EMAN::FRCCmp Class Reference

FRCCmp returns a quality factor based on FRC between images. More...

#include <cmp.h>

Inheritance diagram for EMAN::FRCCmp:

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Collaboration diagram for EMAN::FRCCmp:

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List of all members.

Public Member Functions
float	cmp (EMData *image, EMData *with) const
	To compare 'image' with another image passed in through its parameters.
string	get_name () const
	Get the Cmp's name.
string	get_desc () const
TypeDict	get_param_types () const
	Get Cmp parameter information in a dictionary.
Static Public Member Functions
static Cmp *	NEW ()

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Detailed Description

FRCCmp returns a quality factor based on FRC between images.

Fourier ring correlation (FRC) is a measure of statistical dependency between two averages, computed by comparison of rings in Fourier space. 1 means prefect agreement. 0 means no correlation.

Definition at line 446 of file cmp.h.

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Member Function Documentation

float FRCCmp::cmp	(	EMData *	image,
		EMData *	with
	)			const [virtual]

To compare 'image' with another image passed in through its parameters.

An optional transformation may be used to transform the 2 images.

Parameters:

	image	The first image to be compared.
	with	The second image to be comppared.

Returns:

The comparison result. Smaller better by default

Implements EMAN::Cmp.

Definition at line 919 of file cmp.cpp.

References EMAN::Ctf::apix, EMAN::EMData::calc_fourier_shell_correlation(), EMAN::EMData::calc_radial_dist(), EMAN::Ctf::compute_1d(), EMAN::EMData::copy(), EMAN::Ctf::CTF_SNR, EMAN::EMData::do_fft(), EMAN::EMData::do_fft_inplace(), ENTERFUNC, EXITFUNC, EMAN::EMObject::f, EMAN::EMData::get_attr(), EMAN::EMData::get_attr_default(), EMAN::EMData::get_data(), EMAN::EMData::get_xsize(), EMAN::EMData::get_ysize(), EMAN::EMData::get_zsize(), EMAN::EMData::has_attr(), InvalidCallException, EMAN::EMData::is_complex(), EMAN::Cmp::params, EMAN::EMData::set_attr(), EMAN::Dict::set_default(), EMAN::EMData::update(), EMAN::Cmp::validate_input_args(), and weight.

{
        ENTERFUNC;
        validate_input_args(image, with);

        int snrweight = params.set_default("snrweight", 0);
        int ampweight = params.set_default("ampweight", 0);
        int sweight = params.set_default("sweight", 1);
        int nweight = params.set_default("nweight", 0);
        int zeromask = params.set_default("zeromask",0);

        if (zeromask) {
                image=image->copy();
                with=with->copy();
                
                int sz=image->get_xsize()*image->get_ysize()*image->get_zsize();
                float *d1=image->get_data();
                float *d2=with->get_data();
                
                for (int i=0; i<sz; i++) {
                        if (d1[i]==0.0 || d2[i]==0.0) { d1[i]=0.0; d2[i]=0.0; }
                }
                
                image->update();
                with->update();
                image->do_fft_inplace();
                with->do_fft_inplace();
                image->set_attr("free_me",1); 
                with->set_attr("free_me",1); 
        }


        if (!image->is_complex()) {
                image=image->do_fft(); 
                image->set_attr("free_me",1); 
        }
        if (!with->is_complex()) { 
                with=with->do_fft(); 
                with->set_attr("free_me",1); 
        }

        static vector < float >default_snr;

//      if (image->get_zsize() > 1) {
//              throw ImageDimensionException("2D only");
//      }

//      int nx = image->get_xsize();
        int ny = image->get_ysize();
        int ny2=ny/2+1;

        vector < float >fsc;

                

        fsc = image->calc_fourier_shell_correlation(with,1);

        // The fast hypot here was supposed to speed things up. Little effect
//      if (image->get_zsize()>1) fsc = image->calc_fourier_shell_correlation(with,1);
//      else {
//              double *sxy = (double *)malloc(ny2*sizeof(double)*4);
//              double *sxx = sxy+ny2;
//              double *syy = sxy+2*ny2;
//              double *norm= sxy+3*ny2;
//
//              float *df1=image->get_data();
//              float *df2=with->get_data();
//              int nx2=image->get_xsize();
//
//              for (int y=-ny/2; y<ny/2; y++) {
//                      for (int x=0; x<nx2/2; x++) {
//                              if (x==0 && y<0) continue;      // skip Friedel pair
//                              short r=Util::hypot_fast_int(x,y);
//                              if (r>ny2-1) continue;
//                              int l=x*2+(y<0?ny+y:y)*nx2;
//                              sxy[r]+=df1[l]*df2[l]+df1[l+1]*df2[l+1];
//                              sxx[r]+=df1[l]*df1[l];
//                              syy[r]+=df2[l]*df2[l];
//                              norm[r]+=1.0;
//                      }
//              }
//              fsc.resize(ny2*3);
//              for (int r=0; r<ny2; r++) {
//                      fsc[r]=r*0.5/ny2;
//                      fsc[ny2+r]=sxy[r]/(sqrt(sxx[r])*sqrt(syy[r]));
//                      fsc[ny2*2+r]=norm[r];
//              }
//              free(sxy);
//      }

        vector<float> snr;
        if (snrweight) {
                Ctf *ctf = NULL;
                if (!image->has_attr("ctf")) {
                        if (!with->has_attr("ctf")) throw InvalidCallException("SNR weight with no CTF parameters");
                        ctf=with->get_attr("ctf");
                }
                else ctf=image->get_attr("ctf");

                float ds=1.0f/(ctf->apix*ny);
                snr=ctf->compute_1d(ny,ds,Ctf::CTF_SNR);
                if(ctf) {delete ctf; ctf=0;}
        }

        vector<float> amp;
        if (ampweight) amp=image->calc_radial_dist(ny/2,0,1,0);

        double sum=0.0, norm=0.0;

        for (int i=0; i<ny/2; i++) {
                double weight=1.0;
                if (sweight) weight*=fsc[(ny2)*2+i];
                if (ampweight) weight*=amp[i];
                if (snrweight) weight*=snr[i];
                sum+=weight*fsc[ny2+i];
                norm+=weight;
//              printf("%d\t%f\t%f\n",i,weight,fsc[ny/2+1+i]);
        }

        // This performs a weighting that tries to normalize FRC by correcting from the number of particles represented by the average
        sum/=norm;
        if (nweight && with->get_attr_default("ptcl_repr",0) && sum>=0 && sum<1.0) {
                sum=sum/(1.0-sum);                                                      // convert to SNR
                sum/=(float)with->get_attr_default("ptcl_repr",0);      // divide by ptcl represented
                sum=sum/(1.0+sum);                                                      // convert back to correlation
        }

        if (image->has_attr("free_me")) delete image;
        if (with->has_attr("free_me")) delete with;

        EXITFUNC;


        //.Note the negative! This is because EMAN2 follows the convention that
        // smaller return values from comparitors indicate higher similarity -
        // this enables comparitors to be used in a generic fashion.
        return (float)-sum;
}

string EMAN::FRCCmp::get_name

(

)

const [inline, virtual]

Get the Cmp's name.

Each Cmp is identified by a unique name.

Returns:

The Cmp's name.

Implements EMAN::Cmp.

Definition at line 451 of file cmp.h.

                {
                        return "frc";
                }

string EMAN::FRCCmp::get_desc

(

)

const [inline, virtual]

Implements EMAN::Cmp.

Definition at line 456 of file cmp.h.

                {
                        return "Computes the mean Fourier Ring Correlation between the image and reference (with optional weighting factors).";
                }

static Cmp* EMAN::FRCCmp::NEW

(

)

[inline, static]

Definition at line 461 of file cmp.h.

                {
                        return new FRCCmp();
                }

TypeDict EMAN::FRCCmp::get_param_types

(

)

const [inline, virtual]

Get Cmp 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.

Implements EMAN::Cmp.

Definition at line 466 of file cmp.h.

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

                {
                        TypeDict d;
                        d.put("snrweight", EMObject::INT, "If set, the SNR of 'this' will be used to weight the result. If 'this' lacks CTF info, it will check 'with'. (default=0)");
                        d.put("ampweight", EMObject::INT, "If set, the amplitude of 'this' will be used to weight the result (default=0)");
                        d.put("sweight", EMObject::INT, "If set, weight the (1-D) average by the number of pixels in each ring (default=1)");
                        d.put("nweight", EMObject::INT, "Downweight similarity based on number of particles in reference (default=0)");
                        d.put("zeromask", EMObject::INT, "Treat regions in either image that are zero as a mask");
                        return d;
                }

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

• cmp.h
• cmp.cpp