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BedData.py
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BedData.py
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'''
2018-02-22 New version to include calculations for biomass-prime
To avoid calculating B-prime based on bed-categories, there are lines
of code to delete or undelete. Currently line# 36-41
2018-04-17
Comment out all references to DensDatByDenCat_pr.
These values will not be used or calculated
'''
from ADO import adoBaseClass as OpenDB
from DensDatByRegion import DensDatByRegion
from DensDatByRegion_pr import DensDatByRegion_pr
from norm import norm
from LowHalfNormal import LowHalfNormal
from ProdDistributions import ProdDistributions
import gc
class BedData:
'''Density estimates as recorded in the Dom-database
Called CBedData in previous implementation'''
def __init__(self,ODB,OutMDB,Region=None,DenCat=-1,TableName="[103-All_Beds_w_Area_MeanWt_DenCat_QRegion].",\
QuantileUse=[.005,.025,.050,.125,.5,.875,.950,.975,.995]):
'''BedData(ODB,QuotaCalcRegion=None,stat_area=None,sub_area=None,DenCat=-1)
Reads data from the 103-All_Beds_w_Area_MeanWt_DenCat_QRegion table as conceived in the Dom-database
ODB is a connection to an open Dom-database. Other variables are based upon field-names'''
self.ODB=ODB
self.Region=Region
self.DenCat=DenCat
self.TableName=TableName
self.QuantileUse=QuantileUse
#Bed specific density estimates for the entire region. combined density classes
#DenCat indicates that all density-classes are to be used to calculate distributions
self.DDBR=DensDatByRegion(self.ODB,self.Region,DenCat=-1)
self.DDBR_pr=DensDatByRegion_pr(self.ODB,self.Region,DenCat=-1)
#Consider density-class of surveyed beds
if self.DenCat>-1:
self.DensDatByDenCat =DensDatByRegion( self.ODB,self.Region,DenCat=self.DenCat)
#self.DensDatByDenCat_pr=DensDatByRegion_pr(self.ODB,self.Region,DenCat=self.DenCat)
#in case we don't want to show prime-results for specific density-categories
#self.DensDatByDenCat_pr=[None for t in self.DensDatByDenCat]
else:
self.DensDatByDenCat =self.DDBR
#self.DensDatByDenCat_pr=self.DDBR_pr
#Read bed data (surveyed and unsurveyed)
query=self.MakeQuery()
try:
self.ODB.execute(query)
except:
print('/nBedData 34')
print(query)
self.ODB.execute(query)
self.ODB.DefineFieldNames()
nfield=self.ODB.GetFieldCount()
self.values=[]
if(self.ODB.rs.EOF):return #no data in query
self.ODB.rs.MoveFirst()
gc.disable()
gc.collect()
p=list(map(lambda t:(t+.5)/200,range(200)))
print('\n BedData 50',self.Region,' ',self.DenCat)
while not(self.ODB.rs.EOF):
CurRec=ODB.Get()
try:
#exclude B-prime based on density-categories
self.values+=[BedVal(ODB,CurRec,nfield,self.DensDatByDenCat,\
#self.DensDatByDenCat_pr,\
self.DDBR,self.DDBR_pr,QuantileUse=self.QuantileUse,p=p)]
except:
print()
print('BedData 69')
print('CurRec',CurRec)
print('self.DensDatByDenCat',self.DensDatByDenCat)
#print('self.DensDatByDenCat_pr',self.DensDatByDenCat_pr)
print('self.DDBR',self.DDBR)
print('self.DDBR_pr',self.DDBR_pr)
self.values+=[BedVal(ODB,CurRec,nfield,self.DensDatByDenCat,\
#self.DensDatByDenCat_pr,\
self.DDBR,self.DDBR_pr,QuantileUse=self.QuantileUse,p=p)]
print(self.values[-1]['QuotaCalcRegion'],self.values[-1]['stat_area'],\
self.values[-1]['sub_area'],self.values[-1]['gis_code'],self.values[-1]['description'])
OutMDB.ADDTo_Results(self.values[-1])
gc.collect()
gc.enable()
def MakeQuery(self):
querySelect = "SELECT "
querySelect+=self.TableName+"counter, "
querySelect+=self.TableName+"description, "
querySelect+=self.TableName+"stat_area, "
querySelect+=self.TableName+"sub_area, "
querySelect+=self.TableName+"bed_code, "
querySelect+='cstr('+self.TableName+"TextCode) as TextCode, "
querySelect+=self.TableName+"gis_code, "
querySelect+=self.TableName+"BedArea, "
querySelect+=self.TableName+"BedAreaSE, "
querySelect+=self.TableName+"MeanWt, "
querySelect+=self.TableName+"MeanWtSE, "
querySelect+=self.TableName+"MeanWtSource, "
querySelect+=self.TableName+"QuotaCalcRegion, "
querySelect+=self.TableName+"LicenceRegion, "
querySelect+=self.TableName+"DenCat "
queryFrom= " FROM "+self.TableName[:-1] +" "
WhereString=[]
if self.Region!=None:WhereString+=["("+self.TableName+"QuotaCalcRegion='"+self.Region+"')"]
else :WhereString+=["("+self.TableName+"QuotaCalcRegion is null)"]
if self.DenCat!=-1:
WhereString+=["("+self.TableName+"DenCat="+str(self.DenCat)+")"]
else:
WhereString+=["("+self.TableName+"DenCat is Null)"]
queryWhere=''
if len(WhereString)==1:queryWhere="Where"+WhereString[0]
if len(WhereString)> 1:queryWhere="Where("+ (' and '.join(WhereString)) +')'
queryOrder ="Order by "
queryOrder+=self.TableName+"QuotaCalcRegion, "
queryOrder+=self.TableName+"stat_area, "
queryOrder+=self.TableName+"sub_area, "
queryOrder+=self.TableName+"gis_code; "
try:
query=querySelect+queryFrom+queryWhere+queryOrder
except:
print('\nBedData 77')
print('querySelect',querySelect)
print('queryFrom',queryFrom)
print('queryWhere',queryWhere)
print('queryOrder',queryOrder)
query=querySelect+queryFrom+queryWhere+queryOrder
return(query)
class BedVal(dict):
'''Class to contain information about individual beds'''
#def __new__(self,ODB, CurRec,nfield,DensDatByDenCat,DensDatByDenCat_pr,DDBR,DDBR_pr,QuantileUse,n=100,p=None):
def __new__(self,ODB, CurRec,nfield,DensDatByDenCat,DDBR,DDBR_pr,QuantileUse,n=100,p=None):
self.n=n
self.p=p
if self.p==None:self.p=list(map(lambda t:(t+.5)/self.n,range(self.n)))
CurValue={}
#Values directly from Database
for i in range(nfield):CurValue[ODB.Fname[i]]=CurRec[i]
#Incorporate estimated abundance and confidence bounds into the dictionary
SiteMean=CurValue['BedArea']*10000 #Convert bed-area from hectares to square metres
SiteStDev=CurValue['BedAreaSE']*10000
WeightMean=CurValue['MeanWt']/2.204622622 #Convert mean weight from pounds to kilos
WeightStEr=CurValue['MeanWtSE']/2.204622622
DistWeight=norm( WeightMean,WeightStEr)
DistArea =LowHalfNormal(SiteMean ,SiteStDev)
DistWeightByArea=ProdDistributions(DistWeight,DistArea,p=self.p)
BiomassDC=ProdDistributions(DistWeightByArea,DensDatByDenCat,p=self.p)#Based upon Density-class and region
BiomassQR=ProdDistributions(DistWeightByArea,DDBR,p=self.p)#Based on Region only
#Biomass_pr_DC=ProdDistributions(DistWeightByArea,DensDatByDenCat_pr,p=self.p)#Based upon Density-class and region
Biomass_pr_QR=ProdDistributions(DistWeightByArea,DDBR_pr,p=self.p)#Based on Region only
CurValue['CBBiomassDC'] =BiomassDC.isf(QuantileUse)
CurValue['CBBiomassQR'] =BiomassQR.isf(QuantileUse)
#CurValue['CBBiomass_prDC']=Biomass_pr_DC.isf(QuantileUse)
CurValue['CBBiomass_prQR']=Biomass_pr_QR.isf(QuantileUse)
CurValue['n_DenCat']=len(DensDatByDenCat)
CurValue['n_Region']=len(DDBR)
return(CurValue)
if __name__ == "__main__":
databasepath='h:\\QuotaCalcs\\data\\2014_Quotas.mdb'
ODB=OpenDB(databasepath)
from NewMDB import NewMDB
OUTmdbName='h:\\QuotaCalcs\\data\\testGeorgiaStrait.mdb'
OutMDB=NewMDB(OUTmdbName)
test=BedData(ODB,OutMDB,Region='GeorgiaStrait',DenCat=-1)
print('\ntest.MakeQuery()', test.MakeQuery() )
print('\ndir(test)', dir(test) )
print('len(test.values)', len(test.values) )
print(' \ntest.values[0]', test.values[0])
print(' \ntest.values[-1]', test.values[-1])
#test.CalcResults()