python类write()的实例源码

def loadfromfile(filename, charset=None):
        """Loads data set from filename."""

        try:
            f = open(filename, 'rb')
        except OSError as e:
            stderr.write("Couldn't open data set file, error: {0}\n".format(e))
            return None
        else:
            try:
                dataset = pickle.load(f)
            except Exception as e:
                stderr.write("Couldn't load data set, error: {0}\n".format(e))
                return None
            else:
                stderr.write("Loaded data set from {0}\n".format(filename))
                return dataset
        finally:
            f.close()

def ask_username(config):
    stdout.write("Username: ".encode('utf-8'))
    raw_username = raw_input()
    try:
        get_user(config.temboard['users'], raw_username)
    except HTTPError:
        pass
    except ConfigurationError:
        pass
    else:
        stdout.write("User already exists.\n")
        return ask_username(config)
    try:
        username = raw_username
        validate_parameters({'username': username},
                            [('username', T_USERNAME, False)])
    except HTTPError:
        stdout.write("Invalid username.\n")
        return ask_username(config)
    return username

def print_status(progress, file_size, start):
    """
    This function - when passed as `on_progress` to `Video.download` - prints
    out the current download progress.

    :params progress:
        The lenght of the currently downloaded bytes.
    :params file_size:
        The total size of the video.
    :params start:
        The time when started
    """

    percent_done = int(progress) * 100. / file_size
    done = int(50 * progress / int(file_size))
    dt = (clock() - start)
    if dt > 0:
        stdout.write("\r  [%s%s][%3.2f%%] %s at %s/s " %
                     ('=' * done, ' ' * (50 - done), percent_done,
                      sizeof(file_size), sizeof(progress // dt)))
    stdout.flush()

def extract_all_features(save_dir, data_dir=DATA_DIR, extension=".cell"):
    from naive_bayes import extract_nb_features
    from random_forest import extract_rf_features
    from svc1 import extract_svc1_features
    from svc2 import extract_svc2_features
    import subprocess

    create_dir_if_not_exists(save_dir + '/knn_cells/')
    subprocess.run([
        'go', 'run', dirname + '/kNN.go', '-folder', data_dir + '/',
        '-new_path', save_dir + '/knn_cells/', '-extension', extension]
    )

    # extract_features(extract_nb_features, save_dir + '/nb_cells', data_dir=data_dir, extension=extension, model_name="naive bayes")
    extract_features(extract_rf_features, save_dir + '/rf_cells', data_dir=data_dir, extension=extension, model_name="random forest")
    extract_features(extract_svc1_features, save_dir + '/svc1_cells', data_dir=data_dir, extension=extension, model_name="svc1")
    extract_features(extract_svc2_features, save_dir + '/svc2_cells', data_dir=data_dir, extension=extension, model_name="svc2")

    stdout.write("Finished extracting features\n")

def import_data(data_dir=DATA_DIR, in_memory=True, extension=".cell"):
    """
    Reads all of the files in the `data_dir` and returns all of the contents in a variable.

    @param data_dir is a string with the name of the data directory
    @param in_memory is a boolean value. If true, it pulls all the data into memory

    @return
        if in_memory == True:
            is a tuple with the following format: ([[size, incoming]], [webpage_label])
                where outgoing is 1 is incoming and -1
        else:
            a tuple with the following format: ([paths], [webpage_label])

    """
    stdout.write("Starting data import\n")

    if in_memory:
        return pull_data_in_memory(data_dir, extension)
    else:
        return get_files(data_dir, extension)

def parallel_cone(pipe,cells,time,cone_input,cone_layer,Vis_dark,Vis_resting_potential):
    # Initialize array of cone_response copying cone_input
    cone_response = cone_input

    for cell in cells:
        if multiprocessing.current_process().name=="root":
            progress = 100*(cell-cells[0])/len(cells)
            stdout.write("\r progress: %d %%"% progress)
            stdout.flush()

        # Time-driven simulation
        for t in np.arange(0,time):
            # Update dynamics of the model
            cone_layer[cell].feedInput(cone_input[cell,t])
            cone_layer[cell].update()
            # Record response
            cone_response[cell,t] = (cone_layer[cell].LF_taum.last_values[0] -\
            cone_layer[cell].LF_tauh.last_values[0] - Vis_dark - Vis_resting_potential)

    pipe.send(cone_response[cells,:])
    pipe.close()

#! ================
#! Class runNetwork
#! ================

def update_web_event(self):
        web_event = os.path.join(_base_dir, "web", "events-%s.json" % self._username)

        if not os.path.exists(web_event):
            self.init_event_outfile()

        json_events = self.jsonify_events()
        #self.bot.logger.info('####### Writing %s' % json_events)

        try:
            with open(web_event, "w") as outfile:
                json.dump(json_events, outfile)
        except (IOError, ValueError) as e:
            self.bot.logger.info('[x] Error while opening events file for write: %s' % e, 'red')
        except:
            raise FileIOException("Unexpected error writing to {}".web_event)

def main(argv, environ):
    parser = ArgumentParser(
        prog='temboard-agent-adduser',
        description="Add a new temboard-agent user.",
        argument_default=UNDEFINED_ARGUMENT,
    )
    args = parser.parse_args(argv)
    config = load_configuration(
        specs=list_options_specs(), args=args, environ=environ,
    )

    # Load configuration from the configuration file.
    username = ask_username(config)
    password = ask_password()
    hash_ = hash_password(username, password).decode('utf-8')
    try:
        with open(config.temboard['users'], 'a') as fd:
            fd.write("%s:%s\n" % (username, hash_))
    except IOError as e:
        raise UserError(str(e))
    else:
        stdout.write("Done.\n")

def test(self, dataset, subset='test', name='Test'):
        global g_args
        train_writer = tf.summary.FileWriter(
            os.path.join(hparams.SUMMARY_DIR,
                         str(datetime.datetime.now().strftime("%m%d_%H%M%S")) + ' ' + hparams.SUMMARY_TITLE), g_sess.graph)
        cli_report = {}
        for data_pt in dataset.epoch(
                subset, hparams.BATCH_SIZE * hparams.MAX_N_SIGNAL):
            # note: this disables dropout during test
            to_feed = dict(
                zip(self.train_feed_keys, (
                    np.reshape(data_pt[0], [hparams.BATCH_SIZE, hparams.MAX_N_SIGNAL, -1, hparams.FEATURE_SIZE]),
                    1.)))
            step_summary, step_fetch = g_sess.run(
                self.valid_fetches, to_feed)[:2]
            train_writer.add_summary(step_summary)
            stdout.write('.')
            stdout.flush()
            _dict_add(cli_report, step_fetch)
        stdout.write(name + ': %s\n' % (
            _dict_format(cli_report)))

def before_exit():
    lines_of_code = process_history()

    if not PySession.save or len(lines_of_code) == 0:
        stdout.write(DO_NOTHING)
        return

    filename = expanduser(os.getenv('PYSESSION_FILENAME', 'session.py'))

    if PySession.save_locally:
        stdout.write(SAVING_FILE.format(filename=filename))
        PySession.save_to_file('\n'.join(lines_of_code), filename)
        stdout.write(SUCCESS)
        return

    try:
        stdout.write(SAVING_GIST.format(filename=filename))
        gist_response = PySession.save_to_gist('\n'.join(lines_of_code), filename)
        gist_url = gist_response['html_url']
        PySession.save_gist_url(gist_url)
        webbrowser.open_new_tab(gist_url)
        stdout.write(SUCCESS)
    except:
        stdout.write(FAILED)
        PySession.save_to_file('\n'.join(lines_of_code), filename)

def notify(self, msg):
        if isinstance(msg, basestring):
            print msg
        elif msg[0] == "PROGRESSBAR":
            n = len(self.progressbars)
            name, action = msg[1:]
            if action == "start":
                stdout.write(term.CLRSCR())
                self.progressbars[name] = term.ProgressBar()
            elif action == "end":
                stdout.write(term.CLRSCR())
                del self.progressbars[name]
            else:
                self.progressbars[name].frac = action
            stdout.write(term.CUP(0, 0))
            for name, progressbar in self.progressbars.iteritems():
                stdout.write("%s : %s\n"%(progressbar, name))

def loadfromfile(cls, infile):
        """Load model parameters from file and rebuild model."""

        with np.load(infile) as f:
            # Extract hyperparams and position
            p = f['p']
            hparams = pickle.loads(p.tobytes())
            hyper, epoch, pos = hparams['hyper'], hparams['epoch'], hparams['pos']

            # Load matrices
            pvalues = { n:f[n] for n in cls.pnames }

            # Create instance
            if isinstance(infile, str):
                stdout.write("Loaded model parameters from {0}\n".format(infile))
            stdout.write("Rebuilding model...\n")
            model = cls(hyper, epoch, pos, pvalues)

            return model

def savetofile(self, outfile):
        """Save model parameters to file."""

        # Pickle non-matrix params into bytestring, then convert to numpy byte array
        pklbytes = pickle.dumps({'hyper': self.hyper, 'epoch': self.epoch, 'pos': self.pos}, 
            protocol=pickle.HIGHEST_PROTOCOL)
        p = np.fromstring(pklbytes, dtype=np.uint8)

        # Gather parameter matrices and names
        pvalues = { n:m.get_value() for n, m in self.params.items() }

        # Now save params and matrices to file
        try:
            np.savez_compressed(outfile, p=p, **pvalues)
        except OSError as e:
            raise e
        else:
            if isinstance(outfile, str):
                stdout.write("Saved model parameters to {0}\n".format(outfile))

def build_onehots(self, vocab_size=None):
        """Build one-hot encodings of each sequence."""

        # If we're passed a charset size, great - if not, fall back to inferring vocab size
        if vocab_size:
            self.charsize = vocab_size
            vocab = vocab_size
        else:
            vocab = self.charsize

        stderr.write("Constructing one-hot vector data...")
        stderr.flush()

        time1 = time.time()

        # These can be large, so we don't necessarily want them on the GPU
        # Thus they're not Theano shared vars
        # Also, numpy fancy indexing is fun!
        self.x_onehots = np.eye(vocab, dtype=th.config.floatX)[self.x_array]
        self.y_onehots = np.eye(vocab, dtype=th.config.floatX)[self.y_array]

        time2 = time.time()

        stderr.write("done!\nTook {0:.4f} ms.\n".format((time2 - time1) * 1000.0))

def buildmodelparams(self, hyper, checkpointdir=None):
        """Builds model parameters from given hyperparameters and charset size.
        Optionally saves checkpoint immediately after building if path specified.
        """
        useclass = self.modeltypes[self.modeltype]
        self.model = useclass(hyper)

        if checkpointdir:
            # Compile training functions
            self.model._build_t()

            # Get initial loss estimate
            stderr.write("Calculating initial loss estimate...\n")

            # We don't need anything fancy or long, just a rough baseline
            data_len = self.valid.batchepoch(16)
            loss_len = 20 if data_len >= 20 else data_len
            loss = self.model.calc_loss(self.valid, 0, batchsize=8, num_examples=loss_len)

            stderr.write("Initial loss: {0:.3f}\n".format(loss))
            stderr.write("Initial log loss: {0:.3f}\n".format(log(loss)))

            # Take checkpoint
            self.newcheckpoint(loss, savedir=checkpointdir)

def _vis_graph(graph, points, worker, status):
    total_points = len(points)
    visible_edges = []
    if status:
        t0 = default_timer()
        points_done = 0
    for p1 in points:
        for p2 in visible_vertices(p1, graph, scan='half'):
            visible_edges.append(Edge(p1, p2))
        if status:
            points_done += 1
            avg_time = round((default_timer() - t0) / points_done, 3)
            time_stat = (points_done, total_points-points_done, avg_time)
            status = '\r\033[' + str(21*worker) + 'C[{:4}][{:4}][{:5.3f}] \r'
            stdout.write(status.format(*time_stat))
            stdout.flush()
    return visible_edges

def download_file(self, url: str, filepath: str, fname="", progf=False):
        """Download a file from `url` to `filepath/name`"""
        r = self.session.get(url, stream=True)
        dlen = r.headers.get("content-length")
        step = (100 / int(dlen))
        prog = 0
        if not fname:
            fname = unquote(Path(r.url).name)
        with open(filepath+"/"+fname, 'wb') as f:
            for chunk in r.iter_content(chunk_size=1024):
                if chunk:
                    prog += len(chunk)
                    if progf:
                        progf(int(step * prog))
                    f.write(chunk)
        if progf:
            progf(0)
        return filepath+"/"+fname

def bruteforce(host, port, uname, wordlist):
    try:
        lista = open(wordlist, "r")
    except IOError:
        stdout.write(colored(" [x] Error opening word list\n", "red", attrs=['bold']))
        exit()
    url = "http://"+host+":"+port+"/"
    init = time()
    for l in lista:
        pwd = l.strip()
        try:
            r=get(url, auth=(uname, pwd), timeout=3)
        except:
            stdout.write(colored("\n [-] There was an error connecting to the router %s\n"%(host), "red", attrs=['bold']))
            exit()
        if r.status_code == 200:
            stdout.write(colored("\n\n [+] Cracked => %s:%s\n [+] Duration => %s seconds\n\n"%(uname, pwd, time() - init), "green", attrs=['bold']))
            lista.close()
            exit()
        else:
            stdout.write(colored("\r [-] Current login %s:%s"%(uname, pwd), "yellow", attrs=['bold']))
            stdout.flush()
    print ""
    lista.close()

def file_stats(max_lenght):
    Fl = 7 ** max_lenght
    Fb = (max_lenght + 2) * Fl
    if Fb >= 1000 and Fb < 1000000:
        Fbfinal = Fb / 1000
        stdout.write("[+] Lines : %s\n[+] File size : %i Kb\n\n"%(Fl, Fbfinal))
    elif Fb >= 1000000 and Fb < 1000000000:
        Fbfinal = Fb / 1000000
        stdout.write("[+] Lines : %s\n[+] File size : %i Mb\n\n"%(Fl, Fbfinal))
    elif Fb >= 1000000000:
        setlocale(LC_NUMERIC, '')
        Fbfinal = Fb / 1000000000
        formato = format("%.*f", (0, Fbfinal), True)
        stdout.write("[+] Lines : %s\n[+] File size : "+formato+" Gb\n\n"%(Fl))
    else:
        stdout.write("[+] Lines : %s\n[+] File size : %i bytes\n\n"%(Fl))
    cmd = raw_input("[?] Do you want continue [Y/N] : ")
    if cmd == "N" or cmd == "n":
        exit()

def generator(min_lenght, max_lenght, chars, name):
    lines = 0
    try:
        file=open(name, "w")
    except IOError:
        print "\n[x] Error : %s este caminho nao existe\n"%(name)
        exit()
    file_stats(max_lenght)
    print ""
    for n in range(min_lenght, max_lenght + 1):
        for xs in product(chars, repeat=n):
            lines = lines + 1
            string=''.join(xs)
            file.write(string + "\n")
            stdout.write('\r[+] Saving character `%s`' % string)
            stdout.flush()
    print "\a"
    file.close()

def check_deploy_result(operation, console, appname, auth_header):
    i = 0
    while True:
        s = (i % 3 + 1) * '.'
        if len(s) < 3:
            s = s + (3 - len(s)) * ' '
        i += 1
        stdout.write("\r%s... %s " % (operation, s))
        stdout.flush()
        sleep(0.5)
        result = app_status(console, appname, auth_header)
        if result:
            stdout.write("\r%s... %s. " % (operation, result))
            stdout.flush()
            stdout.write("\n")
            return result

def download_and_uncompress_tarball(base_url, filename, data_dir):

  def _progress(count, block_size, total_size):
    stdout.write('\r>> Downloading %s %.1f%%' % (
        filename, float(count * block_size) / float(total_size) * 100.0))
    stdout.flush()

  tarball_url = base_url + filename
  filepath = osp.join(data_dir, filename)

  if not tf.gfile.Exists( osp.join(download_dir, model_dl) ):
    filepath, _ = urllib.request.urlretrieve(tarball_url, filepath, _progress)
    print()
    statinfo = stat(filepath)
    print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')
  else:
    print('{} tarball already exists -- not downloading'.format(filename))

  tarfile.open(filepath, 'r:*').extractall(data_dir)

def print_rule(self):
        """Print out rules
        """
        stdout.write('======================================================\n')
        stdout.write('Rules:\n')
        for rule in self.rule:
            head = rule[0][0]
            tail = rule[0][1]
            confidence = rule[1]
            stdout.write('(')
            stdout.write(', '.join(head))
            stdout.write(')')
            stdout.write(' ==> ')
            stdout.write('(')
            stdout.write(', '.join(tail))
            stdout.write(')')
            stdout.write('  confidence = {0}\n'.format(round(confidence, 3)))
        stdout.write('======================================================\n')

def optimize(self, X, lmbd, Z=None, max_iter=1, tol=1e-5):
        if Z is None:
            batch_size = X.shape[0]
            K = self.D.shape[0]
            z_curr = np.zeros((batch_size, K))
        else:
            z_curr = np.copy(Z)
        self.train_cost, self.train_z = [], []
        feed = {self.X: X, self.Z: z_curr, self.lmbd: lmbd}
        for k in range(max_iter):
            z_curr[:], dz, cost = self.session.run(
                [self.step_optim, self.dz, self._cost], feed_dict=feed)
            self.train_cost += [cost]
            self.train_z += [np.copy(z_curr)]
            if dz < tol:
                print("\r{} reached optimal solution in {}-iteration"
                      .format(self.name, k))
                break
            out.write("\rIterative optimization ({}): {:7.1%} - {:.4e}"
                      "".format(self.name, k/max_iter, dz))
            out.flush()
        self.train_cost += [self.session.run(self._cost, feed_dict=feed)]
        print("\rIterative optimization ({}): {:7}".format(self.name, "done"))
        return z_curr

def print_progress(self, threshold=0, decimals=1, barLength=100):
        """Print a terminal progress bar."""
        # Based on @Greenstick's reply (https://stackoverflow.com/a/34325723)
        iteration = self.stream.tell()
        if iteration > self.file_size:
            return
        total = self.file_size
        if total == 0:
            return

        progress = 100.0 * iteration / total
        if self.progress and progress - self.progress < threshold:
            return

        self.progress = progress
        percents = ("%03." + str(decimals) + "f") % progress
        filledLength = int(round(barLength * iteration / float(total)))

        barText = '*' * filledLength + '-' * (barLength - filledLength)
        stdout.write('%s| %s%% Completed\r' % (barText, percents))
        stdout.flush()

def __init__(self, parallel):
        self.continuation_prompt = self.prompt
        self.parallel = parallel
        width, height = get_terminal_size() or MIN_TERM_SIZE
        if any(map((lambda s: s[0] < s[1]), zip((height, width), MIN_TERM_SIZE))):
            stdout.write("\x1b[8;{rows};{cols}t".format(rows=max(MIN_TERM_SIZE[0], height),
                                                        cols=max(MIN_TERM_SIZE[1], width)))
        if self.parallel:
            processes = cpu_count()
            self.__last_tasklist = None
            self.tasklist = {}
            self.pool = Pool(processes, lambda: signal(SIGINT, SIG_IGN))
            atexit.register(self.graceful_exit)
        self.reexec = ['status']
        self.__bind_commands()
        super(FrameworkConsole, self).__init__()
        self.do_loglevel('info')
        self.__start_docserver()
        self.do_clear('')

def wait_time(self, data={'waitSeconds': None}):
        def complete(i, wait):
            return ((100 * (float(i) / float(wait))) * 50) / 100

        if data['waitSeconds'] is not None:
            wait = data['waitSeconds'] + (random.randint(2, 4) / 3.33)
            print(I18n.get('Waiting %s seconds') % str(wait))

            c = i = 0
            while c < 50:
                c = complete(i, wait)
                time.sleep(wait - i if i == int(wait) else 1)
                out.write("[{}]\0\r".format('+' * int(c) + '-' * (50 - int(c))))
                out.flush()
                i += 1
            out.write("\n")
            out.flush()
            return data['waitSeconds']
        return 99999999

def __iter__(self):
        try:
            with open(self.__filename, 'rb') as file_in:
                while True:
                    data = file_in.read(self.__chunksize)
                    if not data:
                        if self.__progressbar:
                            stdout.write("\n")
                        break
                    self.__readsofar += len(data)
                    if self.__progressbar:
                        percent = self.__readsofar * 100 / self.__totalsize
                        stdout.write("\r{percent:3.0f}%".format(percent=percent))
                    yield data
        except OSError as error:
            raise exception.SourceNotFound(error)

def create_admin(username):
    from eventit.eventit import db
    from eventit.models import User, Role
    import getpass
    from sys import stdout

    password = None
    password2 = None
    while not password or password != password2:
        password = getpass.getpass()
        password2 = getpass.getpass('Please, repeat your password: ')
        if not password or password != password2:
            stdout.write('Passwords do not match')

    user = User(username=username, email='', is_active=True, password=password)
    role_admin = Role.get_role_obj('admin')
    user.role = role_admin

    db.session.add(user)
    db.session.commit()

def LOG(message=None,type=None):
    if VERBOSITY<=0:
        return
    elif VERBOSITY==1:
        #minimal verbosity ...   dot style output
        if type in MSGSCHEME_MIN:
            message = MSGSCHEME_MIN[type]
            if DO_COLOR and type in COLORSCHEME:
                message = COLORSCHEME[type]%message
            stdout.write("%s"%message)
            stdout.flush()
    else:
        if type in MSGSCHEME:
            message = MSGSCHEME[type]%message
        if DO_COLOR and type in COLORSCHEME:
            message = COLORSCHEME[type]%message
        if MODE_FUZZ:
            stdout.write("[FUZZ] %s\n"% (message))
        else:
            stdout.write("%s\n"% (message))
        stdout.flush()