def test_circular_tuning_model(self):
data = self.sc.parallelize([(1, array([1.5, 2.3, 6.2, 5.1, 3.4, 2.1]))])
s = array([-pi/2, -pi/3, -pi/4, pi/4, pi/3, pi/2])
model = TuningModel.load(s, "circular")
params = model.fit(data)
tol = 1E-4 # to handle rounding errors
assert(allclose(params.map(lambda (_, v): v).collect()[0], array([0.10692, 1.61944]), atol=tol))
def test_gaussian_tuning_model(self):
data = self.sc.parallelize([(1, array([1.5, 2.3, 6.2, 5.1, 3.4, 2.1]))])
s = array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6])
model = TuningModel.load(s, "gaussian")
params = model.fit(data)
tol = 1E-4 # to handle rounding errors
assert(allclose(params.map(lambda (_, v): v).collect()[0], array([0.36262, 0.01836]), atol=tol))
def test_circular_tuning_model(self):
data = self.sc.parallelize([(1, array([1.5, 2.3, 6.2, 5.1, 3.4,
2.1]))])
s = array([-pi / 2, -pi / 3, -pi / 4, pi / 4, pi / 3, pi / 2])
model = TuningModel.load(s, "circular")
params = model.fit(data)
tol = 1E-4 # to handle rounding errors
assert (allclose(params.map(lambda (_, v): v).collect()[0],
array([0.10692, 1.61944]),
atol=tol))
def test_gaussian_tuning_model(self):
data = self.sc.parallelize([(1, array([1.5, 2.3, 6.2, 5.1, 3.4,
2.1]))])
s = array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6])
model = TuningModel.load(s, "gaussian")
params = model.fit(data)
tol = 1E-4 # to handle rounding errors
assert (allclose(params.map(lambda (_, v): v).collect()[0],
array([0.36262, 0.01836]),
atol=tol))
choices=("raw", "dff", "dff-highpass", "sub"),
default="raw",
required=False)
parser.add_argument("--regressmodelfile", type=str)
parser.add_argument("--regressmode",
choices=("linear", "bilinear"),
help="form of regression")
args = parser.parse_args()
sc = SparkContext(args.master, "tuning")
if args.master != "local":
egg = glob.glob(os.path.join(os.environ['THUNDER_EGG'], "*.egg"))
sc.addPyFile(egg[0])
data = load(sc, args.datafile, args.preprocess)
tuningmodel = TuningModel.load(args.tuningmodelfile, args.tuningmode)
if args.regressmodelfile is not None:
# use regression results
regressmodel = RegressionModel.load(args.regressmodelfile,
args.regressmode)
betas, stats, resid = regressmodel.fit(data)
params = tuningmodel.fit(betas)
else:
# use data
params = tuningmodel.fit(data)
outputdir = args.outputdir + "-tuning"
save(params, outputdir, "params", "matlab")
# compute mean map
vals = Stats("mean").calc(data)
save(vals,outputdir,"mean_vals","matlab")
# compute local cor
if args.neighbourhood != 0:
cor = LocalCorr(neighborhood=args.neighbourhood).calc(data)
save(cor,outputdir,"local_corr","matlab")
# if stim argument is not default
if args.stim != '-':
# parse into different stim names
p = re.compile('-')
stims = p.split(args.stim)
# compute regression
for i in range(len(stims)):
modelfile = os.path.join(args.datafolder, args.basename + stims[i])
m = RegressionModel.load(modelfile, args.regressmode)
betas, stats, resid = m.fit(data)
t = TuningModel.load(modelfile, args.tuningmode)
tune = t.fit(betas)
out_name = "stats_" + stims[i]
save(stats, outputdir, out_name, "matlab")
out_name = "tune_" + stims[i]
save(tune, outputdir, out_name, "matlab")
