| import numpy as np | |
| def count2noise(count): | |
| return np.sqrt(np.max(9.3131 * count - np.ones_like(count) * 63.003, 0)) | |
| def spectrum2mutnoise(spectrum, ref=23760, mul=1.75, offset=0.45): #Arrange for your data | |
| spectrum_add = spectrum * mul + offset | |
| obj_count = ref * np.exp(-spectrum_add) | |
| ref_sigma = count2noise(ref) | |
| obj_sigma = count2noise(obj_count) | |
| mut_noise = np.sqrt((ref_sigma / ref) * (ref_sigma / ref) + (obj_sigma / obj_count) * (obj_sigma / obj_count)) | |
| return mut_noise / mul | |