Windowing with Looking back in numpy array in python-CodePudding

following the array I have. I have added my expected result that i am not getting with attached code.

p= array([[0.26650886, 0.6108316 , 0.87093688, 0.56106049],
       [0.27189878, 0.60786972, 0.87653939, 0.54244087],
       [0.27508257, 0.60678571, 0.87979568, 0.5297218 ],
       [0.27582241, 0.60754711, 0.88034473, 0.51667662],
       [0.27606467, 0.60711087, 0.8800212 , 0.51716336],
       [0.27705633, 0.60654571, 0.88044624, 0.52474009],
       [0.27909608, 0.60545549, 0.88164035, 0.52696207],
       [0.28027486, 0.60447923, 0.8821804 , 0.51754806],
       [0.27989394, 0.6036416 , 0.88188837, 0.50952766],
       [0.27953247, 0.6015729 , 0.88151134, 0.51027505]])

Expected result i want, lets say window_size=4 I am not getting this using below code.

[[0.26650886, 0.6108316 , 0.87093688, 0.56106049],
       [0.27189878, 0.60786972, 0.87653939, 0.54244087],
       [0.27508257, 0.60678571, 0.87979568, 0.5297218 ],
       [0.27582241, 0.60754711, 0.88034473, 0.51667662]
    
    
[0.27189878, 0.60786972, 0.87653939, 0.54244087],
       [0.27508257, 0.60678571, 0.87979568, 0.5297218 ],
       [0.27582241, 0.60754711, 0.88034473, 0.51667662],
       [0.27606467, 0.60711087, 0.8800212 , 0.51716336]
    
    
[0.27508257, 0.60678571, 0.87979568, 0.5297218 ],
       [0.27582241, 0.60754711, 0.88034473, 0.51667662],
       [0.27606467, 0.60711087, 0.8800212 , 0.51716336],
       [0.27705633, 0.60654571, 0.88044624, 0.52474009].....]

I did the following code but it not giving me a result that I want.

# df is dataframe with shape(14, 4) 

X = np.zeros(shape=(df.shape[0]-window_size,window_size,df.shape[1]))

for i in range(window_size-1, 10):
    for j in range(i-window_size 1, i 1):
        X[i-window_size 1][window_size-1-i j] = p[j]

Thanks in advance

CodePudding user response：

A more efficient solution would be to use sliding_window_view from numpy.lib.stride_tricks:

from numpy.lib.stride_tricks import sliding_window_view

values_per_arr = 4  # or p.shape[1]
window_size = 4
X = sliding_window_view(p, window_shape=(values_per_arr, window_size)).reshape(-1, values_per_arr, window_size)

Output:

>>> X
array([[[0.26650886, 0.6108316 , 0.87093688, 0.56106049],
        [0.27189878, 0.60786972, 0.87653939, 0.54244087],
        [0.27508257, 0.60678571, 0.87979568, 0.5297218 ],
        [0.27582241, 0.60754711, 0.88034473, 0.51667662]],

       [[0.27189878, 0.60786972, 0.87653939, 0.54244087],
        [0.27508257, 0.60678571, 0.87979568, 0.5297218 ],
        [0.27582241, 0.60754711, 0.88034473, 0.51667662],
        [0.27606467, 0.60711087, 0.8800212 , 0.51716336]],

       [[0.27508257, 0.60678571, 0.87979568, 0.5297218 ],
        [0.27582241, 0.60754711, 0.88034473, 0.51667662],
        [0.27606467, 0.60711087, 0.8800212 , 0.51716336],
        [0.27705633, 0.60654571, 0.88044624, 0.52474009]],

       [[0.27582241, 0.60754711, 0.88034473, 0.51667662],
        [0.27606467, 0.60711087, 0.8800212 , 0.51716336],
        [0.27705633, 0.60654571, 0.88044624, 0.52474009],
        [0.27909608, 0.60545549, 0.88164035, 0.52696207]],

       [[0.27606467, 0.60711087, 0.8800212 , 0.51716336],
        [0.27705633, 0.60654571, 0.88044624, 0.52474009],
        [0.27909608, 0.60545549, 0.88164035, 0.52696207],
        [0.28027486, 0.60447923, 0.8821804 , 0.51754806]],

       [[0.27705633, 0.60654571, 0.88044624, 0.52474009],
        [0.27909608, 0.60545549, 0.88164035, 0.52696207],
        [0.28027486, 0.60447923, 0.8821804 , 0.51754806],
        [0.27989394, 0.6036416 , 0.88188837, 0.50952766]],

       [[0.27909608, 0.60545549, 0.88164035, 0.52696207],
        [0.28027486, 0.60447923, 0.8821804 , 0.51754806],
        [0.27989394, 0.6036416 , 0.88188837, 0.50952766],
        [0.27953247, 0.6015729 , 0.88151134, 0.51027505]]])

CodePudding user response：

Your code with window size of 4:

In [9]: arr =np.arange(4*14).reshape(14,4)
   ...: X = np.zeros(shape=(14-4,4,4),dtype=int)
   ...: for i in range(4-1,10):
   ...:     for j in range(i-4 1,i 1):
   ...:         X[i-4 1][4-1-i j] = arr[j]      # arr instead of p
   ...: 
In [10]: X
Out[10]: 
array([[[ 0,  1,  2,  3],
        [ 4,  5,  6,  7],
        [ 8,  9, 10, 11],
        [12, 13, 14, 15]],

       [[ 4,  5,  6,  7],
        [ 8,  9, 10, 11],
        [12, 13, 14, 15],
        [16, 17, 18, 19]],

       [[ 8,  9, 10, 11],
        [12, 13, 14, 15],
        [16, 17, 18, 19],
        [20, 21, 22, 23]],

       [[12, 13, 14, 15],
        [16, 17, 18, 19],
        [20, 21, 22, 23],
        [24, 25, 26, 27]],

       [[16, 17, 18, 19],
        [20, 21, 22, 23],
        [24, 25, 26, 27],
        [28, 29, 30, 31]],

       [[20, 21, 22, 23],
        [24, 25, 26, 27],
        [28, 29, 30, 31],
        [32, 33, 34, 35]],

       [[24, 25, 26, 27],
        [28, 29, 30, 31],
        [32, 33, 34, 35],
        [36, 37, 38, 39]],

       [[ 0,  0,  0,  0],
        [ 0,  0,  0,  0],
        [ 0,  0,  0,  0],
        [ 0,  0,  0,  0]],

       [[ 0,  0,  0,  0],
        [ 0,  0,  0,  0],
        [ 0,  0,  0,  0],
        [ 0,  0,  0,  0]],

       [[ 0,  0,  0,  0],
        [ 0,  0,  0,  0],
        [ 0,  0,  0,  0],
        [ 0,  0,  0,  0]]])

the source array:

In [11]: arr
Out[11]: 
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11],
       [12, 13, 14, 15],
       [16, 17, 18, 19],
       [20, 21, 22, 23],
       [24, 25, 26, 27],
       [28, 29, 30, 31],
       [32, 33, 34, 35],
       [36, 37, 38, 39],
       [40, 41, 42, 43],
       [44, 45, 46, 47],
       [48, 49, 50, 51],
       [52, 53, 54, 55]])

Other than the fact that it didn't fill in the last 3 blocks, I don't see what's the problem.