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+0df401bc

external/Direct_Access_SWOT_sim_Oceanography.html

@@ -736,7 +736,7 @@ <h1 class="title">Access Sample SWOT Oceanography Data in the Cloud</h1>
 
 </header>
 
-<p>imported on: <strong>2023-06-27</strong></p>
+<p>imported on: <strong>2023-06-28</strong></p>
 <p>
 This notebook is from a different repository in NASA’s PO.DAAC, 2022-SWOT-OCEAN-Cloud-Workshop
 </p>

external/DownloadDopplerScattData.html

@@ -723,7 +723,7 @@ <h1 class="title">S-MODE Workshop: Science Case Study Airborne Part 1</h1>
 
 </header>
 
-<p>imported on: <strong>2023-06-27</strong></p>
+<p>imported on: <strong>2023-06-28</strong></p>
 <p>
 This notebook is from a different repository in NASA’s PO.DAAC, 2022-SMODE-Open-Data-Workshop
 </p>

external/ECCO_cloud_direct_access_s3.html

@@ -732,7 +732,7 @@ <h1 class="title">Direct Access to ECCO V4r4 Datasets in the Cloud</h1>
 
 </header>
 
-<p>imported on: <strong>2023-06-27</strong></p>
+<p>imported on: <strong>2023-06-28</strong></p>
 <p>
 This notebook is from a different repository in NASA’s PO.DAAC, ECCO.
 </p>

external/ECCO_download_data.html

@@ -727,7 +727,7 @@ <h1 class="title">Access to ECCO V4r4 Datasets on a Local Machine</h1>
 
 </header>
 
-<p>imported on: <strong>2023-06-27</strong></p>
+<p>imported on: <strong>2023-06-28</strong></p>
 <p>
 This notebook is from a different repository in NASA’s PO.DAAC, ECCO.
 </p>

external/Introduction_to_xarray.html

@@ -726,7 +726,7 @@ <h1 class="title">Xarray</h1>
 
 </header>
 
-<p>imported on: <strong>2023-06-27</strong></p>
+<p>imported on: <strong>2023-06-28</strong></p>
 <p>
 This notebook is from NASA Openscapes 2021 Cloud Hackathon Repository
 </p>

external/July_2022_Earthdata_Webinar.html

@@ -756,7 +756,7 @@ <h1 class="title">Earthdata Webinar</h1>
 
 </header>
 
-<p>imported on: <strong>2023-06-27</strong></p>
+<p>imported on: <strong>2023-06-28</strong></p>
 <p>
 This notebook is from a different repository in NASA’s PO.DAAC, the-coding-club
 </p>

external/VisualizeDopplerScattData.html

@@ -724,7 +724,7 @@ <h1 class="title">S-MODE Workshop: Science Case Study Airborne Part 2</h1>
 
 </header>
 
-<p>imported on: <strong>2023-06-27</strong></p>
+<p>imported on: <strong>2023-06-28</strong></p>
 <p>
 This notebook is from a different repository in NASA’s PO.DAAC, 2022-SMODE-Open-Data-Workshop
 </p>

external/cof-zarr-reformat.html

@@ -726,7 +726,7 @@ <h1 class="title">COF Zarr Access via Reformat</h1>
 
 </header>
 
-<p>imported on: <strong>2023-06-27</strong></p>
+<p>imported on: <strong>2023-06-28</strong></p>
 <p>
 This notebook is from a different repository in NASA’s PO.DAAC, ECCO.
 </p>

external/insitu_dataviz_demo.html

@@ -728,7 +728,7 @@ <h1 class="title">S-MODE Workshop: Science Case Study In Situ</h1>
 
 </header>
 
-<p>imported on: <strong>2023-06-27</strong></p>
+<p>imported on: <strong>2023-06-28</strong></p>
 <p>
 This notebook is from a different repository in NASA’s PO.DAAC, 2022-SMODE-Open-Data-Workshop
 </p>

external/zarr-eosdis-store.html

@@ -717,7 +717,7 @@ <h1 class="title">Zarr Example</h1>
 
 </header>
 
-<p>imported on: <strong>2023-06-27</strong></p>
+<p>imported on: <strong>2023-06-28</strong></p>
 <p>
 This notebook is from NASA’s Zarr <strong>EOSDIS</strong> store notebook
 </p>

external/zarr_access.html

@@ -736,7 +736,7 @@ <h1 class="title">Zarr Access for NetCDF4 files</h1>
 
 </header>
 
-<p>imported on: <strong>2023-06-27</strong></p>
+<p>imported on: <strong>2023-06-28</strong></p>
 <p>
 This notebook is from NASA Openscapes 2021 Cloud Hackathon Repository
 </p>

notebooks/datasets/OISSS_L4_multimission_monthly_v1.html

@@ -731,7 +731,7 @@ <h1 class="title">Direct S3 Data Access tutorial (Multi-Mission Optimally Interp
 </header>
 
 <p>This tutorial only works in a jupyterhub hosted at AWS US-WEST-2.</p>
-<p><img src="OISSS_L4_multimission_monthly_v1_files/figure-html/0bcccb40-2-419d3300-7fdc-404c-afae-9e97155fb347.png" class="img-fluid" alt="image.png"><img src="OISSS_L4_multimission_monthly_v1_files/figure-html/0bcccb40-1-2438e8b8-d128-41b5-b06d-53fb2efee43a.png" class="img-fluid" alt="image.png"></p>
+<p><img src="OISSS_L4_multimission_monthly_v1_files/figure-html/71f830a1-2-419d3300-7fdc-404c-afae-9e97155fb347.png" class="img-fluid" alt="image.png"><img src="OISSS_L4_multimission_monthly_v1_files/figure-html/71f830a1-1-2438e8b8-d128-41b5-b06d-53fb2efee43a.png" class="img-fluid" alt="image.png"></p>
 <ul>
 <li><strong>User guide</strong>: http://iprc.soest.hawaii.edu/users/oleg/oisss/GLB/OISSS_Product_Notes.pdf</li>
 <li><strong>DOI</strong> <a href="https://podaac.jpl.nasa.gov/dataset/OISSS_L4_multimission_monthly_v1?ids=&amp;values=&amp;search=oisss&amp;provider=POCLOUD">10.5067/SMP10-4UMCS</a></li>

search.json

@@ -347,7 +347,7 @@
     "href": "notebooks/meetings_workshops/workshop_osm_2022/CloudAWS_AmazonRiver_Estuary_Exploration.html#needed-packages",
     "title": "Amazon Estuary Exploration:",
     "section": "Needed Packages",
-    "text": "Needed Packages\n\nimport os\nimport glob\nimport s3fs\nimport requests\nimport numpy as np\nimport pandas as pd\nimport xarray as xr\nimport hvplot.xarray\nimport matplotlib.pyplot as plt\nimport cartopy.crs as ccrs\nimport cartopy\nimport dask\nfrom datetime import datetime\nfrom os.path import isfile, basename, abspath\nimport earthaccess\nfrom earthaccess import Auth, DataCollections, DataGranules, Store\n\n\nauth = earthaccess.login(strategy=\"interactive\", persist=True)\n\nWe are already authenticated with NASA EDL"
+    "text": "Needed Packages\n\nimport os\nimport glob\nimport s3fs\nimport requests\nimport numpy as np\nimport pandas as pd\nimport xarray as xr\nimport hvplot.xarray\nimport matplotlib.pyplot as plt\nimport cartopy.crs as ccrs\nimport cartopy\nimport dask\nfrom datetime import datetime\nfrom os.path import isfile, basename, abspath\nimport earthaccess\nfrom earthaccess import Auth, DataCollections, DataGranules, Store\n\n\n\n\n\n\n\n\n\n\n\n\nauth = earthaccess.login(strategy=\"interactive\", persist=True)"
   },
   {
     "objectID": "notebooks/meetings_workshops/workshop_osm_2022/CloudAWS_AmazonRiver_Estuary_Exploration.html#liquid-water-equivalent-lwe-thickness-grace-grace-fo",
@@ -816,7 +816,7 @@
     "href": "external/July_2022_Earthdata_Webinar.html",
     "title": "Earthdata Webinar",
     "section": "",
-    "text": "imported on: 2023-06-27\nThis notebook is from a different repository in NASA’s PO.DAAC, the-coding-club"
+    "text": "imported on: 2023-06-28\nThis notebook is from a different repository in NASA’s PO.DAAC, the-coding-club"
   },
   {
     "objectID": "external/July_2022_Earthdata_Webinar.html#abstract",
@@ -921,7 +921,7 @@
     "href": "external/ECCO_download_data.html",
     "title": "Access to ECCO V4r4 Datasets on a Local Machine",
     "section": "",
-    "text": "imported on: 2023-06-27\nThis notebook is from a different repository in NASA’s PO.DAAC, ECCO.\nDuped+slightly modified version of the s3 access ipynb. Tested on JPL-issued macbook and my linux box. It starts by setting up a most trusted strategy for batch downloads behind URS ussing curl/wget. Will attempt to add line(s) to your netrc file if needed btw; then it writes your urs cookies to a local file that should effectively “pre-authenticate” future download sessions for those sub domains."
+    "text": "imported on: 2023-06-28\nThis notebook is from a different repository in NASA’s PO.DAAC, ECCO.\nDuped+slightly modified version of the s3 access ipynb. Tested on JPL-issued macbook and my linux box. It starts by setting up a most trusted strategy for batch downloads behind URS ussing curl/wget. Will attempt to add line(s) to your netrc file if needed btw; then it writes your urs cookies to a local file that should effectively “pre-authenticate” future download sessions for those sub domains."
   },
   {
     "objectID": "external/ECCO_download_data.html#quick-start",
@@ -1019,7 +1019,7 @@
     "href": "external/cof-zarr-reformat.html",
     "title": "COF Zarr Access via Reformat",
     "section": "",
-    "text": "imported on: 2023-06-27\nThis notebook is from a different repository in NASA’s PO.DAAC, ECCO."
+    "text": "imported on: 2023-06-28\nThis notebook is from a different repository in NASA’s PO.DAAC, ECCO."
   },
   {
     "objectID": "external/cof-zarr-reformat.html#getting-started",
@@ -1033,7 +1033,7 @@
     "href": "external/insitu_dataviz_demo.html",
     "title": "S-MODE Workshop: Science Case Study In Situ",
     "section": "",
-    "text": "imported on: 2023-06-27\nThis notebook is from a different repository in NASA’s PO.DAAC, 2022-SMODE-Open-Data-Workshop\nimport glob\nfrom netCDF4 import Dataset\nimport xarray as xr\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport gsw"
+    "text": "imported on: 2023-06-28\nThis notebook is from a different repository in NASA’s PO.DAAC, 2022-SMODE-Open-Data-Workshop\nimport glob\nfrom netCDF4 import Dataset\nimport xarray as xr\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport gsw"
   },
   {
     "objectID": "external/insitu_dataviz_demo.html#compare-saildrone-adcp-data-with-rv-oceanus-data",
@@ -1047,7 +1047,7 @@
     "href": "external/VisualizeDopplerScattData.html",
     "title": "S-MODE Workshop: Science Case Study Airborne Part 2",
     "section": "",
-    "text": "imported on: 2023-06-27\nThis notebook is from a different repository in NASA’s PO.DAAC, 2022-SMODE-Open-Data-Workshop\n%load_ext autoreload\n%autoreload 2\nimport sys\nsys.path.append('../src')\nfrom matplotlib import pyplot as plt\n%matplotlib inline\nfrom pathlib import Path\nimport numpy as np\nimport rioxarray\nimport xarray as xr\nfrom plot_dopplerscatt_data import make_streamplot_image\nimport warnings\nwarnings.simplefilter('ignore')"
+    "text": "imported on: 2023-06-28\nThis notebook is from a different repository in NASA’s PO.DAAC, 2022-SMODE-Open-Data-Workshop\n%load_ext autoreload\n%autoreload 2\nimport sys\nsys.path.append('../src')\nfrom matplotlib import pyplot as plt\n%matplotlib inline\nfrom pathlib import Path\nimport numpy as np\nimport rioxarray\nimport xarray as xr\nfrom plot_dopplerscatt_data import make_streamplot_image\nimport warnings\nwarnings.simplefilter('ignore')"
   },
   {
     "objectID": "external/VisualizeDopplerScattData.html#apply-the-good-data-mask-for-all-current-observations",
@@ -1061,7 +1061,7 @@
     "href": "external/Introduction_to_xarray.html",
     "title": "Xarray",
     "section": "",
-    "text": "imported on: 2023-06-27\nThis notebook is from NASA Openscapes 2021 Cloud Hackathon Repository"
+    "text": "imported on: 2023-06-28\nThis notebook is from NASA Openscapes 2021 Cloud Hackathon Repository"
   },
   {
     "objectID": "external/Introduction_to_xarray.html#why-do-we-need-xarray",
@@ -1677,7 +1677,7 @@
     "href": "external/Direct_Access_SWOT_sim_Oceanography.html",
     "title": "Access Sample SWOT Oceanography Data in the Cloud",
     "section": "",
-    "text": "imported on: 2023-06-27\nThis notebook is from a different repository in NASA’s PO.DAAC, 2022-SWOT-OCEAN-Cloud-Workshop"
+    "text": "imported on: 2023-06-28\nThis notebook is from a different repository in NASA’s PO.DAAC, 2022-SWOT-OCEAN-Cloud-Workshop"
   },
   {
     "objectID": "external/Direct_Access_SWOT_sim_Oceanography.html#getting-started",
@@ -1726,7 +1726,7 @@
     "href": "external/DownloadDopplerScattData.html",
     "title": "S-MODE Workshop: Science Case Study Airborne Part 1",
     "section": "",
-    "text": "imported on: 2023-06-27\nThis notebook is from a different repository in NASA’s PO.DAAC, 2022-SMODE-Open-Data-Workshop"
+    "text": "imported on: 2023-06-28\nThis notebook is from a different repository in NASA’s PO.DAAC, 2022-SMODE-Open-Data-Workshop"
   },
   {
     "objectID": "external/DownloadDopplerScattData.html#create-a-netrc-file-if-non-existent.",
@@ -1789,14 +1789,14 @@
     "href": "external/zarr-eosdis-store.html",
     "title": "Zarr Example",
     "section": "",
-    "text": "imported on: 2023-06-27\n\nThis notebook is from NASA’s Zarr EOSDIS store notebook\n\n\nThe original source for this document is https://github.com/nasa/zarr-eosdis-store/blob/main/presentation/example.ipynb\n\n\nzarr-eosdis-store example\nInstall dependencies\n\nimport sys\n\n# zarr and zarr-eosdis-store, the main libraries being demoed\n!{sys.executable} -m pip install zarr zarr-eosdis-store\n\n# Notebook-specific libraries\n!{sys.executable} -m pip install matplotlib\n\nImportant: To run this, you must first create an Earthdata Login account (https://urs.earthdata.nasa.gov) and place your credentials in ~/.netrc e.g.:\n   machine urs.earthdata.nasa.gov login YOUR_USER password YOUR_PASSWORD\nNever share or commit your password / .netrc file!\nBasic usage. After these lines, we work with ds as though it were a normal Zarr dataset\n\nimport zarr\nfrom eosdis_store import EosdisStore\n\nurl = 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210715090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc'\n\nds = zarr.open(EosdisStore(url))\n\nView the file’s variable structure\n\nprint(ds.tree())\n\n/\n ├── analysed_sst (1, 17999, 36000) int16\n ├── analysis_error (1, 17999, 36000) int16\n ├── dt_1km_data (1, 17999, 36000) int16\n ├── lat (17999,) float32\n ├── lon (36000,) float32\n ├── mask (1, 17999, 36000) int16\n ├── sea_ice_fraction (1, 17999, 36000) int16\n ├── sst_anomaly (1, 17999, 36000) int16\n └── time (1,) int32\n\n\nFetch the latitude and longitude arrays and determine start and end indices for our area of interest. In this case, we’re looking at the Great Lakes, which have a nice, recognizeable shape. Latitudes 41 to 49, longitudes -93 to 76.\n\nlats = ds['lat'][:]\nlons = ds['lon'][:]\nlat_range = slice(lats.searchsorted(41), lats.searchsorted(49))\nlon_range = slice(lons.searchsorted(-93), lons.searchsorted(-76))\n\nGet the analysed sea surface temperature variable over our area of interest and apply scale factor and offset from the file metadata. In a future release, scale factor and add offset will be automatically applied.\n\nvar = ds['analysed_sst']\nanalysed_sst = var[0, lat_range, lon_range] * var.attrs['scale_factor'] + var.attrs['add_offset']\n\nDraw a pretty picture\n\nfrom matplotlib import pyplot as plt\n\nplt.rcParams[\"figure.figsize\"] = [16, 8]\nplt.imshow(analysed_sst[::-1, :])\nNone\n\n\n\n\nIn a dozen lines of code and a few seconds, we have managed to fetch and visualize the 3.2 megabyte we needed from a 732 megabyte file using the original archive URL and no processing services"
+    "text": "imported on: 2023-06-28\n\nThis notebook is from NASA’s Zarr EOSDIS store notebook\n\n\nThe original source for this document is https://github.com/nasa/zarr-eosdis-store/blob/main/presentation/example.ipynb\n\n\nzarr-eosdis-store example\nInstall dependencies\n\nimport sys\n\n# zarr and zarr-eosdis-store, the main libraries being demoed\n!{sys.executable} -m pip install zarr zarr-eosdis-store\n\n# Notebook-specific libraries\n!{sys.executable} -m pip install matplotlib\n\nImportant: To run this, you must first create an Earthdata Login account (https://urs.earthdata.nasa.gov) and place your credentials in ~/.netrc e.g.:\n   machine urs.earthdata.nasa.gov login YOUR_USER password YOUR_PASSWORD\nNever share or commit your password / .netrc file!\nBasic usage. After these lines, we work with ds as though it were a normal Zarr dataset\n\nimport zarr\nfrom eosdis_store import EosdisStore\n\nurl = 'https://archive.podaac.earthdata.nasa.gov/podaac-ops-cumulus-protected/MUR-JPL-L4-GLOB-v4.1/20210715090000-JPL-L4_GHRSST-SSTfnd-MUR-GLOB-v02.0-fv04.1.nc'\n\nds = zarr.open(EosdisStore(url))\n\nView the file’s variable structure\n\nprint(ds.tree())\n\n/\n ├── analysed_sst (1, 17999, 36000) int16\n ├── analysis_error (1, 17999, 36000) int16\n ├── dt_1km_data (1, 17999, 36000) int16\n ├── lat (17999,) float32\n ├── lon (36000,) float32\n ├── mask (1, 17999, 36000) int16\n ├── sea_ice_fraction (1, 17999, 36000) int16\n ├── sst_anomaly (1, 17999, 36000) int16\n └── time (1,) int32\n\n\nFetch the latitude and longitude arrays and determine start and end indices for our area of interest. In this case, we’re looking at the Great Lakes, which have a nice, recognizeable shape. Latitudes 41 to 49, longitudes -93 to 76.\n\nlats = ds['lat'][:]\nlons = ds['lon'][:]\nlat_range = slice(lats.searchsorted(41), lats.searchsorted(49))\nlon_range = slice(lons.searchsorted(-93), lons.searchsorted(-76))\n\nGet the analysed sea surface temperature variable over our area of interest and apply scale factor and offset from the file metadata. In a future release, scale factor and add offset will be automatically applied.\n\nvar = ds['analysed_sst']\nanalysed_sst = var[0, lat_range, lon_range] * var.attrs['scale_factor'] + var.attrs['add_offset']\n\nDraw a pretty picture\n\nfrom matplotlib import pyplot as plt\n\nplt.rcParams[\"figure.figsize\"] = [16, 8]\nplt.imshow(analysed_sst[::-1, :])\nNone\n\n\n\n\nIn a dozen lines of code and a few seconds, we have managed to fetch and visualize the 3.2 megabyte we needed from a 732 megabyte file using the original archive URL and no processing services"
   },
   {
     "objectID": "external/zarr_access.html",
     "href": "external/zarr_access.html",
     "title": "Zarr Access for NetCDF4 files",
     "section": "",
-    "text": "imported on: 2023-06-27\nThis notebook is from NASA Openscapes 2021 Cloud Hackathon Repository"
+    "text": "imported on: 2023-06-28\nThis notebook is from NASA Openscapes 2021 Cloud Hackathon Repository"
   },
   {
     "objectID": "external/zarr_access.html#timing",
@@ -1824,7 +1824,7 @@
     "href": "external/ECCO_cloud_direct_access_s3.html",
     "title": "Direct Access to ECCO V4r4 Datasets in the Cloud",
     "section": "",
-    "text": "imported on: 2023-06-27\nThis notebook is from a different repository in NASA’s PO.DAAC, ECCO."
+    "text": "imported on: 2023-06-28\nThis notebook is from a different repository in NASA’s PO.DAAC, ECCO."
   },
   {
     "objectID": "external/ECCO_cloud_direct_access_s3.html#getting-started",