Notebook

In [ ]:

import datetime as dt
import numpy as np
import netCDF4 as nc
import pandas as pd
import glob
from salishsea_tools import geo_tools
import gsw
import os
import pytz
import matplotlib.pyplot as plt
import cmocean as cmo
import warnings
from sqlalchemy import create_engine, case, MetaData
from sqlalchemy.orm import create_session, aliased
from sqlalchemy.ext.automap import automap_base
from sqlalchemy.sql import and_, or_, not_, func
from salishsea_tools import viz_tools

pd.set_option('display.max_colwidth', -1)

%matplotlib inline

In [ ]:

basedir='/ocean/shared/SalishSeaCastData/DFO/CTD/'
dbname='DFO_CTD.sqlite'
datelims=()

In [ ]:

engine = create_engine('sqlite:///' + basedir + dbname, echo = False)

connection = engine.connect()def Sal_PSU_to_SA(S,press,lon,lat): SA=gsw.SA_from_SP(S,press,lon,lat) if SA>=40: print('Err: SA=',SA,', None entered') SA=None elif SA<0: SA=None print('Err: SA=',SA,', None entered') return(SA) def T_to_CT(SA,T,press): CT=gsw.CT_from_t(SA,T,press) if CT>=40: print('Err: CT=',CT,', None entered') CT=None elif CT<-5: CT=None print('Err: CT=',CT,', None entered') return(CT) def p_to_Z(press,lat): Z=-1.0*gsw.z_from_p(press,lat) return(Z) connection.connection.create_function("Sal_PSU_to_SA_DB",4,Sal_PSU_to_SA) connection.connection.create_function("T_to_CT_DB",3,T_to_CT) connection.connection.create_function("p_to_Z_DB",2,p_to_Z)connection.execute("""UPDATE CalcsTBL SET Temperature_CT = T_to_CT_DB(CalcsTBL.Salinity_SA,ObsTBL.Temperature,ObsTBL.Pressure), Temperature_Primary_CT = T_to_CT_DB(CalcsTBL.Salinity_T0_C0_SA,ObsTBL.Temperature,ObsTBL.Pressure), Temperature_Secondary_CT = T_to_CT_DB(CalcsTBL.Salinity_T1_C1_SA,ObsTBL.Temperature,ObsTBL.Pressure), FROM ((CalcsTBL INNER JOIN ObsTBL ON CalcsTBL.ObsID = ObsTBL.ID) INNER JOIN StationTBL ON CalcsTBL.StationID = StationTBL.ID)""")test=connection.execute("""SELECT T_to_CT_DB( CASE WHEN CalcsTBL.Salinity_T1_C1_SA IS NOT NULL THEN CalcsTBL.Salinity_T1_C1_SA WHEN CalcsTBL.Salinity_T0_C0_SA IS NOT NULL THEN CalcsTBL.Salinity_T0_C0_SA WHEN CalcsTBL.Salinity_SA IS NOT NULL THEN CalcsTBL.Salinity_SA ELSE NULL END,ObsTBL.Temperature,ObsTBL.Pressure) as T1, T_to_CT_DB( CASE WHEN CalcsTBL.Salinity_T1_C1_SA IS NOT NULL THEN CalcsTBL.Salinity_T1_C1_SA WHEN CalcsTBL.Salinity_T0_C0_SA IS NOT NULL THEN CalcsTBL.Salinity_T0_C0_SA WHEN CalcsTBL.Salinity_SA IS NOT NULL THEN CalcsTBL.Salinity_SA ELSE NULL END,ObsTBL.Temperature,ObsTBL.Pressure) as T2, T_to_CT_DB( CASE WHEN CalcsTBL.Salinity_T1_C1_SA IS NOT NULL THEN CalcsTBL.Salinity_T1_C1_SA WHEN CalcsTBL.Salinity_T0_C0_SA IS NOT NULL THEN CalcsTBL.Salinity_T0_C0_SA WHEN CalcsTBL.Salinity_SA IS NOT NULL THEN CalcsTBL.Salinity_SA ELSE NULL END,ObsTBL.Temperature,ObsTBL.Pressure) as T3 FROM CalcsTBL INNER JOIN ObsTBL ON CalcsTBL.ObsID = ObsTBL.ID INNER JOIN StationTBL ON CalcsTBL.StationID = StationTBL.ID;""") ii=0 for row in test: ii=ii+1 if (ii>100)&(ii<120): print(row)

In [ ]:

grid = nc.Dataset('/data/eolson/MEOPAR/NEMO-forcing-new/grid/bathymetry_201702.nc')

In [ ]:

md=MetaData()
md.reflect(engine)
Base = automap_base(metadata=md)
# reflect the tables in salish.sqlite:
Base.prepare()
# mapped classes have been created
# existing tables:
StationTBL=Base.classes.StationTBL
ObsTBL=Base.classes.ObsTBL
CalcsTBL=Base.classes.CalcsTBL
AncTBL=Base.classes.AncillaryTBL
#JDFLocsTBL=Base.classes.JDFLocsTBL
session = create_session(bind = engine, autocommit = False, autoflush = True)

In [ ]:

qry=session.query(ObsTBL.Depth,ObsTBL.Pressure,CalcsTBL.Z,ObsTBL.Salinity,ObsTBL.Salinity_T0_C0,ObsTBL.Salinity_T1_C1,CalcsTBL.Salinity_SA,CalcsTBL.Salinity_T0_C0_SA,CalcsTBL.Salinity_T1_C1_SA,
                  CalcsTBL.Temperature_CT,CalcsTBL.Temperature_Primary_CT,CalcsTBL.Temperature_Secondary_CT).\
    select_from(CalcsTBL).join(ObsTBL,ObsTBL.ID==CalcsTBL.ObsTBLID).filter(ObsTBL.Depth==ObsTBL.Depth).all()

In [ ]:

df=pd.DataFrame(qry)

In [ ]:

df.describe()

Look at cases where Depth and Z are different:¶

In [ ]:

np.min((df['Depth']-df['Z'])/(df['Z']+df['Depth'])*2),np.max((df['Depth']-df['Z'])/(df['Z']+df['Depth'])*2)

In [ ]:

df.loc[(np.abs((df['Depth']-df['Z'])/(df['Z']+df['Depth'])*2)>.1)&(np.abs((df['Depth']-df['Z']))>.3)]

List column names for tables:¶

In [ ]:

sorted([x.name for x in md.tables['StationTBL'].columns])

In [ ]:

sorted([x.name for x in md.tables['ObsTBL'].columns])

In [ ]:

sorted([x.name for x in md.tables['CalcsTBL'].columns])

In [ ]:

sorted([x.name for x in md.tables['AncillaryTBL'].columns])

salinity variables: 'Salinity','Salinity_T0_C0', 'Salinity_T1_C1'¶

temperature variables:'Temperature','Temperature_Primary','Temperature_Secondary'¶

How many Depths with no Pressure and vice versa?¶

In [ ]:

print('Z without P:',session.query(ObsTBL.Depth).filter(ObsTBL.Pressure==None).count())
print('P without Z:',session.query(ObsTBL.Pressure).filter(ObsTBL.Depth==None).count())

Other depth info:¶

In [ ]:

print('Z min, max:',session.query(func.min(ObsTBL.Depth)).one(),session.query(func.max(ObsTBL.Depth)).one())
print('P min, max:',session.query(func.min(ObsTBL.Pressure)).one(),session.query(func.max(ObsTBL.Pressure)).one())

Other Variables:¶

In [ ]:

for var in (ObsTBL.Salinity,ObsTBL.Salinity_T0_C0,ObsTBL.Salinity_T1_C1,ObsTBL.Temperature,ObsTBL.Temperature_Primary,ObsTBL.Temperature_Secondary):
    print(var,'min max count:',session.query(func.min(var)).one(),session.query(func.max(var)).one(),session.query(var).filter(var!=None).count())

In [ ]:

vlist=(ObsTBL.Salinity,ObsTBL.Salinity_T0_C0,ObsTBL.Salinity_T1_C1,ObsTBL.Temperature,ObsTBL.Temperature_Primary,ObsTBL.Temperature_Secondary)
ulist=(ObsTBL.Salinity_units,ObsTBL.Salinity_T0_C0_units,ObsTBL.Salinity_T1_C1_units,ObsTBL.Temperature_units,
      ObsTBL.Temperature_Primary_units,ObsTBL.Temperature_Secondary_units)

In [ ]:

for vvar,uvar in zip(vlist,ulist):
    print(uvar,'unique:')
    print('\t',[i for i in session.query(uvar).group_by(uvar).all()])
    print('\t','# missing units:',session.query(vvar,uvar).filter(and_(vvar!=None,uvar==None)).count())

Check which T&S variable combinations are present¶

In [ ]:

df.loc[(~np.isnan(df['Salinity_T1_C1']))&(~np.isnan(df['Temperature_Primary']))]

In [ ]:

df.loc[(~np.isnan(df['Salinity_T1_C1']))&(~np.isnan(df['Temperature']))]

In [ ]:

df.loc[(~np.isnan(df['Salinity_T0_C0']))&(~np.isnan(df['Temperature']))]

In [ ]:

df.loc[(~np.isnan(df['Salinity_T0_C0']))&(~np.isnan(df['Temperature_Secondary']))]

In [ ]:

df.loc[(~np.isnan(df['Salinity']))&(~np.isnan(df['Temperature_Secondary']))]

In [ ]:

df.loc[(~np.isnan(df['Salinity']))&(~np.isnan(df['Temperature_Primary']))]

In [ ]:

len(df.loc[(~np.isnan(df['Salinity']))&(~np.isnan(df['Temperature']))])

In [ ]:

len(df.loc[(~np.isnan(df['Salinity_T0_C0']))&(~np.isnan(df['Temperature_Primary']))])

In [ ]:

len(df.loc[(~np.isnan(df['Salinity_T1_C1']))&(~np.isnan(df['Temperature_Secondary']))])

Plot All T S Data¶

In [ ]:

df=pd.DataFrame(session.query(ObsTBL.Depth,ObsTBL.Pressure,ObsTBL.Salinity,ObsTBL.Salinity_T0_C0,ObsTBL.Salinity_T1_C1,
                              ObsTBL.Temperature,ObsTBL.Temperature_Primary,ObsTBL.Temperature_Secondary,
                              StationTBL.Lat,StationTBL.Lon).select_from(ObsTBL).join(StationTBL,StationTBL.ID==ObsTBL.StationTBLID).all())

fig,ax=plt.subplots(1,3,figsize=(18,6))
ax[0].plot(df['Salinity'],df['Temperature'],'r.')
ax[1].plot(df['Salinity_T0_C0'],df['Temperature_Primary'],'c.')
ax[2].plot(df['Salinity_T1_C1'],df['Temperature_Secondary'],'m.')

In [ ]:

fig,ax=plt.subplots(1,3,figsize=(18,6))
for iax in ax:
    viz_tools.set_aspect(iax, coords = 'map')
    viz_tools.plot_coastline(iax, grid, coords = 'map')
    iax.set_ylim(47, 52)
    iax.set_xlim(-130, -122);
ax[0].plot(df.loc[(df['Salinity']>0)&(df['Temperature']>0),['Lon']],
                    df.loc[(df['Salinity']>0)&(df['Temperature']>0),['Lat']],'ro')
ax[1].plot(df.loc[(df['Salinity_T0_C0']>0)&(df['Temperature_Primary']>0),['Lon']],
                    df.loc[(df['Salinity_T0_C0']>0)&(df['Temperature_Primary']>0),['Lat']],'co')
ax[2].plot(df.loc[(df['Salinity_T1_C1']>0)&(df['Temperature_Secondary']>0),['Lon']],
                    df.loc[(df['Salinity_T1_C1']>0)&(df['Temperature_Secondary']>0),['Lat']],'mo')

Plot all included T&S data¶

In [ ]:

df=pd.DataFrame(session.query(ObsTBL.Depth,ObsTBL.Pressure,ObsTBL.Salinity,ObsTBL.Salinity_T0_C0,
                              ObsTBL.Salinity_T1_C1,ObsTBL.Temperature,ObsTBL.Temperature_Primary,
                              ObsTBL.Temperature_Secondary,StationTBL.Lat,StationTBL.Lon).\
                select_from(ObsTBL).join(StationTBL,StationTBL.ID==ObsTBL.StationTBLID).\
                filter(ObsTBL.Include==True).all())

fig,ax=plt.subplots(1,3,figsize=(18,6))
ax[0].plot(df['Salinity'],df['Temperature'],'r.')
ax[1].plot(df['Salinity_T0_C0'],df['Temperature_Primary'],'c.')
ax[2].plot(df['Salinity_T1_C1'],df['Temperature_Secondary'],'m.')

In [ ]:

fig,ax=plt.subplots(1,3,figsize=(18,6))
for iax in ax:
    viz_tools.set_aspect(iax, coords = 'map')
    viz_tools.plot_coastline(iax, grid, coords = 'map')
    iax.set_ylim(47, 52)
    iax.set_xlim(-130, -122);
ax[0].plot(df.loc[(df['Salinity']>0)&(df['Temperature']>0),['Lon']],
                    df.loc[(df['Salinity']>0)&(df['Temperature']>0),['Lat']],'ro')
ax[1].plot(df.loc[(df['Salinity_T0_C0']>0)&(df['Temperature_Primary']>0),['Lon']],
                    df.loc[(df['Salinity_T0_C0']>0)&(df['Temperature_Primary']>0),['Lat']],'co')
ax[2].plot(df.loc[(df['Salinity_T1_C1']>0)&(df['Temperature_Secondary']>0),['Lon']],
                    df.loc[(df['Salinity_T1_C1']>0)&(df['Temperature_Secondary']>0),['Lat']],'mo')

Restrict to Salish Sea¶

In [ ]:

qry=session.query(StationTBL.StartYear.label('Year'),StationTBL.StartMonth.label('Month'),
                      StationTBL.StartDay.label('Day'),StationTBL.StartHour.label('Hour'),
                      StationTBL.Lat,StationTBL.Lon,
                     ObsTBL.Depth,ObsTBL.Pressure,ObsTBL.Salinity,
                  ObsTBL.Salinity_T0_C0,ObsTBL.Salinity_T1_C1,
                    ObsTBL.Temperature,ObsTBL.Temperature_Primary,ObsTBL.Temperature_Secondary,ObsTBL.sourceFile).\
                select_from(StationTBL).join(ObsTBL,ObsTBL.StationTBLID==StationTBL.ID).\
                filter(and_(StationTBL.Lat>47-3/2.5*(StationTBL.Lon+123.5),
                            StationTBL.Lat<47-3/2.5*(StationTBL.Lon+121)))
df=pd.DataFrame(qry.all())

In [ ]:

fig,ax=plt.subplots(1,3,figsize=(18,6))
ax[0].plot(df['Salinity'],df['Temperature'],'r.')
ax[1].plot(df['Salinity_T0_C0'],df['Temperature_Primary'],'c.')
ax[2].plot(df['Salinity_T1_C1'],df['Temperature_Secondary'],'m.')

In [ ]:

fig,ax=plt.subplots(1,3,figsize=(18,6))
for iax in ax:
    viz_tools.set_aspect(iax, coords = 'map')
    viz_tools.plot_coastline(iax, grid, coords = 'map')
    #iax.set_ylim(48, 50.5)
    #iax.set_xlim(-125.7, -122.5);
    iax.set_ylim(47, 52)
    iax.set_xlim(-130, -122);
ax[0].plot(df.loc[(df['Salinity']>0)&(df['Temperature']>0),['Lon']],
                    df.loc[(df['Salinity']>0)&(df['Temperature']>0),['Lat']],'ro')
ax[1].plot(df.loc[(df['Salinity_T0_C0']>0)&(df['Temperature_Primary']>0),['Lon']],
                    df.loc[(df['Salinity_T0_C0']>0)&(df['Temperature_Primary']>0),['Lat']],'co')
ax[2].plot(df.loc[(df['Salinity_T1_C1']>0)&(df['Temperature_Secondary']>0),['Lon']],
                    df.loc[(df['Salinity_T1_C1']>0)&(df['Temperature_Secondary']>0),['Lat']],'mo')

In [ ]:

models=session.query(AncTBL.MODEL).distinct().all()
models

Display CastAway stations¶

In [ ]:

## Where are CastAway stations?
qry=session.query(StationTBL.StartYear.label('Year'),StationTBL.StartMonth.label('Month'),
                      StationTBL.StartDay.label('Day'),StationTBL.StartHour.label('Hour'),
                      StationTBL.Lat,StationTBL.Lon,
                     ObsTBL.Depth,ObsTBL.Pressure,ObsTBL.Salinity,
                  ObsTBL.Salinity_T0_C0,ObsTBL.Salinity_T1_C1,
                    ObsTBL.Temperature,ObsTBL.Temperature_Primary,ObsTBL.Temperature_Secondary,ObsTBL.sourceFile).\
                select_from(StationTBL).join(ObsTBL,ObsTBL.StationTBLID==StationTBL.ID).\
                join(AncTBL,AncTBL.StationTBLID==StationTBL.ID).\
                filter(AncTBL.MODEL=='CastAway')
df=pd.DataFrame(qry.all())
fig,ax=plt.subplots(1,3,figsize=(18,6))
for iax in ax:
    viz_tools.set_aspect(iax, coords = 'map')
    viz_tools.plot_coastline(iax, grid, coords = 'map')
    #iax.set_ylim(48, 50.5)
    #iax.set_xlim(-125.7, -122.5);
    iax.set_ylim(47, 52)
    iax.set_xlim(-130, -122);
ax[0].plot(df.loc[(df['Salinity']>0)&(df['Temperature']>0),['Lon']],
                    df.loc[(df['Salinity']>0)&(df['Temperature']>0),['Lat']],'ro')
ax[1].plot(df.loc[(df['Salinity_T0_C0']>0)&(df['Temperature_Primary']>0),['Lon']],
                    df.loc[(df['Salinity_T0_C0']>0)&(df['Temperature_Primary']>0),['Lat']],'co')
ax[2].plot(df.loc[(df['Salinity_T1_C1']>0)&(df['Temperature_Secondary']>0),['Lon']],
                    df.loc[(df['Salinity_T1_C1']>0)&(df['Temperature_Secondary']>0),['Lat']],'mo')

Check CastAway profiles are excluded:¶

In [ ]:

qry=session.query(StationTBL.Include).select_from(StationTBL).join(AncTBL,AncTBL.StationTBLID==StationTBL.ID).\
    filter(AncTBL.MODEL=='CastAway').distinct().all()
print('Station Include:',qry)

In [ ]:

qry=session.query(ObsTBL.Include).select_from(ObsTBL).join(AncTBL,AncTBL.StationTBLID==ObsTBL.StationTBLID).\
    filter(AncTBL.MODEL=='CastAway').distinct().all()
print('Obs Include:',qry)

check that this plot is linear or you are likely missing stations from obs table:¶

In [ ]:

test=session.query(ObsTBL.StationTBLID).distinct().order_by(ObsTBL.StationTBLID).all()
plt.plot(test)

Salish Sea with Include=True¶

In [ ]:

qry=session.query(StationTBL.StartYear.label('Year'),StationTBL.StartMonth.label('Month'),
                      StationTBL.StartDay.label('Day'),StationTBL.StartHour.label('Hour'),
                      StationTBL.Lat,StationTBL.Lon,
                     ObsTBL.Depth,ObsTBL.Pressure,ObsTBL.Salinity,
                  ObsTBL.Salinity_T0_C0,ObsTBL.Salinity_T1_C1,
                    ObsTBL.Temperature,ObsTBL.Temperature_Primary,ObsTBL.Temperature_Secondary,ObsTBL.sourceFile).\
                select_from(StationTBL).join(ObsTBL,ObsTBL.StationTBLID==StationTBL.ID).\
                join(AncTBL,AncTBL.StationTBLID==StationTBL.ID).\
                filter(and_(StationTBL.Lat>47-3/2.5*(StationTBL.Lon+123.5),
                            StationTBL.Lat<47-3/2.5*(StationTBL.Lon+121),ObsTBL.Include==True))
df=pd.DataFrame(qry.all())
fig,ax=plt.subplots(1,3,figsize=(18,6))
ax[0].plot(df['Salinity'],df['Temperature'],'r.')
ax[1].plot(df['Salinity_T0_C0'],df['Temperature_Primary'],'c.')
ax[2].plot(df['Salinity_T1_C1'],df['Temperature_Secondary'],'m.')

In [ ]:

fig,ax=plt.subplots(1,3,figsize=(18,6))
for iax in ax:
    viz_tools.set_aspect(iax, coords = 'map')
    viz_tools.plot_coastline(iax, grid, coords = 'map')
    #iax.set_ylim(48, 50.5)
    #iax.set_xlim(-125.7, -122.5);
    iax.set_ylim(47, 52)
    iax.set_xlim(-130, -122);
ax[0].plot(df.loc[(df['Salinity']>0)&(df['Temperature']>0),['Lon']],
                    df.loc[(df['Salinity']>0)&(df['Temperature']>0),['Lat']],'ro')
ax[1].plot(df.loc[(df['Salinity_T0_C0']>0)&(df['Temperature_Primary']>0),['Lon']],
                    df.loc[(df['Salinity_T0_C0']>0)&(df['Temperature_Primary']>0),['Lat']],'co')
ax[2].plot(df.loc[(df['Salinity_T1_C1']>0)&(df['Temperature_Secondary']>0),['Lon']],
                    df.loc[(df['Salinity_T1_C1']>0)&(df['Temperature_Secondary']>0),['Lat']],'mo')

Check for Duplicates¶

In [ ]:

# search for duplicate stations and investigate:
a1=aliased(StationTBL)
a2=aliased(StationTBL)
dupsQRY=session.query(a1.ID.label('ID1'),a1.Include,a2.ID.label('ID2'),a2.Include,a1.sourceFile.label('source1'),a2.sourceFile.label('source2'),
                     a1.EVENT_NUMBER.label('EVENT_NUMBER1'),a2.EVENT_NUMBER.label('EVENT_NUMBER2'),
                     a1.PLATFORM.label('PLATFORM1'),a2.PLATFORM.label('PLATFORM2'),
                     a1.STATION.label('STATION1'),a2.STATION.label('STATION2'),
                     a1.WATER_DEPTH.label('WATER_DEPTH1'),a2.WATER_DEPTH.label('WATER_DEPTH2')).select_from(a1).join(a2,and_(
    a1.StartYear==a2.StartYear,
    a1.StartMonth==a2.StartMonth,
    a1.StartDay==a2.StartDay,
    a1.StartHour-a2.StartHour<0.001,
    a1.StartHour-a2.StartHour>-0.001,
    a1.Lat-a2.Lat<0.001,
    a1.Lat-a2.Lat>-0.001,
    a1.Lon-a2.Lon<0.001,
    a1.Lon-a2.Lon>-0.001,
    a1.ID!=a2.ID)).filter(a1.Include==True,a2.Include==True,a1.ID<a2.ID)

In [ ]:

dfa=pd.DataFrame(dupsQRY.all())

In [ ]:

dfa

In [ ]:

# display previously eliminated duplicates:
a1=aliased(StationTBL)
a2=aliased(StationTBL)
dupsQRY=session.query(a1.ID.label('ID1'),a1.Include,a2.ID.label('ID2'),a2.Include,a1.sourceFile.label('source1'),a2.sourceFile.label('source2'),
                     a1.EVENT_NUMBER.label('EVENT_NUMBER1'),a2.EVENT_NUMBER.label('EVENT_NUMBER2'),
                     a1.PLATFORM.label('PLATFORM1'),a2.PLATFORM.label('PLATFORM2'),
                     a1.STATION.label('STATION1'),a2.STATION.label('STATION2'),
                     a1.WATER_DEPTH.label('WATER_DEPTH1'),a2.WATER_DEPTH.label('WATER_DEPTH2')).select_from(a1).join(a2,and_(
    a1.StartYear==a2.StartYear,
    a1.StartMonth==a2.StartMonth,
    a1.StartDay==a2.StartDay,
    a1.StartHour-a2.StartHour<0.001,
    a1.StartHour-a2.StartHour>-0.001,
    a1.Lat-a2.Lat<0.001,
    a1.Lat-a2.Lat>-0.001,
    a1.Lon-a2.Lon<0.001,
    a1.Lon-a2.Lon>-0.001,
    a1.ID!=a2.ID)).filter(a1.Include==True,a2.Include==False,a1.ID<a2.ID)
dfa=pd.DataFrame(dupsQRY.all())
dfa

In [ ]:

for i,r in dfa.iterrows():
    print(r['source1'],'\n',r['source2'],'\n')

In [ ]:

## check for obs include true where station include false; should be empty with adjusted procedure
test=pd.DataFrame(session.query(StationTBL.ID.label('StationID'),ObsTBL.ID.label('ObsID')).select_from(StationTBL).\
                  join(ObsTBL,ObsTBL.StationTBLID==StationTBL.ID).\
                  filter(and_(StationTBL.Include==False,ObsTBL.Include==True)).all())
test

In [ ]:

grid.close()

In [ ]: