Time Series

Overview

TimeSeries

Extended TimeSeries with all gwexpy functionality.

TimeSeries Class

class gwexpy.timeseries.TimeSeries(data: _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes], *args: Any, **kwargs: Any)

Bases: PlotMixin, TimeSeriesInteropMixin, TimeSeriesAnalysisMixin, TimeSeriesResamplingMixin, TimeSeriesSignalMixin, SignalAnalysisMixin, TimeSeriesSpectralMixin, StatisticsMixin, FittingMixin, PhaseMethodsMixin, TimeSeriesCore

Extended TimeSeries with all gwexpy functionality.

This class combines functionality from multiple modules: - Core operations: is_regular, _check_regular, tail, crop, append, find_peaks - Spectral transforms: fft, psd, cwt, laplace, etc. - Signal processing: hilbert, mix_down, xcorr, etc. - Analysis: impute, standardize, rolling_*, etc. - Interoperability: to_pandas, to_torch, to_xarray, etc.

Inherits from gwpy.timeseries.TimeSeries for full compatibility.

Methods

fft	Compute the Discrete Fourier Transform (DFT).
rfft	Real-valued Fast Fourier Transform.
psd
asd
csd
spectrogram	Compute the average power spectrogram.
coherence
filter	Filter this TimeSeries with an IIR or FIR filter.
resample	Resample the TimeSeries.
detrend	Remove the trend from this TimeSeries.
cepstrum	Compute the cepstrum of the time series.
cwt	Compute the Continuous Wavelet Transform (CWT).
dct	Compute the Discrete Cosine Transform (DCT).
emd	Decompose the TimeSeries using Empirical Mode Decomposition (EMD).
hht	Perform Hilbert-Huang Transform (HHT) on the TimeSeries.
hilbert_analysis	Perform Hilbert analysis to extract instantaneous amplitude, phase, and frequency.

to_simpeg(location: _SupportsArray[dtype[Any]] | _NestedSequence[_SupportsArray[dtype[Any]]] | bool | int | float | complex | str | bytes | _NestedSequence[bool | int | float | complex | str | bytes] | None = None, rx_type: str = 'PointElectricField', orientation: str = 'x', **kwargs: Any) → Any

Convert to SimPEG Data object.

Parameters:

• location (array_like, optional) – Rx location (x, y, z). Default is [0, 0, 0].

• rx_type (str, optional) – Receiver class name. Default “PointElectricField”.

• orientation (str, optional) – Receiver orientation (‘x’, ‘y’, ‘z’). Default ‘x’.

Return type:

simpeg.data.Data

classmethod from_simpeg(data_obj: Any, **kwargs: Any) → TimeSeries

Create TimeSeries from SimPEG Data object.

Parameters:

data_obj (simpeg.data.Data) – Input SimPEG Data.

Return type:

TimeSeries

classmethod from_control(response: Any, **kwargs: Any) → TimeSeries | TimeSeriesDict

Create TimeSeries from python-control TimeResponseData.

Parameters:

• response (control.TimeResponseData) – The simulation result from python-control.

• **kwargs (dict) – Additional arguments passed to the constructor.

Returns:

The converted time-domain data.

Return type:

TimeSeries or TimeSeriesDict

arima(order: tuple[int, int, int] = (1, 0, 0), *, seasonal_order: tuple[int, int, int, int] | None = None, auto: bool = False, **kwargs: Any) → Any

Fit an ARIMA or SARIMAX model to this TimeSeries.

This method wraps statsmodels.tsa.arima.model.ARIMA (or SARIMAX). If auto=True, it uses pmdarima to automatically find the best parameters.

Parameters:

• order (tuple, default=(1, 0, 0)) – The (p,d,q) order of the model.

• seasonal_order (tuple, optional) – The (P,D,Q,s) seasonal order.

• auto (bool, default=False) – If True, perform Auto-ARIMA search (requires pmdarima).

• **kwargs – Additional arguments passed to fit_arima.

Returns:

Object containing the fitted model, with methods .predict(), .forecast(), .plot().

Return type:

ArimaResult

ar(p: int = 1, **kwargs: Any) → Any

Fit an AutoRegressive AR(p) model. Shortcut for .arima(order=(p, 0, 0)).

ma(q: int = 1, **kwargs: Any) → Any

Fit a Moving Average MA(q) model. Shortcut for .arima(order=(0, 0, q)).

arma(p: int = 1, q: int = 1, **kwargs: Any) → Any

Fit an ARMA(p, q) model. Shortcut for .arima(order=(p, 0, q)).

df: Any

channel_names: Any

Module Contents

gwexpy.timeseries - Time series data containers and operations.

class gwexpy.timeseries.TimeSeriesDict

Bases: PlotMixin, DictMapMixin, PhaseMethodsMixin, TimeSeriesDict

Dictionary of TimeSeries objects.

append(other, copy=True, **kwargs) → TimeSeriesDict

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

asd(*args, **kwargs)

Compute ASD for each TimeSeries. Returns a FrequencySeriesDict.

asfreq(*args, **kwargs)

Apply asfreq to each TimeSeries in the dict.

average_fft(*args, **kwargs)

Apply average_fft to each TimeSeries. Returns a FrequencySeriesDict.

baseband(*args, **kwargs)

Apply baseband to each item.

coherence(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(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:

The coherence matrix.

Return type:

FrequencySeriesMatrix

Notes

If include_diagonal is True and diagonal_value is not None, the diagonal is filled with that value without computation. If diagonal_value is None, the diagonal coherence is computed. Uncomputed elements are represented as NaN. The frequency axis is taken from the first computed element without alignment/truncation; dt and fftlength consistency is enforced before computation.

correlation(other=None, **kwargs)

Compute correlation. Vectorized via TimeSeriesMatrix.

crop(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(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(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) – Must be True for CSD matrices; False raises ValueError because the diagonal is always the PSD.

Returns:

The CSD matrix.

Return type:

FrequencySeriesMatrix

Notes

The diagonal of a self-CSD matrix is always computed as the PSD. Any uncomputed elements are represented as complex NaN. The frequency axis is taken from the first computed element without alignment/truncation; dt and fftlength consistency is enforced before computation.

decimate(*args, **kwargs)

Decimate each TimeSeries in the dict.

degree(*args, **kwargs)

Compute instantaneous phase (in degrees) of each item.

detrend(*args, **kwargs)

Detrend each TimeSeries in the dict.

distance_correlation(other, **kwargs)

Compute distance correlation. Vectorized via TimeSeriesMatrix.

envelope(*args, **kwargs)

Apply envelope to each item.

fft(*args, **kwargs)

Apply FFT to each TimeSeries. Returns a FrequencySeriesDict.

filter(*args, **kwargs)

Filter each TimeSeries in the dict.

classmethod 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:

The converted time-domain data.

Return type:

TimeSeriesDict

classmethod from_mne(raw, *, unit_map=None)

Create from mne.io.Raw.

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

Create TimeSeriesDict from pandas.DataFrame.

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

Create TimeSeriesDict from polars.DataFrame.

gate(*args, **kwargs)

Gate each TimeSeries in the dict.

heterodyne(*args, **kwargs)

Apply heterodyne to each item.

hilbert(*args, **kwargs)

Apply Hilbert transform to each item.

histogram(*args, **kwargs)

Compute Histogram for each TimeSeries. Returns a HistogramDict.

ica(*args, **kwargs)

Perform ICA decomposition across channels.

impute(*args, **kwargs)

Apply impute to each item.

instantaneous_frequency(*args, **kwargs)

Apply instantaneous_frequency to each item.

instantaneous_phase(*args, **kwargs)

Apply instantaneous_phase to each item.

is_contiguous(*args, **kwargs)

Check contiguity with another object for each TimeSeries.

ktau(other, **kwargs)

Compute Kendall’s tau. Vectorized via TimeSeriesMatrix.

kurtosis(**kwargs)

Compute kurtosis. Vectorized via TimeSeriesMatrix.

lock_in(*args, **kwargs)

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

mask(*args, **kwargs)

Mask each TimeSeries in the dict.

max(**kwargs)

Compute maximum. Vectorized via TimeSeriesMatrix.

mean(**kwargs)

Compute mean. Vectorized via TimeSeriesMatrix.

mic(other, **kwargs)

Compute MIC. Vectorized via TimeSeriesMatrix.

min(**kwargs)

Compute minimum. Vectorized via TimeSeriesMatrix.

mix_down(*args, **kwargs)

Apply mix_down to each item.

notch(*args, **kwargs)

Notch filter each TimeSeries in the dict.

pca(*args, **kwargs)

Perform PCA decomposition across channels.

pcc(other, **kwargs)

Compute Pearson correlation. Vectorized via TimeSeriesMatrix.

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

Alias for plot(). Plots all series.

prepend(*args, **kwargs) → TimeSeriesDict

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

psd(*args, **kwargs)

Compute PSD for each TimeSeries. Returns a FrequencySeriesDict.

q_transform(*args, **kwargs)

Compute Q-transform for each TimeSeries. Returns a SpectrogramDict.

radian(*args, **kwargs)

Compute instantaneous phase (in radians) of each item.

classmethod read(source, *args: Any, **kwargs: Any)

resample(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(**kwargs)

Compute root-mean-square. Vectorized via TimeSeriesMatrix.

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

Apply rolling max to each item.

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

Apply rolling mean to each item.

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

Apply rolling median to each item.

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

Apply rolling min to each item.

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

Apply rolling std to each item.

shift(*args, **kwargs)

Shift each TimeSeries in the dict.

skewness(**kwargs)

Compute skewness. Vectorized via TimeSeriesMatrix.

spectrogram(*args, **kwargs)

Compute spectrogram for each TimeSeries. Returns a SpectrogramDict.

spectrogram2(*args, **kwargs)

Compute spectrogram2 for each TimeSeries. Returns a SpectrogramDict.

state_segments(*args, **kwargs)

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

std(**kwargs)

Compute standard deviation. Vectorized via TimeSeriesMatrix.

taper(*args, **kwargs)

Taper each TimeSeries in the dict.

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

Convert dictionary to TimeSeriesMatrix with alignment.

to_mne(info=None, picks=None)

Convert to mne.io.RawArray.

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

Convert to pandas.DataFrame.

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

Convert to polars.DataFrame.

to_tmultigraph(name: str | None = None) → Any

Convert to ROOT TMultiGraph.

unwrap_phase(*args, **kwargs)

Apply unwrap_phase to each item.

value_at(*args, **kwargs)

Get value at a specific time for each TimeSeries.

whiten(*args, **kwargs)

Whiten each TimeSeries in the dict.

write(target: str, *args: Any, **kwargs: Any) → Any

zpk(*args, **kwargs)

Apply ZPK filter to each TimeSeries in the dict.

class gwexpy.timeseries.TimeSeriesList(*items: _T)

Bases: PlotMixin, ListMapMixin, PhaseMethodsMixin, TimeSeriesList

List of TimeSeries objects.

asd(*args, **kwargs)

Compute ASD for each TimeSeries. Returns a FrequencySeriesList.

average_fft(*args, **kwargs)

Apply average_fft to each TimeSeries. Returns a FrequencySeriesList.

baseband(*args, **kwargs)

Apply baseband to each item.

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

Compute Coherence Matrix.

Parameters:

• other (TimeSeriesDict or TimeSeriesList, optional) – Other collection.

• fftlength – See TimeSeries.coherence().

• overlap – See TimeSeries.coherence().

• window – See TimeSeries.coherence().

• symmetric (bool, default=True) – If True and other is None, compute only upper triangle and copy to lower.

• include_diagonal (bool, default=True) – Include diagonal.

• diagonal_value (float or None, default=1.0) – Value to fill diagonal if include_diagonal is True. If None, compute diagonal coherence.

Return type:

FrequencySeriesMatrix

Notes

If include_diagonal is True and diagonal_value is not None, the diagonal is filled with that value without computation. If diagonal_value is None, the diagonal coherence is computed. Uncomputed elements are represented as NaN. The frequency axis is taken from the first computed element without alignment/truncation; dt and fftlength consistency is enforced before computation.

correlation(other=None, **kwargs)

Compute correlation. Vectorized via TimeSeriesMatrix.

crop(start=None, end=None, copy=False) → TimeSeriesList

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

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

Compute Cross Spectral Density Matrix.

Parameters:

• other (TimeSeriesDict or TimeSeriesList, optional) – Other collection for cross-CSD.

• fftlength – See TimeSeries.csd() arguments.

• overlap – See TimeSeries.csd() arguments.

• window – See TimeSeries.csd() arguments.

• hermitian (bool, default=True) – If True and other is None, compute only upper triangle and conjugate fill lower.

• include_diagonal (bool, default=True) – Must be True for CSD matrices; False raises ValueError because the diagonal is always the PSD.

Return type:

FrequencySeriesMatrix

Notes

The diagonal of a self-CSD matrix is always computed as the PSD. Any uncomputed elements are represented as complex NaN. The frequency axis is taken from the first computed element without alignment/truncation; dt and fftlength consistency is enforced before computation.

decimate(*args, **kwargs)

Decimate each TimeSeries in the list.

degree(*args, **kwargs)

Compute instantaneous phase (in degrees) of each item.

detrend(*args, **kwargs)

Detrend each TimeSeries in the list.

envelope(*args, **kwargs)

Apply envelope to each item.

fft(*args, **kwargs)

Apply FFT to each TimeSeries. Returns a FrequencySeriesList.

filter(*args, **kwargs)

Filter each TimeSeries in the list.

gate(*args, **kwargs)

Gate each TimeSeries in the list.

heterodyne(*args, **kwargs)

Apply heterodyne to each item.

hilbert(*args, **kwargs)

Apply Hilbert transform to each item.

histogram(*args, **kwargs)

Compute Histogram for each TimeSeries. Returns a HistogramList.

ica(*args, **kwargs)

Perform ICA decomposition across channels.

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

Impute missing data (NaNs) in each TimeSeries.

Parameters:

• method (str, optional) – Imputation method (‘interpolate’, ‘fill’, etc.).

• limit (int, optional) – Maximum number of consecutive NaNs to fill.

• axis (str, optional) – Axis to impute along.

• max_gap (float, optional) – Maximum gap size to fill (in seconds).

• **kwargs – Passed to TimeSeries.impute().

Return type:

TimeSeriesList

instantaneous_frequency(*args, **kwargs)

Apply instantaneous_frequency to each item.

instantaneous_phase(*args, **kwargs)

Apply instantaneous_phase to each item.

is_contiguous(*args, **kwargs)

Check contiguity with another object for each TimeSeries.

kurtosis(**kwargs)

Compute kurtosis. Vectorized via TimeSeriesMatrix.

lock_in(*args, **kwargs)

Apply lock_in to each item.

mask(*args, **kwargs)

Mask each TimeSeries in the list.

max(**kwargs)

Compute maximum. Vectorized via TimeSeriesMatrix.

mean(**kwargs)

Compute mean. Vectorized via TimeSeriesMatrix.

mic(other, **kwargs)

Compute MIC. Vectorized via TimeSeriesMatrix.

min(**kwargs)

Compute minimum. Vectorized via TimeSeriesMatrix.

mix_down(*args, **kwargs)

Apply mix_down to each item.

notch(*args, **kwargs)

Notch filter each TimeSeries in the list.

pca(*args, **kwargs)

Perform PCA decomposition across channels.

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

Alias for plot(). Plots all series.

psd(*args, **kwargs)

Compute PSD for each TimeSeries. Returns a FrequencySeriesList.

q_transform(*args, **kwargs)

Compute Q-transform for each TimeSeries. Returns a SpectrogramList.

radian(*args, **kwargs)

Compute instantaneous phase (in radians) of each item.

classmethod read(source, *args: Any, **kwargs: Any)

resample(*args, **kwargs)

Resample each TimeSeries in the list.

rms(**kwargs)

Compute root-mean-square. Vectorized via TimeSeriesMatrix.

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

Apply rolling max to each element.

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

Apply rolling mean to each element.

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

Apply rolling median to each element.

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

Apply rolling min to each element.

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

Apply rolling std to each element.

shift(*args, **kwargs)

Shift each TimeSeries in the list.

skewness(**kwargs)

Compute skewness. Vectorized via TimeSeriesMatrix.

spectrogram(*args, **kwargs)

Compute spectrogram for each TimeSeries. Returns a SpectrogramList.

spectrogram2(*args, **kwargs)

Compute spectrogram2 for each TimeSeries. Returns a SpectrogramList.

std(**kwargs)

Compute standard deviation. Vectorized via TimeSeriesMatrix.

taper(*args, **kwargs)

Taper each TimeSeries in the list.

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

Convert list to TimeSeriesMatrix with alignment.

Parameters:

• align (str, optional) – Alignment strategy (‘intersection’, ‘union’, etc.). Default ‘intersection’.

• **kwargs – Additional arguments passed to alignment function.

Returns:

Matrix with all series aligned to common time axis.

Return type:

TimeSeriesMatrix

to_pandas(**kwargs)

Convert TimeSeriesList to pandas DataFrame. Each element becomes a column. ASSUMES common time axis.

to_tmultigraph(name: str | None = None) → Any

Convert to ROOT TMultiGraph.

unwrap_phase(*args, **kwargs)

Apply unwrap_phase to each item.

value_at(*args, **kwargs)

Get value at a specific time for each TimeSeries.

whiten(*args, **kwargs)

Whiten each TimeSeries in the list.

write(target: str, *args: Any, **kwargs: Any) → Any

Write TimeSeriesList to file (HDF5, ROOT, etc.).

zpk(*args, **kwargs)

ZPK filter each TimeSeries in the list.

class gwexpy.timeseries.TimeSeriesMatrix(data: Any = None, times: Any = None, dt: Any = None, t0: Any = None, sample_rate: Any = None, epoch: Any = None, **kwargs: Any)

Bases: PhaseMethodsMixin, TimeSeriesMatrixCoreMixin, TimeSeriesMatrixAnalysisMixin, TimeSeriesMatrixSpectralMixin, TimeSeriesMatrixInteropMixin, SeriesMatrix

2D Matrix container for multiple TimeSeries objects sharing a common time axis.

This class represents a 2-dimensional array (rows x columns) where each element corresponds to a TimeSeries data stream. Crucially, all elements in the matrix share the same time array (same t0, dt, and number of samples). It behaves like a multivariate time series organized in a grid structure.

Provides dt, t0, times aliases and constructs FrequencySeriesMatrix via FFT.

auto_coherence(*args, **kwargs)

Apply univariate spectral method TimeSeries.auto_coherence element-wise.

Computes the auto_coherence for each entry in the matrix, returning a FrequencySeriesMatrix containing the results.

bandpass(*args, **kwargs)

Apply TimeSeries.bandpass element-wise to all entries in the matrix.

This method delegates the call to the underlying TimeSeries objects, preserving the matrix structure and per-element metadata while updating the data values and time axis according to the operation.

coherence(other, *args, **kwargs)

Apply TimeSeries.coherence element-wise with another TimeSeries object.

This method delegates the bivariate call to each TimeSeries in the matrix, using the provided other object as the second operand.

csd(other, *args, **kwargs)

Apply TimeSeries.csd element-wise with another TimeSeries object.

This method delegates the bivariate call to each TimeSeries in the matrix, using the provided other object as the second operand.

default_xunit = 's'

default_yunit: str | u.Unit | None = None

detrend(*args, **kwargs)

Apply TimeSeries.detrend element-wise to all entries in the matrix.

This method delegates the call to the underlying TimeSeries objects, preserving the matrix structure and per-element metadata while updating the data values and time axis according to the operation.

dict_class

alias of TimeSeriesDict

filter(*args, **kwargs)

Apply TimeSeries.filter element-wise to all entries in the matrix.

This method delegates the call to the underlying TimeSeries objects, preserving the matrix structure and per-element metadata while updating the data values and time axis according to the operation.

highpass(*args, **kwargs)

Apply TimeSeries.highpass element-wise to all entries in the matrix.

This method delegates the call to the underlying TimeSeries objects, preserving the matrix structure and per-element metadata while updating the data values and time axis according to the operation.

list_class

alias of TimeSeriesList

lowpass(*args, **kwargs)

Apply TimeSeries.lowpass element-wise to all entries in the matrix.

This method delegates the call to the underlying TimeSeries objects, preserving the matrix structure and per-element metadata while updating the data values and time axis according to the operation.

notch(*args, **kwargs)

Apply TimeSeries.notch element-wise to all entries in the matrix.

This method delegates the call to the underlying TimeSeries objects, preserving the matrix structure and per-element metadata while updating the data values and time axis according to the operation.

resample(*args, **kwargs)

Apply TimeSeries.resample element-wise to all entries in the matrix.

This method delegates the call to the underlying TimeSeries objects, preserving the matrix structure and per-element metadata while updating the data values and time axis according to the operation.

series_class

alias of TimeSeries

series_type = 'time'

taper(*args, **kwargs)

Apply TimeSeries.taper element-wise to all entries in the matrix.

This method delegates the call to the underlying TimeSeries objects, preserving the matrix structure and per-element metadata while updating the data values and time axis according to the operation.

transfer_function(other, *args, **kwargs)

Apply TimeSeries.transfer_function element-wise with another TimeSeries object.

This method delegates the bivariate call to each TimeSeries in the matrix, using the provided other object as the second operand.

whiten(*args, **kwargs)

Apply TimeSeries.whiten element-wise to all entries in the matrix.

This method delegates the call to the underlying TimeSeries objects, preserving the matrix structure and per-element metadata while updating the data values and time axis according to the operation.

class gwexpy.timeseries.Transform

Bases: object

Minimal transform interface for TimeSeries-like objects.

fit(x)

Fit the transform to the data. Returns self.

fit_transform(x)

Fit and transform in one step.

inverse_transform(y)

Reverse the transform. Not all transforms support this.

supports_inverse = False

transform(x)

Apply the transform to data. Must be implemented by subclasses.

class gwexpy.timeseries.Pipeline(steps: Sequence[tuple[str, Transform]])

Bases: object

Sequentially apply a list of transforms.

fit(x)

Fit all transforms in sequence.

fit_transform(x)

Fit and transform in one step.

inverse_transform(y, *, strict: bool = True)

Apply inverse transforms in reverse order.

Parameters:

• y (data) – Transformed data.

• strict (bool, optional) – If True, raise error if any step doesn’t support inverse.

transform(x)

Apply all transforms in sequence.

class gwexpy.timeseries.ImputeTransform(method: str = 'interpolate', **kwargs)

Bases: Transform

Impute missing values using existing lower-level helpers.

transform(x)

Apply imputation to TimeSeries, Matrix, or Collections.

class gwexpy.timeseries.StandardizeTransform(method: str = 'zscore', ddof: int = 0, robust: bool = False, axis: str = 'time', *, multivariate: bool = False, align: str = 'intersection')

Bases: Transform

Standardize TimeSeries/Matrix objects with optional robust scaling.

fit(x)

Fit standardization parameters (center, scale) for the input data.

inverse_transform(y)

Reverse standardization transformation.

supports_inverse = True

transform(x)

Apply standardization using fitted parameters.

class gwexpy.timeseries.WhitenTransform(method: Literal['pca', 'zca'] = 'pca', eps: float = 1e-12, n_components: int | None = None, *, multivariate: bool = True, align: str = 'intersection')

Bases: Transform

Whitening using PCA or ZCA on TimeSeriesMatrix-like data.

fit(x)

Fit whitening parameters for the input data.

inverse_transform(y)

Reverse whitening transformation.

supports_inverse = True

transform(x)

Apply whitening transform.

class gwexpy.timeseries.PCATransform(n_components: int | None = None, *, multivariate: bool = True, align: str = 'intersection', **kwargs)

Bases: Transform

PCA wrapper using existing decomposition helpers.

fit(x)

Fit PCA model for the input data.

inverse_transform(y)

Reverse PCA transformation (reconstruct from scores).

supports_inverse = True

transform(x)

Apply PCA transformation (project to scores).

class gwexpy.timeseries.ICATransform(n_components: int | None = None, *, multivariate: bool = True, align: str = 'intersection', **kwargs)

Bases: Transform

ICA wrapper using existing decomposition helpers.

fit(x)

Fit ICA model for the input data.

inverse_transform(y)

Reverse ICA transformation (reconstruct from sources).

supports_inverse = True

transform(x)

Apply ICA transformation (project to sources).