Cannot cast array data from dtype('float64') to dtype('int32') according to the rule 'safe'

Question

I have a numpy array like

result = np.array([[[289, 354, 331],
                    [291, 206,  66],
                    [242,  70, 256]],

                   [[         


        

Answer 1

The problem is that when you divide by 4, you are creating float values, which don't want to go into the array of ints.

If you want to use putmask, and avoid the problem of trying to convert to float, then you can use floor division (//) in order to change your values to int:

np.putmask(result, result>255, result//4)

>>> result
array([[[ 72,  88,  82],
        [ 72, 206,  66],
        [242,  70,  64]],

       [[210,  97,  85],
        [ 68, 113, 218],
        [255,  87,  64]],

       [[127,  85, 173],
        [112,  98, 147],
        [223, 228, 100]]])

Alternative #1:

Convert your result array to a float dtype, and use your original putmask:

result = result.astype(float)

np.putmask(result, result > 255, result/4)

>>> result
array([[[ 72.25,  88.5 ,  82.75],
        [ 72.75, 206.  ,  66.  ],
        [242.  ,  70.  ,  64.  ]],

       [[210.  ,  97.25,  85.5 ],
        [ 68.25, 113.5 , 218.  ],
        [255.  ,  87.  ,  64.  ]],

       [[127.  ,  85.5 , 173.  ],
        [112.5 ,  98.75, 147.  ],
        [223.  , 228.  , 100.25]]])

You can even convert back to int after if desired:

result = result.astype(int)

array([[[ 72,  88,  82],
        [ 72, 206,  66],
        [242,  70,  64]],

       [[210,  97,  85],
        [ 68, 113, 218],
        [255,  87,  64]],

       [[127,  85, 173],
        [112,  98, 147],
        [223, 228, 100]]])

Alternative #2:

Do away with putmask altogether, and you can get your desired results like this:

result[result > 255] = result[result > 255] / 4

>>> result
array([[[ 72,  88,  82],
        [ 72, 206,  66],
        [242,  70,  64]],

       [[210,  97,  85],
        [ 68, 113, 218],
        [255,  87,  64]],

       [[127,  85, 173],
        [112,  98, 147],
        [223, 228, 100]]])