Python cbook.boxplot_stats方法代码示例

# 需要导入模块: from matplotlib import cbook [as 别名]
# 或者: from matplotlib.cbook import boxplot_stats [as 别名]
def test_results_bootstrapped(self):
        results = cbook.boxplot_stats(self.data, bootstrap=10000)
        res = results[0]
        for key in list(self.known_bootstrapped_ci.keys()):
            assert_approx_equal(
                res[key],
                self.known_bootstrapped_ci[key]
            ) 

# 需要导入模块: from matplotlib import cbook [as 别名]
# 或者: from matplotlib.cbook import boxplot_stats [as 别名]
def test_results_whiskers_float(self):
        results = cbook.boxplot_stats(self.data, whis=3)
        res = results[0]
        for key in list(self.known_whis3_res.keys()):
            if key != 'fliers':
                assert_statement = assert_approx_equal
            else:
                assert_statement = assert_array_almost_equal

            assert_statement(
                res[key],
                self.known_whis3_res[key]
            ) 

# 需要导入模块: from matplotlib import cbook [as 别名]
# 或者: from matplotlib.cbook import boxplot_stats [as 别名]
def test_results_whiskers_range(self):
        results = cbook.boxplot_stats(self.data, whis='range')
        res = results[0]
        for key in list(self.known_res_range.keys()):
            if key != 'fliers':
                assert_statement = assert_approx_equal
            else:
                assert_statement = assert_array_almost_equal

            assert_statement(
                res[key],
                self.known_res_range[key]
            ) 

# 需要导入模块: from matplotlib import cbook [as 别名]
# 或者: from matplotlib.cbook import boxplot_stats [as 别名]
def test_results_whiskers_percentiles(self):
        results = cbook.boxplot_stats(self.data, whis=[5, 95])
        res = results[0]
        for key in list(self.known_res_percentiles.keys()):
            if key != 'fliers':
                assert_statement = assert_approx_equal
            else:
                assert_statement = assert_array_almost_equal

            assert_statement(
                res[key],
                self.known_res_percentiles[key]
            ) 

# 需要导入模块: from matplotlib import cbook [as 别名]
# 或者: from matplotlib.cbook import boxplot_stats [as 别名]
def test_label_error(self):
        labels = [1, 2]
        results = cbook.boxplot_stats(self.data, labels=labels) 

# 需要导入模块: from matplotlib import cbook [as 别名]
# 或者: from matplotlib.cbook import boxplot_stats [as 别名]
def test_bad_dims(self):
        data = np.random.normal(size=(34, 34, 34))
        results = cbook.boxplot_stats(data) 

# 需要导入模块: from matplotlib import cbook [as 别名]
# 或者: from matplotlib.cbook import boxplot_stats [as 别名]
def test_results_bootstrapped(self):
        results = cbook.boxplot_stats(self.data, bootstrap=10000)
        res = results[0]
        for key, value in self.known_bootstrapped_ci.items():
            assert_approx_equal(res[key], value) 

# 需要导入模块: from matplotlib import cbook [as 别名]
# 或者: from matplotlib.cbook import boxplot_stats [as 别名]
def test_results_whiskers_float(self):
        results = cbook.boxplot_stats(self.data, whis=3)
        res = results[0]
        for key, value in self.known_whis3_res.items():
            assert_array_almost_equal(res[key], value) 

# 需要导入模块: from matplotlib import cbook [as 别名]
# 或者: from matplotlib.cbook import boxplot_stats [as 别名]
def test_results_whiskers_range(self):
        results = cbook.boxplot_stats(self.data, whis='range')
        res = results[0]
        for key, value in self.known_res_range.items():
            assert_array_almost_equal(res[key], value) 

# 需要导入模块: from matplotlib import cbook [as 别名]
# 或者: from matplotlib.cbook import boxplot_stats [as 别名]
def test_results_whiskers_percentiles(self):
        results = cbook.boxplot_stats(self.data, whis=[5, 95])
        res = results[0]
        for key, value in self.known_res_percentiles.items():
            assert_array_almost_equal(res[key], value) 

# 需要导入模块: from matplotlib import cbook [as 别名]
# 或者: from matplotlib.cbook import boxplot_stats [as 别名]
def test_label_error(self):
        labels = [1, 2]
        with pytest.raises(ValueError):
            results = cbook.boxplot_stats(self.data, labels=labels) 

# 需要导入模块: from matplotlib import cbook [as 别名]
# 或者: from matplotlib.cbook import boxplot_stats [as 别名]
def test_bad_dims(self):
        data = np.random.normal(size=(34, 34, 34))
        with pytest.raises(ValueError):
            results = cbook.boxplot_stats(data) 

# 需要导入模块: from matplotlib import cbook [as 别名]
# 或者: from matplotlib.cbook import boxplot_stats [as 别名]
def test_boxplot_stats_autorange_false(self):
        x = np.zeros(shape=140)
        x = np.hstack([-25, x, 25])
        bstats_false = cbook.boxplot_stats(x, autorange=False)
        bstats_true = cbook.boxplot_stats(x, autorange=True)

        assert bstats_false[0]['whislo'] == 0
        assert bstats_false[0]['whishi'] == 0
        assert_array_almost_equal(bstats_false[0]['fliers'], [-25, 25])

        assert bstats_true[0]['whislo'] == -25
        assert bstats_true[0]['whishi'] == 25
        assert_array_almost_equal(bstats_true[0]['fliers'], []) 

# 需要导入模块: from matplotlib import cbook [as 别名]
# 或者: from matplotlib.cbook import boxplot_stats [as 别名]
def compute_group(cls, data, scales, **params):
        labels = ['x', 'y']
        X = np.array(data[labels])
        res = boxplot_stats(X, whis=params['coef'], labels=labels)[1]
        try:
            n = data['weight'].sum()
        except KeyError:
            n = len(data['y'])

        if len(np.unique(data['x'])) > 1:
            width = np.ptp(data['x']) * 0.9
        else:
            width = params['width']

        if pdtypes.is_categorical(data['x']):
            x = data['x'].iloc[0]
        else:
            x = np.mean([data['x'].min(), data['x'].max()])

        d = {'ymin': res['whislo'],
             'lower': res['q1'],
             'middle': [res['med']],
             'upper': res['q3'],
             'ymax': res['whishi'],
             'outliers': [res['fliers']],
             'notchupper': res['med']+1.58*res['iqr']/np.sqrt(n),
             'notchlower': res['med']-1.58*res['iqr']/np.sqrt(n),
             'x': x,
             'width': width,
             'relvarwidth': np.sqrt(n)}
        return pd.DataFrame(d) 

# 需要导入模块: from matplotlib import cbook [as 别名]
# 或者: from matplotlib.cbook import boxplot_stats [as 别名]
def setup(self):
        np.random.seed(937)
        self.nrows = 37
        self.ncols = 4
        self.data = np.random.lognormal(size=(self.nrows, self.ncols),
                                        mean=1.5, sigma=1.75)
        self.known_keys = sorted([
            'mean', 'med', 'q1', 'q3', 'iqr',
            'cilo', 'cihi', 'whislo', 'whishi',
            'fliers', 'label'
        ])
        self.std_results = cbook.boxplot_stats(self.data)

        self.known_nonbootstrapped_res = {
            'cihi': 6.8161283264444847,
            'cilo': -0.1489815330368689,
            'iqr': 13.492709959447094,
            'mean': 13.00447442387868,
            'med': 3.3335733967038079,
            'fliers': np.array([
                92.55467075,  87.03819018,  42.23204914,  39.29390996
            ]),
            'q1': 1.3597529879465153,
            'q3': 14.85246294739361,
            'whishi': 27.899688243699629,
            'whislo': 0.042143774965502923
        }

        self.known_bootstrapped_ci = {
            'cihi': 8.939577523357828,
            'cilo': 1.8692703958676578,
        }

        self.known_whis3_res = {
            'whishi': 42.232049135969874,
            'whislo': 0.042143774965502923,
            'fliers': np.array([92.55467075, 87.03819018]),
        }

        self.known_res_percentiles = {
            'whislo':   0.1933685896907924,
            'whishi':  42.232049135969874
        }

        self.known_res_range = {
            'whislo': 0.042143774965502923,
            'whishi': 92.554670752188699

        }