How to use the dmsh.Circle function in dmsh

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github nschloe / dmsh / test / test_square_hole_refined.py View on Github external
def test(show=False):
    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, show=show, tol=1.0e-10
    )

    ref_norms = [2.48e02, 1.200e01, 1.0]
    assert_norm_equality(X.flatten(), ref_norms, 1.0e-3)
    return X, cells
github nschloe / dmsh / test / compare-speed.py View on Github external
def dmsh_circle(num_points):
    target_edge_length = 2 * np.pi / _compute_num_boundary_points(num_points)
    geo = dmsh.Circle([0.0, 0.0], 1.0)
    X, cells = dmsh.generate(geo, target_edge_length)
    return X, cells
github nschloe / dmsh / test / test_pacman.py View on Github external
def test_pacman(show=False):
    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, show=show, tol=1.0e-10)

    ref_norms = [3.0385105041432689e02, 1.3644964912810719e01, 1.0]
    assert_norm_equality(X.flatten(), ref_norms, 1.0e-10)
    return X, cells
github nschloe / dmsh / test / test_union_three_circles.py View on Github external
def test_union(show=False):
    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, 0.2, show=show, tol=1.0e-10)

    ref_norms = [4.0372522103229670e02, 2.1155465970807523e01, 1.9999337650692937e00]
    assert_norm_equality(X.flatten(), ref_norms, 1.0e-10)
    return X, cells
github nschloe / optimesh / examples / create_circle.py View on Github external
def dmsh(target_num_points):
    import dmsh

    print("target num points", target_num_points)

    est_num_boundary_nodes = _compute_num_boundary_points(target_num_points)
    est_num_boundary_nodes = 100
    target_edge_length = 2 * np.pi / est_num_boundary_nodes
    print(target_edge_length)
    print("est num boundary", est_num_boundary_nodes)
    geo = dmsh.Circle([0.0, 0.0], 1.0)
    X, cells = dmsh.generate(geo, target_edge_length)
    print("num points", X.shape[0])

    import meshplex

    mesh = meshplex.MeshTri(X, cells)
    print("num boundary points", sum(mesh.is_boundary_node))
    # exit(1)
    return X, cells
github kinnala / scikit-fem / docs / examples / uzawa_cg.py View on Github external
from skfem import *
from skfem.models.poisson import vector_laplace, laplace
from skfem.models.general import divergence, rot

import numpy as np
from scipy.sparse import csr_matrix
from scipy.sparse.linalg import cg, LinearOperator

from sksparse.cholmod import cholesky

import dmsh

mesh = MeshTri(*map(np.transpose,
                    dmsh.generate(dmsh.Circle([0., 0.], 1.), .1)))

element = {'u': ElementVectorH1(ElementTriP2()),
           'p': ElementTriP1()}
basis = {variable: InteriorBasis(mesh, e, intorder=3)
         for variable, e in element.items()}


@linear_form
def body_force(v, dv, w):
    return w.x[0] * v[1]


velocity = np.zeros(basis['u'].N)
A = asm(vector_laplace, basis['u'])
B = asm(divergence, basis['u'], basis['p'])
f = asm(body_force, basis['u'])
github nschloe / orthopy / orthopy / disk / tools.py View on Github external
def plot(f, lcar=1.0e-1):
    """Plot function over a disk.
    """
    import matplotlib
    import matplotlib.pyplot as plt
    import dmsh

    geo = dmsh.Circle([0.0, 0.0], 1.0)
    points, cells = dmsh.generate(geo, 0.1)

    x = points[:, 0]
    y = points[:, 1]
    triang = matplotlib.tri.Triangulation(x, y, cells["triangle"])

    plt.tripcolor(triang, f(points.T), shading="flat")
    plt.colorbar()

    # Choose a diverging colormap such that the zeros are clearly
    # distinguishable.
    plt.set_cmap("coolwarm")
    # Make sure the color map limits are symmetric around 0.
    clim = plt.gci().get_clim()
    mx = max(abs(clim[0]), abs(clim[1]))
    plt.clim(-mx, mx)