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confusion with cppyy for overloaded methods and error handling

I have a c++ class with several constructors:
MyClass(const std::string& configfilename);
MyClass(const MyClass& other);
I have python bindings for this class that were generated with cppyy - I don't do this myself, this is all part of the framework I'm using (CERN ROOT, in case you're wondering).
Now, I have a piece of python code that instantiates my class, with a nice try-except block:
try:
obj = MyClass(args.config)
except ConfigFileNotFoundError:
print("config file "+args.config+" was not found!")
exit(0)
Now, to test, I'm executing this with a wrong config file. But what I get is roughly this:
TypeError: none of the 2 overloaded methods succeeded. Full details:
MyClass(const std::string&) => ConfigFileNotFoundError
MyClass::MyClass(const MyClass&) => TypeError
So I'm wondering:
Since cppyy seems to handle function overloading with a try/except block, is there any reasonable way to do error handling for such applications?
I'd love to actually get the ConfigFileNotFoundError to handle it properly, rather than getting this TypeError. Also, what determines the actual error class I get in the end - does it depend on the order in which the overloads appear in the header file?
Any help, suggestions or pointers on where to find more information on this would be highly appreciated.

cppyy doesn't use try/except for overload resolution, hence there are also no __context__ and __cause__ set. To be more precise: the C++ exception is not an error that occurs during a handler. Rather, as-yet unresolved overloads are prioritized, then tried in order, with no distinction made between a Python failure (e.g. from an argument conversion) or a C++ failure (any exception that was automatically converted into a Python exception). This is a historic artifact predating run-time template instantiation and SFINAE: it allowed for more detailed run-time type matching in pre-instantiated templates.
If all overloads fail (Python or C++), the collected errors are summarized. Python still requires an exception type, however, and if the exception types across the collected types differ, a generic TypeError is raised, with a message string made up of all the collected exceptions. This is what happens here: there is ConfigFileNotFoundError raised by C++ in one overload and TypeError from argument conversion failure in the other.
There's an improvement now in the cppyy repo; to be released with 2.3.0, where in clear cases such as this one (a single overload succeeding in argument match but failing in the callee), you'll get the actual ConfigFileNotFoundError instance as long as its class is publicly derived from std::exception (but I think it already does, otherwise the error report you posted would have looked quite different).
(Note that CERN's ROOT contains an old fork of cppyy that has quite a bit diverged; you'll have to request them for a separate update there if that fork matters to you.)

Is there a way to mark a python function as incomplete / unimplemented in VSCode / pylance?

I'm relatively new to python, using VSCode for python development. As far as I can tell VSCode is using an extension called "pylance" to handle python support features, such as detecting errors in code as you write.
In the last language I spent time with (Scala), there was a great little expression ??? that could be used to mark a method as incomplete / unimplemented, so that it would not generate any errors in the compiler or IDE, and would just throw an exception if encountered at runtime.
Is there any equivalent in python, specifically that would be understood by pylance? The idea would be that there is an unimplemented function, or one with a return type hint that isn't satisfied because it is incomplete, but this wouldn't throw up any errors that would make it harder to find the problems with the part I'm actually working on.
Obviously this isn't critical, just a matter of preferences, but it would be nice to keep the signal-to-noise ratio down! Thank you

You can use use pass inside of a function definition to avoid having to give a function a body, this will not raise exceptions on its own. Alternatively you can use raise(NotImplementedError()) to raise an error when a function is called.
def foo():
pass
def baz():
raise NotImplementedError
EDIT With Update---
Similar to pass in Python 3+ an ellipsis can be used to indicate code that has not yet been written e.g.
def foo(): ...

If you want it to throw an exception at runtime, the standard thing is to just raise NotImplementedError:
def some_fn():
raise NotImplementedError

Following Python Lazy Logging Formatting, but pylint is still showing (logging-not-lazy) messages

I'm logging some info messages and wanted to make sure I was following the PEP standards. Pylint says that this line:
LOG.info('Directories have been made in /home/bushbak2/projects/'+
'system_file_audit/%s/', manu)
Is not following the lazy logging format of python.
Could it be because the line carries over?
This is the message I get from pylint:
auto_audit.py:157:8: W1201: Use lazy % formatting in logging functions (logging-not-lazy)
Why does pylint still raise this message? Am I not appropriately following the PEP standard?
Cheers,
Brendan

The problem with the + between the two strings is that adds an add operation that must be evaluated in runtime before calling LOG.debug.
This can be a performance problem if you have to evaluate this line too often even if the debug logging flag is not set for your program.
This is the abstract syntax tree for logging.debug("this is " + "a test %s", 123):
As you can see, the first argument of the function call always has an add operation.
On the other hand, if we use logging.debug("this is " "a test %s", 123) (with whitespace instead of a + sign). The python compiler produces this other AST:
What happens with f-strings? A lot of people (including me) is in love with python f-strings. If we pass an f-string, the result is similar to the one with the plus sign.
The AST for logging.debug("this is a test {123}") shows python must perform a string operation before the function call:

Your problem comes from the + in your expression. Pylint sees it as a string concatenation and thus, recommends you to do this concatenation lazily.
You can fix it by removing the +:
LOG.info('Directories have been made in /home/bushbak2/projects/'
'system_file_audit/%s/', manu)

Locally patch missing Python type annotations?

Python now supports type hinting, so... yay! It seems like a great method to avoid some of the more obscure runtime bugs.
Sadly, third-party library support remains an issue. Though partially solved by the typeshed project, which is also used by mypy, when trying to port some of my code to use type hints, I ran into issues due to missing stubs.
E.g.
# file:mypytest.py
import lxml.etree as et
tree = et.fromstring('<root><a>1</a><b>2</b><a>3</a></root>')
items = tree.xpath('/root/a')
print([i.text for i in items])
will work perfectly well, but mypy will produce the spurious error message
>>> mypy mypytest.py
mypytest.py:3: error: "_Element" has no attribute "xpath"
because the stub is currently incomplete.
For a larger project, downloading the stub from typeshed, adding the missing entries, and maybe even submitting the corresponding pull request is a no-brainer.
But is there some method to monkey-patch the missing information in quick-and-dirty scenarios?
Bad workaround
The best I was able to come up with was
items = tree.xpath('/root/a') # type: ignore
which silences the error, but also disables type-checking where the variable items is used afterwards. E.g. items[0] + 1 will not cause a warning anymore.
In order to preserve type-checking it is possible to use
items_tmp = tree.xpath('/root/a') # type: ignore
items = items_tmp # type: List[et._Element]
but this seems hackish; It also has to be repeated everywhere the .xpath method is used.
Update from 2017-09-12: Alternatively one can use the syntax
items_tmp : List[et._Element] = tree.xpath('/root/a') # type: ignore

How Can I Find a List of All Exceptions That a Given Library Function Throws in Python?

Sorry for the long title, but it seems most descriptive for my question.
Basically, I'm having a difficult time finding exception information in the official python documentation. For example, in one program I'm currently writing, I'm using the shutil libary's move function:
from shutil import move
move('somefile.txt', '/tmp/somefile.txt')
That works fine, as long as I have write access to /tmp/, there is enough diskspace, and if all other requirements are satisfied.
However, when writing generic code, it is often difficult to guarantee those factors, so one usually uses exceptions:
from shutil import move
try:
move('somefile.txt', '/tmp/somefile.txt')
except:
print 'Move failed for some reason.'
I'd like to actually catch the appropriate exceptions thrown instead of just catching everything, but I simply can't find a list of exceptions thrown for most python modules. Is there a way for me to see which exceptions a given function can throw, and why? This way I can make appropriate cases for each exception, eg:
from shutil import move
try:
move('somefile.txt', '/tmp/somefile.txt')
except PermissionDenied:
print 'No permission.'
except DestinationDoesNotExist:
print "/tmp/ doesn't exist"
except NoDiskSpace:
print 'No diskspace available.'
Answer points go to whoever can either link me to some relevant documentation that I've somehow overlooked in the official docs, or provide a sure-fire way to figure out exactly which exceptions are thrown by which functions, and why.
Thanks!
UPDATE: It seems from the answers given that there really isn't a 100% straight-forward way to figure out which errors are thrown by specific functions. With meta programming, it seems that I can figure out simple cases and list some exceptions, but this is not a particularly useful or convenient method.
I'd like to think that eventually there will be some standard for defining which exceptions are raised by each python function, and that this information will be included in the official documentation. Until then I think I will just allow those exceptions to pass through and error out for my users as it seems like the most sane thing to do.

To amplify Messa, catch what you expect are failure modes that you know how to recover from. Ian Bicking wrote an article that addresses some of the overarching principles as does Eli Bendersky's note.
The problem with the sample code is that it is not handling errors, just prettifying them and discarding them. Your code does not "know" what to do with a NameError and there isn't much it should do other than pass it up, look at Bicking's re-raise if you feel you must add detail.
IOError and OSError are reasonably "expectable" for a shutil.move but not necessarily handleable. And the caller of your function wanted it to move a file and may itself break if that "contract" that Eli writes of is broken.
Catch what you can fix, adorn and re-raise what you expect but can't fix, and let the caller deal with what you didn't expect, even if the code that "deals" is seven levels up the stack in main.

Python doesn't have a mechanism right now for declaring which exceptions are thrown, unlike (for example) Java. (In Java you have to define exactly which exceptions are thrown by what, and if one of your utility methods needs to throw another exception then you need to add it to all of the methods which call it which gets boring quickly!)
So if you want to discover exactly which exceptions are thrown by any given bit of python then you need to examine the documentation and the source.
However python has a really good exception hierarchy.
If you study the exception hierarchy below you'll see that the error superclass you want to catch is called StandardError - this should catch all the errors that might be generated in normal operations. Turning the error into into a string will give a reasonable idea to the user as to what went wrong, so I'd suggest your code above should look like
from shutil import move
try:
move('somefile.txt', '/tmp/somefile.txt')
except StandardError, e:
print 'Move failed: %s' % e
Exception hierarchy
BaseException
|---Exception
|---|---StandardError
|---|---|---ArithmeticError
|---|---|---|---FloatingPointError
|---|---|---|---OverflowError
|---|---|---|---ZeroDivisionError
|---|---|---AssertionError
|---|---|---AttributeError
|---|---|---BufferError
|---|---|---EOFError
|---|---|---EnvironmentError
|---|---|---|---IOError
|---|---|---|---OSError
|---|---|---ImportError
|---|---|---LookupError
|---|---|---|---IndexError
|---|---|---|---KeyError
|---|---|---MemoryError
|---|---|---NameError
|---|---|---|---UnboundLocalError
|---|---|---ReferenceError
|---|---|---RuntimeError
|---|---|---|---NotImplementedError
|---|---|---SyntaxError
|---|---|---|---IndentationError
|---|---|---|---|---TabError
|---|---|---SystemError
|---|---|---TypeError
|---|---|---ValueError
|---|---|---|---UnicodeError
|---|---|---|---|---UnicodeDecodeError
|---|---|---|---|---UnicodeEncodeError
|---|---|---|---|---UnicodeTranslateError
|---|---StopIteration
|---|---Warning
|---|---|---BytesWarning
|---|---|---DeprecationWarning
|---|---|---FutureWarning
|---|---|---ImportWarning
|---|---|---PendingDeprecationWarning
|---|---|---RuntimeWarning
|---|---|---SyntaxWarning
|---|---|---UnicodeWarning
|---|---|---UserWarning
|---GeneratorExit
|---KeyboardInterrupt
|---SystemExit
This also means that when defining your own exceptions you should base them off StandardError not Exception.
Base class for all standard Python exceptions that do not represent
interpreter exiting.

Yes, you can (for simple cases), but you need a bit of meta-programming. Like the other answers have said, a function does not declare that it throws a particular error type, so you need to look at the module and see what exception types it defines, or what exception types it raises. You can either try to grok the documentation or leverage the Python API to do this.
To first find which exception types a module defines, just write a simple script to go through each object in the module dictionary module.__dict__ and see if it ends in the word "Error" or if it is a subclass of Exception:
def listexns(mod):
"""Saved as: http://gist.github.com/402861
"""
module = __import__(mod)
exns = []
for name in module.__dict__:
if (issubclass(module.__dict__[name], Exception) or
name.endswith('Error')):
exns.append(name)
for name in exns:
print '%s.%s is an exception type' % (str(mod), name)
return
If I run this on your example of shutils I get this:
$ python listexn.py shutil
Looking for exception types in module: shutil
shutil.Error is an exception type
shutil.WindowsError is an exception type
$
That tells you which error types are defined, but not which ones are thrown. To achieve the latter, we need to walk over the abstract syntax tree generated when the Python interpreter parses the module, and look for every raise statement, then save a list of names which are raised. The code for this is a little long, so first I'll state the output:
$ python listexn-raised.py /usr/lib/python2.6/shutil.py
Looking for exception types in: /usr/lib/python2.6/shutil.py
/usr/lib/python2.6/shutil.py:OSError is an exception type
/usr/lib/python2.6/shutil.py:Error is an exception type
$
So, now we know that shutil.py defines the error types Error and WindowsError and raises the exception types OSError and Error. If we want to be a bit more complete, we could write another method to check every except clause to also see which exceptions shutil handles.
Here's the code to walk over the AST, it just uses the compiler.visitor interface to create a walker which implements the "visitor pattern" from the Gang of Four book:
class ExceptionFinder(visitor.ASTVisitor):
"""List all exceptions raised by a module.
Saved as: http://gist.github.com/402869
"""
def __init__(self, filename):
visitor.ASTVisitor.__init__(self)
self.filename = filename
self.exns = set()
return
def __visitName(self, node):
"""Should not be called by generic visit, otherwise every name
will be reported as an exception type.
"""
self.exns.add(node.name)
return
def __visitCallFunc(self, node):
"""Should not be called by generic visit, otherwise every name
will be reported as an exception type.
"""
self.__visitName(node.node)
return
def visitRaise(self, node):
"""Visit a raise statement.
Cheat the default dispatcher.
"""
if issubclass(node.expr1, compiler.ast.Name):
self.__visitName(node.expr1)
elif isinstance(node.expr1, compiler.ast.CallFunc):
self.__visitCallFunc(node.expr1)
return

As these operations usually use libc functions and operating system calls, mostly you get IOError or OSError with an errno number; these errors are listed in man pages of that libc/OS calls.
I know this is possibly not a complete answer, it would be good to have all exceptions listed in documentation...