rdown_pe

code_new/Sumit/rdown_pe.py

+# Copyright (C) 2021 Xisco Jimenez Forteza
+#
+# This program is free software; you can redistribute it and/or modify it
+# under the terms of the GNU General Public License as published by the
+# Free Software Foundation; either version 3 of the License, or (at your
+# option) any later version.
+#
+# This program is distributed in the hope that it will be useful, but
+# WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General
+# Public License for more details.
+#
+# You should have received a copy of the GNU General Public License along
+# with this program; if not, write to the Free Software Foundation, Inc.,
+# 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
+
+
+#
+# =============================================================================
+#
+#                                   Preamble
+#
+# =============================================================================
+#
+# Module to run PE on RD data 
+import random
+from multiprocessing import Pool
+import dynesty
+import numpy as np
+import rdown 
+
+class Ringdown_PE:
+    def __init__(self,rdown_fun,data,dim,priors,errors2=1,theta=[],model='w-tau',norm_factor=0,l_int=0):
+        self.dim = dim
+        self.rdown_fun = rdown_fun
+        self.times = data[:,0]
+        self.datare = data[:,1].real
+        self.dataim = data[:,1].imag
+        self.priors = priors
+        self.priors_min = priors[:,0]
+        self.priors_max = priors[:,1]
+        self.prior_dim = len(priors)
+        self.errors2 = errors2
+        self.norm_factor = norm_factor
+        self.model = model
+        self.l_int = l_int
+        self.theta = theta
+        self.dict = {'w-tau':rdown_fun.rd_model_wtau , 'w-q': rdown_fun.rd_model_wq, 'w-tau-fixed':rdown_fun.rd_model_wtau_fixed,'w-tau-fixed-m-af': rdown_fun.rd_model_wtau_m_af}
+
+    #def log_likelihood(self,theta,sigma=1):
+    #    """chi2 likelihood.
+    #    """ 
+    #    modelev = dict[model](theta)
+    #    result = -np.sum(((gwdatanew_re_tsh - modelev.real)**2+(gwdatanew_im_tsh - modelev.imag)**2)/(2*theta[-1]*error_final))
+    #    if np.isnan(result):
+    #        return -np.inf
+    #    return result
+    
+    def log_likelihood(self,theta,sigma=1):
+        """chi2 likelihood.
+        """ 
+        modelev = self.dict[self.model](theta)
+        modelevre= modelev.real
+        modelevim= modelev.imag
+
+        sigma2 = self.errors2 + self.l_int*(self.datare** 2+self.dataim**2) * np.exp(2 * theta[-1])
+
+        result = -0.5*np.sum(((self.datare - modelevre)**2+(self.dataim - modelevim)**2)/sigma2+self.l_int*(2*np.log(sigma2)))-self.l_int*self.norm_factor
+
+        if np.isnan(result):
+            return -np.inf
+        return result
+      
+
+    def prior_transform(self,cube):
+        """RD uniform priors. The values for priors_min and priors_max must be given out of this function.
+        """ 
+        for i in range(self.prior_dim):
+            cube[i] =  self.priors_min[i]+ cube[i]*(self.priors_max[i]-self.priors_min[i])
+        return cube
+
+def load_priors(model,config_parser,nmax,fitnoise=True):
+    # loading priors
+    if model == 'w-q':
+        tau_var_str='q'
+    else:
+        tau_var_str='tau'
+    
+    if model == 'w-tau':
+        w_mins=np.empty(nmax+1)
+        w_maxs=np.empty(nmax+1)
+        tau_mins=np.empty(nmax+1)
+        tau_maxs=np.empty(nmax+1)
+        a_mins=np.empty(nmax+1)
+        a_maxs=np.empty(nmax+1)
+        ph_mins=np.empty(nmax+1)
+        ph_maxs=np.empty(nmax+1)
+
+        for i in range(nmax+1): 
+            wp_min=config_parser.get('prior-w'+str(i),'w'+str(i)+'_min')
+            w_mins[i] = np.float(wp_min)
+
+            wp_max=config_parser.get('prior-w'+str(i),'w'+str(i)+'_max')
+            w_maxs[i] = np.float(wp_max)
+
+            taup_min=config_parser.get('prior-'+tau_var_str+str(i),tau_var_str+str(i)+'_min')
+            tau_mins[i] = np.float(taup_min)
+
+            taup_max=config_parser.get('prior-'+tau_var_str+str(i),tau_var_str+str(i)+'_max')
+            tau_maxs[i] = np.float(taup_max)
+
+            amp0_min=config_parser.get('prior-amp'+str(i),'amp'+str(i)+'_min')
+            a_mins[i] = np.float(amp0_min)
+
+            amp1_max=config_parser.get('prior-amp'+str(i),'amp'+str(i)+'_max')
+            a_maxs[i] = np.float(amp1_max)
+
+            phase_min=config_parser.get('prior-phase'+str(i),'phase'+str(i)+'_min')
+            ph_mins[i] = np.float(phase_min)*2*np.pi
+
+            phase_max=config_parser.get('prior-phase'+str(i),'phase'+str(i)+'_max')
+            ph_maxs[i] = np.float(phase_max)*2*np.pi
+
+        priors_min = np.concatenate((w_mins,tau_mins,a_mins,ph_mins))
+        priors_max = np.concatenate((w_maxs,tau_maxs,a_maxs,ph_maxs))
+        prior_dim = len(priors_min)
+        priors=np.column_stack((priors_min,priors_max))
+
+    if model == 'w-tau-fixed':
+        a_mins=np.empty(nmax+1)
+        a_maxs=np.empty(nmax+1)
+        ph_mins=np.empty(nmax+1)
+        ph_maxs=np.empty(nmax+1)
+
+        for i in range(nmax+1): 
+            amp0_min=config_parser.get('prior-amp'+str(i),'amp'+str(i)+'_min')
+            a_mins[i] = np.float(amp0_min)
+
+            amp1_max=config_parser.get('prior-amp'+str(i),'amp'+str(i)+'_max')
+            a_maxs[i] = np.float(amp1_max)
+
+            phase_min=config_parser.get('prior-phase'+str(i),'phase'+str(i)+'_min')
+            ph_mins[i] = np.float(phase_min)*2*np.pi
+
+            phase_max=config_parser.get('prior-phase'+str(i),'phase'+str(i)+'_max')
+            ph_maxs[i] = np.float(phase_max)*2*np.pi
+
+
+        priors_min = np.concatenate((a_mins,ph_mins))
+        priors_max = np.concatenate((a_maxs,ph_maxs))
+        prior_dim = len(priors_min)
+        priors=np.column_stack((priors_min,priors_max))
+
+    elif model ==  'w-tau-fixed-m-af':
+        a_mins=np.empty(nmax+1)
+        a_maxs=np.empty(nmax+1)
+        ph_mins=np.empty(nmax+1)
+        ph_maxs=np.empty(nmax+1)
+
+        for i in range(nmax+1): 
+            amp0_min=config_parser.get('prior-amp'+str(i),'amp'+str(i)+'_min')
+            a_mins[i] = np.float(amp0_min)
+
+            amp1_max=config_parser.get('prior-amp'+str(i),'amp'+str(i)+'_max')
+            a_maxs[i] = np.float(amp1_max)
+
+            phase_min=config_parser.get('prior-phase'+str(i),'phase'+str(i)+'_min')
+            ph_mins[i] = np.float(phase_min)*2*np.pi
+
+            phase_max=config_parser.get('prior-phase'+str(i),'phase'+str(i)+'_max')
+            ph_maxs[i] = np.float(phase_max)*2*np.pi
+
+        mass_min=[np.float(config_parser.get('prior-mass','mass_min'))]
+        mass_max=[np.float(config_parser.get('prior-mass','mass_max'))]
+        spin_min=[np.float(config_parser.get('prior-spin','spin_min'))]
+        spin_max=[np.float(config_parser.get('prior-spin','spin_max'))]
+        priors_min = np.concatenate((a_mins,ph_mins,mass_min,spin_min))
+        priors_max = np.concatenate((a_maxs,ph_maxs,mass_max,spin_max))
+        prior_dim = len(priors_min)
+        priors=np.column_stack((priors_min,priors_max))
+
+
+    if fitnoise:
+        priors_fit_min=[np.float(config_parser.get('prior-noise','noise_min'))]
+        priors_fit_max=[np.float(config_parser.get('prior-noise','noise_max'))]
+        priors_min = np.concatenate((priors_min,priors_fit_min))
+        priors_max = np.concatenate((priors_max,priors_fit_max))
+        priors=np.column_stack((priors_min,priors_max))
+        prior_dim = len(priors_min)
+
+    return priors
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