def test_ScaleAndShift_test_a(): preprocessing_queue = [preprocessing.ScaleAndShift()] s = ( '[[{"x":232,"y":423,"time":1407885913983},' '{"x":267,"y":262,"time":1407885914315},' '{"x":325,"y":416,"time":1407885914650}],' '[{"x":252,"y":355,"time":1407885915675},' '{"x":305,"y":351,"time":1407885916361}]]' ) a = HandwrittenData(s) a.preprocessing(preprocessing_queue) s = a.get_pointlist() expectation = [ [ {"y": 1.0, "x": 0.0, "time": 0}, {"y": 0.0, "x": 0.2174, "time": 332}, {"y": 0.9565, "x": 0.5776, "time": 667}, ], [ {"y": 0.5776, "x": 0.1242, "time": 1692}, {"y": 0.5528, "x": 0.4534, "time": 2378}, ], ] assert testhelper.compare_pointlists( s, expectation ), f"Got: {s}; expected {expectation}"
def test_ScaleAndShift_test_a_center(): preprocessing_queue = [preprocessing.ScaleAndShift(center=True)] s = ( '[[{"y": 1.0, "x": -0.3655913978494625, "time": 0}, ' '{"y": 0.0, "x": -0.1482000935016364, "time": 332}, ' '{"y": 0.9565, "x": 0.21204835370333253, "time": 667}], ' '[{"y": 0.5776, "x": -0.24136779536499045, "time": 1692}, ' '{"y": 0.5528, "x": 0.08782475121886046, "time": 2378}]]' ) a = HandwrittenData(s) a.preprocessing(preprocessing_queue) s = a.get_pointlist() expectation = [ [ {"y": 1.0, "x": -0.2888198757763975, "time": 0}, {"y": 0.0, "x": -0.07142857142857142, "time": 332}, {"y": 0.9565, "x": 0.2888198757763975, "time": 667}, ], [ {"y": 0.5776, "x": -0.16459627329192547, "time": 1692}, {"y": 0.5528, "x": 0.16459627329192544, "time": 2378}, ], ] assert testhelper.compare_pointlists( s, expectation ), f"Got: {s}; expected {expectation}"
def get_all_symbols_as_handwriting(): handwritings = [] for symbol_file in get_all_symbols(): with open(symbol_file) as f: data = f.read() handwritings.append(HandwrittenData(data)) return handwritings
def test_ScaleAndShift_test_simple_5(): preprocessing_queue = [preprocessing.ScaleAndShift()] s = '[[{"x":42, "y":12, "time": 10}]]' a = HandwrittenData(s) a.preprocessing(preprocessing_queue) s = a.get_pointlist() expectation = [[{"x": 0, "y": 0, "time": 0}]] assert s == expectation, f"Got: {s}; expected {expectation}"
def ScaleAndShift_test_simple_4(): preprocessing_queue = [preprocessing.ScaleAndShift()] s = '[[{"x":0, "y":0, "time": 10}]]' a = HandwrittenData(s) a.preprocessing(preprocessing_queue) s = a.get_pointlist() expectation = [[{"x": 0, "y": 0, "time": 0}]] assert s == expectation, "Got: %s; expected %s" % (s, expectation)
def get_recordings(mysql, symbol_id): """ Parameters ---------- mysql : dict Connection information symbol_id : int ID of a symbol on write-math.com Returns ------- list : A list of HandwrittenData objects """ connection = pymysql.connect(host=mysql['host'], user=mysql['user'], passwd=mysql['passwd'], db=mysql['db'], cursorclass=pymysql.cursors.DictCursor) cursor = connection.cursor() # Get the data recordings = [] sql = ("SELECT `id`, `data`, `is_in_testset`, `wild_point_count`, " "`missing_line`, `user_id` " "FROM `wm_raw_draw_data` " "WHERE `accepted_formula_id` = %s" % str(symbol_id)) cursor.execute(sql) raw_datasets = cursor.fetchall() for raw_data in raw_datasets: try: handwriting = HandwrittenData(raw_data['data'], symbol_id, raw_data['id'], "no formula in latex", raw_data['wild_point_count'], raw_data['missing_line'], raw_data['user_id']) recordings.append(handwriting) except Exception as e: logging.info("Raw data id: %s", raw_data['id']) logging.info(e) return recordings
def main(destination=os.path.join(utils.get_project_root(), "raw-datasets"), dataset='all', renderings=False): """Main part of the backup script.""" time_prefix = time.strftime("%Y-%m-%d-%H-%M") filename = ("%s-handwriting_datasets-%s-raw.pickle" % (time_prefix, dataset.replace('/', '-'))) destination_path = os.path.join(destination, filename) logging.info("Data will be written to '%s'", destination_path) cfg = utils.get_database_configuration() mysql = cfg['mysql_online'] connection = pymysql.connect(host=mysql['host'], user=mysql['user'], passwd=mysql['passwd'], db=mysql['db'], cursorclass=pymysql.cursors.DictCursor) cursor = connection.cursor() formulas = get_formulas(cursor, dataset) logging.info('Received %i formulas.', len(formulas)) handwriting_datasets = [] formula_id2latex = {} # Go through each formula and download every raw_data instance for formula in formulas: formula_id2latex[formula['id']] = formula['formula_in_latex'] sql = (( "SELECT `wm_raw_draw_data`.`id`, `data`, `is_in_testset`, " "`wild_point_count`, `missing_line`, `user_id`, " "`display_name` " "FROM `wm_raw_draw_data` " "JOIN `wm_users` ON " "(`wm_users`.`id` = `wm_raw_draw_data`.`user_id`) " "WHERE `accepted_formula_id` = %s " # "AND `display_name` LIKE 'MfrDB::%%'" ) % str(formula['id'])) cursor.execute(sql) raw_datasets = cursor.fetchall() logging.info("%s (%i)", formula['formula_in_latex'], len(raw_datasets)) for raw_data in raw_datasets: try: handwriting = HandwrittenData( raw_data['data'], formula['id'], raw_data['id'], formula['formula_in_latex'], raw_data['wild_point_count'], raw_data['missing_line'], raw_data['user_id'], user_name=raw_data['display_name']) handwriting_datasets.append({ 'handwriting': handwriting, 'id': raw_data['id'], 'formula_id': formula['id'], 'formula_in_latex': formula['formula_in_latex'], 'is_in_testset': raw_data['is_in_testset'] }) except Exception as e: logging.info("Raw data id: %s", raw_data['id']) logging.info(e) pickle.dump( { 'handwriting_datasets': handwriting_datasets, 'formula_id2latex': formula_id2latex }, open(destination_path, "wb"), 2) if renderings: logging.info("Start downloading SVG renderings...") svgfolder = tempfile.mkdtemp() sql = """SELECT t1.formula_id, t1.svg from wm_renderings t1 LEFT JOIN wm_renderings t2 ON t1.formula_id = t2.formula_id AND t1.creation_time < t2.creation_time WHERE t2.id is null""" cursor.execute(sql) formulas = cursor.fetchall() logging.info("Create svg...") for formula in formulas: filename = os.path.join(svgfolder, "%s.svg" % str(formula['formula_id'])) with open(filename, 'wb') as temp_file: temp_file.write(formula['svg']) logging.info("Tar at %s", os.path.abspath("renderings.tar")) tar = tarfile.open("renderings.tar.bz2", "w:bz2") for fn in os.listdir(svgfolder): filename = os.path.join(svgfolder, fn) if os.path.isfile(filename): print(filename) tar.add(filename, arcname=os.path.basename(filename)) tar.close()
def width_test(): with open(testhelper.get_symbol(97705)) as f: data = f.read() assert HandwrittenData(data).get_width() == 186, \ "Got %i" % HandwrittenData(data).get_width()
def load_symbol_test(): for symbol_file in testhelper.get_all_symbols(): with open(symbol_file) as f: data = f.read() a = HandwrittenData(data) assert isinstance(a, HandwrittenData)
def get_symbol_as_handwriting(raw_data_id): symbol_file = get_symbol(raw_data_id) with open(symbol_file) as f: data = f.read() a = HandwrittenData(data) return a
def main(dataset='all'): """ Parameters ---------- dataset : string Either 'all' or a path to a yaml symbol file. """ cfg = utils.get_database_configuration() mysql = cfg['mysql_online'] connection = pymysql.connect(host=mysql['host'], user=mysql['user'], passwd=mysql['passwd'], db=mysql['db'], cursorclass=pymysql.cursors.DictCursor) cursor = connection.cursor() # TODO: no formulas, only single-symbol ones. formulas = get_formulas(cursor, dataset) prob = {} # Go through each formula and download every raw_data instance for formula in formulas: stroke_counts = [] recordings = [] sql = (( "SELECT `wm_raw_draw_data`.`id`, `data`, `is_in_testset`, " "`wild_point_count`, `missing_line`, `user_id`, " "`display_name` " "FROM `wm_raw_draw_data` " "JOIN `wm_users` ON " "(`wm_users`.`id` = `wm_raw_draw_data`.`user_id`) " "WHERE `accepted_formula_id` = %s " "AND wild_point_count=0 " "AND has_correction=0 " # "AND `display_name` LIKE 'MfrDB::%%'" ) % str(formula['id'])) cursor.execute(sql) raw_datasets = cursor.fetchall() logging.info("%s (%i)", formula['formula_in_latex'], len(raw_datasets)) for raw_data in raw_datasets: try: handwriting = HandwrittenData(raw_data['data'], formula['id'], raw_data['id'], formula['formula_in_latex'], raw_data['wild_point_count'], raw_data['missing_line'], raw_data['user_id']) stroke_counts.append(len(handwriting.get_pointlist())) recordings.append(handwriting) except Exception as e: logging.info("Raw data id: %s", raw_data['id']) logging.info(e) if len(stroke_counts) > 0: logging.info("\t[%i - %i]", min(stroke_counts), max(stroke_counts)) median = numpy.median(stroke_counts) logging.info("\tMedian: %0.2f\tMean: %0.2f\tstd: %0.2f", median, numpy.mean(stroke_counts), numpy.std(stroke_counts)) # Make prob s = sorted(Counter(stroke_counts).items(), key=lambda n: n[1], reverse=True) key = formula['formula_in_latex'] prob[key] = {} for stroke_nr, count in s: prob[key][stroke_nr] = count # Outliers modes = get_modes(stroke_counts) logging.info("\tModes: %s", modes) exceptions = [] for rec in recordings: if len(rec.get_pointlist()) not in modes: url = (("http://www.martin-thoma.de/" "write-math/view/?raw_data_id=%i - " "%i strokes") % (rec.raw_data_id, len(rec.get_pointlist()))) dist = get_dist(len(rec.get_pointlist()), modes) exceptions.append((url, len(rec.get_pointlist()), dist)) print_exceptions(exceptions, max_print=10) else: logging.debug( "No recordings for symbol " "'http://www.martin-thoma.de/" "write-math/symbol/?id=%s'.", formula['id']) write_prob(prob, "prob_stroke_count_by_symbol.yml")