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I'm trying to use the llc2160 outputs to analyze Argo profiles data but haven't been successful. In particular, I want to subsample full-depth, one-year temperature and salinity data with daily resolution at selected grid points around the Kuroshio region. Here are lines of example code for one location.
import xmitgcm.llcreader as llcreader
model = llcreader.ECCOPortalLLC2160Model()
n = 413
ds = model.get_dataset(varnames=['Theta','SALT'],
iter_start=92160+n*1920,iter_stop=92160+(n+365)*1920,iter_step=1920)
pT = ds.Theta.isel(face=7,i=1600,j=320).values
Ideally I want to access hundreds or thousands of grid points corresponding to Argo profile locations. But I'm constantly getting a timeout error or one of these two:
aiohttp.client_exceptions.ClientResponseError: 500, message='Internal Server Error', url=URL('https://data.nas.nasa.gov/ecco/download_data.php?file=/e
ccodata/llc_2160/compressed/0001370880/Theta.0001370880.data.shrunk')
Hello,
I'm trying to use the llc2160 outputs to analyze Argo profiles data but haven't been successful. In particular, I want to subsample full-depth, one-year temperature and salinity data with daily resolution at selected grid points around the Kuroshio region. Here are lines of example code for one location.
import xmitgcm.llcreader as llcreader
model = llcreader.ECCOPortalLLC2160Model()
n = 413
ds = model.get_dataset(varnames=['Theta','SALT'],
iter_start=92160+n*1920,iter_stop=92160+(n+365)*1920,iter_step=1920)
pT = ds.Theta.isel(face=7,i=1600,j=320).values
Ideally I want to access hundreds or thousands of grid points corresponding to Argo profile locations. But I'm constantly getting a timeout error or one of these two:
aiohttp.client_exceptions.ClientResponseError: 500, message='Internal Server Error', url=URL('https://data.nas.nasa.gov/ecco/download_data.php?file=/e
ccodata/llc_2160/compressed/0001370880/Theta.0001370880.data.shrunk')
FileNotFoundError: https://data.nas.nasa.gov/ecco/download_data.php?file=/eccodata/llc_2160/compressed/0001409280/Theta.0001409280.data.shrunk
I would appreciate any suggestions on how to do this efficiently. Thanks in advance!
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