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_Phh(self, space="real"):
"""
Return Phh in the space specified. Only 'real' is currently implemented.
"""
try:
return getattr(self, "_Phh_" + space)
except AttributeError:
d = os.path.join(self.root, "halo")
mass_bins = ["00020_00060", "00060_00180", "00180_00540", "00540_01620"]
redshifts = ["z007", "z005", "z004"]
Pshot = [
[2336.0, 6494.0, 21882.0, 101112.0],
[2849.0, 9174.0, 41152.0, 314465.0],
[4065.0, 16447.0, 111359.0, np.nan],
]
meta = {"box_size": 1600.0, "N1": 0, "N2": 0}
basename = "pkmu_chi_00_h_h_{mass}_{z}_1-3_02-13binaaQ"
data = np.empty((len(redshifts), len(mass_bins)), dtype=object)
for i in range(len(redshifts)):
for j in range(len(mass_bins)):
args = {"mass": mass_bins[j], "z": redshifts[i]}
meta["N1"] = meta["N2"] = meta["box_size"] ** 3 / Pshot[i][j]
filename = os.path.join(d, basename.format(**args))
if os.path.exists(filename):
data[i, j] = load_data(np.loadtxt(filename), space, **meta)
else:
data[i, j] = np.nan
Phh = SpectraSet(data, coords=[self.z, self.mass], dims=["z", "mass"])
setattr(self, "_Phh_" + space, Phh)
return Phh
def get_Phm(self, space="real"):
"""
Return Phm in the space specified, either `real` or `redshift`
"""
if space != "real":
raise NotImplementedError("sorry, only real-space results exist for Phm")
try:
return getattr(self, "_Phm_" + space)
except AttributeError:
d = os.path.join(self.root, "halo-matter")
mass_bins = ["00020_00060", "00060_00180", "00180_00540", "00540_01620"]
redshifts = ["z007", "z005", "z004"]
meta = {"box_size": 1600.0, "N1": np.inf, "N2": np.inf}
basename = "pk_mr0_hr0_{mass}_{z}_linaax"
data = np.empty((len(redshifts), len(mass_bins)), dtype=object)
for i in range(len(redshifts)):
for j in range(len(mass_bins)):
args = {"mass": mass_bins[j], "z": redshifts[i]}
filename = os.path.join(d, basename.format(**args))
if os.path.exists(filename):
data[i, j] = load_data(np.loadtxt(filename), space, **meta)
else:
data[i, j] = np.nan
Phm = SpectraSet(data, coords=[self.z, self.mass], dims=["z", "mass"])
setattr(self, "_Phm_" + space, Phm)
return Phm
def get_P11(self, mu, smooth=False):
"""
Return the dark matter P11[mu^2] or P11[mu^4] correlator in real-space
"""
tag = "_smooth" if smooth else ""
name = "_P11_%s%s" % (mu, smooth)
if mu not in ["mu2", "mu4"]:
raise ValueError("`mu` must be one of [`mu2`, `mu4`]")
try:
return getattr(self, name)
except AttributeError:
redshifts = ["z007", "z005", "z004"]
meta = {"box_size": 1600.0, "N1": np.inf, "N2": np.inf}
if smooth:
if mu == "mu2":
from pyRSD.data import P11_mu2_z_0_000, P11_mu2_z_0_509, P11_mu2_z_0_989
smooth_data = [P11_mu2_z_0_000(), P11_mu2_z_0_509(), P11_mu2_z_0_989()]
else:
from pyRSD.data import P11_mu4_z_0_000, P11_mu4_z_0_509, P11_mu4_z_0_989
smooth_data = [P11_mu4_z_0_000(), P11_mu4_z_0_509(), P11_mu4_z_0_989()]
else:
d = os.path.join(self.root, "dark_matter")
basename = "pkmu_chi_11_m_m_{z}_1-3_02-13binaaQ"
if mu == "mu2":
cols = [-4, -2]
else:
cols = [-3, -1]
data = np.empty(len(redshifts), dtype=object)
for i in range(len(redshifts)):
if not smooth:
filename = os.path.join(d, basename.format(z=redshifts[i]))
if os.path.exists(filename):
x = np.loadtxt(filename)
else:
data[i] = np.nan
continue
else:
x = smooth_data[i]
kedges = make_kedges(x)
data_dict = {}
data_dict["k"] = x[:, 0]
if not smooth:
data_dict["power"] = x[:, cols[0]] * x[:, 0] ** 2
data_dict["error"] = x[:, cols[1]] * x[:, 0] ** 2
else:
data_dict["power"] = x[:, 1]
data[i] = Power1dDataSet(kedges, data_dict, **meta)
toret = SpectraSet(data, coords=[self.z], dims=["z"])
setattr(self, name, toret)
return toret
def get_window(self, scaled=False):
"""
Return the formatted window function for the cutsky challenge mocks
"""
if scaled:
filename = 'window_cmass_ngc_50x_randoms_scaled_ellmax8.dat'
else:
filename = 'window_cmass_ngc_50x_randoms_unscaled_ellmax8.dat'
filename = os.path.join(self.root, 'extra', 'window', filename)
return np.loadtxt(filename)
def get_P01(self, smooth=False):
"""
Return the dark matter P01[mu^2] correlator in real-space
"""
tag = "_smooth" if smooth else ""
name = "_P01%s" % tag
try:
return getattr(self, name)
except AttributeError:
redshifts = ["z007", "z005", "z004"]
meta = {"box_size": 1600.0, "N1": np.inf, "N2": np.inf}
if smooth:
from pyRSD.data import P01_mu2_z_0_000, P01_mu2_z_0_509, P01_mu2_z_0_989
smooth_data = [P01_mu2_z_0_000(), P01_mu2_z_0_509(), P01_mu2_z_0_989()]
else:
d = os.path.join(self.root, "dark_matter")
basename = "pkmu_chi_01_m_m_{z}_1-3_02-13binaaQ"
data = np.empty(len(redshifts), dtype=object)
for i in range(len(redshifts)):
if not smooth:
filename = os.path.join(d, basename.format(z=redshifts[i]))
if os.path.exists(filename):
x = np.loadtxt(filename)
else:
data[i] = np.nan
continue
else:
x = smooth_data[i]
kedges = make_kedges(x)
data_dict = {}
data_dict["k"] = x[:, 0]
if not smooth:
data_dict["power"] = x[:, -2] * 2 * x[:, 0]
data_dict["error"] = x[:, -1] * 2 * x[:, 0]
else:
data_dict["power"] = x[:, 1]
data[i] = Power1dDataSet(kedges, data_dict, **meta)
toret = SpectraSet(data, coords=[self.z], dims=["z"])
setattr(self, name, toret)
return toret
def get_Pmm(self, space="real"):
"""
Return Pmm in the space specified, either `real` or `redshift`
"""
try:
return getattr(self, "_Pmm_" + space)
except AttributeError:
d = os.path.join(self.root, "dark_matter")
redshifts = ["z007", "z005", "z004"]
meta = {"box_size": 1600.0, "N1": np.inf, "N2": np.inf}
basename = "pkmu_chi_00_m_m_{z}_1-3_02-13binaaQ"
data = np.empty(len(redshifts), dtype=object)
for i in range(len(redshifts)):
filename = os.path.join(d, basename.format(z=redshifts[i]))
if os.path.exists(filename):
data[i] = load_data(np.loadtxt(filename), space, **meta)
else:
data[i] = np.nan
Pmm = SpectraSet(data, coords=[self.z], dims=["z"])
setattr(self, "_Pmm_" + space, Pmm)
return Pmm
