def test_whole(self):
data = Orange.data.Table([[1, 5, 1]])
i = LinearBaseline()(data)
np.testing.assert_equal(i.X, [[0, 4, 0]])
data = Orange.data.Table([[4, 1, 2, 4]])
i = LinearBaseline(peak_dir=LinearBaseline.PeakNegative)(data)
np.testing.assert_equal(i.X, [[0, -3, -2, 0]])
def test_whole(self):
data = Table.from_numpy(None, [[1, 5, 1]])
i = LinearBaseline()(data)
np.testing.assert_equal(i.X, [[0, 4, 0]])
data = Table.from_numpy(None, [[4, 1, 2, 4]])
i = LinearBaseline(peak_dir=LinearBaseline.PeakNegative)(data)
np.testing.assert_equal(i.X, [[0, -3, -2, 0]])
def createinstance(params):
baseline_type = params.get("baseline_type", 0)
peak_dir = params.get("peak_dir", 0)
sub = params.get("sub", 0)
zero_points = params.get("zero_points", None)
if baseline_type == 0:
return LinearBaseline(peak_dir=peak_dir,
sub=sub,
zero_points=zero_points)
elif baseline_type == 1:
return RubberbandBaseline(peak_dir=peak_dir, sub=sub)
else:
raise Exception("unknown baseline type")
def test_edgepoints_out_of_data(self):
data = Table.from_numpy(None, [[1, 5, 1]])
i = LinearBaseline(zero_points=[0, 2.000000001])(data)
np.testing.assert_almost_equal(i.X, [[0, 4, 0]])
def test_3points(self):
data = Table.from_numpy(None, [[1, 5, 1, 5]])
i = LinearBaseline(zero_points=[0, 1, 3])(data)
np.testing.assert_equal(i.X, [[0, 0, -4, 0]])
def test_edgepoints_extrapolate(self):
data = Table.from_numpy(None, [[1, 5, 1]])
i = LinearBaseline(zero_points=[0, 1])(data)
np.testing.assert_equal(i.X, [[0, 0, -8]])
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,
int_method=Integrate.PeakMax,
lower=0,
upper=10000),
Normalize(method=Normalize.MinMax),
CurveShift(1),
Despike(threshold=5, cutoff=60, dis=5),
]
xas_norm_collagen = XASnormalization(edge=1630,
preedge_dict={
'from': 1000,
'to': 1300,
'deg': 1
def test_edgepoints_out_of_data(self):
data = Orange.data.Table([[1, 5, 1]])
i = LinearBaseline(zero_points=[0, 2.000000001])(data)
np.testing.assert_almost_equal(i.X, [[0, 4, 0]])
def test_3points(self):
data = Orange.data.Table([[1, 5, 1, 5]])
i = LinearBaseline(zero_points=[0, 1, 3])(data)
np.testing.assert_equal(i.X, [[0, 0, -4, 0]])
def test_edgepoints_extrapolate(self):
data = Orange.data.Table([[1, 5, 1]])
i = LinearBaseline(zero_points=[0, 1])(data)
np.testing.assert_equal(i.X, [[0, 0, -8]])
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()