def get_poles(self, scaled=True):
"""
Return a dictionary holding the configuration space galaxy spectrum
multipoles in redshift space, as measured by nbodykit
"""
tag = '_scaled' if scaled else '_unscaled'
name = '_poles' + tag
try:
return getattr(self, name)
except AttributeError:
basename = 'poles_challenge_box{box}%s.dat' %tag
d = os.path.join(self.root, 'corr_poles')
loader = io.load_correlation
kwargs = {'sum_only':['RR', 'N'], 'force_index_match':True}
poles = SpectraSet.from_files(loader, d, basename, [self.boxes], ['box'], args=('1d',), kwargs=kwargs)
# reindex and add the errors
poles = self.reindex(poles, 'r_cen', self.dr, weights='N')
# now convert
ells = [('corr_0',0), ('corr_2', 2), ('corr_4', 4)]
poles = tools.unstack_multipoles(poles, ells, 'corr')
setattr(self, name, poles)
return poles
def get_shifted_poles(self, name, tag="", average=None, subtract_shot_noise=True):
"""
Return the N-series multipoles in redshift space, which haven been shifted in
space (and computed with Bianchi algorithm)
"""
if name not in ["boss_like", "plane_parallel"]:
raise ValueError("please choose a name from 'boss_like' or 'plane_parallel'")
tag_ = "_" + tag if tag else ""
if tag:
name += tag_
if average is not None:
if isinstance(average, str):
average = [average]
else:
average = []
name_ = name
if len(average):
name_ += "_" + "_".join(average)
try:
return getattr(self, "shifted_poles_%s" % name_)
except AttributeError:
# load the data from file
basename = "poles_challenge_boxN{box}_shifted_unscaled_shift{shift}_%s.dat" % name
coords = [self.boxes, [0, 1, 2]]
d = os.path.join(self.root, "poles")
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, coords, ["box", "shift"], args=("1d",), kwargs=kwargs)
# reindex and add the errors
poles = self.reindex(poles, "k_cen", self.dk, weights="modes")
# now convert
ells = [("mono", 0), ("quad", 2), ("hexadec", 4)]
poles = tools.unstack_multipoles(poles, ells, "power")
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 len(average):
poles = poles.average(axis=average, weights="modes")
setattr(self, name, poles)
return poles
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_poles(self, average=None):
"""
Return the cutsky galaxy multipoles in redshift space
"""
if average is not None:
if isinstance(average, str):
average = [average]
else:
average = []
name = '_poles'
if len(average):
name += '_' + '_'.join(average)
try:
return getattr(self, name)
except AttributeError:
# form the filename and load the data
basename = 'bianchips_qpmdr12_TSC_{box:04d}.dat'
# read in the data
loader = io.read_cutsky_power_poles
kwargs = {'skiprows' : 31, 'sum_only':['modes'], 'force_index_match':True}
poles = SpectraSet.from_files(loader, self.root, basename, [self.boxes], ['box'], kwargs=kwargs)
# 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')
if len(average):
poles = poles.average(axis=average, weights='modes')
setattr(self, name, poles)
return poles
def get_florian_poles(self, scaled=True, average=False):
"""
Return the cutsky galaxy multipoles in redshift space, as
measured by Florian's code
"""
scaled_tag = 'scaled' if scaled else 'unscaled'
name = '_poles_florian_' + scaled_tag
if average: name += '_mean'
try:
return getattr(self, name)
except AttributeError:
# form the filename and load the data
d = os.path.join(self.root, 'data', scaled_tag)
if scaled:
basename = 'bianchips_cutsky_TSC_0.7_{box:d}.dat'
else:
basename = 'bianchips_Ncutsky_{box:02d}.dat'
# read in the data
loader = io.read_cutsky_power_poles
kwargs = {'skiprows' : 31, 'sum_only':['modes'], 'force_index_match':True}
poles = SpectraSet.from_files(loader, d, basename, [self.boxes], ['box'], kwargs=kwargs)
# 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')
if average:
poles = poles.average(axis='box', weights='modes')
setattr(self, name, poles)
return poles
def get_poles(self, spacing="dk005", scaled=False, Nmu=100):
"""
Return the total galaxy spectrum multipoles in redshift space
"""
_spacing = spacing
tag = "unscaled" if not scaled else "scaled"
if spacing:
spacing = "_" + spacing
name = "_poles%s_%s" % (spacing, tag)
try:
return getattr(self, name)
except AttributeError:
# load the data from file
basename = "poles_challenge_box{box}_%s%s_Nmu%d.dat" % (tag, spacing, Nmu)
coords = [self.boxes]
d = os.path.join(self.root, "poles")
loader = io.load_power
columns = ["k", "mono", "quad", "hexadec", "modes"]
kwargs = {"sum_only": ["modes"], "force_index_match": True, "columns": columns}
poles = SpectraSet.from_files(loader, d, basename, coords, ["box"], args=("1d",), kwargs=kwargs)
# reindex and add the errors
poles = self.reindex(poles, "k_cen", self.dk, weights="modes")
# now convert
ells = [("mono", 0), ("quad", 2), ("hexadec", 4)]
toret = tools.unstack_multipoles(poles, ells, "power")
# add the errors
pkmu = self.get_Pgal(scaled=scaled, spacing=_spacing, Nmu=Nmu)
toret.add_power_pole_errors(pkmu)
setattr(self, name, toret)
return toret
def get_Pgal(self, spacing="dk005", scaled=False, Nmu=5):
"""
Return the total galaxy spectrum in redshift space
Parameters
----------
spacing : str, optional
the tag used to identify results with different k spacing, i.e. `dk005`
scaled : bool, optional
return the results that have/haven't been scaled by AP factors
Nmu : int, optional
the number of mu bins
"""
tag = "unscaled" if not scaled else "scaled"
if spacing:
spacing = "_" + spacing
name = "_Pgal%s_%s_Nmu%d" % (spacing, tag, Nmu)
try:
return getattr(self, name)
except AttributeError:
# load the data from file
basename = "pkmu_challenge_box{box}_%s%s_Nmu%d.dat" % (tag, spacing, Nmu)
coords = [self.boxes]
d = os.path.join(self.root, "power")
loader = io.load_power
kwargs = {"sum_only": ["modes"], "force_index_match": True}
Pgal = SpectraSet.from_files(loader, d, basename, coords, ["box"], args=("2d",), kwargs=kwargs)
# reindex and add the errors
Pgal = self.reindex(Pgal, "k_cen", self.dk, weights="modes")
Pgal.add_power_errors()
setattr(self, name, Pgal)
return Pgal
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_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_poles(
self,
space="redshift",
subtract_shot_noise=True,
spacing="dk005",
Nmu=100,
scaled=False,
average=None,
tag="",
include_tetrahex=False,
):
"""
Return the N-series multipoles in redshift space
"""
if space not in ["redshift", "real"]:
raise ValueError("`space` must be real' or 'redshift'")
if average is not None:
if isinstance(average, str):
average = [average]
else:
average = []
# determine the tag
scaled_tag = "scaled" if scaled else "unscaled"
_spacing = spacing
if spacing:
spacing = "_" + spacing
name = "_nseries_poles_%s_%s%s" % (space, scaled_tag, spacing)
if len(average):
name += "_" + "_".join(average)
tag_ = "_" + tag if tag else ""
if tag:
name += tag_
try:
return getattr(self, name)
except AttributeError:
# load the data from file
if space == "redshift":
basename = "poles_challenge_boxN{box}_%s%s_Nmu%d_{los}los%s.dat" % (scaled_tag, spacing, Nmu, tag_)
coords = [self.los, self.boxes]
dims = ["los", "box"]
else:
basename = "poles_challenge_boxN{box}_real_%s%s_Nmu100%s.dat" % (scaled_tag, spacing, tag_)
coords = [self.boxes]
dims = ["box"]
d = os.path.join(self.root, "poles")
loader = io.load_power
mapcols = {"power_0.real": "mono", "power_2.real": "quad", "power_4.real": "hexadec"}
usecols = ["k", "mono", "quad", "hexadec", "modes"]
if include_tetrahex:
mapcols["power_6.real"] = "tetrahex"
usecols.append("tetrahex")
kwargs = {"usecols": usecols, "mapcols": mapcols}
poles = SpectraSet.from_files(loader, d, basename, coords, dims, args=("1d",), kwargs=kwargs)
# reindex and add the errors
poles = self.reindex(poles, "k_cen", self.dk, weights="modes")
# now convert
ells = [("mono", 0), ("quad", 2), ("hexadec", 4)]
if include_tetrahex:
ells.append(("tetrahex", 6))
poles = tools.unstack_multipoles(poles, ells, "power")
if subtract_shot_noise:
for key in poles.ndindex():
if key["ell"] == 0:
p = poles.loc[key].get()
p["power"] = p["power"] - p.attrs["volume"] / p.attrs["N1"]
# average?
if len(average):
poles = poles.average(axis=average, weights="modes")
# add the errors
pkmu = self.get_Pgal(space=space, spacing=_spacing, Nmu=Nmu, average=average, scaled=scaled, tag=tag)
poles.add_power_pole_errors(pkmu)
setattr(self, name, poles)
return poles
def get_Pgal(
self, space="redshift", spacing="dk005", subtract_shot_noise=False, Nmu=100, scaled=False, average=None, tag=""
):
"""
Return the total galaxy spectrum in redshift space
"""
if space not in ["redshift", "real"]:
raise ValueError("`space` must be real' or 'redshift'")
if average is not None:
if isinstance(average, str):
average = [average]
else:
average = []
# determine the tag
scaled_tag = "scaled" if scaled else "unscaled"
_spacing = spacing
if spacing:
spacing = "_" + spacing
name = "_Pgal%s_%s_Nmu%d_%s" % (spacing, space, Nmu, scaled_tag)
if len(average):
name += "_" + "_".join(average)
if tag:
tag = "_" + tag
name += tag
try:
return getattr(self, name)
except AttributeError:
# load the data from file
if space == "redshift":
basename = "pkmu_challenge_boxN{box}_%s%s_Nmu%d_{los}los%s.dat" % (scaled_tag, spacing, Nmu, tag)
coords = [self.los, self.boxes]
dims = ["los", "box"]
else:
basename = "pkmu_challenge_boxN{box}_real_%s%s_Nmu100%s.dat" % (scaled_tag, spacing, tag)
coords = [self.boxes]
dims = ["box"]
d = os.path.join(self.root, "power")
loader = io.load_power
kwargs = {"sum_only": ["modes"], "force_index_match": True}
Pgal = SpectraSet.from_files(loader, d, basename, coords, dims, args=("2d",), kwargs=kwargs)
# take the real part
for key in Pgal.ndindex():
pkmu = (Pgal.loc[key]).get()
pkmu["power"] = pkmu["power"].real
if subtract_shot_noise:
for key in Pgal.ndindex():
p = Pgal.loc[key].get()
p["power"] = p["power"] - p.attrs["volume"] / p.attrs["N1"]
if len(average):
Pgal = Pgal.average(axis=average, weights="modes")
if isinstance(Pgal, SpectraSet):
# reindex and add the errors
Pgal = self.reindex(Pgal, "k_cen", self.dk, weights="modes")
Pgal.add_power_errors()
else:
# reindex
if self.dk is not None:
Pgal = Pgal.reindex("k_cen", self.dk, weights="modes", force=True)
# add errors
Pgal["error"] = (2.0 / Pgal["modes"]) ** 0.5 * Pgal["power"]
setattr(self, name, Pgal)
return Pgal
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
