TimeSeriesDict

Inherits from: PhaseMethodsMixin, TimeSeriesDict

Dictionary of TimeSeries objects.

Methods

`EntryClass`

EntryClass(data, unit=None, t0=None, dt=None, sample_rate=None, times=None, channel=None, name=None, **kwargs)

A time-domain data array.

Parameters

value : array-like input data array

unit : ~astropy.units.Unit, optional physical unit of these data

t0 : ~gwpy.time.LIGOTimeGPS, float, str, optional GPS epoch associated with these data, any input parsable by ~gwpy.time.to_gps is fine

dt : float, ~astropy.units.Quantity, optional time between successive samples (seconds), can also be given inversely via sample_rate

sample_rate : float, ~astropy.units.Quantity, optional the rate of samples per second (Hertz), can also be given inversely via dt

times : array-like the complete array of GPS times accompanying the data for this series. This argument takes precedence over t0 and dt so should be given in place of these if relevant, not alongside

name : str, optional descriptive title for this array

channel : ~gwpy.detector.Channel, str, optional source data stream for these data

dtype : ~numpy.dtype, optional input data type

copy : bool, optional choose to copy the input data to new memory

subok : bool, optional allow passing of sub-classes by the array generator

Notes

The necessary metadata to reconstruct timing information are recorded in the epoch and sample_rate attributes. This time-stamps can be returned via the :attr:~TimeSeries.times property.

All comparison operations performed on a TimeSeries will return a ~gwpy.timeseries.StateTimeSeries - a boolean array with metadata copied from the starting TimeSeries.

Examples

from gwpy.timeseries import TimeSeries

To create an array of random numbers, sampled at 100 Hz, in units of ‘metres’:

from numpy import random series = TimeSeries(random.random(1000), sample_rate=100, unit=’m’)

which can then be simply visualised via

plot = series.plot() plot.show()

`analytic_signal`

analytic_signal(self, *args, **kwargs)

Apply analytic_signal to each item.

`angle`

angle(self, unwrap: bool = False, deg: bool = False, **kwargs: Any) -> Any

Alias for phase(unwrap=unwrap, deg=deg).

`append`

append(self, other, copy=True, **kwargs) -> 'TimeSeriesDict'

Append another mapping of TimeSeries or a single TimeSeries to each item.

`asd`

asd(self, *args, **kwargs)

Compute Amplitude Spectral Density for each TimeSeries in the dict. Returns a FrequencySeriesDict.

`asfreq`

asfreq(self, rule, **kwargs)

Apply asfreq to each TimeSeries in the dict. Returns a new TimeSeriesDict.

`average_fft`

average_fft(self, *args, **kwargs)

Apply averge_fft to each TimeSeries in the dict. Returns a FrequencySeriesDict.

`baseband`

baseband(self, *args, **kwargs)

Apply baseband to each item.

`coherence`

coherence(self, other=None, *args, fftlength=None, overlap=None, window='hann', symmetric=True, include_diagonal=True, diagonal_value=1.0, **kwargs)

Compute coherence for each element or as a matrix depending on other.

`coherence_matrix`

coherence_matrix(self, other=None, *args, fftlength=None, overlap=None, window='hann', symmetric=True, include_diagonal=True, diagonal_value=1.0, **kwargs)

Compute coherence matrix for all pairs.

Parameters

other : TimeSeriesDict or TimeSeriesList, optional Another collection for cross-coherence. fftlength : float, optional FFT length in seconds. overlap : float, optional Overlap between segments in seconds. window : str, optional Window function name (default ‘hann’). symmetric : bool, optional If True, exploit symmetry (default True). include_diagonal : bool, optional Whether to include diagonal elements (default True). diagonal_value : float, optional Value for diagonal elements (default 1.0).

Returns

FrequencySeriesMatrix The coherence matrix.

`copy`

copy(self)

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

`crop`

crop(self, start=None, end=None, copy=False) -> 'TimeSeriesDict'

Crop each TimeSeries in the dict. Accepts any time format supported by gwexpy.time.to_gps (str, datetime, pandas, obspy, etc). Returns a new TimeSeriesDict.

`csd`

csd(self, other=None, *args, fftlength=None, overlap=None, window='hann', hermitian=True, include_diagonal=True, **kwargs)

Compute CSD for each element or as a matrix depending on other.

`csd_matrix`

csd_matrix(self, other=None, *args, fftlength=None, overlap=None, window='hann', hermitian=True, include_diagonal=True, **kwargs)

Compute Cross-Spectral Density matrix for all pairs.

Parameters

other : TimeSeriesDict or TimeSeriesList, optional Another collection for cross-CSD. If None, compute self-CSD matrix. fftlength : float, optional FFT length in seconds. overlap : float, optional Overlap between segments in seconds. window : str, optional Window function name (default ‘hann’). hermitian : bool, optional If True, exploit Hermitian symmetry (default True). include_diagonal : bool, optional Whether to include diagonal elements (default True).

Returns

FrequencySeriesMatrix The CSD matrix.

`decimate`

decimate(self, *args, **kwargs) -> 'TimeSeriesDict'

Decimate each TimeSeries in the dict. Returns a new TimeSeriesDict.

`degree`

degree(self, *args, **kwargs) -> 'TimeSeriesDict'

Compute instantaneous phase (in degrees) of each item.

`detrend`

detrend(self, *args, **kwargs) -> 'TimeSeriesDict'

Detrend each TimeSeries in the dict. Returns a new TimeSeriesDict.

`envelope`

envelope(self, *args, **kwargs)

Apply envelope to each item.

`fetch`

fetch(channels, start, end, host=None, port=None, verify=False, verbose=False, connection=None, pad=None, scaled=None, allow_tape=None, type=None, dtype=None)

Fetch data from NDS for a number of channels.

Parameters

channels : list required data channels.

start : ~gwpy.time.LIGOTimeGPS, float, str GPS start time of required data, any input parseable by ~gwpy.time.to_gps is fine

end : ~gwpy.time.LIGOTimeGPS, float, str, optional GPS end time of required data, defaults to end of data found; any input parseable by ~gwpy.time.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 download progress, if verbose is specified as a string, this defines the prefix for the progress meter

connection : nds2.connection, optional open NDS connection to use.

scaled : bool, optional apply slope and bias calibration to ADC data, for non-ADC data this option has no effect.

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 to match.

dtype : numpy.dtype, str, type, or dict NDS2 data type to match

Returns

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

`fft`

fft(self, *args, **kwargs)

Apply FFT to each TimeSeries in the dict. Returns a FrequencySeriesDict.

`filter`

filter(self, *args, **kwargs) -> 'TimeSeriesDict'

Filter each TimeSeries in the dict. Returns a new TimeSeriesDict.

`find`

find(channels, start, end, frametype=None, frametype_match=None, pad=None, scaled=None, nproc=1, verbose=False, allow_tape=True, observatory=None, **readargs)

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

This method uses :mod:gwdatafind to discover the (file://) URLs that provide the requested data, then reads those files using :meth:TimeSeriesDict.read().

Parameters

channels : list Required data channels.

start : ~gwpy.time.LIGOTimeGPS, float, str GPS start time of required data, any input parseable by ~gwpy.time.to_gps is fine

end : ~gwpy.time.LIGOTimeGPS, float, str GPS end time of required data, defaults to end of data found; any input parseable by ~gwpy.time.to_gps is fine

frametype : str Name of frametype in which this channel is stored; if not given all frametypes discoverable via GWDataFind will be searched for the required channels.

frametype_match : str Regular expression to use for frametype matching.

pad : float Value with which to fill gaps in the source data, by default gaps will result in a ValueError.

scaled : bool Apply slope and bias calibration to ADC data, for non-ADC data this option has no effect.

nproc : int Number of parallel processes to use.

allow_tape : bool Allow reading from frame files on (slow) magnetic tape.

verbose : bool, optional Print verbose output about read progress, if verbose is specified as a string, this defines the prefix for the progress meter.

readargs Any other keyword arguments to be passed to .read().

Raises

requests.exceptions.HTTPError If the GWDataFind query fails for any reason.

RuntimeError If no files are found to read, or if the read operation fails.

`from_control`

from_control(response: Any, **kwargs) -> 'TimeSeriesDict'

Create TimeSeriesDict from python-control TimeResponseData.

Parameters

response : control.TimeResponseData The simulation result from python-control. **kwargs : dict Additional arguments passed to the TimeSeries constructor.

Returns

TimeSeriesDict The converted time-domain data.

`from_mne`

from_mne(raw, *, unit_map=None)

Create from mne.io.Raw.

`from_nds2_buffers`

from_nds2_buffers(buffers, scaled=None, copy=True, **metadata)

Construct a new dict from a list of nds2.buffer objects

Requires: |nds2|_

Parameters

buffers : list of nds2.buffer the input NDS2-client buffers to read

scaled : bool, optional apply slope and bias calibration to ADC data, for non-ADC data this option has no effect.

copy : bool, optional if True, copy the contained data array to new to a new array

**metadata any other metadata keyword arguments to pass to the TimeSeries constructor

Returns

dict : TimeSeriesDict a new TimeSeriesDict containing the data from the given buffers

`from_pandas`

from_pandas(df, *, unit_map=None, t0=None, dt=None)

Create TimeSeriesDict from pandas.DataFrame.

`from_polars`

from_polars(df, *, time_column='time', unit_map=None)

Create TimeSeriesDict from polars.DataFrame.

`gate`

gate(self, *args, **kwargs) -> 'TimeSeriesDict'

Gate each TimeSeries in the dict. Returns a new TimeSeriesDict.

`get`

get(channels, start, end, pad=None, scaled=None, dtype=None, verbose=False, allow_tape=None, **kwargs)

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 : ~gwpy.time.LIGOTimeGPS, float, str GPS start time of required data, any input parseable by ~gwpy.time.to_gps is fine

end : ~gwpy.time.LIGOTimeGPS, float, str, optional GPS end time of required data, defaults to end of data found; any input parseable by ~gwpy.time.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, by default gaps will result in a ValueError.

scaled : bool, optional apply slope and bias calibration to ADC data, for non-ADC data this option has no effect.

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 data access progress, if verbose is specified as a string, this defines the prefix for the progress meter

**kwargs other keyword arguments to pass to either TimeSeriesBaseDict.find (for direct GWF file access) or TimeSeriesBaseDict.fetch for remote NDS2 access

`heterodyne`

heterodyne(self, *args, **kwargs)

Apply heterodyne to each item.

`hht`

hht(self, *args, **kwargs)

Apply hht (Hilbert-Huang Transform) to each item. Returns a dict of results (either dicts or Spectrograms).

`hilbert`

hilbert(self, *args, **kwargs)

Alias for analytic_signal.

`ica`

ica(self, *args, **kwargs)

Perform ICA decomposition across channels.

`impute`

impute(self, *, method='interpolate', limit=None, axis='time', max_gap=None, **kwargs)

Apply impute to each item.

`instantaneous_frequency`

instantaneous_frequency(self, *args, **kwargs)

Apply instantaneous_frequency to each item.

`instantaneous_phase`

instantaneous_phase(self, *args, **kwargs)

Apply instantaneous_phase to each item.

`is_contiguous`

is_contiguous(self, *args, **kwargs)

Check contiguity with another object for each TimeSeries.

`lock_in`

lock_in(self, *args, **kwargs)

Apply lock_in to each item. Returns TimeSeriesDict (if output=’complex’) or tuple of TimeSeriesDicts.

`mask`

mask(self, *args, **kwargs) -> 'TimeSeriesDict'

Mask each TimeSeries in the dict. Returns a new TimeSeriesDict.

`max`

max(self, *args, **kwargs)

Compute maximum for each TimeSeries. Returns pandas.Series of scalars.

`mean`

mean(self, *args, **kwargs)

Compute mean for each TimeSeries. Returns pandas.Series of scalars.

`min`

min(self, *args, **kwargs)

Compute minimum for each TimeSeries. Returns pandas.Series of scalars.

`mix_down`

mix_down(self, *args, **kwargs)

Apply mix_down to each item.

`notch`

notch(self, *args, **kwargs) -> 'TimeSeriesDict'

Notch filter each TimeSeries in the dict. Returns a new TimeSeriesDict.

`pca`

pca(self, *args, **kwargs)

Perform PCA decomposition across channels.

`phase`

phase(self, unwrap: bool = False, deg: bool = False, **kwargs: Any) -> Any

Calculate the phase of the data.

Parameters

unwrap : bool, optional If True, unwrap the phase to remove discontinuities. Default is False. deg : bool, optional If True, return the phase in degrees. Default is False (radians). **kwargs Additional arguments passed to the underlying calculation.

Returns

Series or Matrix or Collection The phase of the data.

`plot`

plot(self, **kwargs: Any)

Plot all series. Delegates to gwexpy.plot.Plot.

`plot_all`

plot_all(self, *args: Any, **kwargs: Any)

Alias for plot(). Plots all series.

`prepend`

prepend(self, *args, **kwargs) -> 'TimeSeriesDict'

Prepend to each TimeSeries in the dict (in-place). Returns self.

`psd`

psd(self, *args, **kwargs)

Compute Power Spectral Density for each TimeSeries in the dict. Returns a FrequencySeriesDict.

`q_transform`

q_transform(self, *args, **kwargs)

Compute Q-transform for each TimeSeries in the dict. Returns a SpectrogramDict.

`radian`

radian(self, *args, **kwargs) -> 'TimeSeriesDict'

Compute instantaneous phase (in radians) of each item.

`read`

read(source, *args, **kwargs)

Read data for multiple channels into a TimeSeriesDict

Parameters

source : str, list Source of data, any of the following:

- `str` path of single data file,
- `str` path of LAL-format cache file,
- `list` of paths.

channels : ~gwpy.detector.channel.ChannelList, list a list of channels to read from the source.

start : ~gwpy.time.LIGOTimeGPS, float, str optional GPS start time of required data, anything parseable by :func:~gwpy.time.to_gps is fine

end : ~gwpy.time.LIGOTimeGPS, float, str, optional GPS end time of required data, anything parseable by :func:~gwpy.time.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.

pad : float, optional value with which to fill gaps in the source data, by default gaps will result in a ValueError.

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 ======== ==== ===== ============= ats Yes No Yes dttxml Yes No Yes gbd Yes No Yes gse2 Yes Yes No knet Yes No No li Yes No No lsf Yes No No mem Yes No No miniseed Yes Yes No orf Yes No No sac Yes Yes No sdb Yes No Yes sqlite Yes No Yes sqlite3 Yes No Yes taffmat Yes No No tdms Yes No Yes wav Yes No Yes wdf Yes No No win Yes No Yes win32 Yes No Yes wvf Yes No No ======== ==== ===== =============

`resample`

resample(self, rate, **kwargs)

Resample items in the TimeSeriesDict. In-place operation (updates the dict contents).

If rate is time-like, performs time-bin resampling. Otherwise performs signal processing resampling (gwpy’s native behavior).

`rms`

rms(self, *args, **kwargs)

Compute RMS for each TimeSeries. Returns pandas.Series of scalars.

`rolling_max`

rolling_max(self, window, *, center=False, min_count=1, nan_policy='omit', backend='auto', ignore_nan=None)

Apply rolling max to each item.

`rolling_mean`

rolling_mean(self, window, *, center=False, min_count=1, nan_policy='omit', backend='auto', ignore_nan=None)

Apply rolling mean to each item.

`rolling_median`

rolling_median(self, window, *, center=False, min_count=1, nan_policy='omit', backend='auto', ignore_nan=None)

Apply rolling median to each item.

`rolling_min`

rolling_min(self, window, *, center=False, min_count=1, nan_policy='omit', backend='auto', ignore_nan=None)

Apply rolling min to each item.

`rolling_std`

rolling_std(self, window, *, center=False, min_count=1, nan_policy='omit', backend='auto', ddof=0, ignore_nan=None)

Apply rolling std to each item.

`shift`

shift(self, *args, **kwargs) -> 'TimeSeriesDict'

Shift each TimeSeries in the dict. Returns a new TimeSeriesDict.

`span`

The GPS [start, stop) extent of data in this dict

:type: ~gwpy.segments.Segment

`spectrogram`

spectrogram(self, *args, **kwargs)

Compute spectrogram for each TimeSeries in the dict. Returns a SpectrogramDict.

`spectrogram2`

spectrogram2(self, *args, **kwargs)

Compute spectrogram2 for each TimeSeries in the dict. Returns a SpectrogramDict.

`state_segments`

state_segments(self, *args, **kwargs)

Run state_segments on each item (returns Series of SegmentLists).

`std`

std(self, *args, **kwargs)

Compute standard deviation for each TimeSeries. Returns pandas.Series of scalars.

`step`

step(self, label='key', where='post', figsize=(12, 4), xscale='auto-gps', **kwargs)

Create a step plot of this dict.

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 :attr:`~TimeSeries.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

`stlt`

stlt(self, *args, **kwargs)

Apply stlt to each item. Returns a dict of TimePlaneTransforms.

Notes

All arguments are forwarded to TimeSeries.stlt(). In particular, you can pass frequencies (Hz) to evaluate STLT at arbitrary frequency points instead of the FFT grid.

`taper`

taper(self, *args, **kwargs) -> 'TimeSeriesDict'

Taper each TimeSeries in the dict. Returns a new TimeSeriesDict.

`to_matrix`

to_matrix(self, *, align='intersection', **kwargs)

Convert dictionary to TimeSeriesMatrix with alignment.

`to_mne`

to_mne(self, info=None, picks=None)

Convert to mne.io.RawArray.

`to_pandas`

to_pandas(self, index='datetime', *, copy=False)

Convert to pandas.DataFrame.

`to_polars`

to_polars(self, time_column='time', time_unit='datetime')

Convert to polars.DataFrame.

`to_tmultigraph`

to_tmultigraph(self, name: Optional[str] = None) -> Any

Convert to ROOT TMultiGraph.

`unwrap_phase`

unwrap_phase(self, *args, **kwargs)

Apply unwrap_phase to each item.

`value_at`

value_at(self, *args, **kwargs)

Get value at a specific time for each TimeSeries.

`whiten`

whiten(self, *args, **kwargs) -> 'TimeSeriesDict'

Whiten each TimeSeries in the dict. Returns a new TimeSeriesDict.

`write`

write(self, target: str, *args: Any, **kwargs: Any) -> Any

Write this TimeSeriesDict to a file

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

CSV/TXT output for multi-channel data uses a directory layout (one file per entry).

tsd.write("out_dir", format="csv")  # writes per-channel CSVs under out_dir/

For HDF5 output you can choose a layout (default is GWpy-compatible dataset-per-entry).

tsd.write("out.h5", format="hdf5")               # GWpy-compatible (default)
tsd.write("out.h5", format="hdf5", layout="group")  # legacy group-per-entry

HDF5 dataset names (for GWpy path=):

Keys are sanitized to be HDF5-friendly (e.g. H1:TEST -> H1_TEST).
If multiple keys sanitize to the same name, a suffix like __1 is added.
The original keys are stored in file attributes, and gwexpy restores them on read.

.. warning:: Never unpickle data from untrusted sources. pickle/shelve can execute arbitrary code on load.

Pickle portability note: pickled gwexpy TimeSeriesDict unpickles as a GWpy TimeSeriesDict (gwexpy not required on the loading side).

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 ============ ==== ===== ============= gwf Yes Yes Yes gwf.framecpp Yes Yes No gwf.framel Yes Yes No gwf.lalframe Yes Yes No hdf5 Yes Yes No ============ ==== ===== =============

(Inherited from TimeSeriesDict)

`zpk`

zpk(self, *args, **kwargs) -> 'TimeSeriesDict'

Apply ZPK filter to each TimeSeries in the dict. Returns a new TimeSeriesDict.

TimeSeriesDict

Methods

EntryClass

Parameters

Notes

Examples

analytic_signal

angle

append

asd

asfreq

average_fft

baseband

coherence

coherence_matrix

Parameters

Returns

copy

crop

csd

csd_matrix

Parameters

Returns

decimate

degree

detrend

envelope

fetch

Parameters

Returns

fft

filter

find

Parameters

Raises

from_control

Parameters

Returns

from_mne

from_nds2_buffers

Parameters

Returns

from_pandas

from_polars

gate

get

Parameters

heterodyne

hht

hilbert

ica

impute

instantaneous_frequency

instantaneous_phase

is_contiguous

lock_in

mask

max

mean

min

mix_down

notch

pca

phase

Parameters

Returns

plot

plot_all

prepend

psd

q_transform

radian

read

Parameters

Returns

Notes

resample

rms

rolling_max

rolling_mean

`EntryClass`

`analytic_signal`

`angle`

`append`

`asd`

`asfreq`

`average_fft`

`baseband`

`coherence`

`coherence_matrix`

`copy`

`crop`

`csd`

`csd_matrix`

`decimate`

`degree`

`detrend`

`envelope`

`fetch`

`fft`

`filter`

`find`

`from_control`

`from_mne`

`from_nds2_buffers`

`from_pandas`

`from_polars`

`gate`

`get`

`heterodyne`

`hht`

`hilbert`

`ica`

`impute`

`instantaneous_frequency`

`instantaneous_phase`

`is_contiguous`

`lock_in`

`mask`

`max`

`mean`

`min`

`mix_down`

`notch`

`pca`

`phase`

`plot`

`plot_all`

`prepend`

`psd`

`q_transform`

`radian`

`read`

`resample`

`rms`

`rolling_max`

`rolling_mean`

`rolling_median`

`rolling_min`

`rolling_std`

`shift`

`span`

`spectrogram`

`spectrogram2`

`state_segments`

`std`

`step`

`stlt`

`taper`

`to_matrix`

`to_mne`

`to_pandas`

`to_polars`

`to_tmultigraph`

`unwrap_phase`

`value_at`

`whiten`

`write`

`zpk`