#apply styles to notebook (rather than doing it locally 
# via the profile custom.css file)
#from IPython.core.display import HTML
#css_file = 'media/style.css'
#HTML(open(css_file, "r").read())

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt
%matplotlib inline

#read in aggregated federal contract totals
contracts = pd.read_csv('data/fed_contract_totals.txt')
contracts = contracts[[
    'state_abbr', 'year', 'total_adj', 'dod_adj', 
    'non-dod_adj', 'total_per_capita_adj', 'dod_per_capita_adj',
    'other_per_capita_adj', 'dod_percent_total', 'other_percent_total'
]]
#take a peek at the data
contracts.head()

plottotal = contracts[['year', 'state_abbr', 'dod_adj', 'non-dod_adj']]
#show total in millions of $ for readability
plottotal['dod_adj'] = plottotal['dod_adj']/1000000
plottotal['non-dod_adj'] = plottotal['non-dod_adj']/1000000
plottotal = pd.melt(plottotal, id_vars=['year', 'state_abbr'], var_name = 'Contract Type', value_name = 'Amount')
fg = sns.factorplot('year', 'Amount', 'Contract Type',data=plottotal[plottotal['state_abbr']=='VA'], kind='bar')
fg.set_axis_labels('Fiscal Year', 'Amount (Millions of 2015 $)')

plotpc = contracts[['year', 'state_abbr', 'dod_per_capita_adj', 'other_per_capita_adj']]
plotpc = pd.melt(plotpc, id_vars=['year', 'state_abbr'], var_name = 'Contract Type', value_name = 'Amount')
fg = sns.factorplot(
    'year', 'Amount', 'Contract Type',
    plotpc[~plotpc['state_abbr'].isin(['DC', 'PR'])],
    col='state_abbr', col_wrap=4, kind='point'
    )
fg.set_axis_labels('Fiscal Year', 'Amount (2015 $)')