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 load_data(root, box):
toret = []
dtype = [('r', float), ('corr', float), ('error', float)]
for kind in ['mono', 'quad']:
data = np.loadtxt(os.path.join(root, '%s_Box%s_rescale.dat' %(kind, box)))
C = np.loadtxt(os.path.join(root, 'covar_%s.dat' %kind)).reshape((data.shape[0], -1))
errs = np.diag(C)**0.5
data = np.concatenate([data, errs[:,None]], axis=1)
meta = {'edges' : make_edges(data[:,0])}
corr = Corr1dDataSet.from_nbkit(data, meta, columns=['r', 'corr', 'error'])
toret.append(corr)
return toret
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
