diff --git a/csep/core/binomial_evaluations.py b/csep/core/binomial_evaluations.py
index ab97f793..58fd7f50 100644
--- a/csep/core/binomial_evaluations.py
+++ b/csep/core/binomial_evaluations.py
@@ -152,7 +152,7 @@ def _binary_likelihood_test(forecast_data, observed_data, num_simulations=1000,
     # data structures to store results
     sim_fore = numpy.zeros(sampling_weights.shape)
     simulated_ll = []
-    n_active_cells = len(np.unique(np.nonzero(observed_data.ravel())))
+    n_active_cells = len(numpy.unique(numpy.nonzero(observed_data.ravel())))
     n_fore = numpy.sum(forecast_data)
     expected_forecast_count = int(n_active_cells)
 
diff --git a/tests/test_evaluations.py b/tests/test_evaluations.py
index 71557478..c7468528 100644
--- a/tests/test_evaluations.py
+++ b/tests/test_evaluations.py
@@ -2,9 +2,8 @@
 import numpy
 import unittest
 
-from csep.core.poisson_evaluations import _simulate_catalog, _poisson_likelihood_test
-from csep.core.binomial_evaluations import binary_joint_log_likelihood_ndarray
-
+import csep.core.poisson_evaluations as poisson
+import csep.core.binomial_evaluations as binary
 
 def get_datadir():
     root_dir = os.path.dirname(os.path.abspath(__file__))
@@ -48,21 +47,21 @@ def test_simulate_catalog(self):
 
         # this is taken from the test likelihood function
         sim_fore = numpy.empty(sampling_weights.shape)
-        sim_fore = _simulate_catalog(num_events, sampling_weights, sim_fore,
+        sim_fore = poisson._simulate_catalog(num_events, sampling_weights, sim_fore,
                                      random_numbers=self.random_matrix)
 
         # final statement
         numpy.testing.assert_allclose(expected_catalog, sim_fore)
 
         # test again to ensure that fill works properply
-        sim_fore = _simulate_catalog(num_events, sampling_weights, sim_fore,
+        sim_fore = poisson._simulate_catalog(num_events, sampling_weights, sim_fore,
                                      random_numbers=self.random_matrix)
 
         # final statement
         numpy.testing.assert_allclose(expected_catalog, sim_fore)
 
     def test_likelihood(self):
-        qs, obs_ll, simulated_ll = _poisson_likelihood_test(self.forecast_data, self.observed_data, num_simulations=1,
+        qs, obs_ll, simulated_ll = poisson._poisson_likelihood_test(self.forecast_data, self.observed_data, num_simulations=1,
                                                             random_numbers=self.random_matrix, use_observed_counts=True)
 
         # very basic result to pass "laugh" test
@@ -78,13 +77,42 @@ def test_likelihood(self):
 class TestBinomialLikelihood(unittest.TestCase):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
+        self.seed = 0
+        numpy.random.seed(self.seed)
         self.forecast_data = numpy.array([[0.1, 0.3, 0.4], [0.2, 0.1, 0.1]])
         self.observed_data = numpy.array([[0, 1, 2], [1, 1, 0]])
+        self.random_matrix = numpy.random.rand(1, 9)
 
     def test_joint_likelihood_calculation(self):
-        bill = binary_joint_log_likelihood_ndarray(self.forecast_data, self.observed_data)
+        bill = binary.binary_joint_log_likelihood_ndarray(self.forecast_data, self.observed_data)
+        numpy.testing.assert_allclose(bill, -6.7197988064)
 
+    def test_simulate_active_cells(self):
+        #With fixed seed we get the same random numbers if we get all the number at once or one by one.
+        #Making sure random number generated by seed 0 match.
+        expected_random_numbers = numpy.array([[0.5488135, 0.71518937, 0.60276338, 0.54488318, 0.4236548, 0.64589411,
+                             0.4375872112626925, 0.8917730007820798, 0.9636627605010293]])
+
+        numpy.testing.assert_allclose(expected_random_numbers, self.random_matrix)
+
+        #We can expect the following catalog, if we get the above random numbers.
+        #We get 4 active cells after 9th random sample.
+        expected_catalog = [0, 0, 1, 1, 1, 1]
+
+        sampling_weights = numpy.cumsum(self.forecast_data.ravel()) / numpy.sum(self.forecast_data)
+        sim_fore = numpy.zeros(sampling_weights.shape)
+        obs_active_cells =  len(numpy.unique(numpy.nonzero(self.observed_data.ravel())))
+        #resetting seed again to 0, to make sure _simulate_catalog uses this.
+        seed = 0
+        numpy.random.seed(seed)
+        sim_fore = binary._simulate_catalog(obs_active_cells, sampling_weights, sim_fore)
+        numpy.testing.assert_allclose(expected_catalog, sim_fore)
+
+    def test_binomial_likelihood(self):
+        qs, bill, simulated_ll = binary._binary_likelihood_test(self.forecast_data,self.observed_data, num_simulations=1,seed=0, verbose=True)
         numpy.testing.assert_allclose(bill, -6.7197988064)
+        numpy.testing.assert_allclose(qs, 1)
+        numpy.testing.assert_allclose(simulated_ll[0], -7.921741654647629)
 
 
 if __name__ == '__main__':