画布上的 Matplotlib RegularPolygon 集合位置
Fer*_*ira 3 python canvas polygon matplotlib
我正在尝试使用 python 绘制特征图 (SOM)。为简单起见，想象一个 2D 绘图，其中每个单元都表示为一个六边形。
正如本主题所示：Python 中的六边形自组织地图，六边形并排放置，形成网格。
我设法编写了以下代码段，它非常适用于一定数量的多边形和仅适用于少数形状（例如 6 x 6 或 10 x 4 六边形）。然而，这种方法的一个重要特性是支持 3 x 3 的任何网格形状。
def plot_map(grid,
d_matrix,
w=10,
title='SOM Hit map'):
"""
Plot hexagon map where each neuron is represented by a hexagon. The hexagon
color is given by the distance between the neurons (D-Matrix) Scaled
hexagons will appear on top of the background image whether the hits array
is provided. They are scaled according to the number of hits on each
neuron.
Args:
- grid: Grid dictionary (keys: centers, x, y ),
- d_matrix: array contaning the distances between each neuron
- w: width of the map in inches
- title: map title
Returns the Matplotlib SubAxis instance
"""
n_centers = grid['centers']
x, y = grid['x'], grid['y']
fig = plt.figure(figsize=(1.05 * w, 0.85 * y * w / x), dpi=100)
ax = fig.add_subplot(111)
ax.axis('equal')
# Discover difference between centers
collection_bg = RegularPolyCollection(
numsides=6, # a hexagon
rotation=0,
sizes=(y * (1.3 * 2 * math.pi * w) ** 2 / x,),
edgecolors = (0, 0, 0, 1),
array= d_matrix,
cmap = cm.gray,
offsets = n_centers,
transOffset = ax.transData,
)
ax.add_collection(collection_bg, autolim=True)
ax.axis('off')
ax.autoscale_view()
ax.set_title(title)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
plt.colorbar(collection_bg, cax=cax)
return ax
我试图制作一些可以自动理解网格形状的东西。它没有用（我不知道为什么）。它总是在六边形之间出现一个不需要的空间
总结：我想使用六边形生成 3x3 或 6x6 或 10x4（等等）网格，给定点之间没有空格并设置绘图宽度。
正如所问的，这是六边形位置的数据。正如你所看到的，它总是相同的模式
3x3
{'centers': array([[ 1.5 , 0.8660254 ],
[ 2.5 , 0.8660254 ],
[ 3.5 , 0.8660254 ],
[ 1. , 1.73205081],
[ 2. , 1.73205081],
[ 3. , 1.73205081],
[ 1.5 , 2.59807621],
[ 2.5 , 2.59807621],
[ 3.5 , 2.59807621]]),
'x': array([ 3.]),
'y': array([ 3.])}
6x6
{'centers': array([[ 1.5 , 0.8660254 ],
[ 2.5 , 0.8660254 ],
[ 3.5 , 0.8660254 ],
[ 4.5 , 0.8660254 ],
[ 5.5 , 0.8660254 ],
[ 6.5 , 0.8660254 ],
[ 1. , 1.73205081],
[ 2. , 1.73205081],
[ 3. , 1.73205081],
[ 4. , 1.73205081],
[ 5. , 1.73205081],
[ 6. , 1.73205081],
[ 1.5 , 2.59807621],
[ 2.5 , 2.59807621],
[ 3.5 , 2.59807621],
[ 4.5 , 2.59807621],
[ 5.5 , 2.59807621],
[ 6.5 , 2.59807621],
[ 1. , 3.46410162],
[ 2. , 3.46410162],
[ 3. , 3.46410162],
[ 4. , 3.46410162],
[ 5. , 3.46410162],
[ 6. , 3.46410162],
[ 1.5 , 4.33012702],
[ 2.5 , 4.33012702],
[ 3.5 , 4.33012702],
[ 4.5 , 4.33012702],
[ 5.5 , 4.33012702],
[ 6.5 , 4.33012702],
[ 1. , 5.19615242],
[ 2. , 5.19615242],
[ 3. , 5.19615242],
[ 4. , 5.19615242],
[ 5. , 5.19615242],
[ 6. , 5.19615242]]),
'x': array([ 6.]),
'y': array([ 6.])}
11x4
{'centers': array([[ 1.5 , 0.8660254 ],
[ 2.5 , 0.8660254 ],
[ 3.5 , 0.8660254 ],
[ 4.5 , 0.8660254 ],
[ 5.5 , 0.8660254 ],
[ 6.5 , 0.8660254 ],
[ 7.5 , 0.8660254 ],
[ 8.5 , 0.8660254 ],
[ 9.5 , 0.8660254 ],
[ 10.5 , 0.8660254 ],
[ 11.5 , 0.8660254 ],
[ 1. , 1.73205081],
[ 2. , 1.73205081],
[ 3. , 1.73205081],
[ 4. , 1.73205081],
[ 5. , 1.73205081],
[ 6. , 1.73205081],
[ 7. , 1.73205081],
[ 8. , 1.73205081],
[ 9. , 1.73205081],
[ 10. , 1.73205081],
[ 11. , 1.73205081],
[ 1.5 , 2.59807621],
[ 2.5 , 2.59807621],
[ 3.5 , 2.59807621],
[ 4.5 , 2.59807621],
[ 5.5 , 2.59807621],
[ 6.5 , 2.59807621],
[ 7.5 , 2.59807621],
[ 8.5 , 2.59807621],
[ 9.5 , 2.59807621],
[ 10.5 , 2.59807621],
[ 11.5 , 2.59807621],
[ 1. , 3.46410162],
[ 2. , 3.46410162],
[ 3. , 3.46410162],
[ 4. , 3.46410162],
[ 5. , 3.46410162],
[ 6. , 3.46410162],
[ 7. , 3.46410162],
[ 8. , 3.46410162],
[ 9. , 3.46410162],
[ 10. , 3.46410162],
[ 11. , 3.46410162]]),
'x': array([ 11.]),
'y': array([ 4.])}
我设法通过根据给定的 dpi 计算英寸的图形大小来找到解决方法。之后，我计算两个相邻点之间的像素距离（通过使用隐藏散点图绘制）。通过这种方式，我可以计算六边形点心并正确估计六边形内圆的大小（如 matplotlib 所期望的那样）。
最终没有任何差距！
import matplotlib.pyplot as plt
from matplotlib import colors, cm
from matplotlib.collections import RegularPolyCollection
from mpl_toolkits.axes_grid1 import make_axes_locatable
import math
import numpy as np
def plot_map(grid,
d_matrix,
w=1080,
dpi=72.,
title='SOM Hit map'):
"""
Plot hexagon map where each neuron is represented by a hexagon. The hexagon
color is given by the distance between the neurons (D-Matrix)
Args:
- grid: Grid dictionary (keys: centers, x, y ),
- d_matrix: array contaning the distances between each neuron
- w: width of the map in inches
- title: map title
Returns the Matplotlib SubAxis instance
"""
n_centers = grid['centers']
x, y = grid['x'], grid['y']
# Size of figure in inches
xinch = (x * w / y) / dpi
yinch = (y * w / x) / dpi
fig = plt.figure(figsize=(xinch, yinch), dpi=dpi)
ax = fig.add_subplot(111, aspect='equal')
# Get pixel size between to data points
xpoints = n_centers[:, 0]
ypoints = n_centers[:, 1]
ax.scatter(xpoints, ypoints, s=0.0, marker='s')
ax.axis([min(xpoints)-1., max(xpoints)+1.,
min(ypoints)-1., max(ypoints)+1.])
xy_pixels = ax.transData.transform(np.vstack([xpoints, ypoints]).T)
xpix, ypix = xy_pixels.T
# In matplotlib, 0,0 is the lower left corner, whereas it's usually the
# upper right for most image software, so we'll flip the y-coords
width, height = fig.canvas.get_width_height()
ypix = height - ypix
# discover radius and hexagon
apothem = .9 * (xpix[1] - xpix[0]) / math.sqrt(3)
area_inner_circle = math.pi * (apothem ** 2)
collection_bg = RegularPolyCollection(
numsides=6, # a hexagon
rotation=0,
sizes=(area_inner_circle,),
edgecolors = (0, 0, 0, 1),
array= d_matrix,
cmap = cm.gray,
offsets = n_centers,
transOffset = ax.transData,
)
ax.add_collection(collection_bg, autolim=True)
ax.axis('off')
ax.autoscale_view()
ax.set_title(title)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="10%", pad=0.05)
plt.colorbar(collection_bg, cax=cax)
return ax
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