update conus404 explore instructions

.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: dea04435187e365e017cf456b70ee111
+config: abd3ddd4b6ad8bb366467d48a3bc9429
 tags: 645f666f9bcd5a90fca523b33c5a78b7

_sources/dataset_access/conus404_explore.ipynb

@@ -32,9 +32,7 @@
    "id": "57354df2-6786-4d1d-859e-1d5099cb85b9",
    "metadata": {},
    "source": [
-    "## 1) Open dataset from Intake Catalog\n",
-    "* Select `on-prem` dataset from /caldera if running on prem (Denali/Tallgrass)\n",
-    "* Select `cloud`/`osn` object store data if running elsewhere"
+    "## 1) Select the Dataset from HyTEST's Intake Catalog"
    ]
   },
   {
@@ -61,6 +59,14 @@
     "list(cat)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "b7cdd988-4d56-49a5-85b4-d5d81c3db89d",
+   "metadata": {},
+   "source": [
+    "**Select a dataset**: If you are unsure of which copy of a particular dataset to use (e.g. `conus404-hourly-?`), please review the [HyTEST JupyterBook](https://hytest-org.github.io/hytest/dataset_catalog/README.html#storage-locations)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -80,7 +86,7 @@
    "source": [
     "## 2) Set Up AWS Credentials (Optional)\n",
     "\n",
-    "This notebook reads data from the OSN pod by default, which is object store data on a high speed internet connection that is free to access from any environment. If you change this notebook to use one of the CONUS404 datasets stored on S3 (options ending in `-cloud`), you will be pulling data from a `requester-pays` S3 bucket. This means you have to set up your AWS credentials, else we won't be able to load the data. Please note that reading the `-cloud` data from S3 may incur charges if you are reading data outside of the us-west-2 region or running the notebook outside of the cloud altogether. If you would like to access one of the `-cloud` options, uncomment and run the following code snippet to set up your AWS credentials. You can find more info about this AWS helper function [here](../environment_set_up/Help_AWS_Credentials.md)."
+    "This notebook reads data from the OSN pod. The OSN pod is object store data on a high speed internet connection with free access from any computing environment. If you change this notebook to use one of the CONUS404 datasets stored on S3 (options ending in `-cloud`), you will be pulling data from a `requester-pays` S3 bucket. This means you have to set up your AWS credentials before you are able to load the data. Please note that reading the `-cloud` data from S3 may incur charges if you are reading data outside of AWS's `us-west-2` region or running the notebook outside of the cloud altogether. If you would like to access one of the `-cloud` options, uncomment and run the following code snippet to set up your AWS credentials. You can find more info about this AWS helper function [here](../environment_set_up/Help_AWS_Credentials.ipynb)."
    ]
   },
   {
@@ -100,9 +106,9 @@
    "id": "ecef85f6-01e4-4d84-9cce-0bf409b30637",
    "metadata": {},
    "source": [
-    "## 3) Parallelize with Dask \n",
+    "## 3) Parallelize with Dask (Optional, but recommended)\n",
     "Some of the steps we will take are aware of parallel clustered compute environments\n",
-    "using `dask`. We're going to start a cluster now so that future steps can take advantage\n",
+    "using `dask`. We will start a cluster so that future steps can take advantage\n",
     "of this ability. \n",
     "\n",
     "This is an optional step, but speed ups data loading significantly, especially \n",
@@ -120,7 +126,7 @@
    },
    "outputs": [],
    "source": [
-    "%run ../environment_set_up/Start_Dask_Cluster_Nebari.ipynb\n",
+    "#%run ../environment_set_up/Start_Dask_Cluster_Nebari.ipynb\n",
     "## If this notebook is not being run on Nebari/ESIP, replace the above \n",
     "## path name with a helper appropriate to your compute environment.  Examples:\n",
     "# %run ../environment_set_up/Start_Dask_Cluster_Denali.ipynb\n",
@@ -144,9 +150,19 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "# read in the dataset and use metpy to parse the crs information on the dataset\n",
     "print(f\"Reading {dataset} metadata...\", end='')\n",
     "ds = cat[dataset].to_dask().metpy.parse_cf()\n",
-    "print(\"done\")\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4487472f-99fd-4bfa-98b5-742f8d935feb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "# Examine the grid data structure for SNOW: \n",
     "ds.SNOW"
    ]
@@ -156,8 +172,7 @@
    "id": "235352e6-face-431f-932b-2f65e0e6beaf",
    "metadata": {},
    "source": [
-    "Looks like this dataset is organized in three coordinates (x, y, and time).  There is a\n",
-    "`metpy_crs` attached:"
+    "Looks like this dataset is organized in three coordinates (x, y, and time), and we have used the `metpy` package to pase the crs information into the `metpy_crs` variable:"
    ]
   },
   {
@@ -215,10 +230,10 @@
    "id": "8dd35652-608d-4eca-94d9-78cfd3591b38",
    "metadata": {},
    "source": [
-    "**SIDE NOTE**\n",
-    "To identify a point, we will start with its lat/lon coordinates.  But the\n",
-    "data is in Lambert Conformal Conic... need to re-project/transform using the\n",
-    "built-in `crs` we examined earlier: "
+    "We will identify a point that we want to pull data for using lat/lon coordinates.\n",
+    "\n",
+    "The CONUS404 data is in a Lambert Conformal Conic projection, so we need to re-project/transform using the\n",
+    "built-in `crs` we examined earlier."
    ]
   },
   {
@@ -240,6 +255,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "# pull out a particulat time slice at the specified coordinates\n",
     "%%time\n",
     "da = ds.PREC_ACC_NC.sel(x=x, y=y, method='nearest').sel(time=slice('2013-01-01 00:00','2013-12-31 00:00')).load()"
    ]
@@ -251,6 +267,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "# plot your time series\n",
     "da.hvplot(x='time', grid=True)"
    ]
   },
@@ -291,7 +308,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.11.8"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {

_sources/dataset_access/conus404_regrid.ipynb

@@ -371,7 +371,7 @@
    "outputs": [],
    "source": [
     "# you will need to update the filepaths and uncomment the following line to save out your data.\n",
-    "# ds_out.to_netcdf(nc_outfile, encoding=encoding, mode='w')"
+    "# ds_out.load().to_netcdf(nc_outfile, encoding=encoding, mode='w')"
    ]
   },
   {
@@ -444,7 +444,7 @@
    "outputs": [],
    "source": [
     "# you will need to update the filepaths and uncomment the following line to save out your data.\n",
-    "# ds_outcl.to_netcdf('CONUS404_DRB_curvilinear.nc', encoding=encoding, mode='w')"
+    "# ds_outcl.load().to_netcdf('CONUS404_DRB_curvilinear.nc', encoding=encoding, mode='w')"
    ]
   },
   {

_sources/dataset_access/conus404_spatial_aggregation.ipynb

@@ -99,7 +99,7 @@
    "outputs": [],
    "source": [
     "## Select the dataset you want to read into your notebook and preview its metadata\n",
-    "dataset = 'conus404-daily-cloud' \n",
+    "dataset = 'conus404-daily-osn' \n",
     "cat[dataset]"
    ]
   },
@@ -164,7 +164,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ds = cat['conus404-daily-cloud'].to_dask()"
+    "ds = cat['conus404-daily-osn'].to_dask()"
    ]
   },
   {
@@ -195,7 +195,7 @@
     "gage_id = '01482100'\n",
     "nldi = NLDI()\n",
     "del_basins = nldi.get_basins(gage_id)\n",
-    "huc12_basins = WaterData('huc12').bygeom(del_basins.geometry[0])"
+    "huc12_basins = WaterData('wdb12').bygeom(del_basins.geometry[0])"
    ]
   },
   {
@@ -568,16 +568,6 @@
     "overlay.geometry.area.groupby(overlay[region_name]).sum().nlargest(10)/1e6  # km2"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "db635849-5077-457f-b8f2-7b1e49391154",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "overlay[overlay[region_name] == \"Delaware Bay-Deep\"].geometry.plot(edgecolor='k', aspect='equal')"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -843,7 +833,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ds_var = var_regridded.sel(name=['Delaware Bay-Deep', 'Black Creek']).resample(time=\"MS\").mean().to_dataset(region_name)"
+    "ds_var = var_regridded.sel(name=['Mallory Brook-West Branch Delaware River', 'Yaleville Brook-Susquehanna River']).resample(time=\"MS\").mean().to_dataset(region_name)"
    ]
   },
   {