Python smaller_data Examples

Example #1

import numpy as np

import Orange
from Orange.widgets.tests.base import WidgetTest
from Orange.preprocess.preprocess import Preprocess

from orangecontrib.spectroscopy.data import getx
from orangecontrib.spectroscopy.tests import spectral_preprocess
from orangecontrib.spectroscopy.tests.spectral_preprocess import pack_editor, wait_for_preview
from orangecontrib.spectroscopy.widgets.owpreprocess import OWPreprocess, PREPROCESSORS, \
    CutEditor, SavitzkyGolayFilteringEditor
from orangecontrib.spectroscopy.widgets.preprocessors.utils import BaseEditorOrange, \
    REFERENCE_DATA_PARAM
from orangecontrib.spectroscopy.tests.util import smaller_data

SMALL_COLLAGEN = smaller_data(Orange.data.Table("collagen"), 70, 4)


class TestAllPreprocessors(WidgetTest):
    def test_allpreproc_indv(self):
        data = Orange.data.Table("peach_juice.dpt")
        for p in PREPROCESSORS:
            self.widget = self.create_widget(OWPreprocess)
            self.send_signal("Data", data)
            self.widget.add_preprocessor(p)
            self.widget.unconditional_commit()
            wait_for_preview(self.widget)
            self.wait_until_finished()

    def test_allpreproc_indv_empty(self):
        data = Orange.data.Table("peach_juice.dpt")[:0]

Example #2

File: test_preprocess.py Project: noahcabanas/orange-spectroscopy

import Orange
from Orange.data import Table
from Orange.preprocess.preprocess import PreprocessorList

from orangecontrib.spectroscopy.data import getx
from orangecontrib.spectroscopy.preprocess import Absorbance, Transmittance, \
    Integrate, Interpolate, Cut, SavitzkyGolayFiltering, \
    GaussianSmoothing, PCADenoising, RubberbandBaseline, \
    Normalize, LinearBaseline, CurveShift, EMSC, MissingReferenceException, \
    WrongReferenceException, NormalizeReference, XASnormalization, ExtractEXAFS, PreprocessException, \
    NormalizePhaseReference, Despike
from orangecontrib.spectroscopy.preprocess.me_emsc import ME_EMSC
from orangecontrib.spectroscopy.tests.util import smaller_data

COLLAGEN = Orange.data.Table("collagen")
SMALL_COLLAGEN = smaller_data(COLLAGEN, 2, 2)
SMALLER_COLLAGEN = smaller_data(COLLAGEN[195:621], 40,
                                4)  # only glycogen and lipids


def preprocessor_data(preproc):
    """
    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

Example #3

File: test_preprocess.py Project: stuart-cls/orange-infrared

import random
import unittest

import numpy as np

import Orange

from orangecontrib.spectroscopy.data import getx
from orangecontrib.spectroscopy.preprocess import Absorbance, Transmittance, \
    Integrate, Interpolate, Cut, SavitzkyGolayFiltering, \
    GaussianSmoothing, PCADenoising, RubberbandBaseline, \
    Normalize, LinearBaseline, CurveShift, EMSC
from orangecontrib.spectroscopy.tests.util import smaller_data

SMALL_COLLAGEN = smaller_data(Orange.data.Table("collagen"), 2, 2)


# 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]]),