Package new_noise_rsf_integration

This is the second step in creating a linear operator. More...

Classes
class	noiseX_Converter
	This is a mapping instance that maps a SLIMpy command into an object that can be run. More...
Variables
tuple	factory = rsfCommandFactory()

---

Detailed Description

This is the second step in creating a linear operator.

There are two substeps to this module.

• create a sfConverter subclass that defines how to use a SLIMpy operator with rsf commands.
• register the converter with the rsf plugin.

 from numpy import ceil
 from slimpy_base.api.Plugins.slim2rsf.sfCommandFactory import rsfCommandFactory
 from slimpy_base.api.Plugins.slim2rsf.sfcommands.sfConverter import sfConverter
 
 ########################################################################
 # Define how noisex behaves
 ########################################################################
 
 # create a Converter class
 class noiseX_Converter( sfConverter ):
     \"""
     This is a mapping instance that maps a SLIMpy command into an object that 
     can be run.
     There are three types of function this class should have.
       - map
       - trans 
       - constr
     Each of these functions can have an optional '_adj' at the end of the name 
     if the operator's adjoint is different than the forward.
     
     \par map should change the arguments of the operator such that it can run as 
     an rsf command line.
     \par trans defines how this operator affects the domain of the input. 
      this is also to calculate the range of the operator at initialization.
     \par constr defines any run time error messages if the vector is not in the domain of 
     the operator.
       
      
     \"""
     # alwas use classmethod or static method
     @classmethod
     def map( cls, source, command ):
         \"""
         map a SLIMpy command to a rsf command
         \"""
         # the RSF bin and 'sf' will be automaticaly prepended to noise executable
         command = cls.default_function( command, "noise" ) 
         #change all python True/False objects to 'y'/'n' strings
         command = cls.truefalseHelper( command )
         #map the adj flage to inv
         command = cls.keywordmap( command, {'adj':'inv'} )
         #alwas must return a CommandPack instance
         return cls.pack( command )
     
 
     @classmethod
     def trans( cls, command, space, *spaces ):
         'define how this operator affect the space'
         n1 = spaces[0]['n1']
         space['n1_noise']= n1
         space["n1"] = int( ceil( n1/2. )+1 )
         space['data_type']='float' 
         return space
     
     @classmethod
     def trans_adj( cls, command, space, *spaces ):
         \"""
         trans_adj will automatically be called in the case
         where the command has an adj keyword that is true
         \"""
         n1 = spaces[0]['n1_noise']
         space['data_type']='float'
         space['n1']= n1
         return space
     
     @classmethod
     def constr( cls, command, space ):
         'make sure the data on the forward command is float'
         cls.match( space, data_type='float' )
         
     
     @classmethod
     def constr_adj( cls, command, space ):
         'make sure the data on the adjoint command is float'
         cls.match( space, data_type='float' )
 
 
 
 #===============================================================================
 # Add it to the list of existing rsf commands by
 # by registering the converter with the rsf plugin. 
 #===============================================================================
 
 factory = rsfCommandFactory()
 # add the converter class to the factory
 # now converter will be invoked when 
 # a linear operator with the 'name' attribute
 # of noiseX
 factory['noiseX'] = noiseX_Converter

---

Variable Documentation

tuple factory = rsfCommandFactory()

Definition at line 185 of file new_noise_rsf_integration.py.