Slice NetCdf Dataset










0















I am looking to take a subset of the netcdf data set bounded by lat/lon coordinates. 



<xarray.Dataset>
Dimensions: (ICcheckNameLen: 72, ICcheckNum: 55, QCcheckNameLen: 60, QCcheckNum: 10, maxAutoStaLen: 6, maxLocationLen: 24, maxMETARLen: 256, maxRepLen: 6, maxSkyCover: 6, maxSkyLen: 8, maxStaNamLen: 5, maxStaticIds: 10000, maxWeatherLen: 25, nInventoryBins: 24, recNum: 8329, totalIdLen: 6)
Dimensions without coordinates: ICcheckNameLen, ICcheckNum, QCcheckNameLen, QCcheckNum, maxAutoStaLen, maxLocationLen, maxMETARLen, maxRepLen, maxSkyCover, maxSkyLen, maxStaNamLen, maxStaticIds, maxWeatherLen, nInventoryBins, recNum, totalIdLen
Data variables:
 nStaticIds int32 ...
 staticIds (maxStaticIds, totalIdLen) |S1 ...
 lastRecord (maxStaticIds) int32 ...
 invTime (recNum) int32 ...
 prevRecord (recNum) int32 ...
 inventory (maxStaticIds) int32 ...
 globalInventory int32 ...
 firstOverflow int32 ...
 isOverflow (recNum) int32 ...
 firstInBin (nInventoryBins) int32 ...
 lastInBin (nInventoryBins) int32 ...
 secondsStage1_2 (recNum) int32 ...
 secondsStage3 (recNum) int32 ...
 wmoId (recNum) int32 ...
 stationName (recNum, maxStaNamLen) |S1 ...
 locationName (recNum, maxLocationLen) |S1 ...
 QCT (QCcheckNum, QCcheckNameLen) |S1 ...
 ICT (ICcheckNum, ICcheckNameLen) |S1 ...
 latitude (recNum) float32 ...
 longitude (recNum) float32 ...
 elevation (recNum) float32 ...


I have tried multiple methods based on Help1 and Help2 to setup the boundaries which should be between latitude[20,53] and longitude[-131,-62]. The dataset can be accessed at NetCDF Data.



When I use the below, it says, "ValueError: dimensions or multi-index levels ['latitude', 'longitude'] do not exist"



import xarray as xr
ds = xr.open_dataset('/home/awips/python-awips/ups/20181110_1600.nc',
 decode_cf=False)
print(ds)
lat_bnds, lon_bnds = [20, 53], [-131, -62]
ds.sel(latitude=slice(*lat_bnds), longitude=slice(*lon_bnds))
ds.to_netcdf(path='/home/awips/python-awips/ups/subset.nc')


When I try the below, it works through the data, but does not remove any data.



import xarray as xr
ds = xr.open_dataset('/home/awips/python-awips/ups/20181110_1600.nc', decode_cf=True)

ds.where((-131 < ds.longitude) & (ds.longitude < -62)
 & (20 < ds.latitude) & (ds.latitude < 53), drop=True)
ds.to_netcdf(path='/home/awips/python-awips/ups/subset.nc')


Any ideas?






python slice netcdf python-xarray







share|improve this question
























  • Your second approach looks like the right way to solve this, for cases where latitude and longitude are not dimensions. I'm surprised it isn't removing any data -- are you sure sure there are records with longitude/latitude outside those bounds?

    – shoyer
    Nov 15 '18 at 21:20











  • I changed the code to ds.where((-95 < ds.longitude) & (ds.longitude < -80) & (30 < ds.latitude) & (ds.latitude < 35), drop=True). The file it creates is twice the size as the original, so something is wrong somewhere. The lat values are still there for -75.

    – WxJack
    Nov 15 '18 at 21:49












  • @shoyer, I was able to remove the data by assigning it to a new variable, but I am not sure how I properly save the new data. What worked as latitude = ds.latitude.where((ds.latitude > 20) & (ds.latitude < 53), drop=True), longitude = ds.longitude.where((ds.longitude > -131) & (ds.longitude < -62), drop=True). Any further ideas?

    – WxJack
    Nov 16 '18 at 1:59















0















I am looking to take a subset of the netcdf data set bounded by lat/lon coordinates. 



<xarray.Dataset>
Dimensions: (ICcheckNameLen: 72, ICcheckNum: 55, QCcheckNameLen: 60, QCcheckNum: 10, maxAutoStaLen: 6, maxLocationLen: 24, maxMETARLen: 256, maxRepLen: 6, maxSkyCover: 6, maxSkyLen: 8, maxStaNamLen: 5, maxStaticIds: 10000, maxWeatherLen: 25, nInventoryBins: 24, recNum: 8329, totalIdLen: 6)
Dimensions without coordinates: ICcheckNameLen, ICcheckNum, QCcheckNameLen, QCcheckNum, maxAutoStaLen, maxLocationLen, maxMETARLen, maxRepLen, maxSkyCover, maxSkyLen, maxStaNamLen, maxStaticIds, maxWeatherLen, nInventoryBins, recNum, totalIdLen
Data variables:
 nStaticIds int32 ...
 staticIds (maxStaticIds, totalIdLen) |S1 ...
 lastRecord (maxStaticIds) int32 ...
 invTime (recNum) int32 ...
 prevRecord (recNum) int32 ...
 inventory (maxStaticIds) int32 ...
 globalInventory int32 ...
 firstOverflow int32 ...
 isOverflow (recNum) int32 ...
 firstInBin (nInventoryBins) int32 ...
 lastInBin (nInventoryBins) int32 ...
 secondsStage1_2 (recNum) int32 ...
 secondsStage3 (recNum) int32 ...
 wmoId (recNum) int32 ...
 stationName (recNum, maxStaNamLen) |S1 ...
 locationName (recNum, maxLocationLen) |S1 ...
 QCT (QCcheckNum, QCcheckNameLen) |S1 ...
 ICT (ICcheckNum, ICcheckNameLen) |S1 ...
 latitude (recNum) float32 ...
 longitude (recNum) float32 ...
 elevation (recNum) float32 ...


I have tried multiple methods based on Help1 and Help2 to setup the boundaries which should be between latitude[20,53] and longitude[-131,-62]. The dataset can be accessed at NetCDF Data.



When I use the below, it says, "ValueError: dimensions or multi-index levels ['latitude', 'longitude'] do not exist"



import xarray as xr
ds = xr.open_dataset('/home/awips/python-awips/ups/20181110_1600.nc',
 decode_cf=False)
print(ds)
lat_bnds, lon_bnds = [20, 53], [-131, -62]
ds.sel(latitude=slice(*lat_bnds), longitude=slice(*lon_bnds))
ds.to_netcdf(path='/home/awips/python-awips/ups/subset.nc')


When I try the below, it works through the data, but does not remove any data.



import xarray as xr
ds = xr.open_dataset('/home/awips/python-awips/ups/20181110_1600.nc', decode_cf=True)

ds.where((-131 < ds.longitude) & (ds.longitude < -62)
 & (20 < ds.latitude) & (ds.latitude < 53), drop=True)
ds.to_netcdf(path='/home/awips/python-awips/ups/subset.nc')


Any ideas?






python slice netcdf python-xarray







share|improve this question
























  • Your second approach looks like the right way to solve this, for cases where latitude and longitude are not dimensions. I'm surprised it isn't removing any data -- are you sure sure there are records with longitude/latitude outside those bounds?

    – shoyer
    Nov 15 '18 at 21:20











  • I changed the code to ds.where((-95 < ds.longitude) & (ds.longitude < -80) & (30 < ds.latitude) & (ds.latitude < 35), drop=True). The file it creates is twice the size as the original, so something is wrong somewhere. The lat values are still there for -75.

    – WxJack
    Nov 15 '18 at 21:49












  • @shoyer, I was able to remove the data by assigning it to a new variable, but I am not sure how I properly save the new data. What worked as latitude = ds.latitude.where((ds.latitude > 20) & (ds.latitude < 53), drop=True), longitude = ds.longitude.where((ds.longitude > -131) & (ds.longitude < -62), drop=True). Any further ideas?

    – WxJack
    Nov 16 '18 at 1:59













0












0








0








I am looking to take a subset of the netcdf data set bounded by lat/lon coordinates. 



<xarray.Dataset>
Dimensions: (ICcheckNameLen: 72, ICcheckNum: 55, QCcheckNameLen: 60, QCcheckNum: 10, maxAutoStaLen: 6, maxLocationLen: 24, maxMETARLen: 256, maxRepLen: 6, maxSkyCover: 6, maxSkyLen: 8, maxStaNamLen: 5, maxStaticIds: 10000, maxWeatherLen: 25, nInventoryBins: 24, recNum: 8329, totalIdLen: 6)
Dimensions without coordinates: ICcheckNameLen, ICcheckNum, QCcheckNameLen, QCcheckNum, maxAutoStaLen, maxLocationLen, maxMETARLen, maxRepLen, maxSkyCover, maxSkyLen, maxStaNamLen, maxStaticIds, maxWeatherLen, nInventoryBins, recNum, totalIdLen
Data variables:
 nStaticIds int32 ...
 staticIds (maxStaticIds, totalIdLen) |S1 ...
 lastRecord (maxStaticIds) int32 ...
 invTime (recNum) int32 ...
 prevRecord (recNum) int32 ...
 inventory (maxStaticIds) int32 ...
 globalInventory int32 ...
 firstOverflow int32 ...
 isOverflow (recNum) int32 ...
 firstInBin (nInventoryBins) int32 ...
 lastInBin (nInventoryBins) int32 ...
 secondsStage1_2 (recNum) int32 ...
 secondsStage3 (recNum) int32 ...
 wmoId (recNum) int32 ...
 stationName (recNum, maxStaNamLen) |S1 ...
 locationName (recNum, maxLocationLen) |S1 ...
 QCT (QCcheckNum, QCcheckNameLen) |S1 ...
 ICT (ICcheckNum, ICcheckNameLen) |S1 ...
 latitude (recNum) float32 ...
 longitude (recNum) float32 ...
 elevation (recNum) float32 ...


I have tried multiple methods based on Help1 and Help2 to setup the boundaries which should be between latitude[20,53] and longitude[-131,-62]. The dataset can be accessed at NetCDF Data.



When I use the below, it says, "ValueError: dimensions or multi-index levels ['latitude', 'longitude'] do not exist"



import xarray as xr
ds = xr.open_dataset('/home/awips/python-awips/ups/20181110_1600.nc',
 decode_cf=False)
print(ds)
lat_bnds, lon_bnds = [20, 53], [-131, -62]
ds.sel(latitude=slice(*lat_bnds), longitude=slice(*lon_bnds))
ds.to_netcdf(path='/home/awips/python-awips/ups/subset.nc')


When I try the below, it works through the data, but does not remove any data.



import xarray as xr
ds = xr.open_dataset('/home/awips/python-awips/ups/20181110_1600.nc', decode_cf=True)

ds.where((-131 < ds.longitude) & (ds.longitude < -62)
 & (20 < ds.latitude) & (ds.latitude < 53), drop=True)
ds.to_netcdf(path='/home/awips/python-awips/ups/subset.nc')


Any ideas?






python slice netcdf python-xarray







share|improve this question
















I am looking to take a subset of the netcdf data set bounded by lat/lon coordinates. 



<xarray.Dataset>
Dimensions: (ICcheckNameLen: 72, ICcheckNum: 55, QCcheckNameLen: 60, QCcheckNum: 10, maxAutoStaLen: 6, maxLocationLen: 24, maxMETARLen: 256, maxRepLen: 6, maxSkyCover: 6, maxSkyLen: 8, maxStaNamLen: 5, maxStaticIds: 10000, maxWeatherLen: 25, nInventoryBins: 24, recNum: 8329, totalIdLen: 6)
Dimensions without coordinates: ICcheckNameLen, ICcheckNum, QCcheckNameLen, QCcheckNum, maxAutoStaLen, maxLocationLen, maxMETARLen, maxRepLen, maxSkyCover, maxSkyLen, maxStaNamLen, maxStaticIds, maxWeatherLen, nInventoryBins, recNum, totalIdLen
Data variables:
 nStaticIds int32 ...
 staticIds (maxStaticIds, totalIdLen) |S1 ...
 lastRecord (maxStaticIds) int32 ...
 invTime (recNum) int32 ...
 prevRecord (recNum) int32 ...
 inventory (maxStaticIds) int32 ...
 globalInventory int32 ...
 firstOverflow int32 ...
 isOverflow (recNum) int32 ...
 firstInBin (nInventoryBins) int32 ...
 lastInBin (nInventoryBins) int32 ...
 secondsStage1_2 (recNum) int32 ...
 secondsStage3 (recNum) int32 ...
 wmoId (recNum) int32 ...
 stationName (recNum, maxStaNamLen) |S1 ...
 locationName (recNum, maxLocationLen) |S1 ...
 QCT (QCcheckNum, QCcheckNameLen) |S1 ...
 ICT (ICcheckNum, ICcheckNameLen) |S1 ...
 latitude (recNum) float32 ...
 longitude (recNum) float32 ...
 elevation (recNum) float32 ...


I have tried multiple methods based on Help1 and Help2 to setup the boundaries which should be between latitude[20,53] and longitude[-131,-62]. The dataset can be accessed at NetCDF Data.



When I use the below, it says, "ValueError: dimensions or multi-index levels ['latitude', 'longitude'] do not exist"



import xarray as xr
ds = xr.open_dataset('/home/awips/python-awips/ups/20181110_1600.nc',
 decode_cf=False)
print(ds)
lat_bnds, lon_bnds = [20, 53], [-131, -62]
ds.sel(latitude=slice(*lat_bnds), longitude=slice(*lon_bnds))
ds.to_netcdf(path='/home/awips/python-awips/ups/subset.nc')


When I try the below, it works through the data, but does not remove any data.



import xarray as xr
ds = xr.open_dataset('/home/awips/python-awips/ups/20181110_1600.nc', decode_cf=True)

ds.where((-131 < ds.longitude) & (ds.longitude < -62)
 & (20 < ds.latitude) & (ds.latitude < 53), drop=True)
ds.to_netcdf(path='/home/awips/python-awips/ups/subset.nc')


Any ideas?






python slice netcdf python-xarray




python slice netcdf python-xarray






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited Nov 15 '18 at 21:25







WxJack

















asked Nov 15 '18 at 20:53









WxJackWxJack

13




13












  • Your second approach looks like the right way to solve this, for cases where latitude and longitude are not dimensions. I'm surprised it isn't removing any data -- are you sure sure there are records with longitude/latitude outside those bounds?

    – shoyer
    Nov 15 '18 at 21:20











  • I changed the code to ds.where((-95 < ds.longitude) & (ds.longitude < -80) & (30 < ds.latitude) & (ds.latitude < 35), drop=True). The file it creates is twice the size as the original, so something is wrong somewhere. The lat values are still there for -75.

    – WxJack
    Nov 15 '18 at 21:49












  • @shoyer, I was able to remove the data by assigning it to a new variable, but I am not sure how I properly save the new data. What worked as latitude = ds.latitude.where((ds.latitude > 20) & (ds.latitude < 53), drop=True), longitude = ds.longitude.where((ds.longitude > -131) & (ds.longitude < -62), drop=True). Any further ideas?

    – WxJack
    Nov 16 '18 at 1:59

















  • Your second approach looks like the right way to solve this, for cases where latitude and longitude are not dimensions. I'm surprised it isn't removing any data -- are you sure sure there are records with longitude/latitude outside those bounds?

    – shoyer
    Nov 15 '18 at 21:20











  • I changed the code to ds.where((-95 < ds.longitude) & (ds.longitude < -80) & (30 < ds.latitude) & (ds.latitude < 35), drop=True). The file it creates is twice the size as the original, so something is wrong somewhere. The lat values are still there for -75.

    – WxJack
    Nov 15 '18 at 21:49












  • @shoyer, I was able to remove the data by assigning it to a new variable, but I am not sure how I properly save the new data. What worked as latitude = ds.latitude.where((ds.latitude > 20) & (ds.latitude < 53), drop=True), longitude = ds.longitude.where((ds.longitude > -131) & (ds.longitude < -62), drop=True). Any further ideas?

    – WxJack
    Nov 16 '18 at 1:59
















Your second approach looks like the right way to solve this, for cases where latitude and longitude are not dimensions. I'm surprised it isn't removing any data -- are you sure sure there are records with longitude/latitude outside those bounds?

– shoyer
Nov 15 '18 at 21:20





Your second approach looks like the right way to solve this, for cases where latitude and longitude are not dimensions. I'm surprised it isn't removing any data -- are you sure sure there are records with longitude/latitude outside those bounds?

– shoyer
Nov 15 '18 at 21:20













I changed the code to ds.where((-95 < ds.longitude) & (ds.longitude < -80) & (30 < ds.latitude) & (ds.latitude < 35), drop=True). The file it creates is twice the size as the original, so something is wrong somewhere. The lat values are still there for -75.

– WxJack
Nov 15 '18 at 21:49






I changed the code to ds.where((-95 < ds.longitude) & (ds.longitude < -80) & (30 < ds.latitude) & (ds.latitude < 35), drop=True). The file it creates is twice the size as the original, so something is wrong somewhere. The lat values are still there for -75.

– WxJack
Nov 15 '18 at 21:49














@shoyer, I was able to remove the data by assigning it to a new variable, but I am not sure how I properly save the new data. What worked as latitude = ds.latitude.where((ds.latitude > 20) & (ds.latitude < 53), drop=True), longitude = ds.longitude.where((ds.longitude > -131) & (ds.longitude < -62), drop=True). Any further ideas?

– WxJack
Nov 16 '18 at 1:59





@shoyer, I was able to remove the data by assigning it to a new variable, but I am not sure how I properly save the new data. What worked as latitude = ds.latitude.where((ds.latitude > 20) & (ds.latitude < 53), drop=True), longitude = ds.longitude.where((ds.longitude > -131) & (ds.longitude < -62), drop=True). Any further ideas?

– WxJack
Nov 16 '18 at 1:59












1 Answer
1






active

oldest

votes


















1














Xarray operations usually return new objects instead of modifying objects inplace. So you need to assign the result of where to a new variable and save that instead, e.g.,



ds2 = ds.where((-131 < ds.longitude) & (ds.longitude < -62)
 & (20 < ds.latitude) & (ds.latitude < 53), drop=True)
ds2.to_netcdf(path='/home/awips/python-awips/ups/subset.nc')





share|improve this answer























  • If I use ds2 = ds.where((ds.latitude > 20) & (ds.latitude < 50) & (ds.longitude > -131) & (ds.longitude < -62), drop=True) ds2.to_netcdf(path='/home/awips/python-awips/ups/subset.nc') the size of the file increases dramatically and has tons of repeat variables that were not there before.

    – WxJack
    Nov 18 '18 at 15:58











  • I don't know what's going on with the extra variables, but read this issue for a discussion of what's (probably) happening with file sizes: github.com/pydata/xarray/issues/1572

    – shoyer
    Nov 19 '18 at 16:10











  • @shoyer this could also be related to the variables not indexed by latitude/longitude being broadcast against the where condition. The syntax you suggest should only be used for the variables in the Dataset that are indexed by latitude and longitude, no?

    – delgadom
    Nov 26 '18 at 2:30











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1 Answer
1






active

oldest

votes








1 Answer
1






active

oldest

votes









active

oldest

votes






active

oldest

votes









1














Xarray operations usually return new objects instead of modifying objects inplace. So you need to assign the result of where to a new variable and save that instead, e.g.,



ds2 = ds.where((-131 < ds.longitude) & (ds.longitude < -62)
 & (20 < ds.latitude) & (ds.latitude < 53), drop=True)
ds2.to_netcdf(path='/home/awips/python-awips/ups/subset.nc')





share|improve this answer























  • If I use ds2 = ds.where((ds.latitude > 20) & (ds.latitude < 50) & (ds.longitude > -131) & (ds.longitude < -62), drop=True) ds2.to_netcdf(path='/home/awips/python-awips/ups/subset.nc') the size of the file increases dramatically and has tons of repeat variables that were not there before.

    – WxJack
    Nov 18 '18 at 15:58











  • I don't know what's going on with the extra variables, but read this issue for a discussion of what's (probably) happening with file sizes: github.com/pydata/xarray/issues/1572

    – shoyer
    Nov 19 '18 at 16:10











  • @shoyer this could also be related to the variables not indexed by latitude/longitude being broadcast against the where condition. The syntax you suggest should only be used for the variables in the Dataset that are indexed by latitude and longitude, no?

    – delgadom
    Nov 26 '18 at 2:30
















1














Xarray operations usually return new objects instead of modifying objects inplace. So you need to assign the result of where to a new variable and save that instead, e.g.,



ds2 = ds.where((-131 < ds.longitude) & (ds.longitude < -62)
 & (20 < ds.latitude) & (ds.latitude < 53), drop=True)
ds2.to_netcdf(path='/home/awips/python-awips/ups/subset.nc')





share|improve this answer























  • If I use ds2 = ds.where((ds.latitude > 20) & (ds.latitude < 50) & (ds.longitude > -131) & (ds.longitude < -62), drop=True) ds2.to_netcdf(path='/home/awips/python-awips/ups/subset.nc') the size of the file increases dramatically and has tons of repeat variables that were not there before.

    – WxJack
    Nov 18 '18 at 15:58











  • I don't know what's going on with the extra variables, but read this issue for a discussion of what's (probably) happening with file sizes: github.com/pydata/xarray/issues/1572

    – shoyer
    Nov 19 '18 at 16:10











  • @shoyer this could also be related to the variables not indexed by latitude/longitude being broadcast against the where condition. The syntax you suggest should only be used for the variables in the Dataset that are indexed by latitude and longitude, no?

    – delgadom
    Nov 26 '18 at 2:30














1












1








1







Xarray operations usually return new objects instead of modifying objects inplace. So you need to assign the result of where to a new variable and save that instead, e.g.,



ds2 = ds.where((-131 < ds.longitude) & (ds.longitude < -62)
 & (20 < ds.latitude) & (ds.latitude < 53), drop=True)
ds2.to_netcdf(path='/home/awips/python-awips/ups/subset.nc')





share|improve this answer













Xarray operations usually return new objects instead of modifying objects inplace. So you need to assign the result of where to a new variable and save that instead, e.g.,



ds2 = ds.where((-131 < ds.longitude) & (ds.longitude < -62)
 & (20 < ds.latitude) & (ds.latitude < 53), drop=True)
ds2.to_netcdf(path='/home/awips/python-awips/ups/subset.nc')






share|improve this answer












share|improve this answer



share|improve this answer










answered Nov 16 '18 at 23:39









shoyershoyer

5,0391533




5,0391533












  • If I use ds2 = ds.where((ds.latitude > 20) & (ds.latitude < 50) & (ds.longitude > -131) & (ds.longitude < -62), drop=True) ds2.to_netcdf(path='/home/awips/python-awips/ups/subset.nc') the size of the file increases dramatically and has tons of repeat variables that were not there before.

    – WxJack
    Nov 18 '18 at 15:58











  • I don't know what's going on with the extra variables, but read this issue for a discussion of what's (probably) happening with file sizes: github.com/pydata/xarray/issues/1572

    – shoyer
    Nov 19 '18 at 16:10











  • @shoyer this could also be related to the variables not indexed by latitude/longitude being broadcast against the where condition. The syntax you suggest should only be used for the variables in the Dataset that are indexed by latitude and longitude, no?

    – delgadom
    Nov 26 '18 at 2:30


















  • If I use ds2 = ds.where((ds.latitude > 20) & (ds.latitude < 50) & (ds.longitude > -131) & (ds.longitude < -62), drop=True) ds2.to_netcdf(path='/home/awips/python-awips/ups/subset.nc') the size of the file increases dramatically and has tons of repeat variables that were not there before.

    – WxJack
    Nov 18 '18 at 15:58











  • I don't know what's going on with the extra variables, but read this issue for a discussion of what's (probably) happening with file sizes: github.com/pydata/xarray/issues/1572

    – shoyer
    Nov 19 '18 at 16:10











  • @shoyer this could also be related to the variables not indexed by latitude/longitude being broadcast against the where condition. The syntax you suggest should only be used for the variables in the Dataset that are indexed by latitude and longitude, no?

    – delgadom
    Nov 26 '18 at 2:30

















If I use ds2 = ds.where((ds.latitude > 20) & (ds.latitude < 50) & (ds.longitude > -131) & (ds.longitude < -62), drop=True) ds2.to_netcdf(path='/home/awips/python-awips/ups/subset.nc') the size of the file increases dramatically and has tons of repeat variables that were not there before.

– WxJack
Nov 18 '18 at 15:58





If I use ds2 = ds.where((ds.latitude > 20) & (ds.latitude < 50) & (ds.longitude > -131) & (ds.longitude < -62), drop=True) ds2.to_netcdf(path='/home/awips/python-awips/ups/subset.nc') the size of the file increases dramatically and has tons of repeat variables that were not there before.

– WxJack
Nov 18 '18 at 15:58













I don't know what's going on with the extra variables, but read this issue for a discussion of what's (probably) happening with file sizes: github.com/pydata/xarray/issues/1572

– shoyer
Nov 19 '18 at 16:10





I don't know what's going on with the extra variables, but read this issue for a discussion of what's (probably) happening with file sizes: github.com/pydata/xarray/issues/1572

– shoyer
Nov 19 '18 at 16:10













@shoyer this could also be related to the variables not indexed by latitude/longitude being broadcast against the where condition. The syntax you suggest should only be used for the variables in the Dataset that are indexed by latitude and longitude, no?

– delgadom
Nov 26 '18 at 2:30






@shoyer this could also be related to the variables not indexed by latitude/longitude being broadcast against the where condition. The syntax you suggest should only be used for the variables in the Dataset that are indexed by latitude and longitude, no?

– delgadom
Nov 26 '18 at 2:30




















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