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A set of classes for managing tiles of geospatial vector data

Project Description

Vector Tile Tools
=================
A set of classes for managing tiles of geospatial vector data

![Build Status](https://travis-ci.org/SkyTruth/vectortile.svg)


Installation and Unittests
--------------------------
Install via pip:

pip install vectortile

Source:

$ git clone https://github.com/SkyTruth/vectortile.git
$ cd vectortile
$ pip install -r requirements.txt
$ nosetests
$ python setup.py install


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)
{
'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 Example ###
See [data-visualization-tools](https://github.com/SkyTruth/data-visualization-tools)

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