mirror of
https://github.com/veekun/pokedex.git
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1358 lines
53 KiB
Python
Executable file
1358 lines
53 KiB
Python
Executable file
#! /usr/bin/env python
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# Encoding: UTF-8
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import sys
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import argparse
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import itertools
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import heapq
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from collections import defaultdict, namedtuple
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from sqlalchemy.orm import aliased
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from sqlalchemy.orm.exc import NoResultFound
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from sqlalchemy.sql.expression import not_, and_, or_
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from pokedex.db import connect, tables, util
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###
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### Illegal Moveset exceptions
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###
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class IllegalMoveCombination(ValueError): pass
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class TooManyMoves(IllegalMoveCombination): pass
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class NoMoves(IllegalMoveCombination): pass
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class MovesNotLearnable(IllegalMoveCombination): pass
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class NoParent(IllegalMoveCombination): pass
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class TargetExcluded(IllegalMoveCombination): pass
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class DuplicateMoves(IllegalMoveCombination): pass
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###
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### Generic helpers
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###
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def powerset(iterable):
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# recipe from: http://docs.python.org/library/itertools.html
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"powerset([1,2,3]) --> () (1,) (2,) (3,) (1,2) (1,3) (2,3) (1,2,3)"
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s = list(iterable)
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return itertools.chain.from_iterable(itertools.combinations(s, r)
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for r in range(len(s)+1))
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###
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### Search information object
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###
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class MovesetSearch(object):
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def __init__(self, session, pokemon, version, moves, level=100, costs=None,
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exclude_versions=(), exclude_pokemon=(), debug_level=False):
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self.generator = None
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if not moves:
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raise NoMoves('No moves specified.')
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elif len(moves) > 4:
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raise NoMoves('Too many moves specified.')
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self.debug_level = debug_level
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self.session = session
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self.sketch = util.get(session, tables.Move, identifier=u'sketch').id
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self.unsketchable = set([
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util.get(session, tables.Move, identifier=u'struggle').id,
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util.get(session, tables.Move, identifier=u'chatter').id,
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])
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self.no_eggs_group = util.get(session, tables.EggGroup,
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identifier=u'no-eggs').id
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self.ditto_group = util.get(session, tables.EggGroup,
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identifier=u'ditto').id
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if costs is None:
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self.costs = default_costs
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else:
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self.costs = costs
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self.load_pokemon()
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self.load_moves()
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self.excluded_families = frozenset(p.evolution_chain_id
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for p in exclude_pokemon)
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if debug_level > 1:
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print 'Specified moves:', [move.id for move in moves]
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self.goal_pokemon = pokemon.id
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self.goal_moves = frozenset(move.id for move in moves)
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self.goal_version_group = version.version_group_id
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self.goal_level = level
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if len(self.goal_moves) < len(moves):
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raise DuplicateMoves('Cannot learn duplicate moves')
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if pokemon:
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self.goal_evolution_chain = pokemon.evolution_chain_id
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if self.goal_evolution_chain in self.excluded_families:
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raise TargetExcluded('The target pokemon was excluded.')
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else:
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self.goal_evolution_chain = None
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# Fill self.generation_id_by_version_group
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self.load_version_groups(version.version_group_id,
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[v.version_group_id for v in exclude_versions])
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self.pokemon_moves = defaultdict( # key: pokemon
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lambda: defaultdict( # key: version_group
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lambda: defaultdict( # key: move
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lambda: defaultdict( # key: method
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list)))) # ordered list of (level, cost)
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self.movepools = defaultdict(dict) # evo chain -> move -> best cost
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self.learnpools = defaultdict(set) # evo chain -> move, w/o egg moves
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easy_moves, non_egg_moves = self.load_pokemon_moves(
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self.goal_evolution_chain, 'family')
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self.hard_moves = self.goal_moves - easy_moves
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self.egg_moves = self.goal_moves - non_egg_moves
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if self.hard_moves:
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# Have to breed!
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self.load_pokemon_moves(self.goal_evolution_chain, 'others')
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self.construct_breed_graph()
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self.find_duplicate_versions()
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self.output_objects = dict()
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kwargs = dict()
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if debug_level:
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self._astar_debug_notify_counter = 0
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kwargs['notify'] = self.astar_debug_notify
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kwargs['estimate_error_callback'] = self.astar_estimate_error
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self.generator = InitialNode(self).find_all_paths(**kwargs)
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def load_version_groups(self, version, excluded):
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"""Load generation_id_by_version_group
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"""
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query = self.session.query(tables.VersionGroup.id,
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tables.VersionGroup.generation_id)
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query = query.join(tables.Version.version_group)
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if excluded:
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query = query.filter(not_(tables.VersionGroup.id.in_(excluded)))
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self.generation_id_by_version_group = dict(query)
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def expand(v2):
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for v1 in self.generation_id_by_version_group:
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if self.trade_cost(v1, v2):
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yield 0, None, v1
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def is_goal(v):
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return True
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goal = self.goal_version_group
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filtered_map = {goal: self.generation_id_by_version_group[goal]}
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for result in a_star(self.goal_version_group, expand, is_goal):
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for cost, transition, version in result:
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filtered_map[version] = (
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self.generation_id_by_version_group[version])
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self.generation_id_by_version_group = filtered_map
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if self.debug_level > 1:
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print 'Excluded version groups:', excluded
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print 'Trade cost table:'
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print '%03s' % '',
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for g1 in sorted(self.generation_id_by_version_group):
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print '%03s' % g1,
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print
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for g1 in sorted(self.generation_id_by_version_group):
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print '%03s' % g1,
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for g2 in sorted(self.generation_id_by_version_group):
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print '%03s' % (self.trade_cost(g1, g2) or '---'),
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print
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def load_pokemon_moves(self, evolution_chain, selection):
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"""Load pokemon_moves, movepools, learnpools, smeargle_families
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`selection`:
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'family' for loading only pokemon in evolution_chain
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'others' for loading only pokemon NOT in evolution_chain
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Returns: (easy_moves, non_egg_moves)
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If `selection` == 'family':
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easy_moves is a set of moves that are easier to obtain than by
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breeding
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non_egg_moves is a set of moves that don't require breeding
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Otherwise, these are empty sets.
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"""
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if self.debug_level > 1:
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print 'Loading pokemon moves, %s %s' % (evolution_chain, selection)
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query = self.session.query(
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tables.PokemonMove.pokemon_id,
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tables.PokemonMove.move_id,
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tables.PokemonMove.version_group_id,
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tables.PokemonMoveMethod.identifier,
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tables.PokemonMove.level,
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tables.Pokemon.evolution_chain_id,
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)
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query = query.join(tables.PokemonMove.pokemon)
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query = query.filter(tables.PokemonMoveMethod.id ==
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tables.PokemonMove.pokemon_move_method_id)
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query = query.filter(tables.PokemonMove.version_group_id.in_(
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set(self.generation_id_by_version_group)))
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query = query.filter(or_(
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tables.PokemonMove.level > 100, # XXX: Chaff?
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tables.PokemonMove.move_id.in_(self.goal_moves),
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tables.PokemonMove.move_id == self.sketch,
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tables.PokemonMove.move_id.in_(
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self.evolution_moves.values()),
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))
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if self.excluded_families:
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query = query.filter(not_(tables.Pokemon.evolution_chain_id.in_(
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self.excluded_families)))
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if evolution_chain:
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if selection == 'family':
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query = query.filter(tables.Pokemon.evolution_chain_id == (
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evolution_chain))
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elif selection == 'others':
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query = query.filter(tables.Pokemon.evolution_chain_id != (
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evolution_chain))
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query = query.order_by(tables.PokemonMove.level)
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easy_moves = set()
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non_egg_moves = set()
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self.smeargle_families = set()
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costs = self.costs
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movepools = self.movepools
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learnpools = self.learnpools
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sketch_cost = costs['sketch']
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breed_cost = costs['breed']
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for pokemon, move, vg, method, level, chain in query:
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if move in self.goal_moves:
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if method == 'level-up':
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cost = costs['level-up']
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else:
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gen = self.generation_id_by_version_group[vg]
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if method == 'machine' and gen < 5:
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cost = costs['machine-once']
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elif method == 'tutor' and gen == 3:
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cost = costs['tutor-once']
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elif method == 'egg':
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cost = costs['breed']
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else:
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cost = costs[method]
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movepools[chain][move] = min(
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movepools[chain].get(move, cost), cost)
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if method != 'egg':
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learnpools[chain].add(move)
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non_egg_moves.add(move)
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if cost < breed_cost:
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easy_moves.add(move)
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elif move == self.sketch:
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cost = sketch_cost
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self.smeargle_families.add(self.evolution_chains[pokemon])
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else:
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# An evolution move. We need to use it anyway if we need
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# the evolution, so the cost can be an arbitrary positive
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# number. But, do check if this family actually needs the move.
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evolution_chain = self.evolution_chains[pokemon]
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if move != self.evolution_moves.get(evolution_chain):
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continue
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cost = 1
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self.pokemon_moves[pokemon][vg][move][method].append((level, cost))
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if self.debug_level > 1 and selection == 'family':
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print 'Easy moves:', sorted(easy_moves)
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print 'Non-egg moves:', sorted(non_egg_moves)
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if self.debug_level > 1:
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print 'Smeargle families:', sorted(self.smeargle_families)
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return easy_moves, non_egg_moves
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def trade_cost(self, version_group_from, version_group_to, max_generation=None):
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"""Return cost of trading between versions, None if impossibble
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`max_generation` should be the maximum generation of the moves traded.
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(also of pokemon, if those aren't checked another way)
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"""
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# XXX: this ignores HM transfer restrictions
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gen_from = self.generation_id_by_version_group[version_group_from]
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gen_to = self.generation_id_by_version_group[version_group_to]
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if gen_from == gen_to:
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return self.costs['trade']
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elif gen_from in (1, 2):
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if max_generation and max_generation > gen_to:
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return None
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elif gen_to in (1, 2):
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return self.costs['trade']
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else:
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return None
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elif gen_to in (1, 2):
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return None
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elif gen_from > gen_to:
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return None
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elif gen_from < gen_to - 1:
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return None
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else:
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return self.costs['trade'] + self.costs['transfer']
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def load_pokemon(self):
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"""Load pokemon breed groups and evolutions
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self.egg_groups: maps evolution chains to their sorted egg groups
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(wil contain empty tuple for no-eggs or ditto)
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self.evolution_chains: maps pokemon to their evolution chains
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self.pokemon_by_evolution_chain: maps evolution chains to their pokemon
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self.unbreedable: set of unbreedable pokemon
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self.evolution_parents[pokemon] = the pre-evolved form
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self.evolutions[pokemon] = list of (trigger, move, level, child)
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self.evolution_moves[evolution_chain] = move required for evolution
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self.babies[egg_group_id] = set of baby pokemon
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self.hatch_counters[pokemon] = hatch counter
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self.gender_rates[evolution_chain] = gender rate
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"""
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eg1 = tables.PokemonEggGroup
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eg2 = aliased(tables.PokemonEggGroup)
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query = self.session.query(
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tables.Pokemon.id,
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tables.Pokemon.evolution_chain_id,
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tables.Pokemon.evolves_from_pokemon_id,
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eg1.egg_group_id,
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eg2.egg_group_id,
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tables.EvolutionChain.baby_trigger_item_id,
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tables.Pokemon.hatch_counter,
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tables.Pokemon.gender_rate,
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)
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query = query.join(tables.Pokemon.evolution_chain)
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query = query.join((eg1, eg1.pokemon_id == tables.Pokemon.id))
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query = query.outerjoin((eg2, and_(
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eg2.pokemon_id == tables.Pokemon.id,
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eg1.egg_group_id < eg2.egg_group_id,
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)))
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bad_groups = (self.no_eggs_group, self.ditto_group)
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unbreedable = dict() # pokemon->evolution chain
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self.evolution_parents = dict()
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self.egg_groups = defaultdict(tuple)
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self.evolution_chains = dict()
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self.pokemon_by_evolution_chain = defaultdict(set)
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self.babies = defaultdict(set)
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self.hatch_counters = dict()
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self.gender_rates = dict()
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item_baby_chains = set() # evolution chains with baby-trigger items
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for pokemon, evolution_chain, parent, g1, g2, baby_item, hatch_counter, gender_rate in query:
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self.hatch_counters[pokemon] = hatch_counter
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self.gender_rates[evolution_chain] = gender_rate
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if g1 in bad_groups:
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unbreedable[pokemon] = evolution_chain
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else:
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groups = (g1, g2) if g2 else (g1, )
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if len(self.egg_groups.get(evolution_chain, ())) <= len(groups):
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self.egg_groups[evolution_chain] = groups
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for group in groups:
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self.babies[group].add(pokemon)
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self.evolution_chains[pokemon] = evolution_chain
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self.pokemon_by_evolution_chain[evolution_chain].add(pokemon)
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if parent:
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self.evolution_parents[pokemon] = parent
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if baby_item:
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item_baby_chains.add(evolution_chain)
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self.unbreedable = frozenset(unbreedable)
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self.evolutions = defaultdict(set)
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self.evolution_moves = dict()
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query = self.session.query(
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tables.PokemonEvolution.evolved_pokemon_id,
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tables.EvolutionTrigger.identifier,
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tables.PokemonEvolution.known_move_id,
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tables.PokemonEvolution.minimum_level,
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)
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query = query.join(tables.PokemonEvolution.trigger)
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for child, trigger, move, level in query:
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self.evolutions[self.evolution_parents[child]].add(
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(trigger, move, level, child))
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if move:
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self.evolution_moves[self.evolution_chains[child]] = move
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if self.debug_level > 1:
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print 'Loaded %s pokemon: %s evo; %s families: %s breedable' % (
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len(self.evolution_chains),
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len(self.pokemon_by_evolution_chain),
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len(self.egg_groups),
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len(self.evolutions),
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)
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print 'Evolution moves: %s' % self.evolution_moves
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# Chains with unbreedable babies
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for baby, evolution_chain in unbreedable.items():
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if baby not in self.evolution_parents:
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groups = self.egg_groups[evolution_chain]
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for group in groups:
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self.babies[group].add(baby)
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# Chains with item-triggered alternate babies
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for item_baby_chain in item_baby_chains:
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for item_baby in self.pokemon_by_evolution_chain[item_baby_chain]:
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if item_baby not in self.evolution_parents:
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for regular_baby in self.evolutions[item_baby]:
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for group in self.egg_groups[item_baby_chain]:
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self.babies[group].add(pokemon)
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def load_moves(self):
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"""Load move_generations"""
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query = self.session.query(
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tables.Move.id,
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tables.Move.generation_id,
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)
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self.move_generations = dict(query)
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if self.debug_level > 1:
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print 'Loaded %s moves' % len(self.move_generations)
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def construct_breed_graph(self):
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"""Fills breeds_required
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breeds_required[egg_group][moveset] = minimum number of breeds needed
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from a pokemon in this group with this moveset to the goal pokemon
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with the goal moveset.
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The score cannot get lower by learning new moves, only by breeding.
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If missing, breeding or raising the pokemon won't do any good.
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Exceptions:
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For pokemon in the target family, breeds_required doesn't apply.
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For the empty moveset just check if any moveset is worthwhile (i.e.
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bool(breeds_required[egg_group])).
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"""
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# Part I. Determining what moves can be passed/learned
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# eg1_movepools[egg_group_id] = set of moves passable by pkmn in that group
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eg1_movepools = defaultdict(set)
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# eg2_movepools[b_g_id1, b_g_id2] = ditto for pkmn in *both* groups
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eg2_movepools = defaultdict(set)
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# non_egg_pools = as eg1_movepools but for *learnable* moves
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learn_pools = defaultdict(set)
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goal_egg_groups = self.egg_groups[self.goal_evolution_chain]
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all_groups = set()
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for family, groups in self.egg_groups.iteritems():
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if not groups:
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continue
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if family == self.goal_evolution_chain:
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continue
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elif family in self.smeargle_families:
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pool = self.goal_moves
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else:
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pool = self.movepools[family]
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pool = set(pool) & self.goal_moves
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learnpool = self.learnpools[family] & pool
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for group in groups:
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eg1_movepools[group].update(pool)
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learn_pools[group].update(learnpool)
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all_groups.add(group)
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if len(groups) >= 2:
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eg2_movepools[groups].update(pool)
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if self.debug_level > 1:
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print 'Egg group summary:'
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for group in sorted(all_groups):
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print "%2s can pass: %s" % (group, sorted(eg1_movepools[group]))
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if learn_pools[group] != eg1_movepools[group]:
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print " but learn: %s" % sorted(learn_pools[group])
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for g2 in sorted(all_groups):
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||
for g1 in sorted(all_groups):
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||
if eg2_movepools[g1, g2]:
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||
print " %2s/%2s pass: %s" % (g1, g2, sorted(eg2_movepools[g1, g2]))
|
||
print 'Goal groups:', goal_egg_groups
|
||
|
||
# Part II. Determining which moves are worthwhile to pass
|
||
|
||
# We want *all* paths, not just shortest ones, so use DFS.
|
||
breeds_required = defaultdict(dict)
|
||
def handle(group, moves, path):
|
||
"""
|
||
group: the group of the parent
|
||
moves: moves the parent should pass down
|
||
path: previously visited groups - to prevent cycles
|
||
"""
|
||
if not moves:
|
||
# No more moves needed to pass down: success!
|
||
return True
|
||
if breeds_required[group].get(moves, 999) <= len(path):
|
||
# Already done
|
||
return True
|
||
success = False
|
||
# Breed some more
|
||
path = path + (group, )
|
||
for new_group in all_groups.difference(path):
|
||
new_groups = tuple(sorted([group, new_group]))
|
||
# Can we pass down all the requested moves?
|
||
if moves.issubset(eg1_movepools[new_group]):
|
||
# Learn some of the moves: they don't have to be passed to us
|
||
for learned in powerset(moves & learn_pools[new_group]):
|
||
new_moves = moves.difference(learned)
|
||
local_success = handle(new_group, new_moves, path)
|
||
# If this chain eventually ended up being successful,
|
||
# it means that it is useful to pass this moveset
|
||
# to this group.
|
||
if local_success:
|
||
breeds_required[group][moves] = min(breeds_required[group].get(moves, 999), len(path) - 1)
|
||
success = True
|
||
return success
|
||
for group in goal_egg_groups:
|
||
handle(group, self.hard_moves, ())
|
||
for moves in powerset(self.goal_moves):
|
||
if moves:
|
||
breeds_required[group][frozenset(moves)] = 1
|
||
self.breeds_required = breeds_required
|
||
|
||
if self.debug_level > 1:
|
||
for group, movesetlist in breeds_required.items():
|
||
print 'From egg group', group
|
||
for moveset, cost in movesetlist.items():
|
||
print " %s breeds with %s" % (cost, sorted(moveset))
|
||
|
||
def find_duplicate_versions(self):
|
||
"""Fill `duplicate_versions`
|
||
|
||
duplicate_versions[pokemon][version_group] = set of version groups that
|
||
are identical as far as the pokemon learning those moves is
|
||
concerned, and are in the same generation.
|
||
Thus, trading between them is unnecessary.
|
||
"""
|
||
self.duplicate_versions = dict()
|
||
|
||
counter = 0
|
||
for pokemon, vg_moves in self.pokemon_moves.items():
|
||
dupes = self.duplicate_versions[pokemon] = dict()
|
||
last = None
|
||
last_moves = None
|
||
last_gen = None
|
||
for version_group, moves in vg_moves.items():
|
||
gen = self.generation_id_by_version_group[version_group]
|
||
if gen == last_gen and moves == last_moves:
|
||
last.add(version_group)
|
||
dupes[version_group] = last
|
||
counter += 1
|
||
else:
|
||
last = set([version_group])
|
||
dupes[version_group] = last
|
||
last_moves = moves
|
||
last_gen = gen
|
||
|
||
if self.debug_level > 1:
|
||
print 'Deduplicated %s version groups' % counter
|
||
|
||
def astar_debug_notify(self, cost, node, setsize, heapsize):
|
||
counter = self._astar_debug_notify_counter
|
||
if counter % 100 == 0:
|
||
print 'A* iteration %s, cost %s; remaining: %s (%s) \r' % (
|
||
counter, cost, setsize, heapsize),
|
||
sys.stdout.flush()
|
||
self._astar_debug_notify_counter += 1
|
||
|
||
def astar_estimate_error(self, result):
|
||
print '**warning: bad A* estimate**'
|
||
print_result(result)
|
||
|
||
def __iter__(self):
|
||
return self.generator
|
||
|
||
def get_by_id(self, table, id):
|
||
key = table, 'id', id
|
||
try:
|
||
return self.output_objects[key]
|
||
except KeyError:
|
||
o = self.output_objects[key] = util.get(self.session, table, id=id)
|
||
return o
|
||
|
||
def get_by_identifier(self, table, ident):
|
||
key = table, 'identifier', ident
|
||
try:
|
||
return self.output_objects[key]
|
||
except KeyError:
|
||
o = self.output_objects[key] = util.get(self.session,
|
||
table, identifier=ident)
|
||
return o
|
||
|
||
def get_list(self, table, ids):
|
||
key = table, 'list', ids
|
||
try:
|
||
return self.output_objects[key]
|
||
except KeyError:
|
||
o = self.output_objects[key] = sorted(
|
||
(util.get(self.session, table, id=id) for id in ids),
|
||
key=lambda x: x.identifier)
|
||
return o
|
||
|
||
###
|
||
### Costs
|
||
###
|
||
|
||
default_costs = {
|
||
# Costs for learning a move in various ways
|
||
'level-up': 20, # The normal way
|
||
'machine': 40, # Machines are slightly inconvenient.
|
||
'machine-once': 2000, # before gen. 5, TMs only work once. Avoid.
|
||
'tutor': 60, # Tutors are slightly more inconvenient than TMs – can't carry them around
|
||
'tutor-once': 2100, # gen III: tutors only work once (well except Emerald frontier ones)
|
||
|
||
# For technical reasons, 'sketch' is also used for learning Sketch and
|
||
# by normal means, if it isn't included in the target moveset.
|
||
# So the actual cost of a sketched move will be double this number.
|
||
'sketch': 1, # Cheap. Exclude Smeargle if you think it's too cheap.
|
||
|
||
# Gimmick moves – we need to use this method to learn the move anyway,
|
||
# so make a big-ish dent in the score if missing
|
||
'stadium-surfing-pikachu': 100,
|
||
'light-ball-egg': 100, # …
|
||
|
||
# Ugh... I don't know?
|
||
'colosseum-purification': 100,
|
||
'xd-shadow': 100,
|
||
'xd-purification': 100,
|
||
'form-change': 100,
|
||
|
||
# Other actions.
|
||
# Breeding should cost more than 3 times a lv-up/machine/tutor move.
|
||
'evolution': 100, # We have to do this anyway, usually.
|
||
'evolution-delayed': 50, # *in addition* to evolution. Who wants to mash B on every level.
|
||
'breed': 400, # Breeding's a pain.
|
||
'trade': 200, # Trading's a pain, but not as much as breeding.
|
||
'transfer': 150, # *in addition* to trade. Keep it below 'trade'.
|
||
'forget': 300, # Deleting a move. (Not needed unless deleting an evolution move.)
|
||
'relearn': 150, # Also a pain, though not as big as breeding.
|
||
'per-level': 1, # Prefer less grinding. This is for all lv-ups but the final “grow”
|
||
|
||
# Breeding for moves the target pokemon leans easily is kind of stupid.
|
||
# (Though not *very* stupid, and since the program considers evolution
|
||
# chains as a group, the penalty should be much smaller than normal move cost.)
|
||
'egg': 3, # General cost of breeding a move
|
||
'per-hatch-counter': 1, # penalty for 1 initial hatch counter point (these range from 5 to 120)
|
||
|
||
# Penalty for *not* breeding a required egg move; this makes parents
|
||
# with more required moves gain a big advantage over the competition
|
||
'breed-penalty': 100,
|
||
}
|
||
|
||
###
|
||
### A*
|
||
###
|
||
|
||
class Node(object):
|
||
"""Node for the A* search algorithm.
|
||
|
||
To get started, implement the `expand` method and call `search`.
|
||
|
||
N.B. Node objects must be hashable.
|
||
"""
|
||
|
||
def expand(self):
|
||
"""Return a list of (costs, transition, next_node) for next states
|
||
|
||
"Next states" are those reachable from this node.
|
||
|
||
May return any finite iterable.
|
||
"""
|
||
raise NotImplementedError
|
||
|
||
def estimate(self, goal):
|
||
"""Return an *optimistic* estimate of the cost to the given goal node.
|
||
|
||
If there are multiple goal states, return the lowest estimate among all
|
||
of them.
|
||
"""
|
||
return 0
|
||
|
||
def is_goal(self, goal):
|
||
"""Return true iff this is a goal node.
|
||
"""
|
||
return self == goal
|
||
|
||
def find_path(self, goal=None, **kwargs):
|
||
"""Return the best path to the goal
|
||
|
||
Returns an iterator of (cost, transition, node) triples, in reverse
|
||
order (i.e. the first element will have the total cost and goal node).
|
||
|
||
If `goal` will be passed to the `estimate` and `is_goal` methods.
|
||
|
||
See a_star for the advanced keyword arguments, `notify` and
|
||
`estimate_error_callback`.
|
||
"""
|
||
paths = self.find_all_paths(goal=goal, **kwargs)
|
||
try:
|
||
return paths.next()
|
||
except StopIteration:
|
||
return None
|
||
|
||
def find_all_paths(self, goal=None, **kwargs):
|
||
"""Yield the best path to each goal
|
||
|
||
Returns an iterator of paths. See the `search` method for how paths
|
||
look.
|
||
|
||
Giving the `goal` argument will cause it to search for that goal,
|
||
instead of consulting the `is_goal` method.
|
||
This means that if you wish to find more than one path, you must not
|
||
pass a `goal` to this method, and instead reimplament `is_goal`.
|
||
|
||
See a_star for the advanced keyword arguments, `notify` and
|
||
`estimate_error_callback`.
|
||
"""
|
||
return a_star(
|
||
initial=self,
|
||
expand=lambda s: s.expand(),
|
||
estimate=lambda s: s.estimate(goal),
|
||
is_goal=lambda s: s.is_goal(goal),
|
||
**kwargs)
|
||
|
||
def a_star(initial, expand, is_goal, estimate=lambda x: 0, notify=None,
|
||
estimate_error_callback=None):
|
||
"""A* search algorithm for a consistent heuristic
|
||
|
||
General background: http://en.wikipedia.org/wiki/A*_search_algorithm
|
||
|
||
This algorithm will work in large or infinite search spaces.
|
||
|
||
This version of the algorithm is modified for multiple possible goals:
|
||
it does not end when it reaches a goal. Rather, it yields the best path
|
||
for each goal.
|
||
(Exhausting the iterator is of course not recommended for large search
|
||
spaces.)
|
||
|
||
Returns an iterable of paths, where each path is an iterable of
|
||
(cummulative cost, transition, node) triples representing the path to
|
||
the goal. The transition is the one leading to the corresponding node.
|
||
The path is in reverse order, thus its first element will contain the
|
||
total cost and the goal node.
|
||
The initial node is not included in the returned path.
|
||
|
||
Arguments:
|
||
|
||
`initial`: the initial node
|
||
|
||
`expand`: function yielding a (cost of transition, transition, next node)
|
||
triple for each node reachable from its argument.
|
||
The `transition` element is application data; it is not touched, only
|
||
returned as part of the best path.
|
||
`estimate`: function(x) returning optimistic estimate of cost from node x
|
||
to a goal. If not given, 0 will be used for estimates.
|
||
`is_goal`: function(x) returning true iff x is a goal node
|
||
|
||
`notify`: If given, if is called at each step with three arguments:
|
||
- current cost (with estimate). The cost to the next goal will not be
|
||
smaller than this.
|
||
- current node
|
||
- open set cardinality: roughly, an estimate of the size of the
|
||
boundary between "explored" and "unexplored" parts of node space
|
||
- debug: stats that be useful for debugging or tuning (in this
|
||
implementation, this is the open heap size)
|
||
The number of calls to notify or the current cost can be useful as
|
||
stopping criteria; the other values may help in tuning estimators.
|
||
|
||
`estimate_error_callback`: function handling cases where an estimate was
|
||
detected not to be optimistic (as A* requires). The function is given a
|
||
path (as would be returned by a_star, except it does not lead to a goal
|
||
node). By default, nothing is done (indeed, an estimate that's not
|
||
strictly optimistic can be useful, esp. if the optimal path is not
|
||
required)
|
||
"""
|
||
# g: best cummulative cost (from initial node) found so far
|
||
# h: optimistic estimate of cost to goal
|
||
# f: g + h
|
||
closed = set() # nodes we don't want to visit again
|
||
est = estimate(initial) # estimate total cost
|
||
opened = _HeapDict() # node -> (f, g, h)
|
||
opened[initial] = (est, 0, est)
|
||
came_from = {initial: None} # node -> (prev_node, came_from[prev_node])
|
||
while True: # _HeapDict will raise StopIteration for us
|
||
x, (f, g, h) = opened.pop()
|
||
closed.add(x)
|
||
|
||
if notify is not None:
|
||
notify(f, x, len(opened.dict), len(opened.heap))
|
||
|
||
if is_goal(x):
|
||
yield _trace_path(came_from[x])
|
||
|
||
for cost, transition, y in expand(x):
|
||
if y in closed:
|
||
continue
|
||
tentative_g = g + cost
|
||
|
||
old_f, old_g, h = opened.get(y, (None, None, None))
|
||
|
||
if old_f is None:
|
||
h = estimate(y)
|
||
elif tentative_g > old_g:
|
||
continue
|
||
|
||
came_from[y] = ((tentative_g, transition, y), came_from[x])
|
||
new_f = tentative_g + h
|
||
|
||
opened[y] = new_f, tentative_g, h
|
||
|
||
if estimate_error_callback is not None and new_f < f:
|
||
estimate_error_callback(_trace_path(came_from[y]))
|
||
|
||
def _trace_path(cdr):
|
||
"""Backtrace an A* result"""
|
||
# Convert a lispy list to a pythony iterator
|
||
while cdr:
|
||
car, cdr = cdr
|
||
yield car
|
||
|
||
class _HeapDict(object):
|
||
"""A custom parallel heap/dict structure -- the best of both worlds.
|
||
|
||
This is NOT a general-purpose class; it only supports what a_star needs.
|
||
"""
|
||
# The dict has the definitive contents
|
||
# The heap has (value, key) pairs. It may have some extra elements.
|
||
def __init__(self):
|
||
self.dict = {}
|
||
self.heap = []
|
||
|
||
def __setitem__(self, key, value):
|
||
self.dict[key] = value
|
||
heapq.heappush(self.heap, (value, key))
|
||
|
||
def __delitem__(self, key):
|
||
del self.dict[key]
|
||
|
||
def get(self, key, default):
|
||
"""Return value for key, or default if not found
|
||
"""
|
||
return self.dict.get(key, default)
|
||
|
||
def pop(self):
|
||
"""Return (key, value) with the smallest value.
|
||
|
||
Raise StopIteration (!!) if empty
|
||
"""
|
||
while True:
|
||
try:
|
||
value, key = heapq.heappop(self.heap)
|
||
if value is self.dict[key]:
|
||
del self.dict[key]
|
||
return key, value
|
||
except KeyError:
|
||
# deleted from dict = not here
|
||
pass
|
||
except IndexError:
|
||
# nothing more to pop
|
||
raise StopIteration
|
||
|
||
###
|
||
### Result objects
|
||
###
|
||
|
||
class Facade(object):
|
||
"""Facade for optput objects
|
||
|
||
The main algorithm uses integers (and tiny strings, and sets, dicts,
|
||
tuples you get the picture...).
|
||
The rest of the world uses ORM objects.
|
||
So, all objects that are returned in results have "object ID" attributes
|
||
ending in an underscore (e.g. pokemon_), and this base class adds
|
||
underscore-less properties that get the underlying object.
|
||
"""
|
||
@property
|
||
def pokemon(self):
|
||
return self.search.session.query(tables.Pokemon).filter_by(id=self.pokemon_).one()
|
||
|
||
@property
|
||
def version_group(self):
|
||
return self.search.get_by_id(tables.VersionGroup, self.version_group_)
|
||
|
||
@property
|
||
def versions(self):
|
||
return self.version_group.versions
|
||
|
||
@property
|
||
def move(self):
|
||
return self.search.get_by_id(tables.Move, self.move_)
|
||
|
||
@property
|
||
def moves(self):
|
||
return self.search.get_list(tables.Move, self.moves_)
|
||
|
||
@property
|
||
def move_method(self):
|
||
return self.search.get_by_identifier(tables.PokemonMoveMethod,
|
||
self.move_method_)
|
||
|
||
@property
|
||
def evolution_trigger(self):
|
||
return self.search.get_by_identifier(tables.EvolutionTrigger,
|
||
self.evolution_trigger_)
|
||
|
||
###
|
||
### Search space transitions
|
||
###
|
||
|
||
class Action(Facade):
|
||
pass
|
||
|
||
class StartAction(Action, namedtuple('StartAcion', 'search pokemon_ version_group_')):
|
||
keyword = 'start'
|
||
|
||
def __unicode__(self):
|
||
vers = ' or '.join(v.name for v in self.versions)
|
||
return u"Start with {0.pokemon.name} in {1}".format(self, vers)
|
||
|
||
class LearnAction(Action, namedtuple('LearnAction', 'search move_ move_method_')):
|
||
keyword = 'start'
|
||
|
||
def __unicode__(self):
|
||
return u"Learn {0.move.name} by {0.move_method.name}".format(self)
|
||
|
||
class RelearnAction(Action, namedtuple('RelearnAction', 'search move_')):
|
||
keyword = 'start'
|
||
|
||
def __unicode__(self):
|
||
return u"Relearn {0.move.name}".format(self)
|
||
|
||
class ForgetAction(Action, namedtuple('ForgetAction', 'search move_')):
|
||
keyword = 'forget'
|
||
|
||
def __unicode__(self):
|
||
return u"Forget {0.move.name}".format(self)
|
||
|
||
class TradeAction(Action, namedtuple('TradeAction', 'search version_group_')):
|
||
keyword = 'trade'
|
||
|
||
def __unicode__(self):
|
||
vers = ' or '.join(v.name for v in self.versions)
|
||
return u"Trade to {1}".format(self, vers)
|
||
|
||
class EvolutionAction(Action, namedtuple('EvolutionAction', 'search pokemon_ evolution_trigger_')):
|
||
keyword = 'evolution'
|
||
|
||
def __unicode__(self):
|
||
return u"Evolve to {0.pokemon.name} by {0.evolution_trigger.name}".format(self)
|
||
|
||
class GrowAction(Action, namedtuple('GrowAction', 'search level')):
|
||
keyword = 'grow'
|
||
|
||
def __unicode__(self):
|
||
return u"Grow to level {0.level}".format(self)
|
||
|
||
class SketchAction(Action, namedtuple('SketchAction', 'search move_')):
|
||
keyword = 'grow'
|
||
|
||
def __unicode__(self):
|
||
return u"Sketch {0.move.name}".format(self)
|
||
|
||
class BreedAction(Action, namedtuple('BreedAction', 'search pokemon_ moves_')):
|
||
keyword = 'grow'
|
||
|
||
def __unicode__(self):
|
||
mvs = ', '.join(m.name for m in self.moves)
|
||
return u"Breed {0.pokemon.name} with {1}".format(self, mvs)
|
||
|
||
###
|
||
### Search space nodes
|
||
###
|
||
|
||
class InitialNode(Node, namedtuple('InitialNode', 'search')):
|
||
def expand(self):
|
||
search = self.search
|
||
for pokemon, version_groups in search.pokemon_moves.items():
|
||
egg_groups = search.egg_groups[search.evolution_chains[pokemon]]
|
||
if any(search.breeds_required[group] for group in egg_groups) or (
|
||
search.evolution_chains[pokemon] == search.goal_evolution_chain):
|
||
for version_group in version_groups:
|
||
action = StartAction(search, pokemon, version_group)
|
||
node = PokemonNode(
|
||
search=search,
|
||
pokemon_=pokemon,
|
||
level=0,
|
||
version_group_=version_group,
|
||
moves_=frozenset(),
|
||
new_level=True,
|
||
)
|
||
yield 0, action, node
|
||
|
||
class PokemonNode(Node, Facade, namedtuple('PokemonNode',
|
||
'search pokemon_ level version_group_ new_level moves_')):
|
||
|
||
def __str__(self):
|
||
return "lv.{level:3}{s} {self.pokemon.identifier:<10.10} in {version_group_:3} with {moves}".format(
|
||
s='*' if self.new_level else ' ',
|
||
moves=','.join(sorted(move.identifier for move in self.moves)) or '---',
|
||
self=self,
|
||
**self._asdict())
|
||
|
||
def expand(self):
|
||
search = self.search
|
||
evo_chain = search.evolution_chains[self.pokemon_]
|
||
if not self.moves_:
|
||
# Learn something first
|
||
# (other expand_* may rely on there being a move)
|
||
return self.expand_learn()
|
||
elif self.moves_.difference(self.search.goal_moves):
|
||
# Learned too much!
|
||
# Moves that aren't in the goal set are either Sketch or evolution
|
||
# moves.
|
||
# For the former, use the sketch; for the latter, evolve and
|
||
# forget the move.
|
||
return itertools.chain(
|
||
self.expand_sketch(),
|
||
self.expand_forget(),
|
||
self.expand_evolutions(),
|
||
)
|
||
elif evo_chain != search.goal_evolution_chain:
|
||
if not any(self.moves_ in search.breeds_required[group]
|
||
for group in search.egg_groups[evo_chain]):
|
||
# It doesn't make sense to train this any more, since there's
|
||
# no way to pass the moves to the goal pokemon.
|
||
return ()
|
||
if len(self.moves_) == 4:
|
||
learns = ()
|
||
else:
|
||
learns = self.expand_learn()
|
||
return itertools.chain(
|
||
learns,
|
||
self.expand_trade(),
|
||
self.expand_grow(),
|
||
self.expand_evolutions(),
|
||
self.expand_breed(),
|
||
)
|
||
|
||
def expand_learn(self):
|
||
search = self.search
|
||
moves = search.pokemon_moves[self.pokemon_][self.version_group_]
|
||
for move, methods in moves.items():
|
||
if move in self.moves_:
|
||
continue
|
||
for method, levels_costs in methods.items():
|
||
if method == 'level-up':
|
||
for level, cost in levels_costs:
|
||
level_difference = level - self.level
|
||
if level_difference > 0 or (
|
||
level_difference == 0 and self.new_level):
|
||
cost += level - self.level * search.costs['per-level']
|
||
yield self._learn(move, method, cost,
|
||
level=level, new_level=True)
|
||
else:
|
||
yield self._learn(move, 'relearn',
|
||
search.costs['relearn'],
|
||
action=RelearnAction(self.search, move),
|
||
new_level=False)
|
||
elif method in 'machine tutor'.split():
|
||
for level, cost in levels_costs:
|
||
yield self._learn(move, method, cost, new_level=False)
|
||
elif method == 'egg':
|
||
# ignored here
|
||
pass
|
||
elif method == 'light-ball-egg':
|
||
if self.level == 0 and self.new_level:
|
||
for level, cost in levels_costs:
|
||
yield self._learn(move, method, cost)
|
||
elif method == 'stadium-surfing-pikachu':
|
||
for level, cost in levels_costs:
|
||
yield self._learn(move, method, cost, new_level=False)
|
||
elif method == 'form-change':
|
||
# XXX: Form changes
|
||
pass
|
||
else:
|
||
raise ValueError('Unknown move method %s' % method)
|
||
|
||
def _learn(self, move, method, cost, action=None, **kwargs):
|
||
kwargs['moves_'] = self.moves_.union([move])
|
||
if action is None:
|
||
action = LearnAction(self.search, move, method)
|
||
return cost, action, self._replace(
|
||
**kwargs)
|
||
|
||
def expand_forget(self):
|
||
cost = self.search.costs['forget']
|
||
for move in self.moves_.difference(self.search.goal_moves):
|
||
yield cost, ForgetAction(self.search, move), self._replace(
|
||
moves_=self.moves_.difference([move]), new_level=False)
|
||
|
||
def expand_trade(self):
|
||
search = self.search
|
||
target_vgs = set(search.pokemon_moves[self.pokemon_])
|
||
target_vgs.add(search.goal_version_group)
|
||
target_vgs.discard(self.version_group_)
|
||
max_generation = max(search.move_generations[m] for m in self.moves_)
|
||
for version_group in target_vgs:
|
||
cost = search.trade_cost(self.version_group_, version_group,
|
||
max_generation)
|
||
if cost is not None:
|
||
yield cost, TradeAction(search, version_group), self._replace(
|
||
version_group_=version_group, new_level=False)
|
||
|
||
def expand_grow(self):
|
||
search = self.search
|
||
if (self.pokemon_ == search.goal_pokemon and
|
||
self.version_group_ == search.goal_version_group and
|
||
self.moves_ == search.goal_moves and
|
||
self.level <= search.goal_level):
|
||
kwargs = self._asdict()
|
||
kwargs['level'] = search.goal_level
|
||
kwargs['new_level'] = True
|
||
yield 0, GrowAction(search, search.goal_level), GoalNode(**kwargs)
|
||
|
||
def expand_evolutions(self):
|
||
search = self.search
|
||
for trigger, move, level, child in search.evolutions[self.pokemon_]:
|
||
kwargs = dict(pokemon_=child)
|
||
cost = search.costs['evolution']
|
||
if move and move not in self.moves_:
|
||
continue
|
||
if level:
|
||
if level > self.level:
|
||
kwargs['level'] = level
|
||
kwargs['new_level'] = True
|
||
elif level == self.level and self.new_level:
|
||
pass
|
||
else:
|
||
cost += search.costs['evolution-delayed']
|
||
if trigger in 'level-up use-item'.split():
|
||
pass
|
||
elif trigger == 'trade':
|
||
kwargs['new_level'] = False
|
||
elif trigger == 'shed':
|
||
# XXX: Shedinja!!
|
||
pass
|
||
else:
|
||
raise ValueError('Unknown evolution trigger %s' % trigger)
|
||
yield cost, EvolutionAction(search, child, trigger), self._replace(
|
||
**kwargs)
|
||
|
||
def expand_breed(self):
|
||
search = self.search
|
||
if self.pokemon_ in search.unbreedable:
|
||
return
|
||
evo_chain = search.evolution_chains[self.pokemon_]
|
||
egg_groups = search.egg_groups[evo_chain]
|
||
breeds_required = search.breeds_required
|
||
moves = self.moves_
|
||
cost = search.costs['breed']
|
||
cost += search.costs['egg'] * len(moves)
|
||
cost += search.costs['breed-penalty'] * len(search.egg_moves - moves)
|
||
gender_rate = search.gender_rates[evo_chain]
|
||
goal_family = search.goal_evolution_chain
|
||
goal_groups = search.egg_groups[goal_family]
|
||
goal_compatible = set(goal_groups).intersection(egg_groups)
|
||
if 0 < gender_rate:
|
||
# Only pokemon that have males can pas down moves to other species
|
||
# (and the other species must have females: checked in BreedNode)
|
||
for group in egg_groups:
|
||
if moves in breeds_required[group]:
|
||
yield cost, None, BreedNode(search=self.search, dummy='b',
|
||
group_=group, version_group_=self.version_group_,
|
||
moves_=self.moves_)
|
||
# Since the target family is not included in our breed graph, we
|
||
# breed with it explicitly. But again, there must be a female to
|
||
# breed with.
|
||
if goal_compatible and search.gender_rates[
|
||
search.goal_evolution_chain] < 8:
|
||
yield cost, None, GoalBreedNode(search=self.search, dummy='g',
|
||
version_group_=self.version_group_, moves_=self.moves_)
|
||
elif evo_chain == search.goal_evolution_chain:
|
||
# Single-gender & genderless pokemon can pass on moves via
|
||
# breeding with Ditto, to produce the same species again. Obviously
|
||
# this is only useful when breeding the goal species.
|
||
yield cost, None, GoalBreedNode(search=self.search, dummy='g',
|
||
version_group_=self.version_group_, moves_=self.moves_)
|
||
|
||
def expand_sketch(self):
|
||
moves = self.moves_
|
||
sketch = self.search.sketch
|
||
if sketch in moves:
|
||
for sketched in sorted(self.search.goal_moves):
|
||
if sketched in self.search.unsketchable:
|
||
continue
|
||
if sketched not in moves:
|
||
moves = set(moves)
|
||
moves.remove(sketch)
|
||
moves.add(sketched)
|
||
action = SketchAction(self.search, sketched)
|
||
cost = self.search.costs['sketch']
|
||
yield cost, action, self._replace(
|
||
new_level=False, moves_=frozenset(moves))
|
||
return
|
||
|
||
def estimate(self, g):
|
||
# Given good estimates, A* finds solutions much faster.
|
||
# However, here it seems we either have easy movesets, which
|
||
# get found pretty easily by themselves, or hard ones, where
|
||
# heuristics don't help too much, or impossible ones where they
|
||
# don't matter at all.
|
||
# So, keep the computations here to a minimum.
|
||
search = self.search
|
||
trade_cost = search.trade_cost(self.version_group_,
|
||
search.goal_version_group)
|
||
if trade_cost is None:
|
||
trade_cost = search.costs['trade'] * 2
|
||
return trade_cost
|
||
|
||
class BaseBreedNode(Node):
|
||
"""Breed node
|
||
This serves to prevent duplicate breeds, by storing only the needed info
|
||
in the namedtuple.
|
||
Also, the base breed cost was already paid, so the breeding tends to happen
|
||
later in the algorithm.
|
||
"""
|
||
def expand(self):
|
||
search = self.search
|
||
vg = self.version_group_
|
||
gen = search.generation_id_by_version_group[vg]
|
||
hatch_level = 5 if (gen < 4) else 1
|
||
for baby in self.babies():
|
||
bred_moves = self.moves_
|
||
moves = search.pokemon_moves[baby][vg]
|
||
if not bred_moves.issubset(moves):
|
||
continue
|
||
if len(bred_moves) < 4:
|
||
for move, methods in moves.items():
|
||
if 'light-ball-egg' in methods:
|
||
bred_moves = bred_moves.union([move])
|
||
cost = search.costs['per-hatch-counter'] * search.hatch_counters[baby]
|
||
yield 0, BreedAction(self.search, baby, bred_moves), PokemonNode(
|
||
search=self.search, pokemon_=baby, level=hatch_level,
|
||
version_group_=vg, moves_=bred_moves, new_level=True)
|
||
|
||
@property
|
||
def pokemon(self):
|
||
return None
|
||
|
||
def estimate(self, g):
|
||
return 0
|
||
|
||
class BreedNode(BaseBreedNode, namedtuple('BreedNode',
|
||
'search dummy group_ version_group_ moves_')):
|
||
def babies(self):
|
||
search = self.search
|
||
for baby in search.babies[self.group_]:
|
||
baby_chain = search.evolution_chains[baby]
|
||
if self.moves_.issubset(search.movepools[baby_chain]) and (
|
||
search.gender_rates[baby_chain] > 0):
|
||
yield baby
|
||
|
||
class GoalBreedNode(BaseBreedNode, namedtuple('GoalBreedNode',
|
||
'search dummy version_group_ moves_')):
|
||
def babies(self):
|
||
search = self.search
|
||
goal_family = search.goal_evolution_chain
|
||
group = search.egg_groups[goal_family][0]
|
||
for baby in search.pokemon_by_evolution_chain[goal_family]:
|
||
if baby in search.babies[group]:
|
||
yield baby
|
||
|
||
class GoalNode(PokemonNode):
|
||
def expand(self):
|
||
return ()
|
||
|
||
def is_goal(self, g):
|
||
return True
|
||
###
|
||
### CLI interface
|
||
###
|
||
|
||
def print_result(result, moves=()):
|
||
template = u"{cost:4} {est:4} {action:45.45}{long:1} {pokemon:10}{level:>3}{nl:1}{versions:2} {moves}"
|
||
print template.format(cost='Cost', est='Est.', action='Action', pokemon='Pokemon',
|
||
long='', level='Lv.', nl='V', versions='er',
|
||
moves=''.join(m.name[0].lower() for m in moves))
|
||
for cost, action, node in reversed(list(result)):
|
||
if action:
|
||
print template.format(
|
||
cost=cost,
|
||
action=action,
|
||
long='>' if (len(unicode(action)) > 45) else '',
|
||
est=node.estimate(None),
|
||
pokemon=node.pokemon.name,
|
||
nl='.' if node.new_level else ' ',
|
||
level=node.level,
|
||
versions=''.join(v.name[0] for v in node.versions),
|
||
moves=''.join('.' if m in node.moves else ' ' for m in moves) +
|
||
''.join(m.name[0].lower() for m in node.moves if m not in moves),
|
||
)
|
||
|
||
def main(argv):
|
||
parser = argparse.ArgumentParser(description=
|
||
'Find out if the specified moveset is valid, and provide a suggestion '
|
||
'on how to obtain it.')
|
||
|
||
parser.add_argument('pokemon', metavar='POKEMON', type=unicode,
|
||
help='Pokemon to check the moveset for')
|
||
|
||
parser.add_argument('move', metavar='MOVE', type=unicode, nargs='*',
|
||
help='Moves in the moveset')
|
||
|
||
parser.add_argument('-l', '--level', metavar='LV', type=int, default=100,
|
||
help='Level of the pokemon')
|
||
|
||
parser.add_argument('-v', '--version', metavar='VER', type=unicode,
|
||
default='black',
|
||
help='Version to search in.')
|
||
|
||
parser.add_argument('-V', '--exclude-version', metavar='VER', type=unicode,
|
||
action='append', default=[],
|
||
help='Versions to exclude (along with their '
|
||
'counterparts, if any, e.g. `black` will also exclude White).')
|
||
|
||
parser.add_argument('-P', '--exclude-pokemon', metavar='PKM', type=unicode,
|
||
action='append', default=[],
|
||
help='Pokemon to exclude (along with their families, e.g. `pichu` '
|
||
'will also exclude Pikachu and Raichu).')
|
||
|
||
parser.add_argument('-d', '--debug', action='append_const', const=1,
|
||
default=[],
|
||
help='Output timing and debugging information (can be specified more '
|
||
'than once).')
|
||
|
||
args = parser.parse_args(argv)
|
||
args.debug = len(args.debug)
|
||
|
||
if args.debug:
|
||
print 'Connecting'
|
||
|
||
session = connect(engine_args={'echo': args.debug > 2})
|
||
|
||
if args.debug:
|
||
print 'Parsing arguments'
|
||
|
||
def _get_list(table, idents, name):
|
||
result = []
|
||
for ident in idents:
|
||
try:
|
||
result.append(util.get(session, table, identifier=ident))
|
||
except NoResultFound:
|
||
print>>sys.stderr, ('%s %s not found. Please use '
|
||
'the identifier.' % (name, ident))
|
||
return False
|
||
return result
|
||
|
||
pokemon = _get_list(tables.Pokemon, [args.pokemon], 'Pokemon')[0]
|
||
moves = _get_list(tables.Move, args.move, 'Move')
|
||
version = _get_list(tables.Version, [args.version], 'Version')[0]
|
||
excl_versions = _get_list(tables.Version, args.exclude_version, 'Version')
|
||
excl_pokemon = _get_list(tables.Pokemon, args.exclude_pokemon, 'Pokemon')
|
||
|
||
if args.debug:
|
||
print 'Starting search'
|
||
|
||
no_results = True
|
||
try:
|
||
search = MovesetSearch(session, pokemon, version, moves, args.level,
|
||
exclude_versions=excl_versions, exclude_pokemon=excl_pokemon,
|
||
debug_level=args.debug)
|
||
except IllegalMoveCombination, e:
|
||
print 'Error:', e
|
||
else:
|
||
if args.debug:
|
||
print 'Setup done'
|
||
|
||
for result in search:
|
||
if args.debug and search.output_objects:
|
||
print '**warning: search looked up output objects**'
|
||
no_results = False
|
||
print '-' * 79
|
||
print_result(result, moves=moves)
|
||
# XXX: Support more than one result
|
||
break
|
||
|
||
if args.debug:
|
||
print
|
||
print 'Done'
|
||
|
||
return (not no_results)
|
||
|
||
if __name__ == '__main__':
|
||
sys.exit(not main(sys.argv[1:]))
|