Welcome to NeoBase’s documentation!

GeoBase. Rebooted.

>>> b = NeoBase()
>>> b.get('ORY', 'city_code_list')
['PAR']
>>> b.get('ORY', 'city_name_list')
['Paris']
>>> b.get('ORY', 'country_code')
'FR'
>>> b.distance('ORY', 'CDG')
34.87...
>>> b.get_location('ORY')
LatLng(lat=48.72..., lng=2.35...)
class neobase.neobase.NeoBase(rows=None)[source]

Bases: object

Main structure, a wrapper around a dict, with dict-like behavior.

DUPLICATES = True
FIELDS = (('iata_code', 0, None), ('name', 6, None), ('lat', 8, None), ('lng', 9, None), ('country_code', 16, None), ('continent_name', 19, None), ('timezone', 31, None), ('city_code_list', 36, <function <lambda>>), ('city_name_list', 37, <function <lambda>>), ('location_type', 41, <type 'list'>))
KEY = 0
__init__(rows=None)[source]

x.__init__(…) initializes x; see help(type(x)) for signature

distance(key_0, key_1)[source]

Compute distance between two elements.

This is just a wrapper between the original haversine function, but it is probably the most used feature :)

Parameters:
  • key_0 – the first key
  • key_1 – the second key
Returns:

the distance (km)

>>> b = NeoBase()
>>> b.distance('ORY', 'CDG')
34.87...
static distance_between_locations(l0, l1)[source]

Great circle distance

Parameters:
  • l0 – the LatLng tuple of the first location
  • l1 – the LatLng tuple of the second location
Returns:

the distance in kilometers

>>> NeoBase.distance_between_locations((48.84, 2.367), (43.70, 7.26)) # Paris -> Nice
683.85...

Case of unknown location.

>>> NeoBase.distance_between_locations(None, (43.70, 7.26)) # returns None
find_closest_from_location(lat_lng, N=1, from_keys=None)[source]

Concept close to find_near_location, but here we do not look for the keys radius-close to a location, we look for the closest key from this location, given by latitude/longitude.

Note that a similar implementation is done in the LocalHelper, to find efficiently N closest location in a graph, from a location (using heaps).

Parameters:
  • lat_lng – the lat_lng of the location
  • N – the N closest results wanted
  • from_keys – if None, it takes all keys in consideration, else takes from_keys iterable of keys to perform find_closest_from_location. This is useful to combine searches
Returns:

one key (like ‘SFO’), or a list if approximate is not None

>>> b = NeoBase()
>>> list(b.find_closest_from_location((43.70, 7.26))) # Nice
[(0.60..., 'NCE@1')]
>>> list(b.find_closest_from_location((43.70, 7.26), N=3)) # Nice
[(0.60..., 'NCE@1'), (5.82..., 'NCE'), (5.89..., 'XBM')]
find_near(key, radius=50, from_keys=None)[source]

Same as find_near_location, except the location is given not by a lat/lng, but with its key, like ORY or SFO. We just look up in the base to retrieve lat/lng, and call find_near_location.

Parameters:
  • key – the key of the location
  • radius – the radius of the search (kilometers)
  • from_keys – if None, it takes all keys in consideration, else takes from_keys iterable of keys to perform search.
Returns:

a list of keys (like [‘ORY’, ‘CDG’])

>>> b = NeoBase()
>>> sorted(b.find_near('ORY', 10)) # Orly, por <= 10km
[(0.0, 'ORY'), (6.94..., 'XJY'), (9.96..., 'QFC')]
find_near_location(lat_lng, radius=50, from_keys=None)[source]

Returns a list of nearby keys from a location (given latidude and longitude), and a radius for the search. Note that the haversine function, which compute distance at the surface of a sphere, here returns kilometers, so the radius should be in kms.

Parameters:
  • lat_lng – the lat_lng of the location
  • radius – the radius of the search (kilometers)
  • from_keys – if None, it takes all keys in consideration, else takes from_keys iterable of keys to perform search.
Returns:

an iterable of keys (like [‘ORY’, ‘CDG’])

>>> b = NeoBase()
>>> # Paris, airports <= 50km
>>> [b.get(k, 'iata_code') for d, k in sorted(b.find_near_location((48.84, 2.367), 5))]
['PAR', 'XGB', 'XHP', 'XPG', 'XEX']
find_with(conditions, from_keys=None, reverse=False)[source]

Get iterator of all keys with particular field.

For example, if you want to know all airports in Paris.

Parameters:
  • conditions – a list of (field, value) conditions
  • reverse – we look keys where the field is not the particular value
Returns:

an iterator of matching keys

Testing several conditions.

>>> b = NeoBase()
>>> c0 = [('city_code_list', ['PAR'])]
>>> c1 = [('location_type', ['H'])]
>>> len(list(b.find_with(c0)))
16
>>> len(list(b.find_with(c0 + c1)))
2
get(key, field=None, default=<object object>)[source]

Get data from structure.

>>> b = NeoBase()
>>> b.get('OR', 'city_code_list', default=None)
>>> b.get('ORY', 'city_code_list')
['PAR']
get_location(key, default=<object object>)[source]

Get None or the geocode.

>>> b = NeoBase()
>>> b.get_location('ORY')
LatLng(lat=48.72..., lng=2.35...)
keys()[source]

Returns iterator of all keys in the base.

Returns:the iterator of all keys
>>> b = NeoBase()
>>> sorted(b.keys())
['AAA', 'AAA@1', 'AAB', ...
classmethod load(f)[source]

Building a dictionary of geographical data from optd_por.

>>> path = op.join(op.dirname(__file__), 'optd_por_public.csv')
>>> with open_(path) as f:
...     b = NeoBase.load(f)
>>> b['ORY']['city_code_list']
['PAR']
set(key, **data)[source]

Set information.

>>> b = NeoBase()
>>> b.get('ORY', 'name')
'Paris Orly Airport'
>>> b.set('ORY', name='test')
>>> b.get('ORY', 'name')
'test'
>>> b.set('Wow!', name='test')
>>> b.get('Wow!', 'name')
'test'
static skip(row)[source]
class neobase.neobase.LatLng(lat, lng)

Bases: tuple

lat

Alias for field number 0

lng

Alias for field number 1