def create_default_tabs(self, solutions, params_to_print, all_params,
run_date, total_seconds, ranks, categorical):
''' Creates all tabs.
Args:
solutions (list of Solution): list with solutions that
must be displayed, can have 1, 2 or 4 elements.
params_to_print (Parameters): parameters that should be
displayed.
all_params (list of str): list with strings that should
appear before printing every solution from solutions.
run_date (datetime): date and time when the problem was solved.
total_seconds (float): time (in seconds) needed to solve
the problem.
ranks (list of dict of str to double):
list that contains dictionaries that map DMU code
to peel the onion rank.
categorical (str): name of the categorical variable
used in categorical analysis.
'''
writer = XLSWriter(params_to_print, self, run_date, total_seconds,
ranks=ranks, categorical=categorical)
nb_models = len(solutions)
# +1 for parameters sheet, it is stored separately
nb_sheets = len(writer.worksheets) + 1
progress_recorder = GuiProgress(self.parent.progress_bar, nb_models,
nb_sheets)
self.last_modified_tab = -1
self.clear_all_tabs()
self.nb_filled_tabs = 0
for count, sol in enumerate(solutions):
writer.write_data(sol, all_params[count], progress_recorder)
self.modify_tab_names()
self.remove_unused_tabs()
progress_recorder.set_position(100)
def test_all_constraints_multiplier_model(data):
model = MultiplierModelBase(data, 0, MultiplierInputOrientedModel())
bounds = {'I1': (None, 0.4)}
model = MultiplierModelWithVirtualWeightRestrictions(model, bounds)
abs_bounds = {'I2': (None, 0.2)}
model = MultiplierModelWithAbsoluteWeightRestrictions(model, abs_bounds)
ratio_bounds = {('I1', 'I2'): (None, 0.4), ('O1', 'O2'): (0.01, None)}
model = MultiplierModelWithPriceRatioConstraints(model, ratio_bounds)
start_time = datetime.datetime.now()
model_solution = model.run()
end_time = datetime.datetime.now()
utils_for_tests.check_if_category_is_within_abs_limits(
model_solution, abs_bounds)
utils_for_tests.check_if_category_is_within_virtual_limits(
model_solution, bounds)
utils_for_tests.check_if_category_is_within_price_ratio_constraints(
model_solution, ratio_bounds)
work_book = Workbook()
writer = XLSWriter(Parameters(), work_book, datetime.datetime.today(),
(end_time - start_time).total_seconds())
writer.write_data(model_solution)
work_book.save('tests/test_all_constraints_multi_output.xls')
def test_maximize_slacks_usual_weakly_disposable_vars():
categories, xls_data, dmu_name, sheet_name = read_data(
'tests/DEA_example2_data.xls')
coefficients, has_same_dmus = convert_to_dictionary(xls_data)
assert has_same_dmus is False
assert validate_data(categories, coefficients) is True
data = construct_input_data_instance(categories, coefficients)
data.add_input_category('I1')
data.add_input_category('I2')
data.add_input_category('I3')
data.add_output_category('O1')
data.add_output_category('O2')
model = EnvelopmentModelVRSDecorator(
EnvelopmentModelBase(
data,
EnvelopmentModelInputOriented(
generate_upper_bound_for_efficiency_score),
DisposableVarsConstraintCreator(set(['I2', 'O1']))))
model = MaximizeSlacksModel(model)
start_time = datetime.datetime.now()
model_solution = model.run()
end_time = datetime.datetime.now()
utils_for_tests.check_optimal_solution_status_and_sizes(
model_solution, data)
dmus = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K']
utils_for_tests.check_efficiency_scores(
dmus, [1, 0.86998617, 1, 1, 1, 1, 1, 1, 1, 0.6386574, 1],
model_solution, data)
work_book = xlwt.Workbook()
writer = XLSWriter(Parameters(), work_book, datetime.datetime.today(),
(end_time - start_time).total_seconds())
writer.write_data(model_solution)
work_book.save('tests/test_max_slacks_weakly_disp_vars_output.xls')
clean_up_pickled_files()
def test_maximize_slacks_usual_weakly_disposable_vars():
categories, xls_data, dmu_name, sheet_name = read_data(
'tests/DEA_example2_data.xls')
coefficients, has_same_dmus = convert_to_dictionary(xls_data)
assert has_same_dmus is False
assert validate_data(categories, coefficients) is True
data = construct_input_data_instance(categories, coefficients)
data.add_input_category('I1')
data.add_input_category('I2')
data.add_input_category('I3')
data.add_output_category('O1')
data.add_output_category('O2')
model = EnvelopmentModelVRSDecorator(
EnvelopmentModelBase(data,
EnvelopmentModelInputOriented(
generate_upper_bound_for_efficiency_score),
DisposableVarsConstraintCreator(
set(['I2', 'O1']))))
model = MaximizeSlacksModel(model)
start_time = datetime.datetime.now()
model_solution = model.run()
end_time = datetime.datetime.now()
utils_for_tests.check_optimal_solution_status_and_sizes(
model_solution, data)
dmus = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K']
utils_for_tests.check_efficiency_scores(dmus, [1, 0.86998617, 1, 1, 1, 1,
1, 1, 1, 0.6386574, 1],
model_solution, data)
work_book = xlwt.Workbook()
writer = XLSWriter(Parameters(), work_book, datetime.datetime.today(),
(end_time - start_time).total_seconds())
writer.write_data(model_solution)
work_book.save('tests/test_max_slacks_weakly_disp_vars_output.xls')
clean_up_pickled_files()
def test_all_constraints_multiplier_model(data):
model = MultiplierModelBase(data, 0,
MultiplierInputOrientedModel())
bounds = {'I1': (None, 0.4)}
model = MultiplierModelWithVirtualWeightRestrictions(model, bounds)
abs_bounds = {'I2': (None, 0.2)}
model = MultiplierModelWithAbsoluteWeightRestrictions(model, abs_bounds)
ratio_bounds = {('I1', 'I2'): (None, 0.4), ('O1', 'O2'): (0.01, None)}
model = MultiplierModelWithPriceRatioConstraints(model, ratio_bounds)
start_time = datetime.datetime.now()
model_solution = model.run()
end_time = datetime.datetime.now()
utils_for_tests.check_if_category_is_within_abs_limits(
model_solution, abs_bounds)
utils_for_tests.check_if_category_is_within_virtual_limits(
model_solution, bounds)
utils_for_tests.check_if_category_is_within_price_ratio_constraints(
model_solution, ratio_bounds)
work_book = xlwt.Workbook()
writer = XLSWriter(Parameters(), work_book, datetime.datetime.today(),
(end_time - start_time).total_seconds())
writer.write_data(model_solution)
work_book.save('tests/test_all_constraints_multi_output.xls')
def test_super_efficiency_medium(DEA_example2_data):
data = DEA_example2_data
data.add_input_category('I1')
data.add_input_category('I2')
data.add_input_category('I3')
data.add_output_category('O1')
data.add_output_category('O2')
model = EnvelopmentModelBase(
data,
EnvelopmentModelInputOriented(generate_supper_efficiency_upper_bound),
DefaultConstraintCreator())
super_efficiency_model = SupperEfficiencyModel(model)
start_time = datetime.datetime.now()
solution = super_efficiency_model.run()
end_time = datetime.datetime.now()
utils_for_tests.check_optimal_solution_status_and_sizes(solution, data)
dmus = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K']
utils_for_tests.check_efficiency_scores(dmus, [
1.652173892, 0.6485875732, 0.909247759, 1.116838534, 0.5490716156,
2.485294106, 1.244945494, 0.824120607, 2.299898658, 0.6267333816,
1.088235274
], solution, data, 1e-6)
work_book = xlwt.Workbook()
writer = XLSWriter(Parameters(), work_book, datetime.datetime.today(),
(end_time - start_time).total_seconds())
writer.write_data(solution)
work_book.save('tests/test_super_efficiency_output.xls')
def test_super_efficiency_medium(DEA_example2_data):
data = DEA_example2_data
data.add_input_category('I1')
data.add_input_category('I2')
data.add_input_category('I3')
data.add_output_category('O1')
data.add_output_category('O2')
model = EnvelopmentModelBase(
data,
EnvelopmentModelInputOriented(generate_supper_efficiency_upper_bound),
DefaultConstraintCreator())
super_efficiency_model = SupperEfficiencyModel(model)
start_time = datetime.datetime.now()
solution = super_efficiency_model.run()
end_time = datetime.datetime.now()
utils_for_tests.check_optimal_solution_status_and_sizes(solution, data)
dmus = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K']
utils_for_tests.check_efficiency_scores(
dmus,
[1.652173892, 0.6485875732, 0.909247759,
1.116838534, 0.5490716156, 2.485294106,
1.244945494, 0.824120607, 2.299898658,
0.6267333816, 1.088235274],
solution, data, 1e-6)
work_book = xlwt.Workbook()
writer = XLSWriter(Parameters(), work_book, datetime.datetime.today(),
(end_time - start_time).total_seconds())
writer.write_data(solution)
work_book.save('tests/test_super_efficiency_output.xls')
def test_run_with_categorical_dmus(categorical_from_book):
data = categorical_from_book
model = MultiplierModelBase(data, 0,
MultiplierInputOrientedModel())
categorical_model = ModelWithCategoricalDMUs(model, 'Category')
start_time = datetime.datetime.now()
solution = categorical_model.run()
end_time = datetime.datetime.now()
dmus = ['L1', 'L2', 'L3', 'L4', 'L5', 'L6', 'L7', 'L8', 'L9', 'L10',
'L11', 'L12',
'L13', 'L14', 'L15', 'L16', 'L17', 'L18', 'L19', 'L20',
'L21', 'L22', 'L23']
utils_for_tests.check_efficiency_scores(dmus, [0.377, 0.879, 0.936, 1, 1, 1,
0.743, 0.648, 1, 0.815,
0.646,
0.835, 0.794, 0.835, 1,
0.687, 1, 0.787, 1,
0.849, 0.787, 0.681, 1],
solution, data, 1e-3)
work_book = xlwt.Workbook()
writer = XLSWriter(Parameters(), work_book, datetime.datetime.today(),
(end_time - start_time).total_seconds(),
categorical='Category')
writer.write_data(solution)
work_book.save('tests/test_categorical_output.xls')
def test_run_with_categorical_dmus(categorical_from_book):
data = categorical_from_book
model = MultiplierModelBase(data, 0,
MultiplierInputOrientedModel())
categorical_model = ModelWithCategoricalDMUs(model, 'Category')
start_time = datetime.datetime.now()
solution = categorical_model.run()
end_time = datetime.datetime.now()
dmus = ['L1', 'L2', 'L3', 'L4', 'L5', 'L6', 'L7', 'L8', 'L9', 'L10',
'L11', 'L12',
'L13', 'L14', 'L15', 'L16', 'L17', 'L18', 'L19', 'L20',
'L21', 'L22', 'L23']
utils_for_tests.check_efficiency_scores(dmus, [0.377, 0.879, 0.936, 1, 1, 1,
0.743, 0.648, 1, 0.815,
0.646,
0.835, 0.794, 0.835, 1,
0.687, 1, 0.787, 1,
0.849, 0.787, 0.681, 1],
solution, data, 1e-3)
work_book = Workbook()
writer = XLSWriter(Parameters(), work_book, datetime.datetime.today(),
(end_time - start_time).total_seconds(),
categorical='Category')
writer.write_data(solution)
work_book.save('tests/test_categorical_output.xls')
def test_write_data_xls(model):
start_time = datetime.datetime.now()
model_solution = model.run()
end_time = datetime.datetime.now()
work_book = xlwt.Workbook()
params = Parameters()
writer = XLSWriter(params, work_book, datetime.datetime.today(),
(end_time - start_time).total_seconds())
writer.write_data(model_solution)
work_book.save('tests/test_output.xls')
def test_abs_weight_restrictions_multiplier_model(data):
base_model = MultiplierModelBase(data, 0, MultiplierInputOrientedModel())
bounds = {'I2': (0.01, 0.5)}
model = MultiplierModelWithAbsoluteWeightRestrictions(base_model, bounds)
start_time = datetime.datetime.now()
model_solution = model.run()
end_time = datetime.datetime.now()
utils_for_tests.check_if_category_is_within_abs_limits(
model_solution, bounds)
work_book = Workbook()
writer = XLSWriter(Parameters(), work_book, datetime.datetime.today(),
(end_time - start_time).total_seconds())
writer.write_data(model_solution)
work_book.save('tests/test_abs_weights_multi_output.xls')
bounds = {'I2': (None, 0.05)}
model = MultiplierModelWithAbsoluteWeightRestrictions(base_model, bounds)
start_time = datetime.datetime.now()
model_solution = model.run()
end_time = datetime.datetime.now()
utils_for_tests.check_if_category_is_within_abs_limits(
model_solution, bounds)
work_book2 = Workbook()
writer = XLSWriter(Parameters(), work_book2, datetime.datetime.today(),
(end_time - start_time).total_seconds())
writer.write_data(model_solution)
work_book2.save('tests/test_abs_weights_upper_bound_multi_output.xls')
def test_virtual_weight_restrictions_multiplier_model(data):
model = MultiplierModelBase(data, 0,
MultiplierInputOrientedModel())
bounds = {'I1': (None, 0.5)}
model = MultiplierModelWithVirtualWeightRestrictions(model, bounds)
start_time = datetime.datetime.now()
model_solution = model.run()
end_time = datetime.datetime.now()
utils_for_tests.check_if_category_is_within_virtual_limits(
model_solution, bounds)
work_book = xlwt.Workbook()
writer = XLSWriter(Parameters(), work_book, datetime.datetime.today(),
(end_time - start_time).total_seconds())
writer.write_data(model_solution)
work_book.save('tests/test_virtual_weights_multi_output.xls')
def test_virual_restrictions_env_model(data):
model = EnvelopmentModelBase(data,
EnvelopmentModelInputOriented(
generate_upper_bound_for_efficiency_score),
DefaultConstraintCreator())
bounds = {'I2': (0.01, 0.5)}
model = EnvelopmentModelWithVirtualWeightRestrictions(model, bounds)
start_time = datetime.datetime.now()
model_solution = model.run()
end_time = datetime.datetime.now()
utils_for_tests.check_if_category_is_within_virtual_limits(
model_solution, bounds)
work_book = xlwt.Workbook()
writer = XLSWriter(Parameters(), work_book, datetime.datetime.today(),
(end_time - start_time).total_seconds())
writer.write_data(model_solution)
work_book.save('tests/test_virtual_constraints_env_output.xls')
def test_price_ratio_restrictions_env_model(data):
model = EnvelopmentModelBase(data,
EnvelopmentModelInputOriented(
generate_upper_bound_for_efficiency_score),
DefaultConstraintCreator())
bounds = {('I1', 'I2'): (None, 0.4), ('O1', 'O2'): (0.01, None)}
model = EnvelopmentModelWithPriceRatioConstraints(model, bounds)
start_time = datetime.datetime.now()
model_solution = model.run()
end_time = datetime.datetime.now()
utils_for_tests.check_if_category_is_within_price_ratio_constraints(
model_solution, bounds)
work_book = xlwt.Workbook()
writer = XLSWriter(Parameters(), work_book, datetime.datetime.today(),
(end_time - start_time).total_seconds())
writer.write_data(model_solution)
work_book.save('tests/test_price_ratio_env_output.xls')
def test_price_ratio_restrictions_env_model(data):
model = EnvelopmentModelBase(
data,
EnvelopmentModelInputOriented(
generate_upper_bound_for_efficiency_score),
DefaultConstraintCreator())
bounds = {('I1', 'I2'): (None, 0.4), ('O1', 'O2'): (0.01, None)}
model = EnvelopmentModelWithPriceRatioConstraints(model, bounds)
start_time = datetime.datetime.now()
model_solution = model.run()
end_time = datetime.datetime.now()
utils_for_tests.check_if_category_is_within_price_ratio_constraints(
model_solution, bounds)
work_book = Workbook()
writer = XLSWriter(Parameters(), work_book, datetime.datetime.today(),
(end_time - start_time).total_seconds())
writer.write_data(model_solution)
work_book.save('tests/test_price_ratio_env_output.xls')
def test_virual_restrictions_env_model(data):
model = EnvelopmentModelBase(
data,
EnvelopmentModelInputOriented(
generate_upper_bound_for_efficiency_score),
DefaultConstraintCreator())
bounds = {'I2': (0.01, 0.5)}
model = EnvelopmentModelWithVirtualWeightRestrictions(model, bounds)
start_time = datetime.datetime.now()
model_solution = model.run()
end_time = datetime.datetime.now()
utils_for_tests.check_if_category_is_within_virtual_limits(
model_solution, bounds)
work_book = Workbook()
writer = XLSWriter(Parameters(), work_book, datetime.datetime.today(),
(end_time - start_time).total_seconds())
writer.write_data(model_solution)
work_book.save('tests/test_virtual_constraints_env_output.xls')
def test_super_efficiency_with_VRS(DEA_example2_data):
data = DEA_example2_data
data.add_input_category('I1')
data.add_input_category('I2')
data.add_input_category('I3')
data.add_output_category('O1')
data.add_output_category('O2')
model = EnvelopmentModelBase(
data,
EnvelopmentModelInputOriented(generate_supper_efficiency_upper_bound),
DefaultConstraintCreator())
model = EnvelopmentModelVRSDecorator(model)
super_efficiency_model = SupperEfficiencyModel(model)
start_time = datetime.datetime.now()
solution = super_efficiency_model.run()
end_time = datetime.datetime.now()
work_book = xlwt.Workbook()
writer = XLSWriter(Parameters(), work_book, datetime.datetime.today(),
(end_time - start_time).total_seconds())
writer.write_data(solution)
work_book.save('tests/test_super_efficiency_with_VRS.xls')
def test_super_efficiency_with_VRS(DEA_example2_data):
data = DEA_example2_data
data.add_input_category('I1')
data.add_input_category('I2')
data.add_input_category('I3')
data.add_output_category('O1')
data.add_output_category('O2')
model = EnvelopmentModelBase(
data,
EnvelopmentModelInputOriented(generate_supper_efficiency_upper_bound),
DefaultConstraintCreator())
model = EnvelopmentModelVRSDecorator(model)
super_efficiency_model = SupperEfficiencyModel(model)
start_time = datetime.datetime.now()
solution = super_efficiency_model.run()
end_time = datetime.datetime.now()
work_book = xlwt.Workbook()
writer = XLSWriter(Parameters(), work_book, datetime.datetime.today(),
(end_time - start_time).total_seconds())
writer.write_data(solution)
work_book.save('tests/test_super_efficiency_with_VRS.xls')
def test_peel_the_onion_CRS_multi_output_oriented(DEA_example2_data):
model = _create_large_model_CRS_multi_output_oriented_with_non_discretionary(
DEA_example2_data)
start_time = datetime.datetime.now()
solution, ranks, state = peel_the_onion_method(model)
end_time = datetime.datetime.now()
_check_large_model_CRS_multi_output_oriented_with_non_discretionary(
solution, model.input_data)
dmus = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K']
expected_ranks = [1, 3, 2, 1, 3, 1, 1, 2, 1, 2, 1]
utils_for_tests.check_onion_ranks(
model.input_data, dmus, expected_ranks, ranks)
work_book = xlwt.Workbook()
ranks_as_list = []
ranks_as_list.append(ranks)
writer = XLSWriter(Parameters(), work_book, datetime.datetime.today(),
(end_time - start_time).total_seconds(),
ranks=ranks_as_list)
writer.write_data(solution)
work_book.save('tests/test_peel_the_onion.xls')
def test_peel_the_onion_CRS_multi_output_oriented(DEA_example2_data):
model = _create_large_model_CRS_multi_output_oriented_with_non_discretionary(
DEA_example2_data)
start_time = datetime.datetime.now()
solution, ranks, state = peel_the_onion_method(model)
end_time = datetime.datetime.now()
_check_large_model_CRS_multi_output_oriented_with_non_discretionary(
solution, model.input_data)
dmus = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K']
expected_ranks = [1, 3, 2, 1, 3, 1, 1, 2, 1, 2, 1]
utils_for_tests.check_onion_ranks(
model.input_data, dmus, expected_ranks, ranks)
work_book = Workbook()
ranks_as_list = []
ranks_as_list.append(ranks)
writer = XLSWriter(Parameters(), work_book, datetime.datetime.today(),
(end_time - start_time).total_seconds(),
ranks=ranks_as_list)
writer.write_data(solution)
work_book.save('tests/test_peel_the_onion.xls')
def post_process_solutions(self, solutions, params, param_strs, all_ranks,
run_date, total_seconds):
''' See base class.
'''
output_file = auto_name_if_needed(self.params, self.output_format,
self.output_dir)
if output_file:
categorical = self.params.get_parameter_value(
'CATEGORICAL_CATEGORY')
if not categorical.strip():
categorical = None
if output_file.endswith('.xls'):
work_book = xlwt.Workbook()
elif output_file.endswith('.xlsx'):
work_book = XlsxWorkbook()
elif output_file.endswith('.csv'):
work_book = TxtWriter(os.path.splitext(output_file)[0])
else:
raise ValueError('File {0} has unsupported output format'.format
(output_file))
writer = XLSWriter(self.params, work_book, run_date, total_seconds,
ranks=all_ranks, categorical=categorical)
try:
for count, sol in enumerate(solutions):
writer.write_data(sol, param_strs[count])
work_book.save(output_file)
except ValueError:
work_book = TxtWriter(os.path.splitext(output_file)[0])
writer = XLSWriter(self.params, work_book, run_date,
total_seconds, ranks=all_ranks,
categorical=categorical)
for count, sol in enumerate(solutions):
writer.write_data(sol, param_strs[count])
work_book.save(output_file)
def test_abs_restrictions_env_model_output(data):
filename = 'tests/DEA_Harish_parameters.txt'
params = parse_parameters_from_file(filename)
categories, data, dmu_name, sheet_name = read_data(
params.get_parameter_value('DATA_FILE'))
coefficients, has_same_dmus = convert_to_dictionary(data)
model_input = construct_input_data_instance(categories, coefficients)
models, all_params = build_models(params, model_input)
assert len(models) == 1 and len(all_params) == 1
model = models[0]
start_time = datetime.datetime.now()
model_solution = model.run()
end_time = datetime.datetime.now()
bounds = {'Urban Roads (%)': (None, 0.003)}
utils_for_tests.check_if_category_is_within_abs_limits(
model_solution, bounds)
work_book = xlwt.Workbook()
writer = XLSWriter(Parameters(), work_book, datetime.datetime.today(),
(end_time - start_time).total_seconds())
writer.write_data(model_solution)
work_book.save('tests/test_abs_constraints_env_outoriented_output.xls')
def test_abs_restrictions_env_model_output(data):
filename = 'tests/DEA_Harish_parameters.txt'
params = parse_parameters_from_file(filename)
categories, data, dmu_name, sheet_name = read_data(
params.get_parameter_value('DATA_FILE'))
coefficients, has_same_dmus = convert_to_dictionary(data)
model_input = construct_input_data_instance(categories, coefficients)
models, all_params = build_models(params, model_input)
assert len(models) == 1 and len(all_params) == 1
model = models[0]
start_time = datetime.datetime.now()
model_solution = model.run()
end_time = datetime.datetime.now()
bounds = {'Urban Roads (%)': (None, 0.003)}
utils_for_tests.check_if_category_is_within_abs_limits(
model_solution, bounds)
work_book = Workbook()
writer = XLSWriter(Parameters(), work_book, datetime.datetime.today(),
(end_time - start_time).total_seconds())
writer.write_data(model_solution)
work_book.save('tests/test_abs_constraints_env_outoriented_output.xls')
def post_process_solutions(self, solutions, params, param_strs, all_ranks,
run_date, total_seconds):
''' See base class.
'''
output_file = auto_name_if_needed(self.params, self.output_format,
self.output_dir)
if output_file:
categorical = self.params.get_parameter_value(
'CATEGORICAL_CATEGORY')
if not categorical.strip():
categorical = None
if output_file.endswith('.xls'):
work_book = xlwt.Workbook()
elif output_file.endswith('.xlsx'):
work_book = XlsxWorkbook()
elif output_file.endswith('.csv'):
work_book = TxtWriter(os.path.splitext(output_file)[0])
else:
raise ValueError(
'File {0} has unsupported output format'.format(
output_file))
writer = XLSWriter(self.params,
work_book,
run_date,
total_seconds,
ranks=all_ranks,
categorical=categorical)
try:
for count, sol in enumerate(solutions):
writer.write_data(sol, param_strs[count])
work_book.save(output_file)
except ValueError:
work_book = TxtWriter(os.path.splitext(output_file)[0])
writer = XLSWriter(self.params,
work_book,
run_date,
total_seconds,
ranks=all_ranks,
categorical=categorical)
for count, sol in enumerate(solutions):
writer.write_data(sol, param_strs[count])
work_book.save(output_file)
def on_save_solution(self):
''' Saves solution to file.
This method is called when save solution button is pressed.
If there is a solution, this method will ask user to provide
a file name where solution should be stored. If a valid name
is provided,
solution is saved to that file. Allowed file formats are:
.xls and .xlsx.
Default file extension is .xlsx.
If the user checked 'csv' as solution output format, then
the user will be asked to choose a directory where all csv files
will be written.
'''
if self.model_solutions is not None:
assert(self.param_strs is not None)
if (self.solution_format_var.get() == 1 or
self.solution_format_var.get() == 2):
file_name = self.ask_file_name_to_save(
self.solution_format_var.get())
dir_name = ''
else:
dir_name = askdirectory()
file_name = ''
if file_name or dir_name:
print(file_name)
self.status_lbl.config(text='Saving solution to file...')
if file_name.endswith('.xls'):
work_book = xlwt.Workbook()
elif file_name.endswith('.xlsx'):
work_book = XlsxWorkbook()
else:
# all not supported formats will be written to csv
assert(dir_name)
work_book = TxtWriter(dir_name)
writer = XLSWriter(self.params, work_book, self.run_date,
self.total_seconds,
ranks=self.ranks,
categorical=self.categorical)
nb_models = len(self.model_solutions)
# +1 for parameters sheet, it is stored separately
nb_sheets = len(writer.worksheets) + 1
progress_recorder = GuiProgress(self.progress_bar, nb_models,
nb_sheets)
try:
for count, sol in enumerate(self.model_solutions):
writer.write_data(sol, self.param_strs[count],
progress_recorder)
work_book.save(file_name)
except ValueError:
# can happen if maximum number of rows is exceeded
self.status_lbl.config(
text='File is too large for xls format,'
' it will be saved to csv instead')
work_book = TxtWriter(os.path.splitext(file_name)[0])
writer = XLSWriter(self.params, work_book, self.run_date,
self.total_seconds,
ranks=self.ranks,
categorical=self.categorical)
progress_recorder.set_position(0)
for count, sol in enumerate(self.model_solutions):
writer.write_data(sol, self.param_strs[count],
progress_recorder)
work_book.save(file_name)
progress_recorder.set_position(100)
self.parent.change_solution_tab_name('Solution')
self.status_lbl.config(text='Solution saved')
def on_save_solution(self):
''' Saves solution to file.
This method is called when save solution button is pressed.
If there is a solution, this method will ask user to provide
a file name where solution should be stored. If a valid name
is provided,
solution is saved to that file. Allowed file formats are:
.xls and .xlsx.
Default file extension is .xlsx.
If the user checked 'csv' as solution output format, then
the user will be asked to choose a directory where all csv files
will be written.
'''
if self.model_solutions is not None:
assert (self.param_strs is not None)
if (self.solution_format_var.get() == 1
or self.solution_format_var.get() == 2):
file_name = self.ask_file_name_to_save(
self.solution_format_var.get())
dir_name = ''
else:
dir_name = askdirectory()
file_name = ''
if file_name or dir_name:
print(file_name)
self.status_lbl.config(text='Saving solution to file...')
if file_name.endswith('.xls'):
work_book = xlwt.Workbook()
elif file_name.endswith('.xlsx'):
work_book = XlsxWorkbook()
else:
# all not supported formats will be written to csv
assert (dir_name)
work_book = TxtWriter(dir_name)
writer = XLSWriter(self.params,
work_book,
self.run_date,
self.total_seconds,
ranks=self.ranks,
categorical=self.categorical)
nb_models = len(self.model_solutions)
# +1 for parameters sheet, it is stored separately
nb_sheets = len(writer.worksheets) + 1
progress_recorder = GuiProgress(self.progress_bar, nb_models,
nb_sheets)
try:
for count, sol in enumerate(self.model_solutions):
writer.write_data(sol, self.param_strs[count],
progress_recorder)
work_book.save(file_name)
except ValueError:
# can happen if maximum number of rows is exceeded
self.status_lbl.config(
text='File is too large for xls format,'
' it will be saved to csv instead')
work_book = TxtWriter(os.path.splitext(file_name)[0])
writer = XLSWriter(self.params,
work_book,
self.run_date,
self.total_seconds,
ranks=self.ranks,
categorical=self.categorical)
progress_recorder.set_position(0)
for count, sol in enumerate(self.model_solutions):
writer.write_data(sol, self.param_strs[count],
progress_recorder)
work_book.save(file_name)
progress_recorder.set_position(100)
self.parent.change_solution_tab_name('Solution')
self.status_lbl.config(text='Solution saved')
