[1, 1, 1, 2, 0],

[1, 1, 2, 1, 0],

[1, 1, 3, 2, 1],

[1, 2, 1, 3, 0],

[1, 2, 2, 0, 1],

[1, 2, 3, 2, 0],

[2, 1, 'a', 0, 0],

[2, 1, 'b', 2, 1],

[2, 1, 'c', 1, 0],

[2, 'q', 'a', 3, 0],

[2, 'q', 'b', 2, 1],

[2, 'q', 'c', 1, 0],

[2, 'w', 'a', 1, 0],

[2, 'w', 'b', 0, 1],

[2, 'w', 'c', 0, 0],

[3, 1, 1, 1, 0],

[3, 1, 2, 2, 1],

[3, 1, 3, 1, 0],

[3, 2, 1, 0, 0],

[3, 2, 2, 3, 1],

[3, 2, 4, 2, 0],

[4, 1, 1, 1, 0],

[4, 1, 2, 2, 1],

[4, 1, 3, 1, 0],

[4, 2, 1, 1, 0],

[4, 2, 2, 2, 1],

[4, 2, 4, 1, 0]

path = "/Users/u15672269/stat"

data_path = "/Users/u15672269/Desktop/For_Kseniya/однородность.xls"

title = "Отчет о показателях качества тестовых заданий по курсу Информатика 2018-2019 учебного года 1 семестра"

KO_I = True

KO_II = True

correlation = True

report = Document()

report.add_heading(title, 0)

if (KO_I or KO_II or correlation):

dictionary = DataReader.read_dictionary_from_excel(data_path)

data = DataReader.read_raw_data_from_excel(data_path, dictionary)

data_KO = []

keys = []

# состав вопросов в тесте

test = {}

for i in data:

if i[2] != '':

question = dictionary[i[0]][0]

val = test.get(question[0])

if val is None:

test[question[0]] = list()

test[question[0]].append(question[1])

else:

if question[1] not in test[question[0]]:

test[question[0]].append(question[1])

key = (question, i[1], i[2])

if key not in keys:

count = sum(elem[0] == i[0] and elem[1] == key[1] and elem[2] == key[2] for elem in data)

data_KO.append([i[0], i[1], i[2], count, i[4], i[5]])

keys.append(key)

print("ok")

if KO_I:

print("KO_I processing started")

formulation_stat = Stat.get_question_formulation_stat(Stat.count_formulation_stat(data_KO, dictionary))

formulation_homogeneity = {}

for key, question_stat in formulation_stat.items():

formulation_homogeneity[key] = Stat.test_formulation_homogeneity(question_stat)

DataPrinter.create_report_KO_I(report, formulation_homogeneity, path)

print("KO_I processing finished")

if KO_II:

print("KO_II processing started")

distractor_frequency_stat = Stat.get_distractor_frequency_stat(data_KO, dictionary)

distractor_homogeneity = Stat.test_distractor_homogeneity(distractor_frequency_stat, 0.05, 0, 100)

DataPrinter.create_report_KO_II(report, distractor_frequency_stat, distractor_homogeneity, path)

print("KO_II processing finished")

if correlation:

print("correlation processing started")

correlation_stat = Stat.get_correlation_matrix(test, Stat.group_stat_by_student(data, dictionary))

DataPrinter.create_report_correlation(report, correlation_stat, path)

print("correlation processing finished")

report.save(os.path.join(path, '{}.docx'.format(title)))