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herjy · Apr 24, 2018 · ce591d5 · ce591d5
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diff --git a/Examples/Image.fits b/Examples/Image.fits
diff --git a/Examples/Noise_levels.fits b/Examples/Noise_levels.fits
diff --git a/Examples/Test_SLIT.py b/Examples/Test_SLIT.py
@@ -2,7 +2,7 @@
 import matplotlib.pyplot as plt 
 import numpy as np
 import matplotlib.cm as cm
-from scipy import signal as scp
+
 import SLIT 
 import time
 from scipy import signal as scp
@@ -75,7 +75,7 @@ def SIE(x0,y0,n1,n2,b,beta,q,xc,theta):
 ################################Running SLIT############################
 #Parameters
 kmax = 5
-niter =50
+niter =100
 levels = [0]
 
 #Comment the following to have the level estimation routine run (takes more time)
@@ -85,7 +85,7 @@ def SIE(x0,y0,n1,n2,b,beta,q,xc,theta):
 start = time.clock()
 
 #Running SLIT
-sourcesl, Imsl = SLIT.SLIT(Image, Fkappa, kmax, niter, size, PSF, PSFconj, levels = levels)
+sourcesl, Imsl = SLIT.SLIT(Image, Fkappa, kmax, niter, size, PSF, PSFconj, levels = levels, fb =1)
 
 #Stop clock
 elapsed = (time.clock()-start)

diff --git a/Examples/Test_SLIT_MCA.py b/Examples/Test_SLIT_MCA.py
@@ -77,11 +77,23 @@ def SIE(x0,y0,n1,n2,b,beta,q,xc,theta):
 #Final simulated image
 Image = simu+np.random.randn(nt1,nt2)*sigma
 
+lensed = slit.lens_one(Fkappa, nt1,nt2, size)
+Test = slit.Lens.image_to_source(I2+gal, size, Fkappa, lensed = lensed)
+
+plt.imshow(Image, cmap = cm.gist_stern, interpolation = 'nearest')
+plt.show()
+plt.imshow(Test, cmap = cm.gist_stern, interpolation = 'nearest')
+plt.show()
+
+hdus = pf.PrimaryHDU(Image)
+lists = pf.HDUList([hdus])
+lists.writeto('Image.fits', clobber=True)
+
 ################################Running SLIT############################
 #Parameters
 kmax = 5
 niter =100
-riter =50
+riter =100
 levels = [0]
 
 #Comment the following to have the level estimation routine run (takes more time)

diff --git a/Examples/Test_sparsity.py b/Examples/Test_sparsity.py
@@ -31,16 +31,16 @@
 lev = slit.level(nt1,nt1)
 
 #Starlet transforms of the lens and source in their respective planes
-wG = mw.wave_transform(G, lvl = 6)/lev
-wS = mw.wave_transform(S, lvl = 6)/lev
+wG = mw.wave_transform(G, lvl = 6, newwave =1)/lev
+wS = mw.wave_transform(S, lvl = 6, newwave =1)/lev
 #Lensed source
 FS = Lens.source_to_image(S, nt1, nt2,Fkappa)
 #Unlensed lens
 FG = Lens.image_to_source(G, size, Fkappa, lensed=lensed)
 #Starlet transform of the unlensed lens
-wFG = mw.wave_transform(FG, 6)/lev
+wFG = mw.wave_transform(FG, 6, newwave =1)/lev
 #Starlet transform of the lensed 
-wFS = mw.wave_transform(FS, 6)/lev
+wFS = mw.wave_transform(FS, 6, newwave =1)/lev
 
 def mk_sort(X):
     Y = np.sort(np.resize(np.abs(X), X.size))

diff --git a/Results_center/Image_0_0.0_3.07609150209.fits b/Results_center/Image_0_0.0_3.07609150209.fits
diff --git a/Results_center/Image_0_0.1_2.56137075894.fits b/Results_center/Image_0_0.1_2.56137075894.fits
diff --git a/Results_center/Image_0_0.2_3.11771718373.fits b/Results_center/Image_0_0.2_3.11771718373.fits
diff --git a/Results_center/Image_0_0.3_0.693895027301.fits b/Results_center/Image_0_0.3_0.693895027301.fits
diff --git a/Results_center/Image_0_0.4_1.28811796808.fits b/Results_center/Image_0_0.4_1.28811796808.fits
diff --git a/Results_center/Image_0_0.5_2.12202401987.fits b/Results_center/Image_0_0.5_2.12202401987.fits
diff --git a/Results_center/Image_10_0.0_1.90306348037.fits b/Results_center/Image_10_0.0_1.90306348037.fits
diff --git a/Results_center/Image_10_0.1_0.715775900426.fits b/Results_center/Image_10_0.1_0.715775900426.fits
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