test(vv): Added more scripts to verify GridFire behavior

validation/ManuscriptFigures/utils/logger.py

@@ -8,8 +8,13 @@ import sys
 
 from gridfire.solver import PointSolverTimestepContext
 from gridfire._gridfire.engine.scratchpads import StateBlob
+
+from fourdst.composition import Composition
 import gridfire
 
+import numpy as np
+import pandas as pd
+
 class LogEntries(Enum):
     Step = "Step"
     t = "t"
@@ -17,6 +22,7 @@ class LogEntries(Enum):
     eps = "eps"
     Composition = "Composition"
     ReactionContributions = "ReactionContributions"
+    MassFractions = "MassFractions"
 
 
 class StepLogger:
@@ -24,17 +30,40 @@ class StepLogger:
         self.num_steps : int = 0
         self.steps : List[Dict[LogEntries, Any]] = []
 
+    # def log_step(self, ctx: PointSolverTimestepContext):
+    #     comp_data: Dict[str, SupportsFloat] = {}
+    #     for species in ctx.engine.getNetworkSpecies(ctx.state_ctx):
+    #         sid = ctx.engine.getSpeciesIndex(ctx.state_ctx, species)
+    #         comp_data[species.name()] = ctx.state[sid]
+    #     entry : Dict[LogEntries, Any] = {
+    #         LogEntries.Step: ctx.num_steps,
+    #         LogEntries.t: ctx.t,
+    #         LogEntries.dt: ctx.dt,
+    #         LogEntries.eps: ctx.state[-1],
+    #         LogEntries.Composition: comp_data,
+    #     }
+    #     self.steps.append(entry)
+    #     self.num_steps += 1
+
     def log_step(self, ctx: PointSolverTimestepContext):
-        comp_data: Dict[str, SupportsFloat] = {}
-        for species in ctx.engine.getNetworkSpecies(ctx.state_ctx):
-            sid = ctx.engine.getSpeciesIndex(ctx.state_ctx, species)
-            comp_data[species.name()] = ctx.state[sid]
+        full_comp = ctx.composition
+
+        comp_data: Dict[str, float] = {}
+        mass_frac: Dict[str, float] = {}
+        for species in full_comp.getRegisteredSpecies():
+            comp_data[species.name()] = full_comp.getMolarAbundance(species)
+            mass_frac[species.name()] = full_comp.getMassFraction(species)
+
+        rhs_calc = ctx.engine.getMostRecentRHSCalculation(ctx.state_ctx)
+        instantaneous_eps = rhs_calc.energy if rhs_calc else 0.0
+
         entry : Dict[LogEntries, Any] = {
             LogEntries.Step: ctx.num_steps,
             LogEntries.t: ctx.t,
             LogEntries.dt: ctx.dt,
-            LogEntries.eps: ctx.state[-1],
+            LogEntries.eps: instantaneous_eps,
             LogEntries.Composition: comp_data,
+            LogEntries.MassFractions: mass_frac,
         }
         self.steps.append(entry)
         self.num_steps += 1
@@ -47,6 +76,7 @@ class StepLogger:
                 LogEntries.dt.value: step[LogEntries.dt],
                 LogEntries.eps.value: step[LogEntries.eps],
                 LogEntries.Composition.value: step[LogEntries.Composition],
+                LogEntries.MassFractions.value: step[LogEntries.MassFractions],
             }
             for step in self.steps
         ]
@@ -67,6 +97,32 @@ class StepLogger:
         with open(filename, 'w') as f:
             json.dump(out_data, f, indent=4)
 
+    @property
+    def t(self) -> np.ndarray:
+        return np.array([step[LogEntries.t] for step in self.steps])
+
+    @property
+    def df(self) -> pd.DataFrame:
+        if not self.steps:
+            return pd.DataFrame()
+
+        flat_data = []
+        for step in self.steps:
+            row = {
+                "Step": step[LogEntries.Step],
+                "t": step[LogEntries.t],
+                "dt": step[LogEntries.dt],
+                "eps": step[LogEntries.eps],
+            }
+            X_dict = {f"X_{sp}": x for sp, x in step[LogEntries.MassFractions].items()}
+            row.update(step[LogEntries.Composition])
+            row.update(X_dict)
+            flat_data.append(row)
+
+        df = pd.DataFrame(flat_data)
+
+        df = df.ffill().fillna(0.0)
+        return df
 
     def summary(self) -> Dict[str, Any]:
         if not self.steps:
@@ -78,3 +134,8 @@ class StepLogger:
             "FinalComposition": final_step[LogEntries.Composition],
         }
         return summary_data
+
+    def reset(self):
+        self.num_steps = 0
+        self.steps : List[Dict[LogEntries, Any]] = []
+