├── example ├── example_output.pdf └── example_log.txt ├── .gitignore ├── README.md ├── helper_scripts ├── ryg_distributions.py ├── response_distributions.py └── convert_Ed_to_MESS.py ├── MESS.py └── LICENSE /example/example_output.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/niemasd/MESS/HEAD/example/example_output.pdf -------------------------------------------------------------------------------- /example/example_log.txt: -------------------------------------------------------------------------------- 1 | [2021-12-30 12:06:48] Running MESS v1.0.0 (Niema Moshiri 2021) 2 | [2021-12-30 12:06:48] MESS Command: ./MESS.py -i example/example_input.tsv -ot example/example_output.tsv -op example/example_output.pdf -rm 0.025 -rM 0.12 -ym 0.0000001 3 | [2021-12-30 12:06:48] Loading exam responses from input file: example/example_input.tsv 4 | [2021-12-30 12:06:49] Successfully loaded responses from 468 students for 40 exam questions 5 | [2021-12-30 12:06:49] Computing MESS scores... 6 | [2021-12-30 12:06:49] Finished computing 109278 pairwise MESS scores 7 | [2021-12-30 12:06:49] Processing MESS scores... 8 | [2021-12-30 12:06:49] Finished processing MESS scores. [min, max] = [0.0, 0.20644166485398668] 9 | [2021-12-30 12:06:49] Performing linear regression from log-scale MESS distribution in MESS range [0.025, 0.12]... 10 | [2021-12-30 12:06:50] Finished performing linear regression. Best fit exponential: rate = 85.1058219002228 (scale = 1/rate = 0.011750077464411188) and loc = 0.012584399110938721 11 | [2021-12-30 12:06:50] Computing theoretical p-values... 12 | [2021-12-30 12:06:50] Finished computing theoretical p-values 13 | [2021-12-30 12:06:50] Performing multiple hypothesis test correction method: Benjamini-Hochberg 14 | [2021-12-30 12:06:50] Finished computing q-values (corrected p-values) 15 | [2021-12-30 12:06:50] Writing output MESS TSV... 16 | [2021-12-30 12:06:51] Finished writing output MESS TSV: example/example_output.tsv 17 | [2021-12-30 12:06:51] Plotting MESS distribution and regression... 18 | [2021-12-30 12:06:51] MESS distribution and regression figure written to PDF: example/example_output.pdf 19 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # MESS: Moshiri Exam Similarity Score 2 | **MESS** (**M**oshiri **E**xam **S**imilarity **S**core; pun on "MOSS") is a scalable Python tool for detecting exam similarity from student responses. For information about the mathematical methods behind the tool, see the [Methods](../../wiki/Methods) section of the [MESS Wiki](../../wiki) as well as the [published paper](https://dl.acm.org/doi/abs/10.5555/3533760.3533761). 3 | 4 | ## Installation 5 | MESS is written in Python 3 and depends on [NumPy](https://numpy.org/), [SciPy](https://scipy.org/), and [seaborn](https://seaborn.pydata.org/), which can be installed using `pip3`: 6 | 7 | ```bash 8 | pip3 install numpy scipy seaborn 9 | ``` 10 | 11 | You can simply download [MESS.py](MESS.py) to your machine and make it executable: 12 | 13 | ```bash 14 | wget "https://raw.githubusercontent.com/niemasd/MESS/main/MESS.py" 15 | chmod a+x MESS.py 16 | sudo mv MESS.py /usr/local/bin/MESS.py # optional step to install globally 17 | ``` 18 | ## Usage 19 | A comprehensive list of MESS arguments can be found below, but we highly recommend following the [Tutorial](../../wiki/Tutorial) section of the [MESS Wiki](../../wiki). 20 | 21 | ``` 22 | usage: MESS.py [-h] -i INPUT -ot OUTPUT_TSV -op OUTPUT_PDF 23 | [--ignore_case] [-c CORRECTION] 24 | [-rm REG_MIN] [-rM REG_MAX] [-rd REG_XDELTA] 25 | [-kc KDE_COLOR] [-kl KDE_LINESTYLE] [-kw KDE_LINEWIDTH] 26 | [-rc REG_COLOR] [-rl REG_LINESTYLE] [-rw REG_LINEWIDTH] 27 | [-t TITLE] [-xl XLABEL] [-xm XMIN] [-xM XMAX] [-yl YLABEL] [-ym YMIN] [-yM YMAX] [--no_ylog] 28 | 29 | optional arguments: 30 | -h, --help show this help message and exit 31 | -i INPUT, --input INPUT Input Exam Responses (TSV) (default: None) 32 | -ot OUTPUT_TSV, --output_tsv OUTPUT_TSV Output MESS Spreadsheet (TSV) (default: None) 33 | -op OUTPUT_PDF, --output_pdf OUTPUT_PDF Output MESS Distribution (PDF) (default: None) 34 | --ignore_case Ignore Case in Student Responses (default: False) 35 | -nt NUM_TESTS, --num_tests NUM_TESTS Number of Significance Tests to Perform (default: None) 36 | -c CORRECTION, --correction CORRECTION Multiple Hypothesis Test Correction (options: benjamini_hochberg, bonferroni, none) (default: benjamini_hochberg) 37 | -rm REG_MIN, --reg_min REG_MIN Minimum MESS for Regression (default: None) 38 | -rM REG_MAX, --reg_max REG_MAX Maximum MESS for Regression (default: None) 39 | -rd REG_XDELTA, --reg_xdelta REG_XDELTA X Delta for Regression (default: 0.0001) 40 | -kc KDE_COLOR, --kde_color KDE_COLOR KDE Color (default: black) 41 | -kl KDE_LINESTYLE, --kde_linestyle KDE_LINESTYLE KDE Linestyle (default: --) 42 | -kw KDE_LINEWIDTH, --kde_linewidth KDE_LINEWIDTH KDE Line Width (default: 0.75) 43 | -rc REG_COLOR, --reg_color REG_COLOR Regression Color (default: black) 44 | -rl REG_LINESTYLE, --reg_linestyle REG_LINESTYLE Regression Linestyle (default: -) 45 | -rw REG_LINEWIDTH, --reg_linewidth REG_LINEWIDTH Regression Line Width (default: None) 46 | -sh, --show_hist Show Histogram (default: False) 47 | -t TITLE, --title TITLE Figure Title (default: MESS Distribution) 48 | -xl XLABEL, --xlabel XLABEL Figure X-Axis Label (default: MESS Score) 49 | -xm XMIN, --xmin XMIN Figure Minimum X (default: 0) 50 | -xM XMAX, --xmax XMAX Figure Maximum X (default: None) 51 | -yl YLABEL, --ylabel YLABEL Figure Y-Axis Label (default: Frequency) 52 | -ym YMIN, --ymin YMIN Figure Minimum Y (default: None) 53 | -yM YMAX, --ymax YMAX Figure Maximum Y (default: None) 54 | --no_ylog Don't Plot Y-Axis in Log-Scale (default: False) 55 | ``` 56 | 57 | # Citing MESS 58 | If you use MESS in your work, please cite: 59 | 60 | > Moshiri N (2022). "A scalable approach for detecting exam similarity in CS courses." *Journal of Computing Sciences in Colleges*. 37(10):8–16. [doi:10.5555/3533760.3533761](https://dl.acm.org/doi/abs/10.5555/3533760.3533761) 61 | -------------------------------------------------------------------------------- /helper_scripts/ryg_distributions.py: -------------------------------------------------------------------------------- 1 | #! /usr/bin/env python3 2 | ''' 3 | Plot the distribution of red, yellow, and green counts across all pairs of students. 4 | 5 | - Red = both students put the same wrong answer 6 | - Yellow = both students got it wrong, but put different wrong answers 7 | - Green = only 1 student got it wrong 8 | ''' 9 | 10 | # imports 11 | from os.path import isfile 12 | from matplotlib.backends.backend_pdf import PdfPages 13 | from seaborn import histplot, kdeplot, regplot 14 | from sys import stderr 15 | import argparse 16 | import matplotlib.pyplot as plt 17 | 18 | # throw error 19 | def error(message, prefix="ERROR: ", out_file=stderr): 20 | print("%s%s" % (prefix, message), file=out_file); exit(1) 21 | 22 | # main content 23 | if __name__ == "__main__": 24 | # parse user args 25 | parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.ArgumentDefaultsHelpFormatter) 26 | parser.add_argument('-i', '--input', required=True, type=str, help="Input MESS Results (TSV)") 27 | parser.add_argument('-o', '--output', required=True, type=str, help="Output Figures (PDF)") 28 | parser.add_argument('-t', '--title', required=False, type=str, default="Red, Yellow, and Green Distributions", help="Figure Title") 29 | args = parser.parse_args() 30 | args.input = args.input.strip() 31 | args.output = args.output.strip() 32 | 33 | # check user args 34 | if not isfile(args.input): 35 | error("Input file not found: %s" % args.input) 36 | if isfile(args.output): 37 | error("Output file exists: %s" % args.output) 38 | 39 | # load responses 40 | red, yellow, green, total_wrong = list(), list(), list(), list() 41 | print("Loading data: %s" % args.input) 42 | with open(args.input) as tsv: 43 | for row_num, l in enumerate(tsv): 44 | if row_num == 0: 45 | continue 46 | r, y, g = [int(v.strip()) for v in l.split('\t')[6:9]] 47 | red.append(r); yellow.append(y); green.append(g); total_wrong.append(r+y+g) 48 | data = [ 49 | (red, 'red', "Same Wrong Answer"), 50 | (yellow, 'yellow', "Different Wrong Answers"), 51 | (green, 'green', "Only 1 Missed"), 52 | ] 53 | max_val = max(max(red),max(yellow),max(green)) 54 | max_tot = max(total_wrong) 55 | bins = list(range(max_val+1)) 56 | 57 | # plot distributions 58 | with PdfPages(args.output) as pdf: 59 | # plot KDE and histogram 60 | for plot_type_s, plot_type in [("KDE",kdeplot), ("Histogram",histplot)]: 61 | print("Plotting %s..." % plot_type_s) 62 | fig, ax = plt.subplots(figsize=(10,5)) 63 | for vals, color, label in data: 64 | if plot_type is histplot: 65 | ylabel = "Number of Pairs" 66 | plot_type(data=vals, color=color, label=label, bins=bins) 67 | else: 68 | if color == 'yellow': 69 | color = 'goldenrod' 70 | ylabel = "Proportion of Pairs" 71 | plot_type(data=vals, color=color, label=label, bw_adjust=5) 72 | plt.xlim(xmin=0, xmax=max_val) 73 | plt.title(args.title) 74 | plt.xlabel("Number of Questions") 75 | plt.ylabel(ylabel) 76 | plt.legend(bbox_to_anchor=(0.995, 0.995), loc='upper right', borderaxespad=0., ncol=1) 77 | plt.tight_layout() 78 | pdf.savefig(plt.gcf()); plt.cla(); plt.clf(); plt.close('all') 79 | 80 | # plot RYG vs. total wrong 81 | print("Plotting RYG vs. total wrong...") 82 | fig, ax = plt.subplots(figsize=(10,5)) 83 | for vals, color, label in data: 84 | kdeplot(x=total_wrong, y=vals, color=color, fill=True, alpha=0.5)#, bw_adjust=1) 85 | for vals, color, label in data: 86 | regplot(x=total_wrong, y=vals, color=color, label=label, ci=None, scatter=False) 87 | plt.xlim(xmin=0, xmax=max_tot) 88 | plt.ylim(ymin=0, ymax=max_val) 89 | plt.title(args.title) 90 | plt.xlabel("Total Number of Questions Either Student Got Wrong (Red+Yellow+Green)") 91 | plt.ylabel("Number of Questions Red, Yellow, or Green") 92 | plt.legend(bbox_to_anchor=(0.005, 0.995), loc='upper left', borderaxespad=0., ncol=1) 93 | plt.tight_layout() 94 | pdf.savefig(plt.gcf()); plt.cla(); plt.clf(); plt.close('all') 95 | -------------------------------------------------------------------------------- /helper_scripts/response_distributions.py: -------------------------------------------------------------------------------- 1 | #! /usr/bin/env python3 2 | ''' 3 | Plot the distribution of student responses for each question from a spreadsheet of student responses in the MESS TSV format. 4 | ''' 5 | 6 | # imports 7 | from collections import Counter 8 | from csv import reader 9 | from matplotlib.backends.backend_pdf import PdfPages 10 | from os.path import isfile 11 | from seaborn import displot 12 | from sys import argv, stderr 13 | from warnings import filterwarnings 14 | import matplotlib.pyplot as plt 15 | filterwarnings("error") 16 | 17 | # throw error 18 | def error(message, prefix="ERROR: ", out_file=stderr): 19 | print("%s%s" % (prefix, message), file=out_file); exit(1) 20 | 21 | # load responses from MESS TSV 22 | def load_mess_responses(mess_tsv_fn): 23 | print("Loading data from: %s" % mess_tsv_fn) 24 | questions = None # list of question labels 25 | responses = dict() # responses[email][i] = response from student `email` for question `questions[i]` 26 | with open(mess_tsv_fn) as mess_tsv: 27 | for row_num, row in enumerate(reader(mess_tsv, delimiter='\t', quotechar='"')): 28 | email = row[0].strip() 29 | question_cols = [v.strip() for v in row[2:]] 30 | if row_num == 0: 31 | questions = question_cols 32 | else: 33 | if email in responses: 34 | error("Duplicate student: %s" % email) 35 | responses[email] = question_cols 36 | if questions is None: 37 | error("Input file is empty: %s" % mess_tsv_fn) 38 | if len(questions) == 0: 39 | error("No question columns in input file: %s" % mess_tsv_fn) 40 | if len(responses) == 0: 41 | error("No student rows in input file: %s" % mess_tsv_fn) 42 | return questions, responses 43 | 44 | # plot response distributions 45 | def plot_response_dists(questions, responses, pdf_fn, xlabel="Response", ylabel="Count", yscale="linear", aspect=2, xtick_rotation=90): 46 | with PdfPages(pdf_fn) as pdf: 47 | for i, q in enumerate(questions): 48 | # parse current question's responses 49 | print("Question %d of %d..." % (i+1, len(questions)), end='\r') 50 | curr_responses = [responses[email][i] for email in responses if len(responses[email][i]) != 0] 51 | curr_counts = Counter(curr_responses) 52 | curr_order = sorted(set(curr_responses), key=lambda x: curr_counts[x], reverse=True) 53 | curr_responses = [v for v in curr_order for _ in range(curr_counts[v])] # sort in descending order of count 54 | if len(curr_responses) == 0: 55 | continue # every response is empty (e.g. Parson problems on EdStem, which don't export to the output file) 56 | 57 | # create current plot 58 | fg = displot(data=curr_responses, aspect=aspect) 59 | plt.title(q) 60 | plt.xlabel(xlabel) 61 | ymin = 0 62 | if yscale != 'linear': 63 | ylabel += (' (%s-scale)' % yscale) 64 | if yscale == 'log': 65 | ymin = 1 66 | plt.ylim(ymin=ymin) 67 | plt.ylabel(ylabel) 68 | plt.yscale(yscale) 69 | plt.xticks(rotation=xtick_rotation) 70 | 71 | # add counts above each bar 72 | spots = zip(fg.ax.patches, curr_order) 73 | for spot in spots: 74 | fg.ax.text(spot[0].get_x()+spot[0].get_width()/4, spot[0].get_height(), str(curr_counts[spot[1]])) 75 | 76 | # finalize and save current plot 77 | try: 78 | plt.tight_layout() 79 | except UserWarning: 80 | plt.gcf().subplots_adjust(bottom=0.5, top=0.92) 81 | pdf.savefig(plt.gcf()) 82 | plt.cla(); plt.clf(); plt.close('all') 83 | print("Response distribution plots written to: %s" % pdf_fn) 84 | 85 | # main content 86 | if __name__ == "__main__": 87 | # parse user args 88 | if len(argv) != 3: 89 | error("%s " % argv[0], prefix="USAGE: ") 90 | if not isfile(argv[1].strip()): 91 | error("Input file not found: %s" % argv[1]) 92 | if isfile(argv[2].strip()): 93 | error("Output file exists: %s" % argv[2]) 94 | 95 | # load responses and plot distributions 96 | questions, responses = load_mess_responses(argv[1]) 97 | plot_response_dists(questions, responses, argv[2]) 98 | -------------------------------------------------------------------------------- /helper_scripts/convert_Ed_to_MESS.py: -------------------------------------------------------------------------------- 1 | #! /usr/bin/env python3 2 | ''' 3 | Convert student responses exported by the Ed platform ("Download Lesson Quiz Responses") into the MESS TSV format. 4 | 5 | Columns assumed in Ed TSVs: 6 | Name, Email, Total Score, Q1 score, Q2 score, ..., Qn score, Q1, Q1 submitted, Q2, Q2 submitted, ..., Qn, Qn submitted 7 | ''' 8 | 9 | # imports 10 | from csv import reader, writer 11 | from os.path import isfile 12 | from sys import argv, stderr 13 | import re 14 | 15 | # definitions 16 | ZERO_THRESH = 0.0001 17 | 18 | # throw error 19 | def error(message, prefix="ERROR: ", out_file=stderr): 20 | print("%s%s" % (prefix, message), file=out_file); exit(1) 21 | 22 | # load responses from Ed CSV(s) 23 | def load_ed_responses(ed_csv_fns): 24 | # prepare for loading data 25 | questions = dict() # questions[fn] = list of (column, label) tuples for questions in `fn` 26 | max_score = dict() # max_score[fn][i] = max score of question `i` in `fn` 27 | responses = dict() # responses[fn][email] = list of responses from student `email` for `fn` 28 | correct = dict() # correct[fn][email] = list of correct question indices from student `email` for `fn` 29 | 30 | # load data and return 31 | for csv_fn in ed_csv_fns: 32 | email_col = None 33 | with open(csv_fn) as csv_f: 34 | for row_num, row in enumerate(reader(csv_f)): 35 | # header row, so initialize this CSV's entry 36 | if row_num == 0: 37 | questions[csv_fn] = [(i,cell.strip()) for i,cell in enumerate(row) if re.match(r'^Q[0-9]+$', cell.strip())] 38 | responses[csv_fn] = dict(); correct[csv_fn] = dict() 39 | continue 40 | 41 | # second row tells us the email column 42 | elif row_num == 1: 43 | email_col = [i for i,cell in enumerate(row) if cell.strip().lower() == 'email'][0] 44 | continue 45 | 46 | # third row has max points 47 | elif row_num == 2 and row[6].strip().lower().startswith('points'): 48 | max_score[csv_fn] = [float(row[qi]) for qi,q in questions[csv_fn]] 49 | continue 50 | 51 | # parse student row 52 | email = row[email_col].strip() 53 | if email in responses[csv_fn]: 54 | error("Duplicate email found: %s in file %s" % (email, csv_fn)) 55 | responses[csv_fn][email] = [row[qi+1].strip() for qi,q in questions[csv_fn]] 56 | correct[csv_fn][email] = [i for i,qtup in enumerate(questions[csv_fn]) if row[qtup[0]].strip() != '' and abs(float(row[qtup[0]]) - max_score[csv_fn][i]) <= ZERO_THRESH] 57 | return questions, responses, correct 58 | 59 | # main content 60 | if __name__ == "__main__": 61 | # parse user args 62 | if len(argv) < 3: 63 | error("%s [input_Ed_responses_CSV_2] ..." % argv[0], prefix="USAGE: ") 64 | if isfile(argv[1].strip()): 65 | error("Output file exists: %s" % argv[1]) 66 | unique_fn = set() 67 | for curr_fn in argv[2:]: 68 | fn = curr_fn.strip() 69 | if ',' in fn: 70 | error("Input filenames cannot have commas: %s" % fn) 71 | if fn in unique_fn: 72 | error("Duplicate input file: %s" % fn) 73 | if not isfile(fn): 74 | error("Input file not found: %s" % fn) 75 | unique_fn.add(fn) 76 | 77 | # load responses and write to output 78 | sorted_ed_csv_fns = sorted([fn.strip() for fn in argv[2:]]) 79 | questions, responses, correct = load_ed_responses(sorted_ed_csv_fns) 80 | sorted_emails = sorted({email for csv_fn in responses for email in responses[csv_fn]}) 81 | with open(argv[1].strip(), 'w') as out_tsv_f: 82 | out_tsv = writer(out_tsv_f, delimiter='\t') 83 | out_tsv.writerow(["Email", "Correct"] + ['%s (%s)' % (csv_fn,q) for csv_fn in sorted(questions.keys()) for qi,q in questions[csv_fn]]) 84 | for email in sorted_emails: 85 | curr_correct = list(); curr_responses = list() 86 | for csv_fn in sorted_ed_csv_fns: 87 | if email in correct[csv_fn]: 88 | curr_correct += ['%s (%s)' % (csv_fn,questions[csv_fn][i][1]) for i in correct[csv_fn][email]] 89 | curr_responses += responses[csv_fn][email] 90 | else: 91 | curr_responses += ['']*len(questions[csv_fn]) 92 | out_tsv.writerow([email, ','.join(curr_correct)] + curr_responses) 93 | -------------------------------------------------------------------------------- /MESS.py: -------------------------------------------------------------------------------- 1 | #! /usr/bin/env python3 2 | ''' 3 | MESS: Moshiri Exam Similarity Score 4 | ''' 5 | 6 | # imports 7 | from csv import reader, writer 8 | from datetime import datetime 9 | from math import log 10 | from numpy import arange, histogram 11 | from os.path import isfile 12 | from scipy.stats import expon, gaussian_kde, linregress 13 | from seaborn import histplot, kdeplot 14 | from statistics import median 15 | from sys import argv, stderr, stdout 16 | from warnings import warn 17 | import argparse 18 | import matplotlib.pyplot as plt 19 | import matplotlib 20 | matplotlib.use("Agg") 21 | 22 | # constants 23 | VERSION = '1.0.9' 24 | 25 | # no correction 26 | def qvalues_nocorrection(pvalues): 27 | return list(pvalues) 28 | 29 | # bonferroni correction 30 | def qvalues_bonferroni(pvalues): 31 | len_p = len(pvalues) 32 | return [min(1, p*len_p) for p in pvalues] 33 | 34 | # benjamini-hochberg correction 35 | def qvalues_benjamini_hochberg(pvalues): 36 | len_p = len(pvalues); qvalues = [None for _ in range(len_p)] 37 | for rank, pair in enumerate(sorted((p,ind) for ind,p in enumerate(pvalues))): 38 | p, ind = pair; qvalues[ind] = min(1, p*len_p/(rank+1)) 39 | return qvalues 40 | 41 | # constants about the correction techniques 42 | CORRECTION = { 43 | 'bonferroni': { 44 | 'name': "Bonferroni", 45 | 'func': qvalues_bonferroni, 46 | }, 47 | 'benjamini_hochberg': { 48 | 'name': "Benjamini-Hochberg", 49 | 'func': qvalues_benjamini_hochberg, 50 | }, 51 | 'none': { 52 | 'name': "No Correction", 53 | 'func': qvalues_nocorrection, 54 | }, 55 | } 56 | 57 | # return the current time as a string 58 | def get_time(): 59 | return datetime.now().strftime("%Y-%m-%d %H:%M:%S") 60 | 61 | # print to stdout log (prefixed by current time) 62 | def print_log(s='', end='\n'): 63 | tmp = "[%s] %s" % (get_time(), s); print(tmp, end=end); stdout.flush() 64 | 65 | # throw error 66 | def error(message, prefix="ERROR: ", out_file=stderr): 67 | print("%s%s" % (prefix, message), file=out_file); exit(1) 68 | 69 | # load input data 70 | def load_input_data(in_tsv_fn, ignore_case=False): 71 | # prepare for loading data 72 | questions = list() # questions = list containing the question labels in the order they appear in the header 73 | question_to_ind = dict() # question_to_ind[question] = index in `questions` (`q_ind`) for `question` 74 | responses = dict() # responses[student][q_ind] = response from `student` for `q_ind` 75 | correct = dict() # correct[student] = set containing the question label indices (`q_ind`) `student` got correct 76 | 77 | # load data and return 78 | with open(in_tsv_fn) as infile: 79 | for row_num, row in enumerate(reader(infile, delimiter='\t')): 80 | # load question labels from header 81 | if row_num == 0: 82 | for q_ind, q_orig in enumerate(row[2:]): 83 | q = q_orig.strip() 84 | if q in question_to_ind: 85 | error("Duplicate question label: %s" % q) 86 | questions.append(q); question_to_ind[q] = q_ind 87 | continue 88 | 89 | # load current student's correct questions 90 | curr_student = row[0].strip(); curr_correct = set() 91 | if len(row[1].strip()) != 0: 92 | for q_orig in row[1].split(','): 93 | q = q_orig.strip() 94 | if q in curr_correct: 95 | error("Duplicate correct question: %s for student %s" % (q, curr_student)) 96 | if q not in question_to_ind: 97 | error("Student correct question label not found in header row: question '%s' for student '%s'" % (q, curr_student)) 98 | curr_correct.add(question_to_ind[q]) 99 | correct[curr_student] = curr_correct 100 | 101 | # load current student's responses 102 | if ignore_case: 103 | curr_responses = [v.strip().lower() for v in row[2:]] # lowercase responses to ignore case 104 | else: 105 | curr_responses = [v.strip() for v in row[2:]] 106 | if len(curr_responses) > len(questions): 107 | error("Row has more responses than there are questions in the header: %s" % curr_student) 108 | elif len(curr_responses) < len(questions): 109 | curr_responses += ['']*(len(questions)-len(curr_responses)) 110 | responses[curr_student] = curr_responses 111 | return questions, responses, correct 112 | 113 | # compute similarity scores 114 | def compute_mess(questions, responses, correct, ignore_case=False): 115 | # prepare helpful variables 116 | sorted_students = sorted(responses.keys()) 117 | num_students = len(sorted_students) 118 | num_questions = len(questions) 119 | 120 | # count the number of unique responses to each question 121 | response_count = [{'correct':dict(),'incorrect':dict()} for _ in range(num_questions)] # response_count[q_ind]['correct'/'incorrect'][response] = number of students who submitted `response` for `questions[q_ind]` 122 | for student in responses: 123 | for q_ind, response in enumerate(responses[student]): 124 | if q_ind in correct[student]: 125 | curr = response_count[q_ind]['correct'] 126 | else: 127 | curr = response_count[q_ind]['incorrect'] 128 | if response in curr: 129 | curr[response] += 1 130 | else: 131 | curr[response] = 1 132 | 133 | # count the number of students who got each question correct and incorrect 134 | correct_count = [0 for _ in range(num_questions)] 135 | for student in correct: 136 | for q_ind in correct[student]: 137 | correct_count[q_ind] += 1 138 | 139 | # compute MESS and proportion identical for all pairs of students 140 | mess = list() # mess = list of (proportion identical, MESS score, student1, student2, red_count, yellow_count, green_count) tuples; red = same wrong answer, yellow = diff wrong answers, green = only 1 student missed 141 | for student1_ind in range(num_students-1): 142 | student1 = sorted_students[student1_ind]; responses1 = responses[student1] 143 | for student2_ind in range(student1_ind+1, num_students): 144 | student2 = sorted_students[student2_ind]; responses2 = responses[student2] 145 | score = 0; prop_identical = 0.; red_count = 0; yellow_count = 0; green_count = 0 146 | for q_ind in range(num_questions): 147 | rs1 = responses1[q_ind]; rs2 = responses2[q_ind] 148 | if rs1 == rs2: # both students put identical answers (regardless of right or wrong) 149 | prop_identical += 1 150 | if q_ind not in correct[student1] and len(rs1) != 0: 151 | # both students put the same non-empty wrong answer 152 | red_count += 1 153 | num_wrong = sum(response_count[q_ind]['incorrect'].values()) 154 | num_diff_wrong = num_wrong - response_count[q_ind]['incorrect'][rs1] 155 | if num_diff_wrong < 0: 156 | error_message = "Number of different wrong answers was negative (%s): question '%s' for students '%s' and '%s' (%d correct, %d incorrect)" % (num_diff_wrong, questions[q_ind], student1, student2, correct_count[q_ind], num_wrong) 157 | if ignore_case: 158 | error("%s\nPerhaps '--ignore_case' is not valid for this question (e.g. correctness is case-dependent)?" % error_message) 159 | else: 160 | error(error_message) 161 | score += (float(num_diff_wrong)/num_wrong) # prop students who put a different wrong answer 162 | elif q_ind not in correct[student1] and q_ind not in correct[student2]: # both students got it wrong, but they put different wrong answers 163 | yellow_count += 1 164 | elif q_ind not in correct[student1] or q_ind not in correct[student2]: # only 1 student got it wrong 165 | green_count += 1 166 | prop_identical /= num_questions; score /= num_questions # normalize by number of questions 167 | mess.append((score, prop_identical, student1, student2, red_count, yellow_count, green_count)) 168 | return mess 169 | 170 | # perform regression on log-scale MESS distribution 171 | def regress_mess(mess_scores, reg_min, reg_max, reg_xdelta): 172 | kde = gaussian_kde(mess_scores) 173 | X = arange(reg_min, reg_max, reg_xdelta) 174 | Y = kde.logpdf(X) 175 | line = linregress(X,Y) # y = ln(L) - Lx, where L = rate parameter (lambda) of Exponential distribution 176 | rate = -1 * line.slope; scale = 1. / rate 177 | loc = (log(rate) - line.intercept)/line.slope 178 | return rate, scale, loc 179 | 180 | # plot MESS distribution + regression 181 | def plot_mess(mess_scores, scale, loc, xdelta, min_mess_test=None, kde_color='black', kde_linestyle='--', kde_linewidth=0.75, reg_color='black', reg_linestyle='-', reg_linewidth=None, title=None, xlabel=None, xmin=0, xmax=None, ylabel=None, ymin=None, ymax=None, ylog=True, show_hist=True, show_num_tests=True): 182 | fig, ax = plt.subplots() 183 | if show_hist: 184 | histplot(mess_scores, stat='density', fill=False) 185 | kdeplot(mess_scores, color=kde_color, linestyle=kde_linestyle, linewidth=kde_linewidth) 186 | if xmax is None: 187 | xmax = ax.get_xlim()[1] 188 | Xplot = arange(loc+xdelta, xmax, xdelta) 189 | Yplot = expon.pdf(Xplot, loc=loc, scale=scale) 190 | plt.plot(Xplot, Yplot, color=reg_color, linestyle=reg_linestyle) 191 | if title is not None: 192 | plt.title(title) 193 | if xlabel is not None: 194 | plt.xlabel(xlabel) 195 | if ylabel is not None: 196 | plt.ylabel(ylabel) 197 | if ylog: 198 | ax.set_yscale('log') 199 | if ymin is None: 200 | ymin = ax.get_ylim()[0] 201 | if ymax is None: 202 | ymax = ax.get_ylim()[1] 203 | if min_mess_test is not None and show_num_tests: 204 | plt.plot([min_mess_test,min_mess_test], [ymin,ymax], color='red', linestyle=':') 205 | plt.xlim(xmin=xmin, xmax=xmax); plt.ylim(ymin=ymin, ymax=ymax) 206 | return fig, ax 207 | 208 | # compute theoretical p-values 209 | def compute_pvals(mess_scores, scale, loc): 210 | unique_pvals = {s:1.-expon.cdf(s,loc=loc,scale=scale) for s in set(mess_scores)} 211 | return [unique_pvals[s] for s in mess_scores] 212 | 213 | # write output TSV 214 | def write_mess_output(output_tsv_fn, mess, p_values, q_values, rate, loc, correction): 215 | with open(output_tsv_fn, 'w') as out_tsv_f: 216 | out_tsv = writer(out_tsv_f, delimiter='\t') 217 | out_tsv.writerow(["Student 1", "Student 2", "MESS", "Proportion Identical", "p-value (rate=%s, loc=%s)" % (rate,loc), "q-value (%d tests; MESS >= %s; correction: %s)" % (args.num_tests, min_mess_test, CORRECTION[correction]['name']), "Red (same wrong answer)", "Yellow (different wrong answers)", "Green (only 1 student missed)"]) 218 | for i in range(len(mess)): 219 | m, ident, u, v, r, y, g = mess[i]; p = p_values[i] 220 | if i < len(q_values): 221 | q = q_values[i] 222 | else: 223 | q = "N/A" 224 | out_tsv.writerow([u, v, m, ident, p, q, r, y, g]) 225 | 226 | # find number of significance tests to perform 227 | def find_num_tests(mess_scores): 228 | hist, bin_edges = histogram(mess_scores, bins='auto') 229 | med = median(mess_scores) 230 | for i in range(len(hist)): 231 | if hist[i] == 0 and bin_edges[i] > med: 232 | min_mess_test = bin_edges[i]; break 233 | return min_mess_test, len([v for v in mess_scores if v > min_mess_test]) 234 | 235 | # parse user args 236 | def parse_args(): 237 | parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.ArgumentDefaultsHelpFormatter) 238 | parser.add_argument('-i', '--input', required=True, type=str, help="Input Exam Responses (TSV)") 239 | parser.add_argument('-ot', '--output_tsv', required=True, type=str, help="Output MESS Spreadsheet (TSV)") 240 | parser.add_argument('-op', '--output_pdf', required=True, type=str, help="Output MESS Distribution (PDF)") 241 | parser.add_argument('--ignore_case', action='store_true', help="Ignore Case in Student Responses") 242 | parser.add_argument('-nt', '--num_tests', required=False, type=int, default=None, help="Number of Significance Tests to Perform") 243 | parser.add_argument('-c', '--correction', required=False, type=str, default='benjamini_hochberg', help="Multiple Hypothesis Test Correction (options: %s)" % ', '.join(sorted(CORRECTION.keys()))) 244 | parser.add_argument('-rm', '--reg_min', required=False, type=float, default=None, help="Minimum MESS for Regression") 245 | parser.add_argument('-rM', '--reg_max', required=False, type=float, default=None, help="Maximum MESS for Regression") 246 | parser.add_argument('-rd', '--reg_xdelta', required=False, type=float, default=0.0001, help="X Delta for Regression") 247 | parser.add_argument('-kc', '--kde_color', required=False, type=str, default='black', help="KDE Color") 248 | parser.add_argument('-kl', '--kde_linestyle', required=False, type=str, default='--', help="KDE Linestyle") 249 | parser.add_argument('-kw', '--kde_linewidth', required=False, type=float, default=0.75, help="KDE Line Width") 250 | parser.add_argument('-rc', '--reg_color', required=False, type=str, default='black', help="Regression Color") 251 | parser.add_argument('-rl', '--reg_linestyle', required=False, type=str, default='-', help="Regression Linestyle") 252 | parser.add_argument('-rw', '--reg_linewidth', required=False, type=str, default=None, help="Regression Line Width") 253 | parser.add_argument('-sh', '--show_hist', action='store_true', help="Show Histogram") 254 | parser.add_argument('-st', '--show_num_tests', action='store_true', help="Show Number of Significance Tests Performed") 255 | parser.add_argument('-t', '--title', required=False, type=str, default="MESS Distribution", help="Figure Title") 256 | parser.add_argument('-xl', '--xlabel', required=False, type=str, default="MESS Score", help="Figure X-Axis Label") 257 | parser.add_argument('-xm', '--xmin', required=False, type=float, default=0, help="Figure Minimum X") 258 | parser.add_argument('-xM', '--xmax', required=False, type=float, default=None, help="Figure Maximum X") 259 | parser.add_argument('-yl', '--ylabel', required=False, type=str, default="Frequency", help="Figure Y-Axis Label") 260 | parser.add_argument('-ym', '--ymin', required=False, type=float, default=None, help="Figure Minimum Y") 261 | parser.add_argument('-yM', '--ymax', required=False, type=float, default=None, help="Figure Maximum Y") 262 | parser.add_argument('--no_ylog', action='store_true', help="Don't Plot Y-Axis in Log-Scale") 263 | return parser.parse_args() 264 | 265 | # main content 266 | if __name__ == "__main__": 267 | # parse and check user args 268 | args = parse_args() 269 | if not isfile(args.input): 270 | error("Input file not found: %s" % args.input) 271 | for fn in [args.output_tsv, args.output_pdf]: 272 | if isfile(fn): 273 | error("Output file exists: %s" % fn) 274 | args.correction = args.correction.lower() 275 | if args.num_tests is not None and args.num_tests < 1: 276 | error("Number of hypothesis tests needs to be positive: %s" % args.num_tests) 277 | if args.correction not in CORRECTION: 278 | error("Invalid multiple hypothesis test correction: %s\nOptions: %s" % (args.correction, ', '.join(sorted(CORRECTION.keys())))) 279 | if args.reg_min is not None: 280 | if args.reg_min <= 0: 281 | error("reg_min must be positive: %s" % args.reg_min) 282 | if args.reg_max is not None: 283 | if args.reg_max <= args.reg_min: 284 | error("reg_max must be greater than reg_min. reg_min: %s and reg_max: %s" % (args.reg_min, args.reg_max)) 285 | if args.reg_xdelta >= (args.reg_max - args.reg_min): 286 | error("reg_xdelta must be smaller than reg_max - reg_min. reg_xdelta: %s and reg_max: %s and reg_min: %s" % (args.reg_xdelta, args.reg_max, args.reg_min)) 287 | 288 | # print run information 289 | print_log("Running MESS v%s (Niema Moshiri 2021)" % VERSION) 290 | print_log("MESS Command: %s" % ' '.join(argv)) 291 | 292 | # load input data 293 | print_log("Loading exam responses from input file: %s" % args.input) 294 | questions, responses, correct = load_input_data(args.input, ignore_case=args.ignore_case) 295 | print_log("Successfully loaded responses from %d students for %d exam questions" % (len(responses), len(questions))) 296 | 297 | # compute MESS scores 298 | print_log("Computing MESS scores...") 299 | mess = compute_mess(questions, responses, correct, args.ignore_case) 300 | print_log("Finished computing %d pairwise MESS scores" % len(mess)) 301 | 302 | # process MESS scores 303 | print_log("Processing MESS scores...") 304 | mess.sort(reverse=True) # sort in descending order of MESS 305 | mess_scores = [m for m,ident,u,v,r,y,g in mess] 306 | min_mess = min(v for v in mess_scores if v > 0); max_mess = max(mess_scores) 307 | if args.reg_min is None: 308 | args.reg_min = min_mess 309 | else: 310 | args.reg_min = max(args.reg_min, min_mess) 311 | if args.reg_max is None: 312 | args.reg_max = max_mess 313 | else: 314 | args.reg_max = min(args.reg_max, max_mess) 315 | print_log("Finished processing MESS scores. [min, max] = [%s, %s]" % (min_mess, max_mess)) 316 | 317 | # perform regression 318 | print_log("Performing linear regression from log-scale MESS distribution in MESS range [%s, %s]..." % (args.reg_min, args.reg_max)) 319 | rate, scale, loc = regress_mess(mess_scores, reg_min=args.reg_min, reg_max=args.reg_max, reg_xdelta=args.reg_xdelta) 320 | print_log("Finished performing linear regression. Best fit exponential: rate = %s (scale = 1/rate = %s) and loc = %s" % (rate, scale, loc)) 321 | 322 | # compute theoretical p-values 323 | print_log("Computing theoretical p-values...") 324 | p_values = compute_pvals(mess_scores, scale, loc) 325 | print_log("Finished computing theoretical p-values") 326 | 327 | # perform multiple hypothesis test correction 328 | if args.num_tests is None: 329 | min_mess_test, args.num_tests = find_num_tests(mess_scores) 330 | else: 331 | min_mess_test = mess_scores[args.num_tests-1] 332 | print_log("Performing %d significance tests... Minimum MESS: %s" % (args.num_tests, min_mess_test)) 333 | print_log("Multiple hypothesis test correction method: %s" % CORRECTION[args.correction]['name']) 334 | q_values = CORRECTION[args.correction]['func'](p_values[:args.num_tests]) 335 | print_log("Finished computing q-values (corrected p-values)") 336 | 337 | # write output TSV 338 | print_log("Writing output MESS TSV...") 339 | write_mess_output(args.output_tsv, mess, p_values, q_values, rate, loc, args.correction) 340 | print_log("Finished writing output MESS TSV: %s" % args.output_tsv) 341 | 342 | # plot MESS distribution + regression 343 | print_log("Plotting MESS distribution and regression...") 344 | fig, ax = plot_mess(mess_scores, scale, loc, args.reg_xdelta, min_mess_test=min_mess_test, kde_color=args.kde_color, kde_linestyle=args.kde_linestyle, kde_linewidth=args.kde_linewidth, reg_color=args.reg_color, reg_linestyle=args.reg_linestyle, reg_linewidth=args.reg_linewidth, title=args.title, xlabel=args.xlabel, xmin=args.xmin, xmax=args.xmax, ylabel=args.ylabel, ymin=args.ymin, ymax=args.ymax, ylog=(not args.no_ylog), show_hist=args.show_hist, show_num_tests=args.show_num_tests) 345 | fig.savefig(args.output_pdf, format='pdf', bbox_inches='tight'); plt.close(fig) 346 | print_log("MESS distribution and regression figure written to PDF: %s" % args.output_pdf) 347 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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You may not convey a covered 525 | work if you are a party to an arrangement with a third party that is 526 | in the business of distributing software, under which you make payment 527 | to the third party based on the extent of your activity of conveying 528 | the work, and under which the third party grants, to any of the 529 | parties who would receive the covered work from you, a discriminatory 530 | patent license (a) in connection with copies of the covered work 531 | conveyed by you (or copies made from those copies), or (b) primarily 532 | for and in connection with specific products or compilations that 533 | contain the covered work, unless you entered into that arrangement, 534 | or that patent license was granted, prior to 28 March 2007. 535 | 536 | Nothing in this License shall be construed as excluding or limiting 537 | any implied license or other defenses to infringement that may 538 | otherwise be available to you under applicable patent law. 539 | 540 | 12. No Surrender of Others' Freedom. 541 | 542 | If conditions are imposed on you (whether by court order, agreement or 543 | otherwise) that contradict the conditions of this License, they do not 544 | excuse you from the conditions of this License. If you cannot convey a 545 | covered work so as to satisfy simultaneously your obligations under this 546 | License and any other pertinent obligations, then as a consequence you may 547 | not convey it at all. For example, if you agree to terms that obligate you 548 | to collect a royalty for further conveying from those to whom you convey 549 | the Program, the only way you could satisfy both those terms and this 550 | License would be to refrain entirely from conveying the Program. 551 | 552 | 13. Use with the GNU Affero General Public License. 553 | 554 | Notwithstanding any other provision of this License, you have 555 | permission to link or combine any covered work with a work licensed 556 | under version 3 of the GNU Affero General Public License into a single 557 | combined work, and to convey the resulting work. The terms of this 558 | License will continue to apply to the part which is the covered work, 559 | but the special requirements of the GNU Affero General Public License, 560 | section 13, concerning interaction through a network will apply to the 561 | combination as such. 562 | 563 | 14. Revised Versions of this License. 564 | 565 | The Free Software Foundation may publish revised and/or new versions of 566 | the GNU General Public License from time to time. Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | --------------------------------------------------------------------------------