Source code for colour.colorimetry.spectrum

# -*- coding: utf-8 -*-
"""
Spectrum
========

Defines the classes and objects handling spectral data computations:

-   :class:`colour.DEFAULT_SPECTRAL_SHAPE`
-   :class:`colour.SpectralShape`
-   :class:`colour.SpectralDistribution`
-   :class:`colour.MultiSpectralDistributions`
-   :func:`colour.colorimetry.sds_and_multi_sds_to_sds`
-   :func:`colour.colorimetry.sds_and_multi_sds_to_multi_sds`

References
----------
-   :cite:`CIETC1-382005e` : CIE TC 1-38. (2005). 9. INTERPOLATION. In CIE
    167:2005 Recommended Practice for Tabulating Spectral Data for Use in
    Colour Computations (pp. 14-19). ISBN:978-3-901906-41-1
-   :cite:`CIETC1-382005g` : CIE TC 1-38. (2005). EXTRAPOLATION. In CIE
    167:2005 Recommended Practice for Tabulating Spectral Data for Use in
    Colour Computations (pp. 19-20). ISBN:978-3-901906-41-1
-   :cite:`CIETC1-482004l` : CIE TC 1-48. (2004). Extrapolation. In CIE
    015:2004 Colorimetry, 3rd Edition (p. 24). ISBN:978-3-901906-33-6
"""

from __future__ import division, unicode_literals

import numpy as np
from six.moves import zip

from colour.algebra import (Extrapolator, CubicSplineInterpolator,
                            SpragueInterpolator)
from colour.constants import DEFAULT_FLOAT_DTYPE
from colour.continuous import Signal, MultiSignals
from colour.utilities import (as_float, as_int, first_item, is_iterable,
                              is_numeric, is_string, is_uniform, interval,
                              runtime_warning, tstack, usage_warning)
from colour.utilities.deprecation import (ObjectRemoved, ObjectRenamed,
                                          handle_arguments_deprecation)

__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__ = [
    'SpectralShape', 'DEFAULT_SPECTRAL_SHAPE', 'SpectralDistribution',
    'MultiSpectralDistributions', 'sds_and_multi_sds_to_sds',
    'sds_and_multi_sds_to_multi_sds'
]


[docs]class SpectralShape(object): """ Defines the base object for spectral distribution shape. Parameters ---------- start : numeric, optional Wavelength :math:`\\lambda_{i}` range start in nm. end : numeric, optional Wavelength :math:`\\lambda_{i}` range end in nm. interval : numeric, optional Wavelength :math:`\\lambda_{i}` range interval. Attributes ---------- start end interval boundaries Methods ------- __str__ __repr__ __hash__ __iter__ __contains__ __len__ __eq__ __ne__ range Examples -------- >>> SpectralShape(360, 830, 1) SpectralShape(360, 830, 1) """ def __init__(self, start=None, end=None, interval=None): self._range = None self._start = None self._end = None self._interval = None self.start = start self.end = end self.interval = interval @property def start(self): """ Getter and setter property for the spectral shape start. Parameters ---------- value : numeric Value to set the spectral shape start with. Returns ------- numeric Spectral shape start. """ return self._start @start.setter def start(self, value): """ Setter for the **self.start** property. """ if value is not None: assert is_numeric(value), ( '"{0}" attribute: "{1}" is not a "numeric"!'.format( 'start', value)) if self._end is not None: assert value < self._end, ( '"{0}" attribute value must be strictly less than ' '"{1}"!'.format('start', self._end)) # Invalidating the *range* cache. if value != self._start: self._range = None self._start = value @property def end(self): """ Getter and setter property for the spectral shape end. Parameters ---------- value : numeric Value to set the spectral shape end with. Returns ------- numeric Spectral shape end. """ return self._end @end.setter def end(self, value): """ Setter for the **self.end** property. """ if value is not None: assert is_numeric(value), ( '"{0}" attribute: "{1}" is not a "numeric"!'.format( 'end', value)) if self._start is not None: assert value > self._start, ( '"{0}" attribute value must be strictly greater than ' '"{1}"!'.format('end', self._start)) # Invalidating the *range* cache. if value != self._end: self._range = None self._end = value @property def interval(self): """ Getter and setter property for the spectral shape interval. Parameters ---------- value : numeric Value to set the spectral shape interval with. Returns ------- numeric Spectral shape interval. """ return self._interval @interval.setter def interval(self, value): """ Setter for the **self.interval** property. """ if value is not None: assert is_numeric(value), ( '"{0}" attribute: "{1}" is not a "numeric"!'.format( 'interval', value)) # Invalidating the *range* cache. if value != self._interval: self._range = None self._interval = value @property def boundaries(self): """ Getter and setter property for the spectral shape boundaries. Parameters ---------- value : array_like Value to set the spectral shape boundaries with. Returns ------- tuple Spectral shape boundaries. """ return self._start, self._end @boundaries.setter def boundaries(self, value): """ Setter for the **self.boundaries** property. """ if value is not None: assert is_iterable(value), ( '"{0}" attribute: "{1}" is not an "iterable"!'.format( 'boundaries', value)) assert len(value) == 2, ( '"{0}" attribute: "{1}" must have exactly ' 'two elements!'.format('boundaries', value)) start, end = value self.start = start self.end = end
[docs] def __str__(self): """ Returns a formatted string representation of the spectral shape. Returns ------- unicode Formatted string representation. """ return '({0}, {1}, {2})'.format(self._start, self._end, self._interval)
[docs] def __repr__(self): """ Returns an evaluable string representation of the spectral shape. Returns ------- unicode Evaluable string representation. """ return 'SpectralShape({0}, {1}, {2})'.format(self._start, self._end, self._interval)
[docs] def __hash__(self): """ Returns the spectral shape hash. Returns ------- int Object hash. """ return hash(repr(self))
[docs] def __iter__(self): """ Returns a generator for the spectral shape data. Returns ------- generator Spectral shape data generator. Examples -------- >>> shape = SpectralShape(0, 10, 1) >>> for wavelength in shape: ... print(wavelength) 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 """ return iter(self.range())
[docs] def __contains__(self, wavelength): """ Returns if the spectral shape contains given wavelength :math:`\\lambda`. Parameters ---------- wavelength : numeric or array_like Wavelength :math:`\\lambda`. Returns ------- bool Is wavelength :math:`\\lambda` contained in the spectral shape. Examples -------- >>> 0.5 in SpectralShape(0, 10, 0.1) True >>> 0.6 in SpectralShape(0, 10, 0.1) True >>> 0.51 in SpectralShape(0, 10, 0.1) False >>> np.array([0.5, 0.6]) in SpectralShape(0, 10, 0.1) True >>> np.array([0.51, 0.6]) in SpectralShape(0, 10, 0.1) False """ tolerance = 10 ** -np.finfo(DEFAULT_FLOAT_DTYPE).precision return np.all( np.in1d( np.around(wavelength / tolerance).astype(np.int64), np.around(self.range() / tolerance).astype(np.int64)))
[docs] def __len__(self): """ Returns the spectral shape wavelength :math:`\\lambda_n` count. Returns ------- int Spectral shape wavelength :math:`\\lambda_n` count. Examples -------- >>> len(SpectralShape(0, 10, 0.1)) 101 """ return len(self.range())
[docs] def __eq__(self, shape): """ Returns the spectral shape equality with given other spectral shape. Parameters ---------- shape : SpectralShape Spectral shape to compare for equality. Returns ------- bool Spectral shape equality. Examples -------- >>> SpectralShape(0, 10, 0.1) == SpectralShape(0, 10, 0.1) True >>> SpectralShape(0, 10, 0.1) == SpectralShape(0, 10, 1) False """ return (isinstance(shape, self.__class__) and np.array_equal(self.range(), shape.range()))
[docs] def __ne__(self, shape): """ Returns the spectral shape inequality with given other spectral shape. Parameters ---------- shape : SpectralShape Spectral shape to compare for inequality. Returns ------- bool Spectral shape inequality. Examples -------- >>> SpectralShape(0, 10, 0.1) != SpectralShape(0, 10, 0.1) False >>> SpectralShape(0, 10, 0.1) != SpectralShape(0, 10, 1) True """ return not (self == shape)
[docs] def range(self, dtype=None): """ Returns an iterable range for the spectral shape. Parameters ---------- dtype : type Data type used to generate the range. Returns ------- ndarray Iterable range for the spectral distribution shape Raises ------ RuntimeError If one of spectral shape *start*, *end* or *interval* attributes is not defined. Examples -------- >>> SpectralShape(0, 10, 0.1).range() array([ 0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. , 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2. , 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3. , 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4. , 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.7, 4.8, 4.9, 5. , 5.1, 5.2, 5.3, 5.4, 5.5, 5.6, 5.7, 5.8, 5.9, 6. , 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 6.9, 7. , 7.1, 7.2, 7.3, 7.4, 7.5, 7.6, 7.7, 7.8, 7.9, 8. , 8.1, 8.2, 8.3, 8.4, 8.5, 8.6, 8.7, 8.8, 8.9, 9. , 9.1, 9.2, 9.3, 9.4, 9.5, 9.6, 9.7, 9.8, 9.9, 10. ]) """ if dtype is None: dtype = DEFAULT_FLOAT_DTYPE if None in (self._start, self._end, self._interval): raise RuntimeError(('One of the spectral shape "start", "end" or ' '"interval" attributes is not defined!')) if self._range is None: samples = as_int( round((self._interval + self._end - self._start) / self._interval)) range_, current_interval = np.linspace( self._start, self._end, samples, retstep=True, dtype=dtype) self._range = range_ if current_interval != self._interval: self._interval = current_interval runtime_warning( ('"{0}" shape could not be honoured, using ' '"{1}"!').format((self._start, self._end, self._interval), self)) return self._range
DEFAULT_SPECTRAL_SHAPE = SpectralShape(360, 780, 1) """ Default spectral shape according to *ASTM E308-15* practise shape. DEFAULT_SPECTRAL_SHAPE : SpectralShape """
[docs]class SpectralDistribution(Signal): """ Defines the spectral distribution: the base object for spectral computations. The spectral distribution will be initialised according to *CIE 15:2004* recommendation: the method developed by *Sprague (1880)* will be used for interpolating functions having a uniformly spaced independent variable and the *Cubic Spline* method for non-uniformly spaced independent variable. Extrapolation is performed according to *CIE 167:2005* recommendation. Parameters ---------- data : Series or Signal, SpectralDistribution or array_like or \ dict_like, optional Data to be stored in the spectral distribution. domain : array_like, optional Values to initialise the :attr:`colour.SpectralDistribution.wavelength` attribute with. If both ``data`` and ``domain`` arguments are defined, the latter will be used to initialise the :attr:`colour.SpectralDistribution.wavelength` attribute. Other Parameters ---------------- name : unicode, optional Spectral distribution name. interpolator : object, optional Interpolator class type to use as interpolating function. interpolator_kwargs : dict_like, optional Arguments to use when instantiating the interpolating function. extrapolator : object, optional Extrapolator class type to use as extrapolating function. extrapolator_kwargs : dict_like, optional Arguments to use when instantiating the extrapolating function. strict_name : unicode, optional Spectral distribution name for figures, default to :attr:`colour.SpectralDistribution.name` attribute value. Attributes ---------- strict_name wavelengths values shape Methods ------- __init__ interpolate extrapolate align trim normalise References ---------- :cite:`CIETC1-382005e`, :cite:`CIETC1-382005g`, :cite:`CIETC1-482004l` Examples -------- Instantiating a spectral distribution with a uniformly spaced independent variable: >>> from colour.utilities import numpy_print_options >>> data = { ... 500: 0.0651, ... 520: 0.0705, ... 540: 0.0772, ... 560: 0.0870, ... 580: 0.1128, ... 600: 0.1360 ... } >>> with numpy_print_options(suppress=True): ... SpectralDistribution(data) # doctest: +ELLIPSIS SpectralDistribution([[ 500. , 0.0651], [ 520. , 0.0705], [ 540. , 0.0772], [ 560. , 0.087 ], [ 580. , 0.1128], [ 600. , 0.136 ]], interpolator=SpragueInterpolator, interpolator_kwargs={}, extrapolator=Extrapolator, extrapolator_kwargs={...}) Instantiating a spectral distribution with a non-uniformly spaced independent variable: >>> data[510] = 0.31416 >>> with numpy_print_options(suppress=True): ... SpectralDistribution(data) # doctest: +ELLIPSIS SpectralDistribution([[ 500. , 0.0651 ], [ 510. , 0.31416], [ 520. , 0.0705 ], [ 540. , 0.0772 ], [ 560. , 0.087 ], [ 580. , 0.1128 ], [ 600. , 0.136 ]], interpolator=CubicSplineInterpolator, interpolator_kwargs={}, extrapolator=Extrapolator, extrapolator_kwargs={...}) Instantiation with a *Pandas* `Series`: >>> from colour.utilities import is_pandas_installed >>> if is_pandas_installed(): ... from pandas import Series ... print(SpectralDistribution(Series(data))) # doctest: +SKIP [[ 5.0000000...e+02 6.5100000...e-02] [ 5.2000000...e+02 7.0500000...e-02] [ 5.4000000...e+02 7.7200000...e-02] [ 5.6000000...e+02 8.7000000...e-02] [ 5.8000000...e+02 1.1280000...e-01] [ 6.0000000...e+02 1.3600000...e-01] [ 5.1000000...e+02 3.1416000...e-01]] """
[docs] def __init__(self, data=None, domain=None, **kwargs): domain, range_ = self.signal_unpack_data(data, domain) uniform = is_uniform(domain) if domain is not None else True # Initialising with *CIE 15:2004* and *CIE 167:2005* recommendations # defaults. kwargs['interpolator'] = kwargs.get( 'interpolator', SpragueInterpolator if uniform else CubicSplineInterpolator) kwargs['interpolator_kwargs'] = kwargs.get('interpolator_kwargs', {}) kwargs['extrapolator'] = kwargs.get('extrapolator', Extrapolator) kwargs['extrapolator_kwargs'] = kwargs.get('extrapolator_kwargs', { 'method': 'Constant', 'left': None, 'right': None }) super(SpectralDistribution, self).__init__(range_, domain, **kwargs) self._strict_name = None self.strict_name = kwargs.get('strict_name')
@property def strict_name(self): """ Getter and setter property for the spectral distribution strict name. Parameters ---------- value : unicode Value to set the spectral distribution strict name with. Returns ------- unicode Spectral distribution strict name. """ if self._strict_name is not None: return self._strict_name else: return self._name @strict_name.setter def strict_name(self, value): """ Setter for **self.strict_name** property. """ if value is not None: assert is_string(value), ( ('"{0}" attribute: "{1}" is not a "string" like object!' ).format('strict_name', value)) self._strict_name = value @property def wavelengths(self): """ Getter and setter property for the spectral distribution wavelengths :math:`\\lambda_n`. Parameters ---------- value : array_like Value to set the spectral distribution wavelengths :math:`\\lambda_n` with. Returns ------- ndarray Spectral distribution wavelengths :math:`\\lambda_n`. """ return self.domain @wavelengths.setter def wavelengths(self, value): """ Setter for the **self.wavelengths** property. """ self.domain = value @property def values(self): """ Getter and setter property for the spectral distribution values. Parameters ---------- value : array_like Value to set the spectral distribution wavelengths values with. Returns ------- ndarray Spectral distribution values. """ return self.range @values.setter def values(self, value): """ Setter for the **self.values** property. """ self.range = value @property def shape(self): """ Getter and setter property for the spectral distribution shape. Returns ------- SpectralShape Spectral distribution shape. Notes ----- - A spectral distribution with a non-uniformly spaced independent variable have multiple intervals, in that case :attr:`colour.SpectralDistribution.shape` attribute returns the *minimum* interval size. Warning ------- :attr:`colour.SpectralDistribution.shape` attribute is read only. Examples -------- Shape of a spectral distribution with a uniformly spaced independent variable: >>> data = { ... 500: 0.0651, ... 520: 0.0705, ... 540: 0.0772, ... 560: 0.0870, ... 580: 0.1128, ... 600: 0.1360 ... } >>> SpectralDistribution(data).shape SpectralShape(500.0, 600.0, 20.0) Shape of a spectral distribution with a non-uniformly spaced independent variable: >>> data[510] = 0.31416 >>> SpectralDistribution(data).shape SpectralShape(500.0, 600.0, 10.0) """ wavelengths_interval = interval(self.wavelengths) if wavelengths_interval.size != 1: runtime_warning(('"{0}" spectral distribution is not uniform, ' 'using minimum interval!'.format(self.name))) return SpectralShape( min(self.wavelengths), max(self.wavelengths), as_float(min(wavelengths_interval)))
[docs] def interpolate(self, shape, interpolator=None, interpolator_kwargs=None, **kwargs): """ Interpolates the spectral distribution in-place according to *CIE 167:2005* recommendation (if the interpolator has not been changed at instantiation time) or given interpolation arguments. The logic for choosing the interpolator class when ``interpolator`` is not given is as follows: .. code-block:: python if self.interpolator not in (SpragueInterpolator, CubicSplineInterpolator): interpolator = self.interpolator elif self.is_uniform(): interpolator = SpragueInterpolator else: interpolator = CubicSplineInterpolator The logic for choosing the interpolator keyword arguments when ``interpolator_kwargs`` is not given is as follows: .. code-block:: python if self.interpolator not in (SpragueInterpolator, CubicSplineInterpolator): interpolator_kwargs = self.interpolator_kwargs else: interpolator_kwargs = {} Parameters ---------- shape : SpectralShape, optional Spectral shape used for interpolation. interpolator : object, optional Interpolator class type to use as interpolating function. interpolator_kwargs : dict_like, optional Arguments to use when instantiating the interpolating function. Other Parameters ---------------- \\**kwargs : dict, optional Keywords arguments for deprecation management. Returns ------- SpectralDistribution Interpolated spectral distribution. Notes ----- - Interpolation will be performed over boundaries range, if you need to extend the range of the spectral distribution use the :meth:`colour.SpectralDistribution.extrapolate` or :meth:`colour.SpectralDistribution.align` methods. Warning ------- - *Cubic Spline* interpolator requires at least 3 wavelengths :math:`\\lambda_n` for interpolation. - *Sprague (1880)* interpolator requires at least 6 wavelengths :math:`\\lambda_n` for interpolation. References ---------- :cite:`CIETC1-382005e` Examples -------- Spectral distribution with a uniformly spaced independent variable uses *Sprague (1880)* interpolation: >>> from colour.utilities import numpy_print_options >>> data = { ... 500: 0.0651, ... 520: 0.0705, ... 540: 0.0772, ... 560: 0.0870, ... 580: 0.1128, ... 600: 0.1360 ... } >>> sd = SpectralDistribution(data) >>> with numpy_print_options(suppress=True): ... print(sd.interpolate(SpectralShape(interval=1))) ... # doctest: +ELLIPSIS [[ 500. 0.0651 ...] [ 501. 0.0653522...] [ 502. 0.0656105...] [ 503. 0.0658715...] [ 504. 0.0661328...] [ 505. 0.0663929...] [ 506. 0.0666509...] [ 507. 0.0669069...] [ 508. 0.0671613...] [ 509. 0.0674150...] [ 510. 0.0676692...] [ 511. 0.0679253...] [ 512. 0.0681848...] [ 513. 0.0684491...] [ 514. 0.0687197...] [ 515. 0.0689975...] [ 516. 0.0692832...] [ 517. 0.0695771...] [ 518. 0.0698787...] [ 519. 0.0701870...] [ 520. 0.0705 ...] [ 521. 0.0708155...] [ 522. 0.0711336...] [ 523. 0.0714547...] [ 524. 0.0717789...] [ 525. 0.0721063...] [ 526. 0.0724367...] [ 527. 0.0727698...] [ 528. 0.0731051...] [ 529. 0.0734423...] [ 530. 0.0737808...] [ 531. 0.0741203...] [ 532. 0.0744603...] [ 533. 0.0748006...] [ 534. 0.0751409...] [ 535. 0.0754813...] [ 536. 0.0758220...] [ 537. 0.0761633...] [ 538. 0.0765060...] [ 539. 0.0768511...] [ 540. 0.0772 ...] [ 541. 0.0775527...] [ 542. 0.0779042...] [ 543. 0.0782507...] [ 544. 0.0785908...] [ 545. 0.0789255...] [ 546. 0.0792576...] [ 547. 0.0795917...] [ 548. 0.0799334...] [ 549. 0.0802895...] [ 550. 0.0806671...] [ 551. 0.0810740...] [ 552. 0.0815176...] [ 553. 0.0820049...] [ 554. 0.0825423...] [ 555. 0.0831351...] [ 556. 0.0837873...] [ 557. 0.0845010...] [ 558. 0.0852763...] [ 559. 0.0861110...] [ 560. 0.087 ...] [ 561. 0.0879383...] [ 562. 0.0889300...] [ 563. 0.0899793...] [ 564. 0.0910876...] [ 565. 0.0922541...] [ 566. 0.0934760...] [ 567. 0.0947487...] [ 568. 0.0960663...] [ 569. 0.0974220...] [ 570. 0.0988081...] [ 571. 0.1002166...] [ 572. 0.1016394...] [ 573. 0.1030687...] [ 574. 0.1044972...] [ 575. 0.1059186...] [ 576. 0.1073277...] [ 577. 0.1087210...] [ 578. 0.1100968...] [ 579. 0.1114554...] [ 580. 0.1128 ...] [ 581. 0.1141333...] [ 582. 0.1154495...] [ 583. 0.1167424...] [ 584. 0.1180082...] [ 585. 0.1192452...] [ 586. 0.1204536...] [ 587. 0.1216348...] [ 588. 0.1227915...] [ 589. 0.1239274...] [ 590. 0.1250465...] [ 591. 0.1261531...] [ 592. 0.1272517...] [ 593. 0.1283460...] [ 594. 0.1294393...] [ 595. 0.1305340...] [ 596. 0.1316310...] [ 597. 0.1327297...] [ 598. 0.1338277...] [ 599. 0.1349201...] [ 600. 0.136 ...]] Spectral distribution with a non-uniformly spaced independent variable uses *Cubic Spline* interpolation: >>> sd = SpectralDistribution(data) >>> sd[510] = np.pi / 10 >>> with numpy_print_options(suppress=True): ... print(sd.interpolate(SpectralShape(interval=1))) ... # doctest: +ELLIPSIS [[ 500. 0.0651 ...] [ 501. 0.1365202...] [ 502. 0.1953263...] [ 503. 0.2423724...] [ 504. 0.2785126...] [ 505. 0.3046010...] [ 506. 0.3214916...] [ 507. 0.3300387...] [ 508. 0.3310962...] [ 509. 0.3255184...] [ 510. 0.3141592...] [ 511. 0.2978729...] [ 512. 0.2775135...] [ 513. 0.2539351...] [ 514. 0.2279918...] [ 515. 0.2005378...] [ 516. 0.1724271...] [ 517. 0.1445139...] [ 518. 0.1176522...] [ 519. 0.0926962...] [ 520. 0.0705 ...] [ 521. 0.0517370...] [ 522. 0.0363589...] [ 523. 0.0241365...] [ 524. 0.0148407...] [ 525. 0.0082424...] [ 526. 0.0041126...] [ 527. 0.0022222...] [ 528. 0.0023421...] [ 529. 0.0042433...] [ 530. 0.0076966...] [ 531. 0.0124729...] [ 532. 0.0183432...] [ 533. 0.0250785...] [ 534. 0.0324496...] [ 535. 0.0402274...] [ 536. 0.0481829...] [ 537. 0.0560870...] [ 538. 0.0637106...] [ 539. 0.0708246...] [ 540. 0.0772 ...] [ 541. 0.0826564...] [ 542. 0.0872086...] [ 543. 0.0909203...] [ 544. 0.0938549...] [ 545. 0.0960760...] [ 546. 0.0976472...] [ 547. 0.0986321...] [ 548. 0.0990942...] [ 549. 0.0990971...] [ 550. 0.0987043...] [ 551. 0.0979794...] [ 552. 0.0969861...] [ 553. 0.0957877...] [ 554. 0.0944480...] [ 555. 0.0930304...] [ 556. 0.0915986...] [ 557. 0.0902161...] [ 558. 0.0889464...] [ 559. 0.0878532...] [ 560. 0.087 ...] [ 561. 0.0864371...] [ 562. 0.0861623...] [ 563. 0.0861600...] [ 564. 0.0864148...] [ 565. 0.0869112...] [ 566. 0.0876336...] [ 567. 0.0885665...] [ 568. 0.0896945...] [ 569. 0.0910020...] [ 570. 0.0924735...] [ 571. 0.0940936...] [ 572. 0.0958467...] [ 573. 0.0977173...] [ 574. 0.0996899...] [ 575. 0.1017491...] [ 576. 0.1038792...] [ 577. 0.1060649...] [ 578. 0.1082906...] [ 579. 0.1105408...] [ 580. 0.1128 ...] [ 581. 0.1150526...] [ 582. 0.1172833...] [ 583. 0.1194765...] [ 584. 0.1216167...] [ 585. 0.1236884...] [ 586. 0.1256760...] [ 587. 0.1275641...] [ 588. 0.1293373...] [ 589. 0.1309798...] [ 590. 0.1324764...] [ 591. 0.1338114...] [ 592. 0.1349694...] [ 593. 0.1359349...] [ 594. 0.1366923...] [ 595. 0.1372262...] [ 596. 0.1375211...] [ 597. 0.1375614...] [ 598. 0.1373316...] [ 599. 0.1368163...] [ 600. 0.136 ...]] """ interpolator_kwargs = handle_arguments_deprecation({ 'ArgumentRenamed': [['interpolator_args', 'interpolator_kwargs']], }, **kwargs).get('interpolator_kwargs', interpolator_kwargs) self_shape = self.shape s_e_i = zip((shape.start, shape.end, shape.interval), (self_shape.start, self_shape.end, self_shape.interval)) shape = SpectralShape( *[x[0] if x[0] is not None else x[1] for x in s_e_i]) # Defining proper interpolation bounds. # TODO: Provide support for fractional interval like 0.1, etc... if (round(self_shape.start) != self_shape.start or round(self_shape.end) != self_shape.end): runtime_warning( 'Fractional bound encountered, rounding will occur!') shape.start = max(shape.start, np.ceil(self_shape.start)) shape.end = min(shape.end, np.floor(self_shape.end)) if interpolator is None: # User has specifically chosen the interpolator thus it is used # instead of those from *CIE 167:2005* recommendation. if self.interpolator not in (SpragueInterpolator, CubicSplineInterpolator): interpolator = self.interpolator elif self.is_uniform(): interpolator = SpragueInterpolator else: interpolator = CubicSplineInterpolator if interpolator_kwargs is None: # User has specifically chosen the interpolator thus its keyword # arguments are used. if self.interpolator not in (SpragueInterpolator, CubicSplineInterpolator): interpolator_kwargs = self.interpolator_kwargs else: interpolator_kwargs = {} interpolator = interpolator(self.wavelengths, self.values, **interpolator_kwargs) self.domain = shape.range() self.range = interpolator(self.domain) return self
[docs] def extrapolate(self, shape, extrapolator=None, extrapolator_kwargs=None, **kwargs): """ Extrapolates the spectral distribution in-place according to *CIE 15:2004* and *CIE 167:2005* recommendations or given extrapolation arguments. Parameters ---------- shape : SpectralShape Spectral shape used for extrapolation. extrapolator : object, optional Extrapolator class type to use as extrapolating function. extrapolator_kwargs : dict_like, optional Arguments to use when instantiating the extrapolating function. Other Parameters ---------------- \\**kwargs : dict, optional Keywords arguments for deprecation management. Returns ------- SpectralDistribution Extrapolated spectral distribution. References ---------- :cite:`CIETC1-382005g`, :cite:`CIETC1-482004l` Examples -------- >>> from colour.utilities import numpy_print_options >>> data = { ... 500: 0.0651, ... 520: 0.0705, ... 540: 0.0772, ... 560: 0.0870, ... 580: 0.1128, ... 600: 0.1360 ... } >>> sd = SpectralDistribution(data) >>> sd.extrapolate(SpectralShape(400, 700)).shape SpectralShape(400.0, 700.0, 20.0) >>> with numpy_print_options(suppress=True): ... print(sd) [[ 400. 0.0651] [ 420. 0.0651] [ 440. 0.0651] [ 460. 0.0651] [ 480. 0.0651] [ 500. 0.0651] [ 520. 0.0705] [ 540. 0.0772] [ 560. 0.087 ] [ 580. 0.1128] [ 600. 0.136 ] [ 620. 0.136 ] [ 640. 0.136 ] [ 660. 0.136 ] [ 680. 0.136 ] [ 700. 0.136 ]] """ extrapolator_kwargs = handle_arguments_deprecation({ 'ArgumentRenamed': [['extrapolator_args', 'extrapolator_kwargs']], }, **kwargs).get('extrapolator_kwargs', extrapolator_kwargs) self_shape = self.shape wavelengths = np.hstack([ np.arange(shape.start, self_shape.start, self_shape.interval), np.arange(self_shape.end + self_shape.interval, shape.end + self_shape.interval, self_shape.interval) ]) if extrapolator is None: extrapolator = Extrapolator if extrapolator_kwargs is None: extrapolator_kwargs = { 'method': 'Constant', 'left': None, 'right': None } self_extrapolator = self.extrapolator self_extrapolator_kwargs = self.extrapolator_kwargs self.extrapolator = extrapolator self.extrapolator_kwargs = extrapolator_kwargs # The following self-assignment is written as intended and triggers the # extrapolation. self[wavelengths] = self[wavelengths] self.extrapolator = self_extrapolator self.extrapolator_kwargs = self_extrapolator_kwargs return self
[docs] def align(self, shape, interpolator=None, interpolator_kwargs=None, extrapolator=None, extrapolator_kwargs=None, **kwargs): """ Aligns the spectral distribution in-place to given spectral shape: Interpolates first then extrapolates to fit the given range. Interpolation is performed according to *CIE 167:2005* recommendation (if the interpolator has not been changed at instantiation time) or given interpolation arguments. The logic for choosing the interpolator class when ``interpolator`` is not given is as follows: .. code-block:: python if self.interpolator not in (SpragueInterpolator, CubicSplineInterpolator): interpolator = self.interpolator elif self.is_uniform(): interpolator = SpragueInterpolator else: interpolator = CubicSplineInterpolator The logic for choosing the interpolator keyword arguments when ``interpolator_kwargs`` is not given is as follows: .. code-block:: python if self.interpolator not in (SpragueInterpolator, CubicSplineInterpolator): interpolator_kwargs = self.interpolator_kwargs else: interpolator_kwargs = {} Parameters ---------- shape : SpectralShape Spectral shape used for alignment. interpolator : object, optional Interpolator class type to use as interpolating function. interpolator_kwargs : dict_like, optional Arguments to use when instantiating the interpolating function. extrapolator : object, optional Extrapolator class type to use as extrapolating function. extrapolator_kwargs : dict_like, optional Arguments to use when instantiating the extrapolating function. Other Parameters ---------------- \\**kwargs : dict, optional Keywords arguments for deprecation management. Returns ------- SpectralDistribution Aligned spectral distribution. Examples -------- >>> from colour.utilities import numpy_print_options >>> data = { ... 500: 0.0651, ... 520: 0.0705, ... 540: 0.0772, ... 560: 0.0870, ... 580: 0.1128, ... 600: 0.1360 ... } >>> sd = SpectralDistribution(data) >>> with numpy_print_options(suppress=True): ... print(sd.align(SpectralShape(505, 565, 1))) ... # doctest: +ELLIPSIS [[ 505. 0.0663929...] [ 506. 0.0666509...] [ 507. 0.0669069...] [ 508. 0.0671613...] [ 509. 0.0674150...] [ 510. 0.0676692...] [ 511. 0.0679253...] [ 512. 0.0681848...] [ 513. 0.0684491...] [ 514. 0.0687197...] [ 515. 0.0689975...] [ 516. 0.0692832...] [ 517. 0.0695771...] [ 518. 0.0698787...] [ 519. 0.0701870...] [ 520. 0.0705 ...] [ 521. 0.0708155...] [ 522. 0.0711336...] [ 523. 0.0714547...] [ 524. 0.0717789...] [ 525. 0.0721063...] [ 526. 0.0724367...] [ 527. 0.0727698...] [ 528. 0.0731051...] [ 529. 0.0734423...] [ 530. 0.0737808...] [ 531. 0.0741203...] [ 532. 0.0744603...] [ 533. 0.0748006...] [ 534. 0.0751409...] [ 535. 0.0754813...] [ 536. 0.0758220...] [ 537. 0.0761633...] [ 538. 0.0765060...] [ 539. 0.0768511...] [ 540. 0.0772 ...] [ 541. 0.0775527...] [ 542. 0.0779042...] [ 543. 0.0782507...] [ 544. 0.0785908...] [ 545. 0.0789255...] [ 546. 0.0792576...] [ 547. 0.0795917...] [ 548. 0.0799334...] [ 549. 0.0802895...] [ 550. 0.0806671...] [ 551. 0.0810740...] [ 552. 0.0815176...] [ 553. 0.0820049...] [ 554. 0.0825423...] [ 555. 0.0831351...] [ 556. 0.0837873...] [ 557. 0.0845010...] [ 558. 0.0852763...] [ 559. 0.0861110...] [ 560. 0.087 ...] [ 561. 0.0879383...] [ 562. 0.0889300...] [ 563. 0.0899793...] [ 564. 0.0910876...] [ 565. 0.0922541...]] """ self.interpolate(shape, interpolator, interpolator_kwargs) self.extrapolate(shape, extrapolator, extrapolator_kwargs) return self
[docs] def trim(self, shape): """ Trims the spectral distribution wavelengths to given spectral shape. Parameters ---------- shape : SpectralShape Spectral shape used for trimming. Returns ------- SpectralDistribution Trimmed spectral distribution. Examples -------- >>> from colour.utilities import numpy_print_options >>> data = { ... 500: 0.0651, ... 520: 0.0705, ... 540: 0.0772, ... 560: 0.0870, ... 580: 0.1128, ... 600: 0.1360 ... } >>> sd = SpectralDistribution(data) >>> sd = sd.interpolate(SpectralShape(interval=1)) >>> with numpy_print_options(suppress=True): ... print(sd.trim(SpectralShape(520, 580, 5))) ... # doctest: +ELLIPSIS [[ 520. 0.0705 ...] [ 521. 0.0708155...] [ 522. 0.0711336...] [ 523. 0.0714547...] [ 524. 0.0717789...] [ 525. 0.0721063...] [ 526. 0.0724367...] [ 527. 0.0727698...] [ 528. 0.0731051...] [ 529. 0.0734423...] [ 530. 0.0737808...] [ 531. 0.0741203...] [ 532. 0.0744603...] [ 533. 0.0748006...] [ 534. 0.0751409...] [ 535. 0.0754813...] [ 536. 0.0758220...] [ 537. 0.0761633...] [ 538. 0.0765060...] [ 539. 0.0768511...] [ 540. 0.0772 ...] [ 541. 0.0775527...] [ 542. 0.0779042...] [ 543. 0.0782507...] [ 544. 0.0785908...] [ 545. 0.0789255...] [ 546. 0.0792576...] [ 547. 0.0795917...] [ 548. 0.0799334...] [ 549. 0.0802895...] [ 550. 0.0806671...] [ 551. 0.0810740...] [ 552. 0.0815176...] [ 553. 0.0820049...] [ 554. 0.0825423...] [ 555. 0.0831351...] [ 556. 0.0837873...] [ 557. 0.0845010...] [ 558. 0.0852763...] [ 559. 0.0861110...] [ 560. 0.087 ...] [ 561. 0.0879383...] [ 562. 0.0889300...] [ 563. 0.0899793...] [ 564. 0.0910876...] [ 565. 0.0922541...] [ 566. 0.0934760...] [ 567. 0.0947487...] [ 568. 0.0960663...] [ 569. 0.0974220...] [ 570. 0.0988081...] [ 571. 0.1002166...] [ 572. 0.1016394...] [ 573. 0.1030687...] [ 574. 0.1044972...] [ 575. 0.1059186...] [ 576. 0.1073277...] [ 577. 0.1087210...] [ 578. 0.1100968...] [ 579. 0.1114554...] [ 580. 0.1128 ...]] """ start = max(shape.start, self.shape.start) end = min(shape.end, self.shape.end) indexes = np.where( np.logical_and(self.domain >= start, self.domain <= end)) wavelengths = self.wavelengths[indexes] values = self.values[indexes] self.wavelengths = wavelengths self.values = values return self
[docs] def normalise(self, factor=1): """ Normalises the spectral distribution using given normalization factor. Parameters ---------- factor : numeric, optional Normalization factor Returns ------- SpectralDistribution Normalised spectral distribution. Examples -------- >>> from colour.utilities import numpy_print_options >>> data = { ... 500: 0.0651, ... 520: 0.0705, ... 540: 0.0772, ... 560: 0.0870, ... 580: 0.1128, ... 600: 0.1360 ... } >>> sd = SpectralDistribution(data) >>> with numpy_print_options(suppress=True): ... print(sd.normalise()) # doctest: +ELLIPSIS [[ 500. 0.4786764...] [ 520. 0.5183823...] [ 540. 0.5676470...] [ 560. 0.6397058...] [ 580. 0.8294117...] [ 600. 1. ...]] """ self *= 1 / max(self.values) * factor return self
# ------------------------------------------------------------------------# # --- API Changes and Deprecation Management ---# # ------------------------------------------------------------------------# @property def title(self): # pragma: no cover # Docstrings are omitted for documentation purposes. usage_warning( str( ObjectRenamed('SpectralPowerDistribution.title', 'SpectralDistribution.strict_name'))) return self.strict_name @title.setter def title(self, value): # pragma: no cover # Docstrings are omitted for documentation purposes. usage_warning( str( ObjectRenamed('SpectralPowerDistribution.title', 'SpectralDistribution.strict_name'))) self.strict_name = value @property def data(self): # pragma: no cover # Docstrings are omitted for documentation purposes. raise AttributeError(str(ObjectRemoved('SpectralDistribution.data'))) @property def items(self): # pragma: no cover # Docstrings are omitted for documentation purposes. raise AttributeError(str(ObjectRemoved('SpectralDistribution.items'))) def __iter__(self): # pragma: no cover # Docstrings are omitted for documentation purposes. raise AttributeError( str(ObjectRemoved('SpectralDistribution.__iter__'))) def get(self): # pragma: no cover # Docstrings are omitted for documentation purposes. raise AttributeError(str(ObjectRemoved('SpectralDistribution.get'))) def zeros(self): # pragma: no cover # Docstrings are omitted for documentation purposes. raise AttributeError(str(ObjectRemoved('SpectralDistribution.zeros'))) def trim_wavelengths(self, shape): # pragma: no cover # Docstrings are omitted for documentation purposes. usage_warning( str( ObjectRenamed('SpectralPowerDistribution.trim_wavelengths', 'SpectralDistribution.trim'))) return self.trim(shape) def clone(self): # pragma: no cover # Docstrings are omitted for documentation purposes. usage_warning( str( ObjectRenamed('SpectralPowerDistribution.clone', 'SpectralDistribution.copy'))) return self.copy()
[docs]class MultiSpectralDistributions(MultiSignals): """ Defines the multi-spectral distributions: the base object for multi spectral computations. It is used to model colour matching functions, display primaries, camera sensitivities, etc... The multi-spectral distributions will be initialised according to *CIE 15:2004* recommendation: the method developed by *Sprague (1880)* will be used for interpolating functions having a uniformly spaced independent variable and the *Cubic Spline* method for non-uniformly spaced independent variable. Extrapolation is performed according to *CIE 167:2005* recommendation. Parameters ---------- data : Series or Dataframe or Signal or MultiSignals or \ MultiSpectralDistributions or array_like or dict_like, optional Data to be stored in the multi-spectral distributions. domain : array_like, optional Values to initialise the multiple :class:`colour.SpectralDistribution` class instances :attr:`colour.continuous.Signal.wavelengths` attribute with. If both ``data`` and ``domain`` arguments are defined, the latter will be used to initialise the :attr:`colour.continuous.Signal.wavelengths` attribute. labels : array_like, optional Names to use for the :class:`colour.SpectralDistribution` class instances. Other Parameters ---------------- name : unicode, optional Multi-spectral distributions name. interpolator : object, optional Interpolator class type to use as interpolating function for the :class:`colour.SpectralDistribution` class instances. interpolator_kwargs : dict_like, optional Arguments to use when instantiating the interpolating function of the :class:`colour.SpectralDistribution` class instances. extrapolator : object, optional Extrapolator class type to use as extrapolating function for the :class:`colour.SpectralDistribution` class instances. extrapolator_kwargs : dict_like, optional Arguments to use when instantiating the extrapolating function of the :class:`colour.SpectralDistribution` class instances. strict_labels : array_like, optional Multi-spectral distributions labels for figures, default to :attr:`colour.MultiSpectralDistributions.labels` attribute value. Attributes ---------- strict_name strict_labels wavelengths values shape Methods ------- interpolate extrapolate align trim normalise to_sds References ---------- :cite:`CIETC1-382005e`, :cite:`CIETC1-382005g`, :cite:`CIETC1-482004l` Examples -------- Instantiating the multi-spectral distributions with a uniformly spaced independent variable: >>> from colour.utilities import numpy_print_options >>> data = { ... 500: (0.004900, 0.323000, 0.272000), ... 510: (0.009300, 0.503000, 0.158200), ... 520: (0.063270, 0.710000, 0.078250), ... 530: (0.165500, 0.862000, 0.042160), ... 540: (0.290400, 0.954000, 0.020300), ... 550: (0.433450, 0.994950, 0.008750), ... 560: (0.594500, 0.995000, 0.003900) ... } >>> labels = ('x_bar', 'y_bar', 'z_bar') >>> with numpy_print_options(suppress=True): ... MultiSpectralDistributions(data, labels=labels) ... # doctest: +ELLIPSIS MultiSpectral...([[ 500. , 0.0049 , 0.323 , 0.272 ], ... [ 510. , 0.0093 , 0.503 , 0.1582 ], ... [ 520. , 0.06327, 0.71 , 0.07825], ... [ 530. , 0.1655 , 0.862 , 0.04216], ... [ 540. , 0.2904 , 0.954 , 0.0203 ], ... [ 550. , 0.43345, 0.99495, 0.00875], ... [ 560. , 0.5945 , 0.995 , 0.0039 ]], ... labels=[...'x_bar', ...'y_bar', ...'z_bar'], ... interpolator=SpragueInterpolator, ... interpolator_kwargs={}, ... extrapolator=Extrapolator, ... extrapolator_kwargs={...}) Instantiating a spectral distribution with a non-uniformly spaced independent variable: >>> data[511] = (0.00314, 0.31416, 0.03142) >>> with numpy_print_options(suppress=True): ... MultiSpectralDistributions(data, labels=labels) ... # doctest: +ELLIPSIS MultiSpectral...([[ 500. , 0.0049 , 0.323 , 0.272 ], ... [ 510. , 0.0093 , 0.503 , 0.1582 ], ... [ 511. , 0.00314, 0.31416, 0.03142], ... [ 520. , 0.06327, 0.71 , 0.07825], ... [ 530. , 0.1655 , 0.862 , 0.04216], ... [ 540. , 0.2904 , 0.954 , 0.0203 ], ... [ 550. , 0.43345, 0.99495, 0.00875], ... [ 560. , 0.5945 , 0.995 , 0.0039 ]], ... labels=[...'x_bar', ...'y_bar', ...'z_bar'], ... interpolator=CubicSplineInterpolator, ... interpolator_kwargs={}, ... extrapolator=Extrapolator, ... extrapolator_kwargs={...}) Instantiation with a *Pandas* `DataFrame`: >>> from colour.utilities import is_pandas_installed >>> if is_pandas_installed(): ... from pandas import DataFrame ... x_bar = [data[key][0] for key in sorted(data.keys())] ... y_bar = [data[key][1] for key in sorted(data.keys())] ... z_bar = [data[key][2] for key in sorted(data.keys())] ... print(MultiSignals( # doctest: +SKIP ... DataFrame( ... dict(zip(labels, [x_bar, y_bar, z_bar])), data.keys()))) [[ 5.0000000...e+02 4.9000000...e-03 3.2300000...e-01 \ 2.7200000...e-01] [ 5.1000000...e+02 9.3000000...e-03 5.0300000...e-01 \ 1.5820000...e-01] [ 5.2000000...e+02 3.1400000...e-03 3.1416000...e-01 \ 3.1420000...e-02] [ 5.3000000...e+02 6.3270000...e-02 7.1000000...e-01 \ 7.8250000...e-02] [ 5.4000000...e+02 1.6550000...e-01 8.6200000...e-01 \ 4.2160000...e-02] [ 5.5000000...e+02 2.9040000...e-01 9.5400000...e-01 \ 2.0300000...e-02] [ 5.6000000...e+02 4.3345000...e-01 9.9495000...e-01 \ 8.7500000...e-03] [ 5.1100000...e+02 5.9450000...e-01 9.9500000...e-01 \ 3.9000000...e-03]] """ def __init__(self, data=None, domain=None, labels=None, **kwargs): signals = self.multi_signals_unpack_data(data, domain, labels) domain = signals[list(signals.keys())[0]].domain if signals else None uniform = is_uniform(domain) if domain is not None else True # Initialising with *CIE 15:2004* and *CIE 167:2005* recommendations # defaults. kwargs['interpolator'] = kwargs.get( 'interpolator', SpragueInterpolator if uniform else CubicSplineInterpolator) kwargs['interpolator_kwargs'] = kwargs.get('interpolator_kwargs', {}) kwargs['extrapolator'] = kwargs.get('extrapolator', Extrapolator) kwargs['extrapolator_kwargs'] = kwargs.get('extrapolator_kwargs', { 'method': 'Constant', 'left': None, 'right': None }) super(MultiSpectralDistributions, self).__init__( signals, domain, signal_type=SpectralDistribution, **kwargs) self._strict_name = None self.strict_name = kwargs.get('strict_name') self._strict_labels = None self.strict_labels = kwargs.get('strict_labels') @property def strict_name(self): """ Getter and setter property for the multi-spectral distributions strict name. Parameters ---------- value : unicode Value to set the multi-spectral distributions strict name with. Returns ------- unicode Multi-spectral distributions strict name. """ if self._strict_name is not None: return self._strict_name else: return self._name @strict_name.setter def strict_name(self, value): """ Setter for **self.strict_name** property. """ if value is not None: assert is_string(value), ( ('"{0}" attribute: "{1}" is not a "string" like object!' ).format('strict_name', value)) self._strict_name = value @property def strict_labels(self): """ Getter and setter property for the multi-spectral distributions strict labels. Parameters ---------- value : array_like Value to set the multi-spectral distributions strict labels with. Returns ------- array_like Multi-spectral distributions strict labels. """ if self._strict_labels is not None: return self._strict_labels else: return self.labels @strict_labels.setter def strict_labels(self, value): """ Setter for **self.strict_labels** property. """ if value is not None: assert is_iterable(value), ( '"{0}" attribute: "{1}" is not an "iterable" like object!'. format('strict_labels', value)) assert len(value) == len( self.labels), ('"{0}" attribute: length must be "{1}"!'.format( 'strict_labels', len(self.labels))) self._strict_labels = value @property def wavelengths(self): """ Getter and setter property for the multi-spectral distributions wavelengths :math:`\\lambda_n`. Parameters ---------- value : array_like Value to set the multi-spectral distributions wavelengths :math:`\\lambda_n` with. Returns ------- ndarray Multi-spectral distributions wavelengths :math:`\\lambda_n`. """ return self.domain @wavelengths.setter def wavelengths(self, value): """ Setter for the **self.wavelengths** property. """ self.domain = value @property def values(self): """ Getter and setter property for the multi-spectral distributions values. Parameters ---------- value : array_like Value to set the multi-spectral distributions wavelengths values with. Returns ------- ndarray Multi-spectral distributions values. """ return self.range @values.setter def values(self, value): """ Setter for the **self.values** property. """ self.range = value @property def shape(self): """ Getter and setter property for the multi-spectral distributions shape. Returns ------- SpectralShape Multi-spectral distributions shape. Notes ----- - Multi-spectral distributions with a non-uniformly spaced independent variable have multiple intervals, in that case :attr:`colour.MultiSpectralDistributions.shape` attribute returns the *minimum* interval size. Warning ------- :attr:`colour.MultiSpectralDistributions.shape` attribute is read only. Examples -------- Shape of the multi-spectral distributions with a uniformly spaced independent variable: >>> from colour.utilities import numpy_print_options >>> data = { ... 500: (0.004900, 0.323000, 0.272000), ... 510: (0.009300, 0.503000, 0.158200), ... 520: (0.063270, 0.710000, 0.078250), ... 530: (0.165500, 0.862000, 0.042160), ... 540: (0.290400, 0.954000, 0.020300), ... 550: (0.433450, 0.994950, 0.008750), ... 560: (0.594500, 0.995000, 0.003900) ... } >>> MultiSpectralDistributions(data).shape SpectralShape(500.0, 560.0, 10.0) Shape of the multi-spectral distributions with a non-uniformly spaced independent variable: >>> data[511] = (0.00314, 0.31416, 0.03142) >>> MultiSpectralDistributions(data).shape SpectralShape(500.0, 560.0, 1.0) """ if self.signals: return first_item(self._signals.values()).shape
[docs] def interpolate(self, shape, interpolator=None, interpolator_kwargs=None, **kwargs): """ Interpolates the multi-spectral distributions in-place according to *CIE 167:2005* recommendation (if the interpolator has not been changed at instantiation time) or given interpolation arguments. The logic for choosing the interpolator class when ``interpolator`` is not given is as follows: .. code-block:: python if self.interpolator not in (SpragueInterpolator, CubicSplineInterpolator): interpolator = self.interpolator elif self.is_uniform(): interpolator = SpragueInterpolator else: interpolator = CubicSplineInterpolator The logic for choosing the interpolator keyword arguments when ``interpolator_kwargs`` is not given is as follows: .. code-block:: python if self.interpolator not in (SpragueInterpolator, CubicSplineInterpolator): interpolator_kwargs = self.interpolator_kwargs else: interpolator_kwargs = {} Parameters ---------- shape : SpectralShape, optional Spectral shape used for interpolation. interpolator : object, optional Interpolator class type to use as interpolating function. interpolator_kwargs : dict_like, optional Arguments to use when instantiating the interpolating function. Other Parameters ---------------- \\**kwargs : dict, optional Keywords arguments for deprecation management. Returns ------- MultiSpectralDistributions Interpolated multi-spectral distributions. Notes ----- - See :meth:`colour.SpectralDistribution.interpolate` method notes section. Warning ------- See :meth:`colour.SpectralDistribution.interpolate` method warning section. References ---------- :cite:`CIETC1-382005e` Examples -------- Multi-spectral distributions with a uniformly spaced independent variable uses *Sprague (1880)* interpolation: >>> from colour.utilities import numpy_print_options >>> data = { ... 500: (0.004900, 0.323000, 0.272000), ... 510: (0.009300, 0.503000, 0.158200), ... 520: (0.063270, 0.710000, 0.078250), ... 530: (0.165500, 0.862000, 0.042160), ... 540: (0.290400, 0.954000, 0.020300), ... 550: (0.433450, 0.994950, 0.008750), ... 560: (0.594500, 0.995000, 0.003900) ... } >>> msds = MultiSpectralDistributions(data) >>> with numpy_print_options(suppress=True): ... print(msds.interpolate(SpectralShape(interval=1))) ... # doctest: +ELLIPSIS [[ 500. 0.0049 ... 0.323 ... 0.272 ...] [ 501. 0.0043252... 0.3400642... 0.2599848...] [ 502. 0.0037950... 0.3572165... 0.2479849...] [ 503. 0.0033761... 0.3744030... 0.2360688...] [ 504. 0.0031397... 0.3916650... 0.2242878...] [ 505. 0.0031582... 0.4091067... 0.2126801...] [ 506. 0.0035019... 0.4268629... 0.2012748...] [ 507. 0.0042365... 0.4450668... 0.1900968...] [ 508. 0.0054192... 0.4638181... 0.1791709...] [ 509. 0.0070965... 0.4831505... 0.1685260...] [ 510. 0.0093 ... 0.503 ... 0.1582 ...] [ 511. 0.0120562... 0.5232543... 0.1482365...] [ 512. 0.0154137... 0.5439717... 0.1386625...] [ 513. 0.0193991... 0.565139 ... 0.1294993...] [ 514. 0.0240112... 0.5866255... 0.1207676...] [ 515. 0.0292289... 0.6082226... 0.1124864...] [ 516. 0.0350192... 0.6296821... 0.1046717...] [ 517. 0.0413448... 0.6507558... 0.0973361...] [ 518. 0.0481727... 0.6712346... 0.0904871...] [ 519. 0.0554816... 0.6909873... 0.0841267...] [ 520. 0.06327 ... 0.71 ... 0.07825 ...] [ 521. 0.0715642... 0.7283456... 0.0728614...] [ 522. 0.0803970... 0.7459679... 0.0680051...] [ 523. 0.0897629... 0.7628184... 0.0636823...] [ 524. 0.0996227... 0.7789004... 0.0598449...] [ 525. 0.1099142... 0.7942533... 0.0564111...] [ 526. 0.1205637... 0.8089368... 0.0532822...] [ 527. 0.1314973... 0.8230153... 0.0503588...] [ 528. 0.1426523... 0.8365417... 0.0475571...] [ 529. 0.1539887... 0.8495422... 0.0448253...] [ 530. 0.1655 ... 0.862 ... 0.04216 ...] [ 531. 0.1772055... 0.8738585... 0.0395936...] [ 532. 0.1890877... 0.8850940... 0.0371046...] [ 533. 0.2011304... 0.8957073... 0.0346733...] [ 534. 0.2133310... 0.9057092... 0.0323006...] [ 535. 0.2256968... 0.9151181... 0.0300011...] [ 536. 0.2382403... 0.9239560... 0.0277974...] [ 537. 0.2509754... 0.9322459... 0.0257131...] [ 538. 0.2639130... 0.9400080... 0.0237668...] [ 539. 0.2770569... 0.9472574... 0.0219659...] [ 540. 0.2904 ... 0.954 ... 0.0203 ...] [ 541. 0.3039194... 0.9602409... 0.0187414...] [ 542. 0.3175893... 0.9660106... 0.0172748...] [ 543. 0.3314022... 0.9713260... 0.0158947...] [ 544. 0.3453666... 0.9761850... 0.0146001...] [ 545. 0.3595019... 0.9805731... 0.0133933...] [ 546. 0.3738324... 0.9844703... 0.0122777...] [ 547. 0.3883818... 0.9878583... 0.0112562...] [ 548. 0.4031674... 0.9907270... 0.0103302...] [ 549. 0.4181943... 0.9930817... 0.0094972...] [ 550. 0.43345 ... 0.99495 ... 0.00875 ...] [ 551. 0.4489082... 0.9963738... 0.0080748...] [ 552. 0.4645599... 0.9973682... 0.0074580...] [ 553. 0.4803950... 0.9979568... 0.0068902...] [ 554. 0.4963962... 0.9981802... 0.0063660...] [ 555. 0.5125410... 0.9980910... 0.0058818...] [ 556. 0.5288034... 0.9977488... 0.0054349...] [ 557. 0.5451560... 0.9972150... 0.0050216...] [ 558. 0.5615719... 0.9965479... 0.0046357...] [ 559. 0.5780267... 0.9957974... 0.0042671...] [ 560. 0.5945 ... 0.995 ... 0.0039 ...]] Multi-spectral distributions with a non-uniformly spaced independent variable uses *Cubic Spline* interpolation: >>> data[511] = (0.00314, 0.31416, 0.03142) >>> msds = MultiSpectralDistributions(data) >>> with numpy_print_options(suppress=True): ... print(msds.interpolate(SpectralShape(interval=1))) ... # doctest: +ELLIPSIS [[ 500. 0.0049 ... 0.323 ... 0.272 ...] [ 501. 0.0300110... 0.9455153... 0.5985102...] [ 502. 0.0462136... 1.3563103... 0.8066498...] [ 503. 0.0547925... 1.5844039... 0.9126502...] [ 504. 0.0570325... 1.6588148... 0.9327429...] [ 505. 0.0542183... 1.6085619... 0.8831594...] [ 506. 0.0476346... 1.4626640... 0.7801312...] [ 507. 0.0385662... 1.2501401... 0.6398896...] [ 508. 0.0282978... 1.0000089... 0.4786663...] [ 509. 0.0181142... 0.7412892... 0.3126925...] [ 510. 0.0093 ... 0.503 ... 0.1582 ...] [ 511. 0.00314 ... 0.31416 ... 0.03142 ...] [ 512. 0.0006228... 0.1970419... -0.0551709...] [ 513. 0.0015528... 0.1469341... -0.1041165...] [ 514. 0.0054381... 0.1523785... -0.1217152...] [ 515. 0.0117869... 0.2019173... -0.1142659...] [ 516. 0.0201073... 0.2840925... -0.0880670...] [ 517. 0.0299077... 0.3874463... -0.0494174...] [ 518. 0.0406961... 0.5005208... -0.0046156...] [ 519. 0.0519808... 0.6118579... 0.0400397...] [ 520. 0.06327 ... 0.71 ... 0.07825 ...] [ 521. 0.0741690... 0.7859059... 0.1050384...] [ 522. 0.0846726... 0.8402033... 0.1207164...] [ 523. 0.0948728... 0.8759363... 0.1269173...] [ 524. 0.1048614... 0.8961496... 0.1252743...] [ 525. 0.1147305... 0.9038874... 0.1174207...] [ 526. 0.1245719... 0.9021942... 0.1049899...] [ 527. 0.1344776... 0.8941145... 0.0896151...] [ 528. 0.1445395... 0.8826926... 0.0729296...] [ 529. 0.1548497... 0.8709729... 0.0565668...] [ 530. 0.1655 ... 0.862 ... 0.04216 ...] [ 531. 0.1765618... 0.858179 ... 0.0309976...] [ 532. 0.1880244... 0.8593588... 0.0229897...] [ 533. 0.1998566... 0.8647493... 0.0177013...] [ 534. 0.2120269... 0.8735601... 0.0146975...] [ 535. 0.2245042... 0.8850011... 0.0135435...] [ 536. 0.2372572... 0.8982820... 0.0138044...] [ 537. 0.2502546... 0.9126126... 0.0150454...] [ 538. 0.2634650... 0.9272026... 0.0168315...] [ 539. 0.2768572... 0.9412618... 0.0187280...] [ 540. 0.2904 ... 0.954 ... 0.0203 ...] [ 541. 0.3040682... 0.9647869... 0.0211987...] [ 542. 0.3178617... 0.9736329... 0.0214207...] [ 543. 0.3317865... 0.9807080... 0.0210486...] [ 544. 0.3458489... 0.9861825... 0.0201650...] [ 545. 0.3600548... 0.9902267... 0.0188525...] [ 546. 0.3744103... 0.9930107... 0.0171939...] [ 547. 0.3889215... 0.9947048... 0.0152716...] [ 548. 0.4035944... 0.9954792... 0.0131685...] [ 549. 0.4184352... 0.9955042... 0.0109670...] [ 550. 0.43345 ... 0.99495 ... 0.00875 ...] [ 551. 0.4486447... 0.9939867... 0.0065999...] [ 552. 0.4640255... 0.9927847... 0.0045994...] [ 553. 0.4795984... 0.9915141... 0.0028313...] [ 554. 0.4953696... 0.9903452... 0.0013781...] [ 555. 0.5113451... 0.9894483... 0.0003224...] [ 556. 0.5275310... 0.9889934... -0.0002530...] [ 557. 0.5439334... 0.9891509... -0.0002656...] [ 558. 0.5605583... 0.9900910... 0.0003672...] [ 559. 0.5774118... 0.9919840... 0.0017282...] [ 560. 0.5945 ... 0.995 ... 0.0039 ...]] """ interpolator_kwargs = handle_arguments_deprecation({ 'ArgumentRenamed': [['interpolator_args', 'interpolator_kwargs']], }, **kwargs).get('interpolator_kwargs', interpolator_kwargs) for signal in self.signals.values(): signal.interpolate(shape, interpolator, interpolator_kwargs) return self
[docs] def extrapolate(self, shape, extrapolator=None, extrapolator_kwargs=None, **kwargs): """ Extrapolates the multi-spectral distributions in-place according to *CIE 15:2004* and *CIE 167:2005* recommendations or given extrapolation arguments. Parameters ---------- shape : SpectralShape Spectral shape used for extrapolation. extrapolator : object, optional Extrapolator class type to use as extrapolating function. extrapolator_kwargs : dict_like, optional Arguments to use when instantiating the extrapolating function. Other Parameters ---------------- \\**kwargs : dict, optional Keywords arguments for deprecation management. Returns ------- MultiSpectralDistributions Extrapolated multi-spectral distributions. References ---------- :cite:`CIETC1-382005g`, :cite:`CIETC1-482004l` Examples -------- >>> from colour.utilities import numpy_print_options >>> data = { ... 500: (0.004900, 0.323000, 0.272000), ... 510: (0.009300, 0.503000, 0.158200), ... 520: (0.063270, 0.710000, 0.078250), ... 530: (0.165500, 0.862000, 0.042160), ... 540: (0.290400, 0.954000, 0.020300), ... 550: (0.433450, 0.994950, 0.008750), ... 560: (0.594500, 0.995000, 0.003900) ... } >>> msds = MultiSpectralDistributions(data) >>> msds.extrapolate(SpectralShape(400, 700)).shape SpectralShape(400.0, 700.0, 10.0) >>> with numpy_print_options(suppress=True): ... print(msds) [[ 400. 0.0049 0.323 0.272 ] [ 410. 0.0049 0.323 0.272 ] [ 420. 0.0049 0.323 0.272 ] [ 430. 0.0049 0.323 0.272 ] [ 440. 0.0049 0.323 0.272 ] [ 450. 0.0049 0.323 0.272 ] [ 460. 0.0049 0.323 0.272 ] [ 470. 0.0049 0.323 0.272 ] [ 480. 0.0049 0.323 0.272 ] [ 490. 0.0049 0.323 0.272 ] [ 500. 0.0049 0.323 0.272 ] [ 510. 0.0093 0.503 0.1582 ] [ 520. 0.06327 0.71 0.07825] [ 530. 0.1655 0.862 0.04216] [ 540. 0.2904 0.954 0.0203 ] [ 550. 0.43345 0.99495 0.00875] [ 560. 0.5945 0.995 0.0039 ] [ 570. 0.5945 0.995 0.0039 ] [ 580. 0.5945 0.995 0.0039 ] [ 590. 0.5945 0.995 0.0039 ] [ 600. 0.5945 0.995 0.0039 ] [ 610. 0.5945 0.995 0.0039 ] [ 620. 0.5945 0.995 0.0039 ] [ 630. 0.5945 0.995 0.0039 ] [ 640. 0.5945 0.995 0.0039 ] [ 650. 0.5945 0.995 0.0039 ] [ 660. 0.5945 0.995 0.0039 ] [ 670. 0.5945 0.995 0.0039 ] [ 680. 0.5945 0.995 0.0039 ] [ 690. 0.5945 0.995 0.0039 ] [ 700. 0.5945 0.995 0.0039 ]] """ extrapolator_kwargs = handle_arguments_deprecation({ 'ArgumentRenamed': [['extrapolator_args', 'extrapolator_kwargs']], }, **kwargs).get('extrapolator_kwargs', extrapolator_kwargs) for signal in self.signals.values(): signal.extrapolate(shape, extrapolator, extrapolator_kwargs) return self
[docs] def align(self, shape, interpolator=None, interpolator_kwargs=None, extrapolator=None, extrapolator_kwargs=None, **kwargs): """ Aligns the multi-spectral distributions in-place to given spectral shape: Interpolates first then extrapolates to fit the given range. Interpolation is performed according to *CIE 167:2005* recommendation (if the interpolator has not been changed at instantiation time) or given interpolation arguments. The logic for choosing the interpolator class when ``interpolator`` is not given is as follows: .. code-block:: python if self.interpolator not in (SpragueInterpolator, CubicSplineInterpolator): interpolator = self.interpolator elif self.is_uniform(): interpolator = SpragueInterpolator else: interpolator = CubicSplineInterpolator The logic for choosing the interpolator keyword arguments when ``interpolator_kwargs`` is not given is as follows: .. code-block:: python if self.interpolator not in (SpragueInterpolator, CubicSplineInterpolator): interpolator_kwargs = self.interpolator_kwargs else: interpolator_kwargs = {} Parameters ---------- shape : SpectralShape Spectral shape used for alignment. interpolator : object, optional Interpolator class type to use as interpolating function. interpolator_kwargs : dict_like, optional Arguments to use when instantiating the interpolating function. extrapolator : object, optional Extrapolator class type to use as extrapolating function. extrapolator_kwargs : dict_like, optional Arguments to use when instantiating the extrapolating function. Other Parameters ---------------- \\**kwargs : dict, optional Keywords arguments for deprecation management. Returns ------- MultiSpectralDistributions Aligned multi-spectral distributions. Examples -------- >>> from colour.utilities import numpy_print_options >>> data = { ... 500: (0.004900, 0.323000, 0.272000), ... 510: (0.009300, 0.503000, 0.158200), ... 520: (0.063270, 0.710000, 0.078250), ... 530: (0.165500, 0.862000, 0.042160), ... 540: (0.290400, 0.954000, 0.020300), ... 550: (0.433450, 0.994950, 0.008750), ... 560: (0.594500, 0.995000, 0.003900) ... } >>> msds = MultiSpectralDistributions(data) >>> with numpy_print_options(suppress=True): ... print(msds.align(SpectralShape(505, 565, 1))) ... # doctest: +ELLIPSIS [[ 505. 0.0031582... 0.4091067... 0.2126801...] [ 506. 0.0035019... 0.4268629... 0.2012748...] [ 507. 0.0042365... 0.4450668... 0.1900968...] [ 508. 0.0054192... 0.4638181... 0.1791709...] [ 509. 0.0070965... 0.4831505... 0.1685260...] [ 510. 0.0093 ... 0.503 ... 0.1582 ...] [ 511. 0.0120562... 0.5232543... 0.1482365...] [ 512. 0.0154137... 0.5439717... 0.1386625...] [ 513. 0.0193991... 0.565139 ... 0.1294993...] [ 514. 0.0240112... 0.5866255... 0.1207676...] [ 515. 0.0292289... 0.6082226... 0.1124864...] [ 516. 0.0350192... 0.6296821... 0.1046717...] [ 517. 0.0413448... 0.6507558... 0.0973361...] [ 518. 0.0481727... 0.6712346... 0.0904871...] [ 519. 0.0554816... 0.6909873... 0.0841267...] [ 520. 0.06327 ... 0.71 ... 0.07825 ...] [ 521. 0.0715642... 0.7283456... 0.0728614...] [ 522. 0.0803970... 0.7459679... 0.0680051...] [ 523. 0.0897629... 0.7628184... 0.0636823...] [ 524. 0.0996227... 0.7789004... 0.0598449...] [ 525. 0.1099142... 0.7942533... 0.0564111...] [ 526. 0.1205637... 0.8089368... 0.0532822...] [ 527. 0.1314973... 0.8230153... 0.0503588...] [ 528. 0.1426523... 0.8365417... 0.0475571...] [ 529. 0.1539887... 0.8495422... 0.0448253...] [ 530. 0.1655 ... 0.862 ... 0.04216 ...] [ 531. 0.1772055... 0.8738585... 0.0395936...] [ 532. 0.1890877... 0.8850940... 0.0371046...] [ 533. 0.2011304... 0.8957073... 0.0346733...] [ 534. 0.2133310... 0.9057092... 0.0323006...] [ 535. 0.2256968... 0.9151181... 0.0300011...] [ 536. 0.2382403... 0.9239560... 0.0277974...] [ 537. 0.2509754... 0.9322459... 0.0257131...] [ 538. 0.2639130... 0.9400080... 0.0237668...] [ 539. 0.2770569... 0.9472574... 0.0219659...] [ 540. 0.2904 ... 0.954 ... 0.0203 ...] [ 541. 0.3039194... 0.9602409... 0.0187414...] [ 542. 0.3175893... 0.9660106... 0.0172748...] [ 543. 0.3314022... 0.9713260... 0.0158947...] [ 544. 0.3453666... 0.9761850... 0.0146001...] [ 545. 0.3595019... 0.9805731... 0.0133933...] [ 546. 0.3738324... 0.9844703... 0.0122777...] [ 547. 0.3883818... 0.9878583... 0.0112562...] [ 548. 0.4031674... 0.9907270... 0.0103302...] [ 549. 0.4181943... 0.9930817... 0.0094972...] [ 550. 0.43345 ... 0.99495 ... 0.00875 ...] [ 551. 0.4489082... 0.9963738... 0.0080748...] [ 552. 0.4645599... 0.9973682... 0.0074580...] [ 553. 0.4803950... 0.9979568... 0.0068902...] [ 554. 0.4963962... 0.9981802... 0.0063660...] [ 555. 0.5125410... 0.9980910... 0.0058818...] [ 556. 0.5288034... 0.9977488... 0.0054349...] [ 557. 0.5451560... 0.9972150... 0.0050216...] [ 558. 0.5615719... 0.9965479... 0.0046357...] [ 559. 0.5780267... 0.9957974... 0.0042671...] [ 560. 0.5945 ... 0.995 ... 0.0039 ...] [ 561. 0.5945 ... 0.995 ... 0.0039 ...] [ 562. 0.5945 ... 0.995 ... 0.0039 ...] [ 563. 0.5945 ... 0.995 ... 0.0039 ...] [ 564. 0.5945 ... 0.995 ... 0.0039 ...] [ 565. 0.5945 ... 0.995 ... 0.0039 ...]] """ interpolator_kwargs = handle_arguments_deprecation({ 'ArgumentRenamed': [['interpolator_args', 'interpolator_kwargs']], }, **kwargs).get('interpolator_kwargs', interpolator_kwargs) extrapolator_kwargs = handle_arguments_deprecation({ 'ArgumentRenamed': [['extrapolator_args', 'extrapolator_kwargs']], }, **kwargs).get('extrapolator_kwargs', extrapolator_kwargs) for signal in self.signals.values(): signal.align(shape, interpolator, interpolator_kwargs, extrapolator, extrapolator_kwargs) return self
[docs] def trim(self, shape): """ Trims the multi-spectral distributions wavelengths to given shape. Parameters ---------- shape : SpectralShape Spectral shape used for trimming. Returns ------- MultiSpectralDistributions Trimmed multi-spectral distributions. Examples -------- >>> from colour.utilities import numpy_print_options >>> data = { ... 500: (0.004900, 0.323000, 0.272000), ... 510: (0.009300, 0.503000, 0.158200), ... 520: (0.063270, 0.710000, 0.078250), ... 530: (0.165500, 0.862000, 0.042160), ... 540: (0.290400, 0.954000, 0.020300), ... 550: (0.433450, 0.994950, 0.008750), ... 560: (0.594500, 0.995000, 0.003900) ... } >>> msds = MultiSpectralDistributions(data) >>> msds = msds.interpolate(SpectralShape(interval=1)) >>> with numpy_print_options(suppress=True): ... print(msds.trim(SpectralShape(520, 580, 5))) ... # doctest: +ELLIPSIS [[ 520. 0.06327 ... 0.71 ... 0.07825 ...] [ 521. 0.0715642... 0.7283456... 0.0728614...] [ 522. 0.0803970... 0.7459679... 0.0680051...] [ 523. 0.0897629... 0.7628184... 0.0636823...] [ 524. 0.0996227... 0.7789004... 0.0598449...] [ 525. 0.1099142... 0.7942533... 0.0564111...] [ 526. 0.1205637... 0.8089368... 0.0532822...] [ 527. 0.1314973... 0.8230153... 0.0503588...] [ 528. 0.1426523... 0.8365417... 0.0475571...] [ 529. 0.1539887... 0.8495422... 0.0448253...] [ 530. 0.1655 ... 0.862 ... 0.04216 ...] [ 531. 0.1772055... 0.8738585... 0.0395936...] [ 532. 0.1890877... 0.8850940... 0.0371046...] [ 533. 0.2011304... 0.8957073... 0.0346733...] [ 534. 0.2133310... 0.9057092... 0.0323006...] [ 535. 0.2256968... 0.9151181... 0.0300011...] [ 536. 0.2382403... 0.9239560... 0.0277974...] [ 537. 0.2509754... 0.9322459... 0.0257131...] [ 538. 0.2639130... 0.9400080... 0.0237668...] [ 539. 0.2770569... 0.9472574... 0.0219659...] [ 540. 0.2904 ... 0.954 ... 0.0203 ...] [ 541. 0.3039194... 0.9602409... 0.0187414...] [ 542. 0.3175893... 0.9660106... 0.0172748...] [ 543. 0.3314022... 0.9713260... 0.0158947...] [ 544. 0.3453666... 0.9761850... 0.0146001...] [ 545. 0.3595019... 0.9805731... 0.0133933...] [ 546. 0.3738324... 0.9844703... 0.0122777...] [ 547. 0.3883818... 0.9878583... 0.0112562...] [ 548. 0.4031674... 0.9907270... 0.0103302...] [ 549. 0.4181943... 0.9930817... 0.0094972...] [ 550. 0.43345 ... 0.99495 ... 0.00875 ...] [ 551. 0.4489082... 0.9963738... 0.0080748...] [ 552. 0.4645599... 0.9973682... 0.0074580...] [ 553. 0.4803950... 0.9979568... 0.0068902...] [ 554. 0.4963962... 0.9981802... 0.0063660...] [ 555. 0.5125410... 0.9980910... 0.0058818...] [ 556. 0.5288034... 0.9977488... 0.0054349...] [ 557. 0.5451560... 0.9972150... 0.0050216...] [ 558. 0.5615719... 0.9965479... 0.0046357...] [ 559. 0.5780267... 0.9957974... 0.0042671...] [ 560. 0.5945 ... 0.995 ... 0.0039 ...]] """ for signal in self.signals.values(): signal.trim(shape) return self
[docs] def normalise(self, factor=1): """ Normalises the multi-spectral distributions with given normalization factor. Parameters ---------- factor : numeric, optional Normalization factor Returns ------- MultiSpectralDistributions Normalised multi- spectral distribution. Notes ----- - The implementation uses the maximum value for each :class:`colour.SpectralDistribution` class instances. Examples -------- >>> from colour.utilities import numpy_print_options >>> data = { ... 500: (0.004900, 0.323000, 0.272000), ... 510: (0.009300, 0.503000, 0.158200), ... 520: (0.063270, 0.710000, 0.078250), ... 530: (0.165500, 0.862000, 0.042160), ... 540: (0.290400, 0.954000, 0.020300), ... 550: (0.433450, 0.994950, 0.008750), ... 560: (0.594500, 0.995000, 0.003900) ... } >>> msds = MultiSpectralDistributions(data) >>> with numpy_print_options(suppress=True): ... print(msds.normalise()) # doctest: +ELLIPSIS [[ 500. 0.0082422... 0.3246231... 1. ...] [ 510. 0.0156434... 0.5055276... 0.5816176...] [ 520. 0.1064255... 0.7135678... 0.2876838...] [ 530. 0.2783852... 0.8663316... 0.155 ...] [ 540. 0.4884777... 0.9587939... 0.0746323...] [ 550. 0.7291000... 0.9999497... 0.0321691...] [ 560. 1. ... 1. ... 0.0143382...]] """ for signal in self.signals.values(): signal.normalise(factor) return self
[docs] def to_sds(self): """ Converts the multi-spectral distributions to a list of spectral distributions and update their name and strict name using the labels and strict labels. Returns ------- list List of spectral distributions. Examples -------- >>> from colour.utilities import numpy_print_options >>> data = { ... 500: (0.004900, 0.323000, 0.272000), ... 510: (0.009300, 0.503000, 0.158200), ... 520: (0.063270, 0.710000, 0.078250), ... 530: (0.165500, 0.862000, 0.042160), ... 540: (0.290400, 0.954000, 0.020300), ... 550: (0.433450, 0.994950, 0.008750), ... 560: (0.594500, 0.995000, 0.003900) ... } >>> msds = MultiSpectralDistributions(data) >>> with numpy_print_options(suppress=True): ... for sd in msds.to_sds(): ... print(sd) # doctest: +ELLIPSIS [[ 500. 0.0049 ...] [ 510. 0.0093 ...] [ 520. 0.06327...] [ 530. 0.1655 ...] [ 540. 0.2904 ...] [ 550. 0.43345...] [ 560. 0.5945 ...]] [[ 500. 0.323 ...] [ 510. 0.503 ...] [ 520. 0.71 ...] [ 530. 0.862 ...] [ 540. 0.954 ...] [ 550. 0.99495...] [ 560. 0.995 ...]] [[ 500. 0.272 ...] [ 510. 0.1582 ...] [ 520. 0.07825...] [ 530. 0.04216...] [ 540. 0.0203 ...] [ 550. 0.00875...] [ 560. 0.0039 ...]] """ sds = [] for i, signal in enumerate(self.signals.values()): signal = signal.copy() signal.name = '{0} - {1}'.format(self.labels[i], signal.name) signal.strict_name = '{0} - {1}'.format(self.strict_labels[i], signal.strict_name) sds.append(signal) return sds
# ------------------------------------------------------------------------# # --- API Changes and Deprecation Management ---# # ------------------------------------------------------------------------# @property def title(self): # pragma: no cover # Docstrings are omitted for documentation purposes. usage_warning( str( ObjectRenamed('TriSpectralPowerDistribution.title', 'SpectralDistribution.strict_name'))) return self.strict_name @title.setter def title(self, value): # pragma: no cover # Docstrings are omitted for documentation purposes. usage_warning( str( ObjectRenamed('TriSpectralPowerDistribution.title', 'SpectralDistribution.strict_name'))) self.strict_name = value @property def data(self): # pragma: no cover # Docstrings are omitted for documentation purposes. raise AttributeError( str(ObjectRemoved('MultiSpectralDistributions.data'))) @property def items(self): # pragma: no cover # Docstrings are omitted for documentation purposes. raise AttributeError( str(ObjectRemoved('MultiSpectralDistributions.items'))) @property def mapping(self): # pragma: no cover # Docstrings are omitted for documentation purposes. raise AttributeError( str(ObjectRemoved('MultiSpectralDistributions.mapping'))) @property def x(self): # pragma: no cover # Docstrings are omitted for documentation purposes. raise AttributeError( str(ObjectRemoved('MultiSpectralDistributions.x'))) @property def y(self): # pragma: no cover # Docstrings are omitted for documentation purposes. raise AttributeError( str(ObjectRemoved('MultiSpectralDistributions.y'))) @property def z(self): # pragma: no cover # Docstrings are omitted for documentation purposes. raise AttributeError( str(ObjectRemoved('MultiSpectralDistributions.z'))) def __iter__(self): # pragma: no cover # Docstrings are omitted for documentation purposes. raise AttributeError( str(ObjectRemoved('MultiSpectralDistributions.__iter__'))) def get(self): # pragma: no cover # Docstrings are omitted for documentation purposes. raise AttributeError( str(ObjectRemoved('MultiSpectralDistributions.get'))) def zeros(self): # pragma: no cover # Docstrings are omitted for documentation purposes. raise AttributeError( str(ObjectRemoved('MultiSpectralDistributions.zeros'))) def trim_wavelengths(self, shape): # pragma: no cover # Docstrings are omitted for documentation purposes. usage_warning( str( ObjectRenamed('TriSpectralPowerDistribution.trim_wavelengths', 'MultiSpectralDistributions.trim'))) return self.trim(shape) def clone(self): # pragma: no cover # Docstrings are omitted for documentation purposes. usage_warning( str( ObjectRenamed('TriSpectralPowerDistribution.clone', 'MultiSpectralDistributions.copy'))) return self.copy()
[docs]def sds_and_multi_sds_to_sds(sds): """ Converts given spectral and multi-spectral distributions to a flat list of spectral distributions. Parameters ---------- sds : array_like Spectral and multi-spectral distributions to convert to a flat list of spectral distributions. Returns ------- list Flat list of spectral distributions. Examples -------- >>> data = { ... 500: 0.0651, ... 520: 0.0705, ... 540: 0.0772, ... 560: 0.0870, ... 580: 0.1128, ... 600: 0.1360 ... } >>> sd_1 = SpectralDistribution(data) >>> sd_2 = SpectralDistribution(data) >>> data = { ... 500: (0.004900, 0.323000, 0.272000), ... 510: (0.009300, 0.503000, 0.158200), ... 520: (0.063270, 0.710000, 0.078250), ... 530: (0.165500, 0.862000, 0.042160), ... 540: (0.290400, 0.954000, 0.020300), ... 550: (0.433450, 0.994950, 0.008750), ... 560: (0.594500, 0.995000, 0.003900) ... } >>> multi_sds_1 = MultiSpectralDistributions(data) >>> multi_sds_2 = MultiSpectralDistributions(data) >>> len(sds_and_multi_sds_to_sds([sd_1, sd_2, multi_sds_1, multi_sds_2])) 8 """ if not len(sds): return if isinstance(sds, MultiSpectralDistributions): sds = sds.to_sds() else: sds = list(sds) for i, sd in enumerate(sds[:]): if isinstance(sd, MultiSpectralDistributions): sds.remove(sd) sds[i:i] = sd.to_sds() return sds
[docs]def sds_and_multi_sds_to_multi_sds(sds): """ Converts given spectral and multi-spectral distributions to multi-spectral distributions. The spectral and multi-spectral distributions will be aligned to the intersection of their spectral shapes. Parameters ---------- sds : array_like Spectral and multi-spectral distributions to convert to multi-spectral distributions. Returns ------- MultiSpectralDistributions Multi-spectral distributions. Examples -------- >>> data = { ... 500: 0.0651, ... 520: 0.0705, ... 540: 0.0772, ... 560: 0.0870, ... 580: 0.1128, ... 600: 0.1360 ... } >>> sd_1 = SpectralDistribution(data) >>> sd_2 = SpectralDistribution(data) >>> data = { ... 500: (0.004900, 0.323000, 0.272000), ... 510: (0.009300, 0.503000, 0.158200), ... 520: (0.063270, 0.710000, 0.078250), ... 530: (0.165500, 0.862000, 0.042160), ... 540: (0.290400, 0.954000, 0.020300), ... 550: (0.433450, 0.994950, 0.008750), ... 560: (0.594500, 0.995000, 0.003900) ... } >>> multi_sds_1 = MultiSpectralDistributions(data) >>> multi_sds_2 = MultiSpectralDistributions(data) >>> from colour.utilities import numpy_print_options >>> with numpy_print_options(suppress=True, linewidth=160): ... sds_and_multi_sds_to_multi_sds( # doctest: +SKIP ... [sd_1, sd_2, multi_sds_1, multi_sds_2]) MultiSpectralDistributions([[ 500. , 0.0651 ...,\ 0.0651 ..., 0.0049 ..., 0.323 ..., 0.272 ...,\ 0.0049 ..., 0.323 ..., 0.272 ...], [ 510. , 0.0676692...,\ 0.0676692..., 0.0093 ..., 0.503 ..., 0.1582 ...,\ 0.0093 ..., 0.503 ..., 0.1582 ...], [ 520. , 0.0705 ...,\ 0.0705 ..., 0.06327 ..., 0.71 ..., 0.07825 ...,\ 0.06327 ..., 0.71 ..., 0.07825 ...], [ 530. , 0.0737808...,\ 0.0737808..., 0.1655 ..., 0.862 ..., 0.04216 ...,\ 0.1655 ..., 0.862 ..., 0.04216 ...], [ 540. , 0.0772 ...,\ 0.0772 ..., 0.2904 ..., 0.954 ..., 0.0203 ...,\ 0.2904 ..., 0.954 ..., 0.0203 ...], [ 550. , 0.0806671...,\ 0.0806671..., 0.43345 ..., 0.99495 ..., 0.00875 ...,\ 0.43345 ..., 0.99495 ..., 0.00875 ...], [ 560. , 0.087 ...,\ 0.087 ..., 0.5945 ..., 0.995 ..., 0.0039 ...,\ 0.5945 ..., 0.995 ..., 0.0039 ...]], labels=['SpectralDistribution (...)', \ 'SpectralDistribution (...)', '0 - SpectralDistribution (...)', \ '1 - SpectralDistribution (...)', '2 - SpectralDistribution (...)', \ '0 - SpectralDistribution (...)', '1 - SpectralDistribution (...)', \ '2 - SpectralDistribution (...)'], interpolator=SpragueInterpolator, interpolator_kwargs={}, extrapolator=Extrapolator, extrapolator_kwargs={...}) """ if not len(sds): return if isinstance(sds, MultiSpectralDistributions): msds = sds elif len(sds) == 1 and isinstance(sds[0], MultiSpectralDistributions): msds = sds[0] else: sds_u = [] shapes = [] for sd in sds: if isinstance(sd, MultiSpectralDistributions): sds_m = sds_and_multi_sds_to_sds(sd) sds_u.extend(sds_m) shapes.extend([sd_m.shape for sd_m in sds_m]) else: sds_u.append(sd) shapes.append(sd.shape) shapes = tuple(set(shapes)) shape = SpectralShape( max([shape.start for shape in shapes]), min([shape.end for shape in shapes]), min([shape.interval for shape in shapes])) values = [] labels = [] strict_labels = [] for sd_u in sds_u: sd_u.align(shape) values.append(sd_u.values) labels.append(sd_u.name) strict_labels.append(sd_u.strict_name) msds = MultiSpectralDistributions( tstack(values), shape.range(), labels, strict_labels=strict_labels) return msds