I'm trying to understand how plt.quiver() works. My issue is as follows:
I plot a simple vector (1,1) as such:
import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure(2)
ax = fig.add_subplot(111)
ax.quiver(0,0, 1, 1, units = 'xy', scale = 1)
plt.xticks(range(-5,6))
plt.yticks(range(-5,6))
plt.grid()
I would expect the arrow to go from (0,0) to (1,1), but the result is slightly off from that:
Similarly, I try and plot an arrow for vector (0,3) and the resulting arrow seems to be for vector (0,3.5)...
My assumption is that this has something to do with the kwargs 'units', 'scale', 'angles', & 'scale_units'. I've read the docs on them but don't fully understand how they work. A sunday school explanation would be greatly appreciated!
If you adjust the aspect ratio of the figure to 1, the vectors are displayed to proper scale:
import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
ax.quiver((0,0), (0,0), (1,0), (1,3), units = 'xy', scale = 1)
plt.axis('equal')
plt.xticks(range(-5,6))
plt.yticks(range(-5,6))
plt.grid()
plt.show()
You can try this code.
import matplotlib.pyplot as plt
fig = plt.figure(2)
ax = fig.add_subplot(111)
ax.quiver(0,0, 1, 1,angles='xy', scale_units='xy', scale = 1)
plt.xticks(range(-5,6))
plt.yticks(range(-5,6))
plt.grid()
plt.draw()
plt.show()
Just remember that the first two arguments of quiver are the x and y coordinates of the tail of the vector, the next two are the lengths of the vector along x and y direction respectively. angle='xy' makes the arrow point from tail of the vector to its tip.
You can find out more about matplotlib.quiver at http://matplotlib.org/1.3.1/api/pyplot_api.html#matplotlib.pyplot.quiver
Related
I need to plot a line plot. I want to plot all parts of the lineplot that are below zero blue, and all parts above red.
Here's what I managed so far:
import numpy as np
import xarray as xr
import matplotlib.pyplot as plt
x = np.linspace(0, 1, 40)
y = np.random.random(len(x))-0.5
da = xr.DataArray(y, dims=('x',), coords={'x':x})
fig = plt.figure(figsize=(12,6))
ax = fig.add_subplot(1, 1, 1)
da.plot(ax=ax, color='red', linewidth=3)
da.where(y<0).plot(ax=ax, color='blue', linewidth=3)
plt.show()
Here's what I get with this script:
But what I want is for the color to change at the threshold of 0, like this example (that I've modified to show what I want):
I've looked at some suggestions here, for example this here: Plot: color all larger than different color
But I get the same figure with that solution. It seems that the solution lies in the fact that all their line segments are incredibly short, so you don't notice that a segment that passes the threshold doesn't change color at the threshold, and only the next segment is drawn in a different color.
Is there a straightforward way to do this? Or do I have to separate the line segments that cross the threshold manually?
Thank you
It seems that the solution lies in the fact that all their line segments are incredibly short, so you don't notice that a segment that passes the threshold doesn't change color at the threshold, and only the next segment is drawn in a different color.
You could just interpolate your data such that this holds true for your data as well.
import numpy as np
import xarray as xr
import matplotlib.pyplot as plt
xx = np.linspace(0, 1, 40)
yy = np.random.random(len(xx))-0.5
x = np.linspace(0, 1, 4000)
y = np.interp(x, xx, yy) # linear piecewise interpolation
da = xr.DataArray(y, dims=('x',), coords={'x':x})
fig = plt.figure(figsize=(12,6))
ax = fig.add_subplot(1, 1, 1)
da.plot(ax=ax, color='red', linewidth=3)
da.where(y<0).plot(ax=ax, color='blue', linewidth=3)
plt.show()
I'm trying to plot a wave function over one dimension but it has real and imaginary parts, so I did a 3D plot animation of it. This is a screenshot:
The main thing I would like to do is to spread it along the x-axis (which now is vertical) so it doesn't look squeezed. Also, it would be nice to set it up in a set of 3 RGB axes that intersect at the point (0,0,0). In the documentation I couldn't find any straight forward way to do this. I'm attaching the part of the code I'm using to animate it:
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import mpl_toolkits.mplot3d.axes3d as p3
fig = plt.figure()
ax = fig.gca(projection='3d')
line, = ax.plot(REAL[0,:],IMAG[0,:],x,"r",linewidth=0.5)
def animacio(i):
ax.collections.clear()
line.set_data(REAL[i,:],IMAG[i,:])
line.set_3d_properties(x, 'z')
return line,
ani = animation.FuncAnimation(fig,animacio,interval=50, frames=Nt,repeat=True)
nom = 'Evolució_'
ani.save(str(nom)+'['+str(V0)+','+str(L)+','+str(l)+','+str(xi)+','+str(sigmax)+','+str(T)+']'+'.mp4', writer="ffmpeg", dpi=300)
plt.show()
print('Animation saved as: '+str(nom)+'['+str(V0)+','+str(L)+','+str(l)+','+str(xi)+','+str(sigmax)+','+str(T)+']'+'.mp4')
You can add colored lines to the plot, just by giving start and end points and assigning a color. The limits for the 'up'-axis can be set by ax.set_zlim. I created a demo curve that roughly resembles yours.
import numpy as np
from mpl_toolkits import mplot3d
import matplotlib.pyplot as plt
x = np.linspace(-10, 10, 1000)
y = np.sin(10*x)/(x*x+1)
z = np.cos(10*x)/(x*x+1)
ax = plt.axes(projection='3d')
ax.plot3D([0,0], [0,0], [-10,10], color='crimson')
ax.plot3D([0,0], [-1,1], [0,0], color='limegreen')
ax.plot3D([-1,1], [0,0], [0,0], color='dodgerblue')
line, = ax.plot3D(y, z, x, color='blueviolet')
ax.set_zlim(-1, 1)
plt.show()
At the left the plot without limiting, at the right with limits:
To get a more elongated view, you could use something like:
plt.gcf().set_size_inches(4, 12)
I am a beginner in Python. I'm trying to plot a circle using matplotlib that has tangent to Z axis. I know how to draw a sphere in 3D but don't know how to draw a circle/ring in 3D plot. Can someone help me with the code? Thanks in advance!
You need the usual imports, plus the 3D toolkit
import matplotlib as mpl
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import matplotlib.pyplot as plt
You need a 3D enabled axes object
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
You need a circle, contained in the plane y-z
theta = np.linspace(0, 2 * np.pi, 201)
y = 10*np.cos(theta)
z = 10*np.sin(theta)
now we can plot the original circle and, as an example, a number of circles rotated about the z-axis and whose centers are also placed at a fixed distance (equal to the c ircles'radius) from the z-axis, so that they are tangent to it
for i in range(18):
phi = i*np.pi/9
ax.plot(y*np.sin(phi)+10*np.sin(phi),
y*np.cos(phi)+10*np.cos(phi), z)
eventually we place a vertical axis and a legend
ax.plot((0,0),(0,0), (-10,10), '-k', label='z-axis')
ax.legend()
It's time to see what we got
plt.show()
mpl_toolkits.mplot3d.art3d
https://matplotlib.org/3.2.1/gallery/mplot3d/pathpatch3d.html was mentioned
in a comment, the example can be minimized to:
#!/usr/bin/env python3
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Circle
import mpl_toolkits.mplot3d.art3d as art3d
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
# Draw a circle on the x=0 'wall'
p = Circle((5, 5), 3)
ax.add_patch(p)
art3d.pathpatch_2d_to_3d(p, z=0, zdir="x")
ax.set_xlim(0, 10)
ax.set_ylim(0, 10)
ax.set_zlim(0, 10)
plt.show()
which gives:
This is a bit nicer than https://stackoverflow.com/a/56871467/895245 as it uses a higher level Circle object directly, instead of requiring you to explicitly plot the lines.
Unfortunately, 3D support in matplotlib is a bit limited as mentioned in the documentation itself, and you have to do some extra work to plot on planes not parallel to the main coordinate plane: How can matplotlib 2D patches be transformed to 3D with arbitrary normals?
Tested on matplotlib==3.2.2.
I am struggling with matplotlib and padding on the x-axis together with a logarithmic scale (see the first picture).
Without a logarithmic scale, the padding applies nicely (see the second one).
Any suggestations how to get a padding between plot lines and the axis line in the bottom left corner so that one can see the points on the line?
Thanks.
The code:
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.pyplot import *
from matplotlib.ticker import ScalarFormatter
style.use('fivethirtyeight')
fig, ax = plt.subplots()
T = np.array([2**x for x in range(0,7+1)])
opt1 = np.array([x for x in range(0,7+1)])
opt2 = np.array([x*2 for x in range(0,7+1)])
opt3 = np.array([x*4 for x in range(0,7+1)])
ax.grid(True)
xlabel("#nodes")
ylabel("time(s)")
legend(loc="best")
title(r"Node start times")
plt.xticks([2**x for x in range(0,7+1)])
plt.plot(T,opt1,"o-", label="opt1")
plt.plot(T,opt2, "s-", label="opt2")
plt.plot(T,opt3, "d-", label="opt2")
plt.legend(loc="upper left")
# This should be called after all axes have been added
plt.tight_layout()
plt.margins(0.05, 0.05)
# 1, 2, 4, ...
ax.set_xscale('log', basex=2)
ax.xaxis.set_major_formatter(matplotlib.ticker.FormatStrFormatter("%d"))
plt.show()
#savefig("plot_1.pdf")
This does not address your padding issue, but you could use clip_on=False to prevent the points from being cut off. It seems you also need to make sure they're above the axes using zorder
plt.plot(T,opt1,"o-", label="opt1", clip_on=False, zorder=10)
plt.plot(T,opt2, "s-", label="opt2", clip_on=False, zorder=10)
plt.plot(T,opt3, "d-", label="opt2", clip_on=False, zorder=10)
I am making a polar scatter plot for a college project with matplotlib and I can't find out how to add a label to the radial axis. Here is my code ( I left out the data because it was read out of a csv)
import matplotlib.pyplot as plt
ax = plt.subplot(111, polar=True)
ax.set_rmax(1)
c = plt.scatter(theta, radii)
ax.set_title("Spread of Abell Cluster Supernova Events as a Function of Fractional Radius", va='bottom')
ax.legend(['Supernova'])
plt.show()
(My plot looks like this. I can't seem to find any straight forward method to do it. Has anyone dealt with this before and have any suggestions?
I don't know of a built in way to do it, but you could use ax.text to make your own. You can get the position of the radial tick labels using ax.get_rlabel_position(), and the mid point of the radial axis using ax.get_rmax()/2.
For example, here's your code (with some random data):
import matplotlib.pyplot as plt
import numpy as np
theta=np.random.rand(40)*np.pi*2.
radii=np.random.rand(40)
ax = plt.subplot(111, polar=True)
ax.set_rmax(1)
c = plt.scatter(theta, radii)
ax.set_title("Spread of Abell Cluster Supernova Events as a Function of Fractional Radius", va='bottom')
ax.legend(['Supernova'])
label_position=ax.get_rlabel_position()
ax.text(np.radians(label_position+10),ax.get_rmax()/2.,'My label',
rotation=label_position,ha='center',va='center')
plt.show()
And here's the output:
I'd be interested to see if there's a more elegant solution, but hopefully this helps you.
from pylab import *
N = 150
r = 2*rand(N)
theta = 2*pi*rand(N)
area = 200*r**2*rand(N)
colors = theta
ax = subplot(111, polar=True)
c = scatter(theta, r, c=colors, s=area, cmap=cm.hsv)
c.set_alpha(0.75)
ax.set_ylabel('Radius', rotation=45, size=11)
show()
A slightly different method from #tom. This uses directly the plt.legend option.
Example:
import matplotlib.pyplot as plt
import numpy as np
theta=np.random.rand(40)*np.pi*2.
radii=np.random.rand(40)
ax = plt.subplot(111, polar=True)
ax.set_rmax(1)
c = plt.scatter(theta, radii,label='Supernova')
ax.set_title("Spread of Abell Cluster Supernova Events as a Function of Fractional Radius", va='bottom')
ax.legend(loc='lower right', scatterpoints=1)
plt.show()
You can change lower right to upper right or even to best to leave the alignment of the legend to matplotlib.