def testMainSingle(self, verbose=VERBOSE.PLOT):
import time
# setup
V = VERBOSE(verbose)
TF = 21
NC = 2
spike_proto_sc = sp.cos(sp.linspace(-sp.pi, 3 * sp.pi, TF))
spike_proto_sc *= sp.hanning(TF)
scale = sp.linspace(0, 2, TF)
xi1 = sp.vstack(
(spike_proto_sc * 5 * scale, spike_proto_sc * 4 * scale)).T
xi2 = sp.vstack((spike_proto_sc * .5 * scale[::-1],
spike_proto_sc * 9 * scale[::-1])).T
templates = sp.asarray([xi1, xi2])
LEN = 2000
noise = sp.randn(LEN, NC)
ce = TimeSeriesCovE(tf_max=TF, nc=NC)
ce.update(noise)
FB = BOTMNode(templates=templates, ce=ce, verbose=V, ovlp_taus=None)
signal = sp.zeros_like(noise)
NPOS = 4
POS = [(int(i * LEN / (NPOS + 1)), 100) for i in xrange(1, NPOS + 1)]
POS.append((100, 2))
POS.append((150, 2))
for pos, tau in POS:
signal[pos:pos + TF] += xi1
signal[pos + tau:pos + tau + TF] += xi2
x = sp.ascontiguousarray(signal + noise, dtype=sp.float32)
# test against
if V.has_print:
print '### constructed spike times ###'
test_u0 = sorted([t_tpl[0] for t_tpl in POS])
test_u1 = sorted([t_tpl[0] + t_tpl[1] for t_tpl in POS])
test_rval = {
0: sp.array(test_u0) + TF / 2,
1: sp.array(test_u1) + TF / 2
}
if V.has_print:
print test_rval
# sort
tic_o = time.clock()
FB(x)
toc_o = time.clock()
if V.has_print:
print '### sorting spike times ###'
print FB.rval
if V.has_plot:
FB.plot_template_set(show=False)
FB.plot_sorting(show=True)
if V.has_print:
print '###'
print 'duration:', toc_o - tic_o
for k in FB.rval:
assert_array_almost_equal(FB.rval[k], test_rval[k], decimal=0)
def test(debug=True):
from spikeplot import plt, mcdata
# setup
TF = 47
signal, noise, ce, temps = load_input_data(TF)
FB = BOTMNode(
templates=temps,
ce=ce,
adapt_templates=10,
learn_noise=False,
verbose=VERBOSE(debug * 10),
ovlp_taus=None,
chunk_size=500)
x = sp.ascontiguousarray(signal + noise, dtype=sp.float32)
# sort
FB.plot_xvft()
FB(x)
FB.plot_sorting()
print FB.rval
plt.show()
def testMainSingle(self, verbose=VERBOSE.PLOT):
import time
# setup
V = VERBOSE(verbose)
TF = 21
NC = 2
spike_proto_sc = sp.cos(sp.linspace(-sp.pi, 3 * sp.pi, TF))
spike_proto_sc *= sp.hanning(TF)
scale = sp.linspace(0, 2, TF)
xi1 = sp.vstack((spike_proto_sc * 5 * scale,
spike_proto_sc * 4 * scale)).T
xi2 = sp.vstack((spike_proto_sc * .5 * scale[::-1],
spike_proto_sc * 9 * scale[::-1])).T
templates = sp.asarray([xi1, xi2])
LEN = 2000
noise = sp.randn(LEN, NC)
ce = TimeSeriesCovE(tf_max=TF, nc=NC)
ce.update(noise)
FB = BOTMNode(
templates=templates,
ce=ce,
verbose=V,
ovlp_taus=None)
signal = sp.zeros_like(noise)
NPOS = 4
POS = [(int(i * LEN / (NPOS + 1)), 100) for i in xrange(1, NPOS + 1)]
POS.append((100, 2))
POS.append((150, 2))
for pos, tau in POS:
signal[pos:pos + TF] += xi1
signal[pos + tau:pos + tau + TF] += xi2
x = sp.ascontiguousarray(signal + noise, dtype=sp.float32)
# test against
if V.has_print:
print '### constructed spike times ###'
test_u0 = sorted([t_tpl[0] for t_tpl in POS])
test_u1 = sorted([t_tpl[0] + t_tpl[1] for t_tpl in POS])
test_rval = {0: sp.array(test_u0) + TF / 2, 1: sp.array(test_u1) + TF / 2}
if V.has_print:
print test_rval
# sort
tic_o = time.clock()
FB(x)
toc_o = time.clock()
if V.has_print:
print '### sorting spike times ###'
print FB.rval
if V.has_plot:
FB.plot_template_set(show=False)
FB.plot_sorting(show=True)
if V.has_print:
print '###'
print 'duration:', toc_o - tic_o
for k in FB.rval:
assert_array_almost_equal(FB.rval[k], test_rval[k], decimal=0)