从下拉菜单或按钮 Python/Plotly 将 sqrt 设置为 yaxis 比例

2023-09-28Python开发问题
3

本文介绍了从下拉菜单或按钮 Python/Plotly 将 sqrt 设置为 yaxis 比例的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着跟版网的小编来一起学习吧!

问题描述

从这里的示例:

但您实际上不必为了更改与 y 轴关联的数据来显示给定系列的平方根.下面的完整片段生成了一个图形,您可以轻松地在显示原始数据和平方根序列之间切换.

图 1:原始数据

情节 2:np.sqrt

完整代码:

将 numpy 导入为 np导入 plotly.graph_objects将 plotly.express 导入为 pxdf = px.data.gapminder().query("year == 2007")# 图形设置fig = go.Figure()fig.add_scatter(mode="markers", x=df["gdpPercap"], y=df["lifeExp"])fig.add_scatter(mode="markers", x=df["gdpPercap"], y=np.sqrt(df["lifeExp"]), visible = False)# 更新菜单按钮按钮 = [dict(method='restyle',标签='线性',可见=真,args=[{'label': '线性','可见':[真,假],}]),dict(method='restyle',标签='sqrt',可见=真,args=[{'label': '线性','可见':[假,真],}])]# 指定更新菜单嗯 = [{'按钮':按钮,方向":向下"}]fig.update_layout(updatemenus=um)# fig.update_yaxes(type="log")图.show()

From this example here : Set linear or log axes from button or dropdown menu I can use a button to change the yaxis from linear to log. However i need to change it to sqrt.

I have looked at from Plotly: reference layout axis I have found that there is no type ("sqrt")

type Code: fig.update_yaxes(type=) Type: enumerated , one of ( "-" | "linear" | "log" | "date" | "category" | "multicategory" ) Default: "-" Sets the axis type. By default, plotly attempts to determined the axis type by looking into the data of the traces that referenced the axis in question.

Here is the exmaple code:

import plotly.graph_objects as go

fig = go.Figure()

fig.add_trace(go.Scatter(
    x=[0, 1, 2, 3, 4, 5, 6, 7, 8],
    y=[8, 7, 6, 5, 4, 3, 2, 1, 0]
))

fig.add_trace(go.Scatter(
    x=[0, 1, 2, 3, 4, 5, 6, 7, 8],
    y=[0, 1, 2, 3, 4, 5, 6, 7, 8]
))

fig.update_layout(title_text="CIR plot ",
                          updatemenus=[
            dict(
                 buttons=list([
                     dict(label="Linear",  method="update", args=[{"yaxis":{"type": "linear"}}]),
                     dict(label="Log", method="update", args=[{"yaxis":{"type": "log"}}]),
                                  ]),
            )])

 #UPDATE Y AXIS HERE
fig.update_layout( updatemenus=[
            dict(
                 buttons=list([
                     dict(label="Linear-ID",  
                          method="relayout", 
                          args=[{"yaxis.type": "linear"}]),
                     dict(label="Log-ID", 
                          method="relayout", 
                          args=[{"yaxis.type": "log"}]),
                                  ]),
            )])
fig.show()

Is there a way to use a button to update the scale to sqrt ?

解决方案

To my knowledge there's currently no way to specify sqrt as scale for the axis that would trigger a visual change to the axis labels like the case is for fig.update_xaxes(type="log"):

But you don't really have to in order to change the data associated with the yaxis to display the square roots of a given series. The complete snippet below produce a figure that will easily let you switch between displaying the raw data and a series with square roots.

Plot 1: Raw data

Plot 2: np.sqrt

Complete code:

import numpy as np
import plotly.graph_objects as go
import plotly.express as px
df = px.data.gapminder().query("year == 2007")

# figure setup
fig = go.Figure()
fig.add_scatter(mode="markers", x=df["gdpPercap"], y=df["lifeExp"])
fig.add_scatter(mode="markers", x=df["gdpPercap"], y=np.sqrt(df["lifeExp"]), visible = False)

# buttons for updatemenu
buttons = [dict(method='restyle',
                label='linear',
                visible=True,
                args=[{'label': 'linear',
                       'visible':[True, False],
                      }
                     ]),
           dict(method='restyle',
                label='sqrt',
                visible=True,
                args=[{'label': 'linear',
                       'visible':[False, True],
                      }
                     ])]
           
# specify updatemenu        
um = [{'buttons':buttons,
      'direction': 'down'}
      ]

fig.update_layout(updatemenus=um)
# fig.update_yaxes(type="log")
fig.show()

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