Source code for colour.corresponding.prediction

# -*- coding: utf-8 -*-
"""
Corresponding Chromaticities Prediction
=======================================

Defines objects to compute corresponding chromaticities prediction.

References
----------
-   :cite:`Breneman1987b` : Breneman, E. J. (1987). Corresponding
    chromaticities for different states of adaptation to complex visual fields.
    Journal of the Optical Society of America A, 4(6), 1115.
    doi:10.1364/JOSAA.4.001115
-   :cite:`CIETC1-321994b` : CIE TC 1-32. (1994). CIE 109-1994 A Method of
    Predicting Corresponding Colours under Different Chromatic and Illuminance
    Adaptations. Commission Internationale de l'Eclairage.
    ISBN:978-3-900734-51-0
-   :cite:`Fairchild1991a` : Fairchild, M. D. (1991). Formulation and testing
    of an incomplete-chromatic-adaptation model. Color Research & Application,
    16(4), 243-250. doi:10.1002/col.5080160406
-   :cite:`Fairchild2013s` : Fairchild, M. D. (2013). FAIRCHILD'S 1990 MODEL.
    In Color Appearance Models (3rd ed., pp. 4418-4495). Wiley. ISBN:B00DAYO8E2
-   :cite:`Fairchild2013t` : Fairchild, M. D. (2013). Chromatic Adaptation
    Models. In Color Appearance Models (3rd ed., pp. 4179-4252). Wiley.
    ISBN:B00DAYO8E2
-   :cite:`Li2002a` : Li, C., Luo, M. R., Rigg, B., & Hunt, R. W. G. (2002).
    CMC 2000 chromatic adaptation transform: CMCCAT2000. Color Research &
    Application, 27(1), 49-58. doi:10.1002/col.10005
-   :cite:`Luo1999` : Luo, M. Ronnier, & Rhodes, P. A. (1999).
    Corresponding-colour datasets. Color Research & Application, 24(4),
    295-296. doi:10.1002/(SICI)1520-6378(199908)24:4<295::AID-COL10>3.0.CO;2-K
-   :cite:`Westland2012k` : Westland, S., Ripamonti, C., & Cheung, V. (2012).
    CMCCAT2000. In Computational Colour Science Using MATLAB (2nd ed., pp.
    83-86). ISBN:978-0-470-66569-5
"""

from __future__ import division, unicode_literals

import numpy as np
from collections import namedtuple

from colour.adaptation import (
    chromatic_adaptation_CIE1994, chromatic_adaptation_CMCCAT2000,
    chromatic_adaptation_Fairchild1990, chromatic_adaptation_VonKries)
from colour.corresponding import (BRENEMAN_EXPERIMENTS,
                                  BRENEMAN_EXPERIMENT_PRIMARIES_CHROMATICITIES)
from colour.models import (Luv_to_uv, Luv_uv_to_xy, XYZ_to_Luv, XYZ_to_xy,
                           xy_to_XYZ, xyY_to_XYZ)
from colour.utilities import (CaseInsensitiveMapping, domain_range_scale,
                              filter_kwargs, full, is_numeric)

__author__ = 'Colour Developers'
__copyright__ = 'Copyright (C) 2013-2020 - Colour Developers'
__license__ = 'New BSD License - https://opensource.org/licenses/BSD-3-Clause'
__maintainer__ = 'Colour Developers'
__email__ = 'colour-developers@colour-science.org'
__status__ = 'Production'

__all__ = [
    'CorrespondingColourDataset', 'CorrespondingChromaticitiesPrediction',
    'convert_experiment_results_Breneman1987',
    'corresponding_chromaticities_prediction_Fairchild1990',
    'corresponding_chromaticities_prediction_CIE1994',
    'corresponding_chromaticities_prediction_CMCCAT2000',
    'corresponding_chromaticities_prediction_VonKries',
    'CORRESPONDING_CHROMATICITIES_PREDICTION_MODELS',
    'corresponding_chromaticities_prediction'
]


[docs]class CorrespondingColourDataset( namedtuple('CorrespondingColourDataset', ('name', 'XYZ_r', 'XYZ_t', 'XYZ_cr', 'XYZ_ct', 'Y_r', 'Y_t', 'B_r', 'B_t', 'metadata'))): """ Defines a corresponding colour dataset. Parameters ---------- name : unicode Corresponding colour dataset name. XYZ_r : array_like *CIE XYZ* tristimulus values of the reference illuminant. XYZ_t : array_like *CIE XYZ* tristimulus values of the test illuminant. XYZ_cr : array_like Corresponding *CIE XYZ* tristimulus values under the reference illuminant. XYZ_ct : array_like Corresponding *CIE XYZ* tristimulus values under the test illuminant. Y_r : numeric Reference white luminance :math:`Y_r` in :math:`cd/m^2`. Y_t : numeric Test white luminance :math:`Y_t` in :math:`cd/m^2`. B_r : numeric Luminance factor :math:`B_r` of reference achromatic background as percentage. B_t : numeric Luminance factor :math:`B_t` of test achromatic background as percentage. metadata : dict Dataset metadata. Notes ----- - This class is compatible with *Luo and Rhodes (1999)* *Corresponding-Colour Datasets* datasets. References ---------- :cite:`Luo1999` """
[docs]class CorrespondingChromaticitiesPrediction( namedtuple('CorrespondingChromaticitiesPrediction', ('name', 'uv_t', 'uv_m', 'uv_p'))): """ Defines a chromatic adaptation model prediction. Parameters ---------- name : unicode Test colour name. uv_t : array_like, (2,) Chromaticity coordinates :math:`uv_t^p` of test colour. uv_m : array_like, (2,) Chromaticity coordinates :math:`uv_m^p` of matching colour. uv_p : array_like, (2,) Chromaticity coordinates :math:`uv_p^p` of predicted colour. """
def convert_experiment_results_Breneman1987(experiment): """ Converts *Breneman (1987)* experiment results to a :class:`colour.CorrespondingColourDataset` class instance. Parameters ---------- experiment : integer {1, 2, 3, 4, 6, 8, 9, 11, 12} *Breneman (1987)* experiment number. Returns ------- CorrespondingColourDataset :class:`colour.CorrespondingColourDataset` class instance. Examples -------- >>> from pprint import pprint >>> pprint(tuple(convert_experiment_results_Breneman1987(2))) ... # doctest: +ELLIPSIS (2, array([ 0.9582463..., 1. , 0.9436325...]), array([ 0.9587332..., 1. , 0.4385796...]), array([[ 388.125 , 405. , 345.625 ], [ 266.8957925..., 135. , 28.5983365...], [ 474.5717821..., 405. , 222.75 ...], [ 538.3899082..., 405. , 24.8944954...], [ 178.7430167..., 135. , 19.6089385...], [ 436.6749547..., 405. , 26.5483725...], [ 124.7746282..., 135. , 36.1965613...], [ 77.0794172..., 135. , 60.5850563...], [ 279.9390889..., 405. , 455.8395127...], [ 149.5808157..., 135. , 498.7046827...], [ 372.1113689..., 405. , 669.9883990...], [ 212.3638968..., 135. , 414.6704871...]]), array([[ 400.1039651..., 405. , 191.7287234...], [ 271.0384615..., 135. , 13.5 ...], [ 495.4705323..., 405. , 119.7290874...], [ 580.7967033..., 405. , 6.6758241...], [ 190.1933701..., 135. , 7.4585635...], [ 473.7184115..., 405. , 10.2346570...], [ 135.4936014..., 135. , 20.2376599...], [ 86.4689781..., 135. , 35.2281021...], [ 283.5396281..., 405. , 258.1775929...], [ 119.7044335..., 135. , 282.6354679...], [ 359.9532224..., 405. , 381.0031185...], [ 181.8271461..., 135. , 204.0661252...]]), array(1500), array(1500), 0.3, 0.3, {}) """ valid_experiment_results = (1, 2, 3, 4, 6, 8, 9, 11, 12) assert experiment in valid_experiment_results, ( '"Breneman (1987)" experiment result must be one of "{0}"!'.format( valid_experiment_results)) samples_luminance = [ 0.270, 0.090, 0.270, 0.270, 0.090, 0.270, 0.090, 0.090, 0.270, 0.090, 0.270, 0.090, ] experiment_results = list(BRENEMAN_EXPERIMENTS[experiment]) illuminant_chromaticities = experiment_results.pop(0) Y_r = Y_t = BRENEMAN_EXPERIMENT_PRIMARIES_CHROMATICITIES[experiment].Y B_r = B_t = 0.3 XYZ_t, XYZ_r = xy_to_XYZ( np.hstack( [Luv_uv_to_xy(illuminant_chromaticities[1:3]), full([2, 1], Y_r)])) / Y_r xyY_cr, xyY_ct = [], [] for i, experiment_result in enumerate(experiment_results): xyY_cr.append( np.hstack([ Luv_uv_to_xy(experiment_result[2]), samples_luminance[i] * Y_r ])) xyY_ct.append( np.hstack([ Luv_uv_to_xy(experiment_result[1]), samples_luminance[i] * Y_t ])) XYZ_cr = xyY_to_XYZ(xyY_cr) XYZ_ct = xyY_to_XYZ(xyY_ct) return CorrespondingColourDataset(experiment, XYZ_r, XYZ_t, XYZ_cr, XYZ_ct, Y_r, Y_t, B_r, B_t, {})
[docs]def corresponding_chromaticities_prediction_Fairchild1990(experiment=1): """ Returns the corresponding chromaticities prediction for *Fairchild (1990)* chromatic adaptation model. Parameters ---------- experiment : integer or CorrespondingColourDataset, optional {1, 2, 3, 4, 6, 8, 9, 11, 12} *Breneman (1987)* experiment number or :class:`colour.CorrespondingColourDataset` class instance. Returns ------- tuple Corresponding chromaticities prediction. References ---------- :cite:`Breneman1987b`, :cite:`Fairchild1991a`, :cite:`Fairchild2013s` Examples -------- >>> from pprint import pprint >>> pr = corresponding_chromaticities_prediction_Fairchild1990(2) >>> pr = [(p.uv_m, p.uv_p) for p in pr] >>> pprint(pr) # doctest: +ELLIPSIS [(array([ 0.207, 0.486]), array([ 0.2089528..., 0.4724034...])), (array([ 0.449, 0.511]), array([ 0.4375652..., 0.5121030...])), (array([ 0.263, 0.505]), array([ 0.2621362..., 0.4972538...])), (array([ 0.322, 0.545]), array([ 0.3235312..., 0.5475665...])), (array([ 0.316, 0.537]), array([ 0.3151391..., 0.5398333...])), (array([ 0.265, 0.553]), array([ 0.2634745..., 0.5544335...])), (array([ 0.221, 0.538]), array([ 0.2211595..., 0.5324470...])), (array([ 0.135, 0.532]), array([ 0.1396949..., 0.5207234...])), (array([ 0.145, 0.472]), array([ 0.1512288..., 0.4533041...])), (array([ 0.163, 0.331]), array([ 0.1715691..., 0.3026264...])), (array([ 0.176, 0.431]), array([ 0.1825792..., 0.4077892...])), (array([ 0.244, 0.349]), array([ 0.2418905..., 0.3413401...]))] """ experiment_results = (convert_experiment_results_Breneman1987(experiment) if is_numeric(experiment) else experiment) with domain_range_scale(1): XYZ_t, XYZ_r = experiment_results.XYZ_t, experiment_results.XYZ_r xy_t, xy_r = XYZ_to_xy([XYZ_t, XYZ_r]) uv_t = Luv_to_uv(XYZ_to_Luv(experiment_results.XYZ_ct, xy_t), xy_t) uv_m = Luv_to_uv(XYZ_to_Luv(experiment_results.XYZ_cr, xy_r), xy_r) Y_n = experiment_results.Y_t XYZ_1 = experiment_results.XYZ_ct XYZ_2 = chromatic_adaptation_Fairchild1990(XYZ_1, XYZ_t, XYZ_r, Y_n) uv_p = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_r), xy_r) return tuple([ CorrespondingChromaticitiesPrediction(experiment_results.name, uv_t[i], uv_m[i], uv_p[i]) for i in range(len(uv_t)) ])
[docs]def corresponding_chromaticities_prediction_CIE1994(experiment=1): """ Returns the corresponding chromaticities prediction for *CIE 1994* chromatic adaptation model. Parameters ---------- experiment : integer or CorrespondingColourDataset, optional {1, 2, 3, 4, 6, 8, 9, 11, 12} *Breneman (1987)* experiment number or :class:`colour.CorrespondingColourDataset` class instance. Returns ------- tuple Corresponding chromaticities prediction. References ---------- :cite:`Breneman1987b`, :cite:`CIETC1-321994b` Examples -------- >>> from pprint import pprint >>> pr = corresponding_chromaticities_prediction_CIE1994(2) >>> pr = [(p.uv_m, p.uv_p) for p in pr] >>> pprint(pr) # doctest: +ELLIPSIS [(array([ 0.207, 0.486]), array([ 0.2273130..., 0.5267609...])), (array([ 0.449, 0.511]), array([ 0.4612181..., 0.5191849...])), (array([ 0.263, 0.505]), array([ 0.2872404..., 0.5306938...])), (array([ 0.322, 0.545]), array([ 0.3489822..., 0.5454398...])), (array([ 0.316, 0.537]), array([ 0.3371612..., 0.5421567...])), (array([ 0.265, 0.553]), array([ 0.2889416..., 0.5534074...])), (array([ 0.221, 0.538]), array([ 0.2412195..., 0.5464301...])), (array([ 0.135, 0.532]), array([ 0.1530344..., 0.5488239...])), (array([ 0.145, 0.472]), array([ 0.1568709..., 0.5258835...])), (array([ 0.163, 0.331]), array([ 0.1499762..., 0.4401747...])), (array([ 0.176, 0.431]), array([ 0.1876711..., 0.5039627...])), (array([ 0.244, 0.349]), array([ 0.2560012..., 0.4546263...]))] """ experiment_results = (convert_experiment_results_Breneman1987(experiment) if is_numeric(experiment) else experiment) with domain_range_scale(1): XYZ_t, XYZ_r = experiment_results.XYZ_t, experiment_results.XYZ_r xy_o1, xy_o2 = XYZ_to_xy([XYZ_t, XYZ_r]) uv_t = Luv_to_uv(XYZ_to_Luv(experiment_results.XYZ_ct, xy_o1), xy_o1) uv_m = Luv_to_uv(XYZ_to_Luv(experiment_results.XYZ_cr, xy_o2), xy_o2) Y_r = experiment_results.B_r E_o1, E_o2 = experiment_results.Y_t, experiment_results.Y_r XYZ_1 = experiment_results.XYZ_ct XYZ_2 = chromatic_adaptation_CIE1994(XYZ_1, xy_o1, xy_o2, Y_r, E_o1, E_o2) uv_p = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_o2), xy_o2) return tuple([ CorrespondingChromaticitiesPrediction(experiment_results.name, uv_t[i], uv_m[i], uv_p[i]) for i in range(len(uv_t)) ])
[docs]def corresponding_chromaticities_prediction_CMCCAT2000(experiment=1): """ Returns the corresponding chromaticities prediction for *CMCCAT2000* chromatic adaptation model. Parameters ---------- experiment : integer or CorrespondingColourDataset, optional {1, 2, 3, 4, 6, 8, 9, 11, 12} *Breneman (1987)* experiment number or :class:`colour.CorrespondingColourDataset` class instance. Returns ------- tuple Corresponding chromaticities prediction. References ---------- :cite:`Breneman1987b`, :cite:`Li2002a`, :cite:`Westland2012k` Examples -------- >>> from pprint import pprint >>> pr = corresponding_chromaticities_prediction_CMCCAT2000(2) >>> pr = [(p.uv_m, p.uv_p) for p in pr] >>> pprint(pr) # doctest: +ELLIPSIS [(array([ 0.207, 0.486]), array([ 0.2083210..., 0.4727168...])), (array([ 0.449, 0.511]), array([ 0.4459270..., 0.5077735...])), (array([ 0.263, 0.505]), array([ 0.2640262..., 0.4955361...])), (array([ 0.322, 0.545]), array([ 0.3316884..., 0.5431580...])), (array([ 0.316, 0.537]), array([ 0.3222624..., 0.5357624...])), (array([ 0.265, 0.553]), array([ 0.2710705..., 0.5501997...])), (array([ 0.221, 0.538]), array([ 0.2261826..., 0.5294740...])), (array([ 0.135, 0.532]), array([ 0.1439693..., 0.5190984...])), (array([ 0.145, 0.472]), array([ 0.1494835..., 0.4556760...])), (array([ 0.163, 0.331]), array([ 0.1563172..., 0.3164151...])), (array([ 0.176, 0.431]), array([ 0.1763199..., 0.4127589...])), (array([ 0.244, 0.349]), array([ 0.2287638..., 0.3499324...]))] """ experiment_results = (convert_experiment_results_Breneman1987(experiment) if is_numeric(experiment) else experiment) with domain_range_scale(1): XYZ_w, XYZ_wr = experiment_results.XYZ_t, experiment_results.XYZ_r xy_w, xy_wr = XYZ_to_xy([XYZ_w, XYZ_wr]) uv_t = Luv_to_uv(XYZ_to_Luv(experiment_results.XYZ_ct, xy_w), xy_w) uv_m = Luv_to_uv(XYZ_to_Luv(experiment_results.XYZ_cr, xy_wr), xy_wr) L_A1 = experiment_results.Y_t L_A2 = experiment_results.Y_r XYZ_1 = experiment_results.XYZ_ct XYZ_2 = chromatic_adaptation_CMCCAT2000(XYZ_1, XYZ_w, XYZ_wr, L_A1, L_A2) uv_p = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_wr), xy_wr) return tuple([ CorrespondingChromaticitiesPrediction(experiment_results.name, uv_t[i], uv_m[i], uv_p[i]) for i in range(len(uv_t)) ])
[docs]def corresponding_chromaticities_prediction_VonKries(experiment=1, transform='CAT02'): """ Returns the corresponding chromaticities prediction for *Von Kries* chromatic adaptation model using given transform. Parameters ---------- experiment : integer or CorrespondingColourDataset, optional {1, 2, 3, 4, 6, 8, 9, 11, 12} *Breneman (1987)* experiment number or :class:`colour.CorrespondingColourDataset` class instance. transform : unicode, optional **{'CAT02', 'XYZ Scaling', 'Von Kries', 'Bradford', 'Sharp', 'Fairchild', 'CMCCAT97', 'CMCCAT2000', 'CAT02_BRILL_CAT', 'Bianco', 'Bianco PC'}**, Chromatic adaptation transform. Returns ------- tuple Corresponding chromaticities prediction. References ---------- :cite:`Breneman1987b`, :cite:`Fairchild2013t` Examples -------- >>> from pprint import pprint >>> pr = corresponding_chromaticities_prediction_VonKries(2, 'Bradford') >>> pr = [(p.uv_m, p.uv_p) for p in pr] >>> pprint(pr) # doctest: +ELLIPSIS [(array([ 0.207, 0.486]), array([ 0.2082014..., 0.4722922...])), (array([ 0.449, 0.511]), array([ 0.4489102..., 0.5071602...])), (array([ 0.263, 0.505]), array([ 0.2643545..., 0.4959631...])), (array([ 0.322, 0.545]), array([ 0.3348730..., 0.5471220...])), (array([ 0.316, 0.537]), array([ 0.3248758..., 0.5390589...])), (array([ 0.265, 0.553]), array([ 0.2733105..., 0.5555028...])), (array([ 0.221, 0.538]), array([ 0.227148 ..., 0.5331318...)), (array([ 0.135, 0.532]), array([ 0.1442730..., 0.5226804...])), (array([ 0.145, 0.472]), array([ 0.1498745..., 0.4550785...])), (array([ 0.163, 0.331]), array([ 0.1564975..., 0.3148796...])), (array([ 0.176, 0.431]), array([ 0.1760593..., 0.4103772...])), (array([ 0.244, 0.349]), array([ 0.2259805..., 0.3465291...]))] """ experiment_results = (convert_experiment_results_Breneman1987(experiment) if is_numeric(experiment) else experiment) with domain_range_scale(1): XYZ_w, XYZ_wr = experiment_results.XYZ_t, experiment_results.XYZ_r xy_w, xy_wr = XYZ_to_xy([XYZ_w, XYZ_wr]) uv_t = Luv_to_uv(XYZ_to_Luv(experiment_results.XYZ_ct, xy_w), xy_w) uv_m = Luv_to_uv(XYZ_to_Luv(experiment_results.XYZ_cr, xy_wr), xy_wr) XYZ_1 = experiment_results.XYZ_ct XYZ_2 = chromatic_adaptation_VonKries(XYZ_1, XYZ_w, XYZ_wr, transform) uv_p = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_wr), xy_wr) return tuple([ CorrespondingChromaticitiesPrediction(experiment_results.name, uv_t[i], uv_m[i], uv_p[i]) for i in range(len(uv_t)) ])
CORRESPONDING_CHROMATICITIES_PREDICTION_MODELS = CaseInsensitiveMapping({ 'CIE 1994': corresponding_chromaticities_prediction_CIE1994, 'CMCCAT2000': corresponding_chromaticities_prediction_CMCCAT2000, 'Fairchild 1990': corresponding_chromaticities_prediction_Fairchild1990, 'Von Kries': corresponding_chromaticities_prediction_VonKries }) CORRESPONDING_CHROMATICITIES_PREDICTION_MODELS.__doc__ = """ Aggregated corresponding chromaticities prediction models. References ---------- :cite:`Breneman1987b`, :cite:`CIETC1-321994b`, :cite:`Fairchild1991a`, :cite:`Fairchild2013s`, :cite:`Fairchild2013t`, :cite:`Li2002a`, :cite:`Westland2012k` CORRESPONDING_CHROMATICITIES_PREDICTION_MODELS : CaseInsensitiveMapping **{'CIE 1994', 'CMCCAT2000', 'Fairchild 1990', 'Von Kries'}** Aliases: - 'vonkries': 'Von Kries' """ CORRESPONDING_CHROMATICITIES_PREDICTION_MODELS['vonkries'] = ( CORRESPONDING_CHROMATICITIES_PREDICTION_MODELS['Von Kries'])
[docs]def corresponding_chromaticities_prediction(experiment=1, model='Von Kries', **kwargs): """ Returns the corresponding chromaticities prediction for given chromatic adaptation model. Parameters ---------- experiment : integer or CorrespondingColourDataset, optional {1, 2, 3, 4, 6, 8, 9, 11, 12} *Breneman (1987)* experiment number or :class:`colour.CorrespondingColourDataset` class instance. model : unicode, optional **{'Von Kries', 'CIE 1994', 'CMCCAT2000', 'Fairchild 1990'}**, Chromatic adaptation model. Other Parameters ---------------- transform : unicode, optional {:func:`colour.corresponding.\ corresponding_chromaticities_prediction_VonKries`}, **{'CAT02', 'XYZ Scaling', 'Von Kries', 'Bradford', 'Sharp', 'Fairchild', 'CMCCAT97', 'CMCCAT2000', 'CAT02_BRILL_CAT', 'Bianco', 'Bianco PC'}**, Chromatic adaptation transform. Returns ------- tuple Corresponding chromaticities prediction. References ---------- :cite:`Breneman1987b`, :cite:`CIETC1-321994b`, :cite:`Fairchild1991a`, :cite:`Fairchild2013s`, :cite:`Fairchild2013t`, :cite:`Li2002a`, :cite:`Westland2012k` Examples -------- >>> from pprint import pprint >>> pr = corresponding_chromaticities_prediction(2, 'CMCCAT2000') >>> pr = [(p.uv_m, p.uv_p) for p in pr] >>> pprint(pr) # doctest: +SKIP [((0.207, 0.486), (0.2083210..., 0.4727168...)), ((0.449, 0.511), (0.4459270..., 0.5077735...)), ((0.263, 0.505), (0.2640262..., 0.4955361...)), ((0.322, 0.545), (0.3316884..., 0.5431580...)), ((0.316, 0.537), (0.3222624..., 0.5357624...)), ((0.265, 0.553), (0.2710705..., 0.5501997...)), ((0.221, 0.538), (0.2261826..., 0.5294740...)), ((0.135, 0.532), (0.1439693..., 0.5190984...)), ((0.145, 0.472), (0.1494835..., 0.4556760...)), ((0.163, 0.331), (0.1563172..., 0.3164151...)), ((0.176, 0.431), (0.1763199..., 0.4127589...)), ((0.244, 0.349), (0.2287638..., 0.3499324...))] """ function = CORRESPONDING_CHROMATICITIES_PREDICTION_MODELS[model] return function(experiment, **filter_kwargs(function, **kwargs))