Read values from ini file

example0.py

@@ -4,7 +4,7 @@ import numbers
 import jaxlib
 import jax.numpy as jnp
 import jax
-
+import configparser
 
 # Output stream management
 cons = borg.console()
@@ -14,25 +14,59 @@ def myprint(x):
     else:
         cons.print_std(repr(x))
 
+def get_cosmopar(ini_file):
+    """
+    Extract cosmological parameters from an ini file
+    
+    Args:
+        :ini_file (str): Path to the ini file
+    
+    Returns:
+        :cpar (borg.cosmo.CosmologicalParameters): Cosmological parameters
+    """
+    
+    config = configparser.ConfigParser()
+    config.read(ini_file)
+        
+    cpar = borg.cosmo.CosmologicalParameters()
+    cpar.default()
+    cpar.fnl = float(config['cosmology']['fnl'])
+    cpar.omega_k = float(config['cosmology']['omega_k'])
+    cpar.omega_m = float(config['cosmology']['omega_m'])
+    cpar.omega_b = float(config['cosmology']['omega_b'])
+    cpar.omega_q = float(config['cosmology']['omega_q'])
+    cpar.h = float(config['cosmology']['h100'])
+    cpar.sigma8 = float(config['cosmology']['sigma8'])
+    cpar.n_s = float(config['cosmology']['n_s'])
+    cpar.w = float(config['cosmology']['w'])
+    cpar.wprime = float(config['cosmology']['wprime'])
+    
+    return cpar
+
+
 class MyLikelihood(borg.likelihood.BaseLikelihood):
     """
     HADES likelihood class
     """
 
-    def __init__(self, fwd: borg.forward.BaseForwardModel):
+    def __init__(self, fwd: borg.forward.BaseForwardModel, ini_fname: str):
 
         self.fwd = fwd
-        self.N = [32, 32, 32]  # Number of grid points per side
-        self.L = [500, 500, 500]  # Box size lenght Mpc/h
 
-        self.sigma_dens = 0.1  # Density scatter
+        # Read the ini file
+        self.ini_fname = ini_fname
+        config = configparser.ConfigParser()
+        config.read(ini_fname)
+        self.N = [int(config['system'][f'N{i}']) for i in range(3)] # Number of grid points per side
+        self.L = [float(config['system'][f'L{i}']) for i in range(3)]  # Box size lenght Mpc/h
+
+        self.sigma_dens = float(config['mock']['sigma_dens'])  # Density scatter
 
         myprint(f"Likelihood initialized with {self.N} grid points and box size {self.L} Mpc/h")
         super().__init__(fwd, self.N, self.L)
 
         # Set up cosmoligical parameters
-        cpar = borg.cosmo.CosmologicalParameters()
-        cpar.default()
+        cpar = get_cosmopar(ini_fname)
         self.updateCosmology(cpar)
         
         # Gradient of the likelihood
@@ -180,6 +214,7 @@ def build_gravity_model(state: borg.likelihood.MarkovState, box: borg.forward.Bo
     global chain
     myprint("Building gravity model")
 
+    # READ FROM INI FILE
     which_model = 'cola'
     ai = 0.05 # Initial scale factor
     af = 1.0 # Final scale factor
@@ -250,8 +285,7 @@ def build_gravity_model(state: borg.likelihood.MarkovState, box: borg.forward.Bo
     chain @= mod
 
     # Cosmological parameters
-    cpar = borg.cosmo.CosmologicalParameters()
-    cpar.default()
+    cpar = get_cosmopar(borg.getIniConfigurationFilename())
     chain.setCosmoParams(cpar)
 
     return chain
@@ -291,5 +325,5 @@ def build_likelihood(state: borg.likelihood.MarkovState, info: borg.likelihood.L
     
     myprint("Building likelihood")
     global likelihood
-    likelihood = MyLikelihood(chain)
+    likelihood = MyLikelihood(chain, borg.getIniConfigurationFilename())
     return likelihood

ini_file.ini

@@ -51,3 +51,14 @@ w = -1
 wprime = 0
 beta = 1.5
 z0 = 0
+
+[mock]
+sigma_dens = 0.1
+
+[gravity]
+which_model = lpt
+ai = 0.05 # Initial scale factor
+af = 1.0 # Final scale factor
+supersampling = 2
+forcesampling = 2
+nsteps = 20 # Number of steps in the PM solver