How do I add a small filled circle or point to a countour plot in matplotlib?
Here is an example, using pylab.Circle:
import numpy as np
import matplotlib.pyplot as plt
e = np.e
X, Y = np.meshgrid(np.linspace(0, 5, 100), np.linspace(0, 5, 100))
F = X ** Y
G = Y ** X
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
circ = plt.Circle((e, e), radius=0.07, color='g')
plt.contour(X, Y, (F - G), [0])
ax.add_patch(circ)
plt.show()
And here is another example (though not a contour plot) from the docs.
Or, you could just use plot:
import numpy as np
import matplotlib.pyplot as plt
e = np.e
X, Y = np.meshgrid(np.linspace(0, 5, 100), np.linspace(0, 5, 100))
F = X ** Y
G = Y ** X
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
plt.contour(X, Y, (F - G), [0])
plt.plot([e], [e], 'g.', markersize=20.0)
plt.show()
Related
I'm trying to align the right edge of both plots and have the colorbar hanging to the right. This is what I have:
import numpy as np
import matplotlib.pyplot as plt
def F(x, y):
u = x
v = y
return u, v
fig = plt.figure(figsize=(8, 8), tight_layout=True)
ax1 = plt.subplot(2, 1, 1)
ax2 = plt.subplot(2, 1, 2)
x, y = np.meshgrid(np.linspace(-1, 1, 10), np.linspace(-1, 1, 10))
u, v = F(x, y)
vres = np.sqrt(u ** 2 + v ** 2)
vector_field = ax1.quiver(x, y, u, v, vres)
fig.colorbar(vector_field, ax=ax1)
plt.show()
And this is what I got:
I've read the colorbar doc but couldn't find what I wanted, can anyone help?
Thanks in advance!
Updated solution:
You can actually just add another colorbar to your second subplot and make it not visible, like this.
fig.colorbar(vector_field, ax=ax2).ax.set_visible(False)
Complete code:
import numpy as np
import matplotlib.pyplot as plt
def F(x, y):
u = x
v = y
return u, v
fig = plt.figure(figsize=(8, 8), tight_layout=True)
ax1 = plt.subplot(2, 1, 1)
ax2 = plt.subplot(2, 1, 2)
x, y = np.meshgrid(np.linspace(-1, 1, 10), np.linspace(-1, 1, 10))
u, v = F(x, y)
vres = np.sqrt(u ** 2 + v ** 2)
vector_field = ax1.quiver(x, y, u, v, vres)
fig.colorbar(vector_field, ax=ax1)
fig.colorbar(vector_field, ax=ax2).ax.set_visible(False)
plt.show()
Old solution with different result:
I did not expext this to work, but is this what you need?
ax from colorbar can actually take a list of axes , not only one: fig.colorbar(vector_field, ax=[ax1, ax2]). You will also have to remove the tight_layout though...tight_layout=False
import numpy as np
import matplotlib.pyplot as plt
def F(x, y):
u = x
v = y
return u, v
fig = plt.figure(figsize=(8, 8), tight_layout=False)
ax1 = plt.subplot(2, 1, 1)
ax2 = plt.subplot(2, 1, 2)
x, y = np.meshgrid(np.linspace(-1, 1, 10), np.linspace(-1, 1, 10))
u, v = F(x, y)
vres = np.sqrt(u ** 2 + v ** 2)
vector_field = ax1.quiver(x, y, u, v, vres)
fig.colorbar(vector_field, ax=[ax1, ax2])
plt.show()
I found a tutorial online for this matplotlib and numpy graph. The code runs smoothly, but there is no output. I have tried to save the graph as a file, but that does not seem to work.
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits import mplot3d
fig = plt.figure()
ax = plt.axes(projection="3d")
zline = np.linspace(0, 15, 1000)
xline = np.sin(zline)
yline = np.cos(zline)
ax.plot3D(xline, yline, zline, "gray") # Data for three-dimensional scattered points
zdata = 15 * np.random.random(100)
xdata = np.sin(zdata) + 0.1 * np.random.randn(100)
ydata = np.cos(zdata) + 0.1 * np.random.randn(100)
ax.scatter3D(xdata, ydata, zdata, c=zdata, cmap="Greens");
def f(x, y):
return np.sin(np.sqrt(x ** 2 + y ** 2))
x = np.linspace(-6, 6, 30)
y = np.linspace(-6, 6, 30)
X, Y = np.meshgrid(x, y)
Z = f(X, Y)
fig = plt.figure()
ax = plt.axes(projection='3d')
ax.contour3D(X, Y, Z, 50, cmap='binary')
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z');
theta = 2 * np.pi * np.random.random(1000)
r = 6 * np.random.random(1000)
x = np.ravel(r * np.sin(theta))
y = np.ravel(r * np.cos(theta))
z = f(x, y)
ax = plt.axes(projection="3d")
ax.plot_trisurf(x, y, z,cmap="viridis", edgecolor="none");
The link to the website is https://www.edureka.co/blog/python-projects/. Surely there is some way to access the graphical user interface to display the plots?
Adding plt.show() at the end will display both of the graphs.
I'm making multiple density plots in 3D with a circle shape, using contourf.
What I want to do is similar to a clip_path for a pcolormesh plot.
How can I clip the plot in 3D in particular for a contourf plot?
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.path import Path
import matplotlib.patches as patches
fig = plt.figure()
ax = fig.gca(projection='3d')
x = np.linspace(0, 1, 100)
X, Y = np.meshgrid(x, x)
levels = np.linspace(-0.1, 0.4, 100) #(z_min,z_max,number of contour),
a=0
b=1
c=2
Z1 = a+.1*np.sin(2*X)*np.sin(4*Y)
Z2 = b+.1*np.sin(3*X)*np.sin(4*Y)
Z3 = c+.1*np.sin(4*X)*np.sin(5*Y)
plt.contourf(X, Y,Z1, levels=a+levels,cmap=plt.get_cmap('rainbow'))
plt.contourf(X, Y,Z2, levels=b+levels,cmap=plt.get_cmap('rainbow'))
plt.contourf(X, Y,Z3, levels=c+levels,cmap=plt.get_cmap('rainbow'))
ax.set_xlim3d(0, 1)
ax.set_ylim3d(0, 1)
ax.set_zlim3d(0, 2)
plt.show()
Are you sure your code generated that output image with the given parameters? I only got some blue squares. Tweaking a bit, it starts looking like yours.
To clip the contours, consider masking the Z arrays. As in:
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import matplotlib.pyplot as plt
import numpy.ma as ma
fig = plt.figure()
ax = fig.gca(projection='3d')
x = np.linspace(0, 1, 100)
X, Y = np.meshgrid(x, x)
levels = np.linspace(-0.1, 0.4, 100) #(z_min,z_max,number of contour),
a = 0
b = 1
c = 2
Z1 = a + .3 * np.sin(2 * X) * np.sin(4 * Y)
Z2 = b + .3 * np.sin(3 * X) * np.sin(4 * Y)
Z3 = c + .3 * np.sin(4 * X) * np.sin(5 * Y)
mask_cond = (X - .5) ** 2 + (Y - .5) ** 2 > .25
Z1 = ma.masked_where(mask_cond, Z1)
Z2 = ma.masked_where(mask_cond, Z2)
Z3 = ma.masked_where(mask_cond, Z3)
plt.contourf(X, Y, Z1, levels=a + levels, cmap='rainbow')
plt.contourf(X, Y, Z2, levels=b + levels, cmap='rainbow')
plt.contourf(X, Y, Z3, levels=c + levels, cmap='rainbow')
ax.set_xlim3d(0, 1)
ax.set_ylim3d(0, 1)
ax.set_zlim3d(0, 3)
plt.show()
I have a polar contour plot, and I want to add cartesian axes to it. How can I do this? There seems to be no documentation for this.
import numpy as np
import matplotlib.pyplot as plt
def f(x, y):
return np.sin(x) ** 10 + np.cos(10 + y * x) * np.cos(x)
x = np.linspace(0, 8, 50)
y = np.linspace(0, 8, 40)
X, Y = np.meshgrid(x, y)
Z = f(X, Y)
fig, ax = plt.subplots(subplot_kw = dict(projection = 'polar'))
plt.axis('off')
ax.contourf(X, Y, Z)
To add cartesian axes to a polar plot, all you have to do is add a second pair of axes at the same position like so:
import numpy as np
import matplotlib.pyplot as plt
def f(x, y):
return np.sin(x) ** 10 + np.cos(10 + y * x) * np.cos(x)
x = np.linspace(0, 8, 50)
y = np.linspace(0, 8, 40)
X, Y = np.meshgrid(x, y)
Z = f(X, Y)
fig, ax = plt.subplots(subplot_kw = dict(projection = 'polar'))
plt.axis('off')
ax.contourf(X, Y, Z)
new_axis = fig.add_axes(ax.get_position(), frameon = False)
new_axis.plot()
plt.show()
I have the following (example) code:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
n_lines = 5
x = np.linspace(0, 10, 100)
y = np.sin(x[:, None] + np.pi * np.linspace(0, 1, n_lines))
c = np.arange(1, n_lines + 1)
norm = mpl.colors.Normalize(vmin=c.min(), vmax=c.max())
cmap = mpl.cm.ScalarMappable(norm=norm, cmap=mpl.cm.jet)
cmap.set_array([])
fig, ax = plt.subplots(dpi=100)
for i, yi in enumerate(y.T):
ax.plot(x, yi, c=cmap.to_rgba(i + 1))
fig.colorbar(cmap, ticks=c)
plt.show();
I would like to find a substirute for cmap.to_rgba that makes the colour of each line come out as a differnet shade of blue. Basically I want to keep the same layout as the result of this code, but using the colour map Blues.
How can I do it?
You need to change your colormap that you are using from jet to Blues.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
n_lines = 5
x = np.linspace(0, 10, 100)
y = np.sin(x[:, None] + np.pi * np.linspace(0, 1, n_lines))
c = np.arange(1, n_lines + 1)
norm = mpl.colors.Normalize(vmin=c.min(), vmax=c.max())
cmap = mpl.cm.ScalarMappable(norm=norm, cmap=mpl.cm.Blues)
cmap.set_array([])
fig, ax = plt.subplots(dpi=100)
for i, yi in enumerate(y.T):
ax.plot(x, yi, c=cmap.to_rgba(i + 1))
fig.colorbar(cmap, ticks=c)
plt.show()
This produces: