moradology

import java.io._

def printToFile(fileNameString: String)(toWrite: Any) {
  val fos = new FileOutputStream(new File(fileNameString), true)
  val p = new java.io.PrintWriter(fos)
  try { p.append(s"\n$toWrite") } finally { p.close() }
}


printToFile("test.txt")("TEST")

moradology

#geotrellis.vector ##Features and Geometries

In addition to working with raster data, Geotrellis provides a number of facilities for the creation, representation, and modification of vector data. The data types central to this functionality (geotrellis.vector.Feature and geotrellis.vector.Geometry) correspond - and not by accident - to certain objects found in the GeoJson spec. Features correspond to the objects listed under features in a geojson FeatureCollection. Geometrys, to geometries in a geojson Feature.

moradology

#geotrellis.vector ##Features and Geometries

In addition to working with raster data, Geotrellis provides a number of facilities for the creation, representation, and modification of vector data. The data types central to this functionality (geotrellis.vector.Feature and geotrellis.vector.Geometry) correspond - and not by accident - to certain objects found in the GeoJson spec. Features correspond to the objects listed under features in a geojson FeatureCollection. Geometrys, to geometries in a geojson Feature.

moradology

#geotrellis.vector ##Features and Geometries

In addition to working with raster data, Geotrellis provides a number of facilities for the creation, representation, and modification of vector data. The data types central to this functionality (geotrellis.vector.Feature and geotrellis.vector.Geometry) correspond - and not by accident - to certain objects found in the GeoJson spec. Features correspond to the objects listed under features in a geojson FeatureCollection. Geometrys, to geometries in a geojson Feature.

moradology

#geotrellis.vector ##Features and Geometries

In addition to working with raster data, Geotrellis provides a number of facilities for the creation, representation, and modification of vector data. The data types central to this functionality (geotrellis.vector.Feature and geotrellis.vector.Geometry) correspond - and not by accident - to certain objects found in the GeoJson spec. Features correspond to the objects listed under features in a geojson FeatureCollection. Geometrys, to geometries in a geojson Feature.

moradology

#geotrellis.vector
##Features and Geometries

In addition to working with raster data, Geotrellis provides a number of
facilities for the creation, representation, and modification of vector
data. The data types central to this functionality (`geotrellis.vector.Feature`
and `geotrellis.vector.Geometry`) correspond - and not by accident - to
certain objects found in [the GeoJson spec](http://geojson.org/geojson-spec.html).
`Feature`s correspond to the objects listed  under `features` in a geojson
`FeatureCollection`. `Geometry`s, to `geometries` in a geojson `Feature`.

moradology

#geotrellis.vector ##Features and Geometries

In addition to working with raster data, Geotrellis provides a number of facilities for the creation, representation, and modification of vector data. The data types central to this functionality (geotrellis.vector.Feature and geotrellis.vector.Geometry) correspond - and not by accident - to certain objects found in the GeoJson spec. Features correspond to the objects listed under features in a geojson FeatureCollection. Geometrys, to geometries in a geojson Feature.

moradology

Base = declarative_base()

engine = create_engine('postgresql://vagrant:vagrant@33.33.33.15:5432/vagrant', echo=True)
engine.connect()
Session = sessionmaker(bind=engine)

class JSchema(ConcreteBase, Base):
    __tablename__ = 'json_schemas'

    id = Column(Integer, primary_key=True)

moradology

router = DefaultRouter()
router.register(r'modelviewpath', module.SomeViewSet)

factory.put('https://botanic.internal.azavea.com:8023/api/modelviewpath/6/', etc, etc)

moradology

  def readTile[K: TileReader: JsonFormat: ClassTag](layerId: LayerId): K => Tile = {
    val accumuloLayerMetaData = metaDataCatalog.read(layerId)
    val keyBounds = attributeStore.read[KeyBounds[K]](layerId, "keyBounds")
    val index = attributeStore.read[KeyIndex[K]](layerId, "keyIndex")
    implicitly[TileReader[K]].read(instance, layerId, accumuloLayerMetaData, index)(_)
  }
  
val tile = (catalog.readTile[SpaceTimeKey](layerId))(key)            // Throws
val tile = catalog.readTile[SpaceTimeKey](layerId)(key)              // Throws