# -*- coding: utf-8 -*-
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"""
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Created on Tue Oct 31 16:12:55 2023
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@author: ZMK
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"""
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import flopy.utils.binaryfile as bf
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import csv
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import Base as base
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import os
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import json
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import subprocess
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import re
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import ModelPeriod
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import numpy as np
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import AchiveReport
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#添加modle
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def addModelJson(model_name,start_time,end_time,remark):
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context=""
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prediction_path = base.prefix + "\\model_list.json"
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with open(prediction_path,encoding='utf-8') as f:
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context = json.load(f)
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array=[]
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for item in context:
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array.append(item)
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dicts={"model_name":model_name,
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"start_time":start_time,"end_time":end_time,"remark":remark}
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array.append(dicts)
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with open(prediction_path, "w",encoding='utf-8') as outfile:
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json.dump(array, outfile,ensure_ascii=False)
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return "保存完毕！";
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#删除model
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def removeModelJson(model_name):
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context=""
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prediction_path = base.prefix + "\\model_list.json"
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with open(prediction_path,encoding='utf-8') as f:
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context = json.load(f)
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array=[]
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for item in context:
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if item["model_name"] != model_name:
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array.append(item)
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with open(prediction_path, "w",encoding='utf-8') as outfile:
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json.dump(array, outfile,ensure_ascii=False)
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return "删除模型完毕！";
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def get_model_json(model_name):
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period_json=""
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prediction_path = base.model_dir + model_name +"\\prediction.json"
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with open(prediction_path,encoding='utf-8') as f:
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period_json = json.load(f)
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return period_json;
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def get_model_period(model_name):
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period_json= get_model_json(model_name)
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start_time = period_json["start_time"]
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end_time = period_json["end_time"]
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months = ModelPeriod.get_months_in_range_ym(start_time, end_time)
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return months;
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def is_cloned_model(model_name):
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paths = base.model_dir + model_name + "\\water_bal.txt"
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if os.path.exists(paths):
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return False
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return True
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#观测井chart
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def obsChartdata(wellId,model_name, row, column):
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row = int(row)-1
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column = int(column)-1
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dir=""
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period_json= get_model_json(model_name)
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start_time = period_json["start_time"]
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end_time = period_json["end_time"]
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xmonths = ModelPeriod.get_months_in_range_ym(start_time, end_time)
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if model_name == base.not_allowed_model:
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dir = base.baseModel2 + "\\modflow.head"
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else:
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dir = base.model_dir + model_name + "\\modflow.head"
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cloned = is_cloned_model(model_name)
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if cloned ==True:
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return {"y_data": [],"y_data2":[],"x_data":xmonths }
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head = bf.HeadFile(dir)
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alldata = head.get_alldata()
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#周期数
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months = int(len(alldata)/3)
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ydata= []
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result = {}
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for month in range(months):
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z1= alldata[int(month+1)*3-1,0,:,:]
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cell_data = float(z1[row][column])
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ydata.append(round(cell_data,2))
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y_data2=[]
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if model_name == base.not_allowed_model:
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array_data = np.loadtxt(base.obswell_data_path, dtype=str,encoding='utf-8')
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y_data2= getObsData(wellId,array_data)
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result = {"y_data": ydata,"y_data2":y_data2,"x_data": xmonths}
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return result
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def getObsData(wellId,array_data):
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result =[]
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new_list =[]
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for item in array_data:
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if item[0]==wellId:
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result.append(item[3])
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for i in range(0,len(result),3):
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data =( float(result[i]) +float(result[i+1])+float(result[i+2]))/3
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data = round(data,2)
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new_list.append(data)
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return new_list;
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def getRowCloumnById(index_id):
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row,column,count = 104,114,0
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for i in range(row):
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for j in range(column):
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if index_id == count:
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return (i,j)
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count = count +1
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return ""
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#地下水信息
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def earthWaterChart(model_name, index_id):
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row_column = getRowCloumnById(index_id)
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row = row_column[0]
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column = row_column[1]
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period_json= get_model_json(model_name)
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start_time = period_json["start_time"]
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end_time = period_json["end_time"]
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xmonths = ModelPeriod.get_months_in_range_ym(start_time, end_time)
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dir = ""
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if model_name == base.not_allowed_model:
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dir = base.baseModel2 + "\\modflow.head"
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else:
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dir = base.model_dir + model_name + "\\modflow.head"
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cloned = is_cloned_model(model_name)
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if cloned ==True:
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return {"y_data": [],"x_data":xmonths }
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head = bf.HeadFile(dir)
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alldata = head.get_alldata()
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#周期数
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months = int(len(alldata)/3)
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ydata= []
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result = {}
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for month in range(months):
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z1= alldata[int(month+1)*3-1,0,:,:]
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cell_data = float(z1[row][column])
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ydata.append(round(cell_data,2))
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result = {"y_data": ydata, "x_data": xmonths}
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return result
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#导出csv文件
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def exportCsV(model_name):
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dir = base.model_dir + model_name + "\\modflow.head"
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out_path = base.model_dir + model_name + "\\output\\"
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if not os.path.exists(out_path):
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os.mkdir(out_path)
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# z1= alldata[int(per1)*3,0,:,:]
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head = bf.HeadFile(dir)
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alldata = head.get_alldata()
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months = int(len(alldata)/3)
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layers = 3
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#例如 0-36月
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for month in range(months):
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for layer in range (layers):
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z_last= alldata[(month+1)*3-1,layer,:,:]
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filename = out_path + str(month+1) + '-' + str(layer+1) + '.csv'
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f = open(filename, 'w', newline='')
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writer = csv.writer(f)
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for p in z_last:
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writer.writerow(p)
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f.close()
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return out_path
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#水均衡计算
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def waterEqu(model_name):
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if model_name == base.not_allowed_model:
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water_equ_path = base.prefix + "\\water_equ.json"
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with open(water_equ_path,encoding='utf-8') as f:
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data = json.load(f)
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return data
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else:
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year = model_name
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title =[year]
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dict ={"title":title}
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jx = get_model_json(model_name)
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dict["start_time"]=jx["start_time"]
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dict["end_time"]=jx["end_time"]
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paths=base.model_dir + model_name +"\\water_bal.txt"
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wat = water_balance(model_name, paths)
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in_data= round(wat[0]+ wat[1]+ wat[2]+ wat[3] , 4)
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out_data= round(wat[4] + wat[5] + wat[6], 4)
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inout = round(in_data-out_data, 4)
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float_data=[wat[0],wat[1],wat[2],wat[3],in_data,
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wat[4],wat[5], wat[6],out_data,inout]
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inarray=[]
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inarray.append({"name":"降水入渗量","value":wat[0]})
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inarray.append({"name":"河流入渗量","value":wat[1]})
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inarray.append({"name":"一层侧向补给量","value":wat[2]})
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inarray.append({"name":"三层侧向补给量","value":wat[3]})
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outarray=[]
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outarray.append({"name":"人工开采量","value":wat[4]})
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outarray.append({"name":"一层侧向流出量","value":wat[5]})
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outarray.append({"name":"三层侧向流出量","value":wat[6]})
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pie1={str(year):inarray}
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pie2={str(year):outarray}
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dict["pie1"]=pie1
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dict["pie2"]=pie2
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array2d=[]
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array2d.append(["数据（亿立方米）"])
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for i in range(len(float_data)):
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tmp=[]
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tmp.append(str(float_data[i]))
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array2d.append(tmp)
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dict["data"]=array2d
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return dict
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def run_zonebudget_bal(model_name):
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# 定义exe文件的路径和参数
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exe_path = base.ZoneBudget64Exe
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txt_path = base.model_dir + model_name + "\\water_bal.txt\n"
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cell_path = base.model_dir + model_name + "\\modflow.flow\n"
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process = subprocess.Popen([exe_path], stdin=subprocess.PIPE,shell = True)
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process.stdin.write(txt_path.encode()) # 输入参数1
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process.stdin.write(cell_path.encode())
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process.stdin.write(b"title\n")
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process.stdin.write(base.water_bal_zones.encode())
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process.stdin.write(b"A\n")
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output, _ = process.communicate()
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print(output)
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def run_zonebudget_res(model_name):
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# 定义exe文件的路径和参数
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exe_path = base.ZoneBudget64Exe
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txt_path = base.model_dir + model_name + "\\water_res.txt\n"
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cell_path = base.model_dir + model_name + "\\modflow.flow\n"
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process = subprocess.Popen([exe_path], stdin=subprocess.PIPE,shell = True)
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process.stdin.write(txt_path.encode()) # 输入参数1
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process.stdin.write(cell_path.encode())
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process.stdin.write(b"title\n")
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process.stdin.write(base.water_res_zones.encode())
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process.stdin.write(b"A\n")
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output, _ = process.communicate()
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print(output)
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def reg_find_int(text):
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numbers = re.findall(r'\d+', text)
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return numbers
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def read_txt(path):
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data =[]
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with open(path, 'r') as file:
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lines = file.readlines()
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for line in lines:
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data.append(line)
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return data
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#解析水均衡数据
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def water_balance(model_name,paths):
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data= read_txt(paths)
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lens = len(data)
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index = 0
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segment=[]
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dicts={}
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flag = 0
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title=""
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while index < lens:
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strs = data[index].strip()
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if strs.startswith("Flow Budget for Zone"):
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segment=[]
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flag = 1
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title=strs
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if strs.startswith("Percent Discrepancy"):
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segment.append(strs)
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numbers = reg_find_int(title)
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key = ','.join(numbers)
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dicts[key]=segment
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flag = 0
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if flag ==1 :
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segment.append(strs)
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index=index+1
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recharge = 0
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for key in dicts:
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array = dicts[key]
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temp=[]
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for item in array:
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if item.startswith("RECHARGE") :
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strs = item.replace(" ", "").replace("RECHARGE=", "")
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temp.append(float(strs))
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recharge += (temp[0]-temp[1])
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#河流入渗量（正） 所有应力期，for Zone 3的WELLS项相加
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zone3 = 0
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for key in dicts:
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if key.startswith("3,"):
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array = dicts[key]
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temp=[]
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for item in array:
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if item.startswith("WELLS") :
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strs = item.replace(" ", "").replace("WELLS=", "")
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temp.append(float(strs))
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zone3 += (temp[0]-temp[1])
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#L1侧向补给量（正） 所有应力期，for Zone 4的IN-WELLS项相加
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Zone4_in_well=0
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for key in dicts:
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if key.startswith("4,"):
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array = dicts[key]
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for item in array:
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if item.startswith("WELLS") :
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strs = item.replace(" ", "").replace("WELLS=", "")
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data = float(strs)
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Zone4_in_well +=data
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break
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#L3侧向补给量（正） 所有应力期，for Zone 8的IN-WELLS项相加
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Zone8_in_well =0
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for key in dicts:
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if key.startswith("8,"):
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array = dicts[key]
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for item in array:
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if item.startswith("WELLS") :
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strs = item.replace(" ", "").replace("WELLS=", "")
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data = float(strs)
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Zone8_in_well +=data
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break
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#人工开采量（负） 所有应力期，for Zone 5的OUT-WELLS项相加
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zone5out =0
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for key in dicts:
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if key.startswith("5,"):
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array = dicts[key]
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for item in array:
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if item.startswith("WELLS") :
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strs = item.replace(" ", "").replace("WELLS=", "")
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data = float(strs)
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zone5out +=data
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#L1侧向流出量（负） 所有应力期，for Zone 4的OUT-WELLS项相加
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Zone4_out_well=0
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for key in dicts:
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if key.startswith("4,"):
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array = dicts[key]
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for item in array:
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if item.startswith("WELLS") :
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strs = item.replace(" ", "").replace("WELLS=", "")
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data = float(strs)
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Zone4_out_well +=data
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# L3=L3侧向流出量（负）
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L3=0.0
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result =[recharge,zone3,Zone4_in_well,Zone8_in_well,zone5out,Zone4_out_well,L3]
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for i in range(len(result)):
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# 每行数据实际上计算 的是 一个step , 1个step =10天
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result[i]= round(result[i]/100000000*10, 4)
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return result
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#解析水资源量数据
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def water_res(model_name,paths):
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data= read_txt(paths)
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lens = len(data)
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index = 0
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segment=[]
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dicts={}
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flag = 0
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title=""
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while index < lens:
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strs = data[index].strip()
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if strs.startswith("Flow Budget for Zone"):
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segment=[]
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flag = 1
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title=strs
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if strs.startswith("Percent Discrepancy"):
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segment.append(strs)
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numbers = reg_find_int(title)
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key = ','.join(numbers)
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dicts[key]=segment
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flag = 0
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if flag ==1 :
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segment.append(strs)
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index=index+1
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# 大气降水 for zone1里所有的recharge项相加（包括in和out）
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zone1_rechage = 0
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for key in dicts:
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if key.startswith("1,"):
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array = dicts[key]
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temp=[]
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for item in array:
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if item.startswith("RECHARGE") :
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strs = item.replace(" ", "").replace("RECHARGE=", "")
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temp.append(float(strs))
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zone1_rechage += (temp[0]-temp[1])
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#永定河渗漏 for zone1里所有的zone2项相加（包括in和out）
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zone1_well = 0
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for key in dicts:
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if key.startswith("1,"):
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array = dicts[key]
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indata,outdata= 0,0
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for item in array:
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if item.startswith("Zone 2 to 1") :
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strs = item.replace(" ", "").replace("Zone2to1=", "")
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indata = float(strs)
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if item.startswith("Zone 1 to 2") :
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strs = item.replace(" ", "").replace("Zone1to2=", "")
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outdata = float(strs)
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zone1_well += (indata-outdata)
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#侧向流入 For zone7 里所有的zone8项相加（包括in和out）
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zone7=0
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for key in dicts:
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if key.startswith("7,"):
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array = dicts[key]
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indata,outdata= 0,0
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for item in array:
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if item.startswith("Zone 8 to 7") :
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strs = item.replace(" ", "").replace("Zone8to7=", "")
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indata = float(strs)
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if item.startswith("Zone 7 to 8") :
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strs = item.replace(" ", "").replace("Zone7to8=", "")
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outdata = float(strs)
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zone7 += (indata-outdata)
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#越流排泄 For zone6 里所有的zone7项相加（包括in和out）
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zone6 =0
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for key in dicts:
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if key.startswith("6,"):
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array = dicts[key]
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indata,outdata= 0,0
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for item in array:
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if item.startswith("Zone 7 to 6") :
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strs = item.replace(" ", "").replace("Zone7to6=", "")
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indata = float(strs)
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if item.startswith("Zone 6 to 7") :
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strs = item.replace(" ", "").replace("Zone6to7=", "")
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outdata = float(strs)
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zone6 += (indata-outdata)
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result =[zone1_rechage,zone1_well,zone7,zone6]
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for i in range(len(result)):
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result[i]= round(result[i]/100000000*10,4)
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return result
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#解析水资源量数据
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def water_res_month(model_name,paths,per):
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data= read_txt(paths)
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lens = len(data)
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index = 0
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segment=[]
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dicts={}
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flag = 0
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title=""
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while index < lens:
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strs = data[index].strip()
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if strs.startswith("Flow Budget for Zone"):
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segment=[]
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flag = 1
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title=strs
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if strs.startswith("Percent Discrepancy"):
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segment.append(strs)
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numbers = reg_find_int(title)
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key = ','.join(numbers)
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dicts[key]=segment
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flag = 0
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if flag ==1 :
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segment.append(strs)
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index=index+1
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# 大气降水 for zone1里所有的recharge项相加（包括in和out）
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zone1_rechage = 0
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zone1_keys=[ "1,1,"+str(per+1),"1,2,"+str(per+1),"1,3,"+str(per+1)]
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for key in zone1_keys:
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array = dicts[key]
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temp=[]
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for item in array:
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if item.startswith("RECHARGE") :
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strs = item.replace(" ", "").replace("RECHARGE=", "")
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temp.append(float(strs))
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zone1_rechage += (temp[0]-temp[1])
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#永定河渗漏 for zone1里所有的zone2项相加（包括in和out）
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zone1_well = 0
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zone1_well_keys=["1,1,"+str(per+1),"1,2,"+str(per+1),"1,3,"+str(per+1)]
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for key in zone1_well_keys:
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array = dicts[key]
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indata,outdata= 0,0
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for item in array:
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if item.startswith("Zone 2 to 1") :
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strs = item.replace(" ", "").replace("Zone2to1=", "")
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indata = float(strs)
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if item.startswith("Zone 1 to 2") :
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strs = item.replace(" ", "").replace("Zone1to2=", "")
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outdata = float(strs)
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zone1_well += (indata-outdata)
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#侧向流入 For zone7 里所有的zone8项相加（包括in和out）
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zone7=0
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zone7_keys=["7,1,"+str(per+1),"7,2,"+str(per+1),"7,3,"+str(per+1)]
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for key in zone7_keys:
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array = dicts[key]
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indata,outdata= 0,0
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for item in array:
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if item.startswith("Zone 8 to 7") :
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strs = item.replace(" ", "").replace("Zone8to7=", "")
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indata = float(strs)
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if item.startswith("Zone 7 to 8") :
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strs = item.replace(" ", "").replace("Zone7to8=", "")
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outdata = float(strs)
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zone7 += (indata-outdata)
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#越流排泄 For zone6 里所有的zone7项相加（包括in和out）
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zone6 =0
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zone6_keys=["6,1,"+str(per+1),"6,2,"+str(per+1),"6,3,"+str(per+1)]
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for key in zone6_keys:
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array = dicts[key]
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indata,outdata= 0,0
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for item in array:
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if item.startswith("Zone 7 to 6") :
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strs = item.replace(" ", "").replace("Zone7to6=", "")
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indata = float(strs)
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if item.startswith("Zone 6 to 7") :
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strs = item.replace(" ", "").replace("Zone6to7=", "")
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outdata = float(strs)
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zone6 += (indata-outdata)
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result =[zone1_rechage,zone1_well,zone7,zone6]
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for i in range(len(result)):
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result[i]= round(result[i]/100000000*10, 4)
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return result
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#多模型的水位
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def water_depth(model_name):
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name_array = model_name.split(",")
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|
yhyMatrix = np.loadtxt(base.water_yhy_path, dtype=str,encoding='utf-8')
|
lshMatrix = np.loadtxt(base.water_lsh_path, dtype=str,encoding='utf-8')
|
dbwMatrix = np.loadtxt(base.water_dbw_path, dtype=str,encoding='utf-8')
|
|
res ={}
|
#颐和园，莲石湖，东北旺的水平线和多模型的多折线值
|
yhydata=[]
|
base1={"name":"地表高程","data":[52]*12}
|
yhydata.append(base1)
|
|
lshdata=[]
|
base2={"name":"地表高程","data":[80]*12}
|
lshdata.append(base2)
|
|
dbwdata=[]
|
base3={"name":"地表高程","data":[49]*12}
|
dbwdata.append(base3)
|
|
months = ModelPeriod.get_months_in_range_ym("2023-01","2023-12")
|
|
for i in range(len(name_array)):
|
if name_array[i] != '':
|
|
index = muiltyModelIndex(name_array[i])
|
array1 = get_column(yhyMatrix,index)
|
array2 = get_column(lshMatrix,index)
|
array3 = get_column(dbwMatrix,index)
|
|
yhydata.append({"name":name_array[i],"data":convertColumnData(array1)})
|
lshdata.append({"name":name_array[i],"data":convertColumnData(array2)})
|
dbwdata.append({"name":name_array[i],"data":convertColumnData(array3)})
|
|
rchMatrix = np.loadtxt(base.prefix + "base_water.ini", dtype=str,encoding='utf-8')
|
riverMatrix = np.loadtxt(base.prefix + "base_river.ini", dtype=str,encoding='utf-8')
|
pumpMatrix = np.loadtxt(base.prefix + "base_mining.ini", dtype=str,encoding='utf-8')
|
|
rchdata=[]
|
rch_base1 = rchMatrix[1]
|
rch_base1_float =[]
|
for i in range (0,len(rch_base1)):
|
float_data = round(float(rch_base1[i])/9,2)
|
rch_base1_float.append(float_data)
|
|
rchdata.append({"name":"基准值","data":rch_base1_float})
|
|
riverdata=[]
|
riverdata.append({"name":"基准值","data":riverMatrix.astype(float).tolist()})
|
|
pumpdata=[]
|
pumpX=pumpMatrix[1]
|
pump_float=[]
|
for i in range (0,len(pumpX)):
|
float_data = round(float(pumpX[i]),2)
|
pump_float.append(float_data)
|
|
pumpdata.append({"name":"基准值","data":pump_float})
|
|
res["xAxis"] = months
|
res["yhy_line"] = yhydata
|
res["lsh_line"] = lshdata
|
res["dbw_line"] = dbwdata
|
|
|
for i in range(len(name_array)):
|
if name_array[i] != '':
|
rchdata.append(rchBaseResult(rchMatrix,name_array[i]))
|
riverdata.append(riverBaseResult(riverMatrix, name_array[i]))
|
pumpdata.append(pumpBaseResult(pumpMatrix, name_array[i]))
|
|
res["rch_line"] = rchdata
|
res["river_line"] = riverdata
|
res["pump_line"] = pumpdata
|
|
yqsdata=[]
|
pyqdata=[]
|
sqdata=[]
|
for i in range(len(name_array)):
|
if name_array[i] != '':
|
paths = base.muiltyModel + name_array[i] + "\\modflow.head"
|
resdata = AchiveReport.getXs3LineChart(paths)
|
pyqdata.append({"name":name_array[i],"data":resdata["pyq"]})
|
sqdata.append({"name":name_array[i],"data":resdata["sq"]})
|
yqsdata.append({"name":name_array[i],"data":resdata["yqs"]})
|
|
res["yqs_line"] = yqsdata
|
res["sq_line"] = sqdata
|
res["pyq_line"] = pyqdata
|
|
return res
|
|
#小场景水位 和变幅
|
def xs_depth(model_name):
|
res={}
|
line1,line2=[],[]
|
paths = base.model_dir + model_name + "\\modflow.head"
|
if model_name == base.not_allowed_model:
|
paths = base.baseModel2 + "\\modflow.head"
|
|
resdata = AchiveReport.getXs3LineChart(paths)
|
line1.append({"name":"平原区","data":roundArray(resdata["pyq"])})
|
line1.append({"name":"山区","data":roundArray(resdata["sq"])})
|
line1.append({"name":"玉泉山地区","data":roundArray(resdata["yqs"])})
|
res["depth"] = line1
|
|
line2.append({"name":"平原区","data":xs_bf(resdata["pyq"])})
|
line2.append({"name":"山区","data":xs_bf(resdata["sq"])})
|
line2.append({"name":"玉泉山地区","data":xs_bf(resdata["yqs"])})
|
res["bf"] = line2
|
return res
|
|
def xs_bf(array):
|
newlist=[]
|
newlist.append(0)
|
lens = len(array)-1
|
for i in range(0,lens):
|
x = array[i+1]-array[i]
|
newlist.append(round(x,2))
|
return newlist
|
|
def roundArray(array):
|
newlist=[]
|
for item in array:
|
item = round(item,2)
|
newlist.append(item)
|
return newlist
|
|
#rch 基准数据
|
def rchBaseResult(rchMatrix,sp):
|
rchDict ={}
|
rch_base1 = rchMatrix[1]
|
rch_base2 = rchMatrix[2]
|
rch_base3 = rchMatrix[3]
|
trump = getmuiltyModelparam(sp)
|
|
types = trump[0]
|
rch_x = trump[1]
|
|
if types ==1:
|
temp = muiltyArray(rch_base1,rch_x)
|
for i in range(0,len(temp)):
|
temp[i] =round(temp[i]/9,2)
|
rchDict={"name":sp,"data":temp}
|
if types ==2:
|
temp = muiltyArray(rch_base2,rch_x)
|
for i in range(0,len(temp)):
|
temp[i] =round(temp[i]/9,2)
|
rchDict={"name":sp,"data":temp}
|
if types ==3:
|
temp = muiltyArray(rch_base3,rch_x)
|
for i in range(0,len(temp)):
|
temp[i] =round(temp[i]/9,2)
|
rchDict={"name":sp,"data":temp}
|
|
return rchDict
|
|
#河流基准 数据
|
def riverBaseResult(riverMatrix,sp):
|
trump = getmuiltyModelparam(sp)
|
river_x = trump[2]
|
riverDict={"name":sp,"data":muiltyArray(riverMatrix,river_x)}
|
return riverDict
|
|
|
#抽水井基准数据
|
def pumpBaseResult(pumpMatrix,sp):
|
trump = getmuiltyModelparam(sp)
|
pump_x =trump[3]
|
pumpDict={"name":sp,"data":muiltyArray(pumpMatrix[1],pump_x)}
|
return pumpDict
|
|
|
def muiltyArray(array,scale):
|
result =[]
|
for item in array:
|
x= round(float(item) * scale,2)
|
result.append(x)
|
return result
|
|
|
def convertColumnData(array):
|
result =[]
|
new_list=[]
|
for i in range(len(array)):
|
if i!= 0:
|
data = transToNum(array[i])
|
result.append(data)
|
for index in range(len(result)):
|
if index % 3 == 0:
|
new_list.append(result[index])
|
return new_list
|
|
def transToNum(str):
|
data = 0
|
try:
|
data= round(float(str),2)
|
return data
|
except ValueError():
|
return 0
|
|
|
#获取预测场景的下标值， 需要从ini 文件中按照列进行读取
|
def muiltyModelIndex(name):
|
models= muiltyModelList()
|
indexs = models.index(name)
|
return indexs
|
|
#预测模型列表
|
def muiltyModelList():
|
models=["SP0-0","SP1-1","SP1-2","SP1-3","SP2-1","SP2-2","SP2-3","SP3-1",
|
"SP3-2","SP3-4","SP3-5","SP3-6","SP3-7","SP4-1","SP4-7"]
|
return models
|
|
|
|
#Np获取某一列的值
|
def get_column(matrix, column_number):
|
column = matrix[:, column_number]
|
return column
|
|
#元组中 1=降雨类型 2=倍数 3,河流倍数 4,抽水井倍数
|
def getmuiltyModelparam(sp):
|
dicts={
|
"SP0-0":(1,1,1,1),
|
"SP1-1":(2,1,1,1),
|
"SP1-2":(3,1,1,1),
|
"SP1-3":(3,1.2,1,1),
|
"SP2-1":(1,1,2,1),
|
"SP2-2":(1,1,5,1),
|
"SP2-3":(1,1,10,1),
|
"SP3-1":(1,1,1,0.25),
|
"SP3-2":(1,1,1,0.5),
|
"SP3-4":(1,1,1,0),
|
"SP3-5":(1,1,1,0.4),
|
"SP3-6":(1,1,1,0.3),
|
"SP3-7":(1,1,1,0.6),
|
|
"SP4-1":(1,1,2,0.5),
|
"SP4-7":(3,1.2,10,0)}
|
return dicts[sp]
|
