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Trying to open a series of netCDF files with using OpenDAP


Error while accessing NOAA CO-OPS ocean model nowcast on IDVImporting and decoding dataset in xarray to avoid conflicting _FillValue and missing_valueViewing specific dates in a time-series of netCDF mapsPython xarray.concat then xarray.to_netcdf generates huge new file sizeHow to open multiple NetCDF files with xarray via OpenDAP?Why are NaNs introduced into dimension variable when loading a netcdf file into xarray datasetxarray automatically applying _FillValue to coordinates on netCDF output[Errno -101] NetCDF: HDF error when opening netcdf fileProblems reopening netcdf file written with xarray/dasktrouble looping xarray dataframe through subdirectories






.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty height:90px;width:728px;box-sizing:border-box;








0















I want to open all the data from 1950-2005 using xarray and open_mfdataset.
https://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/ncep.reanalysis/surface/catalog.html



This is what I have done so far:



source=https://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/ncep.reanalysis/surface/air.sig995.years.nc



 files = [source for years in range(1950,2005,1)]
ds=xr.open_mfdataset(files)
print(ds)


However, I cannot seem to get my list interpreted to be read into the variable years within source.



Any ideas?



Thank you in advance.



EDIT:
path = 'https://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/ncep.reanalysis/surface'
files = ['0/air.sig995.1:04d.nc'.format(path, years) for years in range(1950,2005,1)]
print(files)
nc = netCDF4.MFDataset(files)



This is the code I am using. When I try to open up these files I get an error:



OSError: [Errno -90] NetCDF: file not found: b'https://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/ncep.reanalysis/surface/air.sig995.1948.nc'


Did I not enter the path correctly?










share|improve this question






























    0















    I want to open all the data from 1950-2005 using xarray and open_mfdataset.
    https://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/ncep.reanalysis/surface/catalog.html



    This is what I have done so far:



    source=https://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/ncep.reanalysis/surface/air.sig995.years.nc



     files = [source for years in range(1950,2005,1)]
    ds=xr.open_mfdataset(files)
    print(ds)


    However, I cannot seem to get my list interpreted to be read into the variable years within source.



    Any ideas?



    Thank you in advance.



    EDIT:
    path = 'https://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/ncep.reanalysis/surface'
    files = ['0/air.sig995.1:04d.nc'.format(path, years) for years in range(1950,2005,1)]
    print(files)
    nc = netCDF4.MFDataset(files)



    This is the code I am using. When I try to open up these files I get an error:



    OSError: [Errno -90] NetCDF: file not found: b'https://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/ncep.reanalysis/surface/air.sig995.1948.nc'


    Did I not enter the path correctly?










    share|improve this question


























      0












      0








      0








      I want to open all the data from 1950-2005 using xarray and open_mfdataset.
      https://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/ncep.reanalysis/surface/catalog.html



      This is what I have done so far:



      source=https://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/ncep.reanalysis/surface/air.sig995.years.nc



       files = [source for years in range(1950,2005,1)]
      ds=xr.open_mfdataset(files)
      print(ds)


      However, I cannot seem to get my list interpreted to be read into the variable years within source.



      Any ideas?



      Thank you in advance.



      EDIT:
      path = 'https://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/ncep.reanalysis/surface'
      files = ['0/air.sig995.1:04d.nc'.format(path, years) for years in range(1950,2005,1)]
      print(files)
      nc = netCDF4.MFDataset(files)



      This is the code I am using. When I try to open up these files I get an error:



      OSError: [Errno -90] NetCDF: file not found: b'https://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/ncep.reanalysis/surface/air.sig995.1948.nc'


      Did I not enter the path correctly?










      share|improve this question
















      I want to open all the data from 1950-2005 using xarray and open_mfdataset.
      https://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/ncep.reanalysis/surface/catalog.html



      This is what I have done so far:



      source=https://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/ncep.reanalysis/surface/air.sig995.years.nc



       files = [source for years in range(1950,2005,1)]
      ds=xr.open_mfdataset(files)
      print(ds)


      However, I cannot seem to get my list interpreted to be read into the variable years within source.



      Any ideas?



      Thank you in advance.



      EDIT:
      path = 'https://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/ncep.reanalysis/surface'
      files = ['0/air.sig995.1:04d.nc'.format(path, years) for years in range(1950,2005,1)]
      print(files)
      nc = netCDF4.MFDataset(files)



      This is the code I am using. When I try to open up these files I get an error:



      OSError: [Errno -90] NetCDF: file not found: b'https://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/ncep.reanalysis/surface/air.sig995.1948.nc'


      Did I not enter the path correctly?







      dask netcdf python-xarray noaa






      share|improve this question















      share|improve this question













      share|improve this question




      share|improve this question








      edited Mar 25 at 20:37







      Jeff Coldplume

















      asked Mar 25 at 0:14









      Jeff ColdplumeJeff Coldplume

      175




      175






















          1 Answer
          1






          active

          oldest

          votes


















          0














          All files are named air.sig995.YYYY.nc, so you need something like:



          files = ['air.sig995.0:04d.nc'.format(years) for years in range(1950,2005,1)]


          Which produces:



          In [2]: files
          Out[2]:
          ['air.sig995.1950.nc',
          'air.sig995.1951.nc',
          'air.sig995.1952.nc',
          'air.sig995.1953.nc',
          .....


          You can also easily include a (remote) path here (if required):



          path = '/some/file/path'
          files = ['0/air.sig995.1:04d.nc'.format(path, years) for years in range(1950,2005,1)]


          See https://pyformat.info/ for more information on string formatting in Python.






          share|improve this answer























          • This is what I was looking for. I was unsure how to reference the dates as a variable within the remote path. What would be the best way to combine these into one continuous time series? Maybe xarray?

            – Jeff Coldplume
            Mar 25 at 20:00











          • Exactly xarray. You could use xr.auto_combine.

            – mdurant
            Mar 25 at 20:36











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          1 Answer
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          1 Answer
          1






          active

          oldest

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          active

          oldest

          votes






          active

          oldest

          votes









          0














          All files are named air.sig995.YYYY.nc, so you need something like:



          files = ['air.sig995.0:04d.nc'.format(years) for years in range(1950,2005,1)]


          Which produces:



          In [2]: files
          Out[2]:
          ['air.sig995.1950.nc',
          'air.sig995.1951.nc',
          'air.sig995.1952.nc',
          'air.sig995.1953.nc',
          .....


          You can also easily include a (remote) path here (if required):



          path = '/some/file/path'
          files = ['0/air.sig995.1:04d.nc'.format(path, years) for years in range(1950,2005,1)]


          See https://pyformat.info/ for more information on string formatting in Python.






          share|improve this answer























          • This is what I was looking for. I was unsure how to reference the dates as a variable within the remote path. What would be the best way to combine these into one continuous time series? Maybe xarray?

            – Jeff Coldplume
            Mar 25 at 20:00











          • Exactly xarray. You could use xr.auto_combine.

            – mdurant
            Mar 25 at 20:36















          0














          All files are named air.sig995.YYYY.nc, so you need something like:



          files = ['air.sig995.0:04d.nc'.format(years) for years in range(1950,2005,1)]


          Which produces:



          In [2]: files
          Out[2]:
          ['air.sig995.1950.nc',
          'air.sig995.1951.nc',
          'air.sig995.1952.nc',
          'air.sig995.1953.nc',
          .....


          You can also easily include a (remote) path here (if required):



          path = '/some/file/path'
          files = ['0/air.sig995.1:04d.nc'.format(path, years) for years in range(1950,2005,1)]


          See https://pyformat.info/ for more information on string formatting in Python.






          share|improve this answer























          • This is what I was looking for. I was unsure how to reference the dates as a variable within the remote path. What would be the best way to combine these into one continuous time series? Maybe xarray?

            – Jeff Coldplume
            Mar 25 at 20:00











          • Exactly xarray. You could use xr.auto_combine.

            – mdurant
            Mar 25 at 20:36













          0












          0








          0







          All files are named air.sig995.YYYY.nc, so you need something like:



          files = ['air.sig995.0:04d.nc'.format(years) for years in range(1950,2005,1)]


          Which produces:



          In [2]: files
          Out[2]:
          ['air.sig995.1950.nc',
          'air.sig995.1951.nc',
          'air.sig995.1952.nc',
          'air.sig995.1953.nc',
          .....


          You can also easily include a (remote) path here (if required):



          path = '/some/file/path'
          files = ['0/air.sig995.1:04d.nc'.format(path, years) for years in range(1950,2005,1)]


          See https://pyformat.info/ for more information on string formatting in Python.






          share|improve this answer













          All files are named air.sig995.YYYY.nc, so you need something like:



          files = ['air.sig995.0:04d.nc'.format(years) for years in range(1950,2005,1)]


          Which produces:



          In [2]: files
          Out[2]:
          ['air.sig995.1950.nc',
          'air.sig995.1951.nc',
          'air.sig995.1952.nc',
          'air.sig995.1953.nc',
          .....


          You can also easily include a (remote) path here (if required):



          path = '/some/file/path'
          files = ['0/air.sig995.1:04d.nc'.format(path, years) for years in range(1950,2005,1)]


          See https://pyformat.info/ for more information on string formatting in Python.







          share|improve this answer












          share|improve this answer



          share|improve this answer










          answered Mar 25 at 13:02









          BartBart

          5,04842244




          5,04842244












          • This is what I was looking for. I was unsure how to reference the dates as a variable within the remote path. What would be the best way to combine these into one continuous time series? Maybe xarray?

            – Jeff Coldplume
            Mar 25 at 20:00











          • Exactly xarray. You could use xr.auto_combine.

            – mdurant
            Mar 25 at 20:36

















          • This is what I was looking for. I was unsure how to reference the dates as a variable within the remote path. What would be the best way to combine these into one continuous time series? Maybe xarray?

            – Jeff Coldplume
            Mar 25 at 20:00











          • Exactly xarray. You could use xr.auto_combine.

            – mdurant
            Mar 25 at 20:36
















          This is what I was looking for. I was unsure how to reference the dates as a variable within the remote path. What would be the best way to combine these into one continuous time series? Maybe xarray?

          – Jeff Coldplume
          Mar 25 at 20:00





          This is what I was looking for. I was unsure how to reference the dates as a variable within the remote path. What would be the best way to combine these into one continuous time series? Maybe xarray?

          – Jeff Coldplume
          Mar 25 at 20:00













          Exactly xarray. You could use xr.auto_combine.

          – mdurant
          Mar 25 at 20:36





          Exactly xarray. You could use xr.auto_combine.

          – mdurant
          Mar 25 at 20:36



















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