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import datetime
import time
import math
import pandas as pd
from pot_libs.utils.exc_util import BusinessException
from unify_api import constants
from pot_libs.mysql_util.mysql_util import MysqlUtil
from unify_api.constants import PM2_5, PM10, TSP, SLOTS
from unify_api.utils.common_utils import round_2n
from unify_api.modules.common.dao.common_dao import tsp_by_cid, \
storey_wp_by_cid, storey_pl_by_cid
from unify_api.modules.tsp_water.components.drop_dust_cps import DtResp, \
ThResp, TisResp, DeResp, SaResp, TcdResp, TpdResp, AdResp
from unify_api.modules.tsp_water.dao.tsp_dao import meterdata_tsp_current, \
tsp_histogram_tsp_id, tsp_index_statistics, tsp_aggs_tsp_id, \
tsp_by_tsp_id_dao
from unify_api.modules.tsp_water.dao.tsp_map_dao import \
get_predict_data_day_dao, get_predict_data_month_dao, get_page_data, \
get_contrast_data_day_dao, get_contrast_data_month_dao, get_cid_tsp_dao
from unify_api.modules.tsp_water.procedures.drop_dust_pds import \
pm2_5_trans_grade, pm10_trans_grade, tsp_trans_grade
from unify_api.modules.tsp_water.procedures.tsp_pds import per_hour_wave, \
per_hour_kwh_wave, per_hour_water_wave, per_hour_kwh_wave_new15, \
per_hour_wave_new15
from unify_api.utils import time_format
from unify_api.utils.common_utils import round_2, correlation, round_0
from unify_api.utils.es_query_body import es_process
from unify_api.utils.time_format import esstr_to_dthoutstr, start_end_date
async def real_time_service(tsp_id):
"""TSP信息-实时参数"""
# 1.根据tsp_id获取redis实时数据
tsp_dic = await meterdata_tsp_current(tsp_id)
if not tsp_dic:
raise BusinessException(message=f"tsp_id: {tsp_id} no redis data")
# 2. 判断数据是否在4h之内
now_ts = int(time.time())
tsp_ts = tsp_dic["timestamp"]
if now_ts - tsp_ts > constants.REAL_EXP_TIME:
return DtResp()
pm2_5 = tsp_dic.get("pm25", "")
pm10 = tsp_dic.get("pm10", "")
tsp = tsp_dic.get("tsp", "")
return DtResp(pm2_5=pm2_5, pm10=pm10, tsp=tsp)
async def tsp_history_service(tsp_id, start, end):
interval, slots = time_format.time_pick_transf(start, end)
# 实时数据
pm25_list, pm10_list, tsp_list = await \
get_data_new15(tsp_id, start, end, slots, interval)
# 预测数据
pm25_predict, pm10_predict, tsp_predict, _ = \
await get_predict_data(tsp_id, start, end, slots)
# 对比数据
pm25_contrast, pm10_contrast, _ = \
await get_contrast_data(tsp_id, start, end, slots)
return ThResp(
pm2_5={"threshold": PM2_5, "value_slots": pm25_list},
pm10={"threshold": PM10, "value_slots": pm10_list},
tsp={"threshold": TSP, "value_slots": tsp_list},
time_slots=slots,
pm2_5_predict={"value_slots": pm25_predict},
pm10_predict={"value_slots": pm10_predict},
tsp_predict={"value_slots": tsp_predict},
pm2_5_contrast={"value_slots": pm25_contrast},
pm10_contrast={"value_slots": pm10_contrast},
)
# tsp实时数据
async def get_data(tsp_id, start, end, slots, interval):
"""TSP信息-历史曲线"""
# 1. 查询es
if interval == 24 * 3600:
interval = "day"
fmt = "MM-DD"
elif interval == 15 * 60:
interval = "15m"
fmt = "HH:mm"
else:
raise BusinessException(message="time range not day or month")
es_res = await tsp_histogram_tsp_id(start, end, tsp_id, interval)
if not es_res:
return ThResp(pm2_5={"threshold": PM2_5, "value_slots": []},
pm10={"threshold": PM10, "value_slots": []},
tsp={"threshold": TSP, "value_slots": []},
time_slots=[]
)
es_dic = es_process(es_res, fmat=fmt)
# 2. 组装数据
pm25_list = []
pm10_list = []
tsp_list = []
for slot in slots:
if slot in es_dic:
pm25_value = round_0(es_dic[slot]["pm25"].get("avg"))
pm10_value = round_0(es_dic[slot]["pm10"].get("avg"))
tsp_value = round_0(es_dic[slot]["tsp"].get("avg"))
else:
pm25_value, pm10_value, tsp_value = "", "", ""
pm25_list.append(pm25_value)
pm10_list.append(pm10_value)
tsp_list.append(tsp_value)
return pm25_list, pm10_list, tsp_list
async def get_data_new15(tsp_id, start, end, slots, interval):
if interval == 24 * 3600:
sql = f'SELECT DATE_FORMAT(create_time,"%m-%d") date_time, ' \
f'AVG(pm25_max) pm25,AVG(pm25_max) pm10,AVG(tsp_max) tsp ' \
f'FROM `tsp_day_record` where tsp_id={tsp_id} and ' \
f'create_time BETWEEN "{start}" and "{end}" GROUP BY date_time' \
f' ORDER BY date_time'
elif interval == 15 * 60:
sql = f'SELECT DATE_FORMAT(create_time,"%H:00") date_time, ' \
f'AVG(pm25_max) pm25,AVG(pm25_max) pm10,AVG(tsp_max) tsp ' \
f'FROM `tsp_15min_record` where tsp_id={tsp_id} and ' \
f'create_time BETWEEN "{start}" and "{end}" GROUP BY date_time' \
f' ORDER BY date_time'
else:
raise BusinessException(message="time range not day or month")
async with MysqlUtil() as conn:
datas = await conn.fetchall(sql)
datas_map = {data["date_time"]: data for data in datas}
# 2. 组装数据
pm25_list = []
pm10_list = []
tsp_list = []
for slot in slots:
slot_data = datas_map.get(slot)
if slot_data:
pm25_value = round_2n(slot_data.get("pm25"))
pm10_value = round_2n(slot_data.get("pm10"))
tsp_value = round_2n(slot_data.get("tsp"))
else:
pm25_value, pm10_value, tsp_value = None, None, None
pm25_list.append(pm25_value)
pm10_list.append(pm10_value)
tsp_list.append(tsp_value)
return pm25_list, pm10_list, tsp_list
# tsp预测数据
async def get_predict_data(tsp_id, start, end, slots):
start_f = datetime.datetime.strptime(start, "%Y-%m-%d %H:%M:%S")
end_f = datetime.datetime.strptime(end, "%Y-%m-%d %H:%M:%S")
if start_f.day == end_f.day:
# 返回当天数据 15min数据
predict_data = await get_predict_data_day_dao(tsp_id, start_f, end_f)
predict_slots = ["%02d:%02d" % (data["quarter_time"].hour,
data["quarter_time"].minute)
for data in predict_data]
date_predict = [data["quarter_time"].strftime("%Y-%m-%d %H:%M:%S")
for data in predict_data]
else:
# 返回月份数据 每天数据
predict_data = await get_predict_data_month_dao(tsp_id, start_f, end_f)
predict_slots = [data["quarter_time"][5:] for data in predict_data]
date_predict = [data["quarter_time"] for data in predict_data]
pm25_predict = [round(data["pm25"]) for data in predict_data]
pm10_predict = [round(data["pm10"]) for data in predict_data]
tsp_predict = [round(data["tsp"]) for data in predict_data]
# 针对如果缺少数据处理,基本不会执行
if len(predict_data) != len(slots):
# 缺少时刻的时间轴
lack_slots = list(set(slots) - set(predict_slots))
for slot in lack_slots:
index = slots.index(slot)
pm25_predict.insert(index, "")
pm10_predict.insert(index, "")
tsp_predict.insert(index, "")
date_predict.insert(index, "")
return pm25_predict, pm10_predict, tsp_predict, date_predict
# 对比预测数据
async def get_contrast_data(tsp_id, start, end, slots):
beg_f = datetime.datetime.strptime(start, "%Y-%m-%d %H:%M:%S")
end_f = datetime.datetime.strptime(end, "%Y-%m-%d %H:%M:%S")
if beg_f.day == end_f.day:
# 返回当天数据 15min数据
contrast_data = await get_contrast_data_day_dao(tsp_id, beg_f, end_f)
contrast_slots = ["%02d:%02d" % (data["quarter_time"].hour,
data["quarter_time"].minute)
for data in contrast_data]
date_contrast = [data["quarter_time"].strftime("%Y-%m-%d %H:%M:%S")
for data in contrast_data]
else:
# 返回月份数据 每天数据
contrast_data = await get_contrast_data_month_dao(tsp_id, beg_f, end_f)
contrast_slots = [data["quarter_time"][5:] for data in contrast_data]
date_contrast = [data["quarter_time"] for data in contrast_data]
pm25_contrast = [round(data["pm25"]) for data in contrast_data]
pm10_contrast = [round(data["pm10"]) for data in contrast_data]
# 针对如果缺少数据处理,基本不会执行
if len(contrast_data) != len(slots):
# 缺少时刻的时间轴
lack_slots = list(set(slots) - set(contrast_slots))
for slot in lack_slots:
index = slots.index(slot)
pm25_contrast.insert(index, "")
pm10_contrast.insert(index, "")
date_contrast.insert(index, "")
return pm25_contrast, pm10_contrast, date_contrast
# 预测偏差
async def tsp_predict_deviation_service(tsp_id, start, end):
interval, slots = time_format.time_pick_transf(start, end)
# 实时数据
pm25, pm10, tsp = await get_data_new15(tsp_id, start, end, slots, interval)
# 预测数据
pm25_predict, pm10_predict, tsp_predict, date_predict = \
await get_predict_data(tsp_id, start, end, slots)
pm25_list, pm10_list, tsp_list = [], [], []
pm25_time, pm10_time, tsp_time = [], [], []
for index, value in enumerate(pm25_predict):
if value and pm25[index]:
pm25_time.append(date_predict[index])
pm25_list.append(
round(math.fabs((pm25[index]-value)/pm25[index]), 3))
for index, value in enumerate(pm10_predict):
if value and pm10[index]:
pm10_time.append(date_predict[index])
pm10_list.append(
round(math.fabs((pm10[index]-value)/pm10[index]), 3))
for index, value in enumerate(tsp_predict):
if value and tsp[index]:
tsp_time.append(date_predict[index])
tsp_list.append(round(math.fabs((tsp[index]-value)/tsp[index]), 3))
pm25_max, pm25_min, pm25_avg = "", "", ""
if pm25_list:
pm25_max, pm25_min = max(pm25_list), min(pm25_list)
pm25_avg = round(sum(pm25_list)/len(pm25_list), 3)
pm10_max, pm10_min, pm10_avg = "", "", ""
if pm10_list:
pm10_max, pm10_min = max(pm10_list), min(pm10_list)
pm10_avg = round(sum(pm10_list)/len(pm10_list), 3)
tsp_max, tsp_min, tsp_avg = "", "", ""
if tsp_list:
tsp_max, tsp_min = max(tsp_list), min(tsp_list)
tsp_avg = round(sum(tsp_list) / len(tsp_list), 3)
return TpdResp(pm2_5={
"max": pm25_max, "min": pm25_min, "avg": pm25_avg,
"max_time": pm25_time[pm25_list.index(pm25_max)] if pm25_max != "" else "",
"min_time": pm25_time[pm25_list.index(pm25_min)] if pm25_min != "" else ""
}, pm10={
"max": pm10_max, "min": pm10_min, "avg": pm10_avg,
"max_time": pm10_time[pm10_list.index(pm10_max)] if pm10_max != "" else "",
"min_time": pm10_time[pm10_list.index(pm10_min)] if pm10_min != "" else "",
}, tsp={
"max": tsp_max, "min": tsp_min, "avg": tsp_avg,
"max_time": tsp_time[tsp_list.index(tsp_max)] if tsp_max != "" else "",
"min_time": tsp_time[tsp_list.index(tsp_min)] if tsp_min != "" else ""
})
# 对比偏差
async def tsp_contrast_deviation_service(tsp_id, start, end):
interval, slots = time_format.time_pick_transf(start, end)
# 实时数据
pm25, pm10, tsp = await get_data_new15(tsp_id, start, end, slots, interval)
# 对比数据
pm25_contrast, pm10_contrast, date_contrast = \
await get_contrast_data(tsp_id, start, end, slots)
pm25_list, pm10_list = [], []
pm25_time, pm10_time = [], []
for index, value in enumerate(pm25_contrast):
if value and pm25[index]:
pm25_time.append(date_contrast[index])
pm25_list.append(
round(math.fabs((pm25[index] - value) / pm25[index]), 3))
for index, value in enumerate(pm10_contrast):
if value and pm10[index]:
pm10_time.append(date_contrast[index])
pm10_list.append(
round(math.fabs((pm10[index] - value) / pm10[index]), 3))
pm25_max, pm25_min, pm25_avg = "", "", ""
if pm25_list:
pm25_max, pm25_min = max(pm25_list), min(pm25_list)
pm25_avg = round(sum(pm25_list) / len(pm25_list), 3)
pm10_max, pm10_min, pm10_avg = "", "", ""
if pm10_list:
pm10_max, pm10_min = max(pm10_list), min(pm10_list)
pm10_avg = round(sum(pm10_list) / len(pm10_list), 3)
return TcdResp(pm2_5={
"max": pm25_max, "min": pm25_min, "avg": pm25_avg,
"max_time": pm25_time[pm25_list.index(pm25_max)] if pm25_max != "" else "",
"min_time": pm25_time[pm25_list.index(pm25_min)] if pm25_min != "" else ""
}, pm10={
"max": pm10_max, "min": pm10_min, "avg": pm10_avg,
"max_time": pm10_time[pm10_list.index(pm10_max)] if pm10_max != "" else "",
"min_time": pm10_time[pm10_list.index(pm10_min)] if pm10_min != "" else "",
})
async def tsp_index_statistics_service(tsp_id, start, end):
"""TSP信息-指标统计"""
# 1. 查询es
es_res = await tsp_index_statistics(start, end, tsp_id)
if not es_res:
return TisResp()
# 2.1 pm25
# max
pm25_max = ""
pm25_max_time = ""
pm_25_max_hits = es_res["pm25_max"]["hits"]["hits"]
if pm_25_max_hits:
pm25_max = round_2(pm_25_max_hits[0]["_source"].get("pm25_max"))
pm25_max_time = esstr_to_dthoutstr(
pm_25_max_hits[0]["_source"].get("pm25_max_time"),
format="%Y-%m-%d %H:%M:%S")
# min
pm25_min = ""
pm25_min_time = ""
pm_25_min_hits = es_res["pm25_min"]["hits"]["hits"]
if pm_25_min_hits:
pm25_min = round_2(pm_25_min_hits[0]["_source"].get("pm25_min"))
pm25_min_time = esstr_to_dthoutstr(
pm_25_min_hits[0]["_source"].get("pm25_min_time"),
format="%Y-%m-%d %H:%M:%S")
# avg
pm25_avg = ""
if es_res["pm25_avg"].get("value"):
pm25_avg = round(es_res["pm25_avg"].get("value"))
# 2.2 pm10
# max
pm10_max = ""
pm10_max_time = ""
pm_10_max_hits = es_res["pm10_max"]["hits"]["hits"]
if pm_10_max_hits:
pm10_max = round_2(pm_10_max_hits[0]["_source"].get("pm10_max"))
pm10_max_time = esstr_to_dthoutstr(
pm_10_max_hits[0]["_source"].get("pm10_max_time"),
format="%Y-%m-%d %H:%M:%S")
# min
pm10_min = ""
pm10_min_time = ""
pm_10_min_hits = es_res["pm10_min"]["hits"]["hits"]
if pm_10_min_hits:
pm10_min = round_2(pm_10_min_hits[0]["_source"].get("pm10_min"))
pm10_min_time = esstr_to_dthoutstr(
pm_10_min_hits[0]["_source"].get("pm10_min_time"),
format="%Y-%m-%d %H:%M:%S")
# avg
pm10_avg = ""
if es_res["pm10_avg"].get("value"):
pm10_avg = round(es_res["pm10_avg"].get("value"))
# 2.3 tsp
# max
tsp_max = ""
tsp_max_time = ""
tsp_max_hits = es_res["tsp_max"]["hits"]["hits"]
if tsp_max_hits:
tsp_max = round_2(tsp_max_hits[0]["_source"].get("tsp_max"))
tsp_max_time = esstr_to_dthoutstr(
tsp_max_hits[0]["_source"].get("tsp_max_time"),
format="%Y-%m-%d %H:%M:%S")
# min
tsp_min = ""
tsp_min_time = ""
tsp_min_hits = es_res["tsp_min"]["hits"]["hits"]
if tsp_min_hits:
tsp_min = round_2(tsp_min_hits[0]["_source"].get("tsp_min"))
tsp_min_time = esstr_to_dthoutstr(
tsp_min_hits[0]["_source"].get("tsp_min_time"),
format="%Y-%m-%d %H:%M:%S")
# avg
tsp_avg = ""
if es_res["tsp_avg"].get("value"):
tsp_avg = round(es_res["tsp_avg"].get("value"))
return TisResp(pm2_5={"max": pm25_max,
"max_time": pm25_max_time,
"min": pm25_min,
"min_time": pm25_min_time,
"avg": pm25_avg},
pm10={"max": pm10_max,
"max_time": pm10_max_time,
"min": pm10_min,
"min_time": pm10_min_time,
"avg": pm10_avg},
tsp={"max": tsp_max,
"max_time": tsp_max_time,
"min": tsp_min,
"min_time": tsp_min_time,
"avg": tsp_avg},
)
async def tsp_index_statistics_service_new15(tsp_id, start, end):
now = str(datetime.datetime.now())
if start[:10] == now[:10] and end[:10] == now[:10]:
table_name = "tsp_15min_record"
else:
table_name = "tsp_day_record"
sql = f"SELECT pm25_max,pm25_max_time,pm25_min,pm25_min_time," \
f"pm10_max,pm10_max_time,pm10_min,pm10_min_time," \
f"tsp_max,tsp_max_time,tsp_min,tsp_min_time" \
f" FROM {table_name} where tsp_id=%s and create_time " \
f"BETWEEN '{start}' and '{end}' ORDER BY create_time"
async with MysqlUtil() as conn:
datas = await conn.fetchall(sql, args=(tsp_id, ))
if not datas:
return TisResp()
df = pd.DataFrame(list(datas))
pm25_max = df["pm25_max"].max()
pm25_max, pm25_max_time = get_max_min_time(df, pm25_max, "pm25_max")
pm25_min = df["pm25_min"].min()
pm25_min, pm25_min_time = get_max_min_time(df, pm25_min, "pm25_min")
pm10_max = df["pm10_max"].max()
pm10_max, pm10_max_time = get_max_min_time(df, pm10_max, "pm10_max")
pm10_min = df["pm10_min"].min()
pm10_min, pm10_min_time = get_max_min_time(df, pm10_min, "pm10_min")
tsp_max = df["tsp_max"].max()
tsp_max, tsp_max_time = get_max_min_time(df, tsp_max, "tsp_max")
tsp_min = df["tsp_min"].min()
tsp_min, tsp_min_time = get_max_min_time(df, tsp_min, "tsp_min")
pm25_avg_value = df["pm25_max"].mean()
pm25_avg_value = round(pm25_avg_value, 2) if pm25_avg_value else ""
pm10_avg_value = df["pm10_max"].mean()
pm10_avg_value = round(pm10_avg_value, 2) if pm10_avg_value else ""
tsp_avg_value = df["tsp_max"].mean()
tsp_avg_value = round(tsp_avg_value, 2) if tsp_avg_value else ""
return TisResp(pm2_5={"max": pm25_max,
"max_time": pm25_max_time,
"min": pm25_min,
"min_time": pm25_min_time,
"avg": pm25_avg_value},
pm10={"max": pm10_max,
"max_time": pm10_max_time,
"min": pm10_min,
"min_time": pm10_min_time,
"avg": pm10_avg_value},
tsp={"max": tsp_max,
"max_time": tsp_max_time,
"min": tsp_min,
"min_time": tsp_min_time,
"avg": tsp_avg_value},
)
def get_max_min_time(df, max_value, name):
if not pd.isna(max_value):
max_datas = df.loc[df[name].idxmax()].to_dict()
max_time = max_datas.get(f"{name}_time")
max_time = '' if pd.isnull(max_time) else str(max_time)
max_value = round_2(max_value)
else:
max_value, max_time = "", ""
return max_value, max_time
async def day_env_service(cid):
"""当日环境"""
# 需求逻辑
# 求每个tsp装置pm2.5,pm10,tsp的平均值
# 取平均值高的pm2.5,pm10,tsp
today_start, today_end, m_start, m_end = start_end_date()
# 1. 根据cid取tsp_id_list
tsp_list = await tsp_by_cid(cid)
tsp_id_list = [i["tsp_id"] for i in tsp_list]
# 2. 取es数据
es_res = await tsp_aggs_tsp_id(today_start, today_end, tsp_id_list)
if not es_res:
return DeResp(pm2_5={"data": "", "grade": ""},
pm10={"data": "", "grade": ""},
tsp={"data": "", "grade": ""})
pm2_5_max = 0
pm10_max = 0
tsp_max = 0
for info in es_res:
pm2_5 = round(info["pm25"]["value"])
if pm2_5 > pm2_5_max:
pm2_5_max = pm2_5
pm10 = round(info["pm10"]["value"])
if pm10 > pm10_max:
pm10_max = pm10
tsp = round(info["tsp"]["value"])
if tsp > tsp_max:
tsp_max = tsp
# 调用函数,获取等级
pm2_5_grade = pm2_5_trans_grade(pm2_5_max)
pm10_grade = pm10_trans_grade(pm10_max)
tsp_grade = tsp_trans_grade(tsp_max)
# 3. 返回
return DeResp(
pm2_5={"data": pm2_5_max, "grade": pm2_5_grade},
pm10={"data": pm10_max, "grade": pm10_grade},
tsp={"data": tsp_max, "grade": tsp_grade}
)
async def day_env_service_new15(cid):
"""当日环境"""
# 需求逻辑
# 求每个tsp装置pm2.5,pm10,tsp的平均值
# 取平均值高的pm2.5,pm10,tsp
today_start, today_end, m_start, m_end = start_end_date()
# 1. 根据cid取tsp_id_list
tsp_list = await tsp_by_cid(cid)
tsp_id_list = [i["tsp_id"] for i in tsp_list]
sql_res = await tsp_by_tsp_id_dao(today_start, today_end, tsp_id_list)
if not sql_res:
return DeResp(pm2_5={"data": "", "grade": ""},
pm10={"data": "", "grade": ""},
tsp={"data": "", "grade": ""})
pm2_5_max = 0
pm10_max = 0
tsp_max = 0
for info in sql_res:
pm2_5 = round_2(info["pm25"]) if info["pm25"] else 0
if pm2_5 > pm2_5_max:
pm2_5_max = pm2_5
pm10 = round_2(info["pm10"]) if info["pm10"] else 0
if pm10 > pm10_max:
pm10_max = pm10
tsp = round_2(info["tsp"]) if info["tsp"] else 0
if tsp > tsp_max:
tsp_max = tsp
# 调用函数,获取等级
pm2_5_grade = pm2_5_trans_grade(pm2_5_max)
pm10_grade = pm10_trans_grade(pm10_max)
tsp_grade = tsp_trans_grade(tsp_max)
# 3. 返回
return DeResp(
pm2_5={"data": pm2_5_max, "grade": pm2_5_grade},
pm10={"data": pm10_max, "grade": pm10_grade},
tsp={"data": tsp_max, "grade": tsp_grade}
)
async def stat_analysis_service(cid, tsp_id, start, end):
"""统计分析-扬尘"""
# 1. 查询es, 获取tsp信息
pm25_list, pm10_list, tsp_list, slots = await per_hour_wave_new15(
start, end, tsp_id)
# 2. 获取雾炮电量数据
storey_list = await storey_wp_by_cid(cid)
point_list = [storey["point_id"] for storey in storey_list]
kwh_res, slots = await per_hour_kwh_wave_new15(start, end, point_list)
r_gun_pm25_value, r_gun_pm25_info = correlation(kwh_res, pm25_list)
r_gun_pm10_value, r_gun_pm10_info = correlation(kwh_res, pm10_list)
r_gun_tsp_value, r_gun_tsp_info = correlation(kwh_res, tsp_list)
# 3. 获取喷淋水量数据
water_res = await per_hour_water_wave(start, end)
r_water_pm25_value, r_water_pm25_info = correlation(water_res, pm25_list)
r_water_pm10_value, r_water_pm10_info = correlation(water_res, pm10_list)
r_water_tsp_value, r_water_tsp_info = correlation(water_res, tsp_list)
return SaResp(
pm2_5=pm25_list,
pm10=pm10_list,
tsp=tsp_list,
time_slots=slots,
fog_gun=kwh_res,
water=water_res,
r_gun_pm25={"r": r_gun_pm25_value, "name": r_gun_pm25_info},
r_gun_pm10={"r": r_gun_pm10_value, "name": r_gun_pm10_info},
r_gun_tsp={"r": r_gun_tsp_value, "name": r_gun_tsp_info},
r_water_pm25={"r": r_water_pm25_value, "name": r_water_pm25_info},
r_water_pm10={"r": r_water_pm10_value, "name": r_water_pm10_info},
r_water_tsp={"r": r_water_tsp_value, "name": r_water_tsp_info},
)
async def analysis_describe_service(cid, start, end, page_num, page_size,
measure_type):
data = await get_cid_tsp_dao(cid, start, end, measure_type)
page_date = await get_page_data(cid, start, end, page_num, page_size,
measure_type)
page_list = []
for page in page_date:
start_datetime = page["start_datetime"].strftime("%Y-%m-%d %H:%M:%S")
end_datetime = page["end_datetime"].strftime("%Y-%m-%d %H:%M:%S")
page_list.append({
"datetime": f"{start_datetime[:16]}-{end_datetime[11:16]}",
"effective": page["measure_msg"],
"is_effective": page["is_valid"],
"message": page["effect"]
})
effective_rate = f"{round(data['effect']/data['measures'],2)*100}%" \
if data['measures'] else 0
return AdResp(
all_count=data["measures"] or 0,
effective_count=data["effect"] or 0,
effective_rate=effective_rate,
page_data=page_list
)