00001 """/*!
00002 @page es1step6 Step 6
00003
00004 \par Objectives:
00005 - Make the script more portable and easy to use by using the built-in solve builder
00006
00007 @section Walkthrough
00008 I have replaced the builder with the Built in Solve builder
00009 @par Additional inputs:
00010 - 'lowfweight.rsf'
00011
00012 @par Additional outputs:
00013 - 'residual.rsf'
00014
00015
00016 */"""
00017
00018
00019 from slimproj import *
00020 from SLIMpy.linear_operators import *
00021 from SLIMpy import vector
00022 from SLIMpy import DotTest, VectorSpace
00023 from slimpy_contrib.ana.problems.l1_minimization_problem import l1_min
00024
00025 from rsfproj import *
00026 #===============================================================================
00027 # import local plotting function
00028 #===============================================================================
00029 from os.path import abspath
00030 sys.path.append( abspath('..') )
00031 from swellplot import plot_swell
00032
00033
00034 data = '../data.rsf'
00035 sig = '../sig.rsf'
00036 noise = '../swellnoise.rsf'
00037 weight = 'lowfweight.rsf'
00038 residual = 'residual'
00039
00040 #Global Operators
00041 PC = None
00042 W = None
00043
00044 def transform_callback( target, source, env, space ):
00045
00046 global PC, W
00047
00048 weight = vector( source[1] )
00049
00050 C = Cosine( space )
00051 P = DiagonalWeight( space, weight )
00052
00053 PC = CompoundOperator( [P,C] )
00054 W = dwt( space )
00055
00056 AH = AugOperator( [[PC],
00057 [W]] )
00058
00059 D = DiagonalWeight( AH.domain(), .49 )
00060
00061 AD = CompoundOperator( [AH,D] )
00062
00063 A = AD.H
00064 return A
00065
00066 def recovery_callback( target, source, env, space ):
00067
00068 AH = AugOperator( [[PC.H, 0 ],
00069 [ 0, W.H ]] )
00070
00071 D = DiagonalWeight( AH.range(), .49 )
00072 DA = CompoundOperator( [D,AH] )
00073 return DA
00074
00075 def result_callback( target, source, env, result ):
00076 result.flat[0].setName( str(target[0] ) )
00077 result.flat[1].setName( str(target[1] ) )
00078
00079
00080 #===============================================================================
00081 # Flows
00082 #===============================================================================
00083
00084 Flow ( weight, None,
00085 'math n1=512 output="1-(1*x1*0.1/512.0)" '
00086 )
00087
00088 Solve( ['enoise','esig'], [data,weight],
00089 problem=l1_min,
00090 transform_callback=transform_callback,
00091 inv_callback = recovery_callback,
00092 result_callback=result_callback,
00093 # l1_min problem options
00094 nouter=10, ninner=10, lmax=0.001, lmin=0.99,
00095 # slimpy options
00096 debug=['stat'] , logfile='sep.log'
00097 )
00098
00099
00100 Flow( residual, [ data, 'enoise','esig' ],
00101 ' math en=${SOURCES[1]} es=${SOURCES[2]} output="input-en-es" '
00102 )
00103
00104
00105 #===============================================================================
00106 # RSF plotting functions
00107 #===============================================================================
00108 plot_swell( data, noise, sig , 'enoise', 'esig', 'residual' )
00109
00110 #===============================================================================
00111 # Dot Test
00112 #===============================================================================
00113
00114 @slim_builder
00115 def AugmentedDotTest( target, source, env ):
00116
00117 y = vector(source[0])
00118 AD = transfrom_callback( target, source, env , y.space )
00119 DotTest(AD,2,5)
00120
00121 test = AugmentedDotTest( 'test', [data,weight],
00122 debug=[] , logfile='dottest' )
00123 Alias( 'dottest', test)
00124
