mth5.timeseries.xarray_helpers

Module containing helper functions for working with xarray objects.

Functions

covariance_xr(→ xarray.DataArray)

Compute the covariance matrix with numpy.cov.

initialize_xrda_1d(→ xarray.DataArray)

Returns a 1D xr.DataArray with variable "channel", having values channels named by the input list.

initialize_xrds_2d(→ xarray.Dataset)

Returns a 2D xr.Dataset with the given variables and coordinates.

initialize_xrda_2d(→ xarray.Dataset)

Initialize a 3D xarray DataArray with dimensions from coords plus 'variable'.

initialize_xrda_2d_cov(channels[, dtype, value])

TODO: consider changing nomenclature from dims=["channel_1", "channel_2"],

Module Contents

mth5.timeseries.xarray_helpers.covariance_xr(X: xarray.DataArray, aweights: numpy.ndarray | None | None = None, bias: bool | None = False, rowvar: bool | None = False) → xarray.DataArray[source]

Compute the covariance matrix with numpy.cov.

Parameters:

  • X (xarray.core.dataarray.DataArray) – Multivariate time series as an xarray

  • aweights (array_like, optional) – Passthrough param for np.cov. 1-D array of observation vector weights. These relative weights are typically large for observations considered “important” and smaller for observations considered less “important”. If ddof=0 the array of weights can be used to assign probabilities to observation vectors.

  • bias (bool) – Passthrough param for np.cov. Default normalization (False) is by (N - 1), where N is the number of observations given (unbiased estimate). If bias is True, then normalization is by N. These values can be overridden by using the keyword ddof in numpy versions >= 1.5.

  • rowvar (bool) – Passthrough param for np.cov. If rowvar is True (default), then each row represents a variable, with observations in the columns. Otherwise, the relationship is transposed: each column represents a variable, while the rows contain observations.

Returns:

  • S (xarray.DataArray) – The covariance matrix of the data in xarray form.

  • Development Notes – In case of ValueError: conflicting sizes for dimension ‘channel_1’, this likely means the bool for rowvar should be flipped.

mth5.timeseries.xarray_helpers.initialize_xrda_1d(channels: list, dtype: type | None = None, value: complex | float | bool | None = 0.0) → xarray.DataArray[source]

Returns a 1D xr.DataArray with variable “channel”, having values channels named by the input list.

Parameters:

  • channels (list) – The channels in the multivariate array

  • dtype (type, optional) – The datatype to initialize the array. Common cases are complex, float, and bool

  • value (Union[complex, float, bool], optional) – The default value to assign the array

Returns:

xrda – An xarray container for the channels, initialized to zeros.

Return type:

xarray.core.dataarray.DataArray

mth5.timeseries.xarray_helpers.initialize_xrds_2d(variables: list, coords: dict, dtype: type | None = complex, value: complex | float | bool | None = 0) → xarray.Dataset[source]

Returns a 2D xr.Dataset with the given variables and coordinates.

Parameters:

  • variables (list) – List of variable names to create in the dataset

  • coords (dict) – Dictionary of coordinates for the dataset dimensions

  • dtype (type, optional) – The datatype to initialize the arrays. Common cases are complex, float, and bool

  • value (Union[complex, float, bool], optional) – The default value to assign the arrays

Returns:

xrds – A 2D xarray Dataset with dimensions from coords

Return type:

xr.Dataset

mth5.timeseries.xarray_helpers.initialize_xrda_2d(variables: list, coords: dict, dtype: type = complex, value: int | float = 0.0) → xarray.Dataset[source]

Initialize a 3D xarray DataArray with dimensions from coords plus ‘variable’.

Parameters:

  • variables (list) – List of variable names for the additional dimension.

  • coords (dict) – Dictionary of coordinates for the dataset dimensions.

  • dtype (type, optional) – Data type for the array, by default complex.

  • value (int or float, optional) – Value to initialize the array with, by default 0.

Returns:

A 3D DataArray with dimensions from coords plus ‘variable’.

Return type:

xr.DataArray

mth5.timeseries.xarray_helpers.initialize_xrda_2d_cov(channels, dtype=complex, value: complex | float | bool | None = 0)[source]

TODO: consider changing nomenclature from dims=[“channel_1”, “channel_2”], to dims=[“variable_1”, “variable_2”], to be consistent with initialize_xrda_1d

Parameters
channels: list

The channels in the multivariate array. The covariance matrix will be square with dimensions len(channels) x len(channels).

dtype: type

The datatype to initialize the array. Common cases are complex, float, and bool

value: Union[complex, float, bool]

The default value to assign the array

Returns
xrda: xarray.core.dataarray.DataArray

An xarray container for the channel covariances, initialized to zeros. The matrix is square with dimensions len(channels) x len(channels).