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Merge pull request #90 from andrewdelman/cloud_compatibility
Update with ecco_access tutorials
2 parents 072f329 + a8ee4ae commit 9d28c60

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‎Intro_to_PO_Tutorials/Geostrophic_balance.ipynb‎

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‎Intro_to_PO_Tutorials/ecco_access‎

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../ecco_access

‎Intro_to_PO_Tutorials/ecco_download.py‎

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‎Intro_to_PO_Tutorials/ecco_po_tutorials.py‎

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#----------------------------------------------------------------------------------------
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def geos_vel_compute(dens_press_filename,grid_filename="~/Downloads/ECCO_V4r4_PODAAC/ECCO_L4_GEOMETRY_LLC0090GRID_V4R4/GRID_GEOMETRY_ECCO_V4r4_native_llc0090.nc",fc_filename="llc_13tile_fc.txt"):
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def geos_vel_compute(dens_press_filename,grid_filename="~/Downloads/ECCO_V4r4_PODAAC/ECCO_L4_GEOMETRY_LLC0090GRID_V4R4/GRID_GEOMETRY_ECCO_V4r4_native_llc0090.nc",subset=None):
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"""
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This routine computes geostrophic velocities from an input netCDF file containing ECCO v4r4 density and pressure anomalies on the native llc90 grid.
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dens_press_filename: the name (including path if not in current directory) of the netCDF file containing the density and pressure anomalies
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grid_filename: the name (including path if not in current directory) of the netCDF file containing the latitudes ('YC') and grid parameters ('dxC','dyC') needed to compute horizontal derivatives
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fc_filename: text file containing a Python dictionary specifying the tile/face connections in the ECCO llc90 grid
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subset: dictionary containing dimension names and index values, e.g., {'k': [0,1,2]}. The index values can be integers, slice objects, or arrays. This object is passed directly to xarray.Dataset.isel as an indexer. If not specified, the entire global domain will be used.
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"""
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import numpy as np
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import ecco_v4_py as ecco
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# load file into workspace
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ds_denspress = xr.open_dataset(dens_press_filename, data_vars='minimal',\
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coords='minimal', compat='override')
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ds_denspress = xr.open_dataset(dens_press_filename)
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densanom = ds_denspress.RHOAnoma
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‎Intro_to_PO_Tutorials/ecco_s3_retrieve.py‎

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‎Tutorials_as_Jupyter_Notebooks/ECCO_v4_Example_MHT.ipynb‎

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"- **ECCO_L4_GEOMETRY_LLC0090GRID_V4R4**\n",
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"- **ECCO_L4_OCEAN_3D_TEMPERATURE_FLUX_LLC0090GRID_MONTHLY_V4R4** (1992-2017)\n",
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"\n",
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"If you haven't yet been through the [download tutorial](https://ecco-v4-python-tutorial.readthedocs.io/Downloading_ECCO_Datasets_from_PODAAC_Python.html) or used the [ecco_download module](https://ecco-v4-python-tutorial.readthedocs.io/Downloading_ECCO_Datasets_from_PODAAC_Python.html#ECCO_download-module), it may help you to review that information before downloading the datasets."
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"If you haven't yet been through the [download tutorial](https://ecco-v4-python-tutorial.readthedocs.io/Downloading_ECCO_Datasets_from_PODAAC_Python.html) or used the [ecco_download module](https://ecco-v4-python-tutorial.readthedocs.io/Downloading_ECCO_Datasets_from_PODAAC_Python.html#ECCO_download-module:-the-quick-and-easy-method), it may help you to review that information before downloading the datasets."
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‎Tutorials_as_Jupyter_Notebooks/ECCO_v4_Example_OSNAP.ipynb‎

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"- **ECCO_L4_OCEAN_3D_VOLUME_FLUX_LLC0090GRID_MONTHLY_V4R4** (1992-2017)\n",
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"- **ECCO_L4_OCEAN_3D_TEMPERATURE_FLUX_LLC0090GRID_MONTHLY_V4R4** (1992-2017)\n",
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"\n",
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"If you need to download ECCO output and haven't yet been through the [download tutorial](https://ecco-v4-python-tutorial.readthedocs.io/Downloading_ECCO_Datasets_from_PODAAC_Python.html) or used the [ecco_download module](https://ecco-v4-python-tutorial.readthedocs.io/Downloading_ECCO_Datasets_from_PODAAC_Python.html#ECCO_download-module), it may help you to review that information before downloading the datasets."
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"If you need to download ECCO output and haven't yet been through the [download tutorial](https://ecco-v4-python-tutorial.readthedocs.io/Downloading_ECCO_Datasets_from_PODAAC_Python.html) or used the [ecco_download module](https://ecco-v4-python-tutorial.readthedocs.io/Downloading_ECCO_Datasets_from_PODAAC_Python.html#ECCO_download-module:-the-quick-and-easy-method), it may help you to review that information before downloading the datasets."
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‎Tutorials_as_Jupyter_Notebooks/ECCO_v4_Heat_budget_closure.ipynb‎

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"- **ECCO_L4_SSH_LLC0090GRID_SNAPSHOT_V4R4** (1993/1/1-2017/1/1, 1st of each month)\n",
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"- **ECCO_L4_TEMP_SALINITY_LLC0090GRID_SNAPSHOT_V4R4** (1993/1/1-2017/1/1, 1st of each month)\n",
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"\n",
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"If you haven't yet been through the [download tutorial](https://ecco-v4-python-tutorial.readthedocs.io/Downloading_ECCO_Datasets_from_PODAAC_Python.html) or used the [ecco_download module](https://ecco-v4-python-tutorial.readthedocs.io/Downloading_ECCO_Datasets_from_PODAAC_Python.html#ECCO_download-module), it may help you to review that information before downloading the datasets. If you are downloading the snapshots, you may also find the [snaps_monthly_textlist](https://ecco-v4-python-tutorial.readthedocs.io/ECCO_v4_Salt_and_salinity_budget.html#Load-monthly-snapshots) function helpful; it generates a file list of only the snapshot files for the 1st of each month, which you can then download using `wget`."
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"If you haven't yet been through the [download tutorial](https://ecco-v4-python-tutorial.readthedocs.io/Downloading_ECCO_Datasets_from_PODAAC_Python.html) or used the [ecco_download module](https://ecco-v4-python-tutorial.readthedocs.io/Downloading_ECCO_Datasets_from_PODAAC_Python.html#ECCO_download-module:-the-quick-and-easy-method), it may help you to review that information before downloading the datasets. If you are downloading the snapshots, you may also find the [snaps_monthly_textlist](https://ecco-v4-python-tutorial.readthedocs.io/ECCO_v4_Salt_and_salinity_budget.html#Load-monthly-snapshots) function helpful; it generates a file list of only the snapshot files for the 1st of each month, which you can then download using `wget`."
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‎Tutorials_as_Jupyter_Notebooks/ECCO_v4_Loading_the_ECCOv4_native_model_grid_parameters.ipynb‎

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"The ECCOv4 model grid parameters are provided as a single NetCDF file. It can be downloaded using the `ecco_podaac_download` function as described in the [download tutorial](https://ecco-v4-python-tutorial.readthedocs.io/Downloading_ECCO_Datasets_from_PODAAC_Python.html#ECCO_download-module). The ShortName for the dataset is **ECCO_L4_GEOMETRY_LLC0090GRID_V4R4**. The grid parameters file has no time dimension, but `ecco_podaac_download` requires a StartDate and EndDate to be specified; any date in the range 1992-2017 can be used."
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"The ECCOv4 model grid parameters are provided as a single NetCDF file. It can be downloaded using the `ecco_podaac_download` function as described in the [download tutorial](https://ecco-v4-python-tutorial.readthedocs.io/Downloading_ECCO_Datasets_from_PODAAC_Python.html#ECCO_download-module:-the-quick-and-easy-method). The ShortName for the dataset is **ECCO_L4_GEOMETRY_LLC0090GRID_V4R4**. The grid parameters file has no time dimension, but `ecco_podaac_download` requires a StartDate and EndDate to be specified; any date in the range 1992-2017 can be used."
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