For those doing scientific programming, the IPython notebook is a joyful place for interactive exploration and can be appropriate for document creation. Notebook cells can have code, images, or text, and text can mix Markdown and LaTeX (rendered in the cell via MathJax). Notebooks can be converted to HTML or PDF (via LaTeX), using the nbconvert utility (which depends on pandoc). For serious document production, this is not even remotely a replacement for LaTeX, but it can be a great place for interactive work.
Think it's graphicsx. One of the packages, anyways, lets you include PNGs, JPGs, etc.
It is also not nearly as bad as you depict. Vector drawing is handled nicely by pgf/tik. If you want meta-control of tikz, you can use the wonderful tikz backend for matplotlib. There are also beautiful ways to produce EPS or (better yet imho) PDF for LaTeX, with embedded TeX fonts, including Matplotlib and the amazingly powerful PyX. Btw, the graphics inclusion package is graphicx.
There is a petition to help this student, asking Dawson to reinstate him, make him whole financially, and apologize.
I think Lott is opinionated, inflammatory, and important. I suspect he is probably wrong in his concealed carry conclusions. However in a complex matter like this, as in pretty much any scientific inquiry, those of us who are not specialists should rely on the consensus of the experts. In this case, there is no consensus on how concealed carry affects murder rates, so holding a strong opinion is unjustified. (It follows that we can expect a lot of ideological shouting.)
One thing that is known to raise murder rates is high and sustained unemployment. Neither concealed carry laws nor unemployment rates appear relevant to this shooting, however.
The hard data shows far more crimes prevented by guns than caused by them..
I'm unaware of such "hard data". Cites please. Btw, if you are referring to John Lott's important work, note that the National Academy of Sciences reviewed this and did not back his core claims.
I mostly use python these days [snip] Matlab's syntax is just so slick by comparison:
Matlab: foo = [1 2;3 4] Python: foo= array([[1,2],[3,4]]) R: foo - matrix(c(1,2,3,4),2,2)
NumPy includes a matrix library: foo=mat('1 2;3 4'). In general, Python's syntax beats Matlab's syntax
hands down. (In this particular case there is almost a tie, but a trivial advantage for Matlab. I spend apporximately 0% of my time typing in data for array construction, and I suppose that is true of most users.) For help transitioning, see http://www.scipy.org/NumPy_for_Matlab_Users.
Think its real?"
Link to Original Source
On the contrary, this is quite normal. Ice caps expand and recede all the time and have been for centuries. As MIT climatologist Richard Lindzen pointed out in WSJ today, you're discarding a well-established understanding of the history of the planet by making that claim.
Promoting Lindzen can be counterproductive for climate change deniers:
"In November 2004, climate change skeptic Richard Lindzen was quoted saying he'd be willing to bet that the earth's climate will be cooler in 20 years than it is today. When British climate researcher James Annan contacted him, however, Lindzen would only agree to take the bet if Annan offered a 50-to-1 payout."
I also wonder how many who quote Lindzen on climate change also quote Lindzen on smoking?
R handles non-matrix data structures much, much better than Matlab does
This advantage is even larger for Python. Use the NumPy package for efficient array handling and basic linear algebra. Use SciPy for optimization and statistics. Use Matplotlib for amazingly powerful 2d graphics. And if you occasionally need R, which does have an wonderfully deep statistical library, you can access it with rpy.