There are many scripting language communities claiming that the language can be used for everything but in fact, nearly everybody uses it for one specific thing, e.g.: web development. If I take a look at Ruby, for example, they tell you its general-purpose but actually everybody is using it with rails for web development only..
Can you list me some uses of popular general-purpose scripting languages for the local PC? (except embedding) Are there any?
Is the fast development usually worth having to bring the whole interpreter with your program? Then there would be some language-dependent performance and stability problems too in most cases..
best regards,
lamas
I tend to use Python for most things that aren't compute bound, i.e. they aren't restricted by how many computations you do per second. Some of the things I've used Python for are:
General scripts to manipulate images etc. with the Python Imaging Library.
GUI frontends for command line applications using the pexpect module.
Mathematical modeling of microbial systems.
Bioinformatics.
Some web programming.
etc...
When the program/algorithm is compute bound, I use C together with Python and Ctypes. Does this fit your definition of general purpose? It's certainly useful for a wide variety of applications, but not suitable if the program needs to crunch numbers fast.
Stability: Python 2.5/2.6 is rock solid. Never had a crash that wasn't caused by self-stupidity.
Fast development: It's definitely worth it for me. For the most part, in the field where I work, programmer time is orders of magnitude more valuable than processor time. I'm quite happy to let a program run for hours if I can write it in a few days instead of a few weeks.
I often use PHP for things that I used to use bat files for. Much easier to write. Ironically, the deployment scripts to create installable materials for my web apps from the subversion sources are written in PHP.
Python is popular in the gaming community. EVE Online is written in python.
claiming that they can be used for everything but I often can't find any examples for that
You are basing your question on an incorrect assumption. Although, as pointed out, a Turing complete language will be able to compute what you require ... languages are 'viewed' by most as the sum of their most useful features and productive semantics.
The reality is:
Most scripting languages can do the same things, or support the most common things via libraries.
Some languages make a subset of operations more convenient, take Perl and regular expressions as an example
CPU time is cheap, as is RAM. Simple to understand code is the priority for most people.
The rise of the scripting languages is natural. Trying to assert any one language, approach or level of execution is good for a range of situations is usually fruitless.
What do you want?
What is the best language for that?
Is is fast enough or small enough? Usually the answer is yes
Imagine trying to use Python where you should be using Erlang, or C instead of Lisp because you thought all languages are equal. They aren't, even though, you can achieve the same things in a problem domain, in most languages/platforms with varying levels of ballache dependant on the task.
I often use ruby for what other people would create bash/sh files for. I find Ruby syntax intuitive for batch tasks along with a lot of other sorts of tasks(it's my goto language)
Perl is extremely popular for general scripting in unixes, such as there are package managers and websites and maintenance scripts written in perl.
Python is extremely popular for both web and application use.
VBA Is popular for being abused to write programs inside of Access, and also was once commonly used in ASP for websites (right?)
Nobody mentioned AppleScript!
Hahah, no seriously, Perl runs everywhere, is installed by default on (almost) any Unix-family OS (and is easy to get on Windows), and is extremely useful for gluing things together. And if you browse a bit at CPAN you'll see that it's extremely general-purpose. "Swiss army chainsaw" was intended as a slur but I think of it fondly. Performance is good too, though it hardly ever actually matters. Larry Wall's goal was "make easy things easy and hard things possible".
OK OK, so I'm a fanboy still, sigh.
At work we write a small to moderate amount of scripts to aid us in normal development. We have some people that are more comfortable in python, some in perl, some in php, etc...
Sometimes I think it is best to let people work in a language they are most comfortable with. This can mean that sometimes people can do a better job on a script (as they know more tricks in one language). It can also lead to less development time per script.
Other times I think that we would benefit the most from standardizing so that there might be more shared libraries and so that we don't get into the situation of "I can't work on that script because I don't know python".
Do you think that we should standardize or let people choose for each script they write?
I would be inclined to let people choose, and hire people who are comfortable at learning new things. Gaining basic fluency with almost any language should be easy enough for a good developer. And for small scripts where the original author isn't far away, it's even easier.
The second part of the above is the hard part, of course. But you'll end up with a more flexible, more knowledgeable team.
I would advocate standardizing on a couple languages. "Thou shalt use either Python, or Perl, or Ruby. Not Rexx, nor PHP, nor NewBatch, nor aught other, for thy brethren ought to be able to read thine writing without undue despair or cutting of their skins".
My opinion is that it is just like any other development environment. Do you standardize on one development language? Why? The same should be true for your scripting environment. Not only do I lean toward standardizing on a specific language I think you should standardize all the same things that you are hopefully doing in your programming environment (naming conventions, coding style, etc). Of course there are counter arguments and there will occasionally be exceptions, but hopefully they would be few and for valid reasons.
Like everything there are upsides and downsides.
As a developer I dislike being limited by "official company standards." More often than not these rules tend to constrain and stifle.
I think what is most important is to always have two people who can both read and write a script language on staff so one person can go on vacation or be sick without holding up the works.
For major components of your system it is probably better to standardize on a single language - as much as it pains me to say so.
I think the right place to ask this question is with your own team.
Your team should form a consensus as to whether or not they want a common platform or whether they want the freedom to choose the right tool for the job. There is no single best answer to this question any more than there is a single best answer for "what's the best scripting language".
Certainly there are advantages to each approach. If every team member is free to choose their own language, they might be more productive and your business will reap the benefit. On the other hand, if someone writes a critical tool in a language only she knows and then something happens to her (illness, new job, etc) you can be stuck with a single point of failure that no one knows how to fix.
So long as the languages in question are used for scripting, I agree with other commenters that it should be left to the devteam, and different languages should be tolerated in most cases.
If the language is used for your main codebase, you had better standardize on one.
If some developers complain about a script not written in their language, encourage them to learn it, particularly if it's one of the widespread ones like Ruby, Perl, et al.
No.
Your dev environment is hopefully a living entity that changes and is cultivated. New abilities (scripting languages) should be able to be accommodated, and older places that haven't been visited for years will someday get obviated by tools, or revisited from time to time and an evaluation will take place. Hopefully the dev scripts are a minor amount of development and automation. Chances are the team will drift towards a set of standards (ant, python, etc).
Per Project, anything checked in should probably follow a rule of minimal complexity.
Your team will be gone some day, and someone else will have to come in and maintain this. Please don't make them learn 5 languages or they are going to look for your names in the source code an hunt you down.
When we've picked up hack projects like that, we've pretty much considered the creators complete fools and made fun of them. They couldn't bother themselves to learn one scripting language???
That said, if you don't check it in--who cares?
For starters I should let you guys know what I'm trying to do. The project I'm working on has a requirement that requires a custom scripting system to be built. This will be used by non-programmers who are using the application and should be as close to natural language as possible. An example would be if the user needs to run a custom simulation and plot the output, the code they would write would need to look like
variable input1 is 10;
variable input2 is 20;
variable value1 is AVERAGE(input1, input2);
variable condition1 is true;
if condition1 then PLOT(value1);
Might not make a lot of sense, but its just an example. AVERAGE and PLOT are functions we'd like to define, they shouldn't be allowed to change them or really even see how they work. Is something like this possible with DLR? If not what other options would we have(start with ANTRL to define the grammar and then move on?)? In the future this may need to run using XBAP and WPF too, so this is also something we need to consider, but haven't seen much if anything on dlr & xbap. Thanks, and hopefully this all makes sense.
Lua is not an option as it is to different from what they are already accustomed to.
Ralf, its going to reactive, and to be honest the timeframe for when the results should get back to the user may be 1/100 of a second all the way up to 2 weeks or a month(very complex mathematical functions).
Basically they already have a system they purchased that does some of what they need, and included a custom scripting language that does what I mentioned above and they don't want to have to learn a new one, they basically just want us to copy it and add functionality. I think I'll just start with ANTRL and go from there.
Lua
it's small, fast, easy to embed, portable, extensible, and fun!
Lua is definitly the best choice for soft real-time system (like computer games).
See http://shootout.alioth.debian.org/ for detailed benchmarks.
However, last time I checked, Lua used a mark-and-sweep garbage collector which can lead to deadline-violation and non-deterministic jitter in real-time systems.
I believe that you could use theoretically use the DLR, but I'm unsure about support in an XBAP (partially trusted?) scenario.
If you host the DLR you would quickly be able to take advantage of IronRuby or IronPython scripting. You would want to look at these implementations when creating your own language implementation. If you post your question to the IronPython mailing list I'm sure you would get a better reply around the XBAP scenario, and some of the developers there created ToyScript.
What kind of real-time requirement are you trying to fulfill? Is the simulation a hard real-time simulation (some kind of hardware-in-the-loop simulation ==> deadline is less than 1/1000 second)?
Or do you want the scripting-system to be "reactive" to user-input ==> 1/10 should be sufficient.
I am no expert regarding MS DLR, but as far as I know, it does not support hard real-time systems. You may want to take a look at the real-time specification for Java (RTSJ)
Firstly I think that defining your own language is not the way to go.
Primarily because the biggest productivity gains you can get for programmers or non-programmers are the development tools. You (and 99.9% of the rest of us) are not going to write tools as good as what is out their.
Language design is hard.
Language support and documentation, also hard
I would recommend looking for a pre-built solution. If you could find a language that can lock down some functionality, that would be a good starting point. MatLab would be the first that comes to my mind.
Lastly, ditch the natural language part, BASIC, COBOL and YA-TDWTF-Lang all tried and failed at it.
Full disclosure: I work for a company that is developing a generalized domain specific language "system". It's targeted at data-in/text-out applications so it's not apropos and it's not yet to beta. The result is I'm somewhat knowledgeable and biased.
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Background
Last year, I did an internship in a physics research group at a university. In this group, we mostly used LabVIEW to write programs for controlling our setups, doing data acquisition and analyzing our data. For the first two purposes, that works quite OK, but for data analysis, it's a real pain. On top of that, everyone was mostly self-taught, so code that was written was generally quite a mess (no wonder that every PhD quickly decided to rewrite everything from scratch). Version control was unknown, and impossible to set up because of strict software and network regulations from the IT department.
Now, things actually worked out surprisingly OK, but how do people in the natural sciences do their software development?
Questions
Some concrete questions:
What languages/environments have you used for developing scientific software, especially data analysis? What libraries? (for example, what do you use for plotting?)
Was there any training for people without any significant background in programming?
Did you have anything like version control, and bug tracking?
How would you go about trying to create a decent environment for programming, without getting too much in the way of the individual scientists (especially physicists are stubborn people!)
Summary of answers thus far
The answers (or my interpretation of them) thus far: (2008-10-11)
Languages/packages that seem to be the most widely used:
LabVIEW
Python
with SciPy, NumPy, PyLab, etc. (See also Brandon's reply for downloads and links)
C/C++
MATLAB
Version control is used by nearly all respondents; bug tracking and other processes are much less common.
The Software Carpentry course is a good way to teach programming and development techniques to scientists.
How to improve things?
Don't force people to follow strict protocols.
Set up an environment yourself, and show the benefits to others. Help them to start working with version control, bug tracking, etc. themselves.
Reviewing other people's code can help, but be aware that not everyone may appreciate that.
What languages/environments have you used for developing scientific software, esp. data analysis? What libraries? (E.g., what do you use for plotting?)
I used to work for Enthought, the primary corporate sponsor of SciPy. We collaborated with scientists from the companies that contracted Enthought for custom software development. Python/SciPy seemed to be a comfortable environment for scientists. It's much less intimidating to get started with than say C++ or Java if you're a scientist without a software background.
The Enthought Python Distribution comes with all the scientific computing libraries including analysis, plotting, 3D visualation, etc.
Was there any training for people without any significant background in programming?
Enthought does offer SciPy training and the SciPy community is pretty good about answering questions on the mailing lists.
Did you have anything like version control, bug tracking?
Yes, and yes (Subversion and Trac). Since we were working collaboratively with the scientists (and typically remotely from them), version control and bug tracking were essential. It took some coaching to get some scientists to internalize the benefits of version control.
How would you go about trying to create a decent environment for programming, without getting too much in the way of the individual scientists (esp. physicists are stubborn people!)
Make sure they are familiarized with the tool chain. It takes an investment up front, but it will make them feel less inclined to reject it in favor of something more familiar (Excel). When the tools fail them (and they will), make sure they have a place to go for help — mailing lists, user groups, other scientists and software developers in the organization. The more help there is to get them back to doing physics the better.
The course Software Carpentry is aimed specifically at people doing scientific computing and aims to teach the basics and lessons of software engineering, and how best to apply them to projects.
It covers topics like version control, debugging, testing, scripting and various other issues.
I've listened to about 8 or 9 of the lectures and think it is to be highly recommended.
Edit: The MP3s of the lectures are available as well.
Nuclear/particle physics here.
Major programing work used to be done mostly in Fortran using CERNLIB (PAW, MINUIT, ...) and GEANT3, recently it has mostly been done in C++ with ROOT and Geant4. There are a number of other libraries and tools in specialized use, and LabVIEW sees some use here and there.
Data acquisition in my end of this business has often meant fairly low level work. Often in C, sometimes even in assembly, but this is dying out as the hardware gets more capable. On the other hand, many of the boards are now built with FPGAs which need gate twiddling...
One-offs, graphical interfaces, etc. use almost anything (Tcl/Tk used to be big, and I've been seeing more Perl/Tk and Python/Tk lately) including a number of packages that exist mostly inside the particle physics community.
Many people writing code have little or no formal training, and process is transmitted very unevenly by oral tradition, but most of the software group leaders take process seriously and read as much as necessary to make up their deficiencies in this area.
Version control for the main tools is ubiquitous. But many individual programmers neglect it for their smaller tasks. Formal bug tracking tools are less common, as are nightly builds, unit testing, and regression tests.
To improve things:
Get on the good side of the local software leaders
Implement the process you want to use in your own area, and encourage those you let in to use it too.
Wait. Physicists are empirical people. If it helps, they will (eventually!) notice.
One more suggestion for improving things.
Put a little time in to helping anyone you work directly with. Review their code. Tell them about algorithmic complexity/code generation/DRY or whatever basic thing they never learned because some professor threw a Fortran book at them once and said "make it work". Indoctrinate them on process issues. They are smart people, and they will learn if you give them a chance.
This might be slightly tangential, but hopefully relevant.
I used to work for National Instruments, R&D, where I wrote software for NI RF & Communication toolkits. We used LabVIEW quite a bit, and here are the practices we followed:
Source control. NI uses Perforce. We did the regular thing - dev/trunk branches, continuous integration, the works.
We wrote automated test suites.
We had a few people who came in with a background in signal processing and communication. We used to have regular code reviews, and best practices documents to make sure their code was up to the mark.
Despite the code reviews, there were a few occasions when "software guys", like me had to rewrite some of this code for efficiency.
I know exactly what you mean about stubborn people! We had folks who used to think that pointing out a potential performance improvement in their code was a direct personal insult! It goes without saying that that this calls for good management. I thought the best way to deal with these folks is to go slowly, not press to hard for changes and if necessary be prepared to do the dirty work. [Example: write a test suite for their code].
I'm not exactly a 'natural' scientist (I study transportation) but am an academic who writes a lot of my own software for data analysis. I try to write as much as I can in Python, but sometimes I'm forced to use other languages when I'm working on extending or customizing an existing software tool. There is very little programming training in my field. Most folks are either self-taught, or learned their programming skills from classes taken previously or outside the discipline.
I'm a big fan of version control. I used Vault running on my home server for all the code for my dissertation. Right now I'm trying to get the department to set up a Subversion server, but my guess is I will be the only one who uses it, at least at first. I've played around a bit with FogBugs, but unlike version control, I don't think that's nearly as useful for a one-man team.
As for encouraging others to use version control and the like, that's really the problem I'm facing now. I'm planning on forcing my grad students to use it on research projects they're doing for me, and encouraging them to use it for their own research. If I teach a class involving programming, I'll probably force the students to use version control there too (grading them on what's in the repository). As far as my colleagues and their grad students go, all I can really do is make a server available and rely on gentle persuasion and setting a good example. Frankly, at this point I think it's more important to get them doing regular backups than get them on source control (some folks are carrying around the only copy of their research data on USB flash drives).
1.) Scripting languages are popular these days for most things due to better hardware. Perl/Python/Lisp are prevalent for lightweight applications (automation, light computation); I see a lot of Perl at my work (computational EM) since we like Unix/Linux. For performance stuff, C/C++/Fortran are typically used. For parallel computing, well, we usually manually parallelize runs in EM as opposed to having a program implicitly do it (ie split up the jobs by look angle when computing radar cross sections).
2.) We just kind of throw people into the mix here. A lot of the code we have is very messy, but scientists are typically a scatterbrained bunch that don't mind that sort of thing. Not ideal, but we have things to deliver and we're severely understaffed. We're slowly getting better.
3.) We use SVN; however, we do not have bug tracking software. About as good as it gets for us is a txt file that tells you where bugs specific bugs are.
4.) My suggestion for implementing best practices for scientists: do it slowly. As scientists, we typically don't ship products. No one in science makes a name for himself by having clean, maintainable code. They get recognition from the results of that code, typically. They need to see justification for spending time on learning software practices. Slowly introduce new concepts and try to get them to follow; they're scientists, so after their own empirical evidence confirms the usefulness of things like version control, they will begin to use it all the time!
I'd highly recommend reading "What Every Computer Scientist Should Know About Floating-Point Arithmetic". A lot of problems I encounter on a regular basis come from issues with floating point programming.
I am a physicist working in the field of condensed matter physics, building classical and quantum models.
Languages:
C++ -- very versatile: can be used for anything, good speed, but it can be a bit inconvenient when it comes to MPI
Octave -- good for some supplementary calculations, very convenient and productive
Libraries:
Armadillo/Blitz++ -- fast array/matrix/cube abstractions for C++
Eigen/Armadillo -- linear algebra
GSL -- to use with C
LAPACK/BLAS/ATLAS -- extremely big and fast, but less convenient (and written in FORTRAN)
Graphics:
GNUPlot -- it has very clean and neat output, but not that productive sometimes
Origin -- very convenient for plotting
Development tools:
Vim + plugins -- it works great for me
GDB -- a great debugging tool when working with C/C++
Code::Blocks -- I used it for some time and found it quite comfortable, but Vim is still better in my opinion.
I work as a physicist in a UK university.
Perhaps I should emphasise that different areas of research have different emphasis on programming. Particle physicists (like dmckee) do computational modelling almost exclusively and may collaborate on large software projects, whereas people in fields like my own (condensed matter) write code relatively infrequently. I suspect most scientists fall into the latter camp. I would say coding skills are usually seen as useful in physics, but not essential, much like physics/maths skills are seen as useful for programmers but not essential. With this in mind...
What languages/environments have you used for developing scientific software, esp. data analysis? What libraries? (E.g., what do you use for plotting?)
Commonly data analysis and plotting is done using generic data analysis packages such as IGOR Pro, ORIGIN, Kaleidegraph which can be thought of as 'Excel plus'. These packages typically have a scripting language that can be used to automate. More specialist analysis may have a dedicated utility for the job that generally will have been written a long time ago, no-one has the source for and is pretty buggy. Some more techie types might use the languages that have been mentioned (Python, R, MatLab with Gnuplot for plotting).
Control software is commonly done in LabVIEW, although we actually use Delphi which is somewhat unusual.
Was there any training for people without any significant background in programming?
I've been to seminars on grid computing, 3D visualisation, learning Boost etc. given by both universities I've been at. As an undergraduate we were taught VBA for Excel and MatLab but C/MatLab/LabVIEW is more common.
Did you have anything like version control, bug tracking?
No, although people do have personal development setups. Our code base is in a shared folder on a 'server' which is kept current with a synching tool.
How would you go about trying to create a decent environment for programming, without getting too much in the way of the individual scientists (esp. physicists are stubborn people!)
One step at a time! I am trying to replace the shared folder with something a bit more solid, perhaps finding a SVN client which mimics the current synching tools behaviour would help.
I'd say though on the whole, for most natural science projects, time is generally better spent doing research!
Ex-academic physicist and now industrial physicist UK here:
What languages/environments have you used for developing scientific software, esp. data analysis? What libraries? (E.g., what do you use for plotting?)
I mainly use MATLAB these days (easy to access visualisation functions and maths). I used to use Fortran a lot and IDL. I have used C (but I'm more a reader than a writer of C), Excel macros (ugly and confusing). I'm currently needing to be able to read Java and C++ (but I can't really program in them) and I've hacked Python as well. For my own entertainment I'm now doing some programming in C# (mainly to get portability / low cost / pretty interfaces). I can write Fortran with pretty much any language I'm presented with ;-)
Was there any training for people without any significant background in programming?
Most (all?) undergraduate physics course will have a small programming course usually on C, Fortran or MATLAB but it's the real basics. I'd really like to have had some training in software engineering at some point (revision control / testing / designing medium scale systems)
Did you have anything like version control, bug tracking?
I started using Subversion / TortoiseSVN relatively recently. Groups I've worked with in the past have used revision control. I don't know any academic group which uses formal bug tracking software. I still don't use any sort of systematic testing.
How would you go about trying to create a decent environment for programming, without getting too much in the way of the individual scientists (esp. physicists are stubborn people!)
I would try to introduce some software engineering ideas at undergraduate level and then reinforce them by practice at graduate level, also provide pointers to resources like the Software Carpentry course mentioned above.
I'd expect that a significant fraction of academic physicists will be writing software (not necessarily all though) and they are in dire need of at least an introduction to ideas in software engineering.
What languages/environments have you used for developing scientific software, esp. data analysis? What libraries? (E.g., what do you use for plotting?)
Python, NumPy and pylab (plotting).
Was there any training for people without any significant background in programming?
No, but I was working in a multimedia research lab, so almost everybody had a computer science background.
Did you have anything like version control, bug tracking?
Yes, Subversion for version control, Trac for bug tracing and wiki. You can get free bug tracker/version control hosting from http://www.assembla.com/ if their TOS fits your project.
How would you go about trying to create a decent environment for programming, without getting too much in the way of the individual scientists (esp. physicists are stubborn people!).
Make sure the infrastructure is set up and well maintained and try to sell the benefits of source control.
I'm a statistician at a university in the UK. Generally people here use R for data analysis, it's fairly easy to learn if you know C/Perl. Its real power is in the way you can import and modify data interactively. It's very easy to take a number of say CSV (or Excel) files and merge them, create new columns based on others and then throw that into a GLM, GAM or some other model. Plotting is trivial too and doesn't require knowledge of a whole new language (like PGPLOT or GNUPLOT.) Of course, you also have the advantage of having a bunch of built-in features (from simple things like mean, standard deviation etc all the way to neural networks, splines and GL plotting.)
Having said this, there are a couple of issues. With very large datasets R can become very slow (I've only really seen this with >50,000x30 datasets) and since it's interpreted you don't get the advantage of Fortran/C in this respect. But, you can (very easily) get R to call C and Fortran shared libraries (either from something like netlib or ones you've written yourself.) So, a usual workflow would be to:
Work out what to do.
Prototype the code in R.
Run some preliminary analyses.
Re-write the slow code into C or Fortran and call that from R.
Which works very well for me.
I'm one of the only people in my department (of >100 people) using version control (in my case using git with githuib.com.) This is rather worrying, but they just don't seem to be keen on trying it out and are content with passing zip files around (yuck.)
My suggestion would be to continue using LabView for the acquisition (and perhaps trying to get your co-workers to agree on a toolset for acquisition and making is available for all) and then move to exporting the data into a CSV (or similar) and doing the analysis in R. There's really very little point in re-inventing the wheel in this respect.
What languages/environments have you used for developing scientific software, esp. data analysis? What libraries? (E.g., what do you use for plotting?)
My undergraduate physics department taught LabVIEW classes and used it extensively in its research projects.
The other alternative is MATLAB, in which I have no experience. There are camps for either product; each has its own advantages/disadvantages. Depending on what kind of problems you need to solve, one package may be more preferable than the other.
Regarding data analysis, you can use whatever kind of number cruncher you want. Ideally, you can do the hard calculations in language X and format the output to plot nicely in Excel, Mathcad, Mathematica, or whatever the flavor du jour plotting system is. Don't expect standardization here.
Did you have anything like version control, bug tracking?
Looking back, we didn't, and it would have been easier for us all if we did. Nothing like breaking everything and struggling for hours to fix it!
Definitely use source control for any common code. Encourage individuals to write their code in a manner that could be made more generic. This is really just coding best practices. Really, you should have them teaching (or taking) a computer science class so they can get the basics.
How would you go about trying to create a decent environment for programming, without getting too much in the way of the individual scientists (esp. physicists are stubborn people!)
There is a clear split between data aquisition (DAQ) and data analysis. Meaning, it's possible to standardize on the DAQ and then allow the scientists to play with the data in the program of their choice.
Another good option is Scilab. It has graphic modules à la LabVIEW, it has its own programming language and you can also embed Fortran and C code, for example. It's being used in public and private sectors, including big industrial companies. And it's free.
About versioning, some prefer Mercurial, as it gives more liberties managing and defining the repositories. I have no experience with it, however.
For plotting I use Matplotlib. I will soon have to make animations, and I've seen good results using MEncoder. Here is an example including an audio track.
Finally, I suggest going modular, this is, trying to keep main pieces of code in different files, so code revision, understanding, maintenance and improvement will be easier. I have written, for example, a Python module for file integrity testing, another for image processing sequences, etc.
You should also consider developing with the use a debugger that allows you to check variable contents at settable breakpoints in the code, instead using print lines.
I have used Eclipse for Python and Fortran developing (although I got a false bug compiling a Fortran short program with it, but it may have been a bad configuration) and I'm starting to use the Eric IDE for Python. It allows you to debug, manage versioning with SVN, it has an embedded console, it can do refactoring with Bicycle Repair Man (it can use another one, too), you have Unittest, etc. A lighter alternative for Python is IDLE, included with Python since version 2.3.
As a few hints, I also suggest:
Not using single-character variables. When you want to search appearances, you will get results everywhere. Some argue that a decent IDE makes this easier, but then you will depend on having permanent access to the IDE. Even using ii, jj and kk can be enough, although this choice will depend on your language. (Double vowels would be less useful if code comments are made in Estonian, for instance).
Commenting the code from the very beginning.
For critical applications sometimes it's better to rely on older language/compiler versions (major releases), more stable and better debugged.
Of course you can have more optimized code in later versions, fixed bugs, etc, but I'm talking about using Fortran 95 instead of 2003, Python 2.5.4 instead of 3.0, or so. (Specially when a new version breaks backwards compatibility.) Lots of improvements usually introduce lots of bugs. Still, this will depend on specific application cases!
Note that this is a personal choice, many people could argue against this.
Use redundant and automated backup! (With versioning control).
Definitely, use Subversion to keep current, work-in-progress, and stable snapshot copies of source code. This includes C++, Java etc. for homegrown software tools, and quickie scripts for one-off processing.
With the strong leaning in science and applied engineering toward "lone cowboy" development methodology, the usual practice of organizing the repository into trunk, tag and whatever else it was - don't bother! Scientists and their lab technicians like to twirl knobs, wiggle electrodes and chase vacuum leaks. It's enough of a job to get everyone to agree to, say Python/NumPy or follow some naming convention; forget trying to make them follow arcane software developer practices and conventions.
For source code management, centralized systems such as Subversion are superior for scientific use due to the clear single point of truth (SPOT). Logging of changes and ability to recall versions of any file, without having chase down where to find something, has huge record-keeping advantages. Tools like Git and Monotone: oh my gosh the chaos I can imagine that would follow! Having clear-cut records of just what version of hack-job scripts were used while toying with the new sensor when that Higgs boson went by or that supernova blew up, will lead to happiness.
What languages/environments have you
used for developing scientific
software, esp. data analysis? What
libraries? (E.g., what do you use for
plotting?)
Languages I have used for numerics and sicentific-related stuff:
C (slow development, too much debugging, almost impossible to write reusable code)
C++ (and I learned to hate it -- development isn't as slow as C, but can be a pain. Templates and classes were cool initially, but after a while I realized that I was fighting them all the time and finding workarounds for language design problems
Common Lisp, which was OK, but not widely used fo Sci computing. Not easy to integrate with C (if compared to other languages), but works
Scheme. This one became my personal choice.
My editor is Emacs, although I do use vim for quick stuff like editing configuration files.
For plotting, I usually generate a text file and feed it into gnuplot.
For data analysis, I usually generate a text file and use GNU R.
I see lots of people here using FORTRAN (mostly 77, but some 90), lots of Java and some Python. I don't like those, so I don't use them.
Was there any training for people
without any significant background in
programming?
I think this doesn't apply to me, since I graduated in CS -- but where I work there is no formal training, but people (Engineers, Physicists, Mathematicians) do help each other.
Did you have anything like version
control, bug tracking?
Version control is absolutely important! I keep my code and data in three different machines, in two different sides of the world -- in Git repositories. I sync them all the time (so I have version control and backups!) I don't do bug control, although I may start doing that.
But my colleagues don't BTS or VCS at all.
How would you go about trying to
create a decent environment for
programming, without getting too much
in the way of the individual
scientists (esp. physicists are
stubborn people!)
First, I'd give them as much freedom as possible. (In the University where I work I could chooe between having someone install Ubuntu or Windows, or install my own OS -- I chose to install my own. I don't have support from them and I'm responsible for anything that happens with my machins, including security issues, but I do whatever I want with the machine).
Second, I'd see what they are used to, and make it work (need FORTRAN? We'll set it up. Need C++? No problem. Mathematica? OK, we'll buy a license). Then see how many of them would like to learn "additional tools" to help them be more productive (don't say "different" tools. Say "additional", so it won't seem like anyone will "lose" or "let go" or whatever). Start with editors, see if there are groups who would like to use VCS to sync their work (hey, you can stay home and send your code through SVN or GIT -- wouldn't that be great?) and so on.
Don't impose -- show examples of how cool these tools are. Make data analysis using R, and show them how easy it was. Show nice graphics, and explain how you've created them (but start with simple examples, so you can quickly explain them).
I would suggest F# as a potential candidate for performing science-related manipulations given its strong semantic ties to mathematical constructs.
Also, its support for units-of-measure, as written about here makes a lot of sense for ensuring proper translation between mathematical model and implementation source code.
First of all, I would definitely go with a scripting language to avoid having to explain a lot of extra things (for example manual memory management is - mostly - ok if you are writing low-level, performance sensitive stuff, but for somebody who just wants to use a computer as an upgraded scientific calculator it's definitely overkill). Also, look around if there is something specific for your domain (as is R for statistics). This has the advantage of already working with the concepts the users are familiar with and having specialized code for specific situations (for example calculating standard deviations, applying statistical tests, etc in the case of R).
If you wish to use a more generic scripting language, I would go with Python. Two things it has going for it are:
The interactive shell where you can experiment
Its clear (although sometimes lengthy) syntax
As an added advantage, it has libraries for most of the things you would want to do with it.
I'm no expert in this area, but I've always understood that this is what MATLAB was created for. There is a way to integrate MATLAB with SVN for source control as well.