Python AzimuthalIntegrator.reset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 reset [as 别名]

#.........这里部分代码省略.........
        :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_model.get_raw_img_data())
        elif algorithm == 'Blob':
            if mask is not None:
                self.peak_search_algorithm = BlobDetection(self.img_model.get_raw_img_data() * mask)
            else:
                self.peak_search_algorithm = BlobDetection(self.img_model.get_raw_img_data())
            self.peak_search_algorithm.process()
        else:
            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

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

#.........这里部分代码省略.........
                                    poni1=pyFAI_parameter['poni1'],
                                    poni2=pyFAI_parameter['poni2'],
                                    rot1=pyFAI_parameter['rot1'],
                                    rot2=pyFAI_parameter['rot2'],
                                    rot3=pyFAI_parameter['rot3'],
                                    pixel1=pyFAI_parameter['pixel1'],
                                    pixel2=pyFAI_parameter['pixel2'])

        self.cake_geometry.wavelength = pyFAI_parameter['wavelength']

    def setup_peak_search_algorithm(self, algorithm, mask=None):
        """
        Initializes the peak search algorithm on the current image
        :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