def test_cut_both(self):
d = self.collagen
dcut = Cut(lowlim=0, highlim=2)(d)
self.assertFalse(getx(dcut))
dcut = Cut(lowlim=1000, highlim=1100)(d)
self.assertGreaterEqual(min(getx(dcut)), 1000)
self.assertLessEqual(max(getx(dcut)), 1100)
def test_cut_single_inverse(self):
d = self.collagen
dcut = Cut(lowlim=1000, inverse=True)(d)
self.assertLessEqual(max(getx(dcut)), 1000)
self.assertEqual(min(getx(dcut)), min(getx(d)))
dcut = Cut(highlim=1000, inverse=True)(d)
self.assertGreaterEqual(min(getx(dcut)), 1000)
self.assertEqual(max(getx(dcut)), max(getx(d)))
def test_cut_both_inverse(self):
d = self.collagen
# cutting out of x interval - need all
dcut = Cut(lowlim=0, highlim=2, inverse=True)(d)
np.testing.assert_equal(getx(dcut), getx(d))
# cutting in the middle - edged are the same
dcut = Cut(lowlim=1000, highlim=1100, inverse=True)(d)
dcutx = getx(dcut)
self.assertEqual(min(dcutx), min(getx(d)))
self.assertEqual(max(dcutx), max(getx(d)))
self.assertLess(len(dcutx), len(getx(d)))
np.testing.assert_equal(np.where(dcutx < 1100), np.where(dcutx < 1000))
np.testing.assert_equal(np.where(dcutx > 1100), np.where(dcutx > 1000))
def test_predict_different_domain_interpolation(self):
train, test = separate_learn_test(self.collagen)
aucorig = AUC(TestOnTestData()(train, test, [LogisticRegressionLearner()]))
test = Interpolate(points=getx(test) - 1.)(test) # other test domain
train = Interpolate(points=getx(train))(train) # make train capable of interpolation
aucshift = AUC(TestOnTestData()(train, test, [LogisticRegressionLearner()]))
self.assertAlmostEqual(aucorig, aucshift, delta=0.01) # shift can decrease AUC slightly
test = Cut(1000, 1700)(test)
auccut1 = AUC(TestOnTestData()(train, test, [LogisticRegressionLearner()]))
test = Cut(1100, 1600)(test)
auccut2 = AUC(TestOnTestData()(train, test, [LogisticRegressionLearner()]))
test = Cut(1200, 1500)(test)
auccut3 = AUC(TestOnTestData()(train, test, [LogisticRegressionLearner()]))
# the more we cut the lower precision we get
self.assertTrue(aucorig > auccut1 > auccut2 > auccut3)
orig_data.domain.metas + data.domain.attributes,
)
data_anno = orig_data.transform(dom_anno)
data_anno.metas[:, len(orig_data.domain.metas):] = data.X
progress_interrupt(100)
return data, data_fits, data_resid, data_anno
def on_done(self, results):
fit_params, fits, residuals, annotated_data = results
self.Outputs.fit_params.send(fit_params)
self.Outputs.fits.send(fits)
self.Outputs.residuals.send(residuals)
self.Outputs.annotated_data.send(annotated_data)
def on_exception(self, ex):
try:
super().on_exception(ex)
except ValueError:
self.Error.applying(ex)
self.Outputs.fit_params.send(None)
self.Outputs.fits.send(None)
self.Outputs.residuals.send(None)
self.Outputs.annotated_data.send(None)
if __name__ == "__main__": # pragma: no cover
data = Cut(lowlim=1360, highlim=1700)(Table("collagen")[0:3])
WidgetPreview(OWPeakFit).run(data)
"""
Rerturn appropriate test file for a preprocessor.
Very slow preprocessors should get smaller files.
"""
if isinstance(preproc, ME_EMSC):
return SMALLER_COLLAGEN
return SMALL_COLLAGEN
# 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(),
LinearBaseline(),
Normalize(method=Normalize.Vector),
Normalize(method=Normalize.Area,
import numpy as np
from Orange.widgets.data.utils.preprocess import DescriptionRole
from Orange.widgets.tests.base import WidgetTest
from orangewidget.tests.base import GuiTest
from orangecontrib.spectroscopy.data import getx
from orangecontrib.spectroscopy.preprocess import Cut, LinearBaseline
from orangecontrib.spectroscopy.tests.spectral_preprocess import wait_for_preview
from orangecontrib.spectroscopy.widgets.gui import MovableVline
from orangecontrib.spectroscopy.widgets.owpeakfit import OWPeakFit, fit_peaks, PREPROCESSORS, \
create_model, prepare_params, unique_prefix, create_composite_model, pack_model_editor
from orangecontrib.spectroscopy.widgets.peak_editors import ParamHintBox, VoigtModelEditor, \
PseudoVoigtModelEditor, ExponentialGaussianModelEditor, PolynomialModelEditor
COLLAGEN = Orange.data.Table("collagen")[0:3]
COLLAGEN_2 = LinearBaseline()(Cut(lowlim=1500, highlim=1700)(COLLAGEN))
COLLAGEN_1 = LinearBaseline()(Cut(lowlim=1600, highlim=1700)(COLLAGEN_2))
class TestOWPeakFit(WidgetTest):
def setUp(self):
self.widget = self.create_widget(OWPeakFit)
self.data = COLLAGEN_1
def test_load_unload(self):
self.send_signal("Data", Orange.data.Table("iris.tab"))
self.send_signal("Data", None)
def test_allint_indv(self):
for p in PREPROCESSORS:
with self.subTest(msg=f"Testing model {p.name}"):
self.info_preproc.setText(
self._format_preproc_str(
self.preprocessor).lstrip("\n"))
else:
# only allow readers with tile-by-tile support to run.
reader = None
return reader
elif self.source == self.URL:
url = self.url_combo.currentText().strip()
if url:
return UrlReader(url)
if __name__ == "__main__":
import sys
from orangecontrib.spectroscopy.preprocess import Cut, LinearBaseline
from Orange.preprocess.preprocess import PreprocessorList
import orangecontrib.protospec #load readers
a = QApplication(sys.argv)
# preproc = PreprocessorList([Cut(lowlim=2000, highlim=2006), LinearBaseline()])
preproc = PreprocessorList(
[LinearBaseline(), Cut(lowlim=2000, highlim=2006)])
# preproc = PreprocessorList([Cut(lowlim=2000, highlim=2006), Cut(lowlim=2002, highlim=2006)])
# Test 2nd Deriv superwide
# from orangecontrib.spectroscopy.preprocess import Normalize, Integrate, integrate, SavitzkyGolayFiltering
# preproc = PreprocessorList([Normalize(lower=1591.0484214631633, upper=1719.720747038586, int_method=integrate.IntegrateFeatureSimple), SavitzkyGolayFiltering(window=13, deriv=2), Integrate(methods=integrate.IntegrateFeatureAtPeak, limits=[[1625.0, 1.0], [1152.0, 1.0], [1575.0, 1.0], [1127.0, 1.0]])])
ow = OWTilefile()
ow.update_preprocessor(preproc)
ow.show()
a.exec_()
ow.saveSettings()
