# import pandas as pd
# import numpy as np
# from numpy import array
# import matplotlib.pyplot as plt
# data = pd.read_csv("Multi_P1FWM_turbo_failure_deviation_ano.csv", header=0)
# train_data = data.iloc[:,5:405]
# # print(train_data.head(2))
# print(train_data.shape)
# newdata = array(train_data)
# print(newdata.shape)
# newdata = newdata[1,:]
# print(newdata.shape)
# print(newdata)
import numpy as np
import pandas as pd
from PIL import Image
import plotly.plotly as py
import plotly.figure_factory as ff
from numpy import array
import matplotlib.pyplot as plt
import argparse
parser = argparse.ArgumentParser()
parser.add_argument( '-a', "--anonymous", action='store_true', default=False, help='Hide axes' )
parser.add_argument( '-t', "--truck", type=str, default=None, help='PLot one truck ID' )
parser.add_argument( '-f', "--filename", type=str, default="Multi_P1FWM_turbo_failure_deviation_ano.csv", help='CSV filename' )
parser.add_argument( '-n', "--normalise", action='store_true', default=False, help='Normalise' )
parser.add_argument( "--max_pics", type=int, default=16, help='Max histograms on a plot' )
parser.add_argument( "--cols", type=int, default=4, help='Number of histogram columns in plot' )
parser.add_argument( "--rows", type=int, default=None, help='Number of rows to read from CSV file' )
parser.add_argument( "--start", type=int, default=0, help='Start histogram' )
parser.add_argument( "--trucks", type=str, default=None, help='Multiple chassis IDs, comma seperated' )
args = parser.parse_args()
df = pd.read_csv("Multi_P1FWM_turbo_failure_deviation_ano.csv")
vehicle_1 = df[df['VEHICLE_ID'] == 1]
vehicle_2 = df[df['VEHICLE_ID'] == 2]
vehicle_3 = df[df['VEHICLE_ID'] == 3]
vehicle_4 = df[df['VEHICLE_ID'] == 4]
vehicle_5 = df[df['VEHICLE_ID'] == 5]
#print(vehicle_1)
vehicle_1_time = vehicle_1.SEND_DATETIME
vehicle_2_time = vehicle_2.SEND_DATETIME
vehicle_3_time = vehicle_3.SEND_DATETIME
vehicle_4_time = vehicle_4.SEND_DATETIME
vehicle_5_time = vehicle_5.SEND_DATETIME
#print(vehicle_1_time)
new = df["SEND_DATETIME"].str.split(" ",n=1,expand=True)
df["Date"] =new[0]
df["Time"] =new[1]
df.drop(columns=["SEND_DATETIME"], inplace= True)
# df.dropna(inplace= True)
# df = df.join(df['SEND_DATETIME'].str.split(',',expand=True).add_prefix('Time'))
#print(df.shape)
#print(df)
data = array(df)
# print(data)
#print(type(data))
#print(data[0,4:404])
print('Rows: %d' %data.shape[0])
print('Cols: %d' %data.shape[1])
num_pics = data.shape[0]
if num_pics > args.max_pics:
      st = num_pics - args.max_pics
      num_pics = args.max_pics
print(num_pics)
sp =1
n = 3
cols = args.cols
rows = int(num_pics/cols)+ 1*((num_pics % cols)!=0)
nn = rows * cols
pc = 0
fig = plt.figure(figsize=(cols*3, rows*3)) #plt.figure(figsize=(sz,sz))
plt.subplots_adjust( hspace=0.7, wspace=0.5 )
ax = fig.add_subplot(rows,cols,sp)
im = data[13,4:404]
im = np.reshape(im,(20,20))
im = im.astype(int)
# print(im.shape)
im = np.flipud(im)
# print(im)
if args.normalise:
      _min = 0
      _max = 1
      im += -(np.min(im))
      im /= np.max(im) / (_max - _min)
im_masked = np.ma.masked_where(im == 0 , im)
plt.imshow(im_masked,interpolation='none',cmap="plasma")
ax.set_aspect('equal')
ax.get_xaxis().set_ticks([0,19])
ax.get_yaxis().set_ticks([])
ax.set_xticks(np.arange(-.5, 19, 1), minor=True);
ax.set_yticks(np.arange(-.5, 19, 1), minor=True);
ax.grid(which='minor', color='w', linestyle='-', linewidth=1)
if not args.anonymous:
 ax.set_xlabel( "engine speed" )
 ax.set_ylabel( "engine torque" )
 plt.colorbar(orientation='vertical', ax=ax, format='%.1f', fraction=0.04)
else:
  plt.colorbar(orientation='vertical', ax=ax, ticks=[])

plt.show()

#print(a)
#print(a.shape)
# a = a.reshape(20,20)
# print(a.shape)

# plt.hist(a, 20, density=1,alpha=0.75)
# plt.ylabel('engine torque')
# plt.xlabel('engine speed')
# # plt.title('vehicle_id')
# # plt.grid(True)
# # plt.show()
# xx = a[:19]
# yy = a[19:]
# #plt.hist2d(xx,yy,bins=20,cmap=plt.cm.BuGn_r)
# plt.figure(2)
# plt.plot(xx,'bo',yy,'k')
# plt.show()
# nn = [go.Histogram2d(x=xx,y=yy)]
# py.iplot(nn)
# plt.show()
# plt.figure(3)
# # plt.hexbin(xx,yy,gridsize=(15,10),cmap='Blues')
# # cb=plt.colorbar
# # plt.show()
# hist_data = [xx, yy]
# group_labels=['a','b']
# fig = ff.create_distplot(hist_data,group_labels,bin_size=.2)
# py.iplot(fig)

# print(xx)
# print(yy)
# plt.hist(a,bins=20)
# plt.show()

# newdata = df[:,5:]
# print(newdata.shape)
# print(newdata)