1049 lines
44 KiB
Python
1049 lines
44 KiB
Python
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#!/usr/bin/python
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#
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# Copyright (c) 2011 The WebRTC project authors. All Rights Reserved.
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#
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# Use of this source code is governed by a BSD-style license
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# that can be found in the LICENSE file in the root of the source
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# tree. An additional intellectual property rights grant can be found
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# in the file PATENTS. All contributing project authors may
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# be found in the AUTHORS file in the root of the source tree.
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"""Converts Python data into data for Google Visualization API clients.
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This library can be used to create a google.visualization.DataTable usable by
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visualizations built on the Google Visualization API. Output formats are raw
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JSON, JSON response, and JavaScript.
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See http://code.google.com/apis/visualization/ for documentation on the
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Google Visualization API.
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"""
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__author__ = "Amit Weinstein, Misha Seltzer"
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import cgi
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import datetime
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import types
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class DataTableException(Exception):
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"""The general exception object thrown by DataTable."""
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pass
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class DataTable(object):
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"""Wraps the data to convert to a Google Visualization API DataTable.
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Create this object, populate it with data, then call one of the ToJS...
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methods to return a string representation of the data in the format described.
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You can clear all data from the object to reuse it, but you cannot clear
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individual cells, rows, or columns. You also cannot modify the table schema
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specified in the class constructor.
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You can add new data one or more rows at a time. All data added to an
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instantiated DataTable must conform to the schema passed in to __init__().
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You can reorder the columns in the output table, and also specify row sorting
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order by column. The default column order is according to the original
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table_description parameter. Default row sort order is ascending, by column
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1 values. For a dictionary, we sort the keys for order.
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The data and the table_description are closely tied, as described here:
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The table schema is defined in the class constructor's table_description
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parameter. The user defines each column using a tuple of
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(id[, type[, label[, custom_properties]]]). The default value for type is
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string, label is the same as ID if not specified, and custom properties is
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an empty dictionary if not specified.
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table_description is a dictionary or list, containing one or more column
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descriptor tuples, nested dictionaries, and lists. Each dictionary key, list
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element, or dictionary element must eventually be defined as
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a column description tuple. Here's an example of a dictionary where the key
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is a tuple, and the value is a list of two tuples:
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{('a', 'number'): [('b', 'number'), ('c', 'string')]}
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This flexibility in data entry enables you to build and manipulate your data
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in a Python structure that makes sense for your program.
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Add data to the table using the same nested design as the table's
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table_description, replacing column descriptor tuples with cell data, and
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each row is an element in the top level collection. This will be a bit
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clearer after you look at the following examples showing the
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table_description, matching data, and the resulting table:
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Columns as list of tuples [col1, col2, col3]
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table_description: [('a', 'number'), ('b', 'string')]
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AppendData( [[1, 'z'], [2, 'w'], [4, 'o'], [5, 'k']] )
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Table:
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a b <--- these are column ids/labels
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1 z
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2 w
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4 o
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5 k
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Dictionary of columns, where key is a column, and value is a list of
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columns {col1: [col2, col3]}
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table_description: {('a', 'number'): [('b', 'number'), ('c', 'string')]}
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AppendData( data: {1: [2, 'z'], 3: [4, 'w']}
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Table:
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a b c
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1 2 z
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3 4 w
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Dictionary where key is a column, and the value is itself a dictionary of
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columns {col1: {col2, col3}}
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table_description: {('a', 'number'): {'b': 'number', 'c': 'string'}}
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AppendData( data: {1: {'b': 2, 'c': 'z'}, 3: {'b': 4, 'c': 'w'}}
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Table:
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a b c
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1 2 z
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3 4 w
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"""
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def __init__(self, table_description, data=None, custom_properties=None):
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"""Initialize the data table from a table schema and (optionally) data.
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See the class documentation for more information on table schema and data
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values.
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Args:
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table_description: A table schema, following one of the formats described
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in TableDescriptionParser(). Schemas describe the
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column names, data types, and labels. See
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TableDescriptionParser() for acceptable formats.
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data: Optional. If given, fills the table with the given data. The data
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structure must be consistent with schema in table_description. See
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the class documentation for more information on acceptable data. You
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can add data later by calling AppendData().
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custom_properties: Optional. A dictionary from string to string that
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goes into the table's custom properties. This can be
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later changed by changing self.custom_properties.
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Raises:
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DataTableException: Raised if the data and the description did not match,
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or did not use the supported formats.
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"""
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self.__columns = self.TableDescriptionParser(table_description)
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self.__data = []
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self.custom_properties = {}
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if custom_properties is not None:
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self.custom_properties = custom_properties
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if data:
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self.LoadData(data)
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@staticmethod
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def _EscapeValueForCsv(v):
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"""Escapes the value for use in a CSV file.
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Puts the string in double-quotes, and escapes any inner double-quotes by
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doubling them.
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Args:
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v: The value to escape.
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Returns:
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The escaped values.
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"""
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return '"%s"' % v.replace('"', '""')
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@staticmethod
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def _EscapeValue(v):
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"""Puts the string in quotes, and escapes any inner quotes and slashes."""
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if isinstance(v, unicode):
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# Here we use repr as in the usual case, but on unicode strings, it
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# also escapes the unicode characters (which we want to leave as is).
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# So, after repr() we decode using raw-unicode-escape, which decodes
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# only the unicode characters, and leaves all the rest (", ', \n and
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# more) escaped.
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# We don't take the first character, because repr adds a u in the
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# beginning of the string (usual repr output for unicode is u'...').
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return repr(v).decode("raw-unicode-escape")[1:]
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# Here we use python built-in escaping mechanism for string using repr.
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return repr(str(v))
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@staticmethod
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def _EscapeCustomProperties(custom_properties):
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"""Escapes the custom properties dictionary."""
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l = []
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for key, value in custom_properties.iteritems():
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l.append("%s:%s" % (DataTable._EscapeValue(key),
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DataTable._EscapeValue(value)))
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return "{%s}" % ",".join(l)
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@staticmethod
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def SingleValueToJS(value, value_type, escape_func=None):
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"""Translates a single value and type into a JS value.
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Internal helper method.
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Args:
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value: The value which should be converted
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value_type: One of "string", "number", "boolean", "date", "datetime" or
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"timeofday".
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escape_func: The function to use for escaping strings.
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Returns:
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The proper JS format (as string) of the given value according to the
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given value_type. For None, we simply return "null".
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If a tuple is given, it should be in one of the following forms:
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- (value, formatted value)
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- (value, formatted value, custom properties)
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where the formatted value is a string, and custom properties is a
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dictionary of the custom properties for this cell.
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To specify custom properties without specifying formatted value, one can
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pass None as the formatted value.
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One can also have a null-valued cell with formatted value and/or custom
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properties by specifying None for the value.
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This method ignores the custom properties except for checking that it is a
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dictionary. The custom properties are handled in the ToJSon and ToJSCode
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methods.
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The real type of the given value is not strictly checked. For example,
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any type can be used for string - as we simply take its str( ) and for
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boolean value we just check "if value".
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Examples:
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SingleValueToJS(None, "boolean") returns "null"
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SingleValueToJS(False, "boolean") returns "false"
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SingleValueToJS((5, "5$"), "number") returns ("5", "'5$'")
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SingleValueToJS((None, "5$"), "number") returns ("null", "'5$'")
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Raises:
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DataTableException: The value and type did not match in a not-recoverable
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way, for example given value 'abc' for type 'number'.
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"""
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if escape_func is None:
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escape_func = DataTable._EscapeValue
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if isinstance(value, tuple):
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# In case of a tuple, we run the same function on the value itself and
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# add the formatted value.
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if (len(value) not in [2, 3] or
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(len(value) == 3 and not isinstance(value[2], dict))):
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raise DataTableException("Wrong format for value and formatting - %s." %
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str(value))
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if not isinstance(value[1], types.StringTypes + (types.NoneType,)):
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raise DataTableException("Formatted value is not string, given %s." %
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type(value[1]))
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js_value = DataTable.SingleValueToJS(value[0], value_type)
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if value[1] is None:
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return (js_value, None)
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return (js_value, escape_func(value[1]))
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# The standard case - no formatting.
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t_value = type(value)
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if value is None:
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return "null"
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if value_type == "boolean":
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if value:
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return "true"
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return "false"
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elif value_type == "number":
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if isinstance(value, (int, long, float)):
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return str(value)
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raise DataTableException("Wrong type %s when expected number" % t_value)
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elif value_type == "string":
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if isinstance(value, tuple):
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raise DataTableException("Tuple is not allowed as string value.")
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return escape_func(value)
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elif value_type == "date":
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if not isinstance(value, (datetime.date, datetime.datetime)):
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raise DataTableException("Wrong type %s when expected date" % t_value)
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# We need to shift the month by 1 to match JS Date format
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return "new Date(%d,%d,%d)" % (value.year, value.month - 1, value.day)
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elif value_type == "timeofday":
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if not isinstance(value, (datetime.time, datetime.datetime)):
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raise DataTableException("Wrong type %s when expected time" % t_value)
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return "[%d,%d,%d]" % (value.hour, value.minute, value.second)
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elif value_type == "datetime":
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if not isinstance(value, datetime.datetime):
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raise DataTableException("Wrong type %s when expected datetime" %
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t_value)
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return "new Date(%d,%d,%d,%d,%d,%d)" % (value.year,
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value.month - 1, # To match JS
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value.day,
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value.hour,
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value.minute,
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value.second)
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# If we got here, it means the given value_type was not one of the
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# supported types.
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raise DataTableException("Unsupported type %s" % value_type)
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@staticmethod
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def ColumnTypeParser(description):
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"""Parses a single column description. Internal helper method.
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Args:
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description: a column description in the possible formats:
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'id'
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('id',)
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('id', 'type')
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('id', 'type', 'label')
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('id', 'type', 'label', {'custom_prop1': 'custom_val1'})
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Returns:
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Dictionary with the following keys: id, label, type, and
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custom_properties where:
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- If label not given, it equals the id.
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- If type not given, string is used by default.
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- If custom properties are not given, an empty dictionary is used by
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default.
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Raises:
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DataTableException: The column description did not match the RE, or
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unsupported type was passed.
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"""
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if not description:
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raise DataTableException("Description error: empty description given")
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if not isinstance(description, (types.StringTypes, tuple)):
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raise DataTableException("Description error: expected either string or "
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"tuple, got %s." % type(description))
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if isinstance(description, types.StringTypes):
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description = (description,)
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# According to the tuple's length, we fill the keys
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# We verify everything is of type string
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for elem in description[:3]:
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if not isinstance(elem, types.StringTypes):
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raise DataTableException("Description error: expected tuple of "
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"strings, current element of type %s." %
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type(elem))
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desc_dict = {"id": description[0],
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"label": description[0],
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"type": "string",
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"custom_properties": {}}
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if len(description) > 1:
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desc_dict["type"] = description[1].lower()
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if len(description) > 2:
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desc_dict["label"] = description[2]
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if len(description) > 3:
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if not isinstance(description[3], dict):
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raise DataTableException("Description error: expected custom "
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"properties of type dict, current element "
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"of type %s." % type(description[3]))
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desc_dict["custom_properties"] = description[3]
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if len(description) > 4:
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raise DataTableException("Description error: tuple of length > 4")
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if desc_dict["type"] not in ["string", "number", "boolean",
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"date", "datetime", "timeofday"]:
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raise DataTableException(
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"Description error: unsupported type '%s'" % desc_dict["type"])
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return desc_dict
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@staticmethod
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def TableDescriptionParser(table_description, depth=0):
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"""Parses the table_description object for internal use.
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Parses the user-submitted table description into an internal format used
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by the Python DataTable class. Returns the flat list of parsed columns.
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Args:
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table_description: A description of the table which should comply
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with one of the formats described below.
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depth: Optional. The depth of the first level in the current description.
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Used by recursive calls to this function.
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Returns:
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List of columns, where each column represented by a dictionary with the
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keys: id, label, type, depth, container which means the following:
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- id: the id of the column
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- name: The name of the column
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- type: The datatype of the elements in this column. Allowed types are
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described in ColumnTypeParser().
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- depth: The depth of this column in the table description
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- container: 'dict', 'iter' or 'scalar' for parsing the format easily.
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- custom_properties: The custom properties for this column.
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|
|
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 SingleValueToJS() 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.
|
||
|
|
"""
|
||
|
|
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, DataTable._EscapeCustomProperties(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,
|
||
|
|
col_dict[col]["type"],
|
||
|
|
DataTable._EscapeValue(col_dict[col]["label"]),
|
||
|
|
DataTable._EscapeValue(col_dict[col]["id"]))
|
||
|
|
if col_dict[col]["custom_properties"]:
|
||
|
|
jscode += "%s.setColumnProperties(%d, %s);\n" % (
|
||
|
|
name, i, DataTable._EscapeCustomProperties(
|
||
|
|
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
|
||
|
|
cell_cp = ""
|
||
|
|
if isinstance(row[col], tuple) and len(row[col]) == 3:
|
||
|
|
cell_cp = ", %s" % DataTable._EscapeCustomProperties(row[col][2])
|
||
|
|
value = self.SingleValueToJS(row[col], col_dict[col]["type"])
|
||
|
|
if isinstance(value, tuple):
|
||
|
|
# We have a formatted value or custom property as well
|
||
|
|
if value[1] is None:
|
||
|
|
value = (value[0], "null")
|
||
|
|
jscode += ("%s.setCell(%d, %d, %s, %s%s);\n" %
|
||
|
|
(name, i, j, value[0], value[1], cell_cp))
|
||
|
|
else:
|
||
|
|
jscode += "%s.setCell(%d, %d, %s);\n" % (name, i, j, value)
|
||
|
|
if cp:
|
||
|
|
jscode += "%s.setRowProperties(%d, %s);\n" % (
|
||
|
|
name, i, DataTable._EscapeCustomProperties(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.SingleValueToJS(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(value[1]))
|
||
|
|
else:
|
||
|
|
cells_list.append(cell_template % cgi.escape(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.
|
||
|
|
|
||
|
|
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.
|
||
|
|
"""
|
||
|
|
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(DataTable._EscapeValueForCsv(col_dict[col]["label"]))
|
||
|
|
columns_line = separator.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:
|
||
|
|
value = '""'
|
||
|
|
if col in row and row[col] is not None:
|
||
|
|
value = self.SingleValueToJS(row[col], col_dict[col]["type"],
|
||
|
|
DataTable._EscapeValueForCsv)
|
||
|
|
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(value[1])
|
||
|
|
else:
|
||
|
|
cells_list.append(value[0])
|
||
|
|
else:
|
||
|
|
# We need to quote date types, because they contain commas.
|
||
|
|
if (col_dict[col]["type"] in ["date", "datetime", "timeofday"] and
|
||
|
|
value != '""'):
|
||
|
|
value = '"%s"' % value
|
||
|
|
cells_list.append(value)
|
||
|
|
rows_list.append(separator.join(cells_list))
|
||
|
|
rows = "\n".join(rows_list)
|
||
|
|
|
||
|
|
return "%s\n%s" % (columns_line, rows)
|
||
|
|
|
||
|
|
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").encode("UTF-16LE")
|
||
|
|
|
||
|
|
def ToJSon(self, columns_order=None, order_by=()):
|
||
|
|
"""Writes a JSON 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.
|
||
|
|
"""
|
||
|
|
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 columns jsons
|
||
|
|
cols_jsons = []
|
||
|
|
for col_id in columns_order:
|
||
|
|
d = dict(col_dict[col_id])
|
||
|
|
d["id"] = DataTable._EscapeValue(d["id"])
|
||
|
|
d["label"] = DataTable._EscapeValue(d["label"])
|
||
|
|
d["cp"] = ""
|
||
|
|
if col_dict[col_id]["custom_properties"]:
|
||
|
|
d["cp"] = ",p:%s" % DataTable._EscapeCustomProperties(
|
||
|
|
col_dict[col_id]["custom_properties"])
|
||
|
|
cols_jsons.append(
|
||
|
|
"{id:%(id)s,label:%(label)s,type:'%(type)s'%(cp)s}" % d)
|
||
|
|
|
||
|
|
# Creating the rows jsons
|
||
|
|
rows_jsons = []
|
||
|
|
for row, cp in self._PreparedData(order_by):
|
||
|
|
cells_jsons = []
|
||
|
|
for col in columns_order:
|
||
|
|
# We omit the {v:null} for a None value of the not last column
|
||
|
|
value = row.get(col, None)
|
||
|
|
if value is None and col != columns_order[-1]:
|
||
|
|
cells_jsons.append("")
|
||
|
|
else:
|
||
|
|
value = self.SingleValueToJS(value, col_dict[col]["type"])
|
||
|
|
if isinstance(value, tuple):
|
||
|
|
# We have a formatted value or custom property as well
|
||
|
|
if len(row.get(col)) == 3:
|
||
|
|
if value[1] is None:
|
||
|
|
cells_jsons.append("{v:%s,p:%s}" % (
|
||
|
|
value[0],
|
||
|
|
DataTable._EscapeCustomProperties(row.get(col)[2])))
|
||
|
|
else:
|
||
|
|
cells_jsons.append("{v:%s,f:%s,p:%s}" % (value + (
|
||
|
|
DataTable._EscapeCustomProperties(row.get(col)[2]),)))
|
||
|
|
else:
|
||
|
|
cells_jsons.append("{v:%s,f:%s}" % value)
|
||
|
|
else:
|
||
|
|
cells_jsons.append("{v:%s}" % value)
|
||
|
|
if cp:
|
||
|
|
rows_jsons.append("{c:[%s],p:%s}" % (
|
||
|
|
",".join(cells_jsons), DataTable._EscapeCustomProperties(cp)))
|
||
|
|
else:
|
||
|
|
rows_jsons.append("{c:[%s]}" % ",".join(cells_jsons))
|
||
|
|
|
||
|
|
general_custom_properties = ""
|
||
|
|
if self.custom_properties:
|
||
|
|
general_custom_properties = (
|
||
|
|
",p:%s" % DataTable._EscapeCustomProperties(self.custom_properties))
|
||
|
|
|
||
|
|
# We now join the columns jsons and the rows jsons
|
||
|
|
json = "{cols:[%s],rows:[%s]%s}" % (",".join(cols_jsons),
|
||
|
|
",".join(rows_jsons),
|
||
|
|
general_custom_properties)
|
||
|
|
return json
|
||
|
|
|
||
|
|
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.
|
||
|
|
"""
|
||
|
|
table = self.ToJSon(columns_order, order_by)
|
||
|
|
return ("%s({'version':'0.6', 'reqId':'%s', 'status':'OK', "
|
||
|
|
"'table': %s});") % (response_handler, req_id, table)
|
||
|
|
|
||
|
|
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"])
|