From e00b88c681f0c906576c4cff0ac7db872ce5ff59 Mon Sep 17 00:00:00 2001
From: Edoardo Pasca <edo.paskino@gmail.com>
Date: Sat, 19 Oct 2019 21:18:47 +0100
Subject: Revert "added FGP_dTV (#32)" (#34)

This reverts commit 8839dffdee7ef1fff72eb305bf09fe30917ec238.
---
 Wrappers/Python/ccpi/plugins/regularisers.py | 45 ----------------------------
 1 file changed, 45 deletions(-)

diff --git a/Wrappers/Python/ccpi/plugins/regularisers.py b/Wrappers/Python/ccpi/plugins/regularisers.py
index ef79231..6ed9fb2 100644
--- a/Wrappers/Python/ccpi/plugins/regularisers.py
+++ b/Wrappers/Python/ccpi/plugins/regularisers.py
@@ -91,51 +91,6 @@ class FGP_TV(Function):
             out = x.copy()
             out.fill(res)
         return out
-
-class FGP_dTV(Function):
-    def __init__(self, refdata, regularisation_parameter, iterations,
-                 tolerance, eta_const, methodTV, nonneg, device='cpu'):
-        # set parameters
-        self.lambdaReg = regularisation_parameter
-        self.iterationsTV = iterations
-        self.tolerance = tolerance
-        self.methodTV = methodTV
-        self.nonnegativity = nonneg
-        self.device = device # string for 'cpu' or 'gpu'
-        self.refData = np.asarray(refdata.as_array(), dtype=np.float32)
-        self.eta = eta_const
-        
-    def __call__(self,x):
-        # evaluate objective function of TV gradient
-        EnergyValTV = TV_ENERGY(np.asarray(x.as_array(), dtype=np.float32), np.asarray(x.as_array(), dtype=np.float32), self.lambdaReg, 2)
-        return 0.5*EnergyValTV[0]
-    def proximal(self,x,tau, out=None):
-        pars = {'algorithm' : FGP_dTV, \
-               'input' : np.asarray(x.as_array(), dtype=np.float32),\
-                'regularization_parameter':self.lambdaReg*tau, \
-                'number_of_iterations' :self.iterationsTV ,\
-                'tolerance_constant':self.tolerance,\
-                'methodTV': self.methodTV ,\
-                'nonneg': self.nonnegativity ,\
-                'eta_const' : self.eta,\
-                'refdata':self.refData}
-       #inputData, refdata, regularisation_parameter, iterations,
-       #              tolerance_param, eta_const, methodTV, nonneg, device='cpu' 
-        res , info = regularisers.FGP_dTV(pars['input'],
-              pars['refdata'], 
-              pars['regularization_parameter'],
-              pars['number_of_iterations'],
-              pars['tolerance_constant'],
-              pars['eta_const'], 
-              pars['methodTV'],
-              pars['nonneg'],
-              self.device)
-        if out is not None:
-            out.fill(res)
-        else:
-            out = x.copy()
-            out.fill(res)
-        return out
         
 class SB_TV(Function):
     def __init__(self,lambdaReg,iterationsTV,tolerance,methodTV,printing,device):
-- 
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