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()
Related
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:
I have a issue to interpolate my values "c" on cylindrical surface.
The problem is that possibly I dont understand how to indicate surface for gridding with gridddata function..
>import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import griddata as gd
#Creating data in polar coordinates
phi,d = np.linspace(0, 2* np.pi, 20), np.linspace(0,20,20)
PHI,D = np.meshgrid(phi,d)
R = 2
#Transforming in X Y Z coordinates
X = R * np.cos(PHI)
Y = R * np.sin(PHI)
Z = R * D
T=np.linspace(0,10,400)
c=np.sin(T)*np.cos(T/2) #Value c I would like to interpolate
fig1 = plt.figure()
ax = fig1.add_subplot(1,1,1, projection='3d')
xi=np.array(np.meshgrid(X,Y,Z))
img = ax.scatter(X, Y, Z,c=c, cmap=plt.hot()) #To plot data scatter before interpolation
fig1.colorbar(img)
plt.show()
X1,Y1,Z1 =np.meshgrid(X ,Y ,Z) #To define sufrace for interpolation
int = gd((X,Y,Z), c, (X1,Y1,Z1), method='linear')
fig2 = plt.figure() #trying to plot the answer
ax1 = fig2.add_subplot(1,1,1, projection='3d')
ax1.scatter(int)
img = ax1.scatter(X, Y, Z, c=c, cmap=plt.hot())
`
Its gives error: different number of values and points
I dont know how to indicate (X1,Y1,Z1) surface in griddata function
Thanks a lot for any tips ...
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)
I want to plot some data x and y in which I need the marker size to depend on a third array z. I could plot them separately (i.e., scatter x and y with size = z, and errorbar without marker, fmc = 'none') and this solves it. The problem is that I need the legend to show the errorbar AND the dot, together:
and not
Code is here with some made-up data:
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(1,10,100)
y = 2*x
yerr = np.random(0.5,1.0,100)
z = np.random(1,10,100)
fig, ax = plt.subplots()
plt.scatter(x, y, s=z, facecolors='', edgecolors='red', label='Scatter')
ax.errorbar(x, y, yerr=yerr, xerr=0, fmt='none', mfc='o', color='red', capthick=1, label='Error bar')
plt.legend()
plt.show()
which produces the legend I want to avoid:
In errorbar the argumentmarkersizedoes not accept arrays asscatter` does.
The idea is usually to use a proxy to put into the legend. So while the errorbar in the plot may have no marker, the one in the legend has a marker set.
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(1,10,11)
y = 2*x
yerr = np.random.rand(11)*5
z = np.random.rand(11)*2+5
fig, ax = plt.subplots()
sc = ax.scatter(x, y, s=z**2, facecolors='', edgecolors='red')
errb = ax.errorbar(x, y, yerr=yerr, xerr=0, fmt='none',
color='red', capthick=1, label="errorbar")
proxy = ax.errorbar([], [], yerr=[], xerr=[], marker='o', mfc="none", mec="red",
color='red', capthick=1, label="errorbar")
ax.legend(handles=[proxy], labels=["errorbar"])
plt.show()
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)