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"""
This module provides functions for sample-wise processing of single traces.
"""
from typing import cast
import numpy as np
from public import public
from .trace import Trace
@public
def absolute(trace: Trace) -> Trace:
"""
Apply absolute value to samples of `trace`.
:param trace:
:return:
"""
return trace.with_samples(np.absolute(trace.samples))
@public
def invert(trace: Trace) -> Trace:
"""
Invert(negate) the samples of `trace`.
:param trace:
:return:
"""
return trace.with_samples(np.negative(trace.samples))
@public
def threshold(trace: Trace, value) -> Trace:
"""
Map samples of the `trace` to 1 if they are above `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)
def _rolling_window(samples: np.ndarray, window: int) -> np.ndarray:
shape = samples.shape[:-1] + (samples.shape[-1] - window + 1, window)
strides = samples.strides + (samples.strides[-1],)
return np.lib.stride_tricks.as_strided(samples, shape=shape, strides=strides) # type: ignore[attr-defined]
@public
def rolling_mean(trace: Trace, window: int) -> Trace:
"""
Compute the rolling mean of `trace` using `window`. Shortens the trace by `window` - 1.
:param trace:
:param window:
:return:
"""
return trace.with_samples(
cast(
np.ndarray,
np.mean(_rolling_window(trace.samples, window), -1).astype(
dtype=trace.samples.dtype, copy=False
),
)
)
@public
def offset(trace: Trace, offset) -> Trace:
"""
Offset samples of `trace` by `offset`, sample-wise (Adds `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)))
@public
def recenter(trace: Trace) -> Trace:
"""
Subtract the root mean square of the `trace` from its samples, sample-wise.
:param trace:
:return:
"""
around = _root_mean_square(trace)
return offset(trace, -around)
@public
def normalize(trace: Trace) -> Trace:
"""
Normalize a `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)
)
@public
def normalize_wl(trace: Trace) -> Trace:
"""
Normalize a `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))
)
|
