I'm trying to do a simple polar scatter plot in Matplotlib, here is my code:
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111, polar = True)
c = ax.scatter([-1.3,.4,-.2],[1,1.5,2])
For some reason, the plot doesn't include all of the points, it only shows me the point with radius 1. How do I make it automatically show all of the points, or how can I fix the radius of the plot myself? I am running this in a Jupyter notebook, if that makes any difference.
This is what I suggest if I understand your question correctly this code plots all the points automatically
Here I am just using for loop to loop through all the points
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111, polar = True)
a = [1,1.5,2]
for idx, i in enumerate([-1.3,.4,-.2]):
c = ax.scatter([i],[a[idx]])
plt.show()
Related
I make 3d plots with matplotlib and I always get a weird frame with a normalized scale around my plot. Where does it come from and how can I get rid of it ?
Here is an example code that drives me to the problem :
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib import cm
from mpl_toolkits.mplot3d import Axes3D
x = np.linspace(0,10)
y = np.linspace(0,10)
z = np.linspace(0,10)
# ------------- Figure ---------------
fig, ax = plt.subplots(figsize = (9,6))
ax = fig.gca(projection='3d')
ax.plot(np.sin(x), np.cos(y), z)
plt.show()
And here is the result :
I use plt.subplots() because I want a figure with a 3D and a 2D plot side by side.
You call plt.subplots(...) and this, of course, instantiates an Axes, complete of horizontal and vertical spines, before Matplotlib is informed that you want a 3D enabled Axes.
When you later call plt.gca(...) it's too late…
Simply use
fig, ax = plt.subplots(figsize = (9,6), subplot_kw={"projection" : "3d"})
or
fig = plt.figure(figsize = (9,6))
ax = fig.add_subplot(111, projection='3d')
Addressing OP's comment
Figure.add_subplot is pretty flexible…
fig = plt.figure()
fig.add_subplot(1,5,(1,4), projection='3d')
fig.add_subplot(1,5,5)
fig.tight_layout()
plt.show()
I am a student and I am new to matplotlib animation.
I am trying to figure out how to animate zooming in towards the center of my 3d scatterplot, and I've included my code below. I am trying to get the zeroes to be at the middle of each axis so I am able to see the overall plot as a zoom in. I don't get an error whenever I run my code but when I run the animation the intervals change abruptly and don't seem to go in a certain pattern. Another thing I've noticed is that the zeroes are only sometimes in the middle of the axis, while the plot "glitches out".
Thank You.
import matplotlib.pylab as plt
import matplotlib.animation as animation
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
%matplotlib notebook
x = np.random.rand(100)*100
y = np.random.rand(100)*100
z = np.random.rand(100)*100
#setup figure
fig = plt.figure()
ax = fig.add_subplot(111, facecolor='LightCyan', projection = '3d')
#set up viewing window (in this case the 25 most recent values)
ax.set_xlim([-1, 1])
ax.set_ylim([-1,1])
ax.set_zlim([-1,1])
#sets up list of images for animation
plot = ax.scatter(x, y, z, color='b', marker= '*',)
def func(i):
x_lim = ax.set_xlim(-i,i)
y_lim = ax.set_ylim(-i, i)
z_lim = ax.set_zlim(-i, i)
return plot
ani = animation.FuncAnimation(fig, func, frames=100, interval=1000, blit=True)
I have a scatter plot with a colour scaling where each plotted point is associated with another value. This is a lazy workaround to make a "countour plot" style image without having to regularise data points. To make analysis easier I am using mpldatacursor to generate interactive annotations on the plot, and I have a custom formatter which is displaying co-ordinates just fine:
datacursor(scatter,
formatter='$T=${x:.2f}$^\circ$C\n$I=${y:.2f}$\,$mA\n$\Delta F=$$\,$THz'.format,
draggable=True)
but what I really want is for that third line, $\Delta F=$$\,$THz, to include a statement that returns the value associated with the colour map at that point. Does anyone know what kwargs I should use to achieve this?
EDIT: MWE
from mpldatacursor import datacursor
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure()
ax = fig.add_subplot(111)
scatter = ax.scatter(np.random.random(100),
np.random.random(100),
c=np.random.random(100),
s=0.5)
cb = plt.colorbar(scatter, label="Colour")
datacursor(scatter,
formatter='$T=${x:.2f}$^\circ$C\n$I=${y:.2f}$\,$mA\n$\Delta F=$$\,$THz'.format,
draggable=True)
You will need to convert the index of the picked point to the value to be shown. Therefore the scatter's colors should be publicly available, such that the ind of the pick_event can index it and return the value at the picked point.
from mpldatacursor import datacursor
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure()
ax = fig.add_subplot(111)
x = np.random.random(100)
y = np.random.random(100)
c = np.random.random(100)
scatter = ax.scatter(x, y, c=c, s=1)
cb = plt.colorbar(scatter, label="Colour")
def fmt(**dic):
tx = '$T=${x:.2f}$^\circ$C\n$I=${y:.2f}$\,$mA\n$\Delta F=${z:.2f}$\,$THz'
dic.update({"z" : c[dic["ind"][0]]})
return tx.format(**dic)
datacursor(scatter, formatter=fmt, draggable=True)
plt.show()
I've seen how to plot a color filled ellipse in 2D. I would like to plot ellipses like that, but in a 3D plot.
To do that you need to use PyPlot. Now, PyPlot makes MatPlotLib work like MATLAB. Now, in order to make an ellipse you can try the below example code.
from pylab import figure, show, rand
from matplotlib.patches import Ellipse
NUM = 100
ells = [Ellipse(xy=rand(2)*10, width=rand(), height=rand(), angle=rand()*360)
for i in range(NUM)]
fig = figure()
ax = fig.add_subplot(111, aspect='equal')
for e in ells:
ax.add_artist(e)
e.set_clip_box(ax.bbox)
e.set_alpha(rand())
e.set_facecolor(rand(3))
ax.set_xlim(0, 5)
ax.set_ylim(0, 5)
show()
I am trying to create a plot composed of multiple wireframe spheres using matplotlib. I found a code fragment to plot one such sphere here so I thought it would be easy to extend it to multiple spheres by just calling plot_wireframe multiple times. I was wrong. Here's a code fragment:
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
u, v = np.mgrid[0:2*np.pi:20j, 0:np.pi:10j]
x=np.cos(u)*np.sin(v)
y=np.sin(u)*np.sin(v)
z=np.cos(v)
fig = plt.figure(figsize=(8,6))
ax = fig.gca(projection='3d')
ax.plot_wireframe(x*3.+5., y*3., z*3.,linewidths=.2)
ax.view_init(azim=30,elev=40)
ax.set_aspect("equal")
plt.show()
fig = plt.figure(figsize=(8,6))
ax = fig.gca(projection='3d')
ax.plot_wireframe(x*3.+5., y*3., z*3.,linewidths=.2)
spheres = [ [0,0,0,1], [3,0,0,1.6] ]
for v in spheres:
ax.plot_wireframe(x*v[3]+v[0], y*v[3]+v[1], z*v[3]+v[2],linewidths=.2)
ax.view_init(azim=30,elev=40)
ax.set_aspect("equal")
plt.show()
If you run that code, the first plot will show a nice sphere, while in the second all the spheres are distorted and shifted. I searched to make sure plot_wireframe can be called multiple time on the same axis but couldn't find anything. Also, I'm a Python noob, but I don't think I'm doing anything wrong.
Thank you for the help!
Short answer: adjust the axes limits manually:
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
u, v = np.mgrid[0:2*np.pi:20j, 0:np.pi:10j]
x=np.cos(u)*np.sin(v)
y=np.sin(u)*np.sin(v)
z=np.cos(v)
# I'm not sure what was this for.
'''
fig = plt.figure(figsize=(8,6))
ax = fig.gca(projection='3d')
ax.plot_wireframe(x*3.+5., y*3., z*3.,linewidths=.2)
ax.view_init(azim=30,elev=40)
ax.set_aspect("equal")
plt.show()
'''
fig = plt.figure(figsize=(8,6))
ax = fig.gca(projection='3d')
ax.plot_wireframe(x*3.+5., y*3., z*3.,linewidths=.2)
spheres = [ [0,0,0,1], [3,0,0,1.6] ]
for v in spheres:
ax.plot_wireframe(x*v[3]+v[0], y*v[3]+v[1], z*v[3]+v[2],linewidths=.2)
ax.view_init(azim=30,elev=40)
ax.set_xlim([0,7]) # Like so.
ax.set_ylim([-3,3])
ax.set_zlim([-3,3])
ax.set_aspect("equal")
plt.show()