class TestBootstrapAnalysis(TestCase):
def setUp(self):
self.number_of_resamples = 10000
# import dataset from file
filename = 'flicker.xlsx'
self.__fh = FileHandling()
self.original_data_dict = self.__fh.import_spreadsheet(filename)
# resample dataset
self.__bootstrapper = Bootstrapper(self.original_data_dict, self.number_of_resamples)
#init bootstrap analysis tools
self.analysis = BootstrapAnalysis(self.__bootstrapper)
def check_keys_in_dict(self, dict):
key_count = 0
key_list = []
for key in dict:
result_dict = dict[key]
for key, values in result_dict.items():
print(key)
key_list.append(key)
key_count+= 1
return key_list, key_count
def test_mean_dictionary_keys(self):
"""
Tests if the correct number of keys (experiment column names) is contained in the result dict and
if the contained keys are identical to the column names.
"""
mean_dict = self.analysis.get_bootstrapped_mean()
key_list, key_count = self.check_keys_in_dict(mean_dict)
self.assertEqual(3, key_count)
self.assertTrue(sorted(["Brown", "Green", "Blue"]) == sorted(key_list))
def test_median_dictionary_keys(self):
"""
Tests if the correct number of keys (experiment column names) is contained in the result dict and
if the contained keys are identical to the column names.
"""
median_dict = self.analysis.get_bootstrapped_median()
key_list, key_count = self.check_keys_in_dict(median_dict)
self.assertEqual(3, key_count)
self.assertTrue(sorted(["Brown", "Green", "Blue"]) == sorted(key_list))
def test_SEM(self):
"""
Tests if the correct number of keys (experiment column names) is contained in the result dict and
if the contained keys are identical to the column names.
"""
sem_dict = self.analysis.get_bootstrapped_median()
key_list, key_count = self.check_keys_in_dict(sem_dict)
self.assertEqual(3, key_count)
self.assertTrue(sorted(["Brown", "Green", "Blue"]) == sorted(key_list))
class TestBootstrapAnalysis(TestCase):
def setUp(self):
self.number_of_resamples = 10000
# import dataset from file
filename = 'flicker.xlsx'
self.__fh = FileHandling()
self.original_data_dict = self.__fh.import_spreadsheet(filename)
# resample dataset
self.__bootstrapper = Bootstrapper(self.original_data_dict,
self.number_of_resamples)
#init bootstrap analysis tools
self.analysis = BootstrapAnalysis(self.__bootstrapper)
def check_keys_in_dict(self, dict):
key_count = 0
key_list = []
for key in dict:
result_dict = dict[key]
for key, values in result_dict.items():
print(key)
key_list.append(key)
key_count += 1
return key_list, key_count
def test_mean_dictionary_keys(self):
"""
Tests if the correct number of keys (experiment column names) is contained in the result dict and
if the contained keys are identical to the column names.
"""
mean_dict = self.analysis.get_bootstrapped_mean()
key_list, key_count = self.check_keys_in_dict(mean_dict)
self.assertEqual(3, key_count)
self.assertTrue(sorted(["Brown", "Green", "Blue"]) == sorted(key_list))
def test_median_dictionary_keys(self):
"""
Tests if the correct number of keys (experiment column names) is contained in the result dict and
if the contained keys are identical to the column names.
"""
median_dict = self.analysis.get_bootstrapped_median()
key_list, key_count = self.check_keys_in_dict(median_dict)
self.assertEqual(3, key_count)
self.assertTrue(sorted(["Brown", "Green", "Blue"]) == sorted(key_list))
def test_SEM(self):
"""
Tests if the correct number of keys (experiment column names) is contained in the result dict and
if the contained keys are identical to the column names.
"""
sem_dict = self.analysis.get_bootstrapped_median()
key_list, key_count = self.check_keys_in_dict(sem_dict)
self.assertEqual(3, key_count)
self.assertTrue(sorted(["Brown", "Green", "Blue"]) == sorted(key_list))
def setUp(self):
self.number_of_resamples = 10000
filename = 'flicker.xlsx'
self.bootstrapper = None
fh = FileHandling()
self.data_dict = fh.import_spreadsheet(filename)
self.bootstrapper = Bootstrapper(self.data_dict, self.number_of_resamples)
self.bootstrapped_dict = self.bootstrapper.bootstrapped_data
def setUp(self):
self.number_of_resamples = 10000
filename = 'flicker.xlsx'
self.bootstrapper = None
fh = FileHandling()
self.data_dict = fh.import_spreadsheet(filename)
self.bootstrapper = Bootstrapper(self.data_dict,
self.number_of_resamples)
self.bootstrapped_dict = self.bootstrapper.bootstrapped_data
class Bootstrapit:
"""
The Bootstrapit class builds the API for the Bootstrapit application. The Methods inside this class can directly
be used to interact with the application.
"""
def __init__(self, filename, number_of_resamples=10000):
"""
Initializing the Bootstrapit class executes the resampling of the imported dataset.
:param filename: The filename including filepath to the import data file.
:param number_of_resamples: The number of resamples to perform.
"""
self.number_of_resamples = number_of_resamples
# import dataset from file
self.__fh = FileHandling()
self.original_data_dict = self.__fh.import_spreadsheet(filename)
# resample dataset
self.__bootstrapper = Bootstrapper(self.original_data_dict, number_of_resamples)
#init bootstrap analysis tools
self.__analysis = BootstrapAnalysis(self.__bootstrapper)
#init plotter
self.__plotter = Plotting(self.__fh.export_order)
# TODO: Export bootstrapped data array to json or excel
def export(self, *export_datasets_dicts, filename="bootstrapit_results.xlsx"):
"""
The export method does merge all data analysis result dictionaries and exports them using the FileHandler class.
:param export_datasets_dicts: All result dictionaries from the bootstrapping analysis.
:param filename: the export filename.
"""
merged_dict = self.__merge_dicts(*export_datasets_dicts)
filetype_check = filename.split('.')
filetype = filetype_check[-1]
filename = filetype_check[0]
if filetype == "xlsx":
self.__fh.file_type = FileType.XLSX
elif filetype == "xls":
self.__fh.file_type = FileType.XLS
elif filetype == "csv":
self.__fh.file_type = FileType.CSV
else:
print("Error: Unsupported file type.")
self.__fh.file_name = filename
self.__fh.export(merged_dict)
def __merge_dicts(self, *dict_args):
"""
Helper method to merge multiple result dictionaries.
:param dict_args: List of dictionaries
:return: The merged dictionary containing all results of the analysis.
"""
result = {}
for dictionary in dict_args:
result.update(dictionary)
return result
def mean(self):
return self.__analysis.get_bootstrapped_mean()
def median(self):
return self.__analysis.get_bootstrapped_median()
def SEM(self):
return self.__analysis.get_SEM()
def barchart(self, figure, data_dict, errorbar = {}):
return self.__plotter.plot_barchart(figure, data_dict, errorbar)
def set_axis_label(self, axes, title, xlabel, ylabel):
self.__plotter.set_axis_labels(axes, title, xlabel, ylabel)