def process_mat_into_hdf5(settings, target, data_type, N_jobs):
assert data_type in ('preictal', 'interictal', 'test')
print 'Loading data ...'
timer = Timer()
out_dir = os.path.join(settings.data_dir)
metadata = Metadata()
segments_processed = process_and_merge_segments(target, data_type, out_dir, metadata, N_jobs)
print 'Processed %d segments in %s' % (segments_processed, timer.pretty_str())
print data_type, 'Metadata', metadata
def load_data_mp(settings, target, data_type, pipeline, check_only=False, quiet=False, meta_only=False):
filename_out = single_filename_builder(target, data_type, settings.cache_dir, pipeline.get_name())
filename_out_exists = os.path.exists(filename_out)
if check_only:
return filename_out_exists
input_source = pipeline.get_input_source()
input_source_pipeline = input_source.get_pipeline()
if input_source_pipeline is not None:
if not load_data_mp(settings, target, data_type, input_source_pipeline, check_only=True, quiet=quiet, meta_only=meta_only):
if not quiet: print('Preparing input source', input_source_pipeline.get_name())
load_data_mp(settings, target, data_type, input_source_pipeline, check_only=False, quiet=quiet, meta_only=meta_only)
if not quiet: print('Input source ready')
if not quiet: print('Loading %s data ...' % data_type, end=' ')
timer = Timer()
# TODO(mike): re-implement tmpfile cleanup that isn't really slow in the face of the genetic algorithm
# spamming the disk with cross-validation score files.
# clear cache of tmp files
# regex = re.compile(r""".*\.pid\.(\d+)""")
# for file in glob.glob(os.path.join(settings.cache_dir, '*.tmp')):
# match = regex.match(file)
# assert match is not None
# pid = int(match.group(1))
# try:
# os.getpgid(pid)
# except:
# print 'Removing', file
# os.remove(file)
if not filename_out_exists:
# DEBUG
debug = False
# debug = True
if debug:
print('DEBUG')
process_data_job(settings, target, data_type, 0, 1, pipeline)
print('Done')
else:
pool = Pool(settings.N_jobs)
[pool.apply_async(process_data_job, [settings, target, data_type, i, settings.N_jobs, pipeline]) for i in range(settings.N_jobs)]
pool.close()
pool.join()
accum, accum_meta = accumulate_data(settings, target, data_type, pipeline.get_name(), quiet=quiet, meta_only=meta_only)
if not quiet: print('prepared %d segments in %s %s %s' % (accum_meta.num_segments, timer.pretty_str(), accum_meta.X_shape, pipeline.get_name()))
return accum, accum_meta
def process_mat_into_hdf5(settings, target, data_type, N_jobs):
assert data_type in ('preictal', 'interictal', 'test')
print('Loading data ...')
timer = Timer()
out_dir = os.path.join(settings.data_dir)
metadata = Metadata()
segments_processed = process_and_merge_segments(target, data_type, out_dir,
metadata, N_jobs)
print('Processed %d segments in %s' %
(segments_processed, timer.pretty_str()))
print(data_type, 'Metadata', metadata)
def load_data_mp(settings, target, data_type, pipeline, check_only=False, quiet=False, meta_only=False):
filename_out = single_filename_builder(target, data_type, settings.cache_dir, pipeline.get_name())
filename_out_exists = os.path.exists(filename_out)
if check_only:
return filename_out_exists
input_source = pipeline.get_input_source()
input_source_pipeline = input_source.get_pipeline()
if input_source_pipeline is not None:
if not load_data_mp(settings, target, data_type, input_source_pipeline, check_only=True, quiet=quiet, meta_only=meta_only):
if not quiet: print 'Preparing input source', input_source_pipeline.get_name()
load_data_mp(settings, target, data_type, input_source_pipeline, check_only=False, quiet=quiet, meta_only=meta_only)
if not quiet: print 'Input source ready'
if not quiet: print 'Loading %s data ...' % data_type,
timer = Timer()
# TODO(mike): re-implement tmpfile cleanup that isn't really slow in the face of the genetic algorithm
# spamming the disk with cross-validation score files.
# clear cache of tmp files
# regex = re.compile(r""".*\.pid\.(\d+)""")
# for file in glob.glob(os.path.join(settings.cache_dir, '*.tmp')):
# match = regex.match(file)
# assert match is not None
# pid = int(match.group(1))
# try:
# os.getpgid(pid)
# except:
# print 'Removing', file
# os.remove(file)
if not filename_out_exists:
# DEBUG
debug = False
# debug = True
if debug:
print 'DEBUG'
process_data_job(settings, target, data_type, 0, 1, pipeline)
print 'Done'
else:
pool = Pool(settings.N_jobs)
[pool.apply_async(process_data_job, [settings, target, data_type, i, settings.N_jobs, pipeline]) for i in range(settings.N_jobs)]
pool.close()
pool.join()
accum, accum_meta = accumulate_data(settings, target, data_type, pipeline.get_name(), quiet=quiet, meta_only=meta_only)
if not quiet: print 'prepared %d segments in %s %s %s' % (accum_meta.num_segments, timer.pretty_str(), accum_meta.X_shape, pipeline.get_name())
return accum, accum_meta
