''

users = pd.read_table(DATA_DIR+'username-course-group-redefor-11.csv',sep=',',index_col=0)

def find_username(username,detail):

try:

user = users.xs(username)

return user[detail]

except KeyError:

return 'Nenhum'

username_course = lambda username: find_username(username,'course1')

username_group = lambda username: find_username(username,'group1')

username_role = lambda username: find_username(username,'role1')

username = df['Nome de usuário']

df['Curso'] = username.map(username_course)

df['Grupo'] = username.map(username_group)

df['Papel'] = username.map(username_role)

''

def codpes_sexo(codpes):

try:

codpes = int(codpes)

return mdlib.pessoa(codpes)['sexpes']

except ValueError:

return

codpes = df['NumeroUSP']

df['Sexo'] = codpes.map(codpes_sexo)

''

def codpes_nasc(codpes):

try:

codpes = int(codpes)

return mdlib.pessoa(codpes)['dtanas']

except ValueError:

return

codpes = df['NumeroUSP']

df['DataNasc'] = codpes.map(codpes_nasc)

''

cb = df['classe_cb']

c = Counter(cb)

plt.pie(c.values(),labels=c.keys(),autopct='%1.0f%%')

ax.set_title(u'Critério Brasil')

''

cb = df['classe_cb']

c = Counter(cb)

labels = sorted(c.keys())

values = [c[val] for val in labels]

N = len(labels)

x = np.arange(N)

width = 0.6

ax.bar(x,values,width,color='r')

ax.set_title(u'Critério Brasil')

ax.set_xticks(x+width)

''

ldi = df['ldi15']

plt.hist(ldi,rwidth=0.8)

ax.set_title(u'Indice de Literacia Digital\nEscala de 15 itens (0-4)')

mean = unicode(round(ldi.mean()))

std = unicode(round(ldi.std()))

ax.text(0.6,0.85,u'Média = '+ mean + u'\nDesvio Padrão = ' + std,transform=ax.transAxes)

''

name,ext = os.path.splitext(filename)

fig = plt.figure(figsize =(6,10))

fig.text(0.02,0.95,unicode(name,'utf8'),fontsize=20)

ax1 = plt.subplot(211)

#ax1.set_aspect('equal')

ax1 = graph_cb_bar(df,ax1)

ax2 = plt.subplot(212)

ax2 = graph_ldi(df,ax2)

fig.subplots_adjust(hspace=0.5)

''

itens_pontos = {'tv':[0,1,2,3,4],

'radio':[0,1,2,3,4],

'banheiro':[0,4,5,6,7],

'auto':[0,4,7,9,9],

'empregada':[0,3,4,4,4],

'maqlavar':[0,2,2,2,2],

'dvd':[0,2,2,2,2],

'geladeira':[0,4,4,4,4],

'freezer':[0,2,2,2,2],

'chefe':[0,1,2,3,8]}

def cb1(item):

return lambda quantidade: itens_pontos[item][quantidade]

def pontos_classe(pontos):

if(pontos < 8):

return 'E'

elif(pontos < 14):

return 'D'

elif(pontos < 18):

return 'C2'

elif(pontos < 23):

return 'C1'

elif(pontos < 29):

return 'B2'

elif(pontos < 35):

return 'B1'

elif(pontos < 42):

return 'A2'

else:

return 'A1'

tv = df['Q02_critério brasil 1->Televisão em cores']-1

radio = df['Q02_critério brasil 1->radio']-1

banheiro = df['Q02_critério brasil 1->banheiro'] -1

auto = df['Q02_critério brasil 1->automovel']-1

empregada = df['Q02_critério brasil 1->empregada']-1

maqlavar = df['Q02_critério brasil 1->maquinalavar']-1

dvd = df['Q02_critério brasil 1->vcoudvd']-1

geladeira = df['Q02_critério brasil 1->geladeira']-1

freezer = df['Q02_critério brasil 1->freezer']-1

chefe = df['Q03_critério brasil 2'].map(lambda x: int(x[0]))-1 # only first character

pontos = tv.apply(cb1('tv')) + radio.apply(cb1('radio')) + banheiro.apply(cb1('banheiro')) + auto.apply(cb1('auto')) + empregada.apply(cb1('empregada')) + maqlavar.apply(cb1('maqlavar')) + dvd.apply(cb1('dvd')) + freezer.apply(cb1('freezer')) + geladeira.apply(cb1('geladeira')) + chefe.apply(cb1('chefe'))

df['pontos_cb'] = pontos

df['classe_cb'] = pontos.apply(pontos_classe)

'Veja http://webuse.org/p/a34'

ldi6 = df['Q08_Literacia digital->Busca Avançada'] + df['Q08_Literacia digital->PDF'] + df['Q08_Literacia digital->Spyware'] + df['Q10_Literacia Digital II->Wiki'] + df['Q10_Literacia Digital II->Cache'] + df['Q10_Literacia Digital II->Phishing'] - 6

ldi10 = ldi6 + df['Q10_Literacia Digital II->Palavras-chave'] + df['Q08_Literacia digital->JPEG'] + df['Q08_Literacia digital->Blog'] + df['Q10_Literacia Digital II->Vírus'] - 4

ldi15 = ldi10 + df['Q08_Literacia digital->Preferências'] + df['Q10_Literacia Digital II->Abas no Navegador'] + df['Q10_Literacia Digital II->Firewall'] + df['Q10_Literacia Digital II->Podcast'] + df['Q10_Literacia Digital II->Feeds da Web'] - 5

df['ldi6'] = ldi6

df['ldi10'] = ldi10

df['ldi15'] = ldi15

'''Filter personally identifying information like name, idnumbers, etc.

We map the Moodle userid to make another ID which will allow us to

follow a user between surveys.

But we must be realist: it is very dificult to anonimize data

without making it useless. Vigilance is required!

'''

def RFID_map(seed,N):

'Return dict with some permutation of range(N)'

import random

random.seed(seed)

ids = range(N)

random.shuffle(ids)

return dict(zip(range(N),ids))

del df['Nome de usuário']

del df['Nome completo']

del df['NumeroUSP']

# Aqui indexamos o dataframe com um número mapeado 1-1 com

# o Moodle ID. Assim podemos comparar usuários entre

# enquetes. Deixamos o número USP por agora, para fazer testes.

mapping = RFID_map(config.seed,100000)

df.index = df['ID'].map(mapping)

del df['ID']

del df['Instituição']

del df['Departamento']

''

grouped = df.groupby(field)

index = [gp_keys[0] for gp_keys in grouped.groups.values()]

'''return list of (value,dataframe) tuples, where dataframes

contain only rows grouped by values of field'''

grouped = df.groupby(field)

if np.iterable(include_keys):

dfs = [(key,df.reindex(index)) for key, index in grouped.groups.iteritems() if key in include_keys]

else:

dfs = [(key,df.reindex(index)) for key, index in grouped.groups.iteritems()]

''

if enq_no == 1:

df = criterio_brasil(df)

df = calc_lit_digital_index(df)

elif enq_no == 2:

df = add_curso_e_grupo(df)

elif enq_no == 3:

df = add_curso_e_grupo(df)

df = add_sexo(df)

df = add_nasc(df)

df = deduplicar(df,'ID')

df = anonimizar(df)

''

print "writing to ", filename

rec = df.to_records()

mlab.rec2csv(rec,filename,delimiter='\t')

''

dfs = []

for filename in filenames:

print "converting " + filename

dfs.append(convert2df(filename))

dftotal = dfs[0]

samecolumns = dfs[0].columns == dfs[1].columns

try:

if samecolumns.all():

print "Same columns: trying append vertically to join"

if np.intersect1d(dfs[0].index.tolist(),dfs[1].index.tolist()).any():

print "Warning: some indexes of first two files are the same!"

for i in range(1,len(dfs)):

dftotal = dftotal.append(dfs[i])

except AttributeError:

if not samecolumns:

print "Diferent columns: trying to join horizontally"

for i in range(1,len(dfs)):

dftotal = dftotal.join(dfs[i],how='outer',lsuffix='_left')

#dftotal = dftotal.join(dfs[i],how='inner',lsuffix='_left')

''

if options.join:

if not options.outfile:

print "Need outfile"

return 1

df = joinfiles(filenames)

print "Saving to %s" % options.outfile

writeprocessed(df,options.outfile)

return 0

for filename in filenames:

print "processing: %s" % filename

df = convert2df(filename)

df = process(df,options.enq_no)

if options.splitfield:

dfs = dividir(df,options.splitfield)

for name, df in dfs:

root, ext = os.path.splitext(filename)

outfile = root + '-'+ name + '-processed.csv'

print "Saving to %s" % outfile

writeprocessed(df,outfile)

else:

root, ext = os.path.splitext(filename)

outfile = root + '-processed.csv'

print "Saving to %s" % outfile

writeprocessed(df,outfile)

if options.graph:

root, ext = os.path.splitext(filename)

outfile = root + '-graph.png'

print "Making graphs and saving to ", outfile

make_graphs(df,outfile)