def evalSystem(m, ens=False, bl=True, cid=0):
if not type(m[0]) in [tuple, list]:
nd = int((-5 + np.sqrt(25 + 8 * len(m))) / 2)
m = (m[:nd], m[nd:2 * nd], m[2 * nd:])
if bl:
stim = gwn.getstim('bl')
else:
stim = gwn.getstim('raw')
evts = acell.gmatchCell(stim, m[0], m[1], m[2], cid)
if not ens:
return evts
else:
return ece(stim, evts)
def __init__(self, pars):
self.compress = pars['compress']
self.clevel = pars['clevel']
self.doc = gwn.getstim('doc', pars['slen'])
self.snn = self.doc.find(timeseries_t, True)
self.ac = acell.acell()
self.cm = acell.compare()
self.nd = pars['nd']
Model.__init__(self, pars)
def stacnbackFig(m1, stac=.8):
stim = gwn.getstim('bl')
uc = ucse(stim)
rc = evalSystem(m1, True, cid=0)
iss = istac.istacspace(rc, uc, stac)['vecs']
plt.figure(3)
plt.clf()
_prow(rc, None, 0)
rc1 = np.dot(iss.transpose(), rc)
_prow(rc1, None, 1)
rc2 = np.dot(iss, rc1)
_prow(rc2, None, 2)
f = plt.figure(3)
f.subplots_adjust(left=.04, right=.99, bottom=.05, top=.99, wspace=.05, hspace=.05)
f.canvas.draw()
def llFig(m1, m2, compress='istac', clevel=.85):
if istac:
n = 2
else:
n = 1
stim = gwn.getstim('bl')
evts1 = evalSystem(m1, cid=0)
evts2 = evalSystem(m2, cid=1)
ll = compare(stim, evts1, evts2, compress='no')
f = plt.figure(2)
plt.clf()
plt.subplot(1, n, 1)
_llplt(ll)
if istac:
ll = compare(stim, evts1, evts2, compress=compress, clevel=clevel, report=report)
plt.subplot(1, n, 2)
_llplt(ll)
f.subplots_adjust(left=.05, right=.98, bottom=.1, top=.98, wspace=.05)
f.canvas.draw()
def runSystems(stimuli=STIMS_BAND_OL, systems=(M200_FO,), stimlength=800, length=60, lead=40,
label="A", date="", comments="", edet=(1.0, .01)):
'''
stimuli is a list of "band" parameter tuples. Systems is a list of
(off, mean, cov) test system parameter tuples. "l" is a stimulus length.
Constructs each stimulus in the stimulus list, and passes it to each
system.
'''
if not date:
date = time.strftime("%m%d%y")
s = gd.Doc()
s['label'] = 'iSTACe' + date + label
s['comments'] = comments
s['samplerate'] = 1000.0
s['stimlength'] = stimlength
s['enslength'] = length
s['enslead'] = lead
s['eventgenerator'] = 'peakdet'
for i, b in enumerate(stimuli):
sn = "s%i" % i
s[sn + '._'] = gwn.getstim('bl', stimlength, b)
s[sn + '.band'] = b
un = "u%i" % i
s[un + '._'] = ucse(s[sn + '._'], 10000, length)
for i, p in enumerate(systems):
mn = "m%i" % i
off, mu, sig = p
d = {'mu': mu, 'off': off, 'sigma': sig, 'cid': i, 'ejit': edet[0], 'ethresh': edet[1]}
s.set(mn, d)
for j in range(len(stimuli)):
en = "e%i_%i" % (i, j)
sn = "s%i" % j
d['outpath'] = en
d['stimpath'] = "->" + sn
s = s.fuse(acell(s, d)[0])
s[en + '.stim'] = {'_link': sn}
s[en + '.sys'] = {'_link': mn}
rn = "r%i_%i" % (i, j)
s[rn + '._'] = ece(s[sn + '._'], s[en + '._'], length, lead)
s[rn + '.stim'] = {'_link': sn}
s[rn + '.sys'] = {'_link': mn}
return s
def bigFig(m1, m2, compress='istac', clevel=.85):
stim = gwn.getstim('bl')
uc = ucse(stim)
rcse = evalSystem(m1, True, cid=0)
rcse2 = evalSystem(m2, True, cid=1)
rc = eqrc((rcse, rcse2))
iss = DRMODES[compress](rc, uc, clevel)
report('Using %i components' % iss.shape[0])
plt.figure(3)
plt.clf()
ll = lltest(rcse, rcse2)
_prow(rcse, rcse2, 0, 3, True, ll)
rc1 = np.dot(iss, rcse)
rc2 = np.dot(iss, rcse2)
ll = lltest(rc1, rc2)
_prow(rc1, rc2, 1, 3, True, ll)
rcb1 = np.dot(iss.transpose(), rc1)
rcb2 = np.dot(iss.transpose(), rc2)
#ll = lltest(rcb1, rcb2)
_prow(rcb1, rcb2, 2, 3, True, ll)
f = plt.figure(3)
f.subplots_adjust(left=.04, right=.99, bottom=.05, top=.99, wspace=.05, hspace=.05)
f.canvas.draw()
def run(self, doc, pars, out, messages):
paths = dict([(n, doc[pars[n]]) for n in ['stim1', 'stim2', 'evts1', 'evts2']])
ll = self.callWith(comp2s, paths, pars)
c12 = (ll[0, 0, 0] - ll[0, 1, 0]) / (ll[0, 0, 1] + ll[0, 1, 1])
c21 = (ll[1, 1, 0] - ll[1, 0, 0]) / (ll[1, 0, 1] + ll[1, 1, 1])
out[pars['outpath']] = min(c12, c21)
out[pars['outpath'] + '.raw'] = ll
compare2 = Compare2s()
if __name__ == "__main__":
import gwn
s = gwn.getstim().fuse(gwn.getstim(band=(1, 400)))
s['m1.outpath'] = 'evts1'
s['m1.stimpath'] = 'stim5_150'
d, r = acell(s, 'm1')
for st in r:
print(st)
s['m2.outpath'] = 'evts2'
s['m2.stimpath'] = 'stim1_400'
d2, r = acell(s, 'm2')
for st in r:
print(st)
d = d.fuse(d2)
d['stim1'] = s['stim5_150']
d['stim2'] = s['stim1_400']
cmo = {'evts1': 'evts1', 'evts2': 'evts2', 'stim1': 'stim1', 'stim2': 'stim2'}
print d['evts1']
def run(self, doc, pars, out, messages):
paths = dict([(n, doc[pars[n]]) for n in ['evts1', 'evts2', 'stim']])
ll = self.callWith(iscmp, paths, pars)
c12 = (ll[0, 0, 0] - ll[0, 1, 0]) / (ll[0, 0, 1] + ll[0, 1, 1])
c21 = (ll[1, 1, 0] - ll[1, 0, 0]) / (ll[1, 0, 1] + ll[1, 1, 1])
out[pars['outpath']] = min(c12, c21)
messages.append('ll values %s' % (str(ll[..., 0]),))
compare = Compare()
if __name__ == "__main__":
import gwn
s = gwn.getstim()
s['model.off'] = (9, 5, 1)
s['model.mu'] = (.2, -.5, 1)
s['model.sigma'] = (.3, -1, 1, .2, -1, .1)
s['model.cid'] = 0
s['model.outpath'] = 'events'
s['model.stimpath'] = 'stim5_150'
#print(ac.test(s, 'model'))
d, r = acell(s, 'model')
for st in r:
print(st)
s['model2.off'] = (5, 3, 1)
s['model2.mu'] = (.2, -.5, 1)
s['model2.sigma'] = (.3, -1, 1, .2, -1, .1)
s['model2.cid'] = 0
s['model2.outpath'] = 'events2'
