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all comment changes here so far

Co-authored-by: Thomas Hladish <[email protected]>

config/config.py

@@ -114,6 +114,7 @@ def make_list_if_not(obj):
 
 
 def distribution_converter(dct):
+'''Converts the distribution as specified in JSON config file into a numpyro distribution object'''
     # a distribution is identified by the "distribution" and "params" keys
     if "distribution" in dct.keys() and "params" in dct.keys():
         numpyro_dst = dct["distribution"]
@@ -209,10 +210,10 @@ def age_limit_checks(key, age_limits):
     test_ascending(key, age_limits)
     assert all(
         [isinstance(a, int) for a in age_limits]
-    ), "ages must be int, not float"
+    ), "ages must be int, not float because census age data is specified as int"
     assert (
         age_limits[-1] < MAX_AGE_CENSUS_DATA
-    ), "age limits can not exceed 84 years of age, the last age bin is implied and does not need to be included"
+    ), "age limits can not exceed " + str(MAX_AGE_CENSUS_DATA) + " years of age, the last age bin is implied and does not need to be included"
 
 
 def compare_geq(keys, vals):

example_end_to_end_run.py

@@ -21,7 +21,7 @@
     INITIALIZER_CONFIG_PATH = config_path + "config_initializer_covid.json"
     # defines the running variables, strain R0s, external strain introductions etc.
     RUNNER_CONFIG_PATH = config_path + "config_runner_covid.json"
-    # defines __distributions__ that act like priors to infer runner variables.
+    # defines prior __distributions__ for inferring runner variables.
     INFERER_CONFIG_PATH = config_path + "config_inferer_covid.json"
     # defines how the solution should be viewed, what slices examined, how to save.
     INTERPRETER_CONFIG_PATH = config_path + "config_interpreter_covid.json"
@@ -47,7 +47,7 @@
         ihr = [0.002, 0.004, 0.008, 0.06]
         model_incidence = jnp.sum(solution.ys[3], axis=(2, 3, 4))
         model_incidence = jnp.diff(model_incidence, axis=0)
-        rng = np.random.default_rng(seed=8675399)
+        rng = np.random.default_rng(seed=8675309)
         m = np.asarray(model_incidence) * ihr
         k = 10.0
         p = k / (k + m)