colour.continuous.Signal¶
-
class
colour.continuous.
Signal
(data=None, domain=None, **kwargs)[source]¶ 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 thecolour.continuous.Signal.domain
attribute corresponds with the function dependent and already known range stored in thecolour.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 bothdata
anddomain
arguments are defined, the latter with be used to initialise thecolour.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
¶
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( # doctest: +SKIP ... 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] # doctest: +ELLIPSIS 0.0359701... >>> x = np.linspace(100, 1000, 3) >>> Signal(range_, domain)[x] # doctest: +ELLIPSIS 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] # doctest: +ELLIPSIS 0.0555274... >>> x = np.linspace(100, 1000, 3) >>> Signal( ... range_, ... domain, ... interpolator=CubicSplineInterpolator)[x] # doctest: +ELLIPSIS array([ 0. , 0.4794253..., 0.8414709...])
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__init__
(data=None, domain=None, **kwargs)[source]¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__
([data, domain])Initialize self. arithmetical_operation
(a, operation[, in_place])Performs given arithmetical operation with \(a\) operand, the operation can be either performed on a copy or in-place. copy
()Returns a copy of the sub-class instance. domain_distance
(a)Returns the euclidean distance between given array and independent domain \(x\) closest element. fill_nan
([method, default])Fill NaNs in independent domain \(x\) variable and corresponding range \(y\) variable using given method. is_uniform
()Returns if independent domain \(x\) variable is uniform. signal_unpack_data
([data, domain, dtype])Unpack given data for continuous signal instantiation. to_series
()Converts the continuous signal to a Pandas Series
class instance.Attributes
domain
Getter and setter property for the continuous signal independent domain \(x\) variable. dtype
Getter and setter property for the continuous signal dtype. extrapolator
Getter and setter property for the continuous signal extrapolator type. extrapolator_args
Getter and setter property for the continuous signal extrapolator instantiation time arguments. function
Getter and setter property for the continuous signal callable. interpolator
Getter and setter property for the continuous signal interpolator type. interpolator_args
Getter and setter property for the continuous signal interpolator instantiation time arguments. name
Getter and setter property for the abstract continuous function name. range
Getter and setter property for the continuous signal corresponding range \(y\) variable.