def test_domain_conversion(self):
"""Test whether a domain can be used for conversion."""
data = Transmittance()(Orange.data.Table("collagen.csv"))
absorbance = Absorbance()(data)
nt = Orange.data.Table.from_table(absorbance.domain, data)
self.assertEqual(absorbance.domain, nt.domain)
np.testing.assert_equal(absorbance.X, nt.X)
np.testing.assert_equal(absorbance.Y, nt.Y)
def test_roundtrip(self):
"""Test TR -> AB -> TR calculation"""
# actually AB -> TR -> AB -> TR
data = Transmittance()(Orange.data.Table("collagen.csv"))
calcdata = Transmittance()(Absorbance()(data))
np.testing.assert_allclose(data.X, calcdata.X)
def test_roundtrip(self):
"""Test AB -> TR -> AB calculation"""
data = Orange.data.Table("collagen.csv")
calcdata = Absorbance()(Transmittance()(data))
np.testing.assert_allclose(data.X, calcdata.X)
from orangecontrib.infrared.data import getx
from orangecontrib.infrared.preprocess import Absorbance, Transmittance, \
Integrate, Interpolate, Cut, SavitzkyGolayFiltering, \
GaussianSmoothing, PCADenoising, RubberbandBaseline, \
Normalize
# Preprocessors that work per sample and should return the same
# result for a sample independent of the other samples
PREPROCESSORS_INDEPENDENT_SAMPLES = [
Interpolate(np.linspace(1000, 1700, 100)),
SavitzkyGolayFiltering(window=9, polyorder=2, deriv=2),
Cut(lowlim=1000, highlim=1800),
GaussianSmoothing(sd=3.),
Absorbance(),
Transmittance(),
Integrate(limits=[[900, 100], [1100, 1200], [1200, 1300]]),
Integrate(methods=Integrate.Simple, limits=[[1100, 1200]]),
Integrate(methods=Integrate.Baseline, limits=[[1100, 1200]]),
Integrate(methods=Integrate.PeakMax, limits=[[1100, 1200]]),
Integrate(methods=Integrate.PeakBaseline, limits=[[1100, 1200]]),
Integrate(methods=Integrate.PeakAt, limits=[[1100]]),
Integrate(methods=Integrate.PeakX, limits=[[1100, 1200]]),
Integrate(methods=Integrate.PeakXBaseline, limits=[[1100, 1200]]),
RubberbandBaseline(),
Normalize(method=Normalize.Vector),
Normalize(method=Normalize.Area, int_method=Integrate.PeakMax, lower=0, upper=10000),
]
# Preprocessors that use groups of input samples to infer
# internal parameters.