def __init__(self,
path,
zoom,
tile_size=256,
bands=None,
xy_tile_path="%s/%s.png"):
self.gm = globalmaptiles.GlobalMercator(tileSize=256)
self.resolution = self.gm.Resolution(zoom)
self.zoom = zoom
self.bands = range(1, 4) if bands is None else bands
self.tile_size = tile_size
xsize = 2**zoom * tile_size
ysize = xsize
max_lat = 20037508.342789244 # 1/2 the curcumference of the earther in meters
min_lon = -20037508.342789244
geo_transform = (min_lon, self.resolution, 0., max_lat, 0.,
-self.resolution)
proj = projection_from_epsg(3785) # Sphereical Mercator
grid_size = (2 * max_lat / self.resolution,
2 * max_lat / self.resolution)
tile_structure = "%d/" % zoom + xy_tile_path
super(TiledWebRaster, self).__init__(path,
xsize,
ysize,
geo_transform,
proj,
grid_size=grid_size,
tile_structure=tile_structure)
def to_feature(shp, _id):
if isinstance(shp, BaseGeometry):
return {
"type": "Feature",
"id": _id,
"geometry": shp.__geo_interface__
}
elif isinstance(shp, Geometry):
geom = shp.Clone()
geom.TransformTo(projection_from_epsg())
return {
"type": "Feature",
"id": _id,
"geometry": json.loads(geom.ExportToJson())
}
elif pd.isnull(shp):
return {
"type": "Feature",
"id": _id,
"geometry": {
"type": "Point",
"coordinates": []
}
}
else:
raise ValueError("Unable create feature dict from shp")
def __init__(self, *args, **kwargs):
proj = kwargs.pop("proj", None)
if proj is None:
proj = ut.projection_from_epsg()
if isinstance(args[0], pd.Series):
kwargs.pop("index", None)
super(VectorLayer, self).__init__(*args, **kwargs)
self.proj = proj
self._sindex = None
def __init__(self, *args, **kwargs):
proj = kwargs.pop("proj", None)
if proj is None:
proj = ut.projection_from_epsg()
if isinstance(args[0], pd.Series):
kwargs.pop("index", None)
super(VectorLayer, self).__init__(*args, **kwargs)
self.proj = proj
self._sindex = None
def read_geojson(path_or_str, index=None):
"""Create a vector layer from a geojson object. Assumes that
the data has a projection of EPSG:4326
Parameters
----------
path_or_str: string
path or json string
index: string or iterable
If string, the column in the "properties" of each feature to use
as the index. If iterable, use the iterable as the index.
Returns
-------
Tuple of (VectorLayer, pandas.DataFrame of properties)"""
if "FeatureCollection" not in path_or_str:
geojson_str = fetch_geojson(path_or_str)
else:
geojson_str = path_or_str
feats = pd.io.json.loads(geojson_str)["features"]
if index is None:
try:
ids = map(lambda x: x["id"], feats)
except KeyError:
ids = range(len(feats))
name = "index"
elif isinstance(index, str) or isinstance(index, unicode):
ids = map(lambda x: x["properties"][index], feats)
name = index
else:
raise ValueError("Unable to create index.")
proj = ut.projection_from_epsg()
props = pd.DataFrame(map(lambda x: x["properties"], feats), index=ids)
geoms = pd.Series(map(lambda x: shape(x["geometry"]), feats), index=ids) \
.map(lambda x: to_geometry(x, proj=proj))
props.index.name = name
geoms.index.name = name
return VectorLayer(geoms, proj=proj, index=ids), props
def read_geojson(path_or_str, index=None):
"""Create a vector layer from a geojson object. Assumes that
the data has a projection of EPSG:4326
Parameters
----------
path_or_str: string
path or json string
index: string or iterable
If string, the column in the "properties" of each feature to use
as the index. If iterable, use the iterable as the index.
Returns
-------
Tuple of (VectorLayer, pandas.DataFrame of properties)"""
if "FeatureCollection" not in path_or_str:
geojson_str = fetch_geojson(path_or_str)
else:
geojson_str = path_or_str
feats = pd.io.json.loads(geojson_str)["features"]
if index is None:
try:
ids = map(lambda x: x["id"], feats)
except KeyError:
ids = range(len(feats))
name = "index"
elif isinstance(index, str) or isinstance(index, unicode):
ids = map(lambda x: x["properties"][index], feats)
name = index
else:
raise ValueError("Unable to create index.")
proj = ut.projection_from_epsg()
props = pd.DataFrame(map(lambda x: x["properties"], feats), index=ids)
geoms = pd.Series(map(lambda x: shape(x["geometry"]), feats), index=ids) \
.map(lambda x: to_geometry(x, proj=proj))
props.index.name = name
geoms.index.name = name
return VectorLayer(geoms, proj=proj, index=ids), props
def to_feature(shp, _id):
if isinstance(shp, BaseGeometry):
return {"type": "Feature", "id": _id,
"geometry": shp.__geo_interface__}
elif isinstance(shp, Geometry):
geom = shp.Clone()
geom.TransformTo(projection_from_epsg())
return {"type": "Feature", "id": _id,
"geometry": json.loads(geom.ExportToJson())}
elif pd.isnull(shp):
return {"type": "Feature", "id": _id,
"geometry": {"type": "Point",
"coordinates": []}}
else:
raise ValueError("Unable create feature dict from shp")
def __init__(self, path, zoom, tile_size=256, bands=None,
xy_tile_path="%s/%s.png"):
self.gm = globalmaptiles.GlobalMercator(tileSize=256)
self.resolution = self.gm.Resolution(zoom)
self.zoom = zoom
self.bands = range(1, 4) if bands is None else bands
self.tile_size = tile_size
xsize = 2**zoom*tile_size
ysize = xsize
max_lat = 20037508.342789244 # 1/2 the curcumference of the earther in meters
min_lon = -20037508.342789244
geo_transform = (min_lon, self.resolution, 0., max_lat, 0., -self.resolution)
proj = projection_from_epsg(3785) # Sphereical Mercator
grid_size = (2*max_lat/self.resolution, 2*max_lat/self.resolution)
tile_structure = "%d/" % zoom + xy_tile_path
super(TiledWebRaster, self).__init__(path, xsize, ysize, geo_transform, proj,
grid_size=grid_size, tile_structure=tile_structure)
def setup_class(cls):
path1 = get_path("clu/four_shapes_2il_2ca.geojson")
path2 = get_path("gz_2010_us_040_00_500k.json")
path3 = get_path("bay_area_counties.geojson")
path4 = get_path("bay_area_zips.geojson")
cls.vl1, cls.df1 = vt.read_geojson(path1)
cls.vl2, cls.df2 = vt.read_geojson(path2)
cls.counties, cls.df3 = vt.read_geojson(path3, index="NAME")
cls.sf = "San Francisco"
proj = projection_from_epsg()
rect.AssignSpatialReference(proj)
farallon.AssignSpatialReference(proj)
cls.counties[cls.sf] = cls.counties[cls.sf].Difference(farallon)
cls.zips, cls.df4 = vt.read_geojson(path4, index="ZCTA5CE10")
p = get_path("clu/four_shapes_2il_2ca.p")
cls.df = pickle.load(open(p))
assert isinstance(cls.counties, vt.VectorLayer)
assert isinstance(cls.counties["San Francisco"], ogr.Geometry)
def setup_class(cls):
path1 = get_path("clu/four_shapes_2il_2ca.geojson")
path2 = get_path("gz_2010_us_040_00_500k.json")
path3 = get_path("bay_area_counties.geojson")
path4 = get_path("bay_area_zips.geojson")
cls.vl1, cls.df1 = vt.read_geojson(path1)
cls.vl2, cls.df2 = vt.read_geojson(path2)
cls.counties, cls.df3 = vt.read_geojson(path3, index="NAME")
cls.sf = "San Francisco"
proj = projection_from_epsg()
rect.AssignSpatialReference(proj)
farallon.AssignSpatialReference(proj)
cls.counties[cls.sf] = cls.counties[cls.sf].Difference(farallon)
cls.zips, cls.df4 = vt.read_geojson(path4, index="ZCTA5CE10")
p = get_path("clu/four_shapes_2il_2ca.p")
cls.df = pickle.load(open(p))
assert isinstance(cls.counties, vt.VectorLayer)
assert isinstance(cls.counties["San Francisco"], ogr.Geometry)
def to_wgs84(self):
"""Transform the VectorLayer into WGS84"""
proj = ut.projection_from_epsg()
return self.transform(proj)
def to_wgs84(self, method="nneighbour"):
return self.transform(projection_from_epsg(4326), method=method)
def to_wgs84(self):
"""Transform the VectorLayer into WGS84"""
proj = ut.projection_from_epsg()
return self.transform(proj)
def to_wgs84(self, method="nneighbour"):
return self.transform(projection_from_epsg(4326), method=method)
