colour Package

Sub-Packages

Module Contents

Colour

Colour is a Python colour science package implementing a comprehensive number of colour theory transformations and algorithms.

Subpackages

  • adaptation: Chromatic adaptation models and transformations.
  • algebra: Algebra utilities.
  • appearance: Colour appearance models.
  • biochemistry: Biochemistry computations.
  • characterisation: Colour fitting and camera characterisation.
  • colorimetry: Core objects for colour computations.
  • constants: CIE and CODATA constants.
  • corresponding: Corresponding colour chromaticities computations.
  • difference: Colour difference computations.
  • examples: Examples for the sub-packages.
  • io: Input / output objects for reading and writing data.
  • models: Colour models.
  • notation: Colour notation systems.
  • phenomenons: Computation of various optical phenomenons.
  • plotting: Diagrams, figures, etc...
  • quality: Colour quality computation.
  • recovery: Reflectance recovery.
  • temperature: Colour temperature and correlated colour temperature computation.
  • utilities: Various utilities and data structures.
  • volume: Colourspace volumes computation and optimal colour stimuli.
colour.handle_numpy_errors(**kwargs)[source]

Decorator for handling Numpy errors.

Other Parameters:
 **kwargs (dict, optional) – Keywords arguments.
Returns:
Return type:object

References

[1]Kienzle, P., Patel, N., & Krycka, J. (2011). refl1d.numpyerrors - Refl1D v0.6.19 documentation. Retrieved January 30, 2015, from http://www.reflectometry.org/danse/docs/refl1d/_modules/refl1d/numpyerrors.html

Examples

>>> import numpy
>>> @handle_numpy_errors(all='ignore')
... def f():
...     1 / numpy.zeros(3)
>>> f()
colour.ignore_numpy_errors(function)

Wrapper for given function.

colour.raise_numpy_errors(function)

Wrapper for given function.

colour.print_numpy_errors(function)

Wrapper for given function.

colour.warn_numpy_errors(function)

Wrapper for given function.

colour.ignore_python_warnings(function)[source]

Decorator for ignoring Python warnings.

Parameters:function (object) – Function to decorate.
Returns:
Return type:object

Examples

>>> @ignore_python_warnings
... def f():
...     warnings.warn('This is an ignored warning!')
>>> f()
colour.batch(iterable, k=3)[source]

Returns a batch generator from given iterable.

Parameters:
  • iterable (iterable) – Iterable to create batches from.
  • k (integer) – Batches size.
Returns:

Is string_like variable.

Return type:

bool

Examples

>>> batch(tuple(range(10)))  
<generator object batch at 0x...>
colour.is_openimageio_installed(raise_exception=False)[source]

Returns if OpenImageIO is installed and available.

Parameters:raise_exception (bool) – Raise exception if OpenImageIO is unavailable.
Returns:Is OpenImageIO installed.
Return type:bool
Raises:ImportError – If OpenImageIO is not installed.
colour.is_iterable(a)[source]

Returns if given \(a\) variable is iterable.

Parameters:a (object) – Variable to check the iterability.
Returns:\(a\) variable iterability.
Return type:bool

Examples

>>> is_iterable([1, 2, 3])
True
>>> is_iterable(1)
False
colour.is_string(a)[source]

Returns if given \(a\) variable is a string like variable.

Parameters:a (object) – Data to test.
Returns:Is \(a\) variable a string like variable.
Return type:bool

Examples

>>> is_string('I`m a string!')
True
>>> is_string(['I`m a string!'])
False
colour.is_numeric(a)[source]

Returns if given \(a\) variable is a number.

Parameters:a (object) – Variable to check.
Returns:Is \(a\) variable a number.
Return type:bool

See also

is_integer()

Examples

>>> is_numeric(1)
True
>>> is_numeric((1,))
False
colour.is_integer(a)[source]

Returns if given \(a\) variable is an integer under given threshold.

Parameters:a (object) – Variable to check.
Returns:Is \(a\) variable an integer.
Return type:bool

Notes

  • The determination threshold is defined by the colour.algebra.common.INTEGER_THRESHOLD attribute.

See also

is_numeric()

Examples

>>> is_integer(1)
True
>>> is_integer(1.01)
False
colour.filter_kwargs(function, **kwargs)[source]

Filters keyword arguments incompatible with the given function signature.

Parameters:function (callable) – Callable to filter the incompatible keyword arguments.
Other Parameters:
 **kwargs (dict, optional) – Keywords arguments.
Returns:Filtered keyword arguments.
Return type:dict

Examples

>>> def fn_a(a):
...     return a
>>> def fn_b(a, b=0):
...     return a, b
>>> def fn_c(a, b=0, c=0):
...     return a, b, c
>>> fn_a(1, **filter_kwargs(fn_a, b=2, c=3))
1
>>> fn_b(1, **filter_kwargs(fn_b, b=2, c=3))
(1, 2)
>>> fn_c(1, **filter_kwargs(fn_c, b=2, c=3))
(1, 2, 3)
colour.as_numeric(a, type_=<type 'numpy.float64'>)[source]

Converts given \(a\) variable to numeric. In the event where \(a\) cannot be converted, it is passed as is.

Parameters:
  • a (object) – Variable to convert.
  • type (object) – Type to use for conversion.
Returns:

\(a\) variable converted to numeric.

Return type:

ndarray

See also

as_stack(), as_shape(), auto_axis()

Examples

>>> as_numeric(np.array([1]))
1.0
>>> as_numeric(np.arange(10))
array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.])
colour.as_namedtuple(a, named_tuple)[source]

Converts given \(a\) variable to given namedtuple class instance.

\(a\) can be either a Numpy structured array, a namedtuple, a mapping, or an array_like object. The definition will attempt to convert it to given namedtuple.

Parameters:
  • a (object) – Variable to convert.
  • named_tuple (namedtuple) – namedtuple class.
Returns:

math:a variable converted to namedtuple.

Return type:

namedtuple

Examples

>>> from collections import namedtuple
>>> a_a = 1
>>> a_b = 2
>>> a_c = 3
>>> NamedTuple = namedtuple('NamedTuple', 'a b c')
>>> as_namedtuple(NamedTuple(a=1, b=2, c=3), NamedTuple)
NamedTuple(a=1, b=2, c=3)
>>> as_namedtuple({'a': a_a, 'b': a_b, 'c': a_c}, NamedTuple)
NamedTuple(a=1, b=2, c=3)
>>> as_namedtuple([a_a, a_b, a_c], NamedTuple)
NamedTuple(a=1, b=2, c=3)
colour.closest(a, b)[source]

Returns closest \(a\) variable element to reference \(b\) variable.

Parameters:
  • a (array_like) – Variable to search for the closest element.
  • b (numeric) – Reference variable.
Returns:

Closest \(a\) variable element.

Return type:

numeric

Examples

>>> a = np.array([24.31357115,
...               63.62396289,
...               55.71528816,
...               62.70988028,
...               46.84480573,
...               25.40026416])
>>> closest(a, 63)
62.70988028
colour.normalise_maximum(a, axis=None, factor=1, clip=True)[source]

Normalises given array_like \(a\) variable values by \(a\) variable maximum value and optionally clip them between.

Parameters:
  • a (array_like) – \(a\) variable to normalise.
  • axis (numeric, optional) – Normalization axis.
  • factor (numeric, optional) – Normalization factor.
  • clip (bool, optional) – Clip values between in domain [0, ‘factor’].
Returns:

Maximum normalised \(a\) variable.

Return type:

ndarray

Examples

>>> a = np.array([0.48222001, 0.31654775, 0.22070353])
>>> normalise_maximum(a)  
array([ 1.        ,  0.6564384...,  0.4576822...])
colour.interval(distribution)[source]

Returns the interval size of given distribution.

Parameters:distribution (array_like) – Distribution to retrieve the interval.
Returns:Distribution interval.
Return type:ndarray

Examples

Uniformly spaced variable:

>>> y = np.array([1, 2, 3, 4, 5])
>>> interval(y)
array([1])

Non-uniformly spaced variable:

>>> y = np.array([1, 2, 3, 4, 8])
>>> interval(y)
array([1, 4])
colour.is_uniform(distribution)[source]

Returns if given distribution is uniform.

Parameters:distribution (array_like) – Distribution to check for uniformity.
Returns:Is distribution uniform.
Return type:bool

Examples

Uniformly spaced variable:

>>> a = np.array([1, 2, 3, 4, 5])
>>> is_uniform(a)
True

Non-uniformly spaced variable:

>>> a = np.array([1, 2, 3.1415, 4, 5])
>>> is_uniform(a)
False
colour.in_array(a, b, tolerance=2.2204460492503131e-16)[source]

Tests whether each element of an array is also present in a second array within given tolerance.

Parameters:
  • a (array_like) – Array to test the elements from.
  • b (array_like) – The values against which to test each value of array a.
  • tolerance (numeric, optional) – Tolerance value.
Returns:

A boolean array with a shape describing whether an element of a is present in b within given tolerance.

Return type:

ndarray

References

[1]Yorke, R. (2014). Python: Change format of np.array or allow tolerance in in1d function. Retrieved March 27, 2015, from http://stackoverflow.com/a/23521245/931625

Examples

>>> a = np.array([0.50, 0.60])
>>> b = np.linspace(0, 10, 101)
>>> np.in1d(a, b)
array([ True, False], dtype=bool)
>>> in_array(a, b)
array([ True,  True], dtype=bool)
colour.tstack(a)[source]

Stacks arrays in sequence along the last axis (tail).

Rebuilds arrays divided by tsplit().

Parameters:a (array_like) – Array to perform the stacking.
Returns:
Return type:ndarray

See also

tsplit()

Examples

>>> a = 0
>>> tstack((a, a, a))
array([0, 0, 0])
>>> a = np.arange(0, 6)
>>> tstack((a, a, a))
array([[0, 0, 0],
       [1, 1, 1],
       [2, 2, 2],
       [3, 3, 3],
       [4, 4, 4],
       [5, 5, 5]])
>>> a = np.reshape(a, (1, 6))
>>> tstack((a, a, a))
array([[[0, 0, 0],
        [1, 1, 1],
        [2, 2, 2],
        [3, 3, 3],
        [4, 4, 4],
        [5, 5, 5]]])
>>> a = np.reshape(a, (1, 1, 6))
>>> tstack((a, a, a))
array([[[[0, 0, 0],
         [1, 1, 1],
         [2, 2, 2],
         [3, 3, 3],
         [4, 4, 4],
         [5, 5, 5]]]])
colour.tsplit(a)[source]

Splits arrays in sequence along the last axis (tail).

Parameters:a (array_like) – Array to perform the splitting.
Returns:
Return type:ndarray

See also

tstack()

Examples

>>> a = np.array([0, 0, 0])
>>> tsplit(a)
array([0, 0, 0])
>>> a = np.array([[0, 0, 0],
...               [1, 1, 1],
...               [2, 2, 2],
...               [3, 3, 3],
...               [4, 4, 4],
...               [5, 5, 5]])
>>> tsplit(a)
array([[0, 1, 2, 3, 4, 5],
       [0, 1, 2, 3, 4, 5],
       [0, 1, 2, 3, 4, 5]])
>>> a = np.array([[[0, 0, 0],
...                [1, 1, 1],
...                [2, 2, 2],
...                [3, 3, 3],
...                [4, 4, 4],
...                [5, 5, 5]]])
>>> tsplit(a)
array([[[0, 1, 2, 3, 4, 5]],

       [[0, 1, 2, 3, 4, 5]],

       [[0, 1, 2, 3, 4, 5]]])
colour.row_as_diagonal(a)[source]

Returns the per row diagonal matrices of the given array.

Parameters:a (array_like) – Array to perform the diagonal matrices computation.
Returns:
Return type:ndarray

References

[1]Castro, S. (2014). Numpy: Fastest way of computing diagonal for each row of a 2d array. Retrieved August 22, 2014, from http://stackoverflow.com/questions/26511401/numpy-fastest-way-of-computing-diagonal-for-each-row-of-a-2d-array/26517247#26517247

Examples

>>> a = np.array([[0.25891593, 0.07299478, 0.36586996],
...               [0.30851087, 0.37131459, 0.16274825],
...               [0.71061831, 0.67718718, 0.09562581],
...               [0.71588836, 0.76772047, 0.15476079],
...               [0.92985142, 0.22263399, 0.88027331]])
>>> row_as_diagonal(a)
array([[[ 0.25891593,  0.        ,  0.        ],
        [ 0.        ,  0.07299478,  0.        ],
        [ 0.        ,  0.        ,  0.36586996]],

       [[ 0.30851087,  0.        ,  0.        ],
        [ 0.        ,  0.37131459,  0.        ],
        [ 0.        ,  0.        ,  0.16274825]],

       [[ 0.71061831,  0.        ,  0.        ],
        [ 0.        ,  0.67718718,  0.        ],
        [ 0.        ,  0.        ,  0.09562581]],

       [[ 0.71588836,  0.        ,  0.        ],
        [ 0.        ,  0.76772047,  0.        ],
        [ 0.        ,  0.        ,  0.15476079]],

       [[ 0.92985142,  0.        ,  0.        ],
        [ 0.        ,  0.22263399,  0.        ],
        [ 0.        ,  0.        ,  0.88027331]]])
colour.dot_vector(m, v)[source]

Convenient wrapper around np.einsum() with the following subscripts: ‘...ij,...j->...i’.

It performs the dot product of two arrays where m parameter is expected to be an array of 3x3 matrices and parameter v an array of vectors.

Parameters:
  • m (array_like) – Array of 3x3 matrices.
  • v (array_like) – Array of vectors.
Returns:

Return type:

ndarray

See also

dot_matrix()

Examples

>>> m = np.array([[0.7328, 0.4296, -0.1624],
...               [-0.7036, 1.6975, 0.0061],
...               [0.0030, 0.0136, 0.9834]])
>>> m = np.reshape(np.tile(m, (6, 1)), (6, 3, 3))
>>> v = np.array([0.07049534, 0.10080000, 0.09558313])
>>> v = np.tile(v, (6, 1))
>>> dot_vector(m, v)  
array([[ 0.0794399...,  0.1220905...,  0.0955788...],
       [ 0.0794399...,  0.1220905...,  0.0955788...],
       [ 0.0794399...,  0.1220905...,  0.0955788...],
       [ 0.0794399...,  0.1220905...,  0.0955788...],
       [ 0.0794399...,  0.1220905...,  0.0955788...],
       [ 0.0794399...,  0.1220905...,  0.0955788...]])
colour.dot_matrix(a, b)[source]

Convenient wrapper around np.einsum() with the following subscripts: ‘...ij,...jk->...ik’.

It performs the dot product of two arrays where a parameter is expected to be an array of 3x3 matrices and parameter b another array of of 3x3 matrices.

Parameters:
  • a (array_like) – Array of 3x3 matrices.
  • b (array_like) – Array of 3x3 matrices.
Returns:

Return type:

ndarray

See also

dot_matrix()

Examples

>>> a = np.array([[0.7328, 0.4296, -0.1624],
...               [-0.7036, 1.6975, 0.0061],
...               [0.0030, 0.0136, 0.9834]])
>>> a = np.reshape(np.tile(a, (6, 1)), (6, 3, 3))
>>> b = a
>>> dot_matrix(a, b)  
array([[[ 0.2342420...,  1.0418482..., -0.2760903...],
        [-1.7099407...,  2.5793226...,  0.1306181...],
        [-0.0044203...,  0.0377490...,  0.9666713...]],

       [[ 0.2342420...,  1.0418482..., -0.2760903...],
        [-1.7099407...,  2.5793226...,  0.1306181...],
        [-0.0044203...,  0.0377490...,  0.9666713...]],

       [[ 0.2342420...,  1.0418482..., -0.2760903...],
        [-1.7099407...,  2.5793226...,  0.1306181...],
        [-0.0044203...,  0.0377490...,  0.9666713...]],

       [[ 0.2342420...,  1.0418482..., -0.2760903...],
        [-1.7099407...,  2.5793226...,  0.1306181...],
        [-0.0044203...,  0.0377490...,  0.9666713...]],

       [[ 0.2342420...,  1.0418482..., -0.2760903...],
        [-1.7099407...,  2.5793226...,  0.1306181...],
        [-0.0044203...,  0.0377490...,  0.9666713...]],

       [[ 0.2342420...,  1.0418482..., -0.2760903...],
        [-1.7099407...,  2.5793226...,  0.1306181...],
        [-0.0044203...,  0.0377490...,  0.9666713...]]])
colour.orient(a, orientation)[source]

Orient given array accordingly to given orientation value.

Parameters:
  • a (array_like) – Array to perform the orientation onto.
  • orientation (unicode, optional) – {‘Flip’, ‘Flop’, ‘90 CW’, ‘90 CCW’, ‘180’} Orientation to perform.
Returns:

Oriented array.

Return type:

ndarray

Examples

>>> a = np.tile(np.arange(5), (5, 1))
>>> a
array([[0, 1, 2, 3, 4],
       [0, 1, 2, 3, 4],
       [0, 1, 2, 3, 4],
       [0, 1, 2, 3, 4],
       [0, 1, 2, 3, 4]])
>>> orient(a, '90 CW')
array([[0, 0, 0, 0, 0],
       [1, 1, 1, 1, 1],
       [2, 2, 2, 2, 2],
       [3, 3, 3, 3, 3],
       [4, 4, 4, 4, 4]])
>>> orient(a, 'Flip')
array([[4, 3, 2, 1, 0],
       [4, 3, 2, 1, 0],
       [4, 3, 2, 1, 0],
       [4, 3, 2, 1, 0],
       [4, 3, 2, 1, 0]])
colour.centroid(a)[source]

Computes the centroid indexes of given \(a\) array.

Parameters:a (array_like) – \(a\) array to compute the centroid indexes.
Returns:\(a\) array centroid indexes.
Return type:ndarray

Examples

>>> a = np.tile(np.arange(0, 5), (5, 1))
>>> centroid(a)
array([2, 3])
colour.linear_conversion(a, old_range, new_range)[source]

Performs a simple linear conversion of given array between the old and new ranges.

Parameters:
  • a (array_like) – Array to perform the linear conversion onto.
  • old_range (array_like) – Old range.
  • new_range (array_like) – New range.
Returns:

Return type:

ndarray

Examples

>>> a = np.linspace(0, 1, 10)
>>> linear_conversion(a, np.array([0, 1]), np.array([1, 10]))
array([  1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.])
class colour.ArbitraryPrecisionMapping(data=None, key_decimals=0, **kwargs)[source]

Bases: _abcoll.MutableMapping

Implements a mutable mapping / dict like object where numeric keys are stored with an arbitrary precision.

Parameters:
  • data (dict, optional) – dict of data to store into the mapping at initialisation.
  • key_decimals (int, optional) – Decimals count the keys will be rounded at
Other Parameters:
 

**kwargs (dict, optional) – Key / Value pairs to store into the mapping at initialisation.

key_decimals
__setitem__()[source]
__getitem__()[source]
__delitem__()[source]
__contains__()[source]
__iter__()[source]
__len__()[source]

Examples

>>> data1 = {0.1999999998: 'Nemo', 0.2000000000: 'John'}
>>> apm1 = ArbitraryPrecisionMapping(data1, key_decimals=10)
>>> # Doctests skip for Python 2.x compatibility.
>>> tuple(apm1.keys())  
(0.1999999998, 0.2)
>>> apm2 = ArbitraryPrecisionMapping(data1, key_decimals=7)
>>> # Doctests skip for Python 2.x compatibility.
>>> tuple(apm2.keys())  
(0.2,)
data

Property for self.data attribute.

Returns:ArbitraryPrecisionMapping data structure.
Return type:dict

Warning

ArbitraryPrecisionMapping.data is read only.

key_decimals

Property for self._key_decimals private attribute.

Returns:self._key_decimals.
Return type:unicode
class colour.Lookup[source]

Bases: dict

Extends dict type to provide a lookup by value(s).

first_key_from_value()[source]
keys_from_value()[source]

References

[2]Mansencal, T. (n.d.). Lookup. Retrieved from https://github.com/KelSolaar/Foundations/blob/develop/foundations/data_structures.py

Examples

>>> person = Lookup(first_name='Doe', last_name='John', gender='male')
>>> person.first_key_from_value('Doe')
'first_name'
>>> persons = Lookup(John='Doe', Jane='Doe', Luke='Skywalker')
>>> sorted(persons.keys_from_value('Doe'))
['Jane', 'John']
first_key_from_value(value)[source]

Gets the first key with given value.

Parameters:value (object) – Value.
Returns:Key.
Return type:object
keys_from_value(value)[source]

Gets the keys with given value.

Parameters:value (object) – Value.
Returns:Keys.
Return type:object
class colour.Structure(*args, **kwargs)[source]

Bases: dict

Defines an object similar to C/C++ structured type.

Other Parameters:
 
  • *args (list, optional) – Arguments.
  • **kwargs (dict, optional) – Key / Value pairs.
__getattr__()[source]
__setattr__()[source]
__delattr__()[source]
update()[source]

References

[1]Mansencal, T. (n.d.). Structure. Retrieved from https://github.com/KelSolaar/Foundations/blob/develop/foundations/data_structures.py

Examples

>>> person = Structure(first_name='Doe', last_name='John', gender='male')
>>> # Doctests skip for Python 2.x compatibility.
>>> person.first_name  
'Doe'
>>> sorted(person.keys())
['first_name', 'gender', 'last_name']
>>> # Doctests skip for Python 2.x compatibility.
>>> person['gender']  
'male'
update(*args, **kwargs)[source]

Updates both keys and sibling attributes.

Other Parameters:
 
  • *args (list, optional) – Arguments.
  • **kwargs (dict, optional) – Keywords arguments.

Notes

class colour.CaseInsensitiveMapping(data=None, **kwargs)[source]

Bases: _abcoll.MutableMapping

Implements a case-insensitive mutable mapping / dict object.

Allows values retrieving from keys while ignoring the key case. The keys are expected to be unicode or string-like objects supporting the str.lower() method.

Parameters:data (dict) – dict of data to store into the mapping at initialisation.
Other Parameters:
 **kwargs (dict, optional) – Key / Value pairs to store into the mapping at initialisation.
__setitem__()[source]
__getitem__()[source]
__delitem__()[source]
__contains__()[source]
__iter__()[source]
__len__()[source]
__eq__()[source]
__ne__()[source]
__repr__()[source]
copy()[source]
lower_items()[source]

Warning

The keys are expected to be unicode or string-like objects.

References

[3]Reitz, K. (n.d.). CaseInsensitiveDict. Retrieved from https://github.com/kennethreitz/requests/blob/v1.2.3/requests/structures.py#L37

Examples

>>> methods = CaseInsensitiveMapping({'McCamy': 1, 'Hernandez': 2})
>>> methods['mccamy']
1
copy()[source]

Returns a copy of the mapping.

Returns:Mapping copy.
Return type:CaseInsensitiveMapping

Notes

data

Property for self.data attribute.

Returns:ArbitraryPrecisionMapping data structure.
Return type:dict

Warning

ArbitraryPrecisionMapping.data is read only.

lower_items()[source]

Iterates over the lower items names.

Returns:Lower item names.
Return type:generator
exception colour.ColourWarning[source]

Bases: exceptions.Warning

This is the base class of Colour warnings. It is a subclass of Warning.

colour.message_box(message, width=79, padding=3)[source]

Prints a message inside a box.

Parameters:
  • message (unicode) – Message to print.
  • width (int, optional) – Message box width.
  • padding (unicode) – Padding on each sides of the message.
Returns:

Definition success.

Return type:

bool

Examples

>>> message = ('Lorem ipsum dolor sit amet, consectetur adipiscing elit, '
...     'sed do eiusmod tempor incididunt ut labore et dolore magna '
...     'aliqua.')
>>> message_box(message, width=75)
===========================================================================
*                                                                         *
*   Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do       *
*   eiusmod tempor incididunt ut labore et dolore magna aliqua.           *
*                                                                         *
===========================================================================
True
>>> message_box(message, width=60)
============================================================
*                                                          *
*   Lorem ipsum dolor sit amet, consectetur adipiscing     *
*   elit, sed do eiusmod tempor incididunt ut labore et    *
*   dolore magna aliqua.                                   *
*                                                          *
============================================================
True
>>> message_box(message, width=75, padding=16)
===========================================================================
*                                                                         *
*                Lorem ipsum dolor sit amet, consectetur                  *
*                adipiscing elit, sed do eiusmod tempor                   *
*                incididunt ut labore et dolore magna                     *
*                aliqua.                                                  *
*                                                                         *
===========================================================================
True
colour.warning(*args, **kwargs)[source]

Issues a warning.

Other Parameters:
 
  • *args (list, optional) – Arguments.
  • **kwargs (dict, optional) – Keywords arguments.
Returns:

Definition success.

Return type:

bool

Examples

>>> warning('This is a warning!')  
/Users/.../colour/utilities/verbose.py:132: UserWarning: This is a warning!
colour.filter_warnings(state=True, colour_warnings_only=True)[source]

Filters Colour and also optionally overall Python warnings.

Parameters:
  • state (bool, optional) – Warnings filter state.
  • colour_warnings_only (bool, optional) – Whether to only filter Colour warnings or also overall Python warnings.
Returns:

Definition success.

Return type:

bool

Examples

# Filtering Colour only warnings: >>> filter_warnings() True

# Filtering Colour and also Python warnings: >>> filter_warnings(colour_warnings_only=False) True

colour.chromatic_adaptation_matrix_VonKries(XYZ_w, XYZ_wr, transform=u'CAT02')[source]

Computes the chromatic adaptation matrix from test viewing conditions to reference viewing conditions.

Parameters:
  • XYZ_w (array_like) – Test viewing condition CIE XYZ tristimulus values of whitepoint.
  • XYZ_wr (array_like) – Reference viewing condition CIE XYZ tristimulus values of whitepoint.
  • transform (unicode, optional) – {‘CAT02’, ‘XYZ Scaling’, ‘Von Kries’, ‘Bradford’, ‘Sharp’, ‘Fairchild’, ‘CMCCAT97’, ‘CMCCAT2000’, ‘CAT02_BRILL_CAT’, ‘Bianco’, ‘Bianco PC’}, Chromatic adaptation transform.
Returns:

Chromatic adaptation matrix.

Return type:

ndarray

Raises:

KeyError – If chromatic adaptation method is not defined.

Examples

>>> XYZ_w = np.array([1.09846607, 1.00000000, 0.35582280])
>>> XYZ_wr = np.array([0.95042855, 1.00000000, 1.08890037])
>>> chromatic_adaptation_matrix_VonKries(  
...     XYZ_w, XYZ_wr)
array([[ 0.8687653..., -0.1416539...,  0.3871961...],
       [-0.1030072...,  1.0584014...,  0.1538646...],
       [ 0.0078167...,  0.0267875...,  2.9608177...]])

Using Bradford method:

>>> XYZ_w = np.array([1.09846607, 1.00000000, 0.35582280])
>>> XYZ_wr = np.array([0.95042855, 1.00000000, 1.08890037])
>>> method = 'Bradford'
>>> chromatic_adaptation_matrix_VonKries(  
...     XYZ_w, XYZ_wr, method)
array([[ 0.8446794..., -0.1179355...,  0.3948940...],
       [-0.1366408...,  1.1041236...,  0.1291981...],
       [ 0.0798671..., -0.1349315...,  3.1928829...]])
colour.chromatic_adaptation_VonKries(XYZ, XYZ_w, XYZ_wr, transform=u'CAT02')[source]

Adapts given stimulus from test viewing conditions to reference viewing conditions.

Parameters:
  • XYZ (array_like) – CIE XYZ tristimulus values of stimulus to adapt.
  • XYZ_w (array_like) – Test viewing condition CIE XYZ tristimulus values of whitepoint.
  • XYZ_wr (array_like) – Reference viewing condition CIE XYZ tristimulus values of whitepoint.
  • transform (unicode, optional) – {‘CAT02’, ‘XYZ Scaling’, ‘Von Kries’, ‘Bradford’, ‘Sharp’, ‘Fairchild’, ‘CMCCAT97’, ‘CMCCAT2000’, ‘CAT02_BRILL_CAT’, ‘Bianco’, ‘Bianco PC’}, Chromatic adaptation transform.
Returns:

CIE XYZ_c tristimulus values of the stimulus corresponding colour.

Return type:

ndarray

Examples

>>> XYZ = np.array([0.07049534, 0.10080000, 0.09558313])
>>> XYZ_w = np.array([1.09846607, 1.00000000, 0.35582280])
>>> XYZ_wr = np.array([0.95042855, 1.00000000, 1.08890037])
>>> chromatic_adaptation_VonKries(XYZ, XYZ_w, XYZ_wr)  
array([ 0.0839746...,  0.1141321...,  0.2862554...])

Using Bradford method:

>>> XYZ = np.array([0.07049534, 0.10080000, 0.09558313])
>>> XYZ_w = np.array([1.09846607, 1.00000000, 0.35582280])
>>> XYZ_wr = np.array([0.95042855, 1.00000000, 1.08890037])
>>> method = 'Bradford'
>>> chromatic_adaptation_VonKries(  
...     XYZ, XYZ_w, XYZ_wr, method)
array([ 0.0854032...,  0.1140122...,  0.2972149...])
colour.chromatic_adaptation_Fairchild1990(XYZ_1, XYZ_n, XYZ_r, Y_n, discount_illuminant=False)[source]

Adapts given stimulus CIE XYZ_1 tristimulus values from test viewing conditions to reference viewing conditions using Fairchild (1990) chromatic adaptation model.

Parameters:
  • XYZ_1 (array_like) – CIE XYZ_1 tristimulus values of test sample / stimulus in domain [0, 100].
  • XYZ_n (array_like) – Test viewing condition CIE XYZ_n tristimulus values of whitepoint.
  • XYZ_r (array_like) – Reference viewing condition CIE XYZ_r tristimulus values of whitepoint.
  • Y_n (numeric or array_like) – Luminance \(Y_n\) of test adapting stimulus in \(cd/m^2\).
  • discount_illuminant (bool, optional) – Truth value indicating if the illuminant should be discounted.
Returns:

Adapted CIE XYZ_2 tristimulus values of stimulus.

Return type:

ndarray

Warning

The input domain of that definition is non standard!

Notes

  • Input CIE XYZ_1, CIE XYZ_n and CIE XYZ_r tristimulus values are in domain [0, 100].
  • Output CIE XYZ_2 tristimulus values are in range [0, 100].

Examples

>>> XYZ_1 = np.array([19.53, 23.07, 24.97])
>>> XYZ_n = np.array([111.15, 100.00, 35.20])
>>> XYZ_r = np.array([94.81, 100.00, 107.30])
>>> Y_n = 200
>>> chromatic_adaptation_Fairchild1990(  
...     XYZ_1, XYZ_n, XYZ_r, Y_n)
array([ 23.3252634...,  23.3245581...,  76.1159375...])
class colour.CMCCAT2000_InductionFactors[source]

Bases: colour.adaptation.cmccat2000.CMCCAT2000_InductionFactors

CMCCAT2000 chromatic adaptation model induction factors.

Parameters:F (numeric or array_like) – \(F\) surround condition.

Create new instance of CMCCAT2000_InductionFactors(F,)

colour.chromatic_adaptation_forward_CMCCAT2000(XYZ, XYZ_w, XYZ_wr, L_A1, L_A2, surround=CMCCAT2000_InductionFactors(F=1))[source]

Adapts given stimulus CIE XYZ tristimulus values from test viewing conditions to reference viewing conditions using CMCCAT2000 forward chromatic adaptation model.

Parameters:
  • XYZ (array_like) – CIE XYZ tristimulus values of the stimulus to adapt.
  • XYZ_w (array_like) – Test viewing condition CIE XYZ tristimulus values of the whitepoint.
  • XYZ_wr (array_like) – Reference viewing condition CIE XYZ tristimulus values of the whitepoint.
  • L_A1 (numeric or array_like) – Luminance of test adapting field \(L_{A1}\) in \(cd/m^2\).
  • L_A2 (numeric or array_like) – Luminance of reference adapting field \(L_{A2}\) in \(cd/m^2\).
  • surround (CMCCAT2000_InductionFactors, optional) – Surround viewing conditions induction factors.
Returns:

CIE XYZ_c tristimulus values of the stimulus corresponding colour.

Return type:

ndarray

Warning

The input domain and output range of that definition are non standard!

Notes

  • Input CIE XYZ, CIE XYZ_w and CIE XYZ_wr tristimulus values are in domain [0, 100].
  • Output CIE XYZ_c tristimulus values are in range [0, 100].

Examples

>>> XYZ = np.array([22.48, 22.74, 8.54])
>>> XYZ_w = np.array([111.15, 100.00, 35.20])
>>> XYZ_wr = np.array([94.81, 100.00, 107.30])
>>> L_A1 = 200
>>> L_A2 = 200
>>> chromatic_adaptation_forward_CMCCAT2000(  
...     XYZ, XYZ_w, XYZ_wr, L_A1, L_A2)
array([ 19.5269832...,  23.0683396...,  24.9717522...])
colour.chromatic_adaptation_reverse_CMCCAT2000(XYZ_c, XYZ_w, XYZ_wr, L_A1, L_A2, surround=CMCCAT2000_InductionFactors(F=1))[source]

Adapts given stimulus corresponding colour CIE XYZ tristimulus values from reference viewing conditions to test viewing conditions using CMCCAT2000 reverse chromatic adaptation model.

Parameters:
  • XYZ_c (array_like) – CIE XYZ tristimulus values of the stimulus to adapt.
  • XYZ_w (array_like) – Test viewing condition CIE XYZ tristimulus values of the whitepoint.
  • XYZ_wr (array_like) – Reference viewing condition CIE XYZ tristimulus values of the whitepoint.
  • L_A1 (numeric or array_like) – Luminance of test adapting field \(L_{A1}\) in \(cd/m^2\).
  • L_A2 (numeric or array_like) – Luminance of reference adapting field \(L_{A2}\) in \(cd/m^2\).
  • surround (CMCCAT2000_InductionFactors, optional) – Surround viewing conditions induction factors.
Returns:

CIE XYZ_c tristimulus values of the adapted stimulus.

Return type:

ndarray

Warning

The input domain and output range of that definition are non standard!

Notes

  • Input CIE XYZ_c, CIE XYZ_w and CIE XYZ_wr tristimulus values are in domain [0, 100].
  • Output CIE XYZ tristimulus values are in range [0, 100].

Examples

>>> XYZ_c = np.array([19.53, 23.07, 24.97])
>>> XYZ_w = np.array([111.15, 100.00, 35.20])
>>> XYZ_wr = np.array([94.81, 100.00, 107.30])
>>> L_A1 = 200
>>> L_A2 = 200
>>> chromatic_adaptation_reverse_CMCCAT2000(  
...     XYZ_c, XYZ_w, XYZ_wr, L_A1, L_A2)
array([ 22.4839876...,  22.7419485...,   8.5393392...])
colour.chromatic_adaptation_CMCCAT2000(XYZ, XYZ_w, XYZ_wr, L_A1, L_A2, surround=CMCCAT2000_InductionFactors(F=1), method=u'Forward')[source]

Adapts given stimulus CIE XYZ tristimulus values using given viewing conditions.

This definition is a convenient wrapper around chromatic_adaptation_forward_CMCCAT2000() and chromatic_adaptation_reverse_CMCCAT2000().

Parameters:
  • XYZ (array_like) – CIE XYZ tristimulus values of the stimulus to adapt.
  • XYZ_w (array_like) – Source viewing condition CIE XYZ tristimulus values of the whitepoint.
  • XYZ_wr (array_like) – Target viewing condition CIE XYZ tristimulus values of the whitepoint.
  • L_A1 (numeric or array_like) – Luminance of test adapting field \(L_{A1}\) in \(cd/m^2\).
  • L_A2 (numeric or array_like) – Luminance of reference adapting field \(L_{A2}\) in \(cd/m^2\).
  • surround (CMCCAT2000_InductionFactors, optional) – Surround viewing conditions induction factors.
  • method (unicode, optional) – {‘Forward’, ‘Reverse’}, Chromatic adaptation method.
Returns:

Adapted stimulus CIE XYZ tristimulus values.

Return type:

ndarray

Warning

The input domain and output range of that definition are non standard!

Notes

  • Input CIE XYZ, CIE XYZ_w and CIE XYZ_wr tristimulus values are in domain [0, 100].
  • Output CIE XYZ tristimulus values are in range [0, 100].

Examples

>>> XYZ = np.array([22.48, 22.74, 8.54])
>>> XYZ_w = np.array([111.15, 100.00, 35.20])
>>> XYZ_wr = np.array([94.81, 100.00, 107.30])
>>> L_A1 = 200
>>> L_A2 = 200
>>> chromatic_adaptation_CMCCAT2000(  
...     XYZ, XYZ_w, XYZ_wr, L_A1, L_A2, method='Forward')
array([ 19.5269832...,  23.0683396...,  24.9717522...])

Using the CMCCAT2000 reverse model:

>>> XYZ = np.array([19.52698326, 23.06833960, 24.97175229])
>>> XYZ_w = np.array([111.15, 100.00, 35.20])
>>> XYZ_wr = np.array([94.81, 100.00, 107.30])
>>> L_A1 = 200
>>> L_A2 = 200
>>> chromatic_adaptation_CMCCAT2000(  
...     XYZ, XYZ_w, XYZ_wr, L_A1, L_A2, method='Reverse')
array([ 22.48,  22.74,   8.54])
colour.chromatic_adaptation_CIE1994(XYZ_1, xy_o1, xy_o2, Y_o, E_o1, E_o2, n=1)[source]

Adapts given stimulus CIE XYZ_1 tristimulus values from test viewing conditions to reference viewing conditions using CIE 1994 chromatic adaptation model.

Parameters:
  • XYZ_1 (array_like) – CIE XYZ tristimulus values of test sample / stimulus in domain [0, 100].
  • xy_o1 (array_like) – Chromaticity coordinates \(x_{o1}\) and \(y_{o1}\) of test illuminant and background.
  • xy_o2 (array_like) – Chromaticity coordinates \(x_{o2}\) and \(y_{o2}\) of reference illuminant and background.
  • Y_o (numeric) – Luminance factor \(Y_o\) of achromatic background as percentage in domain [18, 100].
  • E_o1 (numeric) – Test illuminance \(E_{o1}\) in \(cd/m^2\).
  • E_o2 (numeric) – Reference illuminance \(E_{o2}\) in \(cd/m^2\).
  • n (numeric, optional) – Noise component in fundamental primary system.
Returns:

Adapted CIE XYZ_2 tristimulus values of test stimulus.

Return type:

ndarray

Warning

The input domain of that definition is non standard!

Notes

  • Input CIE XYZ_1 tristimulus values are in domain [0, 100].
  • Output CIE XYZ_2 tristimulus values are in range [0, 100].

Examples

>>> XYZ_1 = np.array([28.00, 21.26, 5.27])
>>> xy_o1 = np.array([0.4476, 0.4074])
>>> xy_o2 = np.array([0.3127, 0.3290])
>>> Y_o = 20
>>> E_o1 = 1000
>>> E_o2 = 1000
>>> chromatic_adaptation_CIE1994(  
...     XYZ_1, xy_o1, xy_o2, Y_o, E_o1, E_o2)
array([ 24.0337952...,  21.1562121...,  17.6430119...])
colour.cartesian_to_spherical(a)[source]

Transforms given Cartesian coordinates array \(xyz\) to Spherical coordinates array \(\rho\theta\phi\) (radial distance, inclination or elevation and azimuth).

Parameters:a (array_like) – Cartesian coordinates array \(xyz\) to transform.
Returns:Spherical coordinates array \(\rho\theta\phi\).
Return type:ndarray

Examples

>>> a = np.array([3, 1, 6])
>>> cartesian_to_spherical(a)  
array([ 6.7823299...,  1.0857465...,  0.3217505...])
colour.spherical_to_cartesian(a)[source]

Transforms given Spherical coordinates array \(\rho\theta\phi\) (radial distance, inclination or elevation and azimuth) to Cartesian coordinates array \(xyz\).

Parameters:a (array_like) – Spherical coordinates array \(\rho\theta\phi\) to transform.
Returns:Cartesian coordinates array \(xyz\).
Return type:ndarray

Examples

>>> a = np.array([6.78232998, 1.08574654, 0.32175055])
>>> spherical_to_cartesian(a)  
array([ 3.        ,  0.9999999...,  6.        ])
colour.cartesian_to_polar(a)[source]

Transforms given Cartesian coordinates array \(xy\) to Polar coordinates array \(\rho\phi\) (radial coordinate, angular coordinate).

Parameters:a (array_like) – Cartesian coordinates array \(xy\) to transform.
Returns:Polar coordinates array \(\rho\phi\).
Return type:ndarray

Examples

>>> a = np.array([3, 1])
>>> cartesian_to_polar(a)  
array([ 3.1622776...,  0.3217505...])
colour.polar_to_cartesian(a)[source]

Transforms given Polar coordinates array \(\rho\phi\) (radial coordinate, angular coordinate) to Cartesian coordinates array \(xy\).

Parameters:a (array_like) – Polar coordinates array \(\rho\phi\) to transform.
Returns:Cartesian coordinates array \(xy\).
Return type:ndarray

Examples

>>> a = np.array([3.16227766, 0.32175055])
>>> polar_to_cartesian(a)  
array([ 3.        ,  0.9999999...])
colour.cartesian_to_cylindrical(a)[source]

Transforms given Cartesian coordinates array \(xyz\) to Cylindrical coordinates array \(\rho\phi z\) (azimuth, radial distance and height).

Parameters:a (array_like) – Cartesian coordinates array \(xyz\) to transform.
Returns:Cylindrical coordinates array \(\rho\phi z\).
Return type:ndarray

Examples

>>> a = np.array([3, 1, 6])
>>> cartesian_to_cylindrical(a)  
array([ 3.1622776...,  0.3217505...,  6.        ])
colour.cylindrical_to_cartesian(a)[source]

Transforms given Cylindrical coordinates array \(\rho\phi z\) (azimuth, radial distance and height) to Cartesian coordinates array \(xyz\).

Parameters:a (array_like) – Cylindrical coordinates array \(\rho\phi z\) to transform.
Returns:Cartesian coordinates array \(xyz\).
Return type:ndarray

Examples

>>> a = np.array([3.16227766, 0.32175055, 6.00000000])
>>> cylindrical_to_cartesian(a)  
array([ 3.        ,  0.9999999...,  6.        ])
class colour.Extrapolator(interpolator=None, method=u'Linear', left=None, right=None)[source]

Bases: object

Extrapolates the 1-D function of given interpolator.

The Extrapolator class acts as a wrapper around a given Colour or scipy interpolator class instance with compatible signature. Two extrapolation methods are available:

  • Linear: Linearly extrapolates given points using the slope defined by the interpolator boundaries (xi[0], xi[1]) if x < xi[0] and (xi[-1], xi[-2]) if x > xi[-1].
  • Constant: Extrapolates given points by assigning the interpolator boundaries values xi[0] if x < xi[0] and xi[-1] if x > xi[-1].

Specifying the left and right arguments takes precedence on the chosen extrapolation method and will assign the respective left and right values to the given points.

Parameters:
  • interpolator (object) – Interpolator object.
  • method (unicode, optional) – {‘Linear’, ‘Constant’}, Extrapolation method.
  • left (numeric, optional) – Value to return for x < xi[0].
  • right (numeric, optional) – Value to return for x > xi[-1].
__class__()

Notes

The interpolator must define x and y attributes.

References

[1]sastanin. (n.d.). How to make scipy.interpolate give an extrapolated result beyond the input range? Retrieved August 08, 2014, from http://stackoverflow.com/a/2745496/931625

Examples

Extrapolating a single numeric variable:

>>> from colour.algebra import LinearInterpolator
>>> x = np.array([3, 4, 5])
>>> y = np.array([1, 2, 3])
>>> interpolator = LinearInterpolator(x, y)
>>> extrapolator = Extrapolator(interpolator)
>>> extrapolator(1)
-1.0

Extrapolating an array_like variable:

>>> extrapolator(np.array([6, 7 , 8]))
array([ 4.,  5.,  6.])

Using the Constant extrapolation method:

>>> x = np.array([3, 4, 5])
>>> y = np.array([1, 2, 3])
>>> interpolator = LinearInterpolator(x, y)
>>> extrapolator = Extrapolator(interpolator, method='Constant')
>>> extrapolator(np.array([0.1, 0.2, 8, 9]))
array([ 1.,  1.,  3.,  3.])

Using defined left boundary and Constant extrapolation method:

>>> x = np.array([3, 4, 5])
>>> y = np.array([1, 2, 3])
>>> interpolator = LinearInterpolator(x, y)
>>> extrapolator = Extrapolator(interpolator, method='Constant', left=0)
>>> extrapolator(np.array([0.1, 0.2, 8, 9]))
array([ 0.,  0.,  3.,  3.])
interpolator

Property for self._interpolator private attribute.

Returns:self._interpolator
Return type:object
left

Property for self._left private attribute.

Returns:self._left
Return type:numeric
method

Property for self._method private attribute.

Returns:self._method
Return type:unicode
right

Property for self._right private attribute.

Returns:self._right
Return type:numeric
colour.normalise_vector(a)[source]

Normalises given vector \(a\).

Parameters:a (array_like) – Vector \(a\) to normalise.
Returns:Normalised vector \(a\).
Return type:ndarray

Examples

>>> a = np.array([0.07049534, 0.10080000, 0.09558313])
>>> normalise_vector(a)  
array([ 0.4525410...,  0.6470802...,  0.6135908...])
colour.euclidean_distance(a, b)[source]

Returns the euclidean distance between point arrays \(a\) and \(b\).

Parameters:
  • a (array_like) – Point array \(a\).
  • b (array_like) – Point array \(b\).
Returns:

Euclidean distance.

Return type:

numeric or ndarray

Examples

>>> a = np.array([100.00000000, 21.57210357, 272.22819350])
>>> b = np.array([100.00000000, 426.67945353, 72.39590835])
>>> euclidean_distance(a, b)  
451.7133019...
colour.extend_line_segment(a, b, distance=1)[source]

Extends the line segment defined by point arrays \(a\) and \(b\) by given distance and return the new end point.

Parameters:
  • a (array_like) – Point array \(a\).
  • b (array_like) – Point array \(b\).
  • distance (numeric, optional) – Distance to extend the line segment.
Returns:

New end point.

Return type:

ndarray

References

[1]Saeedn. (n.d.). Extend a line segment a specific distance. Retrieved January 16, 2016, from http://stackoverflow.com/questions/7740507/extend-a-line-segment-a-specific-distance

Notes

  • Input line segment points coordinates are 2d coordinates.

Examples

>>> a = np.array([0.95694934, 0.13720932])
>>> b = np.array([0.28382835, 0.60608318])
>>> extend_line_segment(a, b)  
array([-0.5367248...,  1.1776534...])
class colour.LineSegmentsIntersections_Specification[source]

Bases: colour.algebra.geometry.LineSegmentsIntersections_Specification

Defines the specification for intersection of line segments \(l_1\) and \(l_2\) returned by intersect_line_segments() definition.

Parameters:
  • xy (array_like) – Array of \(l_1\) and \(l_2\) line segments intersections coordinates. Non existing segments intersections coordinates are set with np.nan.
  • intersect (array_like) – Array of bool indicating if line segments \(l_1\) and \(l_2\) intersect.
  • parallel (array_like) – Array of bool indicating if line segments \(l_1\) and \(l_2\) are parallel.
  • coincident (array_like) – Array of bool indicating if line segments \(l_1\) and \(l_2\) are coincident.

Create new instance of LineSegmentsIntersections_Specification(xy, intersect, parallel, coincident)

colour.intersect_line_segments(l_1, l_2)[source]

Computes \(l_1\) line segments intersections with \(l_2\) line segments.

Parameters:
  • l_1 (array_like) – \(l_1\) line segments array, each row is a line segment such as (\(x_1\), \(y_1\), \(x_2\), \(y_2\)) where (\(x_1\), \(y_1\)) and (\(x_2\), \(y_2\)) are respectively the start and end points of \(l_1\) line segments.
  • l_2 (array_like) – \(l_2\) line segments array, each row is a line segment such as (\(x_3\), \(y_3\), \(x_4\), \(y_4\)) where (\(x_3\), \(y_3\)) and (\(x_4\), \(y_4\)) are respectively the start and end points of \(l_2\) line segments.
Returns:

Line segments intersections specification.

Return type:

LineSegmentsIntersections_Specification

References

[2]Bourke, P. (n.d.). Intersection point of two line segments in 2 dimensions. Retrieved January 15, 2016, from http://paulbourke.net/geometry/pointlineplane/
[3]Erdem, U. M. (n.d.). Fast Line Segment Intersection. Retrieved January 15, 2016, from http://www.mathworks.com/matlabcentral/fileexchange/27205-fast-line-segment-intersection

Notes

  • Input line segments points coordinates are 2d coordinates.

Examples

>>> l_1 = np.array([[[0.15416284, 0.7400497],
...                  [0.26331502, 0.53373939]],
...                 [[0.01457496, 0.91874701],
...                  [0.90071485, 0.03342143]]])
>>> l_2 = np.array([[[0.95694934, 0.13720932],
...                  [0.28382835, 0.60608318]],
...                 [[0.94422514, 0.85273554],
...                  [0.00225923, 0.52122603]],
...                 [[0.55203763, 0.48537741],
...                  [0.76813415, 0.16071675]]])
>>> s = intersect_line_segments(l_1, l_2)
>>> s.xy  
array([[[        nan,         nan],
        [ 0.2279184...,  0.6006430...],
        [        nan,         nan]],

       [[ 0.4281451...,  0.5055568...],
        [ 0.3056055...,  0.6279838...],
        [ 0.7578749...,  0.1761301...]]])
>>> s.intersect
array([[False,  True, False],
       [ True,  True,  True]], dtype=bool)
>>> s.parallel
array([[False, False, False],
       [False, False, False]], dtype=bool)
>>> s.coincident
array([[False, False, False],
       [False, False, False]], dtype=bool)
class colour.LinearInterpolator(x=None, y=None)[source]

Bases: object

Linearly interpolates a 1-D function.

Parameters:
  • x (ndarray) – Independent \(x\) variable values corresponding with \(y\) variable.
  • y (ndarray) – Dependent and already known \(y\) variable values to interpolate.
__call__()[source]

Notes

This class is a wrapper around numpy.interp definition.

Examples

Interpolating a single numeric variable:

>>> y = np.array([5.9200,
...               9.3700,
...               10.8135,
...               4.5100,
...               69.5900,
...               27.8007,
...               86.0500])
>>> x = np.arange(len(y))
>>> f = LinearInterpolator(x, y)
>>> # Doctests ellipsis for Python 2.x compatibility.
>>> f(0.5)  
7.64...

Interpolating an array_like variable:

>>> f([0.25, 0.75])
array([ 6.7825,  8.5075])
x

Property for self.__x private attribute.

Returns:self.__x
Return type:array_like
y

Property for self.__y private attribute.

Returns:self.__y
Return type:array_like
class colour.SpragueInterpolator(x=None, y=None)[source]

Bases: object

Constructs a fifth-order polynomial that passes through \(y\) dependent variable.

Sprague (1880) method is recommended by the CIE for interpolating functions having a uniformly spaced independent variable.

Parameters:
  • x (array_like) – Independent \(x\) variable values corresponding with \(y\) variable.
  • y (array_like) – Dependent and already known \(y\) variable values to interpolate.
__call__()[source]

Notes

The minimum number \(k\) of data points required along the interpolation axis is \(k=6\).

References

[1]CIE TC 1-38. (2005). 9.2.4 Method of interpolation for uniformly spaced independent variable. In CIE 167:2005 Recommended Practice for Tabulating Spectral Data for Use in Colour Computations (pp. 1–27). ISBN:978-3-901-90641-1
[2]Westland, S., Ripamonti, C., & Cheung, V. (2012). Interpolation Methods. In Computational Colour Science Using MATLAB (2nd ed., pp. 29–37). ISBN:978-0-470-66569-5

Examples

Interpolating a single numeric variable:

>>> y = np.array([5.9200,
...               9.3700,
...               10.8135,
...               4.5100,
...               69.5900,
...               27.8007,
...               86.0500])
>>> x = np.arange(len(y))
>>> f = SpragueInterpolator(x, y)
>>> f(0.5)  
7.2185025...

Interpolating an array_like variable:

>>> f([0.25, 0.75])  
array([ 6.7295161...,  7.8140625...])
SPRAGUE_C_COEFFICIENTS = array([[ 884, -1960, 3033, -2648, 1080, -180], [ 508, -540, 488, -367, 144, -24], [ -24, 144, -367, 488, -540, 508], [ -180, 1080, -2648, 3033, -1960, 884]])
x

Property for self.__x private attribute.

Returns:self.__x
Return type:array_like
y

Property for self.__y private attribute.

Returns:self.__y
Return type:array_like
colour.lagrange_coefficients(r, n=4)[source]

Computes the Lagrange Coefficients at given point \(r\) for degree \(n\).

Parameters:
  • r (numeric) – Point to get the Lagrange Coefficients at.
  • n (int, optional) – Degree of the Lagrange Coefficients being calculated.
Returns:

Return type:

ndarray

References

[4]Fairman, H. S. (1985). The calculation of weight factors for tristimulus integration. Color Research & Application, 10(4), 199–203. doi:10.1002/col.5080100407
[5]Wikipedia. (n.d.). Lagrange polynomial - Definition. Retrieved January 20, 2016, from https://en.wikipedia.org/wiki/Lagrange_polynomial#Definition

Examples

>>> lagrange_coefficients(0.1)
array([ 0.8265,  0.2755, -0.1305,  0.0285])
colour.is_identity(a, n=3)[source]

Returns if \(a\) array is an identity matrix.

Parameters:
  • a (array_like, (N)) – Variable \(a\) to test.
  • n (int, optional) – Matrix dimension.
Returns:

Is identity matrix.

Return type:

bool

Examples

>>> is_identity(np.array([1, 0, 0, 0, 1, 0, 0, 0, 1]).reshape(3, 3))
True
>>> is_identity(np.array([1, 2, 0, 0, 1, 0, 0, 0, 1]).reshape(3, 3))
False
colour.random_triplet_generator(size, limits=array([[0, 1], [0, 1], [0, 1]]), random_state=<mtrand.RandomState object>)[source]

Returns a generator yielding random triplets.

Parameters:
  • size (integer) – Generator size.
  • limits (array_like, (3, 2)) – Random values limits on each triplet axis.
  • random_state (RandomState) – Mersenne Twister pseudo-random number generator.
Returns:

Random triplets generator.

Return type:

generator

Notes

Examples

>>> from pprint import pprint
>>> prng = np.random.RandomState(4)
>>> pprint(  
...     tuple(random_triplet_generator(10, random_state=prng)))
(array([ 0.9670298...,  0.5472322...,  0.9726843...]),
 array([ 0.7148159...,  0.6977288...,  0.2160895...]),
 array([ 0.9762744...,  0.0062302...,  0.2529823...]),
 array([ 0.4347915...,  0.7793829...,  0.1976850...]),
 array([ 0.8629932...,  0.9834006...,  0.1638422...]),
 array([ 0.5973339...,  0.0089861...,  0.3865712...]),
 array([ 0.0441600...,  0.9566529...,  0.4361466...]),
 array([ 0.9489773...,  0.7863059...,  0.8662893...]),
 array([ 0.1731654...,  0.0749485...,  0.6007427...]),
 array([ 0.1679721...,  0.7333801...,  0.4084438...]))
colour.reaction_rate_MichealisMenten(S, V_max, K_m)[source]

Describes the rate of enzymatic reactions, by relating reaction rate \(v\) to concentration of a substrate \(S\).

Parameters:
  • S (array_like) – Concentration of a substrate \(S\).
  • V_max (array_like) – Maximum rate \(V_{max}\) achieved by the system, at saturating substrate concentration.
  • K_m (array_like) – Substrate concentration \(V_{max}\) at which the reaction rate is half of \(V_{max}\).
Returns:

Reaction rate \(v\).

Return type:

array_like

Examples

>>> reaction_rate_MichealisMenten(0.5, 2.5, 0.8)  
0.9615384...
colour.substrate_concentration_MichealisMenten(v, V_max, K_m)[source]

Describes the rate of enzymatic reactions, by relating concentration of a substrate \(S\) to reaction rate \(v\).

Parameters:
  • v (array_like) – Reaction rate \(v\).
  • V_max (array_like) – Maximum rate \(V_{max}\) achieved by the system, at saturating substrate concentration.
  • K_m (array_like) – Substrate concentration \(V_{max}\) at which the reaction rate is half of \(V_{max}\).
Returns:

Concentration of a substrate \(S\).

Return type:

array_like

Examples

>>> substrate_concentration_MichealisMenten(
...     0.961538461538461, 2.5, 0.8)  
0.4999999...
class colour.SpectralMapping(data=None, wavelength_decimals=10, **kwargs)[source]

Bases: colour.utilities.data_structures.ArbitraryPrecisionMapping

Defines the base mapping for spectral data.

It enables usage of floating point wavelengths as keys by rounding them at a specific decimals count.

Parameters:
  • data (dict or SpectralMapping, optional) – Spectral data in a dict or SpectralMapping as follows: {wavelength \(\lambda_{i}\): value, wavelength \(\lambda_{i+1}\): value, ..., wavelength \(\lambda_{i+n}\): value}
  • wavelength_decimals (int, optional) – Decimals count the keys will be rounded at.
Other Parameters:
 

**kwargs (dict, optional) – Key / Value pairs to store into the mapping at initialisation.

wavelength_decimals

Examples

>>> data1 = {380.1999999998: 0.000039, 380.2000000000: 0.000039}
>>> mapping = SpectralMapping(data1, wavelength_decimals=10)
>>> # Doctests skip for Python 2.x compatibility.
>>> tuple(mapping.keys())  
(380.1999999..., 380.2)
>>> mapping = SpectralMapping(data1, wavelength_decimals=7)
>>> # Doctests skip for Python 2.x compatibility.
>>> tuple(mapping.keys())  
(380.2,)
wavelength_decimals

Property for self.key_decimals attribute.

Returns:self.key_decimals.
Return type:unicode
class colour.SpectralShape(start=None, end=None, interval=None)[source]

Bases: object

Defines the base object for spectral power distribution shape.

Parameters:
  • start (numeric, optional) – Wavelength \(\lambda_{i}\) range start in nm.
  • end (numeric, optional) – Wavelength \(\lambda_{i}\) range end in nm.
  • interval (numeric, optional) – Wavelength \(\lambda_{i}\) range interval.
start
end
interval
boundaries
__str__()[source]
__repr__()[source]
__iter__()[source]
__contains__()[source]
__len__()[source]
__eq__()[source]
__ne__()[source]
range()[source]

Examples

>>> # Doctests skip for Python 2.x compatibility.
>>> SpectralShape(360, 830, 1)  
SpectralShape(360, 830, 1)
boundaries

Property for self._start and self._end private attributes.

Returns:self._start, self._end.
Return type:tuple
end

Property for self._end private attribute.

Returns:self._end.
Return type:numeric
interval

Property for self._interval private attribute.

Returns:self._interval.
Return type:numeric
range()[source]

Returns an iterable range for the spectral power distribution shape.

Returns:Iterable range for the spectral power distribution shape
Return type:ndarray
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. ])
start

Property for self._start private attribute.

Returns:self._start.
Return type:numeric
class colour.SpectralPowerDistribution(name, data, title=None)[source]

Bases: object

Defines the base object for spectral data computations.

Parameters:
  • name (unicode) – Spectral power distribution name.
  • data (dict or SpectralMapping) – Spectral power distribution data in a dict or SpectralMapping as follows: {wavelength \(\lambda_{i}\): value, wavelength \(\lambda_{i+1}\): value, ..., wavelength \(\lambda_{i+n}\): value}
  • title (unicode, optional) – Spectral power distribution title for figures.

Notes

  • Underlying spectral data is stored within a colour.SpectralMapping class mapping which implies that wavelengths keys will be rounded.
name
data
title
wavelengths
values
items
shape
__str__()[source]
__repr__()[source]
__hash__()[source]
__init__()[source]
__getitem__()[source]
__setitem__()[source]
__iter__()[source]
__contains__()[source]
__len__()[source]
__eq__()[source]
__ne__()[source]
__add__()[source]
__iadd__()[source]
__sub__()[source]
__isub__()[source]
__mul__()[source]
__imul__()[source]
__div__()[source]
__idiv__()[source]
__pow__()[source]
__ipow__()[source]
get()[source]
is_uniform()[source]
extrapolate()[source]
interpolate()[source]
align()[source]
trim_wavelengths()[source]
zeros()[source]
normalise()[source]
clone()[source]

Examples

>>> data = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> spd = SpectralPowerDistribution('Sample', data)
>>> # Doctests skip for Python 2.x compatibility.
>>> spd.wavelengths  
array([510, 520, 530, 540])
>>> spd.values
array([ 49.67,  69.59,  81.73,  88.19])
>>> spd.shape  
SpectralShape(510, 540, 10)
align(shape, interpolation_method=None, extrapolation_method=u'Constant', extrapolation_left=None, extrapolation_right=None)[source]

Aligns the spectral power distribution to given spectral shape: Interpolates first then extrapolates to fit the given range.

Parameters:
  • shape (SpectralShape) – Spectral shape used for alignment.
  • interpolation_method (unicode, optional) – {None, ‘Cubic Spline’, ‘Linear’, ‘Pchip’, ‘Sprague’}, Enforce given interpolation method.
  • extrapolation_method (unicode, optional) – {‘Constant’, ‘Linear’}, Extrapolation method.
  • extrapolation_left (numeric, optional) – Value to return for low extrapolation range.
  • extrapolation_right (numeric, optional) – Value to return for high extrapolation range.
Returns:

Aligned spectral power distribution.

Return type:

SpectralPowerDistribution

Examples

>>> data = {
...     510: 49.67,
...     520: 69.59,
...     530: 81.73,
...     540: 88.19,
...     550: 86.26,
...     560: 77.18}
>>> spd = SpectralPowerDistribution('Sample', data)
>>> print(spd.align(SpectralShape(505, 565, 1)))
SpectralPowerDistribution('Sample', (505.0, 565.0, 1.0))
>>> # Doctests skip for Python 2.x compatibility.
>>> spd.wavelengths  
array([505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517,
       518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530,
       531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543,
       544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556,
       557, 558, 559, 560, 561, 562, 563, 564, 565])
>>> spd.values  
array([ 49.67     ...,  49.67     ...,  49.67     ...,  49.67     ...,
        49.67     ...,  49.67     ...,  51.8341162...,  53.9856467...,
        56.1229464...,  58.2366197...,  60.3121800...,  62.3327095...,
        64.2815187...,  66.1448055...,  67.9143153...,  69.59     ...,
        71.1759958...,  72.6627938...,  74.0465756...,  75.3329710...,
        76.5339542...,  77.6647421...,  78.7406907...,  79.7741932...,
        80.7715767...,  81.73     ...,  82.6407518...,  83.507872 ...,
        84.3326333...,  85.109696 ...,  85.8292968...,  86.47944  ...,
        87.0480863...,  87.525344 ...,  87.9056578...,  88.19     ...,
        88.3858347...,  88.4975634...,  88.5258906...,  88.4696570...,
        88.3266460...,  88.0943906...,  87.7709802...,  87.3558672...,
        86.8506741...,  86.26     ...,  85.5911699...,  84.8503430...,
        84.0434801...,  83.1771110...,  82.2583874...,  81.2951360...,
        80.2959122...,  79.2700525...,  78.2277286...,  77.18     ...,
        77.18     ...,  77.18     ...,  77.18     ...,  77.18     ...])
clone()[source]

Clones the spectral power distribution.

Most of the SpectralPowerDistribution class operations are conducted in-place. The SpectralPowerDistribution.clone() method provides a convenient way to copy the spectral power distribution to a new object.

Returns:Cloned spectral power distribution.
Return type:SpectralPowerDistribution

Examples

>>> data = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> spd = SpectralPowerDistribution('Sample', data)
>>> print(spd)  
SpectralPowerDistribution('Sample', (510..., 540..., 10...))
>>> spd_clone = spd.clone()
>>> print(spd_clone)  
SpectralPowerDistribution('Sample (...)', (510..., 540..., 10...))
data

Property for self._data private attribute.

Returns:self._data.
Return type:SpectralMapping
extrapolate(shape, method=u'Constant', left=None, right=None)[source]

Extrapolates the spectral power distribution following CIE 15:2004 recommendation.

Parameters:
  • shape (SpectralShape) – Spectral shape used for extrapolation.
  • method (unicode, optional) – {‘Constant’, ‘Linear’},, Extrapolation method.
  • left (numeric, optional) – Value to return for low extrapolation range.
  • right (numeric, optional) – Value to return for high extrapolation range.
Returns:

Extrapolated spectral power distribution.

Return type:

SpectralPowerDistribution

References

[2]CIE TC 1-48. (2004). Extrapolation. In CIE 015:2004 Colorimetry, 3rd Edition (p. 24). ISBN:978-3-901-90633-6
[3]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:978-3-901-90641-1

Examples

>>> data = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> spd = SpectralPowerDistribution('Sample', data)
>>> spd.extrapolate(  
...     SpectralShape(400, 700)).shape
SpectralShape(400..., 700..., 10...)
>>> spd[400]  
array(49.67...)
>>> spd[700]  
array(88.1...)
get(wavelength, default=nan)[source]

Returns the value for given wavelength \(\lambda\).

Parameters:
  • wavelength (numeric or ndarray) – Wavelength \(\lambda\) to retrieve the value.
  • default (nan or numeric, optional) – Wavelength \(\lambda\) default value.
Returns:

Wavelength \(\lambda\) value.

Return type:

numeric or ndarray

Examples

>>> data = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> spd = SpectralPowerDistribution('Sample', data)
>>> # Doctests ellipsis for Python 2.x compatibility.
>>> spd.get(510)  
array(49.67...)
>>> spd.get(511)
array(nan)
>>> spd.get(np.array([510, 520]))
array([ 49.67,  69.59])
interpolate(shape=SpectralShape(None, None, None), method=None)[source]

Interpolates the spectral power distribution following CIE 167:2005 recommendations: the method developed by Sprague (1880) should be used for interpolating functions having a uniformly spaced independent variable and a Cubic Spline method for non-uniformly spaced independent variable.

Parameters:
  • shape (SpectralShape, optional) – Spectral shape used for interpolation.
  • method (unicode, optional) – {None, ‘Cubic Spline’, ‘Linear’, ‘Pchip’, ‘Sprague’}, Enforce given interpolation method.
Returns:

Interpolated spectral power distribution.

Return type:

SpectralPowerDistribution

Raises:
  • RuntimeError – If Sprague (1880) interpolation method is forced with a non-uniformly spaced independent variable.
  • ValueError – If the interpolation method is not defined.

Notes

Warning

  • If scipy is not unavailable the Cubic Spline method will fallback to legacy Linear interpolation.
  • Cubic Spline interpolator requires at least 3 wavelengths \(\lambda_n\) for interpolation.
  • Linear interpolator requires at least 2 wavelengths \(\lambda_n\) for interpolation.
  • Pchip interpolator requires at least 2 wavelengths \(\lambda_n\) for interpolation.
  • Sprague (1880) interpolator requires at least 6 wavelengths \(\lambda_n\) for interpolation.

References

[4]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:978-3-901-90641-1

Examples

Uniform data is using Sprague (1880) interpolation by default:

>>> data = {
...     510: 49.67,
...     520: 69.59,
...     530: 81.73,
...     540: 88.19,
...     550: 86.26,
...     560: 77.18}
>>> spd = SpectralPowerDistribution('Sample', data)
>>> print(spd.interpolate(SpectralShape(interval=1)))
SpectralPowerDistribution('Sample', (510.0, 560.0, 1.0))
>>> spd[515]  
array(60.3121800...)

Non uniform data is using Cubic Spline interpolation by default:

>>> spd = SpectralPowerDistribution('Sample', data)
>>> spd[511] = 31.41
>>> print(spd.interpolate(SpectralShape(interval=1)))
SpectralPowerDistribution('Sample', (510.0, 560.0, 1.0))
>>> spd[515]  
array(21.4835799...)

Enforcing Linear interpolation:

>>> spd = SpectralPowerDistribution('Sample', data)
>>> print(spd.interpolate(
...     SpectralShape(interval=1), method='Linear'))
SpectralPowerDistribution('Sample', (510.0, 560.0, 1.0))
>>> spd[515]  
array(59.63...)

Enforcing Pchip interpolation:

>>> spd = SpectralPowerDistribution('Sample', data)
>>> print(spd.interpolate(
...     SpectralShape(interval=1), method='Pchip'))
SpectralPowerDistribution('Sample', (510.0, 560.0, 1.0))
>>> spd[515]  
array(60.7204982...)
is_uniform()[source]

Returns if the spectral power distribution has uniformly spaced data.

Returns:Is uniform.
Return type:bool

Examples

>>> data = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> spd = SpectralPowerDistribution('Sample', data)
>>> spd.is_uniform()
True

Breaking the interval by introducing a new wavelength \(\lambda\) value:

>>> spd[511] = 3.1415
>>> spd.is_uniform()
False
items

Property for self.items attribute. This is a convenient attribute used to iterate over the spectral power distribution.

Returns:Spectral power distribution data.
Return type:ndarray
name

Property for self._name private attribute.

Returns:self._name.
Return type:unicode
normalise(factor=1)[source]

Normalises the spectral power distribution with given normalization factor.

Parameters:factor (numeric, optional) – Normalization factor
Returns:Normalised spectral power distribution.
Return type:SpectralPowerDistribution

Examples

>>> data = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> spd = SpectralPowerDistribution('Sample', data)
>>> print(spd.normalise())  
SpectralPowerDistribution('Sample', (510..., 540..., 10...))
>>> spd.values  
array([ 0.5632157...,  0.7890917...,  0.9267490...,  1.        ])
shape

Property for self.shape attribute.

Returns the shape of the spectral power distribution in the form of a SpectralShape class instance.

Returns:Spectral power distribution shape.
Return type:SpectralShape

Notes

  • A non uniform spectral power distribution may will have multiple different interval, in that case SpectralPowerDistribution.shape returns the minimum interval size.

Warning

SpectralPowerDistribution.shape is read only.

Examples

Uniform spectral power distribution:

>>> data = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> SpectralPowerDistribution(  
...     'Sample', data).shape
SpectralShape(510..., 540..., 10...)

Non uniform spectral power distribution:

>>> data = {512.3: 49.67, 524.5: 69.59, 532.4: 81.73, 545.7: 88.19}
>>> # Doctests ellipsis for Python 2.x compatibility.
>>> SpectralPowerDistribution(  
...     'Sample', data).shape
SpectralShape(512.3, 545.7, 7...)
title

Property for self._title private attribute.

Returns:self._title.
Return type:unicode
trim_wavelengths(shape)[source]

Trims the spectral power distribution wavelengths to given spectral shape.

Parameters:shape (SpectralShape) – Spectral shape used for trimming.
Returns:Trimed spectral power distribution.
Return type:SpectralPowerDistribution

Examples

>>> data = {
...     510: 49.67,
...     520: 69.59,
...     530: 81.73,
...     540: 88.19,
...     550: 86.26,
...     560: 77.18}
>>> spd = SpectralPowerDistribution('Sample', data)
>>> print(spd.trim_wavelengths(  
...     SpectralShape(520, 550, 10)))
SpectralPowerDistribution('Sample', (520.0, 550.0, 10.0))
>>> # Doctests skip for Python 2.x compatibility.
>>> spd.wavelengths  
array([ 520.,  530.,  540.,  550.])
values

Property for self.values attribute.

Returns:Spectral power distribution wavelengths \(\lambda_n\) values.
Return type:ndarray

Warning

SpectralPowerDistribution.values is read only.

wavelengths

Property for self.wavelengths attribute.

Returns:Spectral power distribution wavelengths \(\lambda_n\).
Return type:ndarray
zeros(shape=SpectralShape(None, None, None))[source]

Zeros fills the spectral power distribution: Missing values will be replaced with zeros to fit the defined range.

Parameters:shape (SpectralShape, optional) – Spectral shape used for zeros fill.
Returns:Zeros filled spectral power distribution.
Return type:SpectralPowerDistribution
Raises:RuntimeError – If the spectral power distribution cannot be zeros filled.

Examples

>>> data = {
...     510: 49.67,
...     520: 69.59,
...     530: 81.73,
...     540: 88.19,
...     550: 86.26,
...     560: 77.18}
>>> spd = SpectralPowerDistribution('Sample', data)
>>> print(spd.zeros(SpectralShape(505, 565, 1)))
SpectralPowerDistribution('Sample', (505.0, 565.0, 1.0))
>>> spd.values
array([  0.  ,   0.  ,   0.  ,   0.  ,   0.  ,  49.67,   0.  ,   0.  ,
         0.  ,   0.  ,   0.  ,   0.  ,   0.  ,   0.  ,   0.  ,  69.59,
         0.  ,   0.  ,   0.  ,   0.  ,   0.  ,   0.  ,   0.  ,   0.  ,
         0.  ,  81.73,   0.  ,   0.  ,   0.  ,   0.  ,   0.  ,   0.  ,
         0.  ,   0.  ,   0.  ,  88.19,   0.  ,   0.  ,   0.  ,   0.  ,
         0.  ,   0.  ,   0.  ,   0.  ,   0.  ,  86.26,   0.  ,   0.  ,
         0.  ,   0.  ,   0.  ,   0.  ,   0.  ,   0.  ,   0.  ,  77.18,
         0.  ,   0.  ,   0.  ,   0.  ,   0.  ])
class colour.TriSpectralPowerDistribution(name, data, mapping, title=None, labels=None)[source]

Bases: object

Defines the base object for colour matching functions.

A compound of three SpectralPowerDistribution is used to store the underlying axis data.

Parameters:
  • name (unicode) – Tri-spectral power distribution name.
  • data (dict) – Tri-spectral power distribution data.
  • mapping (dict) – Tri-spectral power distribution attributes mapping.
  • title (unicode, optional) – Tri-spectral power distribution title for figures.
  • labels (dict, optional) – Tri-spectral power distribution axis labels mapping for figures.
name
mapping
data
title
labels
x
y
z
wavelengths
values
items
shape
__str__()[source]
__repr__()[source]
__hash__()[source]
__init__()[source]
__getitem__()[source]
__setitem__()[source]
__iter__()[source]
__contains__()[source]
__len__()[source]
__eq__()[source]
__ne__()[source]
__add__()[source]
__iadd__()[source]
__sub__()[source]
__isub__()[source]
__mul__()[source]
__imul__()[source]
__div__()[source]
__idiv__()[source]
__pow__()[source]
__ipow__()[source]
get()[source]
is_uniform()[source]
extrapolate()[source]
interpolate()[source]
align()[source]
trim_wavelengths()[source]
zeros()[source]
normalise()[source]
clone()[source]

Examples

>>> x_bar = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> y_bar = {510: 90.56, 520: 87.34, 530: 45.76, 540: 23.45}
>>> z_bar = {510: 12.43, 520: 23.15, 530: 67.98, 540: 90.28}
>>> data = {'x_bar': x_bar, 'y_bar': y_bar, 'z_bar': z_bar}
>>> mapping = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Observer', data, mapping)
>>> # Doctests skip for Python 2.x compatibility.
>>> tri_spd.wavelengths  
array([510, 520, 530, 540])
>>> tri_spd.values
array([[ 49.67,  90.56,  12.43],
       [ 69.59,  87.34,  23.15],
       [ 81.73,  45.76,  67.98],
       [ 88.19,  23.45,  90.28]])
>>> # Doctests skip for Python 2.x compatibility.
>>> tri_spd.shape  
SpectralShape(510, 540, 10)
align(shape, interpolation_method=None, extrapolation_method=u'Constant', extrapolation_left=None, extrapolation_right=None)[source]

Aligns the tri-spectral power distribution to given shape: Interpolates first then extrapolates to fit the given range.

Parameters:
  • shape (SpectralShape) – Spectral shape used for alignment.
  • interpolation_method (unicode, optional) – {None, ‘Cubic Spline’, ‘Linear’, ‘Pchip’, ‘Sprague’}, Enforce given interpolation method.
  • extrapolation_method (unicode, optional) – {‘Constant’, ‘Linear’}, Extrapolation method.
  • extrapolation_left (numeric, optional) – Value to return for low extrapolation range.
  • extrapolation_right (numeric, optional) – Value to return for high extrapolation range.
Returns:

Aligned tri-spectral power distribution.

Return type:

TriSpectralPowerDistribution

Examples

>>> x_bar = {
...     510: 49.67,
...     520: 69.59,
...     530: 81.73,
...     540: 88.19,
...     550: 89.76,
...     560: 90.28}
>>> y_bar = {
...     510: 90.56,
...     520: 87.34,
...     530: 45.76,
...     540: 23.45,
...     550: 15.34,
...     560: 10.11}
>>> z_bar = {
...     510: 12.43,
...     520: 23.15,
...     530: 67.98,
...     540: 90.28,
...     550: 91.61,
...     560: 98.24}
>>> data = {'x_bar': x_bar, 'y_bar': y_bar, 'z_bar': z_bar}
>>> mapping = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Observer', data, mapping)
>>> print(tri_spd.align(SpectralShape(505, 565, 1)))
TriSpectralPowerDistribution('Observer', (505.0, 565.0, 1.0))
>>> # Doctests skip for Python 2.x compatibility.
>>> tri_spd.wavelengths  
array([505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517,
       518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530,
       531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543,
       544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556,
       557, 558, 559, 560, 561, 562, 563, 564, 565])
>>> tri_spd.values  
array([[ 49.67     ...,  90.56     ...,  12.43     ...],
       [ 49.67     ...,  90.56     ...,  12.43     ...],
       [ 49.67     ...,  90.56     ...,  12.43     ...],
       [ 49.67     ...,  90.56     ...,  12.43     ...],
       [ 49.67     ...,  90.56     ...,  12.43     ...],
       [ 49.67     ...,  90.56     ...,  12.43     ...],
       [ 51.8325938...,  91.2994928...,  12.5377184...],
       [ 53.9841952...,  91.9502387...,  12.7233193...],
       [ 56.1205452...,  92.5395463...,  12.9651679...],
       [ 58.2315395...,  93.0150037...,  13.3123777...],
       [ 60.3033208...,  93.2716331...,  13.8605136...],
       [ 62.3203719...,  93.1790455...,  14.7272944...],
       [ 64.2676077...,  92.6085951...,  16.0282961...],
       [ 66.1324679...,  91.4605335...,  17.8526544...],
       [ 67.9070097...,  89.6911649...,  20.2387677...],
       [ 69.59     ...,  87.34     ...,  23.15     ...],
       [ 71.1837378...,  84.4868033...,  26.5150469...],
       [ 72.6800056...,  81.0666018...,  30.3964852...],
       [ 74.0753483...,  77.0766254...,  34.7958422...],
       [ 75.3740343...,  72.6153870...,  39.6178858...],
       [ 76.5856008...,  67.8490714...,  44.7026805...],
       [ 77.7223995...,  62.9779261...,  49.8576432...],
       [ 78.7971418...,  58.2026503...,  54.8895997...],
       [ 79.8204447...,  53.6907852...,  59.6368406...],
       [ 80.798376 ...,  49.5431036...,  64.0011777...],
       [ 81.73     ...,  45.76     ...,  67.98     ...],
       [ 82.6093606...,  42.2678534...,  71.6460893...],
       [ 83.439232 ...,  39.10608  ...,  74.976976 ...],
       [ 84.2220071...,  36.3063728...,  77.9450589...],
       [ 84.956896 ...,  33.85464  ...,  80.552    ...],
       [ 85.6410156...,  31.7051171...,  82.8203515...],
       [ 86.27048  ...,  29.79448  ...,  84.785184 ...],
       [ 86.8414901...,  28.0559565...,  86.4857131...],
       [ 87.351424 ...,  26.43344  ...,  87.956928 ...],
       [ 87.7999266...,  24.8956009...,  89.2212178...],
       [ 88.19     ...,  23.45     ...,  90.28     ...],
       [ 88.5265036...,  22.1424091...,  91.1039133...],
       [ 88.8090803...,  20.9945234...,  91.6538035...],
       [ 89.0393279...,  20.0021787...,  91.9333499...],
       [ 89.2222817...,  19.1473370...,  91.9858818...],
       [ 89.3652954...,  18.4028179...,  91.8811002...],
       [ 89.4769231...,  17.7370306...,  91.7018000...],
       [ 89.5657996...,  17.1187058...,  91.5305910...],
       [ 89.6395227...,  16.5216272...,  91.4366204...],
       [ 89.7035339...,  15.9293635...,  91.4622944...],
       [ 89.76     ...,  15.34     ...,  91.61     ...],
       [ 89.8094041...,  14.7659177...,  91.8528616...],
       [ 89.8578890...,  14.2129190...,  92.2091737...],
       [ 89.9096307...,  13.6795969...,  92.6929664...],
       [ 89.9652970...,  13.1613510...,  93.2988377...],
       [ 90.0232498...,  12.6519811...,  94.0078786...],
       [ 90.0807467...,  12.1452800...,  94.7935995...],
       [ 90.1351435...,  11.6366269...,  95.6278555...],
       [ 90.1850956...,  11.1245805...,  96.4867724...],
       [ 90.2317606...,  10.6124724...,  97.3566724...],
       [ 90.28     ...,  10.11     ...,  98.24     ...],
       [ 90.28     ...,  10.11     ...,  98.24     ...],
       [ 90.28     ...,  10.11     ...,  98.24     ...],
       [ 90.28     ...,  10.11     ...,  98.24     ...],
       [ 90.28     ...,  10.11     ...,  98.24     ...],
       [ 90.28     ...,  10.11     ...,  98.24     ...]])
clone()[source]

Clones the tri-spectral power distribution.

Most of the TriSpectralPowerDistribution class operations are conducted in-place. The TriSpectralPowerDistribution.clone() method provides a convenient way to copy the tri-spectral power distribution to a new object.

Returns:Cloned tri-spectral power distribution.
Return type:TriSpectralPowerDistribution

Examples

>>> x_bar = {
...     510: 49.67,
...     520: 69.59,
...     530: 81.73,
...     540: 88.19,
...     550: 89.76,
...     560: 90.28}
>>> y_bar = {
...     510: 90.56,
...     520: 87.34,
...     530: 45.76,
...     540: 23.45,
...     550: 15.34,
...     560: 10.11}
>>> z_bar = {
...     510: 12.43,
...     520: 23.15,
...     530: 67.98,
...     540: 90.28,
...     550: 91.61,
...     560: 98.24}
>>> data = {'x_bar': x_bar, 'y_bar': y_bar, 'z_bar': z_bar}
>>> mapping = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Observer', data, mapping)
>>> print(tri_spd)  
TriSpectralPowerDistribution('Observer', (510..., 560..., 10...))
>>> tri_spd_clone = tri_spd.clone()
>>> print(tri_spd_clone)  
TriSpectralPowerDistribution('Observer (...)', (510..., 560..., 10...))
data

Property for self._data private attribute.

Returns:self._data.
Return type:dict
extrapolate(shape, method=u'Constant', left=None, right=None)[source]

Extrapolates the tri-spectral power distribution following CIE 15:2004 recommendation. [2]_ [3]_

Parameters:
  • shape (SpectralShape) – Spectral shape used for extrapolation.
  • method (unicode, optional) – {‘Constant’, ‘Linear’}, Extrapolation method.
  • left (numeric, optional) – Value to return for low extrapolation range.
  • right (numeric, optional) – Value to return for high extrapolation range.
Returns:

Extrapolated tri-spectral power distribution.

Return type:

TriSpectralPowerDistribution

Examples

>>> x_bar = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> y_bar = {510: 90.56, 520: 87.34, 530: 45.76, 540: 23.45}
>>> z_bar = {510: 12.43, 520: 23.15, 530: 67.98, 540: 90.28}
>>> data = {'x_bar': x_bar, 'y_bar': y_bar, 'z_bar': z_bar}
>>> mapping = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Observer', data, mapping)
>>> tri_spd.extrapolate(  
...     SpectralShape(400, 700)).shape
SpectralShape(400..., 700..., 10...)
>>> tri_spd[400]
array([ 49.67,  90.56,  12.43])
>>> tri_spd[700]
array([ 88.19,  23.45,  90.28])
get(wavelength, default=nan)[source]

Returns the values for given wavelength \(\lambda\).

Parameters:
  • wavelength (numeric or array_like) – Wavelength \(\lambda\) to retrieve the values.
  • default (nan, numeric or array_like, optional) – Wavelength \(\lambda\) default values.
Returns:

Wavelength \(\lambda\) values.

Return type:

numeric or array_like

Examples

>>> x_bar = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> y_bar = {510: 90.56, 520: 87.34, 530: 45.76, 540: 23.45}
>>> z_bar = {510: 12.43, 520: 23.15, 530: 67.98, 540: 90.28}
>>> data = {'x_bar': x_bar, 'y_bar': y_bar, 'z_bar': z_bar}
>>> mapping = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Observer', data, mapping)
>>> tri_spd.get(510)
array([ 49.67,  90.56,  12.43])
>>> tri_spd.get(np.array([510, 520]))
array([[ 49.67,  90.56,  12.43],
       [ 69.59,  87.34,  23.15]])
>>> tri_spd.get(511)
array([ nan,  nan,  nan])
>>> tri_spd.get(np.array([510, 520]))
array([[ 49.67,  90.56,  12.43],
       [ 69.59,  87.34,  23.15]])
interpolate(shape=SpectralShape(None, None, None), method=None)[source]

Interpolates the tri-spectral power distribution following CIE 167:2005 recommendations: the method developed by Sprague (1880) should be used for interpolating functions having a uniformly spaced independent variable and a Cubic Spline method for non-uniformly spaced independent variable. [4]_

Parameters:
  • shape (SpectralShape, optional) – Spectral shape used for interpolation.
  • method (unicode, optional) – {None, ‘Cubic Spline’, ‘Linear’, ‘Pchip’, ‘Sprague’}, Enforce given interpolation method.
Returns:

Interpolated tri-spectral power distribution.

Return type:

TriSpectralPowerDistribution

Notes

Warning

See SpectralPowerDistribution.interpolate() method warning section.

Examples

Uniform data is using Sprague (1880) interpolation by default:

>>> x_bar = {
...     510: 49.67,
...     520: 69.59,
...     530: 81.73,
...     540: 88.19,
...     550: 89.76,
...     560: 90.28}
>>> y_bar = {
...     510: 90.56,
...     520: 87.34,
...     530: 45.76,
...     540: 23.45,
...     550: 15.34,
...     560: 10.11}
>>> z_bar = {
...     510: 12.43,
...     520: 23.15,
...     530: 67.98,
...     540: 90.28,
...     550: 91.61,
...     560: 98.24}
>>> data = {'x_bar': x_bar, 'y_bar': y_bar, 'z_bar': z_bar}
>>> mapping = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Observer', data, mapping)
>>> print(tri_spd.interpolate(SpectralShape(interval=1)))
TriSpectralPowerDistribution('Observer', (510.0, 560.0, 1.0))
>>> tri_spd[515]  
array([ 60.3033208...,  93.2716331...,  13.8605136...])

Non uniform data is using Cubic Spline interpolation by default:

>>> data = {'x_bar': x_bar, 'y_bar': y_bar, 'z_bar': z_bar}
>>> mapping = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Observer', data, mapping)
>>> tri_spd[511] = np.array([31.41, 95.27, 15.06])
>>> print(tri_spd.interpolate(SpectralShape(interval=1)))
TriSpectralPowerDistribution('Observer', (510.0, 560.0, 1.0))
>>> tri_spd[515]  
array([  21.4752929...,  100.6436744...,   18.8153985...])

Enforcing Linear interpolation:

>>> data = {'x_bar': x_bar, 'y_bar': y_bar, 'z_bar': z_bar}
>>> mapping = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Observer', data, mapping)
>>> print(tri_spd.interpolate(  
...     SpectralShape(interval=1), method='Linear'))
TriSpectralPowerDistribution('Observer', (510.0, 560.0, 1.0))
>>> tri_spd[515]  
array([ 59.63...,  88.95...,  17.79...])

Enforcing Pchip interpolation:

>>> data = {'x_bar': x_bar, 'y_bar': y_bar, 'z_bar': z_bar}
>>> mapping = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Observer', data, mapping)
>>> print(tri_spd.interpolate(  
...     SpectralShape(interval=1), method='Pchip'))
TriSpectralPowerDistribution('Observer', (510.0, 560.0, 1.0))
>>> tri_spd[515]  
array([ 60.7204982...,  89.6971406...,  15.6271845...])
is_uniform()[source]

Returns if the tri-spectral power distribution has uniformly spaced data.

Returns:Is uniform.
Return type:bool

Examples

>>> x_bar = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> y_bar = {510: 90.56, 520: 87.34, 530: 45.76, 540: 23.45}
>>> z_bar = {510: 12.43, 520: 23.15, 530: 67.98, 540: 90.28}
>>> data = {'x_bar': x_bar, 'y_bar': y_bar, 'z_bar': z_bar}
>>> mapping = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Observer', data, mapping)
>>> tri_spd.is_uniform()
True

Breaking the interval by introducing new wavelength \(\lambda\) values:

>>> tri_spd[511] = np.array([49.6700, 49.6700, 49.6700])
>>> tri_spd.is_uniform()
False
items

Property for self.items attribute. This is a convenient attribute used to iterate over the tri-spectral power distribution.

Notes

Returns:Tri-spectral power distribution data.
Return type:list
labels

Property for self._labels private attribute.

Returns:self._labels.
Return type:dict
mapping

Property for self._mapping private attribute.

Returns:self._mapping.
Return type:dict
name

Property for self._name private attribute.

Returns:self._name.
Return type:unicode
normalise(factor=1)[source]

Normalises the tri-spectral power distribution with given normalization factor.

Parameters:factor (numeric, optional) – Normalization factor
Returns:Normalised tri- spectral power distribution.
Return type:TriSpectralPowerDistribution

Notes

  • The implementation uses the maximum value for all axis.

Examples

>>> x_bar = {
...     510: 49.67,
...     520: 69.59,
...     530: 81.73,
...     540: 88.19,
...     550: 89.76,
...     560: 90.28}
>>> y_bar = {
...     510: 90.56,
...     520: 87.34,
...     530: 45.76,
...     540: 23.45,
...     550: 15.34,
...     560: 10.11}
>>> z_bar = {
...     510: 12.43,
...     520: 23.15,
...     530: 67.98,
...     540: 90.28,
...     550: 91.61,
...     560: 98.24}
>>> data = {'x_bar': x_bar, 'y_bar': y_bar, 'z_bar': z_bar}
>>> mapping = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Observer', data, mapping)
>>> print(tri_spd.normalise())  
TriSpectralPowerDistribution('Observer', (510..., 560..., 10...))
>>> tri_spd.values  
array([[ 0.5055985...,  0.9218241...,  0.1265268...],
       [ 0.7083672...,  0.8890472...,  0.2356473...],
       [ 0.8319421...,  0.4657980...,  0.6919788...],
       [ 0.8976995...,  0.2387011...,  0.9189739...],
       [ 0.9136807...,  0.1561482...,  0.9325122...],
       [ 0.9189739...,  0.1029112...,  1.       ...]])
shape

Property for self.shape attribute.

Returns the shape of the tri-spectral power distribution in the form of a SpectralShape class instance.

Returns:Tri-spectral power distribution shape.
Return type:SpectralShape

Warning

TriSpectralPowerDistribution.shape is read only.

Examples

>>> x_bar = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> y_bar = {510: 90.56, 520: 87.34, 530: 45.76, 540: 23.45}
>>> z_bar = {510: 12.43, 520: 23.15, 530: 67.98, 540: 90.28}
>>> data = {'x_bar': x_bar, 'y_bar': y_bar, 'z_bar': z_bar}
>>> mapping = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Observer', data, mapping)
>>> tri_spd.shape  
SpectralShape(510..., 540..., 10...)
title

Property for self._title private attribute.

Returns:self._title.
Return type:unicode
trim_wavelengths(shape)[source]

Trims the tri-spectral power distribution wavelengths to given shape.

Parameters:shape (SpectralShape) – Spectral shape used for trimming.
Returns:Trimmed tri-spectral power distribution.
Return type:TriSpectralPowerDistribution

Examples

>>> x_bar = {
...     510: 49.67,
...     520: 69.59,
...     530: 81.73,
...     540: 88.19,
...     550: 89.76,
...     560: 90.28}
>>> y_bar = {
...     510: 90.56,
...     520: 87.34,
...     530: 45.76,
...     540: 23.45,
...     550: 15.34,
...     560: 10.11}
>>> z_bar = {
...     510: 12.43,
...     520: 23.15,
...     530: 67.98,
...     540: 90.28,
...     550: 91.61,
...     560: 98.24}
>>> data = {'x_bar': x_bar, 'y_bar': y_bar, 'z_bar': z_bar}
>>> mapping = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Observer', data, mapping)
>>> # Doctests skip for Python 2.x compatibility.
>>> print(tri_spd.trim_wavelengths(  
...     SpectralShape(520, 550, 10)))
TriSpectralPowerDistribution('Observer', (520.0, 550.0, 10.0))
>>> tri_spd.wavelengths  
array([ 520.,  530.,  540.,  550.])
values

Property for self.values attribute.

Returns:Tri-spectral power distribution wavelengths \(\lambda_n\) values.
Return type:ndarray

Warning

TriSpectralPowerDistribution.values is read only.

wavelengths

Property for self.wavelengths attribute.

Returns:Tri-spectral power distribution wavelengths \(\lambda_n\).
Return type:ndarray
x

Property for self.x attribute.

Returns:Spectral power distribution for x axis.
Return type:SpectralPowerDistribution

Warning

TriSpectralPowerDistribution.x is read only.

y

Property for self.y attribute.

Returns:Spectral power distribution for y axis.
Return type:SpectralPowerDistribution

Warning

TriSpectralPowerDistribution.y is read only.

z

Property for self.z attribute.

Returns:Spectral power distribution for z axis.
Return type:SpectralPowerDistribution

Warning

TriSpectralPowerDistribution.z is read only.

zeros(shape=SpectralShape(None, None, None))[source]

Zeros fills the tri-spectral power distribution: Missing values will be replaced with zeros to fit the defined range.

Parameters:shape (SpectralShape, optional) – Spectral shape used for zeros fill.
Returns:Zeros filled tri-spectral power distribution.
Return type:TriSpectralPowerDistribution

Examples

>>> x_bar = {
...     510: 49.67,
...     520: 69.59,
...     530: 81.73,
...     540: 88.19,
...     550: 89.76,
...     560: 90.28}
>>> y_bar = {
...     510: 90.56,
...     520: 87.34,
...     530: 45.76,
...     540: 23.45,
...     550: 15.34,
...     560: 10.11}
>>> z_bar = {
...     510: 12.43,
...     520: 23.15,
...     530: 67.98,
...     540: 90.28,
...     550: 91.61,
...     560: 98.24}
>>> data = {'x_bar': x_bar, 'y_bar': y_bar, 'z_bar': z_bar}
>>> mapping = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd = TriSpectralPowerDistribution('Observer', data, mapping)
>>> print(tri_spd.zeros(SpectralShape(505, 565, 1)))
TriSpectralPowerDistribution('Observer', (505.0, 565.0, 1.0))
>>> tri_spd.values
array([[  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [ 49.67,  90.56,  12.43],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [ 69.59,  87.34,  23.15],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [ 81.73,  45.76,  67.98],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [ 88.19,  23.45,  90.28],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [ 89.76,  15.34,  91.61],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [ 90.28,  10.11,  98.24],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ],
       [  0.  ,   0.  ,   0.  ]])
colour.constant_spd(k, shape=SpectralShape(360.0, 780.0, 1.0))[source]

Returns a spectral power distribution of given spectral shape filled with constant \(k\) values.

Parameters:
  • k (numeric) – Constant \(k\) to fill the spectral power distribution with.
  • shape (SpectralShape, optional) – Spectral shape used to create the spectral power distribution.
Returns:

Constant \(k\) to filled spectral power distribution.

Return type:

SpectralPowerDistribution

Notes

  • By default, the spectral power distribution will use the shape given by DEFAULT_SPECTRAL_SHAPE attribute.

Examples

>>> spd = constant_spd(100)
>>> spd.shape
SpectralShape(360.0, 780.0, 1.0)
>>> spd[400]
array(100.0)
colour.zeros_spd(shape=SpectralShape(360.0, 780.0, 1.0))[source]

Returns a spectral power distribution of given spectral shape filled with zeros.

Parameters:shape (SpectralShape, optional) – Spectral shape used to create the spectral power distribution.
Returns:Zeros filled spectral power distribution.
Return type:SpectralPowerDistribution

See also

constant_spd()

Notes

  • By default, the spectral power distribution will use the shape given by DEFAULT_SPECTRAL_SHAPE attribute.

Examples

>>> spd = zeros_spd()
>>> spd.shape
SpectralShape(360.0, 780.0, 1.0)
>>> spd[400]
array(0.0)
colour.ones_spd(shape=SpectralShape(360.0, 780.0, 1.0))[source]

Returns a spectral power distribution of given spectral shape filled with ones.

Parameters:shape (SpectralShape, optional) – Spectral shape used to create the spectral power distribution.
Returns:Ones filled spectral power distribution.
Return type:SpectralPowerDistribution

See also

constant_spd()

Notes

  • By default, the spectral power distribution will use the shape given by DEFAULT_SPECTRAL_SHAPE attribute.

Examples

>>> spd = ones_spd()
>>> spd.shape
SpectralShape(360.0, 780.0, 1.0)
>>> spd[400]
array(1.0)
colour.blackbody_spd(temperature, shape=SpectralShape(360.0, 780.0, 1.0), c1=3.741771e-16, c2=0.014388, n=1)[source]

Returns the spectral power distribution of the planckian radiator for given temperature \(T[K]\).

Parameters:
  • temperature (numeric) – Temperature \(T[K]\) in kelvin degrees.
  • shape (SpectralShape, optional) – Spectral shape used to create the spectral power distribution of the planckian radiator.
  • c1 (numeric, optional) – The official value of \(c1\) is provided by the Committee on Data for Science and Technology (CODATA) and is \(c1=3,741771x10.16\ W/m_2\) (Mohr and Taylor, 2000).
  • c2 (numeric, optional) – Since \(T\) is measured on the International Temperature Scale, the value of \(c2\) used in colorimetry should follow that adopted in the current International Temperature Scale (ITS-90) (Preston-Thomas, 1990; Mielenz et aI., 1991), namely \(c2=1,4388x10.2\ m/K\).
  • n (numeric, optional) – Medium index of refraction. For dry air at 15°C 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 \(n=1\).
Returns:

Blackbody spectral power distribution.

Return type:

SpectralPowerDistribution

Examples

>>> from colour import STANDARD_OBSERVERS_CMFS
>>> cmfs = STANDARD_OBSERVERS_CMFS['CIE 1931 2 Degree Standard Observer']
>>> print(blackbody_spd(5000, cmfs.shape))
SpectralPowerDistribution('5000K Blackbody', (360.0, 830.0, 1.0))
colour.blackbody_spectral_radiance(wavelength, temperature, c1=3.741771e-16, c2=0.014388, n=1)

Returns the spectral radiance of a blackbody at thermodynamic temperature \(T[K]\) in a medium having index of refraction \(n\).

Parameters:
  • wavelength (numeric or array_like) – Wavelength in meters.
  • temperature (numeric or array_like) – Temperature \(T[K]\) in kelvin degrees.
  • c1 (numeric or array_like, optional) – The official value of \(c1\) is provided by the Committee on Data for Science and Technology (CODATA) and is \(c1=3,741771x10.16\ W/m_2\) (Mohr and Taylor, 2000).
  • c2 (numeric or array_like, optional) – Since \(T\) is measured on the International Temperature Scale, the value of \(c2\) used in colorimetry should follow that adopted in the current International Temperature Scale (ITS-90) (Preston-Thomas, 1990; Mielenz et aI., 1991), namely \(c2=1,4388x10.2\ m/K\).
  • n (numeric or array_like, optional) – Medium index of refraction. For dry air at 15°C 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 \(n=1\).
Returns:

Radiance in watts per steradian per square metre.

Return type:

numeric or ndarray

Notes

  • The following form implementation is expressed in term of wavelength.
  • The SI unit of radiance is watts per steradian per square metre.

References

[1]CIE TC 1-48. (2004). APPENDIX E. INFORMATION ON THE USE OF PLANCK’S EQUATION FOR STANDARD AIR. In CIE 015:2004 Colorimetry, 3rd Edition (pp. 77–82). ISBN:978-3-901-90633-6

Examples

>>> # Doctests ellipsis for Python 2.x compatibility.
>>> planck_law(500 * 1e-9, 5500)  
20472701909806.5...
colour.planck_law(wavelength, temperature, c1=3.741771e-16, c2=0.014388, n=1)[source]

Returns the spectral radiance of a blackbody at thermodynamic temperature \(T[K]\) in a medium having index of refraction \(n\).

Parameters:
  • wavelength (numeric or array_like) – Wavelength in meters.
  • temperature (numeric or array_like) – Temperature \(T[K]\) in kelvin degrees.
  • c1 (numeric or array_like, optional) – The official value of \(c1\) is provided by the Committee on Data for Science and Technology (CODATA) and is \(c1=3,741771x10.16\ W/m_2\) (Mohr and Taylor, 2000).
  • c2 (numeric or array_like, optional) – Since \(T\) is measured on the International Temperature Scale, the value of \(c2\) used in colorimetry should follow that adopted in the current International Temperature Scale (ITS-90) (Preston-Thomas, 1990; Mielenz et aI., 1991), namely \(c2=1,4388x10.2\ m/K\).
  • n (numeric or array_like, optional) – Medium index of refraction. For dry air at 15°C 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 \(n=1\).
Returns:

Radiance in watts per steradian per square metre.

Return type:

numeric or ndarray

Notes

  • The following form implementation is expressed in term of wavelength.
  • The SI unit of radiance is watts per steradian per square metre.

References

[1]CIE TC 1-48. (2004). APPENDIX E. INFORMATION ON THE USE OF PLANCK’S EQUATION FOR STANDARD AIR. In CIE 015:2004 Colorimetry, 3rd Edition (pp. 77–82). ISBN:978-3-901-90633-6

Examples

>>> # Doctests ellipsis for Python 2.x compatibility.
>>> planck_law(500 * 1e-9, 5500)  
20472701909806.5...
class colour.LMS_ConeFundamentals(name, data, title=None)[source]

Bases: colour.colorimetry.spectrum.TriSpectralPowerDistribution

Implements support for the Stockman and Sharpe LMS cone fundamentals colour matching functions.

Parameters:
  • name (unicode) – LMS colour matching functions name.
  • data (dict) – LMS colour matching functions.
  • title (unicode, optional) – LMS colour matching functions title for figures.
l_bar
m_bar
s_bar
l_bar

Property for self.x attribute.

Returns:self.x
Return type:SpectralPowerDistribution

Warning

LMS_ConeFundamentals.l_bar is read only.

m_bar

Property for self.y attribute.

Returns:self.y
Return type:SpectralPowerDistribution

Warning

LMS_ConeFundamentals.m_bar is read only.

s_bar

Property for self.z attribute.

Returns:self.z
Return type:SpectralPowerDistribution

Warning

LMS_ConeFundamentals.s_bar is read only.

class colour.RGB_ColourMatchingFunctions(name, data, title=None)[source]

Bases: colour.colorimetry.spectrum.TriSpectralPowerDistribution

Implements support for the CIE RGB colour matching functions.

Parameters:
  • name (unicode) – CIE RGB colour matching functions name.
  • data (dict) – CIE RGB colour matching functions.
  • title (unicode, optional) – CIE RGB colour matching functions title for figures.
r_bar
g_bar
b_bar
b_bar

Property for self.z attribute.

Returns:self.z
Return type:SpectralPowerDistribution

Warning

RGB_ColourMatchingFunctions.b_bar is read only.

g_bar

Property for self.y attribute.

Returns:self.y
Return type:SpectralPowerDistribution

Warning

RGB_ColourMatchingFunctions.g_bar is read only.

r_bar

Property for self.x attribute.

Returns:self.x
Return type:SpectralPowerDistribution

Warning

RGB_ColourMatchingFunctions.r_bar is read only.

class colour.XYZ_ColourMatchingFunctions(name, data, title=None)[source]

Bases: colour.colorimetry.spectrum.TriSpectralPowerDistribution

Implements support for the CIE Standard Observers XYZ colour matching functions.

Parameters:
  • name (unicode) – CIE Standard Observer XYZ colour matching functions name.
  • data (dict) – CIE Standard Observer XYZ colour matching functions.
  • title (unicode, optional) – CIE Standard Observer XYZ colour matching functions title for figures.
x_bar
y_bar
z_bar
x_bar

Property for self.x attribute.

Returns:self.x
Return type:SpectralPowerDistribution

Warning

XYZ_ColourMatchingFunctions.x_bar is read only.

y_bar

Property for self.y attribute.

Returns:self.y
Return type:SpectralPowerDistribution

Warning

XYZ_ColourMatchingFunctions.y_bar is read only.

z_bar

Property for self.z attribute.

Returns:self.z
Return type:SpectralPowerDistribution

Warning

XYZ_ColourMatchingFunctions.z_bar is read only.

colour.bandpass_correction(spd, method=u'Stearns 1988')[source]

Implements spectral bandpass dependence correction on given spectral power distribution using given method.

Parameters:
  • spd (SpectralPowerDistribution) – Spectral power distribution.
  • method (unicode, optional) – (‘Stearns 1988’, ) Correction method.
Returns:

Spectral bandpass dependence corrected spectral power distribution.

Return type:

SpectralPowerDistribution

colour.bandpass_correction_Stearns1988(spd)[source]

Implements spectral bandpass dependence correction on given spectral power distribution using Stearns and Stearns (1988) method.

Parameters:spd (SpectralPowerDistribution) – Spectral power distribution.
Returns:Spectral bandpass dependence corrected spectral power distribution.
Return type:SpectralPowerDistribution

References

[1]Westland, S., Ripamonti, C., & Cheung, V. (2012). Correction for Spectral Bandpass. In Computational Colour Science Using MATLAB (2nd ed., p. 38). ISBN:978-0-470-66569-5
[2]Stearns, E. I., & Stearns, R. E. (1988). An example of a method for correcting radiance data for Bandpass error. Color Research & Application, 13(4), 257–259. doi:10.1002/col.5080130410

Examples

>>> from colour import SpectralPowerDistribution
>>> data = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> spd = SpectralPowerDistribution('Spd', data)
>>> corrected_spd = bandpass_correction_Stearns1988(spd)
>>> corrected_spd.values  
array([ 48.01664   ,  70.3729688...,  82.0919506...,  88.72618   ])
colour.D_illuminant_relative_spd(xy)[source]

Returns the relative spectral power distribution of given CIE Standard Illuminant D Series using given xy chromaticity coordinates.

References

[1]Wyszecki, G., & Stiles, W. S. (2000). CIE Method of Calculating D-Illuminants. In Color Science: Concepts and Methods, Quantitative Data and Formulae (pp. 145–146). Wiley. ISBN:978-0471399186
[2]Lindbloom, B. (2007). Spectral Power Distribution of a CIE D-Illuminant. Retrieved April 05, 2014, from http://www.brucelindbloom.com/Eqn_DIlluminant.html
Parameters:xy (array_like) – xy chromaticity coordinates.
Returns:CIE Standard Illuminant D Series relative spectral power distribution.
Return type:SpectralPowerDistribution

Examples

>>> xy = np.array([0.34570, 0.35850])
>>> print(D_illuminant_relative_spd(xy))
SpectralPowerDistribution('CIE Standard Illuminant D Series', (300.0, 830.0, 10.0))
colour.CIE_standard_illuminant_A_function(wl)[source]

CIE Standard Illuminant A is intended to represent typical, domestic, tungsten-filament lighting. Its relative spectral power 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:wl (array_like) – Wavelength to evaluate the function at.
Returns:CIE Standard Illuminant A value at given wavelength.
Return type:ndarray

References

[1]CIE TC 1-48. (2004). 3.1 Recommendations concerning standard physical data of illuminants. In CIE 015:2004 Colorimetry, 3rd Edition (pp. 12–13). ISBN:978-3-901-90633-6

Examples

>>> wl = np.array([560, 580, 581.5])
>>> CIE_standard_illuminant_A_function(wl)  
array([ 100.        ,  114.4363383...,  115.5285063...])
colour.mesopic_luminous_efficiency_function(Lp, source=u'Blue Heavy', method=u'MOVE', photopic_lef=SpectralPowerDistribution( 'CIE 1924 Photopic Standard Observer', {360.0: 3.917e-06, 361.0: 4.393581e-06, 362.0: 4.929604e-06, 363.0: 5.532136e-06, 364.0: 6.208245e-06, 365.0: 6.965e-06, 366.0: 7.813219e-06, 367.0: 8.767336e-06, 368.0: 9.839844e-06, 369.0: 1.104323e-05, 370.0: 1.239e-05, 371.0: 1.388641e-05, 372.0: 1.555728e-05, 373.0: 1.744296e-05, 374.0: 1.958375e-05, 375.0: 2.202e-05, 376.0: 2.483965e-05, 377.0: 2.804126e-05, 378.0: 3.153104e-05, 379.0: 3.521521e-05, 380.0: 3.9e-05, 381.0: 4.28264e-05, 382.0: 4.69146e-05, 383.0: 5.15896e-05, 384.0: 5.71764e-05, 385.0: 6.4e-05, 386.0: 7.234421e-05, 387.0: 8.221224e-05, 388.0: 9.350816e-05, 389.0: 0.0001061361, 390.0: 0.00012, 391.0: 0.000134984, 392.0: 0.000151492, 393.0: 0.000170208, 394.0: 0.000191816, 395.0: 0.000217, 396.0: 0.0002469067, 397.0: 0.00028124, 398.0: 0.00031852, 399.0: 0.0003572667, 400.0: 0.000396, 401.0: 0.0004337147, 402.0: 0.000473024, 403.0: 0.000517876, 404.0: 0.0005722187, 405.0: 0.00064, 406.0: 0.00072456, 407.0: 0.0008255, 408.0: 0.00094116, 409.0: 0.00106988, 410.0: 0.00121, 411.0: 0.001362091, 412.0: 0.001530752, 413.0: 0.001720368, 414.0: 0.001935323, 415.0: 0.00218, 416.0: 0.0024548, 417.0: 0.002764, 418.0: 0.0031178, 419.0: 0.0035264, 420.0: 0.004, 421.0: 0.00454624, 422.0: 0.00515932, 423.0: 0.00582928, 424.0: 0.00654616, 425.0: 0.0073, 426.0: 0.008086507, 427.0: 0.00890872, 428.0: 0.00976768, 429.0: 0.01066443, 430.0: 0.0116, 431.0: 0.01257317, 432.0: 0.01358272, 433.0: 0.01462968, 434.0: 0.01571509, 435.0: 0.01684, 436.0: 0.01800736, 437.0: 0.01921448, 438.0: 0.02045392, 439.0: 0.02171824, 440.0: 0.023, 441.0: 0.02429461, 442.0: 0.02561024, 443.0: 0.02695857, 444.0: 0.02835125, 445.0: 0.0298, 446.0: 0.03131083, 447.0: 0.03288368, 448.0: 0.03452112, 449.0: 0.03622571, 450.0: 0.038, 451.0: 0.03984667, 452.0: 0.041768, 453.0: 0.043766, 454.0: 0.04584267, 455.0: 0.048, 456.0: 0.05024368, 457.0: 0.05257304, 458.0: 0.05498056, 459.0: 0.05745872, 460.0: 0.06, 461.0: 0.06260197, 462.0: 0.06527752, 463.0: 0.06804208, 464.0: 0.07091109, 465.0: 0.0739, 466.0: 0.077016, 467.0: 0.0802664, 468.0: 0.0836668, 469.0: 0.0872328, 470.0: 0.09098, 471.0: 0.09491755, 472.0: 0.09904584, 473.0: 0.1033674, 474.0: 0.1078846, 475.0: 0.1126, 476.0: 0.117532, 477.0: 0.1226744, 478.0: 0.1279928, 479.0: 0.1334528, 480.0: 0.13902, 481.0: 0.1446764, 482.0: 0.1504693, 483.0: 0.1564619, 484.0: 0.1627177, 485.0: 0.1693, 486.0: 0.1762431, 487.0: 0.1835581, 488.0: 0.1912735, 489.0: 0.199418, 490.0: 0.20802, 491.0: 0.2171199, 492.0: 0.2267345, 493.0: 0.2368571, 494.0: 0.2474812, 495.0: 0.2586, 496.0: 0.2701849, 497.0: 0.2822939, 498.0: 0.2950505, 499.0: 0.308578, 500.0: 0.323, 501.0: 0.3384021, 502.0: 0.3546858, 503.0: 0.3716986, 504.0: 0.3892875, 505.0: 0.4073, 506.0: 0.4256299, 507.0: 0.4443096, 508.0: 0.4633944, 509.0: 0.4829395, 510.0: 0.503, 511.0: 0.5235693, 512.0: 0.544512, 513.0: 0.56569, 514.0: 0.5869653, 515.0: 0.6082, 516.0: 0.6293456, 517.0: 0.6503068, 518.0: 0.6708752, 519.0: 0.6908424, 520.0: 0.71, 521.0: 0.7281852, 522.0: 0.7454636, 523.0: 0.7619694, 524.0: 0.7778368, 525.0: 0.7932, 526.0: 0.8081104, 527.0: 0.8224962, 528.0: 0.8363068, 529.0: 0.8494916, 530.0: 0.862, 531.0: 0.8738108, 532.0: 0.8849624, 533.0: 0.8954936, 534.0: 0.9054432, 535.0: 0.9148501, 536.0: 0.9237348, 537.0: 0.9320924, 538.0: 0.9399226, 539.0: 0.9472252, 540.0: 0.954, 541.0: 0.9602561, 542.0: 0.9660074, 543.0: 0.9712606, 544.0: 0.9760225, 545.0: 0.9803, 546.0: 0.9840924, 547.0: 0.9874182, 548.0: 0.9903128, 549.0: 0.9928116, 550.0: 0.9949501, 551.0: 0.9967108, 552.0: 0.9980983, 553.0: 0.999112, 554.0: 0.9997482, 555.0: 1.0, 556.0: 0.9998567, 557.0: 0.9993046, 558.0: 0.9983255, 559.0: 0.9968987, 560.0: 0.995, 561.0: 0.9926005, 562.0: 0.9897426, 563.0: 0.9864444, 564.0: 0.9827241, 565.0: 0.9786, 566.0: 0.9740837, 567.0: 0.9691712, 568.0: 0.9638568, 569.0: 0.9581349, 570.0: 0.952, 571.0: 0.9454504, 572.0: 0.9384992, 573.0: 0.9311628, 574.0: 0.9234576, 575.0: 0.9154, 576.0: 0.9070064, 577.0: 0.8982772, 578.0: 0.8892048, 579.0: 0.8797816, 580.0: 0.87, 581.0: 0.8598613, 582.0: 0.849392, 583.0: 0.838622, 584.0: 0.8275813, 585.0: 0.8163, 586.0: 0.8047947, 587.0: 0.793082, 588.0: 0.781192, 589.0: 0.7691547, 590.0: 0.757, 591.0: 0.7447541, 592.0: 0.7324224, 593.0: 0.7200036, 594.0: 0.7074965, 595.0: 0.6949, 596.0: 0.6822192, 597.0: 0.6694716, 598.0: 0.6566744, 599.0: 0.6438448, 600.0: 0.631, 601.0: 0.6181555, 602.0: 0.6053144, 603.0: 0.5924756, 604.0: 0.5796379, 605.0: 0.5668, 606.0: 0.5539611, 607.0: 0.5411372, 608.0: 0.5283528, 609.0: 0.5156323, 610.0: 0.503, 611.0: 0.4904688, 612.0: 0.4780304, 613.0: 0.4656776, 614.0: 0.4534032, 615.0: 0.4412, 616.0: 0.42908, 617.0: 0.417036, 618.0: 0.405032, 619.0: 0.393032, 620.0: 0.381, 621.0: 0.3689184, 622.0: 0.3568272, 623.0: 0.3447768, 624.0: 0.3328176, 625.0: 0.321, 626.0: 0.3093381, 627.0: 0.2978504, 628.0: 0.2865936, 629.0: 0.2756245, 630.0: 0.265, 631.0: 0.2547632, 632.0: 0.2448896, 633.0: 0.2353344, 634.0: 0.2260528, 635.0: 0.217, 636.0: 0.2081616, 637.0: 0.1995488, 638.0: 0.1911552, 639.0: 0.1829744, 640.0: 0.175, 641.0: 0.1672235, 642.0: 0.1596464, 643.0: 0.1522776, 644.0: 0.1451259, 645.0: 0.1382, 646.0: 0.1315003, 647.0: 0.1250248, 648.0: 0.1187792, 649.0: 0.1127691, 650.0: 0.107, 651.0: 0.1014762, 652.0: 0.09618864, 653.0: 0.09112296, 654.0: 0.08626485, 655.0: 0.0816, 656.0: 0.07712064, 657.0: 0.07282552, 658.0: 0.06871008, 659.0: 0.06476976, 660.0: 0.061, 661.0: 0.05739621, 662.0: 0.05395504, 663.0: 0.05067376, 664.0: 0.04754965, 665.0: 0.04458, 666.0: 0.04175872, 667.0: 0.03908496, 668.0: 0.03656384, 669.0: 0.03420048, 670.0: 0.032, 671.0: 0.02996261, 672.0: 0.02807664, 673.0: 0.02632936, 674.0: 0.02470805, 675.0: 0.0232, 676.0: 0.02180077, 677.0: 0.02050112, 678.0: 0.01928108, 679.0: 0.01812069, 680.0: 0.017, 681.0: 0.01590379, 682.0: 0.01483718, 683.0: 0.01381068, 684.0: 0.01283478, 685.0: 0.01192, 686.0: 0.01106831, 687.0: 0.01027339, 688.0: 0.009533311, 689.0: 0.008846157, 690.0: 0.00821, 691.0: 0.007623781, 692.0: 0.007085424, 693.0: 0.006591476, 694.0: 0.006138485, 695.0: 0.005723, 696.0: 0.005343059, 697.0: 0.004995796, 698.0: 0.004676404, 699.0: 0.004380075, 700.0: 0.004102, 701.0: 0.003838453, 702.0: 0.003589099, 703.0: 0.003354219, 704.0: 0.003134093, 705.0: 0.002929, 706.0: 0.002738139, 707.0: 0.002559876, 708.0: 0.002393244, 709.0: 0.002237275, 710.0: 0.002091, 711.0: 0.001953587, 712.0: 0.00182458, 713.0: 0.00170358, 714.0: 0.001590187, 715.0: 0.001484, 716.0: 0.001384496, 717.0: 0.001291268, 718.0: 0.001204092, 719.0: 0.001122744, 720.0: 0.001047, 721.0: 0.0009765896, 722.0: 0.0009111088, 723.0: 0.0008501332, 724.0: 0.0007932384, 725.0: 0.00074, 726.0: 0.0006900827, 727.0: 0.00064331, 728.0: 0.000599496, 729.0: 0.0005584547, 730.0: 0.00052, 731.0: 0.0004839136, 732.0: 0.0004500528, 733.0: 0.0004183452, 734.0: 0.0003887184, 735.0: 0.0003611, 736.0: 0.0003353835, 737.0: 0.0003114404, 738.0: 0.0002891656, 739.0: 0.0002684539, 740.0: 0.0002492, 741.0: 0.0002313019, 742.0: 0.0002146856, 743.0: 0.0001992884, 744.0: 0.0001850475, 745.0: 0.0001719, 746.0: 0.0001597781, 747.0: 0.0001486044, 748.0: 0.0001383016, 749.0: 0.0001287925, 750.0: 0.00012, 751.0: 0.0001118595, 752.0: 0.0001043224, 753.0: 9.73356e-05, 754.0: 9.084587e-05, 755.0: 8.48e-05, 756.0: 7.914667e-05, 757.0: 7.3858e-05, 758.0: 6.8916e-05, 759.0: 6.430267e-05, 760.0: 6e-05, 761.0: 5.598187e-05, 762.0: 5.22256e-05, 763.0: 4.87184e-05, 764.0: 4.544747e-05, 765.0: 4.24e-05, 766.0: 3.956104e-05, 767.0: 3.691512e-05, 768.0: 3.444868e-05, 769.0: 3.214816e-05, 770.0: 3e-05, 771.0: 2.799125e-05, 772.0: 2.611356e-05, 773.0: 2.436024e-05, 774.0: 2.272461e-05, 775.0: 2.12e-05, 776.0: 1.977855e-05, 777.0: 1.845285e-05, 778.0: 1.721687e-05, 779.0: 1.606459e-05, 780.0: 1.499e-05, 781.0: 1.398728e-05, 782.0: 1.305155e-05, 783.0: 1.217818e-05, 784.0: 1.136254e-05, 785.0: 1.06e-05, 786.0: 9.885877e-06, 787.0: 9.217304e-06, 788.0: 8.592362e-06, 789.0: 8.009133e-06, 790.0: 7.4657e-06, 791.0: 6.959567e-06, 792.0: 6.487995e-06, 793.0: 6.048699e-06, 794.0: 5.639396e-06, 795.0: 5.2578e-06, 796.0: 4.901771e-06, 797.0: 4.56972e-06, 798.0: 4.260194e-06, 799.0: 3.971739e-06, 800.0: 3.7029e-06, 801.0: 3.452163e-06, 802.0: 3.218302e-06, 803.0: 3.0003e-06, 804.0: 2.797139e-06, 805.0: 2.6078e-06, 806.0: 2.43122e-06, 807.0: 2.266531e-06, 808.0: 2.113013e-06, 809.0: 1.969943e-06, 810.0: 1.8366e-06, 811.0: 1.71223e-06, 812.0: 1.596228e-06, 813.0: 1.48809e-06, 814.0: 1.387314e-06, 815.0: 1.2934e-06, 816.0: 1.20582e-06, 817.0: 1.124143e-06, 818.0: 1.048009e-06, 819.0: 9.770578e-07, 820.0: 9.1093e-07, 821.0: 8.492513e-07, 822.0: 7.917212e-07, 823.0: 7.380904e-07, 824.0: 6.881098e-07, 825.0: 6.4153e-07, 826.0: 5.980895e-07, 827.0: 5.575746e-07, 828.0: 5.19808e-07, 829.0: 4.846123e-07, 830.0: 4.5181e-07}), scotopic_lef=SpectralPowerDistribution( 'CIE 1951 Scotopic Standard Observer', {380.0: 0.000589, 381.0: 0.000665, 382.0: 0.000752, 383.0: 0.000854, 384.0: 0.000972, 385.0: 0.001108, 386.0: 0.001268, 387.0: 0.001453, 388.0: 0.001668, 389.0: 0.001918, 390.0: 0.002209, 391.0: 0.002547, 392.0: 0.002939, 393.0: 0.003394, 394.0: 0.003921, 395.0: 0.00453, 396.0: 0.00524, 397.0: 0.00605, 398.0: 0.00698, 399.0: 0.00806, 400.0: 0.00929, 401.0: 0.0107, 402.0: 0.01231, 403.0: 0.01413, 404.0: 0.01619, 405.0: 0.01852, 406.0: 0.02113, 407.0: 0.02405, 408.0: 0.0273, 409.0: 0.03089, 410.0: 0.03484, 411.0: 0.03916, 412.0: 0.0439, 413.0: 0.049, 414.0: 0.0545, 415.0: 0.0604, 416.0: 0.0668, 417.0: 0.0736, 418.0: 0.0808, 419.0: 0.0885, 420.0: 0.0966, 421.0: 0.1052, 422.0: 0.1141, 423.0: 0.1235, 424.0: 0.1334, 425.0: 0.1436, 426.0: 0.1541, 427.0: 0.1651, 428.0: 0.1764, 429.0: 0.1879, 430.0: 0.1998, 431.0: 0.2119, 432.0: 0.2243, 433.0: 0.2369, 434.0: 0.2496, 435.0: 0.2625, 436.0: 0.2755, 437.0: 0.2886, 438.0: 0.3017, 439.0: 0.3149, 440.0: 0.3281, 441.0: 0.3412, 442.0: 0.3543, 443.0: 0.3673, 444.0: 0.3803, 445.0: 0.3931, 446.0: 0.406, 447.0: 0.418, 448.0: 0.431, 449.0: 0.443, 450.0: 0.455, 451.0: 0.467, 452.0: 0.479, 453.0: 0.49, 454.0: 0.502, 455.0: 0.513, 456.0: 0.524, 457.0: 0.535, 458.0: 0.546, 459.0: 0.557, 460.0: 0.567, 461.0: 0.578, 462.0: 0.588, 463.0: 0.599, 464.0: 0.61, 465.0: 0.62, 466.0: 0.631, 467.0: 0.642, 468.0: 0.653, 469.0: 0.664, 470.0: 0.676, 471.0: 0.687, 472.0: 0.699, 473.0: 0.71, 474.0: 0.722, 475.0: 0.734, 476.0: 0.745, 477.0: 0.757, 478.0: 0.769, 479.0: 0.781, 480.0: 0.793, 481.0: 0.805, 482.0: 0.817, 483.0: 0.828, 484.0: 0.84, 485.0: 0.851, 486.0: 0.862, 487.0: 0.873, 488.0: 0.884, 489.0: 0.894, 490.0: 0.904, 491.0: 0.914, 492.0: 0.923, 493.0: 0.932, 494.0: 0.941, 495.0: 0.949, 496.0: 0.957, 497.0: 0.964, 498.0: 0.97, 499.0: 0.976, 500.0: 0.982, 501.0: 0.986, 502.0: 0.99, 503.0: 0.994, 504.0: 0.997, 505.0: 0.998, 506.0: 1.0, 507.0: 1.0, 508.0: 1.0, 509.0: 0.998, 510.0: 0.997, 511.0: 0.994, 512.0: 0.99, 513.0: 0.986, 514.0: 0.981, 515.0: 0.975, 516.0: 0.968, 517.0: 0.961, 518.0: 0.953, 519.0: 0.944, 520.0: 0.935, 521.0: 0.925, 522.0: 0.915, 523.0: 0.904, 524.0: 0.892, 525.0: 0.88, 526.0: 0.867, 527.0: 0.854, 528.0: 0.84, 529.0: 0.826, 530.0: 0.811, 531.0: 0.796, 532.0: 0.781, 533.0: 0.765, 534.0: 0.749, 535.0: 0.733, 536.0: 0.717, 537.0: 0.7, 538.0: 0.683, 539.0: 0.667, 540.0: 0.65, 541.0: 0.633, 542.0: 0.616, 543.0: 0.599, 544.0: 0.581, 545.0: 0.564, 546.0: 0.548, 547.0: 0.531, 548.0: 0.514, 549.0: 0.497, 550.0: 0.481, 551.0: 0.465, 552.0: 0.448, 553.0: 0.433, 554.0: 0.417, 555.0: 0.402, 556.0: 0.3864, 557.0: 0.3715, 558.0: 0.3569, 559.0: 0.3427, 560.0: 0.3288, 561.0: 0.3151, 562.0: 0.3018, 563.0: 0.2888, 564.0: 0.2762, 565.0: 0.2639, 566.0: 0.2519, 567.0: 0.2403, 568.0: 0.2291, 569.0: 0.2182, 570.0: 0.2076, 571.0: 0.1974, 572.0: 0.1876, 573.0: 0.1782, 574.0: 0.169, 575.0: 0.1602, 576.0: 0.1517, 577.0: 0.1436, 578.0: 0.1358, 579.0: 0.1284, 580.0: 0.1212, 581.0: 0.1143, 582.0: 0.1078, 583.0: 0.1015, 584.0: 0.0956, 585.0: 0.0899, 586.0: 0.0845, 587.0: 0.0793, 588.0: 0.0745, 589.0: 0.0699, 590.0: 0.0655, 591.0: 0.0613, 592.0: 0.0574, 593.0: 0.0537, 594.0: 0.0502, 595.0: 0.0469, 596.0: 0.0438, 597.0: 0.0409, 598.0: 0.03816, 599.0: 0.03558, 600.0: 0.03315, 601.0: 0.03087, 602.0: 0.02874, 603.0: 0.02674, 604.0: 0.02487, 605.0: 0.02312, 606.0: 0.02147, 607.0: 0.01994, 608.0: 0.01851, 609.0: 0.01718, 610.0: 0.01593, 611.0: 0.01477, 612.0: 0.01369, 613.0: 0.01269, 614.0: 0.01175, 615.0: 0.01088, 616.0: 0.01007, 617.0: 0.00932, 618.0: 0.00862, 619.0: 0.00797, 620.0: 0.00737, 621.0: 0.00682, 622.0: 0.0063, 623.0: 0.00582, 624.0: 0.00538, 625.0: 0.00497, 626.0: 0.00459, 627.0: 0.00424, 628.0: 0.003913, 629.0: 0.003613, 630.0: 0.003335, 631.0: 0.003079, 632.0: 0.002842, 633.0: 0.002623, 634.0: 0.002421, 635.0: 0.002235, 636.0: 0.002062, 637.0: 0.001903, 638.0: 0.001757, 639.0: 0.001621, 640.0: 0.001497, 641.0: 0.001382, 642.0: 0.001276, 643.0: 0.001178, 644.0: 0.001088, 645.0: 0.001005, 646.0: 0.000928, 647.0: 0.000857, 648.0: 0.000792, 649.0: 0.000732, 650.0: 0.000677, 651.0: 0.000626, 652.0: 0.000579, 653.0: 0.000536, 654.0: 0.000496, 655.0: 0.000459, 656.0: 0.000425, 657.0: 0.0003935, 658.0: 0.0003645, 659.0: 0.0003377, 660.0: 0.0003129, 661.0: 0.0002901, 662.0: 0.0002689, 663.0: 0.0002493, 664.0: 0.0002313, 665.0: 0.0002146, 666.0: 0.0001991, 667.0: 0.0001848, 668.0: 0.0001716, 669.0: 0.0001593, 670.0: 0.000148, 671.0: 0.0001375, 672.0: 0.0001277, 673.0: 0.0001187, 674.0: 0.0001104, 675.0: 0.0001026, 676.0: 9.54e-05, 677.0: 8.88e-05, 678.0: 8.26e-05, 679.0: 7.69e-05, 680.0: 7.15e-05, 681.0: 6.66e-05, 682.0: 6.2e-05, 683.0: 5.78e-05, 684.0: 5.38e-05, 685.0: 5.01e-05, 686.0: 4.67e-05, 687.0: 4.36e-05, 688.0: 4.06e-05, 689.0: 3.789e-05, 690.0: 3.533e-05, 691.0: 3.295e-05, 692.0: 3.075e-05, 693.0: 2.87e-05, 694.0: 2.679e-05, 695.0: 2.501e-05, 696.0: 2.336e-05, 697.0: 2.182e-05, 698.0: 2.038e-05, 699.0: 1.905e-05, 700.0: 1.78e-05, 701.0: 1.664e-05, 702.0: 1.556e-05, 703.0: 1.454e-05, 704.0: 1.36e-05, 705.0: 1.273e-05, 706.0: 1.191e-05, 707.0: 1.114e-05, 708.0: 1.043e-05, 709.0: 9.76e-06, 710.0: 9.14e-06, 711.0: 8.56e-06, 712.0: 8.02e-06, 713.0: 7.51e-06, 714.0: 7.04e-06, 715.0: 6.6e-06, 716.0: 6.18e-06, 717.0: 5.8e-06, 718.0: 5.44e-06, 719.0: 5.1e-06, 720.0: 4.78e-06, 721.0: 4.49e-06, 722.0: 4.21e-06, 723.0: 3.951e-06, 724.0: 3.709e-06, 725.0: 3.482e-06, 726.0: 3.27e-06, 727.0: 3.07e-06, 728.0: 2.884e-06, 729.0: 2.71e-06, 730.0: 2.546e-06, 731.0: 2.393e-06, 732.0: 2.25e-06, 733.0: 2.115e-06, 734.0: 1.989e-06, 735.0: 1.87e-06, 736.0: 1.759e-06, 737.0: 1.655e-06, 738.0: 1.557e-06, 739.0: 1.466e-06, 740.0: 1.379e-06, 741.0: 1.299e-06, 742.0: 1.223e-06, 743.0: 1.151e-06, 744.0: 1.084e-06, 745.0: 1.022e-06, 746.0: 9.62e-07, 747.0: 9.07e-07, 748.0: 8.55e-07, 749.0: 8.06e-07, 750.0: 7.6e-07, 751.0: 7.16e-07, 752.0: 6.75e-07, 753.0: 6.37e-07, 754.0: 6.01e-07, 755.0: 5.67e-07, 756.0: 5.35e-07, 757.0: 5.05e-07, 758.0: 4.77e-07, 759.0: 4.5e-07, 760.0: 4.25e-07, 761.0: 4.01e-07, 762.0: 3.79e-07, 763.0: 3.58e-07, 764.0: 3.382e-07, 765.0: 3.196e-07, 766.0: 3.021e-07, 767.0: 2.855e-07, 768.0: 2.699e-07, 769.0: 2.552e-07, 770.0: 2.413e-07, 771.0: 2.282e-07, 772.0: 2.159e-07, 773.0: 2.042e-07, 774.0: 1.932e-07, 775.0: 1.829e-07, 776.0: 1.731e-07, 777.0: 1.638e-07, 778.0: 1.551e-07, 779.0: 1.468e-07, 780.0: 1.39e-07}))[source]

Returns the mesopic luminous efficiency function \(V_m(\lambda)\) for given photopic luminance \(L_p\).

Parameters:
  • Lp (numeric) – Photopic luminance \(L_p\).
  • 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) – \(V(\lambda)\) photopic luminous efficiency function.
  • scotopic_lef (SpectralPowerDistribution, optional) – \(V^\prime(\lambda)\) scotopic luminous efficiency function.
Returns:

Mesopic luminous efficiency function \(V_m(\lambda)\).

Return type:

SpectralPowerDistribution

Examples

>>> print(mesopic_luminous_efficiency_function(0.2))
SpectralPowerDistribution('0.2 Lp Mesopic Luminous Efficiency Function', (380.0, 780.0, 1.0))
colour.mesopic_weighting_function(wavelength, Lp, source=u'Blue Heavy', method=u'MOVE', photopic_lef=SpectralPowerDistribution( 'CIE 1924 Photopic Standard Observer', {360.0: 3.917e-06, 361.0: 4.393581e-06, 362.0: 4.929604e-06, 363.0: 5.532136e-06, 364.0: 6.208245e-06, 365.0: 6.965e-06, 366.0: 7.813219e-06, 367.0: 8.767336e-06, 368.0: 9.839844e-06, 369.0: 1.104323e-05, 370.0: 1.239e-05, 371.0: 1.388641e-05, 372.0: 1.555728e-05, 373.0: 1.744296e-05, 374.0: 1.958375e-05, 375.0: 2.202e-05, 376.0: 2.483965e-05, 377.0: 2.804126e-05, 378.0: 3.153104e-05, 379.0: 3.521521e-05, 380.0: 3.9e-05, 381.0: 4.28264e-05, 382.0: 4.69146e-05, 383.0: 5.15896e-05, 384.0: 5.71764e-05, 385.0: 6.4e-05, 386.0: 7.234421e-05, 387.0: 8.221224e-05, 388.0: 9.350816e-05, 389.0: 0.0001061361, 390.0: 0.00012, 391.0: 0.000134984, 392.0: 0.000151492, 393.0: 0.000170208, 394.0: 0.000191816, 395.0: 0.000217, 396.0: 0.0002469067, 397.0: 0.00028124, 398.0: 0.00031852, 399.0: 0.0003572667, 400.0: 0.000396, 401.0: 0.0004337147, 402.0: 0.000473024, 403.0: 0.000517876, 404.0: 0.0005722187, 405.0: 0.00064, 406.0: 0.00072456, 407.0: 0.0008255, 408.0: 0.00094116, 409.0: 0.00106988, 410.0: 0.00121, 411.0: 0.001362091, 412.0: 0.001530752, 413.0: 0.001720368, 414.0: 0.001935323, 415.0: 0.00218, 416.0: 0.0024548, 417.0: 0.002764, 418.0: 0.0031178, 419.0: 0.0035264, 420.0: 0.004, 421.0: 0.00454624, 422.0: 0.00515932, 423.0: 0.00582928, 424.0: 0.00654616, 425.0: 0.0073, 426.0: 0.008086507, 427.0: 0.00890872, 428.0: 0.00976768, 429.0: 0.01066443, 430.0: 0.0116, 431.0: 0.01257317, 432.0: 0.01358272, 433.0: 0.01462968, 434.0: 0.01571509, 435.0: 0.01684, 436.0: 0.01800736, 437.0: 0.01921448, 438.0: 0.02045392, 439.0: 0.02171824, 440.0: 0.023, 441.0: 0.02429461, 442.0: 0.02561024, 443.0: 0.02695857, 444.0: 0.02835125, 445.0: 0.0298, 446.0: 0.03131083, 447.0: 0.03288368, 448.0: 0.03452112, 449.0: 0.03622571, 450.0: 0.038, 451.0: 0.03984667, 452.0: 0.041768, 453.0: 0.043766, 454.0: 0.04584267, 455.0: 0.048, 456.0: 0.05024368, 457.0: 0.05257304, 458.0: 0.05498056, 459.0: 0.05745872, 460.0: 0.06, 461.0: 0.06260197, 462.0: 0.06527752, 463.0: 0.06804208, 464.0: 0.07091109, 465.0: 0.0739, 466.0: 0.077016, 467.0: 0.0802664, 468.0: 0.0836668, 469.0: 0.0872328, 470.0: 0.09098, 471.0: 0.09491755, 472.0: 0.09904584, 473.0: 0.1033674, 474.0: 0.1078846, 475.0: 0.1126, 476.0: 0.117532, 477.0: 0.1226744, 478.0: 0.1279928, 479.0: 0.1334528, 480.0: 0.13902, 481.0: 0.1446764, 482.0: 0.1504693, 483.0: 0.1564619, 484.0: 0.1627177, 485.0: 0.1693, 486.0: 0.1762431, 487.0: 0.1835581, 488.0: 0.1912735, 489.0: 0.199418, 490.0: 0.20802, 491.0: 0.2171199, 492.0: 0.2267345, 493.0: 0.2368571, 494.0: 0.2474812, 495.0: 0.2586, 496.0: 0.2701849, 497.0: 0.2822939, 498.0: 0.2950505, 499.0: 0.308578, 500.0: 0.323, 501.0: 0.3384021, 502.0: 0.3546858, 503.0: 0.3716986, 504.0: 0.3892875, 505.0: 0.4073, 506.0: 0.4256299, 507.0: 0.4443096, 508.0: 0.4633944, 509.0: 0.4829395, 510.0: 0.503, 511.0: 0.5235693, 512.0: 0.544512, 513.0: 0.56569, 514.0: 0.5869653, 515.0: 0.6082, 516.0: 0.6293456, 517.0: 0.6503068, 518.0: 0.6708752, 519.0: 0.6908424, 520.0: 0.71, 521.0: 0.7281852, 522.0: 0.7454636, 523.0: 0.7619694, 524.0: 0.7778368, 525.0: 0.7932, 526.0: 0.8081104, 527.0: 0.8224962, 528.0: 0.8363068, 529.0: 0.8494916, 530.0: 0.862, 531.0: 0.8738108, 532.0: 0.8849624, 533.0: 0.8954936, 534.0: 0.9054432, 535.0: 0.9148501, 536.0: 0.9237348, 537.0: 0.9320924, 538.0: 0.9399226, 539.0: 0.9472252, 540.0: 0.954, 541.0: 0.9602561, 542.0: 0.9660074, 543.0: 0.9712606, 544.0: 0.9760225, 545.0: 0.9803, 546.0: 0.9840924, 547.0: 0.9874182, 548.0: 0.9903128, 549.0: 0.9928116, 550.0: 0.9949501, 551.0: 0.9967108, 552.0: 0.9980983, 553.0: 0.999112, 554.0: 0.9997482, 555.0: 1.0, 556.0: 0.9998567, 557.0: 0.9993046, 558.0: 0.9983255, 559.0: 0.9968987, 560.0: 0.995, 561.0: 0.9926005, 562.0: 0.9897426, 563.0: 0.9864444, 564.0: 0.9827241, 565.0: 0.9786, 566.0: 0.9740837, 567.0: 0.9691712, 568.0: 0.9638568, 569.0: 0.9581349, 570.0: 0.952, 571.0: 0.9454504, 572.0: 0.9384992, 573.0: 0.9311628, 574.0: 0.9234576, 575.0: 0.9154, 576.0: 0.9070064, 577.0: 0.8982772, 578.0: 0.8892048, 579.0: 0.8797816, 580.0: 0.87, 581.0: 0.8598613, 582.0: 0.849392, 583.0: 0.838622, 584.0: 0.8275813, 585.0: 0.8163, 586.0: 0.8047947, 587.0: 0.793082, 588.0: 0.781192, 589.0: 0.7691547, 590.0: 0.757, 591.0: 0.7447541, 592.0: 0.7324224, 593.0: 0.7200036, 594.0: 0.7074965, 595.0: 0.6949, 596.0: 0.6822192, 597.0: 0.6694716, 598.0: 0.6566744, 599.0: 0.6438448, 600.0: 0.631, 601.0: 0.6181555, 602.0: 0.6053144, 603.0: 0.5924756, 604.0: 0.5796379, 605.0: 0.5668, 606.0: 0.5539611, 607.0: 0.5411372, 608.0: 0.5283528, 609.0: 0.5156323, 610.0: 0.503, 611.0: 0.4904688, 612.0: 0.4780304, 613.0: 0.4656776, 614.0: 0.4534032, 615.0: 0.4412, 616.0: 0.42908, 617.0: 0.417036, 618.0: 0.405032, 619.0: 0.393032, 620.0: 0.381, 621.0: 0.3689184, 622.0: 0.3568272, 623.0: 0.3447768, 624.0: 0.3328176, 625.0: 0.321, 626.0: 0.3093381, 627.0: 0.2978504, 628.0: 0.2865936, 629.0: 0.2756245, 630.0: 0.265, 631.0: 0.2547632, 632.0: 0.2448896, 633.0: 0.2353344, 634.0: 0.2260528, 635.0: 0.217, 636.0: 0.2081616, 637.0: 0.1995488, 638.0: 0.1911552, 639.0: 0.1829744, 640.0: 0.175, 641.0: 0.1672235, 642.0: 0.1596464, 643.0: 0.1522776, 644.0: 0.1451259, 645.0: 0.1382, 646.0: 0.1315003, 647.0: 0.1250248, 648.0: 0.1187792, 649.0: 0.1127691, 650.0: 0.107, 651.0: 0.1014762, 652.0: 0.09618864, 653.0: 0.09112296, 654.0: 0.08626485, 655.0: 0.0816, 656.0: 0.07712064, 657.0: 0.07282552, 658.0: 0.06871008, 659.0: 0.06476976, 660.0: 0.061, 661.0: 0.05739621, 662.0: 0.05395504, 663.0: 0.05067376, 664.0: 0.04754965, 665.0: 0.04458, 666.0: 0.04175872, 667.0: 0.03908496, 668.0: 0.03656384, 669.0: 0.03420048, 670.0: 0.032, 671.0: 0.02996261, 672.0: 0.02807664, 673.0: 0.02632936, 674.0: 0.02470805, 675.0: 0.0232, 676.0: 0.02180077, 677.0: 0.02050112, 678.0: 0.01928108, 679.0: 0.01812069, 680.0: 0.017, 681.0: 0.01590379, 682.0: 0.01483718, 683.0: 0.01381068, 684.0: 0.01283478, 685.0: 0.01192, 686.0: 0.01106831, 687.0: 0.01027339, 688.0: 0.009533311, 689.0: 0.008846157, 690.0: 0.00821, 691.0: 0.007623781, 692.0: 0.007085424, 693.0: 0.006591476, 694.0: 0.006138485, 695.0: 0.005723, 696.0: 0.005343059, 697.0: 0.004995796, 698.0: 0.004676404, 699.0: 0.004380075, 700.0: 0.004102, 701.0: 0.003838453, 702.0: 0.003589099, 703.0: 0.003354219, 704.0: 0.003134093, 705.0: 0.002929, 706.0: 0.002738139, 707.0: 0.002559876, 708.0: 0.002393244, 709.0: 0.002237275, 710.0: 0.002091, 711.0: 0.001953587, 712.0: 0.00182458, 713.0: 0.00170358, 714.0: 0.001590187, 715.0: 0.001484, 716.0: 0.001384496, 717.0: 0.001291268, 718.0: 0.001204092, 719.0: 0.001122744, 720.0: 0.001047, 721.0: 0.0009765896, 722.0: 0.0009111088, 723.0: 0.0008501332, 724.0: 0.0007932384, 725.0: 0.00074, 726.0: 0.0006900827, 727.0: 0.00064331, 728.0: 0.000599496, 729.0: 0.0005584547, 730.0: 0.00052, 731.0: 0.0004839136, 732.0: 0.0004500528, 733.0: 0.0004183452, 734.0: 0.0003887184, 735.0: 0.0003611, 736.0: 0.0003353835, 737.0: 0.0003114404, 738.0: 0.0002891656, 739.0: 0.0002684539, 740.0: 0.0002492, 741.0: 0.0002313019, 742.0: 0.0002146856, 743.0: 0.0001992884, 744.0: 0.0001850475, 745.0: 0.0001719, 746.0: 0.0001597781, 747.0: 0.0001486044, 748.0: 0.0001383016, 749.0: 0.0001287925, 750.0: 0.00012, 751.0: 0.0001118595, 752.0: 0.0001043224, 753.0: 9.73356e-05, 754.0: 9.084587e-05, 755.0: 8.48e-05, 756.0: 7.914667e-05, 757.0: 7.3858e-05, 758.0: 6.8916e-05, 759.0: 6.430267e-05, 760.0: 6e-05, 761.0: 5.598187e-05, 762.0: 5.22256e-05, 763.0: 4.87184e-05, 764.0: 4.544747e-05, 765.0: 4.24e-05, 766.0: 3.956104e-05, 767.0: 3.691512e-05, 768.0: 3.444868e-05, 769.0: 3.214816e-05, 770.0: 3e-05, 771.0: 2.799125e-05, 772.0: 2.611356e-05, 773.0: 2.436024e-05, 774.0: 2.272461e-05, 775.0: 2.12e-05, 776.0: 1.977855e-05, 777.0: 1.845285e-05, 778.0: 1.721687e-05, 779.0: 1.606459e-05, 780.0: 1.499e-05, 781.0: 1.398728e-05, 782.0: 1.305155e-05, 783.0: 1.217818e-05, 784.0: 1.136254e-05, 785.0: 1.06e-05, 786.0: 9.885877e-06, 787.0: 9.217304e-06, 788.0: 8.592362e-06, 789.0: 8.009133e-06, 790.0: 7.4657e-06, 791.0: 6.959567e-06, 792.0: 6.487995e-06, 793.0: 6.048699e-06, 794.0: 5.639396e-06, 795.0: 5.2578e-06, 796.0: 4.901771e-06, 797.0: 4.56972e-06, 798.0: 4.260194e-06, 799.0: 3.971739e-06, 800.0: 3.7029e-06, 801.0: 3.452163e-06, 802.0: 3.218302e-06, 803.0: 3.0003e-06, 804.0: 2.797139e-06, 805.0: 2.6078e-06, 806.0: 2.43122e-06, 807.0: 2.266531e-06, 808.0: 2.113013e-06, 809.0: 1.969943e-06, 810.0: 1.8366e-06, 811.0: 1.71223e-06, 812.0: 1.596228e-06, 813.0: 1.48809e-06, 814.0: 1.387314e-06, 815.0: 1.2934e-06, 816.0: 1.20582e-06, 817.0: 1.124143e-06, 818.0: 1.048009e-06, 819.0: 9.770578e-07, 820.0: 9.1093e-07, 821.0: 8.492513e-07, 822.0: 7.917212e-07, 823.0: 7.380904e-07, 824.0: 6.881098e-07, 825.0: 6.4153e-07, 826.0: 5.980895e-07, 827.0: 5.575746e-07, 828.0: 5.19808e-07, 829.0: 4.846123e-07, 830.0: 4.5181e-07}), scotopic_lef=SpectralPowerDistribution( 'CIE 1951 Scotopic Standard Observer', {380.0: 0.000589, 381.0: 0.000665, 382.0: 0.000752, 383.0: 0.000854, 384.0: 0.000972, 385.0: 0.001108, 386.0: 0.001268, 387.0: 0.001453, 388.0: 0.001668, 389.0: 0.001918, 390.0: 0.002209, 391.0: 0.002547, 392.0: 0.002939, 393.0: 0.003394, 394.0: 0.003921, 395.0: 0.00453, 396.0: 0.00524, 397.0: 0.00605, 398.0: 0.00698, 399.0: 0.00806, 400.0: 0.00929, 401.0: 0.0107, 402.0: 0.01231, 403.0: 0.01413, 404.0: 0.01619, 405.0: 0.01852, 406.0: 0.02113, 407.0: 0.02405, 408.0: 0.0273, 409.0: 0.03089, 410.0: 0.03484, 411.0: 0.03916, 412.0: 0.0439, 413.0: 0.049, 414.0: 0.0545, 415.0: 0.0604, 416.0: 0.0668, 417.0: 0.0736, 418.0: 0.0808, 419.0: 0.0885, 420.0: 0.0966, 421.0: 0.1052, 422.0: 0.1141, 423.0: 0.1235, 424.0: 0.1334, 425.0: 0.1436, 426.0: 0.1541, 427.0: 0.1651, 428.0: 0.1764, 429.0: 0.1879, 430.0: 0.1998, 431.0: 0.2119, 432.0: 0.2243, 433.0: 0.2369, 434.0: 0.2496, 435.0: 0.2625, 436.0: 0.2755, 437.0: 0.2886, 438.0: 0.3017, 439.0: 0.3149, 440.0: 0.3281, 441.0: 0.3412, 442.0: 0.3543, 443.0: 0.3673, 444.0: 0.3803, 445.0: 0.3931, 446.0: 0.406, 447.0: 0.418, 448.0: 0.431, 449.0: 0.443, 450.0: 0.455, 451.0: 0.467, 452.0: 0.479, 453.0: 0.49, 454.0: 0.502, 455.0: 0.513, 456.0: 0.524, 457.0: 0.535, 458.0: 0.546, 459.0: 0.557, 460.0: 0.567, 461.0: 0.578, 462.0: 0.588, 463.0: 0.599, 464.0: 0.61, 465.0: 0.62, 466.0: 0.631, 467.0: 0.642, 468.0: 0.653, 469.0: 0.664, 470.0: 0.676, 471.0: 0.687, 472.0: 0.699, 473.0: 0.71, 474.0: 0.722, 475.0: 0.734, 476.0: 0.745, 477.0: 0.757, 478.0: 0.769, 479.0: 0.781, 480.0: 0.793, 481.0: 0.805, 482.0: 0.817, 483.0: 0.828, 484.0: 0.84, 485.0: 0.851, 486.0: 0.862, 487.0: 0.873, 488.0: 0.884, 489.0: 0.894, 490.0: 0.904, 491.0: 0.914, 492.0: 0.923, 493.0: 0.932, 494.0: 0.941, 495.0: 0.949, 496.0: 0.957, 497.0: 0.964, 498.0: 0.97, 499.0: 0.976, 500.0: 0.982, 501.0: 0.986, 502.0: 0.99, 503.0: 0.994, 504.0: 0.997, 505.0: 0.998, 506.0: 1.0, 507.0: 1.0, 508.0: 1.0, 509.0: 0.998, 510.0: 0.997, 511.0: 0.994, 512.0: 0.99, 513.0: 0.986, 514.0: 0.981, 515.0: 0.975, 516.0: 0.968, 517.0: 0.961, 518.0: 0.953, 519.0: 0.944, 520.0: 0.935, 521.0: 0.925, 522.0: 0.915, 523.0: 0.904, 524.0: 0.892, 525.0: 0.88, 526.0: 0.867, 527.0: 0.854, 528.0: 0.84, 529.0: 0.826, 530.0: 0.811, 531.0: 0.796, 532.0: 0.781, 533.0: 0.765, 534.0: 0.749, 535.0: 0.733, 536.0: 0.717, 537.0: 0.7, 538.0: 0.683, 539.0: 0.667, 540.0: 0.65, 541.0: 0.633, 542.0: 0.616, 543.0: 0.599, 544.0: 0.581, 545.0: 0.564, 546.0: 0.548, 547.0: 0.531, 548.0: 0.514, 549.0: 0.497, 550.0: 0.481, 551.0: 0.465, 552.0: 0.448, 553.0: 0.433, 554.0: 0.417, 555.0: 0.402, 556.0: 0.3864, 557.0: 0.3715, 558.0: 0.3569, 559.0: 0.3427, 560.0: 0.3288, 561.0: 0.3151, 562.0: 0.3018, 563.0: 0.2888, 564.0: 0.2762, 565.0: 0.2639, 566.0: 0.2519, 567.0: 0.2403, 568.0: 0.2291, 569.0: 0.2182, 570.0: 0.2076, 571.0: 0.1974, 572.0: 0.1876, 573.0: 0.1782, 574.0: 0.169, 575.0: 0.1602, 576.0: 0.1517, 577.0: 0.1436, 578.0: 0.1358, 579.0: 0.1284, 580.0: 0.1212, 581.0: 0.1143, 582.0: 0.1078, 583.0: 0.1015, 584.0: 0.0956, 585.0: 0.0899, 586.0: 0.0845, 587.0: 0.0793, 588.0: 0.0745, 589.0: 0.0699, 590.0: 0.0655, 591.0: 0.0613, 592.0: 0.0574, 593.0: 0.0537, 594.0: 0.0502, 595.0: 0.0469, 596.0: 0.0438, 597.0: 0.0409, 598.0: 0.03816, 599.0: 0.03558, 600.0: 0.03315, 601.0: 0.03087, 602.0: 0.02874, 603.0: 0.02674, 604.0: 0.02487, 605.0: 0.02312, 606.0: 0.02147, 607.0: 0.01994, 608.0: 0.01851, 609.0: 0.01718, 610.0: 0.01593, 611.0: 0.01477, 612.0: 0.01369, 613.0: 0.01269, 614.0: 0.01175, 615.0: 0.01088, 616.0: 0.01007, 617.0: 0.00932, 618.0: 0.00862, 619.0: 0.00797, 620.0: 0.00737, 621.0: 0.00682, 622.0: 0.0063, 623.0: 0.00582, 624.0: 0.00538, 625.0: 0.00497, 626.0: 0.00459, 627.0: 0.00424, 628.0: 0.003913, 629.0: 0.003613, 630.0: 0.003335, 631.0: 0.003079, 632.0: 0.002842, 633.0: 0.002623, 634.0: 0.002421, 635.0: 0.002235, 636.0: 0.002062, 637.0: 0.001903, 638.0: 0.001757, 639.0: 0.001621, 640.0: 0.001497, 641.0: 0.001382, 642.0: 0.001276, 643.0: 0.001178, 644.0: 0.001088, 645.0: 0.001005, 646.0: 0.000928, 647.0: 0.000857, 648.0: 0.000792, 649.0: 0.000732, 650.0: 0.000677, 651.0: 0.000626, 652.0: 0.000579, 653.0: 0.000536, 654.0: 0.000496, 655.0: 0.000459, 656.0: 0.000425, 657.0: 0.0003935, 658.0: 0.0003645, 659.0: 0.0003377, 660.0: 0.0003129, 661.0: 0.0002901, 662.0: 0.0002689, 663.0: 0.0002493, 664.0: 0.0002313, 665.0: 0.0002146, 666.0: 0.0001991, 667.0: 0.0001848, 668.0: 0.0001716, 669.0: 0.0001593, 670.0: 0.000148, 671.0: 0.0001375, 672.0: 0.0001277, 673.0: 0.0001187, 674.0: 0.0001104, 675.0: 0.0001026, 676.0: 9.54e-05, 677.0: 8.88e-05, 678.0: 8.26e-05, 679.0: 7.69e-05, 680.0: 7.15e-05, 681.0: 6.66e-05, 682.0: 6.2e-05, 683.0: 5.78e-05, 684.0: 5.38e-05, 685.0: 5.01e-05, 686.0: 4.67e-05, 687.0: 4.36e-05, 688.0: 4.06e-05, 689.0: 3.789e-05, 690.0: 3.533e-05, 691.0: 3.295e-05, 692.0: 3.075e-05, 693.0: 2.87e-05, 694.0: 2.679e-05, 695.0: 2.501e-05, 696.0: 2.336e-05, 697.0: 2.182e-05, 698.0: 2.038e-05, 699.0: 1.905e-05, 700.0: 1.78e-05, 701.0: 1.664e-05, 702.0: 1.556e-05, 703.0: 1.454e-05, 704.0: 1.36e-05, 705.0: 1.273e-05, 706.0: 1.191e-05, 707.0: 1.114e-05, 708.0: 1.043e-05, 709.0: 9.76e-06, 710.0: 9.14e-06, 711.0: 8.56e-06, 712.0: 8.02e-06, 713.0: 7.51e-06, 714.0: 7.04e-06, 715.0: 6.6e-06, 716.0: 6.18e-06, 717.0: 5.8e-06, 718.0: 5.44e-06, 719.0: 5.1e-06, 720.0: 4.78e-06, 721.0: 4.49e-06, 722.0: 4.21e-06, 723.0: 3.951e-06, 724.0: 3.709e-06, 725.0: 3.482e-06, 726.0: 3.27e-06, 727.0: 3.07e-06, 728.0: 2.884e-06, 729.0: 2.71e-06, 730.0: 2.546e-06, 731.0: 2.393e-06, 732.0: 2.25e-06, 733.0: 2.115e-06, 734.0: 1.989e-06, 735.0: 1.87e-06, 736.0: 1.759e-06, 737.0: 1.655e-06, 738.0: 1.557e-06, 739.0: 1.466e-06, 740.0: 1.379e-06, 741.0: 1.299e-06, 742.0: 1.223e-06, 743.0: 1.151e-06, 744.0: 1.084e-06, 745.0: 1.022e-06, 746.0: 9.62e-07, 747.0: 9.07e-07, 748.0: 8.55e-07, 749.0: 8.06e-07, 750.0: 7.6e-07, 751.0: 7.16e-07, 752.0: 6.75e-07, 753.0: 6.37e-07, 754.0: 6.01e-07, 755.0: 5.67e-07, 756.0: 5.35e-07, 757.0: 5.05e-07, 758.0: 4.77e-07, 759.0: 4.5e-07, 760.0: 4.25e-07, 761.0: 4.01e-07, 762.0: 3.79e-07, 763.0: 3.58e-07, 764.0: 3.382e-07, 765.0: 3.196e-07, 766.0: 3.021e-07, 767.0: 2.855e-07, 768.0: 2.699e-07, 769.0: 2.552e-07, 770.0: 2.413e-07, 771.0: 2.282e-07, 772.0: 2.159e-07, 773.0: 2.042e-07, 774.0: 1.932e-07, 775.0: 1.829e-07, 776.0: 1.731e-07, 777.0: 1.638e-07, 778.0: 1.551e-07, 779.0: 1.468e-07, 780.0: 1.39e-07}))[source]

Calculates the mesopic weighting function factor at given wavelength \(\lambda\) using the photopic luminance \(L_p\).

Parameters:
  • wavelength (numeric or array_like) – Wavelength \(\lambda\) to calculate the mesopic weighting function factor.
  • Lp (numeric) – Photopic luminance \(L_p\).
  • 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) – \(V(\lambda)\) photopic luminous efficiency function.
  • scotopic_lef (SpectralPowerDistribution, optional) – \(V^\prime(\lambda)\) scotopic luminous efficiency function.
Returns:

Mesopic weighting function factor.

Return type:

numeric or ndarray

Examples

>>> mesopic_weighting_function(500, 0.2)  
0.7052200...
colour.lightness(Y, method=u'CIE 1976', **kwargs)[source]

Returns the Lightness \(L\) using given method.

Parameters:
  • Y (numeric or array_like) – luminance \(Y\).
  • method (unicode, optional) – {‘CIE 1976’, ‘Glasser 1958’, ‘Wyszecki 1963’, ‘Fairchild 2010’}, Computation method.
Other Parameters:
 
Returns:

Lightness \(L\).

Return type:

numeric or array_like

Notes

  • Input luminance \(Y\) and optional \(Y_n\) are in domain [0, 100] or [0, \(\infty\)].
  • Output Lightness \(L\) is in range [0, 100].

Examples

>>> lightness(10.08)  
array(37.9856290...)
>>> lightness(10.08, Y_n=100)  
array(37.9856290...)
>>> lightness(10.08, Y_n=95)  
array(38.9165987...)
>>> lightness(10.08, method='Glasser 1958')  
36.2505626...
>>> lightness(10.08, method='Wyszecki 1963')  
37.0041149...
>>> lightness(
...     10.08 / 100,
...     epsilon=1.836,
...     method='Fairchild 2010')  
24.9022902...
colour.lightness_Glasser1958(Y)[source]

Returns the Lightness \(L\) of given luminance \(Y\) using Glasser et al. (1958) method.

Parameters:Y (numeric or array_like) – luminance \(Y\).
Returns:Lightness \(L\).
Return type:numeric or array_like

Notes

  • Input luminance \(Y\) is in domain [0, 100].
  • Output Lightness \(L\) is in range [0, 100].

References

[2]Glasser, L. G., McKinney, A. H., Reilly, C. D., & Schnelle, P. D. (1958). Cube-Root Color Coordinate System. J. Opt. Soc. Am., 48(10), 736–740. doi:10.1364/JOSA.48.000736

Examples

>>> lightness_Glasser1958(10.08)  
36.2505626...
colour.lightness_Wyszecki1963(Y)[source]

Returns the Lightness \(W\) of given luminance \(Y\) using Wyszecki (1963) method.

Parameters:Y (numeric or array_like) – luminance \(Y\).
Returns:Lightness \(W\).
Return type:numeric or array_like

Notes

  • Input luminance \(Y\) is in domain [0, 100].
  • Output Lightness \(W\) is in range [0, 100].

References

[3]Wyszecki, G. (1963). Proposal for a New Color-Difference Formula. J. Opt. Soc. Am., 53(11), 1318–1319. doi:10.1364/JOSA.53.001318

Examples

>>> lightness_Wyszecki1963(10.08)  
37.0041149...
colour.lightness_CIE1976(Y, Y_n=100)[source]

Returns the Lightness \(L^*\) of given luminance \(Y\) using given reference white luminance \(Y_n\) as per CIE 1976 recommendation.

Parameters:
  • Y (numeric or array_like) – luminance \(Y\).
  • Y_n (numeric or array_like, optional) – White reference luminance \(Y_n\).
Returns:

Lightness \(L^*\).

Return type:

numeric or array_like

Notes

  • Input luminance \(Y\) and \(Y_n\) are in domain [0, 100].
  • Output Lightness \(L^*\) is in range [0, 100].

References

[4]Wyszecki, G., & Stiles, W. S. (2000). CIE 1976 (L*u*v*)-Space and Color-Difference Formula. In Color Science: Concepts and Methods, Quantitative Data and Formulae (p. 167). Wiley. ISBN:978-0471399186
[5]Lindbloom, B. (2003). A Continuity Study of the CIE L* Function. Retrieved February 24, 2014, from http://brucelindbloom.com/LContinuity.html

Examples

>>> lightness_CIE1976(10.08)  
array(37.9856290...)
colour.lightness_Fairchild2010(Y, epsilon=2)[source]

Computes Lightness \(L_{hdr}\) of given luminance \(Y\) using Fairchild and Wyble (2010) method accordingly to Michealis-Menten kinetics.

Parameters:
  • Y (array_like) – luminance \(Y\).
  • epsilon (numeric or array_like, optional) – \(\epsilon\) exponent.
Returns:

Lightness \(L_{hdr}\).

Return type:

array_like

Warning

The input domain of that definition is non standard!

Notes

  • Input luminance \(Y\) is in domain [0, \(\infty\)].

References

[6]Fairchild, M. D., & Wyble, D. R. (2010). hdr-CIELAB and hdr-IPT: Simple Models for Describing the Color of High-Dynamic-Range and Wide-Color-Gamut Images. In Proc. of Color and Imaging Conference (pp. 322–326). ISBN:9781629932156

Examples

>>> lightness_Fairchild2010(10.08 / 100, 1.836)  
24.9022902...
colour.luminance(LV, method=u'CIE 1976', **kwargs)[source]

Returns the luminance \(Y\) of given Lightness \(L^*\) or given Munsell value \(V\).

Parameters:
  • LV (numeric or array_like) – Lightness \(L^*\) or Munsell value \(V\).
  • method (unicode, optional) – {‘CIE 1976’, ‘Newhall 1943’, ‘ASTM D1535-08’, ‘Fairchild 2010’}, Computation method.
Other Parameters:
 
Returns:

luminance \(Y\).

Return type:

numeric or array_like

Notes

  • Input LV is in domain [0, 100], [0, 10] or [0, 1] and optional luminance \(Y_n\) is in domain [0, 100].
  • Output luminance \(Y\) is in range [0, 100] or [0, math:infty].

Examples

>>> luminance(37.98562910)  
array(10.0800000...)
>>> luminance(37.98562910, Y_n=100)  
array(10.0800000...)
>>> luminance(37.98562910, Y_n=95)  
array(9.5760000...)
>>> luminance(3.74629715, method='Newhall 1943')  
10.4089874...
>>> luminance(3.74629715, method='ASTM D1535-08')  
10.1488096...
>>> luminance(
...     24.902290269546651,
...     epsilon=1.836,
...     method='Fairchild 2010')  
0.1007999...
colour.luminance_Newhall1943(V)[source]

Returns the luminance \(R_Y\) of given Munsell value \(V\) using Newhall et al. (1943) method.

Parameters:V (numeric or array_like) – Munsell value \(V\).
Returns:luminance \(R_Y\).
Return type:numeric or array_like

Notes

  • Input Munsell value \(V\) is in domain [0, 10].
  • Output luminance \(R_Y\) is in range [0, 100].

References

[1]Newhall, S. M., Nickerson, D., & Judd, D. B. (1943). Final report of the OSA subcommittee on the spacing of the munsell colors. JOSA, 33(7), 385. doi:10.1364/JOSA.33.000385

Examples

>>> luminance_Newhall1943(3.74629715382)  
10.4089874...
colour.luminance_ASTMD153508(V)[source]

Returns the luminance \(Y\) of given Munsell value \(V\) using ASTM D1535-08e1 method.

Parameters:V (numeric or array_like) – Munsell value \(V\).
Returns:luminance \(Y\).
Return type:numeric or array_like

Notes

  • Input Munsell value \(V\) is in domain [0, 10].
  • Output luminance \(Y\) is in range [0, 100].

References

[4]ASTM International. (n.d.). ASTM D1535-08e1 Standard Practice for Specifying Color by the Munsell System. doi:10.1520/D1535-08E01

Examples

>>> luminance_ASTMD153508(3.74629715382)  
10.1488096...
colour.luminance_CIE1976(Lstar, Y_n=100)[source]

Returns the luminance \(Y\) of given Lightness \(L^*\) with given reference white luminance \(Y_n\).

Parameters:
  • Lstar (numeric or array_like) – Lightness \(L^*\)
  • Y_n (numeric or array_like) – White reference luminance \(Y_n\).
Returns:

luminance \(Y\).

Return type:

numeric or array_like

Notes

  • Input Lightness \(L^*\) and reference white luminance \(Y_n\) are in domain [0, 100].
  • Output luminance \(Y\) is in range [0, 100].

References

[2]Wyszecki, G., & Stiles, W. S. (2000). CIE 1976 (L*u*v*)-Space and Color-Difference Formula. In Color Science: Concepts and Methods, Quantitative Data and Formulae (p. 167). Wiley. ISBN:978-0471399186
[3]Lindbloom, B. (2003). A Continuity Study of the CIE L* Function. Retrieved February 24, 2014, from http://brucelindbloom.com/LContinuity.html

Examples

>>> luminance_CIE1976(37.98562910)  
array(10.0800000...)
>>> luminance_CIE1976(37.98562910, 95)  
array(9.5760000...)
colour.luminance_Fairchild2010(L_hdr, epsilon=2)[source]

Computes luminance \(Y\) of given Lightness \(L_{hdr}\) using Fairchild and Wyble (2010) method accordingly to Michealis-Menten kinetics.

Parameters:
  • L_hdr (array_like) – Lightness \(L_{hdr}\).
  • epsilon (numeric or array_like, optional) – \(\epsilon\) exponent.
Returns:

luminance \(Y\).

Return type:

array_like

Warning

The output range of that definition is non standard!

Notes

  • Output luminance \(Y\) is in range [0, math:infty].

References

[4]Fairchild, M. D., & Wyble, D. R. (2010). hdr-CIELAB and hdr-IPT: Simple Models for Describing the Color of High-Dynamic-Range and Wide-Color-Gamut Images. In Proc. of Color and Imaging Conference (pp. 322–326). ISBN:9781629932156

Examples

>>> luminance_Fairchild2010(
...     24.902290269546651, 1.836)  
0.1007999...
colour.dominant_wavelength(xy, xy_n, cmfs=XYZ_ColourMatchingFunctions( 'CIE 1931 2 Degree Standard Observer', {u'x_bar': {360.0: 0.0001299, 361.0: 0.000145847, 362.0: 0.0001638021, 363.0: 0.0001840037, 364.0: 0.0002066902, 365.0: 0.0002321, 366.0: 0.000260728, 367.0: 0.000293075, 368.0: 0.000329388, 369.0: 0.000369914, 370.0: 0.0004149, 371.0: 0.0004641587, 372.0: 0.000518986, 373.0: 0.000581854, 374.0: 0.0006552347, 375.0: 0.0007416, 376.0: 0.0008450296, 377.0: 0.0009645268, 378.0: 0.001094949, 379.0: 0.001231154, 380.0: 0.001368, 381.0: 0.00150205, 382.0: 0.001642328, 383.0: 0.001802382, 384.0: 0.001995757, 385.0: 0.002236, 386.0: 0.002535385, 387.0: 0.002892603, 388.0: 0.003300829, 389.0: 0.003753236, 390.0: 0.004243, 391.0: 0.004762389, 392.0: 0.005330048, 393.0: 0.005978712, 394.0: 0.006741117, 395.0: 0.00765, 396.0: 0.008751373, 397.0: 0.01002888, 398.0: 0.0114217, 399.0: 0.01286901, 400.0: 0.01431, 401.0: 0.01570443, 402.0: 0.01714744, 403.0: 0.01878122, 404.0: 0.02074801, 405.0: 0.02319, 406.0: 0.02620736, 407.0: 0.02978248, 408.0: 0.03388092, 409.0: 0.03846824, 410.0: 0.04351, 411.0: 0.0489956, 412.0: 0.0550226, 413.0: 0.0617188, 414.0: 0.069212, 415.0: 0.07763, 416.0: 0.08695811, 417.0: 0.09717672, 418.0: 0.1084063, 419.0: 0.1207672, 420.0: 0.13438, 421.0: 0.1493582, 422.0: 0.1653957, 423.0: 0.1819831, 424.0: 0.198611, 425.0: 0.21477, 426.0: 0.2301868, 427.0: 0.2448797, 428.0: 0.2587773, 429.0: 0.2718079, 430.0: 0.2839, 431.0: 0.2949438, 432.0: 0.3048965, 433.0: 0.3137873, 434.0: 0.3216454, 435.0: 0.3285, 436.0: 0.3343513, 437.0: 0.3392101, 438.0: 0.3431213, 439.0: 0.3461296, 440.0: 0.34828, 441.0: 0.3495999, 442.0: 0.3501474, 443.0: 0.350013, 444.0: 0.349287, 445.0: 0.34806, 446.0: 0.3463733, 447.0: 0.3442624, 448.0: 0.3418088, 449.0: 0.3390941, 450.0: 0.3362, 451.0: 0.3331977, 452.0: 0.3300411, 453.0: 0.3266357, 454.0: 0.3228868, 455.0: 0.3187, 456.0: 0.3140251, 457.0: 0.308884, 458.0: 0.3032904, 459.0: 0.2972579, 460.0: 0.2908, 461.0: 0.2839701, 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820.0: 0.0, 821.0: 0.0, 822.0: 0.0, 823.0: 0.0, 824.0: 0.0, 825.0: 0.0, 826.0: 0.0, 827.0: 0.0, 828.0: 0.0, 829.0: 0.0, 830.0: 0.0}}, 'CIE 1931 2$^\circ$ Standard Observer'), reverse=False)[source]

Returns the dominant wavelength \(\lambda_d\) for given colour stimulus \(xy\) and the related \(xy_wl\) first and \(xy_{cw}\) second intersection coordinates with the spectral locus.

In the eventuality where the \(xy_wl\) first intersection coordinates are on the line of purples, the complementary wavelength will be computed in lieu.

The complementary wavelength is indicated by a negative sign and the \(xy_{cw}\) second intersection coordinates which are set by default to the same value than \(xy_wl\) first intersection coordinates will be set to the complementary dominant wavelength intersection coordinates with the spectral locus.

Parameters:
  • xy (array_like) – Colour stimulus xy chromaticity coordinates.
  • xy_n (array_like) – Achromatic stimulus xy chromaticity coordinates.
  • cmfs (XYZ_ColourMatchingFunctions, optional) – Standard observer colour matching functions.
  • reverse (bool, optional) – Reverse the computation direction to retrieve the complementary wavelength.
Returns:

Dominant wavelength, first intersection point xy chromaticity coordinates, second intersection point xy chromaticity coordinates.

Return type:

tuple

Examples

Dominant wavelength computation:

>>> from pprint import pprint
>>> xy = np.array([0.26415, 0.37770])
>>> xy_n = np.array([0.31270, 0.32900])
>>> cmfs = CMFS['CIE 1931 2 Degree Standard Observer']
>>> pprint(dominant_wavelength(xy, xy_n, cmfs))  
(array(504...),
 array([ 0.0036969...,  0.6389577...]),
 array([ 0.0036969...,  0.6389577...]))

Complementary dominant wavelength is returned if the first intersection is located on the line of purples:

>>> xy = np.array([0.35000, 0.25000])
>>> pprint(dominant_wavelength(xy, xy_n, cmfs))  
(array(-520...),
 array([ 0.4133314...,  0.1158663...]),
 array([ 0.0743553...,  0.8338050...]))
colour.complementary_wavelength(xy, xy_n, cmfs=XYZ_ColourMatchingFunctions( 'CIE 1931 2 Degree Standard Observer', {u'x_bar': {360.0: 0.0001299, 361.0: 0.000145847, 362.0: 0.0001638021, 363.0: 0.0001840037, 364.0: 0.0002066902, 365.0: 0.0002321, 366.0: 0.000260728, 367.0: 0.000293075, 368.0: 0.000329388, 369.0: 0.000369914, 370.0: 0.0004149, 371.0: 0.0004641587, 372.0: 0.000518986, 373.0: 0.000581854, 374.0: 0.0006552347, 375.0: 0.0007416, 376.0: 0.0008450296, 377.0: 0.0009645268, 378.0: 0.001094949, 379.0: 0.001231154, 380.0: 0.001368, 381.0: 0.00150205, 382.0: 0.001642328, 383.0: 0.001802382, 384.0: 0.001995757, 385.0: 0.002236, 386.0: 0.002535385, 387.0: 0.002892603, 388.0: 0.003300829, 389.0: 0.003753236, 390.0: 0.004243, 391.0: 0.004762389, 392.0: 0.005330048, 393.0: 0.005978712, 394.0: 0.006741117, 395.0: 0.00765, 396.0: 0.008751373, 397.0: 0.01002888, 398.0: 0.0114217, 399.0: 0.01286901, 400.0: 0.01431, 401.0: 0.01570443, 402.0: 0.01714744, 403.0: 0.01878122, 404.0: 0.02074801, 405.0: 0.02319, 406.0: 0.02620736, 407.0: 0.02978248, 408.0: 0.03388092, 409.0: 0.03846824, 410.0: 0.04351, 411.0: 0.0489956, 412.0: 0.0550226, 413.0: 0.0617188, 414.0: 0.069212, 415.0: 0.07763, 416.0: 0.08695811, 417.0: 0.09717672, 418.0: 0.1084063, 419.0: 0.1207672, 420.0: 0.13438, 421.0: 0.1493582, 422.0: 0.1653957, 423.0: 0.1819831, 424.0: 0.198611, 425.0: 0.21477, 426.0: 0.2301868, 427.0: 0.2448797, 428.0: 0.2587773, 429.0: 0.2718079, 430.0: 0.2839, 431.0: 0.2949438, 432.0: 0.3048965, 433.0: 0.3137873, 434.0: 0.3216454, 435.0: 0.3285, 436.0: 0.3343513, 437.0: 0.3392101, 438.0: 0.3431213, 439.0: 0.3461296, 440.0: 0.34828, 441.0: 0.3495999, 442.0: 0.3501474, 443.0: 0.350013, 444.0: 0.349287, 445.0: 0.34806, 446.0: 0.3463733, 447.0: 0.3442624, 448.0: 0.3418088, 449.0: 0.3390941, 450.0: 0.3362, 451.0: 0.3331977, 452.0: 0.3300411, 453.0: 0.3266357, 454.0: 0.3228868, 455.0: 0.3187, 456.0: 0.3140251, 457.0: 0.308884, 458.0: 0.3032904, 459.0: 0.2972579, 460.0: 0.2908, 461.0: 0.2839701, 462.0: 0.2767214, 463.0: 0.2689178, 464.0: 0.2604227, 465.0: 0.2511, 466.0: 0.2408475, 467.0: 0.2298512, 468.0: 0.2184072, 469.0: 0.2068115, 470.0: 0.19536, 471.0: 0.1842136, 472.0: 0.1733273, 473.0: 0.1626881, 474.0: 0.1522833, 475.0: 0.1421, 476.0: 0.1321786, 477.0: 0.1225696, 478.0: 0.1132752, 479.0: 0.1042979, 480.0: 0.09564, 481.0: 0.08729955, 482.0: 0.07930804, 483.0: 0.07171776, 484.0: 0.06458099, 485.0: 0.05795001, 486.0: 0.05186211, 487.0: 0.04628152, 488.0: 0.04115088, 489.0: 0.03641283, 490.0: 0.03201, 491.0: 0.0279172, 492.0: 0.0241444, 493.0: 0.020687, 494.0: 0.0175404, 495.0: 0.0147, 496.0: 0.01216179, 497.0: 0.00991996, 498.0: 0.00796724, 499.0: 0.006296346, 500.0: 0.0049, 501.0: 0.003777173, 502.0: 0.00294532, 503.0: 0.00242488, 504.0: 0.002236293, 505.0: 0.0024, 506.0: 0.00292552, 507.0: 0.00383656, 508.0: 0.00517484, 509.0: 0.00698208, 510.0: 0.0093, 511.0: 0.01214949, 512.0: 0.01553588, 513.0: 0.01947752, 514.0: 0.02399277, 515.0: 0.0291, 516.0: 0.03481485, 517.0: 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5.532136e-06, 364.0: 6.208245e-06, 365.0: 6.965e-06, 366.0: 7.813219e-06, 367.0: 8.767336e-06, 368.0: 9.839844e-06, 369.0: 1.104323e-05, 370.0: 1.239e-05, 371.0: 1.388641e-05, 372.0: 1.555728e-05, 373.0: 1.744296e-05, 374.0: 1.958375e-05, 375.0: 2.202e-05, 376.0: 2.483965e-05, 377.0: 2.804126e-05, 378.0: 3.153104e-05, 379.0: 3.521521e-05, 380.0: 3.9e-05, 381.0: 4.28264e-05, 382.0: 4.69146e-05, 383.0: 5.15896e-05, 384.0: 5.71764e-05, 385.0: 6.4e-05, 386.0: 7.234421e-05, 387.0: 8.221224e-05, 388.0: 9.350816e-05, 389.0: 0.0001061361, 390.0: 0.00012, 391.0: 0.000134984, 392.0: 0.000151492, 393.0: 0.000170208, 394.0: 0.000191816, 395.0: 0.000217, 396.0: 0.0002469067, 397.0: 0.00028124, 398.0: 0.00031852, 399.0: 0.0003572667, 400.0: 0.000396, 401.0: 0.0004337147, 402.0: 0.000473024, 403.0: 0.000517876, 404.0: 0.0005722187, 405.0: 0.00064, 406.0: 0.00072456, 407.0: 0.0008255, 408.0: 0.00094116, 409.0: 0.00106988, 410.0: 0.00121, 411.0: 0.001362091, 412.0: 0.001530752, 413.0: 0.001720368, 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Returns the complementary wavelength \(\lambda_c\) for given colour stimulus \(xy\) and the related \(xy_wl\) first and \(xy_{cw}\) second intersection coordinates with the spectral locus.

In the eventuality where the \(xy_wl\) first intersection coordinates are on the line of purples, the dominant wavelength will be computed in lieu.

The dominant wavelength is indicated by a negative sign and the \(xy_{cw}\) second intersection coordinates which are set by default to the same value than \(xy_wl\) first intersection coordinates will be set to the dominant wavelength intersection coordinates with the spectral locus.

Parameters:
  • xy (array_like) – Colour stimulus xy chromaticity coordinates.
  • xy_n (array_like) – Achromatic stimulus xy chromaticity coordinates.
  • cmfs (XYZ_ColourMatchingFunctions, optional) – Standard observer colour matching functions.
Returns:

Complementary wavelength, first intersection point xy chromaticity coordinates, second intersection point xy chromaticity coordinates.

Return type:

tuple

Examples

Complementary wavelength computation:

>>> from pprint import pprint
>>> xy = np.array([0.35000, 0.25000])
>>> xy_n = np.array([0.31270, 0.32900])
>>> cmfs = CMFS['CIE 1931 2 Degree Standard Observer']
>>> pprint(complementary_wavelength(xy, xy_n, cmfs))  
(array(520...),
 array([ 0.0743553...,  0.8338050...]),
 array([ 0.0743553...,  0.8338050...]))

Dominant wavelength is returned if the first intersection is located on the line of purples:

>>> xy = np.array([0.26415, 0.37770])
>>> pprint(complementary_wavelength(xy, xy_n, cmfs))  
(array(-504...),
 array([ 0.4897494...,  0.1514035...]),
 array([ 0.0036969...,  0.6389577...]))
colour.excitation_purity(xy, xy_n, cmfs=XYZ_ColourMatchingFunctions( 'CIE 1931 2 Degree Standard Observer', {u'x_bar': {360.0: 0.0001299, 361.0: 0.000145847, 362.0: 0.0001638021, 363.0: 0.0001840037, 364.0: 0.0002066902, 365.0: 0.0002321, 366.0: 0.000260728, 367.0: 0.000293075, 368.0: 0.000329388, 369.0: 0.000369914, 370.0: 0.0004149, 371.0: 0.0004641587, 372.0: 0.000518986, 373.0: 0.000581854, 374.0: 0.0006552347, 375.0: 0.0007416, 376.0: 0.0008450296, 377.0: 0.0009645268, 378.0: 0.001094949, 379.0: 0.001231154, 380.0: 0.001368, 381.0: 0.00150205, 382.0: 0.001642328, 383.0: 0.001802382, 384.0: 0.001995757, 385.0: 0.002236, 386.0: 0.002535385, 387.0: 0.002892603, 388.0: 0.003300829, 389.0: 0.003753236, 390.0: 0.004243, 391.0: 0.004762389, 392.0: 0.005330048, 393.0: 0.005978712, 394.0: 0.006741117, 395.0: 0.00765, 396.0: 0.008751373, 397.0: 0.01002888, 398.0: 0.0114217, 399.0: 0.01286901, 400.0: 0.01431, 401.0: 0.01570443, 402.0: 0.01714744, 403.0: 0.01878122, 404.0: 0.02074801, 405.0: 0.02319, 406.0: 0.02620736, 407.0: 0.02978248, 408.0: 0.03388092, 409.0: 0.03846824, 410.0: 0.04351, 411.0: 0.0489956, 412.0: 0.0550226, 413.0: 0.0617188, 414.0: 0.069212, 415.0: 0.07763, 416.0: 0.08695811, 417.0: 0.09717672, 418.0: 0.1084063, 419.0: 0.1207672, 420.0: 0.13438, 421.0: 0.1493582, 422.0: 0.1653957, 423.0: 0.1819831, 424.0: 0.198611, 425.0: 0.21477, 426.0: 0.2301868, 427.0: 0.2448797, 428.0: 0.2587773, 429.0: 0.2718079, 430.0: 0.2839, 431.0: 0.2949438, 432.0: 0.3048965, 433.0: 0.3137873, 434.0: 0.3216454, 435.0: 0.3285, 436.0: 0.3343513, 437.0: 0.3392101, 438.0: 0.3431213, 439.0: 0.3461296, 440.0: 0.34828, 441.0: 0.3495999, 442.0: 0.3501474, 443.0: 0.350013, 444.0: 0.349287, 445.0: 0.34806, 446.0: 0.3463733, 447.0: 0.3442624, 448.0: 0.3418088, 449.0: 0.3390941, 450.0: 0.3362, 451.0: 0.3331977, 452.0: 0.3300411, 453.0: 0.3266357, 454.0: 0.3228868, 455.0: 0.3187, 456.0: 0.3140251, 457.0: 0.308884, 458.0: 0.3032904, 459.0: 0.2972579, 460.0: 0.2908, 461.0: 0.2839701, 462.0: 0.2767214, 463.0: 0.2689178, 464.0: 0.2604227, 465.0: 0.2511, 466.0: 0.2408475, 467.0: 0.2298512, 468.0: 0.2184072, 469.0: 0.2068115, 470.0: 0.19536, 471.0: 0.1842136, 472.0: 0.1733273, 473.0: 0.1626881, 474.0: 0.1522833, 475.0: 0.1421, 476.0: 0.1321786, 477.0: 0.1225696, 478.0: 0.1132752, 479.0: 0.1042979, 480.0: 0.09564, 481.0: 0.08729955, 482.0: 0.07930804, 483.0: 0.07171776, 484.0: 0.06458099, 485.0: 0.05795001, 486.0: 0.05186211, 487.0: 0.04628152, 488.0: 0.04115088, 489.0: 0.03641283, 490.0: 0.03201, 491.0: 0.0279172, 492.0: 0.0241444, 493.0: 0.020687, 494.0: 0.0175404, 495.0: 0.0147, 496.0: 0.01216179, 497.0: 0.00991996, 498.0: 0.00796724, 499.0: 0.006296346, 500.0: 0.0049, 501.0: 0.003777173, 502.0: 0.00294532, 503.0: 0.00242488, 504.0: 0.002236293, 505.0: 0.0024, 506.0: 0.00292552, 507.0: 0.00383656, 508.0: 0.00517484, 509.0: 0.00698208, 510.0: 0.0093, 511.0: 0.01214949, 512.0: 0.01553588, 513.0: 0.01947752, 514.0: 0.02399277, 515.0: 0.0291, 516.0: 0.03481485, 517.0: 0.04112016, 518.0: 0.04798504, 519.0: 0.05537861, 520.0: 0.06327, 521.0: 0.07163501, 522.0: 0.08046224, 523.0: 0.08973996, 524.0: 0.09945645, 525.0: 0.1096, 526.0: 0.1201674, 527.0: 0.1311145, 528.0: 0.1423679, 529.0: 0.1538542, 530.0: 0.1655, 531.0: 0.1772571, 532.0: 0.18914, 533.0: 0.2011694, 534.0: 0.2133658, 535.0: 0.2257499, 536.0: 0.2383209, 537.0: 0.2510668, 538.0: 0.2639922, 539.0: 0.2771017, 540.0: 0.2904, 541.0: 0.3038912, 542.0: 0.3175726, 543.0: 0.3314384, 544.0: 0.3454828, 545.0: 0.3597, 546.0: 0.3740839, 547.0: 0.3886396, 548.0: 0.4033784, 549.0: 0.4183115, 550.0: 0.4334499, 551.0: 0.4487953, 552.0: 0.464336, 553.0: 0.480064, 554.0: 0.4959713, 555.0: 0.5120501, 556.0: 0.5282959, 557.0: 0.5446916, 558.0: 0.5612094, 559.0: 0.5778215, 560.0: 0.5945, 561.0: 0.6112209, 562.0: 0.6279758, 563.0: 0.6447602, 564.0: 0.6615697, 565.0: 0.6784, 566.0: 0.6952392, 567.0: 0.7120586, 568.0: 0.7288284, 569.0: 0.7455188, 570.0: 0.7621, 571.0: 0.7785432, 572.0: 0.7948256, 573.0: 0.8109264, 574.0: 0.8268248, 575.0: 0.8425, 576.0: 0.8579325, 577.0: 0.8730816, 578.0: 0.8878944, 579.0: 0.9023181, 580.0: 0.9163, 581.0: 0.9297995, 582.0: 0.9427984, 583.0: 0.9552776, 584.0: 0.9672179, 585.0: 0.9786, 586.0: 0.9893856, 587.0: 0.9995488, 588.0: 1.0090892, 589.0: 1.0180064, 590.0: 1.0263, 591.0: 1.0339827, 592.0: 1.040986, 593.0: 1.047188, 594.0: 1.0524667, 595.0: 1.0567, 596.0: 1.0597944, 597.0: 1.0617992, 598.0: 1.0628068, 599.0: 1.0629096, 600.0: 1.0622, 601.0: 1.0607352, 602.0: 1.0584436, 603.0: 1.0552244, 604.0: 1.0509768, 605.0: 1.0456, 606.0: 1.0390369, 607.0: 1.0313608, 608.0: 1.0226662, 609.0: 1.0130477, 610.0: 1.0026, 611.0: 0.9913675, 612.0: 0.9793314, 613.0: 0.9664916, 614.0: 0.9528479, 615.0: 0.9384, 616.0: 0.923194, 617.0: 0.907244, 618.0: 0.890502, 619.0: 0.87292, 620.0: 0.8544499, 621.0: 0.835084, 622.0: 0.814946, 623.0: 0.794186, 624.0: 0.772954, 625.0: 0.7514, 626.0: 0.7295836, 627.0: 0.7075888, 628.0: 0.6856022, 629.0: 0.6638104, 630.0: 0.6424, 631.0: 0.6215149, 632.0: 0.6011138, 633.0: 0.5811052, 634.0: 0.5613977, 635.0: 0.5419, 636.0: 0.5225995, 637.0: 0.5035464, 638.0: 0.4847436, 639.0: 0.4661939, 640.0: 0.4479, 641.0: 0.4298613, 642.0: 0.412098, 643.0: 0.394644, 644.0: 0.3775333, 645.0: 0.3608, 646.0: 0.3444563, 647.0: 0.3285168, 648.0: 0.3130192, 649.0: 0.2980011, 650.0: 0.2835, 651.0: 0.2695448, 652.0: 0.2561184, 653.0: 0.2431896, 654.0: 0.2307272, 655.0: 0.2187, 656.0: 0.2070971, 657.0: 0.1959232, 658.0: 0.1851708, 659.0: 0.1748323, 660.0: 0.1649, 661.0: 0.1553667, 662.0: 0.14623, 663.0: 0.13749, 664.0: 0.1291467, 665.0: 0.1212, 666.0: 0.1136397, 667.0: 0.106465, 668.0: 0.09969044, 669.0: 0.09333061, 670.0: 0.0874, 671.0: 0.08190096, 672.0: 0.07680428, 673.0: 0.07207712, 674.0: 0.06768664, 675.0: 0.0636, 676.0: 0.05980685, 677.0: 0.05628216, 678.0: 0.05297104, 679.0: 0.04981861, 680.0: 0.04677, 681.0: 0.04378405, 682.0: 0.04087536, 683.0: 0.03807264, 684.0: 0.03540461, 685.0: 0.0329, 686.0: 0.03056419, 687.0: 0.02838056, 688.0: 0.02634484, 689.0: 0.02445275, 690.0: 0.0227, 691.0: 0.02108429, 692.0: 0.01959988, 693.0: 0.01823732, 694.0: 0.01698717, 695.0: 0.01584, 696.0: 0.01479064, 697.0: 0.01383132, 698.0: 0.01294868, 699.0: 0.0121292, 700.0: 0.01135916, 701.0: 0.01062935, 702.0: 0.009938846, 703.0: 0.009288422, 704.0: 0.008678854, 705.0: 0.008110916, 706.0: 0.007582388, 707.0: 0.007088746, 708.0: 0.006627313, 709.0: 0.006195408, 710.0: 0.005790346, 711.0: 0.005409826, 712.0: 0.005052583, 713.0: 0.004717512, 714.0: 0.004403507, 715.0: 0.004109457, 716.0: 0.003833913, 717.0: 0.003575748, 718.0: 0.003334342, 719.0: 0.003109075, 720.0: 0.002899327, 721.0: 0.002704348, 722.0: 0.00252302, 723.0: 0.002354168, 724.0: 0.002196616, 725.0: 0.00204919, 726.0: 0.00191096, 727.0: 0.001781438, 728.0: 0.00166011, 729.0: 0.001546459, 730.0: 0.001439971, 731.0: 0.001340042, 732.0: 0.001246275, 733.0: 0.001158471, 734.0: 0.00107643, 735.0: 0.0009999493, 736.0: 0.0009287358, 737.0: 0.0008624332, 738.0: 0.0008007503, 739.0: 0.000743396, 740.0: 0.0006900786, 741.0: 0.0006405156, 742.0: 0.0005945021, 743.0: 0.0005518646, 744.0: 0.000512429, 745.0: 0.0004760213, 746.0: 0.0004424536, 747.0: 0.0004115117, 748.0: 0.0003829814, 749.0: 0.0003566491, 750.0: 0.0003323011, 751.0: 0.0003097586, 752.0: 0.0002888871, 753.0: 0.0002695394, 754.0: 0.0002515682, 755.0: 0.0002348261, 756.0: 0.000219171, 757.0: 0.0002045258, 758.0: 0.0001908405, 759.0: 0.0001780654, 760.0: 0.0001661505, 761.0: 0.0001550236, 762.0: 0.0001446219, 763.0: 0.0001349098, 764.0: 0.000125852, 765.0: 0.000117413, 766.0: 0.0001095515, 767.0: 0.0001022245, 768.0: 9.539445e-05, 769.0: 8.90239e-05, 770.0: 8.307527e-05, 771.0: 7.751269e-05, 772.0: 7.231304e-05, 773.0: 6.745778e-05, 774.0: 6.292844e-05, 775.0: 5.870652e-05, 776.0: 5.477028e-05, 777.0: 5.109918e-05, 778.0: 4.767654e-05, 779.0: 4.448567e-05, 780.0: 4.150994e-05, 781.0: 3.873324e-05, 782.0: 3.614203e-05, 783.0: 3.372352e-05, 784.0: 3.146487e-05, 785.0: 2.935326e-05, 786.0: 2.737573e-05, 787.0: 2.552433e-05, 788.0: 2.379376e-05, 789.0: 2.21787e-05, 790.0: 2.067383e-05, 791.0: 1.927226e-05, 792.0: 1.79664e-05, 793.0: 1.674991e-05, 794.0: 1.561648e-05, 795.0: 1.455977e-05, 796.0: 1.357387e-05, 797.0: 1.265436e-05, 798.0: 1.179723e-05, 799.0: 1.099844e-05, 800.0: 1.025398e-05, 801.0: 9.559646e-06, 802.0: 8.912044e-06, 803.0: 8.308358e-06, 804.0: 7.745769e-06, 805.0: 7.221456e-06, 806.0: 6.732475e-06, 807.0: 6.276423e-06, 808.0: 5.851304e-06, 809.0: 5.455118e-06, 810.0: 5.085868e-06, 811.0: 4.741466e-06, 812.0: 4.420236e-06, 813.0: 4.120783e-06, 814.0: 3.841716e-06, 815.0: 3.581652e-06, 816.0: 3.339127e-06, 817.0: 3.112949e-06, 818.0: 2.902121e-06, 819.0: 2.705645e-06, 820.0: 2.522525e-06, 821.0: 2.351726e-06, 822.0: 2.192415e-06, 823.0: 2.043902e-06, 824.0: 1.905497e-06, 825.0: 1.776509e-06, 826.0: 1.656215e-06, 827.0: 1.544022e-06, 828.0: 1.43944e-06, 829.0: 1.341977e-06, 830.0: 1.251141e-06}, u'y_bar': {360.0: 3.917e-06, 361.0: 4.393581e-06, 362.0: 4.929604e-06, 363.0: 5.532136e-06, 364.0: 6.208245e-06, 365.0: 6.965e-06, 366.0: 7.813219e-06, 367.0: 8.767336e-06, 368.0: 9.839844e-06, 369.0: 1.104323e-05, 370.0: 1.239e-05, 371.0: 1.388641e-05, 372.0: 1.555728e-05, 373.0: 1.744296e-05, 374.0: 1.958375e-05, 375.0: 2.202e-05, 376.0: 2.483965e-05, 377.0: 2.804126e-05, 378.0: 3.153104e-05, 379.0: 3.521521e-05, 380.0: 3.9e-05, 381.0: 4.28264e-05, 382.0: 4.69146e-05, 383.0: 5.15896e-05, 384.0: 5.71764e-05, 385.0: 6.4e-05, 386.0: 7.234421e-05, 387.0: 8.221224e-05, 388.0: 9.350816e-05, 389.0: 0.0001061361, 390.0: 0.00012, 391.0: 0.000134984, 392.0: 0.000151492, 393.0: 0.000170208, 394.0: 0.000191816, 395.0: 0.000217, 396.0: 0.0002469067, 397.0: 0.00028124, 398.0: 0.00031852, 399.0: 0.0003572667, 400.0: 0.000396, 401.0: 0.0004337147, 402.0: 0.000473024, 403.0: 0.000517876, 404.0: 0.0005722187, 405.0: 0.00064, 406.0: 0.00072456, 407.0: 0.0008255, 408.0: 0.00094116, 409.0: 0.00106988, 410.0: 0.00121, 411.0: 0.001362091, 412.0: 0.001530752, 413.0: 0.001720368, 414.0: 0.001935323, 415.0: 0.00218, 416.0: 0.0024548, 417.0: 0.002764, 418.0: 0.0031178, 419.0: 0.0035264, 420.0: 0.004, 421.0: 0.00454624, 422.0: 0.00515932, 423.0: 0.00582928, 424.0: 0.00654616, 425.0: 0.0073, 426.0: 0.008086507, 427.0: 0.00890872, 428.0: 0.00976768, 429.0: 0.01066443, 430.0: 0.0116, 431.0: 0.01257317, 432.0: 0.01358272, 433.0: 0.01462968, 434.0: 0.01571509, 435.0: 0.01684, 436.0: 0.01800736, 437.0: 0.01921448, 438.0: 0.02045392, 439.0: 0.02171824, 440.0: 0.023, 441.0: 0.02429461, 442.0: 0.02561024, 443.0: 0.02695857, 444.0: 0.02835125, 445.0: 0.0298, 446.0: 0.03131083, 447.0: 0.03288368, 448.0: 0.03452112, 449.0: 0.03622571, 450.0: 0.038, 451.0: 0.03984667, 452.0: 0.041768, 453.0: 0.043766, 454.0: 0.04584267, 455.0: 0.048, 456.0: 0.05024368, 457.0: 0.05257304, 458.0: 0.05498056, 459.0: 0.05745872, 460.0: 0.06, 461.0: 0.06260197, 462.0: 0.06527752, 463.0: 0.06804208, 464.0: 0.07091109, 465.0: 0.0739, 466.0: 0.077016, 467.0: 0.0802664, 468.0: 0.0836668, 469.0: 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737.0: 0.0, 738.0: 0.0, 739.0: 0.0, 740.0: 0.0, 741.0: 0.0, 742.0: 0.0, 743.0: 0.0, 744.0: 0.0, 745.0: 0.0, 746.0: 0.0, 747.0: 0.0, 748.0: 0.0, 749.0: 0.0, 750.0: 0.0, 751.0: 0.0, 752.0: 0.0, 753.0: 0.0, 754.0: 0.0, 755.0: 0.0, 756.0: 0.0, 757.0: 0.0, 758.0: 0.0, 759.0: 0.0, 760.0: 0.0, 761.0: 0.0, 762.0: 0.0, 763.0: 0.0, 764.0: 0.0, 765.0: 0.0, 766.0: 0.0, 767.0: 0.0, 768.0: 0.0, 769.0: 0.0, 770.0: 0.0, 771.0: 0.0, 772.0: 0.0, 773.0: 0.0, 774.0: 0.0, 775.0: 0.0, 776.0: 0.0, 777.0: 0.0, 778.0: 0.0, 779.0: 0.0, 780.0: 0.0, 781.0: 0.0, 782.0: 0.0, 783.0: 0.0, 784.0: 0.0, 785.0: 0.0, 786.0: 0.0, 787.0: 0.0, 788.0: 0.0, 789.0: 0.0, 790.0: 0.0, 791.0: 0.0, 792.0: 0.0, 793.0: 0.0, 794.0: 0.0, 795.0: 0.0, 796.0: 0.0, 797.0: 0.0, 798.0: 0.0, 799.0: 0.0, 800.0: 0.0, 801.0: 0.0, 802.0: 0.0, 803.0: 0.0, 804.0: 0.0, 805.0: 0.0, 806.0: 0.0, 807.0: 0.0, 808.0: 0.0, 809.0: 0.0, 810.0: 0.0, 811.0: 0.0, 812.0: 0.0, 813.0: 0.0, 814.0: 0.0, 815.0: 0.0, 816.0: 0.0, 817.0: 0.0, 818.0: 0.0, 819.0: 0.0, 820.0: 0.0, 821.0: 0.0, 822.0: 0.0, 823.0: 0.0, 824.0: 0.0, 825.0: 0.0, 826.0: 0.0, 827.0: 0.0, 828.0: 0.0, 829.0: 0.0, 830.0: 0.0}}, 'CIE 1931 2$^\circ$ Standard Observer'))[source]

Returns the excitation purity \(P_e\) for given colour stimulus \(xy\).

Parameters:
  • xy (array_like) – Colour stimulus xy chromaticity coordinates.
  • xy_n (array_like) – Achromatic stimulus xy chromaticity coordinates.
  • cmfs (XYZ_ColourMatchingFunctions, optional) – Standard observer colour matching functions.
Returns:

Excitation purity \(P_e\).

Return type:

numeric or array_like

Examples

>>> xy = np.array([0.28350, 0.68700])
>>> xy_n = np.array([0.31270, 0.32900])
>>> cmfs = CMFS['CIE 1931 2 Degree Standard Observer']
>>> excitation_purity(xy, xy_n, cmfs)  
0.9386035...
colour.colorimetric_purity(xy, xy_n, cmfs=XYZ_ColourMatchingFunctions( 'CIE 1931 2 Degree Standard Observer', {u'x_bar': {360.0: 0.0001299, 361.0: 0.000145847, 362.0: 0.0001638021, 363.0: 0.0001840037, 364.0: 0.0002066902, 365.0: 0.0002321, 366.0: 0.000260728, 367.0: 0.000293075, 368.0: 0.000329388, 369.0: 0.000369914, 370.0: 0.0004149, 371.0: 0.0004641587, 372.0: 0.000518986, 373.0: 0.000581854, 374.0: 0.0006552347, 375.0: 0.0007416, 376.0: 0.0008450296, 377.0: 0.0009645268, 378.0: 0.001094949, 379.0: 0.001231154, 380.0: 0.001368, 381.0: 0.00150205, 382.0: 0.001642328, 383.0: 0.001802382, 384.0: 0.001995757, 385.0: 0.002236, 386.0: 0.002535385, 387.0: 0.002892603, 388.0: 0.003300829, 389.0: 0.003753236, 390.0: 0.004243, 391.0: 0.004762389, 392.0: 0.005330048, 393.0: 0.005978712, 394.0: 0.006741117, 395.0: 0.00765, 396.0: 0.008751373, 397.0: 0.01002888, 398.0: 0.0114217, 399.0: 0.01286901, 400.0: 0.01431, 401.0: 0.01570443, 402.0: 0.01714744, 403.0: 0.01878122, 404.0: 0.02074801, 405.0: 0.02319, 406.0: 0.02620736, 407.0: 0.02978248, 408.0: 0.03388092, 409.0: 0.03846824, 410.0: 0.04351, 411.0: 0.0489956, 412.0: 0.0550226, 413.0: 0.0617188, 414.0: 0.069212, 415.0: 0.07763, 416.0: 0.08695811, 417.0: 0.09717672, 418.0: 0.1084063, 419.0: 0.1207672, 420.0: 0.13438, 421.0: 0.1493582, 422.0: 0.1653957, 423.0: 0.1819831, 424.0: 0.198611, 425.0: 0.21477, 426.0: 0.2301868, 427.0: 0.2448797, 428.0: 0.2587773, 429.0: 0.2718079, 430.0: 0.2839, 431.0: 0.2949438, 432.0: 0.3048965, 433.0: 0.3137873, 434.0: 0.3216454, 435.0: 0.3285, 436.0: 0.3343513, 437.0: 0.3392101, 438.0: 0.3431213, 439.0: 0.3461296, 440.0: 0.34828, 441.0: 0.3495999, 442.0: 0.3501474, 443.0: 0.350013, 444.0: 0.349287, 445.0: 0.34806, 446.0: 0.3463733, 447.0: 0.3442624, 448.0: 0.3418088, 449.0: 0.3390941, 450.0: 0.3362, 451.0: 0.3331977, 452.0: 0.3300411, 453.0: 0.3266357, 454.0: 0.3228868, 455.0: 0.3187, 456.0: 0.3140251, 457.0: 0.308884, 458.0: 0.3032904, 459.0: 0.2972579, 460.0: 0.2908, 461.0: 0.2839701, 462.0: 0.2767214, 463.0: 0.2689178, 464.0: 0.2604227, 465.0: 0.2511, 466.0: 0.2408475, 467.0: 0.2298512, 468.0: 0.2184072, 469.0: 0.2068115, 470.0: 0.19536, 471.0: 0.1842136, 472.0: 0.1733273, 473.0: 0.1626881, 474.0: 0.1522833, 475.0: 0.1421, 476.0: 0.1321786, 477.0: 0.1225696, 478.0: 0.1132752, 479.0: 0.1042979, 480.0: 0.09564, 481.0: 0.08729955, 482.0: 0.07930804, 483.0: 0.07171776, 484.0: 0.06458099, 485.0: 0.05795001, 486.0: 0.05186211, 487.0: 0.04628152, 488.0: 0.04115088, 489.0: 0.03641283, 490.0: 0.03201, 491.0: 0.0279172, 492.0: 0.0241444, 493.0: 0.020687, 494.0: 0.0175404, 495.0: 0.0147, 496.0: 0.01216179, 497.0: 0.00991996, 498.0: 0.00796724, 499.0: 0.006296346, 500.0: 0.0049, 501.0: 0.003777173, 502.0: 0.00294532, 503.0: 0.00242488, 504.0: 0.002236293, 505.0: 0.0024, 506.0: 0.00292552, 507.0: 0.00383656, 508.0: 0.00517484, 509.0: 0.00698208, 510.0: 0.0093, 511.0: 0.01214949, 512.0: 0.01553588, 513.0: 0.01947752, 514.0: 0.02399277, 515.0: 0.0291, 516.0: 0.03481485, 517.0: 0.04112016, 518.0: 0.04798504, 519.0: 0.05537861, 520.0: 0.06327, 521.0: 0.07163501, 522.0: 0.08046224, 523.0: 0.08973996, 524.0: 0.09945645, 525.0: 0.1096, 526.0: 0.1201674, 527.0: 0.1311145, 528.0: 0.1423679, 529.0: 0.1538542, 530.0: 0.1655, 531.0: 0.1772571, 532.0: 0.18914, 533.0: 0.2011694, 534.0: 0.2133658, 535.0: 0.2257499, 536.0: 0.2383209, 537.0: 0.2510668, 538.0: 0.2639922, 539.0: 0.2771017, 540.0: 0.2904, 541.0: 0.3038912, 542.0: 0.3175726, 543.0: 0.3314384, 544.0: 0.3454828, 545.0: 0.3597, 546.0: 0.3740839, 547.0: 0.3886396, 548.0: 0.4033784, 549.0: 0.4183115, 550.0: 0.4334499, 551.0: 0.4487953, 552.0: 0.464336, 553.0: 0.480064, 554.0: 0.4959713, 555.0: 0.5120501, 556.0: 0.5282959, 557.0: 0.5446916, 558.0: 0.5612094, 559.0: 0.5778215, 560.0: 0.5945, 561.0: 0.6112209, 562.0: 0.6279758, 563.0: 0.6447602, 564.0: 0.6615697, 565.0: 0.6784, 566.0: 0.6952392, 567.0: 0.7120586, 568.0: 0.7288284, 569.0: 0.7455188, 570.0: 0.7621, 571.0: 0.7785432, 572.0: 0.7948256, 573.0: 0.8109264, 574.0: 0.8268248, 575.0: 0.8425, 576.0: 0.8579325, 577.0: 0.8730816, 578.0: 0.8878944, 579.0: 0.9023181, 580.0: 0.9163, 581.0: 0.9297995, 582.0: 0.9427984, 583.0: 0.9552776, 584.0: 0.9672179, 585.0: 0.9786, 586.0: 0.9893856, 587.0: 0.9995488, 588.0: 1.0090892, 589.0: 1.0180064, 590.0: 1.0263, 591.0: 1.0339827, 592.0: 1.040986, 593.0: 1.047188, 594.0: 1.0524667, 595.0: 1.0567, 596.0: 1.0597944, 597.0: 1.0617992, 598.0: 1.0628068, 599.0: 1.0629096, 600.0: 1.0622, 601.0: 1.0607352, 602.0: 1.0584436, 603.0: 1.0552244, 604.0: 1.0509768, 605.0: 1.0456, 606.0: 1.0390369, 607.0: 1.0313608, 608.0: 1.0226662, 609.0: 1.0130477, 610.0: 1.0026, 611.0: 0.9913675, 612.0: 0.9793314, 613.0: 0.9664916, 614.0: 0.9528479, 615.0: 0.9384, 616.0: 0.923194, 617.0: 0.907244, 618.0: 0.890502, 619.0: 0.87292, 620.0: 0.8544499, 621.0: 0.835084, 622.0: 0.814946, 623.0: 0.794186, 624.0: 0.772954, 625.0: 0.7514, 626.0: 0.7295836, 627.0: 0.7075888, 628.0: 0.6856022, 629.0: 0.6638104, 630.0: 0.6424, 631.0: 0.6215149, 632.0: 0.6011138, 633.0: 0.5811052, 634.0: 0.5613977, 635.0: 0.5419, 636.0: 0.5225995, 637.0: 0.5035464, 638.0: 0.4847436, 639.0: 0.4661939, 640.0: 0.4479, 641.0: 0.4298613, 642.0: 0.412098, 643.0: 0.394644, 644.0: 0.3775333, 645.0: 0.3608, 646.0: 0.3444563, 647.0: 0.3285168, 648.0: 0.3130192, 649.0: 0.2980011, 650.0: 0.2835, 651.0: 0.2695448, 652.0: 0.2561184, 653.0: 0.2431896, 654.0: 0.2307272, 655.0: 0.2187, 656.0: 0.2070971, 657.0: 0.1959232, 658.0: 0.1851708, 659.0: 0.1748323, 660.0: 0.1649, 661.0: 0.1553667, 662.0: 0.14623, 663.0: 0.13749, 664.0: 0.1291467, 665.0: 0.1212, 666.0: 0.1136397, 667.0: 0.106465, 668.0: 0.09969044, 669.0: 0.09333061, 670.0: 0.0874, 671.0: 0.08190096, 672.0: 0.07680428, 673.0: 0.07207712, 674.0: 0.06768664, 675.0: 0.0636, 676.0: 0.05980685, 677.0: 0.05628216, 678.0: 0.05297104, 679.0: 0.04981861, 680.0: 0.04677, 681.0: 0.04378405, 682.0: 0.04087536, 683.0: 0.03807264, 684.0: 0.03540461, 685.0: 0.0329, 686.0: 0.03056419, 687.0: 0.02838056, 688.0: 0.02634484, 689.0: 0.02445275, 690.0: 0.0227, 691.0: 0.02108429, 692.0: 0.01959988, 693.0: 0.01823732, 694.0: 0.01698717, 695.0: 0.01584, 696.0: 0.01479064, 697.0: 0.01383132, 698.0: 0.01294868, 699.0: 0.0121292, 700.0: 0.01135916, 701.0: 0.01062935, 702.0: 0.009938846, 703.0: 0.009288422, 704.0: 0.008678854, 705.0: 0.008110916, 706.0: 0.007582388, 707.0: 0.007088746, 708.0: 0.006627313, 709.0: 0.006195408, 710.0: 0.005790346, 711.0: 0.005409826, 712.0: 0.005052583, 713.0: 0.004717512, 714.0: 0.004403507, 715.0: 0.004109457, 716.0: 0.003833913, 717.0: 0.003575748, 718.0: 0.003334342, 719.0: 0.003109075, 720.0: 0.002899327, 721.0: 0.002704348, 722.0: 0.00252302, 723.0: 0.002354168, 724.0: 0.002196616, 725.0: 0.00204919, 726.0: 0.00191096, 727.0: 0.001781438, 728.0: 0.00166011, 729.0: 0.001546459, 730.0: 0.001439971, 731.0: 0.001340042, 732.0: 0.001246275, 733.0: 0.001158471, 734.0: 0.00107643, 735.0: 0.0009999493, 736.0: 0.0009287358, 737.0: 0.0008624332, 738.0: 0.0008007503, 739.0: 0.000743396, 740.0: 0.0006900786, 741.0: 0.0006405156, 742.0: 0.0005945021, 743.0: 0.0005518646, 744.0: 0.000512429, 745.0: 0.0004760213, 746.0: 0.0004424536, 747.0: 0.0004115117, 748.0: 0.0003829814, 749.0: 0.0003566491, 750.0: 0.0003323011, 751.0: 0.0003097586, 752.0: 0.0002888871, 753.0: 0.0002695394, 754.0: 0.0002515682, 755.0: 0.0002348261, 756.0: 0.000219171, 757.0: 0.0002045258, 758.0: 0.0001908405, 759.0: 0.0001780654, 760.0: 0.0001661505, 761.0: 0.0001550236, 762.0: 0.0001446219, 763.0: 0.0001349098, 764.0: 0.000125852, 765.0: 0.000117413, 766.0: 0.0001095515, 767.0: 0.0001022245, 768.0: 9.539445e-05, 769.0: 8.90239e-05, 770.0: 8.307527e-05, 771.0: 7.751269e-05, 772.0: 7.231304e-05, 773.0: 6.745778e-05, 774.0: 6.292844e-05, 775.0: 5.870652e-05, 776.0: 5.477028e-05, 777.0: 5.109918e-05, 778.0: 4.767654e-05, 779.0: 4.448567e-05, 780.0: 4.150994e-05, 781.0: 3.873324e-05, 782.0: 3.614203e-05, 783.0: 3.372352e-05, 784.0: 3.146487e-05, 785.0: 2.935326e-05, 786.0: 2.737573e-05, 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0.00088688, 599.0: 0.00084256, 600.0: 0.0008, 601.0: 0.00076096, 602.0: 0.00072368, 603.0: 0.00068592, 604.0: 0.00064544, 605.0: 0.0006, 606.0: 0.0005478667, 607.0: 0.0004916, 608.0: 0.0004354, 609.0: 0.0003834667, 610.0: 0.00034, 611.0: 0.0003072533, 612.0: 0.00028316, 613.0: 0.00026544, 614.0: 0.0002518133, 615.0: 0.00024, 616.0: 0.0002295467, 617.0: 0.00022064, 618.0: 0.00021196, 619.0: 0.0002021867, 620.0: 0.00019, 621.0: 0.0001742133, 622.0: 0.00015564, 623.0: 0.00013596, 624.0: 0.0001168533, 625.0: 0.0001, 626.0: 8.613333e-05, 627.0: 7.46e-05, 628.0: 6.5e-05, 629.0: 5.693333e-05, 630.0: 4.999999e-05, 631.0: 4.416e-05, 632.0: 3.948e-05, 633.0: 3.572e-05, 634.0: 3.264e-05, 635.0: 3e-05, 636.0: 2.765333e-05, 637.0: 2.556e-05, 638.0: 2.364e-05, 639.0: 2.181333e-05, 640.0: 2e-05, 641.0: 1.813333e-05, 642.0: 1.62e-05, 643.0: 1.42e-05, 644.0: 1.213333e-05, 645.0: 1e-05, 646.0: 7.733333e-06, 647.0: 5.4e-06, 648.0: 3.2e-06, 649.0: 1.333333e-06, 650.0: 0.0, 651.0: 0.0, 652.0: 0.0, 653.0: 0.0, 654.0: 0.0, 655.0: 0.0, 656.0: 0.0, 657.0: 0.0, 658.0: 0.0, 659.0: 0.0, 660.0: 0.0, 661.0: 0.0, 662.0: 0.0, 663.0: 0.0, 664.0: 0.0, 665.0: 0.0, 666.0: 0.0, 667.0: 0.0, 668.0: 0.0, 669.0: 0.0, 670.0: 0.0, 671.0: 0.0, 672.0: 0.0, 673.0: 0.0, 674.0: 0.0, 675.0: 0.0, 676.0: 0.0, 677.0: 0.0, 678.0: 0.0, 679.0: 0.0, 680.0: 0.0, 681.0: 0.0, 682.0: 0.0, 683.0: 0.0, 684.0: 0.0, 685.0: 0.0, 686.0: 0.0, 687.0: 0.0, 688.0: 0.0, 689.0: 0.0, 690.0: 0.0, 691.0: 0.0, 692.0: 0.0, 693.0: 0.0, 694.0: 0.0, 695.0: 0.0, 696.0: 0.0, 697.0: 0.0, 698.0: 0.0, 699.0: 0.0, 700.0: 0.0, 701.0: 0.0, 702.0: 0.0, 703.0: 0.0, 704.0: 0.0, 705.0: 0.0, 706.0: 0.0, 707.0: 0.0, 708.0: 0.0, 709.0: 0.0, 710.0: 0.0, 711.0: 0.0, 712.0: 0.0, 713.0: 0.0, 714.0: 0.0, 715.0: 0.0, 716.0: 0.0, 717.0: 0.0, 718.0: 0.0, 719.0: 0.0, 720.0: 0.0, 721.0: 0.0, 722.0: 0.0, 723.0: 0.0, 724.0: 0.0, 725.0: 0.0, 726.0: 0.0, 727.0: 0.0, 728.0: 0.0, 729.0: 0.0, 730.0: 0.0, 731.0: 0.0, 732.0: 0.0, 733.0: 0.0, 734.0: 0.0, 735.0: 0.0, 736.0: 0.0, 737.0: 0.0, 738.0: 0.0, 739.0: 0.0, 740.0: 0.0, 741.0: 0.0, 742.0: 0.0, 743.0: 0.0, 744.0: 0.0, 745.0: 0.0, 746.0: 0.0, 747.0: 0.0, 748.0: 0.0, 749.0: 0.0, 750.0: 0.0, 751.0: 0.0, 752.0: 0.0, 753.0: 0.0, 754.0: 0.0, 755.0: 0.0, 756.0: 0.0, 757.0: 0.0, 758.0: 0.0, 759.0: 0.0, 760.0: 0.0, 761.0: 0.0, 762.0: 0.0, 763.0: 0.0, 764.0: 0.0, 765.0: 0.0, 766.0: 0.0, 767.0: 0.0, 768.0: 0.0, 769.0: 0.0, 770.0: 0.0, 771.0: 0.0, 772.0: 0.0, 773.0: 0.0, 774.0: 0.0, 775.0: 0.0, 776.0: 0.0, 777.0: 0.0, 778.0: 0.0, 779.0: 0.0, 780.0: 0.0, 781.0: 0.0, 782.0: 0.0, 783.0: 0.0, 784.0: 0.0, 785.0: 0.0, 786.0: 0.0, 787.0: 0.0, 788.0: 0.0, 789.0: 0.0, 790.0: 0.0, 791.0: 0.0, 792.0: 0.0, 793.0: 0.0, 794.0: 0.0, 795.0: 0.0, 796.0: 0.0, 797.0: 0.0, 798.0: 0.0, 799.0: 0.0, 800.0: 0.0, 801.0: 0.0, 802.0: 0.0, 803.0: 0.0, 804.0: 0.0, 805.0: 0.0, 806.0: 0.0, 807.0: 0.0, 808.0: 0.0, 809.0: 0.0, 810.0: 0.0, 811.0: 0.0, 812.0: 0.0, 813.0: 0.0, 814.0: 0.0, 815.0: 0.0, 816.0: 0.0, 817.0: 0.0, 818.0: 0.0, 819.0: 0.0, 820.0: 0.0, 821.0: 0.0, 822.0: 0.0, 823.0: 0.0, 824.0: 0.0, 825.0: 0.0, 826.0: 0.0, 827.0: 0.0, 828.0: 0.0, 829.0: 0.0, 830.0: 0.0}}, 'CIE 1931 2$^\circ$ Standard Observer'))[source]

Returns the colorimetric purity \(P_c\) for given colour stimulus \(xy\).

Parameters:
  • xy (array_like) – Colour stimulus xy chromaticity coordinates.
  • xy_n (array_like) – Achromatic stimulus xy chromaticity coordinates.
  • cmfs (XYZ_ColourMatchingFunctions, optional) – Standard observer colour matching functions.
Returns:

Colorimetric purity \(P_c\).

Return type:

numeric or array_like

Examples

>>> xy = np.array([0.28350, 0.68700])
>>> xy_n = np.array([0.31270, 0.32900])
>>> cmfs = CMFS['CIE 1931 2 Degree Standard Observer']
>>> colorimetric_purity(xy, xy_n, cmfs)  
0.9705976...
colour.luminous_flux(spd, lef=SpectralPowerDistribution( 'CIE 1924 Photopic Standard Observer', {360.0: 3.917e-06, 361.0: 4.393581e-06, 362.0: 4.929604e-06, 363.0: 5.532136e-06, 364.0: 6.208245e-06, 365.0: 6.965e-06, 366.0: 7.813219e-06, 367.0: 8.767336e-06, 368.0: 9.839844e-06, 369.0: 1.104323e-05, 370.0: 1.239e-05, 371.0: 1.388641e-05, 372.0: 1.555728e-05, 373.0: 1.744296e-05, 374.0: 1.958375e-05, 375.0: 2.202e-05, 376.0: 2.483965e-05, 377.0: 2.804126e-05, 378.0: 3.153104e-05, 379.0: 3.521521e-05, 380.0: 3.9e-05, 381.0: 4.28264e-05, 382.0: 4.69146e-05, 383.0: 5.15896e-05, 384.0: 5.71764e-05, 385.0: 6.4e-05, 386.0: 7.234421e-05, 387.0: 8.221224e-05, 388.0: 9.350816e-05, 389.0: 0.0001061361, 390.0: 0.00012, 391.0: 0.000134984, 392.0: 0.000151492, 393.0: 0.000170208, 394.0: 0.000191816, 395.0: 0.000217, 396.0: 0.0002469067, 397.0: 0.00028124, 398.0: 0.00031852, 399.0: 0.0003572667, 400.0: 0.000396, 401.0: 0.0004337147, 402.0: 0.000473024, 403.0: 0.000517876, 404.0: 0.0005722187, 405.0: 0.00064, 406.0: 0.00072456, 407.0: 0.0008255, 408.0: 0.00094116, 409.0: 0.00106988, 410.0: 0.00121, 411.0: 0.001362091, 412.0: 0.001530752, 413.0: 0.001720368, 414.0: 0.001935323, 415.0: 0.00218, 416.0: 0.0024548, 417.0: 0.002764, 418.0: 0.0031178, 419.0: 0.0035264, 420.0: 0.004, 421.0: 0.00454624, 422.0: 0.00515932, 423.0: 0.00582928, 424.0: 0.00654616, 425.0: 0.0073, 426.0: 0.008086507, 427.0: 0.00890872, 428.0: 0.00976768, 429.0: 0.01066443, 430.0: 0.0116, 431.0: 0.01257317, 432.0: 0.01358272, 433.0: 0.01462968, 434.0: 0.01571509, 435.0: 0.01684, 436.0: 0.01800736, 437.0: 0.01921448, 438.0: 0.02045392, 439.0: 0.02171824, 440.0: 0.023, 441.0: 0.02429461, 442.0: 0.02561024, 443.0: 0.02695857, 444.0: 0.02835125, 445.0: 0.0298, 446.0: 0.03131083, 447.0: 0.03288368, 448.0: 0.03452112, 449.0: 0.03622571, 450.0: 0.038, 451.0: 0.03984667, 452.0: 0.041768, 453.0: 0.043766, 454.0: 0.04584267, 455.0: 0.048, 456.0: 0.05024368, 457.0: 0.05257304, 458.0: 0.05498056, 459.0: 0.05745872, 460.0: 0.06, 461.0: 0.06260197, 462.0: 0.06527752, 463.0: 0.06804208, 464.0: 0.07091109, 465.0: 0.0739, 466.0: 0.077016, 467.0: 0.0802664, 468.0: 0.0836668, 469.0: 0.0872328, 470.0: 0.09098, 471.0: 0.09491755, 472.0: 0.09904584, 473.0: 0.1033674, 474.0: 0.1078846, 475.0: 0.1126, 476.0: 0.117532, 477.0: 0.1226744, 478.0: 0.1279928, 479.0: 0.1334528, 480.0: 0.13902, 481.0: 0.1446764, 482.0: 0.1504693, 483.0: 0.1564619, 484.0: 0.1627177, 485.0: 0.1693, 486.0: 0.1762431, 487.0: 0.1835581, 488.0: 0.1912735, 489.0: 0.199418, 490.0: 0.20802, 491.0: 0.2171199, 492.0: 0.2267345, 493.0: 0.2368571, 494.0: 0.2474812, 495.0: 0.2586, 496.0: 0.2701849, 497.0: 0.2822939, 498.0: 0.2950505, 499.0: 0.308578, 500.0: 0.323, 501.0: 0.3384021, 502.0: 0.3546858, 503.0: 0.3716986, 504.0: 0.3892875, 505.0: 0.4073, 506.0: 0.4256299, 507.0: 0.4443096, 508.0: 0.4633944, 509.0: 0.4829395, 510.0: 0.503, 511.0: 0.5235693, 512.0: 0.544512, 513.0: 0.56569, 514.0: 0.5869653, 515.0: 0.6082, 516.0: 0.6293456, 517.0: 0.6503068, 518.0: 0.6708752, 519.0: 0.6908424, 520.0: 0.71, 521.0: 0.7281852, 522.0: 0.7454636, 523.0: 0.7619694, 524.0: 0.7778368, 525.0: 0.7932, 526.0: 0.8081104, 527.0: 0.8224962, 528.0: 0.8363068, 529.0: 0.8494916, 530.0: 0.862, 531.0: 0.8738108, 532.0: 0.8849624, 533.0: 0.8954936, 534.0: 0.9054432, 535.0: 0.9148501, 536.0: 0.9237348, 537.0: 0.9320924, 538.0: 0.9399226, 539.0: 0.9472252, 540.0: 0.954, 541.0: 0.9602561, 542.0: 0.9660074, 543.0: 0.9712606, 544.0: 0.9760225, 545.0: 0.9803, 546.0: 0.9840924, 547.0: 0.9874182, 548.0: 0.9903128, 549.0: 0.9928116, 550.0: 0.9949501, 551.0: 0.9967108, 552.0: 0.9980983, 553.0: 0.999112, 554.0: 0.9997482, 555.0: 1.0, 556.0: 0.9998567, 557.0: 0.9993046, 558.0: 0.9983255, 559.0: 0.9968987, 560.0: 0.995, 561.0: 0.9926005, 562.0: 0.9897426, 563.0: 0.9864444, 564.0: 0.9827241, 565.0: 0.9786, 566.0: 0.9740837, 567.0: 0.9691712, 568.0: 0.9638568, 569.0: 0.9581349, 570.0: 0.952, 571.0: 0.9454504, 572.0: 0.9384992, 573.0: 0.9311628, 574.0: 0.9234576, 575.0: 0.9154, 576.0: 0.9070064, 577.0: 0.8982772, 578.0: 0.8892048, 579.0: 0.8797816, 580.0: 0.87, 581.0: 0.8598613, 582.0: 0.849392, 583.0: 0.838622, 584.0: 0.8275813, 585.0: 0.8163, 586.0: 0.8047947, 587.0: 0.793082, 588.0: 0.781192, 589.0: 0.7691547, 590.0: 0.757, 591.0: 0.7447541, 592.0: 0.7324224, 593.0: 0.7200036, 594.0: 0.7074965, 595.0: 0.6949, 596.0: 0.6822192, 597.0: 0.6694716, 598.0: 0.6566744, 599.0: 0.6438448, 600.0: 0.631, 601.0: 0.6181555, 602.0: 0.6053144, 603.0: 0.5924756, 604.0: 0.5796379, 605.0: 0.5668, 606.0: 0.5539611, 607.0: 0.5411372, 608.0: 0.5283528, 609.0: 0.5156323, 610.0: 0.503, 611.0: 0.4904688, 612.0: 0.4780304, 613.0: 0.4656776, 614.0: 0.4534032, 615.0: 0.4412, 616.0: 0.42908, 617.0: 0.417036, 618.0: 0.405032, 619.0: 0.393032, 620.0: 0.381, 621.0: 0.3689184, 622.0: 0.3568272, 623.0: 0.3447768, 624.0: 0.3328176, 625.0: 0.321, 626.0: 0.3093381, 627.0: 0.2978504, 628.0: 0.2865936, 629.0: 0.2756245, 630.0: 0.265, 631.0: 0.2547632, 632.0: 0.2448896, 633.0: 0.2353344, 634.0: 0.2260528, 635.0: 0.217, 636.0: 0.2081616, 637.0: 0.1995488, 638.0: 0.1911552, 639.0: 0.1829744, 640.0: 0.175, 641.0: 0.1672235, 642.0: 0.1596464, 643.0: 0.1522776, 644.0: 0.1451259, 645.0: 0.1382, 646.0: 0.1315003, 647.0: 0.1250248, 648.0: 0.1187792, 649.0: 0.1127691, 650.0: 0.107, 651.0: 0.1014762, 652.0: 0.09618864, 653.0: 0.09112296, 654.0: 0.08626485, 655.0: 0.0816, 656.0: 0.07712064, 657.0: 0.07282552, 658.0: 0.06871008, 659.0: 0.06476976, 660.0: 0.061, 661.0: 0.05739621, 662.0: 0.05395504, 663.0: 0.05067376, 664.0: 0.04754965, 665.0: 0.04458, 666.0: 0.04175872, 667.0: 0.03908496, 668.0: 0.03656384, 669.0: 0.03420048, 670.0: 0.032, 671.0: 0.02996261, 672.0: 0.02807664, 673.0: 0.02632936, 674.0: 0.02470805, 675.0: 0.0232, 676.0: 0.02180077, 677.0: 0.02050112, 678.0: 0.01928108, 679.0: 0.01812069, 680.0: 0.017, 681.0: 0.01590379, 682.0: 0.01483718, 683.0: 0.01381068, 684.0: 0.01283478, 685.0: 0.01192, 686.0: 0.01106831, 687.0: 0.01027339, 688.0: 0.009533311, 689.0: 0.008846157, 690.0: 0.00821, 691.0: 0.007623781, 692.0: 0.007085424, 693.0: 0.006591476, 694.0: 0.006138485, 695.0: 0.005723, 696.0: 0.005343059, 697.0: 0.004995796, 698.0: 0.004676404, 699.0: 0.004380075, 700.0: 0.004102, 701.0: 0.003838453, 702.0: 0.003589099, 703.0: 0.003354219, 704.0: 0.003134093, 705.0: 0.002929, 706.0: 0.002738139, 707.0: 0.002559876, 708.0: 0.002393244, 709.0: 0.002237275, 710.0: 0.002091, 711.0: 0.001953587, 712.0: 0.00182458, 713.0: 0.00170358, 714.0: 0.001590187, 715.0: 0.001484, 716.0: 0.001384496, 717.0: 0.001291268, 718.0: 0.001204092, 719.0: 0.001122744, 720.0: 0.001047, 721.0: 0.0009765896, 722.0: 0.0009111088, 723.0: 0.0008501332, 724.0: 0.0007932384, 725.0: 0.00074, 726.0: 0.0006900827, 727.0: 0.00064331, 728.0: 0.000599496, 729.0: 0.0005584547, 730.0: 0.00052, 731.0: 0.0004839136, 732.0: 0.0004500528, 733.0: 0.0004183452, 734.0: 0.0003887184, 735.0: 0.0003611, 736.0: 0.0003353835, 737.0: 0.0003114404, 738.0: 0.0002891656, 739.0: 0.0002684539, 740.0: 0.0002492, 741.0: 0.0002313019, 742.0: 0.0002146856, 743.0: 0.0001992884, 744.0: 0.0001850475, 745.0: 0.0001719, 746.0: 0.0001597781, 747.0: 0.0001486044, 748.0: 0.0001383016, 749.0: 0.0001287925, 750.0: 0.00012, 751.0: 0.0001118595, 752.0: 0.0001043224, 753.0: 9.73356e-05, 754.0: 9.084587e-05, 755.0: 8.48e-05, 756.0: 7.914667e-05, 757.0: 7.3858e-05, 758.0: 6.8916e-05, 759.0: 6.430267e-05, 760.0: 6e-05, 761.0: 5.598187e-05, 762.0: 5.22256e-05, 763.0: 4.87184e-05, 764.0: 4.544747e-05, 765.0: 4.24e-05, 766.0: 3.956104e-05, 767.0: 3.691512e-05, 768.0: 3.444868e-05, 769.0: 3.214816e-05, 770.0: 3e-05, 771.0: 2.799125e-05, 772.0: 2.611356e-05, 773.0: 2.436024e-05, 774.0: 2.272461e-05, 775.0: 2.12e-05, 776.0: 1.977855e-05, 777.0: 1.845285e-05, 778.0: 1.721687e-05, 779.0: 1.606459e-05, 780.0: 1.499e-05, 781.0: 1.398728e-05, 782.0: 1.305155e-05, 783.0: 1.217818e-05, 784.0: 1.136254e-05, 785.0: 1.06e-05, 786.0: 9.885877e-06, 787.0: 9.217304e-06, 788.0: 8.592362e-06, 789.0: 8.009133e-06, 790.0: 7.4657e-06, 791.0: 6.959567e-06, 792.0: 6.487995e-06, 793.0: 6.048699e-06, 794.0: 5.639396e-06, 795.0: 5.2578e-06, 796.0: 4.901771e-06, 797.0: 4.56972e-06, 798.0: 4.260194e-06, 799.0: 3.971739e-06, 800.0: 3.7029e-06, 801.0: 3.452163e-06, 802.0: 3.218302e-06, 803.0: 3.0003e-06, 804.0: 2.797139e-06, 805.0: 2.6078e-06, 806.0: 2.43122e-06, 807.0: 2.266531e-06, 808.0: 2.113013e-06, 809.0: 1.969943e-06, 810.0: 1.8366e-06, 811.0: 1.71223e-06, 812.0: 1.596228e-06, 813.0: 1.48809e-06, 814.0: 1.387314e-06, 815.0: 1.2934e-06, 816.0: 1.20582e-06, 817.0: 1.124143e-06, 818.0: 1.048009e-06, 819.0: 9.770578e-07, 820.0: 9.1093e-07, 821.0: 8.492513e-07, 822.0: 7.917212e-07, 823.0: 7.380904e-07, 824.0: 6.881098e-07, 825.0: 6.4153e-07, 826.0: 5.980895e-07, 827.0: 5.575746e-07, 828.0: 5.19808e-07, 829.0: 4.846123e-07, 830.0: 4.5181e-07}), K_m=683)[source]

Returns the luminous flux for given spectral power distribution using given luminous efficiency function.

Parameters:
Returns:

Luminous flux.

Return type:

numeric

Examples

>>> from colour import LIGHT_SOURCES_RELATIVE_SPDS
>>> spd = LIGHT_SOURCES_RELATIVE_SPDS['Neodimium Incandescent']
>>> luminous_flux(spd)  
23807.6555273...
colour.luminous_efficiency(spd, lef=SpectralPowerDistribution( 'CIE 1924 Photopic Standard Observer', {360.0: 3.917e-06, 361.0: 4.393581e-06, 362.0: 4.929604e-06, 363.0: 5.532136e-06, 364.0: 6.208245e-06, 365.0: 6.965e-06, 366.0: 7.813219e-06, 367.0: 8.767336e-06, 368.0: 9.839844e-06, 369.0: 1.104323e-05, 370.0: 1.239e-05, 371.0: 1.388641e-05, 372.0: 1.555728e-05, 373.0: 1.744296e-05, 374.0: 1.958375e-05, 375.0: 2.202e-05, 376.0: 2.483965e-05, 377.0: 2.804126e-05, 378.0: 3.153104e-05, 379.0: 3.521521e-05, 380.0: 3.9e-05, 381.0: 4.28264e-05, 382.0: 4.69146e-05, 383.0: 5.15896e-05, 384.0: 5.71764e-05, 385.0: 6.4e-05, 386.0: 7.234421e-05, 387.0: 8.221224e-05, 388.0: 9.350816e-05, 389.0: 0.0001061361, 390.0: 0.00012, 391.0: 0.000134984, 392.0: 0.000151492, 393.0: 0.000170208, 394.0: 0.000191816, 395.0: 0.000217, 396.0: 0.0002469067, 397.0: 0.00028124, 398.0: 0.00031852, 399.0: 0.0003572667, 400.0: 0.000396, 401.0: 0.0004337147, 402.0: 0.000473024, 403.0: 0.000517876, 404.0: 0.0005722187, 405.0: 0.00064, 406.0: 0.00072456, 407.0: 0.0008255, 408.0: 0.00094116, 409.0: 0.00106988, 410.0: 0.00121, 411.0: 0.001362091, 412.0: 0.001530752, 413.0: 0.001720368, 414.0: 0.001935323, 415.0: 0.00218, 416.0: 0.0024548, 417.0: 0.002764, 418.0: 0.0031178, 419.0: 0.0035264, 420.0: 0.004, 421.0: 0.00454624, 422.0: 0.00515932, 423.0: 0.00582928, 424.0: 0.00654616, 425.0: 0.0073, 426.0: 0.008086507, 427.0: 0.00890872, 428.0: 0.00976768, 429.0: 0.01066443, 430.0: 0.0116, 431.0: 0.01257317, 432.0: 0.01358272, 433.0: 0.01462968, 434.0: 0.01571509, 435.0: 0.01684, 436.0: 0.01800736, 437.0: 0.01921448, 438.0: 0.02045392, 439.0: 0.02171824, 440.0: 0.023, 441.0: 0.02429461, 442.0: 0.02561024, 443.0: 0.02695857, 444.0: 0.02835125, 445.0: 0.0298, 446.0: 0.03131083, 447.0: 0.03288368, 448.0: 0.03452112, 449.0: 0.03622571, 450.0: 0.038, 451.0: 0.03984667, 452.0: 0.041768, 453.0: 0.043766, 454.0: 0.04584267, 455.0: 0.048, 456.0: 0.05024368, 457.0: 0.05257304, 458.0: 0.05498056, 459.0: 0.05745872, 460.0: 0.06, 461.0: 0.06260197, 462.0: 0.06527752, 463.0: 0.06804208, 464.0: 0.07091109, 465.0: 0.0739, 466.0: 0.077016, 467.0: 0.0802664, 468.0: 0.0836668, 469.0: 0.0872328, 470.0: 0.09098, 471.0: 0.09491755, 472.0: 0.09904584, 473.0: 0.1033674, 474.0: 0.1078846, 475.0: 0.1126, 476.0: 0.117532, 477.0: 0.1226744, 478.0: 0.1279928, 479.0: 0.1334528, 480.0: 0.13902, 481.0: 0.1446764, 482.0: 0.1504693, 483.0: 0.1564619, 484.0: 0.1627177, 485.0: 0.1693, 486.0: 0.1762431, 487.0: 0.1835581, 488.0: 0.1912735, 489.0: 0.199418, 490.0: 0.20802, 491.0: 0.2171199, 492.0: 0.2267345, 493.0: 0.2368571, 494.0: 0.2474812, 495.0: 0.2586, 496.0: 0.2701849, 497.0: 0.2822939, 498.0: 0.2950505, 499.0: 0.308578, 500.0: 0.323, 501.0: 0.3384021, 502.0: 0.3546858, 503.0: 0.3716986, 504.0: 0.3892875, 505.0: 0.4073, 506.0: 0.4256299, 507.0: 0.4443096, 508.0: 0.4633944, 509.0: 0.4829395, 510.0: 0.503, 511.0: 0.5235693, 512.0: 0.544512, 513.0: 0.56569, 514.0: 0.5869653, 515.0: 0.6082, 516.0: 0.6293456, 517.0: 0.6503068, 518.0: 0.6708752, 519.0: 0.6908424, 520.0: 0.71, 521.0: 0.7281852, 522.0: 0.7454636, 523.0: 0.7619694, 524.0: 0.7778368, 525.0: 0.7932, 526.0: 0.8081104, 527.0: 0.8224962, 528.0: 0.8363068, 529.0: 0.8494916, 530.0: 0.862, 531.0: 0.8738108, 532.0: 0.8849624, 533.0: 0.8954936, 534.0: 0.9054432, 535.0: 0.9148501, 536.0: 0.9237348, 537.0: 0.9320924, 538.0: 0.9399226, 539.0: 0.9472252, 540.0: 0.954, 541.0: 0.9602561, 542.0: 0.9660074, 543.0: 0.9712606, 544.0: 0.9760225, 545.0: 0.9803, 546.0: 0.9840924, 547.0: 0.9874182, 548.0: 0.9903128, 549.0: 0.9928116, 550.0: 0.9949501, 551.0: 0.9967108, 552.0: 0.9980983, 553.0: 0.999112, 554.0: 0.9997482, 555.0: 1.0, 556.0: 0.9998567, 557.0: 0.9993046, 558.0: 0.9983255, 559.0: 0.9968987, 560.0: 0.995, 561.0: 0.9926005, 562.0: 0.9897426, 563.0: 0.9864444, 564.0: 0.9827241, 565.0: 0.9786, 566.0: 0.9740837, 567.0: 0.9691712, 568.0: 0.9638568, 569.0: 0.9581349, 570.0: 0.952, 571.0: 0.9454504, 572.0: 0.9384992, 573.0: 0.9311628, 574.0: 0.9234576, 575.0: 0.9154, 576.0: 0.9070064, 577.0: 0.8982772, 578.0: 0.8892048, 579.0: 0.8797816, 580.0: 0.87, 581.0: 0.8598613, 582.0: 0.849392, 583.0: 0.838622, 584.0: 0.8275813, 585.0: 0.8163, 586.0: 0.8047947, 587.0: 0.793082, 588.0: 0.781192, 589.0: 0.7691547, 590.0: 0.757, 591.0: 0.7447541, 592.0: 0.7324224, 593.0: 0.7200036, 594.0: 0.7074965, 595.0: 0.6949, 596.0: 0.6822192, 597.0: 0.6694716, 598.0: 0.6566744, 599.0: 0.6438448, 600.0: 0.631, 601.0: 0.6181555, 602.0: 0.6053144, 603.0: 0.5924756, 604.0: 0.5796379, 605.0: 0.5668, 606.0: 0.5539611, 607.0: 0.5411372, 608.0: 0.5283528, 609.0: 0.5156323, 610.0: 0.503, 611.0: 0.4904688, 612.0: 0.4780304, 613.0: 0.4656776, 614.0: 0.4534032, 615.0: 0.4412, 616.0: 0.42908, 617.0: 0.417036, 618.0: 0.405032, 619.0: 0.393032, 620.0: 0.381, 621.0: 0.3689184, 622.0: 0.3568272, 623.0: 0.3447768, 624.0: 0.3328176, 625.0: 0.321, 626.0: 0.3093381, 627.0: 0.2978504, 628.0: 0.2865936, 629.0: 0.2756245, 630.0: 0.265, 631.0: 0.2547632, 632.0: 0.2448896, 633.0: 0.2353344, 634.0: 0.2260528, 635.0: 0.217, 636.0: 0.2081616, 637.0: 0.1995488, 638.0: 0.1911552, 639.0: 0.1829744, 640.0: 0.175, 641.0: 0.1672235, 642.0: 0.1596464, 643.0: 0.1522776, 644.0: 0.1451259, 645.0: 0.1382, 646.0: 0.1315003, 647.0: 0.1250248, 648.0: 0.1187792, 649.0: 0.1127691, 650.0: 0.107, 651.0: 0.1014762, 652.0: 0.09618864, 653.0: 0.09112296, 654.0: 0.08626485, 655.0: 0.0816, 656.0: 0.07712064, 657.0: 0.07282552, 658.0: 0.06871008, 659.0: 0.06476976, 660.0: 0.061, 661.0: 0.05739621, 662.0: 0.05395504, 663.0: 0.05067376, 664.0: 0.04754965, 665.0: 0.04458, 666.0: 0.04175872, 667.0: 0.03908496, 668.0: 0.03656384, 669.0: 0.03420048, 670.0: 0.032, 671.0: 0.02996261, 672.0: 0.02807664, 673.0: 0.02632936, 674.0: 0.02470805, 675.0: 0.0232, 676.0: 0.02180077, 677.0: 0.02050112, 678.0: 0.01928108, 679.0: 0.01812069, 680.0: 0.017, 681.0: 0.01590379, 682.0: 0.01483718, 683.0: 0.01381068, 684.0: 0.01283478, 685.0: 0.01192, 686.0: 0.01106831, 687.0: 0.01027339, 688.0: 0.009533311, 689.0: 0.008846157, 690.0: 0.00821, 691.0: 0.007623781, 692.0: 0.007085424, 693.0: 0.006591476, 694.0: 0.006138485, 695.0: 0.005723, 696.0: 0.005343059, 697.0: 0.004995796, 698.0: 0.004676404, 699.0: 0.004380075, 700.0: 0.004102, 701.0: 0.003838453, 702.0: 0.003589099, 703.0: 0.003354219, 704.0: 0.003134093, 705.0: 0.002929, 706.0: 0.002738139, 707.0: 0.002559876, 708.0: 0.002393244, 709.0: 0.002237275, 710.0: 0.002091, 711.0: 0.001953587, 712.0: 0.00182458, 713.0: 0.00170358, 714.0: 0.001590187, 715.0: 0.001484, 716.0: 0.001384496, 717.0: 0.001291268, 718.0: 0.001204092, 719.0: 0.001122744, 720.0: 0.001047, 721.0: 0.0009765896, 722.0: 0.0009111088, 723.0: 0.0008501332, 724.0: 0.0007932384, 725.0: 0.00074, 726.0: 0.0006900827, 727.0: 0.00064331, 728.0: 0.000599496, 729.0: 0.0005584547, 730.0: 0.00052, 731.0: 0.0004839136, 732.0: 0.0004500528, 733.0: 0.0004183452, 734.0: 0.0003887184, 735.0: 0.0003611, 736.0: 0.0003353835, 737.0: 0.0003114404, 738.0: 0.0002891656, 739.0: 0.0002684539, 740.0: 0.0002492, 741.0: 0.0002313019, 742.0: 0.0002146856, 743.0: 0.0001992884, 744.0: 0.0001850475, 745.0: 0.0001719, 746.0: 0.0001597781, 747.0: 0.0001486044, 748.0: 0.0001383016, 749.0: 0.0001287925, 750.0: 0.00012, 751.0: 0.0001118595, 752.0: 0.0001043224, 753.0: 9.73356e-05, 754.0: 9.084587e-05, 755.0: 8.48e-05, 756.0: 7.914667e-05, 757.0: 7.3858e-05, 758.0: 6.8916e-05, 759.0: 6.430267e-05, 760.0: 6e-05, 761.0: 5.598187e-05, 762.0: 5.22256e-05, 763.0: 4.87184e-05, 764.0: 4.544747e-05, 765.0: 4.24e-05, 766.0: 3.956104e-05, 767.0: 3.691512e-05, 768.0: 3.444868e-05, 769.0: 3.214816e-05, 770.0: 3e-05, 771.0: 2.799125e-05, 772.0: 2.611356e-05, 773.0: 2.436024e-05, 774.0: 2.272461e-05, 775.0: 2.12e-05, 776.0: 1.977855e-05, 777.0: 1.845285e-05, 778.0: 1.721687e-05, 779.0: 1.606459e-05, 780.0: 1.499e-05, 781.0: 1.398728e-05, 782.0: 1.305155e-05, 783.0: 1.217818e-05, 784.0: 1.136254e-05, 785.0: 1.06e-05, 786.0: 9.885877e-06, 787.0: 9.217304e-06, 788.0: 8.592362e-06, 789.0: 8.009133e-06, 790.0: 7.4657e-06, 791.0: 6.959567e-06, 792.0: 6.487995e-06, 793.0: 6.048699e-06, 794.0: 5.639396e-06, 795.0: 5.2578e-06, 796.0: 4.901771e-06, 797.0: 4.56972e-06, 798.0: 4.260194e-06, 799.0: 3.971739e-06, 800.0: 3.7029e-06, 801.0: 3.452163e-06, 802.0: 3.218302e-06, 803.0: 3.0003e-06, 804.0: 2.797139e-06, 805.0: 2.6078e-06, 806.0: 2.43122e-06, 807.0: 2.266531e-06, 808.0: 2.113013e-06, 809.0: 1.969943e-06, 810.0: 1.8366e-06, 811.0: 1.71223e-06, 812.0: 1.596228e-06, 813.0: 1.48809e-06, 814.0: 1.387314e-06, 815.0: 1.2934e-06, 816.0: 1.20582e-06, 817.0: 1.124143e-06, 818.0: 1.048009e-06, 819.0: 9.770578e-07, 820.0: 9.1093e-07, 821.0: 8.492513e-07, 822.0: 7.917212e-07, 823.0: 7.380904e-07, 824.0: 6.881098e-07, 825.0: 6.4153e-07, 826.0: 5.980895e-07, 827.0: 5.575746e-07, 828.0: 5.19808e-07, 829.0: 4.846123e-07, 830.0: 4.5181e-07}))[source]

Returns the luminous efficiency of given spectral power distribution using given luminous efficiency function.

Parameters:
Returns:

Luminous efficiency.

Return type:

numeric

Examples

>>> from colour import LIGHT_SOURCES_RELATIVE_SPDS
>>> spd = LIGHT_SOURCES_RELATIVE_SPDS['Neodimium Incandescent']
>>> luminous_efficiency(spd)  
0.1994393...
colour.luminous_efficacy(spd, lef=SpectralPowerDistribution( 'CIE 1924 Photopic Standard Observer', {360.0: 3.917e-06, 361.0: 4.393581e-06, 362.0: 4.929604e-06, 363.0: 5.532136e-06, 364.0: 6.208245e-06, 365.0: 6.965e-06, 366.0: 7.813219e-06, 367.0: 8.767336e-06, 368.0: 9.839844e-06, 369.0: 1.104323e-05, 370.0: 1.239e-05, 371.0: 1.388641e-05, 372.0: 1.555728e-05, 373.0: 1.744296e-05, 374.0: 1.958375e-05, 375.0: 2.202e-05, 376.0: 2.483965e-05, 377.0: 2.804126e-05, 378.0: 3.153104e-05, 379.0: 3.521521e-05, 380.0: 3.9e-05, 381.0: 4.28264e-05, 382.0: 4.69146e-05, 383.0: 5.15896e-05, 384.0: 5.71764e-05, 385.0: 6.4e-05, 386.0: 7.234421e-05, 387.0: 8.221224e-05, 388.0: 9.350816e-05, 389.0: 0.0001061361, 390.0: 0.00012, 391.0: 0.000134984, 392.0: 0.000151492, 393.0: 0.000170208, 394.0: 0.000191816, 395.0: 0.000217, 396.0: 0.0002469067, 397.0: 0.00028124, 398.0: 0.00031852, 399.0: 0.0003572667, 400.0: 0.000396, 401.0: 0.0004337147, 402.0: 0.000473024, 403.0: 0.000517876, 404.0: 0.0005722187, 405.0: 0.00064, 406.0: 0.00072456, 407.0: 0.0008255, 408.0: 0.00094116, 409.0: 0.00106988, 410.0: 0.00121, 411.0: 0.001362091, 412.0: 0.001530752, 413.0: 0.001720368, 414.0: 0.001935323, 415.0: 0.00218, 416.0: 0.0024548, 417.0: 0.002764, 418.0: 0.0031178, 419.0: 0.0035264, 420.0: 0.004, 421.0: 0.00454624, 422.0: 0.00515932, 423.0: 0.00582928, 424.0: 0.00654616, 425.0: 0.0073, 426.0: 0.008086507, 427.0: 0.00890872, 428.0: 0.00976768, 429.0: 0.01066443, 430.0: 0.0116, 431.0: 0.01257317, 432.0: 0.01358272, 433.0: 0.01462968, 434.0: 0.01571509, 435.0: 0.01684, 436.0: 0.01800736, 437.0: 0.01921448, 438.0: 0.02045392, 439.0: 0.02171824, 440.0: 0.023, 441.0: 0.02429461, 442.0: 0.02561024, 443.0: 0.02695857, 444.0: 0.02835125, 445.0: 0.0298, 446.0: 0.03131083, 447.0: 0.03288368, 448.0: 0.03452112, 449.0: 0.03622571, 450.0: 0.038, 451.0: 0.03984667, 452.0: 0.041768, 453.0: 0.043766, 454.0: 0.04584267, 455.0: 0.048, 456.0: 0.05024368, 457.0: 0.05257304, 458.0: 0.05498056, 459.0: 0.05745872, 460.0: 0.06, 461.0: 0.06260197, 462.0: 0.06527752, 463.0: 0.06804208, 464.0: 0.07091109, 465.0: 0.0739, 466.0: 0.077016, 467.0: 0.0802664, 468.0: 0.0836668, 469.0: 0.0872328, 470.0: 0.09098, 471.0: 0.09491755, 472.0: 0.09904584, 473.0: 0.1033674, 474.0: 0.1078846, 475.0: 0.1126, 476.0: 0.117532, 477.0: 0.1226744, 478.0: 0.1279928, 479.0: 0.1334528, 480.0: 0.13902, 481.0: 0.1446764, 482.0: 0.1504693, 483.0: 0.1564619, 484.0: 0.1627177, 485.0: 0.1693, 486.0: 0.1762431, 487.0: 0.1835581, 488.0: 0.1912735, 489.0: 0.199418, 490.0: 0.20802, 491.0: 0.2171199, 492.0: 0.2267345, 493.0: 0.2368571, 494.0: 0.2474812, 495.0: 0.2586, 496.0: 0.2701849, 497.0: 0.2822939, 498.0: 0.2950505, 499.0: 0.308578, 500.0: 0.323, 501.0: 0.3384021, 502.0: 0.3546858, 503.0: 0.3716986, 504.0: 0.3892875, 505.0: 0.4073, 506.0: 0.4256299, 507.0: 0.4443096, 508.0: 0.4633944, 509.0: 0.4829395, 510.0: 0.503, 511.0: 0.5235693, 512.0: 0.544512, 513.0: 0.56569, 514.0: 0.5869653, 515.0: 0.6082, 516.0: 0.6293456, 517.0: 0.6503068, 518.0: 0.6708752, 519.0: 0.6908424, 520.0: 0.71, 521.0: 0.7281852, 522.0: 0.7454636, 523.0: 0.7619694, 524.0: 0.7778368, 525.0: 0.7932, 526.0: 0.8081104, 527.0: 0.8224962, 528.0: 0.8363068, 529.0: 0.8494916, 530.0: 0.862, 531.0: 0.8738108, 532.0: 0.8849624, 533.0: 0.8954936, 534.0: 0.9054432, 535.0: 0.9148501, 536.0: 0.9237348, 537.0: 0.9320924, 538.0: 0.9399226, 539.0: 0.9472252, 540.0: 0.954, 541.0: 0.9602561, 542.0: 0.9660074, 543.0: 0.9712606, 544.0: 0.9760225, 545.0: 0.9803, 546.0: 0.9840924, 547.0: 0.9874182, 548.0: 0.9903128, 549.0: 0.9928116, 550.0: 0.9949501, 551.0: 0.9967108, 552.0: 0.9980983, 553.0: 0.999112, 554.0: 0.9997482, 555.0: 1.0, 556.0: 0.9998567, 557.0: 0.9993046, 558.0: 0.9983255, 559.0: 0.9968987, 560.0: 0.995, 561.0: 0.9926005, 562.0: 0.9897426, 563.0: 0.9864444, 564.0: 0.9827241, 565.0: 0.9786, 566.0: 0.9740837, 567.0: 0.9691712, 568.0: 0.9638568, 569.0: 0.9581349, 570.0: 0.952, 571.0: 0.9454504, 572.0: 0.9384992, 573.0: 0.9311628, 574.0: 0.9234576, 575.0: 0.9154, 576.0: 0.9070064, 577.0: 0.8982772, 578.0: 0.8892048, 579.0: 0.8797816, 580.0: 0.87, 581.0: 0.8598613, 582.0: 0.849392, 583.0: 0.838622, 584.0: 0.8275813, 585.0: 0.8163, 586.0: 0.8047947, 587.0: 0.793082, 588.0: 0.781192, 589.0: 0.7691547, 590.0: 0.757, 591.0: 0.7447541, 592.0: 0.7324224, 593.0: 0.7200036, 594.0: 0.7074965, 595.0: 0.6949, 596.0: 0.6822192, 597.0: 0.6694716, 598.0: 0.6566744, 599.0: 0.6438448, 600.0: 0.631, 601.0: 0.6181555, 602.0: 0.6053144, 603.0: 0.5924756, 604.0: 0.5796379, 605.0: 0.5668, 606.0: 0.5539611, 607.0: 0.5411372, 608.0: 0.5283528, 609.0: 0.5156323, 610.0: 0.503, 611.0: 0.4904688, 612.0: 0.4780304, 613.0: 0.4656776, 614.0: 0.4534032, 615.0: 0.4412, 616.0: 0.42908, 617.0: 0.417036, 618.0: 0.405032, 619.0: 0.393032, 620.0: 0.381, 621.0: 0.3689184, 622.0: 0.3568272, 623.0: 0.3447768, 624.0: 0.3328176, 625.0: 0.321, 626.0: 0.3093381, 627.0: 0.2978504, 628.0: 0.2865936, 629.0: 0.2756245, 630.0: 0.265, 631.0: 0.2547632, 632.0: 0.2448896, 633.0: 0.2353344, 634.0: 0.2260528, 635.0: 0.217, 636.0: 0.2081616, 637.0: 0.1995488, 638.0: 0.1911552, 639.0: 0.1829744, 640.0: 0.175, 641.0: 0.1672235, 642.0: 0.1596464, 643.0: 0.1522776, 644.0: 0.1451259, 645.0: 0.1382, 646.0: 0.1315003, 647.0: 0.1250248, 648.0: 0.1187792, 649.0: 0.1127691, 650.0: 0.107, 651.0: 0.1014762, 652.0: 0.09618864, 653.0: 0.09112296, 654.0: 0.08626485, 655.0: 0.0816, 656.0: 0.07712064, 657.0: 0.07282552, 658.0: 0.06871008, 659.0: 0.06476976, 660.0: 0.061, 661.0: 0.05739621, 662.0: 0.05395504, 663.0: 0.05067376, 664.0: 0.04754965, 665.0: 0.04458, 666.0: 0.04175872, 667.0: 0.03908496, 668.0: 0.03656384, 669.0: 0.03420048, 670.0: 0.032, 671.0: 0.02996261, 672.0: 0.02807664, 673.0: 0.02632936, 674.0: 0.02470805, 675.0: 0.0232, 676.0: 0.02180077, 677.0: 0.02050112, 678.0: 0.01928108, 679.0: 0.01812069, 680.0: 0.017, 681.0: 0.01590379, 682.0: 0.01483718, 683.0: 0.01381068, 684.0: 0.01283478, 685.0: 0.01192, 686.0: 0.01106831, 687.0: 0.01027339, 688.0: 0.009533311, 689.0: 0.008846157, 690.0: 0.00821, 691.0: 0.007623781, 692.0: 0.007085424, 693.0: 0.006591476, 694.0: 0.006138485, 695.0: 0.005723, 696.0: 0.005343059, 697.0: 0.004995796, 698.0: 0.004676404, 699.0: 0.004380075, 700.0: 0.004102, 701.0: 0.003838453, 702.0: 0.003589099, 703.0: 0.003354219, 704.0: 0.003134093, 705.0: 0.002929, 706.0: 0.002738139, 707.0: 0.002559876, 708.0: 0.002393244, 709.0: 0.002237275, 710.0: 0.002091, 711.0: 0.001953587, 712.0: 0.00182458, 713.0: 0.00170358, 714.0: 0.001590187, 715.0: 0.001484, 716.0: 0.001384496, 717.0: 0.001291268, 718.0: 0.001204092, 719.0: 0.001122744, 720.0: 0.001047, 721.0: 0.0009765896, 722.0: 0.0009111088, 723.0: 0.0008501332, 724.0: 0.0007932384, 725.0: 0.00074, 726.0: 0.0006900827, 727.0: 0.00064331, 728.0: 0.000599496, 729.0: 0.0005584547, 730.0: 0.00052, 731.0: 0.0004839136, 732.0: 0.0004500528, 733.0: 0.0004183452, 734.0: 0.0003887184, 735.0: 0.0003611, 736.0: 0.0003353835, 737.0: 0.0003114404, 738.0: 0.0002891656, 739.0: 0.0002684539, 740.0: 0.0002492, 741.0: 0.0002313019, 742.0: 0.0002146856, 743.0: 0.0001992884, 744.0: 0.0001850475, 745.0: 0.0001719, 746.0: 0.0001597781, 747.0: 0.0001486044, 748.0: 0.0001383016, 749.0: 0.0001287925, 750.0: 0.00012, 751.0: 0.0001118595, 752.0: 0.0001043224, 753.0: 9.73356e-05, 754.0: 9.084587e-05, 755.0: 8.48e-05, 756.0: 7.914667e-05, 757.0: 7.3858e-05, 758.0: 6.8916e-05, 759.0: 6.430267e-05, 760.0: 6e-05, 761.0: 5.598187e-05, 762.0: 5.22256e-05, 763.0: 4.87184e-05, 764.0: 4.544747e-05, 765.0: 4.24e-05, 766.0: 3.956104e-05, 767.0: 3.691512e-05, 768.0: 3.444868e-05, 769.0: 3.214816e-05, 770.0: 3e-05, 771.0: 2.799125e-05, 772.0: 2.611356e-05, 773.0: 2.436024e-05, 774.0: 2.272461e-05, 775.0: 2.12e-05, 776.0: 1.977855e-05, 777.0: 1.845285e-05, 778.0: 1.721687e-05, 779.0: 1.606459e-05, 780.0: 1.499e-05, 781.0: 1.398728e-05, 782.0: 1.305155e-05, 783.0: 1.217818e-05, 784.0: 1.136254e-05, 785.0: 1.06e-05, 786.0: 9.885877e-06, 787.0: 9.217304e-06, 788.0: 8.592362e-06, 789.0: 8.009133e-06, 790.0: 7.4657e-06, 791.0: 6.959567e-06, 792.0: 6.487995e-06, 793.0: 6.048699e-06, 794.0: 5.639396e-06, 795.0: 5.2578e-06, 796.0: 4.901771e-06, 797.0: 4.56972e-06, 798.0: 4.260194e-06, 799.0: 3.971739e-06, 800.0: 3.7029e-06, 801.0: 3.452163e-06, 802.0: 3.218302e-06, 803.0: 3.0003e-06, 804.0: 2.797139e-06, 805.0: 2.6078e-06, 806.0: 2.43122e-06, 807.0: 2.266531e-06, 808.0: 2.113013e-06, 809.0: 1.969943e-06, 810.0: 1.8366e-06, 811.0: 1.71223e-06, 812.0: 1.596228e-06, 813.0: 1.48809e-06, 814.0: 1.387314e-06, 815.0: 1.2934e-06, 816.0: 1.20582e-06, 817.0: 1.124143e-06, 818.0: 1.048009e-06, 819.0: 9.770578e-07, 820.0: 9.1093e-07, 821.0: 8.492513e-07, 822.0: 7.917212e-07, 823.0: 7.380904e-07, 824.0: 6.881098e-07, 825.0: 6.4153e-07, 826.0: 5.980895e-07, 827.0: 5.575746e-07, 828.0: 5.19808e-07, 829.0: 4.846123e-07, 830.0: 4.5181e-07}))[source]

Returns the luminous efficacy in \(lm\cdot W^{-1}\) of given spectral power distribution using given luminous efficiency function.

Parameters:
Returns:

Luminous efficacy in \(lm\cdot W^{-1}\).

Return type:

numeric

Examples

>>> from colour import LIGHT_SOURCES_RELATIVE_SPDS
>>> spd = LIGHT_SOURCES_RELATIVE_SPDS['Neodimium Incandescent']
>>> luminous_efficacy(spd)  
136.2170803...
colour.RGB_10_degree_cmfs_to_LMS_10_degree_cmfs(wavelength)[source]

Converts Stiles & Burch 1959 10 Degree RGB CMFs colour matching functions into the Stockman & Sharpe 10 Degree Cone Fundamentals spectral sensitivity functions.

Parameters:wavelength (numeric or array_like) – Wavelength \(\lambda\) in nm.
Returns:Stockman & Sharpe 10 Degree Cone Fundamentals spectral tristimulus values.
Return type:ndarray

Notes

  • Data for the Stockman & Sharpe 10 Degree Cone Fundamentals already exists, this definition is intended for educational purpose.

References

[3]CIE TC 1-36. (2006). CIE 170-1:2006 Fundamental Chromaticity Diagram with Physiological Axes - Part 1 (pp. 1–56). ISBN:978-3-901-90646-6

Examples

>>> RGB_10_degree_cmfs_to_LMS_10_degree_cmfs(700)  
array([ 0.0052860...,  0.0003252...,  0.        ])
colour.RGB_2_degree_cmfs_to_XYZ_2_degree_cmfs(wavelength)[source]

Converts Wright & Guild 1931 2 Degree RGB CMFs colour matching functions into the CIE 1931 2 Degree Standard Observer colour matching functions.

Parameters:wavelength (numeric or array_like) – Wavelength \(\lambda\) in nm.
Returns:CIE 1931 2 Degree Standard Observer spectral tristimulus values.
Return type:ndarray

Notes

  • Data for the CIE 1931 2 Degree Standard Observer already exists, this definition is intended for educational purpose.

References

[1]Wyszecki, G., & Stiles, W. S. (2000). Table 1(3.3.3). In Color Science: Concepts and Methods, Quantitative Data and Formulae (pp. 138–139). Wiley. ISBN:978-0471399186

Examples

>>> RGB_2_degree_cmfs_to_XYZ_2_degree_cmfs(700)  
array([ 0.0113577...,  0.004102  ,  0.        ])
colour.RGB_10_degree_cmfs_to_XYZ_10_degree_cmfs(wavelength)[source]

Converts Stiles & Burch 1959 10 Degree RGB CMFs colour matching functions into the CIE 1964 10 Degree Standard Observer colour matching functions.

Parameters:wavelength (numeric or array_like) – Wavelength \(\lambda\) in nm.
Returns:CIE 1964 10 Degree Standard Observer spectral tristimulus values.
Return type:ndarray

Notes

  • Data for the CIE 1964 10 Degree Standard Observer already exists, this definition is intended for educational purpose.

References

[2]Wyszecki, G., & Stiles, W. S. (2000). The CIE 1964 Standard Observer. In Color Science: Concepts and Methods, Quantitative Data and Formulae (p. 141). Wiley. ISBN:978-0471399186

Examples

>>> RGB_10_degree_cmfs_to_XYZ_10_degree_cmfs(700)  
array([  9.6432150...e-03,   3.7526317...e-03,  -4.1078830...e-06])
colour.LMS_2_degree_cmfs_to_XYZ_2_degree_cmfs(wavelength)[source]

Converts Stockman & Sharpe 2 Degree Cone Fundamentals colour matching functions into the CIE 2012 2 Degree Standard Observer colour matching functions.

Parameters:wavelength (numeric or array_like) – Wavelength \(\lambda\) in nm.
Returns:CIE 2012 2 Degree Standard Observer spectral tristimulus values.
Return type:ndarray

Notes

  • Data for the CIE 2012 2 Degree Standard Observer already exists, this definition is intended for educational purpose.

References

[4]CVRL. (n.d.). CIE (2012) 2-deg XYZ “physiologically-relevant” colour matching functions. Retrieved June 25, 2014, from http://www.cvrl.org/database/text/cienewxyz/cie2012xyz2.htm

Examples

>>> LMS_2_degree_cmfs_to_XYZ_2_degree_cmfs(700)  
array([ 0.0109677...,  0.0041959...,  0.        ])
colour.LMS_10_degree_cmfs_to_XYZ_10_degree_cmfs(wavelength)[source]

Converts Stockman & Sharpe 10 Degree Cone Fundamentals colour matching functions into the CIE 2012 10 Degree Standard Observer colour matching functions.

Parameters:wavelength (numeric or array_like) – Wavelength \(\lambda\) in nm.
Returns:CIE 2012 10 Degree Standard Observer spectral tristimulus values.
Return type:ndarray

Notes

  • Data for the CIE 2012 10 Degree Standard Observer already exists, this definition is intended for educational purpose.

References

[5]CVRL. (n.d.). CIE (2012) 10-deg XYZ “physiologically-relevant” colour matching functions. Retrieved June 25, 2014, from http://www.cvrl.org/database/text/cienewxyz/cie2012xyz10.htm

Examples

>>> LMS_10_degree_cmfs_to_XYZ_10_degree_cmfs(700)  
array([ 0.0098162...,  0.0037761...,  0.        ])
colour.spectral_to_XYZ(spd, cmfs=XYZ_ColourMatchingFunctions( 'CIE 1931 2 Degree Standard Observer', {u'x_bar': {360.0: 0.0001299, 361.0: 0.000145847, 362.0: 0.0001638021, 363.0: 0.0001840037, 364.0: 0.0002066902, 365.0: 0.0002321, 366.0: 0.000260728, 367.0: 0.000293075, 368.0: 0.000329388, 369.0: 0.000369914, 370.0: 0.0004149, 371.0: 0.0004641587, 372.0: 0.000518986, 373.0: 0.000581854, 374.0: 0.0006552347, 375.0: 0.0007416, 376.0: 0.0008450296, 377.0: 0.0009645268, 378.0: 0.001094949, 379.0: 0.001231154, 380.0: 0.001368, 381.0: 0.00150205, 382.0: 0.001642328, 383.0: 0.001802382, 384.0: 0.001995757, 385.0: 0.002236, 386.0: 0.002535385, 387.0: 0.002892603, 388.0: 0.003300829, 389.0: 0.003753236, 390.0: 0.004243, 391.0: 0.004762389, 392.0: 0.005330048, 393.0: 0.005978712, 394.0: 0.006741117, 395.0: 0.00765, 396.0: 0.008751373, 397.0: 0.01002888, 398.0: 0.0114217, 399.0: 0.01286901, 400.0: 0.01431, 401.0: 0.01570443, 402.0: 0.01714744, 403.0: 0.01878122, 404.0: 0.02074801, 405.0: 0.02319, 406.0: 0.02620736, 407.0: 0.02978248, 408.0: 0.03388092, 409.0: 0.03846824, 410.0: 0.04351, 411.0: 0.0489956, 412.0: 0.0550226, 413.0: 0.0617188, 414.0: 0.069212, 415.0: 0.07763, 416.0: 0.08695811, 417.0: 0.09717672, 418.0: 0.1084063, 419.0: 0.1207672, 420.0: 0.13438, 421.0: 0.1493582, 422.0: 0.1653957, 423.0: 0.1819831, 424.0: 0.198611, 425.0: 0.21477, 426.0: 0.2301868, 427.0: 0.2448797, 428.0: 0.2587773, 429.0: 0.2718079, 430.0: 0.2839, 431.0: 0.2949438, 432.0: 0.3048965, 433.0: 0.3137873, 434.0: 0.3216454, 435.0: 0.3285, 436.0: 0.3343513, 437.0: 0.3392101, 438.0: 0.3431213, 439.0: 0.3461296, 440.0: 0.34828, 441.0: 0.3495999, 442.0: 0.3501474, 443.0: 0.350013, 444.0: 0.349287, 445.0: 0.34806, 446.0: 0.3463733, 447.0: 0.3442624, 448.0: 0.3418088, 449.0: 0.3390941, 450.0: 0.3362, 451.0: 0.3331977, 452.0: 0.3300411, 453.0: 0.3266357, 454.0: 0.3228868, 455.0: 0.3187, 456.0: 0.3140251, 457.0: 0.308884, 458.0: 0.3032904, 459.0: 0.2972579, 460.0: 0.2908, 461.0: 0.2839701, 462.0: 0.2767214, 463.0: 0.2689178, 464.0: 0.2604227, 465.0: 0.2511, 466.0: 0.2408475, 467.0: 0.2298512, 468.0: 0.2184072, 469.0: 0.2068115, 470.0: 0.19536, 471.0: 0.1842136, 472.0: 0.1733273, 473.0: 0.1626881, 474.0: 0.1522833, 475.0: 0.1421, 476.0: 0.1321786, 477.0: 0.1225696, 478.0: 0.1132752, 479.0: 0.1042979, 480.0: 0.09564, 481.0: 0.08729955, 482.0: 0.07930804, 483.0: 0.07171776, 484.0: 0.06458099, 485.0: 0.05795001, 486.0: 0.05186211, 487.0: 0.04628152, 488.0: 0.04115088, 489.0: 0.03641283, 490.0: 0.03201, 491.0: 0.0279172, 492.0: 0.0241444, 493.0: 0.020687, 494.0: 0.0175404, 495.0: 0.0147, 496.0: 0.01216179, 497.0: 0.00991996, 498.0: 0.00796724, 499.0: 0.006296346, 500.0: 0.0049, 501.0: 0.003777173, 502.0: 0.00294532, 503.0: 0.00242488, 504.0: 0.002236293, 505.0: 0.0024, 506.0: 0.00292552, 507.0: 0.00383656, 508.0: 0.00517484, 509.0: 0.00698208, 510.0: 0.0093, 511.0: 0.01214949, 512.0: 0.01553588, 513.0: 0.01947752, 514.0: 0.02399277, 515.0: 0.0291, 516.0: 0.03481485, 517.0: 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5.532136e-06, 364.0: 6.208245e-06, 365.0: 6.965e-06, 366.0: 7.813219e-06, 367.0: 8.767336e-06, 368.0: 9.839844e-06, 369.0: 1.104323e-05, 370.0: 1.239e-05, 371.0: 1.388641e-05, 372.0: 1.555728e-05, 373.0: 1.744296e-05, 374.0: 1.958375e-05, 375.0: 2.202e-05, 376.0: 2.483965e-05, 377.0: 2.804126e-05, 378.0: 3.153104e-05, 379.0: 3.521521e-05, 380.0: 3.9e-05, 381.0: 4.28264e-05, 382.0: 4.69146e-05, 383.0: 5.15896e-05, 384.0: 5.71764e-05, 385.0: 6.4e-05, 386.0: 7.234421e-05, 387.0: 8.221224e-05, 388.0: 9.350816e-05, 389.0: 0.0001061361, 390.0: 0.00012, 391.0: 0.000134984, 392.0: 0.000151492, 393.0: 0.000170208, 394.0: 0.000191816, 395.0: 0.000217, 396.0: 0.0002469067, 397.0: 0.00028124, 398.0: 0.00031852, 399.0: 0.0003572667, 400.0: 0.000396, 401.0: 0.0004337147, 402.0: 0.000473024, 403.0: 0.000517876, 404.0: 0.0005722187, 405.0: 0.00064, 406.0: 0.00072456, 407.0: 0.0008255, 408.0: 0.00094116, 409.0: 0.00106988, 410.0: 0.00121, 411.0: 0.001362091, 412.0: 0.001530752, 413.0: 0.001720368, 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424.0: 1.0, 425.0: 1.0, 426.0: 1.0, 427.0: 1.0, 428.0: 1.0, 429.0: 1.0, 430.0: 1.0, 431.0: 1.0, 432.0: 1.0, 433.0: 1.0, 434.0: 1.0, 435.0: 1.0, 436.0: 1.0, 437.0: 1.0, 438.0: 1.0, 439.0: 1.0, 440.0: 1.0, 441.0: 1.0, 442.0: 1.0, 443.0: 1.0, 444.0: 1.0, 445.0: 1.0, 446.0: 1.0, 447.0: 1.0, 448.0: 1.0, 449.0: 1.0, 450.0: 1.0, 451.0: 1.0, 452.0: 1.0, 453.0: 1.0, 454.0: 1.0, 455.0: 1.0, 456.0: 1.0, 457.0: 1.0, 458.0: 1.0, 459.0: 1.0, 460.0: 1.0, 461.0: 1.0, 462.0: 1.0, 463.0: 1.0, 464.0: 1.0, 465.0: 1.0, 466.0: 1.0, 467.0: 1.0, 468.0: 1.0, 469.0: 1.0, 470.0: 1.0, 471.0: 1.0, 472.0: 1.0, 473.0: 1.0, 474.0: 1.0, 475.0: 1.0, 476.0: 1.0, 477.0: 1.0, 478.0: 1.0, 479.0: 1.0, 480.0: 1.0, 481.0: 1.0, 482.0: 1.0, 483.0: 1.0, 484.0: 1.0, 485.0: 1.0, 486.0: 1.0, 487.0: 1.0, 488.0: 1.0, 489.0: 1.0, 490.0: 1.0, 491.0: 1.0, 492.0: 1.0, 493.0: 1.0, 494.0: 1.0, 495.0: 1.0, 496.0: 1.0, 497.0: 1.0, 498.0: 1.0, 499.0: 1.0, 500.0: 1.0, 501.0: 1.0, 502.0: 1.0, 503.0: 1.0, 504.0: 1.0, 505.0: 1.0, 506.0: 1.0, 507.0: 1.0, 508.0: 1.0, 509.0: 1.0, 510.0: 1.0, 511.0: 1.0, 512.0: 1.0, 513.0: 1.0, 514.0: 1.0, 515.0: 1.0, 516.0: 1.0, 517.0: 1.0, 518.0: 1.0, 519.0: 1.0, 520.0: 1.0, 521.0: 1.0, 522.0: 1.0, 523.0: 1.0, 524.0: 1.0, 525.0: 1.0, 526.0: 1.0, 527.0: 1.0, 528.0: 1.0, 529.0: 1.0, 530.0: 1.0, 531.0: 1.0, 532.0: 1.0, 533.0: 1.0, 534.0: 1.0, 535.0: 1.0, 536.0: 1.0, 537.0: 1.0, 538.0: 1.0, 539.0: 1.0, 540.0: 1.0, 541.0: 1.0, 542.0: 1.0, 543.0: 1.0, 544.0: 1.0, 545.0: 1.0, 546.0: 1.0, 547.0: 1.0, 548.0: 1.0, 549.0: 1.0, 550.0: 1.0, 551.0: 1.0, 552.0: 1.0, 553.0: 1.0, 554.0: 1.0, 555.0: 1.0, 556.0: 1.0, 557.0: 1.0, 558.0: 1.0, 559.0: 1.0, 560.0: 1.0, 561.0: 1.0, 562.0: 1.0, 563.0: 1.0, 564.0: 1.0, 565.0: 1.0, 566.0: 1.0, 567.0: 1.0, 568.0: 1.0, 569.0: 1.0, 570.0: 1.0, 571.0: 1.0, 572.0: 1.0, 573.0: 1.0, 574.0: 1.0, 575.0: 1.0, 576.0: 1.0, 577.0: 1.0, 578.0: 1.0, 579.0: 1.0, 580.0: 1.0, 581.0: 1.0, 582.0: 1.0, 583.0: 1.0, 584.0: 1.0, 585.0: 1.0, 586.0: 1.0, 587.0: 1.0, 588.0: 1.0, 589.0: 1.0, 590.0: 1.0, 591.0: 1.0, 592.0: 1.0, 593.0: 1.0, 594.0: 1.0, 595.0: 1.0, 596.0: 1.0, 597.0: 1.0, 598.0: 1.0, 599.0: 1.0, 600.0: 1.0, 601.0: 1.0, 602.0: 1.0, 603.0: 1.0, 604.0: 1.0, 605.0: 1.0, 606.0: 1.0, 607.0: 1.0, 608.0: 1.0, 609.0: 1.0, 610.0: 1.0, 611.0: 1.0, 612.0: 1.0, 613.0: 1.0, 614.0: 1.0, 615.0: 1.0, 616.0: 1.0, 617.0: 1.0, 618.0: 1.0, 619.0: 1.0, 620.0: 1.0, 621.0: 1.0, 622.0: 1.0, 623.0: 1.0, 624.0: 1.0, 625.0: 1.0, 626.0: 1.0, 627.0: 1.0, 628.0: 1.0, 629.0: 1.0, 630.0: 1.0, 631.0: 1.0, 632.0: 1.0, 633.0: 1.0, 634.0: 1.0, 635.0: 1.0, 636.0: 1.0, 637.0: 1.0, 638.0: 1.0, 639.0: 1.0, 640.0: 1.0, 641.0: 1.0, 642.0: 1.0, 643.0: 1.0, 644.0: 1.0, 645.0: 1.0, 646.0: 1.0, 647.0: 1.0, 648.0: 1.0, 649.0: 1.0, 650.0: 1.0, 651.0: 1.0, 652.0: 1.0, 653.0: 1.0, 654.0: 1.0, 655.0: 1.0, 656.0: 1.0, 657.0: 1.0, 658.0: 1.0, 659.0: 1.0, 660.0: 1.0, 661.0: 1.0, 662.0: 1.0, 663.0: 1.0, 664.0: 1.0, 665.0: 1.0, 666.0: 1.0, 667.0: 1.0, 668.0: 1.0, 669.0: 1.0, 670.0: 1.0, 671.0: 1.0, 672.0: 1.0, 673.0: 1.0, 674.0: 1.0, 675.0: 1.0, 676.0: 1.0, 677.0: 1.0, 678.0: 1.0, 679.0: 1.0, 680.0: 1.0, 681.0: 1.0, 682.0: 1.0, 683.0: 1.0, 684.0: 1.0, 685.0: 1.0, 686.0: 1.0, 687.0: 1.0, 688.0: 1.0, 689.0: 1.0, 690.0: 1.0, 691.0: 1.0, 692.0: 1.0, 693.0: 1.0, 694.0: 1.0, 695.0: 1.0, 696.0: 1.0, 697.0: 1.0, 698.0: 1.0, 699.0: 1.0, 700.0: 1.0, 701.0: 1.0, 702.0: 1.0, 703.0: 1.0, 704.0: 1.0, 705.0: 1.0, 706.0: 1.0, 707.0: 1.0, 708.0: 1.0, 709.0: 1.0, 710.0: 1.0, 711.0: 1.0, 712.0: 1.0, 713.0: 1.0, 714.0: 1.0, 715.0: 1.0, 716.0: 1.0, 717.0: 1.0, 718.0: 1.0, 719.0: 1.0, 720.0: 1.0, 721.0: 1.0, 722.0: 1.0, 723.0: 1.0, 724.0: 1.0, 725.0: 1.0, 726.0: 1.0, 727.0: 1.0, 728.0: 1.0, 729.0: 1.0, 730.0: 1.0, 731.0: 1.0, 732.0: 1.0, 733.0: 1.0, 734.0: 1.0, 735.0: 1.0, 736.0: 1.0, 737.0: 1.0, 738.0: 1.0, 739.0: 1.0, 740.0: 1.0, 741.0: 1.0, 742.0: 1.0, 743.0: 1.0, 744.0: 1.0, 745.0: 1.0, 746.0: 1.0, 747.0: 1.0, 748.0: 1.0, 749.0: 1.0, 750.0: 1.0, 751.0: 1.0, 752.0: 1.0, 753.0: 1.0, 754.0: 1.0, 755.0: 1.0, 756.0: 1.0, 757.0: 1.0, 758.0: 1.0, 759.0: 1.0, 760.0: 1.0, 761.0: 1.0, 762.0: 1.0, 763.0: 1.0, 764.0: 1.0, 765.0: 1.0, 766.0: 1.0, 767.0: 1.0, 768.0: 1.0, 769.0: 1.0, 770.0: 1.0, 771.0: 1.0, 772.0: 1.0, 773.0: 1.0, 774.0: 1.0, 775.0: 1.0, 776.0: 1.0, 777.0: 1.0, 778.0: 1.0, 779.0: 1.0, 780.0: 1.0}), method=u'ASTM E308-15', **kwargs)[source]

Converts given spectral power distribution to CIE XYZ tristimulus values using given colour matching functions, illuminant and method.

Parameters:
Other Parameters:
 
  • use_practice_range (bool, optional) – {spectral_to_XYZ_ASTME30815()}, Practise ASTM E308-15 working wavelengths range is [360, 780], if True this argument will trim the colour matching functions appropriately.
  • mi_5nm_omission_method (bool, optional) – {spectral_to_XYZ_ASTME30815()}, 5 nm measurement intervals spectral power distribution conversion to tristimulus values will use a 5 nm version of the colour matching functions instead of a table of tristimulus weighting factors.
  • mi_20nm_interpolation_method (bool, optional) – {spectral_to_XYZ_ASTME30815()}, 20 nm measurement intervals spectral power distribution conversion to tristimulus values will use a dedicated interpolation method instead of a table of tristimulus weighting factors.
Returns:

CIE XYZ tristimulus values.

Return type:

ndarray, (3,)

Warning

The output range of that definition is non standard!

Notes

  • Output CIE XYZ tristimulus values are in range [0, 100].

Examples

>>> from colour import (
...     CMFS, ILLUMINANTS_RELATIVE_SPDS, SpectralPowerDistribution)
>>> cmfs = CMFS['CIE 1931 2 Degree Standard Observer']
>>> data = {
...     400: 0.0641,
...     420: 0.0645,
...     440: 0.0562,
...     460: 0.0537,
...     480: 0.0559,
...     500: 0.0651,
...     520: 0.0705,
...     540: 0.0772,
...     560: 0.0870,
...     580: 0.1128,
...     600: 0.1360,
...     620: 0.1511,
...     640: 0.1688,
...     660: 0.1996,
...     680: 0.2397,
...     700: 0.2852}
>>> spd = SpectralPowerDistribution('Sample', data)
>>> illuminant = ILLUMINANTS_RELATIVE_SPDS['D50']
>>> spectral_to_XYZ(  
...     spd, cmfs, illuminant)
array([ 11.5290265...,   9.9502091...,   4.7098882...])
>>> spectral_to_XYZ(  
...     spd, cmfs, illuminant, use_practice_range=False)
array([ 11.5291275...,   9.9502369...,   4.7098811...])
>>> spectral_to_XYZ(  
...     spd, cmfs, illuminant, method='Integration')
array([ 11.5296285...,   9.9499467...,   4.7066079...])
colour.lagrange_coefficients_ASTME202211(interval=10, interval_type=u'inner')[source]

Computes the Lagrange Coefficients for given interval size using practise ASTM E2022-11 method [1]_.

Parameters:
  • interval (int) – Interval size in nm.
  • interval_type (unicode, optional) – {‘inner’, ‘boundary’}, If the interval is an inner interval Lagrange Coefficients are computed for degree 4. Degree 3 is used for a boundary interval.
Returns:

Lagrange Coefficients.

Return type:

ndarray

Examples

>>> lagrange_coefficients_ASTME202211(  
...     10, 'inner')
array([[-0.028...,  0.940...,  0.104..., -0.016...],
       [-0.048...,  0.864...,  0.216..., -0.032...],
       [-0.059...,  0.773...,  0.331..., -0.045...],
       [-0.064...,  0.672...,  0.448..., -0.056...],
       [-0.062...,  0.562...,  0.562..., -0.062...],
       [-0.056...,  0.448...,  0.672..., -0.064...],
       [-0.045...,  0.331...,  0.773..., -0.059...],
       [-0.032...,  0.216...,  0.864..., -0.048...],
       [-0.016...,  0.104...,  0.940..., -0.028...]])
>>> lagrange_coefficients_ASTME202211(  
...     10, 'boundary')
array([[ 0.85...,  0.19..., -0.04...],
       [ 0.72...,  0.36..., -0.08...],
       [ 0.59...,  0.51..., -0.10...],
       [ 0.48...,  0.64..., -0.12...],
       [ 0.37...,  0.75..., -0.12...],
       [ 0.28...,  0.84..., -0.12...],
       [ 0.19...,  0.91..., -0.10...],
       [ 0.12...,  0.96..., -0.08...],
       [ 0.05...,  0.99..., -0.04...]])
colour.tristimulus_weighting_factors_ASTME202211(cmfs, illuminant, shape)[source]

Returns a table of tristimulus weighting factors for given colour matching functions and illuminant using practise ASTM E2022-11 method [1]_.

The computed table of tristimulus weighting factors should be used with spectral data that has been corrected for spectral bandpass dependence.

Parameters:
Returns:

Tristimulus weighting factors table.

Return type:

ndarray

Raises:

ValueError – If the colour matching functions or illuminant intervals are not equal to 1 nm.

Warning

  • The tables of tristimulus weighting factors are cached in _TRISTIMULUS_WEIGHTING_FACTORS_CACHE attribute. Their identifier key is defined by the colour matching functions and illuminant names along the current shape such as: CIE 1964 10 Degree Standard Observer, A, (360.0, 830.0, 10.0) Considering the above, one should be mindful that using similar colour matching functions and illuminant names but with different spectral data will lead to unexpected behaviour.

Notes

  • Input colour matching functions and illuminant intervals are expected to be equal to 1 nm. If the illuminant data is not available at 1 nm interval, it needs to be interpolated using CIE recommendations: The method developed by Sprague (1880) should be used for interpolating functions having a uniformly spaced independent variable and a Cubic Spline method for non-uniformly spaced independent variable.

Examples

>>> from colour import (
...     CMFS,
...     CIE_standard_illuminant_A_function,
...     SpectralPowerDistribution,
...     SpectralShape)
>>> cmfs = CMFS['CIE 1964 10 Degree Standard Observer']
>>> wl = cmfs.shape.range()
>>> A = SpectralPowerDistribution(
...     'A (360, 830, 1)',
...     dict(zip(wl, CIE_standard_illuminant_A_function(wl))))
>>> tristimulus_weighting_factors_ASTME202211(  
...     cmfs, A, SpectralShape(360, 830, 20))
array([[ -2.9816934...e-04,  -3.1709762...e-05,  -1.3301218...e-03],
       [ -8.7154955...e-03,  -8.9154168...e-04,  -4.0743684...e-02],
       [  5.9967988...e-02,   5.0203497...e-03,   2.5650183...e-01],
       [  7.7342255...e-01,   7.7983983...e-02,   3.6965732...e+00],
       [  1.9000905...e+00,   3.0370051...e-01,   9.7554195...e+00],
       [  1.9707727...e+00,   8.5528092...e-01,   1.1486732...e+01],
       [  7.1836236...e-01,   2.1457000...e+00,   6.7845806...e+00],
       [  4.2666758...e-02,   4.8985328...e+00,   2.3208000...e+00],
       [  1.5223302...e+00,   9.6471138...e+00,   7.4306714...e-01],
       [  5.6770329...e+00,   1.4460970...e+01,   1.9581949...e-01],
       [  1.2445174...e+01,   1.7474254...e+01,   5.1826979...e-03],
       [  2.0553577...e+01,   1.7583821...e+01,  -2.6512696...e-03],
       [  2.5331538...e+01,   1.4895703...e+01,   0.0000000...e+00],
       [  2.1571157...e+01,   1.0079661...e+01,   0.0000000...e+00],
       [  1.2178581...e+01,   5.0680655...e+00,   0.0000000...e+00],
       [  4.6675746...e+00,   1.8303239...e+00,   0.0000000...e+00],
       [  1.3236117...e+00,   5.1296946...e-01,   0.0000000...e+00],
       [  3.1753258...e-01,   1.2300847...e-01,   0.0000000...e+00],
       [  7.4634128...e-02,   2.9024389...e-02,   0.0000000...e+00],
       [  1.8299016...e-02,   7.1606335...e-03,   0.0000000...e+00],
       [  4.7942065...e-03,   1.8888730...e-03,   0.0000000...e+00],
       [  1.3293045...e-03,   5.2774591...e-04,   0.0000000...e+00],
       [  4.2546928...e-04,   1.7041978...e-04,   0.0000000...e+00],
       [  9.6251115...e-05,   3.8955295...e-05,   0.0000000...e+00]])
colour.adjust_tristimulus_weighting_factors_ASTME30815(W, shape_r, shape_t)[source]

Adjusts given table of tristimulus weighting factors to account for a shorter wavelengths range of the test spectral shape compared to the reference spectral shape using practise ASTM E308-15 method [2]_: Weights at the wavelengths for which data are not available are added to the weights at the shortest and longest wavelength for which spectral data are available.

Parameters:
  • W (array_like) – Tristimulus weighting factors table.
  • shape_r (SpectralShape) – Reference spectral shape.
  • shape_t (SpectralShape) – Test spectral shape.
Returns:

Adjusted tristimulus weighting factors.

Return type:

ndarray

Examples

>>> from colour import (
...     CMFS,
...     CIE_standard_illuminant_A_function,
...     SpectralPowerDistribution,
...     SpectralShape)
>>> cmfs = CMFS['CIE 1964 10 Degree Standard Observer']
>>> wl = cmfs.shape.range()
>>> A = SpectralPowerDistribution(
...     'A (360, 830, 1)',
...     dict(zip(wl, CIE_standard_illuminant_A_function(wl))))
>>> W = tristimulus_weighting_factors_ASTME202211(
...     cmfs, A, SpectralShape(360, 830, 20))
>>> adjust_tristimulus_weighting_factors_ASTME30815(  
...     W, SpectralShape(360, 830, 20), SpectralShape(400, 700, 20))
array([[  5.0954324...e-02,   4.0970982...e-03,   2.1442802...e-01],
       [  7.7342255...e-01,   7.7983983...e-02,   3.6965732...e+00],
       [  1.9000905...e+00,   3.0370051...e-01,   9.7554195...e+00],
       [  1.9707727...e+00,   8.5528092...e-01,   1.1486732...e+01],
       [  7.1836236...e-01,   2.1457000...e+00,   6.7845806...e+00],
       [  4.2666758...e-02,   4.8985328...e+00,   2.3208000...e+00],
       [  1.5223302...e+00,   9.6471138...e+00,   7.4306714...e-01],
       [  5.6770329...e+00,   1.4460970...e+01,   1.9581949...e-01],
       [  1.2445174...e+01,   1.7474254...e+01,   5.1826979...e-03],
       [  2.0553577...e+01,   1.7583821...e+01,  -2.6512696...e-03],
       [  2.5331538...e+01,   1.4895703...e+01,   0.0000000...e+00],
       [  2.1571157...e+01,   1.0079661...e+01,   0.0000000...e+00],
       [  1.2178581...e+01,   5.0680655...e+00,   0.0000000...e+00],
       [  4.6675746...e+00,   1.8303239...e+00,   0.0000000...e+00],
       [  1.3236117...e+00,   5.1296946...e-01,   0.0000000...e+00],
       [  4.1711096...e-01,   1.6181949...e-01,   0.0000000...e+00]])
colour.spectral_to_XYZ_integration(spd, cmfs=XYZ_ColourMatchingFunctions( 'CIE 1931 2 Degree Standard Observer', {u'x_bar': {360.0: 0.0001299, 361.0: 0.000145847, 362.0: 0.0001638021, 363.0: 0.0001840037, 364.0: 0.0002066902, 365.0: 0.0002321, 366.0: 0.000260728, 367.0: 0.000293075, 368.0: 0.000329388, 369.0: 0.000369914, 370.0: 0.0004149, 371.0: 0.0004641587, 372.0: 0.000518986, 373.0: 0.000581854, 374.0: 0.0006552347, 375.0: 0.0007416, 376.0: 0.0008450296, 377.0: 0.0009645268, 378.0: 0.001094949, 379.0: 0.001231154, 380.0: 0.001368, 381.0: 0.00150205, 382.0: 0.001642328, 383.0: 0.001802382, 384.0: 0.001995757, 385.0: 0.002236, 386.0: 0.002535385, 387.0: 0.002892603, 388.0: 0.003300829, 389.0: 0.003753236, 390.0: 0.004243, 391.0: 0.004762389, 392.0: 0.005330048, 393.0: 0.005978712, 394.0: 0.006741117, 395.0: 0.00765, 396.0: 0.008751373, 397.0: 0.01002888, 398.0: 0.0114217, 399.0: 0.01286901, 400.0: 0.01431, 401.0: 0.01570443, 402.0: 0.01714744, 403.0: 0.01878122, 404.0: 0.02074801, 405.0: 0.02319, 406.0: 0.02620736, 407.0: 0.02978248, 408.0: 0.03388092, 409.0: 0.03846824, 410.0: 0.04351, 411.0: 0.0489956, 412.0: 0.0550226, 413.0: 0.0617188, 414.0: 0.069212, 415.0: 0.07763, 416.0: 0.08695811, 417.0: 0.09717672, 418.0: 0.1084063, 419.0: 0.1207672, 420.0: 0.13438, 421.0: 0.1493582, 422.0: 0.1653957, 423.0: 0.1819831, 424.0: 0.198611, 425.0: 0.21477, 426.0: 0.2301868, 427.0: 0.2448797, 428.0: 0.2587773, 429.0: 0.2718079, 430.0: 0.2839, 431.0: 0.2949438, 432.0: 0.3048965, 433.0: 0.3137873, 434.0: 0.3216454, 435.0: 0.3285, 436.0: 0.3343513, 437.0: 0.3392101, 438.0: 0.3431213, 439.0: 0.3461296, 440.0: 0.34828, 441.0: 0.3495999, 442.0: 0.3501474, 443.0: 0.350013, 444.0: 0.349287, 445.0: 0.34806, 446.0: 0.3463733, 447.0: 0.3442624, 448.0: 0.3418088, 449.0: 0.3390941, 450.0: 0.3362, 451.0: 0.3331977, 452.0: 0.3300411, 453.0: 0.3266357, 454.0: 0.3228868, 455.0: 0.3187, 456.0: 0.3140251, 457.0: 0.308884, 458.0: 0.3032904, 459.0: 0.2972579, 460.0: 0.2908, 461.0: 0.2839701, 462.0: 0.2767214, 463.0: 0.2689178, 464.0: 0.2604227, 465.0: 0.2511, 466.0: 0.2408475, 467.0: 0.2298512, 468.0: 0.2184072, 469.0: 0.2068115, 470.0: 0.19536, 471.0: 0.1842136, 472.0: 0.1733273, 473.0: 0.1626881, 474.0: 0.1522833, 475.0: 0.1421, 476.0: 0.1321786, 477.0: 0.1225696, 478.0: 0.1132752, 479.0: 0.1042979, 480.0: 0.09564, 481.0: 0.08729955, 482.0: 0.07930804, 483.0: 0.07171776, 484.0: 0.06458099, 485.0: 0.05795001, 486.0: 0.05186211, 487.0: 0.04628152, 488.0: 0.04115088, 489.0: 0.03641283, 490.0: 0.03201, 491.0: 0.0279172, 492.0: 0.0241444, 493.0: 0.020687, 494.0: 0.0175404, 495.0: 0.0147, 496.0: 0.01216179, 497.0: 0.00991996, 498.0: 0.00796724, 499.0: 0.006296346, 500.0: 0.0049, 501.0: 0.003777173, 502.0: 0.00294532, 503.0: 0.00242488, 504.0: 0.002236293, 505.0: 0.0024, 506.0: 0.00292552, 507.0: 0.00383656, 508.0: 0.00517484, 509.0: 0.00698208, 510.0: 0.0093, 511.0: 0.01214949, 512.0: 0.01553588, 513.0: 0.01947752, 514.0: 0.02399277, 515.0: 0.0291, 516.0: 0.03481485, 517.0: 0.04112016, 518.0: 0.04798504, 519.0: 0.05537861, 520.0: 0.06327, 521.0: 0.07163501, 522.0: 0.08046224, 523.0: 0.08973996, 524.0: 0.09945645, 525.0: 0.1096, 526.0: 0.1201674, 527.0: 0.1311145, 528.0: 0.1423679, 529.0: 0.1538542, 530.0: 0.1655, 531.0: 0.1772571, 532.0: 0.18914, 533.0: 0.2011694, 534.0: 0.2133658, 535.0: 0.2257499, 536.0: 0.2383209, 537.0: 0.2510668, 538.0: 0.2639922, 539.0: 0.2771017, 540.0: 0.2904, 541.0: 0.3038912, 542.0: 0.3175726, 543.0: 0.3314384, 544.0: 0.3454828, 545.0: 0.3597, 546.0: 0.3740839, 547.0: 0.3886396, 548.0: 0.4033784, 549.0: 0.4183115, 550.0: 0.4334499, 551.0: 0.4487953, 552.0: 0.464336, 553.0: 0.480064, 554.0: 0.4959713, 555.0: 0.5120501, 556.0: 0.5282959, 557.0: 0.5446916, 558.0: 0.5612094, 559.0: 0.5778215, 560.0: 0.5945, 561.0: 0.6112209, 562.0: 0.6279758, 563.0: 0.6447602, 564.0: 0.6615697, 565.0: 0.6784, 566.0: 0.6952392, 567.0: 0.7120586, 568.0: 0.7288284, 569.0: 0.7455188, 570.0: 0.7621, 571.0: 0.7785432, 572.0: 0.7948256, 573.0: 0.8109264, 574.0: 0.8268248, 575.0: 0.8425, 576.0: 0.8579325, 577.0: 0.8730816, 578.0: 0.8878944, 579.0: 0.9023181, 580.0: 0.9163, 581.0: 0.9297995, 582.0: 0.9427984, 583.0: 0.9552776, 584.0: 0.9672179, 585.0: 0.9786, 586.0: 0.9893856, 587.0: 0.9995488, 588.0: 1.0090892, 589.0: 1.0180064, 590.0: 1.0263, 591.0: 1.0339827, 592.0: 1.040986, 593.0: 1.047188, 594.0: 1.0524667, 595.0: 1.0567, 596.0: 1.0597944, 597.0: 1.0617992, 598.0: 1.0628068, 599.0: 1.0629096, 600.0: 1.0622, 601.0: 1.0607352, 602.0: 1.0584436, 603.0: 1.0552244, 604.0: 1.0509768, 605.0: 1.0456, 606.0: 1.0390369, 607.0: 1.0313608, 608.0: 1.0226662, 609.0: 1.0130477, 610.0: 1.0026, 611.0: 0.9913675, 612.0: 0.9793314, 613.0: 0.9664916, 614.0: 0.9528479, 615.0: 0.9384, 616.0: 0.923194, 617.0: 0.907244, 618.0: 0.890502, 619.0: 0.87292, 620.0: 0.8544499, 621.0: 0.835084, 622.0: 0.814946, 623.0: 0.794186, 624.0: 0.772954, 625.0: 0.7514, 626.0: 0.7295836, 627.0: 0.7075888, 628.0: 0.6856022, 629.0: 0.6638104, 630.0: 0.6424, 631.0: 0.6215149, 632.0: 0.6011138, 633.0: 0.5811052, 634.0: 0.5613977, 635.0: 0.5419, 636.0: 0.5225995, 637.0: 0.5035464, 638.0: 0.4847436, 639.0: 0.4661939, 640.0: 0.4479, 641.0: 0.4298613, 642.0: 0.412098, 643.0: 0.394644, 644.0: 0.3775333, 645.0: 0.3608, 646.0: 0.3444563, 647.0: 0.3285168, 648.0: 0.3130192, 649.0: 0.2980011, 650.0: 0.2835, 651.0: 0.2695448, 652.0: 0.2561184, 653.0: 0.2431896, 654.0: 0.2307272, 655.0: 0.2187, 656.0: 0.2070971, 657.0: 0.1959232, 658.0: 0.1851708, 659.0: 0.1748323, 660.0: 0.1649, 661.0: 0.1553667, 662.0: 0.14623, 663.0: 0.13749, 664.0: 0.1291467, 665.0: 0.1212, 666.0: 0.1136397, 667.0: 0.106465, 668.0: 0.09969044, 669.0: 0.09333061, 670.0: 0.0874, 671.0: 0.08190096, 672.0: 0.07680428, 673.0: 0.07207712, 674.0: 0.06768664, 675.0: 0.0636, 676.0: 0.05980685, 677.0: 0.05628216, 678.0: 0.05297104, 679.0: 0.04981861, 680.0: 0.04677, 681.0: 0.04378405, 682.0: 0.04087536, 683.0: 0.03807264, 684.0: 0.03540461, 685.0: 0.0329, 686.0: 0.03056419, 687.0: 0.02838056, 688.0: 0.02634484, 689.0: 0.02445275, 690.0: 0.0227, 691.0: 0.02108429, 692.0: 0.01959988, 693.0: 0.01823732, 694.0: 0.01698717, 695.0: 0.01584, 696.0: 0.01479064, 697.0: 0.01383132, 698.0: 0.01294868, 699.0: 0.0121292, 700.0: 0.01135916, 701.0: 0.01062935, 702.0: 0.009938846, 703.0: 0.009288422, 704.0: 0.008678854, 705.0: 0.008110916, 706.0: 0.007582388, 707.0: 0.007088746, 708.0: 0.006627313, 709.0: 0.006195408, 710.0: 0.005790346, 711.0: 0.005409826, 712.0: 0.005052583, 713.0: 0.004717512, 714.0: 0.004403507, 715.0: 0.004109457, 716.0: 0.003833913, 717.0: 0.003575748, 718.0: 0.003334342, 719.0: 0.003109075, 720.0: 0.002899327, 721.0: 0.002704348, 722.0: 0.00252302, 723.0: 0.002354168, 724.0: 0.002196616, 725.0: 0.00204919, 726.0: 0.00191096, 727.0: 0.001781438, 728.0: 0.00166011, 729.0: 0.001546459, 730.0: 0.001439971, 731.0: 0.001340042, 732.0: 0.001246275, 733.0: 0.001158471, 734.0: 0.00107643, 735.0: 0.0009999493, 736.0: 0.0009287358, 737.0: 0.0008624332, 738.0: 0.0008007503, 739.0: 0.000743396, 740.0: 0.0006900786, 741.0: 0.0006405156, 742.0: 0.0005945021, 743.0: 0.0005518646, 744.0: 0.000512429, 745.0: 0.0004760213, 746.0: 0.0004424536, 747.0: 0.0004115117, 748.0: 0.0003829814, 749.0: 0.0003566491, 750.0: 0.0003323011, 751.0: 0.0003097586, 752.0: 0.0002888871, 753.0: 0.0002695394, 754.0: 0.0002515682, 755.0: 0.0002348261, 756.0: 0.000219171, 757.0: 0.0002045258, 758.0: 0.0001908405, 759.0: 0.0001780654, 760.0: 0.0001661505, 761.0: 0.0001550236, 762.0: 0.0001446219, 763.0: 0.0001349098, 764.0: 0.000125852, 765.0: 0.000117413, 766.0: 0.0001095515, 767.0: 0.0001022245, 768.0: 9.539445e-05, 769.0: 8.90239e-05, 770.0: 8.307527e-05, 771.0: 7.751269e-05, 772.0: 7.231304e-05, 773.0: 6.745778e-05, 774.0: 6.292844e-05, 775.0: 5.870652e-05, 776.0: 5.477028e-05, 777.0: 5.109918e-05, 778.0: 4.767654e-05, 779.0: 4.448567e-05, 780.0: 4.150994e-05, 781.0: 3.873324e-05, 782.0: 3.614203e-05, 783.0: 3.372352e-05, 784.0: 3.146487e-05, 785.0: 2.935326e-05, 786.0: 2.737573e-05, 787.0: 2.552433e-05, 788.0: 2.379376e-05, 789.0: 2.21787e-05, 790.0: 2.067383e-05, 791.0: 1.927226e-05, 792.0: 1.79664e-05, 793.0: 1.674991e-05, 794.0: 1.561648e-05, 795.0: 1.455977e-05, 796.0: 1.357387e-05, 797.0: 1.265436e-05, 798.0: 1.179723e-05, 799.0: 1.099844e-05, 800.0: 1.025398e-05, 801.0: 9.559646e-06, 802.0: 8.912044e-06, 803.0: 8.308358e-06, 804.0: 7.745769e-06, 805.0: 7.221456e-06, 806.0: 6.732475e-06, 807.0: 6.276423e-06, 808.0: 5.851304e-06, 809.0: 5.455118e-06, 810.0: 5.085868e-06, 811.0: 4.741466e-06, 812.0: 4.420236e-06, 813.0: 4.120783e-06, 814.0: 3.841716e-06, 815.0: 3.581652e-06, 816.0: 3.339127e-06, 817.0: 3.112949e-06, 818.0: 2.902121e-06, 819.0: 2.705645e-06, 820.0: 2.522525e-06, 821.0: 2.351726e-06, 822.0: 2.192415e-06, 823.0: 2.043902e-06, 824.0: 1.905497e-06, 825.0: 1.776509e-06, 826.0: 1.656215e-06, 827.0: 1.544022e-06, 828.0: 1.43944e-06, 829.0: 1.341977e-06, 830.0: 1.251141e-06}, u'y_bar': {360.0: 3.917e-06, 361.0: 4.393581e-06, 362.0: 4.929604e-06, 363.0: 5.532136e-06, 364.0: 6.208245e-06, 365.0: 6.965e-06, 366.0: 7.813219e-06, 367.0: 8.767336e-06, 368.0: 9.839844e-06, 369.0: 1.104323e-05, 370.0: 1.239e-05, 371.0: 1.388641e-05, 372.0: 1.555728e-05, 373.0: 1.744296e-05, 374.0: 1.958375e-05, 375.0: 2.202e-05, 376.0: 2.483965e-05, 377.0: 2.804126e-05, 378.0: 3.153104e-05, 379.0: 3.521521e-05, 380.0: 3.9e-05, 381.0: 4.28264e-05, 382.0: 4.69146e-05, 383.0: 5.15896e-05, 384.0: 5.71764e-05, 385.0: 6.4e-05, 386.0: 7.234421e-05, 387.0: 8.221224e-05, 388.0: 9.350816e-05, 389.0: 0.0001061361, 390.0: 0.00012, 391.0: 0.000134984, 392.0: 0.000151492, 393.0: 0.000170208, 394.0: 0.000191816, 395.0: 0.000217, 396.0: 0.0002469067, 397.0: 0.00028124, 398.0: 0.00031852, 399.0: 0.0003572667, 400.0: 0.000396, 401.0: 0.0004337147, 402.0: 0.000473024, 403.0: 0.000517876, 404.0: 0.0005722187, 405.0: 0.00064, 406.0: 0.00072456, 407.0: 0.0008255, 408.0: 0.00094116, 409.0: 0.00106988, 410.0: 0.00121, 411.0: 0.001362091, 412.0: 0.001530752, 413.0: 0.001720368, 414.0: 0.001935323, 415.0: 0.00218, 416.0: 0.0024548, 417.0: 0.002764, 418.0: 0.0031178, 419.0: 0.0035264, 420.0: 0.004, 421.0: 0.00454624, 422.0: 0.00515932, 423.0: 0.00582928, 424.0: 0.00654616, 425.0: 0.0073, 426.0: 0.008086507, 427.0: 0.00890872, 428.0: 0.00976768, 429.0: 0.01066443, 430.0: 0.0116, 431.0: 0.01257317, 432.0: 0.01358272, 433.0: 0.01462968, 434.0: 0.01571509, 435.0: 0.01684, 436.0: 0.01800736, 437.0: 0.01921448, 438.0: 0.02045392, 439.0: 0.02171824, 440.0: 0.023, 441.0: 0.02429461, 442.0: 0.02561024, 443.0: 0.02695857, 444.0: 0.02835125, 445.0: 0.0298, 446.0: 0.03131083, 447.0: 0.03288368, 448.0: 0.03452112, 449.0: 0.03622571, 450.0: 0.038, 451.0: 0.03984667, 452.0: 0.041768, 453.0: 0.043766, 454.0: 0.04584267, 455.0: 0.048, 456.0: 0.05024368, 457.0: 0.05257304, 458.0: 0.05498056, 459.0: 0.05745872, 460.0: 0.06, 461.0: 0.06260197, 462.0: 0.06527752, 463.0: 0.06804208, 464.0: 0.07091109, 465.0: 0.0739, 466.0: 0.077016, 467.0: 0.0802664, 468.0: 0.0836668, 469.0: 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590.0: 1.0, 591.0: 1.0, 592.0: 1.0, 593.0: 1.0, 594.0: 1.0, 595.0: 1.0, 596.0: 1.0, 597.0: 1.0, 598.0: 1.0, 599.0: 1.0, 600.0: 1.0, 601.0: 1.0, 602.0: 1.0, 603.0: 1.0, 604.0: 1.0, 605.0: 1.0, 606.0: 1.0, 607.0: 1.0, 608.0: 1.0, 609.0: 1.0, 610.0: 1.0, 611.0: 1.0, 612.0: 1.0, 613.0: 1.0, 614.0: 1.0, 615.0: 1.0, 616.0: 1.0, 617.0: 1.0, 618.0: 1.0, 619.0: 1.0, 620.0: 1.0, 621.0: 1.0, 622.0: 1.0, 623.0: 1.0, 624.0: 1.0, 625.0: 1.0, 626.0: 1.0, 627.0: 1.0, 628.0: 1.0, 629.0: 1.0, 630.0: 1.0, 631.0: 1.0, 632.0: 1.0, 633.0: 1.0, 634.0: 1.0, 635.0: 1.0, 636.0: 1.0, 637.0: 1.0, 638.0: 1.0, 639.0: 1.0, 640.0: 1.0, 641.0: 1.0, 642.0: 1.0, 643.0: 1.0, 644.0: 1.0, 645.0: 1.0, 646.0: 1.0, 647.0: 1.0, 648.0: 1.0, 649.0: 1.0, 650.0: 1.0, 651.0: 1.0, 652.0: 1.0, 653.0: 1.0, 654.0: 1.0, 655.0: 1.0, 656.0: 1.0, 657.0: 1.0, 658.0: 1.0, 659.0: 1.0, 660.0: 1.0, 661.0: 1.0, 662.0: 1.0, 663.0: 1.0, 664.0: 1.0, 665.0: 1.0, 666.0: 1.0, 667.0: 1.0, 668.0: 1.0, 669.0: 1.0, 670.0: 1.0, 671.0: 1.0, 672.0: 1.0, 673.0: 1.0, 674.0: 1.0, 675.0: 1.0, 676.0: 1.0, 677.0: 1.0, 678.0: 1.0, 679.0: 1.0, 680.0: 1.0, 681.0: 1.0, 682.0: 1.0, 683.0: 1.0, 684.0: 1.0, 685.0: 1.0, 686.0: 1.0, 687.0: 1.0, 688.0: 1.0, 689.0: 1.0, 690.0: 1.0, 691.0: 1.0, 692.0: 1.0, 693.0: 1.0, 694.0: 1.0, 695.0: 1.0, 696.0: 1.0, 697.0: 1.0, 698.0: 1.0, 699.0: 1.0, 700.0: 1.0, 701.0: 1.0, 702.0: 1.0, 703.0: 1.0, 704.0: 1.0, 705.0: 1.0, 706.0: 1.0, 707.0: 1.0, 708.0: 1.0, 709.0: 1.0, 710.0: 1.0, 711.0: 1.0, 712.0: 1.0, 713.0: 1.0, 714.0: 1.0, 715.0: 1.0, 716.0: 1.0, 717.0: 1.0, 718.0: 1.0, 719.0: 1.0, 720.0: 1.0, 721.0: 1.0, 722.0: 1.0, 723.0: 1.0, 724.0: 1.0, 725.0: 1.0, 726.0: 1.0, 727.0: 1.0, 728.0: 1.0, 729.0: 1.0, 730.0: 1.0, 731.0: 1.0, 732.0: 1.0, 733.0: 1.0, 734.0: 1.0, 735.0: 1.0, 736.0: 1.0, 737.0: 1.0, 738.0: 1.0, 739.0: 1.0, 740.0: 1.0, 741.0: 1.0, 742.0: 1.0, 743.0: 1.0, 744.0: 1.0, 745.0: 1.0, 746.0: 1.0, 747.0: 1.0, 748.0: 1.0, 749.0: 1.0, 750.0: 1.0, 751.0: 1.0, 752.0: 1.0, 753.0: 1.0, 754.0: 1.0, 755.0: 1.0, 756.0: 1.0, 757.0: 1.0, 758.0: 1.0, 759.0: 1.0, 760.0: 1.0, 761.0: 1.0, 762.0: 1.0, 763.0: 1.0, 764.0: 1.0, 765.0: 1.0, 766.0: 1.0, 767.0: 1.0, 768.0: 1.0, 769.0: 1.0, 770.0: 1.0, 771.0: 1.0, 772.0: 1.0, 773.0: 1.0, 774.0: 1.0, 775.0: 1.0, 776.0: 1.0, 777.0: 1.0, 778.0: 1.0, 779.0: 1.0, 780.0: 1.0, 781.0: 1.0, 782.0: 1.0, 783.0: 1.0, 784.0: 1.0, 785.0: 1.0, 786.0: 1.0, 787.0: 1.0, 788.0: 1.0, 789.0: 1.0, 790.0: 1.0, 791.0: 1.0, 792.0: 1.0, 793.0: 1.0, 794.0: 1.0, 795.0: 1.0, 796.0: 1.0, 797.0: 1.0, 798.0: 1.0, 799.0: 1.0, 800.0: 1.0, 801.0: 1.0, 802.0: 1.0, 803.0: 1.0, 804.0: 1.0, 805.0: 1.0, 806.0: 1.0, 807.0: 1.0, 808.0: 1.0, 809.0: 1.0, 810.0: 1.0, 811.0: 1.0, 812.0: 1.0, 813.0: 1.0, 814.0: 1.0, 815.0: 1.0, 816.0: 1.0, 817.0: 1.0, 818.0: 1.0, 819.0: 1.0, 820.0: 1.0, 821.0: 1.0, 822.0: 1.0, 823.0: 1.0, 824.0: 1.0, 825.0: 1.0, 826.0: 1.0, 827.0: 1.0, 828.0: 1.0, 829.0: 1.0, 830.0: 1.0}))[source]

Converts given spectral power distribution to CIE XYZ tristimulus values using given colour matching functions and illuminant accordingly to classical integration method.

Parameters:
Returns:

CIE XYZ tristimulus values.

Return type:

ndarray, (3,)

Warning

The output range of that definition is non standard!

Notes

  • Output CIE XYZ tristimulus values are in range [0, 100].

References

[3]Wyszecki, G., & Stiles, W. S. (2000). Integration Replace by Summation. In Color Science: Concepts and Methods, Quantitative Data and Formulae (pp. 158–163). Wiley. ISBN:978-0471399186

Examples

>>> from colour import (
...     CMFS, ILLUMINANTS_RELATIVE_SPDS, SpectralPowerDistribution)
>>> cmfs = CMFS['CIE 1931 2 Degree Standard Observer']
>>> data = {
...     400: 0.0641,
...     420: 0.0645,
...     440: 0.0562,
...     460: 0.0537,
...     480: 0.0559,
...     500: 0.0651,
...     520: 0.0705,
...     540: 0.0772,
...     560: 0.0870,
...     580: 0.1128,
...     600: 0.1360,
...     620: 0.1511,
...     640: 0.1688,
...     660: 0.1996,
...     680: 0.2397,
...     700: 0.2852}
>>> spd = SpectralPowerDistribution('Sample', data)
>>> illuminant = ILLUMINANTS_RELATIVE_SPDS['D50']
>>> spectral_to_XYZ_integration(  
...     spd, cmfs, illuminant)
array([ 11.5296285...,   9.9499467...,   4.7066079...])
colour.spectral_to_XYZ_tristimulus_weighting_factors_ASTME30815(spd, cmfs=XYZ_ColourMatchingFunctions( 'CIE 1931 2 Degree Standard Observer', {u'x_bar': {360.0: 0.0001299, 361.0: 0.000145847, 362.0: 0.0001638021, 363.0: 0.0001840037, 364.0: 0.0002066902, 365.0: 0.0002321, 366.0: 0.000260728, 367.0: 0.000293075, 368.0: 0.000329388, 369.0: 0.000369914, 370.0: 0.0004149, 371.0: 0.0004641587, 372.0: 0.000518986, 373.0: 0.000581854, 374.0: 0.0006552347, 375.0: 0.0007416, 376.0: 0.0008450296, 377.0: 0.0009645268, 378.0: 0.001094949, 379.0: 0.001231154, 380.0: 0.001368, 381.0: 0.00150205, 382.0: 0.001642328, 383.0: 0.001802382, 384.0: 0.001995757, 385.0: 0.002236, 386.0: 0.002535385, 387.0: 0.002892603, 388.0: 0.003300829, 389.0: 0.003753236, 390.0: 0.004243, 391.0: 0.004762389, 392.0: 0.005330048, 393.0: 0.005978712, 394.0: 0.006741117, 395.0: 0.00765, 396.0: 0.008751373, 397.0: 0.01002888, 398.0: 0.0114217, 399.0: 0.01286901, 400.0: 0.01431, 401.0: 0.01570443, 402.0: 0.01714744, 403.0: 0.01878122, 404.0: 0.02074801, 405.0: 0.02319, 406.0: 0.02620736, 407.0: 0.02978248, 408.0: 0.03388092, 409.0: 0.03846824, 410.0: 0.04351, 411.0: 0.0489956, 412.0: 0.0550226, 413.0: 0.0617188, 414.0: 0.069212, 415.0: 0.07763, 416.0: 0.08695811, 417.0: 0.09717672, 418.0: 0.1084063, 419.0: 0.1207672, 420.0: 0.13438, 421.0: 0.1493582, 422.0: 0.1653957, 423.0: 0.1819831, 424.0: 0.198611, 425.0: 0.21477, 426.0: 0.2301868, 427.0: 0.2448797, 428.0: 0.2587773, 429.0: 0.2718079, 430.0: 0.2839, 431.0: 0.2949438, 432.0: 0.3048965, 433.0: 0.3137873, 434.0: 0.3216454, 435.0: 0.3285, 436.0: 0.3343513, 437.0: 0.3392101, 438.0: 0.3431213, 439.0: 0.3461296, 440.0: 0.34828, 441.0: 0.3495999, 442.0: 0.3501474, 443.0: 0.350013, 444.0: 0.349287, 445.0: 0.34806, 446.0: 0.3463733, 447.0: 0.3442624, 448.0: 0.3418088, 449.0: 0.3390941, 450.0: 0.3362, 451.0: 0.3331977, 452.0: 0.3300411, 453.0: 0.3266357, 454.0: 0.3228868, 455.0: 0.3187, 456.0: 0.3140251, 457.0: 0.308884, 458.0: 0.3032904, 459.0: 0.2972579, 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