Recovered history

JsonGenerator.py

@@ -0,0 +1,89 @@
+# Author: Anderrow
+# Read the README.md file for more info.
+# https://github.com/anderrow
+
+import pandas as pd
+import json
+import os
+import re
+
+path = "../ProcessViewMessages.xlsm"
+# Read all sheets into a dictionary of DataFrames
+dfs = pd.read_excel(path, sheet_name=None)
+
+# --------------------------------FILTER DATAFRAMES--------------------------------#
+del dfs["ProtonView"]  # ProtonView sheet is not needed
+del dfs["Pointer overview"]  # Pointer Overview is not needed
+
+df_filtered_dict = {}  # Init
+
+for sheet_name, df in dfs.items():
+    # Replace NaN with an empty string before converting the third column to string
+    df.iloc[:, 2] = df.iloc[:, 2].fillna('')
+
+    # Convert the third column to string to avoid issues with non-string values
+    df.iloc[:, 2] = df.iloc[:, 2].astype(str)
+
+    # Filter dataframe erasing rows that ends with Undefine (Case sensitive is set to false, Nan values are skipped)
+    df_filtered = df[~df.iloc[:, 2].str.contains(r'.*Undefined$', case=False, na=False)]
+
+    # Filter dataframe for 'Unknown message'
+    df_filtered2 = df_filtered[~df_filtered.iloc[:, 2].str.contains('Unkown message', case=False, na=False)]
+
+    # Select the second column (Keys) and the third column (Messages)
+    df_filtered_subset = df_filtered2.iloc[:, 1:9]
+
+    # Erase rows if there is a NaN value (Not message found)
+    df_cleaned = df_filtered_subset[df_filtered_subset.iloc[:, 1] != ""]
+
+    # Keep the dataframe filtered
+    df_filtered_dict[sheet_name] = df_cleaned
+
+    # Print Before and after
+    print("=" * 50)
+    print(f"Sheet: {sheet_name}")
+    print(f"Original length: {len(df)}")
+    print(f"Filtered length: {len(df_cleaned)}")
+    print(f"Data has been reduced by a {100 - len(df_cleaned) * 100 / len(df):.2f}%")
+    print("=" * 50)
+
+# --------------------------------END OF FILTER DATAFRAMES--------------------------------#
+
+# --------------------------------GENERATE JSON FILES--------------------------------#
+
+#Define the column name pattern need it with regex for avoid extrange file names. 
+column_name_pattern = r'^[a-z]{2}-[A-Z]{2}$'
+
+#Verify that the columns have a correct name
+for sheet_name, df in df_filtered_dict.items():
+    for column in range(1, df.shape[1]):
+        column_name = df.columns[column] #Keep name of the column with index column  
+
+        #verify that the column complies with the defined regex pattern
+        if not re.match(column_name_pattern, column_name):
+            raise ValueError(f"\n The column name '{column_name}' doesn't have the requiered format [a-z][a-z]-[A-Z][A-Z]")
+
+# Loop to save the JSON files
+for sheet_name, df in df_filtered_dict.items():
+    # Create the folder in the parent directory if it doesn't exist
+    output_folder = f"../JSON FILES/{sheet_name}"
+    os.makedirs(output_folder, exist_ok=True)
+    print("*"*50)
+
+    #Loop to save each column of the dataframe
+    for column in range(1, df.shape[1]):
+        column_name = df.columns[column] #Keep name of the column with index column  
+
+        column_name_simple = re.sub(r"^([a-zA-Z]+)-.*", r"\1", column_name)
+
+        # Generate the name of the file based on the name of the excel sheet
+        json_filename = os.path.join(output_folder, f"{column_name_simple}.json")
+
+        # Convert the DataFrame to a dictionary with Column1 as the key and Column2 as the value
+        df_to_json = dict(zip(df.iloc[:, 0], df.iloc[:, column]))
+
+        with open(json_filename, 'w') as json_file:
+            json.dump(df_to_json, json_file)  # Saving without indent for a compact format
+
+        print(f"JSON file {json_filename} has been created")
+# --------------------------------END OF GENERATE JSON FILES--------------------------------#

README.md

@@ -1,5 +1,5 @@
 # JSON_Generator
-This is a JSON generator programmed in Python. It automatically generates a Python file based on the information provided in the 'ProcessViewMessages.xlsm' Excel file.
+This is a JSON generator programmed in Python. It automatically generates a JSON file based on the information provided in the 'ProcessViewMessages.xlsm' Excel file.
 ---  
 It's much better to run this program again on the Python engine; anyway:
 If .exe is needed: