statistic.c File Reference

statistical tools module More...

#include <stdint.h>
#include <math.h>
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include <time.h>
#include <gsl/gsl_randist.h>
#include "CommandLineInterface/CLIcore.h"
#include "00CORE/00CORE.h"
#include "COREMOD_memory/COREMOD_memory.h"
#include "statistic/statistic.h"

Include dependency graph for statistic.c:

Data Structures
struct	BIRCHCF

Macros
#define	MODULE_SHORTNAME_DEFAULT   ""
#define	MODULE_DESCRIPTION   "Statistics functions and tools"
#define	MODULE_APPLICATION   "milk"

Functions
CLI bindings
errno_t	statistic_putphnoise_cli ()
errno_t	statistic_putgaussnoise_cli ()
Module initialization
static errno_t	init_module_CLI ()
STATISTIC functions
double	ran1 ()
	Uniform distribution from 0 to 1. More...
double	gauss ()
	Normal distribution, mean=0, sigma=1. More...
double	gauss_trc ()
	truncated (-1/+1) sigma = 1 mean = 0 gaussian probability More...
long	poisson (double mu)
	Poisson distribution. More...
double	cfits_gammaln (double xx)
double	fast_poisson (double mu)
double	better_poisson (double mu)
long	put_poisson_noise (const char *ID_in_name, const char *ID_out_name)
	Apply Poisson noise to image. More...
long	put_gauss_noise (const char *ID_in_name, const char *ID_out_name, double ampl)
	Apply Gaussian noise to image. More...
long	statistic_BIRCH_clustering (__attribute__((unused)) const char *IDin_name, __attribute__((unused)) int B, __attribute__((unused)) double epsilon, __attribute__((unused)) const char *IDout_name)

Detailed Description

statistical tools module

Random numbers, photon noise

Macro Definition Documentation

MODULE_APPLICATION

#define MODULE_APPLICATION   "milk"

MODULE_DESCRIPTION

#define MODULE_DESCRIPTION   "Statistics functions and tools"

MODULE_SHORTNAME_DEFAULT

#define MODULE_SHORTNAME_DEFAULT   ""

Function Documentation

better_poisson()

double better_poisson

(

double

mu

)

cfits_gammaln()

double cfits_gammaln

(

double

xx

)

fast_poisson()

double fast_poisson

(

double

mu

)

gauss()

double gauss

(

)

Normal distribution, mean=0, sigma=1.

gauss_trc()

double gauss_trc

(

)

truncated (-1/+1) sigma = 1 mean = 0 gaussian probability

init_module_CLI()

static errno_t init_module_CLI ( )

static

poisson()

long poisson

(

double

mu

)

Poisson distribution.

Parameters

mu	Distribution mean

put_gauss_noise()

long put_gauss_noise	(	const char *	ID_in_name,
		const char *	ID_out_name,
		double	ampl
	)

Apply Gaussian noise to image.

put_poisson_noise()

long put_poisson_noise	(	const char *	ID_in_name,
		const char *	ID_out_name
	)

Apply Poisson noise to image.

ran1()

double ran1

(

)

Uniform distribution from 0 to 1.

statistic_BIRCH_clustering()

long statistic_BIRCH_clustering	(	__attribute__((unused)) const char *	IDin_name,
		__attribute__((unused)) int	B,
		__attribute__((unused)) double	epsilon,
		__attribute__((unused)) const char *	IDout_name
	)

Purpose

Apply BIRCH clustering to images

Overview

Images input is 3D array, one image per slice
Euclidian distance adopted
B is the number of branches

epsilon is the maximum distance (Euclidian)

Details

statistic_putgaussnoise_cli()

errno_t statistic_putgaussnoise_cli

(

)

statistic_putphnoise_cli()

errno_t statistic_putphnoise_cli

(

)