Fixed likelihood, added GetDist plotting scripts

dev-likelihood-sl/all_param_marg.py

@@ -0,0 +1,47 @@
+# Show plots inline, and load main getdist plot module and samples class
+from __future__ import print_function
+#%matplotlib inline
+#%config InlineBackend.figure_format = 'retina'
+import sys
+# sys.path.insert(0,os.path.realpath(os.path.join(os.getcwd(),'..')))
+import os
+from getdist import plots, MCSamples
+import getdist
+# use this *after* importing getdist if you want to use interactive plots
+# %matplotlib notebook
+import matplotlib.pyplot as plt
+# import IPython
+import numpy as np
+from global_tools import *
+
+
+
+names = ["gamma", "log_{10}(g)", "log_{10}(M[GeV])", "N_a", "N_{conv}", "N_{pr}", "N_{mu}"]
+labels = ["gamma", "log_{10}(g)", "log_{10}(M[GeV])", "N_a", "N_{conv}", "N_{pr}", "N_{mu}"]
+
+gamma, log10_g, log10_M, N_a, N_conv, N_pr, N_mu, something_1, something_2, \
+    something_3 = Read_Data_File(os.getcwd()+'/out/likelihood/ev.dat')
+
+samps = np.array([gamma, log10_g, log10_M, N_a, N_conv, N_pr, N_mu]).T
+
+
+# names = ["gamma", "N_a", "N_{conv}", "N_{pr}", "N_{mu}"]
+# labels = ["gamma", "N_a", "N_{conv}", "N_{pr}", "N_{mu}"]
+
+# gamma, N_a, N_conv, N_pr, N_mu, something_1, something_2, \
+#     something_3 = Read_Data_File(os.getcwd()+'/out/likelihood/ev.dat')
+
+# samps = np.array([gamma, N_a, N_conv, N_pr, N_mu]).T
+
+
+samples = MCSamples(samples = samps, names = names, labels = labels)
+
+
+plt.figure()
+g = plots.get_subplot_plotter()
+samples.updateSettings({'contours': [0.68, 0.90], 'fine_bins_2D': 40, 'smooth_scale_2D': 0.6})
+g.settings.num_plot_contours = 3
+g.triangle_plot(samples, shaded=True)
+#plt.plot(-2.37, -1.80, 'X', color = 'black', markersize=14)
+# g.export(os.getcwd()+'/out/plots/all_param_marg.pdf')
+g.export(os.getcwd()+'/out/plots/all_param_marg.png')

dev-likelihood-sl/full_likelihood.pyx

@@ -6,6 +6,9 @@ import numpy as np
 
 from libc.math cimport log
 from libc.math cimport exp
+
+# from functools import reduce
+
 from global_tools import Write_Data_File
 
 
@@ -17,7 +20,18 @@ from global_tools import Write_Data_File
 #     declination_tr, RA_tr, Med_tr = \
 #     Read_Data_File(os.getcwd()+'/ic_data/data_track.txt')
 
-# ll_den = 273.673 # Log(80!)
+# ll_den = 228.927 # Log(58!*22!)
+
+
+cdef multiply_manual(iterable):
+
+    cdef double prod = 1.0
+    cdef double x
+
+    for x in iterable:
+        prod *= x
+
+    return prod
 
 
 cdef double Partial_Likelihood_Showers(int event_index, double gamma,
@@ -76,21 +90,43 @@ def Log_Likelihood(double gamma, double log10_g, double log10_M,
     cdef double fl_sh
     cdef double fl_tr
     cdef double log_likelihood
+    # cdef double tt
     cdef int i
 
     # Showers
-    fl_sh = sum([Partial_Likelihood_Showers(i, gamma, log10_g, log10_M, N_a,
+    fl_sh = multiply_manual( \
+            [Partial_Likelihood_Showers(i, gamma, log10_g, log10_M, N_a,
                 N_conv, N_pr, N_mu, interp_astro_pdf_sh, pdf_atm_conv_sh,
                 pdf_atm_pr_sh, verbose=verbose) for i in range(num_ic_sh)])
+    # fl_sh = reduce(lambda x, y: x*y, \
+    #         [Partial_Likelihood_Showers(i, gamma, log10_g, log10_M, N_a,
+    #             N_conv, N_pr, N_mu, interp_astro_pdf_sh, pdf_atm_conv_sh,
+    #             pdf_atm_pr_sh, verbose=verbose) for i in range(num_ic_sh)])
+    # fl_sh = np.prod([Partial_Likelihood_Showers(i, gamma, log10_g, log10_M, N_a,
+    #             N_conv, N_pr, N_mu, interp_astro_pdf_sh, pdf_atm_conv_sh,
+    #             pdf_atm_pr_sh, verbose=verbose) for i in range(num_ic_sh)])
 
     # Tracks
-    fl_tr = sum([Partial_Likelihood_Tracks(i, gamma, log10_g, log10_M, N_a,
+    fl_tr = multiply_manual( \
+            [Partial_Likelihood_Tracks(i, gamma, log10_g, log10_M, N_a,
                 N_conv, N_pr, N_mu, interp_astro_pdf_tr, pdf_atm_conv_tr,
                 pdf_atm_pr_tr, pdf_atm_muon_tr, verbose=verbose) \
                 for i in range(num_ic_tr)])
+    # fl_tr = reduce(lambda x, y: x*y, \
+    #         [Partial_Likelihood_Tracks(i, gamma, log10_g, log10_M, N_a,
+    #             N_conv, N_pr, N_mu, interp_astro_pdf_tr, pdf_atm_conv_tr,
+    #             pdf_atm_pr_tr, pdf_atm_muon_tr, verbose=verbose) \
+    #             for i in range(num_ic_tr)])
+    # fl_tr = np.prod([Partial_Likelihood_Tracks(i, gamma, log10_g, log10_M, N_a,
+    #             N_conv, N_pr, N_mu, interp_astro_pdf_tr, pdf_atm_conv_tr,
+    #             pdf_atm_pr_tr, pdf_atm_muon_tr, verbose=verbose) \
+    #             for i in range(num_ic_tr)])
+
 
+    log_likelihood = log(exp(-N_a-N_conv-N_pr-N_mu)*fl_sh*fl_tr) + 400.0 #-ll_den
 
-    log_likelihood = log(exp(-N_a-N_conv-N_pr-N_mu)*fl_sh*fl_tr)
+    # tt = N_a+N_conv+N_pr+N_mu-80.0
+    # log_likelihood = log(exp(-N_a-N_conv-N_pr-N_mu)*fl_sh*fl_tr/tt)-ll_den
 
     return log_likelihood
 

dev-likelihood-sl/gMmarg.py

@@ -0,0 +1,38 @@
+# Show plots inline, and load main getdist plot module and samples class
+from __future__ import print_function
+#%matplotlib inline
+#%config InlineBackend.figure_format = 'retina'
+# import sys, os
+# sys.path.insert(0,os.path.realpath(os.path.join(os.getcwd(),'..')))
+import os
+from getdist import plots, MCSamples
+import getdist
+# use this *after* importing getdist if you want to use interactive plots
+# %matplotlib notebook
+import matplotlib.pyplot as plt
+# import IPython
+import numpy as np
+from global_tools import *
+
+
+
+names = ["gamma", "log_{10}(g)", "log_{10}(M[GeV])", "N_a", "N_conv", "N_pr", "N_mu"]
+labels = ["gamma", "log_{10}(g)", "log_{10}(M[GeV])", "N_a", "N_conv", "N_pr", "N_mu"]
+
+gamma, log10_g, log10_M, N_a, N_conv, N_pr, N_mu, something_1, something_2, \
+    something_3 = Read_Data_File(os.getcwd()+'/out/likelihood/ev.dat')
+
+samps = np.array([gamma, log10_g, log10_M, N_a, N_conv, N_pr, N_mu]).T
+
+samples = MCSamples(samples = samps, names = names, labels = labels)
+
+
+plt.figure()
+g = plots.get_single_plotter()
+samples.updateSettings({'contours': [0.68, 0.90], 'fine_bins_2D': 20, 'smooth_scale_2D': 0.6})
+g.settings.num_plot_contours = 2
+g.plot_2d(samples, 'log_{10}(M[GeV])', 'log_{10}(g)', shaded=True)
+plt.plot(-1.94, -1.16, 'X', color = 'black', markersize=14)
+g.export(os.getcwd()+'/out/plots/gMmarg.png')
+
+

dev-likelihood-sl/likelihood_analysis_parser.py

@@ -51,15 +51,20 @@ def Prior(cube, ndim, nparams):
 
 	# Expected number of astrophysical neutrinos. Uniform distribution between 0 and 80.
 	cube[3] = cube[3]*80
+	# cube[1] = cube[1]*80
+	# cube[3] = 10.**(cube[3]*(1.9084+3.0)-3.0)
 
 	# Expected number of conv. atm. neutrinos. Uniform distribution between 0 and 80.
 	cube[4] = cube[4]*80
+	# cube[4] = 10.**(cube[4]*(1.9084-3.0)+3.0)
 
 	# Expected number of prompt atm. neutrinos. Uniform distribution between 0 and 80.
-	cube[5] = cube[5]*80
+	cube[5] = cube[5]*1
+	# cube[5] = 10.**(cube[5]*(1.9084-3.0)+3.0)
 
 	# Expected number of atm. muons. Uniform distribution between 0 and 80.
 	cube[6] = cube[6]*80
+	# cube[6] = 10.**(cube[6]*(1.9084-3.0)+3.0)
 
 	return 0
 
@@ -95,14 +100,15 @@ def Log_Likelihood_MultiNest(cube, ndim, nparams):
 parameters = ["gamma", "log10_g", "log10_M", "N_a", "N_conv", "N_pr", "N_mu"]
 n_params = len(parameters)
 
-
 # Run MultiNest
 pymultinest.run(Log_Likelihood_MultiNest, Prior, n_params,
 	            outputfiles_basename='out/likelihood/',
 				resume=resume, verbose=verbose, n_live_points=n_live_points,
 				seed=-1, evidence_tolerance=evidence_tolerance,
 				sampling_efficiency=0.8,
-				importance_nested_sampling=True, log_zero=-300.0)
+				importance_nested_sampling=True,
+				const_efficiency_mode=False)
+				#, log_zero=-300.0)
 # const_efficiency_mode=True, sampling_efficiency=1)
 
 json.dump(parameters, open('out/likelihood/params.json', 'w')) # Save parameter names