def handlePath():
print(request.data)
print(request.get_json(force=True)["firstNode"])
print(request.get_json(force=True)["secondNode"])
##
# Data contains:
# startNode is the id number
# endNode is the id number
##
data = request.get_json(force=True)
startnode = int(data["firstNode"])
endnode = int(data["secondNode"])
# make the path here
wet_nodes = []
for node in db.get_nodes():
if node["is_flooded"][utils.cur_date()]:
wet_nodes.append(node["id"])
path_type, path = db.graph().shortest_path(startnode, endnode, wet_nodes)
return jsonify({
"path_type":
path_type,
"path":
path,
"path_coords":
[db.get_node("id", node_id)["coords"] for node_id in path]
})
def report_water_level(node, lvl):
"""
Reports a water level at the given coordinates; adds value to mongoDB database
:param node: node id
:param lvl: integer representing water level @ node
"""
DB.report_rain_level(utils.cur_date(), node, lvl)
def get_nodes(self):
"""
Queries the mongoDB database for all nodes
:return:
"""
for node in self.sorted_nodes():
self._update_avg_level(utils.cur_date(), node)
self.update_is_flooded()
return list(self._nodes.find({}, {'_id': False}).sort("id"))
def server_app(db):
"""
Runner for server.
"""
# db.add_all_nodes()
global linear_model
while True:
# Each day, add date as key to dataset and drop the oldest day
for i in range(7):
time.sleep(86400)
date_to_drop = utils.days_ago(utils.DAYS_TO_KEEP + 1)
nodes = db.get_nodes()
for node in nodes:
node["rain_data"].pop(date_to_drop)
node["avg_levels"].pop(date_to_drop)
node["is_flooded"].pop(date_to_drop)
node["rain_data"][utils.cur_date()] = []
node["avg_levels"][utils.cur_date()] = 0
node["is_flooded"][utils.cur_date()] = 0
# Each week, retrain the models using new data.
# In production, use weather API data. For now, use generated values to test.
# Dump updated nodewise average water level data to depths_train.txt
depths_dict = node_api.get_reported_water_levels()
depths = np.empty(
(len(depths_dict.keys()), len(depths_dict[0].keys())))
for date_idx, date in enumerate(sorted(depths_dict[0].keys())):
for node_id in sorted(depths_dict.keys()):
this_mean = np.mean(depths_dict[node_id][date])
if not this_mean:
this_mean = 0
depths[date_idx][node_id] = this_mean
np.savetxt("depths_train.txt", depths)
# Retrain models
db.linear_model = models.LinearRainModel()
for model in db.XGB_models.values():
model.train()
model.test()
def get_reported_water_levels_today():
"""
Returns reported water levels on the current date.
:return:
"""
today = utils.cur_date()
return {
node["coords"]: node["rain_data"][today]
for node in DB.get_nodes()
}
def update_is_flooded(self):
"""
Predicts is_flooded for all nodes and uploads the data to the mongoDB server.
:return:
"""
reports_plus_predictions = self.predict_is_flooded()
for node in self._nodes.find({}, {'_id': False}):
node["is_flooded"][utils.cur_date()] = reports_plus_predictions[
node["id"]]
update = {"$set": {"is_flooded": node["is_flooded"]}}
self._nodes.update({"id": node["id"]}, update)
def predict_is_flooded(self):
"""
Predicts the value of all nodes with no reported data, using either the linear or XGBoost models.
Uses reported values for those that have it.
"""
queried_nodes = self.sorted_nodes()
reported_is_flooded = [node["is_flooded"] for node in queried_nodes]
linear_predictions = self.linear_model.fit(
weather.get_precipitation(utils.cur_date()))
reported_with_linear = list(reported_is_flooded)
# Insert linear prediction for values with no reported data
for node in queried_nodes:
if node["id"] == 6:
print(node["avg_levels"][utils.cur_date()])
print(node["is_flooded"][utils.cur_date()])
# No reports; pred w linear
if len(node["rain_data"][utils.cur_date()]) == 0:
if linear_predictions[node["id"]] == True:
reported_with_linear[node["id"]] = 2
else:
reported_with_linear[node["id"]] = 0
# Reports; use existing
else:
if reported_is_flooded[node["id"]][utils.cur_date()] == 1:
reported_with_linear[node["id"]] = 1
else:
reported_with_linear[node["id"]] = 0
reported_with_linear_and_XGB = list(reported_with_linear)
# Replace linear predictions with XGB predictions when possible
for node_id in self.XGB_models.keys():
if len(queried_nodes[node_id]["rain_data"][utils.cur_date()]) == 0:
xgb_prediction = self.XGB_models[node_id].predict([
reported_with_linear[:node_id] +
reported_with_linear[node_id + 1:]
])
print(xgb_prediction)
if xgb_prediction:
reported_with_linear_and_XGB[node_id] = 2
else:
reported_with_linear_and_XGB[node_id] = 0
print(reported_with_linear_and_XGB[6])
return reported_with_linear_and_XGB
def on_btn_audit_click(self, shzt: bool, xmzd: str):
'''
:param shzt: 审核/取消审核
:param xmzd: 审核：项目诊断；取消审核：取消原因
:return:
'''
if not self.cur_tjbh:
mes_about(self, '请选择体检顾客！')
return
# 审核
if shzt:
# 更新TJ_TJJLMXB、TJ_EQUIP、TJ_CZJLB
try:
self.session.query(MT_TJ_TJJLMXB).filter(
MT_TJ_TJJLMXB.tjbh == self.cur_tjbh,
MT_TJ_TJJLMXB.zhbh == self.cur_zhbh).update({
MT_TJ_TJJLMXB.zxpb:
'1',
MT_TJ_TJJLMXB.jsbz:
'1',
MT_TJ_TJJLMXB.qzjs:
None,
MT_TJ_TJJLMXB.ycbz:
'1',
MT_TJ_TJJLMXB.shrq:
cur_datetime(),
MT_TJ_TJJLMXB.shys:
self.login_id,
MT_TJ_TJJLMXB.zd:
xmzd
})
data_obj = {
'jllx': '0124',
'jlmc': '%s结果录入' % get_key(self.equips, self.cur_zhbh),
'tjbh': self.cur_tjbh,
'mxbh': self.cur_zhbh,
'czgh': self.login_id,
'czxm': self.login_name,
'czqy': self.login_area,
'jlnr': xmzd,
'bz': None
}
self.session.bulk_insert_mappings(MT_TJ_CZJLB, [data_obj])
self.gp_review_user.statechange(self.login_name, cur_date())
self.session.commit()
mes_about(self, "审核完成！")
except Exception as e:
self.session.rollback()
mes_about(self, "审核失败，错误信息：%s" % e)
return
# 取消审核
else:
bgys = self.table_report_equip.getCurItemValueOfKey('bgys')
if self.login_name != bgys:
mes_about(self, '该报告不是您审核的，您没有权限修改！')
return
try:
self.session.query(MT_TJ_TJJLMXB).filter(
MT_TJ_TJJLMXB.tjbh == self.cur_tjbh,
MT_TJ_TJJLMXB.zhbh == self.cur_zhbh).update({
MT_TJ_TJJLMXB.zxpb:
'0',
MT_TJ_TJJLMXB.jsbz:
'0',
MT_TJ_TJJLMXB.qzjs:
None,
MT_TJ_TJJLMXB.ycbz:
'1',
MT_TJ_TJJLMXB.shrq:
None,
MT_TJ_TJJLMXB.shys:
None,
MT_TJ_TJJLMXB.zd:
None
})
data_obj = {
'jllx': '0128',
'jlmc': '%s结果录入取消' % get_key(self.equips, self.cur_zhbh),
'tjbh': self.cur_tjbh,
'mxbh': self.cur_zhbh,
'czgh': self.login_id,
'czxm': self.login_name,
'czqy': self.login_area,
'jlnr': '%s报告取消审核' % get_key(self.equips, self.cur_zhbh),
'bz': xmzd
}
self.session.bulk_insert_mappings(MT_TJ_CZJLB, [data_obj])
self.session.commit()
self.gp_review_user.statechange()
mes_about(self, "取消审核完成！")
except Exception as e:
self.session.rollback()
mes_about(self, "取消审核失败，错误信息：%s" % e)
return