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from copy import copy
import numpy as np
from public import public
from .trace import Trace
@public
def absolute(trace: Trace) -> Trace:
return Trace(copy(trace.title), copy(trace.data), np.absolute(trace.samples))
@public
def invert(trace: Trace) -> Trace:
return Trace(copy(trace.title), copy(trace.data), np.negative(trace.samples))
@public
def threshold(trace: Trace, value) -> Trace:
result_samples = trace.samples.copy()
result_samples[result_samples <= value] = 0
result_samples[np.nonzero(result_samples)] = 1
return Trace(copy(trace.title), copy(trace.data), 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)
@public
def rolling_mean(trace: Trace, window: int) -> Trace:
return Trace(copy(trace.title), copy(trace.data), np.mean(rolling_window(trace.samples, window), -1).astype(dtype=trace.samples.dtype))
@public
def offset(trace: Trace, offset) -> Trace:
return Trace(copy(trace.title), copy(trace.data), trace.samples + offset)
def root_mean_square(trace: Trace):
return np.sqrt(np.mean(np.square(trace.samples)))
@public
def recenter(trace: Trace) -> Trace:
around = root_mean_square(trace)
return offset(trace, -around)
@public
def normalize(trace: Trace) -> Trace:
return Trace(copy(trace.title), copy(trace.data), (trace.samples - np.mean(trace.samples)) / np.std(trace.samples))
@public
def normalize_wl(trace: Trace) -> Trace:
return Trace(copy(trace.title), copy(trace.data), (trace.samples - np.mean(trace.samples)) / (np.std(trace.samples) * len(trace.samples)))
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