On Wednesday, November 21, 2018 at 3:36:24 PM UTC+1, Raha Asadimehr wrote:

Hello ,
I try to use this example :

https://demo.bokehplots.com/apps/crossfilter

but I could not able to add hover tool for each point .

Could you please give me some help ?

from bokeh.layouts import row, widgetbox

from bokeh.models import Select

from bokeh.palettes import Spectral5

from bokeh.plotting import curdoc, figure

from bokeh.sampledata.autompg import autompg_clean as df3

from bokeh.models import LinearInterpolator, HoverTool,ColumnDataSource

import numpy as np

df = df1.copy()

df=df.rename( columns={“sum”: “n”})

df[‘l1’]= pd.Categorical(df[‘l’], categories=df[‘l’].unique()).codes

df[‘aa_type’]= pd.Categorical(df[‘aa’], categories=df[‘aa’].unique()).codes

df[‘uu_type’]= pd.Categorical(df[‘uu’], categories=df[‘uu’].unique()).codes

df[‘n_range’]= df.n_code.values*5

SIZES = list(set(df.n_range.values))

N_SIZES = len(SIZES)

COLORS =Spectral5

N_COLORS = len(COLORS)

aa= list(set(df.aa.values))

N_aa = len(aa)

uu = list(set(df.uu.values))

N_uu= len(uu)

columns = sorted(df.columns)

discrete = [x for x in columns if df.dtype == object]

continuous = [x for x in columns if x not in discrete]

def create_figure():

xs = df[x.value].values

ys = df[y.value].values

x_title = x.value.title()

y_title = y.value.title()

kw = dict()

if x.value in discrete:

kw[‘x_range’] = sorted(set(xs))

if y.value in discrete:

kw[‘y_range’] = sorted(set(ys))

kw[‘title’] = “%s vs %s” % (x_title, y_title)

source= ColumnDataSource(data=dict(xs=, ys=,RName=, PName=, n=, A=))

new_data = dict(

x=xs,

y=ys,

R=df[‘RName’],

P=df[‘PName’],

N=df[‘n’],

A=df[‘A’],

)

source.data=new_data

hover = HoverTool(tooltips=[ (“R”, “@RName”), (“P”, “@PName”), (“N”, “@n”) , (“A”, “@A”) ])

p = figure(plot_height=800, plot_width=800, tools=‘pan,box_zoom,reset’, **kw)

p.add_tools(hover)

p.xaxis.axis_label = x_title

p.yaxis.axis_label = y_title

if x.value in discrete:

p.xaxis.major_label_orientation = pd.np.pi / 4

sz = 20

if size.value != ‘None’:

if len(set(df[size.value])) > N_SIZES:

groups = pd.qcut(df[size.value].values, N_SIZES, duplicates=‘drop’)

else:

groups = pd.Categorical(df[size.value])

sz = [SIZES[xx] for xx in groups.codes]

c = “#31AADE”

if color.value != ‘None’:

if len(set(df[color.value])) > N_SIZES:

groups = pd.qcut(df[color.value].values, N_COLORS, duplicates=‘drop’)

else:

groups = pd.Categorical(df[color.value])

c = [COLORS[xx] for xx in groups.codes]

a = “I”

if aa.value != ‘All’:

if len(set(df[aa.value])) > N_ASSET:

groups = pd.qcut(df[aa.value].values,N_aa, duplicates=‘drop’)

else:

groups = pd.Categorical(df[aa.value])

a = [aa[xx] for xx in groups.codes]

u = “InterestRate:CrossCurrency:Basis”

if uu.value != ‘All’:

if len(set(df[uu.value])) > N_uu:

groups = pd.qcut(df[uu.value].values,N_uu, duplicates=‘drop’)

else:

groups = pd.Categorical(df[uu.value])

u= [uu[xx] for xx in groups.codes]

p.circle(x=xs, y=ys, color=c, size=sz, line_color=“white”, alpha=0.6, hover_color=‘white’, hover_alpha=0.5)

#,source=source

return p

def update(attr, old, new):

layout.children[1] = create_figure()

x = Select(title=‘X-Axis’, value=‘uu_type’, options=columns)

x.on_change(‘value’, update)

y = Select(title=‘Y-Axis’, value=‘location’, options=columns)

y.on_change(‘value’, update)

asset= Select(title=“aa”, value=“All”, options=list(set(df1.aa.values)))

asset.on_change(‘value’, update)

upi= Select(title=“uu”, value=“All”, options=list(set(df1.uu.values)))

upi.on_change(‘value’, update)

size = Select(title=‘Size’, value=‘n_range’, options=[‘None’] + continuous)

size.on_change(‘value’, update)

color = Select(title=‘Color’, value=‘aa_type’, options=[‘None’] + continuous)

color.on_change(‘value’, update)

controls = widgetbox([x, y, color, size,aa,uu], width=550)

layout = row(controls, create_figure())

curdoc().add_root(layout)

curdoc().title = …

``

Raha Asadi mehr