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 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 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)
