d9t.gis 0.2dev

Measurable Objects

A measurable object has to provide ICoordinate, which also may be provided by an adapter.

Let’s define a generic address class (minimalistic and incomplete, i know).

>>> from zope.interface import implements, Interface
>>> from zope.component import queryUtility, adapts
>>> from d9t.gis.interfaces import ICoordinate, ICoordinateProvider

>>> class Address(object):
...     implements(ICoordinate)
...
...     def __init__(self, country, zip):
...         zip_coordinate_provider = queryUtility(ICoordinateProvider)
...         coordinate = zip_coordinate_provider.coordinate(country, zip)
...         self.latitude = coordinate.latitude
...         self.longitude = coordinate.longitude

Now let’s generate a few addresses.

>>> a1 = Address("DE", "89073")
>>> a2 = Address("DE", "88299")

You can also create simple Coordinates. A demo Coordinate class is included:

>>> from d9t.gis.coordinate import Coordinate
>>> c1 = Coordinate(9.99968113200213, 48.4052825255534)   # DE 89073
>>> c2 = Coordinate(10.0191253966503, 47.8117109627977)   # DE 88299

It should be noted that ZipGis loads the gis data from csv on zope startup. This takes far less then a second and we decided that storing the data in zodb wouldn’t have any advantage.

Calculating Distance

>>> from d9t.gis.interfaces import IDistanceCalculation
>>> distance_util = queryUtility(IDistanceCalculation)

You can measure distance between anything that provides ICoordinate. So: Between Coordinates

>>> distance_util.distance(c1, c2)
65.033485081783084

Between Addresses

>>> distance_util.distance(a1, a2)
65.033485081783084

Between Coordinates and Addresses

>>> distance_util.distance(a1, c2)
65.033485081783084
>>> distance_util.distance(a1, c1)
0.0

Common usage

Probably you already have some address objects which are unaware of gis information. Maybe you want - and this is indeed a good idea - to prevent your address - objects to even know about gis information.

You can have all gis features by providing an ICoordinate Adapter for your objects.

Imagine we have a already existing class which is unaware of any gis info:

>>> class IMyAddress(Interface):
...     """ """

>>> class MyAddress(object):
...     implements(IMyAddress)
...     address = ""
...     zip_code = ""
...     city = ""
...     country = ""
...     def __init__(self, address, zip_code, city, country):
...         self.address, self.zip_code, self.city, self.country = address, zip_code, city, country

You would then have objects of that type:

>>> my_a1 = MyAddress("Illextwiete 12", "22455", "Hamburg", "DE")
>>> my_a2 = MyAddress("Gangweg 2", "80797", "Muenchen", "DE")

For measuring distance, your objects have to provide ICoordinate. So let’s create an adapter which simply uses a ready-to-use utility to get the coordinates.

>>> class MyAddressCoordinate(object):
...     implements(ICoordinate)
...     adapts(IMyAddress)
...
...     def __init__(self, my_address):
...         self.my_address = my_address
...         zip_coordinate_provider = queryUtility(ICoordinateProvider)
...         coordinate = zip_coordinate_provider.coordinate(self.my_address.country, self.my_address.zip_code)
...         self.latitude = coordinate.latitude
...         self.longitude = coordinate.longitude

You would usually provide the adapter in your zcml, but as this is a testcase, we’ll do it here:

>>> from zope.app.testing import ztapi
>>> ztapi.provideAdapter(IMyAddress, ICoordinate, MyAddressCoordinate)

Then you can measure as usual:

>>> distance_util.distance(my_a1, my_a2)
624.79554959923701

Get nearby places

If you want to know which of several addresses are the closest to a given one, just give the utility a list of known ICoordinate and one to look for. You will get back a list of tuples where [0] is the distance and [1] is the original object. Btw: As you will see any adaptable object will be ok and returned as-is.

Let’s measure what are the nearest 3 to Muenchen (my_a2) from this list:

>>> nearest = distance_util.nearest(my_a2, (my_a1, my_a2, c1, c2, a1, a2))
>>> ["%s (%s)" % (n[0], n[1].__class__) for n in nearest]
["0.0 (<class 'MyAddress'>)", "175.137634687 (<class 'Address'>)", "175.137634687 (<class 'd9t.gis.coordinate.Coordinate'>)", "175.24792832 (<class 'Address'>)", "175.24792832 (<class 'd9t.gis.coordinate.Coordinate'>)", "624.795549599 (<class 'MyAddress'>)"]

You can also limit the search to e.g. 3 results (sorted of course):

>>> nearest = distance_util.nearest(my_a2, (my_a1, my_a2, c1, c2, a1, a2), 3)
>>> ["%s (%s)" % (n[0], n[1].__class__) for n in nearest]
["0.0 (<class 'MyAddress'>)", "175.137634687 (<class 'Address'>)", "175.137634687 (<class 'd9t.gis.coordinate.Coordinate'>)"]