def __init__(self,
CAMBGenerator,
smooth_type="hinton2017",
recon_smoothing_scale=15,
mpi_comm=None):
"""
Precomputes certain integrals over the camb power spectrum for efficiency given a list of smoothing scales. Access ks via self.ks, and use get_data for an array
of all the Perturbation Theory integrals.
"""
self.logger = logging.getLogger("barry")
self.smooth_type = smooth_type.lower()
self.mpi_comm = mpi_comm
if not validate_smooth_method(smooth_type):
exit(0)
self.recon_smoothing_scale = recon_smoothing_scale
# We should check here that CAMBGenerator points to some pk_lin.
self.CAMBGenerator = CAMBGenerator
self.data_dir = os.path.dirname(os.path.abspath(
inspect.stack()[0][1])) + os.sep + "data/"
self.filename = self.data_dir + f"PT_{CAMBGenerator.filename_unique}_{self.smooth_type}_{int(self.recon_smoothing_scale * 100)}.pkl"
self.data = None
self.logger.info(
f"Creating PT data with {self.CAMBGenerator.om_resolution} x {self.CAMBGenerator.h0_resolution}"
)
def __init__(self, name="Pk Basic", smooth_type="hinton2017", fix_params=("om"), postprocess=None, smooth=False, correction=None):
""" Generic power spectrum function model
Parameters
----------
name : str, optional
Name of the model
smooth_type : str, optional
The sort of smoothing to use. Either 'hinton2017' or 'eh1998'
fix_params : list[str], optional
Parameter names to fix to their defaults. Defaults to just `[om]`.
postprocess : `Postprocess` object
The class to postprocess model predictions. Defaults to none.
smooth : bool, optional
Whether to generate a smooth model without the BAO feature. Defaults to `false`.
correction : `Correction` enum.
Defaults to `Correction.SELLENTIN
"""
super().__init__(name, postprocess=postprocess, correction=correction)
self.smooth_type = smooth_type.lower()
if not validate_smooth_method(smooth_type):
exit(0)
self.declare_parameters()
self.set_fix_params(fix_params)
# Set up data structures for model fitting
self.smooth = smooth
def __init__(
self,
name="Corr Basic",
smooth_type="hinton2017",
fix_params=("om"),
smooth=False,
correction=None,
isotropic=True,
poly_poles=(0, 2),
marg=None,
):
"""Generic correlation function model
Parameters
----------
name : str, optional
Name of the model
smooth_type : str, optional
The sort of smoothing to use. Either 'hinton2017' or 'eh1998'
fix_params : list[str], optional
Parameter names to fix to their defaults. Defaults to just `[om]`.
smooth : bool, optional
Whether to generate a smooth model without the BAO feature. Defaults to `false`.
correction : `Correction` enum. Defaults to `Correction.SELLENTIN
"""
super().__init__(name,
correction=correction,
isotropic=isotropic,
marg=marg)
self.parent = PowerSpectrumFit(fix_params=fix_params,
smooth_type=smooth_type,
correction=correction,
isotropic=isotropic,
marg=marg)
self.poly_poles = poly_poles
self.smooth_type = smooth_type.lower()
if not validate_smooth_method(smooth_type):
exit(0)
self.declare_parameters()
self.set_fix_params(fix_params)
# Set up data structures for model fitting
self.smooth = smooth
self.nmu = 100
self.mu = np.linspace(0.0, 1.0, self.nmu)
self.pk2xi_0 = None
self.pk2xi_2 = None
self.pk2xi_4 = None
def __init__(self,
name="BAO Correlation Polynomial Fit",
smooth_type="hinton2017",
fix_params=("om"),
smooth=False,
correction=None):
""" Generic correlation function model
Parameters
----------
name : str, optional
Name of the model
smooth_type : str, optional
The sort of smoothing to use. Either 'hinton2017' or 'eh1998'
fix_params : list[str], optional
Parameter names to fix to their defaults. Defaults to just `[om]`.
smooth : bool, optional
Whether to generate a smooth model without the BAO feature. Defaults to `false`.
correction : `Correction` enum. Defaults to `Correction.SELLENTIN
"""
super().__init__(name, correction=correction)
self.smooth_type = smooth_type.lower()
if not validate_smooth_method(smooth_type):
exit(0)
self.declare_parameters()
self.set_fix_params(fix_params)
# Set up data structures for model fitting
self.smooth = smooth
self.camb = None
self.PT = None
self.pk2xi = None
self.recon_smoothing_scale = None
self.cosmology = None
def __init__(
self,
name="Pk Basic",
smooth_type="hinton2017",
fix_params=("om"),
postprocess=None,
smooth=False,
recon=None,
correction=None,
isotropic=True,
poly_poles=(0, 2),
marg=None,
n_poly=0,
n_data=1,
data_share_bias=False,
data_share_poly=False,
):
"""Generic power spectrum function model
Parameters
----------
name : str, optional
Name of the model
smooth_type : str, optional
The sort of smoothing to use. Either 'hinton2017' or 'eh1998'
fix_params : list[str], optional
Parameter names to fix to their defaults. Defaults to just `[om]`.
postprocess : `Postprocess` object
The class to postprocess model predictions. Defaults to none.
smooth : bool, optional
Whether to generate a smooth model without the BAO feature. Defaults to `false`.
correction : `Correction` enum.
Defaults to `Correction.SELLENTIN
"""
super().__init__(name,
postprocess=postprocess,
correction=correction,
isotropic=isotropic,
marg=marg,
n_data=n_data)
self.smooth_type = smooth_type.lower()
if not validate_smooth_method(smooth_type):
exit(0)
self.n_data_bias = 1 if data_share_bias else self.n_data
self.n_data_poly = 1 if data_share_poly else self.n_data
if n_poly > 0 and self.isotropic:
poly_poles = [0]
self.n_poly = n_poly
self.poly_poles = poly_poles
self.data_share_poly = data_share_poly
self.recon = False
self.recon_type = "None"
if recon is not None:
if recon.lower() != "None":
if recon.lower() == "iso":
self.recon_type = "iso"
elif recon.lower() == "ani":
self.recon_type = "ani"
elif recon.lower() == "sym":
self.recon_type = "sym"
else:
raise ValueError(
"recon not recognised, must be 'iso', 'ani' or 'sym'")
self.recon = True
self.declare_parameters()
# Set up data structures for model fitting
self.smooth = smooth
self.nmu = 100
self.mu = np.linspace(0.0, 1.0, self.nmu)
self.kvals = None
self.pksmooth = None
self.pkratio = None