import numpy as np
from numpy import genfromtxt
import matplotlib.pyplot as plt
import argparse

def main():
	parser = argparse.ArgumentParser(description='')
	parser.add_argument('--batch_size', type=int, default=8, help='batch size')
	parser.add_argument('--lr', type=float, default=1e-4, help='learning rate')
	parser.add_argument('--hidden_size', type=int, default=128, help='hidden_size')
	parser.add_argument('--model_type', type=str, default='lstmlast', help='model type')
	parser.add_argument('--N', type=int, default=1, help='number of sequence for inference')
	parser.add_argument('--user', type=int, default=1, help='number of users')
	
	args = parser.parse_args()	
	plot_type = 'bar' # line bar
	act_series = 5

	# read data
	plot_list = []
	for act in range(1,act_series+1):
		user_data_list = []
		for i in range(args.user):
			model_data_list = []
			path = "result/"+"N"+ str(args.N) + "/" + args.model_type + "bs_" + str(args.batch_size) + '_lr_' + str(args.lr) + '_hidden_size_' + str(args.hidden_size) + '_N' + str(args.N) + "_result_user" + str(i) + "_rate__100"  + "_act_" + str(act) +".csv"
			data = genfromtxt(path, delimiter=',', skip_header =1)
			for j in range(7):
				data_temp = data[[1+7*j+j,2+7*j+j,3+7*j+j,4+7*j+j,5+7*j+j,6+7*j+j,7+7*j+j],:][:,[2,4,6,7]]
				model_data_list.append(data_temp)
			model_data_list = np.concatenate(model_data_list, axis=0)
			print(model_data_list.shape)
			user_data_list.append(model_data_list)

		color = ['royalblue', 'lightgreen', 'tomato', 'indigo', 'plum', 'darkorange', 'blue']
		legend = ['rule 1', 'rule 2', 'rule 3', 'rule 4', 'rule 5', 'rule 6', 'rule 7']
		fig, axs = plt.subplots(7, sharex=True, sharey=True)
		fig.set_figheight(14)
		fig.set_figwidth(25)

		for ax in range(7):
			y_total = []
			y_low_total = []
			y_high_total = []
			for j in range(7):
				y= []
				y_low = []
				y_high = []
				for i in range(len(user_data_list)):
					y.append(user_data_list[i][j+ax*7][0])
					y_low.append(user_data_list[i][j+ax*7][2])
					y_high.append(user_data_list[i][j+ax*7][3])
				y_total.append(y)
				y_low_total.append(y_low)	
				y_high_total.append(y_high)
				print()
				print("user mean of mean prob: ", np.mean(y))
				print("user mean of sd prob: ", np.std(y))
				
			for i in range(7):
				if plot_type ==  'line':
					axs[ax].plot(range(args.user), y_total[i], color=color[i], label=legend[i])
					axs[ax].fill_between(range(args.user), y_low_total[i], y_high_total[i], color=color[i],alpha=0.3 )
				if plot_type ==  'bar':
					width = [-0.36, -0.24, -0.12, 0, 0.12, 0.24, 0.36]
					yerror = [np.array(y_total[i])-np.array(y_low_total[i]), np.array(y_high_total[i])-np.array(y_total[i])]
					axs[ax].bar(np.arange(args.user)+width[i], y_total[i], width=0.08, yerr=[np.array(y_total[i])-np.array(y_low_total[i]), np.array(y_high_total[i])-np.array(y_total[i])], label=legend[i], color=color[i])
					axs[ax].tick_params(axis='x', which='both', length=0)
					axs[ax].set_ylabel('prob', fontsize=36) # was 22, 
					for k,x in enumerate(np.arange(args.user)+width[i]):
						y = y_total[i][k] + yerror[1][k]
						axs[ax].annotate(f'{y_total[i][k]:.2f}', (x, y), textcoords='offset points', xytext=(-18,3), fontsize=16) #was 16
					
			axs[0].text(-0.17, 1.2, 'True Intention:', horizontalalignment='center', verticalalignment='center', transform=axs[0].transAxes, fontsize= 46) # was -0.1 0.9 25
			axs[ax].text(-0.17, 0.5, legend[ax], horizontalalignment='center', verticalalignment='center', transform=axs[ax].transAxes, fontsize= 46, color=color[ax]) # was 25 		
			axs[ax].tick_params(axis='both', which='major', labelsize=42) # was 18
			for tick in axs[ax].xaxis.get_major_ticks():
				tick.set_pad(20)
			
		plt.xticks(range(args.user),('1', '2', '3', '4', '5'))
		plt.xlabel('user', fontsize= 42) # was 22
		handles, labels = axs[0].get_legend_handles_labels()

		plt.ylim([0, 1])
		plt.tight_layout()
		if plot_type ==  'line':
			plt.savefig("figure/"+"N"+ str(args.N) + "_ "+ args.model_type + "_bs_" + str(args.batch_size) + '_lr_' + str(args.lr) + '_hidden_size_' + str(args.hidden_size) + '_N' + str(args.N) + "_act_series" + str(act) + "_line_all_individual.png", bbox_inches='tight')
		if plot_type ==  'bar':
			plt.savefig("figure/"+"N"+ str(args.N) + "_ "+ args.model_type + "_bs_" + str(args.batch_size) + '_lr_' + str(args.lr) + '_hidden_size_' + str(args.hidden_size) + '_N' + str(args.N) + "_act_series" + str(act) + "_bar_all_individual.png", bbox_inches='tight')
		
		if plot_type ==  'line':
			plt.savefig("figure/"+"N"+ str(args.N) + "_ "+ args.model_type + "_bs_" + str(args.batch_size) + '_lr_' + str(args.lr) + '_hidden_size_' + str(args.hidden_size) + '_N' + str(args.N) + "_act_series" + str(act) + "_line_all_individual.eps", bbox_inches='tight', format='eps')
		if plot_type ==  'bar':
			plt.savefig("figure/"+"N"+ str(args.N) + "_ "+ args.model_type + "_bs_" + str(args.batch_size) + '_lr_' + str(args.lr) + '_hidden_size_' + str(args.hidden_size) + '_N' + str(args.N) + "_act_series" + str(act) + "_bar_all_individual.eps", bbox_inches='tight', format='eps')			
		#plt.show()
		
if __name__ == '__main__':
	main()