def _detector(self):
"""Return a working detector instance, with added mask regions."""
detector = self._detector_factory.imgCIF_H(self._get_cbf_handle(), "PAD")
for f0, f1, s0, s1 in determine_pilatus_mask(detector):
detector[0].add_mask(f0 - 1, s0 - 1, f1, s1)
m = re.search(
r"^#\s*(\S+)\ssensor, thickness\s*([0-9.]+)\s*m\s*$",
self._cif_header,
re.MULTILINE,
)
if m:
# header gives thickness in metres, we store mm
thickness = float(m.group(2)) * 1000
material = m.group(1)
if material == "Silicon":
material = "Si"
for panel in detector:
panel.set_thickness(thickness)
panel.set_material(material)
try:
# a header only CBF file will not have a beam object
beam = self._beam()
except Exception:
beam = None
if beam:
# attenuation coefficient depends on the beam wavelength
wavelength = beam.get_wavelength()
# this will fail for undefined composite materials
table = attenuation_coefficient.get_table(material)
# mu_at_angstrom returns cm^-1
mu = table.mu_at_angstrom(wavelength) / 10.0
for panel in detector:
panel.set_px_mm_strategy(
ParallaxCorrectedPxMmStrategy(mu, thickness)
)
panel.set_mu(mu)
m = re.search(r"^#\s*Detector:\s+(.*?)\s*$", self._cif_header, re.MULTILINE)
if m and m.group(1):
panel.set_identifier(m.group(1).encode())
size = detector[0].get_image_size()
if size == (2463, 2527):
self.vendortype = "Pilatus-6M"
elif size == (1475, 1679):
self.vendortype = "Pilatus-2M"
elif size == (487, 619):
self.vendortype = "Pilatus-300K"
return detector
def _detector(self):
'''Return a model for a simple detector, presuming no one has
one of these on a two-theta stage. Assert that the beam centre is
provided in the Mosflm coordinate frame.'''
detector = FormatCBFMini._detector(self)
for f0, s0, f1, s1 in determine_pilatus_mask(detector):
detector[0].add_mask(f0, s0, f1, s1)
return detector
def _detector(self):
'''Return a model for a simple detector, presuming no one has
one of these on a two-theta stage. Assert that the beam centre is
provided in the Mosflm coordinate frame.'''
distance = float(
self._cif_header_dictionary['Detector_distance'].split()[0])
beam_xy = self._cif_header_dictionary['Beam_xy'].replace(
'(', '').replace(')', '').replace(',', '').split()[:2]
wavelength = float(
self._cif_header_dictionary['Wavelength'].split()[0])
beam_x, beam_y = map(float, beam_xy)
pixel_xy = self._cif_header_dictionary['Pixel_size'].replace(
'm', '').replace('x', '').split()
pixel_x, pixel_y = map(float, pixel_xy)
thickness = float(
self._cif_header_dictionary['Silicon'].split()[2]) * 1000.0
nx = int(
self._cif_header_dictionary['X-Binary-Size-Fastest-Dimension'])
ny = int(
self._cif_header_dictionary['X-Binary-Size-Second-Dimension'])
overload = dxtbx_overload_scale * int(
self._cif_header_dictionary['Count_cutoff'].split()[0])
underload = -1
# take into consideration here the thickness of the sensor also the
# wavelength of the radiation (which we have in the same file...)
from cctbx.eltbx import attenuation_coefficient
table = attenuation_coefficient.get_table("Si")
mu = table.mu_at_angstrom(wavelength) / 10.0
t0 = thickness
detector = self._detector_factory.simple(
'PAD', distance * 1000.0, (beam_x * pixel_x * 1000.0,
beam_y * pixel_y * 1000.0), '+x', '-y',
(1000 * pixel_x, 1000 * pixel_y),
(nx, ny), (underload, overload), [],
ParallaxCorrectedPxMmStrategy(mu, t0))
for f0, s0, f1, s1 in determine_pilatus_mask(detector):
detector[0].add_mask(f0, s0, f1, s1)
detector[0].set_thickness(thickness)
detector[0].set_material('Si')
detector[0].set_mu(mu)
return detector
def _detector(self):
'''Return a working detector instance, with added mask regions.'''
detector = self._detector_factory.imgCIF_H(self._get_cbf_handle(),
'PAD')
for f0, s0, f1, s1 in determine_pilatus_mask(detector):
detector[0].add_mask(f0, s0, f1, s1)
import re
m = re.search('^#\s*(\S+)\ssensor, thickness\s*([0-9.]+)\s*m\s*$', \
self._cif_header, re.MULTILINE)
if m:
# header gives thickness in metres, we store mm
thickness = float(m.group(2)) * 1000
material = m.group(1)
if material == 'Silicon':
material = 'Si'
for panel in detector:
panel.set_thickness(thickness)
panel.set_material(material)
try:
# a header only CBF file will not have a beam object
beam = self._beam()
except Exception:
beam = None
if beam:
# attenuation coefficient depends on the beam wavelength
wavelength = beam.get_wavelength()
from cctbx.eltbx import attenuation_coefficient
from dxtbx.model import ParallaxCorrectedPxMmStrategy
# this will fail for undefined composite materials
table = attenuation_coefficient.get_table(material)
# mu_at_angstrom returns cm^-1
mu = table.mu_at_angstrom(wavelength) / 10.0
for panel in detector:
panel.set_px_mm_strategy(
ParallaxCorrectedPxMmStrategy(mu, thickness))
panel.set_mu(mu)
m = re.search('^#\s*Detector:\s+(.*?)\s*$', \
self._cif_header, re.MULTILINE)
if m and m.group(1):
panel.set_identifier(m.group(1))
return detector
def _detector(self):
'''Return a working detector instance, with added mask regions.'''
detector = self._detector_factory.imgCIF_H(self._get_cbf_handle(),
'PAD')
for f0, s0, f1, s1 in determine_pilatus_mask(detector):
detector[0].add_mask(f0, s0, f1, s1)
import re
m = re.search('^#\s*(\S+)\ssensor, thickness\s*([0-9.]+)\s*m\s*$', \
self._cif_header, re.MULTILINE)
if m:
# header gives thickness in metres, we store mm
thickness = float(m.group(2)) * 1000
material = m.group(1)
if material == 'Silicon':
material = 'Si'
for panel in detector:
panel.set_thickness(thickness)
panel.set_material(material)
try:
# a header only CBF file will not have a beam object
beam = self._beam()
except Exception:
beam = None
if beam:
# attenuation coefficient depends on the beam wavelength
wavelength = beam.get_wavelength()
from cctbx.eltbx import attenuation_coefficient
from dxtbx.model import ParallaxCorrectedPxMmStrategy
# this will fail for undefined composite materials
table = attenuation_coefficient.get_table(material)
# mu_at_angstrom returns cm^-1
mu = table.mu_at_angstrom(wavelength) / 10.0
for panel in detector:
panel.set_px_mm_strategy(ParallaxCorrectedPxMmStrategy(mu, thickness))
panel.set_mu(mu)
m = re.search('^#\s*Detector:\s+(.*?)\s*$', \
self._cif_header, re.MULTILINE)
if m and m.group(1):
panel.set_identifier(m.group(1))
return detector
def _detector(self):
"""Return a model for a simple detector, presuming no one has
one of these on a two-theta stage. Assert that the beam centre is
provided in the Mosflm coordinate frame."""
if not self._multi_panel:
detector = FormatCBFMini._detector(self)
for f0, f1, s0, s1 in determine_pilatus_mask(detector):
detector[0].add_mask(f0 - 1, s0 - 1, f1, s1)
return detector
# got to here means 60-panel version
d = Detector()
distance = float(
self._cif_header_dictionary["Detector_distance"].split()[0])
beam_xy = (self._cif_header_dictionary["Beam_xy"].replace(
"(", "").replace(")", "").replace(",", "").split()[:2])
beam_x, beam_y = map(float, beam_xy)
wavelength = float(
self._cif_header_dictionary["Wavelength"].split()[0])
pixel_xy = (self._cif_header_dictionary["Pixel_size"].replace(
"m", "").replace("x", "").split())
pixel_x, pixel_y = map(float, pixel_xy)
thickness = float(
self._cif_header_dictionary["Silicon"].split()[2]) * 1000.0
nx = int(
self._cif_header_dictionary["X-Binary-Size-Fastest-Dimension"])
ny = int(self._cif_header_dictionary["X-Binary-Size-Second-Dimension"])
overload = int(self._cif_header_dictionary["Count_cutoff"].split()[0])
underload = -1
# take into consideration here the thickness of the sensor also the
# wavelength of the radiation (which we have in the same file...)
table = attenuation_coefficient.get_table("Si")
mu = table.mu_at_angstrom(wavelength) / 10.0
t0 = thickness
# FIXME would also be very nice to be able to take into account the
# misalignment of the individual modules given the calibration...
# single detector or multi-module detector
pixel_x *= 1000.0
pixel_y *= 1000.0
distance *= 1000.0
beam_centre = matrix.col((beam_x * pixel_x, beam_y * pixel_y, 0))
fast = matrix.col((1.0, 0.0, 0.0))
slow = matrix.col((0.0, -1.0, 0.0))
s0 = matrix.col((0, 0, -1))
origin = (distance * s0) - (fast * beam_centre[0]) - (slow *
beam_centre[1])
root = d.hierarchy()
root.set_local_frame(fast.elems, slow.elems, origin.elems)
det = _DetectorDatabase["Pilatus"]
# Edge dead areas not included, only gaps between modules matter
n_fast, remainder = divmod(nx, det.module_size_fast)
assert (n_fast - 1) * det.gap_fast == remainder
n_slow, remainder = divmod(ny, det.module_size_slow)
assert (n_slow - 1) * det.gap_slow == remainder
mx = det.module_size_fast
my = det.module_size_slow
dx = det.gap_fast
dy = det.gap_slow
xmins = [(mx + dx) * i for i in range(n_fast)]
xmaxes = [mx + (mx + dx) * i for i in range(n_fast)]
ymins = [(my + dy) * i for i in range(n_slow)]
ymaxes = [my + (my + dy) * i for i in range(n_slow)]
self.coords = {}
fast = matrix.col((1.0, 0.0, 0.0))
slow = matrix.col((0.0, 1.0, 0.0))
panel_idx = 0
for ymin, ymax in zip(ymins, ymaxes):
for xmin, xmax in zip(xmins, xmaxes):
xmin_mm = xmin * pixel_x
ymin_mm = ymin * pixel_y
origin_panel = fast * xmin_mm + slow * ymin_mm
panel_name = "Panel%d" % panel_idx
panel_idx += 1
p = d.add_panel()
p.set_type("SENSOR_PAD")
p.set_name(panel_name)
p.set_raw_image_offset((xmin, ymin))
p.set_image_size((xmax - xmin, ymax - ymin))
p.set_trusted_range((underload, overload))
p.set_pixel_size((pixel_x, pixel_y))
p.set_thickness(thickness)
p.set_material("Si")
p.set_mu(mu)
p.set_px_mm_strategy(ParallaxCorrectedPxMmStrategy(mu, t0))
p.set_local_frame(fast.elems, slow.elems, origin_panel.elems)
p.set_raw_image_offset((xmin, ymin))
self.coords[panel_name] = (xmin, ymin, xmax, ymax)
return d
def _detector(self):
"""Detector model, allowing for small offsets in the positions of 60
detector modules"""
# Module positional offsets in x, y, in pixels - for the moment ignoring the
# rotational offsets as these are not well defined. To be honest these
# positional offsets are also not well defined as I do not know how they
# should be applied...
x = {
(0, 0): -0.477546,
(0, 1): 0.130578,
(0, 2): 0.045041,
(0, 3): -0.439872,
(0, 4): -0.382077,
(1, 0): 0.087405,
(1, 1): 0.249597,
(1, 2): 0.184265,
(1, 3): 0.158342,
(1, 4): 0.025225,
(2, 0): -0.179892,
(2, 1): -0.010974,
(2, 2): -0.139207,
(2, 3): 0.282851,
(2, 4): -0.442219,
(3, 0): -0.185027,
(3, 1): 0.218601,
(3, 2): 0.092585,
(3, 3): 0.35862,
(3, 4): -0.29161,
(4, 0): 0.145368,
(4, 1): 0.609289,
(4, 2): 0.396265,
(4, 3): 0.41625,
(4, 4): 0.07152,
(5, 0): 0.247142,
(5, 1): 0.046563,
(5, 2): 0.248714,
(5, 3): -0.044628,
(5, 4): -0.391509,
(6, 0): 0.516643,
(6, 1): 0.358453,
(6, 2): 0.069219,
(6, 3): 0.095861,
(6, 4): -0.167403,
(7, 0): -0.381352,
(7, 1): -0.35338,
(7, 2): 0.348656,
(7, 3): 0.024543,
(7, 4): 0.328706,
(8, 0): 0.150886,
(8, 1): 0.244987,
(8, 2): -0.102911,
(8, 3): 0.16633,
(8, 4): 0.386622,
(9, 0): 0.037924,
(9, 1): 0.314392,
(9, 2): 0.238818,
(9, 3): 0.815028,
(9, 4): -0.048818,
(10, 0): -0.670524,
(10, 1): -0.304119,
(10, 2): 0.252284,
(10, 3): -0.05485,
(10, 4): -0.355264,
(11, 0): -0.404947,
(11, 1): -0.020622,
(11, 2): 0.648473,
(11, 3): -0.277175,
(11, 4): -0.711951,
}
y = {
(0, 0): -0.494797,
(0, 1): -0.212976,
(0, 2): 0.085351,
(0, 3): 0.35494,
(0, 4): 0.571189,
(1, 0): -0.421708,
(1, 1): 0.061914,
(1, 2): 0.238996,
(1, 3): 0.146692,
(1, 4): 0.407145,
(2, 0): -0.313212,
(2, 1): -0.225025,
(2, 2): 0.031613,
(2, 3): -0.047839,
(2, 4): 0.42716,
(3, 0): -0.361193,
(3, 1): 0.057663,
(3, 2): 0.022357,
(3, 3): 0.062717,
(3, 4): 0.150611,
(4, 0): 0.035511,
(4, 1): -0.271567,
(4, 2): 0.007761,
(4, 3): -0.124021,
(4, 4): 0.093017,
(5, 0): -0.238897,
(5, 1): -0.179724,
(5, 2): -0.113608,
(5, 3): 0.017841,
(5, 4): -0.012933,
(6, 0): -0.166337,
(6, 1): -0.272922,
(6, 2): -0.194665,
(6, 3): -0.058535,
(6, 4): -0.405404,
(7, 0): -0.318824,
(7, 1): -0.311276,
(7, 2): -0.205223,
(7, 3): -0.292664,
(7, 4): -0.474762,
(8, 0): -0.039504,
(8, 1): -0.239887,
(8, 2): -0.343485,
(8, 3): -0.459429,
(8, 4): -0.426901,
(9, 0): -0.187805,
(9, 1): 0.282727,
(9, 2): -0.601164,
(9, 3): -0.467605,
(9, 4): -0.589271,
(10, 0): 0.028311,
(10, 1): -0.391571,
(10, 2): -0.463112,
(10, 3): -0.358092,
(10, 4): -0.285396,
(11, 0): 0.01863,
(11, 1): -0.380099,
(11, 2): -0.234953,
(11, 3): -0.593992,
(11, 4): -0.801247,
}
distance = float(self._cif_header_dictionary["Detector_distance"].split()[0])
beam_xy = self._cif_header_dictionary["Beam_xy"].replace("(", "").replace(")", "").replace(",", "").split()[:2]
beam_x, beam_y = map(float, beam_xy)
wavelength = float(self._cif_header_dictionary["Wavelength"].split()[0])
pixel_xy = self._cif_header_dictionary["Pixel_size"].replace("m", "").replace("x", "").split()
pixel_x, pixel_y = map(float, pixel_xy)
thickness = float(self._cif_header_dictionary["Silicon"].split()[2]) * 1000.0
nx = int(self._cif_header_dictionary["X-Binary-Size-Fastest-Dimension"])
ny = int(self._cif_header_dictionary["X-Binary-Size-Second-Dimension"])
overload = int(self._cif_header_dictionary["Count_cutoff"].split()[0])
underload = -1
# take into consideration here the thickness of the sensor also the
# wavelength of the radiation (which we have in the same file...)
from cctbx.eltbx import attenuation_coefficient
table = attenuation_coefficient.get_table("Si")
mu = table.mu_at_angstrom(wavelength) / 10.0
t0 = thickness
# FIXME would also be very nice to be able to take into account the
# misalignment of the individual modules given the calibration...
# single detector or multi-module detector
pixel_x *= 1000.0
pixel_y *= 1000.0
distance *= 1000.0
if single_panel:
detector = self._detector_factory.simple(
"PAD",
distance,
(beam_x * pixel_x, beam_y * pixel_y),
"+x",
"-y",
(pixel_x, pixel_y),
(nx, ny),
(underload, overload),
[],
ParallaxCorrectedPxMmStrategy(mu, t0),
)
for f0, s0, f1, s1 in determine_pilatus_mask(detector):
detector[0].add_mask(f0, s0, f1, s1)
detector[0].set_thickness(thickness)
detector[0].set_material("Si")
detector[0].set_mu(table.mu_at_angstrom(wavelength))
return detector
# got to here means 60-panel version
from dxtbx.model.detector import HierarchicalDetector
from scitbx import matrix
d = HierarchicalDetector()
beam_centre = matrix.col((beam_x * pixel_x, beam_y * pixel_y, 0))
fast = matrix.col((1.0, 0.0, 0.0))
slow = matrix.col((0.0, -1.0, 0.0))
s0 = matrix.col((0, 0, -1))
origin = (distance * s0) - (fast * beam_centre[0]) - (slow * beam_centre[1])
root = d.hierarchy()
root.set_local_frame(fast.elems, slow.elems, origin.elems)
xmins = [0, 494, 988, 1482, 1976]
xmaxes = [487, 981, 1475, 1969, 2463]
ymins = [0, 212, 424, 636, 848, 1060, 1272, 1484, 1696, 1908, 2120, 2332]
ymaxes = [195, 407, 619, 831, 1043, 1255, 1467, 1679, 1891, 2103, 2315, 2527]
self.coords = {}
fast = matrix.col((1.0, 0.0, 0.0))
slow = matrix.col((0.0, 1.0, 0.0))
panel_idx = 0
for ymin, ymax in zip(ymins, ymaxes):
for xmin, xmax in zip(xmins, xmaxes):
xmin_mm = xmin * pixel_x
ymin_mm = ymin * pixel_y
origin_panel = fast * xmin_mm + slow * ymin_mm
panel_name = "Panel%d" % panel_idx
panel_idx += 1
p = d.add_panel()
p.set_name(panel_name)
p.set_image_size((xmax - xmin, ymax - ymin))
p.set_trusted_range((underload, overload))
p.set_pixel_size((pixel_x, pixel_y))
p.set_thickness(thickness)
p.set_material("Si")
p.set_local_frame(fast.elems, slow.elems, origin_panel.elems)
self.coords[panel_name] = (xmin, ymin, xmax, ymax)
root.add_panel(p)
return d
def _detector(self):
'''Detector model, allowing for small offsets in the positions of 60
detector modules'''
# Module positional offsets in x, y, in pixels - for the moment ignoring the
# rotational offsets as these are not well defined. To be honest these
# positional offsets are also not well defined as I do not know how they
# should be applied...
x = {
(0, 0): -0.477546,
(0, 1): 0.130578,
(0, 2): 0.045041,
(0, 3): -0.439872,
(0, 4): -0.382077,
(1, 0): 0.087405,
(1, 1): 0.249597,
(1, 2): 0.184265,
(1, 3): 0.158342,
(1, 4): 0.025225,
(2, 0): -0.179892,
(2, 1): -0.010974,
(2, 2): -0.139207,
(2, 3): 0.282851,
(2, 4): -0.442219,
(3, 0): -0.185027,
(3, 1): 0.218601,
(3, 2): 0.092585,
(3, 3): 0.35862,
(3, 4): -0.29161,
(4, 0): 0.145368,
(4, 1): 0.609289,
(4, 2): 0.396265,
(4, 3): 0.41625,
(4, 4): 0.07152,
(5, 0): 0.247142,
(5, 1): 0.046563,
(5, 2): 0.248714,
(5, 3): -0.044628,
(5, 4): -0.391509,
(6, 0): 0.516643,
(6, 1): 0.358453,
(6, 2): 0.069219,
(6, 3): 0.095861,
(6, 4): -0.167403,
(7, 0): -0.381352,
(7, 1): -0.35338,
(7, 2): 0.348656,
(7, 3): 0.024543,
(7, 4): 0.328706,
(8, 0): 0.150886,
(8, 1): 0.244987,
(8, 2): -0.102911,
(8, 3): 0.16633,
(8, 4): 0.386622,
(9, 0): 0.037924,
(9, 1): 0.314392,
(9, 2): 0.238818,
(9, 3): 0.815028,
(9, 4): -0.048818,
(10, 0): -0.670524,
(10, 1): -0.304119,
(10, 2): 0.252284,
(10, 3): -0.05485,
(10, 4): -0.355264,
(11, 0): -0.404947,
(11, 1): -0.020622,
(11, 2): 0.648473,
(11, 3): -0.277175,
(11, 4): -0.711951
}
y = {
(0, 0): -0.494797,
(0, 1): -0.212976,
(0, 2): 0.085351,
(0, 3): 0.35494,
(0, 4): 0.571189,
(1, 0): -0.421708,
(1, 1): 0.061914,
(1, 2): 0.238996,
(1, 3): 0.146692,
(1, 4): 0.407145,
(2, 0): -0.313212,
(2, 1): -0.225025,
(2, 2): 0.031613,
(2, 3): -0.047839,
(2, 4): 0.42716,
(3, 0): -0.361193,
(3, 1): 0.057663,
(3, 2): 0.022357,
(3, 3): 0.062717,
(3, 4): 0.150611,
(4, 0): 0.035511,
(4, 1): -0.271567,
(4, 2): 0.007761,
(4, 3): -0.124021,
(4, 4): 0.093017,
(5, 0): -0.238897,
(5, 1): -0.179724,
(5, 2): -0.113608,
(5, 3): 0.017841,
(5, 4): -0.012933,
(6, 0): -0.166337,
(6, 1): -0.272922,
(6, 2): -0.194665,
(6, 3): -0.058535,
(6, 4): -0.405404,
(7, 0): -0.318824,
(7, 1): -0.311276,
(7, 2): -0.205223,
(7, 3): -0.292664,
(7, 4): -0.474762,
(8, 0): -0.039504,
(8, 1): -0.239887,
(8, 2): -0.343485,
(8, 3): -0.459429,
(8, 4): -0.426901,
(9, 0): -0.187805,
(9, 1): 0.282727,
(9, 2): -0.601164,
(9, 3): -0.467605,
(9, 4): -0.589271,
(10, 0): 0.028311,
(10, 1): -0.391571,
(10, 2): -0.463112,
(10, 3): -0.358092,
(10, 4): -0.285396,
(11, 0): 0.01863,
(11, 1): -0.380099,
(11, 2): -0.234953,
(11, 3): -0.593992,
(11, 4): -0.801247
}
distance = float(
self._cif_header_dictionary['Detector_distance'].split()[0])
beam_xy = self._cif_header_dictionary['Beam_xy'].replace(
'(', '').replace(')', '').replace(',', '').split()[:2]
beam_x, beam_y = map(float, beam_xy)
wavelength = float(
self._cif_header_dictionary['Wavelength'].split()[0])
pixel_xy = self._cif_header_dictionary['Pixel_size'].replace(
'm', '').replace('x', '').split()
pixel_x, pixel_y = map(float, pixel_xy)
thickness = float(
self._cif_header_dictionary['Silicon'].split()[2]) * 1000.0
nx = int(
self._cif_header_dictionary['X-Binary-Size-Fastest-Dimension'])
ny = int(self._cif_header_dictionary['X-Binary-Size-Second-Dimension'])
overload = int(self._cif_header_dictionary['Count_cutoff'].split()[0])
underload = -1
# take into consideration here the thickness of the sensor also the
# wavelength of the radiation (which we have in the same file...)
from cctbx.eltbx import attenuation_coefficient
table = attenuation_coefficient.get_table("Si")
mu = table.mu_at_angstrom(wavelength) / 10.0
t0 = thickness
# FIXME would also be very nice to be able to take into account the
# misalignment of the individual modules given the calibration information...
detector = self._detector_factory.simple(
'PAD', distance * 1000.0,
(beam_x * pixel_x * 1000.0, beam_y * pixel_y * 1000.0), '+x', '-y',
(1000 * pixel_x, 1000 * pixel_y), (nx, ny), (underload, overload),
[], ParallaxCorrectedPxMmStrategy(mu, t0))
for f0, f1, s0, s1 in determine_pilatus_mask(detector):
detector[0].add_mask(f0 - 1, s0 - 1, f1, s1)
return detector
def _detector(self):
'''Detector model, allowing for small offsets in the positions of 60
detector modules'''
# Module positional offsets in x, y, in pixels - for the moment ignoring the
# rotational offsets as these are not well defined. To be honest these
# positional offsets are also not well defined as I do not know how they
# should be applied...
x = {
(0, 0): -0.477546,
(0, 1): 0.130578,
(0, 2): 0.045041,
(0, 3): -0.439872,
(0, 4): -0.382077,
(1, 0): 0.087405,
(1, 1): 0.249597,
(1, 2): 0.184265,
(1, 3): 0.158342,
(1, 4): 0.025225,
(2, 0): -0.179892,
(2, 1): -0.010974,
(2, 2): -0.139207,
(2, 3): 0.282851,
(2, 4): -0.442219,
(3, 0): -0.185027,
(3, 1): 0.218601,
(3, 2): 0.092585,
(3, 3): 0.35862,
(3, 4): -0.29161,
(4, 0): 0.145368,
(4, 1): 0.609289,
(4, 2): 0.396265,
(4, 3): 0.41625,
(4, 4): 0.07152,
(5, 0): 0.247142,
(5, 1): 0.046563,
(5, 2): 0.248714,
(5, 3): -0.044628,
(5, 4): -0.391509,
(6, 0): 0.516643,
(6, 1): 0.358453,
(6, 2): 0.069219,
(6, 3): 0.095861,
(6, 4): -0.167403,
(7, 0): -0.381352,
(7, 1): -0.35338,
(7, 2): 0.348656,
(7, 3): 0.024543,
(7, 4): 0.328706,
(8, 0): 0.150886,
(8, 1): 0.244987,
(8, 2): -0.102911,
(8, 3): 0.16633,
(8, 4): 0.386622,
(9, 0): 0.037924,
(9, 1): 0.314392,
(9, 2): 0.238818,
(9, 3): 0.815028,
(9, 4): -0.048818,
(10, 0): -0.670524,
(10, 1): -0.304119,
(10, 2): 0.252284,
(10, 3): -0.05485,
(10, 4): -0.355264,
(11, 0): -0.404947,
(11, 1): -0.020622,
(11, 2): 0.648473,
(11, 3): -0.277175,
(11, 4): -0.711951
}
y = {
(0, 0): -0.494797,
(0, 1): -0.212976,
(0, 2): 0.085351,
(0, 3): 0.35494,
(0, 4): 0.571189,
(1, 0): -0.421708,
(1, 1): 0.061914,
(1, 2): 0.238996,
(1, 3): 0.146692,
(1, 4): 0.407145,
(2, 0): -0.313212,
(2, 1): -0.225025,
(2, 2): 0.031613,
(2, 3): -0.047839,
(2, 4): 0.42716,
(3, 0): -0.361193,
(3, 1): 0.057663,
(3, 2): 0.022357,
(3, 3): 0.062717,
(3, 4): 0.150611,
(4, 0): 0.035511,
(4, 1): -0.271567,
(4, 2): 0.007761,
(4, 3): -0.124021,
(4, 4): 0.093017,
(5, 0): -0.238897,
(5, 1): -0.179724,
(5, 2): -0.113608,
(5, 3): 0.017841,
(5, 4): -0.012933,
(6, 0): -0.166337,
(6, 1): -0.272922,
(6, 2): -0.194665,
(6, 3): -0.058535,
(6, 4): -0.405404,
(7, 0): -0.318824,
(7, 1): -0.311276,
(7, 2): -0.205223,
(7, 3): -0.292664,
(7, 4): -0.474762,
(8, 0): -0.039504,
(8, 1): -0.239887,
(8, 2): -0.343485,
(8, 3): -0.459429,
(8, 4): -0.426901,
(9, 0): -0.187805,
(9, 1): 0.282727,
(9, 2): -0.601164,
(9, 3): -0.467605,
(9, 4): -0.589271,
(10, 0): 0.028311,
(10, 1): -0.391571,
(10, 2): -0.463112,
(10, 3): -0.358092,
(10, 4): -0.285396,
(11, 0): 0.01863,
(11, 1): -0.380099,
(11, 2): -0.234953,
(11, 3): -0.593992,
(11, 4): -0.801247
}
distance = float(
self._cif_header_dictionary['Detector_distance'].split()[0])
beam_xy = self._cif_header_dictionary['Beam_xy'].replace(
'(', '').replace(')', '').replace(',', '').split()[:2]
beam_x, beam_y = map(float, beam_xy)
wavelength = float(
self._cif_header_dictionary['Wavelength'].split()[0])
pixel_xy = self._cif_header_dictionary['Pixel_size'].replace(
'm', '').replace('x', '').split()
pixel_x, pixel_y = map(float, pixel_xy)
thickness = float(
self._cif_header_dictionary['Silicon'].split()[2]) * 1000.0
nx = int(
self._cif_header_dictionary['X-Binary-Size-Fastest-Dimension'])
ny = int(self._cif_header_dictionary['X-Binary-Size-Second-Dimension'])
overload = int(self._cif_header_dictionary['Count_cutoff'].split()[0])
underload = -1
# take into consideration here the thickness of the sensor also the
# wavelength of the radiation (which we have in the same file...)
from cctbx.eltbx import attenuation_coefficient
table = attenuation_coefficient.get_table("Si")
mu = table.mu_at_angstrom(wavelength) / 10.0
t0 = thickness
# FIXME would also be very nice to be able to take into account the
# misalignment of the individual modules given the calibration...
# single detector or multi-module detector
pixel_x *= 1000.0
pixel_y *= 1000.0
distance *= 1000.0
if not self._multi_panel:
detector = self._detector_factory.simple(
'PAD', distance, (beam_x * pixel_x, beam_y * pixel_y), '+x',
'-y', (pixel_x, pixel_y), (nx, ny), (underload, overload), [],
ParallaxCorrectedPxMmStrategy(mu, t0))
for f0, f1, s0, s1 in determine_pilatus_mask(detector):
detector[0].add_mask(f0 - 1, s0 - 1, f1, s1)
detector[0].set_thickness(thickness)
detector[0].set_material('Si')
detector[0].set_mu(mu)
return detector
# got to here means 60-panel version
from dxtbx.model import Detector
from scitbx import matrix
d = Detector()
beam_centre = matrix.col((beam_x * pixel_x, beam_y * pixel_y, 0))
fast = matrix.col((1.0, 0.0, 0.0))
slow = matrix.col((0.0, -1.0, 0.0))
s0 = matrix.col((0, 0, -1))
origin = (distance * s0) - (fast * beam_centre[0]) - \
(slow * beam_centre[1])
root = d.hierarchy()
root.set_local_frame(fast.elems, slow.elems, origin.elems)
xmins = [0, 494, 988, 1482, 1976]
xmaxes = [487, 981, 1475, 1969, 2463]
ymins = [
0, 212, 424, 636, 848, 1060, 1272, 1484, 1696, 1908, 2120, 2332
]
ymaxes = [
195, 407, 619, 831, 1043, 1255, 1467, 1679, 1891, 2103, 2315, 2527
]
self.coords = {}
fast = matrix.col((1.0, 0.0, 0.0))
slow = matrix.col((0.0, 1.0, 0.0))
panel_idx = 0
for ymin, ymax in zip(ymins, ymaxes):
for xmin, xmax in zip(xmins, xmaxes):
xmin_mm = xmin * pixel_x
ymin_mm = ymin * pixel_y
origin_panel = fast * xmin_mm + slow * ymin_mm
panel_name = "Panel%d" % panel_idx
panel_idx += 1
p = root.add_panel()
p.set_type("SENSOR_PAD")
p.set_px_mm_strategy(ParallaxCorrectedPxMmStrategy(mu, t0))
p.set_name(panel_name)
p.set_image_size((xmax - xmin, ymax - ymin))
p.set_trusted_range((underload, overload))
p.set_pixel_size((pixel_x, pixel_y))
p.set_thickness(thickness)
p.set_material('Si')
p.set_mu(mu)
p.set_local_frame(fast.elems, slow.elems, origin_panel.elems)
p.set_raw_image_offset((xmin, ymin))
self.coords[panel_name] = (xmin, ymin, xmax, ymax)
return d
def _detector(self):
"""Detector model, allowing for small offsets in the positions of 60
detector modules"""
distance = float(
self._cif_header_dictionary["Detector_distance"].split()[0])
beam_xy = (self._cif_header_dictionary["Beam_xy"].replace(
"(", "").replace(")", "").replace(",", "").split()[:2])
beam_x, beam_y = map(float, beam_xy)
wavelength = float(
self._cif_header_dictionary["Wavelength"].split()[0])
pixel_xy = (self._cif_header_dictionary["Pixel_size"].replace(
"m", "").replace("x", "").split())
pixel_x, pixel_y = map(float, pixel_xy)
thickness = float(
self._cif_header_dictionary["Silicon"].split()[2]) * 1000.0
nx = int(
self._cif_header_dictionary["X-Binary-Size-Fastest-Dimension"])
ny = int(self._cif_header_dictionary["X-Binary-Size-Second-Dimension"])
overload = int(self._cif_header_dictionary["Count_cutoff"].split()[0])
underload = -1
# take into consideration here the thickness of the sensor also the
# wavelength of the radiation (which we have in the same file...)
from cctbx.eltbx import attenuation_coefficient
table = attenuation_coefficient.get_table("Si")
mu = table.mu_at_angstrom(wavelength) / 10.0
t0 = thickness
# FIXME would also be very nice to be able to take into account the
# misalignment of the individual modules given the calibration...
# single detector or multi-module detector
pixel_x *= 1000.0
pixel_y *= 1000.0
distance *= 1000.0
if not self._multi_panel:
detector = self._detector_factory.simple(
"PAD",
distance,
(beam_x * pixel_x, beam_y * pixel_y),
"+x",
"-y",
(pixel_x, pixel_y),
(nx, ny),
(underload, overload),
[],
ParallaxCorrectedPxMmStrategy(mu, t0),
)
for f0, f1, s0, s1 in determine_pilatus_mask(detector):
detector[0].add_mask(f0 - 1, s0 - 1, f1, s1)
detector[0].set_thickness(thickness)
detector[0].set_material("Si")
detector[0].set_mu(mu)
return detector
# got to here means 60-panel version
from dxtbx.model import Detector
from scitbx import matrix
d = Detector()
beam_centre = matrix.col((beam_x * pixel_x, beam_y * pixel_y, 0))
fast = matrix.col((1.0, 0.0, 0.0))
slow = matrix.col((0.0, -1.0, 0.0))
s0 = matrix.col((0, 0, -1))
origin = (distance * s0) - (fast * beam_centre[0]) - (slow *
beam_centre[1])
root = d.hierarchy()
root.set_local_frame(fast.elems, slow.elems, origin.elems)
xmins = [0, 494, 988, 1482, 1976]
xmaxes = [487, 981, 1475, 1969, 2463]
ymins = [
0, 212, 424, 636, 848, 1060, 1272, 1484, 1696, 1908, 2120, 2332
]
ymaxes = [
195, 407, 619, 831, 1043, 1255, 1467, 1679, 1891, 2103, 2315, 2527
]
self.coords = {}
fast = matrix.col((1.0, 0.0, 0.0))
slow = matrix.col((0.0, 1.0, 0.0))
panel_idx = 0
for ymin, ymax in zip(ymins, ymaxes):
for xmin, xmax in zip(xmins, xmaxes):
xmin_mm = xmin * pixel_x
ymin_mm = ymin * pixel_y
origin_panel = fast * xmin_mm + slow * ymin_mm
panel_name = "Panel%d" % panel_idx
panel_idx += 1
p = root.add_panel()
p.set_type("SENSOR_PAD")
p.set_px_mm_strategy(ParallaxCorrectedPxMmStrategy(mu, t0))
p.set_name(panel_name)
p.set_image_size((xmax - xmin, ymax - ymin))
p.set_trusted_range((underload, overload))
p.set_pixel_size((pixel_x, pixel_y))
p.set_thickness(thickness)
p.set_material("Si")
p.set_mu(mu)
p.set_local_frame(fast.elems, slow.elems, origin_panel.elems)
p.set_raw_image_offset((xmin, ymin))
self.coords[panel_name] = (xmin, ymin, xmax, ymax)
return d
def _detector(self):
'''Detector model, allowing for small offsets in the positions of 60
detector modules'''
# Module positional offsets in x, y, in pixels - for the moment ignoring the
# rotational offsets as these are not well defined. To be honest these
# positional offsets are also not well defined as I do not know how they
# should be applied...
x = {
(0, 0): -0.477546, (0, 1): 0.130578, (0, 2): 0.045041,
(0, 3): -0.439872, (0, 4): -0.382077, (1, 0): 0.087405,
(1, 1): 0.249597, (1, 2): 0.184265, (1, 3): 0.158342,
(1, 4): 0.025225, (2, 0): -0.179892, (2, 1): -0.010974,
(2, 2): -0.139207, (2, 3): 0.282851, (2, 4): -0.442219,
(3, 0): -0.185027, (3, 1): 0.218601, (3, 2): 0.092585,
(3, 3): 0.35862, (3, 4): -0.29161, (4, 0): 0.145368,
(4, 1): 0.609289, (4, 2): 0.396265, (4, 3): 0.41625,
(4, 4): 0.07152, (5, 0): 0.247142, (5, 1): 0.046563,
(5, 2): 0.248714, (5, 3): -0.044628, (5, 4): -0.391509,
(6, 0): 0.516643, (6, 1): 0.358453, (6, 2): 0.069219,
(6, 3): 0.095861, (6, 4): -0.167403, (7, 0): -0.381352,
(7, 1): -0.35338, (7, 2): 0.348656, (7, 3): 0.024543,
(7, 4): 0.328706, (8, 0): 0.150886, (8, 1): 0.244987,
(8, 2): -0.102911, (8, 3): 0.16633, (8, 4): 0.386622,
(9, 0): 0.037924, (9, 1): 0.314392, (9, 2): 0.238818,
(9, 3): 0.815028, (9, 4): -0.048818, (10, 0): -0.670524,
(10, 1): -0.304119, (10, 2): 0.252284, (10, 3): -0.05485,
(10, 4): -0.355264, (11, 0): -0.404947, (11, 1): -0.020622,
(11, 2): 0.648473, (11, 3): -0.277175, (11, 4): -0.711951
}
y = {
(0, 0): -0.494797, (0, 1): -0.212976, (0, 2): 0.085351,
(0, 3): 0.35494, (0, 4): 0.571189, (1, 0): -0.421708,
(1, 1): 0.061914, (1, 2): 0.238996, (1, 3): 0.146692,
(1, 4): 0.407145, (2, 0): -0.313212, (2, 1): -0.225025,
(2, 2): 0.031613, (2, 3): -0.047839, (2, 4): 0.42716,
(3, 0): -0.361193, (3, 1): 0.057663, (3, 2): 0.022357,
(3, 3): 0.062717, (3, 4): 0.150611, (4, 0): 0.035511,
(4, 1): -0.271567, (4, 2): 0.007761, (4, 3): -0.124021,
(4, 4): 0.093017, (5, 0): -0.238897, (5, 1): -0.179724,
(5, 2): -0.113608, (5, 3): 0.017841, (5, 4): -0.012933,
(6, 0): -0.166337, (6, 1): -0.272922, (6, 2): -0.194665,
(6, 3): -0.058535, (6, 4): -0.405404, (7, 0): -0.318824,
(7, 1): -0.311276, (7, 2): -0.205223, (7, 3): -0.292664,
(7, 4): -0.474762, (8, 0): -0.039504, (8, 1): -0.239887,
(8, 2): -0.343485, (8, 3): -0.459429, (8, 4): -0.426901,
(9, 0): -0.187805, (9, 1): 0.282727, (9, 2): -0.601164,
(9, 3): -0.467605, (9, 4): -0.589271, (10, 0): 0.028311,
(10, 1): -0.391571, (10, 2): -0.463112, (10, 3): -0.358092,
(10, 4): -0.285396, (11, 0): 0.01863, (11, 1): -0.380099,
(11, 2): -0.234953, (11, 3): -0.593992, (11, 4): -0.801247
}
distance = float(
self._cif_header_dictionary['Detector_distance'].split()[0])
beam_xy = self._cif_header_dictionary['Beam_xy'].replace(
'(', '').replace(')', '').replace(',', '').split()[:2]
beam_x, beam_y = map(float, beam_xy)
wavelength = float(
self._cif_header_dictionary['Wavelength'].split()[0])
pixel_xy = self._cif_header_dictionary['Pixel_size'].replace(
'm', '').replace('x', '').split()
pixel_x, pixel_y = map(float, pixel_xy)
thickness = float(
self._cif_header_dictionary['Silicon'].split()[2]) * 1000.0
nx = int(
self._cif_header_dictionary['X-Binary-Size-Fastest-Dimension'])
ny = int(
self._cif_header_dictionary['X-Binary-Size-Second-Dimension'])
overload = int(
self._cif_header_dictionary['Count_cutoff'].split()[0])
underload = -1
# take into consideration here the thickness of the sensor also the
# wavelength of the radiation (which we have in the same file...)
from cctbx.eltbx import attenuation_coefficient
table = attenuation_coefficient.get_table("Si")
mu = table.mu_at_angstrom(wavelength) / 10.0
t0 = thickness
# FIXME would also be very nice to be able to take into account the
# misalignment of the individual modules given the calibration information...
detector = self._detector_factory.simple(
'PAD', distance * 1000.0, (beam_x * pixel_x * 1000.0,
beam_y * pixel_y * 1000.0), '+x', '-y',
(1000 * pixel_x, 1000 * pixel_y),
(nx, ny), (underload, overload), [],
ParallaxCorrectedPxMmStrategy(mu, t0))
for f0, s0, f1, s1 in determine_pilatus_mask(detector):
detector[0].add_mask(f0, s0, f1, s1)
return detector
