Saving timesteps parameter

src/dynode/config.py

@@ -868,6 +868,16 @@ class is accepted to modify/create the downstream parameters.
         # "validate": do_nothing,
         "type": lambda s: datetime.datetime.strptime(s, "%Y-%m-%d").date(),
     },
+    {
+        # list[date] on which the user wishes to save the state of each
+        # compartment, final_timesteps automatically
+        "name": "COMPARTMENT_SAVE_DATES",
+        # "validate": do_nothing,
+        # type list[date]
+        "type": lambda lst: [
+            datetime.datetime.strptime(s, "%Y-%m-%d").date() for s in lst
+        ],
+    },
     {
         "name": "VACCINATION_SEASON_CHANGE",
         # "validate": do_nothing,

src/dynode/dynode_runner.py

@@ -426,7 +426,7 @@ def save_inference_posteriors(
         self,
         inferer: MechanisticInferer,
         save_filename="checkpoint.json",
-        exclude_prefixes=["final_timestep"],
+        exclude_prefixes=["timestep"],
         save_chains_plot=True,
         save_pairs_correlation_plot=True,
     ) -> None:
@@ -443,7 +443,8 @@ def save_inference_posteriors(
         exclude_prefixes: list[str], optional
             a list of strs that, if found in a sample name,
             are exlcuded from the saved json. This is common for large logging
-            info that will bloat filesize like, by default ["final_timestep"]
+            info that will bloat filesize like, by default ["timestep"]
+            to exclude all timestep deterministic variables.
         save_chains_plot: bool, optional
             whether to save accompanying mcmc chains plot, by default True
         save_pairs_correlation_plot: bool, optional
@@ -480,40 +481,61 @@ def save_inference_final_timesteps(
         self,
         inferer: MechanisticInferer,
         save_filename="final_timesteps.json",
-        final_timestep_identifier="final_timestep",
     ):
-        """saves the `final_timestep` posterior, if it is found in mcmc.get_samples(), otherwise raises a warning
-        and saves nothing
+        """saves the `final_timestep` posterior, if it is found in
+        mcmc.get_samples(), otherwise raises a warning and saves nothing
 
         Parameters
         ----------
         inferer : MechanisticInferer
             inferer that was run with `inferer.infer()`
         save_filename : str, optional
-            output filename, by default "final_timesteps.json"
+            output filename, by default "timesteps.json"
         final_timestep_identifier : str, optional
-            prefix attached to the final_timestep parameter, by default "final_timestep"
+            prefix attached to the final_timestep parameter, by default "timestep"
+        """
+        self.save_inference_timesteps(
+            inferer, save_filename, timestep_identifier="final_timestep"
+        )
+
+    def save_inference_timesteps(
+        self,
+        inferer: MechanisticInferer,
+        save_filename="timesteps.json",
+        timestep_identifier="timestep",
+    ):
+        """saves all `timestep` posteriors, if they are found in
+        mcmc.get_samples(), otherwise raises a warning and saves nothing
+
+        Parameters
+        ----------
+        inferer : MechanisticInferer
+            inferer that was run with `inferer.infer()`
+        save_filename : str, optional
+            output filename, by default "timesteps.json"
+        step_identifier : str, optional
+            identifying token attached to any timestep parameter, by default "timestep"
         """
         # if inference complete, convert jnp/np arrays to list, then json dump
         if inferer.infer_complete:
             samples = inferer.inference_algo.get_samples(group_by_chain=True)
-            final_timesteps = {
+            timesteps = {
                 name: timesteps
                 for name, timesteps in samples.items()
-                if final_timestep_identifier in name
+                if timestep_identifier in name
             }
             # if it is empty, warn the user, save nothing
-            if final_timesteps:
+            if timesteps:
                 save_path = os.path.join(self.azure_output_dir, save_filename)
-                self._save_samples(final_timesteps, save_path)
+                self._save_samples(timesteps, save_path)
             else:
                 warnings.warn(
-                    "attempting to call `save_inference_final_timesteps` but failed to find any final_timesteps with prefix %s"
-                    % final_timestep_identifier
+                    "attempting to call `save_inference_timesteps` but failed to find any timesteps with prefix %s"
+                    % timestep_identifier
                 )
         else:
             warnings.warn(
-                "attempting to call `save_inference_final_timesteps` before inference is complete. Something is likely wrong..."
+                "attempting to call `save_inference_timesteps` before inference is complete. Something is likely wrong..."
             )
 
     def save_inference_timelines(

src/dynode/mechanistic_inferer.py

@@ -4,6 +4,7 @@
 observed metrics.
 """
 
+import datetime
 import json
 from typing import Union
 
@@ -21,6 +22,7 @@
 from .abstract_parameters import AbstractParameters
 from .config import Config
 from .mechanistic_runner import MechanisticRunner
+from .utils import date_to_sim_day
 
 
 class MechanisticInferer(AbstractParameters):
@@ -182,18 +184,7 @@ def likelihood(
         dct = self.run_simulation(tf)
         solution = dct["solution"]
         predicted_metrics = dct["hospitalizations"]
-        numpyro.deterministic(
-            "final_timestep_s", solution.ys[self.config.COMPARTMENT_IDX.S][-1]
-        )
-        numpyro.deterministic(
-            "final_timestep_e", solution.ys[self.config.COMPARTMENT_IDX.E][-1]
-        )
-        numpyro.deterministic(
-            "final_timestep_i", solution.ys[self.config.COMPARTMENT_IDX.I][-1]
-        )
-        numpyro.deterministic(
-            "final_timestep_c", solution.ys[self.config.COMPARTMENT_IDX.C][-1]
-        )
+        self._checkpoint_compartment_sizes(solution)
         predicted_metrics = jnp.maximum(predicted_metrics, 1e-6)
         numpyro.sample(
             "incidence",
@@ -247,6 +238,41 @@ def _debug_likelihood(self, **kwargs) -> bx.Model:
         )
         return bx_model
 
+    def _checkpoint_compartment_sizes(self, solution: Solution):
+        """marks the final_timesteps parameters as well as any
+        requested dates from self.config.COMPARTMENT_SAVE_DATES if the
+        parameter exists. Skipping over any invalid dates.
+
+        This method does not actually save the compartment sizes to a file,
+        instead it stores the values within `self.inference_algo.get_samples()`
+        so that they may be later saved by self.checkpoint() or by the user.
+
+
+        Parameters
+        ----------
+        solution : diffrax.Solution
+            a diffrax Solution object returned by solving ODEs, most often
+            retrieved by `self.run_simulation()`
+        """
+        for compartment in self.config.COMPARTMENT_IDX:
+            numpyro.deterministic(
+                "final_timestep_%s" % compartment.name,
+                solution.ys[compartment][-1],
+            )
+        for date in getattr(self.config, "COMPARTMENT_SAVE_DATES", []):
+            date: datetime.date
+            date_str = date.strftime("%Y_%m_%d")
+            sim_day = date_to_sim_day(date, self.config.INIT_DATE)
+            # ensure user requests a day we actually have in `solution`
+            if sim_day >= 0 and sim_day < len(
+                solution.ys[self.config.COMPARTMENT_IDX.S]
+            ):
+                for compartment in self.config.COMPARTMENT_IDX:
+                    numpyro.deterministic(
+                        "%s_timestep_%s" % (date_str, compartment.name),
+                        solution.ys[compartment][sim_day],
+                    )
+
     def checkpoint(
         self, checkpoint_path: str, group_by_chain: bool = True
     ) -> None:

src/dynode/vis_utils.py

@@ -304,8 +304,8 @@ def plot_checkpoint_inference_correlation_pairs(
         for key, val in posteriors.items()
     }
     posteriors: dict[str, np.ndarray] = flatten_list_parameters(posteriors)
-    # drop any final_timestep parameters in case they snuck in
-    posteriors = drop_keys_with_substring(posteriors, "final_timestep")
+    # drop any timestep parameters in case they snuck in
+    posteriors = drop_keys_with_substring(posteriors, "timestep")
     number_of_samples = posteriors[list(posteriors.keys())[0]].shape[1]
     # if we are dealing with many samples per chain,
     # narrow down to max_samples_calculated samples per chain
@@ -435,8 +435,8 @@ def plot_mcmc_chains(
         for key, val in samples.items()
     }
     samples: dict[str, np.ndarray] = flatten_list_parameters(samples)
-    # drop any final_timestep parameters in case they snuck in
-    samples = drop_keys_with_substring(samples, "final_timestep")
+    # drop any timestep parameters in case they snuck in
+    samples = drop_keys_with_substring(samples, "timestep")
     param_names = list(samples.keys())
     num_params = len(param_names)
     num_chains = samples[param_names[0]].shape[0]