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Christian Claussandcclauss
7bd1e983c3
* updating DIRECTORY.md * ruff rules RET for return statements --------- Co-authored-by: cclauss <cclauss@users.noreply.github.com>
62 lines
1.7 KiB
Python
62 lines
1.7 KiB
Python
from dataclasses import dataclass, field
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from math import floor
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# discrete_convolution
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"""
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* Calculate the discrete convolution of two
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linear discrete sets
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https://en.wikipedia.org/wiki/Convolution
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"""
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@dataclass
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class Signal:
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"""
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A discrete representation of a signal as a n-dimensional vector
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>>> Signal([1.0,3.0,2.0,-1.0])
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Signal(signal=[1.0, 3.0, 2.0, -1.0], n=4)
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"""
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signal: list[float] = field(default_factory=list)
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n: int = 0
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def __post_init__(self) -> None:
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for i in self.signal:
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if not isinstance(i, (float, int)):
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raise TypeError("vector must be a list of numeric values.")
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self.n += 1
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@dataclass
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class DiscreteConvolve1D:
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"""
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1D discrete convolution between two linear signals
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>>> s1 = Signal([1,2,3,4,5])
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>>> s2 = Signal([1,-1,2,-3])
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>>> DiscreteConvolve1D(s1,s2) # doctest: +NORMALIZE_WHITESPACE
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DiscreteConvolve1D(kern=Signal(signal=[1, 2, 3, 4, 5], n=5),
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sig=Signal(signal=[1, -1, 2, -3], n=4))
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"""
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kern: Signal = field(default_factory=Signal)
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sig: Signal = field(default_factory=Signal)
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@property
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def convolve_1d(self) -> Signal:
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conv = Signal()
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for i in range(self.sig.n):
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conv.signal.append(0)
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for j in range(self.kern.n):
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if (
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i + j - floor(self.kern.n / 2) < 0
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or i + j - floor(self.kern.n / 2) >= self.sig.n
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):
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sig_val = 0.0
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else:
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sig_val = float(self.sig.signal[i + j - floor(self.kern.n / 2)])
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conv.signal[i] += self.kern.signal[j] * sig_val
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conv.n += 1
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return conv
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