コード例 #1
0
    def test_compensate(self):
        """Test applying efficiency compensation"""
        # Spectrum
        data = numpy.ones((251, 1, 1, 200, 300), dtype="uint16") + 1
        wld = 433e-9 + numpy.arange(data.shape[0]) * 0.1e-9
        spec = model.DataArray(data, metadata={model.MD_WL_LIST: wld})

        # Background data
        dbckg = numpy.ones(data.shape, dtype=numpy.uint16)
        wl_bckg = 400e-9 + numpy.arange(dbckg.shape[0]) * 10e-9
        obckg = model.DataArray(dbckg, metadata={model.MD_WL_LIST: wl_bckg})
        bckg = calibration.get_spectrum_data([obckg])

        # Compensation data
        dcalib = numpy.array([1, 1.3, 2, 3.5, 4, 5, 0.1, 6, 9.1],
                             dtype=numpy.float)
        dcalib.shape = (dcalib.shape[0], 1, 1, 1, 1)
        wl_calib = 400e-9 + numpy.arange(dcalib.shape[0]) * 10e-9
        calib = model.DataArray(dcalib, metadata={model.MD_WL_LIST: wl_calib})

        compensated = calibration.apply_spectrum_corrections(spec, bckg, calib)

        self.assertEqual(spec.shape, compensated.shape)
        numpy.testing.assert_equal(spec.metadata[model.MD_WL_LIST],
                                   compensated.metadata[model.MD_WL_LIST])

        for i in range(dcalib.shape[0] - 1):
            ca, cb = calib[i], calib[i + 1]
            wla, wlb = wl_calib[i], wl_calib[i + 1]
            # All the values between the 2 wavelengths should be compensated
            # between the 2 factors

            for vo, vb, vc, wl in zip(spec[..., 3, 3], bckg[..., 0, 0],
                                      compensated[..., 3, 3], wld):
                if wla <= wl <= wlb:
                    expa, expb = (vo - vb) * ca, (vo - vb) * cb
                    minc, maxc = min(expa, expb), max(expa, expb)
                    self.assertTrue(minc <= vc <= maxc)
コード例 #2
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    def test_compensate_out(self):
        """Test applying efficiency compensation on an edge of calibration"""
        # Spectrum
        data = numpy.ones((251, 1, 1, 200, 300), dtype="uint16")
        wld = 333e-9 + numpy.arange(data.shape[0]) * 0.1e-9
        spec = model.DataArray(data, metadata={model.MD_WL_LIST: wld})

        # Only from 400 nm => need to use the border (=1) for everything below
        dcalib = numpy.array([1, 1, 2, 3, 4, 5, 1, 6, 9], dtype=numpy.float)
        dcalib.shape = (dcalib.shape[0], 1, 1, 1, 1)
        wl_calib = 400e-9 + numpy.arange(dcalib.shape[0]) * 10e-9
        calib = model.DataArray(dcalib, metadata={model.MD_WL_LIST: wl_calib})

        compensated = calibration.apply_spectrum_corrections(spec, coef=calib)

        self.assertEqual(spec.shape, compensated.shape)
        numpy.testing.assert_equal(spec.metadata[model.MD_WL_LIST],
                                   compensated.metadata[model.MD_WL_LIST])

        # Value before the first calibration wavelength must be estimated
        for vo, vc, wl in zip(spec[..., 3, 3], compensated[..., 3, 3], wld):
            if wl <= wl_calib[0]:
                self.assertEqual(vo * dcalib[0], vc)
コード例 #3
0
ファイル: _static.py プロジェクト: lanery/odemis
    def _updateCalibratedData(self, bckg=None, coef=None):
        """
        Try to update the data with a new calibration. The two parameters are
        the same as apply_spectrum_corrections(). The input data comes from
        .raw and the calibrated data is saved in .calibrated
        :param bckg: (DataArray or None) The background image.
        :param coef: (DataArray or None) The spectrum efficiency correction data.
        :raise ValueError: If the data and calibration data are not valid or
          compatible. In that case the current calibrated data is unchanged.
        """
        data = self.raw[0]  # only one image in .raw for spectrum, temporal spectrum and chronograph

        if data is None:
            self.calibrated.value = None
            return

        if bckg is None and coef is None:
            # make sure to not display any other error
            self.calibrated.value = data
            return

        calibrated = calibration.apply_spectrum_corrections(data, bckg, coef)
        self.calibrated.value = calibrated