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mmann1123 · Sep 25, 2023 · 10574c9 · 10574c9
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diff --git a/pygis/_build/html/_sources/docs/a_intro.md b/pygis/_build/html/_sources/docs/a_intro.md
@@ -7,12 +7,13 @@ kernelspec:
   display_name: Python 3
   language: python
   name: python3
-html_meta:
-  "description lang=en": "Introducing PyGIS an open source book on spatial programming. We cover how to handle points, lines and polygons, including shapefiles, handling remote sensing imagery, and other raster data."
-  "description lang=fr": "Présentation de PyGIS, un livre open source sur la programmation géospatiale. Nous expliquons comment gérer les points, les lignes et les polygones, y compris les fichiers de formes, la gestion des images de télédétection et d'autres données raster."
-  "description lang=es": "Presentamos PyGIS, un libro de código abierto sobre programación geoespacial. Cubrimos cómo manejar puntos, líneas y polígonos, incluidos los shapefiles, el manejo de imágenes de detección remota y otros datos ráster."
-  "keywords": "Shapefiles, points, lines, polygons,raster, Remote Sensing"
-  "property=og:locale": "en_US"
+myst:
+  html_meta:
+      "description lang=en": "Introducing PyGIS an open source book on spatial programming. We cover how to handle points, lines and polygons, including shapefiles, handling remote sensing imagery, and other raster data."
+    "description lang=fr": "Présentation de PyGIS, un livre open source sur la programmation géospatiale. Nous expliquons comment gérer les points, les lignes et les polygones, y compris les fichiers de formes, la gestion des images de télédétection et d'autres données raster."
+    "description lang=es": "Presentamos PyGIS, un libro de código abierto sobre programación geoespacial. Cubrimos cómo manejar puntos, líneas y polígonos, incluidos los shapefiles, el manejo de imágenes de detección remota y otros datos ráster."
+    "keywords": "Shapefiles, points, lines, polygons,raster, Remote Sensing"
+    "property=og:locale": "en_US"
 ---
 
 

diff --git a/pygis/_build/html/_sources/docs/b_about_py.md b/pygis/_build/html/_sources/docs/b_about_py.md
@@ -30,7 +30,16 @@ Python is free and open source, with development coordinated through the
 Python has experienced rapid adoption in the last decade and is now one
 of the most commonly used programming languages.
 
-Popular textbooks on Python programming include {cite}`guttag2016introduction` and {cite}`matthes2019python`.
+Popular textbooks on Python programming include:
+
+Introduction to Computation and Programming Using Python, Second Edition
+With Application to Understanding Data
+By John V. Guttag 
+
+Python Crash Course, 3rd Edition
+A Hands-On, Project-Based Introduction to Programming
+by Eric Matthes
+
 
 ### Common Uses
 
@@ -60,6 +69,9 @@ measure of the relative popularity of Python
 
 ```{figure} /_static/lecture_specific/about_py/python_vs_matlab.png
 :scale: 72%
+:name: python_vs_matlab
+:alt: plot showing increasing popularity of python relative to matlab
+ Python's increasing popularity over time. 
 ```
 
 The figure indicates not only that Python is widely used but also that
@@ -75,6 +87,9 @@ library for data analysis with Python has exploded, as seen here.
 
 ```{figure} /_static/lecture_specific/about_py/pandas_vs_matlab.png
 :scale: 23%
+:name: pandas_vs_matlab
+:alt: plot showing increasing popularity of pandas relative to matlab
+ Pandas increasing popularity over time. 
 ```
 
 Note that pandas takes off in 2012, which is the same year that we see
@@ -91,7 +106,7 @@ Overall, it\'s clear that
 Python is a [high-level language](https://en.wikipedia.org/wiki/High-level_programming_language)
 suitable for rapid development.
 
-It has a relatively small core language supported by many libraries.
+It has a relatively small core language supported by many libraries. For instance run the following [The Zen of Python (poem)](https://www.online-python.com/4MW2mnxufk) online.
 
 Multiple programming styles are supported (procedural, object-oriented, functional, etc.)
 

diff --git a/pygis/_build/html/_sources/docs/b_conda_started2.md b/pygis/_build/html/_sources/docs/b_conda_started2.md
@@ -7,78 +7,103 @@ kernelspec:
   display_name: Python 3
   language: python
   name: python3
-html_meta:
-  "description lang=en": "Learn how install a working python environment for spatial data and remote sensing. Here we utilize Docker to make the process replicable and at least somewhat easy to understand."
-  "description lang=fr": "Découvrez comment installer un environnement python fonctionnel pour les données spatiales et la télédétection. Ici, nous utilisons Docker pour rendre le processus reproductible et au moins quelque peu facile à comprendre."
-  "description lang=es": "Aprenda a instalar un entorno Python que funcione para datos espaciales y detección remota. Aquí utilizamos Docker para hacer que el proceso sea replicable y al menos algo fácil de entender."
-  "keywords": "python environment, Docker, spatial, raster, shapefile, remote sensing"
-  "property=og:locale": "en_US"
+myst:
+  html_meta:
+    "description lang=en": "Learn how install a working python environment for spatial data and remote sensing. Here we utilize Docker to make the process replicable and at least somewhat easy to understand."
+    "description lang=fr": "Découvrez comment installer un environnement python fonctionnel pour les données spatiales et la télédétection. Ici, nous utilisons Docker pour rendre le processus reproductible et au moins quelque peu facile à comprendre."
+    "description lang=es": "Aprenda a instalar un entorno Python que funcione para datos espaciales y detección remota. Aquí utilizamos Docker para hacer que el proceso sea replicable y al menos algo fácil de entender."
+    "keywords": "python environment, Docker, spatial, raster, shapefile, remote sensing"
+    "property=og:locale": "en_US"
 ---
 
-
-
 (conda_started2)=
 
 ----------------
 
 ```{admonition} Learning Objectives
-- Learn basics of anaconda
-- Set up a working environment for spatial modeling
+- Understand the role and significance of package managers in the context of software development and geospatial data science.
+- Install `miniconda` and `mamba` package managers to manage your Python packages.
+- Create and activate a new `conda` environment.
+- Use `conda` and `mamba` to install geospatial packages from the `conda-forge` channel.
+- Recognize key geospatial packages  
 ```
 
 ```{admonition} Review
-* [A normal intro to python environments](b_getting_started.md)
+- [A normal intro to python environments](b_getting_started)
 ```
 ----------------
 
-# Setting up Python for Spatial on Mac, Windows, and Linux
-Spatial analysis requires a pretty broad set of python modules and with it, comes a lot of dependencies. And to be honest, the only thing Python doesn't do well with, is dependencies. Luckily we have a few tricks up our sleeves to help you get to work fast. 
 
+# Geospatial Environment Installation Guide
+
+In the world of software development, package managers have become a fundamental tool for simplifying the installation of software packages and their dependencies. They automate the process of installing, upgrading, configuring, and removing software packages in a consistent manner. In the field of geospatial data science, where several packages need to be installed, package managers ensure a smooth and efficient setup of your environment.
+
+In this guide, we'll talk about `conda` and `mamba`. `Conda` is a powerful package manager and environment manager that you use with command line commands at the Anaconda Prompt for Windows, or in a terminal window for macOS or Linux. `Mamba` is a reimplementation of the `conda` package manager in C++. It's designed to be faster and more robust to network issues or packages with tricky dependencies. When working with complex geospatial packages, `mamba` can often be a more efficient choice.
 
-## Anaconda 
-### Install Anaconda
-Anaconda is a package manager that makes handling dependencies much easier.  Before you begin install:
+This page provides a guide on how to install `miniconda` or `mamba`, and various geospatial packages including `geowombat` and `geowombat-ml`.
 
-- [Anaconda](https://www.anaconda.com/products/distribution)
+## Installing Miniconda
 
-Alternatively, if you don't have much free space in your computer (or if think anaconda is bloated- which is ):
+Firstly, you will need to **choose between** Miniconda & Mamba. Miniconda is a free minimal installer for conda. Please follow the instructions from the [Miniconda page](https://docs.conda.io/en/latest/miniconda.html) to install it on your system.
 
-- [miniconda](https://conda.io/projects/conda/en/latest/user-guide/install/index.html)
+## Installing Mamba
 
-Hit yes and make sure to run the `init` script if prompted. 
+To install `mamba` (prefered), please follow the instructions from the [Mamba page](https://mamba.readthedocs.io/en/latest/).
 
-### Create an Environment Called Spatial
-First thing we need to do is create a conda environment. Think of this as a sandbox for installing a new version of python with only a few chosen modules.
+## Creating and Activating a New Environment
 
-Open `terminal` on a mac/linux or open `anaconda command prompt` or `command prompt` in windows. Then run the following to create an environment called `spatial`:
+You can create a new environment in `conda` or `mamba` using the following command:
 
-``` bash
-conda create -n spatial python=3.9
+```{code-block} console
+conda create --name myenv
 ```
-Now that we have created the environment, let's jump in it! After which you should see `(spatial)` on the left hand side of your terminal. 
+or with `mamba`:
+
+```{code-block} console
+mamba create --name myenv
+```
+Replace `myenv` with the name of your environment.
+
+:::{note}
+For the remainder of this tutorial we will use `mamba`, but `conda` can be exchanged for `mamba` in all commands.
+:::
+
 
-``` bash
-conda activate spatial
+To activate this environment, use:
+
+```{code-block} console
+mamba activate myenv
 ```
-To avoid conflicting dependencies let's add the channel called `conda-forge` (a user maintained list of python modules) and set it to only search there, by setting priority to `strict`. 
 
-``` bash
-conda config --add channels conda-forge
-conda config --set channel_priority strict
+Now, any package you install will be installed in this environment, keeping it isolated from other environments.
+
+## Installing Geospatial Packages
+
+Before we install the packages, we need to add `conda-forge` to your channels:
+
+```{code-block} console
+mamba config --add channels conda-forge
+mamba config --set channel_priority strict
 ```
 
-Now let's install most of the modules we will need in this book:
+Then, we can install `geowombat` and `geowombat-ml` using `conda` or `mamba`. For `mamba`:
 
-``` bash
-conda install geowombat geopandas rasterio matplotlib pandas
+```{code-block} console
+mamba install geowombat geowombat-ml
 ```
-Now install those that aren't available through conda's channels:
+ 
 
-``` bash
-pip install sklearn-xarray  pip-tools rtree ipykernel osmnx PyKrige census us
+Following this, we need to install the following packages: `pyproj`, `geopandas`, `contextily`, `earthpy`, `census`, `geoplot`, `osmnx`, `pykrige`, and `us`.  We will start with the packages available through `conda-forge`:
+
+
+```{code-block} console
+mamba install pyproj geopandas       
 ```
-That's it! To learn more about managing environments in conda please refer to the [docs here](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html).
 
-```{Important}
-Note that every time you want to use your `spatial` environment. You will need to select it in your IDE or open a terminal window and type `conda activate spatial`, you can then launch your IDE via the command line. 
-```
+Finally install those packages that aren't available through `conda-forge`:
+
+```{code-block} console
+pip install census contextily earthpy geoplot osmnx us pykrige
+``
+
+Now you have all the packages installed and ready to be used in your new environment!
diff --git a/pygis/_build/html/_sources/docs/b_getting_started.md b/pygis/_build/html/_sources/docs/b_getting_started.md
@@ -110,7 +110,7 @@ in a Jupyter notebook
 ```{figure} /_static/lecture_specific/getting_started/jp_demo.png
 :scale: 50%
 :name: jp_demo
-
+:alt: A Jupyter notebook viewed in the browser
 A Jupyter notebook viewed in the browser
 ```
 
@@ -136,6 +136,8 @@ If you use the second option, you will see something like this
 
 ```{figure} /_static/lecture_specific/getting_started/starting_nb.png
 :scale: 50%
+:alt: image of an empty terminal window
+Terminal window
 ```
 
 The output tells us the notebook is running at `http://localhost:8888/`
@@ -152,6 +154,8 @@ looks something like this
 
 ```{figure} /_static/lecture_specific/getting_started/nb.png
 :scale: 50%
+:alt: image of a web browser with a jupyter notebook file browser
+ Jupyter notebook file browser
 ```
 
 What you see here is called the Jupyter *dashboard*.
@@ -166,6 +170,9 @@ Here\'s what shows up on our machine:
 
 ```{figure} /_static/lecture_specific/getting_started/nb2.png
 :scale: 50%
+:alt: image of a jupyter notebook cell
+ Jupyter notebook cell
+
 ```
 
 The notebook displays an *active cell*, into which you can type Python
@@ -190,6 +197,8 @@ instead of the usual `Enter`.
 
 ```{figure} /_static/lecture_specific/getting_started/nb3.png
 :scale: 50%
+:alt: image of a jupyter notebook cell with a green border and executed cells
+ Running a cell
 ```
 
 (Note: There are also menu and button options for running code in a cell
@@ -289,6 +298,8 @@ For example, here we type `np.ran` and hit Tab
 
 ```{figure} /_static/lecture_specific/getting_started/nb6.png
 :scale: 50%
+:alt: image of a jupyter notebook cell demonstrating tab completion
+ Tab completion in a notebook
 ```
 
 Jupyter offers up the two possible completions, `random` and `rank`.
@@ -306,6 +317,8 @@ Documentation appears in a split window of the browser, like so
 
 ```{figure} /_static/lecture_specific/getting_started/nb6a.png
 :scale: 50%
+:alt: image of a jupyter notebook cell demonstrating on-line help
+ On-line help in a notebook
 ```
 
 Clicking on the top right of the lower split closes the on-line help.
@@ -320,6 +333,8 @@ code
 
 ```{figure} /_static/lecture_specific/getting_started/nb7.png
 :scale: 50%
+:alt: image of a jupyter notebook cell demonstrating markdown using latex equations
+ Markdown in a notebook
 ```
 
 Next we `Esc` to enter command mode and then type `m` to indicate that
@@ -333,8 +348,11 @@ Now we `Shift+Enter` to produce this
 
 ```{figure} /_static/lecture_specific/getting_started/nb8.png
 :scale: 50%
+:alt: image of a jupyter notebook cell demonstrating markdown rendered using latex equations
+ Markdown rendered in a notebook
 ```
 
+
 ### Sharing Notebooks
 
 Notebook files are just text files structured in
@@ -353,5 +371,74 @@ Save it somewhere, navigate to it from the Jupyter dashboard and then
 run as discussed above.
 
 
+Sure! Let's add a section using Myst Markdown to explain how to access and use Google Colab.
+
+
+
+## Using Google Colab
+
+### Overview
+
+Google Colab is a cloud-based Jupyter notebook environment provided by Google. It allows you to write and execute Python code, similar to Jupyter Notebooks, but without the need for local installations. Google Colab provides free access to computing resources, including GPUs and TPUs, which can be beneficial for resource-intensive tasks.
+
+### Accessing Google Colab
+
+To access Google Colab, follow these steps:
+
+1. Open a web browser and navigate to [Google Colab](https://colab.research.google.com/).
+2. Sign in to your Google account or create one if you don't have it already.
+
+### Creating a New Colab Notebook
+
+Once you are signed in, you can create a new Colab notebook:
+
+1. Click on the "NEW NOTEBOOK" button on the left sidebar or go to "File" > "New notebook."
+
+### Running Code in Colab
+
+Running code in Google Colab is similar to Jupyter Notebooks. You can create code cells and execute them using Shift+Enter or by clicking the "Play" button in the top left of each cell.
+
+```{code-cell} ipython3
+print("Hello, Google Colab!")
+```
+
+```{code-cell} ipython3
+# Basic Python code example
+for i in range(5):
+    print("Count:", i)
+```
+
+### Saving and Sharing Your Colab Notebook
+
+Google Colab automatically saves your notebook in your Google Drive. To save a copy to your local machine or share the notebook with others, go to "File" > "Download" or "File" > "Share."
+
+### GPU and TPU Support
+
+Google Colab provides free access to GPUs and TPUs (Tensor Processing Units) to accelerate computations. To use GPU or TPU, go to "Runtime" > "Change runtime type" and select the desired hardware accelerator.
+
+## Accessing External Data
+
+You can access external data from Google Drive, GitHub, or other online sources directly from a Colab notebook. For instance, you can mount your Google Drive and access files using the following code:
+
+```python
+from google.colab import drive
+drive.mount('/content/drive')
+```
+
+## Installing Additional Libraries
+
+Most popular Python libraries come pre-installed in Google Colab. However, if you need to install additional libraries, you can use the pip package manager directly in a code cell:
+
+```{code-cell} ipython3
+!pip install pandas
+```
+
+## Limitations
+
+While Google Colab is a powerful tool, it does have some limitations. Colab sessions may disconnect after a period of inactivity, and resources like GPU/TPU availability might be limited during peak usage.
+
+**Importantly** some libraries used in this textbook, for instance `geowombat` are not easily installed on google colabs.  
+
+
 ## Attribution
-The above lesson was pulled directly from work by [Thomas J. Sargent & John Stachurski](https://python-programming.quantecon.org/about_py.html)
+The above lesson was pulled and adapted from work by [Thomas J. Sargent & John Stachurski](https://python-programming.quantecon.org/about_py.html)
diff --git a/pygis/_build/html/_sources/docs/b_intro_py.md b/pygis/_build/html/_sources/docs/b_intro_py.md
@@ -13,7 +13,11 @@ kernelspec:
 
 # Welcome - Let's get started
 
+This section is an introduction to spatial Python, starting with the basics of Python programming. It covers the setup of a standard Python environment and provides an introductory example to help learners get acquainted with Python concepts. The section encourages learners to explore further and learn more about spatial Python methods beyond the basics.
+
 This section will answer a few basic questions about python:
 - What is python?
 - How do we use it?
-- How can we extend its functionality?
+- Utilize and extend the programming language python
+
+