def get_panel_fast_slow(self, serial):
"""Get the average x- and y-coordinates of all the ASIC:s in the
panel with serial @p serial. This is done by back-transforming
the centre's of each ASIC to the screen (sort of) coordinate
system. This is more robust than getting the panel positions
directly.
"""
from xfel.cftbx.detector.metrology import get_projection_matrix
center = col([self._asic_focus[0] / 2, self._asic_focus[1] / 2, 1])
fast, nmemb, slow = 0, 0, 0
# Use the pixel size for the ASIC to construct the final
# projection matrix.
for p in self._metrology_params.detector.panel:
if (p.serial != serial):
continue
for s in p.sensor:
for a in s.asic:
E = rec(elems=[+1 / a.pixel_size[0], 0, 0, 0,
0, -1 / a.pixel_size[1], 0, 0],
n=[2, 4])
Pb = get_projection_matrix(a.pixel_size, a.dimension)[1]
Tb = self._matrices[(0, p.serial, s.serial, a.serial)][1]
t = E * Tb * Pb * center
fast += t(0, 0)
slow += t(1, 0)
nmemb += 1
if (nmemb == 0):
return (0, 0)
return (fast / nmemb, slow / nmemb)
def get_flex_image(self, brightness, **kwargs):
# This functionality has migrated to
# rstbx.slip_viewer.tile_generation._get_flex_image_multitile().
# XXX Still used by iotbx/command_line/detector_image_as_png.py
#raise DeprecationWarning(
# "xfel.cftbx.cspad_detector.get_flex_image() is deprecated")
# no kwargs supported at present
from xfel.cftbx.detector.metrology import get_projection_matrix
# E maps picture coordinates onto metric Cartesian coordinates,
# i.e. [row, column, 1 ] -> [x, y, z, 1]. Both frames share the
# same origin, but the first coordinate of the screen coordinate
# system increases downwards, while the second increases towards
# the right. XXX Is this orthographic projection the only one
# that makes any sense?
E = rec(elems=[
0, +self._pixel_size[1], 0, -self._pixel_size[0], 0, 0, 0, 0, 0, 0,
0, 1
],
n=[4, 3])
# P: [x, y, z, 1] -> [row, column, 1]. Note that self._asic_focus
# needs to be flipped.
Pf = get_projection_matrix(
self._pixel_size, (self._asic_focus[1], self._asic_focus[0]))[0]
# XXX Add ASIC:s in order? If a point is contained in two ASIC:s
# simultaneously, it will be assigned to the ASIC defined first.
# XXX Use a Z-buffer instead?
nmemb = 0
for key, asic in six.iteritems(self._tiles):
# Create my_flex_image and rawdata on the first iteration.
if ("rawdata" not in locals()):
rawdata = flex.double(flex.grid(self.size1, self.size2))
my_flex_image = generic_flex_image(
rawdata=rawdata,
binning=1,
size1_readout=self._asic_focus[0],
size2_readout=self._asic_focus[1],
brightness=brightness,
saturation=self._saturation)
rawdata.matrix_paste_block_in_place(block=asic,
i_row=nmemb *
self._asic_padded[0],
i_column=0)
nmemb += 1
# key is guaranteed to exist in self._matrices as per
# readHeader(). Last row of self._matrices[key][0] is always
# [0, 0, 0, 1].
T = Pf * self._matrices[key][0] * E
R = sqr([T(0, 0), T(0, 1), T(1, 0), T(1, 1)])
t = col([T(0, 2), T(1, 2)])
my_flex_image.add_transformation_and_translation(R, t)
my_flex_image.followup_brightness_scale()
return my_flex_image
def get_flex_image(self, brightness, **kwargs):
# This functionality has migrated to
# rstbx.slip_viewer.tile_generation._get_flex_image_multitile().
# XXX Still used by iotbx/command_line/detector_image_as_png.py
#raise DeprecationWarning(
# "xfel.cftbx.cspad_detector.get_flex_image() is deprecated")
# no kwargs supported at present
from xfel.cftbx.detector.metrology import get_projection_matrix
# E maps picture coordinates onto metric Cartesian coordinates,
# i.e. [row, column, 1 ] -> [x, y, z, 1]. Both frames share the
# same origin, but the first coordinate of the screen coordinate
# system increases downwards, while the second increases towards
# the right. XXX Is this orthographic projection the only one
# that makes any sense?
E = rec(elems=[0, +self._pixel_size[1], 0,
-self._pixel_size[0], 0, 0,
0, 0, 0,
0, 0, 1],
n=[4, 3])
# P: [x, y, z, 1] -> [row, column, 1]. Note that self._asic_focus
# needs to be flipped.
Pf = get_projection_matrix(self._pixel_size,
(self._asic_focus[1], self._asic_focus[0]))[0]
# XXX Add ASIC:s in order? If a point is contained in two ASIC:s
# simultaneously, it will be assigned to the ASIC defined first.
# XXX Use a Z-buffer instead?
nmemb = 0
for key, asic in self._tiles.iteritems():
# Create my_flex_image and rawdata on the first iteration.
if ("rawdata" not in locals()):
rawdata = flex.double(flex.grid(self.size1, self.size2))
my_flex_image = generic_flex_image(
rawdata=rawdata,
size1_readout=self._asic_focus[0],
size2_readout=self._asic_focus[1],
brightness=brightness,
saturation=self._saturation)
rawdata.matrix_paste_block_in_place(
block=asic,
i_row=nmemb * self._asic_padded[0],
i_column=0)
nmemb += 1
# key is guaranteed to exist in self._matrices as per
# readHeader(). Last row of self._matrices[key][0] is always
# [0, 0, 0, 1].
T = Pf * self._matrices[key][0] * E
R = sqr([T(0, 0), T(0, 1),
T(1, 0), T(1, 1)])
t = col([T(0, 2), T(1, 2)])
my_flex_image.add_transformation_and_translation(R, t)
my_flex_image.followup_brightness_scale()
return my_flex_image
def readout_coords_as_detector_coords(self, coords):
"""
Convert a 3 tuple coordinates from readout space (x, y, tile number)
to detector space in meters, relative to the detector center
"""
tileno = int(coords[2])
if tileno < 0: return None
try:
T_f, T_b = self._matrices[self._keylist[tileno]]
except IndexError:
return None
assert self._pixel_size is not None
assert self._asic_focus is not None
from xfel.cftbx.detector.metrology import get_projection_matrix
P_f, P_b = get_projection_matrix(self._pixel_size, self._asic_focus)
return T_b * P_b * col([int(coords[0]), int(coords[1]), 1])
def readout_coords_as_detector_coords(self, coords):
"""
Convert a 3 tuple coordinates from readout space (x, y, tile number)
to detector space in meters, relative to the detector center
"""
tileno = int(coords[2])
if tileno < 0: return None
try:
T_f, T_b = self._matrices[self._keylist[tileno]]
except IndexError:
return None
assert self._pixel_size is not None
assert self._asic_focus is not None
from xfel.cftbx.detector.metrology import get_projection_matrix
P_f, P_b = get_projection_matrix(self._pixel_size, self._asic_focus)
return T_b * P_b * col([int(coords[0]), int(coords[1]), 1])
def get_panel_fast_slow(self, serial):
"""Get the average x- and y-coordinates of all the ASIC:s in the
panel with serial @p serial. This is done by back-transforming
the centre's of each ASIC to the screen (sort of) coordinate
system. This is more robust than getting the panel positions
directly.
"""
from xfel.cftbx.detector.metrology import get_projection_matrix
center = col([self._asic_focus[0] / 2, self._asic_focus[1] / 2, 1])
fast, nmemb, slow = 0, 0, 0
# Use the pixel size for the ASIC to construct the final
# projection matrix.
for p in self._metrology_params.detector.panel:
if (p.serial != serial):
continue
for s in p.sensor:
for a in s.asic:
E = rec(elems=[
+1 / a.pixel_size[0], 0, 0, 0, 0, -1 / a.pixel_size[1],
0, 0
],
n=[2, 4])
Pb = get_projection_matrix(a.pixel_size, a.dimension)[1]
Tb = self._matrices[(0, p.serial, s.serial, a.serial)][1]
t = E * Tb * Pb * center
fast += t(0, 0)
slow += t(1, 0)
nmemb += 1
if (nmemb == 0):
return (0, 0)
return (fast / nmemb, slow / nmemb)
def _get_flex_image_multipanel(
panels,
raw_data,
beam,
brightness=1.0,
binning=1,
show_untrusted=False,
color_scheme=0,
):
# From xfel.cftbx.cspad_detector.readHeader() and
# xfel.cftbx.cspad_detector.get_flex_image(). XXX Is it possible to
# merge this with _get_flex_image() above? XXX Move to dxtbx Format
# class (or a superclass for multipanel images)?
from math import ceil
from iotbx.detectors import generic_flex_image
from libtbx.test_utils import approx_equal
from scitbx.array_family import flex
from scitbx.matrix import col, rec, sqr
from xfel.cftbx.detector.metrology import get_projection_matrix
assert len(panels) == len(raw_data), (len(panels), len(raw_data))
# Determine next multiple of eight of the largest panel size.
for data in raw_data:
if "data_max_focus" not in locals():
data_max_focus = data.focus()
else:
data_max_focus = (
max(data_max_focus[0],
data.focus()[0]),
max(data_max_focus[1],
data.focus()[1]),
)
data_padded = (
8 * int(ceil(data_max_focus[0] / 8)),
8 * int(ceil(data_max_focus[1] / 8)),
)
# Assert that all saturated values are equal and not None. While
# dxtbx records a separated trusted_range for each panel,
# generic_flex_image supports only accepts a single common value for
# the saturation.
for panel in panels:
if "saturation" not in locals():
saturation = panel.get_trusted_range()[1]
else:
assert approx_equal(saturation, panel.get_trusted_range()[1])
assert "saturation" in locals() and saturation is not None
# Create rawdata and my_flex_image before populating it.
rawdata = flex.double(
flex.grid(len(panels) * data_padded[0], data_padded[1]))
my_flex_image = generic_flex_image(
rawdata=rawdata,
binning=binning,
size1_readout=data_max_focus[0],
size2_readout=data_max_focus[1],
brightness=brightness,
saturation=saturation,
show_untrusted=show_untrusted,
color_scheme=color_scheme,
)
# Calculate the average beam center across all panels, in meters
# not sure this makes sense for detector which is not on a plane?
beam_center = col((0, 0, 0))
npanels = 0
for panel in panels:
try:
beam_center += col(panel.get_beam_centre_lab(beam.get_s0()))
npanels += 1
except RuntimeError: # catch DXTBX_ASSERT for no intersection
pass
beam_center /= npanels / 1e-3
# XXX If a point is contained in two panels simultaneously, it will
# be assigned to the panel defined first. XXX Use a Z-buffer
# instead?
for i, panel in enumerate(panels):
# Determine the pixel size for the panel (in meters), as pixel
# sizes need not be identical.
data = raw_data[i]
pixel_size = (
panel.get_pixel_size()[0] * 1e-3,
panel.get_pixel_size()[1] * 1e-3,
)
if len(panels) == 24 and panels[0].get_image_size() == (2463, 195):
rawdata.matrix_paste_block_in_place(block=data.as_double(),
i_row=i * data_padded[0],
i_column=0)
# XXX hardcoded panel height and row gap
my_flex_image.add_transformation_and_translation(
(1, 0, 0, 1), (-i * (195 + 17), 0))
continue
elif len(panels) == 120 and panels[0].get_image_size() == (487, 195):
i_row = i // 5
i_col = i % 5
rawdata.matrix_paste_block_in_place(block=data.as_double(),
i_row=i * data_padded[0],
i_column=0)
# XXX hardcoded panel height and row gap
my_flex_image.add_transformation_and_translation(
(1, 0, 0, 1), (-i_row * (195 + 17), -i_col * (487 + 7)))
continue
# Get unit vectors in the fast and slow directions, as well as the
# the locations of the origin and the center of the panel, in
# meters. The origin is taken w.r.t. to average beam center of all
# panels. This avoids excessive translations that can result from
# rotations around the laboratory origin. Related to beam centre above
# and dials#380 not sure this is right for detectors which are not
# coplanar since system derived from first panel...
fast = col(panel.get_fast_axis())
slow = col(panel.get_slow_axis())
origin = col(panel.get_origin()) * 1e-3 - beam_center
center = (origin + (data.focus()[0] - 1) / 2 * pixel_size[1] * slow +
(data.focus()[1] - 1) / 2 * pixel_size[0] * fast)
normal = slow.cross(fast).normalize()
# Determine rotational and translational components of the
# homogeneous transformation that maps the readout indices to the
# three-dimensional laboratory frame.
Rf = sqr((
fast(0, 0),
fast(1, 0),
fast(2, 0),
-slow(0, 0),
-slow(1, 0),
-slow(2, 0),
normal(0, 0),
normal(1, 0),
normal(2, 0),
))
tf = -Rf * center
Tf = sqr((
Rf(0, 0),
Rf(0, 1),
Rf(0, 2),
tf(0, 0),
Rf(1, 0),
Rf(1, 1),
Rf(1, 2),
tf(1, 0),
Rf(2, 0),
Rf(2, 1),
Rf(2, 2),
tf(2, 0),
0,
0,
0,
1,
))
# E maps picture coordinates onto metric Cartesian coordinates,
# i.e. [row, column, 1 ] -> [x, y, z, 1]. Both frames share the
# same origin, but the first coordinate of the screen coordinate
# system increases downwards, while the second increases towards
# the right. XXX Is this orthographic projection the only one
# that makes any sense?
E = rec(
elems=[
0, +pixel_size[1], 0, -pixel_size[0], 0, 0, 0, 0, 0, 0, 0, 1
],
n=[4, 3],
)
# P: [x, y, z, 1] -> [row, column, 1]. Note that data.focus()
# needs to be flipped to give (horizontal, vertical) size,
# i.e. (width, height).
Pf = get_projection_matrix(pixel_size,
(data.focus()[1], data.focus()[0]))[0]
rawdata.matrix_paste_block_in_place(block=data.as_double(),
i_row=i * data_padded[0],
i_column=0)
# Last row of T is always [0, 0, 0, 1].
T = Pf * Tf * E
R = sqr((T(0, 0), T(0, 1), T(1, 0), T(1, 1)))
t = col((T(0, 2), T(1, 2)))
my_flex_image.add_transformation_and_translation(R, t)
my_flex_image.followup_brightness_scale()
return my_flex_image
def _detector(self):
'''The _detector() function returns a model for a CSPAD detector as
used at LCLS's CXI and XPP endstations. It converts the
metrology information in the pure Python object extracted from
the image pickle to DXTBX-style transformation vectors. Only
ASIC:s are considered, since DXTBX metrology is not concerned
with hierarchies.
Merged from xfel.cftbx.detector.cspad_detector.readHeader() and
xfel.cftbx.detector.metrology.metrology_as_dxtbx_vectors().
'''
from dxtbx.model import SimplePxMmStrategy
from dxtbx.model.detector import HierarchicalDetector
from scitbx.matrix import col
# XXX Introduces dependency on cctbx.xfel! Should probably be
# merged into the code here!
from xfel.cftbx.detector.metrology import \
_transform, get_projection_matrix
# Apply the detector distance to the translation of the root
# detector object.
d = self._metrology_params.detector
Tb_d = _transform(
col(d.orientation).normalize(),
col(d.translation) +
col((0, 0, -self._metrology_params.distance * 1e-3)))[1]
self._raw_data = []
detector = HierarchicalDetector()
for p in d.panel:
Tb_p = Tb_d * _transform(
col(p.orientation).normalize(),
col(p.translation))[1]
for s in p.sensor:
Tb_s = Tb_p * _transform(
col(s.orientation).normalize(),
col(s.translation))[1]
for a in s.asic:
Tb_a = Tb_s * _transform(
col(a.orientation).normalize(),
col(a.translation))[1]
Pb = get_projection_matrix(a.pixel_size, a.dimension)[1]
# The DXTBX-style metrology description consists of three
# vectors for each ASIC. The origin vector locates the
# (0, 0)-pixel in the laboratory frame in units of mm.
# The second and third vectors give the directions to the
# pixels immediately next to (0, 0) in the fast and slow
# directions, respectively, in arbitrary units.
origin = Tb_a * Pb * col((0, 0, 1))
fast = Tb_a * Pb * col((0, a.dimension[0], 1)) - origin
slow = Tb_a * Pb * col((a.dimension[1], 0, 1)) - origin
# Convert vector units from meter to millimeter. The
# default, SimplePxMmStrategy applies here. XXX Due to
# dark subtraction, a valid pixel intensity may be
# negative, and this is currently not reflected by
# trusted_range.
key = (d.serial, p.serial, s.serial, a.serial)
panel = detector.add_panel()
panel.set_type("PAD")
panel.set_name('%d:%d:%d:%d' % key)
panel.set_local_frame(
[t * 1e3 for t in fast.elems[0:3]],
[t * 1e3 for t in slow.elems[0:3]],
[t * 1e3 for t in origin.elems[0:3]])
panel.set_pixel_size([t * 1e3 for t in a.pixel_size])
panel.set_image_size(a.dimension)
panel.set_trusted_range((0, a.saturation))
self._raw_data.append(self._tiles[key])
return detector
def _detector(self):
"""The _detector() function returns a model for a CSPAD detector as
used at LCLS's CXI and XPP endstations. It converts the
metrology information in the pure Python object extracted from
the image pickle to DXTBX-style transformation vectors. Only
ASIC:s are considered, since DXTBX metrology is not concerned
with hierarchies.
Merged from xfel.cftbx.detector.cspad_detector.readHeader() and
xfel.cftbx.detector.metrology.metrology_as_dxtbx_vectors().
"""
from dxtbx.model import SimplePxMmStrategy
from dxtbx.model import Detector
from scitbx.matrix import col
# XXX Introduces dependency on cctbx.xfel! Should probably be
# merged into the code here!
from xfel.cftbx.detector.metrology import _transform, get_projection_matrix
# Apply the detector distance to the translation of the root
# detector object.
d = self._metrology_params.detector
Tb_d = _transform(
col(d.orientation).normalize(),
col(d.translation) + col((0, 0, -self._metrology_params.distance * 1e-3)),
)[1]
self._raw_data = []
detector = Detector()
for p in d.panel:
Tb_p = (
Tb_d * _transform(col(p.orientation).normalize(), col(p.translation))[1]
)
for s in p.sensor:
Tb_s = (
Tb_p
* _transform(col(s.orientation).normalize(), col(s.translation))[1]
)
for a in s.asic:
Tb_a = (
Tb_s
* _transform(
col(a.orientation).normalize(), col(a.translation)
)[1]
)
Pb = get_projection_matrix(a.pixel_size, a.dimension)[1]
# The DXTBX-style metrology description consists of three
# vectors for each ASIC. The origin vector locates the
# (0, 0)-pixel in the laboratory frame in units of mm.
# The second and third vectors give the directions to the
# pixels immediately next to (0, 0) in the fast and slow
# directions, respectively, in arbitrary units.
origin = Tb_a * Pb * col((0, 0, 1))
fast = Tb_a * Pb * col((0, a.dimension[0], 1)) - origin
slow = Tb_a * Pb * col((a.dimension[1], 0, 1)) - origin
# Convert vector units from meter to millimeter. The
# default, SimplePxMmStrategy applies here. XXX Due to
# dark subtraction, a valid pixel intensity may be
# negative, and this is currently not reflected by
# trusted_range.
key = (d.serial, p.serial, s.serial, a.serial)
panel = detector.add_panel()
panel.set_type("PAD")
panel.set_name("%d:%d:%d:%d" % key)
panel.set_local_frame(
[t * 1e3 for t in fast.elems[0:3]],
[t * 1e3 for t in slow.elems[0:3]],
[t * 1e3 for t in origin.elems[0:3]],
)
panel.set_pixel_size([t * 1e3 for t in a.pixel_size])
panel.set_image_size(a.dimension)
panel.set_trusted_range((0, a.saturation))
self._raw_data.append(self._tiles[key])
return detector
def _get_flex_image_multipanel(panels,
raw_data,
brightness=1.0,
show_untrusted=False):
# From xfel.cftbx.cspad_detector.readHeader() and
# xfel.cftbx.cspad_detector.get_flex_image(). XXX Is it possible to
# merge this with _get_flex_image() above? XXX Move to dxtbx Format
# class (or a superclass for multipanel images)?
from math import ceil
from iotbx.detectors import generic_flex_image
from libtbx.test_utils import approx_equal
from scitbx.array_family import flex
from scitbx.matrix import col, rec, sqr
from xfel.cftbx.detector.metrology import get_projection_matrix
assert len(panels) == len(raw_data)
# Determine next multiple of eight of the largest panel size.
for data in raw_data:
if 'data_max_focus' not in locals():
data_max_focus = data.focus()
else:
data_max_focus = (max(data_max_focus[0],
data.focus()[0]),
max(data_max_focus[1],
data.focus()[1]))
data_padded = (8 * int(ceil(data_max_focus[0] / 8)),
8 * int(ceil(data_max_focus[1] / 8)))
# Assert that all saturated values are equal and not None. While
# dxtbx records a separated trusted_range for each panel,
# generic_flex_image supports only accepts a single common value for
# the saturation.
for panel in panels:
if 'saturation' not in locals():
saturation = panel.get_trusted_range()[1]
else:
assert approx_equal(saturation, panel.get_trusted_range()[1])
assert 'saturation' in locals() and saturation is not None
# Create rawdata and my_flex_image before populating it.
rawdata = flex.double(
flex.grid(len(panels) * data_padded[0], data_padded[1]))
my_flex_image = generic_flex_image(rawdata=rawdata,
size1_readout=data_max_focus[0],
size2_readout=data_max_focus[1],
brightness=brightness,
saturation=saturation,
show_untrusted=show_untrusted)
# XXX If a point is contained in two panels simultaneously, it will
# be assigned to the panel defined first. XXX Use a Z-buffer
# instead?
for i in range(len(panels)):
# Determine the pixel size for the panel (in meters), as pixel
# sizes need not be identical.
data = raw_data[i]
panel = panels[i]
pixel_size = (panel.get_pixel_size()[0] * 1e-3,
panel.get_pixel_size()[1] * 1e-3)
if len(panels) == 24 and panels[0].get_image_size() == (2463, 195):
#print "DLS I23 12M"
rawdata.matrix_paste_block_in_place(block=data.as_double(),
i_row=i * data_padded[0],
i_column=0)
# XXX hardcoded panel height and row gap
my_flex_image.add_transformation_and_translation(
(1, 0, 0, 1), (-i * (195 + 17), 0))
continue
elif len(panels) == 120 and panels[0].get_image_size() == (487, 195):
i_row = i // 5
i_col = i % 5
#print i_row, i_col
#print data_padded
#print "DLS I23 12M"
rawdata.matrix_paste_block_in_place(block=data.as_double(),
i_row=i * data_padded[0],
i_column=0)
# XXX hardcoded panel height and row gap
my_flex_image.add_transformation_and_translation(
(1, 0, 0, 1), (-i_row * (195 + 17), -i_col * (487 + 7)))
continue
# Get unit vectors in the fast and slow directions, as well as the
# the locations of the origin and the center of the panel, in
# meters.
fast = col(panel.get_fast_axis())
slow = col(panel.get_slow_axis())
origin = col(panel.get_origin()) * 1e-3
# Viewer will show an orthographic projection of the data onto a plane perpendicular to 0 0 1
projection_normal = col((0., 0., 1.))
beam_to_origin_proj = origin.dot(projection_normal) * projection_normal
projected_origin = origin - beam_to_origin_proj
center = projected_origin \
+ (data.focus()[0] - 1) / 2 * pixel_size[1] * slow \
+ (data.focus()[1] - 1) / 2 * pixel_size[0] * fast
normal = slow.cross(fast).normalize()
# Determine rotational and translational components of the
# homogeneous transformation that maps the readout indices to the
# three-dimensional laboratory frame.
Rf = sqr((fast(0, 0), fast(1, 0), fast(2, 0), -slow(0, 0), -slow(1, 0),
-slow(2, 0), normal(0, 0), normal(1, 0), normal(2, 0)))
tf = -Rf * center
Tf = sqr(
(Rf(0, 0), Rf(0, 1), Rf(0, 2), tf(0, 0), Rf(1, 0), Rf(1,
1), Rf(1, 2),
tf(1, 0), Rf(2, 0), Rf(2, 1), Rf(2, 2), tf(2, 0), 0, 0, 0, 1))
# E maps picture coordinates onto metric Cartesian coordinates,
# i.e. [row, column, 1 ] -> [x, y, z, 1]. Both frames share the
# same origin, but the first coordinate of the screen coordinate
# system increases downwards, while the second increases towards
# the right. XXX Is this orthographic projection the only one
# that makes any sense?
E = rec(elems=[
0, +pixel_size[1], 0, -pixel_size[0], 0, 0, 0, 0, 0, 0, 0, 1
],
n=[4, 3])
# P: [x, y, z, 1] -> [row, column, 1]. Note that data.focus()
# needs to be flipped to give (horizontal, vertical) size,
# i.e. (width, height).
Pf = get_projection_matrix(pixel_size,
(data.focus()[1], data.focus()[0]))[0]
rawdata.matrix_paste_block_in_place(block=data.as_double(),
i_row=i * data_padded[0],
i_column=0)
# Last row of T is always [0, 0, 0, 1].
T = Pf * Tf * E
R = sqr((T(0, 0), T(0, 1), T(1, 0), T(1, 1)))
t = col((T(0, 2), T(1, 2)))
#print i,R[0],R[1],R[2],R[3],t[0],t[1]
my_flex_image.add_transformation_and_translation(R, t)
my_flex_image.followup_brightness_scale()
return my_flex_image
# E maps picture coordinates onto metric Cartesian coordinates,
# i.e. [row, column, 1 ] -> [x, y, z, 1]. Both frames share the
# same origin, but the first coordinate of the screen coordinate
# system increases downwards, while the second increases towards
# the right. XXX Is this orthographic projection the only one
# that makes any sense?
E = rec(elems=[
0, +pixel_size[1], 0, -pixel_size[0], 0, 0, 0, 0, 0, 0, 0, 1
],
n=[4, 3])
# P: [x, y, z, 1] -> [row, column, 1]. Note that data.focus()
# needs to be flipped to give (horizontal, vertical) size,
# i.e. (width, height).
Pf = get_projection_matrix(pixel_size,
(data.focus()[1], data.focus()[0]))[0]
rawdata.matrix_paste_block_in_place(block=data.as_double(),
i_row=i * data_padded[0],
i_column=0)
# Last row of T is always [0, 0, 0, 1].
T = Pf * Tf * E
R = sqr((T(0, 0), T(0, 1), T(1, 0), T(1, 1)))
t = col((T(0, 2), T(1, 2)))
my_flex_image.add_transformation_and_translation(R, t)
my_flex_image.followup_brightness_scale()
return my_flex_image
class _Tiles(object):
def _get_flex_image_multipanel(panels, raw_data, brightness=1.0, show_untrusted=False):
# From xfel.cftbx.cspad_detector.readHeader() and
# xfel.cftbx.cspad_detector.get_flex_image(). XXX Is it possible to
# merge this with _get_flex_image() above? XXX Move to dxtbx Format
# class (or a superclass for multipanel images)?
from math import ceil
from iotbx.detectors import generic_flex_image
from libtbx.test_utils import approx_equal
from scitbx.array_family import flex
from scitbx.matrix import col, rec, sqr
from xfel.cftbx.detector.metrology import get_projection_matrix
assert len(panels) == len(raw_data)
# Determine next multiple of eight of the largest panel size.
for data in raw_data:
if 'data_max_focus' not in locals():
data_max_focus = data.focus()
else:
data_max_focus = (max(data_max_focus[0], data.focus()[0]),
max(data_max_focus[1], data.focus()[1]))
data_padded = (8 * int(ceil(data_max_focus[0] / 8)),
8 * int(ceil(data_max_focus[1] / 8)))
# Assert that all saturated values are equal and not None. While
# dxtbx records a separated trusted_range for each panel,
# generic_flex_image supports only accepts a single common value for
# the saturation.
for panel in panels:
if 'saturation' not in locals():
saturation = panel.get_trusted_range()[1]
else:
assert approx_equal(saturation, panel.get_trusted_range()[1])
assert 'saturation' in locals() and saturation is not None
# Create rawdata and my_flex_image before populating it.
rawdata = flex.double(
flex.grid(len(panels) * data_padded[0], data_padded[1]))
my_flex_image = generic_flex_image(
rawdata=rawdata,
size1_readout=data_max_focus[0],
size2_readout=data_max_focus[1],
brightness=brightness,
saturation=saturation,
show_untrusted=show_untrusted
)
# XXX If a point is contained in two panels simultaneously, it will
# be assigned to the panel defined first. XXX Use a Z-buffer
# instead?
for i in range(len(panels)):
# Determine the pixel size for the panel (in meters), as pixel
# sizes need not be identical.
data = raw_data[i]
panel = panels[i]
pixel_size = (panel.get_pixel_size()[0] * 1e-3,
panel.get_pixel_size()[1] * 1e-3)
if len(panels) == 24 and panels[0].get_image_size() == (2463,195):
#print "DLS I23 12M"
rawdata.matrix_paste_block_in_place(
block=data.as_double(),
i_row=i * data_padded[0],
i_column=0)
# XXX hardcoded panel height and row gap
my_flex_image.add_transformation_and_translation((1,0,0,1), (-i*(195+17),0))
continue
elif len(panels) == 120 and panels[0].get_image_size() == (487,195):
i_row = i // 5
i_col = i % 5
#print i_row, i_col
#print data_padded
#print "DLS I23 12M"
rawdata.matrix_paste_block_in_place(
block=data.as_double(),
i_row=i * data_padded[0],
i_column=0)
# XXX hardcoded panel height and row gap
my_flex_image.add_transformation_and_translation(
(1,0,0,1), (-i_row*(195+17),-i_col*(487+7)))
continue
# Get unit vectors in the fast and slow directions, as well as the
# the locations of the origin and the center of the panel, in
# meters.
fast = col(panel.get_fast_axis())
slow = col(panel.get_slow_axis())
origin = col(panel.get_origin()) * 1e-3
# Viewer will show an orthographic projection of the data onto a plane perpendicular to 0 0 1
projection_normal = col((0.,0.,1.))
beam_to_origin_proj = origin.dot(projection_normal)*projection_normal
projected_origin = origin - beam_to_origin_proj
center = projected_origin \
+ (data.focus()[0] - 1) / 2 * pixel_size[1] * slow \
+ (data.focus()[1] - 1) / 2 * pixel_size[0] * fast
normal = slow.cross(fast).normalize()
# Determine rotational and translational components of the
# homogeneous transformation that maps the readout indices to the
# three-dimensional laboratory frame.
Rf = sqr(( fast(0, 0), fast(1, 0), fast(2, 0),
-slow(0, 0), -slow(1, 0), -slow(2, 0),
normal(0, 0), normal(1, 0), normal(2, 0)))
tf = -Rf * center
Tf = sqr((Rf(0, 0), Rf(0, 1), Rf(0, 2), tf(0, 0),
Rf(1, 0), Rf(1, 1), Rf(1, 2), tf(1, 0),
Rf(2, 0), Rf(2, 1), Rf(2, 2), tf(2, 0),
0, 0, 0, 1))
# E maps picture coordinates onto metric Cartesian coordinates,
# i.e. [row, column, 1 ] -> [x, y, z, 1]. Both frames share the
# same origin, but the first coordinate of the screen coordinate
# system increases downwards, while the second increases towards
# the right. XXX Is this orthographic projection the only one
# that makes any sense?
E = rec(elems=[0, +pixel_size[1], 0,
-pixel_size[0], 0, 0,
0, 0, 0,
0, 0, 1],
n=[4, 3])
# P: [x, y, z, 1] -> [row, column, 1]. Note that data.focus()
# needs to be flipped to give (horizontal, vertical) size,
# i.e. (width, height).
Pf = get_projection_matrix(
pixel_size, (data.focus()[1], data.focus()[0]))[0]
rawdata.matrix_paste_block_in_place(
block=data.as_double(),
i_row=i * data_padded[0],
i_column=0)
# Last row of T is always [0, 0, 0, 1].
T = Pf * Tf * E
R = sqr((T(0, 0), T(0, 1),
T(1, 0), T(1, 1)))
t = col((T(0, 2), T(1, 2)))
#print i,R[0],R[1],R[2],R[3],t[0],t[1]
my_flex_image.add_transformation_and_translation(R, t)
my_flex_image.followup_brightness_scale()
return my_flex_image
