77 lines
1.6 KiB
Markdown
77 lines
1.6 KiB
Markdown
# Piecewise
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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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## Top
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Questions to [David Rotermund](mailto:davrot@uni-bremen.de)
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## [numpy.piecewise](https://numpy.org/doc/stable/reference/generated/numpy.piecewise.html)
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```python
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numpy.piecewise(x, condlist, funclist, *args, **kw)
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```
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> Evaluate a piecewise-defined function.
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>
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> Given a set of conditions and corresponding functions, evaluate each function on the input data wherever its condition is true.
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```python
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import numpy as np
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a = np.arange(1, 11)
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b = np.piecewise(a, [a < 5, a == 0, a > 5], [-1, 0, 1])
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print(a) # -> [ 1 2 3 4 5 6 7 8 9 10]
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print(b) # -> [-1 -1 -1 -1 0 1 1 1 1 1]
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```
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Instead of values we can use functions (Or you can use lambda functions...):
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```python
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import numpy as np
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def function_a(input):
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return input**2
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def function_b(input):
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return np.sqrt(input)
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a = np.arange(1, 11)
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b = np.piecewise(a, [a < 5, a == 0, a > 5], [function_a, 0, function_b])
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print(a) # -> [ 1 2 3 4 5 6 7 8 9 10]
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print(b) # -> [ 1 4 9 16 0 2 2 2 3 3]
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```
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**However, the results for the sqrt are strange.** Here we see a case where the automatic dtype switch to float64 failed. This is why I overzealously mange / define the dtypes.
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```python
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import numpy as np
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def function_a(input):
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return input**2
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def function_b(input):
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return np.sqrt(input)
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a = np.arange(1, 11).astype(dtype=np.float32)
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b = np.piecewise(a, [a < 5, a == 0, a > 5], [function_a, 0, function_b])
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print(a) # -> [ 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.]
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print(b) # -> [ 1. 4. 9. 16. 0. 2.45 2.65 2.83 3. 3.16]
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```
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