Is it possible to put the color diagram (which is now on the right side of the original figure) on the top of the figure?
My code:
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d.axes3d import Axes3D, get_test_data
from matplotlib import cm
import numpy as np
# set up a figure twice as wide as it is tall
fig = plt.figure(figsize=plt.figaspect(0.5))
#===============
# First subplot
#===============
# set up the axes for the first plot
ax = fig.add_subplot(1, 2, 1, projection='3d')
# plot a 3D surface like in the example mplot3d/surface3d_demo
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_zlim(-1.01, 1.01)
fig.colorbar(surf, shrink=0.5, aspect=10)
fig.savefig('64bit.png')
You have to add additional axes (add_axes) to put your colorbar at the desired position:
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d.axes3d import Axes3D, get_test_data
from matplotlib import cm
from mpl_toolkits.axes_grid1 import make_axes_locatable
import numpy as np
# set up a figure twice as wide as it is tall
fig = plt.figure(figsize=plt.figaspect(0.5))
#===============
# First subplot
#===============
# set up the axes for the first plot
ax = fig.add_subplot(1, 2, 1, projection='3d')
# plot a 3D surface like in the example mplot3d/surface3d_demo
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_zlim(-1.01, 1.01)
# position of colorbar
# where arg is [left, bottom, width, height]
cax = fig.add_axes([0.15, .87, 0.35, 0.03])
fig.colorbar(surf, orientation='horizontal', cax=cax)
plt.show()
Yes it is, there are multiple answers here in the site showing you how to move the colorbar around like this one: positioning the colorbar
In your case, you want to combine that with the orientation argument. As far as I know, there is no easy way of just placing the colorbar to the top of your figure automatically, you will have to place it manually. Here is my code that replaces your fig.colorbar(surf, shrink=0.5, aspect=10):
cbax = fig.add_axes([0.1, 0.89, 0.5, 0.05])
fig.colorbar(surf, orientation="horizontal", cax=cbax)
The numbers in the list describe some characteristics of the colorbar which are [left, bottom, width, height] as mentioned in the other answer that I have attached.
These numbers came out nicely for your plot, feel free to change them to your liking.
In order to get the colorbar on top of the plot you need to create some axes, designated to host the colorbar.
This can either be done manually by placing a new axes at some given position in figure coordinates,
cax = fig.add_axes([0.2,0.8,0.3,.05])
fig.colorbar(surf, cax=cax, orientation="horizontal")
or, by using a subplot grid (gridspec), which is shown in the following:
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d.axes3d import Axes3D
import matplotlib.gridspec as gridspec
import numpy as np
x = np.arange(-5, 5, 0.25)
X, Y = np.meshgrid(x,x)
Z = np.sin(np.sqrt(X**2 + Y**2))
gs = gridspec.GridSpec(2, 2, height_ratios=[0.05,1])
fig = plt.figure()
ax = fig.add_subplot(gs[1,0], projection='3d')
cax = fig.add_subplot(gs[0,0])
surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap="coolwarm",
linewidth=0, antialiased=False, vmin=-1, vmax=1)
fig.colorbar(surf, cax=cax, orientation="horizontal", ticks=[-1,0,1])
plt.show()
For a method which avoids having to manually create new axes and instead allows us to keep the colorbar linked to an existing plot axis, we can use the location keyword (method adapted initially from here).
The location argument is meant to be used on colorbars which reference multiple axes in a list (and will throw an error if colorbar is given only one axis), but if you simply put your one axis in a list, it will allow you to use the argument. You can use the following code as an example:
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure()
ax = fig.add_subplot(111)
axp = ax.imshow(np.random.randint(0, 100, (100, 100)))
cb = plt.colorbar(axp,ax=[ax],location='top')
plt.show()
which yields this plot. From here, you can edit the colorbar using the typical methods (pad, shrink, etc.) to further tune the appearance of your plot.
Fair warning, I haven't seen this method used many other places and it could be less robust than going through the extra steps of creating a new axis for your colorbar.
Related
I would like to create a smooth plot in Python. Generally, you can make a plot that looks like the one below:
Source
While this is a nice image, it looks as though it's made out of a mesh of polygons, making it look "coarse." In my own plots I have tried increasing the resolution of my function to no avail. I am trying to achieve the following "smooth" look:
Source
How do I achieve this?
Maybe you were missing rcount and ccount?
# This import registers the 3D projection, but is otherwise unused.
from mpl_toolkits.mplot3d import Axes3D # noqa: F401 unused import
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, 0.05)
Y = np.arange(-5, 5, 0.05)
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, Y, Z, cmap=cm.coolwarm,
linewidth=0, antialiased=False, rcount=200, ccount=200)
# Customize the z axis.
ax.set_zlim(-1.01, 1.01)
ax.zaxis.set_major_locator(LinearLocator(10))
ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))
# Add a color bar which maps values to colors.
fig.colorbar(surf, shrink=0.5, aspect=5)
plt.show()
A grid is interpolated with scipy's griddata() and contains values and NaNs. However, when the grid is plotted with mplot3d's plot_surface() there are gaps in the surface (see upper plot). The lower the view height, the more of such gaps are rendered. When the grid is plotted in 2D with imshow() there are no gaps (see lower plot).
Comparison plot_surface and imshow
Here is a close-up:
I couldn't find an answer to this problem and I am wondering whether this is a known issue.
This small example reproduces the problem: (data.txt)
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
Z = np.loadtxt('data.txt')
X = np.linspace(0,3,301)
Y = np.linspace(0,3,301)
X = np.array([X,]*(301)).transpose()
Y = np.array([Y,]*(301))
fig = plt.figure(figsize=(15,15), dpi=100)
ax = fig.add_subplot(2, 1, 1, projection='3d')
plt.hold(True)
ax.view_init(40,300)
surf = ax.plot_surface(X, Y, Z, cmap='jet', rstride=1, cstride=1, antialiased=False, shade=False, alpha=1.0, linewidth=0, vmin=0.25, vmax=0.35);
ax.invert_yaxis()
ax.dist = 11
ax = fig.add_subplot(2, 1, 2)
plt.hold(True)
plt.imshow(Z, cmap='jet', vmin=0.25, vmax=0.35, origin='lower');
plt.show()
I am trying to have two data values drawn on the same set of 3d axis. The value of the two data sets can differ by 1 or 2 orders of magnitude. As a result I want two Z axis similar to the twinx or twiny commands for 2d plots. A rough example is shown in the code below
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from matplotlib import cm
result=[['1', '2', '3'],
['4', '5', '6'],
['7', '8', '9']]
result = np.array(result, dtype=np.int)
fig=plt.figure(figsize=(5, 5), dpi=150)
ax1=fig.add_subplot(111, projection='3d')
xlabels = np.array(['data1x', 'data2x', 'data3x'])
xpos = np.arange(xlabels.shape[0])
ylabels = np.array(['data1y','data2y','data3y'])
ypos = np.arange(ylabels.shape[0])
xposM, yposM = np.meshgrid(xpos, ypos, copy=False)
zpos=result
zpos = zpos.ravel()
dx=0.5
dy=0.5
dz=zpos
ax1.w_xaxis.set_ticks(xpos + dx/2.)
#ax1.w_xaxis.set_ticklabels(xlabels)
ax1.w_yaxis.set_ticks(ypos + dy/2.)
#ax1.w_yaxis.set_ticklabels(ylabels)
values = np.linspace(0.2, 1., xposM.ravel().shape[0])
colors = cm.rainbow(values)
ax1.bar3d(xposM.ravel(), yposM.ravel(), dz*0, dx, dy, dz, color=colors)
X = np.arange(-1, 1, 0.25)
Y = np.arange(-1, 1, 0.25)
X, Y = np.meshgrid(X, Y)
R = np.sqrt(X**2 + Y**2)
Z = 100*np.sin(R)
surf = ax1.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.coolwarm,
linewidth=0, antialiased=False)
plt.show()
How can i change the z scale for one of the plots so I can better see the features of both?
You can scale the data from one of the arrays to correspond to the magnitude of the other. Then, when plotted, the z-extend will be comparable.
In order to inlcude a colorbar for the scaled data that shows the original data range, I used a second set of axis.
Import the following additional libraries:
import matplotlib as mpl # for general access to the colorbar class
import matplotlib.gridspec as gridspec # to set up an axis-grid
Set up your axis:
gs = gridspec.GridSpec(1, 2,
width_ratios=[20,1],
)
ax1 = fig.add_subplot(gs[0], projection='3d')
ax2 = fig.add_subplot(gs[1])
You can adjust the width-ratios to change the width of the colorbar (given by ax2) relative to the data plot (given by ax1).
use numpy's amax to determine the maxima of your two data sets for the scaling (which can be taken care of when calling the surface plot:
surf = ax1.plot_surface(X, Y, Z/np.amax(Z)*np.amax(zpos),
rstride=1, cstride=1, cmap=cm.coolwarm,
linewidth=0, antialiased=False)
using Z/np.amax(Z)*np.amax(zpos) will scale your Z-data to the magnitude of zpos.
Now, plot a colorbar on the second axis:
cbar = mpl.colorbar.ColorbarBase(ax2, cmap = cm.coolwarm,
norm=mpl.colors.Normalize(vmin=np.amin(Z), vmax=np.amax(Z)))
To ensure that the colorbar covers the range of the Z-date, use the norm functionality.
This is your plot:
You can place second - transparent - axes over the first ones and use shared x and y axes.
Set up a second set of axes, including sharex and sharey:
ax2 = fig.add_axes(ax1.get_position(), projection='3d',
sharex=ax1, sharey=ax1)
Make the background transparent and remove the actual axes lines (and ticks, etc.):
ax2.set_axis_off()
ax2.patch.set_facecolor('none')
Plot as before, but specify the second axes (ax2), you can also plot a colorbar without any additional effort:
surf = ax2.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.coolwarm,
linewidth=0, antialiased=False)
cb = fig.colorbar(surf,ax=ax1)
Set xlim and ylim to avoid any misalignment (this can probably be linked to the data):
ax1.set_xlim([-1.0, 3.0])
ax1.set_ylim([-1.0, 3.0])
The resulting plot will be:
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,
)
Is it possible to disable the perspective when plotting in mplot3d, i.e. to use the orthogonal projection?
This is now official included since matplot version 2.2.2 Whats new | github
So for plotting a perspective orthogonal plot you have to add proj_type = 'ortho' then you should have something like that:
fig.add_subplot(121, projection='3d', proj_type = 'ortho')
Example Picture
]2
Example is taken from the official example script and edited
'''
======================
3D surface (color map)
======================
Demonstrates plotting a 3D surface colored with the coolwarm color map.
The surface is made opaque by using antialiased=False.
Also demonstrates using the LinearLocator and custom formatting for the
z axis tick labels.
'''
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
# 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.
fig = plt.figure(figsize=(16,4))
ax.view_init(40, 60)
ax = fig.add_subplot(121, projection='3d')
surf = ax.plot_surface(X, Y, Z, cmap=cm.coolwarm,
linewidth=0, antialiased=False)
ax.set_zlim(-1.01, 1.01)
ax.zaxis.set_major_locator(LinearLocator(10))
ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))
ax = fig.add_subplot(122, projection='3d', proj_type = 'ortho')
# Plot the surface.
surf = ax.plot_surface(X, Y, Z, cmap=cm.viridis, linewidth=0, antialiased=False)
ax.set_zlim(-1.01, 1.01)
ax.zaxis.set_major_locator(LinearLocator(10))
ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))
plt.show()
NOTE: This has been updated see this answer instead.
Sort of, you can run this snippet of code before you plot:
import numpy
from mpl_toolkits.mplot3d import proj3d
def orthogonal_proj(zfront, zback):
a = (zfront+zback)/(zfront-zback)
b = -2*(zfront*zback)/(zfront-zback)
return numpy.array([[1,0,0,0],
[0,1,0,0],
[0,0,a,b],
[0,0,0,zback]])
proj3d.persp_transformation = orthogonal_proj
It is currently an open issue found here.