def etas(t_start, t_stop, mu, alpha, p, c, k, mmin, b):
#Generate background events
bg_spikes = poisson_generator.pg(t_start, t_stop, mu)
cat = np.empty((len(bg_spikes), 2))
cat[:, 0] = bg_spikes
cat[:, 1] = mags.gr_mags(len(bg_spikes), b, mmin)
#Create empty aray for generation information
cat_meta = np.empty((len(bg_spikes), 3))
cat_meta[:, 0] = 0 #generation number
cat_meta[:, 1] = 0 #parent ID
cat_meta[:, 2] = np.arange(len(bg_spikes)) # cluster number
rng = np.random.RandomState()
rng.seed
n_it = 0
while (n_it < np.size(cat, axis=0)):
event_time = cat[n_it, 0]
event_mag = cat[n_it, 1]
event_gen = cat_meta[n_it, 0] + 1
rate_max = rate_funcs.prod(alpha, event_mag,
mmin) * rate_funcs.mol_rate(0.0, k, c, p)
spikes = poisson_generator.pg(event_time, t_stop, rate_max)
#uniform random number on 0,1 for each spike
rn = np.array(rng.uniform(0, 1, len(spikes)))
#instantaneous rate for each spike
spike_rate = rate_funcs.prod(alpha, event_mag,
mmin) * rate_funcs.mol_rate(
spikes - event_time, k, c, p)
thin_spikes = spikes[rn < spike_rate / rate_max]
afters = np.empty((len(thin_spikes), 2))
afters[:, 0] = thin_spikes
afters[:, 1] = mags.gr_mags(len(thin_spikes), b, mmin)
afters_meta = np.empty((len(thin_spikes), 3))
afters_meta[:, 0] = event_gen
afters_meta[:, 1] = n_it
afters_meta[:, 2] = cat_meta[n_it, 2]
cat = np.append(cat, afters, axis=0)
cat_meta = np.append(cat_meta, afters_meta, axis=0)
n_it = n_it + 1
cat = cat[cat[:, 0].argsort()]
return cat
def etas(t_start, t_stop, mu, alpha, p, c, k, mmin, b):
#Generate background events
bg_spikes = poisson_generator.pg(t_start, t_stop, mu)
cat = np.empty((len(bg_spikes),2))
cat[:,0] = bg_spikes
cat[:,1] = mags.gr_mags(len(bg_spikes), b, mmin)
#Create empty aray for generation information
cat_meta = np.empty((len(bg_spikes),3))
cat_meta[:,0] = 0 #generation number
cat_meta[:,1] = 0 #parent ID
cat_meta[:,2] = np.arange(len(bg_spikes)) # cluster number
rng = np.random.RandomState()
rng.seed
n_it = 0
while (n_it < np.size(cat, axis=0)):
event_time = cat[n_it,0]
event_mag = cat[n_it,1]
event_gen = cat_meta[n_it,0] + 1
rate_max = rate_funcs.prod(alpha, event_mag, mmin) * rate_funcs.mol_rate(0.0, k, c, p)
spikes = poisson_generator.pg(event_time, t_stop, rate_max)
#uniform random number on 0,1 for each spike
rn = np.array(rng.uniform(0, 1, len(spikes)))
#instantaneous rate for each spike
spike_rate = rate_funcs.prod(alpha, event_mag, mmin)*rate_funcs.mol_rate(spikes-event_time, k, c, p)
thin_spikes = spikes[rn<spike_rate/rate_max]
afters = np.empty((len(thin_spikes),2))
afters[:,0] = thin_spikes
afters[:,1] = mags.gr_mags(len(thin_spikes), b, mmin)
afters_meta = np.empty((len(thin_spikes),3))
afters_meta[:,0] = event_gen
afters_meta[:,1] = n_it
afters_meta[:,2] = cat_meta[n_it,2]
cat = np.append(cat, afters, axis=0)
cat_meta = np.append(cat_meta, afters_meta, axis=0)
n_it = n_it + 1
cat = cat[cat[:,0].argsort()]
return cat
def iol_poisson_generator(t_start, t_stop, k, t_finish, p):
#set-up random number generator
rng = np.random.RandomState()
rng.seed
#Calculate max rate (here always at t_stop)
rate_max = rate_funcs.iol_rate(t_stop, k, t_finish, p)
#generate spikes for hom. poisson process at maximum rate
spikes = poisson_generator.pg(t_start, t_stop, rate_max)
#uniform random number on 0,1 for each spike
rn = np.array(rng.uniform(0, 1, len(spikes)))
#instantaneous rate for each spike
spike_rate = rate_funcs.iol_rate(spikes, k, t_finish, p)
het_spikes = spikes[rn < spike_rate / rate_max]
return het_spikes
def mol_poisson_generator(t_start, t_stop, k, c, p):
#set-up random number generator
rng = np.random.RandomState()
rng.seed
#Calculate max rate (here always at t_stop)
rate_max = rate_funcs.mol_rate(t_start, k, c, p)
#generate spikes for hom. poisson process at maximum rate
spikes = poisson_generator.pg(t_start, t_stop, rate_max)
#uniform random number on 0,1 for each spike
rn = np.array(rng.uniform(0, 1, len(spikes)))
#instantaneous rate for each spike
spike_rate = rate_funcs.mol_rate(spikes, k, c, p)
het_spikes = spikes[rn<spike_rate/rate_max]
return het_spikes
def CR_sim(t_start, t_stop, rate, mmin, b):
CR_spikes = poisson_generator.pg(t_start, t_stop, rate)
CR_mags = mags.gr_mags(len(CR_spikes), b, mmin)
cat = np.array((np.vstack((CR_spikes, CR_mags)).transpose()))
return cat
def CR_sim(t_start, t_stop, rate, mmin, b):
CR_spikes = poisson_generator.pg(t_start, t_stop, rate)
CR_mags = mags.gr_mags(len(CR_spikes), b, mmin)
cat = np.array((np.vstack((CR_spikes,CR_mags)).transpose()))
return cat