移動標準偏差
移動平均同様、numpyを使って、2次元のマトリックスに対する移動標準偏差の計算。
移動平均も内部で利用。
プログラム
import numpy as np def moving_sum(data_2d,axis=1,windowsize=3): answer = np.zeros((data_2d.shape)) answer[:,:] = np.nan v = np.ones(windowsize,) for i in range(data.shape[axis]): if axis==0: answer[i,windowsize-1:]=np.convolve(data_2d[i,:], v, mode = "valid") if axis==1: answer[windowsize-1:,i]=np.convolve(data_2d[:,i], v, mode = "valid") answer=answer return answer def moving_average(data_2d,axis=1,windowsize=3): return moving_sum(data_2d,axis,windowsize)/windowsize def moving_std(data_2d,axis=1,windowsize=3): answer = np.zeros((data_2d.shape)) answer[:,:] = np.nan answer = moving_sum(np.square(data_2d),axis,windowsize) -\ np.square(moving_average(data_2d,axis,windowsize))*windowsize answer = answer/(windowsize) answer = np.sqrt(answer) return answer #4列のデータが8点 #data=np.arange(32) #data=np.random.rand(32) #data=data.reshape(8,4) #検算のため即値で data=np.array([[0.86619006, 0.9130783, 0.51988756, 0.35008161], [0.12355818, 0.3230697, 0.70366867, 0.74275339], [0.58942652, 0.74948935, 0.30359438, 0.55652164], [0.40820522, 0.85400935, 0.29218585, 0.21874757], [0.06330341, 0.91181499, 0.73940466, 0.88877802], [0.7945424, 0.67662696, 0.44624821, 0.65392414], [0.26358476, 0.43238069, 0.00853011, 0.05989708], [0.89179866, 0.52684014, 0.14116962, 0.6934826 ]]) print("元データ") print(data) print("横方向で平均") answer = moving_average(data,axis=0,windowsize=3) print(answer) print("縦方向で平均") answer = moving_average(data,axis=1,windowsize=3) print(answer) print("横方向で標準偏差") answer=moving_std(data,axis=0,windowsize=3) print(answer) print("縦向で標準偏差") answer=moving_std(data,axis=1,windowsize=3) print(answer) print("検算 縦向 右下の値 標準偏差") print(data[5:8,3]) print(np.std(data[5:8,3]))
実行結果
元データ [[0.86619006 0.9130783 0.51988756 0.35008161] [0.12355818 0.3230697 0.70366867 0.74275339] [0.58942652 0.74948935 0.30359438 0.55652164] [0.40820522 0.85400935 0.29218585 0.21874757] [0.06330341 0.91181499 0.73940466 0.88877802] [0.7945424 0.67662696 0.44624821 0.65392414] [0.26358476 0.43238069 0.00853011 0.05989708] [0.89179866 0.52684014 0.14116962 0.6934826 ]] 横方向で平均 [[ nan nan 0.76638531 0.59434916] [ nan nan 0.38343218 0.58983059] [ nan nan 0.54750342 0.53653512] [ nan nan 0.51813347 0.45498092] [ nan nan 0.57150769 0.84666589] [ nan nan 0.63913919 0.59226644] [ nan nan 0.23483185 0.16693596] [ nan nan 0.51993614 0.45383079]] 縦方向で平均 [[ nan nan nan nan] [ nan nan nan nan] [0.52639159 0.66187912 0.5090502 0.54978555] [0.37372997 0.64218947 0.43314963 0.50600753] [0.35364505 0.8384379 0.44506163 0.55468241] [0.42201701 0.81415043 0.49261291 0.58714991] [0.37381019 0.67360755 0.39806099 0.53419975] [0.64997527 0.5452826 0.19864931 0.46910127]] 横方向で標準偏差 [[ nan nan 0.17534819 0.23579612] [ nan nan 0.24064464 0.18930211] [ nan nan 0.1844338 0.18258364] [ nan nan 0.24217704 0.28374409] [ nan nan 0.36618303 0.07642602] [ nan nan 0.14464027 0.10366564] [ nan nan 0.17422663 0.1888656 ] [ nan nan 0.30648191 0.23131534]] 縦向で標準偏差 [[ nan nan nan nan] [ nan nan nan nan] [0.30643714 0.24870894 0.16350932 0.16037833] [0.1917459 0.22965072 0.19134254 0.21688596] [0.21822617 0.06717765 0.20818406 0.27354188] [0.29868678 0.10006632 0.1854965 0.27758398] [0.30853401 0.19573988 0.30031751 0.34881834] [0.27608926 0.10056226 0.1832616 0.28980139]] 検算 縦向 右下の値 標準偏差 [0.65392414 0.05989708 0.6934826 ] 0.28980139385514914