def get_concave_hull_from_basic_triangulation(in_v_long, in_v_lat, in_range, in_nb_pix_range):
"""
Compute the concave hull of a set of points using classifcal Delauney triangulation.
:param in_v_long: longitudes of points for which to compute the hull
:type in_v_long: 1D-array of float
:param in_v_lat: latitudes of points for which to compute the hull
:type in_v_lat: 1D-array of float
:param in_range: range of points for which to compute the hull
:type in_range: 1D-array of int
:param in_nb_pix_range: maximal number of pixel in range
:type in_nb_pix_range: int
:return the hull of the input set of points
:rtype: OGRMultiPolygon
"""
# Get instance of service config file
cfg = service_config_file.get_instance()
logger = logging.getLogger("my_hull")
nb_pix = in_v_long.size
# ============================ HULL COMPUTATION FOR BIG LAKES ===========================
# ============================ TO CHANGE AS SOON AS POSSIBLE ===========================
# if the lake contains more than 500 000 pixels, the number of pixel is restricted to 500 000. Pixels are randomly selected.
nb_pix_max_delauney = cfg.getint("CONFIG_PARAMS", "NB_PIX_MAX_DELAUNEY")
if nb_pix > nb_pix_max_delauney:
id_selected = np.random.randint(nb_pix, size=int(nb_pix_max_delauney))
logger.warning(
"The number of pixel of the lake is reduced to %d" % (nb_pix_max_delauney))
in_v_long = in_v_long[id_selected]
in_v_lat = in_v_lat[id_selected]
in_range = in_range[id_selected]
alpha_ratio = nb_pix_max_delauney/nb_pix
nb_pix = in_v_long.size
logger.debug("alpha_ratio : " + str(alpha_ratio))
else:
alpha_ratio = 1.
# =======================================================================================
# 1.1 - Gather coordinates in a 2D-array
coords = np.zeros((nb_pix, 2))
# 1.2 - Transform geographical coordinates into utm coordinates
coords[:, 0], coords[:, 1], utm_epsg_code = my_tools.get_utm_coords_from_lonlat(in_v_long, in_v_lat)
# 1.3 - Compute alpha shape
hull_method = cfg.getfloat("CONFIG_PARAMS", "HULL_METHOD")
if hull_method == 1.1: # Without alpha parameter
concave_hull_utm = get_concav_hull_bis(coords)
else: # With alpha parameter
alpha = alpha_ratio*( 0.03 + 0.01 * in_range / in_nb_pix_range) # alpha parameter ranges from 0.03 to 0.04 following the range index
concave_hull_utm = alpha_shape(coords, alpha)
# 1.4 - Transform concave hull polygon into geographical coordinates
concave_hull = my_tools.get_lon_lat_polygon_from_utm(concave_hull_utm, utm_epsg_code)
# 1.5 - Convert Shapely geometry to OGRPolygon or OGRMultiPolygon
return ogr.CreateGeometryFromWkb(concave_hull.wkb)
def __init__(self, in_lake_db_filename, in_poly=None):
"""
Constructor
:param in_lake_db_filename: full path of the prior lake database
:type in_lake_db_filename: string
:param in_poly: polygon to spatially select lakes from DB
:type in_poly: ogr.Polygon
Variables of the object:
- lakedb_id / String: Fieldname of lake id in lakedb
- lake_layer / osgeo.ogr.Layer: lake_layer of a priori lake database
- lake_ds / osgeo.ogr.DataSource: datasource of a priori lake database
"""
logger = logging.getLogger(self.__class__.__name__)
logger.info("Lake DB = %s", in_lake_db_filename)
# 1 - Init LakeDb
super().__init__()
# 2 - Get config file
cfg = service_config_file.get_instance()
# Get lakedb_id parameter
self.lake_db_id = cfg.get("DATABASES", "LAKE_DB_ID")
# 3 - Open database
self.lake_ds, self.lake_layer = self.open_shp(
in_lake_db_filename, in_poly) # Open Lake database
def __init__(self, in_product_type, in_obj_pixc, in_obj_pixc_vec,
in_obj_lake_db, in_layer_name):
"""
Constructor
:param in_product_type: type of product among "SP"=LakeSP and "TILE"=LakeTile
:type in_product_type: string
:param in_obj_pixc: pixel cloud from which to compute lake products
:type in_obj_pixc: proc_pixc.PixelCloud or proc_pixc_sp.PixelCloudSP object
:param in_obj_pixc_vec: pixel cloud complementary file from which to compute lake products
:type in_obj_pixc_vec: proc_pixc_vec.PixelCloudVec or proc_pixc_vec_sp.PixelCloudVecSP object
:param in_obj_lake_db: lake database
:type in_obj_lake_db: lake_db.lakeDb_shp or lake_db.lakeDb_sqlite
:param in_layer_name: name for lake product layer
:type in_layer_name: string
Variables of the object:
- obj_pixc / proc_pixc.PixelCloud or proc_pixc_sp.PixelCloud: pixel cloud from which to compute lake products
- obj_pixc_vec / proc_pixc_vec.PixelCloudVec or proc_pixc_vec_sp.PixelCloudVec: extra info for pixel cloud
- obj_lake_db / lake_db.lakeDb_shp or lake_db.lakeDb_sqlite: lake database
- shp_mem_layer / LakeTileShp_product: shapefile memory layer of the lake product
- uniq_prior_id / set: list of uniq prior identifiers linked to observed objects
"""
# Get instance of service config file
self.cfg = service_config_file.get_instance()
logger = logging.getLogger(self.__class__.__name__)
logger.info("- start -")
# Init variables
# Product type
if (in_product_type != "TILE") and (in_product_type != "SP"):
message = "ERROR = product type is %s ; should be SP or TILE" % in_product_type
raise service_error.ProcessingError(message, logger)
else:
self.type = in_product_type
# Pixel cloud object
self.obj_pixc = in_obj_pixc
# Pixel cloud complementary file object
self.obj_pixc_vec = in_obj_pixc_vec
# Lake database object
self.obj_lake_db = in_obj_lake_db
# Initialize lake product layer
self.shp_mem_layer = shp_file.LakeSPShpProduct(self.type,
in_layer_name)
# Other variables
self.uniq_prior_id = set(
) # List of uniq prior identifiers linked to observed objects
if self.type == "TILE":
# Find tile associated continent
continent = self.obj_lake_db.link_poly_to_continent(
self.obj_pixc.tile_poly)
self.obj_pixc.pixc_metadata["continent"] = continent
self.obj_pixc_vec.pixcvec_metadata["continent"] = continent
def compute_lake_boundaries(in_v_long, in_v_lat, in_range, in_azimuth, in_nb_pix_range):
"""
Compute the hull of a set of points determined by their coordinates given in input parameters
:param in_v_long: longitudes of points for which to compute the hull
:type in_v_long: 1D-array of float
:param in_v_lat: latitudes of points for which to compute the hull
:type in_v_lat: 1D-array of float
:param in_range: range of points for which to compute the hull
:type in_range: 1D-array of int
:param in_azimuth: azimuth of points for which to compute the hull
:type in_azimuth: 1D-array of int
:param in_nb_pix_range: maximal number of pixel in range
:type in_nb_pix_range: int
:return the hull of the input set of points
:rtype: OGRMultiPolygon
"""
# Get instance of service config file
cfg = service_config_file.get_instance()
logger = logging.getLogger("my_hull")
logger.debug("Computing lake boundaries")
hull_method = cfg.getfloat("CONFIG_PARAMS", "HULL_METHOD")
if len(in_v_long) < 4:
logger.debug("Hull computation method : Convex hull because less than 4 pixels")
retour = get_convex_hull(in_v_long, in_v_lat)
elif hull_method == 0: # 0 - CONVEX HULL
logger.debug("Hull computation method : Convex hull")
retour = get_convex_hull(in_v_long, in_v_lat)
elif hull_method == 1.0: # 1.0 : CONCAV HULL - Delaunay triangulation with CGAL
logger.debug("Hull computation method : Concav hull computed in ground geometry, based on Delaunay triangulation - using CGAL library")
retour = get_concave_hull_from_cgal_triangulation(in_v_long, in_v_lat, in_range, in_azimuth, in_nb_pix_range)
elif hull_method == 1.1: # 1.1 - CONCAV HULL - Delaunay triangulation
logger.debug("Hull computation method : Delauney triangulation")
retour = get_concave_hull_from_basic_triangulation(in_v_long, in_v_lat, in_range, in_nb_pix_range)
elif hull_method == 2: # 2 - CONCAV HULL - Radar vectorisation method
logger.debug("Hull computation method : radar vectorization (default)")
retour = get_concave_hull_from_radar_vectorisation(in_range, in_azimuth, in_v_long, in_v_lat)
else:
message = "Concave hull computation method not understood"
raise service_error.ProcessingError(message, logger)
if retour.GetGeometryName() not in ["POLYGON", "MULTIPOLYGON"]:
retour = ogr.Geometry(ogr.wkbPolygon)
return retour
def __init__(self):
"""
Init class ServiceLogger
:param name: name of the logger
"""
THIS.klass = self
cfg = service_config_file.get_instance()
# logging format
# LEVEL : DEBUG, INFO, WARNING, ERROR
# log format :
# YYYY-MM-DDThh:mm:ss.mmm LEVEL:ClassName:FunctionName: message
self.log_formatter = logging.Formatter(
fmt=
'%(asctime)s.%(msecs)03d %(levelname)s | %(name)s::%(funcName)s | %(message)s',
datefmt='%Y-%m-%dT%H:%M:%S')
# set the name of the class in log messages
self.root_logger = logging.getLogger()
# set the logging level from the configuration file
self.root_logger.setLevel(cfg.get('LOGGING', 'logFileLevel'))
if not hasattr(self, 'first'):
# First call to ServiceLogger
self.first = True
# create a log file
self.file_handler = logging.FileHandler(cfg.get(
'LOGGING', 'logFile'),
mode='w')
self.file_handler.setFormatter(self.log_formatter)
self.file_handler.setLevel(cfg.get('LOGGING', 'logFileLevel'))
# create a memory Handler to bufferize SAS log message
self.memory_handler = logging.handlers.MemoryHandler(
1000, target=self.file_handler)
# add logger
self.root_logger.addHandler(self.memory_handler)
if cfg.get('LOGGING', 'logConsole') == 'True':
# logging in console
self.console_handler = logging.StreamHandler()
self.console_handler.setFormatter(self.log_formatter)
self.console_handler.setLevel(
cfg.get('LOGGING', 'logConsoleLevel'))
self.root_logger.addHandler(self.console_handler)
else:
self.console_handler = None
def compute_pixcvec_filename(in_laketile_pixcvec_filename, in_output_dir):
"""
Compute L2_HR_PIXCVec filename from L2_HR_LakeTile_pixcvec filename
:param in_laketile_pixcvec_filename: L2_HR_LakeTile_pixcvec filename to convert
:type IN_laketile_filename: string
:param in_output_dir: output directory
:type in_output_dir: string
"""
# Get config file
cfg = service_config_file.get_instance()
# Init variables
product_counter = 1
# get parameters
pixcvec_pattern = PIXCVEC_PATTERN
pixcvec_prefix = PIXCVEC_PREFIX
pixcvec_suffix = PIXCVEC_SUFFIX
pixcvec_pattern = pixcvec_pattern.replace(
"PIXCVEC_PREFIX + ",
pixcvec_prefix).replace('"', '').replace(" + PIXCVEC_SUFFIX",
pixcvec_suffix)
lake_sp_crid = cfg.get("CRID", "LAKE_SP_CRID")
# Get infos from input LakeTile_pixcvec filename
tmp_dict = get_info_from_filename(in_laketile_pixcvec_filename, "LakeTile")
# Compute associated PIXCVec filename
filename = pixcvec_pattern % (int(tmp_dict["cycle"]), int(tmp_dict["pass"]), tmp_dict["tile_ref"], tmp_dict["start_date"], \
tmp_dict["stop_date"], lake_sp_crid, product_counter)
out_filename = os.path.join(in_output_dir, filename)
while os.path.exists(out_filename):
product_counter += 1
filename = pixcvec_pattern % (int(tmp_dict["cycle"]), int(tmp_dict["pass"]), tmp_dict["tile_ref"], tmp_dict["start_date"], \
tmp_dict["stop_date"], lake_sp_crid, product_counter)
out_filename = os.path.join(in_output_dir, filename)
return out_filename
def open_db(self,
in_lake_db_filename,
table_name,
field_name,
in_poly=None):
"""
Open database, optionnally spatially select polygons and copy layer to memory
:param in_poly: polygon to spatially select lakes from DB
:type in_poly: ogr.Polygon
:param table_name: table_name to load from file
:type table_name: String
:param field_name: field_name to load from table
:type field_name: String
:param in_poly: polygon to spatially select lakes from DB
:type in_poly: ogr.Polygon
"""
logger = logging.getLogger(self.__class__.__name__)
cfg = service_config_file.get_instance()
# Transform 3D geometry into 2D geometry (necessary for spatialite query)
in_poly.FlattenTo2D()
# Open the SQLite database and define the connector
self.db_conn = sqlite3.connect(in_lake_db_filename, timeout=10)
# Load spatialite extension
self.db_conn.enable_load_extension(True)
self.db_conn.execute('SELECT load_extension("mod_spatialite")')
# Define the cursor
self.db_cur = self.db_conn.cursor()
if cfg.get('LOGGING', 'logFileLevel') == 'DEBUG':
(lakes_nb,
) = self.db_cur.execute('SELECT count(*) from lake').fetchone()
logger.debug(" %d features stored in table %s of database" %
(lakes_nb, table_name))
# Create an output datasource in memory
mem_driver = ogr.GetDriverByName('MEMORY') # Memory driver
# Open the memory datasource with write access
ds = mem_driver.CreateDataSource('memData')
# Set spatial projection
srs = ogr.osr.SpatialReference()
srs.ImportFromEPSG(4326)
# Creating memory layer
lyr = ds.CreateLayer(str('layer'), srs=srs, geom_type=ogr.wkbPolygon)
lyr.CreateField(ogr.FieldDefn(field_name, ogr.OFTString))
# Define the layer
lyr_defn = lyr.GetLayerDefn()
if in_poly is not None:
cmd = "SELECT %s, AsText(geometry) FROM %s WHERE MBRIntersects(GeomFromText('%s'), %s.geometry);" % (
field_name, table_name, in_poly.ExportToWkt(), table_name)
self.db_cur.execute(cmd)
else:
cmd = "SELECT %s, AsText(geometry) FROM %s ;" % (field_name,
table_name)
self.db_cur.execute(cmd)
for row in self.db_cur:
# Create empty feature/entity
out_feat = ogr.Feature(lyr_defn)
# Fill feature with ID and geometry from SQLite request
out_feat.SetField(field_name, str(row[0]))
multi_poly = ogr.CreateGeometryFromWkt(row[1])
out_feat.SetGeometry(multi_poly)
lyr.CreateFeature(out_feat)
out_feat.Destroy()
lyr.ResetReading()
# Get memory lake_layer
logger.info("%d features after focus over studied area" %
lyr.GetFeatureCount())
# Close spatialite database
self.db_conn.close()
return ds, lyr
def tmpGetConfigFromServiceConfigFile():
"""
Set global variables from serviceConfgiFile
This function is temporary. It will be delete in the future
when serviceConfigFile will be used by lake_tile
"""
IN_config = service_config_file.get_instance()
logger = logging.getLogger("locnes_variables")
# Lake database
global LAKE_DB
lake_db_file = IN_config.get("DATABASES", "LAKE_DB")
LAKE_DB = lake_db_file
logger.info("> LAKE_DB = %s" % LAKE_DB)
# Lake identifier attribute name in the database
global LAKE_DB_ID
lake_db_id = IN_config.get("DATABASES", "LAKE_DB_ID")
LAKE_DB_ID = lake_db_id
logger.info("> LAKE_DB_ID = %s" % LAKE_DB_ID)
# Shapefile with polygons of continents
global CONTINENT_FILE
continent_file = IN_config.get("DATABASES", "CONTINENT_FILE")
CONTINENT_FILE = continent_file
logger.info("> CONTINENT_FILE = %s" % CONTINENT_FILE)
# Water flags
global FLAG_WATER
FLAG_WATER = IN_config.get("CONFIG_PARAMS", "FLAG_WATER")
logger.info("> FLAG_WATER = %s" % FLAG_WATER)
# Dark water flags
global FLAG_DARK
FLAG_DARK = IN_config.get("CONFIG_PARAMS", "FLAG_DARK")
logger.info("> FLAG_DARK = %s" % FLAG_DARK)
# Hull method
global HULL_METHOD
HULL_METHOD = IN_config.getfloat("CONFIG_PARAMS", "HULL_METHOD")
logger.info("> HULL_METHOD = %s" % HULL_METHOD)
# Maximal standard deviation of height inside a lake
global STD_HEIGHT_MAX
STD_HEIGHT_MAX = IN_config.getfloat("CONFIG_PARAMS", "STD_HEIGHT_MAX")
logger.info("> STD_HEIGHT_MAX = %s" % STD_HEIGHT_MAX)
# Model to deal with big lake processing
global BIGLAKE_MODEL
BIGLAKE_MODEL = IN_config.get("CONFIG_PARAMS", "BIGLAKE_MODEL")
logger.info("> BIGLAKE_MODEL = %s" % BIGLAKE_MODEL)
# Min size for lake to be considered as big
global BIGLAKE_MIN_SIZE
BIGLAKE_MIN_SIZE = IN_config.getfloat("CONFIG_PARAMS", "BIGLAKE_MIN_SIZE")
logger.info("> BIGLAKE_MIN_SIZE = %s" % BIGLAKE_MIN_SIZE)
# Grid spacing for lake height smoothing
global BIGLAKE_GRID_SPACING
BIGLAKE_GRID_SPACING = IN_config.getint("CONFIG_PARAMS",
"BIGLAKE_GRID_SPACING")
logger.info("> BIGLAKE_GRID_SPACING = %s" % BIGLAKE_GRID_SPACING)
# Grid resolution for lake height smoothing
global BIGLAKE_GRID_RES
BIGLAKE_GRID_RES = IN_config.getint("CONFIG_PARAMS", "BIGLAKE_GRID_RES")
logger.info("> BIGLAKE_GRID_RES = %s" % BIGLAKE_GRID_RES)
def __init__(self, in_lake_tile_file_list, in_continent, in_out_dir):
"""
Constructor of LakeSP filenames
:param in_lake_tile_file_list: list of LakeTile_shp files full path
:type in_lake_tile_file_list: list of string
:param in_continent: continent concerning the LakeSP
:type in_continent: string
:param in_out_dir: output directory
:type in_out_dir: string
"""
logger = logging.getLogger(self.__class__.__name__)
logger.info("- start -")
# Get config file
cfg = service_config_file.get_instance()
# 1 - Init variables
self.lake_tile_file_list = in_lake_tile_file_list # list of LakeTile_shp files full path
self.out_dir = in_out_dir # Output directory
self.product_counter = 1 # Product counter
# get parameters
self.lake_sp_pattern = LAKE_SP_PATTERN
self.lake_sp_prefix = LAKE_SP_PREFIX
self.lake_sp_pattern = self.lake_sp_pattern.replace(
"LAKE_SP_PREFIX + ", self.lake_sp_prefix).replace('"', '')
self.lake_sp_pattern_no_cont = LAKE_SP_PATTERN_NO_CONT.replace(
"LAKE_SP_PREFIX + ", self.lake_sp_prefix).replace('"', '')
self.lake_sp_crid = cfg.get("CRID", "LAKE_SP_CRID")
# Lake Id prefix
self.lake_id_prefix = ""
# 2 - Retrieve info from LakeTile filename
tmp_dict = get_info_from_filename(self.lake_tile_file_list[0],
"LakeTile")
# 2.1 - Cycle number
cycle_num = tmp_dict["cycle"]
if cycle_num is None:
self.cycle_num = 999
logger.info(
"WARNING: cycle number has not been found in LakeTile filename %s -> set to default value = %03d",
os.path.basename(self.lake_tile_file_list[0]), self.cycle_num)
else:
self.cycle_num = int(cycle_num)
logger.info("Cycle number = %03d", self.cycle_num)
# 2.2 - Pass number
pass_num = int(tmp_dict["pass"])
if pass_num is None:
self.pass_num = 999
logger.info(
"WARNING: pass number has not been found in LakeTile filename %s -> set to default value = %03d",
os.path.basename(self.lake_tile_file_list[0]), self.pass_num)
else:
self.pass_num = int(pass_num)
logger.info("Pass number = %03d", self.pass_num)
# 2.3 - Continent
if in_continent == "":
self.continent = ""
logger.info("WARNING: no continental split")
else:
self.continent = in_continent
logger.info("Continent = %s", self.continent)
# 3 - Retrieve start and stop dates from LakeTile_shp filenames
self.compute_start_stop_dates()
# 4 - Compute output filenames
self.compute_lake_sp_filename() # LakeSP filename
def __init__(self, in_pixc_file, in_pixc_vec_river_file, in_out_dir):
"""
Constructor of LakeTile filenames
:param IN_pixc_file: PIXC file full path
:type IN_pixc_file: string
:param IN_pixc_vec_river_file: PIXCVecRiver file full path
:type IN_pixc_vec_river_file: string
:param IN_out_dir: output directory
:type IN_out_dir: string
"""
logger = logging.getLogger(self.__class__.__name__)
logger.info("- start -")
# Get config file
cfg = service_config_file.get_instance()
# 1 - Init variables
self.pixc_file = in_pixc_file # PIXC file full path
self.pixc_vec_river_file = in_pixc_vec_river_file # PIXCVecRiver full path
self.out_dir = in_out_dir # Output directory
self.product_counter = 1 # Product counter
# get parameters
self.lake_tile_pattern = LAKE_TILE_PATTERN
self.lake_tile_prefix = LAKE_TILE_PREFIX
self.lake_tile_pattern = self.lake_tile_pattern.replace(
"LAKE_TILE_PREFIX + ", self.lake_tile_prefix).replace('"', '')
self.lake_tile_crid = cfg.get("CRID", "LAKE_TILE_CRID")
self.lake_tile_shp_suffix = LAKE_TILE_SHP_SUFFIX
self.lake_tile_edge_suffix = LAKE_TILE_EDGE_SUFFIX
self.lake_tile_pixcvec_suffix = LAKE_TILE_PIXCVEC_SUFFIX
# 2 - Retrieve info from PIXC filename
tmp_dict = get_info_from_filename(self.pixc_file, "PIXC")
# 2.1 - Cycle number
cycle_num = tmp_dict["cycle"]
if cycle_num is None:
self.cycle_num = 999
logger.info(
"WARNING: cycle number has not been found in PIXC filename %s -> set to default value = %03d",
os.path.basename(self.pixc_file), self.cycle_num)
else:
self.cycle_num = int(cycle_num)
logger.info("Cycle number = %03d", self.cycle_num)
# 2.2 - Pass number
pass_num = int(tmp_dict["pass"])
if pass_num is None:
self.pass_num = 999
logger.info(
"WARNING: pass number has not been found in PIXC filename %s -> set to default value = %03d",
os.path.basename(self.pixc_file), self.pass_num)
else:
self.pass_num = int(pass_num)
logger.info("Pass number = %03d", self.pass_num)
# 2.3 - Tile ref
self.tile_ref = tmp_dict["tile_ref"]
if self.tile_ref is None:
self.tile_ref = "ttts"
logger.info(
"WARNING: tile ref has not been found in PIXC filename %s -> set to default value = %s",
os.path.basename(self.pixc_file), self.tile_ref)
else:
logger.info("Tile ref = %s", self.tile_ref)
# 2.4 - Start date
self.start_date = tmp_dict["start_date"]
if self.start_date is None:
self.start_date = "yyyyMMddThhmmss"
logger.info(
"WARNING: start date has not been found in PIXC filename %s -> set to default value = %s",
os.path.basename(self.pixc_file), self.start_date)
else:
logger.info("Start date = %s", self.start_date)
# 2.5 - Stop date
self.stop_date = tmp_dict["stop_date"]
if self.stop_date is None:
self.stop_date = "yyyyMMddThhmmss"
logger.info(
"WARNING: stop date has not been found in PIXC filename %s -> set to default value = %s",
os.path.basename(self.pixc_file), self.stop_date)
else:
logger.info("Stop date = %s", self.stop_date)
# 3 - Test compatibility of PIXCVecRiver filename with PIXC filename
tmp_ok = self.test_pixc_vec_river_filename()
if tmp_ok:
logger.info(
"PIXCVecRiver basename %s is compatible with PIXC basename %s",
os.path.basename(self.pixc_vec_river_file),
os.path.basename(self.pixc_file))
else:
logger.info(
"WARNING: PIXCVecRiver basename %s IS NOT compatible with PIXC basename %s (cf. above)",
os.path.basename(self.pixc_vec_river_file),
os.path.basename(self.pixc_file))
# 4 - Compute output filenames
self.compute_lake_tile_filename_shp() # LakeTile_shp filename
self.compute_lake_tile_filename_edge() # LakeTile_edge filename
self.compute_lake_tile_filename_pixcvec() # LakeTile_pixcvec filename
def __init__(self, in_lake_tile_pixcvec_file_list, in_obj_pixc_edge_sp,
in_lake_sp_dir, in_continent):
"""
This class is designed to process L2_HR_PIXCVec product over a swath .
All pixels involved in entities covering more than one tile are processed here.
The geolocation of pixels is improved for those pixels and PIXCVec NetCDF file is updated.
The initialization of a PixCVecSP consists in:
- set class attributes
- copy input LakeTile_pixcvec files into PIXCVec files
NP: this object is related to one swath
:param in_lake_tile_pixcvec_file_list: list of LakeTile_pixcvec files full path concerning current swath
:type in_lake_tile_pixcvec_file_list: list of string
:param in_obj_pixc_edge_sp: list of subset of PixC for edge objects current swath
:type in_obj_pixc_edge_sp: proc_pixc_sp.PixCEdgeSP
:param in_lake_sp_dir: output LakeSP directory
:type in_lake_sp_dir: string
:param in_continent: continent covered by the tile (if global var CONTINENT_FILE exists)
:type in_continent: string
Variables of the object:
- From LakeTile_pixcvec file
- longitude_vectorproc / 1D-array of float: improved longitude of water pixels (= variable named longitude_vectorproc in LakeTile_pixcvec file)
- latitude_vectorproc / 1D-array of float: improved latitude of water pixels (= variable named latitude_vectorproc in LakeTile_pixcvec file)
- height_vectorproc / 1D-array of float: improved height of water pixels (= variable named height_vectorproc in LakeTile_pixcvec file)
- river_lake_other_tag / 1D-array of float: tag associated to river and lake databases (= variable named river_lake_other_tag in LakeTile_pixcvec file)
- From process:
- lake_tile_pixcvec_file_list / list of str : list of input LakeTile_pixcvec files
- pixc_vec_file_list / list of str : list of output PIXCVec files
- obj_pixc_edge_sp / proc_pixc_sp.PixCEdgeSP object : subset of PixC related to pixels of objects at top/bottom edge of a PixC tile (output of PGE_LakeTile)
- nb_water_pix / int : number of pixels to process
"""
# 1 - Init variables
# Get instance of service config file
self.cfg = service_config_file.get_instance()
# List of LakeTile_pixcvec files concerning current swath
in_lake_tile_pixcvec_file_list.sort()
self.lake_tile_pixcvec_file_list = in_lake_tile_pixcvec_file_list
# List of output PIXCVec files
self.pixc_vec_file_list = []
# List of PixC_SP objects of current swath
self.obj_pixc_edge_sp = in_obj_pixc_edge_sp
# Continent processed
self.continent = in_continent
# Initialize PIXCVec variables to 0
self.longitude_vectorproc = np.zeros(self.obj_pixc_edge_sp.nb_pixels)
self.latitude_vectorproc = np.zeros(self.obj_pixc_edge_sp.nb_pixels)
self.height_vectorproc = np.zeros(self.obj_pixc_edge_sp.nb_pixels)
self.node_id = np.empty(self.obj_pixc_edge_sp.nb_pixels, dtype=object)
self.node_id[:] = ""
self.lakedb_id = np.empty(self.obj_pixc_edge_sp.nb_pixels,
dtype=object)
self.lakedb_id[:] = ""
self.lakeobs_id = np.empty(self.obj_pixc_edge_sp.nb_pixels,
dtype=object)
self.lakeobs_id[:] = ""
self.flag_ice_climato = np.zeros(self.obj_pixc_edge_sp.nb_pixels,
dtype=np.uint8)
self.flag_ice_dyn = np.zeros(self.obj_pixc_edge_sp.nb_pixels,
dtype=np.uint8)
# Init a list of tiles ref processed in thios class
self.tile_number_list = []
# 2 - List of output files computation
for lake_tile_pixcvec_file in self.lake_tile_pixcvec_file_list:
# 2.1 - Compute output PIXCVec file full path
pixc_vec_file = my_names.compute_pixcvec_filename(
lake_tile_pixcvec_file, in_lake_sp_dir)
# 2.2 - Remove if exists
if os.path.exists(pixc_vec_file):
os.remove(pixc_vec_file)
# 2.3 - Copy LakeTile_pixcvec file full path to PIXCVec file list
self.pixc_vec_file_list.append(pixc_vec_file)
# 2.3 - Extact tile number from PixC Vec file
tile_number = int(
my_names.get_info_from_filename(lake_tile_pixcvec_file,
"LakeTile")["tile_ref"][:-1])
self.tile_number_list.append(tile_number)
def relabel_lake_using_segmentation_heigth(in_x, in_y, in_height, in_std_height_max):
"""
This function main interest is to determine the number of lakes inside a subset of PixC in radar geometry.
In most of the cases, only one lake is located in the given subset, but in some case of different lakes can
be gather inside one because of radar geometric distortions or in the case a of a dam.
Steps :
1. Creates a 2D height matrix from X and Y 1D vectors
2. Unsupervised heigth classification to determine number a classes to get an STD over each classe lower than STD_HEIGHT_MAX
3. Smooth result using a 2D median filter
4. Return labels
:param in_x: X indices of "1" pixels
:type in_x: 1D vector of int
:param in_y: Y indices of "1" pixels
:type in_y: 1D vector of int
:param in_height: height of pixels
:type in_height: 1D vector of float
:param in_std_height_max: maximal standard deviation of height inside a lake
:type in_std_height_max: float
:return: labels recomputed over 1 of several lakes
:rtype: 1D vector of int
"""
# Get instance of service config file
cfg = service_config_file.get_instance()
logger = logging.getLogger("my_tools")
logger.debug("- start -")
# 0 - Deal with exceptions
# 0.1 - Input vectors size must be the same
if (in_x.size != in_y.size) or (in_x.size != in_height.size):
raise ValueError("relabel_lake_using_segmentation_heigth(in_x, in_y, in_height) : in_x and in_y must be the same size ;" + \
"currently : in_x = %d and in_y = %d" % (in_x.size, in_y.size))
else:
nb_pts = in_x.size
# 1 - Compute height matrix
# 1.1 - Init heigth image
logger.debug("Building heigth matrix")
height_img = np.zeros((np.max(in_y) + 1, np.max(in_x) + 1))
message = "> Height matrix size = (X=%d , Y=%d)" % (np.max(in_y.size) + 1, np.max(in_x.size) + 1)
logger.debug(message)
# 1.2 - Put height for every pixels defined by the input vectors
for ind in range(nb_pts):
height_img[in_y[ind], in_x[ind]] = in_height[ind]
logger.debug("K-means processing")
# 2 - Unsupervised clustering to determine number of classes
std_heigth = np.std(in_height)
nb_classes = 1
# 2.1 - Cas with only one class
if std_heigth < in_std_height_max: # If only one lake is in the given pixc subset
retour = np.ones(nb_pts) # Return one unique label for all pixc
else:
# 2.2 - Init a k-means classifier
kmeans_classif = KMeans()
while std_heigth > in_std_height_max:
nb_classes += 1
# 2.3 - Cluster height over nb_classes classes
kmeans_classif = KMeans(n_clusters=nb_classes)
kmeans_classif.fit(in_height.reshape(-1, 1)) # Turn line into column for in_height
# 2.3 - Compute heigth std inside each class
std_heigth = np.max([np.std(in_height[np.where(kmeans_classif.labels_ == curLabel)]) for curLabel in np.unique(kmeans_classif.labels_)])
# If number of classes upper than 10 => stop iteration
if nb_classes > 10:
break
message = "NB classes : %d, max std : %f " % (nb_classes, std_heigth)
logger.debug(message)
# 3 - Format output vector
# 3.1 - Build a labeled matrix
labeled_img = np.zeros(height_img.shape).astype('int')
for ind in range(nb_pts):
labeled_img[in_y[ind], in_x[ind]] = int(kmeans_classif.labels_[ind])
# 3.2 - Median filter on a 3x3 window to smooth output
labeled_img_filted = median_filter(labeled_img.astype('uint8'), square(2).astype('uint8'))
# 3.3 - Init and fill output label array
out_labels = np.zeros(in_height.shape)
for ind in range(nb_pts):
out_labels[ind] = labeled_img_filted[in_y[ind], in_x[ind]] + 1
retour = out_labels
return retour
def get_concave_hull_from_radar_vectorisation(in_range, in_azimuth, in_v_long, in_v_lat):
"""
Compute the concave hull of a set of points using radar vectorisation
:param in_range: range of points
:type in_range: 1D-array of int
:param in_azimuth: azimuth of points
:type in_azimuth: 1D-array of int
:param in_v_long: longitude of points
:type in_v_long: 1D-array of float
:param in_v_lat: latitude of points
:type in_v_lat: 1D-array of float
:return: the hull of the input set of points
:rtype: OGRMultiPolygon
"""
# Get instance of service config file
cfg = service_config_file.get_instance()
# get param nb_pix_max_contour
nb_pix_max_contour = cfg.getint("CONFIG_PARAMS", "NB_PIX_MAX_CONTOUR")
logger = logging.getLogger("my_hull")
# 1 - Get relative range and azimuth (1st pixel = 1)
lake_x = in_range - np.min(in_range) + 1
lake_y = in_azimuth - np.min(in_azimuth) + 1
# 2 - Get image (1 pixel around lake)
lake_img = my_tools.compute_bin_mat(np.max(lake_x) + 2, np.max(lake_y) + 2, lake_x, lake_y)
# 3 - Compute boundaries (there might be more than one if there are islands in lake)
lake_contours = find_contours(lake_img, 0.99999999999)
# 4 - Round contour range and azimuth coordinates to units, since they are indices in input parameters
logger.debug("Removing duplicate points from lake contour")
lake_contour_int = []
for contour in lake_contours:
contour_points = []
[contour_points.append(tuple(np.round(coords, 0))) for coords in contour if
tuple(np.round(coords, 0)) not in contour_points]
lake_contour_int.append(contour_points)
# 5 - Convert (azimuth, range) contour into polygon
logger.debug("Inital polygon contains 1 external ring and %d holes rings " % (len(lake_contour_int) - 1))
# multi_ring_list contains every ring composing the polygon, the first ring contains exterior coordinates, all other rings are holes in the polygon
multi_ring_list = []
for contour in lake_contour_int: # Loop over contours
logger.debug("Building new ring with %d points " % (len(contour)))
# ============================ HULL COMPUTATION FOR BIG LAKES ===========================
# ============================ TO CHANGE AS SOON AS POSSIBLE ===========================
# if the lake contour contains more than 8 000 pixels, the number of pixel is restricted to 8 000..
if len(contour) > nb_pix_max_contour:
logger.warning("Number of contour points is reduced to %d points" % (nb_pix_max_contour))
id_selected_points = np.round(np.linspace(0, len(contour) - 1, nb_pix_max_contour), 0).astype('int')
contour = [contour[idx] for idx in id_selected_points]
# =======================================================================================
list_of_points = []
for (y, x) in contour: # Look over azimuth and range indices
# Retrieve lon/lat coordinates from range and azimtu coordinates
# if current range and azimuth are found in input range and azimuth list
if np.where(np.logical_and(lake_x == x, lake_y == y))[0].any():
point_idx = np.where(np.logical_and(lake_x == x, lake_y == y))[0][0]
lon = in_v_long[point_idx]
lat = in_v_lat[point_idx]
new_point = (lon, lat)
# Add new point :
# - if new_point not in list
# - if list contains more than 3 points : check if new points create a crossing between segments
if new_point not in list_of_points:
if len(list_of_points) < 3:
list_of_points.append(new_point)
else:
list_of_points = add_new_point_to_list_of_point(new_point, list_of_points)
# code commenté : list_of_points = addNewPoint(new_point, list_of_points)
else:
logger.debug("Point of coordinates %d, %d not found -> Point removed" % (y, x))
if not list_of_points:
logger.debug("Ring contains 0 points => Discarded")
continue
# Add first point to the end of list
ring = build_ring_from_list_of_points(list_of_points)
# Check if ring does not intersect multi ring
ring = build_ring_without_integrity_issues_multi_ring(ring, multi_ring_list)
if len(ring) > 3:
logger.debug("Adding new ring containing %d points to multi ring" % (len(ring[:-1])))
multi_ring_list.append(ring)
else:
logger.debug("Ring contains less than 2 points => Discarded")
logger.debug("Building polygon from list of rings")
lake_poly = get_ogr_polygon_from_ring_list(multi_ring_list)
if not lake_poly.IsValid():
logger.debug("Polygon is invalid -> Polygon is downgraded into a valid geometry")
lake_poly = lake_poly.Buffer(0)
else:
logger.debug("Polygon is valid")
return lake_poly
def __init__(self, in_product_type, in_pixcvec_file=None):
"""
Constructor: init variables and set them with data retrieved from pixel cloud complementary file if asked
:param in_product_type: type of product among "SP"=LakeSP and "TILE"=LakeTile
:type in_product_type: string
:param in_pixcvec_file: full path of pixel cloud complementary file
(L2_HR_PIXCVecRiver file if from PGE_RiverTile
or LakeTile_piexcvec if from PGE_LakeTile)
:type in_pixcvec_file: string
Variables of the object:
- From L2_HR_PIXCVecRiver:
- river_idx / 1D-array of int: indices of river pixels within PIXC arrays (= variable named pixc_index in L2_HR_PIXCVecRiver only)
- From both L2_HR_PIXCVecRiver and LakeTile_pixcvec:
- range_idx / 1D-array of int: range indices of water pixels (= variable named range_index in L2_HR_PIXCVecRiver and LakeTile_pixcvec)
- azimuth_idx / 1D-array of int: azimuth indices of water pixels (= variable named azimuth_index in L2_HR_PIXCVecRiver and LakeTile_pixcvec)
- longitude_vectorproc / 1D-array of float: improved longitude of water pixels (= variable named longitude_vectorproc in L2_HR_PIXCVecRiver and LakeTile_pixcvec)
- latitude_vectorproc / 1D-array of float: improved latitude of water pixels (= variable named latitude_vectorproc in L2_HR_PIXCVecRiver and LakeTile_pixcvec)
- height_vectorproc / 1D-array of float: improved height of water pixels (= variable named height_vectorproc in L2_HR_PIXCVecRiver and LakeTile_pixcvec)
- node_id / 1D-array of float: identifier associated to river node database (= variable named node_index in L2_HR_PIXCVecRiver and node_id in LakeTile_pixcvec)
- pixcvec_metadata / dict: dictionary of PIXCVec file metadata
- From L2_HR_PIXC:
- continent / string: continent covered by the tile (if global var CONTINENT_FILE exists)
- From processing:
- nb_water_pix / int: number of water pixels
- reject_index / 1D-array of int: indices of pixels that are river only, ie not reservoirs or dams
- nb_river_pix / int: number of river pixels
- lakedb_id / 1D-array of str: identifier from the lake database (= variable named lakedb_id in LakeTile_pixcvec)
- lakeobs_id / 1D-array of str: identifier associated to unknown object (= variable named lakeobs_id in LakeTile_pixcvec)
- ice_clim_flag / 1D-array of int: climatological ice flag
- ice_dyn_flag / 1D-array of int: dynamical ice flag
"""
# Get instance of service config file
self.cfg = service_config_file.get_instance()
logger = logging.getLogger(self.__class__.__name__)
logger.info("- start -")
# Init type
if in_product_type in ("TILE", "SP"):
self.product_type = in_product_type
else:
logger.debug("Product type %s unknown, set to TILE" %
in_product_type)
self.product_type = "TILE"
# Init dimension
self.nb_water_pix = 0
# Init PIXCVec variables
self.azimuth_index = None
self.range_index = None
self.longitude_vectorproc = None
self.latitude_vectorproc = None
self.height_vectorproc = None
self.node_id = None
self.lakedb_id = None
self.lakeobs_id = None
self.ice_clim_flag = None
self.ice_dyn_flag = None
self.pixcvec_metadata = {}
# Fill variables if filename available
if in_pixcvec_file is not None:
self.set_from_pixcvec_file(in_pixcvec_file)
# Init dictionary of PIXCVec metadata
self.pixcvec_metadata["cycle_number"] = -9999
self.pixcvec_metadata["pass_number"] = -9999
self.pixcvec_metadata["tile_number"] = -9999
self.pixcvec_metadata["swath_side"] = ""
self.pixcvec_metadata["tile_name"] = ""
self.pixcvec_metadata["start_time"] = ""
self.pixcvec_metadata["stop_time"] = ""
self.pixcvec_metadata["inner_first_latitude"] = -9999.0
self.pixcvec_metadata["inner_first_longitude"] = -9999.0
self.pixcvec_metadata["inner_last_latitude"] = -9999.0
self.pixcvec_metadata["inner_last_longitude"] = -9999.0
self.pixcvec_metadata["outer_first_latitude"] = -9999.0
self.pixcvec_metadata["outer_first_longitude"] = -9999.0
self.pixcvec_metadata["outer_last_latitude"] = -9999.0
self.pixcvec_metadata["outer_last_longitude"] = -9999.0
self.pixcvec_metadata["continent"] = ""
self.pixcvec_metadata["ellipsoid_semi_major_axis"] = ""
self.pixcvec_metadata["ellipsoid_flattening"] = ""
self.pixcvec_metadata["xref_static_river_db_file"] = ""
# Variables specific to processing
self.continent = None
# Specific to LakeTile processing
if self.product_type == "TILE":
self.nb_river_pix = 0 # Number of river pixels (used for TILE processing)
self.river_index = None # Indices of pixels processed by RiverTile (used in TILE processing)
self.reject_index = None # Indices of river pixels (not reservoirs)