Amazing NASA simulation of Solar Wind Striping the Martian Atmosphere

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The following video by NASA’s Scientific Visualization Center simulates the Martian atmosphere being striped by incoming solar wind.

More videos and images can be found here.

Mars is a cold and barren desert today, but scientists think that in the ancient past it was warm and wet. The loss of the early Martian atmosphere may have led to this dramatic change, and one of the prime suspects is the solar wind. Unlike Earth, Mars lacks a global magnetic field to deflect the stream of charged particles continuously blowing off the Sun. Instead, the solar wind crashes into the Mars upper atmosphere and can accelerate ions into space. Now, for the first time, NASA’s MAVEN spacecraft has observed this process in action – by measuring the speed and direction of ions escaping from Mars. This data visualization compares simulations of the solar wind and Mars atmospheric escape with new measurements taken by MAVEN.

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OCO-2 scientists @ JPL find patterns in Carbon Dioxide concentrations

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On October 29, 2015 JPL announced that scientists have found patterns in the carbon dioxide concentrations in Earth’s atmosphere. What is most interesting is tha

“…OCO-2 scientists are now beginning to study the net sources of carbon dioxide as well as their “sinks””

 

Another interesting video is this supercomputer model of CO2 levels in Earth’s atmosphere for a whole year (2006):

Full story @ JPL: Excitement Grows as NASA Carbon Sleuth Begins Year Two

Orbiting Carbon Observatory – 2 mission page

Vortices in my coffee and the Perpetual Ocean

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In a previous post I shared a video on mixing and unmixing of fluids. This time I share with you an image I took of my coffee. With some milk fluid dynamics created amazing vortices. Following it is an animation of the time evolution of the vortices.

Milk vortices.jpg
Milk vortices” by AstrobobOwn work. Licensed under CC BY-SA 4.0 via Commons.

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Matlab – Symbolic & Function Handles

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Consider you want to define a function in Matlab, plot it, and differentiate it. This can be done in two ways. Let’s demonstrate the two methods on the function

f(x) = x - 3 * log(x)

whose derivative is

f'(x) = 1 - \frac{3}{x}

The first is using function handles (ie; numerically) that take & return values as input & output. Function handles require the inputs to be initialized. Here’s an example:


x = linspace(0.5, 5.0)'; % range of x as column vector
f = x - 3 * log(x); % returns numerical values
df = diff(f) ./ diff(x); % also numerical values of the derivative

The other is symbolically (ie; like you do in your math class) as such


syms x % define symbolic variables
f = x - 3 * log(x); % symbolic function
df = diff(f, x); % gives the symbolic derivative

which returns

f = x - 3*log(x)
df = 1 - 3/x

But this way you cannot give the funtion numerical inputs and hence can’t plot it. To do so you’ll have to convert the function to a function handle which is easy using matlabFunction():


f_handle = matlabFunction(f); % convert symbolic fn to a handle
f_handle(2); % value of f @ x = 2
x = linspace(0.5, 5.0)'; % define range for x as column vector
plot(x, f_handle(x) ) % plot f on the range x

which return

f_handle = @(x)x-log(x).*3.0
df_handle = @(x)-3.0./x+1.0

Academic Torrents – a torrent sharing website for Academics & Researchers

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Academic Torrents

Sharing data is hard. Emails have size limits, and setting up servers is too much work. We’ve designed a distributed system for sharing enormous datasets – for researchers, by researchers. The result is a scalable, secure, and fault-tolerant repository for data, with blazing fast download speeds.

One aim of this site is to create the infrastructure to allow open access journals to operate at low cost. By facilitating file transfers, the journal can focus on its core mission of providing world class research. After peer review the paper can be indexed on this site and diseminated throughout our system.

Large dataset delivery can be supported by researchers in the field that have the dataset on their machine. A popular large dataset doesn’t need to be housed centrally. Researchers can have part of the dataset they are working on and they can help host it together.

Libraries can host this data to host papers from their own campus without becoming the only source of the data. So even if a library’s system is broken other universities can participate in getting that data into the hands of researchers.

Best Practices for Scientific Computing

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A summary of a very interesting paper on “Best Practices for Scientific Computing” I read a year ago.

andrea cirillo's blog

I reproduce here below principles from the amazing paper Best Practices for Scientific Computing, published on 2012 by a group of US and UK professors. The main purpose of the paper is to “teach”  good programming habits shared from professional developers to people  that weren’t born developer, and became developers just for professional purposes.

Scientists spend an increasing amount of time building and using software. However, most scientists are never taught how to do this efficiently

Best Practices for Scientific Computing

  1. Write programs for people, not computers.

    1. a program should not require its readers to hold more than a handful of facts in memory at once
    2. names should be consistent, distinctive and meaningful
    3. code style and formatting should be consistent
    4. all aspects of software development should be broken down into tasks roughly an hour long
  2. Automate repetitive tasks.

    1. rely on the computer to repeat tasks
    2. save recent commands in…

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Assorted links – Data Science with R

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last updated: 2015-08-29

References & Most helpful commands

Tutorials & Handy packages

Hands-on dplyr tutorial for faster data manipulation in R Interactive Visualizations From R Using Rcharts rMaps – Interactive Maps from R (github repo) (requires “devtools” from cran)
Using R for Psychological Research – Personality Project, William Revelle
DataCamp courses
Try R by Code School (on codeschool)
Introduction to R, Leada

Visualization Packages

see Assorted links – Data Visualization (to be published later)

Papers

Tidy Data, Hadley Wickham [PDF]

Journals

Big Data & Society – Open-access journal

Hacks for better productivity

Sublime and R

Using Sublime Text 2 for R Using R in Sublime Text 3

Books

Video (training) courses

Introduction to Data Science with R, Garrett Grolemund, O’Reilly Media

Lists of Resources by others

Data Mining

Scraping Twitter and Web Data Using R – Pablo Barbera

Numerical Analysis
Interoperability
Data Sources

see Assorted links – Data sources (To be published later)

If you’d like to contribute to this list, please leave them in the comments below.