Python matplotlib.pyplot.tricontour() Examples

The following are 12 code examples of matplotlib.pyplot.tricontour(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module matplotlib.pyplot , or try the search function .
Example #1
Source File: vis.py    From calfem-python with MIT License 6 votes vote down vote up 
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)
Example #2
Source File: vis_mpl.py    From calfem-python with MIT License 6 votes vote down vote up 
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)
Example #3
Source File: vis_mpl.py    From calfem-python with MIT License 6 votes vote down vote up 
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)
Example #4
Source File: vis_mpl.py    From calfem-python with MIT License 5 votes vote down vote up 
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.")
Example #5
Source File: test_triangulation.py    From neural-network-animation with MIT License 5 votes vote down vote up 
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")
Example #6
Source File: test_triangulation.py    From python3_ios with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
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")
Example #7
Source File: test_triangulation.py    From coffeegrindsize with MIT License 5 votes vote down vote up 
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")
Example #8
Source File: test_triangulation.py    From twitter-stock-recommendation with MIT License 5 votes vote down vote up 
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")
Example #9
Source File: test_triangulation.py    From neural-network-animation with MIT License 4 votes vote down vote up 
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.)
Example #10
Source File: test_triangulation.py    From python3_ios with BSD 3-Clause "New" or "Revised" License 4 votes vote down vote up 
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.
Example #11
Source File: test_triangulation.py    From coffeegrindsize with MIT License 4 votes vote down vote up 
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.
Example #12
Source File: test_triangulation.py    From twitter-stock-recommendation with MIT License 4 votes vote down vote up 
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.