本地主机上的 Bokeh Web 服务器应用程序到 html 文件
Bokeh web server app at localhost to html file
我一直在与 bokeh web server 合作。
我创建了一个网络应用程序，使用我自己的数据并遵循以下示例：https://github.com/bokeh/bokeh/blob/master/examples/app/movies/main.py
我已经完成了剧本，一切顺利。我可以使用此命令查看结果：bokeh serve --show main.py
我用来创建网络应用程序的模块是：
from os.path import dirname, join
from pandas import Series, DataFrame
from bokeh.plotting import figure
from bokeh.layouts import layout, widgetbox
from bokeh.models import ColumnDataSource, HoverTool, Div
from bokeh.models.widgets import Slider, Select, TextInput
from bokeh.io import curdoc
from scipy import stats
import numpy as np
import pandas
但是，我的目标是将结果上传到 github 上我的 gh-pages 分支。
如何将 bokeh 的结果保存为 html 文件以便在网页中使用它？
我尝试使用 bokeh.plotting 中的 show，但它显示的本地主机路径与命令 bokeh serve --show main.py 一样。
我可以使用其他命令吗？
如有任何建议，我们将不胜感激！提前致谢。
更新
我使用此代码来获得解决方案。使用这段代码，我得到了一个 html 文件作为输出，但它需要改进。
from os.path import dirname, join
from pandas import Series, DataFrame
from bokeh.plotting import figure
from bokeh.layouts import layout, widgetbox
from bokeh.models import ColumnDataSource, HoverTool, Div
from bokeh.models.widgets import Slider, Select, TextInput
from bokeh.io import curdoc
from bokeh.resources import JSResources
from bokeh.embed import file_html
from bokeh.util.browser import view
from jinja2 import Template
from scipy import stats
import numpy as np
import pandas
csvdata = pandas.read_csv('Alimentacion.csv', low_memory = False, encoding = 'latin-1')
# Convert amount field into int()
def str_to_int(mainList):
for item in mainList:
newList = [(int(item.replace('$', '').replace(',', '')) / (1000000)) for item in mainList]
return newList
# Call str_to_int function
csvdata['CuantiaInt'] = str_to_int(csvdata['Cuantía'])
mean = np.mean(csvdata['CuantiaInt'])
# Assing colors to each contract by mean
csvdata['color'] = np.where(csvdata['CuantiaInt'] > mean, 'red', 'blue')
csvdata['alpha'] = np.where(csvdata['CuantiaInt'] > mean, 0.75, 0.75)
# Replace missing values (NaN) with 0
csvdata.fillna(0, inplace=True)
csvdata['revenue'] = csvdata.CuantiaInt.apply(lambda x: '{:,d}'.format(int(x)))
estados1 = [line.rstrip() for line in open('Estados1.txt')]
estados2 = [line.rstrip() for line in open('Estados2.txt')]
csvdata.loc[csvdata.Estado.isin(estados1), 'color'] = 'grey'
csvdata.loc[csvdata.Estado.isin(estados1), 'alpha'] = 0.75
csvdata.loc[csvdata.Estado.isin(estados2), 'color'] = 'brown'
csvdata.loc[csvdata.Estado.isin(estados2), 'alpha'] = 0.75
csvdata['z score'] = stats.zscore(csvdata['CuantiaInt'])
csvdata['sigma'] = np.std(csvdata['CuantiaInt'])
date_time = pandas.DatetimeIndex(csvdata['Fecha (dd-mm-aaaa)'])
newdates = date_time.strftime('%Y')
newdates = [int(x) for x in newdates]
csvdata['dates'] = newdates
csvdata['Dptos'] = csvdata['Loc dpto']
csvdata['Entidad'] = csvdata['Entidad Compradora']
csvdata['Proceso'] = csvdata['Tipo de Proceso']
axis_map = {
'Cuantía y promedio': 'z score',
'Cuantía (Millones de pesos)': 'CuantiaInt',
'Desviación estándar': 'sigma',
'Fecha del contrato': 'dates',
}
desc = Div(text=open(join(dirname(__file__), 'alimentacion.html')).read(), width=800)
DptosList = [line.rstrip() for line in open('locdpto.txt')]
ProcesosList = [line.rstrip() for line in open('tipoproceso.txt')]
EntidadesList = [line.rstrip() for line in open('entidades.txt')]
# Create Input controls
min_year = Slider(title = 'Año inicial', start = 2012, end = 2015, value = 2013, step = 1)
max_year = Slider(title = 'Año final', start = 2012, end = 2015, value = 2014, step = 1)
boxoffice = Slider(title = 'Costo del contrato (Millones de pesos)', start = 0, end = 77000, value = 0, step = 2)
dptos = Select(title = 'Departamentos', value = 'Todos los departamentos', options = DptosList)
proceso = Select(title = 'Tipo de Proceso', value = 'Todos los procesos', options = ProcesosList)
entidades = Select(title = 'Entidad Compradora', value = 'Todas las entidades', options = EntidadesList)
objeto = TextInput(title='Objeto del contrato')
x_axis = Select(title = 'X Axis', options = sorted(axis_map.keys()), value = 'Fecha del contrato')
y_axis = Select(title = 'Y Axis', options = sorted(axis_map.keys()), value = 'Cuantía (Millones de pesos)')
# Create Column Data Source that will be used by the plot
source = ColumnDataSource(data=dict(x=[], y=[], color=[], entidad=[], year=[], revenue=[], alpha=[]))
hover = HoverTool(tooltips=[
("Entidad", "@entidad"),
("Año", "@year"),
("$", "@revenue" + ' Millones de pesos')
])
p = figure(plot_height=500, plot_width=700, title='', toolbar_location=None, tools=[hover])
p.circle(x = 'x', y = 'y', source = source, size = 7, color = 'color', line_color = None, fill_alpha = 'alpha')
def select_contracts():
dptos_val = dptos.value
proceso_val = proceso.value
entidades_val = entidades.value
objeto_val = objeto.value.strip()
selected = csvdata[
(csvdata.dates >= min_year.value) &
(csvdata.dates <= max_year.value) &
(csvdata.CuantiaInt >= boxoffice.value)
]
if dptos_val != 'Todos los departamentos':
selected = selected[selected.Dptos.str.contains(dptos_val) == True]
if proceso_val != 'Todos los procesos':
selected = selected[selected.Proceso.str.contains(proceso_val) == True]
if entidades_val != 'Todas las entidades':
selected = selected[selected.Entidad.str.contains(entidades_val) == True]
if objeto_val != '':
selected = selected[selected.Objeto.str.contains(objeto_val) == True]
return selected
def update():
df = select_contracts()
x_name = axis_map[x_axis.value]
y_name = axis_map[y_axis.value]
p.xaxis.axis_label = x_axis.value
p.yaxis.axis_label = y_axis.value
p.title.text = '%d contratos seleccionados' % len(df)
source.data = dict(
x = df[x_name],
y = df[y_name],
color = df['color'],
entidad = df['Entidad'],
year = df['dates'],
revenue = df["revenue"],
alpha = df['alpha'],
)
controls = [min_year, max_year, boxoffice, dptos, proceso, entidades, objeto, x_axis, y_axis]
for control in controls:
control.on_change('value', lambda attr, old, new: update())
sizing_mode = 'fixed'
inputs = widgetbox(*controls, sizing_mode=sizing_mode)
l = layout([
[desc],
[inputs, p],
], sizing_mode=sizing_mode)
update()
curdoc().add_root(l)
curdoc().title = "Contratos"
with open('../Contratos/Alimentación/alimentacion.jinja', 'r') as f:
template = Template(f.read())
js_resources = JSResources(mode='inline')
html = file_html(l, resources=(js_resources, None), title="Contracts", template=template)
output_file = '../test.html'
with open(output_file, 'w') as f:
f.write(html)
view(output_file)
如果您的应用在其任何事件回调中调用实际的 python 库（例如，您在上面显示的 numpy 和 pandas）（事实上，如果它甚至有任何 on_change 回调 都 )，则不可能制作 "standalone HTML file"（即可以简单地单独上传）来重现其功能。具体来说：浏览器不能执行python代码，没有numpy和pandas。 Bokeh 服务器的主要目的是成为 python 代码可以 运行 的地方，以响应 UI 事件。您需要在 运行 的某处找到一些实际服务器并托管 Bokeh 服务器。
如果您将 Bokeh 服务器永久托管在某个地方，并且正在询问如何将其上的 Bokeh 应用程序 运行 嵌入到 gh-pages 上的静态页面中，那么答案是使用 autoload_server 或嵌入服务器应用程序 URL 与 <iframe> 效果非常好。
我一直在与 bokeh web server 合作。
我创建了一个网络应用程序，使用我自己的数据并遵循以下示例：https://github.com/bokeh/bokeh/blob/master/examples/app/movies/main.py
我已经完成了剧本，一切顺利。我可以使用此命令查看结果：bokeh serve --show main.py
我用来创建网络应用程序的模块是：
from os.path import dirname, join
from pandas import Series, DataFrame
from bokeh.plotting import figure
from bokeh.layouts import layout, widgetbox
from bokeh.models import ColumnDataSource, HoverTool, Div
from bokeh.models.widgets import Slider, Select, TextInput
from bokeh.io import curdoc
from scipy import stats
import numpy as np
import pandas
但是，我的目标是将结果上传到 github 上我的 gh-pages 分支。
如何将 bokeh 的结果保存为 html 文件以便在网页中使用它？
我尝试使用 bokeh.plotting 中的 show，但它显示的本地主机路径与命令 bokeh serve --show main.py 一样。
我可以使用其他命令吗？
如有任何建议，我们将不胜感激！提前致谢。
更新
我使用此代码来获得解决方案。使用这段代码，我得到了一个 html 文件作为输出，但它需要改进。
from os.path import dirname, join
from pandas import Series, DataFrame
from bokeh.plotting import figure
from bokeh.layouts import layout, widgetbox
from bokeh.models import ColumnDataSource, HoverTool, Div
from bokeh.models.widgets import Slider, Select, TextInput
from bokeh.io import curdoc
from bokeh.resources import JSResources
from bokeh.embed import file_html
from bokeh.util.browser import view
from jinja2 import Template
from scipy import stats
import numpy as np
import pandas
csvdata = pandas.read_csv('Alimentacion.csv', low_memory = False, encoding = 'latin-1')
# Convert amount field into int()
def str_to_int(mainList):
for item in mainList:
newList = [(int(item.replace('$', '').replace(',', '')) / (1000000)) for item in mainList]
return newList
# Call str_to_int function
csvdata['CuantiaInt'] = str_to_int(csvdata['Cuantía'])
mean = np.mean(csvdata['CuantiaInt'])
# Assing colors to each contract by mean
csvdata['color'] = np.where(csvdata['CuantiaInt'] > mean, 'red', 'blue')
csvdata['alpha'] = np.where(csvdata['CuantiaInt'] > mean, 0.75, 0.75)
# Replace missing values (NaN) with 0
csvdata.fillna(0, inplace=True)
csvdata['revenue'] = csvdata.CuantiaInt.apply(lambda x: '{:,d}'.format(int(x)))
estados1 = [line.rstrip() for line in open('Estados1.txt')]
estados2 = [line.rstrip() for line in open('Estados2.txt')]
csvdata.loc[csvdata.Estado.isin(estados1), 'color'] = 'grey'
csvdata.loc[csvdata.Estado.isin(estados1), 'alpha'] = 0.75
csvdata.loc[csvdata.Estado.isin(estados2), 'color'] = 'brown'
csvdata.loc[csvdata.Estado.isin(estados2), 'alpha'] = 0.75
csvdata['z score'] = stats.zscore(csvdata['CuantiaInt'])
csvdata['sigma'] = np.std(csvdata['CuantiaInt'])
date_time = pandas.DatetimeIndex(csvdata['Fecha (dd-mm-aaaa)'])
newdates = date_time.strftime('%Y')
newdates = [int(x) for x in newdates]
csvdata['dates'] = newdates
csvdata['Dptos'] = csvdata['Loc dpto']
csvdata['Entidad'] = csvdata['Entidad Compradora']
csvdata['Proceso'] = csvdata['Tipo de Proceso']
axis_map = {
'Cuantía y promedio': 'z score',
'Cuantía (Millones de pesos)': 'CuantiaInt',
'Desviación estándar': 'sigma',
'Fecha del contrato': 'dates',
}
desc = Div(text=open(join(dirname(__file__), 'alimentacion.html')).read(), width=800)
DptosList = [line.rstrip() for line in open('locdpto.txt')]
ProcesosList = [line.rstrip() for line in open('tipoproceso.txt')]
EntidadesList = [line.rstrip() for line in open('entidades.txt')]
# Create Input controls
min_year = Slider(title = 'Año inicial', start = 2012, end = 2015, value = 2013, step = 1)
max_year = Slider(title = 'Año final', start = 2012, end = 2015, value = 2014, step = 1)
boxoffice = Slider(title = 'Costo del contrato (Millones de pesos)', start = 0, end = 77000, value = 0, step = 2)
dptos = Select(title = 'Departamentos', value = 'Todos los departamentos', options = DptosList)
proceso = Select(title = 'Tipo de Proceso', value = 'Todos los procesos', options = ProcesosList)
entidades = Select(title = 'Entidad Compradora', value = 'Todas las entidades', options = EntidadesList)
objeto = TextInput(title='Objeto del contrato')
x_axis = Select(title = 'X Axis', options = sorted(axis_map.keys()), value = 'Fecha del contrato')
y_axis = Select(title = 'Y Axis', options = sorted(axis_map.keys()), value = 'Cuantía (Millones de pesos)')
# Create Column Data Source that will be used by the plot
source = ColumnDataSource(data=dict(x=[], y=[], color=[], entidad=[], year=[], revenue=[], alpha=[]))
hover = HoverTool(tooltips=[
("Entidad", "@entidad"),
("Año", "@year"),
("$", "@revenue" + ' Millones de pesos')
])
p = figure(plot_height=500, plot_width=700, title='', toolbar_location=None, tools=[hover])
p.circle(x = 'x', y = 'y', source = source, size = 7, color = 'color', line_color = None, fill_alpha = 'alpha')
def select_contracts():
dptos_val = dptos.value
proceso_val = proceso.value
entidades_val = entidades.value
objeto_val = objeto.value.strip()
selected = csvdata[
(csvdata.dates >= min_year.value) &
(csvdata.dates <= max_year.value) &
(csvdata.CuantiaInt >= boxoffice.value)
]
if dptos_val != 'Todos los departamentos':
selected = selected[selected.Dptos.str.contains(dptos_val) == True]
if proceso_val != 'Todos los procesos':
selected = selected[selected.Proceso.str.contains(proceso_val) == True]
if entidades_val != 'Todas las entidades':
selected = selected[selected.Entidad.str.contains(entidades_val) == True]
if objeto_val != '':
selected = selected[selected.Objeto.str.contains(objeto_val) == True]
return selected
def update():
df = select_contracts()
x_name = axis_map[x_axis.value]
y_name = axis_map[y_axis.value]
p.xaxis.axis_label = x_axis.value
p.yaxis.axis_label = y_axis.value
p.title.text = '%d contratos seleccionados' % len(df)
source.data = dict(
x = df[x_name],
y = df[y_name],
color = df['color'],
entidad = df['Entidad'],
year = df['dates'],
revenue = df["revenue"],
alpha = df['alpha'],
)
controls = [min_year, max_year, boxoffice, dptos, proceso, entidades, objeto, x_axis, y_axis]
for control in controls:
control.on_change('value', lambda attr, old, new: update())
sizing_mode = 'fixed'
inputs = widgetbox(*controls, sizing_mode=sizing_mode)
l = layout([
[desc],
[inputs, p],
], sizing_mode=sizing_mode)
update()
curdoc().add_root(l)
curdoc().title = "Contratos"
with open('../Contratos/Alimentación/alimentacion.jinja', 'r') as f:
template = Template(f.read())
js_resources = JSResources(mode='inline')
html = file_html(l, resources=(js_resources, None), title="Contracts", template=template)
output_file = '../test.html'
with open(output_file, 'w') as f:
f.write(html)
view(output_file)
如果您的应用在其任何事件回调中调用实际的 python 库（例如，您在上面显示的 numpy 和 pandas）（事实上，如果它甚至有任何 on_change 回调 都 )，则不可能制作 "standalone HTML file"（即可以简单地单独上传）来重现其功能。具体来说：浏览器不能执行python代码，没有numpy和pandas。 Bokeh 服务器的主要目的是成为 python 代码可以 运行 的地方，以响应 UI 事件。您需要在 运行 的某处找到一些实际服务器并托管 Bokeh 服务器。
如果您将 Bokeh 服务器永久托管在某个地方，并且正在询问如何将其上的 Bokeh 应用程序 运行 嵌入到 gh-pages 上的静态页面中，那么答案是使用 autoload_server 或嵌入服务器应用程序 URL 与 <iframe> 效果非常好。