def test_set_theoretic(self):
proj = projection_from_string(ALBERS_N_AMERICA)
counties = self.counties.transform(proj)
sf = [self.sf]
union_exp = 183.026345584
union_act = counties[sf].union(rect)[self.sf]
assert_almost_equal(union_act.GetArea()/1e6, union_exp)
intersection_exp = 31.0072128793
intersection_act = counties[sf].intersection(rect)[self.sf]
assert_almost_equal(intersection_act.GetArea()/1e6, intersection_exp)
symdifference_exp = 152.019132704
symdifference_act = counties[sf].difference(rect, kind="symmetric")
assert_almost_equal(symdifference_act[self.sf].GetArea()/1e6,
symdifference_exp)
ldifference_exp = 95.5399654593
ldifference_act = counties[sf].difference(rect, kind="left")
assert_almost_equal(ldifference_act[self.sf].GetArea()/1e6,
ldifference_exp)
rdifference_exp = 56.4791672452
rdifference_act = counties[sf].difference(rect, kind="right")
assert_almost_equal(rdifference_act[self.sf].GetArea()/1e6,
rdifference_exp)
def test_set_theoretic(self):
proj = projection_from_string(ALBERS_N_AMERICA)
counties = self.counties.transform(proj)
sf = [self.sf]
union_exp = 183.026345584
union_act = counties[sf].union(rect)[self.sf]
assert_almost_equal(union_act.GetArea()/1e6, union_exp)
intersection_exp = 31.0072128793
intersection_act = counties[sf].intersection(rect)[self.sf]
assert_almost_equal(intersection_act.GetArea()/1e6, intersection_exp)
symdifference_exp = 152.019132704
symdifference_act = counties[sf].difference(rect, kind="symmetric")
assert_almost_equal(symdifference_act[self.sf].GetArea()/1e6,
symdifference_exp)
ldifference_exp = 95.5399654593
ldifference_act = counties[sf].difference(rect, kind="left")
assert_almost_equal(ldifference_act[self.sf].GetArea()/1e6,
ldifference_exp)
rdifference_exp = 56.4791672452
rdifference_act = counties[sf].difference(rect, kind="right")
assert_almost_equal(rdifference_act[self.sf].GetArea()/1e6,
rdifference_exp)
def distances(self, shp, proj=None):
"""Compute the euclidean distances for each of the shapes in the vector
layer. If proj is not none, it will transform shp into proj.
Note: if shp is a shapely object, it is upto to the user
to make sure shp is in the correct coordinate system.
Parameters
----------
proj: string or osr.SpatialReference (default=None)
valid strings are 'albers' or 'utm'. If None, no
transformation of coordinates.
Returns
-------
pandas.Series
Note
----
'utm' should only be used for small polygons when centimeter
level accuraccy is needed. Othewise the area will
be incorrect. Similar issues can happen when polygons cross
utm boundaries.
"""
if proj is None:
shp = to_geometry(shp)
return self.to_geometry(proj=proj).map(lambda x: x.Distance(shp))
if proj == 'utm':
if not self.proj.ExportToProj4() == ut.PROJ_WGS84:
vl = self.transform(ut.projection_from_string())
else:
vl = self
_shp = ops.transform(to_utm, to_shapely(shp))
d = vl.to_shapely() \
.map(lambda x: ops.transform(to_utm, x).distance(_shp))
s = pd.Series(d, index=self.index)
return s
elif proj == 'albers':
proj = ut.projection_from_string(ut.ALBERS_N_AMERICA)
shp = to_geometry(shp, copy=True, proj=proj)
return self.to_geometry(proj=proj).map(lambda x: x.Distance(shp))
def distances(self, shp, proj=None):
"""Compute the euclidean distances for each of the shapes in the vector
layer. If proj is not none, it will transform shp into proj.
Note: if shp is a shapely object, it is upto to the user
to make sure shp is in the correct coordinate system.
Parameters
----------
proj: string or osr.SpatialReference (default=None)
valid strings are 'albers' or 'utm'. If None, no
transformation of coordinates.
Returns
-------
pandas.Series
Note
----
'utm' should only be used for small polygons when centimeter
level accuraccy is needed. Othewise the area will
be incorrect. Similar issues can happen when polygons cross
utm boundaries.
"""
if proj is None:
shp = to_geometry(shp)
return self.to_geometry(proj=proj).map(lambda x: x.Distance(shp))
if proj == 'utm':
if not self.proj.ExportToProj4() == ut.PROJ_WGS84:
vl = self.transform(ut.projection_from_string())
else:
vl = self
_shp = ops.transform(to_utm, to_shapely(shp))
d = vl.to_shapely() \
.map(lambda x: ops.transform(to_utm, x).distance(_shp))
s = pd.Series(d, index=self.index)
return s
elif proj == 'albers':
proj = ut.projection_from_string(ut.ALBERS_N_AMERICA)
shp = to_geometry(shp, copy=True, proj=proj)
return self.to_geometry(proj=proj).map(lambda x: x.Distance(shp))
def createCatalog(src, tile_path, dest, index_path=None, grid=None):
hDataset = gdal.OpenShared(src)
# Get projection.
proj = hDataset.GetProjectionRef()
# Dump to json
catalog = {"Path": src,
"CoordinateSystem": proj,
"GeoTransform": hDataset.GetGeoTransform()}
band = hDataset.GetRasterBand(1)
ctable = band.GetColorTable()
if ctable is not None:
colors = [ctable.GetColorEntry(i) for i in range(256)]
catalog["ColorTable"] = colors
xsize = hDataset.RasterXSize
ysize = hDataset.RasterYSize
catalog["Size"] = (xsize, ysize)
if tile_path is not None:
if os.path.exists(tile_path):
tiles = os.listdir(tile_path)
if len(tiles) == 0:
raise ValueError("%s is empty" % tile_path)
tile = os.path.join(tile_path, tiles[0])
ds = gdal.OpenShared(tile)
if ds is None:
raise ValueError("Unable to open file: %s" % tile)
xsize = ds.RasterXSize
ysize = ds.RasterYSize
if xsize != ysize:
raise ValueError("tiles must have same X and Y size")
catalog["GridSize"] = xsize
catalog["Path"] = tile_path + '/'
else:
raise ValueError("tiles path does not exist: %s" % tile_path)
if index_path is not None:
read = read_geojson if index_path.endswith("json") else read_layer
index = read(index_path)
catalog["Index"] = index.transform(projection_from_string()).to_dict()
if dest is not None:
with open(dest, "w+b") as outf:
outf.write(json.dumps(catalog))
else:
print json.dumps(catalog)
def areas(self, proj=None):
"""Compute the areas for each of the shapes in the vector
layer.
Parameters
----------
proj: string or osr.SpatialReference (default=None)
valid strings are 'albers' or 'utm'. If None, no
transformation of coordinates.
Returns
-------
pandas.Series
Note
----
'utm' should only be used for small polygons when centimeter
level accuraccy is needed. Othewise the area will
be incorrect. Similar issues can happen when polygons cross
utm boundaries.
"""
if proj is None:
return self.map(lambda x: x.GetArea())
if proj == 'utm':
if self.proj.ExportToProj4().strip() != ut.PROJ_WGS84:
vl = self.transform(ut.projection_from_string())
else:
vl = self
shps = vl.to_shapely()
areas = [ops.transform(to_utm, shp).area for shp in shps]
s = pd.Series(areas, index=self.index)
s.name = "area_sqr_m"
return s
elif proj == 'albers':
proj = ut.projection_from_string(ut.ALBERS_N_AMERICA)
return self.transform(proj).areas()
def areas(self, proj=None):
"""Compute the areas for each of the shapes in the vector
layer.
Parameters
----------
proj: string or osr.SpatialReference (default=None)
valid strings are 'albers' or 'utm'. If None, no
transformation of coordinates.
Returns
-------
pandas.Series
Note
----
'utm' should only be used for small polygons when centimeter
level accuraccy is needed. Othewise the area will
be incorrect. Similar issues can happen when polygons cross
utm boundaries.
"""
if proj is None:
return self.map(lambda x: x.GetArea())
if proj == 'utm':
if self.proj.ExportToProj4().strip() != ut.PROJ_WGS84:
vl = self.transform(ut.projection_from_string())
else:
vl = self
shps = vl.to_shapely()
areas = [ops.transform(to_utm, shp).area for shp in shps]
s = pd.Series(areas, index=self.index)
s.name = "area_sqr_m"
return s
elif proj == 'albers':
proj = ut.projection_from_string(ut.ALBERS_N_AMERICA)
return self.transform(proj).areas()
def to_dict(self, df=None):
"""Return a dictionary representation of the object.
Based off the GeoJSON spec. Will transform the vector
layer into WGS84 (EPSG:4326).
Parameters
----------
df: pandas.DataFrame (default=None)
The dataframe to supply the properties of the features.
The index of df must match the ids of the VectorLayer.
Returns
-------
dict
"""
if self.proj.ExportToProj4() != ut.projection_from_string():
vl = self.transform(ut.projection_from_string())
else:
vl = self
res = {"type": "FeatureCollection"}
res["features"] = [
to_feature(f, i).ExportToJson(as_object=True)
for i, f in enumerate(vl)
]
if df is not None:
for i, f in zip(vl.ids, res["features"]):
props = f["properties"]
df_props = df.loc[i].to_dict()
f["properties"] = dict(props.items() + df_props.items())
f["properties"]["__id__"] = i
else:
for i, f in zip(vl.ids, res["features"]):
f["properties"]["__id__"] = i
return res
def to_dict(self, df=None):
"""Return a dictionary representation of the object.
Based off the GeoJSON spec. Will transform the vector
layer into WGS84 (EPSG:4326).
Parameters
----------
df: pandas.DataFrame (default=None)
The dataframe to supply the properties of the features.
The index of df must match the ids of the VectorLayer.
Returns
-------
dict
"""
if self.proj.ExportToProj4() != ut.projection_from_string():
vl = self.transform(ut.projection_from_string())
else:
vl = self
res = {"type": "FeatureCollection"}
res["features"] = [to_feature(f, i).ExportToJson(as_object=True)
for i, f in enumerate(vl)]
if df is not None:
for i, f in zip(vl.ids, res["features"]):
props = f["properties"]
df_props = df.loc[i].to_dict()
f["properties"] = dict(props.items() + df_props.items())
f["properties"]["__id__"] = i
else:
for i, f in zip(vl.ids, res["features"]):
f["properties"]["__id__"] = i
return res
def from_series(geom_series, proj=None):
"""Create a VectorLayer from a pandas.Series object. If
the geometries do not have an spatial reference, EPSG:4326
is assumed.
Parameters
----------
geom_series: pandas.Series
The series object with shapely geometries
proj: osr.SpatialReference
The projection to use, defaults to EPSG:4326
Returns
-------
VectorLayer
"""
proj = ut.projection_from_string() if proj is None else proj
geoms = geom_series.map(lambda x: to_geometry(x, proj=proj))
return VectorLayer(geoms, proj=proj)
def from_series(geom_series, proj=None):
"""Create a VectorLayer from a pandas.Series object. If
the geometries do not have an spatial reference, EPSG:4326
is assumed.
Parameters
----------
geom_series: pandas.Series
The series object with shapely geometries
proj: osr.SpatialReference
The projection to use, defaults to EPSG:4326
Returns
-------
VectorLayer
"""
proj = ut.projection_from_string() if proj is None else proj
geoms = geom_series.map(lambda x: to_geometry(x, proj=proj))
return VectorLayer(geoms, proj=proj)
if args.tile_path is not None:
if os.path.exists(args.tile_path):
tiles = os.listdir(args.tile_path)
if len(tiles) == 0:
raise ValueError("%s is empty" % args.tile_path)
tile = os.path.join(args.tile_path, tiles[0])
ds = gdal.OpenShared(tile)
if ds is None:
raise ValueError("Unable to open file: %s" % tile)
xsize = ds.RasterXSize
ysize = ds.RasterYSize
if xsize != ysize:
raise ValueError("tiles must have same X and Y size")
catalog["GridSize"] = xsize
catalog["Path"] = args.tile_path
else:
raise ValueError("tiles path does not exist: %s" % args.tile_path)
if args.index_path is not None:
read = read_geojson if args.index_path.endswith("json") else read_layer
index = read(args.index_path)
catalog["Index"] = index.transform(projection_from_string()).to_dict()
if args.dest is not None:
with open(args.dest, "w+b") as outf:
outf.write(json.dumps(catalog))
else:
print json.dumps(catalog)
if os.path.exists(args.tile_path):
tiles = os.listdir(args.tile_path)
if len(tiles) == 0:
raise ValueError("%s is empty" % args.tile_path)
tile = os.path.join(args.tile_path, tiles[0])
ds = gdal.OpenShared(tile)
if ds is None:
raise ValueError("Unable to open file: %s" % tile)
xsize = ds.RasterXSize
ysize = ds.RasterYSize
if xsize != ysize:
raise ValueError("tiles must have same X and Y size")
catalog["GridSize"] = xsize
catalog["Path"] = args.tile_path
else:
raise ValueError("tiles path does not exist: %s" % args.tile_path)
if args.index_path is not None:
read = read_geojson if args.index_path.endswith("json") else read_layer
index = read(args.index_path)
catalog["Index"] = index.transform(projection_from_string()).to_dict()
if args.dest is not None:
with open(args.dest, "w+b") as outf:
outf.write(json.dumps(catalog))
else:
print json.dumps(catalog)
