Create README.md

Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>

numpy_save_special/README.md

@@ -0,0 +1,180 @@
+# Numpy: Beyond normal np.save
+{:.no_toc}
+
+<nav markdown="1" class="toc-class">
+* TOC
+{:toc}
+</nav>
+
+## The goal
+Let's [save and load data under numpy](https://numpy.org/doc/stable/reference/routines.io.html). This can be more complicated than expected.
+
+Questions to [David Rotermund](mailto:davrot@uni-bremen.de)
+
+## Normal [np.save](https://numpy.org/doc/stable/reference/generated/numpy.save.html) and [np.load](https://numpy.org/doc/stable/reference/generated/numpy.load.html)
+A normal np.save and np.load cycle may look like this:
+
+```python
+import numpy as np
+
+rng = np.random.default_rng()
+
+a_original: np.ndarray = rng.random((100, 10))
+
+np.save("a.npy", a_original)
+a_load: np.ndarray = np.load("a.npy")
+
+print(np.abs(a_original - a_load).sum()) # -> 0.0
+```
+
+## Saving non-standard numpy data 
+You may have tried this and wondered why it doesn't work
+
+```python
+import numpy as np
+
+rng = np.random.default_rng()
+
+a_original: list = []
+a_original.append(rng.random((100, 10)))
+a_original.append(rng.random((100, 10)))
+a_original.append("now something completely different")
+
+np.save("b.npy", a_original)
+a_load: np.ndarray = np.load("b.npy")
+```
+
+```python
+---------------------------------------------------------------------------
+ValueError                                Traceback (most recent call last)
+/data_1/davrot/MRI_Fruits/test/test.py in line 12
+      26 a_original.append("now something completely different")
+     28 np.save("b.npy", a_original)
+---> 29 a_load: np.ndarray = np.load("b.npy")
+[...]
+```
+This is how you have to do it:
+
+```python
+import numpy as np
+
+rng = np.random.default_rng()
+
+a_original: list = []
+a_original.append(rng.random((100, 10)))
+a_original.append(rng.random((100, 10)))
+a_original.append("now something completely different")
+
+np.save("b.npy", np.array(a_original, dtype=object))
+a_load: np.ndarray = np.load("b.npy", allow_pickle=True)
+
+print(np.abs(a_original[0] - a_load[0]).sum())  # -> 0.0
+print(np.abs(a_original[1] - a_load[1]).sum())  # -> 0.0
+print(a_load[2])  # -> now something completely different
+```
+
+## [np.savez](https://numpy.org/doc/stable/reference/generated/numpy.savez.html)
+
+We can save more than one variable into one file. We need to use np.savez for this. Now the file extension is npz instead of npy. This is required! 
+
+```python
+import numpy as np
+
+rng = np.random.default_rng()
+
+a_original = rng.random((100, 10))
+b_original = rng.random((100, 10))
+c_original = rng.random((100, 10))
+
+np.savez("c.npz", a_original=a_original, b_original=b_original, c_original=c_original)
+
+np_file = np.load("c.npz")
+
+np_file_keys: list = list(np_file.keys())
+print(np_file_keys) # -> ['a_original', 'b_original', 'c_original']
+```
+
+Please don't use savez like this because this can cause human errors down the road:
+
+```python
+import numpy as np
+
+rng = np.random.default_rng()
+
+a_original = rng.random((100, 10))
+b_original = rng.random((100, 10))
+c_original = rng.random((100, 10))
+
+# np.savez("c.npz", a_original=a_original, b_original=b_original, c_original=c_original)
+np.savez("d.npz", a_original, b_original, c_original)
+
+np_file = np.load("d.npz")
+
+np_file_keys: list = list(np_file.keys())
+print(np_file_keys) # -> ['arr_0', 'arr_1', 'arr_2']
+```
+
+You don't need to keep the variable name but keep it human readable:
+
+```python
+import numpy as np
+
+rng = np.random.default_rng()
+
+a_original = rng.random((100, 10))
+b_original = rng.random((100, 10))
+c_original = rng.random((100, 10))
+d_original = rng.random((100, 10))
+
+np.savez("e.npz", what=a_original, a=b_original, nice=c_original, day=d_original)
+
+np_file = np.load("e.npz")
+
+np_file_keys: list = list(np_file.keys())
+print(np_file_keys) # -> ['what', 'a', 'nice', 'day']
+```
+Now we can work with the file and the stored variables: 
+
+```python
+import numpy as np
+
+rng = np.random.default_rng()
+
+a_original = rng.random((100, 10))
+b_original = rng.random((100, 10))
+c_original = rng.random((100, 10))
+
+np.savez("c.npz", a_original=a_original, b_original=b_original, c_original=c_original)
+
+np_file = np.load("c.npz")
+
+print(np.abs(a_original - np_file["a_original"]).sum())  # -> 0.0
+print(np.abs(b_original - np_file["b_original"]).sum())  # -> 0.0
+print(np.abs(c_original - np_file["c_original"]).sum())  # -> 0.0
+```
+
+## [np.savez_compressed](https://numpy.org/doc/stable/reference/generated/numpy.savez_compressed.html)
+
+We can compress the data too:
+
+```python
+import numpy as np
+
+rng = np.random.default_rng()
+
+a_original = rng.random((100, 10))
+b_original = rng.random((100, 10))
+c_original = rng.random((100, 10))
+
+np.savez_compressed(
+    "f.npz", a_original=a_original, b_original=b_original, c_original=c_original
+)
+
+np_file = np.load("f.npz")
+
+print(np.abs(a_original - np_file["a_original"]).sum())  # -> 0.0
+print(np.abs(b_original - np_file["b_original"]).sum())  # -> 0.0
+print(np.abs(c_original - np_file["c_original"]).sum())  # -> 0.0
+```
+
+