#!/usr/bin/env python

######################################################### 
import 	matplotlib.pyplot as plt			#	
import 	matplotlib        as mpl			#
import 	numpy             as np				#
from  	scipy.interpolate import Rbf 			#
from	scipy.optimize    import minimize		#	
from   	math              import *			#
from   	sys		  import stdout			#
#########################################################
#							#
# this code produces a 2D contour plot and the MEP on 	#
# the PES. 						#
# this code is written for a diatomic molecule thinking	#
# about the 2D cut of the potential considering the	#
# interatomic distance (r) and the distance of the 	#
# CoM (Z) from the surface as variables. 		#
# Therefore the data.dat input file must be formatted   # 
# as:		r      Z      E				#
# You can define the variables related to the plot	# 
# apperance and (more important) to the interpolation   #
# and the MEP calculation in the VARIABLES section. 	# 
#							# 	
# Davide Migliorini		      Leiden, 2015	# 				      	
#							#
#-------------------------------------------------------#
# A massive update has been implemented including a way #
# better fitting procedure to find the MEP and a better #
# 2D interpolation function: scipy.interpolate.Rbf	#
# I am too lazy to proper document all the changes	#
# however the comments in the code should make it 	#
# (fairly) self-explainatory.				#
# I hope.						#
#							#
# Davide Migliorini			Leiden, 2018	#
#							#
#-------------------------------------------------------#
# Some changes were implemented to improve the figures, #
# too lazy to document.					#
#							#
# Nick Gerrits				Leiden, 2018	#
#							#
#########################################################

#########################################################
# VARIABLES #############################################

# Title &  file name
inputs 		= [ 'barrier_theta_phi_top.dat', 'barrier_theta_phi_fcc.dat', 'barrier_theta_phi_bridge.dat', 'barrier_theta_phi_TS.dat' ]
titles 		= [ 'Top', 'Fcc', 'Bridge', 'TS' ]  

# contour line
minE    	=  100.			# min and max E shown in the plot
maxE    	=  204.			#	
spacing 	=  10.0		# E spacing between contour lines

# set up plot parameters
mpl.rc("text", usetex=True)
Ncols=4
fig, ax = plt.subplots(nrows=1, ncols=Ncols, figsize=(6.96,4))

########################################################
# Contour plot #########################################
########################################################

images = []
def ReadAndInterpolate(INP, Title, minE, maxE, spacing, plot,bar=False):
	plt.subplot(1,Ncols,plot)

	X = []
	Y = []
	E = []

	lines = open(INP).readlines()
	for i in range(0, len(lines)):
		X.append(float(  lines[i].split()[1]  ))
		Y.append(float(  lines[i].split()[2]  ))
		E.append(float(  lines[i].split()[5]  )) 

	N = len(lines)
	X = np.array(X)
	Y = np.array(Y)
	E = np.array(E)
	print( " -----------------------" )
	print( " file: ", INP )
	print( " title:", Title )
	print( " Energy values (min-max):" )
	print( " (", min(E), max(E), ")  [ eV ]" )

	# CONTOUR LINES PLOT ###################################

	levels = np.arange( minE, maxE , spacing )    #range of energies shown

	# generate a uniform XY grid 
	density = 100	# number of points per dimention in the denser grid
	Xnew, Ynew = np.meshgrid( np.linspace( min(X), max(X), density ),  np.linspace( min(Y), max(Y), density ) )

	# interpolate Z on the XY uniform grid
	F 	= Rbf(X, Y, E, function='cubic', smooth=0. )
	Znew	= F(Xnew, Ynew)	

	# Contour lines ###
#	plt.subplot(1,2,plot)

	areas 	 = plt.contourf(Xnew, Ynew, Znew, levels, zorder=-1, cmap='jet' )# , colors='k' )	# coloured areas
	contours = plt.contour( Xnew, Ynew, Znew, levels, colors='k', zorder=0 )	# black level lines
#	plt.scatter(Xnew,Ynew, s=1)

	# plot appereance ###
	images.append( areas )
#	plt.colorbar(areas)
	
	#plt.axis('scaled')
	plt.xlabel( r'$\theta$ (deg.)' )
	if plot == 1:
		plt.ylabel( r'$\phi$ (deg.)' )
	else:
		plt.tick_params(labelleft='off')
#	plt.axis('equal')
#	plt.xlim(min(X), max(X))
	plt.xlim(90., 149.)
	plt.ylim(min(Y), max(Y))
	plt.yticks( np.arange(0., 420., 60.) )
	plt.title( "%s" % Title )

###################################################################
###################################################################
###################################################################

for i in range(4):
	ReadAndInterpolate(inputs[i], titles[i], minE, maxE, spacing, i+1, True)

plt.tight_layout()
plt.subplots_adjust(wspace=0.0, hspace=-0.0)

cbar_ax = fig.add_axes([0.89, 0.1, 0.025, 0.85]) #xmin, ymin, xwidth, ywidth
fig.colorbar(images[0], cax=cbar_ax) #, ticks=np.arange(0.0,0.7,0.1)
cbar_ax.set_ylabel('Barrier height (kJ/mol)')
fig.subplots_adjust(right=0.87, left=0.08)

plt.savefig('barrier_theta_phi.pdf')
#plt.show()