I have a set of data that I want to use to produce a contour plot in polar co-ordinates using Matplotlib.
My data is the following:
theta
- 1D array of angle valuesradius
- 1D array of radius valuesvalue
- 1D array of values that I want to use for the contours
These are all 1D arrays that align properly - eg:
theta radius value
30 1 2.9
30 2 5.3
35 5 9.2
That is, all of the values are repeated enough times so that each row of this 'table' of three variables defines one point.
How can I create a polar contour plot from these valu开发者_StackOverflowes? I've thought about converting the radius and theta values to x and y values and doing it in cartesian co-ordinates, but the contour function seems to require 2D arrays, and I can't quite understand why.
Any ideas?
Matplotlib's contour()
function expects data to be arranged as a 2D grid of points and corresponding grid of values for each of those grid points. If your data is naturally arranged in a grid you can convert r, theta to x, y and use contour(r*np.cos(theta), r*np.sin(theta), values)
to make your plot.
If your data isn't naturally gridded, you should follow Stephen's advice and used griddata()
to interpolate your data on to a grid.
The following script shows examples of both.
import pylab as plt
from matplotlib.mlab import griddata
import numpy as np
# data on a grid
r = np.linspace(0, 1, 100)
t = np.linspace(0, 2*np.pi, 100)
r, t = np.meshgrid(r, t)
z = (t-np.pi)**2 + 10*(r-0.5)**2
plt.subplot(121)
plt.contour(r*np.cos(t), r*np.sin(t), z)
# ungrid data, then re-grid it
r = r.flatten()
t = t.flatten()
x = r*np.cos(t)
y = r*np.sin(t)
z = z.flatten()
xgrid = np.linspace(x.min(), x.max(), 100)
ygrid = np.linspace(y.min(), y.max(), 100)
xgrid, ygrid = np.meshgrid(xgrid, ygrid)
zgrid = griddata(x,y,z, xgrid, ygrid)
plt.subplot(122)
plt.contour(xgrid, ygrid, zgrid)
plt.show()
I don't know if it's possible to do a polar contour plot directly, but if you convert to cartesian coordinates you can use the griddata
function to convert your 1D arrays to 2D.
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