GNU Astronomy Utilities


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1.2 Science and its tools

History of science indicates that there are always inevitably unseen faults, hidden assumptions, simplifications and approximations in all our theoretical models, data acquisition and analysis techniques. It is precisely these that will ultimately allow future generations to advance the existing experimental and theoretical knowledge through their new solutions and corrections.

In the past, scientists would gather data and process them individually to achieve an analysis thus having a much more intricate knowledge of the data and analysis. The theoretical models also required little (if any) simulations to compare with the data. Today both methods are becoming increasingly more dependent on pre-written software. Scientists are dissociating themselves from the intricacies of reducing raw observational data in experimentation or from bringing the theoretical models to life in simulations. These ‘intricacies’ are precisely those unseen faults, hidden assumptions, simplifications and approximations that define scientific progress.

Unfortunately, most persons who have recourse to a computer for statistical analysis of data are not much interested either in computer programming or in statistical method, being primarily concerned with their own proper business. Hence the common use of library programs and various statistical packages. ... It’s time that was changed.

F. J. Anscombe. The American Statistician, Vol. 27, No. 1. 1973

Anscombe’s quartet demonstrates how four data sets with widely different shapes (when plotted) give nearly identical output from standard regression techniques. Anscombe uses this (now famous) quartet, which was introduced in the paper quoted above, to argue that “Good statistical analysis is not a purely routine matter, and generally calls for more than one pass through the computer”. Echoing Anscombe’s concern after 44 years, some of the highly recognized statisticians of our time (Leek, McShane, Gelman, Colquhoun, Nuijten and Goodman), wrote in Nature that:

We need to appreciate that data analysis is not purely computational and algorithmic — it is a human behaviour....Researchers who hunt hard enough will turn up a result that fits statistical criteria — but their discovery will probably be a false positive.

Five ways to fix statistics, Nature, 551, Nov 2017.

Users of statistical (scientific) methods (software) are therefore not passive (objective) agents in their result. Therefore, it is necessary to actually understand the method, not just use it as a black box. The subjective experience gained by frequently using a method/software is not sufficient to claim an understanding of how the tool/method works and how relevant it is to the data and analysis. This kind of subjective experience is prone to serious misunderstandings about the data, what the software/statistical-method really does (especially as it gets more complicated), and thus the scientific interpretation of the result. This attitude is further encouraged through non-free software3, poorly written (or non-existent) scientific software manuals, and non-reproducible papers4. This approach to scientific software and methods only helps in producing dogmas and an “obscurantist faith in the expert’s special skill, and in his personal knowledge and authority5.

Program or be programmed. Choose the former, and you gain access to the control panel of civilization. Choose the latter, and it could be the last real choice you get to make.

Douglas Rushkoff. Program or be programmed, O/R Books (2010).

It is obviously impractical for any one human being to gain the intricate knowledge explained above for every step of an analysis. On the other hand, scientific data can be large and numerous, for example images produced by telescopes in astronomy. This requires efficient algorithms. To make things worse, natural scientists have generally not been trained in the advanced software techniques, paradigms and architecture that are taught in computer science or engineering courses and thus used in most software. The GNU Astronomy Utilities are an effort to tackle this issue.

Gnuastro is not just a software, this book is as important to the idea behind Gnuastro as the source code (software). This book has tried to learn from the success of the “Numerical Recipes” book in educating those who are not software engineers and computer scientists but still heavy users of computational algorithms, like astronomers. There are two major differences.

The first difference is that Gnuastro’s code and the background information are segregated: the code is moved within the actual Gnuastro software source code and the underlying explanations are given here in this book. In the source code, every non-trivial step is heavily commented and correlated with this book, it follows the same logic of this book, and all the programs follow a similar internal data, function and file structure, see Program source. Complementing the code, this book focuses on thoroughly explaining the concepts behind those codes (history, mathematics, science, software and usage advise when necessary) along with detailed instructions on how to run the programs. At the expense of frustrating “professionals” or “experts”, this book and the comments in the code also intentionally avoid jargon and abbreviations. The source code and this book are thus intimately linked, and when considered as a single entity can be thought of as a real (an actual software accompanying the algorithms) “Numerical Recipes” for astronomy.

The second major, and arguably more important, difference is that “Numerical Recipes” does not allow you to distribute any code that you have learned from it. In other words, it does not allow you to release your software’s source code if you have used their codes, you can only publicly release binaries (a black box) to the community. Therefore, while it empowers the privileged individual who has access to it, it exacerbates social ignorance. Exactly at the opposite end of the spectrum, Gnuastro’s source code is released under the GNU general public license (GPL) and this book is released under the GNU free documentation license. You are therefore free to distribute any software you create using parts of Gnuastro’s source code or text, or figures from this book, see Your rights.

With these principles in mind, Gnuastro’s developers aim to impose the minimum requirements on you (in computer science, engineering and even the mathematics behind the tools) to understand and modify any step of Gnuastro if you feel the need to do so, see Why C programming language? and Program design philosophy.

Without prior familiarity and experience with optics, it is hard to imagine how, Galileo could have come up with the idea of modifying the Dutch military telescope optics to use in astronomy. Astronomical objects could not be seen with the Dutch military design of the telescope. In other words, it is unlikely that Galileo could have asked a random optician to make modifications (not understood by Galileo) to the Dutch design, to do something no astronomer of the time took seriously. In the paradigm of the day, what could be the purpose of enlarging geometric spheres (planets) or points (stars)? In that paradigm only the position and movement of the heavenly bodies was important, and that had already been accurately studied (recently by Tycho Brahe).

In the beginning of his “The Sidereal Messenger” (published in 1610) he cautions the readers on this issue and before describing his results/observations, Galileo instructs us on how to build a suitable instrument. Without a detailed description of how he made his tools and done his observations, no reasonable person would believe his results. Before he actually saw the moons of Jupiter, the mountains on the Moon or the crescent of Venus, Galileo was “evasive”6 to Kepler. Science is defined by its tools/methods, not its results7

The same is true today: science cannot progress with a black box, or poorly released code. Technical knowledge and experience (to experiment on its tools, or software in this context8), is critical to scientific vitality. Scientific research are only considered for peer review and publication if they have a sufficiently high standard of English style. A similar level of quality assessment is necessary regarding the codes/methods scientists use to derive their results. Therefore, when a scientist says “software is not my specialty, I am not a software engineer. So the quality of my code/processing doesn’t matter. Why should I master good coding style, or release my code, when I am hired to do Astronomy/Biology?”. This statement is akin to a French scientist saying that "English is not my language, I am not Shakespeare. So the quality of my English writing doesn’t matter. Why should I master good English style, when I am hired to do Astronomy/Biology?"

Bjarne Stroustrup (creator of the C++ language) says: “Without understanding software, you are reduced to believing in magic”. Ken Thomson (the designer or the Unix operating system) says “I abhor a system designed for the ‘user’ if that word is a coded pejorative meaning ‘stupid and unsophisticated’.” Certainly no scientist (user of a scientific software) would want to be considered a believer in magic, or stupid and unsophisticated.

This can happen when scientists get too distant from the raw data and methods, and are mainly discussing results. In other words, when they feel they have tamed Nature into their own high-level (abstract) models (creations), and are mainly concerned with scaling up, or industrializing those results. Roughly five years before special relativity, and about two decades before quantum mechanics fundamentally changed Physics, Lord Kelvin is quoted as saying:

There is nothing new to be discovered in physics now. All that remains is more and more precise measurement.

William Thomson (Lord Kelvin), 1900

A few years earlier Albert. A. Michelson made the following statement:

The more important fundamental laws and facts of physical science have all been discovered, and these are now so firmly established that the possibility of their ever being supplanted in consequence of new discoveries is exceedingly remote.... Our future discoveries must be looked for in the sixth place of decimals.

Albert. A. Michelson, dedication of Ryerson Physics Lab, U. Chicago 1894

If scientists are considered to be more than mere “puzzle” solvers9 (simply adding to the decimals of existing values or observing a feature in 10, 100, or 100000 more galaxies or stars, as Kelvin and Michelson clearly believed), they cannot just passively sit back and uncritically repeat the previous (observational or theoretical) methods/tools on new data. Today there is a wealth of raw telescope images ready (mostly for free) at the finger tips of anyone who is interested with a fast enough internet connection to download them. The only thing lacking is new ways to analyze this data and dig out the treasure that is lying hidden in them to existing methods and techniques.

New data that we insist on analyzing in terms of old ideas (that is, old models which are not questioned) cannot lead us out of the old ideas. However many data we record and analyze, we may just keep repeating the same old errors, missing the same crucially important things that the experiment was competent to find.

Jaynes, Probability theory, the logic of science. Cambridge U. Press (2003).

Footnotes

(3)

https://www.gnu.org/philosophy/free-sw.html

(4)

Where the authors omit many of the analysis/processing “details” from the paper by arguing that they would make the paper too long/unreadable. However, software engineers have been dealing with such issues for a long time. There are thus software management solutions that allow us to supplement papers with all the details necessary to exactly reproduce the result. For example see zenodo.1163746 and zenodo.1164774 and this general discussion.

(5)

Karl Popper. The logic of scientific discovery. 1959. Larger quote is given at the start of the PDF (for print) version of this book.

(6)

Galileo G. (Translated by Maurice A. Finocchiaro). The essential Galileo. Hackett publishing company, first edition, 2008.

(7)

For example, take the following two results on the age of the universe: roughly 14 billion years (suggested by the current consensus of the standard model of cosmology) and less than 10,000 years (suggested from some interpretations of the Bible). Both these numbers are results. What distinguishes these two results, is the tools/methods that were used to derive them. Therefore, as the term “Scientific method” also signifies, a scientific statement it defined by its method, not its result.

(8)

Of course, this also applies to hardware.

(9)

Thomas S. Kuhn. The Structure of Scientific Revolutions, University of Chicago Press, 1962.


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