test version for cobaya

Author: giorgiazagatti

soliket/halo_model/halo_model_fe/__init__.py

@@ -0,0 +1,5 @@
+from .halo_model_fe import paramfile_halomod
+
+__all__ = {
+    "paramfile_halomod"
+}

soliket/halo_model/halo_model_fe/halo_model_fe.py

@@ -0,0 +1,95 @@
+import numpy as np
+from cobaya.theory import Theory
+from utils import *
+from HODS import *
+from power_spectrum import *
+from lin_matterPS import * 
+from cosmology import *
+
+class HaloModel(Theory):
+    _logz = np.linspace(-3, np.log10(1100), 150)
+    _default_z_sampling = 10**_logz
+    _default_z_sampling[0] = 0
+
+    def initialize(self):
+        self._var_pairs = set()
+        self._required_results = {}
+    
+    def _get_Pk_mm_lin(self):
+        for pair in self._var_pairs:
+            self.k, self.z, Pk_mm = \
+                self.provider.get_Pk_grid(var_pair=pair, nonlinear=False)
+
+        return Pk_mm
+
+    def get_Pk_mm_grid(self):
+
+        return self.current_state["Pk_mm_grid"]
+
+    def get_Pk_gg_grid(self):
+
+        return self.current_state["Pk_gg_grid"]
+
+    def get_Pk_gm_grid(self):
+
+        return self.current_state["Pk_gm_grid"]
+    
+class HaloModel_fe(HaloModel):
+    def initialize(self):
+        super().initialize()
+        self.logmass = np.linspace(self.Mmin, self.mmax, self.nm)
+        self.clust_param = {'sigma_EP': self.sigma_EP,
+                            'sigma_LP': self.sigma_LP,
+                            'scale_EP': self.scale_EP,
+                            'scale_LP': self.scale_LP,
+                            'alpha_EP': self.alpha_EP,
+                            'alpha_LP': self.alpha_LP,
+                            'LogMmin_EP': self.LogMmin_EP,
+                            'LogMmin_LP': self.LogMmin_LP,
+                            'Dc': self.Dc,
+                            }
+        self.isinstance_200 = u_p_nfw_hmf_bias(self.k, self._get_Pk_mm_lin(), self.logmass, self.z, self.Dc)
+        self.instance_HOD = hod_ngal(self.logmass, self.z, self.clust_param, self.instance_200)
+
+    def must_provide(self, **requirements):
+
+        options = requirements.get("halo_model") or {}
+        self._var_pairs.update(
+            set((x, y) for x, y in
+                options.get("vars_pairs", [("delta_tot", "delta_tot")])))
+
+        self.kmax = max(self.kmax, options.get("kmax", self.kmax))
+        self.z = np.unique(np.concatenate(
+                            (np.atleast_1d(options.get("z", self._default_z_sampling)),
+                            np.atleast_1d(self.z))))
+
+        needs = {}
+
+        needs["Pk_grid"] = {
+                "vars_pairs": self._var_pairs,
+                "nonlinear": (False, False),
+                "z": self.z,
+                "k_max": self.kmax
+            }
+
+        needs["sigma_R"] = {"vars_pairs": self._var_pairs,
+                           "z": self.z,
+                           "k_max": self.kmax,
+                           "R": np.linspace(0.14, 66, 256) # list of radii required
+                           }
+        return needs
+
+    def calculate(self, state: dict):
+        spectra = mm_gg_mg_spectra(
+                    self.k,
+                    self._get_Pk_mm_lin(),
+                    self.logmass,
+                    self.z,
+                    self.instance_HOD,
+                    self.instance_200,
+                    self.gal_mod
+                )
+        
+        Pgal = spectra.halo_terms_galaxy()[0]
+
+        state['Pk_gg_grid'] = Pgal