How to use the quantecon.DiscreteRV function in quantecon

""" 
Simulate job / career paths and compute the waiting time to permanent job /
career.  In reading the code, recall that optimal_policy[i, j] = policy at
(theta_i, epsilon_j) = either 1, 2 or 3; meaning 'stay put', 'new job' and
'new life'.
"""
import matplotlib.pyplot as plt
import numpy as np
from quantecon import DiscreteRV, compute_fixed_point, CareerWorkerProblem

wp = CareerWorkerProblem()
v_init = np.ones((wp.N, wp.N))*100
v = compute_fixed_point(wp.bellman,v_init)
optimal_policy = wp.get_greedy(v)
F = DiscreteRV(wp.F_probs)
G = DiscreteRV(wp.G_probs)

def gen_path(T=20):
    i = j = 0  
    theta_index = []
    epsilon_index = []
    for t in range(T):
        if optimal_policy[i, j] == 1:    # Stay put
            pass
        elif optimal_policy[i, j] == 2:  # New job
            j = int(G.draw())
        else:                            # New life
            i, j  = int(F.draw()), int(G.draw())
        theta_index.append(i)
        epsilon_index.append(j)
    return wp.theta[theta_index], wp.epsilon[epsilon_index]

import matplotlib.pyplot as plt
import numpy as np
from quantecon import DiscreteRV, CareerWorkerProblem
from quantecon import compute_fixed_point

wp = CareerWorkerProblem()
v_init = np.ones((wp.N, wp.N))*100
v = compute_fixed_point(wp.bellman, v_init)
optimal_policy = wp.get_greedy(v)
F = DiscreteRV(wp.F_probs)
G = DiscreteRV(wp.G_probs)

def gen_first_passage_time():
    t = 0
    i = j = 0  
    theta_index = []
    epsilon_index = []
    while 1:
        if optimal_policy[i, j] == 1:    # Stay put
            return t
        elif optimal_policy[i, j] == 2:  # New job
            j = int(G.draw())
        else:                            # New life
            i, j  = int(F.draw()), int(G.draw())
        t += 1

M = 25000 # Number of samples

def simulate(self, g1, l1, T):
        """
        Given a policy for consumption (g1) and a policy for continuation
        values (l1) simulate for T periods.
        """
        # Pull out information from class
        ybar, pi_y = self.ybar, self.pi_y
        ns = self.ybar.size

        # Draw random simulation of iid income realizations
        d = qe.DiscreteRV(pi_y)
        y_indexes = d.draw(T)

        # Draw appropriate indexes for policy.
        # We do this by making sure that the indexes are weakly increasing
        # by changing any values less than the previous max to the previous max
        pol_indexes = np.empty(T, dtype=int)
        fix_indexes(ns, T, y_indexes, pol_indexes)

        # Pull off consumption and continuation value sequences
        c = g1[pol_indexes]
        w = l1[pol_indexes]
        y = ybar[y_indexes]

        return c, w, y

import matplotlib.pyplot as plt
import numpy as np
from quantecon import DiscreteRV, CareerWorkerProblem
from quantecon import compute_fixed_point

wp = CareerWorkerProblem()
v_init = np.ones((wp.N, wp.N))*100
v = compute_fixed_point(wp.bellman, v_init)
optimal_policy = wp.get_greedy(v)
F = DiscreteRV(wp.F_probs)
G = DiscreteRV(wp.G_probs)

def gen_first_passage_time():
    t = 0
    i = j = 0  
    theta_index = []
    epsilon_index = []
    while 1:
        if optimal_policy[i, j] == 1:    # Stay put
            return t
        elif optimal_policy[i, j] == 2:  # New job
            j = int(G.draw())
        else:                            # New life
            i, j  = int(F.draw()), int(G.draw())
        t += 1

""" 
Simulate job / career paths and compute the waiting time to permanent job /
career.  In reading the code, recall that optimal_policy[i, j] = policy at
(theta_i, epsilon_j) = either 1, 2 or 3; meaning 'stay put', 'new job' and
'new life'.
"""
import matplotlib.pyplot as plt
import numpy as np
from quantecon import DiscreteRV, compute_fixed_point, CareerWorkerProblem

wp = CareerWorkerProblem()
v_init = np.ones((wp.N, wp.N))*100
v = compute_fixed_point(wp.bellman,v_init)
optimal_policy = wp.get_greedy(v)
F = DiscreteRV(wp.F_probs)
G = DiscreteRV(wp.G_probs)

def gen_path(T=20):
    i = j = 0  
    theta_index = []
    epsilon_index = []
    for t in range(T):
        if optimal_policy[i, j] == 1:    # Stay put
            pass
        elif optimal_policy[i, j] == 2:  # New job
            j = int(G.draw())
        else:                            # New life
            i, j  = int(F.draw()), int(G.draw())
        theta_index.append(i)
        epsilon_index.append(j)
    return wp.theta[theta_index], wp.epsilon[epsilon_index]