# 导入Flask类 from flask import Flask 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 ModelPeriod # strt = ml.bas6.strt # # strs = ml.bas6.strt.__getitem__(1) # # print(strs.get_value()) # mdBase = flopy.modflow.ModflowBas(ml,strt=1.0,ibound=ml.bas6.ibound) # mdBase.write_file(check=False) base_init_year=["2020","2021","2022"] river_start_index = 454 river_end_index =562 #预测周期数 predict_per = 12 #降水量 # def predict_water_chart(base_year,start_time ,end_time): # model_ws = base.baseModel # baseMdoel = flopy.modflow.Modflow.load("modflow.nam", model_ws=model_ws, # exe_name="mf2005", verbose=True, version="mf2005", check=False) # index = 0 # if base_year in base_init_year: # index = base_init_year.index(str(base_year)) # y_data=[] # x_data=[] # satrt_index = index*12 # end_index = satrt_index+12 # for per in range(satrt_index,end_index): # item = baseMdoel.rch.rech.__getitem__(kper=per) # value = item.get_value() # value_float = np.array(value) # avg = value_float.mean() # y_data.append(float (avg)) # start_month = str(base_year) +"-01" # end_month = str(base_year) +"-12" # x_data= ModelPeriod.get_months_in_range_ym(start_month,end_month) # result = {"y_data": y_data, "x_data": x_data} # return result base_water = base.prefix + 'base_water.ini' def predict_water_chart(base_year,start_time ,end_time): water_array = np.loadtxt(base_water, dtype=str,encoding='utf-8') print(water_array) y_data=[] x_data= ModelPeriod.get_months_in_range_ym("2022-01","2022-12") water= water_array[0] for e in water: y_data.append(e) result = {"y_data": y_data, "x_data": x_data} return result #河流的折线图 # def predict_river_chart(base_year,start_time ,end_time): # model_ws = base.baseModel # baseMdoel = flopy.modflow.Modflow.load("modflow.nam", model_ws=model_ws, # exe_name="mf2005", verbose=True, version="mf2005", check=False) # index = 0 # if base_year in base_init_year: # index = base_init_year.index(str(base_year)) # y_data=[] # x_data=[] # satrt_index = index*12 # end_index = satrt_index+12 # for per in range(satrt_index,end_index): # wel = baseMdoel.wel.stress_period_data.__getitem__(kper=per) # arr=[] # for i in range(river_start_index, river_end_index): # Q = wel[i][3] # arr.append(float(Q)) # avg = np.array(arr).mean() # y_data.append(float(avg)) # start_month = str(base_year) +"-01" # end_month = str(base_year) +"-12" # x_data= ModelPeriod.get_months_in_range_ym(start_month,end_month) # result = {"y_data": y_data, "x_data": x_data} # return result base_river = base.prefix + 'base_river.ini' def predict_river_chart(base_year,start_time ,end_time): river_array = np.loadtxt(base_river, dtype=str,encoding='utf-8') print(river_array) y_data=[] x_data= ModelPeriod.get_months_in_range_ym("2022-01","2022-12") for e in river_array: y_data.append(e) result = {"y_data": y_data, "x_data": x_data} return result def run_model_predict(model_name): predictiondata="" prediction_path = base.model_dir + model_name +"\\prediction.json" if os.path.exists(prediction_path): with open(prediction_path,encoding='utf-8') as f: predictiondata = json.load(f) if predictiondata: per = ModelPeriod.get_months_in_range_count( predictiondata["start_time"], predictiondata["end_time"]) # updateDisFile(model_name,per) # updateBase6File(model_name,predictiondata) #updateRchFile(model_name,predictiondata) updateRiverFile(model_name,predictiondata) #updateMineFile(model_name,predictiondata) else: print("prediction.json 预测场景文件为空，无需更改相应文件") # model_ws = base.model_dir + model_name # ml = flopy.modflow.Modflow.load("modflow.nam", model_ws=model_ws, # exe_name="mf2005", verbose=True, version="mf2005", check=False) # ml.run_model(report = True) return jsonify("运行成功！") #更新采集区的数据，分为1.按照区域，2.按照全部 进行更新 def updateMineFile(model_name,predictiondata): start_time =predictiondata["start_time"] end_time = predictiondata["end_time"] base_year = predictiondata["mine"]["base_year"] base_start= str(base_year) + "-" + str(start_time.split("-")[1]) base_end= str(base_year) + "-" + str(end_time.split("-")[1]) start_index = (int)(base.times_month_per_dict[base_start]) end_index = (int)(base.times_month_per_dict[base_end]) pers= end_index-start_index + 1 area= predictiondata["mine"]["area"] flag = check_mine_param(predictiondata) if flag == 'true': baseMdoel = flopy.modflow.Modflow.load("modflow.nam", model_ws= base.baseModel, exe_name="mf2005", verbose=True, version="mf2005", check=False) update_model_ws = base.model_dir + model_name updateMdoel = flopy.modflow.Modflow.load("modflow.nam", model_ws= update_model_ws, exe_name="mf2005", verbose=True, version="mf2005", check=False) district_dict = get_distric_dict() area_dict = get_area_dict(area) lrcq = {} for per in range(pers): wel = [] wel = baseMdoel.wel.stress_period_data.__getitem__(kper = (per + start_index )) array2d = [] count = 1 for Layer, Row, Column, Q in wel: array = [] # 如果是河流的数据范围 if count > river_end_index : r = (float) (get_row_column_ratio(Row, Column, district_dict, area_dict)) array = [Layer, Row, Column, Q * r] else: array = [Layer, Row, Column, Q] array2d.append(array) count +=1 lrcq[per] = array2d flopy.modflow.ModflowWel(updateMdoel,stress_period_data=lrcq) updateMdoel.write_input() else: print("Well--Mine文件无需修改！") #获取 area的 name--> ratio 的结构 def get_area_dict(area): result ={} for i in range(len(area)): name = area[i]["name"] rt = area[i]["ratio"] result[name]= rt return result #获取区县的 row+column --> name结构 def get_distric_dict(): data = base.district result = {} for row ,column ,id ,name in data: key = str(row)+","+str(column) result[key]= name return result #根据 row clomn 获取 ratio def get_row_column_ratio(row, column ,district_dict, area_dict ): key = str(row) +"," + str(column) if area_dict.__contains__("全部区域"): return area_dict["全部区域"] if district_dict.__contains__(key): name = district_dict[key] ratio = area_dict[name] return float(ratio) return float(1.0) def check_mine_param(predictiondata): mine = predictiondata["mine"] if not mine: print("抽水井 预测参数为空，无需要修改") return "false" base_year = predictiondata["mine"]["base_year"] if not base_year : print(" Mine : base_year预测参数为空，无需要修改") return "false" area= predictiondata["mine"]["area"] if not area : print(" Mine : area预测参数为空，无需要修改") return "false" return "true" #更新河流的倍数 # def updateRiverFile(model_name,predictiondata): # start_time =predictiondata["start_time"] # end_time = predictiondata["end_time"] # base_year = predictiondata["river"]["base_year"] # ratio= float(predictiondata["river"]["ratio"]) # base_start= str(base_year) + "-" + str(start_time.split("-")[1]) # base_end= str(base_year) + "-" + str(end_time.split("-")[1]) # start_index = (int)(base.times_month_per_dict[base_start]) # end_index = (int)(base.times_month_per_dict[base_end]) # pers= end_index-start_index + 1 # flag = check_river_param(predictiondata) # if flag == "true": # baseMdoel = flopy.modflow.Modflow.load("modflow.nam", model_ws= base.baseModel, # exe_name="mf2005", verbose=True, version="mf2005", check=False) # update_model_ws = base.model_dir + model_name # updateMdoel = flopy.modflow.Modflow.load("modflow.nam", model_ws= update_model_ws, # exe_name="mf2005", verbose=True, version="mf2005", check=False) # lrcq = {} # for per in range(pers): # wel = [] # wel = baseMdoel.wel.stress_period_data.__getitem__(kper = (per + start_index )) # array2d = [] # count = 1 # for Layer, Row, Column, Q in wel: # array = [] # # 如果是河流的数据范围 # if count > river_start_index and count <= river_end_index: # array = [Layer, Row, Column, Q * ratio] # else: # array = [Layer, Row, Column, Q] # array2d.append(array) # count +=1 # lrcq[per] = array2d # flopy.modflow.ModflowWel(updateMdoel,stress_period_data=lrcq) # updateMdoel.write_input() # else: # print("Well--River文件无需修改！") def updateRiverFile(model_name,predictiondata): start_time =predictiondata["start_time"] end_time = predictiondata["end_time"] river_ratio= float(predictiondata["river"]["ratio"]) rain_ratio = float(predictiondata["rain"]["ratio"]) rain_base_year = predictiondata["rain"]["base_year"] area= predictiondata["mine"]["area"] flag = check_river_param(predictiondata) if flag == "true": ws = base.predictParamModel + rain_base_year baseMdoel = flopy.modflow.Modflow.load("modflow.nam", model_ws= ws, exe_name="mf2005", verbose=True, version="mf2005", check=False) update_model_ws = base.model_dir + model_name updateMdoel = flopy.modflow.Modflow.load("modflow.nam", model_ws= update_model_ws, exe_name="mf2005", verbose=True, version="mf2005", check=False) district_dict = get_distric_dict() area_dict = get_area_dict(area) lrcq = {} for per in range(predict_per): wel = [] wel = baseMdoel.wel.stress_period_data.__getitem__(kper = per) wel_len = len(wel) #侧向边界 for i in range (0,453): wel[i][3] = wel[i][3] * rain_ratio #河流 for i in range(453, 562): wel[i][3] = wel[i][3] * river_ratio #抽水井 for i in range(562,wel_len): r = (float) (get_row_column_ratio(wel[i][1], wel[i][2], district_dict, area_dict)) wel[i][3] = wel[i][3] * r lrcq[per] = wel flopy.modflow.ModflowWel(updateMdoel,stress_period_data=lrcq) updateMdoel.write_input() else: print("Well--River文件无需修改！") def check_river_param(predictiondata): river = predictiondata["river"] if not river: print("River预测参数为空，无需要修改") return "false" base_year = predictiondata["river"]["base_year"] if not base_year : print(" River : base_year预测参数为空，无需要修改") return "false" ratio= predictiondata["river"]["ratio"] if not ratio or ratio == "1" : print(" River : ratio预测参数为空，无需要修改") return "false" return "true" # def updateRchFile(model_name,predictiondata): # start_time =predictiondata["start_time"] # end_time = predictiondata["end_time"] # base_year = predictiondata["rain"]["base_year"] # ratio= float(predictiondata["rain"]["ratio"]) # base_start= str(base_year) + "-" + str(start_time.split("-")[1]) # base_end= str(base_year) + "-" + str(end_time.split("-")[1]) # start_index = (int)(base.times_month_per_dict[base_start]) # end_index = (int)(base.times_month_per_dict[base_end]) # pers= end_index-start_index + 1 # flag = check_rain_param(predictiondata) # if flag == "true": # baseMdoel = flopy.modflow.Modflow.load("modflow.nam", model_ws= base.baseModel, # exe_name="mf2005", verbose=True, version="mf2005", check=False) # update_model_ws = base.model_dir + model_name # updateMdoel = flopy.modflow.Modflow.load("modflow.nam", model_ws= update_model_ws, # exe_name="mf2005", verbose=True, version="mf2005", check=False) # for per in range(pers): # item = baseMdoel.rch.rech.__getitem__(kper = (per + start_index)) # array2d = item.get_value() # array2d_len = len(array2d) # for i in range(array2d_len): # array_len = len(array2d[i]) # for j in range(array_len): # if str(base.area_array[i][j]) != '-9999': # array2d[i][j] = array2d[i][j] * ratio # updateMdoel.rch.rech.__setitem__(key = per, value=array2d) # rch = flopy.modflow.ModflowRch(updateMdoel, rech=updateMdoel.rch.rech) # rch.write_file(check=False) # else: # print("Rch文件无需修改！") def updateRchFile(model_name,predictiondata): start_time =predictiondata["start_time"] end_time = predictiondata["end_time"] #丰水年 枯水年 base_year = predictiondata["rain"]["base_year"] ratio= float(predictiondata["rain"]["ratio"]) flag = check_rain_param(predictiondata) #数据来源的模型文件夹 base_ws= base.predictParamModel + base_year if flag == "true": baseMdoel = flopy.modflow.Modflow.load("modflow.nam", model_ws= base_ws, exe_name="mf2005", verbose=True, version="mf2005", check=False) update_model_ws = base.model_dir + model_name updateMdoel = flopy.modflow.Modflow.load("modflow.nam", model_ws= update_model_ws, exe_name="mf2005", verbose=True, version="mf2005", check=False) for per in range(predict_per): item = baseMdoel.rch.rech.__getitem__(kper = per) array2d = item.get_value() array2d_len = len(array2d) for i in range(array2d_len): array_len = len(array2d[i]) for j in range(array_len): if str(base.area_array[i][j]) != '-9999': array2d[i][j] = array2d[i][j] * ratio updateMdoel.rch.rech.__setitem__(key = per, value=array2d) rch = flopy.modflow.ModflowRch(updateMdoel, rech=updateMdoel.rch.rech) rch.write_file(check=False) else: print("Rch文件无需修改！") def check_rain_param(predictiondata): rain = predictiondata["rain"] if not rain: print("Rch预测参数为空，无需要修改") return "false" base_year = predictiondata["rain"]["base_year"] if not base_year : print(" Rch : base_year预测参数为空，无需要修改") return "false" ratio= predictiondata["rain"]["ratio"] if not ratio or ratio == "1" : print(" Rch : ratio预测参数为空，无需要修改") return "false" return "true" #更新bas6文件 初始水头信息 def updateBase6File(model_name,predictdata): model_ws = base.model_dir + model_name ml = flopy.modflow.Modflow.load("modflow.nam", model_ws=model_ws, exe_name="mf2005", verbose=True, version="mf2005", check=False) #初始水头 init_header = predictdata["initHeader"] dir = base.model_dir + init_header + "\\modflow.head" head = bf.HeadFile(dir) alldata = head.get_alldata() lens = len(alldata) last_index = lens-3 last_array3= alldata[last_index] strt = ml.bas6.strt # strs = ml.bas6.strt.__getitem__(2) # print(strs.get_value()) strt.__setitem__(0,last_array3[0]) strt.__setitem__(1,last_array3[1]) strt.__setitem__(2,last_array3[2]) mfBase6 = flopy.modflow.ModflowBas( ml, strt= strt, ibound=ml.bas6.ibound, hnoflo=ml.bas6.hnoflo, extension="bas6",) mfBase6.write_file(check=False) #修改dis 文件 def updateDisFile(model_name, per): model_ws = base.model_dir + model_name ml = flopy.modflow.Modflow.load("modflow.nam", model_ws=model_ws, exe_name="mf2005", verbose=True, version="mf2005", check=False) mfDis = flopy.modflow.ModflowDis( ml, nlay=ml.dis.nlay, nrow=ml.dis.nrow, ncol=ml.dis.ncol, nper=per, delr=ml.dis.delr, delc=ml.dis.delc, top=ml.dis.top, botm=ml.dis.botm, perlen=ml.dis.perlen, nstp=ml.dis.nstp, tsmult=ml.dis.tsmult, steady=ml.dis.steady, itmuni=ml.dis.itmuni, lenuni=ml.dis.lenuni, extension="dis") mfDis.write_file(check=False)