pad matrix with zeroes to achieve desired shape in python-CodePudding

Hi I have a 19 x 3 matrix, however I want to make it 513 x 3 matrix. So i just need to maintain my 19 rows and the remaining zeroes or ones to make up 513 is this possible?

CodePudding user response：

You can use numpy.pad()

In your case, if you want your 19x3 values in the center of the matrix, you should use:

import numpy as np
padded_matrix = np.pad(your_matrix, ((247, 247), (0, 0)))

247 comes from (513-19)/2

EDIT: If instead, you want to keep the values in the first 19 rows and pad the matrix at the bottom, change the parameters:

padded_matrix = np.pad(your_matrix, ((0, 513-19), (0, 0)))

CodePudding user response：

You could do it like this (check [NumPy]: Assigning values to indexed arrays and [NumPy]: Broadcasting for more details):

>>>
>>> import numpy as np
>>>
>>>
>>> rows_inner, cols_inner = 19, 3
>>> rows_outer, cols_outer = 513, 3
>>>
>>> inner = np.arange(rows_inner * cols_inner).reshape(rows_inner, cols_inner)
>>> inner.shape, inner.dtype
((19, 3), dtype('int32'))
>>> inner
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, 56]])
>>>
>>>
>>> outer = np.zeros((rows_outer, cols_outer), dtype=np.int32)
>>> outer.shape, outer.dtype
((513, 3), dtype('int32'))
>>> outer
array([[0, 0, 0],
       [0, 0, 0],
       [0, 0, 0],
       ...,
       [0, 0, 0],
       [0, 0, 0],
       [0, 0, 0]])
>>>
>>>
>>> outer[0:rows_inner,:] = inner  # !!! Simple Assignment !!!
>>>
>>> outer.shape, outer.dtype
((513, 3), dtype('int32'))
>>> outer
array([[0, 1, 2],
       [3, 4, 5],
       [6, 7, 8],
       ...,
       [0, 0, 0],
       [0, 0, 0],
       [0, 0, 0]])

CodePudding user response：

Use append on axis=0:

arr2 = np.zeros((513-19, 3))
new_arr = np.append(arr1, arr2, axis=0)