def cake_integration(azimuthal_integrator: AzimuthalIntegrator,
data: np.ndarray,
npt_rad: int = 1000,
npt_azim: int = 1000,
polz_factor: float = 0,
unit: Union[str, units.Unit] = "q_A^-1",
radial_range: Tuple[float] = None,
azimuth_range: Tuple[float] = None,
mask: np.ndarray = None,
dark: np.ndarray = None,
flat: np.ndarray = None,
method: str = 'splitbbox',
normalization_factor: float = 1) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
cake, q, chi = azimuthal_integrator.integrate2d(data=data,
npt_rad=npt_rad,
npt_azim=npt_azim,
radial_range=radial_range,
azimuth_range=azimuth_range,
mask=mask,
polarization_factor=polz_factor,
dark=dark,
flat=flat,
method=method,
unit=unit,
normalization_factor=normalization_factor)
return cake, q, chi
def run(self):
ai = AzimuthalIntegrator(dist=self.__distance,
poni1=self.__poni1,
poni2=self.__poni2,
rot1=self.__rotation1,
rot2=self.__rotation2,
rot3=self.__rotation3,
detector=self.__detector,
wavelength=self.__wavelength)
# FIXME error model, method
self.__result1d = ai.integrate1d(
data=self.__image,
npt=self.__nPointsRadial,
unit=self.__radialUnit,
mask=self.__mask,
polarization_factor=self.__polarizationFactor)
self.__result2d = ai.integrate2d(
data=self.__image,
npt_rad=self.__nPointsRadial,
npt_azim=self.__nPointsAzimuthal,
unit=self.__radialUnit,
mask=self.__mask,
polarization_factor=self.__polarizationFactor)
try:
self.__directDist = ai.getFit2D()["directDist"]
except Exception:
# The geometry could not fit this param
_logger.debug("Backtrace", exc_info=True)
self.__directDist = None
if self.__calibrant:
rings = self.__calibrant.get_2th()
try:
rings = utils.from2ThRad(rings, self.__radialUnit,
self.__wavelength, self.__directDist)
except ValueError:
message = "Convertion to unit %s not supported. Ring marks ignored"
_logger.warning(message, self.__radialUnit)
rings = []
# Filter the rings which are not part of the result
rings = filter(
lambda x: self.__result1d.radial[0] <= x <= self.__result1d.
radial[-1], rings)
rings = list(rings)
else:
rings = []
self.__ringAngles = rings
self.__ai = ai
def run(self):
ai = AzimuthalIntegrator(dist=self.__distance,
poni1=self.__poni1,
poni2=self.__poni2,
rot1=self.__rotation1,
rot2=self.__rotation2,
rot3=self.__rotation3,
detector=self.__detector,
wavelength=self.__wavelength)
numberPoint1D = 1024
numberPointRadial = 400
numberPointAzimuthal = 360
# FIXME error model, method
self.__result1d = ai.integrate1d(
data=self.__image,
npt=numberPoint1D,
unit=self.__radialUnit,
mask=self.__mask,
polarization_factor=self.__polarizationFactor)
self.__result2d = ai.integrate2d(
data=self.__image,
npt_rad=numberPointRadial,
npt_azim=numberPointAzimuthal,
unit=self.__radialUnit,
mask=self.__mask,
polarization_factor=self.__polarizationFactor)
if self.__calibrant:
rings = self.__calibrant.get_2th()
rings = filter(lambda x: x <= self.__result1d.radial[-1], rings)
rings = list(rings)
try:
rings = utils.from2ThRad(rings, self.__radialUnit,
self.__wavelength, ai)
except ValueError:
message = "Convertion to unit %s not supported. Ring marks ignored"
_logger.warning(message, self.__radialUnit)
rings = []
else:
rings = []
self.__ringAngles = rings
def run(self):
ai = AzimuthalIntegrator(
dist=self.__distance,
poni1=self.__poni1,
poni2=self.__poni2,
rot1=self.__rotation1,
rot2=self.__rotation2,
rot3=self.__rotation3,
detector=self.__detector,
wavelength=self.__wavelength)
numberPoint1D = 1024
numberPointRadial = 400
numberPointAzimuthal = 360
# FIXME error model, method
self.__result1d = ai.integrate1d(
data=self.__image,
npt=numberPoint1D,
unit=self.__radialUnit,
mask=self.__mask,
polarization_factor=self.__polarizationFactor)
self.__result2d = ai.integrate2d(
data=self.__image,
npt_rad=numberPointRadial,
npt_azim=numberPointAzimuthal,
unit=self.__radialUnit,
mask=self.__mask,
polarization_factor=self.__polarizationFactor)
if self.__calibrant:
rings = self.__calibrant.get_2th()
rings = filter(lambda x: x <= self.__result1d.radial[-1], rings)
rings = list(rings)
try:
rings = utils.from2ThRad(rings, self.__radialUnit, self.__wavelength, ai)
except ValueError:
message = "Convertion to unit %s not supported. Ring marks ignored"
_logger.warning(message, self.__radialUnit)
rings = []
else:
rings = []
self.__ringAngles = rings
def run(self):
ai = AzimuthalIntegrator(dist=self.__distance,
poni1=self.__poni1,
poni2=self.__poni2,
rot1=self.__rotation1,
rot2=self.__rotation2,
rot3=self.__rotation3,
detector=self.__detector,
wavelength=self.__wavelength)
# FIXME Add error model
method = method_registry.Method(0, self.__method.split,
self.__method.algo, self.__method.impl,
None)
method1d = method.fixed(dim=1)
methods = method_registry.IntegrationMethod.select_method(
method=method1d)
if len(methods) == 0:
method1d = method_registry.Method(1, method1d.split, "*", "*",
None)
_logger.warning("Downgrade 1D integration method to %s", method1d)
else:
method1d = methods[0].method
method2d = method.fixed(dim=2)
methods = method_registry.IntegrationMethod.select_method(
method=method2d)
if len(methods) == 0:
method2d = method_registry.Method(2, method2d.split, "*", "*",
None)
_logger.warning("Downgrade 2D integration method to %s", method2d)
else:
method2d = methods[0].method
try:
self.__result1d = ai.integrate1d_ng(
method=method1d,
data=self.__image,
npt=self.__nPointsRadial,
unit=self.__radialUnit,
mask=self.__mask,
polarization_factor=self.__polarizationFactor)
self.__result2d = ai.integrate2d(
method=method2d,
data=self.__image,
npt_rad=self.__nPointsRadial,
npt_azim=self.__nPointsAzimuthal,
unit=self.__radialUnit,
mask=self.__mask,
polarization_factor=self.__polarizationFactor)
# Create an image masked where data exists
self.__resultMask2d = None
if self.__mask is not None:
if self.__mask.shape == self.__image.shape:
maskData = numpy.ones(shape=self.__image.shape,
dtype=numpy.float32)
maskData[self.__mask == 0] = float("NaN")
if self.__displayMask:
self.__resultMask2d = ai.integrate2d(
method=method2d,
data=maskData,
npt_rad=self.__nPointsRadial,
npt_azim=self.__nPointsAzimuthal,
unit=self.__radialUnit,
polarization_factor=self.__polarizationFactor)
else:
_logger.warning(
"Inconsistency between image and mask sizes. %s != %s",
self.__image.shape, self.__mask.shape)
except Exception as e:
_logger.debug("Error while integrating", exc_info=True)
self.__errorMessage = e
# TODO: Could be nice to compute anyway other content (directDist...)
return
try:
self.__directDist = ai.getFit2D()["directDist"]
except Exception:
# The geometry could not fit this param
_logger.debug("Backtrace", exc_info=True)
self.__directDist = None
if self.__calibrant:
rings = self.__calibrant.get_2th()
try:
rings = unitutils.from2ThRad(rings, self.__radialUnit,
self.__wavelength,
self.__directDist)
except ValueError:
message = "Convertion to unit %s not supported. Ring locations ignored."
_logger.warning(message, self.__radialUnit)
self.__errorMessage = message % self.__radialUnit
rings = []
# Filter the rings which are not part of the result
minAngle, maxAngle = self.__result1d.radial[
0], self.__result1d.radial[-1]
rings = [(i, angle) for i, angle in enumerate(rings)
if minAngle <= angle <= maxAngle]
else:
rings = []
self.__rings = rings
self.__ai = ai
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
class TestMultiGeometry(unittest.TestCase):
def setUp(self):
unittest.TestCase.setUp(self)
self.data = fabio.open(UtilsTest.getimage("1788/moke.tif")).data
self.lst_data = [self.data[:250, :300], self.data[250:, :300], self.data[:250, 300:], self.data[250:, 300:]]
self.det = Detector(1e-4, 1e-4)
self.det.max_shape = (500, 600)
self.sub_det = Detector(1e-4, 1e-4)
self.sub_det.max_shape = (250, 300)
self.ai = AzimuthalIntegrator(0.1, 0.03, 0.03, detector=self.det)
self.range = (0, 23)
self.ais = [AzimuthalIntegrator(0.1, 0.030, 0.03, detector=self.sub_det),
AzimuthalIntegrator(0.1, 0.005, 0.03, detector=self.sub_det),
AzimuthalIntegrator(0.1, 0.030, 0.00, detector=self.sub_det),
AzimuthalIntegrator(0.1, 0.005, 0.00, detector=self.sub_det),
]
self.mg = MultiGeometry(self.ais, radial_range=self.range, unit="2th_deg")
self.N = 390
def tearDown(self):
unittest.TestCase.tearDown(self)
self.data = None
self.lst_data = None
self.det = None
self.sub_det = None
self.ai = None
self.ais = None
self.mg = None
def test_integrate1d(self):
tth_ref, I_ref = self.ai.integrate1d(self.data, radial_range=self.range, npt=self.N, unit="2th_deg", method="splitpixel")
obt = self.mg.integrate1d(self.lst_data, self.N)
tth_obt, I_obt = obt
self.assertEqual(abs(tth_ref - tth_obt).max(), 0, "Bin position is the same")
# intensity need to be scaled by solid angle 1e-4*1e-4/0.1**2 = 1e-6
delta = (abs(I_obt * 1e6 - I_ref).max())
self.assertLessEqual(delta, 5e-5, "Intensity is the same delta=%s" % delta)
def test_integrate2d(self):
ref = self.ai.integrate2d(self.data, self.N, 360, radial_range=self.range, azimuth_range=(-180, 180), unit="2th_deg", method="splitpixel", all=True)
obt = self.mg.integrate2d(self.lst_data, self.N, 360, all=True)
self.assertEqual(abs(ref["radial"] - obt["radial"]).max(), 0, "Bin position is the same")
self.assertEqual(abs(ref["azimuthal"] - obt["azimuthal"]).max(), 0, "Bin position is the same")
# intensity need to be scaled by solid angle 1e-4*1e-4/0.1**2 = 1e-6
delta = abs(obt["I"] * 1e6 - ref["I"])[obt["count"] >= 1] # restict on valid pixel
delta_cnt = abs(obt["count"] - ref["count"])
delta_sum = abs(obt["sum"] * 1e6 - ref["sum"])
if delta.max() > 1:
logger.warning("TestMultiGeometry.test_integrate2d gave intensity difference of %s" % delta.max())
if logger.level <= logging.DEBUG:
from matplotlib import pyplot as plt
f = plt.figure()
a1 = f.add_subplot(2, 2, 1)
a1.imshow(ref["sum"])
a2 = f.add_subplot(2, 2, 2)
a2.imshow(obt["sum"])
a3 = f.add_subplot(2, 2, 3)
a3.imshow(delta_sum)
a4 = f.add_subplot(2, 2, 4)
a4.plot(delta_sum.sum(axis=0))
f.show()
raw_input()
self.assertLess(delta_cnt.max(), 0.001, "pixel count is the same delta=%s" % delta_cnt.max())
self.assertLess(delta_sum.max(), 0.03, "pixel sum is the same delta=%s" % delta_sum.max())
self.assertLess(delta.max(), 0.004, "pixel intensity is the same (for populated pixels) delta=%s" % delta.max())
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
class XAnoS_Reducer(QWidget):
"""
This widget is developed to reduce on the fly 2D SAXS data to azimuthally averaged 1D SAXS data
"""
def __init__(self,poniFile=None,dataFile=None, darkFile=None, maskFile=None,extractedFolder='/tmp', npt=1000, azimuthalRange=(-180.0,180.0), parent=None):
"""
poniFile is the calibration file obtained after Q-calibration
"""
QWidget.__init__(self,parent)
self.setup_dict=json.load(open('./SetupData/reducer_setup.txt','r'))
if poniFile is not None:
self.poniFile=poniFile
else:
self.poniFile=self.setup_dict['poniFile']
if maskFile is not None:
self.maskFile=maskFile
else:
self.maskFile=self.setup_dict['maskFile']
self.dataFile=dataFile
if darkFile is None:
self.dark_corrected=False
self.darkFile=''
else:
self.darkFile=darkFile
self.dark_corrected=True
self.curDir=os.getcwd()
self.extractedBaseFolder=extractedFolder
self.npt=npt
self.set_externally=False
#ai=AIWidget()
#self.layout.addWidget(ai)
self.azimuthalRange=azimuthalRange
self.create_UI()
if os.path.exists(self.poniFile):
self.openPoniFile(file=self.poniFile)
if os.path.exists(self.maskFile):
self.openMaskFile(file=self.maskFile)
self.clientRunning=False
def create_UI(self):
"""
Creates the widget user interface
"""
loadUi('UI_Forms/Data_Reduction_Client.ui',self)
self.poniFileLineEdit.setText(str(self.poniFile))
self.maskFileLineEdit.setText(str(self.maskFile))
self.darkFileLineEdit.setText(str(self.darkFile))
self.extractedBaseFolderLineEdit.setText(self.extractedBaseFolder)
self.radialPointsLineEdit.setText(str(self.npt))
self.openDataPushButton.clicked.connect(self.openDataFiles)
self.reducePushButton.clicked.connect(self.reduce_multiple)
self.openDarkPushButton.clicked.connect(self.openDarkFile)
self.openPoniPushButton.clicked.connect(lambda x: self.openPoniFile(file=None))
self.calibratePushButton.clicked.connect(self.calibrate)
self.maskFileLineEdit.returnPressed.connect(self.maskFileChanged)
self.openMaskPushButton.clicked.connect(lambda x: self.openMaskFile(file=None))
self.createMaskPushButton.clicked.connect(self.createMask)
self.extractedFolderPushButton.clicked.connect(self.openFolder)
self.extractedFolderLineEdit.textChanged.connect(self.extractedFolderChanged)
self.polCorrComboBox.currentIndexChanged.connect(self.polarizationChanged)
self.polarizationChanged()
self.radialPointsLineEdit.returnPressed.connect(self.nptChanged)
self.azimuthalRangeLineEdit.returnPressed.connect(self.azimuthalRangeChanged)
self.azimuthalRangeChanged()
#self.statusLabel.setStyleSheet("color:rgba(0,1,0,0)")
self.imageWidget=Image_Widget(zeros((100,100)))
self.cakedImageWidget=Image_Widget(zeros((100,100)))
imgNumberLabel=QLabel('Image number')
self.imgNumberSpinBox=QSpinBox()
self.imgNumberSpinBox.setSingleStep(1)
self.imageWidget.imageLayout.addWidget(imgNumberLabel,row=2,col=1)
self.imageWidget.imageLayout.addWidget(self.imgNumberSpinBox,row=2,col=2)
self.imageView=self.imageWidget.imageView.getView()
self.plotWidget=PlotWidget()
self.plotWidget.setXLabel('Q, Å<sup>-1</sup>',fontsize=5)
self.plotWidget.setYLabel('Intensity',fontsize=5)
self.tabWidget.addTab(self.plotWidget,'Reduced 1D-data')
self.tabWidget.addTab(self.imageWidget,'Masked 2D-data')
self.tabWidget.addTab(self.cakedImageWidget,'Reduced Caked Data')
self.serverAddress=self.serverAddressLineEdit.text()
self.startClientPushButton.clicked.connect(self.startClient)
self.stopClientPushButton.clicked.connect(self.stopClient)
self.serverAddressLineEdit.returnPressed.connect(self.serverAddressChanged)
self.startServerPushButton.clicked.connect(self.startServer)
self.stopServerPushButton.clicked.connect(self.stopServer)
def startServer(self):
serverAddr=self.serverAddressLineEdit.text()
dataDir=QFileDialog.getExistingDirectory(self,'Select data folder',options=QFileDialog.ShowDirsOnly)
self.serverStatusLabel.setText('<font color="Red">Transmitting</font>')
QApplication.processEvents()
self.serverThread=QThread()
self.zeromq_server=ZeroMQ_Server(serverAddr,dataDir)
self.zeromq_server.moveToThread(self.serverThread)
self.serverThread.started.connect(self.zeromq_server.loop)
self.zeromq_server.messageEmitted.connect(self.updateServerMessage)
self.zeromq_server.folderFinished.connect(self.serverDone)
QTimer.singleShot(0,self.serverThread.start)
def updateServerMessage(self,mesg):
#self.serverStatusLabel.setText('<font color="Red">Transmitting</font>')
self.serverMessageLabel.setText('Server sends: %s'%mesg)
QApplication.processEvents()
def serverDone(self):
self.serverStatusLabel.setText('<font color="Green">Idle</font>')
self.zeromq_server.socket.unbind(self.zeromq_server.socket.last_endpoint)
self.serverThread.quit()
self.serverThread.wait()
self.serverThread.deleteLater()
self.zeromq_server.deleteLater()
def stopServer(self):
try:
self.zeromq_server.running=False
self.serverStatusLabel.setText('<font color="Green">Idle</font>')
self.zeromq_server.socket.unbind(self.zeromq_server.socket.last_endpoint)
self.serverThread.quit()
self.serverThread.wait()
self.serverThread.deleteLater()
self.zeromq_server.deleteLater()
except:
QMessageBox.warning(self,'Server Error','Start the server before stopping it')
def enableClient(self,enable=True):
self.startClientPushButton.setEnabled(enable)
self.stopClientPushButton.setEnabled(enable)
def enableServer(self,enable=True):
self.startServerPushButton.setEnabled(enable)
self.stopServerPushButton.setEnabled(enable)
def startClient(self):
if self.clientRunning:
self.stopClient()
else:
self.clientFree=True
self.clientRunning=True
self.files=[]
self.listenerThread = QThread()
addr=self.clientAddressLineEdit.text()
self.zeromq_listener = ZeroMQ_Listener(addr)
self.zeromq_listener.moveToThread(self.listenerThread)
self.listenerThread.started.connect(self.zeromq_listener.loop)
self.zeromq_listener.messageReceived.connect(self.signal_received)
QTimer.singleShot(0, self.listenerThread.start)
QTimer.singleShot(0,self.clientReduce)
self.clientStatusLabel.setText('<font color="red">Connected</font>')
def stopClient(self):
try:
self.clientRunning=False
self.clientFree=False
self.zeromq_listener.messageReceived.disconnect()
self.zeromq_listener.running=False
self.listenerThread.quit()
self.listenerThread.wait()
self.listenerThread.deleteLater()
self.zeromq_listener.deleteLater()
self.clientStatusLabel.setText('<font color="green">Idle</font>')
except:
QMessageBox.warning(self,'Client Error', 'Please start the client first before closing.',QMessageBox.Ok)
def serverAddressChanged(self):
if self.clientRunning:
self.startClient()
def signal_received(self, message):
self.clientMessageLabel.setText('Client receives: %s'%message)
if 'dark.edf' not in message:
self.files.append(message)
def clientReduce(self):
while self.clientFree:
QApplication.processEvents()
if len(self.files)>0:
message=self.files[0]
self.dataFiles=[message]
self.dataFileLineEdit.setText(str(self.dataFiles))
self.extractedBaseFolder=os.path.dirname(message)
self.extractedFolder=os.path.join(self.extractedBaseFolder,self.extractedFolderLineEdit.text())
if not os.path.exists(self.extractedFolder):
os.makedirs(self.extractedFolder)
self.extractedBaseFolderLineEdit.setText(self.extractedBaseFolder)
self.set_externally=True
self.reduce_multiple()
self.set_externally=False
self.files.pop(0)
def closeEvent(self, event):
if self.clientRunning:
self.stopClient()
event.accept()
def polarizationChanged(self):
if self.polCorrComboBox.currentText()=='Horizontal':
self.polarization_factor=1
elif self.polCorrComboBox.currentText()=='Vertical':
self.polarization_factor=-1
elif self.polCorrComboBox.currentText()=='Circular':
self.polarization_factor=0
else:
self.polarization_factor=None
def createMask(self):
"""
Opens a mask-widget to create mask file
"""
fname=str(QFileDialog.getOpenFileName(self,'Select an image file', directory=self.curDir,filter='Image file (*.edf *.tif)')[0])
if fname is not None or fname!='':
img=fb.open(fname).data
self.maskWidget=MaskWidget(img)
self.maskWidget.saveMaskPushButton.clicked.disconnect()
self.maskWidget.saveMaskPushButton.clicked.connect(self.save_mask)
self.maskWidget.show()
else:
QMessageBox.warning(self,'File error','Please import a data file first for creating the mask',QMessageBox.Ok)
def maskFileChanged(self):
"""
Changes the mask file
"""
maskFile=str(self.maskFileLineEdit.text())
if str(maskFile)=='':
self.maskFile=None
elif os.path.exists(maskFile):
self.maskFile=maskFile
else:
self.maskFile=None
def save_mask(self):
"""
Saves the entire mask combining all the shape ROIs
"""
fname=str(QFileDialog.getSaveFileName(filter='Mask Files (*.msk)')[0])
name,extn=os.path.splitext(fname)
if extn=='':
fname=name+'.msk'
elif extn!='.msk':
QMessageBox.warning(self,'File extension error','Please donot provide file extension other than ".msk". Thank you!')
return
else:
tmpfile=fb.edfimage.EdfImage(data=self.maskWidget.full_mask_data.T,header=None)
tmpfile.save(fname)
self.maskFile=fname
self.maskFileLineEdit.setText(self.maskFile)
def calibrate(self):
"""
Opens a calibartion widget to create calibration file
"""
fname=str(QFileDialog.getOpenFileName(self,'Select calibration image',directory=self.curDir, filter='Calibration image (*.edf *.tif)')[0])
if fname is not None:
img=fb.open(fname).data
if self.maskFile is not None:
try:
mask=fb.open(self.maskFile).data
except:
QMessageBox.warning(self,'Mask File Error','Cannot open %s.\n No masking will be done.'%self.maskFile)
mask=None
else:
mask=None
pixel1=79.0
pixel2=79.0
self.calWidget=CalibrationWidget(img,pixel1,pixel2,mask=mask)
self.calWidget.saveCalibrationPushButton.clicked.disconnect()
self.calWidget.saveCalibrationPushButton.clicked.connect(self.save_calibration)
self.calWidget.show()
else:
QMessageBox.warning(self,'File error','Please import a data file first for creating the calibration file',QMessageBox.Ok)
def save_calibration(self):
fname=str(QFileDialog.getSaveFileName(self,'Calibration file',directory=self.curDir,filter='Clibration files (*.poni)')[0])
tfname=os.path.splitext(fname)[0]+'.poni'
self.calWidget.applyPyFAI()
self.calWidget.geo.save(tfname)
self.poniFile=tfname
self.poniFileLineEdit.setText(self.poniFile)
self.openPoniFile(file=self.poniFile)
def openPoniFile(self,file=None):
"""
Select and imports the calibration file
"""
if file is None:
self.poniFile=QFileDialog.getOpenFileName(self,'Select calibration file',directory=self.curDir,filter='Calibration file (*.poni)')[0]
self.poniFileLineEdit.setText(self.poniFile)
else:
self.poniFile=file
if os.path.exists(self.poniFile):
self.setup_dict['poniFile']=self.poniFile
json.dump(self.setup_dict,open('./SetupData/reducer_setup.txt','w'))
fh=open(self.poniFile,'r')
lines=fh.readlines()
self.calib_data={}
for line in lines:
if line[0]!='#':
key,val=line.split(': ')
self.calib_data[key]=float(val)
self.dist=self.calib_data['Distance']
self.pixel1=self.calib_data['PixelSize1']
self.pixel2=self.calib_data['PixelSize2']
self.poni1=self.calib_data['Poni1']
self.poni2=self.calib_data['Poni2']
self.rot1=self.calib_data['Rot1']
self.rot2=self.calib_data['Rot2']
self.rot3=self.calib_data['Rot3']
self.wavelength=self.calib_data['Wavelength']
self.ai=AzimuthalIntegrator(dist=self.dist,poni1=self.poni1,poni2=self.poni2,pixel1=self.pixel1,pixel2=self.pixel2,rot1=self.rot1,rot2=self.rot2,rot3=self.rot3,wavelength=self.wavelength)
#pos=[self.poni2/self.pixel2,self.poni1/self.pixel1]
#self.roi=cake(pos,movable=False)
#self.roi.sigRegionChangeStarted.connect(self.endAngleChanged)
#self.imageView.addItem(self.roi)
else:
QMessageBox.warning(self,'File error','The calibration file '+self.poniFile+' doesnot exists.',QMessageBox.Ok)
def endAngleChanged(self,evt):
print(evt.pos())
def nptChanged(self):
"""
Changes the number of radial points
"""
try:
self.npt=int(self.radialPointsLineEdit.text())
except:
QMessageBox.warning(self,'Value error', 'Please input positive integers only.',QMessageBox.Ok)
def azimuthalRangeChanged(self):
"""
Changes the azimuth angular range
"""
try:
self.azimuthalRange=tuple(map(float, self.azimuthalRangeLineEdit.text().split(':')))
except:
QMessageBox.warning(self,'Value error','Please input min:max angles in floating point numbers',QMessageBox.Ok)
def openDataFile(self):
"""
Select and imports one data file
"""
dataFile=QFileDialog.getOpenFileName(self,'Select data file',directory=self.curDir,filter='Data file (*.edf *.tif)')[0]
if dataFile!='':
self.dataFile=dataFile
self.curDir=os.path.dirname(self.dataFile)
self.dataFileLineEdit.setText(self.dataFile)
self.data2d=fb.open(self.dataFile).data
if self.darkFile is not None:
self.applyDark()
if self.maskFile is not None:
self.applyMask()
self.imageWidget.setImage(self.data2d,transpose=True)
self.tabWidget.setCurrentWidget(self.imageWidget)
if not self.set_externally:
self.extractedFolder=os.path.join(self.curDir,self.extractedFolderLineEdit.text())
if not os.path.exists(self.extractedFolder):
os.makedirs(self.extractedFolder)
def openDataFiles(self):
"""
Selects and imports multiple data files
"""
self.dataFiles=QFileDialog.getOpenFileNames(self,'Select data files', directory=self.curDir,filter='Data files (*.edf *.tif)')[0]
if len(self.dataFiles)!=0:
self.imgNumberSpinBox.valueChanged.connect(self.imageChanged)
self.imgNumberSpinBox.setMinimum(0)
self.imgNumberSpinBox.setMaximum(len(self.dataFiles)-1)
self.dataFileLineEdit.setText(str(self.dataFiles))
self.curDir=os.path.dirname(self.dataFiles[0])
self.extractedBaseFolder=self.curDir
self.extractedFolder=os.path.abspath(os.path.join(self.extractedBaseFolder,self.extractedFolderLineEdit.text()))
if not os.path.exists(self.extractedFolder):
os.makedirs(self.extractedFolder)
self.extractedBaseFolderLineEdit.setText(self.extractedBaseFolder)
self.imgNumberSpinBox.setValue(0)
self.imageChanged()
def imageChanged(self):
self.data2d=fb.open(self.dataFiles[self.imgNumberSpinBox.value()]).data
if self.darkFile is not None:
self.applyDark()
if self.maskFile is not None:
self.applyMask()
self.imageWidget.setImage(self.data2d,transpose=True)
def applyDark(self):
if not self.dark_corrected and self.darkFile!='':
self.dark2d=fb.open(self.darkFile).data
self.data2d=self.data2d-self.dark2d
self.dark_corrected=True
def applyMask(self):
self.mask2d=fb.open(self.maskFile).data
self.data2d=self.data2d*(1+self.mask2d)/2.0
self.mask_applied=True
def openDarkFile(self):
"""
Select and imports the dark file
"""
self.darkFile=QFileDialog.getOpenFileName(self,'Select dark file',directory=self.curDir,filter='Dark file (*.edf)')[0]
if self.darkFile!='':
self.dark_corrected=False
self.darkFileLineEdit.setText(self.darkFile)
if self.dataFile is not None:
self.data2d=fb.open(self.dataFile).data
self.applyDark()
def openMaskFile(self,file=None):
"""
Select and imports the Mask file
"""
if file is None:
self.maskFile=QFileDialog.getOpenFileName(self,'Select mask file',directory=self.curDir,filter='Mask file (*.msk)')[0]
else:
self.maskFile=file
if self.maskFile!='':
self.mask_applied=False
if os.path.exists(self.maskFile):
self.curDir=os.path.dirname(self.maskFile)
self.maskFileLineEdit.setText(self.maskFile)
self.setup_dict['maskFile']=self.maskFile
self.setup_dict['poniFile']=self.poniFile
json.dump(self.setup_dict,open('./SetupData/reducer_setup.txt','w'))
else:
self.openMaskFile(file=None)
if self.dataFile is not None:
self.applyMask()
else:
self.maskFile=None
self.maskFileLineEdit.clear()
def openFolder(self):
"""
Select the folder to save the reduce data
"""
oldfolder=self.extractedBaseFolder.text()
folder=QFileDialog.getExistingDirectory(self,'Select extracted directory',directory=self.curDir)
if folder!='':
self.extractedBaseFolder=folder
self.extractedBaseFolderLineEdit.setText(folder)
self.extractedFolder=os.path.join(folder,self.extractedFolderLineEdit.text())
self.set_externally=True
else:
self.extractedBaseFolder=oldfolder
self.extractedBaseFolderLineEdit.setText(oldfolder)
self.extractedFolder = os.path.join(oldfolder, self.extractedFolderLineEdit.text())
self.set_externally = True
def extractedFolderChanged(self,txt):
self.extractedFolder=os.path.join(self.extractedBaseFolder,txt)
self.set_externally=True
def reduceData(self):
"""
Reduces the 2d data to 1d data
"""
if (self.dataFile is not None) and (os.path.exists(self.dataFile)):
if (self.poniFile is not None) and (os.path.exists(self.poniFile)):
# self.statusLabel.setText('Busy')
# self.progressBar.setRange(0, 0)
imageData=fb.open(self.dataFile)
#self.data2d=imageData.data
#if self.maskFile is not None:
# self.applyMask()
#self.imageWidget.setImage(self.data2d,transpose=True)
#self.tabWidget.setCurrentWidget(self.imageWidget)
self.header=imageData.header
try:
self.ai.set_wavelength(float(self.header['Wavelength'])*1e-10)
except:
self.ai.set_wavelength(self.wavelength)
#print(self.darkFile)
if os.path.exists(self.dataFile.split('.')[0]+'_dark.edf') and self.darkCheckBox.isChecked():
self.darkFile=self.dataFile.split('.')[0]+'_dark.edf'
dark=fb.open(self.darkFile)
self.darkFileLineEdit.setText(self.darkFile)
imageDark=dark.data
self.header['BSDiode_corr']=max([1.0,(float(imageData.header['BSDiode'])-float(dark.header['BSDiode']))])
self.header['Monitor_corr']=max([1.0,(float(imageData.header['Monitor'])-float(dark.header['Monitor']))])
print("Dark File read from existing dark files")
elif self.darkFile is not None and self.darkFile!='' and self.darkCheckBox.isChecked():
dark=fb.open(self.darkFile)
imageDark=dark.data
self.header['BSDiode_corr']=max([1.0,(float(imageData.header['BSDiode'])-float(dark.header['BSDiode']))])
self.header['Monitor_corr']=max([1.0,(float(imageData.header['Monitor'])-float(dark.header['Monitor']))])
print("Dark File from memory subtracted")
else:
imageDark=None
try:
self.header['BSDiode_corr']=float(imageData.header['BSDiode'])
self.header['Monitor_corr']=float(imageData.header['Monitor'])
self.header['Transmission'] = float(imageData.header['Transmission'])
except:
self.normComboBox.setCurrentText('None')
print("No dark correction done")
if str(self.normComboBox.currentText())=='BSDiode':
norm_factor=self.header['BSDiode_corr']#/self.header['Monitor_corr']#float(self.header[
# 'count_time'])
elif str(self.normComboBox.currentText())=='TransDiode':
norm_factor=self.header['Transmission']*self.header['Monitor_corr']
elif str(self.normComboBox.currentText())=='Monitor':
norm_factor=self.header['Monitor_corr']
elif str(self.normComboBox.currentText())=='Image Sum':
norm_factor=sum(imageData.data)
else:
norm_factor=1.0
if self.maskFile is not None:
imageMask=fb.open(self.maskFile).data
else:
imageMask=None
# QApplication.processEvents()
#print(self.azimuthalRange)
self.q,self.I,self.Ierr=self.ai.integrate1d(imageData.data,self.npt,error_model='poisson',mask=imageMask,dark=imageDark,unit='q_A^-1',normalization_factor=norm_factor,azimuth_range=self.azimuthalRange,polarization_factor=self.polarization_factor)
self.plotWidget.add_data(self.q,self.I,yerr=self.Ierr,name='Reduced data')
if not self.set_externally:
cakedI,qr,phir=self.ai.integrate2d(imageData.data,self.npt,mask=imageMask,dark=imageDark,unit='q_A^-1',normalization_factor=norm_factor,polarization_factor=self.polarization_factor)
self.cakedImageWidget.setImage(cakedI,xmin=qr[0],xmax=qr[-1],ymin=phir[0],ymax=phir[-1],transpose=True,xlabel='Q ', ylabel='phi ',unit=['Å<sup>-1</sup>','degree'])
self.cakedImageWidget.imageView.view.setAspectLocked(False)
try:
self.azimuthalRegion.setRegion(self.azimuthalRange)
except:
self.azimuthalRegion=pg.LinearRegionItem(values=self.azimuthalRange,orientation=pg.LinearRegionItem.Horizontal,movable=True,bounds=[-180,180])
self.cakedImageWidget.imageView.getView().addItem(self.azimuthalRegion)
self.azimuthalRegion.sigRegionChanged.connect(self.azimuthalRegionChanged)
self.plotWidget.setTitle(self.dataFile,fontsize=3)
# self.progressBar.setRange(0,100)
# self.progressBar.setValue(100)
# self.statusLabel.setText('Idle')
# QApplication.processEvents()
self.saveData()
#self.tabWidget.setCurrentWidget(self.plotWidget)
else:
QMessageBox.warning(self,'Calibration File Error','Data reduction failed because either no calibration file provided or the provided file or path do not exists',QMessageBox.Ok)
else:
QMessageBox.warning(self,'Data File Error','No data file provided', QMessageBox.Ok)
def azimuthalRegionChanged(self):
minp,maxp=self.azimuthalRegion.getRegion()
self.azimuthalRangeLineEdit.setText('%.1f:%.1f'%(minp,maxp))
self.azimuthalRange=[minp,maxp]
self.set_externally=True
def reduce_multiple(self):
"""
Reduce multiple files
"""
try:
i=0
self.progressBar.setRange(0,len(self.dataFiles))
self.progressBar.setValue(i)
self.statusLabel.setText('<font color="red">Busy</font>')
for file in self.dataFiles:
self.dataFile=file
QApplication.processEvents()
self.reduceData()
i=i+1
self.progressBar.setValue(i)
QApplication.processEvents()
self.statusLabel.setText('<font color="green">Idle</font>')
self.progressBar.setValue(0)
except:
QMessageBox.warning(self,'File error','No data files to reduce',QMessageBox.Ok)
def saveData(self):
"""
saves the extracted data into a file
"""
if not os.path.exists(self.extractedFolder):
os.makedirs(self.extractedFolder)
filename=os.path.join(self.extractedFolder,os.path.splitext(os.path.basename(self.dataFile))[0]+'.txt')
headers='File extracted on '+time.asctime()+'\n'
headers='Files used for extraction are:\n'
headers+='Data file: '+self.dataFile+'\n'
if self.darkFile is not None:
headers+='Dark file: '+self.darkFile+'\n'
else:
headers+='Dark file: None\n'
headers+='Poni file: '+self.poniFile+'\n'
if self.maskFile is not None:
headers+='mask file: '+self.maskFile+'\n'
else:
headers+='mask file: None\n'
for key in self.header.keys():
headers+=key+'='+str(self.header[key])+'\n'
headers+="col_names=['Q (inv Angs)','Int','Int_err']\n"
headers+='Q (inv Angs)\tInt\tInt_err'
data=vstack((self.q,self.I,self.Ierr)).T
savetxt(filename,data,header=headers,comments='#')
class CalibrateDialog(BaseDialog):
def __init__(self, parent=None):
super(CalibrateDialog, self).__init__(parent)
self.plotview = None
self.data = None
self.counts = None
self.points = []
self.pattern_geometry = None
self.cake_geometry = None
self.is_calibrated = False
cstr = str(ALL_CALIBRANTS)
calibrants = sorted(cstr[cstr.index(':') + 2:].split(', '))
self.parameters = GridParameters()
self.parameters.add('calibrant', calibrants, 'Calibrant')
self.parameters['calibrant'].value = 'CeO2'
self.parameters.add('wavelength', 0.5, 'Wavelength (Ang)', False)
self.parameters.add('distance', 100.0, 'Detector Distance (mm)', True)
self.parameters.add('xc', 512, 'Beam Center - x', True)
self.parameters.add('yc', 512, 'Beam Center - y', True)
self.parameters.add('yaw', 0.0, 'Yaw (degrees)', True)
self.parameters.add('pitch', 0.0, 'Pitch (degrees)', True)
self.parameters.add('roll', 0.0, 'Roll (degrees)', True)
self.parameters.add('search_size', 10, 'Search Size (pixels)')
rings = ['Ring1', 'Ring2', 'Ring3', 'Ring4', 'Ring5']
self.rings_box = self.select_box(rings)
self.set_layout(
self.select_entry(self.choose_entry),
self.action_buttons(('Plot Calibration', self.plot_data)),
self.parameters.grid(header=False),
self.make_layout(
self.action_buttons(('Select Points', self.select)),
self.rings_box),
self.action_buttons(('Calibrate', self.calibrate),
('Plot Cake', self.plot_cake),
('Restore', self.restore_parameters),
('Save', self.save_parameters)),
self.close_buttons(close=True))
self.set_title('Calibrating Powder')
def choose_entry(self):
if 'calibration' not in self.entry['instrument']:
raise NeXusError('Please load calibration data to this entry')
self.update_parameters()
self.plot_data()
def update_parameters(self):
self.parameters['wavelength'].value = self.entry[
'instrument/monochromator/wavelength']
detector = self.entry['instrument/detector']
self.parameters['distance'].value = detector['distance']
self.parameters['yaw'].value = detector['yaw']
self.parameters['pitch'].value = detector['pitch']
self.parameters['roll'].value = detector['roll']
if 'beam_center_x' in detector:
self.parameters['xc'].value = detector['beam_center_x']
if 'beam_center_y' in detector:
self.parameters['yc'].value = detector['beam_center_y']
self.data = self.entry['instrument/calibration']
self.counts = self.data.nxsignal.nxvalue
@property
def search_size(self):
return int(self.parameters['search_size'].value)
@property
def ring(self):
return int(self.rings_box.currentText()[-1]) - 1
@property
def ring_color(self):
colors = ['r', 'b', 'g', 'c', 'm']
return colors[self.ring]
def plot_data(self):
if self.plotview is None:
if 'Powder Calibration' in plotviews:
self.plotview = plotviews['Powder Calibration']
else:
self.plotview = NXPlotView('Powder Calibration')
self.plotview.plot(self.data, log=True)
self.plotview.aspect = 'equal'
self.plotview.ytab.flipped = True
self.clear_peaks()
def on_button_press(self, event):
self.plotview.make_active()
if event.inaxes:
self.xp, self.yp = event.x, event.y
else:
self.xp, self.yp = 0, 0
def on_button_release(self, event):
if event.inaxes:
if abs(event.x - self.xp) > 5 or abs(event.y - self.yp) > 5:
return
x, y = self.plotview.inverse_transform(event.xdata, event.ydata)
for i, point in enumerate(self.points):
circle = point[0]
if circle.contains_point(
self.plotview.ax.transData.transform((x, y))):
circle.remove()
for circle in point[2]:
circle.remove()
del self.points[i]
return
idx, idy = self.find_peak(x, y)
points = [(idy, idx)]
circles = []
massif = Massif(self.counts)
extra_points = massif.find_peaks((idy, idx))
for point in extra_points:
points.append(point)
circles.append(self.circle(point[1], point[0], alpha=0.3))
self.points.append(
[self.circle(idx, idy), points, circles, self.ring])
def circle(self, idx, idy, alpha=1.0):
return self.plotview.circle(idx,
idy,
self.search_size,
facecolor=self.ring_color,
edgecolor='k',
alpha=alpha)
def select(self):
self.plotview.cidpress = self.plotview.mpl_connect(
'button_press_event', self.on_button_press)
self.plotview.cidrelease = self.plotview.mpl_connect(
'button_release_event', self.on_button_release)
def find_peak(self, x, y):
s = self.search_size
left = int(np.round(x - s * 0.5))
if left < 0:
left = 0
top = int(np.round(y - s * 0.5))
if top < 0:
top = 0
region = self.counts[top:(top + s), left:(left + s)]
idy, idx = np.where(region == region.max())
idx = left + idx[0]
idy = top + idy[0]
return idx, idy
def clear_peaks(self):
self.points = []
@property
def calibrant(self):
return ALL_CALIBRANTS[self.parameters['calibrant'].value]
@property
def point_array(self):
points = []
for point in self.points:
for p in point[1]:
points.append((p[0], p[1], point[3]))
return np.array(points)
def prepare_parameters(self):
self.parameters.set_parameters()
self.wavelength = self.parameters['wavelength'].value * 1e-10
self.distance = self.parameters['distance'].value * 1e-3
self.yaw = np.radians(self.parameters['yaw'].value)
self.pitch = np.radians(self.parameters['pitch'].value)
self.roll = np.radians(self.parameters['roll'].value)
self.pixel_size = self.entry[
'instrument/detector/pixel_size'].nxvalue * 1e-3
self.xc = self.parameters['xc'].value
self.yc = self.parameters['yc'].value
def calibrate(self):
self.prepare_parameters()
self.orig_pixel1 = self.pixel_size
self.orig_pixel2 = self.pixel_size
self.pattern_geometry = GeometryRefinement(self.point_array,
dist=self.distance,
wavelength=self.wavelength,
pixel1=self.pixel_size,
pixel2=self.pixel_size,
calibrant=self.calibrant)
self.refine()
self.create_cake_geometry()
self.pattern_geometry.reset()
def refine(self):
self.pattern_geometry.data = self.point_array
if self.parameters['wavelength'].vary:
self.pattern_geometry.refine2()
fix = []
else:
fix = ['wavelength']
if not self.parameters['distance'].vary:
fix.append('dist')
self.pattern_geometry.refine2_wavelength(fix=fix)
self.read_parameters()
self.is_calibrated = True
self.create_cake_geometry()
self.pattern_geometry.reset()
def create_cake_geometry(self):
self.cake_geometry = AzimuthalIntegrator()
pyFAI_parameter = self.pattern_geometry.getPyFAI()
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 plot_cake(self):
if 'Cake Plot' in plotviews:
plotview = plotviews['Cake Plot']
else:
plotview = NXPlotView('Cake Plot')
if not self.is_calibrated:
raise NeXusError('No refinement performed')
res = self.cake_geometry.integrate2d(self.counts,
1024,
1024,
method='csr',
unit='2th_deg',
correctSolidAngle=True)
self.cake_data = NXdata(res[0],
(NXfield(res[2], name='azimumthal_angle'),
NXfield(res[1], name='polar_angle')))
self.cake_data['title'] = self.entry['instrument/calibration/title']
plotview.plot(self.cake_data, log=True)
wavelength = self.parameters['wavelength'].value
polar_angles = [
2 * np.degrees(np.arcsin(wavelength / (2 * d)))
for d in self.calibrant.dSpacing
]
plotview.vlines([
polar_angle
for polar_angle in polar_angles if polar_angle < plotview.xaxis.max
],
linestyle=':',
color='r')
def read_parameters(self):
pyFAI = self.pattern_geometry.getPyFAI()
fit2d = self.pattern_geometry.getFit2D()
self.parameters[
'wavelength'].value = self.pattern_geometry.wavelength * 1e10
self.parameters['distance'].value = pyFAI['dist'] * 1e3
self.parameters['yaw'].value = np.degrees(pyFAI['rot1'])
self.parameters['pitch'].value = np.degrees(pyFAI['rot2'])
self.parameters['roll'].value = np.degrees(pyFAI['rot3'])
self.parameters['xc'].value = fit2d['centerX']
self.parameters['yc'].value = fit2d['centerY']
def restore_parameters(self):
self.parameters.restore_parameters()
def save_parameters(self):
if not self.is_calibrated:
raise NeXusError('No refinement performed')
elif 'refinement' in self.entry['instrument/calibration']:
if confirm_action('Overwrite previous refinement?'):
del self.entry['instrument/calibration/refinement']
else:
return
self.entry['instrument/calibration/calibrant'] = self.parameters[
'calibrant'].value
process = NXprocess()
process.program = 'pyFAI'
process.version = pyFAI.version
process.parameters = NXcollection()
process.parameters['Detector'] = self.entry[
'instrument/detector/description']
pyFAI_parameter = self.pattern_geometry.getPyFAI()
process.parameters['PixelSize1'] = pyFAI_parameter['pixel1']
process.parameters['PixelSize2'] = pyFAI_parameter['pixel2']
process.parameters['Distance'] = pyFAI_parameter['dist']
process.parameters['Poni1'] = pyFAI_parameter['poni1']
process.parameters['Poni2'] = pyFAI_parameter['poni2']
process.parameters['Rot1'] = pyFAI_parameter['rot1']
process.parameters['Rot2'] = pyFAI_parameter['rot2']
process.parameters['Rot3'] = pyFAI_parameter['rot3']
process.parameters['Wavelength'] = pyFAI_parameter['wavelength']
self.entry['instrument/calibration/refinement'] = process
self.entry['instrument/monochromator/wavelength'] = self.parameters[
'wavelength'].value
self.entry[
'instrument/monochromator/energy'] = 12.398419739640717 / self.parameters[
'wavelength'].value
detector = self.entry['instrument/detector']
detector['distance'] = self.parameters['distance'].value
detector['yaw'] = self.parameters['yaw'].value
detector['pitch'] = self.parameters['pitch'].value
detector['roll'] = self.parameters['roll'].value
detector['beam_center_x'] = self.parameters['xc'].value
detector['beam_center_y'] = self.parameters['yc'].value
def reject(self):
super(CalibrateDialog, self).reject()
if 'Powder Calibration' in plotviews:
plotviews['Powder Calibration'].close_view()
if 'Cake Plot' in plotviews:
plotviews['Cake Plot'].close_view()
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
class TestMultiGeometry(unittest.TestCase):
def setUp(self):
unittest.TestCase.setUp(self)
self.data = fabio.open(UtilsTest.getimage("1788/moke.tif")).data
self.lst_data = [
self.data[:250, :300], self.data[250:, :300],
self.data[:250, 300:], self.data[250:, 300:]
]
self.det = Detector(1e-4, 1e-4)
self.det.max_shape = (500, 600)
self.sub_det = Detector(1e-4, 1e-4)
self.sub_det.max_shape = (250, 300)
self.ai = AzimuthalIntegrator(0.1, 0.03, 0.03, detector=self.det)
self.range = (0, 23)
self.ais = [
AzimuthalIntegrator(0.1, 0.030, 0.03, detector=self.sub_det),
AzimuthalIntegrator(0.1, 0.005, 0.03, detector=self.sub_det),
AzimuthalIntegrator(0.1, 0.030, 0.00, detector=self.sub_det),
AzimuthalIntegrator(0.1, 0.005, 0.00, detector=self.sub_det),
]
self.mg = MultiGeometry(self.ais,
radial_range=self.range,
unit="2th_deg")
self.N = 390
def tearDown(self):
unittest.TestCase.tearDown(self)
self.data = None
self.lst_data = None
self.det = None
self.sub_det = None
self.ai = None
self.ais = None
self.mg = None
def test_integrate1d(self):
tth_ref, I_ref = self.ai.integrate1d(self.data,
radial_range=self.range,
npt=self.N,
unit="2th_deg",
method="splitpixel")
obt = self.mg.integrate1d(self.lst_data, self.N)
tth_obt, I_obt = obt
self.assertEqual(
abs(tth_ref - tth_obt).max(), 0, "Bin position is the same")
# intensity need to be scaled by solid angle 1e-4*1e-4/0.1**2 = 1e-6
delta = (abs(I_obt * 1e6 - I_ref).max())
self.assert_(delta < 5e-5, "Intensity is the same delta=%s" % delta)
def test_integrate2d(self):
ref = self.ai.integrate2d(self.data,
self.N,
360,
radial_range=self.range,
azimuth_range=(-180, 180),
unit="2th_deg",
method="splitpixel",
all=True)
obt = self.mg.integrate2d(self.lst_data, self.N, 360, all=True)
self.assertEqual(
abs(ref["radial"] - obt["radial"]).max(), 0,
"Bin position is the same")
self.assertEqual(
abs(ref["azimuthal"] - obt["azimuthal"]).max(), 0,
"Bin position is the same")
# intensity need to be scaled by solid angle 1e-4*1e-4/0.1**2 = 1e-6
delta = abs(obt["I"] * 1e6 -
ref["I"])[obt["count"] >= 1e-6] # restrict on valid pixel
delta_cnt = abs(obt["count"] - ref["count"])
delta_sum = abs(obt["sum"] * 1e6 - ref["sum"])
if delta.max() > 0:
logger.warning(
"TestMultiGeometry.test_integrate2d gave intensity difference of %s"
% delta.max())
if logger.level <= logging.DEBUG:
from matplotlib import pyplot as plt
f = plt.figure()
a1 = f.add_subplot(2, 2, 1)
a1.imshow(ref["sum"])
a2 = f.add_subplot(2, 2, 2)
a2.imshow(obt["sum"])
a3 = f.add_subplot(2, 2, 3)
a3.imshow(delta_sum)
a4 = f.add_subplot(2, 2, 4)
a4.plot(delta_sum.sum(axis=0))
f.show()
raw_input()
self.assert_(delta_cnt.max() < 0.001,
"pixel count is the same delta=%s" % delta_cnt.max())
self.assert_(delta_sum.max() < 0.03,
"pixel sum is the same delta=%s" % delta_sum.max())
self.assert_(
delta.max() < 0.004,
"pixel intensity is the same (for populated pixels) delta=%s" %
delta.max())
class CalibrateDialog(NXDialog):
def __init__(self, parent=None):
super().__init__(parent)
self.plotview = None
self.data = None
self.counts = None
self.points = []
self.pattern_geometry = None
self.cake_geometry = None
self.polarization = None
self.is_calibrated = False
self.phi_max = -np.pi
cstr = str(ALL_CALIBRANTS)
calibrants = sorted(cstr[cstr.index(':')+2:].split(', '))
self.parameters = GridParameters()
self.parameters.add('calibrant', calibrants, 'Calibrant')
self.parameters['calibrant'].value = 'CeO2'
self.parameters.add('wavelength', 0.5, 'Wavelength (Ang)', False)
self.parameters.add('distance', 100.0, 'Detector Distance (mm)', True)
self.parameters.add('xc', 512, 'Beam Center - x', True)
self.parameters.add('yc', 512, 'Beam Center - y', True)
self.parameters.add('yaw', 0.0, 'Yaw (degrees)', True)
self.parameters.add('pitch', 0.0, 'Pitch (degrees)', True)
self.parameters.add('roll', 0.0, 'Roll (degrees)', True)
self.parameters.add('search_size', 10, 'Search Size (pixels)')
self.rings_box = self.select_box([f'Ring{i}' for i in range(1, 21)])
self.set_layout(self.select_entry(self.choose_entry),
self.progress_layout(close=True))
self.set_title('Calibrating Powder')
def choose_file(self):
super().choose_file()
powder_file = self.get_filename()
if powder_file:
self.data = load_image(powder_file)
self.counts = self.data.nxsignal.nxvalue
self.plot_data()
def choose_entry(self):
if self.layout.count() == 2:
self.insert_layout(
1, self.filebox('Choose Powder Calibration File'))
self.insert_layout(2, self.parameters.grid(header=False))
self.insert_layout(
3, self.action_buttons(('Select Points', self.select),
('Autogenerate Rings', self.auto),
('Clear Points', self.clear_points)))
self.insert_layout(4, self.make_layout(self.rings_box))
self.insert_layout(
5, self.action_buttons(('Calibrate', self.calibrate),
('Plot Cake', self.plot_cake),
('Restore', self.restore_parameters),
('Save', self.save_parameters)))
self.parameters['wavelength'].value = (
self.entry['instrument/monochromator/wavelength'])
detector = self.entry['instrument/detector']
self.parameters['distance'].value = detector['distance']
self.parameters['yaw'].value = detector['yaw']
self.parameters['pitch'].value = detector['pitch']
self.parameters['roll'].value = detector['roll']
if 'beam_center_x' in detector:
self.parameters['xc'].value = detector['beam_center_x']
if 'beam_center_y' in detector:
self.parameters['yc'].value = detector['beam_center_y']
self.pixel_size = (
self.entry['instrument/detector/pixel_size'].nxvalue * 1e-3)
self.pixel_mask = self.entry['instrument/detector/pixel_mask'].nxvalue
self.ring = self.selected_ring
if 'calibration' in self.entry['instrument']:
self.data = self.entry['instrument/calibration']
self.counts = self.data.nxsignal.nxvalue
self.plot_data()
else:
self.close_plots()
@property
def search_size(self):
return int(self.parameters['search_size'].value)
@property
def selected_ring(self):
return int(self.rings_box.currentText()[4:]) - 1
@property
def ring_color(self):
colors = ['r', 'b', 'g', 'c', 'm'] * 4
return colors[self.ring]
def plot_data(self):
if self.plotview is None:
if 'Powder Calibration' in plotviews:
self.plotview = plotviews['Powder Calibration']
else:
self.plotview = NXPlotView('Powder Calibration')
self.plotview.plot(self.data, log=True)
self.plotview.aspect = 'equal'
self.plotview.ytab.flipped = True
self.clear_points()
def on_button_press(self, event):
self.plotview.make_active()
if event.inaxes:
self.xp, self.yp = event.x, event.y
else:
self.xp, self.yp = 0, 0
def on_button_release(self, event):
self.ring = self.selected_ring
if event.inaxes:
if abs(event.x - self.xp) > 5 or abs(event.y - self.yp) > 5:
return
x, y = self.plotview.inverse_transform(event.xdata, event.ydata)
for i, point in enumerate(self.points):
circle = point[0]
if circle.shape.contains_point(
self.plotview.ax.transData.transform((x, y))):
circle.remove()
for circle in point[2]:
circle.remove()
del self.points[i]
return
self.add_points(x, y)
def circle(self, idx, idy, alpha=1.0):
return self.plotview.circle(idx, idy, self.search_size,
facecolor=self.ring_color, edgecolor='k',
alpha=alpha)
def select(self):
self.plotview.cidpress = self.plotview.mpl_connect(
'button_press_event', self.on_button_press)
self.plotview.cidrelease = self.plotview.mpl_connect(
'button_release_event', self.on_button_release)
def auto(self):
xc, yc = self.parameters['xc'].value, self.parameters['yc'].value
wavelength = self.parameters['wavelength'].value
distance = self.parameters['distance'].value * 1e-3
self.start_progress((0, self.selected_ring+1))
for ring in range(self.selected_ring+1):
self.update_progress(ring)
if len([p for p in self.points if p[3] == ring]) > 0:
continue
self.ring = ring
theta = 2 * np.arcsin(wavelength /
(2*self.calibrant.dSpacing[ring]))
r = distance * np.tan(theta) / self.pixel_size
phi = self.phi_max = -np.pi
while phi < np.pi:
x, y = np.int(xc + r*np.cos(phi)), np.int(yc + r*np.sin(phi))
if ((x > 0 and x < self.data.x.max()) and
(y > 0 and y < self.data.y.max()) and
not self.pixel_mask[y, x]):
self.add_points(x, y, phi)
phi = self.phi_max + 0.2
else:
phi = phi + 0.2
self.stop_progress()
def add_points(self, x, y, phi=0.0):
xc, yc = self.parameters['xc'].value, self.parameters['yc'].value
idx, idy = self.find_peak(x, y)
points = [(idy, idx)]
circles = []
massif = Massif(self.counts)
extra_points = massif.find_peaks((idy, idx))
for point in extra_points:
points.append(point)
circles.append(self.circle(point[1], point[0], alpha=0.3))
phis = np.array([np.arctan2(p[0]-yc, p[1]-xc) for p in points])
if phi < -0.5*np.pi:
phis[np.where(phis > 0.0)] -= 2 * np.pi
self.phi_max = max(*phis, self.phi_max)
self.points.append([self.circle(idx, idy), points, circles, self.ring])
def find_peak(self, x, y):
s = self.search_size
left = int(np.round(x - s * 0.5))
if left < 0:
left = 0
top = int(np.round(y - s * 0.5))
if top < 0:
top = 0
region = self.counts[top:(top+s), left:(left+s)]
idy, idx = np.where(region == region.max())
idx = left + idx[0]
idy = top + idy[0]
return idx, idy
def clear_points(self):
for i, point in enumerate(self.points):
circle = point[0]
circle.remove()
for circle in point[2]:
circle.remove()
self.points = []
@property
def calibrant(self):
return ALL_CALIBRANTS[self.parameters['calibrant'].value]
@property
def point_array(self):
points = []
for point in self.points:
for p in point[1]:
points.append((p[0], p[1], point[3]))
return np.array(points)
def prepare_parameters(self):
self.parameters.set_parameters()
self.wavelength = self.parameters['wavelength'].value * 1e-10
self.distance = self.parameters['distance'].value * 1e-3
self.yaw = np.radians(self.parameters['yaw'].value)
self.pitch = np.radians(self.parameters['pitch'].value)
self.roll = np.radians(self.parameters['roll'].value)
self.xc = self.parameters['xc'].value
self.yc = self.parameters['yc'].value
def calibrate(self):
self.prepare_parameters()
self.orig_pixel1 = self.pixel_size
self.orig_pixel2 = self.pixel_size
self.pattern_geometry = GeometryRefinement(self.point_array,
dist=self.distance,
wavelength=self.wavelength,
pixel1=self.pixel_size,
pixel2=self.pixel_size,
calibrant=self.calibrant)
self.refine()
self.create_cake_geometry()
self.pattern_geometry.reset()
def refine(self):
self.pattern_geometry.data = self.point_array
if self.parameters['wavelength'].vary:
self.pattern_geometry.refine2()
fix = []
else:
fix = ['wavelength']
if not self.parameters['distance'].vary:
fix.append('dist')
self.pattern_geometry.refine2_wavelength(fix=fix)
self.read_parameters()
self.is_calibrated = True
self.create_cake_geometry()
self.pattern_geometry.reset()
def create_cake_geometry(self):
self.cake_geometry = AzimuthalIntegrator()
pyFAI_parameter = self.pattern_geometry.getPyFAI()
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 plot_cake(self):
if 'Cake Plot' in plotviews:
plotview = plotviews['Cake Plot']
else:
plotview = NXPlotView('Cake Plot')
if not self.is_calibrated:
raise NeXusError('No refinement performed')
res = self.cake_geometry.integrate2d(self.counts,
1024, 1024,
method='csr',
unit='2th_deg',
correctSolidAngle=True)
self.cake_data = NXdata(res[0],
(NXfield(res[2], name='azimumthal_angle'),
NXfield(res[1], name='polar_angle')))
self.cake_data['title'] = 'Cake Plot'
plotview.plot(self.cake_data, log=True)
wavelength = self.parameters['wavelength'].value
polar_angles = [2 * np.degrees(np.arcsin(wavelength/(2*d)))
for d in self.calibrant.dSpacing]
plotview.vlines([polar_angle for polar_angle in polar_angles
if polar_angle < plotview.xaxis.max],
linestyle=':', color='r')
def read_parameters(self):
pyFAI = self.pattern_geometry.getPyFAI()
fit2d = self.pattern_geometry.getFit2D()
self.parameters['wavelength'].value = (
self.pattern_geometry.wavelength * 1e10)
self.parameters['distance'].value = pyFAI['dist'] * 1e3
self.parameters['yaw'].value = np.degrees(pyFAI['rot1'])
self.parameters['pitch'].value = np.degrees(pyFAI['rot2'])
self.parameters['roll'].value = np.degrees(pyFAI['rot3'])
self.parameters['xc'].value = fit2d['centerX']
self.parameters['yc'].value = fit2d['centerY']
def restore_parameters(self):
self.parameters.restore_parameters()
def save_parameters(self):
if not self.is_calibrated:
raise NeXusError('No refinement performed')
elif 'calibration' in self.entry['instrument']:
if confirm_action(
"Do you want to overwrite existing calibration data?"):
del self.entry['instrument/calibration']
else:
return
self.entry['instrument/calibration'] = self.data
if 'refinement' in self.entry['instrument/calibration']:
if confirm_action('Overwrite previous refinement?'):
del self.entry['instrument/calibration/refinement']
else:
return
self.entry['instrument/calibration/calibrant'] = (
self.parameters['calibrant'].value)
process = NXprocess()
process.program = 'pyFAI'
process.version = pyFAI.version
process.parameters = NXcollection()
process.parameters['Detector'] = (
self.entry['instrument/detector/description'])
pyFAI_parameter = self.pattern_geometry.getPyFAI()
process.parameters['PixelSize1'] = pyFAI_parameter['pixel1']
process.parameters['PixelSize2'] = pyFAI_parameter['pixel2']
process.parameters['Distance'] = pyFAI_parameter['dist']
process.parameters['Poni1'] = pyFAI_parameter['poni1']
process.parameters['Poni2'] = pyFAI_parameter['poni2']
process.parameters['Rot1'] = pyFAI_parameter['rot1']
process.parameters['Rot2'] = pyFAI_parameter['rot2']
process.parameters['Rot3'] = pyFAI_parameter['rot3']
process.parameters['Wavelength'] = pyFAI_parameter['wavelength']
self.entry['instrument/calibration/refinement'] = process
self.entry['instrument/monochromator/wavelength'] = (
self.parameters['wavelength'].value)
self.entry['instrument/monochromator/energy'] = (
12.398419739640717 / self.parameters['wavelength'].value)
detector = self.entry['instrument/detector']
detector['distance'] = self.parameters['distance'].value
detector['yaw'] = self.parameters['yaw'].value
detector['pitch'] = self.parameters['pitch'].value
detector['roll'] = self.parameters['roll'].value
detector['beam_center_x'] = self.parameters['xc'].value
detector['beam_center_y'] = self.parameters['yc'].value
try:
detector['polarization'] = self.pattern_geometry.polarization(
factor=0.99, shape=detector['mask'].shape)
except Exception:
pass
def close_plots(self):
if 'Powder Calibration' in plotviews:
plotviews['Powder Calibration'].close()
if 'Cake Plot' in plotviews:
plotviews['Cake Plot'].close()
def closeEvent(self, event):
self.close_plots()
event.accept()
def accept(self):
super().accept()
self.close_plots()
def reject(self):
super().reject()
self.close_plots()
def run(self):
ai = AzimuthalIntegrator(
dist=self.__distance,
poni1=self.__poni1,
poni2=self.__poni2,
rot1=self.__rotation1,
rot2=self.__rotation2,
rot3=self.__rotation3,
detector=self.__detector,
wavelength=self.__wavelength)
# FIXME Add error model
method1d = method_registry.Method(1, self.__method.split, self.__method.algo, self.__method.impl, None)
methods = method_registry.IntegrationMethod.select_method(method=method1d)
if len(methods) == 0:
method1d = method_registry.Method(1, method1d.split, "*", "*", None)
_logger.warning("Downgrade 1D integration method to %s", method1d)
else:
method1d = methods[0].method
method2d = method_registry.Method(2, self.__method.split, self.__method.algo, self.__method.impl, None)
methods = method_registry.IntegrationMethod.select_method(method=method2d)
if len(methods) == 0:
method2d = method_registry.Method(2, method2d.split, "*", "*", None)
_logger.warning("Downgrade 2D integration method to %s", method2d)
else:
method2d = methods[0].method
self.__result1d = ai.integrate1d(
method=method1d,
data=self.__image,
npt=self.__nPointsRadial,
unit=self.__radialUnit,
mask=self.__mask,
polarization_factor=self.__polarizationFactor)
self.__result2d = ai.integrate2d(
method=method2d,
data=self.__image,
npt_rad=self.__nPointsRadial,
npt_azim=self.__nPointsAzimuthal,
unit=self.__radialUnit,
mask=self.__mask,
polarization_factor=self.__polarizationFactor)
# Create an image masked where data exists
self.__resultMask2d = None
if self.__mask is not None:
if self.__mask.shape == self.__image.shape:
maskData = numpy.ones(shape=self.__image.shape, dtype=numpy.float32)
maskData[self.__mask == 0] = float("NaN")
if self.__displayMask:
self.__resultMask2d = ai.integrate2d(
method=method2d,
data=maskData,
npt_rad=self.__nPointsRadial,
npt_azim=self.__nPointsAzimuthal,
unit=self.__radialUnit,
polarization_factor=self.__polarizationFactor)
else:
_logger.warning("Inconsistency between image and mask sizes. %s != %s", self.__image.shape, self.__mask.shape)
try:
self.__directDist = ai.getFit2D()["directDist"]
except Exception:
# The geometry could not fit this param
_logger.debug("Backtrace", exc_info=True)
self.__directDist = None
if self.__calibrant:
rings = self.__calibrant.get_2th()
try:
rings = unitutils.from2ThRad(rings, self.__radialUnit, self.__wavelength, self.__directDist)
except ValueError:
message = "Convertion to unit %s not supported. Ring marks ignored"
_logger.warning(message, self.__radialUnit)
rings = []
# Filter the rings which are not part of the result
rings = filter(lambda x: self.__result1d.radial[0] <= x <= self.__result1d.radial[-1], rings)
rings = list(rings)
else:
rings = []
self.__ringAngles = rings
self.__ai = ai
class CalibrateDialog(BaseDialog):
def __init__(self, parent=None):
super(CalibrateDialog, self).__init__(parent)
self.plotview = None
self.data = None
self.counts = None
self.points = []
self.pattern_geometry = None
self.cake_geometry = None
self.is_calibrated = False
cstr = str(ALL_CALIBRANTS)
calibrants = sorted(cstr[cstr.index(':')+2:].split(', '))
self.parameters = GridParameters()
self.parameters.add('calibrant', calibrants, 'Calibrant')
self.parameters['calibrant'].value = 'CeO2'
self.parameters.add('wavelength', 0.5, 'Wavelength (Ang)', False)
self.parameters.add('distance', 100.0, 'Detector Distance (mm)', True)
self.parameters.add('xc', 512, 'Beam Center - x', True)
self.parameters.add('yc', 512, 'Beam Center - y', True)
self.parameters.add('yaw', 0.0, 'Yaw (degrees)', True)
self.parameters.add('pitch', 0.0, 'Pitch (degrees)', True)
self.parameters.add('roll', 0.0, 'Roll (degrees)', True)
self.parameters.add('search_size', 10, 'Search Size (pixels)')
rings = ['Ring%s' % i for i in range(1,21)]
self.rings_box = self.select_box(rings)
self.set_layout(self.select_entry(self.choose_entry),
self.action_buttons(('Plot Calibration', self.plot_data)),
self.parameters.grid(header=False),
self.make_layout(
self.action_buttons(('Select Points', self.select)),
self.rings_box),
self.action_buttons(('Calibrate', self.calibrate),
('Plot Cake', self.plot_cake),
('Restore', self.restore_parameters),
('Save', self.save_parameters)),
self.close_buttons(close=True))
self.set_title('Calibrating Powder')
def choose_entry(self):
if 'calibration' not in self.entry['instrument']:
raise NeXusError('Please load calibration data to this entry')
self.update_parameters()
self.plot_data()
def update_parameters(self):
self.parameters['wavelength'].value = self.entry['instrument/monochromator/wavelength']
detector = self.entry['instrument/detector']
self.parameters['distance'].value = detector['distance']
self.parameters['yaw'].value = detector['yaw']
self.parameters['pitch'].value = detector['pitch']
self.parameters['roll'].value = detector['roll']
if 'beam_center_x' in detector:
self.parameters['xc'].value = detector['beam_center_x']
if 'beam_center_y' in detector:
self.parameters['yc'].value = detector['beam_center_y']
self.data = self.entry['instrument/calibration']
self.counts = self.data.nxsignal.nxvalue
@property
def search_size(self):
return int(self.parameters['search_size'].value)
@property
def ring(self):
return int(self.rings_box.currentText()[4:]) - 1
@property
def ring_color(self):
colors = ['r', 'b', 'g', 'c', 'm'] * 4
return colors[self.ring]
def plot_data(self):
if self.plotview is None:
if 'Powder Calibration' in plotviews:
self.plotview = plotviews['Powder Calibration']
else:
self.plotview = NXPlotView('Powder Calibration')
self.plotview.plot(self.data, log=True)
self.plotview.aspect='equal'
self.plotview.ytab.flipped = True
self.clear_peaks()
def on_button_press(self, event):
self.plotview.make_active()
if event.inaxes:
self.xp, self.yp = event.x, event.y
else:
self.xp, self.yp = 0, 0
def on_button_release(self, event):
if event.inaxes:
if abs(event.x - self.xp) > 5 or abs(event.y - self.yp) > 5:
return
x, y = self.plotview.inverse_transform(event.xdata, event.ydata)
for i, point in enumerate(self.points):
circle = point[0]
if circle.contains_point(self.plotview.ax.transData.transform((x,y))):
circle.remove()
for circle in point[2]:
circle.remove()
del self.points[i]
return
idx, idy = self.find_peak(x, y)
points = [(idy, idx)]
circles = []
massif = Massif(self.counts)
extra_points = massif.find_peaks((idy, idx))
for point in extra_points:
points.append(point)
circles.append(self.circle(point[1], point[0], alpha=0.3))
self.points.append([self.circle(idx, idy), points, circles, self.ring])
def circle(self, idx, idy, alpha=1.0):
return self.plotview.circle(idx, idy, self.search_size,
facecolor=self.ring_color, edgecolor='k',
alpha=alpha)
def select(self):
self.plotview.cidpress = self.plotview.mpl_connect(
'button_press_event', self.on_button_press)
self.plotview.cidrelease = self.plotview.mpl_connect(
'button_release_event', self.on_button_release)
def find_peak(self, x, y):
s = self.search_size
left = int(np.round(x - s * 0.5))
if left < 0:
left = 0
top = int(np.round(y - s * 0.5))
if top < 0:
top = 0
region = self.counts[top:(top+s),left:(left+s)]
idy, idx = np.where(region == region.max())
idx = left + idx[0]
idy = top + idy[0]
return idx, idy
def clear_peaks(self):
self.points = []
@property
def calibrant(self):
return ALL_CALIBRANTS[self.parameters['calibrant'].value]
@property
def point_array(self):
points = []
for point in self.points:
for p in point[1]:
points.append((p[0], p[1], point[3]))
return np.array(points)
def prepare_parameters(self):
self.parameters.set_parameters()
self.wavelength = self.parameters['wavelength'].value * 1e-10
self.distance = self.parameters['distance'].value * 1e-3
self.yaw = np.radians(self.parameters['yaw'].value)
self.pitch = np.radians(self.parameters['pitch'].value)
self.roll = np.radians(self.parameters['roll'].value)
self.pixel_size = self.entry['instrument/detector/pixel_size'].nxvalue * 1e-3
self.xc = self.parameters['xc'].value
self.yc = self.parameters['yc'].value
def calibrate(self):
self.prepare_parameters()
self.orig_pixel1 = self.pixel_size
self.orig_pixel2 = self.pixel_size
self.pattern_geometry = GeometryRefinement(self.point_array,
dist=self.distance,
wavelength=self.wavelength,
pixel1=self.pixel_size,
pixel2=self.pixel_size,
calibrant=self.calibrant)
self.refine()
self.create_cake_geometry()
self.pattern_geometry.reset()
def refine(self):
self.pattern_geometry.data = self.point_array
if self.parameters['wavelength'].vary:
self.pattern_geometry.refine2()
fix = []
else:
fix = ['wavelength']
if not self.parameters['distance'].vary:
fix.append('dist')
self.pattern_geometry.refine2_wavelength(fix=fix)
self.read_parameters()
self.is_calibrated = True
self.create_cake_geometry()
self.pattern_geometry.reset()
def create_cake_geometry(self):
self.cake_geometry = AzimuthalIntegrator()
pyFAI_parameter = self.pattern_geometry.getPyFAI()
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 plot_cake(self):
if 'Cake Plot' in plotviews:
plotview = plotviews['Cake Plot']
else:
plotview = NXPlotView('Cake Plot')
if not self.is_calibrated:
raise NeXusError('No refinement performed')
res = self.cake_geometry.integrate2d(self.counts,
1024, 1024,
method='csr',
unit='2th_deg',
correctSolidAngle=True)
self.cake_data = NXdata(res[0], (NXfield(res[2], name='azimumthal_angle'),
NXfield(res[1], name='polar_angle')))
self.cake_data['title'] = self.entry['instrument/calibration/title']
plotview.plot(self.cake_data, log=True)
wavelength = self.parameters['wavelength'].value
polar_angles = [2 * np.degrees(np.arcsin(wavelength/(2*d)))
for d in self.calibrant.dSpacing]
plotview.vlines([polar_angle for polar_angle in polar_angles
if polar_angle < plotview.xaxis.max],
linestyle=':', color='r')
def read_parameters(self):
pyFAI = self.pattern_geometry.getPyFAI()
fit2d = self.pattern_geometry.getFit2D()
self.parameters['wavelength'].value = self.pattern_geometry.wavelength * 1e10
self.parameters['distance'].value = pyFAI['dist'] * 1e3
self.parameters['yaw'].value = np.degrees(pyFAI['rot1'])
self.parameters['pitch'].value = np.degrees(pyFAI['rot2'])
self.parameters['roll'].value = np.degrees(pyFAI['rot3'])
self.parameters['xc'].value = fit2d['centerX']
self.parameters['yc'].value = fit2d['centerY']
def restore_parameters(self):
self.parameters.restore_parameters()
def save_parameters(self):
if not self.is_calibrated:
raise NeXusError('No refinement performed')
elif 'refinement' in self.entry['instrument/calibration']:
if confirm_action('Overwrite previous refinement?'):
del self.entry['instrument/calibration/refinement']
else:
return
self.entry['instrument/calibration/calibrant'] = self.parameters['calibrant'].value
process = NXprocess()
process.program = 'pyFAI'
process.version = pyFAI.version
process.parameters = NXcollection()
process.parameters['Detector'] = self.entry['instrument/detector/description']
pyFAI_parameter = self.pattern_geometry.getPyFAI()
process.parameters['PixelSize1'] = pyFAI_parameter['pixel1']
process.parameters['PixelSize2'] = pyFAI_parameter['pixel2']
process.parameters['Distance'] = pyFAI_parameter['dist']
process.parameters['Poni1'] = pyFAI_parameter['poni1']
process.parameters['Poni2'] = pyFAI_parameter['poni2']
process.parameters['Rot1'] = pyFAI_parameter['rot1']
process.parameters['Rot2'] = pyFAI_parameter['rot2']
process.parameters['Rot3'] = pyFAI_parameter['rot3']
process.parameters['Wavelength'] = pyFAI_parameter['wavelength']
self.entry['instrument/calibration/refinement'] = process
self.entry['instrument/monochromator/wavelength'] = self.parameters['wavelength'].value
self.entry['instrument/monochromator/energy'] = 12.398419739640717 / self.parameters['wavelength'].value
detector = self.entry['instrument/detector']
detector['distance'] = self.parameters['distance'].value
detector['yaw'] = self.parameters['yaw'].value
detector['pitch'] = self.parameters['pitch'].value
detector['roll'] = self.parameters['roll'].value
detector['beam_center_x'] = self.parameters['xc'].value
detector['beam_center_y'] = self.parameters['yc'].value
def reject(self):
super(CalibrateDialog, self).reject()
if 'Powder Calibration' in plotviews:
plotviews['Powder Calibration'].close_view()
if 'Cake Plot' in plotviews:
plotviews['Cake Plot'].close_view()
