Improve Data Visualization

app.py

@@ -4,7 +4,7 @@ import numpy as np
 from bokeh.plotting import figure
 from bokeh.models import ColumnDataSource, DataTable, TableColumn, CustomJS, Select, Button, HoverTool, LinearColorMapper, ColorBar, FuncTickFormatter, FixedTicker
 from bokeh.layouts import column
-from bokeh.palettes import Reds9, Blues9, Oranges9, Purples9, Greys9, BuGn9, Greens9
+from bokeh.palettes import Reds9, Blues9, Oranges9, Purples9, Greys9, BuGn9, Greens9, RdYlGn11, linear_palette
 from sklearn.decomposition import PCA
 from sklearn.manifold import TSNE, trustworthiness
 from sklearn.metrics import pairwise_distances
@@ -14,6 +14,8 @@ from sklearn.linear_model import LinearRegression
 from scipy.stats import binned_statistic_2d
 import json
 import itertools
+import matplotlib.pyplot as plt
+import matplotlib.colors as mcolors
 
 
 N_COMPONENTS = 3
@@ -1041,6 +1043,17 @@ def run_model(model_name):
         
         # -------------------------------------------------------------------------
         # 4. Cálculo de distancias y scatter plot: Distance vs F1 (usando PC1 y PC2 globales)
+        model_options = ["es-digital-paragraph-degradation-seq", "es-digital-line-degradation-seq", "es-digital-seq", "es-digital-rotation-degradation-seq", "es-digital-zoom-degradation-seq", "es-render-seq"]
+        model_options_with_default = [""]
+        model_options_with_default.extend(model_options)
+
+
+        # Genera una paleta de 256 colores basada en RdYlGn11
+        cmap = plt.get_cmap("RdYlGn")
+        red_green_palette = [mcolors.rgb2hex(cmap(i)) for i in np.linspace(0, 1, 256)]
+
+
+
         real_labels_new = sorted(df_all["real"]['label'].unique().tolist())
         df_distances_new = compute_cluster_distances_synthetic_individual(
             df_all["synthetic"],
@@ -1144,11 +1157,15 @@ def run_model(model_name):
                 selected_feature = st.selectbox("Select heatmap feature:", 
                                                 options=feature_options, key=f"heatmap_{model_name}")
                 select_extra_dataset_hm = st.selectbox("Select a dataset:", 
-                                                options=["-", "es-digital-line-degradation-seq", "es-digital-seq", "es-digital-rotation-degradation-seq", "es-digital-zoom-degradation-seq", "es-render-seq"], key=f"heatmap_extra_dataset_{model_name}")
+                                                options=model_options_with_default, key=f"heatmap_extra_dataset_{model_name}")
                 
                 # Definir el rango de posiciones (x, y)
-                x_min, x_max = df_heatmap['x'].min(), df_heatmap['x'].max()
-                y_min, y_max = df_heatmap['y'].min(), df_heatmap['y'].max()
+                # x_min, x_max = df_heatmap['x'].min(), df_heatmap['x'].max()
+                # y_min, y_max = df_heatmap['y'].min(), df_heatmap['y'].max()
+
+                x_min, x_max = -4, 4
+                y_min, y_max = -4, 4
+
                 
                 grid_size = 50
                 x_bins = np.linspace(x_min, x_max, grid_size + 1)
@@ -1177,7 +1194,15 @@ def run_model(model_name):
                 # Transponer la matriz para alinear correctamente los ejes
                 heatmap_data = heat_stat.T
                 
-                color_mapper = LinearColorMapper(palette="Viridis256", low=np.nanmin(heatmap_data), high=np.nanmax(heatmap_data), nan_color='rgba(0, 0, 0, 0)')
+                if selected_feature in model_options:
+                    color_mapper = LinearColorMapper(
+                        palette=red_green_palette,
+                        low=0,
+                        high=1,
+                        nan_color='rgba(0, 0, 0, 0)'
+                    )
+                else:
+                    color_mapper = LinearColorMapper(palette="Viridis256", low=np.nanmin(heatmap_data), high=np.nanmax(heatmap_data), nan_color='rgba(0, 0, 0, 0)')
                 
                 heatmap_fig = figure(title=f"Heatmap de '{selected_feature}'",
                                     x_range=(x_min, x_max), y_range=(y_min, y_max),
@@ -1223,7 +1248,7 @@ def run_model(model_name):
                         'img': df_extra['img'],
                         'label': df_extra['name']
                     })
-                    extra_renderer = heatmap_fig.circle('x', 'y', size=10, source=source_extra_points, fill_alpha=0, line_alpha=0.5, color="red")
+                    extra_renderer = heatmap_fig.circle('x', 'y', size=5, source=source_extra_points, fill_alpha=0, line_alpha=0.5, color="purple")
                 
                 hover_tool_points = HoverTool(renderers=[invisible_renderer], tooltips=TOOLTIPS)
                 heatmap_fig.add_tools(hover_tool_points)