# coding:utf-8
from keras import models
from keras.models import model_from_json
import numpy as np
import codecs
import csv
import datetime
import matplotlib.pyplot as plt

model=model_from_json(open("train.json").read())
model.load_weights("train.hdf5")
model.summary()

timestep=24

xdata=[]
with codecs.open('juyo_2019_tohoku.csv','r','Shift-JIS', 'ignore') as csvfile:
    reader = csv.reader(csvfile, delimiter = ',')
    cnt=0
    for row in reader:
        if cnt>1:
            ymd=row[0].split('/')
            h=int( row[1].split(':')[0] )
            wd=datetime.datetime(int(ymd[0]),int(ymd[1]),int(ymd[2])).weekday()
            p=float(row[2].strip())/1000.0
            xdata.append([wd,h,p])
        cnt=cnt+1

steps=[]
yvals=[]
ypvals=[]
first_time=True
t_start=4000
t_end=t_start+168
for i in range(t_start,t_end):
    if first_time:
        first_time=False
        x = np.array(xdata[i:i+timestep])
    else:
        x[:,0] = np.roll(x[:,0],-1)
        if x[-1,1]==23:
            x[-1,0] = np.mod( x[-2,0] + 1, 7)
        else:
            x[-1,0] = x[-2,0]
        x[:,1] = np.roll(x[:,1],-1)
        x[-1,1] = np.mod( x[-2,1] + 1, 24)
        x[:,2] = np.roll(x[:,2],-1)
        x[-1,2] = ynext

    xin= np.array([x], dtype=np.float32)    
    ynext = model.predict(xin)[0][0]
    if (i+timestep<len(xdata)):
        yr = xdata[i + timestep][2]
    steps.append(i + timestep)
    yvals.append(yr)
    ypvals.append(ynext)
    
plt.plot(steps,yvals, color=(1.0,0,0.0), linewidth=1.0)
plt.plot(steps,ypvals, color=(0.0,1,1.0), linewidth=1.0)
plt.xlabel('TIME(HR)')
plt.ylabel('10^7 KWH')
plt.grid(True)
plt.show()