Python:如何使用 matplotlib 绘制 Pabon Lasso 图? [英] Python: How to plot Pabon Lasso chart using matplotlib?

问题描述

我正在尝试使用 python 的 matplotlib 库绘制 Pabon-Lasso 图.Pabon-Lasso 是医疗服务效率/性能图.我只在以下位置找到了用于绘图的 R 代码库:

X 轴是指 BOR(床位占用率).Y 轴是指 BTR(床位周转率).顶部和右侧的轴代表 ALOS(平均逗留时间).

该图表分为四个象限，每个象限代表不同的效率/性能水平.图表中间的水平线是平均 BTR.图表中间的垂直线是平均 BOR.图表上的对角线的梯度为 y/x = BTR/BOR.每条对角线都从原点开始.右侧(或顶部)轴上的交点代表 ALOS.

我尝试使用下面的代码，但卡在 ALOS 参数的顶部和右侧轴的标签上.

我的代码:

将 matplotlib.pyplot 导入为 plt将 numpy 导入为 np#[2016、2017]BOR=[57.6, 52.03]比特率=[69.81, 20.08]ALOS = [4.04, 12.68]mean_BTR = np.mean(BTR)mean_BOR = np.mean(BOR)fig=plt.figure()ax=fig.add_subplot(111, label="1")ax2=fig.add_subplot(111, label=2", frame_on=False)#散点ax.scatter([0, 57.6], [0, 52.03], color="C0")ax.annotate('2016', (57.6 + 2, 52.03))ax.scatter([0, 69.81], [0, 20.08], color="C0")ax.annotate('2017', (69.81 - 5, 20.08+2))#绘制对角线ax.axline((0, 0), (57.6, 52.03))ax.axline((0, 0), (69.81, 20.08))#draw象限/平均BOR &比特率ax.vlines(x=mean_BOR, ymin=0, ymax=np.max(BOR) + 20, color='b')ax.hlines(y=mean_BTR, xmin=0, xmax=np.max(BTR) + 20, color='b')ax.set_xlabel(BOR", color=C0")ax.set_ylabel(BTR"，颜色=C0")ax.tick_params(axis='x', 颜色=C0")ax.tick_params(axis='y', colors="C0")ax.set_xlim(xmin=0, xmax=np.max(BTR) + 20)ax.set_ylim(ymin=0, ymax=np.max(BOR) + 20)ax2.xaxis.tick_top()ax2.yaxis.tick_right()ax2.set_xlabel('ALOS', color="C1")ax2.set_ylabel('ALOS', color="C1")ax2.xaxis.set_label_position('top')ax2.yaxis.set_label_position('right')ax2.tick_params(axis='x', 颜色=C1")ax2.tick_params(axis='y', colors="C1")ax2.axes.xaxis.set_ticks([])ax2.axes.yaxis.set_ticks([])plt.show()

有什么帮助吗?或者是否有任何库已经用于此目的.谢谢.

解决方案

您走对了.matplotlib.axes 对象具有 tifying 方法，可添加额外的单轴.

例如:

将 numpy 导入为 np导入 matplotlib.pyplot 作为 plt#2016/2017xmax, ymax = 100, 100ALS = np.array([4.04, 12.68, 2])BOR = [57.6, 69.81, 40]比特率 = [52.03, 20.08, 80]注释 = [2016、2017、2018]mean_BTR = np.mean(BTR)mean_BOR = np.mean(BOR)fig = plt.figure()plot = fig.add_subplot()plot.set_xlim(0, xmax)plot.set_ylim(0, ymax)# 添加行对于 x、y、zip 中的标签(BOR、BTR、注释):plot.axline((0, 0), (x, y))plot.scatter((0, x), (0, y))plot.annotate(label, (x - 10, y))plot.vlines(x=mean_BOR, ymin=0, ymax=ymax, color='b')plot.hlines(y=mean_BTR, xmin=0, xmax=xmax, color='b')# 添加右轴刻度right_axis = plot.twinx()right_axis.set_ylim(0, ymax)right_ticks = np.array([ymax * y/x for x, y in zip(BOR, BTR)])right_axis.set_yticks(right_ticks[right_ticks <= ymax])right_axis.set_yticklabels(ALS[right_ticks <= ymax])# 添加上轴刻度top_axis = plot.twiny()top_axis.set_xlim(0, xmax)top_ticks = np.array([xmax * x/y for x, y in zip(BOR, BTR)])top_axis.set_xticks(top_ticks[top_ticks < xmax])top_axis.set_xticklabels(ALS[top_ticks < xmax])# 添加标签plot.set_xlabel('BOR')plot.set_ylabel('BTR', color='g')right_axis.set_ylabel(ALOS")top_axis.set_xlabel(ALOS")plt.show()

I'm trying to plot a Pabon-Lasso chart using python's matplotlib library. Pabon-Lasso is a healthcare services efficiency/performance plot. I only found the R code library for the plotting at: https://cran.r-project.org/web/packages/PabonLasso/index.html through searching but I have zero R knowledge.

Examples of a Pabon-Lasso chart is as follows:

The X-axis refers to BOR (Bed Occupancy Rate). The Y-axis refers to BTR (Bed Turnover Rate). The top and right-hand side axis represent the ALOS (Average Length of Stay).

The chart is divided into four quadrants with each representing different efficiencies/performance level. The horizontal line on the middle of the chart is the mean BTR. The vertical line on the middle of the chart is the mean BOR. The diagonal lines on the chart are at gradients of y/x = BTR/BOR. Each diagonal lines starts from origin. The intersection on the right(or top) axis represents the ALOS.

I have tried using the code below but stuck on the labelling for the top and right axis for ALOS parameter.

My code:

import matplotlib.pyplot as plt
import numpy as np

#[2016, 2017]
BOR=[57.6, 52.03]
BTR=[69.81, 20.08]
ALOS = [4.04, 12.68]

mean_BTR = np.mean(BTR)
mean_BOR = np.mean(BOR)

fig=plt.figure()
ax=fig.add_subplot(111, label="1")
ax2=fig.add_subplot(111, label="2", frame_on=False)

#scatter points
ax.scatter([0, 57.6], [0, 52.03], color="C0")
ax.annotate('2016', (57.6 + 2, 52.03))
ax.scatter([0, 69.81], [0, 20.08], color="C0")
ax.annotate('2017', (69.81 - 5, 20.08+2))

#draw diagonals
ax.axline((0, 0), (57.6, 52.03))
ax.axline((0, 0), (69.81, 20.08))

#draw quadrants/ mean BOR & BTR
ax.vlines(x=mean_BOR, ymin=0, ymax=np.max(BOR) + 20, color='b')
ax.hlines(y=mean_BTR, xmin=0, xmax=np.max(BTR) + 20, color='b')

ax.set_xlabel("BOR", color="C0")
ax.set_ylabel("BTR", color="C0")
ax.tick_params(axis='x', colors="C0")
ax.tick_params(axis='y', colors="C0")
ax.set_xlim(xmin=0, xmax=np.max(BTR) + 20)
ax.set_ylim(ymin=0, ymax=np.max(BOR) + 20)

ax2.xaxis.tick_top()
ax2.yaxis.tick_right()
ax2.set_xlabel('ALOS', color="C1") 
ax2.set_ylabel('ALOS', color="C1")       
ax2.xaxis.set_label_position('top') 
ax2.yaxis.set_label_position('right') 
ax2.tick_params(axis='x', colors="C1")
ax2.tick_params(axis='y', colors="C1")
ax2.axes.xaxis.set_ticks([])
ax2.axes.yaxis.set_ticks([])

plt.show()

Any help, please? Or are there any libraries already for this purpose. Thank you.

解决方案

You are on the right track. The matplotlib.axes object has twinning methods that adds additional single axes.

For example:

import numpy as np
import matplotlib.pyplot as plt

#2016/2017
xmax, ymax = 100, 100
ALS = np.array([4.04, 12.68, 2])
BOR = [57.6, 69.81, 40]
BTR = [52.03, 20.08, 80]
annotations = [2016, 2017, 2018]
mean_BTR = np.mean(BTR)
mean_BOR = np.mean(BOR)

fig = plt.figure()
plot = fig.add_subplot()
plot.set_xlim(0, xmax)
plot.set_ylim(0, ymax)

# add lines
for x, y, label in zip(BOR, BTR, annotations):
    plot.axline((0, 0), (x, y))
    plot.scatter((0, x), (0, y))
    plot.annotate(label, (x - 10, y))
plot.vlines(x=mean_BOR, ymin=0, ymax=ymax, color='b')
plot.hlines(y=mean_BTR, xmin=0, xmax=xmax, color='b')

# add right axis ticks
right_axis = plot.twinx()
right_axis.set_ylim(0, ymax)
right_ticks = np.array([ymax * y / x for x, y in zip(BOR, BTR)])
right_axis.set_yticks(right_ticks[right_ticks <= ymax])
right_axis.set_yticklabels(ALS[right_ticks <= ymax])

# add top axis ticks
top_axis = plot.twiny()
top_axis.set_xlim(0, xmax)
top_ticks = np.array([xmax * x / y for x, y in zip(BOR, BTR)])
top_axis.set_xticks(top_ticks[top_ticks < xmax])
top_axis.set_xticklabels(ALS[top_ticks < xmax])

# add labels
plot.set_xlabel('BOR')
plot.set_ylabel('BTR', color='g')
right_axis.set_ylabel("ALOS")
top_axis.set_xlabel("ALOS")

plt.show()

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