# -*- coding: utf-8 -*-
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"""
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Created on Wed Mar 20 14:44:36 2024
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@author: ZMK
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"""
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import numpy as np
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import flopy
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from openpyxl import load_workbook
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import os
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import Base as base
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import CalHead
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#河流的分段下标
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riv_seg={0:[454,479],1:[480,505],2:[506,527],3:[528,562]}
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riv_seg_celles=[26,26,22,35]
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#边界的分段下标
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ibound_seg={0:[1,86],1:[87,111],2:[112,142],3:[143,170],4:[171,240],
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5:[241,282],6:[283,354],7:[355,393],8:[394,436],9:[437,453]}
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def base_excel(model_name,types):
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if types =='降雨':
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return rch_excel(model_name)
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if types =='河流':
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return river_excel(model_name)
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if types =='开采量':
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return well_excel(model_name)
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if types =='边界':
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return ibound_excel(model_name)
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return []
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#降雨cxcel
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def rch_excel(model_name):
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paths = base.model_dir + model_name +"\\降雨.xlsx"
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data=[]
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if not os.path.exists(paths):
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return data
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wb = load_workbook(filename = paths)
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ws = wb[wb.sheetnames[0]]
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for row in ws.iter_rows():
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tmp =[]
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for cell in row:
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tmp.append(cell.value)
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data.append(tmp)
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wb.close()
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#分析降水并且更新
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rch_analysis(data,model_name)
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return data
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def rch_analysis(data_array,model_name):
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periods =CalHead.get_model_period(model_name)
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#周期数
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periods_len= len(periods)
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array = np.asarray(data_array)
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fid= array[1:17,2]
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params = array[1:17,3]
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float_params = np.asarray(params,dtype=float)
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#取出来 1-16行 4-col_last列的数据
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col_last = 4 + periods_len
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data = array[1:17,4:col_last]
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float_data = np.asarray(data,dtype=float)
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for i in range(0,len(float_data)):
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for j in range(0,len(float_data[i])):
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float_data[i][j] = float_data[i][j] * float_params[i] /30/100
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rch_update(float_data,fid,model_name,periods_len)
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#更新模型rch数据
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def rch_update(float_data,fids,model_name,periods_len):
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if model_name == base.not_allowed_model or model_name in base.archive_models:
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return "本模型为验证模型,不允许修改！"
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model_ws = base.model_dir + model_name
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mx = flopy.modflow.Modflow.load("modflow.nam", model_ws = model_ws, exe_name="mf2005", verbose=True, version="mf2005", check=False)
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areas= base.getAreas()
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for per in range(periods_len):
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#rch 一个周期的数据
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item = mx.rch.rech.__getitem__(kper=per)
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array2d = item.get_value()
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per_data = float_data[:,per]
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print(per_data)
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for i in range(0,len(per_data)):
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data =round(float(per_data[i]),8)
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fid = fids[i]
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tuples = areas[fid]
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for entity in tuples:
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x = entity[0]
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y = entity[1]
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array2d[x][y]= data
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mx.rch.rech.__setitem__(key=per, value=array2d)
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rch = flopy.modflow.ModflowRch(mx,nrchop=mx.rch.nrchop,
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ipakcb=mx.rch.ipakcb,
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rech=mx.rch.rech,
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irch =mx.rch.irch)
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rch.write_file(check=False)
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#河流数据解析
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def river_excel(model_name):
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paths = base.model_dir + model_name +"\\河流.xlsx"
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data=[]
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if not os.path.exists(paths):
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return data
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wb = load_workbook(filename = paths)
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ws = wb[wb.sheetnames[0]]
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for row in ws.iter_rows():
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tmp =[]
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for cell in row:
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tmp.append(cell.value)
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data.append(tmp)
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wb.close()
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result =[]
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for i in range(1,len(data)):
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result.append(data[i])
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river_analysis(result,model_name)
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return result
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#分析河流数据
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def river_analysis(data_array,model_name):
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periods =CalHead.get_model_period(model_name)
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periods_len= len(periods)
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row_last= 1 + periods_len
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array = np.asarray(data_array)
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data = array[1:row_last,1:2]
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data2 = array[1:row_last,1:4]
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merge_array = np.concatenate((data,data2),axis=1)
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params = array[1:row_last,5:9]
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float_data = np.asarray(merge_array,dtype=float)
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float_params = np.asarray(params,dtype=float)
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for i in range(0,len(float_data)):
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for j in range(0,len(float_data[i])):
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temp = round(float_data[i][j] * float_params[i][j] *30*86400 ,4)
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last = round(temp/riv_seg_celles[j]/30,4)
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float_data[i][j]=last
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print(float_data)
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river_update(float_data,model_name,periods_len)
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#更新河流数据
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def river_update(float_data,model_name,periods_len):
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if model_name == base.not_allowed_model or model_name in base.archive_models:
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return "本模型为验证模型,不允许修改！"
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model_ws = base.model_dir + model_name
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ml = flopy.modflow.Modflow.load("modflow.nam", model_ws = model_ws, exe_name="mf2005", verbose=True, version="mf2005", check=False)
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#循环设置wel文件，更新数据
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# lrcq = {0:[[2, 3, 4, -100.]], 1:[[2, 3, 4, -100.]]}
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lrcq = {}
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for per in range(periods_len):
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wel = []
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wel = ml.wel.stress_period_data.__getitem__(kper=per)
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#河流分段数据
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segment0 = float_data[per][0]
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segment1 = float_data[per][1]
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segment2 = float_data[per][2]
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segment3 = float_data[per][3]
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#存储每个应力期的数据
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array2d = []
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count = 1
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for Layer, Row, Column, Q in wel:
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array = []
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if count>= riv_seg[0][0] and count <= riv_seg[0][1]:
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array = [Layer, Row, Column, segment0]
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elif count>= riv_seg[1][0] and count <= riv_seg[1][1]:
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array = [Layer, Row, Column, segment1]
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elif count>= riv_seg[2][0] and count <= riv_seg[2][1]:
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array = [Layer, Row, Column, segment2]
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elif count>= riv_seg[3][0] and count <= riv_seg[3][1]:
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array = [Layer, Row, Column, segment3]
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else:
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array = [Layer, Row, Column, Q]
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array2d.append(array)
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count +=1
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lrcq[per] = array2d
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flopy.modflow.ModflowWel(ml,ipakcb= ml.wel.ipakcb,
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dtype=ml.wel.dtype,
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options=ml.wel.options,
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stress_period_data=lrcq)
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ml.write_input()
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#解析开采量的数据
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def well_excel(model_name):
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paths = base.model_dir + model_name +"\\开采量.xlsx"
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data=[]
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if not os.path.exists(paths):
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return data
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wb = load_workbook(filename = paths)
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ws = wb[wb.sheetnames[0]]
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for row in ws.iter_rows():
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tmp =[]
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for cell in row:
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tmp.append(cell.value)
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data.append(tmp)
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wb.close()
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result =[]
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for i in range(1,len(data)):
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result.append(data[i])
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well_analysis(result,model_name)
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return result
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#开采量的分析
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def well_analysis(data_array,model_name):
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periods =CalHead.get_model_period(model_name)
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periods_len= len(periods)
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row_last= 1 + periods_len
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array = np.asarray(data_array)
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data = array[1:row_last,1:5]
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float_data= np.asarray(data,dtype=float)
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#转置数组
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zz_array = float_data.transpose()
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zz_data = []
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for i in range(50):
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zz_data.append(zz_array[0])
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for i in range(49):
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zz_data.append(zz_array[1])
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for i in range(18):
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zz_data.append(zz_array[2])
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for i in range(12):
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zz_data.append(zz_array[3])
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zz_data.append(zz_array[0])
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np_data = np.asarray(zz_data,dtype=float)
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well_scale = np.loadtxt(base.well_scale_path, dtype=str)
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float_scale= np.asarray(well_scale,dtype=float)
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##抽水量 = 各城区月开采量*街道比例*10000/(街道井数*30)
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for i in range(0,len(np_data)):
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for j in range(0,len(np_data[i])):
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tmp = np_data[i][j] * float_scale[i][0]*10000/( float_scale[i][1] *30)
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np_data[i][j] = round(tmp,4)
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well_update(np_data,model_name,periods_len)
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#更新开采量
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def well_update(np_data,model_name,periods_len):
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if model_name == base.not_allowed_model or model_name in base.archive_models:
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return "本模型为验证模型,不允许修改！"
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model_ws = base.model_dir + model_name
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ml = flopy.modflow.Modflow.load("modflow.nam", model_ws = model_ws, exe_name="mf2005", verbose=True, version="mf2005", check=False)
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#循环设置wel文件，更新数据
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# lrcq = {0:[[2, 3, 4, -100.]], 1:[[2, 3, 4, -100.]]}
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lrcq = {}
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for per in range(periods_len):
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wel = []
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wel = ml.wel.stress_period_data.__getitem__(kper=per)
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per_data = np_data[:,per]
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#存储每个应力期的数据
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array2d = []
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#well文件中 前562个网格不是井
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# Layer= wel[i][0]
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# Row= wel[i][1]
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# Col = wel[i][2]
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# Q = wel[i][3]
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for i in range(0,562):
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array = [wel[i][0],wel[i][1], wel[i][2],wel[i][3]]
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array2d.append(array)
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for i in range(562,len(wel)):
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indexid = i-562
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update_data=per_data[indexid]
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array = [wel[i][0],wel[i][1], wel[i][2],update_data]
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array2d.append(array)
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lrcq[per] = array2d
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flopy.modflow.ModflowWel(ml,ipakcb= ml.wel.ipakcb,dtype=ml.wel.dtype,
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options=ml.wel.options,
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stress_period_data=lrcq)
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ml.write_input()
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#解析边界的数据
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def ibound_excel(model_name):
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paths = base.model_dir + model_name +"\\边界.xlsx"
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data=[]
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if not os.path.exists(paths):
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return data
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wb = load_workbook(filename = paths)
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ws = wb[wb.sheetnames[0]]
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for row in ws.iter_rows():
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tmp =[]
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for cell in row:
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tmp.append(cell.value)
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data.append(tmp)
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wb.close()
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result =[]
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for i in range(1,len(data)):
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result.append(data[i])
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np_array = ibound_analysis(result,model_name)
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#返回页面展示的内容数组
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view_data = ibound_view_data(np_array)
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periods =CalHead.get_model_period(model_name)
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periods_len= len(periods)
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ibound_update(np_array,model_name,periods_len)
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return view_data
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#更新边界
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def ibound_update(np_array,model_name,periods_len):
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if model_name == base.not_allowed_model or model_name in base.archive_models:
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return "本模型为验证模型,不允许修改！"
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model_ws = base.model_dir + model_name
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ml = flopy.modflow.Modflow.load("modflow.nam", model_ws = model_ws, exe_name="mf2005", verbose=True, version="mf2005", check=False)
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#循环设置wel文件，更新数据
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# lrcq = {0:[[2, 3, 4, -100.]], 1:[[2, 3, 4, -100.]]}
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lrcq = {}
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for per in range(periods_len):
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wel = []
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wel = ml.wel.stress_period_data.__getitem__(kper=per)
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per_data = np_array[:,per]
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#存储每个应力期的数据
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array2d = []
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count = 1
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for Layer, Row, Column, Q in wel:
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array = []
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if count>= ibound_seg[0][0] and count <= ibound_seg[0][1]:
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array = [Layer, Row, Column, per_data[0]]
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elif count>= ibound_seg[1][0] and count <= ibound_seg[1][1]:
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array = [Layer, Row, Column, per_data[1]]
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elif count>= ibound_seg[2][0] and count <= ibound_seg[2][1]:
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array = [Layer, Row, Column, per_data[2]]
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elif count>= ibound_seg[3][0] and count <= ibound_seg[3][1]:
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array = [Layer, Row, Column, per_data[3]]
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elif count>= ibound_seg[4][0] and count <= ibound_seg[4][1]:
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array = [Layer, Row, Column, per_data[4]]
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elif count>= ibound_seg[5][0] and count <= ibound_seg[5][1]:
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array = [Layer, Row, Column, per_data[5]]
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elif count>= ibound_seg[6][0] and count <= ibound_seg[6][1]:
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array = [Layer, Row, Column, per_data[6]]
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elif count>= ibound_seg[7][0] and count <= ibound_seg[7][1]:
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array = [Layer, Row, Column, per_data[7]]
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elif count>= ibound_seg[8][0] and count <= ibound_seg[8][1]:
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array = [Layer, Row, Column, per_data[8]]
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elif count>= ibound_seg[9][0] and count <= ibound_seg[9][1]:
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array = [Layer, Row, Column, per_data[9]]
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else:
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array = [Layer, Row, Column, Q]
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array2d.append(array)
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count +=1
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lrcq[per] = array2d
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flopy.modflow.ModflowWel(ml,ipakcb= ml.wel.ipakcb,
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dtype=ml.wel.dtype,
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options=ml.wel.options,
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stress_period_data=lrcq)
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ml.write_input()
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#边界的分析
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def ibound_analysis(data_array,model_name):
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array = np.asarray(data_array)
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data = array[1:10,2:14]
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#大台 南辛房 上苇甸 三家店 麻裕 温泉 海淀 阳坊 沙河
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float_data= np.asarray(data,dtype=float)
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# 流入1-1 (1-6)大台 南辛房 上苇甸 三家店 麻裕 温泉
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result =[]
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data1= float_data[[0,1,2,3,4,5]]
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sum1 = data1.sum(axis = 0)
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result.append(sum1)
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# 流入1-2()上苇甸
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data2= float_data[2]
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result.append(data2)
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#流入1-3() 大台
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data3 = float_data[0]
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result.append(data3)
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# 流出1-1 大台 南辛房
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data4 = float_data[[0,1]]
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sum4 = data4.sum(axis = 0)
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result.append((-sum4))
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# 流出1-2 大台
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data5 = float_data[0]
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result.append((-data5))
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#流入3-1 大台
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data6 = float_data[0]
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result.append(data6)
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# 流入3-2 大台 南辛房 上苇甸
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data7 = float_data[[0,1,2]]
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sum7 = data7.sum(axis = 0)
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result.append(sum7)
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# 流入3-3 大台 南辛房 上苇甸
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# data8= float_data[[0,1,2]]
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result.append(sum7)
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# 流入3-4 大台 南辛房 上苇甸
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# data9= float_data[[0,1,2]]
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result.append(sum7)
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# 流入3-5 大台 南辛房 上苇甸
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# data10= float_data[[0,1,2]]
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result.append(sum7)
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np_data = np.asarray(result,dtype=float)
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np_data = np.around(np_data, decimals=2)
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return np_data
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def ibound_view_data(np_data):
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names=['流入1-1','流入1-2','流入1-3','流出1-1','流出1-2',
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'流入3-1','流入3-2','流入3-3','流入3-4','流入3-5']
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row_sums = np.sum(np_data, axis=1)
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row_sums= np.around(row_sums,2)
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params=[1,1,1,2.5,1,0.5,0.5,0.5,0.5,0.5]
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cells=[86,47,31,28,50,42,72,39,43,17]
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per=12
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x=30
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water=[]
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for i in range(0,len(names)):
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tmp = round( row_sums[i] * params[i] * cells[i] * per *x , 2)
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water.append(tmp)
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arr=[]
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arr.append(names)
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arr.append(row_sums)
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arr.append(params)
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arr.append(water)
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str_np = np.asarray(arr,dtype=str)
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zz= str_np.transpose()
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title =['边界','降雨量','系数','输入值']
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result =[]
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result.append(title)
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for item in zz:
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result.append(item)
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result = np.asarray(result,dtype=str).tolist()
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return result
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