lexical-core 0.4.6

Lexical, to- and from-string conversion routines.


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Low-level, FFI-compatible, lexical conversion routines for use in a no_std context. This crate by default does not use the Rust standard library. And, as of version 0.3, lexical-core uses minimal unsafe features, limiting the chance of memory-unsafe code.

Getting Started

lexical-core is a low-level, partially FFI-compatible API for number-to-string and string-to-number conversions, without requiring a system allocator. If you would like to use a convenient, high-level API, please look at lexical instead.

Add lexical-core to your Cargo.toml:

lexical-core = "^0.4"

And an introduction through use:

extern crate lexical_core;

// String to number using Rust slices.
// The argument is the byte string parsed.
let f = lexical_core::atof64_slice(b"3.5");   // 3.5
let i = lexical_core::atoi32_slice(b"15");    // 15

// String to number using pointer ranges, for FFI-compatible code.
// The first argument is a pointer to the start of the parsed byte array,
// and the second argument is a pointer to 1-past-the-end. It will process
// bytes in the range [first, last).
unsafe {
    let bytes = b"3.5";
    let first = bytes.as_ptr();
    let last = first.add(bytes.len());
    let f = lexical_core::atof64_range(first, last);

// If and only if the `radix` feature is enabled, you may use the radix
// overloads to parse non-decimal floats and strings.
let f = lexical_core::atof32_radix_slice(2, b"11.1");   // 3.5
let i = lexical_core::atoi32_radix_slice(2, b"1111");   // 15

// The ato*_slice and ato*_range parsers are not checked, they do not
// validate that the input data is entirely correct, and stop parsing
// when invalid data is found, returning whatever was parsed up until
// that point. The explicit behavior is to wrap on overflow, and 
// to discard invalid digits.
let i = lexical_core::atoi8_slice(b"256");    // 0, wraps from 256
let i = lexical_core::atoi8_slice(b"1a5");    // 1, discards "a5"

// You should prefer the checked parsers, whenever possible. These detect 
// numeric overflow, and no invalid trailing digits are present.
// The error code for success is 0, all errors are less than 0.

// Ideally, everything works great.
let res = lexical_core::try_atoi8_slice(b"15");
assert_eq!(res.error.code, lexical_core::ErrorCode::Success);
assert_eq!(res.value, 15);

// However, it detects numeric overflow, setting `res.error.code`
// to the appropriate value.
let res = lexical_core::try_atoi8_slice(b"256");
assert_eq!(res.error.code, lexical_core::ErrorCode::Overflow);

// Errors occurring prematurely terminating the parser due to invalid 
// digits return the index in the buffer where the invalid digit was 
// seen. This may useful in contexts like serde, which require numerical
// parsers from complex data without having to extract a substring 
// containing only numeric data ahead of time. If the error is set
// to a `InvalidDigit`, the value is guaranteed to be accurate up until
// that point. For example, if the trailing data is whitespace,
// the value from an invalid digit may be perfectly valid in some contexts.
let res = lexical_core::try_atoi8_slice(b"15 45");
assert_eq!(res.error.code, lexical_core::ErrorCode::InvalidDigit);
assert_eq!(res.error.index, 2);
assert_eq!(res.value, 15);

// Number to string using slices.
// The first argument is the value, the second argument is the radix,
// and the third argument is the buffer to write to.
// The function returns a subslice of the original buffer, and will
// always start at the same position (`buf.as_ptr() == slc.as_ptr()`).
let mut buf = [b'0'; lexical_core::MAX_I64_SIZE];
let slc = lexical_core::i64toa_slice(15, &mut buf);
assert_eq!(slc, b"15");

// If an insufficiently long buffer is passed, the serializer will panic.
let mut buf = [b'0'; 1];
//let slc = lexical_core::i64toa_slice(15, &mut buf); 

// In order to guarantee the buffer is long enough, always ensure there
// are at least `MAX_*_SIZE`, where * is the type name in upperase,
// IE, for `isize`, `MAX_ISIZE_SIZE`.
let mut buf = [b'0'; lexical_core::MAX_F64_SIZE];
let slc = lexical_core::f64toa_slice(15.1, &mut buf);
assert_eq!(slc, b"15.1");


  • correct Use a correct string-to-float parser. Enabled by default, and may be turned off by setting default-features = false. If neither algorithm_m nor bhcomp is enabled while correct is enabled, lexical uses the bigcomp algorithm.
  • algorithm_m Use Algorithm M for the string-to-float parser. Not recommended. bhcomp must be disabled to use algorithm_m, requiring setting default_features = false. If and only if Algorithm M and radix are both active, lexical-core requires a system allocator.
  • bhcomp Use a comparison between the mantissa digits and the halfway-point for the string-to-float parser. bhcomp is faster for all inputs than any other algorithm. Enabled by default. If and only if bhcomp and radix are both active, lexical-core requires a system allocator.
  • trim_floats Export floats without a fraction as an integer, for example, 0.0f64 will be serialized to "0" and not "0.0", and -0.0 as "0" and not "-0.0".
  • radix Enable lexical conversions to and from non-base10 representations. With radix enabled, any radix from 2 to 36 (inclusive) is valid, otherwise, only 10 is valid.
  • rounding Enable the FLOAT_ROUNDING config variable to dictate how to round IEEE754 floats.
  • ryu Use dtolnay's ryu library for fast and accurate float-to-string conversions.


Lexical-core also includes configuration options that allow you to configure float processing and formatting:

  • NAN_STRING The representation of Not a Number (NaN) as a string (default b"NaN"). For float parsing, lexical-core uses case-insensitive comparisons.
  • INF_STRING The short, default representation of infinity as a string (default b"inf"). For float parsing, lexical-core uses case-insensitive comparisons.
  • INFINITY_STRING The long, backup representation of infinity as a string (default b"infinity"). INFINITY_STRING must be at least as long as INF_STRING, and will only be used during float parsing.
  • EXPONENT_DEFAULT_CHAR - The default character designating the exponent component of a float (default b'e') for strings with a radix less than 15 (including decimal strings). For float parsing, lexical-core uses case-insensitive comparisons. This value should be not be in character set [0-9a-eA-E].
  • EXPONENT_BACKUP_CHAR - (radix only) The backup character designating the exponent component of a float (default b'^') for strings with a radix greater than or equal to 15. This value should not an alpha-numeric character.
  • FLOAT_ROUNDING - The IEEE754 float-rounding scheme to be used during float parsing. In almost every case, this should be set to NearestTieEven.


Lexical-core also includes a few constants to simplify interfacing with number-to-string code. These are named MAX_*_SIZE, and indicate the maximum number of characters a number-to-string function may write. For example, atoi32_range may write up to MAX_I32_SIZE characters. These are provided as Rust constants so they may be used as the size element in arrays. For FFI-code, lexical-core exports unmangled constants named MAX_*_SIZE_FFI, to allow their use in non-Rust code.

FFI Example

First, build lexical-core in release mode from the project home:

cargo build --release

Next, add the shared library to the search path, or load it exactly. For example, to use lexical-core from Python, from the project home directory:

from ctypes import *
import os

# This is the path on Unix, on Windows use *.dll and on MacOS X, use *.dylib.
path = os.path.join(os.getcwd(), "target", "release", "liblexical_core.so")
lib = CDLL(path)

# To access global variables, use $type.in_dll($lib, "$variable")
i8_size = c_size_t.in_dll(lib, "MAX_I8_SIZE_FFI")
print(i8_size)          # c_ulong(4)

exponent_char = c_char.in_dll(lib, "EXPONENT_DEFAULT_CHAR")
print(exponent_char)    # c_char(b'e')

# Define our result types for the error-checked parsers.
class error(Structure):
    _fields_ = [("code", c_int),
                ("index", c_size_t)]

class result_f32(Structure):
    _fields_ = [("value", c_float),
                ("error", error)]

# Need to set the appropriate restypes for our functions, Python assumes
# they're all `c_int`.
lib.atof32_range.restype = c_float
lib.try_atof32_range.restype = result_f32
lib.f32toa_range.restype = POINTER(c_char)

# Call string-to-number parsers. This isn't elegant, because we want
# a valid range of values, but it works.
def to_address(ptr):
    '''Get address from pointer.'''
    return cast(ptr, c_voidp).value

def to_charp(address):
    '''Get char* pointer from address or another pointer.'''
    return cast(address, POINTER(c_char))

def distance(first, last):
    '''Calculate the distance between two ranges'''
    return to_address(last) - to_address(first)

data = b"1.2345"
first = to_charp(data)
last = to_charp(to_address(first) + len(data))
result = lib.atof32_range(first, last)
print(result)               # 1.2345000505447388

result = lib.try_atof32_range(first, last)
print(result.value)         # 1.2345000505447388
print(result.error.code)    # 0

# Call the number-to-string serializers.
# First, create a buffer type of sufficient length.
f32_size = c_size_t.in_dll(lib, "MAX_F32_SIZE_FFI")
F32BufferType = c_char * f32_size.value
buf = F32BufferType()

# Next, initialize our arguments for the call.
value = c_float(1.2345)
first = to_charp(buf)
last = to_charp(to_address(first) + len(buf))

# Call the serializer and create a Python string from our result.
ptr = lib.f32toa_range(value, first, last)
length = distance(first, ptr)
result = string_at(buf, 6)
print(result)               # "1.2345"


Lexical-core's documentation can be found on docs.rs.


Float parsing is difficult to do correctly, and major bugs have been found in implementations from libstdc++'s strtod to Python. In order to validate the accuracy of the lexical, we employ the following external tests:

  1. Hrvoje Abraham's strtod test cases.
  2. Rust's test-float-parse unittests.
  3. Testbase's stress tests for converting from decimal to binary.
  4. Various difficult cases reported on blogs.

Although lexical may contain bugs leading to rounding error, it is tested against a comprehensive suite of random-data and near-halfway representations, and should be fast and correct for the vast majority of use-cases.

Implementation Details

Float to String

For more information on the Grisu2 and Grisu3 algorithms, see Printing Floating-Point Numbers Quickly and Accurately with Integers.

For more information on the Ryu algorithm, see Ryƫ: fast float-to-string conversion.

String to Float

In order to implement an efficient parser in Rust, lexical uses the following steps:

  1. We ignore the sign until the end, and merely toggle the sign bit after creating a correct representation of the positive float.
  2. We handle special floats, such as "NaN", "inf", "Infinity". If we do not have a special float, we continue to the next step.
  3. We parse up to 64-bits from the string for the mantissa, ignoring any trailing digits, and parse the exponent (if present) as a signed 32-bit integer. If the exponent overflows or underflows, we set the value to i32::max_value() or i32::min_value(), respectively.
  4. Fast Path We then try to create an exact representation of a native binary float from parsed mantissa and exponent. If both can be exactly represented, we multiply the two to create an exact representation, since IEEE754 floats mandate the use of guard digits to minimizing rounding error. If either component cannot be exactly represented as the native float, we continue to the next step.
  5. Moderate Path We create an approximate, extended, 80-bit float type (64-bits for the mantissa, 16-bits for the exponent) from both components, and multiplies them together. This minimizes the rounding error, through guard digits. We then estimate the error from the parsing and multiplication steps, and if the float +/- the error differs significantly from b+h, we return the correct representation (b or b+u). If we cannot unambiguously determine the correct floating-point representation, we continue to the next step.
  6. Fallback Moderate Path Next, we create a 128-bit representation of the numerator and denominator for b+h, to disambiguate b from b+u by comparing the actual digits in the input to theoretical digits generated from b+h. This is accurate for ~36 significant digits from a 128-bit approximation with decimal float strings. If the input is less than or equal to 36 digits, we return the value from this step. Otherwise, we continue to the next step.
  7. Slow Path We use arbitrary-precision arithmetic to disambiguate the correct representation without any rounding error. We create an exact representation of the input digits as a big integer, to determine how to round the top 53 bits for the mantissa. If there is a fraction or a negative exponent, we create a representation of the significant digits for b+h and scale the input digits by the binary exponent in b+h, and scale the significant digits in b+h by the decimal exponent, and compare the two to determine if we need to round up or down.

Since arbitrary-precision arithmetic is slow and scales poorly for decimal strings with many digits or exponents of high magnitude, lexical also supports a lossy algorithm, which returns the result from the moderate path. The result from the lossy parser should be accurate to within 1 ULP.

Arbitrary-Precision Arithmetic

Lexical uses arbitrary-precision arithmetic to exactly represent strings between two floating-point representations, and is highly optimized for performance. The following section is a comparison of different algorithms to determine the correct float representation. The arbitrary-precision arithmetic logic is not dependent on memory allocation: it only uses the heap when the radix feature is enabled.

Algorithm Background and Comparison

For close-to-halfway representations of a decimal string s, where s is close between two representations, b and the next float b+u, arbitrary-precision arithmetic is used to determine the correct representation. This means s is close to b+h, where h is the halfway point between b and b+u.

For the following example, we will use the following values for our test case:

  • s = 2.4703282292062327208828439643411068618252990130716238221279284125033775363510437593264991818081799618989828234772285886546332835517796989819938739800539093906315035659515570226392290858392449105184435931802849936536152500319370457678249219365623669863658480757001585769269903706311928279558551332927834338409351978015531246597263579574622766465272827220056374006485499977096599470454020828166226237857393450736339007967761930577506740176324673600968951340535537458516661134223766678604162159680461914467291840300530057530849048765391711386591646239524912623653881879636239373280423891018672348497668235089863388587925628302755995657524455507255189313690836254779186948667994968324049705821028513185451396213837722826145437693412532098591327667236328125001e-324
  • b = 0.0
  • b+h = 2.4703282292062327208828439643411068618252990130716238221279284125033775363510437593264991818081799618989828234772285886546332835517796989819938739800539093906315035659515570226392290858392449105184435931802849936536152500319370457678249219365623669863658480757001585769269903706311928279558551332927834338409351978015531246597263579574622766465272827220056374006485499977096599470454020828166226237857393450736339007967761930577506740176324673600968951340535537458516661134223766678604162159680461914467291840300530057530849048765391711386591646239524912623653881879636239373280423891018672348497668235089863388587925628302755995657524455507255189313690836254779186948667994968324049705821028513185451396213837722826145437693412532098591327667236328125e-324
  • b+u = 5e-324

Algorithm M

Algorithm M represents the significant digits of a float as a fraction of arbitrary-precision integers (a more in-depth description can be found here). For example, 1.23 would be 123/100, while 314.159 would be 314159/1000. We then scale the numerator and denominator by powers of 2 until the quotient is in the range [2^52, 2^53), generating the correct significant digits of the mantissa.

A naive implementation, in Python, is as follows:

def algorithm_m(num, b):
    # Ensure numerator >= 2**52
    bits = int(math.ceil(math.log2(num)))
    if bits <= 53:
        num <<= 53
        b -= 53

    # Track number of steps required (optional).
    steps = 0
    while True:
        steps += 1
        c = num//b
        if c < 2**52:
            b //= 2
        elif c >= 2**53:
            b *= 2

    return (num, b, steps-1)


Bigcomp is the canonical string-to-float parser, which creates an exact representation of b+h as a big integer, and compares the theoretical digits from b+h scaled into the range [1, 10) by a power of 10 to the actual digits in the input string (a more in-depth description can be found here). A maximum of 768 digits need to be compared to determine the correct representation, and the size of the big integers in the ratio does not depend on the number of digits in the input string.

Bigcomp is used as a fallback algorithm for lexical-core when the radix feature is enabled, since the radix-representation of a binary float may never terminate if the radix is not divisible by 2. Since bigcomp uses constant memory, it is used as the default algorithm if more than 2^15 digits are passed and the representation is potentially non-terminating.


Bhcomp is a simple, performant algorithm that compared the significant digits to the theoretical significant digits for b+h. Simply, the significant digits from the string are parsed, creating a ratio. A ratio is generated for b+h, and these two ratios are scaled using the binary and radix exponents.

For example, "2.470328e-324" produces a ratio of 2470328/10^329, while b+h produces a binary ratio of 1/2^1075. We're looking to compare these ratios, so we need to scale them using common factors. Here, we convert this to (2470328*5^329*2^1075)/10^329 and (1*5^329*2^1075)/2^1075, which converts to 2470328*2^746 and 1*5^329.

Our significant digits (real_digits) and b+h (bh_digits) therefore start like:

real_digits = 91438982...
bh_digits   = 91438991...

Since our real digits are below the theoretical halfway point, we know we need to round-down, meaning our literal value is b, or 0.0. This approach allows us to calculate whether we need to round-up or down with a single comparison step, without any native divisions required. This is the default algorithm lexical-core uses.

Other Optimizations

  1. We remove powers of 2 during exponentiation in bhcomp.
  2. We limit the number of parsed digits to the theoretical max number of digits produced by b+h (768 for decimal strings), and merely compare any trailing digits to '0'. This provides an upper-bound on the computation cost.
  3. We use fast exponentiation and multiplication algorithms to scale the significant digits for comparison.
  4. For the fallback bigcomp algorithm, we use a division algorithm optimized for the generation of a single digit from a given radix, by setting the leading bit in the denominator 4 below the most-significant bit (in decimal strings). This requires only 1 native division per digit generated.
  5. The individual "limbs" of the big integers are optimized to the architecture we compile on, for example, u32 on x86 and u64 on x86-64, minimizing the number of native operations required. Currently, 64-bit limbs are used on target architectures aarch64, powerpc64, mips64, and x86_64.

Known Issues

On the ARMVv6 architecture, the stable exponentiation for the fast, incorrect float parser is not fully stable. For example, 1e-300 is correct, while 5e-324 rounds to 0, leading to "5e-324" being incorrectly parsed as 0. This does not affect the default, correct float parser, nor ARMVv7 or ARMVv8 (aarch64) architectures. This bug can compound errors in the incorrect parser (feature-gated by disabling the correct feature`). It is not known if this bug is an artifact of Qemu emulation of ARMv6, or is actually representative the hardware.

Versions of lexical-core prior to 0.4.3 could round parsed floating-point numbers with an error of up to 1 ULP. This occurred for strings with 16 or more digits and a trailing 0 in the fraction, the b+h comparison in the slow-path algorithm incorrectly scales the the theoretical digits due to an over-calculated real exponent. This affects a very small percentage of inputs, however, it is recommended to update immediately.

Version Support

Lexical-core is tested to work from Rustc versions of 1.24-1.35, and should work on newer versions as well. Please report any errors compiling lexical-core for any Rust compiler 1.24.0 or later.


All changes since 2.2.0 are documented in CHANGELOG.


Lexical-core is dual licensed under the Apache 2.0 license as well as the MIT license. See the LICENCE-MIT and the LICENCE-APACHE files for the licenses.

Lexical-core also ports some code from rust (for backwards compatibility), V8, libgo and fpconv, and therefore might be subject to the terms of a 3-clause BSD license or BSD-like license.


Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in lexical by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.