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https://github.com/sigma-py/npx
Some useful extensions for NumPy
https://github.com/sigma-py/npx
numpy python
Last synced: 13 days ago
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Some useful extensions for NumPy
- Host: GitHub
- URL: https://github.com/sigma-py/npx
- Owner: sigma-py
- License: bsd-3-clause
- Created: 2021-03-05T15:54:11.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-06-21T14:46:24.000Z (5 months ago)
- Last Synced: 2024-10-12T00:22:49.442Z (about 1 month ago)
- Topics: numpy, python
- Language: Python
- Homepage:
- Size: 198 KB
- Stars: 26
- Watchers: 4
- Forks: 6
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
# npx
[](https://pypi.org/project/npx/)
[](https://pypi.org/project/npx/)
[](https://github.com/nschloe/npx)
[](https://pepy.tech/project/npx)
[](https://github.com/nschloe/npx/actions?query=workflow%3Aci)
[](https://app.codecov.io/gh/nschloe/npx)
[](https://github.com/psf/black)
[NumPy](https://numpy.org/) is a large library used everywhere in scientific computing.
That's why breaking backwards-compatibility comes at a significant cost and is almost
always avoided, even if the API of some methods is arguably lacking. This package
provides drop-in wrappers "fixing" those.
[scipyx](https://github.com/nschloe/scipyx) does the same for
[SciPy](https://www.scipy.org/).
If you have a fix for a NumPy method that can't go upstream for some reason, feel free
to PR here.
#### `dot`
```python
import npx
import numpy as np
a = np.random.rand(3, 4, 5)
b = np.random.rand(5, 2, 2)
out = npx.dot(a, b)
# out.shape == (3, 4, 2, 2)
```
Forms the dot product between the last axis of `a` and the _first_ axis of `b`.
(Not the second-last axis of `b` as `numpy.dot(a, b)`.)
#### `np.solve`
```python
import npx
import numpy as np
A = np.random.rand(3, 3)
b = np.random.rand(3, 10, 4)
out = npx.solve(A, b)
# out.shape == (3, 10, 4)
```
Solves a linear equation system with a matrix of shape `(n, n)` and an array of shape
`(n, ...)`. The output has the same shape as the second argument.
#### `sum_at`/`add_at`
```python
npx.sum_at(a, idx, minlength=0)
npx.add_at(out, idx, a)
```
Returns an array with entries of `a` summed up at indices `idx` with a minimum length of
`minlength`. `idx` can have any shape as long as it's matching `a`. The output shape is
`(minlength,...)`.
The numpy equivalent `numpy.add.at` is _much_
slower:
Relevant issue reports:
- [ufunc.at (and possibly other methods)
slow](https://github.com/numpy/numpy/issues/11156)
#### `unique`
```python
import npx
a = [0.1, 0.15, 0.7]
a_unique = npx.unique(a, tol=2.0e-1)
assert all(a_unique == [0.1, 0.7])
```
npx's `unique()` works just like NumPy's, except that it provides a parameter
`tol` (default `0.0`) which allows the user to set a tolerance. The real line
is essentially partitioned into bins of size `tol` and at most one
representative of each bin is returned.
#### `unique_rows`
```python
import npx
import numpy as np
a = np.random.randint(0, 5, size=(100, 2))
npx.unique_rows(a, return_inverse=False, return_counts=False)
```
Returns the unique rows of the integer array `a`. The numpy alternative `np.unique(a, axis=0)` is slow.
Relevant issue reports:
- [unique() needlessly slow](https://github.com/numpy/numpy/issues/11136)
#### `isin_rows`
```python
import npx
import numpy as np
a = [[0, 1], [0, 2]]
b = np.random.randint(0, 5, size=(100, 2))
npx.isin_rows(a, b)
```
Returns a boolean array of length `len(a)` specifying if the rows `a[k]` appear in `b`.
Similar to NumPy's own `np.isin` which only works for scalars.
#### `mean`
```python
import npx
a = [1.0, 2.0, 5.0]
npx.mean(a, p=3)
```
Returns the [generalized mean](https://en.wikipedia.org/wiki/Generalized_mean) of a
given list. Handles the cases `+-np.inf` (max/min) and`0` (geometric mean) correctly.
Also does well for large `p`.
Relevant NumPy issues:
- [generalized mean](https://github.com/numpy/numpy/issues/19341)
### License
This software is published under the [BSD-3-Clause
license](https://spdx.org/licenses/BSD-3-Clause.html).