The Good, The Bad and The Stirred Up

Here, I am generating multiple events for a character. Each output has a two sentence storyline.

Jimmy will not be stirred up easily. Jimmy has not insulted others.

Olmec is stirred up easily. Olmec insulted others.

Nemo will not be stirred up easily. Nemo is not insulting others.

#the code
import random 
from itertools import chain

agent_name= "Nemo"

class insulting():
    def __init__(self):
        self.score = random.randint(1,5)
        self.text = ["insulted others","is insulting others","will insult others"]
        self.text_not = ["has not insulted others","is not insulting others","will not insult others"]
        self.factors = ["agreeableness","cooperation","pleasantness","rudeness"]
        
class stirred_up():
    def __init__(self):
        self.score = random.randint(1,5)
        self.text = ["was stirred up easily","is stirred up easily","will be stirred up easily"]
        self.text_not = ["was not stirred up easily","is not stirred up easily","will not be stirred up easily"]
        self.factors = ["anger","neuroticism","tranquility"]
        
def get_factors(kwargs):
    for factor in kwargs:
        print(factor)      
      
stress= random.randint(-10,10)/10
stress_modifier= stress*0.2

def action_probability(score):
    match(score):
        case(1):
            return 0.1
        case(2):
            return 0.2
        case(3):
            return 0.5
        case(4):
            return 0.6
        case(5):
            return 0.8

def action_probability_mod(score):
    match(score):
        case(1):
            return 0.1+(stress*0.1)
        case(2):
            return 0.2+stress_modifier
        case(3):
            return 0.5+stress_modifier
        case(4):
            return 0.6+stress_modifier
        case(5):
            return 0.8+stress_modifier
            
def choose(probability):
   instances=probability*100
   instances_not=100-instances
   yes_occur=int(instances)
   no_occur= int(instances_not)
   my_list=[("yes,"*yes_occur). split (","), ("no," *no_occur).split(",")]
   flat_list = list(chain(*my_list))
   c= random.choice(flat_list)
   return c
  
def text_gen(c, kwarg):
    r= random.randint(0,2)
    match(c):
        case('yes'):
            print(agent_name+" "+kwarg.text[r])
            print ("The probability of this was: "+ str(action_probability_mod(kwarg.score)))
            print ("The base probability of this was: "+ str(action_probability(kwarg.score)))
        case('no'):
            print(agent_name+" "+kwarg.text_not[r])
            print ("The probability of this was: "+ str(1-action_probability_mod(kwarg.score)))
            print ("The base probability of this was: "+ str(1-action_probability(kwarg.score)))
                                       
def get_storyline(kwarg):
    prob=action_probability_mod(kwarg.score)
    choice= choose(prob)
    text_gen(choice, kwarg)
    print("stress modifier: "+ str(stress))
    
print("###")
stirred_up=stirred_up()
print("tendency to be stirred up score: "+str(stirred_up.score))
print("\nfactors: ")
get_factors(stirred_up.factors)
print("###\n")      

get_storyline(stirred_up)

print("\n###")
insult=insulting()
print("tendency to insult score: "+str(insult.score))
print("\nfactors: ")
get_factors(insult.factors)
print("###\n")

get_storyline(insult)

Comments

Post a Comment

Popular posts from this blog

Ledgers, Ledger Objects, Event Scripts, Statuses and Scenarios

Here, ALO and ELO stand for Agent Ledger Object and Environmental Ledger Object, respectively. Both are examples of Entity Ledger Objects (ELOs from here on out). Event Scripts modify the features and associated stats of ELOs. Status is the text description of an ELO state or change. The Scenario is formed by the interaction among one or more ELOs, their Event Scripts and Statuses. This post is a continuation of: https://quantasticjourney.blogspot.com/2024/11/narrative-development-environment.html?m=1
Read more

First Post: Personality Based Simulator

Image
The initial python program, written with Pydroid3, outputted a string (either Jimmy was insulting or Jimmy wasn't insulting) and an associated probability of the string's occurrence. Here, Jimmy beat the odds and became insulting. The final version includes an environmental modifier, Jimmy's stress level. In this output, the stress modifier can lower or raise the base probability of the string occurrence. #The Code import random  from itertools import chain action_score1= random.randint(1,5) action1="insulting others" agent_name= "Jimmy" stress= random.randint(-10,10)/10 stress_modifier= stress*0.2 def action_probability(score):     match(score):         case(1):             return 0.1         case(2):             return 0.2         case(3):             return 0.5         case(4):       ...
Read more