Follow Python's file object semantics. User code is responsible for
closing resources (usually through a context manager) in a deterministic
way.
To achieve this, remove __del__ functions. These functions used to
closed open file handlers in an attempt to silence Python
ResourceWarnings. However, using __del__ has the following drawbacks:
- __del__ isn't called until the object's reference count reaches 0.
Therefore, resource handlers remain open or in use longer than
necessary.
- The __del__ method isn't guaranteed to execute on system exit. See the
Python documentation:
https://docs.python.org/3/reference/datamodel.html#object.__del__
> It is not guaranteed that __del__() methods are called for objects
> that still exist when the interpreter exits.
- Exceptions that occur inside __del__ are ignored instead of raised.
This has the potential of hiding bugs. This is also in the Python
documentation:
> Warning: Due to the precarious circumstances under which __del__()
> methods are invoked, exceptions that occur during their execution
> are ignored, and a warning is printed to sys.stderr instead.
Instead, always close resource handlers when they are no longer in use.
This will close the file handler at a specified point in the user's code
and not wait until the interpreter chooses to. It is always guaranteed
to run. And, if an exception occurs while closing the file handler, the
bug will not be ignored.
Now, when code receives a ResourceWarning, it will highlight an area
that is mishandling resources. It should not simply be silenced, but
fixed by closing resources with a context manager.
All warnings that were emitted during tests have been cleaned up. To
enable warnings, I passed the `-Wa` CLI option to Python. This exposed
some mishandling of resources in ImageFile.__init__() and
SpiderImagePlugin.loadImageSeries(), they too were fixed.
This better follows PEP 8 style guide:
https://www.python.org/dev/peps/pep-0008/#imports
> Imports are always put at the top of the file, just after any module
> comments and docstrings, and before module globals and constants.
This also avoids duplicate import code within the same file.
Similar to the recent adoption of Black. isort is a Python utility to
sort imports alphabetically and automatically separate into sections. By
using isort, contributors can quickly and automatically conform to the
projects style without thinking. Just let the tool do it.
Uses the configuration recommended by the Black to avoid conflicts of
style.
Rewrite TestImageQt.test_deprecated to no rely on import order.
This calculates the entropy for the image, based on the histogram.
Because this uses image histogram data directly, the existing C
function underpinning the `image.histogram()` method was abstracted
into a static function to parse extrema tuple arguments, and a new
C function was added to calculate image entropy, making use of the
new static extrema function.
The extrema-parsing function was written by @homm, based on the
macro abstraction I wrote, during the discussion of my first
entropy-method pull request: https://git.io/fhodS
The new `image.entropy()` method is based on `image.histogram()`,
and will accept the same arguments to calculate the histogram data
it will use to assess the entropy of the image.
The algorithm and methodology is based on existing Python code:
* https://git.io/fhmIU
... A test case in the `Tests/` directory, and doctest lines in
`selftest.py`, have both been added and checked.
Changes proposed in this pull request:
* Added “math.h” include to _imaging.c
* The addition of an `image.entropy()` method to the `Image`
Python class,
* The abstraction of the extrema-parsing logic of of the C
function `_histogram` into a static function, and
* The use of that static function in both the `_histogram` and
`_entropy` C functions.
* Minor documentation addenda in the docstrings for both the
`image.entropy()` and `image.histogram()` methods were also
added.
* Removed outdated boilerplate from testing code
* Removed unused “unittest” import
