xdas.atoms.IIRFilter#

class xdas.atoms.IIRFilter(order, cutoff, btype='bandpass', ftype='butter', stype='sos', rp=None, rs=None, dim='last')[source]#

Pipeline implementation of an IIR filter.

Parameters:

order (int) – The order (number of corners) of the IIR filter
cutoff (float or tuple) – The frequency cut-off of the filter. In the case of a low/high-pass filter, cutoff is a single number. In the case of a bandpass filter, cutoff is a tuple of two number (the upper and lower cut-off frequency, resp.).
btype (str) –
The type of the filter band. Valid options are:
- lowpass: removing frequencies above cutoff
- highpass: removing frequencies below cutoff
- bandpass (default): removing frequencies below cutoff[0] and above cutoff[1]
ftype (str) – The IIR filter type. Default: butter
stype (str) – Form of the output of the filter design. Default: sos
rp – ???. Default: None
rs – ???. Default: None
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, IIRFilter
>>> da = wavelet_wavefronts()

Using IIRFilter directly:

>>> # Highpass > 1.5 Hz
>>> da2 = IIRFilter(order=4, cutoff=1.5, btype="highpass", dim="time")(da)
>>> da2
<xdas.DataArray (time: 300, distance: 401)>
[[ 0.038812 -0.049615  0.061412 ... -0.114737  0.105669 -0.221302]
[-0.104748  0.121279 -0.088378 ...  0.171324 -0.086691  0.216594]
[ 0.082237 -0.120316  0.004964 ... -0.111284 -0.136088  0.185075]
...
[ 0.178379  0.011591 -0.31838  ... -0.228471 -0.314301  0.436016]
[-0.194726 -0.004863  0.116678 ... -0.156696  0.397589 -0.130106]
[ 0.140117  0.197221 -0.268858 ...  0.322317 -0.414973 -0.055147]]
Coordinates:
* time (time): 2023-01-01T00:00:00.000 to 2023-01-01T00:00:05.980
* distance (distance): 0.000 to 10000.000

Using IIRFilter as an atom in Sequential:

>>> # Bandpass between 1 and 10 Hz
>>> sequence = Sequential([
...    IIRFilter(order=6, cutoff=(1.0, 10.0), btype="bandpass", dim="time")
... ])
>>> result = sequence(da)
>>> result
<xdas.DataArray (time: 300, distance: 401)>
[[ 0.00031  -0.000396  0.00049  ... -0.000916  0.000844 -0.001767]
[ 0.001484 -0.001998  0.002966 ... -0.005491  0.005625 -0.011501]
[ 0.001948 -0.003366  0.006708 ... -0.012976  0.014296 -0.028643]
...
[ 0.016432 -0.012658 -0.089414 ... -0.021061  0.168231 -0.118295]
[ 0.004816 -0.044008  0.035511 ... -0.040328  0.144616 -0.064695]
[-0.014048 -0.079786  0.180202 ...  0.013841 -0.048853  0.062074]]
Coordinates:
* time (time): 2023-01-01T00:00:00.000 to 2023-01-01T00:00:05.980
* distance (distance): 0.000 to 10000.000

__init__(order, cutoff, btype='bandpass', ftype='butter', stype='sos', rp=None, rs=None, dim='last')[source]#

Methods

`__init__`(order, cutoff[, btype, ftype, ...])
`call`(da, **flags)	Delegate to the underlying `LFilter` or `SOSFilter` atom.
`initialize`(da, **flags)	Determine the sampling rate from da and recompute the IIR coefficients.
`initialize_from_state`()	Recompute and store the IIR coefficients from the current design parameters.
`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.