In [4]:

    

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import datetime
import os
# from small_script.myFunctions import *
from Bio.PDB.Polypeptide import three_to_one


%matplotlib inline
%load_ext autoreload
%autoreload 2


    

In [13]:

    

plt.rcParams['figure.figsize'] = [16.18033, 10]    #golden ratio
plt.rcParams['figure.facecolor'] = 'w'
plt.rcParams['figure.dpi'] = 100


    

In [6]:

    

from bokeh.io import output_notebook, show
output_notebook()


    

    






    

        
        Loading BokehJS ...
    






    

In [14]:

    

from bokeh.layouts import column
from bokeh.plotting import figure, show, output_file
from bokeh.sampledata.autompg import autompg
from bokeh.transform import jitter

years = sorted(autompg.yr.unique())

p1 = figure(title="Years vs mpg without jittering")
p1.xgrid.grid_line_color = None
p1.xaxis[0].ticker = years
p1.circle(x='yr', y='mpg', size=9, alpha=0.4, source=autompg)

p2 = figure(title="Years vs mpg with jittering")
p2.xgrid.grid_line_color = None
p2.xaxis[0].ticker = years
p2.circle(x=jitter('yr', 0.4), y='mpg', size=9, alpha=0.4, source=autompg)

# output_file("jitter.html")

show(column(p1, p2))


    

In [16]:

    

from bokeh.layouts import column
from bokeh.plotting import figure, show, output_file
from bokeh.sampledata.autompg import autompg
from bokeh.transform import jitter

years = sorted(autompg.yr.unique())

p1 = figure(plot_width=1000, plot_height=300, title="Years vs mpg without jittering")
p1.xgrid.grid_line_color = None
p1.xaxis[0].ticker = years
p1.circle(x='yr', y='mpg', size=9, alpha=0.4, source=autompg)

p2 = figure(plot_width=600, plot_height=300, title="Years vs mpg with jittering")
p2.xgrid.grid_line_color = None
p2.xaxis[0].ticker = years
p2.circle(x=jitter('yr', 0.4), y='mpg', size=9, alpha=0.4, source=autompg)

# output_file("jitter.html")

show(column(p1, p2))


    

In [21]:

    

from bokeh.charts import facet, Bar, Data


    

    




---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
<ipython-input-21-9439b82f6345> in <module>
----> 1 from bokeh.charts import facet, Bar, Data

ModuleNotFoundError: No module named 'bokeh.charts'

In [6]:

    

# Plot a complex chart with intearctive hover in a few lines of code

from bokeh.models import ColumnDataSource, HoverTool
from bokeh.plotting import figure
from bokeh.sampledata.autompg import autompg_clean as df
from bokeh.transform import factor_cmap

df.cyl = df.cyl.astype(str)
df.yr = df.yr.astype(str)

group = df.groupby(by=['cyl', 'mfr'])
source = ColumnDataSource(group)

p = figure(plot_width=800, plot_height=300, title="Mean MPG by # Cylinders and Manufacturer",
           x_range=group, toolbar_location=None, tools="")

p.xgrid.grid_line_color = None
p.xaxis.axis_label = "Manufacturer grouped by # Cylinders"
p.xaxis.major_label_orientation = 1.2

index_cmap = factor_cmap('cyl_mfr', palette=['#2b83ba', '#abdda4', '#ffffbf', '#fdae61', '#d7191c'], 
                         factors=sorted(df.cyl.unique()), end=1)

p.vbar(x='cyl_mfr', top='mpg_mean', width=1, source=source,
       line_color="white", fill_color=index_cmap, 
       hover_line_color="darkgrey", hover_fill_color=index_cmap)

p.add_tools(HoverTool(tooltips=[("MPG", "@mpg_mean"), ("Cyl, Mfr", "@cyl_mfr")]))

show(p)


    

In [7]:

    

# Create and deploy interactive data applications

from IPython.display import IFrame
IFrame('https://demo.bokeh.org/sliders', width=900, height=500)


    

    
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In [8]:

    

from bokeh.plotting import figure, output_file, show

# prepare some data
x = [1, 2, 3, 4, 5]
y = [6, 7, 2, 4, 5]

# output to static HTML file
output_file("lines.html")

# create a new plot with a title and axis labels
p = figure(title="simple line example", x_axis_label='x', y_axis_label='y')

# add a line renderer with legend and line thickness
p.line(x, y, legend="Temp.", line_width=2)

# show the results
show(p)


    

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