diff --git a/TelescopeML/StatVisAnalyzer.py b/TelescopeML/StatVisAnalyzer.py
index 953389b8..378ea8dd 100644
--- a/TelescopeML/StatVisAnalyzer.py
+++ b/TelescopeML/StatVisAnalyzer.py
@@ -14,6 +14,7 @@
 from sklearn.metrics import r2_score, mean_squared_error
 from scipy.interpolate import RegularGridInterpolator
 from scipy.stats import chi2
+import os
 
 import pprint
 
@@ -1340,4 +1341,62 @@ def plot_scatter_x_y (x, y,
     p.grid.grid_line_alpha = 0.5
 
     # Show the plot
-    show(p)
\ No newline at end of file
+    show(p)
+
+
+def plot_filtered_dataframe(dataset, filter_bounds, feature_to_plot, title_label, wl_synthetic, __reference_data__):
+    """
+    Plot a DataFrame with a single x-axis (using column names) and multiple y-axes.
+
+    Parameters:
+        - df (pd.DataFrame): DataFrame containing the data to be plotted.
+    """
+
+    filtered_df = dataset.copy()
+    for feature, bounds in filter_bounds.items():
+        lower_bound, upper_bound = bounds
+        filtered_df = filtered_df[(filtered_df[feature] >= lower_bound) & (filtered_df[feature] <= upper_bound)]
+
+    filtered_df2 = filtered_df.sort_values(feature_to_plot, ascending=False).iloc[::1, 4:-1][::-1]
+
+    fig, ax = plt.subplots(figsize=(10, 3))
+
+    x = filtered_df2.columns
+    df_transposed = filtered_df2.T  # Transpose the DataFrame
+
+    # Define a color palette
+    num_colors = len(df_transposed.columns)  # Number of colors needed (excluding x-axis)
+    colors = sns.color_palette('magma', num_colors)
+
+    for i, col in enumerate(df_transposed.columns):
+        # print(col)
+        if col != 'x':  # Skip the x-axis column
+            ax.semilogy(wl_synthetic, df_transposed[col],
+                        # label=data[col][:4].values,
+                        color=colors[i], alpha=0.7)
+
+    # print(filtered_data.T[col][:4].values[0])
+    ax.set_xlabel('Features (Wavelength [$\mu$m])')
+    ax.set_ylabel(r'F$_{\nu}$  [erg/cm$^2$/s/Hz]')
+    dict_features = {'temperature': 'Effective Temperature', 'gravity': 'Gravity', 'metallicity': 'Metallicity',
+                     'c_o_ratio': 'Carbon-to-oxygen ratio'}
+    ax.set_title(dict_features[feature_to_plot] + " " + title_label)
+    # ax.legend()
+
+    # Get the minimum and maximum values from the data
+    # vmin = df_transposed.values.min()
+    # vmax = df_transposed.values.max()
+
+    # Add colorbar
+    cmap = sns.color_palette('magma', as_cmap=True)
+    cbar = plt.colorbar(plt.cm.ScalarMappable(cmap=cmap,
+                                              norm=plt.Normalize(vmin=filter_bounds[feature_to_plot][0],
+                                                                 vmax=filter_bounds[feature_to_plot][1])), ax=ax)
+    # dict_features2 = {'temperature':'T [K]', 'gravity':'log$g$', 'metallicity':'[M/H]', 'c_o_ratio':'C/O ratio'}
+    dict_features = {'temperature': 'T$_{eff}$ [K]', 'gravity': 'log$g$', 'metallicity': '[M/H]', 'c_o_ratio': 'C/O'}
+    cbar.set_label(dict_features[feature_to_plot])
+
+    plt.savefig(os.path.join(__reference_data__, 'figures', feature_to_plot + "_trainin_examples.pdf"), dpi=500,
+                bbox_inches='tight')
+
+    plt.show()