def init_result():
session = Session()
entry = Session(Result).filter(Result.id == 1).one_or_none()
# 如数据不存在，就新增一条为1的空数据
if not entry:
session.add(Result(modules="", total_time="", data="", dev=""))
session.commit()
else:
pass
session.close()
def set_result(data):
session = Session()
entry = Session(Result).filter(Result.id == data["id"]).one_or_none()
if entry:
entry.modules = data["modules"]
entry.total_time = data["total_time"]
entry.data = data["data"]
entry.dev = data["dev"]
session.commit()
else:
print("实体类不存在")
session.close()
def commit_entries(self, end_batch_id):
session = Session()
current_staged_entry = session.query(StagedEntry).order_by(StagedEntry.entry_id)\
.filter(StagedEntry.entry_id <= end_batch_id)
while current_staged_entry is not None:
self.commit_entry(current_staged_entry)
session.close()
def add_job(self, valid_datetime, job_type, details):
session = Session()
new_job = Job(timestamp_when_valid=valid_datetime,
job_type=job_type,
details=details)
session.add(new_job)
session.commit()
session.close()
def get_lat_and_long(postcode):
session = Session()
postcode_record = session.query(Locations).filter(
Locations.pcds == postcode).first()
session.close()
if postcode_record is not None:
return postcode_record.lat, postcode_record.long
else:
return None, None
def validate_students():
with Session(engine) as session:
students = session.execute(
select(Student).\
order_by(Student.last)
).scalars().all()
q = list(filter(lambda x : not x.today().status, students))
search(q)
def setup_df(input_file):
df = pd.read_csv(input_file, header=None)
f_row = df.iloc[0].str.match(r"([A-Z][-a-zA-Z]*'?[-a-zA-Z]+), ([A-Z][a-z]*'?[-a-zA-Z]+)( [A-Z]'?[-a-zA-Z]*)? (\((\d{6})?\))?")
if not f_row.any(axis=None):
df = df.iloc[1:]
f_col = df.iloc[:,0].str.match(r"([A-Z][-a-zA-Z]*'?[-a-zA-Z]+), ([A-Z][a-z]*'?[-a-zA-Z]+)( [A-Z]'?[-a-zA-Z]*)? (\((\d{6})?\))?")
if not f_col.any(axis=None):
df = df.iloc[:,1:]
dfs = []
for index, series in df.iterrows():
c = series.str.extract(r"([A-Z][-a-zA-Z]*'?[-a-zA-Z]+), ([A-Z][a-z]*'?[-a-zA-Z]+)( [A-Z]'?[-a-zA-Z]*)? (\((\d{6})?\))?")
c.drop(columns=3, inplace=True)
c.dropna(how='all', inplace=True)
c.rename(columns={0:'Last', 1:'First', 2:'Middle', 4:'ID'}, inplace=True)
c.loc[c['ID'] != c.iloc[0, 3], 'Leader_ID'] = c.iloc[0, 3]
c['ID'] = c['ID'].astype(int)
c['Leader_ID'] = c['Leader_ID'].astype(float)
dfs.append(c)
with engine.begin() as conn:
Base.metadata.create_all(conn)
for r in dfs:
r.to_sql("student", con=conn, if_exists='append', index=False)
with Session(engine) as session, session.begin():
for student_id in r.iloc[:, 3]:
attendance = Attendance(student_id=student_id)
session.add(attendance)
with Session(engine) as session:
people = session.execute(
select(Student).\
order_by(Student.last)
).scalars().all()
data = [user.serialize for user in people]
# print(data)
# print("2:", session.execute(select(Attendance)).scalars().all())
eel.create_table(data)
def validate_leaders():
with Session(engine) as session:
leaders = session.execute(
select(Student).\
where(Student.leader_id==None).\
order_by(Student.last)
).scalars().all()
q = list(filter(lambda x : not x.today().status, leaders))
search(q)
def commit_entry(self, staged_entry):
session = Session()
indicator_values = {}
for indicator in self.indicators_metadata:
value_location = self.location_engine.convert_postcode(
staged_entry.postcode,
self.indicators_metadata[indicator]['resolution'])
previous_value_entry = session.query(IndicatorValue)\
.filter(IndicatorValue.indicator == indicator,
IndicatorValue.location == value_location,
IndicatorValue.date < staged_entry.date)\
.order_by(IndicatorValue.date.desc())\
.first()
following_value_entry = session.query(IndicatorValue)\
.filter(IndicatorValue.indicator == indicator,
IndicatorValue.location == value_location,
IndicatorValue.date > staged_entry.date)\
.order_by(IndicatorValue.date)\
.first()
if previous_value_entry is None and following_value_entry is None:
value = None
indicator_date = None
elif following_value_entry is None \
or abs((previous_value_entry.date - staged_entry.date).days) \
< abs((following_value_entry.date - staged_entry.date).days):
value = previous_value_entry.value
indicator_date = previous_value_entry.date
else:
value = following_value_entry.value
indicator_date = following_value_entry.date
# Check if value falls within frequency range
if indicator_date is not None and (abs(staged_entry.date - indicator_date)).days\
> (FREQUENCY_DAY_COUNTS[self.indicators_metadata[indicator]['frequency']] / 2):
value = None
indicator_values[indicator] = value
new_entry = TargetEntry(
entry_id=staged_entry.entry_id,
sale_id=staged_entry.sale_id,
date=staged_entry.date,
value=staged_entry.value,
PDD_type=staged_entry.PDD_type,
postcode=staged_entry.postcode,
town_or_city=staged_entry.town_or_city,
district=staged_entry.district,
county=staged_entry.county,
new_property_flag=staged_entry.new_property_flag,
property_type=staged_entry.property_type,
tenure_type=staged_entry.tenure_type)
for indicator in indicator_values:
setattr(new_entry, indicator, indicator_values[indicator])
session.add(new_entry)
session.commit()
session.close()
def get_model_names(self, dataset_name=None):
session = Session()
model_names = []
if dataset_name is None:
model_records = session.query(ModelEntry).all()
else:
model_records = session.query(ModelEntry).filter(
ModelEntry.dataset == dataset_name)
if model_records is not None:
for model in model_records:
model_names.append(model.name)
session.close()
return model_names
def run(self):
running = True
while running:
session = Session()
current_job = session.query(Job).order_by(
Job.timestamp_when_valid).first()
if current_job is None or current_job.timestamp_when_valid > datetime.datetime.now(
):
sleep(JOB_MANAGER_POLL_DELAY)
else:
self.complete_job(current_job)
current_job.delete()
session.close()
def add_new_model(self, settings):
new_model_name = settings['name']
new_model = Model(settings)
self.models[new_model_name] = new_model
self.save_model(new_model_name)
new_model_record = ModelEntry(name=settings['name'],
type=settings['type'],
dataset=settings['dataset'],
state='untrained')
session = Session()
session.add(new_model_record)
session.commit()
session.close()
def get_trained_model_names(self, dataset_name=None):
model_names = []
session = Session()
if dataset_name is None:
model_records = session.query(
ModelEntry.name).filter(ModelEntry.state == 'trained')
else:
model_records = session.query(ModelEntry.name).filter(
ModelEntry.dataset == dataset_name,
ModelEntry.state == 'trained')
for model in model_records:
model_names.append(model.name)
session.close()
return model_names
def search(absent_list):
global search_bar, width
for index, user in enumerate(absent_list):
name = f'{user.first} {user.last}'
print(f"{len(absent_list) - index} remaining...")
print(f"[?] Searching : {name}")
pug.click(search_bar)
pug.typewrite(name)
wait_label = capture.find_img_coordinates("waiting_room_label.png", "meeting")
in_meeting_label = capture.find_img_coordinates("in_the_meeting_label.png", "meeting")
if wait_label and in_meeting_label:
x, y = wait_label[0] - 20, wait_label[1] + 10
height = in_meeting_label[1] - (wait_label[1] + 25)
wait_list = capture.get_text_coordinates(x, y, width, height)
# MOVE TO NEXT STUDENT IF MORE THAN ONE NAME IS DETECTED
if len(wait_list) > 1:
return f"IMPOSTER DETECTED:<br/>{name}"
print("WAIT LIST:", wait_list)
for person in wait_list:
cv_name = person['Text']
if (cv_name != name) and len(cv_name) == len(name):
if spell_check({name : cv_name}) <= 2:
record(user)
admit(name, wait_list)
elif cv_name == name:
print("NO SPELL:", name)
record(user)
admit(name, wait_list)
close_search()
else:
close_search()
print("Could not locate labels.")
return f'Could not<br/>locate labels.'
with Session(engine) as session:
students = session.execute(
select(Student).\
order_by(Student.last)
).scalars().all()
data = [usr.serialize for usr in students]
eel.create_table(data)
def pull_land_registry(self):
session = Session()
data = request.urlopen(LAND_REGISTRY_URL).read()
data = str(data, 'utf-8')
data_frame = pandas.read_csv(StringIO(data),
header=None,
names=LAND_REGISTRY_DATA_HEADERS)
# Only include entries with PPD type A
# data_frame = [data_frame.PDD_type == 'A']
# Convert date strings to date objects
data_frame['date'] = pandas.to_datetime(
data_frame['date'], format=LAND_REGISTRY_TIMESTAMP_FORMAT)
# Reorder columns
data_frame = data_frame[STAGED_ENTRY_HEADERS]
# Convert old or new character value to boolean
data_frame['new_property_flag'] = data_frame['new_property_flag'].map(
dict(Y=True, N=False))
current_highest_id = -1
latest_entry = session.query(StagedEntry).order_by(
desc(StagedEntry.entry_id)).first()
if latest_entry is not None:
current_highest_id = latest_entry.id
batch_start_id = current_highest_id + 1
batch_end_id = batch_start_id + len(data_frame)
# Add id column
data_frame.insert(0, 'entry_id', range(batch_start_id, batch_end_id))
data_frame.to_sql('staged_entries',
con=self.database_engine,
if_exists='append',
index=False)
# Process deletion entries
deletion_entries = session.query(StagedEntry).filter(
StagedEntry.record_type == 'D')
for deletion_entry in deletion_entries:
session.query(TargetEntry).filter(
TargetEntry.sale_id == deletion_entry.sale_id).delete()
deletion_entry.delete()
# Get update entries
update_entries = session.query(StagedEntry).filter(
StagedEntry.record_type == 'C')
for update_entry in update_entries:
existing_entry = session.query(TargetEntry).filter(
TargetEntry.sale_id == update_entry.sale_id).one()
if existing_entry is not None:
update_entry_from_land_registry(update_entry, existing_entry)
update_entry.delete()
session.commit()
session.close()
return str(batch_end_id)
def delete_model(self, model_name):
session = Session()
model_record = session.query(ModelEntry).filter(
ModelEntry.name == model_name)
model_record.delete()
session.commit()
del self.models[model_name]
os.remove(model_name)
# Update max inputs value
self.max_inputs = 0
for model_name in self.get_trained_model_names():
model_input_count = len(self.get_model_inputs(model_name))
if model_input_count > self.max_inputs:
self.max_inputs = model_input_count
session.close()
def __init__(self, database_engine):
self.database_engine = database_engine
session = Session()
for model_name in self.get_model_names():
self.load_model(model_name)
# Find largest model input count after all models loaded
for model_name in self.get_trained_model_names():
model_input_count = len(self.get_model_inputs(model_name))
if model_input_count > self.max_inputs:
self.max_inputs = model_input_count
# Check for models that were in training when last shut down
for model_record in session.query(ModelEntry).filter(
ModelEntry.state == 'training'):
self.train_model(model_record.name)
session.close()
def get_model_table(self):
session = Session()
models_entries = session.query(ModelEntry)
table = []
for model_entry in models_entries:
dependencies = ", ".join(
self.get_model_dependencies(model_entry.name))
table.append({
'Name': model_entry.name,
'Type': model_entry.type,
'Dataset': model_entry.dataset,
'State': model_entry.state,
'Dependencies': dependencies
})
session.close()
return table
def update_commit_schedule(self, first_pull_date, frequency):
session = Session()
# Calculate when the next pull is that cannot take place immediately
next_scheduled_pull = first_pull_date
while next_scheduled_pull < datetime.datetime.now():
if frequency == 'yearly':
next_scheduled_pull += relativedelta(years=1)
elif frequency == 'monthly':
next_scheduled_pull += relativedelta(months=1)
else:
next_scheduled_pull += relativedelta(
days=FREQUENCY_DAY_COUNTS[frequency])
threshold_date = next_scheduled_pull - relativedelta(
days=FREQUENCY_DAY_COUNTS[frequency] / 2)
commits_before_threshold = session.query(Job).filter(
Job.job_type == COMMIT_JOB,
Job.timestamp_when_valid < threshold_date)
for commit in commits_before_threshold:
commit.timestamp_when_valid = next_scheduled_pull
session.close()
def update_entries_from_source(self, source_entry_date, frequency,
indicators, area_resolution):
session = Session()
frequency_radius_delta = relativedelta(
FREQUENCY_DAY_COUNTS[frequency] / 2)
lower_threshold_date = source_entry_date - frequency_radius_delta
upper_threshold_date = source_entry_date + frequency_radius_delta
# noinspection PyComparisonWithNone
entries_to_update = session.query(TargetEntry).filter(
getattr(TargetEntry, indicators[0]) == None,
TargetEntry.date >= lower_threshold_date,
TargetEntry.date <= upper_threshold_date)
for entry in entries_to_update:
location = self.location_engine.convert_postcode(
entry.postcode, area_resolution)
for indicator in indicators:
value = session.query(IndicatorValue).filter(
IndicatorValue == source_entry_date,
IndicatorValue.location == location)
setattr(entry, indicator, value)
session.commit()
session.close()
def train_model(self, model_name):
session = Session()
model_record = session.query(ModelEntry).filter(ModelEntry.name == model_name).one()
model_record.state = 'training'
session.commit()
parent_models = self.model_manager.models[model_name].input_models
for parent_model in parent_models:
# Train any untrained parent models
if session.query(ModelEntry).filter(ModelEntry.name == parent_model, ModelEntry.state == 'untrained')\
is not None:
self.train_model(parent_model)
# Wait for any parent models that are still in training, in cases where another process started the training
while session.query(ModelEntry).filter(ModelEntry.name == parent_model,
ModelEntry.state == 'training').one_or_none()\
is not None:
time.sleep(30)
model = self.model_manager.models[model_name]
dataset_name = model.dataset
entry_count = model.training_entry_count
if dataset_name == 'core_dataset':
dataframe = pandas.read_sql(session.query(Base.metadata.tables['core_dataset']).order_by(func.rand()).limit(entry_count).statement,
self.database_engine)
else:
dataframe = pandas.read_csv(DEFAULT_DATA_PATH, sep='\s+', names=DEFAULT_DATA_HEADERS)
if entry_count > len(dataframe):
entry_count = len(dataframe)
dataframe = dataframe.sample(entry_count)
for parent_model in parent_models:
self.recursive_process(parent_model, dataframe)
model.train(dataframe)
self.model_manager.save_model(model_name)
model_record.state = 'trained'
session.commit()
# Update the model manager max input count - done here to happen at the end of training
self.model_manager.update_max_inputs(len(self.model_manager.get_model_inputs(model_name)))
session.close()
# create / retrieve / update / delete
from tables import Emp, Students,Session
from sqlalchemy import distinct
from sqlalchemy import or_,desc,func
# 创建到数据库的会话实例
session = Session()
#第1题
print(38*"*")
print('\033[31;1m第一题:查询所有的学生\033[0m')
print('\033[32;1m%-8s %-5s\033[0m' % ('id','学生姓名'))
qset1 = session.query(Students).order_by(Students.id)
for data in qset1:
print('\033[35;1m%-8s %-5s\033[0m' % (data.id, data.name))
print(38*"*")
#第2题
print(38*"*")
print('\033[31;1m第二题:查询学生姓名,语文\033[0m')
print('\033[32;1m%s:%s\033[0m' % ('学生姓名','语文成绩'))
qset2 = session.query(Students).order_by(Students.id)
for data in qset2:
print('\033[35;1m%s:%s\033[0m' % (data.name, data.chinese))
# print(data)
print(38*"*")
#第3题
print(38*"*")
print('\033[31;1m第三题:查询一个公司里面的工作岗位–清除重复数据\033[0m')
print('\033[32;1m公司职务\033[0m')
def record(user):
with Session(engine) as session, session.begin():
t = user.today()
t.status = True
session.add(t)
