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]])
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]])
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]])
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)
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)