'''
Created on Aug 18, 2019
@author: Brett Paufler
(c) Copyright Brett Paufler

Research
    Graphs
    Statistics
    
On one or more logfiles in input

Outputs
    Text
        Research Order Each Game
    Graph
        Culminative Research
        Turn Normalized Research

'''

from os import listdir
from os.path import join as path_join

from civ_parse_logfile import Game
from civ_parse_tech_tree import standard_tech_tree

import matplotlib.pyplot as plt

#################################################
#
#     Pre-Process Games & Tech Tree
#
#################################################

dir_in = './input/'
dir_out = './output/'

log_list = [path_join(dir_in, log_file)
            for log_file in listdir(dir_in)]
games = [Game(log) for log in log_list]

tech_tree = standard_tech_tree()
iCost = tech_tree.icost_from_name

#################################################   
#
#    Process Games for Culminative Research Data
#
#################################################

num_games = len(games)
print num_games

game_names = [game.name for game in games]
print game_names

#For ALL gameS
games_research_culminative_total = []
games_research_order = []

for g in games:
    
    #For Each Game
    total_research = 0
    game_research_culminative_total = []
    game_research_order = []
    
    for turn in g.turns:
        for tech in turn.tech_finished:
            #Comes First For Natural Spelling
            game_research_order.append(tech)
            
            tech = tech.upper()
            tech = tech.replace(' ', '_')
            beakers = iCost(tech)
            total_research += beakers
            
            #print total_research, beakers, tech
            #print game_research_order
        game_research_culminative_total.append(total_research)

    games_research_order.append(list(game_research_order))
    games_research_culminative_total.append(list(game_research_culminative_total))

print games_research_order
print games_research_culminative_total

#################################################
#
#    Output Research Order as Text File
#
#################################################

for n, name in enumerate(game_names):
    print game_research_order[n]
    
    sn = log_list[n].replace(dir_in, dir_out)
    sn = sn.replace('.txt', ' Research Order.txt')
    print sn
    with open(sn, 'w') as f:
        f.write('\n'.join(games_research_order[n]))
    
#################################################
#
#    The Difference Between Two Lists
#        A Difficult Problem
#            Edit Distance
#            Movement Distance
#
#    Not Implemented at this juncture
#    
#    SEE
#        from difflib import SequenceMatcher
#################################################





#################################################
#
#    Graph Culminative Research
#
#        Absolute Values
#
#################################################

#Turn Length of Longest Game
max_turns = max([len(n) for n in games_research_culminative_total])
print max_turns

plt.figure(figsize=(10,5))

for n, game in enumerate(games_research_culminative_total):
    plt.plot(
        range(0, len(game)),
        game,
        label=game_names[n]
        )

plt.legend()
plt.xlim(xmin=0, xmax=max_turns)
plt.title('Absolute Research')
plt.ylabel('Culminative Research')
plt.xlabel('Turn Number')
plt.savefig('./output/culminative_research_absolute.png')
plt.close()
print 'GRAPHED CULMINATIVE RESEARCH'
print '\t ABSOLUTE'

#################################################
#
#    Graph Culminative Research
#
#        Normalized by Turn
#
#################################################


plt.figure(figsize=(10,5))

for n, cul_research in enumerate(games_research_culminative_total):
    
    #Find the Norm Culm/Turn
    norm_research = [0]
    for t,t_r in enumerate(cul_research[1:], 1):
        norm_research.append(t_r/t)
    
    plt.plot(
        range(0, len(norm_research)),
        norm_research,
        label=game_names[n]
        )

plt.legend()
plt.xlim(xmin=0, xmax=max_turns)
plt.title('Normalized Research')
plt.ylabel('Research per Turn')
plt.xlabel('Turn Number')
plt.savefig('./output/culminative_research_turn_normalized.png')
plt.close()

print '\t TURN NORMALIZED'