Commit 9517285b by Matthijs

### folder structure to separate different cases

parent 73a6277f
 ... ... @@ -129,9 +129,8 @@ class SimpleAlgorithm: if hasattr(self, 'truth'): Relerrs = change.copy() # Different algorithms will have different qualities that one # should monitor. Since we take a fixed point perspective, the main # thing to monitor is the change in the iterates. # Different algorithms will have different qualities that one should monitor. # Since we take a fixed point perspective, the main thing to monitor is the change in the iterates. shadow = self.prox1.work(u) ##### LOOP ... ... @@ -143,18 +142,15 @@ class SimpleAlgorithm: u_new = self.evaluate(u) if self.diagnostic: # the next prox operation only gets used in the computation of # the size of the gap. This extra step is not # required in alternating projections, which makes RAAR # the next prox operation only gets used in the computation of the size of the gap. # This extra step is not required in alternating projections, which makes RAAR a bit slower u2 = self.prox2.work(u_new) u1 = self.prox1.work(u2) # compute the normalized change in successive iterates: # change(iter) = sum(sum((feval('P_M',M,u)-tmp).^2))/norm_data; # compute the normalized change in successive iterates tmp_change = 0 tmp_gap = 0 tmp_shadow = 0 # the simple case, where everything can be calculated in one difference if self.product_space_dimension == 1 or (p == 1 and q == 1): tmp_change = phase_offset_compensated_norm(u, u_new, norm_type='fro', norm_factor=norm_data) ** 2 ... ... @@ -216,9 +212,8 @@ class SimpleAlgorithm: # update iterate u = u_new if self.diagnostic: # For Douglas-Rachford,in general it is appropriate to monitor the SHADOWS of the iterates, since in # the convex case these converge even for beta=1. # (see Bauschke-Combettes-Luke, J. Approx. Theory, 2004) # For Douglas-Rachford, in general it is appropriate to monitor the SHADOWS of the iterates, since in # the convex case these converge even for beta=1. (see Bauschke-Combettes-Luke, J. Approx. Theory, 2004) shadow = u2 ##### POSTPROCESSING ... ...
 from .orbitaltomog_data_processor import support_from_autocorrelation from proxtoolbox.Utilities.OrbitalTomog.array_tools import shifted_fft, shifted_ifft from proxtoolbox.Utilities.OrbitalTomog.binning import bin_2d_array, bin_array from proxtoolbox.Problems.OrbitalTomog.planar_molecule.orbitaltomog_data_processor import support_from_autocorrelation from proxtoolbox.Utilities.OrbitalTomog.array_tools import shifted_fft from proxtoolbox.Utilities.OrbitalTomog.binning import bin_array import numpy as np from skimage.io import imread from .Graphics.stack_viewer import XYZStackViewer from proxtoolbox.Problems.OrbitalTomog.Graphics.stack_viewer import XYZStackViewer def data_processor(config): ... ...
 ... ... @@ -103,7 +103,7 @@ new_config = { 'lambda_switch': 100, 'sparsity_parameter': 100, 'use_sparsity_with_support': False, 'use_sparsity_with_support': True, 'symmetry_type': 1, # -1 for antissymmetric functions, 1 for symmetric ones. 'symmetry_axis': -1, # which axis is symmetric. (mirror plane perpendicular to this axis) ... ...
 from proxtoolbox.Problems.OrbitalTomog import coronene_config # base config from proxtoolbox.Problems.OrbitalTomog import orbitaltomog_data_processor # extends config from proxtoolbox.Problems.OrbitalTomog.planar_molecule import coronene_config, orbitaltomog_data_processor from proxtoolbox.Problems.OrbitalTomog.phase import Phase # sys.path.append('../proxtoolbox/Problems/Phase') ... ...
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