fixing utilities

code_new/Sumit/rdown.py

@@ -35,16 +35,16 @@ q_fpars=[[0.584077, 1.52053, -0.480658], [0.00561441, 0.630715, -0.432664], [-0.
 c=2.99792458*10**8;G=6.67259*10**(-11);MS=1.9885*10**30;
 class Ringdown_Spectrum:
     """RDown model generator"""
-    def __init__(self,mf,af,l,m,n=4,s=-2,time=[],fixed=False,qnm_model='berti',rdowndata=[]):
+    def __init__(self,mf,af,l,m,n=4,s=-2,time=[],model='w-tau',qnm_model='berti',rdowndata=[]):
         self.mf = mf
         self.af = af
         self.l = l
         self.m = m
         self.n = n
         self.time = time
+        self.model = model
         self.grav_220 = [qnm.modes_cache(s=s,l=self.l,m=self.m,n=i) for i in range (0,self.n+1)]
         self.dim = self.n+1
-        self.fixed = fixed
         self.qnm_model = qnm_model
         self.dict_omega = {'berti': self.QNM_Berti , 'qnm': self.QNM_spectrum}
         self.rdowndata = rdowndata
@@ -53,7 +53,7 @@ class Ringdown_Spectrum:
         if len(self.time)==0:
             self.time = np.arange(0,100,0.1)
             
-        if self.fixed:
+        if self.model == 'w-tau-fixed':
             omegas_new=np.asarray([self.grav_220[i](a=self.af)[0] for i in range (0,self.dim)])
             self.w = (np.real(omegas_new))/self.mf
             self.tau=-1/(np.imag(omegas_new))*self.mf

code_new/Sumit/rdown_utilities.py

@@ -29,6 +29,8 @@ from dynesty import utils as dyfunc
 import os
 import csv
 import pandas as pd
+import pickle
+import rdown as rd
 
 def posterior_samples(sampler):
     """
@@ -285,10 +287,9 @@ def get_best_amps(pars,parser=None,nr_code=None):
         npamps = np.concatenate((amp_mock,ph_mock)) 
     return npamps
 
-def convert_m_af_2_w_tau_post(res,fitnoise=False):
+def convert_m_af_2_w_tau_post(res,rdown,fitnoise=False):
     
-    samples_2=res.samples
-    samps=f2.results.samples
+    samps=res.samples
     
     if fitnoise:
         fmass_spin=(samps.T)[-3:-1].T
@@ -297,10 +298,22 @@ def convert_m_af_2_w_tau_post(res,fitnoise=False):
         #fmass_spin=new_samples[-2:]
     fmass_spin_dist=[None]*len(fmass_spin)
     weight=np.exp(res.logwt - res.logz[-1])
+    
     for i in range(len(fmass_spin)):
-        fmass_spin_dist[i]=np.concatenate(dict_omega[qnm_model](fmass_spin[i,0],fmass_spin[i,1],2,2))
+        fmass_spin_dist[i]=np.concatenate(rdown.QNM_Berti(fmass_spin[i,0],fmass_spin[i,1]))
         
     fmass_spin_dist_v2=np.asarray(fmass_spin_dist)
     new_samples = dyfunc.resample_equal(fmass_spin_dist_v2, weight)   
     
-    return new_samples
\ No newline at end of file
+    return new_samples
+
+def save_object(obj, filename):
+    with open(filename, 'wb') as output:  # Overwrites any existing file.
+        pickle.dump(obj, output, pickle.HIGHEST_PROTOCOL)
+        
+def rm_files(files):
+    """ rm all old files """
+    for i in files:
+        if os.path.exists(i):
+            os.remove(i)
+        
\ No newline at end of file

code_new/Sumit/read_data.py

@@ -340,5 +340,5 @@ def read_config_file(parser):
     else:
         nm_mock = None
         
-    res = rootpath, simulation_path_1,simulation_path_2, metadata_file , simulation_number, output_folder, export, overwrite, sampler,nr_code, nbcores,tshift,tend,t_align, nmax , npoints, model, error_str, fitnoise, l_int, index_mass,index_spin, error_type, error_val, af, mf,tau_var_str,nm_mock
+    res = rootpath, simulation_path_1,simulation_path_2, metadata_file , simulation_number, output_folder, export, overwrite, sampler,nr_code, nbcores,tshift,tend,t_align, nmax , npoints, model, error_str, fitnoise, l_int, index_mass,index_spin, error_type, error_val, af, mf,tau_var_str,nm_mock,downfactor
     return res
\ No newline at end of file