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