def get_income_oil(targets, depth):
    price = 4.15
    rho = 860
    S = 100
    porosity = 0.7
    diff = np.concatenate([np.asarray([0.0895]) ,np.diff(depth)])
    return porosity*price*rho*S*np.sum(targets*diff)

def get_expected_income_oil(probs, depth):
    price = 4.15
    rho = 860
    S = 100
    porosity = 0.7
    diff = np.concatenate([np.asarray([0.0895]) ,np.diff(depth)])
    return porosity*price*rho*S*np.sum(probs*diff)

def get_costs_research(features, depth):
    costs = {'bk': 2450,
    'GZ1':2050,
    'GZ2':2050,
    'GZ3':2050,
    'GZ4':2050,
    'GZ5':2050,
    'GZ7':2050,
    'DGK':1300,
    'NKTD':2050,
    'NKTM':2050,
    'NKTR':2050,
    'ALPS':1150}
    cost = 0
    diff = np.diff(depth)
    for fea in features:
        cost += costs[fea]
    return np.sum(cost*diff)

def get_check_costs(depth, cost):
    diff = np.diff(depth)
    return np.sum(cost*diff)

def get_overall_income(goals, depth, features, cost):
    research_costs = get_costs_research(features, depth)
    oil_income = get_income_oil(goals, depth)
    check_costs = get_check_costs(depth, cost)
    income =  oil_income - check_costs - research_costs
    return income

def get_decision(probs, depth, feature_names, cost):
    research_costs = get_costs_research(features, depth)
    decision
    oil_income = get_income_oil(decision, depth)
    check_costs = get_check_costs(depth, cost)
    return oil_income

def get_overall_expected_income(probs, depth, features, cost):
    research_costs = get_costs_research(features, depth)
    oil_income = get_expected_income_oil(probs, depth)
    check_costs = get_check_costs(depth, cost)
    income =  oil_income - check_costs - research_costs
    return income