def get_1d_halo_matter(self, kind='small_r', average='box'):
"""
Return the 1D real-space halo-matter correlation functions,
with errors estimated from the scatter in the 10 realizations
"""
valid_kinds = ['small_r', 'fft']
if kind not in valid_kinds:
raise ValueError("valid choices for `kind` are: %s" %str(valid_kinds))
name = '_1d_halo_matter_%s' %kind
if average is not None:
name += '_'+average
try:
return getattr(self, name)
except AttributeError:
basename = 'corr_1d_hm{mass}_runPB_PB{box}_{a}_%s.dat' %kind
d = os.path.join(self.root, 'halo-matter/config/real')
loader = io.load_correlation
coords = [self.a, self.mass, self.box]; dims = ['a', 'mass', 'box']
corr = SpectraSet.from_files(loader, d, basename, coords, dims, args=('1d',))
# reindex and add the errors
corr = self.reindex(corr, 'r_cen', self.dr, weights='N')
# compute errors
errs = {}
for key in corr.ndindex(dims=['a', 'mass']):
xi = corr.sel(**key)
data = []
for x in xi:
x = x.get()
data.append(x['corr'])
subkey = (key['a'], key['mass'])
errs[subkey] = np.diag(np.cov(np.asarray(data).T))**0.5
if average is not None:
errs[subkey] /= (len(self.box))**0.5
# average?
if average is not None:
corr = corr.average(axis=average)
# add the errors
for key in corr.ndindex():
xi = corr.loc[key].get()
xi['error'] = errs[(key['a'], key['mass'])]
setattr(self, name, corr)
return corr
def get_wg_poles(self):
"""
Return a dictionary holding the configuration space galaxy spectrum
multipoles in redshift space, as used by the working group
"""
try:
return self._wg_poles
except AttributeError:
import xarray
root = os.path.join(os.environ['RSD_DIR'], 'CorrelationFunction/Results')
data = np.asarray([load_data(root, box) for box in self.boxes])
ells = [0, 2]
dims = ['box', 'ell']
coords = [self.boxes, ells]
poles = xarray.DataArray(data, dims=dims, coords=coords)
self._wg_poles = poles
return poles
def get_subbox_poles(self, which, average=False, subtract_shot_noise=True):
"""
Return the cutsky galaxy multipoles in redshift space, measured
for the subboxes
"""
name = '_my_poles_subboxes_%s_Mpch' %(which)
if average: name += '_mean'
try:
return getattr(self, name)
except AttributeError:
# form the filename and load the data
d = self.root
basename = 'poles_my_cutskyN1_subbox{box:d}_%s_Mpch_unscaled_no_fkp_dk005.dat' %which
# read in the data
loader = io.load_power
mapcols = {'power_0.real':'mono', 'power_2.real':'quad', 'power_4.real':'hexadec'}
usecols = ['k', 'mono', 'quad', 'hexadec', 'modes']
kwargs = {'usecols':usecols, 'mapcols':mapcols}
if which == '300':
boxes = list(range(94))
else:
boxes = list(range(24))
poles = SpectraSet.from_files(loader, d, basename, [boxes], ['box'], args=('1d',), kwargs=kwargs, ignore_missing=True)
# remove null
valid_boxes = []
for key, p in poles.nditer():
if not p.isnull():
valid_boxes.append(key['box'])
poles = poles.sel(box=valid_boxes)
# reindex
poles = self.reindex(poles, 'k_cen', self.dk, weights='modes')
# unstack the poles
ells = [('mono',0), ('quad', 2), ('hexadec', 4)]
poles = tools.unstack_multipoles(poles, ells, 'power')
# compute errors
errs = {}
for ell in poles['ell'].values:
data = []
for box in poles['box'].values:
p = poles.sel(box=box, ell=ell).get()
data.append(p['power'])
errs[ell] = np.diag(np.cov(np.asarray(data).T))**0.5
if average:
errs[ell] /= (len(boxes))**0.5
if subtract_shot_noise:
for key in poles.ndindex():
if key['ell'] == 0:
p = poles.loc[key].get()
p['power'] = p['power'] - p.attrs['shot_noise']
# average?
if average:
poles = poles.average(axis='box')
# add the errors
for key in poles.ndindex():
p = poles.loc[key].get()
p['error'] = errs[key['ell']]
setattr(self, name, poles)
return poles
def get_my_poles(self, space='redshift', scaled=False, average=False, tag="",
subtract_shot_noise=True):
"""
Return the cutsky galaxy multipoles in redshift space, measured
for my 84 box realizations
"""
if space not in ['real', 'redshift']:
raise ValueError("`space` should be 'real' or 'redshift'")
scaled_tag = 'scaled' if scaled else 'unscaled'
name = '_my_poles_%s_%s' %(space, scaled_tag)
if average: name += '_mean'
if tag:
tag = '_'+tag
name += tag
try:
return getattr(self, name)
except AttributeError:
# form the filename and load the data
d = os.path.join(self.root, 'nbodykit/poles')
if space == 'redshift':
basename = 'poles_my_cutskyN{box:d}_%s_no_fkp_dk005%s.dat' %(scaled_tag, tag)
else:
basename = 'poles_my_cutskyN{box:d}_real_%s_no_fkp_dk005%s.dat' %(scaled_tag, tag)
# read in the data
loader = io.load_power
mapcols = {'power_0.real':'mono', 'power_2.real':'quad', 'power_4.real':'hexadec'}
usecols = ['k', 'mono', 'quad', 'hexadec', 'modes']
kwargs = {'usecols':usecols, 'mapcols':mapcols}
poles = SpectraSet.from_files(loader, d, basename, [self.boxes], ['box'], args=('1d',), kwargs=kwargs, ignore_missing=True)
# remove null
valid_boxes = []
for key, p in poles.nditer():
if not p.isnull():
valid_boxes.append(key['box'])
poles = poles.sel(box=valid_boxes)
# reindex
poles = self.reindex(poles, 'k_cen', self.dk, weights='modes')
# unstack the poles
ells = [('mono',0), ('quad', 2), ('hexadec', 4)]
poles = tools.unstack_multipoles(poles, ells, 'power')
# compute errors
errs = {}
for ell in poles['ell'].values:
data = []
for box in poles['box'].values:
p = poles.sel(box=box, ell=ell).get()
data.append(p['power'])
errs[ell] = np.diag(np.cov(np.asarray(data).T))**0.5
if average:
errs[ell] /= (len(self.boxes))**0.5
if subtract_shot_noise:
for key in poles.ndindex():
if key['ell'] == 0:
p = poles.loc[key].get()
p['power'] = p['power'] - p.attrs['shot_noise']
# average?
if average:
poles = poles.average(axis='box')
# add the errors
for key in poles.ndindex():
p = poles.loc[key].get()
p['error'] = errs[key['ell']]
setattr(self, name, poles)
return poles
def _get_moment(self, moments, los="", save_errors=False, name=None, sel_mu=None, ignore_cache=False):
"""
Internal function to return any (ell, ell_prime) moment
Parameters
----------
moments : list of tuples
list of tuples specifying the velocity moments to load
los : str, {'x', 'y', 'z'} optional
the string specifying the line-of-sight
save_errors : bool, optional
whether to pickle the errors for future loading
name : str, optional
use this string as the attribute name
sel_mu : int, list of int, optional
select specific only mu values
ignore_cache : bool, optional
if `True`, ignore any saved errors
"""
if name is None:
name = "_plus_".join("P%d%d" %(ell, ell_prime) for (ell, ell_prime) in moments)
name_ = "_" + name
if los: name_ += '_%slos' %los
try:
return getattr(self, name_)
except AttributeError:
coords = [self.a, self.mass]
dims = ['a', 'mass']
add_errors = True
# load
loader = io.load_momentum
# loop over all moments
toret = []
for (ell, ell_prime) in moments:
columns = None
if ell == 0 and ell_prime == 0:
d = os.path.join(self.root, 'halo/real/density/power')
tag = self.tag if 'mean' not in self.tag else '10mean'
basename = 'pk_hh{mass}_runPB_%s_{a}.dat' %tag
columns = ['k', 'power', 'modes']
else:
d = os.path.join(self.root, 'halo/real/momentum/poles')
if los:
args = (ell, ell_prime, self.tag, los)
basename = 'poles_P%d%d_hh{mass}_runPB_%s_{a}_%slos.dat' %args
else:
args = (ell, ell_prime, self.tag)
basename = 'poles_P%d%d_hh{mass}_runPB_%s_{a}.dat' %args
kwargs = {'sum_only':['modes'], 'force_index_match':True}
if columns is not None: kwargs['columns'] = columns
poles = SpectraSet.from_files(loader, d, basename, coords, dims, args=(ell,ell_prime,), kwargs=kwargs)
# add the mu dimension
data = np.asarray(poles.values.tolist())
poles = SpectraSet(data, coords=coords+[self.mu], dims=dims+['mu'])
# reindex
poles = self.reindex(poles, 'k_cen', self.dk, weights='modes')
# choose specific mu
if sel_mu is not None:
poles = poles.sel(mu=sel_mu)
# add errors
if ell == 0 and ell_prime == 0:
poles.add_power_errors()
add_errors = False
toret.append(poles)
if len(moments) != 0:
for i in range(1, len(moments)):
for key, P in toret[i].nditer():
subkey = {k:key[k] for k in key if k in toret[0].dims}
P0 = toret[0].sel(**subkey)
if P0.isnull():
continue
else:
P0 = P0.get()
P0['power'] += P.get()['power']
toret = toret[0]
if add_errors:
kw = {'sel_mu':sel_mu, 'save_errors':save_errors, 'name':name, 'ignore_cache':ignore_cache}
self._add_errors(toret, moments, **kw)
setattr(self, name_, toret)
return toret
