Merge branch 'add_uniform_in_log' into 'master'

Add uniform in log prior

See merge request Monash/tupak!42

examples/injection_examples/create_your_own_source_model.py

@@ -24,7 +24,7 @@ def sine_gaussian(f, A, f0, tau, phi0, geocent_time, ra, dec, psi):
 
 
 # We now define some parameters that we will inject and then a waveform generator
-injection_parameters = dict(A=1e-21, f0=10, tau=1, phi0=0, geocent_time=0,
+injection_parameters = dict(A=1e-23, f0=100, tau=1, phi0=0, geocent_time=0,
                             ra=0, dec=0, psi=0)
 waveform_generator = tupak.waveform_generator.WaveformGenerator(time_duration=time_duration,
                                                                 sampling_frequency=sampling_frequency,
@@ -42,15 +42,14 @@ IFOs = [tupak.detector.get_interferometer_with_fake_noise_and_injection(
 # Here we define the priors for the search. We use the injection parameters
 # except for the amplitude, f0, and geocent_time
 prior = injection_parameters.copy()
-prior['A'] = tupak.prior.Uniform(0, 1e-20, 'A')
-prior['f0'] = tupak.prior.Uniform(0, 20, 'f')
-prior['geocent_time'] = tupak.prior.Uniform(-0.01, 0.01, 'geocent_time')
+prior['A'] = tupak.prior.PowerLaw(alpha=-1, minimum=1e-25, maximum=1e-21, name='A')
+prior['f0'] = tupak.prior.Uniform(90, 110, 'f')
 
 likelihood = tupak.likelihood.GravitationalWaveTransient(IFOs, waveform_generator)
 
 result = tupak.sampler.run_sampler(
     likelihood, prior, sampler='dynesty', outdir=outdir, label=label,
-    resume=False, sample='unif')
+    resume=False, sample='unif', injection_parameters=injection_parameters)
 result.plot_walks()
 result.plot_distributions()
 result.plot_corner()

test/prior_tests.py

@@ -92,8 +92,9 @@ class TestPriorClasses(unittest.TestCase):
             tupak.prior.DeltaFunction(name='test', peak=1),
             tupak.prior.Gaussian(name='test', mu=0, sigma=1),
             tupak.prior.PowerLaw(name='test', alpha=0, minimum=0, maximum=1),
-            tupak.prior.PowerLaw(name='test', alpha=-1, minimum=1, maximum=1e2),
+            tupak.prior.PowerLaw(name='test', alpha=2, minimum=1, maximum=1e2),
             tupak.prior.Uniform(name='test', minimum=0, maximum=1),
+            tupak.prior.LogUniform(name='test', minimum=5e0, maximum=1e2),
             tupak.prior.UniformComovingVolume(name='test', minimum=2e2, maximum=5e3),
             tupak.prior.Sine(name='test'),
             tupak.prior.Cosine(name='test'),
@@ -102,26 +103,42 @@ class TestPriorClasses(unittest.TestCase):
             tupak.prior.TruncatedGaussian(name='test', mu=1, sigma=0.4, minimum=-1, maximum=1)
         ]
 
-    def test_rescaling(self):
+    def test_minimum_rescaling(self):
+        """Test the the rescaling works as expected."""
         for prior in self.priors:
-            """Test the the rescaling works as expected."""
             minimum_sample = prior.rescale(0)
             self.assertAlmostEqual(minimum_sample, prior.minimum)
+
+    def test_maximum_rescaling(self):
+        """Test the the rescaling works as expected."""
+        for prior in self.priors:
             maximum_sample = prior.rescale(1)
             self.assertAlmostEqual(maximum_sample, prior.maximum)
+
+    def test_many_sample_rescaling(self):
+        """Test the the rescaling works as expected."""
+        for prior in self.priors:
             many_samples = prior.rescale(np.random.uniform(0, 1, 1000))
             self.assertTrue(all((many_samples >= prior.minimum) & (many_samples <= prior.maximum)))
+
+    def test_out_of_bounds_rescaling(self):
+        """Test the the rescaling works as expected."""
+        for prior in self.priors:
             self.assertRaises(ValueError, lambda: prior.rescale(-1))
 
-    def test_sampling(self):
+    def test_sampling_single(self):
         """Test that sampling from the prior always returns values within its domain."""
         for prior in self.priors:
             single_sample = prior.sample()
             self.assertTrue((single_sample >= prior.minimum) & (single_sample <= prior.maximum))
+
+    def test_sampling_many(self):
+        """Test that sampling from the prior always returns values within its domain."""
+        for prior in self.priors:
             many_samples = prior.sample(1000)
             self.assertTrue(all((many_samples >= prior.minimum) & (many_samples <= prior.maximum)))
 
-    def test_prob(self):
+    def test_probability_above_domain(self):
         """Test that the prior probability is non-negative in domain of validity and zero outside."""
         for prior in self.priors:
             # skip delta function prior in this case
@@ -130,15 +147,36 @@ class TestPriorClasses(unittest.TestCase):
             if prior.maximum != np.inf:
                 outside_domain = np.linspace(prior.maximum + 1, prior.maximum + 1e4, 1000)
                 self.assertTrue(all(prior.prob(outside_domain) == 0))
+
+    def test_probability_below_domain(self):
+        """Test that the prior probability is non-negative in domain of validity and zero outside."""
+        for prior in self.priors:
+            # skip delta function prior in this case
+            if isinstance(prior, tupak.prior.DeltaFunction):
+                continue
             if prior.minimum != -np.inf:
                 outside_domain = np.linspace(prior.minimum - 1e4, prior.minimum - 1, 1000)
                 self.assertTrue(all(prior.prob(outside_domain) == 0))
+
+    def test_probability_in_domain(self):
+        """Test that the prior probability is non-negative in domain of validity and zero outside."""
+        for prior in self.priors:
+            # skip delta function prior in this case
+            if isinstance(prior, tupak.prior.DeltaFunction):
+                continue
             if prior.minimum == -np.inf:
                 prior.minimum = -1e5
             if prior.maximum == np.inf:
                 prior.maximum = 1e5
             domain = np.linspace(prior.minimum, prior.maximum, 1000)
             self.assertTrue(all(prior.prob(domain) >= 0))
+
+    def test_probability_surrounding_domain(self):
+        """Test that the prior probability is non-negative in domain of validity and zero outside."""
+        for prior in self.priors:
+            # skip delta function prior in this case
+            if isinstance(prior, tupak.prior.DeltaFunction):
+                continue
             surround_domain = np.linspace(prior.minimum - 1, prior.maximum + 1, 1000)
             prior.prob(surround_domain)
 
@@ -151,6 +189,7 @@ class TestPriorClasses(unittest.TestCase):
                 domain = np.linspace(-1e2, 1e2, 1000)
             else:
                 domain = np.linspace(prior.minimum, prior.maximum, 1000)
+            print(prior.minimum, prior.maximum)
             self.assertAlmostEqual(np.trapz(prior.prob(domain), domain), 1, 3)
 
 
@@ -189,6 +228,5 @@ class TestFillPrior(unittest.TestCase):
         self.assertIsInstance(self.priors['ra'], tupak.prior.Uniform)
 
 
-
 if __name__ == '__main__':
     unittest.main()

tupak/prior.py

@@ -123,23 +123,6 @@ class Prior(object):
         return label
 
 
-class Uniform(Prior):
-    """Uniform prior"""
-
-    def __init__(self, minimum, maximum, name=None, latex_label=None):
-        Prior.__init__(self, name, latex_label, minimum, maximum)
-        self.support = maximum - minimum
-
-    def rescale(self, val):
-        Prior.test_valid_for_rescaling(val)
-        return self.minimum + val * self.support
-
-    def prob(self, val):
-        """Return the prior probability of val"""
-        in_prior = (val >= self.minimum) & (val <= self.maximum)
-        return 1 / self.support * in_prior
-
-
 class DeltaFunction(Prior):
     """Dirac delta function prior, this always returns peak."""
 
@@ -191,6 +174,24 @@ class PowerLaw(Prior):
                                                                          - self.minimum ** (1 + self.alpha))) * in_prior
 
 
+class Uniform(PowerLaw):
+    """Uniform prior"""
+
+    def __init__(self, minimum, maximum, name=None, latex_label=None):
+        Prior.__init__(self, name, latex_label, minimum, maximum)
+        self.alpha = 0
+
+
+class LogUniform(PowerLaw):
+    """Uniform prior"""
+
+    def __init__(self, minimum, maximum, name=None, latex_label=None):
+        Prior.__init__(self, name, latex_label, minimum, maximum)
+        self.alpha = -1
+        if self.minimum<=0:
+            logging.warning('You specified a uniform-in-log prior with minimum={}'.format(self.minimum))
+
+
 class Cosine(Prior):
 
     def __init__(self, name=None, latex_label=None, minimum=-np.pi / 2, maximum=np.pi / 2):

tupak/sampler.py

@@ -466,7 +466,8 @@ def run_sampler(likelihood, priors=None, label='label', outdir='outdir',
             result.log_bayes_factor = result.logz - result.noise_logz
         if injection_parameters is not None:
             result.injection_parameters = injection_parameters
-            tupak.conversion.generate_all_bbh_parameters(result.injection_parameters)
+            if conversion_function is not None:
+                conversion_function(result.injection_parameters)
         result.fixed_parameter_keys = [key for key in priors if isinstance(key, prior.DeltaFunction)]
         # result.prior = prior  # Removed as this breaks the saving of the data
         result.samples_to_data_frame(likelihood=likelihood, priors=priors, conversion_function=conversion_function)