scikit-spatial

Spatial objects and computations based on NumPy arrays.

https://github.com/ajhynes7/scikit-spatial

Science Score: 44.0%

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Low similarity (11.1%) to scientific vocabulary

Keywords

3d-math linear-algebra matplotlib numpy python spatial visualization

Keywords from Contributors

energy-system-model optimizer mesh molecular-dynamics-simulation hydrology regionalization battery energy-system exoplanet led

Last synced: 4 months ago · JSON representation ·

Repository

Spatial objects and computations based on NumPy arrays.

Basic Info

Host: GitHub
Owner: ajhynes7
License: other
Language: Python
Default Branch: master
Homepage: https://scikit-spatial.readthedocs.io
Size: 2.18 MB

Statistics

Stars: 98
Watchers: 6
Forks: 13
Open Issues: 2
Releases: 0

Topics

3d-math linear-algebra matplotlib numpy python spatial visualization

Created almost 7 years ago · Last pushed 7 months ago

Metadata Files

Readme Changelog Contributing License Citation Authors

Introduction

This package provides spatial objects based on NumPy arrays, as well as computations using these objects. The package includes computations for 2D, 3D, and higher-dimensional space.

The following spatial objects are provided:

Point
Points
Vector
Line
LineSegment
Plane
Circle
Sphere
Triangle
Cylinder

Most of the computations fall into the following categories:

Measurement
Comparison
Projection
Intersection
Fitting
Transformation

All spatial objects are equipped with plotting methods based on matplotlib. Both 2D and 3D plotting are supported. Spatial computations can be easily visualized by plotting multiple objects at once.

Why this instead of `scipy.spatial` or `sympy.geometry`?

This package has little to no overlap with the functionality of scipy.spatial. It can be viewed as an object-oriented extension.

While similar spatial objects and computations exist in the sympy.geometry module, scikit-spatial is based on NumPy rather than symbolic math. The primary objects of scikit-spatial (Point, Points, and Vector) are actually subclasses of the NumPy ndarray. This gives them all the regular functionality of the ndarray, plus additional methods from this package.

```py

from skspatial.objects import Vector

vector = Vector([2, 0, 0])

```

Behaviour inherited from NumPy:

```py

vector.size 3

vector.mean().round(3) np.float64(0.667)

```

Additional methods from scikit-spatial:

```py

vector.norm() np.float64(2.0)

vector.unit() Vector([1., 0., 0.])

```

Because Point and Vector are both subclasses of ndarray, a Vector can be added to a Point. This produces a new Point.

```py

from skspatial.objects import Point

Point([1, 2]) + Vector([3, 4]) Point([4, 6])

```

Point and Vector are based on a 1D NumPy array, and Points is based on a 2D NumPy array, where each row represents a point in space. The Line and Plane objects have Point and Vector objects as attributes.

Note that most methods inherited from NumPy return a regular NumPy object, instead of the spatial object class.

```py

vector.sum() np.int64(2)

```

This is to avoid getting a spatial object with a forbidden shape, like a zero dimension Vector. Trying to convert this back to a Vector causes an exception.

```py

Vector(vector.sum()) Traceback (most recent call last): ValueError: The array must be 1D.

```

Because the computations of scikit-spatial are also based on NumPy, keyword arguments can be passed to NumPy functions. For example, a tolerance can be specified while testing for collinearity. The tol keyword is passed to numpy.linalg.matrix_rank.

```py

from skspatial.objects import Points

points = Points([[1, 2, 3], [4, 5, 6], [7, 8, 8]])

points.are_collinear() False

points.are_collinear(tol=1) True

```

Installation

The package can be installed with pip.

```bash $ pip install scikit-spatial

```

It can also be installed with conda.

```bash $ conda install scikit-spatial -c conda-forge

```

The matplotlib dependency is optional. To enable plotting, you can install scikit-spatial with the extra plotting.

```bash $ pip install 'scikit-spatial[plotting]'

```

Example Usage

Measurement

Measure the cosine similarity between two vectors.

```py

from skspatial.objects import Vector

Vector([1, 0]).cosine_similarity([1, 1]).round(3) np.float64(0.707)

```

Comparison

Check if multiple points are collinear.

```py

from skspatial.objects import Points

points = Points([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])

points.are_collinear() True

```

Projection

Project a point onto a line.

```py

from skspatial.objects import Line

line = Line(point=[0, 0, 0], direction=[1, 1, 0])

line.project_point([5, 6, 7]) Point([5.5, 5.5, 0. ])

```

Intersection

Find the intersection of two planes.

```py

from skspatial.objects import Plane

planea = Plane(point=[0, 0, 0], normal=[0, 0, 1]) planeb = Plane(point=[5, 16, -94], normal=[1, 0, 0])

planea.intersectplane(plane_b) Line(point=Point([5., 0., 0.]), direction=Vector([0, 1, 0]))

```

An error is raised if the computation is undefined.

```py

plane_b = Plane(point=[0, 0, 1], normal=[0, 0, 1])

planea.intersectplane(plane_b) Traceback (most recent call last): ValueError: The planes must not be parallel.

```

Fitting

Find the plane of best fit for multiple points.

```py

points = [[0, 0, 0], [1, 0, 0], [0, 1, 0], [1, 1, 0]]

Plane.best_fit(points) Plane(point=Point([0.5, 0.5, 0. ]), normal=Vector([0., 0., 1.]))

```

Transformation

Transform multiple points to 1D coordinates along a line.

```py

line = Line(point=[0, 0, 0], direction=[1, 2, 0]) points = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]

line.transform_points(points).round(3) array([ 2.236, 6.261, 10.286])

```

Acknowledgment

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

Owner

Name: Andrew Hynes
Login: ajhynes7
Kind: user
Location: St. John's NL, Canada
Company: CoLab Software

Repositories: 4
Profile: https://github.com/ajhynes7

Citation (CITATION.cff)

cff-version: 1.2.0
title: "scikit-spatial: Spatial objects and computations based on NumPy arrays"
message: "If you use this software, please cite it using the metadata from this file."
type: software
authors:
  - given-names: Andrew
    family-names: Hynes
    email: andrewjhynes@gmail.com
license: BSD-3-Clause
url: https://scikit-spatial.readthedocs.io
repository-code: https://github.com/ajhynes7/scikit-spatial

GitHub Events

Total

Issues event: 8
Watch event: 9
Delete event: 2
Issue comment event: 39
Push event: 20
Pull request review event: 2
Pull request event: 7
Create event: 6

Last Year

Issues event: 8
Watch event: 9
Delete event: 2
Issue comment event: 39
Push event: 20
Pull request review event: 2
Pull request event: 7
Create event: 6

Committers

Last synced: 7 months ago

All Time

Total Commits: 1,233
Total Committers: 10
Avg Commits per committer: 123.3
Development Distribution Score (DDS): 0.412

Past Year

Commits: 7
Committers: 2
Avg Commits per committer: 3.5
Development Distribution Score (DDS): 0.143

Top Committers

Name	Email	Commits
Andrew Hynes	a**s@y**m	725
Andrew Hynes	a**s@g**m	403
pyup-bot	g**t@p**o	86
pre-commit-ci[bot]	6****]	8
Cristiano Pizzamiglio	c**o@g**m	5
Louis Lac	l**5@g**m	2
volkoshkursk	3****k	1
martxelo	m**z@g**m	1
dependabot[bot]	4****]	1
EdYazbec	1****c	1

Committer Domains (Top 20 + Academic)

pyup.io: 1 ymail.com: 1

Issues and Pull Requests

Last synced: 5 months ago

All Time

Total issues: 44
Total pull requests: 84
Average time to close issues: 20 days
Average time to close pull requests: 11 days
Total issue authors: 30
Total pull request authors: 11
Average comments per issue: 3.93
Average comments per pull request: 2.14
Merged pull requests: 65
Bot issues: 0
Bot pull requests: 22

Past Year

Issues: 8
Pull requests: 7
Average time to close issues: 21 days
Average time to close pull requests: 18 days
Issue authors: 7
Pull request authors: 2
Average comments per issue: 3.25
Average comments per pull request: 2.29
Merged pull requests: 6
Bot issues: 0
Bot pull requests: 2

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Top Authors

Issue Authors

CristianoPizzamiglio (5)
typhoon71 (5)
pauljurczak (4)
maxim0815 (2)
croemheld (2)
jmspereira (2)
seirios (1)
MikiGrit (1)
3DMiller (1)
mrrezaie (1)
pvtoan (1)
akuznetsov1 (1)
AlfredQin (1)
ArenaGrenade (1)
garyvdm (1)

Pull Request Authors

ajhynes7 (59)
pre-commit-ci[bot] (20)
CristianoPizzamiglio (5)
dependabot[bot] (4)
laclouis5 (4)
EdYazbec (2)
pauljurczak (2)
martxelo (1)
volkoshkursk (1)
Yeok-c (1)
yamila-moreno (1)

Top Labels

Issue Labels

enhancement (2)

Pull Request Labels

dependencies (4) python (2)

Packages

Total packages: 2
Total downloads:
- pypi 53,267 last-month
Total docker downloads: 412

Total dependent packages: 14
(may contain duplicates)
Total dependent repositories: 33
(may contain duplicates)
Total versions: 46
Total maintainers: 1

pypi.org: scikit-spatial

Spatial objects and computations based on NumPy arrays.

Documentation: https://scikit-spatial.readthedocs.io/
License: BSD-3-Clause
Latest release: 9.0.1
published 8 months ago

Versions: 38
Dependent Packages: 14
Dependent Repositories: 32
Downloads: 53,267 Last month
Docker Downloads: 412

Rankings

Dependent packages count: 1.1%

Downloads: 2.4%

Dependent repos count: 2.6%

Docker downloads count: 2.9%

Average: 4.7%

Stargazers count: 8.1%

Forks count: 10.9%

Maintainers (1)

ajhynes7

Last synced: 4 months ago

conda-forge.org: scikit-spatial

Homepage: https://github.com/ajhynes7/scikit-spatial
License: BSD-3-Clause
Latest release: 6.5.0
published over 3 years ago

Versions: 8
Dependent Packages: 0
Dependent Repositories: 1

Rankings

Dependent repos count: 24.4%

Stargazers count: 39.0%

Average: 40.6%

Forks count: 47.5%

Dependent packages count: 51.6%

Last synced: 4 months ago

Dependencies

pyproject.toml pypi

Sphinx 4.2.0
hypothesis 6.8.1
importlib-metadata ~1
matplotlib ^3
numpy ^1.20
numpydoc 1.1.0
pre-commit 2.11.1
pytest 7.1.1
pytest-cov 2.11.1
python ^3.7
sphinx-bootstrap-theme 0.8.0
sphinx-gallery 0.9.0

.github/workflows/main.yml actions

JRubics/poetry-publish v1.8 composite
actions/checkout v2 composite
actions/setup-python v2 composite
codecov/codecov-action v1.2.1 composite
snok/install-poetry v1.3.1 composite

docs/source/requirements.txt pypi

Sphinx ==5.3.0
numpydoc ==1.5.0
sphinx-bootstrap-theme ==0.8.1
sphinx-gallery ==0.9.0

scikit-spatial

Science Score: 44.0%

Keywords

Keywords from Contributors

Repository

Basic Info

Statistics

Topics

Metadata Files

README.md

Introduction

Why this instead of scipy.spatial or sympy.geometry?

Installation

Example Usage

Measurement

Comparison

Projection

Intersection

Fitting

Transformation

Acknowledgment

Owner

Citation (CITATION.cff)

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Packages

pypi.org: scikit-spatial

Rankings

Maintainers (1)

conda-forge.org: scikit-spatial

Rankings

Dependencies

Why this instead of `scipy.spatial` or `sympy.geometry`?