def test_interpolations(
xs, ys, interpolation, k
): # noqa: C901; the method is not complex, it's just a long switch case
# pylint: disable=too-many-branches
"""Tests the functionality of the interpolation functions."""
xs = sorted(xs)
bands = sumpf.Bands(bands=dict(zip(xs, ys)), interpolations=interpolation)
x = xs[0] + k * (xs[1] - xs[0])
if x == xs[0]:
assert bands(x)[0] == ys[0]
elif x == xs[1]:
assert bands(x)[0] == ys[1]
else:
if interpolation is sumpf.Bands.interpolations.ZERO:
assert bands(x)[0] == 0.0
elif interpolation is sumpf.Bands.interpolations.ONE:
assert bands(x)[0] == 1.0
elif interpolation is sumpf.Bands.interpolations.LINEAR:
assert bands(x)[0] == pytest.approx(numpy.interp(x, xs, ys))
elif interpolation is sumpf.Bands.interpolations.LOGARITHMIC:
bands = sumpf.Bands(bands={x: abs(y)
for x, y in zip(xs, ys)},
interpolations=interpolation)
log_xs = numpy.log2(xs)
log_ys = numpy.log(numpy.abs(ys))
assert bands(x)[0] == pytest.approx(numpy.exp(
numpy.interp(numpy.log2(x), log_xs, log_ys)),
nan_ok=True)
elif interpolation is sumpf.Bands.interpolations.LOG_X:
log_xs = numpy.log2(xs)
assert bands(x)[0] == pytest.approx(numpy.interp(
numpy.log2(x), log_xs, ys),
nan_ok=True)
elif interpolation is sumpf.Bands.interpolations.LOG_Y:
bands = sumpf.Bands(bands={x: abs(y)
for x, y in zip(xs, ys)},
interpolations=interpolation)
log_ys = numpy.log(numpy.abs(ys))
assert bands(x)[0] == pytest.approx(numpy.exp(
numpy.interp(x, xs, log_ys)),
nan_ok=True)
elif interpolation is sumpf.Bands.interpolations.STAIRS_LIN:
if k < 0.5:
assert bands(x)[0] == ys[0]
else:
assert bands(x)[0] == ys[1]
elif interpolation is sumpf.Bands.interpolations.STAIRS_LOG:
if numpy.log(x) - numpy.log(xs[0]) < numpy.log(
xs[1]) - numpy.log(x):
assert bands(x)[0] == ys[0]
else:
assert bands(x)[0] == ys[1]
else:
raise ValueError(f"Unknown interpolation: {interpolation}.")
def test_interpolation(
interpolation, xs, ys, k
): # noqa: C901; the function is not complex, it's just a long switch case
# pylint: disable=too-many-branches
"""Tests the computation of an interpolated value."""
func = sumpf_internal.interpolation.get(interpolation)
xs = numpy.array(sorted(xs))
if interpolation in (sumpf_internal.Interpolations.LOGARITHMIC,
sumpf_internal.Interpolations.LOG_X,
sumpf_internal.Interpolations.STAIRS_LOG) and \
min(xs) < 0.0:
xs -= min(xs)
if interpolation in (sumpf_internal.Interpolations.LOGARITHMIC,
sumpf_internal.Interpolations.LOG_Y):
ys = numpy.abs(ys)
x = xs[0] + k * (xs[1] - xs[0])
hypothesis.assume(
x not in xs
) # due to the limited precision of floating point numbers, this can still happen
if interpolation is sumpf_internal.Interpolations.ZERO:
assert func(x, xs, ys) == 0.0
elif interpolation is sumpf_internal.Interpolations.ONE:
assert func(x, xs, ys) == 1.0
elif interpolation is sumpf_internal.Interpolations.LINEAR:
assert func(x, xs, ys) == pytest.approx(numpy.interp(x, xs, ys))
elif interpolation is sumpf_internal.Interpolations.LOGARITHMIC:
log_xs = numpy.log2(xs)
log_ys = numpy.log(numpy.abs(ys))
assert func(x, xs, ys) == pytest.approx(numpy.exp(
numpy.interp(numpy.log2(x), log_xs, log_ys)),
nan_ok=True)
elif interpolation is sumpf_internal.Interpolations.LOG_X:
log_xs = numpy.log2(xs)
assert func(x, xs, ys) == pytest.approx(
numpy.interp(numpy.log2(x), log_xs, ys))
elif interpolation is sumpf_internal.Interpolations.LOG_Y:
log_ys = numpy.log(numpy.abs(ys))
assert func(x, xs,
ys) == pytest.approx(numpy.exp(numpy.interp(x, xs,
log_ys)),
nan_ok=True)
elif interpolation is sumpf_internal.Interpolations.STAIRS_LIN:
if k < 0.5:
assert func(x, xs, ys) == ys[0]
else:
assert func(x, xs, ys) == ys[1]
elif interpolation is sumpf_internal.Interpolations.STAIRS_LOG:
if numpy.log(x) - numpy.log(xs[0]) < numpy.log(xs[1]) - numpy.log(x):
assert func(x, xs, ys) == ys[0]
else:
assert func(x, xs, ys) == ys[1]
else:
raise ValueError(f"Unknown interpolation: {interpolation}.")
def xs_ys(data, interpolation):
"""A helper function, that creates arrays of x and y values from the data pairs,
that have been created by hypothesis.
"""
if data:
xs, ys = map(numpy.array, zip(*sorted(data)))
else:
xs = numpy.empty(0)
ys = numpy.empty(0)
if interpolation in (sumpf_internal.Interpolations.LOGARITHMIC,
sumpf_internal.Interpolations.LOG_X,
sumpf_internal.Interpolations.STAIRS_LOG):
if (xs <= 0).any():
xs -= xs.min()
xs += 1e-15
if interpolation in (sumpf_internal.Interpolations.LOGARITHMIC,
sumpf_internal.Interpolations.LOG_Y):
ys = numpy.abs(ys) + 1e-15
return xs, ys
def test_extrapolation(
xs, ys, interpolation, delta_x
): # noqa: C901; the function is not complex, it's just a long switch case
# pylint: disable=too-many-branches,too-many-statements
"""Tests the computation of an extrapolated value."""
func = sumpf_internal.interpolation.get(interpolation)
xs = numpy.array(sorted(xs))
if interpolation in (sumpf_internal.Interpolations.LOGARITHMIC,
sumpf_internal.Interpolations.LOG_X,
sumpf_internal.Interpolations.STAIRS_LOG) and \
min(xs) < 0.0:
xs -= min(xs)
if interpolation in (sumpf_internal.Interpolations.LOGARITHMIC,
sumpf_internal.Interpolations.LOG_Y):
ys = numpy.abs(ys)
x0 = xs[0] * (1.0 - delta_x) - delta_x
x1 = xs[1] * (1.0 + delta_x) + delta_x
if interpolation is sumpf_internal.Interpolations.ZERO:
assert func(x0, xs, ys) == 0.0
assert func(x1, xs, ys) == 0.0
elif interpolation is sumpf_internal.Interpolations.ONE:
assert func(x0, xs, ys) == 1.0
assert func(x1, xs, ys) == 1.0
elif interpolation is sumpf_internal.Interpolations.LINEAR:
m = (ys[1] - ys[0]) / (xs[1] - xs[0])
n0 = ys[0] - m * xs[0]
n1 = ys[1] - m * xs[1]
assert func(x0, xs, ys) == pytest.approx(m * x0 + n0)
assert func(x1, xs, ys) == pytest.approx(m * x1 + n1)
elif interpolation is sumpf_internal.Interpolations.LOGARITHMIC:
if 0.0 in ys:
assert numpy.isnan(func(x0, xs, ys))
assert numpy.isnan(func(x1, xs, ys))
else:
log_xs = numpy.log2(xs)
log_ys = numpy.log2(ys)
m = (log_ys[1] - log_ys[0]) / (log_xs[1] - log_xs[0])
r0 = numpy.exp2(m * numpy.log2(x0) + log_ys[0] - m * log_xs[0])
r1 = numpy.exp2(m * numpy.log2(x1) + log_ys[1] - m * log_xs[1])
assert (numpy.isnan(func(x0, xs, ys))
and numpy.isnan(r0)) or (func(x0, xs, ys)
== pytest.approx(r0))
assert (numpy.isnan(func(x1, xs, ys))
and numpy.isnan(r1)) or (func(x1, xs, ys)
== pytest.approx(r1))
elif interpolation is sumpf_internal.Interpolations.LOG_X:
log_xs = numpy.log2(xs)
m = (ys[1] - ys[0]) / (log_xs[1] - log_xs[0])
r0 = m * numpy.log2(x0) + ys[0] - m * log_xs[0]
r1 = m * numpy.log2(x1) + ys[1] - m * log_xs[1]
assert (numpy.isnan(func(x0, xs, ys)) and numpy.isnan(r0)) or (func(
x0, xs, ys) == pytest.approx(r0))
assert (numpy.isnan(func(x1, xs, ys)) and numpy.isnan(r1)) or (func(
x1, xs, ys) == pytest.approx(r1))
elif interpolation is sumpf_internal.Interpolations.LOG_Y:
if 0.0 in ys:
assert numpy.isnan(func(x0, xs, ys))
assert numpy.isnan(func(x1, xs, ys))
else:
log_ys = numpy.log2(ys)
m = (log_ys[1] - log_ys[0]) / (xs[1] - xs[0])
n0 = log_ys[0] - m * xs[0]
n1 = log_ys[1] - m * xs[1]
assert func(x0, xs, ys) == pytest.approx(numpy.exp2(m * x0 + n0))
assert func(x1, xs, ys) == pytest.approx(numpy.exp2(m * x1 + n1))
elif interpolation is sumpf_internal.Interpolations.STAIRS_LIN:
assert func(x0, xs, ys) == ys[0]
assert func(x1, xs, ys) == ys[1]
elif interpolation is sumpf_internal.Interpolations.STAIRS_LOG:
assert func(x0, xs, ys) == ys[0]
assert func(x1, xs, ys) == ys[1]
else:
raise ValueError(f"Unknown interpolation: {interpolation}.")
def test_extrapolations(xs, ys, extrapolation, delta_x): # pylint: disable=too-many-statements
"""Tests the functionality of the extrapolation functions."""
xs = sorted(xs)
assert xs[1] - xs[0]
bands = sumpf.Bands(bands=dict(zip(xs, ys)), extrapolations=extrapolation)
if delta_x == 0.0:
assert bands(xs[0])[0] == ys[0]
assert bands(xs[1])[0] == ys[1]
else:
x0 = xs[0] - delta_x
x1 = xs[1] + delta_x
if extrapolation is sumpf.Bands.interpolations.ZERO:
assert bands(x0)[0] == 0.0
assert bands(x1)[0] == 0.0
elif extrapolation is sumpf.Bands.interpolations.ONE:
assert bands(x0)[0] == 1.0
assert bands(x1)[0] == 1.0
elif extrapolation is sumpf.Bands.interpolations.LINEAR:
m = (ys[1] - ys[0]) / (xs[1] - xs[0])
n0 = ys[0] - m * xs[0]
n1 = ys[1] - m * xs[1]
assert bands(x0)[0] == pytest.approx(m * x0 + n0)
assert bands(x1)[0] == pytest.approx(m * x1 + n1)
elif extrapolation is sumpf.Bands.interpolations.LOGARITHMIC:
bands = sumpf.Bands(bands={x: abs(y)
for x, y in zip(xs, ys)},
extrapolations=extrapolation)
log_xs = numpy.log2(xs)
log_ys = numpy.log2(numpy.abs(ys))
m = (log_ys[1] - log_ys[0]) / (log_xs[1] - log_xs[0])
n0 = log_ys[0] - m * log_xs[0]
n1 = log_ys[1] - m * log_xs[1]
r0 = n0 + log_ys[0] - m * log_xs[0]
r1 = n1 + log_ys[1] - m * log_xs[1]
assert bands(x0)[0] == pytest.approx(
numpy.exp2(m * numpy.log2(x0) + n0), nan_ok=True)
assert bands(x1)[0] == pytest.approx(
numpy.exp2(m * numpy.log2(x1) + n1), nan_ok=True)
elif extrapolation is sumpf.Bands.interpolations.LOG_X:
log_xs = numpy.log2(xs)
m = (ys[1] - ys[0]) / (log_xs[1] - log_xs[0])
r0 = m * numpy.log2(x0) + ys[0] - m * log_xs[0]
r1 = m * numpy.log2(x1) + ys[1] - m * log_xs[1]
assert (numpy.isnan(bands(x0)[0])
and numpy.isnan(r0)) or (bands(x0)[0] == pytest.approx(r0))
assert (numpy.isnan(bands(x1)[0])
and numpy.isnan(r1)) or (bands(x1)[0] == pytest.approx(r1))
elif extrapolation is sumpf.Bands.interpolations.LOG_Y:
bands = sumpf.Bands(bands={x: abs(y)
for x, y in zip(xs, ys)},
extrapolations=extrapolation)
log_ys = numpy.log2(numpy.abs(ys))
m = (log_ys[1] - log_ys[0]) / (xs[1] - xs[0])
n0 = log_ys[0] - m * xs[0]
n1 = log_ys[1] - m * xs[1]
assert bands(x0)[0] == pytest.approx(numpy.exp2(m * x0 + n0),
nan_ok=True)
assert bands(x1)[0] == pytest.approx(numpy.exp2(m * x1 + n1),
nan_ok=True)
elif extrapolation is sumpf.Bands.interpolations.STAIRS_LIN:
assert bands(x0)[0] == ys[0]
assert bands(x1)[0] == ys[1]
elif extrapolation is sumpf.Bands.interpolations.STAIRS_LOG:
assert bands(x0)[0] == ys[0]
assert bands(x1)[0] == ys[1]
else:
raise ValueError(f"Unknown extrapolation: {extrapolation}.")