xdas.atoms.ResamplePoly#

class xdas.atoms.ResamplePoly(target, maxfactor=100, window=('kaiser', 5.0), dim='last')[source]#

Pipeline implementation of polyphase-filter resampling.

Resamples from the original sampling rate to the target sampling rate. This is achieved by an upsampling of the data, followed by the application of a low-pass FIR filter, and finally by downsampling of the data. The ratio of the up and downsampling factors equals the target sampling rate over the original sampling rate.

Parameters:

target (float) – The target sampling rate of the new data
maxfactor (int) – Limit the initial upsampling by this factor, to avoid accidental memory overflow. Default: 100
window (str or tuple of string and parameter values) – The window function to apply befor FIR filtering. If a tuple is given, it needs to be compatible with scipy.signal.get_window. Default: ("kaiser", 5.0)
dim (str or int) – The dimension along which the downsampling is applied. This is either an index, time or distance, or last. Default: last

Examples

>>> from xdas.synthetics import wavelet_wavefronts
>>> from xdas.atoms import Sequential, ResamplePoly
>>> da = wavelet_wavefronts()

Using ResamplePoly directly:

>>> # Downsample time to 1 Hz
>>> da2 = ResamplePoly(target=1., dim="time")(da)
>>> da2["time"].values
array(['2022-12-31T23:59:50.000000000', '2022-12-31T23:59:51.000000000',
    '2022-12-31T23:59:52.000000000', '2022-12-31T23:59:53.000000000',
    '2022-12-31T23:59:54.000000000', '2022-12-31T23:59:55.000000000'],
    dtype='datetime64[ns]')

Using ResamplePoly as an atom in Sequential:

>>> # Downsample distance to 100m spacing
>>> sequence = Sequential([
...    ResamplePoly(target=1/100., window=("tukey", 0.1), dim="distance")
... ])
>>> result = sequence(da)
>>> result["distance"].values
array([-1000.,  -900.,  -800.,  -700.,  -600.,  -500.,  -400.,  -300.,
        -200.,  -100.,     0.,   100.,   200.,   300.,   400.,   500.,
        600.,   700.,   800.,   900.,  1000.,  1100.,  1200.,  1300.,
        1400.,  1500.,  1600.,  1700.,  1800.,  1900.,  2000.,  2100.,
        2200.,  2300.,  2400.,  2500.,  2600.,  2700.,  2800.,  2900.,
        3000.,  3100.,  3200.,  3300.,  3400.,  3500.,  3600.,  3700.,
        3800.,  3900.,  4000.,  4100.,  4200.,  4300.,  4400.,  4500.,
        4600.,  4700.,  4800.,  4900.,  5000.,  5100.,  5200.,  5300.,
        5400.,  5500.,  5600.,  5700.,  5800.,  5900.,  6000.,  6100.,
        6200.,  6300.,  6400.,  6500.,  6600.,  6700.,  6800.,  6900.,
        7000.,  7100.,  7200.,  7300.,  7400.,  7500.,  7600.,  7700.,
        7800.,  7900.,  8000.,  8100.,  8200.,  8300.,  8400.,  8500.,
        8600.,  8700.,  8800.,  8900.,  9000.])

Warning

The default dim value last does not work…

__init__(target, maxfactor=100, window=('kaiser', 5.0), dim='last')[source]#

Methods

`__init__`(target[, maxfactor, window, dim])
`call`(da, **flags)	Apply polyphase resampling (upsample → FIR filter → downsample) to da.
`initialize`(da, **flags)	Measure the current sampling rate from da and compute resampling ratios.
`initialize_from_state`()	Recompute the up/down factors and FIR cut-off from the stored sampling rate.
`load_state`(path)	Load the atom state from the NetCDF4 file at path.
`reset`()	Reset all state entries to `...` (uninitialised sentinel).
`save_state`(path)	Serialise the current state to a NetCDF4 file at path.
`set_state`(state)	Restore the atom state from a previously saved state dict.

Attributes

`initialized`	`True` if every state key has been initialised (no `...` sentinels remain).
`state`	Dict of the current state, including nested atom states.