def testDdsCreateWithSubdInfo(self):
"""
Test the :func:`mango.empty` using the :samp:`subdorigin`
and :samp:`subdshape` arguments.
"""
# Create initial fixture.
ddsOrig = \
mango.empty(
shape=(self.shape[0]+3, self.shape[1]+2, self.shape[0]+1),
origin=(-32,-64,-128),
dtype="uint16"
)
np.random.seed((mango.mpi.rank + 1) * 975421)
ddsOrig.asarray()[:] = \
np.random.randint(
np.iinfo(ddsOrig.dtype).min,
np.iinfo(ddsOrig.dtype).max,
ddsOrig.asarray().shape
)
subdZSz = ddsOrig.shape[0] // mango.mpi.size
mySubdZSz = subdZSz
if (mango.mpi.rank == mango.mpi.size - 1):
mySubdZSz += (ddsOrig.shape[0] % mango.mpi.size)
mpidims = sp.array((mango.mpi.size, 1, 1))
subdshape = sp.array((mySubdZSz, ddsOrig.shape[1], ddsOrig.shape[2]))
subdorigin = ddsOrig.origin + sp.array(
(subdZSz, subdshape[1], subdshape[2])) * (mango.mpi.rank, 0, 0)
ddsSubd =\
mango.empty(
shape = ddsOrig.shape,
origin = ddsOrig.origin,
dtype = ddsOrig.dtype,
subdshape = subdshape,
subdorigin = subdorigin,
mpidims = mpidims
)
self.assertTrue(sp.all(ddsSubd.origin == ddsOrig.origin))
self.assertTrue(sp.all(ddsSubd.shape == ddsOrig.shape))
self.assertTrue(sp.all(ddsSubd.subd.origin == subdorigin))
self.assertTrue(sp.all(ddsSubd.subd.shape == subdshape))
if (mpi.haveMpi4py):
self.assertEqual(mango.mpi.world.allreduce(ddsOrig.asarray().size),
mango.mpi.world.allreduce(ddsSubd.asarray().size))
else:
self.assertEqual(ddsOrig.asarray().size, ddsSubd.asarray().size)
logger.info("ddsOrig.origin=%s, ddsOrig.subd.origin=%s" %
(ddsOrig.origin, ddsOrig.subd.origin))
logger.info("ddsSubd.origin=%s, ddsSubd.subd.origin=%s" %
(ddsSubd.origin, ddsSubd.subd.origin))
ddsSubd.fill(ddsOrig)
ddsCmp = mango.zeros_like(ddsOrig)
ddsCmp.fill(ddsSubd)
self.assertTrue(sp.all(ddsOrig.asarray() == ddsCmp.asarray()))
def testDdsCreateFloat16(self):
"""
Test the :func:`mango.empty` and :func:`mango.ones`
and :func:`mango.zeros` methods for :samp:`dtype="float16"`
and :samp:`mtype="float16"`.
"""
if (mango.haveFloat16):
dds = mango.empty(shape=self.shape, dtype="float16")
fVal = np.float16(2.25)
dds.setAllToValue(fVal)
self.assertEqual(fVal, dds[0])
self.assertEqual(fVal, dds[0, 0, 0])
self.assertEqual(fVal, dds[(0, 0, 0)])
dds = mango.zeros(shape=self.shape, mtype="float16")
fVal = np.float16(2.25)
dds.asarray()[...] += fVal
self.assertEqual(fVal, dds[0])
self.assertEqual(fVal, dds[0, 0, 0])
self.assertEqual(fVal, dds[(0, 0, 0)])
dds = mango.ones(shape=self.shape, mtype="float16")
fVal = np.float16(2.25)
dds.asarray()[...] *= fVal
self.assertEqual(fVal, dds[0])
self.assertEqual(fVal, dds[0, 0, 0])
self.assertEqual(fVal, dds[(0, 0, 0)])
def createCheckerDds(shape, checkShape, checkOrigin=(0,0,0), black=0, white=1, dtype="uint8", mtype=None, halo=(0,0,0), mpidims=(0,0,0), origin=(0,0,0)):
"""
Creates a 3D checker-board image.
:type shape: 3-sequence
:param shape: The global shape :samp:`(zSz,ySz,xSz)` of the returned :obj:`mango.Dds` image.
:type checkShape: 3-sequence
:param checkShape: The shape of the checks.
:type checkOrigin: 3-sequence
:param checkOrigin: The origin (relative to the :samp:`origin` parameter) of where the checks
begin to be stacked.
:type black: value
:param black: The value for the *black* checks.
:type white: value
:param white: The value for the *white* checks.
:type dtype: :obj:`numpy.dtype`
:param dtype: The element type of the returned array.
:type mtype: :obj:`mango.mtype`
:param mtype: The mango data type for the checker-board image.
:type halo: 3-sequence
:param halo: The halo size for the returned :obj:`mango.Dds` object.
:type mpidims: 3-sequence
:param mpidims: Cartesian MPI domain decomposition.
:type origin: 3-sequence
:param origin: The global origin index for the returned :obj:`mango.Dds` object.
:rtype: :obj:`mango.Dds`
:return: Checker-board :obj:`mango.Dds`
"""
while (len(shape) <= 2):
shape = (1,) + tuple(shape)
cbDds = mango.empty(shape, dtype=dtype, mtype=mtype, halo=halo, mpidims=mpidims, origin=origin)
checkOrigin = sp.array(checkOrigin) + cbDds.origin
cbArr = cbDds.subd.asarray()
sbeg = sp.array(cbDds.subd.origin)
send = sbeg + cbDds.subd.shape
coords = np.ogrid[sbeg[0]:send[0],sbeg[1]:send[1],sbeg[2]:send[2]]
vals = np.array([black,white],dtype=dtype)
cbArr[...] = \
vals[
(
(coords[0]-checkOrigin[0])//checkShape[0]
+
(coords[1]-checkOrigin[1])//checkShape[1]
+
(coords[2]-checkOrigin[2])//checkShape[2]
)
%
2
]
return cbDds
def gaussian_noise(shape,
mean=0.0,
stdd=1.0,
dtype=None,
mtype=None,
halo=(0, 0, 0),
mpidims=(0, 0, 0),
origin=(0, 0, 0),
subdshape=None,
subdorigin=None):
"""
Generates image of Gaussian (Normal) distributed noise.
:type shape: 3-sequence
:param shape: The global shape :samp:`(zSz,ySz,xSz)` of the returned :obj:`mango.Dds` image.
:type mean: float
:param mean: The mean parameter of the normally distributed noise.
:type stdd: float
:param stdd: The standard-deviation parameter of the normally distributed noise.
:type dtype: :obj:`numpy.dtype`
:param dtype: The element type of the returned array.
:type mtype: :obj:`mango.mtype`
:param mtype: The mango data type for the return noise image.
:type halo: 3-sequence
:param halo: The halo size for the returned :obj:`mango.Dds` object.
:type mpidims: 3-sequence
:param mpidims: Cartesian MPI domain decomposition.
:type origin: 3-sequence
:param origin: The global origin index for the returned :obj:`mango.Dds` object.
:type subdshape: 3-sequence
:param subdshape: Explicitly specify the sub-domain shape for the current MPI process.
:type subdorigin: 3-sequence
:param subdorigin: Explicitly specify the sub-domain origin for the current MPI process.
:rtype: :obj:`mango.Dds`
:return: Noise :obj:`mango.Dds` image.
"""
dds = \
mango.empty(
shape=shape,
dtype=dtype,
mtype=mtype,
halo=halo,
mpidims=mpidims,
origin=origin,
subdshape=subdshape,
subdorigin=subdorigin
)
dds.subd.asarray()[...] = np.random.normal(loc=mean,
scale=stdd,
size=dds.subd.asarray().shape)
return dds
def gaussian_noise(
shape,
mean=0.0,
stdd=1.0,
dtype=None,
mtype=None,
halo=(0,0,0),
mpidims=(0,0,0),
origin=(0,0,0),
subdshape=None,
subdorigin=None
):
"""
Generates image of Gaussian (Normal) distributed noise.
:type shape: 3-sequence
:param shape: The global shape :samp:`(zSz,ySz,xSz)` of the returned :obj:`mango.Dds` image.
:type mean: float
:param mean: The mean parameter of the normally distributed noise.
:type stdd: float
:param stdd: The standard-deviation parameter of the normally distributed noise.
:type dtype: :obj:`numpy.dtype`
:param dtype: The element type of the returned array.
:type mtype: :obj:`mango.mtype`
:param mtype: The mango data type for the return noise image.
:type halo: 3-sequence
:param halo: The halo size for the returned :obj:`mango.Dds` object.
:type mpidims: 3-sequence
:param mpidims: Cartesian MPI domain decomposition.
:type origin: 3-sequence
:param origin: The global origin index for the returned :obj:`mango.Dds` object.
:type subdshape: 3-sequence
:param subdshape: Explicitly specify the sub-domain shape for the current MPI process.
:type subdorigin: 3-sequence
:param subdorigin: Explicitly specify the sub-domain origin for the current MPI process.
:rtype: :obj:`mango.Dds`
:return: Noise :obj:`mango.Dds` image.
"""
dds = \
mango.empty(
shape=shape,
dtype=dtype,
mtype=mtype,
halo=halo,
mpidims=mpidims,
origin=origin,
subdshape=subdshape,
subdorigin=subdorigin
)
dds.subd.asarray()[...] = np.random.normal(loc=mean, scale=stdd, size=dds.subd.asarray().shape)
return dds;
def chi_squared_noise(
shape,
dof=1.0,
dtype=None,
mtype=None,
halo=(0,0,0),
mpidims=(0,0,0),
origin=(0,0,0),
subdshape=None,
subdorigin=None
):
"""
Generates image of Chi-Squared distributed noise.
:type shape: 3-sequence
:param shape: The global shape :samp:`(zSz,ySz,xSz)` of the returned :obj:`mango.Dds` image.
:type dof: float
:param dof: The degrees-of-freedom parameter of the central-Chi-Squared distributed noise.
:type dtype: :obj:`numpy.dtype`
:param dtype: The element type of the returned array.
:type mtype: :obj:`mango.mtype`
:param mtype: The mango data type for the return noise image.
:type halo: 3-sequence
:param halo: The halo size for the returned :obj:`mango.Dds` object.
:type mpidims: 3-sequence
:param mpidims: Cartesian MPI domain decomposition.
:type origin: 3-sequence
:param origin: The global origin index for the returned :obj:`mango.Dds` object.
:type subdshape: 3-sequence
:param subdshape: Explicitly specify the sub-domain shape for the current MPI process.
:type subdorigin: 3-sequence
:param subdorigin: Explicitly specify the sub-domain origin for the current MPI process.
:rtype: :obj:`mango.Dds`
:return: Noise :obj:`mango.Dds` image.
"""
dds = \
mango.empty(
shape=shape,
dtype=dtype,
mtype=mtype,
halo=halo,
mpidims=mpidims,
origin=origin,
subdshape=subdshape,
subdorigin=subdorigin
)
dds.subd.asarray()[...] = np.random.chisquare(df=dof, size=dds.subd.asarray().shape)
return dds;
def testDdsCreateComplex(self):
"""
Test the :func:`mango.empty` and :func:`mango.ones`
and :func:`mango.zeros` methods for dtype=complex64/complex128.
"""
dds = mango.empty(shape=self.shape, dtype="complex64")
cVal = np.complex64(2 + 1j * 4)
dds.setAllToValue(cVal)
self.assertEqual(cVal, dds[0])
self.assertEqual(cVal, dds[0, 0, 0])
self.assertEqual(cVal, dds[(0, 0, 0)])
dds = mango.zeros(shape=self.shape, dtype="complex64")
self.assertEqual(np.complex64(0), dds[0])
self.assertEqual(np.complex64(0), dds[0, 0, 0])
self.assertEqual(np.complex64(0), dds[(0, 0, 0)])
dds = mango.ones(shape=self.shape, dtype="complex64")
self.assertEqual(np.complex64(1), dds[0])
self.assertEqual(np.complex64(1), dds[0, 0, 0])
self.assertEqual(np.complex64(1), dds[(0, 0, 0)])
dds = mango.empty(shape=self.shape, dtype="complex128")
cVal = np.complex128(2 + 1j * 4)
dds.setAllToValue(cVal)
self.assertEqual(cVal, dds[0])
self.assertEqual(cVal, dds[0, 0, 0])
self.assertEqual(cVal, dds[(0, 0, 0)])
dds = mango.zeros(shape=self.shape, dtype="complex128")
self.assertEqual(np.complex128(0), dds[0])
self.assertEqual(np.complex128(0), dds[0, 0, 0])
self.assertEqual(np.complex128(0), dds[(0, 0, 0)])
dds = mango.ones(shape=self.shape, dtype="complex128")
self.assertEqual(np.complex128(1), dds[0])
self.assertEqual(np.complex128(1), dds[0, 0, 0])
self.assertEqual(np.complex128(1), dds[(0, 0, 0)])
def chi_squared_noise(shape,
dof=1.0,
dtype=None,
mtype=None,
halo=(0, 0, 0),
mpidims=(0, 0, 0),
origin=(0, 0, 0),
subdshape=None,
subdorigin=None):
"""
Generates image of Chi-Squared distributed noise.
:type shape: 3-sequence
:param shape: The global shape :samp:`(zSz,ySz,xSz)` of the returned :obj:`mango.Dds` image.
:type dof: float
:param dof: The degrees-of-freedom parameter of the central-Chi-Squared distributed noise.
:type dtype: :obj:`numpy.dtype`
:param dtype: The element type of the returned array.
:type mtype: :obj:`mango.mtype`
:param mtype: The mango data type for the return noise image.
:type halo: 3-sequence
:param halo: The halo size for the returned :obj:`mango.Dds` object.
:type mpidims: 3-sequence
:param mpidims: Cartesian MPI domain decomposition.
:type origin: 3-sequence
:param origin: The global origin index for the returned :obj:`mango.Dds` object.
:type subdshape: 3-sequence
:param subdshape: Explicitly specify the sub-domain shape for the current MPI process.
:type subdorigin: 3-sequence
:param subdorigin: Explicitly specify the sub-domain origin for the current MPI process.
:rtype: :obj:`mango.Dds`
:return: Noise :obj:`mango.Dds` image.
"""
dds = \
mango.empty(
shape=shape,
dtype=dtype,
mtype=mtype,
halo=halo,
mpidims=mpidims,
origin=origin,
subdshape=subdshape,
subdorigin=subdorigin
)
dds.subd.asarray()[...] = np.random.chisquare(
df=dof, size=dds.subd.asarray().shape)
return dds
def createCheckerDds(shape,
checkShape,
checkOrigin=(0, 0, 0),
black=0,
white=1,
dtype="uint8",
mtype=None,
halo=(0, 0, 0),
mpidims=(0, 0, 0),
origin=(0, 0, 0)):
"""
Creates a 3D checker-board image.
:type shape: 3-sequence
:param shape: The global shape :samp:`(zSz,ySz,xSz)` of the returned :obj:`mango.Dds` image.
:type checkShape: 3-sequence
:param checkShape: The shape of the checks.
:type checkOrigin: 3-sequence
:param checkOrigin: The origin (relative to the :samp:`origin` parameter) of where the checks
begin to be stacked.
:type black: value
:param black: The value for the *black* checks.
:type white: value
:param white: The value for the *white* checks.
:type dtype: :obj:`numpy.dtype`
:param dtype: The element type of the returned array.
:type mtype: :obj:`mango.mtype`
:param mtype: The mango data type for the checker-board image.
:type halo: 3-sequence
:param halo: The halo size for the returned :obj:`mango.Dds` object.
:type mpidims: 3-sequence
:param mpidims: Cartesian MPI domain decomposition.
:type origin: 3-sequence
:param origin: The global origin index for the returned :obj:`mango.Dds` object.
:rtype: :obj:`mango.Dds`
:return: Checker-board :obj:`mango.Dds`
"""
while (len(shape) <= 2):
shape = (1, ) + tuple(shape)
cbDds = mango.empty(shape,
dtype=dtype,
mtype=mtype,
halo=halo,
mpidims=mpidims,
origin=origin)
checkOrigin = sp.array(checkOrigin) + cbDds.origin
cbArr = cbDds.subd.asarray()
sbeg = sp.array(cbDds.subd.origin)
send = sbeg + cbDds.subd.shape
coords = np.ogrid[sbeg[0]:send[0], sbeg[1]:send[1], sbeg[2]:send[2]]
vals = np.array([black, white], dtype=dtype)
cbArr[...] = \
vals[
(
(coords[0]-checkOrigin[0])//checkShape[0]
+
(coords[1]-checkOrigin[1])//checkShape[1]
+
(coords[2]-checkOrigin[2])//checkShape[2]
)
%
2
]
return cbDds