estimate.py

# %%
from numpy import random
import matplotlib.pyplot as plt

ALGAE_PROCESSOR_CYCLE_TIME = (5, 0.5)    # Mean, Stddev

CYCLE_TRAVEL_TIME = (4, 0.5)    # Mean, Stddev
CYCLE_COLLECTION_TIME = (4, 0.25)    # Mean, Stddev
CORAL_PLACE_TIME = (6, 0.75)    # Mean, Stddev

def estimate_cycle_coral_eject(level: int):
  return random.normal(*CYCLE_COLLECTION_TIME) + random.normal(*CYCLE_TRAVEL_TIME) + level*random.normal(*CORAL_PLACE_TIME) + random.normal(*CYCLE_TRAVEL_TIME)

def score_sim(strategy_coral, strategy_algae, opp_proc_chance=0.5, **kw):
  points = 0

  state = None
  state_time = 0
  state_timeout = 0

  level_states = [0, 0, 0]

  t = 0
  while t < 2*60+30:
    if state == None:
      state = "cycle"
      state_time = t

      target_level = 0

      if strategy_coral == "high-first":
        if level_states[2] < 12:
          target_level = 1
        elif level_states[1] < 12:
          target_level = 2
      elif strategy_coral == "low-first":
        if level_states[1] < 12:
          target_level = 1
        elif level_states[2] < 12:
          target_level = 2

      state_timeout = estimate_cycle_coral_eject(target_level)
      points += 2 + target_level

      if strategy_algae != "dump-algae":
        points += 6 - (4 if random.rand() < opp_proc_chance else 0)
        state_timeout += random.normal(*ALGAE_PROCESSOR_CYCLE_TIME)
    
    if state_time + state_timeout <= t:
      state = None

    t += 0.1
  
  return points

plt.figure(figsize=(8, 30), dpi=150)

INDEX = 0
def estimate(f, *a, **kw):
  global INDEX
  POINT_POTENTIALS = []

  for i in range(0, 1000):
    POINT_POTENTIALS.append(f(*a, **kw))

  ax = plt.subplot(5, 2, INDEX + 1)
  ax.set_title(", ".join([str(arg) for arg in a]) + "\n" + ", ".join([f"{str(k)}: {str(v)}" for k, v in kw.items()]))
  ax.hist(POINT_POTENTIALS)
  ax.axis([0, 140, 0, 500])
  # plt.show()

  INDEX += 1

estimate(score_sim, "high-first", "dump-algae")
estimate(score_sim, "low-first", "dump-algae")
estimate(score_sim, "high-first", "processor-algae", opp_proc_chance=0.25)
estimate(score_sim, "low-first", "processor-algae", opp_proc_chance=0.25)
estimate(score_sim, "high-first", "processor-algae", opp_proc_chance=0.5)
estimate(score_sim, "low-first", "processor-algae", opp_proc_chance=0.5)
estimate(score_sim, "high-first", "processor-algae", opp_proc_chance=0.75)
estimate(score_sim, "low-first", "processor-algae", opp_proc_chance=0.75)
estimate(score_sim, "high-first", "processor-algae", opp_proc_chance=1)
estimate(score_sim, "low-first", "processor-algae", opp_proc_chance=1)

plt.show()