SimPy with matplotlib: Visualising Simulation Results

Published on February 24, 2026

Numbers tell the story. Visualisations make people believe it. <h2 id="essential-plots">Essential Plots</h2> Every simulation needs: 1. Queue length over time 2. Wait time distribution 3. Utilisation chart 4. Throughput curve <h2 id="setup">Setup</h2> <div class="code-block"><pre><code>import simpy import random import matplotlib.pyplot as plt import numpy as np plt.style.use('seaborn-v0_8-whitegrid') plt.rcParams['figure.figsize'] = (10, 6) plt.rcParams['font.size'] = 12 </code></pre></div> <h2 id="queue-length-over-time">Queue Length Over Time</h2> <div class="code-block"><pre><code>def plot_queue_length(time_series): """Step plot of queue length over simulation time.""" times = [r['time'] for r in time_series] queues = [r['queue'] for r in time_series] fig, ax = plt.subplots() ax.step(times, queues, where='post', linewidth=1, color='steelblue') ax.fill_between(times, queues, step='post', alpha=0.3) ax.set_xlabel('Time') ax.set_ylabel('Queue Length') ax.set_title('Queue Length Over Time') plt.tight_layout() plt.savefig('queue_length.png', dpi=150) plt.show() </code></pre></div> <h2 id="wait-time-distribution">Wait Time Distribution</h2> <div class="code-block"><pre><code>def plot_wait_histogram(wait_times): """Histogram with key statistics marked.""" fig, ax = plt.subplots() ax.hist(wait_times, bins=30, edgecolor='white', alpha=0.7, color='steelblue') # Mark statistics mean_wait = np.mean(wait_times) median_wait = np.median(wait_times) p95 = np.percentile(wait_times, 95) ax.axvline(mean_wait, color='red', linestyle='--', linewidth=2, label=f'Mean: {mean_wait:.1f}') ax.axvline(median_wait, color='green', linestyle='--', linewidth=2, label=f'Median: {median_wait:.1f}') ax.axvline(p95, color='orange', linestyle='--', linewidth=2, label=f'95th pct: {p95:.1f}') ax.set_xlabel('Wait Time') ax.set_ylabel('Frequency') ax.set_title('Distribution of Wait Times') ax.legend() plt.tight_layout() plt.savefig('wait_distribution.png', dpi=150) plt.show() </code></pre></div> <h2 id="cdf-plot">CDF Plot</h2> <div class="code-block"><pre><code>def plot_cdf(data, label='Wait Time'): """Cumulative distribution function.""" sorted_data = np.sort(data) cdf = np.arange(1, len(sorted_data) + 1) / len(sorted_data) fig, ax = plt.subplots() ax.plot(sorted_data, cdf, linewidth=2, color='steelblue') # Mark percentiles for p in [50, 90, 95, 99]: val = np.percentile(data, p) ax.axhline(p/100, color='gray', linestyle=':', alpha=0.5) ax.axvline(val, color='gray', linestyle=':', alpha=0.5) ax.annotate(f'{p}%: {val:.1f}', xy=(val, p/100), xytext=(val + 0.5, p/100 - 0.05), fontsize=10) ax.set_xlabel(label) ax.set_ylabel('Cumulative Probability') ax.set_title(f'CDF of {label}') ax.set_ylim(0, 1.05) plt.tight_layout() plt.savefig('cdf.png', dpi=150) plt.show() </code></pre></div> <h2 id="utilisation-over-time">Utilisation Over Time</h2> <div class="code-block"><pre><code>def plot_utilisation(time_series, capacity): """Resource utilisation over time.""" times = [r['time'] for r in time_series] busy = [r['busy'] for r in time_series] utilisation = [b / capacity for b in busy] fig, ax = plt.subplots() ax.plot(times, utilisation, linewidth=1, color='steelblue') ax.fill_between(times, utilisation, alpha=0.3) # Average line avg_util = np.mean(utilisation) ax.axhline(avg_util, color='red', linestyle='--', label=f'Average: {avg_util:.1%}') ax.set_xlabel('Time') ax.set_ylabel('Utilisation') ax.set_ylim(0, 1.1) ax.set_title('Resource Utilisation Over Time') ax.legend() plt.tight_layout() plt.savefig('utilisation.png', dpi=150) plt.show() </code></pre></div> <h2 id="scenario-comparison-box-plot">Scenario Comparison Box Plot</h2> <div class="code-block"><pre><code>def plot_scenario_comparison(results_dict): """Box plot comparing scenarios.""" fig, ax = plt.subplots() labels = list(results_dict.keys()) data = list(results_dict.values()) bp = ax.boxplot(data, labels=labels, patch_artist=True) # Color boxes for patch in bp['boxes']: patch.set_facecolor('steelblue') patch.set_alpha(0.7) ax.set_ylabel('Wait Time') ax.set_title('Wait Time Comparison Across Scenarios') plt.tight_layout() plt.savefig('comparison.png', dpi=150) plt.show() </code></pre></div> <h2 id="multi-panel-dashboard">Multi-Panel Dashboard</h2> <div class="code-block"><pre><code>def simulation_dashboard(entity_data, time_series, capacity): """Create a multi-panel dashboard.""" fig, axes = plt.subplots(2, 2, figsize=(14, 10)) # Panel 1: Queue length ax1 = axes[0, 0] times = [r['time'] for r in time_series] queues = [r['queue'] for r in time_series] ax1.step(times, queues, where='post', color='steelblue') ax1.fill_between(times, queues, step='post', alpha=0.3) ax1.set_xlabel('Time') ax1.set_ylabel('Queue Length') ax1.set_title('Queue Length Over Time') # Panel 2: Wait distribution ax2 = axes[0, 1] waits = [r['wait'] for r in entity_data] ax2.hist(waits, bins=30, edgecolor='white', color='steelblue', alpha=0.7) ax2.axvline(np.mean(waits), color='red', linestyle='--', label=f'Mean: {np.mean(waits):.1f}') ax2.set_xlabel('Wait Time') ax2.set_ylabel('Frequency') ax2.set_title('Wait Time Distribution') ax2.legend() # Panel 3: Utilisation ax3 = axes[1, 0] util = [r['busy'] / capacity for r in time_series] ax3.plot(times, util, color='steelblue') ax3.fill_between(times, util, alpha=0.3) ax3.axhline(np.mean(util), color='red', linestyle='--') ax3.set_xlabel('Time') ax3.set_ylabel('Utilisation') ax3.set_ylim(0, 1.1) ax3.set_title('Resource Utilisation') # Panel 4: Throughput ax4 = axes[1, 1] departures = sorted([r['departure'] for r in entity_data]) cumulative = range(1, len(departures) + 1) ax4.plot(departures, cumulative, color='steelblue') ax4.set_xlabel('Time') ax4.set_ylabel('Cumulative Departures') ax4.set_title('Throughput') plt.tight_layout() plt.savefig('dashboard.png', dpi=150) plt.show() </code></pre></div> <h2 id="confidence-interval-plot">Confidence Interval Plot</h2> <div class="code-block"><pre><code>def plot_confidence_intervals(scenarios_results): """Bar chart with confidence intervals.""" from scipy import stats fig, ax = plt.subplots() names = list(scenarios_results.keys()) means = [] errors = [] for name, data in scenarios_results.items(): mean = np.mean(data) se = stats.sem(data) ci = 1.96 * se means.append(mean) errors.append(ci) x = range(len(names)) ax.bar(x, means, yerr=errors, capsize=10, color='steelblue', alpha=0.7) ax.set_xticks(x) ax.set_xticklabels(names) ax.set_ylabel('Mean Wait Time') ax.set_title('Scenario Comparison with 95% Confidence Intervals') plt.tight_layout() plt.savefig('confidence.png', dpi=150) plt.show() </code></pre></div> <h2 id="heatmap">Heatmap</h2> <div class="code-block"><pre><code>def plot_heatmap(df, x_col, y_col, value_col): """Heatmap of values by two dimensions.""" pivot = df.pivot_table(values=value_col, index=y_col, columns=x_col, aggfunc='mean') fig, ax = plt.subplots(figsize=(12, 8)) im = ax.imshow(pivot.values, aspect='auto', cmap='YlOrRd') ax.set_xticks(range(len(pivot.columns))) ax.set_xticklabels(pivot.columns) ax.set_yticks(range(len(pivot.index))) ax.set_yticklabels(pivot.index) plt.colorbar(im, label=value_col) ax.set_xlabel(x_col) ax.set_ylabel(y_col) ax.set_title(f'{value_col} by {x_col} and {y_col}') plt.tight_layout() plt.savefig('heatmap.png', dpi=150) plt.show() </code></pre></div> <h2 id="animation-basic">Animation (Basic)</h2> <div class="code-block"><pre><code>from matplotlib.animation import FuncAnimation def animate_queue(time_series, interval=100): """Animate queue length over time.""" fig, ax = plt.subplots() ax.set_xlim(0, time_series[-1]['time']) ax.set_ylim(0, max(r['queue'] for r in time_series) + 1) ax.set_xlabel('Time') ax.set_ylabel('Queue Length') line, = ax.plot([], [], lw=2) def init(): line.set_data([], []) return line, def update(frame): data = time_series[:frame+1] times = [r['time'] for r in data] queues = [r['queue'] for r in data] line.set_data(times, queues) return line, ani = FuncAnimation(fig, update, frames=len(time_series), init_func=init, blit=True, interval=interval) ani.save('queue_animation.gif', writer='pillow') plt.show() </code></pre></div> <h2 id="complete-visualisation-suite">Complete Visualisation Suite</h2> <div class="code-block"><pre><code>class SimulationVisualiser: def __init__(self, entity_data, time_series, config): self.entity_data = entity_data self.time_series = time_series self.config = config def all_plots(self, save_dir='.'): """Generate all standard plots.""" import os # Ensure directory exists os.makedirs(save_dir, exist_ok=True) # Queue length self._plot_queue(f'{save_dir}/queue.png') # Wait distribution self._plot_wait_dist(f'{save_dir}/wait_dist.png') # CDF self._plot_cdf(f'{save_dir}/wait_cdf.png') # Utilisation self._plot_util(f'{save_dir}/utilisation.png') # Dashboard self._plot_dashboard(f'{save_dir}/dashboard.png') print(f"All plots saved to {save_dir}/") def _plot_queue(self, filename): # Implementation as above pass def _plot_wait_dist(self, filename): # Implementation as above pass # ... etc </code></pre></div> <h2 id="summary">Summary</h2> Visualisation essentials: - Queue length over time (step plot) - Wait time distribution (histogram + CDF) - Utilisation (line + fill) - Scenario comparison (box plots) - Confidence intervals (error bars) Show the data. Make it clear. Win the argument. <h2 id="next-steps">Next Steps</h2> <ul> <li><a href="/blog_posts/simpy-with-pandas.html">SimPy with pandas</a></li> <li><a href="/blog_posts/simpy-collect-statistics.html">Collecting Statistics</a></li> <li><a href="/blog_posts/simpy-animation.html">SimPy Animation</a></li> </ul>

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