January 2018 - Pharo-users - lists.pharo.org

Set Rounding mode for IEEE floating point operations
by Steffen MÃ¤rcker May 29, 2018

May 29, 2018

Hi, is there any way to set the rounding mode for IEEE floating point operations? Maybe something like > Double roundToMinusInfWhile: [... code goes here ...]Double > roundToZeroWhile: [... more code here ...] If not, is it possible to add this behavior, e.g., via a custom primitive? Best, Steffen

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Spec on Moose 6.0 error: MessageNotUnderstood: ToolDockingBarMorph>>popUpOwner
by Offray Vladimir Luna CÃ¡rdenas May 15, 2018

May 15, 2018

Hi, I'm getting this error while trying to prototype some interface with Spec on Moose 6.0: MessageNotUnderstood: ToolDockingBarMorph>>popUpOwner it happens anytime that I'm using displaying a MenuModel and I go to the menu items. I imagine that the menu item is complaining about a lacking pop up message. How can I solve this? Thanks, Offray

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#ast vs. #parseTree
by Sean P. DeNigris May 9, 2018

May 9, 2018

Why does CompiledMethod understand both? They are in the same protocol, produce the same kindOf result, and neither has a method comment! :/ (Pharo 4.0) ----- Cheers, Sean -- View this message in context: http://forum.world.st/ast-vs-parseTree-tp4893074.html Sent from the Pharo Smalltalk Users mailing list archive at Nabble.com.

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unsolicited package-cache use
by Hilaire April 3, 2018

April 3, 2018

I am using tonel format to install from and save to a local repository. How to prevent Monticello/Configuration/Metacello to save packages to the image package-cache dir? Even when installing from a configuration with repo on tonel file format, packages get created in the image package-cache directory. I have read there and there, Pharo gets confused and uses wrongly the package-cache versions and not the local repo. -- Dr. Geo http://drgeo.eu

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Looking for small boards and tiny computers which can run Pharo
by Steven Costiou March 29, 2018

March 29, 2018

Hi, i am looking for: - small hardware, boards/computers, "embeddable" devices, etc. that can run Pharo (except Raspberry pi that i already know) - robots, flying drones or things alike with an open linux which can possibly run Pharo Could somebody points me to documentation or web sites where such things can be found ? Thanks, Steven.

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Porting Transducers to Pharo
by Steffen MÃ¤rcker March 21, 2018

March 21, 2018

Hi, I am the developer of the library 'Transducers' for VisualWorks. It was formerly known as 'Reducers', but this name was a poor choice. I'd like to port it to Pharo, if there is any interest on your side. I hope to learn more about Pharo in this process, since I am mainly a VW guy. And most likely, I will come up with a bunch of questions. :-) Meanwhile, I'll cross-post the introduction from VWnc below. I'd be very happy to hear your optinions, questions and I hope we can start a fruitful discussion - even if there is not Pharo port yet. Best, Steffen Transducers are building blocks that encapsulate how to process elements of a data sequence independently of the underlying input and output source. # Overview ## Encapsulate Implementations of enumeration methods, such as #collect:, have the logic how to process a single element in common. However, that logic is reimplemented each and every time. Transducers make it explicit and facilitate re-use and coherent behavior. For example: - #collect: requires mapping: (aBlock1 map) - #select: requires filtering: (aBlock2 filter) ## Compose In practice, algorithms often require multiple processing steps, e.g., mapping only a filtered set of elements. Transducers are inherently composable, and hereby, allow to make the combination of steps explicit. Since transducers do not build intermediate collections, their composition is memory-efficient. For example: - (aBlock1 filter) * (aBlock2 map) "(1.) filter and (2.) map elements" ## Re-Use Transducers are decoupled from the input and output sources, and hence, they can be reused in different contexts. For example: - enumeration of collections - processing of streams - communicating via channels # Usage by Example We build a coin flipping experiment and count the occurrence of heads and tails. First, we associate random numbers with the sides of a coin. scale := [:x | (x * 2 + 1) floor] map. sides := #(heads tails) replace. Scale is a transducer that maps numbers x between 0 and 1 to 1 and 2. Sides is a transducer that replaces the numbers with heads an tails by lookup in an array. Next, we choose a number of samples. count := 1000 take. Count is a transducer that takes 1000 elements from a source. We keep track of the occurrences of heads an tails using a bag. collect := [:bag :c | bag add: c; yourself]. Collect is binary block (reducing function) that collects events in a bag. We assemble the experiment by transforming the block using the transducers. experiment := (scale * sides * count) transform: collect. From left to right we see the steps involved: scale, sides, count and collect. Transforming assembles these steps into a binary block (reducing function) we can use to run the experiment. samples := Random new reduce: experiment init: Bag new. Here, we use #reduce:init:, which is mostly similar to #inject:into:. To execute a transformation and a reduction together, we can use #transduce:reduce:init:. samples := Random new transduce: scale * sides * count reduce: collect init: Bag new. We can also express the experiment as data-flow using #<~. This enables us to build objects that can be re-used in other experiments. coin := sides <~ scale <~ Random new. flip := Bag <~ count. Coin is an eduction, i.e., it binds transducers to a source and understands #reduce:init: among others. Flip is a transformed reduction, i.e., it binds transducers to a reducing function and an initial value. By sending #<~, we draw further samples from flipping the coin. samples := flip <~ coin. This yields a new Bag with another 1000 samples. # Basic Concepts ## Reducing Functions A reducing function represents a single step in processing a data sequence. It takes an accumulated result and a value, and returns a new accumulated result. For example: collect := [:col :e | col add: e; yourself]. sum := #+. A reducing function can also be ternary, i.e., it takes an accumulated result, a key and a value. For example: collect := [:dic :k :v | dict at: k put: v; yourself]. Reducing functions may be equipped with an optional completing action. After finishing processing, it is invoked exactly once, e.g., to free resources. stream := [:str :e | str nextPut: each; yourself] completing: #close. absSum := #+ completing: #abs A reducing function can end processing early by signaling Reduced with a result. This mechanism also enables the treatment of infinite sources. nonNil := [:res :e | e ifNil: [Reduced signalWith: res] ifFalse: [res]]. The primary approach to process a data sequence is the reducing protocol with the messages #reduce:init: and #transduce:reduce:init: if transducers are involved. The behavior is similar to #inject:into: but in addition it takes care of: - handling binary and ternary reducing functions, - invoking the completing action after finishing, and - stopping the reduction if Reduced is signaled. The message #transduce:reduce:init: just combines the transformation and the reducing step. However, as reducing functions are step-wise in nature, an application may choose other means to process its data. ## Reducibles A data source is called reducible if it implements the reducing protocol. Default implementations are provided for collections and streams. Additionally, blocks without an argument are reducible, too. This allows to adapt to custom data sources without additional effort. For example: "XStreams adaptor" xstream := filename reading. reducible := [[xstream get] on: Incomplete do: [Reduced signal]]. "natural numbers" n := 0. reducible := [n := n+1]. ## Transducers A transducer is an object that transforms a reducing function into another. Transducers encapsulate common steps in processing data sequences, such as map, filter, concatenate, and flatten. A transducer transforms a reducing function into another via #transform: in order to add those steps. They can be composed using #* which yields a new transducer that does both transformations. Most transducers require an argument, typically blocks, symbols or numbers: square := Map function: #squared. take := Take number: 1000. To facilitate compact notation, the argument types implement corresponding methods: squareAndTake := #squared map * 1000 take. Transducers requiring no argument are singletons and can be accessed by their class name. flattenAndDedupe := Flatten * Dedupe. # Advanced Concepts ## Data flows Processing a sequence of data can often be regarded as a data flow. The operator #<~ allows define a flow from a data source through processing steps to a drain. For example: squares := Set <~ 1000 take <~ #squared map <~ (1 to: 1000). fileOut writeStream <~ #isSeparator filter <~ fileIn readStream. In both examples #<~ is only used to set up the data flow using reducing functions and transducers. In contrast to streams, transducers are completely independent from input and output sources. Hence, we have a clear separation of reading data, writing data and processing elements. - Sources know how to iterate over data with a reducing function, e.g., via #reduce:init:. - Drains know how to collect data using a reducing function. - Transducers know how to process single elements. ## Reductions A reduction binds an initial value or a block yielding an initial value to a reducing function. The idea is to define a ready-to-use process that can be applied in different contexts. Reducibles handle reductions via #reduce: and #transduce:reduce: For example: sum := #+ init: 0. sum1 := #(1 1 1) reduce: sum. sum2 := (1 to: 1000) transduce: #odd filter reduce: sum. asSet := [:set :e | set add: e; yourself] initializer: [Set new]. set1 := #(1 1 1) reduce: asSet. set2 := #(1 to: 1000) transduce: #odd filter reduce: asSet. By combining a transducer with a reduction, a process can be further modified. sumOdds := sum <~ #odd filter setOdds := asSet <~ #odd filter ## Eductions An eduction combines a reducible data sources with a transducer. The idea is to define a transformed (virtual) data source that needs not to be stored in memory. odds1 := #odd filter <~ #(1 2 3) readStream. odds2 := #odd filter <~ (1 to 1000). Depending on the underlying source, eductions can be processed once (streams, e.g., odds1) or multiple times (collections, e.g., odds2). Since no intermediate data is stored, transducers actions are lazy, i.e., they are invoked each time the eduction is processed. # Origins Transducers is based on the same-named Clojure library and its ideas. Please see: http://clojure.org/transducers

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About validation
by StÃ©phane Ducasse March 11, 2018

March 11, 2018

Hi I read the chapter on validation of the mentoring course book of andres valloud. http://www.lulu.com/fr/fr/shop/andres-valloud/a-mentoring-course-on-smallta… And I love this extension of SUnit. I loved the idea when I saw it for the first time at Lugano (presentation of Leandro Caniglia). I encourage everybody to read it. Now does anybody implemented in Pharo the SUnit extension? I would like to play with it and eventually write a chapter on it for the next book. The idea is so good. Stef

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HMAC-SHA512
by Ben Coman March 8, 2018

March 8, 2018

Can anyone recommend libraries (native Smalltalk or via FFI) to do generate a HMAC-SHA512 ? cheers -ben

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New booklets organisation and new booklet
by Stephane Ducasse Feb. 6, 2018

Feb. 6, 2018

Hi We reorganised the books.pharo.org web site to make the distinction between technology and teaching oriented booklet. We will have specific covers soon and print available on lulu. In addition we got the new booklet from C. Bera that explains Call Stack Management. http://books.pharo.org Stef

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Embedded PDF viewer?
by Manuel Leuenberger Feb. 6, 2018

Feb. 6, 2018

Hi, I want to view a PDF within Pharo. I found that Athens has a PDF canvas, but I have no idea how to use it. Is there a way to view a PDF within Pharo, so that I can scroll, zoom, click links etc. in the PDF? Cheers, Manuel

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