'''
Created on Apr 10, 2019
@author: Brett Paufler
Copyright Brett Paufler

TAKES TWO LOGFILES
    this_log 1.txt
    this_log 2.txt
    
In Research Ration
    Red = First Game
    Black = Second Game
'''

from os import listdir
from os.path import join as path_join
import matplotlib.pyplot as plt
import numpy as np

####################################################
#
#   Logfile IO - Get Raw Text
#
####################################################

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

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

name_proper = ' '.join(
                log_list[0][8:-4].split(' ')[:-1])
save_name_base = '%s%s' % (
                dir_in,
                '_'.join(name_proper.lower().split(' ')
                         ))
save_name_base = save_name_base.replace(dir_in, dir_out)
save_name_ratio = save_name_base + '_research_ratio.png'
save_name_abandoned = save_name_base + '_research_abandoned.png'
save_name_rough = save_name_base + '_research_rough.png'
save_name_cum_sum = save_name_base + '_research_cum_sum.png'
#print log_list
#print name_proper
#print save_name   

with open(log_list[0], 'r') as f:
    log_text_one = f.read()

with open(log_list[1], 'r') as f:
    log_text_two = f.read()

#print log_text_one


####################################################
#
#    Extract Research Data
#
####################################################

#Find Number of Turns
turns_raw = [0, 0]
for n, game in enumerate([log_text_one, log_text_two]):
    for line in game.split('\n'):
    #print line
        if line.startswith('Turn '):
            turn = line.split('/')
            turn = int(turn[0][5:])
            if turns_raw[n] < turn:
                turns_raw[n] = turn
                #print n, turn
#print turns_raw
turns_max = 1 + max(turns_raw)
turns_min = 1 + min(turns_raw)
#print turns_max
#print turns_min

#For Per Turn Research Rate
research_turn = [
    [0] * turns_max,
    [0] * turns_max
    ]
#print research_turn

#To Track Technological Discoveries
discovery_turn = [
    [''] * turns_max,
    [''] * turns_max
    ]
#print discovery_turn

for n, game in enumerate([log_text_one, log_text_two]):
    
    turn = 0

    for line in game.split('\n'):
        
        #What Turn is it?
        if line.startswith('Turn '):
            turn = line.split('/')
            turn = int(turn[0][5:])
            continue
        
        #Per Turn Research Amount
        if (
            (' Research: ' in line)
            and
            (' per turn' in line)):
            research = line.split(': ')[1][:-9]
            research_turn[n][turn] = int(research)
            #print line
            #print research
            continue
        
        #Technologies by Trade
        if 'Tech acquired (trade, light' in line:
            tech = line.split('espionage): ')[1]
            discovery_turn[n][turn] = tech
            #print line
            #print civ_parse_tech_tree
            continue
        
        if 'Tech research finished: ' in line:
            tech = line.split('finished: ')[1]
            discovery_turn[n][turn] = tech
            #print line
            #print civ_parse_tech_tree


###################################################
#
#    Preprocess the Values a Bit
#        I probably did not need to do any of this
#
###################################################


#Chop to Minimum
research_one = research_turn[0][:turns_min]
research_two = research_turn[1][:turns_min]
#print research_one
#print research_two

#But I don't think I will be using this
discover_one = discovery_turn[0][:turns_min]
discover_two = discovery_turn[1][:turns_min]
#print discover_one
#print discover_two


###################################################
#
#    Graph: Research Rate as a Ratio
#
###################################################

ratio_one = []
ratio_two = []

#Reduce Absolute Values to Ratios
for i in range(len(research_one)):

    if research_one[i] >= research_two[i]:
        r = research_one[i]
    else:
        r = research_two[i]
    
    r = float(r)
    ratio_one.append(research_one[i]/r)
    ratio_two.append(research_two[i]/r)
    
plt.figure(figsize=(10,5))

plt.plot(range(turns_min),
         ratio_one,
         color='blue',
         label='Game One')

plt.plot(range(turns_min),
         ratio_two,
         color='red',
         label='Game Two')

plt.legend()

plt.title(name_proper + '\nResearch Ratio')
plt.ylabel('Ratio')

plt.xlabel('Turn Number')
plt.xlim(xmin=0, xmax=turns_min)
#plt.show()

print save_name_abandoned
#plt.savefig(save_name_abandoned)
plt.close()


###################################################
#
#    Graph: as an absolute ratio to the second game
#
###################################################

ratio_two = []

#A Simple Ratio
for i in range(len(research_one)):

    if research_one[i] == 0:
        ratio_two.append(ratio_two[-1])
    else:
        ratio_two.append(
            float(research_two[i]) / float(research_one[i]))
    
plt.figure(figsize=(10,5))

plt.plot(range(turns_min),
         ratio_two,
         color='black')
         
plt.axhline(1.0, color='red')

plt.title(name_proper + '\nResearch Ratio')
plt.ylabel('Ratio')

plt.xlabel('Turn Number')
plt.xlim(xmin=0, xmax=turns_min)
#plt.show()

print save_name_rough
#plt.savefig(save_name_rough)

plt.close()


###################################################
#
#    Graph: Ratio... as above but smooth
#
###################################################

conv = [.01]*100

ratio_one = np.array(research_one, dtype=np.float64)
ratio_one = np.convolve(
    ratio_one, conv, mode='same')
    
ratio_two = np.array(research_two)    
ratio_two = np.convolve(
    ratio_two, conv, mode='same')
    
ratio_two = ratio_two/ratio_one    
abs_max = np.max(np.abs(ratio_one))

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

plt.plot(range(turns_min),
         ratio_two,
         color='black')
         
plt.axhline(1.0, color='red')

plt.title(name_proper + '\nResearch Ratio')
plt.ylabel('Ratio')

plt.xlabel('Turn Number')
plt.xlim(xmin=0, xmax=turns_min)
#plt.ylim(ymin=-abs_max, ymax=abs_max)
#plt.show()

print save_name_ratio
plt.savefig(save_name_ratio)

plt.close()


###################################################
#
#   Graph: Cumulative Sum
#
###################################################

cum_sum_one = np.cumsum(research_one)
cum_sum_two = np.cumsum(research_two)

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

plt.plot(range(turns_min),
         cum_sum_one,
         color='blue',
         label='Game One Total: %d' % sum(research_one))

plt.plot(range(turns_min),
         cum_sum_two,
         color='red',
         label='Game Two Total: %d' % sum(research_two))

plt.legend()

plt.title(name_proper + '\nCumulative Sum')
plt.ylabel('Ratio')

plt.xlabel('Turn Number')
plt.xlim(xmin=0, xmax=turns_min)
#plt.show()

print save_name_cum_sum
plt.savefig(save_name_cum_sum)
plt.close()

###################################################
#
#   Statistics
#
###################################################



print 'Total Research Game One: %d, Turns: %d' % (
    sum(research_one), len(research_one))
    
print 'Total Research Game Two: %d, Turns: %d' % (
    sum(research_two), len(research_one))