Source code for pyecsca.sca.trace.process

"""Provides functions for sample-wise processing of single traces."""
from typing import Any

import numpy as np
from scipy.signal import convolve
from public import public

from .trace import Trace


[docs]@public def absolute(trace: Trace) -> Trace: """ Apply absolute value to samples of :paramref:`~.absolute.trace`. :param trace: :return: """ return trace.with_samples(np.absolute(trace.samples))
[docs]@public def invert(trace: Trace) -> Trace: """ Invert(negate) the samples of :paramref:`~.invert.trace`. :param trace: :return: """ return trace.with_samples(np.negative(trace.samples))
[docs]@public def threshold(trace: Trace, value) -> Trace: """ Map samples of the :paramref:`~.threshold.trace` to ``1`` if they are above :paramref:`~.threshold.value` or to ``0``. :param trace: :param value: :return: """ result_samples = trace.samples.copy() result_samples[result_samples <= value] = 0 result_samples[np.nonzero(result_samples)] = 1 return trace.with_samples(result_samples)
[docs]@public def rolling_mean(trace: Trace, window: int) -> Trace: """ Compute the rolling mean of :paramref:`~.rolling_mean.trace` using :paramref:`~.rolling_mean.window`. Shortens the trace by ``window - 1``. :param trace: :param window: :return: """ return trace.with_samples(convolve(trace.samples, np.ones(window, dtype=trace.samples.dtype), "valid") / window)
[docs]@public def offset(trace: Trace, offset) -> Trace: """ Offset samples of :paramref:`~.offset.trace` by :paramref:`~.offset.offset`, sample-wise. Adds :paramref:`~.offset.offset` to all samples. :param trace: :param offset: :return: """ return trace.with_samples(trace.samples + offset)
def _root_mean_square(trace: Trace): return np.sqrt(np.mean(np.square(trace.samples)))
[docs]@public def recenter(trace: Trace) -> Trace: """ Subtract the root mean square of the :paramref:`~.recenter.trace` from its samples, sample-wise. :param trace: :return: """ around = _root_mean_square(trace) return offset(trace, -around)
[docs]@public def normalize(trace: Trace) -> Trace: """ Normalize a :paramref:`~.normalize.trace` by subtracting its mean and dividing by its standard deviation. :param trace: :return: """ return trace.with_samples( (trace.samples - np.mean(trace.samples)) / np.std(trace.samples) )
[docs]@public def normalize_wl(trace: Trace) -> Trace: """ Normalize a :paramref:`~.normalize_wl.trace` by subtracting its mean and dividing by a multiple (= ``len(trace)``) of its standard deviation. :param trace: :return: """ return trace.with_samples( (trace.samples - np.mean(trace.samples)) / (np.std(trace.samples) * len(trace.samples)) )
[docs]@public def transform(trace: Trace, min_value: Any = 0, max_value: Any = 1) -> Trace: """ Scale a :paramref:`~.transform.trace` so that its minimum is at :paramref:`~.transform.min_value` and its maximum is at :paramref:`~.transform.max_value`. :param trace: :param min_value: :param max_value: :return: """ t_min = np.min(trace.samples) t_max = np.max(trace.samples) t_range = t_max - t_min d = max_value - min_value return trace.with_samples(((trace.samples - t_min) * (d/t_range)) + min_value)