Metadata-Version: 2.1
Name: vg
Version: 1.9.0
Summary: Linear algebra for humans: a very good vector-geometry and linear-algebra toolbelt
Home-page: https://github.com/lace/vg
Author: Metabolize, Body Labs, and other contributors
Author-email: github@paulmelnikow.com
License: UNKNOWN
Project-URL: Issue Tracker, https://github.com/lace/vg/issues
Project-URL: Documentation, https://vgpy.readthedocs.io/en/stable/
Description: vg
        ==
        
        [![version](https://img.shields.io/pypi/v/vg.svg?style=flat-square)][pypi]
        [![python version](https://img.shields.io/pypi/pyversions/vg.svg?style=flat-square)][pypi]
        [![license](https://img.shields.io/pypi/l/vg.svg?style=flat-square)][pypi]
        [![](https://img.shields.io/badge/coverage-100%25-brightgreen.svg?style=flat-square)][coverage]
        [![build](https://img.shields.io/circleci/project/github/lace/vg/master.svg?style=flat-square)][build]
        [![docs build](https://img.shields.io/readthedocs/vgpy.svg?style=flat-square)][docs build]
        [![code style](https://img.shields.io/badge/code%20style-black-black.svg?style=flat-square)][black]
        
        A **v**ery **g**ood vector-geometry and linear-algebra toolbelt. Linear
        algebra for humans. Simple [NumPy][] operations made readable, built to
        scale from prototyping to production.
        
        See the complete API reference: https://vgpy.readthedocs.io/en/latest/
        
        [pypi]: https://pypi.org/project/vg/
        [coverage]: https://github.com/lace/vg/blob/master/.coveragerc
        [build]: https://circleci.com/gh/lace/vg/tree/master
        [docs build]: https://vgpy.readthedocs.io/en/latest/
        [black]: https://black.readthedocs.io/en/stable/
        [lace]: https://github.com/metabolize/lace
        [numpy]: https://www.numpy.org/
        
        Examples
        --------
        
        Normalize a stack of vectors:
        
        ```py
        # 😮
        vs_norm = vs / np.linalg.norm(vs, axis=1)[:, np.newaxis]
        
        # 😀
        vs_norm = vg.normalize(vs)
        ```
        
        Check for the zero vector:
        
        ```py
        # 😣
        is_almost_zero = np.allclose(v, np.array([0.0, 0.0, 0.0]), rtol=0, atol=1e-05)
        
        # 🤓
        is_almost_zero = vg.almost_zero(v, atol=1e-05)
        ```
        
        Find the major axis of variation (first principal component):
        
        ```py
        # 😩
        mean = np.mean(coords, axis=0)
        _, _, pcs = np.linalg.svd(coords - mean)
        first_pc = pcs[0]
        
        # 😍
        first_pc = vg.major_axis(coords)
        ```
        
        Compute pairwise angles between two stacks of vectors:
        
        ```py
        # 😭
        dot_products = np.einsum("ij,ij->i", v1s.reshape(-1, 3), v2s.reshape(-1, 3))
        cosines = dot_products / np.linalg.norm(v1s, axis=1) / np.linalg.norm(v1s, axis=1)
        angles = np.arccos(np.clip(cosines, -1.0, 1.0))
        
        # 🤯
        angles = vg.angle(v1s, v2s)
        ```
        
        Features
        --------
        
        All functions are optionally vectorized, meaning they accept single inputs and
        stacks of inputs interchangeably. They return The Right Thing – a single
        result or a stack of results – without the need to reshape inputs or outputs.
        With the power of NumPy, the vectorized functions are fast.
        
        Installation
        ------------
        
        ```sh
        pip install numpy vg
        ```
        
        
        Usage
        -----
        
        ```py
        import numpy as np
        import vg
        
        projected = vg.scalar_projection(
          np.array([5.0, -3.0, 1.0]),
          onto=vg.basis.neg_y
        )
        ```
        
        
        Design principles
        -----------------
        
        Linear algebra is useful and it doesn't have to be dificult to use. With the
        power of abstractions, simple operations can be made simple, without poring
        through lecture slides, textbooks, inscrutable Stack Overflow answers, or
        dense NumPy docs. Code that uses linear algebra and geometric transformation
        should be readable like English, without compromising efficiency.
        
        These common operations should be abstracted for a few reasons:
        
        1. If a developer is not programming linalg every day, they might forget the
           underlying formula. These forms are easier to remember and more easily
           referenced.
        
        2. These forms tend to be self-documenting in a way that the NumPy forms are
           not. If a developer is not programming linalg every day, this will again
           come in handy.
        
        3. These implementations are more robust. They automatically inspect `ndim`
           on their arguments, so they work equally well if the argument is a vector
           or a stack of vectors. They are more careful about checking edge cases
           like a zero norm or zero cross product and returning a correct result
           or raising an appropriate error.
        
        
        
        Versioning
        ----------
        
        This library adheres to [Semantic Versioning][semver].
        
        [semver]: https://semver.org/
        
        
        Acknowledgements
        ----------------
        
        This collection was developed at Body Labs by [Paul Melnikow][] and extracted
        from the Body Labs codebase and open-sourced as part of [blmath][] by [Alex
        Weiss][]. blmath was subsequently [forked][fork] by Paul Melnikow and later
        the `vx` namespace was broken out into its own package. The project was renamed
        to `vg` to resolve a name conflict.
        
        [paul melnikow]: https://github.com/paulmelnikow
        [blmath]: https://github.com/bodylabs/blmath
        [alex weiss]: https://github.com/algrs
        [fork]: https://github.com/metabolize/blmath
        
        
        License
        -------
        
        The project is licensed under the two-clause BSD license.
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Manufacturing
Classifier: Topic :: Artistic Software
Classifier: Topic :: Multimedia :: Graphics :: 3D Modeling
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/markdown
