I would like to plot a surface with a colormap, wireframe and contours using matplotlib. Something like this:
Notice that I am not asking about the contours that lie in the plane parallel to xy but the ones that are 3D and white in the image.
If I go the naïve way and plot all these things I cannot see the contours (see code and image below).
import numpy as np
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111, projection="3d")
X, Y = np.mgrid[-1:1:30j, -1:1:30j]
Z = np.sin(np.pi*X)*np.sin(np.pi*Y)
ax.plot_surface(X, Y, Z, cmap="autumn_r", lw=0.5, rstride=1, cstride=1)
ax.contour(X, Y, Z, 10, lw=3, cmap="autumn_r", linestyles="solid", offset=-1)
ax.contour(X, Y, Z, 10, lw=3, colors="k", linestyles="solid")
plt.show()
If a add transparency to the surface facets then I can see the contours, but it looks really cluttered (see code and image below)
import numpy as np
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111, projection="3d")
X, Y = np.mgrid[-1:1:30j, -1:1:30j]
Z = np.sin(np.pi*X)*np.sin(np.pi*Y)
ax.plot_surface(X, Y, Z, cmap="autumn_r", lw=0.5, rstride=1, cstride=1, alpha=0.5)
ax.contour(X, Y, Z, 10, lw=3, cmap="autumn_r", linestyles="solid", offset=-1)
ax.contour(X, Y, Z, 10, lw=3, colors="k", linestyles="solid")
plt.show()
Question: Is there a way to obtain this result in matplotlib? The shading is not necessary, though.
Apparently it is a bug, if you try this
import numpy as np
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111, projection="3d")
X, Y = np.mgrid[-1:1:30j, -1:1:30j]
Z = np.sin(np.pi*X)*np.sin(np.pi*Y)
ax.plot_surface(X, Y, Z, cmap="autumn_r", lw=0, rstride=1, cstride=1)
ax.contour(X, Y, Z+1, 10, lw=3, colors="k", linestyles="solid")
plt.show()
And rotate around, you will see the contour lines disappearing when they shouldn't
I think you want to set the offset to the contour :
ax.contour(X, Y, Z, 10, offset=-1, lw=3, colors="k", linestyles="solid", alpha=0.5)
See this example for more:
http://matplotlib.org/examples/mplot3d/contour3d_demo3.html
And the docs here:
http://matplotlib.org/mpl_toolkits/mplot3d/tutorial.html#contour-plots
offset: If specified plot a projection of the contour lines on this position in plane normal to zdir
Note, zdir = 'z' by default, but you can project in the x or y direction be setting the zdir accordingly.
Related
I want to plot a stack of heatmaps, contour, or grid computed over time. The plot should like this,
I have tried this:
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.gca(projection='3d')
x = np.linspace(0, 1, 100)
X, Z = np.meshgrid(x, x)
Y = np.sin(X)*np.sin(Z)
levels = np.linspace(-1, 1, 40)
ax.contourf(X, Y, Z, zdir='y')
ax.contourf(X, Y+3, Z, zdir='y')
ax.contourf(X, Y+7, Z, zdir='y')
ax.legend()
ax.view_init(15,155)
plt.show()
For one my plot looks ugly. It also does not look like what I want. I cannot make a grid there, and the 2d surfaces are tilted.
Any help is really appreciated! I am struggling with this.
Related stackoverflow:
[1] Python plot - stacked image slices
[2] Stack of 2D plot
How about making a series of 3d surface plots, with the data your wish to present in contour plotted as facecolor?
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.ticker import LinearLocator
import numpy as np
fig = plt.figure()
ax = fig.gca(projection='3d')
X = np.arange(-5, 5, 0.25)
Z = np.arange(-5, 5, 0.25)
X, Z = np.meshgrid(X, Z)
C = np.random.random(size=40*40*3).reshape((40, 40, 3))
ax.plot_surface(X, np.ones(shape=X.shape)-1, Z, facecolors=C, linewidth=0)
ax.plot_surface(X, np.ones(shape=X.shape), Z, facecolors=C, linewidth=0)
ax.plot_surface(X, np.ones(shape=X.shape)+1, Z, facecolors=C, linewidth=0)
I have been trying to get the "example" picture (generated with a 3D graphic calc) using Python for a few days now, but keep running into troubles getting the segments of the plot other than the peak in the middle to show up to scale.
I am using this code:
import numpy as np
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111, projection="3d")
X, Y = np.mgrid[-1:1:30j, -1:1:30j]
Z = (2*X*Y) + (1/np.sqrt(X**2+Y**2))
ax.plot_surface(X, Y, Z, cmap="autumn_r", lw=0.5, rstride=1, cstride=1)
ax.contour(X, Y, Z, 10, lw=3, cmap="autumn_r", linestyles="solid", offset=-1)
ax.contour(X, Y, Z, 10, lw=3, colors="k", linestyles="solid")
plt.show()
Which produces this graph.
It is close, but it should look more like this one. When I lower the 30j in attempts to bring it down and hope the flares on the sides are more pronounced, it gets rid of the entire peak. I am trying to get to this.
What if you try the following line?
X, Y = np.mgrid[-7:7:100j, -7:7:100j]
I am trying to "remove the floor" from a 3D surface plot. For example, in this matplotlib demo code:
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
from matplotlib import cm
fig = plt.figure()
ax = fig.gca(projection='3d')
X, Y, Z = axes3d.get_test_data(0.05)
ax.plot_surface(X, Y, Z, rstride=8, cstride=8, alpha=0.3)
cset = ax.contour(X, Y, Z, zdir='z', offset=-100, cmap=cm.coolwarm)
cset = ax.contour(X, Y, Z, zdir='x', offset=-40, cmap=cm.coolwarm)
cset = ax.contour(X, Y, Z, zdir='y', offset=40, cmap=cm.coolwarm)
ax.set_xlabel('X')
ax.set_xlim(-40, 40)
ax.set_ylabel('Y')
ax.set_ylim(-40, 40)
ax.set_zlabel('Z')
ax.set_zlim(-100, 100)
I am trying to just get the top half of the 3d surface, without the blue floor and the bottom hump. I'd like them transparent.
I've tried setting vmin appropriately, and even using a masked array but I still get the "floor" of color in my plots.
Note: My real situation is plotting a KDE generated on some data, on a grid of points and I dont want the entire bottom of my plot to be the same blue color.
The idea can be to set the unwanted part to a transparent color, using a normalization of the colormap.
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
from matplotlib import cm
import matplotlib.colors
fig = plt.figure()
ax = fig.gca(projection='3d')
X, Y, Z = axes3d.get_test_data(0.05)
norm = matplotlib.colors.Normalize(0,100)
cmap = cm.coolwarm
cmap.set_under((0,0,0,0), alpha=0.0)
ax.plot_surface(X, Y, Z, rstride=8, cstride=8, norm=norm, cmap=cmap)
ax.set_xlabel('X')
ax.set_xlim(-40, 40)
ax.set_ylabel('Y')
ax.set_ylim(-40, 40)
ax.set_zlabel('Z')
ax.set_zlim(-100, 100)
plt.show()
Is there a way to plot multiple plots in one window (graphics are displayed qt)?
Sure.
The keyword is subplot. Read this for a basic overview.
Just look at this official example from here:
from mpl_toolkits.mplot3d.axes3d import Axes3D
import matplotlib.pyplot as plt
# imports specific to the plots in this example
import numpy as np
from matplotlib import cm
from mpl_toolkits.mplot3d.axes3d import get_test_data
# Twice as wide as it is tall.
fig = plt.figure(figsize=plt.figaspect(0.5))
#---- First subplot
ax = fig.add_subplot(1, 2, 1, projection='3d')
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)
surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.coolwarm,
linewidth=0, antialiased=False)
ax.set_zlim3d(-1.01, 1.01)
fig.colorbar(surf, shrink=0.5, aspect=10)
#---- Second subplot
ax = fig.add_subplot(1, 2, 2, projection='3d')
X, Y, Z = get_test_data(0.05)
ax.plot_wireframe(X, Y, Z, rstride=10, cstride=10)
plt.show()
Output
I have setup mplot3d to provide a 3D surface plot per the example.
When I plot my data I am seeing that the surface is missing from a ridge running through the surface (see image). I noticed that surface filling appears to follow the stride but the grid-lines make viewing difficult at lower step sizes.
from mpl_toolkits.mplot3d import axes3d
from matplotlib import cm, pyplot
import numpy
Z = data[-300::]
X,Y = numpy.mgrid[:len(Z), :len(Z[0])]
fig = pyplot.figure(figsize=(20, 10), dpi=800)
ax = fig.gca(projection='3d')
surf = ax.plot_surface(X,
Y,
Z,
rstride=len(Z)/5,
cstride=len(Z[0])/10,
alpha=.6,
linewidths=(.5,),
antialiased=True,
cmap=cm.coolwarm,
vmin=124,
vmax=186
)
cset = ax.contourf(X, Y, Z, zdir='z', offset=130, cmap=cm.coolwarm, vmin=124, vmax=186)
ax.set_xlim(len(Z) * 1.2, 0)
ax.set_ylim(0, len(Z[0]) * 1.2)
ax.elev = 25
ax.azim = 20
cb = fig.colorbar(surf, shrink=0.5, aspect=5)
Is there a way to fill the missing surface?
The only way i have found to accomplish this is by setting the stride to one and linewidth to 0. The downside to this is that I appear to lose the grid overlay.
surf = ax.plot_surface(X,
Y,
Z,
shade=True,
rstride=1, cstride=1, linewidth=0,
linewidths=(.5,),
antialiased=True,
)