plotex3.py

# PlotEx: a tool for exploring puzzle plot constraints
#   Version 1.1.1
#   Andrew Plotkin <erkyrath@eblong.com>
#   This script is in the public domain.
#
# For a full description, see <http://eblong.com/zarf/plotex/>

# This is the Python 3 version. (Auto-generated by 2to3.)
# If you have Python 3 installed, you'll have to copy plotex3.py over
# plotex.py, or else change the "import plotex" lines in the examples to
# "import plotex3".

import sys
import optparse

class TrackMetaClass(type):
    '''TrackMetaClass does some Python magic to catalog the members of a
    class as it's being defined. We use this to catalog a scenario and set
    it up properly. (Thanks to Zack Weinberg and Aahz for magic support.)
    '''

    def __init__(cls, name, bases, dict):
        super(TrackMetaClass, cls).__init__(name, bases, dict)
        states = {}
        actions = {}
        tests = {}
        
        for (key, val) in list(dict.items()):
            if (key.startswith('_')):
                continue
            if (isinstance(val, Action)):
                val.name = key
                actions[key] = val
            if (isinstance(val, State)):
                val.name = key
                states[key] = val
            if (isinstance(val, Test)):
                val.name = key
                tests[key] = val

        types = merge_typelists_of(list(actions.values()) + list(states.values()) + list(tests.values()))
            
        senses = {}
        for key in types:
            if (key is None):
                # The Once action sometimes lacks a key. In those cases, it will
                # generate a negative sense boolean.
                continue
            senses[key] = (not key.startswith('_'))

        cls._statemap = states
        cls._actionmap = actions
        cls._testmap = tests
        cls._typemap = types
        cls._sensemap = senses
        
        for val in list(states.values()):
            val.scenario = cls
        for val in list(actions.values()):
            val.set_scenario(cls)
        for val in list(tests.values()):
            val.set_scenario(cls)

def merge_typelists_of(ls):
    '''Given a list of objects (actions and states), pull the type map
    out of each one and return the union of all the maps. If they're not
    all consistent, raise an exception.
    '''
    typedic = {}
    for obj in ls:
        for (key, val) in list(obj.typelist.items()):
            oldval = typedic.get(key)
            if (oldval is None):
                typedic[key] = val
                continue
            if (val != oldval):
                raise Exception('Inconsistent types for key "%s"' % (key,))
    return typedic
    
def infer_typelist(dic):
    '''Given a dictionary of qualities (as you'd see it in a State or
    Set definition), create a type map describing them. Each quality
    must be a number (int), string (str), boolean (bool), or sequence
    (set).
    '''
    res = {}
    for (key, val) in list(dic.items()):
        typ = type(val)
        if (typ in (int, int)):
            res[key] = int
        elif (typ in (str, str)):
            res[key] = str
        elif (typ is bool):
            res[key] = bool
        elif (typ in (tuple, list, set, frozenset)):
            res[key] = set
        else:
            raise Exception('Value must be int, str, set, or bool: %s' % repr(val))
    return res

def parse_states(scenario, optls):
    '''Given a list of strings (as given on the command line), parse them
    as states. Arguments can be separate strings or comma-separated.
    Unrecognized states throw exceptions.
    '''
    res = set()
    for val in optls:
        ls = [ key.strip() for key in val.split(',') ]
        for key in ls:
            state = scenario._statemap.get(key)
            if (not state):
                raise Exception('No such state: "%s"' % (key,))
            res.add(state)
    return res

def parse_actions(scenario, optls):
    '''Given a list of strings (as given on the command line), parse them
    as actions. Arguments can be separate strings or comma-separated.
    Unrecognized actions throw exceptions.
    '''
    res = set()
    for val in optls:
        ls = [ key.strip() for key in val.split(',') ]
        for key in ls:
            action = scenario._actionmap.get(key)
            if (not action):
                raise Exception('No such action: "%s"' % (key,))
            res.add(action)
    return res

def parse_qualities(scenario, optls):
    '''Given a list of strings (as given on the command line), parse them
    as qualities. Arguments can be separate strings or comma-separated.
    Unrecognized qualities throw exceptions.
    '''
    res = set()
    for val in optls:
        ls = [ key.strip() for key in val.split(',') ]
        for key in ls:
            val = scenario._typemap.get(key)
            if (not val):
                raise Exception('No such quality: "%s"' % (key,))
            res.add(key)
    return res

def parse_tests(scenario, optls):
    '''Given a list of strings (as given on the command line), parse them
    as tests. Arguments can be separate strings or comma-separated.
    Unrecognized tests throw exceptions.
    '''
    res = set()
    for val in optls:
        ls = [ key.strip() for key in val.split(',') ]
        for key in ls:
            test = scenario._testmap.get(key)
            if (not test):
                raise Exception('No such test: "%s"' % (key,))
            res.add(test)
    return res

class Graph:
    '''Graph: The context structure for doing a run. You set up a graph
    with some starting states, then tell it to run with some actions.
    The graph object can also display its results neatly.
    '''
    def __init__(self, scenario, states):
        self.scenario = scenario
        self.startstates = states
        self.states = {}
        self.statels = []
        self.seenmaxes = set()
        self.maxls = []

    def run(self, actions, limit=10000, noopt=False):
        '''run(): Do the run. The results are stored within the Graph.
        '''
        improveactions = actions
        changeactions = actions
        if (not noopt):
            improveactions = [ action for action in actions if (action.equivtype != EQUIV_LOSS) ]
            changeactions = [ action for action in actions if (action.equivtype in (EQUIV_LOSS, EQUIV_UNKNOWN)) ]
            #print '%d actions filtered to %d improve, %d change' % (len(actions), len(improveactions), len(changeactions))
        
        newstates = []
        for state in self.startstates:
            newstate = self.find_maximal_state(state, improveactions)
            if (newstate in newstates):
                continue
            newstates.append(newstate)
            self.seenmaxes.add(newstate)
            newnode = self.states[newstate]
            newnode.history = self.states[state].maxing_actions

        while (newstates):
            if (len(self.seenmaxes) >= limit):
                raise Exception('More than %d states!' % (limit,))
            
            oldstate = newstates.pop(0)
            oldnode = self.states[oldstate]
            self.maxls.append(oldstate)
            
            for action in changeactions:
                newstate = action(oldstate)
                if (not newstate):
                    continue
                maxstate = self.find_maximal_state(newstate, improveactions)
                if (maxstate == oldstate):
                    continue
                if (maxstate in oldnode.ancestors):
                    continue

                aclist = (action,) + self.states[newstate].maxing_actions
                maxnode = self.states[maxstate]

                if (maxstate in self.seenmaxes):
                    maxnode.ancestors.update(oldnode.ancestors)
                    maxnode.ancestors.add(oldstate)
                else:
                    newstates.append(maxstate)
                    self.seenmaxes.add(maxstate)
                    maxnode.history = oldnode.history + aclist
                    maxnode.ancestors.update(oldnode.ancestors)
                    maxnode.ancestors.add(oldstate)

                oldnode.children.append( (aclist, maxstate) )
                maxnode.parents.append( (aclist, oldstate) )
            
    def find_maximal_state(self, state, actions):
        '''Do every possible actions that is strictly an improvement --
        that is, every action that produces a better state. Return the
        resulting state.
        '''
        node = self.states.get(state)
        if (node):
            return node.maximal
            
        statechain = []
        actchain = []
        while True:
            node = GraphNode(state)
            self.states[state] = node
            self.statels.append(state)
            statechain.append(state)
            
            found_improvement = False
            for action in actions:
                newstate = action(state)
                if (not newstate):
                    continue
                if (newstate == state):
                    continue
                if not(newstate > state):
                    continue
                
                # That action was an improvement
                actchain.append(action)
                found_improvement = True
                
                if (newstate in self.states):
                    # We've run into a known state. (Might be maximal, or
                    # it might have its own maximal state.)
                    gotstate = newstate
                    gotnode = self.states[gotstate]
                    pos = 0
                    for newstate in statechain:
                        newnode = self.states[newstate]
                        newnode.maximal = gotnode.maximal
                        newnode.maxing_actions = tuple(actchain[pos:]) + gotnode.maxing_actions
                        pos = pos+1
                    return gotnode.maximal
                    
                state = newstate
                break

            if (not found_improvement):
                # This state is maximal.
                pos = 0
                for newstate in statechain:
                    newnode = self.states[newstate]
                    newnode.maximal = state
                    newnode.maxing_actions = tuple(actchain[pos:])
                    pos = pos+1
                self.states[state].is_maximal = True
                return state
                

    def has(self, state):
        return state in self.states

    def showlist(self, showmed=True, filters=[], histories=[]):
        outls = []
        
        for state in self.maxls:
            node = self.states[state]
            if (not showmed):
                if (node.children):
                    continue
            filtered = False
            for filter in filters:
                if (filter not in state.dic):
                    filtered = True
            if (filtered):
                continue
            for histac in histories:
                if (histac not in node.history):
                    filtered = True
            if (filtered):
                continue
            outls.append(state)

        trumped = None
        if (len(outls) <= 20):
            trumped = set()
            for state1 in outls:
                if (state1 in trumped):
                    continue
                for state2 in outls:
                    if (state2 in trumped):
                        continue
                    if (state1 == state2):
                        continue
                    if (state2 < state1):
                        trumped.add(state2)

        return (outls, trumped)
    
    def display(self, showmed=False, showin=False, showout=False, showdiff=False, showcount=False, filters=[], histories=[]):
        (outls, trumped) = self.showlist(showmed, filters, histories)

        if (not showcount):
            difffrom = None
            if (showdiff and len(outls) >= 2):
                difffrom = outls[0]
                for state in outls[1:]:
                    difffrom = difffrom & state
                print('(common state: %s)' % (difffrom,))
                print()
                    
            for state in outls:
                node = self.states[state]
                val = ''
                if (trumped is not None and state not in trumped):
                    val = '*'
                if (difffrom is None):
                    print(val+str(state))
                else:
                    print(val+state.printdiff(difffrom))
                acs = [ ac.name for ac in node.history]
                print('  (%d): %s' % (len(node.history), ', '.join(acs),))
                #print '  ### ancs:', list(node.ancestors)
                if (showin):
                    subls = [ '<= %s : %s' % (substate, ', '.join([ ac.name for ac in acls ])) for (acls, substate) in node.parents ]
                    for val in subls:
                        print('    %s' % (val,))
                if (showout):
                    subls = [ '=> %s : %s' % (', '.join([ ac.name for ac in acls ]), substate) for (acls, substate) in node.children ]
                    for val in subls:
                        print('    %s' % (val,))
                print()
                
        if (showmed):
            summary = '%d maximal states' % (len(outls),)
        else:
            summary = '%d terminal states' % (len(outls),)
        if (trumped):
            val = len(outls) - len(trumped)
            summary += ' (%d preferred)' % (val,)
        if (filters):
            summary += ' with "' + '", "'.join(filters) + '"'
        if (histories):
            subls = [ ac.name for ac in histories ]
            subls.sort()
            summary += ' with ' + ', '.join(subls)
        #print '### (%d intermediate states)' % (len(self.states),)
        print(summary, 'reached')
                    
    def writegv(self, filename, filters=[], histories=[]):
        (outls, trumped) = self.showlist()
        (colorls, _) = self.showlist(True, filters, histories)
        if (len(colorls) >= len(outls)):
            colorls = ()

        extrastart = [ state for state in self.startstates if not self.states[state].is_maximal ]
        outls = extrastart + outls

        nodenames = {}
        pos = 1
        for state in outls:
            nodenames[state] = str(pos)
            pos = pos+1

        fl = open(filename, 'w')
        fl.write('digraph PlotEx {\n')
        fl.write('\n')
        for state in outls:
            node = self.states[state]
            penwidth = 1
            if (node.is_maximal and not node.children):
                penwidth = 3
            color = 'gray75'
            if (state in colorls):
                color = 'forestgreen'
            if (not node.is_maximal):
                color = 'white'
            fl.write('# %s\n' % (state,))
            fl.write('"%s" [ label="", shape=circle, width=0.2, style=filled, fillcolor=%s, penwidth=%d ];\n' % (nodenames[state], color, penwidth))
            fl.write('\n')
            if (node.is_maximal):
                for (acls, child) in node.children:
                    label = '\\n'.join([ ac.name for ac in acls ])
                    fl.write('  "%s" -> "%s" [ label="%s" ];\n' % (nodenames[state], nodenames[child], label))
            else:
                acls = node.maxing_actions
                label = '\\n'.join([ ac.name for ac in acls ])
                fl.write('  "%s" -> "%s" [ label="%s", style=dashed ];\n' % (nodenames[state], nodenames[node.maximal], label))
            fl.write('\n')
            fl.write('\n')
                                                     
        fl.write('}\n')

    def writegml(self, filename, filters=[], histories=[]):
        (outls, trumped) = self.showlist()
        (colorls, _) = self.showlist(True, filters, histories)
        if (len(colorls) >= len(outls)):
            colorls = ()

        extrastart = [ state for state in self.startstates if not self.states[state].is_maximal ]
        outls = extrastart + outls

        nodenames = {}
        pos = 1
        for state in outls:
            nodenames[state] = pos
            pos = pos+1

        fl = open(filename, 'w')
        fl.write('graph [\n')
        fl.write('  directed 1\n\n')
        for state in outls:
            node = self.states[state]
            penwidth = 1
            if (node.is_maximal and not node.children):
                penwidth = 3
            color = 'gray75'
            if (state in colorls):
                color = 'forestgreen'
            if (not node.is_maximal):
                color = 'white'
            fl.write('  comment "%s"\n' % (state,))
            fl.write('  node [ id %d ]\n' % (nodenames[state],))
            fl.write('\n')
            if (node.is_maximal):
                for (acls, child) in node.children:
                    label = ' '.join([ ac.name for ac in acls ])
                    fl.write('  edge [ source %d target %d label "%s" ]\n' % (nodenames[state], nodenames[child], label))
            else:
                acls = node.maxing_actions
                label = ' '.join([ ac.name for ac in acls ])
                fl.write('  edge [ source %d target %d label "%s" ]\n' % (nodenames[state], nodenames[node.maximal], label))
            fl.write('\n')
            fl.write('\n')
                                                     
        fl.write(']\n')


class GraphNode:
    '''GraphNode: Context information for a single state in a Graph.
    (We never store information in the State itself -- that's immutable.)
    '''
    def __init__(self, state):
        self.state = state
        self.maximal = None
        self.is_maximal = False
        self.children = []
        self.parents = []
        self.history = ()
        self.ancestors = set()
            
class State:
    '''State: One state in the plot diagram. A state is set up with a
    bunch of qualities.

    The most interesting thing you can do with states is compare them.
    State1 < state2 if state1's qualities are a subset of state2's.
    This is a partial ordering; it is not necessarily true that
    (x < y or x == y or x > y). Sometimes two states are just different,
    in non-overlapping ways.

    You can also compute state1 & state2, which is the greatest common
    factor (the largest state which is <= both of them). (This doesn't
    quite work out for negative-sense string qualities, but what does,
    really?)

    (Any operation between two states must be within a common Scenario.)

    You will normally create a State as described in the documentation:
    State(key1=val1, key2=val2, ...)

    The constructor supports an alternate form: State(dic, newkeys).
    However, this should only be used internally, by the PlotEx run()
    algorithm. (It skips some of the type-checking and state-fixing,
    on the assumption that the caller has done some of that work
    already.)
    '''
    name = None
    scenario = None
    
    def __init__(self, __dic=None, __newkeys=None, **kargs):
        if (__dic is None):
            __dic = kargs
        if (global_scenario is None):
            self.typelist = infer_typelist(__dic)
        else:
            self.typelist = None
            self.scenario = global_scenario
        self.hashcache = None
        if (not __dic):
            self.dic = {}
            return
        self.dic = __dic
        State.canonize(self.dic, __newkeys)
        
    def __repr__(self):
        keyls = list(self.dic.keys())
        keyls.sort(key=lambda x:x.upper())
        ls = []
        for key in keyls:
            val = self.dic[key]
            if (isinstance(val, frozenset)):
                val = list(val)
                val.sort()
                val = '[' + ','.join(str(subval) for subval in val) + ']'
            if (val is True):
                ls.append('%s' % (key,))
            else:
                ls.append('%s=%s' % (key, val))
        joined = ' '.join(ls)
        if (self.name):
            return '<"%s": %s>' % (self.name, joined)
        else:
            return '<%s>' % (joined,)

    def printdiff(self, other):
        '''Return a string representation of the state, not by itself, but
        in comparison to some other state. Only quality differences will be
        displayed.
        '''
        keyset = set(self.dic.keys()).union(list(other.dic.keys()))
        keyls = list(keyset)
        keyls.sort(key=lambda x:x.upper())
        ls = []
        for key in keyls:
            typ = self.scenario._typemap[key]
            sense = self.scenario._sensemap[key]
            if (typ is bool):
                val = self.dic.get(key)
                otherval = other.dic.get(key)
                if (val and not otherval):
                    ls.append('+%s' % (key,))
                elif (otherval and not val):
                    ls.append('-%s' % (key,))
            elif (typ is str):
                val = self.dic.get(key)
                otherval = other.dic.get(key)
                if (val and otherval != val):
                    ls.append('%s=%s' % (key, val))
                elif (otherval and not val):
                    ls.append('-%s' % (key,))
            elif (typ is int):
                val = self.dic.get(key, 0)
                otherval = other.dic.get(key, 0)
                if (val > otherval):
                    ls.append('%s=+%d' % (key, val-otherval))
                elif (otherval and not val):
                    ls.append('%s=-%d' % (key, otherval-val))
            elif (typ is set):
                val = self.dic.get(key, set())
                otherval = other.dic.get(key, set())
                subls = []
                for subkey in (val - otherval):
                    subls.append('+'+subkey)
                for subkey in (otherval - val):
                    subls.append('-'+subkey)
                subls.sort()
                if (subls):
                    sublsval = '[' + ','.join(str(subval) for subval in subls) + ']'
                    ls.append('%s=%s' % (key, sublsval))
            else:
                ls.append('???')
        joined = ' '.join(ls)
        if (self.name):
            return '<"%s": %s>' % (self.name, joined)
        else:
            return '<%s>' % (joined,)

    def __eq__(self, other):
        return (self.dic == other.dic)
    def __ne__(self, other):
        return (self.dic != other.dic)
    def __gt__(self, other):
        return (self != other) and self.contains(other)
    def __ge__(self, other):
        return self.contains(other)
    def __lt__(self, other):
        return (self != other) and other.contains(self)
    def __le__(self, other):
        return other.contains(self)

    def __and__(self, other):
        dic = {}
        keyset = set(self.dic.keys()).union(list(other.dic.keys()))
        for key in keyset:
            typ = self.scenario._typemap[key]
            sense = self.scenario._sensemap[key]
            val = self.dic.get(key)
            otherval = other.dic.get(key)
            if (sense):
                if ((val is None) or (otherval is None)):
                    continue
                if (typ is bool):
                    dic[key] = (val and otherval)
                if (typ is int):
                    dic[key] = min(val, otherval)
                if (typ is set):
                    dic[key] = val.intersection(otherval)
                if (typ is str):
                    if (val == otherval):
                        dic[key] = val
            else:
                if (val is None):
                    dic[key] = otherval
                    continue
                if (otherval is None):
                    dic[key] = val
                    continue
                if (typ is bool):
                    dic[key] = (val or otherval)
                if (typ is int):
                    dic[key] = max(val, otherval)
                if (typ is set):
                    dic[key] = val.union(otherval)
                if (typ is str):
                    if (val == otherval):
                        dic[key] = val
        res = State(dic) # canonize all keys, this is not speed-critical
        res.scenario = self.scenario
        return res

    def __hash__(self):
        if (self.hashcache is None):
            ls = list(self.dic.items())
            ls.sort()
            self.hashcache = hash(tuple(ls))
        return self.hashcache

    def canonize(dic, changedkeys=None):
        '''Modify a dictionary to be a legal state dict (no false values,
        sets values in frozenset form).
        '''
        if (changedkeys is None):
            changedkeys = list(dic.keys())
        for key in changedkeys:
            val = dic[key]
            if (not val):
                del dic[key]
            elif (type(val) in (tuple, list)):
                dic[key] = frozenset(val)
    canonize = staticmethod(canonize)

    def copy(self):
        res = State()
        res.scenario = self.scenario
        res.dic = dict(self.dic)
        return res

    def addquality(self, key, val):
        '''Return a new state which is a copy of this one, with one quality
        added (or changed). The value must be of the correct type, or castable
        to it.
        '''
        typ = self.scenario._typemap[key]
        dic = dict(self.dic)
        if (typ is bool):
            dic[key] = bool(val)
        elif (typ is int):
            dic[key] = int(val)
        elif (typ is str):
            dic[key] = str(val)
        elif (typ is set):
            dic[key] = dic.get(key, set()).union(set[val])
        res = State(dic)   # canonize all keys
        res.scenario = self.scenario
        return res

    def contains(self, other):
        '''X.contains(Y) is the basic comparison -- X is a subset of (or
        equal to) Y.
        '''
        for (key, oval) in list(other.dic.items()):
            if (not self.scenario._sensemap[key]):
                continue
            if (not self.atleast(key, oval)):
                return False
        for (key, ival) in list(self.dic.items()):
            if (self.scenario._sensemap[key]):
                continue
            if (not other.atleast(key, ival)):
                return False
        return True
        
    def atleast(self, key, val):
        '''X.atleast(key, val) tests whether the key quality is val or better.
        (This does *not* account for negative-sense keys, so don't call it
        on them.)
        '''
        if (not val):
            return True
        ival = self.dic.get(key)
        if (ival is None):
            return False
        typ = self.scenario._typemap[key]
        if (typ is int):
            if (ival < val):
                return False
        elif (typ is set):
            if (not ival.issuperset(frozenset(val))):
                return False
        else:
            if (ival != val):
                return False
        return True
    
    def atmost(self, key, val):
        '''X.atmost(key, val) tests whether the key quality is val or worse.
        (Call this for negative-sense keys.)
        '''
        ival = self.dic.get(key)
        if (not val and not ival):
            return True
        if (not val):
            return False
        if (ival is None):
            return True
        typ = self.scenario._typemap[key]
        if (typ is int):
            if (ival > val):
                return False
        elif (typ is set):
            if (not ival.issubset(frozenset(val))):
                return False
        else:
            if (ival != val):
                return False
        return True

class Test:
    name = '???'
    scenario = None
    def __init__(self, **dic):
        self.startstatelist = []
        val = dic.pop('start', None)
        if (val is not None):
            if (type(val) not in (list, tuple)):
                self.startstatelist.append(val)
            else:
                for state in val:
                    self.startstatelist.append(state)
         
        self.blockactions = set()
        val = dic.pop('block', None)
        if (val is not None):
            if (type(val) not in (list, tuple)):
                self.blockactions.add(val)
            else:
                for ac in val:
                    self.blockactions.add(ac)

        self.includeactions = []
        val = dic.pop('includes', None)
        if (val is not None):
            if (type(val) not in (list, tuple)):
                self.includeactions.append(val)
            else:
                for ac in val:
                    self.includeactions.append(ac)
                
        self.excludeactions = []
        val = dic.pop('excludes', None)
        if (val is not None):
            if (type(val) not in (list, tuple)):
                self.excludeactions.append(val)
            else:
                for ac in val:
                    self.excludeactions.append(ac)
                
        self.canactions = []
        val = dic.pop('can', None)
        if (val is not None):
            if (type(val) not in (list, tuple)):
                self.canactions.append(val)
            else:
                for ac in val:
                    self.canactions.append(ac)
                
        self.cannotactions = []
        val = dic.pop('cannot', None)
        if (val is not None):
            if (type(val) not in (list, tuple)):
                self.cannotactions.append(val)
            else:
                for ac in val:
                    self.cannotactions.append(ac)

        self.getqualities = []
        val = dic.pop('gets', None)
        if (val is not None):
            if (type(val) not in (list, tuple)):
                self.getqualities.append(val)
            else:
                for ac in val:
                    self.getqualities.append(ac)

        self.getnotqualities = []
        val = dic.pop('getsnot', None)
        if (val is not None):
            if (type(val) not in (list, tuple)):
                self.getnotqualities.append(val)
            else:
                for ac in val:
                    self.getnotqualities.append(ac)
        if (dic):
            raise TypeError('Test() got unknown argument: %s' % (', '.join(list(dic.keys())),))

        ls = list(self.blockactions) + self.startstatelist + self.canactions + self.cannotactions
        self.typelist = merge_typelists_of(ls)

    def __repr__(self):
        return '<Test "%s">' % (self.name,)
    def set_scenario(self, scen):
        self.scenario = scen
        for state in self.startstatelist:
            state.scenario = scen
        for ac in self.canactions + self.cannotactions:
            ac.set_scenario(scen)
         
    def startstates(self):
        if (not self.startstatelist):
            state = self.scenario._statemap['Start']
            return [state]
        return self.startstatelist
         
    def actions(self):
        actions = [ action for action in list(self.scenario._actionmap.values()) if action not in self.blockactions ]
        return actions
         
    def verify(self, graph):
        states = list(graph.states.keys())
        for qual in self.getqualities:
            states = [ state for state in states if qual in state.dic ]
            if (not states):
                return False
        for ac in self.canactions:
            states = [ state for state in states if ac(state) ]
            if (not states):
                return False
        for ac in self.includeactions:
            states = [ state for state in states if (ac in graph.states[state].history) ]
            if (not states):
                return False
        for qual in self.getnotqualities:
            ls = [ state for state in states if qual in state.dic ]
            if (ls):
                return False
        for ac in self.cannotactions:
            ls = [ state for state in states if ac(state) ]
            if (ls):
                return False
        for ac in self.excludeactions:
            ls = [ state for state in states if (ac in graph.states[state].history) ]
            if (ls):
                return False
        return True

# When running, it is handy to know whether a given action will strictly
# improve the state (stay in the same maximal class), or always lose
# something (a different maximal class). Often, though, we don't know
# either.
EQUIV_UNKNOWN = '????'   # We don't know
EQUIV_SAME =    'SAME'   # Does not change the state at all
EQUIV_IMPROVE = 'IMPR'   # Definitely an improvement
EQUIV_LOSS =    'LOSS'   # Definitely a loss of something

class Action:
    '''Action: An abstract action in a scenario. Calling Action(State)
    will return a new State, or None if the Action isn't possible in that
    state.
    '''
    name = '???'
    scenario = None
    equivtype = EQUIV_UNKNOWN
    unnamedcount = 0
    def __repr__(self):
        return '<Action "%s">' % (self.name,)
    def __call__(self, state):
        raise NotImplementedError('Action type not implemented')
    def subactions(self):
        return None
    def set_scenario(self, scen):
        self.scenario = scen
        ls = self.subactions()
        if ls:
            for ac in ls:
                ac.set_scenario(scen)

class Set(Action):
    def __init__(self, **dic):
        self.typelist = infer_typelist(dic)
        self.params = dic
        self.keylist = list(dic.keys())
        allbool = True
        pos = 0
        for (key, val) in list(dic.items()):
            if (self.typelist[key] is not bool):
                allbool = False
                break
            if ((not key.startswith('_') and val)
                or (key.startswith('_') and not val)):
                pos += 1
        if (allbool):
            if (pos == len(dic)):
                self.equivtype = EQUIV_IMPROVE
            else:
                self.equivtype = EQUIV_LOSS
    def __call__(self, state):
        dic = state.dic.copy()
        dic.update(self.params)
        return State(dic, self.keylist)

class Reset(Action):
    def __init__(self, **dic):
        self.typelist = infer_typelist(dic)
        self.params = dic
        State.canonize(self.params)
    def __call__(self, state):
        dic = self.params.copy()
        return State(dic, ())

class Has(Action):
    equivtype = EQUIV_SAME
    def __init__(self, **dic):
        self.typelist = infer_typelist(dic)
        self.params = dic
    def __call__(self, state):
        for (key, val) in list(self.params.items()):
            if (self.scenario._sensemap[key]):
                if (not state.atleast(key, val)):
                    return
            else:
                if (not state.atmost(key, val)):
                    return
        return state

class HasAny(Action):
    equivtype = EQUIV_SAME
    def __init__(self, **dic):
        self.typelist = infer_typelist(dic)
        self.params = dic
    def __call__(self, state):
        for (key, val) in list(self.params.items()):
            if (self.scenario._sensemap[key]):
                if (state.atleast(key, val)):
                    return state
            else:
                if (state.atmost(key, val)):
                    return state
        return

class Lose(Action):
    def __init__(self, *keys):
        self.typelist = {}
        self.keys = keys
        pos = 0
        for key in keys:
            if (not key.startswith('_')):
                pos += 1
        if (pos > 0):
            self.equivtype = EQUIV_LOSS
        else:
            self.equivtype = EQUIV_IMPROVE
    def __call__(self, state):
        olddic = state.dic
        for key in self.keys:
            if (key not in olddic):
                return
        dic = olddic.copy()
        for key in self.keys:
            dic.pop(key)
        return State(dic, ())

class Once(Action):
    equivtype = EQUIV_LOSS
    def __init__(self, key=None):
        if (isinstance(key, Action)):
            self.typelist = merge_typelists_of([key])
            self.action = key
            self.key = None
        else:
            self.typelist = { key: bool }
            self.action = None
            self.key = key
    def subactions(self):
        if (self.action):
            return [self.action]
    def set_scenario(self, scen):
        Action.set_scenario(self, scen)
        if (self.key is None):
            if (self.name == '???'):
                Action.unnamedcount = Action.unnamedcount + 1
                self.key = '_did_action_%d' % (Action.unnamedcount,)
            else:
                self.key = '_did_%s' % (self.name.lower())
            scen._typemap[self.key] = bool
            scen._sensemap[self.key] = False
    def __call__(self, state):
        dic = dict(state.dic)
        if (not self.scenario._sensemap[self.key]):
            if (self.key in dic):
                return
            dic[self.key] = True
        else:
            if (self.key not in dic):
                return
            del dic[self.key]
        newstate = State(dic, ())
        if (not self.action):
            return newstate
        else:
            return self.action(newstate)
        
class Increment(Action):
    def __init__(self, key, limit=None):
        self.typelist = { key: int }
        self.key = key
        self.limit = limit
        if (not key.startswith('_')):
            self.equivtype = EQUIV_IMPROVE
        else:
            self.equivtype = EQUIV_LOSS
    def __call__(self, state):
        dic = dict(state.dic)
        val = dic.get(self.key, 0)
        if (self.limit is not None and val >= self.limit):
            return
        dic[self.key] = val+1
        return State(dic, ())

class Decrement(Action):
    def __init__(self, key, limit=0):
        self.typelist = { key: int }
        self.key = key
        self.limit = limit
        if (not key.startswith('_')):
            self.equivtype = EQUIV_LOSS
        else:
            self.equivtype = EQUIV_IMPROVE
    def __call__(self, state):
        dic = dict(state.dic)
        val = dic.get(self.key, 0)
        if (self.limit is not None and val <= self.limit):
            return
        val = val-1
        if (val):
            dic[self.key] = val
        else:
            del dic[self.key]
        return State(dic, ())

class Include(Action):
    def __init__(self, key, *vals):
        self.typelist = { key: set }
        self.key = key
        self.values = frozenset(vals)
    def __call__(self, state):
        dic = dict(state.dic)
        val = dic.get(self.key, frozenset())
        dic[self.key] = val.union(self.values)
        return State(dic, ())

class Exclude(Action):
    def __init__(self, key, *vals):
        self.typelist = { key: set }
        self.key = key
        self.values = frozenset(vals)
    def __call__(self, state):
        dic = dict(state.dic)
        val = dic.get(self.key, frozenset())
        if (not val.issuperset(self.values)):
            return
        val = val.difference(self.values)
        if (val):
            dic[self.key] = val
        else:
            del dic[self.key]
        return State(dic, ())

class Count(Action):
    equivtype = EQUIV_SAME
    def __init__(self, key, count):
        self.typelist = { key: set }
        self.key = key
        self.count = count
    def __call__(self, state):
        val = state.dic.get(self.key, frozenset())
        if (len(val) < self.count):
            return
        return state

class HasDifferent(Action):
    equivtype = EQUIV_SAME
    def __init__(self, key, *vals):
        self.typelist = { key: str }
        self.key = key
        self.values = frozenset(vals)
    def __call__(self, state):
        val = state.dic.get(self.key)
        if (val is None):
            return
        if (val in self.values):
            return
        return state

class Chain(Action):
    def __init__(self, *actions):
        self.typelist = merge_typelists_of(actions)
        self.actions = actions
        losses = 0
        improves = 0
        for action in actions:
            if (action.equivtype == EQUIV_LOSS):
                losses += 1
            if (action.equivtype in (EQUIV_SAME, EQUIV_IMPROVE)):
                improves += 1
        if (losses):
            self.equivtype = EQUIV_LOSS
        elif (improves == len(actions)):
            self.equivtype = EQUIV_IMPROVE
    def subactions(self):
        return self.actions
    def __call__(self, state):
        for action in self.actions:
            state = action(state)
            if not state:
                return
        return state
    
class Choice(Action):
    def __init__(self, *actions):
        self.typelist = merge_typelists_of(actions)
        self.actions = actions
    def subactions(self):
        return self.actions
    def __call__(self, state):
        for action in self.actions:
            newstate = action(state)
            if newstate:
                return newstate
        return

# This is only set while a particular scenario is being processed.
# We can take shortcuts within state generation when global_scenario
# is set, because no new qualities will be introduced.
global_scenario = None
    
def shell(scenario):
    '''This is the top-level function; it processes the command-line options,
    sets up the graph, and does the run.

    Call this, passing your scenario class as the argument.
    '''
    global global_scenario
    global_scenario = scenario
    
    popt = optparse.OptionParser()

    popt.add_option('-s', '--start',
                    action='append', dest='startstates', metavar='STATES',
                    default=[],
                    help='state(s) to begin at (default: Start)')
    popt.add_option('--startwith',
                    action='append', dest='startwith', metavar='QUALITIES',
                    default=[],
                    help='extra boolean quality to add to start states')
    popt.add_option('--block',
                    action='append', dest='blockactions', metavar='ACTIONS',
                    default=[],
                    help='actions to forbid for this run')
    popt.add_option('--withhold',
                    action='append', dest='withholdactions', metavar='ACTIONS',
                    default=[],
                    help='actions to hold until last')
    popt.add_option('-t', '--test',
                    action='append', dest='runtests', metavar='TESTS',
                    default=[],
                    help='test(s) to run')
    popt.add_option('-T', '--alltest', '--alltests',
                    action='store_true', dest='runalltests',
                    help='run all tests')
    popt.add_option('--genlimit',
                    action='store', type=int, dest='genlimit', default=10000,
                    help='maximum number of states to generate')
    popt.add_option('-m', '--showmed',
                    action='store_true', dest='showmed',
                    help='display all intermediate states')
    popt.add_option('--showin',
                    action='store_true', dest='showin',
                    help='display actions into each state')
    popt.add_option('--showout',
                    action='store_true', dest='showout',
                    help='display actions out of each state')
    popt.add_option('-a', '--showall',
                    action='store_true', dest='showall',
                    help='combines --showmed --showin --showout')
    popt.add_option('-d', '--diff',
                    action='store_true', dest='showdiff',
                    help='display only the differences between the found states')
    popt.add_option('-c', '--count',
                    action='store_true', dest='showcount',
                    help='display only the number of states found')
    popt.add_option('--graphviz',
                    action='store', dest='graphviz', metavar='FILE',
                    help='create a graphviz (.gv) file')
    popt.add_option('--graphml',
                    action='store', dest='graphml', metavar='FILE',
                    help='create a GML (.gml) file')
    popt.add_option('-f', '--filter',
                    action='append', dest='filters', metavar='QUALITIES',
                    default=[],
                    help='display only states containing this quality')
    popt.add_option('-H', '--history',
                    action='append', dest='histories', metavar='ACTIONS',
                    default=[],
                    help='display only states that passed through this action')
    popt.add_option('--noopt',
                    action='store_true', dest='noopt',
                    help='do not optimize the run based on action type')

    (opts, args) = popt.parse_args()

    genlimit = getattr(scenario, 'genlimit', opts.genlimit)
    
    if (opts.showall):
        opts.showmed = True
        opts.showin = True
        opts.showout = True
    
    if (not opts.startstates):
        opts.startstates.append('Start')
    startstates = parse_states(scenario, opts.startstates)
    if (opts.startwith):
        for key in parse_qualities(scenario, opts.startwith):
            startstates = [ state.addquality(key, True) for state in startstates ]

    blockactions = parse_actions(scenario, opts.blockactions)

    runtests = parse_tests(scenario, opts.runtests)
    if (opts.runalltests):
        runtests = list(scenario._testmap.values())

    if (runtests):
        runtests = list(runtests)
        runtests.sort(key=lambda ac:ac.name)
        errors = 0
        for test in runtests:
            actions = test.actions()
            actions.sort(key=lambda ac:ac.name)
            graph = Graph(scenario, test.startstates())
            graph.run(actions, limit=genlimit, noopt=opts.noopt)
            if test.verify(graph):
                print('%s: pass' % (test.name,))
            else:
                errors = errors+1
                print('%s: FAIL' % (test.name,))
        if (errors):
            print('%d errors!' % (errors,))
        return
             
    withholdactions = None
    if (opts.withholdactions):
        withholdactions = parse_actions(scenario, opts.withholdactions)
        blockactions = blockactions.union(withholdactions)

    actions = [ action for action in list(scenario._actionmap.values()) if action not in blockactions ]
    actions.sort(key=lambda ac:ac.name)
    graph = Graph(scenario, startstates)
    graph.run(actions, limit=genlimit, noopt=opts.noopt)
    if (withholdactions):
        ls = list(graph.allstates)
        ls.reverse()
        betterls = []
        for state in ls:
            for newstate in betterls:
                if (newstate > state):
                    break
            else:
                betterls.append(state)
        betterls.reverse()
        for action in withholdactions:
            actions.append(action)
        graph = Graph(scenario, betterls)
        graph.run(actions, limit=genlimit, noopt=opts.noopt)

    filters = []
    for val in opts.filters:
        for subval in val.split(','):
            filters.append(subval.strip())
    histories = []
    if (opts.histories):
        histories = parse_actions(scenario, opts.histories)
    graph.display(opts.showmed, opts.showin, opts.showout, opts.showdiff, opts.showcount, filters, histories)

    if (opts.graphviz):
        graph.writegv(opts.graphviz, filters, histories)
    if (opts.graphml):
        graph.writegml(opts.graphml, filters, histories)

    global_scenario = None

class ScenarioClass(metaclass=TrackMetaClass):
    pass

class TestScenario(ScenarioClass):

    # Our actions
    FindSword = Set(sword=True)
    FindLamp = Set(lamp=True)
    EnterCave = Chain(Has(lamp=True), Set(underground=True))
    FeedSelf = Lose('food')
    FeedCyclops = Chain(Has(underground=True), Lose('food'), Set(kitchen=True))
    FeedOrc = Chain(Lose('food'), Set(pants=True))
    KitchenCook = Chain(Has(kitchen=True), Set(food=True))

    # Our (sole) starting state
    Start = State(food=True)

    # Tests to verify the scenario
    Test1 = Test(start=Start, gets='pants')
    Test2 = Test(can=Has(pants=True))
    Test3 = Test(cannot=Has(wand=True))
    Test4 = Test(block=KitchenCook, cannot=Has(pants=True, kitchen=True))
    Test5 = Test(start=State(), getsnot='pants')
    Test6 = Test(includes=KitchenCook)
    Test7 = Test(block=FeedCyclops, excludes=KitchenCook)

if __name__ == '__main__':
    shell(TestScenario)