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simulator.py
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176 lines (157 loc) · 6.11 KB
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import json
from pathlib import Path
import numpy as np
class Simulator:
CASE_ORDER = (
"bestSolution_wspin",
"bestSolution_spin0",
"bestSolution_spin1",
"bestSolution_randSpin",
)
PLOT_STYLES = {
"bestSolution_wspin": ("Best Over All Spins", "blue"),
"bestSolution_spin0": ("All-Zero Spin", "red"),
"bestSolution_spin1": ("All-One Spin", "green"),
"bestSolution_randSpin": ("Random Spin-0/1", "orange"),
}
def __init__(
self,
net,
optimizer,
L,
B,
Ns,
Nu,
satMaxPow,
ueMaxPow,
antSpacing,
nIter,
outfile,
layout_callback=None,
):
self.network = net
self.optimizer = optimizer
self.freqs = L
self.bws = B
self.Ns = Ns
self.Nu = Nu
self.satMaxPow = satMaxPow
self.ueMaxPow = ueMaxPow
self.antSpacing = antSpacing
self.nIter = nIter
self.results_by_case = {case_name: [] for case_name in self.CASE_ORDER}
self.iteration_results = []
self.outfile = outfile
self.layout_callback = layout_callback
@staticmethod
def _write_json_atomic(filename, payload):
filename = Path(filename)
filename.parent.mkdir(parents=True, exist_ok=True)
tmp_path = filename.with_suffix(filename.suffix + ".tmp")
with open(tmp_path, "w", newline="\n") as file:
json.dump(payload, file, indent=2)
tmp_path.replace(filename)
def run(self, checkpoint_file=None, metadata=None, plot_file=None):
self.results_by_case = {case_name: [] for case_name in self.CASE_ORDER}
self.iteration_results = []
checkpoint_path = Path(checkpoint_file) if checkpoint_file is not None else None
plot_path = Path(plot_file) if plot_file is not None else None
try:
for i in range(self.nIter):
print(f"Iteration {i + 1}/{self.nIter}")
if self.layout_callback is not None:
self.layout_callback(i)
self.network.generateLayout(
self.Ns, self.Nu, self.satMaxPow, self.ueMaxPow, self.outfile
)
case_results = self.optimizer.run(
self.network.satellites,
self.network.ues,
self.freqs,
self.bws,
self.network.time,
self.antSpacing,
)
iteration_payload = {"iteration": i + 1}
for case_name in self.CASE_ORDER:
case_value = float(case_results[case_name])
self.results_by_case[case_name].append(case_value)
iteration_payload[case_name] = case_value
iteration_payload["with_spin"] = iteration_payload["bestSolution_wspin"]
iteration_payload["without_spin"] = iteration_payload["bestSolution_randSpin"]
self.iteration_results.append(iteration_payload)
if checkpoint_path is not None:
self.save(
checkpoint_path,
metadata=metadata,
completed_iterations=i + 1,
status="running" if (i + 1) < self.nIter else "completed",
)
if plot_path is not None and self._has_results():
self.plot(plot_path)
except Exception as exc:
if checkpoint_path is not None:
self.save(
checkpoint_path,
metadata=metadata,
completed_iterations=self.completed_iterations,
status="failed",
error_message=str(exc),
)
raise
return {case_name: list(values) for case_name, values in self.results_by_case.items()}
@property
def completed_iterations(self):
return len(self.iteration_results)
def _has_results(self):
return any(self.results_by_case[case_name] for case_name in self.CASE_ORDER)
def save(
self,
filename,
metadata=None,
completed_iterations=None,
status="completed",
error_message=None,
):
payload = {
"metadata": metadata or {},
"status": status,
"completed_iterations": (
self.completed_iterations
if completed_iterations is None
else int(completed_iterations)
),
"target_iterations": int(self.nIter),
"result_case_order": list(self.CASE_ORDER),
"iteration_results": list(self.iteration_results),
"results_by_case": {
case_name: list(self.results_by_case[case_name]) for case_name in self.CASE_ORDER
},
"results_with_spin": list(self.results_by_case["bestSolution_wspin"]),
"results_without_spin": list(self.results_by_case["bestSolution_randSpin"]),
}
if error_message is not None:
payload["error_message"] = error_message
self._write_json_atomic(filename, payload)
def plot(self, output_path):
if not self._has_results():
raise ValueError("No simulation results available to plot.")
import matplotlib.pyplot as plt
output_path = Path(output_path)
output_path.parent.mkdir(parents=True, exist_ok=True)
fmt = output_path.suffix.lstrip(".") or None
plt.figure(figsize=(8, 6))
for case_name in self.CASE_ORDER:
case_values = self.results_by_case[case_name]
if not case_values:
continue
sorted_values = np.sort(case_values)
cdf_values = np.arange(1, len(sorted_values) + 1) / len(sorted_values)
label, color = self.PLOT_STYLES[case_name]
plt.plot(sorted_values, cdf_values, label=label, color=color)
plt.xlabel("Sum Rate")
plt.ylabel("CDF")
plt.grid(True)
plt.legend()
plt.savefig(output_path, format=fmt)
plt.close()