Python: find contour lines from matplotlib.pyplot.

问题:

I'm trying to find (but not draw!) contour lines for some data:

from pprint import pprint 
import matplotlib.pyplot 
z = [[0.350087, 0.0590954, 0.002165], [0.144522, 0.885409, 0.378515], 
     [0.027956, 0.777996, 0.602663], [0.138367, 0.182499, 0.460879], 
     [0.357434, 0.297271, 0.587715]] 
cn = matplotlib.pyplot.contour(z) 

I know cn contains the contour lines I want, but I can't seem to get to them. I've tried several things:

print dir(cn) 
pprint(cn.collections[0]) 
print dir(cn.collections[0]) 
pprint(cn.collections[0].figure) 
print dir(cn.collections[0].figure) 

to no avail. I know cn is a ContourSet, and cn.collections is an array of LineCollections. I would think a LineCollection is an array of line segments, but I can't figure out how to extract those segments.

My ultimate goal is to create a KML file that plots data on a world map, and the contours for that data as well.

However, since some of my data points are close together, and others are far away, I need the actual polygons (linestrings) that make up the contours, not just a rasterized image of the contours.

I'm somewhat surprised qhull doesn't do something like this.

Using Mathematica's ListContourPlot and then exporting as SVG works, but I want to use something open source.

I can't use the well-known CONREC algorithm because my data isn't on a mesh (there aren't always multiple y values for a given x value, and vice versa).

The solution doesn't have to python, but does have to be open source and runnable on Linux.

回答1:

You can get the vertices back by looping over collections and paths and using the iter_segments() method of matplotlib.path.Path.

Here's a function that returns the vertices as a set of nested lists of contour lines, contour sections and arrays of x,y vertices:

import numpy as np

def get_contour_verts(cn):
    contours = []
    # for each contour line
    for cc in cn.collections:
        paths = []
        # for each separate section of the contour line
        for pp in cc.get_paths():
            xy = []
            # for each segment of that section
            for vv in pp.iter_segments():
                xy.append(vv[0])
            paths.append(np.vstack(xy))
        contours.append(paths)

    return contours

---

Edit:

It's also possible to compute the contours without plotting anything using the undocumented matplotlib._cntr C module:

from matplotlib import pyplot as plt
from matplotlib import _cntr as cntr

z = np.array([[0.350087, 0.0590954, 0.002165],
              [0.144522,  0.885409, 0.378515],
              [0.027956,  0.777996, 0.602663],
              [0.138367,  0.182499, 0.460879], 
              [0.357434,  0.297271, 0.587715]])

x, y = np.mgrid[:z.shape[0], :z.shape[1]]
c = cntr.Cntr(x, y, z)

# trace a contour at z == 0.5
res = c.trace(0.5)

# result is a list of arrays of vertices and path codes
# (see docs for matplotlib.path.Path)
nseg = len(res) // 2
segments, codes = res[:nseg], res[nseg:]

fig, ax = plt.subplots(1, 1)
img = ax.imshow(z.T, origin='lower')
plt.colorbar(img)
ax.hold(True)
p = plt.Polygon(segments[0], fill=False, color='w')
ax.add_artist(p)
plt.show()

回答2:

It seems that the contour data is in the .allsegs attribute of the QuadContourSet object returned by the plt.contour() function.

The .allseg attribute is a list of all the levels (which can be specified when calling plt.contour(X,Y,Z,V). For each level you get a list of numpy nx2 arrays.

plt.figure()
plt.contour(X, Y, Z, [0], colors='r')

plt.figure()
for ii, seg in enumerate(C.allsegs[0]):
    plt.plot(seg[:,0], seg[:,1], '.-', label=ii)
plt.legend(fontsize=9, loc='best')

In the above example, only one level is given, so len(C.allsegs) =1. You get:

contour plot

the extracted curves