Python matplotlib.pyplot.tripcolor() Examples

def cortex_cmap_plot_2D(the_map, zs, cmap, vmin=None, vmax=None, axes=None, triangulation=None):
    '''
    cortex_cmap_plot_2D(map, zs, cmap, axes) plots the given cortical map values zs on the given
      axes using the given given color map and yields the resulting polygon collection object.
    cortex_cmap_plot_2D(map, zs, cmap) uses matplotlib.pyplot.gca() for the axes.

    The following options may be passed:
      * triangulation (None) may specify the triangularion object for the mesh if it has already
        been created; otherwise it is generated fresh.
      * axes (None) specify the axes on which to plot; if None, then matplotlib.pyplot.gca() is
        used. If Ellipsis, then a tuple (triangulation, z, cmap) is returned; to recreate the plot,
        one would call:
          axes.tripcolor(triangulation, z, cmap, shading='gouraud', vmin=vmin, vmax=vmax)
      * vmin (default: None) specifies the minimum value for scaling the property when one is passed
        as the color option. None means to use the min value of the property.
      * vmax (default: None) specifies the maximum value for scaling the property when one is passed
        as the color option. None means to use the max value of the property.
    '''
    if triangulation is None:
        triangulation = matplotlib.tri.Triangulation(the_map.coordinates[0], the_map.coordinates[1],
                                                     triangles=the_map.tess.indexed_faces.T)
    if axes is Ellipsis: return (triangulation, zs, cmap)
    return axes.tripcolor(triangulation, zs, cmap=cmap, shading='gouraud', vmin=vmin, vmax=vmax) 

def draw_nodal_values_shaded(values, coords, edof, title=None, dofs_per_node=None, el_type=None, draw_elements=False):
    """Draws element nodal values as shaded triangles. 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.tripcolor(x, y, edof_tri - 1, v.ravel(), shading="gouraud")

    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) 

def test_tripcolor():
    x = np.asarray([0, 0.5, 1, 0,   0.5, 1,   0, 0.5, 1, 0.75])
    y = np.asarray([0, 0,   0, 0.5, 0.5, 0.5, 1, 1,   1, 0.75])
    triangles = np.asarray([
        [0, 1, 3], [1, 4, 3],
        [1, 2, 4], [2, 5, 4],
        [3, 4, 6], [4, 7, 6],
        [4, 5, 9], [7, 4, 9], [8, 7, 9], [5, 8, 9]])

    # Triangulation with same number of points and triangles.
    triang = mtri.Triangulation(x, y, triangles)

    Cpoints = x + 0.5*y

    xmid = x[triang.triangles].mean(axis=1)
    ymid = y[triang.triangles].mean(axis=1)
    Cfaces = 0.5*xmid + ymid

    plt.subplot(121)
    plt.tripcolor(triang, Cpoints, edgecolors='k')
    plt.title('point colors')

    plt.subplot(122)
    plt.tripcolor(triang, facecolors=Cfaces, edgecolors='k')
    plt.title('facecolors') 

def test_tripcolor():
    x = np.asarray([0, 0.5, 1, 0,   0.5, 1,   0, 0.5, 1, 0.75])
    y = np.asarray([0, 0,   0, 0.5, 0.5, 0.5, 1, 1,   1, 0.75])
    triangles = np.asarray([
        [0, 1, 3], [1, 4, 3],
        [1, 2, 4], [2, 5, 4],
        [3, 4, 6], [4, 7, 6],
        [4, 5, 9], [7, 4, 9], [8, 7, 9], [5, 8, 9]])

    # Triangulation with same number of points and triangles.
    triang = mtri.Triangulation(x, y, triangles)

    Cpoints = x + 0.5*y

    xmid = x[triang.triangles].mean(axis=1)
    ymid = y[triang.triangles].mean(axis=1)
    Cfaces = 0.5*xmid + ymid

    plt.subplot(121)
    plt.tripcolor(triang, Cpoints, edgecolors='k')
    plt.title('point colors')

    plt.subplot(122)
    plt.tripcolor(triang, facecolors=Cfaces, edgecolors='k')
    plt.title('facecolors') 

def test_pcolor_heatmap():
    # import matplotlib.tri as tri
    from matplotlib import pyplot as plt

    mesh3D = mesh(100,edges=True)
    mesh2D = proj_to_2D(mesh3D)
    # triangulation = tri.Triangulation(mesh2D) # this is called in tripcolor

    data = np.zeros((3,3))
    data[0,1] += 1

    vals = np.exp(log_dirichlet_density(mesh3D,2.,data=data.sum(0)))
    temp = log_censored_dirichlet_density(mesh3D,2.,data=data)
    censored_vals = np.exp(temp - temp.max())

    plt.figure()
    plt.tripcolor(mesh2D[:,0],mesh2D[:,1],vals)
    plt.title('uncensored')

    plt.figure()
    plt.tripcolor(mesh2D[:,0],mesh2D[:,1],censored_vals)
    plt.title('censored') 

def test_tripcolor():
    x = np.asarray([0, 0.5, 1, 0,   0.5, 1,   0, 0.5, 1, 0.75])
    y = np.asarray([0, 0,   0, 0.5, 0.5, 0.5, 1, 1,   1, 0.75])
    triangles = np.asarray([
        [0, 1, 3], [1, 4, 3],
        [1, 2, 4], [2, 5, 4],
        [3, 4, 6], [4, 7, 6],
        [4, 5, 9], [7, 4, 9], [8, 7, 9], [5, 8, 9]])

    # Triangulation with same number of points and triangles.
    triang = mtri.Triangulation(x, y, triangles)

    Cpoints = x + 0.5*y

    xmid = x[triang.triangles].mean(axis=1)
    ymid = y[triang.triangles].mean(axis=1)
    Cfaces = 0.5*xmid + ymid

    plt.subplot(121)
    plt.tripcolor(triang, Cpoints, edgecolors='k')
    plt.title('point colors')

    plt.subplot(122)
    plt.tripcolor(triang, facecolors=Cfaces, edgecolors='k')
    plt.title('facecolors') 

def test_tripcolor():
    x = np.asarray([0, 0.5, 1, 0,   0.5, 1,   0, 0.5, 1, 0.75])
    y = np.asarray([0, 0,   0, 0.5, 0.5, 0.5, 1, 1,   1, 0.75])
    triangles = np.asarray([
        [0, 1, 3], [1, 4, 3],
        [1, 2, 4], [2, 5, 4],
        [3, 4, 6], [4, 7, 6],
        [4, 5, 9], [7, 4, 9], [8, 7, 9], [5, 8, 9]])

    # Triangulation with same number of points and triangles.
    triang = mtri.Triangulation(x, y, triangles)

    Cpoints = x + 0.5*y

    xmid = x[triang.triangles].mean(axis=1)
    ymid = y[triang.triangles].mean(axis=1)
    Cfaces = 0.5*xmid + ymid

    plt.subplot(121)
    plt.tripcolor(triang, Cpoints, edgecolors='k')
    plt.title('point colors')

    plt.subplot(122)
    plt.tripcolor(triang, facecolors=Cfaces, edgecolors='k')
    plt.title('facecolors') 

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 

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.") 

def plot_many_gll(x, y, z, vmin=None, vmax=None):
    """ Plots values on big 2D unstructured GLL mesh
        (in that case tricontourf does not work)
    """
    r = (max(x) - min(x))/(max(y) - min(y))
    rx = r/np.sqrt(1 + r**2)
    ry = 1/np.sqrt(1 + r**2)

    f = plt.figure(figsize=(10*rx, 10*ry))
    p = plt.tripcolor(x, y, z, vmin=vmin, vmax=vmax)
    plt.axis('image')

    return f, p 

def _plot_rgb_triangle(xy_to_2d, bright=True):
    # plot sRGB triangle
    # discretization points
    n = 50

    # Get all RGB values that sum up to 1.
    rgb_linear, _ = meshzoo.triangle(n)
    if bright:
        # For the x-y-diagram, it doesn't matter if the values are scaled in any way.
        # After all, the tranlation to XYZ is linear, and then to xyY it's (X/(X+Y+Z),
        # Y/(X+Y+Z), Y), so the factor will only be present in the last component which
        # is discarded. To make the plot a bit brighter, scale the colors up as much as
        # possible.
        rgb_linear /= numpy.max(rgb_linear, axis=0)

    srgb_linear = SrgbLinear()
    xyz = srgb_linear.to_xyz100(rgb_linear)
    xyy_vals = xy_to_2d(_xyy_from_xyz100(xyz)[:2])

    # Unfortunately, one cannot use tripcolors with explicit RGB specification
    # (see <https://github.com/matplotlib/matplotlib/issues/10265>). As a
    # workaround, associate range(n) data with the points and create a colormap
    # that associates the integer values with the respective RGBs.
    z = numpy.arange(xyy_vals.shape[1])
    rgb = srgb_linear.to_srgb1(rgb_linear)
    cmap = matplotlib.colors.LinearSegmentedColormap.from_list(
        "gamut", rgb.T, N=len(rgb.T)
    )

    triang = matplotlib.tri.Triangulation(xyy_vals[0], xyy_vals[1])
    plt.tripcolor(triang, z, shading="gouraud", cmap=cmap)
    return 

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() 

def tri_plot(tri, field, title="", levels=12, savefigs=False,
             plt_type="contourf", filename="solution_plot.pdf"):
    """Plot contours over triangulation

    Parameters
    ----------
    tri : ndarray (float)
        Array with number and nodes coordinates:
        `number coordX coordY BCX BCY`
    field : ndarray (float)
        Array with data to be plotted for each node.
    title : string (optional)
        Title of the plot.
    levels : int (optional)
        Number of levels to be used in ``contourf``.
    savefigs : bool (optional)
        Allow to save the figure.
    plt_type : string (optional)
        Plot the field as one of the options: ``pcolor`` or
        ``contourf``
    filename : string (optional)
        Filename to save the figures.
    """
    if plt_type == "pcolor":
        disp_plot = plt.tripcolor
    elif plt_type == "contourf":
        disp_plot = plt.tricontourf
    disp_plot(tri, field, levels, shading="gouraud")
    plt.title(title)
    plt.colorbar(orientation='vertical')
    plt.axis("image")
    if savefigs:
        plt.savefig(filename) 

def cortex_plot_2D(the_map,
                   color=None, cmap=None, vmin=None, vmax=None, alpha=None,
                   underlay='curvature', mask=None, axes=None, triangulation=None):
    '''
    cortex_plot_2D(map) yields a plot of the given 2D cortical mesh, map.

    The following options are accepted:
      * color (default: None) specifies the color to plot for each vertex; this argument may take a
        number of forms:
          * None, do not plot a color over the underlay (the default)
          * a matrix of RGB or RGBA values, one per vertex
          * a property vector or a string naming a property, in which case the cmap, vmin, and vmax
            arguments are used to generate colors
          * a function that, when passed a single argument, a dict of the properties of a single
            vertex, yields an RGB or RGBA list for that vertex.
      * cmap (default: 'eccenflat') specifies the colormap to use in plotting if the color
        argument provided is a property.
      * vmin (default: None) specifies the minimum value for scaling the property when one is passed
        as the color option. None means to use the min value of the property.
      * vmax (default: None) specifies the maximum value for scaling the property when one is passed
        as the color option. None means to use the max value of the property.
      * underlay (default: 'curvature') specifies the default underlay color to plot for the
        cortical surface; it may be None, 'curvature', or a color.
      * alpha (default None) specifies the alpha values to use for the color plot. If None, then
        leaves the alpha values from color unchanged. If a single number, then all alpha values in
        color are multiplied by that value. If a list of values, one per vertex, then this vector
        is multiplied by the alpha values. Finally, any negative value is set instead of multiplied.
        So, for example, if there were 3 vertices with:
          * color = ((0,0,0,1), (0,0,1,0.5), (0,0,0.75,0,8))
          * alpha = (-0.5, 1, 0.5)
        then the resulting colors plotted will be ((0,0,0,0.5), (0,0,1,0.5), (0,0,0.75,0,4)).
      * mask (default: None) specifies a mask to use for the mesh; thi sis passed through map.mask()
        to figure out the masking. Those vertices not in the mask are not plotted (but they will be
        plotted in the underlay if it is not None).
      * axes (default: None) specifies a particular set of matplotlib pyplot axes that should be
        used. If axes is Ellipsis, then instead of attempting to render the plot, a tuple of
        (tri, zs, cmap) is returned; in this case, tri is a matplotlib.tri.Triangulation
        object for the given map and zs and cmap are an array and colormap (respectively) that
        will produce the correct colors. Without axes equal to Ellipsis, these would instead
        be rendered as axes.tripcolor(tri, zs, cmap, shading='gouraud'). If axes is None, then
        uses the current axes.
      * triangulation (default: None) specifies the matplotlib triangulation object to use, if one
        already exists; otherwise a new one is made.
    '''
    # parse the axes
    if axes is None: axes = matplotlib.pyplot.gca()
    # process the colors
    color = cortex_plot_colors(the_map, color=color, cmap=cmap, vmin=vmin, vmax=vmax, alpha=alpha,
                               underlay=underlay, mask=mask)
    # finally, we can make the plot!
    return cortex_rgba_plot_2D(the_map, color, axes=axes, triangulation=triangulation)


# 3D Graphics ######################################################################################

# If we're using Python 2, we're compatible with pysurfer: 

def cortex_plot(mesh, *args, **opts):
    '''
    cortex_plot(mesh) calls either cortex_plot_2D or cortex_plot_3D depending on the dimensionality
      of the given mesh, and yields the resulting graphics object. All optional arguments supported
      by each is supported by cortex plot.

    The following options are accepted:
      * color (default: None) specifies the color to plot for each vertex; this argument may take a
        number of forms:
          * None, do not plot a color over the underlay (the default)
          * a matrix of RGB or RGBA values, one per vertex
          * a property vector or a string naming a property, in which case the cmap, vmin, and vmax
            arguments are used to generate colors
          * a function that, when passed a single argument, a dict of the properties of a single
            vertex, yields an RGB or RGBA list for that vertex.
      * cmap (default: 'eccenflat') specifies the colormap to use in plotting if the color
        argument provided is a property.
      * vmin (default: None) specifies the minimum value for scaling the property when one is passed
        as the color option. None means to use the min value of the property.
      * vmax (default: None) specifies the maximum value for scaling the property when one is passed
        as the color option. None means to use the max value of the property.
      * underlay (default: 'curvature') specifies the default underlay color to plot for the
        cortical surface; it may be None, 'curvature', or a color.
      * alpha (default None) specifies the alpha values to use for the color plot. If None, then
        leaves the alpha values from color unchanged. If a single number, then all alpha values in
        color are multiplied by that value. If a list of values, one per vertex, then this vector
        is multiplied by the alpha values. Finally, any negative value is set instead of multiplied.
        So, for example, if there were 3 vertices with:
          * color = ((0,0,0,1), (0,0,1,0.5), (0,0,0.75,0,8))
          * alpha = (-0.5, 1, 0.5)
        then the resulting colors plotted will be ((0,0,0,0.5), (0,0,1,0.5), (0,0,0.75,0,4)).
      * mask (default: None) specifies a mask to use for the mesh; thi sis passed through map.mask()
        to figure out the masking. Those vertices not in the mask are not plotted (but they will be
        plotted in the underlay if it is not None).
      * hemi (defaut: None) specifies the hemisphere to use. If the passed mesh object is actually a
        subject or mesh pair then this specifies which hemisphere to use. If the passed object is a
        mesh, then this overrides its chirality, if specified in meta_data. If two hemispheres are
        given, then this may be 'both' or 'split' in accordinace with PySurfer's Brain() class.
      * surface (default: 'white') specifies the surface to use if the mesh object passed is in fact
        either a cortex or subject object.
      * axes (default: None) specifies a particular set of matplotlib pyplot axes that should be
        used. If axes is Ellipsis, then instead of attempting to render the plot, a tuple of
        (tri, zs, cmap) is returned; in this case, tri is a matplotlib.tri.Triangulation
        object for the given map and zs and cmap are an array and colormap (respectively) that
        will produce the correct colors. Without axes equal to Ellipsis, these would instead
        be rendered as axes.tripcolor(tri, zs, cmap, shading='gouraud'). If axes is None, then
        uses the current axes.
      * triangulation (default: None) specifies the matplotlib triangulation object to use, if one
        already exists; otherwise a new one is made.
    '''
    if not isinstance(mesh, geo.Mesh) or mesh.coordinates.shape[0] > 2:
        # must be a 3D call
        return cortex_plot_3D(mesh, *args, **opts)
    else:
        return cortex_plot_2D(mesh, *args, **opts)