def __init__(self, img_model=None): """ :param img_model: :type img_model: ImgModel """ super(CalibrationModel, self).__init__() self.img_model = img_model self.points = [] self.points_index = [] self.detector = Detector(pixel1=79e-6, pixel2=79e-6) self.detector_mode = DetectorModes.CUSTOM self._original_detector = None # used for saving original state before rotating or flipping self.pattern_geometry = GeometryRefinement( detector=self.detector, wavelength=0.3344e-10, poni1=0, poni2=0) # default params are necessary, otherwise fails... self.pattern_geometry_img_shape = None self.cake_geometry = None self.cake_geometry_img_shape = None self.calibrant = Calibrant() self.orig_pixel1 = self.detector.pixel1 # needs to be extra stored for applying supersampling self.orig_pixel2 = self.detector.pixel2 self.start_values = { 'dist': 200e-3, 'wavelength': 0.3344e-10, 'polarization_factor': 0.99 } self.fit_wavelength = False self.fixed_values = { } # dictionary for fixed parameters during calibration (keys can be e.g. rot1, poni1 etc. # and values are the values to what the respective parameter will be set self.is_calibrated = False self.use_mask = False self.filename = '' self.calibration_name = 'None' self.polarization_factor = 0.99 self.supersampling_factor = 1 self.correct_solid_angle = True self._calibrants_working_dir = calibrants_path self.distortion_spline_filename = None self.tth = np.linspace(0, 25) self.int = np.sin(self.tth) self.num_points = len(self.int) self.cake_img = np.zeros((2048, 2048)) self.cake_tth = None self.cake_azi = None self.peak_search_algorithm = None
def __init__(self, img_model=None): super(CalibrationModel, self).__init__() """ :param img_model: :type img_model: ImgModel """ self.img_model = img_model self.points = [] self.points_index = [] self.pattern_geometry = AzimuthalIntegrator() self.pattern_geometry_img_shape = None self.cake_geometry = None self.cake_geometry_img_shape = None self.calibrant = Calibrant() self.pattern_geometry.wavelength = 0.3344e-10 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.fit_poni1 = True self.fit_poni2 = True self.fit_rot1 = True self.fit_rot2 = True self.fit_rot3 = 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.correct_solid_angle = True self._calibrants_working_dir = calibrants_path self.distortion_spline_filename = None self.tth = np.linspace(0, 25) self.int = np.sin(self.tth) self.num_points = len(self.int) self.cake_img = np.zeros((2048, 2048)) self.cake_tth = None self.cake_azi = None self.peak_search_algorithm = None
def loadCalibrant(self): dialog = self.createCalibrantDialog("Load calibrant file") result = dialog.exec_() if not result: return filename = dialog.selectedFiles()[0] try: calibrant = Calibrant(filename=filename) except Exception as e: _logger.error(e.args[0]) _logger.debug("Backtrace", exc_info=True) # FIXME Display error dialog return except KeyboardInterrupt: raise try: settings = self.model().experimentSettingsModel() settings.calibrantModel().setCalibrant(calibrant) except Exception as e: _logger.error(e.args[0]) _logger.debug("Backtrace", exc_info=True) # FIXME Display error dialog except KeyboardInterrupt: raise
def test_calib_md(fresh_xrun, exp_hash_uid, glbl, db): xrun = fresh_xrun # calib run sample_md = _sample_name_phase_info_configuration(None, None, "calib") calibrant = os.path.join(glbl["usrAnalysis_dir"], "Ni24.D") detector = "perkin_elmer" _collect_img( 5, True, sample_md, xrun, detector=detector, calibrant=calibrant, ) calib_hdr = db[-1] assert "Ni_calib" == calib_hdr.start["sample_name"] assert detector == calib_hdr.start["detector"] calibrant_obj = Calibrant(calibrant) start_doc = calib_hdr.start assert calibrant_obj.dSpacing == start_doc["dSpacing"] assert start_doc["is_calibration"] for k, expected in sample_md.items(): actual = start_doc[k] assert expected == actual server_uid = start_doc["detector_calibration_server_uid"] client_uid = start_doc["detector_calibration_client_uid"] assert server_uid == exp_hash_uid assert server_uid == client_uid # production run xrun(0, 0) hdr = db[-1] client_uid = hdr.start["detector_calibration_client_uid"] assert client_uid == exp_hash_uid assert "detector_calibration_server_uid" not in hdr.start # new uid new_hash = update_experiment_hash_uid() # production run first xrun(0, 0) hdr = db[-1] client_uid = hdr.start["detector_calibration_client_uid"] assert client_uid == new_hash assert "detector_calibration_server_uid" not in hdr.start # new calib run _collect_img( 5, True, sample_md, xrun, detector=detector, calibrant=calibrant, ) calib_hdr = db[-1] server_uid = calib_hdr.start["detector_calibration_server_uid"] client_uid = calib_hdr.start["detector_calibration_client_uid"] assert server_uid == new_hash assert server_uid == client_uid # md link calib_server_uid = calib_hdr.start["detector_calibration_server_uid"] hdr_client_uid = hdr.start["detector_calibration_client_uid"] assert calib_server_uid == hdr_client_uid
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 __init__(self, img_data=None): self.img_data = img_data self.points = [] self.points_index = [] self.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': 0.95 } self.fit_wavelength = False self.is_calibrated = False self.use_mask = False self.calibration_name = 'None' self.polarization_factor = 0.95 self._calibrants_working_dir = os.path.dirname(Calibrants.__file__)
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 test_load_calibrant(fresh_xrun, bt): xrun = fresh_xrun xrun.beamtime = bt # pyfai factory for k, calibrant_obj in ALL_CALIBRANTS.items(): # light weight callback def check_eq(name, doc): assert calibrant_obj.dSpacing == doc["dSpacing"] assert k == doc["sample_name"] t = xrun.subscribe(check_eq, "start") # execute run_calibration(calibrant=k, phase_info=k, RE_instance=xrun, wait_for_cal=False) # clean xrun.unsubscribe(t) # invalid calibrant with pytest.raises(xpdAcqException): run_calibration(calibrant="pyFAI", phase_info="buggy", RE_instance=xrun, wait_for_cal=False) # filepath pytest_dir = rs_fn("xpdacq", "tests/") src = os.path.join(pytest_dir, "Ni24.D") dst_base = os.path.abspath(str(uuid.uuid4())) os.makedirs(dst_base) fn = str(uuid.uuid4()) dst = os.path.join(dst_base, fn + ".D") shutil.copy(src, dst) c = Calibrant(filename=dst) def check_eq(name, doc): assert c.dSpacing == doc["dSpacing"] assert dst == doc["sample_name"] t = xrun.subscribe(check_eq, "start") # execute run_calibration(calibrant=dst, phase_info="buggy", RE_instance=xrun, wait_for_cal=False) # clean xrun.unsubscribe(t)
def __init__(self, img_data=None): self.img_data = img_data self.points = [] self.points_index = [] self.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': 0.95} self.fit_wavelength = False self.is_calibrated = False self.use_mask = False self.calibration_name = 'None' self.polarization_factor = 0.95 self._calibrants_working_dir = os.path.dirname(Calibrants.__file__)
class CalibrationData(object): def __init__(self, img_data=None): self.img_data = img_data self.points = [] self.points_index = [] self.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': 0.95} self.fit_wavelength = False self.is_calibrated = False self.use_mask = False self.calibration_name = 'None' self.polarization_factor = 0.95 self._calibrants_working_dir = os.path.dirname(Calibrants.__file__) def find_peaks_automatic(self, x, y, peak_ind): 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): 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 setup_peak_search_algorithm(self, algorithm, mask=None): # init the peak search algorithm 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): if not self.is_calibrated: return #transform delta from degree into radians delta_tth = delta_tth / 180.0 * np.pi # get appropiate 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.geometry._ttha is None: tth_array = self.geometry.twoThetaArray(self.img_data.img_data.shape) else: tth_array = self.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)) self.points_index.append(peak_index) def set_calibrant(self, filename): self.calibrant = Calibrant() self.calibrant.load_file(filename) self.geometry.calibrant = self.calibrant def set_start_values(self, start_values): self.start_values = start_values self.polarization_factor = start_values['polarization_factor'] def calibrate(self): self.geometry = GeometryRefinement(self.create_point_array(self.points, self.points_index), dist=self.start_values['dist'], wavelength=self.start_values['wavelength'], pixel1=self.start_values['pixel_width'], pixel2=self.start_values['pixel_height'], calibrant=self.calibrant) self.refine() self.integrate() self.is_calibrated = True self.calibration_name = 'current' def refine(self): self.geometry.data = self.create_point_array(self.points, self.points_index) self.geometry.refine2() if self.fit_wavelength: self.geometry.refine2_wavelength(fix=[]) def integrate(self): self.integrate_1d() self.integrate_2d() def integrate_1d(self, num_points=1400, mask=None, polarization_factor=None, filename=None, unit='2th_deg'): if np.sum(mask) == self.img_data.img_data.shape[0] * self.img_data.img_data.shape[1]: #do not perform integration if the image is completelye masked... return self.tth, self.int if polarization_factor is None: polarization_factor = self.polarization_factor if unit is 'd_A': self.tth, self.int = self.geometry.integrate1d(self.img_data.img_data, num_points, method='lut', unit='2th_deg', mask=mask, polarization_factor=polarization_factor, filename=filename) ind = np.where(self.tth > 0) self.tth = self.geometry.wavelength / (2 * np.sin(self.tth[ind] / 360 * np.pi)) * 1e10 self.int = self.int[ind] else: self.tth, self.int = self.geometry.integrate1d(self.img_data.img_data, num_points, method='lut', unit=unit, mask=mask, polarization_factor=polarization_factor, filename=filename) if self.int.max() > 0: ind = np.where(self.int > 0) self.tth = self.tth[ind] self.int = self.int[ind] return self.tth, self.int def integrate_2d(self, mask=None, polarization_factor=None, unit='2th_deg'): if polarization_factor is None: polarization_factor = self.polarization_factor res = self.geometry.integrate2d(self.img_data.img_data, 2048, 2048, method='lut', mask=mask, unit=unit, polarization_factor=polarization_factor) self.cake_img = res[0] self.cake_tth = res[1] self.cake_azi = res[2] return self.cake_img def create_point_array(self, points, points_ind): res = [] for i, point_list in enumerate(points): if point_list.shape == (2,): res.append([point_list[0], point_list[1], points_ind[i]]) else: for point in point_list: res.append([point[0], point[1], points_ind[i]]) return np.array(res) def get_point_array(self): return self.create_point_array(self.points, self.points_index) def get_calibration_parameter(self): pyFAI_parameter = self.geometry.getPyFAI() pyFAI_parameter['polarization_factor'] = self.polarization_factor try: fit2d_parameter = self.geometry.getFit2D() fit2d_parameter['polarization_factor'] = self.polarization_factor except TypeError: fit2d_parameter = None try: pyFAI_parameter['wavelength'] = self.geometry.wavelength fit2d_parameter['wavelength'] = self.geometry.wavelength except RuntimeWarning: pyFAI_parameter['wavelength'] = 0 return pyFAI_parameter, fit2d_parameter def load(self, filename): self.geometry = GeometryRefinement(np.zeros((2, 3)), dist=self.start_values['dist'], wavelength=self.start_values['wavelength'], pixel1=self.start_values['pixel_width'], pixel2=self.start_values['pixel_height']) self.geometry.load(filename) self.calibration_name = get_base_name(filename) self.is_calibrated = True def save(self, filename): self.geometry.save(filename) self.calibration_name = get_base_name(filename)
def set_calibrant(self, filename): self.calibrant = Calibrant() self.calibrant.load_file(filename) self.spectrum_geometry.calibrant = self.calibrant
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' :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)) self.points_index.append(peak_index) self.set_supersampling() self.spectrum_geometry.reset() def set_calibrant(self, filename): self.calibrant = Calibrant() self.calibrant.load_file(filename) self.spectrum_geometry.calibrant = self.calibrant def set_start_values(self, start_values): self.start_values = start_values self.polarization_factor = start_values['polarization_factor'] def calibrate(self): self.spectrum_geometry = GeometryRefinement(self.create_point_array(self.points, self.points_index), dist=self.start_values['dist'], wavelength=self.start_values['wavelength'], pixel1=self.start_values['pixel_width'], pixel2=self.start_values['pixel_height'], calibrant=self.calibrant) self.orig_pixel1 = self.start_values['pixel_width'] self.orig_pixel2 = self.start_values['pixel_height'] self.refine() self.create_cake_geometry() self.is_calibrated = True self.calibration_name = 'current' self.set_supersampling() # reset the integrator (not the geometric parameters) self.spectrum_geometry.reset() def refine(self): self.reset_supersampling() self.spectrum_geometry.data = self.create_point_array(self.points, self.points_index) fix = ['wavelength'] if self.fit_wavelength: fix = [] if not self.fit_distance: fix.append('dist') if self.fit_wavelength: self.spectrum_geometry.refine2() self.spectrum_geometry.refine2_wavelength(fix=fix) self.create_cake_geometry() self.set_supersampling() # reset the integrator (not the geometric parameters) self.spectrum_geometry.reset() def integrate_1d(self, num_points=None, mask=None, polarization_factor=None, filename=None, unit='2th_deg', method='csr'): if np.sum(mask) == self.img_data.img_data.shape[0] * self.img_data.img_data.shape[1]: # do not perform integration if the image is completely masked... return self.tth, self.int if self.spectrum_geometry._polarization is not None: if self.img_data.img_data.shape != self.spectrum_geometry._polarization.shape: # resetting the integrator if the polarization correction matrix has not the correct shape self.spectrum_geometry.reset() if polarization_factor is None: polarization_factor = self.polarization_factor if num_points is None: num_points = self.calculate_number_of_spectrum_points(2) self.num_points = num_points t1 = time.time() if unit is 'd_A': try: self.tth, self.int = self.spectrum_geometry.integrate1d(self.img_data.img_data, num_points, method=method, unit='2th_deg', mask=mask, polarization_factor=polarization_factor, filename=filename) except NameError: self.tth, self.int = self.spectrum_geometry.integrate1d(self.img_data.img_data, num_points, method=method, unit='2th_deg', mask=mask, polarization_factor=polarization_factor, filename=filename) self.tth = self.spectrum_geometry.wavelength / (2 * np.sin(self.tth / 360 * np.pi)) * 1e10 self.int = self.int else: try: self.tth, self.int = self.spectrum_geometry.integrate1d(self.img_data.img_data, num_points, method=method, unit=unit, mask=mask, polarization_factor=polarization_factor, filename=filename) except NameError: self.tth, self.int = self.spectrum_geometry.integrate1d(self.img_data.img_data, num_points, method='lut', unit=unit, mask=mask, polarization_factor=polarization_factor, filename=filename) logger.info('1d integration of {}: {}s.'.format(os.path.basename(self.img_data.filename), time.time() - t1)) ind = np.where((self.int > 0) & (~np.isnan(self.int))) self.tth = self.tth[ind] self.int = self.int[ind] return self.tth, self.int def integrate_2d(self, mask=None, polarization_factor=None, unit='2th_deg', method='csr', dimensions=(2048, 2048)): if polarization_factor is None: polarization_factor = self.polarization_factor if self.cake_geometry._polarization is not None: if self.img_data.img_data.shape != self.cake_geometry._polarization.shape: # resetting the integrator if the polarization correction matrix has not the same shape as the image self.cake_geometry.reset() t1 = time.time() res = self.cake_geometry.integrate2d(self.img_data._img_data, dimensions[0], dimensions[1], method=method, mask=mask, unit=unit, polarization_factor=polarization_factor) logger.info('2d integration of {}: {}s.'.format(os.path.basename(self.img_data.filename), time.time() - t1)) self.cake_img = res[0] self.cake_tth = res[1] self.cake_azi = res[2] return self.cake_img def create_point_array(self, points, points_ind): res = [] for i, point_list in enumerate(points): if point_list.shape == (2,): res.append([point_list[0], point_list[1], points_ind[i]]) else: for point in point_list: res.append([point[0], point[1], points_ind[i]]) return np.array(res) def get_point_array(self): return self.create_point_array(self.points, self.points_index) def get_calibration_parameter(self): pyFAI_parameter = self.cake_geometry.getPyFAI() pyFAI_parameter['polarization_factor'] = self.polarization_factor try: fit2d_parameter = self.cake_geometry.getFit2D() fit2d_parameter['polarization_factor'] = self.polarization_factor except TypeError: fit2d_parameter = None try: pyFAI_parameter['wavelength'] = self.spectrum_geometry.wavelength fit2d_parameter['wavelength'] = self.spectrum_geometry.wavelength except RuntimeWarning: pyFAI_parameter['wavelength'] = 0 return pyFAI_parameter, fit2d_parameter def calculate_number_of_spectrum_points(self, max_dist_factor=1.5): # calculates the number of points for an integrated spectrum, based on the distance of the beam center to the the #image corners. Maximum value is determined by the shape of the image. fit2d_parameter = self.spectrum_geometry.getFit2D() center_x = fit2d_parameter['centerX'] center_y = fit2d_parameter['centerY'] width, height = self.img_data.img_data.shape if center_x < width and center_x > 0: side1 = np.max([abs(width - center_x), center_x]) else: side1 = width if center_y < height and center_y > 0: side2 = np.max([abs(height - center_y), center_y]) else: side2 = height max_dist = np.sqrt(side1 ** 2 + side2 ** 2) return int(max_dist * max_dist_factor) def load(self, filename): self.spectrum_geometry = AzimuthalIntegrator() self.spectrum_geometry.load(filename) self.orig_pixel1 = self.spectrum_geometry.pixel1 self.orig_pixel2 = self.spectrum_geometry.pixel2 self.calibration_name = get_base_name(filename) self.filename = filename self.is_calibrated = True self.create_cake_geometry() self.set_supersampling() def save(self, filename): self.cake_geometry.save(filename) self.calibration_name = get_base_name(filename) self.filename = filename def create_file_header(self): return self.cake_geometry.makeHeaders(polarization_factor=self.polarization_factor) def set_fit2d(self, fit2d_parameter): print fit2d_parameter self.spectrum_geometry.setFit2D(directDist=fit2d_parameter['directDist'], centerX=fit2d_parameter['centerX'], centerY=fit2d_parameter['centerY'], tilt=fit2d_parameter['tilt'], tiltPlanRotation=fit2d_parameter['tiltPlanRotation'], pixelX=fit2d_parameter['pixelX'], pixelY=fit2d_parameter['pixelY']) self.spectrum_geometry.wavelength = fit2d_parameter['wavelength'] self.create_cake_geometry() self.polarization_factor = fit2d_parameter['polarization_factor'] self.orig_pixel1 = fit2d_parameter['pixelX'] * 1e-6 self.orig_pixel2 = fit2d_parameter['pixelY'] * 1e-6 self.is_calibrated = True self.set_supersampling() def set_pyFAI(self, pyFAI_parameter): self.spectrum_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.spectrum_geometry.wavelength = pyFAI_parameter['wavelength'] self.create_cake_geometry() self.polarization_factor = pyFAI_parameter['polarization_factor'] self.orig_pixel1 = pyFAI_parameter['pixel1'] self.orig_pixel2 = pyFAI_parameter['pixel2'] self.is_calibrated = True self.set_supersampling() def set_supersampling(self, factor=None): if factor is None: factor = self.supersampling_factor self.spectrum_geometry.pixel1 = self.orig_pixel1 / float(factor) self.spectrum_geometry.pixel2 = self.orig_pixel2 / float(factor) if factor != self.supersampling_factor: self.spectrum_geometry.reset() self.supersampling_factor = factor def reset_supersampling(self): self.spectrum_geometry.pixel1 = self.orig_pixel1 self.spectrum_geometry.pixel2 = self.orig_pixel2 def get_two_theta_img(self, x, y): """ Gives the two_theta value for the x,y coordinates on the image :return: two theta in radians """ x = np.array([x]) * self.supersampling_factor y = np.array([y]) * self.supersampling_factor return self.spectrum_geometry.tth(x, y)[0] def get_azi_img(self, x, y): """ Gives chi for position on image. :param x: x-coordinate in pixel :param y: y-coordinate in pixel :return: azimuth in radians """ x *= self.supersampling_factor y *= self.supersampling_factor return self.spectrum_geometry.chi(x, y)[0] def get_two_theta_cake(self, y): """ Gives the two_theta value for the x coordinate in the cake :param x: y-coordinate on image :return: two theta in degree """ return self.cake_tth[np.round(y[0])] def get_azi_cake(self, x): """ Gives the azimuth value for a cake. :param x: x-coordinate in pixel :return: azimuth in degree """ return self.cake_azi[np.round(x[0])] def get_two_theta_array(self): return self.spectrum_geometry._ttha[::self.supersampling_factor, ::self.supersampling_factor] @property def wavelength(self): return self.spectrum_geometry.wavelength
def set_calibrant(self, filename): self.calibrant = Calibrant() self.calibrant.load_file(filename) self.geometry.calibrant = self.calibrant
class CalibrationModel(QtCore.QObject): def __init__(self, img_model=None): super(CalibrationModel, self).__init__() """ :param img_model: :type img_model: ImgModel """ self.img_model = img_model self.points = [] self.points_index = [] self.pattern_geometry = AzimuthalIntegrator() self.pattern_geometry_img_shape = None self.cake_geometry = None self.cake_geometry_img_shape = None self.calibrant = Calibrant() self.pattern_geometry.wavelength = 0.3344e-10 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.fit_poni1 = True self.fit_poni2 = True self.fit_rot1 = True self.fit_rot2 = True self.fit_rot3 = 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.correct_solid_angle = True self._calibrants_working_dir = calibrants_path self.distortion_spline_filename = None self.tth = np.linspace(0, 25) self.int = np.sin(self.tth) self.num_points = len(self.int) self.cake_img = np.zeros((2048, 2048)) self.cake_tth = None self.cake_azi = None self.peak_search_algorithm = None def find_peaks_automatic(self, x, y, peak_ind): """ Searches peaks by using the Massif algorithm :param float x: x-coordinate in pixel - should be from original image (not supersampled x-coordinate) :param float 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((int(np.round(x)), int(np.round(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 int x: x-coordinate in pixel - should be from original image (not supersampled x-coordinate) :param int 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 = int(np.round(x - search_size * 0.5)) if left_ind < 0: left_ind = 0 top_ind = int(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 remove_last_peak(self): if self.points: num_points = int(self.points[-1].size/2) # each peak is x, y so length is twice as number of peaks self.points.pop(-1) self.points_index.pop(-1) return num_points def create_cake_geometry(self): self.cake_geometry = AzimuthalIntegrator(splineFile=self.distortion_spline_filename) pyFAI_parameter = self.pattern_geometry.getPyFAI() pyFAI_parameter['polarization_factor'] = self.polarization_factor pyFAI_parameter['wavelength'] = self.pattern_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' :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.raw_img_data) elif algorithm == 'Blob': if mask is not None: self.peak_search_algorithm = BlobDetection(self.img_model.raw_img_data * mask) else: self.peak_search_algorithm = BlobDetection(self.img_model.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() if ring_index >= len(tth_calibrant_list): raise NotEnoughSpacingsInCalibrant() tth_calibrant = np.float(tth_calibrant_list[ring_index]) # get the calculated two theta values for the whole image tth_array = self.pattern_geometry.twoThetaArray(self.img_model._img_data.shape) # 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 if len(res): self.points.append(np.array(res)) self.points_index.append(ring_index) self.set_supersampling() self.pattern_geometry.reset() def set_calibrant(self, filename): self.calibrant = Calibrant() self.calibrant.load_file(filename) self.pattern_geometry.calibrant = self.calibrant def set_start_values(self, start_values): self.start_values = start_values self.polarization_factor = start_values['polarization_factor'] def calibrate(self): self.pattern_geometry = GeometryRefinement(self.create_point_array(self.points, self.points_index), dist=self.start_values['dist'], wavelength=self.start_values['wavelength'], pixel1=self.start_values['pixel_width'], pixel2=self.start_values['pixel_height'], calibrant=self.calibrant, splineFile=self.distortion_spline_filename) self.orig_pixel1 = self.start_values['pixel_width'] self.orig_pixel2 = self.start_values['pixel_height'] self.refine() self.create_cake_geometry() self.is_calibrated = True self.calibration_name = 'current' self.set_supersampling() # reset the integrator (not the geometric parameters) self.pattern_geometry.reset() def refine(self): self.reset_supersampling() self.pattern_geometry.data = self.create_point_array(self.points, self.points_index) fix = ['wavelength'] if self.fit_wavelength: fix = [] if not self.fit_distance: fix.append('dist') if not self.fit_poni1: fix.append('poni1') if not self.fit_poni2: fix.append('poni2') if not self.fit_rot1: fix.append('rot1') if not self.fit_rot2: fix.append('rot2') if not self.fit_rot3: fix.append('rot3') if self.fit_wavelength: self.pattern_geometry.refine2() self.pattern_geometry.refine2_wavelength(fix=fix) self.create_cake_geometry() self.set_supersampling() # reset the integrator (not the geometric parameters) self.pattern_geometry.reset() def integrate_1d(self, num_points=None, mask=None, polarization_factor=None, filename=None, unit='2th_deg', method='csr'): if np.sum(mask) == self.img_model.img_data.shape[0] * self.img_model.img_data.shape[1]: # do not perform integration if the image is completely masked... return self.tth, self.int if self.pattern_geometry_img_shape != self.img_model.img_data.shape: # if cake geometry was used on differently shaped image before the azimuthal integrator needs to be reset self.pattern_geometry.reset() self.pattern_geometry_img_shape = self.img_model.img_data.shape if polarization_factor is None: polarization_factor = self.polarization_factor if num_points is None: num_points = self.calculate_number_of_pattern_points(2) self.num_points = num_points t1 = time.time() if unit is 'd_A': try: self.tth, self.int = self.pattern_geometry.integrate1d(self.img_model.img_data, num_points, method=method, unit='2th_deg', mask=mask, polarization_factor=polarization_factor, correctSolidAngle=self.correct_solid_angle, filename=filename) except NameError: self.tth, self.int = self.pattern_geometry.integrate1d(self.img_model.img_data, num_points, method='csr', unit='2th_deg', mask=mask, polarization_factor=polarization_factor, correctSolidAngle=self.correct_solid_angle, filename=filename) self.tth = self.pattern_geometry.wavelength / (2 * np.sin(self.tth / 360 * np.pi)) * 1e10 self.int = self.int else: try: self.tth, self.int = self.pattern_geometry.integrate1d(self.img_model.img_data, num_points, method=method, unit=unit, mask=mask, polarization_factor=polarization_factor, correctSolidAngle=self.correct_solid_angle, filename=filename) except NameError: self.tth, self.int = self.pattern_geometry.integrate1d(self.img_model.img_data, num_points, method='csr', unit=unit, mask=mask, polarization_factor=polarization_factor, correctSolidAngle=self.correct_solid_angle, filename=filename) logger.info('1d integration of {0}: {1}s.'.format(os.path.basename(self.img_model.filename), time.time() - t1)) ind = np.where((self.int > 0) & (~np.isnan(self.int))) self.tth = self.tth[ind] self.int = self.int[ind] return self.tth, self.int def integrate_2d(self, mask=None, polarization_factor=None, unit='2th_deg', method='csr', rad_points=None, azimuth_points=360, azimuth_range=None): if polarization_factor is None: polarization_factor = self.polarization_factor if self.cake_geometry_img_shape != self.img_model.img_data.shape: # if cake geometry was used on differently shaped image before the azimuthal integrator needs to be reset self.cake_geometry.reset() self.cake_geometry_img_shape = self.img_model.img_data.shape if rad_points is None: rad_points = self.calculate_number_of_pattern_points(2) self.num_points = rad_points t1 = time.time() res = self.cake_geometry.integrate2d(self.img_model.img_data, rad_points, azimuth_points, azimuth_range=azimuth_range, method=method, mask=mask, unit=unit, polarization_factor=polarization_factor, correctSolidAngle=self.correct_solid_angle) logger.info('2d integration of {0}: {1}s.'.format(os.path.basename(self.img_model.filename), time.time() - t1)) self.cake_img = res[0] self.cake_tth = res[1] self.cake_azi = res[2] return self.cake_img def create_point_array(self, points, points_ind): res = [] for i, point_list in enumerate(points): if point_list.shape == (2,): res.append([point_list[0], point_list[1], points_ind[i]]) else: for point in point_list: res.append([point[0], point[1], points_ind[i]]) return np.array(res) def get_point_array(self): return self.create_point_array(self.points, self.points_index) def get_calibration_parameter(self): pyFAI_parameter = self.pattern_geometry.getPyFAI() pyFAI_parameter['polarization_factor'] = self.polarization_factor try: fit2d_parameter = self.pattern_geometry.getFit2D() fit2d_parameter['polarization_factor'] = self.polarization_factor except TypeError: fit2d_parameter = None pyFAI_parameter['wavelength'] = self.pattern_geometry.wavelength if fit2d_parameter: fit2d_parameter['wavelength'] = self.pattern_geometry.wavelength return pyFAI_parameter, fit2d_parameter def calculate_number_of_pattern_points(self, max_dist_factor=1.5): # calculates the number of points for an integrated pattern, based on the distance of the beam center to the the # image corners. Maximum value is determined by the shape of the image. fit2d_parameter = self.pattern_geometry.getFit2D() center_x = fit2d_parameter['centerX'] center_y = fit2d_parameter['centerY'] width, height = self.img_model.img_data.shape if width > center_x > 0: side1 = np.max([abs(width - center_x), center_x]) else: side1 = width if center_y < height and center_y > 0: side2 = np.max([abs(height - center_y), center_y]) else: side2 = height max_dist = np.sqrt(side1 ** 2 + side2 ** 2) return int(max_dist * max_dist_factor) def load(self, filename): """ Loads a calibration file and and sets all the calibration parameter. :param filename: filename for a *.poni calibration file """ self.pattern_geometry = AzimuthalIntegrator() self.pattern_geometry.load(filename) self.orig_pixel1 = self.pattern_geometry.pixel1 self.orig_pixel2 = self.pattern_geometry.pixel2 self.calibration_name = get_base_name(filename) self.filename = filename self.is_calibrated = True self.create_cake_geometry() self.set_supersampling() def save(self, filename): """ Saves the current calibration parameters into a a text file. Default extension is *.poni """ self.cake_geometry.save(filename) self.calibration_name = get_base_name(filename) self.filename = filename def create_file_header(self): try: # pyFAI version 0.12.0 return self.pattern_geometry.makeHeaders(polarization_factor=self.polarization_factor) except AttributeError: # pyFAI after version 0.12.0 from pyFAI.io import DefaultAiWriter return DefaultAiWriter(None, self.pattern_geometry).make_headers() def set_fit2d(self, fit2d_parameter): """ Reads in a dictionary with fit2d parameters where the fields of the dictionary are: 'directDist', 'centerX', 'centerY', 'tilt', 'tiltPlanRotation', 'pixelX', pixelY', 'polarization_factor', 'wavelength' """ self.pattern_geometry.setFit2D(directDist=fit2d_parameter['directDist'], centerX=fit2d_parameter['centerX'], centerY=fit2d_parameter['centerY'], tilt=fit2d_parameter['tilt'], tiltPlanRotation=fit2d_parameter['tiltPlanRotation'], pixelX=fit2d_parameter['pixelX'], pixelY=fit2d_parameter['pixelY']) self.pattern_geometry.wavelength = fit2d_parameter['wavelength'] self.create_cake_geometry() self.polarization_factor = fit2d_parameter['polarization_factor'] self.orig_pixel1 = fit2d_parameter['pixelX'] * 1e-6 self.orig_pixel2 = fit2d_parameter['pixelY'] * 1e-6 self.is_calibrated = True self.set_supersampling() def set_pyFAI(self, pyFAI_parameter): """ Reads in a dictionary with pyFAI parameters where the fields of dictionary are: 'dist', 'poni1', 'poni2', 'rot1', 'rot2', 'rot3', 'pixel1', 'pixel2', 'wavelength', 'polarization_factor' """ self.pattern_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.pattern_geometry.wavelength = pyFAI_parameter['wavelength'] self.create_cake_geometry() self.polarization_factor = pyFAI_parameter['polarization_factor'] self.orig_pixel1 = pyFAI_parameter['pixel1'] self.orig_pixel2 = pyFAI_parameter['pixel2'] self.is_calibrated = True self.set_supersampling() def load_distortion(self, spline_filename): self.distortion_spline_filename = spline_filename self.pattern_geometry.set_splineFile(spline_filename) if self.cake_geometry: self.cake_geometry.set_splineFile(spline_filename) def reset_distortion_correction(self): self.distortion_spline_filename = None self.pattern_geometry.set_splineFile(None) if self.cake_geometry: self.cake_geometry.set_splineFile(None) def set_supersampling(self, factor=None): """ Sets the supersampling to a specific factor. Whereby the factor determines in how many artificial pixel the original pixel is split. (factor^2) factor n_pixel 1 1 2 4 3 9 4 16 """ if factor is None: factor = self.supersampling_factor self.pattern_geometry.pixel1 = self.orig_pixel1 / float(factor) self.pattern_geometry.pixel2 = self.orig_pixel2 / float(factor) if factor != self.supersampling_factor: self.pattern_geometry.reset() self.supersampling_factor = factor def reset_supersampling(self): self.pattern_geometry.pixel1 = self.orig_pixel1 self.pattern_geometry.pixel2 = self.orig_pixel2 def get_two_theta_img(self, x, y): """ Gives the two_theta value for the x,y coordinates on the image. Be aware that this function will be incorrect for pixel indices, since it does not correct for center of the pixel. :param x: x-coordinate in pixel on the image :type x: ndarray :param y: y-coordinate in pixel on the image :type y: ndarray :return : two theta in radians """ x *= self.supersampling_factor y *= self.supersampling_factor return self.pattern_geometry.tth(x - 0.5, y - 0.5)[0] # deletes 0.5 because tth function uses pixel indices def get_azi_img(self, x, y): """ Gives chi for position on image. :param x: x-coordinate in pixel on the image :type x: ndarray :param y: y-coordinate in pixel on the image :type y: ndarray :return : azimuth in radians """ x *= self.supersampling_factor y *= self.supersampling_factor return self.pattern_geometry.chi(x - 0.5, y - 0.5)[0] def get_two_theta_array(self): return self.pattern_geometry.twoThetaArray(self.img_model.img_data.shape)[::self.supersampling_factor, ::self.supersampling_factor] def get_pixel_ind(self, tth, azi): """ Calculates pixel index for a specfic two theta and azimutal value. :param tth: two theta in radians :param azi: azimuth in radians :return: tuple of index 1 and 2 """ tth_ind = find_contours(self.pattern_geometry.ttha, tth) if len(tth_ind) == 0: return [] tth_ind = np.vstack(tth_ind) azi_values = self.pattern_geometry.chi(tth_ind[:, 0], tth_ind[:, 1]) min_index = np.argmin(np.abs(azi_values - azi)) return tth_ind[min_index, 0], tth_ind[min_index, 1] @property def wavelength(self): return self.pattern_geometry.wavelength
def calibration(img, calibrant_file=None, wavelength=None, calib_collection_uid=None, save_file_name=None, detector=None, gaussian=None): """ run calibration process on a image with geometry correction software current backend is ``pyFAI``. Parameters ---------- img : ndarray image to be calibrated calibrant_file : str, optional calibrant file being used, default is 'Ni.D' under xpdUser/userAnalysis/ wavelength : flot, optional current of x-ray wavelength, in angstrom. Default value is read out from existing xpdacq.Beamtime object calibration_collection_uid : str, optional uid of calibration collection. default is generated from run calibration save_file_name : str, optional file name for yaml that carries resultant calibration parameters detector : pyfai.detector.Detector, optional. instance of detector which defines pixel size in x- and y-direction. Default is set to Perkin Elmer detector gaussian : int, optional gaussian width between rings, Default is 100. """ # default params interactive = True dist = 0.1 _check_obj(_REQUIRED_OBJ_LIST) ips = get_ipython() bto = ips.ns_table['user_global']['bt'] xrun = ips.ns_table['user_global']['xrun'] calibrant = Calibrant() # d-spacing if calibrant_file is not None: calibrant.load_file(calibrant_file) calibrant_name = os.path.split(calibrant_file)[1] calibrant_name = os.path.splitext(calibrant_name)[0] else: calibrant.load_file(os.path.join(glbl.usrAnalysis_dir, 'Ni.D')) calibrant_name = 'Ni' # wavelength if wavelength is None: _wavelength = bto['bt_wavelength'] else: _wavelength = wavelength calibrant.wavelength = _wavelength * 10 ** (-10) # detector if detector is None: detector = Perkin() # calibration timestr = _timestampstr(time.time()) basename = '_'.join(['pyFAI_calib', calibrant_name, timestr]) w_name = os.path.join(glbl.config_base, basename) # poni name c = Calibration(wavelength=calibrant.wavelength, detector=detector, calibrant=calibrant, gaussianWidth=gaussian) c.gui = interactive c.basename = w_name c.pointfile = w_name + ".npt" c.ai = AzimuthalIntegrator(dist=dist, detector=detector, wavelength=calibrant.wavelength) c.peakPicker = PeakPicker(img, reconst=True, mask=detector.mask, pointfile=c.pointfile, calibrant=calibrant, wavelength=calibrant.wavelength) # method=method) if gaussian is not None: c.peakPicker.massif.setValleySize(gaussian) else: c.peakPicker.massif.initValleySize() if interactive: c.peakPicker.gui(log=True, maximize=True, pick=True) update_fig(c.peakPicker.fig) c.gui_peakPicker() c.ai.setPyFAI(**c.geoRef.getPyFAI()) c.ai.wavelength = c.geoRef.wavelength return c.ai
def set_calibrant(self, filename): self.calibrant = Calibrant() self.calibrant.load_file(filename) self.pattern_geometry.calibrant = self.calibrant
class test_peak_picking(unittest.TestCase): """basic test""" calibFile = "1788/moke.tif" # gr = GeometryRefinement() ctrlPt = {0:(300, 230), 1:(300, 212), 2:(300, 195), 3:(300, 177), 4:(300, 159), 5:(300, 140), 6:(300, 123), 7:(300, 105), 8:(300, 87)} tth = numpy.radians(numpy.arange(4, 13)) wavelength = 1e-10 ds = wavelength * 5e9 / numpy.sin(tth / 2) calibrant = Calibrant(dSpacing=ds) maxiter = 100 tmp_dir = os.environ.get("PYFAI_TEMPDIR", os.path.join(os.path.dirname(os.path.abspath(__file__)), "tmp")) logfile = os.path.join(tmp_dir, "testpeakPicking.log") nptfile = os.path.join(tmp_dir, "testpeakPicking.npt") def setUp(self): """Download files""" self.img = UtilsTest.getimage(self.__class__.calibFile) self.pp = PeakPicker(self.img, calibrant=self.calibrant, wavelength=self.wavelength) if not os.path.isdir(self.tmp_dir): os.makedirs(self.tmp_dir) if os.path.isfile(self.logfile): os.unlink(self.logfile) if os.path.isfile(self.nptfile): os.unlink(self.nptfile) def tearDown(self): """Remove temporary files""" unittest.TestCase.tearDown(self) if os.path.isfile(self.logfile): os.unlink(self.logfile) if os.path.isfile(self.nptfile): os.unlink(self.nptfile) def test_peakPicking(self): """first test peak-picking then checks the geometry found is OK""" for i in self.ctrlPt: pts = self.pp.massif.find_peaks(self.ctrlPt[i], stdout=open(self.logfile, "a")) logger.info("point %s at ring #%i (tth=%.1f deg) generated %i points", self.ctrlPt[i], i, self.tth[i], len(pts)) if len(pts) > 0: self.pp.points.append(pts, angle=self.tth[i], ring=i) else: logger.error("point %s caused error (%s) ", i, self.ctrlPt[i]) self.pp.points.save(self.nptfile) lstPeak = self.pp.points.getListRing() # print self.pp.points # print lstPeak logger.info("After peak-picking, we have %s points generated from %s groups ", len(lstPeak), len(self.ctrlPt)) gr = GeometryRefinement(lstPeak, dist=0.01, pixel1=1e-4, pixel2=1e-4, wavelength=self.wavelength, calibrant=self.calibrant) gr.guess_poni() logger.info(gr.__repr__()) last = sys.maxint for i in range(self.maxiter): delta2 = gr.refine2() logger.info(gr.__repr__()) if delta2 == last: logger.info("refinement finished after %s iteration" % i) break last = delta2 self.assertEquals(last < 1e-4, True, "residual error is less than 1e-4, got %s" % last) self.assertAlmostEquals(gr.dist, 0.1, 2, "distance is OK, got %s, expected 0.1" % gr.dist) self.assertAlmostEquals(gr.poni1, 3e-2, 2, "PONI1 is OK, got %s, expected 3e-2" % gr.poni1) self.assertAlmostEquals(gr.poni2, 3e-2, 2, "PONI2 is OK, got %s, expected 3e-2" % gr.poni2) self.assertAlmostEquals(gr.rot1, 0, 2, "rot1 is OK, got %s, expected 0" % gr.rot1) self.assertAlmostEquals(gr.rot2, 0, 2, "rot2 is OK, got %s, expected 0" % gr.rot2) self.assertAlmostEquals(gr.rot3, 0, 2, "rot3 is OK, got %s, expected 0" % gr.rot3)
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'], 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_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 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 if len(res): self.points.append(np.array(res)) self.points_index.append(ring_index) self.set_supersampling() self.spectrum_geometry.reset() def set_calibrant(self, filename): self.calibrant = Calibrant() self.calibrant.load_file(filename) self.spectrum_geometry.calibrant = self.calibrant def set_start_values(self, start_values): self.start_values = start_values self.polarization_factor = start_values['polarization_factor'] def calibrate(self): self.spectrum_geometry = GeometryRefinement(self.create_point_array(self.points, self.points_index), dist=self.start_values['dist'], wavelength=self.start_values['wavelength'], pixel1=self.start_values['pixel_width'], pixel2=self.start_values['pixel_height'], calibrant=self.calibrant) self.orig_pixel1 = self.start_values['pixel_width'] self.orig_pixel2 = self.start_values['pixel_height'] self.refine() self.create_cake_geometry() self.is_calibrated = True self.calibration_name = 'current' self.set_supersampling() # reset the integrator (not the geometric parameters) self.spectrum_geometry.reset() def refine(self): self.reset_supersampling() self.spectrum_geometry.data = self.create_point_array(self.points, self.points_index) fix = ['wavelength'] if self.fit_wavelength: fix = [] if not self.fit_distance: fix.append('dist') if self.fit_wavelength: self.spectrum_geometry.refine2() self.spectrum_geometry.refine2_wavelength(fix=fix) self.create_cake_geometry() self.set_supersampling() # reset the integrator (not the geometric parameters) self.spectrum_geometry.reset() def integrate_1d(self, num_points=None, mask=None, polarization_factor=None, filename=None, unit='2th_deg', method='csr'): if np.sum(mask) == self.img_model.img_data.shape[0] * self.img_model.img_data.shape[1]: # do not perform integration if the image is completely masked... return self.tth, self.int if self.spectrum_geometry._polarization is not None: if self.img_model.img_data.shape != self.spectrum_geometry._polarization.shape: # resetting the integrator if the polarization correction matrix has not the correct shape self.spectrum_geometry.reset() if polarization_factor is None: polarization_factor = self.polarization_factor if num_points is None: num_points = self.calculate_number_of_spectrum_points(2) self.num_points = num_points t1 = time.time() if unit is 'd_A': try: self.tth, self.int = self.spectrum_geometry.integrate1d(self.img_model.img_data, num_points, method=method, unit='2th_deg', mask=mask, polarization_factor=polarization_factor, filename=filename) except NameError: self.tth, self.int = self.spectrum_geometry.integrate1d(self.img_model.img_data, num_points, method='csr', unit='2th_deg', mask=mask, polarization_factor=polarization_factor, filename=filename) self.tth = self.spectrum_geometry.wavelength / (2 * np.sin(self.tth / 360 * np.pi)) * 1e10 self.int = self.int else: try: self.tth, self.int = self.spectrum_geometry.integrate1d(self.img_model.img_data, num_points, method=method, unit=unit, mask=mask, polarization_factor=polarization_factor, filename=filename) except NameError: self.tth, self.int = self.spectrum_geometry.integrate1d(self.img_model.img_data, num_points, method='csr', unit=unit, mask=mask, polarization_factor=polarization_factor, filename=filename) logger.info('1d integration of {0}: {1}s.'.format(os.path.basename(self.img_model.filename), time.time() - t1)) ind = np.where((self.int > 0) & (~np.isnan(self.int))) self.tth = self.tth[ind] self.int = self.int[ind] return self.tth, self.int def integrate_2d(self, mask=None, polarization_factor=None, unit='2th_deg', method='csr', dimensions=(2048, 2048)): if polarization_factor is None: polarization_factor = self.polarization_factor if self.cake_geometry._polarization is not None: if self.img_model.img_data.shape != self.cake_geometry._polarization.shape: # resetting the integrator if the polarization correction matrix has not the same shape as the image self.cake_geometry.reset() t1 = time.time() res = self.cake_geometry.integrate2d(self.img_model._img_data, dimensions[0], dimensions[1], method=method, mask=mask, unit=unit, polarization_factor=polarization_factor) logger.info('2d integration of {0}: {1}s.'.format(os.path.basename(self.img_model.filename), time.time() - t1)) self.cake_img = res[0] self.cake_tth = res[1] self.cake_azi = res[2] return self.cake_img def create_point_array(self, points, points_ind): res = [] for i, point_list in enumerate(points): if point_list.shape == (2,): res.append([point_list[0], point_list[1], points_ind[i]]) else: for point in point_list: res.append([point[0], point[1], points_ind[i]]) return np.array(res) def get_point_array(self): return self.create_point_array(self.points, self.points_index) def get_calibration_parameter(self): pyFAI_parameter = self.cake_geometry.getPyFAI() pyFAI_parameter['polarization_factor'] = self.polarization_factor try: fit2d_parameter = self.cake_geometry.getFit2D() fit2d_parameter['polarization_factor'] = self.polarization_factor except TypeError: fit2d_parameter = None try: pyFAI_parameter['wavelength'] = self.spectrum_geometry.wavelength fit2d_parameter['wavelength'] = self.spectrum_geometry.wavelength except RuntimeWarning: pyFAI_parameter['wavelength'] = 0 return pyFAI_parameter, fit2d_parameter def calculate_number_of_spectrum_points(self, max_dist_factor=1.5): # calculates the number of points for an integrated spectrum, based on the distance of the beam center to the the # image corners. Maximum value is determined by the shape of the image. fit2d_parameter = self.spectrum_geometry.getFit2D() center_x = fit2d_parameter['centerX'] center_y = fit2d_parameter['centerY'] width, height = self.img_model.img_data.shape if center_x < width and center_x > 0: side1 = np.max([abs(width - center_x), center_x]) else: side1 = width if center_y < height and center_y > 0: side2 = np.max([abs(height - center_y), center_y]) else: side2 = height max_dist = np.sqrt(side1 ** 2 + side2 ** 2) return int(max_dist * max_dist_factor) def load(self, filename): """ Loads a calibration file and and sets all the calibration parameter. :param filename: filename for a *.poni calibration file """ self.spectrum_geometry = AzimuthalIntegrator() self.spectrum_geometry.load(filename) self.orig_pixel1 = self.spectrum_geometry.pixel1 self.orig_pixel2 = self.spectrum_geometry.pixel2 self.calibration_name = get_base_name(filename) self.filename = filename self.is_calibrated = True self.create_cake_geometry() self.set_supersampling() def save(self, filename): """ Saves the current calibration parameters into a a text file. Default extension is *.poni """ self.cake_geometry.save(filename) self.calibration_name = get_base_name(filename) self.filename = filename def create_file_header(self): return self.cake_geometry.makeHeaders(polarization_factor=self.polarization_factor) def set_fit2d(self, fit2d_parameter): """ Reads in a dictionary with fit2d parameters where the fields of the dictionary are: 'directDist', 'centerX', 'centerY', 'tilt', 'tiltPlanRotation', 'pixelX', pixelY', 'polarization_factor', 'wavelength' """ self.spectrum_geometry.setFit2D(directDist=fit2d_parameter['directDist'], centerX=fit2d_parameter['centerX'], centerY=fit2d_parameter['centerY'], tilt=fit2d_parameter['tilt'], tiltPlanRotation=fit2d_parameter['tiltPlanRotation'], pixelX=fit2d_parameter['pixelX'], pixelY=fit2d_parameter['pixelY']) self.spectrum_geometry.wavelength = fit2d_parameter['wavelength'] self.create_cake_geometry() self.polarization_factor = fit2d_parameter['polarization_factor'] self.orig_pixel1 = fit2d_parameter['pixelX'] * 1e-6 self.orig_pixel2 = fit2d_parameter['pixelY'] * 1e-6 self.is_calibrated = True self.set_supersampling() def set_pyFAI(self, pyFAI_parameter): """ Reads in a dictionary with pyFAI parameters where the fields of dictionary are: 'dist', 'poni1', 'poni2', 'rot1', 'rot2', 'rot3', 'pixel1', 'pixel2', 'wavelength', 'polarization_factor' """ self.spectrum_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.spectrum_geometry.wavelength = pyFAI_parameter['wavelength'] self.create_cake_geometry() self.polarization_factor = pyFAI_parameter['polarization_factor'] self.orig_pixel1 = pyFAI_parameter['pixel1'] self.orig_pixel2 = pyFAI_parameter['pixel2'] self.is_calibrated = True self.set_supersampling() def set_supersampling(self, factor=None): """ Sets the supersampling to a specific factor. Whereby the factor determines in how many artificial pixel the original pixel is split. (factor^2) factor n_pixel 1 1 2 4 3 9 4 16 """ if factor is None: factor = self.supersampling_factor self.spectrum_geometry.pixel1 = self.orig_pixel1 / float(factor) self.spectrum_geometry.pixel2 = self.orig_pixel2 / float(factor) if factor != self.supersampling_factor: self.spectrum_geometry.reset() self.supersampling_factor = factor def reset_supersampling(self): self.spectrum_geometry.pixel1 = self.orig_pixel1 self.spectrum_geometry.pixel2 = self.orig_pixel2 def get_two_theta_img(self, x, y): """ Gives the two_theta value for the x,y coordinates on the image. Be aware that this function will be incorrect for pixel indices, since it does not correct for center of the pixel. :return: two theta in radians """ x = np.array([x]) * self.supersampling_factor y = np.array([y]) * self.supersampling_factor return self.spectrum_geometry.tth(x - 0.5, y - 0.5)[0] # deletes 0.5 because tth function uses pixel indices def get_azi_img(self, x, y): """ Gives chi for position on image. :param x: x-coordinate in pixel :param y: y-coordinate in pixel :return: azimuth in radians """ x *= self.supersampling_factor y *= self.supersampling_factor return self.spectrum_geometry.chi(x, y)[0] def get_two_theta_cake(self, x): """ Gives the two_theta value for the x coordinate in the cake :param x: x-coordinate on image :return: two theta in degree """ x -= 0.5 cake_step = self.cake_tth[1] - self.cake_tth[0] tth = self.cake_tth[int(np.floor(x))] + (x - np.floor(x)) * cake_step return tth def get_azi_cake(self, x): """ Gives the azimuth value for a cake. :param x: x-coordinate in pixel :return: azimuth in degree """ x -= 0.5 azi_step = self.cake_azi[1] - self.cake_azi[0] azi = self.cake_azi[int(np.floor(x))] + (x - np.floor(x)) * azi_step return azi def get_two_theta_array(self): return self.spectrum_geometry._ttha[::self.supersampling_factor, ::self.supersampling_factor] def get_pixel_ind(self, tth, azi): """ Calculates pixel index for a specfic two theta and azimutal value. :param tth: two theta in radians :param azi: azimuth in radians :return: tuple of index 1 and 2 """ tth_ind = find_contours(self.spectrum_geometry.ttha, tth) tth_ind = np.vstack(tth_ind) azi_values = self.spectrum_geometry.chi(tth_ind[:, 0], tth_ind[:, 1]) min_index = np.argmin(np.abs(azi_values - azi)) return tth_ind[min_index, 0], tth_ind[min_index, 1] @property def wavelength(self): return self.spectrum_geometry.wavelength
def run_calibration(exposure=60, calibrant_file=None, wavelength=None, detector=None, gaussian=None): """ function to run entire calibration process. Entire process includes: collect calibration image, trigger pyFAI calibration process, store calibration parameters as a yaml file under xpdUser/config_base/ and inject uid of calibration image to following scans, until this function is run again. Parameters ---------- exposure : int, optional total exposure time in sec. Default is 60s calibrant_name : str, optional name of calibrant used, different calibrants correspond to different d-spacing profiles. Default is 'Ni'. User can assign different calibrant, given d-spacing file path presents wavelength : flot, optional current of x-ray wavelength, in angstrom. Default value is read out from existing xpdacq.Beamtime object detector : pyfai.detector.Detector, optional. instance of detector which defines pxiel size in x- and y-direction. Default is set to Perkin Elmer detector gaussian : int, optional gaussian width between rings, Default is 100. """ # default params interactive = True dist = 0.1 _check_obj(_REQUIRED_OBJ_LIST) ips = get_ipython() bto = ips.ns_table['user_global']['bt'] prun = ips.ns_table['user_global']['prun'] # print('*** current beamtime info = {} ***'.format(bto.md)) calibrant = Calibrant() # d-spacing if calibrant_file is not None: calibrant.load_file(calibrant_file) calibrant_name = os.path.split(calibrant_file)[1] calibrant_name = os.path.splitext(calibrant_name)[0] else: calibrant.load_file(os.path.join(glbl.usrAnalysis_dir, 'Ni24.D')) calibrant_name = 'Ni' # wavelength if wavelength is None: _wavelength = bto['bt_wavelength'] else: _wavelength = wavelength calibrant.wavelength = _wavelength * 10 ** (-10) # detector if detector is None: detector = Perkin() # scan # simplified version of Sample object calib_collection_uid = str(uuid.uuid4()) calibration_dict = {'sample_name':calibrant_name, 'sample_composition':{calibrant_name :1}, 'is_calibration': True, 'calibration_collection_uid': calib_collection_uid} prun_uid = prun(calibration_dict, ScanPlan(bto, ct, exposure)) light_header = glbl.db[prun_uid[-1]] # last one is always light dark_uid = light_header.start['sc_dk_field_uid'] dark_header = glbl.db[dark_uid] # unknown signature of get_images dark_img = np.asarray( get_images(dark_header, glbl.det_image_field)).squeeze() # dark_img = np.asarray(glbl.get_images(dark_header, glbl.det_image_field)).squeeze() for ev in glbl.get_events(light_header, fill=True): img = ev['data'][glbl.det_image_field] img -= dark_img # calibration timestr = _timestampstr(time.time()) basename = '_'.join(['pyFAI_calib', calibrant_name, timestr]) w_name = os.path.join(glbl.config_base, basename) # poni name c = Calibration(wavelength=calibrant.wavelength, detector=detector, calibrant=calibrant, gaussianWidth=gaussian) c.gui = interactive c.basename = w_name c.pointfile = w_name + ".npt" c.ai = AzimuthalIntegrator(dist=dist, detector=detector, wavelength=calibrant.wavelength) c.peakPicker = PeakPicker(img, reconst=True, mask=detector.mask, pointfile=c.pointfile, calibrant=calibrant, wavelength=calibrant.wavelength) # method=method) if gaussian is not None: c.peakPicker.massif.setValleySize(gaussian) else: c.peakPicker.massif.initValleySize() if interactive: c.peakPicker.gui(log=True, maximize=True, pick=True) update_fig(c.peakPicker.fig) c.gui_peakPicker() c.ai.setPyFAI(**c.geoRef.getPyFAI()) c.ai.wavelength = c.geoRef.wavelength # update until next time glbl.calib_config_dict = c.ai.getPyFAI() Fit2D_dict = c.ai.getFit2D() glbl.calib_config_dict.update(Fit2D_dict) glbl.calib_config_dict.update({'file_name':basename}) glbl.calib_config_dict.update({'time':timestr}) # FIXME: need a solution for selecting desired calibration image # based on calibration_collection_uid later glbl.calib_config_dict.update({'calibration_collection_uid': calib_collection_uid}) # write yaml yaml_name = glbl.calib_config_name with open(os.path.join(glbl.config_base, yaml_name), 'w') as f: yaml.dump(glbl.calib_config_dict, f) return c.ai
class CalibrationData(object): def __init__(self, img_data=None): self.img_data = img_data self.points = [] self.points_index = [] self.geometry = AzimuthalIntegrator() self.geometry.set_wavelength(0.3344e-10) self.calibrant = Calibrant() self.start_values = { 'dist': 200e-3, 'wavelength': 0.3344e-10, 'pixel_width': 79e-6, 'pixel_height': 79e-6, 'polarization_factor': 0.95 } self.fit_wavelength = False self.is_calibrated = False self.use_mask = False self.calibration_name = 'None' self.polarization_factor = 0.95 self._calibrants_working_dir = 'ExampleData/Calibrants' def find_peaks_automatic(self, x, y, peak_ind): 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): 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 search_peaks_on_ring(self, peak_index, delta_tth=0.1, algorithm='Massif', min_mean_factor=1, upper_limit=55000): if not self.is_calibrated: return #transform delta from degree into radians delta_tth = delta_tth / 180.0 * np.pi # get appropiate two theta value for the ring number tth_calibrant_list = self.calibrant.get_2th() tth_calibrant = np.float(tth_calibrant_list[peak_index]) print tth_calibrant # get the calculated two theta values for the whole image if self.geometry._ttha is None: tth_array = self.geometry.twoThetaArray( self.img_data.img_data.shape) else: tth_array = self.geometry._ttha # create mask based on two_theta position mask = abs(tth_array - tth_calibrant) <= delta_tth # init the peak search algorithm if algorithm == 'Massif': peak_search_algorithm = Massif(self.img_data.img_data) elif algorithm == 'Blob': peak_search_algorithm = BlobDetection(self.img_data.img_data * mask) peak_search_algorithm.process() else: return # 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 = 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)) self.points_index.append(peak_index) def set_calibrant(self, filename): self.calibrant = Calibrant() self.calibrant.load_file(filename) self.geometry.calibrant = self.calibrant def set_start_values(self, start_values): self.start_values = start_values self.polarization_factor = start_values['polarization_factor'] def calibrate(self): self.geometry = GeometryRefinement( self.create_point_array(self.points, self.points_index), dist=self.start_values['dist'], wavelength=self.start_values['wavelength'], pixel1=self.start_values['pixel_width'], pixel2=self.start_values['pixel_height'], calibrant=self.calibrant) self.refine() self.integrate() self.is_calibrated = True self.calibration_name = 'current' def refine(self): self.geometry.data = self.create_point_array(self.points, self.points_index) self.geometry.refine2() if self.fit_wavelength: self.geometry.refine2_wavelength(fix=[]) def integrate(self): self.integrate_1d() self.integrate_2d() def integrate_1d(self, num_points=1400, mask=None, polarization_factor=None, filename=None, unit='2th_deg'): if polarization_factor is None: polarization_factor = self.polarization_factor if unit is 'd_A': self.tth, self.int = self.geometry.integrate1d( self.img_data.img_data, num_points, method='lut', unit='2th_deg', mask=mask, polarization_factor=polarization_factor, filename=filename) ind = np.where(self.tth > 0) self.tth = self.geometry.wavelength / ( 2 * np.sin(self.tth[ind] / 360 * np.pi)) * 1e10 self.int = self.int[ind] else: self.tth, self.int = self.geometry.integrate1d( self.img_data.img_data, num_points, method='lut', unit=unit, mask=mask, polarization_factor=polarization_factor, filename=filename) return self.tth, self.int def integrate_2d(self, mask=None, polarization_factor=None, unit='2th_deg'): if polarization_factor is None: polarization_factor = self.polarization_factor res = self.geometry.integrate2d( self.img_data.img_data, 2024, 2024, method='lut', mask=mask, unit=unit, polarization_factor=polarization_factor) self.cake_img = res[0] self.cake_tth = res[1] self.cake_azi = res[2] return self.cake_img def create_point_array(self, points, points_ind): res = [] for i, point_list in enumerate(points): if point_list.shape == (2, ): res.append([point_list[0], point_list[1], points_ind[i]]) else: for point in point_list: res.append([point[0], point[1], points_ind[i]]) return np.array(res) def get_point_array(self): return self.create_point_array(self.points, self.points_index) def get_calibration_parameter(self): pyFAI_parameter = self.geometry.getPyFAI() pyFAI_parameter['polarization_factor'] = self.polarization_factor try: fit2d_parameter = self.geometry.getFit2D() fit2d_parameter['polarization_factor'] = self.polarization_factor except TypeError: fit2d_parameter = None try: pyFAI_parameter['wavelength'] = self.geometry.wavelength fit2d_parameter['wavelength'] = self.geometry.wavelength except RuntimeWarning: pyFAI_parameter['wavelength'] = 0 return pyFAI_parameter, fit2d_parameter def load(self, filename): self.geometry = GeometryRefinement( np.zeros((2, 3)), dist=self.start_values['dist'], wavelength=self.start_values['wavelength'], pixel1=self.start_values['pixel_width'], pixel2=self.start_values['pixel_height'], calibrant=self.calibrant) self.geometry.load(filename) self.calibration_name = get_base_name(filename) self.is_calibrated = True def save(self, filename): self.geometry.save(filename) self.calibration_name = get_base_name(filename)