'''
    DS2000
    Spring 2023
    Sample code from class -- classifier to use in a nearest-neighbor
    prediction model
    
    Our goal -- determine whether a show will win an emmy
    
    Two categories only: emmy / no emmy
'''

from emmyshow import EmmyShow
import csv


SHOW_FILE = "shows.csv"

def read_csv(filename):
    ''' Function: read_csv
        Parameters: filename, a string
        Returns: 2d list of strings
        Does: loops over each line in the file, which is saved as a 
              list of strings, skips the header
    '''
    data = []
    with open(filename, "r") as infile:
        csvfile = csv.reader(infile)
        next(csvfile)
        for row in csvfile:
            data.append(row)
    return data

def make_show_objs(lst):
    ''' Function: make_show_objs
        Parameters: 2d lst of strings
        Returns: lst of emmyShow objects
        Does: iterates over the list, turns each row into an object
    '''
    objs = []
    for row in lst[1:]:
        for i in range(len(row)):
            if row[i].isdigit():
                row[i] = float(row[i])
        tv = EmmyShow(row)
        objs.append(tv)
    return objs
    

def find_min(distances):
    ''' Function: find_min
        Parameters: list of floats
        Return: tuple of float, int
        Does: finds smallest distance in the list, returns
               its value and position
    '''
    min_val = float("inf")
    min_pos = -1
    for i in range(len(distances)):
        if distances[i] < min_val:
            min_val = distances[i]
            min_pos = i
    return min_val, min_pos




def main():
    # gather data - read in the tv show info from the file
    raw_shows = read_csv(SHOW_FILE)
    
    # computation - turn the 2d list of strings into show objects
    tv_objs = make_show_objs(raw_shows)
    
    
main()