def test_read_one(line_name):
l = get_state_examination_data_by_line_name(line_name)
print(enc1(line_name))
print 'check_year unit_1 unit_2 unit_3 unit_4 unit_5 unit_6 unit_7 unit_8 line_state'
for i in l:
print ' %d %05s %05s %05s %05s %05s %05s %05s %05s %05s' % \
(i['check_year'], i['unit_1'], i['unit_2'], i['unit_3'], i['unit_4'], i['unit_5'], i['unit_6'], i['unit_7'], i['unit_8'], i['line_state'])
def test_read_one(line_name):
l = get_state_examination_data_by_line_name(line_name)
print(enc1(line_name))
print 'check_year unit_1 unit_2 unit_3 unit_4 unit_5 unit_6 unit_7 unit_8 line_state'
for i in l:
print ' %d %05s %05s %05s %05s %05s %05s %05s %05s %05s' % \
(i['check_year'], i['unit_1'], i['unit_2'], i['unit_3'], i['unit_4'], i['unit_5'], i['unit_6'], i['unit_7'], i['unit_8'], i['line_state'])
def generate_unit_probability(db):
def get_domains(alist):
d = OrderedDict(alist[0][1])
return d.keys()
def get_desc(alist, name):
ret = []
for i in alist:
if i['parent'] == name:
ret.append(i['name'])
return ';\n'.join(ret)
# client = pymongo.MongoClient('192.168.1.8', 27017)
# db = client['kmgd']
if 'bayesian_nodes' in db.collection_names(False):
db.drop_collection('bayesian_nodes')
path = ur'jiakongztpj.json'
std = None
with open(path) as f:
std = json.loads(f.read())
collection = get_collection('state_examination')
pipeline = [
# {'$unwind':'$line_name'},
{
"$group": {
"_id": "$line_name",
"count": {
"$sum": 1
}
}
},
]
lines = list(collection.aggregate(pipeline))
linenames = map(lambda x: x['_id'], lines)
i = 0
l = []
for line_name in linenames:
data = get_state_examination_data_by_line_name(line_name)
print('%s:%d' % (enc1(line_name), len(data)))
o = calc_probability_unit(data)
# print (o)
for k in o.keys():
o1 = {}
o1['name'] = k
o1['display_name'] = UNIT_NAME_MAPPING[k]
o1['line_name'] = line_name
o1['description'] = get_desc(std, UNIT_NAME_MAPPING[k])
o1['conditions'] = o[k]
o1['domains'] = get_domains(o[k])
l.append(o1)
i += 1
# if i > 30:
# break
# with codecs.open(ur'd:\aaa.json', 'w', 'utf-8-sig') as f:
# f.write(json.dumps(l, ensure_ascii=False, indent=4))
collection = get_collection('bayesian_nodes')
for i in l:
collection.save(i)
def test_find_abnormal():
collection = get_collection('state_examination')
ids = []
for i in list(collection.find({})):
for j in range(1, 9):
if i.has_key('unit_%d' % j):
if ' ' in i['unit_%d' % j]:
print('%s:%d' % (enc1(i['line_name']), i['check_year']))
if i.has_key('line_state' ):
if ' ' in i['line_state']:
print('line_state%s:%d' % (enc1(i['line_name']), i['check_year']))
if '-' in i['line_name']:
print('%s:%d' % (enc1(i['line_name']), i['check_year']))
ids.append(i['_id'])
if not i['line_name'][-1] == u'线':
print('%s:%d' % (enc1(i['line_name']), i['check_year']))
ids.append(i['_id'])
def test_calc_past_year(past_years_list=[]):
def get_domains(alist):
d = OrderedDict(alist[0][1])
return d.keys()
def convert_tuple(adict):
ret = {}
for k in adict.keys():
key = ':'.join([k[0], k[1]])
ret[key] = adict[k]
return ret
collection = get_collection('state_examination')
pipeline = [
# {'$unwind':'$line_name'},
{"$group": {"_id": "$line_name", "count": {"$sum": 1}}},
]
lines = list(collection.aggregate(pipeline))
linenames = map(lambda x:x['_id'], lines)
i = 0
ret = []
for line_name in linenames:
l = []
result = {}
data = get_state_examination_data_by_line_name(line_name, past_years_list)
check_year = []
for i in data:
check_year.append(i['check_year'])
print('%s:%d:%s' % (enc1(line_name), len(data), str(check_year) ) )
if len(check_year)>1:
o = calc_probability_unit(data)
result['line_name'] = line_name
result['check_year'] = check_year
for k in o.keys():
o1 = {}
o1['name'] = k
# o1['display_name'] = UNIT_NAME_MAPPING[k]
# o1['line_name'] = line_name
o1['conditions'] = o[k]
o1['domains'] = get_domains(o[k])
l.append(o1)
cond = {}
cond['line_state'] = []
o = calc_probability_line()
cond['line_state'].extend(o['line_state'])
for node in l:
# name = node['name']
# domains = node['domains']
cond[node['name']] = node['conditions']
g = build_bbn_from_conditionals(cond)
result['II'] = convert_tuple(g.query(line_state = 'II'))
result['III'] = convert_tuple(g.query(line_state = 'III'))
result['IV'] = convert_tuple(g.query(line_state = 'IV'))
ret.append(result)
# break
else:
print('need 2 years or more')
with codecs.open(ur'd:\2010_2014.json', 'w', 'utf-8-sig') as f:
f.write(json.dumps(ret, ensure_ascii=False, indent=4))
def test_find_abnormal():
collection = get_collection('state_examination')
ids = []
for i in list(collection.find({})):
for j in range(1, 9):
if i.has_key('unit_%d' % j):
if ' ' in i['unit_%d' % j]:
print('%s:%d' % (enc1(i['line_name']), i['check_year']))
if i.has_key('line_state'):
if ' ' in i['line_state']:
print('line_state%s:%d' %
(enc1(i['line_name']), i['check_year']))
if '-' in i['line_name']:
print('%s:%d' % (enc1(i['line_name']), i['check_year']))
ids.append(i['_id'])
if not i['line_name'][-1] == u'线':
print('%s:%d' % (enc1(i['line_name']), i['check_year']))
ids.append(i['_id'])
def generate_unit_probability(db):
def get_domains(alist):
d = OrderedDict(alist[0][1])
return d.keys()
def get_desc(alist, name):
ret = []
for i in alist:
if i['parent'] == name:
ret.append(i['name'])
return ';\n'.join(ret)
# client = pymongo.MongoClient('192.168.1.8', 27017)
# db = client['kmgd']
if 'bayesian_nodes' in db.collection_names(False):
db.drop_collection('bayesian_nodes')
path = ur'jiakongztpj.json'
std = None
with open(path) as f:
std = json.loads(f.read())
collection = get_collection('state_examination')
pipeline = [
# {'$unwind':'$line_name'},
{"$group": {"_id": "$line_name", "count": {"$sum": 1}}},
]
lines = list(collection.aggregate(pipeline))
linenames = map(lambda x:x['_id'], lines)
i = 0
l = []
for line_name in linenames:
data = get_state_examination_data_by_line_name(line_name)
print('%s:%d' % (enc1(line_name), len(data) ) )
o = calc_probability_unit(data)
# print (o)
for k in o.keys():
o1 = {}
o1['name'] = k
o1['display_name'] = UNIT_NAME_MAPPING[k]
o1['line_name'] = line_name
o1['description'] = get_desc(std, UNIT_NAME_MAPPING[k])
o1['conditions'] = o[k]
o1['domains'] = get_domains(o[k])
l.append(o1)
i += 1
# if i > 30:
# break
# with codecs.open(ur'd:\aaa.json', 'w', 'utf-8-sig') as f:
# f.write(json.dumps(l, ensure_ascii=False, indent=4))
collection = get_collection('bayesian_nodes')
for i in l:
collection.save(i)
def test_read_all_records():
collection = get_collection('state_examination')
l = list(collection.find({}))
namesmap = {}
for i in l:
if i.has_key('line_state') and i['line_state'] in ['III', 'IV']:
if not namesmap.has_key(i['line_name']):
namesmap[i['line_name']] = []
else:
namesmap[i['line_name']].append(i)
m1 = {}
for k in namesmap.keys():
if len(namesmap[k])>3:
m1[k] = namesmap[k]
for k in m1.keys():
print('%s:%d,%s' % (enc1(k), len(m1[k]), str([i['line_state'] for i in m1[k]])))
def test_read_all_records():
collection = get_collection('state_examination')
l = list(collection.find({}))
namesmap = {}
for i in l:
if i.has_key('line_state') and i['line_state'] in ['III', 'IV']:
if not namesmap.has_key(i['line_name']):
namesmap[i['line_name']] = []
else:
namesmap[i['line_name']].append(i)
m1 = {}
for k in namesmap.keys():
if len(namesmap[k]) > 3:
m1[k] = namesmap[k]
for k in m1.keys():
print('%s:%d,%s' %
(enc1(k), len(m1[k]), str([i['line_state'] for i in m1[k]])))
def test_calc_past_year(past_years_list=[]):
def get_domains(alist):
d = OrderedDict(alist[0][1])
return d.keys()
def convert_tuple(adict):
ret = {}
for k in adict.keys():
key = ':'.join([k[0], k[1]])
ret[key] = adict[k]
return ret
collection = get_collection('state_examination')
pipeline = [
# {'$unwind':'$line_name'},
{
"$group": {
"_id": "$line_name",
"count": {
"$sum": 1
}
}
},
]
lines = list(collection.aggregate(pipeline))
linenames = map(lambda x: x['_id'], lines)
i = 0
ret = []
for line_name in linenames:
l = []
result = {}
data = get_state_examination_data_by_line_name(line_name,
past_years_list)
check_year = []
for i in data:
check_year.append(i['check_year'])
print('%s:%d:%s' % (enc1(line_name), len(data), str(check_year)))
if len(check_year) > 1:
o = calc_probability_unit(data)
result['line_name'] = line_name
result['check_year'] = check_year
for k in o.keys():
o1 = {}
o1['name'] = k
# o1['display_name'] = UNIT_NAME_MAPPING[k]
# o1['line_name'] = line_name
o1['conditions'] = o[k]
o1['domains'] = get_domains(o[k])
l.append(o1)
cond = {}
cond['line_state'] = []
o = calc_probability_line()
cond['line_state'].extend(o['line_state'])
for node in l:
# name = node['name']
# domains = node['domains']
cond[node['name']] = node['conditions']
g = build_bbn_from_conditionals(cond)
result['II'] = convert_tuple(g.query(line_state='II'))
result['III'] = convert_tuple(g.query(line_state='III'))
result['IV'] = convert_tuple(g.query(line_state='IV'))
ret.append(result)
# break
else:
print('need 2 years or more')
with codecs.open(ur'd:\2010_2014.json', 'w', 'utf-8-sig') as f:
f.write(json.dumps(ret, ensure_ascii=False, indent=4))
