I would like to draw multiple independent lines in a 3D plot in Python. It looks like: .
I am new at Python. Would you help me?
You have to work with matplotlib library (mplot3d package).
Here is a little example of 3dr plot with lines:
import matplotlib as mpl
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import matplotlib.pyplot as plt
# make 3d axes
fig = plt.figure()
ax = fig.gca(projection='3d')
# test data
x = np.arange(-1., 1., .1)
y = np.arange(-1., 1., .1)
z1 = x + y
z2 = x * x
z3 = -y * y
# plot test data
ax.plot(x, y, z1)
ax.plot(x, y, z2)
ax.plot(x, y, z3)
# make labels
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
plt.show()
Related
I have computed a lot (~5000) of 3d points (x,y,z) in a quite complicated way so I have no function such that z = f(x,y). I can plot the 3d surface using
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
X = surface_points[:,0]
Y = surface_points[:,1]
Z = surface_points[:,2]
fig = plt.figure()
ax = fig.add_subplot(projection='3d')
surf = ax.plot_trisurf(X, Y, Z, cmap=cm.coolwarm, vmin=np.nanmin(Z), vmax=np.nanmax(Z))
I would like to plot this also in 2d, with a colorbar indicating the z-value. I know there is a simple solution using ax.contour if my z is a matrix, but here I only have a vector.
Attaching the plot_trisurf result when rotated to xy-plane. This is what I what like to achieve without having to rotate a 3d plot. In this, my variable surface_points is an np.array with size 5024 x 3.
I had the same problems in one of my codes, I solved it this way:
import numpy as np
from scipy.interpolate import griddata
import matplotlib.pylab as plt
from matplotlib import cm
N = 10000
surface_points = np.random.rand(N,3)
X = surface_points[:,0]
Y = surface_points[:,1]
Z = surface_points[:,2]
nx = 10*int(np.sqrt(N))
xg = np.linspace(X.min(), X.max(), nx)
yg = np.linspace(Y.min(), Y.max(), nx)
xgrid, ygrid = np.meshgrid(xg, yg)
ctr_f = griddata((X, Y), Z, (xgrid, ygrid), method='linear')
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.contourf(xgrid, ygrid, ctr_f, cmap=cm.coolwarm)
plt.show()
You could use a scatter plot to display a projection of your z color onto the x-y axis.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
N = 10000
surface_points = np.random.rand(N,3)
X = surface_points[:,0]
Y = surface_points[:,1]
Z = surface_points[:,2]
# fig = plt.figure()
# ax = fig.add_subplot(projection='3d')
# surf = ax.plot_trisurf(X, Y, Z, cmap=cm.coolwarm, vmin=np.nanmin(Z), vmax=np.nanmax(Z))
fig = plt.figure()
cmap = cm.get_cmap('coolwarm')
color = cmap(Z)[..., :3]
plt.scatter(X,Y,c=color)
plt.show()
Since you seem to have a 3D shape that is hollow, you could split the projection into two like if you cur the shape in two pieces.
fig = plt.figure()
plt.subplot(121)
plt.scatter(X[Z<0.5],Y[Z<0.5],c=color[Z<0.5])
plt.title('down part')
plt.subplot(122)
plt.scatter(X[Z>=0.5],Y[Z>=0.5],c=color[Z>+0.5])
plt.title('top part')
plt.show()
Is there a way to move tick labels in Matplot3dlib like this?
from mpl_toolkits import mplot3d
import numpy as np
import matplotlib.pyplot as plt
x = np.outer(np.linspace(-2, 2, 30), np.ones(30))
y = x.copy().T # transpose
z = np.cos(x ** 2 + y ** 2)
fig = plt.figure()
ax = plt.axes(projection='3d')
ax.plot_surface(x, y, z,cmap='viridis', edgecolor='none')
ax.set_title('Surface plot')
plt.show()
There are some ways using pad parameters.
However, I want to move more precisely like figure in the link above.
Any help appreciated.
-- Addition --
When I changing PAD parameter like the code below, the tick's label is more closer to the axis. However, I want to move it a little bit more to -x direction.
tick's label position changing
from mpl_toolkits import mplot3d
import numpy as np
import matplotlib.pyplot as plt
x = np.outer(np.linspace(-2, 2, 30), np.ones(30))
y = x.copy().T # transpose
z = np.cos(x ** 2 + y ** 2)
fig = plt.figure()
ax = plt.axes(projection='3d')
ax.plot_surface(x, y, z,cmap='viridis', edgecolor='none')
ax.set_title('Surface plot')
ax.tick_params(axis='x', which='major', pad=-5)
plt.show()
Is there any way available in python to plot multiple 2D contour plots in one 3D plot in python. I am currently using matplotlib for contouring, but not finding any option for what I am searching for. A sample image I have added. But I want to do it on Z-axis.
You can try this.
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.path import Path
import matplotlib.patches as patches
fig = plt.figure()
ax = fig.gca(projection='3d')
x = np.linspace(0, 1, 100)
X, Y = np.meshgrid(x, x)
levels = np.linspace(-0.1, 0.4, 100) #(z_min,z_max,number of contour),
a=0
b=1
c=2
Z1 = a+.1*np.sin(2*X)*np.sin(4*Y)
Z2 = b+.1*np.sin(3*X)*np.sin(4*Y)
Z3 = c+.1*np.sin(4*X)*np.sin(5*Y)
plt.contourf(X, Y,Z1, levels=a+levels,cmap=plt.get_cmap('rainbow'))
plt.contourf(X, Y,Z2, levels=b+levels,cmap=plt.get_cmap('rainbow'))
plt.contourf(X, Y,Z3, levels=c+levels,cmap=plt.get_cmap('rainbow'))
ax.set_xlim3d(0, 1)
ax.set_ylim3d(0, 1)
ax.set_zlim3d(0, 2)
plt.show()
In order to plot true 2-D contour plots in one 3D plot, try this:
import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.gca(projection='3d')
x = np.linspace(0, 1, 100)
X, Y = np.meshgrid(x, x)
Z1 = .1*np.sin(2*X)*np.sin(4*Y)
Z2 = .1*np.sin(3*X)*np.sin(4*Y)
Z3 = .1*np.sin(4*X)*np.sin(5*Y)
levels=np.linspace(Z1.min(), Z1.max(), 100)
ax.contourf(X, Y,Z1, levels=levels, zdir='z', offset=0, cmap=plt.get_cmap('rainbow'))
levels=np.linspace(Z2.min(), Z2.max(), 100)
ax.contourf(X, Y,Z2, levels=levels, zdir='z', offset=1, cmap=plt.get_cmap('rainbow'))
levels=np.linspace(Z3.min(), Z3.max(), 100)
ax.contourf(X, Y,Z3, levels=levels, zdir='z', offset=2, cmap=plt.get_cmap('rainbow'))
ax.set_xlim3d(0, 1)
ax.set_ylim3d(0, 1)
ax.set_zlim3d(0, 2)
plt.show()
enter image description here
I am plotting a 3d plot in python 2.7
When I try to plot a 3d plot with color and marker as in 2D plot() function. I come across an error.
So I tried to plot line separately and measured points with markers separately using scatter() function.
When I create legend entries my legend looks like this
But I don't want to have duplicate legend entries instead
I want my legend entries to group with colour, or
Is it possible have both marker and line as a single entry so that there are only 5 entries in my legend
I found a similar question to this (How to make custom legend in matplotlib) but it does not solve my problem
I am appending a code similar to my problem
import matplotlib as mpl
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import matplotlib.pyplot as plt
mpl.rcParams['legend.fontsize'] = 10
fig = plt.figure()
ax = fig.gca(projection='3d')
theta = np.linspace(-4 * np.pi, 4 * np.pi, 100)
z = np.linspace(-2, 2, 100)
r = z**2 + 1
x = r * np.sin(theta)
y = r * np.cos(theta)
ax.plot(x, y, z, label='parametric curve 1')
ax.scatter(x, y, z, label='parametric curve 1',marker = 'o')
x = r * np.sin(theta + 1)
y = r * np.cos(theta + 1)
ax.plot(x, y, z, label='parametric curve 2')
ax.scatter(x, y, z, label='parametric curve 2',marker = 'o')
ax.legend()
plt.show()
The above code gives me a plot shown below
Plot
But I want my legend to have only two entries
Are you using the standard Matplotlib library to generate these 3D plots? If so, starting from the example in the documentation (http://matplotlib.org/mpl_toolkits/mplot3d/tutorial.html#line-plots) it seems to work fine:
import matplotlib as mpl
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import matplotlib.pyplot as plt
mpl.rcParams['legend.fontsize'] = 10
fig = plt.figure()
ax = fig.gca(projection='3d')
theta = np.linspace(-4 * np.pi, 4 * np.pi, 100)
z = np.linspace(-2, 2, 100)
r = z**2 + 1
x = r * np.sin(theta)
y = r * np.cos(theta)
ax.plot(x, y, z, label='parametric curve 1', marker='o')
x = r * np.sin(theta + 1)
y = r * np.cos(theta + 1)
ax.plot(x, y, z, label='parametric curve 2', marker='o')
ax.legend()
plt.show()
I am trying to add legend to a surface plot but unable to do so. Here is the code.
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import random
def fun(x, y):
return 0.063*x**2 + 0.0628*x*y - 0.15015876*x + 96.1659*y**2 - 74.05284306*y + 14.319143466051
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x = y = np.arange(-1.0, 1.0, 0.05)
X, Y = np.meshgrid(x, y)
zs = np.array([fun(x,y) for x,y in zip(np.ravel(X), np.ravel(Y))])
Z = zs.reshape(X.shape)
ax.plot_surface(X, Y, Z)
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
plt.plot(color='red',label='Lyapunov function on XY plane',linewidth=4) # Adding legend
plt.show()
Kindly help. Thanks in advance.
It is not trivial to make a legend in a 3D axis. You can use the following hack:
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import matplotlib as mpl
import random
def fun(x, y):
return 0.063*x**2 + 0.0628*x*y - 0.15015876*x + 96.1659*y**2 - 74.05284306*y + 14.319143466051
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x = y = np.arange(-1.0, 1.0, 0.05)
X, Y = np.meshgrid(x, y)
zs = np.array([fun(x,y) for x,y in zip(np.ravel(X), np.ravel(Y))])
Z = zs.reshape(X.shape)
ax.plot_surface(X, Y, Z)
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
fake2Dline = mpl.lines.Line2D([0],[0], linestyle="none", c='b', marker = 'o')
ax.legend([fake2Dline], ['Lyapunov function on XY plane'], numpoints = 1)
plt.show()
I would say a title is more appropriate than a legend in this case.
According to this question, the issue is ongoing, and there is a relatively simple workaround. You can manually set the two missing attributes that would allow legend to automatically create the patch for you:
surf = ax.plot_surface(X, Y, Z, label='Lyapunov function on XY plane')
surf._edgecolors2d = surf._edgecolor3d
surf._facecolors2d = surf._facecolor3d
ax.legend()
The attribute names on the right hand side of the assignment are surf._edgecolors3d and surf.facecolors3d for matplotlib < v3.3.3.