def main(argv): # parse input arguments parser = argparse.ArgumentParser() parser.add_argument('-e', '--ens') parser.add_argument('-d', '--dir') parser.add_argument('-x', '--exclude', type=float) args = parser.parse_args() # switch directory if needed if args.dir: os.chdir(args.dir) # load target ensemble ens = wtc.loadEnsemble(args.ens) results = Parallel(n_jobs=-1, verbose=50)(delayed( fitModelFromFile )(traj.h5obj.filename, traj.strain, traj.wormID, excludeEdge=args.exclude) for traj in ens) #print results # keep good results T = { traj.wormID: result.tolist() for traj, result in zip(ens, results) if ~np.isnan(result).all() } # save results ensName = os.path.splitext(args.ens)[0] with open('{0}_cmf.yml'.format(ensName), 'wb') as f: yaml.dump(T, f)
def main(argv): # parse input arguments parser = argparse.ArgumentParser() parser.add_argument('-e', '--ens') parser.add_argument('-d', '--dir') parser.add_argument('-x', '--exclude', type=float) args = parser.parse_args() # switch directory if needed if args.dir: os.chdir(args.dir) # load target ensemble ens = wtc.loadEnsemble(args.ens) results = Parallel(n_jobs=-1, verbose=50)(delayed(fitModelFromFile)(traj.h5obj.filename, traj.strain, traj.wormID, excludeEdge=args.exclude) for traj in ens) #print results # keep good results T = {traj.wormID: result.tolist() for traj, result in zip(ens, results) if ~np.isnan(result).all()} # save results ensName = os.path.splitext(args.ens)[0] with open('{0}_cmf.yml'.format(ensName), 'wb') as f: yaml.dump(T, f)
def main(argv): # parse input arguments parser = argparse.ArgumentParser() parser.add_argument('-e', '--ens') parser.add_argument('-d', '--dir') parser.add_argument('-x', '--exclude', type=float) args = parser.parse_args() # switch directory if needed if args.dir: os.chdir(args.dir) # load target ensemble ens = wtc.loadEnsemble(args.ens) results = Parallel(n_jobs=-1, verbose=50)(delayed(calcCorrelationFunctionsFromFile)(traj.h5obj.filename, traj.strain, traj.wormID, excludeEdge=args.exclude) for traj in ens) # save results ensName = os.path.splitext(args.ens)[0] f = h5py.File('{0}_corrfun.h5'.format(ensName), 'w') f.create_dataset('taus', (len(lags),), dtype=float) f['taus'][...] = lags/11.5 f.create_dataset('x_speed', (len(x),), dtype=float) f['x_speed'][...] = x f.create_dataset('wormID', (len(ens),), dtype='S10') f.create_dataset('strain', (len(ens),), dtype='S10') f.create_dataset('MSD', (len(ens), len(lags)), dtype=float) f.create_dataset('VACF', (len(ens), len(lags)), dtype=float) f.create_dataset('s_ACF', (len(ens), len(lags)), dtype=float) f.create_dataset('psi_ACF', (len(ens), len(lags)), dtype=float) f.create_dataset('dpsi_ACF', (len(ens), len(lags)), dtype=float) f.create_dataset('psi_trend', (len(ens), len(lags)), dtype=float) for i in xrange(len(ens)): result = results[i] traj = ens[i] f['strain'][i] = np.string_(traj.strain) f['wormID'][i] = np.string_(traj.wormID) f['MSD'][i] = result[0].filled(np.nan) f['VACF'][i] = result[1].filled(np.nan) f['s_ACF'][i]= result[2].filled(np.nan) f['psi_ACF'][i] = result[3].filled(np.nan) f['dpsi_ACF'][i] = result[4].filled(np.nan) f['psi_trend'][i] = result[5].filled(np.nan) f['s_dist'][i]= result[6].filled(np.nan) f.close()
def main(argv): # parse input arguments parser = argparse.ArgumentParser() parser.add_argument('-e', '--ens') parser.add_argument('-d', '--dir') parser.add_argument('-x', '--exclude', type=float) args = parser.parse_args() # switch directory if needed if args.dir: os.chdir(args.dir) # load target ensemble ens = wtc.loadEnsemble(args.ens) results = Parallel(n_jobs=-1, verbose=50)(delayed( calcCorrelationFunctionsFromFile )(traj.h5obj.filename, traj.strain, traj.wormID, excludeEdge=args.exclude) for traj in ens) # save results ensName = os.path.splitext(args.ens)[0] f = h5py.File('{0}_corrfun.h5'.format(ensName), 'w') f.create_dataset('taus', (len(lags), ), dtype=float) f['taus'][...] = lags / 11.5 f.create_dataset('x_speed', (len(x), ), dtype=float) f['x_speed'][...] = x f.create_dataset('wormID', (len(ens), ), dtype='S10') f.create_dataset('strain', (len(ens), ), dtype='S10') f.create_dataset('MSD', (len(ens), len(lags)), dtype=float) f.create_dataset('VACF', (len(ens), len(lags)), dtype=float) f.create_dataset('s_ACF', (len(ens), len(lags)), dtype=float) f.create_dataset('psi_ACF', (len(ens), len(lags)), dtype=float) f.create_dataset('dpsi_ACF', (len(ens), len(lags)), dtype=float) f.create_dataset('psi_trend', (len(ens), len(lags)), dtype=float) for i in xrange(len(ens)): result = results[i] traj = ens[i] f['strain'][i] = np.string_(traj.strain) f['wormID'][i] = np.string_(traj.wormID) f['MSD'][i] = result[0].filled(np.nan) f['VACF'][i] = result[1].filled(np.nan) f['s_ACF'][i] = result[2].filled(np.nan) f['psi_ACF'][i] = result[3].filled(np.nan) f['dpsi_ACF'][i] = result[4].filled(np.nan) f['psi_trend'][i] = result[5].filled(np.nan) f['s_dist'][i] = result[6].filled(np.nan) f.close()