for cat in expclassesobj.data:
label = '%s-%s, %s'%(cat.cat,cat.cattype,cat.author)
label = '%s'%(cat.category)
#if cat.rxntype=='aqueous':
# label = None
if label in labels:
label = None
else:
labels.append(label)
#assign cat object a dEa
cat.get_dEa(dEa_guess,cat.T,solv_corr=solv_corr)
#plot experimental data with extrapolated selectivity
modelsel = sel_fun(cat.conv,T_fix,dEa=cat.dEa,dGa=None,error=None)
ax.plot(cat.log_conv,
modelsel,
color=get_color(cat.category),
marker=get_shape(cat.rxntype),
label=label,
fillstyle='full',
markersize=10,
clip_on=False)
ax.set_title('T=%i K; all data (extrapolated)'%(int(T_fix)))
ax.legend(loc=3,fontsize=15)
ax.set_xlabel(r'log(CH$_4$ conversion)')
ax.set_ylabel(r'CH$_3$OH selectivity (%)')
plt.savefig('fig-5-main-SvX-extrapolated.pdf')
#cattype = cat.cattype
if 'HSE' in cat.tag:
#marker = 's'
labeler += '-HSE'
if labeler in classes_idx:
label = '_nolegend_'
else:
label = labeler
classes_idx.add(labeler)
if 'surface-stabilized' in cat.tag:
if surf_stab == False:
continue
clr = 'c'
else:
clr = get_color(labeler)
ch3oh_list.append(cat.ets_ch3oh)
ch4_list.append(cat.ets_ch4)
#for hist
dEa = cat.ets_ch4 - cat.ets_ch3oh
if labeler in dEa_dict.keys():
dEa_dict[labeler]['dEas'].append(dEa)
else:
dEa_dict[labeler] = {}
dEa_dict[labeler]['dEas'] = [dEa]
dEa_dict[labeler]['clr'] = get_color(labeler)
dEa_all.append(dEa)
alpha = 1 ##???
h, = ax.plot(cat.ets_ch3oh,
ax.plot(np.log10(conv_vec), sel_vec, color=clr)
######## Plot EXPERIMENTAL DATA ######
labels = []
for pt in catlistobj.data:
#label = '%s-%s, %s'%(pt.cat,pt.cattype,pt.author)
label = pt.cat
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,
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 #####
ax.set_xlim(-8, 0)
#plt.legend(loc=3,fontsize=10)
#cattype = cat.cattype
if 'HSE' in cat.tag:
#marker = 's'
labeler += '-HSE'
if labeler in classes_idx:
label = '_nolegend_'
else:
label = labeler
classes_idx.add(labeler)
if 'surface-stabilized' in cat.tag:
if surf_stab == False:
continue
clr = 'c'
else:
clr = get_color(labeler)
ch3oh_list.append(cat.ets_ch3oh)
ch4_list.append(cat.ets_ch4)
alpha = 1 ##???
h, = ax.plot(cat.ets_ch3oh,
cat.ets_ch4,
marker=marker,
color=clr,
alpha=alpha,
label=label)
clr_handles.append(copy.copy(h))
shape_handles.append(copy.copy(h))
#ax.text(cat.ets_ch3oh,cat.ets_ch4,cat.cat,fontsize=3,ha='center',va='center')
### BEEF ##
if cat.beef_ch4 != None:
lims=[-0.5,1]
if plttype=='$\Delta$S' :
dGcorr = cat.get_dS(T)
lims=[-0.0002,0.0011]
if plttype=='$\Delta$C' :
dGcorr = cat.get_dC(T)
lims=[-0.05,0.05]
dGcorr_list.append(dGcorr)
print cat.cat, cat.cattype
if cat.cattype in dGcorr_dict.keys():
dGcorr_dict[cat.cattype]['dGcorrs'].append(dGcorr)
else:
dGcorr_dict[cat.cattype] = {}
dGcorr_dict[cat.cattype]['dGcorrs'] = [dGcorr]
dGcorr_dict[cat.cattype]['clr'] = get_color(cat.cattype)
#print cat.cat,'/',cat.cattype,'/',cat.vibs_ch4,'/',cat.vibs_ch3oh
fig = plt.figure(figsize=(5,4))
ax = fig.add_subplot(111)
#ax.set_xlim(-.75,0.25)
ax.set_xlim(lims)
dGcorr_multi = []
labels=[]
colors=[]
for cattype in dGcorr_dict.keys():
dGcorr_multi.append(np.array(dGcorr_dict[cattype]['dGcorrs']))
labels.append(cattype)
colors.append(dGcorr_dict[cattype]['clr'])
dGa = dEa - 4.191e-4*T - 0.0152
k2_k1 = math.exp(dGa/kB/T)
sel = (1-conv-(1-conv)**(k2_k1))/(conv*(k2_k1-1))*100
return sel #in percent
labels=[]
for obj in expclassesobj.data:
if obj.rxntype=='aqueous':
dEa = dEa_theory-solv_corr
else:
dEa = dEa_theory
label=obj.category
if label in labels:
label=None
else:
labels.append(label)
sel_pred = sel_fun(obj.conv,dEa,obj.T)
print obj.sel,sel_pred
#xerr=math.log((-sel_pred+sel_fun(obj.conv,dEa-sigma,obj.T))*0.01,10)
xerr=0
ax.errorbar(math.log(sel_pred*.01,10),math.log(obj.sel*.01,10),xerr=xerr,marker='o',label=label,color=get_color(obj.category))
ax.plot(np.arange(-6,1,1),np.arange(-6,1,1))
ax.set_title(r'$\Delta E^a_{theory} = %4.2f$'%(dEa_theory))
ax.set_ylim([-3,0])
ax.set_xlim([-3,0])
ax.set_ylabel('Experimental log(Selectivity)')
ax.set_xlabel('Model log(Selectivity)')
ax.legend(loc='best',fontsize=9)
plt.savefig('parity-dEa-%4.2f.pdf'%(dEa_theory))
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 #####
ax.set_xlim(-8,0)
plt.legend(loc=3,fontsize=10)
ax.set_ylim(0,100)
ax.set_xlabel(r'log(CH$_4$ conversion)')
ax.set_ylabel(r'CH$_3$OH selectivity (%)')
plt.tight_layout()
plt.savefig('fig-7-aq.pdf')
# Can choose whether to get the distribution using the
# true scaling (EaCH3OH = 0.94EaCH4+0.58) or approximate
# slope = 1. In pub, we approximate slope=1.
if scaled == False:
dEa = mat.ets_ch4 - mat.ets_ch3oh
elif scaled == True:
dEa = (mat.ets_ch3oh * 0.94 + 0.58) - mat.ets_ch3oh
mclass = mat.cattype
if 'HSE' in mat.tag:
mclass = mclass + '-HSE'
if mclass in dEa_dict.keys():
dEa_dict[mclass]['dEas'].append(dEa)
else:
dEa_dict[mclass] = {}
dEa_dict[mclass]['dEas'] = [dEa]
dEa_dict[mclass]['clr'] = get_color(mclass)
#dEa_dict[mclass]['clr'] = mat.color
dEa_all.append(dEa)
#print mat.cat,mat.cattype, mat.ets_ch4,mat.ets_ch3oh,dEa
dEa_multi = []
labels = []
colors = []
hatches = []
for mclass in dEa_dict:
dEa_multi.append(np.array(dEa_dict[mclass]['dEas']))
labels.append(mclass)
colors.append(dEa_dict[mclass]['clr'])
hatches.append('/')
pickle.dump(dEa_all, open("dEa_all.pkl", "wb"))
print "Catalyst: %s ; exp dEa = %4.2f for S=%4.2f and X=%4.2f %4.2f"%(pt.cat+'-'+pt.cattype,pt.dEa,pt.sel,pt.log_conv,10**pt.log_conv)
exp_dEa_list.append(pt.dEa)
dEa_multi = []
labels = []
colors = []
dEa_dict = {}
for pt in catlistobj.data:
if pt.cat in dEa_dict.keys():
dEa_dict[pt.cat].append(pt.dEa)
else:
dEa_dict[pt.cat] = [pt.dEa]
for cat in dEa_dict:
dEa_multi.append(np.array(dEa_dict[cat]))
labels.append(cat)
colors.append(get_color(cat))
#if cat in expclass.clr_dict.keys():
# colors.append(expclass.clr_dict[cat])
#else:
# colors.append('grey')
n,bins,patches = plt.hist(exp_dEa_list,nbins,color=expcolor,normed=normed,alpha=0.5)#kwargs
#n,bins,patches = plt.hist(dEa_multi,nbins,label=labels,color=colors,stacked=True,normed=normed)#kwargs
plt.legend(fontsize=15,loc=2)
lines = []
mu,std = norm.fit(exp_dEa_list)
xmin, xmax = plt.xlim()
x = np.linspace(-.2, 2, 1000)
p = norm.pdf(x, mu, std)
h, = plt.plot(x, p, expcolor, linewidth=2)
lines.append(h)
nbins = 5
nbins = 20
Ea_ch3oh = []
Ea_ch4 = []
for mat in dftobj.data:
dEa = mat.ets_ch4 - mat.ets_ch3oh
mclass = mat.cattype
if mclass in dEa_dict.keys():
dEa_dict[mclass]['dEas'].append(dEa)
else:
dEa_dict[mclass] = {}
dEa_dict[mclass]['dEas'] = [dEa]
dEa_dict[mclass]['clr'] = get_color(mat.cattype)
for b_ch4, b_ch3oh in zip(mat.beef_ets_ch4, mat.beef_ets_ch3oh):
dEa_dict[mclass]['dEas'].append(b_ch4 - b_ch3oh)
dEa_all.append(b_ch4 - b_ch3oh)
Ea_ch4.append(b_ch4)
Ea_ch3oh.append(b_ch3oh)
dEa_all.append(dEa)
print mat.cat, mat.cattype, mat.ets_ch4, mat.ets_ch3oh, dEa
labels = []
colors = []
for mclass in dEa_dict:
labels.append(mclass)
colors.append(dEa_dict[mclass]['clr'])
n, bins, patches = plt.hist(dEa_all,
nbins,
T_hi=T_hi,
facecolor=clr,
color=clr)
######## Plot EXPERIMENTAL DATA ######
labels = []
for pt in catlistobj.data:
label = pt.cat
if label in labels:
label = None
else:
labels.append(label)
ax.plot(pt.log_conv,
pt.sel,
'o',
color=get_color(pt.cat),
marker='o',
label=label,
fillstyle='full',
markersize=ptsize,
clip_on=False)
ax.text(pt.log_conv,
pt.sel,
str(pt.T),
fontsize=11,
ha='center',
va='center',
color='k')
plt.text(-5.6, -10, '(a)', fontsize=30)
###### PLOT PARAMETERS #####