Data

`Data` ¤

Bases: ABC

Base class for all data.

The time domain data are considered the primary entity; the Fourier domain data are derived from an FFT after applying a window. The structure is set up so that td and fd are always Fourier conjugates of each other: the one-sided Fourier series is complete up to the Nyquist frequency.

Attributes:

Name	Type	Description
`name`	`str`	Name of the data instance.
`td`	`Float[Array, n_time]`	Time domain data array.
`fd`	`Complex[Array, n_time // 2 + 1]`	Frequency domain data array.
`start_time`	`float`	GPS start time of the data segment in seconds.
`delta_t`	`FloatLike`	Time step between samples.
`window`	`Float[Array, n_time]`	Window function applied to data.

Methods:

Name	Description
`__bool__`	Check if the data is empty.
`__init__`	Initialize the data class.
`__iter__`	Iterator over the time-domain data.
`__len__`	Returns the length of the time-domain data.
`fft`	Compute the Fourier transform of the data and store it
`frequency_slice`	Slice the data in the frequency domain.
`from_fd`	Create a Data object starting from (potentially incomplete)
`from_file`	Load data from a file.
`from_gwosc`	Pull data from GWOSC.
`set_tukey_window`	Create a Tukey window on the data; the window is stored in the
`to_file`	Save the data to a file.
`to_psd`	Compute a Welch estimate of the power spectral density of the data.

`duration: FloatLike` `property` ¤

Gets duration of the data in seconds.

Returns:

Name	Type	Description
`float`	`FloatLike`	Duration in seconds.

`frequencies: Float[Array, n_time // 2 + 1]` `property` ¤

Gets frequencies of the data.

Returns:

Name	Type	Description
`Array`	`Float[Array, n_time // 2 + 1]`	Array of frequencies in Hz.

`has_fd: bool` `property` ¤

Checks if Fourier domain data exists.

Returns:

Name	Type	Description
`bool`	`bool`	True if Fourier domain data exists, False otherwise.

`is_empty: bool` `property` ¤

Checks if the data is empty.

Returns:

Name	Type	Description
`bool`	`bool`	True if data is empty, False otherwise.

`n_freq: int` `property` ¤

Gets number of frequency samples.

Returns:

Name	Type	Description
`int`	`int`	Number of frequency domain samples.

`n_time: int` `property` ¤

Gets number of time samples.

Returns:

Name	Type	Description
`int`	`int`	Number of time domain samples.

`sampling_frequency: FloatLike` `property` ¤

Gets sampling frequency of the data.

Returns:

Name	Type	Description
`float`	`FloatLike`	Sampling frequency in Hz.

`times: Float[Array, n_time]` `property` ¤

Gets time points of the data.

Returns:

Name	Type	Description
`Array`	`Float[Array, n_time]`	Array of time points in seconds.

`bool() -> bool` ¤

Check if the data is empty.

`init(td: Float[Array, n_time] = jnp.array([]), delta_t: FloatLike = 0.0, start_time: float = 0.0, name: str = '', window: Optional[Float[Array, n_time]] = None) -> None` ¤

Initialize the data class.

Parameters:

Name	Type	Description	Default
`td`	`Float[Array, n_time]`	Time domain data array.	`array([])`
`delta_t`	`FloatLike`	Time step of the data in seconds.	`0.0`
`start_time`	`float`	GPS start time of the segment in seconds (default: 0).	`0.0`
`name`	`str`	Name of the data (default: '').	`''`
`window`	`Optional[Float[Array, n_time]]`	Window function to apply to the data before FFT (default: None).	`None`

`iter()` ¤

Iterator over the time-domain data.

Returns:

Name	Type	Description
`iterator`		Iterator over time domain data.

`len() -> int` ¤

Returns the length of the time-domain data.

Returns:

Name	Type	Description
`int`	`int`	Length of time domain data array.

`fft(window: Optional[Float[Array, n_time]] = None) -> Complex[Array, n_freq]` ¤

Compute the Fourier transform of the data and store it in the fd attribute.

Parameters:

Name	Type	Description	Default
`window`	`Optional[Float[Array, n_time]]`	Window function to apply to the data before FFT (default: None).	`None`

`frequency_slice(f_min: FloatLike, f_max: FloatLike, auto_fft: bool = True) -> tuple[Complex[Array, ' n_sample'], Float[Array, ' n_sample']]` ¤

Slice the data in the frequency domain. This is the main function which interacts with the likelihood.

Parameters:

Name	Type	Description	Default
`f_min`	`FloatLike`	Minimum frequency of the slice in Hz.	required
`f_max`	`FloatLike`	Maximum frequency of the slice in Hz.	required
`auto_fft`	`bool`	Whether to automatically compute FFT if not already done.	`True`

Returns:

Name	Type	Description
`tuple`	`tuple[Complex[Array, ' n_sample'], Float[Array, ' n_sample']]`	Sliced data in the frequency domain and corresponding frequencies.

`from_fd(fd_strain: Complex[Array, n_freq], frequencies: Float[Array, n_freq], start_time: float = 0.0, name: str = '') -> Self` `classmethod` ¤

Create a Data object starting from (potentially incomplete) Fourier domain data.

Parameters:

Name	Type	Description	Default
`fd_strain`	`Complex[Array, n_freq]`	Fourier domain data array.	required
`frequencies`	`Float[Array, n_freq]`	Frequencies of the data in Hz.	required
`start_time`	`float`	GPS start time of the segment in seconds (default: 0).	`0.0`
`name`	`str`	Name of the data (default: '').	`''`

Returns:

Name	Type	Description
`Data`	`Self`	Data object with the Fourier and time domain data.

`from_file(path: str, channel: Optional[str] = None, start_time: Optional[float] = None, end_time: Optional[float] = None) -> Self` `classmethod` ¤

Load data from a file.

Supported formats:

.npz — NumPy archive containing td (time-domain strain), dt (time step in seconds), and start_time (GPS start time).
.gwf / .gwf.gz — LIGO/Virgo Gravitational Wave Frame file. Requires gwpy. Specify channel (e.g. 'H1:GDS-CALIB_STRAIN'); if omitted, common preset channel names are tried automatically.
.hdf5 / .h5 / .hdf — HDF5 file readable by gwpy. channel is required when the file contains multiple channels.
.csv — Two-column CSV time-series file (gwpy format).

Parameters:

Name	Type	Description	Default
`path`	`str`	Path to the file.	required
`channel`	`Optional[str]`	Channel name for frame or HDF5 files (e.g. `'H1:GDS-CALIB_STRAIN'`). Ignored for `.npz`.	`None`
`start_time`	`Optional[float]`	GPS start time to read (optional; for gwpy-backed formats).	`None`
`end_time`	`Optional[float]`	GPS end time to read (optional; for gwpy-backed formats).	`None`

Returns:

Name	Type	Description
`Data`	`Self`	Data object with the loaded time domain data.

`from_gwosc(ifo: str, gps_start_time: float, gps_end_time: float, cache: bool = True, **kws) -> Self` `classmethod` ¤

Pull data from GWOSC.

Parameters:

Name	Type	Description	Default
`ifo`	`str`	Interferometer name.	required
`gps_start_time`	`float`	GPS start time of the data.	required
`gps_end_time`	`float`	GPS end time of the data.	required
`cache`	`bool`	Whether to cache the data (default: True).	`True`
`**kws`		Keyword arguments for `gwpy.timeseries.TimeSeries.fetch_open_data`.	`{}`

Returns:

Name	Type	Description
`Data`	`Self`	Data object with the fetched time domain data.

`set_tukey_window(alpha: float = 0.2) -> None` ¤

Create a Tukey window on the data; the window is stored in the window attribute and only applied when FFTing the data.

Parameters:

Name	Type	Description	Default
`alpha`	`float`	Shape parameter of the Tukey window (default: 0.2); this is the fraction of the segment that is tapered on each side.	`0.2`

`to_file(path: str) -> None` ¤

Save the data to a file.

Supported formats:

.npz — NumPy archive with keys td (time-domain strain), dt (time step in seconds), start_time (GPS), and name.
.txt / .dat — Three-column whitespace-separated text file containing [f, real(h(f)), imag(h(f))] (frequency-domain strain).
.csv — Same as .txt but comma-separated.
.gwf — LIGO/Virgo Gravitational Wave Frame file (time-domain). Requires gwpy.
.hdf5 / .h5 — HDF5 file (time-domain). Requires gwpy.

Parameters:

Name	Type	Description	Default
`path`	`str`	Path to the output file (extension determines format).	required

Raises:

Type	Description
`ValueError`	If the file extension is not supported.

`to_psd(**kws) -> PowerSpectrum` ¤

Compute a Welch estimate of the power spectral density of the data.

Parameters:

Name	Type	Description	Default
`**kws`		Keyword arguments for `scipy.signal.welch`.	`{}`

Returns:

Name	Type	Description
`PowerSpectrum`	`PowerSpectrum`	Power spectral density of the data.

`PowerSpectrum` ¤

Bases: ABC

Class representing a power spectral density.

Attributes:

Name	Type	Description
`name`	`str`	Name of the power spectrum.
`values`	`Float[Array, n_freq]`	Array of PSD values.
`frequencies`	`Float[Array, n_freq]`	Array of frequencies corresponding to PSD values.

Methods:

Name	Description
`__bool__`	Check if the power spectrum is empty.
`__init__`	Initialize PowerSpectrum.
`frequency_slice`	Slice the power spectrum to a frequency range.
`from_file`	Load a power spectrum from a file.
`interpolate`	Interpolate the power spectrum to new frequencies.
`simulate_data`	Simulate noise data based on the power spectrum.
`to_file`	Save the power spectrum to a file in .npz format.

`delta_f: FloatScalar` `property` ¤

Gets frequency resolution.

Returns:

Name	Type	Description
`float`	`FloatScalar`	Frequency resolution in Hz.

`delta_t: FloatScalar` `property` ¤

Gets time resolution.

Returns:

Name	Type	Description
`float`	`FloatScalar`	Time resolution in seconds.

`duration: FloatScalar` `property` ¤

Gets duration of the data.

Returns:

Name	Type	Description
`float`	`FloatScalar`	Duration in seconds.

`is_empty: bool` `property` ¤

Checks if the data is empty.

Returns:

Name	Type	Description
`bool`	`bool`	True if data is empty, False otherwise.

`n_freq: int` `property` ¤

Gets number of frequency samples.

Returns:

Name	Type	Description
`int`	`int`	Number of frequency samples.

`sampling_frequency: FloatScalar` `property` ¤

Gets sampling frequency.

Returns:

Name	Type	Description
`float`	`FloatScalar`	Sampling frequency in Hz.

`bool() -> bool` ¤

Check if the power spectrum is empty.

Returns:

Name	Type	Description
`bool`	`bool`	True if power spectrum contains data, False otherwise.

`init(values: Float[Array, n_freq] = jnp.array([]), frequencies: Float[Array, n_freq] = jnp.array([]), name: Optional[str] = None) -> None` ¤

Initialize PowerSpectrum.

Parameters:

Name	Type	Description	Default
`values`	`Float[Array, n_freq]`	Array of PSD values. Defaults to empty array.	`array([])`
`frequencies`	`Float[Array, n_freq]`	Array of frequencies in Hz. Defaults to empty array.	`array([])`
`name`	`Optional[str]`	Name of the power spectrum. Defaults to None.	`None`

`frequency_slice(f_min: float, f_max: float) -> tuple[Float[Array, ' n_sample'], Float[Array, ' n_sample']]` ¤

Slice the power spectrum to a frequency range.

Parameters:

Name	Type	Description	Default
`f_min`	`float`	Minimum frequency of the slice in Hz.	required
`f_max`	`float`	Maximum frequency of the slice in Hz.	required

Returns:

Name	Type	Description
`tuple`	`tuple[Float[Array, ' n_sample'], Float[Array, ' n_sample']]`	Contains: - values: Sliced PSD values - frequencies: Frequencies corresponding to sliced values

`from_file(path: str, is_asd: bool = False) -> Self` `classmethod` ¤

Load a power spectrum from a file.

Supported formats:

.npz — NumPy archive containing values (PSD, Hz⁻¹) and frequencies arrays. is_asd is ignored.
.txt / .dat — two-column whitespace-separated text file (frequency, value). Set is_asd=True if the second column contains amplitude spectral density (Hz⁻¹/²); it will be squared internally to give the PSD.
.csv — same two-column format as .txt/.dat but comma-separated.

Parameters:

Name	Type	Description	Default
`path`	`str`	Path to the PSD file.	required
`is_asd`	`bool`	If `True`, the file contains ASD values (Hz⁻¹/²) that are squared to obtain the PSD. Applies only to text/CSV files; ignored for `.npz`. Defaults to `False`.	`False`

Returns:

Name	Type	Description
`PowerSpectrum`	`Self`	Loaded power spectrum.

`interpolate(frequencies: Float[Array, ' n_sample'], kind: str = 'linear', **kws) -> PowerSpectrum` ¤

Interpolate the power spectrum to new frequencies.

Parameters:

Name	Type	Description	Default
`f`		Frequencies to interpolate to.	required
`kind`	`str`	Interpolation method. Defaults to 'linear'.	`'linear'`
`**kws`		Additional keyword arguments for scipy.interpolate.interp1d.	`{}`

Returns:

Name	Type	Description
`PowerSpectrum`	`PowerSpectrum`	New power spectrum with interpolated values.

`simulate_data(key: Key) -> Complex[Array, ' n_sample']` ¤

Simulate noise data based on the power spectrum.

Parameters:

Name	Type	Description	Default
`key`	`Key`	JAX PRNG key for random number generation.	required

Returns:

Type	Description
`Complex[Array, ' n_sample']`	Complex frequency series of simulated noise data.

`to_file(path: str)` ¤

Save the power spectrum to a file in .npz format.

Parameters:

Name	Type	Description	Default
`path`	`str`	Path to save the .npz file.	required

Data

Data ¤

duration: FloatLike property ¤

frequencies: Float[Array, n_time // 2 + 1] property ¤

has_fd: bool property ¤

is_empty: bool property ¤

n_freq: int property ¤

n_time: int property ¤

sampling_frequency: FloatLike property ¤

times: Float[Array, n_time] property ¤

__bool__() -> bool ¤

__init__(td: Float[Array, n_time] = jnp.array([]), delta_t: FloatLike = 0.0, start_time: float = 0.0, name: str = '', window: Optional[Float[Array, n_time]] = None) -> None ¤

__iter__() ¤

__len__() -> int ¤

fft(window: Optional[Float[Array, n_time]] = None) -> Complex[Array, n_freq] ¤

frequency_slice(f_min: FloatLike, f_max: FloatLike, auto_fft: bool = True) -> tuple[Complex[Array, ' n_sample'], Float[Array, ' n_sample']] ¤

from_fd(fd_strain: Complex[Array, n_freq], frequencies: Float[Array, n_freq], start_time: float = 0.0, name: str = '') -> Self classmethod ¤

from_file(path: str, channel: Optional[str] = None, start_time: Optional[float] = None, end_time: Optional[float] = None) -> Self classmethod ¤

from_gwosc(ifo: str, gps_start_time: float, gps_end_time: float, cache: bool = True, **kws) -> Self classmethod ¤

set_tukey_window(alpha: float = 0.2) -> None ¤

to_file(path: str) -> None ¤

to_psd(**kws) -> PowerSpectrum ¤

PowerSpectrum ¤

delta_f: FloatScalar property ¤

delta_t: FloatScalar property ¤

duration: FloatScalar property ¤

is_empty: bool property ¤

n_freq: int property ¤

sampling_frequency: FloatScalar property ¤

__bool__() -> bool ¤

__init__(values: Float[Array, n_freq] = jnp.array([]), frequencies: Float[Array, n_freq] = jnp.array([]), name: Optional[str] = None) -> None ¤

frequency_slice(f_min: float, f_max: float) -> tuple[Float[Array, ' n_sample'], Float[Array, ' n_sample']] ¤

from_file(path: str, is_asd: bool = False) -> Self classmethod ¤

interpolate(frequencies: Float[Array, ' n_sample'], kind: str = 'linear', **kws) -> PowerSpectrum ¤

simulate_data(key: Key) -> Complex[Array, ' n_sample'] ¤

to_file(path: str) ¤

`Data` ¤

`duration: FloatLike` `property` ¤

`frequencies: Float[Array, n_time // 2 + 1]` `property` ¤

`has_fd: bool` `property` ¤

`is_empty: bool` `property` ¤

`n_freq: int` `property` ¤

`n_time: int` `property` ¤

`sampling_frequency: FloatLike` `property` ¤

`times: Float[Array, n_time]` `property` ¤

`bool() -> bool` ¤

`init(td: Float[Array, n_time] = jnp.array([]), delta_t: FloatLike = 0.0, start_time: float = 0.0, name: str = '', window: Optional[Float[Array, n_time]] = None) -> None` ¤

`iter()` ¤

`len() -> int` ¤

`fft(window: Optional[Float[Array, n_time]] = None) -> Complex[Array, n_freq]` ¤

`frequency_slice(f_min: FloatLike, f_max: FloatLike, auto_fft: bool = True) -> tuple[Complex[Array, ' n_sample'], Float[Array, ' n_sample']]` ¤

`from_fd(fd_strain: Complex[Array, n_freq], frequencies: Float[Array, n_freq], start_time: float = 0.0, name: str = '') -> Self` `classmethod` ¤

`from_file(path: str, channel: Optional[str] = None, start_time: Optional[float] = None, end_time: Optional[float] = None) -> Self` `classmethod` ¤

`from_gwosc(ifo: str, gps_start_time: float, gps_end_time: float, cache: bool = True, **kws) -> Self` `classmethod` ¤

`set_tukey_window(alpha: float = 0.2) -> None` ¤

`to_file(path: str) -> None` ¤

`to_psd(**kws) -> PowerSpectrum` ¤

`PowerSpectrum` ¤

`delta_f: FloatScalar` `property` ¤

`delta_t: FloatScalar` `property` ¤

`duration: FloatScalar` `property` ¤

`is_empty: bool` `property` ¤

`n_freq: int` `property` ¤

`sampling_frequency: FloatScalar` `property` ¤

`bool() -> bool` ¤

`init(values: Float[Array, n_freq] = jnp.array([]), frequencies: Float[Array, n_freq] = jnp.array([]), name: Optional[str] = None) -> None` ¤

`frequency_slice(f_min: float, f_max: float) -> tuple[Float[Array, ' n_sample'], Float[Array, ' n_sample']]` ¤

`from_file(path: str, is_asd: bool = False) -> Self` `classmethod` ¤

`interpolate(frequencies: Float[Array, ' n_sample'], kind: str = 'linear', **kws) -> PowerSpectrum` ¤

`simulate_data(key: Key) -> Complex[Array, ' n_sample']` ¤

`to_file(path: str)` ¤