散景Python:“选择"下拉列表将更新ColumnDataSource，但不会更新图形 [英] Bokeh Python: Select dropdown is updating ColumnDataSource but not updating the graph

问题描述

我是Bokeh的新手，正在尝试创建交互式的天气数据图.有两个选择"菜单，一个用于传感器ID(132、133，...)，一个用于变量(温度，露点...).当用户更改其中一个值时，该图应使用所选数据进行更新.

I'm new to Bokeh and I'm trying to create an interactive plot of weather data. There are two Select menus, one for sensor ID (132, 133, ...), and one for variable (Temperature, Dew Point, ...). When the user changes either value, the graph is supposed to update with the selected data.

我正在使用的数据在Panda Dataframe中，其中有一个列'dt'，其中包含一分钟间隔的datetime对象，并且这些数据位于遵循命名约定'Temp132'，'Temp 133的列中'，'Dew132'，'Dew133'等.理想情况下，用户将从选择"菜单中选择的值组合在一起，以构成一个字符串，该字符串可用于从df中提取数据系列("Temp" +"132"会用于调用df ['Temp132']).

The data I'm using is in a Panda Dataframe, where there is a column 'dt' which contains the one minute interval datetime objects, and the data are in columns that follow the naming convention 'Temp132', 'Temp 133', 'Dew132', 'Dew133', etc. Ideally, the user selected values from the Select menus would be combined to make a string that could be used to extract a data series out of df ('Temp' + '132' would be used to call df['Temp132']).

使用打印语句，我可以看到当用户在选择"菜单中更改值时，ColumnDataSource正在更新.但是，它不会更新图形.我想我的"make_plot"函数做错了.另外，我正在使用Bokeh服务器(bokeh serve --show bokeh_test.py)运行它.

Using print statements, I can see that when the user changes a value in the Select menu, the ColumnDataSource is getting updated. However, it is not updating the graph. I think I'm doing something wrong with the "make_plot" function. Also, I am running this using the Bokeh server (bokeh serve --show bokeh_test.py).

下面是我的代码的摘录:

Below is an excerpt of my code:

from math import pi
import pandas as pd
from bokeh.io import curdoc
from bokeh.plotting import figure
from bokeh.layouts import row, column
from bokeh.models.widgets import Select
from bokeh.models import DatetimeTickFormatter, ColumnDataSource
import datetime

def get_dataset(src, height, var, dt):
    var_str = var_z[var]['var']
    z_str   = var_z[height]['z']
    df_sub =src[[var_str+z_str]].copy()
    df_sub['dt'] = dt
    df_sub = pd.DataFrame(data=df_sub.values, columns=[var_str+height, 'dt'])
    return ColumnDataSource(data=df_sub)

def make_plot(in_source, y_label_var):
    var = variable_select.value
    z   = height_select.value
    var_str = var_z[var]['var']
    plot = figure(plot_width=800, plot_height=800, title="Data ", x_axis_label = 'Date and Time', y_axis_label = y_label_var)
    plot.line('dt', var_str+z, line_width=2, source=in_source)
    plot.xaxis.formatter=DatetimeTickFormatter( days=["%m/%d/%Y %H:%M"],
        months=["%m/%d/%Y %H:%M"],
        hours=["%m/%d/%Y %H:%M"],
        minutes=["%m/%d/%Y %H:%M"])
    plot.xaxis.major_label_orientation = pi/4

    return plot

def update_plot(attr, old, new):
    var = variable_select.value
    z   = height_select.value
    plot.title.text = var_z[var]['title'] + " " + var_z[z]['title']
    src = get_dataset(df, var_z[z]['z'], var_z[var]['title'], dt)

    print(source.data)
    source.data = src.data
    print(source.data)

#-----------------------------------------------------------------------------

init_height = '132'
init_var    = 'Temperature'

var_z = {'Temperature'        : {'var': 'Temp',         'title': 'Temperature',},
         'Dew Point'          : {'var': 'Dew',          'title': 'Dew Point',},
         'Mean Wind Direction': {'var': 'MeanWindDir',  'title': 'Mean Wind Direction',},
         'Mean Wind Speed'    : {'var': 'MeanWindSpeed','title': 'Mean Wind Speed',},
         'Peak Wind Speed'    : {'var': 'PeakWindSpeed','title': 'Peak Wind Speed',},
         'Peak Wind Direction': {'var': 'PeakWindDir',  'title': 'Peak Wind Direction',},
         'Relative Humidity'  : {'var': 'RH',           'title': 'Relative Humidity',},
         '132' : {'z': '132', 'title': '132',},
         '133' : {'z': '133', 'title': '133',},
         '134' : {'z': '134', 'title': '134',},
         '257' : {'z': '257', 'title': '257',},
         '258' : {'z': '258', 'title': '258',},
         '259' : {'z': '259', 'title': '259',},
         '382' : {'z': '382', 'title': '382',},
         '383' : {'z': '383', 'title': '383',},
         '384' : {'z': '384', 'title': '384',},
         '457' : {'z': '457', 'title': '457',},
         '458' : {'z': '458', 'title': '458',},
         '459' : {'z': '459', 'title': '459',}}

height_select  = Select(value=init_height, title='Height', options = ["132","133","134","257","258","259","382","383","384","457","458","459"])
variable_select= Select(value=init_var, title = 'Variable', options = ["Temperature", "Dew Point", "Mean Wind Direction", "Mean Wind Speed", "Peak Wind Speed", "Peak Wind Direction", "Relative Humidity"] )

df = pd.read_csv('My/File/Path')
dt = df['dt'].to_list()
source = get_dataset(df, init_height, init_var, dt)
plot = make_plot(source, var_z[init_var]['var'])

height_select.on_change('value', update_plot)
variable_select.on_change('value', update_plot)

controls = column(height_select, variable_select)

curdoc().add_root(row(plot,controls))

谢谢您的帮助！

推荐答案

您不应将.data从一个CDS分配给另一个CDS.在即将到来的Bokeh 2.0中，尝试执行此操作将引发一条明确的错误消息.尽管它的行为与dict相似，但事实并非如此.为了支持Python和JS之间的所有自动同步，CDS .data实际上是一种非常专业的数据结构，具有与许多其他事物的链接，并且不支持将它们从一个CDS重定位"到另一个CDS.您只应从简单的python字典

You should not assign the .data from one CDS to another CDS. In the upcoming Bokeh 2.0 trying to do this will raise an explicit error message. Although it behaves similar to a dict, it is not. In order to support all the automatic synchronization between Python and JS the CDS .data is actually a very specialized data structure that has linkages to many other things, and "re-homing" them from one CDS to another is not supported. You should only ever assign to .data from a plain python dict

source.data = { ... } # plain python dict

如果您需要调整DataFrame，则CDS上有一个.from_df方法，该方法将创建适当的纯python dict结构，您可以使用该结构进行分配.

If you need to adapt a DataFrame there is a .from_df method on CDS that will create the appropriate plain python dict structure you can use to assign from.

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