Monte Carlo Package:

Metadata:

Final Project (Monte Carlo Simulator)

Synopsis:

The Monte Carlo Package is a Python package with three classes: Die, Game, and Analyzer. Class descriptions and demonstration code are included below to demo how to install and import the package and how to use the three classes.

Classes and attributes:

Die Class:

A die class with N sides/faces and W weights that can be rolled to select a side/face.

Attributes:

• None

Methods:

__init__(self, faces):

Initializer that takes an array or list of faces of any length, initializes weights to 1.

Parameters:

• faces: NumPy array or a list

change_weight(self, face, new_weight):

A method that changes a side/face weight and checks whether side/face and weight are valid.

Parameters:

• face: str or int
• new_weight: float

Raises:

• ValueError if new_weight is not a float or convertible to a float. This error will also appear if the face is not included in the die.

rolls(self, rolls=1):

A method that rolls the die one or more times but defaults to 1.

Parameters:

• rolls: int

Returns:

• rolled_outcome: a list of the rolled outcomes.

show_die(self):

A method that shows the dataframe of faces and weights.

Parameters:

• None

Returns:

• a Pandas df of faces and weights of the die.

Game Class:

A game class that consists of rolling of one or more dice of the same kind one or more times.

Attributes:

• None

Methods:

__init__(self, die_list):

Initializer that takes a single parameter — a list of already instantiated similar Die objects.

Parameters:

• die_list: list

play(self, rolls):

A method that takes a parameter to specify how many times the dice should be rolled.

Parameters:

• rolls: int

show_play(self, form = 'wide'):

A method that shows the user the results of the most recent play. Takes a parameter to return the dataframe in narrow or wide form. This parameter defaults to wide form. This parameter raises an exception if the user passes an invalid option. The narrow form of the dataframe will have a two-column index with:

• the roll number
• the die number
• a column for the face rolled

The wide form of the dataframe will be a single-column index with:

• the roll number
• each die number as a column

Parameters:

• form: string

Raises:

• ValueError if form is not narrow or wide.

Returns:

A Pandas df with the most recent result from play, including:

• the die number
• the roll number
• the face rolled for each respective roll

Analyzer Class:

An analyzer class that takes the results of a single game and computes various descriptive statistical properties about it. These properties and results are available as attributes of an Analyzer object.

Attributes:

• None

Methods:

__init__(self, game):

Initializer that takes a game object as its input parameter. At initialization time, it also infers the data type of the die faces.

Parameters:

• game: Game object.

face_count(self):

A method that computes how many times a given face is rolled in each event. Stores the results as a dataframe in a public attribute. The dataframe has an index of the roll number and face values as columns (i.e., in wide form).

Parameters:

• None

Returns:

A Pandas df with the counts of each face value per roll. Includes indexes for the roll number and columns showing die face values.

jackpot(self):

A method that computes how many times the game resulted in all faces being identical and returns an integer for the number of times to the user. Stores the results as a dataframe of jackpot results in a public attribute.

Parameters:

• None

Returns:

A Pandas df with the rows for when a jackpot occurred, including:

• the roll number
• the die number
• the respective face rolled

combo(self):

A method that computes the distinct combinations of faces rolled and their counts. Combinations are sorted and saved as a multicolumned index. Stores the results as a dataframe in a public attribute.

Parameters:

• None

Returns:

A Pandas df with combinations where the face values are multi-indexes and columns show the combination count.

---

Installing:

!pip install .

Importing:

from montecarlo import Die, Game, Analyzer

Creating dice:

• Create the die object called myDie:

myDie = Die(['face1', 'face2', 'face3'])

• Change the weight of 'face1':

myDie.change_weight('face1', 3)

• Show the faces and weights of myDie:

myDie.show_die()

• Roll myDie five times:

myDie.rolls(5)

Playing games:

• Create the game object myGame:

myGame = Game([Die([1,2,3]), Die([1,2,3]), Die([1,2,3])])

• Play the game using myGame, input value in the rolls parameter to specify number of times each die is to be rolled:

myGame.play(3)

• Show the results of play, input either 'wide' or 'narrow' for the form parameter to specify the format of the df of results to be shown (defaults to wide).

myGame.show_play()

Analyzing games:

• Create the analyzer object myAnalyzer using myGame:

myAnalyzer = Analyzer(myGame)

• Return a df with counts for the occurrence of each face value per roll:

myAnalyzer.face_count()

• Find out how many times a jackpot occurred:

myAnalyzer.jackpot()

• Find out the combinations of faces rolled and their counts:

myAnalyzer.combo()

---

Manifest:

Files in repo:

• final_project
  • montecarlo.egg-info
    • PKG-INFO
    • SOURCES.txt
    • init
    • dependency_links.txt
    • requires.txt
    • top_level.txt
  • montecarlo
    • init.py
    • montecarlo.py
    • pycache
      • init.cpython-39.pyc
      • montecarlo.cpython-39.pyc
  • montecarlo_demo.ipynb
  • montecarlo_tests.py
  • setup.py
  • test_results.txt
• LICENSE
• README.md