mcgs 0.0.3

A Simple Monte Carlo Simulator for different board games such as Coin flips , Dice rolls , Roman Alphabet , Card shuffle and many more.

Monte Carlo Simulator

Monte Carlo Simulator for different board games such as Coin flips , Dice rolls , Roman Alphabet ,Card shuffle and many more.

Metadata

Author: Brook Tarekegn Assefa

Net UD: rnc3mm

Project name: Monte Carlo Simulator

Synopsis

Installation

pip install mcgs

Importing

from montecarlo import Die, Game, Analyzer 

Creating fair dice objects all of six sides with the faces 1 through 6

from montecarlo import Die

fair_die_faces = [1, 2, 3, 4, 5, 6]
fair_die = Die(fair_die_faces)

Create fair coin object with faces H and T

from montecarlo import Die

coin_faces = ['H', 'T']
fair_coin = Die(coin_faces)

Playing Games with two six sided dice

from montecarlo import Die, Game

fair_die_faces = [1, 2, 3, 4, 5, 6]
fair_die = Die(fair_die_faces)

# Let us Play a game of 10 rolls with 2 fair dice 
dice = []
dice.extend([fair_die, fair_die])  # Playing a Two Fair dice 
dice_game = Game(dice)
dice_game.show(dice_game.play(10), 1)  # Displaying results in Wide Dataframe format (see API List Table)

Playing Games with two coins

from montecarlo import Die, Game

coin_faces = ['H', 'T']
fair_coin = Die(coin_faces)

# Let us Play a game of 10 rolls with 2 fair coins 
coins = []
coins.extend([fair_coin, fair_coin])
coin_game = Game(coins)
coin_game.show(coin_game.play(10), 1) 

Analyzing games with two six sided dice

from montecarlo import Die, Game, Analyzer

fair_die_faces = [1, 2, 3, 4, 5, 6]
fair_die = Die(fair_die_faces)

# Let us Play a game of 10 rolls with 2 fair dice 
# This is similar for Coins as well
dice = []
dice.extend([fair_die, fair_die])
dice_game = Game(dice)
dice_game.show(dice_game.play(10), 1)

# Let us now analyze the game
dice_analyzer = Analyzer(dice_game)

# Get the face count on each roll event 
dice_analyzer.face_count()

# Get how many times the game resulted in all faces being identical (jackpot)
dice_analyzer.jackpot()

# Compute the distinct/unique combinations of faces rolled along with their counts
dice_analyzer.combo()

# Compute how may sequence types were rolled and their counts
dice_analyzer.permutation()

API description

Class Table

Class Name	Method Name	Attributes
Die	Methods init change_weight roll_die show_state	Attributes Description faces Sides of the dice weights Value of assigned to a face faces_weights_df Dataframe of faces and weights
Game	Methods init play show	Attributes Description dice Die Object List passed from Die class cols/columns Headers of the play result dataframe play_df Play result dataframe shape N rolls by M dice number_of_rolls The number of times games/rolls played play_result_df_list List form of the play result
Analyzer	Methods init face_count show jackpot combo permutation	Attributes Description game Game Object passed from Game Class game_df_data_type Holds the primitive type of the Dataframe game_result / game_result_df Game result Dataframe shape N rolls by M dice played face_count_df Face Count result Dataframe shape N rolls by M die faces face_list List form of the face count result jackpot_results_df Jackpot result Dataframe shape N of Jackpots rolls by M die faces jackpot_list List form of the jackpot result jack_pot_indices Index values of where the Jackpot occurred in result Dataframe combination_df Combination result as multi-columned Dataframe shape X of N rolls by Y die faces with Z number of occurrence. combination_list Tuple zipped List form of the Combination result permutation_df Permutation result as multi-columned Dataframe shape X of N rolls by Y die faces with Z number of occurrence. permutation_list Tuple zipped List form of the Permutation result

Method Table

Method Name	Description Docs	Parameters	Return values	Snapshots
change_weight	Change the weight of a single side/face	Data types Defaults Face Value integer,string,float integer(1) string("") float (1.0) New Weight integer,string,float 1.0	Assigns a dataframe (integer,string or float ) with faces and the newly assigned weights.
roll_die	Roll the die one or more times	Data types Defaults Number of rolls integer 1	Return a list of outcomes similar to the face types (integer,string or float ).
show_state	Show the die’s current set of faces and weights	Takes no argument	Returns the dataframe according to the face types (integer,string or float ).
play	Rolls the Dice using the inherited roll_die method from the Die class	Data types Defaults Number of Rolls integer 1	Returns the results of the play as a dataframe of shape N rolls by M dice.
show	Show the results of the most recent play	Data types Defaults Play Result DataFrame pd.DataFrame() DataFrame table Form integer Wide (1) or Narrow(2) 1	Display the result of the latest play result as a dataframe of shape N rolls by M dice.
face_count	Method to compute how many times a given face is rolled in each event	Takes no arguments	Returns the count for each face displayed according to the initial dice face sides given as a dataframe of shape N rolls by M dice. See how the above row show dataframe is displayed by face_count dataframe.
jackpot	Method to compute how many times the game resulted in all faces being identical	Takes no arguments	Returns the count of how many times the game resulted in all faces being identical. You can also access the Jackpot Dataframe using Analyzer Object.jackpot_results_df.
combo	Method to compute the distinct/unique combinations of faces rolled, along with their counts	Takes no arguments	Returns the count of how many times the Game could result in distinct/unique combinations of faces when dice is played/rolled. You can also access the full combination multi-columned Dataframe using Analyzer Object.combination_df.
permutation	Method to compute how may sequence types were rolled and their counts	Takes no arguments	Returns the count of all the possible combinations of faces present when dice is played/rolled. You can also access the permutation multi-columned Dataframe using Analyzer Object.permutation_df.

Montecarlo Simulator Demonstration