def ThetaBox(thetas, sample_id = 1):
D = DataPrinter('')
for theta in thetas:
N_phi = 20;
phi_norms = linspace(1/(2.*N_phi), 1. - 1/ (2.*N_phi), N_phi)
file_name = 'sinusoidal_spike_train_N=1000_critical_theta=%d_%d'%(theta, sample_id)
print file_name
T_thresh = 128.0;
binnedTrain = BinnedSpikeTrain.initFromFile(file_name, phi_norms)
ps = binnedTrain._Train._params
abg_true = array((ps._alpha, ps._beta, ps._gamma))
print 'ps = ', ps.getParams()
regime_name = 'theta=%d'%int(ps._theta)
print regime_name
D.setRegime(regime_name,abg_true, Tsim=-1.0)
binnedTrain.pruneBins(None, N_thresh = 5, T_thresh=T_thresh)
D.addSample(sample_id, binnedTrain.getTf(), binnedTrain.getBinCount(), binnedTrain.getSpikeCount())
abg_init = initialize_right_2std(binnedTrain, cap_beta_gamma=True)
D.addEstimate(sample_id, 'Initializer', abg_init,.0, warnflag = 0)
#RELOAD ALL DATA:
binnedTrain = BinnedSpikeTrain.initFromFile(file_name, phi_norms)
binnedTrain.pruneBins(None, N_thresh = 1, T_thresh=T_thresh)
#Weighted Fortet:
start = time.clock()
abg_est, warnflag = FortetEstimatorSup(binnedTrain, abg_init, verbose = False)
finish = time.clock()
D.addEstimate(sample_id, 'Fortet', abg_est, finish-start, warnflag)
#Weighted F-P:
dx = .025; dt = FPMultiPhiSolver.calculate_dt(dx, abg_true, -1.0)
phis = binnedTrain.bins.keys();
S = FPMultiPhiSolver(binnedTrain.theta, phis,
dx, dt,
binnedTrain.getTf(), X_min = -1.0)
start = time.clock()
abg_est, warnflag = FPSupEstimator(S, binnedTrain, abg_init, verbose = False)
finish = time.clock()
D.addEstimate(sample_id, 'FP', abg_est, finish-start, warnflag)
del D
def CustomEstimate(spike_trains):
D = DataPrinter('')
for spike_train in spike_trains:
regime_name = spike_train[0];
sample_id = spike_train[1]
N_spikes = spike_train[2];
N_phi = spike_train[3];
phi_norms = linspace(1/(2.*N_phi), 1. - 1/ (2.*N_phi), N_phi)
base_name = 'sinusoidal_spike_train_N=%d_'%N_spikes
T_thresh = 128.0;
regime_label = base_name + regime_name
file_name = regime_label + '_' + str(sample_id)
print file_name
binnedTrain = BinnedSpikeTrain.initFromFile(file_name, phi_norms)
ps = binnedTrain._Train._params
abg_true = array((ps._alpha, ps._beta, ps._gamma))
D.setRegime(regime_name,abg_true, Tsim=-1.0)
binnedTrain.pruneBins(None, N_thresh = 5, T_thresh=T_thresh)
D.addSample(sample_id, binnedTrain.getTf(), binnedTrain.getBinCount(), binnedTrain.getSpikeCount())
abg_init = initialize_right_2std(binnedTrain)
abg_init[1] = amax([.1, abg_init[1]])
abg_init[2] = amax([.0, abg_init[2]])
D.addEstimate(sample_id, 'Initializer', abg_init,.0, warnflag = 0)
#RELOAD ALL DATA:
binnedTrain = BinnedSpikeTrain.initFromFile(file_name, phi_norms)
binnedTrain.pruneBins(None, N_thresh = 1, T_thresh=T_thresh)
#Weighted Fortet:
start = time.clock()
abg_est, warnflag = FortetEstimatorSup(binnedTrain, abg_init, verbose = True)
finish = time.clock()
D.addEstimate(sample_id, 'Fortet', abg_est, finish-start, warnflag)
#Weighted F-P:
dx = .025; dt = FPMultiPhiSolver.calculate_dt(dx, abg_true, -1.0)
phis = binnedTrain.bins.keys();
S = FPMultiPhiSolver(binnedTrain.theta, phis,
dx, dt,
binnedTrain.getTf(), X_min = -1.0)
start = time.clock()
abg_est, warnflag = FPSupEstimator(S, binnedTrain, abg_init, verbose = True)
finish = time.clock()
D.addEstimate(sample_id, 'FP', abg_est, finish-start, warnflag)
del D
def TestEstimate(N_spikes = 100, N_trains=5, N_phi=8):
print 'N_phi = ', N_phi
phi_norms = linspace(1/(2.*N_phi), 1. - 1/ (2.*N_phi), N_phi)
batch_start = time.clock()
base_name = 'sinusoidal_spike_train_N=%d_'%N_spikes
D = DataPrinter('')
for regime_name, T_thresh in zip(['superT'],
[64]):
regime_label = base_name + regime_name
for sample_id in xrange(1,N_trains +1):
file_name = regime_label + '_' + str(sample_id)
print file_name
binnedTrain = BinnedSpikeTrain.initFromFile(file_name, phi_norms)
ps = binnedTrain._Train._params
abg_true = array((ps._alpha, ps._beta, ps._gamma))
D.setRegime(regime_name,abg_true, Tsim=-1.0)
binnedTrain.pruneBins(None, N_thresh = 5, T_thresh=T_thresh)
D.addSample(sample_id, binnedTrain.getTf(), binnedTrain.getBinCount(), binnedTrain.getSpikeCount())
abg_init = initialize_right_2std(binnedTrain)
abg_init[1] = amax([.1, abg_init[1]])
abg_init[2] = amax([.0, abg_init[2]])
D.addEstimate(sample_id, 'Initializer', abg_init,.0, warnflag = 0)
#RELOAD ALL DATA:
binnedTrain = BinnedSpikeTrain.initFromFile(file_name, phi_norms)
binnedTrain.pruneBins(None, N_thresh = 1, T_thresh=T_thresh)
#Weighted Fortet:
start = time.clock()
abg_est, warnflag = FortetEstimatorSup(binnedTrain, abg_init, verbose = True)
finish = time.clock()
D.addEstimate(sample_id, 'Fortet', abg_est, finish-start, warnflag)
#Weighted F-P:
dx = .025; dt = FPMultiPhiSolver.calculate_dt(dx, abg_true, -1.0)
phis = binnedTrain.bins.keys();
S = FPMultiPhiSolver(binnedTrain.theta, phis,
dx, dt,
binnedTrain.getTf(), X_min = -1.0)
start = time.clock()
abg_est, warnflag = FPSupEstimator(S, binnedTrain, abg_init, verbose = True)
finish = time.clock()
D.addEstimate(sample_id, 'FP', abg_est, finish-start, warnflag)
del D
print 'batch time = ', (time.clock() - batch_start) / 3600.0, ' hrs'
