If you have time series data and want to calculate Moving-function like Moving Average you can use a Rolling window like shown below … enjoy
import numpy as np
def rolling_window(a, window):
shape = a.shape[:-1] + (a.shape[-1] - window + 1, window)
strides = a.strides + (a.strides[-1],)
return np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides)
a = np.arange(0,10)
print(rolling_window(a,5))
print(np.mean(rolling_window(a,5), axis=1))
-----
[[0 1 2 3 4]
[1 2 3 4 5]
[2 3 4 5 6]
[3 4 5 6 7]
[4 5 6 7 8]
[5 6 7 8 9]]
[2. 3. 4. 5. 6. 7.]
b = np.random.randint(0,100,10)
print(rolling_window(b,5))
print(np.mean(rolling_window(b,5), axis=1))
-----
[[42 93 30 69 53]
[93 30 69 53 93]
[30 69 53 93 61]
[69 53 93 61 22]
[53 93 61 22 53]
[93 61 22 53 71]]
[57.4 67.6 61.2 59.6 56.4 60. ]
or here is another way to do moving average :
import numpy as np
def moving_average(a, n=3) :
ret = np.cumsum(a, dtype=float)
ret[n:] = ret[n:] - ret[:-n]
return ret[n - 1:] / n
print(moving_average([1,2,3,4,5,4,3,2,1]))
-------
[2.0 3.0 4.0 4.33 4.0 3.0 2.0]
