matplotlib.pyplot.tripcolor 如何用随机 RGB 颜色填充三角形？
matplotlib.pyplot.tripcolor how to fill triangles with random RGB colors?
假设我有一堆三角形，我知道如何使用 matplotlib.pyplot.tripcolor 绘制它们，我想知道如何使用整个 RGB 颜色中完全随机的 RGB 颜色填充单个三角形 space（所有 16777216 种颜色）与 x、y 无关，如何完成？
我有这个：
source
import matplotlib.pyplot as plt
import numpy as np
from scipy.spatial import Delaunay
from random import random, randbytes
plt.style.use('_mpl-gallery-nogrid')
pts = np.zeros((360,2))
pts[:,0] = np.random.randint(0,1920,360)
pts[:,1] = np.random.randint(0,1080,360)
tri = Delaunay(pts)
plt.xlim(0, 1920)
plt.ylim(0, 1080)
centers = np.sum(pts[tri.simplices], axis=1, dtype='int')/3.0
colors = np.array([ (x-960)**2 + (y-540)**2 for x,y in centers])
plt.tripcolor(pts[:,0], pts[:,1], tri.simplices.copy(), facecolors=colors, edgecolors='k')
plt.gca().set_aspect('equal')
plt.show()
l = centers.shape[0]
colors = np.random.random(size=l)
plt.tripcolor(pts[:,0], pts[:,1], tri.simplices.copy(), facecolors=colors, edgecolors='k')
plt.gca().set_aspect('equal')
plt.show()
他们工作正常，但两个示例的颜色变化太小。
第一个生成如下内容：
颜色一点都不随机。
第二个生成这个：
第二张图片颜色变化不大。
我尝试了很多方法，都失败了：
colors = np.random.random((l, 3))
plt.tripcolor(pts[:,0], pts[:,1], tri.simplices.copy(), facecolors=colors, edgecolors='k')
plt.gca().set_aspect('equal')
plt.show()
colors = [tuple(randbytes(3)) for i in range(l)]
plt.tripcolor(pts[:,0], pts[:,1], tri.simplices.copy(), facecolors=colors, edgecolors='k')
plt.gca().set_aspect('equal')
plt.show()
colors = [tuple(randbytes(4)) for i in range(l)]
plt.tripcolor(pts[:,0], pts[:,1], tri.simplices.copy(), facecolors=colors, edgecolors='k')
plt.gca().set_aspect('equal')
plt.show()
colors = [tuple([random() for i in range(3)]) for j in range(l)]
plt.tripcolor(pts[:,0], pts[:,1], tri.simplices.copy(), facecolors=colors, edgecolors='k')
plt.gca().set_aspect('equal')
plt.show()
colors = [tuple([random() for i in range(4)]) for j in range(l)]
plt.tripcolor(pts[:,0], pts[:,1], tri.simplices.copy(), facecolors=colors, edgecolors='k')
plt.gca().set_aspect('equal')
plt.show()
colors = [randbytes(3).hex() for j in range(l)]
plt.tripcolor(pts[:,0], pts[:,1], tri.simplices.copy(), facecolors=colors, edgecolors='k')
plt.gca().set_aspect('equal')
plt.show()
colors = [randbytes(4).hex() for j in range(l)]
plt.tripcolor(pts[:,0], pts[:,1], tri.simplices.copy(), facecolors=colors, edgecolors='k')
plt.gca().set_aspect('equal')
plt.show()
colors = ['#'+randbytes(3).hex() for j in range(l)]
plt.tripcolor(pts[:,0], pts[:,1], tri.simplices.copy(), facecolors=colors, edgecolors='k')
plt.gca().set_aspect('equal')
plt.show()
colors = ['#'+randbytes(4).hex() for j in range(l)]
plt.tripcolor(pts[:,0], pts[:,1], tri.simplices.copy(), facecolors=colors, edgecolors='k')
plt.gca().set_aspect('equal')
plt.show()
前七次加注ValueError: Collections can only map rank 1 arrays.
最后两个加注TypeError: Image data of dtype <U6 cannot be converted to float.
如何正确地做到这一点？ facecolors 到底应该放什么？官方文档对它应该是什么非常模糊。
为了完整起见，我的意图是：
from PIL import Image
fig = plt.figure(frameon=False, figsize=(19.2,10.8), dpi=100)
ax = fig.add_subplot(111)
ax.set_axis_off()
...
fig.canvas.draw()
fig.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=0, hspace=0)
plt.axis('scaled')
plt.box(False)
Image.frombytes('RGB', fig.canvas.get_width_height(), fig.canvas.tostring_rgb())
我得到了这个：
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.patches import Polygon
from PIL import Image
from scipy.spatial import Delaunay
from random import random, randbytes
plt.style.use('_mpl-gallery-nogrid')
points = np.zeros((360,2))
points[:,0] = np.random.randint(0,1920,360)
points[:,1] = np.random.randint(0,1080,360)
triangles = points[Delaunay(points).simplices]
fig = plt.figure(frameon=False, figsize=(19.2,10.8), dpi=100)
ax = fig.add_subplot(111)
ax.set_axis_off()
for triangle in triangles:
ax.add_patch(Polygon(triangle, edgecolor='#c0c0c0', facecolor='#'+randbytes(3).hex(), fill=True))
plt.xlim(0, 1920)
plt.ylim(0, 1080)
fig.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=0, hspace=0)
plt.axis('scaled')
plt.box(False)
fig.canvas.draw()
Image.frombytes('RGB', fig.canvas.get_width_height(), fig.canvas.tostring_rgb()).show()
但是我的代码明显比tripcolor效率低很多。
解决此问题的一种方法是创建一个生成随机颜色的颜色图，独立于您分配给 facecolors 或 C 的值。例如：
import matplotlib.pyplot as plt
from matplotlib.colors import Colormap
import numpy as np
from scipy.spatial import Delaunay
class RandomColors(Colormap):
def __init__(self):
pass
def __call__(self, X, alpha=None, bytes=False):
# randomly generate an RGBA [N x 4] matrix of colors
# where N is the number of elements of X
# X is what we assigne to `facecolors` or `C`
X = np.atleast_1d(X)
col = np.random.random((X.shape[0], 4))
# set alpha=1
col[:, -1] = 1
return col
plt.figure()
pts = np.zeros((360,2))
pts[:,0] = np.random.randint(0,1920,360)
pts[:,1] = np.random.randint(0,1080,360)
tri = Delaunay(pts)
plt.xlim(0, 1920)
plt.ylim(0, 1080)
centers = np.sum(pts[tri.simplices], axis=1, dtype='int')/3.0
# Don't really care about what's in this array: our custom colormap
# is going to ignore it!
colors = np.ones(centers.shape[0])
# instantiate the colormap
cmap = RandomColors()
plt.tripcolor(pts[:,0], pts[:,1], tri.simplices.copy(), facecolors=colors, cmap=cmap, edgecolors='k')
plt.gca().set_aspect('equal')
plt.show()
避免一起使用 tripcolor 可能是最简单的方法，除非您需要它的某些特定功能？您可以根据 Delauny 三角剖分创建自己的 PolyCollection，这在格式方面更加灵活。
from matplotlib.collections import PolyCollection
import matplotlib.pyplot as plt
from scipy.spatial import Delaunay
from copy import copy
n_points = 360
pts = np.random.randint(0, 1920, (n_points, 2)).astype(np.float32)
tri = Delaunay(pts)
vertices = np.stack((
tri.points[tri.simplices, 0], # x
tri.points[tri.simplices, 1], # y
), axis=-1)
collection = PolyCollection(vertices, edgecolor="k")
collection.set_facecolor(np.random.rand(len(vertices), 3))
fig, ax = plt.subplots(figsize=(8,8), facecolor="w")
ax.add_collection(copy(collection))
ax.autoscale_view()
假设我有一堆三角形，我知道如何使用 matplotlib.pyplot.tripcolor 绘制它们，我想知道如何使用整个 RGB 颜色中完全随机的 RGB 颜色填充单个三角形 space（所有 16777216 种颜色）与 x、y 无关，如何完成？
我有这个： source
import matplotlib.pyplot as plt
import numpy as np
from scipy.spatial import Delaunay
from random import random, randbytes
plt.style.use('_mpl-gallery-nogrid')
pts = np.zeros((360,2))
pts[:,0] = np.random.randint(0,1920,360)
pts[:,1] = np.random.randint(0,1080,360)
tri = Delaunay(pts)
plt.xlim(0, 1920)
plt.ylim(0, 1080)
centers = np.sum(pts[tri.simplices], axis=1, dtype='int')/3.0
colors = np.array([ (x-960)**2 + (y-540)**2 for x,y in centers])
plt.tripcolor(pts[:,0], pts[:,1], tri.simplices.copy(), facecolors=colors, edgecolors='k')
plt.gca().set_aspect('equal')
plt.show()
l = centers.shape[0]
colors = np.random.random(size=l)
plt.tripcolor(pts[:,0], pts[:,1], tri.simplices.copy(), facecolors=colors, edgecolors='k')
plt.gca().set_aspect('equal')
plt.show()
他们工作正常，但两个示例的颜色变化太小。
第一个生成如下内容：
颜色一点都不随机。
第二个生成这个：
第二张图片颜色变化不大。
我尝试了很多方法，都失败了：
colors = np.random.random((l, 3))
plt.tripcolor(pts[:,0], pts[:,1], tri.simplices.copy(), facecolors=colors, edgecolors='k')
plt.gca().set_aspect('equal')
plt.show()
colors = [tuple(randbytes(3)) for i in range(l)]
plt.tripcolor(pts[:,0], pts[:,1], tri.simplices.copy(), facecolors=colors, edgecolors='k')
plt.gca().set_aspect('equal')
plt.show()
colors = [tuple(randbytes(4)) for i in range(l)]
plt.tripcolor(pts[:,0], pts[:,1], tri.simplices.copy(), facecolors=colors, edgecolors='k')
plt.gca().set_aspect('equal')
plt.show()
colors = [tuple([random() for i in range(3)]) for j in range(l)]
plt.tripcolor(pts[:,0], pts[:,1], tri.simplices.copy(), facecolors=colors, edgecolors='k')
plt.gca().set_aspect('equal')
plt.show()
colors = [tuple([random() for i in range(4)]) for j in range(l)]
plt.tripcolor(pts[:,0], pts[:,1], tri.simplices.copy(), facecolors=colors, edgecolors='k')
plt.gca().set_aspect('equal')
plt.show()
colors = [randbytes(3).hex() for j in range(l)]
plt.tripcolor(pts[:,0], pts[:,1], tri.simplices.copy(), facecolors=colors, edgecolors='k')
plt.gca().set_aspect('equal')
plt.show()
colors = [randbytes(4).hex() for j in range(l)]
plt.tripcolor(pts[:,0], pts[:,1], tri.simplices.copy(), facecolors=colors, edgecolors='k')
plt.gca().set_aspect('equal')
plt.show()
colors = ['#'+randbytes(3).hex() for j in range(l)]
plt.tripcolor(pts[:,0], pts[:,1], tri.simplices.copy(), facecolors=colors, edgecolors='k')
plt.gca().set_aspect('equal')
plt.show()
colors = ['#'+randbytes(4).hex() for j in range(l)]
plt.tripcolor(pts[:,0], pts[:,1], tri.simplices.copy(), facecolors=colors, edgecolors='k')
plt.gca().set_aspect('equal')
plt.show()
前七次加注ValueError: Collections can only map rank 1 arrays.
最后两个加注TypeError: Image data of dtype <U6 cannot be converted to float.
如何正确地做到这一点？ facecolors 到底应该放什么？官方文档对它应该是什么非常模糊。
为了完整起见，我的意图是：
from PIL import Image
fig = plt.figure(frameon=False, figsize=(19.2,10.8), dpi=100)
ax = fig.add_subplot(111)
ax.set_axis_off()
...
fig.canvas.draw()
fig.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=0, hspace=0)
plt.axis('scaled')
plt.box(False)
Image.frombytes('RGB', fig.canvas.get_width_height(), fig.canvas.tostring_rgb())
我得到了这个：
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.patches import Polygon
from PIL import Image
from scipy.spatial import Delaunay
from random import random, randbytes
plt.style.use('_mpl-gallery-nogrid')
points = np.zeros((360,2))
points[:,0] = np.random.randint(0,1920,360)
points[:,1] = np.random.randint(0,1080,360)
triangles = points[Delaunay(points).simplices]
fig = plt.figure(frameon=False, figsize=(19.2,10.8), dpi=100)
ax = fig.add_subplot(111)
ax.set_axis_off()
for triangle in triangles:
ax.add_patch(Polygon(triangle, edgecolor='#c0c0c0', facecolor='#'+randbytes(3).hex(), fill=True))
plt.xlim(0, 1920)
plt.ylim(0, 1080)
fig.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=0, hspace=0)
plt.axis('scaled')
plt.box(False)
fig.canvas.draw()
Image.frombytes('RGB', fig.canvas.get_width_height(), fig.canvas.tostring_rgb()).show()
但是我的代码明显比tripcolor效率低很多。
解决此问题的一种方法是创建一个生成随机颜色的颜色图，独立于您分配给 facecolors 或 C 的值。例如：
import matplotlib.pyplot as plt
from matplotlib.colors import Colormap
import numpy as np
from scipy.spatial import Delaunay
class RandomColors(Colormap):
def __init__(self):
pass
def __call__(self, X, alpha=None, bytes=False):
# randomly generate an RGBA [N x 4] matrix of colors
# where N is the number of elements of X
# X is what we assigne to `facecolors` or `C`
X = np.atleast_1d(X)
col = np.random.random((X.shape[0], 4))
# set alpha=1
col[:, -1] = 1
return col
plt.figure()
pts = np.zeros((360,2))
pts[:,0] = np.random.randint(0,1920,360)
pts[:,1] = np.random.randint(0,1080,360)
tri = Delaunay(pts)
plt.xlim(0, 1920)
plt.ylim(0, 1080)
centers = np.sum(pts[tri.simplices], axis=1, dtype='int')/3.0
# Don't really care about what's in this array: our custom colormap
# is going to ignore it!
colors = np.ones(centers.shape[0])
# instantiate the colormap
cmap = RandomColors()
plt.tripcolor(pts[:,0], pts[:,1], tri.simplices.copy(), facecolors=colors, cmap=cmap, edgecolors='k')
plt.gca().set_aspect('equal')
plt.show()
避免一起使用 tripcolor 可能是最简单的方法，除非您需要它的某些特定功能？您可以根据 Delauny 三角剖分创建自己的 PolyCollection，这在格式方面更加灵活。
from matplotlib.collections import PolyCollection
import matplotlib.pyplot as plt
from scipy.spatial import Delaunay
from copy import copy
n_points = 360
pts = np.random.randint(0, 1920, (n_points, 2)).astype(np.float32)
tri = Delaunay(pts)
vertices = np.stack((
tri.points[tri.simplices, 0], # x
tri.points[tri.simplices, 1], # y
), axis=-1)
collection = PolyCollection(vertices, edgecolor="k")
collection.set_facecolor(np.random.rand(len(vertices), 3))
fig, ax = plt.subplots(figsize=(8,8), facecolor="w")
ax.add_collection(copy(collection))
ax.autoscale_view()