def test_invariance_to_unequal_sampling():
# setup
random = np.random.RandomState(42)
lc_path = os.path.join(DATA_PATH, "FATS_aligned.npz")
with np.load(lc_path) as npz:
lc = dict(npz)
# tests performed to the features in order to check their invariance
# to unequal sampling. To do so, we take random observations of a
# light-curve and compare the resulting features with the ones obtained
# from the original data.
fs = FeatureSpace()
# We calculate the features values for fifty random samples of the
# original light-curve:
features_values = []
for i in range(50):
sample = shuffle(random=random, **lc)
features, values = fs.extract(**sample)
result = dict(zip(features, values))
features_values.append(result)
# We obtain the mean and standard deviation of each calculated feature:
stats = pd.DataFrame(features_values).aggregate([np.mean, np.std])
# Original light-curve:
features, values = fs.extract(
magnitude=lc["mag"],
time=lc["time"],
error=lc["error"],
magnitude2=lc["mag2"],
aligned_magnitude=lc["aligned_mag"],
aligned_magnitude2=lc["aligned_mag2"],
aligned_time=lc["aligned_time"],
aligned_error=lc["aligned_error"],
aligned_error2=lc["aligned_error2"],
)
def normalize(c):
name, value = c.name, c[0]
mean, std = stats[name]["mean"], stats[name]["std"]
normalized = (value - mean) / std
return normalized
original = pd.DataFrame([dict(zip(features, values))])
with warnings.catch_warnings():
warnings.filterwarnings(
action="ignore",
message="invalid value encountered in double_scalars",
)
result = original.apply(normalize)
assert np.abs(result.mean()) < 0.12
assert result.std() < 1.09
def test_StructureFunction(random_walk):
fs = FeatureSpace(only=[
"StructureFunction_index_21",
"StructureFunction_index_31",
"StructureFunction_index_32",
])
result = fs.extract(**random_walk).as_arrays()[1]
assert result[0] >= 1.520 and result[0] <= 2.067
assert result[1] >= 1.821 and result[1] <= 3.162
assert result[2] >= 1.243 and result[2] <= 1.562
def test_Period(periodic_lc):
params = {
"lscargle_kwds": {
"autopower_kwds": {
"normalization": "standard",
"nyquist_factor": 1,
}
}
}
fs = FeatureSpace(only=["PeriodLS"], LombScargle=params)
result = fs.extract(**periodic_lc).as_arrays()[1][0]
assert result >= 19 and result <= 21
def test_FluxPercentile(white_noise):
fs = FeatureSpace(only=[
"FluxPercentileRatioMid20",
"FluxPercentileRatioMid35",
"FluxPercentileRatioMid50",
"FluxPercentileRatioMid65",
"FluxPercentileRatioMid80",
])
result = fs.extract(**white_noise).as_arrays()[1]
assert result[0] >= 0.145 and result[0] <= 0.160
assert result[1] >= 0.260 and result[1] <= 0.290
assert result[2] >= 0.350 and result[2] <= 0.450
assert result[3] >= 0.540 and result[3] <= 0.580
assert result[4] >= 0.760 and result[4] <= 0.800
def test_Con(white_noise):
fs = FeatureSpace(only=["Con"], Con={"consecutiveStar": 1})
result = fs.extract(**white_noise).as_arrays()[1][0]
assert result >= 0.04 and result <= 0.05
def test_Mean(white_noise):
fs = FeatureSpace(only=["Mean"])
result = fs.extract(**white_noise).as_arrays()[1][0]
assert result >= -0.1 and result <= 0.1
def test_Beyond1Std(white_noise):
fs = FeatureSpace(only=["Beyond1Std"])
result = fs.extract(**white_noise).as_arrays()[1][0]
assert result >= 0.30 and result <= 0.40
def test_Gskew(white_noise):
fs = FeatureSpace(only=["Gskew"])
result = fs.extract(**white_noise).as_arrays()[1][0]
assert result >= -0.2 and result <= 0.2
def test_Meanvariance(uniform_lc):
fs = FeatureSpace(only=["Meanvariance"])
result = fs.extract(**uniform_lc).as_arrays()[1][0]
assert result >= 0.575 and result <= 0.580
def test_SmallKurtosis(white_noise):
fs = FeatureSpace(only=["SmallKurtosis"])
result = fs.extract(**white_noise).as_arrays()[1][0]
assert result >= -0.2 and result <= 0.2
def test_Rcs(white_noise):
fs = FeatureSpace(only=["Rcs"])
result = fs.extract(**white_noise).as_arrays()[1][0]
assert result >= 0 and result <= 0.1
def test_Q31(white_noise):
fs = FeatureSpace(only=["Q31"])
result = fs.extract(**white_noise).as_arrays()[1][0]
assert result >= 1.30 and result <= 1.38
def test_PairSlopeTrend(white_noise):
fs = FeatureSpace(only=["PairSlopeTrend"])
result = fs.extract(**white_noise).as_arrays()[1][0]
assert result >= -0.25 and result <= 0.25
def test_MedianAbsDev(white_noise):
fs = FeatureSpace(only=["MedianAbsDev"])
result = fs.extract(**white_noise).as_arrays()[1][0]
assert result >= 0.630 and result <= 0.700
def test_Eta_e(white_noise):
fs = FeatureSpace(only=["Eta_e"])
result = fs.extract(**white_noise).as_arrays()[1][0]
assert result >= 1.9 and result <= 2.1
def test_Std(white_noise):
fs = FeatureSpace(only=["Std"])
result = fs.extract(**white_noise).as_arrays()[1][0]
assert result >= 0.9 and result <= 1.1
def test_LinearTrend(white_noise):
fs = FeatureSpace(only=["LinearTrend"])
result = fs.extract(**white_noise).as_arrays()[1][0]
assert result >= -0.1 and result <= 0.1
