带网格的三角形轮廓
我正在尝试为 Dirichlet 重新创建图表(或者只是等高线图即可)维基百科上使用 matplotlib 和 numpy 的分布。我无法轻松生成三角形轮廓。第一个问题是 meshgrid
不返回点的三角形。即使我得到一个三角形的点,contourf 会处理非矩形输入吗?
这是我到目前为止所拥有的:
#!/usr/bin/env python
from __future__ import division
import matplotlib
matplotlib.use("TkAgg")
matplotlib.rc('text', usetex=True)
matplotlib.rcParams['text.latex.preamble']=r"""\usepackage{amsmath}
"""
import math
import scipy.special
root_three_over_two = np.sqrt(3) / 2
def new_figure():
# 1.45
plt.figure(figsize = [2.6, 2.6 * root_three_over_two], dpi = 1200)
plt.axes([0.05, 0.10, 0.90, 0.90], frameon = False)
xsize = 1.0
ysize = root_three_over_two * xsize
plt.axis([0, xsize, 0, ysize])
resolution = 0.05
R = inclusive_arange(0.0, 1.0, resolution)
x, y = np.meshgrid(inclusive_arange(0.0, 1.0, resolution),
inclusive_arange(0.0, 1.0, resolution))
# UNFORTUNATELY x, and y include a lot of points where x+y>1
x = []
y = []
for yy in R:
x.append(list(inclusive_arange(0.0, 1.0 - yy, resolution)))
y.append([yy for xx in R])
print x
print y
z = 1 - x - y
# We can use these to convert to and from the equilateral triangle.
M = [[1, 0.5], [0, root_three_over_two]]
Mi = np.linalg.inv(M)
def dirichlet(x, y, z, a, b, c):
if z < 0:
return 0
return x ** (a - 1) * y ** (b - 1) * z ** (c - 1) \
* math.gamma(a + b + c) \
/ (math.gamma(a) * math.gamma(b) * math.gamma(c))
dirichlet = np.frompyfunc(dirichlet, 6, 1)
for (dirichlet_parm, filename) in [((5.0, 1.5, 2.5), "dir_small.pdf")]:
new_figure()
height = dirichlet(x, y, z, *dirichlet_parm)
M = np.max(height)
cs = plt.contourf(x, y, height, 50)
S = sum(dirichlet_parm)
plt.savefig(filename)
I'm trying to recreate the diagram (or just a contour plot would be okay) for the Dirichlet distribution that's on Wikipedia using matplotlib and numpy. I am having trouble easily generating a triangular contourf. The first problem is that meshgrid
doesn't return a triangle of points. Even if I get a triangle of points, will contourf
handle the non-rectangular input?
Here's what I have so far:
#!/usr/bin/env python
from __future__ import division
import matplotlib
matplotlib.use("TkAgg")
matplotlib.rc('text', usetex=True)
matplotlib.rcParams['text.latex.preamble']=r"""\usepackage{amsmath}
"""
import math
import scipy.special
root_three_over_two = np.sqrt(3) / 2
def new_figure():
# 1.45
plt.figure(figsize = [2.6, 2.6 * root_three_over_two], dpi = 1200)
plt.axes([0.05, 0.10, 0.90, 0.90], frameon = False)
xsize = 1.0
ysize = root_three_over_two * xsize
plt.axis([0, xsize, 0, ysize])
resolution = 0.05
R = inclusive_arange(0.0, 1.0, resolution)
x, y = np.meshgrid(inclusive_arange(0.0, 1.0, resolution),
inclusive_arange(0.0, 1.0, resolution))
# UNFORTUNATELY x, and y include a lot of points where x+y>1
x = []
y = []
for yy in R:
x.append(list(inclusive_arange(0.0, 1.0 - yy, resolution)))
y.append([yy for xx in R])
print x
print y
z = 1 - x - y
# We can use these to convert to and from the equilateral triangle.
M = [[1, 0.5], [0, root_three_over_two]]
Mi = np.linalg.inv(M)
def dirichlet(x, y, z, a, b, c):
if z < 0:
return 0
return x ** (a - 1) * y ** (b - 1) * z ** (c - 1) \
* math.gamma(a + b + c) \
/ (math.gamma(a) * math.gamma(b) * math.gamma(c))
dirichlet = np.frompyfunc(dirichlet, 6, 1)
for (dirichlet_parm, filename) in [((5.0, 1.5, 2.5), "dir_small.pdf")]:
new_figure()
height = dirichlet(x, y, z, *dirichlet_parm)
M = np.max(height)
cs = plt.contourf(x, y, height, 50)
S = sum(dirichlet_parm)
plt.savefig(filename)
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您不需要直线网格来创建等值线图。您可以定义周长并执行 Delaunay 三角剖分。当然,坐标仍然是直线的,并且(看起来)您需要进行转换。 这个示例应该足以创建 2D 轮廓。我制作了一些具有非矩形周长的 3D 曲面图,您会得到一些难看的边缘伪影。一个非常精细的网格可能会改善其中一些问题。
You do not need a rectilinear meshgrid to create a contour plot. You can define the perimeter and perform a Delaunay triangulation. The coordinates, of course will still be rectilinear and (it appears) you'll need to do a transformation. This example should be enough to create a 2D contour. I have produced some 3D surface plots with a non-rectangle perimeter and you get some edge artifacts that can be unsightly. A very fine grid might ameliorate some of these.