def boxplot_phi_reconstruction_results_for_MIP(table, N):
figure()
THETA = deg2rad(22.5)
DTHETA = deg2rad(5.)
bin_edges = linspace(-180, 180, 18)
x, r_dphi = [], []
d25, d50, d75 = [], [], []
for low, high in zip(bin_edges[:-1], bin_edges[1:]):
rad_low = deg2rad(low)
rad_high = deg2rad(high)
query = '(min_n134 >= N) & (rad_low < reference_phi) & (reference_phi < rad_high) & (abs(reference_theta - THETA) <= DTHETA)'
sel = table.readWhere(query)
dphi = sel[:]['reconstructed_phi'] - sel[:]['reference_phi']
dphi = (dphi + pi) % (2 * pi) - pi
r_dphi.append(rad2deg(dphi))
d25.append(scoreatpercentile(rad2deg(dphi), 25))
d50.append(scoreatpercentile(rad2deg(dphi), 50))
d75.append(scoreatpercentile(rad2deg(dphi), 75))
x.append((low + high) / 2)
#boxplot(r_dphi, positions=x, widths=1 * (high - low), sym='')
fill_between(x, d25, d75, color='0.75')
plot(x, d50, 'o-', color='black')
xlabel(r"$\phi_K$ [deg]")
ylabel(r"$\phi_H - \phi_K$ [deg]")
title(r"$N_{MIP} \geq %d, \quad \theta = 22.5^\circ \pm %d^\circ$" %
(N, rad2deg(DTHETA)))
xticks(linspace(-180, 180, 9))
axhline(0, color='black')
utils.saveplot(N)
graph = GraphArtist()
graph.draw_horizontal_line(0, linestyle='gray')
graph.shade_region(x, d25, d75)
graph.plot(x, d50, linestyle=None)
graph.set_xlabel(r"$\phi_K$ [\si{\degree}]")
graph.set_ylabel(r"$\phi_H - \phi_K$ [\si{\degree}]")
graph.set_xticks([-180, -90, '...', 180])
graph.set_xlimits(-180, 180)
graph.set_ylimits(-23, 23)
artist.utils.save_graph(graph, suffix=N, dirname='plots')
def boxplot_phi_reconstruction_results_for_MIP(table, N):
figure()
THETA = deg2rad(22.5)
DTHETA = deg2rad(5.)
bin_edges = linspace(-180, 180, 18)
x, r_dphi = [], []
d25, d50, d75 = [], [], []
for low, high in zip(bin_edges[:-1], bin_edges[1:]):
rad_low = deg2rad(low)
rad_high = deg2rad(high)
query = '(min_n134 >= N) & (rad_low < reference_phi) & (reference_phi < rad_high) & (abs(reference_theta - THETA) <= DTHETA)'
sel = table.read_where(query)
dphi = sel[:]['reconstructed_phi'] - sel[:]['reference_phi']
dphi = (dphi + pi) % (2 * pi) - pi
r_dphi.append(rad2deg(dphi))
d25.append(scoreatpercentile(rad2deg(dphi), 25))
d50.append(scoreatpercentile(rad2deg(dphi), 50))
d75.append(scoreatpercentile(rad2deg(dphi), 75))
x.append((low + high) / 2)
#boxplot(r_dphi, positions=x, widths=1 * (high - low), sym='')
fill_between(x, d25, d75, color='0.75')
plot(x, d50, 'o-', color='black')
xlabel(r"$\phi_K$ [deg]")
ylabel(r"$\phi_H - \phi_K$ [deg]")
title(r"$N_{MIP} \geq %d, \quad \theta = 22.5^\circ \pm %d^\circ$" % (N, rad2deg(DTHETA)))
xticks(linspace(-180, 180, 9))
axhline(0, color='black')
utils.saveplot(N)
graph = GraphArtist()
graph.draw_horizontal_line(0, linestyle='gray')
graph.shade_region(x, d25, d75)
graph.plot(x, d50, linestyle=None)
graph.set_xlabel(r"$\phi_K$ [\si{\degree}]")
graph.set_ylabel(r"$\phi_H - \phi_K$ [\si{\degree}]")
graph.set_xticks([-180, -90, '...', 180])
graph.set_xlimits(-180, 180)
graph.set_ylimits(-23, 23)
artist.utils.save_graph(graph, suffix=N, dirname='plots')
def boxplot_phi_reconstruction_results_for_MIP(group, N):
table = group.E_1PeV.zenith_22_5
figure()
bin_edges = linspace(-180, 180, 18)
x, r_dphi = [], []
d25, d50, d75 = [], [], []
for low, high in zip(bin_edges[:-1], bin_edges[1:]):
rad_low = deg2rad(low)
rad_high = deg2rad(high)
query = '(min_n134 >= N) & (rad_low < reference_phi) & (reference_phi < rad_high)'
sel = table.read_where(query)
dphi = sel[:]['reconstructed_phi'] - sel[:]['reference_phi']
dphi = (dphi + pi) % (2 * pi) - pi
r_dphi.append(rad2deg(dphi))
d25.append(scoreatpercentile(rad2deg(dphi), 25))
d50.append(scoreatpercentile(rad2deg(dphi), 50))
d75.append(scoreatpercentile(rad2deg(dphi), 75))
x.append((low + high) / 2)
fill_between(x, d25, d75, color='0.75')
plot(x, d50, 'o-', color='black')
xlabel(r"$\phi_{simulated}$ [deg]")
ylabel(r"$\phi_{reconstructed} - \phi_{simulated}$ [deg]")
#title(r"$N_{MIP} \geq %d, \quad \theta = 22.5^\circ$" % N)
xticks(linspace(-180, 180, 9))
axhline(0, color='black')
ylim(-15, 15)
utils.saveplot(N)
graph = GraphArtist()
graph.draw_horizontal_line(0, linestyle='gray')
graph.shade_region(x, d25, d75)
graph.plot(x, d50, linestyle=None)
graph.set_xlabel(r"$\phi_\mathrm{sim}$ [\si{\degree}]")
graph.set_ylabel(r"$\phi_\mathrm{rec} - \phi_\mathrm{sim}$ [\si{\degree}]")
graph.set_title(r"$N_\mathrm{MIP} \geq %d$" % N)
graph.set_xticks([-180, -90, '...', 180])
graph.set_xlimits(-180, 180)
graph.set_ylimits(-17, 17)
artist.utils.save_graph(graph, suffix=N, dirname='plots')
def boxplot_phi_reconstruction_results_for_MIP(group, N):
table = group.E_1PeV.zenith_22_5
figure()
bin_edges = linspace(-180, 180, 18)
x, r_dphi = [], []
d25, d50, d75 = [], [], []
for low, high in zip(bin_edges[:-1], bin_edges[1:]):
rad_low = deg2rad(low)
rad_high = deg2rad(high)
query = '(min_n134 >= N) & (rad_low < reference_phi) & (reference_phi < rad_high)'
sel = table.readWhere(query)
dphi = sel[:]['reconstructed_phi'] - sel[:]['reference_phi']
dphi = (dphi + pi) % (2 * pi) - pi
r_dphi.append(rad2deg(dphi))
d25.append(scoreatpercentile(rad2deg(dphi), 25))
d50.append(scoreatpercentile(rad2deg(dphi), 50))
d75.append(scoreatpercentile(rad2deg(dphi), 75))
x.append((low + high) / 2)
fill_between(x, d25, d75, color='0.75')
plot(x, d50, 'o-', color='black')
xlabel(r"$\phi_{simulated}$ [deg]")
ylabel(r"$\phi_{reconstructed} - \phi_{simulated}$ [deg]")
#title(r"$N_{MIP} \geq %d, \quad \theta = 22.5^\circ$" % N)
xticks(linspace(-180, 180, 9))
axhline(0, color='black')
ylim(-15, 15)
utils.saveplot(N)
graph = GraphArtist()
graph.draw_horizontal_line(0, linestyle='gray')
graph.shade_region(x, d25, d75)
graph.plot(x, d50, linestyle=None)
graph.set_xlabel(r"$\phi_\mathrm{sim}$ [\si{\degree}]")
graph.set_ylabel(r"$\phi_\mathrm{rec} - \phi_\mathrm{sim}$ [\si{\degree}]")
graph.set_title(r"$N_\mathrm{MIP} \geq %d$" % N)
graph.set_xticks([-180, -90, '...', 180])
graph.set_xlimits(-180, 180)
graph.set_ylimits(-17, 17)
artist.utils.save_graph(graph, suffix=N, dirname='plots')
def boxplot_core_distances_for_mips(group):
table = group.E_1PeV.zenith_22_5
figure()
r_list = []
r25, r50, r75 = [], [], []
x = []
for N in range(1, 5):
sel = table.read_where('min_n134 >= N')
r = sel[:]['r']
r_list.append(r)
x.append(N)
r25.append(scoreatpercentile(r, 25))
r50.append(scoreatpercentile(r, 50))
r75.append(scoreatpercentile(r, 75))
fill_between(x, r25, r75, color='0.75')
plot(x, r50, 'o-', color='black')
xticks(range(1, 5))
xlabel("Minimum number of particles")
ylabel("Core distance [m]")
#title(r"$\theta = 22.5^\circ$")
utils.saveplot()
graph = GraphArtist()
graph.shade_region(x, r25, r75)
graph.plot(x, r50, linestyle=None)
graph.set_xlabel("Minimum number of particles")
graph.set_ylabel(r"Core distance [\si{\meter}]")
graph.set_ylimits(min=0)
graph.set_xticks(range(5))
artist.utils.save_graph(graph, dirname='plots')
def boxplot_core_distances_for_mips(group):
table = group.E_1PeV.zenith_22_5
figure()
r_list = []
r25, r50, r75 = [], [], []
x = []
for N in range(1, 5):
sel = table.readWhere('min_n134 >= N')
r = sel[:]['r']
r_list.append(r)
x.append(N)
r25.append(scoreatpercentile(r, 25))
r50.append(scoreatpercentile(r, 50))
r75.append(scoreatpercentile(r, 75))
fill_between(x, r25, r75, color='0.75')
plot(x, r50, 'o-', color='black')
xticks(range(1, 5))
xlabel("Minimum number of particles")
ylabel("Core distance [m]")
#title(r"$\theta = 22.5^\circ$")
utils.saveplot()
graph = GraphArtist()
graph.shade_region(x, r25, r75)
graph.plot(x, r50, linestyle=None)
graph.set_xlabel("Minimum number of particles")
graph.set_ylabel(r"Core distance [\si{\meter}]")
graph.set_ylimits(min=0)
graph.set_xticks(range(5))
artist.utils.save_graph(graph, dirname='plots')
def plot_uncertainty_mip(table):
rec = DirectionReconstruction
# constants for uncertainty estimation
station = table.attrs.cluster.stations[0]
r1, phi1 = station.calc_r_and_phi_for_detectors(1, 3)
r2, phi2 = station.calc_r_and_phi_for_detectors(1, 4)
THETA = deg2rad(22.5)
DTHETA = deg2rad(5.)
DN = .1
LOGENERGY = 15
DLOGENERGY = .5
figure()
x, y, y2 = [], [], []
for N in range(1, 6):
x.append(N)
events = table.read_where('(abs(min_n134 - N) <= DN) & (abs(reference_theta - THETA) <= DTHETA) & (abs(log10(k_energy) - LOGENERGY) <= DLOGENERGY)')
print(len(events),)
errors = events['reference_theta'] - events['reconstructed_theta']
# Make sure -pi < errors < pi
errors = (errors + pi) % (2 * pi) - pi
errors2 = events['reference_phi'] - events['reconstructed_phi']
# Make sure -pi < errors2 < pi
errors2 = (errors2 + pi) % (2 * pi) - pi
#y.append(std(errors))
#y2.append(std(errors2))
y.append((scoreatpercentile(errors, 83) - scoreatpercentile(errors, 17)) / 2)
y2.append((scoreatpercentile(errors2, 83) - scoreatpercentile(errors2, 17)) / 2)
print()
print("mip: min_n134, theta_std, phi_std")
for u, v, w in zip(x, y, y2):
print(u, v, w)
print()
# Simulation data
sx, sy, sy2 = loadtxt(os.path.join(DATADIR, 'DIR-plot_uncertainty_mip.txt'))
# Uncertainty estimate
ex = linspace(1, 5, 50)
phis = linspace(-pi, pi, 50)
phi_errsq = mean(rec.rel_phi_errorsq(pi / 8, phis, phi1, phi2, r1, r2))
theta_errsq = mean(rec.rel_theta1_errorsq(pi / 8, phis, phi1, phi2, r1, r2))
#ey = TIMING_ERROR * std_t(ex) * sqrt(phi_errsq)
#ey2 = TIMING_ERROR * std_t(ex) * sqrt(theta_errsq)
R_list = [30, 20, 16, 14, 12]
with tables.open_file('master-ch4v2.h5') as data2:
mc = my_std_t_for_R(data2, x, R_list)
mc = sqrt(mc ** 2 + 1.2 ** 2 + 2.5 ** 2)
print(mc)
ey = mc * sqrt(phi_errsq)
ey2 = mc * sqrt(theta_errsq)
nx = linspace(1, 4, 100)
ey = spline(x, ey, nx)
ey2 = spline(x, ey2, nx)
# Plots
plot(x, rad2deg(y), '^', label="Theta")
plot(sx, rad2deg(sy), '^', label="Theta (sim)")
plot(nx, rad2deg(ey2))#, label="Estimate Theta")
plot(x, rad2deg(y2), 'v', label="Phi")
plot(sx, rad2deg(sy2), 'v', label="Phi (sim)")
plot(nx, rad2deg(ey))#, label="Estimate Phi")
# Labels etc.
xlabel("$N_{MIP} \pm %.1f$" % DN)
ylabel("Angle reconstruction uncertainty [deg]")
title(r"$\theta = 22.5^\circ \pm %d^\circ \quad %.1f \leq \log(E) \leq %.1f$" % (rad2deg(DTHETA), LOGENERGY - DLOGENERGY, LOGENERGY + DLOGENERGY))
legend(numpoints=1)
xlim(0.5, 4.5)
utils.saveplot()
print
graph = GraphArtist()
graph.plot(x, rad2deg(y), mark='o', linestyle=None)
graph.plot(sx, rad2deg(sy), mark='square', linestyle=None)
graph.plot(nx, rad2deg(ey2), mark=None)
graph.plot(x, rad2deg(y2), mark='*', linestyle=None)
graph.plot(sx, rad2deg(sy2), mark='square*', linestyle=None)
graph.plot(nx, rad2deg(ey), mark=None)
graph.set_xlabel(r"$N_\mathrm{MIP} \pm %.1f$" % DN)
graph.set_ylabel(r"Angle reconstruction uncertainty [\si{\degree}]")
graph.set_xlimits(max=4.5)
graph.set_ylimits(0, 40)
graph.set_xticks(range(5))
artist.utils.save_graph(graph, dirname='plots')
def plot_uncertainty_mip(group):
table = group.E_1PeV.zenith_22_5
rec = DirectionReconstruction
# constants for uncertainty estimation
station = table.attrs.cluster.stations[0]
r1, phi1 = station.calc_r_and_phi_for_detectors(1, 3)
r2, phi2 = station.calc_r_and_phi_for_detectors(1, 4)
R_list = get_median_core_distances_for_mips(group, range(1, 6))
figure()
x, y, y2 = [], [], []
for N in range(1, 5):
x.append(N)
events = table.read_where('min_n134>=%d' % N)
#query = '(n1 == N) & (n3 == N) & (n4 == N)'
#vents = table.read_where(query)
print len(events),
errors = events['reference_theta'] - events['reconstructed_theta']
# Make sure -pi < errors < pi
errors = (errors + pi) % (2 * pi) - pi
errors2 = events['reference_phi'] - events['reconstructed_phi']
# Make sure -pi < errors2 < pi
errors2 = (errors2 + pi) % (2 * pi) - pi
#y.append(std(errors))
#y2.append(std(errors2))
y.append((scoreatpercentile(errors, 83) - scoreatpercentile(errors, 17)) / 2)
y2.append((scoreatpercentile(errors2, 83) - scoreatpercentile(errors2, 17)) / 2)
print "YYY", rad2deg(scoreatpercentile(errors2, 83) - scoreatpercentile(errors2, 17))
plot(x, rad2deg(y), '^', label="Theta")
plot(x, rad2deg(y2), 'v', label="Phi")
Sx = x
Sy = y
Sy2 = y2
print
print "mip: min_n134, theta_std, phi_std"
for u, v, w in zip(x, y, y2):
print u, v, w
print
utils.savedata((x, y, y2))
# Uncertainty estimate
x = [1, 2, 3, 4, 5]
phis = linspace(-pi, pi, 50)
phi_errsq = mean(rec.rel_phi_errorsq(pi / 8, phis, phi1, phi2, r1, r2))
theta_errsq = mean(rec.rel_theta1_errorsq(pi / 8, phis, phi1, phi2, r1, r2))
y = TIMING_ERROR * std_t(x) * sqrt(phi_errsq)
y2 = TIMING_ERROR * std_t(x) * sqrt(theta_errsq)
mc = my_std_t_for_R(data, x, R_list)
for u, v in zip(mc, R_list):
print v, u, sqrt(u ** 2 + 1.2 ** 2), sqrt((.66 * u) ** 2 + 1.2 ** 2)
mc = sqrt(mc ** 2 + 1.2 ** 2)
y3 = mc * sqrt(phi_errsq)
y4 = mc * sqrt(theta_errsq)
nx = linspace(1, 4, 100)
y = spline(x, y, nx)
y2 = spline(x, y2, nx)
y3 = spline(x, y3, nx)
y4 = spline(x, y4, nx)
plot(nx, rad2deg(y), label="Gauss Phi")
plot(nx, rad2deg(y2), label="Gauss Theta")
plot(nx, rad2deg(y3), label="Monte Carlo Phi")
plot(nx, rad2deg(y4), label="Monte Carlo Theta")
# Labels etc.
xlabel("Minimum number of particles")
ylabel("Angle reconstruction uncertainty [deg]")
#title(r"$\theta = 22.5^\circ$")
legend(numpoints=1)
xlim(.5, 4.5)
utils.saveplot()
print
graph = GraphArtist()
graph.plot(Sx, rad2deg(Sy), mark='o', linestyle='only marks')
graph.plot(Sx, rad2deg(Sy2), mark='*', linestyle='only marks')
graph.plot(nx, rad2deg(y), mark=None, linestyle='dashed,smooth')
graph.plot(nx, rad2deg(y2), mark=None, linestyle='dashed,smooth')
graph.set_xlabel("Minimum number of particles")
graph.set_ylabel(r"Reconstruction uncertainty [\si{\degree}]")
graph.set_xticks(range(1, 5))
graph.set_ylimits(0, 32)
artist.utils.save_graph(graph, dirname='plots')
graph.plot(nx, rad2deg(y3), mark=None, linestyle='smooth')
graph.plot(nx, rad2deg(y4), mark=None, linestyle='smooth')
artist.utils.save_graph(graph, suffix='full', dirname='plots')
def plot_uncertainty_mip(table):
rec = DirectionReconstruction
# constants for uncertainty estimation
station = table.attrs.cluster.stations[0]
r1, phi1 = station.calc_r_and_phi_for_detectors(1, 3)
r2, phi2 = station.calc_r_and_phi_for_detectors(1, 4)
THETA = deg2rad(22.5)
DTHETA = deg2rad(5.)
DN = .1
LOGENERGY = 15
DLOGENERGY = .5
figure()
x, y, y2 = [], [], []
for N in range(1, 6):
x.append(N)
events = table.readWhere(
'(abs(min_n134 - N) <= DN) & (abs(reference_theta - THETA) <= DTHETA) & (abs(log10(k_energy) - LOGENERGY) <= DLOGENERGY)'
)
print len(events),
errors = events['reference_theta'] - events['reconstructed_theta']
# Make sure -pi < errors < pi
errors = (errors + pi) % (2 * pi) - pi
errors2 = events['reference_phi'] - events['reconstructed_phi']
# Make sure -pi < errors2 < pi
errors2 = (errors2 + pi) % (2 * pi) - pi
#y.append(std(errors))
#y2.append(std(errors2))
y.append(
(scoreatpercentile(errors, 83) - scoreatpercentile(errors, 17)) /
2)
y2.append(
(scoreatpercentile(errors2, 83) - scoreatpercentile(errors2, 17)) /
2)
print
print "mip: min_n134, theta_std, phi_std"
for u, v, w in zip(x, y, y2):
print u, v, w
print
# Simulation data
sx, sy, sy2 = loadtxt(os.path.join(DATADIR,
'DIR-plot_uncertainty_mip.txt'))
# Uncertainty estimate
ex = linspace(1, 5, 50)
phis = linspace(-pi, pi, 50)
phi_errsq = mean(rec.rel_phi_errorsq(pi / 8, phis, phi1, phi2, r1, r2))
theta_errsq = mean(rec.rel_theta1_errorsq(pi / 8, phis, phi1, phi2, r1,
r2))
#ey = TIMING_ERROR * std_t(ex) * sqrt(phi_errsq)
#ey2 = TIMING_ERROR * std_t(ex) * sqrt(theta_errsq)
R_list = [30, 20, 16, 14, 12]
with tables.openFile('master-ch4v2.h5') as data2:
mc = my_std_t_for_R(data2, x, R_list)
mc = sqrt(mc**2 + 1.2**2 + 2.5**2)
print mc
ey = mc * sqrt(phi_errsq)
ey2 = mc * sqrt(theta_errsq)
nx = linspace(1, 4, 100)
ey = spline(x, ey, nx)
ey2 = spline(x, ey2, nx)
# Plots
plot(x, rad2deg(y), '^', label="Theta")
plot(sx, rad2deg(sy), '^', label="Theta (sim)")
plot(nx, rad2deg(ey2)) #, label="Estimate Theta")
plot(x, rad2deg(y2), 'v', label="Phi")
plot(sx, rad2deg(sy2), 'v', label="Phi (sim)")
plot(nx, rad2deg(ey)) #, label="Estimate Phi")
# Labels etc.
xlabel("$N_{MIP} \pm %.1f$" % DN)
ylabel("Angle reconstruction uncertainty [deg]")
title(
r"$\theta = 22.5^\circ \pm %d^\circ \quad %.1f \leq \log(E) \leq %.1f$"
% (rad2deg(DTHETA), LOGENERGY - DLOGENERGY, LOGENERGY + DLOGENERGY))
legend(numpoints=1)
xlim(0.5, 4.5)
utils.saveplot()
print
graph = GraphArtist()
graph.plot(x, rad2deg(y), mark='o', linestyle=None)
graph.plot(sx, rad2deg(sy), mark='square', linestyle=None)
graph.plot(nx, rad2deg(ey2), mark=None)
graph.plot(x, rad2deg(y2), mark='*', linestyle=None)
graph.plot(sx, rad2deg(sy2), mark='square*', linestyle=None)
graph.plot(nx, rad2deg(ey), mark=None)
graph.set_xlabel(r"$N_\mathrm{MIP} \pm %.1f$" % DN)
graph.set_ylabel(r"Angle reconstruction uncertainty [\si{\degree}]")
graph.set_xlimits(max=4.5)
graph.set_ylimits(0, 40)
graph.set_xticks(range(5))
artist.utils.save_graph(graph, dirname='plots')
def plot_uncertainty_mip(group):
table = group.E_1PeV.zenith_22_5
rec = DirectionReconstruction
# constants for uncertainty estimation
station = table.attrs.cluster.stations[0]
r1, phi1 = station.calc_r_and_phi_for_detectors(1, 3)
r2, phi2 = station.calc_r_and_phi_for_detectors(1, 4)
R_list = get_median_core_distances_for_mips(group, range(1, 6))
figure()
x, y, y2 = [], [], []
for N in range(1, 5):
x.append(N)
events = table.readWhere('min_n134>=%d' % N)
#query = '(n1 == N) & (n3 == N) & (n4 == N)'
#vents = table.readWhere(query)
print len(events),
errors = events['reference_theta'] - events['reconstructed_theta']
# Make sure -pi < errors < pi
errors = (errors + pi) % (2 * pi) - pi
errors2 = events['reference_phi'] - events['reconstructed_phi']
# Make sure -pi < errors2 < pi
errors2 = (errors2 + pi) % (2 * pi) - pi
#y.append(std(errors))
#y2.append(std(errors2))
y.append(
(scoreatpercentile(errors, 83) - scoreatpercentile(errors, 17)) /
2)
y2.append(
(scoreatpercentile(errors2, 83) - scoreatpercentile(errors2, 17)) /
2)
print "YYY", rad2deg(
scoreatpercentile(errors2, 83) - scoreatpercentile(errors2, 17))
plot(x, rad2deg(y), '^', label="Theta")
plot(x, rad2deg(y2), 'v', label="Phi")
Sx = x
Sy = y
Sy2 = y2
print
print "mip: min_n134, theta_std, phi_std"
for u, v, w in zip(x, y, y2):
print u, v, w
print
utils.savedata((x, y, y2))
# Uncertainty estimate
x = [1, 2, 3, 4, 5]
phis = linspace(-pi, pi, 50)
phi_errsq = mean(rec.rel_phi_errorsq(pi / 8, phis, phi1, phi2, r1, r2))
theta_errsq = mean(rec.rel_theta1_errorsq(pi / 8, phis, phi1, phi2, r1,
r2))
y = TIMING_ERROR * std_t(x) * sqrt(phi_errsq)
y2 = TIMING_ERROR * std_t(x) * sqrt(theta_errsq)
mc = my_std_t_for_R(data, x, R_list)
for u, v in zip(mc, R_list):
print v, u, sqrt(u**2 + 1.2**2), sqrt((.66 * u)**2 + 1.2**2)
mc = sqrt(mc**2 + 1.2**2)
y3 = mc * sqrt(phi_errsq)
y4 = mc * sqrt(theta_errsq)
nx = linspace(1, 4, 100)
y = spline(x, y, nx)
y2 = spline(x, y2, nx)
y3 = spline(x, y3, nx)
y4 = spline(x, y4, nx)
plot(nx, rad2deg(y), label="Gauss Phi")
plot(nx, rad2deg(y2), label="Gauss Theta")
plot(nx, rad2deg(y3), label="Monte Carlo Phi")
plot(nx, rad2deg(y4), label="Monte Carlo Theta")
# Labels etc.
xlabel("Minimum number of particles")
ylabel("Angle reconstruction uncertainty [deg]")
#title(r"$\theta = 22.5^\circ$")
legend(numpoints=1)
xlim(.5, 4.5)
utils.saveplot()
print
graph = GraphArtist()
graph.plot(Sx, rad2deg(Sy), mark='o', linestyle='only marks')
graph.plot(Sx, rad2deg(Sy2), mark='*', linestyle='only marks')
graph.plot(nx, rad2deg(y), mark=None, linestyle='dashed,smooth')
graph.plot(nx, rad2deg(y2), mark=None, linestyle='dashed,smooth')
graph.set_xlabel("Minimum number of particles")
graph.set_ylabel(r"Reconstruction uncertainty [\si{\degree}]")
graph.set_xticks(range(1, 5))
graph.set_ylimits(0, 32)
artist.utils.save_graph(graph, dirname='plots')
graph.plot(nx, rad2deg(y3), mark=None, linestyle='smooth')
graph.plot(nx, rad2deg(y4), mark=None, linestyle='smooth')
artist.utils.save_graph(graph, suffix='full', dirname='plots')
