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I have coordinates inside a sphere and I want to plot the whole 3D sphere as scattered points, but alongside I would also like to plot a 2D disk.
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(font_scale=1)
sns.set_style("whitegrid")
from matplotlib import rc
rc('text', usetex=True)
rc('font', family='serif')
from mpl_toolkits.mplot3d import Axes3D
fig, (ax1, ax3) = plt.subplots(1, 2, figsize=(900 / 106, 600 / 106), constrained_layout = False)
# 2D
radius = np.random.uniform(size=5000)
phi = np.random.uniform(size=5000) * 2 * np.pi
x = radius * np.cos(phi)
y = radius * np.sin(phi)
plot = ax1.scatter(x, y, s = 10, marker='.')
ax1.set_xlabel('$x$')
ax1.set_ylabel('$y$')
ax1.set_aspect('equal')
# 2D
radius = np.random.uniform(size=5000)
phi = np.random.uniform(size=5000) * 2 * np.pi
alfa = np.random.uniform(size=5000) * np.pi
x = radius * np.cos(phi) * np.sin(alfa)
y = radius * np.sin(phi) * np.sin(alfa)
z = radius * np.cos(alfa)
ax3 = Axes3D(fig)
plot = ax3.scatter(x, y, z, s = 1, marker='.')
ax3.set_xlabel('$x$')
ax3.set_ylabel('$y$')
ax3.set_zlabel('$z$')
#ax3.view_init(30, 240)
ax3.set_aspect('equal', 'box')
#fig.colorbar(plot, shrink = 0.9, ticks = np.linspace(0, 1, 6), ax = ax3)
fig.tight_layout()
Now the problem with this code above is that the output is not nearly as what I would expect (the 3D is plotted on top).
Any ideas how to do this properly?
Related
I am plotting a polar 2d histogram in Python 3.7 using matplotlib and the following code (adapted from this answer to another question):
import numpy as np
import matplotlib.pyplot as plt
# input data
azimut = np.random.rand(3000)*2*np.pi
radius = np.random.rayleigh(9, size=3000)
# binning
rbins = np.linspace(0, radius.max(), 10)
abins = np.linspace(0, 2*np.pi, 10)
# histogram
hist, _, _ = np.histogram2d(azimut, radius, bins=(abins, rbins))
A, R = np.meshgrid(abins, rbins)
# plot
fig, ax = plt.subplots(subplot_kw=dict(projection="polar"))
pc = ax.pcolormesh(A, R, hist.T, cmap='inferno')
fig.colorbar(pc)
plt.show()
To produce the following plot:
Due to the larger bin sizes, the polar projection is appearing more like a polygon rather than a circle.
Is there any way to plot this so that the bins appear curved rather than straight? I.E. so that the plot is always circular, regardless of the bin size and doesn't become polygon-like when bins are larger?
A matplotlib solution would be preferable, but others are welcome.
Thanks very much for any help.
To get a rounded look, the mesh can be subdivided into more angles. Note that np.linspace(0, 2 * np.pi, 10) creates 9 bins (and 10 boundaries). For the subdivided mesh you need e.g. 90 bins, so 91 boundaries. The histogram values need to be repeated by the same factor.
The code below uses a different colormap for debugging purposes. An optional grid highlights the original boundaries.
import numpy as np
import matplotlib.pyplot as plt
# input data
azimut = np.random.rand(3000) * 2 * np.pi
radius = np.random.rayleigh(9, size=3000)
# binning
rbins = np.linspace(0, radius.max(), 7)
abins = np.linspace(0, 2 * np.pi, 10)
subdivs = 10
abins2 = np.linspace(0, 2 * np.pi, (len(abins) - 1) * subdivs + 1)
# histogram
hist, _, _ = np.histogram2d(azimut, radius, bins=(abins, rbins))
A1, R1 = np.meshgrid(abins, rbins)
A2, R2 = np.meshgrid(abins2, rbins)
fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(10, 4), subplot_kw=dict(projection="polar"))
# plot with original mesh
pc1 = ax1.pcolormesh(A1, R1, hist.T, cmap='hsv')
ax1.tick_params(axis='y', labelcolor='white')
ax1.set_xticks(abins[:-1])
fig.colorbar(pc1, ax=ax1)
# plot with subdivided mesh
pc2 = ax2.pcolormesh(A2, R2, np.repeat(hist.T, subdivs, axis=1), cmap='hsv')
ax2.tick_params(axis='y', labelcolor='white')
ax2.set_xticks(abins[:-1])
ax2.set_yticks(rbins, minor=True)
ax2.grid(axis='x', color='white')
ax2.grid(axis='y', which='minor', color='white')
fig.colorbar(pc2, ax=ax2)
plt.tight_layout()
plt.show()
I am trying to plot a curve on a sphere but I can not plot them at the same time. I identified some points with Euclidean norm 10 for my curve, and some other points to plot the sphere of radius 10, respectively as following.
Points for curve:
random_numbers=[]
basevalues=np.linspace(-0.9,0.9,100)
for i in range(len(basevalues)):
t=random.random()
random_numbers.append(t*10)
xvalues=[random_numbers[i]*np.cos(basevalues[i]) for i in range(len(basevalues))]
yvalues=[random_numbers[i]*np.sin(basevalues[i]) for i in range(len(basevalues))]
zvalues=[np.sqrt(100-xvalues[i]**2-yvalues[i]**2)for i in range(len(basevalues))]
Where xvalues, yvalues and zvalues are our points Euclidean components.
Points for sphere:
u = np.linspace(0, 2 * np.pi, 100)
v = np.linspace(0, np.pi, 100)
x = 10 * np.outer(np.cos(u), np.sin(v))
y = 10 * np.outer(np.sin(u), np.sin(v))
z = 10 * np.outer(np.ones(np.size(u)), np.cos(v))
Where x,y and z are Euclidean components of sphere points.
My problem:
When I try to plot the curve, without plotting sphere, it works. But when I plot them together, then it just return the sphere.
The whole code is the following:
import matplotlib.pyplot as plt
import numpy as np
import random
#Curve points
random_numbers=[]
basevalues=np.linspace(-0.9,0.9,100)
for i in range(len(basevalues)):
t=random.random()
random_numbers.append(t*10)
xvalues=[random_numbers[i]*np.cos(basevalues[i]) for i in range(len(basevalues))]
yvalues=[random_numbers[i]*np.sin(basevalues[i]) for i in range(len(basevalues))]
zvalues=[np.sqrt(100-xvalues[i]**2-yvalues[i]**2)for i in range(len(basevalues))]
# Sphere points
u = np.linspace(0, 2 * np.pi, 100)
v = np.linspace(0, np.pi, 100)
x = 10 * np.outer(np.cos(u), np.sin(v))
y = 10 * np.outer(np.sin(u), np.sin(v))
z = 10 * np.outer(np.ones(np.size(u)), np.cos(v))
# Plot the surface and curve
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
circ = ax.plot(xvalues,yvalues,zvalues, color='green',linewidth=1)
sphere=ax.plot_surface(x, y, z, color='r')
ax.set_zlim(-10, 10)
plt.xlabel("X axes")
plt.ylabel("Y axes")
plt.show()
What I want to occur:
I would like to plot the curve on the sphere, but it dose not happen in my code. I appreciate any hint.
If you use a "." option for plotting the points, like
circ = ax.plot(xvalues, yvalues,zvalues, '.', color='green', linewidth=1)
you will see the points on top of the sphere for certain viewing angles, but disappear sometimes even if they are in front of the sphere. This is a known bug explained in the matplotlib documentation:
My 3D plot doesn’t look right at certain viewing angles:
This is probably the most commonly reported issue with mplot3d. The problem is that – from some viewing angles – a 3D object would appear in front of another object, even though it is physically behind it. This can result in plots that do not look “physically correct.”
In the same doc, the developers recommend to use Mayavi for more advanced use of 3D plots in Python.
Using spherical coordinates, you can easily do that:
## plot a circle on the sphere using spherical coordinate.
import numpy as np
import matplotlib.pyplot as plt
# a complete sphere
R = 10
theta = np.linspace(0, 2 * np.pi, 1000)
phi = np.linspace(0, np.pi, 1000)
x_sphere = R * np.outer(np.cos(theta), np.sin(phi))
y_sphere = R * np.outer(np.sin(theta), np.sin(phi))
z_sphere = R * np.outer(np.ones(np.size(theta)), np.cos(phi))
# a complete circle on the sphere
x_circle = R * np.sin(theta)
y_circle = R * np.cos(theta)
# 3d plot
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(x_sphere, y_sphere, z_sphere, color='blue', alpha=0.2)
ax.plot(x_circle, y_circle, 0, color='green')
plt.show()
I am new to animations with matplotlib, and I am trying to animate a shrinking ellipsoid.
Specifically, I want to animate an ellipsoid that shrinks its axes proportionally. (Mathematically, I'm looking for a shrinking factor of e^(-t) multiplied to each axis, where t is time.)
I have made a function of time t that outputs a static ellipsoid with the code below:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from mpl_toolkits.mplot3d import Axes3D
def param_surface(t):
fig = plt.figure(figsize = (10, 10))
ax = fig.add_subplot(111, projection='3d')
u = np.linspace(0, 2 * np.pi, 100)
v = np.linspace(0, np.pi, 100)
x = axis_a(4 * t / 50) * np.outer(np.cos(u), np.sin(v))
y = axis_a(4 * t / 50) * np.outer(np.sin(u), np.sin(v))
z = axis_b(4 * t / 50) * np.outer(np.ones(np.size(u)), np.cos(v))
return(ax.plot_surface(x, y, z, rstride = 4, cstride = 4))
I have seen animations (such as the one here: https://pythonmatplotlibtips.blogspot.com/2018/11/animation-3d-surface-plot-funcanimation-matplotlib.html) that allow you to animate 3D plots in which z is defined as a function of x, y. However, in the case of the shrinking ellipsoid, I need to use spherical coordinates, which complicates things.
Can someone explain what to add to my code to go from static to the desired shrinking animation?
I want to plot a quantity which is given on a parametric surface in 3d space (for example the temperature distribution on a sphere). I can plot a parametric 3D plot of the sphere (as a function of the two parameters phi and theta) but I don't know how to make the colors of the polygons making up the sphere depend on the parameters theta and phi (normally, the color of a polygon is simply determined by the z-Position of the polygon).
Here's a basic example which plots a torus with colormap:
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
angle = np.linspace(0, 2 * np.pi, 32)
theta, phi = np.meshgrid(angle, angle)
r, R = .25, 1.
X = (R + r * np.cos(phi)) * np.cos(theta)
Y = (R + r * np.cos(phi)) * np.sin(theta)
Z = r * np.sin(phi)
# Display the mesh
fig = plt.figure()
ax = fig.gca(projection = '3d')
ax.set_xlim3d(-1, 1)
ax.set_ylim3d(-1, 1)
ax.set_zlim3d(-1, 1)
ax.plot_surface(X, Y, Z, rstride = 1, cstride = 1,cmap="hot")
plt.show()
However, the colors of the files are given by the z position of the tile, I want the color to be given by a function f(x,y).
Does anyone know how I can achieve this dependency in Matplotlib?
Thanks very much!
Ok, if anyone else is looking for a solution to this problem here's a possible solution:
The colors of the individual faces making up the surface plot can be set using the keyword argument facecolors. The following code will use the function X**2+Y**2 for coloring the faces of the parametric surface:
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.colors as mcolors
from matplotlib import cm
import matplotlib.pyplot as plt
import numpy as np
# Generate torus mesh
angle = np.linspace(0, 2 * np.pi, 32)
theta, phi = np.meshgrid(angle, angle)
r, R = .25, 1.
X = (R + r * np.cos(phi)) * np.cos(theta)
Y = (R + r * np.cos(phi)) * np.sin(theta)
Z = r * np.sin(phi)
colorfunction=(X**2+Y**2)
norm=mcolors.Normalize(colorfunction.min(),colorfunction.max())
# Display the mesh
fig = plt.figure(figsize=(7, 7))
ax = fig.add_subplot(projection='3d')
ax.set_xlim3d(-1, 1)
ax.set_ylim3d(-1, 1)
ax.set_zlim3d(-1, 1)
ax.plot_surface(X, Y, Z, rstride = 1, cstride = 1, facecolors=cm.jet(norm(colorfunction)))
plt.show()
I am running this sample script, with the following modifications:
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')
ax.legend()
ax.set_xlabel('$X$', fontsize=20, rotation=150)
ax.set_ylabel('$Y$')
ax.set_zlabel(r'$\gamma$', fontsize=30, rotation=60)
ax.yaxis._axinfo['label']['space_factor'] = 3.0
plt.show()
How do I adjust the axis ticks to that of my choosing? I.e., how would I get the z-axis to only label 2, 0, and -2, and in the font size that I want? I know how to do this in 2D but not 3D.
The script above produces the following:
Why is the x-axis label distorted, which I wanted to do with this script, but not the z-axis label (gamma)? This does not make sense. I need this axis labeled in the Greek letter. How do I fix this?
How do I adjust the axis ticks to that of my choosing? I.e., how would
I get the z-axis to only label 2, 0, and -2, and in the font size that
I want? I know how to do this in 2D but not 3D.
You have to change properties of zticks.
Why is the x-axis label distorted, which I wanted to do with this
script, but not the z-axis label (gamma)? This does not make sense. I
need this axis labeled in the Greek letter. How do I fix this?
You have to disable autorotation for z axis labels. Look at the code below:
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')
ax.legend()
ax.set_xlabel('$X$', fontsize=20)
ax.set_ylabel('$Y$')
ax.yaxis._axinfo['label']['space_factor'] = 3.0
# set z ticks and labels
ax.set_zticks([-2, 0, 2])
# change fontsize
for t in ax.zaxis.get_major_ticks(): t.label.set_fontsize(10)
# disable auto rotation
ax.zaxis.set_rotate_label(False)
ax.set_zlabel('$\gamma$', fontsize=30, rotation = 0)
plt.show()
The for loop is not necessary, to change the size of your ticks you can use:
ax.zaxis.set_tick_params(labelsize=10)