Nits (#52)

* rename book lock file and other small nb changes

* small changes

Author: e-marshall

book/_config.yml

@@ -13,7 +13,7 @@ only_build_toc_files: false
 # Force re-execution of notebooks on each build.
 # See https://jupyterbook.org/content/execute.html
 execute:
-  execute_notebooks:  cache #'auto'
+  execute_notebooks:   cache #'auto'
   allow_errors: true
   timeout: 1500
   exclude_patterns:

book/endmatter/about_this_book.md

@@ -1,6 +1,5 @@
 # About this book
-
-These tutorials were initially developed while Emma Marshall interned with the Summer Internships in Parallel Computational Sciences ([SIParCS](https://www.cisl.ucar.edu/outreach/internships)) program at the National Center for Atmospheric Research ([NCAR](https://ncar.ucar.edu/)). Jessica Scheick, Scott Henderson, and Deepak Cherian were internship supervisors for this project. The internship was also supported by a NASA Open Source Tools, Frameworks, and Libraries program (Award  80NSSC22K0345), with a specific focus on developing educational resources for working with cloud-hosted data using Xarray. Tutorial development continued after the conclusion of the SIParCS internship when Emma Marshall returned to the University of Utah as a Ph.D. student, where she was supported by a FINESST Fellowship Grant (80NSSC22K1536).
+# ====================
 ## Contributing
 
 If you'd like to contribute to this book, please start a discussion or raise an issue in the GitHub [repository](https://github.com/e-marshall/cloud-open-source-geospatial-datacube-workflows).

book/intro/1_getting_started.md

@@ -56,3 +56,6 @@ This tutorial focuses on another satellite dataset: [Sentinel-1](https://www.esa
 
 A summary of the lessons learned throughout the tutorials and synthesis of these ideas into suggestions and best practices for developing scientific workflows analyzing n-dimensional earth observation data. 
 
+## About this book
+
+These tutorials were initially developed while Emma Marshall interned with the Summer Internships in Parallel Computational Sciences ([SIParCS](https://www.cisl.ucar.edu/outreach/internships)) program at the National Center for Atmospheric Research ([NCAR](https://ncar.ucar.edu/)). Jessica Scheick, Scott Henderson, and Deepak Cherian were internship supervisors for this project. The internship was also supported by a NASA Open Source Tools, Frameworks, and Libraries program (Award  80NSSC22K0345), with a specific focus on developing educational resources for working with cloud-hosted data using Xarray. Tutorial development continued after the conclusion of the SIParCS internship when Emma Marshall returned to the University of Utah as a Ph.D. student, where she was supported by a FINESST Fellowship Grant (80NSSC22K1536).

book/introduction.md

@@ -7,4 +7,9 @@ We focus on data derived from different types of satellite imagery that are publ
 
 The goal of this book is to reduce barriers to entry to working with earth observation data for scientific analysis. It features two stand-alone tutorials, each detailing steps involved in a typical workflow, from data access and organization to exploratory data analysis and visualization. 
 
-Underpinning these examples is a focus on understanding the different components of n-dimensional, gridded datasets, how they relate to the tools we use to work with them (in this case, the Python package Xarray), and how a strong understanding of a scientific dataset within the context of your chosen data model can enable more efficient and intuitive analysis.
+Underpinning these examples is a focus on understanding the different components of n-dimensional, gridded datasets, how they relate to the tools we use to work with them (in this case, the Python package Xarray), and how a strong understanding of a scientific dataset within the context of your chosen data model can enable more efficient and intuitive analysis.
+
+{{break}}
+
+```{figure} background/imgs/cube.png
+```

book/itslive/nbs/4_exploratory_data_analysis_single.ipynb

@@ -287,7 +287,8 @@
     "single_glacier_raster_web.v.mean(dim=\"mid_date\").plot(ax=ax, cmap=\"viridis\", alpha=0.75, add_colorbar=True)\n",
     "single_glacier_vector_web.plot(ax=ax, facecolor=\"None\", edgecolor=\"red\", alpha=0.75)\n",
     "# Add basemap\n",
-    "cx.add_basemap(ax, crs=single_glacier_vector_web.crs, source=cx.providers.Esri.WorldImagery)"
+    "cx.add_basemap(ax, crs=single_glacier_vector_web.crs, source=cx.providers.Esri.WorldImagery)\n",
+    "fig.suptitle('Mean velocity over the time series (m/y) and RGI 7 glacier outline (red) with satellite image basemap from ESRI World Imagery')"
    ]
   },
   {
@@ -355,7 +356,7 @@
     "single_glacier_raster[\"cov\"].plot(ax=ax, linestyle=\"None\", marker=\"x\", alpha=0.75)\n",
     "\n",
     "# Specify axes labels and title\n",
-    "fig.suptitle(\"Velocity data coverage ovver time\", fontsize=16)\n",
+    "fig.suptitle(\"Velocity data coverage over time\", fontsize=16)\n",
     "ax.set_ylabel(\"Coverage (proportion)\", x=-0.05, fontsize=12)\n",
     "ax.set_xlabel(\"Date\", fontsize=12);"
    ]
@@ -1338,7 +1339,7 @@
  "metadata": {
   "celltoolbar": "Tags",
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "geospatial_datacube_book_env",
    "language": "python",
    "name": "python3"
   },

book/sentinel1/nbs/4_read_pc_data.ipynb

@@ -210106,13 +210106,13 @@
    ]
   },
   {
+
    "cell_type": "code",
    "execution_count": 15,
    "id": "ce64e11b",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "da = da.persist()"
+    "Load the data into memory (this may take a few minutes):"
    ]
   },
   {