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multiple subplots

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Sam Hadow
2025-05-06 11:43:49 +02:00
parent a722831d97
commit e21f559c1f
1 changed files with 20 additions and 19 deletions
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39
src/simulation.py
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@@ -149,11 +149,9 @@ def run_single_simulation(args):
except ValueError: # Loss rate too high except ValueError: # Loss rate too high
return None return None
def simulation_wrapper1(simulation_time, num_runs, min_runs, confidence_level, lambda_vals): def simulation_wrapper1(simulation_time, num_runs, min_runs, confidence_level, lambda_vals, ax_rt):
C_values = [1, 2, 3, 6] C_values = [1, 2, 3, 6]
plt.figure(figsize=(12, 8))
mean_at_lambda1 = {} mean_at_lambda1 = {}
loss_data = {} loss_data = {}
@@ -205,8 +203,8 @@ def simulation_wrapper1(simulation_time, num_runs, min_runs, confidence_level, l
print(f"Stopped at λ={lambda_val:.2f} - no successful run") print(f"Stopped at λ={lambda_val:.2f} - no successful run")
break break
plt.plot(lambda_points, means, label=f'C={c}') ax_rt.plot(lambda_points, means, label=f'C={c}')
plt.fill_between(lambda_points, ci_lower, ci_upper, alpha=0.2) ax_rt.fill_between(lambda_points, ci_lower, ci_upper, alpha=0.2)
# store response time for lamba = 1 # store response time for lamba = 1
if 1 in lambda_points: if 1 in lambda_points:
@@ -229,17 +227,18 @@ def simulation_wrapper1(simulation_time, num_runs, min_runs, confidence_level, l
# plot curves # plot curves
if len(lambda_vals)>1: if len(lambda_vals)>1:
plt.xlabel('Arrival Rate (λ)') ax_rt.set_xlim(left=0)
plt.ylabel('Mean Response Time') ax_rt.set_xlabel('Arrival Rate (λ)')
plt.title(f'Mean Response Time vs Arrival Rate ({num_runs} runs, 95% CI)') ax_rt.set_ylabel('Mean Response Time')
plt.legend() ax_rt.set_title(f'Mean Response Time vs Arrival Rate ({num_runs} runs, {int(confidence_level*100)}% CI)')
plt.grid(True) ax_rt.legend()
ax_rt.grid(True)
return loss_data return loss_data
def simulation_wrapper2(simulation_time, num_runs, min_runs, confidence_level, lambda_vals, max_loss_value=0.1): def simulation_wrapper2(simulation_time, num_runs, min_runs, confidence_level, lambda_vals, max_loss_value=0.1):
loss_data = simulation_wrapper1(simulation_time, num_runs, min_runs, confidence_level, lambda_vals) fig, (ax_rt, ax_loss) = plt.subplots(2,1, figsize=(12,10), gridspec_kw={'height_ratios': [4, 3], 'hspace': 0.4}, sharex=False)
plt.show() loss_data = simulation_wrapper1(simulation_time, num_runs, min_runs, confidence_level, lambda_vals, ax_rt)
for c, (lams, losses) in loss_data.items(): for c, (lams, losses) in loss_data.items():
lambda_vals2 = [] lambda_vals2 = []
@@ -300,14 +299,16 @@ def simulation_wrapper2(simulation_time, num_runs, min_runs, confidence_level, l
print(f"Stopped at λ={lambda_val:.4f} - no successful run") print(f"Stopped at λ={lambda_val:.4f} - no successful run")
break break
plt.plot(lambda_points, losses, label=f'C={c}') ax_loss.plot(lambda_points, losses, label=f'C={c}')
plt.fill_between(lambda_points, ci_lower, ci_upper, alpha=0.2) ax_loss.fill_between(lambda_points, ci_lower, ci_upper, alpha=0.2)
ax_loss.set_xlim(left=2)
ax_loss.set_xlabel('Arrival Rate (λ)')
ax_loss.set_ylabel('Mean Loss Rate')
ax_loss.set_title(f'Mean Loss Rate vs Arrival Rate ({num_runs} runs, {int(confidence_level*100)}% CI)')
ax_loss.legend()
ax_loss.grid(True)
plt.xlabel('Arrival Rate (λ)')
plt.ylabel('Mean Loss Rate')
plt.title(f'Mean Loss Rate vs Arrival Rate ({num_runs} runs, 95% CI)')
plt.legend()
plt.grid(True)
plt.show() plt.show()