import json import warnings from bokeh.models import DatetimeTicker, DatetimeTickFormatter warnings.filterwarnings("ignore") import io import os import time import warnings warnings.simplefilter(action='ignore', category=FutureWarning) warnings.simplefilter(action='ignore', category=RuntimeWarning) import pandas as pd import csv import ast from tqdm import tqdm from operator import itemgetter import numpy as np import re import datetime import html from joblib import Parallel, delayed import matplotlib.pyplot as plt import matplotlib.dates as mdates #plt.style.use('seaborn-paper') import holoviews as hv from holoviews import opts, dim from bokeh.sampledata.les_mis import data from bokeh.io import show from bokeh.sampledata.les_mis import data import panel as pn import bokeh from bokeh.resources import INLINE from holoviews.operation.timeseries import rolling, rolling_outlier_std hv.extension('bokeh') from scipy.ndimage import gaussian_filter1d ## LOAD DATASETS data_folder = './data' country_name_df = pd.read_csv(os.path.join(data_folder, 'country_name_map.tsv'), header=0, sep='\t', lineterminator='\n', low_memory=False) country_name_map = dict(zip(country_name_df.Country_Code, country_name_df.Country_Name)) total_publications_time_indexed = pd.read_csv(os.path.join(data_folder, 'total_publications_time_indexed.tsv'), header=0, sep='\t', lineterminator='\n', low_memory=False) country_publications_time_indexed = pd.read_csv(os.path.join(data_folder, 'country_publications_time_indexed.tsv'), header=0, sep='\t', lineterminator='\n', low_memory=False) ## AECO topic over time html file: AECO_topics_over_time_file_path = '/assets/optimized_merged_AECO_topics_over_time_2D_gpt_labels.html' AECO_topics_dendogram_file_path = '/assets/topic_hierarchy_optimal_params.htm' AECO_topic_map_path = '/assets/document_datamap_ver2.html' 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(data_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(data_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(data_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'] def read_top_ent_maps(): reader = csv.reader(open(os.path.join(data_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(data_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(data_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(data_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(data_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(data_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(data_folder, 'dna_relations.tsv'), sep=" ") def load_topic2toptasks(): with open(os.path.join(data_folder+'/time_series','topic2toptasks.json'), "r", encoding="utf-8") as file: mapping = json.load(file) return mapping def loadTaskMethodTimeSeries(topic,task): task_method_ts = pd.read_csv(os.path.join(data_folder+'/time_series', f"""{topic}_{task}_time_series.csv"""), header=0, sep=',', lineterminator='\n', low_memory=False) task_method_ts.set_index(task_method_ts.columns[0], inplace=True) return task_method_ts def loadTaskTimeSeries(topic): #cluster_{cluster_id}_TASK_time_series.csv task_ts = pd.read_csv(os.path.join(data_folder+'/time_series', f"""cluster_{topic}_TASK_time_series.csv"""), header=0, sep=',', lineterminator='\n', low_memory=False) task_ts.set_index(task_ts.columns[0], inplace=True) return task_ts def loadMethodTimeSeries(topic): method_ts = pd.read_csv(os.path.join(data_folder+'/time_series', f"""cluster_{topic}_METHOD_time_series.csv"""), header=0, sep=',', lineterminator='\n', low_memory=False) method_ts.set_index(method_ts.columns[0], inplace=True) return method_ts ################################# CREATE CHARTS ############################ ################################# CREATE CHARTS ############################ # Hook function to customize x-axis for Bokeh def customize_x_axis_bokeh(plot, element): bokeh_plot = plot.state bokeh_plot.xaxis.formatter = DatetimeTickFormatter(months='%m%Y') bokeh_plot.xaxis.ticker.desired_num_ticks = 12 def create_publication_curve_chart(): country_name_df = pd.read_csv(os.path.join(data_folder, 'country_name_map.tsv'), header=0, sep='\t', lineterminator='\n', low_memory=False) country_name_map = dict(zip(country_name_df.Country_Code, country_name_df.Country_Name)) #country_name_map total_publications_time_indexed = pd.read_csv(os.path.join(data_folder, 'total_publications_time_indexed.tsv'), header=0, sep='\t', lineterminator='\n', low_memory=False) total_publications_time_indexed['month_bin'] = pd.to_datetime(total_publications_time_indexed['month_bin']) country_publications_time_indexed = pd.read_csv(os.path.join(data_folder, 'country_publications_time_indexed.tsv'), header=0, sep='\t', lineterminator='\n', low_memory=False) total_publications_time_indexed.id = np.log1p(total_publications_time_indexed.id) country_publications_time_indexed = country_publications_time_indexed.applymap(lambda x: np.log1p(x) if np.issubdtype(type(x), np.number) else x) curve_total = hv.Curve((total_publications_time_indexed.month_bin, total_publications_time_indexed.id), 'Time', 'Publication Counts (log)',label='Total') #Overlay the line plots overlay = curve_total curve_countries = [] for country in country_name_map.keys(): overlay = overlay * hv.Curve((total_publications_time_indexed.month_bin, country_publications_time_indexed[country]), label=country_name_map[country]) overlay.opts(show_legend=True,legend_position='right', width=1400, height=900, hooks=[customize_x_axis_bokeh]) return overlay macro_topics_mapping = {"Energy Efficiency and Thermal Comfort in Building Environments":0, "Indoor Air Quality and Sustainable Air Conditioning Systems":1, "Urban Development Strategies and Sustainable City Planning":2, "Enhancing Child-Friendly Urban Spaces Through Design":3, "Smart city development and urban data management":4, "Urban Resilience and Green Infrastructure in Climate Change Planning":5, "Architectural Integration of Solar Photovoltaic Systems in Buildings":6, "Preservation and Evolution of Traditional Architecture in Modern Contexts":7, "Sustainable Building Construction and Design with Environmental Assessment":8, "Landscape Planning and Design Theory":9, "Urban Sound Environment Research in Architectural Design":10, "Sustainable Construction Materials and Technologies":11, "Utilizing BIM in Construction and Building Information Modeling Industry":12, "Urban Agriculture and Sustainable Food Systems":13, "Sustainable Bridge Design and Construction":14, "Investigation of Cavity Dynamics and Heat Transfer in Various Flow Scenarios":15} macro_topics_active_subset = ["Energy Efficiency and Thermal Comfort in Building Environments","Architectural Integration of Solar Photovoltaic Systems in Buildings","Utilizing BIM in Construction and Building Information Modeling Industry"] def load_institute_network(topic, **kwargs): if topic=='Energy Efficiency and Thermal Comfort in Building Environments': html = """""" html_pane = pn.pane.HTML(html) return html_pane elif topic=='Indoor Air Quality and Sustainable Air Conditioning Systems': html = """""" html_pane = pn.pane.HTML(html) return html_pane elif topic=='Urban Development Strategies and Sustainable City Planning': html = """""" html_pane = pn.pane.HTML(html) return html_pane elif topic=='Enhancing Child-Friendly Urban Spaces Through Design': html = """""" html_pane = pn.pane.HTML(html) return html_pane elif topic=='Smart city development and urban data management': html = """""" html_pane = pn.pane.HTML(html) return html_pane elif topic=='Urban Resilience and Green Infrastructure in Climate Change Planning': html = """""" html_pane = pn.pane.HTML(html) return html_pane elif topic=='Architectural Integration of Solar Photovoltaic Systems in Buildings': html = """""" html_pane = pn.pane.HTML(html) return html_pane elif topic=='Preservation and Evolution of Traditional Architecture in Modern Contexts': html = """""" html_pane = pn.pane.HTML(html) return html_pane elif topic=='Sustainable Building Construction and Design with Environmental Assessment': html = """""" html_pane = pn.pane.HTML(html) return html_pane elif topic=='Landscape Planning and Design Theory': html = """""" html_pane = pn.pane.HTML(html) return html_pane elif topic=='Urban Sound Environment Research in Architectural Design': html = """""" html_pane = pn.pane.HTML(html) return html_pane elif topic=='Sustainable Construction Materials and Technologies': html = """""" html_pane = pn.pane.HTML(html) return html_pane elif topic=='Utilizing BIM in Construction and Building Information Modeling Industry': html = """""" html_pane = pn.pane.HTML(html) return html_pane elif topic=='Urban Agriculture and Sustainable Food Systems': html = """""" html_pane = pn.pane.HTML(html) return html_pane elif topic=='Sustainable Bridge Design and Construction': html = """""" html_pane = pn.pane.HTML(html) return html_pane elif topic=='Investigation of Cavity Dynamics and Heat Transfer in Various Flow Scenarios': html = """""" html_pane = pn.pane.HTML(html) return html_pane def load_country_network(topic, **kwargs): if topic=='Energy Efficiency and Thermal Comfort in Building Environments': html = """""" html_pane = pn.pane.HTML(html) return html_pane elif topic=='Indoor Air Quality and Sustainable Air Conditioning Systems': html = """""" html_pane = pn.pane.HTML(html) return html_pane elif topic=='Urban Development Strategies and Sustainable City Planning': html = """""" html_pane = pn.pane.HTML(html) return html_pane elif topic=='Enhancing Child-Friendly Urban Spaces Through Design': html = """""" html_pane = pn.pane.HTML(html) return html_pane elif topic=='Smart city development and urban data management': html = """""" html_pane = pn.pane.HTML(html) return html_pane elif topic=='Urban Resilience and Green Infrastructure in Climate Change Planning': html = """""" html_pane = pn.pane.HTML(html) return html_pane elif topic=='Architectural Integration of Solar Photovoltaic Systems in Buildings': html = """""" html_pane = pn.pane.HTML(html) return html_pane elif topic=='Preservation and Evolution of Traditional Architecture in Modern Contexts': html = """""" html_pane = pn.pane.HTML(html) return html_pane elif topic=='Sustainable Building Construction and Design with Environmental Assessment': html = """""" html_pane = pn.pane.HTML(html) return html_pane elif topic=='Landscape Planning and Design Theory': html = """""" html_pane = pn.pane.HTML(html) return html_pane elif topic=='Urban Sound Environment Research in Architectural Design': html = """""" html_pane = pn.pane.HTML(html) return html_pane elif topic=='Sustainable Construction Materials and Technologies': html = """""" html_pane = pn.pane.HTML(html) return html_pane elif topic=='Utilizing BIM in Construction and Building Information Modeling Industry': html = """""" html_pane = pn.pane.HTML(html) return html_pane elif topic=='Urban Agriculture and Sustainable Food Systems': html = """""" html_pane = pn.pane.HTML(html) return html_pane elif topic=='Sustainable Bridge Design and Construction': html = """""" html_pane = pn.pane.HTML(html) return html_pane elif topic=='Investigation of Cavity Dynamics and Heat Transfer in Various Flow Scenarios': html = """""" html_pane = pn.pane.HTML(html) return html_pane def create_overlay_plot(subject_df): overlay = hv.Overlay() for obj_column in subject_df.columns: overlay *= hv.Curve((subject_df.index, subject_df[obj_column]), 'Time', 'Frequency', label=obj_column) overlay.opts( show_legend=True, legend_position='right', width=1400, height=900 ) return overlay def create_overlay_plots(df): overlay = hv.Overlay() curves = [] max_y = 0 for i,obj_column in enumerate(df.columns): if max(df[obj_column])>max_y: max_y=max(df[obj_column]) curve = hv.Curve((df.index, np.log1p(df[obj_column])), 'Time', 'Occurrence Ratio', label=obj_column).opts(show_legend=True) curves.append(curve) def hook(plot, element): plot.handles['xaxis'].axis_label_text_color = 'black' plot.handles['yaxis'].axis_label_text_color = 'black' plot.handles['xaxis'].axis_label_text_alpha = 1.0 plot.handles['yaxis'].axis_label_text_alpha = 1.0 plot.handles['xaxis'].axis_line_alpha = 1.0 plot.handles['yaxis'].axis_line_alpha = 1.0 overlay = hv.Overlay(curves).opts( opts.Overlay(bgcolor='white', #padding=-0.1, show_legend=True, legend_position='top_left', legend_opts={ 'click_policy': 'hide', 'background_fill_color': 'white', 'background_fill_alpha': 1.0, 'border_line_color': 'black', 'label_text_color': 'black', 'label_text_font_size': '12pt', }, ylim=(0, max_y + 0.18*max_y), width=1700, height=700, legend_offset=(0, 0), legend_cols=8, hooks=[lambda p, _: p.state.update(border_fill_color='white'), hook] ), opts.Curve( show_grid=True ) ) 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/ button0 = pn.widgets.Button(name="Introduction", button_type="warning", icon="file-info", styles={"width": "100%"}) button1 = pn.widgets.Button(name="Publication Trends", button_type="warning", icon="chart-histogram", styles={"width": "100%"}) button2 = pn.widgets.Button(name="Topic Map", button_type="warning", icon="chart-dots-3", styles={"width": "100%"}) button3 = pn.widgets.Button(name="AECO Macro Topic Hierarchy", button_type="warning", icon="chart-dots-3", styles={"width": "100%"}) button4 = pn.widgets.Button(name="AECO Macro Topics Trends", button_type="warning", icon="chart-histogram", styles={"width": "100%"}) button5 = pn.widgets.Button(name="Research Collaboration Networks: Institutes", button_type="warning", icon="chart-dots-3", styles={"width": "100%"}) button6 = pn.widgets.Button(name="Research Collaboration Networks: Countries", button_type="warning", icon="chart-dots-3", styles={"width": "100%"}) button7 = pn.widgets.Button(name="Research Tasks and Methods Trends", 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.Select(name='Select Macro Topic', value='Energy Efficiency and Thermal Comfort in Building Environments', options=macro_topics_active_subset) # 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) # 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]) button0.on_click(lambda event: show_page("Page0")) 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")) button7.on_click(lambda event: show_page("Page7")) ### CREATE PAGE LAYOUTS def CreatePage0(): return pn.Column(pn.pane.Markdown(""" ## Introduction This is a dashboard for a Data Analytics project regarding research publications in the AECO domain. The source data consists of around 267k English-language research papers gathered from the openalex.org graph database, covering a timeframe from 2011 through early 2024. --------------------------- ## Publication Trends In the "Publication Trends" panel we show monthly time series of the total number of publications and the number of publications per country (both in log scale), for the top 20 countries by number of publications in the dataset. ## Topic Map In the "Topic Map" panel we show a UMAP reduced 2-dimensional visualization of the optimized 52 topic clusters of AECO research papers, embedded using Sentence Transformer model, with the descriptive labels overlayed on the clusters being generated by LLama 2 Large Language Model. Each point in the space represent a paper from the dataset. Hovering over it has the paper title popping up, while clicking on it redirects to the corresponding OpenAlex paper entry page. ## AECO Macro Topic Hierarchy The "AECO Macro Topic Hierarchy" panel allows to explore the dendrogram representation of the optimized clustering, with the leaves of the tree representing the 51 clusters, the intermediate nodes representing merged clusters and the height of the merging (distance from the leaves) indicating topic similarity as based on the cosine distance matrix between topic embeddings. ## AECO Macro Topic Trends The "AECO Macro Topic Trends" panel shows the evolution over time of the 16 AECO macro topics by plotting the semi-annual time series of the absolute numbers of publications per topic. ## Research Collaboration Networks: Institutes The "Research Collaboration Networks: Institutes" panel contains VOSViewer-generated network representations of the research institutions co-authorship connections, for each of the 16 macro clusters. By selecting a macro-cluster from by the drop-down menu, a graph is loaded whose nodes represent research institutions, edges represent co-authorships relations (with edge thickness being proportional to the frequency of the relations) and the color code clustering highlights the partition of the graph in highly interconnected node groups. Open the VOSViewer left panel to customize the visualization and/or search for a target institution in the graph. ## Research Collaboration Networks: Countries The "Research Collaboration Networks: Countries" panel contains VOSViewer-generated network representations of the authors/institutions' country co-authorship connections, for each of the 16 macro clusters. By selecting a macro-cluster from by the drop-down menu, a graph is loaded whose nodes represent authors/institutions' countries, edges represent co-authorships relations (with edge thickness being proportional to the frequency of the relations) and the color code clustering highlights the partition of the graph in highly interconnected node groups. Open the VOSViewer left panel to customize the visualization and/or search for a target country in the graph. """, width=1000), align="center") def CreatePage1(): return pn.Column( pn.pane.Markdown("## Publication Trends "), create_publication_curve_chart(), 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'' # Create an HTML pane to render the content html_pane = pn.pane.HTML(iframe_html , sizing_mode='stretch_both') return pn.Column(pn.pane.Markdown(" ## AECO Topic Map "), html_pane, align="center") def CreatePage3(): # 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'' # Create an HTML pane to render the content html_pane = pn.pane.HTML(iframe_html , sizing_mode='stretch_both') return pn.Column(pn.pane.Markdown(" ## AECO Macro Topics Dendogram "), html_pane, align="center") def CreatePage4(): # 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'' # Create an HTML pane to render the content html_pane = pn.pane.HTML(iframe_html , sizing_mode='stretch_both') return pn.Column(pn.pane.Markdown(" ## AECO Macro Topics "), html_pane, align="center") def CreatePage5(): return pn.Column( macro_topics_button, pn.bind(load_institute_network, macro_topics_button), align="center", ) def CreatePage6(): return pn.Column( macro_topics_button, pn.bind(load_country_network, macro_topics_button), align="center", ) def CreatePage7(): return pn.Column( macro_topics_button, pn.bind(load_Task_Method_trends, macro_topics_button), align="center", ) def load_Task_Method_trends(topic, **kwargs): task_data = dict() # Check if macro_topics_mapping exists if topic not in macro_topics_mapping: raise ValueError(f"Topic '{topic}' not found in macro_topics_mapping") macro_topic_str = str(macro_topics_mapping[topic]) # Create Panel UI with dropdown selection dropdown = pn.widgets.Select( name="Select:", options=["TASK","METHOD"] ) #load the tasks and methods timeseries dataframe for the selected topic task_ts_df = loadTaskTimeSeries(macro_topic_str) method_ts_df = loadMethodTimeSeries(macro_topic_str) # @pn.depends(dropdown.param.value) def load_overlays(value): if value == "TASK": return create_overlay_plots(task_ts_df) elif value == "METHOD": return create_overlay_plots(method_ts_df) return pn.Column( dropdown, pn.bind(load_overlays, dropdown), align="center" ) def load_Task_Method_triple_trends(topic, **kwargs): task_data = dict() # Check if macro_topics_mapping exists if topic not in macro_topics_mapping: raise ValueError(f"Topic '{topic}' not found in macro_topics_mapping") macro_topic_str = str(macro_topics_mapping[topic]) topic2toptasks = load_topic2toptasks() #load the task_timeseries dataframe for each of the task mapped from the selected topic in the dict topic2toptasks for task in topic2toptasks.get(macro_topic_str, []): task_data[task] = loadTaskMethodTimeSeries(macro_topic_str, task) # Create Panel UI with dropdown selection task_dropdown = pn.widgets.Select( name="Select TASK", options=[key for key in task_data.keys()] if task_data else ["No available tasks"] ) #@pn.depends(task_dropdown.param.value) def load_task_overlays(task): if task not in task_data: return hv.Text(0.5, 0.5, "No data available", halign="center") else: task_method_df = task_data.get(task) return create_overlay_plot(task_method_df) return pn.Column( task_dropdown, pn.bind(load_task_overlays, task_dropdown) ) mapping = { "Page0": CreatePage0(), "Page1": CreatePage1(), "Page2": CreatePage2(), "Page3": CreatePage3(), "Page4": CreatePage4(), "Page5": CreatePage5(), "Page6": CreatePage6(), "Page7": CreatePage7() } #################### SIDEBAR LAYOUT ########################## sidebar = pn.Column(pn.pane.Markdown("## Panels"),button0,button1,button2,button3,button4,button5,button6,button7, #button5, #button6, styles={"width": "100%", "padding": "15px"}) #################### MAIN AREA LAYOUT ########################## main_area = pn.Column(mapping["Page0"], 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=330, ## Default is 330 busy_indicator=pn.indicators.BooleanStatus(value=True), ) ### DEPLOY APP # Serve the Panel app template.servable()