GeoBases |travis|_ |cratev|_ |crated|_
======================================

.. _travis : https://travis-ci.org/opentraveldata/geobases
.. |travis| image:: https://api.travis-ci.org/opentraveldata/geobases.png?branch=develop

.. _crated : https://crate.io/packages/GeoBasesDev
.. |crated| image:: https://pypip.in/d/GeoBasesDev/badge.png

.. _cratev : https://crate.io/packages/GeoBasesDev
.. |cratev| image:: https://pypip.in/v/GeoBasesDev/badge.png

Introduction
------------

This project provides tools to play with geographical
data. It also works with non-geographical data, except for map visualizations :).

There are embedded data sources in the project,
but you can easily play with your own data in addition to the available ones.
After data loading, you can:

-  perform various types of queries (find *this key*, or find keys with *this property*)
-  make *fuzzy searches* based on string distance (find things roughly *named like this*)
-  make *phonetic searches* (find things *sounding like this*)
-  make *geographical searches* (find things *next to this place*)
-  get results on a map, or on a graph, or export it as csv data, or as a Python object

This is entirely written in Python. The core part is a Python package,
but there is a command line tool as well! Get it with *easy_install*,
then you can see where are airports with *international* in their name:

.. code-block:: bash

 $ GeoBase --fuzzy international --map

.. figure:: https://raw.github.com/opentraveldata/geobases/public/examples/GeoBases-map-points.png
   :align: center

You can perform all types of queries:

.. code-block:: bash

 $ GeoBase --base cities --fuzzy "san francisko" # typo here :)

Of course, you can use your own data for map display:

.. code-block:: bash

 $ cat coords.csv
 p1,48.22,2.33
 p2,49.33,2.24
 $ cat coords.csv | GeoBase --map

And for every other thing as well:

.. code-block:: bash

 $ cat edges.csv
 A,B
 A,C
 D,A
 $ cat edges.csv | GeoBase --graph

.. figure:: https://raw.github.com/opentraveldata/geobases/public/examples/GeoBases-graph.png
   :align: center

Administrate the data sources:

.. code-block:: bash

 $ GeoBase --admin

We are currently gathering input from the community to define the next version features, so do not hesitate to open issues on the `github page <https://github.com/opentraveldata/geobases>`_.

Documentation
-------------

Here are some useful links:

- the `API documentation <https://geobases.readthedocs.org>`_ for the Python package
- the `wiki pages <https://github.com/opentraveldata/geobases/wiki/_pages>`_ for any question!
- the `twitter account <https://twitter.com/geobasesdev>`_ for the latest news

Installation
------------

Prerequisites
~~~~~~~~~~~~~

These prerequisites are very standard packages which are often installed
by default on Linux distributions. But make sure you have them anyway.

First you need to install *setuptools* (as *root*):

.. code-block:: bash

 $ apt-get install python-setuptools    # for debian
 $ yum install python-setuptools.noarch # for fedora

Then you need some basics compilation stuff to compile dependencies (also as *root*):

.. code-block:: bash

 $ apt-get install python-dev g++    # for debian
 $ yum install python-devel gcc-c++  # for fedora

From PyPI
~~~~~~~~~

You can install it from `PyPI <https://crate.io/packages/GeoBases>`_:

.. code-block:: bash

 $ easy_install --user -U GeoBases

There is a development version also on `PyPI (dev) <https://crate.io/packages/GeoBasesDev>`_:

.. code-block:: bash

 $ easy_install --user -U GeoBasesDev

From Github
~~~~~~~~~~~

You can clone the project from
`github <https://github.com/opentraveldata/geobases.git>`_:

.. code-block:: bash

 $ git clone https://github.com/opentraveldata/geobases.git

Then install the package and its dependencies:

.. code-block:: bash

 $ cd geobases
 $ python setup.py install --user # for user space

Final steps
~~~~~~~~~~~

A script is put in ``~/.local/bin``, to be able to use it, put
that in your ``~/.bashrc`` or ``~/.zshrc``:

.. code-block:: bash

 export PATH=$PATH:$HOME/.local/bin
 export BACKGROUND_COLOR=black # or 'white', your call

If you use zsh and want to get awesome *autocomplete* for the main script, add this to
your ``~/.zshrc``:

.. code-block:: bash

 # Add custom completion scripts
 fpath=(~/.zsh/completion $fpath)
 autoload -U compinit
 compinit


Python 3 and Pypy support
~~~~~~~~~~~~~~~~~~~~~~~~~

There is *Python 3* and *Pypy* support, you can try it
by *changing branch* before installation.

For Python 3, you have to install *setuptools* and *python3-dev* as prerequisites, then:

.. code-block:: bash

 $ git checkout 3000
 $ python3 setup.py install --user

You can also install the package for Python 3 from `PyPI (3K) <https://crate.io/packages/GeoBases3K>`_:

.. code-block:: bash

 $ easy_install-3.2 --user -U GeoBases3K

For Pypy, after *pypy* and *pypy-dev* installation:

.. code-block:: bash

 $ git checkout pypy
 $ sudo pypy setup.py install

You can also install the package for Pypy from `PyPI (pypy) <https://crate.io/packages/GeoBasesPypy>`_:

.. code-block:: bash

 $ easy_install --user -U GeoBasesPypy

Tests
~~~~~

Run the tests:

.. code-block:: bash

 $ python test/test_GeoBases.py -v

Quickstart
----------

.. code-block:: python

 >>> from GeoBases import GeoBase
 >>> geo_o = GeoBase(data='ori_por', verbose=False)
 >>> geo_a = GeoBase(data='airports', verbose=False)
 >>> geo_t = GeoBase(data='stations', verbose=False)

You can provide other values for the *data* parameter.
All data sources are documented in a `single YAML file <https://github.com/opentraveldata/geobases/blob/public/GeoBases/DataSources/Sources.yaml>`_:

-  *data="ori\_por"* will load a local version of
   `this file <https://github.com/opentraveldata/optd/raw/trunk/refdata/ORI/ori_por_public.csv>`_,
   this is the most complete source for airports, use it!
-  *data="airports"* will use geonames as data source for airports
-  *data="stations"* will use RFF data, from `the open data
   website <http://www.data.gouv.fr>`_, as data source for french train
   stations
-  *data="stations\_nls"* will use NLS nomenclature as data source for
   french train stations
-  *data="stations\_uic"* will use UIC nomenclature as data source for
   french train stations
-  *data="countries"* will load data on countries
-  *data="capitals"* will load data on countries capitals
-  *data="continents"* will load data on continents
-  *data="timezones"* will load data on timezones
-  *data="languages"* will load data on languages
-  *data="cities"* will load data on cities, extracted from geonames
-  *data="currencies"* will load data on currencies, extracted from
   wikipedia
-  *data="airlines"* will load data on airlines, extracted from
   `that file <https://raw.github.com/opentraveldata/optd/trunk/refdata/ORI/ori_airlines.csv>`_
-  *data="cabins"* will load data on cabins
-  *data="locales"* will load data on locales
-  *data="location\_types"* will load data on location types
-  *data="feature\_classes"* will load data on feature classes
-  *data="feature\_codes"* will load data on feature codes
-  *data="ori\_por\_non\_iata"* will load some non-iata data excluded
   from *ori\_por*
-  *data="geonames\_MC"* will load MC data of geonames
-  *data="geonames\_FR"* will load FR data of geonames
-  *data="postal\_codes\_MC"* will load MC postal codes data
-  *data="postal\_codes\_FR"* will load FR postal codes data
-  *data="feed"* will create an empty instance

All features are unaware of the underlying data, and are available as long as
the headers are properly set in the configuration file, or from the `Python API <https://geobases.readthedocs.org>`_.
For geographical features, you have to name the latitude field ``lat``, and the
longitude field ``lng``.

Features
--------

Information access
~~~~~~~~~~~~~~~~~~

.. code-block:: python

 >>> geo_o.get('CDG', 'city_code')
 'PAR'
 >>> geo_o.get('BRU', 'name')
 'Bruxelles National'
 >>> geo_t.get('frnic', 'name')
 'Nice-Ville'
 >>> geo_t.get('fr_not_exist', 'name', default='NAME')
 'NAME'

You can put your own data in a ``GeoBase`` class, either by loading
your own file when creating the instance, or by creating an empty instance
and using the ``set`` method.

Find things with properties
~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code-block:: python

 >>> conditions = [('city_code', 'PAR'), ('location_type', ('H',))]
 >>> list(geo_o.findWith(conditions, mode='and'))
 [(2, 'JDP'), (2, 'JPU')]
 >>>
 >>> conditions = [('city_code', 'PAR'), ('city_code', 'LON')]
 >>> len(list(geo_o.findWith(conditions, mode='or')))
 33

Distance computation
~~~~~~~~~~~~~~~~~~~~

.. code-block:: python

 >>> geo_o.distance('CDG', 'NCE')
 694.5162...

Find things near a geocode
~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code-block:: python

 >>> # Paris, airports <= 40km
 >>> [k for _, k in sorted(geo_a.findNearPoint((48.84, 2.367), 40))]
 ['ORY', 'LBG', 'TNF', 'CDG']
 >>>
 >>> # Nice, stations <= 4km
 >>> iterable = geo_t.findNearPoint((43.70, 7.26), 4)
 >>> [geo_t.get(k, 'name') for _, k in iterable]
 ['Nice-Ville', 'Nice-St-Roch', 'Nice-Riquier']

Find things near another thing
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code-block:: python

 >>> sorted(geo_a.findNearKey('ORY', 50)) # Orly, airports <= 50km
 [(0.0, 'ORY'), (18.8..., 'TNF'), (27.8..., 'LBG'), (34.8..., 'CDG')]
 >>>
 >>> sorted(geo_t.findNearKey('frnic', 3)) # Nice station, <= 3km
 [(0.0, 'frnic'), (2.2..., 'fr4342'), (2.3..., 'fr5737')]

Find closest things from a geocode
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code-block:: python

 >>> list(geo_a.findClosestFromPoint((43.70, 7.26))) # Nice
 [(5.82..., 'NCE')]
 >>>
 >>> list(geo_a.findClosestFromPoint((43.70, 7.26), N=3)) # Nice
 [(5.82..., 'NCE'), (30.28..., 'CEQ'), (79.71..., 'ALL')]

Approximate name matching
~~~~~~~~~~~~~~~~~~~~~~~~~

.. code-block:: python

 >>> geo_t.fuzzyFind('Marseille Charles', 'name')[0]
 (0.8..., 'frmsc')
 >>> geo_a.fuzzyFind('paris de gaulle', 'name')[0]
 (0.78..., 'CDG')

Map display
~~~~~~~~~~~

.. code-block:: python

 >>> geo_t.visualize()
 * Added lines for duplicates linking, total 0
 * Could not detect geocode support in join fields.
 > Affecting category None     to color blue    | volume 3190
 <BLANKLINE>
 * Now you may use your browser to visualize:
 ./example_map.html ./example_table.html
 <BLANKLINE>
 * If you want to clean the temporary files:
 rm ./example_map.json ...
 <BLANKLINE>
 (['map', 'table'], (['./example_map.html', './example_table.html'], ['./example_map.json', ...]))

.. figure:: https://raw.github.com/opentraveldata/geobases/public/examples/GeoBases-map-circles.png
   :align: center


Standalone script
-----------------

Installation of the package will also deploy a standalone script named ``GeoBase``:

.. code-block:: bash

 $ GeoBase ORY CDG              # query on the keys ORY and CDG
 $ GeoBase --closest CDG        # closest from CDG
 $ GeoBase --near LIG           # near LIG
 $ GeoBase --fuzzy marseille    # fuzzy search on 'marseille'
 $ GeoBase --admin              # to administrate data sources
 $ GeoBase --ask                # interactive learning mode
 $ GeoBase --help               # your best friend

.. figure:: https://raw.github.com/opentraveldata/geobases/public/examples/GeoBases-CLI.png
   :align: center

In the previous picture, you have an overview of the command line verbose display.
Three displays are available for the command line tool:

-  the verbose display
-  the csv display with ``--quiet``
-  the map display with ``--map``
-  the graph display with ``--graph``

With the verbose display, entries are displayed on each column,
and the available fields on each line. Fields starting with ``__`` like ``__field__`` are
special. This means they were added during data loading:

-  ``__key__`` is the field containing the *id* of the entry. Ids are defined with a list of fields
   in the configuration file.
-  ``__dup__`` is the field containing a list of duplicated keys. Indeed there is mechanism
   handling duplicated keys by default, which creates new keys if the key already exists in the
   ``GeoBase``.
-  ``__par__`` is the field containing the parent key if the key is duplicated.
-  ``__lno__`` is the field containing the line number during loading.
-  ``__gar__`` is the field containing the data which was not loaded on the line (this can be because
   the line was not well formatted, or because there were missing headers).

More examples here, for example how to do a search on a field, like admin\_code (``B8`` is french riviera):

.. code-block:: bash

 $ GeoBase -E adm1_code -e B8

Same with csv output (customized with ``--show``):

.. code-block:: bash

 $ GeoBase -E adm1_code -e B8 --quiet --show __ref__ iata_code  name

Add a fuzzy search:

.. code-block:: bash

 $ GeoBase -E adm1_code -e B8 --fuzzy sur mer

All heliports under 200 km from Paris:

.. code-block:: bash

 $ GeoBase --near PAR -N 200 -E location_type -e 'H'

50 train stations closest to a specific geocode:

.. code-block:: bash

 $ GeoBase -E location_type -e R --closest '48.853, 2.348' -C 50

Countries with non-empty postal code regex:

.. code-block:: bash

 $ GeoBase -b countries -E postal_code_regex -e '' --reverse --quiet

Reading data input on stdin:

.. code-block:: bash

 $ echo -e 'ORY^Orly\nCDG^Charles' | GeoBase

Display on a map:

.. code-block:: bash

 $ GeoBase -b stations --map

Marker-less map for a specific GMT offset:

.. code-block:: bash

 $ GeoBase -E gmt_offset -e 1.0 --map -M _ _ country_code  __none__

Display your data on a map:

.. code-block:: bash

 $ cat coords.csv
 p1,48.22,2.33
 p2,49.33,2.24
 $ cat coords.csv | GeoBase --map

Display your data on a graph:

.. code-block:: bash

 $ cat edges.csv
 A,B
 A,C
 D,A
 $ cat edges.csv | GeoBase --graph


Packaging
---------

The ``MANIFEST.in`` file is used to determine which files will be
included in a source distribution.
``package_data`` directive in ``setup.py`` file is about which file will
be exported in site-package after installation.
So you really need both if you want to produce installable packages like
rpms or zip which can be installed afterwards.

You will also find a `Rakefile <http://rake.rubyforge.org/>`_ at the
root of the project. This can be used to build and deploy the packages.
Deployment can be done using webdav, and the Rakefile expects ``nd`` to be
installed (this is a webdav client).
To install ``nd``, fetch the `sources <https://launchpad.net/ubuntu/+source/nd/0.8.2-2>`_ and compile them.

Virtualenv still has some bugs on 64 bits systems, if you are using such a system,
you absolutely need to upgrade to the very last unreleased version of
virtualenv, before executing rake:

.. code-block:: bash

 $ pip uninstall virtualenv
 $ pip install https://github.com/pypa/virtualenv/tarball/develop

