Enforcing method order in a Python module [closed] - oop

Closed. This question is opinion-based. It is not currently accepting answers.
Want to improve this question? Update the question so it can be answered with facts and citations by editing this post.
Closed last year.
Improve this question
What is the most Pythonic way to deal with a module in which methods must be called in a certain order?
As an example, I have an XML configuration that must be read before doing anything else because the configuration affects behavior.
The parse_config() must be called first with the configuration file provided. Calling other supporting methods, like query_data() won't work until parse_config() has been called.
I first implemented this as a singleton to ensure that a filename for the configuration is passed at the time of initialization, but I was noticing that modules are actually singletons. It's no longer a class, but just a regular module.
What's the best way to enforce the parse_config being called first in a module?
It is worth noting is that the function is actually parse_config(configfile).

If the object isn't valid before it's called, then call that method in __init__ (or use a factory function). You don't need any silly singletons, that's for sure.

The model I have been using is that subsequent functions are only available as methods on the return value of previous functions, like this:
class Second(object):
def two(self):
print "two"
return Third()
class Third(object):
def three(self):
print "three"
def one():
print "one"
return Second()
one().two().three()
Properly designed, this style (which I admit is not terribly Pythonic, yet) makes for fluent libraries to handle complex pipeline operations where later steps in the library require both the results of early calculations and fresh input from the calling function.
An interesting result is error handling. What I've found is the best way of handling well-understood errors in pipeline steps is having a blank Error class that supposedly can handle every function in the pipeline (except initial one) but those functions (except possibly terminal ones) return only self:
class Error(object):
def two(self, *args):
print "two not done because of earlier errors"
return self
def three(self, *args):
print "three not done because of earlier errors"
class Second(object):
def two(self, arg):
if arg == 2:
print "two"
return Third()
else:
print "two cannot be done"
return Error()
class Third(object):
def three(self):
print "three"
def one(arg):
if arg == 1:
print "one"
return Second()
else:
print "one cannot be done"
return Error()
one(1).two(-1).three()
In your example, you'd have the Parser class, which would have almost nothing but a configure function that returned an instance of a ConfiguredParser class, which would do all the thing that only a properly configured parser could do. This gives you access to such things as multiple configurations and handling failed attempts at configuration.

As Cat Plus Plus said in other words, wrap the behaviour/functions up in a class and put all the required setup in the __init__ method.
You might complain that the functions don't seem like they naturally belong together in an object and, hence, this is bad OO design. If that's the case, think of your class/object as a form of name-spacing. It's much cleaner and more flexible than trying to enforce function calling order somehow or using singletons.

The simple requirement that a module needs to be "configured" before it is used is best handled by a class which does the "configuration" in the __init__ method, as in the currently-accepted answer. Other module functions become methods of the class. There is no benefit in trying to make a singleton ... the caller may well want to have two or more differently-configured gadgets operating simultaneously.
Moving on from that to a more complicated requirement, such as a temporal ordering of the methods:
This can be handled in a quite general fashion by maintaining state in attributes of the object, as is usually done in any OOPable language. Each method that has prerequisites must check that those prequisites are satisfied.
Poking in replacement methods is an obfuscation on a par with the COBOL ALTER verb, and made worse by using decorators -- it just wouldn't/shouldn't get past code review.

It comes down to how friendly you want your error messages to be if a function is called before it is configured.
Least friendly is to do nothing extra, and let the functions fail noisily with AttributeErrors, IndexErrors, etc.
Most friendly would be having stub functions that raise an informative exception, such as a custom ConfigError: configuration not initialized. When the ConfigParser() function is called it can then replace the stub functions with real functions.
Something like this:
File config.py
class ConfigError(Exception):
"configuration errors"
def query_data():
raise ConfigError("parse_config() has not been called")
def _query_data():
do_actual_work()
def parse_config(config_file):
load_file(config_file)
if failure:
raise ConfigError("bad file")
all_objects = globals()
for name in ('query_data', ):
working_func = all_objects['_'+name]
all_objects[name] = working_func
If you have very many functions you can add decorators to keep track of the function names, but that's an answer for a different question. ;)
Okay, I couldn't resist -- here is the decorator version, which makes my solution much easier to actually implement:
class ConfigError(Exception):
"various configuration errors"
class NeedsConfig(object):
def __init__(self, module_namespace):
self._namespace = module_namespace
self._functions = dict()
def __call__(self, func):
self._functions[func.__name__] = func
return self._stub
#staticmethod
def _stub(*args, **kwargs):
raise ConfigError("parseconfig() needs to be called first")
def go_live(self):
for name, func in self._functions.items():
self._namespace[name] = func
And a sample run:
needs_parseconfig = NeedsConfig(globals())
#needs_parseconfig
def query_data():
print "got some data!"
#needs_parseconfig
def set_data():
print "set the data!"
def okay():
print "Okay!"
def parse_config(somefile):
needs_parseconfig.go_live()
try:
query_data()
except ConfigError, e:
print e
try:
set_data()
except ConfigError, e:
print e
try:
okay()
except:
print "this shouldn't happen!"
raise
parse_config('config_file')
query_data()
set_data()
okay()
And the results:
parseconfig() needs to be called first
parseconfig() needs to be called first
Okay!
got some data!
set the data!
Okay!
As you can see, the decorator works by remembering the functions it decorates, and instead of returning a decorated function it returns a simple stub that raises a ConfigError if it is ever called. When the parse_config() routine is called, it needs to call the go_live() method which will then replace all the error raising stubs with the actual remembered functions.

A module doesn't do anything it isn't told to do so put your function calls at the bottom of the module so that when you import it, things get ran in the order you specify:
test.py
import testmod
testmod.py
def fun1():
print('fun1')
def fun2():
print('fun2')
fun1()
fun2()
When you run test.py, you'll see fun1 is ran before fun2:
python test.py
fun1
fun2

Related

Are there constructs in Kotlin similar to Swift's #function, #file and #line?

I have a logging function in a Swift class of mine with the following signature:
fun logDetails(msg: String, function: String = #function, file: String = #file, line: Int = #line) {
I like the fact that I can call it from anywhere in my code, and it will fill in the function, file and source code line where it was called.
Is there any way to accomplish this in Kotlin?
There is no direct language feature in Kotlin (that I know of) that could do what you want in a similar way.
The only thing along those lines that I can think of in Kotlin/JVM (or Java) is to use Thread.currentThread().getStackTrace(). This returns an array of StackTraceElements that represent the call stack of the current thread. Each of those elements can provide information about filename, class name, method name and line number.
The problem is that computing such stack traces is costly, so I wouldn't use that extensively for extensive logging purposes (unless you're trying to temporarily debug something without access to a debugger).

How to make a class that inherits the same methods as IO::Path?

I want to build a class in Raku. Here's what I have so far:
unit class Vimwiki::File;
has Str:D $.path is required where *.IO.e;
method size {
return $.file.IO.s;
}
I'd like to get rid of the size method by simply making my class inherit the methods from IO::Path but I'm at a bit of a loss for how to accomplish this. Trying is IO::Path throws errors when I try to create a new object:
$vwf = Vimwiki::File.new(path => 't/test_file.md');
Must specify a non-empty string as a path
in block <unit> at t/01-basic.rakutest line 24
Must specify a non-empty string as a path
I always try a person's code when looking at someone's SO. Yours didn't work. (No declaration of $vwf.) That instantly alerts me that someone hasn't applied Minimal Reproducible Example principles.
So I did and less than 60 seconds later:
IO::Path.new
Yields the same error.
Why?
The doc for IO::Path.new shows its signature:
multi method new(Str:D $path, ...
So, IO::Path's new method expects a positional argument that's a Str. You (and my MRE) haven't passed a positional argument that's a Str. Thus the error message.
Of course, you've declared your own attribute $path, and have passed a named argument to set it, and that's unfortunately confused you because of the coincidence with the name path, but that's the fun of programming.
What next, take #1
Having a path attribute that duplicates IO::Path's strikes me as likely to lead to unnecessary complexity and/or bugs. So I think I'd nix that.
If all you're trying to do is wrap an additional check around the filename, then you could just write:
unit class Vimwiki::File is IO::Path;
method new ($path, |) { $path.IO.e ?? (callsame) !! die 'nope' }
callsame redispatches the ongoing routine call (the new method call), with the exact same arguments, to the next best fitting candidate(s) that would have been chosen if your new one containing the callsame hadn't been called. In this case, the next candidate(s) will be the existing new method(s) of IO::Path.
That seems fine to get started. Then you can add other attributes and methods as you see fit...
What next, take #2
...except for the IO::Path bug you filed, which means you can't initialize attributes in the normal way because IO::Path breaks the standard object construction protocol! :(
Liz shows one way to workaround this bug.
In an earlier version of this answer, I had not only showed but recommended another approach, namely delegation via handles instead of ordinary inheritance. I have since concluded that that was over-complicating things, and so removed it from this answer. And then I read your issue!
So I guess the delegation approach might still be appropriate as a workaround for a bug. So if later readers want to see it in action, follow #sdondley's link to their code. But I'm leaving it out of this (hopefully final! famous last words...) version of this answer in the hope that by the time you (later reader) read this, you just need to do something really simple like take #1.

Understanding how Julia modules can be extended

I'm struggling to understand how exactly modules can be extended in Julia. Specifically, I'd like to create my own LinearAlgebra matrix whose parent class is AbstractMatrix{T} and implement its functionality similar to how the Diagonal or UpperTriangular matrices are implemented in the actual LA package. If I could literally add my matrix to the original package, then I would, but for now I am content creating my own MyLinearAlgebra package that simply imports the original and extends it. Here's what I've got so far in MyLinearAlgebra.jl:
module MyLinearAlgebra
import LinearAlgebra
import Base: getindex, setindex!, size
export
# Types
LocalMatrix,
SolutionVector,
# Functions
issymmetric,
isdiag
# Operators
# Constants
include("SolutionVector.jl")
include("LocalMatrix.jl")
end
Focusing solely on LocalMatrix.jl now, I have:
"""
struct LocalMatrix{T} <: AbstractMatrix{T}
Block diagonal structure for local matrix. `A[:,:,s,iK]` is a block matrix for
state s and element iK
"""
struct LocalMatrix{T} <: AbstractMatrix{T}
data::Array{T,4}
function LocalMatrix{T}(data) where {T}
new{T}(data)
end
end
[... implement size, getindex, setindex! ... all working perfectly]
"""
issymmetric(A::LocalMatrix)
Tests whether a LocalMatrix is symmetric
"""
function issymmetric(A::LocalMatrix)
println("my issymmetric")
all(LinearAlgebra.issymmetric, [#view A.data[:,:,i,j] for i=1:size(A.data,3), j=1:size(A.data,4)])
end
"""
isdiag(A::LocalMatrix)
Tests whether a LocalMatrix is diagonal
"""
function isdiag(A::LocalMatrix)
println("my isdiag")
all(LinearAlgebra.isdiag, [#view A.data[:,:,i,j] for i=1:size(A.data,3), j=1:size(A.data,4)])
end
When I try and run this however, I get
error in method definition: function LinearAlgebra.isdiag must be explicitly imported to be extended
OK not a problem, I can change the definition to function LinearAlgebra.isdiag() instead and it works. But if I also change the definition of the other function to function LinearAlgebra.issymmetric() and run a simple test I now get the error
ERROR: MethodError: no method matching issymmetric(::MyLinearAlgebra.LocalMatrix{Float64})
So I'm stumped. Obviously I have a workaround that lets me continue working for now, but I must be simply misunderstanding how Julia modules work because I can't seem to distinguish between the two functions. Why does one needs to be explicitly extended? Why can the other not? What even is the difference between them in this situation? What is the correct way here to extend a package's module? Thanks for any help.
You need to explicitly state that you are adding new methods to existing functions so it should be:
function LinearAlgebra.issymmetric(A::LocalMatrix)
...
end
function LinearAlgebra.isdiag(A::LocalMatrix)
...
end
The reason that you are getting the error most likely is because you forgot to import LinearAlgebra in the code that is testing your package.
Note that your constructor also should be corrected:
struct LocalMatrix{T} <: AbstractMatrix{T}
data::Array{T,4}
function LocalMatrix(data::Array{T,4}) where {T}
new{T}(data)
end
end
With the current constructor you need to write LocalMatrix{Float64}(some_arr) instead of simply LocalMatrix(some_arr). Even worse, if you provide your constructor with a 3-d array you will get a type conversion error, while when you used the syntax that I am proposing one gets no method matching LocalMatrix(::Array{Int64,3}) which is much more readable for users of your library.

listing all API errors with flask/qwagger [duplicate]

Is there a way knowing (at coding time) which exceptions to expect when executing python code?
I end up catching the base Exception class 90% of the time since I don't know which exception type might be thrown (reading the documentation doesn't always help, since many times an exception can be propagated from the deep. And many times the documentation is not updated or correct).
Is there some kind of tool to check this (like by reading the Python code and libs)?
I guess a solution could be only imprecise because of lack of static typing rules.
I'm not aware of some tool that checks exceptions, but you could come up with your own tool matching your needs (a good chance to play a little with static analysis).
As a first attempt, you could write a function that builds an AST, finds all Raise nodes, and then tries to figure out common patterns of raising exceptions (e. g. calling a constructor directly)
Let x be the following program:
x = '''\
if f(x):
raise IOError(errno.ENOENT, 'not found')
else:
e = g(x)
raise e
'''
Build the AST using the compiler package:
tree = compiler.parse(x)
Then define a Raise visitor class:
class RaiseVisitor(object):
def __init__(self):
self.nodes = []
def visitRaise(self, n):
self.nodes.append(n)
And walk the AST collecting Raise nodes:
v = RaiseVisitor()
compiler.walk(tree, v)
>>> print v.nodes
[
Raise(
CallFunc(
Name('IOError'),
[Getattr(Name('errno'), 'ENOENT'), Const('not found')],
None, None),
None, None),
Raise(Name('e'), None, None),
]
You may continue by resolving symbols using compiler symbol tables, analyzing data dependencies, etc. Or you may just deduce, that CallFunc(Name('IOError'), ...) "should definitely mean raising IOError", which is quite OK for quick practical results :)
You should only catch exceptions that you will handle.
Catching all exceptions by their concrete types is nonsense. You should catch specific exceptions you can and will handle. For other exceptions, you may write a generic catch that catches "base Exception", logs it (use str() function) and terminates your program (or does something else that's appropriate in a crashy situation).
If you really gonna handle all exceptions and are sure none of them are fatal (for example, if you're running the code in some kind of a sandboxed environment), then your approach of catching generic BaseException fits your aims.
You might be also interested in language exception reference, not a reference for the library you're using.
If the library reference is really poor and it doesn't re-throw its own exceptions when catching system ones, the only useful approach is to run tests (maybe add it to test suite, because if something is undocumented, it may change!). Delete a file crucial for your code and check what exception is being thrown. Supply too much data and check what error it yields.
You will have to run tests anyway, since, even if the method of getting the exceptions by source code existed, it wouldn't give you any idea how you should handle any of those. Maybe you should be showing error message "File needful.txt is not found!" when you catch IndexError? Only test can tell.
The correct tool to solve this problem is unittests. If you are having exceptions raised by real code that the unittests do not raise, then you need more unittests.
Consider this
def f(duck):
try:
duck.quack()
except ??? could be anything
duck can be any object
Obviously you can have an AttributeError if duck has no quack, a TypeError if duck has a quack but it is not callable. You have no idea what duck.quack() might raise though, maybe even a DuckError or something
Now supposing you have code like this
arr[i] = get_something_from_database()
If it raises an IndexError you don't know whether it has come from arr[i] or from deep inside the database function. usually it doesn't matter so much where the exception occurred, rather that something went wrong and what you wanted to happen didn't happen.
A handy technique is to catch and maybe reraise the exception like this
except Exception as e
#inspect e, decide what to do
raise
Noone explained so far, why you can't have a full, 100% correct list of exceptions, so I thought it's worth commenting on. One of the reasons is a first-class function. Let's say that you have a function like this:
def apl(f,arg):
return f(arg)
Now apl can raise any exception that f raises. While there are not many functions like that in the core library, anything that uses list comprehension with custom filters, map, reduce, etc. are affected.
The documentation and the source analysers are the only "serious" sources of information here. Just keep in mind what they cannot do.
I ran into this when using socket, I wanted to find out all the error conditions I would run in to (so rather than trying to create errors and figure out what socket does I just wanted a concise list). Ultimately I ended up grep'ing "/usr/lib64/python2.4/test/test_socket.py" for "raise":
$ grep raise test_socket.py
Any exceptions raised by the clients during their tests
raise TypeError, "test_func must be a callable function"
raise NotImplementedError, "clientSetUp must be implemented."
def raise_error(*args, **kwargs):
raise socket.error
def raise_herror(*args, **kwargs):
raise socket.herror
def raise_gaierror(*args, **kwargs):
raise socket.gaierror
self.failUnlessRaises(socket.error, raise_error,
self.failUnlessRaises(socket.error, raise_herror,
self.failUnlessRaises(socket.error, raise_gaierror,
raise socket.error
# Check that setting it to an invalid value raises ValueError
# Check that setting it to an invalid type raises TypeError
def raise_timeout(*args, **kwargs):
self.failUnlessRaises(socket.timeout, raise_timeout,
def raise_timeout(*args, **kwargs):
self.failUnlessRaises(socket.timeout, raise_timeout,
Which is a pretty concise list of errors. Now of course this only works on a case by case basis and depends on the tests being accurate (which they usually are). Otherwise you need to pretty much catch all exceptions, log them and dissect them and figure out how to handle them (which with unit testing wouldn't be to difficult).
There are two ways that I found informative. The first one, run the code in iPython, which will display the exception type.
n = 2
str = 'me '
str + 2
TypeError: unsupported operand type(s) for +: 'int' and 'str'
In the second way we settle for catching too much and improve on it over time. Include a try expression in your code and catch except Exception as err. Print sufficient data to know what exception was thrown. As exceptions are thrown improve your code by adding a more precise except clause. When you feel that you have caught all relevant exceptions remove the all inclusive one. A good thing to do anyway because it swallows programming errors.
try:
so something
except Exception as err:
print "Some message"
print err.__class__
print err
exit(1)
normally, you'd need to catch exception only around a few lines of code. You wouldn't want to put your whole main function into the try except clause. for every few line you always should now (or be able easily to check) what kind of exception might be raised.
docs have an exhaustive list of built-in exceptions. don't try to except those exception that you're not expecting, they might be handled/expected in the calling code.
edit: what might be thrown depends on obviously on what you're doing! accessing random element of a sequence: IndexError, random element of a dict: KeyError, etc.
Just try to run those few lines in IDLE and cause an exception. But unittest would be a better solution, naturally.
This is a copy and pasted answer I wrote for How to list all exceptions a function could raise in Python 3?, I hope that is allowed.
I needed to do something similar and found this post. I decided I
would write a little library to help.
Say hello to Deep-AST. It's very early alpha but it is pip
installable. It has all of the limitations mentioned in this post
and some additional ones but its already off to a really good start.
For example when parsing HTTPConnection.getresponse() from
http.client it parses 24489 AST Nodes. It finds 181 total raised
Exceptions (this includes duplicates) and 8 unique Exceptions were
raised. A working code example.
The biggest flaw is this it currently does work with a bare raise:
def foo():
try:
bar()
except TypeError:
raise
But I think this will be easy to solve and I plan on fixing it.
The library can handle more than just figuring out exceptions, what
about listing all Parent classes? It can handle that too!

Static variable vs class variable vs instance variable vs local variable

Before posting, I read more than a half dozen posts in a search from this site (python variables class static instance local)
https://stackoverflow.com/search?q=python+variables+class+static+instance+local
I bookmarked several of the results for future studying, but none of them seemed to clarify to me if my thinking is right or wrong, so I feel I may be missing something about the basics (see below)...
Are the terms 'class variable' and 'static variable' referring to the same thing? After about three Google searches, reading through ~6 articles per search that I could understand, I've reached a conclusion that class and static variables are the same thing. But, since I'm just learning the fundamentals of Python and OOP, this conclusion may be wrong so I want to find any flaws in my reasoning before I continue learning with the wrong mindset. In the code below:
class Variables():
scVar = 3
def __init__(self, a, b):
self.iVar1 = a
self.iVar2 = b
def getLocalVar3(self):
localVar1 = 17
localVar2 = 100
localVar3 = localVar1 + localVar2
return localVar3
Is 'scVar' both a class variable and a static variable ('class' and 'static' variables being synonymns)?
The second question is to clarify my understanding of differentiating class variables, instance variables, and local variables. In the code above, I'm thinking that scVar is a class variable; iVar1 and iVar2 are instance variables; and localVar1, localVar2, and localVar3 are local variables. Is that correct to say, or is there something that I'm missing?
Thanks cirosantilli, That article that you linked to is one I haven't seen yet. I'm going to look that over. I wonder a bit about Python's point of vew that there's not a distinction between class variables and instance variables. Is this point of view one that I should try to understand correctly right up front, as a beginner, or should I just keep the idea in mind and not worry too much about reconciling my current point of view with that one until I become more experienced? That question probably seems overly vague, and dependent upon my current level of understanding Python. Mostly, I've been running commands on class examples similar to what is in the original post; predicting the output before I press the [enter] key and then studying the output when it's not what I predicted. From that, I'm starting to get some grasp of how things work in Python. Basically, I've just recently started getting a glimpse of how OO works in Python - inching forward, slowly but surely.
"However the above are just conventions: from the language point of view there is no distinction between class variables and instance variables." -- Just to help me understand this better, does the part, 'from the language point of view ...' sort of imply that the Python documentation explains this point of view somewhere within it? If so, I'll re-read through the docs and look specifically for this part, and try to conform my thinking process to it. "... between class variables and instance variables." So, even though there's the difference in the way that class and instance variables can be seen and accessed by 'classobj's and 'instance's, Python's
point of view is that there is no distinction between them? I'm going to keep this idea in mind during future reading so that I can maybe get rid of some confusion on my part. After I run the *.py in the original post, I get the following outputs in IDLE, using Python 2.7.x (only the last line of the traceback error is included, for better readability):
>>> Variables.scVar
3
>>> Variables.iVar1
AttributeError: class Variables has no attribute 'iVar1'
>>> instance = Variables(5, 15)
>>> instance
<__main__.Variables instance at 0x02A0F4E0>
>>> Variables
<class __main__.Variables at 0x02A0D650>
>>> instance.scVar
3
>>> instance.iVar1
5
>>> instance2 = Variables(25, 35)
>>> instance2.scVar
3
>>> Variables.scVar = Variables.scVar * 100
>>> Variables.scVar
300
>>> instance.scVar
300
>>> instance2.scVar
300
>>> instance.scVar = 9999
>>> Variables.scVar
300
>>> instance.scVar
9999
>>> instance2.scVar
300
>>> type(Variables)
<type 'classobj'>
>>> type(instance)
<type 'instance'>
"However the above are just conventions: from the language point of view there is no distinction between class variables and instance variables." -- By using the code from the original post, is there maybe a sequence of commands that illustrates this point? I don't doubt that you know what you're talking about; I just find it difficult to reconcile my current way of thinking with the above statement. But I get the feeling that if I can start to see the difference between the two perspectives, that something important will 'click'.
As an afterthought to the last few sentences, I might be on to seeing things more along the same lines as your statement about 'no distinction between class variables and instance variables', but only if my following assumption is accurate... From the code in the original post (class Variables - 12 lines), are there just the two scopes of global and local involved in that program? Since I've just started to form conclusions about how it all fits together, I think that my limited understanding of scope might be what keeps me from fully grasping the idea that there's no distinction between class variables and instance variables. The only thing I can seem to make of it now is that (only maybe) - 'Python has no distinction between class variables and instance variables; but the differences between global and local scope might make it appear to a novice that there is a distinction between these two types of variables. I don't know, does that statement identify a potential 'hang up' that I could be having about it?
"Everything is an object, including classes and integers:" -- I've read this numerous times. So much so that I take it to be a core belief to understanding OO and Python, but it's not a concept in which I fully realize the implications of yet (I think).
class Foo():
integer = 10
float = 6.37
string = 'hello'
boolean = True
idkyet = None
def __init__(self):
self.a = 'iv_a'
self.b = 'iv_b'
self.c = 'iv_c'
def Func(self):
self.g = 'g'
h = 'h'
i = 'i'
return 'g' + 'h' + 'i'
>>> Foo
<class __main__.Foo at 0x02A1D650>
>>> type(Foo.integer)
<type 'int'>
>>> type(Foo.float)
<type 'float'>
>>> type(Foo.string)
<type 'str'>
>>> type(Foo.boolean)
<type 'bool'>
>>> type(Foo.idkyet)
<type 'NoneType'>
>>> type(Foo)
<type 'classobj'>
>>> import os
>>> type(os.getcwd() + '\\Test.py')
<type 'str'>
>>> type(os)
<type 'module'>
>>> f = Foo()
>>> type(f)
<type 'instance'>
>>> type(f.Func)
<type 'instancemethod'>
>>> type(f.Func())
<type 'str'>
>>> f.Func
<bound method Foo.Func of <__main__.Foo instance at 0x02A25AF8>>
>>> Foo.Func
<unbound method Foo.Func>
>>> type(f.a)
<type 'str'>
>>> type(Foo.a)
AttributeError: class Foo has no attribute 'a'
>>> type(Foo.self.a)
AttributeError: class Foo has no attribute 'self'
When I was about half way through this response, leaving off with the 'class Foo():' code above and the commands ran on it below that, I hit a snag and couldn't quite continue with the other follow-up question that I barely had in mind. So, I stepped away from the problem for awhile and started to read that 'cafepy...' link that you posted (by Shalabh Chaturvedi). That's really interesting. I had seen excerpts from that before but I hadn't read the whole thing, but it seems much more understandable now than it would have been just a week ago. I think I will read the whole thing. Don't mind the last half of this post (after the '***') because I still can't pinpoint exactly what I was trying to ask. ...everything is an object...mainly just a difference in object types???... < That is the note that I had jotted down when I almost had in mind how to frame the last question, but it never came to fruition. I'll have to wait until something else 'clicks' and I can see again what I had in mind.
I'll also keep in mind to stop and re-read if I glance across anything related to 'MRO', bound and unbound methods... I have been picking up just a bit of those three terms lately, in a way that it feels like they won't be too far in the future of my learning process.
I believe that static and class variables are commonly used as synonyms.
What you say about the variables is correct from the convention point of view: this is how you should think about them most of the time.
However the above are just conventions: from the language point of view there is no distinction between class variables and instance variables.
Python is not like C++ or Java.
Everything is an object, including classes and integers:
class C(object): pass
print id(C)
C.a = 1
assert C.__dict__['a'] == 1
There is no clear distinction between methods and instance variables: they are just attributes of an object.
Therefore, there is no language level distinction between instance variables and class variables: they are just attributes of different objects:
instance variables are attributes of the object (self)
class variables are attributes of the Class object.
The real magic happens on the order that the . operator searches for attributes:
__dict__ of the object
__dict__ of the class of the object
MRO up to parent classes
You should read this great article before you get confused in the future.
Also beware of bound vs unbound methods.
EDIT: attempt to address further questions by the OP made in his post.
Wow that was large! I'll try to read everything, but for the future you should try to keep questions more concice. More code, less talk =). You'll get better answers.
should I just keep the idea in mind and not worry too much about reconciling my current point of view with that one until I become more experienced?": I do things.
I do as I feel necessary. When necessity calls, or I can't take magic behaviour anymore, I learn.
sort of imply that the Python documentation explains this point of view somewhere within it?
I don't know about the docs, but the language itself works that way.
Of course, the language was designed to give the impression that syntax works just like in C++ in the common cases, and it adds a thin layer of magic to classes to make it look like so.
But, since that is not how it truly works, you cannot account for all (useful) behaviour by thinking only in terms of C++ class syntax.
By using the code from the original post, is there maybe a sequence of commands that illustrates this point?
I'm not sure it can be illustrated in sequence of commands. The point is: classes are objects, and their attributes are searched by the dot . MRO on the same order as attributes of objects:
class C(object):
i_static = 0
def __init__(self):
self.i = 1
# i is in the __dict__ of object c
c = C()
assert c.__dict__['i'] == 1
assert c.i == 1
# dot finds i_static because MRO looks at class
assert c.__class__.__dict__['i_static'] == 0
assert c.i_static == 0
# i_static is in the __dict__ of object C
assert C.__dict__['i_static'] == 0
assert C.i_static == 0
# __eq__ is in the dict of type, which is the __class__ of C
# By MRO, __eq__ is found. `C,C` because of bound vs unbound.
assert C.__class__.__dict__['__eq__'](C,C)
assert C == C
are there just the two scopes of global and local involved in that program?
This is a point I don't know very clearly.
There is a no global scope in Python, only module level.
Then there is a new local scope inside functions.
The rest is how the . looks for attributes.
can't pinpoint exactly what I was trying to ask
Ask: can I find a difference in syntax between classes, integers or functions?
If you think you have found one, ask: hmmm, how can I make an object with certain attributes that behaves just like that thing which does not look like an object?
You should find an answer every time.
Example:
def f(): pass
class C(object): pass
AHA: f is different than c = C() because I can do f() but notc()`!
But then, no, it is just that the f.__class__.__dict__['__call__'] attribute is defined for f, and can be found via MRO.
But we can do that for c too:
class C(object):
def __call__(self): pass
and now we can do c().
So they were not different in that aspect.