Python stats.Stats类代码示例

class NPC(object):
	def __init__(self, name):
		self.stats = Stats()
		self.name = name

	def show_hp(self):
		print "HP:", self.stats.hit_points

	def attack(self, enemy):
		mod = np.random.uniform(0.9, 1, 1)[0]
		a = self.stats.level*0.3
		b = (float(self.stats.attack)*4/enemy.stats.defense)*4.0 
		c = mod
		damage = max(int(a*b*c), 1)
		enemy.stats.hit_points -= damage
		print "BAM!", enemy.name, "was hit for",  damage
		enemy.stats.hit_points = max(enemy.stats.hit_points, 0)
		return damage

	def level_up(self):
		if self.stats.level < 99:
			self.stats.level += 1
			self.stats.max_hit_points = self.stats.level*8 
			rem_points = 3 
			stat_selector = []
			stat_selector.extend(self.stats.stats_names)
			while(rem_points > 0):
				rand_stat = np.random.choice(stat_selector, 1)
				rand_stat_increase = np.random.randint(1,rem_points+1)
				self.stats.modify_stat(rand_stat, rand_stat_increase) 
				stat_selector.remove(rand_stat)
				rem_points -= rand_stat_increase

	def level_to(self, lvl):
		if lvl > 99:
			lvl = 99
		while(self.stats.level < lvl):
			self.level_up() 

	def show_stats(self):
		print "/"*20
		print self.name, " (LV. " , self.stats.level , ")"
		print self.stats.hit_points , "/", self.stats.max_hit_points
		aux_hp = int((float(self.stats.hit_points)/ self.stats.max_hit_points)*20)
		txt_hp = "+"*aux_hp
		print "[{0:20}]".format(txt_hp)
		print "Att: ", self.stats.attack
		print "Def: ", self.stats.defense
		print "Lck: ", self.stats.luck
		print "/"*20

	def cure(self, hp):
		a = self.stats.hit_points + hp
		if a > self.stats.max_hit_points:
			result = self.stats.max_hit_points - self.stats.hit_points 
			self.stats.hit_points = self.stats.max_hit_points
		else:
			result = hp
			self.stats.hit_points = a
		return result

class Simulation(object):

    def __init__(self, aggregator, strategy, reserved_resources, sleep_time=0):


        self.aggregator = aggregator

        self.strategy = strategy
# 2 year warm up, and 10 year run
        self.stats = Stats(24, 120)
        self.company = Company(20, reserved_resources, self.strategy, self.stats)
        self.sleep_time = sleep_time
        self.reserved_resources = reserved_resources

        print(sim_to_key(self))

    def run(self):

        for t in range(self.stats.runs):

            self.stats.start_month()

            projects = [generate_project() for _ in range(sample.project_count())]
            self.company.decide_projects(projects)

            used_resources = self.company.workflow.work()

            self.stats.end_month(used_resources, self.company.workflow.average_workload())

            if self.sleep_time > 0:
                sleep(self.sleep_time)

            #print(self.stats.monthly_report())

        self.aggregator.add_result(self)

def process(infile, algorithm_name, support, confidence, m, random, partial):
    stats = Stats()
    transactions = TransactionsList(infile)
    stats.record_post_large_sets()
    stats.record_post_rules()
    last_total_time = stats.real_time
    last_user_time = stats.user_time
    stats = Stats()
    if algorithm_name == 'apriori':
        algorithm = Apriori(transactions, support)
    else:
        algorithm = Dic(transactions, support, m, random, partial)
    large_sets, counter = algorithm.get_large_sets_and_counter()
    stats.record_post_large_sets()
    rules = RulesGenerator.generate_rules(large_sets, confidence, counter, transactions)
    stats.record_post_rules()
    large_len = len(large_sets)
    total_time = stats.real_time - last_total_time
    user_time = stats.user_time - last_user_time
    large_sets_time = stats.set_gen_time - last_total_time
    last_total_time = stats.real_time
    last_user_time = stats.user_time
    memory = stats.memory_use
    rules_no = len(rules)

    print "{infile}\t{algorithm_name}\t{support}\t{confidence}\t{m}\t{rules_no}\t{large_len}\t{memory}\t{total_time}\t{user_time}\t{large_sets_time}\t{partial}\t{random}".format(**locals())

    def do_mark(self, subcmd, opts, bamfile, amplicons):
        """${cmd_name}: Mark reads matching amplicons and optionally clip.
            
            Walk a BAM file and mark any matching amplicons using the AM tag.
            Outputs a modified BAM.  Use 'clip' if you want only reads matching 
            amplicons in the output.
            
            ${cmd_usage}
            BAMFILE: input reads (use - for stdin)
            AMPLICONS: a file listing amplicons and trim locations.
            
            ${cmd_option_list}
        """
        samfile = pysam.Samfile(bamfile, "rb")
        stats = Stats(" ".join(sys.argv))
        amplicons = load_amplicons(design, stats, opts, samfile=samfile)
        outfile = pysam.Samfile(opts.outfile, "wb", template=samfile)

        # we need to reopen the file here to get sequential access after computin the pileups
        samfile = pysam.Samfile(bamfile, "rb")
        for read in samfile:

            # TODO: optimisation of the list of amplicons that are considered
            for amp in amplicons:
                if amp.matches(read):
                    amp.clip(read)
                    amp.mark(read)
            outfile.write(read)

        stats.report(sys.stderr)

    def on_shutter(self, state):
        print '-----'

        print 'Time to Start'
        total = Stats()
        for method_name, stats in state.time_to_start.items():
            print "%s avg:%.2fs, max:%.2fs, min: %.2fs" % (method_name, stats.average() or -1.0 , stats.maximum or -1.0, stats.minimum or -1.0)
            total += stats

        print ''
        print 'Time to Process'
        total = Stats()
        for method_name, stats in state.time_to_process.items():
            print "%s avg:%.2fs, max:%.2fs, min: %.2fs" % (method_name, stats.average() or -1.0, stats.maximum or -1.0, stats.minimum or -1.0)
            total += stats
        print "Total: avg:%.2fs, max:%.2fs, min: %.2fs" % (total.average() or -1.0, total.maximum or -1.0, total.minimum or -1.0)

        print ''
        print 'Event Totals'
        for method_name, totals in state.totals.items():
            for total_name, total in totals.items():
                print "%s[%s]: %d" % (method_name, total_name, total)

        print ''
        print 'Queue Sizes'
        print 'Waiting Tasks: %d' % len(state.waiting_tasks)
        print 'Running Tasks: %d' % len(state.running_tasks)

        print ''
        print ''

def main():

    filename = sys.argv[1]

    graph = Graph()
    parser = Parser()

    #parse gdf file
    parser.parse(filename, graph)
#    print graph.__str__()

    stats = Stats(graph)

    #compute popularity
    popularities = stats.computePopularity()
    print "Popularidad"
    print "********************************"
    stats.showPopularities(popularities)

    #compute influences
    influences = stats.computeInfluences()
    print ""
    print "Influencias"
    print "********************************"
    stats.showInfluences(influences)

    #obtain recomendations
    print ""
    print "Recomendaciones"
    print "********************************"
    recommendations = stats.computeRecommendations()
    stats.showRecommendations(recommendations)

	def __init__(self, ciphertext, stats_filenames, dict_file, max_pop = 10, params = default_params, alphabet = default_alphabet, stat_file = None, naive_key = True):
	
		self.params = params
		self.alphabet = alphabet
		self.single_stats_filename, self.double_stats_filename = stats_filenames
		self.ciphertext = ciphertext
		self.max_pop = max_pop
		
		self.exp_stats = Stats(self.alphabet)
		self.exp_stats.load_singlef_from_file(self.single_stats_filename)
		self.exp_stats.load_doublef_from_file(self.double_stats_filename)

		self.ciph_stats = Stats(self.alphabet)
		self.ciph_stats.process_text(self.ciphertext)
		
		self.initialize(naive_key)
		self.stat_file = None
				
		if stat_file != None:
			self.stat_file = open(stat_file, "w")
			self.stat_file.write("gen\tworst\tavg\tbest\tderiv\n")
			
		self.sliding_table = []
		self.end = False
		
		self.dictionary = Dictionary(dict_file)
		self.temp_stats = Stats(self.alphabet)
		
		signal.signal(signal.SIGINT, self.signal_handler)

 def __getattr__(self, name):
         if name == 'stats':
                 stats = Stats()
                 for effect in self.effects:
                         stats.add_stats(effect.stats)
                 return stats
         else:
                 raise AttributeError

    def possible(self, row, col):
        if (row == col):
            return Stats.possible(self, row, col) & self.diag0

        elif (row + col) == 8:
            return Stats.possible(self, row, col) & self.diag8

        else:
            return Stats.possible(self, row, col)

 def test_tb(self):
     """
     Total bases test
     :return:
     """
     # ichiro suzuki(2004)
     single = Stats.single(262, 8, 24, 5)
     tb = Stats.tb(single, 8, 24, 5)
     self.assertEqual(tb, 320)

 def __getattr__(self, name):
         if name == 'stats':
                 stats = Stats()
                 for item in self.equipment:
                         if self.equipment[item]:
                                 stats.add_stats(self.equipment[item].stats)
                 return stats
         else:
                 raise AttributeError

 def test_rc(self):
     """
     Run created test
     :return:
     """
     # ichiro suzuki(2004)
     single = Stats.single(262, 8, 24, 5)
     rc = Stats.rc(262, 49, 4, 11, 6, 3, 2, 36, 63, 704, 19, single, 24, 5, 8)
     self.assertEqual(rc, 136.5)

 def setUp(self):
     self.st = Stats()
     
     self.expect = Stats()
     self.expect.sumsq = 425.1641
     self.expect.sum = 55.84602
     self.expect.min = 0.333
     self.expect.max = 9.678
     self.expect.n = 10

def home(request):
    log.info('Request: home')

    stats = Stats()

    return render(request, 'binary/home.html', {
        'q_values': stats.q.data,
        'runs_latest': stats.runs[-30:],
        'time_frames': stats.summarizeTimeFrames(),
        'trade_bases': stats.summarizeTradeBases(),
        'trade_aims': stats.summarizeTradeAims(),
    })

def main(args):
    states = read_states(args.input)

    stats = Stats(sys.argv)
    cipher = Spritz()

    settings = Settings(args)

    prompt_step = max(1, len(states) // 20)
    i = 0
    for initial_state, revealed_state, prefix_length in states:
        if args.verbosity > 1 and i % prompt_step == 0:
            print('test #:', i)
        i += 1

        KNOWN_KEYSTREAM_SIZE = 3 * initial_state.size
        cipher.initialize_state(initial_state.state)
        known_keystream = cipher.keystream(prefix_length + KNOWN_KEYSTREAM_SIZE)
        settings.prefix_length = prefix_length

        # in case we want to skip less keystream than revealed_state is
        # generate in, we cut off beginning of keystream and move 
        # initial_state apropriatelly
        if args.input and args.force_prefix_length and args.force_prefix_length < prefix_length:
            new_prefix_length = args.force_prefix_length
            new_start_offset = prefix_length - new_prefix_length
            settings.prefix_length = new_prefix_length
            known_keystream = known_keystream[new_start_offset:]   
            cipher.initialize_state(initial_state.state)
            cipher.keystream(new_start_offset)
            initial_state = SpritzState(cipher.state)

        cipher.initialize_state(initial_state.state)
        cipher.keystream(prefix_length)
        found_state, round_stats = backtrack.kpa(
            known_keystream,
            revealed_state,
            settings,
        )

        if found_state and initial_state != found_state:
            print('incorrect result, this should not happen')
            assert False

        stats.add(round_stats)

    stats.print_stats(args.verbosity)
    # dump pickled stats object
    if not args.no_stats_log:
        timestamp = datetime.datetime.today().strftime('%y%m%d_%H%M%S_%f')
        os.makedirs('stats/', exist_ok=True)
        with open('stats/' + timestamp, 'wb') as f:
            pickle.dump(stats, f)