Plotly 图表上的辅助/平行 X 轴 (python) [英] Secondary / Parallel X-Axis on Plotly charts (python)

问题描述

我需要在 Kaplan Meier 图上呈现 at_risk 数字.

最终结果应该是这样的:

我在渲染时遇到的问题是 No.处于风险中的患者数量位于图表底部.此处显示的值对应于 x 轴上的值.所以本质上，它就像一个与 X 平行渲染的 Y 轴.

我一直试图复制在这里找到的多轴(示例 2 - 不同治疗方法的结肠癌

df = pd.read_csv('http://www-eio.upc.edu/~pau/cms/rdata/csv/survival/colon.csv')fig = plotly.tools.make_subplots(rows=2, cols=1, print_grid=False)kmfs = []step = 5 # 应显示有风险的患者数量的时间点数量x_min = 0 # x 轴的最小值，用于确保两个图具有相同的范围x_max = 0 # x 轴的最大值对于 df.rx.unique() 中的 rx:T = df[df.rx == rx]["时间"]E = df[df.rx == rx]["状态"]kmf = lifelines.KaplanMeierFitter()kmf.fit(T, event_observed=E)kmfs.append(kmf)x_max = max(x_max, max(kmf.event_table.index))x_min = min(x_min, min(kmf.event_table.index))fig.append_trace(plotly.graph_objs.Scatter(x=kmf.survival_function_.index,y=kmf.survival_function_.values.flatten(),名称=rx),1, 1)无花果患者 = []对于 s, rx in enumerate(df.rx.unique()):kmf = kmfs[s].event_tablex = []对于 i in range(0, int(x_max), int(x_max/(steps - 1))):x.append(kmf.iloc[np.abs(kmf.index - i).argsort()[0]].name)fig.append_trace(plotly.graph_objs.Scatter(x=x,y=[rx] * len(x),text=[kmfs[s].event_table[kmfs[s].event_table.index == t].at_risk.values[0] for t in x],模式='文本'，显示图例=假)，2、1)# 只是一个用作间隔/标题的虚拟线t = [''] * len(x)t[1] = '有风险的患者'fig.append_trace(plotly.graph_objs.Scatter(x=x,y=[''] * len(x),文字=t，模式='文本'，显示图例=假)，2、1)# 更漂亮的布局x_axis_range = [x_min - x_max * 0.05, x_max * 1.05]fig['layout']['xaxis2']['visible'] = Falsefig['layout']['xaxis2']['range'] = x_axis_rangefig['layout']['xaxis']['range'] = x_axis_rangefig['layout']['yaxis']['domain'] = [0.4, 1]fig['layout']['yaxis2']['domain'] = [0.0, 0.3]fig['layout']['yaxis2']['showgrid'] = Falsefig['layout']['yaxis']['showgrid'] = Falseplotly.offline.iplot(fig)

I need to render at_risk numbers on a Kaplan Meier graph.

The end result should be similar to this:

The bit I am having trouble rendering is the No. of patients at risk at the bottom of the graph. The values displayed there, correspond to the values on the x-axis. So in essence, it's like a Y-axis rendered in parallel with the X.

I have been trying to replicate multiple-axis found here (https://plot.ly/python/multiple-axes/) without success, and also tried having a subplot and hide everything but the X-axis, but then its values do not align with the graph above.

What is the best approach for this?

解决方案

You could plot Kaplan-Meier survival graphs with patients at risk with Plotly by using subplots. The first plot has the survival rate, the second plot is a scatter plot where only the text is shown, i.e. the markers are not shown.

Both plots have the same y-axis and the patients at risk are plotted at the respective x-values.

More examples are here: https://github.com/Ashafix/Kaplan-Meier_Plotly

Example 1 - Lung cancer in female and male patients

import pandas as pd
import lifelines
import plotly
import numpy as np
plotly.offline.init_notebook_mode()

df = pd.read_csv('http://www-eio.upc.edu/~pau/cms/rdata/csv/survival/lung.csv')

fig = plotly.tools.make_subplots(rows=2, cols=1, print_grid=False)
kmfs = []

dict_sex = {1: 'Male', 2: 'Female'}

steps = 5 # the number of time points where number of patients at risk which should be shown

x_min = 0 # min value in x-axis, used to make sure that both plots have the same range
x_max = 0 # max value in x-axis

for sex in df.sex.unique():
    T = df[df.sex == sex]["time"]
    E = df[df.sex == sex]["status"]
    kmf = lifelines.KaplanMeierFitter()

    kmf.fit(T, event_observed=E)
    kmfs.append(kmf)
    x_max = max(x_max, max(kmf.event_table.index))
    x_min = min(x_min, min(kmf.event_table.index))
    fig.append_trace(plotly.graph_objs.Scatter(x=kmf.survival_function_.index,
                                               y=kmf.survival_function_.values.flatten(),  
                                               name=dict_sex[sex]), 
                     1, 1)


for s, sex in enumerate(df.sex.unique()):
    x = []
    kmf = kmfs[s].event_table
    for i in range(0, int(x_max), int(x_max / (steps - 1))):
        x.append(kmf.iloc[np.abs(kmf.index - i).argsort()[0]].name)
    fig.append_trace(plotly.graph_objs.Scatter(x=x, 
                                               y=[dict_sex[sex]] * len(x), 
                                               text=[kmfs[s].event_table[kmfs[s].event_table.index == t].at_risk.values[0] for t in x], 
                                               mode='text', 
                                               showlegend=False), 
                     2, 1)

# just a dummy line used as a spacer/header
t = [''] * len(x)
t[1] = 'Patients at risk'
fig.append_trace(plotly.graph_objs.Scatter(x=x, 
                                           y=[''] * len(x), 
                                           text=t,
                                           mode='text', 
                                           showlegend=False), 
                 2, 1)


# prettier layout
x_axis_range = [x_min - x_max * 0.05, x_max * 1.05]
fig['layout']['xaxis2']['visible'] = False
fig['layout']['xaxis2']['range'] = x_axis_range
fig['layout']['xaxis']['range'] = x_axis_range
fig['layout']['yaxis']['domain'] = [0.4, 1]
fig['layout']['yaxis2']['domain'] = [0.0, 0.3]
fig['layout']['yaxis2']['showgrid'] = False
fig['layout']['yaxis']['showgrid'] = False

plotly.offline.iplot(fig)

Example 2 - Colon cancer with different treatments

df = pd.read_csv('http://www-eio.upc.edu/~pau/cms/rdata/csv/survival/colon.csv')

fig = plotly.tools.make_subplots(rows=2, cols=1, print_grid=False)
kmfs = []

steps = 5 # the number of time points where number of patients at risk which should be shown

x_min = 0 # min value in x-axis, used to make sure that both plots have the same range
x_max = 0 # max value in x-axis

for rx in df.rx.unique():
    T = df[df.rx == rx]["time"]
    E = df[df.rx == rx]["status"]
    kmf = lifelines.KaplanMeierFitter()

    kmf.fit(T, event_observed=E)
    kmfs.append(kmf)
    x_max = max(x_max, max(kmf.event_table.index))
    x_min = min(x_min, min(kmf.event_table.index))
    fig.append_trace(plotly.graph_objs.Scatter(x=kmf.survival_function_.index,
                                               y=kmf.survival_function_.values.flatten(),
                                               name=rx), 
                     1, 1)


fig_patients = []
for s, rx in enumerate(df.rx.unique()):
    kmf = kmfs[s].event_table
    x = []
    for i in range(0, int(x_max), int(x_max / (steps - 1))):
        x.append(kmf.iloc[np.abs(kmf.index - i).argsort()[0]].name)
    fig.append_trace(plotly.graph_objs.Scatter(x=x, 
                                               y=[rx] * len(x), 
                                               text=[kmfs[s].event_table[kmfs[s].event_table.index == t].at_risk.values[0] for t in x], 
                                               mode='text', 
                                               showlegend=False), 
                     2, 1)

# just a dummy line used as a spacer/header
t = [''] * len(x)
t[1] = 'Patients at risk'
fig.append_trace(plotly.graph_objs.Scatter(x=x, 
                                           y=[''] * len(x), 
                                           text=t,
                                           mode='text', 
                                           showlegend=False), 
                 2, 1)


# prettier layout
x_axis_range = [x_min - x_max * 0.05, x_max * 1.05]
fig['layout']['xaxis2']['visible'] = False
fig['layout']['xaxis2']['range'] = x_axis_range
fig['layout']['xaxis']['range'] = x_axis_range
fig['layout']['yaxis']['domain'] = [0.4, 1]
fig['layout']['yaxis2']['domain'] = [0.0, 0.3]
fig['layout']['yaxis2']['showgrid'] = False
fig['layout']['yaxis']['showgrid'] = False

plotly.offline.iplot(fig)

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