diff --git a/.gitignore b/.gitignore
index 6a4dae1..b52fc4b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,2 +1,3 @@
 .DS_Store
 __pycache__
+*.egg-info
diff --git a/holo/test_functions/closed_form/_ehrlich.py b/holo/test_functions/closed_form/_ehrlich.py
index cc5d952..5197891 100644
--- a/holo/test_functions/closed_form/_ehrlich.py
+++ b/holo/test_functions/closed_form/_ehrlich.py
@@ -42,7 +42,11 @@ def __init__(
         self.initial_dist = torch.ones(num_states) / num_states
         bandwidth = int(num_states * 0.4)
         self.transition_matrix = sample_sparse_ergodic_transition_matrix(
-            num_states, bandwidth, softmax_temp=0.5, generator=self._generator, repeats_always_possible=True
+            num_states,
+            bandwidth,
+            softmax_temp=0.5,
+            generator=self._generator,
+            repeats_always_possible=True,
         )
         self.stationary_dist = dmp_stationary_dist(self.transition_matrix)
 
@@ -129,13 +133,11 @@ def optimal_solution(self):
                 ]
             )
             index = spacing.cumsum(0).tolist()
-            print(index)
             motif = motif.tolist()
             for idx in range(index[-1] + 1):
                 if idx in index:
                     next_state = motif.pop(0)
                 soln[position] = next_state
-                # print(position)
                 position += 1
         # fill in remaining states with last state of last motif
         soln[position:] = soln[position - 1]
@@ -143,8 +145,6 @@ def optimal_solution(self):
         # check optimal value
         optimal_value = self.evaluate_true(soln.unsqueeze(0))
         if not optimal_value == self._optimal_value:
-            print(soln)
-            print(optimal_value)
             raise RuntimeError("optimal value not achieved by optimal solution.")
         return soln
 
diff --git a/holo/test_functions/elemental/_discrete_markov_process.py b/holo/test_functions/elemental/_discrete_markov_process.py
index f21d90c..c0ef939 100644
--- a/holo/test_functions/elemental/_discrete_markov_process.py
+++ b/holo/test_functions/elemental/_discrete_markov_process.py
@@ -103,9 +103,7 @@ def sample_sparse_ergodic_transition_matrix(
         generator = torch.Generator()
 
     randn_matrix = torch.randn(num_states, num_states, generator=generator)
-    print(randn_matrix)
     dense_transition_matrix = (randn_matrix / softmax_temp).softmax(dim=-1)
-    print(dense_transition_matrix)
 
     # construct mask as banded matrix
     mask = banded_square_matrix(num_states, bandwidth).bool()
@@ -116,8 +114,6 @@ def sample_sparse_ergodic_transition_matrix(
         # set diagonal entries of mask to True
         mask = mask | torch.eye(num_states, dtype=torch.bool)
 
-    print(mask)
-
     transition_matrix = torch.where(mask, dense_transition_matrix, torch.zeros_like(dense_transition_matrix))
     transition_matrix = transition_matrix / transition_matrix.sum(dim=-1, keepdim=True)