I want to find a fast way (without for loop) in Python to assign reoccuring indices of an array. This is the desired result using a for loop:
import numpy as np
a=np.arange(9, dtype=np.float64).reshape((3,3))
# The array indices: [2,3,4] are identical.
Px = np.uint64(np.array([0,1,1,1,2]))
Py = np.uint64(np.array([0,0,0,0,0]))
# The array to be added at the array indices (may also contain random numbers).
x = np.array([.1,.1,.1,.1,.1])
for m in np.arange(len(x)):
a[Px[m]][Py[m]] += x
print a
%[[ 0.1 1. 2.]
%[ 3.3 4. 5.]
%[ 6.1 7. 8.]]
When I try to add x
to a
at the indices Px,Py
I obviously do not get the same result (3.3 vs. 3.1):
a[Px,Py] += x
print a
%[[ 0.1 1. 2.]
%[ 3.1 4. 5.]
%[ 6.1 7. 8.]]
Is there a way to do this with numpy? Thanks.
NumPy: repeat() function The repeat() function is used to repeat elements of an array. Input array. The number of repetitions for each element. repeats is broadcasted to fit the shape of the given axis.
Compare Two Arrays in Python Using the numpy. array_equiv() Method. The numpy. array_equiv(a1, a2) method takes array a1 and a2 as input and returns True if both arrays' shape and elements are the same; otherwise, returns False .
Element Assignment in NumPy Arrays We can assign new values to an element of a NumPy array using the = operator, just like regular python lists. A few examples are below (note that this is all one code block, which means that the element assignments are carried forward from step to step).
Use numpy. concatenate() to merge the content of two or multiple arrays into a single array. This function takes several arguments along with the NumPy arrays to concatenate and returns a Numpy array ndarray. Note that this method also takes axis as another argument, when not specified it defaults to 0.
Yes, it can be done, but it is a little tricky:
# convert yourmulti-dim indices to flat indices
flat_idx = np.ravel_multi_index((Px, Py), dims=a.shape)
# extract the unique indices and their position
unique_idx, idx_idx = np.unique(flat_idx, return_inverse=True)
# Aggregate the repeated indices
deltas = np.bincount(idx_idx, weights=x)
# Sum them to your array
a.flat[unique_idx] += deltas
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With