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diff --git a/third_party/aom/test/gviz_api.py b/third_party/aom/test/gviz_api.py new file mode 100755 index 0000000000..d3a443dabf --- /dev/null +++ b/third_party/aom/test/gviz_api.py @@ -0,0 +1,1087 @@ +#!/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"]) |