149 add nonlinear galaxy bias models

requirements.txt

@@ -17,4 +17,5 @@ pyccl
 sacc
 fgspectra>=1.1.0
 syslibrary
+fast-pt
 pyhalomodel

setup.cfg

@@ -24,6 +24,7 @@ install_requires =
     sacc
     fgspectra>=1.1.0
     syslibrary
+    fast-pt
     pyhalomodel
 
 [options.package_data]

soliket-tests.yml

@@ -17,3 +17,4 @@ dependencies:
   - camb
   - pip:
       - -r requirements.txt
+      - velocileptors @git+https://github.com/sfschen/velocileptors 

soliket/bias.py

@@ -30,6 +30,8 @@
 from typing import Optional
 from cobaya.theory import Theory
 
+from pyccl import nl_pt as pt
+
 
 class Bias(Theory):
     """Parent class for bias models."""
@@ -97,3 +99,40 @@ def calculate(self, state: dict, want_derived: bool = True,
 
         state["Pk_gg_grid"] = params_values_dict["b_lin"] ** 2. * Pk_mm
         state["Pk_gm_grid"] = params_values_dict["b_lin"] * Pk_mm
+
+
+class PTBias(Bias):
+
+    _bias_models = ['LagrangianPT', 'EulerianPT', 'BACCO', 'anzu']
+
+    def initialize():
+        super.initialize()
+
+        self.bz_model
+        self._initialize_pt()
+
+    def _initialize_pt(self):
+        """
+        Initialize CCL PT calculators.
+        """
+
+        #TODO: Need to decide what we want to expose
+        if self.bz_model == 'LagrangianPT':
+            self.ptc = pt.LagrangianPTCalculator(log10k_min=self.log10k_min,
+                                                 log10k_max=self.log10k_max,
+                                                 nk_per_decade=self.nk_per_decade)
+            # b1_pk_kind='pt', bk2_pk_kind='pt')
+        elif self.bz_model == 'EulerianPT':
+            self.ptc = pt.EulerianPTCalculator(with_NC=True, with_IA=True,
+                                               log10k_min=self.log10k_min, 
+                                               log10k_max=self.log10k_max,
+                                               nk_per_decade=self.nk_per_decade)
+        else:
+            raise LoggedError(self.log,
+                              "Bias model {} not implemented yet.".format(self.bz_model))
+
+    def calculate():
+
+        # do some checking on tracers?
+        state["Pk_gg_grid"] = self.ptc._get_pgg(tr1, tr2)
+        state["Pk_gm_grid"] = self.ptc._get_pgm(tr1)

soliket/ccl.py

@@ -107,7 +107,7 @@ def initialize(self) -> None:
     def get_requirements(self) -> set:
         # These are currently required to construct a CCL cosmology object.
         # Ultimately CCL should depend only on observable not parameters
-        return {'omch2', 'ombh2'}
+        return {'omch2', 'ombh2', 'sigma8', 'ns', 'omnuh2'}
 
     def must_provide(self, **requirements) -> dict:
         # requirements is dictionary of things requested by likelihoods
@@ -169,6 +169,10 @@ def calculate(self, state: dict, want_derived: bool = True,
 
         Omega_c = self.provider.get_param('omch2') / h ** 2
         Omega_b = self.provider.get_param('ombh2') / h ** 2
+        sigma8 = self.provider.get_param('sigma8')
+        n_s = self.provider.get_param('ns')
+        # mnu = self.provider.get_param('mnu')
+        mnu = self.provider.get_param('omnuh2') * 93.014
         # Array z is sorted in ascending order. CCL requires an ascending scale factor
         # as input
         # Flip the arrays to make them a function of the increasing scale factor.
@@ -181,7 +185,6 @@ def calculate(self, state: dict, want_derived: bool = True,
         a = 1. / (1 + self.z[::-1])
         # growth = ccl.background.growth_factor(cosmo, a)
         # fgrowth = ccl.background.growth_rate(cosmo, a)
-
         if self.kmax:
             for pair in self._var_pairs:
                 # Get the matter power spectrum:
@@ -199,35 +202,38 @@ def calculate(self, state: dict, want_derived: bool = True,
                         Omega_c=Omega_c,
                         Omega_b=Omega_b,
                         h=h,
-                        sigma8=0.8,
-                        n_s=0.96,
+                        sigma8=sigma8,
+                        n_s=n_s,
+                        m_nu=mnu,
                         background={'a': a,
                                     'chi': distance,
                                     'h_over_h0': E_of_z},
                         pk_linear={'a': a,
                                    'k': k,
-                                   'delta_matter:delta_matter': Pk_lin},  # noqa E501
+                                   'delta_matter:delta_matter': Pk_lin}, # noqa E501
                         pk_nonlin={'a': a,
                                    'k': k,
-                                   'delta_matter:delta_matter': Pk_nonlin}  # noqa E501
-                    )
+                                   'delta_matter:delta_matter': Pk_nonlin} # noqa E501
+                        )
 
                 else:
                     cosmo = self.ccl.CosmologyCalculator(
                         Omega_c=Omega_c,
                         Omega_b=Omega_b,
                         h=h,
-                        sigma8=0.8,
-                        n_s=0.96,
+                        sigma8=sigma8,
+                        n_s=n_s,
+                        m_nu=mnu,
                         background={'a': a,
                                     'chi': distance,
                                     'h_over_h0': E_of_z},
                         pk_linear={'a': a,
                                    'k': k,
-                                   'delta_matter:delta_matter': Pk_lin}  # noqa E501
-                    )
+                                   'delta_matter:delta_matter': Pk_lin} # noqa E501
+                        )
 
         state['CCL'] = {'cosmo': cosmo, 'ccl': self.ccl}
+
         for required_result, method in self._required_results.items():
             state['CCL'][required_result] = method(cosmo)
 

soliket/cross_correlation/CrossCorrelationLikelihood.yaml

@@ -1,3 +1,4 @@
 datapath: soliket/tests/data/des_s-act_kappa.toy-sim.sacc.fits
 use_spectra: all
 ncovsims: null
+

soliket/cross_correlation/GalaxyKappaLikelihood.yaml

@@ -1,9 +1,15 @@
-params:
-  b1: 
-    prior:
-      min: 0.
-      max: 10.
-    latex: b_1
-  s1: 
-    value: 0.4
-    latex: s_1
+datapath: soliket/tests/data/des_s-act_kappa.toy-sim.sacc.fits
+use_spectra: all
+
+bins: gc_cmass
+bz_model: LagrangianPT
+nz_model: NzShift
+z_cmb: 1030
+input_params_prefix: '_'
+
+log10k_min: -4
+log10k_max: 2
+nk_per_decade: 20
+defaults:
+  lmin: 30
+  lmax: 3000