In [1]:

    

# Basic setup for displaying bokeh plots in jupyter.
import bokeh
from bokeh.plotting import figure 
from bokeh.io import output_notebook, show
output_notebook()


    

    






    

        
        Loading BokehJS ...
    






    

In [2]:

    

# NumPy imports.
import numpy as np
import cPickle as pickle
import os
cwd = os.getcwd()
print(cwd)


    

    




/Users/kevin/dev/nml/paper_platform/scripts

In [3]:

    

# Load parsed data (NumPy array).
array = pickle.load(open('2018-03-27-10-49-30_array.p', 'rb'))
print(array.shape)


    

    




(2, 22052)

In [4]:

    

array_warehouse = pickle.load(open('wh-2018-05-02-22-27-35-098526_array.p','rb'))
print(array_warehouse.shape)


    

    




(2, 1000)

In [5]:

    

# Plot.
p = figure(width=500,
           height=500,
           x_axis_label="Total # of Targets Retrieved per Analysis",
           y_axis_label="Runtime (seconds)",
           title="C. Overall Runtimes for Fisher's Analysis",
           )
p.axis.major_label_text_font_size="12pt"
p.axis.axis_label_text_font='12pt'
p.circle(array[0],array[1], size=1, color="darkslateblue", alpha=0.1)
p.circle(array_warehouse[0],array_warehouse[1], size=1, color="red", alpha=0.9)
show(p)


    

In [7]:

    

import numpy as np
l = pickle.load(open('api-2018-05-03-10-22-35-670254_list.p', 'rb'))
# Plot a histogram of the targets.
x = np.array(l)
print(x.mean())
hist, edges = np.histogram(x, density=True, bins=50)

p1 = figure(title="",tools="save",)

p1.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:],
        fill_color="blue", line_color="#033649", alpha=0.5)

show(p1)


    

    




0.8261259841918945






    







    








    

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