Update app.py

app.py

@@ -844,97 +844,97 @@ def compare_persistence_homology(dna_sequence1, dna_sequence2):
 ui.page_opts(fillable=True)
 
 with ui.navset_card_tab(id="tab"):  
-    with ui.nav_panel("Viral Macrostructure"):
-        ui.page_opts(fillable=True)
-        ui.panel_title("Do viruses have underlying structure?")
-        with ui.layout_columns():
-            with ui.card():
-                ui.input_selectize(  
-                    "virus_selector",  
-                    "Select your viruses:",
-                    virus,
-                    multiple=True,  
-                )  
-            with ui.card():
-                ui.input_selectize(  
-                "plot_type",  
-                "Select your method:",
-                ["Chaos Game Representation", "2D Line", "ColorSquare", "Persistant Homology", "Wens Method"],
-                multiple=False,  
-            )
-                
-        ############################################################# Plotting ########################################################
-        here = Path(__file__).parent
-        import matplotlib as mpl
-        @render.plot
-        def plot():  
-            #ds = load_dataset('Hack90/virus_tiny')
-            df = pd.read_parquet('virus_ds.parquet')
-            df = df[df['Organism_Name'].isin(input.virus_selector())]
-            # group by virus
-            grouped = df.groupby('Organism_Name')['Sequence'].apply(list)
-            mpl.rcParams.update(mpl.rcParamsDefault)
-            
-            # plot the comparison
-            fig = None
-            if input.plot_type() == "2D Line":
-                fig = plot_2d_comparison(grouped, grouped.index)
-            if input.plot_type() == "ColorSquare":
-                filtered_df = df.groupby('Organism_Name').apply(filter_and_select).reset_index(drop=True)
-                fig = plot_color_square(filtered_df['Sequence'], filtered_df['Organism_Name'].unique())
-            if input.plot_type() == "Wens Method":
-                fig = wens_method_heatmap(df, df['Organism_Name'].unique())
-            if input.plot_type() == "Chaos Game Representation":
-                filtered_df = df.groupby('Organism_Name').apply(filter_and_select).reset_index(drop=True)
-                fig = plot_fcgr(filtered_df['Sequence'], df['Organism_Name'].unique())
-            if input.plot_type() == "Persistant Homology":
-                filtered_df = df.groupby('Organism_Name').apply(filter_and_select).reset_index(drop=True)
-                fig = plot_persistence_homology(filtered_df['Sequence'], filtered_df['Organism_Name'])
-            return fig
-   # with ui.nav_panel("Viral Model"):
-  #      gr.load("models/Hack90/virus_pythia_31_1024").launch()
-
-    # with ui.nav_panel("Viral Microstructure"):          
+    # with ui.nav_panel("Viral Macrostructure"):
     #     ui.page_opts(fillable=True)
-    #     ui.panel_title("Kmer Distribution")
+    #     ui.panel_title("Do viruses have underlying structure?")
     #     with ui.layout_columns():
     #         with ui.card():
-    #             ui.input_slider("kmer", "kmer", 0, 10, 5)
-    #             ui.input_slider("top_k", "top:", 0, 1000, 15)
-                
     #             ui.input_selectize(  
-    #         "plot_type",  
-    #         "Select metric:",
-    #         ["percentage", "count"],
-    #         multiple=False,  
-    #     )
-        
-        
+    #                 "virus_selector",  
+    #                 "Select your viruses:",
+    #                 virus,
+    #                 multiple=True,  
+    #             )  
+    #         with ui.card():
+    #             ui.input_selectize(  
+    #             "plot_type",  
+    #             "Select your method:",
+    #             ["Chaos Game Representation", "2D Line", "ColorSquare", "Persistant Homology", "Wens Method"],
+    #             multiple=False,  
+    #         )
+                
+    #     ############################################################# Plotting ########################################################
+    #     here = Path(__file__).parent
+    #     import matplotlib as mpl
     #     @render.plot
     #     def plot():  
-    #         df = pd.read_csv('kmers.csv')
-    #         k = input.kmer()
-    #         top_k = input.top_k()
+    #         #ds = load_dataset('Hack90/virus_tiny')
+    #         df = pd.read_parquet('virus_ds.parquet')
+    #         df = df[df['Organism_Name'].isin(input.virus_selector())]
+    #         # group by virus
+    #         grouped = df.groupby('Organism_Name')['Sequence'].apply(list)
+    #         mpl.rcParams.update(mpl.rcParamsDefault)
+            
+    #         # plot the comparison
     #         fig = None
-    #         if input.plot_type() == "count":
-    #             df = df[df['k'] == k]
-    #             df = df.head(top_k)
-    #             fig, ax = plt.subplots()
-    #             ax.bar(df['kmer'], df['count'])
-    #             ax.set_title(f"Most common {k}-mers")
-    #             ax.set_xlabel("K-mer")
-    #             ax.set_ylabel("Count")
-    #             ax.set_xticklabels(df['kmer'], rotation=90)
-    #         if input.plot_type() == "percentage":
-    #             df = df[df['k'] == k]
-    #             df = df.head(top_k)
-    #             fig, ax = plt.subplots()
-    #             ax.bar(df['kmer'], df['percent']*100)
-    #             ax.set_title(f"Most common {k}-mers")
-    #             ax.set_xlabel("K-mer")
-    #             ax.set_ylabel("Percentage")
-    #             ax.set_xticklabels(df['kmer'], rotation=90)
+    #         if input.plot_type() == "2D Line":
+    #             fig = plot_2d_comparison(grouped, grouped.index)
+    #         if input.plot_type() == "ColorSquare":
+    #             filtered_df = df.groupby('Organism_Name').apply(filter_and_select).reset_index(drop=True)
+    #             fig = plot_color_square(filtered_df['Sequence'], filtered_df['Organism_Name'].unique())
+    #         if input.plot_type() == "Wens Method":
+    #             fig = wens_method_heatmap(df, df['Organism_Name'].unique())
+    #         if input.plot_type() == "Chaos Game Representation":
+    #             filtered_df = df.groupby('Organism_Name').apply(filter_and_select).reset_index(drop=True)
+    #             fig = plot_fcgr(filtered_df['Sequence'], df['Organism_Name'].unique())
+    #         if input.plot_type() == "Persistant Homology":
+    #             filtered_df = df.groupby('Organism_Name').apply(filter_and_select).reset_index(drop=True)
+    #             fig = plot_persistence_homology(filtered_df['Sequence'], filtered_df['Organism_Name'])
     #         return fig
+   # with ui.nav_panel("Viral Model"):
+  #      gr.load("models/Hack90/virus_pythia_31_1024").launch()
+
+    with ui.nav_panel("Viral Microstructure"):          
+        ui.page_opts(fillable=True)
+        ui.panel_title("Kmer Distribution")
+        with ui.layout_columns():
+            with ui.card():
+                ui.input_slider("kmer", "kmer", 0, 10, 5)
+                ui.input_slider("top_k", "top:", 0, 1000, 15)
+                
+                ui.input_selectize(  
+            "plot_type",  
+            "Select metric:",
+            ["percentage", "count"],
+            multiple=False,  
+        )
+        
+        
+        @render.plot
+        def plot():  
+            df = pd.read_csv('kmers.csv')
+            k = input.kmer()
+            top_k = input.top_k()
+            fig = None
+            if input.plot_type() == "count":
+                df = df[df['k'] == k]
+                df = df.head(top_k)
+                fig, ax = plt.subplots()
+                ax.bar(df['kmer'], df['count'])
+                ax.set_title(f"Most common {k}-mers")
+                ax.set_xlabel("K-mer")
+                ax.set_ylabel("Count")
+                ax.set_xticklabels(df['kmer'], rotation=90)
+            if input.plot_type() == "percentage":
+                df = df[df['k'] == k]
+                df = df.head(top_k)
+                fig, ax = plt.subplots()
+                ax.bar(df['kmer'], df['percent']*100)
+                ax.set_title(f"Most common {k}-mers")
+                ax.set_xlabel("K-mer")
+                ax.set_ylabel("Percentage")
+                ax.set_xticklabels(df['kmer'], rotation=90)
+            return fig
 
     # with ui.nav_panel("Viral Model Training"):    
     #     ui.page_opts(fillable=True)