FrequencySeriesMatrix
Inherits from: FrequencySeriesMatrixCoreMixin, FrequencySeriesMatrixAnalysisMixin, SeriesMatrix
Matrix container for multiple FrequencySeries objects.
Inherits from SeriesMatrix and returns FrequencySeries instances when indexed.
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.
apply_response
apply_response(self, response: 'Any', inplace: 'bool' = False) -> 'Any'
Apply a complex frequency response to the matrix.
Extension method (not in GWpy) to support complex filtering/calibration.
Parameters
response : array-like or Quantity Complex frequency response array aligned with self.frequencies. inplace : bool, optional If True, modify in-place.
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.
df
Frequency spacing (dx).
diagonal
diagonal(self, output: 'str' = 'list')
Extract diagonal elements from the matrix.
dict_class
dict_class(*args: 'Any', **kwargs: 'Any')
Ordered mapping of FrequencySeries objects keyed by label.
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.
f0
Starting frequency (x0).
filter
filter(self, *filt: 'Any', **kwargs: 'Any') -> 'Any'
Apply a filter to the FrequencySeriesMatrix.
Matches GWpy FrequencySeries.filter behavior (magnitude-only response) by delegating to gwpy.frequencyseries._fdcommon.fdfilter. Use apply_response() for complex response application.
Parameters
*filt : filter arguments Filter definition. inplace : bool, optional If True, modify in-place. Default False. **kwargs : Additional arguments passed to fdfilter (e.g. analog=True).
Returns
FrequencySeriesMatrix Filtered matrix.
frequencies
Frequency array (xindex).
get_index
get_index(self, key_row: 'Any', key_col: 'Any') -> 'tuple[int, int]'
Get the (row, col) integer indices for given keys.
ifft
ifft(self) -> 'Any'
Compute the inverse FFT of this frequency-domain matrix.
Matches GWpy FrequencySeries.ifft normalization.
Returns
TimeSeriesMatrix The time-domain matrix resulting from the inverse FFT.
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.
list_class
list_class(*items: 'Union[_FS, Iterable[_FS]]')
List of FrequencySeries objects.
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=
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=
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 FrequencySeriesMatrix.
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.
series_class
series_class(data, unit=None, f0=None, df=None, frequencies=None, name=None, epoch=None, channel=None, **kwargs)
Light wrapper of gwpy’s FrequencySeries for compatibility and future extension.
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.
smooth
smooth(self, width: 'int', method: 'str' = 'amplitude', ignore_nan: 'bool' = True) -> 'Any'
Smooth the frequency series matrix along the frequency axis.
Parameters
width : int Full width of the smoothing window in samples. method : str, optional Smoothing method: ‘amplitude’, ‘power’, ‘complex’, ‘db’. Default is ‘amplitude’. ignore_nan : bool, optional If True, ignore NaNs during smoothing. Default is True.
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.