画布上的 Matplotlib RegularPolygon 集合位置答案

【问题标题】：Matplotlib RegularPolygon collection location on the canvas画布上的 Matplotlib RegularPolygon 集合位置
【发布时间】：2014-05-18 20:34:01
【问题描述】：

我正在尝试使用 python 绘制特征图 (SOM)。为简单起见，请想象一个 2D 图，其中每个单位都表示为一个六边形。

正如本主题所示：Hexagonal Self-Organizing map in Python 六边形并排放置，形成网格。

我设法编写了以下代码，它完美地适用于一定数量的多边形并且仅适用于少数形状（例如 6 x 6 或 10 x 4 六边形）。然而，这种方法的一个重要特征是支持 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

我尝试制作一些可以自动理解网格形状的东西。它没有用（我不确定为什么）。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。

如前所述，这里是六边形位置的数据。如您所见，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.])}

【问题讨论】：

对不起，我真的不明白你的问题是什么......
我猜问题出在您用来生成输入的代码中，而不是在 mpl 代码中。
我想知道如何生成类似于第一张图像的东西（六边形并排，没有空格）。六边形中心由算法给出（就像我在另一篇文章中粘贴的那样。）。它是 kohonen R 实现，所以输入很好（R 绘图工作！）。总而言之，我想使用六边形生成 3x3 或 6x6 或 10x4（等等）网格，给定点和绘图宽度大小之间没有空格。现在清楚了吗？
您能否发布完整的代码以及我们可以重现情节的数据？我认为多边形之间的间隙是由于尺寸设置造成的。最近回答了一个在数据空间中用半径画圆的问题：stackoverflow.com/questions/23608691/…
当然我已经添加了数据。出于测试目的，d_matrix 可以是一个零数组。

标签： python canvas matplotlib polygon

【解决方案1】：

我已经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它）。这样我就可以计算六边形 apothem 并正确估计六边形内圆的大小（正如 matplotlib 所期望的那样）。

最后没有差距！

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

【讨论】：