python类ProgressBar()的实例源码

def prepare_inception_data(o_dir, i_dir):
    if not os.path.exists(o_dir):
        os.makedirs(o_dir)
        cnt = 0
        bar = progressbar.ProgressBar(redirect_stdout=True,
                                      max_value=progressbar.UnknownLength)
        for root, subFolders, files in os.walk(i_dir):
            if files:
                for f in files:
                    if 'jpg' in f:
                        f_name = str(cnt) + '_ins.' + f.split('.')[-1]
                        cnt += 1
                        file_dir = os.path.join(root, f)
                        dest_path = os.path.join(o_dir, f)
                        dest_new_name = os.path.join(o_dir, f_name)
                        copy(file_dir, o_dir)
                        os.rename(dest_path, dest_new_name)
                        bar.update(cnt)
        bar.finish()
        print('Total number of files: {}'.format(cnt))

def load_images(o_dir, i_dir, n_images=3000, size=128):
    prepare_inception_data(o_dir, i_dir)
    image_list = []
    done = False
    cnt = 0
    bar = progressbar.ProgressBar(redirect_stdout=True,
                                  max_value=progressbar.UnknownLength)
    for root, dirs, files in os.walk(o_dir):
        if files:
            for f in files:
                cnt += 1
                file_dir = os.path.join(root, f)
                image_list.append(ip.load_image_inception(file_dir, 0))
                bar.update(cnt)
                if len(image_list) == n_images:
                    done = True
                    break
        if done:
            break
    bar.finish()
    print('Finished Loading Files')
    return image_list

def render(self, length=None, progress=False):
        """
        Render this signal into an numpy array of floats. Return the array.

        :param length:      The length to render, in seconds. Optional.
        :param progress:    Whether to show a progress bar for rendering
        """
        if progress and not progressbar:
            print('Install the progressbar module to see a progress bar for rendering')
            progress = False

        duration = self.duration if length is None else length * SAMPLE_RATE
        if duration == float('inf'):
            duration = 3*SAMPLE_RATE
        else:
            duration = int(duration)
        out = numpy.empty((duration, 1))

        pbar = progressbar.ProgressBar(widgets=['Rendering: ', progressbar.Percentage(), ' ', progressbar.Bar(), ' ', progressbar.ETA()], maxval=duration-1).start() if progress else None

        for i in range(duration):
            out[i] = self.amplitude(i)
            if pbar: pbar.update(i)
        if pbar: pbar.finish()
        return out

def multi_core_scrape(num_pages, db_coll):
    '''
    Map the API scrape across number of processors - 1 for performance boost.

    INPUT:
        num_pages: int, number of pages to scrape
        db_coll: pymongo collection object, collection to add documents to

    OUTPUT:
        None, records inserted into MongoDB
    '''
    cpus = cpu_count() - 1
    pool = Pool(processes=cpus)
    pages = range(1, num_pages + 1)
    employers = pool.map(scrape_api_page, pages)
    pool.close()
    pool.join()
    print 'Inserting Employer Records into MongoDB . . .'
    pbar = ProgressBar()
    for page in pbar(employers):
        db_coll.insert_many(page)

def main():
    uri, outfile, dataset = get_arguments()
    fd = tempfile.NamedTemporaryFile()
    progress = ProgressBar(widgets=[Percentage(), ' ', Bar(), ' ', ETA(), ' ', FileTransferSpeed()])

    def update(count, blockSize, totalSize):
        if progress.maxval is None:
            progress.maxval = totalSize
            progress.start()
        progress.update(min(count * blockSize, totalSize))

    urllib.urlretrieve(uri, fd.name, reporthook = update)
    if dataset == 'zinc12':
        df = pandas.read_csv(fd.name, delimiter = '\t')
        df = df.rename(columns={'SMILES':'structure'})
        df.to_hdf(outfile, 'table', format = 'table', data_columns = True)
    elif dataset == 'chembl22':
        df = pandas.read_table(fd.name,compression='gzip')
        df = df.rename(columns={'canonical_smiles':'structure'})
        df.to_hdf(outfile, 'table', format = 'table', data_columns = True)
        pass
    else:
        df = pandas.read_csv(fd.name, delimiter = '\t')
        df.to_hdf(outfile, 'table', format = 'table', data_columns = True)

def main():

    start = timer()
    files = get_file_names()
    smell_results = []

    bar = progressbar.ProgressBar(max_value=len(files))
    processed_files = 0
    with concurrent.futures.ProcessPoolExecutor() as executor:
        for file, smell in zip(files, executor.map(worker, files)):
            smell_results = smell_results + smell
            processed_files += 1
            bar.update(processed_files)
    smell_results = [x for x in smell_results if x]

    end = timer()
    print(end - start)
    dataminer = SmellDataMine()
    dataminer.save_to_database(smell_results)

def knn_masked_data(trX,trY,missing_data_dir, input_shape, k):

    raw_im_data = np.loadtxt(join(script_dir,missing_data_dir,'index.txt'),delimiter=' ',dtype=str)
    raw_mask_data = np.loadtxt(join(script_dir,missing_data_dir,'index_mask.txt'),delimiter=' ',dtype=str)
    # Using 'brute' method since we only want to do one query per classifier
    # so this will be quicker as it avoids overhead of creating a search tree
    knn_m = KNeighborsClassifier(algorithm='brute',n_neighbors=k)
    prob_Y_hat = np.zeros((raw_im_data.shape[0],int(np.max(trY)+1)))
    total_images = raw_im_data.shape[0]
    pbar = progressbar.ProgressBar(widgets=[progressbar.FormatLabel('\rProcessed %(value)d of %(max)d Images '), progressbar.Bar()], maxval=total_images, term_width=50).start()
    for i in range(total_images):
        mask_im=load_image(join(script_dir,missing_data_dir,raw_mask_data[i][0]), input_shape,1).reshape(np.prod(input_shape))
        mask = np.logical_not(mask_im > eps) # since mask is 1 at missing locations
        v_im=load_image(join(script_dir,missing_data_dir,raw_im_data[i][0]), input_shape, 255).reshape(np.prod(input_shape))
        rep_mask = np.tile(mask,(trX.shape[0],1))
        # Corrupt whole training set according to the current mask
        corr_trX = np.multiply(trX, rep_mask)        
        knn_m.fit(corr_trX, trY)
        prob_Y_hat[i,:] = knn_m.predict_proba(v_im.reshape(1,-1))
        pbar.update(i)
    pbar.finish()
    return prob_Y_hat

def preprocess(self, questions: List[QASetting],
                   answers: Optional[List[List[Answer]]] = None,
                   is_eval: bool = False) -> List[XQAAnnotation]:

        if answers is None:
            answers = [None] * len(questions)
        preprocessed = []
        if len(questions) > 1000:
            bar = progressbar.ProgressBar(
                max_value=len(questions),
                widgets=[' [', progressbar.Timer(), '] ', progressbar.Bar(), ' (', progressbar.ETA(), ') '])
            for q, a in bar(zip(questions, answers)):
                preprocessed.append(self.preprocess_instance(q, a))
        else:
            for q, a in zip(questions, answers):
                preprocessed.append(self.preprocess_instance(q, a))

        return preprocessed

def preprocess(self, questions: List[QASetting],
                   answers: Optional[List[List[Answer]]] = None,
                   is_eval: bool = False) -> List[MCAnnotation]:
        if answers is None:
            answers = [None] * len(questions)
        preprocessed = []
        if len(questions) > 1000:
            bar = progressbar.ProgressBar(
                max_value=len(questions),
                widgets=[' [', progressbar.Timer(), '] ', progressbar.Bar(), ' (', progressbar.ETA(), ') '])
            for i, (q, a) in bar(enumerate(zip(questions, answers))):
                preprocessed.append(self.preprocess_instance(i, q, a))
        else:
            for i, (q, a) in enumerate(zip(questions, answers)):
                preprocessed.append(self.preprocess_instance(i, q, a))

        return preprocessed

def evaluate_network(self, ckpt):
        path = config.EVAL_DIR + '/Data/'
        self.filename = path + 'coco_%s_%s_%i.json' % (self.loader.split, args.run_name, ckpt)
        detections = []
        filenames = self.loader.get_filenames()

        bar = progressbar.ProgressBar()
        for i in bar(range(len(filenames))):
            img_id = filenames[i]
            detections.extend(self.process_image(img_id, i))
        with open(self.filename, 'w') as f:
            json.dump(detections, f)
        if args.segment:
            iou = self.compute_mean_iou()
        cocoEval = self.compute_ap()
        return self.compact_results(cocoEval.stats, ckpt)

def to_pairs(self, pairs):
        """Copies labels and scores from self to pairs matrix.

        Args:
            pairs (SimilarityMatrix):

        """
        six.print_('copy labels', flush=True)
        self.build_label_cache()
        pairs.labels.update(self.cache_l2i)

        six.print_('copy matrix to pairs', flush=True)
        limit = self.scores.shape[0]
        bar = ProgressBar()
        for query_id in bar(six.moves.range(0, limit)):
            subjects = self.scores[query_id, ...]
            filled_subjects_ids = subjects.nonzero()[0]
            filled_subjects = [(query_id, i, subjects[i]) for i in filled_subjects_ids if query_id < i]
            if filled_subjects:
                pairs.pairs.table.append(filled_subjects)

def collect_mailids(server):
    folders = server.list_folders()

    #construct progressbar
    progressbar_widgets = [
        '[Searching for mails on server] ',
        progressbar.Percentage(),
        progressbar.Bar(marker=progressbar.RotatingMarker()), ' ']
    progressbar_instance = progressbar.ProgressBar(widgets=progressbar_widgets, maxval=len(folders)).start()

    #collect all mailids for all folders
    folder_contents = {}
    folder_progress = 0
    for flags, delimiter, folder in folders:
        #read all mailids for the folder
        server.select_folder(folder, readonly=True)
        folder_contents[folder] = server.search()

        #update progrssbar
        folder_progress += 1
        progressbar_instance.update(folder_progress)

    progressbar_instance.finish()
    return folder_contents

def download(download_list, total_download_size):
    progressbar_widgets = [
        '[Downloading mails            ] ',
        progressbar.Percentage(),
        progressbar.Bar(marker=progressbar.RotatingMarker()), ' ',
        progressbar.ETA(), ' ',
        bitmath.integrations.BitmathFileTransferSpeed()]
    progressbar_instance = progressbar.ProgressBar(widgets=progressbar_widgets, maxval=int(total_download_size)).start()

    downloaded_size = bitmath.Byte(0)
    for folder, mails in download_list.items():
        server.select_folder(folder, readonly=True)
        for mailid, mailfilename, mailsize in mails:
            #make parent directory
            if not os.path.isdir(os.path.dirname(mailfilename)):
                os.makedirs(os.path.dirname(mailfilename))

            #download mail
            with open(mailfilename, 'wb') as mailfile:
                mailfile.write(server.fetch([mailid], ['RFC822'])[mailid][b'RFC822'])

            #update progressbar
            downloaded_size += mailsize
            progressbar_instance.update(int(downloaded_size))
    progressbar_instance.finish()

def validate(test_data, test_labels, model, batchsize, silent, gpu):
    N_test = test_data.shape[0]
    pbar = ProgressBar(0, N_test)
    sum_accuracy = 0
    sum_loss = 0

    for i in range(0, N_test, batchsize):
        x_batch = test_data[i:i + batchsize]
        y_batch = test_labels[i:i + batchsize]

        if gpu >= 0:
            x_batch = cuda.to_gpu(x_batch.astype(np.float32))
            y_batch = cuda.to_gpu(y_batch.astype(np.int32))

        x = Variable(x_batch)
        t = Variable(y_batch)
        loss, acc = model(x, t, train=False)

        sum_loss += float(cuda.to_cpu(loss.data)) * y_batch.size
        sum_accuracy += float(cuda.to_cpu(acc.data)) * y_batch.size
        if not silent:
            pbar.update(i + y_batch.size)

    return sum_loss, sum_accuracy

def __filter_regions_by_class(self, regions):
        print("Filtering regions...")
        act_regions = []
        region_sub = {}
        bar = progressbar.ProgressBar()
        for region in bar(regions):
            try:
                reg_obj = self.region_objects[region.image.id][region.id]
                reg_obj = frozenset([x.lower()
                                     for x in reg_obj])
            except KeyError:
                reg_obj = frozenset({})

            if reg_obj in self.obj_idx:
                act_regions.append(region)
                if region.image.id not in region_sub:
                    region_sub[region.image.id] = {}
                reg_img = region_sub[region.image.id]
                global_region_img = self.region_objects[region.image.id]
                reg_img[region.id] = global_region_img[region.id]
        return act_regions, region_sub

def load_data(path):
    n_lines = count_lines(path)
    bar = progressbar.ProgressBar()
    train = []
    test = []
    print('loading...: %s' % path)
    with open(path) as f:
        i = 0
        for line in bar(f, max_value=n_lines):
            words = line.strip().split()
            if i < 1000:
                test.append(np.array(words))
                i+=1
            else:
                train.append(np.array(words))
    return train, test

def load_data(word_voc, char_voc, path):
    n_lines = count_lines(path)
    bar = progressbar.ProgressBar()
    data = []
    print('loading...: %s' % path)
    with open(path) as f:
        for line in bar(f, max_value=n_lines):
            words = line.strip().split()
            '''
            array = np.array([word_voc.get(w, UNK) for w in words], dtype=np.int32)
            unk_words = np.array(words)[array==UNK]
            unk_array = np.array([
                np.array([char_voc.get(c, UNK) for c in list(w)], dtype=np.int32)
                for w in unk_words])
            array = np.array([array, unk_array])
            if len(unk_array)!=0:
                print(array)
            '''
            data.append(np.array(words))
    return data

def progressbarize(iterable, progress=False):
    """Construct progressbar for loops if progressbar requested, otherwise return directly iterable.

    :param iterable: iterable to use
    :param progress: True if print progressbar
    """
    if progress:
        # The casting to list is due to possibly yielded value that prevents
        # ProgressBar to compute overall ETA
        return progressbar.ProgressBar(widgets=[
            progressbar.Timer(), ', ',
            progressbar.Percentage(), ', ',
            progressbar.SimpleProgress(), ', ',
            progressbar.ETA()
        ])(list(iterable))

    return iterable

def bruteforce():
    import progressbar
    from time import sleep
    bar = progressbar.ProgressBar(maxval=60, \
        widgets=[progressbar.Bar('==', '[', ']'), ' ', progressbar.Percentage()])
    bar.start()
    for i in xrange(10):
        bar.update(i+1)
        sleep(0.05)
        wordlist = "/root/2fassassin/crack/wordlist/2fa-wordlist.txt"
        target = "/root/2fassassin/loot/*.pfx"
        sign = ""
        sign += "crackpkcs12 -v -b"
        sign += " "
        sign += target
        sign += "| tee crack.log"
        os.system(sign)
    bar.finish()
    sys.exit()

def bruteforce():
    import progressbar
    from time import sleep
    bar = progressbar.ProgressBar(maxval=60, \
        widgets=[progressbar.Bar('=', '[', ']'), ' ', progressbar.Percentage()])
    bar.start()
    for i in xrange(10):
        bar.update(i+1)
        sleep(0.05)
        wordlist = "/root/2fassassin/crack/wordlist/2fa-wordlist.txt"
        target = "/root/2fassassin/loot/*.pfx"
        sign = ""
        sign += "crackpkcs12 -v -b"
        sign += " "
        sign += target
        sign += "| tee crack.log"
        os.system(sign)
    bar.finish()
    sys.exit()

def __init__(self, n_estimators, learning_rate, min_samples_split,
                 min_impurity, max_depth, regression):
        self.n_estimators = n_estimators
        self.learning_rate = learning_rate
        self.min_samples_split = min_samples_split
        self.min_impurity = min_impurity
        self.max_depth = max_depth
        self.regression = regression
        self.bar = progressbar.ProgressBar(widgets=bar_widgets)

        # Square loss for regression
        # Log loss for classification
        self.loss = SquareLoss()
        if not self.regression:
            self.loss = CrossEntropy()

        # Initialize regression trees
        self.trees = []
        for _ in range(n_estimators):
            tree = RegressionTree(
                    min_samples_split=self.min_samples_split,
                    min_impurity=min_impurity,
                    max_depth=self.max_depth)
            self.trees.append(tree)

def __init__(self, n_estimators=100, max_features=None, min_samples_split=2,
                 min_gain=0, max_depth=float("inf")):
        self.n_estimators = n_estimators    # Number of trees
        self.max_features = max_features    # Maxmimum number of features per tree
        self.min_samples_split = min_samples_split
        self.min_gain = min_gain            # Minimum information gain req. to continue
        self.max_depth = max_depth          # Maximum depth for tree
        self.progressbar = progressbar.ProgressBar(widgets=bar_widgets)

        # Initialize decision trees
        self.trees = []
        for _ in range(n_estimators):
            self.trees.append(
                ClassificationTree(
                    min_samples_split=self.min_samples_split,
                    min_impurity=min_gain,
                    max_depth=self.max_depth))

def precompute(db, dir):
    m = megatron.Megatron(db)
    m.database.drop_all()
    m.database.create_database()

    importer = import_book.BookImporter(m)
    progress = progressbar.ProgressBar()
    importer.import_from(dir, progress)

    counting_worker.run(m)

    tfidf = tf_idf.TFIDF(m)
    tfidf.compute_idf()

    tfidf.compute_tfidf()

    tfidf.compute_top_words()

def assembly(overlap_length, percent_identity, threads, wd, verbose):
    """
    """
    manage = Manager()
    queue = manage.Queue()
    pool = Pool(processes=int(threads), maxtasksperchild=10)

    new_commands = []
    for root, dirs, file in os.walk(wd):
        for fasta_file in file:
            complete_data = (fasta_file, percent_identity, overlap_length, wd, verbose, queue)
            new_commands.append(complete_data)
    results = pool.map_async(iAssembler, new_commands)
    with progressbar.ProgressBar(max_value=len(new_commands)) as bar:
        while not results.ready():
            size = queue.qsize()
            bar.update(size)
            time.sleep(1)

def __load_embeding_model(self, file_path, max_vocab_size=100000):
        self.__embed_vectors = dict()
        if not file_path:
            print('Embeddings file not provided')
            return
        if not os.path.exists(file_path):
            print('Embeddings file not found:', file_path)
            return

        print('Loading the embedding model from:', file_path)
        bar = progressbar.ProgressBar(max_value=max_vocab_size)
        with open(file_path, "r") as embed_f:
            for line in embed_f:
                try:
                    tab = line.rstrip().split()
                    word = tab[0].lower()
                    if not word in self.__embed_vectors:
                        vec = numpy.array(tab[1:], dtype=float)
                        self.__embed_vectors[word] = vec
                except ValueError:
                    continue
                bar.update(len(self.__embed_vectors))
                if len(self.__embed_vectors) == max_vocab_size:
                    bar.finish()
                    return

def Steg_brute(ifile, dicc):
    i = 0
    ofile = ifile.split('.')[0] + "_flag.txt"
    nlines = len(open(dicc).readlines())
    with open(dicc, 'r') as passFile:
        pbar = ProgressBar(widgets=[Percentage(), Bar()], maxval=nlines).start()
        for line in passFile.readlines():
            password = line.strip('\n')
            r = commands.getoutput("steghide extract -sf %s -p '%s' -xf %s" % (ifile, password, ofile))
            if not "no pude extraer" in r and not "could not extract" in r:
                print(color.GREEN + "\n\n " + r + color.ENDC)
                print("\n\n [+] " + color.INFO + "Information obtained with password:" + color.GREEN + " %s\n" % password + color.ENDC)
                if check_file(ofile):
                    with open(ofile, 'r') as outfile:
                        for line in outfile.readlines():
                            print(line)
                break
            pbar.update(i + 1)
            i += 1

def emit(self, record):
        import progressbar as pb

        msg = json.loads(record.msg)
        # print(msg)
        if msg[0] == 'SET':
            pass
            self.pbar.update(msg[1])
        elif msg[0] == 'START':
            print(msg[1] + ':', file=sys.stderr)
            self.pbar = pb.ProgressBar(maxval=msg[2], **self.pbar_args)
            self.pbar.start()
        elif msg[0] == 'DONE':
            self.pbar.finish()
            del self.pbar
            print('', file=sys.stderr)

def progress_bar(n):
    import progressbar
    return progressbar.ProgressBar(
        max_value=n,
        widgets=[
            progressxbar.Percentage(),
            ' ',
            '(',
            progressbar.SimpleProgress(),
            ')',
            ' ',
            progressbar.Bar(),
            ' ',
            progressbar.AdaptiveETA(),
        ])


# http://code.activestate.com/recipes/577058/

def sub(self, msg=None, **kwargs):
        """Creates a new progress bar for tracking a sub-process.

        Parameters
        ----------
        msg : str, optional
            Description of sub-process
        """

        if self.sub_bar is not None and self.sub_bar.finished is False:
            self.sub_bar.finish()

        self.sub_bar = ProgressBar(
            present="%s: %s" % (self.present, msg) if msg else self.present,
            **kwargs)
        self.sub_bar.finish = partial(self.sub_bar.finish, end="\r")

        return self.sub_bar

def deleteHostsByHostgroup(groupname):
    hostgroup = zapi.hostgroup.get(output=['groupid'],filter={'name': groupname})
    if hostgroup.__len__() != 1:
        logger.error('Hostgroup not found: %s\n\tFound this: %s' % (groupname,hostgroup))
    groupid = int(hostgroup[0]['groupid'])
    hosts = zapi.host.get(output=['name','hostid'],groupids=groupid)
    total = len(hosts)
    logger.info('Hosts found: %d' % (total))
    if ( args.run ):
        x = 0
        bar = ProgressBar(maxval=total,widgets=[Percentage(), ReverseBar(), ETA(), RotatingMarker(), Timer()]).start()
        logger.echo = False
        for host in hosts:
            x = x + 1
            bar.update(x)
            logger.debug('(%d/%d) >> Removing >> %s' % (x, total, host))
            out = zapi.globo.deleteMonitors(host['name'])
        bar.finish()
        logger.echo = True
    else:
        logger.info('No host removed due to --no-run arg. Full list of hosts:')
        for host in hosts:
            logger.info('%s' % host['name'])
    return