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Dev #28

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Jul 13, 2021
Merged

Dev #28

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24 changes: 11 additions & 13 deletions RiskChanges/ComputeLoss.py
Original file line number Diff line number Diff line change
@@ -1,9 +1,11 @@
import RiskChangesOps.readmeta as readmeta
from RiskChangesOps.readvulnerability import readIntVuln,readSusVuln
import RiskChangesOps.readvector as readvector
import RiskChangesOps.writevector as writevector
import geopandas as gpd
import RiskChangesOps.AggregateData as aggregator
import pandas as pd

from .RiskChangesOps.readvulnerability import readIntVuln,readSusVuln
from .RiskChangesOps import readmeta, readvector, writevector, AggregateData as aggregator



def getHazardMeanIntensity(exposuretable,stepsize,base):
stepsize=stepsize#5 #import from database
base=base#0 #import from database
Expand Down Expand Up @@ -52,13 +54,9 @@ def calculateLoss_spprob(exposuretable,costColumn,spprob):
return losstable_lossonly

def ComputeLoss(con,exposureid,lossid,computeonvalue=True,**kwargs):
try:
is_aggregated=kwargs['is_aggregated']
onlyaggregated=kwargs['only_aggregated']
adminid=kwargs['adminunit_id']
except:
is_aggregated= False
onlyaggregated= False
is_aggregated = kwargs.get('is_aggregated', False)
onlyaggregated = kwargs.get('only_aggregated', False)
adminid = kwargs.get('adminunit_id', None)

metadata=readmeta.computeloss_meta(con,exposureid)
exposure=readvector.prepareExposureForLoss(con,exposureid)
Expand Down Expand Up @@ -86,7 +84,7 @@ def ComputeLoss(con,exposureid,lossid,computeonvalue=True,**kwargs):
writevector.writeLoss(loss,con,schema)

if is_aggregated:
admin_unit=readear.readAdmin(con,adminid)
admin_unit=readvector.readAdmin(con,adminid)
earid=metadata["earID"]
ear= readvector.readear(con,earid)
loss=pd.merge(left=loss, right=ear['id','geom'], left_on='geom_id',right_on='id',right_index=False)
Expand Down
6 changes: 5 additions & 1 deletion RiskChanges/RiskChangesOps/readvector.py
Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,8 @@
from . import readmeta
import psycopg2
import pandas as pd


def readear(connstr,earid):
engine=psycopg2.connect(connstr)
metatable=readmeta.earmeta(connstr,earid)
Expand All @@ -13,6 +15,8 @@ def readear(connstr,earid):
ear_table=gpd.read_postgis(sql,con=engine)
engine.close()
return ear_table


def readexposure(connstr,exposureid,schema):
engine=psycopg2.connect(connstr)
metatable=readmeta.exposuremeta(connstr,exposureid)
Expand All @@ -30,7 +34,7 @@ def prepareExposureForLoss(connstr,exposureid):
pk=metadict["earPK"]
exposuredata=readexposure(connstr,exposureid,schema)
eardatageom=readear(connstr,earid)
eardata=df1 = pd.DataFrame(gdf.drop(columns='geom'))
eardata=pd.DataFrame(eardatageom.drop(columns='geom'))
exposure_all=pd.merge(left=exposuredata, right=eardata, how='left', left_on=['geom_id'], right_on=[pk])
assert not exposure_all.empty , f"The exposure data {exposureid} returned empty from database"
#exposure=readvulnerability.linkvulnerability(connstr,exposure_all)
Expand Down