* do not cache the cupy so the config option can be set later
* getCupy will always return None if the option is not set
* test of failing behavior
* divorce skip from option; restore option after test
* implement rescale using numba
* workaround to pass test_makeARGB.py
* key on (input, output) manually
* remove minimum version check
* signature needs to be a list of signatures
numba considers it a mistake for single-item non-list but works around
it internally
* add numba test to test_makeARGB.py
* add numba as dependency in CI
* handle properly the case where useNumba was already True
* restore useCupy setting after test
* filter numba nan warning
* don't make changes to test_cupy_makeARGB_...
This change makes use of QPointF methods which perform faster than the python
equivalent methods. Furthermore, some tests are added.
* Set __slots__ to empty tuple for pg.Point
* Make Point.angle() behave as Vector.angle()
Static code checker identified multiple places where a file is opened
but is not necessarily closed. This commit addressed that with the
exception of RemoteGraphicsView.py
In a few places in the library, we are doing the conversion from radians
to degrees just so we can call QTransform.rotate(), but there is a
QTransform.rotateRadians() method which would be more suitable, thus
making it so we do not have to handle the conversions ourselves
python any and all are able to break early the moment they come across a
member of the iterable that meets the condition; but having a list
comprehension nested within breaks that ability to exit early, as the
list comprehension has to finish being constructed first before it can
be evaluated
Various places in the library attempt to check if scalars are finite
via numpy methods, which are intended to be used on numpy arrays. Using
the math module equivalent functions on scalars is significantly faster.
In a few places, I also use numpy methods explicitly (np.all vs. all)
Many places in the library were doing radian to degree conversion
via the manual calculation. Doing timeit benchmarks on my system, I
am able to get a 4x speedup by using math.degrees or math.radians
instead
Significant performance issues have been identified with np.clip
and thus clip_array was created to speed up the operation. In addition
clip_scalar was created to clip a scalar value between two other values
this commit replaces many uses of np.clip from operating on scalars to
using clip_scalar instead
Using numpy methods that are intended for vectorized operations is
substantially slower than using the math module, so when feasible the
math module methods should be used.
* NEW features for HistgramLUTItem
* gradientPosition=('left', 'right')
* only paint if item is visible (is faster)
* link hisogram to other histograms
* fixes to be able to merge
* drop linkHistogram (rgba needs to be better integrated)
* make sure defaults to same as current behavior
* draw connecting lines correctly on each side
* add example use
