first draft of pathfunctions

utils/hashfunctions.py

@@ -91,43 +91,3 @@ def categorizeHashes(aug_df: pd.DataFrame, threat_tolerance: int, untrusted_publ
    remaining_df = df[~(mask_needsreview | mask_approved)]
 
    return needsreview_df, approved_df, remaining_df
-
- 
-"""
-def categorizeHashes(aug_df: pd.DataFrame, threat_tolerance: int, untrusted_publishers: list):
-    
-    Categorize hashes into needsreview, approved, and remaining based on publisher and threat level.
-    
-    
-    if untrusted_publishers is None:
-        untrusted_publishers = []
-
-    # Flatten threatlevel from nested reputation dict
-    df = aug_df.copy()  
-
-    # Masks for each category
-    mask_needsreview = ((df["publisher_y"] == "Not Signed") & reputationtool(df)) 
-
-    print(mask_needsreview)
-
-    mask_approved = (df["publisher_y"] != "Not Signed") & (~df["publisher_y"].isin(untrusted_publishers))
-
-    print(mask_approved)
-
-    # Create DataFrames for each category
-    needsreview_df = df[mask_needsreview]
-    approved_df = df[mask_approved]
-    remaining_df = df[~(mask_needsreview | mask_approved)]
-
-    return needsreview_df, approved_df, remaining_df
-
-def approvehashes(approved_df: pd.DataFrame):
-    pass
-
-def reputationtool(df):
-    if df["reputation_scannermatch"] == "N/A":
-        return True
-    if df["reputation_scannermatch"].astype(int) > 3:
-        return True
-    return False
-    """