"""Provides functions for combining traces sample-wise."""
from typing import Callable, Tuple
import numpy as np
from public import public
from .trace import Trace, CombinedTrace
[docs]
@public
def average(*traces: Trace) -> CombinedTrace:
"""
Average :paramref:`~.average.traces`, sample-wise.
:param traces:
:return:
"""
if not traces:
raise ValueError("Nothing to combine")
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)
[docs]
@public
def conditional_average(
*traces: Trace, condition: Callable[[Trace], bool]
) -> CombinedTrace:
"""
Average :paramref:`~.conditional_average.traces` for which the :paramref:`~.conditional_average.condition` is ``True``, sample-wise.
:param traces:
:param condition:
:return:
"""
return average(*filter(condition, traces))
[docs]
@public
def standard_deviation(*traces: Trace) -> CombinedTrace:
"""
Compute the sample standard-deviation of the :paramref:`~.standard_deviation.traces`, sample-wise.
:param traces:
:return:
"""
if not traces:
raise ValueError("Nothing to combine")
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)
[docs]
@public
def variance(*traces: Trace) -> CombinedTrace:
"""
Compute the sample variance of the :paramref:`~.variance.traces`, sample-wise.
:param traces:
:return:
"""
if not traces:
raise ValueError("Nothing to combine")
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)
[docs]
@public
def average_and_variance(*traces) -> Tuple[CombinedTrace, CombinedTrace]:
"""
Compute the average and sample variance of the :paramref:`~.average_and_variance.traces`, sample-wise.
:param traces:
:return:
"""
if not traces:
raise ValueError("Nothing to combine")
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))
[docs]
@public
def add(*traces: Trace) -> CombinedTrace:
"""
Add :paramref:`~.add.traces`, sample-wise.
:param traces:
:return:
"""
if not traces:
raise ValueError("Nothing to combine")
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)
[docs]
@public
def subtract(one: Trace, other: Trace) -> CombinedTrace:
"""
Subtract :paramref:`~.subtract.other` from :paramref:`~.subtract.one`, sample-wise.
:param one:
:param other:
:return:
"""
return CombinedTrace(np.subtract(one.samples, other.samples))
