dashboard.py

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
from bokeh.models import Legend, LegendItem


import os
os.environ['MPLCONFIGDIR'] = '/tmp/matplotlib'

## 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 = """<iframe src="https://tinyurl.com/2d4gl4tl" 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://tinyurl.com/2a2ha2r8" 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://tinyurl.com/2b7sqbdc" 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="https://tinyurl.com/29mkxzep" 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://tinyurl.com/2ynebkcr" 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 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 hook1(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


'''
def legend_hook(plot, element):
    p = plot.state
    if p.legend:
        legend = p.legend[0]
        legend.orientation = 'horizontal'
        legend.location = 'center'
        legend.background_fill_color = 'white'
        legend.border_line_color = 'black'
        legend.label_text_font_size = '12pt'
        legend.label_text_color = 'black'
        legend.spacing = 10
        legend.label_standoff = 5,
        legend.margin = 20
        # Distribute legend items in multiple columns
        legend.columns = 8  # ⚠ This sets 8 columns for Bokeh >= 2.4
        # Move legend below the plot
        p.add_layout(legend, 'below')
'''

def create_overlay_plots(df):
    def move_legend_below(plot, element):
        p = plot.state
        if p.legend:
            legend = p.legend[0]
            legend.orientation = 'horizontal'
            legend.location = 'center'  # center of the below layout, not plot area
            legend.ncols = 4
            legend.background_fill_color = 'white'
            legend.border_line_color = 'black'
            legend.label_text_font_size = '20pt'
            legend.label_text_color = 'black'
            legend.spacing = 10
            legend.margin = 10
            legend.label_standoff = 5

            p.add_layout(legend, 'below')


    line_styles = ['solid', 'dashed', 'dashdot', 'dotted']
    curves = []
    max_y = 0
    for i,obj_column in enumerate(df.columns):
        linestyle = line_styles[i % len(line_styles)]
        curve = hv.Curve((df.index, np.log1p(df[obj_column])), 'Time', 'Occurrence Ratio', label=obj_column).opts(show_legend=True, line_dash=linestyle,fontsize={
        'xlabel': 18,
        'ylabel': 18,
        'xticks': 18,
        'yticks': 18,
    })
        curves.append(curve)



    overlay = hv.Overlay(curves).opts(
    opts.Overlay(bgcolor='white',
            #padding=-0.1,
            show_legend=True,
            #legend_position='top_left',
            #ylim=(0, max_y + 0.18*max_y),
            width=1850,
            height=900,
            #padding=(0.2, 0.2),
            hooks=[move_legend_below,hook1,lambda p, _: p.state.update(border_fill_color='white')],

    ),
    opts.Curve(
        show_grid=True,
        line_width=2,

    )
)
    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.


## Research Tasks and Methods Trend
The "Research Tasks and Methods Trend" shows the evolution over time of the most prominent TASK and METHOD entities extracted from research papers for each of the 16 AECO macro topics. It plots annual time series of the occurrence ratio for each triple target entity (Task or Method) to the number of papers of the macro topic.



""", 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'<iframe src="{AECO_topic_map_path}" width="1400px" height="1200px"></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="1400px" height="1200px"></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="1400px" height="1200px"></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 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()