def __init__(self, array, BoxSize, comm=None, root=0, **kwargs):
if comm.rank == root:
array = numpy.array(array)
if array.dtype.kind == 'c':
# transform to real for the correct shape
array = numpy.fft.irfftn(array)
array[...] *= numpy.prod(array.shape)
shape = array.shape
dtype = array.dtype
else:
array, dtype, shape = [None] * 3
dtype = comm.bcast(dtype, root=root)
shape = comm.bcast(shape, root=root)
assert len(shape) in (2, 3)
Nmesh = shape
empty = numpy.empty((0, ), dtype)
MeshSource.__init__(self, comm, Nmesh, BoxSize, empty.real.dtype)
self.field = self.pm.create(type='real')
if comm.rank != root:
array = empty # ignore data from other ranks.
else:
array = array.ravel()
# fill the field with the array
self.field.unravel(array)
def __init__(self,
Plin,
BoxSize,
Nmesh,
seed=None,
unitary_amplitude=False,
inverted_phase=False,
remove_variance=None,
comm=None):
self.Plin = Plin
# cosmology and communicator
self.comm = comm
self.attrs.update(attrs_to_dict(Plin, 'plin.'))
# set the seed randomly if it is None
if seed is None:
if self.comm.rank == 0:
seed = numpy.random.randint(0, 4294967295)
seed = self.comm.bcast(seed)
self.attrs['seed'] = seed
if remove_variance is not None:
unitary_amplitude = remove_variance
self.attrs['unitary_amplitude'] = unitary_amplitude
self.attrs['inverted_phase'] = inverted_phase
MeshSource.__init__(self,
BoxSize=BoxSize,
Nmesh=Nmesh,
dtype='f4',
comm=comm)
def __init__(self, array, BoxSize, comm=None, root=0, **kwargs):
if comm.rank == root:
array = numpy.array(array)
if array.dtype.kind == 'c':
# transform to real for the correct shape
array = numpy.fft.irfftn(array)
array[...] *= numpy.prod(array.shape)
shape = array.shape
dtype = array.dtype
else:
array, dtype, shape = [None] * 3
dtype = comm.bcast(dtype, root=root)
shape = comm.bcast(shape, root=root)
assert len(shape) in (2, 3)
Nmesh = shape
empty = numpy.empty((0,), dtype)
MeshSource.__init__(self, comm, Nmesh, BoxSize, empty.real.dtype)
self.field = self.pm.create(type='real')
if comm.rank != root:
array = empty # ignore data from other ranks.
else:
array = array.ravel()
# fill the field with the array
self.field.unravel(array)
def __init__(self, source, Nmesh, BoxSize, dtype, selection, position,
weight, value, interlaced, compensated, resampler):
from nbodykit.source.catalog import MultipleSpeciesCatalog
if not isinstance(source, MultipleSpeciesCatalog):
raise TypeError(("the input source for MultipleSpeciesCatalogMesh "
"must be a MultipleSpeciesCatalog"))
MeshSource.__init__(self,
Nmesh=Nmesh,
BoxSize=BoxSize,
dtype=dtype,
comm=source.comm)
self.source = source
self.weight = weight
self.position = position
self.value = value
self.selection = selection
self.interlaced = interlaced
self.compensated = compensated
self.resampler = resampler
self.dtype = dtype
self.species = source.species
def __init__(self, Plin, BoxSize, Nmesh, seed=None,
unitary_amplitude=False,
inverted_phase=False,
remove_variance=None,
comm=None):
self.Plin = Plin
# cosmology and communicator
self.comm = comm
self.attrs.update(attrs_to_dict(Plin, 'plin.'))
# set the seed randomly if it is None
if seed is None:
if self.comm.rank == 0:
seed = numpy.random.randint(0, 4294967295)
seed = self.comm.bcast(seed)
self.attrs['seed'] = seed
if remove_variance is not None:
unitary_amplitude = remove_variance
self.attrs['unitary_amplitude'] = unitary_amplitude
self.attrs['inverted_phase'] = inverted_phase
MeshSource.__init__(self, BoxSize=BoxSize, Nmesh=Nmesh, dtype='f4', comm=comm)
def __init__(self, path, dataset, comm=None, **kwargs):
self.path = path
self.dataset = dataset
self.comm = comm
# update the meta-data
self.attrs.update(kwargs)
with BigFileMPI(comm=self.comm, filename=path)[dataset] as ff:
for key in ff.attrs:
v = ff.attrs[key]
if isinstance(v, string_types) and v.startswith('json://'):
self.attrs[key] = json.loads(v[7:], cls=JSONDecoder)
else:
self.attrs[key] = numpy.squeeze(v)
# fourier space or config space
if ff.dtype.kind == 'c':
self.isfourier = True
if ff.dtype.itemsize == 16:
dtype = 'f8'
else:
dtype = 'f4'
else:
self.isfourier = False
if ff.dtype.itemsize == 8:
dtype = 'f8'
else:
dtype = 'f4'
# determine Nmesh
if 'ndarray.shape' not in self.attrs:
raise ValueError(
"`ndarray.shape` should be stored in the Bigfile `attrs` to determine `Nmesh`"
)
if 'Nmesh' not in self.attrs:
raise ValueError(
"`ndarray.shape` should be stored in the Bigfile `attrs` to determine `Nmesh`"
)
Nmesh = self.attrs['Nmesh']
BoxSize = self.attrs['BoxSize']
MeshSource.__init__(self,
BoxSize=BoxSize,
Nmesh=Nmesh,
dtype=dtype,
comm=comm)
def __new__(cls,
source,
BoxSize,
Nmesh,
dtype,
weight,
value,
selection,
position='Position',
interlaced=False,
compensated=False,
window='cic',
**kwargs):
# source here must be a CatalogSource
assert isinstance(source, CatalogSourceBase)
# new, empty CatalogSource
obj = CatalogSourceBase.__new__(cls, source.comm)
# copy over size from the CatalogSource
obj._size = source.size
obj._csize = source.csize
# copy over the necessary meta-data to attrs
obj.attrs['BoxSize'] = BoxSize
obj.attrs['Nmesh'] = Nmesh
obj.attrs['interlaced'] = interlaced
obj.attrs['compensated'] = compensated
obj.attrs['window'] = window
# copy meta-data from source too
obj.attrs.update(source.attrs)
# store others as straight attributes
obj.dtype = dtype
obj.weight = weight
obj.value = value
obj.selection = selection
obj.position = position
# add in the Mesh Source attributes
MeshSource.__init__(obj, obj.comm, Nmesh, BoxSize, dtype)
# finally set the base as the input CatalogSource
# NOTE: set this AFTER MeshSource.__init__()
obj.base = source
return obj
def __init__(self, path, dataset, comm=None, **kwargs):
self.path = path
self.dataset = dataset
self.comm = comm
# update the meta-data
self.attrs.update(kwargs)
with FileMPI(comm=self.comm, filename=path)[dataset] as ff:
for key in ff.attrs:
v = ff.attrs[key]
if isinstance(v, string_types) and v.startswith('json://'):
self.attrs[key] = json.loads(v[7:], cls=JSONDecoder)
else:
self.attrs[key] = numpy.squeeze(v)
# fourier space or config space
if ff.dtype.kind == 'c':
self.isfourier = True
if ff.dtype.itemsize == 16:
dtype = 'f8'
else:
dtype = 'f4'
else:
self.isfourier = False
if ff.dtype.itemsize == 8:
dtype = 'f8'
else:
dtype = 'f4'
# determine Nmesh
if 'ndarray.shape' not in self.attrs:
raise ValueError("`ndarray.shape` should be stored in the Bigfile `attrs` to determine `Nmesh`")
if 'Nmesh' not in self.attrs:
raise ValueError("`ndarray.shape` should be stored in the Bigfile `attrs` to determine `Nmesh`")
Nmesh = self.attrs['Nmesh']
BoxSize = self.attrs['BoxSize']
MeshSource.__init__(self, BoxSize=BoxSize, Nmesh=Nmesh, dtype=dtype, comm=comm)
def __init__(self,
source,
BoxSize,
Nmesh,
dtype,
weight,
value,
selection,
position='Position',
interlaced=False,
compensated=False,
window='cic',
**kwargs):
# source here must be a CatalogSource
assert isinstance(source, CatalogSourceBase)
# copy over the necessary meta-data to attrs
self.attrs['BoxSize'] = BoxSize
self.attrs['Nmesh'] = Nmesh
self.attrs['interlaced'] = interlaced
self.attrs['compensated'] = compensated
self.attrs['window'] = window
# copy meta-data from source too
self.attrs.update(source.attrs)
self.source = source
# store others as straight attributes
self.dtype = dtype
self.weight = weight
self.value = value
self.selection = selection
self.position = position
# add in the Mesh Source attributes
MeshSource.__init__(self, source.comm, Nmesh, BoxSize, dtype)
def __init__(self,
source,
Nmesh,
BoxSize,
Position,
dtype='f4',
resampler='cic',
compensated=False,
interlaced=False,
Value=None,
Selection=None,
Weight=None,
**kwargs):
from nbodykit.base.catalog import CatalogSourceBase
assert isinstance(source, CatalogSourceBase)
self._columns = {}
# copy meta-data from source too
self.attrs.update(source.attrs)
MeshSource.__init__(self, source.comm, Nmesh, BoxSize, dtype)
self.source = source
# store others as straight attributes
self.dtype = dtype
self.Position = Position
self.Weight = Weight
self.Value = Value
self.Selection = Selection
self.attrs['interlaced'] = interlaced
self.attrs['compensated'] = compensated
self.attrs['resampler'] = str(resampler)
def __init__(self, source, Nmesh, BoxSize,
Position,
dtype='f4',
resampler='cic',
compensated=False,
interlaced=False,
Value=None,
Selection=None,
Weight=None,
**kwargs):
from nbodykit.base.catalog import CatalogSourceBase
assert isinstance(source, CatalogSourceBase)
self._columns = {}
# copy meta-data from source too
self.attrs.update(source.attrs)
MeshSource.__init__(self, source.comm,
Nmesh,
BoxSize,
dtype)
self.source = source
# store others as straight attributes
self.dtype = dtype
self.Position = Position
self.Weight = Weight
self.Value = Value
self.Selection = Selection
self.attrs['interlaced'] = interlaced
self.attrs['compensated'] = compensated
self.attrs['resampler'] = str(resampler)
def save(self, output, dataset='Field', mode='real'):
"""
Save the mesh as a :class:`~nbodykit.source.mesh.bigfile.BigFileMesh`
on disk, either in real or complex space.
Parameters
----------
output : str
name of the bigfile file
dataset : str, optional
name of the bigfile data set where the field is stored
mode : str, optional
real or complex; the form of the field to store
"""
return MeshSource.save(self, output, dataset=dataset, mode=mode)
def __finalize__(self, other):
"""
Finalize the creation of a CatalogMesh object by copying over
attributes from a second CatalogMesh.
This also copies over the relevant MeshSource attributes via a
call to :func:`MeshSource.__finalize__`.
Parameters
----------
obj : CatalogMesh
the second CatalogMesh to copy over attributes from
"""
if isinstance(other, CatalogSourceBase):
self = CatalogSourceBase.__finalize__(self, other)
if isinstance(other, MeshSource):
self = MeshSource.__finalize__(self, other)
return self
def __init__(self,
data,
ran,
Nmesh,
bias=1.0,
f = 0.0,
los = [0, 0, 1],
R=20,
position='Position',
revert_rsd_random=False,
scheme='LGS',
BoxSize=None,
comm=None):
assert scheme in ['LGS', 'LF2', 'LRR']
assert isinstance(data, CatalogSource)
assert isinstance(ran, CatalogSource)
from pmesh.pm import ParticleMesh
if Nmesh is None:
Nmesh = data.attrs['Nmesh']
_Nmesh = numpy.empty(3, dtype='i8')
_Nmesh[...] = Nmesh
if BoxSize is None:
BoxSize = data.attrs['BoxSize']
los = numpy.array(los, dtype='f8', copy=True)
los /= (los ** 2).sum()
assert len(los) == 3
assert (~numpy.isnan(los)).all()
pm = ParticleMesh(BoxSize=BoxSize, Nmesh=_Nmesh, comm=comm)
self.pm = pm
if (self.pm.BoxSize / self.pm.Nmesh).max() > R:
if comm.rank == 0:
warnings.warn("The smoothing radius smaller than the mesh cell size. This may produce undesired numerical results.")
assert position in data.columns
assert position in ran.columns
self.position = position
MeshSource.__init__(self, comm, pm.Nmesh.copy(), pm.BoxSize.copy(), pm.dtype)
self.attrs['bias'] = bias
self.attrs['f'] = f
self.attrs['los'] = los
self.attrs['R'] = R
self.attrs['scheme'] = scheme
self.attrs['revert_rsd_random'] = bool(revert_rsd_random)
self.data = data
self.ran = ran
if self.comm.rank == 0:
self.logger.info("Reconstruction for bias=%g, f=%g, smoothing R=%g los=%s" % (self.attrs['bias'], self.attrs['f'], self.attrs['R'], str(self.attrs['los'])))
self.logger.info("Reconstruction scheme = %s" % (self.attrs['scheme']))
def __init__(self,
data,
ran,
Nmesh,
bias=1.0,
f = 0.0,
los = [0, 0, 1],
R=20,
position='Position',
revert_rsd_random=False,
scheme='LGS',
BoxSize=None):
assert scheme in ['LGS', 'LF2', 'LRR']
assert isinstance(data, CatalogSource)
assert isinstance(ran, CatalogSource)
comm = data.comm
assert data.comm == ran.comm
from pmesh.pm import ParticleMesh
if Nmesh is None:
Nmesh = data.attrs['Nmesh']
_Nmesh = numpy.empty(3, dtype='i8')
_Nmesh[...] = Nmesh
if BoxSize is None:
BoxSize = data.attrs['BoxSize']
los = numpy.array(los, dtype='f8', copy=True)
los /= (los ** 2).sum()
assert len(los) == 3
assert (~numpy.isnan(los)).all()
pm = ParticleMesh(BoxSize=BoxSize, Nmesh=_Nmesh, comm=comm)
self.pm = pm
if (self.pm.BoxSize / self.pm.Nmesh).max() > R:
if comm.rank == 0:
warnings.warn("The smoothing radius smaller than the mesh cell size. This may produce undesired numerical results.")
assert position in data.columns
assert position in ran.columns
self.position = position
MeshSource.__init__(self, comm, pm.Nmesh.copy(), pm.BoxSize.copy(), pm.dtype)
self.attrs['bias'] = bias
self.attrs['f'] = f
self.attrs['los'] = los
self.attrs['R'] = R
self.attrs['scheme'] = scheme
self.attrs['revert_rsd_random'] = bool(revert_rsd_random)
self.data = data
self.ran = ran
if self.comm.rank == 0:
self.logger.info("Reconstruction for bias=%g, f=%g, smoothing R=%g los=%s" % (self.attrs['bias'], self.attrs['f'], self.attrs['R'], str(self.attrs['los'])))
self.logger.info("Reconstruction scheme = %s" % (self.attrs['scheme']))