threshold=4.25, null_grad_thresh=20.0, null_grad_val=0.2)¶

Get the BestNR statistic for each row in the table

get_coinc_chisq()¶

@returns the coincident chisq for each row in the table

get_coinc_snr()¶

get_column(column)¶

get_end()¶

get_new_snr(index=4.0, nhigh=3.0, column='chisq')¶

get_null_chisq()¶

@returns the coherent null chisq for each row in the table

get_null_snr()¶

Get the coherent Null SNR for each row in the table.

get_reduced_bank_chisq()¶

@returns the bank chisq per degree of freedom for each row in this table

get_reduced_chisq()¶

@returns the chisq per degree of freedom for each row in this table

get_reduced_coinc_chisq()¶

@returns the coincident chisq per degree of freedom for each row in the table

get_reduced_cont_chisq()¶

@returns the auto (continuous) chisq per degree of freedom for each row in this table

get_reduced_sngl_chisq(instrument)¶

@returns the single-detector chisq per degree of freedom for each row in this table

get_sigmasq(instrument)¶

Get the single-detector SNR of the given instrument for each row in the table.

get_sigmasqs(instruments=None)¶

Return dictionary of single-detector sigmas for each row in the table.

get_sngl_bank_chisq(instrument)¶

Get the single-detector chi^2 of the given instrument for each row in the table.

get_sngl_bank_chisqs(instruments=None)¶

Get the single-detector chi^2 for each row in the table.

get_sngl_chisq(instrument)¶

Get the single-detector chi^2 of the given instrument for each row in the table.

get_sngl_chisqs(instruments=None)¶

Get the single-detector chi^2 for each row in the table.

get_sngl_cont_chisq(instrument)¶

Get the single-detector chi^2 of the given instrument for each row in the table.

get_sngl_cont_chisqs(instruments=None)¶

Get the single-detector chi^2 for each row in the table.

get_sngl_new_snr(ifo, column='chisq', index=4.0, nhigh=3.0)¶

get_sngl_snr(instrument)¶

Get the single-detector SNR of the given instrument for each row in the table.

get_sngl_snrs(instruments=None)¶

Get the single-detector SNRs for each row in the table.

getslide(slide_num)¶

Return the triggers with a specific slide number. @param slide_num: the slide number to recover (contained in the event_id)

getstat()¶

hist(column, **kwargs)[source]¶

Generate a HistogramPlot of this Table.

Parameters:	column : str name of the column over which to histogram data **kwargs : any other arguments applicable to the HistogramPlot
Returns:	plot : HistogramPlot new plot displaying a histogram of this Table.

plot(*args, **kwargs)[source]¶

Generate an EventTablePlot of this Table.

Parameters:

x : str name of column defining centre point on the X-axis y : str name of column defining centre point on the Y-axis width : str, optional name of column defining width of tile height : str, optional name of column defining height of tile Note The width and height positional arguments should either both not given, in which case a scatter plot will be drawn, or both given, in which case a collections of rectangles will be drawn. color : str, optional, default:None name of column by which to color markers **kwargs : any other arguments applicable to the Plot constructor, and the Table plotter.

Returns:

plot : EventTablePlot new plot for displaying tabular data.

See also

gwpy.plotter.EventTablePlot

for more details.

classmethod read(*args, **kwargs)¶

Read data into a MultiInspiralTable.

Parameters:

f : file, str, CacheEntry, list, Cache object representing one or more files. One of an open file a str pointing to a file path on disk a formatted CacheEntry representing one file a list of str file paths a formatted Cache representing many files columns : list, optional list of column name strings to read, default all. ifo : str, optional prefix of IFO to read Warning the ifo keyword argument is only applicable (but is required) when reading single-interferometer data from a multi-interferometer file filt : function, optional function by which to filter events. The callable must accept as input a row of the table event and return True/False. nproc : int, optional, default: 1 number of parallel processes with which to distribute file I/O, default: serial process. Warning The nproc keyword argument is only applicable when reading a list (or Cache) of files. contenthandler : LIGOLWContentHandler SAX content handler for parsing LIGO_LW documents. Warning The contenthandler keyword argument is only applicable when reading from LIGO_LW documents. **loadtxtkwargs : when reading from ASCII, all other keyword arguments are passed directly to numpy.loadtxt

Returns:

table : MultiInspiralTable MultiInspiralTable of data with given columns filled

Notes

The available built-in formats are:

Format	Read	Write	Auto-identify
ascii	Yes	No	Yes
csv	Yes	No	No
ligolw	Yes	No	Yes
multi_inspiral	Yes	No	No

to_recarray(columns=None, on_attributeerror='raise')[source]¶

Convert this table to a structured numpy.recarray

This returned recarray is a blank data container, mapping columns in the original LIGO_LW table to fields in the output, but mapping none of the instance methods of the origin table.

Parameters:	columns : list of str, optional the columns to populate, if not given, all columns present in the table are mapped on_attributeerror : str how to handle AttributeError when accessing rows, one of ‘raise’ : raise normal exception ‘ignore’ : skip over this column ‘warn’ : print a warning instead of raising error

veto(seglist)¶

vetoed(seglist)¶

Return the inverse of what veto returns, i.e., return the triggers that lie within a given seglist.

class gwpy.table.lsctables.ProcessTable(*args)¶

Bases: glue.ligolw.table.Table

Attributes Summary

constraints
next_id
tableName
validcolumns

Methods Summary

from_recarray(array[, columns])	Create a new table from a numpy.recarray
get_ids_by_program(program)	Return a set containing the process IDs from rows whose program string equals the given program.
hist(column, **kwargs)	Generate a HistogramPlot of this Table.
plot(*args, **kwargs)	Generate an EventTablePlot of this Table.
read(*args, **kwargs)	Read data into a ProcessTable.
to_recarray([columns, on_attributeerror])	Convert this table to a structured numpy.recarray

Attributes Documentation

constraints = 'PRIMARY KEY (process_id)'¶

next_id = <glue.ligolw.ilwd.process_process_id_class object>¶

tableName = 'process:table'¶

validcolumns = {'comment': 'lstring', 'node': 'lstring', 'domain': 'lstring', 'unix_procid': 'int_4s', 'start_time': 'int_4s', 'process_id': 'ilwd:char', 'is_online': 'int_4s', 'ifos': 'lstring', 'jobid': 'int_4s', 'username': 'lstring', 'program': 'lstring', 'end_time': 'int_4s', 'version': 'lstring', 'cvs_repository': 'lstring', 'cvs_entry_time': 'int_4s'}¶

Methods Documentation

classmethod from_recarray(array, columns=None)[source]¶

Create a new table from a numpy.recarray

Parameters:	array : numpy.recarray an array of data column : list of str, optional the columns to populate, if not given, all columns present in the recarray are mapped

Notes

The columns populated in the numpy.recarray must all map exactly to valid columns of the target Table.

get_ids_by_program(program)¶

Return a set containing the process IDs from rows whose program string equals the given program.

hist(column, **kwargs)[source]¶

Generate a HistogramPlot of this Table.

Parameters:	column : str name of the column over which to histogram data **kwargs : any other arguments applicable to the HistogramPlot
Returns:	plot : HistogramPlot new plot displaying a histogram of this Table.

plot(*args, **kwargs)[source]¶

Generate an EventTablePlot of this Table.

Parameters:

x : str name of column defining centre point on the X-axis y : str name of column defining centre point on the Y-axis width : str, optional name of column defining width of tile height : str, optional name of column defining height of tile Note The width and height positional arguments should either both not given, in which case a scatter plot will be drawn, or both given, in which case a collections of rectangles will be drawn. color : str, optional, default:None name of column by which to color markers **kwargs : any other arguments applicable to the Plot constructor, and the Table plotter.

Returns:

plot : EventTablePlot new plot for displaying tabular data.

See also

gwpy.plotter.EventTablePlot

for more details.

classmethod read(*args, **kwargs)¶

Read data into a ProcessTable.

Parameters:

f : file, str, CacheEntry, list, Cache object representing one or more files. One of an open file a str pointing to a file path on disk a formatted CacheEntry representing one file a list of str file paths a formatted Cache representing many files columns : list, optional list of column name strings to read, default all. ifo : str, optional prefix of IFO to read Warning the ifo keyword argument is only applicable (but is required) when reading single-interferometer data from a multi-interferometer file filt : function, optional function by which to filter events. The callable must accept as input a row of the table event and return True/False. nproc : int, optional, default: 1 number of parallel processes with which to distribute file I/O, default: serial process. Warning The nproc keyword argument is only applicable when reading a list (or Cache) of files. contenthandler : LIGOLWContentHandler SAX content handler for parsing LIGO_LW documents. Warning The contenthandler keyword argument is only applicable when reading from LIGO_LW documents. **loadtxtkwargs : when reading from ASCII, all other keyword arguments are passed directly to numpy.loadtxt

Returns:

table : ProcessTable ProcessTable of data with given columns filled

Notes

The available built-in formats are:

Format	Read	Write	Auto-identify
ascii	Yes	No	Yes
csv	Yes	No	No
ligolw	Yes	No	Yes
process	Yes	No	No

to_recarray(columns=None, on_attributeerror='raise')[source]¶

Convert this table to a structured numpy.recarray

This returned recarray is a blank data container, mapping columns in the original LIGO_LW table to fields in the output, but mapping none of the instance methods of the origin table.

Parameters:	columns : list of str, optional the columns to populate, if not given, all columns present in the table are mapped on_attributeerror : str how to handle AttributeError when accessing rows, one of ‘raise’ : raise normal exception ‘ignore’ : skip over this column ‘warn’ : print a warning instead of raising error

class gwpy.table.lsctables.ProcessParamsTable(*args)¶

Bases: glue.ligolw.table.Table

Attributes Summary

how_to_index
tableName
validcolumns

Methods Summary

append(row)
from_recarray(array[, columns])	Create a new table from a numpy.recarray
read(*args, **kwargs)	Read data into a ProcessParamsTable.
to_recarray([columns, on_attributeerror])	Convert this table to a structured numpy.recarray

Attributes Documentation

how_to_index = {'pp_pip_index': ('process_id', 'param')}¶

tableName = 'process_params:table'¶

validcolumns = {'process_id': 'ilwd:char', 'program': 'lstring', 'type': 'lstring', 'value': 'lstring', 'param': 'lstring'}¶

Methods Documentation

append(row)¶

classmethod from_recarray(array, columns=None)[source]¶

Create a new table from a numpy.recarray

Parameters:	array : numpy.recarray an array of data column : list of str, optional the columns to populate, if not given, all columns present in the recarray are mapped

Notes

The columns populated in the numpy.recarray must all map exactly to valid columns of the target Table.

classmethod read(*args, **kwargs)¶

Read data into a ProcessParamsTable.

Parameters:

f : file, str, CacheEntry, list, Cache object representing one or more files. One of an open file a str pointing to a file path on disk a formatted CacheEntry representing one file a list of str file paths a formatted Cache representing many files columns : list, optional list of column name strings to read, default all. ifo : str, optional prefix of IFO to read Warning the ifo keyword argument is only applicable (but is required) when reading single-interferometer data from a multi-interferometer file filt : function, optional function by which to filter events. The callable must accept as input a row of the table event and return True/False. nproc : int, optional, default: 1 number of parallel processes with which to distribute file I/O, default: serial process. Warning The nproc keyword argument is only applicable when reading a list (or Cache) of files. contenthandler : LIGOLWContentHandler SAX content handler for parsing LIGO_LW documents. Warning The contenthandler keyword argument is only applicable when reading from LIGO_LW documents. **loadtxtkwargs : when reading from ASCII, all other keyword arguments are passed directly to numpy.loadtxt

Returns:

table : ProcessParamsTable ProcessParamsTable of data with given columns filled

Notes

The available built-in formats are:

Format	Read	Write	Auto-identify
ascii	Yes	No	Yes
csv	Yes	No	No
ligolw	Yes	No	Yes
process_params	Yes	No	No

to_recarray(columns=None, on_attributeerror='raise')[source]¶

Convert this table to a structured numpy.recarray

This returned recarray is a blank data container, mapping columns in the original LIGO_LW table to fields in the output, but mapping none of the instance methods of the origin table.

Parameters:	columns : list of str, optional the columns to populate, if not given, all columns present in the table are mapped on_attributeerror : str how to handle AttributeError when accessing rows, one of ‘raise’ : raise normal exception ‘ignore’ : skip over this column ‘warn’ : print a warning instead of raising error