def run(algorithm, path, label, pre_simulation_hook=None):
"""
Simulate a reconstruction algorithm.
The simulation results are stored in a HDF5 database rather than returned
by the function.
Parameters
----------
algorithm : function
A function handle to the reconstruction algorithm.
path : str
The path of the HDF5 database where the results should be stored.
label : str
The label assigned to the simulation results
pre_simumlation_hook : callable
A handle to a callable which should be run *just* before the call to
the reconstruction algorithm (the default is None, which implies that
no pre hook is run).
"""
tmp_dir = _split_path(path)[0] + '.tmp' + os.sep
tmp_file = tmp_dir + label.replace('/', '#') + '.hdf5'
if not os.path.isdir(tmp_dir):
os.mkdir(tmp_dir)
if not os.path.isfile(tmp_file):
_backup.create(tmp_file)
done = _backup.get(tmp_file)
shape = [len(_conf['delta']), len(_conf['rho'])]
random.seed(_conf['seed'])
seeds = np.array(random.sample(iter_range,
shape[0] * shape[1])).reshape(shape)
tasks = [(algorithm, (i, j), seeds[i, j], tmp_file, pre_simulation_hook)
for i in range(shape[0]) for j in range(shape[1])
if not done[i, j]]
_process(_simulate, args_list=tasks, maxtasks=_conf['maxpoints'])
with _File(tmp_file, 'r') as f:
stat_time = f.root.time[:]
stat_dist = f.root.dist[:]
stat_mse = f.root.mse[:]
stat_norm = f.root.norm[:]
with _File(path, 'a') as f:
f.create_array('/' + label, 'time', stat_time, createparents=True)
f.create_array('/' + label, 'dist', stat_dist, createparents=True)
f.create_array('/' + label, 'mse', stat_mse, createparents=True)
f.create_array('/' + label, 'norm', stat_norm, createparents=True)
os.remove(tmp_file)
if len(os.listdir(tmp_dir)) == 0:
os.removedirs(tmp_dir)
def run(algorithm, path, label):
"""
Simulate a reconstruction algorithm.
The simulation results are stored in a HDF5 database rather than returned
by the function.
Parameters
----------
algorithm : function
A function handle to the reconstruction algorithm.
path : str
The path of the HDF5 database where the results should be stored.
label : str
The label assigned to the simulation results.
"""
tmp_dir = _split_path(path)[0] + '.tmp' + os.sep
tmp_file = tmp_dir + label.replace('/', '#') + '.hdf5'
if not os.path.isdir(tmp_dir):
os.mkdir(tmp_dir)
if not os.path.isfile(tmp_file):
_backup.create(tmp_file)
done = _backup.get(tmp_file)
shape = _config.get()
shape = [len(shape['delta']), len(shape['rho']), shape['monte_carlo']]
np.random.seed(_config.get('seed'))
seeds = np.random.randint(0, 2**30, shape)
tasks = [(algorithm, (i, j), seeds[i, j], tmp_file)
for i in range(shape[0]) for j in range(shape[1])
if not done[i, j]]
_process(_simulate, args_list=tasks)
with _File(tmp_file, 'r') as f:
stat_time = f.root.time[:]
stat_dist = f.root.dist[:]
with _File(path, 'a') as f:
f.create_array('/' + label, 'time', stat_time, createparents=True)
f.create_array('/' + label, 'dist', stat_dist, createparents=True)
os.remove(tmp_file)
if len(os.listdir(tmp_dir)) == 0:
os.removedirs(tmp_dir)
def _save_output(output_path, name, fig, fig_ext, datasets):
"""
Save figure and data output.
Parameters
----------
output_path : str
The output_path to save to.
name : str
The 'fixed' part of the file name saved to.
fig : matplotlib.figure.Figure
The figure instance to save.
fig_ext : str
The file extension to use for the saved figure.
datasets : dict
The dict of dicts for datasets to save in a HDF database.
"""
@_decorate_validation
def validate_input():
_generic('output_path', 'string')
_generic('name', 'string')
_generic('fig', mpl.figure.Figure)
_generic('fig_ext', 'string')
_levels('datasets', (_generic(None, 'mapping'),
_generic(None, 'mapping')))
validate_input()
if output_path[-1] == os.sep:
path = output_path
prefix = ''
else:
path, prefix, no_ext = _split_path(output_path)
prefix = prefix + '_'
fig.savefig(path + prefix + name + os.path.extsep + fig_ext)
db_path = path + prefix + name + '.hdf5'
_io.create_database(db_path)
with _File(db_path, mode='a') as h5file:
data_group = h5file.create_group('/', 'data', __name__ + ': ' + name)
for dataset in datasets:
set_group = h5file.create_group(data_group, dataset, dataset)
for array in datasets[dataset]:
h5file.create_array(set_group, array, datasets[dataset][array])