import pandas as pd
import requests
# URL of the CSV file
url = "https://titlon.uit.no/hht/data/gapminder.csv"

#sometimes this doesn't work on mac
#g = pd.read_csv(url)#reading data

# Local file path to save the downloaded CSV
local_file_path = "gapminder.csv"


# Download the CSV file using requests
response = requests.get(url)
with open(local_file_path, 'wb') as f:
    f.write(response.content)

# Read the locally saved CSV file into a Pandas DataFrame
g = pd.read_csv(local_file_path)
g

import numpy as np
from matplotlib import pyplot as plt

fig,ax=plt.subplots()

#adding axis lables:
ax.set_ylabel('Forventet antall leveår')
ax.set_xlabel('BNP per innbygger')

#plotting the function:
ax.scatter(np.log(g['gdp_cap']), g['life_exp'],  label='Observasjoner')
ax.legend(loc='lower right',frameon=False)

#creating a dictionary of continents with norwegian translation:
continents={'Africa': 'Afrika', 'Americas': 'Sør/Nord-Amerika', 'Asia': 'Asia', 'Europe': 'Europa', 'Oceania': 'Oseania'}
#assigning a color hex-code to each continent
colors={'Africa': '#E14827', 'Americas': '#84E127', 'Asia': '#2792E1', 'Europe': '#BC27E1', 'Oceania': '#E04A6C'}


g['continent_no']=g['continent'].apply(lambda cont:  continents[cont])
g['colors']=g['continent'].apply(lambda cont: colors[cont])
g

g['size']=g['population'].apply(lambda pop: pop**0.5/300)
display(g[g['country']=='India'])
g

'{:,}'.format(int(23153161365)).replace(',',' ')

g['gdp_capNOK']=g['gdp_cap'].apply(lambda gdp: '{:,}'.format(int(8.9*gdp)).replace(',',' '))
g['life_exp_rnd']=g['life_exp'].apply(lambda l: int(l))
g['pop_mill_str']=g['population'].apply(lambda pop:  '{:,}'.format(int(pop/1000000)))
g

from bokeh.plotting import figure

#creating figure:
p = figure(
        title = "Levealder og BNP per innbygger", 
        x_axis_label = 'BNP per innbygger (NOK)',
        y_axis_label = 'Forventet antall leveår',
    
        x_axis_type = "log",
    
        tools="hover", 
        tooltips = [
            ("Land","@country"),
            ("BNP/innbygger","@gdp_capNOK"),
            ("Forventet alder","@life_exp_rnd"),
            ("Befolkning","@pop_mill_str")
            ],
    
        height = 580,
        width = 980)

from bokeh.io import show, output_notebook
from bokeh.plotting import output_file

p.scatter(
    source=g,
    x= 'gdp_cap', 
    y= 'life_exp',
    
    size='size', 
    color= 'colors',
    legend_field='continent_no',
    alpha= 0.8
    )

#Creating the graph and saving as html
output_notebook()
output_file("BNP_levealder.html")
show(p)

#formatting:
p.xaxis.major_label_overrides = { 1000: '1k', 10000: '10k', 100000: '100k' }
p.legend.location = "top_left"

p.legend[0].border_line_alpha=0
p.outline_line_alpha=0
p.grid[0].grid_line_alpha=0
p.grid[1].grid_line_alpha=0

output_notebook()
output_file("BNP_levealder.html")
show(p)

import pandas as pd
import requests

# URL of the CSV file
url = r"https://titlon.uit.no/hht/data/valgkretser/data.csv"

#sometimes this doesn't work on mac
#g = pd.read_csv(url)#reading data

# Local file path to save the downloaded CSV
local_file_path = "data.csv"


# Download the CSV file using requests
response = requests.get(url)
with open(local_file_path, 'wb') as f:
    f.write(response.content)

data=pd.read_csv(
    local_file_path,
    encoding="latin-1",
    delimiter=";") 
data

import geopandas as gpd
#loading the geographical data
#and converting to a coordinate system that folium understands:

# URL of the CSV file
url = r"https://titlon.uit.no/hht/data/valgkretser.zip"

#sometimes this doesn't work on mac
#g = pd.read_csv(url)#reading data

# Local file path to save the downloaded CSV
local_file_path = "valgkretser.zip"

# Download the CSV file using requests
response = requests.get(url)
with open(local_file_path, 'wb') as f:
    f.write(response.content)

geodata = gpd.read_file("valgkretser.zip")
#Need to specify which cooridinate system to use
geodata=geodata.to_crs('epsg:4326') 
geodata

str(geodata['geometry'][geodata['VKRETS_V_1']=='Sentrum'].iloc[0])[0:300]

geodata=geodata.rename(columns={'VKRETS_V_1':'Bydel'})

#merging the demographic data into the geodata:
geodata_merged=geodata.merge(data,on='Bydel')
#Keeping only the relevant fields

geodata_merged.keys()

geodata_merged=geodata_merged[['Bydel', 'Inntekt', 'Formue', 'Skatt',
       'Alder', 'Skattepliktige', 'Andel med høyere utdanning', 'Rødhet',
       'ApSVR', 'HFrp', 'Mynter', 'Lat', 'Lon',
       'GEOMETRITY',  'geometry']]
geodata_merged

import webbrowser
import folium

#Creating the map
tromso=folium.Map(location=[69.67, 18.98],zoom_start=10)

#Creating the html file
tromso.save('tromso.html')

#Open the html in new tab
webbrowser.open('tromso.html')

f=folium.Choropleth(
    geo_data=geodata_merged,
    name="choropleth",
    columns=["Bydel", "Rødhet"],
    data=geodata_merged,
    key_on="feature.properties.Bydel",
    fill_color="RdBu",
    nan_fill_color='white',
    fill_opacity=0.5,
    legend_name="Valgresultat oppslutning H+Frp minus Ap+SV+Rødt"
).add_to(tromso)

folium.LayerControl().add_to(tromso)

#Saving a new html wtih colour overlay
tromso.save('tromso.html')

#cleaning data
import math

geodata_merged['Andel med høyere utdanning%'] = geodata_merged['Andel med høyere utdanning'].apply(
    
    lambda x: f"{int(x)}%"
    if not math.isnan(x) else 'NA')

geodata_merged['ApSVR%'] = geodata_merged['ApSVR'].apply(
    
    lambda x: f"{int(x*100)}%"  
    if not math.isnan(x) else 'NA')

geodata_merged['HFrp%'] = geodata_merged['HFrp'].apply(
    
    lambda x:  f"{int(x*100)}%"  
    if not math.isnan(x) else 'NA')

geodata_merged[['Bydel', 'Andel med høyere utdanning%', 'ApSVR%', 'HFrp%']]

for i,r in geodata_merged.iterrows():
    #obtaining coordinates:
    lat=float(r['Lat'])
    lon=float(r['Lon'])   

    #tooltips text:
    t=(f"<b>{r['Bydel']}</b><br>"
        f"Inntekt: {          r['Inntekt']                    }<br>"
        f"Alder: {            r['Alder']                      }<br>"
        f"Høyere utdanning: { r['Andel med høyere utdanning%'] }<br>"
        f"Ap+SV+Rødt: {       r['ApSVR%']                     }<br>"
        f"H+FrP: {            r['HFrp%']                       }")
    
    #setting the marker:
    folium.Marker(
        [lat,lon-0.01],
        tooltip=t,
        icon=folium.Icon(color='red')
    ).add_to(tromso)
    
    
#saving the map as a html file:
tromso.save('./tromso.html')

for i,r in geodata_merged.iterrows():
    #obtaining coordinates:
    lat=float(r['Lat'])
    lon=float(r['Lon']) 
    
    #creating pin for wealth. The height of the stack of coins is determined by the 
    #   picture file f"./img/{int(r['Mynter'])}coins.png":
    icon=folium.features.CustomIcon(f"img/coins/{int(r['Mynter'])}coins.png")
    folium.Marker([lat,lon+0.01],tooltip=f"Formue:{r['Formue']}",icon=icon).add_to(tromso)
    
#saving the map as a html file:
tromso.save('./tromso.html')
webbrowser.open('tromso.html')

import geopandas as gpd
#loading the geographical data
#and converting to a coordinate system that folium understands:

# URL of the CSV file
url = r"https://titlon.uit.no/hht/data/fylker_komprimert.json"

#sometimes this doesn't work on mac
#g = pd.read_csv(url)#reading data

# Local file path to save the downloaded CSV
local_file_path = "fylker_komprimert.json"

# Download the CSV file using requests
response = requests.get(url)
with open(local_file_path, 'wb') as f:
    f.write(response.content)

import geopandas as gpd
import numpy as np
import string

#loading the geographical data
#and converting to a coordinate system that folium understands:
geodata = gpd.read_file(local_file_path)
#Need to specify which cooridinate system to use
geodata = geodata[['navn', 'geometry']]
geodata['Fylke'] = geodata['navn'].apply(lambda x: eval(x)[0]['navn'])

#Adding some random data:
geodata['Farge'] = np.random.rand(len(geodata))
s = string.ascii_letters
geodata['Tilfeldig streng'] = geodata['navn'].apply(lambda x: ''.join([s[i] for i in np.random.choice(len(s),8)]))
geodata['Tilfeldig kvm. pris'] = np.array(30000+np.random.rand(len(geodata))*40000, dtype = int)
geodata = geodata[['Fylke','Farge', 'Tilfeldig streng', 'Tilfeldig kvm. pris', 'geometry']]

#Adding cooridnates for symbols that shall be placed in the counties:
coordinates = {
    "Rogaland": (59.1000, 5.7333),
    "Vestfold og Telemark": (59.2800, 9.1100),
    "Nordland": (66.8309, 13.7467),
    "Agder": (58.3405, 7.9715),
    "Innlandet": (61.5000, 10.0000),
    "Møre og Romsdal": (62.7100, 7.3600),
    "Vestland": (60.3913, 5.3221),
    "Oslo": (59.9139, 10.7522),
    "Viken": (60.0, 9.5),
    "Trøndelag": (63.4305, 10.3951),
    "Troms og Finnmark": (70.2500, 25.0000)
}

geodata['lat'] = geodata['Fylke'].map(lambda fylke: coordinates[fylke][0] if fylke in coordinates else None)
geodata['lon'] = geodata['Fylke'].map(lambda fylke: coordinates[fylke][1] if fylke in coordinates else None)

geodata

import webbrowser
import folium

#Creating the map
norge=folium.Map(location=[65, 14],zoom_start=4.5)
f=folium.Choropleth(
    geo_data=geodata.to_json(),
    name="choropleth",
    columns=["Fylke", "Farge"],
    data=geodata,
    key_on="feature.properties.Fylke",
    fill_color="RdBu",
    nan_fill_color='white',
    fill_opacity=0.5,
    legend_name="Tilfeldig farge"
).add_to(norge)

folium.LayerControl().add_to(norge)

#norge.save('norge.html')
#webbrowser.open('norge.html')

for i,r in geodata.iterrows():
    #obtaining coordinates:
    lat=float(r['lat'])
    lon=float(r['lon'])   

    #tooltips text:
    t=(f"<b>{r['Fylke']}</b><br>"
        f"Tilfeldig streng: {          r['Tilfeldig streng']                    }<br>"
)
    
    #setting the marker:
    folium.Marker(
        [lat,lon-0.01],
        tooltip=t,
        icon=folium.Icon(color='blue')
    ).add_to(norge)
    
#norge.save('norge.html')
#webbrowser.open('norge.html')

for i,r in geodata.iterrows():
    #obtaining coordinates:
    lat=float(r['lat'])
    lon=float(r['lon']) 
    
    #creating pin for wealth. The height of the stack of coins is determined by the 
    #   picture file f"./img/{int(r['Mynter'])}coins.png":
    icon=folium.features.CustomIcon(f"img/house.png")
    folium.Marker([lat,lon+0.4],tooltip=f"Kvm.pris:{r['Tilfeldig kvm. pris']}",icon=icon).add_to(norge)
    
norge.save('norge.html')
webbrowser.open('norge.html')