I am using mplot3d from the mpl_toolkits library. When displaying the 3D surface on the figure I'm realized the axis were not positioned as I wished they would.
Let me show, I have added to the following screenshot the position of each axis:
Is there a way to change the position of the axes in order to get this result:
Here's the working code:
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
ax = Axes3D(plt.figure())
def f(x,y) :
return -x**2 - y**2
X = np.arange(-1, 1, 0.02)
Y = np.arange(-1, 1, 0.02)
X, Y = np.meshgrid(X, Y)
Z = f(X, Y)
ax.plot_surface(X, Y, Z, alpha=0.5)
# Hide axes ticks
ax.set_xticks([-1,1])
ax.set_yticks([-1,1])
ax.set_zticks([-2,0])
ax.set_yticklabels([-1,1],rotation=-15, va='center', ha='right')
plt.show()
I have tried using xaxis.set_ticks_position('left') statement, but it doesn't work.
No documented methods, but with some hacking ideas from https://stackoverflow.com/a/15048653/1149007 you can.
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = ax = fig.add_subplot(111, projection='3d')
ax.view_init(30, 30)
def f(x,y) :
return -x**2 - y**2
X = np.arange(-1, 1, 0.02)
Y = np.arange(-1, 1, 0.02)
X, Y = np.meshgrid(X, Y)
Z = f(X, Y)
ax.plot_surface(X, Y, Z, alpha=0.5)
# Hide axes ticks
ax.set_xticks([-1,1])
ax.set_yticks([-1,1])
ax.set_zticks([-2,0])
ax.xaxis._axinfo['juggled'] = (0,0,0)
ax.yaxis._axinfo['juggled'] = (1,1,1)
ax.zaxis._axinfo['juggled'] = (2,2,2)
plt.show()
I can no idea of the meaning of the third number in triples. If set zeros nothing changes in the figure. So should look in the code for further tuning.
You can also look at related question Changing position of vertical (z) axis of 3D plot (Matplotlib)? with low level hacking of _PLANES property.
Something changed, code blow doesn't work, all axis hide...
ax.xaxis._axinfo['juggled'] = (0,0,0)
ax.yaxis._axinfo['juggled'] = (1,1,1)
ax.zaxis._axinfo['juggled'] = (2,2,2)
I suggest using the plot function to create a graph
Related
in mplot3d how do I change my axis limits such gets cut off the at the limites. When using ax.set_xlim3d() the graph continuous out the plot.
Consider the graph generated by:
import numpy as np; import matplotlib.pyplot as plt;
from mpl_toolkits import mplot3d
def func(x, y):
return x ** 2 + 0.5*y ** 3
x = np.linspace(-6, 6, 30)
y = np.linspace(-6, 6, 30)
X, Y =np. meshgrid(x, y)
Z = func(X, Y)
plt.clf()
fig = plt.figure(1)
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(X, Y, Z, rstride=2, cstride=1,
cmap='viridis', edgecolor='none')
Say I want to cut my z = 50, so the last part disappears. ax.set_zlim3d(-100,50) doesn't do the trick. I have a lot of code written in this form so I prefer not to use the method described here mplot3D fill_between extends over axis limits where the core code of the plots are totally different than mine. TI figure there must be a way to fix my problem my adding a line of code to my existing code.
I'm very new in Python and trying to plot a single curve on a surface.
Here is where I came so far and plotted a surface in s domain:
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import cmath
x = np.linspace(-400, 0, 100)
y = np.linspace(-100, 100, 100)
X, Y = np.meshgrid(x,y)
fc=50
wc=2*np.pi*fc
s = X + Y*1j
Z= abs(1/(1+s/wc))
fig = plt.figure()
ax = fig.gca(projection='3d')
surf = ax.plot_surface(X, Y, Z)
ax.plot(X, Y, Z)
plt.ylabel('Im')
plt.show()
I now need to plot the curve for X = 0 in different color which means the curve on the same surface along the imaginary axis. surf = ax.plot_surface(0, Y, Z) did not work. Does anybody have experience with such plot?
I'm assuming you meant you wanted to plot y=0 instead of x=0 (since x=0 would be pretty boring).
Since you want to plot a single slice of your data, you can't use the meshgrid format (or if you can, it would require some weird indexing that I don't want to figure out).
Here's how I would plot the y=0 slice:
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import cmath
x = np.linspace(-400, 0, 100)
y = np.linspace(-100, 100, 100)
X, Y = np.meshgrid(x,y)
fc=50
wc=2*np.pi*fc
s = X + Y*1j
Z= abs(1/(1+s/wc))
fig = plt.figure()
ax = fig.gca(projection='3d')
surf = ax.plot_surface(X, Y, Z)
# create data for y=0
z = abs(1/(1+x/wc))
ax.plot(x,np.zeros(np.shape(x)),z)
plt.ylabel('Im')
plt.show()
So I have an array of values that I am trying to plot using the plot_surface command. Specifically I have a 30x30 array with one in the middle and zeros elsewhere. When I plot it this is what I obtain:
I would like however for the value to be represented as a cuboid. Is that possible?
Thank you
edit: Code that shows the figure
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
N=30
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x = y = np.arange(0, N)
z = np.zeros((N,N))
z[15,15] = 1
X, Y = np.meshgrid(x, y)
ax.plot_surface(X, Y, z, cmap='YlOrBr')
plt.show(block=False)
I think a 3D bar plot will give what you're looking for.
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
N=30
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x = y = np.arange(0, N)
z_bottom = np.zeros((N,N))
z_top = z_bottom.copy()
z_top[15,15] = 1
X, Y = np.meshgrid(x, y)
ax.bar3d(X.ravel(), Y.ravel(), z_bottom.ravel(), 1, 1, z_top.ravel())
plt.show(block=False)
The full documentation is here if you want to play with the colors and so forth.
I am trying to make a 3d surface plot of experimental data using matplotlib. I would like to plot different Z values against the same X and Y axes. When I try the simple code below, I get the error
"plot_surface() missing 1 required positional argument: 'Z' ".
It seems that the Axes3D package only work if Z is given as a function of X and Y, rather than an actual data matrix. Does anybody know a way around this?
Please note that the Zmatrix that I need is actual data, but I just used a random matrix for illustration here.
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
fig = plt.figure()
X=[2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20]
Y= [0,2500,5000,7500,10000,15000,20000,25000,30000,35000,40000,45000,50000,55000,60000,65000,70000]
Zmatrix=np.random.rand(len(X),len(Y))
Axes3D.plot_surface(X,Y,Zmatrix)
There were sone issues with your code:
First you have to get a meshgrid of X and Y (all combinations as matrices). Next swap len(X) and len(Y) inside the Zmatrix. And first define ax = Axes3D(plt.gcf()) and plot_surface afterwards on ax.
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
fig = plt.figure()
X=[2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20]
Y= [0,2500,5000,7500,10000,15000,20000,25000,30000,35000,40000,45000,50000,55000,60000,65000,70000]
Xm, Ym = np.meshgrid(X, Y)
Zmatrix=np.random.rand(len(Y),len(X))
ax = Axes3D(plt.gcf())
ax.plot_surface(Xm, Ym, Zmatrix)
Here is an example of surface plot.
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import random
def fun(x, y):
return x**2 + y
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x = y = np.arange(-3.0, 3.0, 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 Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')
plt.show()
I am trying to plot a surface using matplotlib using the code below:
from matplotlib import cm
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import axes3d, Axes3D
import pylab as p
vima=0.5
fig = plt.figure()
ax = fig.gca(projection='3d')
X = np.arange(0, 16.67, vima)
Y = np.arange(0, 12.5, vima)
X, Y = np.meshgrid(X, Y)
Z = np.sqrt(((1.2*Y+0.6*X)**2+(0.2*Y+1.6*X)**2)/(0.64*Y**2+0.36*X**2))
surf = ax.plot_surface(X, Y, Z,rstride=1, cstride=1, alpha=1,cmap=cm.jet, linewidth=0)
fig.colorbar(surf, shrink=0.5, aspect=5)
plt.show()
If you run it you will see a blue surface, but I want to use the whole color range of jet... I know there is a class "matplotlib.colors.Normalize", but I don't know how to use it. Could you please add the necessary code in order to do it?
I realise that the poster's issue has already been resolved, but the question of normalizing the colors was never dealt with. Since I've figured out how I thought I'd just drop this here for anyone else who might need it.
First you create a norm and pass that to the plotting function, I've tried to add this to the OP's code.
from matplotlib import cm
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import axes3d, Axes3D
import pylab as p
import matplotlib
vima=0.5
fig = plt.figure()
ax = fig.gca(projection='3d')
X = np.arange(0, 16.67, vima)
Y = np.arange(0, 12.5, vima)
X, Y = np.meshgrid(X, Y)
Z = np.sqrt(((1.2*Y+0.6*X)**2+(0.2*Y+1.6*X)**2)/(0.64*Y**2+0.36*X**2))
Z = np.nan_to_num(Z)
# Make the norm
norm = matplotlib.colors.Normalize(vmin = np.min(Z), vmax = np.max(Z), clip = False)
# Plot with the norm
surf = ax.plot_surface(X, Y, Z,rstride=1, cstride=1, norm=norm, alpha=1,cmap=cm.jet, linewidth=0)
fig.colorbar(surf, shrink=0.5, aspect=5)
plt.show()
The norm works the same way for the "imshow" command.
As JoshAdel noted in a comment (credit belongs to him), it appears that the surface plot is improperly ranging the colormap when a NaN is in the Z array. A simple work-around is to simply convert the NaN's to zero or very large or very small numbers so that the colormap can be normalized to the z-axis range.
from matplotlib import cm
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import axes3d, Axes3D
import pylab as p
vima=0.5
fig = plt.figure()
ax = fig.gca(projection='3d')
X = np.arange(0, 16.67, vima)
Y = np.arange(0, 12.5, vima)
X, Y = np.meshgrid(X, Y)
Z = np.sqrt(((1.2*Y+0.6*X)**2+(0.2*Y+1.6*X)**2)/(0.64*Y**2+0.36*X**2))
Z = np.nan_to_num(Z) # added this line
surf = ax.plot_surface(X, Y, Z,rstride=1, cstride=1, alpha=1,cmap=cm.jet, linewidth=0)
fig.colorbar(surf, shrink=0.5, aspect=5)
plt.show()
Replying to an old question, I know, but the answers posted were at least in my case somewhat unsatisfactory. For those still stumbling here, I give a solution that worked for me.
Firstly, I did not want use zeros to replace NaNs, as for me they represent points with missing or undefined data. I'd rather not have anything plotted at these points. Secondly, the whole z range of my data was way above zero, so dotting the plot with zeros would result in an ugly and badly scaled plot.
Solution given by leifdenby was quite close, so +1 for that (though as pointed out, the explicit normalisation does not add to the earlier solution). I just dropped the NaN-to-zero replacement, and used the functions nanmin and nanmax instead of min and max in the color scale normalisation. These functions give the min and max of an array but simply ignore all NaNs. The code now reads:
# Added colors to the matplotlib import list
from matplotlib import cm, colors
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import axes3d, Axes3D
import pylab as p
vima=0.5
fig = plt.figure()
ax = fig.gca(projection='3d')
X = np.arange(0, 16.67, vima)
Y = np.arange(0, 12.5, vima)
X, Y = np.meshgrid(X, Y)
Z = np.sqrt(((1.2*Y+0.6*X)**2+(0.2*Y+1.6*X)**2)/(0.64*Y**2+0.36*X**2))
# MAIN IDEA: Added normalisation using nanmin and nanmax functions
norm = colors.Normalize(vmin = np.nanmin(Z),
vmax = np.nanmax(Z))
# Added the norm=norm parameter
surf = ax.plot_surface(X, Y, Z,rstride=1, cstride=1, alpha=1, norm=norm, cmap=cm.jet, linewidth=0)
fig.colorbar(surf, shrink=0.5, aspect=5)
plt.show()
Running this, I get a correctly scaled plot, with the (0, 0) datapoint missing. This is also the behaviour that I find most preferable, as the limit (x, y) to (0, 0) does not seem to exist for the function in question.
This has been my first contribution to StackOverflow, I hope it was a good one (wink).