3 files changed, 9 insertions, 9 deletions
diff --git a/Wrappers/Python/ccpi/filters/regularisers.py b/Wrappers/Python/ccpi/filters/regularisers.py
index 81deea9..e6814e8 100644
--- a/Wrappers/Python/ccpi/filters/regularisers.py
+++ b/Wrappers/Python/ccpi/filters/regularisers.py
@@ -87,7 +87,7 @@ def FGP_dTV(inputData, refdata, regularisation_parameter, iterations,
         raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
                          .format(device))
 def TNV(inputData, regularisation_parameter, iterations, tolerance_param):
-        return TNV_CPU_pyx(inputData,
+        return TNV_CPU(inputData,
                      regularisation_parameter,
                      iterations, 
                      tolerance_param)
diff --git a/Wrappers/Python/demos/demo_cpu_regularisers.py b/Wrappers/Python/demos/demo_cpu_regularisers.py
index e74fa58..7443b83 100644
--- a/Wrappers/Python/demos/demo_cpu_regularisers.py
+++ b/Wrappers/Python/demos/demo_cpu_regularisers.py
@@ -247,7 +247,7 @@ print ("__________Total nuclear Variation__________")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
 ## plot 
-fig = plt.figure(4)
+fig = plt.figure(5)
 plt.suptitle('Performance of TNV regulariser using the CPU')
 a=fig.add_subplot(1,2,1)
 a.set_title('Noisy Image')
@@ -258,7 +258,7 @@ N = 512
 noisyVol = np.zeros((channelsNo,N,N),dtype='float32')
 idealVol = np.zeros((channelsNo,N,N),dtype='float32')
 
-for i in range (slices):
+for i in range (channelsNo):
     noisyVol[i,:,:] = Im + np.random.normal(loc = 0 , scale = perc * Im , size = np.shape(Im))
     idealVol[i,:,:] = Im
 
@@ -321,7 +321,7 @@ print ("_______________ROF-TV (3D)_________________")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
 ## plot 
-fig = plt.figure(5)
+fig = plt.figure(6)
 plt.suptitle('Performance of ROF-TV regulariser using the CPU')
 a=fig.add_subplot(1,2,1)
 a.set_title('Noisy 15th slice of a volume')
@@ -361,7 +361,7 @@ print ("_______________FGP-TV (3D)__________________")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
 ## plot 
-fig = plt.figure(6)
+fig = plt.figure(7)
 plt.suptitle('Performance of FGP-TV regulariser using the CPU')
 a=fig.add_subplot(1,2,1)
 a.set_title('Noisy Image')
@@ -410,7 +410,7 @@ print ("_______________SB-TV (3D)_________________")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
 ## plot 
-fig = plt.figure(7)
+fig = plt.figure(8)
 plt.suptitle('Performance of SB-TV regulariser using the CPU')
 a=fig.add_subplot(1,2,1)
 a.set_title('Noisy Image')
@@ -457,7 +457,7 @@ print ("_______________FGP-dTV (3D)__________________")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
 ## plot 
-fig = plt.figure(8)
+fig = plt.figure(9)
 plt.suptitle('Performance of FGP-dTV regulariser using the CPU')
 a=fig.add_subplot(1,2,1)
 a.set_title('Noisy Image')
diff --git a/Wrappers/Python/src/cpu_regularisers.pyx b/Wrappers/Python/src/cpu_regularisers.pyx
index 898bb40..abbf3b0 100644
--- a/Wrappers/Python/src/cpu_regularisers.pyx
+++ b/Wrappers/Python/src/cpu_regularisers.pyx
@@ -21,7 +21,7 @@ cimport numpy as np
 cdef extern float TV_ROF_CPU_main(float *Input, float *Output, float lambdaPar, int iterationsNumb, float tau, int dimX, int dimY, int dimZ);
 cdef extern float TV_FGP_CPU_main(float *Input, float *Output, float lambdaPar, int iterationsNumb, float epsil, int methodTV, int nonneg, int printM, int dimX, int dimY, int dimZ);
 cdef extern float SB_TV_CPU_main(float *Input, float *Output, float lambdaPar, int iterationsNumb, float epsil, int methodTV, int printM, int dimX, int dimY, int dimZ);
-cdef extern float TNV_CPU_main(float *Input, float *u, float lambda, int maxIter, float tol, int dimX, int dimY, int dimZ);
+cdef extern float TNV_CPU_main(float *Input, float *u, float lambdaPar, int maxIter, float tol, int dimX, int dimY, int dimZ);
 cdef extern float dTV_FGP_CPU_main(float *Input, float *InputRef, float *Output, float lambdaPar, int iterationsNumb, float epsil, float eta, int methodTV, int nonneg, int printM, int dimX, int dimY, int dimZ);
 
 
@@ -254,7 +254,7 @@ def dTV_FGP_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
 #****************************************************************#
 #*********************Total Nuclear Variation********************#
 #****************************************************************#
-def TNV_CPU_pyx(inputData, regularisation_parameter, iterationsNumb, tolerance_param):
+def TNV_CPU(inputData, regularisation_parameter, iterationsNumb, tolerance_param):
     if inputData.ndim == 2:
         return 
     elif inputData.ndim == 3: