From 827e68d23d1a56dc58f8973b88bd3dbadf256719 Mon Sep 17 00:00:00 2001
From: David Rotermund <54365609+davrot@users.noreply.github.com>
Date: Sat, 30 Dec 2023 01:09:10 +0100
Subject: [PATCH] Create README.md

Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
---
 numpy/iterating/README.md | 113 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 113 insertions(+)
 create mode 100644 numpy/iterating/README.md

diff --git a/numpy/iterating/README.md b/numpy/iterating/README.md
new file mode 100644
index 0000000..8ceb693
--- /dev/null
+++ b/numpy/iterating/README.md
@@ -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]]
+```