We Keep Coding

How to fix "not enough values to unpack" when trying to plot 3D data as colormesh?

With matplotlib I am trying to plot 3D data as a 2D colormap. Each point has a x and a y coordinate, and a 'height' z. This height should determine the color a certain x/y region is colored in.
Here is the code I have been trying:
import random
import numpy as np
import matplotlib.pyplot as plt
x = []
y = []
z = []
for index in range(100):
a = random.random()
b = random.random()
c = np.exp(-a*a - b*b)
x.append(a)
y.append(b)
z.append(c)
cmap = plt.get_cmap('PiYG')
fig, ax = plt.subplots()
ax.pcolormesh(x, y, z, cmap=cmap)
But it gives an error
ValueError: not enough values to unpack (expected 2, got 1)
Maybe I am trying the wrong thing?
Remark: The three lists x,y,z and calculated for the example above, but in reality I have just three lists with "random" numbers in it I want to vizualize. I cannot calculate z given x and y.
I could also use imshow to create the plot I want, but I have to convert my original data into a matrix first. Maybe there is a function I can use?

pcolormesh might not be the choice for this kind of problem. pcolormesh expects ordered cell edges as data rather than random data points. You could do this if you know your grid before hand e.g.
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 1, 51)
# meshgrid makes a 2D grid of points
xx, yy = np.meshgrid(x, x)
z = np.exp(-xx**2 - yy*2)
fig, ax = plt.subplots()
ax.pcolormesh(xx, yy, z, cmap="PiYG")
which will give you
Alternatively, you could use one of the tri functions such as tripcolor with your existing setup
import random
import numpy as np
import matplotlib.pyplot as plt
x = []
y = []
z = []
for index in range(100):
a = random.random()
b = random.random()
c = np.exp(-a*a - b*b)
x.append(a)
y.append(b)
z.append(c)
fig, ax = plt.subplots()
ax.tripcolor(x, y, z, cmap="PiYG")
which will give
Note it would be simpler to use np.random to generate your data
x, y = np.random.random(size=(2, 100))
z = np.exp(-x**2 - y**2)
fig, ax = plt.subplots()
ax.tripcolor(x, y, z, cmap="PiYG")

There is an issue with x, y and z shapes: they have to be 2D arrays (matrices) but they are 1-dimensional.
In order to generate x and y axis, you could use:
x = []
y = []
for index in range(100):
x.append(random.random())
y.append(random.random())
Then you have to create a meshgrid:
X, Y = np.meshgrid(x, y)
Finally you can compute Z over the meshgrid:
Z = np.exp(-X**2 - Y**2)
In this way, your code:
cmap = plt.get_cmap('PiYG')
fig, ax = plt.subplots()
ax.pcolormesh(X, Y, Z, cmap=cmap)
gives:
If you you cannot compute Z on the meshgrid, then you should not use pcolormesh.
Some alternative could be:
3D scatterplot:
import random
import numpy as np
import matplotlib.pyplot as plt
x = []
y = []
z = []
for index in range(100):
a = random.random()
b = random.random()
c = np.exp(-a*a - b*b)
x.append(a)
y.append(b)
z.append(c)
cmap = plt.get_cmap('PiYG')
fig = plt.figure()
ax = fig.add_subplot(projection = '3d')
ax.scatter(x, y, z, cmap=cmap)
plt.show()
2D colored scatterplot:
import random
import numpy as np
import matplotlib.pyplot as plt
x = []
y = []
z = []
for index in range(100):
a = random.random()
b = random.random()
c = np.exp(-a*a - b*b)
x.append(a)
y.append(b)
z.append(c)
cmap = plt.get_cmap('PiYG')
plt.style.use('seaborn-darkgrid')
fig, ax = plt.subplots()
ax.scatter(x, y, c = z, cmap=cmap)
plt.show()

How to make my surface plot appear using Axes3D?

I'm trying to create a surface plot using Python Matplotlib. I've read the documentation in an attempt to figure out where my code was wrong or if I've left anything out, but was having trouble.
The code that I've written is
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
def computeCost(X, y, theta):
m = len(y)
predictions = np.dot(X, theta)
squareErros = (predictions - y) ** 2
J = (1 / (2 * m)) * sum(squareErrors)
return J
data = np.loadtxt("./data1.txt", delimiter=',')
X = data[:, 0].reshape(-1, 1)
y = data[:, 1].reshape(-1, 1)
m = len(y)
X = np.concatenate((np.ones((m, 1)), X), axis=1)
theta0_vals = np.linspace(-10, 10, 100) # size (100,)
theta1_vals = np.linspace(-1, 4, 100) # size (100,)
J_vals = np.zeros((len(theta0_vals), len(theta1_vals)))
for i in range(len(x_values)):
for j in range(len(y_values)):
t = np.array([theta0_vals[i], theta1_vals[j]]).reshape(-1, 1)
J_vals[i][j] = computeCost(X, y, t) # size (100, 100)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
surf = ax.plot_surface(theta0_vals, theta1_vals, J_vals)
plt.show()
When I invoke plt.show() I get no output. The surface plot that I'm expecting to see is similar to this:
Would anybody be kind enough to let me know where my usage of the surface plot library went wrong? Thank you.
EDIT
I've tried to run the demo code provided here and it works fine. Here's the code for that:
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import numpy as np
fig = plt.figure()
ax = fig.gca(projection='3d')
# Make data.
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)
# Plot the surface.
surf = ax.plot_surface(X, Y, Z, cmap=cm.coolwarm,
linewidth=0, antialiased=False)
# Customize the z axis.
ax.set_zlim(-1.01, 1.01)
ax.zaxis.set_major_locator(LinearLocator(10))
ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))
# Add a color bar which maps values to colors.
fig.colorbar(surf, shrink=0.5, aspect=5)
plt.show()

I think I've figured out the issue by changing a couple of the last lines of code from
ax = fig.add_subplot(111, projection='3d')
surf = ax.plot_surface(theta0_vals, theta1_vals, J_vals)
to
ax = plt.axes(projection='3d')
surf = ax.plot_surface(theta0_vals, theta1_vals, J_vals, rstride=1, cstride=1, cmap='viridis', edgecolor='none')
Making this change gives me a surface plot such that:
The link that gave me reference to this was this.

Using matplotlib to make 3D plot

Just trying to make a 3D plot of a constant (0). So I have
width = 1
dx = 0.1
X = np.arange(-width, width, dx)
Y = np.arange(-width, width, dx)
X, Y = np.meshgrid(X, Y)
Z = []
for i in range(len(X)):
Z.append(np.zeros(len(X[i])))
But when I try to run Axes3D.plot_wireframe(X,Y,Z) I get plot_wireframe() missing 1 required positional argument: 'Z'. I need help understanding why this is, because Z is a 2D array like it should be, and I can't find many helpful examples with 3D plotting with matplotlib.

The main point is that you cannot run Axes3D.plot_wireframe(X,Y,Z) by itself. Instead you need to create an instance of Axes3D and call its method [*]. Just like in the 2D case where you wouldn't call matplotlib.axes.Axes.plot(x,y) but ax.plot(x,y) where ax is the instance created e.g. via fig.add_subplot.
An example for the wireframe plot can be found here.
The following code (using the code from the question)
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
width = 1
dx = 0.1
X = np.arange(-width, width, dx)
Y = np.arange(-width, width, dx)
X, Y = np.meshgrid(X, Y)
Z = []
for i in range(len(X)):
Z.append(np.zeros(len(X[i])))
ax.plot_wireframe(X, Y, Z)
plt.show()
produses the following plot
[*] To be precise here; you can call the class method plot_wireframe, but you would then need to supply it with the instance like
Axes3D.plot_wireframe(ax, X, Y, Z)

I found this example online.
I've pasted it in your code example and I got the following code + plot:
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
width = 1
dx = 0.1
X = np.arange(-width, width, dx)
Y = np.arange(-width, width, dx)
X, Y = np.meshgrid(X, Y)
Z = []
for i in range(len(X)):
Z.append(np.zeros(len(X[i])))
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_wireframe(X, Y, Z)
plt.show()
Hope this helps! I ran it with python 3.5, using the spyder IDE.
Cheers,
Dave

Fixing jagged edges of 3D plot, selecting an appropriate mask

So I have some 3D data that I am able to plot just fine except the edges look jagged.
The relevant code:
import numpy as np
from matplotlib import cm
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
x = np.arange(-1, 1, 0.01)
y = np.arange(-1, 1, 0.01)
x, y = np.meshgrid(x, y)
rho = np.sqrt(x**2 + y**2)
# Attempts at masking shown here
# My Mask
row=0
while row<np.shape(x)[0]:
col=0
while col<np.shape(x)[1]:
if rho[row][col] > 1:
rho[row][col] = None
col=col+1
row=row+1
# Calculate & Plot
z = rho**2
fig = plt.figure()
ax = fig.gca(projection='3d')
surf = ax.plot_surface(x, y, z, rstride=8, cstride=8, cmap=cm.bone, alpha=0.15, linewidth=0.25)
plt.show()
Produces:
This is so close to what I want except the edges are jagged.
If I disable my mask in the code above & replace it with rho = np.ma.masked_where(rho > 1, rho) it gives:
It isn't jagged but not want I want in the corners.
Any suggestions on different masking or plotting methods to get rid of this jaggedness?

Did you consider using polar coordinates (like in this example) ?
Something like:
import numpy as np
from matplotlib import cm
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
# create supporting points in polar coordinates
r = np.linspace(0,1.25,50)
p = np.linspace(0,2*np.pi,50)
R,P = np.meshgrid(r,p)
# transform them to cartesian system
x, y = R * np.cos(P), R * np.sin(P)
rho = np.sqrt(x**2 + y**2)
# Calculate & Plot
z = rho**2
fig = plt.figure()
ax = fig.gca(projection='3d')
surf = ax.plot_surface(x, y, z, rstride=1, cstride=1, cmap=cm.bone, alpha=0.15, linewidth=0.25)
plt.show()

Plot surface python with different width in mesh

I' m trying to plot a 3d surface with python in fact i have this code:
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
import matplotlib.pyplot as plt
from numpy import *
def f(x,y):
r=x**2 + y**2
return r
n=4.
b=1.
a=-b
h=(2*b)/n
print h
hx=h ##This line##
fig = plt.figure()
ax = Axes3D(fig)
X = arange(a, b+hx, hx)
Y = arange(a, b+h, h)
n = len(X)
m = len(Y)
Z = zeros([n,m])
for i in arange(n):
for j in arange(m):
Z[i,j] = f(X[i],Y[j])
X, Y = meshgrid(X, Y)
ax.plot_surface(Y, X, Z, rstride=1, cstride=1, cmap=cm.jet)
ax.set_xlabel("X Axis")
ax.set_ylabel("Y Axis")
ax.set_zlabel("Z Axis")
plt.show()
This runs Ok and show me the graph I am looking. But when I change ##This line## into hx=h/2. And run it, the graph goes to hell, it's horrible and impossible to understand. I want to have a closer grid in X than Y axis. How I can do this??
Of course this is an example I am solving a partial differential equation, and i need to have a grid closer in one axis than the other one to have numerical estability.

You have flipped your dimensions
Z = zeros([m,n])
for i in arange(n):
for j in arange(m):
Z[j,i] = f(X[i],Y[j])
X, Y = meshgrid(X, Y)
works for any ratio of n to m.
With the function you have, you can use numpy's broadcasting and write this whole section as
X, Y = meshgrid(X, Y)
Z = f(X,Y)
which is both easier to read and faster.
I would re-write this whole block of code as:
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
import matplotlib.pyplot as plt
from numpy import *
def f(x,y):
r=x**2 + y**2
return r
n = 5
m = 10
b = 1.
a = -b
fig = plt.figure()
ax = Axes3D(fig)
X = linspace(a,b,n)
Y = linspace(a,b,m)
X, Y = meshgrid(X, Y)
Z = f(X,Y)
ax.plot_surface(Y, X, Z, rstride=1, cstride=1, cmap=cm.jet)
ax.set_xlabel("X Axis")
ax.set_ylabel("Y Axis")
ax.set_zlabel("Z Axis")
plt.show()