import numpy as np
import pandas as pd
from UfoDataReader.util.parser import UfoXBRLParser
from bokeh.io import show
from bokeh.models import ColumnDataSource, FactorRange
from bokeh.palettes import Spectral5
from bokeh.plotting import figure
from bokeh.transform import factor_cmap

ufoparser = UfoXBRLParser()

# target XBRL file
file = 'jpcrp030000-asr-001_E04996-000_2017-03-31_01_2017-06-23.xbrl'

xbrl = ufoparser.parse(file)
dei = ufoparser.parseDEI(xbrl)

# getting each years GAAP objects
contexts = ['Prior4', 'Prior3', 'Prior2', 'Prior1', 'Current']
gaaps = [ufoparser.parseGAAP(xbrl, context=context) for context in contexts]

# getting factors
share = np.array([float(gaap.shares_outstanding) for gaap in gaaps])
sale = np.array([float(gaap.netsales) for gaap in gaaps])
cf = np.array([float(gaap.cf_from_operating) +
               float(gaap.cf_from_financing) +
               float(gaap.cf_from_financing) for gaap in gaaps])

df = pd.DataFrame(
    dict(
        year=contexts,
        dps=np.array([float(gaap.dps) for gaap in gaaps]),
        eps=np.array([float(gaap.basic_eps) for gaap in gaaps]),
        cfps=cf / share,
        sps=sale / share
    )
)


# bokeh setting
years = contexts
factors = ['dps', 'eps', 'cfps', 'sps']

x = [(year, factor) for year in years for factor in factors]
amounts = sum(zip(df['dps'], df['eps'], df['cfps'], df['sps']), ())

source = ColumnDataSource(data=dict(x=x, counts=amounts))

p = figure(x_range=FactorRange(*x), plot_height=300, title=dei.company_name)

p.vbar(x='x', top='counts', width=1.0, source=source,
       fill_color=factor_cmap('x', palette=Spectral5, factors=factors, start=1, end=2))

p.y_range.start = 0
p.x_range.range_padding = 0.1
p.xaxis.major_label_orientation = 1
p.xgrid.grid_line_color = None

# open browser
show(p)