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from dataclasses import dataclass, field

from math import floor

# discrete_convolution

"""

* Calculate the discrete convolution of two

linear discrete sets

https://en.wikipedia.org/wiki/Convolution

"""

@dataclass

class Signal:

"""

A discrete representation of a signal as a n-dimensional vector

>>> Signal([1.0,3.0,2.0,-1.0])

Signal(signal=[1.0, 3.0, 2.0, -1.0], n=4)

"""

signal: list[float] = field(default_factory=list)

n: int = 0

def __post_init__(self) -> None:

for i in self.signal:

if not isinstance(i, (float, int)):

raise TypeError("vector must be a list of numeric values.")

self.n += 1

@dataclass

class DiscreteConvolve1D:

"""

1D discrete convolution between two linear signals

>>> s1 = Signal([1,2,3,4,5])

>>> s2 = Signal([1,-1,2,-3])

>>> DiscreteConvolve1D(s1,s2) # doctest: +NORMALIZE_WHITESPACE

DiscreteConvolve1D(kern=Signal(signal=[1, 2, 3, 4, 5], n=5),

sig=Signal(signal=[1, -1, 2, -3], n=4))

"""

kern: Signal = field(default_factory=Signal)

sig: Signal = field(default_factory=Signal)

@property

def convolve_1d(self) -> Signal:

conv = Signal()

for i in range(self.sig.n):

conv.signal.append(0)

for j in range(self.kern.n):

if (

i + j - floor(self.kern.n / 2) < 0

or i + j - floor(self.kern.n / 2) >= self.sig.n

sig_val = 0.0

else:

sig_val = float(self.sig.signal[i + j - floor(self.kern.n / 2)])

conv.signal[i] += self.kern.signal[j] * sig_val

conv.n += 1

return conv

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