Metadata-Version: 2.1
Name: dmsh
Version: 0.1.4
Summary: High-quality 2D mesh generator based on distmesh
Home-page: https://github.com/nschloe/dmsh
Author: Nico Schlömer
Author-email: nico.schloemer@gmail.com
License: License :: OSI Approved :: MIT License
Description: <p align="center">
          <a href="https://github.com/nschloe/dmsh"><img alt="dmsh" src="https://nschloe.github.io/dmsh/logo-with-text.svg" width="50%"></a>
          <p align="center">The worst mesh generator you'll ever use.</p>
        </p>
        
        [![CircleCI](https://img.shields.io/circleci/project/github/nschloe/dmsh/master.svg?style=flat-square)](https://circleci.com/gh/nschloe/dmsh/tree/master)
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        [![PyPi Version](https://img.shields.io/pypi/v/dmsh.svg?style=flat-square)](https://pypi.org/project/dmsh)
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        Inspired by [distmesh](http://persson.berkeley.edu/distmesh/), dmsh
        
         * is slow,
         * requires a lot of memory, and
         * isn't terribly robust either.
        
        On the plus side,
        
         * it's got a usable interface,
         * is pure Python (and hence easily installable on any system), and
         * if it works, it produces pretty high-quality meshes.
        
        Combined with [optimesh](https://github.com/nschloe/optimesh), dmsh produces the
        highest-quality 2D meshes in the west.
        
        ### Examples
        
        #### Primitives
        
        ![circle](https://nschloe.github.io/dmsh/circle.png) |
        ![rectangle](https://nschloe.github.io/dmsh/rectangle.png) |
        ![polygon](https://nschloe.github.io/dmsh/polygon.png)
        |:---:|:---:|:---:|
        
        ```python
        import dmsh
        
        geo = dmsh.Circle([0.0, 0.0], 1.0)
        X, cells = dmsh.generate(geo, 0.1)
        
        # optionally optimize the mesh
        import optimesh
        X, cells = optimesh.cvt.quasi_newton_uniform_full(X, cells, 1.0e-10, 100)
        
        # and write it to a file
        import meshio
        meshio.write_points_cells("circle.vtk", X, {"triangle": cells})
        ```
        
        ```python
        geo = dmsh.Rectangle(-1.0, +2.0, -1.0, +1.0)
        X, cells = dmsh.generate(geo, 0.1)
        ```
        
        ```python
        geo = dmsh.Polygon(
            [
                [0.0, 0.0],
                [1.1, 0.0],
                [1.2, 0.5],
                [0.7, 0.6],
                [2.0, 1.0],
                [1.0, 2.0],
                [0.5, 1.5],
            ]
        )
        X, cells = dmsh.generate(geo, 0.1)
        ```
        
        #### Combinations
        
        ##### Difference
        
        ![difference](https://nschloe.github.io/dmsh/difference.png) |
        ![pacman](https://nschloe.github.io/dmsh/pacman.png) |
        ![square_hole_refined](https://nschloe.github.io/dmsh/square_hole_refined.png)
        :-------------------:|:------------------:|:----:|
        
        ```python
        geo = dmsh.Difference(dmsh.Circle([-0.5, 0.0], 1.0), dmsh.Circle([+0.5, 0.0], 1.0))
        X, cells = dmsh.generate(geo, 0.1)
        ```
        ```python
        geo = dmsh.Difference(
            dmsh.Circle([0.0, 0.0], 1.0),
            dmsh.Polygon([[0.0, 0.0], [1.5, 0.4], [1.5, -0.4]]),
        )
        X, cells = dmsh.generate(geo, 0.1, tol=1.0e-10)
        ```
        
        The following example uses a nonconstant edge length; it depends on the distance to the
        circle `c`.
        ```python
        r = dmsh.Rectangle(-1.0, +1.0, -1.0, +1.0)
        c = dmsh.Circle([0.0, 0.0], 0.3)
        geo = dmsh.Difference(r, c)
        
        X, cells = dmsh.generate(
            geo, lambda pts: numpy.abs(c.dist(pts)) / 5 + 0.05, tol=1.0e-10
        )
        ```
        
        ##### Union
        
        ![union](https://nschloe.github.io/dmsh/union.png) |
        ![union-rect](https://nschloe.github.io/dmsh/union_rectangles.png) |
        ![union-three-circles](https://nschloe.github.io/dmsh/union_three_circles.png) |
        :-------------------:|:------------------:|:----:|
        
        ```python
        geo = dmsh.Union([dmsh.Circle([-0.5, 0.0], 1.0), dmsh.Circle([+0.5, 0.0], 1.0)])
        X, cells = dmsh.generate(geo, 0.15)
        ```
        ```python
        geo = dmsh.Union(
            [dmsh.Rectangle(-1.0, +0.5, -1.0, +0.5), dmsh.Rectangle(-0.5, +1.0, -0.5, +1.0)]
        )
        X, cells = dmsh.generate(geo, 0.15)
        ```
        ```python
        angles = numpy.pi * numpy.array([3.0 / 6.0, 7.0 / 6.0, 11.0 / 6.0])
        geo = dmsh.Union(
            [
                dmsh.Circle([numpy.cos(angles[0]), numpy.sin(angles[0])], 1.0),
                dmsh.Circle([numpy.cos(angles[1]), numpy.sin(angles[1])], 1.0),
                dmsh.Circle([numpy.cos(angles[2]), numpy.sin(angles[2])], 1.0),
            ]
        )
        X, cells = dmsh.generate(geo, 0.15)
        ```
        
        #### Intersection
        
        ![intersection](https://nschloe.github.io/dmsh/intersection.png) |
        ![intersection-three-circles](https://nschloe.github.io/dmsh/intersection_three_circles.png) |
        ![halfspace](https://nschloe.github.io/dmsh/halfspace.png)
        :-------------------:|:------------------:|:----:|
        
        ```python
        geo = dmsh.Intersection(
            [dmsh.Circle([0.0, -0.5], 1.0), dmsh.Circle([0.0, +0.5], 1.0)]
        )
        X, cells = dmsh.generate(geo, 0.1, tol=1.0e-10)
        ```
        
        ```python
        angles = numpy.pi * numpy.array([3.0 / 6.0, 7.0 / 6.0, 11.0 / 6.0])
        geo = dmsh.Intersection(
            [
                dmsh.Circle([numpy.cos(angles[0]), numpy.sin(angles[0])], 1.5),
                dmsh.Circle([numpy.cos(angles[1]), numpy.sin(angles[1])], 1.5),
                dmsh.Circle([numpy.cos(angles[2]), numpy.sin(angles[2])], 1.5),
            ]
        )
        X, cells = dmsh.generate(geo, 0.1, tol=1.0e-10)
        ```
        
        The following uses the `HalfSpace` primtive for cutting of a circle.
        ```python
        geo = dmsh.Intersection(
            [
                dmsh.HalfSpace(numpy.sqrt(0.5) * numpy.array([1.0, 1.0]), 0.0),
                dmsh.Circle([0.0, 0.0], 1.0),
            ]
        )
        X, cells = dmsh.generate(geo, 0.1)
        ```
        
        ### Rotation, translation, scaling
        
        ![rotation](https://nschloe.github.io/dmsh/rotation.png) |
        ![scaling](https://nschloe.github.io/dmsh/scaling.png)
        |:----:|:----:|
        
        ```python
        geo = dmsh.Rotation(dmsh.Rectangle(-1.0, +2.0, -1.0, +1.0), 0.1 * numpy.pi)
        X, cells = dmsh.generate(geo, 0.1, tol=1.0e-10)
        ```
        ```python
        geo = dmsh.Translation(dmsh.Rectangle(-1.0, +2.0, -1.0, +1.0), [1.0, 1.0])
        X, cells = dmsh.generate(geo, 0.1, show=show)
        ```
        ```python
        geo = dmsh.Scaling(dmsh.Rectangle(-1.0, +2.0, -1.0, +1.0), 2.0)
        X, cells = dmsh.generate(geo, 0.1, show=show, tol=1.0e-5)
        ```
        
        ### Local refinement
        
        ![refinement-line](https://nschloe.github.io/dmsh/refinement_line.png)
        
        All objects can be used to refine the mesh according to the distance to the object;
        e.g. a `Path`:
        ```python
        geo = dmsh.Rectangle(0.0, 1.0, 0.0, 1.0)
        
        p1 = dmsh.Path([[0.4, 0.6], [0.6, 0.4]])
        
        def edge_size(x):
            return 0.03 + 0.1 * p1.dist(x)
        
        X, cells = dmsh.generate(geo, edge_size, show=show, tol=1.0e-10)
        ```
        
        
        
        ### Installation
        
        dmsh is [available from the Python Package
        Index](https://pypi.org/project/dmsh/), so simply type
        ```
        pip3 install dmsh --user
        ```
        to install.
        
        ### Testing
        
        To run the dmsh unit tests, check out this repository and type
        ```
        pytest
        ```
        
        ### License
        
        dmsh is published under the [MIT license](https://en.wikipedia.org/wiki/MIT_License).
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Development Status :: 4 - Beta
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Requires-Python: >=3
Description-Content-Type: text/markdown
Provides-Extra: all
Provides-Extra: plot
