In [1]:

    

import pandas as pd
# %matplotlib notebook
%matplotlib inline

from bokeh.io import hplot, output_notebook, show
from bokeh.plotting import figure
output_notebook()


    

    





    
        
        
        
    

        
        BokehJS successfully loaded.
    

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gauss_df = pd.read_table('../gaussian.tsv')
uniform_df = pd.read_table('../uniform.tsv')


    

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gauss_df.seconds - gauss_df.seconds.min()


    

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gauss_df.wait.hist(bins=20)


    

    
Out[3]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f19717af2d0>






    

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# wait over time
gauss_df.plot(x='seconds', y='wait', kind='scatter')


    

    
Out[4]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f198d8a9350>






    

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plot_size={'width': 300, 'height': 300}
gauss_scatter = figure(title='Gaussian delay', **plot_size)
uniform_scatter = figure(title='Uniform delay', **plot_size)
gauss_scatter.circle(gauss_df.seconds - gauss_df.seconds.min(), gauss_df.wait, alpha=0.5)
uniform_scatter.circle((uniform_df.seconds - uniform_df.seconds.min()), uniform_df.wait, color="deeppink", alpha=0.3)
p = hplot(gauss_scatter, uniform_scatter)
show(p)


    

In [6]:

    

# wait times
gauss_df.plot(y='wait')


    

    
Out[6]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f1971a79d90>






    

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