LocalWeightAverager makes an average of a set of images in Fourier space using a per-image radial weight. More...

#include <averager.h>

Inheritance diagram for EMAN::LocalWeightAverager:

Collaboration diagram for EMAN::LocalWeightAverager:

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Public Member Functions
	LocalWeightAverager ()

void	add_image (EMData *image)
	To add an image to the Averager. More...

EMData *	finish ()
	Finish up the averaging and return the result. More...

string	get_name () const
	Get the Averager's name. More...

string	get_desc () const

TypeDict	get_param_types () const
	Get Averager parameter information in a dictionary. More...

Public Member Functions inherited from EMAN::Averager
	Averager ()

virtual	~Averager ()

virtual void	add_image_list (const vector< EMData * > &images)
	To add multiple images to the Averager. More...

virtual void	set_params (const Dict &new_params)
	Set the Averager parameters using a key/value dictionary. More...

virtual void	mult (const float &s)
	Multiply the result image by some floating point constant This is useful when weighting the input images prior to calling add_image - a situation where it is likely you want to divide by the sum of the weights. More...

Static Public Member Functions
static Averager *	NEW ()

Static Public Attributes
static const string	NAME = "localweight"

Private Attributes
std::vector< EMData * >	images

EMData *	normimage

int	freenorm

int	nimg

int	fourier

Additional Inherited Members
Protected Attributes inherited from EMAN::Averager
Dict	params

EMData *	result

Detailed Description

LocalWeightAverager makes an average of a set of images in Fourier space using a per-image radial weight.

The provided XYData object for each inserted image should range from x=0 - 0.5*sqrt(2), and contains the radial weights from 0 - Nyquist at the point. If the x range is insufficient, values will be clamped at the ends of the available x-range. 2-D Images only, but will work with rectangular images.

Parameters

normimage After finish() will contain the sum of the weights in each Fourier location. Size must be ((nx+1)/2,y)

Definition at line 214 of file averager.h.

Constructor & Destructor Documentation

◆ LocalWeightAverager()

LocalWeightAverager::LocalWeightAverager ( )

Definition at line 549 of file averager.cpp.

        : normimage(0), freenorm(0), nimg(0),fourier(0)
{
 
}

Referenced by NEW().

Member Function Documentation

◆ add_image()

void LocalWeightAverager::add_image ( EMData * image )

virtual

To add an image to the Averager.

This image will be averaged in this function.

Parameters

image The image to be averaged.

Reimplemented from EMAN::Averager.

Definition at line 555 of file averager.cpp.

{
        if (!image) {
                return;
        }
        
        nimg++;
 
        int nx = image->get_xsize();
        int ny = image->get_ysize();
        int nz = image->get_zsize();
 
        fourier = params.set_default("fourier", (int)0);
        if (nimg == 1) {
                result = image->copy_head();
                result->set_size(nx, ny, nz);
                result->to_zero();
 
                
                normimage = params.set_default("normimage", (EMData*)0);
                if (normimage==0) { normimage=new EMData(nx,ny,nz); freenorm=1; }
                normimage->to_zero();
                normimage->add(0.0000001f);
        }
 
        images.push_back(image->copy());
}

References fourier, freenorm, images, nimg, normimage, EMAN::Averager::params, EMAN::Averager::result, and EMAN::Dict::set_default().

◆ finish()

EMData * LocalWeightAverager::finish ( )

virtual

Finish up the averaging and return the result.

Returns: The averaged image.

Implements EMAN::Averager.

Definition at line 583 of file averager.cpp.

{
        if (nimg==0) return NULL;
        
        int nx = images.front()->get_xsize();
        int ny = images.front()->get_ysize();
        int nz = images.front()->get_zsize();
        
        float dampnoise = params.set_default("dampnoise", (float)0.5);
        
        // This is the standard mode where local real-space correlation with the average is used to define a local weight (like a mask)
        // applied to each image. If fourier>=2 then the same is done, but in "gaussian bands" in Fourier space
        if (fourier<=1) {
                EMData *stg1 = new EMData(nx,ny,nz);
                
                for (std::vector<EMData*>::iterator im = images.begin(); im!=images.end(); ++im) stg1->add(**im);
                stg1->process_inplace("normalize");
                
        //      std::vector<EMData*> weights;
                for (std::vector<EMData*>::iterator im = images.begin(); im!=images.end(); ++im) {
                        EMData *imc=(*im)->copy();
                        imc->mult(*stg1);
                        imc->process_inplace("filter.lowpass.gauss",Dict("cutoff_freq",0.02f));
                        imc->process_inplace("threshold.belowtozero",Dict("minval",0.0f));
        //              imc->process_inplace("math.sqrt");
        //              imc->process_inplace("math.pow",Dict("pow",0.25));
                        (*im)->mult(*imc);
                        result->add(**im);
                        normimage->add(*imc);
                        delete imc;
                        delete *im;
                }
                
                if (dampnoise>0) {
                        float mean=normimage->get_attr("mean");
                        float max=normimage->get_attr("maximum");
                        normimage->process_inplace("threshold.clampminmax",Dict("minval",mean*dampnoise,"maxval",max));
                }
                
                result->div(*normimage);
                
                result->set_attr("ptcl_repr",nimg);
                
                if (freenorm) { delete normimage; normimage=(EMData*)0; }
                nimg=0;
 
                return result;
        }
        else if (fourier<=ny/2) {
                // we do pretty much the same thing as above, but one Fourier "band" at a time
                
                for (int r=0;  r<fourier; r++) {
                        float cen=r/((float)fourier-1.0)*0.5;
                        float sig=(0.5/fourier)/(2.0*sqrt(log(2.0)));
                        std::vector<EMData*> filt;
 
                        EMData *stg1 = new EMData(nx,ny,nz);
                        for (std::vector<EMData*>::iterator im = images.begin(); im!=images.end(); ++im) {
                                EMData *f=(*im)->process("filter.bandpass.gauss",Dict("center",(float)cen,"sigma",sig));
                                filt.push_back(f);
                                stg1->add(*f);
                                if (r==fourier-1) delete *im;           // we don't actually need the unfiltered images again
                        }
                        stg1->process_inplace("normalize");
                        stg1->process("filter.bandpass.gauss",Dict("center",(float)cen,"sigma",sig));
//                      stg1->write_image("cmp.hdf",r);
                        
                //      std::vector<EMData*> weights;
                        int imn=1;
                        for (std::vector<EMData*>::iterator im = filt.begin(); im!=filt.end(); ++im) {
                                EMData *imc=(*im)->copy();
                                imc->mult(*stg1);
                                imc->process_inplace("filter.lowpass.gauss",Dict("cutoff_freq",0.02f));
                                imc->process_inplace("threshold.belowtozero",Dict("minval",0.0f));
//                              imc->write_image("cmp.hdf",imn*fourier+r);
                //              imc->process_inplace("math.sqrt");
                //              imc->process_inplace("math.pow",Dict("pow",0.25));
                                (*im)->mult(*imc);
                                result->add(**im);
                                normimage->add(*imc);
                                delete *im;
                                delete imc;
                                imn++;
                        }
                        
                        if (dampnoise>0) {
                                float mean=normimage->get_attr("mean");
                                float max=normimage->get_attr("maximum");
                                normimage->process_inplace("threshold.clampminmax",Dict("minval",mean*dampnoise,"maxval",max));
                        }
                        
                }
                result->div(*normimage);
                result->set_attr("ptcl_repr",nimg);
                
                if (freenorm) { delete normimage; normimage=(EMData*)0; }
                nimg=0;
                return result;
        }
        
}

References fourier, freenorm, images, log(), nimg, normimage, EMAN::Averager::params, EMAN::Averager::result, EMAN::Dict::set_default(), and sqrt().

◆ get_desc()

string EMAN::LocalWeightAverager::get_desc ( ) const

inlinevirtual

Implements EMAN::Averager.

Definition at line 227 of file averager.h.

                {
                        return "Average of images weighted by local similarity to unweighted average";
                }

◆ get_name()

string EMAN::LocalWeightAverager::get_name ( ) const

inlinevirtual

Get the Averager's name.

Each Averager is identified by a unique name.

Returns: The Averager's name.

Implements EMAN::Averager.

Definition at line 222 of file averager.h.

                {
                        return NAME;
                }

References NAME.

◆ get_param_types()

TypeDict EMAN::LocalWeightAverager::get_param_types ( ) const

inlinevirtual

Get Averager parameter information in a dictionary.

Each parameter has one record in the dictionary. Each record contains its name, data-type, and description.

Returns: A dictionary containing the parameter info.

Reimplemented from EMAN::Averager.

Definition at line 237 of file averager.h.

                {
                        TypeDict d;
//                      d.put("weight", EMObject::XYDATA, "Radial weight. X: 0 - 0.5*sqrt(2). Y contains weights.");
                        d.put("normimage", EMObject::EMDATA, "After finish() will contain the sum of the weights in real-space");
                        d.put("dampnoise", EMObject::FLOAT, "Will set a minimum mean*x for the norm image, damping regions with poor information. Default = 0.5, 0 disables. ");
                        d.put("fourier", EMObject::INT, "If set does local weighting in Fourier rather than real space ");
                        return d;
                }

References EMAN::EMObject::EMDATA, EMAN::EMObject::FLOAT, EMAN::EMObject::INT, and EMAN::TypeDict::put().

◆ NEW()

static Averager * EMAN::LocalWeightAverager::NEW ( )

inlinestatic

Definition at line 232 of file averager.h.

                {
                        return new LocalWeightAverager();
                }

References LocalWeightAverager().

Member Data Documentation

◆ fourier

int EMAN::LocalWeightAverager::fourier

private

Definition at line 254 of file averager.h.

Referenced by add_image(), and finish().

◆ freenorm

int EMAN::LocalWeightAverager::freenorm

private

Definition at line 252 of file averager.h.

Referenced by add_image(), and finish().

◆ images

std::vector<EMData*> EMAN::LocalWeightAverager::images

private

Definition at line 250 of file averager.h.

Referenced by add_image(), and finish().

◆ NAME

const string LocalWeightAverager::NAME = "localweight"

static

Definition at line 247 of file averager.h.

Referenced by get_name().

◆ nimg

int EMAN::LocalWeightAverager::nimg

private

Definition at line 253 of file averager.h.

Referenced by add_image(), and finish().

◆ normimage

EMData* EMAN::LocalWeightAverager::normimage

private

Definition at line 251 of file averager.h.

Referenced by add_image(), and finish().

The documentation for this class was generated from the following files:

Public Member Functions

Static Public Member Functions

Static Public Attributes

Private Attributes

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ LocalWeightAverager()

Member Function Documentation

◆ add_image()

◆ finish()

◆ get_desc()

◆ get_name()

◆ get_param_types()

◆ NEW()

Member Data Documentation

◆ fourier

◆ freenorm

◆ images

◆ NAME

◆ nimg

◆ normimage