From f4e29c18e5635fee808a60f9165e55ac63047f8f Mon Sep 17 00:00:00 2001
From: Lorenzo Venerandi <lollo.venerandi@gmail.com>
Date: Fri, 28 Feb 2025 20:34:32 +0100
Subject: [PATCH] plot with seaborn

---
 plot_metrics.py | 80 +++++++++++++++++++++++++------------------------
 1 file changed, 41 insertions(+), 39 deletions(-)

diff --git a/plot_metrics.py b/plot_metrics.py
index 19e1299..1a61dc5 100644
--- a/plot_metrics.py
+++ b/plot_metrics.py
@@ -1,5 +1,7 @@
+import seaborn as sns
 import matplotlib.pyplot as plt
 import yaml
+import pandas as pd
 
 # Function to read and parse the file
 def read_metrics(file_path):
@@ -8,47 +10,47 @@ def read_metrics(file_path):
     return data['runs']
 
 # Read metrics from the file
-file_path = 'project/metrics.yaml'  # Replace with the correct file path
+file_path = 'project/metrics.yaml'  # Replace with your file path
 runs = read_metrics(file_path)
 
-# Extracting values
-n_tasks = [run['n_task'] for run in runs]
-build_times = [float(run['build']['components_build_time']) for run in runs]
-gen_times = [float(run['code_gen']['gen_time']) for run in runs]
-deploy_times = [float(run['deploy']['components_deploy_time']) for run in runs]
+#Flatten data into a list of dictionaries
+data = []
+for run in runs:
+    if 'build' in run:
+        data.append({'Task': run['n_task'], 'Type': 'Build Time', 'Time': float(run['build']['components_build_time'])})
 
-# Plotting Build Time
-plt.figure(figsize=(8, 6))
-ax = plt.gca()  # Get current axis
-p = ax.bar(n_tasks, build_times, color='b')
-ax.bar_label(p, label_type='edge')
-ax.set_title('Build Time')
-ax.set_xlabel('Number of Tasks')
-ax.set_ylabel('Build Time (seconds)')
-plt.tight_layout()
-plt.savefig('build_time_plot.png')  # Save the plot as an image
-plt.close()
+    if 'code_gen' in run:
+        data.append({'Task': run['n_task'], 'Type': 'Generation Time', 'Time': float(run['code_gen']['gen_time'])})
+    
+    if 'deploy' in run:
+        data.append({'Task': run['n_task'], 'Type': 'Deployment Time', 'Time': float(run['deploy']['components_deploy_time'])})
 
-# Plotting Code Generation Time
-plt.figure(figsize=(8, 6))
-ax = plt.gca()  # Get current axis
-p = ax.bar(n_tasks, gen_times, color='g')
-ax.bar_label(p, label_type='edge')
-ax.set_title('Code Generation Time')
-ax.set_xlabel('Number of Tasks')
-ax.set_ylabel('Generation Time (seconds)')
-plt.tight_layout()
-plt.savefig('gen_time_plot.png')  # Save the plot as an image
-plt.close()
+# Convert to DataFrame
+df = pd.DataFrame(data)
 
-# Plotting Deployment Time
-plt.figure(figsize=(8, 6))
-ax = plt.gca()  # Get current axis
-p = ax.bar(n_tasks, deploy_times, color='r')
-ax.bar_label(p, label_type='edge')
-ax.set_title('Deployment Time')
-ax.set_xlabel('Number of Tasks')
-ax.set_ylabel('Deployment Time (seconds)')
-plt.tight_layout()
-plt.savefig('deploy_time_plot.png')  # Save the plot as an image
-plt.close()
\ No newline at end of file
+# Function to plot and add median labels
+def plot_metric(metric, filename, color):
+    subset = df[df['Type'] == metric]
+    plt.figure(figsize=(8, 6))
+    ax = sns.boxplot(x='Task', y='Time', data=subset, color=color)
+
+    # Add median labels
+    medians = subset.groupby('Task')['Time'].median()
+    for i, task in enumerate(medians.index):
+        median_value = medians[task]
+        ax.text(i, median_value, f'{median_value:.3f}', ha='center', va='center',
+                fontsize=10, color='white', bbox=dict(facecolor='black', alpha=0.6, boxstyle='round,pad=0.3'))
+
+    plt.title(f'{metric} by Number of Tasks')
+    plt.xlabel('Number of Tasks')
+    plt.ylabel('Time (seconds)')
+    plt.tight_layout()
+    plt.savefig(filename)
+    plt.close()
+
+# Plot each metric separately
+plot_metric('Build Time', 'res/build_time_boxplot.png', 'skyblue')
+plot_metric('Generation Time', 'res/gen_time_boxplot.png', 'lightgreen')
+plot_metric('Deployment Time', 'res/deploy_time_boxplot.png', 'salmon')
+
+print('Plots saved successfully!')
\ No newline at end of file