Python AzimuthalIntegrator.twoThetaArray方法代码示例

# 需要导入模块: from pyFAI.azimuthalIntegrator import AzimuthalIntegrator [as 别名]
# 或者: from pyFAI.azimuthalIntegrator.AzimuthalIntegrator import twoThetaArray [as 别名]
    def diff_tth_X(self, dx=0.1):
        """
        Jerome peux-tu décrire de quoi il retourne ???

        @param dx: ???
        @type: float ???

        @return: ???
        @rtype: ???
        """
        f = self.ai.getFit2D()
        fp = f.copy()
        fm = f.copy()
        fm["centerX"] -= dx / 2.0
        fp["centerX"] += dx / 2.0
        ap = AzimuthalIntegrator()
        am = AzimuthalIntegrator()
        ap.setFit2D(**fp)
        am.setFit2D(**fm)
        dtthX = (ap.twoThetaArray(self.shape) - am.twoThetaArray(self.shape))\
            / dx
        tth, I = self.ai.xrpd(self.img, max(self.shape))
        dI = SGModule.getSavitzkyGolay(I, npoints=5, degree=2, order=1)\
            / (tth[1] - tth[0])
        dImg = self.reconstruct(tth, dI)
        return (dtthX * dImg).sum()

# 需要导入模块: from pyFAI.azimuthalIntegrator import AzimuthalIntegrator [as 别名]
# 或者: from pyFAI.azimuthalIntegrator.AzimuthalIntegrator import twoThetaArray [as 别名]
    def diff_Fit2D(self, axis="all", dx=0.1):
        """
        ???

        @param axis: ???
        @type axis: ???
        @param dx: ???
        @type dx: ???

        @return: ???
        @rtype: ???
        """
        tth, I = self.ai.xrpd(self.img, max(self.shape))
        dI = SGModule.getSavitzkyGolay(I, npoints=5, degree=2, order=1) / (tth[1] - tth[0])
        dImg = self.reconstruct(tth, dI)
        f = self.ai.getFit2D()
        tth2d_ref = self.ai.twoThetaArray(self.shape)  # useless variable ???

        keys = ["centerX", "centerY", "tilt", "tiltPlanRotation"]
        if axis != "all":
            keys = [i for i in keys if i == axis]
        grad = {}
        for key in keys:
            fp = f.copy()
            fp[key] += dx
            ap = AzimuthalIntegrator()
            ap.setFit2D(**fp)
            dtth = (ap.twoThetaArray(self.shape) - self.ai.twoThetaArray(self.shape)) / dx
            grad[key] = (dtth * dImg).sum()
        if axis == "all":
            return grad
        else:
            return grad[axis]

# 需要导入模块: from pyFAI.azimuthalIntegrator import AzimuthalIntegrator [as 别名]
# 或者: from pyFAI.azimuthalIntegrator.AzimuthalIntegrator import twoThetaArray [as 别名]
    def diff_tth_tilt(self, dx=0.1):
        """
        idem ici ???

        @param dx: ???
        @type dx: float ???

        @return: ???
        @rtype: ???
        """
        f = self.ai.getFit2D()
        fp = f.copy()
        fm = f.copy()
        fm["tilt"] -= dx / 2.0
        fp["tilt"] += dx / 2.0
        ap = AzimuthalIntegrator()
        am = AzimuthalIntegrator()
        ap.setFit2D(**fp)
        am.setFit2D(**fm)
        dtthX = (ap.twoThetaArray(self.shape) - am.twoThetaArray(self.shape)) / dx
        tth, I = self.ai.xrpd(self.img, max(self.shape))
        dI = SGModule.getSavitzkyGolay(I, npoints=5, degree=2, order=1) / (tth[1] - tth[0])
        dImg = self.reconstruct(tth, dI)
        return (dtthX * dImg).sum()

# 需要导入模块: from pyFAI.azimuthalIntegrator import AzimuthalIntegrator [as 别名]
# 或者: from pyFAI.azimuthalIntegrator.AzimuthalIntegrator import twoThetaArray [as 别名]
class ObliqueAngleDetectorAbsorptionCorrectionTest(unittest.TestCase):
    def setUp(self):
        # defining geometry
        image_shape = [2048, 2048]  # pixel
        detector_distance = 200  # mm
        wavelength = 0.31  # angstrom
        center_x = 1024  # pixel
        center_y = 1024  # pixel
        self.tilt = 0  # degree
        self.rotation = 0  # degree
        pixel_size = 79  # um
        dummy_tth = np.linspace(0, 35, 2000)
        dummy_int = np.ones(dummy_tth.shape)
        self.geometry = AzimuthalIntegrator()
        self.geometry.setFit2D(directDist=detector_distance,
                               centerX=center_x,
                               centerY=center_y,
                               tilt=self.tilt,
                               tiltPlanRotation=self.rotation,
                               pixelX=pixel_size,
                               pixelY=pixel_size)
        self.geometry.wavelength = wavelength / 1e10
        self.dummy_img = self.geometry.calcfrom1d(dummy_tth, dummy_int, shape=image_shape, correctSolidAngle=True)

        self.tth_array = self.geometry.twoThetaArray(image_shape)
        self.azi_array = self.geometry.chiArray(image_shape)

    def tearDown(self):
        del self.azi_array
        del self.tth_array
        del self.dummy_img
        del self.geometry
        gc.collect()

    def test_that_it_is_correctly_calculating(self):
        oblique_correction = ObliqueAngleDetectorAbsorptionCorrection(
                tth_array=self.tth_array,
                azi_array=self.azi_array,
                detector_thickness=40,
                absorption_length=465.5,
                tilt=self.tilt,
                rotation=self.rotation
        )
        oblique_correction_data = oblique_correction.get_data()
        self.assertGreater(np.sum(oblique_correction_data), 0)
        self.assertEqual(oblique_correction_data.shape, self.dummy_img.shape)
        del oblique_correction

# 需要导入模块: from pyFAI.azimuthalIntegrator import AzimuthalIntegrator [as 别名]
# 或者: from pyFAI.azimuthalIntegrator.AzimuthalIntegrator import twoThetaArray [as 别名]
class CbnCorrectionTest(unittest.TestCase):
    def setUp(self):
        # defining geometry
        image_shape = [2048, 2048]  # pixel
        detector_distance = 200  # mm
        wavelength = 0.31  # angstrom
        center_x = 1024  # pixel
        center_y = 1024  # pixel
        tilt = 0  # degree
        rotation = 0  # degree
        pixel_size = 79  # um
        dummy_tth = np.linspace(0, 35, 2000)
        dummy_int = np.ones(dummy_tth.shape)
        self.geometry = AzimuthalIntegrator()
        self.geometry.setFit2D(directDist=detector_distance,
                               centerX=center_x,
                               centerY=center_y,
                               tilt=tilt,
                               tiltPlanRotation=rotation,
                               pixelX=pixel_size,
                               pixelY=pixel_size)
        self.geometry.wavelength = wavelength / 1e10
        self.dummy_img = self.geometry.calcfrom1d(dummy_tth, dummy_int, shape=image_shape, correctSolidAngle=True)

        self.tth_array = self.geometry.twoThetaArray(image_shape)
        self.azi_array = self.geometry.chiArray(image_shape)

    def tearDown(self):
        del self.tth_array
        del self.azi_array
        del self.dummy_img
        del self.geometry
        gc.collect()

    def test_that_it_is_calculating_correctly(self):
        cbn_correction = CbnCorrection(self.tth_array, self.azi_array,
                                       diamond_thickness=2.2,
                                       seat_thickness=5.3,
                                       small_cbn_seat_radius=0.4,
                                       large_cbn_seat_radius=1.95,
                                       tilt=0,
                                       tilt_rotation=0)
        cbn_correction.update()
        cbn_correction_data = cbn_correction.get_data()
        self.assertGreater(np.sum(cbn_correction_data), 0)
        self.assertEqual(cbn_correction_data.shape, self.dummy_img.shape)

# 需要导入模块: from pyFAI.azimuthalIntegrator import AzimuthalIntegrator [as 别名]
# 或者: from pyFAI.azimuthalIntegrator.AzimuthalIntegrator import twoThetaArray [as 别名]
#some initialization
dummy_tth = np.linspace(0, 35, 2000)
dummy_int = np.ones(dummy_tth.shape)
geometry = AzimuthalIntegrator()
geometry.setFit2D(directDist=detector_distance,
                  centerX=center_x,
                  centerY=center_y,
                  tilt=tilt,
                  tiltPlanRotation=rotation,
                  pixelX=pixel_size,
                  pixelY=pixel_size)
geometry.wavelength = wavelength/1e10
dummy_img = geometry.calcfrom1d(dummy_tth, dummy_int, shape=image_shape, correctSolidAngle=True)


tth_array = geometry.twoThetaArray(image_shape)
azi_array = geometry.chiArray(image_shape)


cbn_correction = CbnCorrection(tth_array, azi_array,
                               diamond_thickness=2.2,
                               seat_thickness=5.3,
                               small_cbn_seat_radius=0.4,
                               large_cbn_seat_radius=1.95,
                               tilt=0,
                               tilt_rotation=0)

t1= time.time()
cbn_correction.get_data()
print("It took {}s".format(time.time()-t1))

# 需要导入模块: from pyFAI.azimuthalIntegrator import AzimuthalIntegrator [as 别名]
# 或者: from pyFAI.azimuthalIntegrator.AzimuthalIntegrator import twoThetaArray [as 别名]
class Refinment2D(object):
    """
    refine the parameters from image itself ...
    (Jerome est-ce que tu peux elaborer un petit peu plus ???)
    """

    def __init__(self, img, ai=None):
        """
        @param img: raw image we are working on
        @type img: ndarray
        @param ai: azimuhal integrator we are working on
        @type ai: pyFAI.azimuthalIntegrator.AzimutalIntegrator
        """
        self.img = img
        if ai is None:
            self.ai = AzimuthalIntegrator()
        else:
            self.ai = ai

    def get_shape(self):
        return self.img.shape

    shape = property(get_shape)

    def reconstruct(self, tth, I):
        """
        Reconstruct a perfect image according to 2th / I given in
        input

        @param tth: 2 theta array
        @type tth: ndarray
        @param I: intensity array
        @type I: ndarray

        @return: a reconstructed image
        @rtype: ndarray
        """
        return numpy.interp(self.ai.twoThetaArray(self.shape), tth, I)

    def diff_tth_X(self, dx=0.1):
        """
        Jerome peux-tu décrire de quoi il retourne ???

        @param dx: ???
        @type: float ???

        @return: ???
        @rtype: ???
        """
        f = self.ai.getFit2D()
        fp = f.copy()
        fm = f.copy()
        fm["centerX"] -= dx / 2.0
        fp["centerX"] += dx / 2.0
        ap = AzimuthalIntegrator()
        am = AzimuthalIntegrator()
        ap.setFit2D(**fp)
        am.setFit2D(**fm)
        dtthX = (ap.twoThetaArray(self.shape) - am.twoThetaArray(self.shape)) / dx
        tth, I = self.ai.xrpd(self.img, max(self.shape))
        dI = SGModule.getSavitzkyGolay(I, npoints=5, degree=2, order=1) / (tth[1] - tth[0])
        dImg = self.reconstruct(tth, dI)
        return (dtthX * dImg).sum()

    def diff_tth_tilt(self, dx=0.1):
        """
        idem ici ???

        @param dx: ???
        @type dx: float ???

        @return: ???
        @rtype: ???
        """
        f = self.ai.getFit2D()
        fp = f.copy()
        fm = f.copy()
        fm["tilt"] -= dx / 2.0
        fp["tilt"] += dx / 2.0
        ap = AzimuthalIntegrator()
        am = AzimuthalIntegrator()
        ap.setFit2D(**fp)
        am.setFit2D(**fm)
        dtthX = (ap.twoThetaArray(self.shape) - am.twoThetaArray(self.shape)) / dx
        tth, I = self.ai.xrpd(self.img, max(self.shape))
        dI = SGModule.getSavitzkyGolay(I, npoints=5, degree=2, order=1) / (tth[1] - tth[0])
        dImg = self.reconstruct(tth, dI)
        return (dtthX * dImg).sum()

    def diff_Fit2D(self, axis="all", dx=0.1):
        """
        ???

        @param axis: ???
        @type axis: ???
        @param dx: ???
        @type dx: ???

        @return: ???
        @rtype: ???
#.........这里部分代码省略.........

# 需要导入模块: from pyFAI.azimuthalIntegrator import AzimuthalIntegrator [as 别名]
# 或者: from pyFAI.azimuthalIntegrator.AzimuthalIntegrator import twoThetaArray [as 别名]

#.........这里部分代码省略.........
            return

    def search_peaks_on_ring(self, ring_index, delta_tth=0.1, min_mean_factor=1,
                             upper_limit=55000, mask=None):
        """
        This function is searching for peaks on an expected ring. It needs an initial calibration
        before. Then it will search for the ring within some delta_tth and other parameters to get
        peaks from the calibrant.

        :param ring_index: the index of the ring for the search
        :param delta_tth: search space around the expected position in two theta
        :param min_mean_factor: a factor determining the minimum peak intensity to be picked up. it is based
                                on the mean value of the search area defined by delta_tth. Pick a large value
                                for larger minimum value and lower for lower minimum value. Therefore, a smaller
                                number is more prone to picking up noise. typical values like between 1 and 3.
        :param upper_limit: maximum intensity for the peaks to be picked
        :param mask: in case the image has to be masked from certain areas, it need to be given here. Default is None.
                     The mask should be given as an 2d array with the same dimensions as the image, where 1 denotes a
                     masked pixel and all others should be 0.
        """
        self.reset_supersampling()
        if not self.is_calibrated:
            return

        # transform delta from degree into radians
        delta_tth = delta_tth / 180.0 * np.pi

        # get appropriate two theta value for the ring number
        tth_calibrant_list = self.calibrant.get_2th()
        tth_calibrant = np.float(tth_calibrant_list[ring_index])

        # get the calculated two theta values for the whole image
        if self.spectrum_geometry._ttha is None:
            tth_array = self.spectrum_geometry.twoThetaArray(self.img_model._img_data.shape)
        else:
            tth_array = self.spectrum_geometry._ttha

        # create mask based on two_theta position
        ring_mask = abs(tth_array - tth_calibrant) <= delta_tth

        if mask is not None:
            mask = np.logical_and(ring_mask, np.logical_not(mask))
        else:
            mask = ring_mask

        # calculate the mean and standard deviation of this area
        sub_data = np.array(self.img_model._img_data.ravel()[np.where(mask.ravel())], dtype=np.float64)
        sub_data[np.where(sub_data > upper_limit)] = np.NaN
        mean = np.nanmean(sub_data)
        std = np.nanstd(sub_data)

        # set the threshold into the mask (don't detect very low intensity peaks)
        threshold = min_mean_factor * mean + std
        mask2 = np.logical_and(self.img_model._img_data > threshold, mask)
        mask2[np.where(self.img_model._img_data > upper_limit)] = False
        size2 = mask2.sum(dtype=int)

        keep = int(np.ceil(np.sqrt(size2)))
        try:
            sys.stdout = DummyStdOut
            res = self.peak_search_algorithm.peaks_from_area(mask2, Imin=mean - std, keep=keep)
            sys.stdout = sys.__stdout__
        except IndexError:
            res = []

        # Store the result

# 需要导入模块: from pyFAI.azimuthalIntegrator import AzimuthalIntegrator [as 别名]
# 或者: from pyFAI.azimuthalIntegrator.AzimuthalIntegrator import twoThetaArray [as 别名]

#.........这里部分代码省略.........
        :param algorithm:
            peak search algorithm used. Possible algorithms are 'Massif' and 'Blob'
        :param mask:
            if a mask is used during the process this is provided here as a 2d array for the image.
        """

        if algorithm == 'Massif':
            self.peak_search_algorithm = Massif(self.img_data._img_data)
        elif algorithm == 'Blob':
            if mask is not None:
                self.peak_search_algorithm = BlobDetection(self.img_data._img_data * mask)
            else:
                self.peak_search_algorithm = BlobDetection(self.img_data._img_data)
            self.peak_search_algorithm.process()
        else:
            return


    def search_peaks_on_ring(self, peak_index, delta_tth=0.1, min_mean_factor=1,
                             upper_limit=55000, mask=None):
        self.reset_supersampling()
        if not self.is_calibrated:
            return

        # transform delta from degree into radians
        delta_tth = delta_tth / 180.0 * np.pi

        # get appropriate two theta value for the ring number
        tth_calibrant_list = self.calibrant.get_2th()
        tth_calibrant = np.float(tth_calibrant_list[peak_index])

        # get the calculated two theta values for the whole image
        if self.spectrum_geometry._ttha is None:
            tth_array = self.spectrum_geometry.twoThetaArray(self.img_data._img_data.shape)
        else:
            tth_array = self.spectrum_geometry._ttha

        # create mask based on two_theta position
        ring_mask = abs(tth_array - tth_calibrant) <= delta_tth

        if mask is not None:
            mask = np.logical_and(ring_mask, np.logical_not(mask))
        else:
            mask = ring_mask

        # calculate the mean and standard deviation of this area
        sub_data = np.array(self.img_data._img_data.ravel()[np.where(mask.ravel())], dtype=np.float64)
        sub_data[np.where(sub_data > upper_limit)] = np.NaN
        mean = np.nanmean(sub_data)
        std = np.nanstd(sub_data)

        # set the threshold into the mask (don't detect very low intensity peaks)
        threshold = min_mean_factor * mean + std
        mask2 = np.logical_and(self.img_data._img_data > threshold, mask)
        mask2[np.where(self.img_data._img_data > upper_limit)] = False
        size2 = mask2.sum(dtype=int)

        keep = int(np.ceil(np.sqrt(size2)))
        try:
            res = self.peak_search_algorithm.peaks_from_area(mask2, Imin=mean - std, keep=keep)
        except IndexError:
            res = []

        # Store the result
        if len(res):
            self.points.append(np.array(res))