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SimpleITK
1.1.0
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Solve an Eikonal equation using Fast Marching. More...
#include <sitkFastMarchingImageFilter.h>
Public Types | |
| typedef BasicPixelIDTypeList | PixelIDTypeList |
| typedef FastMarchingImageFilter | Self |
| Public Types inherited from itk::simple::ImageFilter< 1 > | |
| typedef ImageFilter | Self |
| Public Types inherited from itk::simple::ProcessObject | |
| typedef ProcessObject | Self |
Public Member Functions | |
| Self & | AddTrialPoint (const std::vector< unsigned int > &point) |
| Add TrialPoints point. More... | |
| Self & | ClearTrialPoints () |
| Remove all TrialPoints points. More... | |
| Image | Execute (const Image &image1) |
| Image | Execute (const Image &image1, const std::vector< std::vector< unsigned int > > &trialPoints, double normalizationFactor, double stoppingValue) |
| FastMarchingImageFilter () | |
| std::string | GetName () const |
| double | GetNormalizationFactor () const |
| double | GetStoppingValue () const |
| std::vector< std::vector< unsigned int > > | GetTrialPoints () const |
| Self & | SetNormalizationFactor (double NormalizationFactor) |
| Self & | SetStoppingValue (double StoppingValue) |
| Self & | SetTrialPoints (const std::vector< std::vector< unsigned int > > &TrialPoints) |
| std::string | ToString () const |
| virtual | ~FastMarchingImageFilter () |
| Public Member Functions inherited from itk::simple::ImageFilter< 1 > | |
| ImageFilter () | |
| virtual | ~ImageFilter ()=0 |
| Public Member Functions inherited from itk::simple::ProcessObject | |
| virtual void | Abort () |
| virtual int | AddCommand (itk::simple::EventEnum event, itk::simple::Command &cmd) |
| Add a Command Object to observer the event. More... | |
| virtual float | GetProgress () const |
| An Active Measurement of the progress of execution. More... | |
| virtual bool | HasCommand (itk::simple::EventEnum event) const |
| Query of this object has any registered commands for event. More... | |
| ProcessObject () | |
| virtual void | RemoveAllCommands () |
| Remove all registered commands. More... | |
| virtual | ~ProcessObject () |
| virtual void | DebugOn () |
| virtual void | DebugOff () |
| virtual bool | GetDebug () const |
| virtual void | SetDebug (bool debugFlag) |
| virtual void | SetNumberOfThreads (unsigned int n) |
| virtual unsigned int | GetNumberOfThreads () const |
Private Types | |
| typedef Image(Self::* | MemberFunctionType) (const Image &image1) |
Private Member Functions | |
| template<class TImageType > | |
| Image | ExecuteInternal (const Image &image1) |
Private Attributes | |
| nsstd::auto_ptr< detail::MemberFunctionFactory< MemberFunctionType > > | m_MemberFactory |
| double | m_NormalizationFactor |
| double | m_StoppingValue |
| std::vector< std::vector< unsigned int > > | m_TrialPoints |
Friends | |
| struct | detail::MemberFunctionAddressor< MemberFunctionType > |
Additional Inherited Members | |
| Static Public Member Functions inherited from itk::simple::ProcessObject | |
| static bool | GetGlobalDefaultDebug () |
| static void | GlobalDefaultDebugOff () |
| static void | GlobalDefaultDebugOn () |
| static void | SetGlobalDefaultDebug (bool debugFlag) |
| static void | GlobalWarningDisplayOn () |
| static void | GlobalWarningDisplayOff () |
| static void | SetGlobalWarningDisplay (bool flag) |
| static bool | GetGlobalWarningDisplay () |
| static void | SetGlobalDefaultNumberOfThreads (unsigned int n) |
| static unsigned int | GetGlobalDefaultNumberOfThreads () |
| static double | GetGlobalDefaultCoordinateTolerance () |
| Access the global tolerance to determine congruent spaces. More... | |
| static void | SetGlobalDefaultCoordinateTolerance (double) |
| Access the global tolerance to determine congruent spaces. More... | |
| static double | GetGlobalDefaultDirectionTolerance () |
| Access the global tolerance to determine congruent spaces. More... | |
| static void | SetGlobalDefaultDirectionTolerance (double) |
| Access the global tolerance to determine congruent spaces. More... | |
| Protected Member Functions inherited from itk::simple::ProcessObject | |
| virtual unsigned long | AddITKObserver (const itk::EventObject &, itk::Command *) |
| virtual itk::ProcessObject * | GetActiveProcess () |
| virtual void | OnActiveProcessDelete () |
| virtual void | onCommandDelete (const itk::simple::Command *cmd) SITK_NOEXCEPT |
| virtual void | PreUpdate (itk::ProcessObject *p) |
| virtual void | RemoveITKObserver (EventCommand &e) |
| Protected Member Functions inherited from itk::simple::NonCopyable | |
| NonCopyable () | |
| Static Protected Member Functions inherited from itk::simple::ImageFilter< 1 > | |
| static void | FixNonZeroIndex (TImageType *img) |
| Static Protected Member Functions inherited from itk::simple::ProcessObject | |
| template<class TImageType > | |
| static TImageType::ConstPointer | CastImageToITK (const Image &img) |
| template<class TPixelType , unsigned int VImageDimension, unsigned int VLength, template< typename, unsigned int > class TVector> | |
| static Image | CastITKToImage (itk::Image< TVector< TPixelType, VLength >, VImageDimension > *img) |
| template<class TImageType > | |
| static Image | CastITKToImage (TImageType *img) |
| static const itk::EventObject & | GetITKEventObject (EventEnum e) |
| template<typename T > | |
| static std::ostream & | ToStringHelper (std::ostream &os, const T &v) |
| static std::ostream & | ToStringHelper (std::ostream &os, const char &v) |
| static std::ostream & | ToStringHelper (std::ostream &os, const signed char &v) |
| static std::ostream & | ToStringHelper (std::ostream &os, const unsigned char &v) |
Solve an Eikonal equation using Fast Marching.
Fast marching solves an Eikonal equation where the speed is always non-negative and depends on the position only. Starting from an initial position on the front, fast marching systematically moves the front forward one grid point at a time.
Updates are preformed using an entropy satisfy scheme where only "upwind" neighborhoods are used. This implementation of Fast Marching uses a std::priority_queue to locate the next proper grid position to update.
Fast Marching sweeps through N grid points in (N log N) steps to obtain the arrival time value as the front propagates through the grid.
Implementation of this class is based on Chapter 8 of "Level Set Methods and Fast Marching Methods", J.A. Sethian, Cambridge Press, Second edition, 1999.
This class is templated over the level set image type and the speed image type. The initial front is specified by two containers: one containing the known points and one containing the trial points. Alive points are those that are already part of the object, and trial points are considered for inclusion. In order for the filter to evolve, at least some trial points must be specified. These can for instance be specified as the layer of pixels around the alive points.
The speed function can be specified as a speed image or a speed constant. The speed image is set using the method SetInput() . If the speed image is ITK_NULLPTR, a constant speed function is used and is specified using method the SetSpeedConstant() .
If the speed function is constant and of value one, fast marching results in an approximate distance function from the initial alive points. FastMarchingImageFilter is used in the ReinitializeLevelSetImageFilter object to create a signed distance function from the zero level set.
The algorithm can be terminated early by setting an appropriate stopping value. The algorithm terminates when the current arrival time being processed is greater than the stopping value.
There are two ways to specify the output image information ( LargestPossibleRegion, Spacing, Origin): (a) it is copied directly from the input speed image or (b) it is specified by the user. Default values are used if the user does not specify all the information.
The output information is computed as follows. If the speed image is ITK_NULLPTR or if the OverrideOutputInformation is set to true, the output information is set from user specified parameters. These parameters can be specified using methods SetOutputRegion() , SetOutputSpacing() , SetOutputDirection() , and SetOutputOrigin() . Else if the speed image is not ITK_NULLPTR, the output information is copied from the input speed image.
For an alternative implementation, see itk::FastMarchingImageFilter .
Possible Improvements: In the current implementation, std::priority_queue only allows taking nodes out from the front and putting nodes in from the back. To update a value already on the heap, a new node is added to the heap. The defunct old node is left on the heap. When it is removed from the top, it will be recognized as invalid and not used. Future implementations can implement the heap in a different way allowing the values to be updated. This will generally require some sift-up and sift-down functions and an image of back-pointers going from the image to heap in order to locate the node which is to be updated.
Definition at line 68 of file sitkFastMarchingImageFilter.h.
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private |
Setup for member function dispatching
Definition at line 136 of file sitkFastMarchingImageFilter.h.
Define the pixels types supported by this filter
Definition at line 80 of file sitkFastMarchingImageFilter.h.
Definition at line 70 of file sitkFastMarchingImageFilter.h.
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virtual |
Destructor
| itk::simple::FastMarchingImageFilter::FastMarchingImageFilter | ( | ) |
Default Constructor that takes no arguments and initializes default parameters
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inline |
Add TrialPoints point.
Definition at line 94 of file sitkFastMarchingImageFilter.h.
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inline |
Remove all TrialPoints points.
Definition at line 96 of file sitkFastMarchingImageFilter.h.
Execute the filter on the input image
| Image itk::simple::FastMarchingImageFilter::Execute | ( | const Image & | image1, |
| const std::vector< std::vector< unsigned int > > & | trialPoints, | ||
| double | normalizationFactor, | ||
| double | stoppingValue | ||
| ) |
Execute the filter on the input image with the given parameters
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private |
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inlinevirtual |
Name of this class
Implements itk::simple::ProcessObject.
Definition at line 119 of file sitkFastMarchingImageFilter.h.
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inline |
Set/Get the Normalization Factor for the Speed Image . The values in the Speed Image is divided by this factor. This allows the use of images with integer pixel types to represent the speed.
Definition at line 107 of file sitkFastMarchingImageFilter.h.
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inline |
Get the Fast Marching algorithm Stopping Value.
Definition at line 117 of file sitkFastMarchingImageFilter.h.
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Get the container of Trial Points representing the initial front.
Definition at line 92 of file sitkFastMarchingImageFilter.h.
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inline |
Set/Get the Normalization Factor for the Speed Image . The values in the Speed Image is divided by this factor. This allows the use of images with integer pixel types to represent the speed.
Definition at line 102 of file sitkFastMarchingImageFilter.h.
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inline |
Set the Fast Marching algorithm Stopping Value. The Fast Marching algorithm is terminated when the value of the smallest trial point is greater than the stopping value.
Definition at line 112 of file sitkFastMarchingImageFilter.h.
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Set the container of Trial Points representing the initial front. Trial points are represented as a VectorContainer of LevelSetNodes.
Definition at line 87 of file sitkFastMarchingImageFilter.h.
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virtual |
Print ourselves out
Reimplemented from itk::simple::ProcessObject.
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friend |
Definition at line 140 of file sitkFastMarchingImageFilter.h.
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private |
Definition at line 142 of file sitkFastMarchingImageFilter.h.
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private |
Definition at line 146 of file sitkFastMarchingImageFilter.h.
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private |
Definition at line 147 of file sitkFastMarchingImageFilter.h.
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private |
Definition at line 145 of file sitkFastMarchingImageFilter.h.


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| THE SHEPHERDS brow, fronting forked lightning, owns | |
| The horror and the havoc and the glory | |
| Of it. Angels fall, they are towers, from heavena story | |
| Of just, majestical, and giant groans. | |
| But manwe, scaffold of score brittle bones; | 5 |
| Who breathe, from groundlong babyhood to hoary | |
| Age gasp; whose breath is our memento mori | |
| What bass is our viol for tragic tones? | |
| He!


{{/* The MIT License (MIT) Copyright (c) 2022 M E Leypold Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.  IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */}} {{ define "title" }} {{.File.BaseFileName }} | {{.Site.Params.siteBasename}} {{ end }} {{ define "main" }}
| Author(s) | {{.Params.author}} |
|---|---|
| Title | {{.Params.title}} |
| Publisher | {{if .Params.publisher_formerly}} (formerly) {{ $disclaimers = (append "publisher-formerly" $disclaimers)}} {{end}} {{.Params.publisher}} |
| Year | {{.Params.year}} |
| Keyword(s) | {{range .}} {{.}} {{end}} |
| Homepage | {{with .Params.homepage}} {{.}} {{else}} n/a {{ $disclaimers = (append "homepage-na" $disclaimers)}} {{end}} |
| {{ $disclaimers = (append "online-only" $disclaimers)}} for online reading only at the homepage, see disclaimer "Only for Online Reading". | |
| Direct Download | {{with $page.Params.download_via_homepage}} via homepage only, see disclaimer "No Direct Download". {{ $disclaimers = (append "no-direct-download" $disclaimers)}} {{else}} {{$page.Params.download}} (disclaimer) {{ $disclaimers = (append "direct-download" $disclaimers)}} {{end}} |
| Disclaimers |
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Phil Spencer's 5 questions to ask before you apply for a mortgage
Whether you're looking for your first home, a new home or your current deal is expiring, there are a few key questions you have to ask before you start out
by James Andrews, http://www.facebook.com/jameswritesmoney/Right now mortgages are cheaper than they have been for two years - making bigger loans seem more and more affordable.
In fact, there are even deals on the market charging less than 1% interest.
While that doesn't necessarily mean it's time to dive in and grab one - it does mean that if you're already looking for a loan to buy a home or move to a cheaper deal, now's a good time.
But you also need to make sure it's the right deal for you.
To help, TV property guru Phil Spencer has explained on Zoopla the five key questions people need to ask themselves before signing on the dotted line.
This is what hey are:
1. Can I afford for my mortgage payments to go up?
There are various types of mortgage. And some – such as trackers, and discounts – mean your interest rate, and therefore monthly payments, could go up at short notice.
If there’s no room in your budget for a rise in repayments, or you simply don’t want to worry about it, go for a fixed rate mortgage – even if it starts out a little more expensive.
2. What arrangement fee does the mortgage charge?
Some of the cheapest mortgage rates come with ‘a catch’ – the lender will charge a big arrangement fee to set up the deal. Make sure you factor the cost of this fee into the loan rate. This is a quick way to do it:
- Find out the monthly cost of the mortgage by entering the interest rate alongside the amount you want to borrow into an online mortgage calculator (taking note if it’s repayment or interest-only).
- When you have the monthly cost, multiply it by the number of months in the term – for example a two-year fixed rate would be 24 months and a five-year fixed rate would be 60.
- Add on the stated mortgage fee and you have the true overall cost of the mortgage.
- Do the same again for different deals and compare.
Bear in mind arrangement fees become more significant on shorter-term loans as they work out at a higher cost per year.
3. Am I free to leave or even overpay?
If you sign up to a specific mortgage deal, you’ll have to pay early repayment charges (ERCs) if you want to redeem the loan before the end of the term.
In fact, even if you just wanted to overpay – with an inheritance or bonus for example – most lenders will cap this at 10% each year before ERCs kick in.
ERCs are usually charged at a percentage of the outstanding borrowing. Often this percentage decreases with each year of the deal. In other words, it would be more expensive to leave in year two of a five-year fix than it would in year four.
Think carefully about your plans before tying yourself into a mortgage long-term.
Alternatively, some mortgages, such as some lifetime trackers and or those priced on the lender's SVR, don't come with tie-ins (and therefore ERCs) at all.
4. Should I use my savings?

But if you’ve been a homeowner with a mortgage for a while, you may have built up your savings pot again.
When it comes to switching deals, rather than keeping these savings in a low-interest account, it could make more sense to use them to reduce the amount of your mortgage.
That’s because the lower you can drive down your loan to value (the percentage of the house value that is mortgaged), the cheaper your rate will be. You can find out more about loan to values here . Always do your sums!
5. Have I done enough research?
It’s impossible to predict the future, but at least you can make the right decision based on your circumstances at the time – and this is never truer than when it comes to getting the right mortgage.
Make sure you fully research the current mortgage market using comparison websites like uSwitch.com .
If you need further help or have specific needs such as being self-employed or having a poor credit score, then contact an independent fee-free mortgage broker for advice.


The trends that, aside from Manhattan, all of the other counties have a greater percentage of children under 18 years of age (yellow bar). This suggests that many people move outside of big cities, and into more suburban locations to raise families.


The item handle.
More...
#include <data.h>
The item handle.
Definition at line 149 of file data.h.
§ data
| unsigned char* cbor_item_t::data |
Raw data block - interpretation depends on metadata.
Definition at line 157 of file data.h.
§ metadata
Discriminated by type.
Definition at line 151 of file data.h.
§ refcount
| size_t cbor_item_t::refcount |
Reference count - initialize to 0.
Definition at line 153 of file data.h.
§ type
Major type discriminator.
Definition at line 155 of file data.h.
The documentation for this struct was generated from the following file:


For those who aren't familiar with the ROV industry, ROV stands for Remotely Operated Vehicle. Almost always, an ROV is an underwater vehicle designed to complete tasks that are too dangerous, too boring, or too difficult for a human diver to accomplish. ROVs are piloted remotely with signals sent through a tether and have made it as deep as 35,000 feet below the sea floor. By percent growth, it is one of the fastest growing engineering industries.
The Purdue IEEE ROV team is dedicated to competing in the worldâ€™s largest ROV competition, the MATE (Marine Advanced Technology Education Center) competition. The mission this year focused on simulating submarine rescue ROVs that can deliver fresh air, deliver emergency supplies, survey a submarine for damage, and mate with an escape hatch in a worst-case scenario that submarines need to be brought on-board the ROV.
Including the entry level Ranger class and more technical Explorer class (Purdue choose to compete in the Explorer class despite the fact that most universities are placed in the Ranger class their first year), there were over 340 team from around the world!
Purdue competed on June 25th and placed 5th overall internationally! The team was also the only rookie team to place in the top 10!
This put Purdue ahead of such universities as MIT, Georgia Tech, UNC, Embry-Riddle, and even the defending champions from Newfoundland!
Thanks to all those at Purdue who have helped us get to where we are!
If you'd like to see a video of ROV Osprey in action, please click on the following link: http://www.youtube.com/watch?v=hrX_OsVcW4g
Purdue will be returning to next year's competition which will be in Hawaii!
Please forward this to anyone you may think would be interested!
Thanks for your interest!
Seth Baklor
Team Captain
Sophomore, Industrial Engineering
The Purdue IEEE ROV Team received 5th Place in the 2009 MATE (Marine Advanced Technology Education) Center International ROV Competition in Buzzards Bay, Massachusetts. The team was the only rookie among the returning teams in the top 10, edging out even the defending champions.
ROV Osprey, the first creation of the new Purdue University IEEE (Institute of Electrical and Electronics Engineers) ROV Team, has been built to accomplish and exceed the 2009 MATE International ROV Competition mission requirements. The vehicle was designed with a focus on overall reliability, design simplicity, and speed/agility. All of these goals were to be accomplished within a predetermined final vehicle cost of approximately $2,000 (not including the cost of research/testing or the cost of going to the competition).
The vehicle is designed to aid crippled submarines by surveying them for damage, delivering Emergency Life Support System (ELSS) pods, delivering fresh air, and rescuing crew in the extreme condition that it becomes necessary. Designing submarine rescue vehicles is a crucial and exciting part of the ROV world the team is proud to have experienced.
This report will present the creation and evolution of ROV Osprey and the Purdue team as a whole. Having just been formed, the team has overcome a lack of knowledge, experience, available tools, and support to become better engineers.
Pictures from this season may be found on Facebook. A press release detailing the team's first success may be read above. A video of the submarine rescue mission run may be found on YouTube.


Table of Contents:
The following pages are just a collection of sections within IMP that can be readily modified, customised. The customisability of IMP is important to the project team (http://www.horde.org) so to get the latest information log into their site and download the current release.
Login Prompt. This is a plain text file that is 'echo'd' onto the HTML login page. The text displays below the login prompt, and there is a lot of space for writing.
HTML tags can be used in this file to beautify your message.
I find this a good location to place the E-mail policy statement for the organisation.
Left Margin Links. This is a configuration text file that allows you specify additional items to be placed in the left-margin column.
This column normally has only the IMP logo and the "login" button. The link will cause a new browser window to be started.
Graphics files are expected to be in the ../imp/graphics directory.
Trailer Messages. The contents of this file are echoed to the mail sent by IMP clients.
PHP variables are expanded before messages are sent.
contains standard English text for the login screen. Let's you change the 'welcome to IMP' to another welcome message.
To make this a global change (independent of language) then make the change to ../imp/locale/login.lang. My suggestion is to copy the language file you would normally use into this folder, change the sections you want to keep and remove the other sections.
Top Toolbar. Contains the text for the top toolbar 'Welcome to IMP. Please log in.' Changing this effects the text in the message:
To make this a global change (independent of language) then make the change to ../imp/locale/status.lang. My suggestion is to copy the language file you would normally use into this folder, change the sections you want to keep and remove the other sections.
The page title is defined in ../imp/index.php3. The <title> is defined twice, once for browsers with support for frames, and another for unknown browsers.
Copyright (c) 2000/1/2 Samiuela LV Taufa. All Rights Reserved.
I reserve the right to be totally incorrect even at the best advice of betters.
You are permitted and encouraged to use this guide for fun or for profit as you see fit. If you republish this work in what-ever form, it would be nice (though not enforceable) to be credited.
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IMP Customisations - Changing the face of it all. |
Copyright © 2000/1/2 NoMoa Publishers All rights reserved. Caveat Emptor


Welcome to Chapter 4 of the "Implementing a language with LLVM" tutorial. Chapters 1-3 described the implementation of a simple language and added support for generating LLVM IR. This chapter describes two new techniques: adding optimizer support to your language, and adding JIT compiler support. These additions will demonstrate how to get nice, efficient code for the Kaleidoscope language.
Note: the default IRBuilder now always includes the constant folding optimisations below.
Our demonstration for Chapter 3 is elegant and easy to extend. Unfortunately, it does not produce wonderful code. For example, when compiling simple code, we don't get obvious optimizations:
ready> def test(x) 1+2+x; Read function definition: define double @test(double %x) { entry: %addtmp = fadd double 1.000000e+00, 2.000000e+00 %addtmp1 = fadd double %addtmp, %x ret double %addtmp1 }
This code is a very, very literal transcription of the AST built by parsing the input. As such, this transcription lacks optimizations like constant folding (we'd like to get "add x, 3.0" in the example above) as well as other more important optimizations. Constant folding, in particular, is a very common and very important optimization: so much so that many language implementors implement constant folding support in their AST representation.
With LLVM, you don't need this support in the AST. Since all calls to build LLVM IR go through the LLVM builder, it would be nice if the builder itself checked to see if there was a constant folding opportunity when you call it. If so, it could just do the constant fold and return the constant instead of creating an instruction. This is exactly what the LLVMFoldingBuilder class does.
All we did was switch from LLVMBuilder to LLVMFoldingBuilder. Though we change no other code, we now have all of our instructions implicitly constant folded without us having to do anything about it. For example, the input above now compiles to:
ready> def test(x) 1+2+x; Read function definition: define double @test(double %x) { entry: %addtmp = fadd double 3.000000e+00, %x ret double %addtmp }
Well, that was easy :). In practice, we recommend always using LLVMFoldingBuilder when generating code like this. It has no "syntactic overhead" for its use (you don't have to uglify your compiler with constant checks everywhere) and it can dramatically reduce the amount of LLVM IR that is generated in some cases (particular for languages with a macro preprocessor or that use a lot of constants).
On the other hand, the LLVMFoldingBuilder is limited by the fact that it does all of its analysis inline with the code as it is built. If you take a slightly more complex example:
ready> def test(x) (1+2+x)*(x+(1+2)); ready> Read function definition: define double @test(double %x) { entry: %addtmp = fadd double 3.000000e+00, %x %addtmp1 = fadd double %x, 3.000000e+00 %multmp = fmul double %addtmp, %addtmp1 ret double %multmp }
In this case, the LHS and RHS of the multiplication are the same value. We'd really like to see this generate "tmp = x+3; result = tmp*tmp;" instead of computing "x*3" twice.
Unfortunately, no amount of local analysis will be able to detect and correct this. This requires two transformations: reassociation of expressions (to make the add's lexically identical) and Common Subexpression Elimination (CSE) to delete the redundant add instruction. Fortunately, LLVM provides a broad range of optimizations that you can use, in the form of "passes".
LLVM provides many optimization passes, which do many different sorts of things and have different tradeoffs. Unlike other systems, LLVM doesn't hold to the mistaken notion that one set of optimizations is right for all languages and for all situations. LLVM allows a compiler implementor to make complete decisions about what optimizations to use, in which order, and in what situation.
As a concrete example, LLVM supports both "whole module" passes, which look across as large of body of code as they can (often a whole file, but if run at link time, this can be a substantial portion of the whole program). It also supports and includes "per-function" passes which just operate on a single function at a time, without looking at other functions. For more information on passes and how they are run, see the How to Write a Pass document and the List of LLVM Passes.
For Kaleidoscope, we are currently generating functions on the fly, one at a time, as the user types them in. We aren't shooting for the ultimate optimization experience in this setting, but we also want to catch the easy and quick stuff where possible. As such, we will choose to run a few per-function optimizations as the user types the function in. If we wanted to make a "static Kaleidoscope compiler", we would use exactly the code we have now, except that we would defer running the optimizer until the entire file has been parsed.
In order to get per-function optimizations going, we need to set up a Llvm.PassManager to hold and organize the LLVM optimizations that we want to run. Once we have that, we can add a set of optimizations to run. The code looks like this:
(* Create the JIT. *) let the_execution_engine = ExecutionEngine.create Codegen.the_module in let the_fpm = PassManager.create_function Codegen.the_module in (* Set up the optimizer pipeline. Start with registering info about how the * target lays out data structures. *) TargetData.add (ExecutionEngine.target_data the_execution_engine) the_fpm; (* Do simple "peephole" optimizations and bit-twiddling optzn. *) add_instruction_combining the_fpm; (* reassociate expressions. *) add_reassociation the_fpm; (* Eliminate Common SubExpressions. *) add_gvn the_fpm; (* Simplify the control flow graph (deleting unreachable blocks, etc). *) add_cfg_simplification the_fpm; ignore (PassManager.initialize the_fpm); (* Run the main "interpreter loop" now. *) Toplevel.main_loop the_fpm the_execution_engine stream;
The meat of the matter here, is the definition of "the_fpm". It requires a pointer to the the_module to construct itself. Once it is set up, we use a series of "add" calls to add a bunch of LLVM passes. The first pass is basically boilerplate, it adds a pass so that later optimizations know how the data structures in the program are laid out. The "the_execution_engine" variable is related to the JIT, which we will get to in the next section.
In this case, we choose to add 4 optimization passes. The passes we chose here are a pretty standard set of "cleanup" optimizations that are useful for a wide variety of code. I won't delve into what they do but, believe me, they are a good starting place :).
Once the Llvm.PassManager. is set up, we need to make use of it. We do this by running it after our newly created function is constructed (in Codegen.codegen_func), but before it is returned to the client:
let codegen_func the_fpm = function ... try let ret_val = codegen_expr body in (* Finish off the function. *) let _ = build_ret ret_val builder in (* Validate the generated code, checking for consistency. *) Llvm_analysis.assert_valid_function the_function; (* Optimize the function. *) let _ = PassManager.run_function the_function the_fpm in the_function
As you can see, this is pretty straightforward. The the_fpm optimizes and updates the LLVM Function* in place, improving (hopefully) its body. With this in place, we can try our test above again:
ready> def test(x) (1+2+x)*(x+(1+2)); ready> Read function definition: define double @test(double %x) { entry: %addtmp = fadd double %x, 3.000000e+00 %multmp = fmul double %addtmp, %addtmp ret double %multmp }
As expected, we now get our nicely optimized code, saving a floating point add instruction from every execution of this function.
LLVM provides a wide variety of optimizations that can be used in certain circumstances. Some documentation about the various passes is available, but it isn't very complete. Another good source of ideas can come from looking at the passes that llvm-gcc or llvm-ld run to get started. The "opt" tool allows you to experiment with passes from the command line, so you can see if they do anything.
Now that we have reasonable code coming out of our front-end, lets talk about executing it!
Code that is available in LLVM IR can have a wide variety of tools applied to it. For example, you can run optimizations on it (as we did above), you can dump it out in textual or binary forms, you can compile the code to an assembly file (.s) for some target, or you can JIT compile it. The nice thing about the LLVM IR representation is that it is the "common currency" between many different parts of the compiler.
In this section, we'll add JIT compiler support to our interpreter. The basic idea that we want for Kaleidoscope is to have the user enter function bodies as they do now, but immediately evaluate the top-level expressions they type in. For example, if they type in "1 + 2;", we should evaluate and print out 3. If they define a function, they should be able to call it from the command line.
In order to do this, we first declare and initialize the JIT. This is done by adding a global variable and a call in main:
... let main () = ... (* Create the JIT. *) let the_execution_engine = ExecutionEngine.create Codegen.the_module in ...
This creates an abstract "Execution Engine" which can be either a JIT compiler or the LLVM interpreter. LLVM will automatically pick a JIT compiler for you if one is available for your platform, otherwise it will fall back to the interpreter.
Once the Llvm_executionengine.ExecutionEngine.t is created, the JIT is ready to be used. There are a variety of APIs that are useful, but the simplest one is the "Llvm_executionengine.ExecutionEngine.run_function" function. This method JIT compiles the specified LLVM Function and returns a function pointer to the generated machine code. In our case, this means that we can change the code that parses a top-level expression to look like this:
(* Evaluate a top-level expression into an anonymous function. *) let e = Parser.parse_toplevel stream in print_endline "parsed a top-level expr"; let the_function = Codegen.codegen_func the_fpm e in dump_value the_function; (* JIT the function, returning a function pointer. *) let result = ExecutionEngine.run_function the_function [||] the_execution_engine in print_string "Evaluated to "; print_float (GenericValue.as_float Codegen.double_type result); print_newline ();
Recall that we compile top-level expressions into a self-contained LLVM function that takes no arguments and returns the computed double. Because the LLVM JIT compiler matches the native platform ABI, this means that you can just cast the result pointer to a function pointer of that type and call it directly. This means, there is no difference between JIT compiled code and native machine code that is statically linked into your application.
With just these two changes, lets see how Kaleidoscope works now!
ready> 4+5; define double @""() { entry: ret double 9.000000e+00 } Evaluated to 9.000000
Well this looks like it is basically working. The dump of the function shows the "no argument function that always returns double" that we synthesize for each top level expression that is typed in. This demonstrates very basic functionality, but can we do more?
ready> def testfunc(x y) x + y*2; Read function definition: define double @testfunc(double %x, double %y) { entry: %multmp = fmul double %y, 2.000000e+00 %addtmp = fadd double %multmp, %x ret double %addtmp } ready> testfunc(4, 10); define double @""() { entry: %calltmp = call double @testfunc(double 4.000000e+00, double 1.000000e+01) ret double %calltmp } Evaluated to 24.000000
This illustrates that we can now call user code, but there is something a bit subtle going on here. Note that we only invoke the JIT on the anonymous functions that call testfunc, but we never invoked it on testfunc itself. What actually happened here is that the JIT scanned for all non-JIT'd functions transitively called from the anonymous function and compiled all of them before returning from run_function.
The JIT provides a number of other more advanced interfaces for things like freeing allocated machine code, rejit'ing functions to update them, etc. However, even with this simple code, we get some surprisingly powerful capabilities - check this out (I removed the dump of the anonymous functions, you should get the idea by now :) :
ready> extern sin(x); Read extern: declare double @sin(double) ready> extern cos(x); Read extern: declare double @cos(double) ready> sin(1.0); Evaluated to 0.841471 ready> def foo(x) sin(x)*sin(x) + cos(x)*cos(x); Read function definition: define double @foo(double %x) { entry: %calltmp = call double @sin(double %x) %multmp = fmul double %calltmp, %calltmp %calltmp2 = call double @cos(double %x) %multmp4 = fmul double %calltmp2, %calltmp2 %addtmp = fadd double %multmp, %multmp4 ret double %addtmp } ready> foo(4.0); Evaluated to 1.000000
Whoa, how does the JIT know about sin and cos? The answer is surprisingly simple: in this example, the JIT started execution of a function and got to a function call. It realized that the function was not yet JIT compiled and invoked the standard set of routines to resolve the function. In this case, there is no body defined for the function, so the JIT ended up calling "dlsym("sin")" on the Kaleidoscope process itself. Since "sin" is defined within the JIT's address space, it simply patches up calls in the module to call the libm version of sin directly.
The LLVM JIT provides a number of interfaces (look in the llvm_executionengine.mli file) for controlling how unknown functions get resolved. It allows you to establish explicit mappings between IR objects and addresses (useful for LLVM global variables that you want to map to static tables, for example), allows you to dynamically decide on the fly based on the function name, and even allows you to have the JIT compile functions lazily the first time they're called.
One interesting application of this is that we can now extend the language by writing arbitrary C code to implement operations. For example, if we add:
/* putchard - putchar that takes a double and returns 0. */ extern "C" double putchard(double X) { putchar((char)X); return 0; }
Now we can produce simple output to the console by using things like: "extern putchard(x); putchard(120);", which prints a lowercase 'x' on the console (120 is the ASCII code for 'x'). Similar code could be used to implement file I/O, console input, and many other capabilities in Kaleidoscope.
This completes the JIT and optimizer chapter of the Kaleidoscope tutorial. At this point, we can compile a non-Turing-complete programming language, optimize and JIT compile it in a user-driven way. Next up we'll look into extending the language with control flow constructs, tackling some interesting LLVM IR issues along the way.
Here is the complete code listing for our running example, enhanced with the LLVM JIT and optimizer. To build this example, use:
# Compile ocamlbuild toy.byte # Run ./toy.byte
Here is the code:
<{lexer,parser}.ml>: use_camlp4, pp(camlp4of) <*.{byte,native}>: g++, use_llvm, use_llvm_analysis <*.{byte,native}>: use_llvm_executionengine, use_llvm_target <*.{byte,native}>: use_llvm_scalar_opts, use_bindings
open Ocamlbuild_plugin;; ocaml_lib ~extern:true "llvm";; ocaml_lib ~extern:true "llvm_analysis";; ocaml_lib ~extern:true "llvm_executionengine";; ocaml_lib ~extern:true "llvm_target";; ocaml_lib ~extern:true "llvm_scalar_opts";; flag ["link"; "ocaml"; "g++"] (S[A"-cc"; A"g++"]);; dep ["link"; "ocaml"; "use_bindings"] ["bindings.o"];;
(*===----------------------------------------------------------------------=== * Lexer Tokens *===----------------------------------------------------------------------===*) (* The lexer returns these 'Kwd' if it is an unknown character, otherwise one of * these others for known things. *) type token = (* commands *) | Def | Extern (* primary *) | Ident of string | Number of float (* unknown *) | Kwd of char
(*===----------------------------------------------------------------------=== * Lexer *===----------------------------------------------------------------------===*) let rec lex = parser (* Skip any whitespace. *) | [< ' (' ' | '\n' | '\r' | '\t'); stream >] -> lex stream (* identifier: [a-zA-Z][a-zA-Z0-9] *) | [< ' ('A' .. 'Z' | 'a' .. 'z' as c); stream >] -> let buffer = Buffer.create 1 in Buffer.add_char buffer c; lex_ident buffer stream (* number: [0-9.]+ *) | [< ' ('0' .. '9' as c); stream >] -> let buffer = Buffer.create 1 in Buffer.add_char buffer c; lex_number buffer stream (* Comment until end of line. *) | [< ' ('#'); stream >] -> lex_comment stream (* Otherwise, just return the character as its ascii value. *) | [< 'c; stream >] -> [< 'Token.Kwd c; lex stream >] (* end of stream. *) | [< >] -> [< >] and lex_number buffer = parser | [< ' ('0' .. '9' | '.' as c); stream >] -> Buffer.add_char buffer c; lex_number buffer stream | [< stream=lex >] -> [< 'Token.Number (float_of_string (Buffer.contents buffer)); stream >] and lex_ident buffer = parser | [< ' ('A' .. 'Z' | 'a' .. 'z' | '0' .. '9' as c); stream >] -> Buffer.add_char buffer c; lex_ident buffer stream | [< stream=lex >] -> match Buffer.contents buffer with | "def" -> [< 'Token.Def; stream >] | "extern" -> [< 'Token.Extern; stream >] | id -> [< 'Token.Ident id; stream >] and lex_comment = parser | [< ' ('\n'); stream=lex >] -> stream | [< 'c; e=lex_comment >] -> e | [< >] -> [< >]
(*===----------------------------------------------------------------------=== * Abstract Syntax Tree (aka Parse Tree) *===----------------------------------------------------------------------===*) (* expr - Base type for all expression nodes. *) type expr = (* variant for numeric literals like "1.0". *) | Number of float (* variant for referencing a variable, like "a". *) | Variable of string (* variant for a binary operator. *) | Binary of char * expr * expr (* variant for function calls. *) | Call of string * expr array (* proto - This type represents the "prototype" for a function, which captures * its name, and its argument names (thus implicitly the number of arguments the * function takes). *) type proto = Prototype of string * string array (* func - This type represents a function definition itself. *) type func = Function of proto * expr
(*===---------------------------------------------------------------------=== * Parser *===---------------------------------------------------------------------===*) (* binop_precedence - This holds the precedence for each binary operator that is * defined *) let binop_precedence:(char, int) Hashtbl.t = Hashtbl.create 10 (* precedence - Get the precedence of the pending binary operator token. *) let precedence c = try Hashtbl.find binop_precedence c with Not_found -> -1 (* primary * ::= identifier * ::= numberexpr * ::= parenexpr *) let rec parse_primary = parser (* numberexpr ::= number *) | [< 'Token.Number n >] -> Ast.Number n (* parenexpr ::= '(' expression ')' *) | [< 'Token.Kwd '('; e=parse_expr; 'Token.Kwd ')' ?? "expected ')'" >] -> e (* identifierexpr * ::= identifier * ::= identifier '(' argumentexpr ')' *) | [< 'Token.Ident id; stream >] -> let rec parse_args accumulator = parser | [< e=parse_expr; stream >] -> begin parser | [< 'Token.Kwd ','; e=parse_args (e :: accumulator) >] -> e | [< >] -> e :: accumulator end stream | [< >] -> accumulator in let rec parse_ident id = parser (* Call. *) | [< 'Token.Kwd '('; args=parse_args []; 'Token.Kwd ')' ?? "expected ')'">] -> Ast.Call (id, Array.of_list (List.rev args)) (* Simple variable ref. *) | [< >] -> Ast.Variable id in parse_ident id stream | [< >] -> raise (Stream.Error "unknown token when expecting an expression.") (* binoprhs * ::= ('+' primary)* *) and parse_bin_rhs expr_prec lhs stream = match Stream.peek stream with (* If this is a binop, find its precedence. *) | Some (Token.Kwd c) when Hashtbl.mem binop_precedence c -> let token_prec = precedence c in (* If this is a binop that binds at least as tightly as the current binop, * consume it, otherwise we are done. *) if token_prec < expr_prec then lhs else begin (* Eat the binop. *) Stream.junk stream; (* Parse the primary expression after the binary operator. *) let rhs = parse_primary stream in (* Okay, we know this is a binop. *) let rhs = match Stream.peek stream with | Some (Token.Kwd c2) -> (* If BinOp binds less tightly with rhs than the operator after * rhs, let the pending operator take rhs as its lhs. *) let next_prec = precedence c2 in if token_prec < next_prec then parse_bin_rhs (token_prec + 1) rhs stream else rhs | _ -> rhs in (* Merge lhs/rhs. *) let lhs = Ast.Binary (c, lhs, rhs) in parse_bin_rhs expr_prec lhs stream end | _ -> lhs (* expression * ::= primary binoprhs *) and parse_expr = parser | [< lhs=parse_primary; stream >] -> parse_bin_rhs 0 lhs stream (* prototype * ::= id '(' id* ')' *) let parse_prototype = let rec parse_args accumulator = parser | [< 'Token.Ident id; e=parse_args (id::accumulator) >] -> e | [< >] -> accumulator in parser | [< 'Token.Ident id; 'Token.Kwd '(' ?? "expected '(' in prototype"; args=parse_args []; 'Token.Kwd ')' ?? "expected ')' in prototype" >] -> (* success. *) Ast.Prototype (id, Array.of_list (List.rev args)) | [< >] -> raise (Stream.Error "expected function name in prototype") (* definition ::= 'def' prototype expression *) let parse_definition = parser | [< 'Token.Def; p=parse_prototype; e=parse_expr >] -> Ast.Function (p, e) (* toplevelexpr ::= expression *) let parse_toplevel = parser | [< e=parse_expr >] -> (* Make an anonymous proto. *) Ast.Function (Ast.Prototype ("", [||]), e) (* external ::= 'extern' prototype *) let parse_extern = parser | [< 'Token.Extern; e=parse_prototype >] -> e
(*===----------------------------------------------------------------------=== * Code Generation *===----------------------------------------------------------------------===*) open Llvm exception Error of string let context = global_context () let the_module = create_module context "my cool jit" let builder = builder context let named_values:(string, llvalue) Hashtbl.t = Hashtbl.create 10 let double_type = double_type context let rec codegen_expr = function | Ast.Number n -> const_float double_type n | Ast.Variable name -> (try Hashtbl.find named_values name with | Not_found -> raise (Error "unknown variable name")) | Ast.Binary (op, lhs, rhs) -> let lhs_val = codegen_expr lhs in let rhs_val = codegen_expr rhs in begin match op with | '+' -> build_add lhs_val rhs_val "addtmp" builder | '-' -> build_sub lhs_val rhs_val "subtmp" builder | '*' -> build_mul lhs_val rhs_val "multmp" builder | '<' -> (* Convert bool 0/1 to double 0.0 or 1.0 *) let i = build_fcmp Fcmp.Ult lhs_val rhs_val "cmptmp" builder in build_uitofp i double_type "booltmp" builder | _ -> raise (Error "invalid binary operator") end | Ast.Call (callee, args) -> (* Look up the name in the module table. *) let callee = match lookup_function callee the_module with | Some callee -> callee | None -> raise (Error "unknown function referenced") in let params = params callee in (* If argument mismatch error. *) if Array.length params == Array.length args then () else raise (Error "incorrect # arguments passed"); let args = Array.map codegen_expr args in build_call callee args "calltmp" builder let codegen_proto = function | Ast.Prototype (name, args) -> (* Make the function type: double(double,double) etc. *) let doubles = Array.make (Array.length args) double_type in let ft = function_type double_type doubles in let f = match lookup_function name the_module with | None -> declare_function name ft the_module (* If 'f' conflicted, there was already something named 'name'. If it * has a body, don't allow redefinition or reextern. *) | Some f -> (* If 'f' already has a body, reject this. *) if block_begin f <> At_end f then raise (Error "redefinition of function"); (* If 'f' took a different number of arguments, reject. *) if element_type (type_of f) <> ft then raise (Error "redefinition of function with different # args"); f in (* Set names for all arguments. *) Array.iteri (fun i a -> let n = args.(i) in set_value_name n a; Hashtbl.add named_values n a; ) (params f); f let codegen_func the_fpm = function | Ast.Function (proto, body) -> Hashtbl.clear named_values; let the_function = codegen_proto proto in (* Create a new basic block to start insertion into. *) let bb = append_block context "entry" the_function in position_at_end bb builder; try let ret_val = codegen_expr body in (* Finish off the function. *) let _ = build_ret ret_val builder in (* Validate the generated code, checking for consistency. *) Llvm_analysis.assert_valid_function the_function; (* Optimize the function. *) let _ = PassManager.run_function the_function the_fpm in the_function with e -> delete_function the_function; raise e
(*===----------------------------------------------------------------------=== * Top-Level parsing and JIT Driver *===----------------------------------------------------------------------===*) open Llvm open Llvm_executionengine (* top ::= definition | external | expression | ';' *) let rec main_loop the_fpm the_execution_engine stream = match Stream.peek stream with | None -> () (* ignore top-level semicolons. *) | Some (Token.Kwd ';') -> Stream.junk stream; main_loop the_fpm the_execution_engine stream | Some token -> begin try match token with | Token.Def -> let e = Parser.parse_definition stream in print_endline "parsed a function definition."; dump_value (Codegen.codegen_func the_fpm e); | Token.Extern -> let e = Parser.parse_extern stream in print_endline "parsed an extern."; dump_value (Codegen.codegen_proto e); | _ -> (* Evaluate a top-level expression into an anonymous function. *) let e = Parser.parse_toplevel stream in print_endline "parsed a top-level expr"; let the_function = Codegen.codegen_func the_fpm e in dump_value the_function; (* JIT the function, returning a function pointer. *) let result = ExecutionEngine.run_function the_function [||] the_execution_engine in print_string "Evaluated to "; print_float (GenericValue.as_float Codegen.double_type result); print_newline (); with Stream.Error s | Codegen.Error s -> (* Skip token for error recovery. *) Stream.junk stream; print_endline s; end; print_string "ready> "; flush stdout; main_loop the_fpm the_execution_engine stream
(*===----------------------------------------------------------------------=== * Main driver code. *===----------------------------------------------------------------------===*) open Llvm open Llvm_executionengine open Llvm_target open Llvm_scalar_opts let main () = ignore (initialize_native_target ()); (* Install standard binary operators. * 1 is the lowest precedence. *) Hashtbl.add Parser.binop_precedence '<' 10; Hashtbl.add Parser.binop_precedence '+' 20; Hashtbl.add Parser.binop_precedence '-' 20; Hashtbl.add Parser.binop_precedence '*' 40; (* highest. *) (* Prime the first token. *) print_string "ready> "; flush stdout; let stream = Lexer.lex (Stream.of_channel stdin) in (* Create the JIT. *) let the_execution_engine = ExecutionEngine.create Codegen.the_module in let the_fpm = PassManager.create_function Codegen.the_module in (* Set up the optimizer pipeline. Start with registering info about how the * target lays out data structures. *) TargetData.add (ExecutionEngine.target_data the_execution_engine) the_fpm; (* Do simple "peephole" optimizations and bit-twiddling optzn. *) add_instruction_combination the_fpm; (* reassociate expressions. *) add_reassociation the_fpm; (* Eliminate Common SubExpressions. *) add_gvn the_fpm; (* Simplify the control flow graph (deleting unreachable blocks, etc). *) add_cfg_simplification the_fpm; ignore (PassManager.initialize the_fpm); (* Run the main "interpreter loop" now. *) Toplevel.main_loop the_fpm the_execution_engine stream; (* Print out all the generated code. *) dump_module Codegen.the_module ;; main ()
#include <stdio.h> /* putchard - putchar that takes a double and returns 0. */ extern double putchard(double X) { putchar((char)X); return 0; }


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92% of students perform better or equivalent with non-animal alternatives to dissection.
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Shivers is a utility for SAP/HANA developers to visualize SAP/HANA information views (analytic, attribute and calculation views) and their dependencies. The name shivers stands for Sap Hana Information Viewers.
Shivers can be run from the local file system. You do not need a webserver.
To visualize HANA information views, use the open button on the toolbar. Alternatively, drop HANA information vieww source files into the Shivers toolbar.
You will be prompted to provide the package to which your source files belong. You need to provide the package name yourself because this information cannot be extracted from the files directly. Shivers needs to know the package name in order to resolve dependencies on other information views when visualizing.
After loading information views, shivers will parse the source files and display the loaded objects in the treeview. To visualize any particular information view, simply select one in the treeview by clicking it.
Shivers visualizes dependencies as far as it can. If it finds a reference to an object for which the source file was not already loaded, a warning will be printed in the log tab. You can choose to load the sources for those missing objects afterwards. Tabs that were already opened will not refresh automatically, so if you want to visualize the information view again, close the tab and re-open it.
For more information about shivers, check out this blog: http://rpbouman.blogspot.nl/2017/03/announcing-shivers-visualizing-saphana.html. To contribute to the project, checkout its github repository: https://github.com/just-bi/shivers.


pickle install extension_name- Convert an existing PECL extension configuration to a Pickle configuration file:
pickle convert path/to/directory- Validate a PECL extension:
pickle validate path/to/directory- Package a PECL extension for release:
pickle release path/to/directory


I guys, i'm a newbie about electronics and Raspberry and I like the emonTx V3 project. I need to know if it's possible to connect emonTx V3 to a standard pc using a usb dongle. I already have a low power fedora server and i want to use it.
Many thanks in advance
Re: connecting a emonTx V3 to a standard pc
You can use the programmer from the shop to make a wired connection to your pc's USB port. The emonTx radio uses neither the same frequency nor protocol as WiFi so a WiFi USB dongle won't work.
If you have a serial port on your server, you might be able to make a wired connection to that, similar to the direct wired connection to the Raspberry Pi.
Re: connecting a emonTx V3 to a standard pc
Thanks Robert. This http://www.digitalsmarties.net/products/jeelink stick from jeelabs may work ?


Buttons have three variations in color for different purposes.
Primary - Used for the primary use action
Secondary - Used for an alternate or less important user action
Icon - Used to provide more visual context to a user action
<button type="button"></button>
<button type="button" class="button__primary"></button>
<button type="button" class="button__secondary"></button>
<button type="button" class="button__icon">
Icon
</button>
Buttons have three variations in size for emphasis. Size and color variations can be combined to signify the appropriate level of importance to a given user action.
<button type="button" class="button-lg"></button>
<button type="button" class="button-md"></button>
<button type="button"></button>
Links should have a consistent look and feel not matter how they are technically implmented. The link
class has been created to suppor the use of <button>
tag implementation (anchors are styled implicitly).
Anchor based link
<button type="button" class="link">Button based link</button>


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Class PriorityQueue
Defined in: DataStructures.js.
| Constructor Attributes | Constructor Name and Description |
|---|---|
|
Class for managing a priority queue.
|
| Field Attributes | Field Name and Description |
|---|---|
|
The list of the items in the queue.
|
|
|
The length of the queue.
|
| Method Attributes | Method Name and Description |
|---|---|
|
changePriority(index,
newPriority)
Changes the priority of the item at the position index.
|
|
|
clear()
Removes all the items stored in the queue.
|
|
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clone()
Clones the queue into a new queue.
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Clones the queue into a new queue without cloning duplicated items.
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containsPriority(priority,
callback)
Checks if the queue contains a priority that satisfy the condition represented by
the callback function.
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|
dequeue()
Removes the item at the head of the queue.
|
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enqueue(priority, item)
Adds the item at the tail of the queue.
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execute(callback)
Executes the callback function for each item of the queue.
|
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|
filter(callback)
Returns the items that satisfy the condition determined by the callback.
|
|
|
getItem(index)
Returns the item at the position index.
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getItems(priority)
Returns the items relatives to the priority.
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Returns the iterator relative to the aggregate.
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|
|
Returns the length of the queue.
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isEmpty()
Checks if the queue is empty.
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multiDequeue(times)
Removes the items at the head of the queue.
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multiEnqueue(priority,
items)
Adds the items with the same priority at the tail of the queue.
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peek()
Returns the first item in the queue.
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remove(index, length)
Removes the first length items from the position index.
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toQueue()
Returns the queue created by the priority queue with the items in the same order
but without the priority.
|
Field Detail
items
The list of the items in the queue.
length
The length of the queue.
Method Detail
{void}
changePriority(index, newPriority)
Changes the priority of the item at the position index.
- Parameters:
- index
- {number} The position of the item of which increase the priority.
- newPriority
- {number} The new priority.
- Returns:
- {void}
{void}
clear()
Removes all the items stored in the queue.
- Returns:
- {void}
{PriorityQueue}
clone()
Clones the queue into a new queue.
- Returns:
- {PriorityQueue} The queue cloned from this queue.
{PriorityQueue}
cloneDistinct()
Clones the queue into a new queue without cloning duplicated items.
- Returns:
- {PriorityQueue} The queue cloned from this queue.
{boolean}
containsPriority(priority, callback)
Checks if the queue contains a priority that satisfy the condition represented by the callback function.
- Parameters:
- priority
- {number} The priority to find.
- callback Optional, Default: function(p){return(p===priority);}
- The condition to satisfy. The callback must accept the current priority to check.
- Returns:
- {boolean} True if the queue contains the priority that satisfy the condition, false otherwise.
{*}
dequeue()
Removes the item at the head of the queue.
- Returns:
- {*} The item at the head of the queue. It's undefined if the queue is empty.
{void}
enqueue(priority, item)
Adds the item at the tail of the queue.
- Parameters:
- priority
- {number} The priority of the item.
- item
- {*} The item to add.
- Returns:
- {void}
{void}
execute(callback)
Executes the callback function for each item of the queue.
This method modifies the queue so if you don't need to modify it you must return the same item of the array.
- Parameters:
- callback
- {function} The function to execute for each item. The function must accept the current item on which execute the function.
- Returns:
- {void}
{Array<*>}
filter(callback)
Returns the items that satisfy the condition determined by the callback.
- Parameters:
- callback
- {function} The function that implements the condition.
- Returns:
- {Array<*>} The array that contains the items that satisfy the condition.
{*}
getItem(index)
Returns the item at the position index.
- Parameters:
- index
- {number} The index of the item.
- Returns:
- {*} The item found. It's undefined if the position index is out of bounds.
{Array<*>}
getItems(priority)
Returns the items relatives to the priority.
- Parameters:
- priority
- {number} The priority of the items.
- Returns:
- {Array<*>} The items found.
{Iterator}
getIterator()
Returns the iterator relative to the aggregate.
- Returns:
- {Iterator} The iterator.
{number}
getLength()
Returns the length of the queue.
- Returns:
- {number} The length of the queue.
{boolean}
isEmpty()
Checks if the queue is empty.
- Returns:
- {boolean} True if the queue is empty, false otherwise.
{Array<*>}
multiDequeue(times)
Removes the items at the head of the queue.
- Parameters:
- times
- {number} The number of times to repeat the dequeue method.
- Returns:
- {Array<*>} The items at the head of the queue.
{void}
multiEnqueue(priority, items)
Adds the items with the same priority at the tail of the queue.
- Parameters:
- priority
- {number} The priority of the items.
- items
- {Array<*>} The items to add.
- Returns:
- {void}
{*}
peek()
Returns the first item in the queue. The item is not removed.
- Returns:
- {*} The first item. It's undefined if the queue is empty.
{void}
remove(index, length)
Removes the first length items from the position index.
- Parameters:
- index
- {number} The position where to start to remove the items.
- length Optional, Default: 1
- {number} The number of items to remove.
- Returns:
- {void}
{Queue}
toQueue()
Returns the queue created by the priority queue with the items in the same order but without the priority.
- Returns:
- {Queue} The queue created.


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Dragon Compiler
0.7.0
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#include <stdlib.h>
Go to the source code of this file.
Classes | |
| struct | yyltype |
| store location information More... | |
Macros | |
| #define | YYLTYPE yyltype |
Functions | |
| yyltype * | locdup (yyltype *loc) |
| duplicate a yyltype node More... | |
| int | loccmp (yyltype *l, yyltype *r) |
| compare two location More... | |
Variables | |
| struct yyltype | yylloc |
| location information for current token | |
compare two location
| l | location information |
| r | location information |


001 package org.javasimon.callback.async; 002 003 import java.util.concurrent.Callable; 004 005 /** 006 * Executor is similar to {@link java.util.concurrent.Executor} 007 * or {@link java.util.concurrent.ExecutorService} but simpler (only one method to implement). 008 * 009 * @param <T> 010 * @author gerald 011 * @see Executors Implementations 012 */ 013 public interface Executor<T> { 014 015 /** 016 * Main method of the executor. 017 * 018 * @param callable Piece of code to execute 019 * @return Result of the execution 020 * @throws Throwable Raised when execution failed 021 */ 022 T execute(Callable<T> callable) throws Throwable; 023 }


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Anything else? |
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Bitcoin Price Forces Small Miners Out, Untagged Wallets Dump $20M
by Christine VasilevaMiners made the biggest withdrawal of funds in 2019, just as Bitcoin price was fighting its last to keep above $8,000. The accelerated selling of rewards may look like an episode of miners capitulating.
Bitcoin Price Forces Miners to Sell
The sum of $20 million looks small in comparison to the entire BTC activity, which is above $19 billion per day. However, miners selling their current rewards is a signal that their faith in bitcoin price is shaken.
Larger miners may afford to finance their operations, but small ones may be pressed to close doors, if bitcoin price falls too far down. Currently, miners produce more than 96 quintillion hashes per second, as the network rate is near its peak.
However, most blocks go to the largest pools, and small miners grab a fraction of the rewards. It is possible that small miners are starting to give up, adding to the selling pressure on the market. At current BTC prices of $7,987.88, the profitability of Antminer S9 is not appealing – bringing an annual loss of around $567. Investing in new, more powerful ASIC is happening only gradually, and is seen as a factor in the health of the Bitcoin network.
Halving to Discourage Smaller Miners
Miners moving away is an expected event after the halving, which is expected around May 15. At that point, pools will compete for just 900 BTC in rewards per day. With a bearish outlook, it is possible some miners may have decided to fold months before the halving.
Predictions include large-scale “whale” transactions moving between wallets, as miners tend to sell on OTC markets.
The Bitcoin difficulty moved down with a tiny correction during Thursday’s recalculation. The cut in difficulty is not enough to warrant a much greater ease in mining. But if more miners give up or switch off the equipment, the remaining ones will have a greater chance at winning blocks.
The Bitcoin network is still secured by more than 9,300 nodes, though the decentralization is partial, as some nodes use cloud services and do not rely on independent hardware. The Bitcoin network still carries more than 300,000 transactions per day.
For BTC, mining activity also shrank toward the end of 2018, as prices tumbled from $6,000 to a bottom of around $3,200. December predictions see miners capitulating, as bitcoin price backtracks further.
What do you think about miners giving up on BTC? Share your thoughts in the comments section below!
Images via Shutterstock, Twitter @BTC_JackSparrow @Eliasimos @Crypto_birb
The Rundown


| abc | Parameter estimation with Approximate Bayesian Computation (ABC) |
| abc.return | Parameter estimation with Approximate Bayesian Computation (ABC) |
| cv4abc | Cross validation for Approximate Bayesian Computation (ABC) |
| cv4postpr | Leave-one-our cross validation for model selection ABC |
| expected.deviance | Expected deviance |
| getmode | Summaries of posterior samples generated by ABC algortithms |
| gfit | Goodness of fit |
| gfitpca | Goodness of fit with principal component analysis |
| hist.abc | Posterior histograms |
| plot.abc | Diagnostic plots for ABC |
| plot.cv4abc | Cross-validation plots for ABC |
| plot.cv4postpr | Barplot of model misclassification |
| plot.gfit | Goodness-of-fit plot for ABC |
| postpr | Estimating posterior model probabilities |
| summary.abc | Summaries of posterior samples generated by ABC algortithms |
| summary.cv4abc | Calculates the cross-validation prediction error |
| summary.cv4postpr | Confusion matrix and misclassification probabilities of models |
| summary.gfit | Calculates the p-value of the goodness-of-fit test. |
| summary.postpr | Posterior model probabilities and Bayes factors |


[gecode-users] my first propagator - Nq
Christian Schulte
cschulte at kth.se
Mon Feb 12 10:05:58 CET 2007
Congratulations!
Maybe some comments. Never do this:
if (Nq::post(home, view1, view2) < ES_OK)
home->fail();
as you should not rely on the order of ES_* (actually, they will change in
Gecode 2.0)! The other way is safe, or test for == ES_FAILED. (No matter
what the macros themselves do, as their implementation will change
consistently with changes to ExecStatus).
The cost you give is way too high: you know that if the propagator is
invoked, it will be subsumed. So, PC_UNARY_LO is more like it.
If you assume that an integer view x is assigned, us x.val() rather than
x.min(); it will have an assertion that it is really assumed.
Posting can be smarter: if you know that just one of the views is assigned,
you can immediately do the propagation.
Cheers
Christian
--
Christian Schulte, http://www.imit.kth.se/~schulte/
-----Original Message-----
From: users-bounces at gecode.org [mailto:users-bounces at gecode.org] On Behalf
Of Kilian Sprotte
Sent: Saturday, February 10, 2007 2:34 PM
To: users at gecode.org
Subject: [gecode-users] my first propagator - Nq
Hi,
I have written my first propagator... :)
Its basically a translation of Nq in the java Queens example.
I hope it is correct, I am posting it here, in case anyone might find it
useful as a starting point.
The class is below - you can post it with:
if (Nq::post(home, view1, view2) < ES_OK)
home->fail();
or (if you are inside a void function)
GECODE_ES_FAIL(home, Nq::post(s, view1, view2));
Cheers,
Kilian
--
using namespace Gecode;
class Nq : public BinaryPropagator<Int::IntView, Int::PC_INT_VAL> {
public:
Nq(Space* home, Int::IntView x0, Int::IntView x1)
: BinaryPropagator<Int::IntView,Int::PC_INT_VAL>(home,x0,x1) {
}
Nq(Space* home, bool share, Nq& p)
: BinaryPropagator<Int::IntView,Int::PC_INT_VAL>(home,share,p) {
}
Actor* copy(Space* home, bool share) {
return new (home) Nq(home, share,*this);
}
PropCost cost () {
return PC_BINARY_HI;
}
ExecStatus propagate (Space* home) {
std::cout << "propagate head: "<< x0 << " " << x1 << "\n";
if (x0.assigned()) {
GECODE_ME_CHECK(x1.nq(home,x0.min()));
std::cout << "propagate subsumed: "<< x0 << " " << x1 << "\n";
return ES_SUBSUMED;
} else { // x1 is assigned
GECODE_ME_CHECK(x0.nq(home,x1.min()));
std::cout << "propagate subsumed: "<< x0 << " " << x1 << "\n";
return ES_SUBSUMED;
}
}
static ExecStatus post (Space* home, Int::IntView x0, Int::IntView
x1) {
if (x0.assigned() && x1.assigned() && (x0.min()==x1.min())) {
return ES_FAILED;
}
(void) new (home) Nq(home, x0, x1);
return ES_OK;
}
};
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EVO HTML to PDF Converter allows you to replace in the generated PDF document the original images from HTML document with higher quality images from the local file system.
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Hello, future customer! Please take some time to take this quick survey. This survey will consist of 25 rounds of voting on random products. This research will assist us in providing the best products for the best customer experience as the opening for our new mall location approaches on November 12th, 2016. If you have any questions or suggestions, feel free to email<EMAIL_ADDRESS>Thank you!


I am a Full Stack Web Developer at Start Innovation Hub. I love building solutions, learning new technologies, meeting people, sharing experiences and listening to music.
I am enthusiatic about the Psychology of humans, Machine Learning, Robotics and Artificial Intelligence.


Highcharts is a well-known charting tool designed for day to day, easy to read, fast-built charts. It is a charting library written in JavaScript that supports a series of graphics - like line, area, column, bar, pie, scatter and polar chart types. You can download this library and work with its code, as long as it is not for commercial usage.
For this tutorial, we will use the online version, which is very straight forward and easy.
In this tutorial, you will need to access:
In this tutorial, we will use the tab-separated values (.tsv) data format. Differently from the .csv, the .tsv has the values of its columns separated by tabs (spaces), not commas. Highcharts also accepts .csv.
The example data shows the social media following (Twitter and Facebook) of some New York City-based newsrooms. It was compiled by Volt Data Lab, with reference in June 4, 2016.
WARNING: as cool as Highcharts is, there is still a lot of confusion when using commas and dots to separate thousands. If you use commas to separate thousands, make sure your data is clean and the columns are well aligned. If you want to use dots, don’t - Highcharts does not recognize dots as thousand separators, so, for instance, 1.000.000 will be read as a string, not as a number. In our example, we will use neither, just the plain number, as our data don’t have decimals.
Also, at least in the the online version, Highcharts only accepts string values in the first column of your dataset or the in the column headers. This happens because of the way their code is structure to render reusable charts. If you try to input string values in any column other than the first one, you will likely get this message.
Moving ahead, copy the data and head to the online version of the tool. You can use this link to access the dataset.
After you sign up and login, you will see a button in the upper right corner, that reads “create chart”. Click on it.
Highchart’s interface is pretty simple and detailed, and you will see right away a field to input your data, in the left side. Paste the copied data there.
Immediately a line chart will pop up in the right side of the screen. But that is not the chart we want for this particular dataset, as line charts are better to show values on a time series.
Before we proceed, make sure to check the “use categories” box bellow the dataset. This will ensure that all the data, from all the newsrooms listed, will be used in the chart. It may happen that the chart already plotted all the data, but it is worth checking the box nevertheless.
We are trying to compare the social media following of 20 of the biggest newsrooms in NYC. Proceed to the next step by clicking the green button “Continue to Templates”.
Here is where we will chose the most adequate chart type to render all that data.
As you will see, Highcharts will give you a list of chart types and subtypes to choose from. Feel free to browse around and see how your data look like in different types of graphics.
Right away, we can discard using the line and the area charts, as they are good for time series. In this case, since we are only using one dimension (number of followers) and two categories (Twitter and Facebook), we can also discard the scatter and bubble charts - which can be used when you have more data to display in the same category to show.
Pie charts are used to proportion of a whole. Since it is not our intention to sum up every publication’s social media following and then see how big of a piece each newsroom has, we can leave the pie chart behind. For its turn, polar charts are interesting, but with this kind of data it can be confusing.
This mostly leaves us with bar/column charts. Lets use the horizontal type, as it will give us a good idea of how to compare the sizes of the bars. Lets also select the stacked subtype, as it will render one bar showing two categories and we can compare both by publication clearly.
You chart will look like this, before customization:
After this step, click on the button “continue to customize”. In here, we will make your chart look better.
First, lets give it a title (suggestion: “Top 20 newsrooms in social media following”) and a subtitle (suggestion: Source: Volt Data Lab). You can erase the “values” label, as our data already is market with the M, from million, sign.
In the “general” tab, you can customize width, height and other things. We won’t do anything here, no need for that now, there will be an option to do that later when we export it.
Lets change the basic “appearance”. Assing whatever font you want. We will leave it at 18px and will change the font type to Tahoma.
This example will also assign a light grey (hex color #ededed
) to the background of the map.
The axes are one of the most important components of any chart. In here, we will make sure they best represent our data.
Highcharts allows us to use linear (arithmetic progression) and logarithmic scales (geometric progression). This last one is useful when you data is way too far apart from each point.
For instance, if your data shows 20 million followers in the upper point and mere 500 followers in the lower, you won’t be able to see the smallest bar. It will be there, but will be so small that you can’t see it. The logarithmic scale adjust the scale so you can see all the bars.
In the “axes” tab, try applying the logarithmic scale to the horizontal axis (nothing will happen in the vertical axis because you are using string in that one). The path will be Axes -> Horizontal Axis -> Type -> Logarithmic
. You might notice the bottom values have bigger bars now.
That all being said, we won’t apply the log scale in our example, as we don’t need it: all the values are in million and give a good general look about how dominant a few companies are on social media against others.
One last thing here. It will look better for us to put the X axis in the top, so we can see more clearly. Follow this path Axes -> Horizontal Axis -> Opposite side of chart -> Check the box
.
Proceed to the data series tab to customize the colors you want to use.
To make sure the colors are very different so we can distinguish them immediately, lets apply the a dark blue (hex color #1c126c
) for Twitter and a light yellow for Facebook (hex color #d3e466
).
For the tooltip suffix box, lets put the work “ followers” (don’t forget the space tab before), so every time you hover a bar, this suffix appears after the number.
In the vale labels tab, you can choose to plot in the graphic the numbers of followers in each bar. I don’t recommend, as you graphic will look confusing, full of numbers. Also, as it is interactive, you can hover the bars too see the same number.
Our last adjustment before publishing the chart is to assign the shared tooltip between the two categories, so when you hover the bars you will get both Facebook and Twitter numbers at the same time.
Go to Tooltip -> General -> Shared between series
.
 There are several options here:
Come back to editing your graphic and explore other options we did not use in this tutorial, as changing label formats, the legends, font-sizes, borders, adding a prefix to the tooltip, change the order of the categories and more.
You may even also change the chart type and choose one that you think will be better.
If you want to use other data to explore this tool, Gapminder has some cool ready-to-use datasets.


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High Prefect Finker:
You've already completed your first class transfer, and you come to transfer again? Develop your strength and transfer to an even higher class. Do not squander your time! Use it to build your physical strength! Stoke the fire in your belly!


This is the readme for the model associated with the paper: S. K. Sudhakar, B. Torben-Nielsen, E. De Schutter: Cerebellar nuclear neurons use time and rate coding to transmit Purkinje neuron pauses. PLoS Computational Biology in press The CN neuron model is based on previously published model, V. Steuber, N.W. Schultheiss, R.A. Silver, E. De Schutter, D. Jaeger, Determinants of synaptic integration and heterogeneity in rebound firing explored with data-driven models of deep cerebellar nucleus cells., J. Comput. Neurosci. 30 (2011) 633–58. doi:10.1007/s10827-010-0282-z. Download, extract, and compile the mod files with the "nrnivmodl" command after cd'ing to the extracted folder. For more help on downloading and running NEURON models from ModelDB see: http://senselab.med.yale.edu/ModelDB/NEURON_DwnldGuide.html The following commands reproduces figure 1 from the paper (Sudhakar et al) Fig 1a: nrngui -c "x=1" DCN_init_model2_lowgain.hoc (Note: this is the same simulation as provided in the auto-launch from ModelDB) After a brief wait you should see an image like this on the screen: Fig 1b: nrngui -c "x=1" DCN_init_model2_medgain.hoc Fig 1c: nrngui -c "x=1" DCN_init_model2_highgain.hoc Model output: m2_a1.bin -- Membrane potential traces of the model run for 1000 ms m2_b1.bin -- Spike times of the model run for 1000 ms The following data files represent model input. datasp1.dat : spike times of 200 Purkinje cells for Pause beginning synchronization l1.dat: Length of spike times of each Purkinje cell datasp_ex1.dat: Poisson spike times of 100 mossy fibers l_ex1.dat: Length of spike times of each mossy fiber


|
gtsam
4.0.0
gtsam
|
An implementation of the nonlinear CG method using the template below.
Public Member Functions | |
| NonlinearConjugateGradientOptimizer (const NonlinearFactorGraph &graph, const Values &initialValues, const Parameters ¶ms=Parameters()) | |
| Constructor. | |
| virtual | ~NonlinearConjugateGradientOptimizer () |
| Destructor. | |
| GaussianFactorGraph::shared_ptr | iterate () override |
| Perform a single iteration, returning GaussianFactorGraph corresponding to the linearized factor graph. | |
| const Values & | optimize () override |
| Optimize for the maximum-likelihood estimate, returning a the optimized variable assignments. | |
| Public Member Functions inherited from gtsam::NonlinearOptimizer | |
| const Values & | optimizeSafely () |
| Optimize, but return empty result if any uncaught exception is thrown Intended for MATLAB. More... | |
| double | error () const |
| return error | |
| size_t | iterations () const |
| return number of iterations | |
| const Values & | values () const |
| return values | |
| virtual | ~NonlinearOptimizer () |
| Virtual destructor. | |
| virtual VectorValues | solve (const GaussianFactorGraph &gfg, const NonlinearOptimizerParams ¶ms) const |
| Default function to do linear solve, i.e. More... | |
Public Types | |
| typedef NonlinearOptimizer | Base |
| typedef NonlinearOptimizerParams | Parameters |
| typedef boost::shared_ptr< NonlinearConjugateGradientOptimizer > | shared_ptr |
| Public Types inherited from gtsam::NonlinearOptimizer | |
| typedef boost::shared_ptr< const NonlinearOptimizer > | shared_ptr |
| A shared pointer to this class. | |
Protected Member Functions | |
| const NonlinearOptimizerParams & | _params () const override |
| Protected Member Functions inherited from gtsam::NonlinearOptimizer | |
| void | defaultOptimize () |
| A default implementation of the optimization loop, which calls iterate() until checkConvergence returns true. | |
| NonlinearOptimizer (const NonlinearFactorGraph &graph, std::unique_ptr< internal::NonlinearOptimizerState > state) | |
| Constructor for initial construction of base classes. More... | |
Protected Attributes | |
| Parameters | params_ |
| Protected Attributes inherited from gtsam::NonlinearOptimizer | |
| NonlinearFactorGraph | graph_ |
| The graph with nonlinear factors. | |
| std::unique_ptr< internal::NonlinearOptimizerState > | state_ |
| PIMPL'd state. | |


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Liberals to release fiscal update before Christmas, Morneau says
OTTAWA — Finance Minister Bill Morneau says the government will provide an update on federal finances before Christmas. Morneau is introducing legislation today to provide a tax break for anyone earning under $147,000 a year.
by Canadian PressOTTAWA — Finance Minister Bill Morneau says the government will provide an update on federal finances before Christmas.
Morneau is introducing legislation today to provide a tax break for anyone earning under $147,000 a year.
But he also says the fall report on Ottawa's fiscal status come within the next two weeks.
Over the weekend, the Conservatives called on Morneau to provide a fiscal update immediately as economic concerns mount following a poor jobs report late last week.
The tax cut fulfills a Liberal election promise to make the first $15,000 of income tax-free for most Canadians.
Morneau says the change will be implemented over the next four years and will save the average Canadian family $585 a year.
This report by The Canadian Press was first published Dec. 9, 2019.
The Canadian Press


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--- layout: post tags: [Productivity, Unit Testing] date: 2009-03-05 20:23:05 UTC title: "Constrained Non-Determinism" description: "An alternative to parametrised tests is to use constrained randomised test input." --- {% include JB/setup %}
This may turn out to be the most controversial of my Zero-Friction TDD posts so far, as it supposedly goes against conventional wisdom. However, I have found this approach to be really powerful since I began using it about a year ago.
In my previous post, I explained how Derived Values help ensure that tests act as Executable Specification. In short, a test should clearly specify the relationship between input and outcome, as this test does:
[TestMethod] public void InvertWillReverseText() { // Fixture setup string anonymousText = "ploeh"; string expectedResult = new string(anonymousText.Reverse().ToArray()); MyClass sut = new MyClass(); // Exercise system string result = sut.Invert(anonymousText); // Verify outcome Assert.AreEqual<string>(expectedResult, result, "DoWork"); // Teardown }
However, it is very tempting to just hardcode the expected value. Consistently using Derived Values to establish the relationship between input and outcome requires discipline.
To help myself enforce this discipline, I use well-defined, but essentially random, input, because when the input is random, I don't know the value at design time, and hence, it is impossible for me to accidentally hard-code any assertions.
[TestMethod] public void InvertWillReverseText_Cnd() { // Fixture setup string anonymousText = Guid.NewGuid().ToString(); string expectedResult = new string(anonymousText.Reverse().ToArray()); MyClass sut = new MyClass(); // Exercise system string result = sut.Invert(anonymousText); // Verify outcome Assert.AreEqual<string>(expectedResult, result, "DoWork"); // Teardown }
For strings, I prefer Guids, as the above example demonstrates. For numbers, I often just use the sequence of natural numbers (i.e. 1, 2, 3, 4, 5...). For booleans, I often use an alternating sequence (i.e. true, false, true, false...).
While this technique causes input to become non-deterministic, I always pick the non-deterministic value-generating algorithm in such a way that it creates 'nice' values; I call this principle Constrained Non-Determinism. Values are carefully generated to stay far away from any boundary conditions that may cause the SUT to behave differently in each test run.
Conventional unit testing wisdom dictates that unit tests should be deterministic, so how can I possibly endorse this technique?
To understand this, it's important to know why the rule about deterministic unit tests exist. It exists because we want to be certain that each time we execute a test suite, we verify the exact same behavior as we did the last time (given that no tests changed). Since we also use test suites as regression tests, it's important that we can be confident that each and every test run verifies the exact same specification.
Constrained Non-Determinism doesn't invalidate that goal, because the algorithm that generates the values must be carefully picked to always create values that stay within the input's Equivalence Class.
In a surprisingly large set of APIs, strings, for example, are treated as opaque values that don't influence behavior in themselves. Many enterprise applications mostly store and read data from persistent data stores, and the value of a string in itself is often inconsequential from the point of view of the code's execution path. Data stores may have constraints on the length of strings, so Constrained Non-Determinism dictates that you should pick the generating algorithm so that the string length always stays within (or exceeds, if that's what you want to test) the constraint. Guid.ToString always returns a string with the length of 36, which is fine for a large number of scenarios.
Note that Constrained Non-Determinism is only relevant for Anonymous Variables. For input where the value holds a particular meaning in the context of the SUT, you will still need to hand-pick values as always. E.g. if the input is expected to be an XML string conforming to a particular schema, a Guid string makes no sense.
A secondary benefit of Constrained Non-Determinism is that you don't have to pause to come up with values for Anonymous Variables when you are writing the test.
While this advice may be controversial, I can only recommend it - I've been using this technique for about a year now, and have only become more fond of it as I have gained more experience with it.
Comments
One suggestion/tip when using random values to test, is to output the random value when the test fails. With randomness, you may come across that 1 in 100 value that breaks the test, but you won't have any idea of what it was, which makes it a bit hard to replicate and fix. In my Unit Test project, I have a common method that you pass the value to and it will print it to the output screen when it fails.
As for my question on Twitter. I'm dealing with a really large database (700Gbs), which returns an infinite number of combinations of data through stored procs. There are no INSERT/UPDATE/DELETEs (that's done in a totally different project). What I want to test is how the code handles the data that is returned from the procs, which is based on a user's selection from multiple radio buttons, and drop down boxes (single and multiple selection).
This data that they select from, comes from the database too, which we have little control over if it's valid or not.
So my question on Twitter was, should I create a method(s) that generate random parameters (which come from the DB) that the proc accepts? What I'm testing is not if the data itself is valid, but whether the code handles some weird anomalies? Or should I just use parameters that I know return valid data?
I've already experimented with creating a proc that generates random parameters from the data within the database, and using those values in my test. Amazingly, it's found a lot of issues that I was able to go back and fix.
The reason why I ask, is because I've heard that this goes beyond "Unit Testing".
Thanks,
Carl J
If you can't inject a Test Double that provides all the different data combinations, you should be able to use Moles for that part of the task.


Tests that TextTrack's cues are indexed and updated in order during video playback. Test uses the cuechange event.


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