From 1ae38ab34bcbdff622c7623119ee54bad419f4ed Mon Sep 17 00:00:00 2001
From: zmk <496160012@qq.com>
Date: 星期三, 28 八月 2024 16:11:08 +0800
Subject: [PATCH] 提交代码
---
MainAPI.py | 620 +++++++++++++++++++++++++++----------------------------
1 files changed, 303 insertions(+), 317 deletions(-)
diff --git a/MainAPI.py b/MainAPI.py
index 78b64b3..a1adfdf 100644
--- a/MainAPI.py
+++ b/MainAPI.py
@@ -4,21 +4,21 @@
from flask import jsonify
from flask import request
from flask_cors import CORS
-import sys
import numpy as np
-import pandas as pd
import flopy
-import flopy.utils.binaryfile as bf
-import csv
import time
-from openpyxl import load_workbook
import os
import shutil
+import json
import Base as base
import CalHead
import Predict
-import json
import ModelPeriod
+import AchiveReport as achiveReport
+import BigData
+import OpenExcel
+import DataTransf
+import DataTask
# Flask鍑芥暟鎺ユ敹涓�涓弬鏁癬_name__锛屽畠浼氭寚鍚戠▼搴忔墍鍦ㄧ殑鍖�
app = Flask(__name__)
@@ -33,11 +33,13 @@
#娌虫祦cell鐨勬暟閲�
riverCellSize = 109
-iboundGroupSize = 5
-iboundGroup={1:[1,86],2:[87,111],3:[112,142],4:[143,170],5:[171,240]}
+iboundGroupSize = 10
+iboundGroup={1:[1,86],2:[87,111],3:[112,142],4:[143,170],5:[171,240],
+ 6:[241,282],7:[283,354],8:[355,393],9:[394,436],10:[437,453]}
-iboundGroup3Size = 5
-iboundGroup3={1:[241,282],2:[283,354],3:[355,393],4:[394,436],5:[437,453]}
+iboundName =["瑗夸晶娴佸叆","鍖楅儴娴佸叆","涓滃寳閮ㄦ祦鍏�","涓滈儴娴佸嚭","鍗楅儴娴佸嚭",
+ "灞卞尯娴佸叆","姘稿畾娌虫柇瑁傛祦鍏�","榛勫簞楂樹附鏂娴佸叆","鍏疂灞辨柇瑁傛祦鍏�","鏄嗘槑婀栨柇瑁傚叆娴�"]
+
riverGroupSize = 4
riverGroup={1:[454,479],2:[480,505],3:[506,527],4:[528,562]}
@@ -49,7 +51,7 @@
def getModel(model_name):
model_ws=""
if not model_name:
- model_ws = "202001_202212"
+ model_ws = base.not_allowed_model
else:
model_ws = base.model_dir + model_name
@@ -60,7 +62,7 @@
@app.route('/baseparam/', methods=['GET'])
def baseparam():
- model_name = request.args.get('model_name')
+ model_name = request.args.get('model_name')
ml= getModel(model_name)
nrclp = ml.get_nrow_ncol_nlay_nper()
dict = {"Row": nrclp[0], "Column": nrclp[1],
@@ -71,8 +73,31 @@
months = ModelPeriod.get_months_in_range_ym(start_time, end_time)
dict["months"]=months
+ print(jsondata)
+ if "initHeader" in jsondata:
+ dict["initHead"] = jsondata["initHeader"]
+ else:
+ dict["initHead"] = ""
return jsonify(dict)
+#淇濆瓨鍒濆姘村ご
+@app.route('/saveInitHead', methods=['POST'])
+def saveInitHead():
+ jsondata = request.get_json()
+ model_name = str(jsondata['model_name'])
+ initHeader = str(jsondata['initHead'])
+
+ if model_name == base.not_allowed_model or model_name in base.archive_models:
+ return jsonify("鏈ā鍨嬩负楠岃瘉妯″瀷,涓嶅厑璁镐慨鏀癸紒")
+
+ jsondata= CalHead.get_model_json(model_name)
+ jsondata["initHeader"] = initHeader
+
+ jpath = base.model_dir + model_name +"\\prediction.json"
+ with open(jpath, "w",encoding='utf-8') as outfile:
+ json.dump(jsondata, outfile,ensure_ascii=False)
+
+ return jsonify("淇濆瓨鍒濆姘村ご鎴愬姛锛�")
#璇诲彇wel鏂囦欢 鍙傛暟涓� Period
@app.route('/welList/', methods=['GET'])
@@ -97,11 +122,16 @@
result_len = len(result)
+ ibound_segment={}
+
if layerparam == '1':
#杈圭晫
+
+ ibound_segment={"1":[0,85],"2":[86,110],"3":[111,141],"4":[142,169],"5":[170,239]}
+
for i in range(0, 240):
iboundarray.append(result[i])
- #娌虫祦
+ #娌虫祦
for i in range(453, 562):
riverarray.append(result[i])
@@ -114,12 +144,17 @@
welarray.append(result[i])
elif layerparam == '3':
+
+ ibound_segment={"6":[0,41],"7":[42,113],"8":[114,152],"9":[153,195],"10":[196,212]}
+
for i in range(240, 453):
iboundarray.append(result[i])
+ ibounddict = {"name": "ibound", "data": iboundarray,"segment":ibound_segment}
- ibounddict = {"name": "ibound", "data": iboundarray}
- riverdict = {"name": "river", "data": riverarray}
+ riversgement={"1":[0,25],"2":[26,51],"3":[52,73],"4":[74,108]}
+ riverdict = {"name": "river", "data": riverarray,"segment":riversgement}
+
weldict = {"name": "wel", "data": welarray}
@@ -128,6 +163,7 @@
data.append(ibounddict)
data.append(weldict)
return jsonify(data)
+
#璇诲彇鍗曚釜浜曠殑鏁版嵁
@app.route('/wel/', methods=['GET'])
@@ -169,6 +205,9 @@
# model_name = request.args.get('model_name')
model_name = str(json['model_name'])
+ if model_name == base.not_allowed_model or model_name in base.archive_models:
+ return jsonify("鏈ā鍨嬩负楠岃瘉妯″瀷,涓嶅厑璁镐慨鏀癸紒")
+
ml= getModel(model_name)
@@ -199,119 +238,19 @@
lrcq[per] = array2d
- flopy.modflow.ModflowWel(ml,stress_period_data=lrcq)
+ flopy.modflow.ModflowWel(ml,ipakcb= ml.wel.ipakcb,dtype=ml.wel.dtype,
+ options=ml.wel.options,
+ stress_period_data=lrcq)
ml.write_input()
return jsonify("鏁版嵁鏇存柊瀹屾瘯锛�")
-
-
-#璇诲叆鐢靛瓙琛ㄦ牸鏂囦欢鐢熸垚wel 鏂囦欢
-@app.route('/cellFileInput', methods=['POST'])
-def cellFileInput():
-
- path ='C:\\Users\\ZMK\\Desktop\\寰呭彂閫乗\cell鏂囦欢.xlsx'
-
- data = get_cell_data(path)
-
- lrcq= get_cell_struct(data["excel1"],data["excel2"],data["excel3"])
-
- model_name = request.args.get('model_name')
-
- ml= getModel(model_name)
-
- flopy.modflow.ModflowWel(ml,stress_period_data=lrcq)
- ml.write_input()
-
- return jsonify("sucess")
-
-
-def get_cell_struct(excel1,excel2,excel3):
- lrcq={}
-
- #鍛ㄦ湡鏁伴噺
- period = 7
- start_row_index = 1
-
- #杈圭晫鏁版嵁 excel
- for col in range (0,period):
- array =[]
- for row in range(start_row_index, len(excel1)):
-
- arr = [excel1[row][2]-1,excel1[row][3]-1,excel1[row][4]-1,excel1[row][6+col]]
- array.append(arr)
- lrcq[col]= array
-
- #娌虫祦鏁版嵁 excel
- for col in range (0,period):
- array =[]
- for row in range(start_row_index, len(excel2)):
-
- arr = [excel2[row][2]-1,excel2[row][3]-1,excel2[row][4]-1,excel2[row][6+col]]
- array.append(arr)
-
- lrcq[col].extend(array)
-
- #鎶芥按鏁版嵁 excel
- for col in range (0,period):
-
- array =[]
- for row in range(start_row_index, len(excel3)):
-
- arr = [excel3[row][1]-1,excel3[row][2]-1,excel3[row][3]-1,excel3[row][8+col]]
- array.append(arr)
-
- lrcq[col].extend(array)
-
- return lrcq
-
-
-
-#鑾峰彇cell鏂囦欢
-#file_path 鏂囦欢鐨勮矾寰�
-def get_cell_data(file_path):
-
- workbook = load_workbook(file_path)
- sheetnames = workbook.get_sheet_names()
- #read first sheet
- sheet1 = workbook[sheetnames[0]]
- sheet2 = workbook[sheetnames[1]]
- sheet3 = workbook[sheetnames[2]]
-
- excel1 =[]
- excel2=[]
- excel3=[]
- # 閬嶅巻璇诲彇鏁翠釜宸ヤ綔琛�
- for row in sheet1.iter_rows(values_only=True):
- array=[]
- for cell in row:
- array.append(cell)
- excel1.append(array)
-
- for row in sheet2.iter_rows(values_only=True):
- array=[]
- for cell in row:
- array.append(cell)
- excel2.append(array)
-
- for row in sheet3.iter_rows(values_only=True):
- array=[]
- for cell in row:
- array.append(cell)
- excel3.append(array)
-
- # 鍏抽棴Excel鏂囦欢
- workbook.close()
- data={"excel1":excel1,"excel2":excel2,"excel3":excel3}
-
- return data
-
#璇诲彇wel鏂囦欢 鍙傛暟涓� Period
@app.route('/iboundList/', methods=['GET'])
def iboundList():
- return jsonify(iboundGroupSize)
+ return jsonify(iboundName)
#杈圭晫鐨勫垎缁勬暟鎹�
@@ -360,6 +299,10 @@
data = json['data']
model_name = json['model_name']
+
+ if model_name == base.not_allowed_model or model_name in base.archive_models:
+ return jsonify("鏈ā鍨嬩负楠岃瘉妯″瀷,涓嶅厑璁镐慨鏀癸紒")
+
ml= getModel(model_name)
index = iboundGroup[no]
@@ -392,7 +335,10 @@
lrcq[per] = array2d
- flopy.modflow.ModflowWel(ml,stress_period_data=lrcq)
+ flopy.modflow.ModflowWel(ml,ipakcb= ml.wel.ipakcb,
+ dtype=ml.wel.dtype,
+ options=ml.wel.options,
+ stress_period_data=lrcq)
ml.write_input()
return jsonify("鏁版嵁鏇存柊瀹屾瘯锛�")
@@ -458,6 +404,9 @@
end_index = index[1]
model_name = json['model_name']
+ if model_name == base.not_allowed_model or model_name in base.archive_models:
+ return jsonify("鏈ā鍨嬩负楠岃瘉妯″瀷,涓嶅厑璁镐慨鏀癸紒")
+
ml= getModel(model_name)
periods =CalHead.get_model_period(model_name)
@@ -487,7 +436,11 @@
lrcq[per] = array2d
- flopy.modflow.ModflowWel(ml,stress_period_data=lrcq)
+
+ flopy.modflow.ModflowWel(ml,ipakcb= ml.wel.ipakcb,
+ dtype=ml.wel.dtype,
+ options=ml.wel.options,
+ stress_period_data=lrcq)
ml.write_input()
return jsonify("鏁版嵁鏇存柊瀹屾瘯锛�")
@@ -529,55 +482,14 @@
return jsonify(result)
-#闄嶆按鏁版嵁淇敼
-# @app.route('/precipitationInput', methods=['POST'])
-# def precipitationInput():
-
-# json = request.get_json()
-# model_name= str(json['model_name'])
-# period = int(json['period'])
-# #鏈夊簭鐨勫簲鍔涘懆鏈熷垪琛� json
-# data = json['data']
-# dict = {}
-# for i in range(len(data)):
-# q1 = data[i]['Q1']
-# q2 = data[i]['Q2']
-# dict[q1] = q2
-
-# ml= getModel(model_name)
-
-# item = ml.rch.rech.__getitem__(kper=period)
-# array2d = item.get_value()
-
-# count = 0
-
-# array2d_len = len(array2d)
-
-# for i in range(array2d_len):
-
-# array_len = len(array2d[i])
-
-# for j in range(array_len):
-
-# va = str(array2d[i][j])
-# if va in dict:
-# count += 1
-# array2d[i][j] = float(dict[va])
-
-# ml.rch.rech.__setitem__(key=period, value=array2d)
-
-# rch = flopy.modflow.ModflowRch(ml, rech=ml.rch.rech)
-# rch.write_file(check=False)
-# #ml.write_input()
-
-# return jsonify("闄嶆按鍙傛暟淇敼瀹屾瘯锛�")
-
@app.route('/precipitationInput', methods=['POST'])
def precipitationInput():
json = request.get_json()
model_name= str(json['model_name'])
+ if model_name == base.not_allowed_model or model_name in base.archive_models:
+ return jsonify("鏈ā鍨嬩负楠岃瘉妯″瀷,涓嶅厑璁镐慨鏀癸紒")
period = int(json['period'])
#鏈夊簭鐨勫簲鍔涘懆鏈熷垪琛� json
data = json['data']
@@ -597,7 +509,7 @@
tuples= areas[key]
zblen= len(tuples)
- values = float(dict[key])
+ values = round(float(dict[key]),8)
for i in range(zblen):
x = tuples[i][0]
y = tuples[i][1]
@@ -605,106 +517,14 @@
ml.rch.rech.__setitem__(key=period, value=array2d)
- rch = flopy.modflow.ModflowRch(ml, rech = ml.rch.rech)
+ rch = flopy.modflow.ModflowRch(ml,nrchop=ml.rch.nrchop,
+ ipakcb=ml.rch.ipakcb,
+ rech=ml.rch.rech,
+ irch =ml.rch.irch)
rch.write_file(check=False)
# ml.write_input()
return jsonify("闄嶆按鍙傛暟淇敼瀹屾瘯锛�")
-
-#瀵煎叆csv鏂囦欢
-@app.route('/precipitationInputFile', methods=['POST'])
-def precipitationInputFile():
-
- model_name = request.args.get('model_name')
- ml= getModel(model_name)
- save_path = 'C:/Users/ZMK/Desktop/test1/' + "1111.xlsx"
- file = request.files.get('file')
-
- if file:
- file.save(save_path)
-
- #鑾峰彇璇诲彇鐨別xcel 琛ㄦ牸鏁版嵁
- stations = get_station_struct(save_path)
-
- #寰幆鍛ㄦ湡
- #perd鍛ㄦ湡鍙橀噺
- #array2d 姣忎釜鍛ㄦ湡鐨勪簩缁存暟缁�
- for perd in range(0,36):
- period = perd
- item = ml.rch.rech.__getitem__(kper=period)
- array2d = item.get_value()
-
- array2d_len = len(array2d)
- count = 0
- #寰幆绔欑偣灏嗕竴涓猵eriod鐨勬墍鏈塻tations杩涜瀛楀吀瀛樺偍
- dict = {}
- for k in range(0,len(stations)):
- row = stations[k]["row"]
- column = stations[k]["column"]
-
- data_old = array2d[row][column]
- data_new = stations[k]["data"][perd]
- dict[data_old]= data_new
-
-
- #寰幆璁剧疆姣忎釜period 鐨勫��
- for i in range(array2d_len):
-
- array_len = len(array2d[i])
-
- for j in range(array_len):
-
- va = str(array2d[i][j])
- if va in dict:
- array2d[i][j] = float(dict[va])
-
- #灏哸rray2d閲嶆柊set 瀵瑰簲鐨� item 鍛ㄦ湡鍐�
- ml.rch.rech.__setitem__(key=period, value=array2d)
-
- rch = flopy.modflow.ModflowRch(ml, rech=ml.rch.rech)
- rch.write_file(check=False)
- # ml.write_input()
- return '鏂囦欢涓婁紶鎴愬姛'
- else:
- return '涓婁紶澶辫触锛屾湭閫夋嫨鏂囦欢'
-
-
-#鑾峰彇绔欑偣鐨勬暟鎹紝鏋勯�犳暟鎹粨鏋�
-#file_path 鏂囦欢鐨勮矾寰�
-def get_station_struct(file_path):
-
- workbook = load_workbook(file_path)
- sheetnames = workbook.get_sheet_names()
- #read first sheet
- sheet = workbook[sheetnames[0]]
-
- array2d_excel=[]
- # 閬嶅巻璇诲彇鏁翠釜宸ヤ綔琛�
- for row in sheet.iter_rows(values_only=True):
- array=[]
- for cell in row:
- array.append(cell)
- array2d_excel.append(array)
- # 鍏抽棴Excel鏂囦欢
- workbook.close()
-
- #鏁版嵁鐨勫紑濮嬩笅鏍�
- data_start_index=6
- #淇℃伅鐨勫紑濮嬭鍙�
- start_row_index = 1
- #瀛樺偍绔欑偣淇℃伅
- stations = []
- for i in range (start_row_index,len(array2d_excel)):
- st={"name":array2d_excel[i][1],"row":array2d_excel[i][4],"column":array2d_excel[i][5]}
- data=[]
- for j in range(data_start_index,len(array2d_excel[i])):
- cell_data = array2d_excel[i][j]
- cell_data= cell_data/100/30*0.15
- data.append(round(cell_data, 6))
- st["data"]= data
- stations.append(st)
-
- return stations
#杩愯妯″瀷
@@ -712,22 +532,30 @@
def runModel():
model_name = request.args.get('model_name')
- msg= Predict.run_model_predict(model_name)
- #瀵煎嚭csv鏂囦欢
- csvpath = CalHead.exportCsV(model_name)
+ if model_name == base.not_allowed_model or model_name in base.archive_models:
+ return jsonify("鏈ā鍨嬩负楠岃瘉妯″瀷,涓嶅厑璁镐慨鏀癸紒")
- #鏇存柊妯″瀷涓夌淮缃戞牸閰嶇疆
+ dicts= Predict.run_model_predict(model_name)
+ if dicts["code"] == 400:
+ return dicts["msg"]
+ #瀵煎嚭csv鏂囦欢
+ CalHead.exportCsV(model_name)
+
+ # #鏇存柊妯″瀷涓夌淮缃戞牸閰嶇疆
base.updateModelConfig(model_name)
- #鍒涘缓妯″瀷鐨勪笁缁寸綉鏍�
+ # #鍒涘缓妯″瀷鐨勪笁缁寸綉鏍�
filedir = base.model3d_path + model_name
- print(filedir)
if not os.path.exists(filedir):
os.makedirs(filedir, exist_ok=True)
- base.callModelexe()
- return jsonify(msg)
+ base.callModelexe()
+ #璁＄畻姘磋祫婧愰噺鍜屾按鍧囪
+ CalHead.run_zonebudget_bal(model_name)
+ CalHead.run_zonebudget_res(model_name)
+
+ return jsonify(dicts["msg"])
#鐢熸垚妯″瀷csv 鏂囦欢
@app.route('/runModelCsv/', methods=['GET'])
@@ -739,23 +567,6 @@
return jsonify(result)
-
-#鍒濆姘翠綅淇℃伅
-@app.route('/initWater/', methods=['GET'])
-def initWater():
-
- period = request.args.get('period')
-
- per = int(period)
-
- model_name = request.args.get('model_name')
-
- ml= getModel(model_name)
- item = ml.rch.rech.__getitem__(kper=per)
- value = item.get_value()
- t = np.array(value).tolist()
- return jsonify(t)
-
#鍒涘缓鏂版ā鍨�
@app.route('/saveModel/', methods=['GET'])
def saveModel():
@@ -763,29 +574,65 @@
modelname = request.args.get('name')
startTime = request.args.get('startTime')
endTime = request.args.get('endTime')
+ remark = request.args.get('remark')
file_list = os.listdir(base.model_dir)
for name in file_list:
if name == modelname:
return jsonify("妯″瀷鍚嶇О宸茬粡瀛樺湪锛屼笉鍏佽閲嶅鍒涘缓锛�")
+
+ pers = ModelPeriod.get_months_in_range_count(startTime,endTime)
+ if pers > 60 :
+ return jsonify("妯″瀷鍒涘缓澶辫触锛屾渶澶氬彧鍏佽60涓懆鏈熺殑杩炵画棰勬祴锛�")
dir = base.model_dir + modelname
- shutil.copytree(base.predictModel,dir)
+
+ if pers==12:
+ shutil.copytree(base.predictModel,dir)
+ else:
+ #濡傛灉涓嶆槸涓�骞寸殑棰勬祴锛� 鍙兘涓� 鍏朵粬鐨勫鍛ㄦ湡鐨勯娴� 澶т簬 12 涓懆鏈熸垨鑰� 灏忎簬12涓懆鏈�
+ #棣栧厛鎶�60涓懆鏈熺殑褰撳仛妯″瀷clone涓�浠斤紝 鐒跺悗淇敼 dis wel rch 鏂囦欢
+ #wel 鏂囦欢鍜� rch 鏂囦欢涓嶉渶瑕佷慨鏀�
+ shutil.copytree(base.predictModel60,dir)
+ Predict.updateDisFile(modelname,pers)
+
jsondata={"model_name":modelname,"start_time":startTime,"end_time":endTime}
predictionJson = base.model_dir + modelname +"\\prediction.json"
with open(predictionJson, "w",encoding='utf-8') as outfile:
json.dump(jsondata, outfile,ensure_ascii=False)
-
+
+ CalHead.addModelJson(modelname, startTime, endTime, remark)
return jsonify("鍒涘缓鏂版ā鍨嬪畬姣曪紒")
#鍒涘缓鏂版ā鍨�
@app.route('/ModelList/', methods=['GET'])
def ModelList():
-
file_list = os.listdir(base.model_dir)
return jsonify(file_list)
+#妯″瀷鍒楄〃2
+@app.route('/ModelList2/', methods=['GET'])
+def ModelList2():
+ model_path = base.prefix +"\\model_list.json"
+ model_lsit=""
+ with open(model_path,encoding='utf-8') as f:
+ model_lsit = json.load(f)
+ return jsonify(model_lsit)
+
+#鍒犻櫎妯″瀷
+@app.route('/deleteModel/', methods=['GET'])
+def deleteModel():
+ model_name = request.args.get('model_name')
+ if not model_name:
+ return jsonify({"code":400,"msg":"涓嶅厑璁稿垹闄ょ┖鐩綍锛�"})
+ if model_name == base.not_allowed_model:
+ return jsonify({"code":400,"msg":"楠岃瘉妯″瀷涓嶅厑璁稿垹闄わ紒"})
+
+ paths = base.model_dir + model_name
+ shutil.rmtree(paths)
+ CalHead.removeModelJson(model_name)
+ return jsonify({"code":200,"msg":"妯″瀷鍒犻櫎瀹屾瘯锛�"})
#棰勬祴鍦烘櫙鍙傛暟
@app.route('/prediction', methods=['POST'])
@@ -800,15 +647,19 @@
predictionJson = base.model_dir + model_name +"\\prediction.json"
with open(predictionJson, "w",encoding='utf-8') as outfile:
json.dump(jsondata, outfile,ensure_ascii=False)
-
- return jsonify("淇濆瓨棰勬祴鍦烘櫙鍙傛暟瀹屾瘯锛�")
+
+ #杩愯妯″瀷
+ dicts = Predict.run_model(model_name)
+ return jsonify(dicts["msg"])
+
#棰勬祴鍦烘櫙鍙傛暟
@app.route('/predictionparam', methods=['GET'])
def predictionparam():
- model_name = request.args.get('model_name')
+ model_name = request.args.get('model_name')
+ print(model_name)
file_list = os.listdir(base.model_dir)
if model_name not in file_list:
return jsonify("妯″瀷涓嶅瓨鍦紒")
@@ -862,28 +713,31 @@
def pump_importdata():
model_name = request.form.get('model_name')
-
- # ml= getModel(model_name)
+ types = request.form.get('type')
file = request.files.get('file')
+ print(types)
- save_path = base.model_dir + model_name +"\\extra_cell.xlsx"
+ save_path = base.model_dir + model_name +"\\"+types+".xlsx"
+ print(save_path)
if file:
file.save(save_path)
- resultDict={"code":200,"msg":"淇濆瓨鏁版嵁瀹屾瘯锛�"}
+ data= DataTransf.base_excel(model_name,types)
+
+ resultDict={"code":200,"msg":"淇濆瓨鏁版嵁瀹屾瘯锛�","data":data}
return jsonify(resultDict)
#瑙傛祴浜曞垪琛�
@app.route('/obsWellList', methods=['GET'])
def obsWellList():
obswell= base.obs_well
- dict =[]
- for name , row ,column in obswell:
- obj ={"name":name,"row":row,"column":column,"Layer":1}
- dict.append(obj)
+ dicts =[]
+ for wellId, name , row ,column in obswell:
+ obj ={"wellId":wellId,"name":name,"row":row,"column":column,"Layer":1}
+ dicts.append(obj)
- return jsonify(dict)
+ return jsonify(dicts)
#瑙傛祴浜昪hart鏁版嵁鎺ュ彛
@@ -892,8 +746,9 @@
model_name = request.args.get('model_name')
row = request.args.get('row')
column = request.args.get('column')
+ wellId = request.args.get('wellId')
- result = CalHead.obsChartdata(model_name, row, column)
+ result = CalHead.obsChartdata(wellId,model_name, row, column)
return jsonify(result)
@@ -904,8 +759,8 @@
base_year = request.args.get('base_year')
start_time = request.args.get('start_time')
end_time = request.args.get('end_time')
-
- return jsonify(Predict.predict_river_chart(base_year, start_time, end_time))
+ value = float(request.args.get('value'))
+ return jsonify(Predict.predict_river_chart(base_year, start_time, end_time,value))
#棰勬祴椤甸潰 闄嶆按鍥捐〃
@app.route('/predictWaterChart', methods=['GET'])
@@ -913,15 +768,57 @@
base_year = request.args.get('base_year')
start_time = request.args.get('start_time')
end_time = request.args.get('end_time')
- return jsonify(Predict.predict_water_chart(base_year, start_time, end_time))
+ value = float(request.args.get('value'))
+ return jsonify(Predict.predict_water_chart(base_year, start_time, end_time,value))
-@app.route('/heatmap', methods=['GET'])
-def heatmap():
+#寮�閲囬噺鐨勬姌绾垮浘
+@app.route('/predictWellChart', methods=['POST'])
+def predictWellChart():
+
+ json = request.get_json()
+ base_year = str(json['base_year'])
+ start_time = json['start_time']
+ end_time = json['end_time']
+ data = json['data']
+
+ return jsonify(Predict.predict_well_chart(base_year, start_time, end_time,data))
+
+#娴佸満鍥�
+@app.route('/flowField', methods=['GET'])
+def flowField():
model_name = request.args.get('model_name')
- period = request.args.get('period')
- data = CalHead.heatmapdata(model_name,period)
- maximum_value = np.max(data)
- return jsonify(np.array(data).tolist())
+ flowStartTime = int(request.args.get('flowStartTime'))
+ flowEndTime=int(request.args.get('flowEndTime'))
+ flowType= request.args.get('flowType')
+ # layer = int(request.args.get('layer') )
+
+ pic = str(int(time.time())) +".png"
+ outpath = base.flow_file + pic
+ dicts={}
+ if flowType=="姘翠綅":
+ dicts = achiveReport.flow_field(model_name, flowStartTime, 0,"娴佸満淇℃伅", "online", outpath)
+ if flowType=="鍙樺箙":
+ dicts = achiveReport.getFlowFieldBF(model_name,flowStartTime,flowEndTime,outpath)
+
+ if flowType=="鍩嬫繁":
+ dicts = achiveReport.getFlowFieldDepth(model_name,flowStartTime, 0,"娴佸満淇℃伅", "online", outpath)
+
+ dicts["path"] = "/xishan/xinshanFlow/"+pic
+ return jsonify(dicts)
+
+
+#鍒濆姘翠綅娴佸満淇℃伅
+@app.route('/initWaterFlow/', methods=['GET'])
+def initWater():
+
+ model_name = request.args.get('model_name')
+ layer = int(request.args.get('layer') )
+ pic = str(int(time.time())) +".png"
+ outpath = base.flow_file + pic
+ result= achiveReport.init_flow_field(model_name, layer,"鍒濆娴佸満淇℃伅", "online", outpath)
+ if result == "#":
+ return jsonify("")
+ return jsonify("/xishan/xinshanFlow/"+pic)
#闄嶆按鍧囪
@@ -935,15 +832,104 @@
#鍦扮悆椤甸潰鏁版嵁
@app.route('/earthWaterChart', methods=['GET'])
def earthWaterChart():
-
+ model_name = request.args.get('model_name')
indexId = int(request.args.get('index_id'))
- data = CalHead.earthWaterChart("202001_202212",indexId)
+ data = CalHead.earthWaterChart(model_name,indexId)
return jsonify(data)
+
+#娓楅�忕郴鏁�
+@app.route('/mdLpf', methods=['GET'])
+def mdLpf():
+ lf = base.md_lpf
+ return jsonify(np.array(lf).tolist())
+
+#姘磋祫婧愰噺
+@app.route('/water_res', methods=['GET'])
+def water_res():
+ model_name = request.args.get('model_name')
+ #姘村潎琛�
+ path1=base.muiltyModel + model_name +"\\water_bal.txt"
+ bal = CalHead.water_balance(model_name, path1)
+ path2=base.muiltyModel + model_name +"\\water_res.txt"
+ res = CalHead.water_res(model_name,path2)
+ dicts = BigData.mergeWaterData(bal,res)
+
+ initFlowPNG = achiveReport.getWaterResFiled(model_name,0)
+ dicts["initFlowPNG"]= "/xishan/xinshanFlow/" + initFlowPNG
+
+ FlowPNG2 = achiveReport.getWaterResFiled(model_name,11)
+ dicts["lastFlowPNG"]= "/xishan/xinshanFlow/" + FlowPNG2
+ dicts["initMonth"] ="2023-01"
+ dicts["lastMonth"] ="2023-12"
+
+ #姘磋祫婧愰噺
+ return dicts
+
+
+#澶氭ā鍨嬪湴涓嬫按浣�
+@app.route('/water_depth', methods=['GET'])
+def water_depth():
+ model_name = request.args.get('model_name')
+
+ result = CalHead.water_depth(model_name)
+ #姘磋祫婧愰噺
+ return result
+
+
+#灏忓満鏅� 鍗曚釜妯″瀷姘翠綅 鍜屽彉骞�
+@app.route('/xs_depth', methods=['GET'])
+def xs_depth():
+ model_name = request.args.get('model_name')
+
+ res = CalHead.xs_depth(model_name)
+ jsondata= CalHead.get_model_json(model_name)
+ start_time = jsondata["start_time"]
+ end_time = jsondata["end_time"]
+ months = ModelPeriod.get_months_in_range_ym(start_time, end_time)
+ res["months"] = months
+ #姘磋祫婧愰噺
+ return res
+
+#淇濆瓨鐩戞祴绔欓檷闆ㄩ噺
+@app.route('/sensor_jyl_list', methods=['GET'])
+def sensor_jyl_list():
+ model_name = request.args.get('model_name')
+ data = OpenExcel.read_excel(model_name)
+ return jsonify(data)
+
+#淇濆瓨鐩戞祴绔欓檷闆ㄩ噺
+@app.route('/sensor_jyl_save', methods=['POST'])
+def sensor_jyl_save():
+ json = request.get_json()
+ model_name = str(json['model_name'])
+ data = json['data']
+ OpenExcel.write_excel(model_name,data)
+ return jsonify("淇濆瓨瀹屾瘯")
+
+#鏈堟姤鍐呭
+@app.route('/xs_month_report', methods=['GET'])
+def xs_month_report():
+ model_name = request.args.get('model_name')
+ per = int(request.args.get('period'))
+ res = achiveReport.archive_report_content(model_name,per)
+ return res
+
+
+#瀹炴椂鏁版嵁
+@app.route('/xs_real_data', methods=['GET'])
+def xs_real_data():
+ num = request.args.get('num')
+ start_time = request.args.get('start_time')
+ end_time = request.args.get('end_time')
+ types = request.args.get('types')
+ res = DataTask.get_data(types,num,start_time,end_time)
+ return jsonify(res)
+
+
if __name__ == '__main__':
#app.run() # 鍙互鎸囧畾杩愯鐨勪富鏈篒P鍦板潃锛岀鍙ｏ紝鏄惁寮�鍚皟璇曟ā寮�
- app.run(host="192.168.0.122", port=5000)
-
-
-
+ app.run(host="localhost", port=5000)
+
+
--
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