cacao  Release 0.1.03-dev
Compute And Control For Adaptive Optics
statistic.h
Go to the documentation of this file.
1 
11 #ifndef _STATISTIC_H
12 #define _STATISTIC_H
13 
14 
15 void __attribute__((constructor)) libinit_statistic();
16 
17 
18 
21 double ran1();
22 
23 
24 
27 double gauss();
28 
29 
30 
33 double gauss_trc();
34 
35 
40 long poisson(double mu);
41 
42 
43 
46 double gammaln(double xx);
47 
48 
49 double better_poisson(double mu);
50 
51 double fast_poisson(double mu);
52 
53 
54 
57 long put_poisson_noise(const char *ID_in_name, const char *ID_out_name);
58 
59 
62 long put_gauss_noise(const char *ID_in_name, const char *ID_out_name,
63  double ampl);
64 
65 
66 
67 #endif
gauss
double gauss()
Normal distribution, mean=0, sigma=1.
Definition: statistic.c:216
put_poisson_noise
long put_poisson_noise(const char *ID_in_name, const char *ID_out_name)
Apply Poisson noise to image.
Definition: statistic.c:354
gauss_trc
double gauss_trc()
truncated (-1/+1) sigma = 1 mean = 0 gaussian probability
Definition: statistic.c:230
ran1
double ran1()
Uniform distribution from 0 to 1.
Definition: statistic.c:203
poisson
long poisson(double mu)
Poisson distribution.
Definition: statistic.c:245
__attribute__
void __attribute__((constructor)) libinit_statistic()
Initialize module.
Definition: ImageStreamIO.c:77
gammaln
double gammaln(double xx)
Gamma function.
better_poisson
double better_poisson(double mu)
Definition: statistic.c:303
fast_poisson
double fast_poisson(double mu)
Definition: statistic.c:283
put_gauss_noise
long put_gauss_noise(const char *ID_in_name, const char *ID_out_name, double ampl)
Apply Gaussian noise to image.
Definition: statistic.c:386