I have a 3d plot made using matplotlib. I now want to fill the vertical space between the drawn line and the x,y axis to highlight the height of the line on the z axis. On a 2d plot this would be done with fill_between but there does not seem to be anything similar for a 3d plot. Can anyone help?
here is my current code
from stravalib import Client
import matplotlib as mpl
import numpy as np
import matplotlib.pyplot as plt
... code to get the data ....
mpl.rcParams['legend.fontsize'] = 10
fig = plt.figure()
ax = fig.gca(projection='3d')
zi = alt
x = df['x'].tolist()
y = df['y'].tolist()
ax.plot(x, y, zi, label='line')
ax.legend()
plt.show()
and the current plot
just to be clear I want a vertical fill to the x,y axis intersection NOT this...
You're right. It seems that there is no equivalent in 3D plot for the 2D plot function fill_between. The solution I propose is to convert your data in 3D polygons. Here is the corresponding code:
import math as mt
import matplotlib.pyplot as pl
import numpy as np
import random as rd
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
# Parameter (reference height)
h = 0.0
# Code to generate the data
n = 200
alpha = 0.75 * mt.pi
theta = [alpha + 2.0 * mt.pi * (float(k) / float(n)) for k in range(0, n + 1)]
xs = [1.0 * mt.cos(k) for k in theta]
ys = [1.0 * mt.sin(k) for k in theta]
zs = [abs(k - alpha - mt.pi) * rd.random() for k in theta]
# Code to convert data in 3D polygons
v = []
for k in range(0, len(xs) - 1):
x = [xs[k], xs[k+1], xs[k+1], xs[k]]
y = [ys[k], ys[k+1], ys[k+1], ys[k]]
z = [zs[k], zs[k+1], h, h]
#list is necessary in python 3/remove for python 2
v.append(list(zip(x, y, z)))
poly3dCollection = Poly3DCollection(v)
# Code to plot the 3D polygons
fig = pl.figure()
ax = Axes3D(fig)
ax.add_collection3d(poly3dCollection)
ax.set_xlim([min(xs), max(xs)])
ax.set_ylim([min(ys), max(ys)])
ax.set_zlim([min(zs), max(zs)])
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_zlabel("z")
pl.show()
It produces the following figure:
I hope this will help you.
Related
I am trying to get my 3D python plot into 3 different colors based on the value of Z from a CSV file. I am trying to color a point one specific color, and then points below one color and points above one color. I can get the plot into a color above and below the point, but I can't seem to figure out how to get it into 3 colors.
I have tried to split the Z value into 3 different 3 subsets, but when I tried to plot the plot was just empty. I also tried to write it through an if statement assigning Z to the color but that did not work either. This is the code that works for the 2 color:
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import cm
from mpl_toolkits.mplot3d import Axes3D
import pandas as pd
headers = ['name','ra','x rads','x par','dec','y rads','Parallax','Parallax Error','central distance','Z Max','Z Min']
mergeddata = pd.read_csv(r'C:\Users\GregL\Downloads\mergedata - no neg parallax #s (2).csv')
mergeddata.z = mergeddata['central distance']
mergeddata.x = mergeddata['x par']
mergeddata.y = mergeddata['y rads']
x= mergeddata.x
y= mergeddata.y
z = mergeddata.z
colors = [z <= 1956.783590]
fig = plt.figure(figsize=(10,10))
ax = fig.add_subplot(111, projection='3d')
surf=ax.scatter3D(x,y,z,c=colors, cmap='coolwarm',s=.5,marker='^')
ax.set_title('3D Data Distance Plot')
ax.set_zlim(-100,10000)
ax.set_xlim(-50,50)
ax.set_ylim(-50,50)
ax.set_xlabel('RA')
ax.set_ylabel('DEC')
ax.set_zlabel('CENTRAL DISTANCE')
plt.show()
Which gives me this plot
As mentioned by Claudio on the comment, you can create a color value and then assign a proper colormap. Here, I'm going to create a discrete color map based on Matplotlib's Tab10:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import matplotlib.colors as col
fig = plt.figure()
ax = plt.axes(projection ='3d')
z = np.linspace(0, 1, 100)
x = z * np.sin(25 * z)
y = z * np.cos(25 * z)
# values for color
c = [int(zv / 0.4) for zv in z]
# discrete colormap with 3 colors
cmap=col.ListedColormap(cm.tab10.colors[:len(np.unique(c))])
ax.scatter(x, y, z, c=c, cmap=cmap)
plt.show()
Alternatively, you can create multiple ax.scatter commands, each one plotting a subset. The advantage of this approach is that you can set custom labels or rendering properties to each subset:
fig = plt.figure()
ax = plt.axes(projection ='3d')
i1 = z < 0.3
i2 = (z >= 0.3) & (z < 0.6)
i3 = z >= 0.6
ax.scatter(x[i1], y[i1], z[i1], label="a")
ax.scatter(x[i2], y[i2], z[i2], label="b")
ax.scatter(x[i3], y[i3], z[i3], label="c")
ax.legend()
plt.show()
I have a 3 1D arrays: radius, angle and temperature. Together they form a 2D temperature map of a ring.
The arrays take the form:
r = [0,0,0,1,1,1,2,2,2]
th = [0.,0.78539816,1.57079633,2.35619449,3.14159265,3.92699082,4.71238898,5.49778714,6.28318531]
z = [-1,2,5,2,4,-1,3,2,3]
I don't understand how I can make those z data fall on the right coordinates.
I can make it work, with random numbers, using the following simple code:
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
fig = plt.figure()
ax = Axes3D(fig)
rad = np.linspace(.2, 1, 4)
azm = np.linspace(0, 2 * np.pi, 9)
r, th = np.meshgrid(rad, azm)
z = np.random.rand(9,4) ** th * r
ax0 = plt.subplot(projection="polar")
im = plt.pcolormesh(th, r, z, cmap='bwr')
plt.plot(azm, r, color='k', ls='none')
plt.axis('off')
cbar = fig.colorbar(im)
ax0.set_title('3 radii polar heat map')
This is how my example code comes out
I ended splitting the list z into 3 lists and made a matrix out of them using z_mat = np.array([z1,z2,z3]). I took care that the lists for rad and azm contained one item more than z, which is a requirement for pcolormesh. After that I transposed the matrix to have the same dimensions as r and th using z_mat_trans = z_mat.transpose()
here is the code im using and I've also attached the output. I'd like to plot a two dimensional lognorm function as a 3d surface, the above code is supposed to do this however the output results in the entire plane being skewed rather than just the z values. any help or suggestions would be greatly appreciated.
dx = 90 - (-90)
dy = 90 - (-90)
c = [dx + dx/2.0, dy+dy/2.0]
z = np.zeros((400, 400))
x = np.linspace(-90, 90, 400)
y = x.copy()
for i in range(len(x)):
for j in range(len(y)):
p =[x[i], y[j]]
d = math.sqrt((p[0]-c[0])**2 + (p[1]-c[1])**2)
t = d
z[i][j] = lognorm.pdf(t, 1.2)
fig = plt.figure()
ax = fig.add_subplot(111, projection = '3d')
ax.plot_surface(x,y, z, cmap = 'viridis')
plt.show()
output of the provided code
ideally I'd like for it to look something like this.
this is the image here
I think you wanted to plot a 3D surface and here is an example:
#!/usr/bin/python3
# 2018/10/25 14:44 (+0800)
# Plot a 3D surface
from scipy.stats import norm, lognorm
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
xy = np.linspace(-5, 5, 400)
xx, yy = np.meshgrid(xy)
t = np.sqrt(xx**2 + yy**2)
zz = lognorm.pdf(t, 1.2)
fig = plt.figure()
ax = fig.add_subplot(111, projection = '3d')
ax.plot_surface(xx,yy, zz, cmap = 'viridis')
plt.show()
I am trying to circular contour lines around an array of random values of radius. The result should be a bunch of concentric circles with different radius. However I am not too sure how to plot the theta so that for each radius, all values of theta is plotted to form a line.
import random
import numpy as np
r= sort(np.array([ random.random()*5 for i in arange(100) ]))
len(r)
theta = [t for t in linspace(0,2*pi,100)]
ax = plt.subplot(111, polar=True)
ax.plot(theta, r, 'o',color='r', linewidth=3)
ax.set_rmax(2.0)
ax.grid(True)
Thank you.
Here is a one-line addition that I think does what you want:
import random
import numpy as np
import matplotlib.pyplot as plt
r= np.sort(np.array([ random.random()*5 for i in np.arange(100) ]))
len(r)
theta = [t for t in np.linspace(0,2*np.pi,100)]
ax = plt.subplot(111, polar=True)
ax.plot(theta, r, 'o',color='r', linewidth=3)
ax.set_rmax(2.0)
ax.grid(True)
[ax.plot(theta, rcirc*np.ones(100)) for rcirc in r.max()*np.random.rand(5)]
plt.show()
A quick-and-dirty way to do it would be to use np.linspace to effectively draw a polygon (as I think you were attempting to do):
import numpy as np
from matplotlib import pyplot as plt
# some random radii
r = np.random.rand(10)
# 1000 angles linearly spaced between 0 and 2pi
t = np.linspace(0, 2 * np.pi, 1000)
# broadcast r against t to make each a (1000, 10) array
r, t = np.broadcast_arrays(r[None, :], t[:, None])
# plot the lines
fig, ax = plt.subplots(1, 1, subplot_kw={'polar':True})
ax.plot(t, r, '-')
I'm sure there must be a more elegant way to do this, though.
As the title suggests, I'm trying to plot a Basemap map on the z=0 surface of a matplotlib.mplot3d lineplot. I know the Axes3D object is capable of plotting on the z=0 surface (via Axes3D.plot, Axes3D.scatter, etc.), but I can't figure out how to do so with a Basemap object. Hopefully the code below shows what I need clearly enough. Any ideas would be much appreciated!
import matplotlib.pyplot as pp
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.basemap import Basemap
# make sample data for 3D lineplot
z = np.linspace(-2, 2, 100)
r = z**2 + 1
x = r * np.sin(theta)
y = r * np.cos(theta)
# make the 3D line plot
FIG = ct.pp.figure()
AX = Axes3D(FIG)
AX.plot(x, y, z, '-b')
# make the 2D basemap
### NEEDS TO SOMEHOW BE AT z=0 IN FIG
M = ct.Basemap(projection='stere', width=3700e3, height=2440e3,
lon_0=-5.0, lat_0=71.0, lat_ts=71.0,
area_thresh=100, resolution='c')
PATCHES = M.fillcontinents(lake_color='#888888', color='#282828')
Just add your map as a 3d collection to the Axes3D instance:
import numpy as np
import matplotlib.pyplot as pp
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.basemap import Basemap
theta = np.linspace(-4 * np.pi, 4 * np.pi, 100)
z = np.linspace(-500, 500, 100)
r = z**2 + 1
x = r * np.sin(theta)
y = r * np.cos(theta)
FIG = pp.figure()
AX = Axes3D(FIG)
AX.plot(x, y, z, '-b')
M = Basemap(projection='stere', width=3700e3, height=2440e3,
lon_0=-5.0, lat_0=71.0, lat_ts=71.0,
area_thresh=100, resolution='c')
AX.add_collection3d(M.drawcoastlines())
AX.grid(True)
pp.draw()
pp.show()
AX.add_collection3d(M.drawcoastlines())
works but
PATCHES = M.fillcontinents(lake_color='#888888', color='#282828')
does not work.
As soon as you add color fill you get an error similar to: "AttributeError: 'Polygon' object has no attribute 'do_3d_projection'"
M.fillcontinents(lake_color='#888888', color='#282828')`
returns an array of Polygons, not one of the inputs required by add_collection(). collect.PatchCollection() does not seem to work either.
So what do you use to add `M.fillcontinents(lake_color='#888888', color='#282828') to a 3D plot?