Update aom to v1.0.0

Update aom to commit id d14c5bb4f336ef1842046089849dee4a301fbbf0.

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diff --git a/third_party/aom/test/gviz_api.py b/third_party/aom/test/gviz_api.py
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+#!/usr/bin/python
+#
+# Copyright (c) 2016, Alliance for Open Media. All rights reserved
+#
+# This source code is subject to the terms of the BSD 2 Clause License and
+# the Alliance for Open Media Patent License 1.0. If the BSD 2 Clause License
+# was not distributed with this source code in the LICENSE file, you can
+# obtain it at www.aomedia.org/license/software. If the Alliance for Open
+# Media Patent License 1.0 was not distributed with this source code in the
+# PATENTS file, you can obtain it at www.aomedia.org/license/patent.
+#
+
+"""Converts Python data into data for Google Visualization API clients.
+
+This library can be used to create a google.visualization.DataTable usable by
+visualizations built on the Google Visualization API. Output formats are raw
+JSON, JSON response, JavaScript, CSV, and HTML table.
+
+See http://code.google.com/apis/visualization/ for documentation on the
+Google Visualization API.
+"""
+
+__author__ = "Amit Weinstein, Misha Seltzer, Jacob Baskin"
+
+import cgi
+import cStringIO
+import csv
+import datetime
+try:
+  import json
+except ImportError:
+  import simplejson as json
+import types
+
+
+class DataTableException(Exception):
+  """The general exception object thrown by DataTable."""
+  pass
+
+
+class DataTableJSONEncoder(json.JSONEncoder):
+  """JSON encoder that handles date/time/datetime objects correctly."""
+
+  def __init__(self):
+    json.JSONEncoder.__init__(self,
+                              separators=(",", ":"),
+                              ensure_ascii=False)
+
+  def default(self, o):
+    if isinstance(o, datetime.datetime):
+      if o.microsecond == 0:
+        # If the time doesn't have ms-resolution, leave it out to keep
+        # things smaller.
+        return "Date(%d,%d,%d,%d,%d,%d)" % (
+            o.year, o.month - 1, o.day, o.hour, o.minute, o.second)
+      else:
+        return "Date(%d,%d,%d,%d,%d,%d,%d)" % (
+            o.year, o.month - 1, o.day, o.hour, o.minute, o.second,
+            o.microsecond / 1000)
+    elif isinstance(o, datetime.date):
+      return "Date(%d,%d,%d)" % (o.year, o.month - 1, o.day)
+    elif isinstance(o, datetime.time):
+      return [o.hour, o.minute, o.second]
+    else:
+      return super(DataTableJSONEncoder, self).default(o)
+
+
+class DataTable(object):
+  """Wraps the data to convert to a Google Visualization API DataTable.
+
+  Create this object, populate it with data, then call one of the ToJS...
+  methods to return a string representation of the data in the format described.
+
+  You can clear all data from the object to reuse it, but you cannot clear
+  individual cells, rows, or columns. You also cannot modify the table schema
+  specified in the class constructor.
+
+  You can add new data one or more rows at a time. All data added to an
+  instantiated DataTable must conform to the schema passed in to __init__().
+
+  You can reorder the columns in the output table, and also specify row sorting
+  order by column. The default column order is according to the original
+  table_description parameter. Default row sort order is ascending, by column
+  1 values. For a dictionary, we sort the keys for order.
+
+  The data and the table_description are closely tied, as described here:
+
+  The table schema is defined in the class constructor's table_description
+  parameter. The user defines each column using a tuple of
+  (id[, type[, label[, custom_properties]]]). The default value for type is
+  string, label is the same as ID if not specified, and custom properties is
+  an empty dictionary if not specified.
+
+  table_description is a dictionary or list, containing one or more column
+  descriptor tuples, nested dictionaries, and lists. Each dictionary key, list
+  element, or dictionary element must eventually be defined as
+  a column description tuple. Here's an example of a dictionary where the key
+  is a tuple, and the value is a list of two tuples:
+    {('a', 'number'): [('b', 'number'), ('c', 'string')]}
+
+  This flexibility in data entry enables you to build and manipulate your data
+  in a Python structure that makes sense for your program.
+
+  Add data to the table using the same nested design as the table's
+  table_description, replacing column descriptor tuples with cell data, and
+  each row is an element in the top level collection. This will be a bit
+  clearer after you look at the following examples showing the
+  table_description, matching data, and the resulting table:
+
+  Columns as list of tuples [col1, col2, col3]
+    table_description: [('a', 'number'), ('b', 'string')]
+    AppendData( [[1, 'z'], [2, 'w'], [4, 'o'], [5, 'k']] )
+    Table:
+    a  b   <--- these are column ids/labels
+    1  z
+    2  w
+    4  o
+    5  k
+
+  Dictionary of columns, where key is a column, and value is a list of
+  columns  {col1: [col2, col3]}
+    table_description: {('a', 'number'): [('b', 'number'), ('c', 'string')]}
+    AppendData( data: {1: [2, 'z'], 3: [4, 'w']}
+    Table:
+    a  b  c
+    1  2  z
+    3  4  w
+
+  Dictionary where key is a column, and the value is itself a dictionary of
+  columns {col1: {col2, col3}}
+    table_description: {('a', 'number'): {'b': 'number', 'c': 'string'}}
+    AppendData( data: {1: {'b': 2, 'c': 'z'}, 3: {'b': 4, 'c': 'w'}}
+    Table:
+    a  b  c
+    1  2  z
+    3  4  w
+  """
+
+  def __init__(self, table_description, data=None, custom_properties=None):
+    """Initialize the data table from a table schema and (optionally) data.
+
+    See the class documentation for more information on table schema and data
+    values.
+
+    Args:
+      table_description: A table schema, following one of the formats described
+                         in TableDescriptionParser(). Schemas describe the
+                         column names, data types, and labels. See
+                         TableDescriptionParser() for acceptable formats.
+      data: Optional. If given, fills the table with the given data. The data
+            structure must be consistent with schema in table_description. See
+            the class documentation for more information on acceptable data. You
+            can add data later by calling AppendData().
+      custom_properties: Optional. A dictionary from string to string that
+                         goes into the table's custom properties. This can be
+                         later changed by changing self.custom_properties.
+
+    Raises:
+      DataTableException: Raised if the data and the description did not match,
+                          or did not use the supported formats.
+    """
+    self.__columns = self.TableDescriptionParser(table_description)
+    self.__data = []
+    self.custom_properties = {}
+    if custom_properties is not None:
+      self.custom_properties = custom_properties
+    if data:
+      self.LoadData(data)
+
+  @staticmethod
+  def CoerceValue(value, value_type):
+    """Coerces a single value into the type expected for its column.
+
+    Internal helper method.
+
+    Args:
+      value: The value which should be converted
+      value_type: One of "string", "number", "boolean", "date", "datetime" or
+                  "timeofday".
+
+    Returns:
+      An item of the Python type appropriate to the given value_type. Strings
+      are also converted to Unicode using UTF-8 encoding if necessary.
+      If a tuple is given, it should be in one of the following forms:
+        - (value, formatted value)
+        - (value, formatted value, custom properties)
+      where the formatted value is a string, and custom properties is a
+      dictionary of the custom properties for this cell.
+      To specify custom properties without specifying formatted value, one can
+      pass None as the formatted value.
+      One can also have a null-valued cell with formatted value and/or custom
+      properties by specifying None for the value.
+      This method ignores the custom properties except for checking that it is a
+      dictionary. The custom properties are handled in the ToJSon and ToJSCode
+      methods.
+      The real type of the given value is not strictly checked. For example,
+      any type can be used for string - as we simply take its str( ) and for
+      boolean value we just check "if value".
+      Examples:
+        CoerceValue(None, "string") returns None
+        CoerceValue((5, "5$"), "number") returns (5, "5$")
+        CoerceValue(100, "string") returns "100"
+        CoerceValue(0, "boolean") returns False
+
+    Raises:
+      DataTableException: The value and type did not match in a not-recoverable
+                          way, for example given value 'abc' for type 'number'.
+    """
+    if isinstance(value, tuple):
+      # In case of a tuple, we run the same function on the value itself and
+      # add the formatted value.
+      if (len(value) not in [2, 3] or
+          (len(value) == 3 and not isinstance(value[2], dict))):
+        raise DataTableException("Wrong format for value and formatting - %s." %
+                                 str(value))
+      if not isinstance(value[1], types.StringTypes + (types.NoneType,)):
+        raise DataTableException("Formatted value is not string, given %s." %
+                                 type(value[1]))
+      js_value = DataTable.CoerceValue(value[0], value_type)
+      return (js_value,) + value[1:]
+
+    t_value = type(value)
+    if value is None:
+      return value
+    if value_type == "boolean":
+      return bool(value)
+
+    elif value_type == "number":
+      if isinstance(value, (int, long, float)):
+        return value
+      raise DataTableException("Wrong type %s when expected number" % t_value)
+
+    elif value_type == "string":
+      if isinstance(value, unicode):
+        return value
+      else:
+        return str(value).decode("utf-8")
+
+    elif value_type == "date":
+      if isinstance(value, datetime.datetime):
+        return datetime.date(value.year, value.month, value.day)
+      elif isinstance(value, datetime.date):
+        return value
+      else:
+        raise DataTableException("Wrong type %s when expected date" % t_value)
+
+    elif value_type == "timeofday":
+      if isinstance(value, datetime.datetime):
+        return datetime.time(value.hour, value.minute, value.second)
+      elif isinstance(value, datetime.time):
+        return value
+      else:
+        raise DataTableException("Wrong type %s when expected time" % t_value)
+
+    elif value_type == "datetime":
+      if isinstance(value, datetime.datetime):
+        return value
+      else:
+        raise DataTableException("Wrong type %s when expected datetime" %
+                                 t_value)
+    # If we got here, it means the given value_type was not one of the
+    # supported types.
+    raise DataTableException("Unsupported type %s" % value_type)
+
+  @staticmethod
+  def EscapeForJSCode(encoder, value):
+    if value is None:
+      return "null"
+    elif isinstance(value, datetime.datetime):
+      if value.microsecond == 0:
+        # If it's not ms-resolution, leave that out to save space.
+        return "new Date(%d,%d,%d,%d,%d,%d)" % (value.year,
+                                                value.month - 1,  # To match JS
+                                                value.day,
+                                                value.hour,
+                                                value.minute,
+                                                value.second)
+      else:
+        return "new Date(%d,%d,%d,%d,%d,%d,%d)" % (value.year,
+                                                   value.month - 1,  # match JS
+                                                   value.day,
+                                                   value.hour,
+                                                   value.minute,
+                                                   value.second,
+                                                   value.microsecond / 1000)
+    elif isinstance(value, datetime.date):
+      return "new Date(%d,%d,%d)" % (value.year, value.month - 1, value.day)
+    else:
+      return encoder.encode(value)
+
+  @staticmethod
+  def ToString(value):
+    if value is None:
+      return "(empty)"
+    elif isinstance(value, (datetime.datetime,
+                            datetime.date,
+                            datetime.time)):
+      return str(value)
+    elif isinstance(value, unicode):
+      return value
+    elif isinstance(value, bool):
+      return str(value).lower()
+    else:
+      return str(value).decode("utf-8")
+
+  @staticmethod
+  def ColumnTypeParser(description):
+    """Parses a single column description. Internal helper method.
+
+    Args:
+      description: a column description in the possible formats:
+       'id'
+       ('id',)
+       ('id', 'type')
+       ('id', 'type', 'label')
+       ('id', 'type', 'label', {'custom_prop1': 'custom_val1'})
+    Returns:
+      Dictionary with the following keys: id, label, type, and
+      custom_properties where:
+        - If label not given, it equals the id.
+        - If type not given, string is used by default.
+        - If custom properties are not given, an empty dictionary is used by
+          default.
+
+    Raises:
+      DataTableException: The column description did not match the RE, or
+          unsupported type was passed.
+    """
+    if not description:
+      raise DataTableException("Description error: empty description given")
+
+    if not isinstance(description, (types.StringTypes, tuple)):
+      raise DataTableException("Description error: expected either string or "
+                               "tuple, got %s." % type(description))
+
+    if isinstance(description, types.StringTypes):
+      description = (description,)
+
+    # According to the tuple's length, we fill the keys
+    # We verify everything is of type string
+    for elem in description[:3]:
+      if not isinstance(elem, types.StringTypes):
+        raise DataTableException("Description error: expected tuple of "
+                                 "strings, current element of type %s." %
+                                 type(elem))
+    desc_dict = {"id": description[0],
+                 "label": description[0],
+                 "type": "string",
+                 "custom_properties": {}}
+    if len(description) > 1:
+      desc_dict["type"] = description[1].lower()
+      if len(description) > 2:
+        desc_dict["label"] = description[2]
+        if len(description) > 3:
+          if not isinstance(description[3], dict):
+            raise DataTableException("Description error: expected custom "
+                                     "properties of type dict, current element "
+                                     "of type %s." % type(description[3]))
+          desc_dict["custom_properties"] = description[3]
+          if len(description) > 4:
+            raise DataTableException("Description error: tuple of length > 4")
+    if desc_dict["type"] not in ["string", "number", "boolean",
+                                 "date", "datetime", "timeofday"]:
+      raise DataTableException(
+          "Description error: unsupported type '%s'" % desc_dict["type"])
+    return desc_dict
+
+  @staticmethod
+  def TableDescriptionParser(table_description, depth=0):
+    """Parses the table_description object for internal use.
+
+    Parses the user-submitted table description into an internal format used
+    by the Python DataTable class. Returns the flat list of parsed columns.
+
+    Args:
+      table_description: A description of the table which should comply
+                         with one of the formats described below.
+      depth: Optional. The depth of the first level in the current description.
+             Used by recursive calls to this function.
+
+    Returns:
+      List of columns, where each column represented by a dictionary with the
+      keys: id, label, type, depth, container which means the following:
+      - id: the id of the column
+      - name: The name of the column
+      - type: The datatype of the elements in this column. Allowed types are
+              described in ColumnTypeParser().
+      - depth: The depth of this column in the table description
+      - container: 'dict', 'iter' or 'scalar' for parsing the format easily.
+      - custom_properties: The custom properties for this column.
+      The returned description is flattened regardless of how it was given.
+
+    Raises:
+      DataTableException: Error in a column description or in the description
+                          structure.
+
+    Examples:
+      A column description can be of the following forms:
+       'id'
+       ('id',)
+       ('id', 'type')
+       ('id', 'type', 'label')
+       ('id', 'type', 'label', {'custom_prop1': 'custom_val1'})
+       or as a dictionary:
+       'id': 'type'
+       'id': ('type',)
+       'id': ('type', 'label')
+       'id': ('type', 'label', {'custom_prop1': 'custom_val1'})
+      If the type is not specified, we treat it as string.
+      If no specific label is given, the label is simply the id.
+      If no custom properties are given, we use an empty dictionary.
+
+      input: [('a', 'date'), ('b', 'timeofday', 'b', {'foo': 'bar'})]
+      output: [{'id': 'a', 'label': 'a', 'type': 'date',
+                'depth': 0, 'container': 'iter', 'custom_properties': {}},
+               {'id': 'b', 'label': 'b', 'type': 'timeofday',
+                'depth': 0, 'container': 'iter',
+                'custom_properties': {'foo': 'bar'}}]
+
+      input: {'a': [('b', 'number'), ('c', 'string', 'column c')]}
+      output: [{'id': 'a', 'label': 'a', 'type': 'string',
+                'depth': 0, 'container': 'dict', 'custom_properties': {}},
+               {'id': 'b', 'label': 'b', 'type': 'number',
+                'depth': 1, 'container': 'iter', 'custom_properties': {}},
+               {'id': 'c', 'label': 'column c', 'type': 'string',
+                'depth': 1, 'container': 'iter', 'custom_properties': {}}]
+
+      input:  {('a', 'number', 'column a'): { 'b': 'number', 'c': 'string'}}
+      output: [{'id': 'a', 'label': 'column a', 'type': 'number',
+                'depth': 0, 'container': 'dict', 'custom_properties': {}},
+               {'id': 'b', 'label': 'b', 'type': 'number',
+                'depth': 1, 'container': 'dict', 'custom_properties': {}},
+               {'id': 'c', 'label': 'c', 'type': 'string',
+                'depth': 1, 'container': 'dict', 'custom_properties': {}}]
+
+      input: { ('w', 'string', 'word'): ('c', 'number', 'count') }
+      output: [{'id': 'w', 'label': 'word', 'type': 'string',
+                'depth': 0, 'container': 'dict', 'custom_properties': {}},
+               {'id': 'c', 'label': 'count', 'type': 'number',
+                'depth': 1, 'container': 'scalar', 'custom_properties': {}}]
+
+      input: {'a': ('number', 'column a'), 'b': ('string', 'column b')}
+      output: [{'id': 'a', 'label': 'column a', 'type': 'number', 'depth': 0,
+               'container': 'dict', 'custom_properties': {}},
+               {'id': 'b', 'label': 'column b', 'type': 'string', 'depth': 0,
+               'container': 'dict', 'custom_properties': {}}
+
+      NOTE: there might be ambiguity in the case of a dictionary representation
+      of a single column. For example, the following description can be parsed
+      in 2 different ways: {'a': ('b', 'c')} can be thought of a single column
+      with the id 'a', of type 'b' and the label 'c', or as 2 columns: one named
+      'a', and the other named 'b' of type 'c'. We choose the first option by
+      default, and in case the second option is the right one, it is possible to
+      make the key into a tuple (i.e. {('a',): ('b', 'c')}) or add more info
+      into the tuple, thus making it look like this: {'a': ('b', 'c', 'b', {})}
+      -- second 'b' is the label, and {} is the custom properties field.
+    """
+    # For the recursion step, we check for a scalar object (string or tuple)
+    if isinstance(table_description, (types.StringTypes, tuple)):
+      parsed_col = DataTable.ColumnTypeParser(table_description)
+      parsed_col["depth"] = depth
+      parsed_col["container"] = "scalar"
+      return [parsed_col]
+
+    # Since it is not scalar, table_description must be iterable.
+    if not hasattr(table_description, "__iter__"):
+      raise DataTableException("Expected an iterable object, got %s" %
+                               type(table_description))
+    if not isinstance(table_description, dict):
+      # We expects a non-dictionary iterable item.
+      columns = []
+      for desc in table_description:
+        parsed_col = DataTable.ColumnTypeParser(desc)
+        parsed_col["depth"] = depth
+        parsed_col["container"] = "iter"
+        columns.append(parsed_col)
+      if not columns:
+        raise DataTableException("Description iterable objects should not"
+                                 " be empty.")
+      return columns
+    # The other case is a dictionary
+    if not table_description:
+      raise DataTableException("Empty dictionaries are not allowed inside"
+                               " description")
+
+    # To differentiate between the two cases of more levels below or this is
+    # the most inner dictionary, we consider the number of keys (more then one
+    # key is indication for most inner dictionary) and the type of the key and
+    # value in case of only 1 key (if the type of key is string and the type of
+    # the value is a tuple of 0-3 items, we assume this is the most inner
+    # dictionary).
+    # NOTE: this way of differentiating might create ambiguity. See docs.
+    if (len(table_description) != 1 or
+        (isinstance(table_description.keys()[0], types.StringTypes) and
+         isinstance(table_description.values()[0], tuple) and
+         len(table_description.values()[0]) < 4)):
+      # This is the most inner dictionary. Parsing types.
+      columns = []
+      # We sort the items, equivalent to sort the keys since they are unique
+      for key, value in sorted(table_description.items()):
+        # We parse the column type as (key, type) or (key, type, label) using
+        # ColumnTypeParser.
+        if isinstance(value, tuple):
+          parsed_col = DataTable.ColumnTypeParser((key,) + value)
+        else:
+          parsed_col = DataTable.ColumnTypeParser((key, value))
+        parsed_col["depth"] = depth
+        parsed_col["container"] = "dict"
+        columns.append(parsed_col)
+      return columns
+    # This is an outer dictionary, must have at most one key.
+    parsed_col = DataTable.ColumnTypeParser(table_description.keys()[0])
+    parsed_col["depth"] = depth
+    parsed_col["container"] = "dict"
+    return ([parsed_col] +
+            DataTable.TableDescriptionParser(table_description.values()[0],
+                                             depth=depth + 1))
+
+  @property
+  def columns(self):
+    """Returns the parsed table description."""
+    return self.__columns
+
+  def NumberOfRows(self):
+    """Returns the number of rows in the current data stored in the table."""
+    return len(self.__data)
+
+  def SetRowsCustomProperties(self, rows, custom_properties):
+    """Sets the custom properties for given row(s).
+
+    Can accept a single row or an iterable of rows.
+    Sets the given custom properties for all specified rows.
+
+    Args:
+      rows: The row, or rows, to set the custom properties for.
+      custom_properties: A string to string dictionary of custom properties to
+      set for all rows.
+    """
+    if not hasattr(rows, "__iter__"):
+      rows = [rows]
+    for row in rows:
+      self.__data[row] = (self.__data[row][0], custom_properties)
+
+  def LoadData(self, data, custom_properties=None):
+    """Loads new rows to the data table, clearing existing rows.
+
+    May also set the custom_properties for the added rows. The given custom
+    properties dictionary specifies the dictionary that will be used for *all*
+    given rows.
+
+    Args:
+      data: The rows that the table will contain.
+      custom_properties: A dictionary of string to string to set as the custom
+                         properties for all rows.
+    """
+    self.__data = []
+    self.AppendData(data, custom_properties)
+
+  def AppendData(self, data, custom_properties=None):
+    """Appends new data to the table.
+
+    Data is appended in rows. Data must comply with
+    the table schema passed in to __init__(). See CoerceValue() for a list
+    of acceptable data types. See the class documentation for more information
+    and examples of schema and data values.
+
+    Args:
+      data: The row to add to the table. The data must conform to the table
+            description format.
+      custom_properties: A dictionary of string to string, representing the
+                         custom properties to add to all the rows.
+
+    Raises:
+      DataTableException: The data structure does not match the description.
+    """
+    # If the maximal depth is 0, we simply iterate over the data table
+    # lines and insert them using _InnerAppendData. Otherwise, we simply
+    # let the _InnerAppendData handle all the levels.
+    if not self.__columns[-1]["depth"]:
+      for row in data:
+        self._InnerAppendData(({}, custom_properties), row, 0)
+    else:
+      self._InnerAppendData(({}, custom_properties), data, 0)
+
+  def _InnerAppendData(self, prev_col_values, data, col_index):
+    """Inner function to assist LoadData."""
+    # We first check that col_index has not exceeded the columns size
+    if col_index >= len(self.__columns):
+      raise DataTableException("The data does not match description, too deep")
+
+    # Dealing with the scalar case, the data is the last value.
+    if self.__columns[col_index]["container"] == "scalar":
+      prev_col_values[0][self.__columns[col_index]["id"]] = data
+      self.__data.append(prev_col_values)
+      return
+
+    if self.__columns[col_index]["container"] == "iter":
+      if not hasattr(data, "__iter__") or isinstance(data, dict):
+        raise DataTableException("Expected iterable object, got %s" %
+                                 type(data))
+      # We only need to insert the rest of the columns
+      # If there are less items than expected, we only add what there is.
+      for value in data:
+        if col_index >= len(self.__columns):
+          raise DataTableException("Too many elements given in data")
+        prev_col_values[0][self.__columns[col_index]["id"]] = value
+        col_index += 1
+      self.__data.append(prev_col_values)
+      return
+
+    # We know the current level is a dictionary, we verify the type.
+    if not isinstance(data, dict):
+      raise DataTableException("Expected dictionary at current level, got %s" %
+                               type(data))
+    # We check if this is the last level
+    if self.__columns[col_index]["depth"] == self.__columns[-1]["depth"]:
+      # We need to add the keys in the dictionary as they are
+      for col in self.__columns[col_index:]:
+        if col["id"] in data:
+          prev_col_values[0][col["id"]] = data[col["id"]]
+      self.__data.append(prev_col_values)
+      return
+
+    # We have a dictionary in an inner depth level.
+    if not data.keys():
+      # In case this is an empty dictionary, we add a record with the columns
+      # filled only until this point.
+      self.__data.append(prev_col_values)
+    else:
+      for key in sorted(data):
+        col_values = dict(prev_col_values[0])
+        col_values[self.__columns[col_index]["id"]] = key
+        self._InnerAppendData((col_values, prev_col_values[1]),
+                              data[key], col_index + 1)
+
+  def _PreparedData(self, order_by=()):
+    """Prepares the data for enumeration - sorting it by order_by.
+
+    Args:
+      order_by: Optional. Specifies the name of the column(s) to sort by, and
+                (optionally) which direction to sort in. Default sort direction
+                is asc. Following formats are accepted:
+                "string_col_name"  -- For a single key in default (asc) order.
+                ("string_col_name", "asc|desc") -- For a single key.
+                [("col_1","asc|desc"), ("col_2","asc|desc")] -- For more than
+                    one column, an array of tuples of (col_name, "asc|desc").
+
+    Returns:
+      The data sorted by the keys given.
+
+    Raises:
+      DataTableException: Sort direction not in 'asc' or 'desc'
+    """
+    if not order_by:
+      return self.__data
+
+    proper_sort_keys = []
+    if isinstance(order_by, types.StringTypes) or (
+        isinstance(order_by, tuple) and len(order_by) == 2 and
+        order_by[1].lower() in ["asc", "desc"]):
+      order_by = (order_by,)
+    for key in order_by:
+      if isinstance(key, types.StringTypes):
+        proper_sort_keys.append((key, 1))
+      elif (isinstance(key, (list, tuple)) and len(key) == 2 and
+            key[1].lower() in ("asc", "desc")):
+        proper_sort_keys.append((key[0], key[1].lower() == "asc" and 1 or -1))
+      else:
+        raise DataTableException("Expected tuple with second value: "
+                                 "'asc' or 'desc'")
+
+    def SortCmpFunc(row1, row2):
+      """cmp function for sorted. Compares by keys and 'asc'/'desc' keywords."""
+      for key, asc_mult in proper_sort_keys:
+        cmp_result = asc_mult * cmp(row1[0].get(key), row2[0].get(key))
+        if cmp_result:
+          return cmp_result
+      return 0
+
+    return sorted(self.__data, cmp=SortCmpFunc)
+
+  def ToJSCode(self, name, columns_order=None, order_by=()):
+    """Writes the data table as a JS code string.
+
+    This method writes a string of JS code that can be run to
+    generate a DataTable with the specified data. Typically used for debugging
+    only.
+
+    Args:
+      name: The name of the table. The name would be used as the DataTable's
+            variable name in the created JS code.
+      columns_order: Optional. Specifies the order of columns in the
+                     output table. Specify a list of all column IDs in the order
+                     in which you want the table created.
+                     Note that you must list all column IDs in this parameter,
+                     if you use it.
+      order_by: Optional. Specifies the name of the column(s) to sort by.
+                Passed as is to _PreparedData.
+
+    Returns:
+      A string of JS code that, when run, generates a DataTable with the given
+      name and the data stored in the DataTable object.
+      Example result:
+        "var tab1 = new google.visualization.DataTable();
+         tab1.addColumn("string", "a", "a");
+         tab1.addColumn("number", "b", "b");
+         tab1.addColumn("boolean", "c", "c");
+         tab1.addRows(10);
+         tab1.setCell(0, 0, "a");
+         tab1.setCell(0, 1, 1, null, {"foo": "bar"});
+         tab1.setCell(0, 2, true);
+         ...
+         tab1.setCell(9, 0, "c");
+         tab1.setCell(9, 1, 3, "3$");
+         tab1.setCell(9, 2, false);"
+
+    Raises:
+      DataTableException: The data does not match the type.
+    """
+
+    encoder = DataTableJSONEncoder()
+
+    if columns_order is None:
+      columns_order = [col["id"] for col in self.__columns]
+    col_dict = dict([(col["id"], col) for col in self.__columns])
+
+    # We first create the table with the given name
+    jscode = "var %s = new google.visualization.DataTable();\n" % name
+    if self.custom_properties:
+      jscode += "%s.setTableProperties(%s);\n" % (
+          name, encoder.encode(self.custom_properties))
+
+    # We add the columns to the table
+    for i, col in enumerate(columns_order):
+      jscode += "%s.addColumn(%s, %s, %s);\n" % (
+          name,
+          encoder.encode(col_dict[col]["type"]),
+          encoder.encode(col_dict[col]["label"]),
+          encoder.encode(col_dict[col]["id"]))
+      if col_dict[col]["custom_properties"]:
+        jscode += "%s.setColumnProperties(%d, %s);\n" % (
+            name, i, encoder.encode(col_dict[col]["custom_properties"]))
+    jscode += "%s.addRows(%d);\n" % (name, len(self.__data))
+
+    # We now go over the data and add each row
+    for (i, (row, cp)) in enumerate(self._PreparedData(order_by)):
+      # We add all the elements of this row by their order
+      for (j, col) in enumerate(columns_order):
+        if col not in row or row[col] is None:
+          continue
+        value = self.CoerceValue(row[col], col_dict[col]["type"])
+        if isinstance(value, tuple):
+          cell_cp = ""
+          if len(value) == 3:
+            cell_cp = ", %s" % encoder.encode(row[col][2])
+          # We have a formatted value or custom property as well
+          jscode += ("%s.setCell(%d, %d, %s, %s%s);\n" %
+                     (name, i, j,
+                      self.EscapeForJSCode(encoder, value[0]),
+                      self.EscapeForJSCode(encoder, value[1]), cell_cp))
+        else:
+          jscode += "%s.setCell(%d, %d, %s);\n" % (
+              name, i, j, self.EscapeForJSCode(encoder, value))
+      if cp:
+        jscode += "%s.setRowProperties(%d, %s);\n" % (
+            name, i, encoder.encode(cp))
+    return jscode
+
+  def ToHtml(self, columns_order=None, order_by=()):
+    """Writes the data table as an HTML table code string.
+
+    Args:
+      columns_order: Optional. Specifies the order of columns in the
+                     output table. Specify a list of all column IDs in the order
+                     in which you want the table created.
+                     Note that you must list all column IDs in this parameter,
+                     if you use it.
+      order_by: Optional. Specifies the name of the column(s) to sort by.
+                Passed as is to _PreparedData.
+
+    Returns:
+      An HTML table code string.
+      Example result (the result is without the newlines):
+       <html><body><table border="1">
+        <thead><tr><th>a</th><th>b</th><th>c</th></tr></thead>
+        <tbody>
+         <tr><td>1</td><td>"z"</td><td>2</td></tr>
+         <tr><td>"3$"</td><td>"w"</td><td></td></tr>
+        </tbody>
+       </table></body></html>
+
+    Raises:
+      DataTableException: The data does not match the type.
+    """
+    table_template = "<html><body><table border=\"1\">%s</table></body></html>"
+    columns_template = "<thead><tr>%s</tr></thead>"
+    rows_template = "<tbody>%s</tbody>"
+    row_template = "<tr>%s</tr>"
+    header_cell_template = "<th>%s</th>"
+    cell_template = "<td>%s</td>"
+
+    if columns_order is None:
+      columns_order = [col["id"] for col in self.__columns]
+    col_dict = dict([(col["id"], col) for col in self.__columns])
+
+    columns_list = []
+    for col in columns_order:
+      columns_list.append(header_cell_template %
+                          cgi.escape(col_dict[col]["label"]))
+    columns_html = columns_template % "".join(columns_list)
+
+    rows_list = []
+    # We now go over the data and add each row
+    for row, unused_cp in self._PreparedData(order_by):
+      cells_list = []
+      # We add all the elements of this row by their order
+      for col in columns_order:
+        # For empty string we want empty quotes ("").
+        value = ""
+        if col in row and row[col] is not None:
+          value = self.CoerceValue(row[col], col_dict[col]["type"])
+        if isinstance(value, tuple):
+          # We have a formatted value and we're going to use it
+          cells_list.append(cell_template % cgi.escape(self.ToString(value[1])))
+        else:
+          cells_list.append(cell_template % cgi.escape(self.ToString(value)))
+      rows_list.append(row_template % "".join(cells_list))
+    rows_html = rows_template % "".join(rows_list)
+
+    return table_template % (columns_html + rows_html)
+
+  def ToCsv(self, columns_order=None, order_by=(), separator=","):
+    """Writes the data table as a CSV string.
+
+    Output is encoded in UTF-8 because the Python "csv" module can't handle
+    Unicode properly according to its documentation.
+
+    Args:
+      columns_order: Optional. Specifies the order of columns in the
+                     output table. Specify a list of all column IDs in the order
+                     in which you want the table created.
+                     Note that you must list all column IDs in this parameter,
+                     if you use it.
+      order_by: Optional. Specifies the name of the column(s) to sort by.
+                Passed as is to _PreparedData.
+      separator: Optional. The separator to use between the values.
+
+    Returns:
+      A CSV string representing the table.
+      Example result:
+       'a','b','c'
+       1,'z',2
+       3,'w',''
+
+    Raises:
+      DataTableException: The data does not match the type.
+    """
+
+    csv_buffer = cStringIO.StringIO()
+    writer = csv.writer(csv_buffer, delimiter=separator)
+
+    if columns_order is None:
+      columns_order = [col["id"] for col in self.__columns]
+    col_dict = dict([(col["id"], col) for col in self.__columns])
+
+    writer.writerow([col_dict[col]["label"].encode("utf-8")
+                     for col in columns_order])
+
+    # We now go over the data and add each row
+    for row, unused_cp in self._PreparedData(order_by):
+      cells_list = []
+      # We add all the elements of this row by their order
+      for col in columns_order:
+        value = ""
+        if col in row and row[col] is not None:
+          value = self.CoerceValue(row[col], col_dict[col]["type"])
+        if isinstance(value, tuple):
+          # We have a formatted value. Using it only for date/time types.
+          if col_dict[col]["type"] in ["date", "datetime", "timeofday"]:
+            cells_list.append(self.ToString(value[1]).encode("utf-8"))
+          else:
+            cells_list.append(self.ToString(value[0]).encode("utf-8"))
+        else:
+          cells_list.append(self.ToString(value).encode("utf-8"))
+      writer.writerow(cells_list)
+    return csv_buffer.getvalue()
+
+  def ToTsvExcel(self, columns_order=None, order_by=()):
+    """Returns a file in tab-separated-format readable by MS Excel.
+
+    Returns a file in UTF-16 little endian encoding, with tabs separating the
+    values.
+
+    Args:
+      columns_order: Delegated to ToCsv.
+      order_by: Delegated to ToCsv.
+
+    Returns:
+      A tab-separated little endian UTF16 file representing the table.
+    """
+    return (self.ToCsv(columns_order, order_by, separator="\t")
+            .decode("utf-8").encode("UTF-16LE"))
+
+  def _ToJSonObj(self, columns_order=None, order_by=()):
+    """Returns an object suitable to be converted to JSON.
+
+    Args:
+      columns_order: Optional. A list of all column IDs in the order in which
+                     you want them created in the output table. If specified,
+                     all column IDs must be present.
+      order_by: Optional. Specifies the name of the column(s) to sort by.
+                Passed as is to _PreparedData().
+
+    Returns:
+      A dictionary object for use by ToJSon or ToJSonResponse.
+    """
+    if columns_order is None:
+      columns_order = [col["id"] for col in self.__columns]
+    col_dict = dict([(col["id"], col) for col in self.__columns])
+
+    # Creating the column JSON objects
+    col_objs = []
+    for col_id in columns_order:
+      col_obj = {"id": col_dict[col_id]["id"],
+                 "label": col_dict[col_id]["label"],
+                 "type": col_dict[col_id]["type"]}
+      if col_dict[col_id]["custom_properties"]:
+        col_obj["p"] = col_dict[col_id]["custom_properties"]
+      col_objs.append(col_obj)
+
+    # Creating the rows jsons
+    row_objs = []
+    for row, cp in self._PreparedData(order_by):
+      cell_objs = []
+      for col in columns_order:
+        value = self.CoerceValue(row.get(col, None), col_dict[col]["type"])
+        if value is None:
+          cell_obj = None
+        elif isinstance(value, tuple):
+          cell_obj = {"v": value[0]}
+          if len(value) > 1 and value[1] is not None:
+            cell_obj["f"] = value[1]
+          if len(value) == 3:
+            cell_obj["p"] = value[2]
+        else:
+          cell_obj = {"v": value}
+        cell_objs.append(cell_obj)
+      row_obj = {"c": cell_objs}
+      if cp:
+        row_obj["p"] = cp
+      row_objs.append(row_obj)
+
+    json_obj = {"cols": col_objs, "rows": row_objs}
+    if self.custom_properties:
+      json_obj["p"] = self.custom_properties
+
+    return json_obj
+
+  def ToJSon(self, columns_order=None, order_by=()):
+    """Returns a string that can be used in a JS DataTable constructor.
+
+    This method writes a JSON string that can be passed directly into a Google
+    Visualization API DataTable constructor. Use this output if you are
+    hosting the visualization HTML on your site, and want to code the data
+    table in Python. Pass this string into the
+    google.visualization.DataTable constructor, e.g,:
+      ... on my page that hosts my visualization ...
+      google.setOnLoadCallback(drawTable);
+      function drawTable() {
+        var data = new google.visualization.DataTable(_my_JSon_string, 0.6);
+        myTable.draw(data);
+      }
+
+    Args:
+      columns_order: Optional. Specifies the order of columns in the
+                     output table. Specify a list of all column IDs in the order
+                     in which you want the table created.
+                     Note that you must list all column IDs in this parameter,
+                     if you use it.
+      order_by: Optional. Specifies the name of the column(s) to sort by.
+                Passed as is to _PreparedData().
+
+    Returns:
+      A JSon constructor string to generate a JS DataTable with the data
+      stored in the DataTable object.
+      Example result (the result is without the newlines):
+       {cols: [{id:"a",label:"a",type:"number"},
+               {id:"b",label:"b",type:"string"},
+              {id:"c",label:"c",type:"number"}],
+        rows: [{c:[{v:1},{v:"z"},{v:2}]}, c:{[{v:3,f:"3$"},{v:"w"},{v:null}]}],
+        p:    {'foo': 'bar'}}
+
+    Raises:
+      DataTableException: The data does not match the type.
+    """
+
+    encoder = DataTableJSONEncoder()
+    return encoder.encode(
+        self._ToJSonObj(columns_order, order_by)).encode("utf-8")
+
+  def ToJSonResponse(self, columns_order=None, order_by=(), req_id=0,
+                     response_handler="google.visualization.Query.setResponse"):
+    """Writes a table as a JSON response that can be returned as-is to a client.
+
+    This method writes a JSON response to return to a client in response to a
+    Google Visualization API query. This string can be processed by the calling
+    page, and is used to deliver a data table to a visualization hosted on
+    a different page.
+
+    Args:
+      columns_order: Optional. Passed straight to self.ToJSon().
+      order_by: Optional. Passed straight to self.ToJSon().
+      req_id: Optional. The response id, as retrieved by the request.
+      response_handler: Optional. The response handler, as retrieved by the
+          request.
+
+    Returns:
+      A JSON response string to be received by JS the visualization Query
+      object. This response would be translated into a DataTable on the
+      client side.
+      Example result (newlines added for readability):
+       google.visualization.Query.setResponse({
+          'version':'0.6', 'reqId':'0', 'status':'OK',
+          'table': {cols: [...], rows: [...]}});
+
+    Note: The URL returning this string can be used as a data source by Google
+          Visualization Gadgets or from JS code.
+    """
+
+    response_obj = {
+        "version": "0.6",
+        "reqId": str(req_id),
+        "table": self._ToJSonObj(columns_order, order_by),
+        "status": "ok"
+    }
+    encoder = DataTableJSONEncoder()
+    return "%s(%s);" % (response_handler,
+                        encoder.encode(response_obj).encode("utf-8"))
+
+  def ToResponse(self, columns_order=None, order_by=(), tqx=""):
+    """Writes the right response according to the request string passed in tqx.
+
+    This method parses the tqx request string (format of which is defined in
+    the documentation for implementing a data source of Google Visualization),
+    and returns the right response according to the request.
+    It parses out the "out" parameter of tqx, calls the relevant response
+    (ToJSonResponse() for "json", ToCsv() for "csv", ToHtml() for "html",
+    ToTsvExcel() for "tsv-excel") and passes the response function the rest of
+    the relevant request keys.
+
+    Args:
+      columns_order: Optional. Passed as is to the relevant response function.
+      order_by: Optional. Passed as is to the relevant response function.
+      tqx: Optional. The request string as received by HTTP GET. Should be in
+           the format "key1:value1;key2:value2...". All keys have a default
+           value, so an empty string will just do the default (which is calling
+           ToJSonResponse() with no extra parameters).
+
+    Returns:
+      A response string, as returned by the relevant response function.
+
+    Raises:
+      DataTableException: One of the parameters passed in tqx is not supported.
+    """
+    tqx_dict = {}
+    if tqx:
+      tqx_dict = dict(opt.split(":") for opt in tqx.split(";"))
+    if tqx_dict.get("version", "0.6") != "0.6":
+      raise DataTableException(
+          "Version (%s) passed by request is not supported."
+          % tqx_dict["version"])
+
+    if tqx_dict.get("out", "json") == "json":
+      response_handler = tqx_dict.get("responseHandler",
+                                      "google.visualization.Query.setResponse")
+      return self.ToJSonResponse(columns_order, order_by,
+                                 req_id=tqx_dict.get("reqId", 0),
+                                 response_handler=response_handler)
+    elif tqx_dict["out"] == "html":
+      return self.ToHtml(columns_order, order_by)
+    elif tqx_dict["out"] == "csv":
+      return self.ToCsv(columns_order, order_by)
+    elif tqx_dict["out"] == "tsv-excel":
+      return self.ToTsvExcel(columns_order, order_by)
+    else:
+      raise DataTableException(
+          "'out' parameter: '%s' is not supported" % tqx_dict["out"])