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_args (dict_like, optional) – Arguments to use when instantiating the interpolating function.
extrapolator (object, optional) – Extrapolator class type to use as extrapolating function.
extrapolator_args (dict_like, optional) – Arguments to use when instantiating the extrapolating function.

dtype¶

domain¶

range¶

interpolator¶

interpolator_args¶

extrapolator¶

extrapolator_args¶

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_args

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_args

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