How do I generate a custom filter for laser lock box with scipy?
Laser Lock Box customer filter
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Moku:Go
Moku:Go Arbitrary Waveform Generator Moku:Go Data Logger Moku:Go Frequency Response Analyzer Moku:Go Logic Analyzer & Pattern Generator Moku:Go Oscilloscope & Voltmeter Moku:Go PID Controller Moku:Go Spectrum Analyzer Moku:Go Waveform Generator Moku:Go Power Supplies Moku:Go Digital Filter Box Moku:Go FIR Filter Builder Moku:Go Lock-in Amplifier
- Moku:Lab
- Moku:Pro
- Python API examples
Moku:Lab laser lock box implements a 2 cascaded direct form I second-order stages IIR filter. It has a fixed sampling rate at 31.25 MHz. The coefficients can be set by set_custom_filter function in pymoku or MATLAB API, or directly loaded on the iPad with a 6 by 2 (6 columns, 2 rows) array as the following:
s1 | b0.1 | b1.1 | b2.1 | a1.1 | a2.1 |
s2 | b0.2 | b1.2 | b2.2 | a1.2 | a2.2 |
Each ‘a’ coefficient must be a float in the range [-2.0, +2.0). ‘s’ coefficients are multiplied into each ‘b’ coefficient before being sent to the device. These products (sN x b0.N, sN x b1.N, sN x b2.N) must also fall in the range [-2.0, +2.0). Internally, the ‘a’ and ‘b’ coefficients are represented as signed 32-bit fixed-point numbers, with 30 fractional bits.
For example, we can use scipy package to generate the coefficients for a second order butterworth filter like the following. It returns the coefficients array that can be later used in the set_custom_filter function. Please note negative signs are added to all 'a' coefficients for the direct form I filter.
from scipy import signal def gen_butterworth(corner_frequency): """ Generate coefficients for a second order butterworth low-pass filter. Corner frequencies for laser lock box second harmonic filtering should be in the range: 1 kHz < corner frequency < 31.25 MHz. """ sample_rate = 31.25e6 normalised_corner = corner_frequency / (sample_rate / 2) b, a = signal.butter(2, normalised_corner, 'low', analog = False) coefficient_array = [[1.0, b[0], b[1], b[2], -a[1], -a[2]], [1.0, 1.0, 0.0, 0.0, 0.0, 0.0]] return coefficient_array