#### Function to plot the overproduction factors vs initial mass

#Important: explosive and pre-exp parts are WITHOUT wind contribution

def create_set_plots(setdir,expdir,isotopes=True,plot_log=True,mesaonly=True,mcut='fryer',fallback_coords=[],fallback_masses=[],savepath='set1.2_mesa/elements',filelabel='_set1p2',color=['r','b','g','k'], marker_type=['o','p','s','D'],line_style=['--','-','-.',':']): 

	import nugrid_set as set
	import matplotlib
	import matplotlib.pyplot as plt

	import sys
	sys.setrecursionlimit(50000)
	dum_iso_num=5000

	cycles=[[0,80000,1000],[0,80000,1000],[0,80000,1000],[0,80000,1000]] 
	#dirs=['M1.650Z0.0001', 'M2.000Z0.0001','M3.000Z0.0001','M5.000Z0.0001']
	cycles=20*[[1,-1,200]]

	if isotopes==True:
		isotopes=['allstable']
		elements=[]
	else:
		elements=['allstable']
		isotopes=['allstable'] #to get a length for isotopes variable below for color etc.

	isotopes=['Ge-70']


	#how to use change sets
	#choose the right location
	#adjust the savefig location
	#adjust minm and maxm

	exp_type='delay'

	if mcut == 'fryer':
	    expdirext='ppd_exp'
	elif mcut == 'ye':
	    expdirext='ppd_exp_yemcut'

	xmin=1.
	xmax=25

	#specifiy the set + save point/name

	setse=set.mppnp_set(rundir=setdir)#,multi_dir=rundirs,extra_label=15*[" "])

	#wind
	wind_only=True
	pre_exp=False
	exp_only=False

	setse.get_stellar_ejecta(cycles=cycles,isotopes=isotopes,elements=elements,yields_output=False,GCE_tables=False,plots=True,exp_dir=expdir,exp_type=exp_type,pre_exp=pre_exp,exp_only=exp_only,wind_only=wind_only,plot_log=plot_log,color=dum_iso_num*['r'],marker_type=dum_iso_num*['+'],line_style=dum_iso_num*['-'],markersize=dum_iso_num*[16],line_width=dum_iso_num*[2],title='',label='Wind',withlabel=False)

	##for below
	plot_masses_only=[12,15,20,25]


	#exp delay
	exp_type='delay'
	wind_only=False
	pre_exp=False
	exp_only=True #False

        setse.get_stellar_ejecta(cycles=cycles,isotopes=isotopes,elements=elements,yields_output=False,GCE_tables=False,plots=True,exp_dir=expdir,exp_type=exp_type,pre_exp=pre_exp,exp_only=exp_only,wind_only=wind_only,plot_masses_only=plot_masses_only,plot_log=plot_log,color=dum_iso_num*['g'],marker_type=dum_iso_num*['o'],line_style=dum_iso_num*['--'],markersize=dum_iso_num*[14],line_width=dum_iso_num*[8],title='',label='exp, delay',withlabel=False)


	#exp rapid
        exp_type='rapid'
        wind_only=False
        pre_exp=False
        exp_only=True #False

        setse.get_stellar_ejecta(cycles=cycles,isotopes=isotopes,elements=elements,yields_output=False,GCE_tables=False,plots=True,exp_dir=expdir,exp_type=exp_type,pre_exp=pre_exp,exp_only=exp_only,wind_only=wind_only,plot_masses_only=plot_masses_only,color=dum_iso_num*['b'],marker_type=dum_iso_num*['o'],line_style=dum_iso_num*['--'],markersize=dum_iso_num*[14],line_width=dum_iso_num*[8],title='',label='exp',withlabel=False)


	#pre-exp delay

        exp_type='delay'
        wind_only=False
        pre_exp=True
        exp_only=True #False

        setse.get_stellar_ejecta(cycles=cycles,isotopes=isotopes,elements=elements,yields_output=False,GCE_tables=False,plots=True,exp_dir=expdir,exp_type=exp_type,pre_exp=pre_exp,exp_only=exp_only,wind_only=wind_only,plot_masses_only=plot_masses_only,color=dum_iso_num*['g'],marker_type=dum_iso_num*['o'],line_style=dum_iso_num*['--'],markersize=dum_iso_num*[8],line_width=dum_iso_num*[4],title='',label='pre-exp, delay',withlabel=False)


	#pre-exp rapid

        exp_type='rapid'
        wind_only=False
        pre_exp=True
        exp_only=True #False

        setse.get_stellar_ejecta(cycles=cycles,isotopes=isotopes,elements=elements,yields_output=False,GCE_tables=False,plots=True,exp_dir=expdir,exp_type=exp_type,pre_exp=pre_exp,exp_only=exp_only,wind_only=wind_only,plot_masses_only=plot_masses_only,color=dum_iso_num*['b'],marker_type=dum_iso_num*['o'],line_style=dum_iso_num*['--'],markersize=dum_iso_num*[8],line_width=dum_iso_num*[4],title='',label='pre-exp, rapid',withlabel=False)






	figures=[manager.canvas.figure for manager in matplotlib._pylab_helpers.Gcf.get_all_fig_managers()]


	props = dict(boxstyle='square', facecolor='w', alpha=1)
	for fig in figures:
		isotope=fig.get_label()
		name=isotope+"_prodfac"
		fig=plt.figure(isotope);
		#ax = fig.add_subplot(111)
		#ax.text(0.85, 0.85, isotope, transform=ax.transAxes, fontsize=16,verticalalignment='top', bbox=props)
		plt.xlim(xmin,xmax)
		plt.xscale('log')
		plt.ylabel('Overproduction factor')
		#fig.savefig(savepath+'/'+name+filelabel+'.pdf')
	#plt.close('all')