paper plots

demos/demo.py

@@ -64,7 +64,7 @@ if __name__ == '__main__':
     plt.imsave('point_sex.png', (data_unmod - sex), vmax = lin_max,vmin =lin_min)
 
     alpha = 1.4
-    Starblade = sb.build_starblade(data, alpha=alpha,cg_iterations=100,newton_iterations=10)
+    Starblade = sb.build_starblade(data, alpha=alpha, cg_steps=100, newton_steps=10)
     for i in range(1000):
         Starblade = sb.starblade_iteration(Starblade, samples=5)
 

demos/sextractor/compare_sex.py

@@ -70,7 +70,7 @@ if __name__ == '__main__':
     plt.imsave('log_point_sex.png', np.log((data_unmod - sex).clip(min=0.0001)), vmax = vmax,vmin =vmin)
 
     alpha = 1.4
-    Starblade = sb.build_starblade(data, alpha=alpha,cg_iterations=100,newton_iterations=3)
+    Starblade = sb.build_starblade(data, alpha=alpha, cg_steps=100, newton_steps=3)
     for i in range(10):
         Starblade = sb.starblade_iteration(Starblade, samples=5)
 

paper/DDCAE.py

+from keras.layers import Activation, Dense, Input
+from keras.layers import Conv2D, Flatten
+from keras.layers import Reshape, Conv2DTranspose, BatchNormalization, merge, Dropout
+from keras.models import Model
+
+
+def DDCAE_model(image_size):
+    input_shape = (image_size, image_size,1)
+    batch_size = 32
+    kernel_size = 3
+    latent_dim = 8
+    # Encoder/Decoder number of CNN layers and filters per layer
+    layer_filters = [32,16]
+
+    # Build the Autoencoder Model
+    # First build the Encoder Model
+    inputs = Input(shape=input_shape, name='encoder_input')
+    x = inputs
+    # Stack of Conv2D blocks
+    # Notes:
+    # 1) Use Batch Normalization before ReLU on deep networks
+    # 2) Use MaxPooling2D as alternative to strides>1
+    # - faster but not as good as strides>1
+    for filters in layer_filters:
+        x = Conv2D(filters=filters,
+                   kernel_size=kernel_size,
+                   strides=1,
+                   activation='relu',
+                   padding='same',
+                   data_format='channels_last')(x)
+        # x = BatchNormalization()(x)
+        x = Dropout(0.1)(x)
+
+    for filters in layer_filters[::-1]:
+        x = Conv2DTranspose(filters=filters,
+                            kernel_size=kernel_size,
+                            strides=1,
+                            activation='relu',
+                            padding='same',
+                            data_format='channels_last')(x)
+        # x = BatchNormalization()(x)
+        x = Dropout(0.1)(x)
+
+    x = Conv2DTranspose(filters=1,
+                        kernel_size=kernel_size,
+                        padding='same')(x)
+
+    outputs = Activation('sigmoid', name='decoder_output')(x)
+    outputs = merge([inputs,outputs],mode='mul')
+    return  Model(inputs, outputs, name='autoencoder')

paper/hubble_separation.py

@@ -9,32 +9,40 @@ from matplotlib.colors import LogNorm
 from mpl_toolkits.axes_grid1 import make_axes_locatable
 from mpl_toolkits.axes_grid1 import AxesGrid
 from astropy.io import fits
-
+from scipy.ndimage import zoom
 import starblade as sb
-
-
+from DDCAE import DDCAE_model
 
 
 np.random.seed(42)
 if __name__ == '__main__':
-    path = 'demos/data/hst_05195_01_wfpc2_f702w_pc_sci.fits'
+    path = '../demos/data/hst_05195_01_wfpc2_f702w_pc_sci.fits'
     data = fits.open(path)[1].data
 
-    data = data.clip(min=0.001)
+    # data = data.clip(min=0.001)
 
     data = np.ndarray.astype(data, float)
-    alpha = 1.28
+    #data = zoom(data, 0.25)
+    data = data.clip(min=0.001)
+
+    hdu = fits.PrimaryHDU(data)
+    hdul = fits.HDUList([hdu])
+    hdul.writeto('my_hubble_data.fits',overwrite=True)
+
+
+    alpha = 1.1
 
 
 
     myEnergy = sb.build_starblade(data, alpha=alpha)
-    for i in range(10):
-        myEnergy = sb.starblade_iteration(myEnergy)
-    A = FFTSmoothingOperator(myEnergy.s.domain, sigma=2.)
+    for i in range(30):
+        myEnergy = sb.starblade_iteration(myEnergy, samples=5,newton_steps=30,cg_steps=10, sampling_steps=100)
+        print i
+    A = FFTSmoothingOperator(myEnergy.s.domain, sigma=.002)
     plt.magma()
     size = 15
     vmin = data.min()+0.01
-    vmax = 0.01*data.max()
+    vmax = data.max()*0.01
     plt.figure()
     plt.title('diffuse emission', size=size)
     plt.axis('off')
@@ -81,16 +89,16 @@ if __name__ == '__main__':
 
     plt.suptitle('zoomed in section', size=size)
     # fig.tight_layout()
-    vmin = data.min() + 0.0001
-    vmax = 0.001 * data.max()
-    im = ax[0].imshow(data[600:700, 650:750], norm=LogNorm(vmin=vmin, vmax=vmax))
+    vmin_small = data.min() + 0.0001
+    vmax_small = 0.001 * data.max()
+    im = ax[0].imshow(data[600:700, 650:750], norm=LogNorm(vmin=vmin_small, vmax=vmax_small))
     ax[0].set_title('data', size=15)
     ax[0].axis('off')
-    ax[1].imshow(exp(myEnergy.s).val[600:700, 650:750], norm=LogNorm(vmin=vmin, vmax=vmax))
+    ax[1].imshow(exp(myEnergy.s).val[600:700, 650:750], norm=LogNorm(vmin=vmin_small, vmax=vmax_small))
     ax[1].set_title('diffuse', size=15)
     ax[1].axis('off')
 
-    ax[2].imshow(exp(myEnergy.u).val[600:700, 650:750], norm=LogNorm(vmin=vmin, vmax=vmax))
+    ax[2].imshow(exp(myEnergy.u).val[600:700, 650:750], norm=LogNorm(vmin=vmin_small, vmax=vmax_small))
     ax[2].set_title('point-like', size=15)
     ax[2].axis('off')
 
@@ -140,7 +148,7 @@ if __name__ == '__main__':
 
     k_lengths = power.domain[0].k_lengths
     plt.plot(k_lengths, power.val, 'k-', label='diffuse')
-    plt.plot(k_lengths, power_d.val, 'k:', label='data')
+    plt.plot(k_lengths, power_d.val, 'k+-', label='data')
     plt.legend()
     plt.yscale('log')
     plt.xscale('log')
@@ -148,3 +156,55 @@ if __name__ == '__main__':
     plt.ylabel('power',size=15)
     plt.xlabel('harmonic mode',size=15)
     plt.savefig('hubble_log_power.pdf')
+
+    DDCAE=DDCAE_model(None)
+    DDCAE.load_weights('DDCAC',by_name=True)
+    ddd = data.reshape([1,1000,1000,1])
+    separation = DDCAE.predict(ddd)
+    plt.figure()
+    plt.title('DAE diffuse emission', size=size)
+    plt.axis('off')
+    ax = plt.gca()
+    im = ax.imshow(separation[0,:,:,0],norm=LogNorm(vmin=vmin, vmax=vmax))
+    divider = make_axes_locatable(ax)
+    cax = divider.append_axes("right", size="5%", pad=0.05)
+    cbar = plt.colorbar(im, cax=cax)
+    cbar.set_label('flux', size=size)
+    plt.tight_layout()
+    plt.savefig('DAE_hubble_diffuse.pdf')
+    plt.figure()
+    plt.title('DAE point-like emission', size=size)
+    plt.axis('off')
+    ax = plt.gca()
+    im = ax.imshow(A(ift.Field(myEnergy.position.domain,data-separation[0,:,:,0])).val,norm=LogNorm(vmin=vmin, vmax=vmax))
+    divider = make_axes_locatable(ax)
+    cax = divider.append_axes("right", size="5%", pad=0.05)
+    cbar = plt.colorbar(im, cax=cax)
+    cbar.set_label('flux', size=size)
+    plt.tight_layout()
+    plt.savefig('DAE_hubble_point_like.pdf')
+
+    sextracted = fits.open('check_hubble64.fits')[0].data
+    plt.figure()
+    plt.title('SExtractor diffuse emission', size=size)
+    plt.axis('off')
+    ax = plt.gca()
+    im = ax.imshow(sextracted.clip(0.001),norm=LogNorm(vmin=vmin, vmax=vmax))
+    divider = make_axes_locatable(ax)
+    cax = divider.append_axes("right", size="5%", pad=0.05)
+    cbar = plt.colorbar(im, cax=cax)
+    cbar.set_label('flux', size=size)
+    plt.tight_layout()
+    plt.savefig('SExtractor_hubble_diffuse.pdf')
+
+    plt.figure()
+    plt.title('SExtractor point-like emission', size=size)
+    plt.axis('off')
+    ax = plt.gca()
+    im = ax.imshow(A(ift.Field(myEnergy.position.domain,(data-sextracted))).val.clip(0.001),norm=LogNorm(vmin=vmin, vmax=vmax))
+    divider = make_axes_locatable(ax)
+    cax = divider.append_axes("right", size="5%", pad=0.05)
+    cbar = plt.colorbar(im, cax=cax)
+    cbar.set_label('flux', size=size)
+    plt.tight_layout()
+    plt.savefig('SExtractor_hubble_point_like.pdf')

starblade/starblade_energy.py

@@ -17,7 +17,7 @@
 # Starblade is being developed at the Max-Planck-Institut fuer Astrophysik
 
 from nifty4 import Energy, Field, log, exp, DiagonalOperator,\
-    create_power_operator, SandwichOperator, ScalingOperator, InversionEnabler
+    create_power_operator, SandwichOperator, ScalingOperator, InversionEnabler, SamplingEnabler
 from nifty4.library import WienerFilterCurvature
 from nifty4.library.nonlinearities import PositiveTanh, Tanh
 
@@ -56,6 +56,7 @@ class StarbladeEnergy(Energy):
 
         self.parameters = parameters
         self.inverter = parameters['inverter']
+        self.sampling_inverter = parameters['sampling_inverter']
         self.d = parameters['data']
         self.FFT = parameters['FFT']
         self.power_spectrum = parameters['power_spectrum']
@@ -115,10 +116,11 @@ class StarbladeEnergy(Energy):
         point = self.q * exp(-self.u) * self.u_p ** 2
         # R = self.FFT.inverse * self.s_p
         # N = self.correlation
-        N_inv = DiagonalOperator(point + 1/self.var_x )#+ 2*self.a_p))
+        O_x = DiagonalOperator(Field(self.position.domain,val=1./self.var_x))
+        N_inv = DiagonalOperator(point )#+ 2*self.a_p))
         R = ScalingOperator(1., point.domain)
         S_p = DiagonalOperator(self.s_p)
         my_S_inv = SandwichOperator.make(self.FFT.adjoint.inverse.adjoint * S_p, self.correlation.inverse)
-        curv = InversionEnabler(N_inv + my_S_inv, self.inverter)
+        curv = InversionEnabler(SamplingEnabler(my_S_inv+N_inv, O_x, self.sampling_inverter), self.inverter)
         return curv
         # return WienerFilterCurvature(R=R, N=N, S=S, inverter=self.inverter)

starblade/sugar.py

@@ -23,8 +23,8 @@ import nifty4 as ift
 from .starblade_energy import StarbladeEnergy
 from .starblade_kl import StarbladeKL
 
-def build_starblade(data, alpha=1.5, q=1e-40, cg_iterations=100, newton_iterations = 3,
-                    manual_power_spectrum = None):
+def build_starblade(data, alpha=1.5, q=1e-40, cg_steps=100, newton_steps = 3,
+                    manual_power_spectrum = None, sampling_steps = 100):
     """ Setting up the StarbladeEnergy for the given data and parameters
     Parameters
     ----------
@@ -34,9 +34,9 @@ def build_starblade(data, alpha=1.5, q=1e-40, cg_iterations=100, newton_iteratio
         The slope parameter of the point source prior (default: 1.5).
     q : float
         The cutoff parameter of the point source prior (default: 1e-40).
-    cg_iterations : int
+    cg_steps : int
         Maximum number of conjugate gradient iterations for numerical operator inversion (default: 500).
-    newton_iterations : int
+    newton_steps : int
         Number of consecutive Newton steps within one algorithmic step.(default: 3)
     manual_power_spectrum : None, Field or callable
         Option to set a manual power spectrum which is kept constant during the separation.
@@ -63,21 +63,24 @@ def build_starblade(data, alpha=1.5, q=1e-40, cg_iterations=100, newton_iteratio
     initial_x = ift.Field(s_space, val=-1.)
     alpha = ift.Field(s_space, val=alpha)
     q = ift.Field(s_space, val=q)
-    ICI = ift.GradientNormController(iteration_limit=cg_iterations,
+    ICI = ift.GradientNormController(iteration_limit=cg_steps,
                                      tol_abs_gradnorm=1e-5)
     inverter = ift.ConjugateGradient(controller=ICI)
+    IC_samples =  ift.GradientNormController(iteration_limit=sampling_steps,
+                                     tol_abs_gradnorm=1e-5)
+    sampling_inverter = ift.ConjugateGradient(controller=IC_samples)
 
     parameters = dict(data=data, power_spectrum=initial_spectrum,
                       alpha=alpha, q=q,
                       inverter=inverter, FFT=FFT,
-                      newton_iterations=newton_iterations, update_power=update_power)
+                      newton_iterations=newton_steps, sampling_inverter=sampling_inverter, update_power=update_power)
     Starblade = StarbladeEnergy(position=initial_x, parameters=parameters)
 
 
     return Starblade
 
 
-def starblade_iteration(starblade, samples=3):
+def starblade_iteration(starblade, samples=3, cg_steps=10, newton_steps=3, sampling_steps=100):
     """ Performing one Newton minimization step
     Parameters
     ----------
@@ -88,16 +91,26 @@ def starblade_iteration(starblade, samples=3):
     """
     controller = ift.GradientNormController(name="Newton",
                                             tol_abs_gradnorm=1e-8,
-                                            iteration_limit=starblade.newton_iterations)
+                                            iteration_limit=newton_steps)
     minimizer = ift.RelaxedNewton(controller=controller)
+    ICI = ift.GradientNormController(iteration_limit=cg_steps,
+                                     tol_abs_gradnorm=1e-5)
+    inverter = ift.ConjugateGradient(controller=ICI)
+    IC_samples =  ift.GradientNormController(iteration_limit=sampling_steps,
+                                     tol_abs_gradnorm=1e-5)
+    sampling_inverter = ift.ConjugateGradient(controller=IC_samples)
     # minimizer = ift.VL_BFGS(controller=controller)
-    energy = starblade
+    para = starblade.parameters
+    para['inverter'] = inverter
+    para['sampling_inverter'] = sampling_inverter
+    energy = StarbladeEnergy(starblade.position,parameters=para)
+
     sample_list = []
     for i in range(samples):
         sample = energy.curvature.inverse.draw_sample()
         sample_list.append(sample)
     if len(sample_list)>0:
-        energy = StarbladeKL(starblade.position, samples=sample_list, parameters=starblade.parameters)
+        energy = StarbladeKL(starblade.position, samples=sample_list, parameters=energy.parameters)
     else:
         energy = starblade
     energy, convergence = minimizer(energy)
@@ -140,9 +153,9 @@ def build_multi_starblade(data, alpha=1.5, q=1e-40, cg_iterations=500,
     """
     MultiStarblade = []
     for i in range(data.shape[-1]):
-        starblade = build_starblade(data[...,i],alpha=alpha, q=q,
-                                    cg_iterations=cg_iterations,
-                                    newton_iterations=newton_iterations,
+        starblade = build_starblade(data[...,i], alpha=alpha, q=q,
+                                    cg_steps=cg_iterations,
+                                    newton_steps=newton_iterations,
                                     manual_power_spectrum = manual_power_spectrum)
         MultiStarblade.append(starblade)
     return MultiStarblade