[JTH] solve wavespectra issue to install bluemath with pip in macos a…

bluemath_tk/interpolation/rbf.py

@@ -275,6 +275,9 @@ def __init__(
         self._rbf_coeffs: pd.DataFrame = pd.DataFrame()
         self._opt_sigmas: dict = {}
 
+        # Exclude attributes to .save_model() method
+        self._exclude_attributes = []
+
     @property
     def sigma_min(self) -> float:
         return self._sigma_min

bluemath_tk/wrappers/_base_wrappers.py

@@ -424,13 +424,11 @@ def run_cases(
         cases_to_run : List[int], optional
             The list with the cases to run. Default is None.
         num_workers : int, optional
-            The number of parallel workers. Default is 1.
+            The number of parallel workers. Default is None.
         """
 
         if num_workers is None:
             num_workers = self.num_workers
-        else:
-            self.num_workers = num_workers
 
         # Get launcher command from the available launchers
         launcher = self.list_available_launchers().get(launcher, launcher)
@@ -500,7 +498,7 @@ def run_cases_in_background(
         self,
         launcher: str,
         cases_to_run: List[int] = None,
-        num_workers: int = 1,
+        num_workers: int = None,
     ) -> None:
         """
         Run the cases in the background based on the launcher specified.
@@ -514,13 +512,11 @@ def run_cases_in_background(
         cases_to_run : List[int], optional
             The list with the cases to run. Default is None.
         num_workers : int, optional
-            The number of parallel workers. Default is 1.
+            The number of parallel workers. Default is None.
         """
 
         if num_workers is None:
             num_workers = self.num_workers
-        else:
-            self.num_workers = num_workers
 
         self.status_queue = Queue()
         self.thread = threading.Thread(
@@ -600,7 +596,7 @@ def join_postprocessed_files(
     def postprocess_cases(
         self,
         cases_to_postprocess: List[int] = None,
-        num_workers: int = 1,
+        num_workers: int = None,
         write_output_nc: bool = True,
         clean_after: bool = False,
         force: bool = False,
@@ -613,7 +609,7 @@ def postprocess_cases(
         cases_to_postprocess : List[int], optional
             The list with the cases to postprocess. Default is None.
         num_workers : int, optional
-            The number of parallel workers. Default is 1.
+            The number of parallel workers. Default is None.
         write_output_nc : bool, optional
             Write the output postprocessed file. Default is True.
         clean_after : bool, optional

bluemath_tk/wrappers/swan/swan_wrapper.py

@@ -1,6 +1,8 @@
 import os
+import re
 from typing import List
 
+import pandas as pd
 import scipy.io as sio
 import xarray as xr
 
@@ -30,6 +32,7 @@ class SwanModelWrapper(BaseModelWrapper):
     available_launchers = {
         "bash": "swanrun -input input",
         "docker": "docker run --rm -v .:/case_dir -w /case_dir tausiaj/swan-geoocean:41.51 swanrun -input input",
+        "docker_serial": "docker run --rm -v .:/case_dir -w /case_dir geoocean/rocky8 swash_serial.exe",
     }
 
     output_variables = {
@@ -134,6 +137,55 @@ def _convert_case_output_files_to_nc(
 
         return ds
 
+    def get_case_percentage_from_file(self, output_log_file: str) -> str:
+        """
+        Get the case percentage from the output log file.
+
+        Parameters
+        ----------
+        output_log_file : str
+            The output log file.
+
+        Returns
+        -------
+        str
+            The case percentage.
+        """
+
+        if not os.path.exists(output_log_file):
+            return "0 %"
+
+        progress_pattern = r"OK in\s+(\d+\.\d+)\s*%"
+        with open(output_log_file, "r") as f:
+            for line in reversed(f.readlines()):
+                match = re.search(progress_pattern, line)
+                if match:
+                    return f"{match.group(1)} %"
+
+        return "0 %"  # if no progress is found
+
+    def monitor_cases(self) -> pd.DataFrame:
+        """
+        Monitor the cases and log relevant information.
+
+        Returns
+        -------
+        pd.DataFrame
+            The cases percentage.
+        """
+
+        cases_percentage = {}
+
+        for case_dir in self.cases_dirs:
+            # TODO: Try to generalise the output log file name with launcher??
+            output_log_file = os.path.join(case_dir, "wrapper_out.log")
+            progress = self.get_case_percentage_from_file(
+                output_log_file=output_log_file
+            )
+            cases_percentage[os.path.basename(case_dir)] = progress
+
+        return pd.DataFrame(cases_percentage.items(), columns=["Case", "Percentage"])
+
     def postprocess_case(
         self,
         case_num: int,

pyproject.toml

@@ -43,15 +43,17 @@ dependencies = [
     "minisom",
     "statsmodels",
     "regionmask",
-    "wavespectra",
     "cdsapi",
+    "ipykernel",
+    "ipywidgets",
 ]
 
 [project.optional-dependencies]
 
 docs = ["sphinx", "sphinx-rtd-theme", "jupyter-sphinx"]
 deep = ["tensorflow", "keras"]
 num-models = ["hydromt-sfincs"]
+waves = ["wavespectra"]
 
 [project.urls]