I want to use the slider to change the figures, but it is no use. I think maybe the problem is in the Callback part. but i have no idea how to do it.
month = [1,2,3,1,2,3,1,2,3,1,2,3]
tilts = [1,1,1,2,2,2,3,3,3,4,4,4]
data = [0.1,0.2,0.3,1,2,3,11,12,13,21,22,24]
df = pd.DataFrame({'month':month,'tilt':tilts,'data'=data})
df_default = df[df['tilt']==1]
source = ColumnDataSource({
'x': df_default.month.tolist(),
'y': df_default.data.tolist(),
})
plot = figure(plot_width=400, plot_height=400)
plot.line('x', 'y', source=source, line_width=3, line_alpha=0.6)
callback = CustomJS(args=dict(source=source), code="""
var data = source.data;
var tilt = slider.value;
var x = data['x']
var y = data['y']
'x' = df[df['tilt']==tilt].month.tolist();
'y' = df[df['tilt']==tilt].data;
plot.line(x='x', y='y', source=source, line_width=3,
line_alpha=0.6,
);
source.change.emit();
""")
slider = Slider(start=1, end=4, step=1, value=1, title='tilt')
slider.js_on_change('value',callback)
layout = row(
plot,
column(slider)
)
output_file("slider.html", title="slider.py example")
show(layout)
it can show , but apparently, the callback is not working
There are several problems with this code, one of them is that these lines are nonsensical:
'x' = df[df['tilt']==tilt].month.tolist();
'y' = df[df['tilt']==tilt].data;
These are trying to assign values to string constants, which is invalid JavaScript. However, a bigger issue is that you are trying to use Pandas DataFrames in a CustomJS callback, and this can never work. Pandas DataFrames are a Python object, they do not exist inside browsers. To run real Python code, e.g. use Pandas DataFrames, you will have to create and run a Bokeh Server application.
Related
I wanted to create a plot with bokeh in python which runs quite well so far. But now I wanted to add a Slider and tell him to hide all bars in my vbar plot which are lower than the value of the slider.
current = df[(df['ID'] > num_tokens.value[0])].dropna()
source.data = {
'ID': current.ID
}
I tried to create a variable 'current' and assign it to the 'ID' column so that the plot can update the plot. But I always get a TypeError: Int is not subscriptable. How can I make my slider widget make work?
Thank you in advance
Don't know if we must close this issue or not but I would sugget using a customJS callback:
Create initially a source and a render_soruce from df
source = ColumnDataSource(df)
renderer_source = ColumnDataSource(df)
Then define your callback and your slider
code = """
var slider_value= cb_obj.value; //cb_obj is your slider widget
var data=source.data;
var data_id = data['ID'];
var data_x=data['x'];
var data_y=data['y'];
var render_x=render['x'];
var render_y=render['y'];
var x = [];
var y = [];
render_x=[];
render_y=[];
for (var i=0;i<data_id.length; i++){
if (data_id[i]== slider_valuer) {
x.push(data_x[i]);
y.push(data_y[i]);
}
renderer_source.data['x']=x;
renderer_source.data['y']=y;
renderer_source.change.emit();
"""
callback = CustomJS(args=dict(source=source, renderer_source=renderer_source), code=code)
slider = Slider(start=0, end=(max_value_o_slider), value=1, step=1, title="Frame")
slider.js_on_change('value', callback)
And identify source=renderer_source in your plot
You can achieve this with almost no JavaScript using a view:
from bokeh.io import curdoc
from bokeh.layouts import column
from bokeh.models import ColumnDataSource, CDSView, CustomJSFilter, Slider, CustomJS
from bokeh.plotting import figure
N = 5
x = list(range(1, N + 1))
top = list(range(1, N + 1))
# Specifying manual ranges to prevent range changes when some bars are hidden.
p = figure(x_range=(0, N + 1), y_range=(0, N + 1))
ds = ColumnDataSource(data=dict(x=x, top=top))
s = Slider(start=0, end=N, value=0)
# Making sure that everything that depends on the `ds`
# is notified when the slider value is changed.
s.js_on_change('value', CustomJS(args=dict(ds=ds),
code="ds.change.emit();"))
view = CDSView(source=ds,
filters=[CustomJSFilter(args=dict(ds=ds, slider=s),
code="return ds.data['top'].map((top) => top > slider.value);")])
p.vbar(x='x', width=0.7, top='top', bottom=0, source=ds, view=view)
curdoc().add_root(column(p, s))
I am creating a bokeh plot with a slider to refresh plot accordingly. There are 2 issues with the code posted.
1. The plot is not refreshed as per the slider. Please help in providing a fix for this issue.
2. Plot is not displayed with curdoc() when bokeh serve --show fn.ipynb is used
I'm trying to visualise this CSV file.
import pandas as pd
import numpy as np
from bokeh.models import ColumnDataSource, CategoricalColorMapper, HoverTool, Slider
from bokeh.plotting import figure, curdoc
from bokeh.palettes import viridis
from bokeh.layouts import row, widgetbox
#Importing and processing data file
crop = pd.read_csv('crop_production.csv')
#Cleaning Data
crop.fillna(np.NaN)
crop['Season'] = crop.Season.str.strip()
#Removing Whitespace #Filtering the dataset by Season
crop_season = crop[crop.Season == 'Whole Year']
crop_dt = crop_season.groupby(['State_Name', 'District_Name', 'Crop_Year']).mean().round(1)
#Creating Column Data Source
source = ColumnDataSource({
'x' : crop_dt[crop_dt.index.get_level_values('Year')==2001].loc[(['ABC']), :].Area,
'y' : crop_dt[crop_dt.index.get_level_values('Year')==2001].loc[(['ABC']), :].Production,
'state' : crop_dt[crop_dt.index.get_level_values('Year')==2001].loc[(['ABC']), :].index.get_level_values('State_Name'),
'district' : crop_dt[crop_dt.index.get_level_values('Year')==2001].loc[(['ABC']), :].index.get_level_values('District_Name')
})
#Creating color palette for plot
district_list = crop_dt.loc[(['Tamil Nadu']), :].index.get_level_values('District_Name').unique().tolist()
call_colors = viridis(len(district_list))
color_mapper = CategoricalColorMapper(factors=district_list, palette=call_colors)
# Creating the figure
#xmin, xmax = min(data.Crop_Year), max(data.Crop_Year)
#ymin, ymax = min(data.Production), max(data.Production)
p = figure(
title = 'Crop Area vs Production',
x_axis_label = 'Area',
y_axis_label = 'Production',
plot_height=900,
plot_width=1200,
tools = [HoverTool(tooltips='#district')]
)
p.circle(x='x', y='y', source=source, size=12, alpha=0.7,
color=dict(field='district', transform=color_mapper),
legend='district')
p.legend.location = 'top_right'
def update_plot(attr, old, new):
yr = slider.value
new_data = {
'x' : crop_dt[crop_dt.index.get_level_values('Year')==yr].loc[(['ABC']), :].Area,
'y' : crop_dt[crop_dt.index.get_level_values('Year')==yr].loc[(['ABC']), :].Production,
'state' : crop_dt[crop_dt.index.get_level_values('Year')==yr].loc[(['ABC']), :].index.get_level_values('State_Name'),
'district' : crop_dt[crop_dt.index.get_level_values('Year')==yr].loc[(['ABC']), :].index.get_level_values('District_Name')
}
source.data = new_data
#Creating Slider for Year
start_yr = min(crop_dt.index.get_level_values('Crop_Year'))
end_yr = max(crop_dt.index.get_level_values('Crop_Year'))
slider = Slider(start=start_yr, end=end_yr, step=1, value=start_yr, title='Year')
slider.on_change('value',update_plot)
layout = row(widgetbox(slider), p)
curdoc().add_root(layout)
show(layout)
Also tried a different option using CustomJS as below, but still no luck.
callback = CustomJS(args=dict(source=source), code="""
var data = source.data;
var yr = slider.value;
var x = data['x']
var y = data['y']
'x' = crop_dt[crop_dt.index.get_level_values('Crop_Year')==yr].loc[(['ABC']), :].Area;
'y' = crop_dt[crop_dt.index.get_level_values('Crop_Year')==yr].loc[(['ABC']), :].Production;
p.circle(x='x', y='y', source=source, size=12, alpha=0.7,
color=dict(field='district', transform=color_mapper),
legend='district');
}
source.change.emit();
""")
#Creating Slider for Year
start_yr = min(crop_dt.index.get_level_values('Crop_Year'))
end_yr = max(crop_dt.index.get_level_values('Crop_Year'))
yr_slider = Slider(start=start_yr, end=end_yr, step=1, value=start_yr, title='Year', callback=callback)
callback.args["slider"] = yr_slider
Had a lot of issues trying to execute your code and I have changed some things, so feel free to correct me if did something wrong.
The error was caused by the creation of the ColumnDataSource, I had to change the level value to Crop_Year instead of Year. The loc 'ABC' also caused an error so I removed that too (And I had to add source = ColumnDataSource({, you probably forgot to copy that)
I also added a dropdown menu so it's possible to only show the data from one district.
Also, I'm not quite sure if it's possible to start a bokeh server by supplying a .ipynb file to --serve. But don't pin me down on that, I never use notebooks. I've tested this with a .py file.
#!/usr/bin/python3
import pandas as pd
import numpy as np
from bokeh.models import ColumnDataSource, CategoricalColorMapper, HoverTool
from bokeh.plotting import figure, curdoc
from bokeh.palettes import viridis
from bokeh.layouts import row, widgetbox
from bokeh.models.widgets import Select, Slider
#Importing and processing data file
crop = pd.read_csv('crop_production.csv')
#Cleaning Data
crop.fillna(np.NaN)
crop['Season'] = crop.Season.str.strip()
#Removing Whitespace #Filtering the dataset by Season
crop_season = crop[crop.Season == 'Whole Year']
crop_dt = crop_season.groupby(['State_Name', 'District_Name', 'Crop_Year']).mean().round(1)
crop_dt_year = crop_dt[crop_dt.index.get_level_values('Crop_Year')==2001]
crop_dt_year_state = crop_dt_year[crop_dt_year.index.get_level_values('State_Name')=='Tamil Nadu']
#Creating Column Data Source
source = ColumnDataSource({
'x': crop_dt_year_state.Area.tolist(),
'y': crop_dt_year_state.Production.tolist(),
'state': crop_dt_year_state.index.get_level_values('State_Name').tolist(),
'district': crop_dt_year_state.index.get_level_values('District_Name').tolist()
})
#Creating color palette for plot
district_list = crop_dt.loc[(['Tamil Nadu']), :].index.get_level_values('District_Name').unique().tolist()
call_colors = viridis(len(district_list))
color_mapper = CategoricalColorMapper(factors=district_list, palette=call_colors)
# Creating the figure
p = figure(
title = 'Crop Area vs Production',
x_axis_label = 'Area',
y_axis_label = 'Production',
plot_height=900,
plot_width=1200,
tools = [HoverTool(tooltips='#district')]
)
glyphs = p.circle(x='x', y='y', source=source, size=12, alpha=0.7,
color=dict(field='district', transform=color_mapper),
legend='district')
p.legend.location = 'top_right'
def update_plot(attr, old, new):
#Update glyph locations
yr = slider.value
state = select.value
crop_dt_year = crop_dt[crop_dt.index.get_level_values('Crop_Year')==yr]
crop_dt_year_state = crop_dt_year[crop_dt_year.index.get_level_values('State_Name')==state]
new_data = {
'x': crop_dt_year_state.Area.tolist(),
'y': crop_dt_year_state.Production.tolist(),
'state': crop_dt_year_state.index.get_level_values('State_Name').tolist(),
'district': crop_dt_year_state.index.get_level_values('District_Name').tolist()
}
source.data = new_data
#Update colors
district_list = crop_dt.loc[([state]), :].index.get_level_values('District_Name').unique().tolist()
call_colors = viridis(len(district_list))
color_mapper = CategoricalColorMapper(factors=district_list, palette=call_colors)
glyphs.glyph.fill_color = dict(field='district', transform=color_mapper)
glyphs.glyph.line_color = dict(field='district', transform=color_mapper)
#Creating Slider for Year
start_yr = min(crop_dt.index.get_level_values('Crop_Year'))
end_yr = max(crop_dt.index.get_level_values('Crop_Year'))
slider = Slider(start=start_yr, end=end_yr, step=1, value=start_yr, title='Year')
slider.on_change('value',update_plot)
#Creating drop down for state
options = list(set(crop_dt.index.get_level_values('State_Name').tolist()))
options.sort()
select = Select(title="State:", value="Tamil Nadu", options=options)
select.on_change('value', update_plot)
layout = row(widgetbox(slider, select), p)
curdoc().add_root(layout)
#Jasper Thanks a lot. This works, however it doesnt work with .loc[(['Tamil Nadu']), :]. Reason for having this is to filter the data by adding a bokeh dropdown or radio button object and refresh the plot based on the filters. The below code works only if .loc[(['Tamil Nadu']), :] is removed. Is there any other way to fix this please?
def update_plot(attr, old, new):
yr = slider.value
new_data = {
'x' : crop_dt[crop_dt.index.get_level_values('Crop_Year')==yr].loc[(['Tamil Nadu']), :].Area.tolist(),
'y' : crop_dt[crop_dt.index.get_level_values('Crop_Year')==yr].loc[(['Tamil Nadu']), :].Production.tolist(),
'state' : crop_dt[crop_dt.index.get_level_values('Crop_Year')==yr].loc[(['Tamil Nadu']), :].index.get_level_values('State_Name').tolist(),
'district' : crop_dt[crop_dt.index.get_level_values('Crop_Year')==yr].loc[(['Tamil Nadu']), :].index.get_level_values('District_Name').tolist()
}
source.data = new_data
I have some problem with updating the plot values with Bokeh. Select and Slider don't change the plot. The code is supposed to plot 'budget' along with 'vote_average' in different years. Slider is for showing data (release_date) from 1970 to 2016 years. I'm working in the Jupyter notebook. Code is below:
source = ColumnDataSource(data = {
'x': movies.budget,
'y': movies.vote_average,
'revenue': movies.revenue,
'profit': movies.profit,
'original_title': movies.original_title,
'release_date': movies.release_date
})
p = figure(x_axis_label='Budget in millions $', y_axis_label='Rank',
tools = [HoverTool(tooltips = '#original_title')])
p.circle(x = 'x', y = 'y', source=source)
def update_plot(attr, old, new):
yr = slider.value
# Set new_data
new_data = {
'x' : data.budget.loc[data.release_date == str(yr)].values,
'y' : data.vote_average.loc[data.release_date == str(yr).values
}
# Assign new_data to source.data
source.data = new_data
slider = Slider(start=1970, end=2016, step=1, value=1970, title='Year')
slider.on_change('value', update_plot)
layout = row(widgetbox(slider), p)
show(layout)
What's supposed to be in 'update plot' function? It seems that this func just doesn't work.
Binding widgets in Jupyter notebook requires custom Javascript callbacks as far as I know. Your example would only work on a bokeh serve app. Check out this notebook to see how.
I've been working with the python bokeh function, and I wish to display a graph of a stock when the ticker is entered into the TextInput section. However, in my case the only way I've made this work is to create a new p.line within the update function, which overlays one stock graph on top of another. Is there a way to update my source data or update function such that a graph with only the input stock is shown?
p=figure(
height=400,
x_axis_type='datetime',
title=(company+' ('+tickerstring+') '),
tools='pan, box_zoom, wheel_zoom, reset',
)
p.line('x', 'y', source=source)
line1=p.line(thedates, stockcloseprices)
p.grid.grid_line_color="white"
p.xaxis.axis_label = 'Date'
p.yaxis.axis_label = 'Price'
p.add_tools(HoverTool(
tooltips=[
("Date", "#x{%F}"),
('Close',"#y")
],
formatters={
'x':'datetime', # use 'datetime' formatter for 'date' field
},
mode='vline'
))
source = ColumnDataSource(data=dict(
x=thedates,
y=stockcloseprices
))
div = Div(text='<br><b> Key Points </b><br><br>'+percentagechange+'<br><br>'+performance,
width=200, height=100)
def update(f):
fstocksymbol=str(f.upper())
if fstocksymbol in stocksymbols:
p.title.text = (symbolsdictionary[fstocksymbol]).upper()+' ('+fstocksymbol+')'
tickerstring=fstocksymbol
firstfunction=stockname(tickerstring)
secondfunction=stockdata(firstfunction)
stockdates=[]
stockcloseprices=[]
for value in secondfunction:
stockdates.append(value[0])
stockcloseprices.append(value[4])
thedates = np.array(stockdates, dtype=np.datetime64)
p.line(thedates, stockcloseprices)
push_notebook()
elif fstocksymbol=='':
print('')
else:
print("")
interact(update, f='')
grid = gridplot([p, div, button], ncols=2, plot_width=570, plot_height=400)
show(grid, notebook_handle=True)
There are several example notebooks that show how to update a data source for an existing glyph in the examples directory on GitHub:
https://github.com/bokeh/bokeh/tree/master/examples/howto/notebook_comms
In brief, you want to update the data source:
source.data = new_data_dict
push_notebook()
I am evaluating Bokeh to see if it is ready for more extensive use. I have plotted two columns of a dataframe (code at the end), "Close" and "Adj Close".
I want to put in checkboxes to toggle the display of both the line graphs in the plot. So if the relevant checkbox is unchecked the line does not appear. The Bokeh documentation at http://docs.bokeh.org/en/latest/docs/user_guide/interaction.html does talk about checkbox group but doesn't provide an explicit working example. I would appreciate any help in getting checkboxes working for columns of a dataframe.
import pandas as pd
from bokeh.plotting import figure, output_file, show
IBM = pd.read_csv(
"http://ichart.yahoo.com/table.csv?s=IBM&a=0&b=1&c=2011&d=0&e=1&f=2016",
parse_dates=['Date'])
output_file("datetime.html")
p = figure(width=500, height=250, x_axis_type="datetime")
p.line(IBM['Date'], IBM['Close'], color='navy', alpha=0.5)
p.line(IBM['Date'], IBM['Adj Close'], color='red', alpha=0.5)
show(p)
This is obviously a late reply but I'm currently trying to learn python and bokeh to hack out some sort of data dashboard. I was trying to figure out how the checkboxes worked and I stumbled on your question. This solution only works with bokeh serve . I don't know how to make it work in an HTML output.
I'm only modifying the line visibility and not the source. I didn't try it yet but I'm sure the legends would still show the invisible lines
Apologies for duct tape code.
#-| bokeh serve
#-|
import pandas as pd
from bokeh.io import curdoc,output_file, show
from bokeh.layouts import row, widgetbox
from bokeh.plotting import figure
from bokeh.models import ColumnDataSource
from bokeh.models.widgets import *
#Widgets
ticker = TextInput(title='Ticker Symbol',value='IBM')
button=Button(label='Lookup',button_type='success')
log = Paragraph(text="""log""",
width=200, height=100)
cb_group = CheckboxButtonGroup(labels=['Close', 'Adj Close'],active=[0,1])
cb_group.labels.append('Placebo')
#Plot
p = figure(title='',width=500, height=250, x_axis_type='datetime')
source = ColumnDataSource({'x': [], 'y1': [],'y2': []})
lineClose=p.line('x','y1',source=source, color='navy', alpha=0.5)
lineAdj=p.line('x','y2',source=source, color='red', alpha=0.5)
lines=[lineClose,lineAdj]
#Event handling
def error(msg):
log.text=msg
def update_data():
try:
src='http://ichart.yahoo.com/table.csv?s={symb}&a=0&b=1&c=2011&d=0&e=1&f=2016'.format(symb=ticker.value)
df=pd.read_csv(src,parse_dates=['Date'])
source.data=({'x': df['Date'], 'y1': df['Close'],'y2': df['Adj Close']})
except:
error('Error ticker')
def update_plot(new):
switch=cb_group.active
for x in range(0,len(lines)):
if x in switch:
lines[x].visible=True
else:
lines[x].visible=False
error('<CheckboxButtonGroup>.active = '+str(switch))
button.on_click(update_data)
cb_group.on_click(update_plot)
inputs=widgetbox(ticker,button,cb_group,log)
curdoc().add_root(row(inputs,p,width=800))
curdoc().title = 'Bokeh Checkbox Example'
button.clicks=1
I added the 'Placebo' checkbox to see if I could append to the checkbox group
instead of the typical method so I'm sure there's a way to more elegantly and dynamically add checkboxes.
I haven't been able to get the check boxes to work yet, although I wouldn't be surprised if that functionality is coming soon. In the meantime, here is a workaround using the multiselect widget:
from bokeh.io import vform
from bokeh.models import CustomJS, ColumnDataSource, MultiSelect
from bokeh.plotting import figure, output_file, show
import pandas as pd
IBM = pd.read_csv(
"http://ichart.yahoo.com/table.csv?s=IBM&a=0&b=1&c=2011&d=0&e=1&f=2016",
parse_dates=['Date'])
output_file("datetime.html")
source = ColumnDataSource({'x': IBM['Date'], 'y1': IBM['Close'], \
'y2': IBM['Adj Close'], 'y1p': IBM['Close'], 'y2p': IBM['Adj Close']})
p = figure(width=500, height=250, x_axis_type="datetime")
p.line('x', 'y1', source=source, color='navy', alpha=0.5)
p.line('x', 'y2', source=source, color='red', alpha=0.5)
callback = CustomJS(args=dict(source=source), code="""
var data = source.get('data');
var f = cb_obj.get('value')
y1 = data['y1']
y2 = data['y2']
y1p = data['y1p']
y2p = data['y2p']
if (f == "line2") {
for (i = 0; i < y1.length; i++) {
y1[i] = 'nan'
y2[i] = y2p[i]
}
} else if (f == "line1") {
for (i = 0; i < y2.length; i++) {
y1[i] = y1p[i]
y2[i] = 'nan'
}
} else if (f == "none") {
for (i = 0; i < y2.length; i++) {
y1[i] = 'nan'
y2[i] = 'nan'
}
} else {
for (i = 0; i < y2.length; i++) {
y1[i] = y1p[i]
y2[i] = y2p[i]
}
}
source.trigger('change');
""")
multi_select = MultiSelect(title="Lines to plot:", \
value=["line1", "line2", "none"], \
options=["line1", "line2", "none"], callback=callback)
layout = vform(multi_select, p)
show(layout)
The output looks like this:
For people still looking for this. It was solved by mosc9575. Here is a slightly modified version of mosc9575's solution code:
import numpy as np
from bokeh.layouts import column, row
from bokeh.models import CustomJS, Slider, CheckboxGroup
from bokeh.plotting import ColumnDataSource, figure, show
# initial input data
x = np.linspace(0, 10, 500)
y = np.sin(x)
z = np.cos(x)
name_lst = ['sin', 'cos']
# dataframe
source = ColumnDataSource(data=dict(x=x, y=y, z=z))
# initialize figure
fig = figure()
line_renderer = [
fig.line('x', 'y', source=source, name=name_lst[0]),
fig.line('x', 'z', source=source, name=name_lst[1])
]
line_renderer[0].visible = False
# create a slider and a couple of check boxes
freq_slider = Slider(start=0.1, end=10, value=1, step=.1, title="Frequency")
checkbox = CheckboxGroup(
labels=name_lst,
active=[1, 1],
width=100
)
# callbacks
callback = CustomJS(args=dict(source=source, freq=freq_slider),
code="""
const data = source.data;
const k = freq.value;
const x = data['x'];
const y = data['y'];
const z = data['z'];
for (let i = 0; i < x.length; i++) {
y[i] = Math.sin(k*x[i]);
z[i] = Math.cos(k*x[i]);
}
source.change.emit();
""")
callback2 = CustomJS(args=dict(lines=line_renderer, checkbox=checkbox),
code="""
lines[0].visible = checkbox.active.includes(0);
lines[1].visible = checkbox.active.includes(1);
""")
# changes upon clicking and sliding
freq_slider.js_on_change('value', callback)
checkbox.js_on_change('active', callback2)
layout = row(
fig,
column(freq_slider, checkbox)
)
show(layout)
cos(kx) and a sin(kx) functions can be turned on and off using the checkboxes. With a slider k can be changed. This does not require a bokeh server.