I have 2 bokeh rows. The top row contains a DataTable and a TextInput, both of which are able to stretch_width in order to fit the width of the browser. The bottom row contains a gridplot, which is able to stretch_width, but only does so by distorting the scale of the image. Ideally, I would like the gridplot to update the amount of columns displayed based on the size of the browser.
Consider the following example:
import pandas as pd
from bokeh.models.widgets import DataTable, TableColumn
from bokeh.models import ColumnDataSource, TextInput
from bokeh.plotting import figure, output_file, save
from bokeh.layouts import row, column, gridplot
def get_datatable():
"""this can stretch width without issue"""
df = pd.DataFrame({'a': [0, 1, 2], 'b': [2, 3, 4]})
source = ColumnDataSource(df)
Columns = [TableColumn(field=i, title=i) for i in df.columns]
data_table = DataTable(columns=Columns, source=source, sizing_mode='stretch_width', max_width=9999)
return data_table
def get_text_input():
"""this can stretch width without issue"""
return TextInput(value='Example', title='Title', sizing_mode="stretch_width", max_width=9999)
def get_gridplot():
"""
this requires columns to be hard-coded
stretch_width is an option, but only distorts the images if enabled
"""
figs = []
for _ in range(30):
fig = figure(x_range=(0,10), y_range=(0,10))
_ = fig.image_rgba(image=[], x=0, y=0)
figs.append(fig)
return gridplot(children=figs, ncols=2)
top_row = row([get_datatable(), get_text_input()], max_width=9999, sizing_mode='stretch_width')
bottom_row = row(get_gridplot())
col = column(top_row, bottom_row, sizing_mode="stretch_width")
output_file("example.html")
save(col)
My end goal is to have the gridplot automatically update the amount of columns based on the width of the browser. Is there a way to do this natively in bokeh? If not, is it possible to do this via a CustomJs javascript callback?
Solution
Consider using sizing_mode=“scale_width” when calling figure.
fig = figure(x_range=(0,10), y_range=(0,10), sizing_mode=“scale_width”)
Note
It may be preferable to use scale_width instead of stretch_width more generally.
Bokeh Doc Example: https://docs.bokeh.org/en/latest/docs/user_guide/layout.html#multiple-objects
Related
I plotted a line graphs with using bokeh on Python. I want to highlight and take the values (Max-Min-x, y coordinates) of the selected areas with "Box Select tool" like shown below. when I choose a certain section on the graph with "box select tool" the color of the selected part does not change. How to solve this problem?
Example
import numpy as np
import pandas as pd
from bokeh.plotting import figure,show,output_file
from bokeh.models import ColumnDataSource
output_file("PlottingTest.html")
dataset = pd.read_csv("data.csv")
data = dataset.iloc[:,3]
time = np.linspace(1, 500, num = 500)
TOOLS ="pan,wheel_zoom,reset,hover,poly_select,xbox_select,lasso_select"
s1 = ColumnDataSource(data=dict(x=time, y=data))
p = figure(title = 'Test',x_axis_label = 'time', y_axis_label='csv Data',plot_width=1000, plot_height=500,tools=TOOLS)
p.line ('date', 't1', source=s1, selection_color="orange")
p.line(time, data, legend_label="Current", line_width=1)
p.toolbar.autohide = True
show(p)
here's my data ：https://drive.google.com/drive/folders/1CabmdDQucaKW2XhBxQlXVNOSiNRtkMm-?usp=sharing
i want to use the select to choose the stock i want to show;
and slider to choose the year range i want to show;
and checkboxgroup to choose the index i want to compare with.
the problem is when i adjust the slider, the figure will update, but when i use the select and checkboxgroup, the figure won't update,
what's the reason?
from bokeh.io import curdoc
from bokeh.layouts import column, row
from bokeh.models import ColumnDataSource, Slider, TextInput , Select , Div, CheckboxGroup
from bokeh.plotting import figure
import pandas as pd
import numpy as np
price=pd.read_excel('price.xlsx',index_col=0)
# input control
stock = Select(title='Stock',value='AAPL',options=[x for x in list(price.columns) if x not in ['S&P','DOW']])
yr_1 = Slider(title='Start year',value=2015,start=2000,end=2020,step=1)
yr_2 = Slider(title='End year',value=2020,start=2000,end=2020,step=1)
index = CheckboxGroup(labels=['S&P','DOW'],active=[0,1])
def get_data():
compare_index = [index.labels[i] for i in index.active]
stocks = stock.value
start_year = str(yr_1.value)
end_year = str(yr_2.value)
select_list = []
select_list.append(stocks)
select_list.extend(compare_index)
selected = price[select_list]
selected = selected [start_year:end_year]
for col in selected.columns:
selected[col]=selected[col]/selected[col].dropna()[0]
return ColumnDataSource(selected)
def make_plot(source):
fig=figure(plot_height=600, plot_width=700, title="",sizing_mode="scale_both", x_axis_type="datetime")
data_columns = list(source.data.keys())
for data in data_columns[1:]:
fig.line(x=data_columns[0],y=data,source=source,line_width=3, line_alpha=0.6, legend_label=data)
return fig
def update(attrname, old, new):
new_source = get_data()
source.data.clear()
source.data.update(new_source.data)
#get the initial value and plot
source = get_data()
plot = make_plot(source)
#control_update
stock.on_change('value', update)
yr_1.on_change('value', update)
yr_2.on_change('value', update)
index.on_change('active', update)
# Set up layouts and add to document
inputs = column(stock, yr_1, yr_2, index)
curdoc().add_root(row(inputs, plot, width=800))
curdoc().title = "Stocks"
You're creating a new ColumnDataSource for new data. That's not a good approach.
Instead, create it once and then just assign its data as appropriate.
In your case, I would do it like this:
Create ColumnDataSource just once, as described above
Do not use .update on CDS, just reassign .data
Create the legend manually
For that one line that's susceptible to the select change choose a static x field and use it everywhere instead
Change the first legend item's label when you change the select's value to instead of that x field it has the correct name
I am trying to set the absolute position of a Bokeh Chart inside a Layout so that one of the plots is shown on top of another plot. Right now when I am plotting something like this:
from bokeh.io import curdoc
from bokeh.plotting import figure
from bokeh.layouts import layout
import numpy as np
x = np.arange(1,10.1,0.1)
y = [i**2 for i in x]
categories = ['A', 'B']
values = [1000, 1500]
fig1 = figure(width=600,plot_height=600, title="First Plot")
fig1.line(x=x, y=y)
fig2 = figure(width=200,plot_height=250,x_range=categories,
title="Second Plot") fig2.vbar(x=categories, top=values, width=0.2)
l = layout([[fig1,fig2]])
curdoc().add_root(l)
The result will be this:
What I am searching for is some way to make it look like that:
How can this result be achieved?
Thank you!
This is what I came up with (works for Bokeh v1.0.4). You need to move your mouse over the plot to get the other one jump inside but you could also copy the JS code from the callback and manually add it to the HTML generated by Bokeh so you achieve the same result.
from bokeh.plotting import figure, show
from bokeh.layouts import Row
from bokeh.models import ColumnDataSource, CDSView, BooleanFilter, CustomJS, BoxSelectTool, HoverTool
import pandas as pd
plot = figure(tools = 'hover', tooltips = [("x", "#x"), ("y", "#y")])
circles = plot.circle('x', 'y', size = 20, source = ColumnDataSource({'x': [1, 2, 3], 'y':[1, 2, 3]}))
inner_plot = figure(name = 'inner_plot', plot_width = 200, plot_height = 200)
lines = inner_plot.line('x', 'y', source = ColumnDataSource({'x': [8, 9, 10], 'y':[8, 6, 8]}))
code = """ div = document.getElementsByClassName('bk-root')[0];
tooltip_plot = div.children[0].children[1]
tooltip_plot.style = "position:absolute; left: 340px; top: 350px;"; """
callback = CustomJS(code = code)
plot.js_on_event('mousemove', callback)
show(Row(plot, inner_plot))
Result:
I'm building an histogram plot with running a bokeh server to dynamically change data for an histogram plot. Datasource should change by clicking on button - but it's not working as expected.
from bokeh.plotting import figure
from bokeh.layouts import layout, widgetbox
from bokeh.io import curdoc
from bokeh.transform import factor_cmap
from bokeh.palettes import Spectral6
from bokeh.models import FactorRange, ColumnDataSource
from bokeh.models.widgets import Button
button = Button(label="ChangeValue", button_type="success")
fruits = ['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries']
val = [5, 3, 4, 2, 4, 6]
data_dict = {'x':fruits,'y':val}
source_table_hist = ColumnDataSource(data=data_dict)
h = figure(x_range=data_dict['x'],plot_height=350, title="Histogram")
h.vbar(x='x', top='y', width=0.2, source=source_table_hist, legend="x", line_color='black',
fill_color=factor_cmap('x', palette=Spectral6, factors=data_dict['x']))
h.xgrid.grid_line_color = None
h.y_range.start = 0
inputs = widgetbox(button)
def update():
fruits = ['Banana', 'Orange']
val = [15, 23]
data_dict = {'x':fruits,'y':val}
h.x_range=FactorRange(factors=data_dict['x'])
source_table_hist.data = data_dict
button.on_click(update)
l = layout([inputs,h])
curdoc().add_root(l)
curdoc().title = "Test"
The use-case that Bokeh creators had in mind, and the use case that is therefore currently best-supported, is to set up an app, then update the data and properties in place (i.e. as opposed to replacing whole objects like ranges). Your code works for me if this line
h.x_range=FactorRange(factors=data_dict['x']) # replace entire range (bad)
is changed to this instead:
h.x_range.factors=data_dict['x'] # update existing range (good)
The new bars are rendered as grey, since the color mapper you configure does not know anything about the new factors you changed to. Some options:
configure the color mapper up front for all possible factors that will ever be set
update the properties of the color mapper to adjust for the new factors
do a color mapping in Python (and put a "color" column in the CDS) instead of using LinearColormapper
I have got a pandas dataframe whose columns I want to show as lines in a plot using a Bokeh server. Additionally, I would like to have a slider for shifting one of the lines against the other.
My problem is the update functionality when the slider value changes. I have tried the code from the sliders-example of bokeh, but it does not work.
Here is an example
import pandas as pd
from bokeh.io import vform
from bokeh.plotting import Figure, output_file, show
from bokeh.models import CustomJS, ColumnDataSource, Slider
df = pd.DataFrame([[1,2,3],[3,4,5]])
df = df.transpose()
myindex = list(df.index.values)
mysource = ColumnDataSource(df)
plot = Figure(plot_width=400, plot_height=400)
for i in range(len(mysource.column_names) - 1):
name = mysource.column_names[i]
plot.line(x = myindex, y = str(name), source = mysource)
offset = Slider(title="offset", value=0.0, start=-1.0, end=1.0, step=1)
def update_data(attrname, old, new):
# Get the current slider values
a = offset.value
temp = df[1].shift(a)
#to finish#
offset.on_change('value', update_data)
layout = vform(offset, plot)
show(layout)
Inside the update_data-function I have to update mysource, but I cannot figure out how to do that. Can anybody point me in the right direction?
Give this a try... change a=offset.value to a=cb_obj.get('value')
Then put source.trigger('change') after you do whatever it is you are trying to do in that update_data function instead of offset.on_change('value', update_data).
Also change offset = Slider(title="offset", value=0.0, start=-1.0, end=1.0, step=1, callback=CustomJS.from_py_func(offset))
Note this format I'm using works with flexx installed. https://github.com/zoofio/flexx if you have Python 3.5 you'll have to download the zip file, extract, and type python setup.py install as it isn't posted yet compiled for this version...