QTile

class gwpy.signal.qtransform.QTile(

q: float,

frequency: float,

duration: float,

sampling: float,

mismatch: float = 0.2,

)

Bases: QBase

Representation of a tile with fixed Q and frequency.

Attributes Summary

bandwidth	The bandwidth for tiles in this row.
ntiles	The number of tiles in this row.
padding	The (left, right) padding required for the IFFT.
windowsize	The size of the frequency-domain window for this row.

Methods Summary

get_data_indices()	Return the index array of interesting frequencies for this row.
get_window()	Generate the bi-square window for this row.
transform(fseries, *[, norm, epoch])	Calculate the energy TimeSeries for the given fseries.

Attributes Documentation

bandwidth

The bandwidth for tiles in this row.

ntiles

The number of tiles in this row.

padding

The (left, right) padding required for the IFFT.

windowsize

The size of the frequency-domain window for this row.

Methods Documentation

get_data_indices() → ndarray[tuple[int], dtype[int64]]

Return the index array of interesting frequencies for this row.

get_window() → NDArray[numpy.float64]

Generate the bi-square window for this row.

Returns:

windownumpy.ndarray

transform(

fseries: FrequencySeries,

*,

norm: bool | str = True,

epoch: float | LIGOTimeGPS | None = None,

) → TimeSeries

Calculate the energy TimeSeries for the given fseries.

Parameters:

fseriesFrequencySeries

The complex FFT of a time-series data set.

normbool, str

Normalize the energy of the output by the median (if True or 'median') or the 'mean', if False the output is the energy (power) of the Q-tranform.

epochLIGOTimeGPS, float

The epoch of these data, only used for metadata in the output TimeSeries, and not requires if the input fseries has the epoch populated.

Returns:

energyTimeSeries

A TimeSeries of the energy from the Q-transform of this tile against the data.