= Design Decisions =

The Lichen language design involves some different choices to those taken in

Python's design. Many of these choices are motivated by the following

criteria:

 * To simplify the language and to make what programs do easier to understand

   and to predict

 * To make analysis of programs easier, particularly

   [[../Deduction|deductions]] about the nature of the code

 * To simplify and otherwise reduce the [[../Representations|representations]]

   employed and the operations performed at run-time

Lichen is in many ways a restricted form of Python. In particular,

restrictions on the attribute names supported by each object help to clearly

define the object types in a program, allowing us to identify those objects

when they are used. Consequently, optimisations that can be employed in a

Lichen program become possible in situations where they would have been

difficult or demanding to employ in a Python program.

Some design choices evoke memories of earlier forms of Python. Removing nested

scopes simplifies the [[../Inspection|inspection]] of programs and run-time

[[../Representations|representations]] and mechanisms. Other choices seek to

remedy difficult or defective aspects of Python, notably the behaviour of

Python's [[../Imports|import]] system.

<<TableOfContents(2,3)>>

== Attributes ==

{{{#!table

'''Lichen''' || '''Python''' || '''Rationale'''

==

Objects have a fixed set of attribute names

|| Objects can gain and lose attributes at run-time

|| Having fixed sets of attributes helps identify object types

==

Instance attributes may not shadow class attributes

|| Instance attributes may shadow class attributes

|| Forbidding shadowing simplifies access operations

==

Attributes are simple members of object structures

|| Dynamic handling and computation of attributes is supported

|| Forbidding dynamic attributes simplifies access operations

}}}

=== Fixed Attribute Names ===

Attribute names are bound for classes through assignment in the class

namespace, for modules in the module namespace, and for instances in methods

through assignment to `self`. Class and instance attributes are propagated to

descendant classes and instances of descendant classes respectively. Once

bound, attributes can be modified, but new attributes cannot be bound by other

means, such as the assignment of an attribute to an arbitrary object that

would not already support such an attribute.

{{{#!python numbers=disable

class C:

    a = 123

    def __init__(self):

        self.x = 234

C.b = 456 # not allowed (b not bound in C)

C().y = 567 # not allowed (y not bound for C instances)

}}}

Permitting the addition of attributes to objects would then require that such

addition attempts be associated with particular objects, leading to a

potentially iterative process involving object type deduction and

modification, also causing imprecise results.

=== No Shadowing ===

Instances may not define attributes that are provided by classes.

{{{#!python numbers=disable

class C:

    a = 123

    def shadow(self):

        self.a = 234 # not allowed (attribute shadows class attribute)

}}}

Permitting this would oblige instances to support attributes that, when

missing, are provided by consulting their classes but, when not missing, may

also be provided directly by the instances themselves.

=== No Dynamic Attributes ===

Instance attributes cannot be provided dynamically, such that any missing

attribute would be supplied by a special method call to determine the

attribute's presence and to retrieve its value.

{{{#!python numbers=disable

class C:

    def __getattr__(self, name): # not supported

        if name == "missing":

            return 123

}}}

Permitting this would require object types to potentially support any

attribute, undermining attempts to use attributes to identify objects.

== Naming ==

{{{#!table

'''Lichen''' || '''Python''' || '''Rationale'''

==

Names may be local, global or built-in: nested namespaces must be initialised

explicitly

|| Names may also be non-local, permitting closures

|| Limited name scoping simplifies program inspection and run-time mechanisms

==

`self` is a reserved name and is optional in method parameter lists

|| `self` is a naming convention, but the first method parameter must always

.. refer to the accessed object

|| Reserving `self` assists deduction; making it optional is a consequence of

.. the method binding behaviour

==

Instance attributes can be initialised using `.name` parameter notation

|| [[https://stackoverflow.com/questions/1389180/automatically-initialize-instance-variables|Workarounds]]

.. involving decorators and introspection are required for similar brevity

|| Initialiser notation eliminates duplication in program code and is convenient

}}}

=== Traditional Local, Global and Built-In Scopes Only ===

Namespaces reside within a hierarchy within modules: classes containing

classes or functions; functions containing other functions. Built-in names are

exposed in all namespaces, global names are defined at the module level and

are exposed in all namespaces within the module, locals are confined to the

namespace in which they are defined.

However, locals are not inherited by namespaces from surrounding or enclosing

namespaces.

{{{#!python numbers=disable

def f(x):

    def g(y):

        return x + y # not permitted: x is not inherited from f in Lichen (it is in Python)

    return g

def h(x):

    def i(y, x=x): # x is initialised but held in the namespace of i

        return x + y # succeeds: x is defined

    return i

}}}

Needing to access outer namespaces in order to access any referenced names

complicates the way in which such dynamic namespaces would need to be managed.

Although the default initialisation technique demonstrated above could be

automated, explicit initialisation makes programs easier to follow and avoids

mistakes involving globals having the same name.

=== Reserved Self ===

The `self` name can be omitted in method signatures, but in methods it is

always initialised to the instance on which the method is operating.

{{{#!python numbers=disable

class C:

    def f(y): # y is not the instance

        self.x = y # self is the instance

}}}

The assumption in methods is that `self` must always be referring to an

instance of the containing class or of a descendant class. This means that

`self` cannot be initialised to another kind of value, which Python permits

through the explicit invocation of a method with the inclusion of the affected

instance as the first argument. Consequently, `self` becomes optional in the

signature because it is not assigned in the same way as the other parameters.

=== Instance Attribute Initialisers ===

In parameter lists, a special notation can be used to indicate that the given

name is an instance attribute that will be assigned the argument value

corresponding to the parameter concerned.

{{{#!python numbers=disable

class C:

    def f(self, .a, .b, c): # .a and .b indicate instance attributes

        self.c = c # a traditional assignment using a parameter

}}}

To use the notation, such dot-qualified parameters must appear only in the

parameter lists of methods, not plain functions. The qualified parameters are

represented as locals having the same name, and assignments to the

corresponding instance attributes are inserted into the generated code.

{{{#!python numbers=disable

class C:

    def f1(self, .a, .b): # equivalent to f2, below

        pass

    def f2(self, a, b):

        self.a = a

        self.b = b

    def g(self, .a, .b, a): # not permitted: a appears twice

        pass

}}}

Naturally, `self`, being a reserved name in methods, can also be omitted from

such parameter lists. Moreover, such initialising parameters can have default

values.

{{{#!python numbers=disable

class C:

    def __init__(.a=1, .b=2):

        pass

c1 = C()

c2 = C(3, 4)

print c1.a, c1.b # 1 2

print c2.a, c2.b # 3 4

}}}

== Inheritance and Binding ==

{{{#!table

'''Lichen''' || '''Python''' || '''Rationale'''

==

Class attributes are propagated to class hierarchy members during

initialisation: rebinding class attributes does not affect descendant class

attributes

|| Class attributes are propagated live to class hierarchy members and must be

.. looked up by the run-time system if not provided by a given class

|| Initialisation-time propagation simplifies access operations and attribute

.. table storage

==

Unbound methods must be bound using a special function taking an instance

|| Unbound methods may be called using an instance as first argument

|| Forbidding instances as first arguments simplifies the invocation mechanism

==

Functions assigned to class attributes do not become unbound methods

|| Functions assigned to class attributes become unbound methods

|| Removing method assignment simplifies deduction: methods are always defined

.. in place

==

Base classes must be well-defined

|| Base classes may be expressions

|| Well-defined base classes are required to establish a well-defined

.. hierarchy of types

==

Classes may not be defined in functions

|| Classes may be defined in any kind of namespace

|| Forbidding classes in functions prevents the definition of countless class

.. variants that are awkward to analyse

}}}

=== Inherited Class Attributes ===

Class attributes that are changed for a class do not change for that class's

descendants.

{{{#!python numbers=disable

class C:

    a = 123

class D(C):

    pass

C.a = 456

print D.a # remains 123 in Lichen, becomes 456 in Python

}}}

Permitting this requires indirection for all class attributes, requiring them

to be treated differently from other kinds of attributes. Meanwhile, class

attribute rebinding and the accessing of inherited attributes changed in this

way is relatively rare.

=== Unbound Methods ===

Methods are defined on classes but are only available via instances: they are

instance methods. Consequently, acquiring a method directly from a class and

then invoking it should fail because the method will be unbound: the "context"

of the method is not an instance. Furthermore, the Python technique of

supplying an instance as the first argument in an invocation to bind the

method to an instance, thus setting the context of the method, is not

supported. See [[#Reserved Self|"Reserved Self"]] for more information.

{{{#!python numbers=disable

class C:

    def f(self, x):

        self.x = x

    def g(self):

        C.f(123) # not permitted: C is not an instance

        C.f(self, 123) # not permitted: self cannot be specified in the argument list

        get_using(C.f, self)(123) # binds C.f to self, then the result is called

}}}

Binding methods to instances occurs when acquiring methods via instances or

explicitly using the `get_using` built-in. The built-in checks the

compatibility of the supplied method and instance. If compatible, it provides

the bound method as its result.

Normal functions are callable without any further preparation, whereas unbound

methods need the binding step to be performed and are not immediately

callable. Were functions to become unbound methods upon assignment to a class

attribute, they would need to be invalidated by having the preparation

mechanism enabled on them. However, this invalidation would only be relevant

to the specific case of assigning functions to classes and this would need to

be tested for. Given the added complications, such functionality is arguably

not worth supporting.

=== Assigning Functions to Class Attributes ===

Functions can be assigned to class attributes but do not become unbound

methods as a result.

{{{#!python numbers=disable

class C:

    def f(self): # will be replaced

        return 234

def f(self):

    return self

C.f = f # makes C.f a function, not a method

C().f() # not permitted: f requires an explicit argument

C().f(123) # permitted: f has merely been exposed via C.f

}}}

Methods are identified as such by their definition location, they contribute

information about attributes to the class hierarchy, and they employ certain

structure details at run-time to permit the binding of methods. Since

functions can defined in arbitrary locations, no class hierarchy information

is available, and a function could combine `self` with a range of attributes

that are not compatible with any class to which the function might be

assigned.

=== Well-Defined Base Classes ===

Base classes must be clearly identifiable as well-defined classes. This

facilitates the cataloguing of program objects and further analysis on them.

{{{#!python numbers=disable

class C:

    x = 123

def f():

    return C

class D(f()): # not permitted: f could return anything

    pass

}}}

If base class identification could only be done reliably at run-time, class

relationship information would be very limited without running the program or

performing costly and potentially unreliable analysis. Indeed, programs

employing such dynamic base classes are arguably resistant to analysis, which

is contrary to the goals of a language like Lichen.

=== Class Definitions and Functions ===

Classes may not be defined in functions because functions provide dynamic

namespaces, but Lichen relies on a static namespace hierarchy in order to

clearly identify the principal objects in a program. If classes could be

defined in functions, despite seemingly providing the same class over and over

again on every invocation, a family of classes would, in fact, be defined.

{{{#!python numbers=disable

def f(x):

    class C: # not permitted: this describes one of potentially many classes

        y = x

    return f

}}}

Moreover, issues of namespace nesting also arise, since the motivation for

defining classes in functions would surely be to take advantage of local state

to parameterise such classes.

== Modules and Packages ==

{{{#!table

'''Lichen''' || '''Python''' || '''Rationale'''

==

Modules are independent: package hierarchies are not traversed when importing

|| Modules exist in hierarchical namespaces: package roots must be imported

.. before importing specific submodules

|| Eliminating module traversal permits more precise imports and reduces

.. superfluous code

==

Only specific names can be imported from a module or package using the `from`

statement

|| Importing "all" from a package or module is permitted

|| Eliminating "all" imports simplifies the task of determining where names in

.. use have come from

==

Modules must be specified using absolute names

|| Imports can be absolute or relative

|| Using only absolute names simplifies the import mechanism

==

Modules are imported independently and their dependencies subsequently

resolved

|| Modules are imported as import statements are encountered

|| Statically-initialised objects can be used declaratively, although an

.. initialisation order may still need establishing

}}}

=== Independent Modules ===

The inclusion of modules in a program affects only explicitly-named modules:

they do not have relationships implied by their naming that would cause such

related modules to be included in a program.

{{{#!python numbers=disable

from compiler import consts # defines consts

import compiler.ast # defines ast, not compiler

ast # is defined

compiler # is not defined

consts # is defined

}}}

Where modules should have relationships, they should be explicitly defined

using `from` and `import` statements which target the exact modules required.

In the above example, `compiler` is not routinely imported because modules

within the `compiler` package have been requested.

=== Specific Name Imports Only ===

Lichen, unlike Python, also does not support the special `__all__` module

attribute.

{{{#!python numbers=disable

from compiler import * # not permitted

from compiler import ast, consts # permitted

interpreter # undefined in compiler (yet it might be thought to reside there) and in this module

}}}

The `__all__` attribute supports `from ... import *` statements in Python, but

without identifying the module or package involved and then consulting

`__all__` in that module or package to discover which names might be involved

(which might require the inspection of yet other modules or packages), the

names imported cannot be known. Consequently, some names used elsewhere in the

module performing the import might be assumed to be imported names when, in

fact, they are unknown in both the importing and imported modules. Such

uncertainty hinders the inspection of individual modules.

=== Modules Imported Independently ===

When indicating an import using the `from` and `import` statements, the

[[../Toolchain|toolchain]] does not attempt to immediately import other

modules. Instead, the imports act as declarations of such other modules or

names from other modules, resolved at a later stage. This permits mutual

imports to a greater extent than in Python.

{{{#!python numbers=disable

# Module M

from N import C # in Python: fails attempting to re-enter N

class D(C):

    y = 456

# Module N

from M import D # in Python: causes M to be entered, fails when re-entered from N

class C:

    x = 123

class E(D):

    z = 789

# Main program

import N

}}}

Such flexibility is not usually needed, and circular importing usually

indicates issues with program organisation. However, declarative imports can

help to decouple modules and avoid combining import declaration and module

initialisation order concerns.

== Syntax and Control-Flow ==

{{{#!table

'''Lichen''' || '''Python''' || '''Rationale'''

==

If expressions and comprehensions are not supported

|| If expressions and comprehensions are supported

|| Omitting such syntactic features simplifies program inspection and

.. translation

==

The `with` statement is not supported

|| The `with` statement offers a mechanism for resource allocation and

.. deallocation using context managers

|| This syntactic feature can be satisfactorily emulated using existing

.. constructs

==

Generators are not supported

|| Generators are supported

|| Omitting generator support simplifies run-time mechanisms

==

Only positional and keyword arguments are supported

|| Argument unpacking (using `*` and `**`) is supported

|| Omitting unpacking simplifies generic invocation handling

==

All parameters must be specified

|| Catch-all parameters (`*` and `**`) are supported

|| Omitting catch-all parameter population simplifies generic invocation

.. handling

}}}

=== No If Expressions or Comprehensions ===

In order to support the classic [[WikiPedia:?:|ternary operator]], a construct

was [[https://www.python.org/dev/peps/pep-0308/|added]] to the Python syntax

that needed to avoid problems with the existing grammar and notation.

Unfortunately, it reorders the components from the traditional form:

{{{#!python numbers=disable

# Not valid in Lichen, only in Python.

# In C: condition ? true_result : false_result

true_result if condition else false_result

# In C: (condition ? inner_true_result : inner_false_result) ? true_result : false_result

true_result if (inner_true_result if condition else inner_false_result) else false_result

}}}

Since if expressions may participate within expressions, they cannot be

rewritten as if statements. Nor can they be rewritten as logical operator

chains in general.

{{{#!python numbers=disable

# Not valid in Lichen, only in Python.

a = 0 if x else 1 # x being true yields 0

# Here, x being true causes (x and 0) to complete, yielding 0.

# But this causes ((x and 0) or 1) to complete, yielding 1.

a = x and 0 or 1 # not valid

}}}

But in any case, it would be more of a motivation to support the functionality

if a better syntax could be adopted instead. However, if expressions are not

particularly important in Python, and despite enhancement requests over many

years, everybody managed to live without them.

List and generator comprehensions are more complicated but share some

characteristics of if expressions: their syntax contradicts the typical

conventions established by the rest of the Python language; they create

implicit state that is perhaps most appropriately modelled by a separate

function or similar object. Since Lichen does not support generators at all,

it will obviously not support generator expressions.

Meanwhile, list comprehensions quickly encourage barely-readable programs:

{{{#!python numbers=disable

# Not valid in Lichen, only in Python.

x = [0, [1, 2, 0], 0, 0, [0, 3, 4]]

a = [z for y in x if y for z in y if z]

}}}

Supporting the creation of temporary functions to produce list comprehensions,

while also hiding temporary names from the enclosing scope, adds complexity to

the toolchain for situations where programmers would arguably be better

creating their own functions and thus writing more readable programs.

=== No With Statement ===

The

[[https://docs.python.org/2.7/reference/compound_stmts.html#the-with-statement|with

statement]] introduced the concept of

[[https://docs.python.org/2.7/reference/datamodel.html#context-managers|context

managers]] in Python 2.5, with such objects supporting a

[[https://docs.python.org/2.7/library/stdtypes.html#typecontextmanager|programming

interface]] that aims to formalise certain conventions around resource

management. For example:

{{{#!python numbers=disable

# Not valid in Lichen, only in Python.

with connection = db.connect(connection_args):

    with cursor = connection.cursor():

        cursor.execute(query, args)

}}}

Although this makes for readable code, it must be supported by objects which

define the `__enter__` and `__exit__` special methods. Here, the `connect`

method invoked in the first `with` statement must return such an object;

similarly, the `cursor` method must also provide an object with such

characteristics.

However, the "pre-with" solution is as follows:

{{{#!python numbers=disable

connection = db.connect(connection_args)

try:

    cursor = connection.cursor()

    try:

        cursor.execute(query, args)

    finally:

        cursor.close()

finally:

    connection.close()

}}}

Although this seems less readable, its behaviour is more obvious because magic

methods are not being called implicitly. Moreover, any parameterisation of the

acts of resource deallocation or closure can be done in the `finally` clauses

where such parameterisation would seem natural, rather than being specified

through some kind of context manager initialisation arguments that must then

be propagated to the magic methods so that they may take into consideration

contextual information that is readily available in the place where the actual

resource operations are being performed.

=== No Generators ===

[[https://www.python.org/dev/peps/pep-0255/|Generators]] were

[[https://docs.python.org/release/2.3/whatsnew/section-generators.html|added]]

to Python in the 2.2 release and became fully part of the language in the 2.3

release. They offer a convenient way of writing iterator-like objects,

capturing execution state instead of obliging the programmer to manage such

state explicitly.

{{{#!python numbers=disable

# Not valid in Lichen, only in Python.

def fib():

    a, b = 0, 1

    while 1:

        yield b

        a, b = b, a+b

# Alternative form valid in Lichen.

class fib:

    def __init__(self):

        self.a, self.b = 0, 1

    def next(self):

        result = self.b

        self.a, self.b = self.b, self.a + self.b

        return result

# Main program.

seq = fib()

i = 0

while i < 10:

    print seq.next()

    i += 1

}}}

However, generators make additional demands on the mechanisms provided to

support program execution. The encapsulation of the above example generator in

a separate class illustrates the need for state that persists outside the

execution of the routine providing the generator's results. Generators may

look like functions, but they do not necessarily behave like them, leading to

potential misunderstandings about their operation even if the code is

superficially tidy and concise.

=== Positional and Keyword Arguments Only ===