#![cfg_attr(feature = "const-generics", feature(const_generics))]

//! This crates exposes a single struct, [`IIDDistr`], parametrized by a [`Distribution`] `D`.
//!
//! If D is a `Distribution<T>` (meaning that it can be used to sample random variate of type `T`)
//! then `IIDDistr<D>` is a `Distribution<[T;n]>` for *n*
//! between 0 and 31 included (by default),
//! and up to 512 included if the feature `more_array_sizes` is activated.
//! `IIDDistr<D>` can be used to sample arrays whose elements are
//! Independently Identically Distributed (i.i.d.) according to `D`.
//!
//! # Handling of panic
//!
//! If sampling from the underlying distributon `D` panics, sampling
//! from `IIDDistr<D>` panics as well, but no memory is leaked.
//! This guarantee actually comes from the
//! [`array_init`](https://docs.rs/array-init/0.1.1/array_init/) crate.
//!
//! # Examples
//!
//! ## An array of normally distributed scalars
//! ```rust
//! # use rand_array_iid::IIDDistr;
//! # use rand_distr::Distribution;
//! # use rand_distr::StandardNormal;
//! let distr = IIDDistr::new(StandardNormal);
//! let mut rng = rand::thread_rng();
//! // Each of x element is distributed according to StandardNormal.
//! let x : [f64; 10] = distr.sample(&mut rng);
//! ```
//!
//! ## An array of 3D vectors sampled from the unit sphere
//! ```rust
//! # use rand_array_iid::IIDDistr;
//! # use rand_distr::Distribution;
//! # use rand_distr::UnitSphere;
//! let distr = IIDDistr::new(UnitSphere);
//! let mut rng = rand::thread_rng();
//! // Each of x element is sampled uniformly from the surface of the 3D unit sphere.
//! let x : [[f64; 3]; 10] = distr.sample(&mut rng);
//! ```
//!
//! # Why only arrays?
//!
//! Collections such as [`Vec`] that implement [`std::iter::FromIterator`] bear
//! no information on their size in their type, hence the distribution would have
//! to be restricted to a given size. They can also be sampled as follow:
//!
//! ```rust
//! # use rand_distr::Distribution;
//! # use rand::Rng;
//! fn sample_iid<D,R, Col>(dist: D, rng: &mut R, n: usize) -> Col
//! where
//!     R: Rng + ?Sized,
//!     Col: std::iter::IntoIterator,
//!     Col: std::iter::FromIterator<<Col as std::iter::IntoIterator>::Item>,
//!     D: Distribution<<Col as std::iter::IntoIterator>::Item>,
//! {
//!     dist.sample_iter(rng).take(n).collect()
//! }
//! ```
#![no_std]
use array_init::array_init;
use rand::Rng;
use rand_distr::Distribution;

#[derive(Debug, Eq, PartialEq, Copy, Clone, Default, Hash)]
/// A distribution on arrays whose elements are i.i.d. with distribution `D`.
///
/// See crate-level documentation for more information.
pub struct IIDDistr<D> {
    distribution: D,
}

impl<D> IIDDistr<D> {
    /// Create an i.i.d. distribution, where each array element is distributed according to `D`.
    ///
    /// # Examples
    /// ```rust
    /// # use rand_array_iid::IIDDistr;
    /// # use rand_distr::Distribution;
    /// # use rand_distr::StandardNormal;
    /// let distr = IIDDistr::new(StandardNormal);
    /// let mut rng = rand::thread_rng();
    /// // Each of x element is distributed according to StandardNormal
    /// let x : [f64; 10] = distr.sample(&mut rng);
    /// ```
    pub const fn new(d: D) -> Self {
        IIDDistr { distribution: d }
    }
}

#[cfg(feature = "const-generics")]
mod const_gen {
    use super::*;

    impl<D, T, const N: usize> Distribution<[T; N]> for IIDDistr<D>
    where
        D: Distribution<T>,
        T: Sized,
    {
        fn sample<R>(&self, rng: &mut R) -> [T; N]
        where
            R: Rng + ?Sized,
        {
            array_init(|_| self.distribution.sample(rng))
        }
    }

    #[cfg(feature = "more-array-sizes")]
    compile_error!(
        r#"Feature "more-array-sizes" is redundant when feature "const-generics" is activated."#
    );
}

#[cfg(not(feature = "const-generics"))]
mod macro_impl {
    use super::*;

    macro_rules! impl_distr_array {
        ($($n: literal)+) => ($(
            impl<D,T> Distribution<[T;$n]> for IIDDistr<D>
            where
                D: Distribution<T>,
                T: Sized
            {
                fn sample<R>(&self, rng: &mut R) -> [T;$n]
                where
                    R: Rng + ?Sized,
                {
                   array_init(
                       |_| self.distribution.sample(rng)
                    )
                }
            }
        )+)
    }

    impl_distr_array! {
        0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15
        16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
    }

    #[cfg(feature = "more-array-sizes")]
    impl_distr_array! {
        32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47
        48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63
        64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79
        80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95
        96 97 98 99 100 101 102 103 104 105 106 107 108
        109 110 111 112 113 114 115 116 117 118 119 120
        121 122 123 124 125 126 127 128 129 130 131 132
        133 134 135 136 137 138 139 140 141 142 143 144
        145 146 147 148 149 150 151 152 153 154 155 156
        157 158 159 160 161 162 163 164 165 166 167 168
        169 170 171 172 173 174 175 176 177 178 179 180
        181 182 183 184 185 186 187 188 189 190 191 192
        193 194 195 196 197 198 199 200 201 202 203 204
        205 206 207 208 209 210 211 212 213 214 215 216
        217 218 219 220 221 222 223 224 225 226 227 228
        229 230 231 232 233 234 235 236 237 238 239 240
        241 242 243 244 245 246 247 248 249 250 251 252
        253 254 255 256 257 258 259 260 261 262 263 264
        265 266 267 268 269 270 271 272 273 274 275 276
        277 278 279 280 281 282 283 284 285 286 287 288
        289 290 291 292 293 294 295 296 297 298 299 300
        301 302 303 304 305 306 307 308 309 310 311 312
        313 314 315 316 317 318 319 320 321 322 323 324
        325 326 327 328 329 330 331 332 333 334 335 336
        337 338 339 340 341 342 343 344 345 346 347 348
        349 350 351 352 353 354 355 356 357 358 359 360
        361 362 363 364 365 366 367 368 369 370 371 372
        373 374 375 376 377 378 379 380 381 382 383 384
        385 386 387 388 389 390 391 392 393 394 395 396
        397 398 399 400 401 402 403 404 405 406 407 408
        409 410 411 412 413 414 415 416 417 418 419 420
        421 422 423 424 425 426 427 428 429 430 431 432
        433 434 435 436 437 438 439 440 441 442 443 444
        445 446 447 448 449 450 451 452 453 454 455 456
        457 458 459 460 461 462 463 464 465 466 467 468
        469 470 471 472 473 474 475 476 477 478 479 480
        481 482 483 484 485 486 487 488 489 490 491 492
        493 494 495 496 497 498 499 500 501 502 503 504
        505 506 507 508 509 510 511 512
    }
}

/// The multivariate standard normal distribution.
///
/// # Examples
/// ```rust
/// # use rand_array_iid::STANDARD_MULTI_NORMAL;
/// # use rand_distr::Distribution;
/// # use rand_distr::StandardNormal;
/// let mut rng = rand::thread_rng();
/// // Each of x element is distributed according to StandardNormal
/// let x : [f64; 10] = STANDARD_MULTI_NORMAL.sample(&mut rng);
/// ```
pub const STANDARD_MULTI_NORMAL: IIDDistr<rand_distr::StandardNormal> =
    IIDDistr::new(rand_distr::StandardNormal);

#[cfg(test)]
mod tests {
    use super::*;
    use rand::rngs::StdRng;
    use rand::SeedableRng;

    type TestRng = StdRng;

    fn create_rng() -> TestRng {
        TestRng::seed_from_u64(0x0)
    }
    #[test]
    #[allow(clippy::float_cmp)]
    fn sample_standard_array_3() {
        let mut rng = create_rng();
        let x: [f64; 3] = rng.sample(&STANDARD_MULTI_NORMAL);
        let mut rng = create_rng();
        let y: [f64; 3] = rng.sample(&STANDARD_MULTI_NORMAL);
        assert_eq!(x, y);
    }
}