mplot3d

I am designing a three-dimensional illustration using Matplotlib. All is working nicely, except that the (red) parametric curve gets the wrong zorder while the (green) parametric surface is drawn completely correctly. Output generated by code below: I know that Matplotlib has limited capabilities for accurately computing the zorder of objects, but since it can do it for the parametric surface, it seems like a bug in Matplotlib. That said, is there any way to force correct z-ordering just to get things to work quickly? It seems that all I have to be able to say is that the right transparent

I'm new to 3d plotting. I just want to build a pyramid out of 5 Points and cut a plane through it. My problem is I don't know how to fill the sides. points = np.array([[-1, -1, -1], [1, -1, -1 ], [1, 1, -1], [-1, 1, -1], [0, 0 , 1]]) fig = plt.figure() ax = fig.add_subplot(111, projection='3d') r = [-1,1] X, Y = np.meshgrid(r, r) ax.plot_surface(X,Y,-1, alpha=0.5) ax.plot_surface(X,Y,0.5, alpha=0.5, facecolors='r') ax.scatter3D(points[:, 0], points[:, 1], points[:, 2]) ax.set_xlabel('X') ax.set_ylabel('Y') ax.set_zlabel('Z') plt.show() Any help is appreciated. You have to use polygons: from

I have a set of 3D points which I've used scipy.spatial.Delaunay to do the triangulation / tetrahedralization. I now have a set of unique faces of all of the tetrahedra, and would like to visualize these in 3D. Are there any Python libraries (or libraries with a Python wrapper) that can do this? nullas Try mayavi.mlab.triangular_mesh() import numpy as np from mayavi import mlab vertices = np.array([[0, 1, 0, 0],[0, 0, 1, 0],[0, 0, 0, 1]]) faces = np.array([[0, 1, 0, 0],[1, 2, 1, 2],[2, 3, 3, 3]]) mlab.triangular_mesh(vertices[0,:], vertices[1,:], vertices[2,:], faces.T) mlab.show() It can also

I have a simple task that should have a simple solution, but I have been trying for days now. I try to be specific. I try to plot a surface using matplotlib's mplot3d and plot_surface. When I plot the surface of a dataset 'z' and try to scale the colormap to a certain maximum value I change the 'vmax' property to this value. That works great. When I try to plot a surface of one dataset (z) and use the facecolors of a second dataset (fc), this also works fine. When I want to scale the colormap of the facecolors, the vmax property is overruled by the facecolors values. Vmax therefore has no

I want to have colors corresponding to a colormap in my 3d quiver plot. The 2d version of the plot has an optional array that is used to map colors to the arrows. How can I create the same effect in the 3d version? 3D quiver plots are a brand-new feature in 1.4 it (and it's documentation) might still be a bit rough around the edges. In this case we can try to use the fact that the quiver is implemented as a LineCollection which (eventually) inherits from ScalarMappable which means it knows what a colormap is and the returned artist has the method set_array . Building on the docs here from mpl

I am trying to animate a 3D scatter plot using mplotlib in Python. I am able to graph the data and redraw every time, but this results in a frame rate of less than 1 FPS, and I need to scale to upwards of 30 FPS. When I run my code: import serial import numpy import matplotlib.pyplot as plt #import matplotlib library from mpl_toolkits.mplot3d import Axes3D from drawnow import * import matplotlib.animation import time ser = serial.Serial('COM7',9600,timeout=5) ser.flushInput() time.sleep(5) ser.write(bytes(b's1000')) x=list() y=list() z=list() #plt.ion() fig = plt.figure(figsize=(16,12)) ax =

I'm trying to change the thickness and transparency of the lines that make up the grid in the background of a surface plot like this example from Matplotlib's website : Here's the source code: from mpl_toolkits.mplot3d import Axes3D import matplotlib.pyplot as plt from matplotlib import cm from matplotlib.ticker import LinearLocator, FormatStrFormatter import numpy as np fig = plt.figure() ax = fig.gca(projection='3d') # Make data. X = np.arange(-5, 5, 0.25) Y = np.arange(-5, 5, 0.25) X, Y = np.meshgrid(X, Y) R = np.sqrt(X**2 + Y**2) Z = np.sin(R) # Plot the surface. surf = ax.plot_surface(X,