import matplotlib.pyplot as plt
from matplotlib import pyplot
from sklearn.datasets import make_regression
from sklearn.preprocessing import MinMaxScaler
import sklearn
import numpy as np
from scipy.stats import pearsonr
import csv
import numpy as np
import pandas as pd
import json
import random

class DataExporter:
    
    def __init__(self, path):
        self.path = path
    
    def get_correlation_string(self, correlation):
        if correlation == -0.8:
            return 'N'
        elif correlation == 0.8:
            return 'P'
        else:
            return 'NC'

    def get_monotonic_type_string(self, monotonic, type_monotonic):
        if monotonic:
            if type_monotonic == 1:
                return 'MI'
            else:
                return 'MD'
        else:
            return 'NM'

    def get_outfile_name(self, r, monotonic, type_monotonic, r_exception, monotonic_exception, type_monotonic_exception, geotemp_config):
        
        ######### BASE #########
        name = 'usstates_'
        
        ######### ADD PATTERN BEHAVIOR ###########
        name += 'PB-'
        
        ######### CORRELATION #########
        corr_string = self.get_correlation_string(r)
        name += corr_string + '_'
        
        ######### MONOTONIC BEHAVIOR #########
        name += self.get_monotonic_type_string(monotonic, type_monotonic)
        name += '_'
        
        ######### ADD NOISE BEHAVIOR ##########
        name += 'NB-'
        
        ######### CORRELATION #########
        corr_string = self.get_correlation_string(r_exception)
        name += corr_string + '_'
        
        ######### MONOTONIC BEHAVIOR #########
        name += self.get_monotonic_type_string(monotonic_exception, type_monotonic_exception)
        name += '_'
        
        ######### NOISE GEO #########
        percentage = round(geotemp_config.locations_noise_perc * 100)
        name += 'NG' + str(percentage)
        
        ######### FORCED LOCATION #########
        location = geotemp_config.location_forced
        if location is not None:
            name += '_F!GL-' + str(location)
        
        ######### FORCED LOCATION RANGE #########
        zone = geotemp_config.location_range_forced
        if zone is not None:
            name += '_F!GR-' + str(zone)
        
        ######### NOISE TIME #########
        percentage = round(geotemp_config.times_noise_perc * 100)
        name += '_NT' + str(percentage)
        
        ######### FORCED TIME #########
        time = geotemp_config.time_forced
        if time is not None:
            name += '_F!T-' + str(time)
        
        ######### FORCED TIME RANGE #########
        period = geotemp_config.time_range_forced
        if period is not None:
            name += '_F!TR-' + str(period)

        return name
    
    def export(self, df, r, monotonic, type_monotonic, r_exception, monotonic_exception, type_monotonic_exception, geotemp_config):
        output = self.get_outfile_name(r, monotonic, type_monotonic, r_exception, monotonic_exception, type_monotonic_exception, geotemp_config)
        print(output)
        self.generate_csv(df, output + '.csv')
        self.generate_json(df, output + '.json')       
        
    def generate_csv(self, df, filename):
        df.to_csv(self.path + filename)
    
    def generate_json(self, df, filename):
        copy = df.copy()
        copy.drop('notnoise_geo', 1, inplace = True)
        copy.drop(df.index[len(df)-1], inplace = True)
        ids = pd.read_json('VEGA/data/obesity.json')
        ids.head()

        data2 = pd.merge(copy, ids, on='state')
        data2.drop(['rate'], axis=1, inplace=True)
        data2.head()
        data2.to_json(self.path + filename, orient='records')
        centers = pd.read_csv('us_states_codes_centers.csv')
        with open(self.path + filename) as json_file:  
            data = json.load(json_file)
            for d in data:
                this_row = (centers[centers.Abbreviation == d['state'] ]).iloc[0]
                geometries = { 'coordinates': [this_row.x, this_row.y]}
                d['geometries'] = geometries
            with open(self.path + filename, 'w') as outfile:  
                json.dump(data, outfile)