Esempio n. 1
0
    def _convert_asm_shot(self,
                          descriptor,
                          x_pixel_size=109.9,
                          y_pixel_size=109.9,
                          bragg_peak_radius=458):
        """
        Convert a Kitty-generated assembled image h5 file to an odin.xray.Shot.
        """

        logger.info('Loading assembled image in: %s' % descriptor['data_file'])

        for field in self.essential_fields:
            if field not in descriptor.keys():
                raise ValueError('Essential data field %s not in YAML file!' %
                                 field)

        energy = float(descriptor['photon_eV']) / 1000.0  # eV -> keV
        path_length = float(descriptor['detector_mm']) * 1000.0  # mm -> um
        logger.debug('Energy:      %f keV' % energy)
        logger.debug('Path length: %f microns' % path_length)

        # extract intensity data
        f = tables.File(descriptor['data_file'])
        try:
            path = descriptor['i_asm']
        except KeyError as e:
            raise ValueError('Essential data field `i_asm` not in YAML file!')
        i = f.getNode(path).read()
        f.close()
        logger.debug('Read field %s in file: %s' %
                     (descriptor['i_asm'], descriptor['data_file']))

        # find the center (center is in pixel units)
        #dgeo = xray.DetectorGeometry(i)
        center = (853., 861.)  #dgeo.center
        corner = (-center[0] * x_pixel_size, -center[1] * y_pixel_size, 0.0)
        logger.debug('Found center: %s' % str(center))

        # compile a grid_list object
        basis = (x_pixel_size, y_pixel_size, 0.0)
        shape = (i.shape[0], i.shape[1], 1)
        grid_list = [(basis, shape, corner)]

        # generate the detector object
        b = xray.Beam(100, energy=energy)
        d = xray.Detector.from_basis(grid_list, path_length, b.k)

        logger.debug('Generated detector object...')

        # generate the shot
        s = xray.Shot(i.flatten(), d)

        return s
Esempio n. 2
0
 def _extract_energy(self, entry):
     """
     Scrape the following important metadata out of the .cxi file:
     -- beam energy
     -- 
     """
     energy_nodes = self._get_nodes('energy', root=entry, strict=True)
     if len(energy_nodes) > 1:
         raise RuntimeError(
             'Ambiguity in beam energy... %d energy nodes found' %
             len(energy_nodes))
     energy = float(energy_nodes[0].read()) / 1000.0
     beam = xray.Beam(1., energy=energy)  # todo: compute flux
     return beam
Esempio n. 3
0
    def test_evaluate_qmag(self):
        # doubles as a test for _evaluate_theta
        x = np.zeros((5, 3))
        x[:, 0] = np.random.randn(5)
        x[:, 2] = self.l

        S = x.copy()
        S = S / np.sqrt(np.sum(np.power(S, 2), axis=1))[:, None]
        S -= self.d.beam_vector

        b = xray.Beam(1, energy=self.energy)
        qref = b.k * np.sqrt(np.sum(np.power(S, 2), axis=1))

        qmag = self.d.evaluate_qmag(x)
        assert_allclose(qref, qmag)
Esempio n. 4
0
    def _convert_raw_shot(self, descriptor):
        """
        Convert a Kitty-generated raw image h5 file to an odin.xray.Shot.
        """

        logger.info('Loading raw image in: %s' % descriptor['data_file'])

        for field in self.essential_fields:
            if field not in descriptor.keys():
                raise ValueError('Essential data field %s not in YAML file!' %
                                 field)

        energy = float(descriptor['photon_eV'])
        path_length = float(descriptor['detector_mm']) * 1000.0  # mm -> um

        # load all the relevant data from many h5s... (dumb)
        f = tables.File(descriptor['x_raw'])
        x = f.root.data.data.read()
        f.close()

        f = tables.File(descriptor['y_raw'])
        y = f.root.data.data.read()
        f.close()

        z = np.zeros_like(x)

        f = tables.File(descriptor['data_file'])
        path = descriptor['i_raw']
        i = f.getNode(path).read()
        f.close()

        # turn that data into a basis representation
        grid_list, flat_i = self._lcls_raw_to_basis(x, y, z, i)

        # generate the detector object
        b = xray.Beam(100, energy=energy)
        d = xray.Detector.from_basis(grid_list, path_length, b.k)

        # generate the shot
        s = xray.Shot(flat_i, d)

        return s
Esempio n. 5
0
    def as_shotset(self):
        """
        Convert the CBF file to an ODIN shotset representation.
        
        Returns
        -------
        cbf : odin.xray.Shotset
            The CBF file as an ODIN shotset.
        """

        p = np.array(list(self.corner) + [self.path_length])
        f = np.array([self.pixel_size[0], 0.0, 0.0])  # fast is x
        s = np.array([0.0, self.pixel_size[1], 0.0])  # slow is y

        bg = xray.BasisGrid()
        bg.add_grid(p, s, f, self.intensities_shape)

        # todo better value for photons
        b = xray.Beam(1e4, wavelength=self.wavelength)
        d = xray.Detector(bg, b.k)
        s = xray.Shotset(self.intensities.flatten().astype(np.float64), d,
                         self.mask)

        return s
Esempio n. 6
0
 def test_unit_convs(self):
     beam = xray.Beam(self.n_photons, energy=1.0)
     assert_allclose(beam.wavelength, 12.398, rtol=1e-3)
     assert_allclose(beam.frequency, 2.4190e17, rtol=1e-3)
     assert_allclose(beam.wavenumber, (2.0 * np.pi) / 12.398, rtol=1e-3)