I have about 50 functions which should consume only even positive numbers. Right now I am checking each time with an "if" whether the number put in is zero or not:
function grof(x::Int)
if (x % 2) == 0
println("good")
else
throw("x is not an even number!!!!!!!!!!!!! Stupid programmer!")
end
end
Ideally, I would like to have a datatype which produces this automatically, i.e.
function grof(x::EvenInt)
println("good")
end
However, I am not able to produce this datatype by my own since I am unable to understand the documentary. Thanks for your help!
Best, v.
I don't think creating a type is warranted in such a situation: I would simply #assert that the condition is verified at the beginning of the function(s). (Funnily enough, checking the whether a number is even is the example that was chosen in the documentation to illustrate the effect of #assert)
For example:
julia> function grof(x::Int)
#assert iseven(x) "Stupid programmer!"
println("good")
end
grof (generic function with 1 method)
julia> grof(2)
good
julia> grof(3)
ERROR: AssertionError: Stupid programmer!
Stacktrace:
[1] grof(::Int64) at ./REPL[5]:2
[2] top-level scope at REPL[7]:1
EDIT: If you really want to create a type enforcing such a constraint, it is possible. The way to do this would be to
create a type (possibly subtyping one of the Number abstract types; maybe Signed)
define an inner constructor ensuring that such a type cannot hold an odd value
A very simple example to build upon would be along the lines of:
# A wrapper around an even integer value
struct EvenInt
val :: Int
# inner constructor
function EvenInt(val)
#assert iseven(val)
new(val)
end
end
# Accessor to the value of an EvenInt
val(x::EvenInt) = x.val
# A method working only on even numbers
grof(x::EvenInt) = println("good: $(val(x)) is even")
You'd use this like so:
julia> x = EvenInt(42)
EvenInt(42)
julia> grof(x)
good: 42 is even
julia> y = EvenInt(1)
ERROR: AssertionError: iseven(val)
Stacktrace:
[1] EvenInt(::Int64) at ./REPL[1]:5
[2] top-level scope at REPL[6]:1
but note that you can't do anything on EvenInts yet: you need to either unwrap them (using val() in this case), or define operations on them (a task which can be vastly simplified if you make EvenInt a subtype of one of the abstract number types and follow the relevant interface).
All integers multiplied by two are even, so redefine your function to take half the number it currently takes.
function grof2(halfx::Int)
x=2*halfx
println("good")
end
>>> (1).__str__!=(2).__str__
True
Is there any technical reason why they've been made seperate objects instead of refering to a single object? It seems like it would be more efficient
That is because you don't get the __str__ function, but a method wrapper for it.
>>> x = (1).__str__
>>> type(x)
<class 'method-wrapper'>
>>> x()
'1'
>>> x.__self__
1
>>> x = (2).__str__
>>> x()
'2'
>>> x.__self__
2
A method wrapper is an object that keeps a reference to self. So it has to be different for every instance.
For more details, check this question.
Is there a way to enforce a dictionary being constant?
I have a function which reads out a file for parameters (and ignores comments) and stores it in a dict:
function getparameters(filename::AbstractString)
f = open(filename,"r")
dict = Dict{AbstractString, AbstractString}()
for ln in eachline(f)
m = match(r"^\s*(?P<key>\w+)\s+(?P<value>[\w+-.]+)", ln)
if m != nothing
dict[m[:key]] = m[:value]
end
end
close(f)
return dict
end
This works just fine. Since i have a lot of parameters, which i will end up using on different places, my idea was to let this dict be global. And as we all know, global variables are not that great, so i wanted to ensure that the dict and its members are immutable.
Is this a good approach? How do i do it? Do i have to do it?
Bonus answerable stuff :)
Is my code even ok? (it is the first thing i did with julia, and coming from c/c++ and python i have the tendencies to do things differently.) Do i need to check whether the file is actually open? Is my reading of the file "julia"-like? I could also readall and then use eachmatch. I don't see the "right way to do it" (like in python).
Why not use an ImmutableDict? It's defined in base but not exported. You use one as follows:
julia> id = Base.ImmutableDict("key1"=>1)
Base.ImmutableDict{String,Int64} with 1 entry:
"key1" => 1
julia> id["key1"]
1
julia> id["key1"] = 2
ERROR: MethodError: no method matching setindex!(::Base.ImmutableDict{String,Int64}, ::Int64, ::String)
in eval(::Module, ::Any) at .\boot.jl:234
in macro expansion at .\REPL.jl:92 [inlined]
in (::Base.REPL.##1#2{Base.REPL.REPLBackend})() at .\event.jl:46
julia> id2 = Base.ImmutableDict(id,"key2"=>2)
Base.ImmutableDict{String,Int64} with 2 entries:
"key2" => 2
"key1" => 1
julia> id.value
1
You may want to define a constructor which takes in an array of pairs (or keys and values) and uses that algorithm to define the whole dict (that's the only way to do so, see the note at the bottom).
Just an added note, the actual internal representation is that each dictionary only contains one key-value pair, and a dictionary. The get method just walks through the dictionaries checking if it has the right value. The reason for this is because arrays are mutable: if you did a naive construction of an immutable type with a mutable field, the field is still mutable and thus while id["key1"]=2 wouldn't work, id.keys[1]=2 would. They go around this by not using a mutable type for holding the values (thus holding only single values) and then also holding an immutable dict. If you wanted to make this work directly on arrays, you could use something like ImmutableArrays.jl but I don't think that you'd get a performance advantage because you'd still have to loop through the array when checking for a key...
First off, I am new to Julia (I have been using/learning it since only two weeks). So do not put any confidence in what I am going to say unless it is validated by others.
The dictionary data structure Dict is defined here
julia/base/dict.jl
There is also a data structure called ImmutableDict in that file. However as const variables aren't actually const why would immutable dictionaries be immutable?
The comment states:
ImmutableDict is a Dictionary implemented as an immutable linked list,
which is optimal for small dictionaries that are constructed over many individual insertions
Note that it is not possible to remove a value, although it can be partially overridden and hidden
by inserting a new value with the same key
So let us call what you want to define as a dictionary UnmodifiableDict to avoid confusion. Such object would probably have
a similar data structure as Dict.
a constructor that takes a Dict as input to fill its data structure.
specialization (a new dispatch?) of the the method setindex! that is called by the operator [] =
in order to forbid modification of the data structure. This should be the case of all other functions that end with ! and hence modify the data.
As far as I understood, It is only possible to have subtypes of abstract types. Therefore you can't make UnmodifiableDict as a subtype of Dict and only redefine functions such as setindex!
Unfortunately this is a needed restriction for having run-time types and not compile-time types. You can't have such a good performance without a few restrictions.
Bottom line:
The only solution I see is to copy paste the code of the type Dict and its functions, replace Dict by UnmodifiableDict everywhere and modify the functions that end with ! to raise an exception if called.
you may also want to have a look at those threads.
https://groups.google.com/forum/#!topic/julia-users/n-lqjybIO_w
https://github.com/JuliaLang/julia/issues/1974
REVISION
Thanks to Chris Rackauckas for pointing out the error in my earlier response. I'll leave it below as an illustration of what doesn't work. But, Chris is right, the const declaration doesn't actually seem to improve performance when you feed the dictionary into the function. Thus, see Chris' answer for the best resolution to this issue:
D1 = [i => sind(i) for i = 0.0:5:3600];
const D2 = [i => sind(i) for i = 0.0:5:3600];
function test(D)
for jdx = 1:1000
# D[2] = 2
for idx = 0.0:5:3600
a = D[idx]
end
end
end
## Times given after an initial run to allow for compiling
#time test(D1); # 0.017789 seconds (4 allocations: 160 bytes)
#time test(D2); # 0.015075 seconds (4 allocations: 160 bytes)
Old Response
If you want your dictionary to be a constant, you can use:
const MyDict = getparameters( .. )
Update Keep in mind though that in base Julia, unlike some other languages, it's not that you cannot redefine constants, instead, it's just that you get a warning when doing so.
julia> const a = 2
2
julia> a = 3
WARNING: redefining constant a
3
julia> a
3
It is odd that you don't get the constant redefinition warning when adding a new key-val pair to the dictionary. But, you still see the performance boost from declaring it as a constant:
D1 = [i => sind(i) for i = 0.0:5:3600];
const D2 = [i => sind(i) for i = 0.0:5:3600];
function test1()
for jdx = 1:1000
for idx = 0.0:5:3600
a = D1[idx]
end
end
end
function test2()
for jdx = 1:1000
for idx = 0.0:5:3600
a = D2[idx]
end
end
end
## Times given after an initial run to allow for compiling
#time test1(); # 0.049204 seconds (1.44 M allocations: 22.003 MB, 5.64% gc time)
#time test2(); # 0.013657 seconds (4 allocations: 160 bytes)
To add to the existing answers, if you like immutability and would like to get performant (but still persistent) operations which change and extend the dictionary, check out FunctionalCollections.jl's PersistentHashMap type.
If you want to maximize performance and take maximal advantage of immutability, and you don't plan on doing any operations on the dictionary whatsoever, consider implementing a perfect hash function-based dictionary. In fact, if your dictionary is a compile-time constant, these can even be computed ahead of time (using metaprogramming) and precompiled.
Robot is telling me that I'm providing too many arguments to my keyword. I've boiled it down to a base case where I have a keyword that should do nothing:
def do_nothing():
"""
Does absolutly nothing
"""
Calling this keywork like this:
*** Test Cases ***
testCaseOne
do_nothing
Give this result:
TypeError: do_nothing() takes no arguments (1 given)
Adding a parameter to the keyword definition fixes the problem. Why does robot seem to pass 1 parameter to each keyword, even if there are no parameters in the test case?
I found the answer here.
The issue has nothing to do with the robotframework, and has every thing to do with Python; Python implicitly passes the current instance of the class to method calls, but I needed to explicitly declare the parameter. This is customarily named self:
def do_nothing(self):
This test runs.
I have code:
let join a ~with':b ~by:key =
let rec a' = link a ~to':b' ~by:key
and b' = link b ~to':a' ~by:(Key.opposite key) in
a'
and compilation result for it is:
Error: This kind of expression is not allowed as right-hand side of
`let rec' build complete
I can rewrite it to:
let join a ~with':b ~by:key =
let rec a'() = link a ~to':(b'()) ~by:key
and b'() = link b ~to':(a'()) ~by:(Key.opposite key) in
a'()
It is compilable variant, but implemented function is infinitely recursive and it is not what I need.
My questions: Why is first implementation invalid? How to call two functions and use their results as arguments for each other?
My compiler version = 4.01.0
The answer to your first question is given in Section 7.3 of the OCaml manual. Here's what it says:
Informally, the class of accepted definitions consists of those definitions where the defined names occur only inside function bodies or as argument to a data constructor.
Your names appear as function arguments, which isn't supported.
I suspect the reason is that you can't assign a semantics otherwise. It seems to me the infinite computation that you see is impossible to avoid in general.