##  Gets the data from external website - refreshes whenever the programme is called.
## Urllib2 required module
##  csv to make life easier handling the data

import urllib2
import csv
import sys  
import math
# import sqlite3    #don't need this just now, will probably run Django with MySQL when it comes to it
# import MySQLdb Likewise, don't need this just now.
#python3
import atexit
from time import time
from datetime import timedelta

def secondsToStr(t):
    return str(timedelta(seconds=t))

line ="="*40
def log(s, elapsed=None):
    print(line)
    print(secondsToStr(time()), '-', s)
    if elapsed:
        print("Elapsed time:", elapsed)
    print(line)
    print()

def endlog():
    end = time()
    elapsed = end-start
    log("End Program", secondsToStr(elapsed))

def now():
    return secondsToStr(time())

start = time()
atexit.register(endlog)
log("Start Program")
def open_external_source():
    # Checks if the core file's been modified since the last time we used it - if it hasn't, then we skip all of the file reading stuff.
    #need to change this to just pull the headers the first time.  
    master_data_file = urllib2.urlopen("http://www.football-data.co.uk/mmz4281/1213/E0.csv","GET")
    print master_data_file
    headers = master_data_file.info()
    last_mod = headers["last-modified"]
    settings = open ("settings.csv","r+")
    historic_last_mod = settings.readline() #this only works when the setting is a 1 line file
    print"Local file version:" + historic_last_mod
    print"Server file version:" +last_mod
    if last_mod == historic_last_mod :
        print"It's the same, file not loaded"
        return true
    else :
        return false
    settings.close()

#the if statement's commented out because it was messing up the variables into the function
#if open_external_source == False:
master_data_file = urllib2.urlopen("http://www.football-data.co.uk/mmz4281/1213/E0.csv","GET")
data = list(tuple(rec) for rec in csv.reader(master_data_file, delimiter=','))
print len(data)
print"printing full file"
print data
league_list = ["Arsenal","Chelsea","Liverpool","Man City","Man United","Newcastle","Newcastle","Norwich","Reading","Southampton","Stoke","Sunderland","Swansea","Tottenham","West Brom","West Ham","Wigan"]

league_stats = league_list

#for teams in league_list: - come back to this, will do this as a split and append.


#call the next set of functions to skip the data reading stuff
#This is the data reading section, that puts the data into our system
#If we do proceed, then we redo all of the calculations, and read the data file in again, in case of any corrections, etc.  

#Column references:
#Home Goals 4
#Away Goals 5
#Full Time Result 6
#Home Shots 10
#Away Shots 11
#Home Shots on Target 12
#Away Shots on Target 13


#Calculates the average for a given team at home, columns are 4 Home Goals, 5 Away Goa
def CalcAverageHome(team, column, data):
    total = 0
    count = 0
    n=0
    for row in data:
        if  data[count][2] == team:
            total += int(data[count][column])
            n+=1
        count += 1      
    try:
        average = float(total) / n
    except ZeroDivisionError:
        average = 'Not played'
    return average

def CalcAverageAway(team, column, data):
    total = 0
    count = 0
    n=0
    for row in data:
        if  data[count][3] == team:
            total += int(data[count][column])
            n+=1
        count += 1      
    try:
        average = float(total) / n
    except ZeroDivisionError:
        average = 'Not played'  
    return average


home_team ="Chelsea"
away_team ="Newcastle"
print"Here's the Average number of goals scored Home"
home_goals = CalcAverageHome(home_team, 4, data)
away_goals = CalcAverageAway(home_team, 5, data)
home_conceded = CalcAverageHome(home_team, 5, data)
away_conceded = CalcAverageAway(away_team, 4, data)
adjusted_home = home_goals * away_conceded
adjusted_away = away_goals * home_conceded

print home_team, home_goals, home_conceded, adjusted_home
print away_team, away_goals, away_conceded, adjusted_away

print"starting to try and work the league averages out here."

def poisson_probability(actual, mean):
    # naive:   math.exp(-mean) * mean**actual / factorial(actual)

    # iterative, to keep the components from getting too large or small:
    p = math.exp(-mean)
    for i in xrange(actual):
        p *= mean
        p /= i+1
    return p

for i in range (10):
    print str((100*poisson_probability(i,adjusted_home)))+"%"


league_list = ["Arsenal","Chelsea","Liverpool","Man City","Man United","Newcastle","Newcastle","Norwich","Reading","Southampton","Stoke","Sunderland","Swansea","Tottenham","West Brom","West Ham","Wigan"]


# just assign the league list to the stats for now -
# eventually each team entry will become the first column of a new sublist

def LeagueAverages(data,column):
    total = 0
    n = 0
    for row in data :
        string = row[column]
        if string.isdigit() == True:
            total = total + int(row[column])
            n += 1
    league_average = float(total) / n
    return league_average


print"League home goals average is:", LeagueAverages(data, 4)
print"League away goals average is:", LeagueAverages(data, 5)

print"finished that loop..."




league_stats = []
test_team ="Arsenal"

# Function iterates through the league teams and calculates the averages
# and places them in one long list.
for team in league_list:
    league_stats.append(team)
    league_stats.append(CalcAverageHome(team, 4, data))
    print CalcAverageHome(team, 4, data)
    league_stats.append(CalcAverageHome(team, 5, data))
    CalcAverageHome(team, 5, data)
    league_stats.append(CalcAverageHome(team, 7, data))
    CalcAverageHome(team, 7, data)
    league_stats.append(CalcAverageHome(team, 8, data))
    CalcAverageHome(team, 8, data)
    league_stats.append(CalcAverageHome(team, 10, data))
    CalcAverageHome(team, 10, data)
    league_stats.append(CalcAverageHome(team, 11, data))
    CalcAverageHome(team, 11, data)
    league_stats.append(CalcAverageHome(team, 12, data))
    CalcAverageHome(team, 12, data)
    league_stats.append(CalcAverageHome(team, 13, data))
    CalcAverageHome(team, 13, data)

# This function should chunk the 'file', as when we run the above code,
# we'll end up with one incredibly long list that contains every team on the same line
def chunker(seq, size):
    return (seq[pos:pos + size] for pos in xrange(0, len(seq), size))

chunker (league_stats, 9)

final_stats = []
for group in chunker(league_stats, 9):
   print repr(group)
   final_stats.append(repr(group))

#retrieve a particular value from the final stats array
"""
    for row in final_stats:
        if  data[count][2] == team:
            total += int(data[count][column])
            n+=1
        count += 1  
"""

def create_probability_table(hometeam, awayteam, final_stats):
#reads in the home and away sides, calculates their performance adjusted
#ratings and then calculates the likelihood of each team scoring a particular
#number of goals (from 0-10)
#those likelihoods are then combined to provide an 11x11 matrix of probabilities
    poisson_array = []
    poisson_list_home = []
    poisson_list_away = []
    goals_home = 0
    conceded_home = 0
    goals_away = 0
    conceded_away = 0

    for team in final_stats:
        if team == hometeam:
            goals_home = team[1]
            conceded_home = team [3]
            print"home Goals, Home Conceded"
            print goals_home, conceded_home
        elif team == awayteam:
            goals_away = team[2]
            conceded_away = team[4]
            print"Away Goals, Away Conceded"
            print goals_away, conceded_away,
        else:          
            pass

    adjusted_goals_home = goals_home * conceded_away
    adjusted_goals_away = goals_away * conceded_home

    #this section creates the two probability lists for home and away num goals scored      
    for i in range (10):

        poisson_list_home.append = (100*poisson_probability(i,adjusted_goals_home))
        poisson_list_away.append = (100*poisson_probability(i,adjusted_goals_away))

    print poisson_list_home
    print poisson_list_away

    for number in poisson_list_home:
        for number in poisson_list_away:
            probability_table.append(poisson_list_home[number] * poisson_list_away[number])
    return probability_table

create_probability_table("Arsenal","Chelsea", final_stats)

#and this section cross multiplies them into a new list

#   for i in range (10):

# print data_frame [0:100] prints to console to provide visual check

master_data_file.close()

line 272, in create_probability_table
adjusted_goals_home = goals_home * conceded_away UnboundLocalError: local variable 'conceded_away' referenced before assignment

condeded_away = 0
   ^

teams = {
    'team1': [...],
    'team2': [...],
    ...
}

stats = teams['team2']