Python pyplot.triplot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 triplot [as 別名]
def show(pts, cells, geo, title=None, full_screen=True):
    import matplotlib.pyplot as plt

    eps = 1.0e-10
    is_inside = geo.dist(pts.T) < eps
    plt.plot(pts[is_inside, 0], pts[is_inside, 1], ".")
    plt.plot(pts[~is_inside, 0], pts[~is_inside, 1], ".", color="r")
    plt.triplot(pts[:, 0], pts[:, 1], cells)
    plt.axis("square")

    if full_screen:
        figManager = plt.get_current_fig_manager()
        try:
            figManager.window.showMaximized()
        except AttributeError:
            # Some backends have no window (e.g., Agg)
            pass

    if title is not None:
        plt.title(title)

    try:
        geo.show(level_set=False)
    except AttributeError:
        pass 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import triplot [as 別名]
def trace_roi(hemi, map_proj, axes, closed=True, event_handlers=None, plot_list=None, **kw):
    '''
    trace_roi(hemi, map_proj, axes) creates an ROIDrawer object that controls the tracing of lines
      around an ROI in a 2D matplotlib plot and returns a not-yet-persistent immutable PathTrace
      object with the ROIDrawer in its meta_data. The path trace is persisted as soon as the user
      finished drawing their line; if the line is canceled, then the trace is never persisted.

    ROI tracing is very simple: any point in the plot is appended to the path as it is clicked; in
    order to eliminate the previous point, hold shift while clicking. To end the path, hold control
    while clicking. To abort the path, hold both shift and control while clicking. (Double-clicking
    should be equivalent to control-clicking, but this does not work in all setups.) In order to use
    the ROI tracing, `%matplotlib notebook` is recommended.

    The trace_roi() function accepts all options that can be passed to cortex_plot() as well as the
    following options:
      * closed (default: True) specifies whether the path-trace that is constructed should be closed
        (True) or open (False).
      * event_handlers (default: None) specifies additional event handlers (named by key) for the
        ROIDrawer().
      * plot_list (default: None) specifies a list of alternate TriMesh objects that can be plotted
        cyclically when the user presses tab. TriMesh objects can be created by pyplot.triplot and
        pyplot.tripcolor, which are used by the neuropythy cortex_plot function as well. If the
        plot_list is not empty, then the first item of the list is immediately plotted on the axes.
        Unlike in the ROIDrawer function itself, the plot_list may contain maps whose keys are
        the various arguments (aside from the initial mesh argument) to cortex_plot.
    '''
    # okay, first off, if the plot_list has maps in it, we convert them using cortex_plot:
    if plot_list is not None:
        if geo.is_flatmap(hemi):                  fmap = hemi
        elif geo.is_flatmap(map_proj):            fmap = map_proj
        elif not geo.is_map_projection(map_proj): fmap = geo.to_map_projection(map_proj)(hemi)
        else:                                     fmap = map_proj(hemi)
        plot_list = [cortex_plot(fmap, axes=axes, **p) if pimms.is_map(p) else p
                     for p in plot_list]
    # next, make the roi drawer
    rd = ROIDrawer(axes, map_proj, closed=closed,
                   event_handlers=event_handlers, plot_list=plot_list)
    return rd.trace 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import triplot [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 triplot [as 別名]
def test_triplot_return():
    # Check that triplot returns the artists it adds
    from matplotlib.figure import Figure
    ax = Figure().add_axes([0.1, 0.1, 0.7, 0.7])
    triang = mtri.Triangulation(
        [0.0, 1.0, 0.0, 1.0], [0.0, 0.0, 1.0, 1.0],
        triangles=[[0, 1, 3], [3, 2, 0]])
    if ax.triplot(triang, "b-") is None:
        raise AssertionError("triplot should return the artist it adds") 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import triplot [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 triplot [as 別名]
def test_triplot_return():
    # Check that triplot returns the artists it adds
    from matplotlib.figure import Figure
    ax = Figure().add_axes([0.1, 0.1, 0.7, 0.7])
    triang = mtri.Triangulation(
        [0.0, 1.0, 0.0, 1.0], [0.0, 0.0, 1.0, 1.0],
        triangles=[[0, 1, 3], [3, 2, 0]])
    assert ax.triplot(triang, "b-") is not None, \
        'triplot should return the artist it adds' 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import triplot [as 別名]
def plot(self):
		if self.__numberOfVertices != 3: raise RuntimeError('Plotting only supported in 2D')
		matrix = self.testPoints[0:self.iterations, :]

		x = matrix[:,0].flat
		y = matrix[:,1].flat
		z = matrix[:,2].flat

		coords = []
		acquisitions = []

		for triangle in self.queue:
			coords.append(triangle.pointIndices)
			acquisitions.append(-1 * triangle.acquisitionValue)


		plotter.figure()
		plotter.tricontourf(x, y, coords, z)
		plotter.triplot(x, y, coords, color='white', lw=0.5)
		plotter.colorbar()


		plotter.figure()
		plotter.tripcolor(x, y, coords, acquisitions)
		plotter.triplot(x, y, coords, color='white', lw=0.5)
		plotter.colorbar()

		plotter.show() 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import triplot [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. 

# 需要導入模塊: from matplotlib import pyplot [as 別名]
# 或者: from matplotlib.pyplot import triplot [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.)