I have a NumPy array of X * Y elements, represented as a flatted array (arr = np.array(x * y)).
Given the following values:
X = 832
Y = 961
I need to access elements of the array in the following sequence:
arr[0:832:2]
arr[1:832:2]
arr[832:1664:2]
arr[833:1664:2]
...
arr[((Y-1) * X):(X * Y):2]
I'm not sure, mathematically, how to achieve the start and stop for each iteration in a loop.
This should do the trick
Y = 961
X = 832
all_ = np.random.rand(832*961)
# Iterating over the values of y
for i in range(1,Y):
# getting the indicies from the array we need
# i - 1 = Start
# X*i = END
# 2 is the step
indicies = list(range(i-1,X*i,2))
# np.take slice values from the array or get values corresponding to the list of indicies we prepared above
required_array = np.take(indices=indices)
To anybody interested in this solution (per iteration, not incrementing the shift each iteration):
for i in range(Y):
shift = X * (i // 2)
begin = (i % 2) + shift
end = X + shift
print(f'{begin}:{end}:2')
Y*2 in the for loop. When you use Y it only runs till 399360(Y-1) * X), (X * Y):2 which is 798720: 799552 but your loop ends with 399360:400192:2Let's say you have an array of shape (x * y,) that you want to process in chunks of x. You can simply reshape your array to shape (y, x) and process the rows:
>>> x = 832
>>> y = 961
>>> arr = np.arange(x * y)
Now reshape, and process in bulk. In the following example, I take the mean of each row. You can apply whatever functions you want to the entire array this way:
>>> arr = arr.reshape(y, x)
>>> np.mean(arr[:, ::2], axis=1)
>>> np.mean(arr[:, 1::2], axis=1)
The reshape operation does not alter the data in your array. The buffer it points to is the same as the original. You can invert the reshape by calling ravel on the array.
[start:stop:step], such asarr[1:832:2] = 4.arris a structured array representing a hexagon tessellation.arrhas a shape of(X * Y, )and each element has a shape of(7, 2). I'm iterating over theY(column) range to assign values to the tessellation since each column shares x-axis values.