Sparse vectors optimized for memory and NumPy 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 as hash sets and maps. All native operations are optimized and release the GIL. Objects are safe for concurrent reads across threads; writers must not overlap readers or other writers on the same object.
- conversion between sparse
numpyarrays - conversion between dense
numpyarrays - binary set operations
- binary math operations
map,filter, andreduceoperations withnumpyuniversal functions
Usage
indices
A sparse boolean array with a set interface.
>>> 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.
>>> 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.
>>> 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
pip install spector
Tests
100% branch coverage.
pytest [--cov]
1 maintainer
Metadata
1 maintainer
Release files for spector 1.7
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| spector-1.7.tar.gz | 262.8 kB | With provenance | Details |
Built distributions (wheels)
| File | Reset | |||
|---|---|---|---|---|
| spector-1.7-cp311-abi3-win_amd64.whl | CPython 3.11 | abi3 | Windows x86-64 | Details |
| spector-1.7-cp311-abi3-win32.whl | CPython 3.11 | abi3 | Windows x86-32 | Details |
| spector-1.7-cp311-abi3-musllinux_1_2_x86_64.whl | CPython 3.11 | abi3 | Linux musl 1.2+ x86-64 | Details |
| spector-1.7-cp311-abi3-musllinux_1_2_aarch64.whl | CPython 3.11 | abi3 | Linux musl 1.2+ ARM64 | Details |
| spector-1.7-cp311-abi3-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl | CPython 3.11 | abi3 | Linux glibc 2.28+ x86-64, Linux glibc 2.24+ x86-64 | Details |
| spector-1.7-cp311-abi3-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl | CPython 3.11 | abi3 | Linux glibc 2.28+ ARM64, Linux glibc 2.24+ ARM64 | Details |
| spector-1.7-cp311-abi3-macosx_11_0_arm64.whl | CPython 3.11 | abi3 | macOS 11.0+ ARM64 | Details |
| spector-1.7-cp311-abi3-macosx_10_9_x86_64.whl | CPython 3.11 | abi3 | macOS 10.9+ x86-64 | Details |
Total release size: 10.1 MB
Release files / spector-1.7.tar.gz
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