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Methods

Bases: ABC

This class defines the structure of a centering method used to determine the center of diffraction patterns.

Source code in bblib/methods.py 
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class CenteringMethod(ABC):
    """
    This class defines the structure of a centering method used to determine the center of diffraction patterns.

    """
    @abstractmethod
    def __init__(self, **kwargs) -> None: ...

    @abstractmethod
    def _prep_for_centering(self, **kwargs) -> None: ...

    @abstractmethod
    def _run_centering(self) -> tuple: ...

    def __call__(self, **kwargs) -> tuple:
        self._prep_for_centering(**kwargs)
        return self._run_centering()

    def centering_converged(self, center) -> bool:
        if center == [-1, -1]:
            return False
        else:
            return True

Bases: CenteringMethod

This class determine the center of a diffraction pattern by calculating the center of mass of the image.

Source code in bblib/methods.py 
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class CenterOfMass(CenteringMethod):
    """
    This class determine the center of a diffraction pattern by calculating the center of mass of the image.

    """
    def __init__(self, config: dict, PF8Config: PF8Info, plots_info: dict = None):
        """
        This method initializes the CenterOfMass centering method.

        Args:
            config (dict): A configuration dictionary in a format expected by beambusters.
            PF8Config (PF8Info): Peakfinder8 parameters.
            plots_info (dict): Configuration parameters to generate plots.
        """

        self.config = config
        self.PF8Config = PF8Config
        self.plots_info = plots_info

        if config["plots_flag"] and not plots_info:
            raise ValueError(
                "From config you want to save plots, please indicate the information to save the plots."
            )

        # Creates an empty plots_info dictionary.
        if not config["plots_flag"] and not plots_info:
            plots_info = {"filename": "", "folder_name": "", "root_path": ""}

    def _prep_for_centering(self, data: np.ndarray):

        self.initial_detector_center = self.PF8Config.get_detector_center()

        # Determine Bragg peaks position.
        pf8 = PF8(self.PF8Config)
        peak_list = pf8.get_peaks_pf8(data=data)
        peak_list_x_in_frame, peak_list_y_in_frame = pf8.peak_list_in_slab(peak_list)
        row_indexes = np.zeros(peak_list["num_peaks"], dtype=int)
        col_indexes = np.zeros(peak_list["num_peaks"], dtype=int)

        # Calculate the peaks index in the data array.
        for idx, k in enumerate(peak_list_y_in_frame):
            row_peak = int(k + self.initial_detector_center[1])
            col_peak = int(peak_list_x_in_frame[idx] + self.initial_detector_center[0])
            row_indexes[idx] = row_peak
            col_indexes[idx] = col_peak
        peaks_indexes = (row_indexes, col_indexes)

        # Assemble data and mask
        data_visualize = geometry.DataVisualizer(pixel_maps=self.PF8Config.pixel_maps)

        with h5py.File(f"{self.PF8Config.bad_pixel_map_filename}", "r") as f:
            mask = np.array(f[f"{self.PF8Config.bad_pixel_map_hdf5_path}"])

        if (
            self.PF8Config.pf8_detector_info["nasics_x"]
            * self.PF8Config.pf8_detector_info["nasics_y"]
            > 1
        ):
            self.visual_data = data_visualize.visualize_data(data=data)
            visual_mask = data_visualize.visualize_data(data=mask).astype(int)
        else:

            self.visual_data = visualize_single_panel(
                data, self.PF8Config.transformation_matrix, self.PF8Config.ss_in_rows
            )
            visual_mask = visualize_single_panel(
                mask, self.PF8Config.transformation_matrix, self.PF8Config.ss_in_rows
            )

        # JF for safety
        visual_mask[np.where(self.visual_data < 0)] = 0

        # Mask Bragg peaks
        peaks_mask = mask_peaks(
            visual_mask,
            peaks_indexes,
            bragg=self.config["bragg_peaks_positions_for_center_of_mass_calculation"],
            n=self.config["pixels_for_mask_of_bragg_peaks"],
        )
        self.mask_for_center_of_mass = peaks_mask * visual_mask

    def _run_centering(self) -> tuple:

        center = center_of_mass(self.visual_data, self.mask_for_center_of_mass)
        if self.centering_converged(center):
            center[0] += self.config["offset"]["x"]
            center[1] += self.config["offset"]["y"]

        if self.config["plots_flag"]:
            visual_img = self.visual_data * self.mask_for_center_of_mass
            fig, ax1 = plt.subplots(1, 1, figsize=(10, 10))
            if self.plots_info["value_auto"]:
                color_map = copy.copy(
                    matplotlib.colormaps[self.plots_info["color_map"]]
                )
                color_map.set_bad(color_map(0))
                pos = ax1.imshow(
                    visual_img, norm=LogNorm(), cmap=color_map, origin="lower"
                )
            else:

                color_map = copy.copy(
                    matplotlib.colormaps[self.plots_info["color_map"]]
                )
                color_map.set_bad(color_map(0))
                pos = ax1.imshow(
                    visual_img,
                    norm=LogNorm(
                        self.plots_info["value_min"], self.plots_info["value_max"]
                    ),
                    cmap=color_map,
                    origin="lower",
                )
            ax1.scatter(
                self.initial_detector_center[0],
                self.initial_detector_center[1],
                s=self.plots_info["marker_size"],
                color="blue",
                marker="o",
                edgecolor="black",
                linewidth=0.5,
                label=f"Initial detector center: ({np.round(self.initial_detector_center[0])}, {np.round(self.initial_detector_center[1])})",
            )
            ax1.scatter(
                center[0],
                center[1],
                s=self.plots_info["marker_size"],
                color="r",
                marker="^",
                edgecolor="black",
                linewidth=0.5,
                label=f"Refined detector center: ({center[0]}, {center[1]})",
            )
            ax1.legend(fontsize=14, loc=1, markerscale=1)
            plt.tick_params(axis="both", which="major", labelsize=16)
            ax1.set_xlabel("x (pixel)", fontsize=20)
            ax1.set_ylabel("y (pixel)", fontsize=20)

            cbar = fig.colorbar(pos, ax=ax1, shrink=0.6)
            cbar.ax.tick_params(labelsize=20)

            path = pathlib.Path(
                f'{self.plots_info["root_path"]}/center_refinement/plots/{self.plots_info["folder_name"]}/center_of_mass/'
            )
            path.mkdir(parents=True, exist_ok=True)
            if not self.plots_info["axis_lim_auto"]:
                ax1.set_xlim(self.plots_info["xlim_min"], self.plots_info["xlim_max"])
                ax1.set_ylim(self.plots_info["ylim_min"], self.plots_info["ylim_max"])
            plt.savefig(
                f'{self.plots_info["root_path"]}/center_refinement/plots/{self.plots_info["folder_name"]}/center_of_mass/{self.plots_info["filename"]}.png'
            )
            plt.close()
        return np.round(center, 0)

__init__(config, PF8Config, plots_info=None)

This method initializes the CenterOfMass centering method.

Parameters:

Name Type Description Default
config dict

A configuration dictionary in a format expected by beambusters.

 required
PF8Config PF8Info

Peakfinder8 parameters.

 required
plots_info dict

Configuration parameters to generate plots.

 None
Source code in bblib/methods.py 
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def __init__(self, config: dict, PF8Config: PF8Info, plots_info: dict = None):
    """
    This method initializes the CenterOfMass centering method.

    Args:
        config (dict): A configuration dictionary in a format expected by beambusters.
        PF8Config (PF8Info): Peakfinder8 parameters.
        plots_info (dict): Configuration parameters to generate plots.
    """

    self.config = config
    self.PF8Config = PF8Config
    self.plots_info = plots_info

    if config["plots_flag"] and not plots_info:
        raise ValueError(
            "From config you want to save plots, please indicate the information to save the plots."
        )

    # Creates an empty plots_info dictionary.
    if not config["plots_flag"] and not plots_info:
        plots_info = {"filename": "", "folder_name": "", "root_path": ""}

Bases: CenteringMethod

This class determines the center of a diffraction pattern as the center of a circle contained in the image.

Source code in bblib/methods.py 
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class CircleDetection(CenteringMethod):
    """
    This class determines the center of a diffraction pattern as the center of a circle contained in the image.
    """
    def __init__(self, config: dict, PF8Config: PF8Info, plots_info: dict = None):
        """
        This method initializes the CircleDetection centering method.

        Args:
            config (dict): A configuration dictionary in a format expected by beambusters.
            PF8Config (PF8Info): Peakfinder8 parameters.
            plots_info (dict): Configuration parameters to generate plots.
        """
        self.config = config
        self.PF8Config = PF8Config
        self.plots_info = plots_info
        if config["plots_flag"] and not plots_info:
            raise ValueError(
                "From config you want to save plots, please indicate the information to save the plots."
            )

        if not config["plots_flag"] and not plots_info:
            plots_info = {"filename": "", "folder_name": "", "root_path": ""}

    def _prep_for_centering(self, data: np.ndarray) -> None:
        self.initial_detector_center = self.PF8Config.get_detector_center()
        ## Find peaks
        pf8 = PF8(self.PF8Config)
        peak_list = pf8.get_peaks_pf8(data=data)
        peak_list_x_in_frame, peak_list_y_in_frame = pf8.peak_list_in_slab(peak_list)
        row_indexes = np.zeros(peak_list["num_peaks"], dtype=int)
        col_indexes = np.zeros(peak_list["num_peaks"], dtype=int)

        for idx, k in enumerate(peak_list_y_in_frame):
            row_peak = int(k + self.initial_detector_center[1])
            col_peak = int(peak_list_x_in_frame[idx] + self.initial_detector_center[0])
            row_indexes[idx] = row_peak
            col_indexes[idx] = col_peak
        peaks_indexes = (row_indexes, col_indexes)

        # Assemble data and mask
        data_visualize = geometry.DataVisualizer(pixel_maps=self.PF8Config.pixel_maps)

        with h5py.File(f"{self.PF8Config.bad_pixel_map_filename}", "r") as f:
            mask = np.array(f[f"{self.PF8Config.bad_pixel_map_hdf5_path}"])

        if (
            self.PF8Config.pf8_detector_info["nasics_x"]
            * self.PF8Config.pf8_detector_info["nasics_y"]
            > 1
        ):
            self.visual_data = data_visualize.visualize_data(data=data)
            visual_mask = data_visualize.visualize_data(data=mask).astype(int)
        else:

            self.visual_data = visualize_single_panel(
                data, self.PF8Config.transformation_matrix, self.PF8Config.ss_in_rows
            )
            visual_mask = visualize_single_panel(
                mask, self.PF8Config.transformation_matrix, self.PF8Config.ss_in_rows
            )

        # JF for safety
        visual_mask[np.where(self.visual_data < 0)] = 0
        only_peaks_mask = mask_peaks(
            visual_mask,
            peaks_indexes,
            bragg=0,
            n=self.config["pixels_for_mask_of_bragg_peaks"],
        )
        self.mask_for_circle_detection = only_peaks_mask * visual_mask

    def _run_centering(self, **kwargs) -> tuple:
        ## Scikit-image circle detection
        edges = canny(
            self.visual_data,
            mask=self.mask_for_circle_detection,
            sigma=self.config["canny"]["sigma"],
            use_quantiles=True,
            low_threshold=self.config["canny"]["low_threshold"],
            high_threshold=self.config["canny"]["high_threshold"],
        )

        if self.config["plots_flag"]:
            fig, ax1 = plt.subplots(1, 1, figsize=(10, 10))
            color_map = copy.copy(matplotlib.colormaps[self.plots_info["color_map"]])
            color_map.set_bad(color_map(0))
            pos = ax1.imshow(edges, origin="lower", cmap=color_map)
            plt.tick_params(axis="both", which="major", labelsize=16)
            ax1.set_xlabel("x (pixel)", fontsize=20)
            ax1.set_ylabel("y (pixel)", fontsize=20)
            cbar = fig.colorbar(pos, ax=ax1, shrink=0.6)
            cbar.ax.tick_params(labelsize=20)

            path = pathlib.Path(
                f'{self.plots_info["root_path"]}/center_refinement/plots/{self.plots_info["folder_name"]}/edges/'
            )
            path.mkdir(parents=True, exist_ok=True)
            plt.savefig(
                f'{self.plots_info["root_path"]}/center_refinement/plots/{self.plots_info["folder_name"]}/edges/{self.plots_info["filename"]}.png'
            )
            plt.close()
        # Detect radii
        hough_radii = np.arange(
            self.config["peak_region"]["min"], self.config["peak_region"]["max"], 1
        )
        hough_res = hough_circle(edges, hough_radii)
        # Select the most prominent circle given the hough_rank chosen
        accums, xc, yc, radii = hough_circle_peaks(
            hough_res, hough_radii, total_num_peaks = int(self.config["hough_rank"])
        )

        if len(xc) > 0:
            xc = xc[int(self.config["hough_rank"] - 1)] + self.config["offset"]["x"]
            yc = yc[int(self.config["hough_rank"] - 1)] + self.config["offset"]["y"]
        else:
            xc = -1
            yc = -1

        center = [xc, yc]
        if self.config["plots_flag"]:
            visual_img = self.visual_data * self.mask_for_circle_detection
            fig, ax1 = plt.subplots(1, 1, figsize=(10, 10))
            if self.plots_info["value_auto"]:
                color_map = copy.copy(
                    matplotlib.colormaps[self.plots_info["color_map"]]
                )
                color_map.set_bad(color_map(0))
                pos = ax1.imshow(
                    visual_img,
                    norm=LogNorm(),
                    origin="lower",
                    cmap=color_map,
                )
            else:
                color_map = copy.copy(
                    matplotlib.colormaps[self.plots_info["color_map"]]
                )
                color_map.set_bad(color_map(0))
                pos = ax1.imshow(
                    visual_img,
                    norm=LogNorm(
                        self.plots_info["value_min"], self.plots_info["value_max"]
                    ),
                    origin="lower",
                    cmap=color_map,
                )
            ax1.scatter(
                self.initial_detector_center[0],
                self.initial_detector_center[1],
                s=self.plots_info["marker_size"],
                color="blue",
                marker="o",
                edgecolor="black",
                linewidth=0.5,
                label=f"Initial detector center: ({np.round(self.initial_detector_center[0])}, {np.round(self.initial_detector_center[1])})",
            )
            ax1.scatter(
                center[0],
                center[1],
                s=self.plots_info["marker_size"],
                color="r",
                marker="^",
                edgecolor="black",
                linewidth=0.5,
                label=f"Refined detector center: ({center[0]}, {center[1]})",
            )
            path = pathlib.Path(
                f'{self.plots_info["root_path"]}/center_refinement/plots/{self.plots_info["folder_name"]}/center_circle_detection/'
            )
            path.mkdir(parents=True, exist_ok=True)
            ax1.legend(fontsize=14, loc=1, markerscale=1)
            cbar = fig.colorbar(pos, ax=ax1, shrink=0.6)
            cbar.ax.tick_params(labelsize=20)
            plt.tick_params(axis="both", which="major", labelsize=16)
            ax1.set_xlabel("x (pixel)", fontsize=20)
            ax1.set_ylabel("y (pixel)", fontsize=20)

            if not self.plots_info["axis_lim_auto"]:
                ax1.set_xlim(self.plots_info["xlim_min"], self.plots_info["xlim_max"])
                ax1.set_ylim(self.plots_info["ylim_min"], self.plots_info["ylim_max"])
            plt.savefig(
                f'{self.plots_info["root_path"]}/center_refinement/plots/{self.plots_info["folder_name"]}/center_circle_detection/{self.plots_info["filename"]}.png'
            )
            plt.close()
        return center

__init__(config, PF8Config, plots_info=None)

This method initializes the CircleDetection centering method.

Parameters:

Name Type Description Default
config dict

A configuration dictionary in a format expected by beambusters.

 required
PF8Config PF8Info

Peakfinder8 parameters.

 required
plots_info dict

Configuration parameters to generate plots.

 None
Source code in bblib/methods.py 
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def __init__(self, config: dict, PF8Config: PF8Info, plots_info: dict = None):
    """
    This method initializes the CircleDetection centering method.

    Args:
        config (dict): A configuration dictionary in a format expected by beambusters.
        PF8Config (PF8Info): Peakfinder8 parameters.
        plots_info (dict): Configuration parameters to generate plots.
    """
    self.config = config
    self.PF8Config = PF8Config
    self.plots_info = plots_info
    if config["plots_flag"] and not plots_info:
        raise ValueError(
            "From config you want to save plots, please indicate the information to save the plots."
        )

    if not config["plots_flag"] and not plots_info:
        plots_info = {"filename": "", "folder_name": "", "root_path": ""}

Bases: CenteringMethod

This class determines the center of a diffraction pattern by minimizing the FWHM of a Gaussian curve fitted to an interval in the azimuthal integration.

Source code in bblib/methods.py 
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class MinimizePeakFWHM(CenteringMethod):
    """
    This class determines the center of a diffraction pattern by minimizing the FWHM of a Gaussian curve fitted to an interval in the azimuthal integration.
    """

    def __init__(self, config: dict, PF8Config: PF8Info, plots_info: dict = None):
        """
        This method initializes the MinimizePeakFWHM centering method.

        Args:
            config (dict): A configuration dictionary in a format expected by beambusters.
            PF8Config (PF8Info): Peakfinder8 parameters.
            plots_info (dict): Configuration parameters to generate plots.
        """
        self.config = config
        self.PF8Config = PF8Config
        self.plots_info = plots_info
        self.plot_fwhm_flag = False
        if config["plots_flag"] and not plots_info:
            raise ValueError(
                "From config you want to save plots, please indicate the information to save the plots."
            )

        if not config["plots_flag"] and not plots_info:
            plots_info = {"filename": "", "folder_name": "", "root_path": ""}

    def _calculate_fwhm(self, coordinate: tuple) -> dict:
        center_to_radial_average = coordinate
        try:
            x_all, y_all = azimuthal_average(
                self.visual_data,
                center=center_to_radial_average,
                mask=self.mask_for_fwhm_min,
            )
        except IndexError:
            return {
                "xc": center_to_radial_average[0],
                "yc": center_to_radial_average[1],
                "fwhm": 10000,
                "r_squared": 0,
            }

        if self.plot_fwhm_flag:
            fig, ax1 = plt.subplots(1, 1, figsize=(8, 8))
            plt.plot(x_all, y_all)
            ax1.set_xlabel("Radial distance (pixel)", fontsize=20)
            ax1.set_ylabel("Intensity (ADU)", fontsize=20)
            plt.tick_params(axis="both", which="major", labelsize=16)

        ## Define background peak region
        x_min = self.config["peak_region"]["min"]
        x_max = self.config["peak_region"]["max"]
        x = x_all[x_min:x_max]
        y = y_all[x_min:x_max]
        ## Estimation of initial parameters

        m0 = 0
        n0 = 2
        y_linear = m0 * x + n0
        y_gaussian = y - y_linear

        try:
            mean = sum(x * y_gaussian) / sum(y_gaussian)
            sigma = np.sqrt(sum(y_gaussian * (x - mean) ** 2) / sum(y_gaussian))
            popt, pcov = curve_fit(
                gaussian_lin, x, y, p0=[max(y_gaussian), mean, sigma, m0, n0]
            )
            fwhm = popt[2] * math.sqrt(8 * np.log(2))
            ## Divide by radius of the peak to get shasrpness ratio
            fwhm_over_radius = fwhm / popt[1]

            ##Calculate residues
            residuals = y - gaussian_lin(x, *popt)
            ss_res = np.sum(residuals**2)
            ss_tot = np.sum((y - np.mean(y)) ** 2)
            r_squared = 1 - (ss_res / ss_tot)
        except (ZeroDivisionError, RuntimeError):
            r_squared = 0
            fwhm = 10000
            popt = []

        ## Display plots
        if self.plot_fwhm_flag and len(popt) > 0:
            x_fit = x.copy()
            y_fit = gaussian_lin(x_fit, *popt)

            plt.vlines([x[0], x[-1]], 0, round(popt[0]) * 10, "r")

            plt.plot(
                x_fit,
                y_fit,
                "r--",
                label=f"gaussian fit \n a:{round(popt[0],2)} \n x0:{round(popt[1],2)} \n sigma:{round(popt[2],2)} \n R² {round(r_squared, 4)}\n FWHM : {round(fwhm,3)}",
            )

            plt.legend(fontsize=14, loc=1, markerscale=1)
            path = pathlib.Path(
                f'{self.plots_info["root_path"]}/center_refinement/plots/{self.plots_info["folder_name"]}/radial_average/'
            )
            path.mkdir(parents=True, exist_ok=True)
            plt.savefig(
                f'{self.plots_info["root_path"]}/center_refinement/plots/{self.plots_info["folder_name"]}/radial_average/{self.plots_info["filename"]}.png'
            )
            plt.close()

        return {
            "xc": center_to_radial_average[0],
            "yc": center_to_radial_average[1],
            "fwhm": fwhm,
            "r_squared": r_squared,
        }

    def _prep_for_centering(self, data: np.ndarray, initial_guess: tuple) -> None:
        self.initial_guess = initial_guess
        self.initial_detector_center = self.PF8Config.get_detector_center()
        non_shifted_pixel_maps_for_visualization = self.PF8Config.pixel_maps.copy()
        ## Find peaks
        self.PF8Config.update_pixel_maps(
            initial_guess[0] - self.initial_detector_center[0],
            initial_guess[1] - self.initial_detector_center[1],
        )
        pf8 = PF8(self.PF8Config)
        # Assemble data and mask
        data_visualize = geometry.DataVisualizer(
            pixel_maps=non_shifted_pixel_maps_for_visualization
        )

        with h5py.File(f"{self.PF8Config.bad_pixel_map_filename}", "r") as f:
            mask = np.array(f[f"{self.PF8Config.bad_pixel_map_hdf5_path}"])

        if not self.config["polarization"]["apply_polarization_correction"]:
            peak_list = pf8.get_peaks_pf8(data=data)
            if (
                self.PF8Config.pf8_detector_info["nasics_x"]
                * self.PF8Config.pf8_detector_info["nasics_y"]
                > 1
            ):
                self.visual_data = data_visualize.visualize_data(data=data)
                visual_mask = data_visualize.visualize_data(data=mask).astype(int)
            else:
                self.visual_data = visualize_single_panel(
                    data,
                    self.PF8Config.transformation_matrix,
                    self.PF8Config.ss_in_rows,
                )
                visual_mask = visualize_single_panel(
                    mask,
                    self.PF8Config.transformation_matrix,
                    self.PF8Config.ss_in_rows,
                )
        else:
            pol_corrected_data, pol_array_map = correct_polarization(
                self.PF8Config.pixel_maps["x"],
                self.PF8Config.pixel_maps["y"],
                float(
                    np.mean(self.PF8Config.pixel_maps["z"])
                    * self.PF8Config.pixel_resolution
                ),
                data,
                mask=mask,
                polarization_axis=self.config["polarization"]["axis"],
                p=self.config["polarization"]["value"],
            )
            peak_list = pf8.get_peaks_pf8(data=pol_corrected_data)
            if (
                self.PF8Config.pf8_detector_info["nasics_x"]
                * self.PF8Config.pf8_detector_info["nasics_y"]
                > 1
            ):
                self.visual_data = data_visualize.visualize_data(
                    data=pol_corrected_data
                )
                visual_mask = data_visualize.visualize_data(data=mask).astype(int)
            else:
                self.visual_data = visualize_single_panel(
                    pol_corrected_data,
                    self.PF8Config.transformation_matrix,
                    self.PF8Config.ss_in_rows,
                )
                visual_mask = visualize_single_panel(
                    mask,
                    self.PF8Config.transformation_matrix,
                    self.PF8Config.ss_in_rows,
                )

        peak_list_x_in_frame, peak_list_y_in_frame = pf8.peak_list_in_slab(peak_list)
        row_indexes = np.zeros(peak_list["num_peaks"], dtype=int)
        col_indexes = np.zeros(peak_list["num_peaks"], dtype=int)

        for idx, k in enumerate(peak_list_y_in_frame):
            row_peak = int(k + self.initial_guess[1])
            col_peak = int(peak_list_x_in_frame[idx] + self.initial_guess[0])
            row_indexes[idx] = row_peak
            col_indexes[idx] = col_peak
        peaks_indexes = (row_indexes, col_indexes)

        # JF for safety
        visual_mask[np.where(self.visual_data < 0)] = 0

        only_peaks_mask = mask_peaks(
            visual_mask,
            peaks_indexes,
            bragg=0,
            n=self.config["pixels_for_mask_of_bragg_peaks"],
        )
        self.mask_for_fwhm_min = only_peaks_mask * visual_mask

        self.pixel_step = 1
        xx, yy = np.meshgrid(
            np.arange(
                self.initial_guess[0] - 20,
                self.initial_guess[0] + 21,
                self.pixel_step,
                dtype=int,
            ),
            np.arange(
                self.initial_guess[1] - 20,
                self.initial_guess[1] + 21,
                self.pixel_step,
                dtype=int,
            ),
        )
        coordinates = np.column_stack((np.ravel(xx), np.ravel(yy)))

        pool = multiprocessing.Pool()
        with pool:
            self.fwhm_summary = pool.map(self._calculate_fwhm, coordinates)

    def _run_centering(self, **kwargs) -> tuple:
        if self.config["plots_flag"]:
            path = pathlib.Path(
                f'{self.plots_info["root_path"]}/center_refinement/plots/{self.plots_info["folder_name"]}/fwhm_map/'
            )
            path.mkdir(parents=True, exist_ok=True)

        xc, yc = get_fwhm_map_global_min(
            self.fwhm_summary,
            f'{self.plots_info["root_path"]}/center_refinement/plots/{self.plots_info["folder_name"]}',
            f'{self.plots_info["filename"]}',
            self.pixel_step,
            self.config["plots_flag"],
        )

        xc += self.config["offset"]["x"]
        yc += self.config["offset"]["y"]
        center = [xc, yc]

        if self.centering_converged(center):
            self.plot_fwhm_flag = True
            self._calculate_fwhm(center)
            self.plot_fwhm_flag = False

        if self.config["plots_flag"]:
            visual_img = self.visual_data * self.mask_for_fwhm_min
            fig, ax1 = plt.subplots(1, 1, figsize=(10, 10))
            if self.plots_info["value_auto"]:
                color_map = copy.copy(
                    matplotlib.colormaps[self.plots_info["color_map"]]
                )
                color_map.set_bad(color_map(0))
                pos = ax1.imshow(
                    visual_img,
                    norm=LogNorm(),
                    origin="lower",
                    cmap=color_map,
                )
            else:
                color_map = copy.copy(
                    matplotlib.colormaps[self.plots_info["color_map"]]
                )
                color_map.set_bad(color_map(0))
                pos = ax1.imshow(
                    visual_img,
                    norm=LogNorm(
                        self.plots_info["value_min"], self.plots_info["value_max"]
                    ),
                    origin="lower",
                    cmap=color_map,
                )

            ax1.scatter(
                self.initial_guess[0],
                self.initial_guess[1],
                s=self.plots_info["marker_size"],
                color="blue",
                marker="o",
                edgecolor="black",
                linewidth=0.5,
                label=f"Initial guess: ({np.round(self.initial_guess[0])}, {np.round(self.initial_guess[1])})",
            )
            ax1.scatter(
                center[0],
                center[1],
                s=self.plots_info["marker_size"],
                color="r",
                marker="^",
                edgecolor="black",
                linewidth=0.5,
                label=f"Refined detector center: ({center[0]}, {center[1]})",
            )
            path = pathlib.Path(
                f'{self.plots_info["root_path"]}/center_refinement/plots/{self.plots_info["folder_name"]}/center_fwhm_minimization/'
            )
            path.mkdir(parents=True, exist_ok=True)
            ax1.legend(fontsize=14, loc=1, markerscale=1)
            plt.tick_params(axis="both", which="major", labelsize=16)
            ax1.set_xlabel("x (pixel)", fontsize=20)
            ax1.set_ylabel("y (pixel)", fontsize=20)

            cbar = fig.colorbar(pos, ax=ax1, shrink=0.6)
            cbar.ax.tick_params(labelsize=20)

            if not self.plots_info["axis_lim_auto"]:
                ax1.set_xlim(self.plots_info["xlim_min"], self.plots_info["xlim_max"])
                ax1.set_ylim(self.plots_info["ylim_min"], self.plots_info["ylim_max"])
            plt.savefig(
                f'{self.plots_info["root_path"]}/center_refinement/plots/{self.plots_info["folder_name"]}/center_fwhm_minimization/{self.plots_info["filename"]}.png'
            )
            plt.close()

        return center

__init__(config, PF8Config, plots_info=None)

This method initializes the MinimizePeakFWHM centering method.

Parameters:

Name Type Description Default
config dict

A configuration dictionary in a format expected by beambusters.

 required
PF8Config PF8Info

Peakfinder8 parameters.

 required
plots_info dict

Configuration parameters to generate plots.

 None
Source code in bblib/methods.py 
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def __init__(self, config: dict, PF8Config: PF8Info, plots_info: dict = None):
    """
    This method initializes the MinimizePeakFWHM centering method.

    Args:
        config (dict): A configuration dictionary in a format expected by beambusters.
        PF8Config (PF8Info): Peakfinder8 parameters.
        plots_info (dict): Configuration parameters to generate plots.
    """
    self.config = config
    self.PF8Config = PF8Config
    self.plots_info = plots_info
    self.plot_fwhm_flag = False
    if config["plots_flag"] and not plots_info:
        raise ValueError(
            "From config you want to save plots, please indicate the information to save the plots."
        )

    if not config["plots_flag"] and not plots_info:
        plots_info = {"filename": "", "folder_name": "", "root_path": ""}

Bases: CenteringMethod

This class determines the center of a diffraction pattern through the selection of Friedel pairs candidates and matching of their coordinates.

Source code in bblib/methods.py 
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class FriedelPairs(CenteringMethod):
    """
    This class determines the center of a diffraction pattern through the selection of Friedel pairs candidates and matching of their coordinates.
    """

    def __init__(self, config: dict, PF8Config: PF8Info, plots_info: dict = None):
        """
        This method initializes the FriedelPairs centering method.

        Args:
            config (dict): A configuration dictionary in a format expected by beambusters.
            PF8Config (PF8Info): Peakfinder8 parameters.
            plots_info (dict): Configuration parameters to generate plots.
        """
        self.config = config
        self.PF8Config = PF8Config
        self.plots_info = plots_info
        if config["plots_flag"] and not plots_info:
            raise ValueError(
                "From config you want to save plots, please indicate the information to save the plots."
            )

        if not config["plots_flag"] and not plots_info:
            plots_info = {"filename": "", "folder_name": "", "root_path": ""}

    def _select_closest_peaks(self, peaks_list: list, inverted_peaks: list) -> list:
        pairs_list = []
        for i in peaks_list:
            radius = 0.1
            found_peak = False
            while not found_peak and radius <= self.config["search_radius"]:
                found_peak = self._find_a_peak_in_the_surrounding(
                    i, inverted_peaks, radius
                )
                radius += 0.1
            if found_peak:
                pairs_list.append((i, found_peak))
        pairs_list = self._check_paired_reflections(pairs_list)
        return pairs_list

    def _find_a_peak_in_the_surrounding(
        self, peak: list, inverted_peaks_list: list, radius: float
    ) -> list:
        cut_peaks_list = []
        cut_peaks_list = [
            (
                inverted_peak,
                math.sqrt(
                    (peak[0] - inverted_peak[0]) ** 2
                    + (peak[1] - inverted_peak[1]) ** 2
                ),
            )
            for inverted_peak in inverted_peaks_list
            if math.sqrt(
                (peak[0] - inverted_peak[0]) ** 2 + (peak[1] - inverted_peak[1]) ** 2
            )
            <= radius
        ]
        cut_peaks_list.sort(key=lambda x: x[1])

        if cut_peaks_list == []:
            return False
        else:
            return cut_peaks_list[0][0]

    def _check_paired_reflections(self, pairs_list: list) -> list:
        # Check if the reversed peak is also on the list
        filtered_pairs = []

        for original_peak, inverted_peak in pairs_list:
            inverted_peak_inverted_twice = (
                -1 * inverted_peak[0],
                -1 * inverted_peak[1],
            )
            original_peak_inverted_twice = (
                -1 * original_peak[0],
                -1 * original_peak[1],
            )
            inverted_pair = (inverted_peak_inverted_twice, original_peak_inverted_twice)
            if inverted_pair in pairs_list:
                filtered_pairs.append((original_peak, inverted_peak))

        return filtered_pairs

    def _prep_for_centering(self, data: np.ndarray, initial_guess: tuple) -> None:

        self.initial_guess = initial_guess
        self.initial_detector_center = self.PF8Config.get_detector_center()
        non_shifted_pixel_maps_for_visualization = self.PF8Config.pixel_maps.copy()

        # Find Bragg  peaks
        self.PF8Config.update_pixel_maps(
            initial_guess[0] - self.initial_detector_center[0],
            initial_guess[1] - self.initial_detector_center[1],
        )

        pf8 = PF8(self.PF8Config)

        # Assemble data and mask
        data_visualize = geometry.DataVisualizer(
            pixel_maps=non_shifted_pixel_maps_for_visualization
        )

        with h5py.File(f"{self.PF8Config.bad_pixel_map_filename}", "r") as f:
            mask = np.array(f[f"{self.PF8Config.bad_pixel_map_hdf5_path}"])

        if not self.config["polarization"]["apply_polarization_correction"]:
            peak_list = pf8.get_peaks_pf8(data=data)
            if (
                self.PF8Config.pf8_detector_info["nasics_x"]
                * self.PF8Config.pf8_detector_info["nasics_y"]
                > 1
            ):
                self.visual_data = data_visualize.visualize_data(data=data)
                self.visual_mask = data_visualize.visualize_data(data=mask).astype(int)
            else:
                self.visual_data = visualize_single_panel(
                    data,
                    self.PF8Config.transformation_matrix,
                    self.PF8Config.ss_in_rows,
                )
                self.visual_mask = visualize_single_panel(
                    mask,
                    self.PF8Config.transformation_matrix,
                    self.PF8Config.ss_in_rows,
                )
        else:
            pol_corrected_data, pol_array_map = correct_polarization(
                self.PF8Config.pixel_maps["x"],
                self.PF8Config.pixel_maps["y"],
                float(
                    np.mean(self.PF8Config.pixel_maps["z"])
                    * self.PF8Config.pixel_resolution
                ),
                data,
                mask=mask,
                polarization_axis=self.config["polarization"]["axis"],
                p=self.config["polarization"]["value"],
            )
            peak_list = pf8.get_peaks_pf8(data=pol_corrected_data)
            if (
                self.PF8Config.pf8_detector_info["nasics_x"]
                * self.PF8Config.pf8_detector_info["nasics_y"]
                > 1
            ):
                self.visual_data = data_visualize.visualize_data(
                    data=pol_corrected_data
                )
                self.visual_mask = data_visualize.visualize_data(data=mask).astype(int)
            else:
                self.visual_data = visualize_single_panel(
                    pol_corrected_data,
                    self.PF8Config.transformation_matrix,
                    self.PF8Config.ss_in_rows,
                )
                self.visual_mask = visualize_single_panel(
                    mask,
                    self.PF8Config.transformation_matrix,
                    self.PF8Config.ss_in_rows,
                )

        peak_list_in_slab = pf8.peak_list_in_slab(peak_list)
        self.peak_list_x_in_frame, self.peak_list_y_in_frame = peak_list_in_slab

    def _run_centering(self, **kwargs) -> tuple:

        peak_list_x_in_frame = self.peak_list_x_in_frame.copy()
        peak_list_y_in_frame = self.peak_list_y_in_frame.copy()

        peaks = list(zip(peak_list_x_in_frame, peak_list_y_in_frame))
        inverted_peaks_x = [-1 * k for k in peak_list_x_in_frame]
        inverted_peaks_y = [-1 * k for k in peak_list_y_in_frame]
        inverted_peaks = list(zip(inverted_peaks_x, inverted_peaks_y))
        pairs_list = self._select_closest_peaks(peaks, inverted_peaks)

        # Calculcate the direct beam shift

        self.peaks_list_original = [x for x, y in pairs_list]
        self.peaks_list_inverted = [y for x, y in pairs_list]

        if len(pairs_list) > 0:
            print(
                f"--------------  Friedel pairs search --------------\nNumber of Friedel Pairs in frame: {len(pairs_list)/2}"
            )
            print(f"Pairs list for analysis:")
            print(pairs_list)

            friedel_coordinates_in_x = [x for x, y in self.peaks_list_original]
            friedel_coordinates_in_y = [y for x, y in self.peaks_list_original]

            print(f"Friedel pairs position before center correction in pixels:")
            print(self.peaks_list_original)

            shift_x = sum(friedel_coordinates_in_x) / len(friedel_coordinates_in_x)
            shift_y = sum(friedel_coordinates_in_y) / len(friedel_coordinates_in_y)

            print("Center shift in x", shift_x)
            print("Center shift in y", shift_y)
            center = [
                np.round(self.initial_guess[0] + shift_x, 1),
                np.round(self.initial_guess[1] + shift_y, 1),
            ]

            print(f"Friedel pairs position after center correction in pixels:")
            pairs_list_after_correction = [
                (np.round(x[0] - shift_x, 1), np.round(x[1] - shift_y, 1))
                for x in self.peaks_list_original
            ]
            print(pairs_list_after_correction)
            print(f"All reflections after center correction in pixels:")
            peaks_list_after_correction = [
                (np.round(x[0] - shift_x, 1), np.round(x[1] - shift_y, 1))
                for x in peaks
            ]
            print(peaks_list_after_correction)
            print(f"-- End --")

            center[0] += self.config["offset"]["x"]
            center[1] += self.config["offset"]["y"]
        else:
            center = [-1, -1]

        if self.config["plots_flag"] and self.centering_converged(center):

            fig, ax1 = plt.subplots(1, 1, figsize=(10, 10))
            if self.plots_info["value_auto"]:
                color_map = copy.copy(
                    matplotlib.colormaps[self.plots_info["color_map"]]
                )
                color_map.set_bad(color_map(0))
                pos = ax1.imshow(
                    self.visual_data * self.visual_mask,
                    norm=LogNorm(),
                    cmap=color_map,
                    origin="lower",
                )
            else:
                color_map = copy.copy(
                    matplotlib.colormaps[self.plots_info["color_map"]]
                )
                color_map.set_bad(color_map(0))
                pos = ax1.imshow(
                    self.visual_data * self.visual_mask,
                    norm=LogNorm(
                        self.plots_info["value_min"], self.plots_info["value_max"]
                    ),
                    cmap=color_map,
                    origin="lower",
                )

            ax1.scatter(
                self.initial_guess[0],
                self.initial_guess[1],
                color="b",
                marker="o",
                edgecolor="black",
                linewidth=0.5,
                s=self.plots_info["marker_size"],
                label=f"Initial guess:({np.round(self.initial_guess[0],1)},{np.round(self.initial_guess[1], 1)})",
            )

            ax1.scatter(
                center[0],
                center[1],
                color="r",
                marker="^",
                edgecolor="black",
                linewidth=0.5,
                s=self.plots_info["marker_size"],
                label=f"Refined detector center:({np.round(center[0],1)}, {np.round(center[1],1)})",
            )

            cbar = fig.colorbar(pos, ax=ax1, shrink=0.6)
            cbar.ax.tick_params(labelsize=20)
            ax1.legend(fontsize=14, loc=1, markerscale=1)
            plt.tick_params(axis="both", which="major", labelsize=16)
            ax1.set_xlabel("x (pixel)", fontsize=20)
            ax1.set_ylabel("y (pixel)", fontsize=20)

            if not self.plots_info["axis_lim_auto"]:
                ax1.set_xlim(self.plots_info["xlim_min"], self.plots_info["xlim_max"])
                ax1.set_ylim(self.plots_info["ylim_min"], self.plots_info["ylim_max"])

            path = pathlib.Path(
                f'{self.plots_info["root_path"]}/center_refinement/plots/{self.plots_info["folder_name"]}/centered_friedel/'
            )
            path.mkdir(parents=True, exist_ok=True)
            plt.savefig(
                f'{self.plots_info["root_path"]}/center_refinement/plots/{self.plots_info["folder_name"]}/centered_friedel/{self.plots_info["filename"]}.png'
            )
            plt.close("all")

            original_peaks_x = [
                np.round(k + self.initial_guess[0]) for k in peak_list_x_in_frame
            ]
            original_peaks_y = [
                np.round(k + self.initial_guess[1]) for k in peak_list_y_in_frame
            ]

            inverted_non_shifted_peaks_x = [
                np.round(k[0] + self.initial_guess[0]) for k in self.peaks_list_inverted
            ]
            inverted_non_shifted_peaks_y = [
                np.round(k[1] + self.initial_guess[1]) for k in self.peaks_list_inverted
            ]
            inverted_shifted_peaks_x = [
                np.round(k[0] + self.initial_guess[0] + shift_x)
                for k in self.peaks_list_inverted
            ]
            inverted_shifted_peaks_y = [
                np.round(k[1] + self.initial_guess[1] + shift_y)
                for k in self.peaks_list_inverted
            ]

            # Check pairs alignement
            fig, ax1 = plt.subplots(1, 1, figsize=(10, 10))
            if self.plots_info["value_auto"]:
                color_map = copy.copy(
                    matplotlib.colormaps[self.plots_info["color_map"]]
                )
                color_map.set_bad(color_map(0))
                pos = ax1.imshow(
                    self.visual_data * self.visual_mask,
                    norm=LogNorm(),
                    cmap=color_map,
                    origin="lower",
                )
            else:
                color_map = copy.copy(
                    matplotlib.colormaps[self.plots_info["color_map"]]
                )
                color_map.set_bad(color_map(0))
                pos = ax1.imshow(
                    self.visual_data * self.visual_mask,
                    norm=LogNorm(
                        self.plots_info["value_min"], self.plots_info["value_max"]
                    ),
                    cmap=color_map,
                    origin="lower",
                )

            ax1.scatter(
                original_peaks_x,
                original_peaks_y,
                facecolor="none",
                s=0.8 * self.plots_info["marker_size"],
                marker="o",
                edgecolor="red",
                linewidth=1.8,
                label="original peaks",
            )

            ax1.scatter(
                inverted_non_shifted_peaks_x,
                inverted_non_shifted_peaks_y,
                s=1.2 * self.plots_info["marker_size"],
                facecolor="none",
                marker="s",
                edgecolor="blue",
                linewidth=1.8,
                label="inverted peaks",
                alpha=1,
            )
            ax1.scatter(
                inverted_shifted_peaks_x,
                inverted_shifted_peaks_y,
                facecolor="none",
                s=1.2 * self.plots_info["marker_size"],
                marker="D",
                linewidth=1.8,
                alpha=1,
                edgecolor="green",
                label="inverted peaks shifted",
            )

            if not self.plots_info["axis_lim_auto"]:
                ax1.set_xlim(self.plots_info["xlim_min"], self.plots_info["xlim_max"])
                ax1.set_ylim(self.plots_info["ylim_min"], self.plots_info["ylim_max"])

            cbar = fig.colorbar(pos, ax=ax1, shrink=0.6)
            cbar.ax.tick_params(labelsize=20)
            ax1.legend(fontsize=14, loc=1, markerscale=1)
            plt.tick_params(axis="both", which="major", labelsize=16)
            ax1.set_xlabel("x (pixel)", fontsize=20)
            ax1.set_ylabel("y (pixel)", fontsize=20)
            path = pathlib.Path(
                f'{self.plots_info["root_path"]}/center_refinement/plots/{self.plots_info["folder_name"]}/peaks/'
            )
            path.mkdir(parents=True, exist_ok=True)
            plt.savefig(
                f'{self.plots_info["root_path"]}/center_refinement/plots/{self.plots_info["folder_name"]}/peaks/{self.plots_info["filename"]}.png'
            )
            plt.close()
        return center

__init__(config, PF8Config, plots_info=None)

This method initializes the FriedelPairs centering method.

Parameters:

Name Type Description Default
config dict

A configuration dictionary in a format expected by beambusters.

 required
PF8Config PF8Info

Peakfinder8 parameters.

 required
plots_info dict

Configuration parameters to generate plots.

 None
Source code in bblib/methods.py 
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def __init__(self, config: dict, PF8Config: PF8Info, plots_info: dict = None):
    """
    This method initializes the FriedelPairs centering method.

    Args:
        config (dict): A configuration dictionary in a format expected by beambusters.
        PF8Config (PF8Info): Peakfinder8 parameters.
        plots_info (dict): Configuration parameters to generate plots.
    """
    self.config = config
    self.PF8Config = PF8Config
    self.plots_info = plots_info
    if config["plots_flag"] and not plots_info:
        raise ValueError(
            "From config you want to save plots, please indicate the information to save the plots."
        )

    if not config["plots_flag"] and not plots_info:
        plots_info = {"filename": "", "folder_name": "", "root_path": ""}