def sim_to_data_Qdeviation(spotDataA, spotDataB, refls, detector, beam):
"""
:param spotDataA:
:param spotDataB:
:param refls:
:param detector:
:param beam:
:return:
"""
reflsPP = spot_utils.refls_by_panelname(refls)
data_qvecs = []
for pid in reflsPP:
R = reflsPP[pid]
x, y, z = spot_utils.xyz_from_refl(R)
qvecs = spot_utils.xy_to_q(x, y, detector[pid], beam)
data_qvecs.append(qvecs)
data_qvecs = np.vstack(data_qvecs)
simA_qvecs = q_vecs_from_spotData(spotDataA)
simB_qvecs = q_vecs_from_spotData(spotDataB)
treeA = cKDTree(simA_qvecs)
treeB = cKDTree(simB_qvecs)
distA, idxA = treeA.query(data_qvecs, k=1)
distB, idxB = treeB.query(data_qvecs, k=1)
nnA = treeA.data[idxA]
nnB = treeB.data[idxB]
return distA, nnA, distB, nnB
def integrate(R, badmask, data, gain=28, fit_bg=True):
"""
get the crystal scatter, background scatter, and photon counting noise
for the reflections listed in the table R
:param R: reflection table
:param badmask: mask in numpy format, same shape as data
:param data: data
:param gain: detector gain per photon
:return: 3 arrays, one for signal, background and noise
"""
Nrefl = len(R)
Rpp = spot_utils.refls_by_panelname(
R
) # this is a dictionary whose key (0-63) unlock that panels reflections
allspotmask = {}
bgtilt = {}
for pid in Rpp:
# load the spot mask for all strong spots for this panel
allspotmask[pid] = spot_utils.strong_spot_mask(Rpp[pid], (185, 194))
if fit_bg:
bgtilt[pid], _, _ = tilting_plane(data[pid],
mask=(~allspotmask[pid]) *
badmask[pid],
zscore=8)
signa = np.zeros(Nrefl)
bg = np.zeros(Nrefl)
noise = np.zeros_like(signa)
for i_r, refl in enumerate(R):
pid = refl['panel']
spotmask = refl['shoebox'].mask.as_numpy_array() == 5
f1, f2, s1, s2, _, _ = refl[
'shoebox'].bbox # fast scan and slow scan edges of bounding box
thisspotmask = np.zeros_like(allspotmask[pid])
thisspotmask[s1:s2, f1:f2] = spotmask
#smask = (thisspotmask ^ allspotmask[pid])
signa[i_r] = data[pid][thisspotmask].sum()
if fit_bg:
bg[i_r] = bgtilt[pid][thisspotmask].sum()
# else bg[i_r] is going to remain 0
signa[i_r] = (signa[i_r] - bg[i_r]) / gain
noise[i_r] = np.sqrt(signa[i_r])
return signa, bg, noise
# and the computer will know to preserve that for all beam colors
FLUX = [1e12, 1e12] # fluxes of the beams
waveA = parameters.ENERGY_CONV / ENERGIES[0]
waveB = parameters.ENERGY_CONV / ENERGIES[1]
exp_lst = ExperimentListFactory.from_json_file(exp_name) #, check_format=False)
iset = exp_lst.imagesets()[0]
detector = iset.get_detector(0)
data = utils.open_flex( data_name)
beamA = deepcopy(iset.get_beam())
beamB = deepcopy(iset.get_beam())
beamA.set_wavelength(waveA)
beamB.set_wavelength(waveB)
refls_strong =data["refls_strong"]
crystalAB = data["crystalAB"]
reflsPP = spot_utils.refls_by_panelname(refls_strong)
pids = [i for i in reflsPP if len(reflsPP[i]) > 0] # refine on these panels only
pan_imgs = [iset.get_raw_data(0)[pid].as_numpy_array()
for pid in pids]
#Nper_pid = [ len(reflsPP[i]) for i in pids]
#order_pid = np.argsort( Nper_pid)[::-1]
#pids = np.array(pids)[order_pid][:10] # keep 10 most spot-populated panels
# helper wrapper for U-matrix grid search based refinement
# `scanZ = ...` can also be passed as an argument, to jitter rotation
# about the Z (beam) axis
if jitter:
jitt_out = jitter_refine.jitter_panels(
panel_ids=pids,
drop_meA.append(iA)
drop_meB.append(iB)
if drop_meA:
keep_meA = [i not in drop_meA for i in range(NreflA)]
refl_simA = refl_simA.select(flex.bool(keep_meA))
if drop_meB:
keep_meB = [i not in drop_meB for i in range(NreflB)]
refl_simB = refl_simB.select(flex.bool(keep_meB))
# ---- Done with edge case filters#
# reflections per panel
rpp = spot_utils.refls_by_panelname(refl_data)
rppA = spot_utils.refls_by_panelname(refl_simA)
rppB = spot_utils.refls_by_panelname(refl_simB)
DATA = {"D": [], "IA": [], "IB": [], "h2": [], "k2": [], "l2": [],
"h": [], "k": [], "l": [], "PA": [], "PB": [], "FA": [],
"FB": [], "iA": [], "iB": [], "Nstrong": [], "pid": []}
all_int_me = []
sz = 5 # integration fudge factor to include spots that dont overlap perfectly with predictions
# now set up boundboxes and integrate
for pid in rpp:
R = rpp[pid]
if pid in rppA: # are there A-channel reflections on this panel
inA = True
RA = rppA[pid]
all_dAorB.append(dAorB)
all_qAnotB.append(qAnotB)
all_qBnotA.append(qBnotA)
all_qAandB.append(qAandB)
all_qAorB.append(qAorB)
nA = AnotB.sum()
nB = BnotA.sum()
nAandB = AandB.sum()
nAorB = AorB.sum()
Nidx = sum(AorB)
frac_idx = float(Nidx) / Nref
Rpp = spot_utils.refls_by_panelname(refls_data.select(flex.bool(AorB)))
nC = 0
for pid in Rpp:
r = Rpp[pid]
x, y, _ = spot_utils.xyz_from_refl(r)
C = distance.pdist(zip(x, y))
nC += np.sum((1 < C) & (C < 7))
dist_out3.append([
nC, i_f, d['rmsd_v1'], f, run, shot_idx, frac_idx, Nref, Nidx, nA, nB,
nAandB, nAorB
])
x, y, _ = spot_utils.xyz_from_refl(refls_data)
# flag whether a reflection was in the low gain region
is_low.append([
def jitter_panels(panel_ids,
crystal,
refls,
det,
beam,
FF,
en,
data_imgs,
flux,
ret_best=False,
scanX=None,
scanY=None,
scanZ=None,
mos_dom=1,
mos_spread=0.01,
szx=30,
szy=30,
**kwargs):
"""
Helper function for doing fast refinements by rocking the U-matrix
portion of the crystal A matrix
NOTE, additional kwargs are passed to the PatternFactor instantiator
:param panel_ids: a list of panels ids where refinement
if None we will refine using all panels with 1 or more reflections
:param crystal: dxtbx crystal model
:param refls: reflection table with strong spots
:param det: dxtxb multi panel detector model
:param beam: dxtbx beam model. Note this beam is only used to
specify the beam direction but not the its energy, we will
override the energy.
:param FF: a cctbc miller array instance or a list of them
corresponding to the energies list (same length)
:param en: a float or list of floats specifying energies
:param data_imgs: the data imgs corresponding to the
:param flux: float or list of floats, the flux of each energy
and it should be same length as en
:param ret_best, whether to return the best Amatrix or all the data
from each scan
:param scanX: scan angles for the X-rotation matrix
(same for scanY and Z, note these are lab frame notataions)
:param mos_dom, number os mosaic domains in crystal
:param mos_spread, angular spread of reflections from mosaicity (tangential component)
:return: best A matrix , or else a bunch of data for each panel
that can be used to select best A matrix
"""
if isinstance(en, float) or isinstance(en, int):
en = [en]
FF = [FF]
flux = [flux]
else:
# TODO: assert iterables
assert (len(en) == len(FF) == len(flux))
if scanX is None:
scanX = np.arange(-0.3, 0.3, 0.025)
if scanY is None:
scanY = np.arange(-0.3, 0.3, 0.025)
# leave Z scanning off by default
R = spot_utils.refls_by_panelname(refls)
out = {}
for i_color in range(len(en)):
for i_pid, (pid, dat) in enumerate(izip(panel_ids, data_imgs)):
print i_color + 1, len(en), i_pid + 1, len(panel_ids)
P = sim_utils.PatternFactory(detector=det,
beam=beam,
panel_id=pid,
recenter=True,
**kwargs)
P.adjust_mosaicity(mos_dom, mos_spread)
P.primer(crystal, en[i_color], flux[i_color], FF[i_color])
JR = JitterFactory(crystal, P, R[pid], dat, szx=szx, szy=szy)
if pid not in out: # initialize
out[pid] = JR.jitter_Amat(scanX, scanY, scanZ, plot=False)
else: # just add the overlay from the second color
out_temp = JR.jitter_Amat(scanX, scanY, scanZ, plot=False)
out[pid]['overlaps'] += out_temp['overlaps']
if ret_best:
max_overlay_pos = np.sum([out[i]['overlaps'] for i in out],
axis=0).argmax()
bestA = out[out.keys()[0]]["A_seq"][
max_overlay_pos] # its the same sequence for each panel
return bestA
else:
return out
def main(rank):
device_Id = rank % ngpu
worker_Id = node_id * ngpu + rank
import os
import sys
from copy import deepcopy
import glob
from itertools import izip
from scipy.spatial import distance
import h5py
import scipy.ndimage
from IPython import embed
import numpy as np
import pandas
from scipy.spatial import cKDTree
from simtbx.nanoBragg import shapetype, nanoBragg
from libtbx.phil import parse
from scitbx.matrix import sqr
import dxtbx
from dxtbx.model.experiment_list import ExperimentListFactory
from dxtbx.model.crystal import CrystalFactory
from dials.algorithms.indexing.compare_orientation_matrices \
import rotation_matrix_differences
from dials.array_family import flex
from dials.command_line.find_spots import phil_scope as find_spots_phil_scope
from cxid9114.refine import metrics
from cxid9114 import utils
from cxid9114.geom import geom_utils
from cxid9114.spots import integrate, spot_utils
from cxid9114 import parameters
from cxid9114.sim import sim_utils
from cctbx import miller, sgtbx
from cxid9114 import utils
from cxid9114.bigsim import sim_spectra
from cxid9114.refine.jitter_refine import make_param_list
spot_par = find_spots_phil_scope.fetch(source=parse("")).extract()
spot_par.spotfinder.threshold.dispersion.global_threshold = 40
spot_par.spotfinder.threshold.dispersion.gain = 28
spot_par.spotfinder.threshold.dispersion.kernel_size = [2, 2]
spot_par.spotfinder.threshold.dispersion.sigma_strong = 1
spot_par.spotfinder.threshold.dispersion.sigma_background = 6
spot_par.spotfinder.filter.min_spot_size = 3
spot_par.spotfinder.force_2d = True
odir = args.odir
odirj = os.path.join(odir, "job%d" % worker_Id)
#all_pkl_files = [s for sl in \
# [ files for _,_, files in os.walk(odir)]\
# for s in sl if s.endswith("pkl")]
#print "Found %d pkl files already in %s!" \
# % (len(all_pkl_files), odir)
if not os.path.exists(odirj):
os.makedirs(odirj)
hkl_tol = .15
run = 61
shot_idx = 0
ENERGIES = [parameters.ENERGY_LOW,
parameters.ENERGY_HIGH] # colors of the beams
FF = [10000, None]
cryst_descr = {
'__id__': 'crystal',
'real_space_a': (79, 0, 0),
'real_space_b': (0, 79, 0),
'real_space_c': (0, 0, 38),
'space_group_hall_symbol': '-P 4 2'
}
crystalAB = CrystalFactory.from_dict(cryst_descr)
sfall_main = sim_spectra.load_spectra("../bigsim/test_sfall.h5")
FFdat = [sfall_main[19], sfall_main[110]]
FLUX = [1e11, 1e11] # fluxes of the beams
chanA_flux = 1e11
chanB_flux = 1e11
FLUXdat = [chanA_flux, chanB_flux]
GAIN = 1
waveA = parameters.ENERGY_CONV / ENERGIES[0]
waveB = parameters.ENERGY_CONV / ENERGIES[1]
from cxid9114.bigsim.bigsim_geom import DET, BEAM
detector = DET
print("Rank %d Begin" % worker_Id)
for i_data in range(args.num_trials):
pklname = "%s_rank%d_data%d.pkl" % (ofile, worker_Id, i_data)
pklname = os.path.join(odirj, pklname)
print("<><><><><><><")
print("Job %d: trial %d / %d" %
(worker_Id, i_data + 1, args.num_trials))
print("<><><><><><><")
if (worker_Id == 0 and i_data % smi_stride == 0 and cuda):
print("GPU status")
os.system("nvidia-smi")
print("\n\n")
print("CPU memory usage")
mem_usg = """ps -U dermen --no-headers -o rss | awk '{ sum+=$1} END {print int(sum/1024) "MB consumed by CPU user"}'"""
os.system(mem_usg)
beamA = deepcopy(BEAM)
beamB = deepcopy(BEAM)
beamA.set_wavelength(waveA)
beamB.set_wavelength(waveB)
np.random.seed(args.seed)
crystalAB = CrystalFactory.from_dict(cryst_descr)
randnums = np.random.random(3)
Rrand = random_rotation(1, randnums)
crystalAB.set_U(Rrand.ravel())
#pert = np.random.uniform(0.0001/2/np.pi, 0.0003 / 2. /np.pi)
#print("PERT %f" % pert)
#Rsmall = random_rotation(0.00001, randnums ) #pert)
params_lst = make_param_list(crystalAB,
DET,
BEAM,
1,
rot=0.08,
cell=.0000001,
eq=(1, 1, 0),
min_Ncell=23,
max_Ncell=24,
min_mos_spread=0.02,
max_mos_spread=0.08)
Ctruth = params_lst[0]['crystal']
print Ctruth.get_unit_cell().parameters()
print crystalAB.get_unit_cell().parameters()
init_comp = rotation_matrix_differences((Ctruth, crystalAB))
init_rot = float(init_comp.split("\n")[-2].split()[2])
if use_data_spec:
print "NOT IMPLEMENTED, Using a phony 2col spectrum to simulate the data"
data_fluxes = FLUXdat
data_energies = [parameters.ENERGY_LOW, parameters.ENERGY_HIGH]
data_ff = FFdat
else:
print "Using a phony two color spectrum to simulate the data"
data_fluxes = FLUXdat
data_energies = [parameters.ENERGY_LOW, parameters.ENERGY_HIGH]
data_ff = FFdat
print("Truth crystal Misorientation deviation: %f deg" % init_rot)
if args.truth_cryst:
print "Using truth crystal"
dataCryst = Ctruth
else:
print "Not using truth crystal"
dataCryst = crystalAB
if not make_background:
print "SIMULATING Flat-Fhkl IMAGES"
simsAB = sim_utils.sim_twocolors2(
crystalAB,
detector,
BEAM,
FF, [parameters.ENERGY_LOW, parameters.ENERGY_HIGH],
FLUX,
pids=None,
Gauss=Gauss,
cuda=cuda,
oversample=oversample,
Ncells_abc=Ncells_abc,
mos_dom=mos_doms,
mos_spread=mos_spread,
exposure_s=exposure_s,
beamsize_mm=beamsize_mm,
device_Id=device_Id,
boost=boost)
if make_background:
print("MAKING BACKGROUND")
spec_file = h5py.File("../bigsim/simMe_data_run62.h5", "r")
ave_spec = np.mean(spec_file["hist_spec"][()], axis=0)
data_fluxes = [ave_spec[19], ave_spec[110]]
data_energies = spec_file["energy_bins"][()][[19, 110]]
data_ff = [1, 1] #*len(data_energies)
only_water = True
else:
only_water = False
print "SIULATING DATA IMAGE"
print data_fluxes
simsDataSum = sim_utils.sim_twocolors2(dataCryst,
detector,
BEAM,
data_ff,
data_energies,
data_fluxes,
pids=None,
Gauss=Gauss,
cuda=cuda,
oversample=oversample,
Ncells_abc=Ncells_abc,
accumulate=True,
mos_dom=mos_doms,
mos_spread=mos_spread,
boost=boost,
exposure_s=exposure_s,
beamsize_mm=beamsize_mm,
only_water=only_water,
device_Id=device_Id)
simsDataSum = np.array(simsDataSum)
if make_background:
bg_out = h5py.File(bg_name, "w")
bg_out.create_dataset("bigsim_d9114", data=simsDataSum[0])
print "Background made! Saved to file %s" % bg_name
sys.exit()
if add_background:
print("ADDING BG")
background = h5py.File(bg_name, "r")['bigsim_d9114'][()]
bg_scale = np.sum([39152412349.12075, 32315440627.406036])
bg_scale = np.sum(data_fluxes) / bg_scale
print "%.3e backgorund scale" % bg_scale
print "BG shape", background.shape
simsDataSum[0] += background * bg_scale
if add_noise:
print("ADDING NOISE")
for pidx in range(1):
SIM = nanoBragg(detector=DET, beam=BEAM, panel_id=pidx)
SIM.exposure_s = exposure_s
SIM.beamsize_mm = beamsize_mm
SIM.flux = np.sum(data_fluxes)
SIM.detector_psf_kernel_radius_pixels = 5
SIM.detector_psf_type = shapetype.Unknown # for CSPAD
SIM.detector_psf_fwhm_mm = 0
SIM.quantum_gain = 28
SIM.raw_pixels = flex.double(simsDataSum[pidx].ravel())
SIM.add_noise()
simsDataSum[pidx] = SIM.raw_pixels.as_numpy_array()\
.reshape(simsDataSum[0].shape)
SIM.free_all()
del SIM
if args.write_img:
print "SAVING DATAFILE"
h5name = "%s_rank%d_data%d.h5" % (ofile, worker_Id, i_data)
h5name = os.path.join(odirj, h5name)
fout = h5py.File(h5name, "w")
fout.create_dataset("bigsim_d9114", data=simsDataSum[0])
fout.create_dataset("crystalAB", data=crystalAB.get_A())
fout.create_dataset("dataCryst", data=dataCryst.get_A())
fout.close()
if args.write_sim_img:
print "SAVING DATAFILE"
for i_sim in simsAB:
sim_h5name = "%s_rank%d_sim%d_%d.h5" % (ofile, worker_Id,
i_data, i_sim)
sim_h5name = os.path.join(odirj, sim_h5name)
from IPython import embed
embed()
fout = h5py.File(sim_h5name, "w")
fout.create_dataset("bigsim_d9114", data=simsAB[i_sim][0])
fout.create_dataset("crystalAB", data=crystalAB.get_A())
fout.create_dataset("dataCryst", data=dataCryst.get_A())
fout.close()
print "RELFS FROM SIMS"
refl_simA = spot_utils.refls_from_sims(simsAB[0],
detector,
beamA,
thresh=thresh)
refl_simB = spot_utils.refls_from_sims(simsAB[1],
detector,
beamB,
thresh=thresh)
if use_dials_spotter:
print("DIALS SPOTTING")
El = utils.explist_from_numpyarrays(simsDataSum, DET, beamA)
refl_data = flex.reflection_table.from_observations(El, spot_par)
print("Found %d refls using DIALS spot finder" % len(refl_data))
else:
refl_data = spot_utils.refls_from_sims(simsDataSum, detector, beamA,\
thresh=thresh)
print("Found %d refls using threshold" % len(refl_data))
if len(refl_data) == 0:
print "Rank %d: No reflections found! " % (worker_Id)
continue
residA = metrics.check_indexable2(refl_data, refl_simA, detector,
beamA, crystalAB, hkl_tol)
residB = metrics.check_indexable2(refl_data, refl_simB, detector,
beamB, crystalAB, hkl_tol)
sg96 = sgtbx.space_group(" P 4nw 2abw")
FA = sfall_main[19] # utils.open_flex('SA.pkl') # ground truth values
FB = sfall_main[110] #utils.open_flex('SB.pkl') # ground truth values
HA = tuple([hkl for hkl in FA.indices()])
HB = tuple([hkl for hkl in FB.indices()])
HA_val_map = {h: data for h, data in izip(FA.indices(), FA.data())}
HB_val_map = {h: data for h, data in izip(FB.indices(), FB.data())}
def get_val_at_hkl(hkl, val_map):
poss_equivs = [
i.h() for i in miller.sym_equiv_indices(sg96, hkl).indices()
]
in_map = False
for hkl2 in poss_equivs:
if hkl2 in val_map: # fast lookup
in_map = True
break
if in_map:
return hkl2, val_map[hkl2]
else:
return (None, None, None), -1
filt = 1 #True #`False #True
if filt:
_, all_HiA, _ = spot_utils.refls_to_hkl(refl_simA,
detector,
beamA,
crystal=crystalAB,
returnQ=True)
all_treeA = cKDTree(all_HiA)
nnA = all_treeA.query_ball_point(all_HiA, r=1e-7)
_, all_HiB, _ = spot_utils.refls_to_hkl(refl_simB,
detector,
beamB,
crystal=crystalAB,
returnQ=True)
all_treeB = cKDTree(all_HiB)
nnB = all_treeB.query_ball_point(all_HiB, r=1e-7)
NreflA = len(refl_simA)
NreflB = len(refl_simB)
drop_meA = []
for i, vals in enumerate(nnA):
if i in drop_meA:
continue
if len(vals) > 1:
pids = [refl_simA[v]['panel'] for v in vals]
if len(set(pids)) == 1:
refl_vals = refl_simA.select(
flex.bool(
[i_v in vals for i_v in np.arange(NreflA)]))
x, y, z = spot_utils.xyz_from_refl(refl_vals)
allI = [r['intensity.sum.value'] for r in refl_vals]
allI = sum(allI)
xm = np.mean(x)
ym = np.mean(y)
zm = np.mean(z)
drop_meA.extend(vals[1:])
x1b, x2b, y1b, y2b, z1b, z2b = zip(
*[r['bbox'] for r in refl_vals])
keep_me = vals[0]
# indexing order is important to modify as reference
refl_simA['intensity.sum.value'][keep_me] = allI
refl_simA['xyzobs.px.value'][keep_me] = (xm, ym, zm)
refl_simA['bbox'][keep_me] = (min(x1b), max(x2b),\
min(y1b), max(y2b), min(z1b), max(z2b))
else:
drop_meA.append(vals)
print vals
if drop_meA:
keep_meA = np.array([i not in drop_meA for i in range(NreflA)])
refl_simA = refl_simA.select(flex.bool(keep_meA))
NreflA = len(refl_simA)
drop_meB = []
for i, vals in enumerate(nnB):
if i in drop_meB:
continue
if len(vals) > 1:
pids = [refl_simB[v]['panel'] for v in vals]
if len(set(pids)) == 1:
print vals
# merge_spots(vals)
refl_vals = refl_simB.select(
flex.bool(
[i_v in vals for i_v in np.arange(NreflB)]))
x, y, z = spot_utils.xyz_from_refl(refl_vals)
allI = [r['intensity.sum.value'] for r in refl_vals]
allI = sum(allI)
xm = np.mean(x)
ym = np.mean(y)
zm = np.mean(z)
drop_meB.extend(vals[1:])
x1b, x2b, y1b, y2b, z1b, z2b = zip(
*[r['bbox'] for r in refl_vals])
keep_me = vals[0]
refl_simB['intensity.sum.value'][keep_me] = allI
refl_simB['xyzobs.px.value'][keep_me] = (xm, ym, zm)
refl_simB['bbox'][keep_me] = (min(x1b), max(x2b), min(y1b),\
max(y2b), min(z1b), max(z2b))
else:
drop_meB.append(vals)
print vals
if drop_meB:
keep_meB = [i not in drop_meB for i in range(NreflB)]
refl_simB = refl_simB.select(flex.bool(keep_meB))
NreflB = len(refl_simB)
## remake the trees given the drops
_, all_HiA = spot_utils.refls_to_hkl(refl_simA,
detector,
beamA,
crystal=crystalAB,
returnQ=False)
all_treeA = cKDTree(all_HiA)
_, all_HiB = spot_utils.refls_to_hkl(refl_simB,
detector,
beamB,
crystal=crystalAB,
returnQ=False)
#all_treeB = cKDTree(all_HiB)
## CHECK if same HKL, indexed by both colors
# exists on multiple panels, and if so, delete...
nnAB = all_treeA.query_ball_point(all_HiB, r=1e-7)
drop_meA = []
drop_meB = []
for iB, iA_vals in enumerate(nnAB):
if len(iA_vals) > 0:
assert (len(iA_vals) == 1)
iA = iA_vals[0]
pidA = refl_simA[iA]['panel']
pidB = refl_simB[iB]['panel']
if pidA != pidB:
drop_meA.append(iA)
drop_meB.append(iB)
if drop_meA:
keep_meA = [i not in drop_meA for i in range(NreflA)]
refl_simA = refl_simA.select(flex.bool(keep_meA))
if drop_meB:
keep_meB = [i not in drop_meB for i in range(NreflB)]
refl_simB = refl_simB.select(flex.bool(keep_meB))
# ---- Done with edge case filters#
# reflections per panel
rpp = spot_utils.refls_by_panelname(refl_data)
rppA = spot_utils.refls_by_panelname(refl_simA)
rppB = spot_utils.refls_by_panelname(refl_simB)
DATA = {
"D": [],
"IA": [],
"IB": [],
"h2": [],
"k2": [],
"l2": [],
"h": [],
"k": [],
"l": [],
"PA": [],
"PB": [],
"FA": [],
"FB": [],
"iA": [],
"iB": [],
"Nstrong": [],
"pid": [],
"delta_pix": [],
"deltaX": [],
"deltaY": []
}
all_int_me = []
# now set up boundboxes and integrate
if tilt_plane_integration:
mask = np.ones(simsDataSum.shape).astype(np.bool)
print "Using tilt plane integration!"
else:
print "Not using tilt plane integration, just basic spot thresh integration "
for pid in rpp:
if tilt_plane_integration:
Is, Ibk, noise, pix_per = \
integrate.integrate3(
rpp[pid],
mask[pid],
simsDataSum[pid],
gain=28) #nom_gain)
R = rpp[pid]
if pid in rppA: # are there A-channel reflections on this panel
inA = True
RA = rppA[pid]
xA, yA, _ = spot_utils.xyz_from_refl(RA)
pointsA = np.array(zip(xA, yA))
HA, HiA, QA = spot_utils.refls_to_hkl(RA,
detector,
beamA,
crystal=crystalAB,
returnQ=True)
else:
inA = False
if pid in rppB: # are there B channel reflections on this channel
inB = True
RB = rppB[pid]
xB, yB, _ = spot_utils.xyz_from_refl(RB)
pointsB = np.array(zip(xB, yB))
HB, HiB, QB = spot_utils.refls_to_hkl(RB,
detector,
beamB,
crystal=crystalAB,
returnQ=True)
else:
inB = False
x, y, _ = spot_utils.xyz_from_refl(R)
x = np.array(x)
y = np.array(y)
panX, panY = detector[pid].get_image_size()
mergesA = []
mergesB = []
if inA and inB: # are there both A and B channel reflections ? If so, lets find out which ones have same hkl
# make tree structure for merging the spots
treeA = cKDTree(pointsA)
treeB = cKDTree(pointsB)
QA = geom_utils.res_on_panel(detector[pid], beamA)
QAmag = np.linalg.norm(QA, axis=2) * 2 * np.pi
detdist = detector[pid].get_distance()
pixsize = detector[pid].get_pixel_size()[0]
merge_me = []
for p in pointsA:
iix, iiy = int(p[0]), int(p[1])
q = QAmag[iiy, iix]
radA = detdist * np.tan(
2 * np.arcsin(q * waveA / 4 / np.pi)) / pixsize
radB = detdist * np.tan(
2 * np.arcsin(q * waveB / 4 / np.pi)) / pixsize
rmax = np.abs(radA - radB)
split_spot_pairs = treeB.query_ball_point(x=p, r=rmax + sz)
merge_me.append(split_spot_pairs)
#rmax = geom_utils.twocolor_deltapix(detector[pid], beamA, beamB)
#merge_me = treeA.query_ball_tree(treeB, r=rmax + sz)
for iA, iB in enumerate(merge_me):
if not iB:
continue
iB = iB[0]
# check that the miller indices are the same
if not all([i == j for i, j in zip(HiA[iA], HiB[iB])]):
continue
x1A, x2A, y1A, y2A, _, _ = RA[iA]['bbox'] # shoebox'].bbox
x1B, x2B, y1B, y2B, _, _ = RB[iB]['bbox'] # shoebox'].bbox
xlow = max([0, min((x1A, x1B)) - sz])
xhigh = min([panX, max((x2A, x2B)) + sz])
ylow = max([0, min((y1A, y1B)) - sz])
yhigh = min([panY, max((y2A, y2B)) + sz])
#if iA==79:
# embed()
# integrate me if I am in the bounding box!
int_me = np.where((xlow < x) & (x < xhigh) & (ylow < y)
& (y < yhigh))[0]
if not int_me.size:
continue
mergesA.append(iA)
mergesB.append(iB)
# integrate the spot, this will change depending on data or simulation
totalI = 0
totalCOM = 0
for ref_idx in int_me:
if tilt_plane_integration:
totalI += Is[ref_idx]
else:
totalI += rpp[pid][ref_idx]["intensity.sum.value"]
totalCOM += np.array(
rpp[pid][ref_idx]["xyzobs.px.value"])
totalCOM /= len(int_me)
PA = RA[iA]['intensity.sum.value']
PB = RB[iB]['intensity.sum.value']
# get the hkl structure factor, and the sym equiv hkl
(h, k, l) = HiA[iA] # NOTE: same for A and B channels
(h2, k2, l2), FA = get_val_at_hkl((h, k, l), HA_val_map)
_, FB = get_val_at_hkl(
(h, k, l),
HB_val_map) # NOTE: no need to return h2,k2,l2 twice
#if FB==-1 or FA==-1:
# continue
DATA['h'].append(h)
DATA['k'].append(k)
DATA['l'].append(l)
DATA['h2'].append(h2)
DATA['k2'].append(k2)
DATA['l2'].append(l2)
DATA['D'].append(totalI)
DATA['PA'].append(PA)
DATA['PB'].append(PB)
DATA['FA'].append(FA)
DATA['FB'].append(FB)
DATA['IA'].append(abs(FA)**2)
DATA['IB'].append(abs(FB)**2)
DATA['pid'].append(pid)
DATA["Nstrong"].append(int_me.size)
DATA["iA"].append(iA)
DATA["iB"].append(iB)
all_int_me.append(int_me)
# NOTE: stash the sim-data distance (COM to COM)
posA = RA[iA]['xyzobs.px.value']
posB = RB[iB]['xyzobs.px.value']
simCOM = np.mean([posA, posB], axis=0)
DATA["delta_pix"].append(
distance.euclidean(totalCOM[:2], simCOM[:2]))
DATA["deltaX"].append(totalCOM[0] - simCOM[0])
DATA["deltaY"].append(totalCOM[1] - simCOM[1])
if inA:
for iA, ref in enumerate(RA):
if iA in mergesA:
continue
x1A, x2A, y1A, y2A, _, _ = RA[iA][
'bbox'] # ['shoebox'].bbox
xlow = max((0, x1A - sz))
xhigh = min((panX, x2A + sz))
ylow = max((0, y1A - sz))
yhigh = min((panY, y2A + sz))
int_me = np.where((xlow < x) & (x < xhigh) & (ylow < y)
& (y < yhigh))[0]
if not int_me.size:
continue
totalI = 0
totalCOM = 0
for ref_idx in int_me:
if tilt_plane_integration:
totalI += Is[ref_idx]
else:
totalI += rpp[pid][ref_idx]["intensity.sum.value"]
totalCOM += np.array(
rpp[pid][ref_idx]["xyzobs.px.value"])
totalCOM /= len(int_me)
PA = RA[iA]['intensity.sum.value']
PB = 0 # crucial ;)
# get the hkl structure factor, and the sym equiv hkl
(h, k, l) = HiA[iA] # NOTE: same for A and B channels
(h2, k2, l2), FA = get_val_at_hkl((h, k, l), HA_val_map)
_, FB = get_val_at_hkl(
(h, k, l),
HB_val_map) # NOTE: no need to return h2,k2,l2 twice
#if FA==-1 or FB==-1:
# continue
DATA['h'].append(h)
DATA['k'].append(k)
DATA['l'].append(l)
DATA['h2'].append(h2)
DATA['k2'].append(k2)
DATA['l2'].append(l2)
DATA['D'].append(totalI)
DATA['PA'].append(PA)
DATA['PB'].append(PB)
DATA['FA'].append(FA)
DATA['FB'].append(FB)
DATA['IA'].append(abs(FA)**2)
DATA['IB'].append(abs(FB)**2)
DATA['pid'].append(pid)
DATA["Nstrong"].append(int_me.size)
DATA["iA"].append(iA)
DATA["iB"].append(np.nan)
all_int_me.append(int_me)
# NOTE: stash the sim-data distance (COM to COM)
simCOM = np.array(RA[iA]['xyzobs.px.value'])
DATA["delta_pix"].append(
distance.euclidean(totalCOM[:2], simCOM[:2]))
DATA["deltaX"].append(totalCOM[0] - simCOM[0])
DATA["deltaY"].append(totalCOM[1] - simCOM[1])
if inB:
for iB, ref in enumerate(RB):
if iB in mergesB:
continue
x1B, x2B, y1B, y2B, _, _ = RB[iB]['bbox'] # shoebox'].bbox
xlow = max((0, x1B - sz))
xhigh = min((panX, x2B + sz))
ylow = max((0, y1B - sz))
yhigh = min((panY, y2B + sz))
# subimg = simsDataSum[pid][ylow:yhigh, xlow:xhigh]
# bg = 0
int_me = np.where((xlow < x) & (x < xhigh) & (ylow < y)
& (y < yhigh))[0]
if not int_me.size:
continue
totalI = 0
totalCOM = 0
for ref_idx in int_me:
if tilt_plane_integration:
totalI += Is[ref_idx]
else:
totalI += rpp[pid][ref_idx]["intensity.sum.value"]
totalCOM += np.array(
rpp[pid][ref_idx]["xyzobs.px.value"])
totalCOM /= len(int_me)
PA = 0 # crucial ;)
PB = RB[iB]['intensity.sum.value']
# get the hkl structure factor, and the sym equiv hkl
(h, k, l) = HiB[iB] # NOTE: same for A and B channels
(h2, k2, l2), FB = get_val_at_hkl((h, k, l), HB_val_map)
_, FA = get_val_at_hkl(
(h, k, l),
HA_val_map) # NOTE: no need to return h2,k2,l2 twice
#if FA==-1 or FB==-1:
# continue
DATA['h'].append(h)
DATA['k'].append(k)
DATA['l'].append(l)
DATA['h2'].append(h2)
DATA['k2'].append(k2)
DATA['l2'].append(l2)
DATA['D'].append(totalI)
DATA['PA'].append(PA)
DATA['PB'].append(PB)
DATA['FA'].append(FA)
DATA['FB'].append(FB)
DATA['IA'].append(abs(FA)**2)
DATA['IB'].append(abs(FB)**2)
DATA['pid'].append(pid)
DATA["Nstrong"].append(int_me.size)
DATA["iA"].append(np.nan)
DATA["iB"].append(iB)
all_int_me.append(int_me)
# NOTE: stash the sim-data distance (COM to COM)
simCOM = np.array(RB[iB]['xyzobs.px.value'])
DATA["delta_pix"].append(
distance.euclidean(totalCOM[:2], simCOM[:2]))
DATA["deltaX"].append(totalCOM[0] - simCOM[0])
DATA["deltaY"].append(totalCOM[1] - simCOM[1])
df = pandas.DataFrame(DATA)
df["run"] = run
df["shot_idx"] = shot_idx
df['gain'] = GAIN
if use_data_spec:
print "Setting LA, LB as sums over flux regions A,B"
df['LA'] = data_fluxes[:75].sum()
df['LB'] = data_fluxes[75:].sum()
else:
print "Setting LA LB as data_fluxes"
df['LA'] = data_fluxes[0]
df["LB"] = data_fluxes[1]
df['K'] = FF[0]**2 * FLUX[0]
df["rhs"] = df.gain * (df.IA * df.LA * (df.PA / df.K) + df.IB * df.LB *
(df.PB / df.K))
df["lhs"] = df.D
#df['data_name'] = data_name
df['init_rot'] = init_rot
df.to_pickle(pklname)
print("PLOT")
if args.plot:
import pylab as plt
plt.plot(df.lhs, df.rhs, '.')
plt.show()
print("DonDonee")
simsDataSum = simsAB[0] + simsAB[1]
refl_data = refls_strong = spot_utils.refls_from_sims(simsDataSum,
det,
beamA,
thresh=1e-3)
###########
###########
###########
###########
outputname = os.path.join(outdir, output_basename)
h5_fname = outputname + ".h5py"
reflsPP = spot_utils.refls_by_panelname(refl_data)
pids = reflsPP.keys()
roi_pp = []
counts_pp = []
img_sh = (185, 194)
Malls = {}
for pid in pids:
panel = det[pid]
rois = spot_utils.get_spot_roi(reflsPP[pid],
dxtbx_image_size=panel.get_image_size(),
szx=szx,
szy=szy)
counts = spot_utils.count_roi_overlap(rois, img_size=img_sh)
run = 63
root = "/reg/d/psdm/cxi/cxid9114/scratch/dermen/idx/mad/"
dump_file = "%s/results/run%d/dump_%d_feb8th.pkl" % (root, run, idx)
f = h5py.File(
"%s/videos/run%d/shot%d%s/dump_%d_feb8th_jitt.h5py" %
(root, run, idx, tag, idx), "r")
pids = f['pids'].value
#pan_imgs = f['pan_imgs'].value
# for the simulated images
keys = [k for k in f.keys() if k.startswith("sim_imgs_")]
Ntrials = len(keys)
d = utils.open_flex(dump_file)
R = d['refls_strong']
Rpp = spot_utils.refls_by_panelname(R)
Masks = spot_utils.strong_spot_mask(R, (185, 194), as_composite=False)
min_prob = 0.1 / Ntrials
Ntotal_bad = Ntotal_spots = 0
for pidx, pid in enumerate(pids):
sim = np.array([f[k][pidx] for k in keys])
im = sim.mean(0)
im /= im.max() # make it a probability
M0 = spot_utils.strong_spot_mask(Rpp[pid], (185, 194), as_composite=False)
vals = []
for m in M0:
all_AnotB.append( resAnotB)
all_BnotA.append( resBnotA)
all_AandB.append( resAandB)
nA = np.logical_and( idxA, ~idxB).sum()
nB = np.logical_and( ~idxA, idxB).sum()
nAB = np.logical_and( idxA, idxB).sum()
R = d['refls_strong']
Nref = len(R)
Nidx = sum( good)
frac_idx = float(Nidx) / Nref
Rpp = spot_utils.refls_by_panelname(R.select( flex.bool(good)))
nC = 0
for pid in Rpp:
r = Rpp[pid]
x,y,_ = spot_utils.xyz_from_refl(r)
C = distance.pdist(zip(x,y))
nC += np.sum( (1 < C) & (C < 7))
run_num = int(f.split("/")[1].split("run")[1])
shot_idx = int(f.split("_")[1])
dist_out3.append( [nC, i_f, d['rmsd_v1'], f, run_num, shot_idx,
frac_idx, Nref, Nidx, nA, nB, nAB] )
print i_f, Nf
def main(jid):
import sys
import glob
import os
from copy import deepcopy
import numpy as np
import pandas
from scipy.spatial import cKDTree, distance
from IPython import embed
import h5py
from cctbx import miller, sgtbx
from cxid9114 import utils
from dials.array_family import flex
import dxtbx
from cxid9114 import utils
from cxid9114.geom import geom_utils
from cxid9114.spots import integrate, spot_utils
from cxid9114 import parameters
from cxid9114.sim import sim_utils
from cxid9114.solvers import setup_inputs
from cxid9114.refine import metrics
from LS49.sim.step4_pad import microcrystal
assert (iglob is not None)
spec_f = h5py.File("simMe_data_run62.h5", "r")
spec_data = spec_f["hist_spec"][()]
sg96 = sgtbx.space_group(" P 4nw 2abw")
ofile = "%s_liftoff_betelgeuse%d.%d.pdpkl" % (tag, jid + 1, Njobs)
ofile = os.path.join(odir, ofile)
print(ofile)
file_list = glob.glob(iglob)
Nfiles = len(file_list)
ENERGIES = [parameters.ENERGY_LOW, parameters.ENERGY_HIGH]
FF = [1e4, None]
FLUX = [1e12, 1e12]
beamsize_mm = 0.001
Deff_A = 2200
length_um = 2.2
detector = utils.open_flex("bigsim_detect.pkl")
beam = utils.open_flex("bigsim_beam.pkl")
beamA = deepcopy(beam)
beamB = deepcopy(beam)
waveA = parameters.ENERGY_CONV / ENERGIES[0]
waveB = parameters.ENERGY_CONV / ENERGIES[1]
beamA.set_wavelength(waveA)
beamB.set_wavelength(waveB)
file_list_idx = np.array_split(np.arange(Nfiles), Njobs)
crystal = microcrystal(Deff_A=Deff_A,
length_um=length_um,
beam_diameter_um=beamsize_mm * 1000,
verbose=False)
all_dfs = []
idxstart = file_list_idx[jid][0]
Nfiles = len(file_list_idx[jid])
if max_files is not None:
Nfiles = min(max_files, Nfiles)
for idx in range(idxstart, idxstart + Nfiles):
if jid == 0 and idx % smi_stride == 0:
print("GPU status")
os.system("nvidia-smi")
print("\n\n")
print("CPU memory usage")
mem_usg = """ps -U dermen --no-headers -o rss | awk '{ sum+=$1} END {print int(sum/1024) "MB consumed by CPU user"}'"""
os.system(mem_usg)
data_name = file_list[idx]
data = utils.open_flex(data_name)
shot_idx = int(data["img_f"].split("_")[-1].split(".")[0])
print "Data file %s" % data_name
shot_idx = int(shot_idx)
shot_spectrum = spec_data[shot_idx]
chanA_flux = shot_spectrum[10:25].sum()
chanB_flux = shot_spectrum[100:115].sum()
crystalAB = data["crystalAB"]
print "Doing the basic simulation.."
simsAB = sim_utils.sim_twocolors2(
crystalAB,
detector,
beam,
FF, [parameters.ENERGY_LOW, parameters.ENERGY_HIGH],
FLUX,
Gauss=True,
oversample=0,
Ncells_abc=(25, 25, 25),
mos_dom=1000,
mos_spread=0.015,
cuda=cuda,
device_Id=jid,
beamsize_mm=beamsize_mm,
boost=crystal.domains_per_crystal,
exposure_s=1)
print "Done!"
refl_data = data["refls_strong"]
#
print "\n\n\n#######\nProcessing %d reflections read from the data file \n#####\n\n" % len(
refl_data)
refl_simA = spot_utils.refls_from_sims(simsAB[0],
detector,
beamA,
thresh=thresh)
refl_simB = spot_utils.refls_from_sims(simsAB[1],
detector,
beamB,
thresh=thresh)
residA = metrics.check_indexable2(refl_data, refl_simA, detector,
beamA, crystalAB, hkl_tol)
residB = metrics.check_indexable2(refl_data, refl_simB, detector,
beamB, crystalAB, hkl_tol)
print "Initial metrics suggest that:"
print "\t %d reflections could be indexed by channeL A" % residA[
'indexed'].sum()
print "\t %d reflections could be indexed by channeL B" % residB[
'indexed'].sum()
print "\t NOw we can check for outliers.. "
if plot_overlap:
spot_utils.plot_overlap(refl_simA, refl_simB, refl_data, detector)
d = {
"crystalAB": crystalAB,
"residA": residA,
"residB": residB,
"beamA": beamA,
"beamB": beamB,
"detector": detector,
"refls_simA": refl_simA,
"refls_simB": refl_simB,
"refls_data": refl_data
}
# integrate with tilt plane subtraction
print("LOADING THE FINE IMAGE")
loader = dxtbx.load(data["img_f"])
pan_data = np.array([loader.get_raw_data().as_numpy_array()])
print(data["img_f"])
# make a dummie mask
mask = np.ones_like(pan_data).astype(np.bool)
# before processing we need to check edge cases
print "Checking the simulations edge cases, basically to do with the spot detection of simulations... \n\t such a pain.. "
filt = True
if filt:
_, all_HiA, _ = spot_utils.refls_to_hkl(refl_simA,
detector,
beamA,
crystal=crystalAB,
returnQ=True)
all_treeA = cKDTree(all_HiA)
nnA = all_treeA.query_ball_point(all_HiA, r=1e-7)
_, all_HiB, _ = spot_utils.refls_to_hkl(refl_simB,
detector,
beamB,
crystal=crystalAB,
returnQ=True)
all_treeB = cKDTree(all_HiB)
nnB = all_treeB.query_ball_point(all_HiB, r=1e-7)
NreflA = len(refl_simA)
NreflB = len(refl_simB)
drop_meA = []
for i, vals in enumerate(nnA):
if i in drop_meA:
continue
if len(vals) > 1:
pids = [refl_simA[v]['panel'] for v in vals]
if len(set(pids)) == 1:
refl_vals = refl_simA.select(
flex.bool(
[i_v in vals for i_v in np.arange(NreflA)]))
x, y, z = spot_utils.xyz_from_refl(refl_vals)
allI = [r['intensity.sum.value'] for r in refl_vals]
allI = sum(allI)
xm = np.mean(x)
ym = np.mean(y)
zm = np.mean(z)
drop_meA.extend(vals[1:])
x1b, x2b, y1b, y2b, z1b, z2b = zip(
*[r['bbox'] for r in refl_vals])
keep_me = vals[0]
# indexing order is important to modify as reference
refl_simA['intensity.sum.value'][keep_me] = allI
refl_simA['xyzobs.px.value'][keep_me] = (xm, ym, zm)
refl_simA['bbox'][keep_me] = (min(x1b), max(x2b),\
min(y1b), max(y2b), min(z1b), max(z2b))
else:
drop_meA.append(vals)
print vals
if drop_meA:
keep_meA = np.array([i not in drop_meA for i in range(NreflA)])
refl_simA = refl_simA.select(flex.bool(keep_meA))
NreflA = len(refl_simA)
drop_meB = []
for i, vals in enumerate(nnB):
if i in drop_meB:
continue
if len(vals) > 1:
pids = [refl_simB[v]['panel'] for v in vals]
if len(set(pids)) == 1:
print vals
# merge_spots(vals)
refl_vals = refl_simB.select(
flex.bool(
[i_v in vals for i_v in np.arange(NreflB)]))
x, y, z = spot_utils.xyz_from_refl(refl_vals)
allI = [r['intensity.sum.value'] for r in refl_vals]
allI = sum(allI)
xm = np.mean(x)
ym = np.mean(y)
zm = np.mean(z)
drop_meB.extend(vals[1:])
x1b, x2b, y1b, y2b, z1b, z2b = zip(
*[r['bbox'] for r in refl_vals])
keep_me = vals[0]
refl_simB['intensity.sum.value'][keep_me] = allI
refl_simB['xyzobs.px.value'][keep_me] = (xm, ym, zm)
refl_simB['bbox'][keep_me] = (min(x1b), max(x2b), min(y1b),\
max(y2b), min(z1b), max(z2b))
else:
drop_meB.append(vals)
print vals
if drop_meB:
keep_meB = [i not in drop_meB for i in range(NreflB)]
refl_simB = refl_simB.select(flex.bool(keep_meB))
NreflB = len(refl_simB)
## remake the trees given the drops
_, all_HiA = spot_utils.refls_to_hkl(refl_simA,
detector,
beamA,
crystal=crystalAB,
returnQ=False)
all_treeA = cKDTree(all_HiA)
_, all_HiB = spot_utils.refls_to_hkl(refl_simB,
detector,
beamB,
crystal=crystalAB,
returnQ=False)
#all_treeB = cKDTree(all_HiB)
## CHECK if same HKL, indexed by both colors
# exists on multiple panels, and if so, delete...
nnAB = all_treeA.query_ball_point(all_HiB, r=1e-7)
drop_meA = []
drop_meB = []
for iB, iA_vals in enumerate(nnAB):
if len(iA_vals) > 0:
assert (len(iA_vals) == 1)
iA = iA_vals[0]
pidA = refl_simA[iA]['panel']
pidB = refl_simB[iB]['panel']
if pidA != pidB:
drop_meA.append(iA)
drop_meB.append(iB)
if drop_meA:
keep_meA = [i not in drop_meA for i in range(NreflA)]
refl_simA = refl_simA.select(flex.bool(keep_meA))
if drop_meB:
keep_meB = [i not in drop_meB for i in range(NreflB)]
refl_simB = refl_simB.select(flex.bool(keep_meB))
# ---- Done with edge case filters#
print "<><><><>\nI am doing checking the simulations for edge cases!\n<><><><>"
# reflections per panel
rpp = spot_utils.refls_by_panelname(refl_data)
rppA = spot_utils.refls_by_panelname(refl_simA)
rppB = spot_utils.refls_by_panelname(refl_simB)
DATA = {
"D": [],
"Dnoise": [],
"h": [],
"k": [],
"l": [],
"is_pos": [],
"hAnom": [],
"kAnom": [],
"lAnom": [],
"horig": [],
"korig": [],
"lorig": [],
"PA": [],
"PB": [],
"iA": [],
"iB": [],
"Nstrong": [],
"pid": [],
"delta_pix": []
} # NOTE: added in the delta pix
# for comparing sim and data center of masses
all_int_me = []
sz_fudge = sz = 5 # integration fudge factor to include spots that dont overlap perfectly with predictions
# double define for convenience cause sz is easier to type than sz_fudge
# now set up boundboxes and integrate
for idx_pid, pid in enumerate(rpp):
# NOTE: integrate the spots for this panel
Is, Ibk, noise, pix_per = integrate.integrate3(rpp[pid],
mask[pid],
pan_data[pid],
gain=nom_gain)
print "Processing peaks on CSPAD panel %d (%d / %d)" % (
pid, idx_pid, len(rpp))
R = rpp[pid]
if pid in rppA: # are there A-channel reflections on this panel
inA = True
RA = rppA[pid]
xA, yA, _ = spot_utils.xyz_from_refl(RA)
pointsA = np.array(zip(xA, yA))
HA, HiA, QA = spot_utils.refls_to_hkl(RA,
detector,
beamA,
crystal=crystalAB,
returnQ=True)
else:
inA = False
if pid in rppB: # are there B channel reflections on this channel
inB = True
RB = rppB[pid]
xB, yB, _ = spot_utils.xyz_from_refl(RB)
pointsB = np.array(zip(xB, yB))
HB, HiB, QB = spot_utils.refls_to_hkl(RB,
detector,
beamB,
crystal=crystalAB,
returnQ=True)
else:
inB = False
x, y, _ = spot_utils.xyz_from_refl(R)
x = np.array(x)
y = np.array(y)
panX, panY = detector[pid].get_image_size()
mergesA = []
mergesB = []
if inA and inB: # are there both A and B channel reflections ? If so, lets find out which ones have same hkl
# make tree structure for merging the spots
treeA = cKDTree(pointsA)
treeB = cKDTree(pointsB)
# how far apart should the two color spots be ?
# NOTE: this is the critical step - are the spots within rmax - and if so they are considered indexed..
rmax = geom_utils.twocolor_deltapix(detector[pid], beamA,
beamB)
merge_me = treeA.query_ball_tree(
treeB, r=rmax + sz_fudge) # slap on some fudge
# if pixels points in treeA are within rmax + sz_fugde of
# points in treeB, then these points are assumed to be overlapped
for iA, iB in enumerate(merge_me):
if not iB:
continue
iB = iB[0]
# check that the miller indices are the same
if not all([i == j for i, j in zip(HiA[iA], HiB[iB])]):
continue
x1A, x2A, y1A, y2A, _, _ = RA[iA]['bbox'] # shoebox'].bbox
x1B, x2B, y1B, y2B, _, _ = RB[iB]['bbox'] # shoebox'].bbox
xlow = max([0, min((x1A, x1B)) - sz])
xhigh = min([panX, max((x2A, x2B)) + sz])
ylow = max([0, min((y1A, y1B)) - sz])
yhigh = min([panY, max((y2A, y2B)) + sz])
# integrate me if I am in the bounding box!
int_me = np.where((xlow < x) & (x < xhigh) & (ylow < y)
& (y < yhigh))[0]
if not int_me.size:
continue
mergesA.append(iA)
mergesB.append(iB)
# integrate the spot, this will change depending on data or simulation
#NOTE : adding in the data-spot center of mass here as well
totalCOM = np.zeros(3) # NOTE: x,y,z
totalI = 0
totalNoise = 0
for ref_idx in int_me:
# TODO implement the spot intensity version here
# which fits the background plane!
totalI += Is[
ref_idx] #rpp[pid][ref_idx]["intensity.sum.value"]
totalNoise += noise[ref_idx]**2
totalCOM += np.array(
rpp[pid][ref_idx]["xyzobs.px.value"])
totalCOM /= len(int_me)
totalNoise = np.sqrt(totalNoise)
PA = RA[iA]['intensity.sum.value']
PB = RB[iB]['intensity.sum.value']
# NOTE: added the simulated spot(s) center of mass
posA = RA[iA]['xyzobs.px.value']
posB = RB[iB]['xyzobs.px.value']
simCOM = np.mean([posA, posB], axis=0)
# get the hkl structure factor, and the sym equiv hkl
(horig, korig,
lorig) = HiA[iA] # NOTE: same for A and B channels
h, k, l = setup_inputs.single_to_asu((horig, korig, lorig),
ano=False)
hAnom, kAnom, lAnom = setup_inputs.single_to_asu(
(horig, korig, lorig), ano=True)
if h == hAnom and k == kAnom and l == lAnom:
is_pos = True
else:
is_pos = False
DATA['is_pos'].append(is_pos)
DATA['horig'].append(horig)
DATA['korig'].append(korig)
DATA['lorig'].append(lorig)
DATA['h'].append(h)
DATA['k'].append(k)
DATA['l'].append(l)
DATA['hAnom'].append(hAnom)
DATA['kAnom'].append(kAnom)
DATA['lAnom'].append(lAnom)
DATA['D'].append(totalI)
DATA['Dnoise'].append(totalNoise)
DATA['PA'].append(PA)
DATA['PB'].append(PB)
DATA['pid'].append(pid)
DATA["Nstrong"].append(int_me.size)
DATA["iA"].append(iA)
DATA["iB"].append(iB)
all_int_me.append(int_me)
# NOTE: stash the sim-data distance (COM to COM)
DATA["delta_pix"].append(
distance.euclidean(totalCOM[:2], simCOM[:2]))
# this spot was both colors, overlapping
# find center of mass of all spots inside the integration box
# and find its distance to the center of mass of the simulation spots
if inA:
for iA, ref in enumerate(RA):
if iA in mergesA:
# this sim spot was already treated above
continue
x1A, x2A, y1A, y2A, _, _ = RA[iA][
'bbox'] # ['shoebox'].bbox
xlow = max((0, x1A - sz))
xhigh = min((panX, x2A + sz))
ylow = max((0, y1A - sz))
yhigh = min((panY, y2A + sz))
int_me = np.where((xlow < x) & (x < xhigh) & (ylow < y)
& (y < yhigh))[0]
if not int_me.size:
continue
# NOTE: added in the total sim calc
totalCOM = np.zeros(3)
totalI = 0
totalNoise = 0
for ref_idx in int_me:
# TODO implement the spot intensity version here
# which fits the background plane!
totalI += Is[
ref_idx] #rpp[pid][ref_idx]["intensity.sum.value"]
totalNoise += noise[ref_idx]**2
totalCOM += np.array(
rpp[pid][ref_idx]["xyzobs.px.value"])
totalCOM /= len(int_me)
totalNoise = np.sqrt(totalNoise)
PA = RA[iA]['intensity.sum.value']
PB = 0 # crucial ;)
# NOTE: added the simulated spot center of mass, for spotA
simCOM = np.array(RA[iA]['xyzobs.px.value'])
# get the hkl structure factor, and the sym equiv hkl
(horig, korig, lorig) = HiA[iA]
h, k, l = setup_inputs.single_to_asu((horig, korig, lorig),
ano=False)
hAnom, kAnom, lAnom = setup_inputs.single_to_asu(
(horig, korig, lorig), ano=True)
if h == hAnom and k == kAnom and l == lAnom:
is_pos = True
else:
is_pos = False
DATA['is_pos'].append(is_pos)
DATA['horig'].append(horig)
DATA['korig'].append(korig)
DATA['lorig'].append(lorig)
DATA['h'].append(h)
DATA['k'].append(k)
DATA['l'].append(l)
DATA['hAnom'].append(hAnom)
DATA['kAnom'].append(kAnom)
DATA['lAnom'].append(lAnom)
DATA['D'].append(totalI)
DATA['Dnoise'].append(totalNoise)
DATA['PA'].append(PA)
DATA['PB'].append(PB)
DATA['pid'].append(pid)
DATA["Nstrong"].append(int_me.size)
DATA["iA"].append(iA)
DATA["iB"].append(np.nan)
all_int_me.append(int_me)
# NOTE: stash the sim-data distance (COM to COM)
DATA["delta_pix"].append(
distance.euclidean(totalCOM[:2], simCOM[:2]))
if inB:
for iB, ref in enumerate(RB):
if iB in mergesB:
continue
x1B, x2B, y1B, y2B, _, _ = RB[iB]['bbox'] # shoebox'].bbox
xlow = max((0, x1B - sz))
xhigh = min((panX, x2B + sz))
ylow = max((0, y1B - sz))
yhigh = min((panY, y2B + sz))
# subimg = simsDataSum[pid][ylow:yhigh, xlow:xhigh]
# bg = 0
int_me = np.where((xlow < x) & (x < xhigh) & (ylow < y)
& (y < yhigh))[0]
if not int_me.size:
continue
# NOTE: added in the total COM calc
totalCOM = np.zeros(3)
totalI = 0
totalNoise = 0
for ref_idx in int_me:
# TODO implement the spot intensity version here
# which fits the background plane!
totalI += Is[
ref_idx] #rpp[pid][ref_idx]["intensity.sum.value"]
totalNoise += noise[ref_idx]**2
totalCOM += np.array(
rpp[pid][ref_idx]["xyzobs.px.value"])
totalCOM /= len(int_me)
totalNoise = np.sqrt(totalNoise)
PA = 0 # crucial ;)
PB = RB[iB]['intensity.sum.value']
# NOTE: added the simulated spot center of mass, for spotB only
simCOM = np.array(RB[iB]['xyzobs.px.value'])
# get the hkl structure factor, and the sym equiv hkl
(horig, korig, lorig) = HiB[iB]
h, k, l = setup_inputs.single_to_asu((horig, korig, lorig),
ano=False)
hAnom, kAnom, lAnom = setup_inputs.single_to_asu(
(horig, korig, lorig), ano=True)
if h == hAnom and k == kAnom and l == lAnom:
is_pos = True
else:
is_pos = False
DATA['is_pos'].append(is_pos)
DATA['horig'].append(horig)
DATA['korig'].append(korig)
DATA['lorig'].append(lorig)
DATA['h'].append(h)
DATA['k'].append(k)
DATA['l'].append(l)
DATA['hAnom'].append(hAnom)
DATA['kAnom'].append(kAnom)
DATA['lAnom'].append(lAnom)
DATA['D'].append(totalI)
DATA['Dnoise'].append(totalNoise)
DATA['PA'].append(PA)
DATA['PB'].append(PB)
DATA['pid'].append(pid)
DATA["Nstrong"].append(int_me.size)
DATA["iA"].append(np.nan)
DATA["iB"].append(iB)
all_int_me.append(int_me)
# NOTE: stash the sim-data distance (COM to COM)
DATA["delta_pix"].append(
distance.euclidean(totalCOM[:2], simCOM[:2]))
df = pandas.DataFrame(DATA)
df["run"] = run
df["shot_idx"] = shot_idx
df['LA'] = chanA_flux
df["LB"] = chanB_flux
df['K'] = FF[0]**2 * FLUX[0]
df['nominal_gain'] = nom_gain
all_dfs.append(df)
print("Saved %d partial structure factor measurements in file %s" %
(len(df), ofile))
DF = pandas.concat(all_dfs)
DF.to_pickle(ofile)
def integrate2(R, badmask, data, gain=28, fit_bg=True, zscore=8, sz=8):
"""
get the crystal scatter, background scatter, and photon counting noise
for the reflections listed in the table R
:param R: reflection table
:param badmask: mask in numpy format, same shape as data
:param data: data
:param gain: detector gain per photon
:return: 3 arrays, one for signal, background and noise
"""
from dials.algorithms.shoebox import MaskCode
fg_code = MaskCode.Foreground.real
Nrefl = len(R)
fs_dim = 194
ss_dim = 185
Rpp = spot_utils.refls_by_panelname(
R
) # this is a dictionary whose key (0-63) unlock that panels reflections
allspotmask = {}
for pid in Rpp:
# load the spot mask for all strong spots for this panel
allspotmask[pid] = spot_utils.strong_spot_mask(Rpp[pid],
(ss_dim, fs_dim))
signa = np.zeros(Nrefl)
bg = np.zeros(Nrefl)
noise = np.zeros_like(signa)
pix_per = np.zeros(Nrefl, int)
for i_r, refl in enumerate(R):
pid = refl['panel']
spotmask = refl['shoebox'].mask.as_numpy_array() & fg_code == fg_code
f1, f2, s1, s2, _, _ = refl[
'shoebox'].bbox # fast scan and slow scan edges of bounding box
icent, jcent, _ = refl['xyzobs.px.value']
thisspotmask = np.zeros_like(allspotmask[pid])
thisspotmask[s1:s2, f1:f2] = spotmask
i1 = int(max(icent - .5 - sz, 0))
i2 = int(min(icent - .5 + sz, fs_dim))
j1 = int(max(jcent - .5 - sz, 0))
j2 = int(min(jcent - .5 + sz, ss_dim))
sub_data = data[pid][j1:j2, i1:i2]
sub_mask = ((~allspotmask[pid]) * badmask[pid])[j1:j2, i1:i2]
sub_thisspotmask = thisspotmask[j1:j2, i1:i2]
Is = sub_data[sub_thisspotmask].sum()
if fit_bg:
tilt, bgmask, coeff = tilting_plane(sub_data,
mask=sub_mask,
zscore=zscore)
bg_fit_mask = np.logical_and(~bgmask, sub_mask)
m = sub_thisspotmask.sum()
n = bg_fit_mask.sum()
m2n = float(m) / float(
n
) # ratio of number of background to number of strong spot pixels
# modifuf Is according to background plane fit
Is = Is - tilt[sub_thisspotmask].sum()
# store background pix according to Leslie 99
Ibg = m2n * sub_data[bg_fit_mask].sum()
else:
Ibg = 0
signa[i_r] = Is # signal in the spot
bg[i_r] = Ibg # background around the spot
noise[i_r] = (Is + Ibg + m2n * Ibg) / gain
pix_per[i_r] = thisspotmask.sum()
return signa, bg, noise, pix_per
