def main():
parser = OptionParser(usage=description)
parser.add_option("--saccade_data", help="Main data directory",
default='saccade_data')
parser.add_option("--db", help='Location of output Flydra db.')
(options, args) = parser.parse_args() #@UnusedVariable
if not options.db:
raise Exception('Please define FlydraDB directory using `--db`.')
verbose = True
flydra_db = FlydraDB(options.db, create=True)
matlab_dir = options.saccade_data
for group in os.listdir(matlab_dir):
group_dir = os.path.join(matlab_dir, group)
if not os.path.isdir(group_dir):
continue
if verbose:
print("Opening {0}".format(group))
for file in [file for file in os.listdir(group_dir)
if (file.startswith('magno_')) \
and file.endswith('.mat')]:
sample = file[file.index('_') + 1:file.index('.')]
if verbose:
print(" - Considering sample {0}".format(sample.__repr__()))
if not flydra_db.has_sample(sample):
flydra_db.add_sample(sample)
flydra_db.add_sample_to_group(sample, group)
# flydra_db.add_sample_to_group(sample, 'ros')
filename = os.path.join(group_dir, file)
exp_data, attributes = read_raw_data(filename)
consider_importing_processed(flydra_db, sample, exp_data, attributes)
flydra_db.set_attr(sample, 'species', attributes['species'])
flydra_db.set_attr(sample, 'background', attributes['background'])
flydra_db.set_table(sample, EXP_DATA_TABLE, exp_data)
flydra_db.close()
def main_filter(args):
parser = LenientOptionParser()
parser.add_option("--db", default='flydra_db', help="FlydraDB directory")
parser.add_option("--min_frames_per_track", default=400,
help="Minimum number of frames per track [= %default]")
parser.add_option("--confirm_problems",
help="Stop interactively on problems with log files'\
'(e.g.: cannot find valid obj_ids) [default: %default]",
default=False, action="store_true")
parser.add_option("--dynamic_model_name",
help="Smoothing dynamical model [default: %default]",
default="mamarama, units: mm")
parser.add_option("--debug_output", help="Creates debug figures.",
default=False, action="store_true")
parser.add_option("--nocache", help="Ignores already computed results.",
default=False, action="store_true")
parser.add_option("--smoothing", help="Uses Kalman-smoothed data.",
default=False, action="store_true")
(options, args) = parser.parse_args(args)
table_name = 'rows' # TODO: use constant
table_version = "smooth" if options.smoothing else "kf"
if not args:
raise UserError('No files or directories specified.')
if not os.path.exists(options.db):
os.makedirs(options.db)
db = FlydraDB(options.db)
good_files = get_good_files(where=args, pattern="*.kh5",
confirm_problems=options.confirm_problems)
if len(good_files) == 0:
logger.error("No good files to process")
sys.exit(1)
n = len(good_files)
for i in range(n):
(filename, obj_ids, stim_fname) = good_files[i]
logger.info('Sample %s/%s: %s' % (i + 1, n, filename))
# only maintain basename
stim = os.path.splitext(os.path.basename(stim_fname))[0]
sample_id = os.path.splitext(os.path.basename(filename))[0]
logger.info("File %d/%d %s %s %s " %
(i, n, str(filename), str(obj_ids), stim_fname))
if (db.has_sample(sample_id)
and db.has_table(sample_id, table_name, table_version)
and not options.nocache):
logger.info('Sample %r already computed; skipping.'
' (use --nocache to ignore)' % sample_id)
continue
all_data = []
for obj_id, rows in get_good_smoothed_tracks(#@UnusedVariable
filename=filename,
obj_ids=obj_ids,
min_frames_per_track=options.min_frames_per_track,
dynamic_model_name=options.dynamic_model_name,
use_smoothing=options.smoothing):
filtered = filter_rows(rows, options)
all_data.append(filtered)
if not all_data:
logger.info('Not enough data found for %r; skipping.' % sample_id)
continue
if not db.has_sample(sample_id):
db.add_sample(sample_id)
db.set_attr(sample_id, 'stim_fname', stim_fname)
db.set_attr(sample_id, 'stimulus', stim)
stim_xml = open(stim_fname).read()
db.set_attr(sample_id, 'stimulus_xml', stim_xml)
geometry = get_posts_info(stim_xml)
print(geometry)
db.set_attr(sample_id, 'posts', geometry['posts'])
if 'arena' in geometry:
db.set_attr(sample_id, 'arena', geometry['arena'])
db.add_sample_to_group(sample_id, stim)
if stim != 'nopost':
db.add_sample_to_group(sample_id, 'posts')
rows = numpy.concatenate(all_data)
db.set_table(sample=sample_id,
table=table_name,
data=rows,
version=table_version)
db.set_attr(sample_id, 'filter_time', datetime.now().strftime("%Y%m%d_%H%M%S"))
db.set_attr(sample_id, 'filter_host', platform.node())
db.set_attr(sample_id, 'filter_user', get_user())
db.set_attr(sample_id, 'filter_python_version', platform.python_version())
db.set_attr(sample_id, 'filter_numpy_version', numpy.version.version)
db.close()
