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Solve the following python programming problem given the following code:
# -*- coding: utf-8 -*-

# -------------------------------------------------------------------------
#  class Updater
# -------------------------------------------------------------------------

import numpy as np

class Updater():
    #
    #
    # METHODS
    #
    def get_identifier(self):
        return self.identifier

    # -------------------------------------------------------------------------
    # __init__
    # -------------------------------------------------------------------------
    def __init__(self, environment, runner):
        self.environment = environment
        self.runner = runner

        self.results_df = 0
        self.prices = np.array([])
        self.rates={}

        self.system_equity = 0
        self.system_assets = 0

        self.delta_pGB = 0
        self.delta_pGB = 0

    # -------------------------------------------------------------------------
    # -------------------------------------------------------------------------
    # do_update
    # -------------------------------------------------------------------------
    def do_update_benchmark(self, environment, current_step, scenario):
        import pandas as pd

        if current_step < 1:
            self.add_rates(environment)
            self.initialize_prices(environment, current_step)

            print "***In t=:", current_step , " This is the price matrix***\n", self.prices, "\n",

            self.initialize_assets_all_agents(current_step, environment)

            self.profit_all_agents(environment, current_step)

        else:

            self.update_all_agents_balance_sheets(environment, current_step, scenario)

Given the above code, generate some markdown instructions for it.
assistant
#Here is Python program to print "***In t=:", current_step , " This is the price matrix***\n", self.

Here you can change the initital interest rates and prices for the marketable assets and also the shocks. It's possible to assign them to other variables and change them later in the updater. It's also possible to not read them in at all (just delete the lines) and define those in the updater script, but in the current setup they get read into the dictionary in the environment, environment.exogenous variables = {price_GB : 1.0, ... }. 
What's the difference between num_simulations and num_sweeps? For the time iterations current_step it's more convenient to use num_sweeps rather than num_simulations for now. num_sweeps are the time steps for the model algorithm.  With num_simulations, one can  initiate a different environment with different classes, so for now it's better to stick with num_sweeps to handle the time steps in the model. 
Once all the agents' objects and configuration files are instantiated, the code moves on to the actual model algorithm which is stored in the updater class under \src\
    updater.py
The updater can be thought as the class that handels the transition.  
Updater.py
Here 

We take over the initial prices and interest rates given by the enviroment config and save them in updater specific    dictionaries (updater.prices and updater.rates by using the method def add_rates(self, environment):

def initialize_prices and def updade_prices

The really important method is the one below: 
```ruby 
def do_update_benchmark(self, environment, current_step, scenario):
    import pandas as pd
if current_step &lt; 1:
    self.add_rates(environment)
    self.initialize_prices(environment, current_step)

    print "***In t=:", current_step , " This is the price matrix***\n", self.prices, "\n",

    self.initialize_assets_all_agents(current_step, environment)

    self.profit_all_agents(environment, current_step)

else:

    self.update_all_agents_balance_sheets(environment, current_step, scenario)```


def write_to_csv  puts the output in to a result dataframe self.results_df.