_PHOTORECEPTORS = ['l-cone', 'm-cone', 's-cone', 'rod', 'iprgc']
_Ee_SYMBOLS = ['Ee,lc', 'Ee,mc', 'Ee,sc', 'Ee,r', 'Ee,z']
_E_SYMBOLS = ['E,lc', 'E,mc', 'E,sc', 'E,r', 'E,z']
_Q_SYMBOLS = ['Q,lc', 'Q,mc', 'Q,sc', 'Q,r', 'Q,z']
_Ee_UNITS = ['W⋅m−2', 'W⋅m−2', 'W⋅m−2', 'W⋅m−2', 'W⋅m−2']
_E_UNITS = ['lux', 'lux', 'lux', 'lux', 'lux']
_Q_UNITS = [
'photons/m2/s', 'photons/m2/s', 'photons/m2/s', 'photons/m2/s',
'photons/m2/s'
]
_QUANTITIES = ['erythropic', 'chloropic', 'cyanopic', 'rhodopic',
'melanopic'] #irradiance, illuminance
_ACTIONSPECTRA = getdata(_PKG_PATH + _SEP + 'toolboxes' + _SEP +
'photbiochem' + _SEP + 'data' + _SEP +
'cie_tn003_2015_SI_action_spectra.dat',
header='infer',
kind='np',
verbosity=0).T
# Calculate correction factor for Km in standard air:
na = _BB['na'] # n for standard air
c = _BB['c'] # m/s light speed
lambdad = c / (na * 54 * 1e13) / (1e-9) # 555 nm lambda in standard air
Km_correction_factor = 1 / (1 - (1 - 0.9998567) * (lambdad - 555)
) # correction factor for Km in standard air
def spd_to_aopicE(sid,
Ee=None,
E=None,
Q=None,
from luxpy.utils import np, sp, plt, _PKG_PATH, _SEP, getdata
__all__ = [
'_INDVCMF_DATA_PATH', '_INDVCMF_DATA', '_INDVCMF_STD_DEV_ALL_PARAM',
'_INDVCMF_CATOBSPFCTR', '_INDVCMF_M_2d', '_INDVCMF_M_10d'
]
__all__ += [
'cie2006cmfsEx', 'getMonteCarloParam', 'genMonteCarloObs', 'getCatObs'
]
_INDVCMF_DATA_PATH = _PKG_PATH + _SEP + 'toolboxes' + _SEP + 'indvcmf' + _SEP + 'data' + _SEP
# Load data from files:
_INDVCMF_DATA = {}
_INDVCMF_DATA['rmd'] = getdata(_INDVCMF_DATA_PATH +
'asano_cie2006_RelativeMacularDensity.dat',
header=None).T
_INDVCMF_DATA['LMSa'] = getdata(_INDVCMF_DATA_PATH + 'asano_cie2006_Alms.dat',
header=None).T
_INDVCMF_DATA['docul'] = getdata(_INDVCMF_DATA_PATH +
'asano_cie2006_docul.dat',
header=None).T
_INDVCMF_DATA['USCensus2010population'] = getdata(
_INDVCMF_DATA_PATH + 'asano_USCensus2010Population.dat',
header='infer',
verbosity=0).T
_INDVCMF_DATA['CatObsPfctr'] = getdata(_INDVCMF_DATA_PATH +
'asano_CatObsPfctr.dat',
header=None).T
# Store var of. physiological parameters in dict:
def spd_to_xyz(data,
relative=True,
rfl=None,
cieobs=_CIEOBS,
K=None,
out=None,
cie_std_dev_obs=None):
"""
Calculates xyz tristimulus values from spectral data.
Args:
:data:
| ndarray or pandas.dataframe with spectral data
| (.shape = (number of spectra + 1, number of wavelengths))
| Note that :data: is never interpolated, only CMFs and RFLs.
| This way interpolation errors due to peaky spectra are avoided.
| Conform CIE15-2018.
:relative:
| True or False, optional
| Calculate relative XYZ (Yw = 100) or absolute XYZ (Y = Luminance)
:rfl:
| ndarray with spectral reflectance functions.
| Will be interpolated if wavelengths do not match those of :data:
:cieobs:
| luxpy._CIEOBS or str, optional
| Determines the color matching functions to be used in the
| calculation of XYZ.
:K:
| None, optional
| e.g. K = 683 lm/W for '1931_2' (relative == False)
| or K = 100/sum(spd*dl) (relative == True)
:out:
| None or 1 or 2, optional
| Determines number and shape of output. (see :returns:)
:cie_std_dev_obs:
| None or str, optional
| - None: don't use CIE Standard Deviate Observer function.
| - 'f1': use F1 function.
Returns:
:returns:
| If rfl is None:
| If out is None: ndarray of xyz values
| (.shape = (data.shape[0],3))
| If out == 1: ndarray of xyz values
| (.shape = (data.shape[0],3))
| If out == 2: (ndarray of xyz, ndarray of xyzw) values
| Note that xyz == xyzw, with (.shape = (data.shape[0],3))
| If rfl is not None:
| If out is None: ndarray of xyz values
| (.shape = (rfl.shape[0],data.shape[0],3))
| If out == 1: ndarray of xyz values
| (.shape = (rfl.shape[0]+1,data.shape[0],3))
| The xyzw values of the light source spd are the first set
| of values of the first dimension. The following values
| along this dimension are the sample (rfl) xyz values.
| If out == 2: (ndarray of xyz, ndarray of xyzw) values
| with xyz.shape = (rfl.shape[0],data.shape[0],3)
| and with xyzw.shape = (data.shape[0],3)
References:
1. `CIE15:2018, “Colorimetry,” CIE, Vienna, Austria, 2018. <https://doi.org/10.25039/TR.015.2018>`_
"""
data = getdata(data,
kind='np') if isinstance(data, pd.DataFrame) else np2d(
data) # convert to np format and ensure 2D-array
# get wl spacing:
dl = getwld(data[0])
# get cmf,k for cieobs:
if isinstance(cieobs, str):
if K is None: K = _CMF[cieobs]['K']
scr = 'dict'
else:
scr = 'cieobs'
if (K is None) & (relative == False): K = 1
# Interpolate to wl of data:
cmf = xyzbar(cieobs=cieobs, scr=scr, wl_new=data[0], kind='np')
# Add CIE standard deviate observer function to cmf if requested:
if cie_std_dev_obs is not None:
cmf_cie_std_dev_obs = xyzbar(cieobs='cie_std_dev_obs_' +
cie_std_dev_obs.lower(),
scr=scr,
wl_new=data[0],
kind='np')
cmf[1:] = cmf[1:] + cmf_cie_std_dev_obs[1:]
# Rescale xyz using k or 100/Yw:
if relative == True: K = 100.0 / np.dot(data[1:], cmf[2, :] * dl)
# Interpolate rfls to lambda range of spd and calculate xyz:
if rfl is not None:
rfl = cie_interp(data=np2d(rfl), wl_new=data[0], kind='rfl')
rfl = np.concatenate((np.ones((1, data.shape[1])),
rfl[1:])) #add rfl = 1 for light source spectrum
xyz = K * np.array(
[np.dot(rfl, (data[1:] * cmf[i + 1, :] * dl).T)
for i in range(3)]) #calculate tristimulus values
rflwasnotnone = 1
else:
rfl = np.ones((1, data.shape[1]))
xyz = (K * (np.dot((cmf[1:] * dl), data[1:].T))[:, None, :])
rflwasnotnone = 0
xyz = np.transpose(xyz, [1, 2, 0]) #order [rfl,spd,xyz]
# Setup output:
if out == 2:
xyzw = xyz[0, ...]
xyz = xyz[rflwasnotnone:, ...]
if rflwasnotnone == 0: xyz = np.squeeze(xyz, axis=0)
return xyz, xyzw
elif out == 1:
if rflwasnotnone == 0: xyz = np.squeeze(xyz, axis=0)
return xyz
else:
xyz = xyz[rflwasnotnone:, ...]
if rflwasnotnone == 0: xyz = np.squeeze(xyz, axis=0)
return xyz
def spd(data = None, interpolation = None, kind = 'np', wl = None,\
columns = None, sep = ',',header = None, datatype = 'S', \
norm_type = None, norm_f = None):
"""
| All-in-one function that can:
| 1. Read spectral data from data file or take input directly
as pandas.dataframe or ndarray.
| 2. Convert spd-like data from ndarray to pandas.dataframe and back.
| 3. Interpolate spectral data.
| 4. Normalize spectral data.
Args:
:data:
| - str with path to file containing spectral data
| - ndarray with spectral data
| - pandas.dataframe with spectral data
| (.shape = (number of spectra + 1, number of original wavelengths))
:interpolation:
| None, optional
| - None: don't interpolate
| - str with interpolation type or spectrum type
:kind:
| str ['np','df'], optional
| Determines type(:returns:), np: ndarray, df: pandas.dataframe
:wl:
| None, optional
| New wavelength range for interpolation.
| Defaults to wavelengths specified by luxpy._WL3.
:columns:
| - None or list[str] of column names for dataframe, optional
:header:
| None or 'infer', optional
| - None: no header in file
| - 'infer': infer headers from file
:sep:
| ',' or '\t' or other char, optional
| Column separator in case :data: specifies a data file.
:datatype':
| 'S' (light source) or 'R' (reflectance) or other, optional
| Specifies a type of spectral data.
| Is used when creating column headers when :column: is None.
:norm_type:
| None, optional
| - 'lambda': make lambda in norm_f equal to 1
| - 'area': area-normalization times norm_f
| - 'max': max-normalization times norm_f
| - 'ru': to :norm_f: radiometric units
| - 'pu': to :norm_f: photometric units
| - 'pusa': to :norm_f: photometric units (with Km corrected
| to standard air, cfr. CIE TN003-2015)
| - 'qu': to :norm_f: quantal energy units
:norm_f:
| 1, optional
| Normalization factor that determines the size of normalization
for 'max' and 'area'
or which wavelength is normalized to 1 for 'lambda' option.
Returns:
:returns:
| ndarray or pandas.dataframe
| with interpolated and/or normalized spectral data.
"""
transpose = True if isinstance(
data, str
) else False #when spd comes from file -> transpose (columns in files should be different spectra)
# Wavelength definition:
wl = getwlr(wl)
# Data input:
if data is not None:
if (interpolation is None) & (norm_type is None):
data = getdata(data=data,
kind='np',
columns=columns,
sep=sep,
header=header,
datatype=datatype,
copy=True)
if (transpose == True): data = data.T
else:
data = getdata(
data=data,
kind='np',
columns=columns,
sep=sep,
header=header,
datatype=datatype,
copy=True) #interpolation requires np-array as input
if (transpose == True): data = data.T
data = cie_interp(data=data, wl_new=wl, kind=interpolation)
data = spd_normalize(data,
norm_type=norm_type,
norm_f=norm_f,
wl=True)
if isinstance(data, pd.DataFrame):
columns = data.columns #get possibly updated column names
else:
data = np2d(wl)
if ((data.shape[0] - 1) == 0): columns = None #only wavelengths
if kind == 'df': data = data.T
# convert to desired kind:
data = getdata(
data=data, kind=kind, columns=columns, datatype=datatype,
copy=False) # already copy when data is not None, else new anyway
return data
Reference:
AS/NZS1680.2.5 (1997). INTERIOR LIGHTING PART 2.5: HOSPITAL AND MEDICAL TASKS.
.. codeauthor:: Kevin A.G. Smet (ksmet1977 at gmail.com)
"""
from luxpy import deltaE, _CIE_ILLUMINANTS, spd_to_xyz, blackbody, xyz_to_cct
from luxpy.utils import np, _PKG_PATH, _SEP, getdata
__all__ = [
'_COI_RFL_BLOOD', '_COI_CIEOBS', '_COI_CSPACE', 'spd_to_COI_ASNZS1680'
]
# Reflectance spectra of 100% and 50% oxygenated blood
_COI_RFL_BLOOD = getdata(_PKG_PATH + _SEP + 'toolboxes' + _SEP +
'photbiochem' + _SEP + 'data' + _SEP +
'ASNZS_1680.2.5_1997_cyanosisindex_100_50.dat',
header=None,
kind='np',
verbosity=0).T
_COI_CIEOBS = '1931_2' # default CMF set
_COI_CSPACE = 'lab'
_COI_REF = blackbody(4000, )
def spd_to_COI_ASNZS1680(S=None,
tf=_COI_CSPACE,
cieobs=_COI_CIEOBS,
out='COI,cct',
extrapolate_rfl=False):
Light. Res. Technol. 44, 516.
<https://doi.org/10.1177/1477153512467607>`_
Also see notes in doc_string of spd_to_CS_CLa_lrc()
.. codeauthor:: Kevin A.G. Smet (ksmet1977 at gmail.com)
"""
from luxpy import _CIE_ILLUMINANTS, getwld, cie_interp, _IESTM3015, blackbody, spd_to_power
from luxpy.utils import np, _PKG_PATH, _SEP, getdata
from scipy import integrate
__all__=['_LRC_CLA_CS_CONST','spd_to_CS_CLa_lrc']
_LRC_CLA_CS_EFF_FCN = getdata(_PKG_PATH + _SEP + 'toolboxes' + _SEP + 'photbiochem' + _SEP + 'data' + _SEP + 'LRC2012_CS_CLa_efficiency_functions.dat', header = 'infer', kind ='np', verbosity = 0).T
_LRC_CLA_CS_CONST = {'CLa_2012' : {'Norm' : 1622, 'k': 0.2616, 'a_b_y':0.6201, 'a_rod' : 3.2347, 'RodSat' : 6.52,\
'Vphotl': _LRC_CLA_CS_EFF_FCN[1], 'Vscotl': _LRC_CLA_CS_EFF_FCN[2], \
'Vl_mpl': _LRC_CLA_CS_EFF_FCN[3], 'Scl_mpl': _LRC_CLA_CS_EFF_FCN[4],\
'Mcl' : _LRC_CLA_CS_EFF_FCN[5], 'WL' : _LRC_CLA_CS_EFF_FCN[0]},\
'CLa': {'Norm' : 1547.9, 'k': 0.2616, 'a_b_y':0.7, 'a_rod' : 3.3, 'RodSat' : 6.5215,\
'Vphotl': _LRC_CLA_CS_EFF_FCN[1], 'Vscotl': _LRC_CLA_CS_EFF_FCN[2], \
'Vl_mpl': _LRC_CLA_CS_EFF_FCN[3], 'Scl_mpl': _LRC_CLA_CS_EFF_FCN[4],\
'Mcl' : _LRC_CLA_CS_EFF_FCN[5], 'WL' : _LRC_CLA_CS_EFF_FCN[0]}}
def fCLa(wl, Elv, integral, Norm = None, k = None, a_b_y = None, a_rod = None, RodSat = None,\
Vphotl = None, Vscotl = None, Vl_mpl = None, Scl_mpl = None, Mcl = None, WL = None):