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 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_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 _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
def get_flex_image_multipanel(
detector,
image_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 iotbx.detectors import generic_flex_image
from libtbx.test_utils import approx_equal
assert len(detector) == len(image_data), (len(detector), len(image_data))
# Determine next multiple of eight of the largest panel size.
data_max_focus = None
for data in image_data:
if data_max_focus is None:
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(math.ceil(data_max_focus[0] / 8)),
8 * int(math.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.
saturation = None
for panel in detector:
if saturation is None:
saturation = panel.get_trusted_range()[1]
else:
assert approx_equal(saturation, panel.get_trusted_range()[1])
assert saturation is not None
# Create rawdata and flex_image_multipanel before populating it.
rawdata = flex.double(
flex.grid(len(detector) * data_padded[0], data_padded[1]))
flex_image_multipanel = 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 = scitbx.matrix.col((0, 0, 0))
npanels = 0
for panel in detector:
try:
beam_center += scitbx.matrix.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(detector):
# Determine the pixel size for the panel (in meters), as pixel
# sizes need not be identical.
data = image_data[i]
rawdata.matrix_paste_block_in_place(block=data.as_double(),
i_row=i * data_padded[0],
i_column=0)
# If the panel already has a 2d projection then use it
if panel.get_projection_2d():
panel_r, panel_t = panel.get_projection_2d()
else:
if getattr(detector, "projection", "lab") == "image":
# Get axes from precalculated 2D projection.
origin_2d, fast_2d, slow_2d = detector.projection_2d_axes
fast = scitbx.matrix.col(fast_2d[i] + (0, ))
slow = scitbx.matrix.col(slow_2d[i] + (0, ))
origin = scitbx.matrix.col(origin_2d[i] + (0, )) * 1e-3
else:
# 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 = scitbx.matrix.col(panel.get_fast_axis())
slow = scitbx.matrix.col(panel.get_slow_axis())
origin = scitbx.matrix.col(
panel.get_origin()) * 1e-3 - beam_center
panel_r, panel_t = get_panel_projection_2d_from_axes(
panel, data, fast, slow, origin)
flex_image_multipanel.add_transformation_and_translation(
panel_r, panel_t)
flex_image_multipanel.followup_brightness_scale()
return flex_image_multipanel
def _get_flex_image_multipanel(panels,
raw_data,
beam,
brightness=1.0,
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,
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, e: # catch DXTBX_ASSERT for no intersection
pass
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
