def get_closest_node(lon, lat, session):
c_node = session.query(Ways).order_by(
Ways.the_geom.ST_Distance(
func.ST_SetSRID(func.ST_Point(lon, lat), 4326))).limit(1).all()
dist_source = session.query(
func.ST_Distance(
func.ST_SetSRID(func.ST_Point(c_node[0].x1, c_node[0].y1), 4326),
func.ST_SetSRID(func.ST_Point(lon, lat), 4326))).all()
dist_target = session.query(
func.ST_Distance(
func.ST_SetSRID(func.ST_Point(c_node[0].x2, c_node[0].y2), 4326),
func.ST_SetSRID(func.ST_Point(lon, lat), 4326))).all()
# distances return only one value
if dist_source[0][0] < dist_target[0][0]:
return c_node[0].source
else:
return c_node[0].target
def list_cube_items(self,
cube_id: str,
bbox: str = None,
start: str = None,
end: str = None,
tiles: str = None,
page: int = 1,
per_page: int = 10):
"""Retrieve all data cube items done."""
cube = self.get_cube_or_404(cube_id=cube_id)
where = [Item.collection_id == cube.id, Tile.id == Item.tile_id]
# temporal filter
if start:
where.append(Item.start_date >= start)
if end:
where.append(Item.end_date <= end)
# tile(string) filter
if tiles:
tiles = tiles.split(',') if isinstance(tiles, str) else tiles
where.append(Tile.name.in_(tiles))
# spatial filter
if bbox:
xmin, ymin, xmax, ymax = [
float(coord) for coord in bbox.split(',')
]
where.append(
func.ST_Intersects(
func.ST_SetSRID(Item.geom, 4326),
func.ST_MakeEnvelope(xmin, ymin, xmax, ymax, 4326)))
paginator = db.session.query(Item).filter(*where).order_by(
Item.start_date.desc()).paginate(int(page),
int(per_page),
error_out=False)
result = []
for item in paginator.items:
obj = Serializer.serialize(item)
obj['geom'] = None
obj['min_convex_hull'] = None
obj['tile_id'] = item.tile.name
if item.assets.get('thumbnail'):
obj['quicklook'] = item.assets['thumbnail']['href']
del obj['assets']
result.append(obj)
return dict(items=result,
page=page,
per_page=page,
total_items=paginator.total,
total_pages=paginator.pages), 200
def random_point_from_district(d_name: str):
district_cat_id = 163
sql = sqlalchemy.select([
func.ST_SetSRID(func.ST_GeneratePoints(table.model.c.geom, 1), 4326)
]).where(
and_(table.model.c.name == d_name,
table.model.c.category_id == district_cat_id))
result = connection.execute(sql).fetchone()
if result is not None:
return result[0]
else:
return print('None')
def get_grs_schema(cls,
grs_id,
bbox: Tuple[float, float, float, float] = None,
tiles=None):
"""Retrieve a Grid Schema definition with tiles associated."""
schema: GridRefSys = GridRefSys.query().filter(
GridRefSys.id == grs_id).first()
if schema is None:
return 'GRS {} not found.'.format(grs_id), 404
geom_table = schema.geom_table
srid_column = get_srid_column(geom_table.c, default_srid=4326)
where = []
if bbox is not None:
x_min, y_min, x_max, y_max = bbox
where.append(
func.ST_Intersects(
func.ST_MakeEnvelope(x_min, y_min, x_max, y_max, 4326),
func.ST_Transform(
func.ST_SetSRID(geom_table.c.geom, srid_column),
4326)))
if tiles:
where.append(geom_table.c.tile.in_(tiles))
tiles = db.session.query(
geom_table.c.tile,
func.ST_AsGeoJSON(
func.ST_Transform(
func.ST_SetSRID(geom_table.c.geom, srid_column), 4326), 6,
3).cast(
sqlalchemy.JSON).label('geom_wgs84')).filter(*where).all()
dump_grs = Serializer.serialize(schema)
dump_grs['tiles'] = [
dict(id=t.tile, geom_wgs84=t.geom_wgs84) for t in tiles
]
return dump_grs, 200
def orchestrate(self):
"""Orchestrate datacube defintion and prepare temporal resolutions."""
self.datacube = Collection.query().filter(
Collection.name == self.params['datacube']).one()
temporal_schema = self.datacube.temporal_composition_schema
cube_parameters: CubeParameters = CubeParameters.query().filter(
CubeParameters.collection_id == self.datacube.id).first()
if cube_parameters is None:
raise RuntimeError(
f'No parameters configured for data cube "{self.datacube.id}"')
# This step acts like first execution. When no stac_url defined in cube parameters but it was given, save it.
if self.properties.get(
'stac_url') and not cube_parameters.metadata_.get('stac_url'):
logging.debug(
f'No "stac_url"/"token" configured yet for cube parameters.'
f'Using {self.properties["stac_url"]}')
meta = cube_parameters.metadata_.copy()
meta['stac_url'] = self.properties['stac_url']
meta['token'] = self.properties.get('token')
cube_parameters.metadata_ = meta
cube_parameters.save(commit=True)
# Validate parameters
cube_parameters.validate()
# Pass the cube parameters to the data cube functions arguments
props = deepcopy(cube_parameters.metadata_)
props.update(self.properties)
self.properties = props
dstart = self.params['start_date']
dend = self.params['end_date']
if self.datacube.composite_function.alias == 'IDT':
timeline = [[dstart, dend]]
else:
if self.datacube.composite_function.alias == 'STK':
warnings.warn(
'The composite function STK is deprecated. Use LCF (Least Cloud Cover First) instead.',
DeprecationWarning,
stacklevel=2)
timeline = Timeline(**temporal_schema,
start_date=dstart,
end_date=dend).mount()
where = [Tile.grid_ref_sys_id == self.datacube.grid_ref_sys_id]
if self.params.get('tiles'):
where.append(Tile.name.in_(self.params['tiles']))
self.tiles = db.session.query(Tile).filter(*where).all()
self.bands = Band.query().filter(
Band.collection_id == self.warped_datacube.id).all()
bands = self.datacube_bands
self.band_map = {
b.name: dict(name=b.name,
data_type=b.data_type,
nodata=b.nodata,
min_value=b.min_value,
max_value=b.max_value)
for b in bands
}
if self.properties.get('reuse_from'):
warnings.warn(
'The parameter `reuse_from` is deprecated and will be removed in next version. '
'Use `reuse_data_cube` instead.')
common_bands = _common_bands()
collection_bands = [
b.name for b in self.datacube.bands
if b.name not in common_bands
]
reused_collection_bands = [b.name for b in self.bands]
# The input cube (STK/MED) must have all bands of reused. Otherwise raise Error.
if not set(collection_bands).issubset(
set(reused_collection_bands)):
raise RuntimeError(
f'Reused data cube {self.warped_datacube.name} must have all bands of {self.datacube.name}'
)
# Extra filter to only use bands of Input data cube.
self.bands = [b for b in self.bands if b.name in collection_bands]
if cube_parameters.reuse_cube:
self.reused_datacube = cube_parameters.reuse_cube
for tile in self.tiles:
tile_name = tile.name
grs: GridRefSys = tile.grs
grid_geom: sqlalchemy.Table = grs.geom_table
srid_column = get_srid_column(grid_geom.c)
# TODO: Raise exception when using a native grid argument
# Use bands resolution and match with SRID context (degree x degree) etc.
tile_stats = db.session.query(
(func.ST_XMin(grid_geom.c.geom)).label('min_x'),
(func.ST_YMax(grid_geom.c.geom)).label('max_y'),
(func.ST_XMax(grid_geom.c.geom) -
func.ST_XMin(grid_geom.c.geom)).label('dist_x'),
(func.ST_YMax(grid_geom.c.geom) -
func.ST_YMin(grid_geom.c.geom)).label('dist_y'),
(func.ST_Transform(
func.ST_SetSRID(grid_geom.c.geom, srid_column),
4326)).label('feature')).filter(
grid_geom.c.tile == tile_name).first()
self.mosaics[tile_name] = dict(periods=dict())
for interval in timeline:
startdate = interval[0]
enddate = interval[1]
if dstart is not None and startdate < dstart:
continue
if dend is not None and enddate > dend:
continue
period = f'{startdate}_{enddate}'
self.mosaics[tile_name]['periods'][period] = {}
self.mosaics[tile_name]['periods'][period][
'start'] = startdate.strftime('%Y-%m-%d')
self.mosaics[tile_name]['periods'][period][
'end'] = enddate.strftime('%Y-%m-%d')
self.mosaics[tile_name]['periods'][period][
'dist_x'] = tile_stats.dist_x
self.mosaics[tile_name]['periods'][period][
'dist_y'] = tile_stats.dist_y
self.mosaics[tile_name]['periods'][period][
'min_x'] = tile_stats.min_x
self.mosaics[tile_name]['periods'][period][
'max_y'] = tile_stats.max_y
self.mosaics[tile_name]['periods'][period][
'feature'] = tile_stats.feature
if self.properties.get('shape', None):
self.mosaics[tile_name]['periods'][period][
'shape'] = self.properties['shape']
