def test_UL():
# Signal definition
sig = harp.HARPix().add_iso(16)
sig.add_func(lambda d: np.exp(-d**2 / 2 / 20**2),
mode='dist',
center=(0, 0))
# Background definition
bg = harp.zeros_like(sig)
bg.add_func(lambda l, b: 0. / (b**2 + 1.)**0.5 + 1.0)
fluxes, noise, systematics, exposure = get_model_input(
[sig], bg, [dict(err=bg * 0.1, sigmas=[
20.,
], Sigma=None, nside=16)], bg * 100.)
m = Swordfish(fluxes, noise, systematics, exposure, solver='cg')
I = m.fishermatrix()
print I
ec = EffectiveCounts(m)
UL = ec.upperlimit(0.05, 0)
ULg = ec.upperlimit(0.05, 0, gaussian=True)
s, b = ec.effectivecounts(0, 1.0)
print "Total signal counts (theta = 1):", ec.counts(0, 1.0)
print "Eff. signal counts (theta = 1):", s
print "Eff. bkg counts (theta = 1) :", b
print "Upper limit on theta :", UL
print "Upper limit on theta (gaussian):", ULg
def test_covariance():
nside = 8
# Signal definition
sig = harp.HARPix().add_iso(nside).add_singularity((50, 50), .1, 50, n=100)
sig.add_func(lambda d: np.exp(-d**2 / 2 / 20**2),
mode='dist',
center=(0, 0))
sig.mul_sr()
# Covariance matrix definition
cov = HARPix_Sigma(sig)
bg = sig * .0
bg.data += 1
cov.add_systematics(err=bg * 0.1, sigmas=[
20.,
], Sigma=None, nside=nside)
x = np.zeros_like(sig.data)
x[901] = 1.
y = cov.dot(x)
sig.data = y
sig.div_sr()
z = sig.get_healpix(128)
hp.mollview(z, nest=True)
plt.savefig('test.eps')
def test_MW_dSph():
nside = 32
def plot_harp(h, filename):
m = h.get_healpix(128)
vmax = np.log10(m).max()
vmin = vmax - 2
hp.mollview(np.log10(m), nest=True, min=vmin, max=vmax)
plt.savefig(filename)
dims = ()
# Signal definition
spec = 1.
MW = harp.HARPix(dims=dims).add_iso(2).add_singularity((0, 0),
0.1,
2.,
n=10)
MW.add_func(lambda d: spec / (1 + d)**2, mode='dist', center=(0, 0))
pos = (50, 40)
# dSph = harp.HARPix(dims = dims).add_singularity(pos, 0.1, 20, n = 10)
# dSph.add_func(lambda d: 0.1*spec/(.1+d)**2, mode = 'dist', center=pos)
sig = MW #+ dSph
sig.data += .001 # EGBG
plot_harp(sig, 'sig.eps')
# Background definition
bg = harp.HARPix(dims=dims).add_iso(64)
bg.add_func(lambda l, b: 1 / (b + 1)**2)
plot_harp(bg, 'bg.eps')
# Covariance matrix definition
cov = harpix_Sigma(sig)
cov.add_systematics(err=bg * 0.1, sigma=10., Sigma=None, nside=0)
# Set up swordfish
fluxes = [sig.data.flatten()]
noise = bg.get_formatted_like(sig).data.flatten()
systematics = cov
exposure = np.ones_like(noise) * 0.001
m = sf.Swordfish(fluxes, noise, systematics, exposure, solver='direct')
F = m.effectiveinfoflux(0)
f = harp.HARPix.from_data(sig, F)
plot_harp(f, 'test.eps')
def test_infoflux():
sig = harp.HARPix().add_singularity((0, 0), .1, 50, n=100)
sig.data += 1.
bkg = sig
fluxes, noise, systematics, exposure = get_model_input([sig], bkg, None,
1.)
m = Swordfish(fluxes, noise, systematics, exposure)
F = m.effectiveinfoflux(0)
f = harp.HARPix.from_data(sig, F, div_sr=True)
m = f.get_healpix(256)
hp.mollview(m, nest=True)
plt.savefig('test.eps')
def test_simple():
def plot_harp(h, filename):
m = h.get_healpix(128)
hp.mollview(m, nest=True)
plt.savefig(filename)
dims = ()
nside = 16
# Signal definition
spec = 1.
sig = harp.HARPix(dims=dims).add_iso(nside).add_singularity((50, 50),
.1,
20,
n=100)
sig.add_func(lambda d: np.exp(-d**2 / 2 / 20**2),
mode='dist',
center=(0, 0))
sig.add_func(lambda d: 1 / (d + 1)**1, mode='dist', center=(50, 50))
sig.data += 0.1 # EGBG
plot_harp(sig, 'sig.eps')
sig.mul_sr()
# Background definition
bg = harp.zeros_like(sig)
bg.add_func(lambda l, b: 0. / (b**2 + 1.)**0.5 + 1.0)
plot_harp(bg, 'bg.eps')
bg.mul_sr()
#bg.print_info()
# Covariance matrix definition
cov = HARPix_Sigma(sig)
cov.add_systematics(err=bg * 0.1, sigmas=[
20.,
], Sigma=None, nside=64)
# Set up swordfish
fluxes = [sig.data.flatten()]
noise = bg.data.flatten()
systematics = cov
exposure = np.ones_like(noise) * 10000.
m = Swordfish(fluxes, noise, systematics, exposure, solver='cg')
F = m.effectiveinfoflux(0, thetas=[0.000], psi=1.)
f = harp.HARPix.from_data(sig, F)
f.div_sr()
plot_harp(f, 'test.eps')
quit()
def test():
H = harp.HARPix().add_iso(4).add_disc((80, 00), 40, 16).add_disc((-80, 00),
40, 16)
H.data += 1.
# Covariance matrix definition
cov = harpix_Sigma(H)
flat = harp.HARPix().add_iso(16, fill=1.)
cov.add_systematics(err=flat * .1, sigma=3, Sigma=None, nside=128)
# Set up swordfish
fluxes = [H.get_data(mul_sr=True)]
noise = fluxes[0]
expmap = noise * 0. + 1e5
systematics = cov
m = sf.Swordfish(fluxes, noise, systematics, expmap, solver='cg')
F = m.effectiveinfoflux(0)
f = harp.HARPix.from_data(H, F, div_sr=True)
print min(f.data), max(f.data)
m = f.get_healpix(128)
hp.mollview(m, nest=True)
plt.savefig("MW.eps")
def get_Jmap():
Interp_sig = get_los()
#sig = harp.HARPix(dims=dims).add_singularity((0,0), 1, 20, n = 10)
Jmap = harp.HARPix().add_iso(32).add_disc((0, 0), 20, 64)
Jmap.add_func(lambda d: Interp_sig(d), mode='dist', center=(0, 0))
return Jmap
def MW_dSph():
nside = 32
bg_hpx, exposure_hpx = get_BG()
dims = ()
J = get_Jmap()
Jex = get_extragalactic()
J = J + Jex
# Signal definition
spec = 1.
dSph = harp.HARPix(dims=dims).add_iso(1, fill=1.)
pos_list = [(50, 40), (-20, 30), (80, -80), (42, -15)]
Jlist = [3e21, 3e21, 6e21, 2e21]
for J0, pos in zip(Jlist, pos_list):
dSph = dSph.add_singularity(pos, 0.1, 5, n=10)
#dSph = harp.HARPix(dims = dims).add_disc(pos, 10, 62).add_disc((50,-40), 10, 32)
dSph.add_func(lambda d: J0 / (.1 + d)**2, mode='dist', center=pos)
sig = J + dSph
#sig.data += 1e21 # EGBG
plot_harp(sig, 'sig.eps', maxfac=1.0)
# Background definition
#bg = harp.HARPix(dims = dims).add_iso(64)
bg = harp.HARPix.from_healpix(bg_hpx, nest=False)
plot_harp(bg, 'bg.eps', maxfac=0.01)
exposure = harp.HARPix.from_healpix(exposure_hpx, nest=False)
# Covariance matrix definition
cov = harpix_Sigma(sig)
bg_flat = deepcopy(bg)
bg_flat.data *= 0
bg_flat.data += 1e-8
# cov.add_systematics(err = bg_flat*0.1, sigma = .25, Sigma = None, nside =64)
# cov.add_systematics(err = bg_flat*0.1, sigma = .5, Sigma = None, nside =16)
# cov.add_systematics(err = bg_flat*0.1, sigma = 1., Sigma = None, nside =16)
# cov.add_systematics(err = bg_flat*0.1, sigma = 2., Sigma = None, nside =64)
# cov.add_systematics(err = bg_flat*0.1, sigma = 3., Sigma = None, nside =64)
# cov.add_systematics(err = bg_flat*0.1, sigma = 4., Sigma = None, nside =64)
# cov.add_systematics(err = bg_flat*0.1, sigma = 7., Sigma = None, nside =64)
cov.add_systematics(err=bg_flat * 0.1, sigma=10., Sigma=None, nside=64)
cov.add_systematics(err=bg_flat * 0.3, sigma=1e10, Sigma=None, nside=8)
cov.add_systematics(err=bg_flat * 0.1, sigma=20., Sigma=None, nside=64)
#cov.add_systematics(err = bg_flat*0.1, sigma = 25., Sigma = None, nside = 1)
#cov.add_systematics(err = bg_flat*0.1, sigma = 30., Sigma = None, nside = 1)
#cov.add_systematics(err = bg_flat*0.1, sigma = 35., Sigma = None, nside = 1)
#cov.add_systematics(err = bg_flat*0.1, sigma = 40., Sigma = None, nside = 1)
# Set up swordfish
fluxes = [sig.get_data(mul_sr=True)]
noise = bg.get_formatted_like(sig).get_data(mul_sr=True)
expmap = exposure.get_formatted_like(sig).get_data() * 1e-4
systematics = cov
#systematics = None
m = sf.Swordfish(fluxes, noise, systematics, expmap, solver='cg')
F = m.effectiveinfoflux(0)
f = harp.HARPix.from_data(sig, F, div_sr=True)
plot_harp(f, 'MW.eps', maxfac=0.1)
def test_3d():
def plot_harp(h, filename, dims=()):
m = h.get_healpix(128, idxs=dims)
hp.mollview(m, nest=True)
plt.savefig(filename)
def plot_rock(h, filename):
nside = 32
N = h.dims[0]
npix = hp.nside2npix(nside)
rings = hp._pixelfunc.pix2ring(nside, np.arange(npix), nest=True)
T = harp.get_trans_matrix(h, nside)
data = harp.trans_data(T, h.data)
out = []
for r in range(1, 4 * nside):
mask = rings == r
out.append(data[mask].sum(axis=0))
out = np.array(out)
plt.imshow(out, aspect=0.8 * N / (4 * nside))
plt.xlabel("Energy [AU]")
plt.ylabel("Latitude [AU]")
plt.savefig(filename)
x = np.linspace(0, 10, 20)
nside = 16
dims = (len(x), )
# Signal definition
spec_sig = np.exp(-(x - 5)**2 / 2)
sig = harp.HARPix(dims=dims).add_iso(nside).add_singularity((50, 50),
1,
20,
n=10)
sig.add_func(lambda d: spec_sig * np.exp(-d**2 / 2 / 20**2),
mode='dist',
center=(0, 0))
sig.add_func(lambda d: spec_sig / (d + 1)**1, mode='dist', center=(50, 50))
sig.mul_sr()
# Background definition
bg = harp.zeros_like(sig)
spec_bg = x * 0. + 1.
bg.add_func(lambda l, b: spec_bg * (0. / (b**2 + 1.)**0.5 + 0.1))
bg.mul_sr()
# Covariance matrix definition
cov = HARPix_Sigma(sig)
corr = lambda x, y: np.exp(-(x - y)**2 / 2 / 3**2)
Sigma = get_sigma(x, corr)
cov.add_systematics(err=bg * 0.1, sigmas=[
20.,
], Sigma=Sigma, nside=16)
# Set up swordfish
fluxes = [sig.data.flatten()]
noise = bg.data.flatten()
systematics = cov
exposure = np.ones_like(noise) * 100.0
m = Swordfish(fluxes, noise, systematics, exposure, solver='cg')
F = m.effectiveinfoflux(0)
f = harp.HARPix.from_data(sig, F)
f.div_sr()
plot_harp(f, 'test.eps', dims=(11, ))
plot_rock(f, 'test2.eps')