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var data = {"files":[{"path":["/","home","srv","hg","icefox","srht","icefox","oorandom","src","lib.rs"],"content":"//! A tiny, robust PRNG implementation.\n//!\n//! More specifically, it implements a single GOOD PRNG algorithm,\n//! which is currently a permuted congruential generator. It has two\n//! implementations, one that returns `u32` and one that returns\n//! `u64`. It also has functions that return floats or integer\n//! ranges. And that's it. What more do you need?\n//!\n//! For more info on PCG generators, see http://www.pcg-random.org/\n//!\n//! This was designed as a minimalist utility for video games. No\n//! promises are made about its quality, and if you use it for\n//! cryptography you will get what you deserve.\n//!\n//! Works with `#![no_std]`, has no global state, no dependencies\n//! apart from some in the unit tests, and is generally neato.\n\n#![forbid(unsafe_code)]\n#![forbid(missing_docs)]\n#![forbid(missing_debug_implementations)]\n#![forbid(unused_results)]\n#![no_std]\nuse core::ops::Range;\n\n/// A PRNG producing a 32-bit output.\n///\n/// The current implementation is `PCG-XSH-RR`.\n#[derive(Copy, Clone, Debug, PartialEq)]\npub struct Rand32 {\n state: u64,\n inc: u64,\n}\n\nimpl Rand32 {\n /// The default value for `increment`.\n /// This is basically arbitrary, it comes from the\n /// PCG reference C implementation:\n /// https://github.com/imneme/pcg-c/blob/master/include/pcg_variants.h#L284\n pub const DEFAULT_INC: u64 = 1442695040888963407;\n\n /// This is the number that you have to Really Get Right.\n ///\n /// The value used here is from the PCG C implementation:\n /// https://github.com/imneme/pcg-c/blob/master/include/pcg_variants.h#L278\n pub(crate) const MULTIPLIER: u64 = 6364136223846793005;\n\n /// Creates a new PRNG with the given seed and a default increment.\n pub fn new(seed: u64) -\u003e Self {\n Self::new_inc(seed, Self::DEFAULT_INC)\n }\n\n /// Creates a new PRNG. The two inputs, `seed` and `increment`,\n /// determine what you get; `increment` basically selects which\n /// sequence of all those possible the PRNG will produce, and the\n /// `seed` selects where in that sequence you start.\n ///\n /// Both are arbitrary; increment must be an odd number but this\n /// handles that for you\n pub fn new_inc(seed: u64, increment: u64) -\u003e Self {\n let mut rng = Self {\n state: 0,\n inc: increment.wrapping_shl(1) | 1,\n };\n // This initialization song-and-dance is a little odd,\n // but seems to be just how things go.\n let _ = rng.rand_u32();\n rng.state = rng.state.wrapping_add(seed);\n let _ = rng.rand_u32();\n rng\n }\n\n /// Returns the internal state of the PRNG. This allows\n /// you to save a PRNG and create a new one that will resume\n /// from the same spot in the sequence.\n pub fn state(\u0026self) -\u003e (u64, u64) {\n (self.state, self.inc)\n }\n\n /// Creates a new PRNG from a saved state from `Rand32::state()`.\n /// This is NOT quite the same as `new_inc()` because `new_inc()` does\n /// a little extra setup work to initialize the state.\n pub fn from_state(state: (u64, u64)) -\u003e Self {\n let (state, inc) = state;\n Self { state, inc }\n }\n\n /// Produces a random `u32` in the range `[0, u32::MAX]`.\n pub fn rand_u32(\u0026mut self) -\u003e u32 {\n let oldstate: u64 = self.state;\n self.state = oldstate\n .wrapping_mul(Self::MULTIPLIER)\n .wrapping_add(self.inc);\n let xorshifted: u32 = (((oldstate \u003e\u003e 18) ^ oldstate) \u003e\u003e 27) as u32;\n let rot: u32 = (oldstate \u003e\u003e 59) as u32;\n xorshifted.rotate_right(rot)\n }\n\n /// Produces a random `i32` in the range `[i32::MIN, i32::MAX]`.\n pub fn rand_i32(\u0026mut self) -\u003e i32 {\n self.rand_u32() as i32\n }\n\n /// Produces a random `f32` in the range `[0.0, 1.0)`.\n pub fn rand_float(\u0026mut self) -\u003e f32 {\n // This impl was taken more or less from `rand`, see\n // \u003chttps://docs.rs/rand/0.7.0/src/rand/distributions/float.rs.html#104-117\u003e\n // There MAY be better ways to do this, see:\n // https://mumble.net/~campbell/2014/04/28/uniform-random-float\n // https://mumble.net/~campbell/2014/04/28/random_real.c\n // https://github.com/Lokathor/randomize/issues/34\n const TOTAL_BITS: u32 = 32;\n const PRECISION: u32 = core::f32::MANTISSA_DIGITS + 1;\n const MANTISSA_SCALE: f32 = 1.0 / ((1u32 \u003c\u003c PRECISION) as f32);\n let mut u = self.rand_u32();\n u \u003e\u003e= TOTAL_BITS - PRECISION;\n u as f32 * MANTISSA_SCALE\n }\n\n /// Produces a random within the given bounds. Like any `Range`,\n /// it includes the lower bound and excludes the upper one.\n ///\n /// This should be faster than `Self::rand() % end + start`, but the\n /// real advantage is it's more convenient. Requires that\n /// `range.end \u003c= range.start`.\n pub fn rand_range(\u0026mut self, range: Range\u003cu32\u003e) -\u003e u32 {\n // This is harder to do well than it looks, it seems. I don't\n // trust Lokathor's implementation 'cause I don't understand\n // it, so I went to numpy's, which points me to \"Lemire's\n // rejection algorithm\": http://arxiv.org/abs/1805.10941\n //\n // Algorithms 3, 4 and 5 in that paper all seem fine modulo\n // minor performance differences, so this is algorithm 5.\n // It uses numpy's implementation, `buffered_bounded_lemire_uint32()`\n\n debug_assert!(range.start \u003c range.end);\n let range_starting_from_zero = 0..(range.end - range.start);\n\n let s: u32 = range_starting_from_zero.end;\n let mut m: u64 = u64::from(self.rand_u32()) * u64::from(s);\n let mut leftover: u32 = (m \u0026 0xFFFF_FFFF) as u32;\n\n if leftover \u003c s {\n // TODO: verify the wrapping_neg() here\n let threshold: u32 = s.wrapping_neg() % s;\n while leftover \u003c threshold {\n m = u64::from(self.rand_u32()).wrapping_mul(u64::from(s));\n leftover = (m \u0026 0xFFFF_FFFF) as u32;\n }\n }\n (m \u003e\u003e 32) as u32 + range.start\n }\n}\n\n/// A PRNG producing a 64-bit output.\n///\n/// The current implementation is `PCG-XSH-RR`.\n// BUGGO: The recommended algorithm is PCG-XSL-RR?\n// See https://github.com/imneme/pcg-c/blob/master/include/pcg_variants.h#L2405\n// Not sure if it matters?\n#[derive(Copy, Clone, Debug, PartialEq)]\npub struct Rand64 {\n state: u128,\n inc: u128,\n}\n\nimpl Rand64 {\n /// The default value for `increment`.\n ///\n /// The value used here is from the PCG default C implementation: http://www.pcg-random.org/download.html\n pub const DEFAULT_INC: u128 = 0x2FE0E169_FFBD06E3_5BC307BD_4D2F814F;\n\n /// This is the number that you have to Really Get Right.\n ///\n /// The value used here is from the PCG C implementation:\n /// https://github.com/imneme/pcg-c/blob/master/include/pcg_variants.h#L288\n pub(crate) const MULTIPLIER: u128 = 47026247687942121848144207491837523525;\n\n /// Creates a new PRNG with the given seed and a default increment.\n pub fn new(seed: u128) -\u003e Self {\n Self::new_inc(seed, Self::DEFAULT_INC)\n }\n\n /// Same as `Rand32::new_inc()`\n fn new_inc(seed: u128, increment: u128) -\u003e Self {\n let mut rng = Self {\n state: 0,\n inc: increment.wrapping_shl(1) | 1,\n };\n let _ = rng.rand_u64();\n rng.state = rng.state.wrapping_add(seed);\n let _ = rng.rand_u64();\n rng\n }\n\n /// Returns the internal state of the PRNG. This allows\n /// you to save a PRNG and create a new one that will resume\n /// from the same spot in the sequence.\n pub fn state(\u0026self) -\u003e (u128, u128) {\n (self.state, self.inc)\n }\n\n /// Creates a new PRNG from a saved state from `Rand32::state()`.\n /// This is NOT quite the same as `new_inc()` because `new_inc()` does\n /// a little extra setup work to initialize the state.\n pub fn from_state(state: (u128, u128)) -\u003e Self {\n let (state, inc) = state;\n Self { state, inc }\n }\n\n /// Produces a random `u64` in the range`[0, u64::MAX]`.\n pub fn rand_u64(\u0026mut self) -\u003e u64 {\n let oldstate: u128 = self.state;\n self.state = oldstate\n .wrapping_mul(Self::MULTIPLIER)\n .wrapping_add(self.inc);\n let xorshifted: u64 = (((oldstate \u003e\u003e 29) ^ oldstate) \u003e\u003e 58) as u64;\n let rot: u32 = (oldstate \u003e\u003e 122) as u32;\n xorshifted.rotate_right(rot)\n }\n\n /// Produces a random `i64` in the range `[i64::MIN, i64::MAX]`.\n pub fn rand_i64(\u0026mut self) -\u003e i64 {\n self.rand_u64() as i64\n }\n\n /// Produces a random `f64` in the range `[0.0, 1.0)`.\n pub fn rand_float(\u0026mut self) -\u003e f64 {\n const TOTAL_BITS: u32 = 64;\n const PRECISION: u32 = core::f64::MANTISSA_DIGITS + 1;\n const MANTISSA_SCALE: f64 = 1.0 / ((1u64 \u003c\u003c PRECISION) as f64);\n let mut u = self.rand_u64();\n u \u003e\u003e= TOTAL_BITS - PRECISION;\n u as f64 * MANTISSA_SCALE\n }\n\n /// Produces a random within the given bounds. Like any `Range`,\n /// it includes the lower bound and excludes the upper one.\n ///\n /// This should be faster than `Self::rand() % end + start`, but the\n /// real advantage is it's more convenient. Requires that\n /// `range.end \u003c= range.start`.\n pub fn rand_range(\u0026mut self, range: Range\u003cu64\u003e) -\u003e u64 {\n // Same as `Rand32::rand_range()`\n debug_assert!(range.start \u003c range.end);\n let range_starting_from_zero = 0..(range.end - range.start);\n\n let s: u64 = range_starting_from_zero.end;\n let mut m: u128 = u128::from(self.rand_u64()) * u128::from(s);\n let mut leftover: u64 = (m \u0026 0xFFFFFFFF_FFFFFFFF) as u64;\n\n if leftover \u003c s {\n // TODO: Verify the wrapping_negate() here\n let threshold: u64 = s.wrapping_neg() % s;\n while leftover \u003c threshold {\n m = u128::from(self.rand_u64()) * u128::from(s);\n leftover = (m \u0026 0xFFFFFFFF_FFFFFFFF) as u64;\n }\n }\n (m.wrapping_shr(64)) as u64 + range.start\n }\n}\n\n#[cfg(test)]\nmod tests {\n use super::*;\n use randomize::{self, PCG32, PCG64};\n\n #[test]\n fn test_rand32_vs_randomize() {\n // Generate some random numbers and validate them against\n // a known-good generator.\n {\n let seed = 54321;\n let mut r1 = Rand32::new(seed);\n let mut r2 = PCG32::seed(seed, Rand32::DEFAULT_INC);\n for _ in 0..1000 {\n assert_eq!(r1.rand_u32(), r2.next_u32());\n assert_eq!(r1.rand_i32(), r2.next_u32() as i32);\n }\n }\n\n {\n let seed = 3141592653;\n let inc = 0xDEADBEEF;\n let mut r1 = Rand32::new_inc(seed, inc);\n let mut r2 = PCG32::seed(seed, inc);\n for _ in 0..1000 {\n assert_eq!(r1.rand_u32(), r2.next_u32());\n assert_eq!(r1.rand_i32(), r2.next_u32() as i32);\n }\n }\n }\n\n #[test]\n fn test_rand64_vs_randomize() {\n // Generate some random numbers and validate them against\n // a known-good generator.\n {\n let seed = 54321;\n let mut r1 = Rand64::new(seed);\n let mut r2 = PCG64::seed(seed, Rand64::DEFAULT_INC);\n for _ in 0..1000 {\n assert_eq!(r1.rand_u64(), r2.next_u64());\n assert_eq!(r1.rand_i64(), r2.next_u64() as i64);\n }\n }\n\n {\n let seed = 3141592653;\n let inc = 0xDEADBEEF;\n let mut r1 = Rand64::new_inc(seed, inc);\n let mut r2 = PCG64::seed(seed, inc);\n for _ in 0..1000 {\n assert_eq!(r1.rand_u64(), r2.next_u64());\n assert_eq!(r1.rand_i64(), r2.next_u64() as i64);\n }\n }\n }\n\n #[test]\n fn test_float32() {\n {\n let seed = 2718281828;\n let mut r1 = Rand32::new(seed);\n let mut r2 = PCG32::seed(seed, Rand32::DEFAULT_INC);\n for _ in 0..1000 {\n // First just make sure they both work with randomize's\n // f32 conversion function -- sanity checks.\n let i1 = r1.rand_u32();\n let i2 = r2.next_u32();\n assert_eq!(i1, i2);\n let f1 = randomize::f32_half_open_right(i1);\n let f2 = randomize::f32_half_open_right(i2);\n // We can directly compare floats 'cause we do no math, it's\n // literally the same bitwise algorithm with the same inputs.\n assert_eq!(f1, f2);\n\n // Make sure result is in [0.0, 1.0)\n assert!(f1 \u003e= 0.0);\n assert!(f1 \u003c 1.0);\n }\n\n // At least make sure our float's from rand_float() are in the valid range.\n for _ in 0..1000 {\n let f1 = r1.rand_float();\n assert!(f1 \u003e= 0.0);\n assert!(f1 \u003c 1.0);\n }\n\n /*\n TODO: Randomize changed its int-to-float conversion functions and now they don't\n match ours.\n for _ in 0..1000 {\n // Now make sure our own float conversion function works.\n let f1 = r1.rand_float();\n //let f2 = randomize::f32_half_open_right(r2.next_u32());\n let f2 = randomize::f32_open(r2.next_u32());\n assert_eq!(f1, f2);\n assert!(f1 \u003e= 0.0);\n assert!(f1 \u003c 1.0);\n }\n */\n }\n }\n\n #[test]\n fn test_float64() {\n {\n let seed = 2718281828;\n let mut r1 = Rand64::new(seed);\n let mut r2 = PCG64::seed(seed, Rand64::DEFAULT_INC);\n for _ in 0..1000 {\n // First just make sure they both work with randomize's\n // f64 conversion function -- sanity checks.\n let i1 = r1.rand_u64();\n let i2 = r2.next_u64();\n assert_eq!(i1, i2);\n let f1 = randomize::f64_half_open_right(i1);\n let f2 = randomize::f64_half_open_right(i2);\n // We can directly compare floats 'cause we do no math, it's\n // literally the same bitwise algorithm with the same inputs.\n assert_eq!(f1, f2);\n\n // Make sure result is in [0.0, 1.0)\n assert!(f1 \u003e= 0.0);\n assert!(f1 \u003c 1.0);\n }\n\n // At least make sure our float's from rand_float() are in the valid range.\n for _ in 0..1000 {\n let f1 = r1.rand_float();\n assert!(f1 \u003e= 0.0);\n assert!(f1 \u003c 1.0);\n }\n\n /*\n TODO: Randomize changed its int-to-float conversion functions and now they don't\n match ours.\n for _ in 0..1000 {\n // Now make sure our own float conversion function works.\n let f1 = r1.rand_float();\n //let f2 = randomize::f32_half_open_right(r2.next_u32());\n let f2 = randomize::f32_open(r2.next_u32());\n assert_eq!(f1, f2);\n assert!(f1 \u003e= 0.0);\n assert!(f1 \u003c 1.0);\n }\n */\n }\n }\n\n #[test]\n fn test_randrange32() {\n // Make sure ranges are valid and in the given range\n let seed = 2342_3141;\n let mut r1 = Rand32::new(seed);\n for _ in 0..1000 {\n // Generate our bounds at random\n let a = r1.rand_u32();\n let b = r1.rand_u32();\n if a == b {\n continue;\n }\n let (low, high) = if a \u003c b { (a, b) } else { (b, a) };\n\n // Get a number in that range\n let in_range = r1.rand_range(low..high);\n assert!(in_range \u003e= low);\n assert!(in_range \u003c high);\n }\n }\n\n #[test]\n fn test_randrange64() {\n // Make sure ranges are valid and in the given range\n let seed = 2342_2718;\n let mut r1 = Rand64::new(seed);\n for _ in 0..1000 {\n // Generate our bounds at random\n let a = r1.rand_u64();\n let b = r1.rand_u64();\n if a == b {\n continue;\n }\n let (low, high) = if a \u003c b { (a, b) } else { (b, a) };\n\n // Get a number in that range\n let in_range = r1.rand_range(low..high);\n assert!(in_range \u003e= low);\n assert!(in_range \u003c high);\n }\n }\n\n #[test]\n fn test_rand32_vs_rand() {\n use rand_core::RngCore;\n use rand_pcg;\n {\n let seed = 54321;\n let mut r1 = Rand32::new(seed);\n let mut r2 = rand_pcg::Pcg32::new(seed, Rand32::DEFAULT_INC);\n for _ in 0..1000 {\n assert_eq!(r1.rand_u32(), r2.next_u32());\n }\n }\n\n {\n let seed = 3141592653;\n let inc = 0xDEADBEEF;\n let mut r1 = Rand32::new_inc(seed, inc);\n let mut r2 = rand_pcg::Pcg32::new(seed, inc);\n for _ in 0..1000 {\n assert_eq!(r1.rand_u32(), r2.next_u32());\n }\n }\n }\n\n // This doesn't work 'cause for 64-bit output `rand` uses\n // PCG-XSL-RR\n // and we use\n // PCG-XSH-RR\n /*\n #[test]\n fn test_rand64_vs_rand() {\n use rand_pcg;\n use rand_core::RngCore;\n {\n let seed = 54321;\n let mut r1 = Rand64::new(seed);\n let mut r2 = rand_pcg::Pcg64::new(seed, Rand64::DEFAULT_INC);\n for _ in 0..1000 {\n assert_eq!(r1.rand(), r2.next_u64());\n }\n }\n\n {\n let seed = 3141592653;\n let inc = 0xDEADBEEF;\n let mut r1 = Rand64::new_inc(seed, inc);\n let mut r2 = rand_pcg::Pcg64::new(seed, inc);\n for _ in 0..1000 {\n assert_eq!(r1.rand(), r2.next_u64());\n }\n }\n }\n */\n\n // Test vs. random-fast-rng, which I will call rfr\n // rfr's float conversion uses yet a different algorithm\n // than ours, so we can't really check that.\n #[test]\n fn test_rand32_vs_rfr() {\n use random_fast_rng as rfr;\n use rfr::Random;\n {\n let seed = 54321;\n let mut r1 = Rand32::new(seed);\n let mut r2 = rfr::FastRng::seed(seed, Rand32::DEFAULT_INC);\n for _ in 0..1000 {\n assert_eq!(r1.rand_u32(), r2.get_u32());\n }\n }\n\n {\n let seed = 3141592653;\n let inc = 0xDEADBEEF;\n let mut r1 = Rand32::new_inc(seed, inc);\n let mut r2 = rfr::FastRng::seed(seed, inc);\n for _ in 0..1000 {\n assert_eq!(r1.rand_u32(), r2.get_u32());\n }\n }\n }\n\n /// Make sure that saving a RNG state and restoring\n /// it works.\n /// See https://todo.sr.ht/~icefox/oorandom/1\n #[test]\n fn test_save_restore() {\n {\n let seed = 54321;\n let mut r1 = Rand32::new(seed);\n let s1 = r1.state();\n let mut r2 = Rand32::from_state(s1);\n assert_eq!(r1, r2);\n for _ in 0..1000 {\n assert_eq!(r1.rand_u32(), r2.rand_u32());\n }\n }\n\n {\n let seed = 3141592653;\n let inc = 0xDEADBEEF;\n let mut r1 = Rand32::new_inc(seed, inc);\n let s1 = r1.state();\n let mut r2 = Rand32::from_state(s1);\n assert_eq!(r1, r2);\n for _ in 0..1000 {\n assert_eq!(r1.rand_u32(), r2.rand_u32());\n }\n }\n\n {\n let seed = 54321;\n let mut r1 = Rand64::new(seed);\n let s1 = r1.state();\n let mut r2 = Rand64::from_state(s1);\n assert_eq!(r1, r2);\n for _ in 0..1000 {\n assert_eq!(r1.rand_u64(), r2.rand_u64());\n }\n }\n\n {\n let seed = 3141592653;\n let inc = 0xDEADBEEF;\n let mut r1 = Rand64::new_inc(seed, inc);\n let s1 = r1.state();\n let mut r2 = Rand64::from_state(s1);\n assert_eq!(r1, r2);\n for _ in 0..1000 {\n assert_eq!(r1.rand_u64(), r2.rand_u64());\n }\n }\n 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var previousData = {"files":[{"path":["/","home","srv","hg","icefox","srht","icefox","oorandom","src","lib.rs"],"content":"//! A tiny, robust PRNG implementation.\n//!\n//! More specifically, it implements a single GOOD PRNG algorithm,\n//! which is currently a permuted congruential generator. It has two\n//! implementations, one that returns `u32` and one that returns\n//! `u64`. It also has functions that return floats or integer\n//! ranges. And that's it. What more do you need?\n//!\n//! For more info on PCG generators, see http://www.pcg-random.org/\n//!\n//! This was designed as a minimalist utility for video games. No\n//! promises are made about its quality, and if you use it for\n//! cryptography you will get what you deserve.\n//!\n//! Works with `#![no_std]`, has no global state, no dependencies\n//! apart from some in the unit tests, and is generally neato.\n\n#![forbid(unsafe_code)]\n#![forbid(missing_docs)]\n#![forbid(missing_debug_implementations)]\n#![forbid(unused_results)]\n#![no_std]\nuse core::ops::Range;\n\n/// A PRNG producing a 32-bit output.\n///\n/// The current implementation is `PCG-XSH-RR`.\n#[derive(Copy, Clone, Debug, PartialEq)]\npub struct Rand32 {\n state: u64,\n inc: u64,\n}\n\nimpl Rand32 {\n /// The default value for `increment`.\n /// This is basically arbitrary, it comes from the\n /// PCG reference C implementation:\n /// https://github.com/imneme/pcg-c/blob/master/include/pcg_variants.h#L284\n pub const DEFAULT_INC: u64 = 1442695040888963407;\n\n /// This is the number that you have to Really Get Right.\n ///\n /// The value used here is from the PCG C implementation:\n /// https://github.com/imneme/pcg-c/blob/master/include/pcg_variants.h#L278\n pub(crate) const MULTIPLIER: u64 = 6364136223846793005;\n\n /// Creates a new PRNG with the given seed and a default increment.\n pub fn new(seed: u64) -\u003e Self {\n Self::new_inc(seed, Self::DEFAULT_INC)\n }\n\n /// Creates a new PRNG. The two inputs, `seed` and `increment`,\n /// determine what you get; `increment` basically selects which\n /// sequence of all those possible the PRNG will produce, and the\n /// `seed` selects where in that sequence you start.\n ///\n /// Both are arbitrary; increment must be an odd number but this\n /// handles that for you\n pub fn new_inc(seed: u64, increment: u64) -\u003e Self {\n let mut rng = Self {\n state: 0,\n inc: increment.wrapping_shl(1) | 1,\n };\n // This initialization song-and-dance is a little odd,\n // but seems to be just how things go.\n let _ = rng.rand_u32();\n rng.state = rng.state.wrapping_add(seed);\n let _ = rng.rand_u32();\n rng\n }\n\n /// Returns the internal state of the PRNG. This allows\n /// you to save a PRNG and create a new one that will resume\n /// from the same spot in the sequence.\n pub fn state(\u0026self) -\u003e (u64, u64) {\n (self.state, self.inc)\n }\n\n /// Creates a new PRNG from a saved state from `Rand32::state()`.\n /// This is NOT quite the same as `new_inc()` because `new_inc()` does\n /// a little extra setup work to initialize the state.\n pub fn from_state(state: (u64, u64)) -\u003e Self {\n let (state, inc) = state;\n Self { state, inc }\n }\n\n /// Produces a random `u32` in the range `[0, u32::MAX]`.\n pub fn rand_u32(\u0026mut self) -\u003e u32 {\n let oldstate: u64 = self.state;\n self.state = oldstate\n .wrapping_mul(Self::MULTIPLIER)\n .wrapping_add(self.inc);\n let xorshifted: u32 = (((oldstate \u003e\u003e 18) ^ oldstate) \u003e\u003e 27) as u32;\n let rot: u32 = (oldstate \u003e\u003e 59) as u32;\n xorshifted.rotate_right(rot)\n }\n\n /// Produces a random `i32` in the range `[i32::MIN, i32::MAX]`.\n pub fn rand_i32(\u0026mut self) -\u003e i32 {\n self.rand_u32() as i32\n }\n\n /// Produces a random `f32` in the range `[0.0, 1.0)`.\n pub fn rand_float(\u0026mut self) -\u003e f32 {\n // This impl was taken more or less from `rand`, see\n // \u003chttps://docs.rs/rand/0.7.0/src/rand/distributions/float.rs.html#104-117\u003e\n // There MAY be better ways to do this, see:\n // https://mumble.net/~campbell/2014/04/28/uniform-random-float\n // https://mumble.net/~campbell/2014/04/28/random_real.c\n // https://github.com/Lokathor/randomize/issues/34\n const TOTAL_BITS: u32 = 32;\n const PRECISION: u32 = core::f32::MANTISSA_DIGITS + 1;\n const MANTISSA_SCALE: f32 = 1.0 / ((1u32 \u003c\u003c PRECISION) as f32);\n let mut u = self.rand_u32();\n u \u003e\u003e= TOTAL_BITS - PRECISION;\n u as f32 * MANTISSA_SCALE\n }\n\n /// Produces a random within the given bounds. Like any `Range`,\n /// it includes the lower bound and excludes the upper one.\n ///\n /// This should be faster than `Self::rand() % end + start`, but the\n /// real advantage is it's more convenient. Requires that\n /// `range.end \u003c= range.start`.\n pub fn rand_range(\u0026mut self, range: Range\u003cu32\u003e) -\u003e u32 {\n // This is harder to do well than it looks, it seems. I don't\n // trust Lokathor's implementation 'cause I don't understand\n // it, so I went to numpy's, which points me to \"Lemire's\n // rejection algorithm\": http://arxiv.org/abs/1805.10941\n //\n // Algorithms 3, 4 and 5 in that paper all seem fine modulo\n // minor performance differences, so this is algorithm 5.\n // It uses numpy's implementation, `buffered_bounded_lemire_uint32()`\n\n debug_assert!(range.start \u003c range.end);\n let range_starting_from_zero = 0..(range.end - range.start);\n\n let s: u32 = range_starting_from_zero.end;\n let mut m: u64 = u64::from(self.rand_u32()) * u64::from(s);\n let mut leftover: u32 = (m \u0026 0xFFFF_FFFF) as u32;\n\n if leftover \u003c s {\n // TODO: verify the wrapping_neg() here\n let threshold: u32 = s.wrapping_neg() % s;\n while leftover \u003c threshold {\n m = u64::from(self.rand_u32()).wrapping_mul(u64::from(s));\n leftover = (m \u0026 0xFFFF_FFFF) as u32;\n }\n }\n (m \u003e\u003e 32) as u32 + range.start\n }\n}\n\n/// A PRNG producing a 64-bit output.\n///\n/// The current implementation is `PCG-XSH-RR`.\n// BUGGO: The recommended algorithm is PCG-XSL-RR?\n// See https://github.com/imneme/pcg-c/blob/master/include/pcg_variants.h#L2405\n// Not sure if it matters?\n#[derive(Copy, Clone, Debug, PartialEq)]\npub struct Rand64 {\n state: u128,\n inc: u128,\n}\n\nimpl Rand64 {\n /// The default value for `increment`.\n ///\n /// The value used here is from the PCG default C implementation: http://www.pcg-random.org/download.html\n pub const DEFAULT_INC: u128 = 0x2FE0E169_FFBD06E3_5BC307BD_4D2F814F;\n\n /// This is the number that you have to Really Get Right.\n ///\n /// The value used here is from the PCG C implementation:\n /// https://github.com/imneme/pcg-c/blob/master/include/pcg_variants.h#L288\n pub(crate) const MULTIPLIER: u128 = 47026247687942121848144207491837523525;\n\n /// Creates a new PRNG with the given seed and a default increment.\n pub fn new(seed: u128) -\u003e Self {\n Self::new_inc(seed, Self::DEFAULT_INC)\n }\n\n /// Same as `Rand32::new_inc()`\n fn new_inc(seed: u128, increment: u128) -\u003e Self {\n let mut rng = Self {\n state: 0,\n inc: increment.wrapping_shl(1) | 1,\n };\n let _ = rng.rand_u64();\n rng.state = rng.state.wrapping_add(seed);\n let _ = rng.rand_u64();\n rng\n }\n\n /// Returns the internal state of the PRNG. This allows\n /// you to save a PRNG and create a new one that will resume\n /// from the same spot in the sequence.\n pub fn state(\u0026self) -\u003e (u128, u128) {\n (self.state, self.inc)\n }\n\n /// Creates a new PRNG from a saved state from `Rand32::state()`.\n /// This is NOT quite the same as `new_inc()` because `new_inc()` does\n /// a little extra setup work to initialize the state.\n pub fn from_state(state: (u128, u128)) -\u003e Self {\n let (state, inc) = state;\n Self { state, inc }\n }\n\n /// Produces a random `u64` in the range`[0, u64::MAX]`.\n pub fn rand_u64(\u0026mut self) -\u003e u64 {\n let oldstate: u128 = self.state;\n self.state = oldstate\n .wrapping_mul(Self::MULTIPLIER)\n .wrapping_add(self.inc);\n let xorshifted: u64 = (((oldstate \u003e\u003e 29) ^ oldstate) \u003e\u003e 58) as u64;\n let rot: u32 = (oldstate \u003e\u003e 122) as u32;\n xorshifted.rotate_right(rot)\n }\n\n /// Produces a random `i64` in the range `[i64::MIN, i64::MAX]`.\n pub fn rand_i64(\u0026mut self) -\u003e i64 {\n self.rand_u64() as i64\n }\n\n /// Produces a random `f64` in the range `[0.0, 1.0)`.\n pub fn rand_float(\u0026mut self) -\u003e f64 {\n const TOTAL_BITS: u32 = 64;\n const PRECISION: u32 = core::f64::MANTISSA_DIGITS + 1;\n const MANTISSA_SCALE: f64 = 1.0 / ((1u64 \u003c\u003c PRECISION) as f64);\n let mut u = self.rand_u64();\n u \u003e\u003e= TOTAL_BITS - PRECISION;\n u as f64 * MANTISSA_SCALE\n }\n\n /// Produces a random within the given bounds. Like any `Range`,\n /// it includes the lower bound and excludes the upper one.\n ///\n /// This should be faster than `Self::rand() % end + start`, but the\n /// real advantage is it's more convenient. Requires that\n /// `range.end \u003c= range.start`.\n pub fn rand_range(\u0026mut self, range: Range\u003cu64\u003e) -\u003e u64 {\n // Same as `Rand32::rand_range()`\n debug_assert!(range.start \u003c range.end);\n let range_starting_from_zero = 0..(range.end - range.start);\n\n let s: u64 = range_starting_from_zero.end;\n let mut m: u128 = u128::from(self.rand_u64()) * u128::from(s);\n let mut leftover: u64 = (m \u0026 0xFFFFFFFF_FFFFFFFF) as u64;\n\n if leftover \u003c s {\n // TODO: Verify the wrapping_negate() here\n let threshold: u64 = s.wrapping_neg() % s;\n while leftover \u003c threshold {\n m = u128::from(self.rand_u64()) * u128::from(s);\n leftover = (m \u0026 0xFFFFFFFF_FFFFFFFF) as u64;\n }\n }\n (m.wrapping_shr(64)) as u64 + range.start\n }\n}\n\n#[cfg(test)]\nmod tests {\n use super::*;\n use randomize::{self, PCG32, PCG64};\n\n #[test]\n fn test_rand32_vs_randomize() {\n // Generate some random numbers and validate them against\n // a known-good generator.\n {\n let seed = 54321;\n let mut r1 = Rand32::new(seed);\n let mut r2 = PCG32::seed(seed, Rand32::DEFAULT_INC);\n for _ in 0..1000 {\n assert_eq!(r1.rand_u32(), r2.next_u32());\n assert_eq!(r1.rand_i32(), r2.next_u32() as i32);\n }\n }\n\n {\n let seed = 3141592653;\n let inc = 0xDEADBEEF;\n let mut r1 = Rand32::new_inc(seed, inc);\n let mut r2 = PCG32::seed(seed, inc);\n for _ in 0..1000 {\n assert_eq!(r1.rand_u32(), r2.next_u32());\n assert_eq!(r1.rand_i32(), r2.next_u32() as i32);\n }\n }\n }\n\n #[test]\n fn test_rand64_vs_randomize() {\n // Generate some random numbers and validate them against\n // a known-good generator.\n {\n let seed = 54321;\n let mut r1 = Rand64::new(seed);\n let mut r2 = PCG64::seed(seed, Rand64::DEFAULT_INC);\n for _ in 0..1000 {\n assert_eq!(r1.rand_u64(), r2.next_u64());\n assert_eq!(r1.rand_i64(), r2.next_u64() as i64);\n }\n }\n\n {\n let seed = 3141592653;\n let inc = 0xDEADBEEF;\n let mut r1 = Rand64::new_inc(seed, inc);\n let mut r2 = PCG64::seed(seed, inc);\n for _ in 0..1000 {\n assert_eq!(r1.rand_u64(), r2.next_u64());\n assert_eq!(r1.rand_i64(), r2.next_u64() as i64);\n }\n }\n }\n\n #[test]\n fn test_float32() {\n {\n let seed = 2718281828;\n let mut r1 = Rand32::new(seed);\n let mut r2 = PCG32::seed(seed, Rand32::DEFAULT_INC);\n for _ in 0..1000 {\n // First just make sure they both work with randomize's\n // f32 conversion function -- sanity checks.\n let i1 = r1.rand_u32();\n let i2 = r2.next_u32();\n assert_eq!(i1, i2);\n let f1 = randomize::f32_half_open_right(i1);\n let f2 = randomize::f32_half_open_right(i2);\n // We can directly compare floats 'cause we do no math, it's\n // literally the same bitwise algorithm with the same inputs.\n assert_eq!(f1, f2);\n\n // Make sure result is in [0.0, 1.0)\n assert!(f1 \u003e= 0.0);\n assert!(f1 \u003c 1.0);\n }\n\n // At least make sure our float's from rand_float() are in the valid range.\n for _ in 0..1000 {\n let f1 = r1.rand_float();\n assert!(f1 \u003e= 0.0);\n assert!(f1 \u003c 1.0);\n }\n\n /*\n TODO: Randomize changed its int-to-float conversion functions and now they don't\n match ours.\n for _ in 0..1000 {\n // Now make sure our own float conversion function works.\n let f1 = r1.rand_float();\n //let f2 = randomize::f32_half_open_right(r2.next_u32());\n let f2 = randomize::f32_open(r2.next_u32());\n assert_eq!(f1, f2);\n assert!(f1 \u003e= 0.0);\n assert!(f1 \u003c 1.0);\n }\n */\n }\n }\n\n #[test]\n fn test_float64() {\n {\n let seed = 2718281828;\n let mut r1 = Rand64::new(seed);\n let mut r2 = PCG64::seed(seed, Rand64::DEFAULT_INC);\n for _ in 0..1000 {\n // First just make sure they both work with randomize's\n // f64 conversion function -- sanity checks.\n let i1 = r1.rand_u64();\n let i2 = r2.next_u64();\n assert_eq!(i1, i2);\n let f1 = randomize::f64_half_open_right(i1);\n let f2 = randomize::f64_half_open_right(i2);\n // We can directly compare floats 'cause we do no math, it's\n // literally the same bitwise algorithm with the same inputs.\n assert_eq!(f1, f2);\n\n // Make sure result is in [0.0, 1.0)\n assert!(f1 \u003e= 0.0);\n assert!(f1 \u003c 1.0);\n }\n\n // At least make sure our float's from rand_float() are in the valid range.\n for _ in 0..1000 {\n let f1 = r1.rand_float();\n assert!(f1 \u003e= 0.0);\n assert!(f1 \u003c 1.0);\n }\n\n /*\n TODO: Randomize changed its int-to-float conversion functions and now they don't\n match ours.\n for _ in 0..1000 {\n // Now make sure our own float conversion function works.\n let f1 = r1.rand_float();\n //let f2 = randomize::f32_half_open_right(r2.next_u32());\n let f2 = randomize::f32_open(r2.next_u32());\n assert_eq!(f1, f2);\n assert!(f1 \u003e= 0.0);\n assert!(f1 \u003c 1.0);\n }\n */\n }\n }\n\n #[test]\n fn test_randrange32() {\n // Make sure ranges are valid and in the given range\n let seed = 2342_3141;\n let mut r1 = Rand32::new(seed);\n for _ in 0..1000 {\n // Generate our bounds at random\n let a = r1.rand_u32();\n let b = r1.rand_u32();\n if a == b {\n continue;\n }\n let (low, high) = if a \u003c b { (a, b) } else { (b, a) };\n\n // Get a number in that range\n let in_range = r1.rand_range(low..high);\n assert!(in_range \u003e= low);\n assert!(in_range \u003c high);\n }\n }\n\n #[test]\n fn test_randrange64() {\n // Make sure ranges are valid and in the given range\n let seed = 2342_2718;\n let mut r1 = Rand64::new(seed);\n for _ in 0..1000 {\n // Generate our bounds at random\n let a = r1.rand_u64();\n let b = r1.rand_u64();\n if a == b {\n continue;\n }\n let (low, high) = if a \u003c b { (a, b) } else { (b, a) };\n\n // Get a number in that range\n let in_range = r1.rand_range(low..high);\n assert!(in_range \u003e= low);\n assert!(in_range \u003c high);\n }\n }\n\n #[test]\n fn test_rand32_vs_rand() {\n use rand_core::RngCore;\n use rand_pcg;\n {\n let seed = 54321;\n let mut r1 = Rand32::new(seed);\n let mut r2 = rand_pcg::Pcg32::new(seed, Rand32::DEFAULT_INC);\n for _ in 0..1000 {\n assert_eq!(r1.rand_u32(), r2.next_u32());\n }\n }\n\n {\n let seed = 3141592653;\n let inc = 0xDEADBEEF;\n let mut r1 = Rand32::new_inc(seed, inc);\n let mut r2 = rand_pcg::Pcg32::new(seed, inc);\n for _ in 0..1000 {\n assert_eq!(r1.rand_u32(), r2.next_u32());\n }\n }\n }\n\n // This doesn't work 'cause for 64-bit output `rand` uses\n // PCG-XSL-RR\n // and we use\n // PCG-XSH-RR\n /*\n #[test]\n fn test_rand64_vs_rand() {\n use rand_pcg;\n use rand_core::RngCore;\n {\n let seed = 54321;\n let mut r1 = Rand64::new(seed);\n let mut r2 = rand_pcg::Pcg64::new(seed, Rand64::DEFAULT_INC);\n for _ in 0..1000 {\n assert_eq!(r1.rand(), r2.next_u64());\n }\n }\n\n {\n let seed = 3141592653;\n let inc = 0xDEADBEEF;\n let mut r1 = Rand64::new_inc(seed, inc);\n let mut r2 = rand_pcg::Pcg64::new(seed, inc);\n for _ in 0..1000 {\n assert_eq!(r1.rand(), r2.next_u64());\n }\n }\n }\n */\n\n // Test vs. random-fast-rng, which I will call rfr\n // rfr's float conversion uses yet a different algorithm\n // than ours, so we can't really check that.\n #[test]\n fn test_rand32_vs_rfr() {\n use random_fast_rng as rfr;\n use rfr::Random;\n {\n let seed = 54321;\n let mut r1 = Rand32::new(seed);\n let mut r2 = rfr::FastRng::seed(seed, Rand32::DEFAULT_INC);\n for _ in 0..1000 {\n assert_eq!(r1.rand_u32(), r2.get_u32());\n }\n }\n\n {\n let seed = 3141592653;\n let inc = 0xDEADBEEF;\n let mut r1 = Rand32::new_inc(seed, inc);\n let mut r2 = rfr::FastRng::seed(seed, inc);\n for _ in 0..1000 {\n assert_eq!(r1.rand_u32(), r2.get_u32());\n }\n }\n }\n\n /// Make sure that saving a RNG state and restoring\n /// it works.\n /// See https://todo.sr.ht/~icefox/oorandom/1\n #[test]\n fn test_save_restore() {\n {\n let seed = 54321;\n let mut r1 = Rand32::new(seed);\n let s1 = r1.state();\n let mut r2 = Rand32::from_state(s1);\n assert_eq!(r1, r2);\n for _ in 0..1000 {\n assert_eq!(r1.rand_u32(), r2.rand_u32());\n }\n }\n\n {\n let seed = 3141592653;\n let inc = 0xDEADBEEF;\n let mut r1 = Rand32::new_inc(seed, inc);\n let s1 = r1.state();\n let mut r2 = Rand32::from_state(s1);\n assert_eq!(r1, r2);\n for _ in 0..1000 {\n assert_eq!(r1.rand_u32(), r2.rand_u32());\n }\n }\n\n {\n let seed = 54321;\n let mut r1 = Rand64::new(seed);\n let s1 = r1.state();\n let mut r2 = Rand64::from_state(s1);\n assert_eq!(r1, r2);\n for _ in 0..1000 {\n assert_eq!(r1.rand_u64(), r2.rand_u64());\n }\n }\n\n {\n let seed = 3141592653;\n let inc = 0xDEADBEEF;\n let mut r1 = Rand64::new_inc(seed, inc);\n let s1 = r1.state();\n let mut r2 = Rand64::from_state(s1);\n assert_eq!(r1, r2);\n for _ in 0..1000 {\n assert_eq!(r1.rand_u64(), r2.rand_u64());\n }\n }\n 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});
return [
...folders,
...files.filter(file => file.path.length === 1),
];
}
class App extends React.Component {
constructor(...args) {
super(...args);
this.state = {
current: [],
};
}
componentDidMount() {
this.updateStateFromLocation();
window.addEventListener("hashchange", () => this.updateStateFromLocation(), false);
}
updateStateFromLocation() {
if (window.location.hash.length > 1) {
const current = window.location.hash.substr(1).split('/');
this.setState({current});
} else {
this.setState({current: []});
}
}
getCurrentPath() {
let file = this.props.root;
let path = [file];
for (let p of this.state.current) {
file = file.children.find(file => file.path[0] === p);
if (!file) {
return path;
}
path.push(file);
}
return path;
}
render() {
const path = this.getCurrentPath();
const file = path[path.length - 1];
let w = null;
if (file.is_folder) {
w = e(FilesList, {
folder: file,
onSelectFile: this.selectFile.bind(this),
onBack: path.length > 1 ? this.back.bind(this) : null,
});
} else {
w = e(DisplayFile, {
file,
onBack: this.back.bind(this),
});
}
return e('div', {className: 'app'}, w);
}
selectFile(file) {
this.setState(({current}) => {
return {current: [...current, file.path[0]]};
}, () => this.updateHash());
}
back(file) {
this.setState(({current}) => {
return {current: current.slice(0, current.length - 1)};
}, () => this.updateHash());
}
updateHash() {
if (!this.state.current || !this.state.current.length) {
window.location = '#';
} else {
window.location = '#' + this.state.current.join('/');
}
}
}
function FilesList({folder, onSelectFile, onBack}) {
let files = folder.children;
return e('div', {className: 'display-folder'},
e(FileHeader, {file: folder, onBack}),
e('table', {className: 'files-list'},
e('thead', {className: 'files-list__head'},
e('tr', null,
e('th', null, "Path"),
e('th', null, "Coverage")
)
),
e('tbody', {className: 'files-list__body'},
files.map(file => e(File, {file, onClick: onSelectFile}))
)
)
);
}
function File({file, onClick}) {
const coverage = file.coverable ? file.covered / file.coverable * 100 : -1;
const coverageDelta = file.prevRun &&
(file.covered / file.coverable * 100 - file.prevRun.covered / file.prevRun.coverable * 100);
return e('tr', {
className: 'files-list__file'
+ (coverage >= 0 && coverage < 50 ? ' files-list__file_low': '')
+ (coverage >= 50 && coverage < 80 ? ' files-list__file_medium': '')
+ (coverage >= 80 ? ' files-list__file_high': '')
+ (file.is_folder ? ' files-list__file_folder': ''),
onClick: () => onClick(file),
},
e('td', null, pathToString(file.path)),
e('td', null,
file.covered + ' / ' + file.coverable +
(coverage >= 0 ? ' (' + coverage.toFixed(2) + '%)' : ''),
e('span', {title: 'Change from the previous run'},
(coverageDelta ? ` (${coverageDelta > 0 ? '+' : ''}${coverageDelta.toFixed(2)}%)` : ''))
)
);
}
function DisplayFile({file, onBack}) {
return e('div', {className: 'display-file'},
e(FileHeader, {file, onBack}),
e(FileContent, {file})
);
}
function FileHeader({file, onBack}) {
const coverage = file.covered / file.coverable * 100;
const coverageDelta = file.prevRun && (coverage - file.prevRun.covered / file.prevRun.coverable * 100);
return e('div', {className: 'file-header'},
onBack ? e('a', {className: 'file-header__back', onClick: onBack}, 'Back') : null,
e('div', {className: 'file-header__name'}, pathToString([...file.parent, ...file.path])),
e('div', {className: 'file-header__stat'},
'Covered: ' + file.covered + ' of ' + file.coverable +
(file.coverable ? ' (' + coverage.toFixed(2) + '%)' : ''),
e('span', {title: 'Change from the previous run'},
(coverageDelta ? ` (${coverageDelta > 0 ? '+' : ''}${coverageDelta.toFixed(2)}%)` : ''))
)
);
}
function FileContent({file}) {
return e('div', {className: 'file-content'},
file.content.split(/\r?\n/).map((line, index) => {
const trace = file.traces.find(trace => trace.line === index + 1);
const covered = trace && trace.stats.Line;
const uncovered = trace && !trace.stats.Line;
return e('pre', {
className: 'code-line'
+ (covered ? ' code-line_covered' : '')
+ (uncovered ? ' code-line_uncovered' : ''),
title: trace ? JSON.stringify(trace.stats, null, 2) : null,
}, line);
})
);
}
(function(){
const commonPath = findCommonPath(data.files);
const prevFilesMap = new Map();
previousData && previousData.files.forEach((file) => {
const path = file.path.slice(commonPath.length).join('/');
prevFilesMap.set(path, file);
});
const files = data.files.map((file) => {
const path = file.path.slice(commonPath.length);
const { covered = 0, coverable = 0 } = prevFilesMap.get(path.join('/')) || {};
return {
...file,
path,
parent: commonPath,
prevRun: { covered, coverable },
};
});
const children = findFolders(files);
const root = {
is_folder: true,
children,
path: commonPath,
parent: [],
covered: children.reduce((sum, file) => sum + file.covered, 0),
coverable: children.reduce((sum, file) => sum + file.coverable, 0),
prevRun: {
covered: children.reduce((sum, file) => sum + file.prevRun.covered, 0),
coverable: children.reduce((sum, file) => sum + file.prevRun.coverable, 0),
}
};
ReactDOM.render(e(App, {root, prevFilesMap}), document.getElementById('root'));
}());
</script>
</body>
</html>