Can this be written as a single line?
assertThat(actualDeltas)
.filteredOn(delta -> delta instanceof Replacement)
.asInstanceOf(InstanceOfAssertFactories.list(Replacement.class))
I expected asInstanceOf to do the filtering. Alternatively, I searched for extractors or other concepts, but couldn't find any simple solution.
Is that possible with assertj?
By design, the purpose of asInstanceOf is only to provide type-narrowed assertions for cases where the type of the object under assertion is not visible at compile time.
When you provide InstanceOfAssertFactories.list(Replacement.class) as a parameter for asInstanceOf, you are telling AssertJ that you expect the object under assertion to be a List with elements of type Replacement.
While asInstanceOf will make sure that the object under test is a List, it will neither filter nor enforce that all the list elements are of type Replacement. The Replacement will ensure type-safety with subsequent methods that can be chained, for example with extracting(Function).
Currently, filteredOn(Predicate) or any other filteredOn variant is the right way to take out elements that should not be part of the assertion. If the filtering would happen outside (e.g., via Stream API), no asInstanceOf call would be needed as assertThat() could detect the proper element type based on the input declaration.
Related
I can see that Subject.eraseToAnySubject() returns the concrete Subject type AnySubject. I'm assuming this is using a type eraser pattern.
However, the apple docs provide almost no details: https://developer.apple.com/documentation/combine/passthroughsubject/3241547-erasetoanysubject
Can anyone explain how this works and where it should be used?
Also, would it be possible to use the some keyword to avoid using AnySubject?
In Combine, as you chain Publishers to Operators, the return type becomes complicated very quickly since it includes specific detail about each publisher in the chain.
For example a simple string Publisher with a filter and map Operator attached will have a return type of: <Filter<Map<Published<String, Error>>>>
eraseToAny uses a type eraser pattern to capture what's actually important about the return type. In the example given, adding an eraseToAnyPublisher will shorten the type to a more succinct <AnyPublisher<String, Error>>
In the syntax analysis phase, an imperative compiler can build an AST out of nodes that already contain a type field that is set to null during construction, and then later, in the semantic analysis phase, fill in the types by assigning the declared/inferred types into the type fields.
How do purely functional languages handle this, where you do not have the luxury of assignment? Is the type-less AST mapped to a different kind of type-enriched AST? Does that mean I need to define two types per AST node, one for the syntax phase, and one for the semantic phase?
Are there purely functional programming tricks that help the compiler writer with this problem?
I usually rewrite a source (or an already several steps lowered) AST into a new form, replacing each expression node with a pair (tag, expression).
Tags are unique numbers or symbols which are then used by the next pass which derives type equations from the AST. E.g., a + b will yield something like { numeric(Tag_a). numeric(Tag_b). equals(Tag_a, Tag_b). equals(Tag_e, Tag_a).}.
Then types equations are solved (e.g., by simply running them as a Prolog program), and, if successful, all the tags (which are variables in this program) are now bound to concrete types, and if not, they're left as type parameters.
In a next step, our previous AST is rewritten again, this time replacing tags with all the inferred type information.
The whole process is a sequence of pure rewrites, no need to replace anything in your AST destructively. A typical compilation pipeline may take a couple of dozens of rewrites, some of them changing the AST datatype.
There are several options to model this. You may use the same kind of nullable data fields as in your imperative case:
data Exp = Var Name (Maybe Type) | ...
parse :: String -> Maybe Exp -- types are Nothings here
typeCheck :: Exp -> Maybe Exp -- turns Nothings into Justs
or even, using a more precise type
data Exp ty = Var Name ty | ...
parse :: String -> Maybe (Exp ())
typeCheck :: Exp () -> Maybe (Exp Type)
I cant speak for how it is supposed to be done, but I did do this in F# for a C# compiler here
The approach was basically - build an AST from the source, leaving things like type information unconstrained - So AST.fs basically is the AST which strings for the type names, function names, etc.
As the AST starts to be compiled to (in this case) .NET IL, we end up with more type information (we create the types in the source - lets call these type-stubs). This then gives us the information needed to created method-stubs (the code may have signatures that include type-stubs as well as built in types). From here we now have enough type information to resolve any of the type names, or method signatures in the code.
I store that in the file TypedAST.fs. I do this in a single pass, however the approach may be naive.
Now we have a fully typed AST you could then do things like compile it, fully analyze it, or whatever you like with it.
So in answer to the question "Does that mean I need to define two types per AST node, one for the syntax phase, and one for the semantic phase?", I cant say definitively that this is the case, but it is certainly what I did, and it appears to be what MS have done with Roslyn (although they have essentially decorated the original tree with type info IIRC)
"Are there purely functional programming tricks that help the compiler writer with this problem?"
Given the ASTs are essentially mirrored in my case, it would be possible to make it generic and transform the tree, but the code may end up (more) horrendous.
i.e.
type 'type AST;
| MethodInvoke of 'type * Name * 'type list
| ....
Like in the case when dealing with relational databases, in functional programming it is often a good idea not to put everything in a single data structure.
In particular, there may not be a data structure that is "the AST".
Most probably, there will be data structures that represent parsed expressions. One possible way to deal with type information is to assign a unique identifier (like an integer) to each node of the tree already during parsing and have some suitable data structure (like a hash map) that associates those node-ids with types. The job of the type inference pass, then, would be just to create this map.
I've been reading a lot of other people's Scala code recently, and one of the things that I have difficultly with (coming from Java) is a lack of explicit type annotations.
It's certainly convenient when writing code to be able to leave out type annotations -- however when reading code I often find that explicit type annotations help me to understand at a glance what code is doing more easily.
The Scala style guide (http://docs.scala-lang.org/style/types.html) doesn't seem to provide any definitive guidance on this, stating:
Use type inference where possible, but put clarity first, and favour explicitness in public APIs.
To my mind, this is a bit contradictory. While it's clearly obvious what type this variable is:
val tokens = new HashMap[String, Int]
It's not so obvious what type this one is:
val tokens = readTokens()
So, if I was putting clarity first I would probably annotate all variables where the type is not already declared on the same line.
Do any Scala practitioners have guidance on this? Am I crazy to be considering adding type annotations to my local variables? I'm particularly interested in hearing from folks who spend a lot of time reading scala code (for example, in code reviews), as well as writing it.
It's not so obvious what type this one is:
val tokens = readTokens()
Good names are important: the name is plural, ergo it returns some collection of some kind. The most general collection types in Scala are Traversable and Iterator, and they mostly share a common interface, so it's not really important which one of the two it is. The name also talks about "reading tokens", ergo it obviously should return Tokens in some fashion. And last but not least, the method call has parentheses, which according to the style guide means it has side-effects, so I wouldn't count on being able to traverse the collection more than once.
Ergo, the return type is something like
Traversable[Token]
or
Iterator[Token]
and which of the two it is doesn't really matter because their client interfaces are mostly identical.
Note also that the latter constraint (only traversing the collection once) isn't even captured in the type, even if you were providing an explicit type, you would still have to look at the name and the style!
Is it possible to compose an arbitrary function with an Enumerator or EnumeratorM, so that each individual item of data being pushed into the iteratee is first preprocessed by applying the function?
With Scalaz 6 at least, no - not if the function has a return type that is different from (and not a subtype of) its argument type, because EnumeratorM's type doesn't allow it to change the input type of the iteratee.
However, it is possible to "pre-compose" arbitrary functions with an iteratee, so I think this is the way to go. Could also maybe use an enumeratee, but this abstraction is not provided in Scalaz 6.
I have read the blog post recommended me here. Now I wonder what some those methods are useful for. Can you show examples of using forall (as opposed to foreach) and toList of Option?
map: Allows you to transform a value "inside" an Option, as you probably already know for Lists. This operation makes Option a functor (you can say "endofunctor" if you want to scare your colleagues)
flatMap: Option is actually a monad, and flatMap makes it one (together with something like a constuctor for a single value). This method can be used if you have a function which turns a value into an Option, but the value you have is already "wrapped" in an Option, so flatMap saves you the unwrapping before applying the function. E.g. if you have an Option[Map[K,V]], you can write mapOption.flatMap(_.get(key)). If you would use a simple map here, you would get an Option[Option[V]], but with flatMap you get an Option[V]. This method is cooler than you might think, as it allows to chain functions together in a very flexible way (which is one reason why Haskell loves monads).
flatten: If you have a value of type Option[Option[T]], flatten turns it into an Option[T]. It is the same as flatMap(identity(_)).
orElse: If you have several alternatives wrapped in Options, and you want the first one that holds actually a value, you can chain these alternatives with orElse: steakOption.orElse(hamburgerOption).orElse(saladOption)
getOrElse: Get the value out of the Option, but specify a default value if it is empty, e.g. nameOption.getOrElse("unknown").
foreach: Do something with the value inside, if it exists.
isDefined, isEmpty: Determine if this Option holds a value.
forall, exists: Tests if a given predicate holds for the value. forall is the same as option.map(test(_)).getOrElse(true), exists is the same, just with false as default.
toList: Surprise, it converts the Option to a List.
Many of the methods on Option may be there more for the sake of uniformity (with collections) rather than for their usefulness, as they are all very small functions and so do not spare much effort, yet they serve a purpose, and their meanings are clear once you are familiar with the collection framework (as is often said, Option is like a list which cannot have more than one element).
forall checks a property of the value inside an option. If there is no value, the check pass. For example, if in a car rental, you are allowed one additionalDriver: Option[Person], you can do
additionalDriver.forall(_.hasDrivingLicense)
exactly the same thing that you would do if several additional drivers were allowed and you had a list.
toList may be a useful conversion. Suppose you have options: List[Option[T]], and you want to get a List[T], with the values of all of the options that are Some. you can do
for(option <- options; value in option.toList) yield value
(or better options.flatMap(_.toList))
I have one practical example of toList method. You can find it in scaldi (my Scala dependency injection framework) in Module.scala at line 72:
https://github.com/OlegIlyenko/scaldi/blob/f3697ecaa5d6e96c5486db024efca2d3cdb04a65/src/main/scala/scaldi/Module.scala#L72
In this context getBindings method can return either Nil or List with only one element. I can retrieve it as Option with discoverBinding. I find it convenient to be able to convert Option to List (that either empty or has one element) with toList method.