Verify use of cache via logging output

Inspecting the logging output allows us to detect whether a forecast
resumed from a cached state, so we can test whether the desired caching
behaviour was produced.

tests/test_cached_prng_states.py

@@ -22,7 +22,7 @@ def test_cached_prng_states(caplog):
     t0 = 0.0
     t1 = 15.0
     t2 = 30.0
-    (state_1, state_2) = run_two_forecasts(time_scale, t0, t1, t2)
+    (state_1, state_2) = run_two_forecasts(caplog, time_scale, t0, t1, t2)
 
     # Extract the relevant summary tables for each forecast.
     cints_1 = state_1[t1]['summary']['model_cints']
@@ -84,7 +84,7 @@ def run_forecast(time_scale, t0, t1, t2, cache_file, x_tbl, obs):
     return pypfilt.forecast(params, [obs], [t1], filename=None)
 
 
-def run_two_forecasts(time_scale, t0, t1, t2):
+def run_two_forecasts(caplog, time_scale, t0, t1, t2):
     """
     Run the same forecast twice; the first time with an empty cache, and the
     second resuming from a cached state.
@@ -112,8 +112,16 @@ def run_two_forecasts(time_scale, t0, t1, t2):
     # forecast from the cached state produces identical model dynamics.
     assert not os.path.isfile(cache_file)
     state_1 = run_forecast(time_scale, t0, t1, t2, cache_file, sim_obs, obs)
+    # Check that no cached state was used.
+    assert 'No estimation pass needed' not in caplog.text
+    # NOTE: this message has *two spaces* so that it aligns with
+    # 'Forecasting from' messages.
+    assert 'Estimating  from' in caplog.text
+
     assert os.path.isfile(cache_file)
     state_2 = run_forecast(time_scale, t0, t1, t2, cache_file, sim_obs, obs)
+    # Check that a cached state was used.
+    assert 'No estimation pass needed' in caplog.text
 
     os.remove(cache_file)