def aer_simple_stats(log):
events = collect_all(aer_load_log_generic(log))
sign = events["sign"]
h_all = aer_histogram(events)
h_plus = aer_histogram(events[sign == +1])
h_minus = aer_histogram(events[sign == -1])
return dict(h_all=h_all, h_plus=h_plus, h_minus=h_minus)
def aer_simple_stats(log):
events = collect_all(aer_load_log_generic(log))
sign = events['sign']
h_all = aer_histogram(events)
h_plus = aer_histogram(events[sign == +1])
h_minus = aer_histogram(events[sign == -1])
return dict(h_all=h_all, h_plus=h_plus, h_minus=h_minus)
def aer_stats_events_meat(log):
# events = collect_all(aer_load_log_generic(log))
events = aer_raw_events_from_file_all(log)
hist = aer_histogram(events)
_, coords = md_argmax(hist)
r = Report('index')
with r.subsection('sub') as sub:
report_for_one(sub, events, coords)
return r
def aer_stats_events_meat(log):
# events = collect_all(aer_load_log_generic(log))
events = aer_raw_events_from_file_all(log)
hist = aer_histogram(events)
_, coords = md_argmax(hist)
r = Report('index')
with r.subsection('sub') as sub:
report_for_one(sub, events, coords)
return r
def report_for_one(r, events, coords):
f = r.figure(caption='For coordinate %s' % str(coords))
h = aer_histogram(events).astype('float')
h[coords[0], coords[1]] = np.nan
one = get_for_one(events, coords)
min_f = 20.0
max_f = 5000.0
min_d = 1.0 / max_f
max_d = 1.0 / min_f
f.data_rgb('where', scale(h))
if False:
with f.plot('frequency') as pylab:
fbins = np.linspace(min_f, max_f, 1000)
pylab.hist(one['frequency'], bins=fbins)
pylab.xlabel('1/interval (Hz)')
with f.plot('delta') as pylab:
dbins = (1.0 / fbins)[::-1]
pylab.hist(one['delta'], bins=dbins)
ticks = np.linspace(min_d, max_d, 10)
labels = ['%d' % (x * 1000000) for x in ticks]
pylab.xticks(ticks, labels)
pylab.xlabel('interval (microseconds)')
time = one['timestamp']
time = time - time[0]
with f.plot('history') as pylab:
plus = one['sign'] == 1
minus = one['sign'] == -1
ones = np.ones(one.size)
pylab.plot(time[plus], 1 * ones[plus], 'rs')
pylab.plot(time[minus], -1 * ones[minus], 'bs')
t0 = time[0]
delta = 0.01
pylab.axis((t0, t0 + delta, -1.2, 1.2))
def report_for_one(r, events, coords):
f = r.figure(caption='For coordinate %s' % str(coords))
h = aer_histogram(events).astype('float')
h[coords[0], coords[1]] = np.nan
one = get_for_one(events, coords)
min_f = 20.0
max_f = 5000.0
min_d = 1.0 / max_f
max_d = 1.0 / min_f
f.data_rgb('where', scale(h))
if False:
with f.plot('frequency') as pylab:
fbins = np.linspace(min_f, max_f, 1000)
pylab.hist(one['frequency'], bins=fbins)
pylab.xlabel('1/interval (Hz)')
with f.plot('delta') as pylab:
dbins = (1.0 / fbins)[::-1]
pylab.hist(one['delta'], bins=dbins)
ticks = np.linspace(min_d, max_d, 10)
labels = ['%d' % (x * 1000000) for x in ticks]
pylab.xticks(ticks, labels)
pylab.xlabel('interval (microseconds)')
time = one['timestamp']
time = time - time[0]
with f.plot('history') as pylab:
plus = one['sign'] == 1
minus = one['sign'] == -1
ones = np.ones(one.size)
pylab.plot(time[plus], 1 * ones[plus], 'rs')
pylab.plot(time[minus], -1 * ones[minus], 'bs')
t0 = time[0]
delta = 0.01
pylab.axis((t0, t0 + delta, -1.2, 1.2))
def show_heat(pylab, events):
if events.size > 0:
h = aer_histogram(events)
else:
h = np.zeros((128, 128))
pylab.imshow(h)
def aer_events_hist(events):
return aer_histogram(events)
def aer_events_hist(events):
return aer_histogram(events)
def show_heat(pylab, events):
if events.size > 0:
h = aer_histogram(events)
else:
h = np.zeros((128, 128))
pylab.imshow(h)