simsAB = sim_utils.sim_twocolors2( crystalAB, detector, iset.get_beam(0), FF, [parameters.ENERGY_LOW, parameters.ENERGY_HIGH], FLUX, pids=None, Gauss=False, oversample=8, Ncells_abc=(22, 22, 22), mos_dom=1, mos_spread=0.0) simsData = sim_utils.sim_twocolors2( crystalAB, detector, iset.get_beam(0), FFdat, [parameters.ENERGY_LOW, parameters.ENERGY_HIGH], FLUXdat, pids=None, Gauss=False, oversample=8, Ncells_abc=(22, 22, 22), mos_dom=1, mos_spread=0.) simsDataSum = GAIN * (np.array(simsData[0]) + np.array(simsData[1])) refl_simA = spot_utils.refls_from_sims(simsAB[0], detector, beamA) refl_simB = spot_utils.refls_from_sims(simsAB[1], detector, beamB) # This only uses the beam to instatiate an imageset / datablock # but otherwise the return value (refl_data) is indepent of the # beam object passed refl_data = spot_utils.refls_from_sims(simsDataSum, detector, beamA) 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 = utils.open_flex('SA.pkl') FB = utils.open_flex('SB.pkl') HA = tuple([hkl for hkl in FA.indices()])
max_pos] # just grab the first A_seq cause same sequence is tested on all panels optCrystal = deepcopy(crystalAB) optCrystal.set_A(optA) else: optCrystal = crystalAB overlap = None simsAB = sim_utils.sim_twocolors2( optCrystal, detector, iset.get_beam(0), FF, [parameters.ENERGY_LOW, parameters.ENERGY_HIGH], FLUX, pids=None, Gauss=False, oversample=2, Ncells_abc=(7, 7, 7), mos_dom=20, mos_spread=0.0) refl_simA = spot_utils.refls_from_sims(simsAB[0], detector, beamA) refl_simB = spot_utils.refls_from_sims(simsAB[1], detector, beamB) residA = metrics.check_indexable( refls_strong, refl_simA, detector, beamA, optCrystal, hkl_tol) residB = metrics.check_indexable( refls_strong, refl_simB, detector, beamB, optCrystal, hkl_tol) simsAB_old = sim_utils.sim_twocolors2( crystalAB, detector, iset.get_beam(0), FF, [parameters.ENERGY_LOW, parameters.ENERGY_HIGH], FLUX, pids=None, Gauss=False, oversample=2, Ncells_abc=(7, 7, 7), mos_dom=20, mos_spread=0.0) refl_simA_old = spot_utils.refls_from_sims(simsAB_old[0], detector, beamA) refl_simB_old = spot_utils.refls_from_sims(simsAB_old[1], detector, beamB) residA_old = metrics.check_indexable(
FF, [parameters.ENERGY_LOW, parameters.ENERGY_HIGH], FLUX, pids=None, Gauss=False, oversample=0, Ncells_abc=(22, 22, 22), mos_dom=1, mos_spread=0.0) refl_data = data["refls_strong"] print "\n\n\n#######\nProcessing %d reflections deemed strong\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) # NOTE load some dummie structure factor data # these are calculated using the PDB 4bs7.pdb, Lyso Yterbium derivitive # solved at a synchrotron, room temp FA = utils.open_flex(
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 refl_data['panel'] = flex.size_t(refl_panel) ########### ########### outputname = os.path.join(outdir, output_basename) #reflsPP = spot_utils.refls_by_panelname(refl_data)
energies=ENERGIES, fluxes=FLUX, pids=None, profile='tophat', oversample=0, Ncells_abc=(10, 10, 10), mos_dom=1, verbose=1, mos_spread=0.0) beamA = deepcopy(beam) beamB = deepcopy(beam) beamA.set_wavelength(parameters.WAVELEN_LOW) beamB.set_wavelength(parameters.WAVELEN_HIGH) refl_simA = spot_utils.refls_from_sims(simsAB[0], det, beamA, thresh=1e-3) refl_simB = spot_utils.refls_from_sims(simsAB[1], det, beamB, thresh=1e-3) 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)
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")
def run_paramList(Ntrials, odir, tag, rank, n_jobs, pkl_file): import os import sys from copy import deepcopy import numpy as np import h5py from IPython import embed from scipy.ndimage.morphology import binary_dilation import scitbx from scitbx.array_family import flex from scitbx.matrix import sqr,col from simtbx.nanoBragg import shapetype from simtbx.nanoBragg import nanoBragg import libtbx.load_env # possibly implicit from libtbx.development.timers import Profiler from dxtbx.model.crystal import CrystalFactory from cctbx import crystal,crystal_orientation from cxid9114 import utils from cxid9114.sim import sim_utils from cxid9114.spots import spot_utils from cxid9114.bigsim.bigsim_geom import DET,BEAM from cxid9114.parameters import ENERGY_CONV, ENERGY_HIGH, ENERGY_LOW from cxid9114.refine.jitter_refine import make_param_list from cxid9114.bigsim import sim_spectra from LS49.sim.step4_pad import microcrystal data_pack = utils.open_flex(pkl_file) CRYST = data_pack['crystalAB'] mos_spread_deg=0.015 mos_doms=1000 beam_size_mm=0.001 exposure_s=1 use_microcrystal=True Deff_A = 2200 length_um = 2.2 timelog = False crystal = microcrystal(Deff_A = Deff_A, length_um = length_um, beam_diameter_um = beam_size_mm*1000, verbose=False) spec_file = h5py.File("simMe_data_run62.h5", "r") spec_data = spec_file["hist_spec"] Umat_data = spec_file["Umats"] en_chans = spec_file["energy_bins"][()] ilow = np.abs(en_chans - ENERGY_LOW).argmin() ihigh = np.abs(en_chans - ENERGY_HIGH).argmin() wave_chans = ENERGY_CONV/en_chans sfall_main = sim_spectra.load_spectra("test_sfall.h5") refls_strong = data_pack['refls_strong'] strong_mask_img = spot_utils.strong_spot_mask( refls_strong, (1800,1800) ) # edge detection in the ground truth strong mask image reference_img = (binary_dilation(strong_mask_img, iterations=1).astype(int) - strong_mask_img.astype(int) ).astype(bool) param_fileout = os.path.join( odir, "rank%d_%s.pkl" % (rank, tag)) param_list = make_param_list( CRYST, DET, BEAM, Ntrials, rot=0.09, cell=0.1, eq=(1,1,0), min_Ncell=20, max_Ncell=40, min_mos_spread=0.005, max_mos_spread=0.02) for p in param_list: print(p['crystal'].get_unit_cell().parameters()) shot_idx = int(data_pack['img_f'].split("_")[-1].split(".")[0]) Fluxes = spec_data[2] #shot_idx] Pmax = param_list[0] F1max = 0 for i_trial in range(Ntrials): print ("<><><><><><><><><><><><><><>") print ("Job %d; Trial %d / %d" % (rank, i_trial+1, Ntrials)) print ("<><><><><><><><><><><><><><>") if (rank==0 and i_trial % 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) assert (len(wave_chans)==len(Fluxes)==len(sfall_main)) if np.sum(Fluxes)==0: print ("Cannot simulate with an all-zeros spectrum!") sys.exit() N = crystal.number_of_cells(sfall_main[0].unit_cell()) Ncells_abc = (N,N,N) if force_twocolor: Fluxes *= 0 Fluxes[ilow] = 1e12 Fluxes[ihigh]=1e12 P = param_list[i_trial] simsAB = sim_utils.sim_twocolors2( P['crystal'], DET, BEAM, sfall_main, en_chans, Fluxes, pids = None, profile="gauss", oversample=0, Ncells_abc = Ncells_abc, #P['Ncells_abc'], mos_dom=mos_doms, verbose=verbose, mos_spread=mos_spread_deg, #@mos_spread=P['mos_spread'], cuda=True, device_Id=rank, beamsize_mm=beamsize_mm, exposure_s=exposure_s, boost=crystal.domains_per_crystal) out = np.sum( [ simsAB[i][0] for i in simsAB.keys() if simsAB[i]], axis=0) if out.shape==(): print("This simsAB output has an empty shape, something is wrong!") sys.exit() trial_refls = spot_utils.refls_from_sims([out], DET, BEAM, thresh=thresh) trial_spotmask = spot_utils.strong_spot_mask( trial_refls, (1800,1800) ) trial_img = (binary_dilation(trial_spotmask, iterations=1).astype(int) - trial_spotmask.astype(int) ).astype(bool) comp_img = trial_img.astype(int) + reference_img.astype(int)*2 Nfalse_neg = (comp_img==2).sum() # reference has signal but trial doesnt Nfalse_pos = (comp_img==1).sum() # trial has signal but reference doesnt Ntrue_pos = (comp_img==3).sum() # Precision = float(Ntrue_pos) / (Ntrue_pos + Nfalse_pos) Recall = float(Ntrue_pos) / (Ntrue_pos + Nfalse_neg) F1 = 2.*(Precision*Recall) / (Precision+Recall) if F1 > F1max: Pmax = {'crystal': P['crystal'], 'mos_spread': P['mos_spread'], 'Ncells_abc': P['Ncells_abc'], "F1": F1} F1max = F1 print("Rank %d, Trial %d: F1score = %.5f" % (rank, i_trial, F1)) utils.save_flex(Pmax, param_fileout) return F1max
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 = GAIN 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 = np.random.uniform(1e11,1e12) chanB_flux = np.random.uniform(1e11,1e12) FLUXdat = [chanA_flux, chanB_flux] 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) SCALE = np.random.uniform(0.1,10) 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 = SCALE*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.Gauss SIM.detector_psf_type=shapetype.Unknown # for CSPAD SIM.detector_psf_fwhm_mm=0 SIM.quantum_gain = GAIN 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) 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) 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())} Hmaps = [HA_val_map, HB_val_map] def get_val_at_hkl(hkl, val_map): sg96 = sgtbx.space_group(" P 4nw 2abw") 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 if use_data_spec: print "Setting LA, LB as sums over flux regions A,B" LA = data_fluxes[:75].sum() LB = data_fluxes[75:].sum() else: print "Setting LA LB as data_fluxes" LA = data_fluxes[0] LB = data_fluxes[1] K=FF[0] ** 2 * FLUX[0] L_at_color = [LA,LB] out = spot_utils.integrate_boxes( refl_data, simsDataSum[0], [refl_simA, refl_simB], DET, [beamA,beamB], crystalAB, delta_q=args.deltaq, gain=GAIN ) Nh = len(out[0]) rhs = [] lhs = [] all_H2 = [] all_PA = [] all_PB = [] all_FA = [] all_FB = [] for i in range(Nh): HKL = out[0][i] yobs = out[1][i] Pvals = out[2][i] ycalc = 0 for i_P, P in enumerate(Pvals): L = L_at_color[i_P] H2, F = get_val_at_hkl(HKL, Hmaps[i_P]) if i_P==0: all_FA.append(F) else: all_FB.append(F) ycalc += SCALE*L*P*abs(F)**2/K all_PA.append( Pvals[0]) all_PB.append( Pvals[1]) all_H2.append(H2) rhs.append(ycalc) lhs.append(yobs) df = pandas.DataFrame({"rhs":rhs, "lhs": lhs, "PA":all_PA, "PB":all_PB, "FA": all_FA, "FB": all_FB}) df["run"] = run df["shot_idx"] = shot_idx df['gain'] = SCALE df['LA'] = LA df['LB'] = LB df['K'] = K df['init_rot'] = init_rot h,k,l = zip(*all_H2) df['h2'] = h df['k2'] = k df['l2'] = l df.to_pickle(pklname) if args.plot: print("PLOT") import pylab as plt plt.plot( df.lhs, df.rhs, '.') plt.show() print("DonDonee")
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 msi(n_jobs, jid, out_dir, tag, glob_str ): """ msi stands for mad --> spot --> index Here we load a data image that has crystal diffraction then we spot peaks and index the crystal image """ import sys import numpy as np import glob import os from copy import deepcopy from dials.command_line.find_spots import phil_scope as find_spots_phil_scope import dxtbx from dxtbx.datablock import DataBlockFactory from dxtbx.model.experiment_list import ExperimentListFactory, ExperimentList, Experiment from dials.array_family import flex from libtbx.utils import Sorry as Sorry from libtbx.phil import parse from cxi_xdr_xes.two_color import two_color_indexer indexer_two_color = two_color_indexer.indexer_two_color from cxid9114 import utils from cctbx import crystal from cxid9114 import parameters from cxid9114.spots import spot_utils from dials.command_line.stills_process import phil_scope\ as indexer_phil_scope from cxi_xdr_xes.command_line.two_color_process import two_color_phil_scope from dials.algorithms.indexing.lattice_search import basis_vector_search_phil_scope indexer_phil_scope.adopt_scope(two_color_phil_scope) indexer_phil_scope.adopt_scope(basis_vector_search_phil_scope) mad_index_params = indexer_phil_scope.extract() fnames = glob.glob(glob_str) out_dir =os.path.join( out_dir , "job%d" % jid) if not os.path.exists(out_dir): os.makedirs(out_dir) # track shots that indexed, or shots that # had too few spots to index, so can change parameters and try again def load_tracker_f(fname): data = [] if os.path.exists(fname): data = np.loadtxt(fname, str) if data.size and not data.shape: data = list(set(data[None].astype(int))) else: data = list(set(data.astype(int))) return data skip_weak = False skip_failed = False skip_indexed = False weak_shots_f = os.path.join(out_dir, "weak_shots.txt") failed_idx_f = os.path.join(out_dir, "failed_shots.txt") indexed_f = os.path.join(out_dir, "indexed_shots.txt") weak_shots = load_tracker_f(weak_shots_f) failed_shots = load_tracker_f(failed_idx_f) indexed_shots = load_tracker_f(indexed_f) if not os.path.exists(out_dir): os.makedirs(out_dir) # --- spotting parameters 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 # ------ indexing parameters KNOWN_SYMMETRY = crystal.symmetry("79,79,38,90,90,90", "P43212") #KNOWN_SYMMETRY = crystal.symmetry("78.95,78.95,38.13,90,90,90", "P1") #KNOWN_SYMMETRY = crystal.symmetry("78.95,78.95,38.13,90,90,90", "P43212") mad_index_params.refinement.parameterisation.beam.fix = "all" mad_index_params.refinement.parameterisation.detector.fix = "all" mad_index_params.refinement.verbosity = 3 #mad_index_params.refinement.reflections.outlier.algorithm = "null" mad_index_params.indexing.stills.refine_all_candidates = args.stills_refine mad_index_params.indexing.optimise_initial_basis_vectors = args.basis_refine #mad_index_params.indexing.stills.refine_candidates_with_known_symmetry = False mad_index_params.indexing.known_symmetry.space_group = KNOWN_SYMMETRY.space_group_info() mad_index_params.indexing.known_symmetry.unit_cell = KNOWN_SYMMETRY.unit_cell() mad_index_params.indexing.refinement_protocol.d_min_start = None mad_index_params.indexing.debug = True mad_index_params.indexing.real_space_grid_search.characteristic_grid = 0.02 mad_index_params.indexing.known_symmetry.absolute_angle_tolerance = 5.0 mad_index_params.indexing.known_symmetry.relative_length_tolerance = 0.3 mad_index_params.indexing.stills.rmsd_min_px = 20000 mad_index_params.indexing.refinement_protocol.n_macro_cycles = 1 mad_index_params.indexing.multiple_lattice_search.max_lattices = 1 mad_index_params.indexing.basis_vector_combinations.max_refine = 10000000000 #mad_index_params.indexing.basis_vector_combinations.max_combinations = 150 #mad_index_params.indexing.stills.candidate_outlier_rejection = False mad_index_params.indexing.refinement_protocol.mode = "ignore" mad_index_params.indexing.two_color.high_energy = parameters.ENERGY_HIGH mad_index_params.indexing.two_color.low_energy = parameters.ENERGY_LOW mad_index_params.indexing.two_color.avg_energy = parameters.ENERGY_LOW * .5 + parameters.ENERGY_HIGH * .5 #mad_index_params.indexing.two_color.spiral_method = (1., 100000) # 1000000) mad_index_params.indexing.two_color.n_unique_v = 22 #mad_index_params.indexing.two_color.block_size = 25 #mad_index_params.indexing.two_color.filter_by_mag = (10,3) # ------ N = len(fnames) idx_split = np.array_split( np.arange(N), n_jobs) n_idx = 0 # number indexed for idx in idx_split[jid]: img_f = fnames[idx] loader = dxtbx.load(img_f) iset = loader.get_imageset( loader.get_image_file() ) DET = loader.get_detector() BEAM = loader.get_beam() El = ExperimentListFactory.from_imageset_and_crystal( iset, crystal=None) img_data = loader.get_raw_data().as_numpy_array() if idx in weak_shots and skip_weak: print("Skipping weak shots %d" % idx) continue if idx in failed_shots and skip_failed: print("Skipping failed idx shots %d" % idx) continue if idx in indexed_shots and skip_indexed: print("Skipping already idx shots %d" % idx) continue if use_dials_spotter: refls_strong = flex.reflection_table.from_observations(El, spot_par) else: refls_strong = spot_utils.refls_from_sims([img_data], DET, BEAM, thresh=1e-2) refls_strong['id'] = flex.int( np.zeros( len(refls_strong))) if len(refls_strong) < min_spot_per_pattern: print("Not enough spots shot %d, continuing!" % idx) weak_shots.append(idx) try: np.savetxt(weak_shots_f, weak_shots, fmt="%d") except: pass continue # ================================== # 2 color indexer of 2 color pattern # ================================== try: orientAB = indexer_two_color( reflections=spot_utils.as_single_shot_reflections(refls_strong, inplace=False), experiments=El, params=mad_index_params) orientAB.index() except (Sorry, RuntimeError, AssertionError) as error: print("####\nIndexingFailed T_T \n####") print (error) failed_shots.append( idx) try: np.savetxt(failed_idx_f, failed_shots, fmt="%d") except: pass continue print("####\n *_* IndexingWORKED %d *_* \n####" % idx) n_idx += 1 indexed_shots.append(idx) try: np.savetxt(indexed_f, indexed_shots, fmt="%d") except: pass crystalAB = orientAB.refined_experiments.crystals()[0] refl_pkl = os.path.join(out_dir, "refl_%d_%s.pkl" % (idx, tag)) utils.save_flex(refls_strong, refl_pkl) dump = {"crystalAB": crystalAB, "img_f":img_f, "refined_refls_v1": orientAB.refined_reflections, "refls_strong": refls_strong} dump_pkl = os.path.join(out_dir, "dump_%d_%s.pkl" % (idx, tag)) utils.save_flex(dump, dump_pkl)
crystalAB, detector, iset.get_beam(0), FFdat, [parameters.ENERGY_LOW, parameters.ENERGY_HIGH], FLUX, pids=None, Gauss=False, oversample=2, Ncells_abc=(7, 7, 7), mos_dom=1, mos_spread=0.) simsDataSum = GAIN * (np.array(simsData[0]) + np.array(simsData[1])) simsDataTruthSum = np.array(simsDataTruth[0]) + np.array(simsDataTruth[1]) refl_simA = spot_utils.refls_from_sims(simsAB[0], detector, beamA) refl_simB = spot_utils.refls_from_sims(simsAB[1], detector, beamB) refl_truthA = spot_utils.refls_from_sims(simsDataTruth[0], detector, beamA) refl_truthB = spot_utils.refls_from_sims(simsDataTruth[1], detector, beamB) # This only uses the beam to instatiate an imageset / datablock # but otherwise the return value (refl_data) is indepent of the # beam object passed refl_data = spot_utils.refls_from_sims(simsDataSum, detector, beamA) refl_dataNoScale = spot_utils.refls_from_sims(simsDataTruthSum, detector, beamA) residA = metrics.check_indexable2(refl_data, refl_simA, detector, beamA, crystalAB, hkl_tol) residB = metrics.check_indexable2(refl_data, refl_simB, detector, beamB,