def sd_Jakob2019(
coefficients: ArrayLike, shape: SpectralShape = SPECTRAL_SHAPE_JAKOB2019
) -> SpectralDistribution:
"""
Return a spectral distribution following the spectral model given by
*Jakob and Hanika (2019)*.
Parameters
----------
coefficients
Dimensionless coefficients for *Jakob and Hanika (2019)* reflectance
spectral model.
shape
Shape used by the spectral distribution.
Returns
-------
:class:`colour.SpectralDistribution`
*Jakob and Hanika (2019)* spectral distribution.
References
----------
:cite:`Jakob2019`
Examples
--------
>>> from colour.utilities import numpy_print_options
>>> with numpy_print_options(suppress=True):
... sd_Jakob2019([-9e-05, 8.5e-02, -20], SpectralShape(400, 700, 20))
... # doctest: +ELLIPSIS
SpectralDistribution([[ 400. , 0.3143046...],
[ 420. , 0.4133320...],
[ 440. , 0.4880034...],
[ 460. , 0.5279562...],
[ 480. , 0.5319346...],
[ 500. , 0.5 ...],
[ 520. , 0.4326202...],
[ 540. , 0.3373544...],
[ 560. , 0.2353056...],
[ 580. , 0.1507665...],
[ 600. , 0.0931332...],
[ 620. , 0.0577434...],
[ 640. , 0.0367011...],
[ 660. , 0.0240879...],
[ 680. , 0.0163316...],
[ 700. , 0.0114118...]],
interpolator=SpragueInterpolator,
interpolator_kwargs={},
extrapolator=Extrapolator,
extrapolator_kwargs={...})
"""
c_0, c_1, c_2 = as_float_array(coefficients)
wl = shape.range()
U = c_0 * wl**2 + c_1 * wl + c_2
R = 1 / 2 + U / (2 * np.sqrt(1 + U**2))
name = f"{coefficients!r} (COEFF) - Jakob (2019)"
return SpectralDistribution(R, wl, name=name)
def msds_constant(
k: Floating,
labels: Sequence,
shape: SpectralShape = SPECTRAL_SHAPE_DEFAULT,
**kwargs: Any,
) -> MultiSpectralDistributions:
"""
Return the multi-spectral distributions with given labels and given
spectral shape filled with constant :math:`k` values.
Parameters
----------
k
Constant :math:`k` to fill the multi-spectral distributions with.
labels
Names to use for the :class:`colour.SpectralDistribution` class
instances.
shape
Spectral shape used to create the multi-spectral distributions.
Other Parameters
----------------
kwargs
{:class:`colour.MultiSpectralDistributions`},
See the documentation of the previously listed class.
Returns
-------
:class:`colour.MultiSpectralDistributions`
Constant :math:`k` filled multi-spectral distributions.
Notes
-----
- By default, the multi-spectral distributions will use the shape given
by :attr:`colour.SPECTRAL_SHAPE_DEFAULT` attribute.
Examples
--------
>>> msds = msds_constant(100, labels=['a', 'b', 'c'])
>>> msds.shape
SpectralShape(360.0, 780.0, 1.0)
>>> msds[400]
array([ 100., 100., 100.])
>>> msds.labels # doctest: +SKIP
['a', 'b', 'c']
"""
settings = {"name": f"{k} Constant"}
settings.update(kwargs)
wavelengths = shape.range()
values = full((len(wavelengths), len(labels)), k)
return MultiSpectralDistributions(values,
wavelengths,
labels=labels,
**settings)
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()
def sd_gaussian_fwhm(
peak_wavelength: Floating,
fwhm: Floating,
shape: SpectralShape = SPECTRAL_SHAPE_DEFAULT,
**kwargs: Any,
) -> SpectralDistribution:
"""
Return a gaussian spectral distribution of given spectral shape at given
peak wavelength and full width at half maximum.
Parameters
----------
peak_wavelength
Wavelength the gaussian spectral distribution will peak at.
fwhm
Full width at half maximum, i.e. width of the gaussian spectral
distribution measured between those points on the *y* axis which are
half the maximum amplitude.
shape
Spectral shape used to create the spectral distribution.
Other Parameters
----------------
kwargs
{:class:`colour.SpectralDistribution`},
See the documentation of the previously listed class.
Returns
-------
:class:`colour.SpectralDistribution`
Gaussian spectral distribution.
Notes
-----
- By default, the spectral distribution will use the shape given by
:attr:`colour.SPECTRAL_SHAPE_DEFAULT` attribute.
Examples
--------
>>> sd = sd_gaussian_fwhm(555, 25)
>>> sd.shape
SpectralShape(360.0, 780.0, 1.0)
>>> sd[555]
1.0
>>> sd[530] # doctest: +ELLIPSIS
0.3678794...
"""
settings = {"name": f"{peak_wavelength}nm - {fwhm} FWHM - Gaussian"}
settings.update(kwargs)
wavelengths = shape.range()
values = np.exp(-(((wavelengths - peak_wavelength) / fwhm)**2))
return SpectralDistribution(values, wavelengths, **settings)
def sd_gaussian_normal(
mu: Floating,
sigma: Floating,
shape: SpectralShape = SPECTRAL_SHAPE_DEFAULT,
**kwargs: Any,
) -> SpectralDistribution:
"""
Return a gaussian spectral distribution of given spectral shape at
given mean wavelength :math:`\\mu` and standard deviation :math:`sigma`.
Parameters
----------
mu
Mean wavelength :math:`\\mu` the gaussian spectral distribution will
peak at.
sigma
Standard deviation :math:`sigma` of the gaussian spectral distribution.
shape
Spectral shape used to create the spectral distribution.
Other Parameters
----------------
kwargs
{:class:`colour.SpectralDistribution`},
See the documentation of the previously listed class.
Returns
-------
:class:`colour.SpectralDistribution`
Gaussian spectral distribution.
Notes
-----
- By default, the spectral distribution will use the shape given by
:attr:`colour.SPECTRAL_SHAPE_DEFAULT` attribute.
Examples
--------
>>> sd = sd_gaussian_normal(555, 25)
>>> sd.shape
SpectralShape(360.0, 780.0, 1.0)
>>> sd[555] # doctest: +ELLIPSIS
1.0000000...
>>> sd[530] # doctest: +ELLIPSIS
0.6065306...
"""
settings = {"name": f"{mu}nm - {sigma} Sigma - Gaussian"}
settings.update(kwargs)
wavelengths = shape.range()
values = np.exp(-((wavelengths - mu)**2) / (2 * sigma**2))
return SpectralDistribution(values, wavelengths, **settings)
def mesopic_luminous_efficiency_function(
Lp,
source="Blue Heavy",
method="MOVE",
photopic_lef=PHOTOPIC_LEFS.get("CIE 1924 Photopic Standard Observer"),
scotopic_lef=SCOTOPIC_LEFS.get("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
--------
>>> mesopic_luminous_efficiency_function(0.2) # doctest: +ELLIPSIS
<colour.colorimetry.spectrum.SpectralPowerDistribution object at 0x...>
"""
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.steps, scotopic_lef_shape.steps),
)
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()
def sd_constant(k: Floating,
shape: SpectralShape = SPECTRAL_SHAPE_DEFAULT,
**kwargs: Any) -> SpectralDistribution:
"""
Return a spectral distribution of given spectral shape filled with
constant :math:`k` values.
Parameters
----------
k
Constant :math:`k` to fill the spectral distribution with.
shape
Spectral shape used to create the spectral distribution.
Other Parameters
----------------
kwargs
{:class:`colour.SpectralDistribution`},
See the documentation of the previously listed class.
Returns
-------
:class:`colour.SpectralDistribution`
Constant :math:`k` filled spectral distribution.
Notes
-----
- By default, the spectral distribution will use the shape given by
:attr:`colour.SPECTRAL_SHAPE_DEFAULT` attribute.
Examples
--------
>>> sd = sd_constant(100)
>>> sd.shape
SpectralShape(360.0, 780.0, 1.0)
>>> sd[400]
100.0
"""
settings = {"name": f"{k} Constant"}
settings.update(kwargs)
wavelengths = shape.range()
values = full(len(wavelengths), k)
return SpectralDistribution(values, wavelengths, **settings)
def sd_mesopic_luminous_efficiency_function(
Lp,
source='Blue Heavy',
method='MOVE',
photopic_lef=SDS_LEFS_PHOTOPIC['CIE 1924 Photopic Standard Observer'],
scotopic_lef=SDS_LEFS_SCOTOPIC['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 : SpectralDistribution, optional
:math:`V(\\lambda)` photopic luminous efficiency function.
scotopic_lef : SpectralDistribution, optional
:math:`V^\\prime(\\lambda)` scotopic luminous efficiency function.
Returns
-------
SpectralDistribution
Mesopic luminous efficiency function :math:`V_m(\\lambda)`.
References
----------
:cite:`Wikipedia2005d`
Examples
--------
>>> from colour.utilities import numpy_print_options
>>> with numpy_print_options(suppress=True):
... sd_mesopic_luminous_efficiency_function(0.2) # doctest: +ELLIPSIS
SpectralDistribution([[ 380. , 0.000424 ...],
[ 381. , 0.0004781...],
[ 382. , 0.0005399...],
[ 383. , 0.0006122...],
[ 384. , 0.0006961...],
[ 385. , 0.0007929...],
[ 386. , 0.000907 ...],
[ 387. , 0.0010389...],
[ 388. , 0.0011923...],
[ 389. , 0.0013703...],
[ 390. , 0.0015771...],
[ 391. , 0.0018167...],
[ 392. , 0.0020942...],
[ 393. , 0.0024160...],
[ 394. , 0.0027888...],
[ 395. , 0.0032196...],
[ 396. , 0.0037222...],
[ 397. , 0.0042957...],
[ 398. , 0.0049531...],
[ 399. , 0.0057143...],
[ 400. , 0.0065784...],
[ 401. , 0.0075658...],
[ 402. , 0.0086912...],
[ 403. , 0.0099638...],
[ 404. , 0.0114058...],
[ 405. , 0.0130401...],
[ 406. , 0.0148750...],
[ 407. , 0.0169310...],
[ 408. , 0.0192211...],
[ 409. , 0.0217511...],
[ 410. , 0.0245342...],
[ 411. , 0.0275773...],
[ 412. , 0.0309172...],
[ 413. , 0.0345149...],
[ 414. , 0.0383998...],
[ 415. , 0.0425744...],
[ 416. , 0.0471074...],
[ 417. , 0.0519322...],
[ 418. , 0.0570541...],
[ 419. , 0.0625466...],
[ 420. , 0.0683463...],
[ 421. , 0.0745255...],
[ 422. , 0.0809440...],
[ 423. , 0.0877344...],
[ 424. , 0.0948915...],
[ 425. , 0.1022731...],
[ 426. , 0.109877 ...],
[ 427. , 0.1178421...],
[ 428. , 0.1260316...],
[ 429. , 0.1343772...],
[ 430. , 0.143017 ...],
[ 431. , 0.1518128...],
[ 432. , 0.1608328...],
[ 433. , 0.1700088...],
[ 434. , 0.1792726...],
[ 435. , 0.1886934...],
[ 436. , 0.1982041...],
[ 437. , 0.2078032...],
[ 438. , 0.2174184...],
[ 439. , 0.2271147...],
[ 440. , 0.2368196...],
[ 441. , 0.2464623...],
[ 442. , 0.2561153...],
[ 443. , 0.2657160...],
[ 444. , 0.2753387...],
[ 445. , 0.2848520...],
[ 446. , 0.2944648...],
[ 447. , 0.3034902...],
[ 448. , 0.3132347...],
[ 449. , 0.3223257...],
[ 450. , 0.3314513...],
[ 451. , 0.3406129...],
[ 452. , 0.3498117...],
[ 453. , 0.3583617...],
[ 454. , 0.3676377...],
[ 455. , 0.3762670...],
[ 456. , 0.3849392...],
[ 457. , 0.3936540...],
[ 458. , 0.4024077...],
[ 459. , 0.4111965...],
[ 460. , 0.4193298...],
[ 461. , 0.4281803...],
[ 462. , 0.4363804...],
[ 463. , 0.4453117...],
[ 464. , 0.4542949...],
[ 465. , 0.4626509...],
[ 466. , 0.4717570...],
[ 467. , 0.4809300...],
[ 468. , 0.4901776...],
[ 469. , 0.4995075...],
[ 470. , 0.5096145...],
[ 471. , 0.5191293...],
[ 472. , 0.5294259...],
[ 473. , 0.5391316...],
[ 474. , 0.5496217...],
[ 475. , 0.5602103...],
[ 476. , 0.5702197...],
[ 477. , 0.5810207...],
[ 478. , 0.5919093...],
[ 479. , 0.6028683...],
[ 480. , 0.6138806...],
[ 481. , 0.6249373...],
[ 482. , 0.6360619...],
[ 483. , 0.6465989...],
[ 484. , 0.6579538...],
[ 485. , 0.6687841...],
[ 486. , 0.6797939...],
[ 487. , 0.6909887...],
[ 488. , 0.7023827...],
[ 489. , 0.7133032...],
[ 490. , 0.7244513...],
[ 491. , 0.7358470...],
[ 492. , 0.7468118...],
[ 493. , 0.7580294...],
[ 494. , 0.7694964...],
[ 495. , 0.7805225...],
[ 496. , 0.7917805...],
[ 497. , 0.8026123...],
[ 498. , 0.8130793...],
[ 499. , 0.8239297...],
[ 500. , 0.8352251...],
[ 501. , 0.8456342...],
[ 502. , 0.8564818...],
[ 503. , 0.8676921...],
[ 504. , 0.8785021...],
[ 505. , 0.8881489...],
[ 506. , 0.8986405...],
[ 507. , 0.9079322...],
[ 508. , 0.9174255...],
[ 509. , 0.9257739...],
[ 510. , 0.9350656...],
[ 511. , 0.9432365...],
[ 512. , 0.9509063...],
[ 513. , 0.9586931...],
[ 514. , 0.9658413...],
[ 515. , 0.9722825...],
[ 516. , 0.9779924...],
[ 517. , 0.9836106...],
[ 518. , 0.9883465...],
[ 519. , 0.9920964...],
[ 520. , 0.9954436...],
[ 521. , 0.9976202...],
[ 522. , 0.9993457...],
[ 523. , 1. ...],
[ 524. , 0.9996498...],
[ 525. , 0.9990487...],
[ 526. , 0.9975356...],
[ 527. , 0.9957615...],
[ 528. , 0.9930143...],
[ 529. , 0.9899559...],
[ 530. , 0.9858741...],
[ 531. , 0.9814453...],
[ 532. , 0.9766885...],
[ 533. , 0.9709363...],
[ 534. , 0.9648947...],
[ 535. , 0.9585832...],
[ 536. , 0.952012 ...],
[ 537. , 0.9444916...],
[ 538. , 0.9367089...],
[ 539. , 0.9293506...],
[ 540. , 0.9210429...],
[ 541. , 0.9124772...],
[ 542. , 0.9036604...],
[ 543. , 0.8945958...],
[ 544. , 0.8845999...],
[ 545. , 0.8750500...],
[ 546. , 0.8659457...],
[ 547. , 0.8559224...],
[ 548. , 0.8456846...],
[ 549. , 0.8352499...],
[ 550. , 0.8253229...],
[ 551. , 0.8152079...],
[ 552. , 0.8042205...],
[ 553. , 0.7944209...],
[ 554. , 0.7837466...],
[ 555. , 0.7735680...],
[ 556. , 0.7627808...],
[ 557. , 0.7522710...],
[ 558. , 0.7417549...],
[ 559. , 0.7312909...],
[ 560. , 0.7207983...],
[ 561. , 0.7101939...],
[ 562. , 0.6996362...],
[ 563. , 0.6890656...],
[ 564. , 0.6785599...],
[ 565. , 0.6680593...],
[ 566. , 0.6575697...],
[ 567. , 0.6471578...],
[ 568. , 0.6368208...],
[ 569. , 0.6264871...],
[ 570. , 0.6161541...],
[ 571. , 0.6058896...],
[ 572. , 0.5957000...],
[ 573. , 0.5855937...],
[ 574. , 0.5754412...],
[ 575. , 0.5653883...],
[ 576. , 0.5553742...],
[ 577. , 0.5454680...],
[ 578. , 0.5355972...],
[ 579. , 0.5258267...],
[ 580. , 0.5160152...],
[ 581. , 0.5062322...],
[ 582. , 0.4965595...],
[ 583. , 0.4868746...],
[ 584. , 0.4773299...],
[ 585. , 0.4678028...],
[ 586. , 0.4583704...],
[ 587. , 0.4489722...],
[ 588. , 0.4397606...],
[ 589. , 0.4306131...],
[ 590. , 0.4215446...],
[ 591. , 0.4125681...],
[ 592. , 0.4037550...],
[ 593. , 0.3950359...],
[ 594. , 0.3864104...],
[ 595. , 0.3778777...],
[ 596. , 0.3694405...],
[ 597. , 0.3611074...],
[ 598. , 0.3528596...],
[ 599. , 0.3447056...],
[ 600. , 0.3366470...],
[ 601. , 0.3286917...],
[ 602. , 0.3208410...],
[ 603. , 0.3130808...],
[ 604. , 0.3054105...],
[ 605. , 0.2978225...],
[ 606. , 0.2903027...],
[ 607. , 0.2828727...],
[ 608. , 0.2755311...],
[ 609. , 0.2682900...],
[ 610. , 0.2611478...],
[ 611. , 0.2541176...],
[ 612. , 0.2471885...],
[ 613. , 0.2403570...],
[ 614. , 0.2336057...],
[ 615. , 0.2269379...],
[ 616. , 0.2203527...],
[ 617. , 0.2138465...],
[ 618. , 0.2073946...],
[ 619. , 0.2009789...],
[ 620. , 0.1945818...],
[ 621. , 0.1881943...],
[ 622. , 0.1818226...],
[ 623. , 0.1754987...],
[ 624. , 0.1692476...],
[ 625. , 0.1630876...],
[ 626. , 0.1570257...],
[ 627. , 0.151071 ...],
[ 628. , 0.1452469...],
[ 629. , 0.1395845...],
[ 630. , 0.1341087...],
[ 631. , 0.1288408...],
[ 632. , 0.1237666...],
[ 633. , 0.1188631...],
[ 634. , 0.1141075...],
[ 635. , 0.1094766...],
[ 636. , 0.1049613...],
[ 637. , 0.1005679...],
[ 638. , 0.0962924...],
[ 639. , 0.0921296...],
[ 640. , 0.0880778...],
[ 641. , 0.0841306...],
[ 642. , 0.0802887...],
[ 643. , 0.0765559...],
[ 644. , 0.0729367...],
[ 645. , 0.0694345...],
[ 646. , 0.0660491...],
[ 647. , 0.0627792...],
[ 648. , 0.0596278...],
[ 649. , 0.0565970...],
[ 650. , 0.0536896...],
[ 651. , 0.0509068...],
[ 652. , 0.0482444...],
[ 653. , 0.0456951...],
[ 654. , 0.0432510...],
[ 655. , 0.0409052...],
[ 656. , 0.0386537...],
[ 657. , 0.0364955...],
[ 658. , 0.0344285...],
[ 659. , 0.0324501...],
[ 660. , 0.0305579...],
[ 661. , 0.0287496...],
[ 662. , 0.0270233...],
[ 663. , 0.0253776...],
[ 664. , 0.0238113...],
[ 665. , 0.0223226...],
[ 666. , 0.0209086...],
[ 667. , 0.0195688...],
[ 668. , 0.0183056...],
[ 669. , 0.0171216...],
[ 670. , 0.0160192...],
[ 671. , 0.0149986...],
[ 672. , 0.0140537...],
[ 673. , 0.0131784...],
[ 674. , 0.0123662...],
[ 675. , 0.0116107...],
[ 676. , 0.0109098...],
[ 677. , 0.0102587...],
[ 678. , 0.0096476...],
[ 679. , 0.0090665...],
[ 680. , 0.0085053...],
[ 681. , 0.0079567...],
[ 682. , 0.0074229...],
[ 683. , 0.0069094...],
[ 684. , 0.0064213...],
[ 685. , 0.0059637...],
[ 686. , 0.0055377...],
[ 687. , 0.0051402...],
[ 688. , 0.00477 ...],
[ 689. , 0.0044263...],
[ 690. , 0.0041081...],
[ 691. , 0.0038149...],
[ 692. , 0.0035456...],
[ 693. , 0.0032984...],
[ 694. , 0.0030718...],
[ 695. , 0.0028639...],
[ 696. , 0.0026738...],
[ 697. , 0.0025000...],
[ 698. , 0.0023401...],
[ 699. , 0.0021918...],
[ 700. , 0.0020526...],
[ 701. , 0.0019207...],
[ 702. , 0.001796 ...],
[ 703. , 0.0016784...],
[ 704. , 0.0015683...],
[ 705. , 0.0014657...],
[ 706. , 0.0013702...],
[ 707. , 0.001281 ...],
[ 708. , 0.0011976...],
[ 709. , 0.0011195...],
[ 710. , 0.0010464...],
[ 711. , 0.0009776...],
[ 712. , 0.0009131...],
[ 713. , 0.0008525...],
[ 714. , 0.0007958...],
[ 715. , 0.0007427...],
[ 716. , 0.0006929...],
[ 717. , 0.0006462...],
[ 718. , 0.0006026...],
[ 719. , 0.0005619...],
[ 720. , 0.0005240...],
[ 721. , 0.0004888...],
[ 722. , 0.0004561...],
[ 723. , 0.0004255...],
[ 724. , 0.0003971...],
[ 725. , 0.0003704...],
[ 726. , 0.0003455...],
[ 727. , 0.0003221...],
[ 728. , 0.0003001...],
[ 729. , 0.0002796...],
[ 730. , 0.0002604...],
[ 731. , 0.0002423...],
[ 732. , 0.0002254...],
[ 733. , 0.0002095...],
[ 734. , 0.0001947...],
[ 735. , 0.0001809...],
[ 736. , 0.0001680...],
[ 737. , 0.0001560...],
[ 738. , 0.0001449...],
[ 739. , 0.0001345...],
[ 740. , 0.0001249...],
[ 741. , 0.0001159...],
[ 742. , 0.0001076...],
[ 743. , 0.0000999...],
[ 744. , 0.0000927...],
[ 745. , 0.0000862...],
[ 746. , 0.0000801...],
[ 747. , 0.0000745...],
[ 748. , 0.0000693...],
[ 749. , 0.0000646...],
[ 750. , 0.0000602...],
[ 751. , 0.0000561...],
[ 752. , 0.0000523...],
[ 753. , 0.0000488...],
[ 754. , 0.0000456...],
[ 755. , 0.0000425...],
[ 756. , 0.0000397...],
[ 757. , 0.0000370...],
[ 758. , 0.0000346...],
[ 759. , 0.0000322...],
[ 760. , 0.0000301...],
[ 761. , 0.0000281...],
[ 762. , 0.0000262...],
[ 763. , 0.0000244...],
[ 764. , 0.0000228...],
[ 765. , 0.0000213...],
[ 766. , 0.0000198...],
[ 767. , 0.0000185...],
[ 768. , 0.0000173...],
[ 769. , 0.0000161...],
[ 770. , 0.0000150...],
[ 771. , 0.0000140...],
[ 772. , 0.0000131...],
[ 773. , 0.0000122...],
[ 774. , 0.0000114...],
[ 775. , 0.0000106...],
[ 776. , 0.0000099...],
[ 777. , 0.0000092...],
[ 778. , 0.0000086...],
[ 779. , 0.0000080...],
[ 780. , 0.0000075...]],
interpolator=SpragueInterpolator,
interpolator_kwargs={},
extrapolator=Extrapolator,
extrapolator_kwargs={...})
"""
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()
sd_data = dict(
zip(
wavelengths,
mesopic_weighting_function(wavelengths, Lp, source, method,
photopic_lef, scotopic_lef)))
sd = SpectralDistribution(
sd_data, name='{0} Lp Mesopic Luminous Efficiency Function'.format(Lp))
return sd.normalise()
def sd_rayleigh_scattering(
shape: SpectralShape = SPECTRAL_SHAPE_DEFAULT,
CO2_concentration: FloatingOrArrayLike = CONSTANT_STANDARD_CO2_CONCENTRATION,
temperature: FloatingOrArrayLike = CONSTANT_STANDARD_AIR_TEMPERATURE,
pressure: FloatingOrArrayLike = CONSTANT_AVERAGE_PRESSURE_MEAN_SEA_LEVEL,
latitude: FloatingOrArrayLike = CONSTANT_DEFAULT_LATITUDE,
altitude: FloatingOrArrayLike = CONSTANT_DEFAULT_ALTITUDE,
avogadro_constant: FloatingOrArrayLike = CONSTANT_AVOGADRO,
n_s_function: Callable = air_refraction_index_Bodhaine1999,
F_air_function: Callable = F_air_Bodhaine1999,
) -> SpectralDistribution:
"""
Return the *Rayleigh* spectral distribution for given spectral shape.
Parameters
----------
shape
Spectral shape used to create the *Rayleigh* scattering spectral
distribution.
CO2_concentration
:math:`CO_2` concentration in parts per million (ppm).
temperature
Air temperature :math:`T[K]` in kelvin degrees.
pressure
Surface pressure :math:`P` of the measurement site.
latitude
Latitude of the site in degrees.
altitude
Altitude of the site in meters.
avogadro_constant
*Avogadro*'s number (molecules :math:`mol^{-1}`).
n_s_function
Air refraction index :math:`n_s` computation method.
F_air_function
:math:`(6+3_p)/(6-7_p)`, the depolarisation term :math:`F(air)` or
*King Factor* computation method.
Returns
-------
:class:`colour.SpectralDistribution`
*Rayleigh* optical depth spectral distribution.
References
----------
:cite:`Bodhaine1999a`, :cite:`Wikipedia2001c`
Examples
--------
>>> from colour.utilities import numpy_print_options
>>> with numpy_print_options(suppress=True):
... sd_rayleigh_scattering() # doctest: +ELLIPSIS
SpectralDistribution([[ 360. , 0.5991013...],
[ 361. , 0.5921706...],
[ 362. , 0.5853410...],
[ 363. , 0.5786105...],
[ 364. , 0.5719774...],
[ 365. , 0.5654401...],
[ 366. , 0.5589968...],
[ 367. , 0.5526460...],
[ 368. , 0.5463860...],
[ 369. , 0.5402153...],
[ 370. , 0.5341322...],
[ 371. , 0.5281354...],
[ 372. , 0.5222234...],
[ 373. , 0.5163946...],
[ 374. , 0.5106476...],
[ 375. , 0.5049812...],
[ 376. , 0.4993939...],
[ 377. , 0.4938844...],
[ 378. , 0.4884513...],
[ 379. , 0.4830934...],
[ 380. , 0.4778095...],
[ 381. , 0.4725983...],
[ 382. , 0.4674585...],
[ 383. , 0.4623891...],
[ 384. , 0.4573889...],
[ 385. , 0.4524566...],
[ 386. , 0.4475912...],
[ 387. , 0.4427917...],
[ 388. , 0.4380568...],
[ 389. , 0.4333856...],
[ 390. , 0.4287771...],
[ 391. , 0.4242302...],
[ 392. , 0.4197439...],
[ 393. , 0.4153172...],
[ 394. , 0.4109493...],
[ 395. , 0.4066391...],
[ 396. , 0.4023857...],
[ 397. , 0.3981882...],
[ 398. , 0.3940458...],
[ 399. , 0.3899576...],
[ 400. , 0.3859227...],
[ 401. , 0.3819402...],
[ 402. , 0.3780094...],
[ 403. , 0.3741295...],
[ 404. , 0.3702996...],
[ 405. , 0.366519 ...],
[ 406. , 0.3627868...],
[ 407. , 0.3591025...],
[ 408. , 0.3554651...],
[ 409. , 0.3518740...],
[ 410. , 0.3483286...],
[ 411. , 0.344828 ...],
[ 412. , 0.3413716...],
[ 413. , 0.3379587...],
[ 414. , 0.3345887...],
[ 415. , 0.3312609...],
[ 416. , 0.3279747...],
[ 417. , 0.3247294...],
[ 418. , 0.3215245...],
[ 419. , 0.3183593...],
[ 420. , 0.3152332...],
[ 421. , 0.3121457...],
[ 422. , 0.3090962...],
[ 423. , 0.3060841...],
[ 424. , 0.3031088...],
[ 425. , 0.3001699...],
[ 426. , 0.2972668...],
[ 427. , 0.2943989...],
[ 428. , 0.2915657...],
[ 429. , 0.2887668...],
[ 430. , 0.2860017...],
[ 431. , 0.2832697...],
[ 432. , 0.2805706...],
[ 433. , 0.2779037...],
[ 434. , 0.2752687...],
[ 435. , 0.2726650...],
[ 436. , 0.2700922...],
[ 437. , 0.2675500...],
[ 438. , 0.2650377...],
[ 439. , 0.2625551...],
[ 440. , 0.2601016...],
[ 441. , 0.2576770...],
[ 442. , 0.2552807...],
[ 443. , 0.2529124...],
[ 444. , 0.2505716...],
[ 445. , 0.2482581...],
[ 446. , 0.2459713...],
[ 447. , 0.2437110...],
[ 448. , 0.2414768...],
[ 449. , 0.2392683...],
[ 450. , 0.2370851...],
[ 451. , 0.2349269...],
[ 452. , 0.2327933...],
[ 453. , 0.2306841...],
[ 454. , 0.2285989...],
[ 455. , 0.2265373...],
[ 456. , 0.2244990...],
[ 457. , 0.2224838...],
[ 458. , 0.2204912...],
[ 459. , 0.2185211...],
[ 460. , 0.2165730...],
[ 461. , 0.2146467...],
[ 462. , 0.2127419...],
[ 463. , 0.2108583...],
[ 464. , 0.2089957...],
[ 465. , 0.2071536...],
[ 466. , 0.2053320...],
[ 467. , 0.2035304...],
[ 468. , 0.2017487...],
[ 469. , 0.1999865...],
[ 470. , 0.1982436...],
[ 471. , 0.1965198...],
[ 472. , 0.1948148...],
[ 473. , 0.1931284...],
[ 474. , 0.1914602...],
[ 475. , 0.1898101...],
[ 476. , 0.1881779...],
[ 477. , 0.1865633...],
[ 478. , 0.1849660...],
[ 479. , 0.1833859...],
[ 480. , 0.1818227...],
[ 481. , 0.1802762...],
[ 482. , 0.1787463...],
[ 483. , 0.1772326...],
[ 484. , 0.1757349...],
[ 485. , 0.1742532...],
[ 486. , 0.1727871...],
[ 487. , 0.1713365...],
[ 488. , 0.1699011...],
[ 489. , 0.1684809...],
[ 490. , 0.1670755...],
[ 491. , 0.1656848...],
[ 492. , 0.1643086...],
[ 493. , 0.1629468...],
[ 494. , 0.1615991...],
[ 495. , 0.1602654...],
[ 496. , 0.1589455...],
[ 497. , 0.1576392...],
[ 498. , 0.1563464...],
[ 499. , 0.1550668...],
[ 500. , 0.1538004...],
[ 501. , 0.1525470...],
[ 502. , 0.1513063...],
[ 503. , 0.1500783...],
[ 504. , 0.1488628...],
[ 505. , 0.1476597...],
[ 506. , 0.1464687...],
[ 507. , 0.1452898...],
[ 508. , 0.1441228...],
[ 509. , 0.1429675...],
[ 510. , 0.1418238...],
[ 511. , 0.1406916...],
[ 512. , 0.1395707...],
[ 513. , 0.1384610...],
[ 514. , 0.1373624...],
[ 515. , 0.1362747...],
[ 516. , 0.1351978...],
[ 517. , 0.1341316...],
[ 518. , 0.1330759...],
[ 519. , 0.1320306...],
[ 520. , 0.1309956...],
[ 521. , 0.1299707...],
[ 522. , 0.1289559...],
[ 523. , 0.1279511...],
[ 524. , 0.1269560...],
[ 525. , 0.1259707...],
[ 526. , 0.1249949...],
[ 527. , 0.1240286...],
[ 528. , 0.1230717...],
[ 529. , 0.1221240...],
[ 530. , 0.1211855...],
[ 531. , 0.1202560...],
[ 532. , 0.1193354...],
[ 533. , 0.1184237...],
[ 534. , 0.1175207...],
[ 535. , 0.1166263...],
[ 536. , 0.1157404...],
[ 537. , 0.1148630...],
[ 538. , 0.1139939...],
[ 539. , 0.1131331...],
[ 540. , 0.1122804...],
[ 541. , 0.1114357...],
[ 542. , 0.1105990...],
[ 543. , 0.1097702...],
[ 544. , 0.1089492...],
[ 545. , 0.1081358...],
[ 546. , 0.1073301...],
[ 547. , 0.1065319...],
[ 548. , 0.1057411...],
[ 549. , 0.1049577...],
[ 550. , 0.1041815...],
[ 551. , 0.1034125...],
[ 552. , 0.1026507...],
[ 553. , 0.1018958...],
[ 554. , 0.1011480...],
[ 555. , 0.1004070...],
[ 556. , 0.0996728...],
[ 557. , 0.0989453...],
[ 558. , 0.0982245...],
[ 559. , 0.0975102...],
[ 560. , 0.0968025...],
[ 561. , 0.0961012...],
[ 562. , 0.0954062...],
[ 563. , 0.0947176...],
[ 564. , 0.0940352...],
[ 565. , 0.0933589...],
[ 566. , 0.0926887...],
[ 567. , 0.0920246...],
[ 568. , 0.0913664...],
[ 569. , 0.0907141...],
[ 570. , 0.0900677...],
[ 571. , 0.0894270...],
[ 572. , 0.0887920...],
[ 573. , 0.0881627...],
[ 574. , 0.0875389...],
[ 575. , 0.0869207...],
[ 576. , 0.0863079...],
[ 577. , 0.0857006...],
[ 578. , 0.0850986...],
[ 579. , 0.0845019...],
[ 580. , 0.0839104...],
[ 581. , 0.0833242...],
[ 582. , 0.0827430...],
[ 583. , 0.082167 ...],
[ 584. , 0.0815959...],
[ 585. , 0.0810298...],
[ 586. , 0.0804687...],
[ 587. , 0.0799124...],
[ 588. , 0.0793609...],
[ 589. , 0.0788142...],
[ 590. , 0.0782722...],
[ 591. , 0.0777349...],
[ 592. , 0.0772022...],
[ 593. , 0.0766740...],
[ 594. , 0.0761504...],
[ 595. , 0.0756313...],
[ 596. , 0.0751166...],
[ 597. , 0.0746063...],
[ 598. , 0.0741003...],
[ 599. , 0.0735986...],
[ 600. , 0.0731012...],
[ 601. , 0.072608 ...],
[ 602. , 0.0721189...],
[ 603. , 0.0716340...],
[ 604. , 0.0711531...],
[ 605. , 0.0706763...],
[ 606. , 0.0702035...],
[ 607. , 0.0697347...],
[ 608. , 0.0692697...],
[ 609. , 0.0688087...],
[ 610. , 0.0683515...],
[ 611. , 0.0678981...],
[ 612. , 0.0674485...],
[ 613. , 0.0670026...],
[ 614. , 0.0665603...],
[ 615. , 0.0661218...],
[ 616. , 0.0656868...],
[ 617. , 0.0652555...],
[ 618. , 0.0648277...],
[ 619. , 0.0644033...],
[ 620. , 0.0639825...],
[ 621. , 0.0635651...],
[ 622. , 0.0631512...],
[ 623. , 0.0627406...],
[ 624. , 0.0623333...],
[ 625. , 0.0619293...],
[ 626. , 0.0615287...],
[ 627. , 0.0611312...],
[ 628. , 0.0607370...],
[ 629. , 0.0603460...],
[ 630. , 0.0599581...],
[ 631. , 0.0595733...],
[ 632. , 0.0591917...],
[ 633. , 0.0588131...],
[ 634. , 0.0584375...],
[ 635. , 0.0580649...],
[ 636. , 0.0576953...],
[ 637. , 0.0573286...],
[ 638. , 0.0569649...],
[ 639. , 0.0566040...],
[ 640. , 0.0562460...],
[ 641. , 0.0558909...],
[ 642. , 0.0555385...],
[ 643. , 0.0551890...],
[ 644. , 0.0548421...],
[ 645. , 0.0544981...],
[ 646. , 0.0541567...],
[ 647. , 0.053818 ...],
[ 648. , 0.0534819...],
[ 649. , 0.0531485...],
[ 650. , 0.0528176...],
[ 651. , 0.0524894...],
[ 652. , 0.0521637...],
[ 653. , 0.0518405...],
[ 654. , 0.0515198...],
[ 655. , 0.0512017...],
[ 656. , 0.0508859...],
[ 657. , 0.0505726...],
[ 658. , 0.0502618...],
[ 659. , 0.0499533...],
[ 660. , 0.0496472...],
[ 661. , 0.0493434...],
[ 662. , 0.0490420...],
[ 663. , 0.0487428...],
[ 664. , 0.0484460...],
[ 665. , 0.0481514...],
[ 666. , 0.0478591...],
[ 667. , 0.0475689...],
[ 668. , 0.0472810...],
[ 669. , 0.0469953...],
[ 670. , 0.0467117...],
[ 671. , 0.0464302...],
[ 672. , 0.0461509...],
[ 673. , 0.0458737...],
[ 674. , 0.0455986...],
[ 675. , 0.0453255...],
[ 676. , 0.0450545...],
[ 677. , 0.0447855...],
[ 678. , 0.0445185...],
[ 679. , 0.0442535...],
[ 680. , 0.0439905...],
[ 681. , 0.0437294...],
[ 682. , 0.0434703...],
[ 683. , 0.0432131...],
[ 684. , 0.0429578...],
[ 685. , 0.0427044...],
[ 686. , 0.0424529...],
[ 687. , 0.0422032...],
[ 688. , 0.0419553...],
[ 689. , 0.0417093...],
[ 690. , 0.0414651...],
[ 691. , 0.0412226...],
[ 692. , 0.0409820...],
[ 693. , 0.0407431...],
[ 694. , 0.0405059...],
[ 695. , 0.0402705...],
[ 696. , 0.0400368...],
[ 697. , 0.0398047...],
[ 698. , 0.0395744...],
[ 699. , 0.0393457...],
[ 700. , 0.0391187...],
[ 701. , 0.0388933...],
[ 702. , 0.0386696...],
[ 703. , 0.0384474...],
[ 704. , 0.0382269...],
[ 705. , 0.0380079...],
[ 706. , 0.0377905...],
[ 707. , 0.0375747...],
[ 708. , 0.0373604...],
[ 709. , 0.0371476...],
[ 710. , 0.0369364...],
[ 711. , 0.0367266...],
[ 712. , 0.0365184...],
[ 713. , 0.0363116...],
[ 714. , 0.0361063...],
[ 715. , 0.0359024...],
[ 716. , 0.0357000...],
[ 717. , 0.0354990...],
[ 718. , 0.0352994...],
[ 719. , 0.0351012...],
[ 720. , 0.0349044...],
[ 721. , 0.0347090...],
[ 722. , 0.0345150...],
[ 723. , 0.0343223...],
[ 724. , 0.0341310...],
[ 725. , 0.0339410...],
[ 726. , 0.0337523...],
[ 727. , 0.033565 ...],
[ 728. , 0.0333789...],
[ 729. , 0.0331941...],
[ 730. , 0.0330106...],
[ 731. , 0.0328284...],
[ 732. , 0.0326474...],
[ 733. , 0.0324677...],
[ 734. , 0.0322893...],
[ 735. , 0.0321120...],
[ 736. , 0.0319360...],
[ 737. , 0.0317611...],
[ 738. , 0.0315875...],
[ 739. , 0.0314151...],
[ 740. , 0.0312438...],
[ 741. , 0.0310737...],
[ 742. , 0.0309048...],
[ 743. , 0.0307370...],
[ 744. , 0.0305703...],
[ 745. , 0.0304048...],
[ 746. , 0.0302404...],
[ 747. , 0.0300771...],
[ 748. , 0.0299149...],
[ 749. , 0.0297538...],
[ 750. , 0.0295938...],
[ 751. , 0.0294349...],
[ 752. , 0.0292771...],
[ 753. , 0.0291203...],
[ 754. , 0.0289645...],
[ 755. , 0.0288098...],
[ 756. , 0.0286561...],
[ 757. , 0.0285035...],
[ 758. , 0.0283518...],
[ 759. , 0.0282012...],
[ 760. , 0.0280516...],
[ 761. , 0.0279030...],
[ 762. , 0.0277553...],
[ 763. , 0.0276086...],
[ 764. , 0.027463 ...],
[ 765. , 0.0273182...],
[ 766. , 0.0271744...],
[ 767. , 0.0270316...],
[ 768. , 0.0268897...],
[ 769. , 0.0267487...],
[ 770. , 0.0266087...],
[ 771. , 0.0264696...],
[ 772. , 0.0263314...],
[ 773. , 0.0261941...],
[ 774. , 0.0260576...],
[ 775. , 0.0259221...],
[ 776. , 0.0257875...],
[ 777. , 0.0256537...],
[ 778. , 0.0255208...],
[ 779. , 0.0253888...],
[ 780. , 0.0252576...]],
interpolator=SpragueInterpolator,
interpolator_kwargs={},
extrapolator=Extrapolator,
extrapolator_kwargs={...})
"""
wavelengths = shape.range()
return SpectralDistribution(
rayleigh_optical_depth(
wavelengths * 10e-8,
CO2_concentration,
temperature,
pressure,
latitude,
altitude,
avogadro_constant,
n_s_function,
F_air_function,
),
wavelengths,
name=(
"Rayleigh Scattering - "
f"{CO2_concentration!r} ppm, "
f"{temperature!r} K, "
f"{pressure!r} Pa, "
f"{latitude!r} Degrees, "
f"{altitude!r} m"
),
)
def sd_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 : SpectralDistribution, optional
:math:`V(\\lambda)` photopic luminous efficiency function.
scotopic_lef : SpectralDistribution, optional
:math:`V^\\prime(\\lambda)` scotopic luminous efficiency function.
Returns
-------
SpectralDistribution
Mesopic luminous efficiency function :math:`V_m(\\lambda)`.
References
----------
:cite:`Wikipedia2005d`
Examples
--------
>>> from colour.utilities import numpy_print_options
>>> with numpy_print_options(suppress=True):
... sd_mesopic_luminous_efficiency_function(0.2) # doctest: +ELLIPSIS
SpectralDistribution([[ 380. , 0.000424 ...],
[ 381. , 0.0004781...],
[ 382. , 0.0005399...],
[ 383. , 0.0006122...],
[ 384. , 0.0006961...],
[ 385. , 0.0007929...],
[ 386. , 0.000907 ...],
[ 387. , 0.0010389...],
[ 388. , 0.0011923...],
[ 389. , 0.0013703...],
[ 390. , 0.0015771...],
[ 391. , 0.0018167...],
[ 392. , 0.0020942...],
[ 393. , 0.0024160...],
[ 394. , 0.0027888...],
[ 395. , 0.0032196...],
[ 396. , 0.0037222...],
[ 397. , 0.0042957...],
[ 398. , 0.0049531...],
[ 399. , 0.0057143...],
[ 400. , 0.0065784...],
[ 401. , 0.0075658...],
[ 402. , 0.0086912...],
[ 403. , 0.0099638...],
[ 404. , 0.0114058...],
[ 405. , 0.0130401...],
[ 406. , 0.0148750...],
[ 407. , 0.0169310...],
[ 408. , 0.0192211...],
[ 409. , 0.0217511...],
[ 410. , 0.0245342...],
[ 411. , 0.0275773...],
[ 412. , 0.0309172...],
[ 413. , 0.0345149...],
[ 414. , 0.0383998...],
[ 415. , 0.0425744...],
[ 416. , 0.0471074...],
[ 417. , 0.0519322...],
[ 418. , 0.0570541...],
[ 419. , 0.0625466...],
[ 420. , 0.0683463...],
[ 421. , 0.0745255...],
[ 422. , 0.0809440...],
[ 423. , 0.0877344...],
[ 424. , 0.0948915...],
[ 425. , 0.1022731...],
[ 426. , 0.109877 ...],
[ 427. , 0.1178421...],
[ 428. , 0.1260316...],
[ 429. , 0.1343772...],
[ 430. , 0.143017 ...],
[ 431. , 0.1518128...],
[ 432. , 0.1608328...],
[ 433. , 0.1700088...],
[ 434. , 0.1792726...],
[ 435. , 0.1886934...],
[ 436. , 0.1982041...],
[ 437. , 0.2078032...],
[ 438. , 0.2174184...],
[ 439. , 0.2271147...],
[ 440. , 0.2368196...],
[ 441. , 0.2464623...],
[ 442. , 0.2561153...],
[ 443. , 0.2657160...],
[ 444. , 0.2753387...],
[ 445. , 0.2848520...],
[ 446. , 0.2944648...],
[ 447. , 0.3034902...],
[ 448. , 0.3132347...],
[ 449. , 0.3223257...],
[ 450. , 0.3314513...],
[ 451. , 0.3406129...],
[ 452. , 0.3498117...],
[ 453. , 0.3583617...],
[ 454. , 0.3676377...],
[ 455. , 0.3762670...],
[ 456. , 0.3849392...],
[ 457. , 0.3936540...],
[ 458. , 0.4024077...],
[ 459. , 0.4111965...],
[ 460. , 0.4193298...],
[ 461. , 0.4281803...],
[ 462. , 0.4363804...],
[ 463. , 0.4453117...],
[ 464. , 0.4542949...],
[ 465. , 0.4626509...],
[ 466. , 0.4717570...],
[ 467. , 0.4809300...],
[ 468. , 0.4901776...],
[ 469. , 0.4995075...],
[ 470. , 0.5096145...],
[ 471. , 0.5191293...],
[ 472. , 0.5294259...],
[ 473. , 0.5391316...],
[ 474. , 0.5496217...],
[ 475. , 0.5602103...],
[ 476. , 0.5702197...],
[ 477. , 0.5810207...],
[ 478. , 0.5919093...],
[ 479. , 0.6028683...],
[ 480. , 0.6138806...],
[ 481. , 0.6249373...],
[ 482. , 0.6360619...],
[ 483. , 0.6465989...],
[ 484. , 0.6579538...],
[ 485. , 0.6687841...],
[ 486. , 0.6797939...],
[ 487. , 0.6909887...],
[ 488. , 0.7023827...],
[ 489. , 0.7133032...],
[ 490. , 0.7244513...],
[ 491. , 0.7358470...],
[ 492. , 0.7468118...],
[ 493. , 0.7580294...],
[ 494. , 0.7694964...],
[ 495. , 0.7805225...],
[ 496. , 0.7917805...],
[ 497. , 0.8026123...],
[ 498. , 0.8130793...],
[ 499. , 0.8239297...],
[ 500. , 0.8352251...],
[ 501. , 0.8456342...],
[ 502. , 0.8564818...],
[ 503. , 0.8676921...],
[ 504. , 0.8785021...],
[ 505. , 0.8881489...],
[ 506. , 0.8986405...],
[ 507. , 0.9079322...],
[ 508. , 0.9174255...],
[ 509. , 0.9257739...],
[ 510. , 0.9350656...],
[ 511. , 0.9432365...],
[ 512. , 0.9509063...],
[ 513. , 0.9586931...],
[ 514. , 0.9658413...],
[ 515. , 0.9722825...],
[ 516. , 0.9779924...],
[ 517. , 0.9836106...],
[ 518. , 0.9883465...],
[ 519. , 0.9920964...],
[ 520. , 0.9954436...],
[ 521. , 0.9976202...],
[ 522. , 0.9993457...],
[ 523. , 1. ...],
[ 524. , 0.9996498...],
[ 525. , 0.9990487...],
[ 526. , 0.9975356...],
[ 527. , 0.9957615...],
[ 528. , 0.9930143...],
[ 529. , 0.9899559...],
[ 530. , 0.9858741...],
[ 531. , 0.9814453...],
[ 532. , 0.9766885...],
[ 533. , 0.9709363...],
[ 534. , 0.9648947...],
[ 535. , 0.9585832...],
[ 536. , 0.952012 ...],
[ 537. , 0.9444916...],
[ 538. , 0.9367089...],
[ 539. , 0.9293506...],
[ 540. , 0.9210429...],
[ 541. , 0.9124772...],
[ 542. , 0.9036604...],
[ 543. , 0.8945958...],
[ 544. , 0.8845999...],
[ 545. , 0.8750500...],
[ 546. , 0.8659457...],
[ 547. , 0.8559224...],
[ 548. , 0.8456846...],
[ 549. , 0.8352499...],
[ 550. , 0.8253229...],
[ 551. , 0.8152079...],
[ 552. , 0.8042205...],
[ 553. , 0.7944209...],
[ 554. , 0.7837466...],
[ 555. , 0.7735680...],
[ 556. , 0.7627808...],
[ 557. , 0.7522710...],
[ 558. , 0.7417549...],
[ 559. , 0.7312909...],
[ 560. , 0.7207983...],
[ 561. , 0.7101939...],
[ 562. , 0.6996362...],
[ 563. , 0.6890656...],
[ 564. , 0.6785599...],
[ 565. , 0.6680593...],
[ 566. , 0.6575697...],
[ 567. , 0.6471578...],
[ 568. , 0.6368208...],
[ 569. , 0.6264871...],
[ 570. , 0.6161541...],
[ 571. , 0.6058896...],
[ 572. , 0.5957000...],
[ 573. , 0.5855937...],
[ 574. , 0.5754412...],
[ 575. , 0.5653883...],
[ 576. , 0.5553742...],
[ 577. , 0.5454680...],
[ 578. , 0.5355972...],
[ 579. , 0.5258267...],
[ 580. , 0.5160152...],
[ 581. , 0.5062322...],
[ 582. , 0.4965595...],
[ 583. , 0.4868746...],
[ 584. , 0.4773299...],
[ 585. , 0.4678028...],
[ 586. , 0.4583704...],
[ 587. , 0.4489722...],
[ 588. , 0.4397606...],
[ 589. , 0.4306131...],
[ 590. , 0.4215446...],
[ 591. , 0.4125681...],
[ 592. , 0.4037550...],
[ 593. , 0.3950359...],
[ 594. , 0.3864104...],
[ 595. , 0.3778777...],
[ 596. , 0.3694405...],
[ 597. , 0.3611074...],
[ 598. , 0.3528596...],
[ 599. , 0.3447056...],
[ 600. , 0.3366470...],
[ 601. , 0.3286917...],
[ 602. , 0.3208410...],
[ 603. , 0.3130808...],
[ 604. , 0.3054105...],
[ 605. , 0.2978225...],
[ 606. , 0.2903027...],
[ 607. , 0.2828727...],
[ 608. , 0.2755311...],
[ 609. , 0.2682900...],
[ 610. , 0.2611478...],
[ 611. , 0.2541176...],
[ 612. , 0.2471885...],
[ 613. , 0.2403570...],
[ 614. , 0.2336057...],
[ 615. , 0.2269379...],
[ 616. , 0.2203527...],
[ 617. , 0.2138465...],
[ 618. , 0.2073946...],
[ 619. , 0.2009789...],
[ 620. , 0.1945818...],
[ 621. , 0.1881943...],
[ 622. , 0.1818226...],
[ 623. , 0.1754987...],
[ 624. , 0.1692476...],
[ 625. , 0.1630876...],
[ 626. , 0.1570257...],
[ 627. , 0.151071 ...],
[ 628. , 0.1452469...],
[ 629. , 0.1395845...],
[ 630. , 0.1341087...],
[ 631. , 0.1288408...],
[ 632. , 0.1237666...],
[ 633. , 0.1188631...],
[ 634. , 0.1141075...],
[ 635. , 0.1094766...],
[ 636. , 0.1049613...],
[ 637. , 0.1005679...],
[ 638. , 0.0962924...],
[ 639. , 0.0921296...],
[ 640. , 0.0880778...],
[ 641. , 0.0841306...],
[ 642. , 0.0802887...],
[ 643. , 0.0765559...],
[ 644. , 0.0729367...],
[ 645. , 0.0694345...],
[ 646. , 0.0660491...],
[ 647. , 0.0627792...],
[ 648. , 0.0596278...],
[ 649. , 0.0565970...],
[ 650. , 0.0536896...],
[ 651. , 0.0509068...],
[ 652. , 0.0482444...],
[ 653. , 0.0456951...],
[ 654. , 0.0432510...],
[ 655. , 0.0409052...],
[ 656. , 0.0386537...],
[ 657. , 0.0364955...],
[ 658. , 0.0344285...],
[ 659. , 0.0324501...],
[ 660. , 0.0305579...],
[ 661. , 0.0287496...],
[ 662. , 0.0270233...],
[ 663. , 0.0253776...],
[ 664. , 0.0238113...],
[ 665. , 0.0223226...],
[ 666. , 0.0209086...],
[ 667. , 0.0195688...],
[ 668. , 0.0183056...],
[ 669. , 0.0171216...],
[ 670. , 0.0160192...],
[ 671. , 0.0149986...],
[ 672. , 0.0140537...],
[ 673. , 0.0131784...],
[ 674. , 0.0123662...],
[ 675. , 0.0116107...],
[ 676. , 0.0109098...],
[ 677. , 0.0102587...],
[ 678. , 0.0096476...],
[ 679. , 0.0090665...],
[ 680. , 0.0085053...],
[ 681. , 0.0079567...],
[ 682. , 0.0074229...],
[ 683. , 0.0069094...],
[ 684. , 0.0064213...],
[ 685. , 0.0059637...],
[ 686. , 0.0055377...],
[ 687. , 0.0051402...],
[ 688. , 0.00477 ...],
[ 689. , 0.0044263...],
[ 690. , 0.0041081...],
[ 691. , 0.0038149...],
[ 692. , 0.0035456...],
[ 693. , 0.0032984...],
[ 694. , 0.0030718...],
[ 695. , 0.0028639...],
[ 696. , 0.0026738...],
[ 697. , 0.0025000...],
[ 698. , 0.0023401...],
[ 699. , 0.0021918...],
[ 700. , 0.0020526...],
[ 701. , 0.0019207...],
[ 702. , 0.001796 ...],
[ 703. , 0.0016784...],
[ 704. , 0.0015683...],
[ 705. , 0.0014657...],
[ 706. , 0.0013702...],
[ 707. , 0.001281 ...],
[ 708. , 0.0011976...],
[ 709. , 0.0011195...],
[ 710. , 0.0010464...],
[ 711. , 0.0009776...],
[ 712. , 0.0009131...],
[ 713. , 0.0008525...],
[ 714. , 0.0007958...],
[ 715. , 0.0007427...],
[ 716. , 0.0006929...],
[ 717. , 0.0006462...],
[ 718. , 0.0006026...],
[ 719. , 0.0005619...],
[ 720. , 0.0005240...],
[ 721. , 0.0004888...],
[ 722. , 0.0004561...],
[ 723. , 0.0004255...],
[ 724. , 0.0003971...],
[ 725. , 0.0003704...],
[ 726. , 0.0003455...],
[ 727. , 0.0003221...],
[ 728. , 0.0003001...],
[ 729. , 0.0002796...],
[ 730. , 0.0002604...],
[ 731. , 0.0002423...],
[ 732. , 0.0002254...],
[ 733. , 0.0002095...],
[ 734. , 0.0001947...],
[ 735. , 0.0001809...],
[ 736. , 0.0001680...],
[ 737. , 0.0001560...],
[ 738. , 0.0001449...],
[ 739. , 0.0001345...],
[ 740. , 0.0001249...],
[ 741. , 0.0001159...],
[ 742. , 0.0001076...],
[ 743. , 0.0000999...],
[ 744. , 0.0000927...],
[ 745. , 0.0000862...],
[ 746. , 0.0000801...],
[ 747. , 0.0000745...],
[ 748. , 0.0000693...],
[ 749. , 0.0000646...],
[ 750. , 0.0000602...],
[ 751. , 0.0000561...],
[ 752. , 0.0000523...],
[ 753. , 0.0000488...],
[ 754. , 0.0000456...],
[ 755. , 0.0000425...],
[ 756. , 0.0000397...],
[ 757. , 0.0000370...],
[ 758. , 0.0000346...],
[ 759. , 0.0000322...],
[ 760. , 0.0000301...],
[ 761. , 0.0000281...],
[ 762. , 0.0000262...],
[ 763. , 0.0000244...],
[ 764. , 0.0000228...],
[ 765. , 0.0000213...],
[ 766. , 0.0000198...],
[ 767. , 0.0000185...],
[ 768. , 0.0000173...],
[ 769. , 0.0000161...],
[ 770. , 0.0000150...],
[ 771. , 0.0000140...],
[ 772. , 0.0000131...],
[ 773. , 0.0000122...],
[ 774. , 0.0000114...],
[ 775. , 0.0000106...],
[ 776. , 0.0000099...],
[ 777. , 0.0000092...],
[ 778. , 0.0000086...],
[ 779. , 0.0000080...],
[ 780. , 0.0000075...]],
interpolator=SpragueInterpolator,
interpolator_args={},
extrapolator=Extrapolator,
extrapolator_args={...})
"""
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()
sd_data = dict(
zip(
wavelengths,
mesopic_weighting_function(wavelengths, Lp, source, method,
photopic_lef, scotopic_lef)))
sd = SpectralDistribution(
sd_data, name='{0} Lp Mesopic Luminous Efficiency Function'.format(Lp))
return sd.normalise()
def sd_CIE_standard_illuminant_A(
shape: SpectralShape = SPECTRAL_SHAPE_DEFAULT,
) -> SpectralDistribution:
"""
*CIE Standard Illuminant A* is intended to represent typical, domestic,
tungsten-filament lighting.
Its spectral distribution is that of a Planckian radiator at a temperature
of approximately 2856 K. *CIE Standard Illuminant A* should be used in all
applications of colorimetry involving the use of incandescent lighting,
unless there are specific reasons for using a different illuminant.
Parameters
----------
shape
Spectral shape used to create the spectral distribution of the
*CIE Standard Illuminant A*.
Returns
-------
:class:`colour.SpectralDistribution`
*CIE Standard Illuminant A*. spectral distribution.
References
----------
:cite:`CIETC1-482004n`
Examples
--------
>>> from colour import SpectralShape
>>> sd_CIE_standard_illuminant_A(SpectralShape(400, 700, 10))
... # doctest: +ELLIPSIS
SpectralDistribution([[ 400. , 14.7080384...],
[ 410. , 17.6752521...],
[ 420. , 20.9949572...],
[ 430. , 24.6709226...],
[ 440. , 28.7027304...],
[ 450. , 33.0858929...],
[ 460. , 37.8120566...],
[ 470. , 42.8692762...],
[ 480. , 48.2423431...],
[ 490. , 53.9131532...],
[ 500. , 59.8610989...],
[ 510. , 66.0634727...],
[ 520. , 72.4958719...],
[ 530. , 79.1325945...],
[ 540. , 85.9470183...],
[ 550. , 92.9119589...],
[ 560. , 100. ...],
[ 570. , 107.1837952...],
[ 580. , 114.4363383...],
[ 590. , 121.7312009...],
[ 600. , 129.0427389...],
[ 610. , 136.3462674...],
[ 620. , 143.6182057...],
[ 630. , 150.8361944...],
[ 640. , 157.9791857...],
[ 650. , 165.0275098...],
[ 660. , 171.9629200...],
[ 670. , 178.7686175...],
[ 680. , 185.4292591...],
[ 690. , 191.9309499...],
[ 700. , 198.2612232...]],
interpolator=SpragueInterpolator,
interpolator_kwargs={},
extrapolator=Extrapolator,
extrapolator_kwargs={...})
"""
wavelengths = shape.range()
values = (
100
* (560 / wavelengths) ** 5
* (
np.expm1((1.435 * 10**7) / (2848 * 560))
/ np.expm1((1.435 * 10**7) / (2848 * wavelengths))
)
)
return SpectralDistribution(
values, wavelengths, name="CIE Standard Illuminant A"
)
[0.35760870152308100, 0.32914390227898000, 0.31324730130288600],
[0.34871280010839300, 0.33080872680368200, 0.32047832512463300],
[0.34488011934469100, 0.33148268992224300, 0.32363699470796100],
[0.34191787732329100, 0.33198455035238900, 0.32609730884690000],
[0.33953109298712900, 0.33234117252254500, 0.32812736934018400],
[0.33716950377436700, 0.33291200941553900, 0.32991797595888800],
[0.33617201852771700, 0.33291927969521400, 0.33090790121664900],
[0.33516744343336300, 0.33302767257885600, 0.33180363309599500],
[0.33442162530646300, 0.33317970467326000, 0.33239662725536100],
[0.33400876037640200, 0.33324703097454900, 0.33274078072682400],
[0.33391579279008200, 0.33325934921060100, 0.33282085708148900],
[0.33381845494636700, 0.33327505027938300, 0.33290173128344400],
[0.33367277492845600, 0.33329432844873200, 0.33302596748863200],
[0.33356951340559100, 0.33330942495777500, 0.33311108308149700],
])
MSDS_BASIS_FUNCTIONS_sRGB_MALLETT2019 = MultiSpectralDistributions(
DATA_BASIS_FUNCTIONS_sRGB_MALLETT2019,
SPECTRAL_SHAPE_sRGB_MALLETT2019.range(),
name='Basis Functions - sRGB - Mallett 2019',
labels=('red', 'green', 'blue'))
"""
*Mallett and Yuksel (2019)* basis functions for the *sRGB* colourspace.
References
----------
:cite:`Mallett2019`
MSDS_BASIS_FUNCTIONS_sRGB_MALLETT2019 : MultiSpectralDistributions
"""
def sd_reference_illuminant(CCT: Floating,
shape: SpectralShape) -> SpectralDistribution:
"""
Compute the reference illuminant for a given correlated colour temperature
:math:`T_{cp}` for use in *CIE 2017 Colour Fidelity Index* (CFI)
computation.
Parameters
----------
CCT
Correlated colour temperature :math:`T_{cp}`.
shape
Desired shape of the returned spectral distribution.
Returns
-------
:class:`colour.SpectralDistribution`
Reference illuminant for *CIE 2017 Colour Fidelity Index* (CFI)
computation.
Examples
--------
>>> from colour.utilities import numpy_print_options
>>> with numpy_print_options(suppress=True):
... sd_reference_illuminant( # doctest: +ELLIPSIS
... 4224.469705295263300, SpectralShape(380, 780, 20))
SpectralDistribution([[ 380. , 0.0034089...],
[ 400. , 0.0044208...],
[ 420. , 0.0053260...],
[ 440. , 0.0062857...],
[ 460. , 0.0072767...],
[ 480. , 0.0080207...],
[ 500. , 0.0086590...],
[ 520. , 0.0092242...],
[ 540. , 0.0097686...],
[ 560. , 0.0101444...],
[ 580. , 0.0104475...],
[ 600. , 0.0107642...],
[ 620. , 0.0110439...],
[ 640. , 0.0112535...],
[ 660. , 0.0113922...],
[ 680. , 0.0115185...],
[ 700. , 0.0113155...],
[ 720. , 0.0108192...],
[ 740. , 0.0111582...],
[ 760. , 0.0101299...],
[ 780. , 0.0105638...]],
interpolator=SpragueInterpolator,
interpolator_kwargs={},
extrapolator=Extrapolator,
extrapolator_kwargs={...})
"""
if CCT <= 5000:
sd_planckian = sd_blackbody(CCT, shape)
if CCT >= 4000:
xy = CCT_to_xy_CIE_D(CCT)
sd_daylight = sd_CIE_illuminant_D_series(xy).align(shape)
if CCT < 4000:
sd_reference = sd_planckian
elif 4000 <= CCT <= 5000:
# Planckian and daylight illuminant must be normalised so that the
# mixture isn't biased.
sd_planckian /= sd_to_XYZ(sd_planckian)[1] # type: ignore[misc]
sd_daylight /= sd_to_XYZ(sd_daylight)[1] # type: ignore[misc]
# Mixture: 4200K should be 80% Planckian, 20% CIE Illuminant D Series.
m = (CCT - 4000) / 1000
values = linstep_function(m, sd_planckian.values, sd_daylight.values)
name = (f"{as_int_scalar(CCT)}K "
f"Blackbody & CIE Illuminant D Series Mixture - "
f"{as_float_scalar(100 * m):.1f}%")
sd_reference = SpectralDistribution(values, shape.range(), name=name)
elif CCT > 5000:
sd_reference = sd_daylight
return sd_reference
def mesopic_luminous_efficiency_function(
Lp,
source='Blue Heavy',
method='MOVE',
photopic_lef=PHOTOPIC_LEFS.get(
'CIE 1924 Photopic Standard Observer'),
scotopic_lef=SCOTOPIC_LEFS.get(
'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()
def sd_blackbody(
temperature: Floating,
shape: SpectralShape = SPECTRAL_SHAPE_DEFAULT,
c1: Floating = CONSTANT_C1,
c2: Floating = CONSTANT_C2,
n: Floating = CONSTANT_N,
) -> SpectralDistribution:
"""
Return the spectral distribution of the planckian radiator for given
temperature :math:`T[K]` with values in
*watts per steradian per square metre per nanometer* (:math:`W/sr/m^2/nm`).
Parameters
----------
temperature
Temperature :math:`T[K]` in kelvin degrees.
shape
Spectral shape used to create the spectral distribution of the
planckian radiator.
c1
The official value of :math:`c1` is provided by the Committee on Data
for Science and Technology (CODATA) and is
:math:`c1=3,741771x10.16\\ W/m_2` *(Mohr and Taylor, 2000)*.
c2
Since :math:`T` is measured on the International Temperature Scale,
the value of :math:`c2` used in colorimetry should follow that adopted
in the current International Temperature Scale (ITS-90)
*(Preston-Thomas, 1990; Mielenz et aI., 1991)*, namely
:math:`c2=1,4388x10.2\\ m/K`.
n
Medium index of refraction. For dry air at 15C and 101 325 Pa,
containing 0,03 percent by volume of carbon dioxide, it is
approximately 1,00028 throughout the visible region although
*CIE 15:2004* recommends using :math:`n=1`.
Returns
-------
:class:`colour.SpectralDistribution`
Blackbody spectral distribution with values in
*watts per steradian per square metre per nanometer*
(:math:`W/sr/m^2/nm`).
Examples
--------
>>> from colour.utilities import numpy_print_options
>>> with numpy_print_options(suppress=True):
... sd_blackbody(5000) # doctest: +ELLIPSIS
SpectralDistribution([[ 360. , 6654.2782706...],
[ 361. , 6709.6052792...],
[ 362. , 6764.8251215...],
[ 363. , 6819.9330786...],
[ 364. , 6874.9244898...],
[ 365. , 6929.7947526...],
[ 366. , 6984.5393232...],
[ 367. , 7039.1537166...],
[ 368. , 7093.6335071...],
[ 369. , 7147.9743284...],
[ 370. , 7202.1718736...],
[ 371. , 7256.2218956...],
[ 372. , 7310.1202073...],
[ 373. , 7363.8626816...],
[ 374. , 7417.4452515...],
[ 375. , 7470.8639102...],
[ 376. , 7524.1147113...],
[ 377. , 7577.1937686...],
[ 378. , 7630.0972565...],
[ 379. , 7682.8214094...],
[ 380. , 7735.3625224...],
[ 381. , 7787.7169506...],
[ 382. , 7839.8811097...],
[ 383. , 7891.8514754...],
[ 384. , 7943.6245836...],
[ 385. , 7995.1970300...],
[ 386. , 8046.5654705...],
[ 387. , 8097.7266205...],
[ 388. , 8148.6772551...],
[ 389. , 8199.4142089...],
[ 390. , 8249.9343757...],
[ 391. , 8300.2347083...],
[ 392. , 8350.3122185...],
[ 393. , 8400.1639766...],
[ 394. , 8449.7871113...],
[ 395. , 8499.1788096...],
[ 396. , 8548.3363163...],
[ 397. , 8597.2569337...],
[ 398. , 8645.9380216...],
[ 399. , 8694.3769968...],
[ 400. , 8742.5713329...],
[ 401. , 8790.5185599...],
[ 402. , 8838.2162638...],
[ 403. , 8885.6620864...],
[ 404. , 8932.8537251...],
[ 405. , 8979.7889322...],
[ 406. , 9026.4655149...],
[ 407. , 9072.8813344...],
[ 408. , 9119.0343064...],
[ 409. , 9164.9223997...],
[ 410. , 9210.5436366...],
[ 411. , 9255.8960922...],
[ 412. , 9300.9778938...],
[ 413. , 9345.7872209...],
[ 414. , 9390.3223045...],
[ 415. , 9434.5814267...],
[ 416. , 9478.5629206...],
[ 417. , 9522.2651692...],
[ 418. , 9565.6866057...],
[ 419. , 9608.8257125...],
[ 420. , 9651.6810212...],
[ 421. , 9694.2511118...],
[ 422. , 9736.5346124...],
[ 423. , 9778.5301986...],
[ 424. , 9820.2365935...],
[ 425. , 9861.6525666...],
[ 426. , 9902.7769336...],
[ 427. , 9943.6085564...],
[ 428. , 9984.1463416...],
[ 429. , 10024.3892411...],
[ 430. , 10064.3362510...],
[ 431. , 10103.9864112...],
[ 432. , 10143.3388051...],
[ 433. , 10182.3925589...],
[ 434. , 10221.1468414...],
[ 435. , 10259.6008633...],
[ 436. , 10297.7538768...],
[ 437. , 10335.6051749...],
[ 438. , 10373.1540914...],
[ 439. , 10410.3999999...],
[ 440. , 10447.3423137...],
[ 441. , 10483.9804852...],
[ 442. , 10520.3140051...],
[ 443. , 10556.3424025...],
[ 444. , 10592.0652439...],
[ 445. , 10627.4821331...],
[ 446. , 10662.5927104...],
[ 447. , 10697.3966524...],
[ 448. , 10731.8936712...],
[ 449. , 10766.0835144...],
[ 450. , 10799.9659640...],
[ 451. , 10833.5408365...],
[ 452. , 10866.8079821...],
[ 453. , 10899.7672843...],
[ 454. , 10932.4186594...],
[ 455. , 10964.7620561...],
[ 456. , 10996.7974551...],
[ 457. , 11028.5248683...],
[ 458. , 11059.9443388...],
[ 459. , 11091.0559402...],
[ 460. , 11121.8597759...],
[ 461. , 11152.3559791...],
[ 462. , 11182.5447121...],
[ 463. , 11212.4261658...],
[ 464. , 11242.0005596...],
[ 465. , 11271.2681403...],
[ 466. , 11300.2291822...],
[ 467. , 11328.8839867...],
[ 468. , 11357.2328813...],
[ 469. , 11385.2762197...],
[ 470. , 11413.0143813...],
[ 471. , 11440.4477705...],
[ 472. , 11467.5768165...],
[ 473. , 11494.4019726...],
[ 474. , 11520.9237164...],
[ 475. , 11547.1425485...],
[ 476. , 11573.0589928...],
[ 477. , 11598.6735959...],
[ 478. , 11623.9869264...],
[ 479. , 11648.9995750...],
[ 480. , 11673.7121534...],
[ 481. , 11698.1252948...],
[ 482. , 11722.2396526...],
[ 483. , 11746.0559008...],
[ 484. , 11769.5747329...],
[ 485. , 11792.7968621...],
[ 486. , 11815.7230205...],
[ 487. , 11838.3539591...],
[ 488. , 11860.6904469...],
[ 489. , 11882.7332712...],
[ 490. , 11904.4832366...],
[ 491. , 11925.9411650...],
[ 492. , 11947.1078953...],
[ 493. , 11967.9842826...],
[ 494. , 11988.5711984...],
[ 495. , 12008.8695298...],
[ 496. , 12028.8801795...],
[ 497. , 12048.6040651...],
[ 498. , 12068.0421192...],
[ 499. , 12087.1952887...],
[ 500. , 12106.0645344...],
[ 501. , 12124.6508312...],
[ 502. , 12142.9551672...],
[ 503. , 12160.9785437...],
[ 504. , 12178.7219748...],
[ 505. , 12196.1864870...],
[ 506. , 12213.3731190...],
[ 507. , 12230.2829214...],
[ 508. , 12246.9169563...],
[ 509. , 12263.2762971...],
[ 510. , 12279.3620282...],
[ 511. , 12295.1752445...],
[ 512. , 12310.7170514...],
[ 513. , 12325.9885643...],
[ 514. , 12340.9909086...],
[ 515. , 12355.7252189...],
[ 516. , 12370.1926394...],
[ 517. , 12384.3943230...],
[ 518. , 12398.3314315...],
[ 519. , 12412.0051350...],
[ 520. , 12425.4166118...],
[ 521. , 12438.5670483...],
[ 522. , 12451.4576382...],
[ 523. , 12464.0895830...],
[ 524. , 12476.4640911...],
[ 525. , 12488.5823780...],
[ 526. , 12500.4456657...],
[ 527. , 12512.0551828...],
[ 528. , 12523.4121640...],
[ 529. , 12534.5178499...],
[ 530. , 12545.3734871...],
[ 531. , 12555.9803275...],
[ 532. , 12566.3396282...],
[ 533. , 12576.4526517...],
[ 534. , 12586.3206651...],
[ 535. , 12595.9449403...],
[ 536. , 12605.3267534...],
[ 537. , 12614.4673849...],
[ 538. , 12623.3681194...],
[ 539. , 12632.0302452...],
[ 540. , 12640.4550541...],
[ 541. , 12648.6438417...],
[ 542. , 12656.5979064...],
[ 543. , 12664.3185499...],
[ 544. , 12671.8070768...],
[ 545. , 12679.0647943...],
[ 546. , 12686.0930120...],
[ 547. , 12692.8930419...],
[ 548. , 12699.4661982...],
[ 549. , 12705.8137971...],
[ 550. , 12711.9371564...],
[ 551. , 12717.8375957...],
[ 552. , 12723.5164362...],
[ 553. , 12728.9750001...],
[ 554. , 12734.2146109...],
[ 555. , 12739.2365933...],
[ 556. , 12744.0422724...],
[ 557. , 12748.6329745...],
[ 558. , 12753.0100260...],
[ 559. , 12757.1747541...],
[ 560. , 12761.1284859...],
[ 561. , 12764.8725489...],
[ 562. , 12768.4082704...],
[ 563. , 12771.7369777...],
[ 564. , 12774.8599976...],
[ 565. , 12777.7786567...],
[ 566. , 12780.4942809...],
[ 567. , 12783.0081955...],
[ 568. , 12785.3217250...],
[ 569. , 12787.4361930...],
[ 570. , 12789.3529220...],
[ 571. , 12791.0732335...],
[ 572. , 12792.5984474...],
[ 573. , 12793.9298826...],
[ 574. , 12795.0688562...],
[ 575. , 12796.0166840...],
[ 576. , 12796.7746799...],
[ 577. , 12797.3441559...],
[ 578. , 12797.7264224...],
[ 579. , 12797.9227874...],
[ 580. , 12797.9345572...],
[ 581. , 12797.7630356...],
[ 582. , 12797.4095241...],
[ 583. , 12796.8753220...],
[ 584. , 12796.1617260...],
[ 585. , 12795.2700302...],
[ 586. , 12794.2015261...],
[ 587. , 12792.9575025...],
[ 588. , 12791.5392453...],
[ 589. , 12789.9480374...],
[ 590. , 12788.1851590...],
[ 591. , 12786.2518870...],
[ 592. , 12784.1494952...],
[ 593. , 12781.8792543...],
[ 594. , 12779.4424316...],
[ 595. , 12776.8402910...],
[ 596. , 12774.0740932...],
[ 597. , 12771.1450952...],
[ 598. , 12768.0545506...],
[ 599. , 12764.8037091...],
[ 600. , 12761.3938171...],
[ 601. , 12757.8261171...],
[ 602. , 12754.1018476...],
[ 603. , 12750.2222435...],
[ 604. , 12746.1885357...],
[ 605. , 12742.0019511...],
[ 606. , 12737.6637126...],
[ 607. , 12733.1750389...],
[ 608. , 12728.5371449...],
[ 609. , 12723.7512409...],
[ 610. , 12718.8185333...],
[ 611. , 12713.7402241...],
[ 612. , 12708.5175109...],
[ 613. , 12703.1515870...],
[ 614. , 12697.6436414...],
[ 615. , 12691.9948585...],
[ 616. , 12686.2064183...],
[ 617. , 12680.2794963...],
[ 618. , 12674.2152632...],
[ 619. , 12668.0148855...],
[ 620. , 12661.6795247...],
[ 621. , 12655.2103378...],
[ 622. , 12648.6084770...],
[ 623. , 12641.8750899...],
[ 624. , 12635.0113192...],
[ 625. , 12628.0183029...],
[ 626. , 12620.8971740...],
[ 627. , 12613.6490609...],
[ 628. , 12606.2750869...],
[ 629. , 12598.7763704...],
[ 630. , 12591.1540251...],
[ 631. , 12583.4091595...],
[ 632. , 12575.5428771...],
[ 633. , 12567.5562766...],
[ 634. , 12559.4504515...],
[ 635. , 12551.2264904...],
[ 636. , 12542.8854766...],
[ 637. , 12534.4284886...],
[ 638. , 12525.8565997...],
[ 639. , 12517.1708779...],
[ 640. , 12508.3723863...],
[ 641. , 12499.4621828...],
[ 642. , 12490.4413201...],
[ 643. , 12481.3108457...],
[ 644. , 12472.0718019...],
[ 645. , 12462.7252260...],
[ 646. , 12453.2721498...],
[ 647. , 12443.7136000...],
[ 648. , 12434.0505982...],
[ 649. , 12424.2841606...],
[ 650. , 12414.4152982...],
[ 651. , 12404.4450167...],
[ 652. , 12394.3743166...],
[ 653. , 12384.2041931...],
[ 654. , 12373.9356362...],
[ 655. , 12363.5696306...],
[ 656. , 12353.1071555...],
[ 657. , 12342.5491851...],
[ 658. , 12331.8966883...],
[ 659. , 12321.1506285...],
[ 660. , 12310.3119640...],
[ 661. , 12299.3816476...],
[ 662. , 12288.3606272...],
[ 663. , 12277.2498448...],
[ 664. , 12266.0502378...],
[ 665. , 12254.7627377...],
[ 666. , 12243.3882711...],
[ 667. , 12231.9277592...],
[ 668. , 12220.3821179...],
[ 669. , 12208.7522577...],
[ 670. , 12197.0390841...],
[ 671. , 12185.2434970...],
[ 672. , 12173.3663914...],
[ 673. , 12161.4086567...],
[ 674. , 12149.3711771...],
[ 675. , 12137.2548318...],
[ 676. , 12125.0604945...],
[ 677. , 12112.7890338...],
[ 678. , 12100.4413128...],
[ 679. , 12088.0181898...],
[ 680. , 12075.5205176...],
[ 681. , 12062.9491438...],
[ 682. , 12050.3049109...],
[ 683. , 12037.5886562...],
[ 684. , 12024.8012117...],
[ 685. , 12011.9434044...],
[ 686. , 11999.016056 ...],
[ 687. , 11986.0199830...],
[ 688. , 11972.9559971...],
[ 689. , 11959.8249045...],
[ 690. , 11946.6275064...],
[ 691. , 11933.3645990...],
[ 692. , 11920.0369733...],
[ 693. , 11906.6454152...],
[ 694. , 11893.1907055...],
[ 695. , 11879.6736202...],
[ 696. , 11866.0949300...],
[ 697. , 11852.4554007...],
[ 698. , 11838.7557929...],
[ 699. , 11824.9968625...],
[ 700. , 11811.1793602...],
[ 701. , 11797.3040317...],
[ 702. , 11783.3716180...],
[ 703. , 11769.3828548...],
[ 704. , 11755.3384733...],
[ 705. , 11741.2391993...],
[ 706. , 11727.0857541...],
[ 707. , 11712.878854 ...],
[ 708. , 11698.6192103...],
[ 709. , 11684.3075296...],
[ 710. , 11669.9445138...],
[ 711. , 11655.5308596...],
[ 712. , 11641.0672593...],
[ 713. , 11626.5544002...],
[ 714. , 11611.9929648...],
[ 715. , 11597.3836310...],
[ 716. , 11582.7270720...],
[ 717. , 11568.0239562...],
[ 718. , 11553.2749471...],
[ 719. , 11538.4807040...],
[ 720. , 11523.6418811...],
[ 721. , 11508.7591283...],
[ 722. , 11493.8330905...],
[ 723. , 11478.8644085...],
[ 724. , 11463.8537180...],
[ 725. , 11448.8016505...],
[ 726. , 11433.7088327...],
[ 727. , 11418.5758871...],
[ 728. , 11403.4034314...],
[ 729. , 11388.1920788...],
[ 730. , 11372.9424382...],
[ 731. , 11357.6551141...],
[ 732. , 11342.3307063...],
[ 733. , 11326.9698104...],
[ 734. , 11311.5730175...],
[ 735. , 11296.1409144...],
[ 736. , 11280.6740836...],
[ 737. , 11265.1731031...],
[ 738. , 11249.6385466...],
[ 739. , 11234.0709837...],
[ 740. , 11218.4709796...],
[ 741. , 11202.8390952...],
[ 742. , 11187.1758873...],
[ 743. , 11171.4819083...],
[ 744. , 11155.7577065...],
[ 745. , 11140.0038261...],
[ 746. , 11124.2208070...],
[ 747. , 11108.4091852...],
[ 748. , 11092.5694922...],
[ 749. , 11076.7022559...],
[ 750. , 11060.8079996...],
[ 751. , 11044.8872430...],
[ 752. , 11028.9405016...],
[ 753. , 11012.9682867...],
[ 754. , 10996.9711059...],
[ 755. , 10980.9494627...],
[ 756. , 10964.9038566...],
[ 757. , 10948.8347833...],
[ 758. , 10932.7427345...],
[ 759. , 10916.6281979...],
[ 760. , 10900.4916576...],
[ 761. , 10884.3335937...],
[ 762. , 10868.1544824...],
[ 763. , 10851.9547962...],
[ 764. , 10835.7350038...],
[ 765. , 10819.4955701...],
[ 766. , 10803.2369563...],
[ 767. , 10786.9596199...],
[ 768. , 10770.6640145...],
[ 769. , 10754.3505902...],
[ 770. , 10738.0197934...],
[ 771. , 10721.6720668...],
[ 772. , 10705.3078497...],
[ 773. , 10688.9275774...],
[ 774. , 10672.5316819...],
[ 775. , 10656.1205916...],
[ 776. , 10639.6947313...],
[ 777. , 10623.2545223...],
[ 778. , 10606.8003824...],
[ 779. , 10590.3327259...],
[ 780. , 10573.8519636...]],
interpolator=SpragueInterpolator,
interpolator_kwargs={},
extrapolator=Extrapolator,
extrapolator_kwargs={...})
"""
wavelengths = shape.range()
return SpectralDistribution(
planck_law(wavelengths * 1e-9, temperature, c1, c2, n) * 1e-9,
wavelengths,
name=f"{temperature}K Blackbody",
)
