else:
clr='c'
label = "non-single site"
if label in labels:
label = None
else:
labels.append(label)
cat.get_dEa(dEa_guess,cat.T,solv_corr=solv_corr)
#extrapolate selectivity
modelsel = cat.sel_fun(T_fix)
#plot experimental data with extrapolated selectivity
ax.plot(cat.log_conv,
modelsel,
color=clr,
marker='o',
label=label,
fillstyle='full',
markersize=10,
clip_on=False)
#### Plot Model selectivity ####
conv_vec = np.logspace(-5,-.01,num=1e2,base=10)
plot_sel(ax,conv_vec,0.55,T_fix,facecolor='k',color='k')
plt.text(-5.7,-12,'(b)',fontsize=30)
ax.legend(loc='best',fontsize=14)
ax.set_xlabel(r'log(CH$_4$ conversion)')
ax.set_ylabel(r'CH$_3$OH selectivity (%)')
plt.tight_layout()
plt.savefig('fig-9b-extrap-ss-nss.pdf')
else:
condns = ['gas']
for condn in condns:
labels = []
if condn == 'aqueous':
dEa = dEa_theory - solv_corr
else:
dEa = dEa_theory
plt.close('all')
fig = plt.figure(1, figsize=size)
ax = fig.add_subplot(111)
plot_sel(ax,
conv_vec,
dEa,
T,
T_low=T - step / 2.,
T_hi=T + step / 2.,
facecolor='c',
color='c')
#ax.plot(np.log10(conv_vec),sel_fun(conv_vec,dEa,T))
#ax.fill_between(np.log10(conv_vec),sel_fun(conv_vec,dEa,T,error='-'),sel_fun(conv_vec,dEa,T,error='+'),alpha=0.2)
#V old error V
#ax.fill_between(np.log10(conv_vec),sel_fun(conv_vec,dEa+sigma,T),sel_fun(conv_vec,dEa-sigma,T),alpha=0.2)
count = 0
for cat in expclassesobj.data:
if cat.T >= T - step / 2. and cat.T <= T + step / 2. and cat.rxntype == condn:
count += 1
label = '%s' % (cat.category)
#if cat.rxntype=='aqueous':
# label = None
if label in labels:
catlistobj = catlistobj.classfilter(lambda x: x.rxntype =='aqueous')
###### Model Selectivity #######
plugerr=err
condns={}
condns['323']={'T':[323,323],'line':'-','color':'c','solv_corr':solv_corr}
condns['323g']={'T':[323,323],'line':'-','color':'b','solv_corr':0}
for cond in condns:
T_low = condns[cond]['T'][0]
T_hi = condns[cond]['T'][1]
T_av = np.array(condns[cond]['T']).mean()
clr = condns[cond]['color']
solv_corr = condns[cond]['solv_corr']
plot_sel(ax,conv_vec,dEa-solv_corr,T_av,T_low=T_low,T_hi=T_hi,facecolor=clr,color=clr)
######## Plot EXPERIMENTAL DATA ######
catlistobj = catlistobj.classfilter(lambda x: x.cattype !='MMO')
labels=[]
for pt in catlistobj.data:
#label = '%s-%s, %s'%(pt.cat,pt.cattype,pt.author)
label = pt.category
if label in labels:
label = None
else:
labels.append(label)
ax.plot(pt.log_conv,pt.sel,'o',color=get_color(pt.category),marker='o',label=label,fillstyle='full',markersize=ptsize,clip_on=False)
#ax.text(pt.log_conv,pt.sel,str(pt.T),fontsize=7,ha='center',va='center',color='k')
###### PLOT PARAMETERS #####
for cat in expclassesobj.data:
label = '%s-%s, %s'%(cat.cat,cat.cattype,cat.author)
label = '%s'%(cat.category)
if label in labels:
label = None
else:
labels.append(label)
dEa = cat.get_dEa(dEa_guess,cat.T,solv_corr=0)
#extrapolate selectivity
modelsel = cat.sel_fun(T_fix) #rethink, plots model
print modelsel
#plot experimental data with extrapolated selectivity
ax.plot(cat.log_conv,
modelsel,
color=get_color(cat.category),
marker='o',
label=label,
fillstyle='full',
markersize=15,
clip_on=False)
#### Plot Model selectivity ####
conv_vec = np.logspace(-8,-.01,num=1e2,base=10)
plot_sel(ax,conv_vec,0.55-solv_corr,T_fix,facecolor='c',color='c')
ax.legend(loc=3,fontsize=10)
ax.set_xlabel(r'log(CH$_4$ conversion)')
ax.set_ylabel(r'CH$_3$OH selectivity (%)')
ax.set_xlim([-8,0])
plt.savefig('fig-S13-aq-extrap.pdf')