add test_cpue_2 and test_reset()

tests/test_methods.py

@@ -5,34 +5,75 @@
 def test_action_units():
     env = greenCrabSimplifiedEnv()
     env.reset()
-    action = np.array([-1,-1,-1])
+    action = np.array([-1,-1,-1]) # no traps
     natural_units = np.maximum( env.max_action * (1 + action)/2 , 0.)
-    assert np.array_equal(natural_units, np.array([0,0,0]))
+    assert np.array_equal(natural_units, np.array([0,0,0])) # check if no crab exist and no change in population when do nothing
 
 def test_no_harvest():
     env = greenCrabSimplifiedEnv()
     env.reset()
 
-    steps = 3
+    steps = 10
     for i in range(steps):
         observation, rew, term, trunc, info  = env.step(np.array([-1,-1, -1]))
-
-    assert info == {}
-    assert trunc == False
-    assert term == False
-    assert rew < 0
-    assert sum(env.state) > 0
+        assert info == {}
+        assert trunc == False
+        assert term == False
+        assert rew < 0
+        assert sum(env.state) > 0
+        assert observation[0] == -1 and observation[1] == -1 #check if catch rate is zero in observation
 
 
 def test_full_harvest():
     env = greenCrabSimplifiedEnv()
     env.reset()
 
     steps = env.Tmax
+    prev_state = env.state # store the state before taking step
     for i in range(steps):
         observation, rew, term, trunc, info  = env.step(np.array([1,1, 1]))
+        # if crab population drop, catch rate should not be zero 
+        if (sum(prev_state) > sum(env.state)):
+            assert observation[0] != -1 or observation[1] != -1
 
     assert info == {}
     assert trunc == False
     assert term == False
     assert sum(env.state) < 100000
+
+def test_cpue_2():
+    env = greenCrabSimplifiedEnv()
+    env.reset()
+    max_action = 2000
+
+    # check if return 0 when sum(action_natural_units) = 0
+    action = np.array([-1,-1, -1])
+    action_natural_units = np.maximum( max_action * (1 + action)/2 , 0.) # action_natural_units = 0
+    observation = env.observations
+    assert all((env.cpue_2(observation,  action_natural_units))== np.float32([0,0]))
+
+    # when sum(action_natural_units) > 0
+    action = np.array([1,1, 1])
+    action_natural_units = np.maximum( max_action * (1 + action)/2 , 0.)
+    observation = env.observations
+    cpue_2_value = env.cpue_2(observation,  action_natural_units)
+    assert -1<cpue_2_value[0]<1 
+    assert -1<cpue_2_value[1]<1 
+
+def test_reset():
+    env = greenCrabSimplifiedEnv()
+    steps = env.Tmax 
+    # run the simulation util the 
+    for i in range(steps):
+        env.step(np.array([0,0, 0])) # set constant amount of trap every year
+
+    curr_ob_space = env.observation_space
+
+    # if final observation space is [-1, -1], set it to other obseravtion space for test
+    if curr_ob_space == np.array([-1,-1]):
+        curr_ob_space = np.array([1,1])
+
+    # reset the obseravtion environment
+    new_ob_space, new_info = env.reset()
+
+    assert np.array_equal(new_ob_space, np.array([-1, -1]))