Plantilla

.gitignore

+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+_build
+
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build
+examples/_build
+jupyter_book/book_template/_build
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# VSCode config
+.vscode
+
+# PyCharm config
+.idea
+
+# OSX
+.DS_Store

.gitlab-ci.yml

+# requiring the environment of python
+image: python:3.9-buster
+
+before_script:
+  - pip install -r requirements.txt
+
+test:
+  stage: test
+  script:
+    - jupyter-book build . # build to public path
+  only:
+    - branches # this job will affect every branch except 'master'
+  except:
+    - master
+    
+# the 'pages' job will deploy and build your site to the 'public' path
+pages:
+  stage: deploy
+  script:
+    - jupyter-book build . # build to public path
+  artifacts:
+    paths:
+      - _build
+    expire_in: 10 mins
+  only:
+    - master # this job will affect only the 'master' branch

README.md

-# plantillaJupyterBook
+# Plantilla Jupyter Book
 
+Plantilla para la creación de documentación usando JupyterBook
 
-
-## Getting started
+Ejemplo en [https://class.redclara.net/cyted/course2](https://class.redclara.net/cyted/course2)
\ No newline at end of file
-
-To make it easy for you to get started with GitLab, here's a list of recommended next steps.
-
-Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
-
-## Add your files
-
-- [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
-- [ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:
-
-```
-cd existing_repo
-git remote add origin https://gitmilab.redclara.net/hackathon-bella/plantillajupyterbook.git
-git branch -M main
-git push -uf origin main
-```
-
-## Integrate with your tools
-
-- [ ] [Set up project integrations](https://gitmilab.redclara.net/hackathon-bella/plantillajupyterbook/-/settings/integrations)
-
-## Collaborate with your team
-
-- [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)
-- [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
-- [ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
-- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/)
-- [ ] [Set auto-merge](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html)
-
-## Test and Deploy
-
-Use the built-in continuous integration in GitLab.
-
-- [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)
-- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing(SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
-- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
-- [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
-- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
-
-***
-
-# Editing this README
-
-When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thank you to [makeareadme.com](https://www.makeareadme.com/) for this template.
-
-## Suggestions for a good README
-Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
-
-## Name
-Choose a self-explaining name for your project.
-
-## Description
-Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.
-
-## Badges
-On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge.
-
-## Visuals
-Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.
-
-## Installation
-Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.
-
-## Usage
-Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.
-
-## Support
-Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.
-
-## Roadmap
-If you have ideas for releases in the future, it is a good idea to list them in the README.
-
-## Contributing
-State if you are open to contributions and what your requirements are for accepting them.
-
-For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
-
-You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
-
-## Authors and acknowledgment
-Show your appreciation to those who have contributed to the project.
-
-## License
-For open source projects, say how it is licensed.
-
-## Project status
-If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.

_config.yml

+# Book settings
+# Learn more at https://jupyterbook.org/customize/config.html
+
+title: Plantilla Jupyter Book
+author: MiLab
+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://gitmilab.redclara.net/hackathon-bella/plantillajupyterbook  # Online location of your book
+  #path_to_book: docs  # Optional path to your book, relative to the repository root
+  branch: main  # 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: docs/intro
+parts:
+  - caption: Sesiones
+    numbered: True  # Only applies to chapters in Part 1.
+    chapters:
+    - file: docs/sesion01/milab

docs/intro.md

+# Creación de cursos con MkDocs
+
+```{tableofcontents}
+```
+
+Este es un curso en el servicio [MiLab Pages](https://milab.redclara.net/). Puede revisar su [código fuente](https://gitlab.com/pages/mkdocs) y solicitar un despliegue nuevo con esta plantilla para sus proyectos llenando el siguiente formulario: [https://forms.gle/y33t6xb1vSKXQV4n8](https://forms.gle/y33t6xb1vSKXQV4n8).
+
+## Tutorial de edición
+
+> Esta plantilla se edita en formato MarkDown, puede usar la guía creada por [Matt Cone](https://github.com/mattcone) para conocer su sintaxis en [Guía rápida MarkDown](markdown.md) 
+
+Para la edición de los cursos se debe seguir estos pasos:
+
+1. Dirigirse al repositorio del curso. Por ejemplo https://gitmilab.redclara.net/cyted/course1
+2. Ingresar al editor web dando clic al botón **Web IDE**
+![upload](images/cyted1.png "Web IDE")
+3. Para editar archivos existentes, localizarlos en la carpeta `docs/` y realizar las ediciones necesarias.
+4. Para nuevas paginas:
+    1. Se debe crear un nuevo archivo con extensión `.md` en el directorio `docs/`. Ejemplo `docs/documentacion.md`
+    2. Se debe agregar al menú desde el archivo `mkdocs.yml` en la sección `nav:` siguiendo el formato:  
+
+        ```
+        nav:
+        - Título: docs/archivo.md
+        ```
+        
+5. Se pueden agregar imagenes, documentos o cualquier material complementario cargandolos desde el editor en el ícono ![upload](images/upload.png "Upload icon from https://www.flaticon.com/free-icon/upload_747416")
+6. Para guardar los cambios:  
+    1. Se da clíc al botón **Create Commit**
+    2. Se agrega una descripción de los cambios en el campo *Commit message*.  
+        > Una frase corta que describe a grandes rasgos las mejoras añadidas
+    3. Se selecciona, Commit to **master** branch.
+    4. Se da clic al botón **Commit**
+
+> A partir del commmit los cambios se reflejarán en la versión web en aproximadamente 5 minutos.

docs/sesion01/milab.md

+# Reproducibilidad `MiLab`
+
+Plataforma MiLab, control de versiones, mensajería en escenarios de investigación, calculo computacional y datos de investigación para la ciencia abierta y reproducible.
+
+

markdown-notebooks.md

+---
+jupytext:
+  cell_metadata_filter: -all
+  formats: md:myst
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.13
+    jupytext_version: 1.11.5
+kernelspec:
+  display_name: Python 3
+  language: python
+  name: python3
+---
+
+# Notebooks with MyST Markdown
+
+Jupyter Book also lets you write text-based notebooks using MyST Markdown.
+See [the Notebooks with MyST Markdown documentation](https://jupyterbook.org/file-types/myst-notebooks.html) for more detailed instructions.
+This page shows off a notebook written in MyST Markdown.
+
+## An example cell
+
+With MyST Markdown, you can define code cells with a directive like so:
+
+```{code-cell}
+print(2 + 2)
+```
+
+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.
+
+```{seealso}
+Jupyter Book uses [Jupytext](https://jupytext.readthedocs.io/en/latest/) to convert text-based files to notebooks, and can support [many other text-based notebook files](https://jupyterbook.org/file-types/jupytext.html).
+```
+
+## Create a notebook with MyST Markdown
+
+MyST Markdown notebooks are defined by two things:
+
+1. YAML metadata that is needed to understand if / how it should convert text files to notebooks (including information about the kernel needed).
+   See the YAML at the top of this page for example.
+2. The presence of `{code-cell}` directives, which will be executed with your book.
+
+That's all that is needed to get started!
+
+## Quickly add YAML metadata for MyST Notebooks
+
+If you have a markdown file and you'd like to quickly add YAML metadata to it, so that Jupyter Book will treat it as a MyST Markdown Notebook, run the following command:
+
+```
+jupyter-book myst init path/to/markdownfile.md
+```

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**.
+This is a simple file to help you get started and show off some syntax.
+
+## 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.
+
+For more about MyST, see [the MyST Markdown Overview](https://jupyterbook.org/content/myst.html).
+
+## Sample Roles and Directivs
+
+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.
+
+Here is a "note" directive:
+
+```{note}
+Here is a note
+```
+
+It will be rendered in a special box when you build your book.
+
+Here is an inline directive to refer to a document: {doc}`markdown-notebooks`.
+
+
+## Citations
+
+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`.
+
+Moreover, 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}
+```
+
+## Learn more
+
+This is just a simple starter to get you started.
+You can learn a lot more at [jupyterbook.org](https://jupyterbook.org).

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-wide.svg)\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