phase.py 10.4 KB
Newer Older
1
2
# -*- coding: utf-8 -*-

3
from proxtoolbox.Problems.problems import Problem
4
5
6
from proxtoolbox import Algorithms
from proxtoolbox import ProxOperators
from proxtoolbox.ProxOperators.proxoperators import ProxOperator
7
from proxtoolbox.Problems.Phase.JWST_graphics import JWST_graphics
8
from numpy.linalg import norm
9
10
import numpy as np
import h5py
11
from numpy import square, sqrt, nonzero
12
13


14
class Phase(Problem):
15
    """
16
    Phase Problem
17
18
19
20
21
22
    """
    config = {
    }
    
    def __init__(self, new_config={}):
        """
23
        The initialization of a Phase instance takes the default configuration
24
25
26
27
28
29
30
        and updates the parameters with the arguments in new_config.
        
        Parameters
        ----------
        new_config : dict, optional - Parameters to initialize the problem. If unspecified, the default config is used.
        """
        self.config.update(new_config)
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48


        #call data processor, read data

        module = __import__(self.config['data_filename'])
        data_processor = getattr(module, self.config['data_filename'])
        data_processor(self.config)
        

        #reshape and rename the data 
        self.config['data_sq'] = self.config['data'];
        self.config['data'] = self.config['rt_data'];
        tmp = self.config['data'].shape;
        if(tmp[0]==1 or tmp[1]==1):
            self.config['data_sq'] = self.config['data_sq'].reshape((self.config['Nx'],self.config['Ny']));
            #the projection algorithms work with the square root of the measurement:
            self.config['data'] = self.config['data'].reshape((self.config['Nx'],self.config['Ny']));

49
        self.config['normM']=self.config['norm_rt_data']; #previously (in matlab) norm_data
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
        if 'Nz' not in self.config:
            self.config[Nz] = 1;


        #If method_config[formulation is does not exist, i.e. not specified in 
        #the *_in.m file, use the product space as the default.
        if 'formulation' in self.config:
            formulation = self.config['formulation'];
        else:
            formulation = 'product space';

        # Set the projectors and inputs based on the types of constraints and 
        # experiments
        proxoperators = ['','',''];

        if self.config['constraint'] == 'hybrid':
            proxoperators[0] = 'P_cP'; # This will be problem specific
        elif self.config['constraint'] == 'support only':
            proxoperators[0] = 'P_S';
        elif self.config['constraint'] == 'real and support':
            proxoperators[0] ='P_S_real';
        elif self.config['constraint'] =='nonnegative and support':
            proxoperators[0] ='P_SP';
        elif self.config['constraint'] =='amplitude only':
            proxoperators[0] ='P_amp';
        elif self.config['constraint'] =='minimum amplitude':
            proxoperators[0] = 'P_min_amp';
        elif self.config['constraint'] =='sparse':
            proxoperators[0] = 'not in yet';  
        elif self.config['constraint'] =='phaselift':
            proxoperators[0] = 'P_mean_SP';
        elif self.config['constraint'] =='phaselift2':
            proxoperators[0] ='P_liftM';
            proxoperators[2] ='Approx_PM_Poisson'; # Patrick: This is just to monitor the change of phases!  

        if self.config['experiment'] == 'single diffraction':
            if self.config[distance] == 'far field':
                if self.config['constraint'] == 'phaselift':
                    proxoperators[1] = 'P_Rank1';
                elif self.config['constraint'] == 'phaselift2':
                    proxoperators[1] = 'P_rank1_SR';
                else:
                    if self.config['noise'] == 'Poisson':
                        proxoperators[1] ='Approx_PM_Poisson';
                    else:
                        proxoperators[1] ='Approx_PM_Gaussian';
            else:
                proxoperators[1]='P_Fresnel';

        # The following selects the projectors for diversity diffraction not
        # performed in the product space. So far only used for RCAAR.
        elif self.config['experiment'] == 'diversity diffraction' and formulation == 'sequential':
            proxoperators[1] = 'Approx_P_RCAAR_JWST_Poisson';
            proxoperators[0] = proxoperators[1];
        elif self.config['experiment'] == 'JWST': 
            proxoperators[1] = 'Approx_P_JWST_Poisson';  
            proxoperators[2] = proxoperators[0];
            proxoperators[0] = 'P_diag';
        elif self.config['experiment'] == 'CDP':
            proxoperators[1] = 'P_CDP';  
            proxoperators[2] = proxoperators[0];
            proxoperators[0] = 'P_diag';
        elif self.config['experiment'] == 'ptychography':
            proxoperators[1] = 'not in yet';
        elif self.config['experiment'] == 'complex':
            proxoperators[1] = 'not in yet';
        elif self.config['constraint'] == 'phaselift':
            proxoperators[1] ='P_PL_lowrank';

        self.config['proxoperators'] = [];

        for prox in proxoperators:
            self.config['proxoperators'].append(getattr(ProxOperators, prox))

        # input.Proj1_input.F=F;  % is it any more expensive to pass everything
        # into the projectors rather than just a selection of data and
        # parameters?  If not, and we pass everything anyway, there is no need
        # to create a new structure element.

        if 'product_space_dimension' not in self.config:
            self.config['product_space_dimension'] = 1;

        # set the animation program:
        self.config['animation']='Phase_animation';
        #
        # if you are only working with two sets but
        # want to do averaged projections
        # (= alternating projections on the product space)
        # or RAAR on the product space (=swarming), then
        # you will want to change product_space_dimension=2
        # and adjust your input files and projectors accordingly. 
        # you could also do this within the data processor

143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
        self.config['TOL2'] = 1e-15;

        #To estimate the gap in the sequential formulation, we build the
        # appropriate point in the product space. This allows for code reuse.
        # Note for sequential diversity diffraction, input.Proj1 is the "RCAAR"
        # version of the function.
        if formulation == 'sequential':
            for j in range(self.config['product_space_dimension']):
                self.config['proj_iter'] =j;
                proj1 = self.config['proxoperators'][0](self.config)
                u_1[:,:,j]= proj1.work(self.config['u_0']);
                self.config['proj_iter'] = mod(j,config['product_space_dimension'])+1;
                proj1 = self.config['proxoperators'][0](self.config)
                u_1[:,:,j]= proj1.work(self.config['u_0']);
            end;
        else: #i.e. formulation=='product space'
            proj1 = self.config['proxoperators'][0](self.config)
            u_1 = proj1.work(self.config['u_0']);
            proj2 = self.config['proxoperators'][1](self.config)
162
            u_2 = proj2.work(u_1);
163

164

165
166
        # estimate the gap in the relevant metric
        if self.config['Nx'] ==1 or self.config['Ny']==1 :
167
            tmp_gap = square(norm(u_1-u_2)/self.config['norm_rt_data']);
168
169
170
171
        else:
            tmp_gap=0;
            for j in range(self.config['product_space_dimension']):
                # compute (||P_Sx-P_Mx||/normM)^2:
172
                tmp_gap = tmp_gap+(norm(u_1[:,:,j]-u_2[:,:,j])/square(self.config['norm_rt_data']));
173
174
175
176
177
178
179
180

        gap_0=sqrt(tmp_gap);

        # sets the set fattening to be a percentage of the
        # initial gap to the unfattened set with 
        # respect to the relevant metric (KL or L2), 
        # that percentage given by
        # input.data_ball input by the user.
181
        self.config['data_ball']=self.config['data_ball']*gap_0;
182
183
        # the second tolerance relative to the oder of 
        # magnitude of the metric
184
        self.config['TOL2'] = self.config['data_ball']*1e-15; 
185
186

        self.algorithm = getattr(Algorithms, self.config['algorithm'])(self.config);
187
188
189
190
191
192
193
        
    
    
    def _presolve(self):
        """
        Prepares argument for actual solving routine
        """
194
        
195
196
197
198
199
200
201
    
    def _solve(self):
        """
        Runs the algorithm to solve the given sudoku problem
        """
#        algorithm = self.config['algorithm'](self.config)
        
202
203
204
        self.output = dict();
        
        self.output['u1'],self.output['u2'],self.output['iter'],self.output['change'],self.output['gap'] = \
205
            self.algorithm.run(self.config['u_0'],self.config['TOL'],self.config['MAXIT'])
206

207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
    
    def _postsolve(self):
        """
        Processes the solution and generates the output
        """
        
        
        
    def show(self):
        """
        Generates graphical output from the solution
        """
        
        print("Calculation time:")
        print(self.elapsed_time)
        JWST_graphics(self.config,self.output)

    def compare_to_matlab(self):
        """
        Routine to test and verify results by comparing to matlab
        Note that this is only for development and should not be used by a normal user
        For result to match u_0 should be chosen as np.multiply(config['abs_illumination'],exp(1j*2*pi*0.5*np.ones(newres)))'] =
        """
        if self.config['MAXIT'] == 1:
            f = h5py.File('Phase_test_data/u1_1.mat')
        elif self.config['MAXIT'] == 500 :
            f = h5py.File('Phase_test_data/u1_500.mat')
        else:
            print("No file available to compare to.")
            return
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
        u1 = f['u1'].value.view(np.complex)
        u1 =np.array(u1)
        u1 = u1.T
        print("Compare u1:")
        print("Nonzero indices matlab:")
        print(nonzero(u1))
        print("Nonzero indices python:")
        print(nonzero(self.output['u1']))
        print("Nonzero indices equal:")
        print(np.array_equal(nonzero(u1),nonzero(self.output['u1'])))
        print("Nonzero values matlab:")
        print(u1[nonzero(u1)])
        print("Nonzero values python:")
        print(self.output['u1'][nonzero(self.output['u1'])])
        print("Difference at nonzero values:")
        nonz = nonzero(u1)
        diff = u1 - self.output['u1']
        print(diff[nonz])
        print("Maximum of absolute value of difference:")
        print(np.amax(abs(diff)));
        print("Frobenius of difference:")
        print(norm(diff))
        print("Frobenius norm of matlab u1:")
        print(norm(u1))
        print("Frobenius norm of python u1:")
        print(norm(self.output['u1']))
263