fix upstream time collisions in ChiRho (#400)

setup.cfg

@@ -10,7 +10,7 @@ install_requires =
     jupyter
     torch >= 1.8.0
     mira @ git+https://github.com/indralab/[email protected]
-    chirho @ git+https://github.com/BasisResearch/chirho@f44731d416c20cbf1615147f5e0ba6ef3afed78d
+    chirho @ git+https://github.com/BasisResearch/chirho@f3019d4b22f4e49261efbf8da90a30095af2afbc
     sympytorch
     torchdiffeq
 

tests/fixtures.py

@@ -30,7 +30,7 @@
 START_TIMES = [0.0, 1.0, 2.0]
 END_TIMES = [3.0, 4.0, 5.0]
 
-LOGGING_STEP_SIZES = [0.09]
+LOGGING_STEP_SIZES = [0.1]
 
 NUM_SAMPLES = [2]
 
@@ -68,7 +68,8 @@ def check_result_sizes(
         if k[:5] == "state":
             assert v.shape == (
                 num_samples,
-                len(torch.arange(start_time, end_time, logging_step_size)),
+                len(torch.arange(start_time, end_time, logging_step_size))
+                - 1,  # Does not include start_time
             )
         else:
             assert v.shape == (num_samples,)

tests/test_interfaces.py

@@ -25,7 +25,6 @@ def test_simulate_no_interventions(
 ):
     result = simulate(url, start_time, end_time, logging_step_size, num_samples)
     assert isinstance(result, dict)
-
     check_result_sizes(result, start_time, end_time, logging_step_size, num_samples)
 
 
@@ -43,7 +42,7 @@ def test_simulate_with_static_interventions(
     intervened_state_1 = {k: v + 1 for k, v in initial_state.items()}
     intervened_state_2 = {k: v + 2 for k, v in initial_state.items()}
 
-    intervention_time_1 = (end_time + start_time) / 2.000001  # Midpoint
+    intervention_time_1 = (end_time + start_time) / 2  # Midpoint
     intervention_time_2 = (end_time + intervention_time_1) / 2  # 3/4 point
     static_interventions = {
         intervention_time_1: intervened_state_1,
@@ -82,7 +81,7 @@ def test_simulate_with_dynamic_interventions(
     intervened_state_1 = {k: v + 1 for k, v in initial_state.items()}
     intervened_state_2 = {k: v + 2 for k, v in initial_state.items()}
 
-    intervention_time_1 = (end_time + start_time) / 2.000001  # Midpoint
+    intervention_time_1 = (end_time + start_time) / 2  # Midpoint
     intervention_time_2 = (end_time + intervention_time_1) / 2  # 3/4 point
 
     def intervention_event_fn_1(time: torch.Tensor, *args, **kwargs):
@@ -128,7 +127,7 @@ def test_simulate_with_static_and_dynamic_interventions(
     intervened_state_1 = {k: v + 1 for k, v in initial_state.items()}
     intervened_state_2 = {k: v + 2 for k, v in initial_state.items()}
 
-    intervention_time_1 = (end_time + start_time) / 2.000001  # Midpoint
+    intervention_time_1 = (end_time + start_time) / 2  # Midpoint
     intervention_time_2 = (end_time + intervention_time_1) / 2  # 3/4 point
 
     def intervention_event_fn_1(time: torch.Tensor, *args, **kwargs):