def task_validate_rawdata_file(pk):
"""validates a rawdata file - that is an archive holding data to be analysed
:type pk: int
:param pk: pk for Data entity
:returns: bool -- True if Data validates, False else. Processing
log, including errors, will be written to the Data entity.
"""
# init and checks
valid = False
logger = Logger.get_logger(StringIO())
try:
df = Data.objects.get(id=pk)
assert df.kind == 'rd_file'
tr = df.content_object
except:
logger.log('ERROR')
return valid
try:
logger.log('looking at raw data file with pk: %s' % pk)
rd, sr = read_hdf5_arc(df.file.path)
logger.log('found rd_file: %s' % df.name)
len_rd_sec = rd.shape[0] / sr
logger.log('found data in %d channels, for %d sec' % (
rd.shape[1], len_rd_sec))
# TODO: more checks?
logger.log('rd_file passed all checks')
valid = True
except Exception, ex:
logger.log('ERROR: rawdata file check: %s' % str(ex))
def task_run_modules(ev, **kwargs):
"""core function to run all modules for an evaluation
:type ev: Evaluation
:param ev: Evaluation entity
:keyword: any, will be passed to modules as parameters
:returns: True on success, False on error
"""
success = None
try:
ev.status = ev.STATUS.running
ev.save()
mod_list = ev.trial.benchmark.module_set.all()
logger = Logger.get_logger(StringIO())
except:
success = False
else:
try:
logger.log_delimiter_line()
rd_file = ev.trial.rd_file
gt_file = ev.trial.gt_file
ev_file = ev.ev_file
logger.log('processing: %s' % ev)
logger.log('reading input files')
rd, sampling_rate = read_hdf5_arc(rd_file.file.path)
if sampling_rate is not None:
kwargs.update(sampling_rate=sampling_rate)
ev_sts = read_gdf_sts(ev_file.file.path)
gt_sts = read_gdf_sts(gt_file.file.path)
logger.log('done reading input files')
logger.log_delimiter_line()
# modules
assert len(mod_list), 'Module list is empty!'
for mod in mod_list:
logger.log('starting module: %s' % mod)
module_pkg = importlib.import_module('spike.module.%s' % mod.path)
_tick_ = datetime.now()
module = module_pkg.module_cls(rd, gt_sts, ev_sts, logger, **kwargs)
module.apply()
module.save(mod, ev)
_tock_ = datetime.now()
logger.log('finished: %s' % str(_tock_ - _tick_))
logger.log_delimiter_line()
del module, module_pkg
except Exception, ex:
logger.log('ERROR: (%s) %s' % (ex.__class__.__name__, str(ex)))
success = False
ev.status = ev.STATUS.failure
else:
def task_validate_spiketrain_file(pk):
"""validate a spiketrain file - that is a text file in gdf format (space separated, 2col, [key,time])
:type pk: int
:param pk: pk for Data entity
:returns: bool -- True if Data validates, False else. Processing
log, including errors, will be written to the Data entity.
"""
# init and checks
valid = False
logger = Logger.get_logger(StringIO())
try:
df = Data.objects.get(id=pk)
assert df.kind == 'st_file'
tr = df.content_object
except:
logger.log('ERROR')
return valid
try:
logger.log('looking at spiketrain file with pk: %s' % df.id)
sts = read_gdf_sts(df.file.path)
logger.log('found st_file: %s' % df.name)
for st in sts:
if not isinstance(sts[st], sp.ndarray):
raise TypeError('spike train %s not ndarray' % st)
if not sts[st].ndim == 1:
raise ValueError('spike trains have to be ndim==1')
# TODO: more checks?
logger.log('st_file passed all checks')
valid = True
except Exception, ex:
logger.log('ERROR: spiketrain file check: %s' % str(ex))
