def region(self, request):
"""Return region shape file."""
extensions = [".gml", ".shp", ".gpkg", ".geojson", ".json"]
region_vector = request.inputs.pop("region_vector")[0].file
return single_file_check(
archive_sniffer(region_vector,
working_dir=self.workdir,
extensions=extensions))
def _handler(self, request, response):
shape_url = request.inputs["shape"][0].file
projected_crs = request.inputs["projected_crs"][0].data
extensions = [".gml", ".shp", ".gpkg", ".geojson", ".json"]
vector_file = single_file_check(
archive_sniffer(shape_url,
working_dir=self.workdir,
extensions=extensions))
shape_crs = crs_sniffer(vector_file)
try:
projection = CRS.from_epsg(projected_crs)
if projection.is_geographic:
msg = (
f"Desired CRS {projection.to_epsg()} is geographic. "
"Areal analysis values will be in decimal-degree units.")
LOGGER.warning(msg)
except Exception as e:
msg = f"{e}: Failed to parse CRS definition. Exiting."
LOGGER.error(msg)
raise Exception(msg)
# TODO: It would be good to one day refactor this to make use of RavenPy utils and gis utilities
properties = list()
try:
for i, layer_name in enumerate(fiona.listlayers(vector_file)):
with fiona.open(vector_file, "r", crs=shape_crs,
layer=i) as src:
for feature in src:
geom = shape(feature["geometry"])
multipolygon_check(geom)
transformed = geom_transform(geom,
source_crs=shape_crs,
target_crs=projection)
prop = {"id": feature["id"]}
prop.update(feature["properties"])
prop.update(geom_prop(transformed))
# Recompute the centroid location using the original projection
prop["centroid"] = geom_prop(geom)["centroid"]
properties.append(prop)
except Exception as e:
msg = f"{e}: Failed to extract features from shape {vector_file}."
LOGGER.error(msg)
raise Exception(msg)
response.outputs["properties"].data = json.dumps(properties)
return response
def _handler(self, request, response):
shape_url = request.inputs["shape"][0].file
simple_categories = request.inputs["simple_categories"][0].data
band = request.inputs["band"][0].data
touches = request.inputs["select_all_touching"][0].data
vectors = [".gml", ".shp", ".gpkg", ".geojson", ".json"]
vector_file = single_file_check(
archive_sniffer(shape_url,
working_dir=self.workdir,
extensions=vectors))
vec_crs = crs_sniffer(vector_file)
response.update_status("Accessed vector", status_percentage=5)
# For raster files using the UNFAO Land Cover Classification System (19 types)
if "raster" in request.inputs:
rasters = [".tiff", ".tif"]
raster_url = request.inputs["raster"][0].file
raster_file = single_file_check(
archive_sniffer(raster_url,
working_dir=self.workdir,
extensions=rasters))
ras_crs = crs_sniffer(raster_file)
if vec_crs != ras_crs:
msg = f"CRS for files {vector_file} and {raster_file} are not the same. Reprojecting..."
LOGGER.warning(msg)
# Reproject full vector to preserve feature attributes
projected = tempfile.NamedTemporaryFile(
prefix="reprojected_",
suffix=".json",
delete=False,
dir=self.workdir,
).name
generic_vector_reproject(vector_file,
projected,
source_crs=vec_crs,
target_crs=ras_crs)
else:
projected = vector_file
else:
raster_url = None
# using the NALCMS data from GeoServer
projected = tempfile.NamedTemporaryFile(prefix="reprojected_",
suffix=".json",
delete=False,
dir=self.workdir).name
generic_vector_reproject(vector_file,
projected,
source_crs=vec_crs,
target_crs=NALCMS_PROJ4)
raster_file = gather_dem_tile(
vector_file,
self.workdir,
geographic=False,
raster="public:CEC_NALCMS_LandUse_2010",
)
data_type = raster_datatype_sniffer(raster_file)
response.update_status("Accessed raster", status_percentage=10)
categories = SIMPLE_CATEGORIES if simple_categories else TRUE_CATEGORIES
summary_stats = SUMMARY_ZONAL_STATS
try:
# Use zonalstats to produce a GeoJSON
stats = zonal_stats(
projected,
raster_file,
stats=summary_stats,
band=band,
categorical=True,
all_touched=touches,
geojson_out=True,
raster_out=False,
)
land_use = list()
for stat in stats:
lu = defaultdict(lambda: 0)
prop = stat["properties"]
# Rename/aggregate land-use categories
for k, v in categories.items():
lu[v] += prop.get(k, 0)
prop.update(lu)
land_use.append(lu)
# prop['mini_raster_array'] = pickle.dumps(prop['mini_raster_array'], protocol=0).decode()
# Use zonalstats to produce sets of raster grids
raster_subset = zonal_stats(
projected,
raster_file,
stats=summary_stats,
band=band,
categorical=True,
all_touched=touches,
geojson_out=False,
raster_out=True,
)
raster_out = zonalstats_raster_file(
raster_subset,
working_dir=self.workdir,
data_type=data_type,
crs=NALCMS_PROJ4,
zip_archive=False,
)
ml = MetaLink4(
"rasters_out",
"Metalink to series of GeoTIFF raster files",
workdir=self.workdir,
)
for r in raster_out:
mf = MetaFile(Path(r).name,
"Raster subset",
fmt=FORMATS.GEOTIFF)
mf.file = r
ml.append(mf)
feature_collect = {"type": "FeatureCollection", "features": stats}
response.outputs["features"].data = json.dumps(feature_collect)
response.outputs["statistics"].data = json.dumps(land_use)
response.outputs["raster"].data = ml.xml
except Exception as e:
msg = f"Failed to perform raster subset using {shape_url}{f' and {raster_url} ' if raster_url else ''}: {e}"
LOGGER.error(msg)
raise Exception(msg) from e
return response
def _handler(self, request, response):
shape_url = request.inputs["shape"][0].file
simple_categories = request.inputs["simple_categories"][0].data
band = request.inputs["band"][0].data
touches = request.inputs["select_all_touching"][0].data
vectors = [".gml", ".shp", ".gpkg", ".geojson", ".json"]
vector_file = single_file_check(
archive_sniffer(shape_url,
working_dir=self.workdir,
extensions=vectors))
vec_crs = crs_sniffer(vector_file)
response.update_status("Accessed vector", status_percentage=5)
if (
"raster" in request.inputs
): # For raster files using the UNFAO Land Cover Classification System (19 types)
rasters = [".tiff", ".tif"]
raster_url = request.inputs["raster"][0].file
raster_file = single_file_check(
archive_sniffer(raster_url,
working_dir=self.workdir,
extensions=rasters))
ras_crs = crs_sniffer(raster_file)
if vec_crs != ras_crs:
msg = f"CRS for files {vector_file} and {raster_file} are not the same. Reprojecting..."
LOGGER.warning(msg)
# Reproject full vector to preserve feature attributes
projected = tempfile.NamedTemporaryFile(
prefix="reprojected_",
suffix=".json",
delete=False,
dir=self.workdir,
).name
generic_vector_reproject(vector_file,
projected,
source_crs=vec_crs,
target_crs=ras_crs)
else:
projected = vector_file
else: # using the NALCMS data from GeoServer
projected = tempfile.NamedTemporaryFile(prefix="reprojected_",
suffix=".json",
delete=False,
dir=self.workdir).name
generic_vector_reproject(vector_file,
projected,
source_crs=vec_crs,
target_crs=NALCMS_PROJ4)
bbox = get_bbox(projected)
raster_url = "public:CEC_NALCMS_LandUse_2010"
raster_bytes = geoserver.get_raster_wcs(bbox,
geographic=False,
layer=raster_url)
raster_file = tempfile.NamedTemporaryFile(prefix="wcs_",
suffix=".tiff",
delete=False,
dir=self.workdir).name
with open(raster_file, "wb") as f:
f.write(raster_bytes)
response.update_status("Accessed raster", status_percentage=10)
categories = SIMPLE_CATEGORIES if simple_categories else TRUE_CATEGORIES
summary_stats = SUMMARY_ZONAL_STATS
try:
stats = zonal_stats(
projected,
raster_file,
stats=summary_stats,
band=band,
categorical=True,
all_touched=touches,
geojson_out=True,
raster_out=False,
)
land_use = list()
for stat in stats:
lu = defaultdict(lambda: 0)
prop = stat["properties"]
# Rename/aggregate land-use categories
for k, v in categories.items():
lu[v] += prop.get(k, 0)
prop.update(lu)
land_use.append(lu)
# prop['mini_raster_array'] = pickle.dumps(prop['mini_raster_array'], protocol=0).decode()
feature_collect = {"type": "FeatureCollection", "features": stats}
response.outputs["features"].data = json.dumps(feature_collect)
response.outputs["statistics"].data = json.dumps(land_use)
except Exception as e:
msg = f"Failed to perform zonal statistics using {shape_url} and {raster_url}: {e}"
LOGGER.error(msg)
raise Exception(msg) from e
return response
def _handler(self, request, response):
level = 12 # request.inputs['level'][0].data
lakes = True # request.inputs['lakes'][0].data
collect_upstream = request.inputs["aggregate_upstream"][0].data
lon, lat = parse_lonlat(request.inputs["location"][0].data)
bbox = (lon, lat, lon, lat)
shape_url = tempfile.NamedTemporaryFile(
prefix="hybas_", suffix=".gml", delete=False, dir=self.workdir
).name
domain = geoserver.select_hybas_domain(bbox)
hybas_gml = geoserver.get_hydrobasins_location_wfs(
bbox, lakes=lakes, level=level, domain=domain
)
if isinstance(hybas_gml, str):
write_flags = "w"
else:
write_flags = "wb"
with open(shape_url, write_flags) as f:
f.write(hybas_gml)
response.update_status("Found downstream watershed", status_percentage=10)
extensions = [".gml", ".shp", ".gpkg", ".geojson", ".json"]
shp = single_file_check(
archive_sniffer(shape_url, working_dir=self.workdir, extensions=extensions)
)
shape_crs = crs_sniffer(shp)
# Find HYBAS_ID
src = fiona.open(shp, "r", crs=shape_crs)
feat = next(iter(src))
hybas_id = feat["properties"]["HYBAS_ID"]
gml_id = feat["properties"]["gml_id"]
if collect_upstream:
main_bas = feat["properties"]["MAIN_BAS"]
if lakes is False or level != 12:
raise InvalidParameterValue("Set lakes to True and level to 12.")
# Collect features from GeoServer
response.update_status("Collecting relevant features", status_percentage=70)
region_url = geoserver.get_hydrobasins_attributes_wfs(
attribute="MAIN_BAS",
value=main_bas,
lakes=lakes,
level=level,
domain=domain,
)
# Read table of relevant features sharing main basin
df = gpd.read_file(region_url)
# TODO: Load and keep this data in memory; Figure out how to better handle encoding and column names.
# Identify upstream sub-basins and write to a new file
up = geoserver.hydrobasins_upstream_ids(hybas_id, df)
upfile = tempfile.NamedTemporaryFile(
prefix="hybas_", suffix=".json", delete=False, dir=self.workdir
).name
up.to_file(upfile, driver="GeoJSON")
# Aggregate upstream features into a single geometry.
gdf = gpd.read_file(upfile)
agg = geoserver.hydrobasins_aggregate(gdf)
# The aggregation returns a FeatureCollection with one feature. We select the first feature so that the
# output is a Feature whether aggregate is True or False.
afeat = json.loads(agg.to_json())["features"][0]
response.outputs["feature"].data = json.dumps(afeat)
response.outputs["upstream_ids"].data = json.dumps(up["id"].tolist())
else:
response.outputs["feature"].data = json.dumps(feat)
response.outputs["upstream_ids"].data = json.dumps([gml_id])
src.close()
return response
def _handler(self, request, response):
# Process inputs
# ---------------
shape_url = request.inputs["shape"][0].file
destination_crs = request.inputs["projected_crs"][0].data
touches = request.inputs["select_all_touching"][0].data
# Checks for valid CRS and that CRS is projected
# -----------------------------------------------
projection = CRS.from_user_input(destination_crs)
if not projection.is_projected:
msg = f"Destination CRS {projection.to_epsg()} is not projected. Terrain analysis values will not be valid."
LOGGER.error(ValueError(msg))
raise ValueError(msg)
# Collect and process the shape
# -----------------------------
vectors = [".gml", ".shp", ".gpkg", ".geojson", ".json"]
vector_file = single_file_check(
archive_sniffer(shape_url,
working_dir=self.workdir,
extensions=vectors))
vec_crs = crs_sniffer(vector_file)
# Check that boundaries within 60N and 60S
boundary_check(vector_file)
if "raster" in request.inputs:
raster_url = request.inputs["raster"][0].file
rasters = [".tiff", ".tif"]
raster_file = single_file_check(
archive_sniffer(raster_url,
working_dir=self.workdir,
extensions=rasters))
else:
# Assuming that the shape coordinate are in WGS84
raster_file = gather_dem_tile(vector_file, self.workdir)
ras_crs = crs_sniffer(raster_file)
# Reproject raster
# ----------------
if ras_crs != projection.to_epsg():
msg = f"CRS for {raster_file} is not {projection}. Reprojecting raster..."
LOGGER.warning(msg)
warped_fn = tempfile.NamedTemporaryFile(prefix="warped_",
suffix=".tiff",
delete=False,
dir=self.workdir).name
generic_raster_warp(raster_file, warped_fn, projection)
else:
warped_fn = raster_file
# Perform the terrain analysis
# ----------------------------
rpj = tempfile.NamedTemporaryFile(prefix="reproj_",
suffix=".json",
delete=False,
dir=self.workdir).name
generic_vector_reproject(vector_file,
rpj,
source_crs=vec_crs,
target_crs=projection.to_epsg())
with open(rpj) as src:
geo = json.load(src)
features = [sgeo.shape(feat["geometry"]) for feat in geo["features"]]
union = ops.unary_union(features)
clipped_fn = tempfile.NamedTemporaryFile(prefix="clipped_",
suffix=".tiff",
delete=False,
dir=self.workdir).name
# Ensure that values for regions outside of clip are kept
generic_raster_clip(
raster=warped_fn,
output=clipped_fn,
geometry=union,
touches=touches,
fill_with_nodata=True,
padded=True,
)
# Compute DEM properties for each feature.
properties = []
for i in range(len(features)):
properties.append(
dem_prop(clipped_fn, geom=features[i], directory=self.workdir))
properties.append(dem_prop(clipped_fn, directory=self.workdir))
response.outputs["properties"].data = json.dumps(properties)
response.outputs["dem"].file = clipped_fn
return response
def _handler(self, request, response):
shape_url = request.inputs["shape"][0].file
band = request.inputs["band"][0].data
touches = request.inputs["select_all_touching"][0].data
vectors = [".gml", ".shp", ".gpkg", ".geojson", ".json"]
vector_file = single_file_check(
archive_sniffer(shape_url,
working_dir=self.workdir,
extensions=vectors))
if "raster" in request.inputs:
raster_url = request.inputs["raster"][0].file
rasters = [".tiff", ".tif"]
raster_file = single_file_check(
archive_sniffer(raster_url,
working_dir=self.workdir,
extensions=rasters))
else:
raster_url = None
# Assuming that the shape coordinate are in WGS84
raster_file = gather_dem_tile(vector_file,
self.workdir,
geographic=True)
vec_crs, ras_crs = crs_sniffer(vector_file), crs_sniffer(raster_file)
if ras_crs != vec_crs:
msg = f"CRS for files {vector_file} and {raster_file} are not the same. Reprojecting raster..."
LOGGER.warning(msg)
projected = tempfile.NamedTemporaryFile(prefix="reprojected_",
suffix=".json",
delete=False,
dir=self.workdir).name
generic_raster_warp(raster_file, projected, target_crs=vec_crs)
raster_file = projected
data_type = raster_datatype_sniffer(raster_file)
try:
stats = zonal_stats(
vector_file,
raster_file,
band=band,
all_touched=touches,
raster_out=True,
)
raster_files = zonalstats_raster_file(
stats,
working_dir=self.workdir,
data_type=data_type,
crs=vec_crs or ras_crs,
)
if len(raster_files) > 1:
ml = MetaLink4(
"test-ml-1",
"MetaLink with links to raster files.",
workdir=self.workdir,
)
for i, file in enumerate(raster_files):
# Create a MetaFile instance, which instantiates a ComplexOutput object.
mf = MetaFile(file.name,
description="Raster file",
fmt=FORMATS.GEOTIFF)
mf.file = (
file.as_posix()
) # or mf.file = <path to file> or mf.url = <url>
ml.append(mf)
response.outputs["raster"].data = ml.xml
else:
response.outputs["raster"].file = raster_files[0]
except Exception as e:
msg = f"Failed to perform raster subset using {shape_url}{f' and {raster_url} ' if raster_url else ''}: {e}"
LOGGER.error(msg)
raise Exception(msg)
return response
def _handler(self, request, response):
shape_url = request.inputs["shape"][0].file
band = request.inputs["band"][0].data
categorical = request.inputs["categorical"][0].data
touches = request.inputs["select_all_touching"][0].data
vectors = [".gml", ".shp", ".gpkg", ".geojson", ".json"]
vector_file = single_file_check(
archive_sniffer(shape_url,
working_dir=self.workdir,
extensions=vectors))
rasters = [".tiff", ".tif"]
if "raster" in request.inputs:
raster_url = request.inputs["raster"][0].file
raster_file = single_file_check(
archive_sniffer(raster_url,
working_dir=self.workdir,
extensions=rasters))
else:
raster_url = None
# Assuming that the shape coordinate are in WGS84
raster_file = gather_dem_tile(vector_file,
self.workdir,
geographic=True)
vec_crs, ras_crs = crs_sniffer(vector_file), crs_sniffer(raster_file)
if ras_crs != vec_crs:
msg = f"CRS for files {vector_file} and {raster_file} are not the same. Reprojecting vector..."
LOGGER.warning(msg)
# Reproject full vector to preserve feature attributes
projected = tempfile.NamedTemporaryFile(prefix="reprojected_",
suffix=".json",
delete=False,
dir=self.workdir).name
generic_vector_reproject(vector_file,
projected,
source_crs=vec_crs,
target_crs=ras_crs)
vector_file = projected
summary_stats = SUMMARY_ZONAL_STATS
try:
stats = zonal_stats(
vector_file,
raster_file,
stats=summary_stats,
band=band,
categorical=categorical,
all_touched=touches,
geojson_out=True,
raster_out=False,
)
feature_collect = {"type": "FeatureCollection", "features": stats}
response.outputs["statistics"].data = json.dumps(feature_collect)
except Exception as e:
msg = f"Failed to perform raster subset using {shape_url}{f' and {raster_url} ' if raster_url else ''}: {e}"
LOGGER.error(msg)
raise Exception(msg) from e
return response