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) 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])
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 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 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 #np.random.seed(41107) np.random.seed() 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 DET = utils.open_flex('ref1_det.pkl') BEAM = utils.open_flex('ref3_beam.pkl') 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): 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() 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() #Ctruth = CrystalFactory.from_dict(cryst_descr) #Ctruth.set_U(Rsmall.ravel()) init_comp = rotation_matrix_differences((Ctruth, crystalAB)) init_rot = float(init_comp.split("\n")[-2].split()[2]) #U = crystalAB.get_U() #img_f = data['img_f'] #if rel_dir is not None: # img_f = os.path.join( rel_dir, img_f) # if not os.path.exists(img_f): # print ("Image file %s does not exists" % img_f) # sys.exit() #loader = dxtbx.load(img_f) 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) 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, 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("sim64_d9114_images", data=[simsDataSum]) print "Background made! Saved to file %s" % bg_name sys.exit() if add_background: print("ADDING BG") background = h5py.File(bg_name, "r")['sim64_d9114_images'][()] simsDataSum += background[0] if add_noise: print("ADDING NOISE") for pidx in range(64): 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("sim64_d9114_images", data=[simsDataSum]) 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: assert (args.write_img) print("DIALS SPOTTING") loader = dxtbx.load(h5name) iset = loader.get_imageset(loader.get_image_file())[:1] #iset.set_detector(DET) iset.set_beam(BEAM) El = ExperimentListFactory.from_imageset_and_crystal(iset, crystal=None) 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 for pid in 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) 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: 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: 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: 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 fix_edge_cases(d): refls_data = d['refls_data'] resA = d['residA'] resB = d['residB'] det = d['detector'] beamA = d['beamA'] beamB = d['beamB'] # its assumed hereafter that beamA is the lower energy beam assert (beamA.get_wavelength() > beamB.get_wavelength()) spot_h = map(tuple, resA['hkl']) h_multiples = \ [h for h,count in Counter(spot_h).items() if count > 1] idxA, idxB = resA['indexed'], resB['indexed'] print "spots by A only: %d" % sum(np.logical_and(idxA, ~idxB)) print "spots by B only: %d" % sum(np.logical_and(~idxA, idxB)) print "spots by A and B: %d" % sum(np.logical_and(idxA, idxB)) fudge = .5 # 1/2 pixel fudge factor for pixels being considered close for h, k, l in h_multiples: is_same = [all((hh == h, kk == k, ll == l)) for hh, kk, ll in spot_h] if sum(is_same) > 2: # NOTE: not sure what to do with >2 refls.. print "Boo!" ii = np.where(is_same)[0] print ii resA['indexed'][ii] = False resB['indexed'][ii] = False continue # the reflection indices! i1, i2 = np.where(is_same)[0] # NOTE: guarenteed to be 2 here # the reflection data r1, r2 = refls_data[i1], refls_data[i2] pid1, pid2 = r1['panel'], r2['panel'] if pid1 != pid2: print "on separate panels, weird" print i1, i2 resA['indexed'][[i1, i2]] = False resB['indexed'][[i1, i2]] = False continue pid = pid1 = pid2 node = det[pid] pixsize = node.get_pixel_size()[0] max_deltapix = geom_utils.twocolor_deltapix(node, beamA, beamB) x1, y1, _ = r1['xyzobs.px.value'] x2, y2, _ = r2['xyzobs.px.value'] deltapix = distance.euclidean((x1, y1), (x2, y2)) if deltapix > max_deltapix + fudge: print " not close enough, not sure what to do" print i1, i2 resA['indexed'][[i1, i2]] = False resB['indexed'][[i1, i2]] = False continue # NOTE: if we made it this far, the two # spots are likely neighboring slit spots # and we should assign i1 / i2 to inner, outer lab1 = node.get_pixel_lab_coord((x1, y1)) lab2 = node.get_pixel_lab_coord((x2, y2)) lab_cent = node.get_beam_centre_lab(beamA.get_s0()) # NOTE : can use beamA or beamB so long as they are colinear # to define cent radial1 = distance.euclidean(lab1, lab_cent) radial2 = distance.euclidean(lab2, lab_cent) if abs(radial1 - radial2) / pixsize < max_deltapix / 2.: # NOTE: if the spots are close but not radially separated... print "close by not cigar" print i1, i2 resA['indexed'][[i1, i2]] = False resB['indexed'][[i1, i2]] = False continue if radial1 < radial2: inner = i1 outer = i2 else: inner = i2 outer = i1 # was the ref indexed by colorA/B inner_byA = resA['indexed'][inner] inner_byB = resB['indexed'][inner] outer_byA = resA['indexed'][outer] outer_byB = resB['indexed'][outer] # NOTE we care about the cases when byA and byB are both True # for the same h if inner_byA and inner_byB and outer_byA and outer_byB: # NOTE in this case assign the inner peak to B resA['indexed'][inner] = False resB['indexed'][outer] = False print "case 1" print i1, i2, inner, outer elif inner_byA and inner_byB and outer_byA and not outer_byB: print "case 2" resA['indexed'][inner] = False print i1, i2, inner, outer elif not inner_byA and inner_byB and outer_byA and outer_byB: print "case 3" resB['indexed'][outer] = False print i1, i2, inner, outer else: # NOTE: in all other cases, they shouldnt arise, so set indexed to false print "case 0" resA['indexed'][[i1, i2]] = False resB['indexed'][[i1, i2]] = False print i1, i2, inner, outer idxA, idxB = resA['indexed'], resB['indexed'] print "spots by A only: %d" % sum(np.logical_and(idxA, ~idxB)) print "spots by B only: %d" % sum(np.logical_and(~idxA, idxB)) print "spots by A and B: %d" % sum(np.logical_and(idxA, idxB)) return d