I have two numpy arrays:
- A 1D array called t of shape (70L,) with element called let s say ti
- A 3D array called I with shape (70L, 1024L, 1024L), with each elements called Ii. Ii are thus of dimension (1024L, 1024L)
I would like to make a product of the two array along the first dimension, i.e.:
tI = t1*I1,t2*I2,...,tN*IN
such as to obtain again a new array of dimension (70L, 1024L, 1024L) and then take the sum along the first dimension in order to obtain an array of dimension (1024L, 1024L):
tsum = t1*I1 + t2*I2 + ... +tN*IN
For the moment I am satisfied with doing the following:
tI = np.asarray([t[i]*I[i,:,:] for i in range(t.shape[0])])
tsum = np.sum(tI,axis=0)
But it is going to be a bit slow is the dimensions of my array are increasing. I was wondering if there exist a numpy or scipy function, more optimized for that particular task?
Thanks in advance of any link or information.
Greg