ActivePapers for Python

Posted September 27, 2013 by Konrad Hinsen
Categories: Reproducible research

Today I have published the first release of ActivePapers for Python, available on PyPI or directly from the Mercurial repository on Bitbucket. The release coincides with the publication of my first scientific paper for which the complete code and data is in the supplementary material, available through the J. Chem. Phys. Web site or from Figshare. There is a good chance that this is the first fully reproducible paper in the field of biomolecular simulation, but it is of course difficult to verify such a claim.

ActivePapers is a framework for doing and publishing reproducible research. An ActivePaper is a file that contains code (Python modules and scripts) and data (HDF5 datasets), plus the dependency information between all these pieces. You can change a script and re-run all the computations that depend on it, for example. Once your project is finished, you can publish the ActivePaper as supplementary material to your standard paper. You can also re-use code and data from a published ActivePaper by using DOI-based links, although for the moment this works only for ActivePapers stored on Figshare.

I consider this first release of ActivePapers quite usable (I use it, after all), but it’s definitely for “early adopters”. You should be comfortable working with command-line tools, for example, and of course you need some experience with writing Python scripts if you want to create your own ActivePaper. For inspecting data, you can use any HDF5-based tool, such as HDFView, though this makes sense only for data that generic tools can handle. My first published ActivePaper contains lots of protein structures, which HDFView doesn’t understand at all. I expect tool support for ActivePapers to improve significantly in the near future.

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Platforms for reproducible research

Posted August 14, 2013 by Konrad Hinsen
Categories: Computational science, Reproducible research

This post was motivated by Ian Gent’s recomputation manifesto and his blog post about it. While I agree with pretty much everything said there, there is one point that I strongly disagree with, and here I’d like to explain the reasons in some detail. The point in question is “The only way to ensure recomputability is to provide virtual machines”. To be fair, the manifesto specifies that it’s the only way “at least for now”, so perhaps our disagreement is not as pronounced as it may seem.

I’ll start with a quote from the manifesto that shows that we have similar ideas of the time scales over which computational research should be reproducible:
“It may be true that code you make available today can be built with only minor pain by many people on current computers. That is unlikely to be true in 5 years, and hardly credible in 20.”

So the question is: how can we best ensure that the software used in our computational studies can still be run, with reasonable effort, 20 years from now. To answer that question, we have to look at the possible platforms for computational research.

By a “platform”, I mean the combination of hardware and software that is required to use a given piece of digital information. For example, Flash video requires a Flash player and a computer plus operating system that the Flash player can run on. That’s what defines the “Flash platform”. Likewise, today’s “Web platform” (a description that requires a date stamp to be precise, because Web standards evolve so quickly) consists of HTML5, JavaScript, and a couple of related standards. If you want to watch a Flash video in 20 years, you will need a working Flash platform, and if you want to use an archived copy of a 2013 Web site, you need the 2013 Web platform.

If you plan to distribute some piece of digital information with the hope that it will make sense 20 years from now, you must either have confidence in the longevity of the platform, or be willing and able to ensure its long-term maintenance yourself. For the Flash platform, that means confidence in Adobe and its willingness to keep Flash alive (I wouldn’t bet on that). For the 2013 Web platform, you may hope that its sheer popularity will motivate someone to keep it alive, but I wouldn’t bet on it either. The Web platform is too complex and too ill-defined to be kept alive reliably when no one uses it in daily life any more.

Back to computational science. 20 years ago, most scientific software was written in Fortran 77, often with extensions specific to a machine or compiler. Much software from that era relied on libraries as well, but they were usually written in the same language, so as long as their source code remains available, the platform for all that is a Fortran compiler compatible with the one from back then. For standard Fortran 77, that’s not much of a problem, whereas most of the vendor-specific extensions have disappeared since. Much of that 20-year-old software can in fact still be used today. However, reproducing a computational study based on that software is a very different problem: it also requires all the input data and an executable description of the computational protocol. Even in the rare case that all that information is available, it is likely to depend on lots of other software pieces that may not be easy to get hold of any more. The total computational platform for a given research project is in fact as ill-defined as the 2013 Web platform.

Today’s situation is worse, because we use more diverse software written in more different languages, and also use more interactive software whose use is notoriously non-reproducible. The only aspect where we have gained in standardization is the underlying hardware and OS layer: pretty much all computational science is done today on x86 processors running Linux. Hence the idea of conserving the full operating environment in the form of a virtual machine. Just fire up VirtualBox (or one of the other virtual machine managers) and run an exact copy of the original study’s work environment.

But what is the platform required to run today’s virtual machines? It’s VirtualBox, or one of its peers. Note however that it’s not “any of today’s virtual machine managers” because compatibility between their virtual machine formats is not perfect. It may work, or it may not. For simplicity I will use VirtualBox in the following, but you can substitute another name and the basic arguments still hold.

VirtualBox is a highly non-trivial piece of software, and it has very stringent hardware requirements. Those hardware requirements are met by the vast majority of today’s computing equipment used in computational science, but the x86 platform is losing market share rapidly on the wider computing device market. VirtualBox doesn’t run on an iPad, for example, and probably it never will. Is VirtualBox likely to be around in 20 years? I won’t dare a prediction. If x86 survives for another 20 years AND if Oracle sees a continuing interest in this product, then it will. I won’t bet on it though.

What we really need for long-term recomputability is a simple platform. A platform that is simple enough that the scientific community alone can afford to keep it alive for its own needs, even if no one else in the world cares about it.

Unfortunately there is no suitable platform today, to the best of my knowledge. Which is why virtual machines are perhaps the best option right now, for lack of a satisfactory one. But if we care about recomputability, we should design and develop a good supporting platform, starting as soon as possible.

For a more detailed discussion of this issue, see this paper written by yours truly. It comes to the conclusion that the closest existing approximation to a good platform is the Java virtual machine. What we’d want ideally is something similar to the JVM, but designed and optimized for scientific applications. A basic JVM implementation is quite simple (the complex JIT stuff is not a requirement), a few orders of magnitude simpler than VirtualBox, and it has no specific hardware dependencies. It’s even simpler than many of today’s scientific software packages, so the scientific community can definitely afford to keep it alive, The tough part is… no, it’s not designing or writing the required software, it’s agreeing on a specification. Perhaps it will never happen. Perhaps virtual machines will remain the best choice for lack of a satisfactory one. Or perhaps we will end up compiling our software to asm.js and run in the browser, just because someone else will keep that platform alive for us, no matter how ill-adapted it is to our needs. But don’t say you haven’t been warned.

Bye bye Address Book, welcome BBDB

Posted June 3, 2013 by Konrad Hinsen
Categories: Uncategorized

About two years ago I wrote a post about why and how I abandoned Apple’s iCal for my agenda management and moved to Emacs org-mode instead. Now I am in the process of making the second step in the same direction: I am abandoning Apple’s Address Book and starting to use the “Big Brother DataBase“, the most popular contact management system from the Emacs universe.

What started to annoy me seriously about Address Book is a bug that makes the database and its backups grow over time, even if no contacts are added, because the images for the contacts keep getting copied and never deleted under certain circumstances. I ended up having address book backups of 200 MB for just 500 contacts, which is ridiculous. A quick Web search shows that the problem has been known for years but has not yet been fixed.

When I upgraded from MacOS 10.6 to 10.7 about a year ago (I am certainly not an early adopter of new MacOS versions), I had a second reason to dislike Address Book: the user interface had been completely re-designed and become a mess in the process. Every time I use it I have to figure out again how to navigate groups and contacts.

I had been considering moving to BBDB for a while, but I hadn’t found any good solution for synchronizing contacts with my Android phone. That changed when I discovered ASynK, which does a bi-directional synchronization between a BBDB database and a Google Contacts account. That setup actually works better than anything I ever tried to synchronize Address Book with Google Contacts, so I gained more than I expected in the transition.

At first glance, it may seem weird to move from technology of the 2000’s to technology of the 1970’s. But the progress over that period in managing rather simple data such as contact information has been negligible. The big advantage of the Emacs platform over the MacOS platform is that it doesn’t try to take control over my data. A BBDB database is just a plain text file whose structure is apparent after five minutes of study, whereas an Address Book database is stored in a proprietary format. A second advantage is that the Emacs developer community fixes bugs a lot faster than Apple does. A less shiny (but perfectly usable) user interface is a small price to pay.

A critical view of altmetrics

Posted May 8, 2013 by Konrad Hinsen
Categories: Science

Altmetrics is one of the hotly debated topics in the Open Science movement today. In summary, the idea is that traditional bibliometric measures (citation counts, impact factors, h factors, …) are too limited because they miss all the scientific activity that happens outside of the traditional journals. That includes the production of scientific contributions that are not traditional papers (i.e. datasets, software, blog posts, etc.) and the references to scientific contributions that are not in the citation list of a traditional paper (blogs, social networks, etc.). Note that the altmetrics manifesto describes altmetrics as a tool to help find scientists publications worth reading. I find it hard to believe that its authors have not thought of applications in evaluation of researchers and institutions, which will inevitably happen if altmetrics ever takes off.

At first sight, altmetrics appear as an evident “update” to traditional bibliometry. It sounds pretty obvious that, as scientific communication moves on to new media and finds new forms of expressions, bibliometry should adapt. On the other hand, bibliometry is considered a more less necessary evil by most scientists. Many deplore today’s “publish or perish” culture and correctly observe that it is harmful to science in the long term, giving more importance to the marketing of research studies than to their careful design and meticulous execution. I haven’t yet seen any discussion of this aspect in the context of altmetrics, so I’d like to start such a discussion with this post.

First of all, why is bibliometry so popular, and why is it harmful in the long run? Second, how will this change if and when altmetrics are adopted by the scientific community?

Bibliometry provides measures of scientific activity that have two important advantages: they are objective, based on data that anyone can check in principle, and they can be evaluated by anyone, even by a computer, without any need to understand the contents of scientific papers. On the downside, those measures can only indirectly represent scientific quality precisely because they ignore the contents. Bibliometry makes the fundamental assumption that the way specific articles are received by the scientific community can be used as a proxy for quality. That assumption is, of course, wrong, and that’s how bibliometry ultimately harms the progress of science.

The techniques that people use to improve their bibliometrical scores without contributing to scientific progress are well known: dilution of content (more articles with less content per article), dilution of authorship (agreements between scientists to add each others’ names to their works), marketing campaigns for getting more citations, application of a single technique to lots of very similar applications even if that adds no insight whatsoever. Altmetrics will cause the same techniques to be applied to datasets and software. For example, I expect scientific software developers to take Open Source libraries and re-publish them with small modifications under a new name, in order to have their name attached to them. Unless we come up with better techniques for software installation and deployment, this will probably make the management of scientific software a bit more complicated because we will have to deal with lots of small libraries. That’s a technical problem that can and should be solved with a technical solution.

However, these most direct and most discussed negative consequences of bibliometry are not the only ones and perhaps not the worst. The replacement of expert judgement by majority vote, which is the basis of bibliometry, also in its altmetrics incarnation, leads to a phenomenon which I will call “scientiic bubbles” in analogy to market bubbles in economy. A market bubble occurs if the price of a good is determined not by the people who buy it to satisfy some need, but by traders and speculators who try to estimate the future price of the good and make a profit from a rise or fall relative to the current price. In science, the “client” whose “need” is fulfilled by a scientific study is mainly future science, plus in the case of applied research engineering and product development. The role of traders and speculators is taken by referees and journal editors. A scientific bubble is a fashionable topic that many people work on not because of its scientific interest but because of the chance it provides to get a highly visible publication. Like market bubbles, scientific bubbles eventually explode when people realize that the once fashionable topic was a dead end. But before exploding, a bubble has wasted much money and intellectual energy. It may also have blocked alternative and ultimately more fruitful research projects that were refused funding because they were in contradiction with the dominating fashionable point of view.

My prediction is that altmetrics will make bubbles more numerous and more severe. One reason is the wider basis of sources from which references are counted. In today’s citation-based bibliometry, citations come from articles that went through some journal’s peer-reviewing process. No matter how imperfect peer review is, it does sort out most of the unfounded and obviously wrong contributions.  To get a paper published in a journal whose citations count, you need a minimum of scientific competence. In contrast, anyone can publish an opinion on Twitter or Facebook. Since for any given topic the number of experts is much smaller than the number of people with just some interest, a wider basis for judgement automatically means less competence on average. As a consequence, high altmetrics scores are best obtained by writing articles that appeal to the masses who can understand what the work is about but not judge if it is well-founded. Another reason why altmetrics will contribute to bubbles is the positive feedback loop created by people reading and citing publications because they are already widely read and cited. That effect is dampened in traditional bibliometry because of the slowness of the publishing and citation mechanism.

My main argument ends here, but I will try to anticipate some criticisms and reply to them immediately.

One objection I expect is that the analysis of citation graphs can be used to assign a kind of reputation to each source and weight references by this reputation. That is the principle of Google’s famous PageRank algorithm. However, any analysis of the citation graph suffers from the same fundamental problem as bibliometry itself: a method that only looks at relations between publications but not at their contents can’t distinguish a gem from a shiny bubble. There will be reputation bubbles just like there are topic bubbles. No purely quantitative analysis can ever make a statement about quality. The situation is similar to mathematical formalisms, with citation graph analysis taking the role of formal proof and scientific quality the role of truth in Gödel’s incompleteness theorem.

Another likely criticism is that the concept of the scientific bubble is dubious. Many paths of scientific explorations have turned out to be failures, but no one could possibly have predicted this in the beginning. In fact, many ultimately successful strategies have initially been criticized as hopeless. Moreover, exploration of a wrong path can still lead to scientific progress, once the mistake has been understood. How can one distinguish promising but ultimately wrong ideas from bubbles? The borderline is indeed fuzzy, but that doesn’t mean that the concept of a bubble is useless. It’s the same for market bubbles, which exist but are less severe when a good is traded both for consumption and for speculation. My point is that the bubble phenomenon exists and is detrimental to scientific progress.

Lessons from sixteen years of molecular simulation in Python

Posted April 10, 2013 by Konrad Hinsen
Categories: Computational science

Tags: , ,

A while ago I was chatting with two users of my Molecular Modelling Toolkit (MMTK), a library for molecular simulations written in Python. One of them asked me what I would do differently if I were to write MMTK today. That’s an interesting question, but not the kind of question I can answer in a sentence or two, so I promised to write a blog post about this. Here it is.

First, a bit of history. The first version of MMTK was released about 16 years ago. I don’t have the exact data, but the first message on the MMTK mailing list, announcing MMTK release 1.0b2, is dated 29 May 1997. Back then Python 1.4 was the state of the art and Numerical Python was a young project that was just beginning to stabilize. MMTK was one of the first domain-specific scientific libraries written in Python, at a time when the scientific Python community was very small and its members were mostly considered cranks by their peers. MMTK was designed from the start as a Python library, with relatively small bits of C code for the time-critical stuff (mainly energy evaluation and MD integration), with NumPy arrays at the Python-C interface. This has since become one of the two main approaches to using Python in scientific computing, the other one being wrapper code around libraries written in C/C++ or Fortran.

So what would I do differently if I were to start writing MMTK today? Many things, for different reasons. Lets first get the obvious stuff out of the way: the Python ecosystem has evolved significantly since 1997, and of course I would use Python 3, and Cython instead of C for the time-critical parts. I would also adopt many of the conventions that the community has developed but which weren’t around in 1997. I might even be tempted to use bleeding-edge tools like Numba, although with hesitation: Numba is not only a moving target at this time, but also requires dependencies (I am thinking mostly of LLVM) which are big and non-trivial to install. One lesson I have learned in 16 years of scientific Python is that dependencies can cause more trouble than they are worth. It’s nice in theory to re-use existing tested code, but it also makes installation and deployment more cumbersome.

So far for changes in the Python ecosystem. What has changed as well, though at a slower pace, is the role of computation in science and in particular in molecular simulation. Back in 1997, there were a few molecular simulation ecosystems that operated almost in isolation. The big players were the CHARMM, AMBER, and GROMOS/GROMACS communities. Each of them had their own software, their own file formats, and their own force fields. Members of these communities would of course talk about science to each other, but not share any software or data. Developing new computational methods required a serious investment into one of these ecosystems. That was in fact my main motivation for developing MMTK: I figured that I would be more efficient (not to mention more satisfied) writing a new system from scratch using modern development tools than trying to get familiar with crufty Fortran code. But I adopted basically the same approach with MMTK: I created a new ecosystem without much regard to sharing code or data with the rest of the world. As an illustration, MMTK defines its own trajectory format which I still consider superior to what the rest of the world is doing, but which is undeniably hard to use without MMTK, given that the definition of a universe is stored as an executable Python expression. MMTK also encourages storing data as Python pickle files, which are even harder to deal with for other programs.

Today we are seeing a change in attitude in computational science that I am sure will soon reach the molecular simulation community as well. People are starting to realize that computational results have serious reliability problems. The most publicized case in the structural biology community was the retraction of a few important published protein structures following the discovery of a bug in the data processing software that lead to completely wrong final structures. This and similar events point to the urgent need for better validation of computational results. One aspect of validation is re-running the same computation with different tools. Another aspect is publishing both software and raw data, enabling other scientists to inspect them and check their validity. Technology for sharing scientific code and data exists today (have a look at Github, Bitbucket, and figshare, for example). But in molecular simulation, there are still important practical barriers to such validation attempts, in particular the use of program-specific and badly documented file formats. While MMTK’s file formats are documented, they are still program-specific and thus incompatible with the requirements of the future.

The sentence that I would like to write now is “If I were to rewrite MMTK today, I would use the exchange data formats accepted by the molecular simulation community”. But those formats don’t exist yet, although there are a few initiatives to develop them. My own contribution to this effort is the Mosaic data model and data formats – if you are interested in this subject, please have a look at it and send me your feedback. Mosaic will of course find its way into future versions of MMTK.

Finally, there are things I would do differently because the experience with MMTK has shown that a few initial design decisions were not the best ones. Number one is the absence of stable atom numbers. In MMTK, each atom and molecule is represented by a unique Python object, and there are ways to refer uniquely to everything by using Python expressions. But there is no such thing as a unique order of atoms that would assign a number to each one. Atoms do have numbers by which the low-level C code refers to them, but these numbers can be different every time you run a Python script. My original design goal was to discourage the use of numbers to refer to atoms, because this is an important source of mistakes if the simulated system undergoes changes. But every other molecular simulation program out there uses numbers to refer to atoms, so people are used to them. For interoperability with other programs, atom numbers are fundamental. There are ways to handle such situations, of course, but it’s a constant source of headaches.

The other design aspect that I would change if I were to rewrite MMTK today is the hierarchy of chemical objects. MMTK has Atoms, Groups, Molecules, and Complexes, plus specializations such as AminoAcidResidue (a special Group), PeptideChain (a special Molecule), and Protein (a special Complex). While all of these correspond to some chemical reality, the system is more complex than required for molecular simulation, leading in some situations to code that is bloated by irrelevant special cases. Today I’d go for just Atoms and Groups, with special features of specific kinds of groups indicated by attributes rather than specific classes.

Integrating scientific software and datasets into the citation record

Posted November 14, 2012 by Konrad Hinsen
Categories: Uncategorized

This morning I read C. Titus Brown’s blog post on how science could be so much better if scientitic data and the software used to work with it were openly available for reuse. One problem he mentions, like many others have done before, is the lack of incentive for publishing anything else but standard scientific papers. What matters for a scientist’s career and for grant applications is papers, papers, papers. Any contribution that’s not in a scientific journal with a reputation and an impact factor is usually ignored, even if its real impact exceeds that of many papers that nobody really wants to read.

Ideally, published scientific data and software should be treated just like a paper: it should be citeable and it should appear in the citation databases that are used to calculate impact factors, h factors, and whatever other metrics bibliometrists come up with and evaluation committees appreciate for their ease of use.

Treating text (i.e. papers), data, and code identically also happens to be useful for making scientific publications more useful to the reader, by adding interactive visualization and exploration of procedures (such as varying parameters) to the static presentation of results in a standard paper. This idea of “executable papers” has generated a lot of interest recently, as shown by Elsevier’s Executable Paper Challenge and the Beyond the PDF workshop. For a technical description of how this can be achieved, see my ActivePapers project and/or the paper describing it. In the ActivePapers framework, a reference to code being called, or to a dataset being reused, is exactly identical to a reference to a published paper. It would then be much easier for citation databases to include all references rather than filter out the ones that are “classical” citations. And that’s a good motivation to finally treat all scientific contributions equally.

Since the executable papers idea is much easier to sell than the idea of an upated incentive system, a seemingly innocent choice in technology could end up helping to change the way scientists and research projects are evaluated.

The ultimate calculator for Android and iOS

Posted September 7, 2012 by Konrad Hinsen
Categories: Computational science, Programming

Calculators are among the most popular applications for smartphones, and therefore it is not surprising that the Google Play Store has more than 1000 calculators for the Android platform. Having used HP’s scientific calculators for more than 20 years, I picked RealCalc when I got my Android phone and set it to RPN mode. It works fine, I have no complaints about it. But I no longer use it because I found something much more powerful.

It’s called “J“, which isn’t exactly a very descriptive name. And that’s probably a good idea because describing it it not so easy. J is much more than a calculator, but it does the calculator job very well. It’s actually a full programming language, but one that differs substantially from everything else that goes by that label. The best description for J I can come up with is “executable mathematical notation”. You type an expression, and you get the result. That’s in fact not very different from working interactively with Python or Matlab, except that the expressions are very different. You can write traditional programs in J, using loops, conditionals, etc., but you can a lot of work done without ever using these features.

The basic data structure in J is the array, which can have any number of dimensions. Array elements can be numbers, characters, or other arrays. Numbers (zero-dimensional arrays) and text strings (one-dimensional arrays of characters) are just special cases. In J jargon, which takes its inspiration from linguistics, data items are called “nouns”. Standard mathematical operators (such as + or -) are called “verbs” and can have one or two arguments (one left, one right). An expression is called a “sentence”. There are no precedence rules, the right argument of any verb being everything to its right. Given the large number of verbs in J, this initially unfamiliar rule makes a lot of sense. A simple example (also showing the use of arrays) is

   2 * 3 + 10 20 30
26 46 66

Up to here, J expressions are not very different from Python or Matlab expressions. What J doesn’t have is functions with the familiar f(x, y, z) syntax, accepting any number of arguments. There are only verbs, with one or two arguments. But what makes J really different from the well-known languages for scientific computing are the “parts of speech” that have no simple equivalent elsewhere: adverbs and conjunctions.

An adverb takes a verb argument and produces a derived verb from it. For example, the adverb ~ takes a two-argument verb (a dyad in J jargon) and turns it into a one-argument verb (a monad) that’s equivalent to using the dyad with two equal arguments. With + standing for plain addition, +~ thus doubles its argument:

   +~ 1 5 10 20
2 10 20 40

meaning it is the same as

   1 5 10 20 + 1 5 10 20
2 10 20 40

A conjunction combines a verb with a noun or another verb to produce a derived verb. An example is ^:, the power conjunction, which applies a verb several times:

   +~(^:2) 1 5 10 20
4 20 40 80
   +~(^:3) 1 5 10 20
8 40 80 160

The parentheses are required to separate the argument of the power conjunction (2 or 3) from the array that is the argument to the resulting derived verb. To see the real power of the power conjunction, consider that it accepts negative arguments as well:

   +~(^:_1) 1 5 10 20
0.5 2.5 5 10

You have seen right: J can figure out that the inverse of adding a number to itself is dividing that number by two!

Pretty much any programming language permits you to assign values to names for re-use in later expressions. J is no exception:

   data =. 1 5 10 20
   double =. +~
   double data
2 10 20 40
   inv =. ^:_1
   halve =. double inv
   halve data
0.5 2.5 5 10

As you can see, names can be given not just to nouns (i.e. data), but also to verbs, adverbs, and conjunctions. Most J programs are just pieces of expressions that are assigned to names. Which means that the short summary of J that I have given here could well be all you ever need to know about the language – apart from the fact that you will have to acquire a working knowledge of many more verbs, adverbs, and conjunctions.

Before you rush off to the Play Store looking for J, let me add that J is not yet there, although it’s supposed to arrive soon. For now, you have to download the APK and install it yourself, using your preferred Android file manager. I should also point out that J is not just for Android. It’s been around for more than 20 years, and you can get J for all the common computing platforms from Jsoftware. There’s also an iOS version for the iPhone and iPad. J’s extreme terseness is a perfect fit for smartphones, where screen space is a scarce resource and where every character you don’t have to type saves you a lot of time.