First commit, generate sample book

_config.yml

+# Book settings
+# Learn more at https://jupyterbook.org/customize/config.html
+
+title: My sample book
+author: The Jupyter Book Community
+logo: logo.png
+
+# Force re-execution of notebooks on each build.
+# See https://jupyterbook.org/content/execute.html
+execute:
+  execute_notebooks: force
+
+# Define the name of the latex output file for PDF builds
+latex:
+  latex_documents:
+    targetname: book.tex
+
+# Add a bibtex file so that we can create citations
+bibtex_bibfiles:
+  - references.bib
+
+# Information about where the book exists on the web
+repository:
+  url: https://github.com/executablebooks/jupyter-book  # Online location of your book
+  path_to_book: docs  # Optional path to your book, relative to the repository root
+  branch: master  # Which branch of the repository should be used when creating links (optional)
+
+# Add GitHub buttons to your book
+# See https://jupyterbook.org/customize/config.html#add-a-link-to-your-repository
+html:
+  use_issues_button: true
+  use_repository_button: true

_toc.yml

+# Table of contents
+# Learn more at https://jupyterbook.org/customize/toc.html
+
+format: jb-book
+root: intro
+chapters:
+- file: markdown
+- file: notebooks

intro.md

+# Welcome to your Jupyter Book
+
+This is a small sample book to give you a feel for how book content is
+structured.
+
+:::{note}
+Here is a note!
+:::
+
+And here is a code block:
+
+```
+e = mc^2
+```
+
+Check out the content pages bundled with this sample book to see more.

markdown.md

+# Markdown Files
+
+Whether you write your book's content in Jupyter Notebooks (`.ipynb`) or
+in regular markdown files (`.md`), you'll write in the same flavor of markdown
+called **MyST Markdown**.
+
+## What is MyST?
+
+MyST stands for "Markedly Structured Text". It
+is a slight variation on a flavor of markdown called "CommonMark" markdown,
+with small syntax extensions to allow you to write **roles** and **directives**
+in the Sphinx ecosystem.
+
+## What are roles and directives?
+
+Roles and directives are two of the most powerful tools in Jupyter Book. They
+are kind of like functions, but written in a markup language. They both
+serve a similar purpose, but **roles are written in one line**, whereas
+**directives span many lines**. They both accept different kinds of inputs,
+and what they do with those inputs depends on the specific role or directive
+that is being called.
+
+### Using a directive
+
+At its simplest, you can insert a directive into your book's content like so:
+
+````
+```{mydirectivename}
+My directive content
+```
+````
+
+This will only work if a directive with name `mydirectivename` already exists
+(which it doesn't). There are many pre-defined directives associated with
+Jupyter Book. For example, to insert a note box into your content, you can
+use the following directive:
+
+````
+```{note}
+Here is a note
+```
+````
+
+This results in:
+
+```{note}
+Here is a note
+```
+
+In your built book.
+
+For more information on writing directives, see the
+[MyST documentation](https://myst-parser.readthedocs.io/).
+
+
+### Using a role
+
+Roles are very similar to directives, but they are less-complex and written
+entirely on one line. You can insert a role into your book's content with
+this pattern:
+
+```
+Some content {rolename}`and here is my role's content!`
+```
+
+Again, roles will only work if `rolename` is a valid role's name. For example,
+the `doc` role can be used to refer to another page in your book. You can
+refer directly to another page by its relative path. For example, the
+role syntax `` {doc}`intro` `` will result in: {doc}`intro`.
+
+For more information on writing roles, see the
+[MyST documentation](https://myst-parser.readthedocs.io/).
+
+
+### Adding a citation
+
+You can also cite references that are stored in a `bibtex` file. For example,
+the following syntax: `` {cite}`holdgraf_evidence_2014` `` will render like
+this: {cite}`holdgraf_evidence_2014`.
+
+Moreoever, you can insert a bibliography into your page with this syntax:
+The `{bibliography}` directive must be used for all the `{cite}` roles to
+render properly.
+For example, if the references for your book are stored in `references.bib`,
+then the bibliography is inserted with:
+
+````
+```{bibliography}
+```
+````
+
+Resulting in a rendered bibliography that looks like:
+
+```{bibliography}
+```
+
+
+### Executing code in your markdown files
+
+If you'd like to include computational content inside these markdown files,
+you can use MyST Markdown to define cells that will be executed when your
+book is built. Jupyter Book uses *jupytext* to do this.
+
+First, add Jupytext metadata to the file. For example, to add Jupytext metadata
+to this markdown page, run this command:
+
+```
+jupyter-book myst init markdown.md
+```
+
+Once a markdown file has Jupytext metadata in it, you can add the following
+directive to run the code at build time:
+
+````
+```{code-cell}
+print("Here is some code to execute")
+```
+````
+
+When your book is built, the contents of any `{code-cell}` blocks will be
+executed with your default Jupyter kernel, and their outputs will be displayed
+in-line with the rest of your content.
+
+For more information about executing computational content with Jupyter Book,
+see [The MyST-NB documentation](https://myst-nb.readthedocs.io/).

notebooks.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Content with notebooks\n",
+    "\n",
+    "You can also create content with Jupyter Notebooks. This means that you can include\n",
+    "code blocks and their outputs in your book.\n",
+    "\n",
+    "## Markdown + notebooks\n",
+    "\n",
+    "As it is markdown, you can embed images, HTML, etc into your posts!\n",
+    "\n",
+    "![](https://myst-parser.readthedocs.io/en/latest/_static/logo.png)\n",
+    "\n",
+    "You can also $add_{math}$ and\n",
+    "\n",
+    "$$\n",
+    "math^{blocks}\n",
+    "$$\n",
+    "\n",
+    "or\n",
+    "\n",
+    "$$\n",
+    "\\begin{aligned}\n",
+    "\\mbox{mean} la_{tex} \\\\ \\\\\n",
+    "math blocks\n",
+    "\\end{aligned}\n",
+    "$$\n",
+    "\n",
+    "But make sure you \\$Escape \\$your \\$dollar signs \\$you want to keep!\n",
+    "\n",
+    "## MyST markdown\n",
+    "\n",
+    "MyST markdown works in Jupyter Notebooks as well. For more information about MyST markdown, check\n",
+    "out [the MyST guide in Jupyter Book](https://jupyterbook.org/content/myst.html),\n",
+    "or see [the MyST markdown documentation](https://myst-parser.readthedocs.io/en/latest/).\n",
+    "\n",
+    "## Code blocks and outputs\n",
+    "\n",
+    "Jupyter Book will also embed your code blocks and output in your book.\n",
+    "For example, here's some sample Matplotlib code:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib import rcParams, cycler\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "plt.ion()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Fixing random state for reproducibility\n",
+    "np.random.seed(19680801)\n",
+    "\n",
+    "N = 10\n",
+    "data = [np.logspace(0, 1, 100) + np.random.randn(100) + ii for ii in range(N)]\n",
+    "data = np.array(data).T\n",
+    "cmap = plt.cm.coolwarm\n",
+    "rcParams['axes.prop_cycle'] = cycler(color=cmap(np.linspace(0, 1, N)))\n",
+    "\n",
+    "\n",
+    "from matplotlib.lines import Line2D\n",
+    "custom_lines = [Line2D([0], [0], color=cmap(0.), lw=4),\n",
+    "                Line2D([0], [0], color=cmap(.5), lw=4),\n",
+    "                Line2D([0], [0], color=cmap(1.), lw=4)]\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(10, 5))\n",
+    "lines = ax.plot(data)\n",
+    "ax.legend(custom_lines, ['Cold', 'Medium', 'Hot']);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There is a lot more that you can do with outputs (such as including interactive outputs)\n",
+    "with your book. For more information about this, see [the Jupyter Book documentation](https://jupyterbook.org)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.0"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

references.bib

+---
+---
+
+@inproceedings{holdgraf_evidence_2014,
+	address = {Brisbane, Australia, Australia},
+	title = {Evidence for {Predictive} {Coding} in {Human} {Auditory} {Cortex}},
+	booktitle = {International {Conference} on {Cognitive} {Neuroscience}},
+	publisher = {Frontiers in Neuroscience},
+	author = {Holdgraf, Christopher Ramsay and de Heer, Wendy and Pasley, Brian N. and Knight, Robert T.},
+	year = {2014}
+}
+
+@article{holdgraf_rapid_2016,
+	title = {Rapid tuning shifts in human auditory cortex enhance speech intelligibility},
+	volume = {7},
+	issn = {2041-1723},
+	url = {http://www.nature.com/doifinder/10.1038/ncomms13654},
+	doi = {10.1038/ncomms13654},
+	number = {May},
+	journal = {Nature Communications},
+	author = {Holdgraf, Christopher Ramsay and de Heer, Wendy and Pasley, Brian N. and Rieger, Jochem W. and Crone, Nathan and Lin, Jack J. and Knight, Robert T. and Theunissen, Frédéric E.},
+	year = {2016},
+	pages = {13654},
+	file = {Holdgraf et al. - 2016 - Rapid tuning shifts in human auditory cortex enhance speech intelligibility.pdf:C\:\\Users\\chold\\Zotero\\storage\\MDQP3JWE\\Holdgraf et al. - 2016 - Rapid tuning shifts in human auditory cortex enhance speech intelligibility.pdf:application/pdf}
+}
+
+@inproceedings{holdgraf_portable_2017,
+	title = {Portable learning environments for hands-on computational instruction using container-and cloud-based technology to teach data science},
+	volume = {Part F1287},
+	isbn = {978-1-4503-5272-7},
+	doi = {10.1145/3093338.3093370},
+	abstract = {© 2017 ACM. There is an increasing interest in learning outside of the traditional classroom setting. This is especially true for topics covering computational tools and data science, as both are challenging to incorporate in the standard curriculum. These atypical learning environments offer new opportunities for teaching, particularly when it comes to combining conceptual knowledge with hands-on experience/expertise with methods and skills. Advances in cloud computing and containerized environments provide an attractive opportunity to improve the effciency and ease with which students can learn. This manuscript details recent advances towards using commonly-Available cloud computing services and advanced cyberinfrastructure support for improving the learning experience in bootcamp-style events. We cover the benets (and challenges) of using a server hosted remotely instead of relying on student laptops, discuss the technology that was used in order to make this possible, and give suggestions for how others could implement and improve upon this model for pedagogy and reproducibility.},
+	booktitle = {{ACM} {International} {Conference} {Proceeding} {Series}},
+	author = {Holdgraf, Christopher Ramsay and Culich, A. and Rokem, A. and Deniz, F. and Alegro, M. and Ushizima, D.},
+	year = {2017},
+	keywords = {Teaching, Bootcamps, Cloud computing, Data science, Docker, Pedagogy}
+}
+
+@article{holdgraf_encoding_2017,
+	title = {Encoding and decoding models in cognitive electrophysiology},
+	volume = {11},
+	issn = {16625137},
+	doi = {10.3389/fnsys.2017.00061},
+	abstract = {© 2017 Holdgraf, Rieger, Micheli, Martin, Knight and Theunissen. Cognitive neuroscience has seen rapid growth in the size and complexity of data recorded from the human brain as well as in the computational tools available to analyze this data. This data explosion has resulted in an increased use of multivariate, model-based methods for asking neuroscience questions, allowing scientists to investigate multiple hypotheses with a single dataset, to use complex, time-varying stimuli, and to study the human brain under more naturalistic conditions. These tools come in the form of “Encoding” models, in which stimulus features are used to model brain activity, and “Decoding” models, in which neural features are used to generated a stimulus output. Here we review the current state of encoding and decoding models in cognitive electrophysiology and provide a practical guide toward conducting experiments and analyses in this emerging field. Our examples focus on using linear models in the study of human language and audition. We show how to calculate auditory receptive fields from natural sounds as well as how to decode neural recordings to predict speech. The paper aims to be a useful tutorial to these approaches, and a practical introduction to using machine learning and applied statistics to build models of neural activity. The data analytic approaches we discuss may also be applied to other sensory modalities, motor systems, and cognitive systems, and we cover some examples in these areas. In addition, a collection of Jupyter notebooks is publicly available as a complement to the material covered in this paper, providing code examples and tutorials for predictive modeling in python. The aimis to provide a practical understanding of predictivemodeling of human brain data and to propose best-practices in conducting these analyses.},
+	journal = {Frontiers in Systems Neuroscience},
+	author = {Holdgraf, Christopher Ramsay and Rieger, J.W. and Micheli, C. and Martin, S. and Knight, R.T. and Theunissen, F.E.},
+	year = {2017},
+	keywords = {Decoding models, Encoding models, Electrocorticography (ECoG), Electrophysiology/evoked potentials, Machine learning applied to neuroscience, Natural stimuli, Predictive modeling, Tutorials}
+}
+
+@book{ruby,
+  title     = {The Ruby Programming Language},
+  author    = {Flanagan, David and Matsumoto, Yukihiro},
+  year      = {2008},
+  publisher = {O'Reilly Media}
+}

requirements.txt

+jupyter-book
+matplotlib
+numpy