SeriesMatrix

Inherits from: RegularityMixin, InteropMixin, SeriesMatrixCoreMixin, SeriesMatrixIndexingMixin, SeriesMatrixIOMixin, SeriesMatrixMathMixin, SeriesMatrixAnalysisMixin, SeriesMatrixStructureMixin, SeriesMatrixVisualizationMixin, SeriesMatrixValidationMixin, StatisticalMethodsMixin, ndarray

Methods

MetaDataMatrix

Metadata matrix containing per-element metadata.

N_samples

Number of samples along the x-axis.

T

Transpose of the matrix (rows and columns swapped).

append

append(self, other, inplace=True, pad=None, gap=None, resize=True)

Append another matrix along the sample axis.

append_exact

append_exact(self, other, inplace=False, pad=None, gap=None, tol=3.814697265625e-06)

Append another matrix with strict contiguity checking.

astype

astype(self, dtype, copy=True)

Cast matrix data to a specified type.

channel_names

Flattened list of all element names.

channels

2D array of channel identifiers for each matrix element.

col_index

col_index(self, key: 'Any') -> 'int'

Get the integer index for a column key.

col_keys

col_keys(self) -> 'tuple[Any, ...]'

Get the keys (labels) for all columns.

conj

conj(self)

Complex conjugate of the matrix.

copy

copy(self, order='C')

Create a deep copy of this matrix.

crop

crop(self, start=None, end=None, copy=False)

Crop the matrix to a specified range along the sample axis.

det

det(self)

Compute the determinant of the matrix at each sample point.

diagonal

diagonal(self, output: 'str' = 'list')

Extract diagonal elements from the matrix.

diff

diff(self, n=1, axis=None)

Calculate the n-th discrete difference along the sample axis.

duration

Duration covered by the samples.

dx

Step size between samples on the x-axis.

get_index

get_index(self, key_row: 'Any', key_col: 'Any') -> 'tuple[int, int]'

Get the (row, col) integer indices for given keys.

imag

imag(self)

Imaginary part of the matrix.

interpolate

interpolate(self, xindex, **kwargs)

Interpolate the matrix to a new sample axis.

inv

inv(self, swap_rowcol: 'bool' = True)

Compute the matrix inverse at each sample point.

is_compatible

is_compatible(self, other: 'Any') -> 'bool'

Compatibility check.

is_compatible_exact

is_compatible_exact(self, other: 'Any') -> 'bool'

Check strict compatibility with another matrix.

is_contiguous

is_contiguous(self, other: 'Any', tol: 'float' = 3.814697265625e-06) -> 'int'

Check if this matrix is contiguous with another.

is_contiguous_exact

is_contiguous_exact(self, other: 'Any', tol: 'float' = 3.814697265625e-06) -> 'int'

Check contiguity with strict shape matching.

is_regular

Return True if this series has a regular grid (constant spacing).

keys

keys(self) -> 'tuple[tuple[Any, ...], tuple[Any, ...]]'

Get both row and column keys.

loc

Label-based indexer for direct value access.

max

max(self, axis=None, out=None, keepdims=False, initial=None, where=True, ignore_nan=True)

a.max(axis=None, out=None, keepdims=False, initial=, where=True)

Return the maximum along a given axis.

Refer to numpy.amax for full documentation.

See Also

numpy.amax : equivalent function

(Inherited from ndarray)

mean

mean(self, axis=None, dtype=None, out=None, keepdims=False, *, where=True, ignore_nan=True)

a.mean(axis=None, dtype=None, out=None, keepdims=False, *, where=True)

Returns the average of the array elements along given axis.

Refer to numpy.mean for full documentation.

See Also

numpy.mean : equivalent function

(Inherited from ndarray)

median

median(self, axis=None, out=None, overwrite_input=False, keepdims=False, ignore_nan=True)

No documentation available.

min

min(self, axis=None, out=None, keepdims=False, initial=None, where=True, ignore_nan=True)

a.min(axis=None, out=None, keepdims=False, initial=, where=True)

Return the minimum along a given axis.

Refer to numpy.amin for full documentation.

See Also

numpy.amin : equivalent function

(Inherited from ndarray)

names

2D array of names for each matrix element. Alias for channel_names if 1D.

pad

pad(self, pad_width, **kwargs)

Pad the matrix along the sample axis.

plot

plot(self, **kwargs: 'Any') -> 'Any'

Plot this SeriesMatrix using gwexpy.plot.Plot.

prepend

prepend(self, other, inplace=True, pad=None, gap=None, resize=True)

Prepend another matrix at the beginning along the sample axis.

prepend_exact

prepend_exact(self, other, inplace=False, pad=None, gap=None, tol=3.814697265625e-06)

Prepend another matrix with strict contiguity checking.

read

read(source, format=None, **kwargs)

Read a SeriesMatrix from file.

Parameters

source : str or path-like Path to file to read. format : str, optional File format. If None, inferred from extension. **kwargs Additional arguments passed to the reader.

Returns

SeriesMatrix The loaded matrix.

The available built-in formats are:

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

real

real(self)

Real part of the matrix.

reshape

reshape(self, shape, order='C')

Reshape the matrix dimensions.

rms

rms(self, axis=None, keepdims=False, ignore_nan=True)

No documentation available.

row_index

row_index(self, key: 'Any') -> 'int'

Get the integer index for a row key.

row_keys

row_keys(self) -> 'tuple[Any, ...]'

Get the keys (labels) for all rows.

schur

schur(self, keep_rows, keep_cols=None, eliminate_rows=None, eliminate_cols=None)

Compute the Schur complement of a block matrix.

shape3D

Shape of the matrix as a 3-tuple (n_rows, n_cols, n_samples). For 4D matrices (spectrograms), the last dimension is likely frequency, so n_samples is determined by _x_axis_index.

shift

shift(self, delta)

Shift the sample axis by a constant offset.

std

std(self, axis=None, dtype=None, out=None, ddof=0, keepdims=False, *, where=True, ignore_nan=True)

a.std(axis=None, dtype=None, out=None, ddof=0, keepdims=False, *, where=True)

Returns the standard deviation of the array elements along given axis.

Refer to numpy.std for full documentation.

See Also

numpy.std : equivalent function

(Inherited from ndarray)

step

step(self, where: 'str' = 'post', **kwargs: 'Any') -> 'Any'

Plot the matrix as a step function.

submatrix

submatrix(self, row_keys, col_keys)

Extract a submatrix by selecting specific rows and columns.

to_cupy

to_cupy(self, dtype=None) -> Any

Convert to CuPy Array.

to_dask

to_dask(self, chunks='auto') -> Any

Convert to Dask Array.

to_dict

to_dict(self) -> 'Any'

Convert matrix to an appropriate collection dict (e.g. TimeSeriesDict). Follows the matrix structure (row, col) unless it’s a 1-column matrix.

to_dict_flat

to_dict_flat(self) -> 'dict[str, Series]'

Convert matrix to a flat dictionary mapping name to Series.

to_hdf5

to_hdf5(self, filepath, **kwargs)

Write matrix to HDF5 file.

to_jax

to_jax(self) -> Any

Convert to JAX Array.

to_list

to_list(self) -> 'Any'

Convert matrix to an appropriate collection list (e.g. TimeSeriesList).

to_pandas

to_pandas(self, format='wide')

Convert matrix to a pandas DataFrame.

to_series_1Dlist

to_series_1Dlist(self) -> 'list[Series]'

Convert matrix to a flat 1D list of Series objects.

to_series_2Dlist

to_series_2Dlist(self) -> 'list[list[Series]]'

Convert matrix to a 2D nested list of Series objects.

to_tensorflow

to_tensorflow(self, dtype: Any = None) -> Any

Convert to tensorflow.Tensor.

to_torch

to_torch(self, device: Optional[str] = None, dtype: Any = None, requires_grad: bool = False, copy: bool = False) -> Any

Convert to torch.Tensor.

to_zarr

to_zarr(self, store, path=None, **kwargs) -> Any

Save to Zarr storage.

trace

trace(self)

Compute the trace of the matrix (sum of diagonal elements).

transpose

transpose(self, *axes)

Transpose rows and columns, preserving sample axis as 2.

units

2D array of units for each matrix element.

update

update(self, other, inplace=True, pad=None, gap=None)

Update matrix by appending without resizing (rolling buffer style).

value

Underlying numpy array of data values.

value_at

value_at(self, x)

Get the matrix values at a specific x-axis location.

var

var(self, axis=None, dtype=None, out=None, ddof=0, keepdims=False, *, where=True, ignore_nan=True)

a.var(axis=None, dtype=None, out=None, ddof=0, keepdims=False, *, where=True)

Returns the variance of the array elements, along given axis.

Refer to numpy.var for full documentation.

See Also

numpy.var : equivalent function

(Inherited from ndarray)

write

write(self, target, format=None, **kwargs)

Write matrix to file.

x0

Starting value of the sample axis.

xarray

Return the sample axis values.

xindex

Sample axis index array.

xspan

Full extent of the sample axis as a tuple (start, end).

xunit

No documentation available.