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andrewdelmanandrewdelman
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Some ecco_access updates to intro to PO tutorials
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‎Intro_to_PO_Tutorials/Geostrophic_balance.ipynb‎

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"metadata": {},
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"source": [
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"# Part 1: Geostrophic balance\n",
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"Andrew Delman, updated 2024-10-15.\n",
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"Andrew Delman, updated 2024-10-16.\n",
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"\n",
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"## Objectives\n",
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"\n",
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"\n",
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"*ecco_access* provides a convenient function, `ecco_podaac_to_xrdataset` that accesses the requested output from PO.DAAC's cloud storage **and** opens an *xarray* dataset.\n",
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"\n",
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"We also can specify a \"mode\" option, which tells the function how to access these datasets. The default mode is `download_ifspace`, which downloads these tutorials to your local machine (under `~/Downloads/ECCO_V4r4_PODAAC/`) *if* the downloaded files would occupy less than 50% of available disk space.\n",
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"We also can specify a \"mode\" option, which tells the function how to access these datasets. The default mode is `download_ifspace`, which downloads these tutorials to your local machine (under `~/Downloads/ECCO_V4r4_PODAAC/`) *if* the downloaded files would occupy less than 50% of available disk space. See the [access modes](https://ecco-v4-python-tutorial.readthedocs.io/ECCO_access_modes.html) tutorial for more information on these modes.\n",
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"\n",
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"> Tip: If you are working in the AWS Cloud, you can specify `incloud_access = True`, which will then use mode = `s3_get_ifspace` by default. The `s3_get_ifspace` mode downloads the files to your local instance if they would occupy less than 50% of available disk space; otherwise the data are accessed remotely (typically slower).\n",
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"\n",
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"import ecco_v4_py as ecco\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"# import ecco_access\n",
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"import ecco_access as ea\n",
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"\n",
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"# specify location to store downloaded files\n",
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"\n",
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"# ~/Downloads/ECCO_V4r4_PODAAC is also the default path used for download_root_dir; \n",
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"# change as needed\n",
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"download_root_dir = join(user_home_dir,'Downloads','ECCO_V4r4_PODAAC')\n",
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"\n",
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"# import ecco_access\n",
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"import ecco_access as ea"
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"download_root_dir = join(user_home_dir,'Downloads','ECCO_V4r4_PODAAC')"
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]
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},
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{
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.3"
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"version": "3.11.9"
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},
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"vscode": {
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"interpreter": {

‎Intro_to_PO_Tutorials/Steric_height.ipynb‎

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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.8"
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"version": "3.11.9"
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}
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},
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"nbformat": 4,

‎Intro_to_PO_Tutorials/Thermal_wind.ipynb‎

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"metadata": {},
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"source": [
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"# Part 2: Thermal Wind\n",
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"Andrew Delman, updated 2024-04-04.\n",
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"Andrew Delman, updated 2024-10-16.\n",
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"\n",
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"## Objectives\n",
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"\n",
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"\n",
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"- **ECCO_L4_OCEAN_VEL_LLC0090GRID_MONTHLY_V4R4** (Jan 2000)\n",
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"- **ECCO_L4_DENS_STRAT_PRESS_LLC0090GRID_MONTHLY_V4R4** (Jan 2000)\n",
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"- **ECCO_L4_GEOMETRY_LLC0090GRID_V4R4** (no time dimension; can use any time 1992-2017 in download functions)\n",
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"- **ECCO_L4_GEOMETRY_LLC0090GRID_V4R4** (no time dimension)\n",
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"\n",
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"To download these datasets to your local machine, you can use the [ecco_download.py](https://raw.githubusercontent.com/ECCO-GROUP/ECCO-v4-Python-Tutorial/master/ecco_access/ecco_download.py) module. See the [previous tutorial](https://ecco-v4-python-tutorial.readthedocs.io/Geostrophic_balance.html#Download-the-ECCO-output) or the [ECCO download](https://ecco-v4-python-tutorial.readthedocs.io/Downloading_ECCO_Datasets_from_PODAAC_Python.html) for more info on how to use this module. If you are working on an AWS instance, just set `incloud_access = True` in the 2nd cell below and the datasets will be downloaded or opened remotely using the `ecco_s3_retrieve.py` module.\n",
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"To download these datasets to your local machine, you can use the `ecco_access` library; see the [*ecco_access* intro tutorial](https://ecco-v4-python-tutorial.readthedocs.io/ECCO_access_intro.html) for instructions on how to [set up](https://ecco-v4-python-tutorial.readthedocs.io/ECCO_access_intro.html#Setting-up-ecco_access) this library in your Python path. The \"access mode\" for retrieving the datasets is `download_ifspace` by default, which downloads the data (under `~/Downloads/ECCO_V4r4_PODAAC/`) only if it will occupy <50% of available storage. See the [access modes](https://ecco-v4-python-tutorial.readthedocs.io/ECCO_access_modes.html) tutorial for more information on these modes.\n",
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"\n",
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"> Tip: If you are working in the AWS Cloud, you can specify `incloud_access = True`, which will then use mode = `s3_get_ifspace` by default. The `s3_get_ifspace` mode downloads the files to your local instance if they would occupy less than 50% of available disk space; otherwise the data are accessed remotely (typically slower).\n",
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"\n",
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"We are going to load the density dataset and plot density on longitude-depth axes following the line in the grid nearest to $26^{o}$ N, i.e., ```i = 73```."
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]
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"metadata": {},
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"outputs": [],
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"source": [
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"# specify incloud_access = True if working in the AWS Cloud, otherwise False\n",
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"incloud_access = False\n",
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"\n",
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"if incloud_access:\n",
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" access_mode = 's3_get_ifspace'\n",
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"else:\n",
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" access_mode = 'download_ifspace'\n",
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"\n",
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"# first import needed packages\n",
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"import numpy as np\n",
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"import xarray as xr\n",
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"sys.path.append(join(user_home_dir,'ECCOv4-py')) # only needed if ecco_v4_py files are stored under this directory\n",
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"import matplotlib.pyplot as plt\n",
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"import ecco_v4_py as ecco\n",
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"import ecco_access as ea\n",
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"from ecco_po_tutorials import * # import from ecco_po_tutorials.py module downloaded in the last tutorial\n",
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"\n",
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"\n",
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"# ShortNames\n",
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"vel_monthly_shortname = \"ECCO_L4_OCEAN_VEL_LLC0090GRID_MONTHLY_V4R4\"\n",
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"denspress_monthly_shortname = \"ECCO_L4_DENS_STRAT_PRESS_LLC0090GRID_MONTHLY_V4R4\"\n",
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"metadata": {},
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"outputs": [],
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"source": [
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"# if working in the AWS cloud, set incloud_access = True and run this cell\n",
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"# specify location to store downloaded files\n",
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"\n",
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"incloud_access = False\n",
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"\n",
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"ShortNames_list = [vel_monthly_shortname,denspress_monthly_shortname,grid_params_shortname]\n",
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"if incloud_access == True:\n",
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" from ecco_s3_retrieve import ecco_podaac_s3_get_diskaware\n",
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" # download files to default path ~/Downloads/ECCO_V4r4_PODAAC/\n",
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" files_dict = ecco_podaac_s3_get_diskaware(ShortNames=ShortNames_list,\\\n",
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" StartDate='2000-01',EndDate='2000-01',\\\n",
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" max_avail_frac=0.5,\\\n",
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" download_root_dir=None)"
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"# ~/Downloads/ECCO_V4r4_PODAAC is also the default path used for download_root_dir; \n",
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"# change as needed\n",
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"download_root_dir = join(user_home_dir,'Downloads','ECCO_V4r4_PODAAC')"
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]
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},
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{
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"# now open density and grid parameter files\n",
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"\n",
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"# density file\n",
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"download_root_dir = join(user_home_dir,'Downloads','ECCO_V4r4_PODAAC')\n",
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"download_dir = join(download_root_dir,denspress_monthly_shortname)\n",
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"if incloud_access == True:\n",
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" curr_denspress_file = files_dict[denspress_monthly_shortname]\n",
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"else:\n",
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" curr_denspress_file = list(glob.glob(join(download_dir,'*2000-01*.nc')))\n",
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"ds_dens = xr.open_dataset(curr_denspress_file[0])\n",
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"ds_dens = xr.ecco_podaac_to_xrdataset(denspress_monthly_shortname,\\\n",
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" StartDate=\"2000-01\",EndDate=\"2000-01\",\\\n",
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" mode=access_mode,\\\n",
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" download_root_dir=download_root_dir)\n",
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"ds_dens"
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]
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},
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],
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"source": [
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"# load grid parameters file\n",
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"grid_params_file = \"GRID_GEOMETRY_ECCO_V4r4_native_llc0090.nc\"\n",
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"if incloud_access == True:\n",
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" grid_params_file_path = files_dict[grid_params_shortname]\n",
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"else:\n",
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" grid_params_file_path = join(download_root_dir,grid_params_shortname,grid_params_file)\n",
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"ds_grid = xr.open_dataset(grid_params_file_path)\n",
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"ds_grid = xr.ecco_podaac_to_xrdataset(grid_params_shortname,\\\n",
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" mode=access_mode,\\\n",
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" download_root_dir=download_root_dir)\n",
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"\n",
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"# find index closest to target latitude\n",
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"lat_target = 26.\n",
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"outputs": [],
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"source": [
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"# load velocity dataset\n",
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"download_dir = join(download_root_dir,vel_monthly_shortname)\n",
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"if incloud_access == True:\n",
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" curr_vel_file = files_dict[vel_monthly_shortname]\n",
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"else:\n",
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" curr_vel_file = list(glob.glob(join(download_dir,'*2000-01*.nc')))\n",
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"ds_vel = xr.open_dataset(curr_vel_file[0])\n",
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"ds_vel = xr.ecco_podaac_to_xrdataset(vel_monthly_shortname,\\\n",
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" StartDate='2000-01',EndDate='2000-01',\\\n",
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" mode=access_mode,\\\n",
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" download_root_dir=download_root_dir)\n",
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"\n",
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"# interpolate velocities to center of grid cells\n",
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"ds_vel.UVEL.values[np.isnan(ds_vel.UVEL.values)] = 0\n",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.8"
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"version": "3.11.9"
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}
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},
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"nbformat": 4,

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