def main(args): vol1 , _, _ = mrc.parse_mrc(args.vol1) vol2 , _, _ = mrc.parse_mrc(args.vol2) if args.mask: mask = mrc.parse_mrc(args.mask)[0] vol1 *= mask vol2 *= mask D = vol1.shape[0] x = np.arange(-D//2, D//2) x2, x1, x0 = np.meshgrid(x,x,x, indexing='ij') coords = np.stack((x0,x1,x2), -1) r = (coords**2).sum(-1)**.5 assert r[D//2, D//2, D//2] == 0.0 vol1 = fft.fftn_center(vol1) vol2 = fft.fftn_center(vol2) #log(r[D//2, D//2, D//2:]) prev_mask = np.zeros((D,D,D), dtype=bool) fsc = [1.0] for i in range(1,D//2): mask = r < i shell = np.where(mask & np.logical_not(prev_mask)) v1 = vol1[shell] v2 = vol2[shell] p = np.vdot(v1,v2) / (np.vdot(v1,v1)*np.vdot(v2,v2))**.5 fsc.append(p.real) prev_mask = mask fsc = np.asarray(fsc) x = np.arange(D//2)/D res = np.stack((x,fsc),1) if args.o: np.savetxt(args.o, res) else: log(res) w = np.where(fsc < 0.5) if w: log('0.5: {}'.format(1/x[w[0]]*args.Apix)) w = np.where(fsc < 0.143) if w: log('0.143: {}'.format(1/x[w[0]]*args.Apix)) if args.plot: plt.plot(x,fsc) plt.ylim((0,1)) plt.show()
def main(args): stack,_,_ = mrc.parse_mrc(args.input,lazy=True) print('{} {}x{} images'.format(len(stack), *stack[0].get().shape)) stack = [stack[x].get() for x in range(9)] analysis.plot_projections(stack) if args.o: plt.savefig(args.o) else: plt.show()
def main(args): assert args.input.endswith('.mrc'), "Input volume must be .mrc file" assert args.o.endswith('.mrc'), "Output volume must be .mrc file" x, _, _ = mrc.parse_mrc(args.input) D = args.apix if args.invert: x *= -1 if args.flip: x = x[::-1] mrc.write(args.o, x, ax=D, ay=D, az=D) log(f'Wrote {args.o}')
def load_particles(mrcs_txt_star, lazy=False, datadir=None): ''' Load particle stack from either a .mrcs file, a .star file, a .txt file containing paths to .mrcs files, or a cryosparc particles.cs file lazy (bool): Return numpy array if True, or return list of LazyImages datadir (str or None): Base directory overwrite for .star or .cs file parsing ''' if mrcs_txt_star.endswith('.txt'): particles = mrc.parse_mrc_list(mrcs_txt_star, lazy=lazy) elif mrcs_txt_star.endswith('.star'): # not exactly sure what the default behavior should be for the data paths if parsing a starfile try: particles = starfile.Starfile.load(mrcs_txt_star).get_particles(datadir=datadir, lazy=lazy) except Exception as e: if datadir is None: datadir = os.path.dirname(mrcs_txt_star) # assume .mrcs files are in the same director as the starfile particles = starfile.Starfile.load(mrcs_txt_star).get_particles(datadir=datadir, lazy=lazy) else: raise RuntimeError(e) elif mrcs_txt_star.endswith('.cs'): particles = starfile.csparc_get_particles(mrcs_txt_star, datadir, lazy) else: particles, _, _ = mrc.parse_mrc(mrcs_txt_star, lazy=lazy) return particles
def main(args): np.random.seed(args.seed) log('RUN CMD:\n' + ' '.join(sys.argv)) log('Arguments:\n' + str(args)) if args.Nimg is None: log('Loading all particles') particles = mrc.parse_mrc(args.particles, lazy=False)[0] Nimg = len(particles) else: Nimg = args.Nimg log('Lazy loading ' + str(args.Nimg) + ' particles') particle_list = mrc.parse_mrc(args.particles, lazy=True, Nimg=Nimg)[0] particles = np.array([i.get() for i in particle_list]) D, D2 = particles[0].shape assert D == D2, 'Images must be square' log('Loaded {} images'.format(Nimg)) #if not args.rad: args.rad = D/2 #x0, x1 = np.meshgrid(np.arange(-D/2,D/2),np.arange(-D/2,D/2)) #mask = np.where((x0**2 + x1**2)**.5 < args.rad) if args.s1 is not None: assert args.s2 is not None, "Need to provide both --s1 and --s2" if args.s1 is None: Nstd = min(100, Nimg) mask = np.where(particles[:Nstd] > 0) std = np.std(particles[mask]) s1 = std / np.sqrt(args.snr1) else: s1 = args.s1 if s1 > 0: log('Adding noise with stdev {}'.format(s1)) particles = add_noise(particles, D, s1) log('Calculating the CTF') ctf, defocus_list = compute_full_ctf(D, Nimg, args) log('Applying the CTF') particles = add_ctf(particles, ctf) if args.s2 is None: std = np.std(particles[mask]) # cascading of noise processes according to Frank and Al-Ali (1975) & Baxter (2009) snr2 = (1 + 1 / args.snr1) / (1 / args.snr2 - 1 / args.snr1) log('SNR2 target {} for total snr of {}'.format(snr2, args.snr2)) s2 = std / np.sqrt(snr2) else: s2 = args.s2 if s2 > 0: log('Adding noise with stdev {}'.format(s2)) particles = add_noise(particles, D, s2) if args.normalize: log('Normalizing particles') particles = normalize(particles) if not (args.noinvert): log('Inverting particles') particles = invert(particles) log('Writing image stack to {}'.format(args.o)) mrc.write(args.o, particles.astype(np.float32)) if args.out_star is None: args.out_star = f'{args.o}.star' log(f'Writing associated .star file to {args.out_star}') if args.ctf_pkl: params = pickle.load(open(args.ctf_pkl, 'rb')) try: assert len(params) == Nimg except AssertionError: log('Note that the input ctf.pkl contains ' + str(len(params)) + ' particles, but that you have only chosen to output the first ' + str(Nimg) + ' particle') params = params[:Nimg] args.kv = params[0][5] args.cs = params[0][6] args.wgh = params[0][7] args.Apix = params[0][1] write_starfile(args.out_star, args.o, Nimg, defocus_list, args.kv, args.wgh, args.cs, args.Apix) if not args.ctf_pkl: if args.out_pkl is None: args.out_pkl = f'{args.o}.pkl' log(f'Writing CTF params pickle to {args.out_pkl}') params = np.ones((Nimg, 9), dtype=np.float32) params[:, 0] = D params[:, 1] = args.Apix params[:, 2:4] = defocus_list params[:, 4] = args.ang params[:, 5] = args.kv params[:, 6] = args.cs params[:, 7] = args.wgh params[:, 8] = args.ps log(params[0]) with open(args.out_pkl, 'wb') as f: pickle.dump(params, f)
import numpy as np import sys, os import argparse import pickle import matplotlib.pyplot as plt import torch import torch.nn as nn sys.path.insert(0,'../lib-python') import fft import models import mrc from lattice import Lattice imgs,_,_ = mrc.parse_mrc('data/hand.mrcs') img = imgs[0] D = img.shape[0] ht = fft.ht2_center(img) ht = fft.symmetrize_ht(ht) D += 1 lattice = Lattice(D) model = models.FTSliceDecoder(D**2, D, 10,10,nn.ReLU) coords = lattice.coords[...,0:2]/2 ht = torch.tensor(ht.astype(np.float32)).view(1,-1) trans = torch.tensor([5.,10.]).view(1,1,2) ht_shifted = lattice.translate_ht(ht, trans) ht_np = ht_shifted.view(D,D).numpy()[0:-1, 0:-1]
# coding: utf-8 import sys, os DIR = os.path.dirname(os.path.abspath(__file__)) sys.path.insert(0,'{}/../lib-python'.format(DIR)) import mrc import numpy as np data, _, _ = mrc.parse_mrc('{}/data/toy_projections.mrcs'.format(DIR), lazy=True) data2, _, _ = mrc.parse_mrc('{}/data/toy_projections.mrcs'.format(DIR), lazy=False) data1=np.asarray([x.get() for x in data]) assert (data1==data2).all() print('ok') import dataset data2 = dataset.load_particles('{}/data/toy_projections.star'.format(DIR)) assert (data1==data2).all() print('ok') data2 = dataset.load_particles('{}/data/toy_projections.txt'.format(DIR)) assert (data1==data2).all() print('ok') print('all ok')
def main(args): log(args) torch.set_grad_enabled(False) use_cuda = torch.cuda.is_available() log('Use cuda {}'.format(use_cuda)) if use_cuda: torch.set_default_tensor_type(torch.cuda.FloatTensor) t1 = time.time() ref, _, _ = mrc.parse_mrc(args.ref) log('Loaded {} volume'.format(ref.shape)) vol, _, _ = mrc.parse_mrc(args.vol) log('Loaded {} volume'.format(vol.shape)) projector = VolumeAligner(vol, vol_ref=ref, maxD=args.max_D, flip=args.flip) if use_cuda: projector.use_cuda() r_resol = args.r_resol quats = so3_grid.grid_SO3(r_resol) q_id = np.arange(len(quats)) q_id = np.stack([q_id // (6 * 2**r_resol), q_id % (6 * 2**r_resol)], -1) rots = GridPose(quats, q_id) t_resol = 0 T_EXTENT = vol.shape[0] / 16 if args.t_extent is None else args.t_extent T_NGRID = args.t_grid trans = shift_grid3.base_shift_grid(T_EXTENT, T_NGRID) t_id = np.stack(shift_grid3.get_base_id(np.arange(len(trans)), T_NGRID), -1) trans = GridPose(trans, t_id) max_keep_r = args.keep_r max_keep_t = args.keep_t #rot_tracker = MinPoseTracker(max_keep_r, 4, 2) #tr_tracker = MinPoseTracker(max_keep_t, 3, 3) for it in range(args.niter): log('Iteration {}'.format(it)) log('Generating {} rotations'.format(len(rots))) log('Generating {} translations'.format(len(trans))) pose_err = np.empty((len(rots), len(trans)), dtype=np.float32) #rot_tracker.clear() #tr_tracker.clear() r_iterator = data.DataLoader(rots, batch_size=args.rb, shuffle=False) t_iterator = data.DataLoader(trans, batch_size=args.tb, shuffle=False) r_it = 0 for rot, r_id in r_iterator: if use_cuda: rot = rot.cuda() vr, vi = projector.rotate(rot) t_it = 0 for tr, t_id in t_iterator: if use_cuda: tr = tr.cuda() vtr, vti = projector.translate( vr, vi, tr.expand(rot.size(0), *tr.shape)) # todo: check volume err = projector.compute_err(vtr, vti) # R x T pose_err[r_it:r_it + len(rot), t_it:t_it + len(tr)] = err.cpu().numpy() #r_err = err.min(1)[0] #min_r_err, min_r_i = r_err.sort() #rot_tracker.add(min_r_err[:max_keep_r], rot[min_r_i][:max_keep_r], r_id[min_r_i][:max_keep_r]) #t_err= err.min(0)[0] #min_t_err, min_t_i = t_err.sort() #tr_tracker.add(min_t_err[:max_keep_t], tr[min_t_i][:max_keep_t], t_id[min_t_i][:max_keep_t]) t_it += len(tr) r_it += len(rot) r_err = pose_err.min(1) r_err_argmin = r_err.argsort()[:max_keep_r] t_err = pose_err.min(0) t_err_argmin = t_err.argsort()[:max_keep_t] # lstart #r = rots.pose[r_err_argmin[0]] #t = trans.pose[t_err_argmin[0]] #log('Best rot: {}'.format(r)) #log('Best trans: {}'.format(t)) #vr, vi = projector_full.rotate(torch.tensor(r).unsqueeze(0)) #vr, vi = projector_full.translate(vr, vi, torch.tensor(t).view(1,1,3)) #err = projector_full.compute_err(vr,vi) #w = np.where(r_err[r_err_argmin] > err.item())[0] rots, rots_id = subdivide_r(rots.pose[r_err_argmin], rots.pose_id[r_err_argmin], r_resol) rots = GridPose(rots, rots_id) t_err = pose_err.min(0) t_err_argmin = t_err.argsort()[:max_keep_t] trans, trans_id = subdivide_t(trans.pose_id[t_err_argmin], t_resol, T_EXTENT, T_NGRID) trans = GridPose(trans, trans_id) r_resol += 1 t_resol += 1 vlog(r_err[r_err_argmin]) vlog(t_err[t_err_argmin]) #log(rot_tracker.min_errs) #log(tr_tracker.min_errs) r = rots.pose[r_err_argmin[0]] t = trans.pose[t_err_argmin[0]] * vol.shape[0] / args.max_D log('Best rot: {}'.format(r)) log('Best trans: {}'.format(t)) t *= 2 / vol.shape[0] projector = VolumeAligner(vol, vol_ref=ref, maxD=vol.shape[0], flip=args.flip) if use_cuda: projector.use_cuda() vr = projector.real_tform( torch.tensor(r).unsqueeze(0), torch.tensor(t).view(1, 1, 3)) v = vr.squeeze().cpu().numpy() log('Saving {}'.format(args.o)) mrc.write(args.o, v.astype(np.float32)) td = time.time() - t1 log('Finished in {}s'.format(td))
def main(args): for out in (args.o, args.out_png, args.out_pose): if not out: continue mkbasedir(out) warnexists(out) if args.t_extent == 0.: log('Not shifting images') else: assert args.t_extent > 0 if args.seed is not None: np.random.seed(args.seed) torch.manual_seed(args.seed) use_cuda = torch.cuda.is_available() log('Use cuda {}'.format(use_cuda)) if use_cuda: torch.set_default_tensor_type(torch.cuda.FloatTensor) t1 = time.time() vol, _ , _ = mrc.parse_mrc(args.mrc) log('Loaded {} volume'.format(vol.shape)) if args.tilt: theta = args.tilt*np.pi/180 args.tilt = np.array([[1.,0.,0.], [0, np.cos(theta), -np.sin(theta)], [0, np.sin(theta), np.cos(theta)]]).astype(np.float32) projector = Projector(vol, args.tilt) if use_cuda: projector.lattice = projector.lattice.cuda() projector.vol = projector.vol.cuda() if args.grid is not None: rots = GridRot(args.grid) log('Generating {} rotations at resolution level {}'.format(len(rots), args.grid)) else: log('Generating {} random rotations'.format(args.N)) rots = RandomRot(args.N) log('Projecting...') imgs = [] iterator = data.DataLoader(rots, batch_size=args.b) for i, rot in enumerate(iterator): vlog('Projecting {}/{}'.format((i+1)*len(rot), args.N)) projections = projector.project(rot) projections = projections.cpu().numpy() imgs.append(projections) rots = rots.rots.cpu().numpy() imgs = np.vstack(imgs) td = time.time()-t1 log('Projected {} images in {}s ({}s per image)'.format(args.N, td, td/args.N )) if args.t_extent: log('Shifting images between +/- {} pixels'.format(args.t_extent)) trans = np.random.rand(args.N,2)*2*args.t_extent - args.t_extent imgs = np.asarray([translate_img(img, t) for img,t in zip(imgs,trans)]) # convention: we want the first column to be x shift and second column to be y shift # reverse columns since current implementation of translate_img uses scipy's # fourier_shift, which is flipped the other way # convention: save the translation that centers the image trans = -trans[:,::-1] # convert translation from pixel to fraction D = imgs.shape[-1] assert D % 2 == 0 trans /= D log('Saving {}'.format(args.o)) mrc.write(args.o,imgs.astype(np.float32)) log('Saving {}'.format(args.out_pose)) with open(args.out_pose,'wb') as f: if args.t_extent: pickle.dump((rots,trans),f) else: pickle.dump(rots, f) if args.out_png: log('Saving {}'.format(args.out_png)) plot_projections(args.out_png, imgs[:9])