#!/usr/bin/env python2
#io_python
#plots 

import numpy as np
import matplotlib.pyplot as plt
import sys, os



dt = 0.1
t1 = 8970          #stimulation at time 900ms (tstim = 9000*dt )
t2 = 10500         #PC: 10500,  ACC: 10500, BC: 10500 , BSC: 10500, OLM: 10500, VIPCCK: 10500 , VIPCR: 10500 


#select the target cell 
cell = sys.argv[1]  # EC_CA3_to_PC or  Pyramidal  or  Axoaxonic  or Basket or Bistratified or OLM or VIPCCK or VIPCR

fname = 'SynapticProperties'

os.system('mkdir -p '+fname+'/plots/')
#               ___    ___
############## |___|  |    ################
############## |      |    ################# 
############## |      |___ #################

if cell == 'EC_CA3_to_PC':
	print "EC and CA3 ampa/nmda to Pyramidal"

	#PC cell 
	with open(fname+'/ampaECtoPC_i_rec_soma.txt','r', 0) as f1:    
		vec1 = f1.readlines()
	with open(fname+'/ampaCA3toapicalPC_i_rec_soma.txt','r', 0) as f2:    
		vec2 = f2.readlines()
	with open(fname+'/ampaCA3tobasalPC_i_rec_soma.txt','r', 0) as f3:    
		vec3 = f3.readlines()
	with open(fname+'/nmdaECtoPC_i_rec_soma.txt','r', 0) as f4:    
		vec4 = f4.readlines()
	with open(fname+'/nmdaCA3toapicalPC_i_rec_soma.txt','r', 0) as f5:    
		vec5 = f5.readlines()
	with open(fname+'/nmdaCA3tobasalPC_i_rec_soma.txt','r', 0) as f6:    
		vec6 = f6.readlines()



	# Make strings as floats with list comprehension
	vec1 = [float(x1) for x1 in vec1]
	vec2 = [float(x2) for x2 in vec2]
	vec3 = [float(x3) for x3 in vec3]
	vec4 = [float(x4) for x4 in vec4]
	vec5 = [float(x5) for x5 in vec5]
	vec6 = [float(x6) for x6 in vec6]

	# To convert in pA
	vec1_pA = [1000*x1 for x1 in vec1]
	vec2_pA = [1000*x2 for x2 in vec2]
	vec3_pA = [1000*x3 for x3 in vec3]
	vec4_pA = [1000*x4 for x4 in vec4]
	vec5_pA = [1000*x5 for x5 in vec5]
	vec6_pA = [1000*x6 for x6 in vec6]

	t2 = t1+5000  #more time because of slow kinetics of NMDA

	M1 = vec1_pA[t1]
	M2 = vec2_pA[t1]
	M3 = vec3_pA[t1]

	M4 = vec4_pA[t1]
	M5 = vec5_pA[t1]
	M6 = vec6_pA[t1]

	vec1n_pA = [x1-M1 for x1 in vec1_pA]
	vec2n_pA = [x2-M2 for x2 in vec2_pA]
	vec3n_pA = [x3-M3 for x3 in vec3_pA]

	vec4n_pA = [x4-M4 for x4 in vec4_pA]
	vec5n_pA = [x5-M5 for x5 in vec5_pA]
	vec6n_pA = [x6-M6 for x6 in vec6_pA]

	a = range(1,len(vec1_pA)+1)# time bins
	# print a
	tb = [x*dt for x in a]

	fig = plt.figure(1,figsize=(10,10))


	plt.plot(tb[t1:t2],vec1n_pA[t1:t2], label='EC AMPA EPSC')
	plt.plot(tb[t1:t2],vec2n_pA[t1:t2], label='CA3 AMPA EPSC (apical)')
	plt.plot(tb[t1:t2],vec3n_pA[t1:t2], label='CA3 AMPA EPSC (basal)')
	plt.plot(tb[t1:t2],vec4n_pA[t1:t2], label='EC NMDA EPSC')
	plt.plot(tb[t1:t2],vec5n_pA[t1:t2], label='CA3 NMDA EPSC (apical)')
	plt.plot(tb[t1:t2],vec6n_pA[t1:t2], label='CA3 NMDA EPSC (basal)')
	

	mn = min(vec3n_pA[t1:t2]) #min epsc/ipsc
	mx = max(vec1n_pA[t1:t2]) #max epsc/ipsc
	rg = int(mx-mn)/10
	rg = rg - rg % 10
	# print rg

	
	plt.plot([1030, 1030], [mn+5, mn+5+1], linewidth = 1.6, color = 'black') # 5 pA vertical 
	plt.plot([1030, 1130], [mn+5, mn+5], linewidth = 1.6, color = 'black')  # 10 ms

	plt.text(1050, mn+5.2, r'1 pA',fontweight='bold')
	plt.text(1052, mn+4.2, r'100 ms',fontweight='bold')

	plt.ylabel('Current (pA)')
	plt.xlabel('Time (ms)')
	plt.suptitle('      Post-synaptic ampa & nmda currents from EC & CA3 to Pyramidal', fontsize=14, fontweight='bold')
	plt.legend(frameon = False)

	plt.savefig(fname+'/plots/EC_CA3_AMPA_NMDAtoPC_currents.pdf',format='pdf',dpi=600)

	#plt.show()

elif cell == 'Pyramidal':
	print "Pyramidal Cell plot"

	#PC cell 
	with open(fname+'/BCtoPC_i_rec_soma.txt','r', 0) as f1:    
		vec1 = f1.readlines()
	with open(fname+'/VIPCCKtoPC_i_rec_soma.txt','r', 0) as f2:   
		vec2 = f2.readlines()
	with open(fname+'/AACtoPC_i_rec_soma.txt','r', 0) as f3:    
		vec3 = f3.readlines()
	with open(fname+'/gabaaOLMtoPC_i_rec_soma.txt','r', 0) as f4:   
		vec4 = f4.readlines()
	with open(fname+'/gabaaBSCtoPC_i_rec_soma.txt','r', 0) as f5:    
		vec5 = f5.readlines()
	with open(fname+'/gabaaBSCtobasalPC_i_rec_soma.txt','r', 0) as f6:    
		vec6 = f6.readlines()
	with open(fname+'/PCtoPC_i_rec_soma.txt','r', 0) as f7:  
		vec7 = f7.readlines()


	# Make strings as floats with list comprehension
	vec1 = [float(x1) for x1 in vec1]
	vec2 = [float(x2) for x2 in vec2]
	vec3 = [float(x3) for x3 in vec3]
	vec4 = [float(x4) for x4 in vec4]
	vec5 = [float(x5) for x5 in vec5]
	vec6 = [float(x6) for x6 in vec6]
	vec7 = [float(x7) for x7 in vec7]


	# To convert in pA
	vec1_pA = [1000*x1 for x1 in vec1]
	vec2_pA = [1000*x2 for x2 in vec2]
	vec3_pA = [1000*x3 for x3 in vec3]
	vec4_pA = [1000*x4 for x4 in vec4]
	vec5_pA = [1000*x5 for x5 in vec5]
	vec6_pA = [1000*x6 for x6 in vec6]
	vec7_pA = [1000*x7 for x7 in vec7]

	
	a = range(1,len(vec1_pA)+1)# time bins
	# print a
	tb = [x*dt for x in a]

	fig = plt.figure(1,figsize=(10,10))

	
	plt.plot(tb[t1:t2],vec1_pA[t1:t2], label='BC IPSC')
	plt.plot(tb[t1:t2],vec2_pA[t1:t2], label='VIPCCK IPSC')
	plt.plot(tb[t1:t2],vec3_pA[t1:t2], label='ACC IPSC')
	plt.plot(tb[t1:t2],vec4_pA[t1:t2], label='GABAA OLM IPSC')
	plt.plot(tb[t1:t2],vec5_pA[t1:t2], label='GABAA BSC (apical) IPSC')
	plt.plot(tb[t1:t2],vec6_pA[t1:t2], label='GABAA BSC (basal) IPSC')
	plt.plot(tb[t1:t2],vec7_pA[t1:t2], label='PC EPSC')


	mn = min(vec7_pA[t1:t2]) #min epsc/ipsc
	mx = max(vec3_pA[t1:t2]) #max epsc/ipsc
	rg = int(mx-mn)/10
	rg = rg - rg % 10
	# print rg

	
	
	plt.plot([1000, 1000], [mn+150, mn+150+20], linewidth = 1.6, color = 'black') # 5 pA vertical 
	plt.plot([1000, 1010], [mn+150, mn+150], linewidth = 1.6, color = 'black')  # 10 ms

	plt.text(985, mn+154, r'20 pA',fontweight='bold')
	plt.text(1002, mn+140, r'10 ms',fontweight='bold')

	plt.ylabel('Current (pA)')
	plt.xlabel('Time (ms)')
	plt.suptitle('      Post-synaptic currents from all cells except EC & CA3 ', fontsize=14, fontweight='bold')
	plt.legend(frameon = False)

	plt.savefig(fname+'/plots/all_except_ec_ca3_toPC_currents.pdf',format='pdf',dpi=600)

	#plt.show()


#                           ___
############## /\     /\   |    ##############
############# /__\   /__\  |    ################# 
############ /    \ /    \ |___ #################


elif cell == 'Axoaxonic':
	print "Axoaxonic Cell plot"

	#AAC cell 
	with open(fname+'/ECtoAAC_i_rec_soma.txt','r', 0) as f1:    
		vec1 = f1.readlines()
	with open(fname+'/CA3toAAC_i_rec_soma.txt','r', 0) as f2:    
		vec2 = f2.readlines()
	with open(fname+'/PCtoAAC_i_rec_soma.txt','r', 0) as f3:   
		vec3 = f3.readlines()
	with open(fname+'/BCtoAAC_i_rec_soma.txt','r', 0) as f4:   
		vec4 = f4.readlines()
	with open(fname+'/BSCtoAAC_i_rec_soma.txt','r', 0) as f5:    
		vec5 = f5.readlines()
	with open(fname+'/OLMtoAAC_i_rec_soma.txt','r', 0) as f6:   
		vec6 = f6.readlines()
	with open(fname+'/VIPCCKtoAAC_i_rec_soma.txt','r', 0) as f7:  
		vec7 = f7.readlines()



	# Make strings as floats with list comprehension
	vec1 = [float(x1) for x1 in vec1]
	vec2 = [float(x2) for x2 in vec2]
	vec3 = [float(x3) for x3 in vec3]
	vec4 = [float(x4) for x4 in vec4]
	vec5 = [float(x5) for x5 in vec5]
	vec6 = [float(x6) for x6 in vec6]
	vec7 = [float(x7) for x7 in vec7]
	

	# To convert in pA
	vec1_pA = [1000*x1 for x1 in vec1]
	vec2_pA = [1000*x2 for x2 in vec2]
	vec3_pA = [1000*x3 for x3 in vec3]
	vec4_pA = [1000*x4 for x4 in vec4]
	vec5_pA = [1000*x5 for x5 in vec5]
	vec6_pA = [1000*x6 for x6 in vec6]
	vec7_pA = [1000*x7 for x7 in vec7]

	a = range(1,len(vec1_pA)+1)# time bins
	# print a
	tb = [x*dt for x in a]
	# print tb

	fig = plt.figure(1,figsize=(10,10))

	plt.plot(tb[t1:t2],vec1_pA[t1:t2], label='EC EPSC')
	plt.plot(tb[t1:t2],vec2_pA[t1:t2], label='CA3 EPSC')
	plt.plot(tb[t1:t2],vec3_pA[t1:t2], label='PC EPSC')
	plt.plot(tb[t1:t2],vec4_pA[t1:t2], label='BC IPSC')
	plt.plot(tb[t1:t2],vec5_pA[t1:t2], label='BSC IPSC')
	plt.plot(tb[t1:t2],vec6_pA[t1:t2], label='OLM IPSC')
	plt.plot(tb[t1:t2],vec7_pA[t1:t2], label='VIPCCK IPSC')
	
	mn = min(vec2_pA[t1:t2]) 
	mx = max(vec7_pA[t1:t2])
	rg = int(mx-mn)/10
	rg = rg - rg % 10
	# print mx,mn, rg

	plt.text(1025, mn+2.2, r'1 pA',fontweight='bold')
	plt.text(1042, mn+1.5, r'10 ms',fontweight='bold')

	plt.plot([1040, 1040], [mn+2, mn+2+ 1], linewidth = 1.6, color = 'black') # 1 pA vertical 
	plt.plot([1040, 1050], [mn+2, mn+2], linewidth = 1.6, color = 'black')  # 10 ms

	plt.ylabel('Current (pA)')
	plt.xlabel('Time (ms)')
	plt.suptitle('      Post-synaptic currents to AAC ', fontsize=14, fontweight='bold')
	plt.legend(frameon = False)


	plt.savefig(fname+'/plots/toAAC_post_synaptic_currents.pdf',format='pdf',dpi=600)


	#plt.show()

#              ___    ___
############# |   |  |    #################
############# |~~~   |    ################# 
############# |___|  |___ #################


elif cell=="Basket":
	print "Basket Cell plot"

	#BC cell 
	with open(fname+'/ECtoBC_i_rec_soma.txt','r', 0) as f1:
		vec1 = f1.readlines()
	with open(fname+'/CA3toBC_i_rec_soma.txt','r', 0) as f2:   
		vec2 = f2.readlines()
	with open(fname+'/PCtoBC_i_rec_soma.txt','r', 0) as f3:    
		vec3 = f3.readlines()
	with open(fname+'/BCtoBC_i_rec_soma.txt','r', 0) as f4: 
		vec4 = f4.readlines()
	with open(fname+'/BSCtoBC_i_rec_soma.txt','r', 0) as f5:    
		vec5 = f5.readlines()
	with open(fname+'/OLMtoBC_i_rec_soma.txt','r', 0) as f6: 
		vec6 = f6.readlines()
	with open(fname+'/VIPCRtoBC_i_rec_soma.txt','r', 0) as f7: 
		vec7 = f7.readlines()



	# Make strings as floats with list comprehension
	vec1 = [float(x1) for x1 in vec1]
	vec2 = [float(x2) for x2 in vec2]
	vec3 = [float(x3) for x3 in vec3]
	vec4 = [float(x4) for x4 in vec4]
	vec5 = [float(x5) for x5 in vec5]
	vec6 = [float(x6) for x6 in vec6]
	vec7 = [float(x7) for x7 in vec7]


	# To convert in pA
	vec1_pA = [1000*x1 for x1 in vec1]
	vec2_pA = [1000*x2 for x2 in vec2]
	vec3_pA = [1000*x3 for x3 in vec3]
	vec4_pA = [1000*x4 for x4 in vec4]
	vec5_pA = [1000*x5 for x5 in vec5]
	vec6_pA = [1000*x6 for x6 in vec6]
	vec7_pA = [1000*x7 for x7 in vec7]

	a = range(1,len(vec1_pA)+1)# time bins
	# print a
	tb = [x*dt for x in a]

	fig = plt.figure(1,figsize=(10,10))


	plt.plot(tb[t1:t2],vec1_pA[t1:t2], label='EC EPSC')
	plt.plot(tb[t1:t2],vec2_pA[t1:t2], label='CA3 EPSC')
	plt.plot(tb[t1:t2],vec3_pA[t1:t2], label='PC EPSC')
	plt.plot(tb[t1:t2],vec4_pA[t1:t2], label='BC IPSC')
	plt.plot(tb[t1:t2],vec5_pA[t1:t2], label='BSC IPSC')
	plt.plot(tb[t1:t2],vec6_pA[t1:t2], label='OLM IPSC')
	plt.plot(tb[t1:t2],vec7_pA[t1:t2], label='VIPCR IPSC')

	mn = min(vec3_pA[t1:t2]) #min PC
	mx = max(vec7_pA[t1:t2]) #max VIPCR
	rg = int(mx-mn)/10
	rg = rg - rg % 10
	# print mx,mn, rg
	
	plt.text(1015, mn+105, r'60 pA',fontweight='bold')
	plt.text(1032, mn+65, r'10 ms',fontweight='bold')

	plt.plot([1030, 1030], [mn+90, mn+90+ rg], linewidth = 1.6, color = 'black') # 60 pA vertical 
	plt.plot([1030, 1040], [mn+90, mn+90], linewidth = 1.6, color = 'black')  # 10 ms


	plt.ylabel('Current (pA)')
	plt.xlabel('Time (ms)')
	plt.suptitle('      Post-synaptic currents to BC ', fontsize=14, fontweight='bold')
	plt.legend(frameon = False)
	
	plt.savefig(fname+'/plots/toBC_post_synaptic_currents.pdf',format='pdf',dpi=600)

	#plt.show()

#              ___    __    ___
############# |   |  |__   |    ###########
############# |---      |  |    ################# 
############# |___|  ___|  |___ #################



elif cell=="Bistratified":
	print "Bistratified Cell plot"

	#BC cell 
	with open(fname+'/ECtoBSC_i_rec_soma.txt','r', 0) as f1:  
		vec1 = f1.readlines()
	with open(fname+'/CA3toBSC_i_rec_soma.txt','r', 0) as f2:  
		vec2 = f2.readlines()
	with open(fname+'/PCtoBSC_i_rec_soma.txt','r', 0) as f3:   
		vec3 = f3.readlines()
	with open(fname+'/BCtoBSC_i_rec_soma.txt','r', 0) as f4:
		vec4 = f4.readlines()
	with open(fname+'/BSCtoBSC_i_rec_soma.txt','r', 0) as f5:   
		vec5 = f5.readlines()
	with open(fname+'/OLMtoBSC_i_rec_soma.txt','r', 0) as f6:    
		vec6 = f6.readlines()
	with open(fname+'/VIPCCKtoBSC_i_rec_soma.txt','r', 0) as f7:
		vec7 = f7.readlines()



	# Make strings as floats with list comprehension
	vec1 = [float(x1) for x1 in vec1]
	vec2 = [float(x2) for x2 in vec2]
	vec3 = [float(x3) for x3 in vec3]
	vec4 = [float(x4) for x4 in vec4]
	vec5 = [float(x5) for x5 in vec5]
	vec6 = [float(x6) for x6 in vec6]
	vec7 = [float(x7) for x7 in vec7]


	# To convert in pA
	vec1_pA = [1000*x1 for x1 in vec1]
	vec2_pA = [1000*x2 for x2 in vec2]
	vec3_pA = [1000*x3 for x3 in vec3]
	vec4_pA = [1000*x4 for x4 in vec4]
	vec5_pA = [1000*x5 for x5 in vec5]
	vec6_pA = [1000*x6 for x6 in vec6]
	vec7_pA = [1000*x7 for x7 in vec7]

	a = range(1,len(vec1_pA)+1)# time bins
	# print a
	tb = [x*dt for x in a]

	fig = plt.figure(1,figsize=(10,10))


	plt.plot(tb[t1:t2],vec1_pA[t1:t2], label='EC EPSC')
	plt.plot(tb[t1:t2],vec2_pA[t1:t2], label='CA3 EPSC')
	plt.plot(tb[t1:t2],vec3_pA[t1:t2], label='PC EPSC')
	plt.plot(tb[t1:t2],vec4_pA[t1:t2], label='BC IPSC')
	plt.plot(tb[t1:t2],vec5_pA[t1:t2], label='BSC IPSC')
	plt.plot(tb[t1:t2],vec6_pA[t1:t2], label='OLM IPSC')
	plt.plot(tb[t1:t2],vec7_pA[t1:t2], label='VIPCR IPSC')

	mn = min(vec3_pA[t1:t2]) #min PC
	mx = max(vec4_pA[t1:t2]) #max VIPCR
	rg = int(mx-mn)/10
	rg = rg - rg % 10
	# print mx,mn, rg
	
	plt.text(1005, mn+53, r'10 pA',fontweight='bold')
	plt.text(1023, mn+47, r'10 ms',fontweight='bold')


	plt.plot([1020, 1020], [mn+50, mn+50+ rg], linewidth = 1.6, color = 'black') # 10 pA vertical or pA
	plt.plot([1020, 1030], [mn+50, mn+50], linewidth = 1.6, color = 'black')  # 10 ms


	plt.ylabel('Current (pA)')
	plt.xlabel('Time (ms)')
	plt.suptitle('      Post-synaptic currents to BSC ', fontsize=14, fontweight='bold')
	plt.legend(frameon = False)

	plt.savefig(fname+'/plots/toBSC_post_synaptic_currents.pdf',format='pdf',dpi=600)

	#plt.show()



#              ___           
############# |   | |     |\  /| ###########
############# |   | |     | \/ | ############## 
############# |___| |____ |    | #################



elif cell=="OLM":
	print "OLM Cell plot"

	#BC cell 
	with open(fname+'/CA3toOLM_i_rec_soma.txt','r', 0) as f1:   
		vec1 = f1.readlines()
	with open(fname+'/PCtoOLM_i_rec_soma.txt','r', 0) as f2:   
		vec2 = f2.readlines()
	with open(fname+'/BSCtoOLM_i_rec_soma.txt','r', 0) as f3:    
		vec3 = f3.readlines()
	with open(fname+'/OLMtoOLM_i_rec_soma.txt','r', 0) as f4:    
		vec4 = f4.readlines()
	with open(fname+'/VIPCRtoOLM_i_rec_soma.txt','r', 0) as f5:   
		vec5 = f5.readlines()



	# Make strings as floats with list comprehension
	vec1 = [float(x1) for x1 in vec1]
	vec2 = [float(x2) for x2 in vec2]
	vec3 = [float(x3) for x3 in vec3]
	vec4 = [float(x4) for x4 in vec4]
	vec5 = [float(x5) for x5 in vec5]
	

	# To convert in pA
	vec1_pA = [1000*x1 for x1 in vec1]
	vec2_pA = [1000*x2 for x2 in vec2]
	vec3_pA = [1000*x3 for x3 in vec3]
	vec4_pA = [1000*x4 for x4 in vec4]
	vec5_pA = [1000*x5 for x5 in vec5]
	
	a = range(1,len(vec1_pA)+1)# time bins
	# print a
	tb = [x*dt for x in a]


	fig = plt.figure(1,figsize=(10,10))


	plt.plot(tb[t1:t2],vec1_pA[t1:t2], label='CA3 EPSC')
	plt.plot(tb[t1:t2],vec2_pA[t1:t2], label='PC EPSC')
	plt.plot(tb[t1:t2],vec3_pA[t1:t2], label='BSC IPSC')
	plt.plot(tb[t1:t2],vec4_pA[t1:t2], label='OLM IPSC')
	plt.plot(tb[t1:t2],vec5_pA[t1:t2], label='VIPCR IPSC')

	mn = min(vec2_pA[t1:t2]) #min PC
	mx = max(vec5_pA[t1:t2]) #max VIPCR
	rg = int(mx-mn)/10
	rg = rg - rg % 10
	# print mx,mn, rg
	
	plt.text(1011, mn+4.5, r'1 pA',fontweight='bold')
	plt.text(1028, mn+3.5, r'10 ms',fontweight='bold')


	plt.plot([1025, 1025], [mn+4, mn+4+ 1], linewidth = 1.6, color = 'black') # 1 pA vertical or pA
	plt.plot([1025, 1035], [mn+4, mn+4], linewidth = 1.6, color = 'black')  # 10 ms


	plt.ylabel('Current (pA)')
	plt.xlabel('Time (ms)')
	plt.suptitle('      Post-synaptic currents to OLM ', fontsize=14, fontweight='bold')
	plt.legend(frameon = False)

	plt.savefig(fname+'/plots/toOLM_post_synaptic_currents.pdf',format='pdf',dpi=600)

	#plt.show()


#                    ___     ___   ___
########### \    / | |___|  |     |     |  / ###########
############ \  /  | |      |     |     |<   ############## 
############# \/   | |      |___  |___  |  \ ################

elif cell=="VIPCCK":
	print "VIPCCK Cell plot"

	#VIPCCK cell 
	with open(fname+'/ECtoVIPCCK_i_rec_soma.txt','r', 0) as f1:
		vec1 = f1.readlines()
	with open(fname+'/CA3toVIPCCK_i_rec_soma.txt','r', 0) as f2:   
		vec2 = f2.readlines()
	with open(fname+'/PCtoVIPCCK_i_rec_soma.txt','r', 0) as f3:    
		vec3 = f3.readlines()
	with open(fname+'/BCtoVIPCCK_i_rec_soma.txt','r', 0) as f4: 
		vec4 = f4.readlines()
	with open(fname+'/BSCtoVIPCCK_i_rec_soma.txt','r', 0) as f5:    
		vec5 = f5.readlines()
	with open(fname+'/OLMtoVIPCCK_i_rec_soma.txt','r', 0) as f6: 
		vec6 = f6.readlines()
	with open(fname+'/VIPCCKtoVIPCCK_i_rec_soma.txt','r', 0) as f7: 
		vec7 = f7.readlines()



	# Make strings as floats with list comprehension
	vec1 = [float(x1) for x1 in vec1]
	vec2 = [float(x2) for x2 in vec2]
	vec3 = [float(x3) for x3 in vec3]
	vec4 = [float(x4) for x4 in vec4]
	vec5 = [float(x5) for x5 in vec5]
	vec6 = [float(x6) for x6 in vec6]
	vec7 = [float(x7) for x7 in vec7]


	# To convert in pA
	vec1_pA = [1000*x1 for x1 in vec1]
	vec2_pA = [1000*x2 for x2 in vec2]
	vec3_pA = [1000*x3 for x3 in vec3]
	vec4_pA = [1000*x4 for x4 in vec4]
	vec5_pA = [1000*x5 for x5 in vec5]
	vec6_pA = [1000*x6 for x6 in vec6]
	vec7_pA = [1000*x7 for x7 in vec7]

	a = range(1,len(vec1_pA)+1)# time bins
	# print a
	tb = [x*dt for x in a]

	fig = plt.figure(1,figsize=(10,10))


	plt.plot(tb[t1:t2],vec1_pA[t1:t2], label='EC EPSC')
	plt.plot(tb[t1:t2],vec2_pA[t1:t2], label='CA3 EPSC')
	plt.plot(tb[t1:t2],vec3_pA[t1:t2], label='PC EPSC')
	plt.plot(tb[t1:t2],vec4_pA[t1:t2], label='BC IPSC')
	plt.plot(tb[t1:t2],vec5_pA[t1:t2], label='BSC IPSC')
	plt.plot(tb[t1:t2],vec6_pA[t1:t2], label='OLM IPSC')
	plt.plot(tb[t1:t2],vec7_pA[t1:t2], label='VIPCR IPSC')

	mn = min(vec3_pA[t1:t2]) #min epsc (PC)
	mx = max(vec4_pA[t1:t2]) #max ipsc (VIPCR)
	rg = int(mx-mn)/10
	rg = rg - rg % 10
	# print mx,mn, rg
	
	plt.text(1027, mn+21.5, r'5 pA',fontweight='bold')
	plt.text(1047, mn+18.5, r'10 ms',fontweight='bold')


	plt.plot([1045, 1045], [mn+20, mn+20+ 5], linewidth = 1.6, color = 'black') # 5 pA vertical or pA
	plt.plot([1045, 1055], [mn+20, mn+20], linewidth = 1.6, color = 'black')  # 10 ms


	plt.ylabel('Current (pA)')
	plt.xlabel('Time (ms)')
	plt.suptitle('      Post-synaptic currents to VIPCCK ', fontsize=14, fontweight='bold')
	plt.legend(frameon = False)

	plt.savefig(fname+'/plots/toVIPCCK_post_synaptic_currents.pdf',format='pdf',dpi=600)


	#plt.show()




#                    ___     ___   ___
########### \    / | |___|  |     |___| ###########
############ \  /  | |      |     | \    ############## 
############# \/   | |      |___  |  \  ################



elif cell=="VIPCR":
	print "VIPCR Cell plot"

	#VIPCR cell 
	with open(fname+'/ECtoVIPCR_i_rec_soma.txt','r', 0) as f1:    
		vec1 = f1.readlines()
	with open(fname+'/CA3toVIPCR_i_rec_soma.txt','r', 0) as f2:  
		vec2 = f2.readlines()
	with open(fname+'/PCtoVIPCR_i_rec_soma.txt','r', 0) as f3:    
		vec3 = f3.readlines()
	



	# Make strings as floats with list comprehension
	vec1 = [float(x1) for x1 in vec1]
	vec2 = [float(x2) for x2 in vec2]
	vec3 = [float(x3) for x3 in vec3]
	

	# To convert in pA
	vec1_pA = [1000*x1 for x1 in vec1]
	vec2_pA = [1000*x2 for x2 in vec2]
	vec3_pA = [1000*x3 for x3 in vec3]
	
	a = range(1,len(vec1_pA)+1)# time bins
	# print a
	tb = [x*dt for x in a]

	fig = plt.figure(1,figsize=(10,10))


	plt.plot(tb[t1:t2],vec1_pA[t1:t2], label='EC EPSC')
	plt.plot(tb[t1:t2],vec2_pA[t1:t2], label='CA3 EPSC')
	plt.plot(tb[t1:t2],vec3_pA[t1:t2], label='PC EPSC')
	
	mn = min(vec3_pA[t1:t2]) #min PC
	mx = 0 #max 0
	rg = int(mx-mn)/10
	rg = rg - rg % 10
	# print mx,mn, rg
	
	plt.text(1016, mn+21.5, r'5 pA',fontweight='bold')
	plt.text(1033, mn+18.5, r'10 ms',fontweight='bold')


	plt.plot([1030, 1030], [mn+20, mn+20+ 5], linewidth = 1.6, color = 'black') # 5 pA vertical or pA
	plt.plot([1030, 1040], [mn+20, mn+20], linewidth = 1.6, color = 'black')  # 10 ms


	plt.ylabel('Current (pA)')
	plt.xlabel('Time (ms)')
	plt.suptitle('      Post-synaptic currents to VIPCR ', fontsize=14, fontweight='bold')
	plt.legend(frameon = False)

	plt.savefig(fname+'/plots/toVIPCR_post_synaptic_currents.pdf',format='pdf',dpi=600)

	#plt.show()


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


else:
	print "oops!! something is going wrong! Not a valid Input argument."