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Changes
=======
v1.1.2
------
* **BUG AFFECTING CALCULATIONS:** As a result of the scrutiny
triggered by the v1.1.1 release, we discovered that the reference
article that colorspacious (and other libraries) was using as a
source for the ``CIECAM02Surround.DIM`` constant values was *itself*
incorrect. It has now been corrected, and verified against CIE
159:2004. See `#14
<https://github.com/njsmith/colorspacious/issues/14>`__ for details,
and thanks to Thomas Mansencal and Mark Fairchild for helping sort
this out.
Like the bug fix in v1.1.1, this doesn't affect most calculations;
it only matters if you were explicitly choosing the
``CIECAM02Surround.DIM`` viewing conditions.
v1.1.1
------
.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.1211038.svg
:target: https://doi.org/10.5281/zenodo.1211038
* **BUG AFFECTING CALCULATIONS:** In previous versions, the constants
``CIECAM02Surround.DIM`` and ``CIECAM02Surround.DARK`` were
incorrect due to a data entry error. Fortunately,
``CIECAM02Surround.AVERAGE`` *was* correct, and this is the default,
so unless you have explicitly been requesting ``DIM`` or ``DARK``
surrounds in your CIECAM02 calculations, then you should be
unaffected by this bug. (Thanks to `Brian Deiterle
<https://github.com/briend>`__ for catching this!)
v1.1.0
------
.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.165867.svg
:target: https://doi.org/10.5281/zenodo.165867
* **BUG AFFECTING CALCULATIONS:** In previous versions, it turns out
that the CAM02-LCD and CAM02-SCD spaces were accidentally swapped –
so if you asked for CAM02-LCD you got SCD, and vice-versa. This has
now been corrected. (Thanks to Github user TFiFiE for catching
this!)
* Fixed setup.py to be compatible with both python 2 and python 3.
* Miscellaneous documentation improvements.
v1.0.0
------
.. image:: https://zenodo.org/badge/doi/10.5281/zenodo.33086.svg
:target: http://dx.doi.org/10.5281/zenodo.33086
Notable changes since v0.1.0 include:
* **BUG AFFECTING CALCULATIONS:** the sRGB viewing conditions
(``colorspacious.CIECAM02Space.sRGB``), which are used by default in
all calculations involving CIECAM02 or CAM02-UCS, were previously
incorrect -- the :math:`L_A` parameter was supposed to be :math:`(64
/ \pi) / 5`, but instead was incorrectly calculated as :math:`(64 /
\pi) * 5`. The effect of this was to assume much brighter ambient
lighting than actually specified by the sRGB standard (i.e., the
sRGB standard assumes that you are looking at your monitor in a dim
environment, like a movie theatre; we were calculating as if you
were looking at your monitor in an environment that was 125 times
lighter -- something like, outside on an overcast day). This bug is
corrected in this release.
Fortunately this turns out to have had a negligible effect on
viridis and the other matplotlib colormaps that were computed using
the buggy code. Once the bug is corrected, the old colormaps'
perceptual uniformity is no long analytically exactly perfect, but
the deviations are numerically negligible, so there's no need to
regenerate the colormaps. (Indeed, the buggy viewing conditions,
while different from those specified in IEC 61966-2-1:1999, are
probably still within the range of realistic viewing conditions
where these colormaps will be used.)
If it is necessary to reproduce results using the old code, then
this can be accomplished by instantiating a custom
:class:`CIECAM02Space` object::
from colorspacious import CIECAM02Space
# almost, but not quite, the sRGB viewing conditions:
buggy_space = CIECAM02Space(
XYZ100_w="D65",
Y_b=20,
# bug: should be (64 / np.pi) / 5
L_A=(64 / np.pi) * 5)
This can be used directly, or to create custom colorspace
specifications to use with :func:`cspace_convert`. E.g., to convert
from sRGB1 to JCh using the buggy viewing conditions::
cspace_convert(..., "sRGB1",
{"name": "JCh", "ciecam02_space": buggy_space})
Or to convert from XYZ100 to CAM02-UCS using the buggy viewing
conditions::
cspace_convert(..., "XYZ100",
{"name": "CAM02-UCS", "ciecam02_space": buggy_space})
Similar code has been added to `viscm
<https://github.com/matplotlib/viscm>`_ to allow reproduction and
editing of viridis and related colormaps that were designed using
the old code.
* :func:`colorspacious.deltaE` is now available as a convenience
function for computing the perceptual distance between colors.
* Substantially improved docs (i.e. there is now actually a
comprehensive manual).
* Better test coverage (currently at 100% statement and branch
coverage).
* Miscellaneous bug fixes.
v0.1.0
------
Initial release.
|
