# 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 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 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') 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 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') 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: try: updateDisFile(model_name,predict_per) updateBase6File(model_name,predictiondata) updateRchFile(model_name,predictiondata) updateRiverFile(model_name,predictiondata) except: return "请检查初始水头、降水量、永定河入渗量、开采量等参数是否填写完整！" 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 "预测模型运行成功！" #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 updateRiverFile(model_name,predictiondata): flag = check_rain_param(predictiondata) if flag == "true": rain_ratio = float(predictiondata["rain"]["ratio"]) rain_base_year = predictiondata["rain"]["base_year"] river_ratio= float(predictiondata["river"]["ratio"]) area= predictiondata["mine"]["area"] 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 = [] array2d = [] 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 #重置数组 for Layer, Row, Column, Q in wel: array = [Layer, Row, Column, Q] array2d.append(array) flex_data= getFlexdata(model_name) for i in range(len(flex_data)): array2d.append(flex_data[i]) lrcq[per] = array2d flopy.modflow.ModflowWel(updateMdoel,stress_period_data=lrcq) updateMdoel.write_input() else: print("Well--River文件无需修改！") #追加复杂原汇项信息 def getFlexdata(model_name): welldata="" well_path = base.model_dir + model_name +"\\pump_well.json" data=[] if os.path.exists(well_path): with open(well_path,encoding='utf-8') as f: welldata = json.load(f) wel= welldata["well"] for i in range (len(wel)): layer = int (wel[i]['layer'])-1 row= int(wel[i]['row'])-1 column = int(wel[i]['column'])-1 v = float(wel[i]['value']) arr = [layer,row, column, v] data.append(arr) return data def updateRchFile(model_name,predictiondata): flag = check_rain_param(predictiondata) if flag == "true": #丰水年 枯水年 base_year = predictiondata["rain"]["base_year"] ratio= float(predictiondata["rain"]["ratio"]) #数据来源的模型文件夹 base_ws= base.predictParamModel + base_year 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 : 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)