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Python AzimuthalIntegrator.tth方法代码示例

本文整理汇总了Python中pyFAI.azimuthalIntegrator.AzimuthalIntegrator.tth方法的典型用法代码示例。如果您正苦于以下问题:Python AzimuthalIntegrator.tth方法的具体用法?Python AzimuthalIntegrator.tth怎么用?Python AzimuthalIntegrator.tth使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在pyFAI.azimuthalIntegrator.AzimuthalIntegrator的用法示例。


在下文中一共展示了AzimuthalIntegrator.tth方法的2个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: CalibrationModel

# 需要导入模块: from pyFAI.azimuthalIntegrator import AzimuthalIntegrator [as 别名]
# 或者: from pyFAI.azimuthalIntegrator.AzimuthalIntegrator import tth [as 别名]
class CalibrationModel(object):
    def __init__(self, img_model=None):
        """
        :param img_model:
        :type img_model: ImgModel
        """
        self.img_model = img_model
        self.points = []
        self.points_index = []
        self.spectrum_geometry = AzimuthalIntegrator()
        self.cake_geometry = None
        self.calibrant = Calibrant()
        self.start_values = {'dist': 200e-3,
                             'wavelength': 0.3344e-10,
                             'pixel_width': 79e-6,
                             'pixel_height': 79e-6,
                             'polarization_factor': 0.99}
        self.orig_pixel1 = 79e-6
        self.orig_pixel2 = 79e-6
        self.fit_wavelength = False
        self.fit_distance = True
        self.is_calibrated = False
        self.use_mask = False
        self.filename = ''
        self.calibration_name = 'None'
        self.polarization_factor = 0.99
        self.supersampling_factor = 1
        self._calibrants_working_dir = os.path.dirname(calibrants.__file__)

        self.cake_img = np.zeros((2048, 2048))
        self.tth = np.linspace(0, 25)
        self.int = np.sin(self.tth)
        self.num_points = len(self.int)

        self.peak_search_algorithm = None

    def find_peaks_automatic(self, x, y, peak_ind):
        """
        Searches peaks by using the Massif algorithm
        :param x:
            x-coordinate in pixel - should be from original image (not supersampled x-coordinate)
        :param y:
            y-coordinate in pixel - should be from original image (not supersampled y-coordinate)
        :param peak_ind:
            peak/ring index to which the found points will be added
        :return:
            array of points found
        """
        massif = Massif(self.img_model._img_data)
        cur_peak_points = massif.find_peaks([x, y], stdout=DummyStdOut())
        if len(cur_peak_points):
            self.points.append(np.array(cur_peak_points))
            self.points_index.append(peak_ind)
        return np.array(cur_peak_points)

    def find_peak(self, x, y, search_size, peak_ind):
        """
        Searches a peak around the x,y position. It just searches for the maximum value in a specific search size.
        :param x:
            x-coordinate in pixel - should be from original image (not supersampled x-coordinate)
        :param y:
            y-coordinate in pixel - should be form original image (not supersampled y-coordinate)
        :param search_size:
            the length of the search rectangle in pixels in all direction in which the algorithm searches for
            the maximum peak
        :param peak_ind:
            peak/ring index to which the found points will be added
        :return:
            point found (as array)
        """
        left_ind = np.round(x - search_size * 0.5)
        if left_ind < 0:
            left_ind = 0
        top_ind = np.round(y - search_size * 0.5)
        if top_ind < 0:
            top_ind = 0
        search_array = self.img_model.img_data[left_ind:(left_ind + search_size),
                       top_ind:(top_ind + search_size)]
        x_ind, y_ind = np.where(search_array == search_array.max())
        x_ind = x_ind[0] + left_ind
        y_ind = y_ind[0] + top_ind
        self.points.append(np.array([x_ind, y_ind]))
        self.points_index.append(peak_ind)
        return np.array([np.array((x_ind, y_ind))])

    def clear_peaks(self):
        self.points = []
        self.points_index = []

    def create_cake_geometry(self):
        self.cake_geometry = AzimuthalIntegrator()

        pyFAI_parameter = self.spectrum_geometry.getPyFAI()
        pyFAI_parameter['polarization_factor'] = self.polarization_factor
        pyFAI_parameter['wavelength'] = self.spectrum_geometry.wavelength

        self.cake_geometry.setPyFAI(dist=pyFAI_parameter['dist'],
                                    poni1=pyFAI_parameter['poni1'],
                                    poni2=pyFAI_parameter['poni2'],
                                    rot1=pyFAI_parameter['rot1'],
#.........这里部分代码省略.........
开发者ID:knilav,项目名称:Dioptas,代码行数:103,代码来源:CalibrationModel.py

示例2: CalibrationData

# 需要导入模块: from pyFAI.azimuthalIntegrator import AzimuthalIntegrator [as 别名]
# 或者: from pyFAI.azimuthalIntegrator.AzimuthalIntegrator import tth [as 别名]
class CalibrationData(object):
    def __init__(self, img_data=None):
        self.img_data = img_data
        self.points = []
        self.points_index = []
        self.spectrum_geometry = AzimuthalIntegrator()
        self.calibrant = Calibrant()
        self.start_values = {'dist': 200e-3,
                             'wavelength': 0.3344e-10,
                             'pixel_width': 79e-6,
                             'pixel_height': 79e-6,
                             'polarization_factor': 0.99}
        self.orig_pixel1 = 79e-6
        self.orig_pixel2 = 79e-6
        self.fit_wavelength = False
        self.fit_distance = True
        self.is_calibrated = False
        self.use_mask = False
        self.filename = ''
        self.calibration_name = 'None'
        self.polarization_factor = 0.99
        self.supersampling_factor = 1
        self._calibrants_working_dir = os.path.dirname(Calibrants.__file__)

        self.cake_img = np.zeros((2048, 2048))
        self.tth = np.linspace(0, 25)
        self.int = np.sin(self.tth)

    def find_peaks_automatic(self, x, y, peak_ind):
        """
        Searches peaks by using the Massif algorithm
        :param x:
            x-coordinate in pixel - should be from original image (not supersampled x-coordinate)
        :param y:
            y-coordinate in pixel - should be from original image (not supersampled y-coordinate)
        :param peak_ind:
            peak/ring index to which the found points will be added
        :return:
            array of points found
        """
        massif = Massif(self.img_data._img_data)
        cur_peak_points = massif.find_peaks([x, y])
        if len(cur_peak_points):
            self.points.append(np.array(cur_peak_points))
            self.points_index.append(peak_ind)
        return np.array(cur_peak_points)

    def find_peak(self, x, y, search_size, peak_ind):
        """
        Searches a peak around the x,y position. It just searches for the maximum value in a specific search size.
        :param x:
            x-coordinate in pixel - should be from original image (not supersampled x-coordinate)
        :param y:
            y-coordinate in pixel - should be form original image (not supersampled y-coordinate)
        :param search_size:
            the amount of pixels in all direction in which the algorithm searches for the maximum peak
        :param peak_ind:
            peak/ring index to which the found points will be added
        :return:
            point found (as array)
        """
        left_ind = np.round(x - search_size * 0.5)
        top_ind = np.round(y - search_size * 0.5)
        x_ind, y_ind = np.where(self.img_data._img_data[left_ind:(left_ind + search_size),
                                top_ind:(top_ind + search_size)] == \
                                self.img_data._img_data[left_ind:(left_ind + search_size),
                                top_ind:(top_ind + search_size)].max())
        x_ind = x_ind[0] + left_ind
        y_ind = y_ind[0] + top_ind
        self.points.append(np.array([x_ind, y_ind]))
        self.points_index.append(peak_ind)
        return np.array([np.array((x_ind, y_ind))])

    def clear_peaks(self):
        self.points = []
        self.points_index = []

    def create_cake_geometry(self):
        self.cake_geometry = AzimuthalIntegrator()

        pyFAI_parameter = self.spectrum_geometry.getPyFAI()
        pyFAI_parameter['polarization_factor'] = self.polarization_factor
        pyFAI_parameter['wavelength'] = self.spectrum_geometry.wavelength

        self.cake_geometry.setPyFAI(dist=pyFAI_parameter['dist'],
                                    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'
#.........这里部分代码省略.........
开发者ID:ggarba,项目名称:Dioptas,代码行数:103,代码来源:CalibrationData.py


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