def __init__(self, sample: Sample, std: Standard):
self.sample = sample
self.std = std
self.distance = [sequence_distance_1(self.std.seq_max0, seq_max0) for seq_max0 in self.sample.seq_max0]
self.va = [plot_image(visual_analysis, xr) for xr in self.distance]
self.stat = [stat_analysis(xr) for xr in self.distance]
self.ntest = [test_normal(xr, qq=True) for xr in self.distance]
self.distance_apl = [sequence_distance_1(self.std.seq_max_apl, seq_max0) for seq_max0 in self.sample.seq_max0]
self.va_apl = [plot_image(visual_analysis, xr) for xr in self.distance_apl]
self.stat_apl = [stat_analysis(xr) for xr in self.distance_apl]
self.ntest_apl = [test_normal(xr, qq=True) for xr in self.distance_apl]
self.sample_name = self.sample.display()
def __init__(self, std: Standard, samples: list):
self.std = std
self.samples = samples[:]
self.distance = [[
sequence_distance_1(sample.seq_max[factor], self.std.seq_max)
for sample in self.samples
] for factor in range(4)]
self.max_list = []
for factor in range(4):
max_list_factor = []
for sample_num in range(len(self.samples)):
max_list_factor.append(
np.mean(self.distance[factor][sample_num]))
self.max_list.append(max_list_factor)
self.va = [
plot_image(visual_analysis, max_list_factor)
for max_list_factor in self.max_list
]
self.stat = [
stat_analysis(max_list_factor) for max_list_factor in self.max_list
]
self.ntest = [
test_normal(max_list_factor, qq=True)
for max_list_factor in self.max_list
]
def __init__(self, sample: Sample, factor: int, std: Standard):
self.sample = sample
self.factor = factor
self.std = std
self.distance = sequence_distance_1(self.std.seq_max0, self.sample.seq_max0[factor])
self.va = plot_image(visual_analysis, self.distance)
self.stat = stat_analysis(self.distance)
self.ntest = test_normal(self.distance, qq=True)
self.distance_apl = sequence_distance_1(self.std.seq_max_apl, self.sample.seq_max0[factor])
self.va_apl = plot_image(visual_analysis, self.distance_apl)
self.stat_apl = stat_analysis(self.distance_apl)
self.ntest_apl = test_normal(self.distance_apl, qq=True)
self.sample_name = self.sample.display()
self.factor_name = FACTORS_L[self.factor]
def __init__(self, stds: list, sample: Sample, factor: int):
self.stds = stds[:]
self.sample = sample
self.factor = factor
self.distance = [
sequence_distance_1(self.sample.seq_max[factor], std.seq_max)
for std in self.stds
]
self.va = [plot_image(visual_analysis, xr) for xr in self.distance]
self.stat = [stat_analysis(xr) for xr in self.distance]
self.ntest = [test_normal(xr, qq=True) for xr in self.distance]
self.sample_name = self.sample.display()
self.factor_name = FACTORS_L[self.factor]
def __init__(self, stds: list, sample: Sample):
self.stds = stds[:]
self.sample = sample
self.distance = [[
sequence_distance_1(self.sample.seq_max[factor], std.seq_max)
for factor in range(4)
] for std in self.stds]
self.va = [[plot_image(visual_analysis, factor) for factor in xr]
for xr in self.distance]
self.stat = [[stat_analysis(factor) for factor in xr]
for xr in self.distance]
self.ntest = [[test_normal(factor, qq=True) for factor in xr]
for xr in self.distance]
self.sample_name = self.sample.display()
def __init__(self, stds: list, samples: list, factor: int):
self.stds = stds[:]
self.samples = samples[:]
self.factor = factor
self.distance = [[
sequence_distance_1(sample.seq_max[self.factor], std.seq_max)
for sample in self.samples
] for std in self.stds]
self.max_list = [[
np.mean(std[sample_num]) for sample_num in range(len(self.samples))
] for std in self.distance]
self.va = [plot_image(visual_analysis, xr) for xr in self.max_list]
self.stat = [stat_analysis(xr) for xr in self.max_list]
self.ntest = [test_normal(xr, qq=True) for xr in self.max_list]
self.factor_name = FACTORS_L[self.factor]
def __init__(self, std: Standard, samples: list, factor: int):
self.std = std
self.samples = samples[:]
self.factor = factor
self.distance = [
sequence_distance_1(sample.seq_max[self.factor], self.std.seq_max)
for sample in self.samples
]
self.max_list = []
for sample_num in range(len(self.samples)):
self.max_list.append(np.mean(self.distance[sample_num]))
self.va = plot_image(visual_analysis, self.max_list)
self.stat = stat_analysis(self.max_list)
self.ntest = test_normal(self.max_list, qq=True)
self.factor_name = FACTORS_L[self.factor]
def __init__(self, stds: list, samples: list):
self.stds = stds[:]
self.samples = samples[:]
self.distance = [[[
sequence_distance_1(factor, std.seq_max)
for factor in sample.seq_max
] for sample in self.samples] for std in self.stds]
self.max_list = [[[
np.mean(std[sample_num][factor])
for sample_num in range(len(self.samples))
] for factor in range(4)] for std in self.distance]
self.va = [[plot_image(visual_analysis, factor) for factor in xr]
for xr in self.max_list]
self.stat = [[stat_analysis(factor) for factor in std]
for std in self.max_list]
self.ntest = [[test_normal(factor, qq=True) for factor in std]
for std in self.max_list]
def report_ntest(report, doc: Printer):
res_ok = "пройден"
res_nok = "не пройден"
shapiro = report["shapiro"]
doc.add_paragraph("Тест нормальности Шапиро-Вилка: {}".format(res_ok if shapiro["res"] else res_nok))
agostino = report["agostino"]
doc.add_paragraph("Тест нормальности Д'Агостино и Пирсона: из {0} прогонов доля {1}/{0} = {2:.2f} отклоняет "
"гипотезу о нормальности на уровне отклонения {3}"
.format(agostino["num_tests"], agostino["num_rejects"], agostino["ratio"], agostino["alpha"]))
ks = report["ks"]
doc.add_paragraph("Тест нормальности Колмогорова-Смирнова: из {0} прогонов доля {1}/{0} = {2:.2f} отклоняет "
"гипотезу о нормальности на уровне отклонения {3}"
.format(ks["num_tests"], ks["num_rejects"], ks["ratio"], ks["alpha"]))
if report['qq'] and doc.destination == "doc":
img = plot_image(test_normal_plot, report)
doc.add_paragraph("QQ-тест:")
doc.add_picture(img, width=Cm(12.5))