Package: vectortile
Vector Tile ToolsA set of classes for managing tiles of geospatial vector dataInstallation and UnittestsInstall via pip:pip install vectortileSource:$ git clone https://github.com/SkyTruth/vectortile.git
$ cd vectortile
$ pip install -r requirements.txt
$ nosetests
$ python setup.py installTypedMatrixTypedMatrix 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 DetailsA 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:Int32Float32Special 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 sectioncols: an array of column definitions.  The length of this array indicates the number of columns in each rowFor 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)
{
    'cols': [
        {
            'name': 'float',
            'type': 'Float32'
        },
        {
            'name': 'int',
            'type': 'Int32'
        },
        {
            'name': 'timestamp',
            'type': 'Float32'
        }
    ],
    'length': 2
}

>>> pprint(data)
[
    {
        'float': 1.0,
        'int': 1,
        'timestamp': 1388534431744.0
    },
    {
        'float': 2.0,
        'int': 2,
        '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)
{
    'cols': [
        {
            'name': 'float',
            'type': 'Float32'
        },
        {
            'name': 'int',
            'type': 'Int32'
        },
        {
            'name': 'timestamp',
            'type': 'Float32'
        }
    ],
    'length': 2
}

>>> pprint(data)
[
    {
        'float': 1.0,
        'int': 1,
        'timestamp': 1388534431744.0
    },
    {
        'float': 2.0,
        'int': 2,
        'timestamp': 1388620808192.0
    }
]Javascript ExampleSeedata-visualization-tools