Being a huge movie fan (remember your job at LucasFilms), you decide to start storing information on good movies with the help of lists.
Start by creating a list for the movie "The Shining". We have already created the variables mov
, act
and rev
in your R workspace. Feel free to check them out in the console.
Complete the code on the right to create shining_list
; it contains three elements:
mov
)act
)rev
)Do not forget to name the list components accordingly (names are moviename, actors and reviews).
mov <- "The Shining"
act <- c("Jack Nicholson","Shelley Duvall","Danny Lloyd","Scatman Crothers","Barry Nelson")
sources <- c("IMDb1","IMDb2","IMDb3")
comments <- c("Best Horror Film I Have Ever Seen","A truly brilliant and scary film from Stanley Kubrick","A masterpiece of psychological horror")
scores <- c(4.5,4,5)
rev <- data.frame(scores, sources, comments)
rm(scores, sources, comments)
# The variables mov, act and rev are available
# Finish the code to build shining_list
shining_list <- list(moviename = mov)
# The variables mov, act and rev are available
# Finish the code to build shining_list
shining_list <- list(moviename = mov, actors = act, reviews = rev)
msg = "Do not remove or change the definition of the pre-set variables!"
lapply(c("mov", "rev", "act"), test_object, undefined_msg = msg, incorrect_msg = msg)
test_object("shining_list",
incorrect_msg = "It looks like `shining_list` does not contain the correct elements: the first element should be `mov`, the second element `act`, and the third `rev`.")
test_object("shining_list", eq_condition = "equal",
incorrect_msg = "It looks like `shining_list` does not contain the correct naming for the components: name the first element `moviename`, the second element `actors`, and the third element `reviews`.");
success_msg("Wonderful! You now know how to construct and name lists. As in the previous chapters, let's look at how to select elements for lists. Head over to the next exercise")
shining_list <- list(moviename = mov)
is only part of the solution; it's your job to also add act
and rev
to the list, with the appropriate names.


Welcome to Recursive Symmetry Aware Materials Microstructure Explorer’s documentation!¶
Recursive Symmetry Aware Materials Microstructure Explorer is a tool to enable the recursive searching of images by creating projections of images and allowing interactive filtering. This tool is packaged with two feature extractors, a VGG-16 based model and a symmetry aware model based on ResNet-34
Installation
To install use
pip install Recursive_Symmetry_Aware_Materials_Microstructure_Explorer
It is recommended that you install the package on a new conda env
1. conda create -name bokeh
2. conda install -c anaconda ipykernel
3. python -m ipykernel install --user --name=bokeh
4. conda activate bokeh
5. pip install Recursive_Symmetry_Aware_Materials_Microstructure_Explorer
# Examples
We include one example of this package.
Within the repositiory download the two files in the Examples
folder:
Example_Image_Scraping_and_Collating.ipynb
- This is a notebook that scrapes images from google, this also has the code
to collate the images from the folders.
bokeh_server.py
- This contains the script for the graphical user interface.
To spin up a server,
bokeh serve bokeh_server.py --args -p path
The Bokeh demo will be served at the local host: http://localhost:5006/bokeh_server


- EchoCommand - Class in org.drupal.project.computing.common
-
A simple DCommand implementation that echos the "input" string into "output".
- EchoCommand() - Constructor for class org.drupal.project.computing.common.EchoCommand
-
- encodeURLQueryParameters(Properties) - Method in class org.drupal.project.computing.DUtils
-
Encode URL query parameters.
- endpoint - Variable in class org.drupal.project.computing.DRestfulJsonServices
-
- execute() - Method in class org.drupal.project.computing.common.EchoCommand
-
- execute() - Method in class org.drupal.project.computing.DCommand
-
This is the core of DCommand.
- execute(String[], String) - Method in class org.drupal.project.computing.DDrush
-
Execute Drush command, and returns STDOUT results.
- execute(String[]) - Method in class org.drupal.project.computing.DDrush
-
- executeShell(CommandLine, File, String) - Method in class org.drupal.project.computing.DUtils
-
Execute a command in the working dir, and return the output as a String.
- executeShell(CommandLine, String) - Method in class org.drupal.project.computing.DUtils
-
- executeShell(CommandLine) - Method in class org.drupal.project.computing.DUtils
-
- executeShell(String) - Method in class org.drupal.project.computing.DUtils
-
- executeShell(CommandLine, File, byte[], Charset) - Method in class org.drupal.project.computing.DUtils
-
This is the underlying System Exec entry


Preparation time
less than 30 mins
Cooking time
over 2 hours
Serves
Serves 4
Braise shin of beef with chorizo and red wine for this long and you won’t be disappointed.
For the beef, make sure there are two available shelves in your oven and preheat the oven to 160C/320F/Gas 3. Heat the olive oil in a flameproof casserole and brown the beef. (You will need to do this in batches, so don't overcrowd the pan.) Using a slotted spoon, lift out the meat and set aside.
Add the chorizo and onion to the pan and fry until softened and the fat runs from the chorizo.
Add the carrots and parsnips and continue to cook until tinged with colour and beginning to soften. Sprinkle in the flour and cook for 2-3 minutes, stirring frequently. Pour over the wine and stirring constantly, bring to the boil. Stir in the beef stock.
Return the beef to the pan and season with salt and freshly ground black pepper. Cover with a tight fitting lid and braise on the bottom shelf of the oven for two hours, or until the meat is tender - check during the cooking time to ensure that the meat doesn't become too dry. If it does, add a splash of boiled water.
For the dumplings, line a baking tray with non-stick baking paper. Sift the flour into a bowl with a pinch of salt. Add the suet and rosemary and bind together with enough cold water to form a soft dough.
Using floured hands and working as lightly fingered as possible, divide the mixture into eight and shape into oval dumplings. Place on a baking tray slightly spaced apart.
Bake the dumplings on the shelf above the beef for the last 25 minutes of the beef’s cooking time, until they are puffed up and cooked through.
Meanwhile, for the mash, boil the potatoes in a pan of boiling, salted water for 15 minutes, or until tender. Drain well and press through a potato ricer into a warm bowl. Mix in the horseradish, butter and cream. Season to taste with salt and freshly ground black pepper.
Serve the mash with the beef and dumplings.


| 018:022 Khan | : |
(Some) say they were three, the dog being the fourth among them; (others) say they were five, the dog being the sixth, guessing at the unseen; (yet others) say they were seven, the dog being the eighth. Say (O Muhammad SAW): "My Lord knows best their number; none knows them but a few." So debate not (about their number, etc.) except with the clear proof (which We have revealed to you). And consult not any of them (people of the Scripture, Jews and Christians) about (the affair of) the people of the Cave. |
| 018:022 Maulana | : |
(Some) say: (They were) three, the fourth of them their dog; and (others) say: Five, the sixth of them their dog, making conjectures about the unseen. And (others) say: Seven, and the eighth of them their dog. Say: My Lord best knows their number -- none knows them but a few. So contend not in their matter but with an outward contention, and question not any of them concerning them. |
| 018:022 Pickthal | : |
(Some) will say: They were three, their dog the fourth, and (some) say: Five, their dog the sixth, guessing at random; and (some) say: Seven, and their dog the eighth. Say (O Muhammad): My Lord is Best Aware of their number. None knoweth them save a few. So contend not concerning them except with an outward contending, and ask not any of them to pronounce concerning them. |
| 018:022 Rashad | : |
Some would say, "They were three; their dog being the fourth," while others would say, "Five; the sixth being their dog," as they guessed. Others said, "Seven," and the eighth was their dog. Say, "My Lord is the best knower of their number." Only a few knew the correct number. Therefore, do not argue with them; just go along with them. You need not consult anyone about this. |
| 018:022 Sarwar | : |
(With regard to the number of the youths) some say, "There were three and the dog was the fourth one," Others say, "There were five and the dog was the sixth one." In reality, they are just feeling around in the dark. Still some of them say, "There were seven and the dog was the eighth one." (Muhammad), say, "My Lord has the best knowledge of their number. You know very little about it." Do not insist on arguing with them, but merely tell them the story as it has been revealed to you and do not ask anyone about them. |
| 018:022 Shakir | : |
(Some) say: (They are) three, the fourth of them being their dog; and (others) say: Five, the sixth of them being their dog, making conjectures at what is unknown; and (others yet) say: Seven, and the eighth of them is their dog. Say: My Lord best knows their number, none knows them but a few; therefore contend not in the matter of them but with an outward contention, and do not question concerning them any of them. |
| 018:022 Sherali | : |
Some say, 'They were three, the fourth of them was their dog;' and others say, 'They were five, the sixth of them was their dog,' guessing at random. And yet others say, 'They were seven, the eighth of them was their dog.' Say, 'My Lord knows best their correct number. None knows them except a few.' So argue not concerning them except with unanswerable arguments, nor seek information concerning them from anyone of them. |
| 018:022 Yusufali | : |
(Some) say they were three, the dog being the fourth among them; (others) say they were five, the dog being the sixth,- doubtfully guessing at the unknown; (yet others) say they were seven, the dog being the eighth. Say thou: "My Lord knoweth best their number; It is but few that know their (real case)." Enter not, therefore, into controversies concerning them, except on a matter that is clear, nor consult any of them about (the affair of) the Sleepers. |
Concepts/Themes Covered: أركان الإسلام > التوحيد > توحيد الله تعالي > صفات الله تعالى > أعلم Pillars of Islam> Islamic> Oneness of Allah> His Glory's Characteristics> The One who Knows Better القصص والتاريخ > أصحاب الكهف The Stories and the History>Cave People
This tool uses eight English parallel translations for the Holy Quran imported from Mr. Clay Smith. You can also visit Qurany Concepts for browsing the Quran themes or topics. If you want to search the Holy Quran for keywords, please visit Qurany Keywords.


STEFANIE TAM
INFORMATION
03.2018; 8.5"x11", 5.25"x6.75"
GOODS + SERVICES is a 3D product catalogue of the future! Featuring a special edition issue with items curated from Craigslist's "BEST OF" section and four issues from their "FOR SALE" section, consumers can access 3D models of posted items to view and purchase! Find weird Craigslist postings that range between bellybutton lint, top ramen chicken flavor packets, and a Calder- esque dildo mobile!
G+S archives the cost, description, location, and post date of 49 Craigslist items over the span of 5 issues, each with a relative insert. Each item has a 3D modeled representation that's accessible through the Augment app in combination with the print issues.


An {@link org.mcarthur.sandy.gwt.event.list.client.EventList} implementation that noticed when elments in it change and fires events accordingly.
To add this to your project you will need the GWT-Stuff-EventList and GWT-Stuff-PropertyChange jars added to your classpath and add the following to your GWT module file:
<inherits name="org.mcarthur.sandy.gwt.event.list.property.PropertyChangeEventList"/>
Use the ObservingEventList when you have a collection of objects that implement {@link org.mcarthur.sandy.gwt.event.property.client.PropertyChangeSource}.
EventList eventList = new ObservingEventList();


I'm really into Github.
I do many things at work. I am very interesting.
Carrier pigeon is the best way to contact me.


{% extends './layout_index.html' %} {% block page_title %} Chlamydia - NHS.UK {% endblock %} {% block pageTitle %} Chlamydia {% endblock %} {% block pageSubTitle %} If it's not treated {% endblock %} {% block navContent %}
If you have chlamydia and don't treat it, it can spread to other parts of the body and lead to serious health problems. These are known as complications.
The more times you have chlamydia, the more likely you are to develop complications.


Web service template v1 features
Essential structure only, all five typical requests
No data, just string handling


[gecode-users] Extending Integer Variable
Filip Konvička
filip.konvicka at logis.cz
Tue Apr 8 10:13:07 CEST 2008
> Well, nothing is easy ;-)
>
> As it comes to double one should keep in mind that most CPUs have parallel
> functional units for floats: so, that just runs in parallel to the integer
> and address arithmetic. I once did a little profiling to see what effect it
> has for double and it was considerably less than what I had expected. The
> big problem with doubles start if you store them in memory, bandwidth and
> alignment are the typical problems. But for intermediate results that should
> be okay. But I agree, 64 bit should also be fine. The reason why we didn't
> go for that is that I looked at the assembly code that was generated by gcc
> at that time (maybe three years ago)... It just made me cry. As soon as long
> long becomes C++ standard (maybe relying on the de-facto C++0x standard -
> most likely C++09 - would already be good enough) we will go for 64 bits.
> The main motivation for us is that we use double only for integers which
> limits precision to 48 bits.
:-) I think I've seen some discussions on the gcc mailing lists
concerning long long arithmetics, maybe some year ago or so...so maybe
it is better now.
> 128 bits can be tinkered okay. Either rely on compiler extensions and if
> they are not available hack it yourself.
>
> Yes, however small the set of propagators might be, we will be interested. I
> would believe that much of the IntVar stuff can serve as a good starting
> point for you. If one gets really serious about float intervals, it becomes
> tricky: it requires symbolic representation of the arithmetic expressions as
> well as variable centered propagation. Quite different.
That's interesting. (Read: I don't understand that :-)) What is
variable-centered propagation? Do you have any papers about the matter?
The only implementation of floatvars I've seen so far is the XRI library
for Mozart, so I was not thinking about doing anything special yet.
I can see that symbolic representation can be useful, but it depends on
what quality/performance ratio you target. Personally I target
performance (the tradeoff I see here is that I might not get bit-precise
filtering).
> Watch out: for some x64 it is still that long is 32 bits (Windows). On
> others it is 64 (MacOS, Linux?). Only long long is 64 at least.
Yes, I was referring to gcc. On Windows things tend to be
backwards-compatible as *long* as possible :-D
Cheers,
Filip
More information about the gecode-users
mailing list


You're viewing this page over insecure channel. You can give it a try and connect over HTTPS, if that succeeds all subsequents accesses of this page will go over HTTPS.
Click here to fetch the certificate of this authority. Alternatively install certificate on Fedora or Ubuntu with following copy-pastable snippet:
{% include "snippets/store-authority.sh" %}
{% include "snippets/update-trust.sh" %}


About
Sono is a high-level object-oriented and procedural scripting language developed with linguistic capabilities in mind. For that it supports multiple operators revolving around phonological analysis based upon distinctive features.
The language is capable of functioning moreover as a general scripting language, with a growing number of libraries and the capacity to interface with Java files. The language is small right now and has very little following, if any at all, but it is a language made to fill a niche that has not yet been assessed. Every day, the gap between the sciences and programming becomes smaller and smaller, and the sadly neglected science of Linguistics is equally deserving of its time in the world of programming. If not by this language does it become widepsread, than at least may it be that this language inspire another to take up that mantle.
Installation
There are currently binaries for Windows only, but one can compile the source directly from the Github repo:
$ git clone https://github.com/Nallantli/Sono $ cd Sono
For Windows:
$ ./build-jar $ ./build-lib
For OSX/Linux:
$ sh build-jar.sh $ sh build-lib.sh
These commands will place all necessary files into their locations in /bin
, which can be
then added to the system path.
Once the system path is updated, all one needs to do is offer the interpreter a dataset consisting of
base segmental values. The repo includes a slightly adjusted version of Bruce Hayes' feature chart under
assets/
:
$ sono -d assets/hayes.tsv
This will generate the cache, stored in your home directory under .sono/
. Now you can entire
the console or run a file!
$ sono Sono {VERSION} Phonological Data Loaded From <{DATA}> Loaded System Library >
$ sono examples/hello.so Hello World!
Hello World
Below is the classic "hello, world!" script; with one line the task is done:
println("Hello World!");
Phonology
There are multiple examples to be seen in the repo, but a few minor demonstrations can never be so bad.
var rule = S |> [-|syl, +|cons, +|voice, -|nasal] -> [-|voice] // null .. "#";
That value above is a Rule, a phonological transformation rule. When applied to a Word, it transforms that value accordingly. The rule above is a final-consonant devoicing rule, so any consonant at the end of a Word that is not a nasal will become [-voice].
println(`taːg` >> rule); println(`taːgə` >> rule);
Output:
taːk taːgə
Note that the second Word is unaffected - it does not end with a consonant. Rules can be much more powerful and versatile using a variety of tokens and parameters further explained in the repo (but soon to be transported here).


Back
Colchicum hungaricum is a species of lily plant . First described by Victor von Janka. Colchicum hungaricum belongs to the genus Colchicum in the family Colchicaceae. To date no ticks have been listed under him.


public interface IFlowTable
| Modifier and Type | Method and Description |
|---|---|
java.util.List |
acceptSources()
Returns all accept sources in table.
|
void |
addAcceptFlow(IFlowNodeHandle source,
UUID remoteRepoIdentifier,
java.lang.String remoteRepoURI,
java.util.Collection componentScopes,
java.lang.String description)
Creates a flow entry (with description) adding the given workspace as an accept source, and declaring a list of component filters (can be empty) to be used when determining which change sets to be accepted.
|
void |
addDeliverFlow(IFlowNodeHandle target,
UUID remoteRepoIdentifier,
java.lang.String remoteRepoURI,
java.util.Collection componentSyncs,
java.lang.String description)
Creates a flow entry (with description) adding the given workspace as a deliver target.
|
java.util.List |
deliverTargets()
Returns all deliver targets in table.
|
IFlowEntry |
getAcceptFlow(IFlowNodeHandle source)
Get the accept flow entry which has the specified flow node as
an accept source.
|
IFlowEntry |
getAcceptFlow(IFlowNodeHandle source,
java.util.Collection componentScopes)
Get the accept flow entry which has the specified workspace & component scopes as
an accept source.
|
java.util.Collection |
getComponentScopes(IFlowNodeHandle node)
Returns the component scopes for the given flow node.
|
IFlowEntry |
getCurrentAcceptFlow()
Gets the current accept flow entry for the workspace.
|
IFlowEntry |
getCurrentAcceptFlow(IComponentHandle component)
Gets the workspace's current accept flow entry for the given component.
|
IFlowEntry |
getCurrentDeliverFlow()
Gets the current deliver flow entry for the workspace.
|
IFlowEntry |
getCurrentDeliverFlow(IComponentHandle component)
Gets the workspace's current deliver flow entry for the component.
|
IFlowEntry |
getDefaultAcceptFlow()
Gets the default accept flow entry for the workspace.
|
IFlowEntry |
getDefaultAcceptFlow(IComponentHandle component)
Gets the workspace's default accept flow entry for the given component.
|
IFlowEntry |
getDefaultDeliverFlow()
Gets the default deliver flow entry for the workspace.
|
IFlowEntry |
getDefaultDeliverFlow(IComponentHandle component)
Gets the workspace's default deliver flow entry for the component.
|
IFlowEntry |
getDeliverFlow(IFlowNodeHandle target)
Get the deliver flow entry which has the specified flow node as
a deliver target.
|
IFlowEntry |
getDeliverFlow(IFlowNodeHandle target,
java.util.Collection componentScopes)
Get the deliver flow entry which has the specified workspace & component scopes as
a deliver target.
|
IFlowTable |
getWorkingCopy()
Returns a modifiable copy of this flow table.
|
void |
removeAcceptFlow(IFlowNodeHandle source)
Removes flow entries that have the given node as an accept source.
|
void |
removeAll(IFlowNodeHandle target)
Removes all flow entries that have the given node as a deliver target or as an accept source.
|
void |
removeDeliverFlow(IFlowNodeHandle target)
Removes flow entries that have the given node as a deliver target.
|
void |
setComponentScopes(IFlowNodeHandle node,
java.util.Collection componentScopes)
Sets the component scopes for the given flow node.
|
void |
setCurrent(IFlowEntry entry)
Indicates that the specified entry is the current one for the workspace
for this direction (Accept or Deliver).
|
void |
setCurrent(IFlowEntry entry,
IComponentHandle component)
Indicates that the specified entry is the current one for a particular
component in the workspace for this direction (Accept or Deliver).
|
void |
setDefault(IFlowEntry entry)
Indicates that the specified entry is the default one for the workspace
for this direction (Accept or Deliver).
|
void |
setDefault(IFlowEntry entry,
IComponentHandle component)
Indicates that the specified entry is the default one for a particular
component in the workspace for this direction (Accept or Deliver).
|
void |
setFlowEntryDescription(IFlowEntry entry,
java.lang.String description)
For the specified entry, set the specified flow entry's description attribute to the
string value specified.
|
void |
unsetCurrentAcceptFlow()
Clear the workspace's current accept flow.
|
void |
unsetCurrentAcceptFlow(IComponentHandle component)
Clear the current accept flow setting for the specified component.
|
void |
unsetCurrentDeliverFlow()
Clear the workspace's current deliver flow.
|
void |
unsetCurrentDeliverFlow(IComponentHandle component)
Clear the current deliver flow setting for the specified component.
|
void |
unsetDefaultAcceptFlow()
Clear the workspace's default accept flow.
|
void |
unsetDefaultAcceptFlow(IComponentHandle component)
Clear the default accept flow setting for the specified component.
|
void |
unsetDefaultDeliverFlow()
Clear the workspace's default deliver flow.
|
void |
unsetDefaultDeliverFlow(IComponentHandle component)
Clear the default deliver flow setting for the specified component.
|
IFlowTable getWorkingCopy()
null
java.util.List deliverTargets()
IFlowEntry
; never null
java.util.List acceptSources()
IFlowEntry
; never null
.void addDeliverFlow(IFlowNodeHandle target, UUID remoteRepoIdentifier, java.lang.String remoteRepoURI, java.util.Collection componentSyncs, java.lang.String description)
setComponentScopes(IFlowNodeHandle, Collection)
to set the components scopes for deliver flow target.target
- flow node to serve as the accept source for the entry; cannot be null
.remoteRepoIdentifier
- : the identifier which represents the repository id for the deliver flow target if the
the deliver target is from a different repositoryremoteRepoURI
- : the expected repository uri which represents the URI for the deliver flow target if the
the deliver target is from a different repositorycomponentSyncs
- : collection (element type: IComponentHandle
) which is used internally to manage list
of common components between source and target; Can be null
.description
- client specified description to associate with this flow entry; null
is permittedvoid addAcceptFlow(IFlowNodeHandle source, UUID remoteRepoIdentifier, java.lang.String remoteRepoURI, java.util.Collection componentScopes, java.lang.String description)
source
- flow node to serve as the accept source for the entry; cannot be null
.remoteRepoIdentifier
- : the identifier which represents the repository id for the accept flow target if the
the accept source is from a different repositoryremoteRepoURI
- : the expected repository uri which represents the URI for the accept flow target if the
the accept source is from a different repositorycomponentScopes
- collection (element type: IComponentHandle
) which specify
the additional component-scoped sources for flow; null
implies no component scopesdescription
- client specified description to associate with this flow entry; null
is permittedvoid removeDeliverFlow(IFlowNodeHandle target)
target
- flow node which is to be removed from all deliver flows;
cannot be null
.void removeAcceptFlow(IFlowNodeHandle source)
source
- flow node which is to be removed from all accept flows;
cannot be null
.void removeAll(IFlowNodeHandle target)
target
- source or target flow node which is to be removed from all accept and accept flows;
cannot be null
.IFlowEntry getAcceptFlow(IFlowNodeHandle source)
source
- flow node to serve as the accept source for the entry; cannot be null
.null
otherwise.IFlowEntry getAcceptFlow(IFlowNodeHandle source, java.util.Collection componentScopes)
source
- flow node to serve as the accept source for the entry; cannot be null
.componentScopes
- collection (element type: IComponentHandle
) which specify
the additional component-scoped sources for flow; cannot be null
.null
otherwise.IFlowEntry getDeliverFlow(IFlowNodeHandle target)
target
- flow node to serve as the deliver target for the entry; cannot be null
.null
otherwise.IFlowEntry getDeliverFlow(IFlowNodeHandle target, java.util.Collection componentScopes)
target
- flow node to serve as the deliver target for the entry; cannot be null
.componentScopes
- collection (element type: IComponentHandle
) which specify
the additional component-scoped targets for flow; cannot be null
.null
otherwise.void setFlowEntryDescription(IFlowEntry entry, java.lang.String description)
entry
- a flow entry that exists in the table; cannot be null
.description
- a description for the given flow entryIFlowEntry getCurrentAcceptFlow()
null
.IFlowEntry getCurrentAcceptFlow(IComponentHandle component)
component
- a component in the flow; cannot be null
.null
.IFlowEntry getCurrentDeliverFlow()
null
.IFlowEntry getCurrentDeliverFlow(IComponentHandle component)
component
- a component in the flow; cannot be null
.null
.void unsetCurrentAcceptFlow()
void unsetCurrentAcceptFlow(IComponentHandle component)
component
- a component in the flow; cannot be null
.void unsetCurrentDeliverFlow()
void unsetCurrentDeliverFlow(IComponentHandle component)
component
- a component in the flow; cannot be null
.void setCurrent(IFlowEntry entry)
If there is already a current flow entry in this direction, it is cleared.
entry
- a flow entry that exists in the table; cannot be null
.void setCurrent(IFlowEntry entry, IComponentHandle component)
If there is already a current flow entry for this component in this direction, it is cleared.
entry
- a flow entry that exists in the table; cannot be null
.IFlowEntry getDefaultAcceptFlow()
null
.IFlowEntry getDefaultAcceptFlow(IComponentHandle component)
component
- a component in the flow; cannot be null
.null
.IFlowEntry getDefaultDeliverFlow()
null
.IFlowEntry getDefaultDeliverFlow(IComponentHandle component)
component
- a component in the flow; cannot be null
.null
.void unsetDefaultAcceptFlow()
void unsetDefaultAcceptFlow(IComponentHandle component)
component
- a component in the flow; cannot be null
.void unsetDefaultDeliverFlow()
void unsetDefaultDeliverFlow(IComponentHandle component)
component
- a component in the flow; cannot be null
.void setDefault(IFlowEntry entry)
If there is already a default flow entry in this direction, it is cleared.
entry
- a flow entry that exists in the table; cannot be null
.void setDefault(IFlowEntry entry, IComponentHandle component)
If there is already a default flow entry for this component in this direction, it is cleared.
entry
- a flow entry that exists in the table; cannot be null
.java.util.Collection getComponentScopes(IFlowNodeHandle node)
node
- the flow node handle (not null)IComponentHandle
) of the
flow, never null
, empty list means unscoped (i.e. process all components)void setComponentScopes(IFlowNodeHandle node, java.util.Collection componentScopes)
node
- the flow node handle (not null)componentScopes
- the collection of component scopes (element type: IComponentHandle
) of the
flow, never null
, empty list means unscoped (i.e. process all components)


Getting Involved with the KLEE Project
If you are interested in following development of KLEE, or would like to contribute, here are some resources that may prove useful.
Mailing Lists
Currently the main list for KLEE discussion (both for users and developers) is klee-dev.
Commit messages to the KLEE repository go to klee-commits. This is also the place to send patches if you are interested in contributing to KLEE.
Bug Reports
If you find a bug in KLEE, please report it on klee-dev and also fill a bug report on Bugzilla under the klee product.
Working with the Code
KLEE developer documentation is written in doxygen and you may have it online here: doxygen (this is updated nightly from the source tree).
Many parts of KLEE rely on the LLVM infrastructure, you can find more information about programming for LLVM here: LLVM's General Programming Documentation.


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From: @email.from.format
To: @email.to.map(_.format).mkString(", ")
Subject: @email.subject
@email.content match { case EmailSrv.TextContent(text) => {
Text content:
@text
Html content:
@Html(html)


{% extends 'base.html' %} {% load isingroup %} {% block content %} Your balance {% if balance == 0 %} € 0,- {% else %} {{ balance.balance }} {% endif %} {% if balance.balance.amount > 5 %} (Positive balance, so you have money left to spend). {% else %} {% if balance.balance.amount >= 0 %} So there little, if any, left in your acount. Thererfore you may want to make a deposit before this runs out. As it takes a few days (or more) before the monies wired will show up in your account. {%else%} Negative, so you owe the space money; and should make a deposit as soon as possible. {% endif %} {% endif %} {% if last_import %} The last import/synchronisation with the bank account was done by {{ last_import.by }} on {{ last_import.date }}.
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FileManager is "static" class which manages loading/saving from/to files.
More...
#include <FileManager.h>
FileManager is "static" class which manages loading/saving from/to files.
◆ isValidChar()
| bool FileManager::isValidChar |
( |
char |
c | ) |
|
|
static |
checks whether given char c is valid game map char
- Returns
- true if is valid, false otherwise
◆ loadHighScore()
| int FileManager::loadHighScore |
( |
| ) |
|
|
static |
loads high score from a file
- Returns
- int loaded score on success, 0 if any error has occurred
◆ loadMap()
| int FileManager::loadMap |
( |
const int & |
map_id, |
|
|
Map & |
map |
|
) |
| |
|
static |
loads and validates map
- Parameters
-
| [in] | map_id | determines ID of the map which matches with file map[map_id] |
| [in] | map | - reference to a map to fill in loaded data |
- Returns
- 0 on success, 1 if ifstream was not opened, 4 if there are too many/too little chars in the line, 5 if there are any non-valid chars, 6 if any of the inital player position is not steppable
◆ saveHighScore()
| bool FileManager::saveHighScore |
( |
int |
score | ) |
|
|
static |
saves high score in the highscore file
- Parameters
-
- Returns
- bool on success, false if something failed while saving
The documentation for this class was generated from the following files:


|
Lisp
is the second-oldest high-level programming language
(after Fortran
). Richard Stallman said in one of his
articles: “The most powerful programming language is
Lisp
. If you don't know Lisp
(or its variant,
Scheme
), you don't know what it means for a programming
language to be powerful and elegant. Once you learn Lisp
,
you will see what is lacking in most other languages.” This made
me curious and motivated me to learn Lisp
begining from
September of 2015. “Powerful” in the world of
Turing-complete programming languages means that you can do more with
the language in a finite amount of time.
(Another big name in Lisp community is Paul Graham, who wrote many inspiring essays on Lisp[5]. I read most of his essays and found they provide new insights on many issues, not just programming.)
I primarily use Guile
, a GNU implementation of the
Scheme
. I also use Racket
(previously known as
PLT scheme), another famous implementation. Scheme
is
mostly functional (but not purely functional). A program being
functional means that the program accomplishes its task by evaluating
various expressions. Being functional also means that a function itself
is a type of value, which can be, for example, stored in a variable,
passed as an argument to a function, returned from a function
invocation, just like a traditional value such as an integer.
All function invocations (including basic arithmetic operations) in Lisp
are based on the parenthezed-prefix-notation, i.e., (operator
operands1 operands2)
. For example (+ 1 2)
is a
function call. This notation is also used for other syntax structures
(called special forms) in Lisp such as the if
conditional
structure:
(if my_test exp1 exp2)
A subexpression in the above can also be another function call (or special form), which will introduce another pair of round-brackets. This kind of nest can be of arbitary levels. As a result, Lisp programs are full of round-brackets, which makes Lisp source codes look messy and not so readable if without strong support from a text editor (e.g. automatic indenting).
The parentheses (round-brackets) are also used as the external representation of the list data structure in Lisp. Therefore, people often say that souce codes of Lisp take the same form as the list data structure.
Multiple whitespaces (including line-breaks) are equivalent to a single whithespace in Lisp.
On my Ubuntu desktop computer, I used aptitude
to install
Common-Lisp, clisp
. Later I switched to guile
and Racket. I will primarily use guile
in this note.
In a terminal, type guile
to invoke the scheme
interpreter:
yj@pic:~$ guile
guile> (display "Hello World! \n")
Hello World!
Users interact with a Scheme interpreter though a read-evaluate-print loop (REPL). Scheme waits for the user to type an expression, reads it, evaluates it, and prints the return value.
The readline support for guile
command line is not loaded
by default and can be loaded and activated by adding the following two
lines of code in the init file ~/.guile
:
(use-modules (ice-9 readline)) (activate-readline)
Create a file called h.scm
with the following content:
;This line is a comment (display "Hello, World! \n")
Then run the above script at command line:
$ guile -s h.scm
Like Perl, Python, or any shell, Guile can interpret script files. A
Guile script is simply a file of Scheme code with some extra information
at the beginning which tells the operating system how to invoke Guile,
and then tells Guile how to handle the Scheme code. Add the interpreter
at the beginning of h.scm
:
#!/usr/bin/guile -s !#
and then make the file executable
chmod u+x h.scm
Then we can run it directly by ./h.scm
(assuming
h.scm
is in the current directory).
Note that there is an additional line starting with !#
.
This is because #!..!#
indicates multiline comments (block
comments) in guile
. When bash sees #!
at the
first line of a file, it considers the name following #!
as
an interpreter and invoke the interpreter with the name of the present
file as an argument. After guile
gets control, it reads the
file again from the begining. In this case, guile
see
#!..!#
, which is block comment and so is ignored by
guile
. That is the reason why another line with
!#
is needed in the script.
In dynamically typed languages such as Scheme, types are used to categorize values, rather than variables. The term “value” can be used exchangeably with “data”, which is often used when making a comparison with “program”. Scheme provides the following value types:
Simple value types: boolean, number, char, symbol.
Compound value types: string, vector, pair, procedure, port
Type of a value can be tested by the corresponding predicates:
boolean? number? char? symbol? string? vector? pair? procedure? port?
Boolean: #f
is logical false, #t
is logical
true. Although there is a separate boolean type, any Scheme value can be
used as a boolean value for the purpose of a conditional test: all
values count as true in such a test except for #f
.
number–>complex–>real–>rational–>integer.
Scheme numbers can be integers (eg, 42
), rationals
(22/7
), reals (3.1416
), or complex
(2+3i
). In scheme, an integer is a rational, is a real, is
a complex number, is a number. Predicates exist for testing the various
kinds of numberness:
(number? 42) => #t (number? #t) => #f (complex? 2+3i) => #t (real? 2+3i) => #f (real? 3.1416) => #t (real? 22/7) => #t (real? 42) => #t (rational? 2+3i) => #f (rational? 3.1416) => #t (rational? 22/7) => #t (integer? 22/7) => #f (integer? 42) => #t
Integers need not be specified in decimal (base 10) format. They can be
specified in binary by prefixing the numeral with #b
. Thus
#b1100
is the number twelve. The octal prefix is
#o
and the hex prefix is #x
. (The optional
decimal prefix is #d
.)
Character data are represented by prefixing the character with
#\
. Thus, #\c
is the character c
.
Some non-graphic characters have more descriptive names, e.g.,
#\newline
, #\tab
. The character for space can
be written #\
, or more readably, #\space
. The
character predicate is char?
Symbol is a sequence of characters that can not be confused with other
values, namely, characters, booleans, numbers, compound data. Thus,
this‑is‑a‑symbol
, i8n
,
<=>
, and $!#*
are all symbols; whereas
6
, ‑i
(a complex number),
#t
,
"this‑is‑a‑string"
, and
(barf)
(a list) are not. Symbols and strings are separate
data types.
Symbols are also atomic, we cannot split them apart. The primary operation we perform on symbols is comparison (determining whether two symbols are the same).
A symbol denotes only itself. Unlike other simple values (booleans, characters, numbers), symbols are not self-evaluating. This design is because of the practice that a sequence of characters that is a symbol is reserved by Scheme as an identifier (rather than a value), and is evaluated to the value the identifier is bound (if it is bound, otherwise raises an error), rather than the symbol literal itself.
When we want to refer to something as a value (data) involved in a computation, rather than as a program (specifically, the name of some other value, an expression to be evaluated), we put an apostrophe (usually pronounced “quote”) in front of it. In effect, by quoting something, we're telling Scheme to take it literally and without further interpretation or evaluation. You can quote many different data, not only limitted to symbols. For example:
(symbol? '(1 2 3)) ;=>#f (symbol? 'sample) ;=>#t (symbol? '2) ;=>#f (integer? '2) ;=>#t
In effect, an apostrophe introduces data i.e., values, which can be of any types. What follows the apostrophe and ends at a proper location (determined by Lisp syntax) is the data itself; apostrophe itself is not a part of the data. For the practical purpose of distinguishing between data and program for programmers, the apostrophe can be considered as a part of external representation of the data, as an indicator of data. For values of boolean, number, char and string, they are self-evaluating, and thus it is not necessary to quote them, but in order to have a sharp distinguishment between data and program, it is instructive to quote them.
To be unified with Lisp's parentheses prefix syntax, Lisp also introduces the quote special form:
(symbol? (quote sample)) ;=>#t
string: a sequence of characters enclosed by double quotation markers is a string. Strings evaluates to themselves:
"hello" => "hello"
The characters in a given string can be individually accessed and
modified. The procedure string‑ref
takes a string
and a (0-based) index, and returns the character at that index:
(string-ref "Hello" 0) => #\H
Other useful string methods include string-append
,
make-string
, string-set!
.
Vectors are sequences like strings, but their elements can be anything, not just characters. Indeed, the elements can be vectors themselves, which is a good way to generate multidimensional vectors.
Scheme's representation of a vector value: a sharper sign #
followed by the vector's contents enclosed in parentheses. eg.
#(0 1 2)
Here's a way to create a vector of the first five integers:
(vector 0 1 2 3 4) => #(0 1 2 3 4)
In analogy with make‑string
, the procedure
make‑vector
makes a vector of a specific length:
(make-vector 5)
The procedures vector‑ref
and
vector‑set!
access and modify vector elements. The
predicate for checking if something is a vector is vector?
.
The following pair:
(a . (b . (c . d)))
is equivalent to the following list:
(a b c d)
List predicat is list?
This value is returned by a lambda expression (discussed later). Procedure values do not have external representations for readin syntax.
Port value do not have external representations for readin syntax.
Like other functional programming languages, basic elements of Scheme code are expressions. Expressions can be evaluated, producing a value. The most fundamental expressions are literal expressions:
#t ;=>#t 23 ;=>23
This notation means that the expression #t
evaluates to
#t
, that is, the value for “true”, and that the
expression 23
evaluates to a number object representing the
number 23
.
 The first subexpression identifies an operation; the remaining subexpressions are operands to the operation:
(+ 23 42) ;=>65 (+ 14 (* 23 42)) ;=>980 (remainder 5 2) ; => 1
Compound expressions can be nested, as the second example shows. As these examples indicate, compound expressions in Scheme are always written using the same prefix notation: operator first then operands follow. As a consequence, the parentheses are needed to indicate structure. Another consequence is that, “superfluous” parentheses, which are often permissible in mathematical notation and also in many programming languages, are not allowed in Scheme. This is because adding a pair of superfluous parentheses to an expression corresponds to forming a new compound expression and letting Lisp treat the original expression as a sub-expression and use the value of this sub-expression as an operator and evaluate the compound expression (function invocation with no argument), the value of which is usually different from the original expression (if we are lucky to get a value at all).
Multiple sub-expressions of a compound expression are separated by white-space. In Scheme, line-ending is equivalent to whithe spaces, and multiple whitespaces or line-endings are equivalent to a single whitespace.
Not all expressions are valid programs. If you typed (1 2)
at the Scheme listener, you will get an error because (1,2
)
is a list, which is valid data but is not an expression that can be
evaluated to give an value.
Compound expressions are a subcategory of combinations (in Guile) or forms (in R5RS). This ditinguishment is necessary becasue, in scheme, some combinations are not considered to be expressions. One example is the variable binding and initialization structure:
(define x 23)
which is not expression. While definitions are not expressions, definitions and compound expressions exhibit similar syntactic structure:
(define x 23) (* x 2)
While the first line contains a definition, and the second an
expression, this distinction depends on the bindings for
define
and *
. At the purely syntactical level,
both are forms, and form is the general name for a syntactic part of a
Scheme program. In particular, 23
is a subform of the form
(define x 23)
.
Scheme evaluates a list form by examining the first element, or head, of
the form. If the head evaluates to a procedure, the rest of the form is
evaluated to get the procedure's arguments, and the procedure is applied
to the arguments. If the head of the form is a special form, how the
remaing sub-form are evalued are determined by that special form. Some
special forms are begin
, define
, and
set!
. define
introduces and initializes a
variable. set!
assigns a new value to a variable.
begin
causes its subforms to be evaluated in order, the
result of the entire form being the result of the last subform.
Empty combination () is considered to be an invalid program in Guile
and Racket (it is still a valid data, i.e., empty list) because it is
missing the procedure expression. In GNU clisp, empty list is a valid
program, which evaluates to NIL
(boolean value false).
All function calls (including basic arithmetic operations) in Lips are
based on the parenthezed-prefix-notation, i.e., (operator
operands1 operands2)
. Many predefined operations of Scheme are
provided not by syntax, but by variables whose values are procedures.
The + operation, for example, which receives special syntactic treatment
in many other languages, is just a regular identifier in Scheme, bound
to a procedure that adds number objects. The following are some
arithmetic operations:
(+ 2 3) ;=> 5 (- 2 3) ;=> -1 (* 2 3) ;=> 6 (/ 2 3) ;=> 2/3 (expt 2 3) ;=> 8 (quotient 5 2) ; => 2 (remainder 5 2) ; => 1 (/ 35 5) ; => 7 (/ 1 3) ; => 1/3 (exact->inexact 1/3) ; => 0.3333333333333333 (+ 1+2i 2-3i) ; => 3-1i
A list in lisp can be treated as program or data. If a list is given to
a lisp interpreter, by default, the list is treated as program and lisp
evaluates each elements of the list. To prevent a list from being
evaluated (i.e, to treat them literally), we can use the
quote
special form. For example
guile>(quote (1 2 3)) (1 2 3) guile> (quote foo) foo
quote is called a “special form” because, unlike most other lisp operations, it doesn't evaluate its argument.
There is a shortcut (syntax sugar) which provides an alternative form of
calling the quote
procedure: instead of using the standard
list form: a single quotation marker before an expression have the
effect of preventing evaluation. For example
guile> '(f 2 3) (f 2 3) guile> 'foo foo
In Scheme, there are very few restrictions on what kind of sequence of characters can be used as a variable name. Any character sequence that can not be confused with Scheme's value types can be used a variable name.
The restriction on variable names in Lisp can be put in the following
way: All literals that are symbols (i.e., the predicate
symbol?
return true) can be used as identifiers (variable
names, names of locations, names of values).
Scheme has latent as opposed to manifest types. Types are associated with objects(also called values) rather than with variables. (Some authors refer to languages with latent types as untyped, weakly typed or dynamically typed languages.) Other languages with latent types are Python, Ruby, Smalltalk, and other dialects of Lisp. Languages with manifest types (sometimes referred to as strongly typed or statically typed languages) include Fortran, C/C++, C#, Java, Haskell, and ML.
Lisp
is a language with dynamically typed variables, which
means the type of a variable can change during runtime and hence there
is no type declaration for variables. More accurately, typing is
associated with the value that a variable assumes rather than the
variable itself.
In guile
, a variable (a name) is bound to a value (data
object) by using define
(define a 123) (define a "hello")
More accurately speeaking, define
bind a name with a
location (if the name is not yet bound) and assign a value to that
location. When a variable is bound to a location, its value can also be
modified by using set!
, e.g.,
(set! a 456)
For a bound variable, using define
and set!
are equivalent to each other. If we use set!
on a unbound
variable, the lisp will raise error message, reminding us that a
variable must be first bound to a location (using define
)
before we can set its value.
Scheme
uses lexical (static) scope. (Originally Lisp used
dynamic scoping, Emacs lisp can swich between lexical and dynamic
scoping by seting an option.) In lexical scope, each use of a variable
is associated with a lexically apparent binding of that variable.
Scheme is a statically scoped language with block structure. To each place in a top-level program or library body where an identifier is bound, there corresponds a region of code within which the binding is visible. The region is determined by the particular binding construct that establishes the binding; if the binding is established by a lambda expression, for example, then its region is the entire lambda expression. Every mention of an identifier refers to the binding of the identifier that establishes the innermost of the regions containing the use. If a use of an identifier appears in a place where none of the surrounding expressions contains a binding for the identifier, the use may refer to a binding established by a definition or import at the top of the enclosing library or top-level program (see chapter 7). If there is no binding for the identifier, it is said to be unbound.
In dynamic scoping, a function may reference local variables defined in the lexical scope of the calling unit, which means, if a programmer declares a variable within the lexical scope of a function, it is available to subroutines called from within that function. Originally, this was intended as an optimization; lexical scoping was still uncommon and of uncertain performance. Somebody asked RMS (Richard Stallman) why it was dynamically scoped when he was implementing emacs lisp and his exact reply was that lexical scope was too inefficient. Dynamic scoping was also meant to provide greater flexibility for user customizations. However, dynamic scoping has several disadvantages. It can easily lead to bugs in large programs, due to unintended interactions between variables in different functions.
With the constructs for local binding (let, let*, letrec, and letrec*), the Scheme language has a block structure like most other programming languages since the days of ALGOL 60. Readers familiar to languages like C or Java should already be used to this concept.
The most basic local binding construct is let
. syntax: let
bindings body
bindings has the form
( (variable1 init1) ...)
that is zero or more two-element lists of a variable and an arbitrary
expression each. All variable names must be distinct. A let
expression is evaluated as follows. All init expressions are evaluated.
New storage is allocated for the variables. The values of the init
expressions are stored into the variables.
The expressions in body are evaluated in order, and the value of the last expression is returned as the value of the let expression.
Local binding: ‘me' is bound to "Bob" only within the (let …)
(let ((me "Bob")(you "Tom")
)me);; => "Bob"
The other binding constructs are variations on the same theme: making new values, binding them to variables, and executing a body in that in the newly extended lexical context.
All objects created in the course of a Scheme computation, including procedures and continuations, have unlimited extent. No Scheme object is ever destroyed. The reason that implementations of Scheme do not (usually!) run out of storage is that they are permitted to reclaim the storage occupied by an object if they can prove that the object cannot possibly matter to any future computation. Other languages in which most objects have unlimited extent include C#, Java, Haskell, most Lisp dialects, ML, Python, Ruby, and Smalltalk
“A conditional is an if-then-else construct. We take these for granted now. They were invented in the course of developing Lisp. (Fortran at that time only had a conditional goto, closely based on the branch instruction in the underlying hardware.)[5].
The conditionals in Scheme take the following forms:
(if test consequent alternate) (if test consequent)
where test
, consequent
, and
alternate
are expressions. An if
expression is
evaluated as follows: first, test
is evaluated. If it
yields a true value, then consequent
is evaluated and its
values are returned. Otherwise alternate
is evaluated and
its values are returned. If test
yields a false value and
no alternate
is specified, then the result of the
if
expression is unspecified.
Example:
(if (= 5 (+ 2 3)) "equal" "inequal") ;==>"equal"
if
is a special form because it does not automatically
evaluate all of its arguments:
(if (= 0 1) (/ 1 0) (+ 2 3)) ;==> 5
Note that the (/ 1 0)
is not evaluated. Another interesting
example:
((if #f + *) 3 4) ;===> 12
In scheme standard[2], if
construct is the
primitive construct. There is a derived conditional, the
cond
construct:
(cond (test1 expr1) (test2 expr2) … (testN exprN))
As soon as cond
find a test that evaluates to true, then it
evaluates the corresponding expression and return its value. The
remaining tests and expressions are not evaluated. If none of the tests
evaluate to ture, then the return value of cond
is not
defined. To get an “else” part , we provide a test that is
gurrantee to be true as the last clause of the cond
structure.
(cond (test1 expr1) (test2 expr2) … (#t exprN))
Consider the fact “else” part is often used,
guile
provide a syntax sugar which uses else
as a keyword:
(cond (test1 expr1) (test2 expr2) … (else exprN))
If none of the tests evaluate to ture then cond
evaluate
the else
part and return its value (the else
part can be left off, but it's not good style, since this may make
return value undefined).
Logical operators: and
/or
. For example:
(and #f #t) ;=> flase (or #f #t) ;=> true
The lambda expression define a (anonymous) function and, when it is evaluated, return that function object as the returned value. Let us examine an example:
(lambda (x y) (+ x y))
which will return a function object that takes two arguments and what the function does is to sum the two arguments.
Note that function/procedure is just another type of value and the
importance of lambda
is that it can return this type of
value. Using the word “lambda” instead of
“funtion” or “procedure” is due to Lisp's
history. In practice, when we see the word lambda
in source
codes, we can safely translate (in our mind) the word to
“function”.
The returned function by the above lambda
call is unnamed.
How do we use the returned function? We can use it wherever a function
name can appear. For example
((lambda (x y) (+ x y)) 2 3)
or, equivalently, binding the function objet to a variable:
(define foo (lambda (x y) (+ x y)))
and then calling the function using the variable name:
(foo 2 3)
In the latter case, we define a unnamed function by using
lambda
and then bind it to the name foo
. This
amounts to that we are defining a named function. Since defining named
functions is a very common task, scheme
provides this
special version of define
that doesn't use
lambda
explicitly:
(define (foo x y) (+ x y))
which can be considered as an abbreviation for (define foo (lambda
(x y) (+ x y)))
, i.e., defining a unnamed function and then
binding it to the variable foo
.
Similar to C, Scheme
function call uses pass by value
(rather than by reference) with pointer semantics. Let us examine an
example:
(define (func x) (define x 4) (* x 2)) (define a 3) (func a) (display a) ;=> 3
where
8.2General syntax of lambda
expression:
(lambda <formals> <body>)
<formals>
have one of the following forms:
(variable_1 variable_2 ... variable_n)
The
procedure takes a fixed number of arguments; when the procedure is
called, the values of actual arguments will be stored in newly
allocated locations to which the formal arguments are bound.
variable
The procedure takes any
number of arguments; when the procedure is called, the sequence of
actual arguments is converted into a newly allocated list, and the
list is stroed in a fresh location to which variable
is bound.
(variable_1 … variable_n.
variable_n+1)
If a space-delimited period precedes the last
variable, then the procedure takes n or more arguments,
where n is the number of formal arguments before the
period. The value stored in the binding of the last variable will
be a newly allocated list of the actual arguments left over after
all the other actual arguments have been matched up against the
other fromal arguments.
What is important about lambda
expression is that it
returns an enviroment (e.g., values of the free variables in the lambda
expression) to perform calculations and this environment can be changed
and is remembered (persitent variables) by the closure. Let us see an
example of a serial number generator:
(define make-serial-number-generator (lambda () (let ((current-serial-number 0)) (lambda () (set! current-serial-number (+ current-serial-number 1)) current-serial-number)))) (define entry-sn-generator (make-serial-number-generator)) (define entry-sn-generator2 (make-serial-number-generator)) (display (entry-sn-generator)) ;==>1 (newline) (display (entry-sn-generator)) ;==>2 (newline) (display (entry-sn-generator2)) ;==>1 (newline) (display (entry-sn-generator2)) ;==>2 (newline)
This example create a procedure with a variable binding that is private to the procedure, like a local variable, but whose value persists between procedure calls.
When make-serial-number-generator is called, it creates a local environment with a binding for current-serial-number whose initial value is 0, then, within this environment, creates a procedure. The local environment is stored within the created procedure object and so persists for the lifetime of the created procedure.
Note that make-serial-number-generator can be called again to create a second serial number generator that is independent of the first. Every new invocation of make-serial-number-generator creates a new local let environment and returns a new procedure object with an association to this environment.
In summary, closure is the capture of an environment, containing persistent variable bindings, within the definition of a procedure. This is rather similar to the idea in some object oriented languages of encapsulating a set of related data variables inside an “object”, together with a set of “methods” that operate on the encapsulated data. The following example shows how closure can be used to emulate the ideas of objects, methods and encapsulation in Scheme.
(define (make-account) (let ((balance 0)) (define (get-balance) balance) (define (deposit amount) (set! balance (+ balance amount)) balance) (define (withdraw amount) (deposit (- amount))) (lambda args (apply (case (car args) ((get-balance) get-balance) ((deposit) deposit) ((withdraw) withdraw) (else (error "Invalid method!"))) (cdr args)))))
Scheme was one of the first languages to support procedures as objects in their own right. Procedures can be created dynamically, stored in data structures, returned as results of procedures, and so on. Other languages with these properties include Common Lisp, Haskell, ML, Ruby, and Smalltalk.
One distinguishing feature of Scheme is that continuations, which in most other languages only operate behind the scenes, also have “first-class” status. First-class continuations are useful for implementing a wide variety of advanced control constructs, including non-local exits, backtracking, and coroutines.
In Scheme, the argument expressions of a procedure call are evaluated before the procedure gains control, whether the procedure needs the result of the evaluation or not. C, C#, Common Lisp, Python, Ruby, and Smalltalk are other languages that always evaluate argument expressions before invoking a procedure. This is distinct from the lazy-evaluation semantics of Haskell, or the call-by-name semantics of Algol 60, where an argument expression is not evaluated unless its value is needed by the procedure.
The power of lambda expression is better shown when they are used as an anonymous function inside another function.
Let us see some simple examples of recursion.
(define (factorial n) (if (= 0 n) 1 (* n (factorial (- n 1)))))
which is the classic factorial function.
;; (double-each '(1 3 4)) => (2 6 8) (define (double-each s) (if (null? s) '() (cons (* 2 (car s)) (double-each (cdr s)))))
which doubles each number in a list.
Rules for writing recursive functions:
1. Know when to stop (the base case)
2. Decide how to take one step towards the base case
3. Phrase the solution in terms of one step, and a smaller version of the original problem.
For numbers, the base case is usually a small integer constant, and a
smaller version of the problem is something like n-1
.
For lists, the base case is usually the empty list, and a smaller
version of the problem is usually the rest (i.e., cdr
) of
the list. Here is a template for most recursive functions:
(define (fn args) (if base-case base-value (compute-result (fn (smaller args)))))
Implementations of Scheme must be properly tail-recursive, which means that the execution of an iterative computation in constant space, even if the iterative computation is described by a syntactically recursive procedure. Thus with a properly tail-recursive implementation, iteration can be expressed using the ordinary procedure-call mechanics without runtime penality, so that special iteration constructs are useful only as syntactic sugar.
Scheme has no expressions designed for looping at a general level. The only easy way to do this is recursion, which forces a particular mindset but is actually a natural way for iteratation. The following example illustrates a Scheme script that iterates from 0 to 9, then prints done. This example uses what in Scheme is called tail recursion. Note that the function being defined is invoked at the end of the block. This recursion is called tail recursion. In traditional languages, this recursion eats away at the stack to maintain a history of the calls; in Scheme, it's different. The last call (the tail) simply invokes the function without any procedure call or stack maintenance overhead. This is often stated in the following words: Implementations of Scheme must be properly tail-recursive
(let countup ((i 0)) (if (= i 10) (begin (display "done") (newline)) (begin (display i) (newline) (countup (+ i 1)))))
Scheme is “properly tail recursive”, meaning that tail calls or recursions from certain contexts do not consume stack space or other resources and can therefore be used on arbitrarily large data or for an arbitrarily long calculation.
Scheme
supports functions as first class citizens,
which means Scheme
supports passing functions as arguments
to other functions, returning them as the values from other functions,
and assigning them to variables or storing them in data structures. A
programming language is said to have first-class functions if it treats
functions as first-class citizens.
Scheme gets much of its expressiveness and capacity for abstraction by supporting an applicative programming style using higher order functions – functions that take other functions as arguments, or that return functions as the result. All other functions are first-order functions. In mathematics higher-order functions are called operators or functionals. The differential operator in calculus is a common example, since it maps a function to its derivative, also a function.
First-class functions are a necessity for the functional programming style, in which the use of higher-order functions is a standard practice. A simple example of a higher-ordered function is the map function, which takes, as its arguments, a function and a list, and returns the list formed by applying the function to each member of the list. For a language to support map, it must support passing a function as an argument. Let us see an example:
(map (lambda (x) (* x 2)) '(1 2 3))
which doubles all elements in a numerical list. Let us define our own
version of map
, my-map
, which is a higher
order function:
(define (my-map fun alist) (if (null? alist) '() (cons (fun (car alist)) (my-map fun (cdr alist)))) )
There are certain implementation difficulties in passing functions as arguments or returning them as results, especially in the presence of non-local variables introduced in nested and anonymous functions. Historically, these were termed the funarg problems, the name coming from "function argument". In early imperative languages these problems were avoided by either not supporting functions as result types (e.g. ALGOL 60, Pascal) or omitting nested functions and thus non-local variables (e.g. C). The early functional language Lisp took the approach of dynamic scoping, where non-local variables refer to the closest definition of that variable at the point where the function is executed, instead of where it was defined. Proper support for lexically scoped first-class functions was introduced in Scheme and requires handling references to functions as closures instead of bare function pointers, which in turn makes garbage collection a necessity.
A function object is also known as a closure.
Memory management is one of Scheme's strong points. Unlike languages like C, programmers of scheme do not have to deal with complicated memory management. Scheme uses garbage collection, so you do not have to worry about. This means that the Scheme environment frees memory for you when the last reference to an object is destroyed. Simply stop referring to an object (e.g. re-assigning a variable) and the value is marked as free and garbage collected. Languages like Java also do this. Garbage collection is a very powerful feature. The source of many bugs (in languages that you must manually manage memory in) is memory management. Why have every program deal with memory management manually when you can do it all in one place? Of course, many people will disagree, but whether garbage collection is the answer is usually something that is true for some apps but false for others.
1.7 Procedure calls and syntactic keywords
Whereas (+ 23 42)
and ((lambda (x) (+ x 42))
23)
are all examples of procedure calls, lambda
and
let
forms are not. This is because let
, even
though it is an identifier, is not a variable, but is instead a
syntactic keyword. A form that has a syntactic keyword as its first
subform obeys special rules determined by the keyword. The
define
identifier in a definition is also a syntactic
keyword. Hence, definitions are also not procedure calls.
The rules for the lambda
keyword specify that the first
subform is a list of parameters, and the remaining subforms are the body
of the procedure. The rules for the let
keyword specify
that the first subform is a list of binding specifications, and the
remaining subforms constitute a body of expressions.
Procedure calls can generally be distinguished from these special forms by looking for a syntactic keyword in the first position of an form: if the first position does not contain a syntactic keyword, the expression is a procedure call. (So-called identifier macros allow creating other kinds of special forms, but are comparatively rare.) The set of syntactic keywords of Scheme is fairly small, which usually makes this task fairly simple. However, it is possible to create new bindings for syntactic keywords
5.8.7 The R5RS syntax-rules System
R5RS defines an alternative system for macro and syntax transformations
using the keywords define-syntax
, let-syntax
,
letrec-syntax
and syntax-rules
. The main
difference between the R5RS system and the traditional macros is how the
transformation is specified. In R5RS, rather than permitting a macro
definition to return an arbitrary expression, the transformation is
specified in a pattern language that (1) does not require complicated
quoting and extraction of components of the source expression using
caddr etc. (2) is designed such that the bindings associated with
identifiers in the transformed expression are well defined, and such
that it is impossible for the transformed expression to construct new
identifiers. The last point is commonly referred to as being hygienic,
i.e., the R5RS syntax-case system provides hygienic macros.
In Guile, the syntax-rules system is provided by the (ice-9
syncase)
module. To make these facilities available in your code,
include the expression (use-syntax (ice-9 syncase))
. For
example:
#!/usr/bin/guile -s !# (use-syntax (ice-9 syncase)) (define-syntax def (syntax-rules () ( (def f (p …) body) (define (f p …) body) ) ) ) (def f(x) (+ x 1)) (display (f 3)) (newline)
Pattern Language: The syntax-rules pattern language.
Define-Syntax: Top level syntax definitions.
Let-Syntax: Local syntax definitions.
Reading
Writing
File ports
Automatic opening and closing of file ports
String ports
Useful Scheme programs often need to interact with the underlying operating system.
file‑exists?
checks if its argument string names a
file. delete‑file
deletes its argument file. These
procedures are not part of the Scheme standard, but are available in
most implementations (e.g. gule
). These procedures work
reliably only for files that are not directories. (Their behavior on
directories is dialect-specific.)
The system procedure executes its argument string as an operating-system command.1 It returns true if the command executed successfully with an exit status 0, and false if it failed to execute or exited with a non-zero status. Any output generated by the command goes to standard output.
(system "ls") ;lists current directory (define fname "spot") (system (string-append "test -f " fname)) ;tests if file ‘spot' exists (system (string-append "rm -f " fname)) ;removes ‘spot'
The last two forms are equivalent to
(file-exists? fname) (delete-file fname)
The getenv
procedure returns the setting of an
operating-system environment variable. Eg,
(getenv "HOME") => "/home/yj"
reference: https://ds26gte.github.io/tyscheme/
Let us consider using Runge-Kuta method to integrate a system of ordinary differential equations (ODEs). First let us construct some basic facilities (some data structures and operations on them) which will be used in our simulation. We use a Scheme vector to store a state of the system. If we have a system of 3 ODEs, then the vector will be of size 3, with one element of the vector standing for the value of one variable of the 3 ODEs.
(define (generate-vector size proc) (let ((ans (make-vector size))) (letrec ((loop (lambda (i) (cond ((= i size) ans) (else (vector-set! ans i (proc i)) (loop (+ i 1))))))) (loop 0)))) (define (elementwise f) (lambda vectors (generate-vector (vector-length (car vectors)) (lambda (i) (apply f (map (lambda (v) (vector-ref v i)) vectors)))))) (define add-vectors (elementwise +)) (define (scale-vector s) (elementwise (lambda (x) (* x s))))
Here vectors
is a list of vectors.
Next, define the core procedure which uses runge-Kutta method to push a given system one step forward:
(define (runge-kutta-4 f h) (let ((*h (scale-vector h)) (*2 (scale-vector 2)) (*1/2 (scale-vector (/ 1 2))) (*1/6 (scale-vector (/ 1 6)))) (lambda (y) ;; y is a system state (let* ((k0 (*h (f y))) (k1 (*h (f (add-vectors y (*1/2 k0))))) (k2 (*h (f (add-vectors y (*1/2 k1))))) (k3 (*h (f (add-vectors y k2))))) (add-vectors y (*1/6 (add-vectors k0 (*2 k1) (*2 k2) k3)))))))
Here h
is time-step size, f
is a function
that calculates the time derivates for a given system state.
Specifically, function invocation (f y)
, with the
y
being a vector standing for the system state, returns the
time derivates.
Next, let us consider a specific system, a damped oscillator:
|
(1) |
|
(2) |
The above time derivatives are implemented as:
(define (damped-oscillator R L C) (lambda (state) (let ((Vc (vector-ref state 0)) (Il (vector-ref state 1))) (vector (- 0 (+ (/ Vc (* R C)) (/ Il C))) (/ Vc L)))))
Finally, let us implement the time iteration:
(define (integrate-system system-derivative initial-state h nsteps) (let ((next (runge-kutta-4 system-derivative h)) (old_state initial-state) (new_state initial-state)) (letrec ((countdown (lambda (i) (if (= i 0) 'liftoff (begin (set! new_state (next old_state)) (display (vector-ref new_state 0)) (newline) (set! old_state new_state) (countdown (- i 1))))))) (countdown nsteps))))
And invoke the above procedure for a specific damped oscillator with
R = 10000, L = 1000, C = 0.01, initial
state vector #(1 0)
, time-step size h = 0.01,
number of time-steps nstep = 5000:
(integrate-system (damped-oscillator 10000 1000 .001) '#(1 0) 0.01 5000) (display "finish") (newline)
The above example is a revised version of the example given in R7RS[3]. Store the above codes in a file and run it in a linux shell:
$ guile -s rk_yj.scm > t.txt
Then plot the result stored in t.txt
by using
GNUplot
:
GNUplot]
plot '/home/yj/project_new/test_space/lisp/t.txt' using 1
w l
GNUplot]
LISP
source
code
Each function call in LISP
source code is organized as a
(nested) list. Since each list is delimited by a pair of parentheses
()
, for a nested list with several depth, the number of
parenthesis will increase to a level that makes many people consider
LISP
as weird/unreadable at first glance. The reason is
obvious: by intuition, people do not distinguish a nested structure by
parentheses. People's intuition tends to distinguish logical structure
through the spatial structure, e.g. line break and indent. This is the
reason why Python uses indent to delimit structure, which increase the
readability of the source code.
There is no difficulty for a computer to recognize the structure
whatever delimiting marks a language chooses to use. Source code editors
of LISP
can transform the structure defined by the
parenthesis to the appropriate indent loved by human's eyes and brain
(the messy parentheses are still there, just do not bother with them and
occasionally use them to get useful information for the structures.)
As mentioned above, Lisp
source code is a list, which is a
one-dimensional structure, and line-breaks are equivalent to white
spaces. We can add new lines appropriately to provide vertical spacial
structure in order to enhance readability.
two things will vastly improve your experience with Emacs and Guile:
The first is Taylor Campbell's Paredit. You should not code in any dialect of Lisp without Paredit. (They say that unopinionated writing is boring—hence this tone—but it's the truth, regardless.) Paredit is the bee's knees.
When developing these notes, I read the following materials: [4][1]
https://people.eecs.berkeley.edu/~bh/ssch0/preface.html
, (2) program and data are equivalent, which makes it easy to write Scheme programs that process/produce other programs, e.g. compilers, structure editors, debuggers, etc.
Lisp code is easy to write, but hard to read by pepole other than the code author (due to using macoros)
Metaprogramming is a programming technique in which computer programs have the ability to treat other programs as their data. It means that a program can be designed to read, generate, analyze or transform other programs, and even modify itself while running
Metaprogramming can be used to move computations from run-time to compile-time, to generate code using compile time computations, and to enable self-modifying code. The language in which the metaprogram is written is called the metalanguage. The language of the programs that are manipulated is called the attribute-oriented programming language. The ability of a programming language to be its own metalanguage is called reflection or “reflexivity”. Reflection is a valuable language feature to facilitate metaprogramming.
Metaprogramming was popular in the 1970s and 1980s using LISP languages. LISP hardware machines were popular in the 1980s and enabled applications that could process code.
A large number of programmers would have a tendency to learn a new programming language whenever they get a chance. Typically, decent programmers can pick up a new Language and write nontrivial programs in that language within a few days. This high efficiency of learning a new language is achieved by explicitly asking ourselves several questions about general language features:
static type or dynamic type or mixed?
what is the primitive type (e.g., real and integer numbers) and compound type (e.g., arrays and lists),
what is the syntax for name binding (i.e., variable/function definition)?
what is the syntax of calling functions?
lexical scope or dynamic scope? (most languages adopts lexical scope),
the flow control: what is the syntax for conditional structures and loop structures?
With these questions in mind, we can quickly find the answers by searching online or a handbook of that language. Then we can write codes and test the syntax with a compiler/interpreter.
https://schemers.org/Documents/Standards/R5RS/HTML
Scheme has latent (as opposed to manifest) types. Types are associated with values (also called objects) rather than with variables. (Some authors refer to languages with latent types as weakly typed or dynamically typed languages.) Other languages with latent types include python and javascript. Languages with manifest types (sometimes referred to as strongly typed or statically typed languages) include Fortran, and C.
Arguments to Scheme procedures are always passed by value, which means that the actual argument expressions are evaluated before the procedure gains control, whether the procedure needs the evaluation result or not. ML, C, and APL are three other languages that always pass arguments by value. This is distinct from the lazy-evaluation semantics of Haskell, or the call-by-name semantics of Algol 60, where an argument expression is not evaluated unless its value is needed by the procedure.
Scheme's model of arithmetic is designed to remain as independent as possible of the particular ways in which numbers are represented within a computer. In Scheme, every integer is a rational number, every rational is a real, and every real is a complex number. Thus the distinction between integer and real arithmetic, which is important to many programming languages, does not appear in Scheme. In its place is a distinction between exact arithmetic, which corresponds to the mathematical ideal, and inexact arithmetic on approximations. As in Common Lisp, exact arithmetic is not limited to integers.
Note that the sequence of characters (+ 2 6)
is not an
external representation of the integer 8, even though it is an
expression evaluating to the integer 8; rather, it is an external
representation of a three-element list, the elements of which are the
symbol + and the integers 2 and 6. Scheme's syntax has the property that
any sequence of characters that is an expression is also the
external representation of some object. This can lead to
confusion, since it may not be obvious out of context whether a given
sequence of characters is intended to denote data or program, but it is
also a source of power, since it facilitates writing programs such as
interpreters and compilers that treat programs as data (or vice versa).
An identifier may name a type of syntax, or it may name a location where a value can be stored. An identifier that names a type of syntax is called a syntactic keyword and is said to be bound to that syntax. An identifier that names a location is called a variable and is said to be bound to that location. The value stored in the location to which a variable is bound is called the variable's value. By abuse of terminology, the variable is sometimes said to name the value or to be bound to the value. This is not quite accurate, but confusion rarely results from this practice.
Certain expression types are used to create new kinds of syntax and bind syntactic keywords to those new syntaxes (these expression types are called keyword binding constructs.), while other expression types create new locations and bind variables to those locations (these expression types are called binding constructs).
The most fundamental of the variable binding constructs is the lambda expression, because all other variable binding constructs can be explained in terms of lambda expressions. The other variable binding constructs are let, let*, letrec, and do expressions.
In Lisp, list delimiter is chosen to be parentheses, i.e., round brackets (for comparison, square brackets are chosen in python). The whitespace is chosen as the saperator between different elements of a list (cf., commas are chosen by python and whitespace is ignored by the interpreter). In lisp, a newline is equivalant to a whitespace. The following is a lisp list:
(1 2 3 a b c)
For comparison, the following is a python list:
Fortran's new array constructor (introduced in Fortran 2003) can also
adopt the python style, e.g.
The standard form of fortran array constructor is
[1, 2, 3, a, b, c]
integer :: a(3), i
a=[1, 2, 3]
a=[(i, i=1,3)] !implied do-loop
(/ ...
/)
, which is hard to remember and read.
The so-called “lambda calculus” is a formal system for expressing computation based on function abstraction and application using variable binding and substitution. Althouth the name contains “calculus”, the lambda calculus has nothing to do with the calculus in mathematics (i.e., integration and differential). Then why the name “calculus”? Is the name here to frighten newbies? Maybe paritally yes. The remaining part, I guess, is related to the applicative programming style using higher order functions – functions that take other functions as arguments. In mathematics higher-order functions are called operators or functionals. The differential operator in calculus is a common example, since it maps a function to its derivative, which is also a function. Due to this similarity with the differential operators in calculus, this formal computational system is called “lambda calculus”, where lambda can be understood as “function”.
The lambda calculus can be called as the smallest programming language of the world. It gives the definition of what is computable. Anything that can be computed by lambda calculus is computable. It provides a theoretical framework for describing functions and their evaluation.
Do not get frightened by the fancy/mysterious nonmenclatures. Calm down and assume that they probably refer to a very simple thing that you already understand. The author use them maybe because he/she want to impress readers. They are simple concepts in disguise.
Scheme
would still be useful as a notation for expressing
computational methods even in the absence of a mechanical
implementation.
One of the great simplifications of Scheme
is that a
procedure is just another type of value, and that procedure values can
be passed around and stored in variables in exactly the same way as, for
example, strings and lists.
a function that, when invoked, return a function.
a function that return a function upon invocation.
History
Alonzo Church formalized lambda calculus, a language based on pure abstraction, in the 1930s. Lambda functions are also referred to as lambda abstractions, a direct reference to the abstraction model of Alonzo Church's original creation.
Lambda calculus can encode any computation. It is Turing complete, but contrary to the concept of a Turing machine, it is pure and does not keep any state.
Functional languages get their origin in mathematical logic and lambda calculus, while imperative programming languages embrace the state-based model of computation invented by Alan Turing. The two models of computation, lambda calculus and Turing machines, can be translated into each another. This equivalence is known as the Church-Turing hypothesis.
Functional languages directly inherit the lambda calculus philosophy, adopting a declarative approach of programming that emphasizes abstraction, data transformation, composition, and purity (no state and no side effects). Examples of functional languages include Haskell, Lisp, or Erlang.
By contrast, the Turing Machine led to imperative programming found in languages like Fortran, C, or Python.
The imperative style consists of programming with statements, driving the flow of the program step by step with detailed instructions. This approach promotes mutation and requires managing state.
The separation in both families presents some nuances, as some functional languages incorporate imperative features, like OCaml, while functional features have been permeating the imperative family of languages in particular with the introduction of lambda functions in Java, or Python.
Python is not inherently a functional language, but it adopted some functional concepts early on. In January 1994, map(), filter(), reduce(), and the lambda operator were added to the language.
Operation on List Data structure.
List is one the built in data type in Scheme
. Lists in
Scheme
can contain items of different types:
(1 1.5 x (a) “hello”)
The intrinsic functions that create or operate on a list:
list
, car
, cdr
,
cons
, and append
.
(list 1 2 3) ==>create a list
(car (list 1 2 3)) ==> choose the first element of a list
(car '(1 2 3)) ==> the same as the above
(cdr '(1 2 3)) ==> create a list by excluding the first element (cons 'foo '(1 2 3)) ==> add a new cell to a list: (append '(1 2) '(3)) ===>concatenate two or more lists==>(1 2 3)
The process that led to the R6RS standard brought a split in the Scheme community to the surface. The implementors that wrote R6RS considered that it was impossible to write useful, portable programs in R5RS, and that only an ambitious standard could solve this problem. However, part of the Scheme world saw the R6RS effort as too broad, and as having included some components that would never be adopted by more minimalistic Scheme implementations. This second group succeeded in taking control of the official Scheme standardization track and in 2013 released a more limited R7RS, essentially consisting of R5RS, plus a module system. Guile supports R7RS also.
As a Scheme program runs, values of all types pop in and out of existence. Sometimes values are stored in variables, but more commonly they pass seamlessly from being the result of one computation to being one of the parameters for the next.
Scheme programmers prefer to avoid assignment statements because assignment is a mutating operation, which is not prefered in (pure) functional programming pardigam. In most cases, values pass seamlessly from being the result of one computation to being one of the parameters for the next, rather than being stroed in an temporary variable.
Part of what we mean when we talk about “creating a variable” is in fact establishing an association between a name, or identifier, that is used by the Scheme program code, and the variable location to which that name refers. Although the value that is stored in that location may change, the location to which a given name refers is always the same.
Why is not Lisp booming now that there is an AI boom in 2010s?
The reason is very simple, Lisp was created for the school of Symbolic AI, and the AI that is growing in popularity right now, is a totally different school, the school of Machine Learning, which is a highly numerical domain (numerical statistics). You could say that Symbolic AI looks for a deductive approach while Machine Learning an Inductive approach.
Lisp is definitely no longer the language for AI, because AI itself has moved into a highly numerical domain which has traditionally been more of stronghold of C/C++. With current emphasis on GPU computing for maximizing compute power, C/C++ is the right vehicle to build AI systems because GPU computing is still a very low-level exercise.
Additionally, for programmers seeking a higher level of abstraction (by using libraries), Python has become the language of choice, because of its accessibility and widespread support, and the necessary packages and bindings becoming available for compute intensive numeric operations.
DrScheme automatically indents according to the standard style when you type Enter in a program or REPL expression. For example, if you hit Enter after typing (define (greet name), then DrScheme automatically inserts two spaces for the next line. If you change a region of code, you can select it in DrScheme and hit Tab, and DrScheme will re-indent the code (without inserting any line breaks). Editors like Emacs offer a Scheme mode with similar indentation support.
Line breaks and indentation are not significant for parsing Scheme programs, but most Scheme programmers use a standard set of conventions to make code more readable. For example, the body of a definition is typically indented under the first line of the definition. Identifiers are written immediately after an open parenthesis with no extra space, and closing parentheses never go on their own line.
[1] Alan Borning. Programming Languages . https://courses.cs.washington.edu/courses/cse341/03wi/scheme/, 2013. [Online; accessed 16-Feb-2019].
[2] Richard Kelsey et al. Revised5 Report on the Algorithmic Language Scheme. https://schemers.org/Documents/Standards/R5RS/HTML/r5rs.html, 1998. [Online].
[3] Steven Ganz et al. Revised7 Report on the Algorithmic Language Scheme. https://small.r7rs.org/attachment/r7rs.pdf, 2013. [Online].
[4] Free Software Foundation. GNU guile manual. https://www.gnu.org/software/guile/manual/, 2019. [Online].
[5] Paul Graham. What made lisp different. http://www.paulgraham.com/diff.html.


File DCTverify.str
Version 1.0
This file contains functions that check the correctness of various Discrete Cosine Transforms and their inverses. When reference is made to the IEEE DCT specification, it is refering to the IEEE DCT specification used by both MPEG and JPEG. A definition of what makes an 8x8 DCT conform to the IEEE specification, as well as a pseudocode implementation, can be found in Appendix A of the MPEG-2 specification (ISO/IEC 13818-2) on P. 125.
Please contact the author (Matthew Drake) for more information.


Options are resolved from top to bottom, using a context dependent route.
config.type
]dataset.type
defaults to config.type
, if not specified.
dataset.type
]config.type
].datasets[dataset.type
]dataset.type
]dataset.type
].animationconfig.type
].datasets[dataset.type
].animationdataset.type
].animationEach scope is looked up with elementType
prefix in the option name first, then wihtout the prefix. For example, radius
for point
element is looked up using pointRadius
and if that does not hit, then radius
.
dataset.type
]dataset.type
].elements[elementType
]elementType
]config.type
].datasets[dataset.type
]config.type
].datasets[dataset.type
].elements[elementType
]dataset.type
]dataset.type
].elements[elementType
]elementType
]config.type
].scalesA plugin can provide additionalOptionScopes
array of paths to additionally look for its options in. For root scope, use empty string: ''
. Most core plugins also take options from root scope.
plugin.id
]...plugin.additionalOptionScopes
])config.type
].plugins[plugin.id
]plugin.id
]...plugin.additionalOptionScopes
])Scriptable options also accept a function which is called for each of the underlying data values and that takes the unique argument context
representing contextual information (see option context). A resolver is passed as second parameter, that can be used to access other options in the same context.
:::tip Note
The context
argument should be validated in the scriptable function, because the function can be invoked in different contexts. The type
field is a good candidate for this validation.
:::
Example:
color: function(context) {
var index = context.dataIndex;
var value = context.dataset.data[index];
return value < 0 ? 'red' : // draw negative values in red
index % 2 ? 'blue' : // else, alternate values in blue and green
'green';
},
borderColor: function(context, options) {
var color = options.color; // resolve the value of another scriptable option: 'red', 'blue' or 'green'
return Chart.helpers.color(color).lighten(0.2);
}
Indexable options also accept an array in which each item corresponds to the element at the same index. Note that if there are less items than data, the items are looped over. In many cases, using a function is more appropriate if supported.
Example:
color: [
"red", // color for data at index 0
"blue", // color for data at index 1
"green", // color for data at index 2
"black", // color for data at index 3
//...
];
The option context is used to give contextual information when resolving options and currently only applies to scriptable options. The object is preserved, so it can be used to store and pass information between calls.
There are multiple levels of context objects:
chart
dataset
data
scale
tick
tooltip
Each level inherits its parent(s) and any contextual information stored in the parent is available through the child.
The context object contains the following properties:
chart
: the associated charttype
: 'chart'
In addition to chart
active
: true if element is active (hovered)dataset
: dataset at index datasetIndex
datasetIndex
: index of the current datasetindex
: same as datasetIndex
mode
: the update modetype
: 'dataset'
In addition to dataset
active
: true if element is active (hovered)dataIndex
: index of the current dataparsed
: the parsed data values for the given dataIndex
and datasetIndex
raw
: the raw data values for the given dataIndex
and datasetIndex
element
: the element (point, arc, bar, etc.) for this dataindex
: same as dataIndex
type
: 'data'
In addition to chart
scale
: the associated scaletype
: 'scale'
In addition to scale
tick
: the associated tick objectindex
: tick indextype
: 'tick'
In addition to chart
tooltip
: the tooltip objecttooltipItems
: the items the tooltip is displaying


Simulate invocation of alt+enter and pressing item in the list defined by parameter. Optional: it is possible to only open pop up and check if item from pa is presented in list.
Syntax: %altEnter <item in list>(Optional)|<invoke>
Example: %altEnter Find cause|true


The quadratic curve called with Q, is actually a simpler curve than the cubic one. It requires one control point which determines the slope of the curve at both the start point and the end point. It takes two arguments: the control point and the end point of the curve. https://developer.mozilla.org/en-US/docs/Web/SVG/Tutorial/Paths#Bezier_Curves