%pylab inline

import os as os
import pickle as pickle
import pandas as pd

print 'changing to source dirctory'

os.chdir('../src')

from Stats.Scipy import *
from Stats.Survival import *

from Processing.Helpers import *

from Figures.Helpers import *
from Figures.Pandas import *
from Figures.Boxplots import *
from Figures.Survival import draw_survival_curve, survival_and_stats
from Figures.Survival import draw_survival_curves
from Figures.Survival import survival_stat_plot

import Data.Firehose as FH

from IPython import utils  
from IPython.display import HTML 

css_file = 'profile_default/static/custom/custom.css'
base = utils.path.get_ipython_dir()
styles = "<style>\n%s\n</style>" % (open(os.path.join(base, css_file),'r').read())
display(HTML(styles))

pd.set_option('precision', 3)
pd.set_option('display.width', 300)
plt.rcParams['font.size'] = 12

'''Color schemes for paper taken from http://colorbrewer2.org/'''
colors = plt.rcParams['axes.color_cycle']
colors_st = ['#CA0020', '#F4A582', '#92C5DE', '#0571B0']
colors_th = ['#E66101', '#FDB863', '#B2ABD2', '#5E3C99']

def get_run(firehose_dir, version='Latest'):
    '''
    Helper to get a run from the file-system. 
    '''
    path = '{}/ucsd_analyses'.format(firehose_dir)
    if version is 'Latest':
        version = sorted(os.listdir(path))[-1]
    run = pickle.load(open('{}/{}/RunObject.p'.format(path, version), 'rb'))
    return run

OUT_PATH = '../Data'
RUN_DATE = '2014_01_15'
VERSION = 'all'
CANCER = 'HNSC'
FIGDIR = '../Figures/'
if not os.path.isdir(FIGDIR):
    os.makedirs(FIGDIR)

print 'populating namespace with data'

run_path  = '{}/Firehose__{}/'.format(OUT_PATH, RUN_DATE)
run = get_run(run_path, 'Run_' + VERSION)
cancer = run.load_cancer(CANCER)
clinical = cancer.load_clinical()

mut = cancer.load_data('Mutation')
mut.uncompress()
cn = cancer.load_data('CN_broad')
cn.uncompress()

rna = pickle.load(open(cancer.path + '/mRNASeq/store/no_hpv2.p', 'rb'))
mirna = pickle.load(open(cancer.path + '/miRNASeq/store/no_hpv2.p', 'rb'))

hpv = clinical.hpv
surv = clinical.survival.survival_5y
age = clinical.clinical.age.astype(float)
old = pd.Series(1.*(age>=75), name='old')

pats = [true_index(hpv == 0), mut.features.columns, cn.features.columns,
        surv.unstack().index, rna.features.columns, mirna.features.columns,
        true_index(age < 85)]
keepers_o = reduce(set.intersection, map(set, pats))
keepers_o = pd.Index(keepers_o)
len(keepers_o)