import icon as icon
import numpy
import matplotlib.pyplot as plt
rkind = 'property'
sim = icon.sim(50,500,0.9,1,rkind)
simHO = sim['ho']
simHE = sim['he']
ab_rv_HO = icon.revenue_by_ability(simHO['firms'],rkind)
ab_rv_HE = icon.revenue_by_ability(simHE['firms'],rkind)
nn_rv_HO = icon.revenue_by_nn(simHO['firms'],rkind)
nn_rv_HE = icon.revenue_by_nn(simHE['firms'],rkind)
##### plot the sucka
fig1, ((ax,cx),(bx,dx)) = plt.subplots(nrows=2, ncols=2)
fig1.set_facecolor("#ffffff")
ax.set_title("Homogeneous")
ax.hist(icon.revenues(simHO['firms'],rkind))
ax.set_xlabel("revenue (knowledge uncovered)")
ax.set_ylabel("frequency")
bx.plot(range(0,len(simHO['progress'])),simHO['progress'])
bx.set_xlabel("time")
bx.set_ylabel("% discovered")
cx.set_title("Heterogeneous")
cx.hist(icon.revenues(simHE['firms'],rkind))
cx.set_xlabel("revenue (knowledge uncovered)")
cx.set_ylabel("frequency")
dx.plot(range(0,len(simHE['progress'])),simHE['progress'])
dx.set_xlabel("time")
dx.set_ylabel("% discovered")
if (len(simHE['progress']) > len(runs[name]['he']['progress'])):
runs[name]['he']['progress'] = numpy.add(runs[name]['he']['progress'],simHE['progress'][0:len(runs[name]['he']['progress'])])
else:
runs[name]['he']['progress'] = numpy.add(runs[name]['he']['progress'][0:len(simHE['progress'])],simHE['progress'])
tmp = icon.revenue_by_ability(simHO['firms'],rkind)
runs[name]['ho']['ab_rv']['lrevenues'] += tmp['lrevenues']
runs[name]['ho']['ab_rv']['labilities'] += tmp['labilities']
runs[name]['ho']['ab_rv']['revenues'] += tmp['revenues']
runs[name]['ho']['ab_rv']['abilities'] += tmp['abilities']
tmp = icon.revenue_by_ability(simHE['firms'],rkind)
runs[name]['he']['ab_rv']['lrevenues'] += tmp['lrevenues']
runs[name]['he']['ab_rv']['labilities'] += tmp['labilities']
runs[name]['he']['ab_rv']['revenues'] += tmp['revenues']
runs[name]['he']['ab_rv']['abilities'] += tmp['abilities']
tmp = icon.revenue_by_nn(simHO['firms'],rkind)
runs[name]['ho']['nn_rv']['lrevenues'] += tmp['lrevenues']
runs[name]['ho']['nn_rv']['ldistances'] += tmp['ldistances']
runs[name]['ho']['nn_rv']['lrevenues'] += tmp['lrevenues']
runs[name]['ho']['nn_rv']['ldistances'] += tmp['ldistances']
tmp = icon.revenue_by_nn(simHE['firms'],rkind)
runs[name]['he']['nn_rv']['lrevenues'] += tmp['lrevenues']
runs[name]['he']['nn_rv']['ldistances'] += tmp['ldistances']
runs[name]['he']['nn_rv']['revenues'] += tmp['revenues']
runs[name]['he']['nn_rv']['distances'] += tmp['distances']
runs[name]['ho']['progress'] = _div(runs[name]['ho']['progress'],10)
runs[name]['he']['progress'] = _div(runs[name]['he']['progress'],10)
pickle.dump(runs, open( "runs.pickle", "wb" ) )
