def test_kalman_vf(self):
te = dbfn.TaskEntry(2424, dbname='testing')
dec_record = te.decoder_record
dec = dec_record.load()
training_block = dbfn.TaskEntry(dec_record.entry_id, dbname='testing')
files = training_block.datafiles
print(files)
from riglib.bmi import extractor
extractor_cls = extractor.BinnedSpikeCountsExtractor
extractor_kwargs = dict()
extractor_kwargs['n_subbins'] = dec.n_subbins
dec_new = train.train_KFDecoder(files, extractor_cls, extractor_kwargs, train.get_plant_pos_vel, dec.ssm, dec.units, update_rate=dec.binlen, kin_source='task', pos_key='cursor', vel_key=None, tslice=dec.tslice)
for attr in dec.filt.model_attrs:
old_attr = getattr(dec.filt, attr)
new_attr = getattr(dec_new.filt, attr)
self.assertTrue(np.all((old_attr - new_attr) < 1e-10))
self.assertTrue(np.all(np.abs(dec_new.mFR - dec.mFR) < 1e-10))
self.assertTrue(np.all(np.abs(dec_new.sdFR - dec.sdFR) < 1e-10))
self.assertTrue(np.array_equal(dec_new.units, dec.units))
self.assertTrue(np.array_equal(dec_new.bounding_box[0], dec.bounding_box[0]))
self.assertTrue(np.array_equal(dec_new.bounding_box[1], dec.bounding_box[1]))
self.assertTrue(dec_new.states == dec.states)
self.assertTrue(dec_new.states_to_bound == dec.states_to_bound)
self.assertTrue(np.array_equal(dec_new.drives_neurons, dec.drives_neurons))
def load_decoder(self):
'''
Instantiate the neural encoder and "train" the decoder
'''
try:
self.encoder = pickle.load(
open(
os.path.expandvars(
'/storage/task_data/JoystickDrivenCursorOPS/encoder1.pkl'
)))
self.decoder = pickle.load(
open(
os.path.expandvars(
'/storage/task_data/JoystickDrivenCursorOPS/decoder1.pkl'
)))
except:
print "recreating encoder/decoder"
from riglib.bmi import sim_neurons
from riglib.bmi import train
n_units = 5
encoder = sim_neurons.CosEnc(n_neurons=n_units,
baselines=2,
mod_depth=3)
n_samples = 10000
vel = np.random.normal(size=(n_samples, 2)) * 4
spike_counts = np.zeros([n_units, n_samples])
for k in range(n_samples):
spike_counts[:, k] = encoder(vel[k])
ssm = train.endpt_2D_state_space
kin = np.zeros([6, n_samples])
kin[ssm.train_inds, :] = vel.T
units = np.vstack([np.arange(n_units), np.ones(n_units)]).T
self.decoder = train.train_KFDecoder(ssm, kin, spike_counts, units)
self.encoder = encoder
pickle.dump(
self.encoder,
open(
os.path.expandvars(
'/storage/task_data/JoystickDrivenCursorOPS/encoder1.pkl'
), 'w'))
pickle.dump(
self.decoder,
open(
os.path.expandvars(
'/storage/task_data/JoystickDrivenCursorOPS/decoder1.pkl'
), 'w'))
extractor_cls = extractor.LFPButterBPFPowerExtractor
extractor_kwargs = dict()
extractor_kwargs['channels'] = channels
# list of DataFile objects
datafiles = models.DataFile.objects.filter(entry_id=entry)
# key: a string representing a system name (e.g., 'plexon', 'blackrock', 'task', 'hdf')
# value: a single filename, or a list of filenames if there are more than one for that system
files = dict()
system_names = set(d.system.name for d in datafiles)
for system_name in system_names:
filenames = [
d.get_path() for d in datafiles if d.system.name == system_name
]
if system_name == 'blackrock':
files[system_name] = filenames # list of (one or more) files
else:
assert (len(filenames) == 1)
files[system_name] = filenames[0] # just one file
dec = train.train_KFDecoder(files,
extractor_cls,
extractor_kwargs,
train.get_plant_pos_vel,
ssm,
units,
update_rate=binlen,
tslice=tslice,
pos_key=pos_key)
if TRAIN_SPIKE:
channels = range(1, 7)
units = np.hstack([np.array(channels).reshape(-1,1), np.ones((len(channels),1), dtype=np.int)])
extractor_cls = extractor.BinnedSpikeCountsExtractor
extractor_kwargs = dict()
extractor_kwargs['units'] = units
extractor_kwargs['n_subbins'] = 1
else: # train an LFP decoder
channels = np.array(range(1, 9))
units = np.hstack([channels.reshape(-1, 1), np.zeros(channels.reshape(-1, 1).shape, dtype=np.int32)])
extractor_cls = extractor.LFPButterBPFPowerExtractor
extractor_kwargs = dict()
extractor_kwargs['channels'] = channels
# list of DataFile objects
datafiles = models.DataFile.objects.filter(entry_id=entry)
# key: a string representing a system name (e.g., 'plexon', 'blackrock', 'task', 'hdf')
# value: a single filename, or a list of filenames if there are more than one for that system
files = dict()
system_names = set(d.system.name for d in datafiles)
for system_name in system_names:
filenames = [d.get_path() for d in datafiles if d.system.name == system_name]
if system_name == 'blackrock':
files[system_name] = filenames # list of (one or more) files
else:
assert(len(filenames) == 1)
files[system_name] = filenames[0] # just one file
dec = train.train_KFDecoder(files, extractor_cls, extractor_kwargs, train.get_plant_pos_vel, ssm, units, update_rate=binlen, tslice=tslice, pos_key=pos_key)
import pickle
from riglib.bmi import train
pkl = pickle.load(open('/home/tecnalia/code/bmi3d/tests/ibmi/dec_br.p'))
from ismore.ismore_bmi_lib import StateSpaceArmAssist
import namelist
kin_ext = namelist.kin_extractors[pkl['kin_extractor']]
files = pkl['files']
extractor_cls = pkl['extractor_cls']
extractor_kwargs = pkl['extractor_kwargs']
kin_extractor = kin_ext
ssm = pkl['ssm']
units = pkl['units']
update_rate=0.1
tslice=None
kin_source='task'
pos_key='plant_pos'
vel_key=None
dec = train.train_KFDecoder(pkl['files'],
pkl['extractor_cls'],
pkl['extractor_kwargs'],
kin_ext,
StateSpaceArmAssist(),
pkl['units'],
update_rate=0.1,
tslice=None,
kin_source='task',
pos_key='plant_pos',
vel_key=None)