.. _dootle.actions.random:
   
    
=====================
dootle.actions.random
=====================

   
.. py:module:: dootle.actions.random

       
 

   
 

 

 
   
        

           

 
 

           
   
             
  
 

Functions
---------

.. autoapisummary::

    dootle.actions.random.rng_fresh
    dootle.actions.random.rng_spawn
    dootle.actions.random.rng_job_spawn
    dootle.actions.random.rng_ints
    dootle.actions.random.rng_draw
    dootle.actions.random.rng_shuffle
           
 
  
           
 
      
 
Module Contents
===============

 
.. py:function:: rng_fresh(spec, state, seed, seed_path)

   Create a new (maybe seeded) random number generator,
   added to state._rng

   Uses PCG-64 bitgenerator by default
   With no seed, or seed = -1, uses secrets.randbits(128)


 
.. py:function:: rng_spawn(spec, state, _rng, num, _update)

   Spawn independent sub rngs (eg: for passing to job children)


 
.. py:function:: rng_job_spawn(spec, state, _rng, _onto)

   For n generated subtasks, spawn n new RNGs and inject them


 
.. py:function:: rng_ints(spec, state, _rng, count, _min, _max, _update)

   Use the rng to get a count of integers from min to max


 
.. py:function:: rng_draw(spec, state, _rng, dist, shape, args, _update)

   Use the rng to draw from a distribution
   Use 'args', 'shape', and 'dist'

   see https://numpy.org/doc/stable/reference/random/generator.html

   Distributions include:
   - beta(a, b)
   - binomial(n,p)
   - chisquare(df)
   - dirichlet(alpha)
   - exponential(scale(
   - f(dfnum, dfden)
   - gamma(shape, scale)
   - geometric(p)
   - logistic(loc, scale)
   - lognormal(mean, sigma)
   - multinomial(n, pvals)
   - normal(loc, scale)
   - pareto(a)
   - poisson(lam)
   - power(a)
   - standrd_t(df)
   - uniform(low, high)



 
.. py:function:: rng_shuffle(spec, state, _rng, base, form, _update)

   For shuffling and permuting a state value