def __init__(self, N, BoxSize, max_speed=500., mu_logM=13.5, sigma_logM=0.5, seed=None):
self.N = N
self.BoxSize = BoxSize
self.max_speed = max_speed
self.mu_logM = mu_logM
self.sigma_logM = sigma_logM
self.seed = seed
# create the local random seed from the global seed and comm size
self.local_seed = utils.local_random_seed(self.seed, self.comm)
def __init__(self, datasource, collision_radius=62/60./60., seed=None):
# set the input parameters
self.datasource = datasource
self.collision_radius = collision_radius
self.seed = seed
# store collision radius in radians
self._collision_radius_rad = self.collision_radius * numpy.pi/180.
if self.comm.rank == 0:
self.logger.info("collision radius in degrees = %.4f" %collision_radius)
# create the local random seed from the global seed and comm size
self.local_seed = utils.local_random_seed(self.seed, self.comm)
self.logger.info("local_seed = %d" %self.local_seed)
def __init__(self, nbar, redshift, BoxSize, Nmesh, bias=2., rsd=None, seed=None):
self.nbar = nbar
self.redshift = redshift
self.BoxSize = BoxSize
self.Nmesh = Nmesh
self.bias = bias
self.rsd = rsd
self.seed = seed
# create the local random seed from the global seed and comm size
self.local_seed = utils.local_random_seed(self.seed, self.comm)
# crash if no cosmology provided
if self.cosmo is None:
raise ValueError("a cosmology must be specified via the `cosmo` keyword to use %s" %self.plugin_name)
def __init__(self,
N,
BoxSize,
max_speed=500.,
mu_logM=13.5,
sigma_logM=0.5,
seed=None):
self.N = N
self.BoxSize = BoxSize
self.max_speed = max_speed
self.mu_logM = mu_logM
self.sigma_logM = sigma_logM
self.seed = seed
# create the local random seed from the global seed and comm size
self.local_seed = utils.local_random_seed(self.seed, self.comm)
def run(self):
"""
Run the Subsample algorithm
"""
import mpsort
from astropy.utils.misc import NumpyRNGContext
if self.smoothing is None:
self.smoothing = self.datasource.BoxSize[0] / self.Nmesh[0]
elif (self.datasource.BoxSize / self.Nmesh > self.smoothing).any():
raise ValueError("smoothing is too small")
def Smoothing(pm, complex):
k = pm.k
k2 = 0
for ki in k:
ki2 = ki**2
complex[:] *= numpy.exp(-0.5 * ki2 * self.smoothing**2)
def NormalizeDC(pm, complex):
""" removes the DC amplitude. This effectively
divides by the mean
"""
w = pm.w
comm = pm.comm
ind = []
value = 0.0
found = True
for wi in w:
if (wi != 0).all():
found = False
break
ind.append((wi == 0).nonzero()[0][0])
if found:
ind = tuple(ind)
value = numpy.abs(complex[ind])
value = comm.allreduce(value)
complex[:] /= value
# open the datasource and keep the cache
with self.datasource.keep_cache():
painter = Painter.create("DefaultPainter", paintbrush='cic')
real, stats = painter.paint(self.pm, self.datasource)
complex = real.r2c()
for t in [Smoothing, NormalizeDC]:
t(self.pm, complex)
complex.c2r(real)
columns = ['Position', 'ID', 'Velocity']
local_seed = utils.local_random_seed(self.seed, self.comm)
dtype = numpy.dtype([
('Position', ('f4', 3)),
('Velocity', ('f4', 3)),
('ID', 'u8'),
('Density', 'f4'),
])
subsample = [numpy.empty(0, dtype=dtype)]
with self.datasource.open() as stream:
for Position, ID, Velocity in stream.read(columns):
with NumpyRNGContext(local_seed):
u = numpy.random.uniform(size=len(ID))
keep = u < self.ratio
Nkeep = keep.sum()
if Nkeep == 0: continue
data = numpy.empty(Nkeep, dtype=dtype)
data['Position'][:] = Position[keep]
data['Velocity'][:] = Velocity[keep]
data['ID'][:] = ID[keep]
layout = self.pm.decompose(data['Position'])
pos1 = layout.exchange(data['Position'])
density1 = real.readout(pos1)
density = layout.gather(density1)
# normalize the position after reading out density!
data['Position'][:] /= self.datasource.BoxSize
data['Velocity'][:] /= self.datasource.BoxSize
data['Density'][:] = density
subsample.append(data)
subsample = numpy.concatenate(subsample)
mpsort.sort(subsample, orderby='ID', comm=self.comm)
return subsample
def run(self):
"""
Run the Subsample algorithm
"""
import mpsort
from astropy.utils.misc import NumpyRNGContext
if self.smoothing is None:
self.smoothing = self.datasource.BoxSize[0] / self.Nmesh[0]
elif (self.datasource.BoxSize / self.Nmesh > self.smoothing).any():
raise ValueError("smoothing is too small")
def Smoothing(pm, complex):
k = pm.k
k2 = 0
for ki in k:
ki2 = ki ** 2
complex[:] *= numpy.exp(-0.5 * ki2 * self.smoothing ** 2)
def NormalizeDC(pm, complex):
""" removes the DC amplitude. This effectively
divides by the mean
"""
w = pm.w
comm = pm.comm
ind = []
value = 0.0
found = True
for wi in w:
if (wi != 0).all():
found = False
break
ind.append((wi == 0).nonzero()[0][0])
if found:
ind = tuple(ind)
value = numpy.abs(complex[ind])
value = comm.allreduce(value)
complex[:] /= value
# open the datasource and keep the cache
with self.datasource.keep_cache():
painter = Painter.create("DefaultPainter", paintbrush="cic")
real, stats = painter.paint(self.pm, self.datasource)
complex = real.r2c()
for t in [Smoothing, NormalizeDC]:
t(self.pm, complex)
complex.c2r(real)
columns = ["Position", "ID", "Velocity"]
local_seed = utils.local_random_seed(self.seed, self.comm)
dtype = numpy.dtype([("Position", ("f4", 3)), ("Velocity", ("f4", 3)), ("ID", "u8"), ("Density", "f4")])
subsample = [numpy.empty(0, dtype=dtype)]
with self.datasource.open() as stream:
for Position, ID, Velocity in stream.read(columns):
with NumpyRNGContext(local_seed):
u = numpy.random.uniform(size=len(ID))
keep = u < self.ratio
Nkeep = keep.sum()
if Nkeep == 0:
continue
data = numpy.empty(Nkeep, dtype=dtype)
data["Position"][:] = Position[keep]
data["Velocity"][:] = Velocity[keep]
data["ID"][:] = ID[keep]
layout = self.pm.decompose(data["Position"])
pos1 = layout.exchange(data["Position"])
density1 = real.readout(pos1)
density = layout.gather(density1)
# normalize the position after reading out density!
data["Position"][:] /= self.datasource.BoxSize
data["Velocity"][:] /= self.datasource.BoxSize
data["Density"][:] = density
subsample.append(data)
subsample = numpy.concatenate(subsample)
mpsort.sort(subsample, orderby="ID", comm=self.comm)
return subsample
