问题
My data is regularly spaced, but not quite a grid - each row of points is slightly offset from the one below.
The data is in the form of 3 1D arrays, x, y, z, with each index corresponding to a point. It is smoothly varying data - approximately Gaussian.
The point density is quite high. What is the best way to plot this data?
I tried meshgrid, but it gives me some bad contours through regions that have no data points near the contour's value.
I have tried rbf interpolation according to this post: Python : 2d contour plot from 3 lists : x, y and rho? but this just gives me nonsense - all the contours are on one edge - does not reflect the data at all.
Any other ideas for what I can try. Maybe I should be using some sort of nearest neighbour interpolation? Here is a picture of about a 1/4 of my data points: http://imgur.com/a/b00R6
I'm surprised it is causing me such difficulty - it seems like it should be fairly easy to plot.
回答1:
The easiest way to plot ungridded data is probably tricontour or tricontourf (a filled tricontour plot).
Having 1D arrays of the x, y and z coordinates x, y and z, you'd simply call
plt.tricontourf(x,y,z, n, ...)
to obtain n levels of contours.
The other quick method is to interpolate on a grid using matplotlib.mlab.griddata to obtain a regular grid from the irregular points.
Both methods are compared in an example on the matplotlib page: Tricontour vs. griddata
回答2:
Found the answer: needed to rescale my data.
来源：https://stackoverflow.com/questions/42494642/python-problems-contour-plotting-offset-grid-of-data