了解matplotlib转换 [英] Understanding matplotlib verts

问题描述

我正在尝试在matplotlib中为散点图创建自定义标记，其中标记是具有固定高度和可变宽度的矩形.每个标记的宽度是y值的函数.我使用此代码作为模板，并假设如果给定了verts，就这样尝试了N个2-D元组的列表，它以相应的第一个值的宽度和第二个值的高度绘制矩形(也许这已经是错误的，但是我还应该怎么做?).

I'm trying to create custom markers in matplotlib for a scatter plot, where the markers are rectangles with fix height and varying width. The width of each marker is a function of the y-value. I tried it like this using this code as a template and assuming that if verts is given a list of N 2-D tuples it plots rectangles with the width of the corresponing first value and the height of the second (maybe this is already wrong, but then how else do I accomplish that?).

我有一个x和y值的列表，每个值都包含以度为单位的角度.然后，我通过以下方式计算每个标记的宽度和高度:

I have a list of x and y values, each containing angles in degrees. Then, I compute the width and height of each marker by

field_size = 2.
symb_vec_x = [(field_size / np.cos(i * np.pi / 180.)) for i in y]
symb_vec_y = [field_size for i in range(len(y))]

并建立版本列表并使用

symb_vec = list(zip(symb_vec_x, symb_vec_y))
fig = plt.figure(1, figsize=(14.40, 9.00))
ax = fig.add_subplot(1,1,1)
sc = ax.scatter(ra_i, dec_i, marker='None', verts=symb_vec)

但是结果图是空的，但是没有错误消息.谁能告诉我定义顶点时做错了什么以及如何正确做? 谢谢！

But the resulting plot is empty, no error message however. Can anyone tell me what I did wrong with defining the verts and how to do it right? Thanks!

推荐答案

我从matplotlib用户邮件列表的Ryan获得了解决方案.非常优雅，所以我在这里分享他的例子:

I got the solution from Ryan of the matplotlib users mailing list. It's quite elegant, so I will share his example here:

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
from matplotlib.collections import PatchCollection

n = 100

# Get your xy data points, which are the centers of the rectangles.
xy = np.random.rand(n,2)

# Set a fixed height
height = 0.02
# The variable widths of the rectangles
widths = np.random.rand(n)*0.1

# Get a color map and make some colors
cmap = plt.cm.hsv
colors = np.random.rand(n)*10.
# Make a normalized array of colors
colors_norm = colors/colors.max()
# Here's where you have to make a ScalarMappable with the colormap
mappable = plt.cm.ScalarMappable(cmap=cmap)
# Give it your non-normalized color data
mappable.set_array(colors)

rects = []
for p, w in zip(xy, widths):
    xpos = p[0] - w/2 # The x position will be half the width from the center
    ypos = p[1] - height/2 # same for the y position, but with height
    rect = Rectangle( (xpos, ypos), w, height ) # Create a rectangle
    rects.append(rect) # Add the rectangle patch to our list

# Create a collection from the rectangles
col = PatchCollection(rects)
# set the alpha for all rectangles
col.set_alpha(0.3)
# Set the colors using the colormap
col.set_facecolor( cmap(colors_norm) )
# No lines
col.set_linewidth( 0 )
#col.set_edgecolor( 'none' )

# Make a figure and add the collection to the axis.
fig = plt.figure()
ax = fig.add_subplot(111)
ax.add_collection(col)
# Add your ScalarMappable to a figure colorbar
fig.colorbar(mappable)
plt.show()

谢谢您，瑞安(Ryan)和所有贡献自己想法的人！

Thank you, Ryan, and everyone who contributed their ideas!

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