gh actions file

.github/workflows/deploy.yml

@@ -0,0 +1,62 @@
+name: deploy-book
+
+name: deploy-book
+
+# Run this when the master or main branch changes
+on:
+  push:
+    branches:
+    - master
+    - main
+    # If your git repository has the Jupyter Book within some-subfolder next to
+    # unrelated files, you can make this run only if a file within that specific
+    # folder has been modified.
+    #
+    # paths:
+    # - some-subfolder/**
+
+# This job installs dependencies, builds the book, and pushes it to `gh-pages`
+jobs:
+  deploy-book:
+    runs-on: ubuntu-latest
+    permissions:
+      pages: write
+      id-token: write
+    steps:
+    - uses: actions/checkout@v3
+
+    # Install dependencies
+    - name: Set up Python 3.11
+      uses: actions/setup-python@v4
+      with:
+        python-version: 3.11
+
+    - name: Install dependencies
+      run: |
+        pip install -r requirements.txt
+
+    # (optional) Cache your executed notebooks between runs
+    # if you have config:
+    # execute:
+    #   execute_notebooks: cache
+    - name: cache executed notebooks
+      uses: actions/cache@v3
+      with:
+        path: _build/.jupyter_cache
+        key: jupyter-book-cache-${{ hashFiles('requirements.txt') }}
+
+    # Build the book
+    - name: Build the book
+      run: |
+        jupyter-book build .
+
+    # Upload the book's HTML as an artifact
+    - name: Upload artifact
+      uses: actions/upload-pages-artifact@v2
+      with:
+        path: "_build/html"
+
+    # Deploy the book's HTML to GitHub Pages
+    - name: Deploy to GitHub Pages
+      id: deployment
+      uses: actions/deploy-pages@v2

software.ipynb

@@ -0,0 +1,125 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "71327c0e",
+   "metadata": {},
+   "source": [
+    "# Software and Data\n",
+    "\n",
+    "On this page you'll find information about the computing environment and datasets that we'll be using in this tutorial.   \n",
+    "\n",
+    "\n",
+    "## Computing environment\n",
+    "\n",
+    "Below, you'lll see a list of the python libraries we'll be using in this example. This is the full list of libraries across all notebooks."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "446021e3",
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import json\n",
+    "import urllib.request\n",
+    "import numpy as np\n",
+    "import xarray as xr\n",
+    "import rioxarray as rxr\n",
+    "import geopandas as gpd\n",
+    "import pandas as pd\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.ticker as mticker\n",
+    "\n",
+    "from shapely.geometry import Polygon\n",
+    "from shapely.geometry import Point\n",
+    "import cartopy.crs as ccrs\n",
+    "from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n",
+    "import cartopy\n",
+    "import cartopy.feature as cfeature\n",
+    "\n",
+    "from geocube.api.core import make_geocube\n",
+    "import flox\n",
+    "import s3fs\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d43c7d3b",
+   "metadata": {},
+   "source": [
+    "This tutorial also uses several functions that are stored in the script [`itslivetools.py`](https://github.com/e-marshall/itslive/blob/master/itslivetools.py). It is located in the github repo for this tutorial. If you clone the repo, it should be available to import to the tutorial notebooks. Otherwise, if you would like to use `itslivetools.py`, download the script and move it to your working directory. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35049ecb",
+   "metadata": {},
+   "source": [
+    "## Running tutorial on the cloud\n",
+    "\n",
+    "This link will launch a preconfigured jupyterlab environment on mybinder.org:\n",
+    "\n",
+    "[https://mybinder.org/v2/gh/e-marshall/itslive/HEAD?labpath=accessing_s3_data.ipynb](https://mybinder.org/v2/gh/e-marshall/itslive/HEAD?labpath=accessing_s3_data.ipynb)\n",
+    "\n",
+    "## Running tutorial material locally\n",
+    "\n",
+    "To run the notebooks contained in this tutorial on your local machine\n",
+    "\n",
+    "create the `itslivetools_env` conda environment (`conda env create -f environment-unpinned.yml`) based on the `environment.yml` file [here](https://github.com/e-marshall/mynewbook/blob/master/environment.yml). This should work on any platform (linux, osx, windows) and will install the latest versions of all dependencies.\n",
+    "\n",
+    "Alternatively, the code repository for this tutorial (https://github.com/e-marshall/itslive) also contains \"lock\" files for Linux (conda-linux-64.lock.yml) and MacOS (conda-osx-64.lock.yml) that pin exact versions of all required python packages for a [reproducible computing environment](https://mybinder.readthedocs.io/en/latest/tutorials/reproducibility.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "29b773e4",
+   "metadata": {},
+   "source": [
+    "## Data\n",
+    "\n",
+    "The velocity data that we'll be using is from the  [ITS_LIVE dataset](https://its-live.jpl.nasa.gov/#access). This dataset contains global coverage of land ice velocity data at various temporal frequencies and in various formats. Follow the link to explore the data that's available for a particular region you may be interested in. **ITS_LIVE** has multiple options for data access; this example will focus on using zarr datacubes that are stored in s3 buckets on AWS.\n",
+    "\n",
+    "**ITS_LIVE** velocity data is accessed in a raster format and the data covers a large swath of terrain covering land that is glaciated and non-glaciated. We want to select just the pixels that cover glaciated surfaces; to do this, we use glacier outlines from the [Randolph Glacier Inventory](https://www.glims.org/RGI/). The RGI region used in this tutorial is made available as a [GeoParquet](https://geoparquet.org/) file in the tutorial [repository](https://github.com/e-marshall/itslive/blob/master/rgi7_region15_south_asia_east.parquet). \n",
+    "\n",
+    "Head to the next page to see how we start accessing and working with this data \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "38108842-c077-4174-84c5-7cc44cc3c6f8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}