Python stats.StatsAnalyzer類代碼示例

 def test_GIVEN_divide_by_zeros_WHEN_rel_stddev_THEN_rel_stddev_correct(self):
     # Zeros in the first array cause undefined values in the relative calculations.
     data_with_zeros = self.data1
     data_with_zeros.data[0][1] = 0
     stats = StatsAnalyzer(data_with_zeros, self.missing2)
     res = stats.rel_stddev()
     assert_that(res[0].rel_stddev, close_to(5.8725242578, 1e-5))

def stats_cmd(main_arguments):
    """
    Main routine for handling calls to the statistics command.

    :param main_arguments: The command line arguments (minus the stats command)
    """
    from cis.stats import StatsAnalyzer
    from cis.data_io.gridded_data import GriddedDataList
    data_reader = DataReader()
    data_list = data_reader.read_datagroups(main_arguments.datagroups)
    analyzer = StatsAnalyzer(*data_list)
    results = analyzer.analyze()
    header = "RESULTS OF STATISTICAL COMPARISON:"
    note = "Compared all points which have non-missing values in both variables"
    header_length = max(len(header), len(note))
    print(header_length * '=')
    print(header)
    print(header_length * '-')
    print(note)
    print(header_length * '=')
    for result in results:
        print(result.pprint())
    if main_arguments.output:
        cubes = GriddedDataList([result.as_cube() for result in results])
        variables = []
        filenames = []
        for datagroup in main_arguments.datagroups:
            variables.extend(datagroup['variables'])
            filenames.extend(datagroup['filenames'])
        history = "Statistical comparison performed using CIS version " + __version__ + \
                  "\n variables: " + str(variables) + \
                  "\n from files: " + str(set(filenames))
        cubes.add_history(history)
        cubes.save_data(main_arguments.output)

 def test_GIVEN_missing_values_WHEN_analyze_THEN_original_data_unchanged(self):
     # We perform some manipulation on the data masks, but we don't want the
     # original data to be changed.
     stats = StatsAnalyzer(self.missing1, self.missing2)
     results = stats.analyze()
     assert_that(len(self.missing1.data.compressed()), is_(7))
     assert_that(len(self.missing2.data.compressed()), is_(7))

 def test_GIVEN_divide_by_zeros_WHEN_rel_mean_THEN_rel_mean_correct(self):
     # Zeros in the first array cause undefined values in the relative calculations.
     data_with_zeros = self.data1
     data_with_zeros.data[0][1] = 0
     stats = StatsAnalyzer(data_with_zeros, self.missing2)
     res = stats.rel_mean()
     assert_that(res[0].rel_mean, close_to(-2.3518849206, 1e-5))

 def test_GIVEN_flattened_and_unflattened_datasets_WHEN_analyze_THEN_StatisticsResults_returned(self):
     data1 = mock.make_regular_2d_ungridded_data()
     data2 = mock.make_regular_2d_ungridded_data()
     data2._data = data2.data_flattened
     for coord in data2.coords():
         coord._data = coord.data_flattened
     stats = StatsAnalyzer(data1, data2)
     results = stats.analyze()
     assert_that(len(results), is_(14))

 def test_GIVEN_one_masked_one_nparray_WHEN_lin_regression_THEN_regression_correct(self):
     stats = StatsAnalyzer(self.data1, self.missing2)
     res = stats.linear_regression()
     expected_res = [-5.1404761905, 12.3595238095, -0.4079085869, 5.14561290806]
     actual_res = res[0].grad, res[1].intercept, res[2].r, res[3].stderr
     assert_that(np.allclose(actual_res, expected_res))

 def test_GIVEN_missing_vals_WHEN_lin_regression_THEN_regression_correct(self):
     stats = StatsAnalyzer(self.missing1, self.missing2)
     res = stats.linear_regression()
     expected_res = [1.1920369653, -0.6908343017, 0.999845219, 0.0104877890357]
     actual_res = res[0].grad, res[1].intercept, res[2].r, res[3].stderr
     assert_that(np.allclose(actual_res, expected_res))

 def test_GIVEN_no_missing_vals_WHEN_abs_stddev_THEN_abs_stddev_correct(self):
     stats = StatsAnalyzer(self.data1, self.data2)
     res = stats.abs_stddev()
     assert_that(res[0].abs_stddev, close_to(0.2643650675, 1e-5))

 def test_GIVEN_missing_vals_WHEN_abs_mean_THEN_abs_mean_correct(self):
     stats = StatsAnalyzer(self.missing1, self.missing2)
     res = stats.abs_mean()
     assert_that(res[0].abs_mean, close_to(-3.2833333333, 1e-5))

 def test_GIVEN_missing_vals_WHEN_stddev_THEN_stddev_correct(self):
     stats = StatsAnalyzer(self.missing1, self.missing2)
     res = stats.stddevs()
     assert_that(res[0].stddev, close_to(42.5099988238, 1e-5))
     assert_that(res[1].stddev, close_to(50.6813344997, 1e-5))

 def test_GIVEN_one_masked_one_nparray_WHEN_abs_stddev_THEN_abs_stddev_correct(self):
     stats = StatsAnalyzer(self.data1, self.missing2)
     res = stats.abs_stddev()
     assert_that(res[0].abs_stddev, close_to(48.7984582114, 1e-5))

 def test_GIVEN_one_masked_one_nparray_WHEN_rel_stddev_THEN_rel_stddev_correct(self):
     stats = StatsAnalyzer(self.data1, self.missing2)
     res = stats.rel_stddev()
     assert_that(res[0].rel_stddev, close_to(5.4371807462, 1e-5))

 def test_GIVEN_missing_vals_WHEN_rel_stddev_THEN_rel_stddev_correct(self):
     stats = StatsAnalyzer(self.missing1, self.missing2)
     res = stats.rel_stddev()
     assert_that(res[0].rel_stddev, close_to(0.1930820326, 1e-5))

 def test_GIVEN_no_missing_vals_WHEN_rel_stddev_THEN_rel_stddev_correct(self):
     stats = StatsAnalyzer(self.data1, self.data2)
     res = stats.rel_stddev()
     assert_that(res[0].rel_stddev, close_to(0.1097392069, 1e-5))

 def test_GIVEN_one_masked_one_nparray_WHEN_rel_mean_THEN_rel_mean_correct(self):
     stats = StatsAnalyzer(self.data1, self.missing2)
     res = stats.rel_mean()
     assert_that(res[0].rel_mean, close_to(-2.0087585034, 1e-5))