davrot/pytutorial
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

Create README.md

Browse source 
Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
This commit is contained in:
David Rotermund 2023-12-30 01:09:10 +01:00 committed by GitHub
parent dd8c2d60b2
commit 827e68d23d
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
1 changed files with 113 additions and 0 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

113
numpy/iterating/README.md Normal file
View file

@ -0,0 +1,113 @@
# Iterating over an array
{:.no_toc}
<nav markdown="1" class="toc-class">
* TOC
{:toc}
</nav>
## The goal
Questions to [David Rotermund](mailto:davrot@uni-bremen.de)
{: .topic-optional}
This is an optional topic!
## [numpy.apply_along_axis](https://numpy.org/doc/stable/reference/generated/numpy.apply_along_axis.html)
```python
numpy.apply_along_axis(func1d, axis, arr, *args, **kwargs)
```
> Apply a function to 1-D slices along the given axis.
>
> Execute func1d(a, *args, **kwargs) where func1d operates on 1-D arrays and a is a 1-D slice of arr along axis.
>
> This is equivalent to (but faster than) the following use of ndindex and s_, which sets each of ii, jj, and kk to a tuple of indices:
```python
Ni, Nk = a.shape[:axis], a.shape[axis+1:]
for ii in ndindex(Ni):
for kk in ndindex(Nk):
f = func1d(arr[ii + s_[:,] + kk])
Nj = f.shape
for jj in ndindex(Nj):
out[ii + jj + kk] = f[jj]
```
> Equivalently, eliminating the inner loop, this can be expressed as:
```python
Ni, Nk = a.shape[:axis], a.shape[axis+1:]
for ii in ndindex(Ni):
for kk in ndindex(Nk):
out[ii + s_[...,] + kk] = func1d(arr[ii + s_[:,] + kk])
```
### Example
```python
import numpy as np
def function_1d(input):
print(f"input shape: {input.shape}, input: {input}")
return input + input.shape[0]
a = np.arange(1, 13).reshape(3, 4)
print(a)
print(a.shape) # -> (3, 4)
print()
print("******")
b = np.apply_along_axis(function_1d, axis=0, arr=a)
print("******")
print()
print(b)
print(b.shape) # -> (3, 4)
print()
print("++++++")
b = np.apply_along_axis(function_1d, axis=1, arr=a)
print("++++++")
print()
print(b)
print(b.shape) # -> (3, 4)
```
Output:
```python
[[ 1 2 3 4]
[ 5 6 7 8]
[ 9 10 11 12]]
******
input shape: (3,), input: [1 5 9]
input shape: (3,), input: [ 2 6 10]
input shape: (3,), input: [ 3 7 11]
input shape: (3,), input: [ 4 8 12]
******
[[ 4 5 6 7]
[ 8 9 10 11]
[12 13 14 15]]
++++++
input shape: (4,), input: [1 2 3 4]
input shape: (4,), input: [5 6 7 8]
input shape: (4,), input: [ 9 10 11 12]
++++++
[[ 5 6 7 8]
[ 9 10 11 12]
[13 14 15 16]]
```