#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Luminous Efficiency Functions Spectral Power Distributions
==========================================================
Defines luminous efficiency functions computation related objects.
See Also
--------
`Luminous Efficiency Functions Jupyter Notebook
<http://nbviewer.jupyter.org/github/colour-science/colour-notebooks/\
blob/master/notebooks/colorimetry/lefs.ipynb>`_
colour.colorimetry.dataset.lefs,
colour.colorimetry.spectrum.SpectralPowerDistribution
References
----------
.. [1] Wikipedia. (n.d.). Mesopic weighting function. Retrieved June 20,
2014, from http://en.wikipedia.org/wiki/\
Mesopic_vision#Mesopic_weighting_function
"""
from __future__ import division, unicode_literals
from colour.colorimetry import (PHOTOPIC_LEFS, SCOTOPIC_LEFS,
SpectralPowerDistribution, SpectralShape)
from colour.colorimetry.dataset.lefs import MESOPIC_X_DATA
from colour.utilities import closest
__author__ = 'Colour Developers'
__copyright__ = 'Copyright (C) 2013-2017 - Colour Developers'
__license__ = 'New BSD License - http://opensource.org/licenses/BSD-3-Clause'
__maintainer__ = 'Colour Developers'
__email__ = 'colour-science@googlegroups.com'
__status__ = 'Production'
__all__ = [
'mesopic_weighting_function', 'mesopic_luminous_efficiency_function'
]
[docs]def mesopic_weighting_function(
wavelength,
Lp,
source='Blue Heavy',
method='MOVE',
photopic_lef=PHOTOPIC_LEFS['CIE 1924 Photopic Standard Observer'],
scotopic_lef=SCOTOPIC_LEFS['CIE 1951 Scotopic Standard Observer']):
"""
Calculates the mesopic weighting function factor at given wavelength
:math:`\lambda` using the photopic luminance :math:`L_p`.
Parameters
----------
wavelength : numeric or array_like
Wavelength :math:`\lambda` to calculate the mesopic weighting function
factor.
Lp : numeric
Photopic luminance :math:`L_p`.
source : unicode, optional
**{'Blue Heavy', 'Red Heavy'}**,
Light source colour temperature.
method : unicode, optional
**{'MOVE', 'LRC'}**,
Method to calculate the weighting factor.
photopic_lef : SpectralPowerDistribution, optional
:math:`V(\lambda)` photopic luminous efficiency function.
scotopic_lef : SpectralPowerDistribution, optional
:math:`V^\prime(\lambda)` scotopic luminous efficiency function.
Returns
-------
numeric or ndarray
Mesopic weighting function factor.
Examples
--------
>>> mesopic_weighting_function(500, 0.2) # doctest: +ELLIPSIS
0.7052200...
"""
mesopic_x_luminance_values = sorted(MESOPIC_X_DATA.keys())
index = mesopic_x_luminance_values.index(
closest(mesopic_x_luminance_values, Lp))
x = MESOPIC_X_DATA[mesopic_x_luminance_values[index]][source][method]
Vm = ((1 - x) * scotopic_lef.get(wavelength) +
x * photopic_lef.get(wavelength))
return Vm
[docs]def mesopic_luminous_efficiency_function(
Lp,
source='Blue Heavy',
method='MOVE',
photopic_lef=PHOTOPIC_LEFS['CIE 1924 Photopic Standard Observer'],
scotopic_lef=SCOTOPIC_LEFS['CIE 1951 Scotopic Standard Observer']):
"""
Returns the mesopic luminous efficiency function :math:`V_m(\lambda)` for
given photopic luminance :math:`L_p`.
Parameters
----------
Lp : numeric
Photopic luminance :math:`L_p`.
source : unicode, optional
**{'Blue Heavy', 'Red Heavy'}**,
Light source colour temperature.
method : unicode, optional
**{'MOVE', 'LRC'}**,
Method to calculate the weighting factor.
photopic_lef : SpectralPowerDistribution, optional
:math:`V(\lambda)` photopic luminous efficiency function.
scotopic_lef : SpectralPowerDistribution, optional
:math:`V^\prime(\lambda)` scotopic luminous efficiency function.
Returns
-------
SpectralPowerDistribution
Mesopic luminous efficiency function :math:`V_m(\lambda)`.
Examples
--------
>>> print(mesopic_luminous_efficiency_function(0.2))
SpectralPowerDistribution(\
'0.2 Lp Mesopic Luminous Efficiency Function', (380.0, 780.0, 1.0))
"""
photopic_lef_shape = photopic_lef.shape
scotopic_lef_shape = scotopic_lef.shape
shape = SpectralShape(
max(photopic_lef_shape.start, scotopic_lef_shape.start),
min(photopic_lef_shape.end, scotopic_lef_shape.end),
max(photopic_lef_shape.interval, scotopic_lef_shape.interval))
wavelengths = shape.range()
spd_data = dict(
zip(wavelengths,
mesopic_weighting_function(wavelengths, Lp, source, method,
photopic_lef, scotopic_lef)))
spd = SpectralPowerDistribution(
'{0} Lp Mesopic Luminous Efficiency Function'.format(Lp), spd_data)
return spd.normalise()