Can I set a lower corner frequency for the lowpass filter in the laser lock box?

Moku:Go
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 Moku:Lab
 Moku:Pro
Moku:Lab laser lock box uses a 2stage secondorder IIR filter. In the iPad app, the filter defaults to 1stage or 2stage secondorder filter only. As we have a limited number of bits during the calculation, this limited us to a ~1 kHz corner. In order to get a filter with a lower corner frequency, it is possible to manually load a 2stage firstorder filter, which should work at or below 1 Hz.
To manually load a custom filter, select the custom filter type in the filter configurator, and then load the filter coefficients from a .txt file in the SD card, clipboard, or My Files, as shown in the screenshot below.
The .txt file should have two rows, six columns with the following format:
s1  b0.1  b1.1  b2.1  a1.1  a2.1 
s2  b0.2  b1.2  b2.2  a1.2  a2.2 
s1 and s2 refer to the overall gain for stage 1 and stage 2. bx.1 to ax.1 represent the filter coefficients for stage 1, and bx.2 to ax.2 represent the filter coefficients for stage 2. These filter coefficients can be generated by Python scipy or MATLAB with Signal Processing Toolbox. Here, we provide a Python script to generate the .txt file.
import numpy as np import matplotlib.pyplot as plt from scipy import signal import math def main(): corner_frequency = 1 #in Hz filter_type = 1 #1 for first order 2stage, 2 for second order 1stage generate_filter_array(corner_frequency,filter_type, plot = False) def generate_filter_array(corner_frequency,filter_order,plot): sample_rate = 31.25e6 normalised_corner = corner_frequency / (sample_rate / 2) if filter_order == 1: b, a = signal.butter(1, normalised_corner, 'low', analog = False) a = np.append(a,0) b = np.append(b,0) gen_coe_array(b, a, filter_order,str(corner_frequency)+'Hz'+str(filter_order)+'order_filter') if plot == True: plot_fra(b,a,filter_order) elif filter_order == 2: b, a = signal.butter(2, normalised_corner, 'low', analog = False) gen_coe_array(b,a,filter_order,str(corner_frequency)+'Hz'+str(filter_order)+'order_filter') if plot == True: plot_fra(b,a,filter_order) def plot_fra(b,a,order): w, h = signal.freqz(b, a, worN = 2048*2048) if order == 1: h = np.multiply(h,h) plt.semilogx(w*sample_rate/math.pi/2, 20 * np.log10(np.absolute(h))) plt.title('Butterworth filter frequency response') plt.xlabel('Frequency [Hz]') plt.ylabel('Amplitude [dB]') plt.grid(which='both', axis='both') plt.show() def gen_coe_array(b,a,order,name): if order == 1: coefficient_array = [[1.0, b[0], b[1], 0, a[1], 0], [1.0, b[0], b[1], 0, a[1], 0]] elif order == 2: 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]] np.savetxt(name +'.txt',coefficient_array, delimiter = ',') if __name__ == '__main__': main()