paul j3 <[email protected]> added the comment:
This kind of file read can be done just as easily after parsing. For example
using the function in my previous post:
In [127]: parser = argparse.ArgumentParser()
In [128]: parser.add_argument('-d','--data');
In [129]: args = parser.parse_args(['-d','@foo.json'])
In [130]: args
Out[130]: Namespace(data='@foo.json')
In [131]: args.data = readdata(args.data)
In [132]: args
Out[132]: Namespace(data={'foo': 12, 'bar': 'twelve'})
I've pointed out in various SO answers that using the 'type' parameter has just
a few benefits.
- If you have many arguments that require this conversion, using type is a
little more streamlined. But it's not hard to iterate of a list of 'dest'.
- Using type routes the errors through the standard argparse mechanism,
including the display of usage and system exit. But the type function has to
raise TypeError, ValueError, or ArgumentTypeError. But you can also use
'parser.error(...)' in the post parsing processing.
Error handling is the make-or-break-it issue. What kinds of errors do we want
to handle? What if the file name is bad or not accessible? What if the file
is poorly formatted JSON? How do other APIs handle these errors?
For example the function that I defined can raise a FileNotFoundError if the
file isn't found, or a JSONDecodeError if the file is badly formed.
JSONDecodeError is a subclass of ValueError, but IO errors are not.
If such a type function is added to argparse, the unittest file,
test_argparse.py will have to have a number of test cases. It will have to
create a valid json test file, and possibly an invalid one. It will have test
working cases, and various error cases. The amount of testing code is likely
to be many times larger than the function code itself. And we can't overlook
the documentation additions.
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