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Commit b6ed499e authored by Jakob Knollmueller's avatar Jakob Knollmueller
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playing around, definetly working!

parent d3ef2730
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paper/1d_separation.py
View file @ b6ed499e
......@@ -15,7 +15,6 @@ if __name__ == '__main__':
h_space = s_space.get_default_codomain()
FFT = ift.FFTOperator(h_space)
p_spec = lambda k: (1./(1+k)**4)
mod_p_spec = lambda k: p_spec(k*1024)*1024**4
binbounds = ift.PowerSpace.useful_binbounds(h_space,logarithmic=False)#, nbin=100)
p_space = ift.PowerSpace(h_space,binbounds=binbounds)
k_lengths=p_space.k_lengths
......@@ -39,26 +38,30 @@ if __name__ == '__main__':
sextracted8 = fits.open('check8.fits')
sextracted8 = sextracted8[0].data
sextracted8 = sextracted8.clip(0.001)
dcnn_diffuse = np.load('tf_diffuse.npy')
dcnn_points = np.load('tf_points.npy')
dcnn_diffuse = dcnn_diffuse.clip(0.001)
dcnn_points = dcnn_points.clip(0.001)
energy1 = sb.build_starblade(data,1.0, newton_iterations=200, cg_iterations=3,
energy1 = sb.build_starblade(data,1.0, newton_iterations=200, cg_iterations=10,
q=q)#, manual_power_spectrum= p_spec)
energy2 = sb.build_starblade(data,1.5, newton_iterations=200, cg_iterations=3,
energy2 = sb.build_starblade(data,1.5, newton_iterations=200, cg_iterations=10,
q=q)#, manual_power_spectrum= p_spec)
energy3 = sb.build_starblade(data,3., newton_iterations=200, cg_iterations=3,
energy3 = sb.build_starblade(data,3., newton_iterations=200, cg_iterations=10,
q=q)#, manual_power_spectrum= p_spec)
# ift.extra.check_value_gradient_consistency(energy1, tol=1e-3)
for i in range(10):
energy1 = sb.starblade_iteration(energy1, samples=5)
energy2 = sb.starblade_iteration(energy2, samples=5)
energy3 = sb.starblade_iteration(energy3, samples=5)
print "error energy1:", np.sqrt(((ift.exp(energy1.s).val - ift.exp(s).val) ** 2).mean())
print "error energy2:", np.sqrt(((ift.exp(energy2.s).val - ift.exp(s).val) ** 2).mean())
print "error energy3:", np.sqrt(((ift.exp(energy3.s).val - ift.exp(s).val) ** 2).mean())
for i in range(5):
energy1 = sb.starblade_iteration(energy1, samples=3)
energy2 = sb.starblade_iteration(energy2, samples=3)
energy3 = sb.starblade_iteration(energy3, samples=3)
print "error energy1:", np.sqrt(((1 - ift.exp(s).val/ift.exp(energy1.s).val) ** 2).mean())
print "error energy2:", np.sqrt(((1 - ift.exp(s).val/ift.exp(energy2.s).val) ** 2).mean())
print "error energy3:", np.sqrt(((1 - ift.exp(s).val/ift.exp(energy3.s).val) ** 2).mean())
samples = []
n=30
for i in range(n):
samples.append(energy1.curvature.inverse.draw_sample())
samples.append(energy2.curvature.inverse.draw_sample())
m = 0
v = 0
s_s=0
......@@ -66,7 +69,7 @@ if __name__ == '__main__':
pos_tanh = ift.library.nonlinearities.PositiveTanh()
p=0
for sample in samples:
a_s = pos_tanh(sample)
a_s = pos_tanh(energy2.position+sample)
m += a_s
v += a_s**2
s_s += ift.log(d*(1-a_s))
......@@ -94,7 +97,7 @@ if __name__ == '__main__':
plt.savefig('2d_data.pdf')
# plt.figure(figsize=(8, 8))
# plt.plot(data, 'k-')
f, (ax0, ax1,ax2,ax3,ax4) = plt.subplots(5, sharex=True, sharey=True,figsize=(8, 10))
f, (ax0, ax1,ax2,ax3,ax4,ax5) = plt.subplots(6, sharex=True, sharey=True,figsize=(8, 12))
plt.suptitle('diffuse components', size=size)
for i in range(energy1.s.val.shape[0]):
......@@ -119,12 +122,17 @@ if __name__ == '__main__':
for i in range(energy1.s.val.shape[0]):
ax4.plot((sextracted8)[i].clip(0.0001), 'k-', alpha=(0.15 / (energy1.s.val.shape[0]) * i))
ax4.yaxis.set_label_position("right")
ax4.set_ylabel(r'sextractor'+'\n' + r'\textsf{BACK\_SIZE}'+r'$=8$', size=size)
ax5.set_ylabel(r'sextractor'+'\n' + r'\textsf{BACK\_SIZE}'+r'$=8$', size=size)
for i in range(energy1.s.val.shape[0]):
ax5.plot((dcnn_diffuse)[i].clip(0.0001), 'k-', alpha=(0.15 / (energy1.s.val.shape[0]) * i))
ax5.yaxis.set_label_position("right")
ax5.set_ylabel(r'DCAE', size=size)
plt.savefig('1d_diffuse.pdf')
plt.figure()
f, (ax0, ax1,ax2,ax3,ax4) = plt.subplots(5, sharex=True, sharey=True,figsize=(8, 10))
f, (ax0, ax1,ax2,ax3,ax4,ax5) = plt.subplots(6, sharex=True, sharey=True,figsize=(8, 12))
plt.suptitle('point-like components', size=size)
for i in range(energy1.s.val.shape[0]):
......@@ -149,6 +157,10 @@ if __name__ == '__main__':
ax4.plot((data-sextracted8)[i].clip(0.0001), 'k-', alpha=(0.15 / (energy1.s.val.shape[0]) * i))
ax4.yaxis.set_label_position("right")
ax4.set_ylabel(r'sextractor'+'\n' + r'\textsf{BACK\_SIZE}'+r'$=8$', size=size)
for i in range(energy1.s.val.shape[0]):
ax5.plot((dcnn_points)[i].clip(0.0001), 'k-', alpha=(0.15 / (energy1.s.val.shape[0]) * i))
ax5.set_ylabel(r'DCAE', size=size)
ax5.yaxis.set_label_position("right")
ax0.set_yscale("log")
......@@ -157,7 +169,7 @@ if __name__ == '__main__':
# plt.ylim(1e-0)
plt.savefig('1d_points.pdf')
plt.figure()
f, (ax0, ax1,ax2) = plt.subplots(3, sharex=True, sharey=True)
f, (ax0, ax1,ax2) = plt.subplots(3, sharex=True, sharey=True,figsize=(8, 6))
plt.suptitle('data and true components', size=size)
for i in range(energy1.s.val.shape[0]):
ax0.plot(data[i], 'k-', alpha=(0.15 / (energy1.s.val.shape[0]) * i))
......@@ -207,10 +219,23 @@ if __name__ == '__main__':
plt.ylabel('power',size=15)
plt.xlabel('harmonic mode',size=15)
plt.savefig('1d_power.pdf')
plt.figure()
plt.scatter(s.val, np.log(dcnn_diffuse), alpha = 0.2, c='k', label='DDCAE')
plt.scatter(s.val, energy2.s.val, alpha = 0.2, c='r', label = 'starblade')
plt.title('diffuse truth vs DDCAE and starblade')
plt.legend()
plt.savefig('scatter.pdf')
plt.close('all')
print "error energy1:", np.sqrt(((ift.exp(energy1.s).val-ift.exp(s).val)**2).mean())
print "error energy2:", np.sqrt(((ift.exp(energy2.s).val-ift.exp(s).val)**2).mean())
print "error energy3:", np.sqrt(((ift.exp(energy3.s).val-ift.exp(s).val)**2).mean())
print "error back_size8:", np.sqrt(((sextracted8-ift.exp(s).val)**2).mean())
print "error back_size64:", np.sqrt(((sextracted64-ift.exp(s).val)**2).mean())
print "flux error energy1:", np.sqrt((((1 - ift.exp(s).val / ift.exp(energy1.s).val) ** 2)*d.val/d.sum()).mean())
print "flux error energy2:", np.sqrt((((1 - ift.exp(s).val / ift.exp(energy2.s).val) ** 2)*d.val/d.sum()).mean())
print "flux error energy3:", np.sqrt((((1 - ift.exp(s).val / ift.exp(energy3.s).val) ** 2)*d.val/d.sum()).mean())
print "flux error back_size8:", (np.sqrt(((1-sextracted8/ift.exp(s).val)**2)*d.val/d.sum()).mean())
print "flux error back_size64:", (np.sqrt(((1-sextracted64/ift.exp(s).val)**2)*d.val/d.sum()).mean())
print "flux error dcnn:", (np.sqrt(((1-dcnn_diffuse/ift.exp(s).val)**2)*d.val/d.sum()).mean())
print "class error energy1:", np.sqrt(((1 - ift.exp(energy1.s).val/ift.exp(s).val) ** 2).mean())
print "class error energy2:", np.sqrt(((1 - ift.exp(energy2.s).val/ift.exp(s).val) ** 2).mean())
print "class error energy3:", np.sqrt(((1 - ift.exp(energy3.s).val/ift.exp(s).val) ** 2).mean())
print "class error back_size8:", np.sqrt(((1-sextracted8/ift.exp(s).val)**2).mean())
print "class error back_size64:", np.sqrt((((1-sextracted64/ift.exp(s).val)**2)).mean())
print "class error dcnn:", np.sqrt((((1-dcnn_diffuse/ift.exp(s).val)**2)).mean())
starblade/sugar.py
View file @ b6ed499e
......@@ -52,8 +52,8 @@ def build_starblade(data, alpha=1.5, q=1e-40, cg_iterations=100, newton_iteratio
if manual_power_spectrum is None:
initial_spectrum = ift.power_analyze(FFT.adjoint(ift.log(data)),
binbounds=p_space.binbounds)
initial_spectrum /= 100*(p_space.k_lengths+1.)**4
# initial_spectrum = ift.Field(p_space,val=1e-3)
# initial_spectrum /= 100*(p_space.k_lengths+1.)**4
initial_spectrum = ift.Field(p_space,val=1e-8)
update_power = True
......
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