# The Yhat Blog

machine learning, data science, engineering

# Rodeo 1.1 - Markdown, Autoupdates, Feedback

### So what's new?!?

In 1.1, we've added 4 main features:

• Rendering markdown and exporting to pdf
• Auto-updating
• Integration with Intercom for updates, feature requests and feedback
• Created a Slack channel for getting help w/ Rodeo and meeting other users

### Markdown Rendering

We've added a markdown renderer to the text editor so you can render markdown, code, images, Latex, and output it all into pdf!

#### Regular ole markdown

Plain Jane markdown renders as you'd expect in Rodeo. Check out the Daring Fireball Markdown Guide for more info on syntax and features.

A First Level Header
====================

---------------------

Now is the time for all good men to come to
the aid of their country. This is just a
regular paragraph.

The quick brown fox jumped over the lazy
dogs back.

> This is a blockquote.
>
> This is the second paragraph in the blockquote.
>
> ## This is an H2 in a blockquote


#### Code Cells

In addition to basic markdown, Rodeo also supports "Python infused" markdown. In your markdown file, define a code cell block using the "{python}". Write your code as per ususal and any output (including plots!) will be included in the output.

{python}
import numpy as np
import seaborn as sns

sns.distplot(np.random.rand(100).tolist())


To render this code in markdown:

1) Highlight it

2) Click the markdown icon below:

And the markdown output:

#### Latex, gif/images

If that's not enough, you can also embed Latex! Rodeo uses MathJax for rendering Latex, so anything you can do with Latex, you can do in Rodeo. All you need to do is define a code block which contains "{mathjax}".

And guess what? Gifs work too! Use the ![]() syntax to embed one into your markdown file. Need inspiration? There are plenty of gifs on giphy.

### Render Latex

{mathjax}
$$\begin{array}{r l} \max & z = \sum_{j=1}^{n} c_j x_j & \\ \text{s.t.} & \sum_{j=1}^{n} \bar a_{ij} x_{j} + \max_{S_i \subseteq J_i, |S_i| = \Gamma_i} { \sum_{j \in S_i} \hat a_{ij} y_j } \leq b_i & \forall\, i=1,\dotsc,m \\ & x_j \leq y_j & \forall\, j=1,\dotsc,n\\ & x_j \geq 0,y_j \geq 0 & \forall\, j=1,\dotsc,n \end{array}$$


### Render images and gifs

![](http://i.giphy.com/j5QcmXoFWl4Q0.gif)



The output can then be exported by clicking the "Export to PDF" button.

### Auto-updating

Features releases, bug fixes, and general product improvements can now be downloaded directly from within the application. Simply click the notification shown below and we'll automatically download and update Rodeo.

### Intercom and Slack channel

We've added an integration with Intercom. This means that whenever you have a request for a new feature, just click the "?" in the bottom right corner, type down your thoughts, and we'll add it to the feature roadmap.

If you're having trouble getting setup or want to chat w/ other users, we've also setup a public Slack channel at http://slack.yhat.com/ Come meet the Yhat team and Rodeo users!

### Wrap Up

In addition to quite a few bug fixes, that about wraps up the latest version of Rodeo. We'd love to hear your feedback, feature requests or any thoughts you have on Rodeo.

Feel free to email them to info [at] yhathq.com or message us directly within Rodeo.

#### Our Products

Rodeo: a native Python editor built for doing data science on your desktop.

ScienceOps: deploy predictive models in production applications without IT.

Yhat (pronounced Y-hat) provides data science solutions that let data scientists deploy and integrate predictive models into applications without IT or custom coding.