Ejemplo n.º 1
0
    def test_3d_kwargs(self):
        a = arange(12).reshape(2, 2, 3)

        def myfunc(b, offset=0):
            return b[1 + offset]

        xa = apply_along_axis(myfunc, 2, a, offset=1)
        assert_equal(xa, [[2, 5], [8, 11]])
Ejemplo n.º 2
0
    def test_3d(self):
        a = arange(12.).reshape(2, 2, 3)

        def myfunc(b):
            return b[1]

        xa = apply_along_axis(myfunc, 2, a)
        assert_equal(xa, [[1, 4], [7, 10]])
Ejemplo n.º 3
0
    def test_3d_kwargs(self):
        a = arange(12).reshape(2, 2, 3)

        def myfunc(b, offset=0):
            return b[1+offset]

        xa = apply_along_axis(myfunc, 2, a, offset=1)
        assert_equal(xa, [[2, 5], [8, 11]])
Ejemplo n.º 4
0
    def test_3d(self):
        a = arange(12.).reshape(2, 2, 3)

        def myfunc(b):
            return b[1]

        xa = apply_along_axis(myfunc, 2, a)
        assert_equal(xa, [[1, 4], [7, 10]])
Ejemplo n.º 5
0
def idealfourths(data, axis=None):
    """This function returns an estimate of the lower and upper quartiles of the data along
    the given axis, as computed with the ideal fourths. This function was taken
    from scipy.stats.mstat_extra.py (http://projects.scipy.org/scipy/browser/trunk/scipy/stats/mstats_extras.py?rev=6392)
    """
    def _idf(data):
        x = data.compressed()
        n = len(x)
        if n < 3:
            return [numpy_nan,numpy_nan]
        (j,h) = divmod(n/4. + 5/12.,1)
        qlo = (1-h)*x[j-1] + h*x[j]
        k = n - j
        qup = (1-h)*x[k] + h*x[k-1]
        return [qlo, qup]
    data = numpy_sort(data, axis=axis).view(MaskedArray)
    if (axis is None):
        return _idf(data)
    else:
        return apply_along_axis(_idf, axis, data)
Ejemplo n.º 6
0
def idealfourths(data, axis=None):
    """This function returns an estimate of the lower and upper quartiles of the data along
    the given axis, as computed with the ideal fourths. This function was taken
    from scipy.stats.mstat_extra.py (http://projects.scipy.org/scipy/browser/trunk/scipy/stats/mstats_extras.py?rev=6392)
    """
    def _idf(data):
        x = data.compressed()
        n = len(x)
        if n < 3:
            return [numpy_nan, numpy_nan]
        (j, h) = divmod(n / 4. + 5 / 12., 1)
        qlo = (1 - h) * x[j - 1] + h * x[j]
        k = n - j
        qup = (1 - h) * x[k] + h * x[k - 1]
        return [qlo, qup]

    data = numpy_sort(data, axis=axis).view(MaskedArray)
    if (axis is None):
        return _idf(data)
    else:
        return apply_along_axis(_idf, axis, data)