Moku:Lab laser lock box uses a 2-stage second-order IIR filter. In the iPad app, the filter defaults to 1-stage or 2-stage second-order 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 2-stage first-order 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 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 2-stage, 2 for second order 1-stage
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()