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Centering annotation on a log scale with matplotlib

I have the following, self-explanatory situation; please see the figure and working example pasted below.
I wonder how to center text in the middle of the dimension line.
import numpy as np
import matplotlib.pyplot as plt
# Dimension line
def annotation_line(ax, xmin, xmax, y, text, ytext=0, linecolor='black', linewidth=1, fontsize=12):
ax.annotate('', xy=(xmin, y), xytext=(xmax, y), xycoords='data', textcoords='data', arrowprops={'arrowstyle': '|-|', 'color':linecolor, 'linewidth':linewidth})
ax.annotate('', xy=(xmin, y), xytext=(xmax, y), xycoords='data', textcoords='data', arrowprops={'arrowstyle': '<->', 'color':linecolor, 'linewidth':linewidth})
xcenter = xmin + (xmax - xmin) / 2
if ytext==0:
ytext = y + ( ax.get_ylim()[1] - ax.get_ylim()[0] ) / 20
ax.annotate(text, xy=(xcenter, ytext), ha='center', va='bottom', fontsize=fontsize)
# Toy data
N = 8
y = np.zeros(N)
x1 = np.linspace(1, 1000, N, endpoint=True)
fig, ax = plt.subplots(figsize=(10, 6))
ax.plot(x1, y, 'o')
annotation_line(ax=ax, text='TEXT 1', xmin=1, xmax=100, y=0.01, ytext=0, linewidth=1, linecolor='gray', fontsize=12)
ax.set_xscale('log')

The simple solution is to do as #JohanC suggests and calculate the midpoint in log coordinates.
An other solution is to use the coordinates of the arrow to find it's midpoint. There are drawbacks to that method however. First, you need to explicitly draw the figure at an intermediate step, since the coordinates will only be valid at the time of drawing, and second, you need to set the log-scale before drawing the annotation. On the other hand, the code works regardless of the scaling of the axes
import numpy as np
import matplotlib.pyplot as plt
# Dimension line
def annotation_line(ax, xmin, xmax, y, text, ytext=0, linecolor='black', linewidth=1, fontsize=12):
an = ax.annotate('', xy=(xmin, y), xytext=(xmax, y), xycoords='data', textcoords='data', arrowprops={'arrowstyle': '|-|', 'color':linecolor, 'linewidth':linewidth})
ax.annotate('', xy=(xmin, y), xytext=(xmax, y), xycoords='data', textcoords='data', arrowprops={'arrowstyle': '<->', 'color':linecolor, 'linewidth':linewidth})
ax.figure.canvas.draw() # draw to get actual coordinates
p = an.arrow_patch.get_path().transformed(ax.transAxes.inverted())
xmin, xmax = np.min(p.vertices[:,0]),np.max(p.vertices[:,0])
xcenter = xmin+(xmax-xmin)/2
if ytext==0:
ytext = y + ( ax.get_ylim()[1] - ax.get_ylim()[0] ) / 20
ax.annotate(text, xy=(xcenter, ytext), xycoords=('axes fraction','data'), ha='center', va='bottom', fontsize=fontsize)
return an
# Toy data
N = 8
y = np.zeros(N)
x1 = np.linspace(1, 1000, N, endpoint=True)
fig, ax = plt.subplots(figsize=(10, 6))
ax.plot(x1, y, 'o')
ax.set_xscale('log') # must do before the call to annotation_line
an = annotation_line(ax=ax, text='TEXT 1', xmin=1, xmax=100, y=0.01, ytext=0, linewidth=1, linecolor='gray', fontsize=12)

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()

Python manipulate axis (x and y) update

we measure the radius over an entire device (each degree, 360 points), which is around 148mm. It should be between 146 and 150.
If you plot the data with the corresponding limits, you get this:
CirclPlot
I like to change the axis that between -145 and 145 is small, and between 145- 150 / -145 - -150 is large. So I can see the measured value nice in between the limits.
Is that possible with python?
import matplotlib.pyplot as plt
import matplotlib.scale as mscale
import pandas as pd
#read CSV
EBRData = pd.read_csv('C://Users/vanderey/Documents/MATLAB/EBRTest2.csv', header = 0)
# Define data
Dates = EBRData['Date']
Rx = EBRData['xCoat']
Ry = EBRData['yCoat']
RLSLx = EBRData['xCoat_LSL']
RLSLy = EBRData['yCoat_LSL']
RUSLx = EBRData['xCoat_USL']
RUSLy = EBRData['yCoat_USL']
#Create plot
my_dpi=96
plt.figure(figsize=(480/my_dpi, 480/my_dpi), dpi=my_dpi)
plt.plot(Rx, Ry, color='blue', marker='.', linewidth=1, alpha=0.4)
plt.plot(RLSLx, RLSLy, color='red', marker='.', linewidth=1, alpha=0.4)
plt.plot(RUSLx, RUSLy, color='red', marker='.', linewidth=1, alpha=0.4)
plt.title('EBR')
plt.show()

If radius is what you want to show, I'd also recommend to calculate R from x and y measurements and put that into a plot together with the target limits.
You can do so by calculating the complete polar coordinates from your x/y-values
phi = np.arctan2(df.yCoat, df.xCoat)
R = pd.DataFrame(np.sqrt(df.xCoat.values**2 + df.yCoat.values**2), columns=['R'], index=phi)
If you rather like to plot over the nominal angular values instead of the actual measured angle positions, you could set phi also to e.g.
phi = np.linspace(-np.pi, np.pi, 360, endpoint=False)
However, this can be plotted simply as a normal line plot with two indicated limit lines like
R.plot()
plt.hlines(146, -np.pi, np.pi, 'k')
plt.hlines(150, -np.pi, np.pi, 'k')
or e.g. as a polar plot
f = plt.figure()
ax = f.add_subplot(111, projection='polar')
ax.set_rlim(144, 152)
plt.plot(R, 'b.-')
ax.fill_between(np.linspace(-np.pi, np.pi, 360), 140, 146, color='gray')
ax.fill_between(np.linspace(-np.pi, np.pi, 360), 150, 160, color='gray')
To show samples outside the wanted range, you can simply add e.g.
plt.plot(R[R<146], 'r.')
plt.plot(R[R>150], 'r.')
to immediately see if there's a problem:

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 get build a consistent discrete colormap/colorbar with "upper" and "lower" values

An image is worth a thousand words :
https://www.harrisgeospatial.com/docs/html/images/colorbars.png
I want to obtain the same color bar than the one on the right with matplotlib.
Default behavior use the same color for "upper"/"lower" and adjacent cell...
Thank you for your help!
Here is the code I have:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as colors
N = 100
X, Y = np.mgrid[-3:3:complex(0, N), -2:2:complex(0, N)]
Z1 = np.exp(-X**2 - Y**2)
Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
Z = (Z1 - Z2) * 2
fig, ax = plt.subplots(1, 1, figsize=(8, 8))
# even bounds gives a contour-like effect
bounds = np.linspace(-1, 1, 10)
norm = colors.BoundaryNorm(boundaries=bounds, ncolors=256)
pcm = ax.pcolormesh(X, Y, Z,
norm=norm,
cmap='RdBu_r')
fig.colorbar(pcm, ax=ax, extend='both', orientation='vertical')

In order to have the "over"/"under"-color of a colormap take the first/last color of that map but still be different from the last color inside the colormapped range you can get one more color from a colormap than you have boundaries in the BoundaryNorm and use the first and last color as the respective colors for the "over"/"under"-color.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
N = 100
X, Y = np.mgrid[-3:3:complex(0, N), -2:2:complex(0, N)]
Z1 = np.exp(-X**2 - Y**2)
Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
Z = (Z1 - Z2) * 2
fig, ax = plt.subplots(1, 1, figsize=(8, 8))
# even bounds gives a contour-like effect
bounds = np.linspace(-1, 1, 11)
# get one more color than bounds from colormap
colors = plt.get_cmap('RdBu_r')(np.linspace(0,1,len(bounds)+1))
# create colormap without the outmost colors
cmap = mcolors.ListedColormap(colors[1:-1])
# set upper/lower color
cmap.set_over(colors[-1])
cmap.set_under(colors[0])
# create norm from bounds
norm = mcolors.BoundaryNorm(boundaries=bounds, ncolors=len(bounds)-1)
pcm = ax.pcolormesh(X, Y, Z, norm=norm, cmap=cmap)
fig.colorbar(pcm, ax=ax, extend='both', orientation='vertical')
plt.show()

As suggested in my comment you can change the color map with
pcm = ax.pcolormesh(X, Y, Z, norm=norm, cmap='rainbow_r')
That gives:
You can define your own color map as shown here: Create own colormap using matplotlib and plot color scale