haskell / random / 28

{-# LANGUAGE BangPatterns #-}

{-# LANGUAGE DataKinds #-}

{-# LANGUAGE FlexibleInstances #-}

{-# LANGUAGE FunctionalDependencies #-}

{-# LANGUAGE GeneralizedNewtypeDeriving #-}

{-# LANGUAGE MultiParamTypeClasses #-}

{-# LANGUAGE RankNTypes #-}

{-# LANGUAGE Trustworthy #-}

{-# LANGUAGE TypeFamilies #-}

{-# LANGUAGE UndecidableInstances #-}

-- |

-- Module      :  System.Random.Stateful

-- Copyright   :  (c) The University of Glasgow 2001

-- License     :  BSD-style (see the file LICENSE in the 'random' repository)

-- Maintainer  :  libraries@haskell.org

-- Stability   :  stable

--

-- This library deals with the common task of pseudo-random number generation.

module System.Random.Stateful

  (

  -- * Pure Random Generator

  module System.Random

  -- * Monadic Random Generator

  -- $introduction

  -- * Usage

  -- $usagemonadic

  -- * Mutable pseudo-random number generator interfaces

  -- $interfaces

  , StatefulGen(..)

  , FrozenGen(..)

  , RandomGenM(..)

  , withMutableGen

  , withMutableGen_

  , randomM

  , randomRM

  , splitGenM

  -- * Monadic adapters for pure pseudo-random number generators #monadicadapters#

  -- $monadicadapters

  -- ** Pure adapter

  , StateGen(..)

  , StateGenM(..)

  , runStateGen

  , runStateGen_

  , runStateGenT

  , runStateGenT_

  , runStateGenST

  -- ** Mutable adapter with atomic operations

  , AtomicGen(..)

  , AtomicGenM(..)

  , newAtomicGenM

  , applyAtomicGen

  -- ** Mutable adapter in 'IO'

  , IOGen(..)

  , IOGenM(..)

  , newIOGenM

  , applyIOGen

  -- ** Mutable adapter in 'ST'

  , STGen(..)

  , STGenM(..)

  , newSTGenM

  , applySTGen

  , runSTGen

  , runSTGen_

  -- * Pseudo-random values of various types

  -- $uniform

  , Uniform(..)

  , uniformListM

  , uniformViaFiniteM

  , UniformRange(..)

  -- * Generators for sequences of pseudo-random bytes

  , genShortByteStringIO

  , genShortByteStringST

  , uniformByteStringM

  , uniformDouble01M

  , uniformDoublePositive01M

  , uniformFloat01M

  , uniformFloatPositive01M

  -- * Appendix

  -- ** How to implement 'StatefulGen'

  -- $implementmonadrandom

  -- ** Floating point number caveats #fpcaveats#

  -- $floating

  -- * References

  -- $references

  ) where

import Control.DeepSeq

import Control.Monad.IO.Class

import Control.Monad.ST

import Control.Monad.State.Strict

import Data.IORef

import Data.STRef

import Foreign.Storable

import System.Random

import System.Random.Internal

-- $introduction

--

-- This module provides type classes and instances for the following concepts:

--

-- [Monadic pseudo-random number generators] 'StatefulGen' is an interface to

--     monadic pseudo-random number generators.

--

-- [Monadic adapters] 'StateGenM', 'AtomicGenM', 'IOGenM' and 'STGenM' turn a

--     'RandomGen' instance into a 'StatefulGen' instance.

--

-- [Drawing from a range] 'UniformRange' is used to generate a value of a

--     type uniformly within a range.

--

--     This library provides instances of 'UniformRange' for many common

--     numeric types.

--

-- [Drawing from the entire domain of a type] 'Uniform' is used to generate a

--     value of a type uniformly over all possible values of that type.

--

--     This library provides instances of 'Uniform' for many common bounded

--     numeric types.

--

-- $usagemonadic

--

-- In monadic code, use the relevant 'Uniform' and 'UniformRange' instances to

-- generate pseudo-random values via 'uniformM' and 'uniformRM', respectively.

--

-- As an example, @rollsM@ generates @n@ pseudo-random values of @Word@ in the

-- range @[1, 6]@ in a 'StatefulGen' context; given a /monadic/ pseudo-random

-- number generator, you can run this probabilistic computation as follows:

--

-- >>> :{

-- let rollsM :: StatefulGen g m => Int -> g -> m [Word]

--     rollsM n = replicateM n . uniformRM (1, 6)

-- in do

--     monadicGen <- MWC.create

--     rollsM 10 monadicGen :: IO [Word]

-- :}

-- [3,4,3,1,4,6,1,6,1,4]

--

-- Given a /pure/ pseudo-random number generator, you can run the monadic

-- pseudo-random number computation @rollsM@ in an 'IO' or 'ST' context by

-- applying a monadic adapter like 'AtomicGenM', 'IOGenM' or 'STGenM'

-- (see [monadic-adapters](#monadicadapters)) to the pure pseudo-random number

-- generator.

--

-- >>> :{

-- let rollsM :: StatefulGen g m => Int -> g -> m [Word]

--     rollsM n = replicateM n . uniformRM (1, 6)

--     pureGen = mkStdGen 42

-- in

--     newIOGenM pureGen >>= rollsM 10 :: IO [Word]

-- :}

-- [1,1,3,2,4,5,3,4,6,2]

-------------------------------------------------------------------------------

-- Pseudo-random number generator interfaces

-------------------------------------------------------------------------------

-- $interfaces

--

-- Pseudo-random number generators come in two flavours: /pure/ and /monadic/.

--

-- ['System.Random.RandomGen': pure pseudo-random number generators]

--     See "System.Random" module.

--

-- ['StatefulGen': monadic pseudo-random number generators] These generators

--     mutate their own state as they produce pseudo-random values. They

--     generally live in 'ST' or 'IO' or some transformer that implements

--     @PrimMonad@.

--

-------------------------------------------------------------------------------

-- Monadic adapters

-------------------------------------------------------------------------------

-- $monadicadapters

--

-- Pure pseudo-random number generators can be used in monadic code via the

-- adapters 'StateGenM', 'AtomicGenM', 'IOGenM' and 'STGenM'.

--

-- *   'StateGenM' can be used in any state monad. With strict 'StateT' there is

--     no performance overhead compared to using the 'RandomGen' instance

--     directly. 'StateGenM' is /not/ safe to use in the presence of exceptions

--     and concurrency.

--

-- *   'AtomicGenM' is safe in the presence of exceptions and concurrency since

--     it performs all actions atomically.

--

-- *   'IOGenM' is a wrapper around an 'IORef' that holds a pure generator.

--     'IOGenM' is safe in the presence of exceptions, but not concurrency.

--

-- *   'STGenM' is a wrapper around an 'STRef' that holds a pure generator.

--     'STGenM' is safe in the presence of exceptions, but not concurrency.

-- | Interface to operations on 'RandomGen' wrappers like 'IOGenM' and 'StateGenM'.

--

-- @since 1.2.0

class (RandomGen r, StatefulGen g m) => RandomGenM g r m | g -> r where

  applyRandomGenM :: (r -> (a, r)) -> g -> m a

-- | Splits a pseudo-random number generator into two. Overwrites the mutable

-- wrapper with one of the resulting generators and returns the other.

--

-- @since 1.2.0

splitGenM :: (Splittable r ~ 'True, RandomGenM g r m) => g -> m r

instance (RandomGen r, MonadIO m) => RandomGenM (IOGenM r) r m where

instance (RandomGen r, MonadIO m) => RandomGenM (AtomicGenM r) r m where

instance (RandomGen r, MonadState r m) => RandomGenM (StateGenM r) r m where

instance RandomGen r => RandomGenM (STGenM r s) r (ST s) where

-- | Runs a mutable pseudo-random number generator from its 'Frozen' state.

--

-- ====__Examples__

--

-- >>> import Data.Int (Int8)

-- >>> withMutableGen (IOGen (mkStdGen 217)) (uniformListM 5) :: IO ([Int8], IOGen StdGen)

-- ([-74,37,-50,-2,3],IOGen {unIOGen = StdGen {unStdGen = SMGen 4273268533320920145 15251669095119325999}})

--

-- @since 1.2.0

withMutableGen :: FrozenGen f m => f -> (MutableGen f m -> m a) -> m (a, f)

-- | Same as 'withMutableGen', but only returns the generated value.

--

-- ====__Examples__

--

-- >>> import System.Random.Stateful

-- >>> let pureGen = mkStdGen 137

-- >>> withMutableGen_ (IOGen pureGen) (uniformRM (1 :: Int, 6 :: Int))

-- 4

--

-- @since 1.2.0

withMutableGen_ :: FrozenGen f m => f -> (MutableGen f m -> m a) -> m a

-- | Generates a list of pseudo-random values.

--

-- ====__Examples__

--

-- >>> import System.Random.Stateful

-- >>> let pureGen = mkStdGen 137

-- >>> g <- newIOGenM pureGen

-- >>> uniformListM 10 g :: IO [Bool]

-- [True,True,True,True,False,True,True,False,False,False]

--

-- @since 1.2.0

uniformListM :: (StatefulGen g m, Uniform a) => Int -> g -> m [a]

-- | Generates a pseudo-random value using monadic interface and `Random` instance.

--

-- ====__Examples__

--

-- >>> import System.Random.Stateful

-- >>> let pureGen = mkStdGen 137

-- >>> g <- newIOGenM pureGen

-- >>> randomM g :: IO Double

-- 0.5728354935654512

--

-- @since 1.2.0

randomM :: (RandomGenM g r m, Random a) => g -> m a

-- | Generates a pseudo-random value using monadic interface and `Random` instance.

--

-- ====__Examples__

--

-- >>> import System.Random.Stateful

-- >>> let pureGen = mkStdGen 137

-- >>> g <- newIOGenM pureGen

-- >>> randomRM (1, 100) g :: IO Int

-- 52

--

-- @since 1.2.0

randomRM :: (RandomGenM g r m, Random a) => (a, a) -> g -> m a

-- | Wraps an 'IORef' that holds a pure pseudo-random number generator. All

-- operations are performed atomically.

--

-- *   'AtomicGenM' is safe in the presence of exceptions and concurrency.

-- *   'AtomicGenM' is the slowest of the monadic adapters due to the overhead

--     of its atomic operations.

--

-- @since 1.2.0

-- | Frozen version of mutable `AtomicGenM` generator

--

-- @since 1.2.0

-- | Creates a new 'AtomicGenM'.

--

-- @since 1.2.0

newAtomicGenM :: MonadIO m => g -> m (AtomicGenM g)

instance (RandomGen g, MonadIO m) => StatefulGen (AtomicGenM g) m where

  {-# INLINE uniformWord32R #-}

  {-# INLINE uniformWord64R #-}

  {-# INLINE uniformWord8 #-}

  {-# INLINE uniformWord16 #-}

  {-# INLINE uniformWord32 #-}

  {-# INLINE uniformWord64 #-}

instance (RandomGen g, MonadIO m) => FrozenGen (AtomicGen g) m where

  type MutableGen (AtomicGen g) m = AtomicGenM g

-- | Atomically applies a pure operation to the wrapped pseudo-random number

-- generator.

--

-- ====__Examples__

--

-- >>> import System.Random.Stateful

-- >>> let pureGen = mkStdGen 137

-- >>> g <- newAtomicGenM pureGen

-- >>> applyAtomicGen random g :: IO Int

-- 7879794327570578227

--

-- @since 1.2.0

applyAtomicGen :: MonadIO m => (g -> (a, g)) -> (AtomicGenM g) -> m a

{-# INLINE applyAtomicGen #-}

-- | Wraps an 'IORef' that holds a pure pseudo-random number generator.

--

-- *   'IOGenM' is safe in the presence of exceptions, but not concurrency.

-- *   'IOGenM' is slower than 'StateGenM' due to the extra pointer indirection.

-- *   'IOGenM' is faster than 'AtomicGenM' since the 'IORef' operations used by

--     'IOGenM' are not atomic.

--

-- An example use case is writing pseudo-random bytes into a file:

--

-- >>> import UnliftIO.Temporary (withSystemTempFile)

-- >>> import Data.ByteString (hPutStr)

-- >>> let ioGen g = withSystemTempFile "foo.bin" $ \_ h -> uniformRM (0, 100) g >>= flip uniformByteStringM g >>= hPutStr h

--

-- and then run it:

--

-- >>> newIOGenM (mkStdGen 1729) >>= ioGen

--

-- @since 1.2.0

-- | Frozen version of mutable `IOGenM` generator

--

-- @since 1.2.0

-- | Creates a new 'IOGenM'.

--

-- @since 1.2.0

newIOGenM :: MonadIO m => g -> m (IOGenM g)

instance (RandomGen g, MonadIO m) => StatefulGen (IOGenM g) m where

  {-# INLINE uniformWord32R #-}

  {-# INLINE uniformWord64R #-}

  {-# INLINE uniformWord8 #-}

  {-# INLINE uniformWord16 #-}

  {-# INLINE uniformWord32 #-}

  {-# INLINE uniformWord64 #-}

instance (RandomGen g, MonadIO m) => FrozenGen (IOGen g) m where

  type MutableGen (IOGen g) m = IOGenM g

-- | Applies a pure operation to the wrapped pseudo-random number generator.

--

-- ====__Examples__

--

-- >>> import System.Random.Stateful

-- >>> let pureGen = mkStdGen 137

-- >>> g <- newIOGenM pureGen

-- >>> applyIOGen random g :: IO Int

-- 7879794327570578227

--

-- @since 1.2.0

applyIOGen :: MonadIO m => (g -> (a, g)) -> IOGenM g -> m a

{-# INLINE applyIOGen #-}

-- | Wraps an 'STRef' that holds a pure pseudo-random number generator.

--

-- *   'STGenM' is safe in the presence of exceptions, but not concurrency.

-- *   'STGenM' is slower than 'StateGenM' due to the extra pointer indirection.

--

-- @since 1.2.0

-- | Frozen version of mutable `STGenM` generator

--

-- @since 1.2.0

-- | Creates a new 'STGenM'.

--

-- @since 1.2.0

newSTGenM :: g -> ST s (STGenM g s)

instance RandomGen g => StatefulGen (STGenM g s) (ST s) where

  {-# INLINE uniformWord32R #-}

  {-# INLINE uniformWord64R #-}

  {-# INLINE uniformWord8 #-}

  {-# INLINE uniformWord16 #-}

  {-# INLINE uniformWord32 #-}

  {-# INLINE uniformWord64 #-}

instance RandomGen g => FrozenGen (STGen g) (ST s) where

  type MutableGen (STGen g) (ST s) = STGenM g s

-- | Applies a pure operation to the wrapped pseudo-random number generator.

--

-- ====__Examples__

--

-- >>> import System.Random.Stateful

-- >>> let pureGen = mkStdGen 137

-- >>> (runSTGen pureGen (\g -> applySTGen random g)) :: (Int, StdGen)

-- (7879794327570578227,StdGen {unStdGen = SMGen 11285859549637045894 7641485672361121627})

--

-- @since 1.2.0

applySTGen :: (g -> (a, g)) -> STGenM g s -> ST s a

{-# INLINE applySTGen #-}

-- | Runs a monadic generating action in the `ST` monad using a pure

-- pseudo-random number generator.

--

-- ====__Examples__

--

-- >>> import System.Random.Stateful

-- >>> let pureGen = mkStdGen 137

-- >>> (runSTGen pureGen (\g -> applySTGen random g)) :: (Int, StdGen)

-- (7879794327570578227,StdGen {unStdGen = SMGen 11285859549637045894 7641485672361121627})

--

-- @since 1.2.0

runSTGen :: RandomGen g => g -> (forall s . STGenM g s -> ST s a) -> (a, g)

-- | Runs a monadic generating action in the `ST` monad using a pure

-- pseudo-random number generator. Returns only the resulting pseudo-random

-- value.

--

-- ====__Examples__

--

-- >>> import System.Random.Stateful

-- >>> let pureGen = mkStdGen 137

-- >>> (runSTGen_ pureGen (\g -> applySTGen random g)) :: Int

-- 7879794327570578227

--

-- @since 1.2.0

runSTGen_ :: RandomGen g => g -> (forall s . STGenM g s -> ST s a) -> a

-- $uniform

--

-- This library provides two type classes to generate pseudo-random values:

--

-- *   'UniformRange' is used to generate a value of a type uniformly within a

--     range.

-- *   'Uniform' is used to generate a value of a type uniformly over all

--     possible values of that type.

--

-- Types may have instances for both or just one of 'UniformRange' and

-- 'Uniform'. A few examples illustrate this:

--

-- *   'Int', 'Word16' and 'Bool' are instances of both 'UniformRange' and

--     'Uniform'.

-- *   'Integer', 'Float' and 'Double' each have an instance for 'UniformRange'

--     but no 'Uniform' instance.

-- *   A hypothetical type @Radian@ representing angles by taking values in the

--     range @[0, 2π)@ has a trivial 'Uniform' instance, but no 'UniformRange'

--     instance: the problem is that two given @Radian@ values always span /two/

--     ranges, one clockwise and one anti-clockwise.

-- *   It is trivial to construct a @Uniform (a, b)@ instance given

--     @Uniform a@ and @Uniform b@ (and this library provides this tuple

--     instance).

-- *   On the other hand, there is no correct way to construct a

--     @UniformRange (a, b)@ instance based on just @UniformRange a@ and

--     @UniformRange b@.

-------------------------------------------------------------------------------

-- Notes

-------------------------------------------------------------------------------

-- $floating

--

-- The 'UniformRange' instances for 'Float' and 'Double' use the following

-- procedure to generate a random value in a range for @uniformRM (a, b) g@:

--

-- If \(a = b\), return \(a\). Otherwise:

--

-- 1.  Generate \(x\) uniformly such that \(0 \leq x \leq 1\).

--

--     The method by which \(x\) is sampled does not cover all representable

--     floating point numbers in the unit interval. The method never generates

--     denormal floating point numbers, for example.

--

-- 2.  Return \(x \cdot a + (1 - x) \cdot b\).

--

--     Due to rounding errors, floating point operations are neither

--     associative nor distributive the way the corresponding operations on

--     real numbers are. Additionally, floating point numbers admit special

--     values @NaN@ as well as negative and positive infinity.

--

-- For pathological values, step 2 can yield surprising results.

--

-- *   The result may be greater than @max a b@.

--

--     >>> :{

--     let (a, b, x) = (-2.13238e-29, -2.1323799e-29, 0.27736077)

--         result = x * a + (1 - x) * b :: Float

--     in (result, result > max a b)

--     :}

--     (-2.1323797e-29,True)

--

-- *   The result may be smaller than @min a b@.

--

--     >>> :{

--     let (a, b, x) = (-1.9087862, -1.908786, 0.4228573)

--         result = x * a + (1 - x) * b :: Float

--     in (result, result < min a b)

--     :}

--     (-1.9087863,True)

--

-- What happens when @NaN@ or @Infinity@ are given to 'uniformRM'? We first

-- define them as constants:

--

-- >>> nan = read "NaN" :: Float

-- >>> inf = read "Infinity" :: Float

--

-- *   If at least one of \(a\) or \(b\) is @NaN@, the result is @NaN@.

--

--     >>> let (a, b, x) = (nan, 1, 0.5) in x * a + (1 - x) * b

--     NaN

--     >>> let (a, b, x) = (-1, nan, 0.5) in x * a + (1 - x) * b

--     NaN

--

-- *   If \(a\) is @-Infinity@ and \(b\) is @Infinity@, the result is @NaN@.

--

--     >>> let (a, b, x) = (-inf, inf, 0.5) in x * a + (1 - x) * b

--     NaN

--

-- *   Otherwise, if \(a\) is @Infinity@ or @-Infinity@, the result is \(a\).

--

--     >>> let (a, b, x) = (inf, 1, 0.5) in x * a + (1 - x) * b

--     Infinity

--     >>> let (a, b, x) = (-inf, 1, 0.5) in x * a + (1 - x) * b

--     -Infinity

--

-- *   Otherwise, if \(b\) is @Infinity@ or @-Infinity@, the result is \(b\).

--

--     >>> let (a, b, x) = (1, inf, 0.5) in x * a + (1 - x) * b

--     Infinity

--     >>> let (a, b, x) = (1, -inf, 0.5) in x * a + (1 - x) * b

--     -Infinity

--

-- Note that the [GCC 10.1.0 C++ standard library](https://gcc.gnu.org/git/?p=gcc.git;a=blob;f=libstdc%2B%2B-v3/include/bits/random.h;h=19307fbc3ca401976ef6823e8fda893e4a263751;hb=63fa67847628e5f358e7e2e7edb8314f0ee31f30#l1859),

-- the [Java 10 standard library](https://docs.oracle.com/javase/10/docs/api/java/util/Random.html#doubles%28double,double%29)

-- and [CPython 3.8](https://github.com/python/cpython/blob/3.8/Lib/random.py#L417)

-- use the same procedure to generate floating point values in a range.

--

-- $implementmonadrandom

--

-- Typically, a monadic pseudo-random number generator has facilities to save

-- and restore its internal state in addition to generating pseudo-random numbers.

--

-- Here is an example instance for the monadic pseudo-random number generator

-- from the @mwc-random@ package:

--

-- > instance (s ~ PrimState m, PrimMonad m) => StatefulGen (MWC.Gen s) m where

-- >   uniformWord8 = MWC.uniform

-- >   uniformWord16 = MWC.uniform

-- >   uniformWord32 = MWC.uniform

-- >   uniformWord64 = MWC.uniform

-- >   uniformShortByteString n g = unsafeSTToPrim (genShortByteStringST n (MWC.uniform g))

--

-- > instance PrimMonad m => FrozenGen MWC.Seed m where

-- >   type MutableGen MWC.Seed m = MWC.Gen (PrimState m)

-- >   thawGen = MWC.restore

-- >   freezeGen = MWC.save

--

-- === @FrozenGen@

--

-- `FrozenGen` gives us ability to use any stateful pseudo-random number generator in its

-- immutable form, if one exists that is. This concept is commonly known as a seed, which

-- allows us to save and restore the actual mutable state of a pseudo-random number

-- generator. The biggest benefit that can be drawn from a polymorphic access to a

-- stateful pseudo-random number generator in a frozen form is the ability to serialize,

-- deserialize and possibly even use the stateful generator in a pure setting without

-- knowing the actual type of a generator ahead of time. For example we can write a

-- function that accepts a frozen state of some pseudo-random number generator and

-- produces a short list with random even integers.

--

-- >>> import Data.Int (Int8)

-- >>> :{

-- myCustomRandomList :: FrozenGen f m => f -> m [Int8]

-- myCustomRandomList f =

--   withMutableGen_ f $ \gen -> do

--     len <- uniformRM (5, 10) gen

--     replicateM len $ do

--       x <- uniformM gen

--       pure $ if even x then x else x + 1

-- :}

--

-- and later we can apply it to a frozen version of a stateful generator, such as `STGen`:

--

-- >>> print $ runST $ myCustomRandomList (STGen (mkStdGen 217))

-- [-50,-2,4,-8,-58,-40,24,-32,-110,24]

--

-- or a @Seed@ from @mwc-random@:

--

-- >>> import Data.Vector.Primitive as P

-- >>> print $ runST $ myCustomRandomList (MWC.toSeed (P.fromList [1,2,3]))

-- [24,40,10,40,-8,48,-78,70,-12]

--

-- Alternatively, instead of discarding the final state of the generator, as it happens

-- above, we could have used `withMutableGen`, which together with the result would give

-- us back its frozen form. This would allow us to store the end state of our generator

-- somewhere for the later reuse.

--

--

-- $references

--

-- 1. Guy L. Steele, Jr., Doug Lea, and Christine H. Flood. 2014. Fast

-- splittable pseudorandom number generators. In Proceedings of the 2014 ACM

-- International Conference on Object Oriented Programming Systems Languages &

-- Applications (OOPSLA '14). ACM, New York, NY, USA, 453-472. DOI:

-- <https://doi.org/10.1145/2660193.2660195>

-- $setup

-- >>> import Control.Monad.Primitive

-- >>> import qualified System.Random.MWC as MWC

--

-- >>> :set -XFlexibleContexts

-- >>> :set -XFlexibleInstances

-- >>> :set -XMultiParamTypeClasses

-- >>> :set -XTypeFamilies

-- >>> :set -XUndecidableInstances

--

-- >>> :{

-- instance (s ~ PrimState m, PrimMonad m) => StatefulGen (MWC.Gen s) m where

--   uniformWord8 = MWC.uniform

--   uniformWord16 = MWC.uniform

--   uniformWord32 = MWC.uniform

--   uniformWord64 = MWC.uniform

--   uniformShortByteString n g = unsafeSTToPrim (genShortByteStringST n (MWC.uniform g))

-- instance PrimMonad m => FrozenGen MWC.Seed m where

--   type MutableGen MWC.Seed m = MWC.Gen (PrimState m)

--   thawGen = MWC.restore

--   freezeGen = MWC.save

-- :}

--