#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Spectrum
========
Defines the classes handling spectral data computation:
- :class:`SpectralMapping`
- :class:`SpectralShape`
- :class:`SpectralPowerDistribution`
- :class:`TriSpectralPowerDistribution`
See Also
--------
`Spectrum IPython Notebook
<http://nbviewer.ipython.org/github/colour-science/colour-ipython/blob/master/notebooks/colorimetry/spectrum.ipynb>`_ # noqa
"""
from __future__ import division, unicode_literals
import copy
import itertools
import numpy as np
from colour.algebra import (
Extrapolator1d,
LinearInterpolator,
SpragueInterpolator,
CubicSplineInterpolator,
PchipInterpolator)
from colour.utilities import (
ArbitraryPrecisionMapping,
is_iterable,
is_numeric,
is_string,
is_uniform,
steps,
tstack,
warning)
__author__ = 'Colour Developers'
__copyright__ = 'Copyright (C) 2013 - 2015 - Colour Developers'
__license__ = 'New BSD License - http://opensource.org/licenses/BSD-3-Clause'
__maintainer__ = 'Colour Developers'
__email__ = 'colour-science@googlegroups.com'
__status__ = 'Production'
__all__ = ['DEFAULT_WAVELENGTH_DECIMALS',
'SpectralMapping',
'SpectralShape',
'SpectralPowerDistribution',
'TriSpectralPowerDistribution',
'DEFAULT_SPECTRAL_SHAPE',
'constant_spd',
'zeros_spd',
'ones_spd']
DEFAULT_WAVELENGTH_DECIMALS = 10
"""
Default wavelength precision decimals.
DEFAULT_WAVELENGTH_DECIMALS : int
"""
[docs]class SpectralMapping(ArbitraryPrecisionMapping):
"""
Defines the base mapping for spectral data.
It enables usage of floating point wavelengths as keys by rounding them at
a specfic decimals count.
Parameters
----------
data : dict or SpectralMapping, optional
Spectral data in a *dict* or *SpectralMapping* as follows:
{wavelength :math:`\lambda_{i}`: value,
wavelength :math:`\lambda_{i+1}`: value,
...,
wavelength :math:`\lambda_{i+n}`: value}
wavelength_decimals : int, optional
Decimals count the keys will be rounded at.
\*\*kwargs : dict
Key / Value pairs to store into the mapping at initialisation.
Attributes
----------
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()) # doctest: +SKIP
(380.1999999998, 380.2)
>>> mapping = SpectralMapping(data1, wavelength_decimals=7)
>>> # Doctests skip for Python 2.x compatibility.
>>> tuple(mapping.keys()) # doctest: +SKIP
(380.2,)
"""
def __init__(self,
data=None,
wavelength_decimals=DEFAULT_WAVELENGTH_DECIMALS,
**kwargs):
super(SpectralMapping, self).__init__(
data, wavelength_decimals, **kwargs)
@property
def wavelength_decimals(self):
"""
Property for **self.key_decimals** attribute.
Returns
-------
unicode
self.key_decimals.
"""
return self.key_decimals
@wavelength_decimals.setter
[docs] def wavelength_decimals(self, value):
"""
Setter for **self.key_decimals** attribute.
Parameters
----------
value : unicode
Attribute value.
"""
if value is not None:
assert type(value) is int, (
'"{0}" attribute: "{1}" type is not "int"!').format(
'wavelength_decimals', value)
self.key_decimals = value
[docs]class SpectralShape(object):
"""
Defines the base object for spectral power distribution shape.
Parameters
----------
start : numeric, optional
Wavelength :math:`\lambda_{i}` range start in nm.
end : numeric, optional
Wavelength :math:`\lambda_{i}` range end in nm.
steps : numeric, optional
Wavelength :math:`\lambda_{i}` range steps.
Attributes
----------
start
end
steps
Methods
-------
__repr__
__contains__
__len__
__eq__
__ne__
range
Examples
--------
>>> # Doctests skip for Python 2.x compatibility.
>>> SpectralShape(360, 830, 1) # doctest: +SKIP
SpectralShape(360, 830, 1)
"""
def __init__(self, start=None, end=None, steps=None):
# Attribute storing the spectral shape range for caching purpose.
self.__range = None
self.__start = None
self.__end = None
self.__steps = None
self.start = start
self.end = end
self.steps = steps
@property
def start(self):
"""
Property for **self.__start** private attribute.
Returns
-------
numeric
self.__start.
"""
return self.__start
@start.setter
[docs] def start(self, value):
"""
Setter for **self.__start** private attribute.
Parameters
----------
value : unicode
Attribute value.
"""
if value is not None:
assert is_numeric(value), (
'"{0}" attribute: "{1}" type is not "numeric"!'.format(
'start', value))
value = round(value, DEFAULT_WAVELENGTH_DECIMALS)
if self.__end is not None:
assert value < self.__end, (
'"{0}" attribute value must be strictly less than '
'"{1}"!'.format('start', self.__end))
# Invalidating the *range* cache.
if value != self.__start:
self.__range = None
self.__start = value
@property
def end(self):
"""
Property for **self.__end** private attribute.
Returns
-------
numeric
self.__end.
"""
return self.__end
@end.setter
[docs] def end(self, value):
"""
Setter for **self.__end** private attribute.
Parameters
----------
value : unicode
Attribute value.
"""
if value is not None:
assert is_numeric(value), (
'"{0}" attribute: "{1}" type is not "numeric"!'.format(
'end', value))
value = round(value, DEFAULT_WAVELENGTH_DECIMALS)
if self.__start is not None:
assert value > self.__start, (
'"{0}" attribute value must be strictly greater than '
'"{1}"!'.format('end', self.__start))
# Invalidating the *range* cache.
if value != self.__end:
self.__range = None
self.__end = value
@property
def steps(self):
"""
Property for **self.__steps** private attribute.
Returns
-------
numeric
self.__steps.
"""
return self.__steps
@steps.setter
[docs] def steps(self, value):
"""
Setter for **self.__steps** private attribute.
Parameters
----------
value : unicode
Attribute value.
"""
if value is not None:
assert is_numeric(value), (
'"{0}" attribute: "{1}" type is not "numeric"!'.format(
'steps', value))
value = round(value, DEFAULT_WAVELENGTH_DECIMALS)
# Invalidating the *range* cache.
if value != self.__steps:
self.__range = None
self.__steps = value
[docs] def __str__(self):
"""
Returns a nice formatted string representation.
Returns
-------
unicode
Nice formatted string representation.
"""
return '({0}, {1}, {2})'.format(self.__start,
self.__end,
self.__steps)
[docs] def __repr__(self):
"""
Returns a formatted string representation.
Returns
-------
unicode
Formatted string representation.
"""
return 'SpectralShape({0}, {1}, {2})'.format(self.__start,
self.__end,
self.__steps)
[docs] def __iter__(self):
"""
Returns a generator for the spectral power distribution data.
Returns
-------
generator
Spectral power distribution data generator.
Notes
-----
- Reimplements the :meth:`object.__iter__` method.
Examples
--------
>>> shape = SpectralShape(0, 10, 1)
>>> for wavelength in shape: print(wavelength)
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
"""
return iter(self.range())
[docs] def __contains__(self, wavelength):
"""
Returns if the spectral shape contains given wavelength
:math:`\lambda`.
Parameters
----------
wavelength : numeric or array_like
Wavelength :math:`\lambda`.
Returns
-------
bool
Is wavelength :math:`\lambda` contained in the spectral shape.
Warning
-------
*wavelength* argument is tested to be contained in the spectral shape
within the tolerance defined by :attr:`colour.constants.common.EPSILON`
attribute value.
Notes
-----
- Reimplements the :meth:`object.__contains__` method.
Examples
--------
>>> 0.5 in SpectralShape(0, 10, 0.1)
True
>>> 0.6 in SpectralShape(0, 10, 0.1)
True
>>> 0.51 in SpectralShape(0, 10, 0.1)
False
>>> np.array([0.5, 0.6]) in SpectralShape(0, 10, 0.1)
True
>>> np.array([0.51, 0.6]) in SpectralShape(0, 10, 0.1)
False
"""
return np.all(np.in1d(wavelength, self.range()))
[docs] def __len__(self):
"""
Returns the spectral shape wavelength :math:`\lambda_n` count.
Returns
-------
int
Spectral shape wavelength :math:`\lambda_n` count.
Notes
-----
- Reimplements the :meth:`object.__len__` method.
Examples
--------
>>> len(SpectralShape(0, 10, 0.1))
101
"""
return len(self.range())
[docs] def __eq__(self, shape):
"""
Returns the spectral shape equality with given other spectral shape.
Parameters
----------
shape : SpectralShape
Spectral shape to compare for equality.
Returns
-------
bool
Spectral shape equality.
Notes
-----
- Reimplements the :meth:`object.__eq__` method.
Examples
--------
>>> SpectralShape(0, 10, 0.1) == SpectralShape(0, 10, 0.1)
True
>>> SpectralShape(0, 10, 0.1) == SpectralShape(0, 10, 1)
False
"""
return (isinstance(shape, self.__class__) and
np.array_equal(self.range(), shape.range()))
[docs] def __ne__(self, shape):
"""
Returns the spectral shape inequality with given other spectral shape.
Parameters
----------
shape : SpectralShape
Spectral shape to compare for inequality.
Returns
-------
bool
Spectral shape inequality.
Notes
-----
- Reimplements the :meth:`object.__ne__` method.
Examples
--------
>>> SpectralShape(0, 10, 0.1) != SpectralShape(0, 10, 0.1)
False
>>> SpectralShape(0, 10, 0.1) != SpectralShape(0, 10, 1)
True
"""
return not (self == shape)
[docs] def range(self):
"""
Returns an iterable range for the spectral power distribution shape.
Returns
-------
ndarray
Iterable range for the spectral power distribution shape
Raises
------
RuntimeError
If one of spectral shape *start*, *end* or *steps* attributes is
not defined.
Examples
--------
>>> SpectralShape(0, 10, 0.1).range()
array([ 0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8,
0.9, 1. , 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7,
1.8, 1.9, 2. , 2.1, 2.2, 2.3, 2.4, 2.5, 2.6,
2.7, 2.8, 2.9, 3. , 3.1, 3.2, 3.3, 3.4, 3.5,
3.6, 3.7, 3.8, 3.9, 4. , 4.1, 4.2, 4.3, 4.4,
4.5, 4.6, 4.7, 4.8, 4.9, 5. , 5.1, 5.2, 5.3,
5.4, 5.5, 5.6, 5.7, 5.8, 5.9, 6. , 6.1, 6.2,
6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 6.9, 7. , 7.1,
7.2, 7.3, 7.4, 7.5, 7.6, 7.7, 7.8, 7.9, 8. ,
8.1, 8.2, 8.3, 8.4, 8.5, 8.6, 8.7, 8.8, 8.9,
9. , 9.1, 9.2, 9.3, 9.4, 9.5, 9.6, 9.7, 9.8,
9.9, 10. ])
"""
if None in (self.__start, self.__end, self.__steps):
raise RuntimeError(('One of the spectral shape "start", "end" or '
'"steps" attributes is not defined!'))
if self.__range is None:
samples = round(
(self.__steps + self.__end - self.__start) / self.__steps)
range, current_steps = np.linspace(
self.__start, self.__end, samples, retstep=True)
self.__range = np.around(range, DEFAULT_WAVELENGTH_DECIMALS)
if current_steps != self.__steps:
self.__steps = current_steps
warning(('"{0}" shape could not be honored, using '
'"{1}"!').format((self.__start,
self.__end,
self.__steps),
self))
return self.__range
[docs]class SpectralPowerDistribution(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 :math:`\lambda_{i}`: value,
wavelength :math:`\lambda_{i+1}`: value,
...,
wavelength :math:`\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.
Attributes
----------
name
data
title
wavelengths
values
items
shape
Methods
-------
__init__
__getitem__
__setitem__
__iter__
__contains__
__len__
__eq__
__ne__
__add__
__sub__
__mul__
__div__
__truediv__
__pow__
get
is_uniform
extrapolate
interpolate
align
zeros
normalise
clone
Examples
--------
>>> data = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> spd = SpectralPowerDistribution('Spd', data)
>>> # Doctests skip for Python 2.x compatibility.
>>> spd.wavelengths # doctest: +SKIP
array([510, 520, 530, 540])
>>> spd.values
array([ 49.67, 69.59, 81.73, 88.19])
>>> spd.shape # doctest: +SKIP
SpectralShape(510, 540, 10)
"""
[docs] def __init__(self, name, data, title=None):
self.__name = None
self.name = name
self.__data = None
self.data = data
self.__title = None
self.title = title
@property
def name(self):
"""
Property for **self.__name** private attribute.
Returns
-------
unicode
self.__name.
"""
return self.__name
@name.setter
[docs] def name(self, value):
"""
Setter for **self.__name** private attribute.
Parameters
----------
value : unicode
Attribute value.
"""
if value is not None:
assert type(value) in (str, unicode), ( # noqa
('"{0}" attribute: "{1}" type is not '
'"str" or "unicode"!').format('name', value))
self.__name = value
@property
def data(self):
"""
Property for **self.__data** private attribute.
Returns
-------
SpectralMapping
self.__data.
"""
return self.__data
@data.setter
[docs] def data(self, value):
"""
Setter for **self.__data** private attribute.
Parameters
----------
value : dict or SpectralMapping
Attribute value.
"""
if value is not None:
assert type(value) in (dict, SpectralMapping), (
'"{0}" attribute: "{1}" type is not "dict" or '
'"SpectralMapping"!'.format('data', value))
self.__data = SpectralMapping(value)
@property
def title(self):
"""
Property for **self.__title** private attribute.
Returns
-------
unicode
self.__title.
"""
if self.__title is not None:
return self.__title
else:
return self.__name
@title.setter
[docs] def title(self, value):
"""
Setter for **self.__title** private attribute.
Parameters
----------
value : unicode
Attribute value.
"""
if value is not None:
assert type(value) in (str, unicode), ( # noqa
('"{0}" attribute: "{1}" type is not '
'"str" or "unicode"!').format('title', value))
self.__title = value
@property
def wavelengths(self):
"""
Property for **self.wavelengths** attribute.
Returns
-------
ndarray
Spectral power distribution wavelengths :math:`\lambda_n`.
Warning
-------
:attr:`SpectralPowerDistribution.wavelengths` is read only.
"""
return np.array(sorted(self.__data.keys()))
@wavelengths.setter
[docs] def wavelengths(self, value):
"""
Setter for **self.wavelengths** attribute.
Parameters
----------
value : object
Attribute value.
"""
raise AttributeError(
'"{0}" attribute is read only!'.format('wavelengths'))
@property
def values(self):
"""
Property for **self.values** attribute.
Returns
-------
ndarray
Spectral power distribution wavelengths :math:`\lambda_n` values.
Warning
-------
:attr:`SpectralPowerDistribution.values` is read only.
"""
return np.array([self.get(wavelength)
for wavelength in self.wavelengths])
@values.setter
[docs] def values(self, value):
"""
Setter for **self.values** attribute.
Parameters
----------
value : object
Attribute value.
"""
raise AttributeError('"{0}" attribute is read only!'.format('values'))
@property
def items(self):
"""
Property for **self.items** attribute. This is a convenient attribute
used to iterate over the spectral power distribution.
Returns
-------
generator
Spectral power distribution data generator.
"""
return self.__iter__()
@items.setter
[docs] def items(self, value):
"""
Setter for **self.items** attribute.
Parameters
----------
value : object
Attribute value.
"""
raise AttributeError('"{0}" attribute is read only!'.format('items'))
@property
def shape(self):
"""
Property for **self.shape** attribute.
Returns the shape of the spectral power distribution in the form of a
:class:`SpectralShape` class instance.
Returns
-------
SpectralShape
Spectral power distribution shape.
See Also
--------
SpectralPowerDistribution.is_uniform
Notes
-----
- A non uniform spectral power distribution may will have multiple
different steps, in that case
:attr:`SpectralPowerDistribution.shape` returns the *minimum* steps
size.
Warning
-------
:attr:`SpectralPowerDistribution.shape` is read only.
Examples
--------
Uniform spectral power distribution:
>>> data = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> SpectralPowerDistribution('Spd', data).shape # doctest: +ELLIPSIS
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('Spd', data).shape # doctest: +ELLIPSIS
SpectralShape(512.3, 545.7, 7...)
"""
return SpectralShape(min(self.data.keys()),
max(self.data.keys()),
min(steps(self.wavelengths)))
@shape.setter
[docs] def shape(self, value):
"""
Setter for **self.shape** attribute.
Parameters
----------
value : object
Attribute value.
"""
raise AttributeError('"{0}" attribute is read only!'.format('shape'))
[docs] def __hash__(self):
"""
Returns the spectral power distribution hash value.
Returns
-------
int
Object hash.
Notes
-----
- Reimplements the :meth:`object.__hash__` method.
Warning
-------
:class:`SpectralPowerDistribution` class is mutable and should not be
hashable. However, so that it can be used as a key in some data caches,
we provide a *__hash__* implementation, **assuming that the underlying
data will not change for those specific cases**.
References
----------
.. [1] Hettinger, R. (n.d.). Python hashable dicts. Retrieved August
08, 2014, from http://stackoverflow.com/a/16162138/931625
"""
return hash(frozenset(self.__data))
[docs] def __getitem__(self, wavelength):
"""
Returns the value for given wavelength :math:`\lambda`.
Parameters
----------
wavelength: numeric, array_like or slice
Wavelength :math:`\lambda` to retrieve the value.
Returns
-------
numeric or ndarray
Wavelength :math:`\lambda` value.
See Also
--------
SpectralPowerDistribution.get
Notes
-----
- Reimplements the :meth:`object.__getitem__` method.
Examples
--------
>>> data = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> spd = SpectralPowerDistribution('Spd', data)
>>> # Doctests ellipsis for Python 2.x compatibility.
>>> spd[510] # doctest: +ELLIPSIS
array(49.67...)
>>> spd[np.array([510, 520])]
array([ 49.67, 69.59])
>>> spd[:]
array([ 49.67, 69.59, 81.73, 88.19])
"""
if type(wavelength) is slice:
return self.values[wavelength]
else:
wavelength = np.asarray(wavelength)
value = [self.data.__getitem__(x) for x in np.ravel(wavelength)]
value = np.reshape(value, wavelength.shape)
return value
[docs] def __setitem__(self, wavelength, value):
"""
Sets the wavelength :math:`\lambda` with given value.
Parameters
----------
wavelength : numeric, array_like or slice
Wavelength :math:`\lambda` to set.
value : numeric or array_like
Value for wavelength :math:`\lambda`.
Warning
-------
*value* parameter is resized to match *wavelength* parameter size.
Notes
-----
- Reimplements the :meth:`object.__setitem__` method.
Examples
--------
>>> spd = SpectralPowerDistribution('Spd', {})
>>> spd[510] = 49.67
>>> spd.values
array([ 49.67])
>>> spd[np.array([520, 530])] = np.array([69.59, 81.73])
>>> spd.values
array([ 49.67, 69.59, 81.73])
>>> spd[np.array([540, 550])] = 88.19
>>> spd.values
array([ 49.67, 69.59, 81.73, 88.19, 88.19])
>>> spd[:] = 49.67
>>> spd.values
array([ 49.67, 49.67, 49.67, 49.67, 49.67])
"""
if is_numeric(wavelength) or is_iterable(wavelength):
wavelengths = np.ravel(wavelength)
elif type(wavelength) is slice:
wavelengths = self.wavelengths[wavelength]
else:
raise NotImplementedError(
'"{0}" type is not supported for indexing!'.format(
type(wavelength)))
values = np.resize(value, wavelengths.shape)
for i in range(len(wavelengths)):
self.__data.__setitem__(wavelengths[i], values[i])
[docs] def __iter__(self):
"""
Returns a generator for the spectral power distribution data.
Returns
-------
generator
Spectral power distribution data generator.
Notes
-----
- Reimplements the :meth:`object.__iter__` method.
Examples
--------
>>> data = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> spd = SpectralPowerDistribution('Spd', data)
>>> # Doctests ellipsis for Python 2.x compatibility.
>>> for wavelength, value in spd: # doctest: +SKIP
... print((wavelength, value))
(510, 49.6...)
(520, 69.5...)
(530, 81.7...)
(540, 88.1...)
"""
return iter(sorted(self.__data.items()))
[docs] def __contains__(self, wavelength):
"""
Returns if the spectral power distribution contains given wavelength
:math:`\lambda`.
Parameters
----------
wavelength : numeric or array_like
Wavelength :math:`\lambda`.
Returns
-------
bool
Is wavelength :math:`\lambda` contained in the spectral power
distribution.
Warning
-------
*wavelength* argument is tested to be contained in the spectral power
distribution within the tolerance defined by
:attr:`colour.constants.common.EPSILON` attribute value.
Notes
-----
- Reimplements the :meth:`object.__contains__` method.
Examples
--------
>>> data = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> spd = SpectralPowerDistribution('Spd', data)
>>> 510 in spd
True
>>> np.array([510, 520]) in spd
True
>>> np.array([510, 520, 521]) in spd
False
"""
return np.all(np.in1d(wavelength, self.wavelengths))
[docs] def __len__(self):
"""
Returns the spectral power distribution wavelengths :math:`\lambda_n`
count.
Returns
-------
int
Spectral power distribution wavelengths :math:`\lambda_n` count.
Notes
-----
- Reimplements the :meth:`object.__len__` method.
Examples
--------
>>> data = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> spd = SpectralPowerDistribution('Spd', data)
>>> len(spd)
4
"""
return len(self.__data)
[docs] def __eq__(self, spd):
"""
Returns the spectral power distribution equality with given other
spectral power distribution.
Parameters
----------
spd : SpectralPowerDistribution
Spectral power distribution to compare for equality.
Returns
-------
bool
Spectral power distribution equality.
Notes
-----
- Reimplements the :meth:`object.__eq__` method.
Examples
--------
>>> data1 = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> data2 = {510: 48.6700, 520: 69.5900, 530: 81.7300, 540: 88.1900}
>>> spd1 = SpectralPowerDistribution('Spd', data1)
>>> spd2 = SpectralPowerDistribution('Spd', data2)
>>> spd3 = SpectralPowerDistribution('Spd', data2)
>>> spd1 == spd2
False
>>> spd2 == spd3
True
"""
return isinstance(spd, self.__class__) and spd.data == self.data
[docs] def __ne__(self, spd):
"""
Returns the spectral power distribution inequality with given other
spectral power distribution.
Parameters
----------
spd : SpectralPowerDistribution
Spectral power distribution to compare for inequality.
Returns
-------
bool
Spectral power distribution inequality.
Notes
-----
- Reimplements the :meth:`object.__ne__` method.
Examples
--------
>>> data1 = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> data2 = {510: 48.6700, 520: 69.5900, 530: 81.7300, 540: 88.1900}
>>> spd1 = SpectralPowerDistribution('Spd', data1)
>>> spd2 = SpectralPowerDistribution('Spd', data2)
>>> spd3 = SpectralPowerDistribution('Spd', data2)
>>> spd1 != spd2
True
>>> spd2 != spd3
False
"""
return not (self == spd)
def __format_operand(self, x):
"""
Formats given :math:`x` variable operand to *numeric* or *ndarray*.
This method is a convenient method to prepare given :math:`x` variable
for the arithmetic operations below.
Parameters
----------
x : numeric or ndarray or SpectralPowerDistribution
Variable to format.
Returns
-------
numeric or ndarray
Formatted operand.
"""
if issubclass(type(x), SpectralPowerDistribution):
x = x.values
elif is_iterable(x):
x = np.atleast_1d(x)
return x
[docs] def __add__(self, x):
"""
Implements support for spectral power distribution addition.
Parameters
----------
x : numeric or array_like or SpectralPowerDistribution
Variable to add.
Returns
-------
SpectralPowerDistribution
Variable added spectral power distribution.
See Also
--------
SpectralPowerDistribution.__sub__, SpectralPowerDistribution.__mul__,
SpectralPowerDistribution.__div__
Notes
-----
- Reimplements the :meth:`object.__add__` method.
Warning
-------
The addition operation happens in place.
Examples
--------
Adding a single *numeric* variable:
>>> data = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> spd = SpectralPowerDistribution('Spd', data)
>>> spd + 10 # doctest: +ELLIPSIS
<...SpectralPowerDistribution object at 0x...>
>>> spd.values
array([ 59.67, 79.59, 91.73, 98.19])
Adding an *array_like* variable:
>>> spd + [1, 2, 3, 4] # doctest: +ELLIPSIS
<...SpectralPowerDistribution object at 0x...>
>>> spd.values
array([ 60.67, 81.59, 94.73, 102.19])
Adding a :class:`SpectralPowerDistribution` class variable:
>>> spd_alternate = SpectralPowerDistribution('Spd', data)
>>> spd + spd_alternate # doctest: +ELLIPSIS
<...SpectralPowerDistribution object at 0x...>
>>> spd.values
array([ 110.34, 151.18, 176.46, 190.38])
"""
self.__data = SpectralMapping(
zip(self.wavelengths, self.values + self.__format_operand(x)))
return self
[docs] def __sub__(self, x):
"""
Implements support for spectral power distribution subtraction.
Parameters
----------
x : numeric or array_like or SpectralPowerDistribution
Variable to subtract.
Returns
-------
SpectralPowerDistribution
Variable subtracted spectral power distribution.
See Also
--------
SpectralPowerDistribution.__add__, SpectralPowerDistribution.__mul__,
SpectralPowerDistribution.__div__
Notes
-----
- Reimplements the :meth:`object.__sub__` method.
Warning
-------
The subtraction operation happens in place.
Examples
--------
Subtracting a single *numeric* variable:
>>> data = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> spd = SpectralPowerDistribution('Spd', data)
>>> spd - 10 # doctest: +ELLIPSIS
<...SpectralPowerDistribution object at 0x...>
>>> spd.values
array([ 39.67, 59.59, 71.73, 78.19])
Subtracting an *array_like* variable:
>>> spd - [1, 2, 3, 4] # doctest: +ELLIPSIS
<...SpectralPowerDistribution object at 0x...>
>>> spd.values
array([ 38.67, 57.59, 68.73, 74.19])
Subtracting a :class:`SpectralPowerDistribution` class variable:
>>> spd_alternate = SpectralPowerDistribution('Spd', data)
>>> spd - spd_alternate # doctest: +ELLIPSIS
<...SpectralPowerDistribution object at 0x...>
>>> spd.values
array([-11., -12., -13., -14.])
"""
return self + (-self.__format_operand(x))
[docs] def __mul__(self, x):
"""
Implements support for spectral power distribution multiplication.
Parameters
----------
x : numeric or array_like or SpectralPowerDistribution
Variable to multiply.
Returns
-------
SpectralPowerDistribution
Variable multiplied spectral power distribution.
See Also
--------
SpectralPowerDistribution.__add__, SpectralPowerDistribution.__sub__,
SpectralPowerDistribution.__div__
Notes
-----
- Reimplements the :meth:`object.__mul__` method.
Warning
-------
The multiplication operation happens in place.
Examples
--------
Multiplying a single *numeric* variable:
>>> data = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> spd = SpectralPowerDistribution('Spd', data)
>>> spd * 10 # doctest: +ELLIPSIS
<...SpectralPowerDistribution object at 0x...>
>>> spd.values
array([ 496.7, 695.9, 817.3, 881.9])
Multiplying an *array_like* variable:
>>> spd * [1, 2, 3, 4] # doctest: +ELLIPSIS
<...SpectralPowerDistribution object at 0x...>
>>> spd.values
array([ 496.7, 1391.8, 2451.9, 3527.6])
Multiplying a :class:`SpectralPowerDistribution` class variable:
>>> spd_alternate = SpectralPowerDistribution('Spd', data)
>>> spd * spd_alternate # doctest: +ELLIPSIS
<...SpectralPowerDistribution object at 0x...>
>>> spd.values
array([ 24671.089, 96855.362, 200393.787, 311099.044])
"""
self.__data = SpectralMapping(
zip(self.wavelengths, self.values * self.__format_operand(x)))
return self
[docs] def __div__(self, x):
"""
Implements support for spectral power distribution division.
Parameters
----------
x : numeric or array_like or SpectralPowerDistribution
Variable to divide.
Returns
-------
SpectralPowerDistribution
Variable divided spectral power distribution.
See Also
--------
SpectralPowerDistribution.__add__, SpectralPowerDistribution.__sub__,
SpectralPowerDistribution.__mul__
Notes
-----
- Reimplements the :meth:`object.__div__` method.
Warning
-------
The division operation happens in place.
Examples
--------
Dividing a single *numeric* variable:
>>> data = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> spd = SpectralPowerDistribution('Spd', data)
>>> spd / 10 # doctest: +ELLIPSIS
<...SpectralPowerDistribution object at 0x...>
>>> spd.values
array([ 4.967, 6.959, 8.173, 8.819])
Dividing an *array_like* variable:
>>> spd / [1, 2, 3, 4] # doctest: +ELLIPSIS
<...SpectralPowerDistribution object at 0x...>
>>> spd.values
array([ 4.967 , 3.4795 , 2.72433333, 2.20475 ])
Dividing a :class:`SpectralPowerDistribution` class variable:
>>> spd_alternate = SpectralPowerDistribution('Spd', data)
>>> spd / spd_alternate # doctest: +ELLIPSIS
<...SpectralPowerDistribution object at 0x...>
>>> spd.values # doctest: +ELLIPSIS
array([ 0.1 , 0.05 , 0.0333333..., 0.025 ])
"""
self.__data = SpectralMapping(
zip(self.wavelengths,
self.values * (1 / self.__format_operand(x))))
return self
# Python 3 compatibility.
__truediv__ = __div__
[docs] def __pow__(self, x):
"""
Implements support for spectral power distribution exponentiation.
Parameters
----------
x : numeric or array_like or SpectralPowerDistribution
Variable to exponentiate by.
Returns
-------
SpectralPowerDistribution
Spectral power distribution raised by power of x.
See Also
--------
SpectralPowerDistribution.__add__, SpectralPowerDistribution.__sub__,
SpectralPowerDistribution.__mul__, SpectralPowerDistribution.__div__
Notes
-----
- Reimplements the :meth:`object.__pow__` method.
Warning
-------
The power operation happens in place.
Examples
--------
Exponentiation by a single *numeric* variable:
>>> data = {510: 1.67, 520: 2.59, 530: 3.73, 540: 4.19}
>>> spd = SpectralPowerDistribution('Spd', data)
>>> spd ** 2 # doctest: +ELLIPSIS
<...SpectralPowerDistribution object at 0x...>
>>> spd.values
array([ 2.7889, 6.7081, 13.9129, 17.5561])
Exponentiation by an *array_like* variable:
>>> spd ** [1, 2, 3, 4] # doctest: +ELLIPSIS
<...SpectralPowerDistribution object at 0x...>
>>> spd.values # doctest: +ELLIPSIS
array([ 2.7889000...e+00, 4.4998605...e+01, 2.6931031...e+03,
9.4997501...e+04])
Exponentiation by a :class:`SpectralPowerDistribution` class variable:
>>> spd_alternate = SpectralPowerDistribution('Spd', data)
>>> spd ** spd_alternate # doctest: +ELLIPSIS
<...SpectralPowerDistribution object at 0x...>
>>> spd.values # doctest: +ELLIPSIS
array([ 5.5446356...e+00, 1.9133109...e+04, 6.2351033...e+12,
7.1880990...e+20])
"""
self.__data = SpectralMapping(
zip(self.wavelengths, self.values ** self.__format_operand(x)))
return self
[docs] def get(self, wavelength, default=np.nan):
"""
Returns the value for given wavelength :math:`\lambda`.
Parameters
----------
wavelength : numeric or ndarray
Wavelength :math:`\lambda` to retrieve the value.
default : nan or numeric, optional
Wavelength :math:`\lambda` default value.
Returns
-------
numeric or ndarray
Wavelength :math:`\lambda` value.
See Also
--------
SpectralPowerDistribution.__getitem__
Examples
--------
>>> data = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> spd = SpectralPowerDistribution('Spd', data)
>>> # Doctests ellipsis for Python 2.x compatibility.
>>> spd.get(510) # doctest: +ELLIPSIS
array(49.67...)
>>> spd.get(511)
array(nan)
>>> spd.get(np.array([510, 520]))
array([ 49.67, 69.59])
"""
wavelength = np.asarray(wavelength)
value = [self.data.get(x, default) for x in np.ravel(wavelength)]
value = np.reshape(value, wavelength.shape)
return value
[docs] def interpolate(self, shape=SpectralShape(), method=None):
"""
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
-------
SpectralPowerDistribution
Interpolated spectral power distribution.
Raises
------
RuntimeError
If Sprague (1880) interpolation method is forced with a
non-uniformly spaced independent variable.
ValueError
If the interpolation method is not defined.
See Also
--------
SpectralPowerDistribution.align
Notes
-----
- Interpolation will be conducted over boundaries range, if you need
to extend the range of the spectral power distribution use the
:meth:`SpectralPowerDistribution.extrapolate` or
:meth:`SpectralPowerDistribution.align` methods.
- Sprague (1880) interpolator cannot be used for interpolating
functions having a non-uniformly spaced independent variable.
Warning
-------
- If *scipy* is not unavailable the *Cubic Spline* method will
fallback to legacy *Linear* interpolation.
- *Cubic Spline* interpolator requires at least 3 wavelengths
:math:`\lambda_n` for interpolation.
- *Linear* interpolator requires at least 2 wavelengths
:math:`\lambda_n` for interpolation.
- *Pchip* interpolator requires at least 2 wavelengths
:math:`\lambda_n` for interpolation.
- Sprague (1880) interpolator requires at least 6 wavelengths
:math:`\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('Spd', data)
>>> spd.interpolate(SpectralShape(steps=1)) # doctest: +ELLIPSIS
<...SpectralPowerDistribution object at 0x...>
>>> spd[515] # doctest: +ELLIPSIS
array(60.3121800...)
Non uniform data is using *Cubic Spline* interpolation by default:
>>> spd = SpectralPowerDistribution('Spd', data)
>>> spd[511] = 31.41
>>> spd.interpolate(SpectralShape(steps=1)) # doctest: +ELLIPSIS
<...SpectralPowerDistribution object at 0x...>
>>> spd[515] # doctest: +ELLIPSIS
array(21.4792222...)
Enforcing *Linear* interpolation:
>>> spd = SpectralPowerDistribution('Spd', data)
>>> spd.interpolate( # doctest: +ELLIPSIS
... SpectralShape(steps=1), method='Linear')
<...SpectralPowerDistribution object at 0x...>
>>> spd[515] # doctest: +ELLIPSIS
array(59.63...)
Enforcing *Pchip* interpolation:
>>> spd = SpectralPowerDistribution('Spd', data)
>>> spd.interpolate( # doctest: +ELLIPSIS
... SpectralShape(steps=1), method='Pchip')
<...SpectralPowerDistribution object at 0x...>
>>> spd[515] # doctest: +ELLIPSIS
array(58.8173260...)
"""
spd_shape = self.shape
boundaries = zip((shape.start, shape.end, shape.steps),
(spd_shape.start, spd_shape.end, spd_shape.steps))
boundaries = [x[0] if x[0] is not None else x[1] for x in boundaries]
shape = SpectralShape(*boundaries)
# Defining proper interpolation bounds.
# TODO: Provide support for fractional steps like 0.1, etc...
shape.start = max(shape.start, np.ceil(spd_shape.start))
shape.end = min(shape.end, np.floor(spd_shape.end))
wavelengths, values = self.wavelengths, self.values
is_uniform = self.is_uniform()
if is_string(method):
method = method.lower()
if method is None:
if is_uniform:
interpolator = SpragueInterpolator
else:
interpolator = CubicSplineInterpolator
elif method == 'cubic spline':
interpolator = CubicSplineInterpolator
elif method == 'linear':
interpolator = LinearInterpolator
elif method == 'pchip':
interpolator = PchipInterpolator
elif method == 'sprague':
if is_uniform:
interpolator = SpragueInterpolator
else:
raise RuntimeError(
('"Sprague" interpolator can only be used for '
'interpolating functions having a uniformly spaced '
'independent variable!'))
else:
raise ValueError(
'Undefined "{0}" interpolator!'.format(method))
interpolator = interpolator(wavelengths, values)
self.__data = SpectralMapping(
[(wavelength, float(interpolator(wavelength)))
for wavelength in shape])
return self
[docs] def align(self,
shape,
method='Constant',
left=None,
right=None):
"""
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.
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
-------
SpectralPowerDistribution
Aligned spectral power distribution.
See Also
--------
SpectralPowerDistribution.extrapolate,
SpectralPowerDistribution.interpolate
Examples
--------
>>> data = {
... 510: 49.67,
... 520: 69.59,
... 530: 81.73,
... 540: 88.19,
... 550: 86.26,
... 560: 77.18}
>>> spd = SpectralPowerDistribution('Spd', data)
>>> spd.align(SpectralShape(505, 565, 1)) # doctest: +ELLIPSIS
<...SpectralPowerDistribution object at 0x...>
>>> # Doctests skip for Python 2.x compatibility.
>>> spd.wavelengths # doctest: +SKIP
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 # doctest: +ELLIPSIS
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 ...])
"""
self.interpolate(shape)
self.extrapolate(shape, method, left, right)
return self
[docs] def zeros(self, shape=SpectralShape()):
"""
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
-------
SpectralPowerDistribution
Zeros filled spectral power distribution.
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('Spd', data)
>>> spd.zeros(SpectralShape(505, 565, 1)) # doctest: +ELLIPSIS
<...SpectralPowerDistribution object at 0x...>
>>> 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. ])
"""
spd_shape = self.shape
boundaries = zip((shape.start, shape.end, shape.steps),
(spd_shape.start, spd_shape.end, spd_shape.steps))
boundaries = [x[0] if x[0] is not None else x[1] for x in boundaries]
shape = SpectralShape(*boundaries)
data = SpectralMapping(
[(wavelength, self.get(wavelength, 0))
for wavelength in shape])
values_s = max(self.shape.start, shape.start)
values_e = min(self.shape.end, shape.end)
values = [self[wavelength] for wavelength in self.wavelengths
if values_s <= wavelength <= values_e]
if not np.all(np.in1d(values, list(data.values()))):
raise RuntimeError(('"{0}" cannot be zeros filled using "{1}" '
'shape!').format(self, shape))
else:
self.__data = data
return self
[docs] def normalise(self, factor=1):
"""
Normalises the spectral power distribution with given normalization
factor.
Parameters
----------
factor : numeric, optional
Normalization factor
Returns
-------
SpectralPowerDistribution
Normalised spectral power distribution.
Examples
--------
>>> data = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> spd = SpectralPowerDistribution('Spd', data)
>>> spd.normalise() # doctest: +ELLIPSIS
<...SpectralPowerDistribution object at 0x...>
>>> spd.values # doctest: +ELLIPSIS
array([ 0.5632157..., 0.7890917..., 0.9267490..., 1. ])
"""
return (self * (1 / max(self.values))) * factor
[docs] def clone(self):
"""
Clones the spectral power distribution.
Most of the :class:`SpectralPowerDistribution` class operations are
conducted in-place. The :meth:`SpectralPowerDistribution.clone` method
provides a convenient way to copy the spectral power distribution to a
new object.
Returns
-------
SpectralPowerDistribution
Cloned spectral power distribution.
Examples
--------
>>> data = {510: 49.67, 520: 69.59, 530: 81.73, 540: 88.19}
>>> spd = SpectralPowerDistribution('Spd', data)
>>> print(spd) # doctest: +ELLIPSIS
<...SpectralPowerDistribution object at 0x...>
>>> spd_clone = spd.clone()
>>> print(spd_clone) # doctest: +ELLIPSIS
<...SpectralPowerDistribution object at 0x...>
"""
return copy.deepcopy(self)
[docs]class TriSpectralPowerDistribution(object):
"""
Defines the base object for colour matching functions.
A compound of three :class:`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.
Attributes
----------
name
mapping
data
title
labels
x
y
z
wavelengths
values
items
shape
Methods
-------
__init__
__getitem__
__setitem__
__iter__
__contains__
__len__
__eq__
__ne__
__add__
__sub__
__mul__
__div__
__truediv__
get
is_uniform
extrapolate
interpolate
align
zeros
normalise
clone
See Also
--------
colour.colorimetry.cmfs.LMS_ConeFundamentals,
colour.colorimetry.cmfs.RGB_ColourMatchingFunctions,
colour.colorimetry.cmfs.XYZ_ColourMatchingFunctions
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('Tri Spd', data, mapping)
>>> # Doctests skip for Python 2.x compatibility.
>>> tri_spd.wavelengths # doctest: +SKIP
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 # doctest: +SKIP
SpectralShape(510, 540, 10)
"""
[docs] def __init__(self, name, data, mapping, title=None, labels=None):
self.__name = None
self.name = name
self.__mapping = mapping
self.__data = None
self.data = data
self.__title = None
self.title = title
self.__labels = None
self.labels = labels
@property
def name(self):
"""
Property for **self.__name** private attribute.
Returns
-------
unicode
self.__name.
"""
return self.__name
@name.setter
[docs] def name(self, value):
"""
Setter for **self.__name** private attribute.
Parameters
----------
value : unicode
Attribute value.
"""
if value is not None:
assert type(value) in (str, unicode), ( # noqa
('"{0}" attribute: "{1}" type is not '
'"str" or "unicode"!').format('name', value))
self.__name = value
@property
def mapping(self):
"""
Property for **self.__mapping** private attribute.
Returns
-------
dict
self.__mapping.
"""
return self.__mapping
@mapping.setter
[docs] def mapping(self, value):
"""
Setter for **self.__mapping** private attribute.
Parameters
----------
value : dict
Attribute value.
"""
if value is not None:
assert type(value) is dict, (
'"{0}" attribute: "{1}" type is not "dict"!'.format(
'mapping', value))
for axis in ('x', 'y', 'z'):
assert axis in value.keys(), (
'"{0}" attribute: "{1}" axis label is missing!'.format(
'mapping', axis))
self.__mapping = value
@property
def data(self):
"""
Property for **self.__data** private attribute.
Returns
-------
dict
self.__data.
"""
return self.__data
@data.setter
[docs] def data(self, value):
"""
Setter for **self.__data** private attribute.
Parameters
----------
value : dict
Attribute value.
"""
if value is not None:
assert type(value) is dict, (
'"{0}" attribute: "{1}" type is not "dict"!'.format(
'data', value))
for axis in ('x', 'y', 'z'):
assert self.__mapping.get(axis) in value.keys(), (
'"{0}" attribute: "{1}" axis is missing!'.format(
'data', axis))
data = {}
for axis in ('x', 'y', 'z'):
data[axis] = SpectralPowerDistribution(
self.__mapping.get(axis),
value.get(self.__mapping.get(axis)))
np.testing.assert_almost_equal(
data['x'].wavelengths,
data['y'].wavelengths,
err_msg=('"{0}" attribute: "{1}" and "{2}" wavelengths are '
'different!').format('data',
self.__mapping.get('x'),
self.__mapping.get('y')))
np.testing.assert_almost_equal(
data['x'].wavelengths,
data['z'].wavelengths,
err_msg=('"{0}" attribute: "{1}" and "{2}" wavelengths are '
'different!').format('data',
self.__mapping.get('x'),
self.__mapping.get('z')))
self.__data = data
else:
self.__data = None
@property
def title(self):
"""
Property for **self.__title** private attribute.
Returns
-------
unicode
self.__title.
"""
if self.__title is not None:
return self.__title
else:
return self.__name
@title.setter
[docs] def title(self, value):
"""
Setter for **self.__title** private attribute.
Parameters
----------
value : unicode
Attribute value.
"""
if value is not None:
assert type(value) in (str, unicode), ( # noqa
('"{0}" attribute: "{1}" type is not '
'"str" or "unicode"!').format('title', value))
self.__title = value
@property
def labels(self):
"""
Property for **self.__labels** private attribute.
Returns
-------
dict
self.__labels.
"""
if self.__labels is not None:
return self.__labels
else:
return self.__mapping
@labels.setter
[docs] def labels(self, value):
"""
Setter for **self.__labels** private attribute.
Parameters
----------
value : dict
Attribute value.
"""
if value is not None:
assert type(value) is dict, (
'"{0}" attribute: "{1}" type is not "dict"!'.format(
'labels', value))
for axis in ('x', 'y', 'z'):
assert axis in value.keys(), (
'"{0}" attribute: "{1}" axis label is missing!'.format(
'labels', axis))
self.__labels = value
@property
def x(self):
"""
Property for **self.x** attribute.
Returns
-------
SpectralPowerDistribution
Spectral power distribution for *x* axis.
Warning
-------
:attr:`TriSpectralPowerDistribution.x` is read only.
"""
return self.__data.get('x')
@x.setter
[docs] def x(self, value):
"""
Setter for **self.x** attribute.
Parameters
----------
value : object
Attribute value.
"""
raise AttributeError('"{0}" attribute is read only!'.format('x'))
@property
def y(self):
"""
Property for **self.y** attribute.
Returns
-------
SpectralPowerDistribution
Spectral power distribution for *y* axis.
Warning
-------
:attr:`TriSpectralPowerDistribution.y` is read only.
"""
return self.__data.get('y')
@y.setter
[docs] def y(self, value):
"""
Setter for **self.y** attribute.
Parameters
----------
value : object
Attribute value.
"""
raise AttributeError('"{0}" attribute is read only!'.format('y'))
@property
def z(self):
"""
Property for **self.z** attribute.
Returns
-------
SpectralPowerDistribution
Spectral power distribution for *z* axis.
Warning
-------
:attr:`TriSpectralPowerDistribution.z` is read only.
"""
return self.__data.get('z')
@z.setter
[docs] def z(self, value):
"""
Setter for **self.z** attribute.
Parameters
----------
value : object
Attribute value.
"""
raise AttributeError('"{0}" attribute is read only!'.format('z'))
@property
def wavelengths(self):
"""
Property for **self.wavelengths** attribute.
Returns
-------
ndarray
Tri-spectral power distribution wavelengths :math:`\lambda_n`.
Warning
-------
:attr:`TriSpectralPowerDistribution.wavelengths` is read only.
"""
return self.x.wavelengths
@wavelengths.setter
[docs] def wavelengths(self, value):
"""
Setter for **self.wavelengths** attribute.
Parameters
----------
value : object
Attribute value.
"""
raise AttributeError(
'"{0}" attribute is read only!'.format('wavelengths'))
@property
def values(self):
"""
Property for **self.values** attribute.
Returns
-------
ndarray
Tri-spectral power distribution wavelengths :math:`\lambda_n`
values.
Warning
-------
:attr:`TriSpectralPowerDistribution.values` is read only.
"""
return np.array([self.get(wavelength)
for wavelength in self.wavelengths])
@values.setter
[docs] def values(self, value):
"""
Setter for **self.values** attribute.
Parameters
----------
value : object
Attribute value.
"""
raise AttributeError('"{0}" attribute is read only!'.format('values'))
@property
def items(self):
"""
Property for **self.items** attribute. This is a convenient attribute
used to iterate over the tri-spectral power distribution.
Returns
-------
generator
Tri-spectral power distribution data generator.
"""
return self.__iter__()
@items.setter
[docs] def items(self, value):
"""
Setter for **self.items** attribute.
Parameters
----------
value : object
Attribute value.
"""
raise AttributeError('"{0}" attribute is read only!'.format('items'))
@property
def shape(self):
"""
Property for **self.shape** attribute.
Returns the shape of the tri-spectral power distribution in the form of
a :class:`SpectralShape` class instance.
Returns
-------
SpectralShape
Tri-spectral power distribution shape.
See Also
--------
SpectralPowerDistribution.is_uniform,
TriSpectralPowerDistribution.is_uniform
Warning
-------
:attr:`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('Tri Spd', data, mapping)
>>> tri_spd.shape # doctest: +ELLIPSIS
SpectralShape(510..., 540..., 10...)
"""
return self.x.shape
@shape.setter
[docs] def shape(self, value):
"""
Setter for **self.shape** attribute.
Parameters
----------
value : object
Attribute value.
"""
raise AttributeError('"{0}" attribute is read only!'.format('shape'))
[docs] def __hash__(self):
"""
Returns the spectral power distribution hash value. [1]_
Returns
-------
int
Object hash.
Notes
-----
- Reimplements the :meth:`object.__hash__` method.
Warning
-------
See :meth:`SpectralPowerDistribution.__hash__` method warning section.
"""
return hash((frozenset(self.__data.get('x')),
frozenset(self.__data.get('y')),
frozenset(self.__data.get('z'))))
[docs] def __getitem__(self, wavelength):
"""
Returns the values for given wavelength :math:`\lambda`.
Parameters
----------
wavelength: numeric, array_like or slice
Wavelength :math:`\lambda` to retrieve the values.
Returns
-------
ndarray
Wavelength :math:`\lambda` values.
See Also
--------
TriSpectralPowerDistribution.get
Notes
-----
- Reimplements the :meth:`object.__getitem__` method.
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('Tri Spd', data, mapping)
>>> tri_spd[510]
array([ 49.67, 90.56, 12.43])
>>> tri_spd[np.array([510, 520])]
array([[ 49.67, 90.56, 12.43],
[ 69.59, 87.34, 23.15]])
>>> tri_spd[:]
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]])
"""
value = tstack((np.asarray(self.x[wavelength]),
np.asarray(self.y[wavelength]),
np.asarray(self.z[wavelength])))
return value
[docs] def __setitem__(self, wavelength, value):
"""
Sets the wavelength :math:`\lambda` with given value.
Parameters
----------
wavelength : numeric, array_like or slice
Wavelength :math:`\lambda` to set.
value : array_like
Value for wavelength :math:`\lambda`.
Warning
-------
*value* parameter is resized to match *wavelength* parameter size.
Notes
-----
- Reimplements the :meth:`object.__setitem__` method.
Examples
--------
>>> x_bar = {}
>>> y_bar = {}
>>> z_bar = {}
>>> 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('Tri Spd', data, mapping)
>>> tri_spd[510] = np.array([49.67, 49.67, 49.67])
>>> tri_spd.values
array([[ 49.67, 49.67, 49.67]])
>>> tri_spd[np.array([520, 530])] = np.array([[69.59, 69.59, 69.59],
... [81.73, 81.73, 81.73]])
>>> tri_spd.values
array([[ 49.67, 49.67, 49.67],
[ 69.59, 69.59, 69.59],
[ 81.73, 81.73, 81.73]])
>>> tri_spd[np.array([540, 550])] = 88.19
>>> tri_spd.values
array([[ 49.67, 49.67, 49.67],
[ 69.59, 69.59, 69.59],
[ 81.73, 81.73, 81.73],
[ 88.19, 88.19, 88.19],
[ 88.19, 88.19, 88.19]])
>>> tri_spd[:] = 49.67
>>> tri_spd.values
array([[ 49.67, 49.67, 49.67],
[ 49.67, 49.67, 49.67],
[ 49.67, 49.67, 49.67],
[ 49.67, 49.67, 49.67],
[ 49.67, 49.67, 49.67]])
"""
if is_numeric(wavelength) or is_iterable(wavelength):
wavelengths = np.ravel(wavelength)
elif type(wavelength) is slice:
wavelengths = self.wavelengths[wavelength]
else:
raise NotImplementedError(
'"{0}" type is not supported for indexing!'.format(
type(wavelength)))
value = np.resize(value, (wavelengths.shape[0], 3))
self.x.__setitem__(wavelengths, value[..., 0])
self.y.__setitem__(wavelengths, value[..., 1])
self.z.__setitem__(wavelengths, value[..., 2])
[docs] def __iter__(self):
"""
Returns a generator for the tri-spectral power distribution data.
Returns
-------
generator
Tri-spectral power distribution data generator.
Notes
-----
- Reimplements the :meth:`object.__iter__` method.
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('Tri Spd', data, mapping)
>>> # Doctests skip for Python 2.x compatibility.
>>> for wavelength, value in tri_spd: # doctest: +SKIP
... print((wavelength, value))
(510, array([ 49.67, 90.56, 12.43]))
(520, array([ 69.59, 87.34, 23.15]))
(530, array([ 81.73, 45.76, 67.98]))
(540, array([ 88.19, 23.45, 90.28]))
"""
return itertools.izip(self.wavelengths, self.values)
[docs] def __contains__(self, wavelength):
"""
Returns if the tri-spectral power distribution contains given
wavelength :math:`\lambda`.
Parameters
----------
wavelength : numeric or array_like
Wavelength :math:`\lambda`.
Returns
-------
bool
Is wavelength :math:`\lambda` contained in the tri-spectral power
distribution.
Warning
-------
*wavelength* argument is tested to be contained in the tri-spectral
power distribution within the tolerance defined by
:attr:`colour.constants.common.EPSILON` attribute value.
Notes
-----
- Reimplements the :meth:`object.__contains__` method.
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('Tri Spd', data, mapping)
>>> 510 in tri_spd
True
>>> np.array([510, 520]) in tri_spd
True
>>> np.array([510, 520, 521]) in tri_spd
False
"""
return wavelength in self.x
[docs] def __len__(self):
"""
Returns the tri-spectral power distribution wavelengths
:math:`\lambda_n` count.
Returns
-------
int
Tri-Spectral power distribution wavelengths :math:`\lambda_n`
count.
Notes
-----
- Reimplements the :meth:`object.__len__` method.
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('Tri Spd', data, mapping)
>>> len(tri_spd)
4
"""
return len(self.x)
[docs] def __eq__(self, tri_spd):
"""
Returns the tri-spectral power distribution equality with given other
tri-spectral power distribution.
Parameters
----------
spd : TriSpectralPowerDistribution
Tri-spectral power distribution to compare for equality.
Returns
-------
bool
Tri-spectral power distribution equality.
Notes
-----
- Reimplements the :meth:`object.__eq__` method.
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}
>>> data1 = {'x_bar': x_bar, 'y_bar': y_bar, 'z_bar': z_bar}
>>> data2 = {'x_bar': y_bar, 'y_bar': x_bar, 'z_bar': z_bar}
>>> mapping = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd1 = TriSpectralPowerDistribution('Tri Spd', data1, mapping)
>>> tri_spd2 = TriSpectralPowerDistribution('Tri Spd', data2, mapping)
>>> tri_spd3 = TriSpectralPowerDistribution('Tri Spd', data1, mapping)
>>> tri_spd1 == tri_spd2
False
>>> tri_spd1 == tri_spd3
True
"""
if not isinstance(tri_spd, self.__class__):
return False
equality = True
for axis in self.__mapping:
equality *= getattr(self, axis) == getattr(tri_spd, axis)
return bool(equality)
[docs] def __ne__(self, tri_spd):
"""
Returns the tri-spectral power distribution inequality with given other
tri-spectral power distribution.
Parameters
----------
spd : TriSpectralPowerDistribution
Tri-spectral power distribution to compare for inequality.
Returns
-------
bool
Tri-spectral power distribution inequality.
Notes
-----
- Reimplements the :meth:`object.__eq__` method.
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}
>>> data1 = {'x_bar': x_bar, 'y_bar': y_bar, 'z_bar': z_bar}
>>> data2 = {'x_bar': y_bar, 'y_bar': x_bar, 'z_bar': z_bar}
>>> mapping = {'x': 'x_bar', 'y': 'y_bar', 'z': 'z_bar'}
>>> tri_spd1 = TriSpectralPowerDistribution('Tri Spd', data1, mapping)
>>> tri_spd2 = TriSpectralPowerDistribution('Tri Spd', data2, mapping)
>>> tri_spd3 = TriSpectralPowerDistribution('Tri Spd', data1, mapping)
>>> tri_spd1 != tri_spd2
True
>>> tri_spd1 != tri_spd3
False
"""
return not (self == tri_spd)
def __format_operand(self, x):
"""
Formats given :math:`x` variable operand to *numeric* or *ndarray*.
This method is a convenient method to prepare given :math:`x` variable
for the arithmetic operations below.
Parameters
----------
x : numeric or ndarray or TriSpectralPowerDistribution
Variable to format.
Returns
-------
numeric or ndarray
Formatted operand.
"""
if issubclass(type(x), TriSpectralPowerDistribution):
x = x.values
elif is_iterable(x):
x = np.atleast_1d(x)
return x
[docs] def __add__(self, x):
"""
Implements support for tri-spectral power distribution addition.
Parameters
----------
x : numeric or array_like or TriSpectralPowerDistribution
Variable to add.
Returns
-------
TriSpectralPowerDistribution
Variable added tri-spectral power distribution.
See Also
--------
TriSpectralPowerDistribution.__sub__,
TriSpectralPowerDistribution.__mul__,
TriSpectralPowerDistribution.__div__
Notes
-----
- Reimplements the :meth:`object.__add__` method.
Warning
-------
The addition operation happens in place.
Examples
--------
Adding a single *numeric* variable:
>>> 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('Tri Spd', data, mapping)
>>> tri_spd + 10 # doctest: +ELLIPSIS
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd.values
array([[ 59.67, 100.56, 22.43],
[ 79.59, 97.34, 33.15],
[ 91.73, 55.76, 77.98],
[ 98.19, 33.45, 100.28]])
Adding an *array_like* variable:
>>> tri_spd + [(1, 2, 3)] * 4 # doctest: +ELLIPSIS
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd.values
array([[ 60.67, 102.56, 25.43],
[ 80.59, 99.34, 36.15],
[ 92.73, 57.76, 80.98],
[ 99.19, 35.45, 103.28]])
Adding a :class:`TriSpectralPowerDistribution` class variable:
>>> data1 = {'x_bar': z_bar, 'y_bar': x_bar, 'z_bar': y_bar}
>>> tri_spd1 = TriSpectralPowerDistribution('Tri Spd', data1, mapping)
>>> tri_spd + tri_spd1 # doctest: +ELLIPSIS
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd.values
array([[ 73.1 , 152.23, 115.99],
[ 103.74, 168.93, 123.49],
[ 160.71, 139.49, 126.74],
[ 189.47, 123.64, 126.73]])
"""
values = self.values + self.__format_operand(x)
for i, axis in enumerate(('x', 'y', 'z')):
self.__data[axis].data = SpectralMapping(
zip(self.wavelengths, values[..., i]))
return self
[docs] def __sub__(self, x):
"""
Implements support for tri-spectral power distribution subtraction.
Parameters
----------
x : numeric or array_like or TriSpectralPowerDistribution
Variable to subtract.
Returns
-------
TriSpectralPowerDistribution
Variable subtracted tri-spectral power distribution.
See Also
--------
TriSpectralPowerDistribution.__add__,
TriSpectralPowerDistribution.__mul__,
TriSpectralPowerDistribution.__div__
Notes
-----
- Reimplements the :meth:`object.__sub__` method.
Warning
-------
The subtraction operation happens in place.
Examples
--------
Subtracting a single *numeric* variable:
>>> 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('Tri Spd', data, mapping)
>>> tri_spd - 10 # doctest: +ELLIPSIS
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd.values
array([[ 39.67, 80.56, 2.43],
[ 59.59, 77.34, 13.15],
[ 71.73, 35.76, 57.98],
[ 78.19, 13.45, 80.28]])
Subtracting an *array_like* variable:
>>> tri_spd - [(1, 2, 3)] * 4 # doctest: +ELLIPSIS
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd.values
array([[ 38.67, 78.56, -0.57],
[ 58.59, 75.34, 10.15],
[ 70.73, 33.76, 54.98],
[ 77.19, 11.45, 77.28]])
Subtracting a :class:`TriSpectralPowerDistribution` class variable:
>>> data1 = {'x_bar': z_bar, 'y_bar': x_bar, 'z_bar': y_bar}
>>> tri_spd1 = TriSpectralPowerDistribution('Tri Spd', data1, mapping)
>>> tri_spd - tri_spd1 # doctest: +ELLIPSIS
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd.values
array([[ 26.24, 28.89, -91.13],
[ 35.44, 5.75, -77.19],
[ 2.75, -47.97, 9.22],
[-13.09, -76.74, 53.83]])
"""
return self + (-self.__format_operand(x))
[docs] def __mul__(self, x):
"""
Implements support for tri-spectral power distribution multiplication.
Parameters
----------
x : numeric or array_like or TriSpectralPowerDistribution
Variable to multiply.
Returns
-------
TriSpectralPowerDistribution
Variable multiplied tri-spectral power distribution.
See Also
--------
TriSpectralPowerDistribution.__add__,
TriSpectralPowerDistribution.__sub__,
TriSpectralPowerDistribution.__div__
Notes
-----
- Reimplements the :meth:`object.__mul__` method.
Warning
-------
The multiplication operation happens in place.
Examples
--------
Multiplying a single *numeric* variable:
>>> 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('Tri Spd', data, mapping)
>>> tri_spd * 10 # doctest: +ELLIPSIS
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd.values
array([[ 496.7, 905.6, 124.3],
[ 695.9, 873.4, 231.5],
[ 817.3, 457.6, 679.8],
[ 881.9, 234.5, 902.8]])
Multiplying an *array_like* variable:
>>> tri_spd * [(1, 2, 3)] * 4 # doctest: +ELLIPSIS
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd.values
array([[ 1986.8, 7244.8, 1491.6],
[ 2783.6, 6987.2, 2778. ],
[ 3269.2, 3660.8, 8157.6],
[ 3527.6, 1876. , 10833.6]])
Multiplying a :class:`TriSpectralPowerDistribution` class variable:
>>> data1 = {'x_bar': z_bar, 'y_bar': x_bar, 'z_bar': y_bar}
>>> tri_spd1 = TriSpectralPowerDistribution('Tri Spd', data1, mapping)
>>> tri_spd * tri_spd1 # doctest: +ELLIPSIS
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd.values
array([[ 24695.924, 359849.216, 135079.296],
[ 64440.34 , 486239.248, 242630.52 ],
[ 222240.216, 299197.184, 373291.776],
[ 318471.728, 165444.44 , 254047.92 ]])
"""
values = self.values * self.__format_operand(x)
for i, axis in enumerate(('x', 'y', 'z')):
self.__data[axis].data = SpectralMapping(
zip(self.wavelengths, values[..., i]))
return self
[docs] def __div__(self, x):
"""
Implements support for tri-spectral power distribution division.
Parameters
----------
x : numeric or array_like or TriSpectralPowerDistribution
Variable to divide.
Returns
-------
TriSpectralPowerDistribution
Variable divided tri-spectral power distribution.
See Also
--------
TriSpectralPowerDistribution.__add__,
TriSpectralPowerDistribution.__sub__,
TriSpectralPowerDistribution.__mul__
Notes
-----
- Reimplements the :meth:`object.__mul__` method.
Warning
-------
The division operation happens in place.
Examples
--------
Dividing a single *numeric* variable:
>>> 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('Tri Spd', data, mapping)
>>> tri_spd / 10 # doctest: +ELLIPSIS
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd.values
array([[ 4.967, 9.056, 1.243],
[ 6.959, 8.734, 2.315],
[ 8.173, 4.576, 6.798],
[ 8.819, 2.345, 9.028]])
Dividing an *array_like* variable:
>>> tri_spd / [(1, 2, 3)] * 4 # doctest: +ELLIPSIS
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd.values # doctest: +ELLIPSIS
array([[ 19.868 , 18.112 , 1.6573333...],
[ 27.836 , 17.468 , 3.0866666...],
[ 32.692 , 9.152 , 9.064 ...],
[ 35.276 , 4.69 , 12.0373333...]])
Dividing a :class:`TriSpectralPowerDistribution` class variable:
>>> data1 = {'x_bar': z_bar, 'y_bar': x_bar, 'z_bar': y_bar}
>>> tri_spd1 = TriSpectralPowerDistribution('Tri Spd', data1, mapping)
>>> tri_spd / tri_spd1 # doctest: +ELLIPSIS
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd.values # doctest: +ELLIPSIS
array([[ 1.5983909..., 0.3646466..., 0.0183009...],
[ 1.2024190..., 0.2510130..., 0.0353408...],
[ 0.4809061..., 0.1119784..., 0.1980769...],
[ 0.3907399..., 0.0531806..., 0.5133191...]])
"""
return self * (1 / self.__format_operand(x))
# Python 3 compatibility.
__truediv__ = __div__
[docs] def __pow__(self, x):
"""
Implements support for tri-spectral power distribution exponentiation.
Parameters
----------
x : numeric or array_like or TriSpectralPowerDistribution
Variable to exponentiate by.
Returns
-------
TriSpectralPowerDistribution
TriSpectral power distribution raised by power of x.
See Also
--------
TriSpectralPowerDistribution.__add__,
TriSpectralPowerDistribution.__sub__,
TriSpectralPowerDistribution.__mul__,
TriSpectralPowerDistribution.__div__
Notes
-----
- Reimplements the :meth:`object.__pow__` method.
Warning
-------
The power operation happens in place.
Examples
--------
Exponentiation by a single *numeric* variable:
>>> x_bar = {510: 1.67, 520: 1.59, 530: 1.73, 540: 1.19}
>>> y_bar = {510: 1.56, 520: 1.34, 530: 1.76, 540: 1.45}
>>> z_bar = {510: 1.43, 520: 1.15, 530: 1.98, 540: 1.28}
>>> 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('Tri Spd', data, mapping)
>>> tri_spd ** 1.1 # doctest: +ELLIPSIS
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd.values # doctest: +ELLIPSIS
array([[ 1.7578755..., 1.6309365..., 1.4820731...],
[ 1.6654700..., 1.3797972..., 1.1661854...],
[ 1.8274719..., 1.8623612..., 2.1199797...],
[ 1.2108815..., 1.5048901..., 1.3119913...]])
Exponentiation by an *array_like* variable:
>>> tri_spd ** ([(1, 2, 3)] * 4) # doctest: +ELLIPSIS
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd.values # doctest: +ELLIPSIS
array([[ 1.7578755..., 2.6599539..., 3.2554342...],
[ 1.6654700..., 1.9038404..., 1.5859988...],
[ 1.8274719..., 3.4683895..., 9.5278547...],
[ 1.2108815..., 2.2646943..., 2.2583585...]])
Exponentiation by a :class:`TriSpectralPowerDistribution`
class variable:
>>> data1 = {'x_bar': z_bar, 'y_bar': x_bar, 'z_bar': y_bar}
>>> tri_spd1 = TriSpectralPowerDistribution('Tri Spd', data1, mapping)
>>> tri_spd ** tri_spd1 # doctest: +ELLIPSIS
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd.values # doctest: +ELLIPSIS
array([[ 2.2404384..., 5.1231818..., 6.3047797...],
[ 1.7979075..., 2.7836369..., 1.8552645...],
[ 3.2996236..., 8.5984706..., 52.8483490...],
[ 1.2775271..., 2.6452177..., 3.2583647...]])
"""
values = self.values ** self.__format_operand(x)
for i, axis in enumerate(('x', 'y', 'z')):
self.__data[axis].data = SpectralMapping(
zip(self.wavelengths, values[..., i]))
return self
[docs] def get(self, wavelength, default=np.nan):
"""
Returns the values for given wavelength :math:`\lambda`.
Parameters
----------
wavelength : numeric or array_like
Wavelength :math:`\lambda` to retrieve the values.
default : nan, numeric or array_like, optional
Wavelength :math:`\lambda` default values.
Returns
-------
numeric or array_like
Wavelength :math:`\lambda` values.
See Also
--------
TriSpectralPowerDistribution.__getitem__
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('Tri Spd', 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]])
"""
wavelength = np.asarray(wavelength)
default = np.resize(default, 3)
value = np.array([(self.x.get(x, default[0]),
self.y.get(x, default[1]),
self.z.get(x, default[2]))
for x in np.ravel(wavelength)])
value = np.reshape(value, wavelength.shape + (3,))
return value
[docs] def interpolate(self, shape=SpectralShape(), method=None):
"""
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
-------
TriSpectralPowerDistribution
Interpolated tri-spectral power distribution.
See Also
--------
TriSpectralPowerDistribution.align
Notes
-----
- See :meth:`SpectralPowerDistribution.interpolate` method
notes section.
Warning
-------
See :meth:`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('Tri Spd', data, mapping)
>>> tri_spd.interpolate(SpectralShape(steps=1)) # doctest: +ELLIPSIS
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd[515] # doctest: +ELLIPSIS
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('Tri Spd', data, mapping)
>>> tri_spd[511] = np.array([31.41, 95.27, 15.06])
>>> tri_spd.interpolate(SpectralShape(steps=1)) # doctest: +ELLIPSIS
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd[515] # doctest: +ELLIPSIS
array([ 21.4710405..., 100.6430015..., 18.8165196...])
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('Tri Spd', data, mapping)
>>> tri_spd.interpolate( # doctest: +ELLIPSIS
... SpectralShape(steps=1), method='Linear')
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd[515] # doctest: +ELLIPSIS
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('Tri Spd', data, mapping)
>>> tri_spd.interpolate( # doctest: +ELLIPSIS
... SpectralShape(steps=1), method='Pchip')
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd[515] # doctest: +ELLIPSIS
array([ 58.8173260..., 89.4355596..., 16.4545683...])
"""
for i in self.__mapping.keys():
getattr(self, i).interpolate(shape, method)
return self
[docs] def align(self,
shape,
method='Constant',
left=None,
right=None):
"""
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.
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
-------
TriSpectralPowerDistribution
Aligned tri-spectral power distribution.
See Also
--------
TriSpectralPowerDistribution.extrapolate,
TriSpectralPowerDistribution.interpolate
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('Tri Spd', data, mapping)
>>> tri_spd.align(SpectralShape(505, 565, 1)) # doctest: +ELLIPSIS
<...TriSpectralPowerDistribution object at 0x...>
>>> # Doctests skip for Python 2.x compatibility.
>>> tri_spd.wavelengths # doctest: +SKIP
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 # doctest: +ELLIPSIS
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 ...]])
"""
for i in self.__mapping.keys():
getattr(self, i).align(shape, method, left, right)
return self
[docs] def zeros(self, shape=SpectralShape()):
"""
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
-------
TriSpectralPowerDistribution
Zeros filled tri-spectral power distribution.
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('Tri Spd', data, mapping)
>>> tri_spd.zeros(SpectralShape(505, 565, 1)) # doctest: +ELLIPSIS
<...TriSpectralPowerDistribution object at 0x...>
>>> 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. ]])
"""
for i in self.__mapping.keys():
getattr(self, i).zeros(shape)
return self
[docs] def normalise(self, factor=1):
"""
Normalises the tri-spectral power distribution with given normalization
factor.
Parameters
----------
factor : numeric, optional
Normalization factor
Returns
-------
TriSpectralPowerDistribution
Normalised tri- spectral power distribution.
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('Tri Spd', data, mapping)
>>> tri_spd.normalise() # doctest: +ELLIPSIS
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd.values # doctest: +ELLIPSIS
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. ...]])
"""
maximum = max(np.ravel(self.values))
for i in self.__mapping.keys():
getattr(self, i) * (1 / maximum) * factor
return self
[docs] def clone(self):
"""
Clones the tri-spectral power distribution.
Most of the :class:`TriSpectralPowerDistribution` class operations are
conducted in-place. The :meth:`TriSpectralPowerDistribution.clone`
method provides a convenient way to copy the tri-spectral power
distribution to a new object.
Returns
-------
TriSpectralPowerDistribution
Cloned tri-spectral power distribution.
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('Tri Spd', data, mapping)
>>> print(tri_spd) # doctest: +ELLIPSIS
<...TriSpectralPowerDistribution object at 0x...>
>>> tri_spd_clone = tri_spd.clone()
>>> print(tri_spd_clone) # doctest: +ELLIPSIS
<...TriSpectralPowerDistribution object at 0x...>
"""
return copy.deepcopy(self)
DEFAULT_SPECTRAL_SHAPE = SpectralShape(360, 830, 1)
"""
Default spectral shape using the shape of
*CIE 1931 2 Degree Standard Observer*.
DEFAULT_SPECTRAL_SHAPE : SpectralShape
"""
[docs]def constant_spd(k,
shape=DEFAULT_SPECTRAL_SHAPE):
"""
Returns a spectral power distribution of given spectral shape filled with
constant :math:`k` values.
Parameters
----------
k : numeric
Constant :math:`k` to fill the spectral power distribution with.
shape : SpectralShape, optional
Spectral shape used to create the spectral power distribution.
Returns
-------
SpectralPowerDistribution
Constant :math:`k` to filled spectral power distribution.
Notes
-----
- By default, the spectral power distribution will use the shape given
by :attr:`DEFAULT_SPECTRAL_SHAPE` attribute.
Examples
--------
>>> spd = constant_spd(100)
>>> spd.shape
SpectralShape(360.0, 830.0, 1.0)
>>> spd[400]
array(100.0)
"""
wavelengths = shape.range()
values = np.full(len(wavelengths), k)
name = '{0} Constant'.format(k)
return SpectralPowerDistribution(
name, SpectralMapping(zip(wavelengths, values)))
[docs]def zeros_spd(shape=DEFAULT_SPECTRAL_SHAPE):
"""
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
-------
SpectralPowerDistribution
Zeros filled spectral power distribution.
See Also
--------
constant_spd
Notes
-----
- By default, the spectral power distribution will use the shape given
by :attr:`DEFAULT_SPECTRAL_SHAPE` attribute.
Examples
--------
>>> spd = zeros_spd()
>>> spd.shape
SpectralShape(360.0, 830.0, 1.0)
>>> spd[400]
array(0.0)
"""
return constant_spd(0, shape)
[docs]def ones_spd(shape=DEFAULT_SPECTRAL_SHAPE):
"""
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
-------
SpectralPowerDistribution
Ones filled spectral power distribution.
See Also
--------
constant_spd
Notes
-----
- By default, the spectral power distribution will use the shape given
by :attr:`DEFAULT_SPECTRAL_SHAPE` attribute.
Examples
--------
>>> spd = ones_spd()
>>> spd.shape
SpectralShape(360.0, 830.0, 1.0)
>>> spd[400]
array(1.0)
"""
return constant_spd(1, shape)
Colour 0.3