colour.continuous.Signal

class colour.continuous.Signal(data=None, domain=None, **kwargs)[source]

Bases: colour.continuous.abstract.AbstractContinuousFunction

Defines the base class for continuous signal.

The class implements the Signal.function() method so that evaluating the function for any independent domain \(x \in\mathbb{R}\) variable returns a corresponding range \(y \in\mathbb{R}\) variable. It adopts an interpolating function encapsulated inside an extrapolating function. The resulting function independent domain, stored as discrete values in the colour.continuous.Signal.domain attribute corresponds with the function dependent and already known range stored in the colour.continuous.Signal.range attribute.

Parameters
  • data (Series or Signal or array_like or dict_like, optional) – Data to be stored in the continuous signal.

  • domain (array_like, optional) – Values to initialise the colour.continuous.Signal.domain attribute with. If both data and domain arguments are defined, the latter with be used to initialise the colour.continuous.Signal.domain attribute.

Other Parameters
  • name (unicode, optional) – Continuous signal name.

  • dtype (type, optional) – {np.float16, np.float32, np.float64, np.float128}, Floating point data type.

  • interpolator (object, optional) – Interpolator class type to use as interpolating function.

  • interpolator_kwargs (dict_like, optional) – Arguments to use when instantiating the interpolating function.

  • extrapolator (object, optional) – Extrapolator class type to use as extrapolating function.

  • extrapolator_kwargs (dict_like, optional) – Arguments to use when instantiating the extrapolating function.

dtype
domain
range
interpolator
interpolator_kwargs
extrapolator
extrapolator_kwargs
function
__str__()[source]
__repr__()[source]
__hash__()[source]
__getitem__()[source]
__setitem__()[source]
__contains__()[source]
__eq__()[source]
__ne__()[source]
arithmetical_operation()[source]
signal_unpack_data()[source]
fill_nan()[source]
to_series()[source]

Examples

Instantiation with implicit domain:

>>> range_ = np.linspace(10, 100, 10)
>>> print(Signal(range_))
[[   0.   10.]
 [   1.   20.]
 [   2.   30.]
 [   3.   40.]
 [   4.   50.]
 [   5.   60.]
 [   6.   70.]
 [   7.   80.]
 [   8.   90.]
 [   9.  100.]]

Instantiation with explicit domain:

>>> domain = np.arange(100, 1100, 100)
>>> print(Signal(range_, domain))
[[  100.    10.]
 [  200.    20.]
 [  300.    30.]
 [  400.    40.]
 [  500.    50.]
 [  600.    60.]
 [  700.    70.]
 [  800.    80.]
 [  900.    90.]
 [ 1000.   100.]]

Instantiation with a dict:

>>> print(Signal(dict(zip(domain, range_))))
[[  100.    10.]
 [  200.    20.]
 [  300.    30.]
 [  400.    40.]
 [  500.    50.]
 [  600.    60.]
 [  700.    70.]
 [  800.    80.]
 [  900.    90.]
 [ 1000.   100.]]

Instantiation with a Pandas Series:

>>> if is_pandas_installed():
...     from pandas import Series
...     print(Signal(  
...         Series(dict(zip(domain, range_)))))
[[  100.    10.]
 [  200.    20.]
 [  300.    30.]
 [  400.    40.]
 [  500.    50.]
 [  600.    60.]
 [  700.    70.]
 [  800.    80.]
 [  900.    90.]
 [ 1000.   100.]]

Retrieving domain y variable for arbitrary range x variable:

>>> x = 150
>>> range_ = np.sin(np.linspace(0, 1, 10))
>>> Signal(range_, domain)[x]  
0.0359701...
>>> x = np.linspace(100, 1000, 3)
>>> Signal(range_, domain)[x]  
array([  ...,   4.7669395...e-01,   8.4147098...e-01])

Using an alternative interpolating function:

>>> x = 150
>>> from colour.algebra import CubicSplineInterpolator
>>> Signal(
...     range_,
...     domain,
...     interpolator=CubicSplineInterpolator)[x]  
0.0555274...
>>> x = np.linspace(100, 1000, 3)
>>> Signal(
...     range_,
...     domain,
...     interpolator=CubicSplineInterpolator)[x]  
array([ 0.        ,  0.4794253...,  0.8414709...])
arithmetical_operation(a, operation, in_place=False)[source]

Performs given arithmetical operation with \(a\) operand, the operation can be either performed on a copy or in-place.

Parameters
  • a (numeric or ndarray or Signal) – Operand.

  • operation (object) – Operation to perform.

  • in_place (bool, optional) – Operation happens in place.

Returns

Continuous signal.

Return type

Signal

Examples

Adding a single numeric variable:

>>> range_ = np.linspace(10, 100, 10)
>>> signal_1 = Signal(range_)
>>> print(signal_1)
[[   0.   10.]
 [   1.   20.]
 [   2.   30.]
 [   3.   40.]
 [   4.   50.]
 [   5.   60.]
 [   6.   70.]
 [   7.   80.]
 [   8.   90.]
 [   9.  100.]]
>>> print(signal_1.arithmetical_operation(10, '+', True))
[[   0.   20.]
 [   1.   30.]
 [   2.   40.]
 [   3.   50.]
 [   4.   60.]
 [   5.   70.]
 [   6.   80.]
 [   7.   90.]
 [   8.  100.]
 [   9.  110.]]

Adding an array_like variable:

>>> a = np.linspace(10, 100, 10)
>>> print(signal_1.arithmetical_operation(a, '+', True))
[[   0.   30.]
 [   1.   50.]
 [   2.   70.]
 [   3.   90.]
 [   4.  110.]
 [   5.  130.]
 [   6.  150.]
 [   7.  170.]
 [   8.  190.]
 [   9.  210.]]

Adding a colour.continuous.Signal class:

>>> signal_2 = Signal(range_)
>>> print(signal_1.arithmetical_operation(signal_2, '+', True))
[[   0.   40.]
 [   1.   70.]
 [   2.  100.]
 [   3.  130.]
 [   4.  160.]
 [   5.  190.]
 [   6.  220.]
 [   7.  250.]
 [   8.  280.]
 [   9.  310.]]
property domain

Getter and setter property for the continuous signal independent domain \(x\) variable.

Parameters

value (array_like) – Value to set the continuous signal independent domain \(x\) variable with.

Returns

Continuous signal independent domain \(x\) variable.

Return type

ndarray

property dtype

Getter and setter property for the continuous signal dtype.

Parameters

value (type) – Value to set the continuous signal dtype with.

Returns

Continuous signal dtype.

Return type

type

property extrapolator

Getter and setter property for the continuous signal extrapolator type.

Parameters

value (type) – Value to set the continuous signal extrapolator type with.

Returns

Continuous signal extrapolator type.

Return type

type

property extrapolator_kwargs

Getter and setter property for the continuous signal extrapolator instantiation time arguments.

Parameters

value (dict) – Value to set the continuous signal extrapolator instantiation time arguments to.

Returns

Continuous signal extrapolator instantiation time arguments.

Return type

dict

fill_nan(method='Interpolation', default=0)[source]

Fill NaNs in independent domain \(x\) variable and corresponding range \(y\) variable using given method.

Parameters
  • method (unicode, optional) – {‘Interpolation’, ‘Constant’}, Interpolation method linearly interpolates through the NaNs, Constant method replaces NaNs with default.

  • default (numeric, optional) – Value to use with the Constant method.

Returns

NaNs filled continuous signal.

Return type

Signal

Examples

>>> range_ = np.linspace(10, 100, 10)
>>> signal = Signal(range_)
>>> signal[3:7] = np.nan
>>> print(signal)
[[   0.   10.]
 [   1.   20.]
 [   2.   30.]
 [   3.   nan]
 [   4.   nan]
 [   5.   nan]
 [   6.   nan]
 [   7.   80.]
 [   8.   90.]
 [   9.  100.]]
>>> print(signal.fill_nan())
[[   0.   10.]
 [   1.   20.]
 [   2.   30.]
 [   3.   40.]
 [   4.   50.]
 [   5.   60.]
 [   6.   70.]
 [   7.   80.]
 [   8.   90.]
 [   9.  100.]]
>>> signal[3:7] = np.nan
>>> print(signal.fill_nan(method='Constant'))
[[   0.   10.]
 [   1.   20.]
 [   2.   30.]
 [   3.    0.]
 [   4.    0.]
 [   5.    0.]
 [   6.    0.]
 [   7.   80.]
 [   8.   90.]
 [   9.  100.]]
property function

Getter and setter property for the continuous signal callable.

Parameters

value (object) – Attribute value.

Returns

Continuous signal callable.

Return type

callable

Notes

  • This property is read only.

property interpolator

Getter and setter property for the continuous signal interpolator type.

Parameters

value (type) – Value to set the continuous signal interpolator type with.

Returns

Continuous signal interpolator type.

Return type

type

property interpolator_kwargs

Getter and setter property for the continuous signal interpolator instantiation time arguments.

Parameters

value (dict) – Value to set the continuous signal interpolator instantiation time arguments to.

Returns

Continuous signal interpolator instantiation time arguments.

Return type

dict

property range

Getter and setter property for the continuous signal corresponding range \(y\) variable.

Parameters

value (array_like) – Value to set the continuous signal corresponding range \(y\) variable with.

Returns

Continuous signal corresponding range \(y\) variable.

Return type

ndarray

static signal_unpack_data(data=None, domain=None, dtype=<class 'numpy.float64'>)[source]

Unpack given data for continuous signal instantiation.

Parameters
  • data (Series or Signal or array_like or dict_like, optional) – Data to unpack for continuous signal instantiation.

  • domain (array_like, optional) – Values to initialise the colour.continuous.Signal.domain attribute with. If both data and domain arguments are defined, the latter will be used to initialise the colour.continuous.Signal.domain attribute.

  • dtype (type, optional) – {np.float16, np.float32, np.float64, np.float128}, Floating point data type.

Returns

Independent domain \(x\) variable and corresponding range \(y\) variable unpacked for continuous signal instantiation.

Return type

tuple

Examples

Unpacking using implicit domain:

>>> range_ = np.linspace(10, 100, 10)
>>> domain, range_ = Signal.signal_unpack_data(range_)
>>> print(domain)
[ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9.]
>>> print(range_)
[  10.   20.   30.   40.   50.   60.   70.   80.   90.  100.]

Unpacking using explicit domain:

>>> domain = np.arange(100, 1100, 100)
>>> domain, range = Signal.signal_unpack_data(range_, domain)
>>> print(domain)
[  100.   200.   300.   400.   500.   600.   700.   800.   900.  1000.]
>>> print(range_)
[  10.   20.   30.   40.   50.   60.   70.   80.   90.  100.]

Unpacking using a dict:

>>> domain, range_ = Signal.signal_unpack_data(
...     dict(zip(domain, range_)))
>>> print(domain)
[  100.   200.   300.   400.   500.   600.   700.   800.   900.  1000.]
>>> print(range_)
[  10.   20.   30.   40.   50.   60.   70.   80.   90.  100.]

Unpacking using a Pandas Series:

>>> if is_pandas_installed():
...     from pandas import Series
...     domain, range = Signal.signal_unpack_data(
...         Series(dict(zip(domain, range_))))
... 
>>> print(domain)  
[  100.   200.   300.   400.   500.   600.   700.   800.   900.  1000.]
>>> print(range_)  
[  10.   20.   30.   40.   50.   60.   70.   80.   90.  100.]

Unpacking using a colour.continuous.Signal class:

>>> domain, range_ = Signal.signal_unpack_data(
...     Signal(range_, domain))
>>> print(domain)
[  100.   200.   300.   400.   500.   600.   700.   800.   900.  1000.]
>>> print(range_)
[  10.   20.   30.   40.   50.   60.   70.   80.   90.  100.]
to_series()[source]

Converts the continuous signal to a Pandas Series class instance.

Returns

Continuous signal as a Pandas Series class instance.

Return type

Series

Examples

>>> if is_pandas_installed():
...     range_ = np.linspace(10, 100, 10)
...     signal = Signal(range_)
...     print(signal.to_series())  
0.0     10.0
1.0     20.0
2.0     30.0
3.0     40.0
4.0     50.0
5.0     60.0
6.0     70.0
7.0     80.0
8.0     90.0
9.0    100.0
Name: Signal (...), dtype: float64