From 258986b0e4195e748f441dc803596ccb94af52d9 Mon Sep 17 00:00:00 2001
From: David Rotermund <54365609+davrot@users.noreply.github.com>
Date: Fri, 29 Dec 2023 17:29:35 +0100
Subject: [PATCH] Create README.md

Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
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+# Stack and Split
+{:.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.column_stack](https://numpy.org/doc/stable/reference/generated/numpy.column_stack.html)
+
+```python
+numpy.column_stack(tup)
+```
+
+> Stack 1-D arrays as columns into a 2-D array.
+> 
+> Take a sequence of 1-D arrays and stack them as columns to make a single 2-D array. 2-D arrays are stacked as-is, just like with hstack. 1-D arrays are turned into 2-D columns first.
+
+```python
+import numpy as np
+
+a = np.arange(0, 10)
+print(a) # -> [0 1 2 3 4 5 6 7 8 9]
+print(a.shape)  # -> (10,)
+
+b = np.column_stack((a, a))
+print(b)
+print(b.shape) # -> (10, 2)
+```
+Output:
+
+```python
+[[0 0]
+ [1 1]
+ [2 2]
+ [3 3]
+ [4 4]
+ [5 5]
+ [6 6]
+ [7 7]
+ [8 8]
+ [9 9]]
+```
+
+## [numpy.row_stack](https://numpy.org/doc/stable/reference/generated/numpy.row_stack.html)
+
+```python
+numpy.row_stack(tup, *, dtype=None, casting='same_kind')
+```
+
+> Stack arrays in sequence vertically (row wise).
+> 
+> This is equivalent to concatenation along the first axis after 1-D arrays of shape (N,) have been reshaped to (1,N). Rebuilds arrays divided by vsplit.
+> 
+> This function makes most sense for arrays with up to 3 dimensions. For instance, for pixel-data with a height (first axis), width (second axis), and r/g/b channels (third axis). The functions concatenate, stack and block provide more general stacking and concatenation operations.
+> 
+> np.row_stack is an alias for vstack. They are the same function.
+
+```python
+import numpy as np
+
+a = np.arange(0, 10)
+print(a)  # -> [0 1 2 3 4 5 6 7 8 9]
+print(a.shape)  # -> (10,)
+
+b = np.row_stack((a, a))
+print(b)
+print(b.shape)  # -> (2, 10)
+```
+
+Output:
+
+```python
+[[0 1 2 3 4 5 6 7 8 9]
+ [0 1 2 3 4 5 6 7 8 9]]
+```
+