[](https://pypi.org/project/spector/)
[](https://python3statement.org)
[](https://pepy.tech/project/spector)
[](https://github.com/coady/spector/actions/workflows/build.yml)
[](https://codecov.io/gh/coady/spector/)
[](https://github.com/coady/spector/actions/workflows/github-code-scanning/codeql)
[](https://codspeed.io/coady/spector)
[](https://github.com/astral-sh/ruff)
[](https://mypy-lang.org/)
Sparse vectors optimized for memory and [NumPy](https://www.numpy.org) integrations.
`numpy` handles densely populated n-dimemsional arrays. `scipy.sparse` handles sparsely populated 2-dimensional arrays, i.e., matrices. What's missing from the ecosystem is sparsely populated 1-dimensional arrays, i.e., vectors.
NumPy | Python | Spector
----- | ------ | -------
1-dim bool `numpy.array` | `set[int]` | `spector.indices`
1-dim float `numpy.array` | `dict[int, float]` | `spector.vector`
`scipy.sparse.dok_matrix` | `dict[int, dict[int, float]]` | `spector.matrix`
Indices and vectors are implemented in [Cython](https://cython.org) as hash sets and maps. All native operations are optimized and release the [GIL](https://docs.python.org/3/glossary.html#term-global-interpreter-lock).
* conversion between sparse `numpy` arrays
* conversion between dense `numpy` arrays
* binary set operations
* binary math operations
* `map`, `filter`, and `reduce` operations with `numpy` universal functions
## Usage
### indices
A sparse boolean array with a set interface.
```python
>>> from spector import indices
>>> ind = indices([0, 2])
>>> ind
indices([2 0])
>>> 1 in ind
False
>>> ind.add(1)
True
>>> ind.todense()
array([ True, True, True])
>>> ind.fromdense(_)
indices([2 1 0])
```
### vector
A sparse float array with a mapping interface.
```python
>>> from spector import vector
>>> vec = vector({0: 1.0, 2: 2.0, 4: 1.0})
>>> vec
vector([4 2 0], [1. 2. 1.])
>>> vec[2] += 1.0
>>> vec[2]
3.0
>>> vec.todense()
array([1., 0., 3., 0., 1.])
>>> vector.fromdense(_)
vector([4 2 0], [1. 3. 1.])
>>> vec.sum()
5.0
>>> vec + vec
vector([0 2 4], [2. 6. 2.])
```
Vectors support math operations with scalars, and with vectors if the set method is unambiguous.
vector operation | set method | ufunc
---------------- | ---------- | -----
`+` | union | add
`*` | intersection | multiply
`-` | | subtract
`/` | | true_divide
`**` | | power
`\|` | union | max
`&` | intersection | min
`^` | symmetric_difference |
`difference` | difference |
### matrix
A mapping of keys to vectors.
```python
>>> from spector import matrix
>>> mat = matrix({0: {1: 2.0}})
>>> mat
matrix(<class 'spector.vector.vector'>, {0: vector([1], [2.])})
>>> mat.row, mat.col, mat.data
(array([0]), array([1]), array([2.]))
```
## Installation
```console
% pip install spector
```
## Tests
100% branch coverage.
```console
% pytest [--cov]
```
Raw data
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"description": "[](https://pypi.org/project/spector/)\n[](https://python3statement.org)\n[](https://pepy.tech/project/spector)\n\n[](https://github.com/coady/spector/actions/workflows/build.yml)\n[](https://codecov.io/gh/coady/spector/)\n[](https://github.com/coady/spector/actions/workflows/github-code-scanning/codeql)\n[](https://codspeed.io/coady/spector)\n[](https://github.com/astral-sh/ruff)\n[](https://mypy-lang.org/)\n\nSparse vectors optimized for memory and [NumPy](https://www.numpy.org) integrations.\n\n`numpy` handles densely populated n-dimemsional arrays. `scipy.sparse` handles sparsely populated 2-dimensional arrays, i.e., matrices. What's missing from the ecosystem is sparsely populated 1-dimensional arrays, i.e., vectors.\n\nNumPy | Python | Spector\n----- | ------ | -------\n1-dim bool `numpy.array` | `set[int]` | `spector.indices`\n1-dim float `numpy.array` | `dict[int, float]` | `spector.vector`\n`scipy.sparse.dok_matrix` | `dict[int, dict[int, float]]` | `spector.matrix`\n\nIndices and vectors are implemented in [Cython](https://cython.org) as hash sets and maps. All native operations are optimized and release the [GIL](https://docs.python.org/3/glossary.html#term-global-interpreter-lock).\n\n* conversion between sparse `numpy` arrays\n* conversion between dense `numpy` arrays\n* binary set operations\n* binary math operations\n* `map`, `filter`, and `reduce` operations with `numpy` universal functions\n\n## Usage\n### indices\nA sparse boolean array with a set interface.\n\n```python\n>>> from spector import indices\n>>> ind = indices([0, 2])\n>>> ind\nindices([2 0])\n>>> 1 in ind\nFalse\n>>> ind.add(1)\nTrue\n>>> ind.todense()\narray([ True, True, True])\n>>> ind.fromdense(_)\nindices([2 1 0])\n```\n\n### vector\nA sparse float array with a mapping interface.\n\n```python\n>>> from spector import vector\n>>> vec = vector({0: 1.0, 2: 2.0, 4: 1.0})\n>>> vec\nvector([4 2 0], [1. 2. 1.])\n>>> vec[2] += 1.0\n>>> vec[2]\n3.0\n>>> vec.todense()\narray([1., 0., 3., 0., 1.])\n>>> vector.fromdense(_)\nvector([4 2 0], [1. 3. 1.])\n>>> vec.sum()\n5.0\n>>> vec + vec\nvector([0 2 4], [2. 6. 2.])\n```\n\nVectors support math operations with scalars, and with vectors if the set method is unambiguous.\n\nvector operation | set method | ufunc\n---------------- | ---------- | -----\n`+` | union | add\n`*` | intersection | multiply\n`-` | | subtract\n`/` | | true_divide\n`**` | | power\n`\\|` | union | max\n`&` | intersection | min\n`^` | symmetric_difference |\n`difference` | difference |\n\n### matrix\nA mapping of keys to vectors.\n\n```python\n>>> from spector import matrix\n>>> mat = matrix({0: {1: 2.0}})\n>>> mat\nmatrix(<class 'spector.vector.vector'>, {0: vector([1], [2.])})\n>>> mat.row, mat.col, mat.data\n(array([0]), array([1]), array([2.]))\n```\n\n## Installation\n```console\n% pip install spector\n```\n\n## Tests\n100% branch coverage.\n\n```console\n% pytest [--cov]\n```\n",
"bugtrack_url": null,
"license": "Copyright 2022 Aric Coady Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ",
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