In [1]:

    

from collections import OrderedDict
import math
import datetime
import time

import matplotlib.pyplot as plt
import pandas as pd
from IPython.core.display import HTML, display

import bokeh.io
import bokeh.plotting
import bokeh.models
import bokeh.palettes

import fto
from fto import stats


    

In [2]:

    

%load_ext autoreload
%autoreload 2
bokeh.io.output_notebook()


    

    






    

        
        Loading BokehJS ...
    






    

In [3]:

    

class HTMLDict(OrderedDict):
    def _repr_html_(self):
        output_string_list = ["<table><tbody>"]
        for key, val in self.items():
            output_string_list.append('<tr><th>%s</th><td>%s</td></tr>' % (key, val))
        output_string_list.append("</tbody></table>")
        return ''.join(output_string_list)


    

In [4]:

    

df = fto.load_dataframe("/home/hugh/data/fto-stats.csv")
monthly_df = fto.stats.generate_monthly_dataframe(df)


    

In [5]:

    

display(HTML(monthly_df.to_html(index=False)))


    

    






  

    

      
Month
      
Births
      
Deaths
      
Pregnancies
    
  
  

    

      
March 2016
      
54
      
63
      
21
    
    

      
April 2016
      
68
      
44
      
29
    
    

      
May 2016
      
45
      
43
      
18
    
    

      
June 2016
      
58
      
68
      
28
    
    

      
July 2016
      
79
      
59
      
28
    
    

      
August 2016
      
37
      
67
      
16
    
    

      
September 2016
      
37
      
49
      
19
    
  

In [6]:

    

def generate_montly_figure(monthly_df):
    monthly_df.index = month_to_timestamp(monthly_df.Month)
    monthly_df.index.name = "TS"
    max_val = find_maximum_value(monthly_df)
    palette = bokeh.palettes.Set2_3
    source = bokeh.models.ColumnDataSource(monthly_df)
    fig = bokeh.plotting.figure(x_axis_type="datetime", width=800)
    x_axis = monthly_df.index.to_series()
    PROPS = {'line_width': 5, 'line_cap': 'round', 'source': source}
    #fig.line("TS", "Births", source=source)
    fig.line("TS", "Births", color=palette[0], legend="Births", **PROPS)
    fig.line("TS", "Deaths", color=palette[1], legend="Deaths", **PROPS)
    fig.line("TS", "Pregnancies", color=palette[2], legend="Pregnancies", **PROPS)
    fig.y_range = bokeh.models.Range1d(0, max_val, bounds="auto")
    fig.x_range = bokeh.models.Range1d(monthly_df.index.min(), monthly_df.index.max(), bounds="auto")
    fig.toolbar.logo = None
    configure_legend(fig.legend)  
    fig.yaxis.minor_tick_line_alpha = 0
    return fig


def find_maximum_value(monthly_df):
    max_series = monthly_df.max()
    max_val = max_series[max_series.apply(lambda val: not isinstance(val, str))].max()
    max_val_roundup = math.ceil(max_val/10) * 10
    return max_val_roundup

def month_to_timestamp(month_series):
    return month_series.apply(lambda val: pd.Timestamp.strptime(val, "%B %Y"))

def configure_legend(legend):
    legend.background_fill_alpha = 0
    legend.border_line_alpha = 0
    legend.spacing = 0
    legend.margin = 0
    legend.padding = 5

monthly_df = stats.generate_monthly_dataframe(df)
fig = generate_montly_figure(monthly_df)
bokeh.io.show(fig)


    

In [7]:

    

import bokeh.charts


    

In [8]:

    

records = stats.average_stats(df, monthly_df)
record_list = stats.RecordFormatList(records)
HTMLDict(record_list)


    

    
Out[8]:




Average Birth Queue Time
3.8 months
Projected Birth Queue Time Entering Now
4.9 months
Average Number of Babies per Pregnancy
2.4 babies

In [9]:

    

from fto import fto_web


    

In [37]:

    

layout = fto_web.run(df)
bokeh.io.show(layout)


    

In [13]:

    

import bokeh.themes
import yaml
m = layout.children[1]


    

In [16]:

    

m


    

In [20]:

    

layout.apply_theme?


    

In [21]:

    

from bokeh.themes import Theme


    

In [68]:

    

bokeh.io.show(layout)


    

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