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zmk

2024-08-28 1ae38ab34bcbdff622c7623119ee54bad419f4ed

 Predict.py

@@ -1,23 +1,14 @@

# 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
import OpenExcel


base_init_year=["2020","2021","2022"]
@@ -30,16 +21,15 @@
#降水量

base_water = base.prefix + 'base_water.ini'
def predict_water_chart(base_year,start_time ,end_time):
    
    
def predict_water_chart(base_year,start_time ,end_time,value):   
     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)
     x_data= ModelPeriod.get_months_in_year()
     water= water_array[int(base_year)]
     for data in water:
         float_data = float(data)/9
         float_data= round(float_data*value,2)
         y_data.append(float_data)
    
     result = {"y_data": y_data, "x_data": x_data}
     return result
@@ -47,43 +37,104 @@
#河流的折线图

base_river = base.prefix + 'base_river.ini'
def predict_river_chart(base_year,start_time ,end_time):
     
    
def predict_river_chart(base_year,start_time ,end_time,value):
     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)
     x_data= ModelPeriod.get_months_in_year()
     for data in river_array:
         #万立方米
         float_data = float(data)/4/10000
         float_data= round(float_data*value,2)
         y_data.append(float_data)
         
     result = {"y_data": y_data, "x_data": x_data}
     return result


#开采量折线图
base_mining = base.prefix + 'base_mining.ini'
def predict_well_chart(base_year,start_time ,end_time,data):
     #   2022/海淀/昌平/门头沟/石景山
     area_names=["全部区域","海淀区","昌平区","门头沟区","石景山区"]
     mining_array = np.loadtxt(base_mining, dtype=str,encoding='utf-8')
     x_data= ModelPeriod.get_months_in_year()
     result = {"x_data": x_data}   
     y_data=[]
     for item in data:
         name = item["area_name"]
         value = float(item["value"]) 
         if name =='朝阳区':
             continue
         index = area_names.index(name)   
         row_data = mining_array[index]
         float_data=[]
         for item in row_data:
             x = round(float(item)/10000*value,2)
             float_data.append(x)
         dicts={"name":name,"data":float_data}
         y_data.append(dicts)
     result["y_data"] = y_data
     return result
 
    
def run_model(model_name):
    if model_name == base.not_allowed_model or model_name in base.archive_models:
        return "本模型为验证模型,不允许修改！"
    
    dicts= run_model_predict(model_name)
    if dicts["code"] == 400:
        return dicts
    #导出csv文件
    CalHead.exportCsV(model_name)
    
    # #更新模型三维网格配置
    base.updateModelConfig(model_name)
    
    # #创建模型的三维网格
    filedir = base.model3d_path + model_name
    
    if not os.path.exists(filedir):
        os.makedirs(filedir, exist_ok=True)
        
    base.callModelexe() 
    #计算水资源量和水均衡
    CalHead.run_zonebudget_bal(model_name)
    CalHead.run_zonebudget_res(model_name)
    dicts= {"code":200,"msg":"预测模型运行成功！" } 
    return dicts


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)
              predictiondata = json.load(f)
    
    if predictiondata:
                    
    periods =CalHead.get_model_period(model_name)
    periods_len= len(periods)
    
    if predictiondata:      
        updateDisFile(model_name,periods_len)         
        try:
            updateDisFile(model_name,predict_per)
            
            updateBase6File(model_name,predictiondata)
           flag =  updateBase6File(model_name,predictiondata)
           if bool(flag)==False:
                dicts= {"code":400,"msg":"请检查模型的初始水是否设置！" } 
                return  dicts             
        except:      
             dicts= {"code":400,"msg":"请检查模型的初始水是否设置！" } 
             return  dicts       
 
            updateRchFile(model_name,predictiondata)
        try:
            updateRchFile(model_name,predictiondata)   
        except:      
            print("RchFile无预测参数，无需修改！")
            
        try:
            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！"
            
            
        except:      
            print("RiverFile无预测参数，无需修改！")    
    else:
        print("prediction.json 预测场景文件为空，无需更改相应文件")
    
@@ -93,7 +144,8 @@
    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！"
    dicts= {"code":200,"msg":"预测模型运行成功！" } 
    return dicts


    
@@ -142,11 +194,21 @@
        
        rain_ratio = float(predictiondata["rain"]["ratio"])
        rain_base_year = predictiondata["rain"]["base_year"]
        if rain_base_year=='4':
            rain_base_year="1"
        
        river_ratio= float(predictiondata["river"]["ratio"])
        area= predictiondata["mine"]["area"] 
        
        ws = base.predictParamModel + rain_base_year
        # ws = base.predictParamModel + rain_base_year
        ws=""
        start_time = predictiondata["start_time"]
        end_time =  predictiondata["end_time"]
        count = ModelPeriod.get_months_in_range_count(start_time, end_time)
        if count==12:
             ws=  base.predictParamModel + rain_base_year     
        else:
             ws=  base.predictModel60  
        
        baseMdoel = flopy.modflow.Modflow.load("modflow.nam", model_ws= ws,
                                    exe_name="mf2005", verbose=True,  version="mf2005", check=False)
@@ -161,7 +223,10 @@
        
        lrcq = {}
        
        for per in range(predict_per):
        periods =CalHead.get_model_period(model_name)
        periods_len= len(periods)
        
        for per in range(periods_len):
            wel = [] 
            array2d = [] 
            
@@ -188,14 +253,21 @@
                array = [Layer, Row, Column, Q]
                array2d.append(array)
                
            #追加井
            flex_data= getFlexdata(model_name)
            print("==============")
            print(flex_data)
    
            for i in range(len(flex_data)):
                array2d.append(flex_data[i])
          
            lrcq[per] = array2d 
            
        flopy.modflow.ModflowWel(updateMdoel,stress_period_data=lrcq)
        flopy.modflow.ModflowWel(updateMdoel,
                                 ipakcb= baseMdoel.wel.ipakcb,
                                 dtype=baseMdoel.wel.dtype,
                                 options=baseMdoel.wel.options,
                                 stress_period_data=lrcq)
        updateMdoel.write_input()          
                               
    else:     
@@ -227,11 +299,30 @@
    if flag == "true":
         #丰水年 枯水年
        base_year = predictiondata["rain"]["base_year"]  
        ratio= float(predictiondata["rain"]["ratio"])
        if base_year =="1" or base_year =="2" or base_year =="3":
            updateRchBaseYear(model_name,predictiondata)
        elif  base_year =="4":
            #接入传感器数据
            updateRchRealData(model_name,predictiondata)
        
    else:   
        print("Rch文件无需修改！")

#更新平水年 丰水年 枯水年数据
def updateRchBaseYear(model_name,predictiondata):
      #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
        
        base_ws=""
        start_time = predictiondata["start_time"]
        end_time =  predictiondata["end_time"]
        count = ModelPeriod.get_months_in_range_count(start_time, end_time)
        if count==12:
             base_ws=  base.predictParamModel + base_year     
        else:
             base_ws=  base.predictModel60    
            
        baseMdoel = flopy.modflow.Modflow.load("modflow.nam", model_ws= base_ws,
                                    exe_name="mf2005", verbose=True,  version="mf2005", check=False)
        
@@ -239,28 +330,107 @@
        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):
            
           
        periods =CalHead.get_model_period(model_name)
        periods_len= len(periods)
        for per in range(periods_len):       
            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':
                    
                  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)
            updateMdoel.rch.rech.__setitem__(key = per, value=array2d)         
        rch = flopy.modflow.ModflowRch(updateMdoel,
                                       nrchop=baseMdoel.rch.nrchop,
                                       ipakcb=baseMdoel.rch.ipakcb,
                                       rech=updateMdoel.rch.rech,
                                       irch =baseMdoel.rch.irch)
         
        rch.write_file(check=False)    

    
#接入传感器数据
def updateRchRealData(model_name,predictiondata):
    
       # 取平水年的rch文件
        base_year = "1"  
        ratio= float(predictiondata["rain"]["ratio"])   
        if not ratio:
            ratio = 1
        excel_data = OpenExcel.read_jyl_excel(model_name) 
        array_data =[]
        
    else:   
        print("Rch文件无需修改！")      
        if not excel_data:
            print("降水模板数据未获取到！")
            return "降水模板数据未获取到！" 
        for i in range(1,len(excel_data)):
            temp =[]
            for j in range(1,len(excel_data[i])):
                data = round(float(excel_data[i][j]),8) 
                temp.append(data)
            array_data.append(temp)
        
        base_ws=""
        start_time = predictiondata["start_time"]
        end_time =  predictiondata["end_time"]
        count = ModelPeriod.get_months_in_range_count(start_time, end_time)
        if count==12:
             base_ws=  base.predictParamModel + base_year     
        else:
             base_ws=  base.predictModel60      
   
        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)
        
        #降雨分区
        area_dicts = base.getAreas()
        
        
        periods =CalHead.get_model_period(model_name)
        periods_len= len(periods)
        
        for per in range(periods_len):   
            #16个分区某个周期的 数据 下标(0,15) 和area_dicts字典降雨分区的key对应
            water_per_data = get_Array2_column(array_data,per)   

            item = baseMdoel.rch.rech.__getitem__(kper = per)
            array2d = item.get_value()
            for key in area_dicts:  
                tuples= area_dicts[key]
                values = water_per_data[int(key)] 
                for i in range(len(tuples)):
                    x = tuples[i][0]
                    y = tuples[i][1]
                    array2d[x][y]= values*ratio
            
            updateMdoel.rch.rech.__setitem__(key = per, value=array2d)   
            
        rch = flopy.modflow.ModflowRch(updateMdoel,
                                       nrchop=baseMdoel.rch.nrchop,
                                       ipakcb=baseMdoel.rch.ipakcb,
                                       rech=updateMdoel.rch.rech,
                                       irch =baseMdoel.rch.irch)
         
        rch.write_file(check=False) 
        print("降水分区数据更新完毕!")
        return "降水分区数据更新完毕！" 

#获取列数据
def get_Array2_column(array_data,column):
    arr = np.array(array_data)
    column_data = arr[:, column]
    return column_data
    
        
def check_rain_param(predictiondata):
    
@@ -287,18 +457,18 @@
     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"
     if "initHeader" not in predictdata:
         print("=============has no  initHeader=============")
         return False
     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_index = len(alldata)-1    
     last_array3= alldata[last_index]

     strt = ml.bas6.strt
@@ -317,6 +487,7 @@
          extension="bas6",)
     
     mfBase6.write_file(check=False)
     return True


#修改dis 文件
@@ -345,3 +516,5 @@
        extension="dis")

    mfDis.write_file(check=False)