Mtb updates

data_loader/LEA_MTB/permeability/load_LEA_MTB_permeability_data.py → data_loader/LEA_MTB/permeability/load_single_material_permeability_data_from_LEA_MTB.py

@@ -1,4 +1,5 @@
 """Load permeability data in the mdb."""
+
 import logging
 
 from pathlib import Path
@@ -13,24 +14,27 @@
 logger = logging.getLogger(__name__)
 
 # configure logging to show terminal output
-logging.basicConfig(format='%(levelname)s:%(message)s', level=logging.INFO)
+logging.basicConfig(format="%(levelname)s:%(message)s", level=logging.INFO)
 
 
 # - - - - - - - - - - - - - - - -
 # Define paths and load probe data
 # - - - - - - - - - - - - - - - -
 
+
 def create_paths_to_LEA_MTB_measurements_from_config():
     """
     Create paths to permeability measurements from config file.
 
     :return: path2processed_measurement_data and path2probe_database
     """
     # Path to the directory, where the measurement data from the LEA Magnetics Testbench are located
-    path2measurement_data = Path(mdb.get_user_paths().external_material_data).joinpath("LEA_MTB")
+    path2measurement_data = Path(mdb.get_user_paths().external_material_data).joinpath(
+        "LEA_MTB"
+    )
 
     # Path to the directory of the processed measurement data
-    path2processed_measurement_data = path2measurement_data.joinpath("permeability_temporary").joinpath("processed")
+    path2processed_measurement_data = path2measurement_data.joinpath("permeability")
 
     # Path to the file, where all probes' data is stored centrally
     path2probe_database = path2measurement_data.joinpath("probe_database.csv")
@@ -39,22 +43,28 @@ def create_paths_to_LEA_MTB_measurements_from_config():
 
 
 # Paths to the measurement data of the LEA Magnetics testbench
-path2processed_measurement_data, path2probe_database = create_paths_to_LEA_MTB_measurements_from_config()
+path2processed_measurement_data, path2probe_database = (
+    create_paths_to_LEA_MTB_measurements_from_config()
+)
 
 # Read in the probe database as a pd.DataFrame
 df_probe_database = pd.read_csv(path2probe_database)
 print(df_probe_database)
 
 # Path to the directory, where the data shall be stored inside the materialdatabase
-path2mdb_data = Path(mdb.get_user_paths().material_data).joinpath("complex_permeability").joinpath("LEA_MTB")
+path2mdb_data = (
+    Path(mdb.get_user_paths().material_data)
+    .joinpath("complex_permeability")
+    .joinpath("LEA_MTB")
+)
 
 
 # - - - - - - - - - - - - - - - -
 # Iterate over materials:
 # - - - - - - - - - - - - - - - -
 
 # Name of the ferrite material to be processed
-material_name = Material._3F46.value
+material_name = Material.N49.value
 
 # Path to the directory, where the processed measurement data of the specified material is located
 path2processed_material_data = path2processed_measurement_data.joinpath(material_name)
@@ -65,29 +75,40 @@ def create_paths_to_LEA_MTB_measurements_from_config():
 
 else:
     # Find the directories inside the folder where the material data is stored
-    directories_in_path2processed_data = [f for f in listdir(path2processed_material_data) if isdir(path2processed_material_data.joinpath(str(f)))]
-    logger.info(f"Found {len(directories_in_path2processed_data)} directories (probe names) in {path2processed_material_data} : "
-                f"{directories_in_path2processed_data}")
+    directories_in_path2processed_data = [
+        f
+        for f in listdir(path2processed_material_data)
+        if isdir(path2processed_material_data.joinpath(str(f)))
+    ]
+    logger.info(
+        f"Found {len(directories_in_path2processed_data)} directories (probe names) in {path2processed_material_data} : "
+        f"{directories_in_path2processed_data}"
+    )
 
     # initialize an empty dataframe
     df_material = pd.DataFrame()
 
     # Iterate over probes
     for directory in directories_in_path2processed_data:
-
         # Check whether the directory name matches any entry in the probe_database.csv
         if directory in df_probe_database["probe_code"].values:
             probe_code = directory
         else:
-            logger.warning(f"The directory name {directory} does not match any probe name in the probe_database.csv \n"
-                           f"-> ({path2probe_database})")
+            logger.warning(
+                f"The directory name {directory} does not match any probe name in the probe_database.csv \n"
+                f"-> ({path2probe_database})"
+            )
             break
 
         # create path to the directory of the probe's measurement data
         path2processed_probe_data = path2processed_material_data.joinpath(probe_code)
 
         # Get all files in given directory.
-        files_in_path2processed_probe_data = [f for f in listdir(path2processed_probe_data) if isfile(path2processed_probe_data.joinpath(str(f)))]
+        files_in_path2processed_probe_data = [
+            f
+            for f in listdir(path2processed_probe_data)
+            if isfile(path2processed_probe_data.joinpath(str(f)))
+        ]
 
         # If only a single file is in the specified path, assume that it contains the correct measurement:
         if len(files_in_path2processed_probe_data) != 1:
@@ -97,7 +118,12 @@ def create_paths_to_LEA_MTB_measurements_from_config():
                 logger.warning("Too many files in path", path2processed_probe_data)
         else:
             # read in the measurement data
-            df_probe = pd.read_csv(path2processed_probe_data.joinpath(str(files_in_path2processed_probe_data[0])), encoding="latin1")
+            df_probe = pd.read_csv(
+                path2processed_probe_data.joinpath(
+                    str(files_in_path2processed_probe_data[0])
+                ),
+                encoding="latin1",
+            )
 
             # add probe column (since it's not in the CSV)
             df_probe["probe"] = probe_code
@@ -108,11 +134,15 @@ def create_paths_to_LEA_MTB_measurements_from_config():
             # append to material dataframe
             df_material = pd.concat([df_material, df_probe], ignore_index=True)
 
-            logger.info(f"Write permeability data of material {material_name} measured on probe {probe_code} into the materialdatabase.")
+            logger.info(
+                f"Write permeability data of material {material_name} measured on probe {probe_code} into the materialdatabase."
+            )
 
         # Check if reading the data was successful
         if df_material.empty:
-            logger.warning('DataFrame is empty!')
+            logger.warning("DataFrame is empty!")
         else:
             # Store processed data in csv file
-            df_material.to_csv(path2mdb_data.joinpath(f"{material_name}.csv"), index=False)
+            df_material.to_csv(
+                path2mdb_data.joinpath(f"{material_name}.csv"), index=False
+            )