Please help with the problem of paraboloid 3d plotting using from the elements in an array.
Below code gives a nice parabolid:
from matplotlib import *
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
from pylab import *
import math
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
a=2
t = np.arange(0, 1, 0.01)
th = np.arange(0, 2*pi, 0.01)
t,th = np.meshgrid(r, T)
#Parametrise it
X = r*cos(T)
Y = r*sin(T)
Z = a*r**2
ax.plot_surface(X, Y, Z, alpha=0.9, rstride=10, cstride=10, linewidth=0.5, cmap=cm.summer)
plt.show()
But I want something like this:
th,t,a = symbols('th t a')
rotz=np.matrix([[cos(th),-sin(th),0,0],
[sin(th),cos(th),0,0],
[0,0,1,0],
[0,0,0,1]])
g=np.matrix([t,0,a*(t**2),1])
g=np.transpose(g)
M = rotz*g
print(M)
Now M is a 4x1 matrix with the 1st three elements to parametrize the paraboloid code
[[t*cos(th)]
[t*sin(th)]
[a*t**2]
[1]]
Now I want to use the x=M[0,:],y=M[1,:],z=M[2,:] and replace it in the parabolid code and plot the paraboloid.
I want this way so that I could do some transformation to the matrix as needed.
Related
I want to adjust colobar scale from my current figure1 to the desired figure2 !!
My colorbar scale range is -1 to 1, but I want it in exponential form and for that I tried levels = np.linspace(-100e-2,100e-2) as well, but it also doesn't give the desired scale2
import xarray as xr
import numpy as np
import matplotlib.pyplot as plt
ds = xr.open_dataset('PL_Era_Tkt.nc')
wp = ds.w.mean(dim=['longitude','latitude']).plot.contourf(x='time',cmap='RdBu',add_colorbar=False,extend='both')
wpcb = plt.colorbar(wp)
wpcb.set_label(label='Pa.s${^{-1}}$',size=13)
plt.gca().invert_yaxis()
plt.title('Vertical Velocity',size=15)
My current scale
My desired scale
Since the data is not presented, I added normalized color bars with the data from the graph sample here. I think the color bar scales will also be in log format with this setup. Please note that the data used is not large, so I have not been able to confirm this.
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib as mpl
import matplotlib.ticker as ticker
import numpy as np
plt.style.use('seaborn-white')
def f(x, y):
return np.sin(x) ** 10 + np.cos(10 + y * x) * np.cos(x)
x = np.linspace(0, 5, 50)
y = np.linspace(0, 5, 40)
X, Y = np.meshgrid(x, y)
Z = f(X, Y)
fig, ax = plt.subplots()
ax.contourf(X, Y, Z, 20, cmap='RdGy')
cmap = mpl.cm.RdGy
norm = mpl.colors.Normalize(vmin=-1, vmax=1.0)
fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap),
ax=ax, orientation='vertical', label='Some Units', extend='both', ticks=ticker.LogLocator())
plt.show()
I want to add R^2 and R in my line chart.But I just know how to add equation in the chart.Here is my code.Thanks a lot!
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from sympy import S, symbols, printing
import pylab as pl
# Data for plotting
y = df["rain"]
x = df["SITE"]
fig, ax = plt.subplots()
ax.plot(x, y)
ax.set(xlabel='year', ylabel='P',
title='rain')
ax.grid()
z = np.polyfit(x, y, 1)
p = np.poly1d(z)
pylab.plot(x,p(x),"y--")
pl.plot(x, y, 'og-', label=("y=%.6fx+(%.6f)"%(z[0],z[1])))
pl.legend()
At the moment I have a figure that looks like this,
Generated by the code:
import matplotlib.pyplot as plt
import numpy as np
data = np.random.rand(20,5)
rows,cols = data.shape
plt.imshow(data, interpolation='nearest', extent=[0.5, 0.5+cols, 0.5, 0.5+cols], cmap='bwr')
plt.show()
However I would like to 'fold' this up into a 3D cylinder, joining the left and right edges (as one would do with a sheet of paper). In other words the left edge and the right edges are actually the same edge and so I would like to join them together to form a cylinder.
How would I go about doing this?
The Poly3DCollection is the method of choice for arbitrary 3D polygons in mplot3d.
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
import numpy as np
fig = plt.figure()
ax = fig.gca(projection='3d')
nphi,nz=12,20
r=1 # radius of cylinder
phi = np.linspace(0,360, nphi)/180.0*np.pi
z= np.linspace(0,1.0,nz)
print z
facecolors=['r','g','b','y']
cols=[]
verts2 = []
for i in range(len(phi)-1):
cp0= r*np.cos(phi[i])
cp1= r*np.cos(phi[i+1])
sp0= r*np.sin(phi[i])
sp1= r*np.sin(phi[i+1])
for j in range(len(z)-1):
z0=z[j]
z1=z[j+1]
verts=[]
verts.append((cp0, sp0, z0))
verts.append((cp1, sp1, z0))
verts.append((cp1, sp1, z1))
verts.append((cp0, sp0, z1))
verts2.append(verts)
value=np.random.rand()
print value
col=plt.cm.bwr(value)
print col
cols.append(col)
poly3= Poly3DCollection(verts2, facecolor=cols )
poly3.set_alpha(0.8)
ax.add_collection3d(poly3)
ax.set_xlabel('X')
ax.set_xlim3d(-1, 1)
ax.set_ylabel('Y')
ax.set_ylim3d(-1, 1)
ax.set_zlabel('Z')
ax.set_zlim3d(0, 1)
plt.show()
You could use plot_surface:
import numpy as np
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d.axes3d as axes3d
np.random.seed(2016)
data = np.random.rand(12, 20)
h, w = data.shape
theta, z = np.linspace(0, 2 * np.pi, w), np.linspace(0, 1, h)
THETA, Z = np.meshgrid(theta, z)
X = np.cos(THETA)
Y = np.sin(THETA)
fig = plt.figure()
ax = fig.add_subplot(1,1,1, projection='3d')
cmap = plt.get_cmap('bwr')
plot = ax.plot_surface(
X, Y, Z, rstride=1, cstride=1, facecolors=cmap(data),
linewidth=0, antialiased=False, alpha=0.75)
plt.show()
yields
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.
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()