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38 #ifndef GMX_RANDOM_RANDOM_H
39 #define GMX_RANDOM_RANDOM_H
41 #include "gromacs/utility/basedefinitions.h"
42 #include "gromacs/utility/real.h"
48 /*! Fixed random number seeds for different types of RNG */
49 #define RND_SEED_UPDATE 1 /**< For coordinate update (sd, bd, ..) */
50 #define RND_SEED_REPLEX 2 /**< For replica exchange */
51 #define RND_SEED_VRESCALE 3 /**< For V-rescale thermostat */
52 #define RND_SEED_ANDERSEN 4 /**< For Andersen thermostat */
53 #define RND_SEED_TPI 5 /**< For test particle insertion */
54 #define RND_SEED_EXPANDED 6 /**< For expanded emseble methods */
56 /*! \brief Abstract datatype for a random number generator
58 * This is a handle to the full state of a random number generator.
59 * You can not access anything inside the gmx_rng structure outside this
62 typedef struct gmx_rng *
66 /*! \brief Returns the size of the RNG integer data structure
68 * Returns the size of the RNG integer data structure.
75 /*! \brief Create a new RNG, seeded from a single integer.
77 * If you dont want to pick a seed, just call it as
78 * rng=gmx_rng_init(gmx_rng_make_seed()) to seed it from
79 * the system time or a random device.
81 * \param seed Random seed, unsigned 32-bit integer.
83 * \return Reference to a random number generator, or NULL if there was an
89 gmx_rng_init(unsigned int seed);
92 /*! \brief Generate a 'random' RNG seed.
94 * This routine tries to get a seed from /dev/random if present,
95 * and if not it uses time-of-day and process id to generate one.
97 * \return 32-bit unsigned integer random seed.
99 * Tip: If you use this in your code, it is a good idea to write the
100 * returned random seed to a logfile, so you can recreate the exact sequence
101 * of random number if you need to reproduce your run later for one reason
107 gmx_rng_make_seed(void);
110 /*! \brief Initialize a RNG with 624 integers (>32 bits of entropy).
112 * The Mersenne twister RNG used in Gromacs has an extremely long period,
113 * but when you only initialize it with a 32-bit integer there are only
114 * 2^32 different possible sequences of number - much less than the generator
117 * If you really need the full entropy, this routine makes it possible to
118 * initialize the RNG with up to 624 32-bit integers, which will give you
119 * up to 2^19968 bits of entropy.
121 * \param seed Array of unsigned integers to form a seed
122 * \param seed_length Number of integers in the array, up to 624 are used.
124 * \return Reference to a random number generator, or NULL if there was an
130 gmx_rng_init_array(unsigned int seed[],
134 /*! \brief Release resources of a RNG
136 * This routine destroys a random number generator and releases all
137 * resources allocated by it.
139 * \param rng Handle to random number generator previously returned by
140 * gmx_rng_init() or gmx_rng_init_array().
142 * \threadsafe Function itself is threadsafe, but you should only destroy a
143 * certain RNG once (i.e. from one thread).
146 gmx_rng_destroy(gmx_rng_t rng);
149 /*! \brief Get the state of a RNG
151 * This routine stores the random state in \p mt and \p mti.
153 * \param[in] rng Handle to random number generator previously returned by
154 * gmx_rng_init() or gmx_rng_init_array().
155 * \param[out] mt Array of at least 624 integers to receive state.
156 * \param[out] mti Pointer to an integer to receive state.
159 gmx_rng_get_state(gmx_rng_t rng, unsigned int *mt, int *mti);
162 /*! \brief Set the state of a RNG
164 * This routine sets the random state from \p mt and \p mti.
166 * \param rng Handle to random number generator previously returned by
167 * gmx_rng_init() or gmx_rng_init_array().
168 * \param[in] mt Array of at least 624 integers.
169 * \param[in] mti Additional integer.
172 gmx_rng_set_state(gmx_rng_t rng, unsigned int *mt, int mti);
175 /*! \brief Random 32-bit integer from a uniform distribution
177 * This routine returns a random integer from the random number generator
178 * provided, and updates the state of that RNG.
180 * \param rng Handle to random number generator previously returned by
181 * gmx_rng_init() or gmx_rng_init_array().
183 * \return 32-bit unsigned integer from a uniform distribution.
185 * \threadsafe Function yes, input data no. You should not call this function
186 * from two different threads using the same RNG handle at the
187 * same time. For performance reasons we cannot lock the handle
188 * with a mutex every time we need a random number - that would
189 * slow the routine down a factor 2-5. There are two simple
190 * solutions: either use a mutex and lock it before calling
191 * the function, or use a separate RNG handle for each thread.
194 gmx_rng_uniform_uint32(gmx_rng_t rng);
197 /*! \brief Random gmx_real_t 0<=x<1 from a uniform distribution
199 * This routine returns a random floating-point number from the
200 * random number generator provided, and updates the state of that RNG.
202 * \param rng Handle to random number generator previously returned by
203 * gmx_rng_init() or gmx_rng_init_array().
205 * \return floating-point number 0<=x<1 from a uniform distribution.
207 * \threadsafe Function yes, input data no. You should not call this function
208 * from two different threads using the same RNG handle at the
209 * same time. For performance reasons we cannot lock the handle
210 * with a mutex every time we need a random number - that would
211 * slow the routine down a factor 2-5. There are two simple
212 * solutions: either use a mutex and lock it before calling
213 * the function, or use a separate RNG handle for each thread.
216 gmx_rng_uniform_real(gmx_rng_t rng);
219 /*! \brief Random gmx_real_t from a gaussian distribution
221 * This routine returns a random floating-point number from the
222 * random number generator provided, and updates the state of that RNG.
224 * The Box-Muller algorithm is used to provide gaussian random numbers. This
225 * is not the fastest known algorithm for gaussian numbers, but in contrast
226 * to the alternatives it is very well studied and you can trust the returned
227 * random numbers to have good properties and no correlations.
229 * \param rng Handle to random number generator previously returned by
230 * gmx_rng_init() or gmx_rng_init_array().
232 * \return Gaussian random floating-point number with average 0.0 and
233 * standard deviation 1.0. You can get any average/mean you want
234 * by first multiplying with the desired average and then adding
235 * the average you want.
237 * \threadsafe Function yes, input data no. You should not call this function
238 * from two different threads using the same RNG handle at the
239 * same time. For performance reasons we cannot lock the handle
240 * with a mutex every time we need a random number - that would
241 * slow the routine down a factor 2-5. There are two simple
242 * solutions: either use a mutex and lock it before calling
243 * the function, or use a separate RNG handle for each thread.
245 * It works perfectly to mix calls to get uniform and gaussian random numbers
246 * from the same generator, but since it will affect the sequence of returned
247 * numbers it is probably better to use separate random number generator
251 gmx_rng_gaussian_real(gmx_rng_t rng);
255 /* Return a new gaussian random number with expectation value
256 * 0.0 and standard deviation 1.0. This routine uses a table
257 * lookup for maximum speed.
259 * WARNING: The lookup table is 16k by default, which means
260 * the granularity of the random numbers is coarser
261 * than what you get from gmx_rng_gauss_real().
262 * In most cases this is no problem whatsoever,
263 * and it is particularly true for BD/SD integration.
264 * Note that you will NEVER get any really extreme
265 * numbers: the maximum absolute value returned is
271 gmx_rng_gaussian_table(gmx_rng_t rng);
274 /* The stateless cycle based random number generators below,
275 * which all use threefry2x64, take the following arguments:
277 * ctr1: In mdrun the step counter, in tools the frame(-step)
278 * counter, so we can ensure reproducible results, even
279 * we starting at different steps/frames. Might need to be
280 * multiplied by a constant if we need more random numbers.
281 * ctr2: A local counter, in mdrun often a global atom index.
282 * If any algorithm needs a variable number of random numbers,
283 * the second counter is usually a function of the local
285 * key1: A user provided random seed.
286 * key2: A fixed seed which is particular for the algorithm,
287 * as defined at the top of this file, to ensure different
288 * random sequences when the same user seed is used for
289 * different algorithms.
292 /* Return two uniform random numbers with 2^53 equally
293 * probable values between 0 and 1 - 2^-53.
294 * It uses a stateless counter based random number generator
298 gmx_rng_cycle_2uniform(gmx_int64_t ctr1, gmx_int64_t ctr2,
299 gmx_int64_t key1, gmx_int64_t key2,
302 /* Return three Gaussian random numbers with expectation value
303 * 0.0 and standard deviation 1.0. This routine uses a table
304 * lookup for maximum speed. It uses a stateless counter
305 * based random number generator (threefry2x64). See
306 * gmx_rng_gaussian_table for a warning about accuracy of the table.
311 gmx_rng_cycle_3gaussian_table(gmx_int64_t ctr1, gmx_int64_t ctr2,
312 gmx_int64_t key1, gmx_int64_t key2,
315 /* As gmx_rng_3gaussian_table, but returns 6 Gaussian numbers. */
317 gmx_rng_cycle_6gaussian_table(gmx_int64_t ctr1, gmx_int64_t ctr2,
318 gmx_int64_t key1, gmx_int64_t key2,