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main.py

+
+import time
+import pandas as pd
+import knime.extension as knext
+import numpy as np
+import logging
+import plotly.graph_objects as go
+import pycountry
+from geopy.geocoders import Nominatim
+LOGGER = logging.getLogger(__name__)
+
+
+category = knext.category("/community", "Supply_chain_visualization_node", "Supplychain visualization node", "Supplychain visualization node for KNIME",icon="icons/icon.png")		
+@knext.node(name="Supplychain visualization node", node_type=knext.NodeType.LEARNER, category=category, icon_path="icons/icon.png")	
+@knext.input_table(name="Input Data 1",description="Input data")
+@knext.output_table(name="Output Data",  description="Output data")
+@knext.output_view(name="Output view", description="Output view")
+
+class TemplateNode:
+    def configure(self, configure_context, input_schema_1):
+        return input_schema_1
+
+    def execute(self, exec_context, input_1):
+        df = input_1.to_pandas() # Transform the input table to some processable format (pandas or pyarrow)
+        if df.empty:
+          LOGGER.warning("Input table is empty.")
+          return None, None
+
+        df = df.rename(columns={'Country (from)': 'C_From', 'Country (to)': 'C_To', 'Value (thousands of €)' : 'Value' })
+
+        countries = pd.concat([df['C_From'], df['C_To']], axis=0, ignore_index=True)
+        countries = pd.DataFrame(countries)
+        countries.columns = ['Name']
+        
+        countries.drop_duplicates(inplace=True)
+
+
+        # Apply function to DataFrame to get latitude and longitude for each country
+        countries['Latitude'], countries['Longitude'] = zip(*countries['Name'].apply(get_lat_long))
+
+        countries['Country_Code'] = countries['Name'].apply(get_country_code)
+
+        country_code_array = np.array(countries['Country_Code'])
+        country_name_array = np.array(countries['Name'])
+
+
+        country_colour=[]
+        broj_drzava = len(country_code_array)
+        for i in range(0,broj_drzava):
+            country_colour.append(i)
+
+
+
+
+        countries = countries.set_index('Name')
+        df['Value_Normalized']=round(df['Value']*10/max(df['Value']))+1
+
+
+
+        import plotly.graph_objects as go
+
+        # Define your data for the choropleth map
+        data = [
+            go.Choropleth(
+                locations=country_code_array,  # Country codes or names
+                z=country_colour,  # Values for coloring the map
+                text=country_name_array,  # Country names
+                # colorscale='Blues',  # Color scale for the map
+                autocolorscale=False,  # Disable automatic scaling of colors
+                showscale=False,  # Hide the color scale legend
+                # geojson='https://raw.githubusercontent.com/python-visualization/folium/master/examples/data/world-countries.json'  # GeoJSON file URL
+            )
+        ]
+
+        # Define the layout of the figure
+        layout = go.Layout(
+            title='Supplychain visualization',
+            geo=dict(
+                showframe=True,  # Show the frame around the map
+                showcoastlines=True,  # Show coastlines
+                projection_type='natural earth'  # Set the projection type
+            )
+        )
+
+        # Create the figure
+        fig = go.Figure(data=data, layout=layout)
+
+        # Display the figure
+        # fig.show()
+
+
+        for index, row in df.iterrows():
+            c_from = row['C_From']
+            c_to = row['C_To']
+            c_th = row['Value_Normalized']
+           
+            # Introduce a delay between geocoding requests
+            time.sleep(0.2)  # Wait for a second before each geocoding request
+    
+            nacrtaj_arrow(fig, countries, c_from, c_to, c_th)
+
+        fig.update_layout(
+            # width=900, height=650,
+                showlegend = False,
+                geo = dict(
+                    scope = 'world',
+                    
+                    # landcolor = 'rgb(217, 217, 217)'
+                    )
+                    )
+
+
+
+
+
+        # x = input_1_pandas.to_html()
+
+        x = countries.to_html()
+
+        # return knext.Table.from_pandas(input_1_pandas), knext.view(input_1_pandas) ### Tutorial step 13: Uncomment
+        # return knext.Table.from_pandas(df), knext.view_html(x) ### Tutorial step 13: Uncomment
+        return knext.Table.from_pandas(df), knext.view(fig)  ### Tutorial step 13: Uncomment
+        
+
+
+# Define function to get latitude and longitude for a country
+
+def _code(country_name):
+    country = pycountry.countries.get(name=country_name.upper())
+    if country:
+        return country.alpha_3
+    else:
+        LOGGER.warning(f"Country code not found for {country_name}. Skipping this country.")
+        return None
+
+   
+
+# Define function to get latitude and longitude for a country
+# Modify get_lat_long function
+def get_lat_long(country):
+    geolocator = Nominatim(user_agent="my_app")
+    try:
+        location = geolocator.geocode(country, timeout=10)
+        if location:
+            return (location.latitude, location.longitude)
+        else:
+            LOGGER.warning(f"Location not found for country: {country}")
+            return (None, None)
+    except GeocoderTimedOut:
+        LOGGER.warning(f"Geocoding request timed out for country: {country}")
+        return (None, None)
+
+
+# Modify get_country_code function
+def get_country_code(country_name):
+    country = pycountry.countries.get(name=country_name.upper())
+    if country:
+        return country.alpha_3
+    else:
+        LOGGER.warning(f"Country code not found for: {country_name}. Skipping this country.")
+        return None
+
+
+def nacrtaj_arrow(fig, countries, c1, c2, thickness):
+    fig.add_trace(go.Scattergeo(
+        lat = [countries.loc[c1][0], countries.loc[c2][0]], 
+        lon = [countries.loc[c1][1], countries.loc[c2][1]],
+        mode = 'lines',
+        
+        line = dict(width = thickness, color = 'blue'),
+    ))
+
+    #Workaround to get the arrow at the end of an edge AB
+
+    l = 1.1  # the arrow length
+    widh =  0.035  #2*widh is the width of the arrow base as triangle
+
+    B = np.array([countries.loc[c2][1], countries.loc[c2][0]])
+    A = np.array([countries.loc[c1][1], countries.loc[c1][0]])
+    # A = np.array([locations['lon'][4], locations['lat'][4]])
+    # B = np.array([locations['lon'][0], locations['lat'][0]])
+    v = B-A
+    w = v/np.linalg.norm(v)  
+    # u = np.array([-v[1], v[0]])  #u orthogonal on  w
+    u = np.array([-w[1], w[0]])*10
+            
+    P = B-l*w
+    S = P - widh*u
+    T = P + widh*u
+
+    fig.add_trace(go.Scattergeo(lon = [S[0], T[0], B[0], S[0]], 
+                                lat =[S[1], T[1], B[1], S[1]], 
+                                mode='lines', 
+                                fill='toself', 
+                                fillcolor='blue', 
+                                line_color='blue'))
+    # #------Display your text at the middle of the segment AB
+    # fig.add_trace(go.Scattergeo(lon =[0.5*(A+B)[0]], lat = [0.5*(A+B)[1]], mode='text', text='Your text'))
+