TimeSeriesDict¶

class gwpy.timeseries.TimeSeriesDict(*args, **kwds)[source]¶

Bases: gwpy.timeseries.core.TimeSeriesBaseDict

Ordered key-value mapping of named TimeSeries objects

This object is designed to hold data for many different sources (channels) for a single time span.

The main entry points for this object are the read() and fetch() data access methods.

Methods Summary

append(other[, copy])	Append the dict other to this one
clear(() -> None.  Remove all items from od.)
copy()	Return a copy of this dict with each value copied to new memory
crop([start, end, copy])	Crop each entry of this dict
fetch(channels, start, end[, host, port, …])	Fetch data from NDS for a number of channels.
find(channels, start, end[, frametype, …])	Find and read data from frames for a number of channels.
fromkeys((S[, …)	If not specified, the value defaults to None.
get(channels, start, end[, pad, dtype, …])	Retrieve data for multiple channels from frames or NDS
has_key((k) -> True if D has a key k, else False)
items(() -> list of (key, value) pairs in od)
iteritems()	od.iteritems -> an iterator over the (key, value) pairs in od
iterkeys(() -> an iterator over the keys in od)
itervalues()	od.itervalues -> an iterator over the values in od
keys(() -> list of keys in od)
plot([label])	Plot the data for this TimeSeriesBaseDict.
pop((k[,d]) -> v, …)	value. If key is not found, d is returned if given, otherwise KeyError
popitem(() -> (k, v), …)	Pairs are returned in LIFO order if last is true or FIFO order if false.
prepend(other, **kwargs)	Prepend the dict other to this one
read(source, *args, **kwargs)	Read data for multiple channels into a TimeSeriesDict
resample(rate, **kwargs)	Resample items in this dict.
setdefault((k[,d]) -> od.get(k,d), …)
update(([E, …)	If E present and has a .keys() method, does: for k in E: D[k] = E[k]
values(() -> list of values in od)
viewitems(…)
viewkeys(…)
viewvalues(…)
write(target, *args, **kwargs)	Write this TimeSeriesDict to a file

Methods Documentation

append(other, copy=True, **kwargs)[source]¶

Append the dict other to this one

Parameters:	other : dict of TimeSeries the container to append to this one copy : bool, optional if True copy data from other before storing, only affects those keys in other that aren’t in self **kwargs other keyword arguments to send to TimeSeries.append

See also

TimeSeries.append

for details of the underlying series append operation

clear() → None. Remove all items from od.¶

copy()[source]¶

Return a copy of this dict with each value copied to new memory

crop(start=None, end=None, copy=False)[source]¶

Crop each entry of this dict

This method calls the crop() method of all entries and modifies this dict in place.

Parameters:	start : LIGOTimeGPS, float, str GPS start time of required data, any input parseable by to_gps is fine end : LIGOTimeGPS, float, str, optional GPS end time of required data, defaults to end of data found; any input parseable by to_gps is fine

See also

TimeSeries.crop

for more details

fetch(channels, start, end, host=None, port=None, verify=False, verbose=False, connection=None, pad=None, allow_tape=None, type=None, dtype=None)[source]¶

Fetch data from NDS for a number of channels.

Parameters:

channels : list required data channels. start : LIGOTimeGPS, float, str GPS start time of required data, any input parseable by to_gps is fine end : LIGOTimeGPS, float, str, optional GPS end time of required data, defaults to end of data found; any input parseable by to_gps is fine host : str, optional URL of NDS server to use, if blank will try any server (in a relatively sensible order) to get the data port : int, optional port number for NDS server query, must be given with host. verify : bool, optional, default: True check channels exist in database before asking for data verbose : bool, optional print verbose output about NDS progress. connection : nds2.connection, optional open NDS connection to use. allow_tape : bool, optional allow data access from slow tapes. If host or connection is given, the default is to do whatever the server default is, otherwise servers will be searched in logical order allowing tape access if necessary to retrieve the data type : int, str, optional NDS2 channel type integer or string name. dtype : numpy.dtype, str, type, or dict numeric data type for returned data, e.g. numpy.float, or dict of (channel, dtype) pairs

Returns:

data : TimeSeriesBaseDict a new TimeSeriesBaseDict of (str, TimeSeries) pairs fetched from NDS.

find(channels, start, end, frametype=None, frametype_match=None, pad=None, dtype=None, nproc=1, verbose=False, allow_tape=True, observatory=None, **readargs)[source]¶

Find and read data from frames for a number of channels.

Parameters:

channels : list required data channels. start : LIGOTimeGPS, float, str GPS start time of required data, any input parseable by to_gps is fine end : LIGOTimeGPS, float, str, optional GPS end time of required data, defaults to end of data found; any input parseable by to_gps is fine frametype : str, optional name of frametype in which this channel is stored, by default will search for all required frame types frametype_match : str, optional regular expression to use for frametype matching pad : float, optional value with which to fill gaps in the source data, defaults to ‘don’t fill gaps’ dtype : numpy.dtype, str, type, or dict numeric data type for returned data, e.g. numpy.float, or dict of (channel, dtype) pairs nproc : int, optional, default: 1 number of parallel processes to use, serial process by default. allow_tape : bool, optional, default: True allow reading from frame files on (slow) magnetic tape verbose : bool, optional print verbose output about NDS progress. **readargs any other keyword arguments to be passed to read()

fromkeys(S[, v]) → New ordered dictionary with keys from S.¶

If not specified, the value defaults to None.

get(channels, start, end, pad=None, dtype=None, verbose=False, allow_tape=None, **kwargs)[source]¶

Retrieve data for multiple channels from frames or NDS

This method dynamically accesses either frames on disk, or a remote NDS2 server to find and return data for the given interval

Parameters:

channels : list required data channels. start : LIGOTimeGPS, float, str GPS start time of required data, any input parseable by to_gps is fine end : LIGOTimeGPS, float, str, optional GPS end time of required data, defaults to end of data found; any input parseable by to_gps is fine frametype : str, optional name of frametype in which this channel is stored, by default will search for all required frame types pad : float, optional value with which to fill gaps in the source data, only used if gap is not given, or gap='pad' is given dtype : numpy.dtype, str, type, or dict numeric data type for returned data, e.g. numpy.float, or dict of (channel, dtype) pairs nproc : int, optional, default: 1 number of parallel processes to use, serial process by default. allow_tape : bool, optional, default: None allow the use of frames that are held on tape, default is None to attempt to allow the TimeSeries.fetch method to intelligently select a server that doesn’t use tapes for data storage (doesn’t always work), but to eventually allow retrieving data from tape if required verbose : bool, optional print verbose output about NDS progress. **kwargs other keyword arguments to pass to either TimeSeriesBaseDict.find (for direct GWF file access) or TimeSeriesBaseDict.fetch for remote NDS2 access

has_key(k) → True if D has a key k, else False¶

items() → list of (key, value) pairs in od¶

iteritems()¶

od.iteritems -> an iterator over the (key, value) pairs in od

iterkeys() → an iterator over the keys in od¶

itervalues()¶

od.itervalues -> an iterator over the values in od

keys() → list of keys in od¶

plot(label='key', **kwargs)[source]¶

Plot the data for this TimeSeriesBaseDict.

Parameters:	label : str, optional labelling system to use, or fixed label for all elements Special values include 'key': use the key of the TimeSeriesBaseDict, 'name': use the name of each element If anything else, that fixed label will be used for all lines. **kwargs all other keyword arguments are passed to the plotter as appropriate

pop(k[, d]) → v, remove specified key and return the corresponding¶

value. If key is not found, d is returned if given, otherwise KeyError is raised.

popitem() → (k, v), return and remove a (key, value) pair.¶

Pairs are returned in LIFO order if last is true or FIFO order if false.

prepend(other, **kwargs)[source]¶

Prepend the dict other to this one

Parameters:	other : dict of TimeSeries the container to prepend to this one copy : bool, optional if True copy data from other before storing, only affects those keys in other that aren’t in self **kwargs other keyword arguments to send to TimeSeries.prepend

See also

TimeSeries.prepend

for details of the underlying series prepend operation

read(source, *args, **kwargs)[source]¶

Read data for multiple channels into a TimeSeriesDict

Parameters:

source : str, Cache a single file path str, or a Cache containing a contiguous list of files. channels : ChannelList, list a list of channels to read from the source. start : LIGOTimeGPS, float, str optional GPS start time of required data, anything parseable by to_gps() is fine end : LIGOTimeGPS, float, str, optional GPS end time of required data, anything parseable by to_gps() is fine format : str, optional source format identifier. If not given, the format will be detected if possible. See below for list of acceptable formats. nproc : int, optional number of parallel processes to use, serial process by default. Note Parallel frame reading, via the nproc keyword argument, is only available when giving a Cache of frames, or using the format='cache' keyword argument. gap : str, optional how to handle gaps in the cache, one of ‘ignore’: do nothing, let the undelying reader method handle it ‘warn’: do nothing except print a warning to the screen ‘raise’: raise an exception upon finding a gap (default) ‘pad’: insert a value to fill the gaps pad : float, optional value with which to fill gaps in the source data, only used if gap is not given, or gap='pad' is given

Returns:

tsdict : TimeSeriesDict a TimeSeriesDict of (channel, TimeSeries) pairs. The keys are guaranteed to be the ordered list channels as given.

Notes

The available built-in formats are:

Format	Read	Write	Auto-identify
framecpp	Yes	Yes	No
gwf	Yes	Yes	Yes
gwf.framecpp	Yes	Yes	No
gwf.lalframe	Yes	Yes	No
hdf5	Yes	No	No
lalframe	Yes	Yes	No

resample(rate, **kwargs)[source]¶

Resample items in this dict.

This operation over-writes items inplace.

Parameters:	rate : dict, float either a dict of (channel, float) pairs for key-wise resampling, or a single float/int to resample all items. **kwargs other keyword arguments to pass to each item’s resampling method.

setdefault(k[, d]) → od.get(k,d), also set od[k]=d if k not in od¶

update([E, ]**F) → None. Update D from mapping/iterable E and F.¶

If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v

values() → list of values in od¶

viewitems() → a set-like object providing a view on od's items¶

viewkeys() → a set-like object providing a view on od's keys¶

viewvalues() → an object providing a view on od's values¶

write(target, *args, **kwargs)[source]¶

Write this TimeSeriesDict to a file

Arguments and keywords depend on the output format, see the online documentation for full details for each format.

Parameters:	target : str output filename format : str, optional output format identifier. If not given, the format will be detected if possible. See below for list of acceptable formats.

Notes

The available built-in formats are:

Format	Read	Write	Auto-identify
framecpp	Yes	Yes	No
gwf	Yes	Yes	Yes
gwf.framecpp	Yes	Yes	No
gwf.lalframe	Yes	Yes	No
hdf5	Yes	Yes	No
lalframe	Yes	Yes	No

EntryClass[source]¶

alias of TimeSeries