Source code for colour.models.rgb.transfer_functions.pivoted_log
"""
Pivoted Log Encoding
====================
Defines the *Pivoted Log* encoding:
- :func:`colour.models.log_encoding_PivotedLog`
- :func:`colour.models.log_decoding_PivotedLog`
References
----------
- :cite:`SonyImageworks2012a` : Sony Imageworks. (2012). make.py. Retrieved
November 27, 2014, from
https://github.com/imageworks/OpenColorIO-Configs/blob/master/\
nuke-default/make.py
"""
from __future__ import annotations
import numpy as np
from colour.hints import ArrayLike, NDArrayFloat
from colour.utilities import as_float, from_range_1, to_domain_1
__author__ = "Colour Developers"
__copyright__ = "Copyright 2013 Colour Developers"
__license__ = "BSD-3-Clause - https://opensource.org/licenses/BSD-3-Clause"
__maintainer__ = "Colour Developers"
__email__ = "colour-developers@colour-science.org"
__status__ = "Production"
__all__ = [
"log_encoding_PivotedLog",
"log_decoding_PivotedLog",
]
[docs]
def log_encoding_PivotedLog(
x: ArrayLike,
log_reference: float = 445,
linear_reference: float = 0.18,
negative_gamma: float = 0.6,
density_per_code_value: float = 0.002,
) -> NDArrayFloat:
"""
Define the *Josh Pines* style *Pivoted Log* log encoding curve /
opto-electronic transfer function.
Parameters
----------
x
Linear data :math:`x`.
log_reference
Log reference.
linear_reference
Linear reference.
negative_gamma
Negative gamma.
density_per_code_value
Density per code value.
Returns
-------
:class:`numpy.ndarray`
Non-linear data :math:`y`.
Notes
-----
+------------+-----------------------+---------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+============+=======================+===============+
| ``x`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
+------------+-----------------------+---------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+============+=======================+===============+
| ``y`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
References
----------
:cite:`SonyImageworks2012a`
Examples
--------
>>> log_encoding_PivotedLog(0.18) # doctest: +ELLIPSIS
0.4349951...
"""
x = to_domain_1(x)
y = (
log_reference
+ np.log10(x / linear_reference) / (density_per_code_value / negative_gamma)
) / 1023
return as_float(from_range_1(y))
[docs]
def log_decoding_PivotedLog(
y: ArrayLike,
log_reference: float = 445,
linear_reference: float = 0.18,
negative_gamma: float = 0.6,
density_per_code_value: float = 0.002,
) -> NDArrayFloat:
"""
Define the *Josh Pines* style *Pivoted Log* log decoding curve /
electro-optical transfer function.
Parameters
----------
y
Non-linear data :math:`y`.
log_reference
Log reference.
linear_reference
Linear reference.
negative_gamma
Negative gamma.
density_per_code_value
Density per code value.
Returns
-------
:class:`numpy.ndarray`
Linear data :math:`x`.
Notes
-----
+------------+-----------------------+---------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+============+=======================+===============+
| ``y`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
+------------+-----------------------+---------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+============+=======================+===============+
| ``x`` | [0, 1] | [0, 1] |
+------------+-----------------------+---------------+
References
----------
:cite:`SonyImageworks2012a`
Examples
--------
>>> log_decoding_PivotedLog(0.434995112414467) # doctest: +ELLIPSIS
0.1...
"""
y = to_domain_1(y)
x = (
10 ** ((y * 1023 - log_reference) * (density_per_code_value / negative_gamma))
* linear_reference
)
return as_float(from_range_1(x))