Emily Boudreaux
84ff182717
python hooks to make getting base composition more reliable; further, a number of small changes made to aid in my analysis in response to ref report 1
81 lines
2.7 KiB
Python
81 lines
2.7 KiB
Python
from enum import Enum
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from typing import Dict, List, Any, SupportsFloat
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import json
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from datetime import datetime
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import os
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import sys
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from gridfire.solver import PointSolverTimestepContext
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from gridfire._gridfire.engine.scratchpads import StateBlob
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import gridfire
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class LogEntries(Enum):
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Step = "Step"
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t = "t"
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dt = "dt"
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eps = "eps"
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Composition = "Composition"
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ReactionContributions = "ReactionContributions"
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class StepLogger:
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def __init__(self):
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self.num_steps : int = 0
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self.steps : List[Dict[LogEntries, Any]] = []
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def log_step(self, ctx: PointSolverTimestepContext):
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comp_data: Dict[str, SupportsFloat] = {}
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for species in ctx.engine.getNetworkSpecies(ctx.state_ctx):
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sid = ctx.engine.getSpeciesIndex(ctx.state_ctx, species)
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comp_data[species.name()] = ctx.state[sid]
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entry : Dict[LogEntries, Any] = {
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LogEntries.Step: ctx.num_steps,
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LogEntries.t: ctx.t,
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LogEntries.dt: ctx.dt,
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LogEntries.eps: ctx.state[-1],
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LogEntries.Composition: comp_data,
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}
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self.steps.append(entry)
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self.num_steps += 1
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def to_json(self, filename: str, **kwargs):
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serializable_steps : List[Dict[str, Any]] = [
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{
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LogEntries.Step.value: step[LogEntries.Step],
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LogEntries.t.value: step[LogEntries.t],
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LogEntries.dt.value: step[LogEntries.dt],
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LogEntries.eps.value: step[LogEntries.eps],
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LogEntries.Composition.value: step[LogEntries.Composition],
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}
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for step in self.steps
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]
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out_data : Dict[str, Any] = {
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"Metadata": {
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"NumSteps": self.num_steps,
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**kwargs,
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"DateCreated": datetime.now().isoformat(),
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"GridFireVersion": gridfire.__version__,
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"Author": "Emily M. Boudreaux",
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"OS": os.uname().sysname,
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"ClangVersion": os.popen("clang --version").read().strip(),
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"GccVersion": os.popen("gcc --version").read().strip(),
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"PythonVersion": sys.version,
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},
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"Steps": serializable_steps
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}
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with open(filename, 'w') as f:
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json.dump(out_data, f, indent=4)
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def summary(self) -> Dict[str, Any]:
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if not self.steps:
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return {}
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final_step = self.steps[-1]
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summary_data : Dict[str, Any] = {
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"TotalSteps": self.num_steps,
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"FinalTime": final_step[LogEntries.t],
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"FinalComposition": final_step[LogEntries.Composition],
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}
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return summary_data
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