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Python numpy.histogram_bin_edges方法代码示例

本文整理汇总了Python中numpy.histogram_bin_edges方法的典型用法代码示例。如果您正苦于以下问题:Python numpy.histogram_bin_edges方法的具体用法?Python numpy.histogram_bin_edges怎么用?Python numpy.histogram_bin_edges使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在numpy的用法示例。


在下文中一共展示了numpy.histogram_bin_edges方法的5个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: test_histogram_bin_edges

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import histogram_bin_edges [as 别名]
def test_histogram_bin_edges(self):
        x = np.array([1.1, 1.2, 1.3, 2.1, 5.1]) * u.m
        out_b = np.histogram_bin_edges(x)
        expected_b = np.histogram_bin_edges(x.value) * x.unit
        assert np.all(out_b == expected_b)
        # With bins
        out2_b = np.histogram_bin_edges(x, [125, 200] * u.cm)
        expected2_b = np.histogram_bin_edges(x.value, [1.25, 2.]) * x.unit
        assert np.all(out2_b == expected2_b)
        with pytest.raises(u.UnitsError):
            np.histogram_bin_edges(x, [125, 200] * u.s)

        with pytest.raises(u.UnitsError):
            np.histogram_bin_edges(x, [125, 200])

        with pytest.raises(u.UnitsError):
            np.histogram_bin_edges(x.value, [125, 200] * u.s) 
开发者ID:holzschu,项目名称:Carnets,代码行数:19,代码来源:test_quantity_non_ufuncs.py

示例2: histogram_bin_edges

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import histogram_bin_edges [as 别名]
def histogram_bin_edges(a, bins=10, range=None, weights=None):
    # weights is currently unused
    a = _as_quantity(a)
    if not isinstance(bins, str):
        bins = _check_bins(bins, a.unit)

    return (a.value, bins, range, weights), {}, a.unit, None 
开发者ID:holzschu,项目名称:Carnets,代码行数:9,代码来源:function_helpers.py

示例3: _hist_bin_auto

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import histogram_bin_edges [as 别名]
def _hist_bin_auto(x, range):
    """
    Histogram bin estimator that uses the minimum width of the
    Freedman-Diaconis and Sturges estimators if the FD bandwidth is non zero
    and the Sturges estimator if the FD bandwidth is 0.

    The FD estimator is usually the most robust method, but its width
    estimate tends to be too large for small `x` and bad for data with limited
    variance. The Sturges estimator is quite good for small (<1000) datasets
    and is the default in the R language. This method gives good off the shelf
    behaviour.

    .. versionchanged:: 1.15.0
    If there is limited variance the IQR can be 0, which results in the
    FD bin width being 0 too. This is not a valid bin width, so
    ``np.histogram_bin_edges`` chooses 1 bin instead, which may not be optimal.
    If the IQR is 0, it's unlikely any variance based estimators will be of
    use, so we revert to the sturges estimator, which only uses the size of the
    dataset in its calculation.

    Parameters
    ----------
    x : array_like
        Input data that is to be histogrammed, trimmed to range. May not
        be empty.

    Returns
    -------
    h : An estimate of the optimal bin width for the given data.

    See Also
    --------
    _hist_bin_fd, _hist_bin_sturges
    """
    fd_bw = _hist_bin_fd(x, range)
    sturges_bw = _hist_bin_sturges(x, range)
    del range  # unused
    if fd_bw:
        return min(fd_bw, sturges_bw)
    else:
        # limited variance, so we return a len dependent bw estimator
        return sturges_bw

# Private dict initialized at module load time 
开发者ID:Frank-qlu,项目名称:recruit,代码行数:46,代码来源:histograms.py

示例4: testHistogramBinEdgesExecution

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import histogram_bin_edges [as 别名]
def testHistogramBinEdgesExecution(self):
        rs = np.random.RandomState(0)

        raw = rs.randint(10, size=(20,))
        a = tensor(raw, chunk_size=3)

        # range provided
        for range_ in [(0, 10), (3, 11), (3, 7)]:
            bin_edges = histogram_bin_edges(a, range=range_)
            result = self.executor.execute_tensor(bin_edges)[0]
            expected = np.histogram_bin_edges(raw, range=range_)
            np.testing.assert_array_equal(result, expected)

        ctx, executor = self._create_test_context(self.executor)
        with ctx:
            raw2 = rs.randint(10, size=(1,))
            b = tensor(raw2)
            raw3 = rs.randint(10, size=(0,))
            c = tensor(raw3)
            for t, r in [(a, raw), (b, raw2), (c, raw3), (sort(a), raw)]:
                test_bins = [10, 'stone', 'auto', 'doane', 'fd',
                             'rice', 'scott', 'sqrt', 'sturges']
                for bins in test_bins:
                    bin_edges = histogram_bin_edges(t, bins=bins)

                    if r.size > 0:
                        with self.assertRaises(TilesError):
                            executor.execute_tensor(bin_edges)

                    result = executor.execute_tensors([bin_edges])[0]
                    expected = np.histogram_bin_edges(r, bins=bins)
                    np.testing.assert_array_equal(result, expected)

                test_bins = [[0, 4, 8], tensor([0, 4, 8], chunk_size=2)]
                for bins in test_bins:
                    bin_edges = histogram_bin_edges(t, bins=bins)
                    result = executor.execute_tensors([bin_edges])[0]
                    expected = np.histogram_bin_edges(r, bins=[0, 4, 8])
                    np.testing.assert_array_equal(result, expected)

            raw = np.arange(5)
            a = tensor(raw, chunk_size=3)
            bin_edges = histogram_bin_edges(a)
            result = executor.execute_tensors([bin_edges])[0]
            expected = np.histogram_bin_edges(raw)
            self.assertEqual(bin_edges.shape, expected.shape)
            np.testing.assert_array_equal(result, expected) 
开发者ID:mars-project,项目名称:mars,代码行数:49,代码来源:test_statistics_execute.py

示例5: _hist_bin_auto

# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import histogram_bin_edges [as 别名]
def _hist_bin_auto(x):
    """
    Histogram bin estimator that uses the minimum width of the
    Freedman-Diaconis and Sturges estimators if the FD bandwidth is non zero
    and the Sturges estimator if the FD bandwidth is 0.

    The FD estimator is usually the most robust method, but its width
    estimate tends to be too large for small `x` and bad for data with limited
    variance. The Sturges estimator is quite good for small (<1000) datasets
    and is the default in the R language. This method gives good off the shelf
    behaviour.

    .. versionchanged:: 1.15.0
    If there is limited variance the IQR can be 0, which results in the
    FD bin width being 0 too. This is not a valid bin width, so
    ``np.histogram_bin_edges`` chooses 1 bin instead, which may not be optimal.
    If the IQR is 0, it's unlikely any variance based estimators will be of
    use, so we revert to the sturges estimator, which only uses the size of the
    dataset in its calculation.

    Parameters
    ----------
    x : array_like
        Input data that is to be histogrammed, trimmed to range. May not
        be empty.

    Returns
    -------
    h : An estimate of the optimal bin width for the given data.

    See Also
    --------
    _hist_bin_fd, _hist_bin_sturges
    """
    fd_bw = _hist_bin_fd(x)
    sturges_bw = _hist_bin_sturges(x)
    if fd_bw:
        return min(fd_bw, sturges_bw)
    else:
        # limited variance, so we return a len dependent bw estimator
        return sturges_bw

# Private dict initialized at module load time 
开发者ID:ryfeus,项目名称:lambda-packs,代码行数:45,代码来源:histograms.py


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