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()
