def get_object_by_data2(obj,usr):
sql='select objeto,i from '+mdTb.table_object+' where lower(objeto)=lower(?) and USERNAME = ? '
resultSet = conn3.sqlX (sql,([obj,usr]))
obj_nm=None
uid=None
for results in resultSet:
obj_nm=results[0]
uid=results[1]
break
#-----------
lay=mdNeural.mdLayer ()
if obj_nm == None: obj_nm=obj
lay.name=obj_nm
#-----------
if True :
resultSet =conn3.sql ( 'select trim(DT),trim(TOPICO) from '+mdTb.table_dt+' where \"UID\" = ' + str(uid) )
obj_nm=None
for results in resultSet:
DT=results[0]
TOP=results[1]
nr= lay.set_nr(DT)
#tps=lay.get_topico(TOP)
#if tps == None:
# tps=lay.set_topico(TOP)
#----
tps=lay.set_topico(TOP)
tps.connect_to(lay,nr,'Composicao')
return lay
def get_object_by_data(obj,usr,conn):
cursor = conn.cursor ()
sql='select objeto from SEMANTIC_OBJECT where i=%s and USERNAME = %s '
cursor.execute (sql,(obj,usr))
resultSet = cursor.fetchall()
obj_nm=None
for results in resultSet:
obj_nm=results[0]
#-----------
lay=mdNeural.mdLayer()
lay.name=obj_nm
#-----------
cursor = conn.cursor ()
sql='select DT,TOPICO from SEMANTIC_OBJECT_DT where OBJECT=%s and USERNAME = %s '
cursor.execute (sql,(obj,usr))
resultSet = cursor.fetchall()
obj_nm=None
for results in resultSet:
DT=results[0]
TOP=results[1]
nr= lay.set_nr(DT)
tps=lay.get_topico(TOP)
if tps == None:
tps=lay.set_topico(TOP)
tps.connect_to(lay,nr,'Composicao')
return lay
def runc_layer(layer):
objects=[]
ref_opcode=''
# Relaction-oper-opcode opcode original dos qualificadores se nao especificado
#=====================================================
def get_o_ob():
for s in objects:
if s.name==ob: return s
return None
#=====================================================
if True:
print 'RTS-REF(1):------------'
r=layer
for s in r.topicos:
print 'Topico:',s.dt
print 'SNS:++++++++++++++++++'
for s1 in s.sinapses:
print s1.nr.dt
print '++++++++++++++++++'
print 'RTS-REF(1)(END):------------'
obj_foco=[]
#print 'N tops:' , len(layer.topicos)
for t in layer.topicos:
tp=t
nr_t=t.dt[0]
if nr_t == '': continue
if 'referencial.source' in t.dt :
sn_dt=''
for sn in tp.sinapses:
if 'indicador' not in sn.nr.dt:
for s1 in sn.nr.dt:
sn_dt+=(s1+' ')
if umisc.trim(sn_dt) != '$$id$$' and umisc.trim(sn_dt) != '':
print 'referencial.source:',sn_dt
if len(obj_foco) > 0:
lay=obj_foco[0]
lay.name+=(sn_dt)
else:
lay=mdNeural.mdLayer()
lay.name=sn_dt
#====
objects.append(lay)
obj_foco.append(lay)
rel1=False
for t in layer.topicos:
topico_rsf=t
#==============================================
for dt_top in topico_rsf.dt:
if dt_top in [ 'referencial'] or ref_opcode in [ 'refer']:
for ob1 in obj_foco:
ob1.set_topico_nr(topico_rsf)
break
print 'Obj.foco.refer:',obj_foco
return obj_foco
def get_object_by_data(obj, usr):
sql = 'select trim(objeto),i from SEMANTIC_OBJECT3 where objeto=? and USERNAME = ? '
resultSet = conn3.sqlX(sql, ([obj, usr]))
obj_nm = None
uid = None
for results in resultSet:
obj_nm = results[0]
uid = results[1]
break
#-----------
lay = mdNeural.mdLayer()
if obj_nm == None: obj_nm = obj
lay.name = obj_nm
tpc2 = lay.set_topico('identificador')
nrc2 = lay.set_nr(lay.name)
tpc2.connect_to(lay, nrc2, 'Composicao')
#print lay.topicos,'....................'
#print 'Read object:',obj,' uid:',uid
#-----------
if True:
resultSet = conn3.sql(
'select trim(DT),trim(TOPICO),i from SEMANTIC_OBJECT_DT3 where "UID" = '
+ str(uid) + ' and lev <=1 ')
obj_nm = None
for results in resultSet:
DT = results[0]
TOP = results[1]
nr = lay.set_nr(DT)
ic = results[2]
if ic == None: ic = 0
#tps=lay.get_topico(TOP)
#if tps == None:
# tps=lay.set_topico(TOP)
# ---
tps = lay.set_topico(TOP)
#===
#print 'Set topico:',TOP,' for layer:',obj_nm,' uid:',uid
# ---
tps.connect_to(lay, nr, 'Composicao')
if True:
#==
resultSet = conn3.sql(
'select id_top,trim(DT),trim(TOPICO) from SEMANTIC_OBJECT_DT3 where UID = '
+ str(uid) + ' and lev > 1 and id_top=' + str(ic) +
'and i <> ' + str(ic), )
obj_nm = None
for results in resultSet:
DT = results[0]
TOP = results[1]
nrc = lay.set_nr(DT)
#==
nr.connect_to(lay, nrc, 'Composicao')
return lay
def run(layers,relacionado,startL,usr,stack):
rts=[]
for lr in layers:
identif=''
cenario=''
# achar cenario + i do lr
#
#_get_ontology(self,aliases,purposes,usr)
objects=lr._get_ontology([identif],[cenario],usr)
for l in objects:
layer_r=mdNeural.mdLayer()
layer_r.name=l.name
rts.append(layer_r)
links=l.get_links('REFERENCIAL-CLASS')
for l in links:
tps=l.lr.get_all()
for s in tps:
layer_r.set_topico_nr(s)
layers=rts
def get_object_by_data(obj, usr):
sql = 'select objeto from SEMANTIC_OBJECT where i=? and USERNAME = ? '
resultSet = conn3.sqlX(sql, ([obj, usr]))
obj_nm = None
for results in resultSet:
obj_nm = results[0]
#-----------
lay = mdNeural.mdLayer()
if obj_nm == None: obj_nm = obj
lay.name = obj_nm
#-----------
sql = 'select DT,TOPICO from SEMANTIC_OBJECT_DT where OBJECT=? and USERNAME = ? '
resultSet = conn4.sqlX(sql, ([obj, usr]))
obj_nm = None
for results in resultSet:
DT = results[0]
TOP = results[1]
nr = lay.set_nr(DT)
tps = lay.get_topico(TOP)
if tps == None:
tps = lay.set_topico(TOP)
tps.connect_to(lay, nr, 'Composicao')
return lay
def get_object_by_data29(obj,usr,max_purposes):
sql='select objeto,i from '+mdTb.table_object+' where lower(objeto)=lower(?) and USERNAME = ? '
resultSet = conn3.sqlX (sql,([obj,usr]))
obj_nm=None
uid=None
for results in resultSet:
obj_nm=results[0]
uid=results[1]
break
#-----------
lay=mdNeural.mdLayer ()
if obj_nm == None: obj_nm=obj
lay.name=obj_nm
#-----------
if True :
rattings=0
#print 'SQl: conn:','select trim(DT),trim(TOPICO) from '+mdTb.table_dt+' where \"UID\" = ' + str(uid)
resultSet =conn3.sql ( 'select trim(DT),trim(TOPICO) from '+mdTb.table_dt+' where \"UID\" = ' + str(uid) )
obj_nm=None
for results in resultSet:
DT=results[0]
TOP=results[1]
nr= lay.set_nr(DT)
#print '->>:',DT,TOP
#tps=lay.get_topico(TOP)
#if tps == None:
# tps=lay.set_topico(TOP)
#----
tps=lay.set_topico(TOP)
tps.connect_to(lay,nr,'Composicao')
for d in max_purposes:
if TOP.lower() == d.lower():
rattings+=1
break
return [lay,rattings]
def run( layers,relactionado,startL,usr,stack):
print 'Start compile cenary:----------'
cur_cen=init_cenario_class_cliente(stack)
lrK=mdNeural.mdLayer()
#set_topico_a
for l in layers:
ids=l.name
for u in l.topicos :
if 'object' in u.dt:
u.dt=['identificador']
lrK.set_topico_nr(u)
print 'DIS:',u.sinapses
lrK.name='__TST__'
#lrK.set_topico_aq(['quality',''])
#lrK.set_topico_aq(['rule','mean'])
lrK.set_topico_aq(['alias',''])
lrK.set_topico_aq(['opcode','mean'])
layers_param=[lrK]
for cn in cur_cen:
for rct in cn.rcts:
print 'ALiass:',rct
second_Rcts=mdER.get_RactionLines2C(rct,usr)
print 'len.rcts(O) [',len(second_Rcts),']'
print '......................................'
for s in second_Rcts:
print 'S:',s
for l in s:
if type(l).__name__ == 'instance':
if l.__class__.__name__ == 'mdLayer':
print 'RCT-fnd:',l.name
nrt=l.set_topico('purpose')
l.set_nr_ch(nrt,'class','composicao')
print 'RCT Ask Call-TO-Process:',l,l.name
stack.proc_pg.call_process2(usr,[l],layers_param)
print '--------------------------------------'
def get_object_by_data223(obj,usr,plus):
sql='select objeto,i from '+mdTb.table_object+' where lower(objeto)=lower(?) and USERNAME = ? '
resultSet = conn3.sqlX (sql,([obj,usr]))
obj_nm=None
uid=None
for results in resultSet:
obj_nm=results[0]
uid=results[1]
break
#-----------
lay=mdNeural.mdLayer ()
if obj_nm == None: obj_nm=obj
lay.name=obj_nm
#-----------
plus_fnd=0
if True :
resultSet = conn3.sql ( 'select trim(DT),trim(TOPICO) from '+mdTb.table_dt+' where \"UID\" = ' + str(uid) )
obj_nm=None
for results in resultSet:
DT=results[0]
TOP=results[1]
nr= lay.set_nr(DT)
plusf=False
for sd in plus:
if TOP == 'need' and DT == sd:
plusf=True
if plusf:
plus_fnd+=1
#tps=lay.get_topico(TOP)
#if tps == None:
# tps=lay.set_topico(TOP)
#----
tps=lay.set_topico(TOP)
tps.connect_to(lay,nr,'Composicao')
return [lay,plus_fnd]
def __init__(self, usr, purpose):
inpt_word = mdNeural.mdLayer() # interpretacao de palavras
#==
[self.nodesER,
self.all_purpouses] = mdER.get_RactionLines(purpose, usr)
def get_object_by_data(obj,usr,uid):
#========
if uid != '':
resultSet=tb_object.get(uid)
key1=uid
else:
resultSet=tb_object.get(obj)
key1=uid
#
#
obj_nm=None
uid=None
cenar=0
cnts_all_tps=0
if True:
results = resultSet
obj_nm=results[u'objeto']
uid=key1
cenar=results[u'cenar']
cnts_all_tps=results[u'conts_n']
#-----------
lay=mdNeural.mdLayer ()
if obj_nm == None: obj_nm=obj
lay.name=obj_nm
tpc2=lay.set_topico('identificador')
tpc2.uuid=cenar
nrc2= lay.set_nr(lay.name)
nrc2.uuid=cenar
tpc2.connect_to(lay,nrc2,'Composicao')
#print lay.topicos,'....................'
print 'Read object(g1):',obj,' uid:',uid
#-----------
def cached_dt(objectkey,cnts_all_tp):
'''
cached=[]
keyc=objectkey
i=1
while i <= cnts_all_tp:
try:
c1=tb_object_dt.get(keyc+"|"+str(i))
cached.append([keyc+"|"+str(i),c1])
except: break
i+=1
'''
cached=[]
keyc=objectkey
i=1
keys=[]
while i <= int(cnts_all_tp):
keys.append( keyc+"|"+str(i) )
i+=1
i=0
if True:
try:
c1=tb_object_dt.multiget(keys,column_count=10000)
for kd in c1.items():
cached.append([kd[0],kd[1]])
i+=1
except Exception,e:
print 'ERROR:',e
pass
return cached
def run(layers, relactionado, startL, usr, stack):
print 'Start compile cenary:----------'
cur_cen = init_cenario_class_facebook(stack)
lrK = mdNeural.mdLayer()
#set_topico_a
obs_defs = []
for l in layers:
print 'FACE-LR-DUMP:============'
ids = l.name
for u in l.topicos:
#print 'dt:(t):',u.dt,'\n'
if 'with' in u.dt:
for d in u.sinapses:
for dt in d.nr.dt:
obs_defs.append(dt)
if 'object' in u.dt:
u.dt = ['identificador']
lrK.set_topico_nr(u)
print 'DIS:', u.sinapses
lrK.name = '__TST__'
print 'FACE-LR-DUMP:(END)========='
#lrK.set_topico_aq(['quality',''])
#lrK.set_topico_aq(['rule','mean'])
# inicializar extrutura do cenario a ser procurado no facebook===
print '[obs_defs]:{', obs_defs, '}'
obs = []
secs = []
for nm in obs_defs:
print 'ALiass(obj-def):', nm
second_Rcts = mdER.get_RactionLines2C(nm, usr)
print 'len.rcts(O) [', len(second_Rcts), ']'
print '(obj-def)......................................:)'
secs.append(second_Rcts)
if len(secs) == 0:
return
to_Search = []
to_Search_C = []
to_Search_A = []
for s1 in secs:
for s in s1:
#print 'S:',s
for l in s:
if type(l).__name__ == 'instance':
if l.__class__.__name__ == 'mdLayer':
print 'LR-FCB-RETURN:', l, l.name
fcts = l.get_links('FACT')
for fct in fcts:
for u in fct.lr.topicos:
if 'composicao' in u.dt:
for d in u.sinapses:
for dt in d.nr.dt:
to_Search_C.append(dt)
if 'action' in u.dt:
for d in u.sinapses:
for dt in d.nr.dt:
to_Search_A.append(dt)
for u in fct.lr.topicos:
if 'identificador' in u.dt:
for d in u.sinapses:
for dt in d.nr.dt:
if len(to_Search_A) == 0:
for c in to_Search_C:
dt2 = dt + ' ' + c + ' brasil'
to_Search.append(dt2)
elif len(to_Search_C) == 0:
for c in to_Search_A:
dt2 = dt + ' ' + c + ' brasil'
to_Search.append(dt2)
else:
for c2 in to_Search_A:
for c in to_Search_C:
dt2 = dt + ' ' + c + ' ' + c2 + ' brasil'
to_Search.append(
dt2)
import collect_it_fcb
print 'to_Search:{', to_Search, '}'
if len(to_Search) == 0: return
collect_it_fcb.clean_marcador()
collect_it_fcb.entry_Sents(to_Search, usr)
return
def runc_layer(layer, ex_Data):
objects = []
ref_opcode = ''
not_in = [
'alias', 'object3', 'class', 'indicator', 'defin', 'simple-collect',
'sample', 'equilibrio', 'know', 'mean', 'qtd-', 'time', 'indicative',
'interact', 'state', 'accept', 'way', 'need', 'intensity', 'grow',
'order', 'simple-', 'value', 'enum', 'event', 'news', 'referencial',
'compare', 'number'
]
# Relaction-oper-opcode opcode original dos qualificadores se nao especificado
#=====================================================
def get_o_ob():
for s in objects:
if s.name == ob: return s
return None
#=====================================================
class_fnd = False
if True:
print 'RTS(1-S.2):------------', '[', ex_Data, ']'
r = layer
for s in r.topicos:
for kdata in s.dt:
if kdata.lower().startswith(
'defs') or kdata.lower().startswith('interact.'):
class_fnd = True
break
if 'category' in s.dt or 'class' in s.dt:
class_fnd = True
if 'object' in s.dt:
for ds in s.sinapses:
if 'category' in ds.nr.dt or 'defs' in ds.nr.dt:
class_fnd = True
print 'Topico:', s.dt
#====================================================
tp = s
if 'object' in tp.dt:
sn_dt = ''
lst = []
print 'obj.alias.snp:', tp.sinapses
has_spaced = False
analiz = []
for skl1 in tp.sinapses:
for dp in skl1.nr.dt:
analiz.append(dp)
if umisc.trim(dp) != '' and umisc.trim(
dp) != '\n' and umisc.trim(
dp) != 'start' and umisc.trim(dp) != '.':
has_spaced = True
print 'has_spaced:', has_spaced, analiz
if len(tp.sinapses) == 0 or (not has_spaced):
for d in ex_Data:
sn_dt = ex_Data[0]
sn_dt = sn_dt.replace('\n', '')
sn_dt = sn_dt.replace('\t', '')
sn_dt = sn_dt.replace('.', ' ')
#sn_dt = filter(lambda x: x in string.printable, sn_dt)
for sn in tp.sinapses:
for a in sn.nr.dt:
if a.lower() == 'start' or a.lower() == 'end':
tp.sinapses.remove(sn)
break
for sn in tp.sinapses:
ah = False
for a in sn.nr.dt:
for cin in not_in:
if cin in a.lower() and a.lower(
) != 'evento' and a.lower() != 'eventos' and (
not a.lower().startswith('interact.')):
ah = True
continue
sn_dt = umisc.trim(sn_dt) + (' ' + a + ' ')
if ah:
lst.append(sn.nr)
continue
sn_dt = umisc.trim(sn_dt)
print 'Collect layer.sn_dt:', sn_dt
if umisc.trim(sn_dt) != '$$id$$' and umisc.trim(sn_dt) != '':
new_k = sn_dt
if umisc.trim(new_k) not in ex_Data:
ex_Data.append(umisc.trim(new_k))
print 'ex:data:', ex_Data
#====================================================
print 'SNS:------------------------------------------'
for s1 in s.sinapses:
print ' ', s1.nr.dt
for s2 in s1.nr.sinapses:
print ' ', s2.nr.dt
for s3 in s2.nr.sinapses:
print ' ', s3.nr.dt
print '----------------------------------------------'
print 'RTS(1-S.2)(END):------------'
#===============================
print 'need_fnd(2):', class_fnd
if not class_fnd:
return
obj_foco = []
ex_Data2 = []
#=
#print 'N tops:' , len(layer.topicos)
found_obj_1 = False
first_t = True
print 'dump.layer:', layer.name
layer.dump_layer()
for t in layer.topicos:
tp = t
nr_t = t.dt[0]
nr_t = nr_t.replace('\n', '')
if umisc.trim(nr_t) == '' or umisc.trim(nr_t) == '\n': continue
#======================================
if 'object' in t.dt:
sn_dt = ''
lst = []
print 'obj.alias.snp:', tp.sinapses
has_spaced = False
analiz = []
for skl1 in tp.sinapses:
for dp in skl1.nr.dt:
analiz.append(dp)
if umisc.trim(dp) != '' and umisc.trim(
dp) != '\n' and umisc.trim(
dp) != 'start' and umisc.trim(dp) != '.':
has_spaced = True
print 'has_spaced:', has_spaced, analiz
if len(tp.sinapses) == 0 or (not has_spaced):
for d in ex_Data:
sn_dt = ex_Data[0]
sn_dt = sn_dt.replace('\t', '')
sn_dt = sn_dt.replace('\n', '')
sn_dt = sn_dt.replace('.', ' ')
#sn_dt = filter(lambda x: x in string.printable, sn_dt)
for sn in tp.sinapses:
ah = False
for a in sn.nr.dt:
if (a.lower().startswith('interact.')):
lst.append(sn.nr)
continue
for cin in not_in:
if cin in a.lower() and a.lower(
) != 'evento' and a.lower() != 'eventos' and a.lower(
) != 'start' and a.lower() != 'end':
ah = True
continue
sn_dt = umisc.trim(sn_dt) + (' ' + a + ' ')
if ah:
lst.append(sn.nr)
continue
sn_dt = umisc.trim(sn_dt)
print 'Collect layer.sn_dt(2):', sn_dt
if umisc.trim(sn_dt) != '$$id$$' and umisc.trim(sn_dt) != '':
lay = mdNeural.mdLayer()
new_k = sn_dt
if umisc.trim(new_k) not in ex_Data:
ex_Data.append(umisc.trim(new_k))
print 'ex:data:', ex_Data
lay.name = umisc.trim(new_k)
if umisc.trim(sn_dt) == '':
if len(ex_Data) > 0:
print 'ex_Data(1)-->', ex_Data
for s in ex_Data:
if umisc.trim(s.name) == '':
lay.name = s.name
break
for topico_rsfd in lst:
lay.set_topico_nr(topico_rsfd)
#====
#print 'lay-name:',lay.name
objects.append(lay)
# verificar se tem mais de 3 words no nome do objeto, restringir ate achar ['o','a']
#lay.name=sep_multiple_data_unkwon(lay.name)
print 'Collect layer(2):', lay.name
obj_foco.append(lay)
ex_Data.append(lay)
found_obj_1 = True
first_t = False
if len(obj_foco) > 0 and (not found_obj_1):
print 'ex_Data-->', obj_foco
lay = obj_foco[len(obj_foco) - 1]
for topico_rsfd in lst:
lay.set_topico_nr(topico_rsfd)
elif len(ex_Data) > 0 and (not found_obj_1):
print 'ex_Data-->', ex_Data
rel1 = False
# verificar se tem os campos mandatorios( caracts necessarias )
mdn = False
for t in layer.topicos:
topico_rsf = t
rel1 = False
#=======================================================
ref_opcode = t.dt[0]
#==============================================
if ref_opcode not in ['', None, 'object']:
for ob1 in obj_foco:
ob1.set_topico_nr(topico_rsf)
#=============================================
if not mdn and layer in obj_foco:
obj_foco.remove(layer)
for o_f in obj_foco:
#print 'o_f.name:',o_f.name,len(o_f.name)
if o_f.name == 'undef' or umisc.trim(o_f.name) == '':
obj_foco.remove(o_f)
print 'OBS.return:', len(obj_foco)
return obj_foco
def runc_layer(layer, ex_Data):
objects = []
ref_opcode = ''
# Relaction-oper-opcode opcode original dos qualificadores se nao especificado
#=====================================================
def get_o_ob():
for s in objects:
if s.name == ob: return s
return None
#=====================================================
class_fnd = False
if True:
print 'RTS(1EV-S):------------', '[', ex_Data, ']'
r = layer
for s in r.topicos:
for kdt in s.dt:
if ('event' in kdt
or 'startssssssss' in kdt) and not 'event-stop' in kdt:
class_fnd = True
if 'object' in s.dt:
for ds in s.sinapses:
if ('event' in ds.nr.dt or 'startsssssssssss'
in ds.nr.dt) and not 'event-stop' in ds.nr.dt:
class_fnd = True
print 'Topico:', s.dt
print 'SNS:++++++++++++++++++'
for s1 in s.sinapses:
print s1.nr.dt
print '++++++++++++++++++'
print 'RTS(1EV-S)(END):------------'
#===============================
if not class_fnd:
return
obj_foco = []
#print 'N tops:' , len(layer.topicos)
found_obj_1 = False
first_t = True
lst = []
way_mid = False
for t in layer.topicos:
if first_t and 'indicador' in t.dt:
pass
elif 'event-link' in t.dt:
pass
elif 'object' in t.dt:
pass
elif 'way.mid' in t.dt: #[way.mid,,way.mid]
way_mid = True
pass
else:
lst.append(t)
for t in layer.topicos:
if 'event-link' in t.dt and way_mid:
lst.append(t)
for t in layer.topicos:
tp = t
nr_t = t.dt[0]
if nr_t == '': continue
#======================================
if (not first_t) and 'indicador' in t.dt:
if len(obj_foco) > 0:
o = obj_foco[len(obj_foco) - 1]
t.dt = ['pacient']
o.set_topico_nr(t)
#====================================== indicador em inicio de sentenca detem o significado de agente e object
if (('event-link' in t.dt or 'start' in t.dt)
and not way_mid) or 'way.mid' in t.dt:
not_in = [
'alias', 'estimate', 'object3', 'place', 'class', 'indicador',
'indicator', 'defin', 'simple-collect', 'sample', 'equilibrio',
'know', 'mean', 'qtd-', 'time', 'indicative', 'interact',
'state', 'accept', 'way', 'need', 'intensity', 'grow', 'order',
'simple-', 'value', 'enum', 'event', 'news', 'referencial',
'compare', 'number', 'ele', 'ela'
]
if way_mid:
not_in = [
'alias', 'estimate', 'object3', 'place', 'class',
'indicador', 'defin', 'simple-collect', 'sample',
'equilibrio', 'know', 'mean', 'qtd-', 'time', 'indicative',
'interact', 'state', 'accept', 'need', 'intensity', 'grow',
'order', 'simple-', 'value', 'enum', 'event', 'news',
'referencial', 'compare', 'number', 'ele', 'ela'
]
sn_dt = ''
for sn in t.sinapses:
ah = False
for a in sn.nr.dt:
for cin in not_in:
if cin in a.lower():
ah = True
continue
if ah:
continue
email_ad = False
for a in sn.nr.dt:
if a in 'site-address':
email_ad = True
if email_ad:
for k in sn.nr.sinapses:
for k1 in k.nr.dt:
sn_dt += (k1)
if not email_ad:
for a in sn.nr.dt:
sn_dt += (a + ' ')
print 'T.dt:', t.dt, '->[', sn_dt, ']'
if True:
lay = mdNeural.mdLayer()
lay.name = umisc.trim(sn_dt)
#======================
for topico_rsfd in lst:
lay.set_topico_nr(topico_rsfd)
lst = []
#====
objects.append(lay)
obj_foco.append(lay)
return obj_foco
def runc_layer(layer):
objects=[]
ref_opcode=''
# Relaction-oper-opcode opcode original dos qualificadores se nao especificado
#=====================================================
def get_o_ob():
for s in objects:
if s.name==ob: return s
return None
#=====================================================
obj_foco=[]
#print 'N tops:' , len(layer.topicos)
for t in layer.topicos:
tp=t
nr_t=t.dt[0]
if len(tp.sinapses) > 1 :
print '{',t.dt,'....','[',len(t.sinapses),']','}'
if len(tp.sinapses) > 100:
for ss in tp.sinapses:
print ss.nr.dt
if 'identificador' in t.dt:
for sn in tp.sinapses:
sn_dt=''
for s1 in sn.nr.dt:
sn_dt+=s1
lay=mdNeural.mdLayer()
lay.name=sn_dt
objects.append(lay)
obj_foco.append(lay)
if 'realid2' in t.dt:
print '{realid2}:',len(tp.sinapses)
for sn in tp.sinapses:
sn_dt=''
for s1 in sn.nr.dt:
sn_dt =s1
lay=mdNeural.mdLayer()
lay.name=sn_dt
print '{{',sn_dt,'}}'
lay.set_topico('identificador')
lay.set_topico('{realid}')
objects.append(lay)
obj_foco.append(lay)
#print 'obj:',sn_dt
rel1=False
for t in layer.topicos:
topico_rsf=t
#=======================================================
ref_opcode=t.dt[0]
if 'composicao' in t.dt or 'hierarchy' in t.dt :
rel1=False
#==============================================
if ref_opcode in ['quality-good','need','way','guess','class','referencial','action','indicador','choice','decison','option','guess','state']:
rel1=True
if rel1:
for sn in tp.sinapses:
sn_dt=None
for s1 in sn.nr.dt:
sn_dt=s1
break
opc=ref_opcode
for ob1 in obj_foco:
referencia_c=False
# referencia,link -> achar objeto e linkar , senao, apeas conecta
if ref_opcode in ['refers','links','needs','ways','reference','referencial','referencia'] : referencia_c = True
# ====
if not referencia_c:
ob1.set_topico_nr(topico_rsf)
else :
print '{Connect reference...}'
obj_k=get_o(sn_dt)
if obj_k == None:
#criar obj
obj_k=mdNeural.mdLayer()
obj_k.name=sn_dt
ob1.set_link_ds(obj_k,opc,[],[])
#=============================================
if 'composicao' in t.dt or 'hierarchy' in t.dt :
sn_dt=''
for ob1 in obj_foco:
ob1.set_topico_nr(t)
return objects
def process_termo(termo, usr, purp, start_c, path_j):
pages = []
ths2 = []
no_start = False
if start_c == 0:
no_start = True
clean_Trace(usr)
start_c = 1
all_pgs = get_pages_len(usr, termo, purp)
ib = 0
lb = all_pgs
print 'Process pages:', (all_pgs)
w_fnds = True
while ib < all_pgs:
if ib < 15:
start_c += 15
w_fnds = False
else:
#start_job_sec(ib+1,path_j)
print 'Start collection at:', start_c - 15
ths2.append(thread_cntl())
thread.start_new_thread(
start_job_sec,
((start_c - 15), usr, path_j, ths2[len(ths2) - 1]))
w_fnds = True
ib += 15
if not w_fnds:
print 'Start collection at:', start_c - 15
ths2.append(thread_cntl())
thread.start_new_thread(
start_job_sec,
((start_c - 15), usr, path_j, ths2[len(ths2) - 1]))
w_fnds = False
else:
pass
#========================================================================================================================================
#========================================================================================================================================
if no_start:
ind_col = 0
while True and len(ths2) > 0:
print 'wait for secondary process...', len(ths2) - ind_col
fnds_t = False
ind_col = 0
for ths1 in ths2:
if not ths1.finished: fnds_t = True
if ths1.finished: ind_col += 1
if fnds_t:
time.sleep(10)
continue
else:
break
if no_start:
clean_Trace(usr)
finish_Trace(usr) #finish info cache
#========================================================================================================================================
if no_start:
return
#========================================================================================================================================
job_index = int(start_c / 100)
job_index += 1
pages = get_pages(usr, start_c, termo, purp)
print 'Process pages:(', len(pages), ')'
#=====================================
[onto_basis, [allp, allp2]] = mount_node(termo, usr, purp)
#========================================
cind2 = 0
threads_fd = []
result_onto_tree_er = [] # caracteristicas,descricoes,etc...
result_onto_tree_bpm = [] # fluxo de acoes, sequencia de actions
result_linked = []
cnt_process = 0
addresses = []
all_size_pg = len(pages)
pg_index = start_c
d1 = 1
print '================================================'
print 'Init Process tree for pages:', len(pages), ' Start at:', start_c
print '================================================'
for pagina in pages:
if pagina.dt1 == None: continue
all_p = []
endereco = pagina.dt1
lines_doc = pagina.dt2
all_p.append([endereco, lines_doc])
cnt_process += 1
d1 += 1
cind2 += 1
if len(all_p) > 0:
print 'Process page num:', pg_index
process_page(all_p, usr, purp, cind2, None, cind2, pg_index,
all_size_pg, job_index, addresses,
result_onto_tree_er, onto_basis)
#================
print 'End page:', pg_index, ' of total:', len(pages)
#================
pg_index += 1
# processar informacoes ( encontrar os ractionlines linkdas por 'CALL' )
rts = []
ind = 0
def get_result_links(lr):
for s in result_linked:
for s2 in s:
if s2 == lr:
return s
return []
if len(pages) <= 0: # <1>
return
print 'Prepare to call rcts:', start_c
for data_c in result_onto_tree_er:
runc = result_onto_tree_bpm[
ind] # rct original, ractionline q processou as informacoes
#sd_link=result_linked[ind]
sd_link = get_result_links(data_c)
runc.process_Call_RCT(conn, data_c, usr, sd_link)
rts.append(data_c)
ind += 1
#========================================================================================================================================
print 'ER:', len(result_onto_tree_er)
for e in result_onto_tree_er:
print '----------------------------------------'
for t in e.topicos:
print t.dt
print '**************************'
for s in t.sinapses:
print s.nr.dt
print '**************************'
print '----------------------------------------'
#break
# retorno:
'''
->gravar na ontologia semantic_objects2, os qualificadores de definicao,composicao( achar objectos com destinacao(info-composicao), que contem as informacoes de propriedades )
->gravar na ontologia semantic_objects2, os qualificadores de estado( achar objectos com destinacao(info-state), que contem as informacoes de estado->links para cada momento das propriedades )
->gravar na ontologiao links_objetcts os qualificadores definidores de relacao , link ,conexao, etc.. ( achar objectos com destinacao(info-relaction), que contem as informacoes de relacoes )
->os qualificadores de filtro,foco informados na sentenca do clipping seram utilizados para o retorno no clipping
'''
layers_processed = []
for e in result_onto_tree_er:
#================
topicos_composicao = [] # ->semantic_objects2
topicos_relaction = [] # ->semantic_relactions2
topicos_infostate = [] # ->semantic_infostate
objs_prs = get_objs_purp(e.name, 'COMPOSICAO', usr)
tops_cmp_prs = get_objectdt_by(objs_prs, usr)
objs_prs = get_objs_purp(e.name, 'INFO-STATE', usr)
tops_infos_prs = get_objectdt_by(objs_prs, usr)
objs_prs = get_objs_purp(e.name, 'RELACTION', usr)
tops_rel_prs = get_objectdt_by(objs_prs, usr)
objets_Real_post = [
] # REAL-ONTOLOGY-POST=> qualificadores que indicam se deve ou nao fazer parte da ontologia primaria(ontologia principal de conhecimento )->instrucao privilegiada
#================
layer_post = mdNeural.mdLayer()
layers_processed.append(layer_post)
foco_h = [
] #-> usado para focar os elementos pertinentes Я observacao para o historico
foco_h2 = []
#========================
for t in e.topicos:
for d in t.dt:
if d.upper() == 'FOCO':
for s in t.sinases:
foco_h2.append(s)
for d2 in s.nr.dt:
foco_h.append(d2)
#========================
for t in e.topicos:
for d in t.dt:
if d.upper() == 'REAL-ONTOLOGY-POST':
for s in t.sinases:
for d2 in s.nr.dt: #[objecto]
objets_Real_post.append([d2])
#==================================
fnd = False
for d in t.dt:
if not fnd:
if d.upper() == 'IDENTIFICADOR':
for s in t.sinases:
if not fnd:
for d2 in s.nr.dt:
obj_nm = d2
layer_post.name = obj_nm
break
#==================================
fnd = False
for d in t.dt:
if not fnd:
for d2 in tops_cmp_prs:
if d.upper() == d2.upper():
fnd = True
break
if fnd:
topicos_composicao.append(t)
#==================================
fnd = False
for d in t.dt:
if not fnd:
for d2 in tops_rel_prs:
if d.upper() == d2.upper():
fnd = True
break
if fnd:
topicos_relaction.append(t)
#==================================
fnd = False
for d in t.dt:
if not fnd:
for d2 in tops_infos_prs:
if d.upper() == d2.upper():
fnd = True
break
if fnd:
topicos_infostate.append(t)
#=
for t_com in topicos_composicao:
for s in t_com.sinapses:
layer_post.set_topico_nr(t_com.nr)
#=====================
#
links = []
for lnk_s in topicos_relaction:
dt = []
for s in t_com.sinapses:
for d in nr.dt:
o = get_object_by_data(dt, usr)
if o != None:
layer_post.set_link(
o, s.opcode) # opcode=> link, motivo da conexao
if len(objets_Real_post) == 0:
post_object_by_data2(layer_post, usr, termo, foco_h2)
else: # se estiver na lista objets_Real_post, postar como ontologia principal
post_object_by_data(layer_post, usr, termo, foco_h2)
# historico
for t_hist in topicos_infostate: # historico, identificador do referencial
state_type = t_hist.dt
composicao = []
rels = []
#=========
if len(foco_h) == 0:
for t_com in topicos_composicao:
composicao.append(t_com)
else:
for t_com in topicos_composicao:
fnd = False
for f in foco_h: #composicao, elementos considerados
if not fnd:
for s in t_com.sinapses:
if f.upper() in s.dt:
composicao.append(t_com)
fnd = True
break
else:
break
#========= lnks
if len(foco_h) == 0:
for t_com in topicos_relaction:
rels.append(t_com)
else:
for t_com in topicos_relaction:
fnd = False
for f in foco_h: #rels, links considerados
if not fnd:
for s in t_com.sinapses:
if f.upper() in s.dt:
rels.append(t_com)
fnd = True
break
else:
break
post_datah_state(state_type, layer_post.name, composicao, rels,
usr)
#==================================
# filtro,foco para indicar o que extrair dos objetos acima postados
#==================================
s_foco = []
s_restricao = []
s_filtro = []
s_m = [] # msg ou clipping
for lay in onto_basis.nodesER:
nodes2 = lay.get_links('FACT')
for lay in nodes2:
lay = lay.lr
for top in lay.topicos:
for dts in top.dt:
if dts.upper() == "FILTRO":
for s in top.sinapses:
s_filtro.append(s.nr)
if dts.upper() == "FOCO":
for s in top.sinapses:
s_foco.append(s.nr)
if dts.upper() == "RESTRICAO":
for s in top.sinapses:
s_restricao.append(s.nr)
if dts.upper() == "PURPOSE-DST":
for s in top.sinapses:
for ds1 in s.nr.dt:
s_m.append(ds1)
#==================================
'''
1 -setar foco nos objectos
2 -caracteristicas + links
3 -layout-out com interesses
4 -layout-code para extrair informacao
5 -inserir no clipping return
'''
for lr in layers_processed:
lr_name = lr.name
#foco
consid = []
#============================================
for f in s_foco:
for nr2 in lr.topicos:
if lr.compare_dt_depend(conn, usr, purp, f, nr2, []):
consid.append(nr2)
#============================================
for f in s_filtro:
for nr2 in lr.topicos:
if lr.compare_dt_depend(conn, usr, purp, f, nr2, []):
consid.append(nr2)
#============================================
for f in s_restricao:
for nr2 in lr.topicos:
if lr.compare_dt_depend(conn, usr, purp, f, nr2, []):
consid.append(nr2)
#============================================
for c in consid:
lr.put_publish(c)
#============================================
#========================================================================================================================================
#========================================================================================================================================
data_return = []
# char layoutcode
ind_c = 0
for data_c in result_onto_tree_er:
#==========
cmps_nr = layers_processed[ind_c].get_all()
ind_c += 1
#==========
runc = result_onto_tree_bpm[
ind] # rct original, ractionline q processou as informacoes
addr_url = addresses[ind]
layout_codes = runc.get_links('LAYOUT-CODE')
nms = []
for d in result_onto_tree_bpm:
nms.append(d.lr.name)
rts2 = prepare_search_customlayouts(nms, cmps_nr,
usr) #purposes,dts,usr
data_return.append(rts2)
#========================================================================================================================================
#
#====================================
all_txt = ''
for dt in data_return: # post no clipping
all_txt += (' ' + dt)
all_txt = umisc.trim(all_txt)
if all_txt != "":
#== classificacao/categoria
#class=get_class(purpose) - > retorna o label do purpose
class_c = get_class(purp, usr)
#== post no clipping
if len(s_m) <= 0:
cursor.execute(
" insert into clipping_return(username,retorno,categoria,href) values ( %s , %s , %s , %s )",
(usr, all_txt, class_c, ''))
else:
has_clipp = False
has_msg = False
for d in s_m: # verificar onde inserir
if d.upper() == "CLIPPING":
if not has_clipp:
has_clipp = True
cursor.execute(
" insert into clipping_return(username,retorno,categoria,href) values ( %s , %s , %s , %s )",
(usr, all_txt, class_c, ''))
elif d.upper() == "FEED":
if not has_msg:
has_msg = True
cursor.execute(
" insert into INBOX_MSG (username,MSG,SOURCE,REFERENCIAS) values ( %s , %s , %s , %s )",
(usr, all_txt, class_c, addr_url))
def runc_layer(layer, usr):
#===============================================
def translate_year(mn):
if mn == '$this-year':
return str(date2.datetime.today().year)
elif mn == '$next-year':
return str(date2.datetime.today().year + 1)
elif mn == '$next-two-year':
return str(date2.datetime.today().year + 2)
elif mn == '$next-three-year':
return str(date2.datetime.today().year + 3)
elif mn == '$next-four-year':
return str(date2.datetime.today().year + 4)
elif mn == '$next-five-year':
return str(date2.datetime.today().year + 5)
elif mn == '$next-six-year':
return str(date2.datetime.today().year + 6)
elif mn == '$next-seven-year':
return str(date2.datetime.today().year + 7)
elif mn == '$next-eigth-year':
return str(date2.datetime.today().year + 8)
elif mn == '$next-nine-year':
return str(date2.datetime.today().year + 9)
elif mn == '$next-ten-year':
return str(date2.datetime.today().year + 10)
else:
return mn
def translate_month(mn):
if mn.lower() == 'janeiro':
return 1
elif mn.lower() == 'fevereiro':
return 2
elif mn.lower() == 'março' or mn.lower() == 'marco':
return 3
elif mn.lower() == 'abril':
return 4
elif mn.lower() == 'maio':
return 5
elif mn.lower() == 'junho':
return 6
elif mn.lower() == 'julho':
return 7
elif mn.lower() == 'agosto':
return 8
elif mn.lower() == 'setembro':
return 9
elif mn.lower() == 'outubro':
return 10
elif mn.lower() == 'novembro':
return 11
elif mn.lower() == 'dezembro':
return 12
else:
return mn
#===============================================
not_in = [
'event', 'start', 'number', 'class-past', 'person', 'assunto',
'simple-collect', 'sample', 'equilibrio', 'know', 'mean', 'qtd-',
'time', 'indicative', 'interact', 'state', 'accept', 'way', 'need',
'intensity', 'grow', 'order', 'simple-', 'value', 'enum', 'event',
'news', 'referencial', 'compare', 'number', 'ele', 'ela'
]
not_in2 = ['class-past', 'start-past']
not_in3 = ['e', 'do', 'da', 'dos', 'das']
not_in4 = ['state', 'event', 'start']
ref_opcode = ''
global_uuid = 0
if True:
print 'RTS(1--2-I)(' + layer.name + '):------------'
r = layer
r.dump_layer()
print 'RTS(1--2-I)(END):------------'
obj_foco = []
prop_l = False
abs_cnt = 1
#print 'N tops:' , len(layer.topicos)
topics = []
reins_tps = []
for t in layer.topicos:
if 'object' in t.dt or 'identificador' in t.dt:
for sn in t.sinapses:
ah = False
for a in sn.nr.dt:
for cin in not_in:
if cin in a.lower():
ah = True
continue
if ah:
reins_tps.append(sn.nr)
for t in layer.topicos:
if 'tstatic' in t.dt: # se tstatic, avalia apenas os topicos abaixo desse
for s1 in t.sinapses:
topics.append(s1.nr)
else:
topics.append(t)
found_by_name = False
if True:
if umisc.trim(layer.name) != '$$id$$' and umisc.trim(layer.name) != '':
lay = mdNeural.mdLayer()
lay.name = layer.name
#===
obj_foco.append(lay)
found_by_name = True
for t in topics:
tp = t
nr_t = t.dt[0]
if nr_t == '': continue
if 'object' in t.dt or 'identificador' in t.dt and not found_by_name:
sn_dt = ''
for sn in tp.sinapses:
for s1 in sn.nr.dt:
print 'Object.fnd:', s1, '->>', t.dt
sn_dt += (s1 + ' ')
rts = layer.s_get_ontology_ponderate(sn_dt, [], [], usr)
for [ratting, la] in rts:
obj_foco.append(la)
break
if len(rts) == 0:
if umisc.trim(sn_dt) != '$$id$$':
lay = mdNeural.mdLayer()
lay.name = sn_dt
#===
obj_foco.append(lay)
#===
if len(obj_foco) == 0: # nao descreve um layer, é o proprio layer
obj_foco.append(layer)
prop_l = True
i_need = []
i_sugest = []
i_guess = []
acts = []
event = []
for t in layer.topicos:
reins_tps.append(t)
start_data = None
end_data = None
for t in reins_tps:
topico_rsf = t
rel1 = False
ref_opcode = t.dt[0]
dt1 = ''
dt2 = ''
#==============================================
if ref_opcode in ['interact.state.moment.startday']:
for s in t.sinapses:
for ds in s.nr.dt:
d1 = ds
if ds != '': d1 += '-'
break
#==============================================
if ref_opcode in ['interact.state.moment.endday']:
for s in t.sinapses:
for ds in s.nr.dt:
d2 = ds
if ds != '': d2 += '-'
break
#==============================================
if ref_opcode in ['interact.state.moment.month']:
for s in t.sinapses:
for ds in s.nr.dt:
d1 += str(translate_month(ds))
d2 += str(translate_month(ds))
if ds != '':
d2 += '-'
d1 += '-'
break
#==============================================
if ref_opcode in ['interact.state.moment.year']:
for s in t.sinapses:
for ds in s.nr.dt:
d1 += translate_year(ds)
d2 += translate_year(ds)
#===========
start_data = d1
end_data = d2
#===========
break
#==============================================
if ref_opcode in ['interact.state.moment'
] or ref_opcode in ['interact.state.moment.mult']:
event.append(t)
#==============================================
if ref_opcode in ['need'] or ref_opcode in [
'interact.need'
] or ref_opcode in ['interact.info.value'
] or ref_opcode in ['interact.want']:
i_need.append(t)
#=============================================
if ref_opcode in ['interact.can'] or ref_opcode in [
'interaction.comunic.ask.can'
] or ref_opcode in ['interact.find'
] or ref_opcode in ['interesting-ask']:
i_sugest.append(t)
if ref_opcode in ['interact.guess']:
i_guess.append(t)
if ref_opcode in ['interact.get.action']:
acts.append(t)
#==============================================
#===========================================================================
# need
#===========================================================================
cl_fnd = False
cn_cl = []
ls_link = []
print 'Process need:', i_need
if not cl_fnd: # criar um objeto __abstract__ e implementar com o class recolhido
dtnm = ''
ah = False
ah2 = False
to_class_impl = []
for c_i in i_need:
for sn in c_i.sinapses:
to_class_impl.append(sn.nr)
if True:
print 'Parse need layers:', to_class_impl
if len(to_class_impl) > 0:
laycl = mdNeural.mdLayer()
laycl.name = '[__need__]:' + str(global_uuid)
#======================================================
for cl_impl_nr in to_class_impl:
tps = laycl.set_topico('defs')
tps.uuid = global_uuid
laycl.set_nr_ch_a2(tps, cl_impl_nr, 'Composicao')
for ev in event:
laycl.set_topico_nr(ev)
#======================================================
for a in acts:
c = laycl.set_topico_nr(a)
c.uuid = global_uuid
#======================================================
if start_data != None and end_data != None:
c = laycl.set_topico('interact.state.moment.start-data')
c.uuid = global_uuid
laycl.set_nr_ch(c, start_data, 'defs')
#============
c = laycl.set_topico('interact.state.moment.end-data')
c.uuid = global_uuid
laycl.set_nr_ch(c, end_data, 'defs')
#=======================================================
ls_link.append(laycl)
print '[global_uuid]:', global_uuid
for la in obj_foco:
for laycl in ls_link:
laycl.name = la.name + '[__need__]:' + str(global_uuid)
print 'Process i_need link :', laycl.name
la.set_link_ds(laycl, 'need', '', '')
#===========================================================================
# guess
#===========================================================================
ls_link = []
cl_fnd = False
cn_cl = []
ls_link = []
print 'Process guess:', i_guess
if not cl_fnd: # criar um objeto __abstract__ e implementar com o class recolhido
dtnm = ''
ah = False
ah2 = False
to_class_impl = []
for c_i in i_guess:
for sn in c_i.sinapses:
to_class_impl.append(sn.nr)
if True:
print 'Parse guess layers:', to_class_impl
if len(to_class_impl) > 0:
laycl = mdNeural.mdLayer()
laycl.name = '[__guess__]:' + str(global_uuid)
#======================================================
for cl_impl_nr in to_class_impl:
tps = laycl.set_topico('defs')
tps.uuid = global_uuid
laycl.set_nr_ch_a2(tps, cl_impl_nr, 'Composicao')
#======================================================
for a in acts:
r1 = laycl.set_topico_nr(a)
r1.uuid = global_uuid
#======================================================
ls_link.append(laycl)
for la in obj_foco:
for laycl in ls_link:
laycl.name = la.name + '[__guess__]:' + str(global_uuid)
print 'Process i_guess(' + la.name + ') link :', laycl.name
la.set_link_ds(laycl, 'guess', '', '')
#===========================================================================
# sugest
#===========================================================================
cl_fnd = False
cn_cl = []
ls_link = []
print 'Process sugest:', i_sugest
if not cl_fnd: # criar um objeto __abstract__ e implementar com o class recolhido
dtnm = ''
ah = False
ah2 = False
to_class_impl = []
for c_i in i_sugest:
for sn in c_i.sinapses:
to_class_impl.append(sn.nr)
if True:
print 'Parse sugest layers:', to_class_impl
if len(to_class_impl) > 0:
laycl = mdNeural.mdLayer()
laycl.name = '[__sugest__]:' + str(global_uuid)
#======================================================
for cl_impl_nr in to_class_impl:
tps = laycl.set_topico('defs')
tps.uuid = global_uuid
laycl.set_nr_ch_a2(tps, cl_impl_nr, 'Composicao')
#======================================================
for a in acts:
c = laycl.set_topico_nr(a)
c.uuid = global_uuid
#======================================================
ls_link.append(laycl)
for la in obj_foco:
for laycl in ls_link:
laycl.name = la.name + '[__sugest__]:' + str(global_uuid)
print 'Process i_sugest(' + la.name + ') link :', laycl.name
la.set_link_ds(laycl, 'sugest', '', '')
#===========================================================================
for lr_p in obj_foco:
if umisc.trim(lr_p.name) != '':
print 'Prepare post object:', lr_p.name
az = mdTb.Zeus_Mode
mdTb.Zeus_Mode = False
lr_p.s_post_object_by_data_es(lr_p, usr)
mdTb.Zeus_Mode = az
return obj_foco
def runc_layer(layer, usr):
return
ref_opcode = ''
'''
if True:
print 'RTS(12):------------'
r=layer
for s in r.topicos:
print 'Topico:',s.dt
print 'SNS:++++++++++++++++++'
for s1 in s.sinapses:
print s1.nr.dt
print '++++++++++++++++++'
print 'RTS(12)(END):------------'
'''
obj_foco = []
i_quality = []
prop_l = False
#print 'N tops:' , len(layer.topicos)
topics = []
for t in layer.topicos:
if 'tstatic' in t.dt: # se tstatic, avalia apenas os topicos abaixo desse
for s1 in t.sinapses:
topics.append(s1.nr)
else:
topics.append(t)
for t in topics:
tp = t
nr_t = t.dt[0]
if nr_t == '': continue
if 'object' in t.dt or 'identificador' in t.dt:
sn_dt = ''
for sn in tp.sinapses:
for s1 in sn.nr.dt:
print 'Object.fnd:', s1, '->>', t.dt
sn_dt += (s1 + ' ')
rts = layer.s_get_ontology_ponderate(sn_dt, [], [], usr)
for [ratting, la] in rts:
obj_foco.append(la)
break
if len(rts) == 0:
if umisc.trim(sn_dt) != '$$id$$':
lay = mdNeural.mdLayer()
lay.name = sn_dt
#===
obj_foco.append(lay)
#===
if len(obj_foco) == 0: # nao descreve um layer, é o proprio layer
obj_foco.append(layer)
prop_l = True
'''
'''
i_ev = []
i_ev_data = []
i_state = []
i_data_f = []
i_data_interv = []
i_ev_tps = []
dt_ini = None
i_month = None
i_year = None
i_day = None
for t in layer.topicos:
topico_rsf = t
rel1 = False
ref_opcode = t.dt[0]
if 'object' in t.dt or 'identificador' in t.dt:
continue
#==============================================
if 'quality' in t.dt:
i_quality.append(t)
if 'event' in t.dt:
i_ev.append(t)
elif 'event-data' in t.dt:
i_ev_data.append(t)
elif 'event-data' in t.dt:
i_ev_data.append(t)
elif 'event-data-month' in t.dt:
i_month = (t)
#=========================
elif 'event-data-year' in t.dt:
i_year = (t)
i_data_f.append([i_day, i_month, i_year])
i_day = None
i_month = None
i_year = None
#=========================
elif 'event-data-day' in t.dt:
i_day = (t)
#=========================
elif 'state' in t.dt:
i_state.append(t)
elif 'event-data-ini' in t.dt:
dt_ini = t
elif 'event-data-end' in t.dt:
if dt_ini != none:
i_data_interv.append([dt_ini, t])
dt_ini = None
else:
i_ev_tps.append(t)
#==============================================
#===========================================================================
# mount event
#===========================================================================
cl_fnd = False
cn_cl = []
ls_link = []
# ==
for c_i in i_ev:
# ==
if prop_l:
layer.topicos.remove(c_i)
#====
dt = c_i.dt
# por classe e por caracts
obs = layer.s_get_ontology_s(dt[0], [], [], usr)
if len(obs) > 0:
cl_fnd = True
for i in obs:
ls_link.append(o)
else:
cn_cl.append(dt[0])
if not cl_fnd:
for dt in cn_cl:
# procurar as informacoes nas caracts do obj
obs = layer.s_get_ontology_s(dt, cn_cl, [], usr)
if len(obs) > 0:
cl_fnd = True
for i in obs:
ls_link.append(o)
if not cl_fnd: # criar um objeto __abstract__ e implementar com o class recolhido
laycl = None
if len(i_data_interv):
for [ini, fim] in i_data_f:
c_ini = ''
c_fim = ''
if ini != None:
for s in ini.sinapses:
for d in s.nr.dt:
if umisc.trim(c_ini) != '':
c_ini += (' ')
c_ini += (d)
#==
if c_fim == None: continue
for s in fim.sinapses:
for d in s.nr.dt:
if umisc.trim(c_fim) != '':
c_fim += (' ')
c_fim += (d)
laycl = mdNeural.mdLayer()
laycl.name = c_ini + '|' + c_fim
ls_link.append(laycl)
if len(i_data_f):
for [day, month, year] in i_data_f:
c_day = ''
c_month = ''
c_year = ''
if day != None:
for s in day.sinapses:
for d in s.nr.dt:
if umisc.trim(c_day) != '':
c_day += (' ')
c_day += (d)
#==
if month != None:
for s in month.sinapses:
for d in s.nr.dt:
if umisc.trim(c_month) != '':
c_month += (' ')
c_month += (d)
#==
if year == None: continue
for s in year.sinapses:
for d in s.nr.dt:
if umisc.trim(c_year) != '':
c_year += (' ')
c_year += (d)
laycl = mdNeural.mdLayer()
laycl.name = c_day + '|' + c_month + '|' + c_year
ls_link.append(laycl)
elif len(i_ev):
ev = ''
for y in i_ev:
for s in y.sinapses:
for d in s.nr.dt:
if umisc.trim(ev) != '':
ev += (' ')
ev += (d)
laycl = mdNeural.mdLayer()
laycl.name = ev
ls_link.append(laycl)
elif len(i_state):
ev = ''
for y in i_state:
for s in y.sinapses:
for d in s.nr.dt:
if umisc.trim(ev) != '':
ev += (' ')
ev += (d)
laycl = mdNeural.mdLayer()
laycl.name = ev
ls_link.append(laycl)
elif len(i_ev_data):
ev = ''
for y in i_state:
for s in y.sinapses:
for d in s.nr.dt:
if umisc.trim(ev) != '':
ev += (' ')
ev += (d)
laycl = mdNeural.mdLayer()
laycl.name = ev
ls_link.append(laycl)
if laycl != None:
for c_i in i_ev_tps:
laycl.set_topico_nr(c_i)
for la in obj_foco:
for laycl in ls_link:
if len(i_quality) == 0:
la.set_link(laycl, 'history')
else:
for q in i_quality:
la.set_link(laycl, q.dt[0])
for lr_p in obj_foco:
if umisc.trim(lr_p.name) != '':
print 'Prepare post object:', lr_p.name
lr_p.s_post_object_by_data_es(lr_p, usr)
return obj_foco
def runc_layer(layer, ex_Data):
objects = []
ref_opcode = ''
# Relaction-oper-opcode opcode original dos qualificadores se nao especificado
#=====================================================
def get_o_ob():
for s in objects:
if s.name == ob: return s
return None
#=====================================================
class_fnd = False
if True:
print 'RTS(1-S):------------', '[', ex_Data, ']'
r = layer
for s in r.topicos:
if 'class' in s.dt or 'classe' in s.dt or 'classificacao' in s.dt or 'defs' in s.dt:
class_fnd = True
if 'object' in s.dt:
for ds in s.sinapses:
if 'class' in ds.nr.dt or 'classe' in ds.nr.dt or 'classificacao' in ds.nr.dt or 'defs' in ds.nr.dt:
class_fnd = True
print 'Topico:', s.dt
print 'SNS:++++++++++++++++++'
for s1 in s.sinapses:
print s1.nr.dt
print '++++++++++++++++++'
print 'RTS(1-S)(END):------------'
#===============================
print 'class_fnd:', class_fnd
if not class_fnd:
return
obj_foco = []
ex_Data2 = []
#print 'N tops:' , len(layer.topicos)
found_obj_1 = False
first_t = True
for t in layer.topicos:
tp = t
nr_t = t.dt[0]
if nr_t == '': continue
#======================================
if (not first_t) and 'indicator' in t.dt:
if len(obj_foco) > 0:
o = obj_foco[len(obj_foco) - 1]
t.dt = ['pacient']
o.set_topico_nr(t)
#====================================== indicador em inicio de sentenca detem o significado de agente e object
if first_t and 'indicator' in t.dt:
sn_dt = ''
lst = []
t.dt = ['agent']
for sn in tp.sinapses:
ah = False
for a in sn.nr.dt:
if 'way' in a:
ah = True
continue
if 'simple-collect' in a:
ah = True
continue
if ah: continue
if 'intensity' in sn.nr.dt:
lst.append(sn.nr)
continue
for s1 in sn.nr.dt:
if umisc.trim(s1) in [
',', ';', '.', '?', '!', 'with', 'e', 'E', 'dos',
'das', 'do', 'da'
]:
s1 = ''
sn_dt += (s1 + ' ')
if umisc.trim(sn_dt) != '$$id$$':
lay = mdNeural.mdLayer()
if umisc.trim(sn_dt) in [
',', ';', '.', '?', '!', 'with', 'e', 'E', 'dos',
'das', 'do', 'da'
]:
sn_dt = ''
sn_dt = sn_dt.replace(',', '')
sn_dt = sn_dt.replace('.', '')
sn_dt = sn_dt.replace(';', '')
sn_dt = sn_dt.replace('!', '')
sn_dt = sn_dt.replace('?', '')
sn_dt = sn_dt.replace('"', '')
if umisc.trim(sn_dt).lower() == 'e':
sn_dt = ''
lay.name = umisc.trim(sn_dt)
if umisc.trim(sn_dt) == '':
if len(ex_Data) > 0:
print 'ex_Data(1)-->', ex_Data
for s in ex_Data:
if umisc.trim(s.name) != '':
lay.name = s.name
break
for topico_rsfd in lst:
lay.set_topico_nr(topico_rsfd)
lay.set_topico_nr(t)
#====
objects.append(lay)
obj_foco.append(lay)
ex_Data.append(lay)
found_obj_1 = True
#===================================================
if 'object' in t.dt:
sn_dt = ''
lst = []
not_in = [
'alias', 'object3', 'class', 'indicator', 'defin',
'simple-collect', 'sample', 'equilibrio', 'know', 'mean',
'qtd-', 'time', 'indicative', 'interact', 'state', 'accept',
'way', 'need', 'intensity', 'grow', 'order', 'simple-',
'value', 'enum', 'event', 'news', 'referencial', 'compare',
'number'
]
for sn in tp.sinapses:
ah = False
for a in sn.nr.dt:
for cin in not_in:
if cin in a.lower(
) and a.lower() != 'evento' and a.lower() != 'eventos':
ah = True
continue
if ah:
lst.append(sn.nr)
continue
if 'place' in sn.nr.dt:
lst.append(sn.nr)
sn_dt = ''
for sn2 in sn.nr.sinapses:
for a in sn2.nr.dt:
sn_dt += (' ' + a)
if umisc.trim(sn_dt) != '$$id$$' and len(ex_Data2) == 0:
lay = mdNeural.mdLayer()
new_k = sn_dt
lay.name = umisc.trim(new_k)
if umisc.trim(sn_dt) == '':
if len(ex_Data) > 0:
print 'ex_Data(1.2)-->', ex_Data
for s in ex_Data:
if umisc.trim(s.name) != '':
lay.name = s.name
break
#====
objects.append(lay)
#
lay.name = sep_multiple_data_unkwon(lay.name)
#
obj_foco.append(lay)
ex_Data.append(lay)
ex_Data2.append(lay)
#
else:
lay = ex_Data2[0]
lay.name += (sn_dt)
#==============================================
for topico_rsfd in lst:
lay.set_topico_nr(topico_rsfd)
topico_rsfd = []
continue
if ('simple-collect-place2'
in sn.nr.dt) or ('place-this'
in sn.nr.dt) or ('place' in sn.nr.dt):
lst.append(sn.nr)
continue
if ('intensity' in sn.nr.dt) or ('person'
in sn.nr.dt) or ('event'
in sn.nr.dt):
lst.append(sn.nr)
continue
for s1 in sn.nr.dt:
if umisc.trim(s1) in [',', '"']:
s1 = ''
sn_dt += (s1 + ' ')
if umisc.trim(sn_dt) != '$$id$$':
lay = mdNeural.mdLayer()
new_k = sn_dt
lay.name = umisc.trim(new_k)
if umisc.trim(sn_dt) == '':
if len(ex_Data) > 0:
print 'ex_Data(1)-->', ex_Data
for s in ex_Data:
if umisc.trim(s.name) != '':
lay.name = s.name
break
for topico_rsfd in lst:
lay.set_topico_nr(topico_rsfd)
#====
#print 'lay-name:',lay.name
objects.append(lay)
# verificar se tem mais de 3 words no nome do objeto, restringir ate achar ['o','a']
lay.name = sep_multiple_data_unkwon(lay.name)
obj_foco.append(lay)
ex_Data.append(lay)
found_obj_1 = True
first_t = False
if len(obj_foco) > 0 and (not found_obj_1):
print 'ex_Data-->', obj_foco
lay = obj_foco[len(obj_foco) - 1]
for topico_rsfd in lst:
lay.set_topico_nr(topico_rsfd)
elif len(ex_Data) > 0 and (not found_obj_1):
print 'ex_Data-->', ex_Data
rel1 = False
# verificar se tem os campos mandatorios( caracts necessarias )
mdn = False
for t in layer.topicos:
topico_rsf = t
rel1 = False
#=======================================================
ref_opcode = t.dt[0]
#==============================================
if ref_opcode not in ['', None, 'object']:
for ob1 in obj_foco:
ob1.set_topico_nr(topico_rsf)
#=============================================
if not mdn and layer in obj_foco:
obj_foco.remove(layer)
'''
print 'DUMP:------------------!!'
for ok in obj_foco:
for te in ok.topicos:
print 'TP:-------------'
print te.dt
print 'SNS:','-----------'
for sg in te.sinapses:
print sg.nr.dt
print 'TP(END):-------------'
print '-----------------------!!'
'''
for o_f in obj_foco:
#print 'o_f.name:',o_f.name,len(o_f.name)
if o_f.name == 'undef' or umisc.trim(o_f.name) == '':
obj_foco.remove(o_f)
return obj_foco
#EXEC
def __init__(self):
inpt_word = mdNeural.mdLayer() # interpretacao de palavras
#==
[self.nodesER, self.all_purpouses] = [[], []]
def process_termo(termo,usr,purp,start_c,path_j):
pages=[]
ths2=[]
if start_c == 0:
start_c=1
all_pgs=get_pages_len(usr)
ib=0
lb=all_pgs
print 'Process pages:',all_pgs
while ib < all_pgs:
if ib < 100:
print 'Start collection at:',start_c
pages=get_pages(usr,start_c)
start_c+=100
else:
#start_job_sec(ib+1,path_j)
ths2.append(thread_cntl())
thread.start_new_thread(start_job_sec,(ib+1,path_j,ths2[len(ths2)-1]) )
ib+=100
else:
print 'Start collection on:',start_c
#========================================================================================================================================
#========================================================================================================================================
ind_col=0
while True and len(ths2) > 0:
print 'wait for secondary process...',len(ths2)-ind_col
fnds_t=False
ind_col=0
for ths1 in ths2:
if not ths1.finished:fnds_t=True
if ths1.finished: ind_col+=1
if fnds_t:
time.sleep(10)
continue
else:
break
#========================================================================================================================================
#========================================================================================================================================
#=====================================
[onto_basis,allp]=mount_node(termo,usr,purp)
#========================================
cind2=0
threads_fd=[]
result_onto_tree_er=[] # caracteristicas,descricoes,etc...
result_onto_tree_bpm=[] # fluxo de acoes, sequencia de actions
result_linked=[]
#print 'Init Process tree for pages:',len(pages)
cnt_process=0
all_p=[]
addresses=[]
for pagina in pages:
cind2+=1
if pagina.dt1 == None: continue
endereco=pagina.dt1
lines_doc=pagina.dt2
def process_page(all_ps,id,purpose,pgs,finish,th):
ln_o=''
conn= MySQLdb.connect(host='dbmy0023.whservidor.com', user='******' , passwd='acc159753', db='mindnet')
for lines_doc2_ in all_ps:
#try:
endereco_url=lines_doc2_[0]
lines_doc2=lines_doc2_[1]
l2=Identify.prepare_layout(conn,id,purpose)
if True :
for s in lines_doc2:
ln_o=s
addresses.append(endereco_url)
if umisc.trim(ln_o) == '':
finish.finished=True
return
ir=Identify.process_data(conn,l2,ln_o,onto_basis,purpose,id,th)
if ir[0] != None :
# procura identificador ---
fnd_ident=False
for es in ir[0].topicos:
if ir[0].es_compare_dt(es,'identificador'):
fnd_ident=True
if not fnd_ident:
ind=len(result_onto_tree_er)-1
fond_cs=False
while ind >=0 and not fond_cs:
for es2 in result_onto_tree_er[ind].topicos:
if ir[0].es_compare_dt(es2,'identificador'):
ir[0].set_topico_nr(es2)
fond_cs=True
break
ind-=1
# verificar se nao tem somente identificadores(elemento fact invalido)
oth=False
for es in ir[0].topicos:
if ir[0].es_compare_dt(es,'identificador'):
pass
else:
oth=True
if not oth:
continue
result_onto_tree_er.append(ir[0])
# procurar group
ind=len(result_onto_tree_er)-1
while ind >=0 :
if ir[0] != result_onto_tree_er[ind]:
for es2 in result_onto_tree_er[ind].topicos:
if ir[0].es_compare_dt(es2,'identificador'):
for top in ir[0].topicos:
if ir[0].compare_dt_depend(conn,usr,purpose,es2,top,['']):
# encontrou referencias do mesmo identificador, incluir nos objetos linkados
rt=None
fnd_new=False
for k1 in result_linked:
for k2 in k1:
if k1 == ir[0]:
fnd_new=True
rt=k1
#=
if not fnd_new:
result_linked.append([ir[0]])
rt= result_linked[len(result_linked)-1 ]
#=======================================
fnd_new=False
for k2 in rt:
if k2 == result_onto_tree_er[ind]:
fnd_new=True
if not fnd_new:
rt.append(result_onto_tree_er[ind])
ind-=1
#==========================
if ir[1] != None:
result_onto_tree_bpm.append(ir[1])
#except Exception ,err:
# print 'Except:',err
# finish.finished=True
finish.finished=True
conn.close()
#print 'Thread ',pgs,' was finished.','Len:',len(ln_o),' process:',start_c/10
if cnt_process >= 10:
#print 'Start thread job :',cind2,' process:',
threads_fd.append(thread_cntl())
#process_page(all_p,usr,purp,cind2,threads_fd[len(threads_fd)-1],cind2)
thread.start_new_thread(process_page, (all_p,usr,purp,cind2,threads_fd[len(threads_fd)-1],cind2) )
all_p=[]
cnt_process=0
continue
else:
all_p.append([endereco,lines_doc])
cnt_process+=1
#--
ind_fs=0
while True:
if (len(threads_fd)-ind_fs) == 0: break
#print 'Processing...',len(threads_fd)-ind_fs,',ind_fs:',ind_fs
ind_fs=0
time.sleep(10)
conti=False
for d in threads_fd:
if not d.finished: conti=True
if d.finished: ind_fs+=1
if conti: continue
# processar informacoes ( encontrar os ractionlines linkdas por 'CALL' )
rts=[]
ind=0
def get_result_links(lr):
for s in result_linked:
for s2 in s:
if s2 == lr:
return s
return []
conn= MySQLdb.connect(host='dbmy0023.whservidor.com', user='******' , passwd='acc159753', db='mindnet')
for data_c in result_onto_tree_er:
runc=result_onto_tree_bpm[ind] # rct original, ractionline q processou as informacoes
#sd_link=result_linked[ind]
sd_link=get_result_links(data_c)
runc.process_Call_RCT(conn,data_c,usr,sd_link)
rts.append(data_c)
ind+=1
#========================================================================================================================================
print 'ER:',len(result_onto_tree_er)
for e in result_onto_tree_er:
print '----------------------------------------'
for t in e.topicos:
print t.dt
print '**************************'
for s in t.sinapses:
print s.nr.dt
print '**************************'
print '----------------------------------------'
#break
# retorno:
'''
->gravar na ontologia semantic_objects2, os qualificadores de definicao,composicao( achar objectos com destinacao(info-composicao), que contem as informacoes de propriedades )
->gravar na ontologia semantic_objects2, os qualificadores de estado( achar objectos com destinacao(info-state), que contem as informacoes de estado->links para cada momento das propriedades )
->gravar na ontologiao links_objetcts os qualificadores definidores de relacao , link ,conexao, etc.. ( achar objectos com destinacao(info-relaction), que contem as informacoes de relacoes )
->os qualificadores de filtro,foco informados na sentenca do clipping seram utilizados para o retorno no clipping
'''
layers_processed=[]
for e in result_onto_tree_er:
#================
topicos_composicao=[] # ->semantic_objects2
topicos_relaction=[] # ->semantic_relactions2
topicos_infostate=[] # ->semantic_infostate
objs_prs=get_objs_purp(e.name,'COMPOSICAO',usr,conn)
tops_cmp_prs=get_objectdt_by(objs_prs,usr,conn)
objs_prs=get_objs_purp(e.name,'INFO-STATE',usr,conn)
tops_infos_prs=get_objectdt_by(objs_prs,usr,conn)
objs_prs=get_objs_purp(e.name,'RELACTION',usr,conn)
tops_rel_prs=get_objectdt_by(objs_prs,usr,conn)
objets_Real_post=[] # REAL-ONTOLOGY-POST=> qualificadores que indicam se deve ou nao fazer parte da ontologia primaria(ontologia principal de conhecimento )->instrucao privilegiada
#================
layer_post=mdNeural.mdLayer()
layers_processed.append(layer_post)
foco_h=[] #-> usado para focar os elementos pertinentes Я observacao para o historico
foco_h2=[]
#========================
for t in e.topicos:
for d in t.dt:
if d.upper() == 'FOCO':
for s in t.sinases:
foco_h2.append(s)
for d2 in s.nr.dt:
foco_h.append(d2)
#========================
for t in e.topicos:
for d in t.dt:
if d.upper() == 'REAL-ONTOLOGY-POST':
for s in t.sinases:
for d2 in s.nr.dt: #[objecto]
objets_Real_post.append([d2])
#==================================
fnd=False
for d in t.dt:
if not fnd:
if d.upper() == 'IDENTIFICADOR':
for s in t.sinases:
if not fnd:
for d2 in s.nr.dt:
obj_nm=d2
layer_post.name=obj_nm
break
#==================================
fnd=False
for d in t.dt:
if not fnd:
for d2 in tops_cmp_prs:
if d.upper() == d2.upper():
fnd=True
break
if fnd:
topicos_composicao.append(t)
#==================================
fnd=False
for d in t.dt:
if not fnd:
for d2 in tops_rel_prs:
if d.upper() == d2.upper():
fnd=True
break
if fnd:
topicos_relaction.append(t)
#==================================
fnd=False
for d in t.dt:
if not fnd:
for d2 in tops_infos_prs:
if d.upper() == d2.upper():
fnd=True
break
if fnd:
topicos_infostate.append(t)
#=
for t_com in topicos_composicao:
for s in t_com.sinapses:
layer_post.set_topico_nr(t_com.nr)
#=====================
#
links=[]
for lnk_s in topicos_relaction:
dt=[]
for s in t_com.sinapses:
for d in nr.dt:
o= get_object_by_data(dt,usr,conn)
if o != None:
layer_post.set_link(o,s.opcode) # opcode=> link, motivo da conexao
if len(objets_Real_post)==0:
post_object_by_data2(layer_post,usr,conn,termo,foco_h2)
else:# se estiver na lista objets_Real_post, postar como ontologia principal
post_object_by_data(layer_post,usr,conn,termo,foco_h2)
# historico
for t_hist in topicos_infostate: # historico, identificador do referencial
state_type=t_hist.dt
composicao=[]
rels=[]
#=========
if len(foco_h)==0:
for t_com in topicos_composicao:
composicao.append(t_com)
else:
for t_com in topicos_composicao:
fnd=False
for f in foco_h: #composicao, elementos considerados
if not fnd:
for s in t_com.sinapses:
if f.upper() in s.dt :
composicao.append(t_com)
fnd=True
break
else: break
#========= lnks
if len(foco_h)==0:
for t_com in topicos_relaction:
rels.append(t_com)
else:
for t_com in topicos_relaction:
fnd=False
for f in foco_h: #rels, links considerados
if not fnd:
for s in t_com.sinapses:
if f.upper() in s.dt :
rels.append(t_com)
fnd=True
break
else: break
post_datah_state(state_type,layer_post.name,composicao,rels,usr,conn)
#==================================
# filtro,foco para indicar o que extrair dos objetos acima postados
#==================================
s_foco=[]
s_restricao=[]
s_filtro=[]
s_m=[] # msg ou clipping
for lay in onto_basis.nodesER:
nodes2=lay.get_links('FACT')
for lay in nodes2:
lay=lay.lr
for top in lay.topicos:
for dts in top.dt:
if dts.upper() == "FILTRO":
for s in top.sinapses:
s_filtro.append(s.nr)
if dts.upper() == "FOCO":
for s in top.sinapses:
s_foco.append(s.nr)
if dts.upper() == "RESTRICAO":
for s in top.sinapses:
s_restricao.append(s.nr)
if dts.upper() == "PURPOSE-DST":
for s in top.sinapses:
for ds1 in s.nr.dt:
s_m.append(ds1)
#==================================
'''
1 -setar foco nos objectos
2 -caracteristicas + links
3 -layout-out com interesses
4 -layout-code para extrair informacao
5 -inserir no clipping return
'''
for lr in layers_processed:
lr_name=lr.name
#foco
consid=[]
#============================================
for f in s_foco:
for nr2 in lr.topicos:
if lr.compare_dt_depend(conn,usr,purp,f,nr2,[]):
consid.append(nr2)
#============================================
for f in s_filtro:
for nr2 in lr.topicos:
if lr.compare_dt_depend(conn,usr,purp,f,nr2,[]):
consid.append(nr2)
#============================================
for f in s_restricao:
for nr2 in lr.topicos:
if lr.compare_dt_depend(conn,usr,purp,f,nr2,[]):
consid.append(nr2)
#============================================
for c in consid:
lr.put_publish(c)
#============================================
#========================================================================================================================================
#========================================================================================================================================
data_return=[]
# char layoutcode
ind_c=0
for data_c in result_onto_tree_er:
#==========
cmps_nr=layers_processed[ind_c].get_all()
ind_c+=1
#==========
runc=result_onto_tree_bpm[ind] # rct original, ractionline q processou as informacoes
addr_url=addresses[ind]
layout_codes=runc.get_links('LAYOUT-CODE')
nms=[]
for d in result_onto_tree_bpm:
nms.append(d.lr.name)
rts2=prepare_search_customlayouts(nms,cmps_nr,usr,conn) #purposes,dts,usr
data_return.append(rts2)
#========================================================================================================================================
#
#====================================
all_txt=''
for dt in data_return: # post no clipping
all_txt+=(' '+dt)
all_txt=umisc.trim(all_txt)
if all_txt != "":
#== classificacao/categoria
#class=get_class(purpose) - > retorna o label do purpose
class_c = get_class(purp,usr,conn)
#== post no clipping
if len(s_m) <=0 :
cursor.execute (" insert into clipping_return(username,retorno,categoria,href) values ( %s , %s , %s , %s )", (usr, all_txt,class_c,''))
else:
has_clipp=False
has_msg=False
for d in s_m: # verificar onde inserir
if d.upper()=="CLIPPING":
if not has_clipp:
has_clipp=True
cursor.execute (" insert into clipping_return(username,retorno,categoria,href) values ( %s , %s , %s , %s )", (usr, all_txt,class_c,''))
elif d.upper()=="FEED":
if not has_msg:
has_msg=True
cursor.execute (" insert into INBOX_MSG (username,MSG,SOURCE,REFERENCIAS) values ( %s , %s , %s , %s )", (usr, all_txt,class_c,addr_url))
#===========================================================================================================================================
conn.close()
