Implemented CorrelatedGaussianPrior and CorrelatedUniformPrior

examples/other_examples/correlated_prior.py

+import matplotlib.pyplot as plt
+import numpy as np
+import corner
+
 import bilby.gw.prior
 
-mass_1 = bilby.core.prior.Uniform(5, 100)
-mass_2 = bilby.gw.prior.CorrelatedSecondaryMassPrior(minimum=5, maximum=100)
+# mass_1 = bilby.core.prior.Uniform(5, 100)
+# mass_2 = bilby.gw.prior.CorrelatedSecondaryMassPrior(minimum=5, maximum=100)
+#
+# correlated_priors = bilby.core.prior.CorrelatedPriorDict(dictionary=dict(mass_1=mass_1, mass_2=mass_2))
+#
+# samples = correlated_priors.sample(10)
+#
+# primary_masses = samples['mass_1']
+# secondary_masses = samples['mass_2']
+# for i in range(len(primary_masses)):
+#     if primary_masses[i] > secondary_masses[i]:
+#         print('True')
+#     else:
+#         print('False')
+#
+# sample = dict(mass_1=25, mass_2=20)
+# print(correlated_priors.prob(sample))
+
+
+def correlation_func_a(mu, a=0):
+    return mu + a**2 + 2*a + 3
+
+
+def correlation_func_b(mu, a=0, b=0):
+    return mu + 0.01*a**2 + 0.01*b**2 + 0.01*a*b + 0.1*b + 3
+
+
+a = bilby.core.prior.Gaussian(mu=0., sigma=1)
+b = bilby.core.prior.CorrelatedGaussian(mu=0., sigma=1, correlation_func=correlation_func_a)
+c = bilby.core.prior.CorrelatedGaussian(mu=0, sigma=1, correlation_func=correlation_func_b)
+
+correlated_uniform = bilby.core.prior.CorrelatedPriorDict(dictionary=dict(a=a, b=b, c=c))
+
+samples = correlated_uniform.sample(1000000)
+print(samples)
+
+# plt.plot(samples['a'], samples['b'], 'C0o')
+
+# plt.show()
+samples = np.array([samples['a'], samples['b'], samples['c']]).T
+corner.corner(np.array(samples))
+plt.show()
+
+
+a = bilby.core.prior.Uniform(minimum=0, maximum=1)
+b = bilby.core.prior.CorrelatedUniform(minimum=0, maximum=1, correlation_func=correlation_func_a)
+c = bilby.core.prior.CorrelatedUniform(minimum=0, maximum=1, correlation_func=correlation_func_b)
 
-correlated_priors = bilby.core.prior.CorrelatedPriorDict(dictionary=dict(mass_1=mass_1, mass_2=mass_2))
+correlated_uniform = bilby.core.prior.CorrelatedPriorDict(dictionary=dict(a=a, b=b, c=c))
 
-samples = correlated_priors.sample(10)
+samples = correlated_uniform.sample(1000000)
+print(samples)
 
-primary_masses = samples['mass_1']
-secondary_masses = samples['mass_2']
-for i in range(len(primary_masses)):
-    if primary_masses[i] > secondary_masses[i]:
-        print('True')
-    else:
-        print('False')
+# plt.plot(samples['a'], samples['b'], 'C0o')
 
-sample = dict(mass_1=25, mass_2=20)
-print(correlated_priors.prob(sample))
+# plt.show()
+samples = np.array([samples['a'], samples['b'], samples['c']]).T
+corner.corner(np.array(samples))
+plt.show()