# -*- coding: utf-8 -*-
"""
Created on Fri Nov 18 13:38:33 2022

@author: Stephan Bogs (RWTH Aachen, Chair of Operations Management)
"""

import geopandas as gpd
import matplotlib.pyplot as plt


def plot_shares(transport, production):
    # Pie chart, where the slices will be ordered and plotted counter-clockwise:
    labels = 'Transport', 'Produktion'
    sizes = [transport, production]
    
    fig, ax = plt.subplots()
    fig.set_dpi(100)
    ax.pie(sizes, labels=labels, autopct='%1.1f%%',
        shadow=True, startangle=90)
    ax.axis('equal')
    
    plt.show()
    fig.savefig('pie_chart.png')
    
def plot_totals_bar(transport, production):

    fig, ax = plt.subplots()
    
    labels = ['Transport', 'Fixed']
    counts = [transport, production]

    ax.bar(labels, counts)
    
    fig.set_dpi(100)
    plt.show()
    fig.savefig('totals.png')


production = [('Krefeld',51.33489008436512, 6.568047013473666),
              ('Herne',51.53771045684371, 7.194962256766569),
              ('Paderborn',51.71909942364076, 8.75057976047261)]

sinks = [('Koeln',50.93386518777527, 6.958973923969869),
         ('Bonn',50.740625840775465, 7.0952987924814),
         ('Duisburg',51.43082091590227, 6.76912922060523),
         ('Muenster',51.96498607848209, 7.617203294554034),
         ('Unna',51.537604178977716, 7.690509013834595),
         ('Bielefeld',52.02833269735831, 8.527475896723134)]

def plot_potential_modules(fig, ax):
    x_values = [value[2] for value in production]
    y_values = [value[1] for value in production]
    ax.scatter(x_values, y_values, c="y", label="Produktion")
    
def plot_sinks(fig, ax):
    x_values = [value[2] for value in sinks]
    y_values = [value[1] for value in sinks]
    ax.scatter(x_values, y_values, c="r", label="Kunden")
    
def plot_map():
    fp = "landkreise.geojson"
    data_nrw = gpd.read_file(fp)
    
    fig, ax = plt.subplots()
    data_nrw.plot(ax=ax, facecolor="none", edgecolor="black")
    

def plot_problem():
    fp = "landkreise.geojson"
    data_nrw = gpd.read_file(fp)
    
    fig, ax = plt.subplots()
    
    # Skalierung des Bildes
    scale = 1.5
    Size = fig.get_size_inches()
    fig.set_size_inches(Size[0]*scale, Size[1]*scale, forward=True) 
    # Anpassung der Auflösug
    fig.set_dpi(100)
    
    # Plotten der Karte und aller potenziellen Fabriken und Abnahmezenter
    data_nrw.plot(ax=ax, facecolor="none", edgecolor="black")
    plot_potential_modules(fig, ax)
    plot_sinks(fig,ax)
    
    # Positionierung der Legende
    box = ax.get_position()
    ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
    ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
    
    fig.savefig('problem.png')

def plot_solution(x):
    fp = "landkreise.geojson"
    data_nrw = gpd.read_file(fp)
    
    fig, ax = plt.subplots()
    
    # Skalierung des Bildes
    scale = 2
    Size = fig.get_size_inches()
    fig.set_size_inches(Size[0]*scale, Size[1]*scale, forward=True) 
    # Anpassung der Auflösug
    fig.set_dpi(100)
    
    # Plotten der Karte und aller potenziellen Fabriken und Abnahmezenter
    data_nrw.plot(ax=ax, facecolor="none", edgecolor="black")
    plot_potential_modules(fig, ax)
    plot_sinks(fig,ax)
    
    # Plotten der Ergebnisse. Eine Verbindung wenn von Fabrik i zu Abnahme j 
    # geliefert wird
    for i in range(len(production)):    
        for j in range(len(sinks)):
            if(x[i, j]. x > 0.001):
                ax.plot([production[i][2], sinks[j][2]],
                        [production[i][1], sinks[j][1]], c='k')
    
    fig.savefig('solution.png')