scipy.stats.signaltonoise(arr, axis=0, ddof=0) function computes the signal-to-noise ratio of the input data.
Its formula :
Parameters :
arr : [array_like]Input array or object having the elements to calculate the signal-to-noise ratio
axis : Axis along which the mean is to be computed. By default axis = 0.
ddof : Degree of freedom correction for Standard Deviation.Results : mean to standard deviation ratio i.e. signal-to-noise ratio.
Code #1: Working
# stats.signaltonoise() method import numpy as np from scipy import stats arr1 = [[20, 2, 7, 1, 34], [50, 12, 12, 34, 4]] arr2 = [50, 12, 12, 34, 4] print ("\narr1 : ", arr1) print ("\narr2 : ", arr2) print ("\nsignaltonoise ratio for arr1 : ", stats.signaltonoise(arr1, axis = 0, ddof = 0)) print ("\nsignaltonoise ratio for arr1 : ", stats.signaltonoise(arr1, axis = 1, ddof = 0)) print ("\nsignaltonoise ratio for arr1 : ", stats.signaltonoise(arr2, axis = 0, ddof = 0)) |
Output :
arr1 : [[20, 2, 7, 1, 34], [50, 12, 12, 34, 4]]
arr2 : [50, 12, 12, 34, 4]
signaltonoise ratio for arr1 : [2.33333333 1.4 3.8 1.06060606 1.26666667]
signaltonoise ratio for arr1 : [1.01779811 1.31482934]
signaltonoise ratio for arr2 : 1.3148293369202024
Code #2 : How to implement
def signaltonoise(a, axis, ddof): a = np.asanyarray(a) m = a.mean(axis) sd = a.std(axis = axis, ddof = ddof) return np.where(sd == 0, 0, m / sd) print ("\nsignaltonoise ratio for arr1 : ", signaltonoise(arr1, axis = 0, ddof = 0)) print ("\nsignaltonoise ratio for arr1 : ", signaltonoise(arr1, axis = 1, ddof = 0)) print ("\nsignaltonoise ratio for arr2 : ", signaltonoise(arr2, axis = 0, ddof = 0)) |
Output :
signaltonoise ratio for arr1 : [2.33333333 1.4 3.8 1.06060606 1.26666667]
signaltonoise ratio for arr1 : [1.01779811 1.31482934]
signaltonoise ratio for arr2 : 1.3148293369202024
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