doing work

1 files changed, 258 insertions, 213 deletions

diff --git a/src/Python/test/test_regularizers_3d.py b/src/Python/test/test_regularizers_3d.py
index a2e3027..2d11a7e 100644
--- a/src/Python/test/test_regularizers_3d.py
+++ b/src/Python/test/test_regularizers_3d.py
@@ -5,17 +5,17 @@ Created on Fri Aug  4 11:10:05 2017
 @author: ofn77899
 """
 
-from ccpi.viewer.CILViewer2D import Converter
-import vtk
+#from ccpi.viewer.CILViewer2D import Converter
+#import vtk
 
-import regularizers
 import matplotlib.pyplot as plt
 import numpy as np
 import os    
 from enum import Enum
 import timeit
-
-from Regularizer import Regularizer
+#from PIL import Image
+#from Regularizer import Regularizer
+from ccpi.imaging.Regularizer import Regularizer
 
 ###############################################################################
 #https://stackoverflow.com/questions/13875989/comparing-image-in-url-to-image-in-filesystem-in-python/13884956#13884956
@@ -46,77 +46,303 @@ def nrmse(im1, im2):
 # u0 = Im + .05*randn(size(Im)); u0(u0 < 0) = 0;
 # u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
 
+
 #filename = r"C:\Users\ofn77899\Documents\GitHub\CCPi-FISTA_reconstruction\data\lena_gray_512.tif"
+filename = r"/home/ofn77899/Reconstruction/CCPi-FISTA_Reconstruction/data/lena_gray_512.tif"
+#filename = r'/home/algol/Documents/Python/STD_test_images/lena_gray_512.tif'
+
 #reader = vtk.vtkTIFFReader()
 #reader.SetFileName(os.path.normpath(filename))
 #reader.Update()
-##vtk returns 3D images, let's take just the one slice there is as 2D
-#Im = Converter.vtk2numpy(reader.GetOutput()).T[0]/255
+Im = plt.imread(filename)                     
+#Im = Image.open('/home/algol/Documents/Python/STD_test_images/lena_gray_512.tif')/255
+#img.show()
+Im = np.asarray(Im, dtype='float32')
+
+# create a 3D image by stacking N of this images
+
+
+#imgplot = plt.imshow(Im)
+perc = 0.05
+u_n = Im + (perc* np.random.normal(size=np.shape(Im)))
+y,z = np.shape(u_n)
+u_n = np.reshape(u_n , (1,y,z))
+
+u0 = u_n.copy()
+for i in range (19):
+    u_n = Im + (perc* np.random.normal(size=np.shape(Im)))
+    u_n = np.reshape(u_n , (1,y,z))
+
+    u0 = np.vstack ( (u0, u_n) )
+
+# map the u0 u0->u0>0
+f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
+u0 = f(u0).astype('float32')
+
+print ("Passed image shape {0}".format(np.shape(u0)))
+
+## plot 
+fig = plt.figure()
+#a=fig.add_subplot(3,3,1)
+#a.set_title('Original')
+#imgplot = plt.imshow(Im)
+sliceno = 10
+
+a=fig.add_subplot(2,3,1)
+a.set_title('noise')
+imgplot = plt.imshow(u0[sliceno],cmap="gray")
+
+reg_output = []
+##############################################################################
+# Call regularizer
+
+####################### SplitBregman_TV #####################################
+# u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
+
+use_object = True
+if use_object:
+    reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
+    print (reg.pars)
+    reg.setParameter(input=u0)    
+    reg.setParameter(regularization_parameter=10.)
+    # or 
+    # reg.setParameter(input=u0, regularization_parameter=10., #number_of_iterations=30,
+              #tolerance_constant=1e-4, 
+              #TV_Penalty=Regularizer.TotalVariationPenalty.l1)
+    plotme = reg() [0]
+    pars = reg.pars
+    textstr = reg.printParametersToString() 
+    
+    #out = reg(input=u0, regularization_parameter=10., #number_of_iterations=30,
+              #tolerance_constant=1e-4, 
+    #          TV_Penalty=Regularizer.TotalVariationPenalty.l1)
+    
+#out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10., number_of_iterations=30,
+#          tolerance_constant=1e-4, 
+#          TV_Penalty=Regularizer.TotalVariationPenalty.l1)
+
+else:
+    out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10. )
+    pars = out2[2]
+    reg_output.append(out2)
+    plotme = reg_output[-1][0]
+    textstr = out2[-1]
+
+a=fig.add_subplot(2,3,2)
+
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+# place a text box in upper left in axes coords
+a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+        verticalalignment='top', bbox=props)
+imgplot = plt.imshow(plotme[sliceno],cmap="gray")
+
+###################### FGP_TV #########################################
+# u = FGP_TV(single(u0), 0.05, 100, 1e-04);
+out2 = Regularizer.FGP_TV(input=u0, regularization_parameter=0.0005,
+                          number_of_iterations=50)
+pars = out2[-2]
+
+reg_output.append(out2)
+
+a=fig.add_subplot(2,3,3)
+
+textstr = out2[-1]
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+# place a text box in upper left in axes coords
+a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(reg_output[-1][0][sliceno])
+# place a text box in upper left in axes coords
+a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+        verticalalignment='top', bbox=props)
+imgplot = plt.imshow(reg_output[-1][0][sliceno],cmap="gray")
+
+###################### LLT_model #########################################
+# * u0 = Im + .03*randn(size(Im)); % adding noise
+# [Den] = LLT_model(single(u0), 10, 0.1, 1);
+#Den = LLT_model(single(u0), 25, 0.0003, 300, 0.0001, 0); 
+#input, regularization_parameter , time_step, number_of_iterations,
+#                  tolerance_constant, restrictive_Z_smoothing=0
+out2 = Regularizer.LLT_model(input=u0, regularization_parameter=25,
+                          time_step=0.0003,
+                          tolerance_constant=0.0001,
+                          number_of_iterations=300)
+pars = out2[-2]
+
+reg_output.append(out2)
+
+a=fig.add_subplot(2,3,4)
+
+textstr = out2[-1]
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+# place a text box in upper left in axes coords
+a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(reg_output[-1][0][sliceno],cmap="gray")
+
+
+# ###################### PatchBased_Regul #########################################
+# # Quick 2D denoising example in Matlab:   
+# #   Im = double(imread('lena_gray_256.tif'))/255;  % loading image
+# #   u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
+# #   ImDen = PB_Regul_CPU(single(u0), 3, 1, 0.08, 0.05); 
+
+out2 = Regularizer.PatchBased_Regul(input=u0, regularization_parameter=0.05,
+                       searching_window_ratio=3,
+                       similarity_window_ratio=1,
+                       PB_filtering_parameter=0.08)
+pars = out2[-2]
+reg_output.append(out2)
+
+a=fig.add_subplot(2,3,5)
+
+
+textstr = out2[-1]
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+# place a text box in upper left in axes coords
+a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+        verticalalignment='top', bbox=props)
+imgplot = plt.imshow(reg_output[-1][0][sliceno],cmap="gray")
+
+
+# ###################### TGV_PD #########################################
+# # Quick 2D denoising example in Matlab:   
+# #   Im = double(imread('lena_gray_256.tif'))/255;  % loading image
+# #   u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
+# #   u = PrimalDual_TGV(single(u0), 0.02, 1.3, 1, 550);
+
+
+out2 = Regularizer.TGV_PD(input=u0, regularization_parameter=0.05,
+                           first_order_term=1.3,
+                           second_order_term=1,
+                           number_of_iterations=550)
+pars = out2[-2]
+reg_output.append(out2)
+
+a=fig.add_subplot(2,3,6)
+
+
+textstr = out2[-1]
+
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+# place a text box in upper left in axes coords
+a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(reg_output[-1][0][sliceno],cmap="gray")
+
+
+plt.show()
+
+################################################################################
+##
+##  3D Regularizers
+##
+################################################################################
+##Example:
+## figure;
+## Im = double(imread('lena_gray_256.tif'))/255;  % loading image
+## u0 = Im + .05*randn(size(Im)); u0(u0 < 0) = 0;
+## u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
+#
+##filename = r"C:\Users\ofn77899\Documents\GitHub\CCPi-Reconstruction\python\test\reconstruction_example.mha"
+#filename = r"C:\Users\ofn77899\Documents\GitHub\CCPi-Simpleflex\data\head.mha"
 #
+#reader = vtk.vtkMetaImageReader()
+#reader.SetFileName(os.path.normpath(filename))
+#reader.Update()
+##vtk returns 3D images, let's take just the one slice there is as 2D
+#Im = Converter.vtk2numpy(reader.GetOutput())
+#Im = Im.astype('float32')
 ##imgplot = plt.imshow(Im)
 #perc = 0.05
 #u0 = Im + (perc* np.random.normal(size=np.shape(Im)))
 ## map the u0 u0->u0>0
 #f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
 #u0 = f(u0).astype('float32')
+#converter = Converter.numpy2vtkImporter(u0, reader.GetOutput().GetSpacing(),
+#                                        reader.GetOutput().GetOrigin())
+#converter.Update()
+#writer = vtk.vtkMetaImageWriter()
+#writer.SetInputData(converter.GetOutput())
+#writer.SetFileName(r"C:\Users\ofn77899\Documents\GitHub\CCPi-FISTA_reconstruction\data\noisy_head.mha")
+##writer.Write()
+#
 #
 ### plot 
-#fig = plt.figure()
+#fig3D = plt.figure()
 ##a=fig.add_subplot(3,3,1)
 ##a.set_title('Original')
 ##imgplot = plt.imshow(Im)
+#sliceNo = 32
 #
-#a=fig.add_subplot(2,3,1)
+#a=fig3D.add_subplot(2,3,1)
 #a.set_title('noise')
-#imgplot = plt.imshow(u0)
+#imgplot = plt.imshow(u0.T[sliceNo])
+#
+#reg_output3d = []
 #
-#reg_output = []
 ###############################################################################
 ## Call regularizer
 #
 ######################## SplitBregman_TV #####################################
 ## u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
+#
 ##reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
-##
+#
 ##out = reg(input=u0, regularization_parameter=10., #number_of_iterations=30,
 ##          #tolerance_constant=1e-4, 
 ##          TV_Penalty=Regularizer.TotalVariationPenalty.l1)
 #
-##out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10., number_of_iterations=30,
-##          tolerance_constant=1e-4, 
-##          TV_Penalty=Regularizer.TotalVariationPenalty.l1)
-#out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10. )
-#pars = out2[2]
-#reg_output.append(out2)
+#out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10., number_of_iterations=30,
+#          tolerance_constant=1e-4, 
+#          TV_Penalty=Regularizer.TotalVariationPenalty.l1)
+#
+#
+#pars = out2[-2]
+#reg_output3d.append(out2)
+#
+#a=fig3D.add_subplot(2,3,2)
 #
-#a=fig.add_subplot(2,3,2)
 #
 #textstr = out2[-1]
+#
+#
 ## these are matplotlib.patch.Patch properties
 #props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
 ## place a text box in upper left in axes coords
 #a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
 #        verticalalignment='top', bbox=props)
-#imgplot = plt.imshow(reg_output[-1][0])
+#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
 #
 ####################### FGP_TV #########################################
 ## u = FGP_TV(single(u0), 0.05, 100, 1e-04);
 #out2 = Regularizer.FGP_TV(input=u0, regularization_parameter=0.005,
 #                          number_of_iterations=200)
 #pars = out2[-2]
+#reg_output3d.append(out2)
 #
-#reg_output.append(out2)
+#a=fig3D.add_subplot(2,3,2)
 #
-#a=fig.add_subplot(2,3,3)
 #
 #textstr = out2[-1]
 #
+#
 ## these are matplotlib.patch.Patch properties
 #props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
 ## place a text box in upper left in axes coords
 #a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
 #        verticalalignment='top', bbox=props)
-#imgplot = plt.imshow(reg_output[-1][0])
+#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
 #
 ####################### LLT_model #########################################
 ## * u0 = Im + .03*randn(size(Im)); % adding noise
@@ -129,17 +355,20 @@ def nrmse(im1, im2):
 #                          tolerance_constant=0.0001,
 #                          number_of_iterations=300)
 #pars = out2[-2]
+#reg_output3d.append(out2)
+#
+#a=fig3D.add_subplot(2,3,2)
 #
-#reg_output.append(out2)
 #
-#a=fig.add_subplot(2,3,4)
 #textstr = out2[-1]
+#
+#
 ## these are matplotlib.patch.Patch properties
 #props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
 ## place a text box in upper left in axes coords
 #a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
 #        verticalalignment='top', bbox=props)
-#imgplot = plt.imshow(reg_output[-1][0])
+#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
 #
 ####################### PatchBased_Regul #########################################
 ## Quick 2D denoising example in Matlab:   
@@ -152,136 +381,6 @@ def nrmse(im1, im2):
 #                          similarity_window_ratio=1,
 #                          PB_filtering_parameter=0.08)
 #pars = out2[-2]
-#reg_output.append(out2)
-#
-#a=fig.add_subplot(2,3,5)
-#
-#
-#textstr = out2[-1]
-#
-## these are matplotlib.patch.Patch properties
-#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-## place a text box in upper left in axes coords
-#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
-#        verticalalignment='top', bbox=props)
-#imgplot = plt.imshow(reg_output[-1][0])
-#
-#
-####################### TGV_PD #########################################
-## Quick 2D denoising example in Matlab:   
-##   Im = double(imread('lena_gray_256.tif'))/255;  % loading image
-##   u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
-##   u = PrimalDual_TGV(single(u0), 0.02, 1.3, 1, 550);
-#
-#
-#out2 = Regularizer.TGV_PD(input=u0, regularization_parameter=0.05,
-#                          first_order_term=1.3,
-#                          second_order_term=1,
-#                          number_of_iterations=550)
-#pars = out2[-2]
-#reg_output.append(out2)
-#
-#a=fig.add_subplot(2,3,6)
-#
-#
-#textstr = out2[-1]
-#
-#
-## these are matplotlib.patch.Patch properties
-#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-## place a text box in upper left in axes coords
-#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
-#        verticalalignment='top', bbox=props)
-#imgplot = plt.imshow(reg_output[-1][0])
-#
-
-###############################################################################
-#
-#  3D Regularizers
-#
-###############################################################################
-#Example:
-# figure;
-# Im = double(imread('lena_gray_256.tif'))/255;  % loading image
-# u0 = Im + .05*randn(size(Im)); u0(u0 < 0) = 0;
-# u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
-
-#filename = r"C:\Users\ofn77899\Documents\GitHub\CCPi-Reconstruction\python\test\reconstruction_example.mha"
-filename = r"C:\Users\ofn77899\Documents\GitHub\CCPi-Simpleflex\data\head.mha"
-
-reader = vtk.vtkMetaImageReader()
-reader.SetFileName(os.path.normpath(filename))
-reader.Update()
-#vtk returns 3D images, let's take just the one slice there is as 2D
-Im = Converter.vtk2numpy(reader.GetOutput())
-Im = Im.astype('float32')
-#imgplot = plt.imshow(Im)
-perc = 0.05
-u0 = Im + (perc* np.random.normal(size=np.shape(Im)))
-# map the u0 u0->u0>0
-f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
-u0 = f(u0).astype('float32')
-converter = Converter.numpy2vtkImporter(u0, reader.GetOutput().GetSpacing(),
-                                        reader.GetOutput().GetOrigin())
-converter.Update()
-writer = vtk.vtkMetaImageWriter()
-writer.SetInputData(converter.GetOutput())
-writer.SetFileName(r"C:\Users\ofn77899\Documents\GitHub\CCPi-FISTA_reconstruction\data\noisy_head.mha")
-#writer.Write()
-
-
-## plot 
-fig3D = plt.figure(figsize=(20,16))
-
-#a=fig.add_subplot(3,3,1)
-#a.set_title('Original')
-#imgplot = plt.imshow(Im)
-sliceNo = 32
-
-a=fig3D.add_subplot(2,3,1)
-a.set_title('noise')
-imgplot = plt.imshow(u0.T[sliceNo])
-
-reg_output3d = []
-
-##############################################################################
-# Call regularizer
-
-####################### SplitBregman_TV #####################################
-# u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
-
-#reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
-
-#out = reg(input=u0, regularization_parameter=10., #number_of_iterations=30,
-#          #tolerance_constant=1e-4, 
-#          TV_Penalty=Regularizer.TotalVariationPenalty.l1)
-
-out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10., number_of_iterations=30,
-          tolerance_constant=1e-4, 
-          TV_Penalty=Regularizer.TotalVariationPenalty.l1)
-
-
-pars = out2[-2]
-reg_output3d.append(out2)
-
-a=fig3D.add_subplot(2,3,2)
-
-
-textstr = out2[-1]
-
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-# place a text box in upper left in axes coords
-a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
-        verticalalignment='top', bbox=props)
-imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
-
-###################### FGP_TV #########################################
-# u = FGP_TV(single(u0), 0.05, 100, 1e-04);
-#out2 = Regularizer.FGP_TV(input=u0, regularization_parameter=0.005,
-#                          number_of_iterations=200)
-#pars = out2[-2]
 #reg_output3d.append(out2)
 #
 #a=fig3D.add_subplot(2,3,2)
@@ -296,67 +395,15 @@ imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
 #a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
 #        verticalalignment='top', bbox=props)
 #imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
+#
 
-###################### LLT_model #########################################
-# * u0 = Im + .03*randn(size(Im)); % adding noise
-# [Den] = LLT_model(single(u0), 10, 0.1, 1);
-#Den = LLT_model(single(u0), 25, 0.0003, 300, 0.0001, 0); 
-#input, regularization_parameter , time_step, number_of_iterations,
-#                  tolerance_constant, restrictive_Z_smoothing=0
-out2 = Regularizer.LLT_model(input=u0, regularization_parameter=25,
-                          time_step=0.0003,
-                          tolerance_constant=0.0001,
-                          number_of_iterations=300)
-pars = out2[-2]
-reg_output3d.append(out2)
-
-a=fig3D.add_subplot(2,3,3)
-
-
-textstr = out2[-1]
-
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-# place a text box in upper left in axes coords
-a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
-        verticalalignment='top', bbox=props)
-imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
-
-###################### PatchBased_Regul #########################################
+###################### TGV_PD #########################################
 # Quick 2D denoising example in Matlab:   
 #   Im = double(imread('lena_gray_256.tif'))/255;  % loading image
 #   u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
-#   ImDen = PB_Regul_CPU(single(u0), 3, 1, 0.08, 0.05); 
-
-out2 = Regularizer.PatchBased_Regul(input=u0, regularization_parameter=0.05,
-                          searching_window_ratio=3,
-                          similarity_window_ratio=1,
-                          PB_filtering_parameter=0.08)
-pars = out2[-2]
-reg_output3d.append(out2)
-
-a=fig3D.add_subplot(2,3,4)
-
-
-textstr = out2[-1]
+#   u = PrimalDual_TGV(single(u0), 0.02, 1.3, 1, 550);
 
 
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
-# place a text box in upper left in axes coords
-a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
-        verticalalignment='top', bbox=props)
-imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
-
-
-####################### TGV_PD #########################################
-## Quick 2D denoising example in Matlab:   
-##   Im = double(imread('lena_gray_256.tif'))/255;  % loading image
-##   u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
-##   u = PrimalDual_TGV(single(u0), 0.02, 1.3, 1, 550);
-#
-#
 #out2 = Regularizer.TGV_PD(input=u0, regularization_parameter=0.05,
 #                          first_order_term=1.3,
 #                          second_order_term=1,
@@ -376,5 +423,3 @@ imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
 #a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
 #        verticalalignment='top', bbox=props)
 #imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
-fig3D.savefig('test\\3d.png')
-plt.close(fig3D)
\ No newline at end of file