def export_all(format, path, datas):
"""
将所有结果数据导出到一个文件
:param str format: 导出文件格式
:param str path: 导出文件路径
:param list datas: 待导出的结果数据
"""
format = check_format(format, len(datas))
timestamp = get_timestamp()
name = f'all_subdomain_result_{timestamp}'
path = check_path(path, name, format)
logger.log('INFOR', f'所有主域的子域结果 {path}')
row_list = list()
for row in datas:
row.pop('header')
row.pop('response')
row.pop('module')
row.pop('source')
row.pop('elapsed')
row.pop('count')
keys = row.keys()
values = row.values()
if format in {'xls', 'xlsx'}:
values = check_value(values)
row_list.append(Record(keys, values))
rows = RecordCollection(iter(row_list))
content = rows.export(format)
save_data(path, content)
def query(self, sql, columns=None, **kwargs):
headers = {
"Accept":
"text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8",
"Accept-Language":
"zh-CN,zh;q=0.8",
"Connection":
"keep-alive",
"Host":
"192.168.0.159:8007",
"Referer":
"http://192.168.0.159:8007/clustering",
"User-Agent":
"Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/61.0.3163.79 Safari/537.36"
}
self.params.update({"q": sql})
rep = requests.get(self.db_url, params=self.params, headers=headers)
content = rep.text.split('\n')
rows_gen = (Record(json.loads(row).keys(),
json.loads(row).values()) for row in content
if row.strip())
results = RecordCollection(rows_gen)
return results
def recordsHandler():
if not g.user:
return redirect('/')
if request.method == "POST":
app.logger.info(request.method)
req = request.form
# Logout
if 'logoutBtn' in req:
app.logger.info("Logout user: "******"No medical records found.")
return redirect('/medic')
resList = []
for res in result:
print(res)
resList.append(Record(res[0], res[1], res[2], res[3]))
return render_template('records.html',
list=resList,
client_name=resList[0].name,
client_tel=resList[0].tel)
def add():
form = RecordForm()
if form.validate_on_submit():
record = Record()
record.id = uuid.uuid4()
record.value = request.form['value']
record.timestamp = datetime.now()
RECORDS.append(record)
return redirect(url_for('home'))
return render_template('add.html', form=form)
def query(self, sql, columns=None, **kwargs):
rows = self.conn.execute(sql)
row_gen = (Record(columns, row) for row in rows)
# Convert psycopg2 results to RecordCollection.
results = RecordCollection(row_gen)
# # # Fetch all results if desired.
# if fetchall:
# results.all()
return results
def iquery(self, query, batches=100):
cursor = self._conn.execute(text(query))
columns = cursor.keys()
history = []
for i, row in enumerate(cursor, start=1):
history.extend(
list(RecordCollection(
(Record(columns, _row) for _row in (row, )))))
if i % batches == 0:
yield history
history.clear()
if history:
yield history
def checkDuplicates(self, line):
# Returns true for unique records, stores duplicates
ret = True
cancer = False
s = line.strip().split(self.d)
if self.col.Patient and s[self.col.Patient] in self.reps.ids:
if self.col.Code and "8" in s[self.col.Code]:
cancer = True
# Sort duplicates and store for later
rec = Record(s[self.col.Sex], s[self.col.Age], s[self.col.Patient],
s[self.col.Species], cancer, s[self.col.ID])
self.reps.sortReplicates(rec)
self.dups[s[self.col.ID]] = line
ret = False
return ret
def query(self, sql, columns=None, **kwargs):
try:
dsl = json.loads(sql)
index_name = kwargs.pop("index_name", None)
type_name = kwargs.pop("type_name", None)
data_gen = (Record(line['_source'].keys(),
line['_source'].values())
for line in self.db.search(body=dsl,
index=index_name,
doc_type=type_name,
_source_include=columns)
['hits']['hits'])
result = RecordCollection(data_gen)
return result
except Exception as e:
print(e)
def export_all_results(path, name, format, datas):
path = check_path(path, name, format)
logger.log('ALERT', f'The subdomain result for all main domains: {path}')
row_list = list()
for row in datas:
if 'header' in row:
row.pop('header')
if 'response' in row:
row.pop('response')
keys = row.keys()
values = row.values()
if format in {'xls', 'xlsx'}:
values = check_value(values)
row_list.append(Record(keys, values))
rows = RecordCollection(iter(row_list))
content = rows.export(format)
save_data(path, content)
def process_docs(docs_dataset):
invert_index = {}
for row in docs_dataset:
record = Record(keys=docs_dataset.headers, values=row)
logging.info("处理文档: %s" % record.id)
if not record.doc.strip():
logging.warning("文档内容为空")
continue
# 分词并获取词性
words_pos = word_segment(record.doc)
# 清洗单词
words = clean_words(words_pos)
word_frequency = get_word_frequency(words)
logging.info("文档词频统计结果: %s" % word_frequency)
for word, frequency in word_frequency.items():
if word in invert_index:
invert_index[word].append((record.id, frequency))
else:
invert_index[word] = [(record.id, frequency)]
return invert_index
def visit(elem, depth=0):
content = elem[CONTENT_INDEX]
# 解析名字
name = content.text
if name is None or name.strip() == "":
name = get_name()
# 如果是a标签, 则需提取url
if content.tag == "a":
urls = content.get("href"),
url = urls[0]
createds = content.get("add_date"),
created = createds[0]
record = Record(keys=["url", "created"], values=[url, created])
else:
record = None
elem_obj = Element(name=name, data=record)
for child in elem.findall(CHILD_XPATH):
elem_obj.add_child(visit(child, depth + 1))
return elem_obj
def export_all(format, datas):
format = check_format(format, len(datas))
dpath = check_dpath()
timestamp = get_timestamp()
fpath = dpath.joinpath(f'all_subdomain_{timestamp}.{format}')
row_list = list()
for row in datas:
row.pop('header')
row.pop('response')
row.pop('module')
row.pop('source')
row.pop('elapsed')
row.pop('count')
keys = row.keys()
values = row.values()
if format in {'xls', 'xlsx'}:
values = check_value(values)
row_list.append(Record(keys, values))
rows = RecordCollection(iter(row_list))
content = rows.export(format)
save_data(fpath, content)
def giveInsulin(self, amount: float):
print("Trying to deliver {} units of insulin.".format(amount))
from records import Record
r = Record()
scrollRate = r.getScrollRate()
lastDC = r.getDutyCycle()
ratio = .03 # Not a set ratio, I have to design the gearbox first.
try:
import RPi.GPIO as GPIO
GPIO.setmode(GPIO.BCM)
GPIO.setup(17, GPIO.OUT)
servo = GPIO.PWM(17, 50)
servo.start(lastDC)
for i in range(amount / scrollRate):
dutycycle = lastDC + (i * ratio)
servo.ChangeDutyCycle(dutycycle)
print(
"Servo dutycycle is now {}.\n{} units out of {} of insulin delivered as of now."
.format(dutycycle, i * scrollRate, amount))
r.setDutyCycle(dutycycle)
sleep(.5)
servo.stop()
GPIO.cleanup()
except ImportError or ModuleNotFoundError:
print(
"This is likely not running on a Raspberry Pi.\nIf it is, make sure RPi is installed for Python 3.\n\nRunning print loop now instead of sending servo commands."
)
print(amount / scrollRate)
for i in range(int(amount / scrollRate)):
dutycycle = lastDC + (i * ratio)
print(
"Servo dutycycle is now {}.\n{} units out of {} of insulin delivered as of now."
.format(dutycycle, i * scrollRate, amount))
r.setDutyCycle(dutycycle)
sleep(.5)
dist = 50
limit = 100
color_list = ("b.", "r.", "g.", "m.", "c.", "y.")
with open('jsondata.json') as f:
x = json.load(f, object_hook=lambda d: SimpleNamespace(**d))
print(x.features[2].properties.filename)
X = np.zeros((1, 2))
list_rec=[]
weight_list = []
for i in range(len(x.features)):
list_rec.append(Record(i, x.features[i].properties))
X = np.vstack((X, [list_rec[i].lat, list_rec[i].lon]))
list_rec[i].calc_weight()
weight_list.append(list_rec[i].weight)
X = X[1:-1]
weight_list = weight_list[0:-1]
#print(np.unique(cat_list,return_index=True))
#print(x.features[6].properties)
cluster = sklearn.DBSCAN(eps=0.0001, min_samples=100).fit(X,y=None,sample_weight=weight_list)
dumps = []
cluster_count = np.unique(cluster.labels_)
def recordsPage(current_page):
current_page.frame.pack_forget()
current_page = Record(window, patientInfoPage)
