def test_list_stat_values(self): """Test analysis.get_statistics() with list output""" statistics_list = analysis.get_statistics(self.tseries, output='list') self.assertEqual(type(statistics_list), list) self.assertAlmostEqual(statistics_list[0], self.mu, 0) self.assertAlmostEqual(statistics_list[1], self.sigma, 0) self.assertEqual(statistics_list[2], self.data.max()) self.assertEqual(statistics_list[3], self.data.min()) self.assertEqual(statistics_list[4], self.data.size)
def test_dict_stat_values(self): """Test analysis.get_statistics() with dictionary output""" statistics_dict = analysis.get_statistics(self.tseries) self.assertEqual(type(statistics_dict), dict) self.assertAlmostEqual(statistics_dict['mean'], self.mu, 0) self.assertAlmostEqual(statistics_dict['std'], self.sigma, 0) self.assertEqual(statistics_dict['max'], self.data.max()) self.assertEqual(statistics_dict['min'], self.data.min()) self.assertEqual(statistics_dict['size'], self.data.size)
def test_weibull_params(self): """Testing analysis.get_weibull_params()""" ## Generate single variable weibull distribution c, k = 1., 1.5 weibull_data = numpy.array(numpy.random.weibull(k, 10000)) stats = analysis.get_statistics(weibull_data) ## generate params from distribution sample statistics test_c, test_k = analysis.get_weibull_params(stats['mean'],stats['std']) self.assertAlmostEqual(k, test_k, 1) self.assertAlmostEqual(c, test_c, 1)
def test_weibull_params(self): """Testing analysis.get_weibull_params()""" ## Generate single variable weibull distribution c, k = 1., 1.5 weibull_data = numpy.array(numpy.random.weibull(k, 10000)) stats = analysis.get_statistics(weibull_data) ## generate params from distribution sample statistics test_c, test_k = analysis.get_weibull_params(stats['mean'], stats['std']) self.assertAlmostEqual(k, test_k, 1) self.assertAlmostEqual(c, test_c, 1)