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https://github.com/quansight-labs/ndindex
A Python library for manipulating indices of ndarrays
https://github.com/quansight-labs/ndindex
numpy numpy-arrays python
Last synced: 4 days ago
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A Python library for manipulating indices of ndarrays
- Host: GitHub
- URL: https://github.com/quansight-labs/ndindex
- Owner: Quansight-Labs
- License: mit
- Created: 2020-03-09T22:20:06.000Z (almost 5 years ago)
- Default Branch: main
- Last Pushed: 2024-10-09T10:51:47.000Z (3 months ago)
- Last Synced: 2024-12-28T20:03:40.700Z (11 days ago)
- Topics: numpy, numpy-arrays, python
- Language: Python
- Homepage: https://quansight-labs.github.io/ndindex/
- Size: 8.76 MB
- Stars: 99
- Watchers: 15
- Forks: 13
- Open Issues: 37
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
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README
# ndindex
A Python library for manipulating indices of ndarrays.
The documentation for ndindex can be found at https://quansight-labs.github.io/ndindex/
ndindex is a library that allows representing and manipulating objects that
can be valid indices to numpy arrays, i.e., slices, integers, ellipses,
None, integer and boolean arrays, and tuples thereof. The goals of the library
are
- Provide a uniform API to manipulate these objects. Unlike the standard index
objects themselves like `slice`, `int`, and `tuple`, which do not share any
methods in common related to being indices, ndindex classes can all be
manipulated uniformly. For example, `idx.args` always gives the arguments
used to construct `idx`.
- Give 100% correct semantics as defined by numpy's ndarray. This means that
ndindex will not make a transformation on an index object unless it is
correct for all possible input array shapes. The only exception to this rule
is that ndindex assumes that any given index will not raise IndexError (for
instance, from an out of bounds integer index or from too few dimensions).
For those operations where the array shape is known, there is a `reduce()`
method to reduce an index to a simpler index that is equivalent for the
given shape.
- Enable useful transformation and manipulation functions on index objects.
## Examples
**Canonicalize a slice (over a given shape, or independent of array shape)**
```py
>>> from ndindex import *
>>> Slice(-2, 10, 3).reduce()
Slice(-2, 10, 2)
>>> Slice(-2, 10, 3).reduce(5)
Slice(3, 4, 1)
```
**Compute the maximum length of a sliced axis**
```py
>>> import numpy as np
>>> len(Slice(2, 10, 3))
3
>>> len(np.arange(10)[2:10:3])
3
```
**Compute the shape of an array of shape `(10, 20)` indexed by `[0, 0:10]`**
```py
>>> Tuple(0, slice(0, 10)).newshape((10, 20))
(10,)
>>> np.ones((10, 20))[0, 0:10].shape
(10,)
```
**Check if an indexed array would be empty**
```py
>>> Tuple(0, ..., Slice(10, 20)).isempty((3, 4, 5))
True
>>> np.ones((3, 4, 5))[0,...,10:20]
array([], shape=(4, 0), dtype=float64)
```
See the [documentation](https://quansight-labs.github.io/ndindex/) for full details
on what ndindex can do.
## License
[MIT License](LICENSE)
## Acknowledgments
ndindex development is supported by [Quansight
Labs](https://labs.quansight.org/) and is sponsored in part by [the D. E.
Shaw group](https://www.deshaw.com/). The D. E. Shaw group collaborates with
Quansight on numerous open source projects, including Numba, Dask and Project
Jupyter.
