Python 复杂bokeh图中CheckboxButtonGroup与图例的交互_Python_Pandas_Bokeh

Python 复杂bokeh图中CheckboxButtonGroup与图例的交互

python pandas

Python 复杂bokeh图中CheckboxButtonGroup与图例的交互,python,pandas,bokeh,Python,Pandas,Bokeh,我有一个复杂的多元数据集，其结构与此类似： import pandas as pd import numpy as np import datetime as dt from itertools import cycle, islice N = 24 start_date = dt.date(2016,1,1) nbdays = int(365 / N) df = pd.DataFrame({'Date': [start_date + dt.timedelta(days=i*nbdays)

我有一个复杂的多元数据集，其结构与此类似：

import pandas as pd
import numpy as np
import datetime as dt
from itertools import cycle, islice

N = 24
start_date = dt.date(2016,1,1)
nbdays = int(365 / N)

df = pd.DataFrame({'Date': [start_date + dt.timedelta(days=i*nbdays) for i in range(1,N+1)], 
                   'Rating':    [(100/N)*i for i in range(1,N+1)], 
                   'Plot':      list(islice(cycle(range(1, 9)), 0, N)), 
                   'Treatment': list(islice(cycle(range(1, 7)), 0, N)), 
                   'Trial':     list(islice(cycle(range(1, 4)), 0, N)), 
                   'Name':      list(islice(cycle("ABCDEF"), 0, N)), 
                   'Target':    list(islice(cycle("JKLMNOP"), 0, N)), 
                   'Part':      list(islice(cycle("WXYZ"), 0, N)) 
                   })

我想：

绘制
```
日期
```
与
```
评级
```
，用
```
治疗
```
创建交互式图例，以便单击治疗可以切换治疗的可见性
在其他参数（
```
绘图
```
，
```
试验
```
，
```
名称
```
，
```
目标
```
，
```
零件
```
）的绘图侧面设置按钮，以便单击按钮切换相应点的可见性
将鼠标悬停在点上时显示所有参数的步骤

这是我拥有的代码（变量

df

中来自上面的数据集）：

当您运行

bokeh-serve-show main.py

（

bokeh

0.12.10版）时，它是这样的：

工作原理：

单击图例可切换处理的可见性

什么不起作用：

鼠标悬停标签中显示的信息不正确（前6个点的鼠标悬停标签中的信息相同，后6个点的鼠标悬停标签也相同，依此类推）
单击右侧按钮会弄乱绘图：轴标签不可见，第二个图例显示在绘图上方而不是下方）

如何解决最后两点？

以下是一些想法：

使用
```
CustomJSFilter
```
包装一个javascript函数来处理数据过滤器
只需调用一次
```
p.circle（）
```
即可绘制所有圆
使用
```
因子cmap
```
将治疗列映射到颜色
使用
```
标记
```
属性以在Python中保存数据并在javascript中读取数据

由于只有一个

GlyphRenderer

，因此可见性切换不适用于其图例

要解决此问题，请创建一个虚拟

ColumnDataSource

，并多次调用

p.circle（）

，以创建虚拟

GlyphRenderer

列表。为这些虚拟

GlyphRenderer

创建图例，并将其可见属性更改链接到调用

source.change.emit（）的CustomJS
重新绘制图形
因为所有的过滤器计算都是由javascript执行的，所以您可以创建一个静态html文件，该文件可以与用户输入交互
这是笔记本：

非常好。我从来都不知道回调在笔记本中起作用，我的印象是每次都需要使用bokeh服务器。谢谢你，它帮助了我@海利
from bokeh.plotting import figure
from bokeh.models import ColumnDataSource
from bokeh.palettes import Set1 
from bokeh.models import (CDSView, BooleanFilter, Legend,
                          DatetimeTickFormatter, Range1d,
                          HoverTool)
from bokeh.models.widgets import CheckboxButtonGroup, Div
from bokeh.layouts import widgetbox, layout
from bokeh.io import curdoc

columns = ['Treatment', 'Plot', 'Trial', 'Name', 'Target', 'Part']
categories = [sorted(df[column].unique()) for column in columns]
all_columns = ['Date', 'Rating'] + columns

treatment_colormap = dict(zip(categories[0], Set1[6])) 

# Create Input controls
divs = [Div(text=column+':') for column in columns[1:]]
controls   = [CheckboxButtonGroup(labels=list(map(str, category)), active=list(range(len(category)))) for category in categories[1:]]

# Create Column Data Source that will be used by the plot
source = ColumnDataSource(data=dict((column, []) for column in all_columns))


def select():
    actives = [control.active for control in controls]
    actives_names = [[category[a] for a in active] for (active, category) in zip(actives, categories[1:])]

    presence = [df[column].isin(active_names) for (column, active_names) in zip(columns[1:], actives_names)]
    result = df[np.logical_and.reduce(presence)] # https://stackoverflow.com/a/49027984/50065
    return result

def update():
    sdf = select()
    source.data = dict((column, sdf[column]) for column in all_columns)

    glyphs = []
    selected_treatments = sorted(sdf['Treatment'].unique())
    for treatment in selected_treatments:
        booleans = [value == treatment for value in source.data['Treatment']]
        view = CDSView(source=source, filters=[BooleanFilter(booleans)])
        color = treatment_colormap[treatment]
        glyphs.append(p.circle(x='Date', y='Rating', source=source, view=view, line_color=color, fill_color=color))

    legend = Legend(items=[
        ("treatment {}".format(treatment), [glyph]) for treatment, glyph
        in zip(selected_treatments, glyphs)
        ])

    p.add_layout(legend, 'below')
    p.legend.click_policy='hide'
    p.legend.location = 'bottom_center'
    p.legend.orientation = 'horizontal'

for control in controls:
    control.on_change('active', lambda attr, old, new: update())

def datetime_in_miliseconds(date):
    date = dt.datetime.strptime(date, '%d/%m/%Y')
    epoch = dt.datetime.utcfromtimestamp(0)
    return (date - epoch).total_seconds() * 1000.0

hover = HoverTool(tooltips=[('Date', '@Date{%d/%m/%Y}')] + [(column, '@'+column) 
                            for column in all_columns[1:]], formatters={
                             'Date': 'datetime', # use 'datetime' formatter for 'Date' field
                             })


p = figure(x_axis_type="datetime", tools=[hover])
p.title.text = 'Date vs Rating'
p.xaxis.axis_label = 'Date'
p.xaxis.formatter = DatetimeTickFormatter(days = ['%d/%m/%y'])
start = datetime_in_miliseconds('01/01/2016')
end = datetime_in_miliseconds('31/12/2016')
p.x_range=Range1d(start, end)
p.yaxis.axis_label = 'Rating'
p.ygrid.band_fill_color="olive"
p.ygrid.band_fill_alpha = 0.1
p.y_range=Range1d(0,100)

sizing_mode = 'scale_width'
inputs = widgetbox(*sum(zip(divs, controls), tuple()), sizing_mode=sizing_mode)

l = layout([[p, inputs]], sizing_mode=sizing_mode)
update()  # initial load of the data
curdoc().add_root(l)

from bokeh.plotting import figure
from bokeh.models import ColumnDataSource, CustomJS, CDSView, CustomJSFilter, HoverTool
from bokeh.models.widgets import CheckboxButtonGroup
from bokeh.io import show, output_notebook
from bokeh.palettes import Set1 
from bokeh.transform import factor_cmap
from bokeh.layouts import widgetbox, layout

import pandas as pd
import numpy as np
import datetime as dt
from itertools import cycle, islice

output_notebook()

N = 24
start_date = dt.date(2016,1,1)
nbdays = int(365 / N)

df = pd.DataFrame({'Date': [start_date + dt.timedelta(days=i*nbdays) for i in range(1,N+1)], 
                   'Rating':    [(100/N)*i for i in range(1,N+1)], 
                   'Plot':      list(islice(cycle(range(1, 9)), 0, N)), 
                   'Treatment': list(islice(cycle(range(1, 7)), 0, N)), 
                   'Trial':     list(islice(cycle(range(1, 4)), 0, N)), 
                   'Name':      list(islice(cycle("ABCDEF"), 0, N)), 
                   'Target':    list(islice(cycle("JKLMNOP"), 0, N)), 
                   'Part':      list(islice(cycle("WXYZ"), 0, N)) 
                   })

columns = 'Plot', 'Trial', 'Name', 'Target', 'Part'
unique_items = [df[col].unique() for col in columns]

df["Treatment"] = df["Treatment"].astype(str)

source = ColumnDataSource(data=df)
dummy_source = ColumnDataSource(data={"x":[], "y":[]})

hover = HoverTool(tooltips=[('Date', '@Date{%d/%m/%Y}')] + [(column, '@'+column) 
                            for column in columns], formatters={
                             'Date': 'datetime', # use 'datetime' formatter for 'Date' field
                             })

p = figure(x_axis_type="datetime", tools=[hover])
color = factor_cmap("Treatment", Set1[9], df.Treatment.unique())

for i, label in enumerate(df.Treatment.unique()):
    dummy_circle = p.circle(x="x", y="y", source=dummy_source, legend="Treatment {}".format(label), color=Set1[9][i])
    dummy_circle.tags = [label]

p.legend.location = "bottom_right"
p.legend.click_policy = "hide"

def source_change(source=source):
    source.change.emit()

callback_source_change = CustomJS.from_py_func(source_change)

for item in p.legend[0].items:
    item.renderers[0].js_on_change("visible", callback_source_change)

controls = [CheckboxButtonGroup(labels=items.astype(str).tolist(), active=list(range(len(items)))) for items in unique_items]
widgets = widgetbox(*controls)

for name, control in zip(columns, controls):
    control.tags = [name]

def func_filter(source=source, legend=p.legend[0], widgets=widgets):
    window.widgets = widgets
    visible_treatments = [item.renderers[0].tags for item in legend.items if item.renderers[0].visible]
    date = source.data['Date']
    treatments = source.data['Treatment']
    res = []

    selectors = {}
    for widget in widgets.children:
        col = widget.tags[0]
        selectors[col] = dict([(widget.labels[i], i) for i in widget.active])

    for i in range(len(date)):
        flag = treatments[i] in visible_treatments
        for key, val in selectors.items():
            if source.data[key][i] not in val:
                flag = False
                break
        res.append(flag)
    return res

view = CDSView(source=source, filters=[CustomJSFilter.from_py_func(func_filter)])
p.circle(x='Date', y='Rating', source=source, view=view, line_color=color, fill_color=color)  

for control in controls:
    control.js_on_change("active", callback_source_change)

show(layout([[p, widgets]]))