# Python plugin This plugin allows you to write data sources in the Python programming language. This is useful if you want to rapidly prototype a plugin, perform some custom manipulation on data or if you want to bind mapnik to a datasource which is most conveniently accessed through Python. The plugin may be used from the existing mapnik Python bindings or it can embed the Python interpreter directly allowing it to be used from C++, XML or even JavaScript. ## Rationale Mapnik already has excellent Python bindings but they only directly support calling *into* mapnik *from* Python. This forces mapnik and its input plugins to be the lowest layer of the stack. The role of this plugin is to allow mapnik to call *into* Python itself. This allows mapnik to sit as rendering middleware between a custom Python frontend and a custom Python datasource. This increases the utility of mapnik as a component in a larger system. There already exists MemoryDatasource which can be used to dynamically create geometry in Python. It suffers from the problem that it does not allow generating only the geometry which is seen by a particular query. Similarly the entire geometry must exist in memory before rendering can progress. By using a custom iterator object or by using generator expressions this plugin allows geometry to be created on demand and to be destroyed after use. This can have a great impact on memory efficiency. Since geometry is generated on-demand as rendering progresses there can be arbitrarily complex 'cleverness' optimising the geometry generated for a particular query. Obvious examples of this would be generating only geometry within the query bounding box and generating geometry with an appropriate level of detail for the output resolution. ## Initialization Only the `factory` parameter is required. This is of the form `[module:]callable`. If `module` is present then `module` will be imported and its attribute named `callable` will be used as a factory callable. If `module` is omitted, then `__main__` is used. Any other parameter aside from `factory` or `type` will be passed directly to the callable as keyword arguments. Note that these will always be passed as strings even if the parameter can be parsed as an integer of floating point value. The callable should return an object with the following required attributes: * `envelope` - a 4-tuple giving the (minx, miny, maxx, maxy) extent of the datasource; * `data_type` - a `mapnik.DataType` instance giving the type of data stored in this datasource. This will usually be one of `mapnik.DataType.Vector` or `mapnik.DataType.Raster`. The following attributes are optional: * `geometry_type` - if the dataset is a vector dataset, this is an instance of `mapnik.DataGeometryType` giving the type of geometry returned by the datasource. The following methods must be present: * `features(query)` - takes a single argument which is an instance of `mapnik.Query` and returns an iterable of `mapnik.Feature` instances for that query. * `features_at_point(point)` - almost never used. Takes a single argument which is an instance of `mapnik.Point` (I think) and returns an iterable of features associated with that point. ## Convenience classes The standard `mapnik` module provides a convenience class called `mapnik.PythonDatasource` which has default implementations for the required methods and accepts the geometry type, data type and envelope as constructor arguments. It also provides some convenience class methods which take care of constructing features for you: * `mapnik.PythonDatasource.wkb_features` - constructs features from well-known-binary (WKB) format geometry. Takes two keyword arguments: `keys` which is a sequence of keys associated with each feature and `features` which is a sequence of pairs. The first element in each pair is the WKB representation of the feature and the second element is a dictionary mapping keys to values. # Caveats * If used directly from C++, `Py_Initialize()` must have been called before the plugin is loaded to initialise the interpreter correctly. * When inside the interpreter the global interpreter lock is held each time a feature is fetched and so multi-threaded rendering performance may suffer. You can mitigate this by making sure that the feature iterator yields its value as quickly as possible, potentially from an in-memory buffer filled fom another process over IPC. # Examples In XML: ```xml style python test:TestDatasource ``` In Python using the shapely geometry library: ```python import mapnik from shapely.geometry import * class TestDatasource(mapnik.PythonDatasource): def __init__(self): super(TestDatasource, self).__init__() def features(self, query): return mapnik.PythonDatasource.wkb_features( keys = ('label',), features = ( ( Point(5,6).wkb, { 'label': 'foo-bar'} ), ( Point(100,60).wkb, { 'label': 'buzz-quux'} ), ) ) if __name__ == '__main__': m = mapnik.Map(1280,1024) m.background = mapnik.Color('white') s = mapnik.Style() r = mapnik.Rule() r.symbols.append(mapnik.PointSymbolizer()) t = mapnik.TextSymbolizer(mapnik.Expression("[label]"),"DejaVu Sans Book",10,mapnik.Color('black')) t.displacement = (5,5) r.symbols.append(t) s.rules.append(r) m.append_style('point_style',s) ds = mapnik.Python(factory='TestDatasource') layer = mapnik.Layer('python') layer.datasource = ds layer.styles.append('point_style') m.layers.append(layer) m.zoom_all() mapnik.render_to_file(m,'map.png', 'png') ``` A more complex Python example which makes use of iterators to generate geometry dynamically: ```python """A more complex example which renders an infinite series of concentric circles centred on a point. The circles are represented by a Python iterator which will yield only the circles which intersect the query's bounding box. The advantage of this approach over a MemoryDatasource is that a) only those circles which intersect the viewport are actually generated and b) only the memory for the largest circle need be available since each circle is created on demand and destroyed when finished with. """ import math import mapnik from shapely.geometry import * def box2d_to_shapely(box): import shapely.geometry return shapely.geometry.box(box.minx, box.miny, box.maxx, box.maxy) class ConcentricCircles(object): def __init__(self, centre, bounds, step=1): self.centre = centre self.bounds = bounds self.step = step class Iterator(object): def __init__(self, container): self.container = container centre = self.container.centre bounds = self.container.bounds step = self.container.step if centre.within(bounds): self.radius = 0 else: self.radius = math.ceil(centre.distance(bounds) / float(step)) * step def next(self): circle = self.container.centre.buffer(self.radius) self.radius += self.container.step # has the circle grown so large that the boundary is entirely within it? if circle.contains(self.container.bounds): raise StopIteration() return ( circle.wkb, { } ) def __iter__(self): return ConcentricCircles.Iterator(self) class TestDatasource(mapnik.PythonDatasource): def __init__(self): super(TestDatasource, self).__init__(geometry_type=mapnik.DataGeometryType.Polygon) def features(self, query): # Get the query bounding-box as a shapely bounding box bounding_box = box2d_to_shapely(query.bbox) centre = Point(-20, 0) return mapnik.PythonDatasource.wkb_features( keys = (), features = ConcentricCircles(centre, bounding_box, 0.5) ) if __name__ == '__main__': m = mapnik.Map(640, 320) m.background = mapnik.Color('white') s = mapnik.Style() r = mapnik.Rule() r.symbols.append(mapnik.LineSymbolizer()) s.rules.append(r) m.append_style('point_style',s) ds = mapnik.Python(factory='TestDatasource') layer = mapnik.Layer('python') layer.datasource = ds layer.styles.append('point_style') m.layers.append(layer) box = mapnik.Box2d(-60, -60, 0, -30) m.zoom_to_box(box) mapnik.render_to_file(m,'map.png', 'png') ```