deprecated.py

def deprecated_get_num_passes(filepath):
    """
    computes the number of passed tests by reading the top lines of the checker output,
    namely by counting the number of .'s

    this number will be incorrect if there's screenout in the middle of the string
    this can only be prevented by capturing all warnings and printing during all tests,
    which makes the test output significantly less useful.
    """
    with open(filepath,'r') as file:
        data = file.read()


    as_lines = data.split('\n')

    topline = as_lines.pop(0)

    while len(set(topline)-set('.EF'))>0:
        if 'Warning' in topline:
            as_lines.pop(0) # the next line is a follow-on to the warning we're skipping
            topline = as_lines.pop(0)
        else:
            topline = as_lines.pop(0)

    num_fails = len(topline) - topline.count('.')
    return topline.count('.'), topline





def deprecated_tests_to_feedback(filename):
    """
    feed this function a filename, read from a unittest file
    """
    with open(filename,'r') as file:
        raw_data = file.read()
        data = raw_data.split('\n')

    if 'Traceback' in data[0]:
        return "Your file has a critical error in it, such that the autograder was not able to execute your code, and thus was unable to run any tests:\n\n"+raw_data

    feedback = ''
    topline = data.pop(0)
    num_warnings = 0
    num_weird = 0

    found_warnings = ''
    found_weird = ''
    while len(set(topline)-set('.EF'))>0:

        if 'Warning' in topline:
            if num_warnings == 0:
                found_warnings = found_warnings+'### Detected warnings your code generated:'
            found_warnings = found_warnings+"\n\n===> Your code has a warning in it!!!  Don't ignore warnings!!!!\n\n"+topline+'\n'+data.pop(0)
            topline = data.pop(0)
            num_warnings = num_warnings+1
        else:
            if num_weird==0:
                found_weird=found_weird+'found some unexpected lines from the checker.'
            found_weird = found_weird+topline
            topline = data.pop(0)

    feedback = found_warnings + ("\n\n" if num_warnings>0 else "") + found_weird + ("\n\n" if num_weird>0 else "")
    num_fails = len(topline) - topline.count('.')

    encountered_fails = 0
    linenum=0
    while linenum < len(data):
        line = data[linenum]
        if line.startswith('FAIL: ') or line.startswith('ERROR: '):
            encountered_fails = encountered_fails+1

            #if encountered_fails==1:
            #   feedback = feedback+'### Autograder tests failed in this autochecker file:\n\n'
            d = line.split(' ')
            test_name = d[1]
            feedback = feedback+f'{encountered_fails}: {test_name}\n'
            linenum+=5
            feedback = feedback+f'\t --reason: failed {data[linenum]}\n\n'
        linenum+=1


    if encountered_fails!=num_fails:
        raise RuntimeError(f'mismatched number of not-passed tests, expected {num_fails}, found {encountered_fails}, in {filename}')
    return feedback

def deprecated_collect_num_passes_txt_format(path):
    """
    constructs a data frame, holding the number of passes, etc.
    scans all .txt files in the folder, which were presumably
    generated by running a checker against students' code.
    """

    name = []
    student_id = []
    file_number = []
    num_passes = []
    success_string = []
    feedback = []
    # canvas_name = []
    # section = []

    files = os.listdir(path)
    for f in files:
        if f.endswith('.txt'):
            d = process_filename(f)
            name.append(d['name'])
            student_id.append(int(d['student_id']))
            file_number.append(int(d['file_number']))
            q = deprecated_get_num_passes(join(path,f))
            num_passes.append(q[0])
            success_string.append(q[1])
            feedback.append(tests_to_feedback(join(path,f)))

    df = pd.DataFrame({'name':name, "student_id":student_id,
        "file_number":file_number, "num_passes":num_passes,
        "success_string":success_string, 'feedback':feedback})

    df['num_tests'] = df['success_string'].map(lambda x: len(x))
    df['percent_pass'] = df['num_passes']/df['num_tests']
    return df















def deprecated_combine_feedback(row):
    """
    a function to apply, to merge two feedbacks -- from pre and post.
    """

    if row['percent_pass_pre']==1 and row['percent_pass_post']==1:
        return "\nNice work, all automatically run tests passed!\n\n# Manual grading comments:\n\n"

    feedback = '# Manual grading comments:\n\n\n\n'
    feedback = feedback + '# Automatically generated feedback on your last submitted code'
    if row['percent_pass_pre']<1:
        feedback = feedback + '\n\n## While grading, we detected that the following issues from the provided assignment checker file:\n\n'
        feedback = feedback + row['auto_feedback_pre']

    if row['percent_pass_post']<1:
        feedback = feedback + '\n\n## While grading, we detected that the following issues from an instructor-only checker file:\n\n'
        feedback = feedback + row['auto_feedback_post']

    # put line of code indicating autograder raw score here
    return feedback



def deprecated_format_feedback(row):
    """
    a function to apply to the data frame, adding some stuff to end of the feedback
    """
    val = '\n{}\n\n{}\n'.format(row['sortable_name'],row['auto_feedback_combined'])
    val = val + '\nEnd of code feedback for {}.\n\n'.format(row['sortable_name'])
    val = val + '\n**********************\nNext student\n===================\n'
    return val


def deprecated_save_feedback(feedback, filename):
    """
    saves the feedback data frame to a file named `filename`
    """

    with open(filename,'w') as file:  # i'm not sure this needs to be here.  can remove?
        formatted = feedback.apply(format_feedback,axis=1)
        formatted.to_csv(filename,index=False,header=False)