def main():
# :: handle program parameters
arg_parser = handle_arg()
args = arg_parser.parse_args()
# fix verbosity in case of 'quiet'
if args.quiet:
args.verbose = VERB_LVL['none']
# :: print debug info
if args.verbose >= VERB_LVL['debug']:
arg_parser.print_help()
msg('\nARGS: ' + str(vars(args)), args.verbose, VERB_LVL['debug'])
if not args.indirect:
numex.interactive_tk_mpl.plotting(plot_rho_b1t_mp2rage_seq,
SEQ_INTERACTIVES,
resources_path=PATH['resources'],
title=TITLE,
about=__doc__)
else:
numex.interactive_tk_mpl.plotting(plot_rho_b1t_mp2rage_acq,
ACQ_INTERACTIVES,
resources_path=PATH['resources'],
title=TITLE,
about=__doc__)
elapsed(__file__[len(PATH['base']) + 1:])
msg(report())
def main():
# :: handle program parameters
arg_parser = handle_arg()
args = arg_parser.parse_args()
# fix verbosity in case of 'quiet'
if args.quiet:
args.verbose = VERB_LVL['none']
# :: print debug info
if args.verbose >= VERB_LVL['debug']:
arg_parser.print_help()
msg('\nARGS: ' + str(vars(args)), args.verbose, VERB_LVL['debug'])
if not args.name:
msg('Choose your playground:')
msg('Available playgrounds: {AVAILABLES}'.format_map(globals()))
args.name = input(': ')
if args.name in AVAILABLES:
numex.interactive_tk_mpl.plotting(globals()[PREFIX + args.name],
PARAMS[args.name],
title=TITLE_BASE + ' - ' +
TITLE[args.name],
about=__doc__)
elapsed(__file__[len(PATH['base']) + 1:])
msg(report())
else:
msg(fmtm('Plot `{args.name}` not valid.'))
def _to_be_checked():
msg(__doc__.strip())
test()
# todo: move this test to unittest
s = '/scr/beryllium1/mr16/RM/lcmLONGc_128avg' \
'/LONGc_128avg160419_RL6T_MET20_Step01_WAT.txt'
d = read_output(s)
for k, v in sorted(d['metabolites'].items()):
print('{} : {}'.format(k, v))
print()
for k, v in sorted(d['extra'].items()):
print('{} : {}'.format(k, v))
print()
if 'data_cs' in d:
import matplotlib.pyplot as plt
plt.figure()
plt.plot(d['data_cs'], d['data_s'], '-b')
plt.plot(d['data_cs'], d['data_fit'], '-r')
plt.plot(d['data_cs'], d['data_bg'], '-y')
plt.show()
s = '/scr/beryllium1/mr16/RM/lcmLONGc_112avg' \
'/LONGc_112avg160419_RL6T_MET20_Step01'
c = read_input(s)
print(c['data'].shape, c)
elapsed('test lcmodel i/o')
msg(report())
print()
def main():
# :: handle program parameters
arg_parser = handle_arg()
args = arg_parser.parse_args()
# fix verbosity in case of 'quiet'
if args.quiet:
args.verbose = VERB_LVL['none']
# :: print debug info
if args.verbose >= VERB_LVL['debug']:
arg_parser.print_help()
msg('\nARGS: ' + str(vars(args)), args.verbose, VERB_LVL['debug'])
if args.verbose >= VERB_LVL['medium']:
print("II: Using method/options: '{}' / '{}'".format(
args.method, args.options))
if args.method:
preset_func_name = _func_name('preset', args.method)
sources_func_name = _func_name('sources', args.method)
compute_func_name = _func_name('compute', args.method)
# use preset if available
opts = json.loads(args.options) if args.options else {}
if preset_func_name in vars(pmc):
new_opts = vars(pmc)[preset_func_name]()
new_opts.update(opts)
opts = new_opts
# source extraction
sources_func = vars(pmc)[sources_func_name] \
if sources_func_name in vars(pmc) else pmc.sources_generic
sources_args = [opts, args.force, args.verbose]
sources_kwargs = {}
# computation
compute_func = vars(pmc)[compute_func_name] \
if compute_func_name in vars(pmc) else pmc.compute_generic
compute_args = [opts, args.force, args.verbose]
compute_kwargs = {}
# inform on the actual functions
if args.verbose > VERB_LVL['none']:
print('II: Mode: {} / {} / {}'.format(args.method,
sources_func.__name__,
compute_func.__name__))
print('II: Opts: {}'.format(json.dumps(opts)))
# proceed with computation on selected sources
if opts != {}:
pmc.compute(sources_func, sources_args, sources_kwargs,
compute_func, compute_args, compute_kwargs, args.input,
args.output, args.recursive, args.meta_subpath,
args.data_subpath, args.verbose)
else:
print('EE: Mode / options combination not supported.')
else:
print('WW: Method not specified.')
fc.elapsed('compute')
msg(report(), args.verbose, VERB_LVL['medium'])
def main():
# :: handle program parameters
arg_parser = handle_arg()
args = arg_parser.parse_args()
# fix verbosity in case of 'quiet'
if args.quiet:
args.verbose = VERB_LVL['none']
# :: print debug info
if args.verbose >= VERB_LVL['debug']:
arg_parser.print_help()
msg('\nARGS: ' + str(vars(args)), args.verbose, VERB_LVL['debug'])
msg(__doc__.strip())
extract_nifti(args.dir, args.extradir, args.force, args.verbose)
elapsed('extract_nifit_bruker')
msg(report())
def main():
# :: handle program parameters
arg_parser = handle_arg()
args = arg_parser.parse_args()
# fix verbosity in case of 'quiet'
if args.quiet:
args.verbose = VERB_LVL['none']
# :: print debug info
if args.verbose >= VERB_LVL['debug']:
arg_parser.print_help()
msg('\nARGS: ' + str(vars(args)), args.verbose, VERB_LVL['debug'])
msg(__doc__.strip())
kws = vars(args)
kws.pop('quiet')
pml.check_correlation(**kws)
elapsed(__file__[len(PATH['base']) + 1:])
msg(report())
def main():
# :: handle program parameters
arg_parser = handle_arg()
args = arg_parser.parse_args()
# fix verbosity in case of 'quiet'
if args.quiet:
args.verbose = VERB_LVL['none']
# :: print debug info
if args.verbose >= VERB_LVL['debug']:
arg_parser.print_help()
msg('\nARGS: ' + str(vars(args)), args.verbose, VERB_LVL['debug'])
x_vars = set([x.lower() for x in args.mode])
filtered_interactives = INTERACTIVES.copy()
for k in list(filtered_interactives.keys()):
if k[:2] not in x_vars:
filtered_interactives.pop(k)
if x_vars == {'t1', 'tr'}:
numex.interactive_tk_mpl.plotting(
plot_flash_ernst_angle_t1_tr,
filtered_interactives, resources_path=PATH['resources'],
title=TITLE, about=__doc__)
elif x_vars == {'fa', 't1'}:
numex.interactive_tk_mpl.plotting(
plot_flash_ernst_angle_fa_t1,
filtered_interactives, resources_path=PATH['resources'],
title=TITLE, about=__doc__)
elif x_vars == {'fa', 't1'}:
numex.interactive_tk_mpl.plotting(
filtered_interactives, resources_path=PATH['resources'],
title=TITLE, about=__doc__)
elapsed(__file__[len(PATH['base']) + 1:])
msg(report())
# check_dynamics_operator()
# fc.elapsed'check_dynamics_operator')
# check_mt_sequence()
# fc.elapsed'check_mt_sequence')
# check_approx_propagator()
# fc.elapsed'check_approx_propagator')
# check_z_spectrum(
# SpinModel(100.0, (0.5, 0.3, 0.1, 0.1), (GAMMA['1H'] * B0,) * 4,
# (0.25, 0.8, 0.001, 1.0), (20.0, 60.0, 8e4, 5e4),
# (1.0, 0.3, 0.0, 1.0, 0.5, 1.0),
# (None, None, 'superlorenz_approx', 'superlorenz_approx')))
x1 = check_z_spectrum()
elapsed('check_z_spectrum')
x2 = check_z_spectrum2()
elapsed('check_z_spectrum2')
x3 = check_z_spectrum_sparse()
elapsed('check_z_spectrum_sparse')
# print(x2[0].ravel() / x1[0].ravel())
# print(x3[0].ravel() / x1[0].ravel())
# check_fit_spin_model()
# fc.elapsed('check_fit_spin_model')
msg(report())
# profile.run('check_z_spectrum()', sort=1)
plt.show()
def _test(use_cache=True):
# x = np.linspace(1, 40, 5)
x = np.array([2, 5, 7, 20, 40])
tau_arr = np.linspace(2, 1000, 4000)
a_arr = np.linspace(500, 4000, 4000)
import pymrt.util
import os
base_dir = fc.realpath('~/hd1/TEMP')
filepath = os.path.join(base_dir, 'tau_arr.npz')
if os.path.isfile(filepath) and use_cache:
y = np.load(filepath)['y']
else:
y = np.zeros((len(tau_arr), len(a_arr), len(x)))
for i, a in enumerate(a_arr):
for j, tau in enumerate(tau_arr):
y[j, i] = func_exp_decay(x, tau, a)
np.savez(filepath, y=y)
def eval_dist(a, b, axis=-1):
mu = np.nanmean(a, axis) - b
std = np.nanstd(a, axis)
return np.mean(mu), np.mean(std)
elapsed('gen_tau_phantom')
snr = 20
p = 1 / snr
n = np.max(a_arr) * p * (np.random.random(y.shape) - 0.5)
m = [True, True, False, False, False]
# print(fit_exp_loglin(y + n, x)['tau'])
# print(fit_exp_loglin(y + n, x, weighted=False)['tau'])
# print(fit_exp_tau_quadr(y + n, x))
print('quad', eval_dist(fit_exp_quad(y + n, x, m)['tau'], tau_arr))
elapsed('quad')
print('diff', eval_dist(fit_exp_diff(y + n, x, m)['tau'], tau_arr))
elapsed('diff')
print('quadr', eval_dist(fit_exp_quadr(y + n, x, m)['tau'], tau_arr))
elapsed('quadr')
print('quadr_w2',
eval_dist(fit_exp_quadr(y + n, x, m, window_size=2)['tau'], tau_arr))
elapsed('quadr_w2')
print('quadr_w3',
eval_dist(fit_exp_quadr(y + n, x, m, window_size=3)['tau'], tau_arr))
elapsed('quadr_w3')
print('arlo', eval_dist(fit_exp_arlo(y + n, x, m)['tau'], tau_arr))
elapsed('arlo')
print('loglin', eval_dist(fit_exp_loglin(y + n, x, m)['tau'], tau_arr))
elapsed('loglin')
print(
'loglin_w',
eval_dist(
fit_exp_loglin(y + n, x, m, variant='weighted_reverse')['tau'],
tau_arr))
elapsed('loglin_w')
# print('leasq',
# eval_dist(fit_exp_curve_fit(y + n, x, init=[5, 4000])['tau'],
# tau_arr))
# elapsed('leasq')
msg(report())
