devices.xylo.syns63300.imuif.BandPassFilter

class devices.xylo.syns63300.imuif.BandPassFilter(B_b: int = 6, B_wf: int = 8, B_af: int = 9, a1: int = -36565, a2: int = 31754, scale_out: float | None = None)[source]

Bases: object

Class that instantiates a single quantised band-pass filter, as implemented on Xylo IMU hardware

This class will design the best filter that meets a pass-band specification, using the class method from_specification(). Alternatively, you can specify the a1 and a2 taps directly as a signed integer representation, as well as specifying the number of bits needed for specifying the filter.

Attributes overview

`B_af`	Bits needed for encoding the fractional parts of taps
`B_b`	Bits needed for scaling b0
`B_wf`	Bits needed for fractional part of the filter output
`a1`	Integer representation of a1 tap
`a2`	Integer representation of a2 tap
`scale_out`	A virtual scaling factor that is applied to the output of the filter, NOT IMPLEMENTED ON HARDWARE! That shows the surplus scaling needed in the output (accepted range is [0.5, 1.0])

Methods overview

`__init__`([B_b, B_wf, B_af, a1, a2, scale_out])
`compute_AR`(signal)	Compute the AR part of the filter in the block-diagram with the given parameters.
`compute_MA`(signal)	Compute the MA part of the filter in the block-diagram representation.
`from_specification`(low_cut_off, high_cut_off)	Create a filter with the given upper and lower cut-off frequencies.

B_af: int = 9: Bits needed for encoding the fractional parts of taps

B_b: int = 6: Bits needed for scaling b0

B_wf: int = 8: Bits needed for fractional part of the filter output

__init__(B_b: int = 6, B_wf: int = 8, B_af: int = 9, a1: int = -36565, a2: int = 31754, scale_out: float | None = None) → None

a1: int = -36565: Integer representation of a1 tap

a2: int = 31754: Integer representation of a2 tap

compute_AR(signal: ndarray) → ndarray[source]

Compute the AR part of the filter in the block-diagram with the given parameters.

Parameters:: signal (np.ndarray) – the quantized input signal in python-object integer format.
Raises:: OverflowError – if any overflow happens during the filter computation.
Returns:: the output signal of the AR filter.
Return type:: np.ndarray

compute_MA(signal: ndarray) → ndarray[source]

Compute the MA part of the filter in the block-diagram representation.

Parameters:: signal (np.ndarray) – input signal (in this case output of AR part) of datatype pyton.object.
Raises:: OverflowError – if any overflow happens during the filter computation.
Returns:: quantized filtered output signal.
Return type:: np.ndarray

classmethod from_specification(low_cut_off: float, high_cut_off: float, fs: float = 200) → BandPassFilter[source]

Create a filter with the given upper and lower cut-off frequencies. Note that the hardware filter WOULD NOT BE EXACTLY THE SAME as the one specified here. This script finds the closest one possible

Parameters:

low_cut_off (float) – The low cut-off frequency of the band-pass filter.
high_cut_off (float) – The high cut-off frequency of the band-pass filter.
fs (float) – The clock rate of the chip running the filters (in Hz). Defaults to 200.

Returns:

the filter with the given cut-off frequencies.

Return type:

BandPassFilter

scale_out: float | None = None: A virtual scaling factor that is applied to the output of the filter, NOT IMPLEMENTED ON HARDWARE! That shows the surplus scaling needed in the output (accepted range is [0.5, 1.0])