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attributeSampling.py
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attributeSampling.py
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from multiprocessing import Value
from symbol import parameters
from osgeo import ogr, osr
#import matplotlib.pyplot as plt
# %matplotlib inline
import numpy as np
import os
# import matplotlib.pyplot as plt
import pandas as pd
import pymc as pm
from osgeo import ogr,gdal
import math
import main as M
import GetRasterEnt
#点插值属性的不确定性。
def GetFeas(inshp,fieldName_arr,fieldsType_arr):
print('inshp:', inshp)
driver = ogr.GetDriverByName('ESRI Shapefile')
gdal.SetConfigOption("GDAL_FILENAME_IS_UTF8", "YES")
gdal.SetConfigOption('SHAPE_ENCODING', "CP936") # 属性表支持中文字段
ds = driver.Open(inshp)
lyr = ds.GetLayer()
prjfile = open(inshp.replace('.shp', '.prj'), 'w')
Feacount = lyr.GetFeatureCount()
geoX_arr=[]
geoY_arr=[]
geoFieldsValue_arr=[]
lyrDefn = lyr.GetLayerDefn()
feature0=lyr.GetFeature(0)
featureDefn = feature0.GetDefnRef()
if fieldsType_arr == []:
for fieldName in fieldName_arr:
field_index = lyr.FindFieldIndex(fieldName, True)
fieldDefn = lyrDefn.GetFieldDefn(field_index)
field_type = fieldDefn.GetTypeName()
if field_type=='Real':
field_type=ogr.OFTReal
elif field_type=='String':
field_type=ogr.OFTString
print(field_type)
fieldsType_arr.append(field_type)
for i in range(Feacount):
fea = lyr.GetFeature(i)
feat_geom = fea.GetGeometryRef()
geoX=feat_geom.GetX()
geoY=feat_geom.GetY()
geoX_arr.append(geoX)
geoY_arr.append(geoY)
FieldsValue_arr = []
for fieldName in fieldName_arr:
field_index = fea.GetFieldIndex(fieldName)
Value = fea.GetField(field_index)
FieldsValue_arr.append(Value)
geoFieldsValue_arr.append(FieldsValue_arr)
return geoX_arr,geoY_arr,geoFieldsValue_arr,fieldsType_arr
def BuildModel(AttributesArr,sigma_arr):
for i in range(len(AttributesArr)):#每个geom
AttributeTraceArr = []
print("geoAttrArr:", AttributesArr[i])
for j in range(len(AttributesArr[i])):#每个字段
if (AttributesArr[i][j]==0):
AttributesArr[i][j]=sigma_arr[j] #避免抽样出现负值，若初始值为0，则将初始值 更改为sigma大小
print("geo{},Field{}".format(i,j))
trace_npy=M.MC_ATTRIBUTE+r'\attribute_Test{}{}.npy'.format(i,j)
if os.path.exists(trace_npy)==False:
m=AttributesArr[i][j]
v = sigma_arr[j]
# mu=math.log((m**2)/(math.sqrt(v+m**2)))
# sigma=math.sqrt(math.log(v/(m**2)+1))
mu = np.log(m / np.sqrt(1 + (v / m)**2))
sigma = np.sqrt(np.log(1 + (v / m)**2))
tau=1/(sigma*sigma)
Attribute = pm.Normal("attribute{}{}".format(i,j), mu=mu, tau=tau)
model = pm.Model([Attribute])
runner = pm.MCMC(model)
# 进行5000次抽样，取后2000次
runner.sample(iter=5000, burn=2000)
AttributeTrace = runner.trace("attribute{}{}".format(i,j))[:]
test = np.array(AttributeTrace)
np.save(trace_npy, test)
geoAttributesTrace_arr=[]
for i0 in range(len(AttributesArr)):#每个geom
AttributeTraceArr = []
#print("geoAttrArr:", AttributesArr[i])
for j0 in range(len(AttributesArr[i])):#每个字段
trace_npy=M.MC_ATTRIBUTE+r'\attribute_Test{}{}.npy'.format(i0,j0)
trace = np.load(trace_npy)
AttributeTraceArr.append(trace)
geoAttributesTrace_arr.append(AttributeTraceArr)
return geoAttributesTrace_arr
def GB15618(k,PH,waterField):
if k == 0: # Cr 铬
if PH <= 5.5:
if waterField == -1: # -1代表为水田
background = 250
else:
background = 150
elif PH <= 6.5:
if waterField == -1:
background = 250
else:
background = 150
elif PH <= 7.5:
if waterField == -1:
background = 300
else:
background = 200
else:
if waterField == -1:
background = 350
else:
background = 250
if k == 1: # Cd 镉
if PH <= 5.5:
if waterField == -1:
background = 0.3
else:
background = 0.3
elif PH <= 6.5:
if waterField == -1:
background = 0.4
else:
background = 0.3
elif PH <= 7.5:
if waterField == -1:
background = 0.6
else:
background = 0.3
else:
if waterField == -1:
background = 0.8
else:
background = 0.6
if k == 2: # As 砷
if PH <= 5.5:
if waterField == -1:
background = 30
else:
background = 40
elif PH <= 6.5:
if waterField == -1:
background = 30
else:
background = 40
elif PH <= 7.5:
if waterField == -1:
background = 25
else:
background = 30
else:
if waterField == -1:
background = 20
else:
background = 25
if k == 3: # Pb 铅
if PH <= 5.5:
if waterField == -1:
background = 80
else:
background = 70
elif PH <= 6.5:
if waterField == -1:
background = 100
else:
background = 90
elif PH <= 7.5:
if waterField == -1:
background = 140
else:
background = 120
else:
if waterField == -1:
background = 240
else:
background = 170
if k == 4: # Hg 汞
if PH <= 5.5:
if waterField == -1:
background = 0.5
else:
background = 1.3
elif PH <= 6.5:
if waterField == -1:
background = 0.5
else:
background = 1.8
elif PH <= 7.5:
if waterField == -1:
background = 0.6
else:
background = 2.4
else:
if waterField == -1:
background = 1.0
else:
background = 3.4
return background
#各元素非致癌风险
def HQ(kind,CS,BW_A,BW_K,ED_A,ED_K,IRs_A,IRs_K,SA_A,SA_K,AF_A,AF_K):
#暴露途径：口摄 s，皮肤接触 d；人群：A表示为成人，K表示为儿童；
# IRs_A=100#摄入率
# IRs_K=200#
#AFd=0.2
TF=10**-6
EF_A=350#
EF_K=350
# ED_A=24
# ED_K=6
# BW_A=63
# BW_K=29
AT_A=365*ED_A
AT_K=365*ED_K
AT_C=365*70#致癌
# SA_A=1.6*10**4
# SA_K=2800
if kind=='Cr':
ABS=0.001
RfDs=3*10**-3
RfDd=6*10**-5
elif kind=='Cd':
ABS=0.001
RfDs=1*10**-3
RfDd=1*10**-5
elif kind=='As':
ABS=0.03
RfDs=3*10**-4
RfDd=1.23*10**-4
elif kind=='Pb':
ABS=0.001
RfDs=3.5*10**-3
RfDd=5.25*10**-4
elif kind=='Hg':
ABS=0.001
RfDs=3*10**-4
RfDd=2.1*10**-5
#非致癌
ADDs_A=CS*IRs_A*TF*EF_A*ED_A/(BW_A*AT_A)
ADDs_K=CS*IRs_K*TF*EF_K*ED_K/(BW_K*AT_K)
ADDd_A=CS*AF_A*TF*SA_A*ABS*EF_A*ED_A/(BW_A*AT_A)
ADDd_K=CS*AF_K*TF*SA_K*ABS*EF_K*ED_A/(BW_K*AT_K)
#致癌
ADDs_AC=CS*IRs_A*TF*EF_A*ED_A/(BW_A*AT_C)
ADDs_KC=CS*IRs_K*TF*EF_K*ED_K/(BW_K*AT_C)
ADDd_AC=CS*AF_A*TF*SA_A*ABS*EF_A*ED_A/(BW_A*AT_C)
ADDd_KC=CS*AF_K*TF*SA_K*ABS*EF_K*ED_A/(BW_K*AT_C)
HQs_A=ADDs_A/RfDs
HQs_K=ADDs_K/RfDs
HQd_A=ADDd_A/RfDd
HQd_K=ADDd_K/RfDd
return ADDs_A,ADDs_K,ADDd_A,ADDd_K,ADDs_AC,ADDs_KC,ADDd_AC,ADDd_KC,HQs_A,HQs_K,HQd_A,HQd_K
#致癌
def CR(kind,ADDs_AC,ADDs_KC,ADDd_AC,ADDd_KC):
if kind=='Cr':
SFs=5*10**-1
SFd=20 ##
elif kind=='Cd':
SFs=5.01*10**-1
SFd=20
elif kind=='As':
SFs=1.5
SFd=3.66
elif kind=='Pb':#无用，后续不讨论Pb的致癌风险，故在后续代码中将其统一赋值为-999
SFs=1
SFd=1
elif kind=='Hg':#无用，后续不讨论Pb的致癌风险，故在后续代码中将其统一赋值为-999
SFs=1
SFd=1
CRs_A=ADDs_AC*SFs
CRs_K=ADDs_KC*SFs
CRd_A=ADDd_AC*SFd
CRd_K=ADDd_KC*SFd
return CRs_A,CRs_K,CRd_A,CRd_K
def CalHI_CR(shpPath,shpName,BW_ATrace,BW_KTrace,ED_ATrace,ED_KTrace,IRs_ATrace,IRs_KTrace,SA_ATrace,SA_KTrace,AF_ATrace,AF_KTrace):
FieldNameArr=['Cr','Cd','As','Pb','Hg'] #元素字段
fieldsType_arr=[]
FieldsValue_arr=[]
#读取100个shp各要素各字段的值
for i0 in range(100):
inshp = shpPath + '/' + shpName + r"{}.shp".format(i0 + 1)
#print('inshp:', inshp)
geoX_arr,geoY_arr,geoFieldsValue_arr,fieldsType_arr=GetFeas(inshp, FieldNameArr,fieldsType_arr)
FieldsValue_arr.append(geoFieldsValue_arr)
Value100Arr=[]
#每个shp
for i1 in range(100):
# 计算每个点
ValueArr=[]
for j in range(len(FieldsValue_arr[i1])):
FeaIndexValueArr=[]
# 计算每种重金属的HQ、CR
for k1 in range(len(FieldNameArr)):
ADDs_A,ADDs_K,ADDd_A,ADDd_K,ADDs_AC,ADDs_KC,ADDd_AC,ADDd_KC,HQs_A,HQs_K,HQd_A,HQd_K=HQ(FieldNameArr[k1],FieldsValue_arr[i1][j][k1],BW_ATrace[i1],BW_KTrace[i1],ED_ATrace[i1],ED_KTrace[i1],IRs_ATrace[i1],IRs_KTrace[i1],SA_ATrace[i1],SA_KTrace[i1],AF_ATrace[i1],AF_KTrace[i1])
HI_A=HQs_A+HQd_A
HI_K=HQs_K+HQd_K
if FieldNameArr[k1] =='Pb' or FieldNameArr[k1] =='Hg' :
CR_A=-999
CR_K=-999
else:
CRs_A,CRs_K,CRd_A,CRd_K=CR(FieldNameArr[k1],ADDs_AC,ADDs_KC,ADDd_AC,ADDd_KC)
CR_A=CRs_A+CRd_A
CR_K=CRs_K+CRd_K
FeaIndexValueArr.append(HQs_A)
FeaIndexValueArr.append(HQs_K)
FeaIndexValueArr.append(HQd_A)
FeaIndexValueArr.append(HQd_K)
FeaIndexValueArr.append(CRs_A)
FeaIndexValueArr.append(CRs_K)
FeaIndexValueArr.append(CRd_A)
FeaIndexValueArr.append(CRd_K)
FeaIndexValueArr.append(HI_A)
FeaIndexValueArr.append(HI_K)
FeaIndexValueArr.append(CR_A)
FeaIndexValueArr.append(CR_K)
ValueArr.append(FeaIndexValueArr)
Value100Arr.append(ValueArr)
npy_Value100Arr=M.HQ_CR+r"\Value100Arr_2.npy"
# print("Value100arr shape:",Value100Arr)
if os.path.exists(npy_Value100Arr)==False:
np.save(npy_Value100Arr, Value100Arr,allow_pickle=True)
Value100Arr = np.load(npy_Value100Arr)
npy_value=M.HQ_CR+r"\HQ_CR_2.npy"
if os.path.exists(npy_value)==False:
TransformData = Value100Arr.transpose(1, 2, 0)
print("TransformData shape:",TransformData.shape)
np.save(npy_value, TransformData,allow_pickle=True)
TransformData = np.load(npy_value)
print(TransformData.shape)
return
def calNemerow(shpPath,shpName,Si):
FieldNameArr=['Cr','Cd','As','Pb','Hg','PH',u"水田"]
if Si == 'GB15816':
fieldName_arr=['CrIndex','CdIndex','AsIndex','PbIndex','HgIndex','Nemerow']
fieldsType_arr=[]
FieldsValue_arr=[]
for i0 in range(100):
inshp = shpPath + '/{}/'.format(shpName) + shpName + r"{}.shp".format(i0 + 1)
#inshp = shpPath + '/soilTest/' + shpName + r".shp"
#print('inshp:', inshp)
geoX_arr,geoY_arr,geoFieldsValue_arr,fieldsType_arr=GetFeas(inshp, FieldNameArr,fieldsType_arr)
FieldsValue_arr.append(geoFieldsValue_arr)
IndexValue100Arr=[]
for i1 in range(100):
# 计算每个点
ValueArr=[]
for j in range(len(FieldsValue_arr[i1])):
FeaIndexValueArr=[]
# 计算每种重金属的单因素污染指数
PH = FieldsValue_arr[i1][j][5]
waterField = FieldsValue_arr[i1][j][6]
for k in range(5):
if Si=='GB15816':
background=GB15618(k, PH, waterField)
value=FieldsValue_arr[i1][j][k]
SingleIndex=value/background
FeaIndexValueArr.append(SingleIndex)
maxSingle=np.max(FeaIndexValueArr)
meanSingle=np.sum(FeaIndexValueArr)/len(FeaIndexValueArr)
NemerowIndex=np.sqrt((maxSingle*maxSingle+meanSingle*meanSingle)/2)
FeaIndexValueArr.append(NemerowIndex)
ValueArr.append(FeaIndexValueArr)
IndexValue100Arr.append(ValueArr)
Trans_value=M.MC_SHP+'/'+shpName+r'\Trans_value.npy'
if os.path.exists(Trans_value)==False:
TansformData=GetRasterEnt.TransformDataSet(IndexValue100Arr)
#print("geoAttributesTrace_arr",geoAttributesTrace_arr)
test = np.array(TansformData)
np.save(Trans_value, test)
TansformData = np.load(Trans_value)
WriteField(shpPath, shpName, TansformData, fieldName_arr,fieldsType_arr[0:6])
#给图层中多个字段同时赋值，适用于土壤点抽样后数据赋值
def WriteField(shpPath,shpName,geoFieldsValue_arr,fieldName_arr,field_typeArr):
gdal.SetConfigOption("GDAL_FILENAME_IS_UTF8", "YES")
gdal.SetConfigOption('SHAPE_ENCODING', "CP936") # 属性表支持中文字段
driver = ogr.GetDriverByName('ESRI Shapefile')
for n in range(0,100):
#inshp = shpPath +'/'+shpName+ r"{}.shp".format(n + 1)
inshp = shpPath + '/{}/'.format(shpName) + shpName + r"{}.shp".format(n + 1)
print('inshp:', inshp)
ds = driver.Open(inshp,1)
lyr = ds.GetLayer()
lyrDefinition = lyr.GetLayerDefn()
fieldCount = lyrDefinition.GetFieldCount()
for j in range(len(fieldName_arr)):
# print(j)
print("fieldName:", fieldName_arr[j])
fieldIndex = lyrDefinition.GetFieldIndex(fieldName_arr[j])
if fieldIndex < 0:
Field = ogr.FieldDefn(fieldName_arr[j], field_typeArr[j])
lyr.CreateField(Field, 1)
print("创建字段完成！")
# print("value:",geoFieldsValue_arr[i][j][n])
ndim = np.array(geoFieldsValue_arr).ndim
# print("数组维度：", ndim)
for i in range(len(geoFieldsValue_arr)):
print(i)
feature=lyr.GetFeature(i)
if ndim >2:
#土壤的对数按正态分布抽样后，反对数变换
value=math.exp(geoFieldsValue_arr[i][j][n])
feature.SetField(fieldName_arr[j],round(value,4))
lyr.SetFeature(feature)
else:
feature.SetField(fieldName_arr[j], geoFieldsValue_arr[i][j])
lyr.SetFeature(feature)
# 反距离权重插值,也可使用arcpy、arcgis软件批量处理
def IDWInter(inshp, outshp, fieldName_arr, outName_arr):
for j in range(len(fieldName_arr)):
# 设置输出结果
outPath = outshp + r'\{}'.format(outName_arr[j])
if os.path.exists(outPath):
print("目录已存在。")
else:
os.makedirs(outPath)
print("创建成功！")
for i in range(100):
# Set local variables
inPointFeatures = inshp + r"\12PCDW_T{}.shp".format(i + 1)
print('inPointFeatures:', inPointFeatures)
outname = fieldName_arr[j] + "{}.tif".format(i + 1)
outRaster = outPath + '\{}'.format(outname)
print('outRaster', outRaster)
zField = fieldName_arr[j]
print("zField",zField)
"""
idw空间插值
:param output_file:插值结果
:param point_station_file: 矢量站点数据
spatFilter=(minX,minY,maxX,maxY),spatFilter=(37280398.2975199,2725633.33920358,37823398.2975199,37823398.2975199),
outputBounds=(37280398.2975199,2725633.3392,37823398.2975,3334633.3392)
spatFilter=(37280399.1274,2725201.5327,37825399.1274,3335201.5327)
:return:
"""
# 代码调用
opts = gdal.GridOptions(algorithm='invdist:power=2:smoothing=0.2:radius1=0.0:radius2=0.0:angle=0.0:max_points=0:min_points=0:nodata=0.0',\
format="GTiff", outputType=gdal.GDT_Float32, width=543,height=609,outputSRS='EPSG:4525',\
outputBounds=(37280398.2975199,2725633.3392,37823398.2975,3334633.3392),zfield=zField,noData=-9999)
gdal.Grid(destName=outRaster, srcDS=inPointFeatures,options=opts)
print("插值完成")
def ExtractMask(RasterPath,Shp,OutPath,fieldName_arr):
for i in range(0,100):
# Set local variables
for fieldName in fieldName_arr:
#rasterName=fieldName +"{}.tif".format(i+1)
rasterName=fieldName +"Ent.tif"#.format(i+1)
# 设置输入栅格
inRaster = RasterPath+r'\{}'.format(fieldName)+'\{}'.format(rasterName)
print('inRaster',inRaster)
# Set local variables
#inRaster = Raster
inMaskData = Shp
OutName=fieldName+"{}_m.tif".format(i+1)
OutRaster = OutPath+r'\{}'.format(fieldName)+'\{}'.format(OutName)
print("OutRaster",OutRaster)
# Execute ExtractByMask
ds = gdal.Warp(OutRaster , inRaster, format='GTiff',\
cutlineDSName=inMaskData, dstNodata=-9999, dstSRS='EPSG:4546',srcSRS='EPSG:4525')
print(u"保存掩膜结果成功")
def main(inshp,outshp,fieldName_arr,sigma_arr):
FieldName_arr = ['Cr', 'Pb', 'Cd', 'As', 'Hg','PH']
#FieldName_arr = [u'水田']
fieldsTypeArr=[]
#获取原始字段取值
geoX_arr,geoY_arr,geoFieldsValue_arr,fieldsType_arr=GetFeas(inshp,fieldName_arr,fieldsTypeArr)
npy_value=outshp+r"\traceValue0.2.npy"
if os.path.exists(npy_value)==False:
geoAttributesTrace_arr=BuildModel(geoFieldsValue_arr,sigma_arr)
#print("geoAttributesTrace_arr",geoAttributesTrace_arr)
test = np.array(geoAttributesTrace_arr)
np.save(npy_value, test)
trace = np.load(npy_value)
WriteField(M.MC_SHP,'12PCDW_T',trace, FieldName_arr, fieldsType_arr)
# AF Beta抽样
def BetaSample(alpha,beta):
B=pm.Beta('B', alpha=alpha,beta=beta)#alpha+beta越大则抽样数值越集中，alpha/(alpha+beta)越小越靠近0
model = pm.Model([B])
runner = pm.MCMC(model)
# 进行5000次抽样，取后3000次
runner.sample(iter=5000, burn=2000)
BTrace = runner.trace("B")[:]
return BTrace
#
def LognormalSample(mu,sigma):
L=pm.Lognormal('L', mu=mu,tau=1/(sigma*sigma))#
model = pm.Model([L])
runner = pm.MCMC(model)
# 进行5000次抽样，取后3000次
runner.sample(iter=5000, burn=2000)
LTrace = runner.trace("L")[:]
print("小于1:",LTrace[LTrace<1])
if len(LTrace[LTrace<1])>0:
LTrace[LTrace<1]=np.random.choice(LTrace[LTrace>1],len(LTrace[LTrace<1]))#随机选择进行替换
print("小于1_new:",LTrace[LTrace<1])
print(len(LTrace))
LTrace=[math.log(x) for x in LTrace]
print('第50分位数:{}'.format(np.percentile(LTrace,50)))
print('第95分位数:{}'.format(np.percentile(LTrace,95)))
return LTrace
def SA_LognormalSample(m,v):
# mu=math.log((m**2)/(math.sqrt(v+m**2)))
# sigma=math.sqrt(math.log(v/(m**2)+1))
mu = np.log(m / np.sqrt(1 + (v / m)**2))
sigma = np.sqrt(np.log(1 + (v / m)**2))
tau=1/(sigma*sigma)
L=pm.Normal('L', mu=mu,tau=tau)#
model = pm.Model([L])
runner = pm.MCMC(model)
# 进行5000次抽样，取后3000次
runner.sample(iter=5000, burn=2000)
LTrace = runner.trace("L")[:]
LTrace=[math.exp(x) for x in LTrace]
print('第50分位数:{}'.format(np.percentile(LTrace,50)))
print('第95分位数:{}'.format(np.percentile(LTrace,95)))
return LTrace
def UniformSample(low,up):
U=pm.Uniform('U', lower=low, upper=up)#
model = pm.Model([U])
runner = pm.MCMC(model)
# 进行5000次抽样，取后3000次
runner.sample(iter=5000, burn=2000)
UTrace = runner.trace("U")[:]
print(UTrace)
return UTrace