Update dashboard.py

dashboard.py

@@ -1,4 +1,5 @@
 
+from flask import Flask, send_from_directory
 import warnings
 warnings.filterwarnings("ignore")
 import io
@@ -31,416 +32,433 @@ from bokeh.resources import INLINE
 from holoviews.operation.timeseries import rolling, rolling_outlier_std
 hv.extension('bokeh')
 
-## LOAD DATASETS
 
-#static_folder = '/static'
-dna_folder = './data'
+# Create a Flask app
+app = Flask(__name__)
 
+# Serve the HTML file from the 'static' directory
+@app.route('/static/<path:path>')
+def serve_static(path):
+    return send_from_directory('static', path)
 
-macro_topics = ["Energy-Efficient Building Design for Thermal Comfort and Sustainability","Indoor Air Quality and Energy Efficiency in Low-Energy Houses","Urban Planning and Development in China\'s Cities", "Design Thinking and Sustainable Product Development", "Smart Cities and Urban Computing", "Urban Resilience and Water Management","Renewable Energy Systems: Solar PV & Building Applications","Exploring the Intersection of Traditional Heritage and Modern Steel Architecture in Historical Buildings","Green Building Assessment and Design","Landscape Design, Planning, and Research: Integrating Cultural, Ecological, and Rural Perspectives", "Noise and Acoustic Design in Urban Development","Sustainable Building Materials: Wood & 3D Printing Innovations","BIM in AEC: Trends, Challenges, and Opportunities","Urban Food Systems: Community Development and Social Sustainability in Cities","Innovative Bridge Design and Construction: Trends and Case Studies", "Cavity Flow and Heat Transfer"]
 
-
-
-## AECO topic over time html file:
-AECO_topics_over_time_file_path = '/static/optimized_merged_AECO_topics_over_time_2D.html'
-
-#### full data unfiltered:
-
-dna_articles_unfiltered_eu_time_indexed_resampled = pd.read_csv(os.path.join(dna_folder, 'dna_articles_unfiltered_eu_time_indexed_resampled.tsv'),sep='\t',header=0)
-dna_articles_unfiltered_us_time_indexed_resampled = pd.read_csv(os.path.join(dna_folder, 'dna_articles_unfiltered_us_time_indexed_resampled.tsv'),sep='\t',header=0)
-dna_articles_unfiltered_eu_us_time_indexed_resampled = pd.read_csv(os.path.join(dna_folder, 'dna_articles_unfiltered_eu_us_time_indexed_resampled.tsv'),sep='\t',header=0)
-
-#### classifier filtered articles:
-
-dh_ration_df_eu = pd.read_csv(os.path.join(dna_folder, 'dh_ration_df_eu.tsv'),sep='\t',header=0)
-dh_ration_df_us = pd.read_csv(os.path.join(dna_folder, 'dh_ration_df_us.tsv'),sep='\t',header=0)
-dh_ration_df_eu_us = pd.read_csv(os.path.join(dna_folder, 'dh_ration_df_eu_us.tsv'),sep='\t',header=0)
-
-regions = ['eu', 'us', 'eu_us']
-
-sorted_ent_type_freq_map_eu=dict()
-sorted_ent_type_freq_map_us=dict()
-sorted_ent_type_freq_map_eu_us=dict()
-
-def read_top_ent_types():
-    reader = csv.reader(open(os.path.join(dna_folder, 'sorted_ent_type_freq_map_eu.tsv'), 'r'))
-    for i,row in enumerate(reader):
-        if i < 20:
-            k, v = row
-            sorted_ent_type_freq_map_eu[k] = int(v)
-    del sorted_ent_type_freq_map_eu['Entity']
-    reader = csv.reader(open(os.path.join(dna_folder, 'sorted_ent_type_freq_map_us.tsv'), 'r'))
-    for i, row in enumerate(reader):
-        if i < 20:
+# Panel-based dashboard
+@app.route('/')
+def dashboard():
+    
+    ## LOAD DATASETS
+    
+    #static_folder = '/static'
+    dna_folder = './data'
+    
+    
+    macro_topics = ["Energy-Efficient Building Design for Thermal Comfort and Sustainability","Indoor Air Quality and Energy Efficiency in Low-Energy Houses","Urban Planning and Development in China\'s Cities", "Design Thinking and Sustainable Product Development", "Smart Cities and Urban Computing", "Urban Resilience and Water Management","Renewable Energy Systems: Solar PV & Building Applications","Exploring the Intersection of Traditional Heritage and Modern Steel Architecture in Historical Buildings","Green Building Assessment and Design","Landscape Design, Planning, and Research: Integrating Cultural, Ecological, and Rural Perspectives", "Noise and Acoustic Design in Urban Development","Sustainable Building Materials: Wood & 3D Printing Innovations","BIM in AEC: Trends, Challenges, and Opportunities","Urban Food Systems: Community Development and Social Sustainability in Cities","Innovative Bridge Design and Construction: Trends and Case Studies", "Cavity Flow and Heat Transfer"]
+    
+    
+    
+    ## AECO topic over time html file:
+    AECO_topics_over_time_file_path = '/static/optimized_merged_AECO_topics_over_time_2D.html'
+    
+    #### full data unfiltered:
+    
+    dna_articles_unfiltered_eu_time_indexed_resampled = pd.read_csv(os.path.join(dna_folder, 'dna_articles_unfiltered_eu_time_indexed_resampled.tsv'),sep='\t',header=0)
+    dna_articles_unfiltered_us_time_indexed_resampled = pd.read_csv(os.path.join(dna_folder, 'dna_articles_unfiltered_us_time_indexed_resampled.tsv'),sep='\t',header=0)
+    dna_articles_unfiltered_eu_us_time_indexed_resampled = pd.read_csv(os.path.join(dna_folder, 'dna_articles_unfiltered_eu_us_time_indexed_resampled.tsv'),sep='\t',header=0)
+    
+    #### classifier filtered articles:
+    
+    dh_ration_df_eu = pd.read_csv(os.path.join(dna_folder, 'dh_ration_df_eu.tsv'),sep='\t',header=0)
+    dh_ration_df_us = pd.read_csv(os.path.join(dna_folder, 'dh_ration_df_us.tsv'),sep='\t',header=0)
+    dh_ration_df_eu_us = pd.read_csv(os.path.join(dna_folder, 'dh_ration_df_eu_us.tsv'),sep='\t',header=0)
+    
+    regions = ['eu', 'us', 'eu_us']
+    
+    sorted_ent_type_freq_map_eu=dict()
+    sorted_ent_type_freq_map_us=dict()
+    sorted_ent_type_freq_map_eu_us=dict()
+    
+    def read_top_ent_types():
+        reader = csv.reader(open(os.path.join(dna_folder, 'sorted_ent_type_freq_map_eu.tsv'), 'r'))
+        for i,row in enumerate(reader):
+            if i < 20:
+                k, v = row
+                sorted_ent_type_freq_map_eu[k] = int(v)
+        del sorted_ent_type_freq_map_eu['Entity']
+        reader = csv.reader(open(os.path.join(dna_folder, 'sorted_ent_type_freq_map_us.tsv'), 'r'))
+        for i, row in enumerate(reader):
+            if i < 20:
+                k, v = row
+                sorted_ent_type_freq_map_us[k] = int(v)
+        del sorted_ent_type_freq_map_us['Entity']
+        reader = csv.reader(open(os.path.join(dna_folder, 'sorted_ent_type_freq_map_eu_us.tsv'), 'r'))
+        for i, row in enumerate(reader):
+            if i < 20:
+                k, v = row
+                sorted_ent_type_freq_map_eu_us[k] = int(v)
+        del sorted_ent_type_freq_map_eu_us['Entity']
+    
+    read_top_ent_types()
+    
+    top_type_filtered_eu = ['DBpedia:Country', 'DBpedia:Organisation', 'DBpedia:Company', 'DBpedia:Person', 'DBpedia:Disease', 'DBpedia:ChemicalSubstance', 'DBpedia:Drug', 'DBpedia:GovernmentAgency', 'DBpedia:City', 'DBpedia:MonoclonalAntibody']
+    top_type_filtered_us = ['DBpedia:Organisation', 'DBpedia:Company', 'DBpedia:Disease', 'DBpedia:ChemicalSubstance', 'DBpedia:Person', 'DBpedia:Drug', 'DBpedia:Country', 'DBpedia:Region', 'DBpedia:MonoclonalAntibody', 'DBpedia:City', 'DBpedia:Biomolecule']
+    top_type_filtered_eu_us =  ['DBpedia:Organisation', 'DBpedia:Company', 'DBpedia:ChemicalSubstance', 'DBpedia:Drug', 'DBpedia:Country', 'DBpedia:Person', 'DBpedia:Disease', 'DBpedia:MonoclonalAntibody', 'DBpedia:GovernmentAgency', 'DBpedia:Biomolecule', 'DBpedia:Gene']
+    
+    dna_healthtech_articles_eu_time_indexed_resampled=pd.read_csv(os.path.join(dna_folder, 'dna_healthtech_articles_eu_time_indexed_resampled.tsv'),sep='\t',header=0)
+    dna_healthtech_articles_us_time_indexed_resampled=pd.read_csv(os.path.join(dna_folder, 'dna_healthtech_articles_us_time_indexed_resampled.tsv'),sep='\t',header=0)
+    dna_healthtech_articles_eu_us_time_indexed_resampled=pd.read_csv(os.path.join(dna_folder, 'dna_healthtech_articles_eu_us_time_indexed_resampled.tsv'),sep='\t',header=0)
+    
+    def read_top_ent_maps():
+        reader = csv.reader(open(os.path.join(dna_folder, 'sorted_ent_freq_map_eu.tsv'), 'r'), delimiter='\t')
+        for row in reader:
+            k,v = row
+            lista = ast.literal_eval(v)
+            dizionario = dict()
+            for pair in lista:
+                dizionario[pair[0]]=pair[1]
+            dizionario = sorted(dizionario.items(), key=lambda x: x[1], reverse=True)
+            ent_freq_maps_eu[k]=dizionario
+    
+        reader = csv.reader(open(os.path.join(dna_folder, 'sorted_ent_freq_map_us.tsv'), 'r'), delimiter='\t')
+        for row in reader:
             k, v = row
-            sorted_ent_type_freq_map_us[k] = int(v)
-    del sorted_ent_type_freq_map_us['Entity']
-    reader = csv.reader(open(os.path.join(dna_folder, 'sorted_ent_type_freq_map_eu_us.tsv'), 'r'))
-    for i, row in enumerate(reader):
-        if i < 20:
+            lista = ast.literal_eval(v)
+            dizionario = dict()
+            for pair in lista:
+                dizionario[pair[0]] = pair[1]
+            dizionario = sorted(dizionario.items(), key=lambda x: x[1], reverse=True)
+            ent_freq_maps_us[k] = dizionario
+    
+        reader = csv.reader(open(os.path.join(dna_folder, 'sorted_ent_freq_map_eu_us.tsv'), 'r'), delimiter='\t')
+        for row in reader:
             k, v = row
-            sorted_ent_type_freq_map_eu_us[k] = int(v)
-    del sorted_ent_type_freq_map_eu_us['Entity']
-
-read_top_ent_types()
-
-top_type_filtered_eu = ['DBpedia:Country', 'DBpedia:Organisation', 'DBpedia:Company', 'DBpedia:Person', 'DBpedia:Disease', 'DBpedia:ChemicalSubstance', 'DBpedia:Drug', 'DBpedia:GovernmentAgency', 'DBpedia:City', 'DBpedia:MonoclonalAntibody']
-top_type_filtered_us = ['DBpedia:Organisation', 'DBpedia:Company', 'DBpedia:Disease', 'DBpedia:ChemicalSubstance', 'DBpedia:Person', 'DBpedia:Drug', 'DBpedia:Country', 'DBpedia:Region', 'DBpedia:MonoclonalAntibody', 'DBpedia:City', 'DBpedia:Biomolecule']
-top_type_filtered_eu_us =  ['DBpedia:Organisation', 'DBpedia:Company', 'DBpedia:ChemicalSubstance', 'DBpedia:Drug', 'DBpedia:Country', 'DBpedia:Person', 'DBpedia:Disease', 'DBpedia:MonoclonalAntibody', 'DBpedia:GovernmentAgency', 'DBpedia:Biomolecule', 'DBpedia:Gene']
-
-dna_healthtech_articles_eu_time_indexed_resampled=pd.read_csv(os.path.join(dna_folder, 'dna_healthtech_articles_eu_time_indexed_resampled.tsv'),sep='\t',header=0)
-dna_healthtech_articles_us_time_indexed_resampled=pd.read_csv(os.path.join(dna_folder, 'dna_healthtech_articles_us_time_indexed_resampled.tsv'),sep='\t',header=0)
-dna_healthtech_articles_eu_us_time_indexed_resampled=pd.read_csv(os.path.join(dna_folder, 'dna_healthtech_articles_eu_us_time_indexed_resampled.tsv'),sep='\t',header=0)
-
-def read_top_ent_maps():
-    reader = csv.reader(open(os.path.join(dna_folder, 'sorted_ent_freq_map_eu.tsv'), 'r'), delimiter='\t')
-    for row in reader:
-        k,v = row
-        lista = ast.literal_eval(v)
-        dizionario = dict()
-        for pair in lista:
-            dizionario[pair[0]]=pair[1]
-        dizionario = sorted(dizionario.items(), key=lambda x: x[1], reverse=True)
-        ent_freq_maps_eu[k]=dizionario
-
-    reader = csv.reader(open(os.path.join(dna_folder, 'sorted_ent_freq_map_us.tsv'), 'r'), delimiter='\t')
-    for row in reader:
-        k, v = row
-        lista = ast.literal_eval(v)
-        dizionario = dict()
-        for pair in lista:
-            dizionario[pair[0]] = pair[1]
-        dizionario = sorted(dizionario.items(), key=lambda x: x[1], reverse=True)
-        ent_freq_maps_us[k] = dizionario
-
-    reader = csv.reader(open(os.path.join(dna_folder, 'sorted_ent_freq_map_eu_us.tsv'), 'r'), delimiter='\t')
-    for row in reader:
-        k, v = row
-        lista = ast.literal_eval(v)
-        dizionario = dict()
-        for pair in lista:
-            dizionario[pair[0]] = pair[1]
-        dizionario = sorted(dizionario.items(), key=lambda x: x[1], reverse=True)
-        ent_freq_maps_eu_us[k] = dizionario
-
-ent_freq_maps_eu = dict()
-ent_freq_maps_us = dict()
-ent_freq_maps_eu_us = dict()
-
-read_top_ent_maps()
-
-
-def read_type_filtered_triples():
-    for t in top_type_filtered_eu:
-        df = pd.read_csv(dna_folder+'/filtered_rows/eu/'+t.replace(':','_')+'.tsv', sep="	", header=0)
-        df.drop(columns=['Unnamed: 0'], inplace=True)
-        top_type_filtered_triples_eu[t]=df
-    for t in top_type_filtered_us:
-        df = pd.read_csv(dna_folder+'/filtered_rows/us/'+t.replace(':','_')+'.tsv', sep="	")
-        df.drop(columns=['Unnamed: 0'], inplace=True)
-        top_type_filtered_triples_us[t]=df
-    for t in top_type_filtered_eu_us:
-        df = pd.read_csv(dna_folder+'/filtered_rows/eu_us/'+t.replace(':','_')+'.tsv', sep="	")
-        df.drop(columns=['Unnamed: 0'], inplace=True)
-        top_type_filtered_triples_eu_us[t]=df
-
-
-
-top_type_filtered_triples_eu = dict()
-top_type_filtered_triples_us = dict()
-top_type_filtered_triples_eu_us = dict()
-
-read_type_filtered_triples()
-
-grouping_filtered = pd.read_csv(os.path.join(dna_folder, 'dna_relations.tsv'), sep="	")
-################################# CREATE CHARTS ############################
-def create_curve_chart():
-    # Create the 3 line plots
-    curve_eu = hv.Curve((dh_ration_df_eu.index, dh_ration_df_eu.ids/dna_articles_unfiltered_eu_time_indexed_resampled.ids), 'Time', 'Digital Health News Ratio',label='EU')
-    curve_us = hv.Curve((dh_ration_df_us.index, dh_ration_df_us.ids/dna_articles_unfiltered_us_time_indexed_resampled.ids),'Time', 'Digital Health News Ratio', label='US')
-    curve_eu_us = hv.Curve((dh_ration_df_eu_us.index, dh_ration_df_eu_us.ids/dna_articles_unfiltered_eu_us_time_indexed_resampled.ids),'Time', 'Digital Health News Ratio', label='EU-US')
-    #Overlay the line plots
-    overlay = curve_eu * curve_us * curve_eu_us
-    overlay.opts(show_legend = True, legend_position='top_left', width=1200, height=600)
-    return overlay
-
-
-def create_bar_charts(region, **kwargs):
-  if region=='eu':
-    sliced = sorted_ent_type_freq_map_eu
-    return hv.Bars(sliced, hv.Dimension('Entity Types'), 'Frequency').opts( framewise=True, xrotation=45,width=1200, height=600)
-  elif region=='us':
-    sliced = sorted_ent_type_freq_map_us
-    return hv.Bars(sliced, hv.Dimension('Entity Types'), 'Frequency').opts(framewise=True, xrotation=45,width=1200, height=600)
-  elif region=='eu_us':
-    sliced = sorted_ent_type_freq_map_eu_us
-    return hv.Bars(sliced, hv.Dimension('Entity Types'), 'Frequency').opts(framewise=True, xrotation=45,width=1200, height=600)
-
-
-
-# Define a function to generate Curve based on selected values
-def generate_entity_curves(region_value, type_value, **kwargs):
-  if region_value=='eu':
-    top20Ents = ent_freq_maps_eu[type_value]
-    curveList = []
-    for ent in top20Ents:
-      entityTriples =  top_type_filtered_triples_eu[type_value][(top_type_filtered_triples_eu[type_value]['subjEntityLinks']==ent[0]) | (top_type_filtered_triples_eu[type_value]['objEntityLinks']==ent[0])]
-      entityTriples_time_indexed = entityTriples.set_index(pd.DatetimeIndex(entityTriples['timestamp']), inplace=False)
-      del entityTriples_time_indexed['timestamp']
-      entityTriples_time_indexed_resampled = entityTriples_time_indexed.resample("Y").count()
-      #print(entityTriples_time_indexed_resampled)
-      entityTriples_time_indexed_resampled = entityTriples_time_indexed_resampled.reindex(dna_healthtech_articles_eu_time_indexed_resampled.index, fill_value=0)
-      curve = hv.Curve((entityTriples_time_indexed_resampled.index, (entityTriples_time_indexed_resampled['doc_id']/dna_healthtech_articles_eu_time_indexed_resampled['ids'])), 'Time', 'Key Entity Occurrence', label=ent[0])
-      curve.opts(autorange='y')
-      #curve.opts(logy=True)
-      curveList.append(curve)
-    overlay = hv.Overlay(curveList)
-    overlay.opts(legend_muted=False, legend_cols=4, show_legend = True, legend_position='top_left', fontsize={'legend':13},width=1200, height=800)
-    return overlay
-
-  elif region_value=='us':
-    top20Ents = ent_freq_maps_us[type_value]
-    curveList = []
-    for ent in top20Ents:
-      entityTriples =  top_type_filtered_triples_us[type_value][(top_type_filtered_triples_us[type_value]['subjEntityLinks']==ent[0]) | (top_type_filtered_triples_us[type_value]['objEntityLinks']==ent[0])]
-      entityTriples_time_indexed = entityTriples.set_index(pd.DatetimeIndex(entityTriples['timestamp']), inplace=False)
-      del entityTriples_time_indexed['timestamp']
-      entityTriples_time_indexed_resampled = entityTriples_time_indexed_resampled.reindex(dna_healthtech_articles_us_time_indexed_resampled.index, fill_value=0)
-      curve = hv.Curve((entityTriples_time_indexed_resampled.index, (entityTriples_time_indexed_resampled['doc_id']/dna_healthtech_articles_us_time_indexed_resampled['ids'])), 'Time', 'Key Entity Occurrence', label=ent[0])
-      curve.opts(autorange='y')
-      curveList.append(curve)
-    overlay = hv.Overlay(curveList)
-    overlay.opts(legend_muted=False, legend_cols=4, show_legend = True, legend_position='top_left', fontsize={'legend':13},width=1200, height=800)
-    return overlay
-
-  elif region_value=='eu_us':
-    top20Ents = ent_freq_maps_eu_us[type_value]
-    curveList = []
-    for ent in top20Ents:
-      entityTriples =  top_type_filtered_triples_eu_us[type_value][(top_type_filtered_triples_eu_us[type_value]['subjEntityLinks']==ent[0]) | (top_type_filtered_triples_eu_us[type_value]['objEntityLinks']==ent[0])]
-      entityTriples_time_indexed = entityTriples.set_index(pd.DatetimeIndex(entityTriples['timestamp']), inplace=False)
-      del entityTriples_time_indexed['timestamp']
-      entityTriples_time_indexed_resampled = entityTriples_time_indexed_resampled.reindex(dna_healthtech_articles_eu_us_time_indexed_resampled.index, fill_value=0)
-      curve = hv.Curve((entityTriples_time_indexed_resampled.index, (entityTriples_time_indexed_resampled['doc_id']/dna_healthtech_articles_eu_us_time_indexed_resampled['ids'])), 'Time', 'Key Entity Occurrence', label=ent[0])
-      curve.opts(autorange='y')
-      curveList.append(curve)
-    overlay = hv.Overlay(curveList)
-    overlay.opts(legend_muted=False, legend_cols=4, show_legend = True, legend_position='top_left', fontsize={'legend':13},width=1200, height=800)
-    return overlay
-
-
-############################# WIDGETS & CALLBACK ###########################################
-
-def filter_data0(df, min_value):
-    filtered_df = df[df['value'] >= min_value]
-    return filtered_df
-
-
-def plot_chord0_new(df,min_value):
-    filtered_df = filter_data0(df, min_value)
-  # Create a Holoviews Dataset for nodes
-    nodes = hv.Dataset(filtered_df, 'index')
-    nodes.data.head()
-    chord = hv.Chord(filtered_df, ['source', 'target'], ['value'])
-    return chord.opts(opts.Chord(cmap='Category20', edge_cmap='Category20', label_text_color="white",  node_color = hv.dim('index').str(),  edge_color = hv.dim('source').str(), labels = 'index', tools=['hover'],   width=800, height=800))
-
-
-def retrieveRegionTypes(region):
-  if region == 'eu':
-    return top_type_filtered_eu
-  elif region == 'us':
-    return top_type_filtered_us
-  elif region == 'eu_us':
-    return top_type_filtered_eu_us
-
-
-def filter_region(region):
-    if region == 'eu':
-        region_grouping = grouping_filtered[grouping_filtered['region'] == 'eu']
-    elif region == 'us':
-        region_grouping = grouping_filtered[grouping_filtered['region'] == 'us']
-    elif region == 'eu_us':
-        region_grouping = grouping_filtered[grouping_filtered['region'] == 'eu_us']
-
-    #print(len(region_grouping))
-    # Define range for minimum value slider
-    min_value_range = region_grouping['value'].unique()
-    min_value_range.sort()
-
-    # Define HoloMap with minimum value and attribute as key dimensions
-    holomap = hv.HoloMap({min_value: plot_chord0_new(region_grouping, min_value)
-                          for min_value in min_value_range},
-                         kdims=['Show triples with support greater than']
-                         )
-    return holomap
-
-
-# Define a function to generate Entity List RadioButtonGroup based on Region selection
-def generate_radio_buttons(value):
-    if value == 'eu':
-        return pn.widgets.RadioButtonGroup(options=retrieveRegionTypes(value), value='DBpedia:Company', name='eu', orientation='vertical')
-    elif value == 'us':
-        return pn.widgets.RadioButtonGroup(options=retrieveRegionTypes(value), value='DBpedia:Disease', name='us', orientation='vertical')
-    elif value == 'eu_us':
-        return pn.widgets.RadioButtonGroup(options=retrieveRegionTypes(value), value='DBpedia:Person', name='eu_us', orientation='vertical')
-
-
-
-# https://tabler-icons.io/
-button1 = pn.widgets.Button(name="Introduction", button_type="warning", icon="file-info", styles={"width": "100%"})
-button2 = pn.widgets.Button(name="AECO Macro Topics", button_type="warning",  icon="chart-histogram", styles={"width": "100%"})
-#button3 = pn.widgets.Button(name="Top Entity Types", button_type="warning", icon="chart-bar", styles={"width": "100%"})
-#button4 = pn.widgets.Button(name="Top Key Entities", button_type="warning", icon="chart-dots-filled", styles={"width": "100%"})
-#button5 = pn.widgets.Button(name="Entity Chord Diagrams", button_type="warning", icon="chart-dots-filled", styles={"width": "100%"})
-#button3 = pn.widgets.Button(name="Research Collaboration Networks: Institutes", button_type="warning", icon="chart-dots-3", styles={"width": "100%"})
-#button4 = pn.widgets.Button(name="Research Collaboration Networks: Authors", button_type="warning", icon="chart-dots-3", styles={"width": "100%"})
-
-
-region1 = pn.widgets.RadioButtonGroup(name='### Select News Region', options=regions)
-
-macro_topics_button = pn.widgets.RadioButtonGroup(name='### Select Macro Topic', value='Energy-Efficient Building Design for Thermal Comfort and Sustainability', options=macro_topics)
-
-
-# Initial RadioButtonGroup
-radio_buttons_regions =  pn.widgets.RadioButtonGroup(options=regions,value='eu',name='Select region')
-# Generate initial dynamic RadioButtonGroup
-radio_buttons_types  = generate_radio_buttons(radio_buttons_regions.value)
-
-
-
-# Define a callback function to update the panel dynamically
-def update_radio_group(event):
-  #print(event.new)
-  #print(retrieveRegionTypes(event.new))
-  radio_buttons_types.options = retrieveRegionTypes(event.new)
-
-
-# bind the function to the widget(s)
-dmap2 = hv.DynamicMap(pn.bind(generate_entity_curves, radio_buttons_regions,radio_buttons_types))
-# Bind the selected value of the first RadioButtonGroup to update the second RadioButtonGroup
-radio_buttons_regions.param.watch(update_radio_group, 'value')
-
-# Define the callback function to update the HoloMap
-def update_holomap(event):
-    initial_holomap.object = filter_region(event.new)
-
-region_radio_button = pn.widgets.RadioButtonGroup(options=regions, value='eu', name='Select Region')
-
-# Create the initial HoloMap
-initial_holomap = filter_region(region_radio_button.value)
-
-# Bind the callback function to the value change event of the RadioButton widget
-region_radio_button.param.watch(update_holomap, 'value')
-
-
-
-def show_page(page_key):
-    main_area.clear()
-    main_area.append(mapping[page_key])
-
-button1.on_click(lambda event: show_page("Page1"))
-button2.on_click(lambda event: show_page("Page2"))
-#button3.on_click(lambda event: show_page("Page3"))
-#button4.on_click(lambda event: show_page("Page4"))
-#button5.on_click(lambda event: show_page("Page5"))
-#button6.on_click(lambda event: show_page("Page6"))
-
-
-### CREATE PAGE LAYOUTS
-
-def CreatePage1():
-    return pn.Column(pn.pane.Markdown("""
-
-This is a dashboard for a Research Analysis project regarding research and technology in the AECO domain. The source data consists of around 
-276k English-language research papers gathered from the openalex.org graph database, covering a timeframe from 2011 through 2024.
-
----------------------------
-
-## 1. AECO Macro Topics
-In the AECO Macro Topics panel we present the 6-month-sampled time series depicting the number of published research papers 
-for the 16 macro-topics automatically detected by an optimized BerTopic model and ppst-processed for manual topic merging.
-
-
-### 2. Research Collaboration Networks: Institutes
-
-### 3. Research Collaboration Networks: Authors
-""", width=800), align="center")
-
-def CreatePage2():
-    # Load the HTML content from the local file
-    #with open(AECO_topics_over_time_file_path, 'r', encoding='utf-8') as file:
-    #    html_content = file.read()
-    # Use an iframe to load the local HTML file
-    iframe_html = f'<iframe src="{AECO_topics_over_time_file_path}" width="100%" height="600px"></iframe>'
-    # Create an HTML pane to render the content
-    html_pane = pn.pane.HTML(iframe_html , sizing_mode='stretch_width')
-    return pn.Column(pn.pane.Markdown(" ## AECO Macro Topics "), html_pane, align="center")
-
-def CreatePage3():
-    return pn.Column(
-    macro_topics_button,
-        pn.bind(create_bar_charts, region1),
-        align="center",
+            lista = ast.literal_eval(v)
+            dizionario = dict()
+            for pair in lista:
+                dizionario[pair[0]] = pair[1]
+            dizionario = sorted(dizionario.items(), key=lambda x: x[1], reverse=True)
+            ent_freq_maps_eu_us[k] = dizionario
+    
+    ent_freq_maps_eu = dict()
+    ent_freq_maps_us = dict()
+    ent_freq_maps_eu_us = dict()
+    
+    read_top_ent_maps()
+    
+    
+    def read_type_filtered_triples():
+        for t in top_type_filtered_eu:
+            df = pd.read_csv(dna_folder+'/filtered_rows/eu/'+t.replace(':','_')+'.tsv', sep="	", header=0)
+            df.drop(columns=['Unnamed: 0'], inplace=True)
+            top_type_filtered_triples_eu[t]=df
+        for t in top_type_filtered_us:
+            df = pd.read_csv(dna_folder+'/filtered_rows/us/'+t.replace(':','_')+'.tsv', sep="	")
+            df.drop(columns=['Unnamed: 0'], inplace=True)
+            top_type_filtered_triples_us[t]=df
+        for t in top_type_filtered_eu_us:
+            df = pd.read_csv(dna_folder+'/filtered_rows/eu_us/'+t.replace(':','_')+'.tsv', sep="	")
+            df.drop(columns=['Unnamed: 0'], inplace=True)
+            top_type_filtered_triples_eu_us[t]=df
+    
+    
+    
+    top_type_filtered_triples_eu = dict()
+    top_type_filtered_triples_us = dict()
+    top_type_filtered_triples_eu_us = dict()
+    
+    read_type_filtered_triples()
+    
+    grouping_filtered = pd.read_csv(os.path.join(dna_folder, 'dna_relations.tsv'), sep="	")
+    ################################# CREATE CHARTS ############################
+    def create_curve_chart():
+        # Create the 3 line plots
+        curve_eu = hv.Curve((dh_ration_df_eu.index, dh_ration_df_eu.ids/dna_articles_unfiltered_eu_time_indexed_resampled.ids), 'Time', 'Digital Health News Ratio',label='EU')
+        curve_us = hv.Curve((dh_ration_df_us.index, dh_ration_df_us.ids/dna_articles_unfiltered_us_time_indexed_resampled.ids),'Time', 'Digital Health News Ratio', label='US')
+        curve_eu_us = hv.Curve((dh_ration_df_eu_us.index, dh_ration_df_eu_us.ids/dna_articles_unfiltered_eu_us_time_indexed_resampled.ids),'Time', 'Digital Health News Ratio', label='EU-US')
+        #Overlay the line plots
+        overlay = curve_eu * curve_us * curve_eu_us
+        overlay.opts(show_legend = True, legend_position='top_left', width=1200, height=600)
+        return overlay
+    
+    
+    def create_bar_charts(region, **kwargs):
+      if region=='eu':
+        sliced = sorted_ent_type_freq_map_eu
+        return hv.Bars(sliced, hv.Dimension('Entity Types'), 'Frequency').opts( framewise=True, xrotation=45,width=1200, height=600)
+      elif region=='us':
+        sliced = sorted_ent_type_freq_map_us
+        return hv.Bars(sliced, hv.Dimension('Entity Types'), 'Frequency').opts(framewise=True, xrotation=45,width=1200, height=600)
+      elif region=='eu_us':
+        sliced = sorted_ent_type_freq_map_eu_us
+        return hv.Bars(sliced, hv.Dimension('Entity Types'), 'Frequency').opts(framewise=True, xrotation=45,width=1200, height=600)
+    
+    
+    
+    # Define a function to generate Curve based on selected values
+    def generate_entity_curves(region_value, type_value, **kwargs):
+      if region_value=='eu':
+        top20Ents = ent_freq_maps_eu[type_value]
+        curveList = []
+        for ent in top20Ents:
+          entityTriples =  top_type_filtered_triples_eu[type_value][(top_type_filtered_triples_eu[type_value]['subjEntityLinks']==ent[0]) | (top_type_filtered_triples_eu[type_value]['objEntityLinks']==ent[0])]
+          entityTriples_time_indexed = entityTriples.set_index(pd.DatetimeIndex(entityTriples['timestamp']), inplace=False)
+          del entityTriples_time_indexed['timestamp']
+          entityTriples_time_indexed_resampled = entityTriples_time_indexed.resample("Y").count()
+          #print(entityTriples_time_indexed_resampled)
+          entityTriples_time_indexed_resampled = entityTriples_time_indexed_resampled.reindex(dna_healthtech_articles_eu_time_indexed_resampled.index, fill_value=0)
+          curve = hv.Curve((entityTriples_time_indexed_resampled.index, (entityTriples_time_indexed_resampled['doc_id']/dna_healthtech_articles_eu_time_indexed_resampled['ids'])), 'Time', 'Key Entity Occurrence', label=ent[0])
+          curve.opts(autorange='y')
+          #curve.opts(logy=True)
+          curveList.append(curve)
+        overlay = hv.Overlay(curveList)
+        overlay.opts(legend_muted=False, legend_cols=4, show_legend = True, legend_position='top_left', fontsize={'legend':13},width=1200, height=800)
+        return overlay
+    
+      elif region_value=='us':
+        top20Ents = ent_freq_maps_us[type_value]
+        curveList = []
+        for ent in top20Ents:
+          entityTriples =  top_type_filtered_triples_us[type_value][(top_type_filtered_triples_us[type_value]['subjEntityLinks']==ent[0]) | (top_type_filtered_triples_us[type_value]['objEntityLinks']==ent[0])]
+          entityTriples_time_indexed = entityTriples.set_index(pd.DatetimeIndex(entityTriples['timestamp']), inplace=False)
+          del entityTriples_time_indexed['timestamp']
+          entityTriples_time_indexed_resampled = entityTriples_time_indexed_resampled.reindex(dna_healthtech_articles_us_time_indexed_resampled.index, fill_value=0)
+          curve = hv.Curve((entityTriples_time_indexed_resampled.index, (entityTriples_time_indexed_resampled['doc_id']/dna_healthtech_articles_us_time_indexed_resampled['ids'])), 'Time', 'Key Entity Occurrence', label=ent[0])
+          curve.opts(autorange='y')
+          curveList.append(curve)
+        overlay = hv.Overlay(curveList)
+        overlay.opts(legend_muted=False, legend_cols=4, show_legend = True, legend_position='top_left', fontsize={'legend':13},width=1200, height=800)
+        return overlay
+    
+      elif region_value=='eu_us':
+        top20Ents = ent_freq_maps_eu_us[type_value]
+        curveList = []
+        for ent in top20Ents:
+          entityTriples =  top_type_filtered_triples_eu_us[type_value][(top_type_filtered_triples_eu_us[type_value]['subjEntityLinks']==ent[0]) | (top_type_filtered_triples_eu_us[type_value]['objEntityLinks']==ent[0])]
+          entityTriples_time_indexed = entityTriples.set_index(pd.DatetimeIndex(entityTriples['timestamp']), inplace=False)
+          del entityTriples_time_indexed['timestamp']
+          entityTriples_time_indexed_resampled = entityTriples_time_indexed_resampled.reindex(dna_healthtech_articles_eu_us_time_indexed_resampled.index, fill_value=0)
+          curve = hv.Curve((entityTriples_time_indexed_resampled.index, (entityTriples_time_indexed_resampled['doc_id']/dna_healthtech_articles_eu_us_time_indexed_resampled['ids'])), 'Time', 'Key Entity Occurrence', label=ent[0])
+          curve.opts(autorange='y')
+          curveList.append(curve)
+        overlay = hv.Overlay(curveList)
+        overlay.opts(legend_muted=False, legend_cols=4, show_legend = True, legend_position='top_left', fontsize={'legend':13},width=1200, height=800)
+        return overlay
+    
+    
+    ############################# WIDGETS & CALLBACK ###########################################
+    
+    def filter_data0(df, min_value):
+        filtered_df = df[df['value'] >= min_value]
+        return filtered_df
+    
+    
+    def plot_chord0_new(df,min_value):
+        filtered_df = filter_data0(df, min_value)
+      # Create a Holoviews Dataset for nodes
+        nodes = hv.Dataset(filtered_df, 'index')
+        nodes.data.head()
+        chord = hv.Chord(filtered_df, ['source', 'target'], ['value'])
+        return chord.opts(opts.Chord(cmap='Category20', edge_cmap='Category20', label_text_color="white",  node_color = hv.dim('index').str(),  edge_color = hv.dim('source').str(), labels = 'index', tools=['hover'],   width=800, height=800))
+    
+    
+    def retrieveRegionTypes(region):
+      if region == 'eu':
+        return top_type_filtered_eu
+      elif region == 'us':
+        return top_type_filtered_us
+      elif region == 'eu_us':
+        return top_type_filtered_eu_us
+    
+    
+    def filter_region(region):
+        if region == 'eu':
+            region_grouping = grouping_filtered[grouping_filtered['region'] == 'eu']
+        elif region == 'us':
+            region_grouping = grouping_filtered[grouping_filtered['region'] == 'us']
+        elif region == 'eu_us':
+            region_grouping = grouping_filtered[grouping_filtered['region'] == 'eu_us']
+    
+        #print(len(region_grouping))
+        # Define range for minimum value slider
+        min_value_range = region_grouping['value'].unique()
+        min_value_range.sort()
+    
+        # Define HoloMap with minimum value and attribute as key dimensions
+        holomap = hv.HoloMap({min_value: plot_chord0_new(region_grouping, min_value)
+                              for min_value in min_value_range},
+                             kdims=['Show triples with support greater than']
+                             )
+        return holomap
+    
+    
+    # Define a function to generate Entity List RadioButtonGroup based on Region selection
+    def generate_radio_buttons(value):
+        if value == 'eu':
+            return pn.widgets.RadioButtonGroup(options=retrieveRegionTypes(value), value='DBpedia:Company', name='eu', orientation='vertical')
+        elif value == 'us':
+            return pn.widgets.RadioButtonGroup(options=retrieveRegionTypes(value), value='DBpedia:Disease', name='us', orientation='vertical')
+        elif value == 'eu_us':
+            return pn.widgets.RadioButtonGroup(options=retrieveRegionTypes(value), value='DBpedia:Person', name='eu_us', orientation='vertical')
+    
+    
+    
+    # https://tabler-icons.io/
+    button1 = pn.widgets.Button(name="Introduction", button_type="warning", icon="file-info", styles={"width": "100%"})
+    button2 = pn.widgets.Button(name="AECO Macro Topics", button_type="warning",  icon="chart-histogram", styles={"width": "100%"})
+    #button3 = pn.widgets.Button(name="Top Entity Types", button_type="warning", icon="chart-bar", styles={"width": "100%"})
+    #button4 = pn.widgets.Button(name="Top Key Entities", button_type="warning", icon="chart-dots-filled", styles={"width": "100%"})
+    #button5 = pn.widgets.Button(name="Entity Chord Diagrams", button_type="warning", icon="chart-dots-filled", styles={"width": "100%"})
+    #button3 = pn.widgets.Button(name="Research Collaboration Networks: Institutes", button_type="warning", icon="chart-dots-3", styles={"width": "100%"})
+    #button4 = pn.widgets.Button(name="Research Collaboration Networks: Authors", button_type="warning", icon="chart-dots-3", styles={"width": "100%"})
+    
+    
+    region1 = pn.widgets.RadioButtonGroup(name='### Select News Region', options=regions)
+    
+    macro_topics_button = pn.widgets.RadioButtonGroup(name='### Select Macro Topic', value='Energy-Efficient Building Design for Thermal Comfort and Sustainability', options=macro_topics)
+    
+    
+    # Initial RadioButtonGroup
+    radio_buttons_regions =  pn.widgets.RadioButtonGroup(options=regions,value='eu',name='Select region')
+    # Generate initial dynamic RadioButtonGroup
+    radio_buttons_types  = generate_radio_buttons(radio_buttons_regions.value)
+    
+    
+    
+    # Define a callback function to update the panel dynamically
+    def update_radio_group(event):
+      #print(event.new)
+      #print(retrieveRegionTypes(event.new))
+      radio_buttons_types.options = retrieveRegionTypes(event.new)
+    
+    
+    # bind the function to the widget(s)
+    dmap2 = hv.DynamicMap(pn.bind(generate_entity_curves, radio_buttons_regions,radio_buttons_types))
+    # Bind the selected value of the first RadioButtonGroup to update the second RadioButtonGroup
+    radio_buttons_regions.param.watch(update_radio_group, 'value')
+    
+    # Define the callback function to update the HoloMap
+    def update_holomap(event):
+        initial_holomap.object = filter_region(event.new)
+    
+    region_radio_button = pn.widgets.RadioButtonGroup(options=regions, value='eu', name='Select Region')
+    
+    # Create the initial HoloMap
+    initial_holomap = filter_region(region_radio_button.value)
+    
+    # Bind the callback function to the value change event of the RadioButton widget
+    region_radio_button.param.watch(update_holomap, 'value')
+    
+    
+    
+    def show_page(page_key):
+        main_area.clear()
+        main_area.append(mapping[page_key])
+    
+    button1.on_click(lambda event: show_page("Page1"))
+    button2.on_click(lambda event: show_page("Page2"))
+    #button3.on_click(lambda event: show_page("Page3"))
+    #button4.on_click(lambda event: show_page("Page4"))
+    #button5.on_click(lambda event: show_page("Page5"))
+    #button6.on_click(lambda event: show_page("Page6"))
+    
+    
+    ### CREATE PAGE LAYOUTS
+    
+    def CreatePage1():
+        return pn.Column(pn.pane.Markdown("""
+    
+    This is a dashboard for a Research Analysis project regarding research and technology in the AECO domain. The source data consists of around 
+    276k English-language research papers gathered from the openalex.org graph database, covering a timeframe from 2011 through 2024.
+    
+    ---------------------------
+    
+    ## 1. AECO Macro Topics
+    In the AECO Macro Topics panel we present the 6-month-sampled time series depicting the number of published research papers 
+    for the 16 macro-topics automatically detected by an optimized BerTopic model and ppst-processed for manual topic merging.
+    
+    
+    ### 2. Research Collaboration Networks: Institutes
+    
+    ### 3. Research Collaboration Networks: Authors
+    """, width=800), align="center")
+    
+    def CreatePage2():
+        # Load the HTML content from the local file
+        #with open(AECO_topics_over_time_file_path, 'r', encoding='utf-8') as file:
+        #    html_content = file.read()
+        # Use an iframe to load the local HTML file
+        iframe_html = f'<iframe src="{AECO_topics_over_time_file_path}" width="100%" height="600px"></iframe>'
+        # Create an HTML pane to render the content
+        html_pane = pn.pane.HTML(iframe_html , sizing_mode='stretch_width')
+        return pn.Column(pn.pane.Markdown(" ## AECO Macro Topics "), html_pane, align="center")
+    
+    def CreatePage3():
+        return pn.Column(
+        macro_topics_button,
+            pn.bind(create_bar_charts, region1),
+            align="center",
+        )
+    
+    def CreatePage4():
+        return pn.Column(
+            pn.pane.Markdown("## Top Key Entities "),
+            pn.Row(pn.Column(radio_buttons_regions, radio_buttons_types), dmap2),
+            align="center", )
+    
+    def CreatePage5():
+        return pn.Column(
+            pn.pane.Markdown("## Entity Chord Diagrams "),
+            pn.Row(region_radio_button, pn.bind(filter_region, region_radio_button)),
+            align="center", )
+    
+    
+    def CreatePage6():
+        html = """<iframe src="https://app.vosviewer.com/?json=https%3A%2F%2Fdrive.google.com%2Fuc%3Fid%3D16q1oLQyEeMosAgeD9UkC9hSrpzAYX_-n" width="800" height="800"></iframe>"""
+    
+        html_pane = pn.pane.HTML(html)
+        #url = 'https://app.vosviewer.com/?json=https%3A%2F%2Fdrive.google.com%2Fuc%3Fid%3D16q1oLQyEeMosAgeD9UkC9hSrpzAYX_-n'
+        return html_pane
+        #panel.show()
+        #return pn.Column(
+        #    pn.pane.Markdown("## VOSViewer Network "),
+         #   pn.Row(panel)
+         #   )
+    
+        
+    mapping = {
+        "Page1": CreatePage1(),
+        "Page2": CreatePage2(),
+        #"Page3": CreatePage3(),
+        #"Page4": CreatePage4(),
+        #"Page5": CreatePage5(),
+        "Page6": CreatePage6()
+    }
+    
+    #################### SIDEBAR LAYOUT ##########################
+    sidebar = pn.Column(pn.pane.Markdown("## Pages"), button1,button2,#button3,
+                        #button4,
+                        #button5,
+                        #button6,
+    					styles={"width": "100%", "padding": "15px"})
+    
+    #################### MAIN AREA LAYOUT ##########################
+    main_area = pn.Column(mapping["Page1"], styles={"width":"100%"})
+    
+    ###################### APP LAYOUT ##############################
+    template = pn.template.BootstrapTemplate(
+        title=" AECO Tech Dashboard",
+        sidebar=[sidebar],
+        main=[main_area],
+        header_background="black",
+        #site="Charting the Landscape of AECO Research",
+        theme=pn.template.DarkTheme,
+        sidebar_width=250, ## Default is 330
+        busy_indicator=pn.indicators.BooleanStatus(value=True),
     )
-
-def CreatePage4():
-    return pn.Column(
-        pn.pane.Markdown("## Top Key Entities "),
-        pn.Row(pn.Column(radio_buttons_regions, radio_buttons_types), dmap2),
-        align="center", )
-
-def CreatePage5():
-    return pn.Column(
-        pn.pane.Markdown("## Entity Chord Diagrams "),
-        pn.Row(region_radio_button, pn.bind(filter_region, region_radio_button)),
-        align="center", )
-
-
-def CreatePage6():
-    html = """<iframe src="https://app.vosviewer.com/?json=https%3A%2F%2Fdrive.google.com%2Fuc%3Fid%3D16q1oLQyEeMosAgeD9UkC9hSrpzAYX_-n" width="800" height="800"></iframe>"""
-
-    html_pane = pn.pane.HTML(html)
-    #url = 'https://app.vosviewer.com/?json=https%3A%2F%2Fdrive.google.com%2Fuc%3Fid%3D16q1oLQyEeMosAgeD9UkC9hSrpzAYX_-n'
-    return html_pane
-    #panel.show()
-    #return pn.Column(
-    #    pn.pane.Markdown("## VOSViewer Network "),
-     #   pn.Row(panel)
-     #   )
-
     
-mapping = {
-    "Page1": CreatePage1(),
-    "Page2": CreatePage2(),
-    #"Page3": CreatePage3(),
-    #"Page4": CreatePage4(),
-    #"Page5": CreatePage5(),
-    "Page6": CreatePage6()
-}
-
-#################### SIDEBAR LAYOUT ##########################
-sidebar = pn.Column(pn.pane.Markdown("## Pages"), button1,button2,#button3,
-                    #button4,
-                    #button5,
-                    #button6,
-					styles={"width": "100%", "padding": "15px"})
-
-#################### MAIN AREA LAYOUT ##########################
-main_area = pn.Column(mapping["Page1"], styles={"width":"100%"})
-
-###################### APP LAYOUT ##############################
-template = pn.template.BootstrapTemplate(
-    title=" AECO Tech Dashboard",
-    sidebar=[sidebar],
-    main=[main_area],
-    header_background="black",
-    #site="Charting the Landscape of AECO Research",
-    theme=pn.template.DarkTheme,
-    sidebar_width=250, ## Default is 330
-    busy_indicator=pn.indicators.BooleanStatus(value=True),
-)
-
-### DEPLOY APP
+    ### DEPLOY APP
+    
+    # Serve the Panel app
+    template.servable()
 
-# Serve the Panel app
-template.servable()
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=7860)