On Tue, Oct 16, 2012 at 2:34 AM, Damon McDougall <damon.mcdoug...@gmail.com> wrote: > On Tue, Oct 16, 2012 at 8:04 AM, T J <tjhn...@gmail.com> wrote: >> I'm interested in clipping the result of plt.contour (and >> plt.contourf) to a patch. However, QuadContourSet does not have a >> set_clip_path() method. Is there a way to do this? >> >> Here is an example plot that I have generated. >> >> http://imgur.com/pybIf >> >> For the curious, it plots contours of a function on the 2-simplex. >> The way I've gone about computing this is, unfortunately, convoluted. >> I generate a regular grid in 2D and treat each point as a projection >> of a 3D probability vector into 2D. Then, I invert the projection so >> that I have "distributions" and then compute the Z value for each >> point. The contours are then calculated, but now, I need to clip >> everything outside the triangle, as only points within the triangle >> correspond to actual distributions. >> >> Is there a more direct way to calculate contours on a restricted set? >> >> Thanks. > > The contour functions support masked regions. I think that might be > what you're looking for. Since the region you want to mask is a > triangle, maybe even use a masked triangulated contour plot? Here's > the call signature: > http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.tricontour > > Does that help? >
Yes, that looks to be exactly what I am looking for. Note, the mask kwarg is barely mentioned in that docstring, but I think I get it. However, I am having some trouble. tricontour seems to fail when computing the Triangulation() object. Here is my code: http://codepad.org/cVB7YP9r This is a set of 152 points on a triangle. delaunay is mentioned to have problems for some pathological cases. Is a complete triangular grid considered as such a case? Code is shown below as well. ------------ import numpy as np import matplotlib.pyplot as plt points = np.array([ [0.8660254037844384, -0.5000000000000004], [0.7577722283113836, -0.5000000000000004], [0.6495190528383288, -0.5000000000000003], [0.5412658773652739, -0.5000000000000003], [0.4330127018922191, -0.5000000000000002], [0.3247595264191642, -0.5000000000000002], [0.21650635094610943, -0.5000000000000002], [0.10825317547305463, -0.5000000000000001], [-2.220446049250313e-16, -0.5000000000000001], [-0.10825317547305507, -0.5], [-0.21650635094610993, -0.5], [-0.32475952641916467, -0.5], [-0.4330127018922195, -0.4999999999999999], [-0.5412658773652743, -0.4999999999999999], [-0.6495190528383291, -0.49999999999999983], [-0.7577722283113839, -0.4999999999999998], [-0.8660254037844388, -0.4999999999999997], [0.811898816047911, -0.40625000000000044], [0.7036456405748561, -0.4062500000000004], [0.5953924651018013, -0.40625000000000033], [0.48713928962874653, -0.4062500000000003], [0.3788861141556917, -0.4062500000000002], [0.2706329386826369, -0.4062500000000002], [0.16237976320958203, -0.4062500000000001], [0.05412658773652723, -0.4062500000000001], [-0.05412658773652762, -0.40625000000000006], [-0.16237976320958242, -0.40625], [-0.2706329386826372, -0.40625], [-0.37888611415569207, -0.40624999999999994], [-0.4871392896287469, -0.4062499999999999], [-0.5953924651018018, -0.40624999999999983], [-0.7036456405748566, -0.4062499999999998], [-0.8118988160479114, -0.4062499999999997], [0.7577722283113836, -0.3125000000000004], [0.6495190528383288, -0.31250000000000033], [0.541265877365274, -0.31250000000000033], [0.4330127018922191, -0.3125000000000002], [0.3247595264191643, -0.3125000000000002], [0.21650635094610948, -0.31250000000000017], [0.10825317547305463, -0.3125000000000001], [-1.6653345369377348e-16, -0.31250000000000006], [-0.10825317547305502, -0.3125], [-0.21650635094610982, -0.3125], [-0.3247595264191646, -0.31249999999999994], [-0.43301270189221946, -0.3124999999999999], [-0.5412658773652743, -0.31249999999999983], [-0.6495190528383292, -0.3124999999999998], [-0.7577722283113839, -0.3124999999999998], [0.7036456405748562, -0.21875000000000036], [0.5953924651018013, -0.2187500000000003], [0.48713928962874653, -0.21875000000000028], [0.37888611415569173, -0.21875000000000022], [0.2706329386826369, -0.21875000000000017], [0.16237976320958208, -0.21875000000000014], [0.05412658773652723, -0.21875000000000008], [-0.054126587736527565, -0.21875000000000006], [-0.16237976320958242, -0.21875], [-0.2706329386826372, -0.21874999999999994], [-0.378886114155692, -0.21874999999999992], [-0.48713928962874686, -0.21874999999999986], [-0.5953924651018017, -0.2187499999999998], [-0.7036456405748566, -0.21874999999999978], [0.6495190528383288, -0.12500000000000033], [0.541265877365274, -0.12500000000000028], [0.43301270189221913, -0.12500000000000025], [0.32475952641916433, -0.1250000000000002], [0.2165063509461095, -0.12500000000000017], [0.10825317547305469, -0.1250000000000001], [-1.6653345369377348e-16, -0.12500000000000006], [-0.10825317547305496, -0.125], [-0.21650635094610982, -0.12499999999999997], [-0.3247595264191646, -0.12499999999999993], [-0.4330127018922194, -0.12499999999999989], [-0.5412658773652743, -0.12499999999999983], [-0.6495190528383291, -0.12499999999999979], [0.5953924651018014, -0.031250000000000305], [0.4871392896287466, -0.03125000000000028], [0.37888611415569173, -0.031250000000000194], [0.27063293868263694, -0.03125000000000017], [0.1623797632095821, -0.03125000000000014], [0.05412658773652729, -0.03125000000000008], [-0.054126587736527565, -0.03125000000000003], [-0.16237976320958236, -0.031249999999999986], [-0.2706329386826372, -0.031249999999999944], [-0.378886114155692, -0.031249999999999903], [-0.4871392896287468, -0.031249999999999854], [-0.5953924651018017, -0.03124999999999981], [0.541265877365274, 0.06249999999999972], [0.4330127018922192, 0.06249999999999975], [0.3247595264191644, 0.06249999999999983], [0.21650635094610954, 0.06249999999999986], [0.10825317547305471, 0.06249999999999989], [-1.1102230246251565e-16, 0.062499999999999944], [-0.10825317547305496, 0.0625], [-0.21650635094610976, 0.06250000000000003], [-0.3247595264191646, 0.06250000000000008], [-0.4330127018922194, 0.06250000000000012], [-0.5412658773652742, 0.06250000000000017], [0.4871392896287466, 0.15624999999999975], [0.3788861141556918, 0.15624999999999978], [0.27063293868263694, 0.15624999999999986], [0.16237976320958214, 0.1562499999999999], [0.054126587736527315, 0.15624999999999992], [-0.05412658773652751, 0.15625], [-0.16237976320958236, 0.15625000000000003], [-0.27063293868263716, 0.15625000000000006], [-0.378886114155692, 0.1562500000000001], [-0.4871392896287468, 0.15625000000000017], [0.43301270189221924, 0.24999999999999978], [0.3247595264191644, 0.2499999999999998], [0.21650635094610957, 0.2499999999999999], [0.10825317547305474, 0.24999999999999992], [-8.326672684688674e-17, 0.24999999999999994], [-0.10825317547305491, 0.25], [-0.2165063509461097, 0.25000000000000006], [-0.32475952641916456, 0.2500000000000001], [-0.43301270189221935, 0.2500000000000001], [0.37888611415569184, 0.3437499999999998], [0.270632938682637, 0.34374999999999983], [0.16237976320958217, 0.3437499999999999], [0.05412658773652734, 0.34374999999999994], [-0.05412658773652748, 0.34375], [-0.1623797632095823, 0.34375000000000006], [-0.2706329386826371, 0.3437500000000001], [-0.37888611415569196, 0.3437500000000001], [0.32475952641916445, 0.43749999999999983], [0.2165063509461096, 0.4374999999999999], [0.10825317547305478, 0.4374999999999999], [-5.551115123125783e-17, 0.4374999999999999], [-0.10825317547305488, 0.43750000000000006], [-0.2165063509461097, 0.4375000000000001], [-0.3247595264191645, 0.4375000000000001], [0.27063293868263705, 0.5312499999999999], [0.1623797632095822, 0.5312499999999999], [0.054126587736527385, 0.5312499999999999], [-0.054126587736527426, 0.53125], [-0.16237976320958225, 0.5312500000000001], [-0.2706329386826371, 0.5312500000000001], [0.21650635094610965, 0.6249999999999999], [0.10825317547305482, 0.6249999999999999], [-1.3877787807814457e-17, 0.625], [-0.10825317547305482, 0.625], [-0.21650635094610965, 0.6250000000000001], [0.16237976320958225, 0.7187499999999999], [0.05412658773652742, 0.71875], [-0.0541265877365274, 0.71875], [-0.16237976320958222, 0.71875], [0.10825317547305485, 0.8125], [2.7755575615628914e-17, 0.8125], [-0.1082531754730548, 0.8125], [0.054126587736527454, 0.90625], [-0.05412658773652737, 0.90625], [6.123233995736766e-17, 1.0]]) def test1(x=None): # Failure for x > 23 slc = slice(None,x,None) plt.scatter(points[slc,0], points[slc,1]) matplotlib.delaunay.Triangulation(points[slc,0], points[slc,1]) def test2(x=None): # Randomizing the rows seems to have no effect # Failure for x > 23 slc = slice(None,x,None) idx = np.arange(points.shape[0]) np.random.shuffle(idx) points2 = np.array([points[i] for i in idx]) plt.scatter(points2[slc,0], points2[slc,1]) matplotlib.delaunay.Triangulation(points[slc,0], points[slc,1]) if __name__ == '__main__': test2() #test2(22) ------------------------------------------------------------------------------ Don't let slow site performance ruin your business. 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