def getUSCensusAgeDist():
"""
Get US Census Age Distribution
"""
t_num = _INDVCMF_DATA['USCensus2010population']
list_AgeCensus = t_num[0]
freq_AgeCensus = np.round(
t_num[1] / 1000
) # Reduce # of populations to manageable number, this doesn't change probability
# Remove age < 10 and 70 < age:
freq_AgeCensus[:10] = 0
freq_AgeCensus[71:] = 0
list_Age = []
for k in range(len(list_AgeCensus)):
list_Age = np.hstack(
(list_Age, np.repeat(list_AgeCensus[k], freq_AgeCensus[k])))
return list_Age
def genMonteCarloObs(n_obs=1,
fieldsize=10,
list_Age=[32],
out='LMS',
wl=None,
allow_negative_values=False):
"""
Monte-Carlo generation of individual observer cone fundamentals.
Args:
:n_obs:
| 1, optional
| Number of observer CMFs to generate.
:list_Age:
| list of observer ages or str, optional
| Defaults to 32 (cfr. CIE2006 CMFs)
| If 'us_census': use US population census of 2010
to generate list_Age.
:fieldsize:
| fieldsize in degrees (between 2° and 10°), optional
| Defaults to 10°.
:out:
| 'LMS' or str, optional
| Determines output.
:wl:
| None, optional
| Interpolation/extraplation of :LMS: output to specified wavelengths.
| None: output original _WL = np.array([390,780,5])
:allow_negative_values:
| False, optional
| Cone fundamentals or color matching functions
| should not have negative values.
| If False: X[X<0] = 0.
Returns:
:returns:
| LMS [,var_age, vAll]
| - LMS: ndarray with population LMS functions.
| - var_age: ndarray with population observer ages.
| - vAll: dict with population physiological factors (see .keys())
References:
1. `Asano Y, Fairchild MD, and Blondé L (2016).
Individual Colorimetric Observer Model.
PLoS One 11, 1–19.
<http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0145671>`_
2. `Asano Y, Fairchild MD, Blondé L, and Morvan P (2016).
Color matching experiment for highlighting interobserver variability.
Color Res. Appl. 41, 530–539.
<https://onlinelibrary.wiley.com/doi/abs/10.1002/col.21975>`_
3. `CIE, and CIE (2006).
Fundamental Chromaticity Diagram with Physiological Axes - Part I
(Vienna: CIE).
<http://www.cie.co.at/publications/fundamental-chromaticity-diagram-physiological-axes-part-1>`_
4. `Asano's Individual Colorimetric Observer Model
<https://www.rit.edu/cos/colorscience/re_AsanoObserverFunctions.php>`_
"""
# Scale down StdDev by scalars optimized using Asano's 75 observers
# collected in Germany:
stdDevAllParam = _INDVCMF_STD_DEV_ALL_PARAM.copy()
scale_factors = [0.98, 0.98, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
scale_factors = dict(zip(list(stdDevAllParam.keys()), scale_factors))
stdDevAllParam = {
k: v * scale_factors[k]
for (k, v) in stdDevAllParam.items()
}
# Get Normally-distributed Physiological Factors:
vAll = getMonteCarloParam(n_obs=n_obs)
if list_Age is 'us_census':
list_Age = getUSCensusAgeDist()
# Generate Random Ages with the same probability density distribution
# as color matching experiment:
sz_interval = 1
list_AgeRound = np.round(np.array(list_Age) / sz_interval) * sz_interval
h = math.histogram(list_AgeRound,
bins=np.unique(list_AgeRound),
bin_center=True)[0]
p = h / h.sum() # probability density distribution
var_age = np.random.choice(np.unique(list_AgeRound), \
size = n_obs, replace = True,\
p = p)
# Set requested wavelength range:
if wl is not None:
wl = getwlr(wl3=wl)
else:
wl = _WL
LMS_All = np.nan * np.ones((3 + 1, wl.shape[0], n_obs))
for k in range(n_obs):
t_LMS, t_trans_lens, t_trans_macula, t_sens_photopig = cie2006cmfsEx(age = var_age[k], fieldsize = fieldsize, wl = wl,\
var_od_lens = vAll['od_lens'][k], var_od_macula = vAll['od_macula'][k], \
var_od_L = vAll['od_L'][k], var_od_M = vAll['od_M'][k], var_od_S = vAll['od_S'][k],\
var_shft_L = vAll['shft_L'][k], var_shft_M = vAll['shft_M'][k], var_shft_S = vAll['shft_S'][k],\
out = 'LMS,trans_lens,trans_macula,sens_photopig')
LMS_All[:, :, k] = t_LMS
# listout = out.split(',')
# if ('trans_lens' in listout) | ('trans_macula' in listout) | ('trans_photopig' in listout):
# trans_lens[:,k] = t_trans_lens
# trans_macula[:,k] = t_trans_macula
# sens_photopig[:,:,k] = t_sens_photopig
if n_obs == 1:
LMS_All = np.squeeze(LMS_All, axis=2)
if ('xyz' in out.lower().split(',')):
LMS_All = lmsb_to_xyzb(LMS_All,
fieldsize,
out='xyz',
allow_negative_values=allow_negative_values)
out = out.replace('xyz', 'LMS').replace('XYZ', 'LMS')
if ('lms' in out.lower().split(',')):
out = out.replace('lms', 'LMS')
if (out == 'LMS'):
return LMS_All
elif (out == 'LMS,var_age,vAll'):
return LMS_All, var_age, vAll
else:
return eval(out)