有没有办法保存散景数据 table 内容
is there a way to save bokeh data table content
我正在网页中试验 bokeh data table to display data embedded。它工作得很好。
有没有办法从显示的数据 table 中保存 table 的内容？其他散景图有各种功能的工具栏，包括保存，但 DataTable 似乎没有附带。我对散景数据 table 使用的 javascript 或 slickgrid 知之甚少。并想知道是否可以完成。
谢谢！
编辑 - 看来我原来的问题不够清楚。希望以下图片可以帮助说明：
散景图有关联的工具栏：
但是数据table默认没有，也不会带'tools'参数:
是否可以将 'save' 按钮添加到数据 table 以便查看 table 的人可以下载制表符分隔文件或 csv 文件？不一定要长得一模一样，但保存功能要一样。
如果 bokeh 提供一个工具按钮，用于 saving/exporting 数据 table 到 csv / txt / excel 文件，那就太好了。如果它已经存在，我还没有在文档中找到它。
与此同时，一个可能的答案是使用原生 javascript 将 js 数组（位于散景数据 table 下方）导出到 CSV。它已被描述 here and here。
添加：bokeh 有使用 js 的回调。简单的描述就是here。仍在阅读它......
编辑：它可能已经存在了一段时间，但我刚刚注意到 Bokeh 网站上的一个示例 saving csv from data table。
2021 更新：调整后的代码适用于 python 3.8 和 bokeh 2.2.3
对于那些在调整或在 bokeh 网站上找不到示例或者只是非常懒惰的人，下面的代码完成了最少的工作：
from bokeh.models import ColumnDataSource, CustomJS
from bokeh.models.widgets import Button
from bokeh.io import show
import os
source = ColumnDataSource({'list1':[0,1,2,3],'list2':[4,5,6,7]})
button = Button(label="Download", button_type="success")
button.js_on_click(CustomJS(args=dict(source=source),code=open(os.path.join(os.path.dirname(__file__),"download.js")).read()))
show(button)
和文件 download.js:
function table_to_csv(source) {
const columns = Object.keys(source.data)
const nrows = source.get_length()
const lines = [columns.join(',')]
for (let i = 0; i < nrows; i++) {
let row = [];
for (let j = 0; j < columns.length; j++) {
const column = columns[j]
row.push(source.data[column][i].toString())
}
lines.push(row.join(','))
}
return lines.join('\n').concat('\n')
}
const filename = 'data_result.csv'
const filetext = table_to_csv(source)
const blob = new Blob([filetext], { type: 'text/csv;charset=utf-8;' })
//addresses IE
if (navigator.msSaveBlob) {
navigator.msSaveBlob(blob, filename)
} else {
const link = document.createElement('a')
link.href = URL.createObjectURL(blob)
link.download = filename
link.target = '_blank'
link.style.visibility = 'hidden'
link.dispatchEvent(new MouseEvent('click'))
}
与我对 的回复有关。回复复制如下：
这是一个使用 Python 3.7.5 和 Bokeh 1.4.0
的工作示例
public github link 到这个 jupyter 笔记本：
https://github.com/surfaceowl-ai/python_visualizations/blob/master/notebooks/bokeh_save_linked_plot_data.ipynb
环境报告：
虚拟环境 python 版本：Python 3.7.5
虚拟环境 ipython 版本：7.9.0
水印包报告：
散景 1.4.0
木星 1.0.0
麻木 1.17.4
pandas 0.25.3
上升 5.6.0
水印 2.0.2
# Generate linked plots + TABLE displaying data + save button to export cvs of selected data
from random import random
from bokeh.io import output_notebook # prevent opening separate tab with graph
from bokeh.io import show
from bokeh.layouts import row
from bokeh.layouts import grid
from bokeh.models import CustomJS, ColumnDataSource
from bokeh.models import Button # for saving data
from bokeh.models.widgets import DataTable, DateFormatter, TableColumn
from bokeh.models import HoverTool
from bokeh.plotting import figure
# create data
x = [random() for x in range(500)]
y = [random() for y in range(500)]
# create first subplot
plot_width = 400
plot_height = 400
s1 = ColumnDataSource(data=dict(x=x, y=y))
fig01 = figure(
plot_width=plot_width,
plot_height=plot_height,
tools=["lasso_select", "reset", "save"],
title="Select Here",
)
fig01.circle("x", "y", source=s1, alpha=0.6)
# create second subplot
s2 = ColumnDataSource(data=dict(x=[], y=[]))
# demo smart error msg: `box_zoom`, vs `BoxZoomTool`
fig02 = figure(
plot_width=400,
plot_height=400,
x_range=(0, 1),
y_range=(0, 1),
tools=["box_zoom", "wheel_zoom", "reset", "save"],
title="Watch Here",
)
fig02.circle("x", "y", source=s2, alpha=0.6, color="firebrick")
# create dynamic table of selected points
columns = [
TableColumn(field="x", title="X axis"),
TableColumn(field="y", title="Y axis"),
]
table = DataTable(
source=s2,
columns=columns,
width=400,
height=600,
sortable=True,
selectable=True,
editable=True,
)
# fancy javascript to link subplots
# js pushes selected points into ColumnDataSource of 2nd plot
# inspiration for this from a few sources:
# credit: https://whosebug.com/users/1097752/iolsmit via:
# credit: https://whosebug.com/users/8412027/joris via:
s1.selected.js_on_change(
"indices",
CustomJS(
args=dict(s1=s1, s2=s2, table=table),
code="""
var inds = cb_obj.indices;
var d1 = s1.data;
var d2 = s2.data;
d2['x'] = []
d2['y'] = []
for (var i = 0; i < inds.length; i++) {
d2['x'].push(d1['x'][inds[i]])
d2['y'].push(d1['y'][inds[i]])
}
s2.change.emit();
table.change.emit();
var inds = source_data.selected.indices;
var data = source_data.data;
var out = "x, y\n";
for (i = 0; i < inds.length; i++) {
out += data['x'][inds[i]] + "," + data['y'][inds[i]] + "\n";
}
var file = new Blob([out], {type: 'text/plain'});
""",
),
)
# create save button - saves selected datapoints to text file onbutton
# inspriation for this code:
# credit:
# note: savebutton line `var out = "x, y\n";` defines the header of the exported file, helpful to have a header for downstream processing
savebutton = Button(label="Save", button_type="success")
savebutton.callback = CustomJS(
args=dict(source_data=s1),
code="""
var inds = source_data.selected.indices;
var data = source_data.data;
var out = "x, y\n";
for (i = 0; i < inds.length; i++) {
out += data['x'][inds[i]] + "," + data['y'][inds[i]] + "\n";
}
var file = new Blob([out], {type: 'text/plain'});
var elem = window.document.createElement('a');
elem.href = window.URL.createObjectURL(file);
elem.download = 'selected-data.txt';
document.body.appendChild(elem);
elem.click();
document.body.removeChild(elem);
""",
)
# add Hover tool
# define what is displayed in the tooltip
tooltips = [
("X:", "@x"),
("Y:", "@y"),
("static text", "static text"),
]
fig02.add_tools(HoverTool(tooltips=tooltips))
# display results
# demo linked plots
# demo zooms and reset
# demo hover tool
# demo table
# demo save selected results to file
layout = grid([fig01, fig02, table, savebutton], ncols=3)
output_notebook()
show(layout)
# things to try:
# select random shape of blue dots with lasso tool in 'Select Here' graph
# only selected points appear as red dots in 'Watch Here' graph -- try zooming, saving that graph separately
# selected points also appear in the table, which is sortable
# click the 'Save' button to export a csv
# TODO: export from Bokeh to pandas dataframe
我正在网页中试验 bokeh data table to display data embedded。它工作得很好。
有没有办法从显示的数据 table 中保存 table 的内容？其他散景图有各种功能的工具栏，包括保存，但 DataTable 似乎没有附带。我对散景数据 table 使用的 javascript 或 slickgrid 知之甚少。并想知道是否可以完成。
谢谢！
编辑 - 看来我原来的问题不够清楚。希望以下图片可以帮助说明：
散景图有关联的工具栏：
但是数据table默认没有，也不会带'tools'参数:
是否可以将 'save' 按钮添加到数据 table 以便查看 table 的人可以下载制表符分隔文件或 csv 文件？不一定要长得一模一样，但保存功能要一样。
如果 bokeh 提供一个工具按钮，用于 saving/exporting 数据 table 到 csv / txt / excel 文件，那就太好了。如果它已经存在，我还没有在文档中找到它。
与此同时，一个可能的答案是使用原生 javascript 将 js 数组（位于散景数据 table 下方）导出到 CSV。它已被描述 here and here。
添加：bokeh 有使用 js 的回调。简单的描述就是here。仍在阅读它......
编辑：它可能已经存在了一段时间，但我刚刚注意到 Bokeh 网站上的一个示例 saving csv from data table。
2021 更新：调整后的代码适用于 python 3.8 和 bokeh 2.2.3
对于那些在调整或在 bokeh 网站上找不到示例或者只是非常懒惰的人，下面的代码完成了最少的工作：
from bokeh.models import ColumnDataSource, CustomJS
from bokeh.models.widgets import Button
from bokeh.io import show
import os
source = ColumnDataSource({'list1':[0,1,2,3],'list2':[4,5,6,7]})
button = Button(label="Download", button_type="success")
button.js_on_click(CustomJS(args=dict(source=source),code=open(os.path.join(os.path.dirname(__file__),"download.js")).read()))
show(button)
和文件 download.js:
function table_to_csv(source) {
const columns = Object.keys(source.data)
const nrows = source.get_length()
const lines = [columns.join(',')]
for (let i = 0; i < nrows; i++) {
let row = [];
for (let j = 0; j < columns.length; j++) {
const column = columns[j]
row.push(source.data[column][i].toString())
}
lines.push(row.join(','))
}
return lines.join('\n').concat('\n')
}
const filename = 'data_result.csv'
const filetext = table_to_csv(source)
const blob = new Blob([filetext], { type: 'text/csv;charset=utf-8;' })
//addresses IE
if (navigator.msSaveBlob) {
navigator.msSaveBlob(blob, filename)
} else {
const link = document.createElement('a')
link.href = URL.createObjectURL(blob)
link.download = filename
link.target = '_blank'
link.style.visibility = 'hidden'
link.dispatchEvent(new MouseEvent('click'))
}
与我对
这是一个使用 Python 3.7.5 和 Bokeh 1.4.0
的工作示例public github link 到这个 jupyter 笔记本：
https://github.com/surfaceowl-ai/python_visualizations/blob/master/notebooks/bokeh_save_linked_plot_data.ipynb
环境报告：
虚拟环境 python 版本：Python 3.7.5
虚拟环境 ipython 版本：7.9.0
水印包报告：
散景 1.4.0
木星 1.0.0
麻木 1.17.4
pandas 0.25.3
上升 5.6.0
水印 2.0.2
# Generate linked plots + TABLE displaying data + save button to export cvs of selected data
from random import random
from bokeh.io import output_notebook # prevent opening separate tab with graph
from bokeh.io import show
from bokeh.layouts import row
from bokeh.layouts import grid
from bokeh.models import CustomJS, ColumnDataSource
from bokeh.models import Button # for saving data
from bokeh.models.widgets import DataTable, DateFormatter, TableColumn
from bokeh.models import HoverTool
from bokeh.plotting import figure
# create data
x = [random() for x in range(500)]
y = [random() for y in range(500)]
# create first subplot
plot_width = 400
plot_height = 400
s1 = ColumnDataSource(data=dict(x=x, y=y))
fig01 = figure(
plot_width=plot_width,
plot_height=plot_height,
tools=["lasso_select", "reset", "save"],
title="Select Here",
)
fig01.circle("x", "y", source=s1, alpha=0.6)
# create second subplot
s2 = ColumnDataSource(data=dict(x=[], y=[]))
# demo smart error msg: `box_zoom`, vs `BoxZoomTool`
fig02 = figure(
plot_width=400,
plot_height=400,
x_range=(0, 1),
y_range=(0, 1),
tools=["box_zoom", "wheel_zoom", "reset", "save"],
title="Watch Here",
)
fig02.circle("x", "y", source=s2, alpha=0.6, color="firebrick")
# create dynamic table of selected points
columns = [
TableColumn(field="x", title="X axis"),
TableColumn(field="y", title="Y axis"),
]
table = DataTable(
source=s2,
columns=columns,
width=400,
height=600,
sortable=True,
selectable=True,
editable=True,
)
# fancy javascript to link subplots
# js pushes selected points into ColumnDataSource of 2nd plot
# inspiration for this from a few sources:
# credit: https://whosebug.com/users/1097752/iolsmit via:
# credit: https://whosebug.com/users/8412027/joris via:
s1.selected.js_on_change(
"indices",
CustomJS(
args=dict(s1=s1, s2=s2, table=table),
code="""
var inds = cb_obj.indices;
var d1 = s1.data;
var d2 = s2.data;
d2['x'] = []
d2['y'] = []
for (var i = 0; i < inds.length; i++) {
d2['x'].push(d1['x'][inds[i]])
d2['y'].push(d1['y'][inds[i]])
}
s2.change.emit();
table.change.emit();
var inds = source_data.selected.indices;
var data = source_data.data;
var out = "x, y\n";
for (i = 0; i < inds.length; i++) {
out += data['x'][inds[i]] + "," + data['y'][inds[i]] + "\n";
}
var file = new Blob([out], {type: 'text/plain'});
""",
),
)
# create save button - saves selected datapoints to text file onbutton
# inspriation for this code:
# credit:
# note: savebutton line `var out = "x, y\n";` defines the header of the exported file, helpful to have a header for downstream processing
savebutton = Button(label="Save", button_type="success")
savebutton.callback = CustomJS(
args=dict(source_data=s1),
code="""
var inds = source_data.selected.indices;
var data = source_data.data;
var out = "x, y\n";
for (i = 0; i < inds.length; i++) {
out += data['x'][inds[i]] + "," + data['y'][inds[i]] + "\n";
}
var file = new Blob([out], {type: 'text/plain'});
var elem = window.document.createElement('a');
elem.href = window.URL.createObjectURL(file);
elem.download = 'selected-data.txt';
document.body.appendChild(elem);
elem.click();
document.body.removeChild(elem);
""",
)
# add Hover tool
# define what is displayed in the tooltip
tooltips = [
("X:", "@x"),
("Y:", "@y"),
("static text", "static text"),
]
fig02.add_tools(HoverTool(tooltips=tooltips))
# display results
# demo linked plots
# demo zooms and reset
# demo hover tool
# demo table
# demo save selected results to file
layout = grid([fig01, fig02, table, savebutton], ncols=3)
output_notebook()
show(layout)
# things to try:
# select random shape of blue dots with lasso tool in 'Select Here' graph
# only selected points appear as red dots in 'Watch Here' graph -- try zooming, saving that graph separately
# selected points also appear in the table, which is sortable
# click the 'Save' button to export a csv
# TODO: export from Bokeh to pandas dataframe