def make_detector():
"""Make a dummy 4 panel detector with not many pixels to ensure test runs
quickly"""
pixel_size_x = 0.1
pixel_size_y = 0.1
npixels_per_panel_x = 50
npixels_per_panel_y = 50
distance = 100
fast = matrix.col((1, 0, 0))
slow = matrix.col((0, -1, 0))
s0u = matrix.col((0, 0, -1))
shift_x = -1.0 * npixels_per_panel_x * pixel_size_x * fast
shift_y = -1.0 * npixels_per_panel_y * pixel_size_y * slow
beam_centre = distance * s0u
orig = beam_centre + shift_x + shift_y
d = Detector()
root = d.hierarchy()
root.set_local_frame(fast.elems, slow.elems, orig.elems)
local_fast = matrix.col((1.0, 0.0, 0.0))
local_slow = matrix.col((0.0, 1.0, 0.0))
span_x = npixels_per_panel_x * pixel_size_x * local_fast
span_y = npixels_per_panel_y * pixel_size_y * local_slow
local_origins = [matrix.col((0, 0, 0)), span_x, span_y, span_x + span_y]
for i, lo in enumerate(local_origins):
p = d.add_panel()
p.set_image_size((npixels_per_panel_x, npixels_per_panel_y))
p.set_pixel_size((pixel_size_x, pixel_size_y))
p.set_local_frame(local_fast.elems, local_slow.elems, lo.elems)
return d
def _detector(self, index=None):
from dxtbx.model.detector import Detector
from scitbx import matrix
import math
wavelength = self.get_beam(index).get_wavelength()
from dxtbx.model import ParallaxCorrectedPxMmStrategy
from cctbx.eltbx import attenuation_coefficient
table = attenuation_coefficient.get_table("Si")
mu = table.mu_at_angstrom(wavelength) / 10.0
px_mm = ParallaxCorrectedPxMmStrategy(mu, self.thickness)
if self.RECONST_MODE:
return self._detector_factory.simple(
sensor="PAD",
distance=self.distance,
beam_centre=(
self.RECONST_SIZE / 2 * self.PIXEL_SIZE,
self.RECONST_SIZE / 2 * self.PIXEL_SIZE,
),
fast_direction="-x",
slow_direction="-y",
pixel_size=(self.PIXEL_SIZE, self.PIXEL_SIZE),
image_size=(self.RECONST_SIZE, self.RECONST_SIZE),
trusted_range=(-1, 65535),
mask=[],
) # TODO: add gaps
detector = Detector()
root = detector.hierarchy()
root.set_frame((-1, 0, 0), (0, 1, 0), (0, 0, -self.distance))
for i in range(8):
angle = math.pi * self.panel_rotations[i] / 180.0
fast = matrix.col((math.cos(angle), math.sin(angle), 0))
slow = matrix.col((-math.sin(angle), math.cos(angle), 0))
normal = fast.cross(slow)
origin = (matrix.col((
-self.panel_origins[i][0],
self.panel_origins[i][1],
self.panel_origins[i][2],
)) / 1000.0)
p = root.add_panel()
p.set_type("SENSOR_PAD")
p.set_name("Panel%d" % i)
p.set_image_size((512, 1024))
p.set_trusted_range((-1, 65535))
p.set_pixel_size((self.PIXEL_SIZE, self.PIXEL_SIZE))
p.set_thickness(self.thickness)
p.set_local_frame(fast.elems, slow.elems, origin.elems)
p.set_px_mm_strategy(px_mm)
p.set_gain(10)
return detector
def _detector(self):
"""Return a working detector instance."""
cbf = self._get_cbf_handle()
d = Detector()
for i in xrange(cbf.count_elements()):
ele_id = cbf.get_element_id(i)
cbf.find_category("diffrn_data_frame")
cbf.find_column("detector_element_id")
cbf.find_row(ele_id)
cbf.find_column("array_id")
array_id = cbf.get_value()
cbf_detector = cbf.construct_detector(i)
p = d.add_panel()
p.set_name(array_id)
# code adapted below from dxtbx.model.detector.DetectorFactory.imgCIF_H
pixel = (
cbf_detector.get_inferred_pixel_size(1),
cbf_detector.get_inferred_pixel_size(2),
)
fast = cbf_detector.get_detector_axes()[0:3]
slow = cbf_detector.get_detector_axes()[3:6]
origin = cbf_detector.get_pixel_coordinates_fs(0, 0)
size = tuple(reversed(cbf.get_image_size(0)))
try:
cbf.find_category("array_intensities")
cbf.find_column("undefined_value")
underload = cbf.get_doublevalue()
overload = cbf.get_overload(0)
trusted_range = (underload, overload)
except Exception:
trusted_range = (0.0, 0.0)
cbf_detector.__swig_destroy__(cbf_detector)
del cbf_detector
p.set_local_frame(fast, slow, origin)
p.set_pixel_size(tuple(map(float, pixel)))
p.set_image_size(size)
p.set_trusted_range(tuple(map(float, trusted_range)))
# p.set_px_mm_strategy(px_mm) FIXME
return d
def _detector(self):
'''Return a working detector instance.'''
cbf = self._get_cbf_handle()
d = Detector()
for i in xrange(cbf.count_elements()):
ele_id = cbf.get_element_id(i)
cbf.find_category("diffrn_data_frame")
cbf.find_column("detector_element_id")
cbf.find_row(ele_id)
cbf.find_column("array_id")
array_id = cbf.get_value()
cbf_detector = cbf.construct_detector(i)
p = d.add_panel()
p.set_name(array_id)
# code adapted below from dxtbx.model.detector.detector_factory.imgCIF_H
pixel = (cbf_detector.get_inferred_pixel_size(1),
cbf_detector.get_inferred_pixel_size(2))
fast = cbf_detector.get_detector_axes()[0:3]
slow = cbf_detector.get_detector_axes()[3:6]
origin = cbf_detector.get_pixel_coordinates_fs(0,0)
size = tuple(reversed(cbf.get_image_size(0)))
try:
cbf.find_category('array_intensities')
cbf.find_column('undefined_value')
underload = cbf.get_doublevalue()
overload = cbf.get_overload(0)
trusted_range = (underload, overload)
except: # intentional
trusted_range = (0.0, 0.0)
cbf_detector.__swig_destroy__(cbf_detector)
del(cbf_detector)
p.set_local_frame(fast, slow, origin)
p.set_pixel_size(tuple(map(float, pixel)))
p.set_image_size(size)
p.set_trusted_range(tuple(map(float, trusted_range)))
#p.set_px_mm_strategy(px_mm) FIXME
return d
def _detector(self):
"""Dummy detector"""
from scitbx import matrix
# 55 mu pixels
pixel_size = 0.055, 0.055
trusted_range = (-1, 65535)
material = "Si"
thickness = 0.3 # assume 300 mu thick. This is actually in the header too
# so could take it from there
# Initialise detector frame - dummy origin to place detector at the header
# distance along the canonical beam direction
distance = (float(
self._cif_header_dictionary["Detector_distance"].split()[0]) *
1000)
fast = matrix.col((1.0, 0.0, 0.0))
slow = matrix.col((0.0, -1.0, 0.0))
cntr = matrix.col((0.0, 0.0, -100.0))
# shifts to go from the centre to the origin
off_x = (self._array_size[0] / 2) * pixel_size[0]
shift_x = -1.0 * fast * off_x
off_y = (self._array_size[1] / 2) * pixel_size[1]
shift_y = -1.0 * slow * off_y
orig = cntr + shift_x + shift_y
d = Detector()
root = d.hierarchy()
root.set_local_frame(fast.elems, slow.elems, orig.elems)
self.coords = {}
panel_idx = 0
# set panel extent in pixel numbers and x, y mm shifts. Origins taken from
# an XDS.INP
pnl_data = []
pnl_data.append({
"xmin": 0,
"ymin": 0,
"xmax": 516,
"ymax": 516,
"xmin_mm": 0,
"ymin_mm": 0
})
pnl_data.append({
"xmin": 516,
"ymin": 0,
"xmax": 1032,
"ymax": 516,
"xmin_mm": (516 + 163.0) * pixel_size[0],
"ymin_mm": -3.6969 * pixel_size[1],
})
pnl_data.append({
"xmin": 0,
"ymin": 516,
"xmax": 516,
"ymax": 1032,
"xmin_mm": -2.5455 * pixel_size[0],
"ymin_mm": (516 + 35.0) * pixel_size[1],
})
pnl_data.append({
"xmin": 516,
"ymin": 516,
"xmax": 1032,
"ymax": 1032,
"xmin_mm": (516 + 165.866) * pixel_size[0],
"ymin_mm": (516 + 32.4545) * pixel_size[1],
})
# redefine fast, slow for the local frame
fast = matrix.col((1.0, 0.0, 0.0))
slow = matrix.col((0.0, 1.0, 0.0))
for ipanel, pd in enumerate(pnl_data):
xmin = pd["xmin"]
xmax = pd["xmax"]
ymin = pd["ymin"]
ymax = pd["ymax"]
xmin_mm = pd["xmin_mm"]
ymin_mm = pd["ymin_mm"]
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(trusted_range)
p.set_pixel_size((pixel_size[0], pixel_size[1]))
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))
p.set_gain(3.0) # exact gain
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 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, 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
# got to here means 60-panel version
from dxtbx.model.detector 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 = 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 run(args):
if '-h' in args or '--help' in args or '-c' in args:
print(help_str)
phil_scope.show(attributes_level=2)
return
user_phil = []
for arg in args:
if os.path.isfile(arg):
user_phil.append(parse("geom_path=%s" % arg))
else:
try:
user_phil.append(parse(arg))
except Exception as e:
raise Sorry("Unrecognized argument: %s" % arg)
params = phil_scope.fetch(sources=user_phil).extract()
if params.distance is None:
raise Usage("Please specify detector distance")
geom = {}
for line in open(params.geom_path):
if len(line.split("=")) != 2: continue
if "rigid_group" in line and not "collection" in line:
geom[line.split("=")[1].strip()] = {}
else:
for key in geom:
if line.startswith("%s/" % key):
geom[key][line.split("=")[0].split("/")
[1].strip()] = line.split("=")[-1].strip()
detector = Detector()
root = detector.hierarchy()
root.set_frame((1, 0, 0), (0, 1, 0), (0, 0, -params.distance))
for i, key in enumerate(sorted(geom)):
fs_x, fs_y = geom[key]['fs'].split(" ")
ss_x, ss_y = geom[key]['ss'].split(" ")
fast = matrix.col(
(-float(fs_x.rstrip('x')), float(fs_y.rstrip('y')), 0.0))
slow = matrix.col(
(-float(ss_x.rstrip('x')), float(ss_y.rstrip('y')), 0.0))
origin = matrix.col(
(-float(geom[key]['corner_x']) * params.pixel_size,
float(geom[key]['corner_y']) * params.pixel_size, 0.0))
# OBS! you need to set the panel to a root before set local frame...
p = root.add_panel()
p.set_name('panel-%s' % key)
p.set_image_size((512, 1024))
p.set_trusted_range((-1, 1000000))
p.set_pixel_size((params.pixel_size, params.pixel_size))
p.set_local_frame(fast.elems, slow.elems, origin.elems)
from dxtbx.model import BeamFactory
wavelength = params.wavelength
beam = BeamFactory.simple(wavelength)
from dxtbx.model import Experiment, ExperimentList
from dxtbx.model.experiment_list import ExperimentListDumper
experiments = ExperimentList()
experiment = Experiment(detector=detector, beam=beam)
experiments.append(experiment)
dump = ExperimentListDumper(experiments)
dump.as_json("geometry.json")
def _detector(self):
'''4 panel detector, 55 micron pixels except for pixels at the outer
edge of each chip, which are 165 microns wide.'''
from scitbx import matrix
# expect 55 mu pixels here, but make it general anyway
pixel_size = tuple([float(self._header_dictionary['PIXEL_SIZE'])] * 2)
image_size = (int(self._header_dictionary['SIZE1']),
int(self._header_dictionary['SIZE2']))
panel_size = tuple([int(e / 2) for e in image_size])
# outer pixels have three times the width
panel_size_mm = (pixel_size[0] * 3 +
(panel_size[0] - 2) * pixel_size[0],
pixel_size[1] * 3 +
(panel_size[1] - 2) * pixel_size[1])
image_size_mm = (panel_size_mm[0] * 2, panel_size_mm[1] * 2)
trusted_range = (-1, 65535)
material = 'Si'
thickness = 0.3 # assume 300 mu thick
# Initialise detector frame
fast = matrix.col((1.0, 0.0, 0.0))
slow = matrix.col((0.0, -1.0, 0.0))
beam_centre = (float(self._header_dictionary['BEAM_CENTER_X']),
float(self._header_dictionary['BEAM_CENTER_Y']))
bx_px, by_px = beam_centre
# the beam centre is in pixels. We want to convert to mm, taking the
# different size of outer pixels into account. Use this local function
# to do that
def px_to_mm(px, px_size_1d, panel_size_1d):
mm = 0
if px > 1: # add first outer pixel
mm += px_size_1d * 3
else: # or fraction of first outer pixel
mm += px * px_size_1d * 3
return mm
if px > panel_size_1d - 1: # add full panel of inner pixels
mm += (panel_size_1d - 2) * px_size_1d
else: # or fraction of inner pixels
mm += (px - 1) * px_size_1d
return mm
if px > panel_size_1d: # add second outer pixel
mm += px_size_1d * 3
else: # or fraction of second outer pixel
mm += (px - (panel_size_1d - 1)) * px_size_1d * 3
return mm
if px > panel_size_1d + 1: # add first outer pixel of second panel
mm += px_size_1d * 3
else: # or fraction of first outer pixel of second panel
mm += (px - panel_size_1d) * px_size_1d * 3
return mm
if px > (2 * panel_size_1d -
1): # add second full panel of inner pixels
mm += (panel_size_1d - 2) * px_size_1d
# plus remaining fraction of the second outer pixel
mm += (px - (2 * panel_size_1d - 1)) * px_size_1d * 3
else: # or fraction of inner pixels of the second panel
mm += (px - panel_size_1d - 1) * px_size_1d
return mm
bx_mm = px_to_mm(bx_px, pixel_size[0], panel_size[0])
by_mm = px_to_mm(by_px, pixel_size[1], panel_size[1])
beam_centre_mm = (bx_mm, by_mm)
# the beam centre is defined from the origin along fast, slow. To determine
# the lab frame origin we place the beam centre down the -z axis
dist = float(self._header_dictionary['DISTANCE'])
cntr = matrix.col((0.0, 0.0, -1 * dist))
orig = cntr - bx_mm * fast - by_mm * slow
d = Detector()
root = d.hierarchy()
root.set_local_frame(fast.elems, slow.elems, orig.elems)
self.coords = {}
panel_idx = 0
# set panel extent in pixel numbers and x, y mm shifts. Note that the
# outer pixels are 0.165 mm in size. These are excluded from the panel
# extents.
pnl_data = []
pnl_data.append({
'xmin': 1,
'ymin': 1,
'xmax': 255,
'ymax': 255,
'xmin_mm': 1 * 0.165,
'ymin_mm': 1 * 0.165
})
pnl_data.append({
'xmin': 257,
'ymin': 1,
'xmax': 511,
'ymax': 255,
'xmin_mm': 3 * 0.165 + (511 - 257) * pixel_size[0],
'ymin_mm': 1 * 0.165
})
pnl_data.append({
'xmin': 1,
'ymin': 257,
'xmax': 255,
'ymax': 511,
'xmin_mm': 1 * 0.165,
'ymin_mm': 3 * 0.165 + (511 - 257) * pixel_size[1]
})
pnl_data.append({
'xmin': 257,
'ymin': 257,
'xmax': 511,
'ymax': 511,
'xmin_mm': 3 * 0.165 + (511 - 257) * pixel_size[0],
'ymin_mm': 3 * 0.165 + (511 - 257) * pixel_size[1]
})
# redefine fast, slow for the local frame
fast = matrix.col((1.0, 0.0, 0.0))
slow = matrix.col((0.0, 1.0, 0.0))
for ipanel, pd in enumerate(pnl_data):
xmin = pd['xmin']
xmax = pd['xmax']
ymin = pd['ymin']
ymax = pd['ymax']
xmin_mm = pd['xmin_mm']
ymin_mm = pd['ymin_mm']
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(trusted_range)
p.set_pixel_size((pixel_size[0], pixel_size[1]))
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 main():
# ---------
SIM = simtbx.nanoBragg.nanoBragg(whole_det, beam)
SIM.Amatrix = sqr(cryst.get_A()).transpose().elems
SIM.default_F = 1
SIM.F000 = 10
SIM.oversample = 2
SIM.Ncells_abc = (5, 5, 5)
SIM.show_params()
SIM.add_nanoBragg_spots()
SIM.raw_pixels *= 2000
wholepix = SIM.raw_pixels.as_numpy_array()
# ---------
# now make 2 smaller detectors
# that equal the whole detector above
# then append them as a dials multi panel
origin0 = (25.6, -12.8, -100)
origin1 = (25.6, 0, -100)
panel_size = (512, 128)
pan0 = {
'fast_axis': (-1.0, 0.0, 0.0),
'gain': 1.0,
'identifier': '',
'image_size': (512, 128),
'mask': [],
'material': '',
'mu': 0.0,
'name': 'panel1',
'origin': origin0,
'pedestal': 0.0,
'pixel_size': (0.1, 0.1),
'px_mm_strategy': {
'type': 'SimplePxMmStrategy'
},
'raw_image_offset': (0, 0),
'slow_axis': (0.0, 1.0, 0.0),
'thickness': 0.0,
'trusted_range': (0.0, 65536.0),
'type': ''
}
pan1 = copy.deepcopy(pan0)
pan1["origin"] = origin1
# combine the panels into a multi-panel detector
parts_det = Detector()
parts_det.add_panel(Panel.from_dict(pan0))
parts_det.add_panel(Panel.from_dict(pan1))
SIMs = {}
for i_pan in range(2):
SIM = simtbx.nanoBragg.nanoBragg(parts_det, beam, panel_id=i_pan)
SIM.Amatrix = sqr(cryst.get_A()).transpose().elems
SIM.default_F = 1
SIM.F000 = 10
SIM.oversample = 2
SIM.Ncells_abc = (5, 5, 5)
SIM.add_nanoBragg_spots()
SIM.raw_pixels *= 2000
SIMs[i_pan] = SIM
# check comparison with whole camera
# ----------------------------------
# combine the two panels:
pix0 = SIMs[0].raw_pixels.as_numpy_array()
pix1 = SIMs[1].raw_pixels.as_numpy_array()
# FIXME: not sure why but in this case the first pixel in memory is off by 0.05
# but the others are all close..
pix1[0, 0] = wholepix[128, 0] # pix1[0,0] = 1.217 and pix[128,0] = 1.264
pix01 = np.vstack((pix0, pix1))
assert (np.allclose(wholepix, pix01))
verbose=1,
mos_spread=mos_spread,
omp=True,
gimmie_Patt=True,
add_water=True,
add_noise=True,
boost=Iboost)
print "TIME: %.4f" % (time.time() - t)
sims = np.sum([simsAB[k] for k in simsAB], axis=0)
np.savez(outputname + "_spotdata.pkl", sims=sims, panel_ids=panel_ids)
print "Done!"
exit()
det2 = Detector()
det2_panel_mapping = {}
for i_pan, pid in enumerate(panel_ids):
det2.add_panel(det[pid])
det2_panel_mapping[i_pan] = pid
refl_data = refls_strong = spot_utils.refls_from_sims(sims,
det2,
beam,
thresh=10)
refl_panel = refl_data['panel'].as_numpy_array()
for i_pan, pid in det2_panel_mapping.items():
sel = refl_panel == i_pan
refl_panel[sel] = pid
params = phil_scope.fetch(sources=user_phil).extract()
if params.distance is None:
raise Usage("Please specify detector distance")
geom = {}
for line in open(params.geom_path):
if len(line.split("=")) != 2: continue
if "rigid_group" in line and not "collection" in line:
geom[line.split("=")[1].strip()] = {}
else:
for key in geom:
if line.startswith("%s/" % key):
geom[key][line.split("=")[0].split("/")
[1].strip()] = line.split("=")[-1].strip()
detector = Detector()
root = detector.hierarchy()
root.set_frame((1, 0, 0), (0, 1, 0), (0, 0, -params.distance))
for i, key in enumerate(sorted(geom)):
fs_x, fs_y = geom[key]['fs'].split(" ")
ss_x, ss_y = geom[key]['ss'].split(" ")
fast = matrix.col(
(-float(fs_x.rstrip('x')), float(fs_y.rstrip('y')), 0.0))
slow = matrix.col(
(-float(ss_x.rstrip('x')), float(ss_y.rstrip('y')), 0.0))
origin = matrix.col(
(-float(geom[key]['corner_x']) * params.pixel_size,
float(geom[key]['corner_y']) * params.pixel_size, 0.0))
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, 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
# got to here means 60-panel version
from dxtbx.model.detector 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 = 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):
"""Dummy detector"""
from scitbx import matrix
# 55 mu pixels
pixel_size = 0.055, 0.055
image_size = 512, 512
trusted_range = (-1, 65535)
material = "Si"
thickness = 0.3 # assume 300 mu thick
# Initialise detector frame - dummy origin to place detector at 100 mm
# along canonical beam direction
fast = matrix.col((1.0, 0.0, 0.0))
slow = matrix.col((0.0, -1.0, 0.0))
cntr = matrix.col((0.0, 0.0, -100.0))
# shifts to go from the centre to the origin - outer pixels are 0.165 mm
off_x = (image_size[0] / 2 - 2) * pixel_size[0]
off_x += 2 * 0.165
shift_x = -1.0 * fast * off_x
off_y = (image_size[1] / 2 - 2) * pixel_size[1]
off_y += 2 * 0.165
shift_y = -1.0 * slow * off_y
orig = cntr + shift_x + shift_y
d = Detector()
root = d.hierarchy()
root.set_local_frame(fast.elems, slow.elems, orig.elems)
self.coords = {}
panel_idx = 0
# set panel extent in pixel numbers and x, y mm shifts. Note that the
# outer pixels are 0.165 mm in size. These are excluded from the panel
# extents.
pnl_data = []
pnl_data.append({
"xmin": 1,
"ymin": 1,
"xmax": 255,
"ymax": 255,
"xmin_mm": 1 * 0.165,
"ymin_mm": 1 * 0.165,
})
pnl_data.append({
"xmin": 257,
"ymin": 1,
"xmax": 511,
"ymax": 255,
"xmin_mm": 3 * 0.165 + (511 - 257) * pixel_size[0],
"ymin_mm": 1 * 0.165,
})
pnl_data.append({
"xmin": 1,
"ymin": 257,
"xmax": 255,
"ymax": 511,
"xmin_mm": 1 * 0.165,
"ymin_mm": 3 * 0.165 + (511 - 257) * pixel_size[1],
})
pnl_data.append({
"xmin": 257,
"ymin": 257,
"xmax": 511,
"ymax": 511,
"xmin_mm": 3 * 0.165 + (511 - 257) * pixel_size[0],
"ymin_mm": 3 * 0.165 + (511 - 257) * pixel_size[1],
})
# redefine fast, slow for the local frame
fast = matrix.col((1.0, 0.0, 0.0))
slow = matrix.col((0.0, 1.0, 0.0))
for ipanel, pd in enumerate(pnl_data):
xmin = pd["xmin"]
xmax = pd["xmax"]
ymin = pd["ymin"]
ymax = pd["ymax"]
xmin_mm = pd["xmin_mm"]
ymin_mm = pd["ymin_mm"]
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(trusted_range)
p.set_pixel_size((pixel_size[0], pixel_size[1]))
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
'pixel_size': (0.1, 0.1),
'px_mm_strategy': {
'type': 'SimplePxMmStrategy'
},
'raw_image_offset': (0, 0),
'slow_axis': (0.0, 1.0, 0.0),
'thickness': 0.0,
'trusted_range': (0.0, 65536.0),
'type': ''
}
pan1 = copy.deepcopy(pan0)
pan1["origin"] = origin1
# combine the panels into a multi-panel detector
parts_det = Detector()
parts_det.add_panel(Panel.from_dict(pan0))
parts_det.add_panel(Panel.from_dict(pan1))
SIMs = {}
for i_pan in range(2):
SIM = simtbx.nanoBragg.nanoBragg(parts_det, beam, panel_id=i_pan)
SIM.Amatrix = sqr(cryst.get_A()).transpose().elems
SIM.default_F = 1
SIM.F000 = 10
SIM.oversample = 2
SIM.Ncells_abc = (5, 5, 5)
SIM.add_nanoBragg_spots()
SIM.raw_pixels *= 2000
SIMs[i_pan] = SIM
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.detector 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 = 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
detector
detector.to_dict
detector.to_dict()
det
det = detector
det
det[0]
#@det.add_panel
#det.rotate_around_origin
dir(det)
det.to_dict()
D = det.to_dict()
D.keys()
D['hierarchy']
from dxtbx.model.detector import Detector
Detector()
D2 = Detector()
for i in [32,34,50]:
D2.add_panel( detector[i])
D2
params = []
for i,C in enumerate(Cabc):
param = {}
param['crystal'] = C
param['ENERGIES'] = en_spec
param['FLUX']= flux
print C.get_unit_cell().parameters()
param['shape'] = 'tophat'
param['Ncells_abc'] = (10,10,10)
param['beam'] = beamA
raise Sorry("Unrecognized argument: %s"%arg)
params = phil_scope.fetch(sources=user_phil).extract()
if params.distance is None:
raise Usage("Please specify detector distance")
geom = {}
for line in open(params.geom_path):
if len(line.split("=")) != 2: continue
if "rigid_group" in line and not "collection" in line:
geom[line.split("=")[1].strip()] = {}
else:
for key in geom:
if line.startswith("%s/"%key):
geom[key][line.split("=")[0].split("/")[1].strip()] = line.split("=")[-1].strip()
detector = Detector()
root = detector.hierarchy()
root.set_frame(
(1, 0, 0),
(0, 1, 0),
(0, 0, - params.distance))
for i, key in enumerate(sorted(geom)):
fs_x, fs_y = geom[key]['fs'].split(" ")
ss_x, ss_y = geom[key]['ss'].split(" ")
fast = matrix.col((-float(fs_x.rstrip('x')),float(fs_y.rstrip('y')), 0.0))
slow = matrix.col((-float(ss_x.rstrip('x')),float(ss_y.rstrip('y')), 0.0))
origin = matrix.col((-float(geom[key]['corner_x']) * params.pixel_size,
float(geom[key]['corner_y']) * params.pixel_size,
0.0))
