vectortile 1.2.6

# Vector Tile Tools A set of classes for managing tiles of geospatial vector data ## Installation TODO: Make this pip-installable ## Test Tests are built using unittest2 and can be executed using nose cd vectortile nosetests ## TypedMatrix TypedMatrix is a binary coded format optimized for delivering large amounts of tabular data from a web server to a javascript client without the need for parsing in javascript on the client side. The vectortile.TypedMatrix module provides functions to read and write typed-matrix formatted strings. ### Format details A TypedMatrix is a packed 2 dimensional array of typed values suitable for typecasting to a set of javascript arrays. Currently two fundamental data types are supported Int32 Float32 Special handling is provided for converting datetime values to Float32 The format includes a header containing a json object, which can contain arbitrary content. The header must contain at least: - length: indicated the number of rows in the data section - cols: an array of column definitions. The length of this array indicates the number of columns in each row For example, a TypedMatrix with 2 rows and 3 columns: {'length': 2, 'cols': [ {'type': 'Float32', 'name': 'float'}, {'type': 'Int32', 'name': 'int'}, {'type': 'Float32', 'name': 'timestamp'}] } ### Usage Examples >>> from vectortile import TypedMatrix >>> from datetime import datetime >>> from pprint import pprint # create two rows of 3 columns each: int, float and datetime >>> data = [{'int':1, 'float':1.0, 'timestamp': datetime(2014,1,1)}] >>> data.append ({'int':2, 'float':2.0, 'timestamp':datetime(2014,1,2)}) >>> t_str = TypedMatrix.pack(data) # Typedmatrix is now coded as a binary string, packed row-wise >>> t_str 'tmtx\x01\x00\x00\x00r\x89\x00\x00\x00{"length": 2, "cols": [{"type": "Float32", "name": "float"}, {"type": "Int32", "name": "int"}, {"type": "Float32", "name": "timestamp"}]}\x00\x00\x80?\x01\x00\x00\x00\x8d\xa5\xa1S\x00\x00\x00@\x02\x00\x00\x00 \xa8\xa1S' >>> header, data = TypedMatrix.unpack(t_str) >>> pprint(header) {u'cols': [{u'name': u'float', u'type': u'Float32'}, {u'name': u'int', u'type': u'Int32'}, {u'name': u'timestamp', u'type': u'Float32'}], u'length': 2} >>> pprint(data) [{u'float': 1.0, u'int': 1, u'timestamp': 1388534431744.0}, {u'float': 2.0, u'int': 2, u'timestamp': 1388620808192.0}] # pack data column-wise >>> TypedMatrix.pack(data,orientation='columnwise') 'tmtx\x01\x00\x00\x00c\x89\x00\x00\x00{"length": 2, "cols": [{"type": "Float32", "name": "float"}, {"type": "Int32", "name": "int"}, {"type": "Float32", "name": "timestamp"}]}\x00\x00\x80?\x00\x00\x00@\x01\x00\x00\x00\x02\x00\x00\x00\x8d\xa5\xa1S \xa8\xa1S' >>> header, data = TypedMatrix.unpack(t_str) >>> pprint(header) {u'cols': [{u'name': u'float', u'type': u'Float32'}, {u'name': u'int', u'type': u'Int32'}, {u'name': u'timestamp', u'type': u'Float32'}], u'length': 2} >>> pprint(data) [{u'float': 1.0, u'int': 1, u'timestamp': 1388534431744.0}, {u'float': 2.0, u'int': 2, u'timestamp': 1388620808192.0}] ### Javascript example see data-visualization-tools/js/app/Data/TypedMatrixParser.js