… is very worth a look. Continue reading Grant Imahara’s Engineering Big Ideas…
cPanel is a widely used front end used on servers for people to operate things like their blog, email system, other web related functions, etc. It is a way to avoid using the command line. So, for instance, you might buy into a hosing package, and that may allow you to set up a WordPress site. Your host give you the info to log into your own cPanel. There, you press the “one click install” button and whoosh, you have a web site. Continue reading ALERT!! cPanel is ruining the Internet!
First, what is “pixel art?”
Is that just art that is rendered in raster? Not exactly. Pixel art is the sort of art you draw for digital cartoons or similar things. The skills and tools of making pixel art would apply to designing icons or logos used in electronic products as well.
To demonstrate what pixel art is, I’m including a few examples from the newly published Make Your Own Pixel Art: Create Graphics for Games, Animations, and More! by Jennifer Dawe and Matthew Humphries.
This book will give you an introduction to the tricks of the trade of making technologically simply but artistically potent drawings, including ways to animate them.
The non-OpenSource (boo) software that is used throughout the book is not expensive and is easy to use, and yes, OpenSource alternatives are suggested and briefly discussed. The book relies on Aseprite and Pro Motion, with GraphcsGale (Windows only, boo) being a free alternative.
Techniques covered include shading, texture, proper use of color, motion and animation, and making things look sentient. Apparently, you can make money doing this sort of thing! This book is probably a good investment, at the very least to see if you have the talent and interest.
Author Jennifer Dawe is an animator and character designer who has been a professional pixel artist for the past 15 years. Author Matthew Humphries is Senior Editor at PCMag.com and a professional game designer.
All keyboards are “mechanical” in some sense, or at least most of them, in that something moves. But what we call a “mechanical keyboard” is one that has individual switches under each key cap, instead of some sort of silly squishy membrane. This gives the keys a different tactile sense, and often, a sound.
This post — Mechanical Keyboards What Are They And Which One Do You Want — is a little, but not too much, out of date. The basic information is correct. There are one or two more kinds of keys than described, and there are emerging manufacturers that may or may not be making good switches, and there are many more offerings of el-cheapo keyboards. And, still, the DasKeyboard is still one of the better (and more expensive) options.
My Avant Stellar keyboard finally broke in enough places (I’m tough on keyboards) to require major repair or replacement. I looked briefly at really old Northgates (20-30 years old?) on ebay, bid on a few, but was outbid and decided not to spend an exorbitant amount of money on a decades old untested machine. I also realized that I have two computers sitting next to each other, and when I change between them, it feels wrong, because they have two entirely different kinds of keyboards. But, I realized, if I get a new DasKeyboard for my Linux machine, since I have a Mac DasKeyboard on the Mac, then I would quickly become accustomed to switching back and forth and all would be well. So, I got the Das Keyboard Model S Professional Cherry MX Blue for the Linux to match the Das Keyboard Model S Pro for Mac, and now everything is good.
Except possibly one thing. You may recal that I had earlier complained about the font used on the key caps on the DasKeyboards. At the time, I used stick on labels to upgrade the DasKeyboard to how I like it. For some reason, as I sit here typing on the new DasKeyboard with the small typeface that I don’t like much, I’m not bothered by it, so I may not make that change. We’ll see.
So now all is well in keyboard land, and my pile of no longer in use keyboards available for spare parts grows.
I have two excellent things on my desk, a Linux Journal article by Andy Wills, and a newly published book by Stefano Allesina and Madlen Wilmes.
Computing Skills for Biologists: A Toolbox by Stefano Allesina and Madlen Wilmes, Princeton University Press.
Open Science, Open Source, and R, by Andy Wills, Linux Journal
OpenSource science means, among other things, using OpenSource software to do the science. For some aspects of software this is not important. It does not matter too much if a science lab uses Microsoft Word or if they use LibreOffice Write.
However, since it does matter if you use LibreOffice Calc as your spreadsheet, as long as you are eschewing proprietary spreadsheets, you might as well use the OpenSource office package LibreOffice or equivalent, and then use the OpenSource presentation software, word processor, and spreadsheet.
OpenSource programs like Calc, R (a stats package), and OpenSource friendly software development tools like Python and the GPL C Compilers, etc. do matter. Why? Because your science involves calculating things, and software is a magic calculating box. You might be doing actual calculations, or production of graphics, or management of data, or whatever. All of the software that does this stuff is on the surface a black box, and just using it does not give you access to what is happening under the hood.
But, if you use OpenSoucre software, you have both direct and indirect access to the actual technologies that are key to your science project. You can see exactly how the numbers are calculated or the graphic created, if you want to. It might not be easy, but at least you don’t have to worry about the first hurdle in looking under the hood that happens with commercial software: they won’t let you do it.
Direct access to the inner workings of the software you use comes in the form of actually getting involved in the software development and maintenance. For most people, this is not something you are going to do in your scientific endeavor, but you could get involved with some help from a friend or colleague. For example, if you are at a University, there is a good chance that somewhere in your university system there is a computer department that has an involvement in OpenSource software development. See what they are up to, find out what they know about the software you are using. Who knows, maybe you can get a special feature included in your favorite graphics package by helping your new found computer friends cop an internal University grant! You might be surprised as to what is out there, as well as what is in there.
In any event, it is explicitly easy to get involved in OpenSource software projects because they are designed that way. Or, usually are and always should be.
The indirect benefit comes from the simple fact that these projects are OpenSource. Let me give you an example form the non scientific world. (it is a made up example, but it could reflect reality and is highly instructive.)
Say there is an operating system or major piece of software competing in a field of other similar products. Say there is a widely used benchmark standard that compares the applications and ranks them. Some of the different products load up faster than others, and use less RAM. That leaves both time (for you) and RAM (for other applications) that you might value a great deal. All else being equal, pick the software that loads faster in less space, right?
Now imagine a group of trollish deviants meeting in a smoky back room of the evile corporation that makes one of these products. They have discovered that if they leave a dozen key features that all the competitors use out of the loading process, so they load later, they can get a better benchmark. Without those standard components running, the software will load fast and be relatively small. It happens to be the case, however, that once all the features are loaded, this particular product is the slowest of them all, and takes up the most RAM. Also, the process of holding back functionality until it is needed is annoying to the user and sometimes causes memory conflicts, causing crashes.
In one version of this scenario, the concept of selling more of the product by using this performance tilting trick is considered a good idea, and someone might even get a promotion for thinking of it. That would be something that could potentially happen in the world of proprietary software.
In a different version of this scenario the idea gets about as far as the water cooler before it is taken down by a heavy tape dispenser to the head and kicked to death. That would be what would certainly happen in the OpenSource world.
So, go OpenSource! And, read the paper from Linux Journal, which by the way has been producing some great articles lately, on this topic.
The Scientists Workflow and Software
You collect and manage data. You write code to process or analyze data. You use statistical tools to turn data into analytically meaningful numbers. You make graphs and charts. You write stuff and integrate the writing with the pretty pictures, and produce a final product.
The first thing you need to understand if you are developing or enhancing the computer side of your scientific endevour is that you need the basic GNU tools and command line access that comes automatically if you use Linux. You can get the same stuff with a few extra steps if you use Windows. The Apple Mac system is in between with the command line tools already built in, but not quite as in your face available.
You may need to have an understanding of Regular Expressions, and how to use them on the command line (using sed or awk, perhaps) and in programming, perhaps in python.
You will likely want to master the R environment because a) it is cool and powerful and b) a lot of your colleagues use R so you will want to have enough under your belt to share code and data now and then. You will likely want to master Python, which is becoming the default scientific programming language. It is probably true that anything you can do in R you can do in Python using the available tools, but it is also true that the most basic statistical stuff you might be doing is easier in R than Python since R is set up for it. The two systems are relatively easy to use and very powerful, so there is no reason to not have both in your toolbox. If you don’t chose the Python route, you may want to supplement R with gnu plotting tools.
You will need some sort of relational database setup in your lab, some kind of OpenSource SQL lanaguge based system.
You will have to decide on your own if you are into LaTex. If you have no idea what I’m talking about, don’t worry, you don’t need to know. If you do know what I’m talking about, you probably have the need to typeset math inside your publications.
Finally, and of utmost importance, you should be willing to spend the upfront effort making your scientific work flow into scripts. Say you have a machine (or a place on the internet or an email stream if you are working collaboratively) where some raw data spits out. These data need some preliminary messing around with to discard what you don’t want, convert numbers to a proper form, etc. etc. Then, this fixed-up data goes through a series of analyses, possibly several parallel streams of analysis, to produce a set of statistical outputs, tables, graphics, or a new highly transformed data set you send on to someone else.
If this is something you do on a regular basis, and it likely is because your lab or field project is set up to get certain data certain ways, then do certain things to it, then ideally you would set up a script, likely in bash but calling gnu tools like sed or awk, or running Python programs or R programs, and making various intermediate files and final products and stuff. You will want to bother with making the first run of these operations take three times longer to set up, so that all the subsequent runs take one one hundredth of the time to carry out, or can be run unattended.
Nothing, of course, is so simple as I just suggested … you will be changing the scripts and Python programs (and LaTeX specs) frequently, perhaps. Or you might have one big giant complex operation that you only need to run once, but you KNOW it is going to screw up somehow … a value that is entered incorrectly or whatever … so the entire thing you need to do once is actually something you have to do 18 times. So make the whole process a script.
Aside form convenience and efficiency, a script does something else that is vitally important. It documents the process, both for you and others. This alone is probably more important than the convenience part of scripting your science, in many cases.
Being small in a world of largeness
Here is a piece of advice you wont get from anyone else. As you develop your computer working environment, the set of software tools and stuff that you use to run R or Python and all that, you will run into opportunities to install some pretty fancy and sophisticated developments systems that have many cool bells and whistles, but that are really designed for team development of large software projects, and continual maintenance over time of versions of that software as it evolves as a distributed project.
Don’t do that unless you need to. Scientific computing often not that complex or team oriented. Sure, you are working with a team, but probably not a team of a dozen people working on the same set of Python programs. Chances are, much of the code you write is going to be tweaked to be what you need it to be then never change. There are no marketing gurus coming along and asking you to make a different menu system to attract millennials. You are not competing with other products in a market of any sort. You will change your software when your machine breaks and you get a new one, and the new one produces output in a more convenient style than the old one. Or whatever.
In other words, if you are running an enterprise level operation, look into systems like Anaconda. If you are a handful of scientists making and controlling your own workflow, stick with the simple scripts and avoid the snake. The setup and maintenance of an enterprise level system for using R and Python is probably more work before you get your first t-test or histogram than it is worth. This is especially true if you are more or less working on your own.
Another piece of advice. Some software decisions are based on deeply rooted cultural norms or fetishes that make no sense. I’m an emacs user. This is the most annoying, but also, most powerful, of all text editors. Here is an example of what is annoying about emac. In the late 70s, computer keyboards had a “meta” key (it was actually called that) which is now the alt key. Emacs made use of the metakey. No person has seen or used a metakey since about 1979, but emacs refuses to change its documentation to use the word “alt” for this key. Rather, the documentation says somethin like “here, use the meta key, which on some keyboards is the alt key.” That is a cultural fetish.
Using LaTeX might be a fetish as well. Obliviously. It is possible that for some people, using R is a fetish and they should rethink and switch to using Python for what they are doing. The most dangerous fetish, of course, is using proprietary scientific software because you think only if you pay hundreds of dollars a year to use SPSS or BMD for stats, as opposed to zero dollars a year for R, will your numbers be acceptable. In fact, the reverse is true. Only with an OpenSource stats package can you really be sure how the stats or other values are calculated.
And my final piece of advice is to get and use this book: Computing Skills for Biologists: A Toolbox by Allesina and Wilmes.
This book focuses on Python and not R, and covers Latex which, frankly, will not be useful for many. This also means that the regular expression work in the book is not as useful for all applications, as might be the case with a volume like Mastering Regular Expressions. But overall, this volume does a great job of mapping out the landscape of scripting-oriented scientific computing, using excellent examples from biology.
Mastering Regular Expressions can and should be used as a textbook for an advanced high school level course to prep young and upcoming investigators for when they go off and apprentice in labs at the start of their career. It can be used as a textbook in a short seminar in any advanced program to get everyone in a lab on the same page. I suppose it would be treat if Princeton came out with a version for math and physical sciences, or geosciences, but really, this volume can be generalized beyond biology.
Stefano Allesina is a professor in the Department of Ecology and Evolution at the University of Chicago and a deputy editor of PLoS Computational Biology. Madlen Wilmes is a data scientist and web developer.
Project oriented programming books, books that help you develop actual working programs while you learn to program, are the thing, and the new Impractical Python Projects: Playful Programming Activities to Make You Smarter is an excellent example. Continue reading Impractical Python Programming For Fun
A computer program is like a memo. Often, a vague memo.
You are the boss. You want a pile of files to be put away. You could do it yourself, but instead you instruct someone else to do it. There are a lot of them and they are all mixed up. So you write a memo to an employee that says “put the files away” and sis-bam-boom you’re all set.
The new Chrome browser by Google, Chrome 69, is probably an important improvement in browser functionality, look and feel, and security. But, as you might expect, the first version available for general users is buggy, perhaps very buggy. I would wait a little while for the bugs to get all hunted down and exterminated. How long? A week or two should do it.
What is new in the new Google Chrome 69 Browser?
Terms used in the personal computer world that begin with the letter “D” Continue reading Computer Terms Definitions — “D”
It is a conspiracy!!!!
I love the way the parrot responds to Alexa’s response by first making bleepy-bloopy techie noises, then imitating the sound of all the manual light switches being thrown.
But wait, there’s more. This parrot made an Alexa shopping list:
Some time ago it dawned on me that a future Microsoft operating system, a version of Windows, would be based on Linux. It only makes sense. There is no better operating system to base a desktop, server, or other specialized OS on, for normal hardware. Eventually, this would dawn on Microsoft. I thought it might have a few years ago when Microsoft went from being openly aggressive against Linux and OpenSource, to being neutral, to being nice, and eventually contributing.
Natasha Ravinand is the founder of “She Dreams in Code,” a nonprofit focused on increasing opportunities for middle school girls to engage in coding. She is also the author of Girls With Dreams: Inspiring Girls to Code and Create in the New Generation. In this book, Ntasha interviews several women in engineering and technology in order to assemble a compendium of inspiration for others like her, who want to engage in technology without the usual and common obstacles.
Here’s two facts you need to know. 1) Only 25% of the adults engaged in science and technology (STEM) are women. 2) This is a HUGE percentage compared to what it was only a few years ago. So, we are in a bad place, but also, we are moving quickly out of that place. Continue reading Girls With Dreams and Women With Cards
Do you worry that your kid is going to be rejected from civilization, or, at least, college or the boy scouts or something, because of dumb stuff they do on line? Do you see evidence that your children are copying the jerky characters that grace our TV screens and movies, and are becoming too annoying, compared to how we all were when we grew up? Do you want to just tell the up coming generation to GET OFF THE LAWN!!!!
Here is a way to do that. Continue reading How to keep your kids out of trouble in this modern age
A while back, I reviewed mechanical keyboards, and found several good ones. Indeed, mechanical keyboards are so much better than membrane keyboards (their name even sounds better) that it is hard to go wrong. Still, when the choices remain in the range of $60 to $200, details matter. Continue reading Making Das Keyboard Closer to Perfect