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"""
This module provides functions for combining traces sample-wise.
"""
from typing import Callable, Optional, Tuple
import numpy as np
from public import public
from .trace import Trace, CombinedTrace
@public
def average(*traces: Trace) -> Optional[CombinedTrace]:
"""
Average `traces`, sample-wise.
:param traces:
:return:
"""
if not traces:
return None
if len(traces) == 1:
return CombinedTrace(traces[0].samples.copy())
min_samples = min(map(len, traces))
s = np.zeros(min_samples, dtype=np.float64)
for t in traces:
s = np.add(s, t.samples[:min_samples])
avg = ((1 / len(traces)) * s)
del s
return CombinedTrace(avg)
@public
def conditional_average(*traces: Trace, condition: Callable[[Trace], bool]) -> Optional[CombinedTrace]:
"""
Average `traces` for which the `condition` is True, sample-wise.
:param traces:
:param condition:
:return:
"""
return average(*filter(condition, traces))
@public
def standard_deviation(*traces: Trace) -> Optional[CombinedTrace]:
"""
Compute the sample standard-deviation of the `traces`, sample-wise.
:param traces:
:return:
"""
if not traces:
return None
if len(traces) == 1:
return CombinedTrace(np.zeros(len(traces[0]), dtype=np.float64))
min_samples = min(map(len, traces))
s = np.zeros(min_samples, dtype=np.float64)
for t in traces:
s = np.add(s, t.samples[:min_samples])
s = (1 / len(traces)) * s
ts = np.zeros(min_samples, dtype=np.float64)
for t in traces:
d = np.subtract(t.samples[:min_samples], s)
ts = np.add(ts, np.multiply(d, d, dtype=np.float64))
std = np.sqrt((1 / (len(traces) - 1)) * ts)
del s
del ts
return CombinedTrace(std)
@public
def variance(*traces: Trace) -> Optional[CombinedTrace]:
"""
Compute the sample variance of the `traces`, sample-wise.
:param traces:
:return:
"""
if not traces:
return None
if len(traces) == 1:
return CombinedTrace(np.zeros(len(traces[0]), dtype=np.float64))
min_samples = min(map(len, traces))
s = np.zeros(min_samples, dtype=np.float64)
for t in traces:
s = np.add(s, t.samples[:min_samples])
s = (1 / len(traces)) * s
ts = np.zeros(min_samples, dtype=np.float64)
for t in traces:
d = np.subtract(t.samples[:min_samples], s)
ts = np.add(ts, np.multiply(d, d, dtype=np.float64))
var = (1 / (len(traces) - 1)) * ts
del s
del ts
return CombinedTrace(var)
@public
def average_and_variance(*traces) -> Optional[Tuple[CombinedTrace, CombinedTrace]]:
"""
Compute the average and sample variance of the `traces`, sample-wise.
:param traces:
:return:
"""
if not traces:
return None
if len(traces) == 1:
return (CombinedTrace(traces[0].samples.copy()),
CombinedTrace(np.zeros(len(traces[0]), dtype=np.float64)))
min_samples = min(map(len, traces))
s = np.zeros(min_samples, dtype=np.float64)
for t in traces:
s = np.add(s, t.samples[:min_samples])
s = (1 / len(traces)) * s
ts = np.zeros(min_samples, dtype=np.float64)
for t in traces:
d = np.subtract(t.samples[:min_samples], s)
ts = np.add(ts, np.multiply(d, d, dtype=np.float64))
var = (1 / (len(traces) - 1)) * ts
del ts
return (CombinedTrace(s), CombinedTrace(var))
@public
def add(*traces: Trace) -> Optional[CombinedTrace]:
"""
Add `traces`, sample-wise.
:param traces:
:return:
"""
if not traces:
return None
if len(traces) == 1:
return CombinedTrace(traces[0].samples.copy())
min_samples = min(map(len, traces))
s = np.zeros(min_samples, dtype=np.float64)
for t in traces:
s = np.add(s, t.samples[:min_samples])
return CombinedTrace(s)
@public
def subtract(one: Trace, other: Trace) -> CombinedTrace:
"""
Subtract `other` from `one`, sample-wise.
:param one:
:param other:
:return:
"""
return CombinedTrace(np.subtract(one.samples, other.samples))
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