Merge pull request #46 from IMMM-SFA/dev

Migrate Jupyter to AWS

.github/workflows/02_deploy.yml

@@ -38,6 +38,16 @@ jobs:
       - name: Get current datetime in ISO format
         id: date
         run: echo "::set-output name=date::$(date -u +'%Y-%m-%d')"
+      - uses: jakejarvis/s3-sync-action@master
+        with:
+          args: --delete
+        env:
+          SOURCE_DIR: 'notebooks'
+          DEST_DIR: 'notebooks'
+          AWS_S3_BUCKET: ${{ secrets.AWS_S3_BUCKET }}
+          AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
+          AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
+          AWS_REGION: ${{ secrets.AWS_REGION }}
       - name: Create GitHub release
         id: gh_release
         uses: softprops/action-gh-release@v1

docs/source/conf.py

@@ -24,10 +24,10 @@
 # -- Notebook URLs -----------------------------------------------------------
 
 rst_prolog = """
-.. _nb_logistic_regression: https://mybinder.org/v2/gh/IMMM-SFA/msd_uncertainty_ebook/main?labpath=notebooks%2Fbasin_users_logistic_regression.ipynb
-.. _nb_saltelli_sobol: https://mybinder.org/v2/gh/IMMM-SFA/msd_uncertainty_ebook/main?labpath=notebooks%2Fsa_saltelli_sobol_ishigami.ipynb
-.. _nb_hymod: https://mybinder.org/v2/gh/IMMM-SFA/msd_uncertainty_ebook/main?labpath=notebooks%2Fhymod.ipynb
-.. _nb_fishery_dynamics: https://mybinder.org/v2/gh/IMMM-SFA/msd_uncertainty_ebook/main?labpath=notebooks%2Ffishery_dynamics.ipynb
+.. _nb_logistic_regression: https://uc-ebook.msdlive.org/user-redirect/lab/tree/examples/basin_users_logistic_regression.ipynb
+.. _nb_saltelli_sobol: https://uc-ebook.msdlive.org/user-redirect/lab/tree/examples/sa_saltelli_sobol_ishigami.ipynb
+.. _nb_hymod: https://uc-ebook.msdlive.org/user-redirect/lab/tree/examples/hymod.ipynb
+.. _nb_fishery_dynamics: https://uc-ebook.msdlive.org/user-redirect/lab/tree/examples/fishery_dynamics.ipynb
 """
 
 

notebooks/basin_users_logistic_regression.ipynb

@@ -30,6 +30,19 @@
     "In this tutorial, we will be loading in data that has been produced in Hadjimichael et al. (2020). Before we start our analysis, we'll load the relevant Python libraries, example data, and information for the three users."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### **NOTE:**  If you are running this notebook locally, run the following command to install the required package data:\n",
+    "\n",
+    "```python\n",
+    "msdbook.install_package_data()\n",
+    "```\n",
+    "\n",
+    "##### Otherwise, proceed with the following"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -51,9 +64,6 @@
     "import matplotlib as mpl\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "# load example data from Hadjimichael et al. (2020)\n",
-    "msdbook.install_package_data()\n",
-    "\n",
     "# Select the IDs for the three users that we will perform the analysis for\n",
     "all_IDs = ['7000550','7200799','3704614'] \n",
     "usernames = ['Medium seniority irrigation',\n",

notebooks/fishery_dynamics.ipynb

@@ -62,6 +62,19 @@
     "In this tutorial, we will be loading in data that has been produced in Hadjimichael et al. (2020). Before we start our analysis, we'll load the relevant Python libraries. __NOTE__: To step through the notebook, execute each gray (code) box by typing \"Shift+Enter\"."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### **NOTE:**  If you are running this notebook locally, run the following command to install the required package data:\n",
+    "\n",
+    "```python\n",
+    "msdbook.install_package_data()\n",
+    "```\n",
+    "\n",
+    "##### Otherwise, proceed with the following"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -81,9 +94,6 @@
     "from SALib.analyze import sobol\n",
     "from matplotlib import patheffects as pe\n",
     "\n",
-    "# load example data\n",
-    "msdbook.install_package_data()\n",
-    "\n",
     "%matplotlib inline\n",
     "%config InlineBackend.print_figure_kwargs = {'bbox_inches':None}\n"
    ]

notebooks/hymod.ipynb

@@ -104,6 +104,19 @@
     "In the following section of code, we'll load the necessary python libraries and read in the input file. For this exercise we'll only use the first eleven years of data. The first five rows of the input dataset are printed to show what they look like:"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### **NOTE:**  If you are running this notebook locally, run the following command to install the required package data:\n",
+    "\n",
+    "```python\n",
+    "msdbook.install_package_data()\n",
+    "```\n",
+    "\n",
+    "##### Otherwise, proceed with the following"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -120,9 +133,6 @@
     "from sklearn import metrics\n",
     "from matplotlib import pyplot as plt\n",
     "\n",
-    "# load example data\n",
-    "msdbook.install_package_data()\n",
-    "\n",
     "# load the Leaf River HYMOD input file\n",
     "leaf_data = msdbook.load_hymod_input_file()\n",
     "\n",