I have 3 contours, generated by the following:
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from scipy import stats
mean0 = [ 3.1627717, 2.74815376]
cov0 = [[0.44675818, -0.04885433], [-0.04885433, 0.52484173]]
mean1 = [ 6.63373967, 6.82700035]
cov1 = [[ 0.46269969, 0.11528141], [0.11528141, 0.50237073]]
mean2 = [ 7.20726944, 2.61513787]
cov2 = [[ 0.38486096, -0.13042758], [-0.13042758, 0.40928813]]
x = np.linspace(0, 10, 100)
y = np.linspace(0, 10, 100)
X, Y = np.meshgrid(x, y)
Z0 = np.random.random((len(x),len(y)))
Z1 = np.random.random((len(x),len(y)))
Z2 = np.random.random((len(x),len(y)))
def pdf0(arg1,arg2):
return (stats.multivariate_normal.pdf((arg1,arg2), mean0, cov0))
def pdf1(arg1,arg2):
return (stats.multivariate_normal.pdf((arg1,arg2), mean1, cov1))
def pdf2(arg1,arg2):
return (stats.multivariate_normal.pdf((arg1,arg2), mean2, cov2))
for i in range (0, len(x)):
for j in range(0,len(y)):
Z0[i,j] = pdf0(x[i],y[j])
Z1[i,j] = pdf1(x[i],y[j])
Z2[i,j] = pdf2(x[i],y[j])
Z0=Z0.T
Z1=Z1.T
Z2=Z2.T
fig3 = plt.figure()
ax3 = fig3.add_subplot(111)
ax3.contour(X,Y,Z0)
ax3.contour(X,Y,Z1)
ax3.contour(X,Y,Z2)
plt.show()
Which, visually, is plotted as the following:
I am wishing to plot all of these in a 3D plot, but when I try do so with:
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1, projection='3d')
# 3D plots for each contour.
surf1 = ax.plot_surface(X, Y, Z0, cmap=cm.coolwarm, linewidth=0, antialiased=False)
surf2 = ax.plot_surface(X, Y, Z1, cmap=cm.coolwarm, linewidth=0, antialiased=False)
surf3 = ax.plot_surface(X, Y, Z2, cmap=cm.coolwarm, linewidth=0, antialiased=False)
ax.contour(X, Y, Z0, zdir='z', offset=-0.5)
ax.contour(X, Y, Z1, zdir='z', offset=-0.5)
ax.contour(X, Y, Z2, zdir='z', offset=-0.5)
ax.set_zlim(-0.5, 0.31)
plt.show()
The resulting graph is this:
How can I get the other two 3D contours to show nicely?
There is no general solution to this problem. Matplotlib cannot decide to show part of an object more in front than another part of it. See e.g. the FAQ, or other questions, like How to obscure a line behind a surface plot in matplotlib?
One may of course split up the object in several parts if necessary. Here, however, it seems sufficient to add the functions up.
surf1 = ax.plot_surface(X, Y, Z0+Z1+Z2, cmap=plt.cm.coolwarm,
linewidth=0, antialiased=False)
ax.contour(X, Y, Z0+Z1+Z2, zdir='z', offset=-0.5)
Related
I am plotting a function of two parameters with matplotlib. I copied an example in matplotlib tutorial and transformed with my own input data: vectors X and Y (equally spaces numbers in -3:3) and Z=peaks(X,Y) with peaks a function that I defined befohand. What is wrong?
def peaks(x,y):
xsq=x**2
ysq=y**2
xsq_one=(x+1)**2
ysq_one=(y+1)**2
m1=3*(1-x)**2
m2=10*(x/5-x**3-y**5)
m3=1/3
return m1*numpy.exp(-xsq-ysq_one)-m2*numpy.exp(-xsq-ysq)-m3*numpy.exp(-xsq_one-ysq)
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.gca(projection='3d')
X=Y=numpy.arange(-3,3,0.01).tolist()
Z=[]
for i in range(len(X)):
Z.append(peaks(X[i],Y[i]))
ax.plot_surface(X, Y, Z, rstride=8, cstride=8, alpha=0.3)
cset = ax.contour(X, Y, Z, zdir='z', offset=-100)
cset = ax.contour(X, Y, Z, zdir='x', offset=-40)
cset = ax.contour(X, Y, Z, zdir='y', offset=40)
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()
Thanks for advice!
You need to generate the meshgrid. X,Y and Z must be 2D arrays
import numpy
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d
def peaks(x,y):
return x * numpy.sin(y)
fig = plt.figure()
ax = fig.gca(projection='3d')
X = Y= numpy.arange(-3, 3, 0.1).tolist()
X, Y = numpy.meshgrid(X, Y)
Z = []
for i in range(len(X)):
Z.append(peaks(X[i],Y[i]))
# Z must be an array
Z = numpy.array(Z)
ax.plot_surface(X, Y, Z, rstride=8, cstride=8, alpha=0.3)
cset = ax.contour(X, Y, Z, zdir='z', offset=-8)
cset = ax.contour(X, Y, Z, zdir='x', offset=-8)
cset = ax.contour(X, Y, Z, zdir='y', offset=8)
ax.set_xlabel('X')
ax.set_xlim(-8, 8)
ax.set_ylabel('Y')
ax.set_ylim(-8, 8)
ax.set_zlabel('Z')
ax.set_zlim(-8, 8)
plt.show()
The accepted answer no longer works. Sadly reviewers rejected my suggested edit which would have made it a working asnwer. So here is the same answer again but with the small change necessary to make it work in the current release of matplotlib.
import numpy
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d
def peaks(x,y):
return x * numpy.sin(y)
fig = plt.figure()
ax = fig.gca(projection='3d')
X = Y= numpy.arange(-3, 3, 0.1).tolist()
X, Y = numpy.meshgrid(X, Y)
Z = numpy.zeros(X.shape)
for i in range(len(X)):
for j in range(len(Y)):
Z[i,j] = peaks(X[i,j],Y[i,j])
ax.plot_surface(X, Y, Z, rstride=8, cstride=8, alpha=0.3)
cset = ax.contour(X, Y, Z, zdir='z', offset=-8)
cset = ax.contour(X, Y, Z, zdir='x', offset=-8)
cset = ax.contour(X, Y, Z, zdir='y', offset=8)
ax.set_xlabel('X')
ax.set_xlim(-8, 8)
ax.set_ylabel('Y')
ax.set_ylim(-8, 8)
ax.set_zlabel('Z')
ax.set_zlim(-8, 8)
plt.show()
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 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.
I am plotting a function of two parameters with matplotlib. I copied an example in matplotlib tutorial and transformed with my own input data: vectors X and Y (equally spaces numbers in -3:3) and Z=peaks(X,Y) with peaks a function that I defined befohand. What is wrong?
def peaks(x,y):
xsq=x**2
ysq=y**2
xsq_one=(x+1)**2
ysq_one=(y+1)**2
m1=3*(1-x)**2
m2=10*(x/5-x**3-y**5)
m3=1/3
return m1*numpy.exp(-xsq-ysq_one)-m2*numpy.exp(-xsq-ysq)-m3*numpy.exp(-xsq_one-ysq)
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.gca(projection='3d')
X=Y=numpy.arange(-3,3,0.01).tolist()
Z=[]
for i in range(len(X)):
Z.append(peaks(X[i],Y[i]))
ax.plot_surface(X, Y, Z, rstride=8, cstride=8, alpha=0.3)
cset = ax.contour(X, Y, Z, zdir='z', offset=-100)
cset = ax.contour(X, Y, Z, zdir='x', offset=-40)
cset = ax.contour(X, Y, Z, zdir='y', offset=40)
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()
Thanks for advice!
You need to generate the meshgrid. X,Y and Z must be 2D arrays
import numpy
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d
def peaks(x,y):
return x * numpy.sin(y)
fig = plt.figure()
ax = fig.gca(projection='3d')
X = Y= numpy.arange(-3, 3, 0.1).tolist()
X, Y = numpy.meshgrid(X, Y)
Z = []
for i in range(len(X)):
Z.append(peaks(X[i],Y[i]))
# Z must be an array
Z = numpy.array(Z)
ax.plot_surface(X, Y, Z, rstride=8, cstride=8, alpha=0.3)
cset = ax.contour(X, Y, Z, zdir='z', offset=-8)
cset = ax.contour(X, Y, Z, zdir='x', offset=-8)
cset = ax.contour(X, Y, Z, zdir='y', offset=8)
ax.set_xlabel('X')
ax.set_xlim(-8, 8)
ax.set_ylabel('Y')
ax.set_ylim(-8, 8)
ax.set_zlabel('Z')
ax.set_zlim(-8, 8)
plt.show()
The accepted answer no longer works. Sadly reviewers rejected my suggested edit which would have made it a working asnwer. So here is the same answer again but with the small change necessary to make it work in the current release of matplotlib.
import numpy
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d
def peaks(x,y):
return x * numpy.sin(y)
fig = plt.figure()
ax = fig.gca(projection='3d')
X = Y= numpy.arange(-3, 3, 0.1).tolist()
X, Y = numpy.meshgrid(X, Y)
Z = numpy.zeros(X.shape)
for i in range(len(X)):
for j in range(len(Y)):
Z[i,j] = peaks(X[i,j],Y[i,j])
ax.plot_surface(X, Y, Z, rstride=8, cstride=8, alpha=0.3)
cset = ax.contour(X, Y, Z, zdir='z', offset=-8)
cset = ax.contour(X, Y, Z, zdir='x', offset=-8)
cset = ax.contour(X, Y, Z, zdir='y', offset=8)
ax.set_xlabel('X')
ax.set_xlim(-8, 8)
ax.set_ylabel('Y')
ax.set_ylim(-8, 8)
ax.set_zlabel('Z')
ax.set_zlim(-8, 8)
plt.show()