Reloading a module

[Tom]

In article <mailman.1033441502.25759.python-list at python.org>,
 Jeff Epler <jepler at unpythonic.net> wrote:


> 
> As for .pyc vs .py, Python follows these steps when importing a module:
>     if a .pyc file exists, read the magic number, version, and the
> 	stored timestamp.  Compare the version to this Python version,
> 	and the stored timestamp to the .py file's timestamp.  If these
> 	both match, or the version matches and the .py file doesn't exist,
> 	use the .pyc file.
>     otherwise, compile the .py file and rebuild the .pyc file along the way
> 
> The only circumstance that would interfere with this is NFS or another
> network filesystem making the check of the timestamp on the .py file
> unreliable---i.e., python can fail to detect that the .py file has been
> updated, because the OS gives it stale data.  Using a filesystem that has
> no timestamp at all (or a very granular timestamp, so that two revisions of
> "silly.py" could share the same timestamp) could cause the same problem,
> though I don't know of any such filesystems offhand.
> 
> (That said, I rarely actually notice problems of this type over NFS)
> 
> It's possible that you've discovered some sort of new problem with the way
> .pyc caching or reloading work, but the idea is so simple that I can't see it
> failing...
> 
> Jeff
> 

Jeff,


Try this:
Here's a file called randomclass.py:

class randomClass:
   def printit(self):
      print "This is rather silly"

myRandomValue = "Foo"


>>>import randomclass
>>> dir(randomclass)
['__builtins__', '__doc__', '__file__', '__name__', 'myRandomValue', 
'randomClass']

>>>m = randomclass
>>>print m.myRandomValue
Foo

>>> n = randomclass
>>> n.randomClass().printit()
This is rather silly

Now change the file by removing the line:

myRandomValue = "Foo"

...and then save the change.

>>> reload(randomclass)
<module 'randomclass' from 'randomclass.py'>

>>> dir(randomclass)
['__builtins__', '__doc__', '__file__', '__name__', 'myRandomValue', 
'randomClass']

Odd...the reference to myRandomValue is still there, and it behaves as 
though the modification was never made:

>>> n = randomclass
>>> print n.myRandomValue
Foo

It doesn't seem like this should be happening. It happens the same on 
both Linux and the Mac (os 9).

Regards,
Tom