C API for random

Access to various distributions below is available via Cython or C-wrapper libraries like CFFI. All the functions accept a bitgen_t as their first argument. To access these from Cython or C, you must link with the npyrandom library which is part of the NumPy distribution, located in numpy/random/lib.


The bitgen_t holds the current state of the BitGenerator and pointers to functions that return standard C types while advancing the state.

struct bitgen:
    void *state
    npy_uint64 (*next_uint64)(void *st) nogil
    uint32_t (*next_uint32)(void *st) nogil
    double (*next_double)(void *st) nogil
    npy_uint64 (*next_raw)(void *st) nogil

ctypedef bitgen bitgen_t

See Extending for examples of using these functions.

The functions are named with the following conventions:

  • “standard” refers to the reference values for any parameters. For instance “standard_uniform” means a uniform distribution on the interval 0.0 to 1.0

  • “fill” functions will fill the provided out with cnt values.

  • The functions without “standard” in their name require additional parameters to describe the distributions.

  • zig in the name are based on a ziggurat lookup algorithm is used instead of calculating the log, which is significantly faster. The non-ziggurat variants are used in corner cases and for legacy compatibility.

double random_standard_uniform(bitgen_t *bitgen_state)
void random_standard_uniform_fill(bitgen_t* bitgen_state, npy_intp cnt, double *out)
double random_standard_exponential(bitgen_t *bitgen_state)
void random_standard_exponential_fill(bitgen_t *bitgen_state, npy_intp cnt, double *out)
double random_standard_normal(bitgen_t* bitgen_state)
void random_standard_normal_fill(bitgen_t *bitgen_state, npy_intp count, double *out)
void random_standard_normal_fill_f(bitgen_t *bitgen_state, npy_intp count, float *out)
double random_standard_gamma(bitgen_t *bitgen_state, double shape)
float random_standard_uniform_f(bitgen_t *bitgen_state)
void random_standard_uniform_fill_f(bitgen_t* bitgen_state, npy_intp cnt, float *out)
float random_standard_exponential_f(bitgen_t *bitgen_state)
void random_standard_exponential_fill_f(bitgen_t *bitgen_state, npy_intp cnt, float *out)
float random_standard_normal_f(bitgen_t* bitgen_state)
float random_standard_gamma_f(bitgen_t *bitgen_state, float shape)
double random_normal(bitgen_t *bitgen_state, double loc, double scale)
double random_gamma(bitgen_t *bitgen_state, double shape, double scale)
float random_gamma_f(bitgen_t *bitgen_state, float shape, float scale)
double random_exponential(bitgen_t *bitgen_state, double scale)
double random_uniform(bitgen_t *bitgen_state, double lower, double range)
double random_beta(bitgen_t *bitgen_state, double a, double b)
double random_chisquare(bitgen_t *bitgen_state, double df)
double random_f(bitgen_t *bitgen_state, double dfnum, double dfden)
double random_standard_cauchy(bitgen_t *bitgen_state)
double random_pareto(bitgen_t *bitgen_state, double a)
double random_weibull(bitgen_t *bitgen_state, double a)
double random_power(bitgen_t *bitgen_state, double a)
double random_laplace(bitgen_t *bitgen_state, double loc, double scale)
double random_gumbel(bitgen_t *bitgen_state, double loc, double scale)
double random_logistic(bitgen_t *bitgen_state, double loc, double scale)
double random_lognormal(bitgen_t *bitgen_state, double mean, double sigma)
double random_rayleigh(bitgen_t *bitgen_state, double mode)
double random_standard_t(bitgen_t *bitgen_state, double df)
double random_noncentral_chisquare(bitgen_t *bitgen_state, double df, double nonc)
double random_noncentral_f(bitgen_t *bitgen_state, double dfnum, double dfden, double nonc)
double random_wald(bitgen_t *bitgen_state, double mean, double scale)
double random_vonmises(bitgen_t *bitgen_state, double mu, double kappa)
double random_triangular(bitgen_t *bitgen_state, double left, double mode, double right)
npy_int64 random_poisson(bitgen_t *bitgen_state, double lam)
npy_int64 random_negative_binomial(bitgen_t *bitgen_state, double n, double p)
typedef struct s_binomial_t {
  int has_binomial; /* !=0: following parameters initialized for binomial */
  double psave;
  RAND_INT_TYPE nsave;
  double r;
  double q;
  double fm;
  double p1;
  double xm;
  double xl;
  double xr;
  double c;
  double laml;
  double lamr;
  double p2;
  double p3;
  double p4;
} binomial_t;
npy_int64 random_binomial(bitgen_t *bitgen_state, double p, npy_int64 n, binomial_t *binomial)
npy_int64 random_logseries(bitgen_t *bitgen_state, double p)
npy_int64 random_geometric_inversion(bitgen_t *bitgen_state, double p)
npy_int64 random_geometric(bitgen_t *bitgen_state, double p)
npy_int64 random_zipf(bitgen_t *bitgen_state, double a)
npy_int64 random_hypergeometric(bitgen_t *bitgen_state, npy_int64 good, npy_int64 bad, npy_int64 sample)
npy_uint64 random_interval(bitgen_t *bitgen_state, npy_uint64 max)
void random_multinomial(bitgen_t *bitgen_state, npy_int64 n, npy_int64 *mnix, double *pix, npy_intp d, binomial_t *binomial)
int random_multivariate_hypergeometric_count(bitgen_t *bitgen_state, npy_int64 total, size_t num_colors, npy_int64 *colors, npy_int64 nsample, size_t num_variates, npy_int64 *variates)
void random_multivariate_hypergeometric_marginals(bitgen_t *bitgen_state, npy_int64 total, size_t num_colors, npy_int64 *colors, npy_int64 nsample, size_t num_variates, npy_int64 *variates)

Generate a single integer

npy_int64 random_positive_int64(bitgen_t *bitgen_state)
npy_int32 random_positive_int32(bitgen_t *bitgen_state)
npy_int64 random_positive_int(bitgen_t *bitgen_state)
npy_uint64 random_uint(bitgen_t *bitgen_state)

Generate random uint64 numbers in closed interval [off, off + rng].

npy_uint64 random_bounded_uint64(bitgen_t *bitgen_state, npy_uint64 off, npy_uint64 rng, npy_uint64 mask, bool use_masked)