Python math.ceil() Examples

def __iter__(self):
        indices = []
        for i, size in enumerate(self.group_sizes):
            if size == 0:
                continue
            indice = np.where(self.flag == i)[0]
            assert len(indice) == size
            np.random.shuffle(indice)
            num_extra = int(np.ceil(size / self.samples_per_gpu)
                            ) * self.samples_per_gpu - len(indice)
            indice = np.concatenate(
                [indice, np.random.choice(indice, num_extra)])
            indices.append(indice)
        indices = np.concatenate(indices)
        indices = [
            indices[i * self.samples_per_gpu:(i + 1) * self.samples_per_gpu]
            for i in np.random.permutation(
                range(len(indices) // self.samples_per_gpu))
        ]
        indices = np.concatenate(indices)
        indices = indices.astype(np.int64).tolist()
        assert len(indices) == self.num_samples
        return iter(indices) 

def save_image(data, epoch, image_size, batch_size, output_dir, padding=2):
    """ save image """
    data = data.asnumpy().transpose((0, 2, 3, 1))
    datanp = np.clip(
        (data - np.min(data))*(255.0/(np.max(data) - np.min(data))), 0, 255).astype(np.uint8)
    x_dim = min(8, batch_size)
    y_dim = int(math.ceil(float(batch_size) / x_dim))
    height, width = int(image_size + padding), int(image_size + padding)
    grid = np.zeros((height * y_dim + 1 + padding // 2, width *
                     x_dim + 1 + padding // 2, 3), dtype=np.uint8)
    k = 0
    for y in range(y_dim):
        for x in range(x_dim):
            if k >= batch_size:
                break
            start_y = y * height + 1 + padding // 2
            end_y = start_y + height - padding
            start_x = x * width + 1 + padding // 2
            end_x = start_x + width - padding
            np.copyto(grid[start_y:end_y, start_x:end_x, :], datanp[k])
            k += 1
    imageio.imwrite(
        '{}/fake_samples_epoch_{}.png'.format(output_dir, epoch), grid) 

def main(_):
  shard_urls = fetch.get_urls_for_shard(FLAGS.urls_dir, FLAGS.shard_id)
  num_groups = int(math.ceil(len(shard_urls) / fetch.URLS_PER_CLIENT))
  tf.logging.info("Launching get_references_web_single_group sequentially for "
                  "%d groups in shard %d. Total URLs: %d",
                  num_groups, FLAGS.shard_id, len(shard_urls))
  command_prefix = FLAGS.command.split() + [
      "--urls_dir=%s" % FLAGS.urls_dir,
      "--shard_id=%d" % FLAGS.shard_id,
      "--debug_num_urls=%d" % FLAGS.debug_num_urls,
  ]
  with utils.timing("all_groups_fetch"):
    for i in range(num_groups):
      command = list(command_prefix)
      out_dir = os.path.join(FLAGS.out_dir, "process_%d" % i)
      command.append("--out_dir=%s" % out_dir)
      command.append("--group_id=%d" % i)
      try:
        # Even on 1 CPU, each group should finish within an hour.
        sp.check_call(command, timeout=60*60)
      except sp.TimeoutExpired:
        tf.logging.error("Group %d timed out", i) 

def make_even_size(x):
  """Pad x to be even-sized on axis 1 and 2, but only if necessary."""
  x_shape = x.get_shape().as_list()
  assert len(x_shape) > 2, "Only 3+-dimensional tensors supported."
  shape = [dim if dim is not None else -1 for dim in x_shape]
  new_shape = x_shape  # To make sure constant shapes remain constant.
  if x_shape[1] is not None:
    new_shape[1] = 2 * int(math.ceil(x_shape[1] * 0.5))
  if x_shape[2] is not None:
    new_shape[2] = 2 * int(math.ceil(x_shape[2] * 0.5))
  if shape[1] % 2 == 0 and shape[2] % 2 == 0:
    return x
  if shape[1] % 2 == 0:
    x, _ = pad_to_same_length(x, x, final_length_divisible_by=2, axis=2)
    x.set_shape(new_shape)
    return x
  if shape[2] % 2 == 0:
    x, _ = pad_to_same_length(x, x, final_length_divisible_by=2, axis=1)
    x.set_shape(new_shape)
    return x
  x, _ = pad_to_same_length(x, x, final_length_divisible_by=2, axis=1)
  x, _ = pad_to_same_length(x, x, final_length_divisible_by=2, axis=2)
  x.set_shape(new_shape)
  return x 

def _count_batch(self, samples, batch_size):
        relations = zip(*samples)[0]
        relations_counts = Counter(relations)
        num_batches = [ceil(1. * x / batch_size) for x in relations_counts.values()]
        return int(sum(num_batches)) 

def step(self, amt=1):
        center = float(self._maxLed) / 2
        center_floor = math.floor(center)
        center_ceil = math.ceil(center)

        if self._centerOut:
            self.layout.fill(
                self.palette(self._step), int(center_floor - self._current), int(center_floor - self._current))
            self.layout.fill(
                self.palette(self._step), int(center_ceil + self._current), int(center_ceil + self._current))
        else:
            self.layout.fill(
                self.palette(self._step), int(self._current), int(self._current))
            self.layout.fill(
                self.palette(self._step), int(self._maxLed - self._current), int(self._maxLed - self._current))

        self._step += amt + self._rainbowInc

        if self._current == center_floor:
            self._current = self._minLed
        else:
            self._current += amt 

def compute_a(sigma, q, lmbd, verbose=False):
  lmbd_int = int(math.ceil(lmbd))
  if lmbd_int == 0:
    return 1.0

  a_lambda_first_term_exact = 0
  a_lambda_second_term_exact = 0
  for i in xrange(lmbd_int + 1):
    coef_i = scipy.special.binom(lmbd_int, i) * (q ** i)
    s1, s2 = 0, 0
    for j in xrange(i + 1):
      coef_j = scipy.special.binom(i, j) * (-1) ** (i - j)
      s1 += coef_j * np.exp((j * j - j) / (2.0 * (sigma ** 2)))
      s2 += coef_j * np.exp((j * j + j) / (2.0 * (sigma ** 2)))
    a_lambda_first_term_exact += coef_i * s1
    a_lambda_second_term_exact += coef_i * s2

  a_lambda_exact = ((1.0 - q) * a_lambda_first_term_exact +
                    q * a_lambda_second_term_exact)
  if verbose:
    print "A: by binomial expansion    {} = {} + {}".format(
        a_lambda_exact,
        (1.0 - q) * a_lambda_first_term_exact,
        q * a_lambda_second_term_exact)
  return _to_np_float64(a_lambda_exact) 

def _format_widgets(self):
        result = []
        expanding = []
        width = self.term_width

        for index, widget in enumerate(self.widgets):
            if isinstance(widget, widgets.WidgetHFill):
                result.append(widget)
                expanding.insert(0, index)
            else:
                widget = widgets.format_updatable(widget, self)
                result.append(widget)
                width -= len(widget)

        count = len(expanding)
        while count:
            portion = max(int(math.ceil(width * 1. / count)), 0)
            index = expanding.pop()
            count -= 1

            widget = result[index].update(self, portion)
            width -= len(widget)
            result[index] = widget

        return result 

def get_urls_for_shard_group(urls_dir, shard_id, group_id):
  shard_urls = get_urls_for_shard(urls_dir, shard_id)

  # Deterministic sort and shuffle to prepare for sharding
  shard_urls.sort()
  random.seed(123)
  random.shuffle(shard_urls)
  groups = shard(shard_urls, int(math.ceil(len(shard_urls) / URLS_PER_CLIENT)))
  group_urls = groups[group_id]
  if FLAGS.debug_num_urls:
    group_urls = group_urls[:FLAGS.debug_num_urls]
  return group_urls 

def ceildiv(a, b):
    return int(math.ceil(a / float(b))) 

def frame_width(self):
    width = self.env.observation_space.shape[1]
    return int(math.ceil(width / self.autoencoder_factor)) 

def frame_height(self):
    height = self.env.observation_space.shape[0]
    ae_height = int(math.ceil(height / self.autoencoder_factor))
    return ae_height 

def frame_width(self):
    width = self.env.observation_space.shape[1]
    return int(math.ceil(width / self.autoencoder_factor)) 

def encode(self, text):
		result = ""
		for i in range(0,int(math.ceil(float(len(text)) / 7.0))):
			result += self.encodeblock(text[i*7:(i+1)*7])

		return result 

def __init__(self, batch_env):
    super(AutoencoderWrapper, self).__init__(batch_env)
    batch_size, height, width, _ = self._batch_env.observ.get_shape().as_list()
    ae_height = int(math.ceil(height / self.autoencoder_factor))
    ae_width = int(math.ceil(width / self.autoencoder_factor))
    ae_channels = 24  # TODO(piotrmilos): make it better
    observ_shape = (batch_size, ae_height, ae_width, ae_channels)
    self._observ = self._observ = tf.Variable(
        tf.zeros(observ_shape, tf.float32), trainable=False)
    with tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE):
      autoencoder_hparams = autoencoders.autoencoder_discrete_pong()
      self.autoencoder_model = autoencoders.AutoencoderOrderedDiscrete(
          autoencoder_hparams, tf.estimator.ModeKeys.EVAL)
    self.autoencoder_model.set_mode(tf.estimator.ModeKeys.EVAL) 

def get_inception_score(images, splits=10, get_split=False):
    assert (type(images) == list)
    assert (type(images[0]) == np.ndarray)
    assert (len(images[0].shape) == 3)
    assert (np.max(images[0]) > 10)
    assert (np.min(images[0]) >= 0.0)
    inps = []
    for img in images:
        img = img.astype(np.float32)
        inps.append(np.expand_dims(img, 0))
    bs = 100
    with tf.Session() as sess:
        preds = []
        n_batches = int(math.ceil(float(len(inps)) / float(bs)))
        for i in range(n_batches):
            sys.stdout.write(".")
            sys.stdout.flush()
            inp = inps[(i * bs):min((i + 1) * bs, len(inps))]
            inp = np.concatenate(inp, 0)
            pred = sess.run(softmax, {'ExpandDims:0': inp})
            preds.append(pred)
        preds = np.concatenate(preds, 0)
        scores = []
        objectness = []
        diversity = []
        for i in range(splits):
            part = preds[(i * preds.shape[0] // splits):((i + 1) * preds.shape[0] // splits), :]
            kl = part * (np.log(part) - np.log(np.expand_dims(np.mean(part, 0), 0)))
            o = -part * np.log(part)
            d = -part * np.log(np.expand_dims(np.mean(part, 0), 0))
            kl = np.mean(np.sum(kl, 1))
            objectness.append(np.exp(np.mean(np.sum(o, 1))))
            diversity.append(np.exp(np.mean(np.sum(d, 1))))
            scores.append(np.exp(kl))
        if get_split:
            return np.mean(scores), np.std(scores), np.mean(objectness), np.mean(diversity)
        return np.mean(scores), np.std(scores)


# This function is called automatically. 

def plotOutput(layer,feed_dict,fieldShape=None,channel=None,figOffset=1,cmap=None):
	# Output summary
	W = layer.output
	wp = W.eval(feed_dict=feed_dict);
	if len(np.shape(wp)) < 4:		# Fully connected layer, has no shape
		temp = np.zeros(np.product(fieldShape)); temp[0:np.shape(wp.ravel())[0]] = wp.ravel()
		fields = np.reshape(temp,[1]+fieldShape)
	else:			# Convolutional layer already has shape
		wp = np.rollaxis(wp,3,0)
		features, channels, iy,ix = np.shape(wp)
		if channel is not None:
			fields = wp[:,channel,:,:]
		else:
			fields = np.reshape(wp,[features*channels,iy,ix])

	perRow = int(math.floor(math.sqrt(fields.shape[0])))
	perColumn = int(math.ceil(fields.shape[0]/float(perRow)))
	fields2 = np.vstack([fields,np.zeros([perRow*perColumn-fields.shape[0]] + list(fields.shape[1:]))])
	tiled = []
	for i in range(0,perColumn*perRow,perColumn):
		tiled.append(np.hstack(fields2[i:i+perColumn]))

	tiled = np.vstack(tiled)
	if figOffset is not None:
		mpl.figure(figOffset); mpl.clf();

	mpl.imshow(tiled,cmap=cmap); mpl.title('%s Output' % layer.name); mpl.colorbar(); 

def perplexity(model, data_loader, args):
    model.eval()
    # Setup average meters
    data_time_meter = metrics.AverageMeter()
    batch_time_meter = metrics.AverageMeter()
    perplexity_meter = metrics.AverageMeter()

    timestamp = time.time()
    for i, (input, target, prev_absolutes, next_absolutes,
            _) in enumerate(data_loader):
        batch_size = input.size(0)
        input = Variable(input.cuda(async=True), volatile=True)
        target = Variable(target.cuda(async=True), volatile=True)
        data_time_meter.update(time.time() - timestamp)

        perplexity = model.perplexity(input, target).data
        perplexity_meter.update(perplexity, batch_size)

        batch_time_meter.update(time.time() - timestamp)
        # Log report
        logging.info(
            'Perplexity: [{}/{}]\t'.format(
                (i // args.break_batch) +
                1, math.ceil(len(data_loader) / args.break_batch)) +
            'Time {batch_time.val:.2f} ({batch_time.avg:.2f})   '
            'Data {data_time.val:.2f} ({data_time.avg:.2f})   '
            'Perplexity [{cur_perplexity}] ([{avg_perplexity}])'.format(
                batch_time=batch_time_meter, data_time=data_time_meter,
                cur_perplexity=', '.join(
                    '{:.2f}'.format(val)
                    for val in perplexity_meter.val), avg_perplexity=', '.join(
                        '{:.2f}'.format(avg) for avg in perplexity_meter.avg)))
    logging.info(
        'Final Perplexity: \tTime {batch_time.sum:.2f}   '
        'Data {data_time.sum:.2f}   Perplexity [{perplexity}] Average Perplexity {avg_perplexity}'.
        format(
            batch_time=batch_time_meter, data_time=data_time_meter,
            perplexity=', '.join(
                '{:.2f}'.format(avg) for avg in perplexity_meter.avg),
            avg_perplexity=perplexity_meter.avg.mean())) 

def mapping_all(self, entries=None):
        """Retrieves cross references for more than one entry

        :param entries: list of values entries (e.g., returned by the :meth:`lookfor` method.)
            if not provided, this method looks for all entries.
        :returns: list of dictionaries with keys being all entry names. Values is a
            dictionary of cross references.

        .. warning:: takes 10 minutes

        """
        from math import ceil
        results = {}

        if entries is None:
            print("First, get all entries")
            entries = self.lookfor('*')

        names = [entry['xlink:title'] for entry in entries]
        N = len(names)

        # split query in sets of 300 names

        dn = 300
        N = len(names)
        n = int(ceil(N/float(dn)))
        for i in range(0, n):
            print("Completed ", i+1, "/", n)
            query  = ";".join(names[i*dn:(i+1)*dn])
            xml = self.get_xml(query)
            genes = xml.findAll("gene")
            for gene in genes:
                res = self._get_xref(gene, None)
                #acc = gene.attrs['acc'] not needed. can be access from ['HGNC']['xkey']
                name = gene.attrs['symbol']
                results[name] = res.copy()
        return results 

def plotFields(layer,fieldShape=None,channel=None,figOffset=1,cmap=None,padding=0.01):
	# Receptive Fields Summary
	try:
		W = layer.W
	except:
		W = layer
	wp = W.eval().transpose();
	if len(np.shape(wp)) < 4:		# Fully connected layer, has no shape
		fields = np.reshape(wp,list(wp.shape[0:-1])+fieldShape)	
	else:			# Convolutional layer already has shape
		features, channels, iy, ix = np.shape(wp)
		if channel is not None:
			fields = wp[:,channel,:,:]
		else:
			fields = np.reshape(wp,[features*channels,iy,ix])

	perRow = int(math.floor(math.sqrt(fields.shape[0])))
	perColumn = int(math.ceil(fields.shape[0]/float(perRow)))

	fig = mpl.figure(figOffset); mpl.clf()
	
	# Using image grid
	from mpl_toolkits.axes_grid1 import ImageGrid
	grid = ImageGrid(fig,111,nrows_ncols=(perRow,perColumn),axes_pad=padding,cbar_mode='single')
	for i in range(0,np.shape(fields)[0]):
		im = grid[i].imshow(fields[i],cmap=cmap); 

	grid.cbar_axes[0].colorbar(im)
	mpl.title('%s Receptive Fields' % layer.name)
	
	# old way
	# fields2 = np.vstack([fields,np.zeros([perRow*perColumn-fields.shape[0]] + list(fields.shape[1:]))])
	# tiled = []
	# for i in range(0,perColumn*perRow,perColumn):
	# 	tiled.append(np.hstack(fields2[i:i+perColumn]))
	# 
	# tiled = np.vstack(tiled)
	# mpl.figure(figOffset); mpl.clf(); mpl.imshow(tiled,cmap=cmap); mpl.title('%s Receptive Fields' % layer.name); mpl.colorbar();
	mpl.figure(figOffset+1); mpl.clf(); mpl.imshow(np.sum(np.abs(fields),0),cmap=cmap); mpl.title('%s Total Absolute Input Dependency' % layer.name); mpl.colorbar() 

def pack_record_hostname(self, data):
		hostname = ""
		for j in range(0,int(math.ceil(float(len(data))/63.0))):
			hostname += data[j*63:(j+1)*63]+"."

		return hostname 

def decode(self, text):
		result = ""
		for i in range(0,int(math.ceil(float(len(text)) / 8.0))):
			result += self.decodeblock(text[i*8:(i+1)*8])
		return result 

def get_epoch_size(args, kv):
    return math.ceil(int(args.num_examples / kv.num_workers) / args.batch_size) 

def compute_b_mp(sigma, q, lmbd, verbose=False):
  lmbd_int = int(math.ceil(lmbd))
  if lmbd_int == 0:
    return 1.0

  mu0, _, mu = distributions_mp(sigma, q)

  b_lambda_fn = lambda z: mu0(z) * (mu0(z) / mu(z)) ** lmbd_int
  b_lambda = integral_inf_mp(b_lambda_fn)

  m = sigma ** 2 * (mp.log((2 - q) / (1 - q)) + 1 / (2 * (sigma ** 2)))
  b_fn = lambda z: ((mu0(z) / mu(z)) ** lmbd_int -
                    (mu(-z) / mu0(z)) ** lmbd_int)
  if verbose:
    print "M =", m
    print "f(-M) = {} f(M) = {}".format(b_fn(-m), b_fn(m))
    assert b_fn(-m) < 0 and b_fn(m) < 0

  b_lambda_int1_fn = lambda z: mu0(z) * (mu0(z) / mu(z)) ** lmbd_int
  b_lambda_int2_fn = lambda z: mu0(z) * (mu(z) / mu0(z)) ** lmbd_int
  b_int1 = integral_bounded_mp(b_lambda_int1_fn, -m, m)
  b_int2 = integral_bounded_mp(b_lambda_int2_fn, -m, m)

  a_lambda_m1 = compute_a_mp(sigma, q, lmbd - 1)
  b_bound = a_lambda_m1 + b_int1 - b_int2

  if verbose:
    print "B by numerical integration", b_lambda
    print "B must be no more than    ", b_bound
  assert b_lambda < b_bound + 1e-5
  return _to_np_float64(b_lambda) 

def run_eval(eval_ops, summary_writer, saver):
  """Runs evaluation over FLAGS.num_examples examples.

  Args:
    eval_ops: dict<metric name, tuple(value, update_op)>
    summary_writer: Summary writer.
    saver: Saver.

  Returns:
    dict<metric name, value>, with value being the average over all examples.
  """
  sv = tf.train.Supervisor(logdir=FLAGS.eval_dir, saver=None, summary_op=None)
  with sv.managed_session(
      master=FLAGS.master, start_standard_services=False) as sess:
    if not restore_from_checkpoint(sess, saver):
      return
    sv.start_queue_runners(sess)

    metric_names, ops = zip(*eval_ops.items())
    value_ops, update_ops = zip(*ops)

    value_ops_dict = dict(zip(metric_names, value_ops))

    # Run update ops
    num_batches = int(math.ceil(FLAGS.num_examples / FLAGS.batch_size))
    tf.logging.info('Running %d batches for evaluation.', num_batches)
    for i in range(num_batches):
      if (i + 1) % 10 == 0:
        tf.logging.info('Running batch %d/%d...', i + 1, num_batches)
      if (i + 1) % 50 == 0:
        _log_values(sess, value_ops_dict)
      sess.run(update_ops)

    _log_values(sess, value_ops_dict, summary_writer=summary_writer) 

def __call__(self, param):
        """Callback to Show progress bar."""
        count = param.nbatch
        filled_len = int(round(self.bar_len * count / float(self.total)))
        percents = math.ceil(100.0 * count / float(self.total))
        prog_bar = '=' * filled_len + '-' * (self.bar_len - filled_len)
        logging.info('[%s] %s%s\r', prog_bar, percents, '%') 

def __init__(self, title, varieties, width, height,
                 anim=True, data_func=None, is_headless=False, legend_pos=4):
        """
        Setup a scatter plot.
        varieties contains the different types of
        entities to show in the plot, which
        will get assigned different colors
        """
        global anim_func

        self.scats = None
        self.anim = anim
        self.data_func = data_func
        self.s = ceil(4096 / width)
        self.headless = is_headless

        fig, ax = plt.subplots()
        ax.set_xlim(0, width)
        ax.set_ylim(0, height)
        self.create_scats(varieties)
        ax.legend(loc = legend_pos)
        ax.set_title(title)
        plt.grid(True)

        if anim and not self.headless:
            anim_func = animation.FuncAnimation(fig,
                                    self.update_plot,
                                    frames=1000,
                                    interval=500,
                                    blit=False) 

def get_epoch_size(args, kv):
    return math.ceil(int(args.num_examples / kv.num_workers) / args.batch_size) 

def to_target(x):
    target = np.zeros((1, num_classes))
    ceil = int(math.ceil(x))
    floor = int(math.floor(x))
    if ceil==floor:
        target[0][floor-1] = 1
    else:
        target[0][floor-1] = ceil - x
        target[0][ceil-1] = x - floor
    return mx.nd.array(target) 

def visual(X, show=True):
    X = X.transpose((0, 2, 3, 1))
    N = X.shape[0]
    n = int(math.ceil(math.sqrt(N)))
    h = X.shape[1]
    w = X.shape[2]
    buf = np.zeros((h*n, w*n, X.shape[3]), dtype=np.uint8)
    for i in range(N):
        x = i%n
        y = i//n
        buf[h*y:h*(y+1), w*x:w*(x+1), :] = X[i]
    if show:
        cv2.imshow('a', buf)
        cv2.waitKey(1)
    return buf