def save_new_dec(task_entry_id, dec_obj, suffix):
'''
Summary: Method to save decoder to DB -- saves to TE that original decoder came from
Input param: task_entry_id: original task to save decoder to
Input param: dec_obj: KF decoder new
Input param: suffix:
Output param:
'''
te = dbfn.TaskEntry(task_entry_id)
try:
te_id = te.te_id
except:
dec_nm = te.name
te_ix = re.search('te[0-9]',dec_nm)
ix = te_ix.start() + 2
sub_dec_nm = dec_nm[ix:]
te_ix_end = sub_dec_nm.find('_')
if te_ix_end == -1:
te_ix_end = len(sub_dec_nm)
te_id = int(sub_dec_nm[:te_ix_end])
old_dec_obj = te.decoder_record
if old_dec_obj is None:
old_dec_obj = faux_decoder_obj(task_entry_id)
trainbmi.save_new_decoder_from_existing(dec_obj, old_dec_obj, suffix=suffix)
def save_new_dec(task_entry_id, dec_obj, suffix):
'''
Summary: Method to save decoder to DB -- saves to TE that original decoder came from
Input param: task_entry_id: original task to save decoder to
Input param: dec_obj: KF decoder new
Input param: suffix:
Output param:
'''
te = dbfn.TaskEntry(task_entry_id)
try:
te_id = te.te_id
except:
dec_nm = te.name
te_ix = re.search('te[0-9]',dec_nm)
ix = te_ix.start() + 2
sub_dec_nm = dec_nm[ix:]
te_ix_end = sub_dec_nm.find('_')
if te_ix_end == -1:
te_ix_end = len(sub_dec_nm)
te_id = int(sub_dec_nm[:te_ix_end])
old_dec_obj = te.decoder_record
if old_dec_obj is None:
old_dec_obj = faux_decoder_obj(task_entry_id)
trainbmi.save_new_decoder_from_existing(dec_obj, old_dec_obj, suffix=suffix)
def add_fa_dict_to_decoder(decoder_training_te, dec_ix, fa_te, return_dec=False):
# First make sure we're training from the correct task entry: spike counts n_units == BMI units
from db import dbfunctions as dbfn
te = dbfn.TaskEntry(fa_te)
hdf = te.hdf
sc_n_units = hdf.root.task[0]["spike_counts"].shape[0]
from db.tracker import models
te_arr = models.Decoder.objects.filter(entry=decoder_training_te)
search_flag = 1
for te in te_arr:
ix = te.path.find("_")
if search_flag:
if int(te.path[ix + 1 : ix + 3]) == dec_ix:
decoder_old = te
search_flag = 0
if search_flag:
raise Exception("No decoder from ", str(decoder_training_te), " and matching index: ", str(dec_ix))
from tasks.factor_analysis_tasks import FactorBMIBase
FA_dict = FactorBMIBase.generate_FA_matrices(fa_te)
import pickle
dec = pickle.load(open(decoder_old.filename))
dec.trained_fa_dict = FA_dict
dec_n_units = dec.n_units
if dec_n_units != sc_n_units:
raise Exception("Cant use TE for BMI training and FA training -- n_units mismatch")
if return_dec:
return dec
else:
from db import trainbmi
trainbmi.save_new_decoder_from_existing(dec, decoder_old, suffix="_w_fa_dict_from_" + str(fa_te))
def conv_KF_to_splitFA_dec(decoder_training_te, dec_ix, fa_te, search_suffix="w_fa_dict_from_"):
from db import dbfunctions as dbfn
te = dbfn.TaskEntry(fa_te)
hdf = te.hdf
sc_n_units = hdf.root.task[0]["spike_counts"].shape[0]
from db.tracker import models
te_arr = models.Decoder.objects.filter(entry=decoder_training_te)
search_flag = 1
for te in te_arr:
ix = te.path.find("_")
if search_flag:
if int(te.path[ix + 1 : ix + 3]) == dec_ix:
decoder = pickle.load(open(te.filename))
if hasattr(decoder, "trained_fa_dict"):
ix = te.path.find("w_fa_dict_from_")
if ix > 1:
fa_te_train = te.path[ix + len(search_suffix) : ix + len(search_suffix) + 4]
if int(fa_te_train) == fa_te:
decoder_old = te
# search_flag = 0
# if search_flag:
# raise Exception('No decoder from ', str(decoder_training_te), ' and matching index: ', str(dec_ix), ' with FA training from: ',str(fa_te))
# else:
print "Using old decoder: ", decoder_old.path
decoder = pickle.load(open(decoder_old.filename))
if hasattr(decoder, "trained_fa_dict"):
FA_dict = decoder.trained_fa_dict
else:
raise Exception("Make an FA dict decoder first, then re-train that")
from db import dbfunctions as dbfn
te_id = dbfn.TaskEntry(fa_te)
files = dict(plexon=te_id.plx_filename, hdf=te_id.hdf_filename)
extractor_cls = decoder.extractor_cls
extractor_kwargs = decoder.extractor_kwargs
extractor_kwargs["discard_zero_units"] = False
kin_extractor = get_plant_pos_vel
ssm = decoder.ssm
update_rate = binlen = decoder.binlen
units = decoder.units
tslice = (0.0, te_id.length)
## get kinematic data
kin_source = "task"
tmask, rows = _get_tmask(files, tslice, sys_name=kin_source)
kin = kin_extractor(files, binlen, tmask, pos_key="cursor", vel_key=None)
## get neural features
neural_features, units, extractor_kwargs = get_neural_features(
files, binlen, extractor_cls.extract_from_file, extractor_kwargs, tslice=tslice, units=units, source=kin_source
)
# Get main shared input:
T = neural_features.shape[0]
demean = neural_features.T - np.tile(FA_dict["fa_mu"], [1, T])
# Neural features in time x spikes:
z = FA_dict["u_svd"].T * FA_dict["uut_psi_inv"] * demean
shar_z = FA_dict["fa_main_shared"] * demean
priv = demean - shar_z
# Time by features:
neural_features2 = np.vstack((z, priv))
decoder_split = train_KFDecoder_abstract(ssm, kin.T, neural_features2, units, update_rate, tslice=tslice)
decoder_split.n_features = len(units)
decoder_split.trained_fa_dict = FA_dict
decoder_split.extractor_cls = extractor_cls
decoder_split.extractor_kwargs = extractor_kwargs
from db import trainbmi
trainbmi.save_new_decoder_from_existing(decoder_split, decoder_old, suffix="_split")
