Habits...
...a pattern of repeated behaviour
What causes a habit???
Experts aren't always sure what causes a habit, but do know that they're learned behaviors that usually provide a positive outcome for the child. Habits may develop as entertainment for a bored child or, more commonly, as a coping mechanism to soothe an anxious one. The next time you see nail biting or hair twirling, try to recall if your child has recently had a stressful experience. If so, the behavior might be your child's attempt to relieve tension, much as you would by working out at the gym. On the other hand, some kids engage in habits when they're relaxed, such as before falling to sleep or quietly listening to music. Some habits may be leftovers from infancy. In infants, thumb sucking is a common self-comfort behavior that has pleasurable associations with feedings and the end of hunger. So it may linger into childhood because of its positive associations. Or perhaps the explanation for your child's nail biting is in your mirror. Do you bite your nails? Studies suggest that nail biting may have a strong familial or genetic component. Other kids engage in habits to attract attention or to manipulate their parents. If kids feel that their parents are ignoring them, they may engage in the annoying habit because they know that it will get a reaction from Mom or Dad.
The basics, always with you throughout your life.
When Is a Habit No Longer Just a Habit?
In some cases, a habit is the result or the cause of a physical or psychological problem. For example, a nose-picker might be uncomfortable because there's actually an object stuck in the nose. And the habits themselves can cause some medical complications, such as:
- nosebleeds in the nose picker
- ingrown or infected nails in the nail biter
- dental problems, such as malocclusion (the failure of the teeth in the upper and lower jaws to meet properly), or thumb or finger infections in the thumb sucker
A habit may no longer be a simple habit if it negatively affects a child's social relationships or interferes with daily functioning.
 If kids are the subject of teasing at school or have difficulty talking because they won't take their thumbs out of their mouths, the behavior has gone beyond a simple habit. Kids who pull their hair out may have trichotillomania, a condition that results in hair loss. And habits that are in response to obsessive thoughts may be a sign of OCD.
However, most habits don't cause any significant problems and tend to improve as kids get older. But if you're concerned about your child's habits, talk with your doctor.
It's never too late, but best if you start early!
GOOD HABITS
Eat Healthy Foods:
This one's probably the toughest of them all.
- Explain the importance of healthy eating and how junk foods could be harmful for health.
- Make homemade versions of fast food snacks like noodles, pasta and pizza for your child occasionally.
Table Manners:
This is definitely one of the most important points among the list of good habits you need to educate your child about. It’s now time to start treating your child like an adult as he sits at the table for dinner.
Brushing Twice a Day:
Younger children are often lazy when it comes to brushing their teeth.
- Make sure you take a few minutes to educate him about the importance of proper brushing.
- Try and make brushing a fun-filled activity by occasionally allowing him some sweet treats if he promises to brush right after he has them.

- It will be the most important when your child starts going to school.
- Stick to a regular bedtime yourself. Your child is less likely to be awake if you aren't.
Play Outdoors:
It's easy to get glued to the TV screen watching cartoons or playing screen games, but that's not what you would want your child to be doing.
- Get out with him for some fresh outdoor activities every day.
- Encourage physical activity.
Cleaning Up The Mess:
It’s always a good idea to get your child clean up the mess in his room.
- Allot him a span of 10-15 minutes a day to make sure all his toys, books and stuff are placed at their appropriate places.
- You can always reward him with his favourite home-cooked snack afterward!
Be Responsible With Money:
If your child is old enough that you start to offer him pocket money, educate him about being responsible with money.

- Encourage him to save his money and manage his expenses within a given budget (you do it all the time- and it's time to let your child know that too).
Say 'Please' and 'Thank You':
Another basic rule you need to educate your child about. 'Please' and 'Thank You' are magical words that can handle most jobs without any hassles, and that's what you would want your child to know.
Sharing Is Caring:
It is one of the very important good habits.
- If your child is an only child, you will need to explain how sharing transforms as an important part of life.
- Educate him about how sharing doubles up the joy and how he is lucky enough to have a particular toy or object while others are not.
- Siblings usually have no problem with this one (in most cases). If there are issues then you know how best to teach your children the art of sharing.

Help your child, following these simple habits, will surely go a long way in developing his habits and shaping his personality. Explain him how following these simple good habits at school will make him a better person.
"All bad habits start slowly and gradually and before you know you have the habit,the habit has you."
BAD HABITS
Nail Biting
If nails chewed to the nub are familiar to you, you're not alone. Nail biting or picking is one of the most common childhood habits. An estimated 30% to 60% of kids and teens chew on one or more fingernails. And, occasionally, a child may also bite his or her toenails. Boys and girls appear equally prone to the habit in earlier years; however, as they get older, boys are more likely to be nail biters.
 Most kids who twist, stroke, or pull their hair are girls. Hair twirling may appear in early childhood as a precursor to hair pulling, either with or without hair loss. But many hair twirlers and pullers stop as they get older. For those who don't, simple behavior modification can help them break the habit. However, for those who start hair pulling as older kids or teens, the habit is harder to break and may be a sign of anxiety, depression, or obsessive-compulsive disorder (OCD).
Nose Picking
Nose picking appears to be a habit that, although it usually begins in childhood, may actually linger into adulthood. If you find that hard to believe, consider that a 1995 study of adults found that 91% picked their noses regularly — and about 8% of them reported that they eat what they pick!
    But frequent or intense thumb sucking beyond 4 to 5 years of age can cause problems, including dental issues (such as an overbite), thumb or finger infections, and being teased.
"Always follow the good ones"
Coping With Your Child's Habit
The good news is that most habits disappear, usually by the time a child reaches school age, because the child no longer needs it or outgrows it.
But if you think it's time to help your child break a habit, consider these steps:
- Calmly point out what you don't like about the behavior and why. This approach can be used with kids as young as 3 or 4 to help increase awareness of the problem. Say something like, "I don't like it when you bite your nails. It doesn't look nice. Could you try to stop doing that?" Most important, the next time you see the nail biting, don't scold or lecture. Punishment, ridicule, or criticism could cause the behavior to increase.
- Involve your child in the process of breaking the habit. If your 5-year-old comes home crying from kindergarten because the other kids made fun of his thumb sucking, understand that this is a way of asking you for help. Parents can ask their kids what they think they could do to stop the habit or if they want to stop the habit. Come up with some ways to work on breaking the unwanted habit together.
- Suggest alternative behaviors.  This will increase awareness of the habit and may serve as a reminder. To occupy your child's attention, try providing a distraction, like helping you in the kitchen or working on a craft.
- Reward and praise self-control. For example, allow your little girl to use nail polish if she lets her nails grow.
- Be consistent in rewarding good behavior. If you fail to notice good behavior, it will disappear over time. The new, positive habit must be firmly established before the old one will disappear.
For the best success, it's important that kids be motivated to break the habit. And because habits take time to develop, they're also going to take time to be replaced by alternative behavior, so be patient.


Leveraging modern technologies to analyze past data, enabling machine learning, performing algorithms to promote fault tolerance and implementing natural language processing for sentiment analysis. Winning more than losing.
Learning from data, discovering solutions for real world problems, and driving decision making to create real world value. As a data scientist I am dedicated to lowering costs and/or increasing revenue while using tools to predict the future with...
Fully understanding what each predictive model does, which models and algorithms to choose, properly cleaning and preparing any input data, then harnessing machine learning to compute efficiently and effectively so that computers can do better work, for us.
I programmed a self driving car at the age of 8, and the technology fundamentally isn't much different over 20 years later. The goal is to make the world a more enjoyable place for all to live by utilizing AI from ML. As usual, start by utilizing simple models, and then scale them. Notice how the skills build.


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public interface FileItemStream extends FileItemHeadersSupport
This interface provides access to a file or form item that was
received within a multipart/form-data
POST request.
The items contents are retrieved by calling openStream()
.
Instances of this class are created by accessing the
iterator, returned by
FileUploadBase.getItemIterator(RequestContext)
.
Note: There is an interaction between the iterator and
its associated instances of FileItemStream
: By invoking
Iterator.hasNext()
on the iterator, you discard all data,
which hasn't been read so far from the previous data.
| Modifier and Type | Interface and Description |
|---|---|
static class |
FileItemStream.ItemSkippedException
This exception is thrown, if an attempt is made to read
data from the
InputStream , which has been returned
by openStream() , after
Iterator.hasNext() has been invoked on the
iterator, which created the FileItemStream . |
| Modifier and Type | Method and Description |
|---|---|
String |
getContentType()
Returns the content type passed by the browser or
null if
not defined. |
String |
getFieldName()
Returns the name of the field in the multipart form corresponding to
this file item.
|
String |
getName()
Returns the original filename in the client's filesystem, as provided by
the browser (or other client software).
|
boolean |
isFormField()
Determines whether or not a
FileItem instance represents
a simple form field. |
InputStream |
openStream()
Creates an
InputStream , which allows to read the
items contents. |
getHeaders, setHeaders
InputStream openStream() throws IOException
InputStream
, which allows to read the
items contents.IllegalStateException
- The method was already invoked on
this item. It is not possible to recreate the data stream.IOException
- An I/O error occurred.FileItemStream.ItemSkippedException
String getContentType()
null
if
not defined.null
if
not defined.String getName()
String getFieldName()
boolean isFormField()
FileItem
instance represents
a simple form field.true
if the instance represents a simple form
field; false
if it represents an uploaded file.Copyright © 2002–2018 The Apache Software Foundation. All rights reserved.


mirtCAT: Computerized Adaptive Testing with Multidimensional Item
Response Theory
Provides tools to generate an HTML interface for creating adaptive
and non-adaptive educational and psychological tests using the shiny
package (Chalmers (2016) <doi:10.18637/jss.v071.i05>).
Suitable for applying unidimensional and multidimensional
computerized adaptive tests (CAT) using item response theory methodology and for
creating simple questionnaires forms to collect response data directly in R.
Additionally, optimal test designs (e.g., "shadow testing") are supported
for tests which contain a large number of item selection constraints.
Finally, package contains tools useful for performing Monte Carlo simulations
for studying the behavior of computerized adaptive test banks.
| Version: |
1.8 |
| Depends: |
mirt (≥ 1.25), shiny (≥ 1.0.1) |
| Imports: |
lattice, stats, Rcpp, methods, markdown, pbapply, lpSolve |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Suggests: |
shinythemes, parallel, SimDesign, knitr |
| Published: |
2018-10-22 |
| Author: |
Phil Chalmers [aut, cre] |
| Maintainer: |
Phil Chalmers <rphilip.chalmers at gmail.com> |
| BugReports: |
https://github.com/philchalmers/mirtCAT/issues?state=open |
| License: |
GPL (≥ 3) |
| URL: |
https://github.com/philchalmers/mirtCAT,
https://github.com/philchalmers/mirtCAT/wiki,
https://groups.google.com/forum/#!forum/mirt-package |
| NeedsCompilation: |
yes |
| Citation: |
mirtCAT citation info |
| Materials: |
README NEWS |
| In views: |
Psychometrics |
| CRAN checks: |
mirtCAT results |
Downloads:
Reverse dependencies:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=mirtCAT
to link to this page.


I’ve been in the tryhard coffee game for almost a decade now, so I’m laying down my best practices for friends and coworkers.
Brewing
I brew with a pour over cone, but I know a little bit about other popular methods. Here’s what you really need to know to begin making a decent cup of coffee on your own:
- Water temperature should be in the range of 195-205F (90-96C)
- The ratio of coffee to water should be between 1:15 (strong) and 1:17 (normal)
- Grounds should be saturated with hot water for 30-45 seconds before more water is added (the “bloom”—a good rule of thumb is to use double the weight of the ground coffee)
Brew times vary depending on the method. For example, pour over should take between 3-4 minutes (with the bloom). French press takes around 4 minutes, and AeroPress is in the 1.5-2 minute range. These are my yardsticks for my typical cup, which is usually 18.5g coffee and 300ml water. These aren’t hard rules, so your preferences may lead you to brew longer or make a weaker cup. I’d argue that this is a good starting place, though.
FAQ
Q: Is there an optimal freshness for beans?
A: Freshly roasted beans are considered to be at their peak from about 3 days after the roast, until around 2 weeks. This doesn’t mean that beans are ‘bad’ after that period; they just produce a cup that’s a little less nuanced.
Q: Should I grind at home, or at the point of purchase?
A: Grind at home immediately before brewing if you have a good burr grinder. The entry level price for these machines is around $120 USD, which is a price many people aren’t willing to pay for something that grinds coffee. If you buy from a specialty roaster, their $1200 Mazzer will grind to a consistency that I think is preferable to grinding at home with a low quality machine.
Q: What does ‘process’ refer to?
A: When beans are harvested, they’re either stripped away from the coffee fruit and washed before drying in the sun (appropriately called washed process), or the fruit is left on, and ferments in the hot sun while its sugars seep into the green coffee bean. This is the ‘natural’ process. There are other variations you might see, like ‘honey,’ where the fruit is removed, but the beans remain unwashed during the drying process. These methods produce very different results; natural process beans are known for their very fruity, acidic flavor. Try one if you’ve never had one!
Q: What differences are there between origins?
A: This is always difficult to answer, but it’s like wine. Some countries are better suited for coffee production because of climate and soil conditions, or they have a very developed infrastructure for producing it. Usually single origin coffees are sold with a name indicating their country of origin, and some subsidiary, like a growing region or farm. Your mileage may vary.
Q: Is dark roast good? What about cold brew?
A: Invariably, these tend to taste the same. So, naturally, dark roasts are ideally suited for making cold brew. Maybe I’ll provide a recipe in a future post?
Q: What’s a geisha?
A: A waste of money.


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This is one in a series of guides discussing software tools and their use in Lever projects. This guide gives you the basics of contributing to Lever.
This guide will walk you through compiling the lever runtime and creating a distributable from it. It will also show how to implement a missing functionality, or how to improve documentation on the website and in the repository.
In this guide we use Visual Studio Code. We assume you use Windows on some points.
Since the introduction of libuv and zlib, this guide is a bit outdated. You may need to also read the pypy documentation to get this project compiled: Translating on Windows
You may have to read these articles first, if you haven't done it yet:
First get yourself an online repository where to push your work for review. You can make one by pressing the 'fork' button at the Lever's github repository. You need to sign up to fork.
Now you have online repository for lever where you can push your changes when you are done.
Next you should clone your online repository to your computer. Easiest way is to do 'Git Bash Here' then type "git clone " and paste the URL of your repository after.
The git clone created a lever repository to where you issued it. Next you should 'File' -> 'Open Folder' to get it into your editor:
There are various things one could do to Lever. So at this point this guide has been divided into many parts. But there is one important thing to discuss before we go ahead.
If you have been following this guide this far. You shouldn't stop.
I have programmed Lever myself. I have been programming for nearly 20 years. I know fears of different kinds can put people to stop what they wanted to do. If you don't have such fears, here are some to start with:
You may also stumble upon difficulties:
And sometimes the outcome is some of the following:
You may want to study these thoughts. They are common, they are meant to keep you on the safe side. Sometimes they prevent you from going ahead.
You can learn new things when you try. And things you try don't need to be in your profession to help you.
You have some motivation why you've read so far into this post. If it's easy to write it down, write it on post-it-note and glue to where you see it.
Also I could really use some non-programmers here. Some people who know very little and like to learn things and won't stop to the first obstacles they see would be really good help at writing documentation. Or if you handle some profession, oh it's interesting to hear what you think and what you write down.
There is real possibility that I reject commits. This shouldn't discourage you. If I ever reject a commit, I will explain why I rejected it and what can be done to get it through. I will try to be rational about this and minimize drama.
There is also a possibility that people ridicule you. But that really also happens even if you did nothing.  Don't let that to affect your mood or motivation. Don't get angered or agravated. This will require lots of training. You should do it.
Whatever you're going to do, try to do it well. Just don't fear to push just because it went bad.
There are some really easy places to contribute when you look around. Lever is straight out missing functionality at places. For example, we are supposed to support most methods for base types that are in Python. But there are lots of omissions.
For example, there was dict.update() missing some while ago.
Now there are some interesting things to note before you get to implement a missing functionality you find.
Some things in Lever are done slightly different ways. For example. The very first person who contributed to Lever wrote a 'len()' function. Lever uses '.length' -convention from Javascript. While he was working he noticed that we got '.length'. And implemented the 'len()' in terms of '.length'. Well I took his commit in. Though I should have really rejected it. I've kept it in the runtime as an easter egg. :-)
Your best bet here is to do some analysis. Is there a Lever way to do the thing this function did? Is there a purpose for this function? In this case '.update' is redundant. It is a good idea to write down your thoughts when analyse, that increases the chances that you succeed.
dict.update() puts the values into the dictionary. The implementation in Lever would be possible:
update = (self, other): if isinstance(other, dict): for key in other self[key] = other[key] else for item in other self[item[0]] = item[1]
dict.update() is part of the list comprehension elements in Python. Lever also appreciates list comprehensions as they are very expressive constructs.
There is a set.update() of similar behavior.
So the function is redundant, but every other reason points out it should be in Lever. Therefore implementing it will drive Lever forward.
I expect that people will contribute from need to extend lever while using it. Some explanation and analysis for the feature are even then very welcome.
It can happen that your analysis reveals that you don't need to extend Lever. If at the beginning you believed you did, it's likely that someone else thoughts same. It will be valuable to commit a report of your analysis as a .txt file, into analysis/ -directory of Lever.
If you edit lib/ it usually can be tested out with lever runtime.
When changing the runtime or compiler of Lever, you should get yourself the tools to compile a new runtime.
Go to Python download page
Next thing is important as RPython cannot translate to 64-bit executables on Windows, and it depends on bit-width of the Python. You need to download the "Windows x86 installer" from the download page that is for 32-bit environment. Also be sure that it is the Python 2.7 installer. The version number should appear in the start of the install.
It's okay to install the Python into C:\Python27 because it's usually expected to be there.
It's good idea to configure python to add itself into the PATH. Although it may be you have to restart your computer to have the PATH rule go to effect.
Next you need to download & install Microsoft Visual C++ Compiler for Python 2.7 The Microsoft tends to mess up their URLs from time to time. If the link doesn't work, do a search to find this installer. Just make sure it comes from Microsoft owned -url. Install it just like usual.
Now you should be able to open a command line for this compiler from the start menu of your Windows.
You need the libuv 1.x for translate to succeed. You can obtain it from the libuv.org release page. Remember to do or download x86 build on Windows. The Lever expects to find libuv include and libuv.lib in Lever's folder, under lever/libuv/.
This is a recent requirement for the build. This page will perhaps get an illustration of simplest way to do this later.
The prerequisite for implementing a function into Lever is that you know how to write Python code. Also you may want to read the RPython manual to know what kind of limitations the Lever runtime requires.
Come up with a sample program into samples/ -directory first. It can be simple and descriptive.
The internal documentation points out the rest of dictionary methods can be found from the runtime/space/dictionary.py
Write your implementation for Dict_update.
Here's a mini-guide for test your program after compiling. Press F5 and select 'Lever Runtime'
Put "${file}" into the "program" -option, so that the currently shown file gets run instead of the 'main.lc'. You may also like to change the "lever_path" into "${workspaceRoot}", because we are developing Lever.
You should get this kind of ENOENT -error when you try to run your 'dictionaries.lc'. This happens because your runtime doesn't have lever executable yet.
If you change the "lever_path" back to proper runtime, you should get the following error. It is clear that .update isn't there. If you do this, switch the lever's path back to this project, because you are going to compile a new runtime next.
There has been situation that my anti-virus software interferes with the debugger. If that happens then the debug console doesn't open. Restarting the Visual Studio Code seem to have helped when that happens.
To verify your implementation works, you need to translate the Lever runtime.
To do it, you will do that from the command line tool obtained two sections back. With the "Visual C++ 2008 32-bit Command Prompt". Navigate to your lever directory with a 'cd' -command.
Run the "python setup.py compile" -command, if it says 'is not recognized', check a troubleshoot for this in the screenshot below.
This triggers a full compile to your runtime and gives you a lever executable you can use to develop lib/ -features. If you're only working on the lib/, it is also useful to know the 'compile-lib' and 'compile-lib-all' commands. These commands use the bootstrapping compiler to compile everything in the lib/ -directory. The later command forces recompile for all files in the lib/.
There is also 'compile-nojit' command to provide shorter compile time. But the resulting interpreter won't be able to JIT the code.
And if it runs successfully, it should look about like this.
Now verify that your lever path refers to this runtime. You're about done with the implementation once your sample program runs properly.
If you made the recompile to get a distributable, there's the win32_dist.py -script that can create a new package with Lever.
If you implemented a feature, remember to update the internal_documentation.txt to let people know that there's a feature for that now. This is all what feature implementation requires.
Lever follows documentation-flows-upwards convention. In this convention the libraries and programs get a detailed description. Every file gets a description about why it is there, what led to its creation, what it implements and so on. These are collected into categories.
Reference documentation for functions will be written into doc/ and it will get a machine-readable format to allow it to be supplied into runtime from there. For now we have no reference documentation.
If you need to add images to the documentation. You can add them as usual. The preferred format is PNG and it's nice, but optional to use optipng on them before committing.
Guides are directly written into html files at guides/ -directory. Creation of a new guide requires a similar analysis as what code did.
This part of a guide will shorten considerably the time for you to get help with Lever. When you stumble upon an issue. Capture the logs and copy them. In the console you can do this by right clicking the window and pressing 'select all'.
The screen gets coloured white. In this mode press 'enter'. The screen recovers to what it was.
Go into gist.github.com and paste your error log there. Press 'create public gist'. You must be logged into github to do this. Any other pastebin site works as well.
On our issues page, press 'new issue' button. Describe the problem you encountered. Add a link to the paste you created. Explain what changes you've done and how you've diverted from the instructions of the guide. You can use the 'snip' -tool on windows 10 to provide us screenshots.
Details are fun. They provide us something we can react upon and provide the knowledge we need to troubleshoot.
This will let us know that you have a problem, and we will get to help you out asap.
You can do several commits, but please don't commit all the time. At worst, commit files once in a day after a productive session.
Push the files using your preferred way to you server.
When you think your work can be contributed, you can then finally write a pull request. There should be a 'new pull request' -button on the Lever repository. Go push that and step through.
Next you get to wait for my response.
If you've worked this through, you should be ready to contribute on Lever development. Chances are you are ready to contribute to most other RPython -related open source projects, including PyPy itself.
To have no trouble with them, please follow the respective conventions they have with their projects. For example. The documentation cycle Lever follows is unique to Lever and Lever-related projects. The compiling tools Lever uses are unique to RPython related projects, but only Lever has setup.py -script wired like this.
You will discover which conventions each project follows by reading their documentation.


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The Jackal (1997)
A movie review by Michael J. Legeros Copyright 1997 by Michael J. Legeros
(Universal) Directed by Michael Caton-Jones Written by Chuck Pfarrer based on the motion picture screenplay THE DAY OF THE JACKAL by Kenneth Ross Cast Bruce Willis, Richard Gere, Sydney Poitier, Diane Venora, Mathilda May, Tess Harper MPAA Rating "R" (for strong violence and language) Running Time 122 minutes Reviewed at Mission Valley Cinemas, Raleigh, NC 11NOV97
==
THE JACKAL is a high-tech, low-believability thriller, starring Bruce Willis as the title character, an expert assassin and master of many disguises, who's contracted by a Russian mob boss for a hit on the head of the F.B.I. One of the few who can actually identify the Big J. is a former I.R.A. operative (Richard Gere), imprisoned in the U.S. and ready to cut a deal if it means a chance to get out and about and maybe escape, or see his old girlfriend (Steven Tyler lookalike Mathilda May), or, hell, just be awarded a transfer to a minimum-security prison. So, the guy with the good looks and giggle-inducing accent is paired with a pair of agents, one from Washington (Sidney Poiter) and the other from Moscow (Diane Venora). And a-travellin' they go, trotting the continent for clues, while their target gets ever-closer to his quarry.
As a loose and largely unremarkable remake of THE DAY OF THE JACKAL, the film has a few things going for it. Let's see... several of the action sequences are legitimate pulse-pounders, even if they rarely serve the story well. The scenery and spacious sets are also quite eye-catching.  And then there's Bruce, the coolest cat on the block and the only guy in the movie who has a pair. (Gere looks good and that's about it. Poitier brings grace, but not enough weight. Only Ms. Venora displays the requisite intensity required of their roles.)
 (The high low point? I gotta go with Bad Death Scene #1. You may need to leave the theater for that one.) And, let's see, some of the effects are weak, such as a blue-screen bit set in a subway tunnel. (But doesn't Gere grimace convincingly?) Oh, and, Heaven help us, there's even a stranger-in-the-house sequence-- the same scene I swear saw in SWITCH- BACK, KISS THE GIRLS, and I KNOW WHAT YOU DID LAST SUMMER. Sigh. Well, at least Willis' disguises are fun. His portly Canuck is a sight, eh?, but be back from the bathroom before Bruce hits a gay bar as a frisky swinger. Even if it (and its follow-up scene) fails to amuse, at least you'll know what they're talking about, when the inevitable outrage erupts. Directed by Michael Caton-Jones (ROB ROY).
Grade: B-
-- Mike Legeros - Movie Hell http://www.nonvirtual.com/hell
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Over the two decades that I’ve been in software, there have been all number of innovations, techniques, tools, tricks and plain fads which purport to help you build applications better - model-driven code generation, unit testing, documenting everything, documenting nothing, pair programming, SOAP, REST, microservice architectures to name a few. I can understand why most of them (with the exception of SOAP, which is an abomination) could be thought to deliver some sort of benefit, but for me there’s one which stands above all the others: Test-Driven Development.
In honesty, I came to the TDD party pretty late. Of course, I’d known about unit testing and automated testing for a long time, but I’d never seen it actually work. What tended to happen was that a conscientious newcomer to the team would write some tests for existing code, and then they’d be maintained for some period of time before falling into (for want of a better word) disrepair, spending as much time failing as passing, and eventually being deleted or ignored. When I first came into a TDD environment it opened my eyes to how to do automated testing effectively, and when I look back to the Before-TDD era, I see nothing but missed opportunities to do things better.
I’ve done TDD with JUnit, ScalaTest, PyTest, Nose and Cucumber, and it works well with any and all of them. It’s not my place to tell you which tools you should use - just pick one that you like and run with it. Even if you’re still writing programs in COBOL for mainframes, there are options out there. TDD is the important bit, not the tools.
I don’t think I would have attempted Plus-F if I didn’t know about TDD. A processor is basically a large collection of small behaviours which, although individually simple, combine together into a single ball of near-indecipherable state. The only way to tackle it was one operation at a time, verifying each one did what the Z80 User Manual said it would, and moving on to the next. Dull, yes, but also entirely necessary. Being (at its heart) a program that runs programs, I shuddered at the thought of having to debug problems in games (specifically Match Day II, of course) without having some confidence that the individual processor operations themselves were doing what they should. As it is, having taken the TDD approach, the major problems that I’ve found (there are plenty of minor problems but I don’t care so much about them) have been reasonably obvious and easy to debug.
By now, you may have read about the thwarted attempt to write Plus-F in Python. I’ll confess that the thought of having to start again from scratch did make me think twice about what I was doing, but what pushed me on was that I had one thing in my favour - a good set of tests for all of the processor emulation. And, given that I hadn’t got much beyond that part, that meant I had a good set of tests for more or less everything that I needed to rewrite. Admittedly, those tests were in Nose in Python, but picking up on something else I’d learned over the last year or two, a typical test looked like this:
def test_and_a_with_other_reg_giving_zero_result(self):
# given
self.given_register_contains_value('a', 0b10101010)
self.given_register_contains_value('b', 0b01010101)
self.given_next_instruction_is(0xa0)
# when
self.processor.execute()
# then
self.assert_register('a').equals(0b00000000)
self.assert_register('b').equals(0b01010101)
self.assert_flag('s').is_reset()
self.assert_flag('z').is_set()
self.assert_flag('h').is_set()
self.assert_flag('p').is_set()
self.assert_flag('n').is_reset()
self.assert_flag('c').is_reset()
In other words, it read sort of like a mini-DSL. A bit of judicious search-and-replace meant that I could port tests like these easily to an equivalent in ScalaTest like this:
"and <reg>" should "calculate a zero result when a and <reg> have no shared bits" in new Machine {
// given
registerContainsValue("a", binary("10101010"))
registerContainsValue("b", binary("01010101"))
nextInstructionIs(0xa0)
// when
processor.execute()
// then
registerValue("a") shouldBe binary("00000000")
registerValue("b") shouldBe binary("01010101")
flag("s").value shouldBe false
flag("z").value shouldBe true
flag("h").value shouldBe true
flag("p").value shouldBe true
flag("n").value shouldBe false
flag("c").value shouldBe false
flag("f3").value shouldBe false
flag("f5").value shouldBe false
}
And in doing so, I had a full set of tests available from the outset against which I could code the Java version of Plus-F. I won’t lie by saying that the process of porting to Java was a mere operation in handle-turning, but neither was it complicated. (It was boring.)
So where’s the advertised gaffe that I normally end my articles with?
Well, processor emulation isn’t the only complex thing I had to tackle when writing Plus-F. It turns out that, for all
that it was slow and infuriating, and that I spent a large part of my formative years dreading seeing the message
R Tape loading error, 0:1
, it’s actually pretty fiddly to supply the correct input to the Spectrum’s tape loading
routines. The main supported format is TZX, which is remarkably well documented,
but as anyone who has read a few specs knows, what looks straightforward on paper isn’t always easy to turn into working
code.
This is exactly the sort of thing that TDD was made for. However, by this stage, I had a lot of Plus-F working and I’d become a bit cavalier in my attitude again. (My unfortunate dalliance with Python was by this stage some six months prior. How quickly we forget.) Certain that I was only a few commits away from a fully-working application, I pinned my ears back and frenziedly wrote the TZX-handling routines with only a smattering of tests. Most of it worked, granted, but it was a laughably inefficient (the screen refresh rate slowed noticeably when a tape was being played) first stab at it. It desperately needed knocking into shape, as do many first attempts.
And that’s where the problem began. Inefficient as it may have been, the first TZX routine did actually work acceptably well. But because I didn’t have a thorough set of tests which allowed me to make changes and still verify that each bit worked, any time I did something that made it run faster, I also introduced a bug somewhere else. In a bit to stop the endless to-and-fro of fixing one bug and introducing another, I resorted to cobbling together a “regression test” based on this class from the original, inefficient, implementation and testing all of my fixes with respect to that.
It was a large enough piece of wallpaper to cover the many cracks, but has no other redeeming features to speak of.
Thankfully, I haven’t had to touch the TZX-handling routines much of late, but I dread the day that I have to. I think it’s pretty clear to see, when comparing its code with the Z80 emulation code that was developed with a TDD approach, that not building it by TDD has caused a lot of problems there needn’t have been.
The lesson? It’s all too easy to consider brushing TDD aside in an effort to do something faster, but it’s just cutting a corner, no matter how much you try to rationalise it. Code is usually more complex than you think it will be, you will need to go back and fix things, and the time you don’t spend up-front writing tests is multiplied times over after the fact.


Cyclical
Colors/Lines
Rhythm
The abuse of pomp, strangeness of metaphor, a new vocabulary or harmony go together with colours and lines: characteristics of any revival.


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stroke: (drawnPoints, tool) -> this.EligibleToSync = true @perform 'stroke', drawnPoints: drawnPoints, tool: tool
def stroke (data) ActionCable.server.broadcast "paint_channel:#{params[:canvas_name]}", drawnPoints: data['drawnPoints'], tool: data['tool'] end
received: (data) -> drawLineSeg(data['drawnPoints'], data['tool'])
// Called from user subscription to InitSyncChannel in browser sendSyncRequest: -> @perform 'send_sync_request', request_to_sync: true, canvas_name: canvasName
# Called from InitSyncChannel object in Ruby def send_sync_request(data) if ActionCable.server.connections.length < 3 close_channel else ActionCable.server.broadcast "paint_channel:#{data['canvas_name']}", request_to_sync: data['request_to_sync'] end end
received: (data) -> if data['request_to_sync'] if this.EligibleToSync this.syncCanvas(this.getCanvasData())
def sync_with_initial(data) ActionCable.server.broadcast "initial_canvas_sync_channel", canvas_data: data['canvas_data'] end
received: (data) -> encodedCanvasData = Uint8ClampedArray.from(data['canvas_data']) this.setCanvas(encodedCanvasData) @perform 'close_channel'


MFeval is an open source MATLAB toolbox that contains the Magic Formula 5.2, 6.1 and 6.2 tyre model equations.
The idea for the creation of MFeval emerged after observing different outputs from different software using Magic Formula 6.1 equations under the same conditions. An investigation into different solvers and their hard coded equations was conducted. This investigation showed that there are several different approaches to the implementation of Magic Formula 6.1 equations.
MFeval is intended to standardise the implementation of Magic Formula equations. The objective is to use the same equations during the fitting process of the model and during the simulations carried out in different software such as MATLAB, Simulink, Simpack and CarMaker.
The toolbox contains a Simulink library with blocks formulated in the common standards: the Contact Point Interface (CPI) and the Standard Tire Interface (STI). Both interfaces can be compiled in C-code to facilitate the integration of MFeval in other software.


DESCRIPTION
The m.swim.subbasins module provides flexible interface to the r.watershed
module allowing the delineation of subbasins within a specified
subbasin size range, constraining them to any amount gauging stations
as well as including predefined subbasins and the production of rich
ancillary maps and subbasin information (provided in the subbasins
vector table). The module works in two steps:
- It first calculates the accumulation, drainage and streams for the entire region. This step can be skipped by setting the -d flag
to save time when recalculating the subbasins, but the accumulation,
drainage and streams raster must exist in that case. This step also
outputs the slope steepness and slope length rasters needed for the m.swim.substats module (names given by default).
- Then it calculates the catchments and subbasins for each station given in stations, cleans the subbasin maps and produces the additional subbasin information and outputs the subbasin statistics to stdout.
Subbasin size thresholds
The upper subbasin size threshold given either for all catchments in upthresh or for each station's catchment individually by reading the station's column given in upthreshcolumn
is a soft limit, i.e. it may exceed this and should be more considered
a mean subbasin size, as it is mostly underestimated (see the
r.watershed manual for more details). The lothresh argument, however, is a hard lower limit, i.e. no subbasins smaller than this should exist in the resultant subbasin map.
NOTES
Although
this module has been tested, it is still in beta mode and hasn't been
extensively error coded. Please report unexplained errors to the author
(see below).
EXAMPLE
Setting up a SWIM project in the North Carolina (basic) testing location:
Create a new mapset and set region:
g.mapset -c mapset=subbasins
g.region rast=elevation@PERMANENT
Stations
Create a stations point vector map:
echo "x|y|name|subbsize
640579|215607|outlet|0.1
640388|216617|headwater|0.05" > stations.dat
v.in.ascii input=stations.dat out=stations x=1 y=2 columns='x int, y int, name varchar(20), subbsize double' skip=1
Subbasins
Make simple subbasins (default output arguments):
m.swim.subbasins elevation=elevation@PERMANENT stations=stations upthresh=0.1 subbasins=subbasins
In
addition to the subbasins vector and raster map, this also produces
catchment raster and vector maps, accumulation, drainage, streams,
slopesteepness and slopelength rasters (as needed by the subsequent
m.swim.* modules) by default.
Subbasins with varying subbasin thresholds and defined lower threshold:
m.swim.subbasins elevation=elevation@PERMANENT stations=stations upthreshcolumn=subbsize lothresh=0.001 subbasins=subbasins
SEE ALSO
m.swim.hydrotopes,
r.watershed, m.swim.substats, m.swim.routing
Documentation [external]: m.swim
AUTHORS
Michel Wortmann, Potsdam Institute for Climate Impact Research (wortmann@pik-potsdam.de)


Fits linear discrete logit choice models with l2 regularization. To handle reasonably sized datasets, we employ an accelerated version of generalized gradient descent.
| Version: | 0.9.4 |
| Published: | 2014-09-03 |
| Author: | Michael Lim |
| Maintainer: | Michael Lim <michael626 at gmail.com> |
| License: | GPL-2 |
| NeedsCompilation: | yes |
| CRAN checks: | logitchoice results |
| Reference manual: | logitchoice.pdf |
| Package source: | logitchoice_0.9.4.tar.gz |
| Windows binaries: | r-devel: logitchoice_0.9.4.zip, r-release: logitchoice_0.9.4.zip, r-oldrel: logitchoice_0.9.4.zip |
| OS X binaries: | r-release: logitchoice_0.9.4.tgz, r-oldrel: logitchoice_0.9.4.tgz |
| Old sources: | logitchoice archive |
Please use the canonical form https://CRAN.R-project.org/package=logitchoice to link to this page.


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