productionise

simulator.py

@@ -0,0 +1,65 @@
+from typing import Callable
+import matplotlib.pyplot as plt
+from dataclasses import dataclass
+from copy import deepcopy
+
+MetricsContainer = dict[str, float]
+
+@dataclass
+class MetricConfig:
+    initial_value: float
+    period_calculator: Callable[[MetricsContainer], float]
+    plot: bool = True
+
+def create_metrics_container(
+    metric_configs: dict[str, MetricConfig]
+) -> MetricsContainer:
+    metrics_container = MetricsContainer()
+
+    for metric_name, metric_config in metric_configs.items():
+        metrics_container[metric_name] = metric_config.initial_value
+
+    return metrics_container
+
+def plot_metric_histories(
+    metric_histories: list[MetricsContainer], 
+    metric_configs: dict[str, MetricConfig]
+):
+    plot_data = {}
+
+    for metric_name, metric_config in metric_configs.items():
+        if not metric_config.plot:
+            continue
+
+        plot_data[metric_name] = [metric_history[metric_name] for 
+                                  metric_history in metric_histories]
+
+    fig, ax = plt.subplots(figsize=(14, 8))
+
+    for label, values in plot_data.items():
+        ax.plot(values, label=label)
+
+    ax.legend()
+
+    ax.set_title("Metrics Over Simulation Period")
+    ax.set_xlabel("Period")
+    ax.set_ylabel("Value")
+
+    ax.grid(True)
+
+    plt.show()
+
+def simulate(
+    num_periods: int,
+    metric_configs: dict[str, MetricConfig]
+):
+    metrics = create_metrics_container(metric_configs)
+    metric_histories = [deepcopy(metrics)]
+
+    for period in range(num_periods):
+        for metric_name, metric_config in metric_configs.items():
+            metrics[metric_name] = metric_config.period_calculator(metrics)
+
+        metric_histories.append(deepcopy(metrics))
+
+    plot_metric_histories(metric_histories, metric_configs)