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# 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/images/cyted1.png

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docs/images/upload.png

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

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