Python pyplot.tricontour方法代碼示例

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import tricontour [as 別名]
def eliso2_mpl(ex, ey, ed):

    plt.axis('equal')
    
    print(np.shape(ex))    
    print(np.shape(ey))
    print(np.shape(ed))
    
    gx = []
    gy = []
    gz = []


    for elx, ely, scl in zip(ex, ey, ed):
        for x in elx:
           gx.append(x) 
        for y in ely:
           gy.append(y) 
        for z in ely:
           gz.append(y) 

    plt.tricontour(gx, gy, gz, 5) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import tricontour [as 別名]
def draw_nodal_values_contour(values, coords, edof, levels=12, title=None, dofs_per_node=None, el_type=None, draw_elements=False):
    """Draws element nodal values as filled contours. Element topologies
    supported are triangles, 4-node quads and 8-node quads."""

    edof_tri = topo_to_tri(edof)

    ax = plt.gca()
    ax.set_aspect('equal')

    x, y = coords.T
    v = np.asarray(values)
    plt.tricontour(x, y, edof_tri - 1, v.ravel(), levels)

    if draw_elements:
        if dofs_per_node != None and el_type != None:
            draw_mesh(coords, edof, dofs_per_node,
                      el_type, color=(0.2, 0.2, 0.2))
        else:
            info("dofs_per_node and el_type must be specified to draw the mesh.")

    if title != None:
        ax.set(title=title) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import tricontour [as 別名]
def eliso2_mpl(ex, ey, ed):

    plt.axis('equal')

    print(np.shape(ex))
    print(np.shape(ey))
    print(np.shape(ed))

    gx = []
    gy = []
    gz = []

    for elx, ely, scl in zip(ex, ey, ed):
        for x in elx:
            gx.append(x)
        for y in ely:
            gy.append(y)
        for z in ely:
            gz.append(y)

    plt.tricontour(gx, gy, gz, 5) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import tricontour [as 別名]
def topo_to_tri(edof):
    """Converts 2d element topology to triangle topology to be used
    with the matplotlib functions tricontour and tripcolor."""

    if edof.shape[1] == 3:
        return edof
    elif edof.shape[1] == 4:
        new_edof = np.zeros((edof.shape[0]*2, 3), int)
        new_edof[0::2, 0] = edof[:, 0]
        new_edof[0::2, 1] = edof[:, 1]
        new_edof[0::2, 2] = edof[:, 2]
        new_edof[1::2, 0] = edof[:, 2]
        new_edof[1::2, 1] = edof[:, 3]
        new_edof[1::2, 2] = edof[:, 0]
        return new_edof
    elif edof.shape[1] == 8:
        new_edof = np.zeros((edof.shape[0]*6, 3), int)
        new_edof[0::6, 0] = edof[:, 0]
        new_edof[0::6, 1] = edof[:, 4]
        new_edof[0::6, 2] = edof[:, 7]
        new_edof[1::6, 0] = edof[:, 4]
        new_edof[1::6, 1] = edof[:, 1]
        new_edof[1::6, 2] = edof[:, 5]
        new_edof[2::6, 0] = edof[:, 5]
        new_edof[2::6, 1] = edof[:, 2]
        new_edof[2::6, 2] = edof[:, 6]
        new_edof[3::6, 0] = edof[:, 6]
        new_edof[3::6, 1] = edof[:, 3]
        new_edof[3::6, 2] = edof[:, 7]
        new_edof[4::6, 0] = edof[:, 4]
        new_edof[4::6, 1] = edof[:, 6]
        new_edof[4::6, 2] = edof[:, 7]
        new_edof[5::6, 0] = edof[:, 4]
        new_edof[5::6, 1] = edof[:, 5]
        new_edof[5::6, 2] = edof[:, 6]
        return new_edof
    else:
        error("Element topology not supported.") 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import tricontour [as 別名]
def test_tri_smooth_contouring():
    # Image comparison based on example tricontour_smooth_user.
    n_angles = 20
    n_radii = 10
    min_radius = 0.15

    def z(x, y):
        r1 = np.sqrt((0.5-x)**2 + (0.5-y)**2)
        theta1 = np.arctan2(0.5-x, 0.5-y)
        r2 = np.sqrt((-x-0.2)**2 + (-y-0.2)**2)
        theta2 = np.arctan2(-x-0.2, -y-0.2)
        z = -(2*(np.exp((r1/10)**2)-1)*30. * np.cos(7.*theta1) +
              (np.exp((r2/10)**2)-1)*30. * np.cos(11.*theta2) +
              0.7*(x**2 + y**2))
        return (np.max(z)-z)/(np.max(z)-np.min(z))

    # First create the x and y coordinates of the points.
    radii = np.linspace(min_radius, 0.95, n_radii)
    angles = np.linspace(0 + n_angles, 2*np.pi + n_angles,
                         n_angles, endpoint=False)
    angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
    angles[:, 1::2] += np.pi/n_angles
    x0 = (radii*np.cos(angles)).flatten()
    y0 = (radii*np.sin(angles)).flatten()
    triang0 = mtri.Triangulation(x0, y0)  # Delaunay triangulation
    z0 = z(x0, y0)
    xmid = x0[triang0.triangles].mean(axis=1)
    ymid = y0[triang0.triangles].mean(axis=1)
    mask = np.where(xmid*xmid + ymid*ymid < min_radius*min_radius, 1, 0)
    triang0.set_mask(mask)

    # Then the plot
    refiner = mtri.UniformTriRefiner(triang0)
    tri_refi, z_test_refi = refiner.refine_field(z0, subdiv=4)
    levels = np.arange(0., 1., 0.025)
    plt.triplot(triang0, lw=0.5, color='0.5')
    plt.tricontour(tri_refi, z_test_refi, levels=levels, colors="black") 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import tricontour [as 別名]
def test_tri_smooth_contouring():
    # Image comparison based on example tricontour_smooth_user.
    n_angles = 20
    n_radii = 10
    min_radius = 0.15

    def z(x, y):
        r1 = np.sqrt((0.5-x)**2 + (0.5-y)**2)
        theta1 = np.arctan2(0.5-x, 0.5-y)
        r2 = np.sqrt((-x-0.2)**2 + (-y-0.2)**2)
        theta2 = np.arctan2(-x-0.2, -y-0.2)
        z = -(2*(np.exp((r1/10)**2)-1)*30. * np.cos(7.*theta1) +
              (np.exp((r2/10)**2)-1)*30. * np.cos(11.*theta2) +
              0.7*(x**2 + y**2))
        return (np.max(z)-z)/(np.max(z)-np.min(z))

    # First create the x and y coordinates of the points.
    radii = np.linspace(min_radius, 0.95, n_radii)
    angles = np.linspace(0 + n_angles, 2*np.pi + n_angles,
                         n_angles, endpoint=False)
    angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
    angles[:, 1::2] += np.pi/n_angles
    x0 = (radii*np.cos(angles)).flatten()
    y0 = (radii*np.sin(angles)).flatten()
    triang0 = mtri.Triangulation(x0, y0)  # Delaunay triangulation
    z0 = z(x0, y0)
    triang0.set_mask(np.hypot(x0[triang0.triangles].mean(axis=1),
                              y0[triang0.triangles].mean(axis=1))
                     < min_radius)

    # Then the plot
    refiner = mtri.UniformTriRefiner(triang0)
    tri_refi, z_test_refi = refiner.refine_field(z0, subdiv=4)
    levels = np.arange(0., 1., 0.025)
    plt.triplot(triang0, lw=0.5, color='0.5')
    plt.tricontour(tri_refi, z_test_refi, levels=levels, colors="black") 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import tricontour [as 別名]
def test_tri_smooth_gradient():
    # Image comparison based on example trigradient_demo.

    def dipole_potential(x, y):
        """ An electric dipole potential V """
        r_sq = x**2 + y**2
        theta = np.arctan2(y, x)
        z = np.cos(theta)/r_sq
        return (np.max(z)-z) / (np.max(z)-np.min(z))

    # Creating a Triangulation
    n_angles = 30
    n_radii = 10
    min_radius = 0.2
    radii = np.linspace(min_radius, 0.95, n_radii)
    angles = np.linspace(0, 2*np.pi, n_angles, endpoint=False)
    angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
    angles[:, 1::2] += np.pi/n_angles
    x = (radii*np.cos(angles)).flatten()
    y = (radii*np.sin(angles)).flatten()
    V = dipole_potential(x, y)
    triang = mtri.Triangulation(x, y)
    xmid = x[triang.triangles].mean(axis=1)
    ymid = y[triang.triangles].mean(axis=1)
    mask = np.where(xmid*xmid + ymid*ymid < min_radius*min_radius, 1, 0)
    triang.set_mask(mask)

    # Refine data - interpolates the electrical potential V
    refiner = mtri.UniformTriRefiner(triang)
    tri_refi, z_test_refi = refiner.refine_field(V, subdiv=3)

    # Computes the electrical field (Ex, Ey) as gradient of -V
    tci = mtri.CubicTriInterpolator(triang, -V)
    (Ex, Ey) = tci.gradient(triang.x, triang.y)
    E_norm = np.sqrt(Ex**2 + Ey**2)

    # Plot the triangulation, the potential iso-contours and the vector field
    plt.figure()
    plt.gca().set_aspect('equal')
    plt.triplot(triang, color='0.8')

    levels = np.arange(0., 1., 0.01)
    cmap = cm.get_cmap(name='hot', lut=None)
    plt.tricontour(tri_refi, z_test_refi, levels=levels, cmap=cmap,
                   linewidths=[2.0, 1.0, 1.0, 1.0])
    # Plots direction of the electrical vector field
    plt.quiver(triang.x, triang.y, Ex/E_norm, Ey/E_norm,
               units='xy', scale=10., zorder=3, color='blue',
               width=0.007, headwidth=3., headlength=4.) 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import tricontour [as 別名]
def test_tri_smooth_gradient():
    # Image comparison based on example trigradient_demo.

    def dipole_potential(x, y):
        """ An electric dipole potential V """
        r_sq = x**2 + y**2
        theta = np.arctan2(y, x)
        z = np.cos(theta)/r_sq
        return (np.max(z)-z) / (np.max(z)-np.min(z))

    # Creating a Triangulation
    n_angles = 30
    n_radii = 10
    min_radius = 0.2
    radii = np.linspace(min_radius, 0.95, n_radii)
    angles = np.linspace(0, 2*np.pi, n_angles, endpoint=False)
    angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
    angles[:, 1::2] += np.pi/n_angles
    x = (radii*np.cos(angles)).flatten()
    y = (radii*np.sin(angles)).flatten()
    V = dipole_potential(x, y)
    triang = mtri.Triangulation(x, y)
    triang.set_mask(np.hypot(x[triang.triangles].mean(axis=1),
                             y[triang.triangles].mean(axis=1))
                    < min_radius)

    # Refine data - interpolates the electrical potential V
    refiner = mtri.UniformTriRefiner(triang)
    tri_refi, z_test_refi = refiner.refine_field(V, subdiv=3)

    # Computes the electrical field (Ex, Ey) as gradient of -V
    tci = mtri.CubicTriInterpolator(triang, -V)
    (Ex, Ey) = tci.gradient(triang.x, triang.y)
    E_norm = np.sqrt(Ex**2 + Ey**2)

    # Plot the triangulation, the potential iso-contours and the vector field
    plt.figure()
    plt.gca().set_aspect('equal')
    plt.triplot(triang, color='0.8')

    levels = np.arange(0., 1., 0.01)
    cmap = cm.get_cmap(name='hot', lut=None)
    plt.tricontour(tri_refi, z_test_refi, levels=levels, cmap=cmap,
                   linewidths=[2.0, 1.0, 1.0, 1.0])
    # Plots direction of the electrical vector field
    plt.quiver(triang.x, triang.y, Ex/E_norm, Ey/E_norm,
               units='xy', scale=10., zorder=3, color='blue',
               width=0.007, headwidth=3., headlength=4.)
    # We are leaving ax.use_sticky_margins as True, so the
    # view limits are the contour data limits.