I'm trying to find a fast way to fill a Numpy array with rotation symmetric values. Imagine an array of zeros containing a cone shaped area. I have a 1D array of values and want to rotate it 360° around the center of the array. There is no 2D function like z=f(x,y), so I can't calculate the 2D values explicitly. I have something that works, but the for-loop is too slow for big arrays. This should make a circle:
values = np.ones(100)
x = np.arange(values.size)-values.size/2+0.5
y = values.size/2-0.5-np.arange(values.size)
x,y = np.meshgrid(x,y)
grid = np.rint(np.sqrt(x**2+y**2))
arr = np.zeros_like(grid)
for i in np.arange(values.size/2):
arr[grid==i] = values[i+values.size/2]
My 1D array is of course not as simple. Can someone think of a way to get rid of the for-loop?
Update: I want to make a circular filter for convolutional blurring. Before I used np.outer(values,values)
which gave me a rectangular filter. David's hint allows me to create a circular filter very fast. See below:
You can use fancy indexing to achieve this:
Here,
inside
select the indices that lie inside the circle, since only these items can be derived fromvalues
.