def test_gauss(self):
x, N, mu, sigma = (1., 1., 0., 1.)
self.assertEqual(utils.gauss(x, N, mu, sigma), self.gaussian(x, N, mu, sigma))
N = 2.
self.assertEqual(utils.gauss(x, N, mu, sigma), self.gaussian(x, N, mu, sigma))
sigma = 2.
self.assertEqual(utils.gauss(x, N, mu, sigma), self.gaussian(x, N, mu, sigma))
x = 1e5
self.assertEqual(utils.gauss(x, N, mu, sigma), 0.)
def analyse(name):
data = genfromtxt('data/%s.tsv' % name, delimiter='\t', dtype=None,
names=['ext_timestamp', 'time_delta'])
time_delta = data['time_delta']
# Plot distribution
counts, bins = histogram(time_delta, bins=arange(-10.5, 11.5, 1))
plot = Plot()
plot.histogram(counts, bins)
x = (bins[1:] + bins[:-1]) / 2.
popt, pcov = curve_fit(gauss, x, counts, p0=(sum(counts), 0., 2.5))
plot.plot(x, gauss(x, *popt), mark=None)
print popt
plot.set_ylimits(min=0)
plot.set_ylabel('Counts')
plot.set_xlabel(r'Time delta [\si{\nano\second}]')
plot.save_as_pdf(name)
# Plot moving average
n = 5000
skip = 100
moving_average = convolve(time_delta, ones((n,)) / n, mode='valid')
plot = Plot()
timestamps = (data['ext_timestamp'][:-n + 1:skip] -
data['ext_timestamp'][0]) / 1e9 / 3600.
plot.plot(timestamps, moving_average[::skip], mark=None)
plot.set_xlimits(min=0)
plot.set_ylabel(r'time delta [\si{\nano\second}]')
plot.set_xlabel('timestamp [\si{\hour}]')
plot.save_as_pdf('moving_average_%s' % name)
def test_gauss_array(self):
"""Test for arrays of random values"""
n = 10000
x, N, mu = random.uniform(-100, 100, size=(3, n))
# sigma can not be 0
sigma = random.uniform(1e-15, 100, size=n)
value1 = utils.gauss(x, N, mu, sigma)
value2 = self.gaussian(x, N, mu, sigma)
self.assertTrue(all(abs(value1 - value2) < 1e-10))
def mod_ciampa_conv_full(a, b, d):
""" Based on Ciampa 1998: zenith angle distribution
Positive C parameter
"""
x = arange(0.1, 85, 0.1)
rx = radians(x)
result = sum([
gauss(rx, mod_ciampa(xi, a, b), rxi, radians(d / cos(rxi)))
for xi, rxi in zip(x, rx)
],
axis=0)
return rx, result
def plot_delta_histogram(ids, **kwargs):
""" Plot a histogram of the deltas
"""
if type(ids) is int:
ids = [ids]
# Bin width
bin_size = 1 # 2.5*n
# Begin Figure
plot = Plot()
for id in ids:
ext_timestamps, deltas = get(id)
low = floor(min(deltas))
high = ceil(max(deltas))
bins = np.arange(low - .5 * bin_size, high + bin_size, bin_size)
n, bins = np.histogram(deltas, bins)
bin_centers = (bins[:-1] + bins[1:]) / 2
popt, pcov = curve_fit(gauss,
bin_centers,
n,
p0=[.15, np.mean(deltas),
np.std(deltas)])
plot.histogram(n, bins)
plot.plot(bin_centers,
gauss(bin_centers, *popt),
mark=None,
linestyle='gray')
if kwargs.keys():
plot.set_title('Tijdtest ' + kwargs[kwargs.keys()[0]])
plot.set_label(r'$\mu={1:.1f}$, $\sigma={2:.1f}$'.format(*popt))
plot.set_xlabel(r'Time difference [ns]')
plot.set_ylabel(r'Counts')
plot.set_xlimits(low, high)
plot.set_ylimits(min=0.)
# Save Figure
if len(ids) == 1:
name = 'delta_histogram/tt_delta_hist_%03d' % ids[0]
elif kwargs.keys():
name = 'delta_histogram/tt_delta_hist_' + kwargs[kwargs.keys()[0]]
plot.save_as_pdf(PLOT_PATH + name)
print 'tt_analyse: Plotted histogram'
def plot_offset_distribution(ids, **kwargs):
"""Offset distribution"""
# Begin Figure
plot = Plot()
offsets = [
np.average([x for x in get(id)[1] if abs(x) < 100]) for id in ids
]
bins = np.arange(-70, 70, 2)
n, bins = np.histogram(offsets, bins)
plot.histogram(n, bins)
bin_centers = (bins[:-1] + bins[1:]) / 2
popt, pcov = curve_fit(gauss,
bin_centers,
n,
p0=[1., np.mean(offsets),
np.std(offsets)])
plot.plot(bin_centers,
gauss(bin_centers, *popt),
mark=None,
linestyle='gray')
if kwargs.keys():
plot.set_title('Tijdtest offset distribution ' +
kwargs[kwargs.keys()[0]])
plot.set_label(r'$\mu={1:.1f}$, $\sigma={2:.1f}$'.format(*popt))
plot.set_xlabel(r'Offset [\si{\nano\second}]')
plot.set_ylabel(r'Counts')
plot.set_ylimits(min=0)
# Save Figure
name = 'box/tt_offset_distribution'
if kwargs.keys():
name += kwargs[kwargs.keys()[0]]
plot.save_as_pdf(PLOT_PATH + name)
print 'tt_analyse: Plotted offsets'
def determine_station_timing_offsets(data):
"""Determine the offsets between the stations."""
c = .3
ref_station_number = 501
ref_d_off = DETECTOR_OFFSETS[ref_station_number]
ref_events = data.root.hisparc.cluster_amsterdam.station_501.events
ref_t = array([
where(
ref_events.col('t1') == -999, 9000,
ref_events.col('t1') - ref_d_off[0]),
where(
ref_events.col('t2') == -999, 9000,
ref_events.col('t2') - ref_d_off[1]),
where(
ref_events.col('t3') == -999, 9000,
ref_events.col('t3') - ref_d_off[2]),
where(
ref_events.col('t4') == -999, 9000,
ref_events.col('t4') - ref_d_off[3])
])
ref_min_t = ref_t.min(axis=0)
station_number = 510
d_off = DETECTOR_OFFSETS[station_number]
events = data.root.hisparc.cluster_amsterdam.station_510.events
t = array([
where(events.col('t1') == -999, 90000,
events.col('t1') - d_off[0]),
where(events.col('t2') == -999, 90000,
events.col('t2') - d_off[1]),
where(events.col('t3') == -999, 90000,
events.col('t3') - d_off[2]),
where(events.col('t4') == -999, 90000,
events.col('t4') - d_off[3])
])
min_t = t.min(axis=0)
dt = []
for event, ref_event in itertools.izip(events, ref_events):
if (ref_event['t_trigger'] in ERR or event['t_trigger'] in ERR):
dt.append(nan)
continue
dt.append((int(event['ext_timestamp']) -
int(ref_event['ext_timestamp'])) -
(event['t_trigger'] - ref_event['t_trigger']))
dt = array(dt)
dt = dt + (min_t - ref_min_t)
plot = Plot()
bins = linspace(-50, 50, 100)
y, bins = histogram(dt, bins=bins)
plot.histogram(y, bins)
x = (bins[:-1] + bins[1:]) / 2
try:
popt, pcov = curve_fit(gauss, x, y, p0=(len(dt), 0., 10))
station_offset = popt[1]
plot.draw_vertical_line(station_offset)
bins = linspace(-50, 50, 1000)
plot.plot(bins, gauss(bins, *popt), mark=None, linestyle='gray')
except RuntimeError:
station_offset = 0.
print station_offset
plot.set_title('Time difference, station 510-501')
plot.set_xlimits(-50, 50)
plot.set_ylimits(min=0)
plot.set_xlabel('$\Delta t$ [ns]')
plot.set_ylabel('Counts')
plot.save_as_pdf('station_offsets')
def plot_fit(x, y, popt, graph):
graph.plot(x - BIN_WIDTH / 2, y, mark=None, use_steps=True)
fit_x = arange(min(x), max(x), 0.1)
graph.plot(fit_x, gauss(fit_x, *popt), mark=None, linestyle='gray')
def combine_delta_data():
delta_data = genfromtxt('data/time_delta.tsv',
delimiter='\t',
dtype=None,
names=['ext_timestamp', 'time_delta'])
delta_data = {ets: td for ets, td in delta_data}
tm = []
ts = []
td = []
te = []
with tables.open_file('data/data.h5', 'r') as data:
for row in data.root.s99.events:
ets = row['ext_timestamp']
try:
dt = delta_data[ets]
except KeyError:
continue
tm.append(row['t1'])
ts.append(row['t3'])
td.append(dt)
te.append(ets)
tm = array(tm)
ts = array(ts)
td = array(td)
te = array(te)
filter = (tm >= 0) & (ts >= 0)
tm = tm.compress(filter)
ts = ts.compress(filter)
td = td.compress(filter)
te = te.compress(filter)
bins = arange(-10.25, 10.26, .5)
mplot = MultiPlot(3, 2, width=r'.4\linewidth', height=r'.3\linewidth')
mplot.show_xticklabels_for_all([(2, 0), (2, 1)])
mplot.show_yticklabels_for_all([(0, 0), (1, 1), (2, 0)])
mplot.set_xlimits_for_all(min=-11, max=11)
mplot.set_ylimits_for_all(min=0, max=1500)
mplot.set_ylabel('Counts')
mplot.set_xlabel(r'Time difference [\si{\nano\second}]')
plot = mplot.get_subplot_at(0, 0)
counts, bins = histogram(tm - ts, bins=bins)
popt = fit_timing_offset(counts / 3., bins)
plot.set_label(r'$t_{M} - t_{S}$, $%.1f\pm%.1f$\si{\nano\second}, scaled' %
(popt[1], popt[2]))
plot.histogram(counts / 3., bins)
plot.plot(bins, gauss(bins, *popt), mark=None, linestyle='gray')
plot = mplot.get_subplot_at(1, 0)
plot.set_label(r'$t_{\delta}$')
plot.histogram(*histogram(td, bins=bins))
plot = mplot.get_subplot_at(2, 0)
counts, bins = histogram(tm - (ts + td), bins=bins)
popt = fit_timing_offset(counts, bins)
plot.set_label(
r'$t_{M} - (t_{S} + t_{\delta})$, $%.1f\pm%.1f$\si{\nano\second}' %
(popt[1], popt[2]))
plot.histogram(counts, bins)
plot.plot(bins, gauss(bins, *popt), mark=None, linestyle='gray')
plot = mplot.get_subplot_at(0, 1)
plot.set_label(r'$t_{\delta}$ where $t_{M} - t_{S} == -5$')
plot.draw_vertical_line(-5, linestyle='gray')
plot.histogram(*histogram(td.compress(tm - ts == -5), bins=bins))
plot = mplot.get_subplot_at(1, 1)
plot.set_label(r'$t_{\delta}$ where $t_{M} - t_{S} == -2.5$')
plot.draw_vertical_line(-2.5, linestyle='gray')
plot.histogram(*histogram(td.compress(tm - ts == -2.5), bins=bins))
plot = mplot.get_subplot_at(2, 1)
plot.set_label(r'$t_{\delta}$ where $t_{M} - t_{S} == 0$')
plot.draw_vertical_line(0, linestyle='gray')
plot.histogram(*histogram(td.compress(tm - ts == 0), bins=bins))
mplot.save_as_pdf('dt_time_delta')
plot = Plot()
counts, xbins, ybins = histogram2d(tm - ts, td, bins=bins)
plot.histogram2d(counts, xbins, ybins, bitmap=True, type='reverse_bw')
plot.set_xlabel(r'$t_{M} - t_{S}$ [\si{\nano\second}]')
plot.set_ylabel(r'$t_{\delta}$ [\si{\nano\second}]')
plot.save_as_pdf('dt_vs_time_delta')
def plot_data_and_fit(x, y, popt, plot):
plot.plot(x - BIN_WIDTH / 2, y, mark=None, use_steps=True)
fit_x = arange(min(x), max(x), 0.1)
plot.plot(fit_x, gauss(fit_x, *popt), mark=None, linestyle='gray')