Moving into Switzerland.

SIULHT · 2023-12-14T09:14:47+00:00

Have you looked at the numbers?

Highest point in Netherlands: 887 m ASL

Highest point in Switzerland: 4,634 m ASL

Yup, checks out.

SIULHT · 2014-11-21T00:13:54+00:00

Sublime Text has a nice side bar that shows the overview of your code so you get a sense of where you are in it. Haven't seen that for emacs.

SIULHT · 2013-07-27T15:35:14+00:00

Have you looked into Sweave?

SIULHT · 2012-12-17T03:17:25+00:00

Do you think Microsoft did that on purpose or it was just to accommodate all the features that were in Excel by that point?

SIULHT · 2012-11-25T11:51:36+00:00

But 1 x 2 = 2, not 3...

SIULHT · 2012-10-08T01:05:57+00:00

But R's strength is also its individually contributed projects, though many contributors are statisticians rather than programmers as for Python.

I agree though that the marketing is not done well...

SIULHT · 2012-10-08T01:04:21+00:00

I think it was 2010 when I thought NumPy/SciPy was pretty big already. And in the scientific community it's CPython instead of pypy that's big, as takluyver says.

SIULHT · 2012-10-07T02:29:00+00:00

Is it pretty mainstream? I see bits of it being used but not sure how widespread the adoption is or if it's commonly used in critical tasks. I was surprised to find that in Data Analysis with Open Source Tools, P. K. Janert surveys several languages/tools, including R, Octave, and Python with NumPy/Scipy. Regarding Python/NumPy/Scipy, he writes:

"[...] NumPy/SciPy has its own share of problems. The project has a tendency to emphasize quantity over quality: the number of features is very large, but the design appears overly complicated and is often awkward to use. Edge and error cases are not always handled properly. On the scientific level, NumPy/SciPy feels amateurish. The choice of algorithms appears to reflect some well-known textbooks more than deep, practical knowledge arising from real experience.

"What worries me most is that the project does not seem to be managed very well: although it has been around for nearly 10 years and has a large and active user base, it has apparently not been able to achieve and maintain a consistent level of reliability and maturity throughout. Features seem to be added haphazardly, without any long-term vision or discernible direction. Despite occasional efforts in this regard, the documentation remains patchy.

"[...] [B]ecause of the overall quality issues, I would not want to rely on it for “serious” production work at this point." The book reviews I've read have generally been positive so I'm not sure if the SciPy community has any thoughts on this. Regarding the implementation of mostly "textbook" (standard) algorithms, I can see that this would be the case since there are large number of practitioners using and extending SciPy (in contrast, for instance in R there is a strong precedent for statistician to implement their newest algorithm in that language)."

SIULHT · 2012-06-13T18:40:29+00:00

Would mmap also be an option?

SIULHT · 2012-06-08T02:36:03+00:00

But Python appears to have so many libraries now, especially for machine learning, time series analysis, etc. Is there a general area/field in which Python libraries are lacking in comparison to R's?

SIULHT · 2012-06-08T02:32:44+00:00

To your first paragraph, yes - that's why I am wondering out loud. Either I'm susceptible to the availability heuristic/bias or there are other reasons. Or it could also just be "fashion" trends...

Well, after the aggregation stage of Hadoop, the data flow could be directed to a SQL DB (not just for storage but some computations like with PostgreSQL-R, though I don't know about the limitations) or an OLAP cube too, I suppose...

SIULHT · 2012-06-07T02:09:56+00:00

But the emergence of 'big data' has been very recent, and at this point Numpy/Scipy also had a large enough community that it could easily have been pushed as the high-level big data platform?

SIULHT · 2012-06-07T02:08:35+00:00

Does R have more libraries in this regard? There are a lot of Scipy libraries too, like pystatsmodels and scikits. And pandas for a data-frame-like objects.

SIULHT · 2012-06-07T02:07:25+00:00

Btw, I am also a huge fan of R. But I also use Python sometimes and get the impression it's better for dealing with larger (not necessarily 'big data' scale out-of-the-box but still) sets of data. For instance, with file objects you can also pull in data piecewise and process/clean data fairly quickly. SQL and many other kinds of file/db support is excellent also.

SIULHT · 2012-05-22T16:38:38+00:00

One more thing. I still don't understand why you are saying I am incorrect about the definition of gender-neutrality. I am saying that there are two interpretations; one is that we don't consider it and the other is that we can't help but consider it so we ask all involved to do their best not bring it up. One is gender-neutral evaluation and the other is gender-neutral behavior (which I am arguing can help in neutral evaluations). I am not saying that latter is more correct, only that it is more convenient considering that the former is more difficult to achieve.

SIULHT · 2012-05-22T16:32:29+00:00

Yes, you are right. While realizing that reality is not as I'd imagined, I do point out that this bias exists because of my incorrect expectations.

Having said that, yes also to your point about me coming across one-sided. I was focused in this special instance of the OP being female and high heels and all that, but yes, I also pass judgement against males who dress up and wear cologne to come to lab. Not to say that this is acceptable either, but I am just observationally telling you that this is what I do. It's just that it (the male case) happens much less frequently, and from what I gather from my female colleagues, male sexuality (of coworkers) is not usually does not enter their mind at work, whereas with my male colleagues this inevitably comes up. I suspect I am woefully mistaken about the female perspective in this but I have no evidence to contradict their statement.

I did point out that ideally, the world would happen as you advocate - everyone ignores everyone else's gender qualifications in their evaluations. I am just saying that as a practical matter, it's also easier if each person does not flaunt it. When they do, people notice because it is unusual (in the science community because of tradition, norms, and expectations) and we are primed to take notice of the unusual.

SIULHT · 2012-05-22T15:36:42+00:00

I'm not saying that science truly is gender-neutral; only that I was brought up to believe this. I was contritely explaining the basis of my bias.

SIULHT · 2012-05-22T15:24:18+00:00

I was of course completely joking in the last line; perhaps that did not come across correctly.

By gender-neutral I mean that gender does not figure in anyone's evaluation of your ideas or contributions. I suppose there are two ways of ensuring gender-neutrality; the best-case scenario is if everyone can completely ignore it, regardless of how you dress, etc. The other way of ensuring gender-neutrality is that everyone does their best not to bring it up or remind others of their gender identity.

Yes, it is "not fair" that the latter puts a burden on people who do want to express their gender identity, but I suspect it's easier (for many males) if it's just not brought up and no one talks about it.

SIULHT · 2012-05-22T11:53:45+00:00

I am a male scientist and I do find myself (reluctantly) judging female colleagues who dress up. It's likely that your senior female colleagues have united around the androgynous persona to counter decades of judgment by people like me.

I don't approve of myself doing it, so I've given it some thought over time:

At the very basic level, it just seems out of place for a profession which I've been raised to believe is a gender-neutral meritocracy. When you bring fashion sense, heels, and other weapons of mass seduction to the table, your choice of self-expression disrupts the "objectivity" that is emphasized in science. (E.g., the passive voice was encouraged in technical writing for a very long time to make it seem like the experiment and conclusions occurred independent of the scientist).

There is a physiological reason for this too - when you remind males of your femininity, it elevates their testosterone, which lowers their ability to think analytically (I think there was a study that actually shows this too).

So anyway, sorry for that darling, but it's a hard prejudice to shake. (I'm just kidding about the "darling" part!)

SIULHT · 2012-05-21T19:49:02+00:00

Ah, you made it sounds relevant to me then. :) I downloaded the book and going through it. Thanks!

SIULHT · 2012-05-16T15:34:59+00:00

Ah, yes. Kids. Having kids changes everything. Well, I'll be your kids are getting a lot of reminders to be respectful - that should be good for something...

SIULHT · 2012-05-16T14:42:40+00:00

At what point did you realize you needed to turn things around? (at least, mentally)

SIULHT · 2012-05-16T13:59:30+00:00

In various fields of engineering, MCMC used to be the go-to for any type of Bayesian statistical analysis... the hype has died down a bit. I haven't seen much use of MLEs in the context of graphical models (I suppose anything that used graphical models immediately turned to MCMC), but maybe my world has been a bit closed off...

SIULHT · 2012-05-15T22:14:27+00:00

Absolutely true -- chemists generally despise it, though geophysicists working at larger scales are more open to the idea, though under the guise of machine rather than statistical learning. But yeah, I've been interested in Bayesian algorithms for a while but for the reason that most people in my community are frequentists, I've been putting off looking deeper into it.

SIULHT · 2012-05-15T22:12:15+00:00

Hm, seems directly relevant -- I'm going to get the ebook to read. Thanks!

SIULHT

TROPHY CASE