How can we make spheres of radius R centered at given coordinates(x,y,z). Like if there are 10 set of coordinates for the centers of the spheres and correspondingly 10 different values of the radii. How can we plot it in python ? Is there any way to do this in python ,like in MATLAB this can be done using surf command. In python how can we achieve this ?
This will solve your problem
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
list_center = [(1,2,3),(-4,-5,6), (5,5,6)]
list_radius = [1,2,1]
def plt_sphere(list_center, list_radius):
for c, r in zip(list_center, list_radius):
ax = fig.gca(projection='3d')
# draw sphere
u, v = np.mgrid[0:2*np.pi:50j, 0:np.pi:50j]
x = r*np.cos(u)*np.sin(v)
y = r*np.sin(u)*np.sin(v)
z = r*np.cos(v)
ax.plot_surface(x-c[0], y-c[1], z-c[2], color=np.random.choice(['g','b']), alpha=0.5*np.random.random()+0.5)
fig = plt.figure()
plt_sphere(list_center, list_radius)
Related
I have a 3d plot made using matplotlib. I now want to fill the vertical space between the drawn line and the x,y axis to highlight the height of the line on the z axis. On a 2d plot this would be done with fill_between but there does not seem to be anything similar for a 3d plot. Can anyone help?
here is my current code
from stravalib import Client
import matplotlib as mpl
import numpy as np
import matplotlib.pyplot as plt
... code to get the data ....
mpl.rcParams['legend.fontsize'] = 10
fig = plt.figure()
ax = fig.gca(projection='3d')
zi = alt
x = df['x'].tolist()
y = df['y'].tolist()
ax.plot(x, y, zi, label='line')
ax.legend()
plt.show()
and the current plot
just to be clear I want a vertical fill to the x,y axis intersection NOT this...
You're right. It seems that there is no equivalent in 3D plot for the 2D plot function fill_between. The solution I propose is to convert your data in 3D polygons. Here is the corresponding code:
import math as mt
import matplotlib.pyplot as pl
import numpy as np
import random as rd
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
# Parameter (reference height)
h = 0.0
# Code to generate the data
n = 200
alpha = 0.75 * mt.pi
theta = [alpha + 2.0 * mt.pi * (float(k) / float(n)) for k in range(0, n + 1)]
xs = [1.0 * mt.cos(k) for k in theta]
ys = [1.0 * mt.sin(k) for k in theta]
zs = [abs(k - alpha - mt.pi) * rd.random() for k in theta]
# Code to convert data in 3D polygons
v = []
for k in range(0, len(xs) - 1):
x = [xs[k], xs[k+1], xs[k+1], xs[k]]
y = [ys[k], ys[k+1], ys[k+1], ys[k]]
z = [zs[k], zs[k+1], h, h]
#list is necessary in python 3/remove for python 2
v.append(list(zip(x, y, z)))
poly3dCollection = Poly3DCollection(v)
# Code to plot the 3D polygons
fig = pl.figure()
ax = Axes3D(fig)
ax.add_collection3d(poly3dCollection)
ax.set_xlim([min(xs), max(xs)])
ax.set_ylim([min(ys), max(ys)])
ax.set_zlim([min(zs), max(zs)])
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_zlabel("z")
pl.show()
It produces the following figure:
I hope this will help you.
Let's say I have a 3D plane equation:
ax+by+cz=d
How can I plot this in python matplotlib?
I saw some examples using plot_surface, but it accepts x,y,z values as 2D array. I don't understand how can I convert my equation into the parameter inputs to plot_surface or any other functions in matplotlib that can be used for this.
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
a,b,c,d = 1,2,3,4
x = np.linspace(-1,1,10)
y = np.linspace(-1,1,10)
X,Y = np.meshgrid(x,y)
Z = (d - a*X - b*Y) / c
fig = plt.figure()
ax = fig.gca(projection='3d')
surf = ax.plot_surface(X, Y, Z)
I am trying to rotate a figure horizontally in python. With the mouse I am only able to rotate through one degree of freedom as well as being able to spin the figure, for example this is what I can do at the momement:
These movements are only vertical. I would like to be able to rotate the figure in all directions, for example:
This would allow me to put the figure on its side etc. However at the moment I cannot do this, all I can do it rotate along one degree of freedom.
Here is my code:
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
import numpy as np
import math
fig = plt.figure()
ax = fig.gca(projection='3d')
nphi,nz= 13, 101
r=1
phi = np.linspace(0,360, nphi)/180.0*np.pi
z= np.linspace(0,350,nz)
cols=[]
verts2 = []
for i in range(len(phi)-1):
cp0= r*np.cos(phi[i])
cp1= r*np.cos(phi[i+1])
sp0= r*np.sin(phi[i])
sp1= r*np.sin(phi[i+1])
for j in range(len(z)-1):
z0=z[j]
z1=z[j+1]
verts=[]
verts.append((cp0, sp0, z0))
verts.append((cp1, sp1, z0))
verts.append((cp1, sp1, z1))
verts.append((cp0, sp0, z1))
verts2.append(verts)
col=plt.cm.Blues(0.4)
cols.append(col)
poly3 = Poly3DCollection(verts2, facecolor=cols ,edgecolor = "red" )
poly3.set_alpha(0.5)
ax.add_collection3d(poly3)
ax.set_xlabel('X')
ax.set_xlim3d(-3, 3)
ax.set_ylabel('Y')
ax.set_ylim3d(-3, 3)
ax.set_zlabel('Z')
ax.set_zlim3d(0, 300)
plt.axis('off')
ax.axes.get_xaxis().set_visible(False)
ax.axes.get_yaxis().set_visible(False)
plt.show()
Unfortunately, I don't think you can; From the documentation:
‘elev’ stores the elevation angle in the z plane. ‘azim’ stores the
azimuth angle in the x,y plane
So you've not actually got 3 degrees of freedom here, because you can only rotate in the xy plane, rather than around each axis independently.
It's a bit clearer if you display the axes:
I have some Fortran code which outputs the polar coordinates of a grid on the surface of a sphere in theta, phi format. It also outputs a value associated with each of these points (specifically meant to represent the voltage at that point on the sphere's surface).
Now I want to read this data into Python, plot a sphere, and then colour it according to the voltage data values. I know how to do this for a latitude-longitude grid, but my grid points are not ordered in any specific way.
The code I'm trying is as follows:
import matplotlib.pyplot as plt
from matplotlib import cm, colors
from mpl_toolkits.mplot3d import Axes3D
import option_d
import numpy as np
# Create a sphere
r = 1.0
pi = np.pi
cos = np.cos
sin = np.sin
#Read in grid points
data = np.genfromtxt('grid.txt')
phi, theta = np.hsplit(data, 2)
#Convert grid points to cartesian
x = r*sin(phi)*cos(theta)
y = r*sin(phi)*sin(theta)
z = r*cos(phi)
#Import data from initial state
colorfunction = np.genfromtxt('sphere_init.txt')
print np.shape(colorfunction)
#Normalise the colour map to the initial data
newcm = option_d.test_cm
norm=colors.Normalize(vmin = -np.max(colorfunction), vmax = np.max(colorfunction), clip = False)
#Plot the surface
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(
x,y,z,rstride=1,cstride=1,cmap=newcm,facecolors=newcm(norm(colorfunction)))
#Set axes and display or save
ax.set_aspect("equal")
plt.tight_layout()
plt.show()
The file 'grid.txt' contains two columns, each 770 in length, representing the phi, theta coordinates of each point. The file 'sphere_init.txt' contains a single column of length 770, which are the corresponding data values. However, this does not work - it just throws error messages at me. Is it even possible to plot a sphere from disordered grid points? Any help much appreciated. Thanks.
Edit
Here is the error message:
Traceback (most recent call last):
File "sphere.py", line 43, in <module>
x,y,z,rstride=1,cstride=1, cmap=newcm,facecolors=newcm(norm(colorfunction)))
File "/usr/lib/pymodules/python2.7/mpl_toolkits/mplot3d/axes3d.py", line 1611, in plot_surface
colset.append(fcolors[rs][cs])
IndexError: index out of bounds
I believe I have solved my problem. I read in my irregular grid data, and then also create a regular latitude-longitude grid. I then interpolate from the irregular grid to the lat-long grid:
import matplotlib.pyplot as plt
import matplotlib.mlab as ml
from matplotlib import cm, colors
from mpl_toolkits.mplot3d import Axes3D
import option_d
import numpy as np
import time
#Read in lebedev grid points
data = np.genfromtxt('grid.txt')
u, v = np.hsplit(data, 2)
phi, theta = u[:,0], v[:,0]
#Import data from initial state
colorfunction = np.genfromtxt('sphere_init.txt')
#Generate a lat-long grid to interpolate on
p = np.linspace(0,np.pi, 770)
t = np.linspace(-np.pi, np.pi, 770)
p, t = np.meshgrid(p, t)
#Interpolate using delaunay triangularization
zi = ml.griddata(phi, theta, colorfunction, p, t)
#Convert the lat-long grid points to cartesian
x = np.sin(p)*np.cos(t)
y = np.sin(p)*np.sin(t)
z = np.cos(p)
#Normalize the interpolated colourfunction
#Use fancy new colourmap
newcm = option_d.test_cm
norm=colors.Normalize(vmin = -np.max(zi), vmax = np.max(zi), clip = False)
#Plot the surface
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(
x,y,z,rstride=1,cstride=1, cmap=newcm,facecolors=newcm(norm(zi)))
#Display
ax.set_aspect("equal")
ax.set_xlim([-1,1])
ax.set_ylim([-1,1])
ax.set_zlim([-1,1])
plt.tight_layout()
plt.show()
Edit
I have run into a new problem with this method. It causes a chunk to be missing from the back of my sphere:
Any ideas why?
As the title suggests, I'm trying to plot a Basemap map on the z=0 surface of a matplotlib.mplot3d lineplot. I know the Axes3D object is capable of plotting on the z=0 surface (via Axes3D.plot, Axes3D.scatter, etc.), but I can't figure out how to do so with a Basemap object. Hopefully the code below shows what I need clearly enough. Any ideas would be much appreciated!
import matplotlib.pyplot as pp
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.basemap import Basemap
# make sample data for 3D lineplot
z = np.linspace(-2, 2, 100)
r = z**2 + 1
x = r * np.sin(theta)
y = r * np.cos(theta)
# make the 3D line plot
FIG = ct.pp.figure()
AX = Axes3D(FIG)
AX.plot(x, y, z, '-b')
# make the 2D basemap
### NEEDS TO SOMEHOW BE AT z=0 IN FIG
M = ct.Basemap(projection='stere', width=3700e3, height=2440e3,
lon_0=-5.0, lat_0=71.0, lat_ts=71.0,
area_thresh=100, resolution='c')
PATCHES = M.fillcontinents(lake_color='#888888', color='#282828')
Just add your map as a 3d collection to the Axes3D instance:
import numpy as np
import matplotlib.pyplot as pp
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.basemap import Basemap
theta = np.linspace(-4 * np.pi, 4 * np.pi, 100)
z = np.linspace(-500, 500, 100)
r = z**2 + 1
x = r * np.sin(theta)
y = r * np.cos(theta)
FIG = pp.figure()
AX = Axes3D(FIG)
AX.plot(x, y, z, '-b')
M = Basemap(projection='stere', width=3700e3, height=2440e3,
lon_0=-5.0, lat_0=71.0, lat_ts=71.0,
area_thresh=100, resolution='c')
AX.add_collection3d(M.drawcoastlines())
AX.grid(True)
pp.draw()
pp.show()
AX.add_collection3d(M.drawcoastlines())
works but
PATCHES = M.fillcontinents(lake_color='#888888', color='#282828')
does not work.
As soon as you add color fill you get an error similar to: "AttributeError: 'Polygon' object has no attribute 'do_3d_projection'"
M.fillcontinents(lake_color='#888888', color='#282828')`
returns an array of Polygons, not one of the inputs required by add_collection(). collect.PatchCollection() does not seem to work either.
So what do you use to add `M.fillcontinents(lake_color='#888888', color='#282828') to a 3D plot?