I'm receiving this error when trying to assign an array to another array specific position. I was doing this before creating simple lists and doing such assignment. But Numpy is faster than simple lists and I was trying to use it now.
The problem is cause I have a 2D array that stores some data and, in my code, I have, e.g., to calculate the gradient for each position value, so I create another 2D array where each position stores the gradient for its value.
import numpy as np
cols = 2
rows = 3
# This works
matrix_a = []
for i in range(rows):
matrix_a.append([0.0] * cols)
print matrix_a
matrix_a[0][0] = np.matrix([[0], [0]])
print matrix_a
# This doesn't work
matrix_b = np.zeros((rows, cols))
print matrix_b
matrix_b[0, 0] = np.matrix([[0], [0]])
What happens is 'cause I have a class defining a np.zeros((rows, cols)) object, that stores information about some data, simplifying, e.g., images data.
class Data2D(object):
def __init__(self, rows=200, cols=300):
self.cols = cols
self.rows = rows
# The 2D data structure
self.data = np.zeros((rows, cols))
In a specific method, I have to calculate the gradient for this data, which is a 2 x 2 matrix (cause of this I would like to use ndarray, and not a simple array), and, to do this, I create another instance of this object to store this new data, in which each point (pixel) should store its gradient. I was using simple lists, which works, but I though I could gain some performance with numpy.
There is a way to work around this? Or a better way to do such thing? I know that I can define the array type to object, but I don't know if I lose performance doing such thing.
Thank you.
