def test_transform_geom_gdal22():
"""Enabling `antimeridian_cutting` has no effect on GDAL 2.2.0 or newer
where antimeridian cutting is always enabled. This could produce
unexpected geometries, so an exception is raised.
"""
geom = {"type": "Point", "coordinates": [0, 0]}
with pytest.raises(GDALVersionError):
transform_geom("EPSG:4326", "EPSG:3857", geom, antimeridian_cutting=False)
def test_transform_geom():
geom = {
'type': 'Polygon',
'coordinates': (
((798842.3090855901, 6569056.500655151),
(756688.2826828464, 6412397.888771972),
(755571.0617232556, 6408461.009397383),
(677605.2284582685, 6425600.39266733),
(677605.2284582683, 6425600.392667332),
(670873.3791649605, 6427248.603432341),
(664882.1106069803, 6407585.48425362),
(663675.8662823177, 6403676.990080649),
(485120.71963574126, 6449787.167760638),
(485065.55660851026, 6449802.826920689),
(485957.03982722526, 6452708.625101285),
(487541.24541826674, 6457883.292107048),
(531008.5797472061, 6605816.560367976),
(530943.7197027118, 6605834.9333479265),
(531888.5010308184, 6608940.750411527),
(533299.5981959199, 6613962.642851984),
(533403.6388841148, 6613933.172096095),
(576345.6064638699, 6761983.708069147),
(577649.6721159086, 6766698.137844516),
(578600.3589008929, 6770143.99782289),
(578679.4732294685, 6770121.638265098),
(655836.640492081, 6749376.357102599),
(659913.0791150068, 6764770.1314677475),
(661105.8478791204, 6769515.168134831),
(661929.4670843681, 6772800.8565198565),
(661929.4670843673, 6772800.856519875),
(661975.1582566603, 6772983.354777632),
(662054.7979028501, 6772962.86384242),
(841909.6014891531, 6731793.200435557),
(840726.455490463, 6727039.8672589315),
(798842.3090855901, 6569056.500655151)),
)
}
result = transform_geom('EPSG:3373', 'EPSG:4326', geom)
assert result['type'] == 'Polygon'
assert len(result['coordinates']) == 1
result = transform_geom(
'EPSG:3373', 'EPSG:4326', geom, antimeridian_cutting=True)
assert result['type'] == 'MultiPolygon'
assert len(result['coordinates']) == 2
result = transform_geom(
'EPSG:3373',
'EPSG:4326',
geom,
antimeridian_cutting=True,
antimeridian_offset=0)
assert result['type'] == 'MultiPolygon'
assert len(result['coordinates']) == 2
result = transform_geom('EPSG:3373', 'EPSG:4326', geom, precision=1)
assert int(result['coordinates'][0][0][0] * 10) == -1778
def test_transform_geom_gdal22():
"""Enabling `antimeridian_cutting` has no effect on GDAL 2.2.0 or newer
where antimeridian cutting is always enabled. This could produce
unexpected geometries, so an exception is raised.
"""
geom = {
'type': 'Point',
'coordinates': [0, 0]
}
with pytest.raises(GDALBehaviorChangeException):
transform_geom(
'EPSG:4326', 'EPSG:3857', geom, antimeridian_cutting=False)
def test_transform_geom_polygon_offset(polygon_3373):
geom = polygon_3373
result = transform_geom(
"EPSG:3373", "EPSG:4326", geom, antimeridian_cutting=True, antimeridian_offset=0
)
assert result["type"] == "MultiPolygon"
assert len(result["coordinates"]) == 2
def test_transform_geom_linearring_precision(polygon_3373):
ring = polygon_3373["coordinates"][0]
geom = {"type": "LinearRing", "coordinates": ring}
result = transform_geom(
"EPSG:3373", "EPSG:4326", geom, precision=1, antimeridian_cutting=True
)
assert all(round(x, 1) == x for x in flatten_coords(result["coordinates"]))
def test_transform_geom_linestring_precision_z(polygon_3373):
ring = polygon_3373['coordinates'][0]
x, y = zip(*ring)
ring = list(zip(x, y, [0.0 for i in range(len(x))]))
geom = {'type': 'LineString', 'coordinates': ring}
result = transform_geom('EPSG:3373', 'EPSG:3373', geom, precision=1)
assert int(result['coordinates'][0][0] * 10) == 7988423
assert int(result['coordinates'][0][2] * 10) == 0
def test_transform_geom_linestring_precision_z(polygon_3373):
ring = polygon_3373["coordinates"][0]
x, y = zip(*ring)
ring = list(zip(x, y, [0.0 for i in range(len(x))]))
geom = {"type": "LineString", "coordinates": ring}
result = transform_geom("EPSG:3373", "EPSG:3373", geom, precision=1)
assert int(result["coordinates"][0][0] * 10) == 7988423
assert int(result["coordinates"][0][2] * 10) == 0
def test_issue_1446():
"""Confirm resolution of #1446"""
g = transform_geom(
CRS.from_epsg(4326),
CRS.from_epsg(32610),
{"type": "Point", "coordinates": (-122.51403808499907, 38.06106733107932)},
)
assert round(g["coordinates"][0], 1) == 542630.9
assert round(g["coordinates"][1], 1) == 4212702.1
def test_transform_geom_polygon_offset(polygon_3373):
geom = polygon_3373
result = transform_geom(
'EPSG:3373',
'EPSG:4326',
geom,
antimeridian_cutting=True,
antimeridian_offset=0)
assert result['type'] == 'MultiPolygon'
assert len(result['coordinates']) == 2
def geojson_tile_burn(tile, features, srid, ts, burn_value=1):
"""Burn tile with GeoJSON features."""
crs = (CRS.from_epsg(srid), CRS.from_epsg(3857))
shapes = ((transform_geom(*crs, feature["geometry"]), burn_value)
for feature in features)
try:
return rasterize(shapes,
out_shape=ts,
transform=from_bounds(*tile_bbox(tile, mercator=True),
*ts))
except:
return None
def reproject(shapes, bounds):
with rasterio.Env(OGR_ENABLE_PARTIAL_REPROJECTION=True):
for g, val in shapes:
# TODO this produces garbage on the left edge of the world
g = warp.transform_geom(bounds.crs, WGS84_CRS, g)
xs, ys = zip(*coords(g))
yield {
"type": "Feature",
"properties": {
"value": val
},
"bbox": [min(xs), min(ys), max(xs),
max(ys)],
"geometry": g,
}
def water_mask(case_study, crs, transform, width, height):
"""Water mask from OSM database (water bodies + seas & oceans)."""
db = osm.OSMDatabase(DB_NAME, DB_USER, DB_PASS, DB_HOST)
polygons = db.water(case_study.aoi_latlon)
geoms = [feature['geometry'] for feature in polygons]
geoms = [transform_geom(WGS84, crs, geom) for geom in geoms]
if len(geoms) == 0:
return np.zeros(shape=(height, width), dtype=np.bool)
water = rasterize(
shapes=geoms,
out_shape=(height, width),
transform=transform,
dtype=np.uint8).astype(np.bool)
db.connection.close()
return water
def create_cutline(
src_dst: Union[DatasetReader, DatasetWriter, WarpedVRT],
geometry: Dict,
geometry_crs: CRS = None,
) -> str:
"""
Create WKT Polygon Cutline for GDALWarpOptions.
Ref: https://gdal.org/api/gdalwarp_cpp.html?highlight=vrt#_CPPv415GDALWarpOptions
Args:
src_dst (rasterio.io.DatasetReader or rasterio.io.DatasetWriter or rasterio.vrt.WarpedVRT): Rasterio dataset.
geometry (dict): GeoJSON feature or GeoJSON geometry. By default the coordinates are considered to be in the dataset CRS. Use `geometry_crs` to set a specific CRS.
geometry_crs (rasterio.crs.CRS, optional): Input geometry Coordinate Reference System
Returns:
str: WKT geometry in form of `POLYGON ((x y, x y, ...)))
"""
if "geometry" in geometry:
geometry = geometry["geometry"]
if not is_valid_geom(geometry):
raise RioTilerError("Invalid geometry")
geom_type = geometry["type"]
if geom_type not in ["Polygon", "MultiPolygon"]:
raise RioTilerError(
"Invalid geometry type: {geom_type}. Should be Polygon or MultiPolygon"
)
if geometry_crs:
geometry = transform_geom(geometry_crs, src_dst.crs, geometry)
polys = []
geom = ([geometry["coordinates"]]
if geom_type == "Polygon" else geometry["coordinates"])
for p in geom:
xs, ys = zip(*coords(p))
src_y, src_x = rowcol(src_dst.transform, xs, ys)
src_x = [max(0, min(src_dst.width, x)) for x in src_x]
src_y = [max(0, min(src_dst.height, y)) for y in src_y]
poly = ", ".join([f"{x} {y}" for x, y in list(zip(src_x, src_y))])
polys.append(f"(({poly}))")
str_poly = ",".join(polys)
return (f"POLYGON {str_poly}"
if geom_type == "Polygon" else f"MULTIPOLYGON ({str_poly})")
def test_transform_geom_wrap():
geom = {
'type':
'Polygon',
'coordinates':
(((798842.3090855901, 6569056.500655151), (756688.2826828464,
6412397.888771972),
(755571.0617232556, 6408461.009397383), (677605.2284582685,
6425600.39266733),
(677605.2284582683, 6425600.392667332), (670873.3791649605,
6427248.603432341),
(664882.1106069803, 6407585.48425362), (663675.8662823177,
6403676.990080649),
(485120.71963574126, 6449787.167760638), (485065.55660851026,
6449802.826920689),
(485957.03982722526, 6452708.625101285), (487541.24541826674,
6457883.292107048),
(531008.5797472061, 6605816.560367976), (530943.7197027118,
6605834.9333479265),
(531888.5010308184, 6608940.750411527), (533299.5981959199,
6613962.642851984),
(533403.6388841148,
6613933.172096095), (576345.6064638699,
6761983.708069147), (577649.6721159086,
6766698.137844516),
(578600.3589008929,
6770143.99782289), (578679.4732294685,
6770121.638265098), (655836.640492081,
6749376.357102599),
(659913.0791150068,
6764770.1314677475), (661105.8478791204,
6769515.168134831), (661929.4670843681,
6772800.8565198565),
(661929.4670843673,
6772800.856519875), (661975.1582566603,
6772983.354777632), (662054.7979028501,
6772962.86384242),
(841909.6014891531,
6731793.200435557), (840726.455490463,
6727039.8672589315), (798842.3090855901,
6569056.500655151)), )
}
result = transform_geom('EPSG:3373',
'EPSG:4326',
geom,
antimeridian_cutting=True)
assert result['type'] == 'MultiPolygon'
assert len(result['coordinates']) == 2
def _add_zones(self, source, name, uid, species, path, zone_field):
with fiona.open(path, 'r') as shp:
for feature in shp:
try:
zone_id = feature['properties'][zone_field]
except KeyError:
zone_id = feature['properties'][zone_field.lower()]
if zone_id is None:
continue
zone_id = int(zone_id)
if zone_id == 0:
continue
if hasattr(name, '__call__'):
object_id = feature['properties'].get('OBJECTID')
zone_name = name(zone_id, object_id)
else:
zone_name = name.format(zone_id=zone_id, species=SPECIES_NAMES.get(species))
geometry = transform_geom(shp.crs, {'init': 'EPSG:4326'}, feature['geometry'])
if feature['geometry']['type'] == 'MultiPolygon':
geometry['coordinates'] = itertools.chain(*geometry['coordinates'])
polygon = Polygon(*[LinearRing(x) for x in geometry['coordinates']])
uid_suffix = 0
while True:
zone_uid = uid.format(zone_id=zone_id)
if uid_suffix > 0:
zone_uid += '_{}'.format(uid_suffix)
try:
with transaction.atomic():
SeedZone.objects.create(
source=source, name=zone_name, species=species, zone_id=zone_id, zone_uid=zone_uid,
polygon=polygon
)
break
except IntegrityError:
if uid_suffix > 10:
raise
uid_suffix += 1
def crop_to_box(image, gps_bbox):
'''
This differs from crop to shape in that:
1 - the bbox coords are WGS84 lat lon (GPS)
2 - we want the result to be a 'square' image (not a diamond)
(we're assuming the image is more-or-less aligned N/S)
'''
# for some reason the gps_bbox cannot be a shapely object; it has to be a geojson dict
crs_bbox = transform_geom('WGS84', image.crs, mapping(gps_bbox))
pixel_bbox = [image.index(*xy) for xy in crs_bbox['coordinates'][0]]
xs, ys = zip(*pixel_bbox)
# maybe we should do something smarter here to ensure we always have data?
# like take the miniumum of the maximal values, etc.
pixel_bbox = box(min(xs), min(ys), max(xs), max(ys))
crs_bbox = Polygon([image.xy(*xy) for xy in pixel_bbox.exterior.coords])
return crop_to_shape(image, crs_bbox)
def __enter__(self):
"""Support using with Context Managers."""
self.scene_params = s2_sceneid_parser(self.sceneid)
prefix = self._prefix.format(**self.scene_params)
self.tileInfo = json.loads(
get_object(self._hostname,
f"{prefix}/tileInfo.json",
request_pays=True))
input_geom = self.tileInfo["tileDataGeometry"]
input_crs = CRS.from_user_input(
input_geom["crs"]["properties"]["name"])
self.datageom = transform_geom(input_crs, constants.WGS84_CRS,
input_geom)
self.bounds = featureBounds(self.datageom)
return self
def grid(ovr_level: int = Query(...)):
"""return geojson."""
options = {"OVERVIEW_LEVEL": ovr_level - 1} if ovr_level else {}
with rasterio.open(self.src_path, **options) as src_dst:
feats = []
for _, window in list(src_dst.block_windows(1)):
geom = bbox_to_feature(src_dst.window_bounds(window))
geom = transform_geom(
src_dst.crs,
WGS84_CRS,
geom.geometry.dict(),
)
feats.append(
{"type": "Feature", "geometry": geom, "properties": {}}
)
grids = {"type": "FeatureCollection", "features": feats}
return grids
def rasterize_geojson(geojson,
template_path,
out_path,
crs=None,
save_geojson=False):
"""
Creates transition spatial multipliers from a GeoJSON dictionary or list of dictionaries.
:param geojson: GeoJSON-formatted dictionary.
:param template_path: Path to the template raster to constrain the shapes to.
:param out_path: Path to the outputted raster with burned shapes into it.
:param crs: CRS of the input geometry. Default is EPSG:4326.
:param save_geojson: If True, save a copy of the rasterized GeoJSON next to the file.
"""
if crs is None:
crs = {'init': 'EPSG:4326'}
# Handle single geometries as well as lists
if type(geojson) is dict:
geojson = [geojson]
if save_geojson:
ext = '.{}'.format(out_path.split('.')[-1])
json_path = out_path.replace(ext, '.json')
with open(json_path, 'w') as f:
json.dump(geojson, f)
with rasterio.open(template_path, 'r') as template:
if not os.path.exists(os.path.dirname(out_path)):
os.makedirs(os.path.dirname(out_path))
with rasterio.open(out_path, 'w', **template.meta.copy()) as dest:
image = features.rasterize(
(
(transform_geom(crs, template.crs, g), 255.0
) # Todo, should value be 255 or 100?
for g in [f['geometry'] for f in geojson]),
out_shape=template.shape,
transform=template.transform,
dtype='float64')
image[numpy.where(
image == 0
)] = 1.0 # Where a polygon wasn't drawn, set multiplier to 1.0
dest.write(image, 1)
def get_zones(self):
for file in self.config.files:
src_filename = os.path.join(self.dir, file)
with fiona.open(src_filename) as shp:
reproject = True
if shp.crs and "init" in shp.crs and shp.crs["init"].lower(
) == "epsg:4326":
reproject = False
else:
warnings.warn(
"{} is not in WGS84 coordinates, it will be reprojected on the fly, which may be slow!"
.format(src_filename),
UserWarning,
)
for feature in Bar(f"Processing {self.name}",
max=len(shp)).iter(shp):
geometry = feature["geometry"]
if reproject:
geometry = transform_geom(shp.crs,
{"init": "EPSG:4326"},
geometry)
if feature["geometry"]["type"] == "MultiPolygon":
polygon = MultiPolygon(*[
Polygon(*[LinearRing(x) for x in g])
for g in geometry["coordinates"]
])
else:
polygon = Polygon(
*[LinearRing(x) for x in geometry["coordinates"]])
info = self.config.get_zone_info(feature, file)
if info is not None:
yield polygon, info
else:
raise ValueError(
"Zone info is not valid for input feature",
feature["properties"])
def geojson_parse_geometry(zoom, srid, feature_map, geometry, buffer):
if buffer:
geometry = transform_geom(CRS.from_epsg(srid), CRS.from_epsg(3857),
geometry) # be sure to be planar
geometry = mapping(shape(geometry).buffer(buffer))
srid = 3857
if geometry["type"] == "Polygon":
feature_map = geojson_parse_polygon(zoom, srid, feature_map,
geometry)
elif geometry["type"] == "MultiPolygon":
for polygon in geometry["coordinates"]:
feature_map = geojson_parse_polygon(zoom, srid, feature_map, {
"type": "Polygon",
"coordinates": polygon
})
return feature_map
def handle(self, name, file, *args, **options):
name = name[0]
file = file[0]
if Region.objects.filter(name__iexact=name).exists():
message = (
'WARNING: This will replace an existing region with the same name: {}. Do you want to continue? [y/n]'
).format(name)
if input(message).lower() not in {'y', 'yes'}:
return
temp_dir = None
try:
if file.endswith('.zip'):
temp_dir = mkdtemp()
with ZipFile(file) as zf:
zf.extractall(temp_dir)
try:
file = glob.glob(os.path.join(temp_dir, '*.shp'))[0]
except IndexError:
raise ValueError('No shapefile in zip archive')
polygons = []
with fiona.open(file, 'r') as shp:
for feature in shp:
geometry = transform_geom(shp.crs, {'init': 'EPSG:4326'}, feature['geometry'])
polygons.append(Polygon(*[LinearRing(x) for x in geometry['coordinates']]))
with transaction.atomic():
Region.objects.filter(name__iexact=name).delete()
Region.objects.create(name=name, polygons=MultiPolygon(polygons))
finally:
if temp_dir is not None:
try:
shutil.rmtree(temp_dir)
except OSError:
pass
def create_cutline(src_dst: DataSet,
geometry: Dict,
geometry_crs: CRS = None) -> str:
"""
Create WKT Polygon Cutline for GDALWarpOptions.
Ref: https://gdal.org/api/gdalwarp_cpp.html?highlight=vrt#_CPPv415GDALWarpOptions
Attributes
----------
src_dst: rasterio.io.DatasetReader
rasterio.io.DatasetReader object
geometry: dict
GeoJSON feature or GeoJSON geometry
geometry_crs: CRS or str, optional
Specify bounds coordinate reference system, default is same as input dataset.
Returns
-------
wkt: str
Cutline WKT geometry in form of `POLYGON ((x y, x y, ...)))
"""
if "geometry" in geometry:
geometry = geometry["geometry"]
geom_type = geometry["type"]
if not geom_type == "Polygon":
raise RioTilerError(
"Invalid geometry type: {geom_type}. Should be Polygon")
if geometry_crs:
geometry = transform_geom(geometry_crs, src_dst.crs, geometry)
xs, ys = zip(*coords(geometry))
src_y, src_x = rowcol(src_dst.transform, xs, ys)
src_x = [max(0, min(src_dst.width, x)) for x in src_x]
src_y = [max(0, min(src_dst.height, y)) for y in src_y]
poly = ", ".join([f"{x} {y}" for x, y in list(zip(src_x, src_y))])
return f"POLYGON (({poly}))"
def test_issue_1446_b():
"""Confirm that lines aren't thrown as reported in #1446"""
src_crs = CRS.from_epsg(4326)
dst_crs = CRS(
{
"proj": "sinu",
"lon_0": 350.85607029556,
"x_0": 0,
"y_0": 0,
"a": 3396190,
"b": 3396190,
"units": "m",
"no_defs": True,
}
)
collection = json.load(open("tests/data/issue1446.geojson"))
geoms = {f["properties"]["fid"]: f["geometry"] for f in collection["features"]}
transformed_geoms = {
k: transform_geom(src_crs, dst_crs, g) for k, g in geoms.items()
}
# Before the fix, this geometry was thrown eastward of 0.0. It should be between -350 and -250.
assert all([-350 < x < -150 for x, y in transformed_geoms[183519]["coordinates"]])
def transform(shape,
source_crs,
destination_crs=None,
src_affine=None,
dst_affine=None):
"""Transforms shape from one CRS to another.
Parameters
----------
shape : shapely.geometry.base.BaseGeometry
Shape to transform.
source_crs : dict or str
Source CRS in the form of key/value pairs or proj4 string.
destination_crs : dict or str, optional
Destination CRS, EPSG:4326 if not given.
src_affine: Affine, optional.
input shape in relative to this affine
dst_affine: Affine, optional.
output shape in relative to this affine
Returns
-------
shapely.geometry.base.BaseGeometry
Transformed shape.
"""
if destination_crs is None:
destination_crs = WGS84_CRS
if src_affine is not None:
shape = ops.transform(lambda r, q: ~src_affine * (r, q), shape)
if source_crs != destination_crs:
shape = make_shape(transform_geom(source_crs, destination_crs, shape))
if dst_affine is not None:
shape = ops.transform(lambda r, q: dst_affine * (r, q), shape)
return shape
def test_issue_1446_b():
"""Confirm that lines aren't thrown as reported in #1446"""
src_crs = CRS({"init": "epsg:4326"})
dst_crs = CRS(
{
"proj": "sinu",
"lon_0": 350.85607029556,
"x_0": 0,
"y_0": 0,
"a": 3396190,
"b": 3396190,
"units": "m",
"no_defs": True,
}
)
collection = json.load(open("tests/data/issue1446.geojson"))
geoms = {f["properties"]["fid"]: f["geometry"] for f in collection["features"]}
transformed_geoms = {
k: transform_geom(src_crs, dst_crs, g) for k, g in geoms.items()
}
# Before the fix, this geometry was thrown eastward of 0.0. It should be between -350 and -250.
assert all([-350 < x < -150 for x, y in transformed_geoms[183519]["coordinates"]])
def geojson(self, crs=_GEOJSON_EPSG_4326_STRING):
""" Return this Tile's geometry as GeoJSON
Parameters
----------
crs : rasterio.crs.CRS
Coordinate reference system of output. Defaults to EPSG:4326 per
GeoJSON standard (RFC7946). If ``None``, will return
geometries in Tile's CRS
Returns
-------
dict
This tile's geometry and crs represented as GeoJSON
References
----------
.. [1] https://tools.ietf.org/html/rfc7946#page-12
"""
gj = shapely.geometry.mapping(self.bbox)
if crs is not None:
from rasterio.warp import transform_geom
crs_ = convert.to_crs(crs)
if crs_ != self.crs:
gj = transform_geom(self.crs, crs_, gj)
else:
logger.debug('Not reprojecting GeoJSON since output CRS '
'is the same as the Tile CRS')
return {
'type': 'Feature',
'properties': {
'horizontal': self.horizontal,
'vertical': self.vertical
},
'geometry': gj
}
def reproject_features(features, src_crs, dst_crs):
"""Reproject a list of GeoJSON-like features.
Parameters
----------
features : iterable of dict
An iterable of GeoJSON-like features.
src_crs : CRS
Source CRS.
dst_crs : CRS
Target CRS.
Returns
-------
out_features : iterable of dict
Iterable of reprojected GeoJSON-like features.
"""
out_features = []
for feature in features:
out_feature = feature.copy()
out_geom = transform_geom(src_crs, dst_crs, feature['geometry'])
out_feature['geometry'] = out_geom
out_features.append(out_feature)
return out_features
def test_transform_geom_polygon_precision(polygon_3373):
geom = polygon_3373
result = transform_geom(
"EPSG:3373", "EPSG:4326", geom, precision=1, antimeridian_cutting=True
)
assert all(round(x, 1) == x for x in flatten_coords(result["coordinates"]))
def test_transform_geom_linestring_precision_iso(polygon_3373):
ring = polygon_3373["coordinates"][0]
geom = {"type": "LineString", "coordinates": ring}
result = transform_geom("EPSG:3373", "EPSG:3373", geom, precision=1)
assert int(result["coordinates"][0][0] * 10) == 7988423
from tilezilla import geoutils, products, stores, tilespec
# Get tilespec, find product, retrieve intersecting tiles
weld_conus = tilespec.TILESPECS['WELD_CONUS']
product = products.ESPALandsat('../tests/data/LT50120312002300-SC20151009172149/')
tile = list(weld_conus.bounds_to_tiles(product.bounding_box(weld_conus.crs)))[0]
crs.to_string(weld_conus.crs)
weld_conus.crs
geoutils.bounds_to_polygon(tile.bounds)
# TODO: geom_geojson looks a little off
warp.transform_geom('EPSG:4326', 'EPSG:5070', json.dumps(tile.geom_geojson))
# Init the data store
# TODO: switch on some storage format metadata configuration values
store = stores.GeoTIFFStore('tests/data/stores/GeoTIFF', tile, product)
# TODO: allow override driver metadata options (section "creation_options:")
store.meta_options
# TODO: user input / configuration to define desired variables
to_store = _util.multiple_filter(
[b.long_name for b in product.bands],
('.*surface reflectance.*', '.*brightness temperature.*', '^cfmask_band$',),
True)
to_store
def test_transform_geom_multipolygon(polygon_3373):
geom = {
'type': 'MultiPolygon', 'coordinates': [polygon_3373['coordinates']]}
result = transform_geom('EPSG:3373', 'EPSG:4326', geom, precision=1)
assert int(result['coordinates'][0][0][0][0] * 10) == -1778
def test_transform_geom_polygon(polygon_3373):
geom = polygon_3373
result = transform_geom('EPSG:3373', 'EPSG:4326', geom)
assert result['type'] == 'Polygon'
assert len(result['coordinates']) == 1
def dataset_features(
src,
bidx=None,
sampling=1,
band=True,
as_mask=False,
with_nodata=False,
geographic=True,
precision=-1):
"""Yield GeoJSON features for the dataset
The geometries are polygons bounding contiguous regions of the same raster value.
Parameters
----------
src: Rasterio Dataset
bidx: int
band index
sampling: int (DEFAULT: 1)
Inverse of the sampling fraction; a value of 10 decimates
band: boolean (DEFAULT: True)
extract features from a band (True) or a mask (False)
as_mask: boolean (DEFAULT: False)
Interpret band as a mask and output only one class of valid data shapes?
with_nodata: boolean (DEFAULT: False)
Include nodata regions?
geographic: str (DEFAULT: True)
Output shapes in EPSG:4326? Otherwise use the native CRS.
precision: int (DEFAULT: -1)
Decimal precision of coordinates. -1 for full float precision output
Yields
------
GeoJSON-like Feature dictionaries for shapes found in the given band
"""
if bidx is not None and bidx > src.count:
raise ValueError('bidx is out of range for raster')
img = None
msk = None
# Adjust transforms.
transform = src.transform
if sampling > 1:
# Determine the target shape (to decimate)
shape = (int(math.ceil(src.height / sampling)),
int(math.ceil(src.width / sampling)))
# Calculate independent sampling factors
x_sampling = src.width / shape[1]
y_sampling = src.height / shape[0]
# Decimation of the raster produces a georeferencing
# shift that we correct with a translation.
transform *= Affine.translation(
src.width % x_sampling, src.height % y_sampling)
# And follow by scaling.
transform *= Affine.scale(x_sampling, y_sampling)
# Most of the time, we'll use the valid data mask.
# We skip reading it if we're extracting every possible
# feature (even invalid data features) from a band.
if not band or (band and not as_mask and not with_nodata):
if sampling == 1:
msk = src.read_masks(bidx)
else:
msk_shape = shape
if bidx is None:
msk = np.zeros(
(src.count,) + msk_shape, 'uint8')
else:
msk = np.zeros(msk_shape, 'uint8')
msk = src.read_masks(bidx, msk)
if bidx is None:
msk = np.logical_or.reduce(msk).astype('uint8')
# Possibly overridden below.
img = msk
# Read the band data unless the --mask option is given.
if band:
if sampling == 1:
img = src.read(bidx, masked=False)
else:
img = np.zeros(
shape,
dtype=src.dtypes[src.indexes.index(bidx)])
img = src.read(bidx, img, masked=False)
# If as_mask option was given, convert the image
# to a binary image. This reduces the number of shape
# categories to 2 and likely reduces the number of
# shapes.
if as_mask:
tmp = np.ones_like(img, 'uint8') * 255
tmp[img == 0] = 0
img = tmp
if not with_nodata:
msk = tmp
# Prepare keyword arguments for shapes().
kwargs = {'transform': transform}
if not with_nodata:
kwargs['mask'] = msk
src_basename = os.path.basename(src.name)
# Yield GeoJSON features.
for i, (g, val) in enumerate(
rasterio.features.shapes(img, **kwargs)):
if geographic:
g = warp.transform_geom(
src.crs, 'EPSG:4326', g,
antimeridian_cutting=True, precision=precision)
xs, ys = zip(*coords(g))
yield {
'type': 'Feature',
'id': "{0}:{1}".format(src_basename, i),
'properties': {
'val': val,
'filename': src_basename
},
'bbox': [min(xs), min(ys), max(xs), max(ys)],
'geometry': g
}
def test_transform_geom_linestring_precision_iso(polygon_3373):
ring = polygon_3373['coordinates'][0]
geom = {'type': 'LineString', 'coordinates': ring}
result = transform_geom('EPSG:3373', 'EPSG:3373', geom, precision=1)
assert int(result['coordinates'][0][0] * 10) == 7988423
def test_transform_geom_multipolygon(polygon_3373):
geom = {"type": "MultiPolygon", "coordinates": [polygon_3373["coordinates"]]}
result = transform_geom("EPSG:3373", "EPSG:4326", geom, precision=1)
assert all(round(x, 1) == x for x in flatten_coords(result["coordinates"]))
def _compute_image_stats_chunked(
dataset: 'DatasetReader') -> Optional[Dict[str, Any]]:
"""Compute statistics for the given rasterio dataset by looping over chunks."""
from rasterio import features, warp, windows
from shapely import geometry
total_count = valid_data_count = 0
tdigest = TDigest()
sstats = SummaryStats()
convex_hull = geometry.Polygon()
block_windows = [w for _, w in dataset.block_windows(1)]
for w in block_windows:
with warnings.catch_warnings():
warnings.filterwarnings('ignore',
message='invalid value encountered.*')
block_data = dataset.read(1, window=w, masked=True)
total_count += int(block_data.size)
valid_data = block_data.compressed()
if valid_data.size == 0:
continue
valid_data_count += int(valid_data.size)
if np.any(block_data.mask):
hull_candidates = RasterDriver._hull_candidate_mask(
~block_data.mask)
hull_shapes = [
geometry.shape(s) for s, _ in features.shapes(
np.ones(hull_candidates.shape, 'uint8'),
mask=hull_candidates,
transform=windows.transform(w, dataset.transform))
]
else:
w, s, e, n = windows.bounds(w, dataset.transform)
hull_shapes = [
geometry.Polygon([(w, s), (e, s), (e, n), (w, n)])
]
convex_hull = geometry.MultiPolygon([convex_hull,
*hull_shapes]).convex_hull
tdigest.update(valid_data)
sstats.update(valid_data)
if sstats.count() == 0:
return None
convex_hull_wgs = warp.transform_geom(dataset.crs, 'epsg:4326',
geometry.mapping(convex_hull))
return {
'valid_percentage': valid_data_count / total_count * 100,
'range': (sstats.min(), sstats.max()),
'mean': sstats.mean(),
'stdev': sstats.std(),
'percentiles': tdigest.quantile(np.arange(0.01, 1, 0.01)),
'convex_hull': convex_hull_wgs
}
def mask(
input,
output,
variable,
like,
netcdf3,
all_touched,
invert,
zip):
"""
Create a NetCDF mask from a shapefile.
Values are equivalent to a numpy mask: 0 for unmasked areas, and 1 for masked areas.
Template NetCDF dataset must have a valid projection defined or be inferred from dimensions (e.g., lat / long)
"""
with Dataset(like) as template_ds:
template_varname = data_variables(template_ds).keys()[0]
template_variable = template_ds.variables[template_varname]
template_crs = get_crs(template_ds, template_varname)
if template_crs:
template_crs = CRS.from_string(template_crs)
elif is_geographic(template_ds, template_varname):
template_crs = CRS({'init': 'EPSG:4326'})
else:
raise click.UsageError('template dataset must have a valid projection defined')
spatial_dimensions = template_variable.dimensions[-2:]
mask_shape = template_variable.shape[-2:]
template_y_name, template_x_name = spatial_dimensions
coords = SpatialCoordinateVariables.from_dataset(
template_ds,
x_name=template_x_name,
y_name=template_y_name,
projection=Proj(**template_crs.to_dict())
)
with fiona.open(input, 'r') as shp:
transform_required = CRS(shp.crs) != template_crs
# Project bbox for filtering
bbox = coords.bbox
if transform_required:
bbox = bbox.project(Proj(**shp.crs), edge_points=21)
geometries = []
for f in shp.filter(bbox=bbox.as_list()):
geom = f['geometry']
if transform_required:
geom = transform_geom(shp.crs, template_crs, geom)
geometries.append(geom)
click.echo('Converting {0} features to mask'.format(len(geometries)))
if invert:
fill_value = 0
default_value = 1
else:
fill_value = 1
default_value = 0
with rasterio.Env():
# Rasterize features to 0, leaving background as 1
mask = rasterize(
geometries,
out_shape=mask_shape,
transform=coords.affine,
all_touched=all_touched,
fill=fill_value,
default_value=default_value,
dtype=numpy.uint8
)
format = 'NETCDF3_CLASSIC' if netcdf3 else 'NETCDF4'
dtype = 'int8' if netcdf3 else 'uint8'
with Dataset(output, 'w', format=format) as out:
coords.add_to_dataset(out, template_x_name, template_y_name)
out_var = out.createVariable(variable, dtype, dimensions=spatial_dimensions, zlib=zip,
fill_value=get_fill_value(dtype))
out_var[:] = mask
def test_transform_geom_multipolygon(polygon_3373):
geom = {
'type': 'MultiPolygon', 'coordinates': [polygon_3373['coordinates']]}
result = transform_geom('EPSG:3373', 'EPSG:4326', geom, precision=1)
assert int(result['coordinates'][0][0][0][0] * 10) == -1778
def test_transform_geom_polygon_precision(polygon_3373):
geom = polygon_3373
result = transform_geom('EPSG:3373', 'EPSG:4326', geom, precision=1)
assert int(result['coordinates'][0][0][0] * 10) == -1778
def test_transform_geom_polygon_cutting(polygon_3373):
geom = polygon_3373
result = transform_geom(
'EPSG:3373', 'EPSG:4326', geom, antimeridian_cutting=True)
assert result['type'] == 'MultiPolygon'
assert len(result['coordinates']) == 2
def test_transform_geom_polygon_precision(polygon_3373):
geom = polygon_3373
result = transform_geom('EPSG:3373', 'EPSG:4326', geom, precision=1)
assert int(result['coordinates'][0][0][0] * 10) == -1778
def test_transform_geom_linestring_precision_iso(polygon_3373):
ring = polygon_3373["coordinates"][0]
geom = {"type": "LineString", "coordinates": ring}
result = transform_geom("EPSG:3373", "EPSG:3373", geom, precision=1)
assert int(result["coordinates"][0][0] * 10) == 7988423
def test_transform_geom_polygon(polygon_3373):
geom = polygon_3373
result = transform_geom('EPSG:3373', 'EPSG:4326', geom)
assert result['type'] == 'Polygon'
assert len(result['coordinates']) == 1
def test_transform_geom_array(polygon_3373):
geom = [polygon_3373 for _ in range(10)]
result = transform_geom("EPSG:3373", "EPSG:4326", geom, precision=1)
assert isinstance(result, list)
assert len(result) == 10
def dataset_features(src,
bidx=None,
sampling=1,
band=True,
as_mask=False,
with_nodata=False,
geographic=True,
precision=-1):
"""Yield GeoJSON features for the dataset
The geometries are polygons bounding contiguous regions of the same raster value.
Parameters
----------
src: Rasterio Dataset
bidx: int
band index
sampling: int (DEFAULT: 1)
Inverse of the sampling fraction; a value of 10 decimates
band: boolean (DEFAULT: True)
extract features from a band (True) or a mask (False)
as_mask: boolean (DEFAULT: False)
Interpret band as a mask and output only one class of valid data shapes?
with_nodata: boolean (DEFAULT: False)
Include nodata regions?
geographic: str (DEFAULT: True)
Output shapes in EPSG:4326? Otherwise use the native CRS.
precision: int (DEFAULT: -1)
Decimal precision of coordinates. -1 for full float precision output
Yields
------
GeoJSON-like Feature dictionaries for shapes found in the given band
"""
if bidx is not None and bidx > src.count:
raise ValueError('bidx is out of range for raster')
img = None
msk = None
# Adjust transforms.
transform = src.transform
if sampling > 1:
# Determine the target shape (to decimate)
shape = (int(math.ceil(src.height / sampling)),
int(math.ceil(src.width / sampling)))
# Calculate independent sampling factors
x_sampling = src.width / shape[1]
y_sampling = src.height / shape[0]
# Decimation of the raster produces a georeferencing
# shift that we correct with a translation.
transform *= Affine.translation(src.width % x_sampling,
src.height % y_sampling)
# And follow by scaling.
transform *= Affine.scale(x_sampling, y_sampling)
# Most of the time, we'll use the valid data mask.
# We skip reading it if we're extracting every possible
# feature (even invalid data features) from a band.
if not band or (band and not as_mask and not with_nodata):
if sampling == 1:
msk = src.read_masks(bidx)
else:
msk_shape = shape
if bidx is None:
msk = np.zeros((src.count, ) + msk_shape, 'uint8')
else:
msk = np.zeros(msk_shape, 'uint8')
msk = src.read_masks(bidx, msk)
if bidx is None:
msk = np.logical_or.reduce(msk).astype('uint8')
# Possibly overridden below.
img = msk
# Read the band data unless the --mask option is given.
if band:
if sampling == 1:
img = src.read(bidx, masked=False)
else:
img = np.zeros(shape, dtype=src.dtypes[src.indexes.index(bidx)])
img = src.read(bidx, img, masked=False)
# If as_mask option was given, convert the image
# to a binary image. This reduces the number of shape
# categories to 2 and likely reduces the number of
# shapes.
if as_mask:
tmp = np.ones_like(img, 'uint8') * 255
tmp[img == 0] = 0
img = tmp
if not with_nodata:
msk = tmp
# Prepare keyword arguments for shapes().
kwargs = {'transform': transform}
if not with_nodata:
kwargs['mask'] = msk
src_basename = os.path.basename(src.name)
# Yield GeoJSON features.
for i, (g, val) in enumerate(rasterio.features.shapes(img, **kwargs)):
if geographic:
g = warp.transform_geom(src.crs,
'EPSG:4326',
g,
antimeridian_cutting=True,
precision=precision)
xs, ys = zip(*coords(g))
yield {
'type': 'Feature',
'id': "{0}:{1}".format(src_basename, i),
'properties': {
'val': val,
'filename': src_basename
},
'bbox': [min(xs), min(ys), max(xs),
max(ys)],
'geometry': g
}
def zones(
input,
output,
variable,
attribute,
like,
netcdf3,
zip):
"""
Create zones in a NetCDF from features in a shapefile. This is intended
to be used as input to zonal statistics functions; it is not intended
as a direct replacement for rasterizing geometries into NetCDF.
Only handles < 65,535 features for now.
If --attribute is provided, any features that do not have this will not be
assigned to zones.
A values lookup will be used to store values. The zones are indices of
the unique values encountered when extracting features.
The original values are stored in an additional variable with the name of
the zones variable plus '_values'.
Template NetCDF dataset must have a valid projection defined or be inferred
from dimensions (e.g., lat / long).
"""
with Dataset(like) as template_ds:
template_varname = list(data_variables(template_ds).keys())[0]
template_variable = template_ds.variables[template_varname]
template_crs = get_crs(template_ds, template_varname)
if template_crs:
template_crs = CRS.from_string(template_crs)
elif is_geographic(template_ds, template_varname):
template_crs = CRS({'init': 'EPSG:4326'})
else:
raise click.UsageError('template dataset must have a valid projection defined')
spatial_dimensions = template_variable.dimensions[-2:]
out_shape = template_variable.shape[-2:]
template_y_name, template_x_name = spatial_dimensions
coords = SpatialCoordinateVariables.from_dataset(
template_ds,
x_name=template_x_name,
y_name=template_y_name,
projection=Proj(**template_crs.to_dict())
)
with fiona.open(input, 'r') as shp:
if attribute:
if not attribute in shp.meta['schema']['properties']:
raise click.BadParameter('{0} not found in dataset'.format(attribute),
param='--attribute', param_hint='--attribute')
att_dtype = shp.meta['schema']['properties'][attribute].split(':')[0]
if not att_dtype in ('int', 'str'):
raise click.BadParameter('integer or string attribute required'.format(attribute),
param='--attribute', param_hint='--attribute')
transform_required = CRS(shp.crs) != template_crs
geometries = []
values = set()
values_lookup = {}
# Project bbox for filtering
bbox = coords.bbox
if transform_required:
bbox = bbox.project(Proj(**shp.crs), edge_points=21)
index = 0
for f in shp.filter(bbox=bbox.as_list()):
value = f['properties'].get(attribute) if attribute else int(f['id'])
if value is not None:
geom = f['geometry']
if transform_required:
geom = transform_geom(shp.crs, template_crs, geom)
geometries.append((geom, index))
if not value in values:
values.add(value)
values_lookup[index] = value
index += 1
# Otherwise, these will not be rasterized
num_geometries = len(geometries)
# Save a slot at the end for nodata
if num_geometries < 255:
dtype = numpy.dtype('uint8')
elif num_geometries < 65535:
dtype = numpy.dtype('uint16')
else:
raise click.UsageError('Too many features to rasterize: {0}, Exceptioning...'.format(num_geometries))
fill_value = get_fill_value(dtype)
click.echo('Rasterizing {0} features into zones'.format(num_geometries))
with rasterio.Env():
zones = rasterize(
geometries,
out_shape=out_shape,
transform=coords.affine,
all_touched=False, # True produces undesirable results for adjacent polygons
fill=fill_value,
dtype=dtype
)
format = 'NETCDF4'
out_dtype = dtype
if netcdf3:
format = 'NETCDF3_CLASSIC'
if dtype == numpy.uint8:
out_dtype = numpy.dtype('int16')
elif dtype == numpy.uint16:
out_dtype = numpy.dtype('int32')
# Have to convert fill_value to mask since we changed data type
zones = numpy.ma.masked_array(zones, mask=(zones == fill_value))
with Dataset(output, 'w', format=format) as out:
values_varname = '{0}_values'.format(variable)
coords.add_to_dataset(out, template_x_name, template_y_name)
out_var = out.createVariable(variable, out_dtype,
dimensions=spatial_dimensions,
zlib=zip,
fill_value=get_fill_value(out_dtype))
out_var.setncattr('values', values_varname)
out_var[:] = zones
out_values = numpy.array([values_lookup[k] for k in range(0, len(values_lookup))])
if netcdf3 and out_values.dtype == numpy.int64:
out_values = out_values.astype('int32')
out.createDimension(values_varname, len(out_values))
values_var = out.createVariable(values_varname, out_values.dtype,
dimensions=(values_varname, ),
zlib=zip)
values_var[:] = out_values
easting_list = []
northing_list = []
for i in highest_east:
easting_list.append(i)
for i in highest_north:
northing_list.append(i)
buffer_coordinates = generate_coordinates(
easting_list,
northing_list)
# Warp the coordinates
warped_elevation_coordinates = warp.transform_geom(
{'init': 'EPSG:27700'},
elevation.crs,
{"type": "Polygon",
"coordinates": [buffer_coordinates]})
# create an 3d array containing the elevation data masked to the buffer zone
elevation_mask, out_transform = mask.mask(elevation,
[warped_elevation_coordinates],
crop=False)
# Search for the highest point in the buffer zone
highest_point = np.amax(elevation_mask)
# Extract the index of the highest point in pixel coordinates
z, highest_east, highest_north = np.where(highest_point == elevation_mask)
# Isolate the first value from the list
def test_transform_geom_multipolygon(polygon_3373):
geom = {
'type': 'MultiPolygon', 'coordinates': [polygon_3373['coordinates']]}
result = transform_geom('EPSG:3373', 'EPSG:4326', geom, precision=1)
assert all(round(x, 1) == x for x in flatten_coords(result['coordinates']))
def test_transform_geom_polygon_precision(polygon_3373):
geom = polygon_3373
result = transform_geom(
"EPSG:3373", "EPSG:4326", geom, precision=1, antimeridian_cutting=True
)
assert all(round(x, 1) == x for x in flatten_coords(result["coordinates"]))
def test_transform_geom_dst_crs_none():
with pytest.raises(CRSError):
transform_geom(WGS84_crs, None, None)
def test_transform_geom_multipolygon(polygon_3373):
geom = {"type": "MultiPolygon", "coordinates": [polygon_3373["coordinates"]]}
result = transform_geom("EPSG:3373", "EPSG:4326", geom, precision=1)
assert all(round(x, 1) == x for x in flatten_coords(result["coordinates"]))
def test_transform_geom_linestring_precision_iso(polygon_3373):
ring = polygon_3373['coordinates'][0]
geom = {'type': 'LineString', 'coordinates': ring}
result = transform_geom('EPSG:3373', 'EPSG:3373', geom, precision=1)
assert int(result['coordinates'][0][0] * 10) == 7988423
def test_transform_geom_dst_crs_none():
with pytest.raises(CRSError):
transform_geom(WGS84_crs, None, None)
def test_transform_geom_linestring_precision(polygon_3373):
ring = polygon_3373['coordinates'][0]
geom = {'type': 'LineString', 'coordinates': ring}
result = transform_geom('EPSG:3373', 'EPSG:4326', geom, precision=1, antimeridian_cutting=True)
assert all(round(x, 1) == x for x in flatten_coords(result['coordinates']))
def test_transform_geom_polygon_cutting(polygon_3373):
geom = polygon_3373
result = transform_geom("EPSG:3373", "EPSG:4326", geom, antimeridian_cutting=True)
assert result["type"] == "MultiPolygon"
assert len(result["coordinates"]) == 2
def transform(feat):
dst_crs = 'epsg:4326' if projection == 'geographic' else crs
geom = transform_geom(crs, dst_crs, feat['geometry'],
precision=precision)
feat['geometry'] = geom
return feat
