mysticmomba/AirlockTools
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Changed how the path exclusions get read back in after approval, now paths can be combined in the review process

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2025-08-29 09:08:05 -04:00
parent 9a7de412f6
commit fa4fc6bd7e
2 changed files with 3 additions and 3 deletions
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AirlockTools.py
+2 -2
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@@ -372,8 +372,8 @@ def menu_prepare_to_enforce():
if os.path.exists(f"dataframe_csv\\allinfo_{first_policy}_{second_policy}.csv") and os.path.exists(f"manuallyapproved\\lcf_needs_approved_{first_policy}_{second_policy}.csv"): if os.path.exists(f"dataframe_csv\\allinfo_{first_policy}_{second_policy}.csv") and os.path.exists(f"manuallyapproved\\lcf_needs_approved_{first_policy}_{second_policy}.csv"):
df1 = tryToReadCSV(f"dataframe_csv\\allinfo_{first_policy}_{second_policy}.csv") df1 = tryToReadCSV(f"dataframe_csv\\allinfo_{first_policy}_{second_policy}.csv")
pathexclusions,allowbyhash = utils.pathfunctions.mask_from_csv(df1, f"manuallyapproved\\lcf_needs_approved_{first_policy}_{second_policy}.csv","longestcfp") allowbyhash = utils.pathfunctions.mask_from_csv(df1, f"manuallyapproved\\lcf_needs_approved_{first_policy}_{second_policy}.csv","longestcfp")
pathexclusions = tryToReadCSV(f"manuallyapproved\\lcf_needs_approved_{first_policy}_{second_policy}.csv")
pathexclusions.to_csv(f"preflight\\final_path_exclusions_{first_policy}_{second_policy}.csv", index=False) pathexclusions.to_csv(f"preflight\\final_path_exclusions_{first_policy}_{second_policy}.csv", index=False)
ct.style_dataframe_dark(pathexclusions, f"preflight\\final_path_exclusions_{first_policy}_{second_policy}.html") ct.style_dataframe_dark(pathexclusions, f"preflight\\final_path_exclusions_{first_policy}_{second_policy}.html")
utils/pathfunctions.py
+1 -1
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@@ -116,7 +116,7 @@ def mask_from_csv(df, csv_path, filepath_col):
# Keep only rows in df that are in approved_files # Keep only rows in df that are in approved_files
masked_df = df[df[filepath_col].isin(approved_files)].copy() masked_df = df[df[filepath_col].isin(approved_files)].copy()
remainder = df[~df[filepath_col].isin(approved_files)].copy() remainder = df[~df[filepath_col].isin(approved_files)].copy()
return masked_df, remainder return remainder
def filter_and_drop(approved, eligiblepaths, min_hashes): def filter_and_drop(approved, eligiblepaths, min_hashes):