add fig plots

scripts/utils/plots.py

@@ -0,0 +1,43 @@
+import matplotlib.pyplot as plt
+import seaborn as sns
+from pandas.api.types import CategoricalDtype
+
+def plot_metrics(metrics, condition_to_latex, title, color_palette):
+    # Add a new column to your dataframe with the latex representation
+    metrics['condition_latex'] = metrics['condition'].map(condition_to_latex)
+
+    # Order condition_latex as per the condition_to_latex dictionary
+    cat_type = CategoricalDtype(categories=condition_to_latex.values(), ordered=True)
+    metrics['condition_latex'] = metrics['condition_latex'].astype(cat_type)
+
+    # Compute mean and std for each condition for each metric
+    grouped = metrics.groupby('condition_latex')[['mel', 'frechet']].agg(['mean', 'std'])
+
+    fig, axs = plt.subplots(2, 1, figsize=(7, 5.25))
+
+    # Set the main title for the figure
+    fig.suptitle(title, fontsize=16)
+
+    # Get color for each bar in the plot
+    bar_colors = [color_palette[condition] for condition in grouped.index]
+
+    # Plot mel
+    sns.boxplot(x='condition_latex', y='mel', data=metrics, ax=axs[0], palette=color_palette, showfliers=False)
+    axs[0].set_ylabel('Mel Spectrogram Loss \u2190')
+    axs[0].set_xlabel('') # Remove x-axis label
+    axs[0].set_xticklabels(grouped.index, rotation=0, ha='center')
+
+    # Plot frechet
+    axs[1].bar(grouped.index, grouped['frechet']['mean'], yerr=grouped['frechet']['std'], color=bar_colors)
+    axs[1].set_ylabel('FAD \u2190')
+    axs[1].set_xlabel('') # Remove x-axis label
+    axs[1].set_xticklabels(grouped.index, rotation=0, ha='center')
+
+    # Adjust the space between plots
+    plt.subplots_adjust(hspace=0.1)
+
+    # Remove any unnecessary space around the plot
+    plt.tight_layout(rect=[0, 0, 1, 0.96])
+
+    # Reduce the space between suptitle and the plot
+    plt.subplots_adjust(top=0.92)