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How to Close the Surface of this Half Cylinder with Python Matplotlib?

I have this half cylinder plot, but it is not closed on the surface. How to make it close?
Is it possible to plot cylinder from vertices and sides? With 2 vertices become an arc?
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d.art3d import Poly3DCollection, Line3DCollection
import numpy as np
def data_for_cylinder_along_z(center_x,center_y,radius,height_z):
z = np.linspace(0, height_z, 50)
theta = np.linspace(0, 1*np.pi, 50)
theta_grid, z_grid=np.meshgrid(theta, z)
x_grid = radius*np.cos(theta_grid) + center_x
y_grid = radius*np.sin(theta_grid) + center_y
return x_grid,y_grid,z_grid
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
Xc,Yc,Zc = data_for_cylinder_along_z(0.2,0.2,0.05,0.1)
ax.plot_surface(Xc, Yc, Zc, alpha=0.5)
# Annotation
ax.set_title("Half Cylinder"))
plt.show()

If you can accept manually plotting each plane to enclose the shape, here's the code:
def data_for_horizontal_plane(center_x, center_y, radius, height_z):
# define the horizontal surface using polar coordinates
_radius = np.linspace(0, radius)
_theta = np.linspace(0, np.pi)
R, T = np.meshgrid(_radius, _theta)
# convert polar to cartesian coordinates and add translation
X = R*np.cos(T) + center_x
Y = R*np.sin(T) + center_y
Z = np.zeros(X.shape) + height_z
return X, Y, Z
def data_for_vertical_plane(center_x, center_y, radius, height_z):
# define the vertical rectangle on the X-Z plane
x = np.linspace(center_x - radius, center_x + radius)
z = np.linspace(0, height_z)
X, Z = np.meshgrid(x, z)
Y = np.zeros(X.shape) + center_y
return X, Y, Z
X, Y, Z = data_for_horizontal_plane(0.2, 0.2, 0.05, 0.0)
ax.plot_surface(X, Y, Z, alpha=0.5)
X, Y, Z = data_for_horizontal_plane(0.2, 0.2, 0.05, 0.1)
ax.plot_surface(X, Y, Z, alpha=0.5)
X, Y, Z = data_for_vertical_plane(0.2, 0.2, 0.05, 0.1)
ax.plot_surface(X, Y, Z, alpha=0.5)

Get distinct boundaries in matplolib contourf

I am trying to linearly interpolate values using scipy of sets of coordinates, thereafter plotting in matplotlib. How can I achieve the distinct boundaries between each region?
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import numpy as np
np.random.seed(42)
from scipy.interpolate import griddata
x = np.random.random(20)
y = np.random.random(20)
z = np.random.random(20)
meshSize = 50
extensionFact = 10
xi, yi, = np.meshgrid(
np.linspace(np.min(x) - np.average(x) / extensionFact, np.max(x) + np.average(x) / extensionFact, meshSize),
np.linspace(np.min(y) - np.average(y) / extensionFact, np.max(y) + np.average(y) / extensionFact, meshSize))
zi = griddata((x, y), z, (xi, yi), method='nearest')
fig = plt.figure(figsize=(8, 6))
ax1 = fig.add_subplot(111)
bounds1 = np.linspace(np.nanmin(zi), np.nanmax(zi), 11)
colors1 = plt.get_cmap('jet')(np.linspace(0, 1, len(bounds1) + 1))
cmap1 = mcolors.ListedColormap(colors1[1:-1])
norm1 = mcolors.BoundaryNorm(boundaries=bounds1, ncolors=len(bounds1) - 1)
im1 = ax1.contourf(xi, yi, zi, levels=bounds1, cmap=cmap1, alpha=1)
fig.colorbar(im1, orientation='vertical', shrink=1, aspect=30, pad=0.03, ticks=bounds1)
plt.scatter(x, y, marker='x', c='k')
plt.show()
Presently the intersection of two regions are blurred.

How to combine two matplotlib (python) colormaps from scatter plot

I am trying to combine two colourmap legends in one. Colour values are defined from third (z) data.
I am trying plot one legend colormap with two color scheme.
from scipy.optimize import curve_fit
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.read_excel('C:\\Users\user1\\PycharmProjects\\untitled\\Python_test.xlsx')
x = df['Vp_dry']
y = df['Vs_dry']
q = df['Vp_wet']
w = df['Vs_wet']
fig, ax = plt.subplots()
popt, pcov = curve_fit(lambda fx, a, b: a * fx ** -b, x, y)
x_linspace = np.linspace(min(x - 100), max(x + 100), 100)
power_y = popt[0]*x_linspace ** -popt[1]
ax1 = plt.scatter(x, y, c=df['Porosity'], cmap=plt.cm.Greys, vmin=2, vmax=df['Porosity'].max(), edgecolors="#B6BBBD")
plt.plot(x_linspace, power_y, color='grey', label='Dry')
popt, pcov = curve_fit(lambda fx, a, b: a * fx ** -b, q, w)
q_linspace = np.linspace(min(q - 100), max(q + 100), 100)
power_w = popt[0]*q_linspace ** -popt[1]
ax2 = plt.scatter(q, w, c=df['Porosity'], cmap=plt.cm.Blues, vmin=2, vmax=df['Porosity'].max(), edgecolors="#3D83C1")
plt.plot(q_linspace, power_w, label='Wet')
cbar = fig.colorbar(ax2)
cbar = fig.colorbar(ax1)
cbar.set_label("Porosity (%)")
plt.xlabel('Vp (m/s)')
plt.ylabel('Vs (m/s)')
plt.grid()
plt.legend()
plt.show()
Desired result:

You seem to need a colorbar with two color maps combined, one of them reversed, and have the ticks changed to percentage values.
An approach is to manually create a second subplot, use two images and make it look like a colorbar:
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
import numpy as np
# first create some dummy data to plot
N = 100
x = np.random.uniform(0, 10, N)
y = np.random.normal(15, 2, N)
q = np.random.uniform(0, 10, N)
w = np.random.normal(10, 2, N)
df_porosity = np.random.uniform(0, 5, N)
fig, (ax, ax2) = plt.subplots(ncols=2, figsize=(6, 4), gridspec_kw={"width_ratios": [1, 0.08]})
plot1 = ax.scatter(x, y, c=df_porosity, cmap=plt.cm.Greys, vmin=2, vmax=df_porosity.max(), edgecolors="#B6BBBD")
plot2 = ax.scatter(q, w, c=df_porosity, cmap=plt.cm.Blues, vmin=2, vmax=df_porosity.max(), edgecolors="#3D83C1")
img_cbar = np.linspace(0, 1, 256).reshape(256, 1)
ax2.imshow(img_cbar, cmap=plt.cm.Blues, extent=[0, 1, 1, 0]) # aspect='auto')
ax2.imshow(img_cbar, cmap=plt.cm.Greys, extent=[0, 1, -1, 0])
ax2.set_ylim(-1, 1)
ax2.set_aspect(10)
ax2.set_ylabel("Porosity (%)")
ax2.yaxis.set_label_position("right")
ax2.set_xticks([])
ax2.yaxis.tick_right()
# optionally show the ticks as percentage, where 1.0 corresponds to 100 %
ax2.yaxis.set_major_formatter(mtick.PercentFormatter(1.0))
plt.tight_layout()
plt.show()

3D plot of the CONE using matplotlib

I'm looking for help to draw a 3D cone using matplotlib.
My goal is to draw a HSL cone, then base on the vertex coordinats i will select the color.
from matplotlib import cm
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')
theta1 = np.linspace(0, 2*np.pi, 100)
r1 = np.linspace(-2, 0, 100)
t1, R1 = np.meshgrid(theta1, r1)
X1 = R1*np.cos(t1)
Y1 = R1*np.sin(t1)
Z1 = 5+R1*2.5
theta2 = np.linspace(0, 2*np.pi, 100)
r2 = np.linspace(0, 2, 100)
t2, R2 = np.meshgrid(theta2, r2)
X2 = R2*np.cos(t2)
Y2 = R2*np.sin(t2)
Z2 = -5+R2*2.5
ax.set_xlabel('x axis')
ax.set_ylabel('y axis')
ax.set_zlabel('z axis')
# ax.set_xlim(-2.5, 2.5)
# ax.set_ylim(-2.5, 2.5)
# ax.set_zlim(0, 5)
ax.set_aspect('equal')
ax.plot_surface(X1, Y1, Z1, alpha=0.8, color="blue")
ax.plot_surface(X2, Y2, Z2, alpha=0.8, color="blue")
# ax.plot_surface(X, Y, Z, alpha=0.8)
#fig. savefig ("Cone.png", dpi=100, transparent = False)
plt.show()
HSL CONE
My cone
So my question now is how to define color of each element.

i have found a solution, maybe it will be usefull for others.
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
import matplotlib.pyplot as plt
import numpy as np
import colorsys
from matplotlib.tri import Triangulation
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
n_angles = 80
n_radii = 20
# An array of radii
# Does not include radius r=0, this is to eliminate duplicate points
radii = np.linspace(0.0, 0.5, n_radii)
# An array of angles
angles = np.linspace(0, 2*np.pi, n_angles, endpoint=False)
# Repeat all angles for each radius
angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
# Convert polar (radii, angles) coords to cartesian (x, y) coords
# (0, 0) is added here. There are no duplicate points in the (x, y) plane
x = np.append(0, (radii*np.cos(angles)).flatten())
y = np.append(0, (radii*np.sin(angles)).flatten())
# Pringle surface
z = 1+-np.sqrt(x**2+y**2)*2
print(x.shape, y.shape, angles.shape, radii.shape, z.shape)
# NOTE: This assumes that there is a nice projection of the surface into the x/y-plane!
tri = Triangulation(x, y)
triangle_vertices = np.array([np.array([[x[T[0]], y[T[0]], z[T[0]]],
[x[T[1]], y[T[1]], z[T[1]]],
[x[T[2]], y[T[2]], z[T[2]]]]) for T in tri.triangles])
x2 = np.append(0, (radii*np.cos(angles)).flatten())
y2 = np.append(0, (radii*np.sin(angles)).flatten())
# Pringle surface
z2 = -1+np.sqrt(x**2+y**2)*2
# NOTE: This assumes that there is a nice projection of the surface into the x/y-plane!
tri2 = Triangulation(x2, y2)
triangle_vertices2 = np.array([np.array([[x2[T[0]], y2[T[0]], z2[T[0]]],
[x2[T[1]], y2[T[1]], z2[T[1]]],
[x2[T[2]], y2[T[2]], z2[T[2]]]]) for T in tri2.triangles])
triangle_vertices = np.concatenate([triangle_vertices, triangle_vertices2])
midpoints = np.average(triangle_vertices, axis=1)
def find_color_for_point(pt):
c_x, c_y, c_z = pt
angle = np.arctan2(c_x, c_y)*180/np.pi
if (angle < 0):
angle = angle + 360
if c_z < 0:
l = 0.5 - abs(c_z)/2
#l=0
if c_z == 0:
l = 0.5
if c_z > 0:
l = (1 - (1-c_z)/2)
if c_z > 0.97:
l = (1 - (1-c_z)/2)
col = colorsys.hls_to_rgb(angle/360, l, 1)
return col
facecolors = [find_color_for_point(pt) for pt in midpoints] # smooth gradient
# facecolors = [np.random.random(3) for pt in midpoints] # random colors
coll = Poly3DCollection(
triangle_vertices, facecolors=facecolors, edgecolors=None)
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.add_collection(coll)
ax.set_xlim(-1, 1)
ax.set_ylim(-1, 1)
ax.set_zlim(-1, 1)
ax.elev = 50
plt.show()

Inspired from Jake Vanderplas with Python Data Science Handbook, when you are drawing some 3-D plot whose base is a circle, it is likely that you would try:
# Actually not sure about the math here though:
u, v = np.mgrid[0:2*np.pi:100j, 0:np.pi:20j]
x = np.cos(u)*np.sin(v)
y = np.sin(u)*np.sin(v)
and then think about the z-axis. Since viewing from the z-axis the cone is just a circle, so the relationships between z and x and y is clear, which is simply: z = np.sqrt(x ** 2 + y ** 2). Then you can draw the cone based on the codes below:
from mpl_toolkits import mplot3d
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
def f(x, y):
return np.sqrt(x ** 2 + y ** 2)
fig = plt.figure()
ax = plt.axes(projection='3d')
# Can manipulate with 100j and 80j values to make your cone looks different
u, v = np.mgrid[0:2*np.pi:100j, 0:np.pi:80j]
x = np.cos(u)*np.sin(v)
y = np.sin(u)*np.sin(v)
z = f(x, y)
ax.plot_surface(x, y, z, cmap=cm.coolwarm)
# Some other effects you may want to try based on your needs:
# ax.plot_surface(x, y, -z, cmap=cm.coolwarm)
# ax.scatter3D(x, y, z, color="b")
# ax.plot_wireframe(x, y, z, color="b")
# ax.plot_wireframe(x, y, -z, color="r")
# Can set your view from different angles.
ax.view_init(azim=15, elev=15)
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_zlabel("z")
plt.show()
ax.set_ylabel("y")
ax.set_zlabel("z")
plt.show()
And from my side, the cone looks like:
and hope it helps.

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.