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Re: python philosophical question - strong vs duck typing

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  Re: python philosophical question - strong vs duck typing Terry Reedy <tjreedy@udel.edu> - 2012-01-04 15:22 -0500
    Re: python philosophical question - strong vs duck typing 88888 Dihedral <dihedral88888@googlemail.com> - 2012-01-07 07:43 -0800
    Re: python philosophical question - strong vs duck typing 88888 Dihedral <dihedral88888@googlemail.com> - 2012-01-07 07:43 -0800

#18511 — Re: python philosophical question - strong vs duck typing

On 1/4/2012 1:37 AM, Terry Reedy wrote:
> On 1/3/2012 8:04 PM, Devin Jeanpierre wrote:

>> [ An example of a simple dependently typed program:
>> http://codepad.org/eLr7lLJd ]
>
> Just got it after a minute delay.

A followup now that I have read it. Removing the 40 line comment, the 
function itself is

fun getitem{n,m:nat}(arr : array(int, n) ,
  length : int(n), index : int m) : int =
     if index < length then
         arr[index]
     else
         ~1 (* -1, error *)

where n,m are compiler variables used to define the dependent 
(paramaterized) types array(int,n) and int(n)/ The double use of n means 
that the compiler checks that length n of the array equals the length 
passed.

My response: in Python, there is no need to pass concrete collection 
sizes because they are packaged with the collection at runtime as an 
attribute. So:

1) In Python, there is no need for such checking. In addition, the 
for-loop construct, 'for item in iterable:', removes the possibility of 
indexing errors.

2) Python classes are, in a sense, or in effect, runtime dependent 
types. While the formal implementation type of a 'list' is just 'list', 
the effective computation type is 'mutable sequence of length n'. The 
type of an iterator is 'read-only sequence of indefinite length'. I find 
this an interesting way to look at Python.

-- 
Terry Jan Reedy

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#18648

Terry Reedy於 2012年1月5日星期四UTC+8上午4時22分03秒寫道：
> On 1/4/2012 1:37 AM, Terry Reedy wrote:
> > On 1/3/2012 8:04 PM, Devin Jeanpierre wrote:
> 
> >> [ An example of a simple dependently typed program:
> >> http://codepad.org/eLr7lLJd ]
> >
> > Just got it after a minute delay.
> 
> A followup now that I have read it. Removing the 40 line comment, the 
> function itself is
> 
> fun getitem{n,m:nat}(arr : array(int, n) ,
>   length : int(n), index : int m) : int =
>      if index < length then
>          arr[index]
>      else
>          ~1 (* -1, error *)
> 
> where n,m are compiler variables used to define the dependent 
> (paramaterized) types array(int,n) and int(n)/ The double use of n means 
> that the compiler checks that length n of the array equals the length 
> passed.
> 
> My response: in Python, there is no need to pass concrete collection 
> sizes because they are packaged with the collection at runtime as an 
> attribute. So:
> 
> 1) In Python, there is no need for such checking. In addition, the 
> for-loop construct, 'for item in iterable:', removes the possibility of 
> indexing errors.
> 
> 2) Python classes are, in a sense, or in effect, runtime dependent 
> types. While the formal implementation type of a 'list' is just 'list', 
> the effective computation type is 'mutable sequence of length n'. The 
> type of an iterator is 'read-only sequence of indefinite length'. I find 
> this an interesting way to look at Python.

Also it is easy to turn an indexed object to be an iterator by a
function decorator that returns a generator in the object level 
without declaring a new class from a class written by others.

Thus this can lead to  a decoupled design of software among many contributors
in an elegant way.  

I prefer a factorized decoupled blocks of modules to be completed 
by several programmers to build a package.  


> 
> -- 
> Terry Jan Reed

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#18649

Terry Reedy於 2012年1月5日星期四UTC+8上午4時22分03秒寫道：
> On 1/4/2012 1:37 AM, Terry Reedy wrote:
> > On 1/3/2012 8:04 PM, Devin Jeanpierre wrote:
> 
> >> [ An example of a simple dependently typed program:
> >> http://codepad.org/eLr7lLJd ]
> >
> > Just got it after a minute delay.
> 
> A followup now that I have read it. Removing the 40 line comment, the 
> function itself is
> 
> fun getitem{n,m:nat}(arr : array(int, n) ,
>   length : int(n), index : int m) : int =
>      if index < length then
>          arr[index]
>      else
>          ~1 (* -1, error *)
> 
> where n,m are compiler variables used to define the dependent 
> (paramaterized) types array(int,n) and int(n)/ The double use of n means 
> that the compiler checks that length n of the array equals the length 
> passed.
> 
> My response: in Python, there is no need to pass concrete collection 
> sizes because they are packaged with the collection at runtime as an 
> attribute. So:
> 
> 1) In Python, there is no need for such checking. In addition, the 
> for-loop construct, 'for item in iterable:', removes the possibility of 
> indexing errors.
> 
> 2) Python classes are, in a sense, or in effect, runtime dependent 
> types. While the formal implementation type of a 'list' is just 'list', 
> the effective computation type is 'mutable sequence of length n'. The 
> type of an iterator is 'read-only sequence of indefinite length'. I find 
> this an interesting way to look at Python.

Also it is easy to turn an indexed object to be an iterator by a
function decorator that returns a generator in the object level 
without declaring a new class from a class written by others.

Thus this can lead to  a decoupled design of software among many contributors
in an elegant way.  

I prefer a factorized decoupled blocks of modules to be completed 
by several programmers to build a package.  


> 
> -- 
> Terry Jan Reed

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