def ltmgrid(df,acertos,ncols=5,nrows=9,fig=None):
''
if not fig:
fig = plt.figure()
qn = 1
provid = df['ID_PROVA_CN'].values[0]
for row in range(nrows):
for col in range(ncols):
ax = plt.subplot2grid((nrows,ncols),(row,col))
itemstats, teststats = stats(acertos,hs = 'notapadrao',df=df)
hscale = itemstats['hscale']
icc = itemstats['icc'][qn-1]
hbin = icc[:,0]
nbin = icc[:,1]
acertos_no_bin = icc[:,2]
prob = icc[:,3]
err = icc[:,4]
ax.errorbar(hbin,prob,yerr=err,fmt='o')
x = np.linspace(0.9*min(hscale),1.1*max(hscale),200)
ltmdif,ltmdisc = ltmfitparams(provid)
a = ltmdisc['value'][qn]
b = ltmdif['value'][qn]
p = invlogit(1.0*a*(x-b))
ax.plot(x,p,'k-')
ax.set_ylim(0,1)
ax.set_yticks([0,0.5,1])
qn += 1
fig.subplots_adjust(left=0.1,right=0.95,bottom=0.05,top=0.9,wspace=0.4,hspace=0.4)
return fig
def iccgraph(df,acertos,qn,hs = "scores",fig=None,ax=None):
''
if not fig:
fig = plt.figure()
if not ax:
ax = fig.add_subplot(111)
ax.set_title(u"Curva Característica do Item "+str(qn))
itemstats, teststats = stats(acertos,hs = hs,df=df)
hscale = itemstats['hscale']
icc = itemstats['icc'][qn-1]
hbin = icc[:,0]
nbin = icc[:,1]
acertos_no_bin = icc[:,2]
prob = icc[:,3]
err = icc[:,4]
const,sconst,nota,snota,itemd,sitemd = itemstats['iccfitsparam'][qn-1]
ax.errorbar(hbin,prob,yerr=err,fmt='o')
x = np.linspace(0.9*min(hscale),1.1*max(hscale),200)
p = invlogit(const+nota*x)
ax.plot(x,p,'g-')
ax.set_ylim(0,1)
ax.set_yticks([0,0.5,1])
if hs == 'scores':
ax.set_xlabel(u"Acertos")
else:
ax.set_xlabel(u"Escore Enem")
ax.set_ylabel(u"Probabilidade")
return fig, ax
def ltmfitgraph(df,acertos,qn1,qn2,fig=None):
'Só usar com prova 89 ou 49!'
provid = df['ID_PROVA_CN'].values[0]
if not fig:
fig = plt.figure()
fig.suptitle(u"Curvas Características dos Itens "+str(qn1)+" e "+str(qn2)+" (+ ajuste 2PL TRI)")
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)
for qn,ax in [(qn1,ax1),(qn2,ax2)]:
itemstats, teststats = stats(acertos,hs = 'notapadrao',df=df)
hscale = itemstats['hscale']
icc = itemstats['icc'][qn-1]
hbin = icc[:,0]
nbin = icc[:,1]
acertos_no_bin = icc[:,2]
prob = icc[:,3]
err = icc[:,4]
ax.errorbar(hbin,prob,yerr=err,fmt='o')
x = np.linspace(0.9*min(hscale),1.1*max(hscale),200)
ltmdif,ltmdisc = ltmfitparams(provid)
a = ltmdisc['value'][qn]
b = ltmdif['value'][qn]
p = invlogit(1.0*a*(x-b))
ax.plot(x,p,'k-')
ax.set_ylim(0,1)
ax.set_yticks([0,0.5,1])
ax.set_xlabel(u"Escore Enem Padronizada")
ax.set_ylabel(u"Probabilidade")
ax2.set_ylabel('')
ax1.text(0.05,0.9,'Q'+str(qn1),transform = ax1.transAxes,fontsize='medium',weight='bold')
ax2.text(0.05,0.9,'Q'+str(qn2),transform = ax2.transAxes,fontsize='medium',weight='bold')
return fig
