380 remove useless statements from log file

flood_adapt/integrator/fiat_adapter.py

@@ -1,4 +1,5 @@
 import gc
+import logging
 from pathlib import Path
 from typing import List, Optional, Union
 
@@ -25,8 +26,8 @@ class FiatAdapter:
     def __init__(self, model_root: str, database_path: str) -> None:
         """Load FIAT model based on a root directory."""
         # Load FIAT template
-        self._logger = FloodAdaptLogging.getLogger(__name__)
-        self.fiat_model = FiatModel(root=model_root, mode="r", logger=self._logger)
+        self._logger = FloodAdaptLogging.getLogger(__name__, level=logging.INFO)
+        self.fiat_model = FiatModel(root=model_root, mode="r")
         self.fiat_model.read()
 
         # Get site information

flood_adapt/integrator/sfincs_adapter.py

@@ -1,4 +1,5 @@
 import gc
+import logging
 import os
 from pathlib import Path
 from typing import Optional, Union
@@ -41,8 +42,8 @@ def __init__(self, site: Site, model_root: Optional[str] = None):
         Args:
             model_root (str, optional): Root directory of overland sfincs model. Defaults to None.
         """
-        self._logger = FloodAdaptLogging.getLogger(__name__)
-        self.sf_model = SfincsModel(root=model_root, mode="r+", logger=self._logger)
+        self._logger = FloodAdaptLogging.getLogger(__name__, level=logging.INFO)
+        self.sf_model = SfincsModel(root=model_root, mode="r+")
         self.sf_model.read()
         self.site = site
 

flood_adapt/log.py

@@ -65,11 +65,32 @@ def remove_file_handler(cls, file_path: str) -> None:
                 cls.getLogger().removeHandler(handler)
 
     @classmethod
-    def getLogger(cls, name: str = None) -> logging.Logger:
+    def getLogger(cls, name: str = None, level: int = None) -> logging.Logger:
+        """Get a logger with the specified name. If no name is provided, return the root logger.
+
+        If the logger does not exist, it is created with the specified level. If no level is provided, the logger inherits the level of the root logger.
+
+        Parameters
+        ----------
+        name : str, optional
+            The name of the logger. If not provided, the root logger is returned.
+        level : int, optional
+            The level of the logger. If not provided, the logger inherits the level of the root logger.
+
+        Returns
+        -------
+        logging.Logger
+            The logger with the specified name.
+        """
         if name is None:
-            return cls._root_logger
+            logger = cls._root_logger
+        else:
+            logger = logging.getLogger(f"FloodAdapt.{name}")
+
+        if level is not None:
+            logger.setLevel(level)
 
-        return logging.getLogger(f"FloodAdapt.{name}")
+        return logger
 
     @classmethod
     def shutdown(cls):

flood_adapt/object_model/hazard/hazard.py

@@ -55,7 +55,6 @@ class Hazard:
 
     def __init__(self, scenario: ScenarioModel, database, results_dir: Path) -> None:
         self._logger = FloodAdaptLogging.getLogger(__name__)
-
         self._mode: Mode
         self.simulation_paths: List[Path]
         self.simulation_paths_offshore: List[Path]
@@ -273,6 +272,9 @@ def run_sfincs(self):
 
         run_success = True
         for simulation_path in self.simulation_paths:
+            self._logger.info(
+                f"Running SFINCS model for {'-'.join(simulation_path.parts[-2:])}."
+            )
             with cd(simulation_path):
                 sfincs_log = "sfincs.log"
                 # with open(results_dir.joinpath(f"{self.name}.log"), "a") as log_handler:
@@ -977,7 +979,7 @@ def calculate_rp_floodmaps(self):
             np.copy(zb), len(floodmap_rp), 1
         )  # if not flooded (i.e. not in valid_cells) revert to bed_level, read from SFINCS results so it is the minimum bed level in a grid cell
 
-        self._logger.info("Calculating flood risk maps, this may take some time...")
+        self._logger.info("Calculating flood risk map data, this may take some time...")
         for jj in valid_cells:  # looping over all non-masked cells.
             # linear interpolation for all return periods to evaluate
             h[:, jj] = np.interp(
@@ -998,6 +1000,7 @@ def calculate_rp_floodmaps(self):
         h[dry] = np.nan
 
         for ii, rp in enumerate(floodmap_rp):
+            self._logger.info(f"Creating flood risk map for return period {rp} years.")
             # #create single nc
             zs_rp_single = xr.DataArray(
                 data=h[ii, :], coords={"z": zs["z"]}, attrs={"units": "meters"}