def getRings(self):
"""
Returns polygons of rings
:returns: List of ring angle with the associated polygon
:rtype: List[Tuple[float,List[numpy.ndarray]]]
"""
tth = self.__geoRef.twoThetaArray(self.__peakPicker.shape)
result = collections.OrderedDict()
tth_max = tth.max()
tth_min = tth.min()
if not self.__calibrant:
return result
angles = [
i for i in self.__calibrant.get_2th()
if (i is not None) and (i >= tth_min) and (i <= tth_max)
]
if len(angles) == 0:
return result
ms = marchingsquares.MarchingSquaresMergeImpl(tth,
self.__mask,
use_minmax_cache=True)
rings = []
for angle in angles:
polygons = ms.find_contours(angle)
rings.append((angle, polygons))
return rings
def update(self, calibration):
"""Update the state from a current calibration process.
:param RingCalibration calibration: A calibration process
"""
mask = calibration.getMask()
self.__geoRef = calibration.getPyfaiGeometry()
self.__geometry = None
self.__rings = calibration.getRings()
self.__previousRms = self.__rms
self.__rms = calibration.getRms()
tth = calibration.getTwoThetaArray()
# Make sure there is no more copy
self.__tth = numpy.ascontiguousarray(tth)
self.__poni = calibration.getPoni()
self.__beamCenter = calibration.getBeamCenter()
self.__empty = False
ms = marchingsquares.MarchingSquaresMergeImpl(tth,
mask,
use_minmax_cache=True)
self.__ms = ms
self.__ringPolygons = {}
self.changed.emit()
def extract(self,
peaks,
method="massif",
maxRings=None,
pointPerDegree=1.0):
"""
Performs an automatic keypoint extraction:
Can be used in recalib or in calib after a first calibration has been performed.
# FIXME pts_per_deg
"""
fixed = pyFAI.utils.FixedParameters()
fixed.add("wavelength")
geoRef = self.__createGeoRef(peaks, fixed=fixed)
self.__geoRef = geoRef
peakPicker = PeakPicker(data=self.__image,
mask=self.__mask,
calibrant=self.__calibrant,
wavelength=self.__wavelength,
detector=self.__detector,
method=method)
peakPicker.reset()
peakPicker.init(method, False)
# if geoRef:
# ai.setPyFAI(**geoRef.getPyFAI())
tth = numpy.array(
[i for i in self.__calibrant.get_2th() if i is not None])
tth = numpy.unique(tth)
tth_min = numpy.zeros_like(tth)
tth_max = numpy.zeros_like(tth)
delta = (tth[1:] - tth[:-1]) / 4.0
tth_max[:-1] = delta
tth_max[-1] = delta[-1]
tth_min[1:] = -delta
tth_min[0] = -delta[0]
tth_max += tth
tth_min += tth
ttha = geoRef.get_ttha()
chia = geoRef.get_chia()
if (ttha is None) or (ttha.shape != peakPicker.data.shape):
ttha = geoRef.twoThetaArray(peakPicker.data.shape)
if (chia is None) or (chia.shape != peakPicker.data.shape):
chia = geoRef.chiArray(peakPicker.data.shape)
rings = 0
peakPicker.sync_init()
if maxRings is None:
maxRings = tth.size
ms = marchingsquares.MarchingSquaresMergeImpl(ttha,
self.__mask,
use_minmax_cache=True)
for i in range(tth.size):
if rings >= maxRings:
break
mask = numpy.logical_and(ttha >= tth_min[i], ttha < tth_max[i])
# if self.mask is not None:
# mask = numpy.logical_and(mask, numpy.logical_not(self.mask))
size = mask.sum(dtype=int)
if (size > 0):
rings += 1
peakPicker.massif_contour(mask)
# if self.gui:
# update_fig(self.peakPicker.fig)
sub_data = peakPicker.data.ravel()[numpy.where(mask.ravel())]
mean = sub_data.mean(dtype=numpy.float64)
std = sub_data.std(dtype=numpy.float64)
upper_limit = mean + std
mask2 = numpy.logical_and(peakPicker.data > upper_limit, mask)
size2 = mask2.sum(dtype=int)
if size2 < 1000:
upper_limit = mean
mask2 = numpy.logical_and(peakPicker.data > upper_limit,
mask)
size2 = mask2.sum()
# length of the arc:
# Coords in points are y, x
points = ms.find_pixels(tth[i])
seeds = set((i[0], i[1]) for i in points if mask2[i[0], i[1]])
# max number of points: 360 points for a full circle
azimuthal = chia[points[:,
0].clip(0, peakPicker.data.shape[0]),
points[:,
1].clip(0, peakPicker.data.shape[1])]
nb_deg_azim = numpy.unique(
numpy.rad2deg(azimuthal).round()).size
keep = int(nb_deg_azim * pointPerDegree)
if keep == 0:
continue
dist_min = len(seeds) / 2.0 / keep
# why 3.0, why not ?
msg = "Extracting datapoint for ring %s (2theta = %.2f deg); "\
"searching for %i pts out of %i with I>%.1f, dmin=%.1f"
_logger.info(msg, i, numpy.degrees(tth[i]), keep, size2,
upper_limit, dist_min)
_res = peakPicker.peaks_from_area(mask=mask2,
Imin=upper_limit,
keep=keep,
method=method,
ring=i,
dmin=dist_min,
seed=seeds)
# self.peakPicker.points.save(self.basename + ".npt")
# if self.weighted:
# self.data = self.peakPicker.points.getWeightedList(self.peakPicker.data)
# else:
# self.data = peakPicker.points.getList()
return peakPicker.points.getList()
def extract_cp(self, max_rings=None, pts_per_deg=1.0, Imin=0):
"""Performs an automatic keypoint extraction and update the geometry refinement part
:param max_ring: extract at most N rings from the image
:param pts_per_deg: number of control points per azimuthal degree (increase for better precision)
"""
if self.massif is None:
self.massif = Massif(self.image)
tth = numpy.array(
[i for i in self.calibrant.get_2th() if i is not None])
tth = numpy.unique(tth)
tth_min = numpy.zeros_like(tth)
tth_max = numpy.zeros_like(tth)
delta = (tth[1:] - tth[:-1]) / 4.0
tth_max[:-1] = delta
tth_max[-1] = delta[-1]
tth_min[1:] = -delta
tth_min[0] = -delta[0]
tth_max += tth
tth_min += tth
shape = self.image.shape
ttha = self.geometry_refinement.twoThetaArray(shape)
chia = self.geometry_refinement.chiArray(shape)
rings = 0
cp = ControlPoints(calibrant=self.calibrant)
if max_rings is None:
max_rings = tth.size
ms = marchingsquares.MarchingSquaresMergeImpl(
ttha,
mask=self.geometry_refinement.detector.mask,
use_minmax_cache=True)
for i in range(tth.size):
if rings >= max_rings:
break
mask = numpy.logical_and(ttha >= tth_min[i], ttha < tth_max[i])
if self.detector.mask is not None:
mask = numpy.logical_and(
mask,
numpy.logical_not(self.geometry_refinement.detector.mask))
size = mask.sum(dtype=int)
if (size > 0):
rings += 1
sub_data = self.image.ravel()[numpy.where(mask.ravel())]
mean = sub_data.mean(dtype=numpy.float64)
std = sub_data.std(dtype=numpy.float64)
upper_limit = mean + std
mask2 = numpy.logical_and(self.image > upper_limit, mask)
size2 = mask2.sum(dtype=int)
if size2 < 1000:
upper_limit = mean
mask2 = numpy.logical_and(self.image > upper_limit, mask)
size2 = mask2.sum()
# length of the arc:
points = ms.find_pixels(tth[i])
seeds = set((i[0], i[1]) for i in points if mask2[i[0], i[1]])
# max number of points: 360 points for a full circle
azimuthal = chia[points[:, 0].clip(0, shape[0]),
points[:, 1].clip(0, shape[1])]
nb_deg_azim = numpy.unique(
numpy.rad2deg(azimuthal).round()).size
keep = int(nb_deg_azim * pts_per_deg)
if keep == 0:
continue
dist_min = len(seeds) / 2.0 / keep
# why 3.0, why not ?
logger.info(
"Extracting datapoint for ring %s (2theta = %.2f deg); " +
"searching for %i pts out of %i with I>%.1f, dmin=%.1f", i,
numpy.degrees(tth[i]), keep, size2, upper_limit, dist_min)
res = self.massif.peaks_from_area(mask2,
Imin=Imin,
keep=keep,
dmin=dist_min,
seed=seeds,
ring=i)
cp.append(res, i)
self.control_points = cp
self.geometry_refinement.data = numpy.asarray(cp.getList(),
dtype=numpy.float64)
return cp
def _extract(self, peaks=None, geometryModel=None):
"""
Performs an automatic keypoint extraction:
Can be used in recalib or in calib after a first calibration has been performed.
:param List[int] ringNumbers: If set, extraction will only be done on
rings number contained in this list (the number 0 identify the first
ring)
"""
assert (numpy.logical_xor(peaks is not None, geometryModel
is not None))
method = "massif"
maxRings = self.__maxRings
ringNumbers = self.__ringNumbers
pointPerDegree = self.__pointPerDegree
if ringNumbers is not None:
ringNumbers = set(ringNumbers)
if peaks is not None:
# Energy from from experiment settings
wavelength = self.__wavelength
self.__calibrant.setWavelength_change2th(wavelength)
geoRef = self.__createGeoRefFromPeaks(peaks)
elif geometryModel is not None:
# Fitted energy
assert (geometryModel.isValid())
wavelength = geometryModel.wavelength().value()
self.__calibrant.setWavelength_change2th(wavelength)
geoRef = self.__createGeoRefFromGeometry(geometryModel)
self.__geoRef = geoRef
peakPicker = PeakPicker(data=self.__image,
mask=self.__mask,
calibrant=self.__calibrant,
wavelength=wavelength,
detector=self.__detector,
method=method)
peakPicker.reset()
peakPicker.init(method, False)
tth = numpy.array(
[i for i in self.__calibrant.get_2th() if i is not None])
tth = numpy.unique(tth)
tth_min = numpy.zeros_like(tth)
tth_max = numpy.zeros_like(tth)
delta = (tth[1:] - tth[:-1]) / 4.0
tth_max[:-1] = delta
tth_max[-1] = delta[-1]
tth_min[1:] = -delta
tth_min[0] = -delta[0]
tth_max += tth
tth_min += tth
ttha = geoRef.get_ttha()
chia = geoRef.get_chia()
if (ttha is None) or (ttha.shape != peakPicker.data.shape):
ttha = geoRef.twoThetaArray(peakPicker.data.shape)
if (chia is None) or (chia.shape != peakPicker.data.shape):
chia = geoRef.chiArray(peakPicker.data.shape)
rings = 0
peakPicker.sync_init()
if maxRings is None:
maxRings = tth.size
ms = marchingsquares.MarchingSquaresMergeImpl(ttha,
self.__mask,
use_minmax_cache=True)
for i in range(tth.size):
if ringNumbers is not None:
if i not in ringNumbers:
continue
if rings >= maxRings:
break
mask = numpy.logical_and(ttha >= tth_min[i], ttha < tth_max[i])
size = mask.sum(dtype=int)
if (size > 0):
rings += 1
self._updateProcessingLocation(mask)
sub_data = peakPicker.data.ravel()[numpy.where(mask.ravel())]
mean = sub_data.mean(dtype=numpy.float64)
std = sub_data.std(dtype=numpy.float64)
upper_limit = mean + std
mask2 = numpy.logical_and(peakPicker.data > upper_limit, mask)
size2 = mask2.sum(dtype=int)
if size2 < 1000:
upper_limit = mean
mask2 = numpy.logical_and(peakPicker.data > upper_limit,
mask)
size2 = mask2.sum()
# Coords in points are y, x
points = ms.find_pixels(tth[i])
seeds = set((i[0], i[1]) for i in points if mask2[i[0], i[1]])
# Max number of points: 360 points for a full circle
azimuthal = chia[points[:,
0].clip(0, peakPicker.data.shape[0]),
points[:,
1].clip(0, peakPicker.data.shape[1])]
nb_deg_azim = numpy.unique(
numpy.rad2deg(azimuthal).round()).size
keep = int(nb_deg_azim * pointPerDegree)
if keep == 0:
continue
dist_min = len(seeds) / 2.0 / keep
# why 3.0, why not ?
msg = "Extracting datapoint for ring %s (2theta = %.2f deg); "\
"searching for %i pts out of %i with I>%.1f, dmin=%.1f"
_logger.info(msg, i, numpy.degrees(tth[i]), keep, size2,
upper_limit, dist_min)
_res = peakPicker.peaks_from_area(mask=mask2,
Imin=upper_limit,
keep=keep,
method=method,
ring=i,
dmin=dist_min,
seed=seeds)
return peakPicker.points.getList()