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| import warnings | |
| 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') | |
| ## 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_ver0.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=" ") | |
| ################################# 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.major_label_orientation = 45 # Rotate x-axis labels | |
| 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) | |
| 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=1200, height=500, hooks=[customize_x_axis_bokeh]) | |
| return overlay | |
| macro_topics = ["Energy Efficiency and Thermal Comfort in Building Environments","Indoor Air Quality and Sustainable Air Conditioning Systems","Urban Development Strategies and Sustainable City Planning", "Enhancing Child-Friendly Urban Spaces Through Design", "Smart city development and urban data management", "Urban Resilience and Green Infrastructure in Climate Change Planning","Architectural Integration of Solar Photovoltaic Systems in Buildings","Preservation and Evolution of Traditional Architecture in Modern Contexts","Sustainable Building Construction and Design with Environmental Assessment","Landscape Planning and Design Theory", "Urban Sound Environment Research in Architectural Design","Sustainable Construction Materials and Technologies","Utilizing BIM in Construction and Building Information Modeling Industry","Urban Agriculture and Sustainable Food Systems","Sustainable Bridge Design and Construction", "Investigation of Cavity Dynamics and Heat Transfer in Various Flow Scenarios"] | |
| macro_topics_active_subset = ["Energy Efficiency and Thermal Comfort in Building Environments","Indoor Air Quality and Sustainable Air Conditioning Systems","Urban Development Strategies and Sustainable City Planning", "Enhancing Child-Friendly Urban Spaces Through Design", "Smart city development and urban data management", "Urban Resilience and Green Infrastructure in Climate Change Planning","Architectural Integration of Solar Photovoltaic Systems in Buildings","Preservation and Evolution of Traditional Architecture in Modern Contexts","Sustainable Building Construction and Design with Environmental Assessment","Landscape Planning and Design Theory", "Urban Sound Environment Research in Architectural Design","Sustainable Construction Materials and Technologies","Utilizing BIM in Construction and Building Information Modeling Industry","Urban Agriculture and Sustainable Food Systems","Sustainable Bridge Design and Construction", "Investigation of Cavity Dynamics and Heat Transfer in Various Flow Scenarios"] | |
| def load_institute_network(topic, **kwargs): | |
| if topic=='Energy Efficiency and Thermal Comfort in Building Environments': | |
| html = """<iframe src="https://app.vosviewer.com/?json=https%3A%2F%2Fdrive.google.com%2Fuc%3Fid%3D16q1oLQyEeMosAgeD9UkC9hSrpzAYX_-n" width="1000" height="800"></iframe>""" | |
| html_pane = pn.pane.HTML(html) | |
| return html_pane | |
| elif topic=='Indoor Air Quality and Sustainable Air Conditioning Systems': | |
| html = """<iframe src="https://app.vosviewer.com/?json=https%3A%2F%2Fdrive.google.com%2Fuc%3Fid%3D1rqPx3X_9Hnv9mTq2bMCbWWh5VIOw9CRh" width="1000" height="800"></iframe>""" | |
| html_pane = pn.pane.HTML(html) | |
| return html_pane | |
| elif topic=='Urban Development Strategies and Sustainable City Planning': | |
| html = """<iframe src="" width="1000" height="800"></iframe>""" | |
| html_pane = pn.pane.HTML(html) | |
| return html_pane | |
| elif topic=='Enhancing Child-Friendly Urban Spaces Through Design': | |
| html = """<iframe src="" width="1000" height="800"></iframe>""" | |
| html_pane = pn.pane.HTML(html) | |
| return html_pane | |
| elif topic=='Smart city development and urban data management': | |
| html = """<iframe src="" width="1000" height="800"></iframe>""" | |
| html_pane = pn.pane.HTML(html) | |
| return html_pane | |
| elif topic=='Urban Resilience and Green Infrastructure in Climate Change Planning': | |
| html = """<iframe src="" width="1000" height="800"></iframe>""" | |
| html_pane = pn.pane.HTML(html) | |
| return html_pane | |
| elif topic=='Architectural Integration of Solar Photovoltaic Systems in Buildings': | |
| html = """<iframe src="https://app.vosviewer.com/?json=https%3A%2F%2Fdrive.google.com%2Fuc%3Fid%3D1BC0Dbmayyxv3G9wLt2fUSTiUL2vwzuLD" width="1000" height="800"></iframe>""" | |
| html_pane = pn.pane.HTML(html) | |
| return html_pane | |
| elif topic=='Preservation and Evolution of Traditional Architecture in Modern Contexts': | |
| html = """<iframe src="" width="1000" height="800"></iframe>""" | |
| html_pane = pn.pane.HTML(html) | |
| return html_pane | |
| elif topic=='Sustainable Building Construction and Design with Environmental Assessment': | |
| html = """<iframe src="" width="1000" height="800"></iframe>""" | |
| html_pane = pn.pane.HTML(html) | |
| return html_pane | |
| elif topic=='Landscape Planning and Design Theory': | |
| html = """<iframe src="" width="1000" height="800"></iframe>""" | |
| html_pane = pn.pane.HTML(html) | |
| return html_pane | |
| elif topic=='Urban Sound Environment Research in Architectural Design': | |
| html = """<iframe src="" width="1000" height="800"></iframe>""" | |
| html_pane = pn.pane.HTML(html) | |
| return html_pane | |
| elif topic=='Sustainable Construction Materials and Technologies': | |
| html = """<iframe src="" width="1000" height="800"></iframe>""" | |
| html_pane = pn.pane.HTML(html) | |
| return html_pane | |
| elif topic=='Utilizing BIM in Construction and Building Information Modeling Industry': | |
| html = """<iframe src="https://app.vosviewer.com/?json=https%3A%2F%2Fdrive.google.com%2Fuc%3Fid%3D1V-Cto19dxV_GR3MtNP6Yk642CnTQkjEK" width="1000" height="800"></iframe>""" | |
| html_pane = pn.pane.HTML(html) | |
| return html_pane | |
| elif topic=='Urban Agriculture and Sustainable Food Systems': | |
| html = """<iframe src="" width="1000" height="800"></iframe>""" | |
| html_pane = pn.pane.HTML(html) | |
| return html_pane | |
| elif topic=='Sustainable Bridge Design and Construction': | |
| html = """<iframe src="" width="1000" height="800"></iframe>""" | |
| html_pane = pn.pane.HTML(html) | |
| return html_pane | |
| elif topic=='Investigation of Cavity Dynamics and Heat Transfer in Various Flow Scenarios': | |
| html = """<iframe src="" width="1000" height="800"></iframe>""" | |
| 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 = """<iframe src="https://app.vosviewer.com/?json=https%3A%2F%2Fdrive.google.com%2Fuc%3Fid%3D1W-ZjXP5vpJ7pwaCT7KFjh1txNgBkIT6h" width="1000" height="800"></iframe>""" | |
| html_pane = pn.pane.HTML(html) | |
| return html_pane | |
| elif topic=='Indoor Air Quality and Sustainable Air Conditioning Systems': | |
| html = """<iframe src="" width="1000" height="800"></iframe>""" | |
| html_pane = pn.pane.HTML(html) | |
| return html_pane | |
| elif topic=='Urban Development Strategies and Sustainable City Planning': | |
| html = """<iframe src="" width="1000" height="800"></iframe>""" | |
| html_pane = pn.pane.HTML(html) | |
| return html_pane | |
| elif topic=='Enhancing Child-Friendly Urban Spaces Through Design': | |
| html = """<iframe src="" width="1000" height="800"></iframe>""" | |
| html_pane = pn.pane.HTML(html) | |
| return html_pane | |
| elif topic=='Smart city development and urban data management': | |
| html = """<iframe src="" width="1000" height="800"></iframe>""" | |
| html_pane = pn.pane.HTML(html) | |
| return html_pane | |
| elif topic=='Urban Resilience and Green Infrastructure in Climate Change Planning': | |
| html = """<iframe src="" width="1000" height="800"></iframe>""" | |
| html_pane = pn.pane.HTML(html) | |
| return html_pane | |
| elif topic=='Architectural Integration of Solar Photovoltaic Systems in Buildings': | |
| html = """<iframe src="" width="1000" height="800"></iframe>""" | |
| html_pane = pn.pane.HTML(html) | |
| return html_pane | |
| elif topic=='Preservation and Evolution of Traditional Architecture in Modern Contexts': | |
| html = """<iframe src="" width="1000" height="800"></iframe>""" | |
| html_pane = pn.pane.HTML(html) | |
| return html_pane | |
| elif topic=='Sustainable Building Construction and Design with Environmental Assessment': | |
| html = """<iframe src="" width="1000" height="800"></iframe>""" | |
| html_pane = pn.pane.HTML(html) | |
| return html_pane | |
| elif topic=='Landscape Planning and Design Theory': | |
| html = """<iframe src="" width="1000" height="800"></iframe>""" | |
| html_pane = pn.pane.HTML(html) | |
| return html_pane | |
| elif topic=='Urban Sound Environment Research in Architectural Design': | |
| html = """<iframe src="" width="1000" height="800"></iframe>""" | |
| html_pane = pn.pane.HTML(html) | |
| return html_pane | |
| elif topic=='Sustainable Construction Materials and Technologies': | |
| html = """<iframe src="" width="1000" height="800"></iframe>""" | |
| html_pane = pn.pane.HTML(html) | |
| return html_pane | |
| elif topic=='Utilizing BIM in Construction and Building Information Modeling Industry': | |
| html = """<iframe src="" width="1000" height="800"></iframe>""" | |
| html_pane = pn.pane.HTML(html) | |
| return html_pane | |
| elif topic=='Urban Agriculture and Sustainable Food Systems': | |
| html = """<iframe src="" width="1000" height="800"></iframe>""" | |
| html_pane = pn.pane.HTML(html) | |
| return html_pane | |
| elif topic=='Sustainable Bridge Design and Construction': | |
| html = """<iframe src="" width="1000" height="800"></iframe>""" | |
| html_pane = pn.pane.HTML(html) | |
| return html_pane | |
| elif topic=='Investigation of Cavity Dynamics and Heat Transfer in Various Flow Scenarios': | |
| html = """<iframe src="" width="1000" height="800"></iframe>""" | |
| html_pane = pn.pane.HTML(html) | |
| return html_pane | |
| ############################# 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 Topics 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%"}) | |
| region1 = pn.widgets.RadioButtonGroup(name='### Select News Region', options=regions) | |
| macro_topics_button = pn.widgets.Select(name='Select Macro Topic', value='Energy-Efficient Building Design for Thermal Comfort and Sustainability', 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")) | |
| #button6.on_click(lambda event: show_page("Page6")) | |
| ### CREATE PAGE LAYOUTS | |
| def CreatePage0(): | |
| 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. | |
| --------------------------- | |
| ## AECO Topic Map | |
| In the AECO Topic Map panel we show 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. | |
| ## 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. | |
| ### Research Collaboration Networks: Institutes | |
| ### Research Collaboration Networks: Authors | |
| """, width=800), 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'<iframe src="{AECO_topic_map_path}" width="1200px" height="1800px"></iframe>' | |
| # 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'<iframe src="{AECO_topics_dendogram_file_path}" width="1200px" height="1800px"></iframe>' | |
| # 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'<iframe src="{AECO_topics_over_time_file_path}" width="1200px" height="1800px"></iframe>' | |
| # 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 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 = { | |
| "Page0": CreatePage0(), | |
| "Page1": CreatePage1(), | |
| "Page2": CreatePage2(), | |
| "Page3": CreatePage3(), | |
| "Page4": CreatePage4(), | |
| "Page5": CreatePage5(), | |
| "Page6": CreatePage6() | |
| } | |
| #################### SIDEBAR LAYOUT ########################## | |
| sidebar = pn.Column(pn.pane.Markdown("## Pages"),button0,button1,button2,button3,button4,button5,button6, | |
| #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=330, ## Default is 330 | |
| busy_indicator=pn.indicators.BooleanStatus(value=True), | |
| ) | |
| ### DEPLOY APP | |
| # Serve the Panel app | |
| template.servable() |