PreProcessor

PreProcessor#

class patato.PreProcessor(time_factor: int = 3, detector_factor: int = 2, irf: bool = True, hilbert: bool = True, lp_filter: float | None = None, hp_filter: float | None = None, filter_window_size: int = 512, window: str = 'hann', absolute: str | None = None, universal_backprojection=False)#

Bases: TimeSeriesProcessingAlgorithm

Preprocesses MSOT time series data. Uses JAX in the background.

__init__(time_factor: int = 3, detector_factor: int = 2, irf: bool = True, hilbert: bool = True, lp_filter: float | None = None, hp_filter: float | None = None, filter_window_size: int = 512, window: str = 'hann', absolute: str | None = None, universal_backprojection=False)[source]#

Methods

__init__([time_factor, detector_factor, ...])

add_child(child)

get_algorithm_name()

Get the name of the algorithm.

get_hdf5_group_name()

Return the name of the group in the HDF5 file

pre_compute_filter(n_samples, fs[, irf])

Precompute the filter to be applied.

run(time_series[, pa_data, irf, detectors])

Run the preprocessing step on a given time series and detectors.
static get_algorithm_name() → str | None[source]#

Get the name of the algorithm.

Return type:

str or None

static get_hdf5_group_name() → str | None[source]#

Return the name of the group in the HDF5 file

Return type:

str or None

pre_compute_filter(n_samples: int, fs: float, irf: ndarray[Any, dtype[_ScalarType_co]] | None = None)[source]#

Precompute the filter to be applied.

Parameters:

  • n_samples (int)

  • fs (float)

  • irf (Array)

run(time_series, pa_data=None, irf=None, detectors=None, **kwargs) → Tuple[PATimeSeries, Dict, list | None][source]#

Run the preprocessing step on a given time series and detectors. This allows batch processing, e.g. if the data doesn’t fit into memory.

Parameters:

  • time_series

  • pa_data

  • irf

  • detectors

  • kwargs

Return type:

tuple of PATimeSeries, dict, list

Contents