vangef/BBGradebookOrganiser
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added inspect_gradebook & code restructure for 'inspect by hash' feature

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vangef
2023-03-10 12:50:14 +00:00
parent aff2644676
commit 9ab782dcc9
3 changed files with 83 additions and 32 deletions
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86
utils/inspector.py
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@@ -3,8 +3,9 @@ from datetime import datetime
import csv
import hashlib
import pandas as pd
from functools import partial
CSV_DIR = os.path.join(os.getcwd(), 'csv')
from utils.settings import CSV_DIR
def load_excluded_filenames(submissions_dir_name: str) -> list[str]: # helper function for hashing all files
@@ -38,50 +39,85 @@ def get_hashes_in_dir(dir_path: str, excluded_filenames: list = []) -> list: #
return hash_list
def hash_submissions(submissions_dir_path: str) -> str: # main function for hashing all files
os.makedirs(CSV_DIR, exist_ok=True)
submissions_dir_name = os.path.abspath(submissions_dir_path).split(os.path.sep)[-1] # get name of submission/assignment by separating path and use rightmost part
excluded_filenames = load_excluded_filenames(submissions_dir_name)
def generate_hashes_gradebook(gradebook_dir_path: str) -> str: # main function for hashing all files in gradebook
gradebook_dir_name = os.path.abspath(gradebook_dir_path).split(os.path.sep) # get name of gradebook by separating path and use rightmost part
if not os.path.isdir(gradebook_dir_path):
exit(f'Directory {gradebook_dir_path} does not exist.\nMake sure "{gradebook_dir_name}" exists in "BB_gradebooks".\n')
dicts_with_hashes_list = get_hashes_in_dir(gradebook_dir_path)
for hash_dict in dicts_with_hashes_list:
student_id = hash_dict['filename'].split('_attempt_')[0].split('_')[-1]
del hash_dict['filepath']
hash_dict.update({'Student ID': student_id})
csv_file_name = f'{submissions_dir_name}_file_hashes_{datetime.now().strftime("%Y%m%d-%H%M%S")}.csv'
os.makedirs(CSV_DIR, exist_ok=True)
csv_file_name = f'{gradebook_dir_name}_gradebook_file_hashes_{datetime.now().strftime("%Y%m%d-%H%M%S")}.csv'
csv_file_path = os.path.join(CSV_DIR, csv_file_name)
with open(csv_file_path, 'w', newline='') as csvfile: # open the output CSV file for writing
fieldnames = ['Student ID', 'filename', 'sha256 hash']
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
writer.writeheader()
writer.writerows(dicts_with_hashes_list)
print(f'[INFO] Created CSV file with all files & hashes in gradebook: {gradebook_dir_name}\nCSV file: {csv_file_path}')
return csv_file_path
def generate_hashes_submissions(submissions_dir_path: str) -> str: # main function for hashing all files in submissions
submissions_dir_name = os.path.abspath(submissions_dir_path).split(os.path.sep)[-1] # get name of submission/assignment by separating path and use rightmost part
if not os.path.isdir(submissions_dir_path):
exit(f'Directory {submissions_dir_path} does not exist.\nMake sure "{submissions_dir_name}" exists in "BB_submissions".\n')
excluded_filenames = load_excluded_filenames(submissions_dir_name)
dicts_with_hashes_list = []
for student_dir_name in os.listdir(submissions_dir_path): # loop through each student dir to get hashes for all files per student
student_dir_path = os.path.join(submissions_dir_path, student_dir_name)
student_dicts_with_hashes_list = get_hashes_in_dir(student_dir_path, excluded_filenames) # dict with hashes for all student files - except for 'excluded' file names
student_dicts_list = []
for hash_dict in student_dicts_with_hashes_list:
hash_dict.update({'Student ID': student_dir_name}) # update hash records with student id
student_dicts_list.append(hash_dict) # append file dict to student list of dict for csv export
dicts_with_hashes_list.append(student_dicts_list) # append student hashes to main list with all submissions
os.makedirs(CSV_DIR, exist_ok=True)
csv_file_name = f'{submissions_dir_name}_submissions_file_hashes_{datetime.now().strftime("%Y%m%d-%H%M%S")}.csv'
csv_file_path = os.path.join(CSV_DIR, csv_file_name)
with open(csv_file_path, 'w', newline='') as csvfile: # open the output CSV file for writing
fieldnames = ['Student ID', 'filepath', 'filename', 'sha256 hash']
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
writer.writeheader()
for student_dir_name in os.listdir(submissions_dir_path): # loop through each student dir to get hashes for all files per student
student_dir_path = os.path.join(submissions_dir_path, student_dir_name)
hashes_dict = get_hashes_in_dir(student_dir_path, excluded_filenames) # dict with hashes for all student files - except for 'excluded' file names
for d in hashes_dict:
d.update({'Student ID': student_dir_name}) # update hash records with student id
writer.writerows(hashes_dict)
print(f'[INFO] Created CSV file with all files & hashes in {submissions_dir_name}\nCSV file: {csv_file_path}')
for student_dict in dicts_with_hashes_list:
writer.writerows(student_dict)
print(f'[INFO] Created CSV file with all files & hashes for submissions in: {submissions_dir_name}\nCSV file: {csv_file_path}')
return csv_file_path
def inspect_for_duplicate_hashes(hashes_csv_file_path: str): # main function for finding duplicate / suspicious hashes
def generate_duplicate_hashes_generic(hashes_csv_file_path: str, drop_columns: list[str]):
csv = pd.read_csv(hashes_csv_file_path)
df = pd.DataFrame(csv) # df with all files and their hashes
drop_columns = ['filepath', 'filename'] # only need to keep 'student id' and 'sha256 hash' for groupby later
df_clean = df.drop(columns=drop_columns) # clear not needed columns
duplicate_hash = df_clean.loc[df_clean.duplicated(subset=['sha256 hash'], keep=False), :] # all files with duplicate hash - incl. files from the same student id
# agg() for 'Student ID' True if more than 1 in groupby (= files with the same hash by multiple student ids)
# False if unique (= files from the same student id with the same hash)
hash_with_multiple_student_ids = duplicate_hash.groupby('sha256 hash').agg(lambda x: len(x.unique())>1)
# list with duplicate hashes - only if different student id (doesn't include files from same student id)
suspicious_hashes_list = hash_with_multiple_student_ids[hash_with_multiple_student_ids['Student ID']==True].index.to_list()
duplicate_hashes_list = hash_with_multiple_student_ids[hash_with_multiple_student_ids['Student ID']==True].index.to_list()
files_with_suspicious_hash = df[df['sha256 hash'].isin(suspicious_hashes_list)] # df with all files with duplicate/suspicious hash, excludes files from the same student id
df_suspicious = files_with_suspicious_hash.sort_values(['sha256 hash', 'Student ID']) # sort before output to csv
files_with_duplicate_hash = df[df['sha256 hash'].isin(duplicate_hashes_list)] # df with all files with duplicate hash, excludes files from the same student id
df_duplicate = files_with_duplicate_hash.sort_values(['sha256 hash', 'Student ID']) # sort before output to csv
gradebook_or_submissions_str = os.path.basename(hashes_csv_file_path).split('_file_hashes_')[0].split('_')[-1] # 'gradebook' or 'submissions' depending on which files hashes csv is read
assignment_name = os.path.basename(hashes_csv_file_path).split(f'_{gradebook_or_submissions_str}_')[0]
csv_out = hashes_csv_file_path.rsplit('_', 1)[0].replace('file_hashes', 'duplicate_') + datetime.now().strftime("%Y%m%d-%H%M%S") + '.csv'
try:
submissions_dir_name = os.path.basename(hashes_csv_file_path).split('_file_hashes_')[0]
csv_out = hashes_csv_file_path.rsplit('_', 1)[0].replace('file_hashes', 'suspicious_') + datetime.now().strftime("%Y%m%d-%H%M%S") + '.csv'
df_suspicious.to_csv(csv_out, index=False)
print(f'[INFO] Created CSV file with duplicate/suspicious hashes in {submissions_dir_name}\nCSV file: {csv_out}')
df_duplicate.to_csv(csv_out, index=False)
print(f'[INFO] Created CSV file with duplicate hashes in {gradebook_or_submissions_str}: {assignment_name}\nCSV file: {csv_out}')
except Exception as e:
exit(f'[ERROR] Something went wrong while trying to save csv file with suspicious hashes\nError message: {e}')
exit(f'[ERROR] Something went wrong while trying to save csv file with duplicate hashes\nError message: {e}')
# partials for generate_duplicate_hashes_generic(), setting the appropriate drop_columns for gradebook / submissions
generate_duplicate_hashes_gradebook = partial(generate_duplicate_hashes_generic, drop_columns=['filename'])
generate_duplicate_hashes_submissions = partial(generate_duplicate_hashes_generic, drop_columns=['filepath', 'filename'])