Mercurial > sypikslang
view gamelib/lab.py @ 45:1e8f7e694f0c
Refactor missions and sciences a bit to reduce duplication.
author | Jeremy Thurgood <firxen@gmail.com> |
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date | Mon, 07 May 2012 20:44:27 +0200 |
parents | efc4f90cfd63 |
children | d3d277a42ac6 |
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# -*- test-case-name: gamelib.tests.test_lab -*- from random import random, choice from gamelib import research, products from gamelib.game_base import get_subclasses class Lab(object): BASIC_RESEARCH_SUCCESS_RATE = 0.05 BASIC_RESEARCH_SUCCESS_MULTIPLIER = 2 def __init__(self, init_data=None): self.science = [] self.new_research = get_subclasses(research.ResearchArea) self.new_products = get_subclasses(products.Product) if init_data is not None: # Load stored state. self._load_data(init_data) else: # New game. self._choose_initial_science() def _load_data(self, init_data): for name, points in init_data['science'].iteritems(): science_type, cls = name.split('.') if science_type == 'product': science = getattr(products, cls) elif science_type == 'research': science = getattr(research, cls) else: raise ValueError("Unknown science type: %s" % (science_type,)) self._gain_science(science(points)) def save_data(self): data = {'science': {}} for science in self.science: name = "%s.%s" % (science.SCIENCE_TYPE, type(science).__name__) data['science'][name] = science.points return data def _choose_initial_science(self): # We always get all starting products. for product in self.new_products[:]: if product.STARTING_PRODUCT: self._gain_science(product()) # We get three random sciences with no prerequisites. new_science = [] for _ in range(3): science = choice(self.find_new_research())() self._gain_science(science) new_science.append(science) # Add a point to each of our sciences, and see if we get products. self.spend_points(new_science, 0) def _gain_science(self, science): self.science.append(science) if isinstance(science, research.ResearchArea): self.new_research.remove(type(science)) elif isinstance(science, products.Product): self.new_products.remove(type(science)) def spend_points(self, things, basic_research): breakthroughs = [] # First, allocate the points. for thing in things: assert thing in self.science assert thing.can_spend(self) thing.spend_point() # Next, check for product breakthroughs and upgrades for thing in things: if isinstance(thing, research.ResearchArea): breakthroughs.extend(self.apply_area_research(thing)) # Finally, check for research breakthroughs. breakthroughs.extend(self.apply_basic_research(basic_research)) return breakthroughs def _get_science(self, science_class): for science in self.science: if isinstance(science, science_class): return science return None def meet_requirements(self, science_class, extra=0): total_points = 0 base_points = 0 for science, level in science_class.PREREQUISITES: my_science = self._get_science(science) if my_science is None: return False if my_science.points < level: return False base_points += level total_points += my_science.points return total_points - base_points >= extra def find_new_products(self): available_products = [] for product_class in self.new_products: if self.meet_requirements(product_class): available_products.append(product_class) return available_products def find_new_research(self): available_research = [] for research_class in self.new_research: if self.meet_requirements(research_class): available_research.append(research_class) return available_research def apply_area_research(self, research): options = [product for product in self.find_new_products() if type(research) in [p[0] for p in product.PREREQUISITES]] breakthroughs = [product for product in options if random() < product.ACQUISITION_CHANCE] if breakthroughs: breakthrough = choice(breakthroughs)() self._gain_science(breakthrough) breakthroughs = [breakthrough] return breakthroughs def apply_basic_research(self, basic_research): if basic_research <= 1: return [] options = self.find_new_research() success_chance = self.BASIC_RESEARCH_SUCCESS_RATE * ( self.BASIC_RESEARCH_SUCCESS_MULTIPLIER ** basic_research) breakthroughs = [research for research in options if random() < success_chance] if breakthroughs: breakthrough = choice(breakthroughs)(1) self._gain_science(breakthrough) breakthroughs = [breakthrough] return breakthroughs