text:
"""Library for loading and preprocessing CIFAR-10 training and testing data."""

import collections
from typing import Callable, Sequence, Tuple, Union

import numpy as np
import tensorflow as tf
import tensorflow_datasets as tfds
import tensorflow_federated as tff

CIFAR_SHAPE = (32, 32, 3)
TOTAL_FEATURE_SIZE = 32 * 32 * 3
NUM_CLASSES = 10
TRAIN_EXAMPLES = 50000
TEST_EXAMPLES = 10000
# Number of training examples per class: 50,000 / 10.
TRAIN_EXAMPLES_PER_LABEL = 5000
# Number of test examples per class: 10,000 / 10.
TEST_EXAMPLES_PER_LABEL = 1000

def load_cifar10_federated(
    dirichlet_parameter: float = 1,
    num_clients: int = 10,
) -> Tuple[tff.simulation.datasets.ClientData,
           tff.simulation.datasets.ClientData]:
  """Construct a federated dataset from the centralized CIFAR-10.

  Sampling based on Dirichlet distribution over categories, following the paper
  Measuring the Effects of Non-Identical Data Distribution for
  Federated Visual Classification (https://arxiv.org/abs/1909.06335).

  Args:
    dirichlet_parameter: Parameter of Dirichlet distribution. Each client
      samples from this Dirichlet to get a multinomial distribution over
      classes. It controls the data heterogeneity of clients. If approaches 0,
      then each client only have data from a single category label. If
      approaches infinity, then the client distribution will approach IID
      partitioning.
    num_clients: The number of clients the examples are going to be partitioned
      on.

  Returns:
    A tuple of `tff.simulation.datasets.ClientData` representing unpreprocessed
    train data and test data.
  """
  train_images, train_labels = tfds.as_numpy(
      tfds.load(
          name='cifar10',
          split='train',
          batch_size=-1,
          as_supervised=True,
      ))
  test_images, test_labels = tfds.as_numpy(
      tfds.load(
          name='cifar10',
          split='test',
          batch_size=-1,
          as_supervised=True,
      ))
  train_clients = collections.OrderedDict()
  test_clients = collections.OrderedDict()

  train_multinomial_vals = []
  test_multinomial_vals = []
  # Each client has a multinomial distribution over classes drawn from a
  # Dirichlet.
  for i in range(num_clients):
    proportion = np.random.dirichlet(dirichlet_parameter *
                                     np.ones(NUM_CLASSES,))
    train_multinomial_vals.append(proportion)
    test_multinomial_vals.append(proportion)

  train_multinomial_vals = np.array(train_multinomial_vals)
  test_multinomial_vals = np.array(test_multinomial_vals)

  train_example_indices = []
  test_indices = []
  for k in range(NUM_CLASSES):
    train_label_k = np.where(train_labels == k)[0]
    np.random.shuffle(train_label_k)
    train_example_indices.append(train_label_k)
    test_label_k = np.where(test_labels == k)[0]
    np.random.shuffle(test_label_k)
    test_indices.append(test_label_k)

  train_example_indices = np.array(train_example_indices)
  test_indices = np.array(test_indices)

  train_client_samples = [[] for _ in range(num_clients)]
  test_client_samples = [[] for _ in range(num_clients)]
  train_count = np.zeros(NUM_CLASSES).astype(int)
  test_count = np.zeros(NUM_CLASSES).astype(int)

  train_examples_per_client = int(TRAIN_EXAMPLES / num_clients)
  test_examples_per_client = int(TEST_EXAMPLES / num_clients)
  for k in range(num_clients):

    for i in range(train_examples_per_client):
      sampled_label = np.argwhere(
          np.random.multinomial(1, train_multinomial_vals[k, :]) == 1)[0][0]
      train_client_samples[k].append(
          train_example_indices[sampled_label, train_count[sampled_label]])
      train_count[sampled_label] += 1
      if train_count[sampled_label] == TRAIN_EXAMPLES_PER_LABEL:
        train_multinomial_vals[:, sampled_label] = 0
        train_multinomial_vals = (
            train_multinomial_vals /
            train_multinomial_vals.sum(axis=1)[:, None])

    for i in range(test_examples_per_client):
      sampled_label = np.argwhere(
          np.random.multinomial(1, test_multinomial_vals[k, :]) == 1)[0][0]
      test_client_samples[k].append(test_indices[sampled_label,
                                                 test_count[sampled_label]])
      test_count[sampled_label] += 1
      if test_count[sampled_label] == TEST_EXAMPLES_PER_LABEL:
        test_multinomial_vals[:, sampled_label] = 0
        test_multinomial_vals = (
            test_multinomial_vals / test_multinomial_vals.sum(axis=1)[:, None])

  for i in range(num_clients):
    client_name = str(i)
    x_train = train_images[np.array(train_client_samples[i])]
    y_train = train_labels[np.array(
        train_client_samples[i])].astype('int64').squeeze()
    train_data = collections.OrderedDict(
        (('image', x_train), ('label', y_train)))
    train_clients[client_name] = train_data

    x_test = test_images[np.array(test_client_samples[i])]
    y_test = test_labels[np.array(
        test_client_samples[i])].astype('int64').squeeze()
    test_data = collections.OrderedDict((('image', x_test), ('label', y_test)))
    test_clients[client_name] = test_data

  train_dataset = tff.simulation.datasets.TestClientData(train_clients)
  test_dataset = tff.simulation.datasets.TestClientData(test_clients)

  return train_dataset, test_dataset

def build_image_map(
    crop_shape: Union[tf.Tensor, Sequence[int]],
    distort: bool = False
) -> Callable[[tf.Tensor], Tuple[tf.Tensor, tf.Tensor]]:
  """Builds a function that crops and normalizes CIFAR-10 elements.

  The image is first converted to a `tf.float32`, then cropped (according to
  the `distort` argument). Finally, its values are normalized via
  `tf.image.per_image_standardization`.

  Args:
    crop_shape: A tuple (crop_height, crop_width, channels) specifying the
      desired crop shape for pre-processing batches. This cannot exceed (32, 32,
      3) element-wise. The element in the last index should be set to 3 to
      maintain the RGB image structure of the elements.
    distort: A boolean indicating whether to distort the image via random crops
      and flips. If set to False, the image is resized to the `crop_shape` via
      `tf.image.resize_with_crop_or_pad`.

  Returns:
    A callable accepting a tensor and performing the crops and normalization
    discussed above.
  """

  if distort:

    def crop_fn(image):
      image = tf.image.random_crop(image, size=crop_shape)
      image = tf.image.random_flip_left_right(image)
      return image

  else:

    def crop_fn(image):
      return tf.image.resize_with_crop_or_pad(
          image, target_height=crop_shape[0], target_width=crop_shape[1])

  def image_map(example):
    image = tf.cast(example['image'], tf.float32)
    image = crop_fn(image)
    image = tf.image.per_image_standardization(image)
    return (image, example['label'])

  return image_map

def create_preprocess_fn(
    num_epochs: int,
    batch_size: int,
    shuffle_buffer_size: int,
    crop_shape: Tuple[int, int, int] = CIFAR_SHAPE,
    distort_image=False,
    num_parallel_calls: int = tf.data.experimental.AUTOTUNE
) -> Callable[[tf.data.Dataset], tf.data.Dataset]:
  """Creates a preprocessing function for CIFAR-10 client datasets.

  Args:
    num_epochs: An integer representing the number of epochs to repeat the
      client datasets.
    batch_size: An integer representing the batch size on clients.
    shuffle_buffer_size: An integer representing the shuffle buffer size on
      clients. If set to a number <= 1, no shuffling occurs.
    crop_shape: A tuple (crop_height, crop_width, num_channels) specifying the
      desired crop shape for pre-processing. This tuple cannot have elements
      exceeding (32, 32, 3), element-wise. The element in the last index should
      be set to 3 to maintain the RGB image structure of the elements.
    distort_image: A boolean indicating whether to perform preprocessing that
      includes image distortion, including random crops and flips.
    num_parallel_calls: An integer representing the number of parallel calls
      used when performing `tf.data.Dataset.map`.

  Returns:
    A callable performing the preprocessing described above.
  """
  if num_epochs < 1:
    raise ValueError('num_epochs must be a positive integer.')
  if shuffle_buffer_size <= 1:
    shuffle_buffer_size = 1

  image_map_fn = build_image_map(crop_shape, distort_image)

  def preprocess_fn(dataset):
    return (
        dataset.shuffle(shuffle_buffer_size).repeat(num_epochs)
        # We map before batching to ensure that the cropping occurs
        # at an image level (eg. we do not perform the same crop on
        # every image within a batch)
        .map(image_map_fn,
             num_parallel_calls=num_parallel_calls).batch(batch_size))

  return preprocess_fn

def get_federated_datasets(train_client_batch_size: int = 20,
                           test_client_batch_size: int = 100,
                           train_client_epochs_per_round: int = 1,
                           test_client_epochs_per_round: int = 1,
                           train_shuffle_buffer_size: int = 1000,
                           test_shuffle_buffer_size: int = 1,
                           crop_shape: Tuple[int, int, int] = CIFAR_SHAPE,
                           serializable: bool = False):
  """Loads and preprocesses federated CIFAR10 training and testing sets.

  Args:
    train_client_batch_size: The batch size for all train clients.
    test_client_batch_size: The batch size for all test clients.
    train_client_epochs_per_round: The number of epochs each train client should
      iterate over their local dataset, via `tf.data.Dataset.repeat`. Must be
      set to a positive integer.
    test_client_epochs_per_round: The number of epochs each test client should
      iterate over their local dataset, via `tf.data.Dataset.repeat`. Must be
      set to a positive integer.
    train_shuffle_buffer_size: An integer representing the shuffle buffer size
      (as in `tf.data.Dataset.shuffle`) for each train client's dataset. By
      default, this is set to the largest dataset size among all clients. If set
      to some integer less than or equal to 1, no shuffling occurs.
    test_shuffle_buffer_size: An integer representing the shuffle buffer size
      (as in `tf.data.Dataset.shuffle`) for each test client's dataset. If set
      to some integer less than or equal to 1, no shuffling occurs.
    crop_shape: An iterable of integers specifying the desired crop shape for
      pre-processing. Must be convertable to a tuple of integers (CROP_HEIGHT,
      CROP_WIDTH, NUM_CHANNELS) which cannot have elements that exceed (32, 32,
      3), element-wise. The element in the last index should be set to 3 to
      maintain the RGB image structure of the elements.
    serializable: Boolean indicating whether the returned datasets are intended
      to be serialized and shipped across RPC channels. If `True`, stateful
      transformations will be disallowed.

  Returns:
    A tuple (cifar_train, cifar_test) of `tff.simulation.datasets.ClientData`
    instances representing the federated training and test datasets.
  """
  if not isinstance(crop_shape, collections.abc.Iterable):
    raise TypeError('Argument crop_shape must be an iterable.')
  crop_shape = tuple(crop_shape)
  if len(crop_shape) != 3:
    raise ValueError('The crop_shape must have length 3, corresponding to a '
                     'tensor of shape [height, width, channels].')
  if not isinstance(serializable, bool):
    raise TypeError(
        'serializable must be a Boolean; you passed {} of type {}.'.format(
            serializable, type(serializable)))
  if train_client_epochs_per_round < 1:
    raise ValueError(
        'train_client_epochs_per_round must be a positive integer.')
  if test_client_epochs_per_round < 0:
    raise ValueError('test_client_epochs_per_round must be a positive integer.')
  if train_shuffle_buffer_size <= 1:
    train_shuffle_buffer_size = 1
  if test_shuffle_buffer_size <= 1:
    test_shuffle_buffer_size = 1

  cifar_train, cifar_test = load_cifar10_federated()

  train_preprocess_fn = create_preprocess_fn(
      num_epochs=train_client_epochs_per_round,
      batch_size=train_client_batch_size,
      shuffle_buffer_size=train_shuffle_buffer_size,
      crop_shape=crop_shape,
      distort_image=not serializable)

  test_preprocess_fn = create_preprocess_fn(
      num_epochs=test_client_epochs_per_round,
      batch_size=test_client_batch_size,
      shuffle_buffer_size=test_shuffle_buffer_size,
      crop_shape=crop_shape,
      distort_image=False)

  cifar_train = cifar_train.preprocess(train_preprocess_fn)
  cifar_test = cifar_test.preprocess(test_preprocess_fn)
  return cifar_train, cifar_test

def get_centralized_datasets(
    train_batch_size: int = 20,
    test_batch_size: int = 100,
    train_shuffle_buffer_size: int = 10000,
    test_shuffle_buffer_size: int = 1,
    crop_shape: Tuple[int, int, int] = CIFAR_SHAPE
) -> Tuple[tf.data.Dataset, tf.data.Dataset]:
  """Loads and preprocesses centralized CIFAR10 training and testing sets.

  Args:
    train_batch_size: The batch size for the training dataset.
    test_batch_size: The batch size for the test dataset.
    train_shuffle_buffer_size: An integer specifying the buffer size used to
      shuffle the train dataset via `tf.data.Dataset.shuffle`. If set to an
      integer less than or equal to 1, no shuffling occurs.
    test_shuffle_buffer_size: An integer specifying the buffer size used to
      shuffle the test dataset via `tf.data.Dataset.shuffle`. If set to an
      integer less than or equal to 1, no shuffling occurs.
    crop_shape: An iterable of integers specifying the desired crop shape for
      pre-processing. Must be convertable to a tuple of integers (CROP_HEIGHT,
      CROP_WIDTH, NUM_CHANNELS) which cannot have elements that exceed (32, 32,
      3), element-wise. The element in the last index should be set to 3 to
      maintain the RGB image structure of the elements.

  Returns:
    A tuple (cifar_train, cifar_test) of `tf.data.Dataset` instances
    representing the centralized training and test datasets.
  """
  try:
    crop_shape = tuple(crop_shape)
  except:
    raise ValueError(
        'Argument crop_shape must be able to coerced into a length 3 tuple.')
  if len(crop_shape) != 3:
    raise ValueError('The crop_shape must have length 3, corresponding to a '
                     'tensor of shape [height, width, channels].')
  if train_shuffle_buffer_size <= 1:
    train_shuffle_buffer_size = 1
  if test_shuffle_buffer_size <= 1:
    test_shuffle_buffer_size = 1

  cifar_train, cifar_test = load_cifar10_federated()
  cifar_train = cifar_train.create_tf_dataset_from_all_clients()
  cifar_test = cifar_test.create_tf_dataset_from_all_clients()

  train_preprocess_fn = create_preprocess_fn(
      num_epochs=1,
      batch_size=train_batch_size,
      shuffle_buffer_size=train_shuffle_buffer_size,
      crop_shape=crop_shape,
      distort_image=True)
  cifar_train = train_preprocess_fn(cifar_train)

  test_preprocess_fn = create_preprocess_fn(
      num_epochs=1,
      batch_size=test_batch_size,
      shuffle_buffer_size=test_shuffle_buffer_size,
      crop_shape=crop_shape,
      distort_image=False)
  cifar_test = test_preprocess_fn(cifar_test)

  return cifar_train, cifar_test


text:
package org.slc.sli.api.resources.v1.aggregation;

import java.util.List;
import java.util.Map;

import javax.ws.rs.GET;
import javax.ws.rs.QueryParam;
import javax.ws.rs.core.Response;

import org.springframework.context.annotation.Scope;
import org.springframework.stereotype.Component;

import org.slc.sli.api.config.EntityDefinition;
import org.slc.sli.domain.CalculatedData;
import org.slc.sli.domain.CalculatedDatum;

/**
 * Resource for displaying aggregate listings
 *
 * @author nbrown
 * @param <T> type of data to return
 *
 */
@Component
@Scope("request")
public class AggregateDataListingResource<T> {

    private final EntityDefinition entityDefinition;
    private final String entityId;

    public AggregateDataListingResource(final String entityId, final EntityDefinition entityDefinition) {
        this.entityId = entityId;
        this.entityDefinition = entityDefinition;
    }

    /**
     * Get the aggregates for a particular entity
     *
     * @param type filter by the aggregate type (assessment, attendance, etc)
     * @param window the window to look in (average, most recent, highest ever, etc)
     * @param methodology the part of the aggregated entity to pull in (ScaleScore, Percentile, etc)
     * @param name the name of the thing being aggregated (SAT, ACT, etc)
     * @return
     */
    @GET
    public Response getAggregatedValues(@QueryParam("type") final String type,
                                        @QueryParam("window") final String window,
                                        @QueryParam("method") final String methodology,
                                        @QueryParam("name") final String name) {

        final CalculatedData<Map<String, Integer>> data = entityDefinition.getService().getAggregates(entityId);
        final List<CalculatedDatum<Map<String, Integer>>> aggs = data.getCalculatedValues(type, window, methodology, name);
        return Response.ok(aggs).build();
    }

}

redpajama_set_name: RedPajamaGithub


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      <provider selected="true" editor-type-id="text-editor">
        <state relative-caret-position="0">
          <caret line="0" column="0" lean-forward="false" selection-start-line="0" selection-start-column="0" selection-end-line="0" selection-end-column="0" />
          <folding />
        </state>
      </provider>
    </entry>
    <entry file="file://$PROJECT_DIR$/js/tile.js">
      <provider selected="true" editor-type-id="text-editor">
        <state relative-caret-position="0">
          <caret line="0" column="0" lean-forward="false" selection-start-line="0" selection-start-column="0" selection-end-line="0" selection-end-column="0" />
          <folding />
        </state>
      </provider>
    </entry>
    <entry file="file://$PROJECT_DIR$/js/grid.js">
      <provider selected="true" editor-type-id="text-editor">
        <state relative-caret-position="1616">
          <caret line="101" column="24" lean-forward="false" selection-start-line="101" selection-start-column="15" selection-end-line="101" selection-end-column="24" />
          <folding />
        </state>
      </provider>
    </entry>
    <entry file="file://$PROJECT_DIR$/README.md">
      <provider selected="true" editor-type-id="text-editor">
        <state relative-caret-position="48">
          <caret line="3" column="60" lean-forward="false" selection-start-line="3" selection-start-column="60" selection-end-line="3" selection-end-column="60" />
          <folding />
        </state>
      </provider>
    </entry>
    <entry file="file://$PROJECT_DIR$/js/game_manager.js">
      <provider selected="true" editor-type-id="text-editor">
        <state relative-caret-position="272">
          <caret line="17" column="4" lean-forward="true" selection-start-line="17" selection-start-column="4" selection-end-line="17" selection-end-column="4" />
          <folding />
        </state>
      </provider>
    </entry>
    <entry file="file://$PROJECT_DIR$/style/main.scss">
      <provider selected="true" editor-type-id="text-editor">
        <state relative-caret-position="0">
          <caret line="0" column="0" lean-forward="false" selection-start-line="0" selection-start-column="0" selection-end-line="0" selection-end-column="0" />
          <folding />
        </state>
      </provider>
    </entry>
    <entry file="file://$PROJECT_DIR$/js/application.js">
      <provider selected="true" editor-type-id="text-editor">
        <state relative-caret-position="128">
          <caret line="8" column="33" lean-forward="false" selection-start-line="8" selection-start-column="33" selection-end-line="8" selection-end-column="33" />
          <folding />
        </state>
      </provider>
    </entry>
    <entry file="file://$PROJECT_DIR$/js/IA.js">
      <provider selected="true" editor-type-id="text-editor">
        <state relative-caret-position="0">
          <caret line="0" column="0" lean-forward="false" selection-start-line="0" selection-start-column="0" selection-end-line="0" selection-end-column="0" />
          <folding />
        </state>
      </provider>
    </entry>
    <entry file="file://$PROJECT_DIR$/README.md">
      <provider selected="true" editor-type-id="text-editor">
        <state relative-caret-position="32">
          <caret line="2" column="60" lean-forward="false" selection-start-line="2" selection-start-column="60" selection-end-line="2" selection-end-column="60" />
          <folding />
        </state>
      </provider>
    </entry>
    <entry file="file://$PROJECT_DIR$/style/main.scss">
      <provider selected="true" editor-type-id="text-editor">
        <state relative-caret-position="0">
          <caret line="0" column="0" lean-forward="false" selection-start-line="0" selection-start-column="0" selection-end-line="0" selection-end-column="0" />
          <folding />
        </state>
      </provider>
    </entry>
    <entry file="file://$PROJECT_DIR$/js/IA.js">
      <provider selected="true" editor-type-id="text-editor">
        <state relative-caret-position="0">
          <caret line="0" column="0" lean-forward="false" selection-start-line="0" selection-start-column="0" selection-end-line="0" selection-end-column="0" />
          <folding />
        </state>
      </provider>
    </entry>
    <entry file="file://$PROJECT_DIR$/style/main.scss">
      <provider selected="true" editor-type-id="text-editor">
        <state relative-caret-position="0">
          <caret line="0" column="0" lean-forward="false" selection-start-line="0" selection-start-column="0" selection-end-line="0" selection-end-column="0" />
          <folding />
        </state>
      </provider>
    </entry>
    <entry file="file://$PROJECT_DIR$/js/application.js">
      <provider selected="true" editor-type-id="text-editor">
        <state relative-caret-position="128">
          <caret line="8" column="33" lean-forward="true" selection-start-line="8" selection-start-column="33" selection-end-line="8" selection-end-column="33" />
          <folding />
        </state>
      </provider>
    </entry>
    <entry file="file://$PROJECT_DIR$/js/game_manager.js">
      <provider selected="true" editor-type-id="text-editor">
        <state relative-caret-position="928">
          <caret line="58" column="2" lean-forward="false" selection-start-line="58" selection-start-column="2" selection-end-line="58" selection-end-column="2" />
        </state>
      </provider>
    </entry>
    <entry file="file://$PROJECT_DIR$/index.html">
      <provider selected="true" editor-type-id="text-editor">
        <state relative-caret-position="528">
          <caret line="33" column="47" lean-forward="false" selection-start-line="33" selection-start-column="47" selection-end-line="33" selection-end-column="47" />
          <folding />
        </state>
      </provider>
    </entry>
    <entry file="file://$PROJECT_DIR$/js/application.js">
      <provider selected="true" editor-type-id="text-editor">
        <state relative-caret-position="208">
          <caret line="13" column="9" lean-forward="true" selection-start-line="13" selection-start-column="9" selection-end-line="13" selection-end-column="9" />
          <folding />
        </state>
      </provider>
    </entry>
    <entry file="file://$PROJECT_DIR$/js/html_actuator.js">
      <provider selected="true" editor-type-id="text-editor">
        <state relative-caret-position="0">
          <caret line="0" column="0" lean-forward="false" selection-start-line="0" selection-start-column="0" selection-end-line="0" selection-end-column="0" />
        </state>
      </provider>
    </entry>
    <entry file="file://$PROJECT_DIR$/js/local_storage_manager.js">
      <provider selected="true" editor-type-id="text-editor">
        <state relative-caret-position="352">
          <caret line="22" column="37" lean-forward="false" selection-start-line="22" selection-start-column="26" selection-end-line="22" selection-end-column="37" />
        </state>
      </provider>
    </entry>
    <entry file="file://$PROJECT_DIR$/style/main.css">
      <provider selected="true" editor-type-id="text-editor">
        <state relative-caret-position="0">
          <caret line="0" column="0" lean-forward="false" selection-start-line="0" selection-start-column="0" selection-end-line="0" selection-end-column="0" />
        </state>
      </provider>
    </entry>
    <entry file="file://$PROJECT_DIR$/index.html">
      <provider selected="true" editor-type-id="text-editor">
        <state relative-caret-position="336">
          <caret line="21" column="22" lean-forward="false" selection-start-line="21" selection-start-column="17" selection-end-line="21" selection-end-column="22" />
          <folding />
        </state>
      </provider>
    </entry>
    <entry file="file://$PROJECT_DIR$/style/main.scss">
      <provider selected="true" editor-type-id="text-editor">
        <state relative-caret-position="0">
          <caret line="0" column="0" lean-forward="false" selection-start-line="0" selection-start-column="0" selection-end-line="0" selection-end-column="0" />
          <folding />
        </state>
      </provider>
    </entry>
    <entry file="file://$PROJECT_DIR$/README.md">
      <provider selected="true" editor-type-id="text-editor">
        <state relative-caret-position="48">
          <caret line="3" column="60" lean-forward="false" selection-start-line="3" selection-start-column="60" selection-end-line="3" selection-end-column="60" />
          <folding />
        </state>
      </provider>
    </entry>
    <entry file="file://$PROJECT_DIR$/js/tile.js">
      <provider selected="true" editor-type-id="text-editor">
        <state relative-caret-position="0">
          <caret line="0" column="0" lean-forward="false" selection-start-line="0" selection-start-column="0" selection-end-line="0" selection-end-column="0" />
          <folding />
        </state>
      </provider>
    </entry>
    <entry file="file://$PROJECT_DIR$/js/grid.js">
      <provider selected="true" editor-type-id="text-editor">
        <state relative-caret-position="1616">
          <caret line="101" column="24" lean-forward="false" selection-start-line="101" selection-start-column="15" selection-end-line="101" selection-end-column="24" />
          <folding />
        </state>
      </provider>
    </entry>
    <entry file="file://$PROJECT_DIR$/js/game_manager.js">
      <provider selected="true" editor-type-id="text-editor">
        <state relative-caret-position="272">
          <caret line="17" column="4" lean-forward="false" selection-start-line="17" selection-start-column="4" selection-end-line="17" selection-end-column="4" />
          <folding />
        </state>
      </provider>
    </entry>
    <entry file="file://$PROJECT_DIR$/js/application.js">
      <provider selected="true" editor-type-id="text-editor">
        <state relative-caret-position="128">
          <caret line="8" column="33" lean-forward="false" selection-start-line="8" selection-start-column="33" selection-end-line="8" selection-end-column="33" />
          <folding />
        </state>
      </provider>
    </entry>
    <entry file="file://$PROJECT_DIR$/js/IA.js">
      <provider selected="true" editor-type-id="text-editor">
        <state relative-caret-position="689">
          <caret line="374" column="39" lean-forward="true" selection-start-line="374" selection-start-column="39" selection-end-line="374" selection-end-column="39" />
          <folding />
        </state>
      </provider>
    </entry>
  </component>
</project>


text:
/**
 * @author Matthew Ferreira
 * @file A mock DiagnosticCollection used to abstract out vscode.
 */

import { Diagnostic, DiagnosticCollection, Uri } from 'vscode';

export class MockDiagnosticCollection implements DiagnosticCollection {
  /** Indicates whether the 'set' function was called. */
  wasSet: boolean = false;

  /** Indicates whether the 'clear' function was called. */
  wasClear: boolean = false;

  name: string;

  set(_uri: any, _diagnostics?: any) {
    this.wasSet = true;
  }

  clear(): void {
    this.wasClear = true;
  }

  delete(_uri: Uri): void { }

  dispose(): void { }

  forEach(_callback: (uri: Uri, diagnostics: Diagnostic[], collection: DiagnosticCollection) => any, _thisArg?: any): void { }

  get(_uri: Uri): Diagnostic[] {
    return [];
  }

  has(_uri: Uri): boolean {
    return false;
  }
}


text:
goog.provide("jchemhub.view.MoleculeDrawing");
goog.require("jchemhub.view.AtomDrawing");
goog.require("jchemhub.view.SingleBondDownDrawing");
goog.require("jchemhub.view.SingleBondDrawing");
goog.require("jchemhub.view.SingleBondUpDrawing");
goog.require("jchemhub.view.SingleBondEitherDrawing");
goog.require("jchemhub.view.DoubleBondDrawing");
goog.require("jchemhub.view.TripleBondDrawing");
goog.require("goog.math.Box");

/**
 * A molecule graphical element in the reaction editor.
 *
 * @param {jchemhub.model.Molecule}
 *            molecule. The molecule to render.
 *
 * @constructor
 * @extends {jchemhub.view.Drawing}
 */
jchemhub.view.MoleculeDrawing = function(molecule) {
	jchemhub.view.Drawing.call(this);
	this.molecule = molecule;

	goog.array.forEach(molecule.bonds, function(bond) {
		this.add(jchemhub.controller.Controller.createBondDrawing(bond));
	}, this);
	goog.array.forEach(molecule.atoms, function(atom) {
		this.add(new jchemhub.view.AtomDrawing(atom));
	}, this);

	// this.addEventListener(goog.events.EventType.CLICK, this.toggleHighlight);
//	this.addEventListener(goog.events.EventType.MOUSEDOWN, this.drag);

};
goog.inherits(jchemhub.view.MoleculeDrawing, jchemhub.view.Drawing);

/**
 * @override does not call children's layout function, just sets their transform
 */
jchemhub.view.MoleculeDrawing.prototype.layoutChildren = function() {
	goog.array.forEach(this.getChildren(), function(child) {
		child.setTransform(this.getTransform());
	}, this);
};

/**
 * render this drawing and all its children
 */
jchemhub.view.MoleculeDrawing.prototype.render = function() {
	this.renderChildren();
};

jchemhub.view.MoleculeDrawing.prototype.toggleHighlight = function(e) {
	if (!this.isSelected) {
		this.isSelected = true;
	} else {
		this.isSelected = false;
	}
	goog.array.forEach(this.getChildren(), function(child) {
		child.toggleHighlight();
	});
};

jchemhub.view.MoleculeDrawing.prototype.drag = function(e) {
	var mol = e.currentTarget;
	var d = new goog.fx.Dragger(mol.getGroup().getElement());
	d._prevX = e.clientX;
	d._prevY = e.clientY;
	d._startX = e.clientX;
	d._startY = e.clientY;

	d.molecule = mol;
	d.addEventListener(goog.fx.Dragger.EventType.DRAG, function(e) {

		goog.array.forEach(d.molecule.getChildren(), function(child) {
			var g_trans = child.getGroup().getTransform();
			var newX = e.clientX - d._prevX + g_trans.getTranslateX();
			var newY = e.clientY - d._prevY + g_trans.getTranslateY();
			child.getGroup().setTransformation(newX, newY, 0, 0, 0);
		});

		d._prevX = e.clientX;
		d._prevY = e.clientY;

	});
	d.addEventListener(goog.fx.Dragger.EventType.END, function(e) {
		var trans = new goog.graphics.AffineTransform.getTranslateInstance(
				e.clientX - d._startX, e.clientY - d._startY);

		d.molecule.transformDrawing(trans);
		d.dispose();
	});
	d.startDrag(e);
};

jchemhub.view.MoleculeDrawing.prototype.getRect = function(){
	return jchemhub.view.MoleculeDrawing.boundingRect([this.molecule]);
}

/**
 * finds bounding box of an array of molecules
 *
 * @param molecules
 * @return goog.math.Box
 */

jchemhub.view.MoleculeDrawing.boundingBox = function(molecules) {
	var atoms = goog.array.flatten(goog.array.map(molecules, function(mol) {
		return mol.atoms;
	}));
	var coords = goog.array.map(atoms, function(a) {
		return a.coord;
	})
	if(coords.length>0){
		return goog.math.Box.boundingBox.apply(null, coords);
	} else{
		return new goog.math.Box(0,0,0,0);
	}
}

jchemhub.view.MoleculeDrawing.boundingRect = function(molecules){
	return goog.math.Rect.createFromBox(this.boundingBox(molecules));
}

jchemhub.view.MoleculeDrawing.prototype.transformDrawing=function(trans){
	goog.array.forEach(this.getChildren(), function(child){
		if(child instanceof jchemhub.view.AtomDrawing){
			child.transformDrawing(trans);
		}
	});
}


text:
Readium.Models.ReadiumOptions = Backbone.Model.extend({

	initialize: function() {
		this.set("id", "singleton");
	},

	defaults: {
		hijack_epub_urls: false,
		verbose_unpacking: true,
		display_page_numbers: true,
		font_size: 10,
		font_face: "default",
		speech_rate: 1.0,
		current_margin: 3,
		current_theme: "default-theme",
		pagination_mode: 'scrolling', //values are scrolling, single, facing
		show_beeline_alert: 'true'
	},

	sync: Readium.Utils.LocalStorageAdaptor("READIUM_OPTIONS"),

	applyOptions: function() {
		var controller = this.get("controller");

		// set everything but two_up
		controller.set({
			"font_size": 			this.get("font_size"),
			"font_face": 			this.get("font_face"),
			"display_page_numbers":	this.get("display_page_numbers"),
			"current_theme": 		this.get("current_theme"),
			"beeline_theme": 		this.get("beeline_theme"),
			"current_margin": 		this.get("current_margin"),
			"speech_rate":          this.get("speech_rate"),
			"voice_uri":		    this.get("voice_uri")
		});

		if (this.get("pagination_mode") == 'scrolling') {
			controller.set('should_scroll', true);
		} else {
			controller.set('should_scroll', false);
			var two_up = (this.get("pagination_mode") == 'facing');
			var shouldToggleTwoUp = two_up !== controller.get("two_up");
			if (shouldToggleTwoUp) {
				controller.set("two_up", !controller.get("two_up"));
			}
		}
		this.save();
	},

	resetOptions: function() {
		// save the controller reference
		var controller = this.get("controller");
		this.fetch();
		this.set("controller", controller);
	}
}, {
	getInstance: function() {
		var instance = new Readium.Models.ReadiumOptions();
		instance.fetch({
			error: function() {
				localStorage.setItem("READIUM_OPTIONS", "");
				instance.save();
			}
		});
		return instance;
	}
});


text:
%IMOMENTS Image moments
%
% F = IMOMENTS(IM) is a RegionFeature object that describes the greyscale
% moments of the image IM.
%
% F = IMOMENTS(U, V) as above but the moments are computed from the pixel
% coordinates given as vectors U (Nx1) and V (Nx1).  All pixels are equally
% weighted and is effectively a binary image.
%
% F = IMOMENTS(U, V, W) as above but the pixels have weights given by the
% vector W and is effectively a greyscale image.
%
% Properties::
% The RegionFeature object has many properties including:
%
%  uc        centroid, horizontal coordinate
%  vc        centroid, vertical coordinate
%  area      the number of pixels
%  a         major axis length of equivalent ellipse
%  b         minor axis length of equivalent ellipse
%  theta     angle of major ellipse axis to horizontal axis
%  shape     aspect ratio b/a (always <= 1.0)
%  moments   a structure containing moments of order 0 to 2, the elements
%            are m00, m10, m01, m20, m02, m11.
%
% See RegionFeature help for more details.
%
% Notes::
% - For a binary image the zeroth moment is the number of non-zero pixels, or
%   its area.
% - This function does not perform connectivity it considers all non-zero
%   pixels in the image.  If connected regions are required then use IBLOBS
%   instead.
%
% See also RegionFeature, IBLOBS.

% Copyright (C) 1993-2011, by Peter I. Corke
%
% This file is part of The Machine Vision Toolbox for Matlab (MVTB).
%
% MVTB is free software: you can redistribute it and/or modify
% it under the terms of the GNU Lesser General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% MVTB is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
% GNU Lesser General Public License for more details.
%
% You should have received a copy of the GNU Leser General Public License
% along with MVTB.  If not, see <http://www.gnu.org/licenses/>.

function F = imoments(varargin)

    opt.aspect = 1;

    [opt,args] = tb_optparse(opt, varargin);

    if length(args) == 1
        im = args{1};
        [v,u] = find(im);
        w = im(find(im > 0));
    else
        u = args{1};
        v = args{2};
        if length(args) == 3
            w = args{3};
        else
            w = ones(size(u));
        end
    end

    % compute basic moments
    m00 = sum(w);
    m10 = sum(u.*w);
    m01 = sum(v.*w*opt.aspect);
    m20 = sum((u.^2).*w);
    m02 = sum((v.^2).*w*opt.aspect^2);
    m11 = sum((u.*v).*w*opt.aspect);

    if m00 > 0
        % figure some central moments
        u20 = m20 - m10^2/m00;
        u02 = m02 - m01^2/m00;
        u11 = m11 - m10*m01/m00;

        % figure the equivalent axis lengths, function of the principal axis lengths
        [x,e] = eig([u20 u11; u11 u02]);
        a = 2*sqrt(max(diag(e))/m00);
        b = 2*sqrt(min(diag(e))/m00);
        v = x(:,end);
        th = atan2(v(2),v(1));

        %th = 0.5*atan2(2*u11, u20-u02);
    else
        u20 = NaN;
        u02 = NaN;
        u11 = NaN;

        a = NaN;
        b = NaN;
        th = NaN;
    end

    %F = [m00 m10/m00 m01/m00 a b th];
    F = RegionFeature;
    F.area_ = m00;
    if m00 > 0
        F.uc_ = m10/m00;
        F.vc_ = m01/m00;
    else
        F.uc_ = NaN;
        F.vc_ = NaN;
    end
    F.a = a;
    F.b = b;
    F.shape_ = b/a;
    F.theta_ = th;
    F.moments.m00 = m00;
    F.moments.m01 = m01;
    F.moments.m10 = m10;
    F.moments.m02 = m02;
    F.moments.m20 = m20;
    F.moments.m11 = m11;
    F.moments.u20 = u20;
    F.moments.u02 = u02;
    F.moments.u11 = u11;


text:
function clickDialog(e) {
	toShow = $(e.currentTarget).attr("href")

	var count = $(toShow).find(".entry").length

  $(toShow).dialog({height: $(window).height() - 50})
  $(toShow).dialog({width: $(window).width() - 50})

	$(toShow).dialog("open");

	$(".dialogHide").hide();
	return false;
}

function dismissDialog(e, ui) {
	$(".dialogHide").show();
}

$(document).ready(function() {
	$( ".dialog" ).dialog({
		autoOpen: false,
		show: {
			effect: "blind",
			duration: 500},
		hide: {
			effect: "fade",
			duration: 250},
		height: 700,
		width: 700,
		modal: true,
		beforeClose: dismissDialog});

	$("a.dialogLauncher").click(clickDialog);
  $("div#content").show();
});


text:
<?php

/**
 * @see Zend_Paginator_Adapter_Interface
 */
// require_once 'Zend/Paginator/Adapter/Interface.php';

/**
 * @see Zend_Db
 */
// require_once 'Zend/Db.php';

/**
 * @see Zend_Db_Select
 */
// require_once 'Zend/Db/Select.php';

/**
 * @category   Zend
 * @package    Zend_Paginator
 * @copyright  Copyright (c) 2005-2010 Zend Technologies USA Inc. (http://www.zend.com)
 * @license    http://framework.zend.com/license/new-bsd     New BSD License
 */
class Zend_Paginator_Adapter_DbSelect implements Zend_Paginator_Adapter_Interface
{
    /**
     * Name of the row count column
     *
     * @var string
     */
    const ROW_COUNT_COLUMN = 'zend_paginator_row_count';

    /**
     * The COUNT query
     *
     * @var Zend_Db_Select
     */
    protected $_countSelect = null;

    /**
     * Database query
     *
     * @var Zend_Db_Select
     */
    protected $_select = null;

    /**
     * Total item count
     *
     * @var integer
     */
    protected $_rowCount = null;

    /**
     * Constructor.
     *
     * @param Zend_Db_Select $select The select query
     */
    public function __construct(Zend_Db_Select $select)
    {
        $this->_select = $select;
    }

    /**
     * Sets the total row count, either directly or through a supplied
     * query.  Without setting this, {@link getPages()} selects the count
     * as a subquery (SELECT COUNT ... FROM (SELECT ...)).  While this
     * yields an accurate count even with queries containing clauses like
     * LIMIT, it can be slow in some circumstances.  For example, in MySQL,
     * subqueries are generally slow when using the InnoDB storage engine.
     * Users are therefore encouraged to profile their queries to find
     * the solution that best meets their needs.
     *
     * @param  Zend_Db_Select|integer $totalRowCount Total row count integer
     *                                               or query
     * @return Zend_Paginator_Adapter_DbSelect $this
     * @throws Zend_Paginator_Exception
     */
    public function setRowCount($rowCount)
    {
        if ($rowCount instanceof Zend_Db_Select) {
            $columns = $rowCount->getPart(Zend_Db_Select::COLUMNS);

            $countColumnPart = $columns[0][1];

            if ($countColumnPart instanceof Zend_Db_Expr) {
                $countColumnPart = $countColumnPart->__toString();
            }

            $rowCountColumn = $this->_select->getAdapter()->foldCase(self::ROW_COUNT_COLUMN);

            // The select query can contain only one column, which should be the row count column
            if (false === strpos($countColumnPart, $rowCountColumn)) {
                /**
                 * @see Zend_Paginator_Exception
                 */
                // require_once 'Zend/Paginator/Exception.php';

                throw new Zend_Paginator_Exception('Row count column not found');
            }

            $result = $rowCount->query(Zend_Db::FETCH_ASSOC)->fetch();

            $this->_rowCount = count($result) > 0 ? $result[$rowCountColumn] : 0;
        } else if (is_integer($rowCount)) {
            $this->_rowCount = $rowCount;
        } else {
            /**
             * @see Zend_Paginator_Exception
             */
            // require_once 'Zend/Paginator/Exception.php';

            throw new Zend_Paginator_Exception('Invalid row count');
        }

        return $this;
    }

    /**
     * Returns an array of items for a page.
     *
     * @param  integer $offset Page offset
     * @param  integer $itemCountPerPage Number of items per page
     * @return array
     */
    public function getItems($offset, $itemCountPerPage)
    {
        $this->_select->limit($itemCountPerPage, $offset);

        return $this->_select->query()->fetchAll();
    }

    /**
     * Returns the total number of rows in the result set.
     *
     * @return integer
     */
    public function count()
    {
        if ($this->_rowCount === null) {
            $this->setRowCount(
                $this->getCountSelect()
            );
        }

        return $this->_rowCount;
    }

    /**
     * Get the COUNT select object for the provided query
     *
     * TODO: Have a look at queries that have both GROUP BY and DISTINCT specified.
     * In that use-case I'm expecting problems when either GROUP BY or DISTINCT
     * has one column.
     *
     * @return Zend_Db_Select
     */
    public function getCountSelect()
    {
        /**
         * We only need to generate a COUNT query once. It will not change for
         * this instance.
         */
        if ($this->_countSelect !== null) {
            return $this->_countSelect;
        }

        $rowCount = clone $this->_select;
        $rowCount->__toString(); // Workaround for ZF-3719 and related

        $db = $rowCount->getAdapter();

        $countColumn = $db->quoteIdentifier($db->foldCase(self::ROW_COUNT_COLUMN));
        $countPart   = 'COUNT(1) AS ';
        $groupPart   = null;
        $unionParts  = $rowCount->getPart(Zend_Db_Select::UNION);

        /**
         * If we're dealing with a UNION query, execute the UNION as a subquery
         * to the COUNT query.
         */
        if (!empty($unionParts)) {
            $expression = new Zend_Db_Expr($countPart . $countColumn);

            $rowCount = $db->select()->from($rowCount, $expression);
        } else {
            $columnParts = $rowCount->getPart(Zend_Db_Select::COLUMNS);
            $groupParts  = $rowCount->getPart(Zend_Db_Select::GROUP);
            $havingParts = $rowCount->getPart(Zend_Db_Select::HAVING);
            $isDistinct  = $rowCount->getPart(Zend_Db_Select::DISTINCT);

            /**
             * If there is more than one column AND it's a DISTINCT query, more
             * than one group, or if the query has a HAVING clause, then take
             * the original query and use it as a subquery os the COUNT query.
             */
            if (($isDistinct && count($columnParts) > 1) || count($groupParts) > 1 || !empty($havingParts)) {
                $rowCount = $db->select()->from($this->_select);
            } else if ($isDistinct) {
                $part = $columnParts[0];

                if ($part[1] !== Zend_Db_Select::SQL_WILDCARD && !($part[1] instanceof Zend_Db_Expr)) {
                    $column = $db->quoteIdentifier($part[1], true);

                    if (!empty($part[0])) {
                        $column = $db->quoteIdentifier($part[0], true) . '.' . $column;
                    }

                    $groupPart = $column;
                }
            } else if (!empty($groupParts) && $groupParts[0] !== Zend_Db_Select::SQL_WILDCARD &&
                       !($groupParts[0] instanceof Zend_Db_Expr)) {
                $groupPart = $db->quoteIdentifier($groupParts[0], true);
            }

            /**
             * If the original query had a GROUP BY or a DISTINCT part and only
             * one column was specified, create a COUNT(DISTINCT ) query instead
             * of a regular COUNT query.
             */
            if (!empty($groupPart)) {
                $countPart = 'COUNT(DISTINCT ' . $groupPart . ') AS ';
            }

            /**
             * Create the COUNT part of the query
             */
            $expression = new Zend_Db_Expr($countPart . $countColumn);

            $rowCount->reset(Zend_Db_Select::COLUMNS)
                     ->reset(Zend_Db_Select::ORDER)
                     ->reset(Zend_Db_Select::LIMIT_OFFSET)
                     ->reset(Zend_Db_Select::GROUP)
                     ->reset(Zend_Db_Select::DISTINCT)
                     ->reset(Zend_Db_Select::HAVING)
                     ->columns($expression);
        }

        $this->_countSelect = $rowCount;

        return $rowCount;
    }
}


text:
$ErrorActionPreference = "Stop"

# Remove the service if it already exists.  Note that Remove-Service isn't
# available until PowerShell 6, and we can't guarantee that anything more
# recent than 5.1 is installed.
$service = Get-WmiObject -Class Win32_Service -Filter "Name='shaka-lab-node'"
if ($service) {
  echo "Stopping service..."
  Stop-Service -Name shaka-lab-node
  echo "Deleting service..."
  $service.delete()
}


text:
<?php

/* AngularBundle:Default:index.html.twig */
class __TwigTemplate_7060affdb2aeadf942d2109d6d1187fab94e58be25c75967fc484df82dd64640 extends Twig_Template
{
    public function __construct(Twig_Environment $env)
    {
        parent::__construct($env);

        $this->parent = false;

        $this->blocks = array(
        );
    }

    protected function doDisplay(array $context, array $blocks = array())
    {
        $__internal_2ebb6ee7327c244b56dbd6572405874b1bcb0a585ffb5391b4c1d67f40ffd397 = $this->env->getExtension("Symfony\\Bridge\\Twig\\Extension\\ProfilerExtension");
        $__internal_2ebb6ee7327c244b56dbd6572405874b1bcb0a585ffb5391b4c1d67f40ffd397->enter($__internal_2ebb6ee7327c244b56dbd6572405874b1bcb0a585ffb5391b4c1d67f40ffd397_prof = new Twig_Profiler_Profile($this->getTemplateName(), "template", "AngularBundle:Default:index.html.twig"));

        // line 1
        echo "<!doctype html>

        <title>Angular JS</title>
        <link rel=\"stylesheet\" href=\"";
        // line 5
        echo twig_escape_filter($this->env, $this->env->getExtension('Symfony\Bridge\Twig\Extension\AssetExtension')->getAssetUrl("bower_components/angular-material/angular-material.css"), "html", null, true);
        echo "\">
        <link rel=\"stylesheet\" href=\"";
        // line 6
        echo twig_escape_filter($this->env, $this->env->getExtension('Symfony\Bridge\Twig\Extension\AssetExtension')->getAssetUrl("bower_components/angular-growl-v2/build/angular-growl.min.css"), "html", null, true);
        echo "\">
        <link rel=\"stylesheet\" href=\"";
        // line 7
        echo twig_escape_filter($this->env, $this->env->getExtension('Symfony\Bridge\Twig\Extension\AssetExtension')->getAssetUrl("bower_components/bootstrap/dist/css/bootstrap.min.css"), "html", null, true);
        echo "\">
        <link rel=\"stylesheet\" href=\"";
        // line 8
        echo twig_escape_filter($this->env, $this->env->getExtension('Symfony\Bridge\Twig\Extension\AssetExtension')->getAssetUrl("bundles/angular/css/style.css"), "html", null, true);
        echo "\">

";
        // line 34
        echo "

        <login-status status=";
        // line 39
        echo twig_escape_filter($this->env, (isset($context["loggedIn"]) ? $context["loggedIn"] : $this->getContext($context, "loggedIn")), "html", null, true);
        echo "></login-status>

";

        $__internal_2ebb6ee7327c244b56dbd6572405874b1bcb0a585ffb5391b4c1d67f40ffd397->leave($__internal_2ebb6ee7327c244b56dbd6572405874b1bcb0a585ffb5391b4c1d67f40ffd397_prof);

    }

    public function getTemplateName()
    {
        return "AngularBundle:Default:index.html.twig";
    }

    public function isTraitable()
    {
        return false;
    }

    public function getDebugInfo()
    {
        return array (  124 => 39,  117 => 34,  112 => 29,  108 => 28,  104 => 27,  100 => 26,  95 => 24,  90 => 22,  86 => 21,  82 => 20,  78 => 19,  74 => 18,  70 => 17,  66 => 16,  62 => 15,  58 => 14,  54 => 13,  50 => 12,  46 => 11,  40 => 8,  36 => 7,  32 => 6,  28 => 5,  22 => 1,);
    }

    /** @deprecated since 1.27 (to be removed in 2.0). Use getSourceContext() instead */
    public function getSource()
    {
        @trigger_error('The '.__METHOD__.' method is deprecated since version 1.27 and will be removed in 2.0. Use getSourceContext() instead.', E_USER_DEPRECATED);

        return $this->getSourceContext()->getCode();
    }

    public function getSourceContext()
    {
        return new Twig_Source("<!doctype html>

        <title>Angular JS</title>
        <link rel=\"stylesheet\" href=\"{{asset(\"bower_components/angular-material/angular-material.css\")}}\">
        <link rel=\"stylesheet\" href=\"{{asset(\"bower_components/angular-growl-v2/build/angular-growl.min.css\")}}\">
        <link rel=\"stylesheet\" href=\"{{asset(\"bower_components/bootstrap/dist/css/bootstrap.min.css\")}}\">
        <link rel=\"stylesheet\" href=\"{{asset(\"bundles/angular/css/style.css\")}}\">

{#

#}

        <login-status status={{loggedIn}}></login-status>

", "AngularBundle:Default:index.html.twig", "/home/michelk9/public_html/myBackEnd/src/AngularBundle/Resources/views/Default/index.html.twig");
    }
}


text:
/**
 * This manager runs daily (timer) to rotate records
 */

module.exports.init=init;
var timer = require("./timer");
var lastRun = null;
var models = require("../data/mongoose/allModel");
var checkPeriod = 24 * 3600 * 1000; //TODO env var
var log = require("./log");
var rotationRunning = false;
var RunModel = models["Run"];
var CheckModel = models["Check"];
var async = require("async");
var env=require("./env.js");

function init(cb) {
  log.info("Rotation Manager subscribe to timer.");
  timer.onTime(onTimer);
  setTimeout(function() {
    onTimer();
  }, 0);
  cb();
}

function onTimer() {
  var now = new Date().getTime();
  if (!lastRun || (now - lastRun) > checkPeriod) {
    if (rotationRunning === false) {
      rotationRunning = true;
      rotate(function(err) {
        if (err) {
          log.error("Rotation error");
          log.error(err);
        }
        log.info("Records rotation finished.");
        lastRun = now;
        rotationRunning = false;
      });
    } else {
      log.info("Rotation in progress. Skip.");
    }
  }
}

function rotate(cb) {
  CheckModel.find({

  }, {
    _id: 1,
    recordRotation: 1,
  }, function(err, allChecks) {
    if (err) {
      cb(err);
    } else {
      async.each(allChecks, function(obj, rotateCb) {
        var checkId=obj._id;
        var rotateDay=obj.recordRotation===undefined?env.get("DEF_RECORD_ROTATION",20):obj.recordRotation;//TODO env var for default rotate day
        var deadLine=new Date(new Date().getTime()-rotateDay*24*3600*1000);
        log.info("Rotate records for check: "+checkId+" where rocords are older than: "+deadLine);
        RunModel.remove({
          startDate:{"$lt":deadLine}
        },rotateCb);
      }, cb);
    }
  });
}


text:
package org.apache.spark.examples.streaming

import org.apache.spark.SparkConf
import org.apache.spark.storage.StorageLevel
import org.apache.spark.streaming._
import org.apache.spark.streaming.flume._
import org.apache.spark.util.IntParam
import java.net.InetSocketAddress

/**
 *  Produces a count of events received from Flume.
 *
 *  This should be used in conjunction with the Spark Sink running in a Flume agent. See
 *  the Spark Streaming programming guide for more details.
 *
 *  Usage: FlumePollingEventCount <host> <port>
 *    `host` is the host on which the Spark Sink is running.
 *    `port` is the port at which the Spark Sink is listening.
 *
 *  To run this example:
 *    `$ bin/run-example org.apache.spark.examples.streaming.FlumePollingEventCount [host] [port] `
 */
object FlumePollingEventCount {
  def main(args: Array[String]) {
    if (args.length < 2) {
      System.err.println(
        "Usage: FlumePollingEventCount <host> <port>")
      System.exit(1)
    }

    StreamingExamples.setStreamingLogLevels()

    val Array(host, IntParam(port)) = args

    val batchInterval = Milliseconds(2000)

    // Create the context and set the batch size
    val sparkConf = new SparkConf().setAppName("FlumePollingEventCount")
    val ssc = new StreamingContext(sparkConf, batchInterval)

    // Create a flume stream that polls the Spark Sink running in a Flume agent
    val stream = FlumeUtils.createPollingStream(ssc, host, port)

    // Print out the count of events received from this server in each batch
    stream.count().map(cnt => "Received " + cnt + " flume events." ).print()

    ssc.start()
    ssc.awaitTermination()
  }
}


text:
TodoL10nVernacular (Xamarin.Forms)
=============

Sample removed.