#pragma once #include // array #include // signbit, isfinite #include // intN_t, uintN_t #include // memcpy, memmove #include // numeric_limits #include // conditional #include namespace nlohmann { namespace detail { /*! @brief implements the Grisu2 algorithm for binary to decimal floating-point conversion. This implementation is a slightly modified version of the reference implementation which may be obtained from http://florian.loitsch.com/publications (bench.tar.gz). The code is distributed under the MIT license, Copyright (c) 2009 Florian Loitsch. For a detailed description of the algorithm see: [1] Loitsch, "Printing Floating-Point Numbers Quickly and Accurately with Integers", Proceedings of the ACM SIGPLAN 2010 Conference on Programming Language Design and Implementation, PLDI 2010 [2] Burger, Dybvig, "Printing Floating-Point Numbers Quickly and Accurately", Proceedings of the ACM SIGPLAN 1996 Conference on Programming Language Design and Implementation, PLDI 1996 */ namespace dtoa_impl { template Target reinterpret_bits(const Source source) { static_assert(sizeof(Target) == sizeof(Source), "size mismatch"); Target target; std::memcpy(&target, &source, sizeof(Source)); return target; } struct diyfp // f * 2^e { static constexpr int kPrecision = 64; // = q std::uint64_t f = 0; int e = 0; constexpr diyfp(std::uint64_t f_, int e_) noexcept : f(f_), e(e_) {} /*! @brief returns x - y @pre x.e == y.e and x.f >= y.f */ static diyfp sub(const diyfp& x, const diyfp& y) noexcept { JSON_ASSERT(x.e == y.e); JSON_ASSERT(x.f >= y.f); return {x.f - y.f, x.e}; } /*! @brief returns x * y @note The result is rounded. (Only the upper q bits are returned.) */ static diyfp mul(const diyfp& x, const diyfp& y) noexcept { static_assert(kPrecision == 64, "internal error"); // Computes: // f = round((x.f * y.f) / 2^q) // e = x.e + y.e + q // Emulate the 64-bit * 64-bit multiplication: // // p = u * v // = (u_lo + 2^32 u_hi) (v_lo + 2^32 v_hi) // = (u_lo v_lo ) + 2^32 ((u_lo v_hi ) + (u_hi v_lo )) + 2^64 (u_hi v_hi ) // = (p0 ) + 2^32 ((p1 ) + (p2 )) + 2^64 (p3 ) // = (p0_lo + 2^32 p0_hi) + 2^32 ((p1_lo + 2^32 p1_hi) + (p2_lo + 2^32 p2_hi)) + 2^64 (p3 ) // = (p0_lo ) + 2^32 (p0_hi + p1_lo + p2_lo ) + 2^64 (p1_hi + p2_hi + p3) // = (p0_lo ) + 2^32 (Q ) + 2^64 (H ) // = (p0_lo ) + 2^32 (Q_lo + 2^32 Q_hi ) + 2^64 (H ) // // (Since Q might be larger than 2^32 - 1) // // = (p0_lo + 2^32 Q_lo) + 2^64 (Q_hi + H) // // (Q_hi + H does not overflow a 64-bit int) // // = p_lo + 2^64 p_hi const std::uint64_t u_lo = x.f & 0xFFFFFFFFu; const std::uint64_t u_hi = x.f >> 32u; const std::uint64_t v_lo = y.f & 0xFFFFFFFFu; const std::uint64_t v_hi = y.f >> 32u; const std::uint64_t p0 = u_lo * v_lo; const std::uint64_t p1 = u_lo * v_hi; const std::uint64_t p2 = u_hi * v_lo; const std::uint64_t p3 = u_hi * v_hi; const std::uint64_t p0_hi = p0 >> 32u; const std::uint64_t p1_lo = p1 & 0xFFFFFFFFu; const std::uint64_t p1_hi = p1 >> 32u; const std::uint64_t p2_lo = p2 & 0xFFFFFFFFu; const std::uint64_t p2_hi = p2 >> 32u; std::uint64_t Q = p0_hi + p1_lo + p2_lo; // The full product might now be computed as // // p_hi = p3 + p2_hi + p1_hi + (Q >> 32) // p_lo = p0_lo + (Q << 32) // // But in this particular case here, the full p_lo is not required. // Effectively we only need to add the highest bit in p_lo to p_hi (and // Q_hi + 1 does not overflow). Q += std::uint64_t{1} << (64u - 32u - 1u); // round, ties up const std::uint64_t h = p3 + p2_hi + p1_hi + (Q >> 32u); return {h, x.e + y.e + 64}; } /*! @brief normalize x such that the significand is >= 2^(q-1) @pre x.f != 0 */ static diyfp normalize(diyfp x) noexcept { JSON_ASSERT(x.f != 0); while ((x.f >> 63u) == 0) { x.f <<= 1u; x.e--; } return x; } /*! @brief normalize x such that the result has the exponent E @pre e >= x.e and the upper e - x.e bits of x.f must be zero. */ static diyfp normalize_to(const diyfp& x, const int target_exponent) noexcept { const int delta = x.e - target_exponent; JSON_ASSERT(delta >= 0); JSON_ASSERT(((x.f << delta) >> delta) == x.f); return {x.f << delta, target_exponent}; } }; struct boundaries { diyfp w; diyfp minus; diyfp plus; }; /*! Compute the (normalized) diyfp representing the input number 'value' and its boundaries. @pre value must be finite and positive */ template boundaries compute_boundaries(FloatType value) { JSON_ASSERT(std::isfinite(value)); JSON_ASSERT(value > 0); // Convert the IEEE representation into a diyfp. // // If v is denormal: // value = 0.F * 2^(1 - bias) = ( F) * 2^(1 - bias - (p-1)) // If v is normalized: // value = 1.F * 2^(E - bias) = (2^(p-1) + F) * 2^(E - bias - (p-1)) static_assert(std::numeric_limits::is_iec559, "internal error: dtoa_short requires an IEEE-754 floating-point implementation"); constexpr int kPrecision = std::numeric_limits::digits; // = p (includes the hidden bit) constexpr int kBias = std::numeric_limits::max_exponent - 1 + (kPrecision - 1); constexpr int kMinExp = 1 - kBias; constexpr std::uint64_t kHiddenBit = std::uint64_t{1} << (kPrecision - 1); // = 2^(p-1) using bits_type = typename std::conditional::type; const auto bits = static_cast(reinterpret_bits(value)); const std::uint64_t E = bits >> (kPrecision - 1); const std::uint64_t F = bits & (kHiddenBit - 1); const bool is_denormal = E == 0; const diyfp v = is_denormal ? diyfp(F, kMinExp) : diyfp(F + kHiddenBit, static_cast(E) - kBias); // Compute the boundaries m- and m+ of the floating-point value // v = f * 2^e. // // Determine v- and v+, the floating-point predecessor and successor if v, // respectively. // // v- = v - 2^e if f != 2^(p-1) or e == e_min (A) // = v - 2^(e-1) if f == 2^(p-1) and e > e_min (B) // // v+ = v + 2^e // // Let m- = (v- + v) / 2 and m+ = (v + v+) / 2. All real numbers _strictly_ // between m- and m+ round to v, regardless of how the input rounding // algorithm breaks ties. // // ---+-------------+-------------+-------------+-------------+--- (A) // v- m- v m+ v+ // // -----------------+------+------+-------------+-------------+--- (B) // v- m- v m+ v+ const bool lower_boundary_is_closer = F == 0 && E > 1; const diyfp m_plus = diyfp(2 * v.f + 1, v.e - 1); const diyfp m_minus = lower_boundary_is_closer ? diyfp(4 * v.f - 1, v.e - 2) // (B) : diyfp(2 * v.f - 1, v.e - 1); // (A) // Determine the normalized w+ = m+. const diyfp w_plus = diyfp::normalize(m_plus); // Determine w- = m- such that e_(w-) = e_(w+). const diyfp w_minus = diyfp::normalize_to(m_minus, w_plus.e); return {diyfp::normalize(v), w_minus, w_plus}; } // Given normalized diyfp w, Grisu needs to find a (normalized) cached // power-of-ten c, such that the exponent of the product c * w = f * 2^e lies // within a certain range [alpha, gamma] (Definition 3.2 from [1]) // // alpha <= e = e_c + e_w + q <= gamma // // or // // f_c * f_w * 2^alpha <= f_c 2^(e_c) * f_w 2^(e_w) * 2^q // <= f_c * f_w * 2^gamma // // Since c and w are normalized, i.e. 2^(q-1) <= f < 2^q, this implies // // 2^(q-1) * 2^(q-1) * 2^alpha <= c * w * 2^q < 2^q * 2^q * 2^gamma // // or // // 2^(q - 2 + alpha) <= c * w < 2^(q + gamma) // // The choice of (alpha,gamma) determines the size of the table and the form of // the digit generation procedure. Using (alpha,gamma)=(-60,-32) works out well // in practice: // // The idea is to cut the number c * w = f * 2^e into two parts, which can be // processed independently: An integral part p1, and a fractional part p2: // // f * 2^e = ( (f div 2^-e) * 2^-e + (f mod 2^-e) ) * 2^e // = (f div 2^-e) + (f mod 2^-e) * 2^e // = p1 + p2 * 2^e // // The conversion of p1 into decimal form requires a series of divisions and // modulos by (a power of) 10. These operations are faster for 32-bit than for // 64-bit integers, so p1 should ideally fit into a 32-bit integer. This can be // achieved by choosing // // -e >= 32 or e <= -32 := gamma // // In order to convert the fractional part // // p2 * 2^e = p2 / 2^-e = d[-1] / 10^1 + d[-2] / 10^2 + ... // // into decimal form, the fraction is repeatedly multiplied by 10 and the digits // d[-i] are extracted in order: // // (10 * p2) div 2^-e = d[-1] // (10 * p2) mod 2^-e = d[-2] / 10^1 + ... // // The multiplication by 10 must not overflow. It is sufficient to choose // // 10 * p2 < 16 * p2 = 2^4 * p2 <= 2^64. // // Since p2 = f mod 2^-e < 2^-e, // // -e <= 60 or e >= -60 := alpha constexpr int kAlpha = -60; constexpr int kGamma = -32; struct cached_power // c = f * 2^e ~= 10^k { std::uint64_t f; int e; int k; }; /*! For a normalized diyfp w = f * 2^e, this function returns a (normalized) cached power-of-ten c = f_c * 2^e_c, such that the exponent of the product w * c satisfies (Definition 3.2 from [1]) alpha <= e_c + e + q <= gamma. */ inline cached_power get_cached_power_for_binary_exponent(int e) { // Now // // alpha <= e_c + e + q <= gamma (1) // ==> f_c * 2^alpha <= c * 2^e * 2^q // // and since the c's are normalized, 2^(q-1) <= f_c, // // ==> 2^(q - 1 + alpha) <= c * 2^(e + q) // ==> 2^(alpha - e - 1) <= c // // If c were an exact power of ten, i.e. c = 10^k, one may determine k as // // k = ceil( log_10( 2^(alpha - e - 1) ) ) // = ceil( (alpha - e - 1) * log_10(2) ) // // From the paper: // "In theory the result of the procedure could be wrong since c is rounded, // and the computation itself is approximated [...]. In practice, however, // this simple function is sufficient." // // For IEEE double precision floating-point numbers converted into // normalized diyfp's w = f * 2^e, with q = 64, // // e >= -1022 (min IEEE exponent) // -52 (p - 1) // -52 (p - 1, possibly normalize denormal IEEE numbers) // -11 (normalize the diyfp) // = -1137 // // and // // e <= +1023 (max IEEE exponent) // -52 (p - 1) // -11 (normalize the diyfp) // = 960 // // This binary exponent range [-1137,960] results in a decimal exponent // range [-307,324]. One does not need to store a cached power for each // k in this range. For each such k it suffices to find a cached power // such that the exponent of the product lies in [alpha,gamma]. // This implies that the difference of the decimal exponents of adjacent // table entries must be less than or equal to // // floor( (gamma - alpha) * log_10(2) ) = 8. // // (A smaller distance gamma-alpha would require a larger table.) // NB: // Actually this function returns c, such that -60 <= e_c + e + 64 <= -34. constexpr int kCachedPowersMinDecExp = -300; constexpr int kCachedPowersDecStep = 8; static constexpr std::array kCachedPowers = { { { 0xAB70FE17C79AC6CA, -1060, -300 }, { 0xFF77B1FCBEBCDC4F, -1034, -292 }, { 0xBE5691EF416BD60C, -1007, -284 }, { 0x8DD01FAD907FFC3C, -980, -276 }, { 0xD3515C2831559A83, -954, -268 }, { 0x9D71AC8FADA6C9B5, -927, -260 }, { 0xEA9C227723EE8BCB, -901, -252 }, { 0xAECC49914078536D, -874, -244 }, { 0x823C12795DB6CE57, -847, -236 }, { 0xC21094364DFB5637, -821, -228 }, { 0x9096EA6F3848984F, -794, -220 }, { 0xD77485CB25823AC7, -768, -212 }, { 0xA086CFCD97BF97F4, -741, -204 }, { 0xEF340A98172AACE5, -715, -196 }, { 0xB23867FB2A35B28E, -688, -188 }, { 0x84C8D4DFD2C63F3B, -661, -180 }, { 0xC5DD44271AD3CDBA, -635, -172 }, { 0x936B9FCEBB25C996, -608, -164 }, { 0xDBAC6C247D62A584, -582, -156 }, { 0xA3AB66580D5FDAF6, -555, -148 }, { 0xF3E2F893DEC3F126, -529, -140 }, { 0xB5B5ADA8AAFF80B8, -502, -132 }, { 0x87625F056C7C4A8B, -475, -124 }, { 0xC9BCFF6034C13053, -449, -116 }, { 0x964E858C91BA2655, -422, -108 }, { 0xDFF9772470297EBD, -396, -100 }, { 0xA6DFBD9FB8E5B88F, -369, -92 }, { 0xF8A95FCF88747D94, -343, -84 }, { 0xB94470938FA89BCF, -316, -76 }, { 0x8A08F0F8BF0F156B, -289, -68 }, { 0xCDB02555653131B6, -263, -60 }, { 0x993FE2C6D07B7FAC, -236, -52 }, { 0xE45C10C42A2B3B06, -210, -44 }, { 0xAA242499697392D3, -183, -36 }, { 0xFD87B5F28300CA0E, -157, -28 }, { 0xBCE5086492111AEB, -130, -20 }, { 0x8CBCCC096F5088CC, -103, -12 }, { 0xD1B71758E219652C, -77, -4 }, { 0x9C40000000000000, -50, 4 }, { 0xE8D4A51000000000, -24, 12 }, { 0xAD78EBC5AC620000, 3, 20 }, { 0x813F3978F8940984, 30, 28 }, { 0xC097CE7BC90715B3, 56, 36 }, { 0x8F7E32CE7BEA5C70, 83, 44 }, { 0xD5D238A4ABE98068, 109, 52 }, { 0x9F4F2726179A2245, 136, 60 }, { 0xED63A231D4C4FB27, 162, 68 }, { 0xB0DE65388CC8ADA8, 189, 76 }, { 0x83C7088E1AAB65DB, 216, 84 }, { 0xC45D1DF942711D9A, 242, 92 }, { 0x924D692CA61BE758, 269, 100 }, { 0xDA01EE641A708DEA, 295, 108 }, { 0xA26DA3999AEF774A, 322, 116 }, { 0xF209787BB47D6B85, 348, 124 }, { 0xB454E4A179DD1877, 375, 132 }, { 0x865B86925B9BC5C2, 402, 140 }, { 0xC83553C5C8965D3D, 428, 148 }, { 0x952AB45CFA97A0B3, 455, 156 }, { 0xDE469FBD99A05FE3, 481, 164 }, { 0xA59BC234DB398C25, 508, 172 }, { 0xF6C69A72A3989F5C, 534, 180 }, { 0xB7DCBF5354E9BECE, 561, 188 }, { 0x88FCF317F22241E2, 588, 196 }, { 0xCC20CE9BD35C78A5, 614, 204 }, { 0x98165AF37B2153DF, 641, 212 }, { 0xE2A0B5DC971F303A, 667, 220 }, { 0xA8D9D1535CE3B396, 694, 228 }, { 0xFB9B7CD9A4A7443C, 720, 236 }, { 0xBB764C4CA7A44410, 747, 244 }, { 0x8BAB8EEFB6409C1A, 774, 252 }, { 0xD01FEF10A657842C, 800, 260 }, { 0x9B10A4E5E9913129, 827, 268 }, { 0xE7109BFBA19C0C9D, 853, 276 }, { 0xAC2820D9623BF429, 880, 284 }, { 0x80444B5E7AA7CF85, 907, 292 }, { 0xBF21E44003ACDD2D, 933, 300 }, { 0x8E679C2F5E44FF8F, 960, 308 }, { 0xD433179D9C8CB841, 986, 316 }, { 0x9E19DB92B4E31BA9, 1013, 324 }, } }; // This computation gives exactly the same results for k as // k = ceil((kAlpha - e - 1) * 0.30102999566398114) // for |e| <= 1500, but doesn't require floating-point operations. // NB: log_10(2) ~= 78913 / 2^18 JSON_ASSERT(e >= -1500); JSON_ASSERT(e <= 1500); const int f = kAlpha - e - 1; const int k = (f * 78913) / (1 << 18) + static_cast(f > 0); const int index = (-kCachedPowersMinDecExp + k + (kCachedPowersDecStep - 1)) / kCachedPowersDecStep; JSON_ASSERT(index >= 0); JSON_ASSERT(static_cast(index) < kCachedPowers.size()); const cached_power cached = kCachedPowers[static_cast(index)]; JSON_ASSERT(kAlpha <= cached.e + e + 64); JSON_ASSERT(kGamma >= cached.e + e + 64); return cached; } /*! For n != 0, returns k, such that pow10 := 10^(k-1) <= n < 10^k. For n == 0, returns 1 and sets pow10 := 1. */ inline int find_largest_pow10(const std::uint32_t n, std::uint32_t& pow10) { // LCOV_EXCL_START if (n >= 1000000000) { pow10 = 1000000000; return 10; } // LCOV_EXCL_STOP if (n >= 100000000) { pow10 = 100000000; return 9; } if (n >= 10000000) { pow10 = 10000000; return 8; } if (n >= 1000000) { pow10 = 1000000; return 7; } if (n >= 100000) { pow10 = 100000; return 6; } if (n >= 10000) { pow10 = 10000; return 5; } if (n >= 1000) { pow10 = 1000; return 4; } if (n >= 100) { pow10 = 100; return 3; } if (n >= 10) { pow10 = 10; return 2; } pow10 = 1; return 1; } inline void grisu2_round(char* buf, int len, std::uint64_t dist, std::uint64_t delta, std::uint64_t rest, std::uint64_t ten_k) { JSON_ASSERT(len >= 1); JSON_ASSERT(dist <= delta); JSON_ASSERT(rest <= delta); JSON_ASSERT(ten_k > 0); // <--------------------------- delta ----> // <---- dist ---------> // --------------[------------------+-------------------]-------------- // M- w M+ // // ten_k // <------> // <---- rest ----> // --------------[------------------+----+--------------]-------------- // w V // = buf * 10^k // // ten_k represents a unit-in-the-last-place in the decimal representation // stored in buf. // Decrement buf by ten_k while this takes buf closer to w. // The tests are written in this order to avoid overflow in unsigned // integer arithmetic. while (rest < dist && delta - rest >= ten_k && (rest + ten_k < dist || dist - rest > rest + ten_k - dist)) { JSON_ASSERT(buf[len - 1] != '0'); buf[len - 1]--; rest += ten_k; } } /*! Generates V = buffer * 10^decimal_exponent, such that M- <= V <= M+. M- and M+ must be normalized and share the same exponent -60 <= e <= -32. */ inline void grisu2_digit_gen(char* buffer, int& length, int& decimal_exponent, diyfp M_minus, diyfp w, diyfp M_plus) { static_assert(kAlpha >= -60, "internal error"); static_assert(kGamma <= -32, "internal error"); // Generates the digits (and the exponent) of a decimal floating-point // number V = buffer * 10^decimal_exponent in the range [M-, M+]. The diyfp's // w, M- and M+ share the same exponent e, which satisfies alpha <= e <= gamma. // // <--------------------------- delta ----> // <---- dist ---------> // --------------[------------------+-------------------]-------------- // M- w M+ // // Grisu2 generates the digits of M+ from left to right and stops as soon as // V is in [M-,M+]. JSON_ASSERT(M_plus.e >= kAlpha); JSON_ASSERT(M_plus.e <= kGamma); std::uint64_t delta = diyfp::sub(M_plus, M_minus).f; // (significand of (M+ - M-), implicit exponent is e) std::uint64_t dist = diyfp::sub(M_plus, w ).f; // (significand of (M+ - w ), implicit exponent is e) // Split M+ = f * 2^e into two parts p1 and p2 (note: e < 0): // // M+ = f * 2^e // = ((f div 2^-e) * 2^-e + (f mod 2^-e)) * 2^e // = ((p1 ) * 2^-e + (p2 )) * 2^e // = p1 + p2 * 2^e const diyfp one(std::uint64_t{1} << -M_plus.e, M_plus.e); auto p1 = static_cast(M_plus.f >> -one.e); // p1 = f div 2^-e (Since -e >= 32, p1 fits into a 32-bit int.) std::uint64_t p2 = M_plus.f & (one.f - 1); // p2 = f mod 2^-e // 1) // // Generate the digits of the integral part p1 = d[n-1]...d[1]d[0] JSON_ASSERT(p1 > 0); std::uint32_t pow10{}; const int k = find_largest_pow10(p1, pow10); // 10^(k-1) <= p1 < 10^k, pow10 = 10^(k-1) // // p1 = (p1 div 10^(k-1)) * 10^(k-1) + (p1 mod 10^(k-1)) // = (d[k-1] ) * 10^(k-1) + (p1 mod 10^(k-1)) // // M+ = p1 + p2 * 2^e // = d[k-1] * 10^(k-1) + (p1 mod 10^(k-1)) + p2 * 2^e // = d[k-1] * 10^(k-1) + ((p1 mod 10^(k-1)) * 2^-e + p2) * 2^e // = d[k-1] * 10^(k-1) + ( rest) * 2^e // // Now generate the digits d[n] of p1 from left to right (n = k-1,...,0) // // p1 = d[k-1]...d[n] * 10^n + d[n-1]...d[0] // // but stop as soon as // // rest * 2^e = (d[n-1]...d[0] * 2^-e + p2) * 2^e <= delta * 2^e int n = k; while (n > 0) { // Invariants: // M+ = buffer * 10^n + (p1 + p2 * 2^e) (buffer = 0 for n = k) // pow10 = 10^(n-1) <= p1 < 10^n // const std::uint32_t d = p1 / pow10; // d = p1 div 10^(n-1) const std::uint32_t r = p1 % pow10; // r = p1 mod 10^(n-1) // // M+ = buffer * 10^n + (d * 10^(n-1) + r) + p2 * 2^e // = (buffer * 10 + d) * 10^(n-1) + (r + p2 * 2^e) // JSON_ASSERT(d <= 9); buffer[length++] = static_cast('0' + d); // buffer := buffer * 10 + d // // M+ = buffer * 10^(n-1) + (r + p2 * 2^e) // p1 = r; n--; // // M+ = buffer * 10^n + (p1 + p2 * 2^e) // pow10 = 10^n // // Now check if enough digits have been generated. // Compute // // p1 + p2 * 2^e = (p1 * 2^-e + p2) * 2^e = rest * 2^e // // Note: // Since rest and delta share the same exponent e, it suffices to // compare the significands. const std::uint64_t rest = (std::uint64_t{p1} << -one.e) + p2; if (rest <= delta) { // V = buffer * 10^n, with M- <= V <= M+. decimal_exponent += n; // We may now just stop. But instead look if the buffer could be // decremented to bring V closer to w. // // pow10 = 10^n is now 1 ulp in the decimal representation V. // The rounding procedure works with diyfp's with an implicit // exponent of e. // // 10^n = (10^n * 2^-e) * 2^e = ulp * 2^e // const std::uint64_t ten_n = std::uint64_t{pow10} << -one.e; grisu2_round(buffer, length, dist, delta, rest, ten_n); return; } pow10 /= 10; // // pow10 = 10^(n-1) <= p1 < 10^n // Invariants restored. } // 2) // // The digits of the integral part have been generated: // // M+ = d[k-1]...d[1]d[0] + p2 * 2^e // = buffer + p2 * 2^e // // Now generate the digits of the fractional part p2 * 2^e. // // Note: // No decimal point is generated: the exponent is adjusted instead. // // p2 actually represents the fraction // // p2 * 2^e // = p2 / 2^-e // = d[-1] / 10^1 + d[-2] / 10^2 + ... // // Now generate the digits d[-m] of p1 from left to right (m = 1,2,...) // // p2 * 2^e = d[-1]d[-2]...d[-m] * 10^-m // + 10^-m * (d[-m-1] / 10^1 + d[-m-2] / 10^2 + ...) // // using // // 10^m * p2 = ((10^m * p2) div 2^-e) * 2^-e + ((10^m * p2) mod 2^-e) // = ( d) * 2^-e + ( r) // // or // 10^m * p2 * 2^e = d + r * 2^e // // i.e. // // M+ = buffer + p2 * 2^e // = buffer + 10^-m * (d + r * 2^e) // = (buffer * 10^m + d) * 10^-m + 10^-m * r * 2^e // // and stop as soon as 10^-m * r * 2^e <= delta * 2^e JSON_ASSERT(p2 > delta); int m = 0; for (;;) { // Invariant: // M+ = buffer * 10^-m + 10^-m * (d[-m-1] / 10 + d[-m-2] / 10^2 + ...) * 2^e // = buffer * 10^-m + 10^-m * (p2 ) * 2^e // = buffer * 10^-m + 10^-m * (1/10 * (10 * p2) ) * 2^e // = buffer * 10^-m + 10^-m * (1/10 * ((10*p2 div 2^-e) * 2^-e + (10*p2 mod 2^-e)) * 2^e // JSON_ASSERT(p2 <= (std::numeric_limits::max)() / 10); p2 *= 10; const std::uint64_t d = p2 >> -one.e; // d = (10 * p2) div 2^-e const std::uint64_t r = p2 & (one.f - 1); // r = (10 * p2) mod 2^-e // // M+ = buffer * 10^-m + 10^-m * (1/10 * (d * 2^-e + r) * 2^e // = buffer * 10^-m + 10^-m * (1/10 * (d + r * 2^e)) // = (buffer * 10 + d) * 10^(-m-1) + 10^(-m-1) * r * 2^e // JSON_ASSERT(d <= 9); buffer[length++] = static_cast('0' + d); // buffer := buffer * 10 + d // // M+ = buffer * 10^(-m-1) + 10^(-m-1) * r * 2^e // p2 = r; m++; // // M+ = buffer * 10^-m + 10^-m * p2 * 2^e // Invariant restored. // Check if enough digits have been generated. // // 10^-m * p2 * 2^e <= delta * 2^e // p2 * 2^e <= 10^m * delta * 2^e // p2 <= 10^m * delta delta *= 10; dist *= 10; if (p2 <= delta) { break; } } // V = buffer * 10^-m, with M- <= V <= M+. decimal_exponent -= m; // 1 ulp in the decimal representation is now 10^-m. // Since delta and dist are now scaled by 10^m, we need to do the // same with ulp in order to keep the units in sync. // // 10^m * 10^-m = 1 = 2^-e * 2^e = ten_m * 2^e // const std::uint64_t ten_m = one.f; grisu2_round(buffer, length, dist, delta, p2, ten_m); // By construction this algorithm generates the shortest possible decimal // number (Loitsch, Theorem 6.2) which rounds back to w. // For an input number of precision p, at least // // N = 1 + ceil(p * log_10(2)) // // decimal digits are sufficient to identify all binary floating-point // numbers (Matula, "In-and-Out conversions"). // This implies that the algorithm does not produce more than N decimal // digits. // // N = 17 for p = 53 (IEEE double precision) // N = 9 for p = 24 (IEEE single precision) } /*! v = buf * 10^decimal_exponent len is the length of the buffer (number of decimal digits) The buffer must be large enough, i.e. >= max_digits10. */ JSON_HEDLEY_NON_NULL(1) inline void grisu2(char* buf, int& len, int& decimal_exponent, diyfp m_minus, diyfp v, diyfp m_plus) { JSON_ASSERT(m_plus.e == m_minus.e); JSON_ASSERT(m_plus.e == v.e); // --------(-----------------------+-----------------------)-------- (A) // m- v m+ // // --------------------(-----------+-----------------------)-------- (B) // m- v m+ // // First scale v (and m- and m+) such that the exponent is in the range // [alpha, gamma]. const cached_power cached = get_cached_power_for_binary_exponent(m_plus.e); const diyfp c_minus_k(cached.f, cached.e); // = c ~= 10^-k // The exponent of the products is = v.e + c_minus_k.e + q and is in the range [alpha,gamma] const diyfp w = diyfp::mul(v, c_minus_k); const diyfp w_minus = diyfp::mul(m_minus, c_minus_k); const diyfp w_plus = diyfp::mul(m_plus, c_minus_k); // ----(---+---)---------------(---+---)---------------(---+---)---- // w- w w+ // = c*m- = c*v = c*m+ // // diyfp::mul rounds its result and c_minus_k is approximated too. w, w- and // w+ are now off by a small amount. // In fact: // // w - v * 10^k < 1 ulp // // To account for this inaccuracy, add resp. subtract 1 ulp. // // --------+---[---------------(---+---)---------------]---+-------- // w- M- w M+ w+ // // Now any number in [M-, M+] (bounds included) will round to w when input, // regardless of how the input rounding algorithm breaks ties. // // And digit_gen generates the shortest possible such number in [M-, M+]. // Note that this does not mean that Grisu2 always generates the shortest // possible number in the interval (m-, m+). const diyfp M_minus(w_minus.f + 1, w_minus.e); const diyfp M_plus (w_plus.f - 1, w_plus.e ); decimal_exponent = -cached.k; // = -(-k) = k grisu2_digit_gen(buf, len, decimal_exponent, M_minus, w, M_plus); } /*! v = buf * 10^decimal_exponent len is the length of the buffer (number of decimal digits) The buffer must be large enough, i.e. >= max_digits10. */ template JSON_HEDLEY_NON_NULL(1) void grisu2(char* buf, int& len, int& decimal_exponent, FloatType value) { static_assert(diyfp::kPrecision >= std::numeric_limits::digits + 3, "internal error: not enough precision"); JSON_ASSERT(std::isfinite(value)); JSON_ASSERT(value > 0); // If the neighbors (and boundaries) of 'value' are always computed for double-precision // numbers, all float's can be recovered using strtod (and strtof). However, the resulting // decimal representations are not exactly "short". // // The documentation for 'std::to_chars' (https://en.cppreference.com/w/cpp/utility/to_chars) // says "value is converted to a string as if by std::sprintf in the default ("C") locale" // and since sprintf promotes floats to doubles, I think this is exactly what 'std::to_chars' // does. // On the other hand, the documentation for 'std::to_chars' requires that "parsing the // representation using the corresponding std::from_chars function recovers value exactly". That // indicates that single precision floating-point numbers should be recovered using // 'std::strtof'. // // NB: If the neighbors are computed for single-precision numbers, there is a single float // (7.0385307e-26f) which can't be recovered using strtod. The resulting double precision // value is off by 1 ulp. #if 0 const boundaries w = compute_boundaries(static_cast(value)); #else const boundaries w = compute_boundaries(value); #endif grisu2(buf, len, decimal_exponent, w.minus, w.w, w.plus); } /*! @brief appends a decimal representation of e to buf @return a pointer to the element following the exponent. @pre -1000 < e < 1000 */ JSON_HEDLEY_NON_NULL(1) JSON_HEDLEY_RETURNS_NON_NULL inline char* append_exponent(char* buf, int e) { JSON_ASSERT(e > -1000); JSON_ASSERT(e < 1000); if (e < 0) { e = -e; *buf++ = '-'; } else { *buf++ = '+'; } auto k = static_cast(e); if (k < 10) { // Always print at least two digits in the exponent. // This is for compatibility with printf("%g"). *buf++ = '0'; *buf++ = static_cast('0' + k); } else if (k < 100) { *buf++ = static_cast('0' + k / 10); k %= 10; *buf++ = static_cast('0' + k); } else { *buf++ = static_cast('0' + k / 100); k %= 100; *buf++ = static_cast('0' + k / 10); k %= 10; *buf++ = static_cast('0' + k); } return buf; } /*! @brief prettify v = buf * 10^decimal_exponent If v is in the range [10^min_exp, 10^max_exp) it will be printed in fixed-point notation. Otherwise it will be printed in exponential notation. @pre min_exp < 0 @pre max_exp > 0 */ JSON_HEDLEY_NON_NULL(1) JSON_HEDLEY_RETURNS_NON_NULL inline char* format_buffer(char* buf, int len, int decimal_exponent, int min_exp, int max_exp) { JSON_ASSERT(min_exp < 0); JSON_ASSERT(max_exp > 0); const int k = len; const int n = len + decimal_exponent; // v = buf * 10^(n-k) // k is the length of the buffer (number of decimal digits) // n is the position of the decimal point relative to the start of the buffer. if (k <= n && n <= max_exp) { // digits[000] // len <= max_exp + 2 std::memset(buf + k, '0', static_cast(n) - static_cast(k)); // Make it look like a floating-point number (#362, #378) buf[n + 0] = '.'; buf[n + 1] = '0'; return buf + (static_cast(n) + 2); } if (0 < n && n <= max_exp) { // dig.its // len <= max_digits10 + 1 JSON_ASSERT(k > n); std::memmove(buf + (static_cast(n) + 1), buf + n, static_cast(k) - static_cast(n)); buf[n] = '.'; return buf + (static_cast(k) + 1U); } if (min_exp < n && n <= 0) { // 0.[000]digits // len <= 2 + (-min_exp - 1) + max_digits10 std::memmove(buf + (2 + static_cast(-n)), buf, static_cast(k)); buf[0] = '0'; buf[1] = '.'; std::memset(buf + 2, '0', static_cast(-n)); return buf + (2U + static_cast(-n) + static_cast(k)); } if (k == 1) { // dE+123 // len <= 1 + 5 buf += 1; } else { // d.igitsE+123 // len <= max_digits10 + 1 + 5 std::memmove(buf + 2, buf + 1, static_cast(k) - 1); buf[1] = '.'; buf += 1 + static_cast(k); } *buf++ = 'e'; return append_exponent(buf, n - 1); } } // namespace dtoa_impl /*! @brief generates a decimal representation of the floating-point number value in [first, last). The format of the resulting decimal representation is similar to printf's %g format. Returns an iterator pointing past-the-end of the decimal representation. @note The input number must be finite, i.e. NaN's and Inf's are not supported. @note The buffer must be large enough. @note The result is NOT null-terminated. */ template JSON_HEDLEY_NON_NULL(1, 2) JSON_HEDLEY_RETURNS_NON_NULL char* to_chars(char* first, const char* last, FloatType value) { static_cast(last); // maybe unused - fix warning JSON_ASSERT(std::isfinite(value)); // Use signbit(value) instead of (value < 0) since signbit works for -0. if (std::signbit(value)) { value = -value; *first++ = '-'; } #ifdef __GNUC__ #pragma GCC diagnostic push #pragma GCC diagnostic ignored "-Wfloat-equal" #endif if (value == 0) // +-0 { *first++ = '0'; // Make it look like a floating-point number (#362, #378) *first++ = '.'; *first++ = '0'; return first; } #ifdef __GNUC__ #pragma GCC diagnostic pop #endif JSON_ASSERT(last - first >= std::numeric_limits::max_digits10); // Compute v = buffer * 10^decimal_exponent. // The decimal digits are stored in the buffer, which needs to be interpreted // as an unsigned decimal integer. // len is the length of the buffer, i.e. the number of decimal digits. int len = 0; int decimal_exponent = 0; dtoa_impl::grisu2(first, len, decimal_exponent, value); JSON_ASSERT(len <= std::numeric_limits::max_digits10); // Format the buffer like printf("%.*g", prec, value) constexpr int kMinExp = -4; // Use digits10 here to increase compatibility with version 2. constexpr int kMaxExp = std::numeric_limits::digits10; JSON_ASSERT(last - first >= kMaxExp + 2); JSON_ASSERT(last - first >= 2 + (-kMinExp - 1) + std::numeric_limits::max_digits10); JSON_ASSERT(last - first >= std::numeric_limits::max_digits10 + 6); return dtoa_impl::format_buffer(first, len, decimal_exponent, kMinExp, kMaxExp); } } // namespace detail } // namespace nlohmann