{{plugin.About}}
Created {{project.DateCreated | date:"longDate"}} by {{project.Author.Person.Name}} Number of submissions {{project.Submissions}} Git Repository


| uuid | title | priority | dueDate | details | process | postpone |
|---|---|---|---|---|---|---|
| {{task.uuid}} | {{task.title}} | {{task.priority}} | {{toISOstring(task.dueDate)}} |
I took the all the freedom that was given to me with interpreting the requirements, hence here are some specifications in order for a better user experience to be achieved
This is a view of all pending tasks. By clicking process you are sending them to a process queue. After the processing is finished, a push notification is sent to all subscribed clients so they can remove the processed task and prevent a stale view. You can try this with multiple tabs/browser windows.
While you are enjoying this beautifull CSS marvel, a scheduler is creating tasks in the background. New tasks created by the scheduler will be pushed on top of this list view. If you don't want to wait for that to happen, you can force a random task creation via this link. It works even when called from another tab
There is far more in a task then it meets the eye on this screen. Click the details button to go to a more detailed view of a task that includes editing options as well as all the fields from the requirements (Status,Priority,CreatedAt,UpdatedAt,UUID,DueDate....etc)
Clicking on process will send the task to a queue that will eventually process it. Once it has been processed it will be removed from all active clients via a push notification. Feel free to try this from diferent browsers/tabs.
Clicking postpone will send you to a screen for choosing a postpone date. Back in the first paragraph I gently mislead you. The list of awesomness doesn't show all the tasks. It ONLY shows pending tasks which have not been postponed to a date after now. A scheduler takes care of all clients getting a push notification when such postponed task becomes due. If you are interested in seeing all the tasks in a plain JSON list as they're persisted visitthis
This button prints the array of task JSONs in the console. I would, however stay out of the console view.


Don't let your links rot! Type in any series of links, or text with links, and this tool will automatically make them permanent!
Supports Markdown, HTML, or any sort of text or links.


Conservatives suspend members over Islamophobia allegations
The Conservatives have suspended a number of members over claims of Islamophobic social media posts.
The Guardian says it has seen details of racist content posted, shared or endorsed by 25 sitting and ex-Tory councillors.
The Conservatives say they take "swift action... on not just anti-Muslim discrimination, but discrimination of any kind".
But in a tweet Labour accused the party of "showing their true colours".
The Guardian says it handed a dossier containing the allegations, produced by an anonymous Twitter user, to the Conservative Party.
Cabinet Office minister Michael Gove reiterated the party's pledge to hold an investigation into Islamophobia and other forms of prejudice within the party, and promised it would start before the end of the year.
A Conservative spokesperson said the individuals named in the dossier had been "suspended immediately, pending investigation".
"The Conservative Party will never stand by when it comes to prejudice and discrimination of any kind.
"That's why we are already establishing the terms of an investigation to make sure that such instances are isolated and robust processes are in place to stamp them out as and when they occur."
It is understood not all the names provided in the dossier are members of the Conservative Party, but the party was unable to say how many members had been suspended.
Responding to the disclosures, the Muslim Council of Britain said Islamophobia was "endemic" in the Conservative Party.
It comes as the Conservative Party faces calls to hold an independent inquiry into Islamophobia following incidents highlighted to the party and in the media.
Earlier this year a number of Conservative Party members were suspended, after the BBC highlighted 20 cases to the party of members posting or endorsing Islamophobic material online.


MQTTCFSocketTransport
@interface MQTTCFSocketTransport
: MQTTTransport <MQTTTransport, MQTTCFSocketDecoderDelegate,
MQTTCFSocketEncoderDelegate>
MQTTCFSocketTransport
- implements an MQTTTransport on top of CFNetwork
-
streamSSLLevel an NSString containing the security level for read and write streams
- For list of possible values see:
- https://developer.apple.com/documentation/corefoundation/cfstream/cfstream_socket_security_level_constants
- Please also note that kCFStreamSocketSecurityLevelTLSv1_2 is not in a list
- and cannot be used as constant, but you can use it as a string value
- defaults to kCFStreamSocketSecurityLevelNegotiatedSSL
Declaration
Objective-C
@property (readwrite, strong, nonatomic) NSString *streamSSLLevel;
Swift
var streamSSLLevel: String! { get set }
-
host an NSString containing the hostName or IP address of the host to connect to
- defaults to @
localhost
Declaration
Objective-C
@property (readwrite, strong, nonatomic) NSString *host;
Swift
var host: String! { get set }
- defaults to @
-
port an unsigned 32 bit integer containing the IP port number to connect to
- defaults to 1883
Declaration
Objective-C
@property (assign, readwrite, nonatomic) UInt32 port;
Swift
var port: UInt32 { get set }
-
tls a boolean indicating whether the transport should be using security
- defaults to NO
Declaration
Objective-C
@property (assign, readwrite, nonatomic) BOOL tls;
Swift
var tls: Bool { get set }
-
Require for VoIP background service
- defaults to NO
Declaration
Objective-C
@property (assign, readwrite, nonatomic) BOOL voip;
Swift
var voip: Bool { get set }
-
certificates An identity certificate used to reply to a server requiring client certificates according
- to the description given for SSLSetCertificate(). You may build the certificates array yourself or use the
- sundry method clientCertFromP12.
Declaration
Objective-C
@property (readwrite, strong, nonatomic) NSArray *certificates;
Swift
var certificates: [Any]! { get set }
-
reads the content of a PKCS12 file and converts it to an certificates array for initWith…
@code NSString *path = [[NSBundle bundleForClass:[MQTTClientTests class]] pathForResource:@
filename
ofType:@p12
];NSArray *myCerts = [MQTTCFSocketTransport clientCertsFromP12:path passphrase:@
passphrase
]; if (myCerts) {self.session = [[MQTTSession alloc] init]; … self.session.certificates = myCerts;
[self.session connect]; … }
@endcode
Declaration
Objective-C
+ (NSArray *)clientCertsFromP12:(NSString *)path passphrase:(NSString *)passphrase;
Swift
class func clientCerts(fromP12 path: String!, passphrase: String!) -> [Any]!
Parameters
path
the path to a PKCS12 file
passphrase
the passphrase to unlock the PKCS12 file @returns a certificates array or nil if an error occured


LEGEND
Tumours
Metastatic lymph nodes
Tumour has spread to lymph nodes AND
5 – 7 cm OR
Larger than 7 cm or spreads to the diaphragm or other nearby organs OR
Has spread to the chest wall or near the heart
Clear
Print


{% extends "common-base.html" %} {% load custom_tags %} {% block title %}Coming soon!{% endblock %}endblock %} {% block common_content %}
This page is not ready yet. We are working on it and it should be available to you soon.
Go back to the homepage or read our documentation to continue.
{% endblock %}


Margin is the space between an element and other elements. For example, you can change the margin to move each div around or away from another div.
Border is the outline of surrounding an element. When you set border to be visible, it forms a box around your text. General, you set the Border to visible by selecting thickness of the border (1x, 2x), the type of line (solid, dashed), and color (black, blue, etc.)
Padding is the space between the text inside the box and the border. So if you don't want the text to be squished so close to your border, just increase your padding. In other words, the spaces around the text increases and the border may move away from the text.


Below the URLs accessibles for the API
- GET /songs
- Returns a list of songs with some details on them
- Possibility to paginate by adding the parameters page_size and page_num
- Page_size is 20 by default
- Gives back in the header X-total-count of the total number of documents
- Gives back in the header Link url to the first / next / prev / last pages if available
- GET /songs/avg/difficulty
- Takes an optional parameter "level" to select only songs from a specific level.
- Returns the average difficulty for all songs.
- GET /songs/search
- Takes in parameter a 'message' string to search.
- Return a list of songs. The search should take into account song's artist and title.
- POST /songs/rating
- Takes in parameter a "song_id" and a "rating"
- This call adds a rating to the song. Ratings should be between 1 and 5.


Press n or j to go to the next uncovered block, b, p or k for the previous block.
| File | Statements | Branches | Functions | Lines | |||||
|---|---|---|---|---|---|---|---|---|---|
| src |
|
73.85% | 48/65 | 23.08% | 3/13 | 58.82% | 10/17 | 73.33% | 44/60 |
| src/errors |
|
100% | 6/6 | 100% | 0/0 | 100% | 2/2 | 100% | 6/6 |
| src/jobs |
|
84.38% | 27/32 | 0% | 0/2 | 57.14% | 4/7 | 83.87% | 26/31 |
| src/models |
|
100% | 5/5 | 100% | 0/0 | 100% | 0/0 | 100% | 5/5 |
| src/routes |
|
100% | 17/17 | 100% | 6/6 | 100% | 2/2 | 100% | 16/16 |


WITNESS LIST Licensing & Administrative Procedures Committee April 1, 1997-10:30A HB 325Committee Substitute (GOOLSBY) Registering, but not testifying: For: BILL STINSON (TAR) On: RANDY YARBROUGH (TABC) HB 1135 Registering, but not testifying: For: JAMES L. WALKER (SELF/FED CPA'S,IRS) KATHLEEN WEISS (SELF/FED ASSOC CPA'S) HB 1321 Committee Substitute (WILSON) For: BRAD DAVIS (SELF/SIMPSON GRP) JACK HASTON (SELF/TX COUN ENG LABS) MAHESH M. NAIK (SELF - CIV ENG) RICHARD W. FURLONG (SELF - CIV ENG) ROBERT C. DAVIS (SELF - CIV ENG) STEVE STAGNER (CONSUL ENG COUN TX) Against: BURT CARTER (SELF) On: JOHN R. SPEED (TX ST BD REG PROF ENG) Registering, but not testifying: For: DURWARD CURLEE (ASS'D GEN CONTR'R/TBB) GERHARDT SCHULLE, JR (TX ASSOC PROF ENG) SUSAN ROCHA (SELF/CITY OF LUBBOCK) Against: ERVIN L. COONROD (ENV HEALTH SUPRVSN) HB 1465 Committee Substitute (PICKETT) For: CARL H. KLEIMANN (NAPEA & 1ST ODYSSEY GRP) LARRY E. KOSTA (INESCO) ROBERT C RICE (NAPEA) On: MICHAEL E. YUHR (TDLR) Registering, but not testifying: For: CORDELL HULL (NAAS) GAYLORD ARMSTRONG (ADMINISTAFF INC.) PAT TAMMARO (INESCO) HB 1496 For: BRENT LEFTWICH (CONTRACT LAND STAFF) JOSEPH MORAHAN (RE/RWA) KERRY J. BRIGGS (BRIGGS FIELD SVCS) ROLANDO I. TREVINO (EL PASO NAT GAS CO) STEPHEN BUSH (SWBT) TIMOTHY J. SUTTLE (MOBIL) Against: BILL STINSON (TAR) JOHN NICHOLS (TAR) RONALD J. WALKER (TAR) Registering, but not testifying: For: BOB STOUT (MITCHELL ENERGY&DEV CRP) BRAD E. DENTON (SPRINT) DAN R ROBERTSON (ARCO PIPELINE) DAVID W. SINCLAIR (ENRON) HASKEL T. ROGERS (SELF) HELEN S. KNAGGS (GTE) HENRY G. FLORES (TX TELEPONE ASSOC) JULIE KLUMPYAN (ULTRAMAR DIAMOND SHAM) LYNN NABERS (WILLIAMS CO) PATRICK NUGENT (TX NAT GAS PL ASSOC) ROBERT CHOZICK (MCI) WILLIAM NIKOLIS (TEPPCO) HB 1621 Committee Substitute (PICKETT) For: RONALD WALKER (TAR) Registering, but not testifying: For: BILL STINSON (TAR) GEORGE B. ALLEN (TX APT ASSOC) HB 2770 Committee Substitute (GOOLSBY) For: JAMES E. MANKINS (ALCOHOL ALERT SYS) KEN STOLL (ALCOHOL ALERT SYS) Registering, but not testifying: For: PHIL CATES (ALCOHOL ALERT SYS) TOM NOWASKI (SELF)


2019 - Charlotte Hornets HoneyBess Presented by BollyWoodMagic USA wearing RituSelects
2019 - Phoenix Risen - TZStudio Photoshoot
2018 - York County Fashion Week
2018 - HeartFelf Fashion Showcase
2017 - Phoenix Risen - Bride & Groom Photoshoot
2017 - Charlotte International Fashion Week
2016 - Bridal Launch @ Bollywood Redefined
2015 - RituSelects Diwali Gala
2015 - Charlotte International Fashion Week
2014 - Heartfelt @ Panthers
2014 - Charlotte Guild Style Week


We extend metrics!
- productize transparent bandwidth
- expedite cross-platform niches
- innovate virtual mindshare
- transition world-class interfaces
- strategize enterprise e-services
- enhance front-end ROI
- repurpose out-of-the-box portals
- visualize robust systems
- harness holistic platforms
- morph dot-com initiatives
- seize cross-platform web services
- enhance proactive relationships
- generate world-class models


Class LocalOutputContext
- java.lang.Object
-
- org.apache.nemo.runtime.executor.transfer.LocalTransferContext
-
- org.apache.nemo.runtime.executor.transfer.LocalOutputContext
-
- All Implemented Interfaces:
java.lang.AutoCloseable
,OutputContext
public final class LocalOutputContext extends LocalTransferContext implements OutputContext
This class provides a data transfer interface to the sender side when both the sender and the receiver are in the same executor. Since data serialization is unnecessary, the sender sends data without serializing them. A single local output context represents a data transfer between two tasks.
-
-
Constructor Summary
Constructors Constructor Description LocalOutputContext(java.lang.String executorId, java.lang.String edgeId, int srcTaskIndex, int dstTaskIndex)
Creates a new local output context.
-
Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description void
close()
Closes this local output context.java.util.concurrent.LinkedBlockingQueue
getQueue()
Accessor method for the queue in this local output context.boolean
isClosed()
Checks whether the context has been closed.TransferOutputStream
newOutputStream()
Creates a new output stream to which the sender sends its data.-
Methods inherited from class org.apache.nemo.runtime.executor.transfer.LocalTransferContext
getDstTaskIndex, getEdgeId, getExecutorId, getSrcTaskIndex
-
-
-
-
Constructor Detail
-
LocalOutputContext
public LocalOutputContext(java.lang.String executorId, java.lang.String edgeId, int srcTaskIndex, int dstTaskIndex)
Creates a new local output context.- Parameters:
executorId
- id of the executor to which this context belongedgeId
- id of the DAG edgesrcTaskIndex
- source task indexdstTaskIndex
- destination task index
-
-
Method Detail
-
close
public void close()
Closes this local output context.- Specified by:
close
in interfacejava.lang.AutoCloseable
- Specified by:
close
in interfaceOutputContext
-
getQueue
public java.util.concurrent.LinkedBlockingQueue getQueue() throws java.lang.RuntimeException
Accessor method for the queue in this local output context.- Returns:
- queue to which the sender writes its data.
- Throws:
java.lang.RuntimeException
- if the context has already been closed.
-
isClosed
public boolean isClosed()
Checks whether the context has been closed. It is for testing purpose.- Returns:
- true if the context has been closed.
-
newOutputStream
public TransferOutputStream newOutputStream()
Creates a new output stream to which the sender sends its data.- Specified by:
newOutputStream
in interfaceOutputContext
- Returns:
- output stream of this local output context
-
-


!=(aValue)
Returns true the receiver is not equal to aValue, false otherwise.
(expression)
The '' method evaluates the argument and returns the result.
-(aNumber)
Returns the negative version of aNumber.
Raises an exception if argument is not a number.
..(arg)
.. is an alias for: method(arg, self asString append(arg asString))
<(expression)
Evaluates argument and returns self if self is less or Nil if not.
<=(expression)
Evaluates argument and returns self if self is less
than or equal to it, or Nil if not.
==(aValue)
Returns true if receiver and aValue are equal, false otherwise.
>(expression)
Evaluates argument and returns self if self is greater than it, or Nil if not.
>=(expression)
Evaluates argument and returns self if self is greater
than or equal to it, or Nil if not.
?(aMessage)
description: Sends the message aMessage to the receiver if it can respond to it. Example:
MyObject test // performs test
MyObject ?test // performs test if MyObject has a slot named test
The search for the slot only follows the receivers proto chain.
@
Sends asynchronous message to an object, returns a FutureProxy.
Caller coroutine is paused when proxy is accessed (i.e. message is sent)
till result is ready. Proxy will become an actual result when it is ready.
See IoGuide for more information.
Usage: obj @someMethod(a, b, c)
@@
Same as Object @, but returns nil instead of FutureProxy.
Might be useful in a command line or as a last expression in a block/method when
you don't want to return a future.
NullCharacter
A sequence with a single zero byte.
actorProcessQueue
Processes each message in a queue, yielding between each message.
actorRun
Starts actor mode if not started already. Basically, sets actorProcessQueue for later execution.
addTrait
Takes another object, whose slots will be copied into the receiver. Optionally takes a second argument, a Map object containing string -> string pairs, holding conflicting slot names and names to rename them to. I.e., if you have two objects A and B, both have a slot named foo, you issue A addTrait(B, Map clone atPut("foo", "newFoo")) the value of B foo will be placed in A newFoo.
ancestorWithSlot(slotName)
Returns the first ancestor of the receiver that contains
a slot of the specified name or Nil if none is found.
ancestors
Returns a list of all of the receiver's ancestors as found by recursively following the protos links.
and(arg)
Evaluates argument and returns the result.
appendProto(anObject)
Appends anObject to the receiver's proto list. Returns self.
apropos
Prints out Protos Core slot descriptions.
argIsActivationRecord
Note: seems to be an obsolete method.
argIsCall(arg)
Returns true if arg is an activation context (i.e. Call object)
Note: this is used internally in one place only (Coroutine callStack).
Refactoring should be considered.
asSimpleString
Returns _ string.
asString
Same as slotSummary.
become(anotherObject)
Replaces receiver with anotherObject and returns self.
Useful for implementing transparent proxies. See also FutureProxy and Object @.
Note: primitives cannot become new values.
block(args..., body)
Creates a block and binds it to the sender context (i.e. lexical context).
In other words, block locals' proto is sender's locals.
args is a list of formal arguments (can be empty).
body is evaluated in the context of Locals object.
See also Object method.
Io> block(x, x*2) scope == thisContext
==> true
break(optionalReturnValue)
Break the current loop, if any.
checkMemory()
Accesses memory in the IoObjectData struct that should be accessible. Should cause a memory access
exception if memory is corrupt.
clone
Returns a clone of the receiver.
cloneWithoutInit
Returns a clone of the receiver but does not call init.
compact
Compact the memory for the object if possible. Returns self.
compactState
Attempt to compact the memory of the IoState if possible.
compare(anObject)
Returns a number containing the comparison value of the target with anObject.
contextWithSlot(slotName)
Returns the first context (starting with the receiver and following the lookup path)
that contains a slot of the specified name or Nil if none is found.
continue
Skip the rest of the current loop iteration and start on
the next, if any.
coroDo(code)
Creates a new coro to be run in a context of sender and yields to it.
Returns a coro.
coroDoLater(code)
Returns a new coro to be run in a context of sender.
New coro is moved to the top of the yieldingCoros queue to be executed
when current coro yields.
Note: run target is self (i.e. receiver), not call sender as in coroDo.
coroFor(code)
Returns a new coro to be run in a context of sender.
coroWith(code)
Returns a new coro to be run in a context of receiver.
currentCoro
Returns the currently running coroutine.
debugOff
Sets debugWriteln to nil. Returns self.
debugOn
Sets debugWriteln to writeln. Returns self.
debugWriteln
Method for writing debug messages (nil/writeln by default)
deprecatedWarning(optionalNewName)
Prints a warning message that the current method is deprecated.
If optionalNewName is supplied, the warning will suggest using that instead.
Returns self.
do(expression)
Evaluates the message in the context of the receiver. Returns self.
doFile(pathString)
Evaluates the File in the context of the receiver. Returns the result.
pathString is relative to the current working directory.
doMessage(aMessage, optionalContext)
Evaluates the message object in the context of the receiver.
Returns the result. optionalContext can be used to specific the locals
context in which the message is evaluated.
doRelativeFile(pathString)
Evaluates the File in the context of the receiver. Returns the result.
pathString is relative to the file calling doRelativeFile. (Duplicate of relativeDoFile)
doString(aString)
Evaluates the string in the context of the receiver. Returns the result.
doURL(urlString)
Fetches the URL and evals it in the context of the receiver.
evalArg(expression)
The '' method evaluates the argument and returns the result.
evalArgAndReturnNil(expression)
Evaluates the argument and returns nil.
evalArgAndReturnSelf(expression)
Evaluates the argument and returns the target.
for(<counter>, <start>, <end>, <do message>)
A for-loop control structure. See the io Programming Guide for a full description.
foreach([name,] value, message)
For each slot, set name to the slot's
name and value to the slot's value and execute message. Examples:
myObject foreach(n, v,
writeln("slot ", n, " = ", v type)
)
myObject foreach(v,
writeln("slot type ", v type)
)
foreachSlot(slotName, slotValue, code)
Iterates over all the slots in a receiver. Provides slotValue (non-activated)
along with slotName. Code is executed in context of sender. slotName and slotValue
become visible in the receiver (no Locals created! Maybe, it is not the best decision).
Io> thisContext foreachSlot(n, v, n println)
Lobby
Protos
exit
forward
n
v
==> false
forward
Called when the receiver is sent a message it doesn't recognize.
Default implementation raises an "Object doesNotRespond" exception.
Subclasses can override this method to implement proxies or special error handling.
Example:
myProxy forward = method(
messageName := thisMessage name
arguments := thisMessage arguments
myObject doMessage(thisMessage)
)
getLocalSlot(slotNameString)
Returns the value of the slot named slotNameString
(not looking in the object's protos) or nil if no such slot is found.
getSlot(slotNameString)
Returns the value of the slot named slotNameString
(following the lookup path) or nil if no such slot is found.
handleActorException(exception)
Callback for handling exceptions during asynchronous message processing.
Default value: method(e, e showStack)
hasLocalSlot(slotNameString)
Returns true if the slot exists in the receiver or false otherwise.
hasProto(anObject)
Returns true if anObject is found in the proto path of the target, false otherwise.
hasSlot(name)
Returns true if slot is found somewhere in the inheritance chain
(including receiver itself).
if(<condition>, <trueMessage>, <optionalFalseMessage>)
Evaluates trueMessage if condition evaluates to a non-Nil.
Otherwise evaluates optionalFalseMessage if it is present.
Returns the result of the evaluated message or Nil if none was evaluated.
ifDebug(code)
Executes code if debugWriteln is not nil.
ifError
Does nothing, returns self.
ifNil(arg)
Does nothing, returns self.
ifNilEval(arg)
Does nothing, returns self.
ifNonNil(arg)
Evaluates argument and returns self.
ifNonNilEval(arg)
Evaluates argument and returns the result.
in(aList)
Same as: aList contains(self)
inlineMethod
Creates a method which is executed directly in a receiver (no Locals object is created).
Io> m := inlineMethod(x := x*2)
Io> x := 1
==> 1
Io> m
==> 2
Io> m
==> 4
Io> m
==> 8
isActivatable
Returns true if the receiver is activatable, false otherwise.
isError
Returns false if not an error.
isIdenticalTo(aValue)
Returns true if the receiver is identical to aValue, false otherwise.
isKindOf(anObject)
Returns true if anObject is in the receiver's ancestors.
isLaunchScript
Returns true if the current file was run on the command line. Io's version of Python's __file__ == "__main__"
isNil
Returns false.
isTrue
Returns true.
justSerialized(stream)
Writes serialized representation to a SerializationStream. Returns stream contents.
[This is unintended side effect! Returned value may change in the future.]
launchFile(pathString)
Eval file at pathString as if from the command line in its folder.
lazySlot(code)
Defines a slot with a lazy initialization code.
Code is run only once: the first time slot is accessed.
Returned value is stored in a regular slot.
Io> x := lazySlot("Evaluated!" println; 17)
Io> x
Evaluated!
==> 17
Io> x
==> 17
Io> x
==> 17
Another form is lazySlot(name, code):
Io> lazySlot("x", "Evaluated!" println; 17)
Io> x
Evaluated!
==> 17
Io> x
==> 17
Io> x
==> 17
lexicalDo(expression)
Evaluates the message in the context of the receiver.
The lexical context is added as a proto of the receiver while the argument is evaluated.
Returns self.
list(...)
Returns a List containing the arguments.
localsForward
CFunction used by Locals prototype for forwarding.
localsUpdateSlot(slotNameString, valueObject)
Local's version of updateSlot mthod.
loop(expression)
Keeps evaluating message until a break.
markClean
Cleans object's slots.
memorySize
Return the amount of memory used by the object.
memorySizeOfState
Returns the number of bytes in the IoState
(this may not include memory allocated by C libraries).
message(expression)
Return the message object for the argument or Nil if there is no argument.
Note: returned object is a mutable singleton. Use "message(foo) clone" if
you wish to modify it.
method(args..., body)
Creates a method.
args is a list of formal arguments (can be empty).
body is evaluated in the context of Locals object.
Locals' proto is a message receiver (i.e. self).
Slot with a method is activatable.
See also Object block.
newSlot(slotName, aValue)
Creates a getter and setter for the slot with the name slotName
and sets its default value to aValue. Returns self. For example,
newSlot("foo", 1) would create slot named foo with the value 1 as well as a setter method setFoo().
not
Returns nil.
or(arg)
Returns true.
ownsSlots
A debug method.
pSlots
PDB extension to set a list of slots to persist with PDB.
Creates the specified slots using newSlot and sets them to nil.
pause
Removes current coroutine from the yieldingCoros queue and
yields to another coro. Exits if no coros left.
See Coroutine documentation for more details.
perform(methodName, <arg1>, <arg2>, ...)
Performs the method corresponding to methodName with the arguments supplied.
performWithArgList(methodName, argList)
Performs the method corresponding to methodName with the arguments in the argList.
persist
Force immediate persistence of this object with PDB.
persistData
Force immediate persistence of this object's serialized form (using asSerialization)
into PDB, if possible.
persistMetaData
Force immediate persistence of this object's type data into PDB
persistSlots
Force immediate persistence of this object's dirty slots into PDB.
ppid
This PDB extension returns a unique identifier for this object and registers it
for persistence with PDB.
prependProto(anObject)
Prepends anObject to the receiver's proto list. Returns self.
print
Prints a string representation of the object. Returns Nil.
println
Same as print, but also prints a new line. Returns self.
proto
Same as; method(self protos first)
protos
Returns a copy of the receiver's protos list.
raiseIfError
Does nothing, returns self.
relativeDoFile(pathString)
Evaluates the File in the context of the receiver. Returns the result.
pathString is relative to the file calling doRelativeFile. (Duplicate of doRelativeFile)
removeAllProtos
Removes all of the receiver's protos. Returns self.
removeAllSlots
Removes all of the receiver's slots. Returns self.
removeProto(anObject)
Removes anObject from the receiver's proto list if it
is present. Returns self.
removeSlot(slotNameString)
Removes the specified slot (only) in the receiver if it exists. Returns self.
resend
Send the message used to activate the current method to the Object's proto.
For example:
Dog := Mammal clone do(
init := method(
resend
)
)
Calling Dog init will send an init method to Mammal, but using the Dog's context.
return(anObject)
Return anObject from the current execution block.
returnIfError
Does nothing, returns self.
returnIfNonNil
Returns the receiver from the current execution block if it is non nil.
Otherwise returns the receiver locally.
self
Returns self.
serialized
Returns a serialized representation of the receiver.
Io> Object clone do(x:=1) serialized
==> Object clone do(
x := 1
)
serializedSlots(stream)
Writes all slots to a stream.
serializedSlotsWithNames(names, stream)
Writes selected slots to a stream.
setIsActivatable(aValue)
When called with a non-Nil aValue, sets the object
to call its activate slot when accessed as a value. Turns this behavior
off if aValue is Nil. Only works on Objects which are not Activatable
Primitives (such as CFunction or Block). Returns self.
setPpid
PDB extension to set the value returned by ppid.
setProto(anObject)
Sets the first proto of the receiver to anObject, replacing the
current one, if any. Returns self.
setProtos(aList)
Replaces the receiver's protos with a copy of aList. Returns self.
setSlot(slotNameString, valueObject)
Sets the slot slotNameString in the receiver to
hold valueObject. Returns valueObject.
setSlotWithType(slotNameString, valueObject)
Sets the slot slotNameString in the receiver to
hold valueObject and sets the type slot of valueObject
to be slotNameString. Returns valueObject.
shallowCopy
Returns a shallow copy of the receiver.
shouldPersistByDefault
PDB boolean flag indicating whether this object should be considered for persistence
when persistence has not been specifically requested by calling ppid() or PDB addObjectToPersist.
Always false by default for Object.
slotDescriptionMap
Returns raw map of slot names and short values' descriptions.
See also Object slotSummary.
slotNames
Returns a list of strings containing the names of the
slots in the receiver (but not in its lookup path).
slotSummary
Returns a formatted slotDescriptionMap.
Io> slotSummary
==> Object_0x30c590:
Lobby = Object_0x30c590
Protos = Object_0x30c880
exit = method(...)
forward = method(...)
slotValues
Returns a list of the values held in the slots of the receiver.
stopStatus
Returns the internal IoState->stopStatus.
super(aMessage)
Sends the message aMessage to the receiver's proto with the context of self. Example:
self test(1, 2) // performs test(1, 2) on self
super(test(1, 2)) // performs test(1, 2) on self proto but with the context of self
switch(<key1>, <expression1>, <key2>, <expression2>, ...)
Execute an expression depending on the value of the caller. (This is an equivalent to C switch/case)
hour := Date hour switch(
12, "midday",
0, "midnight",
17, "teatime",
Date hour asString
)
thisContext
Synonym to self.
thisLocalContext
Returns current locals.
thisMessage
Returns the calling message (i.e. thisMessage itself, huh).
try(code)
Executes particular code in a new coroutine.
Returns exception or nil if no exception is caught.
See also documentation for Exception catch and pass.
type
Returns a string containing the name of the type of Object (Number, String, etc).
uniqueHexId
Returns uniqueId in a hexadecimal form (with a "0x" prefix)
Io> Object uniqueId
==> 3146784
Io> Object uniqueHexId
==> 0x300420
uniqueId
Returns a Number containing a unique id for the receiver.
unpersist
PDB extension to populate this object with the data associated with this object's ppid from PDB.
updateSlot(slotNameString, valueObject)
Same as setSlot(), but raises an error if the slot does not
already exist in the receiver's slot lookup path.
wait(s)
Pauses current coroutine for at least s seconds.
Note: current coroutine may wait much longer than designated number of seconds
depending on circumstances.
while(<condition>, expression)
Keeps evaluating message until condition return Nil.
Returns the result of the last message evaluated or Nil if none were evaluated.
write(<any number of arguments>)
Sends a print message to the evaluated result of each argument. Returns Nil.
writeln(<any number of arguments>)
Same as write() but also writes a return character at the end. Returns Nil.
yield
Yields to another coroutine. Does nothing if yieldingCoros queue is empty.
See Coroutine documentation for more details.
|


"Workshop" is an assembled group of ten to twenty five persons who share a common interest or problem. They meet together to improve their individual skill of a subject through intensive study, research, practice and discussion.
"Seminar" is a form of academic instruction, either at an academic institution or offered by a commercial or professional organization.
It has the function of bringing together small groups for recurring meetings, focusing each time on some particular subject, in which everyone present is requested to participate.
| sl.no | duration | Subject | Destination | Time period |
|---|---|---|---|---|
| 1 | 1 Week | Computational Mechanics and Modelling (CMM 2016) | NIT Silchar Under TEQIP - II |
12 - 16th November 2016 |
| 2 | 1 Week | Computational Combustion | NIT Silchar Under TEQIP - II |
31st October - 5th November |
| 3 | 1 Week | Advanced Joining Technologies | NIT Silchar Under TEQIP - II |
17 -21st October 2016 |
| 4 | 1 Week | (GIAN) course on Suface Engineering | NIT Silchar Under MHRD |
13 -17th September 2016 |
| 5 | 1 Week | Application of probability theories optimization techniques | NIT Silchar Under TEQIP - II |
9 - 13th December 2013 |
| 6 | 3 Days | Application of Soft Computing Technique in Engineering | NIT Silchar Under TEQIP - II |
9 - 11th November 2013 |
| 7 | 3 Days | Advanced Numerical Techniques using MATLAB | NIT Agartala Under TEQIP - II |
7 - 9th October 2013 |
| 8 | 1 Week | Failure Analysis of Engineering Materials | NIT Silchar Under TEQIP - II |
23 - 27th September 2013 |
| 9 | 1 Week | Joining of Materials | NIT Silchar Under TEQIP - II |
9 - 13th August 2013 |
| 10 | 1 Day Awarness Programme |
Industry Oriented Piping Engineering | NIT Silchar Under TEQIP - II |
2nd February 2013 |
| 11 | 3 Days | Personality development, Career building, website designing & Mini project execution |
NIT Agartala Dept. of Mechanical Engineering |
19 - 21st May 2009 |


| Top |
The tag functions provide the ability to specify hyperlinks and document logical structure on supported backends. The following tags are supported:
Link - Create a hyperlink
Destinations - Create a hyperlink destination
Document Structure Tags - Create PDF Document Structure
A hyperlink is specified by enclosing the hyperlink text with the CAIRO_TAG_LINK
tag.
For example:
1 2 3 4 |
cairo_tag_begin (cr, CAIRO_TAG_LINK, "uri='https://cairographics.org'"); cairo_move_to (cr, 50, 50); cairo_show_text (cr, "This is a link to the cairo website."); cairo_tag_end (cr, CAIRO_TAG_LINK); |
The PDF backend uses one or more rectangles to define the clickable area of the link. By default cairo will use the extents of the drawing operations enclosed by the begin/end link tags to define the clickable area. In some cases, such as a link split across two lines, the default rectangle is undesirable.
rect
: [optional] The "rect" attribute allows the application to
specify one or more rectangles that form the clickable region. The
value of this attribute is an array of floats. Each rectangle is
specified by four elements in the array: x, y, width, height. The
array size must be a multiple of four.
An example of creating a link with user specified clickable region:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 |
cairo_font_extents_t font_extents; cairo_text_extents_t text1_extents; cairo_text_extents_t text2_extents; char attribs[100]; const char *text1 = "This link is split"; const char *text2 = "across two lines"; cairo_font_extents (cr, &font_extents); cairo_move_to (cr, 450, 50); cairo_text_extents (cr, text1, &text1_extents); cairo_move_to (cr, 50, 70); cairo_text_extents (cr, text2, &text2_extents); sprintf (attribs, "rect=[%f %f %f %f %f %f %f %f] uri='https://cairographics.org'", text1_extents.x_bearing, text1_extents.y_bearing, text1_extents.width, text1_extents.height, text2_extents.x_bearing, text2_extents.y_bearing, text2_extents.width, text2_extents.height); cairo_tag_begin (cr, CAIRO_TAG_LINK, attribs); cairo_show_text (cr, "This is a link to the cairo website"); cairo_move_to (cr, 450, 50); cairo_show_text (cr, text1); cairo_move_to (cr, 50, 70); cairo_show_text (cr, text2); cairo_tag_end (cr, CAIRO_TAG_LINK); |
There are three types of links. Each type has its own attributes as detailed below.
Internal Links - A link to a location in the same document
URI Links - A link to a Uniform resource identifier
File Links - A link to a location in another document
An internal link is a link to a location in the same document. The destination is specified with either:
dest
: a UTF-8 string specifying the destination in the PDF file to link
to. Destinations are created with the CAIRO_TAG_DEST
tag.
or the two attributes:
page
: An integer specifying the page number in the PDF file to link to.
pos
: [optional] An array of two floats specifying the x,y position
on the page.
An example of the link attributes to link to a page and x,y position:
"page=3 pos=[3.1 6.2]"
A URI link is a link to a Uniform Resource Identifier (RFC 2396).
A URI is specified with the following attribute:
uri
: An ASCII string specifying the URI.
An example of the link attributes to the cairo website:
"uri='https://cairographics.org'"
A file link is a link a location in another PDF file.
The file attribute (required) specifies the name of the PDF file:
file
: File name of PDF file to link to.
The position is specified by either:
dest
: a UTF-8 string specifying the named destination in the PDF file.
or
page
: An integer specifying the page number in the PDF file.
pos
: [optional] An array of two floats specifying the x,y
position on the page. Position coordinates in external files are in PDF
coordinates (0,0 at bottom left).
An example of the link attributes to PDF file:
"file='document.pdf' page=16 pos=[25 40]"
A destination is specified by enclosing the destination drawing
operations with the CAIRO_TAG_DEST
tag.
name
: [required] A UTF-8 string specifying the name of this destination.
x
: [optional] A float specifying the x coordinate of destination
position on this page. If not specified the default
x coordinate is the left side of the extents of the
operations enclosed by the CAIRO_TAG_DEST
begin/end tags. If
no operations are enclosed, the x coordidate is 0.
y
: [optional] A float specifying the y coordinate of destination
position on this page. If not specified the default
y coordinate is the top of the extents of the
operations enclosed by the CAIRO_TAG_DEST
begin/end tags. If
no operations are enclosed, the y coordidate is 0.
internal
: A boolean that if true, the destination name may be
omitted from PDF where possible. In this case, links
refer directly to the page and position instead of via
the named destination table. Note that if this
destination is referenced by another PDF (see File Links),
this attribute must be false. Default is false.
1 2 3 4 5 6 7 8 9 10 11 |
/* Create a hyperlink */ cairo_tag_begin (cr, CAIRO_TAG_LINK, "dest='mydest' internal"); cairo_move_to (cr, 50, 50); cairo_show_text (cr, "This is a hyperlink."); cairo_tag_end (cr, CAIRO_TAG_LINK); /* Create a destination */ cairo_tag_begin (cr, CAIRO_TAG_DEST, "name='mydest'"); cairo_move_to (cr, 50, 250); cairo_show_text (cr, "This paragraph is the destination of the above link."); cairo_tag_end (cr, CAIRO_TAG_DEST); |
The document structure tags provide a means of specifying structural information such as headers, paragraphs, tables, and figures. The inclusion of structural information facilitates:
Extraction of text and graphics for copy and paste
Reflow of text and graphics in the viewer
Processing text eg searching and indexing
Conversion to other formats
Accessability support
The list of structure types is specified in section 14.8.4 of the PDF Reference.
Note the PDF "Link" structure tag is the same as the cairo CAIRO_TAG_LINK
tag.
The following example creates a document structure for a document containing two section, each with a header and a paragraph.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 |
cairo_tag_begin (cr, "Document", NULL); cairo_tag_begin (cr, "Sect", NULL); cairo_tag_begin (cr, "H1", NULL); cairo_show_text (cr, "Heading 1"); cairo_tag_end (cr, "H1"); cairo_tag_begin (cr, "P", NULL); cairo_show_text (cr, "Paragraph 1"); cairo_tag_end (cr, "P"); cairo_tag_end (cr, "Sect"); cairo_tag_begin (cr, "Sect", NULL); cairo_tag_begin (cr, "H1", NULL); cairo_show_text (cr, "Heading 2"); cairo_tag_end (cr, "H1"); cairo_tag_begin (cr, "P", NULL); cairo_show_text (cr, "Paragraph 2"); cairo_tag_end (cr, "P"); cairo_tag_end (cr, "Sect"); cairo_tag_end (cr, "Document"); |
void cairo_tag_begin (cairo_t *cr
,const char *tag_name
,const char *attributes
);
Marks the beginning of the tag_name
structure. Call
cairo_tag_end()
with the same tag_name
to mark the end of the
structure.
The attributes string is of the form "key1=value2 key2=value2 ...". Values may be boolean (true/false or 1/0), integer, float, string, or an array.
String values are enclosed in single quotes ('). Single quotes and backslashes inside the string should be escaped with a backslash.
Boolean values may be set to true by only specifying the key. eg the attribute string "key" is the equivalent to "key=true".
Arrays are enclosed in '[]'. eg "rect=[1.2 4.3 2.0 3.0]".
If no attributes are required, attributes
can be an empty string or NULL.
See Tags and Links Description for the list of tags and attributes.
Invalid nesting of tags or invalid attributes will cause cr
to
shutdown with a status of CAIRO_STATUS_TAG_ERROR
.
See cairo_tag_end()
.
Since: 1.16
void cairo_tag_end (cairo_t *cr
,const char *tag_name
);
Marks the end of the tag_name
structure.
Invalid nesting of tags will cause cr
to shutdown with a status of
CAIRO_STATUS_TAG_ERROR
.
See cairo_tag_begin()
.
Since: 1.16


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TensorFlow Estimator
Introduction
Analytics Zoo Orca Tenorflow Estimator provides a set APIs for running TensorFlow model on Spark in a distributed fashion.
Remarks:
- You need to install tensorflow==1.15.0 on your driver node.
- Your operating system (OS) is required to be one of the following 64-bit systems: Ubuntu 16.04 or later and macOS 10.12.6 or later.
- To run on other systems, you need to manually compile the TensorFlow source code. Instructions can be found here.
Orca TF Estimator
Orca TF Estimator is an estimator to do Tensorflow training/evaluation/prediction on Spark in a distributed fashion.
It can support various data types, like XShards, Spark DataFrame, tf.data.Dataset, numpy.ndarrays, etc.
It supports both native Tensorflow Graph model and Tensorflow Keras model in the unified APIs.
For native Tensorflow Graph model, you can reference our graph model example.
For Tensorflow Keras model, you can reference our keras model example.
Create Estimator with graph model
You can creating Orca TF Estimator with native Tensorflow graph model.
from zoo.orca.learn.tf.estimator import Estimator
Estimator.from_graph(*, inputs, outputs=None, labels=None, loss=None, optimizer=None,
clip_norm=None, clip_value=None,
metrics=None, updates=None,
sess=None, model_dir=None, backend="bigdl")
inputs
: input tensorflow tensors.outputs
: output tensorflow tensors.labels
: label tensorflow tensors.loss
: The loss tensor of the TensorFlow model, should be a scalaroptimizer
: tensorflow optimization method.clip_norm
: float >= 0. Gradients will be clipped when their L2 norm exceeds this value.clip_value
: a float >= 0 or a tuple of two floats.
If clip_value
is a float, gradients will be clipped when their absolute value exceeds this value.
If clip_value
is a tuple of two floats, gradients will be clipped when their value less than clip_value[0]
or larger than clip_value[1]
.
metrics
: dictionary of {metric_name: metric tensor}.sess
: the current TensorFlow Session, if you want to used a pre-trained model, you should use the Session to load the pre-trained variables and pass it to estimatormodel_dir
: location to save model checkpoint and summaries.backend
: backend for estimator. Now it only can be "bigdl".
This method returns an Estimator object.
E.g. To create Orca TF Estimator with tf graph:
from zoo.orca.learn.tf.estimator import Estimator
class SimpleModel(object):
def __init__(self):
self.user = tf.placeholder(dtype=tf.int32, shape=(None,))
self.item = tf.placeholder(dtype=tf.int32, shape=(None,))
self.label = tf.placeholder(dtype=tf.int32, shape=(None,))
feat = tf.stack([self.user, self.item], axis=1)
self.logits = tf.layers.dense(tf.to_float(feat), 2)
self.loss = tf.reduce_mean(tf.losses.sparse_softmax_cross_entropy(logits=self.logits,
labels=self.label))
model = SimpleModel()
est = Estimator.from_graph(
inputs=[model.user, model.item],
labels=[model.label],
outputs=[model.logits],
loss=model.loss,
optimizer=tf.train.AdamOptimizer(),
metrics={"loss": model.loss})
Train graph model with Estimator
After an Estimator created, you can call estimator API to train Tensorflow graph model:
fit(data,
epochs=1,
batch_size=32,
feature_cols=None,
label_cols=None,
validation_data=None,
session_config=None,
feed_dict=None,
checkpoint_trigger=None
)
data
: train data. It can be XShards, Spark DataFrame, tf.data.Dataset.
If data
is XShards, each element can be Pandas Dataframe or {'x': a feature numpy array
or a tuple of feature numpy arrays, 'y': a label numpy array or a tuple of
label numpy arrays}
If data
is tf.data.Dataset, each element is a tuple of input tensors.
epochs
: number of epochs to train.batch_size
: total batch size for each iteration.feature_cols
: feature column names if train data is Spark DataFrame.label_cols
: label column names if train data is Spark DataFrame.validation_data
: validation data. Validation data type should be the same as train data.session_config
: tensorflow session configuration for training. Should be object of tf.ConfigProtofeed_dict
: a dictionary. The key is TensorFlow tensor, usually a placeholder, the value of the dictionary is a tuple of two elements.
The first one of the tuple is the value to feed to the tensor in training phase and the second one is the value to feed to the tensor in validation phase.
* checkpoint_trigger
: when to trigger checkpoint during training. Should be bigdl optimzer trigger, like EveryEpoch(), SeveralIteration(num_iterations),etc.
Example of Train with Orca TF Estimator.
- Train data is tf.data.DataSet. E.g.
dataset = tf.data.Dataset.from_tensor_slices((np.random.randint(0, 200, size=(100,)),
np.random.randint(0, 50, size=(100,)),
np.ones(shape=(100,), dtype=np.int32)))
est.fit(data=dataset,
batch_size=8,
epochs=10,
validation_data=dataset)
- Train data is Spark DataFrame. E.g.
est.fit(data=df,
batch_size=8,
epochs=10,
feature_cols=['user', 'item'],
label_cols=['label'],
validation_data=df)
- Train data is XShards. E.g.
file_path = os.path.join(resource_path, "orca/learn/ncf.csv")
data_shard = zoo.orca.data.pandas.read_csv(file_path)
def transform(df):
result = {
"x": (df['user'].to_numpy(), df['item'].to_numpy()),
"y": df['label'].to_numpy()
}
return result
data_shard = data_shard.transform_shard(transform)
est.fit(data=data_shard,
batch_size=8,
epochs=10,
validation_data=data_shard)
Create Estimator with Keras model
You can creating Orca TF Estimator with Tensorflow Keras model. The model must be compiled.
from zoo.orca.learn.tf.estimator import Estimator
Estimator.from_keras(keras_model, metrics=None, model_dir=None, backend="bigdl")
keras_model
: the tensorflow.keras model, which must be compiled.metrics
: user specified metric.model_dir
: location to save model checkpoint and summaries.backend
: backend for estimator. Now it only can be "bigdl".
This method returns an Estimator object.
E.g. To create Orca TF Estimator with tf keras model:
from zoo.orca.learn.tf.estimator import Estimator
user = tf.keras.layers.Input(shape=[1])
item = tf.keras.layers.Input(shape=[1])
feat = tf.keras.layers.concatenate([user, item], axis=1)
predictions = tf.keras.layers.Dense(2, activation='softmax')(feat)
model = tf.keras.models.Model(inputs=[user, item], outputs=predictions)
model.compile(optimizer='rmsprop',
oss='sparse_categorical_crossentropy',
metrics=['accuracy'])
est = Estimator.from_keras(keras_model=model)
Train Keras model with Estimator
After an Estimator created, you can call estimator's API to train Tensorflow Keras model:
fit(data,
epochs=1,
batch_size=32,
feature_cols=None,
label_cols=None,
validation_data=None,
session_config=None,
checkpoint_trigger=None
)
data
: train data. It can be XShards, Spark DataFrame, tf.data.Dataset.
If data
is XShards, each element needs to be {'x': a feature numpy array
or a tuple of feature numpy arrays, 'y': a label numpy array or a tuple of
label numpy arrays}
If data
is tf.data.Dataset, each element is a tuple of input tensors.
epochs
: number of epochs to train.batch_size
: total batch size for each iteration.feature_cols
: feature column names if train data is Spark DataFrame.label_cols
: label column names if train data is Spark DataFrame.validation_data
: validation data. Validation data type should be the same as train data.session_config
: tensorflow session configuration for training. Should be object of tf.ConfigProto-
checkpoint_trigger
: when to trigger checkpoint during training. Should be bigdl optimzer trigger, like EveryEpoch(), SeveralIteration(num_iterations),etc. -
Train data is tf.data.DataSet. E.g.
dataset = tf.data.Dataset.from_tensor_slices((np.random.randint(0, 200, size=(100,)),
np.random.randint(0, 50, size=(100,)),
np.ones(shape=(100,), dtype=np.int32)))
dataset = dataset.map(lambda user, item, label: [(user, item), label])
est.fit(data=dataset,
batch_size=8,
epochs=10,
validation_data=dataset)
- Train data is Spark DataFrame. E.g.
est.fit(data=df,
batch_size=8,
epochs=10,
feature_cols=['user', 'item'],
label_cols=['label'],
validation_data=df)
- If train data is XShards, e.g.
file_path = os.path.join(self.resource_path, "orca/learn/ncf.csv")
data_shard = zoo.orca.data.pandas.read_csv(file_path)
def transform(df):
result = {
"x": (df['user'].to_numpy().reshape([-1, 1]),
df['item'].to_numpy().reshape([-1, 1])),
"y": df['label'].to_numpy()
}
return result
data_shard = data_shard.transform_shard(transform)
est.fit(data=data_shard,
batch_size=8,
epochs=10,
validation_data=data_shard)
Evaluate with Estimator
You can call estimator's API to evaluate Tensorflow graph model or keras model.
evaluate(data, batch_size=4,
feature_cols=None,
label_cols=None,
)
data
: evaluation data. It can be XShards, Spark DataFrame, tf.data.Dataset.
If data
is XShards, each element needs to be {'x': a feature numpy array or a tuple of feature numpy arrays, 'y': a label numpy array or a tuple of label numpy arrays}
If data
is tf.data.Dataset, each element is [feature tensor tuple, label tensor tuple]
batch_size
: batch size per thread.feature_cols
: feature_cols: feature column names if train data is Spark DataFrame.label_cols
: label column names if train data is Spark DataFrame.
This method returns evaluation result as a dictionary in the format of {'metric name': metric value}
Predict with Estimator
You can call estimator's such APIs to predict with trained model.
predict(data, batch_size=4,
feature_cols=None,
):
-
data
: data to be predicted. It can be XShards, Spark DataFrame, or tf.data.Dataset.If
data
is XShard, each element needs to be {'x': a feature numpy array or a tuple of feature numpy arrays}.If
data
is tf.data.Dataset, each element is feature tensor tuple. -
batch_size
: batch size per thread feature_cols
: list of feature column names if input data is Spark DataFrame.
This method returns a predicted result.
- Predict data is tf.data.DataSet. The prediction result should be an XShards and each element is {'prediction': predicted numpy array or list of predicted numpy arrays}.
dataset = tf.data.Dataset.from_tensor_slices((np.random.randint(0, 200, size=(100, 1)),
np.random.randint(0, 50, size=(100, 1))))
predictions = est.predict(dataset)
prediction_shards = est.predict(data_shard)
predictions = prediction_shards.collect()
assert 'prediction' in predictions[0]
- Predict data is Spark DataFrame. The predict result is a DataFrame which includes original columns plus
prediction
column. Theprediction
column can be FloatType, VectorUDT or Array of VectorUDT depending on model outputs shape.
prediction_df = est.predict(df, batch_size=4, feature_cols=['user', 'item'])
assert 'prediction' in prediction_df.columns
- Predict data is XShards. The prediction result should be an XShards and each element is {'prediction': predicted numpy array or list of predicted numpy arrays}.
file_path = os.path.join(resource_path, "orca/learn/ncf.csv")
data_shard = zoo.orca.data.pandas.read_csv(file_path)
def transform(df):
result = {
"x": (df['user'].to_numpy(), df['item'].to_numpy())
}
return result
data_shard = data_shard.transform_shard(transform)
prediction_shards = est.predict(data_shard)
predictions = prediction_shards.collect()
assert 'prediction' in predictions[0]
Checkpointing and Resume Training
During training, Orca TF Estimator would save Orca checkpoint every epoch. You can also specify checkpoint_trigger
in fit() to set checkpoint interval. The Orca checckpoints are saved in model_dir
which is specified when you create estimator. You can load previous Orca checkpoint and resume train with it with such APIs:
load_orca_checkpoint(path, version=None)
- path: directory containing Orca checkpoint files.
With version=None
, this method load latest checkpoint under specified directory.
E.g.
est = Estimator.from_keras(keras_model=model, model_dir=model_dir)
est.load_orca_checkpoint(model_dir, version=None)
est.fit(data=data_shard,
batch_size=8,
epochs=10,
validation_data=data_shard,
checkpoint_trigger=SeveralIteration(4))
If you want to load specified version of checkpoint, you can use:
load_orca_checkpoint(path, version)
- path: checkpoint directory which contains model. and optimMethod-TFParkTraining. files.
- version: checkpoint version, which is the suffix of model.* file, i.e., for modle.4 file, the version is 4.
After loading checkpoint, you can resume training with fit().
Tensorboard support
During training and validation, Orca TF Estimator would save Tensorflow summary data under model_dir
which is specified when you create estimator. This data can be visualized in TensorBoard, or you can use estimator's APIs to retrieve it. If you want to save train/validation summary data to different directory or you don't specify model_dir
, you can set the logdir and app name with such API:
set_tensorboard(log_dir, app_name)
log_dir
: The base directory path to store training and validation logs.app_name
: The name of the application.
This method sets summary information during the training process for visualization purposes. Saved summary can be viewed via TensorBoard. In order to take effect, it needs to be called before fit.
Training summary will be saved to 'log_dir/app_name/train' and validation summary (if any) will be saved to 'log_dir/app_name/validation'.
E.g. Set tensorboard for the estimator:
est = Estimator.from_keras(keras_model=model)
log_dir = os.path.join(temp, "log")
est.set_tensorboard(log_dir, "test")
Now, you can see the summary data in TensorBoard. Or else, you can get summary data with such APIs:
get_train_summary(tag)
tag
: The string variable represents the scalar wanted. It can only be "Loss", "LearningRate", or "Throughput".
This method get the scalar from model train summary. Return list of summary data of [iteration_number, scalar_value, timestamp].
E.g.
est.fit(data=data_shard,
batch_size=8,
epochs=10,
validation_data=data_shard)
assert os.path.exists(os.path.join(log_dir, "test/train"))
assert os.path.exists(os.path.join(log_dir, "test/validation"))
train_loss = est.get_train_summary("Loss")
get_validation_summary(tag)
tag
: The string variable represents the scalar wanted.
This method gets the scalar from model validation summary. Return list of summary data of [iteration_number, scalar_value, timestamp]
E.g.
val_scores = est.get_validation_summary("Loss")
Save model
After training, you can save model in the estimator:
Save Tensorflow checkpoint
save_tf_checkpoint(path)
path
: tensorflow checkpoint path.
If you use tensorflow graph model in this estimator, this method would save tensorflow checkpoint.
E.g.
temp = tempfile.mkdtemp()
model_checkpoint = os.path.join(temp, 'test.ckpt')
est.save_tf_checkpoint(model_checkpoint)
Save TF Keras model
save_keras_model(path, overwrite=True)
path
: keras model save path.overwrite
: Boolean. Whether to silently overwrite any existing file at the target location. Default: True.
If you use tensorflow keras model in this estimator, this method would save keras model in specified path.
E.g.
temp = tempfile.mkdtemp()
model_path = os.path.join(temp, 'test.h5')
est.save_keras_model(model_path, overwrite=True)


--- layout: about title: about published: true ---
I'm a proud dad! On the weekends I'm also a photographer and love having coffee and traveling with my family.
In a nutshell, I'm candid, a deep thinker and imaginative.
Originally an industrial engineer from Lima, Perú, I’ve worked in marketing in 3M, digital marketing in an ‘Amazon clone’ and HR.
After years of feeling the need to ‘create’ and learning web development on my own, I started work in a web development agency called Laboratoria and fell into UX there. Read more on this transition here.
I enjoy figuring out the structure and the strategy behind building the actual system.
Design with data, qualitative and quantitative, letting it be the guide.
Focus on making interactions functional, understandable, and interesting to users.


About Me
Write a brief introduction about yourself here.
My Projects
-
Project Title
A short description of the project.
View Project -
Project Title
A short description of the project.
View Project -
Project Title
A short description of the project.
View Project


AngularJS - components
I - guides
angular official component devguide
Refactoring Angular Apps to Component Style : teropa.info
Lifecycle hooks in Angular 1.5 : toddmotto.com
Jun 3, 2016
- $onInit
- Using $onInit
- $onInit + “require”
- Real world $onInit + “require”
- $postLink
- Using $postLink
- Real world $postLink
- What $postLink is not
- $onChanges
- What calls $onChanges?
- Using $onChanges
- Cloning “change” hashes for “immutable” bindings
- One-way dataflow + events
- Is two-way binding through “=” syntax dead?
- Using isFirstChange()
- $onDestroy
- Using $onDestroy
One-way data-binding in Angular 1.5 : toddmotto.com
With a parent / child relation ship between component where the parent pass data to the child, it is the practice of binding uni-directionnal instead of bi-directionnal.
bi-directionnal (two way) is
bindings: { obj: '=' }
uni-directionnal (one way) isbindings: { obj: '<' }
Using one way binding break the link with the parent scope. So the child cannot mutate the parent state.
/!\
If an object reference is bound to the child from the parent, if the child mutate a property it will work like two way binding.Another way to exprime difference between two-way and one-way bindings is :
- One way binding is binding the data from the model to the view.
- Two way binding is binding the data from the model to the view and the view to the model. (Also called MVVM)
II - best practices
A - Todd Motto best practices
one-way data flow
- data goes from the top level component to the lower levels.
- child components modifications on bindings doesn’t reflect on parents components.
- callback function are used to handle events, they are used to update the parents components data modified by children.
stateful components
- referred to as smart/container components
- how things work
- fetches state (through API, so no
bindings
) - does not directly mutate state
- renders child components that mutate state
- the classic case is a view component for example an entity list
stateless components
- referred to as dumb/presentational components
- how things look
- has defined inputs and outputs using
bindings: {}
- data enters the component through attribute bindings (inputs)
- data leaves the component through events (outputs)
- mutates state, passes data back up on-demand (such as a click or submit event)
- doesn’t care where data comes from - it’s stateless
- are highly reusable components
So TLDR is :
- the webapp is a tree of components
- theses components are stateful or stateless
- views are stateful
- stateful components fetches and store data (it is the state)
- stateful components are an aggregation of stateless components
- stateless components renders data only, so they are reusables
See section component design section in inter-component communication part for this design flaws.
B - Components types
The 3 component categories in Angular
Presentational and Container Components : Dan Abramov (ReactJS team, Redux author)
- View Components (or Routing components) (or Stateful components for Todd Motto)
- Container Components (or Smart components) (or Stateful components for Todd Motto)
- Dumb Components (or Stateless components for Todd Motto)
Responsabilities table
| Responsabilities per component | View Comp. | Container Comp. | Dumb Comp. |
|---|---|---|---|
| Read route parameters | YES | NO | NO |
| Uses stores and data services | NO | YES | NO |
| Display and styling | NO | NO | YES |
III - feedback
A - NG 1.5 from the trenches at velesin.io
- 1/7 Starting a new app in Angular 1.5 – does it make sense?
- 2/7 Angular 1.5 app as a tree of components
- 3/7 Communication between Angular 1.5 components (and with the API)
- 4/7 A flexible Angular 1.5 project structure (the “fractal” architecture)
- 5/7 Writing Angular 1.5 project in ES6/ES2015
- 6/7 Unit testing Angular 1.5 components – a detailed guide
- 7/7 E2E testing of component-oriented Angular 1.5 app [COMING SOON]
IV - inter-component communication
A - angular 2 parent & child communication via service
Parent and children communicate via a service : angular.io
B - practices overview
src Angular 1.5. Components communication. Best practice : stackoverflow.com
I’m using Angular 1.5.9.
Right now I have two components which need to communicate. If I change something in the first one (for example choose item in the list) => second one needs to be changed (item that was chosen needs to be displayed) and vice versa (changing in the second one => change in the first one) I have read about a lot of approaches, so I can manage this in few ways:
- Communication via shareable Service
- Using two way data binding (“=” option in component bindings). Example: https://jsfiddle.net/peter_drinnan/t4q4nrfp/27/
- Using one way data binding with input/output approach. Example: http://stackoverflow.com/questions/36033940/how-to-pass-data-between-child-components-in-angular-1-5-not-using-scope
- Using “require” (some parent component to share data). Example: http://stackoverflow.com/questions/36645065/component-communication-in-angular-1-5
Maybe I miss something? What is the best practice?
Thank you.
C - component design
1 - avoid event bubbling & extraneous props
2 big flaws :
- Custom Event Bubbling
- Extraneous Properties (input are props so if tree is deeper then 2 comps, there is lots of props passing through for nothing)
2 - cross component communication design comparison
AngularJS: Cross Component Communication
- Communicating with inherited scopes
- Communicating with events
- Communicating with services
3 - event based design is cancer
Events are no more then GOTO in an asynchronous context. (<- assert by me)
Worse then goto :
- async
- many place in source code to register an event
GOTO still considered harmful? : stackoverflow.com
Add Angular1 Event Patterns/Best Practices
D - articles
Front-end Application Libraries and Component Architectures
Parent component trigger function in child component
component communication and triggers
Component Interaction in Angular : kfarst.github.io 20170329


Before Change
def test_remote_install(self):
Verify that installing from a local package works as expected.
with patch("quilt3.packages.get_from_config") as get_config_mock, \
patch("quilt3.Package.push") as push_mock:
remote_registry = "."
get_config_mock.return_value = remote_registry
pkg = Package()
pkg.build("Quilt/nice-name")
quilt3.Package.install("Quilt/nice-name", dest="./")
push_mock.assert_called_once_with(dest="./", name="Quilt/nice-name", registry=remote_registry)
def test_package_fetch(self):
Package.fetch() on nested, relative keys
package_ = Package().set_dir("/", DATA_DIR / "nested")
After Change
def test_remote_install(self):
Verify that installing from a local package works as expected.
remote_registry = Path(".").resolve().as_uri()
quilt3.config(
default_local_registry=remote_registry,
default_remote_registry=remote_registry
)


These methods will be included into any Active Job object, adding helpers for de/serialization and creation of job instances.
Methods
- D
- S
Instance Public methods
deserialize(job_data)
Link
Creates a new job instance from a hash created with serialize
Source: show
# File<EMAIL_ADDRESS>line 24 def deserialize(job_data) job = job_data['job_class'].constantize.new job.job_id = job_data['job_id'] job.queue_name = job_data['queue_name'] job.serialized_arguments = job_data['arguments'] job end
set(options={})
Link
Creates a job preconfigured with the given options. You can call perform_later with the job arguments to enqueue the job with the preconfigured options
Options
-
:wait
- Enqueues the job with the specified delay -
:wait_until
- Enqueues the job at the time specified -
:queue
- Enqueues the job on the specified queue
Examples
VideoJob.set(queue: :some_queue).perform_later(Video.last)
VideoJob.set(wait: 5.minutes).perform_later(Video.last)
VideoJob.set(wait_until: Time.now.tomorrow).perform_later(Video.last)
VideoJob.set(queue: :some_queue, wait: 5.minutes).perform_later(Video.last)
VideoJob.set(queue: :some_queue, wait_until: Time.now.tomorrow).perform_later(Video.last)
Source: show
# File<EMAIL_ADDRESS>line 48 def set(options={}) ConfiguredJob.new(self, options) end


Impact Scrum for Trello is a Chrome Extension that adds a little bit of functionality to Trello.com to make it perfect to use in SCRUM based projects. Impact Scrum for Trello allows you to add story points to cards and accumulates the story points per list.
Story points are written in between curly braces in the title of a card.
{5} upon an error I want to see an awesome 404 page.
Upon saving the card, Trello Scrum picks up the assigned points and shows them
as a blue badge in the lower left corner of the card.
Actual Story points are written in between pipe characters in the title of a card.
{5} |10| upon an error I want to see an awesome 404 page.
Upon saving the card, Trello Scrum picks up the actual points and shows them
as a gold badge in the lower left corner of the card.
When you flip a card to add more information to that card, you are also presented with a story point picker upon editing the title. This allows for easier entry. The points shown are shown in the commonly used semi-fibonacci sequence: 1, 5, 12, 30, 70, 150.
Allowed entries for story points are all numbers, decimals (like .5 or 4.2) and the question mark (?) for unknowns.


| write.mitmlSPSS {mitml} | R Documentation |
mitml
objects to SPSS compatible formatSaves objects of class mitml
as a text and a syntax file which can be processed by the statistical software SPSS (IBM Corp., 2013).
write.mitmlSPSS(x, filename, sep = "\t", dec = ".", na.value = -999, syntax = TRUE, locale = NULL)
x |
An object of class |
filename |
File base name of the data and syntax files, specified without file extension. |
sep |
The field separator. |
dec |
The decimal separator. |
na.value |
A numeric value coding the missing data in the resulting data file. |
syntax |
A logical flag indicating if an SPSS syntax file should be generated. This file contains instructions for SPSS for reading in the data file. Default is |
locale |
(optional) A character string specifying the localization to be used in SPSS (e.g., |
In SPSS, multiply imputed data are contained in a single file, in which an Imputation_
variable separates the original data and the various imputed data sets.
During export, factors are converted to numeric, whereas character variables are left “as is”.
By default, write.mitmlSPSS
generates a raw text file containing the data, along with a syntax file containing instructions for SPSS.
This syntax file mimics SPSS's functionality to read text files with sensible settings.
In order to read in the data, the syntax file must be opened and executed using SPSS, or open using the GUI.
Manual changes to the syntax file can be required, for example, if the file path of the data file is not correctly represented in the syntax.
The locale
argument can be used to ensure that SPSS reads the data in the correct locale.
Alternatively, write.mitmlSAV
may be used for exporting directly to the SPSS native .sav
format.
None (invisible NULL
).
Simon Grund
IBM Corp. IBM SPSS Statistics for Windows. Armonk, NY: IBM Corp
panImpute
, jomoImpute
, mitmlComplete
, write.mitmlSAV
## Not run: data(studentratings) fml <- ReadDis + SES ~ ReadAchiev + (1|ID) imp <- panImpute(studentratings, formula = fml, n.burn = 1000, n.iter = 100, m = 5) # write data file and SPSS syntax write.mitmlSPSS(imp, filename = "imputation", sep = "\t", dec = ".", na.value = -999, locale = "en_US") ## End(Not run)


Since 2019, I have been teaching at the University of Toronto's Faculty of Information, in the Master of Information program. The faculty is home to a small but keen community of information practitioners and scholars in fields such as museum studies, library science, archives and records management, culture & technology, information systems design, and user experienc design.
The User Experience Design concentration of the MI program is designed to prepare emerging professionals in UX with a two-year curriculum of theory, studio practice, co-operative employment, and portfolio building. As part of this concentration, I taught two key courses:
Spring 2019 (2 x 45 students)
Spring 2020 (2 x 65 students)
This is a required second-year MI course for the UXD concentration in which students apply their understanding of user-centred design and the design process to creating and iterating on user interfaces. From initial ideation and layout, through design system adherence, to final polish, design critique, and accessibility, students engage in weekly practice workshops and produce high-fidelity interface prototypes.
I taught this course in a technology-enhanced active learning (TEAL) classroom that allows students to share and present their work in small groups, building students' critique skills alongside their design abilities. Alongside high fidelity considerations such as colour selection, typography, and design system templates, students practice critical assessment, usability, and working with industry standard tools such as Figma.
Fall 2019 (32 students)
This course examines the particular roles of User Experience Design (UXD) and User Experience Research (UXR) in video games and the video games industry, and highlights how core UX research methods can be adapted to this unique domain in which some friction is necessary for engagement.
Throughout the course, students analyze and design interfaces for video games and conduct usability, appreciation, and challenge testing using industry-standard user research tools. Inclusivity, accessibility, and making video games for everyone is a recurring theme that reflects the current state of the industry.
I developed this course from scratch as a special topics elective. It has since been integrated into the curriculum as a regular course for the UXD concentration due to positive student feedback and high interest. This course gives students the opportunity to practice research protocols alongside the unique considerations of designing a user experience that does not remove intended, engaging challenge. Students engage in weekly design workshops and complete individual and group assignments in gameplay observation, HUD design, and heuristic evaluation.


Subaqueous Landslide Morphometrics Database for Global Outreach and Crowd-Sourced Data Collection
Subaqueous landslides are common deposits occurring in environments ranging from lakes and coasts to deepwater ocean basins. Their occurrence can trigger destructive tsunamigenic waves affecting coastal populations, damaging and disrupting communication and other industrial infrastructure (e.g. oil and gas, offshore wind farms, etc), and upsetting biotic communities. In addition, ancient subaqueous landslides play an important role in the development and distribution of non-renewable natural resources (i.e., oil and gas), and impact the safety of exploring for deep subsurface resources.
Despite recognizing the broad scientific importance of subaqueous landslides, most of the research has concentrated on the qualitative description of these events/deposits. Most recently, the subaqueous landslide community has come to a consensus to standardize the collection of quantitative data associated with the morphometry (e.g., length, area, volume, runout distance, etc.), setting and physical nature of these deposits (Clare et al., 2018). It is our hope that this website will:
- hosts this burgeoning community database, and
- promote collection of standardized, quantitative data for subaqueous landslide deposits
We will also (eventually) provide a set of python-based tools for subaqueous landslide analysis that can be used for a variety of modeling efforts including tsunami and geohazard risk assessment, paleoclimate and organic carbon modeling, and many other applications. We see this database and analysis toolset as a bridge to better data collection practices, modeling efforts, and collaborations not only within the subaqueous landslide community, but also with the subaerial landslide community.


public static final class R.string
extends java.lang.Object
| Constructor and Description |
|---|
string() |
public static final int app_name
public static final int buttons_rect_bottom_center
public static final int buttons_rect_bottom_left
public static final int buttons_rect_bottom_right
public static final int buttons_rect_left_bottom
public static final int buttons_rect_left_center
public static final int buttons_rect_left_top
public static final int buttons_rect_right_bottom
public static final int buttons_rect_right_center
public static final int buttons_rect_right_top
public static final int buttons_rect_top_center
public static final int buttons_rect_top_left
public static final int buttons_rect_top_right
public static final int buttons_round_bottom_center
public static final int buttons_round_bottom_left
public static final int buttons_round_bottom_left_lower
public static final int buttons_round_bottom_left_upper
public static final int buttons_round_bottom_right
public static final int buttons_round_bottom_right_lower
public static final int buttons_round_bottom_right_upper
public static final int buttons_round_center_left
public static final int buttons_round_center_right
public static final int buttons_round_top_center
public static final int buttons_round_top_left
public static final int buttons_round_top_left_lower
public static final int buttons_round_top_left_upper
public static final int buttons_round_top_right
public static final int buttons_round_top_right_lower
public static final int buttons_round_top_right_upper
public static final int common_google_play_services_enable_button
public static final int common_google_play_services_enable_text
public static final int common_google_play_services_enable_title
public static final int common_google_play_services_install_button
public static final int common_google_play_services_install_text
public static final int common_google_play_services_install_title
public static final int common_google_play_services_notification_ticker
public static final int common_google_play_services_unknown_issue
public static final int common_google_play_services_unsupported_text
public static final int common_google_play_services_update_button
public static final int common_google_play_services_update_text
public static final int common_google_play_services_update_title
public static final int common_google_play_services_updating_text
public static final int common_google_play_services_wear_update_text
public static final int common_open_on_phone
public static final int common_signin_button_text
public static final int common_signin_button_text_long
public static final int complicationDrawable_noDataText
public static final int complicationDrawable_textTypeface
public static final int complicationDrawable_titleTypeface
public static final int dismiss_overlay_button_label
public static final int generic_cancel
public static final int generic_yes
public static final int message_left
public static final int message_right
public static final int my_analog_name
public static final int navigation_drawer_content_description
public static final int status_bar_notification_info_overflow
public static final int time_difference_now
public static final int time_difference_short_days_and_hours
public static final int time_difference_short_hours_and_minutes


A Cartoon-iriffic Minimalist Survival Sidescroller set in an Alien Rainforest
Collect fruits and animal parts from an alien rainforest!
Craft bizzare foodstuff and equipment.
Choose your own loot system, RNG begone!
Dynamic deadly fights, no hack-n-slash here.
Store items or risk losing it all!
Play Normal or Hardcore death mode!


The current release is a beta release. We welcome any feedback using this form.
| Beta | v1.0 (December, 2019) |
|---|---|
| 10 furniture models, 6 background scenes | 80+ furniture models, programmatic background scenes |
| Baxter, Sawyer, Cursor agents | More robots (e.g. Jaco, Fetch) |
| IK and Impedance Control | Support for 3D controllers |
| RL/IL benchmarks |
Furniture Assembly
Complex long-horizon manipulation task
Diversity
80+ furniture models, customizable background, lighting and textures
Robots
Baxter, Sawyer, and more
Easy to Use
OpenAI Gym interface for imitation / reinforcement learning


How to work with bevels on curves
by Simon Gangl25games writes:
Hey guys.
Today I show you how to create an mesh (bevel) along an bezier curve. It is very easy to create pipes or any other kind of organic round shapes (horns, tree trunk, ... ). I will explain how to do the setup and how to modify your curve for your needs.
Maybe this will help you in any kind of way.
Cheers!


| DOSE: |
3 lines |
insufflated |
Cocaine |
(powder / crystals) |
It was a Friday night and all of my friends and I were ready to go to the club. At about 8 PM a friend of mine asked me if I wanted to do some cocaine. I laughed because I thought he was joking, but I soon stopped when he pulled out a small baggie of white powder cocaine. He soon asked me again if I wanted to do any and I said sure, why not.
At first I put a small amount in my mouth to see what it felt and tasted like. After my mouth went numb I was ready to do a line. Before I even snorted a line my heart was racing as I anticipated what the drug would do to me. I imagined how great of a feeling it would be. Snort! Line one up the nose. My top teeth were numb along with my tongue. I waited about 30 seconds and I started to feel it. All I wanted to do was jump and dance.
After a few minutes I did another line, and then another. By this time my nose was extremely itchy and runny. My friend took me to the bathroom and we put some water up our noses which made them feel better. I was feeling pretty good, but it wasn't what I expected. From all the things I have heard and read about cocaine, I thought the experience would be tremendous. I don't see how cocaine could be addictive at all. I really didn't like it at all. I felt pretty good for about 30 minutes and was really hyper, but besides that there wasn't much more to it. Although I'm sure cocaine is extremely addictive, I felt no craving for it later that night. I had a pretty good time at the club because I had a lot of energy. That night I lay in bed from about 1-4 trying to go to sleep, but it was hard.
Itís just a waste of time and money. Cocaine is highly over rated and much too expensive. If I was offered cocaine again I would reject it.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


GUEST INFORMATION
ACCOMODATION
The Bridal Party and their families will be staying at the Secura Lifestyle holiday park and they would love for you to join them. There are various 2 bedroom cabins and campsites available to book. We are staying 4 nights from Wednesday 19th Feb until Sunday 23rd Feb.
All cabins have been reserved for the wedding party. To book a cabin, please call the holiday park directly on 02 6555 5511 to enquire on availability. Let them know it's for the Tyson and Jess wedding to receive a 15% discount (do not try to book online as it shows fully booked). If the holiday park is unable to find suitable accomdation, contact Jess and she can help!
If Secura Lifestyle cabins or campsite options do not accommodate your needs, there are plenty of other accommodation nearby.
forsterpalmsmotel.com.au
hotelforster.com.au
islandpalms.com.au
airbnb.com.au/s/Forster--Australia
TRANSPORTATION
Forster is a 3.5 hour drive north of Sydney airport and 1.5 / 2 hour drive north of Newcastle airport.We recommend from Sydney & Newcastle hiring a car to get to the venue. You can also fly into Port Macquarie but the flights and car hire are twice the price and is 1 hour from the venue.
CHILDREN
However much we would love to share our day with both you and your children, we regrettably need to limit children to our immediate family, babies in arms and those travelling from overseas.


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