Merge pull request #28 from ashokdahal/dev

Dev

RiskChanges/ComputeLoss.py

@@ -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
@@ -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)
@@ -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)

RiskChanges/RiskChangesOps/readvector.py

@@ -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)
@@ -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)
@@ -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)