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import bokeh
bokeh.load_notebook()


    

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from collections import OrderedDict

import numpy as np
from bokeh.charts import Histogram

mu, sigma = 0, 0.5
normal = np.random.normal(mu, sigma, 1000)
lognormal = np.random.lognormal(mu, sigma, 1000)

distributions = OrderedDict(normal=normal, lognormal=lognormal)
normal_dist = OrderedDict(normal=normal)

hist = Histogram(normal_dist, bins=50, mu=mu, sigma=sigma,
                 title="kwargs, dict_input", ylabel="frequency", legend="top_left",
                 width=400, height=350, notebook=True)
hist.show()


    

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import pandas as pd

df = pd.DataFrame(normal_dist)
hist = Histogram(df, bins=50, mu=mu, sigma=sigma,
                 title="no_tools, df_input", ylabel="frequency", legend="top_left",
                 tools=True, width=400, height=350, notebook=True)
hist.show()


    

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from bokeh.plotting import output_notebook, show

output_notebook()
# Testing with 1D array of scalars
hist = Histogram(normal, bins=50, mu=mu, sigma=sigma,
                 title="kwargs, dict_input", ylabel="frequency", legend="top_left",
                 width=500, height=350, notebook=True)
show(hist)


    

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distributions = OrderedDict(normal=normal, lognormal=lognormal)

hist2 = Histogram(distributions, bins=50, title="kwargs, dict_input", ylabel="frequency", legend="top_left",
                 width=500, height=350, notebook=True)
show(hist2)


    

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df = pd.DataFrame(distributions)

hist = Histogram(df, bins=50,title="kwargs, dict_input", ylabel="frequency", legend="top_left",
                 width=500, height=350, notebook=True)
show(hist)


    

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