def test_linearrings_invalid_shape(shape):
coords = np.ones(shape)
with pytest.raises(ValueError):
pygeos.linearrings(coords)
# make sure the first coordinate != second coordinate
coords[..., 1] += 1
with pytest.raises(ValueError):
pygeos.linearrings(coords)
def test_polygon_with_none_hole():
actual = pygeos.polygons(
pygeos.linearrings(box_tpl(0, 0, 10, 10)),
[
pygeos.linearrings(box_tpl(1, 1, 2, 2)),
None,
pygeos.linearrings(box_tpl(3, 3, 4, 4)),
],
)
assert pygeos.area(actual) == 98.0
import numpy as np
import pygeos
point_polygon_testdata = (
pygeos.points(np.arange(6), np.arange(6)),
pygeos.box(2, 2, 4, 4),
)
point = pygeos.points(2, 3)
line_string = pygeos.linestrings([(0, 0), (1, 0), (1, 1)])
linear_ring = pygeos.linearrings([(0, 0), (1, 0), (1, 1), (0, 1), (0, 0)])
polygon = pygeos.polygons([(0, 0), (2, 0), (2, 2), (0, 2), (0, 0)])
multi_point = pygeos.multipoints([(0, 0), (1, 2)])
multi_line_string = pygeos.multilinestrings([[(0, 0), (1, 2)]])
multi_polygon = pygeos.multipolygons([
[(0, 0), (1, 0), (1, 1), (0, 1), (0, 0)],
[(2.1, 2.1), (2.2, 2.1), (2.2, 2.2), (2.1, 2.2), (2.1, 2.1)],
])
geometry_collection = pygeos.geometrycollections(
[pygeos.points(51, -1),
pygeos.linestrings([(52, -1), (49, 2)])])
point_z = pygeos.points(1.0, 1.0, 1.0)
polygon_with_hole = pygeos.Geometry(
"POLYGON((0 0, 0 10, 10 10, 10 0, 0 0), (2 2, 2 4, 4 4, 4 2, 2 2))")
all_types = (
point,
line_string,
linear_ring,
polygon,
multi_point,
multi_line_string,
def test_linearrings_unclosed():
actual = pygeos.linearrings(box_tpl(0, 0, 1, 1)[:-1])
assert str(actual) == "LINEARRING (1 0, 1 1, 0 1, 0 0, 1 0)"
def test_linearrings_from_xy():
actual = pygeos.linearrings([0, 1, 2, 0], [3, 4, 5, 3])
assert str(actual) == "LINEARRING (0 3, 1 4, 2 5, 0 3)"
def test_polygon_from_linearring():
actual = pygeos.polygons(pygeos.linearrings(box_tpl(0, 0, 1, 1)))
assert str(actual) == "POLYGON ((1 0, 1 1, 0 1, 0 0, 1 0))"
def test_linearrings(coordinates):
actual = pygeos.linearrings(coordinates, indices=len(coordinates) * [0])
assert_geometries_equal(actual, pygeos.linearrings(coordinates))
def test_linearrings_invalid(coordinates):
# attempt to construct linestrings with 1 coordinate
with pytest.raises(pygeos.GEOSException):
pygeos.linearrings(coordinates, indices=np.zeros(len(coordinates)))
@pytest.mark.parametrize("holes",
[[1, 2], None, [linear_ring, point], "hello"])
def test_polygons_invalid_holes(holes):
with pytest.raises((TypeError, ValueError, pygeos.GEOSException)):
pygeos.polygons([linear_ring, linear_ring], holes, indices=[0, 1])
@pytest.mark.parametrize("indices", [[1, 2], [point], "hello", [1], [-1]])
def test_polygons_invalid_indices(indices):
with pytest.raises((TypeError, ValueError)):
pygeos.polygons([linear_ring], [linear_ring], indices=indices)
hole_1 = pygeos.linearrings([(0.2, 0.2), (0.2, 0.4), (0.4, 0.4)])
hole_2 = pygeos.linearrings([(0.6, 0.6), (0.6, 0.8), (0.8, 0.8)])
poly = pygeos.polygons(linear_ring)
poly_hole_1 = pygeos.polygons(linear_ring, holes=[hole_1])
poly_hole_2 = pygeos.polygons(linear_ring, holes=[hole_2])
poly_hole_1_2 = pygeos.polygons(linear_ring, holes=[hole_1, hole_2])
@pytest.mark.parametrize(
"holes,indices,expected",
[
([None], [1], [poly, poly]),
([hole_1], [0], [poly_hole_1, poly]),
([hole_1], [1], [poly, poly_hole_1]),
([hole_1, hole_2], [0, 0], [poly_hole_1_2, poly]),
([hole_1, hole_2], [0, 1], [poly_hole_1, poly_hole_2]),
def test_linearrings():
actual = pygeos.linearrings(box_tpl(0, 0, 1, 1))
assert actual.to_wkt() == "LINEARRING (1 0, 1 1, 0 1, 0 0, 1 0)"
def fix(dem, coastline, **kwargs):
# ---------------------------------------------------------------------
logger.info("adjust dem\n")
# ---------------------------------------------------------------------
# define coastline
try:
shp = gp.GeoDataFrame.from_file(coastline)
except:
shp = gp.GeoDataFrame(coastline)
if "ival" in dem.data_vars:
xp = dem.ilons.values
yp = dem.ilats.values
else:
xp = dem.longitude.values
yp = dem.latitude.values
minlon = xp.min()
maxlon = xp.max()
minlat = yp.min()
maxlat = yp.max()
if xp.mean() < 0 and xp.min() < -180.0:
flag = -1
elif xp.mean() > 0 and xp.max() > 180.0:
flag = 1
else:
flag = 0
if flag == 1:
block1 = shp.cx[minlon:180, minlat:maxlat].copy()
block2 = shp.cx[-180:(maxlon - 360.0), minlat:maxlat].copy()
for idx, poly in block2.iterrows():
block2.loc[idx,
"geometry"] = shapely.ops.transform(lambda x, y, z=None:
(x + 360.0, y),
poly.geometry)
block = pd.concat([block1, block2])
elif flag == -1:
block1 = shp.cx[minlon + 360:180, minlat:maxlat].copy()
block2 = shp.cx[-180:maxlon, minlat:maxlat].copy()
for idx, poly in block1.iterrows():
block1.loc[idx,
"geometry"] = shapely.ops.transform(lambda x, y, z=None:
(x - 360.0, y),
poly.geometry)
block = pd.concat([block1, block2])
else:
block = shp.cx[minlon:maxlon, minlat:maxlat]
try:
block = gp.GeoDataFrame(geometry=list(block.unary_union.geoms))
except:
pass
# ---------------------------------------------------------------------
logger.debug("compute water and land\n")
# ---------------------------------------------------------------------
# create a polygon of the lat/lon window
grp = shapely.geometry.Polygon([(minlon, minlat), (minlon, maxlat),
(maxlon, maxlat), (maxlon, minlat)])
grp = grp.buffer(0.5) # buffer it to get also the boundary points
g = block.unary_union.symmetric_difference(grp) # get the diff
try:
t = gp.GeoDataFrame({"geometry": g})
except:
t = gp.GeoDataFrame({"geometry": [g]})
t["length"] = t["geometry"][:].length # optional
t = t.sort_values(by="length", ascending=0) # use the length to list them
t = t.reset_index(drop=True)
t["in"] = gp.GeoDataFrame(geometry=[grp.buffer(0.001)] *
t.shape[0]).contains(
t) # find the largest of boundaries
try:
idx = np.where(
t["in"] == True)[0][0] # first(largest) boundary within lat/lon
b = t.iloc[idx].geometry # get the largest
except:
b = shapely.geometry.GeometryCollection()
# define wet/dry
water = b
land = grp - b
if (not land) | (not water):
# ---------------------------------------------------------------------
logger.debug("only water/land present...\n")
# ---------------------------------------------------------------------
if "ival" in dem.data_vars:
dem = dem.assign(fval=dem.ival)
else:
dem = dem.assign(adjusted=dem.elevation)
return dem
if "ival" in dem.data_vars:
df = pd.DataFrame({
"longitude": dem.ilons.values.flatten(),
"latitude": dem.ilats.values.flatten(),
"elevation": dem.ival.values.flatten(),
})
else:
df = dem.elevation.to_dataframe().reset_index()
# ---------------------------------------------------------------------
logger.debug("invoke pygeos\n")
# ---------------------------------------------------------------------
spoints_ = pygeos.points(list(df.loc[:, ["longitude", "latitude"]].values)
) # create pygeos objects for the points
# Add land boundaries to a pygeos object
try:
lbs = []
for l in range(len(land.boundary.geoms)):
z = pygeos.linearrings(land.boundary.geoms[l].coords[:])
lbs.append(z)
except:
lbs = pygeos.linearrings(land.boundary.coords[:])
bp = pygeos.polygons(lbs)
# ---------------------------------------------------------------------
logger.debug("find wet and dry masks\n")
# ---------------------------------------------------------------------
# find the points on land
tree = pygeos.STRtree(spoints_)
try:
wl = []
for l in range(len(land.boundary.geoms)):
wl.append(tree.query(bp[l], predicate="contains").tolist())
ns = [j for i in wl for j in i]
except:
wl = tree.query(bp, predicate="contains").tolist()
ns = wl
lmask = np.zeros(spoints_.shape, dtype=bool)
lmask[ns] = True
wmask = ~lmask # invert for wet mask
# ---------------------------------------------------------------------
logger.debug("fix wet points\n")
# ---------------------------------------------------------------------
# Now see if the wet points have indeed negative values
pw_mask = df.loc[wmask, "elevation"] > 0
if pw_mask.sum() > 0:
pw = df.loc[wmask][
pw_mask] # problematic points: bathymetry > 0 in wet area
# Resample to fix that ...
xw = pw.longitude.values
yw = pw.latitude.values
# Define points with positive bathymetry
x, y = np.meshgrid(dem.longitude, dem.latitude) #!!!!!!!!
if flag == 1:
xw = xw - 180.0
x = x - 180.0
elif flag == -1:
xw = xw + 180.0
x = x + 180.0
# wet.fill_value = 0.
mx = np.ma.masked_array(x, dem.elevation.values > 0)
my = np.ma.masked_array(y, dem.elevation.values > 0)
# fill the nan, if present, with values in order to compute values there if needed.
dem.elevation.data[np.isnan(dem.elevation.values)] = 9999.0
# mask positive bathymetry
wet_dem = np.ma.masked_array(dem.elevation, dem.elevation.values > 0)
orig = pyresample.geometry.SwathDefinition(
lons=mx, lats=my) # original bathymetry points
targ = pyresample.geometry.SwathDefinition(lons=xw,
lats=yw) # wet points
bw = pyresample.kd_tree.resample_nearest(orig,
wet_dem,
targ,
radius_of_influence=100000,
fill_value=np.nan)
df.loc[pw.index, "elevation"] = bw # replace in original dataset
# ---------------------------------------------------------------------
logger.debug("fix dry points\n")
# ---------------------------------------------------------------------
# .. the same for dry points
pl_mask = df.loc[lmask, "elevation"] < 0
if pl_mask.sum() > 0:
pl = df.loc[lmask][
pl_mask] # problematic points: bathymetry <0 in dry area
## Resample to fix that
xl = pl.longitude.values
yl = pl.latitude.values
x, y = np.meshgrid(dem.longitude, dem.latitude)
if flag == 1:
xl = xl - 180.0
x = x - 180.0
elif flag == -1:
xl = xl + 180.0
x = x + 180.0
# wet.fill_value = 0.
dx = np.ma.masked_array(x, dem.elevation.values < 0)
dy = np.ma.masked_array(y, dem.elevation.values < 0)
# fill the nan, if present, with values in order to compute values there if needed.
dem.elevation.data[np.isnan(dem.elevation.values)] = 9999.0
# mask positive bathymetry
dry_dem = np.ma.masked_array(dem.elevation, dem.elevation.values < 0)
orig = pyresample.geometry.SwathDefinition(
lons=dx, lats=dy) # original bathymetry points
targ = pyresample.geometry.SwathDefinition(lons=xl,
lats=yl) # wet points
bd = pyresample.kd_tree.resample_nearest(orig,
dry_dem,
targ,
radius_of_influence=100000,
fill_value=np.nan)
df.loc[pl.index, "elevation"] = bd # replace in original dataset
# ---------------------------------------------------------------------
logger.debug("assemble dataset \n")
# ---------------------------------------------------------------------
# reassemble dataset
if "ival" in dem.data_vars:
if len(dem.ival.shape) == 1:
new_dem = df.elevation.to_xarray()
new_dem = xr.merge([new_dem])
new_dem = new_dem.rename({"elevation": "fval"})
new_dem.fval.attrs = {"coastline": "based on coastline"}
new_dem = new_dem.rename({"index": "k"}).drop_vars("k")
else:
new_dem = (df.set_index(["latitude",
"longitude"]).to_xarray().rename({
"longitude":
"l",
"latitude":
"k",
"elevation":
"fval"
}).drop_vars(["k", "l"]))
else:
df_new = df.set_index(["latitude", "longitude"])
new_dem = df_new.to_xarray()
new_dem = new_dem.rename({"elevation": "adjusted"})
new_dem.attrs = {"coastline": "based on coastline"}
cdem = xr.merge([dem, new_dem])
return cdem
def _create_mosaic(
cls,
features: Sequence[Dict],
minzoom: int,
maxzoom: int,
quadkey_zoom: Optional[int] = None,
accessor: Callable[[Dict], str] = default_accessor,
asset_filter: Callable = default_filter,
version: str = "0.0.2",
quiet: bool = True,
**kwargs,
):
"""Create mosaic definition content.
Attributes:
features (list): List of GeoJSON features.
minzoom (int): Force mosaic min-zoom.
maxzoom (int): Force mosaic max-zoom.
quadkey_zoom (int): Force mosaic quadkey zoom (optional).
accessor (callable): Function called on each feature to get its identifier (default is feature["properties"]["path"]).
asset_filter (callable): Function to filter features.
version (str): mosaicJSON definition version (default: 0.0.2).
quiet (bool): Mask processing steps (default is True).
kwargs (any): Options forwarded to `asset_filter`
Returns:
mosaic_definition (MosaicJSON): Mosaic definition.
Examples:
>>> MosaicJSON._create_mosaic([], 12, 14)
"""
quadkey_zoom = quadkey_zoom or minzoom
if not quiet:
click.echo(f"Get quadkey list for zoom: {quadkey_zoom}", err=True)
dataset_geoms = polygons([
linearrings(feat["geometry"]["coordinates"][0])
for feat in features
])
bounds = tuple(total_bounds(dataset_geoms))
tiles = burntiles.burn(features, quadkey_zoom)
tiles = [mercantile.Tile(*tile) for tile in tiles]
mosaic_definition: Dict[str, Any] = dict(
mosaicjson=version,
minzoom=minzoom,
maxzoom=maxzoom,
quadkey_zoom=quadkey_zoom,
bounds=bounds,
center=((bounds[0] + bounds[2]) / 2, (bounds[1] + bounds[3]) / 2,
minzoom),
tiles={},
version="1.0.0",
)
if not quiet:
click.echo("Feed Quadkey index", err=True)
# Create tree and find assets that overlap each tile
tree = STRtree(dataset_geoms)
with ExitStack() as ctx:
fout = ctx.enter_context(open(os.devnull,
"w")) if quiet else sys.stderr
with click.progressbar( # type: ignore
tiles,
file=fout,
show_percent=True,
label="Iterate over quadkeys") as bar:
for tile in bar:
quadkey = mercantile.quadkey(tile)
tile_geom = polygons(
mercantile.feature(tile)["geometry"]["coordinates"][0])
# Find intersections from rtree
intersections_idx = sorted(
tree.query(tile_geom, predicate="intersects"))
if len(intersections_idx) == 0:
continue
intersect_dataset, intersect_geoms = zip(
*[(features[idx], dataset_geoms[idx])
for idx in intersections_idx])
dataset = asset_filter(tile, intersect_dataset,
intersect_geoms, **kwargs)
if dataset:
mosaic_definition["tiles"][quadkey] = [
accessor(f) for f in dataset
]
return cls(**mosaic_definition)
def test_linearrings_all_nan():
coords = np.full((4, 2), np.nan)
with pytest.raises(pygeos.GEOSException):
pygeos.linearrings(coords)
def test_linearrings_invalid_shape_scalar():
with pytest.raises(ValueError):
pygeos.linearrings((1, 1))
import numpy as np
import pygeos
point_polygon_testdata = (
pygeos.points(np.arange(6), np.arange(6)),
pygeos.box(2, 2, 4, 4),
)
point = pygeos.points(2, 2)
line_string = pygeos.linestrings([[0, 0], [1, 0], [1, 1]])
linear_ring = pygeos.linearrings(((0, 0), (0, 1), (1, 1), (1, 0), (0, 0)))
polygon = pygeos.polygons(
((0.0, 0.0), (0.0, 2.0), (2.0, 2.0), (2.0, 0.0), (0.0, 0.0)))
multi_point = pygeos.multipoints([[0.0, 0.0], [1.0, 2.0]])
multi_line_string = pygeos.multilinestrings([[[0.0, 0.0], [1.0, 2.0]]])
multi_polygon = pygeos.multipolygons([
((0.0, 0.0), (0.0, 1.0), (1.0, 1.0), (1.0, 0.0)),
((0.1, 0.1), (0.1, 0.2), (0.2, 0.2), (0.2, 0.1)),
])
geometry_collection = pygeos.geometrycollections(
[pygeos.points(51, -1),
pygeos.linestrings([(52, -1), (49, 2)])])
point_z = pygeos.points(1.0, 1.0, 1.0)
all_types = (
point,
line_string,
linear_ring,
polygon,
multi_point,
multi_line_string,
multi_polygon,
def test_linearrings(coordinates):
actual = pygeos.linearrings(
np.array(coordinates, dtype=np.float64),
indices=np.zeros(len(coordinates), dtype=np.intp),
)
assert_geometries_equal(actual, pygeos.linearrings(coordinates))