Develop Reference

How to draw intersecting polygons in 3d?

I want to plot (filled) polygons that are given by a sequence of points that define the boundary in 3d. Unfortunately these polygons intersect eachother.
Here is a minimal example that shows two squares that intersect, with the issue that they are not plotted correctly. In my actual application these polygons are generated on the fly, so it is also not possible to manually define a triangulation of the polygon. I'm aware that with Poly3DCollection, there is no chance of doing this correctly as the polygons will only be filled after the projection.
Can anyone recommend another method that allows drawing polygons in 3d with correct intersections?
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
import matplotlib.pyplot as plt
fig = plt.figure()
ax = Axes3D(fig)
verts1 = np.array([
[0, 0, 0.5],
[1, 0, 0.5],
[1, 1, 0.5],
[0, 1, 0.5]
])
verts2 = verts1[:, [1, 2, 0]]
verts = [verts1, verts2]
ax.add_collection3d(p3c := Poly3DCollection(verts))
p3c.set_facecolor([(1, 0, 0), (0, 1, 0)])
plt.show()

matplotlib's 3d scaling bugged?

I am trying to work around the limitation of matplotlib that it cannot not scale the axes individually. In trying to achieve that by putting a dot on each axis. Weirdly it is telling me
.../matplotlib/collections.py:1003:RuntimeWarning: invalid value encountered in sqrt
scale = np.sqrt(self._sizes) * dpi / 72.0 * self._factor
Here is my code
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from itertools import permutations
fig = plt.figure()
ax = Axes3D(fig)
a = np.array(list(set(permutations((1, 0, 0), 3)) | set(permutations((-1, 0, 0), 3))))
plt.scatter(a[:, 0], a[:, 1], a[:, 2])

How to create a custom colormap with three colors?

I want to create a custom colormap with three colors, red-white-blue, using Matplotlib.
I know how to create a custom colormap with two colors. Here from red to blue:
import numpy as np
from matplotlib.colors import ListedColormap
N = 1024
vals = np.zeros((N, 4))
vals[:, 0] = np.linspace(1.0, 0.0, N) # red
vals[:, 2] = np.linspace(0.0, 1.0, N) # blue
vals[:, 3] = 1.0
my_cmap = ListedColormap(vals)
But how do I add the color white between red and blue?

You can just specify the colors in the ListedColormap, like this:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.colors import ListedColormap
# make something to plot
x = np.linspace(0, 10, 400)
y = np.linspace(0, 10, 400)
xv, yv = np.meshgrid(x, y)
z = np.sin(xv*yv)
# make the color map:
cmp = ListedColormap(['red', 'white', 'blue'])
# do the plot
plt.pcolormesh(z, cmap=cmp)

I used this adapted code from the matplotlib documentation
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.colors import ListedColormap
def plot_examples(colormaps):
"""
Helper function to plot data with associated colormap.
"""
np.random.seed(19680801)
data = np.random.randn(30, 30)
n = len(colormaps)
fig, axs = plt.subplots(1, n, figsize=(n * 2 + 2, 3),
constrained_layout=True, squeeze=False)
for [ax, cmap] in zip(axs.flat, colormaps):
psm = ax.pcolormesh(data, cmap=cmap, rasterized=True, vmin=-4, vmax=4)
fig.colorbar(psm, ax=ax)
plt.show()
# Red, Green, Blue
N = 256
vals = np.ones((N, 4))
# Red stays constant until middle of colormap all other channels increas
# to result in white
# from middle of colormap we decrease to 0, 0, 255 which is blue
vals[:, 0] = np.concatenate((np.linspace(1, 1, N//2), np.linspace(1, 0, N//2)), axis=None)
vals[:, 1] = np.concatenate((np.linspace(0, 1, N//2), np.linspace(1, 0, N//2)), axis=None)
vals[:, 2] = np.concatenate((np.linspace(0, 1, N//2), np.linspace(1, 1, N//2)), axis=None)
newcmp = ListedColormap(vals)
plot_examples([newcmp])
The output of this script looks similar to this

drawing grid lines beween points in python

I have 6 points in the (x,y) plane: x=[x1,x2,x3,x4,x5,x6] and y=[y1,y2,y3,y4,y5,y6]
import matplotlib.pyplot as plt
x = [0, 2, 4, 0, 2, 4, 0, 2, 4]
y = [0, 0, 0, 3, 3, 3, 7, 7, 7]
plt.scatter(x, y)
plt.show()
I want to between the points, draw entirely parallel lines on each axis x,y(like photo). and how to hide x and y axis on diagram. I want to draw a 2D view of the beams and columns of 3 story building; does matplotlib bring me to my goal or should I go to other libraries?

Absolutely matplotlib can do this. Take a look at their Rectangle Patch:
Example usage (you'll have to modify this to your needs):
import matplotlib.pyplot as plt
import matplotlib.patches as patches
fig = plt.figure()
ax = fig.add_subplot()
rect = patches.Rectangle(
(0.1, 0.1),
0.5,
0.5,
fill=False
)
ax.add_patch(rect)
fig.show()

Custom Colorbar-like plot with matplotlib

I'm looking to make a colorbar like plot, like so:
but with a controllable color, for example I have the following x and y arrays:
x = [0,1,2,4,7,8]
y = [1,2,1,3,4,5]
Then I would have a colorbar like the above picture, but when y=1, it would color red, y=2: green, y=3: blue, y=4:black, etc.
Here is the python code that I modified from matplotlib's gallery:
from matplotlib import pyplot
import matplotlib as mpl
fig = pyplot.figure(figsize=(8,1))
ax2 = fig.add_axes([0.05, 0.25, 0.9, 0.5])
cmap = mpl.cm.Accent
norm = mpl.colors.Normalize(vmin=5, vmax=10)
bounds = [1, 2, 4, 7, 8]
norm = mpl.colors.BoundaryNorm(bounds, cmap.N)
cb2 = mpl.colorbar.ColorbarBase(ax2, cmap=cmap,
norm=norm,
boundaries=[0]+bounds+[13],
ticks=bounds, # optional
spacing='proportional',
orientation='horizontal')

After adapting your code I managed to obtain something like you described.
In this case the colormap is generated using ListedColormap and I added the yellow color for y=5.
It is important to notice that while calculating the BoundaryNorm I am using the intervals that contain the values you described for y.
from matplotlib import pyplot,colors
import matplotlib as mpl
fig = pyplot.figure(figsize=(8,1))
ax2 = fig.add_axes([0.05, 0.25, 0.9, 0.5])
cmap = colors.ListedColormap(['r', 'g', 'b', 'k','y'])
bounds = [0, 1, 2, 4, 7, 8, 13]
yVals = [ 1, 2, 1, 3, 4, 5]
cBounds = [i+0.5 for i in range(6)]
norm = mpl.colors.BoundaryNorm(cBounds, cmap.N)
cb2 = mpl.colorbar.ColorbarBase(ax2, cmap=cmap,
norm=norm,
values=yVals,
boundaries=bounds,
ticks=bounds[1:-1], # optional
spacing='proportional',
orientation='horizontal')
-- Edited 14 of Jan (mrcl) --
Alternatively, you can use pcolormesh to plot your colormap and have a colorbar as your legend, such as in the example below.
from pylab import *
from matplotlib import pyplot,colors
import matplotlib as mpl
fig = pyplot.figure(figsize=(8,1.5))
ax1 = fig.add_axes([0.05, 0.25, 0.82, 0.5])
cmap = colors.ListedColormap(['r', 'g', 'b', 'k','y'])
xBounds = array([0, 1, 2, 4, 7, 8, 13])
yBounds = array([0, 1])
Vals = array([[ 1, 2, 1, 3, 4, 5]])
cBounds = [i+0.5 for i in arange(amax(Vals)+1)]
norm = mpl.colors.BoundaryNorm(cBounds, cmap.N)
c = ax1.pcolormesh(xBounds,yBounds,Vals,cmap=cmap,norm=norm)
ax1.set_xticks(xBounds[1:-1])
ax1.set_yticks([])
ax1.set_xlim(xBounds[0],xBounds[-1])
ax1.set_ylim(yBounds[0],yBounds[-1])
ax2 = fig.add_axes([0.9, 0.25, 0.05, 0.5])
colorbar(c,cax=ax2,ticks=arange(amax(Vals))+1)
Hope it helps.
Cheers

Well, I sort of tinkering with other ways:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cmx
import matplotlib.colors as colors
close('all')
def ColorPlot(x,y):
figure()
jet = plt.get_cmap('jet')
cNorm = colors.Normalize(vmin=min(y), vmax=max(y))
scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=jet)
if len(x) == len(y):
x.insert(0,0)
for kk in range(len(x)-1):
colorVal = scalarMap.to_rgba(y[kk])
plt.axvspan(x[kk], x[kk+1], facecolor=colorVal,
alpha=0.5,label=colorVal)
plt.yticks([])
plt.xticks(x)
xlim([x[0],x[-1]])
plt.show()
x = [1,3,5,6,10,12]
y = [1,3,4,1,4,3]
ColorPlot(x,y)