range_arr[:, 0] = -0.5
range_arr[:, 1] = 0.5
range_dict[param_type] = range_arr
# dataset
input = InputFile(input_file)
input_params = input.read()
tlen = input_params['tlen']
nspc = input_params['nspc']
dataset, _ = work_parameters.get_dataset_syntest2(tlen=tlen,
nspc=nspc,
mode=0,
add_noise=False,
noise_normalized_std=2.)
na = NeighbouhoodAlgorithm.from_file(input_file, model_ref, model_params,
range_dict, dataset, comm)
# run
log_path = os.path.join(na.out_dir, 'log_{}'.format(rank))
log = open(log_path, 'w', buffering=1)
result = na.compute(comm, log)
# plot inverted model
if rank == 0:
fig, ax = plt.subplots(1, 1, figsize=(4, 6))
result.plot_models(types=types,
n_best=1,
ax=ax,
color='black',
label='best model')
if param_type == ParameterType.VSH:
range_arr[:, 0] = -0.3
range_arr[:, 1] = 0.3
range_dict[param_type] = range_arr
# dataset
input = InputFile(input_file)
input_params = input.read()
tlen = input_params['tlen']
nspc = input_params['nspc']
dataset, _ = work_parameters.get_dataset_syntest3(tlen=tlen, nspc=nspc,
mode=2, add_noise=False,
noise_normalized_std=1.)
na = NeighbouhoodAlgorithm.from_file(
input_file, model_ref, model_params, range_dict,
dataset, comm)
# run
log_path = os.path.join(
na.out_dir, 'log_{}'.format(rank))
log = open(log_path, 'w', buffering=1)
result = na.compute(comm, log)
# plot inverted model
if rank == 0:
fig, ax = plt.subplots(1, 1, figsize=(3.5, 6))
result.plot_models(
types=types, n_best=1, ax=ax,
color='black', label='best model')
indices_better = [0]
for imod in range(1, result.meta['n_mod']):
i_best = result.get_indices_best_models(n_best=1, n_mod=imod+1)
if i_best != indices_better[-1]:
indices_better.append(imod)
models = [result.models[i] for i in indices_better]
else:
models = None
outputs = result.compute_models(models)
# plot results
if rank == 0:
out_dir = 'output_' + utilities.get_temporary_str()
os.mkdir(out_dir)
points = NeighbouhoodAlgorithm.get_points_for_voronoi(
result.perturbations,
result.meta['range_dict'],
types)
points = points[:, free_indices]
misfits = result.misfit_dict['variance'].mean(axis=1)
n_r = result.meta['n_r']
n_s = result.meta['n_s']
n_mod = points.shape[0]
i_out = 0
for imod in range(n_mod):
figpath = os.path.join(
out_dir, 'voronoi_{:05d}.png'.format(imod))
fig = plt.figure(figsize=(13*figscale, 5*figscale))
gs = gridspec.GridSpec(1, 3, width_ratios=[2,1,1])
# plot voronoi cells
ax0 = fig.add_subplot(gs[0])
_, _, colormap = NeighbouhoodAlgorithm.plot_voronoi_2d(
range_arr[:, 0] = -100
range_arr[:, 1] = 100
range_dict[p_type] = range_arr
# dataset
input = InputFile(input_file)
input_params = input.read()
dataset, _ = work_parameters.get_dataset_syntest_vshvsv_4(
tlen=input_params['tlen'],
nspc=input_params['nspc'],
mode=input_params['mode'],
add_noise=False,
noise_normalized_std=2.)
na = NeighbouhoodAlgorithm.from_file(
input_file, model, model_params, range_dict,
dataset, comm)
# run
log_path = os.path.join(
na.out_dir, 'log_{}'.format(rank))
log = open(log_path, 'w', buffering=1)
result = na.compute(comm, log)
# plot inverted model
if rank == 0:
fig, ax = plt.subplots(1, 1, figsize=(4,6))
result.plot_models(
types=types, n_best=1, ax=ax,
color='black', label='best model')