Add python script for rate coefficients conversion

scripts_python/rate_coef.py

@@ -0,0 +1,66 @@
+import re
+import pandas as pd
+
+file_path = "rate_coef_Sn.dat"
+
+with open(file_path, "r") as f:
+    content = f.read()
+
+lines = content.splitlines()
+
+# Dictionary to store tables with their respective dataframes
+tables = {}
+current_table = None
+headers = []
+data_rows = []
+
+# Mapping for the short names to full descriptions
+full_name_map = {
+    "ci": "electron_impact_direct_ionization",
+    "ea": "electron_impact_excitation_autoionization",
+    "rr": "radiative_recombination",
+    "dr": "dielectronic_recombination",
+    "bb": "bound_bound_radiative_loss_rates",
+    "bf": "recombination_radiative_loss_rates",
+    "csd": "charge_state_distribution"
+}
+
+# Regular expression to detect a table header (e.g., "ci temp Sn0+ Sn1+ ...")
+header_pattern = re.compile(r'^(ci|ea|rr|dr|bb|bf|csd)\s+temp')
+
+# Iterate through each line in the file
+for line in lines:
+    if header_pattern.match(line):
+        if current_table and headers and data_rows:
+            headers = ['temp' if h == 'temp' else re.sub(r'\D', '', h) for h in headers]
+            df = pd.DataFrame(data_rows, columns=headers)
+            df.iloc[:, 0] = pd.to_numeric(df.iloc[:, 0], errors='coerce')  # Convert temp column to numeric
+            df.set_index(headers[0], inplace=True)
+            tables[current_table] = df
+
+        parts = line.strip().split()
+        current_table = parts[0]
+        headers = parts[1:]
+        data_rows = []
+    elif current_table and line.strip() and not line.strip().startswith("#"):
+        parts = line.strip().split()
+        if len(parts) == len(headers) + 1:
+            data_rows.append(parts)
+        elif len(parts) == len(headers): 
+            data_rows.append(parts)
+
+# Save the last table
+if current_table and headers and data_rows:
+    df = pd.DataFrame(data_rows, columns=headers)
+    df.iloc[:, 0] = pd.to_numeric(df.iloc[:, 0], errors='coerce')
+    df.set_index(headers[0], inplace=True)
+    tables[current_table] = df
+
+# Save all tables to CSV files with full descriptive names
+csv_paths = {}
+for name, df in tables.items():
+    full_name = full_name_map.get(name, name)  # Default to name if no mapping found
+    path = f"{full_name}.csv"
+    df.to_csv(path)
+    csv_paths[name] = path
+