def testMinMax(self):
# Scalar
xouter = MV2.outerproduct(MV2.arange(10), MV2.arange(10))
maxval = MV2.maximum(xouter)
minval = MV2.minimum(xouter)
self.assertEqual(maxval, 81)
self.assertEqual(minval, 0)
# Do an elementwise maximum
xmax = MV2.maximum(self.u_file, self.v_file)
xmin = MV2.minimum(self.u_file, self.v_file)
self.assertTrue(((xmax - self.u_file) >= 0).all())
self.assertTrue(((xmax - self.v_file) >= 0).all())
self.assertTrue(((xmin - self.u_file) <= 0).all())
self.assertTrue(((xmin - self.v_file) <= 0).all())
# Reduce along axes
slicer = [Ellipsis for i in range(len(self.u_file.shape))]
for axis_index, axis_length in enumerate(self.u_file.shape):
amax = MV2.maximum.reduce(self.u_file, axis=axis_index)
amin = MV2.minimum.reduce(self.u_file, axis=axis_index)
s = list(slicer)
for i in range(axis_length):
s[axis_index] = i
ind_slice = self.u_file.subSlice(*s, squeeze=True)
self.assertTrue(((ind_slice - amax) <= 0).all())
self.assertTrue(((ind_slice - amin) >= 0).all())
t1 = MV2.TransientVariable([1., 2.])
t2 = MV2.TransientVariable([1., 10.])
t3 = MV2.minimum.outer(t1, t2)
self.assertTrue(MV2.allequal(t3, [[1, 1], [1, 2]]))
t3 = MV2.maximum.outer(t1, t2)
self.assertTrue(MV2.allequal(t3, [[1, 10], [2, 10]]))
def testChoose(self):
ct1 = MV2.TransientVariable([1, 1, 2, 0, 1])
ctr = MV2.choose(ct1, [numpy.ma.masked, 10, 20, 30, 40])
self.assertTrue(MV2.allclose(ctr, [10, 10, 20, 100, 10]))
ctx = MV2.TransientVariable([1, 2, 3, 150, 4])
cty = -MV2.TransientVariable([1, 2, 3, 150, 4])
ctr = MV2.choose(MV2.greater(ctx, 100), (ctx, 100))
self.assertTrue(MV2.allclose(ctr, [1, 2, 3, 100, 4]))
ctr = MV2.choose(MV2.greater(ctx, 100), (ctx, cty))
self.assertTrue(MV2.allclose(ctr, [1, 2, 3, -150, 4]))
def testTranspose(self):
## transpose(a, axes=None)
# transpose(a, axes=None) reorder dimensions per tuple axes
xtr = MV2.transpose(self.u_file)
xtr = numpy.arange(24)
xtr.shape = (4, 6)
xtr1 = MV2.transpose(xtr)
xtr2 = MV2.transpose(MV2.TransientVariable(xtr))
self.assertTrue(xtr2.shape == (6, 4))
xtr3 = numpy.transpose(xtr)
self.assertTrue(MV2.allclose(xtr1, xtr3))
self.assertTrue(MV2.allclose(xtr2, xtr3))
## If weights are given, result is sum(a*weights)/sum(weights), with
## all elements masked in a or in weights ignored.
## weights if given must have a's shape.
## Denominator is multiplied by 1.0 to prevent truncation for integers.
## returned governs return of second quantity, the weights.
xav = MV2.average(xones, axis=1)
xav2 = MV2.average(ud)
xav3 = MV2.average(udat)
xav4, wav4 = MV2.average(udat,
weights=MV2.ones(udat.shape, numpy.float),
returned=1)
## choose(indices, t)
## Shaped like indices, values t[i] where at indices[i]
## If t[j] is masked, special treatment to preserve type.
ct1 = MV2.TransientVariable([1, 1, 2, 0, 1])
ctr = MV2.choose(ct1, [numpy.ma.masked, 10, 20, 30, 40])
if not MV2.allclose(ctr, [10, 10, 20, 100, 10]): markError('choose error 1')
ctx = MV2.TransientVariable([1, 2, 3, 150, 4])
cty = -MV2.TransientVariable([1, 2, 3, 150, 4])
ctr = MV2.choose(MV2.greater(ctx, 100), (ctx, 100))
if not MV2.allclose(ctr, [1, 2, 3, 100, 4]): markError('choose error 2')
ctr = MV2.choose(MV2.greater(ctx, 100), (ctx, cty))
if not MV2.allclose(ctr, [1, 2, 3, -150, 4]): markError('choose error 3')
## concatenate(arrays, axis=0, axisid=None, axisattributes=None)
## Concatenate the arrays along the given axis. Give the extended axis the id and
## attributes provided - by default, those of the first array.
try:
xcon = MV2.concatenate((ud, vd))
def testDiagnoal(self):
xdiag = MV2.TransientVariable([[1, 2, 3], [4, 5, 6]])
self.assertTrue(MV2.allclose(MV2.diagonal(xdiag, 1), [2, 6]))
