Merge branch 'sampler_code_cleanup'

requirements.txt

@@ -13,3 +13,4 @@ NRSur7dq2
 nestle
 deepdish
 ptemcee
+mock

test/prior_tests.py

 from context import tupak
 import unittest
+from mock import Mock
 import numpy as np
 
 
@@ -153,5 +154,41 @@ class TestPriorClasses(unittest.TestCase):
             self.assertAlmostEqual(np.trapz(prior.prob(domain), domain), 1, 3)
 
 
+class TestFillPrior(unittest.TestCase):
+
+    def setUp(self):
+        self.likelihood = Mock()
+        self.likelihood.parameters = dict(a=0, b=0, c=0, d=0, asdf=0, ra=1)
+        self.priors = dict(a=1, b=1.1, c='string', d=tupak.prior.Uniform(0, 1))
+        self.priors = tupak.prior.fill_priors(self.priors, self.likelihood)
+
+    def tearDown(self):
+        del self.likelihood
+        del self.priors
+
+    def test_prior_instances_are_not_changed_by_parsing(self):
+        self.assertIsInstance(self.priors['d'], tupak.prior.Uniform)
+
+    def test_parsing_ints_to_delta_priors_class(self):
+        self.assertIsInstance(self.priors['a'], tupak.prior.DeltaFunction)
+
+    def test_parsing_ints_to_delta_priors_with_right_value(self):
+        self.assertEqual(self.priors['a'].peak, 1)
+
+    def test_parsing_floats_to_delta_priors_class(self):
+        self.assertIsInstance(self.priors['b'], tupak.prior.DeltaFunction)
+
+    def test_parsing_floats_to_delta_priors_with_right_value(self):
+        self.assertAlmostEqual(self.priors['b'].peak, 1.1, 1e-8)
+
+    def test_without_available_default_priors_no_prior_is_set(self):
+        with self.assertRaises(KeyError):
+            print(self.priors['asdf'])
+
+    def test_with_available_default_priors_a_default_prior_is_set(self):
+        self.assertIsInstance(self.priors['ra'], tupak.prior.Uniform)
+
+
+
 if __name__ == '__main__':
     unittest.main()

tupak/sampler.py

@@ -45,12 +45,12 @@ class Sampler(object):
         self.outdir = outdir
         self.use_ratio = use_ratio
         self.external_sampler = external_sampler
+        self.external_sampler_function = None
 
         self.__search_parameter_keys = []
         self.__fixed_parameter_keys = []
         self.initialise_parameters()
         self.verify_parameters()
-        self.ndim = len(self.__search_parameter_keys)
         self.kwargs = kwargs
 
         self.result = result
@@ -88,6 +88,17 @@ class Sampler(object):
     def fixed_parameter_keys(self):
         return self.__fixed_parameter_keys
 
+    @property
+    def ndim(self):
+        return len(self.__search_parameter_keys)
+
+    @property
+    def kwargs(self):
+        return self.__kwargs
+
+    @kwargs.setter
+    def kwargs(self, kwargs):
+        self.__kwargs = kwargs
 
     @property
     def external_sampler(self):
@@ -107,14 +118,6 @@ class Sampler(object):
             raise TypeError('sampler must either be a string referring to built in sampler or a custom made class that '
                             'inherits from sampler')
 
-    @property
-    def kwargs(self):
-        return self.__kwargs
-
-    @kwargs.setter
-    def kwargs(self, kwargs):
-        self.__kwargs = kwargs
-
     def verify_kwargs_against_external_sampler_function(self):
         args = inspect.getargspec(self.external_sampler_function).args
         bad_keys = []
@@ -453,7 +456,7 @@ def run_sampler(likelihood, priors=None, label='label', outdir='outdir',
         else:
             result.log_bayes_factor = result.logz - result.noise_logz
         result.injection_parameters = injection_parameters
-        result.fixed_parameter_keys = [key for key in priors if isinstance(key, prior.DeltaFunction)]
+        result.fixed_parameter_keys = sampler.fixed_parameter_keys
         result.priors = priors
         result.kwargs = sampler.kwargs
         result.samples_to_data_frame()