Added all phasepack demos

demos/phasepack_AvP.py

+
+import SetProxPythonPath
+from proxtoolbox.experiments.phase.Phasepack_Experiment import Phasepack_Experiment
+
+phasepack = Phasepack_Experiment(algorithm='AvP', formulation='cyclic', MAXIT=2000)
+phasepack.run()
+phasepack.show()

demos/phasepack_CDR.py

+
+import SetProxPythonPath
+from proxtoolbox.experiments.phase.Phasepack_Experiment import Phasepack_Experiment
+
+phasepack = Phasepack_Experiment(algorithm='CDRl', formulation='cyclic', 
+                                 MAXIT=2000, lambda_0=1.0, lambda_max=1.0)
+phasepack.run()
+phasepack.show()

demos/phasepack_CDRl.py

+
+import SetProxPythonPath
+from proxtoolbox.experiments.phase.Phasepack_Experiment import Phasepack_Experiment
+
+phasepack = Phasepack_Experiment(algorithm='CDRl', formulation='cyclic', 
+                                 MAXIT=200, lambda_0=0.75, lambda_max=0.75)
+phasepack.run()
+phasepack.show()

demos/phasepack_CP.py

+
+import SetProxPythonPath
+from proxtoolbox.experiments.phase.Phasepack_Experiment import Phasepack_Experiment
+
+phasepack = Phasepack_Experiment(algorithm='CP', formulation='cyclic')
+phasepack.run()
+phasepack.show()

demos/phasepack_DR.py

+
+import SetProxPythonPath
+from proxtoolbox.experiments.phase.Phasepack_Experiment import Phasepack_Experiment
+
+phasepack = Phasepack_Experiment(algorithm='DRl', formulation='cyclic', 
+                                 MAXIT=2000, lambda_0=1.0, lambda_max=1.0)
+phasepack.run()
+phasepack.show()

demos/phasepack_DRAP.py

+
+import SetProxPythonPath
+from proxtoolbox.experiments.phase.Phasepack_Experiment import Phasepack_Experiment
+
+phasepack = Phasepack_Experiment(algorithm='DRAP', formulation='cyclic', 
+                                 MAXIT=2000, lambda_0=0.02, lambda_max=0.02)
+phasepack.run()
+phasepack.show()

demos/phasepack_DRl.py

+
+import SetProxPythonPath
+from proxtoolbox.experiments.phase.Phasepack_Experiment import Phasepack_Experiment
+
+phasepack = Phasepack_Experiment(algorithm='DRl', formulation='cyclic', 
+                                 MAXIT=2000, lambda_0=0.7, lambda_max=0.7)
+phasepack.run()
+phasepack.show()

demos/phasepack_DyRePr.py

+
+import SetProxPythonPath
+from proxtoolbox.experiments.phase.Phasepack_Experiment import Phasepack_Experiment
+
+phasepack = Phasepack_Experiment(algorithm='DyRePr', formulation='cyclic', 
+                                 MAXIT=2000, lambda_0=1.0, lambda_max=1.0)
+phasepack.run()
+phasepack.show()

demos/phasepack_QNAvP.py

+
+import SetProxPythonPath
+from proxtoolbox.experiments.phase.Phasepack_Experiment import Phasepack_Experiment
+
+phasepack = Phasepack_Experiment(algorithm='QNAvP', formulation='cyclic')
+phasepack.run()
+phasepack.show()

demos/phasepack_Wirt.py

+
+import SetProxPythonPath
+from proxtoolbox.experiments.phase.Phasepack_Experiment import Phasepack_Experiment
+
+phasepack = Phasepack_Experiment(algorithm='Wirtinger', formulation='cyclic')
+phasepack.run()
+phasepack.show()

proxtoolbox/algorithms/Wirt_IterateMonitor.py

@@ -88,14 +88,19 @@ class Wirt_IterateMonitor(IterateMonitor):
             if self.diagnostic:
                 tmp_gap = sum(abs(tmp1 - tmp_u)**2)/normM**2
                 if self.truth is not None:
-                    if self.truth_dim[0] == 1:
-                        z = u[0,:]
-                        z = reshape(z, (1,len(z)))  # we want a true matrix not just a vector
-                    elif self.truth_dim[1] == 1:
-                        z = u[:,0]
+                    if u.ndim == 1:
+                        # u is a vector
+                        z = u
                         z = reshape(z, (len(z),1))  # we want a true matrix not just a vector
-                    else:
-                        z = u[:,:,0]
+                    else: # u is a multidimensional array
+                        if self.truth_dim[0] == 1:
+                            z = u[0,:]
+                            z = reshape(z, (1,len(z)))  # we want a true matrix not just a vector
+                        elif self.truth_dim[1] == 1:
+                            z = u[:,0]
+                            z = reshape(z, (len(z),1))  # we want a true matrix not just a vector
+                        else:
+                            z = u[:,:,0]
         if self.diagnostic and self.truth is not None:
             rel_error = norm(self.truth - exp(-1j*angle(trace(self.truth.T.conj() @ z))) * z) / self.norm_truth
 

proxtoolbox/algorithms/Wirtinger.py

@@ -23,12 +23,19 @@ class Wirtinger(Algorithm):
 
     def __init__(self, experiment, iterateMonitor, accelerator):
         super(Wirtinger, self).__init__(experiment, iterateMonitor, accelerator)
-        self.masks = experiment.masks
-        if hasattr(experiment, 'masks_conj'):
-            self.masks_conj = experiment.masks_conj
-        else:
-            self.masks_conj = conj(self.masks)
-        self.data_sq = experiment.data_sq
+        if experiment.experiment_name == 'Phasepack':
+            self.isPhasepack = True
+            self.data = experiment.data
+            self.phasepack_A = experiment.phasepack_A
+            self.phasepack_Apinv = experiment.phasepack_Apinv
+        else:    
+            self.isPhasepack = False
+            self.masks = experiment.masks
+            if hasattr(experiment, 'masks_conj'):
+                self.masks_conj = experiment.masks_conj
+            else:
+                self.masks_conj = conj(self.masks)
+            self.data_sq = experiment.data_sq
 
         self.tau0 = 330
         self.mu = lambda t: min(1-exp(-t/self.tau0), 0.4)
@@ -47,6 +54,9 @@ class Wirtinger(Algorithm):
         u_new : ndarray
             The new iterate (same type as input parameter `u`).
         """
+        if self.isPhasepack:
+            return self.evaluatePhasePack(u)
+
         L = self.product_space_dimension
         get, set, data_shape = accessors(u, self.Nx, self.Ny, L)
 
@@ -86,4 +96,15 @@ class Wirtinger(Algorithm):
         return grad
 
 
+    def evaluatePhasePack(self, u):
+        u_new = np.empty(u.shape, dtype=np.complex128)
+        Bz = self.phasepack_A @ u
+        C = (abs(Bz)**2 - self.data) * Bz
+        grad = self.phasepack_Apinv @ C
+        self.step = self.mu(self.iter+1)/self.norm_data**2 * grad
+        u_new = u - self.step
+        return u_new
+
+        
+