I'm trying to create a vector field and some curve, I've created a vector field as shown
import matplotlib.pyplot as plt
import numpy as np
x,y = np.meshgrid(np.arange(-3,3,.35),np.arange(-3,3,.35))
u = x
v = y
plt.quiver(x, y, u, v, color = 'black')
plt.show()
But I want to add the curve $y=x^2$ in the same plot, how could I do that?
I've tryeid to add plt.plot and the curve but the result is weird.
You probably want to keep the y-axis limit as was in the mesh grid. plt.ylim is helpful in that case
import matplotlib.pyplot as plt
import numpy as np
x, y = np.meshgrid(np.arange(-3, 3, .35), np.arange(-3, 3, .35))
u = x
v = y
plt.quiver(x, y, u, v, color = 'black')
x = np.linspace(-3, 3, 100)
ylim = plt.ylim()
plt.plot(x, x**2)
plt.ylim(ylim)
plt.show()
Output:
Related
import numpy as np
from matplotlib import pyplot as plt
data = np.random.normal(0,1,[100,3])
x = data[:,0]
y = data[:,1]
z = data[:,2]
plt.contour([x,y],z)
When I run this code with dummy data I get:
ValueError: Contour levels must be increasing
Do you have any idea what would this mean and how I could fix it?
plt.contour is a bit particular about its input, the z values must be on values on a rectangular 2D grid, see for example:
import matplotlib.pyplot as plt
import numpy as np
x = np.expand_dims(np.arange(1,11,1), axis=1)
y = np.expand_dims(np.arange(2,21,2), axis=0)
z = y * x
print(x.shape)
print(y.shape)
print(z.shape)
plt.figure()
plt.contour(z)
plt.show()
You can also provide x and y values for plt.contour by using np.meshgrid :
XX,YY = np.meshgrid(x,y)
plt.figure()
plt.contour(XX, YY, z)
plt.show()
If you have z-values with irregular values for x and y, you might use plt.tricontour, see the following example:
from matplotlib.tri import Triangulation
data = np.random.normal(0,1,[100,3])
x = data[:,0]
y = data[:,1]
#z = data[:,2]
z = x * y
tri = Triangulation(x,y)
plt.figure()
plt.tricontour(tri, z, )
plt.scatter(x,y, c=z)
plt.show()
Edit: from JohanC's comment i learned that this can be simplified without importing matplotlib.tri by:
plt.figure()
plt.tricontour(x,y,z)
plt.scatter(x,y, c=z)
plt.show()
import numpy as np
import matplotlib.pyplot as plt
x, y = np.array([[x, y] for x in range(5) for y in range(x+1)]).T
z = 1/ (5*x + 5)
fig = plt.figure()
ax = fig.gca(projection = '3d')
ax.bar3d(x, y, np.zeros_like(z), dx = 1, dy = 1, dz = z)
yields
How do I get the face at (1,0) to display properly?
There is currently no good solution to this. Fortunately though, it happens only for some viewing angles. So you can choose an angle where it plots fine, e.g.
ax.view_init(azim=-60, elev=25)
This is an extension of a related question.
I intend to make a contour plot, with labeled contours, then change the axes scales to 'log'.
This works fine except that the rotation of the contour labels is not adjusted. Can this be fixed?
loglog = False
import matplotlib.pyplot as plt
import numpy as np
x = (np.linspace(0, 10))
y = (np.linspace(0, 10))
X, Y = np.meshgrid(x, y)
C = plt.contour(X, Y, np.sqrt(X) * Y)
plt.clabel(C, inline=1, fontsize=10)
plt.xlim(1, 10)
plt.ylim(1, 10)
if loglog: plt.xscale('log')
if loglog: plt.yscale('log')
plt.show()
The fist plot is obtained with loglog=False in the second loglog=True:
So the answer is actually obvious. Changing the the axes scale types in advance helps, of course.
Edit:
I think it makes sense to use logspace instead of linspace here.
import matplotlib.pyplot as plt
import numpy as np
x = np.logspace(0, 1, 100, base=10)
y = np.logspace(0, 1, 100, base=10)
X, Y = np.meshgrid(x, y)
plt.xlim(1, 10)
plt.ylim(1, 10)
plt.xscale('log')
plt.yscale('log')
C = plt.contour(X, Y, np.sqrt(X) * Y)
plt.clabel(C, inline=1, fontsize=10)
I have a file 'mydata.tmp' which contains 3 colums like this:
3.81107 0.624698 0.000331622
3.86505 0.624698 0.000131237
3.91903 0.624698 5.15136e-05
3.97301 0.624698 1.93627e-05
1.32802 0.874721 1.59245
1.382 0.874721 1.542
1.43598 0.874721 1.572
1.48996 0.874721 4.27933
etc.
Then I want to make a heatmap color plot where the first two columns are coordinates, and the third column are the values of that coordinates.
Also, I would like to set the third column in log scale.
I have done this
import pandas as pd
import matplotlib.pyplot as plt
import scipy.interpolate
import numpy as np
import matplotlib.colors as colors
# import data
df = pd.read_csv('mydata.tmp', delim_whitespace=True,
comment='#',header=None,
names=['1','2','3'])
x = df['1']
y = df['2']
z = df['3']
spacing = 500
xi, yi = np.linspace(x.min(), x.max(), spacing), np.linspace(y.min(),
y.max(), spacing)
XI, YI = np.meshgrid(xi, yi)
rbf = scipy.interpolate.Rbf(x, y, z, function='linear')
ZI = rbf(XI, YI)
fig, ax = plt.subplots()
sc = ax.imshow(ZI, vmin=z.min(), vmax=z.max(), origin='lower',
extent=[x.min(), x.max(), y.min(),
y.max()], cmap="GnBu", norm=colors.LogNorm(vmin=ZI.min(),
vmax=ZI.max()))
fig.colorbar(sc, ax=ax, fraction=0.05, pad=0.01)
plt.show()
And I get this Image
which has all these empty pixels.
I am looking for something like this instead (I have done this other picture with GNUplot):
How can I do it?
You could use cmap.set_bad to define a color for the NaN values:
import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import griddata
import matplotlib.colors as colors
from matplotlib import cm
import copy
# Some data
x = np.array([0, 1, 3, 0, 2, 4])
y = np.array([0, 0, 0, 1, 1, 1])
z = np.array([2, 2, 3, 2, 3, 4])
# Interpolation on a grid:
nrb_points = 101
xi = np.linspace(-.5, 4.5, nrb_points)
yi = np.linspace(-.5, 1.5, nrb_points)
XI, YI = np.meshgrid(xi, yi)
xy = np.vstack((x, y)).T
XY = (XI.ravel(), YI.ravel())
ZI = griddata(points, z, XY,
method='linear',
fill_value=np.nan) # Value used [for] points
# outside of the convex hull
# of the input points.
ZI = ZI.reshape(XI.shape)
# Color map:
cmap = copy.copy(cm.jet)
cmap.set_bad('grey', 1.)
# Graph:
plt.pcolormesh(xi, yi, ZI,
#norm=colors.LogNorm(),
cmap=cmap);
plt.colorbar(label='z');
plt.plot(x, y, 'ko');
plt.xlabel('x'); plt.ylabel('y');
the result is:
I would also use griddata instead of RBF method for the interpolation. Then, point outside the input data area (i.e. the convex hull) can be set to NaN.
I plan to create a figure in matplotlib, with a 3D surface on the left and its corresponding contour map on the right.
I used subplots but it only show the contour map (with blank space for the surface), and a separate figure for the surface.
Is it possible to create these plots in one figure side-by side?
EDIT: The code is as follows:
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d
import numpy as np
x = np.arange(-5, 5, 0.25)
y = np.arange(-5, 5, 0.25)
x, y = np.meshgrid(x, y)
r = np.sqrt(x**2 + y**2)
z = np.sin(r)
fig, (surf, cmap) = plt.subplots(1, 2)
fig = plt.figure()
surf = fig.gca(projection='3d')
surf.plot_surface(x,y,z)
cmap.contourf(x,y,z,25)
plt.show()
I guess it's hard to use plt.subplots() in order to create a grid of plots with different projections.
So the most straight forward solution is to create each subplot individually with plt.subplot.
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d
import numpy as np
x = np.arange(-5, 5, 0.25)
y = np.arange(-5, 5, 0.25)
x, y = np.meshgrid(x, y)
r = np.sqrt(x**2 + y**2)
z = np.sin(r)
ax = plt.subplot(121, projection='3d')
ax.plot_surface(x,y,z)
ax2 = plt.subplot(122)
ax2.contourf(x,y,z,25)
plt.show()
Of course one may also use the gridspec capabilities for more sophisticated grid structures.