# Primitive Type f32

1.0.0 ·## Expand description

A 32-bit floating point type (specifically, the “binary32” type defined in IEEE 754-2008).

This type can represent a wide range of decimal numbers, like `3.5`

, `27`

,
`-113.75`

, `0.0078125`

, `34359738368`

, `0`

, `-1`

. So unlike integer types
(such as `i32`

), floating point types can represent non-integer numbers,
too.

However, being able to represent this wide range of numbers comes at the
cost of precision: floats can only represent some of the real numbers and
calculation with floats round to a nearby representable number. For example,
`5.0`

and `1.0`

can be exactly represented as `f32`

, but `1.0 / 5.0`

results
in `0.20000000298023223876953125`

since `0.2`

cannot be exactly represented
as `f32`

. Note, however, that printing floats with `println`

and friends will
often discard insignificant digits: `println!("{}", 1.0f32 / 5.0f32)`

will
print `0.2`

.

Additionally, `f32`

can represent some special values:

- −0.0: IEEE 754 floating point numbers have a bit that indicates their sign, so −0.0 is a possible value. For comparison −0.0 = +0.0, but floating point operations can carry the sign bit through arithmetic operations. This means −0.0 × +0.0 produces −0.0 and a negative number rounded to a value smaller than a float can represent also produces −0.0.
- ∞ and
−∞: these result from calculations
like
`1.0 / 0.0`

. - NaN (not a number): this value results from
calculations like
`(-1.0).sqrt()`

. NaN has some potentially unexpected behavior:- It is not equal to any float, including itself! This is the reason
`f32`

doesn’t implement the`Eq`

trait. - It is also neither smaller nor greater than any float, making it
impossible to sort by the default comparison operation, which is the
reason
`f32`

doesn’t implement the`Ord`

trait. - It is also considered
*infectious*as almost all calculations where one of the operands is NaN will also result in NaN. The explanations on this page only explicitly document behavior on NaN operands if this default is deviated from. - Lastly, there are multiple bit patterns that are considered NaN. Rust does not currently guarantee that the bit patterns of NaN are preserved over arithmetic operations, and they are not guaranteed to be portable or even fully deterministic! This means that there may be some surprising results upon inspecting the bit patterns, as the same calculations might produce NaNs with different bit patterns.

- It is not equal to any float, including itself! This is the reason

When a primitive operation (addition, subtraction, multiplication, or division) is performed on this type, the result is rounded according to the roundTiesToEven direction defined in IEEE 754-2008. That means:

- The result is the representable value closest to the true value, if there is a unique closest representable value.
- If the true value is exactly half-way between two representable values, the result is the one with an even least-significant binary digit.
- If the true value’s magnitude is ≥
`f32::MAX`

+ 2^{(f32::MAX_EXP − f32::MANTISSA_DIGITS − 1)}, the result is ∞ or −∞ (preserving the true value’s sign). - If the result of a sum exactly equals zero, the outcome is +0.0 unless
both arguments were negative, then it is -0.0. Subtraction
`a - b`

is regarded as a sum`a + (-b)`

.

For more information on floating point numbers, see Wikipedia.

## Implementations§

source§### impl f32

### impl f32

1.0.0 · source#### pub fn round(self) -> f32

#### pub fn round(self) -> f32

Returns the nearest integer to `self`

. If a value is half-way between two
integers, round away from `0.0`

.

This function always returns the precise result.

##### §Examples

```
let f = 3.3_f32;
let g = -3.3_f32;
let h = -3.7_f32;
let i = 3.5_f32;
let j = 4.5_f32;
assert_eq!(f.round(), 3.0);
assert_eq!(g.round(), -3.0);
assert_eq!(h.round(), -4.0);
assert_eq!(i.round(), 4.0);
assert_eq!(j.round(), 5.0);
```

Run1.77.0 · source#### pub fn round_ties_even(self) -> f32

#### pub fn round_ties_even(self) -> f32

Returns the nearest integer to a number. Rounds half-way cases to the number with an even least significant digit.

This function always returns the precise result.

##### §Examples

```
let f = 3.3_f32;
let g = -3.3_f32;
let h = 3.5_f32;
let i = 4.5_f32;
assert_eq!(f.round_ties_even(), 3.0);
assert_eq!(g.round_ties_even(), -3.0);
assert_eq!(h.round_ties_even(), 4.0);
assert_eq!(i.round_ties_even(), 4.0);
```

Run1.0.0 · source#### pub fn trunc(self) -> f32

#### pub fn trunc(self) -> f32

Returns the integer part of `self`

.
This means that non-integer numbers are always truncated towards zero.

This function always returns the precise result.

##### §Examples

```
let f = 3.7_f32;
let g = 3.0_f32;
let h = -3.7_f32;
assert_eq!(f.trunc(), 3.0);
assert_eq!(g.trunc(), 3.0);
assert_eq!(h.trunc(), -3.0);
```

Run1.0.0 · source#### pub fn fract(self) -> f32

#### pub fn fract(self) -> f32

Returns the fractional part of `self`

.

This function always returns the precise result.

##### §Examples

```
let x = 3.6_f32;
let y = -3.6_f32;
let abs_difference_x = (x.fract() - 0.6).abs();
let abs_difference_y = (y.fract() - (-0.6)).abs();
assert!(abs_difference_x <= f32::EPSILON);
assert!(abs_difference_y <= f32::EPSILON);
```

Run1.0.0 · source#### pub fn signum(self) -> f32

#### pub fn signum(self) -> f32

Returns a number that represents the sign of `self`

.

`1.0`

if the number is positive,`+0.0`

or`INFINITY`

`-1.0`

if the number is negative,`-0.0`

or`NEG_INFINITY`

- NaN if the number is NaN

##### §Examples

```
let f = 3.5_f32;
assert_eq!(f.signum(), 1.0);
assert_eq!(f32::NEG_INFINITY.signum(), -1.0);
assert!(f32::NAN.signum().is_nan());
```

Run1.35.0 · source#### pub fn copysign(self, sign: f32) -> f32

#### pub fn copysign(self, sign: f32) -> f32

Returns a number composed of the magnitude of `self`

and the sign of
`sign`

.

Equal to `self`

if the sign of `self`

and `sign`

are the same, otherwise
equal to `-self`

. If `self`

is a NaN, then a NaN with the sign bit of
`sign`

is returned. Note, however, that conserving the sign bit on NaN
across arithmetical operations is not generally guaranteed.
See explanation of NaN as a special value for more info.

##### §Examples

```
let f = 3.5_f32;
assert_eq!(f.copysign(0.42), 3.5_f32);
assert_eq!(f.copysign(-0.42), -3.5_f32);
assert_eq!((-f).copysign(0.42), 3.5_f32);
assert_eq!((-f).copysign(-0.42), -3.5_f32);
assert!(f32::NAN.copysign(1.0).is_nan());
```

Run1.0.0 · source#### pub fn mul_add(self, a: f32, b: f32) -> f32

#### pub fn mul_add(self, a: f32, b: f32) -> f32

Fused multiply-add. Computes `(self * a) + b`

with only one rounding
error, yielding a more accurate result than an unfused multiply-add.

Using `mul_add`

*may* be more performant than an unfused multiply-add if
the target architecture has a dedicated `fma`

CPU instruction. However,
this is not always true, and will be heavily dependant on designing
algorithms with specific target hardware in mind.

##### §Precision

The result of this operation is guaranteed to be the rounded
infinite-precision result. It is specified by IEEE 754 as
`fusedMultiplyAdd`

and guaranteed not to change.

##### §Examples

```
let m = 10.0_f32;
let x = 4.0_f32;
let b = 60.0_f32;
assert_eq!(m.mul_add(x, b), 100.0);
assert_eq!(m * x + b, 100.0);
let one_plus_eps = 1.0_f32 + f32::EPSILON;
let one_minus_eps = 1.0_f32 - f32::EPSILON;
let minus_one = -1.0_f32;
// The exact result (1 + eps) * (1 - eps) = 1 - eps * eps.
assert_eq!(one_plus_eps.mul_add(one_minus_eps, minus_one), -f32::EPSILON * f32::EPSILON);
// Different rounding with the non-fused multiply and add.
assert_eq!(one_plus_eps * one_minus_eps + minus_one, 0.0);
```

Run1.38.0 · source#### pub fn div_euclid(self, rhs: f32) -> f32

#### pub fn div_euclid(self, rhs: f32) -> f32

Calculates Euclidean division, the matching method for `rem_euclid`

.

This computes the integer `n`

such that
`self = n * rhs + self.rem_euclid(rhs)`

.
In other words, the result is `self / rhs`

rounded to the integer `n`

such that `self >= n * rhs`

.

##### §Precision

The result of this operation is guaranteed to be the rounded infinite-precision result.

##### §Examples

```
let a: f32 = 7.0;
let b = 4.0;
assert_eq!(a.div_euclid(b), 1.0); // 7.0 > 4.0 * 1.0
assert_eq!((-a).div_euclid(b), -2.0); // -7.0 >= 4.0 * -2.0
assert_eq!(a.div_euclid(-b), -1.0); // 7.0 >= -4.0 * -1.0
assert_eq!((-a).div_euclid(-b), 2.0); // -7.0 >= -4.0 * 2.0
```

Run1.38.0 · source#### pub fn rem_euclid(self, rhs: f32) -> f32

#### pub fn rem_euclid(self, rhs: f32) -> f32

Calculates the least nonnegative remainder of `self (mod rhs)`

.

In particular, the return value `r`

satisfies `0.0 <= r < rhs.abs()`

in
most cases. However, due to a floating point round-off error it can
result in `r == rhs.abs()`

, violating the mathematical definition, if
`self`

is much smaller than `rhs.abs()`

in magnitude and `self < 0.0`

.
This result is not an element of the function’s codomain, but it is the
closest floating point number in the real numbers and thus fulfills the
property `self == self.div_euclid(rhs) * rhs + self.rem_euclid(rhs)`

approximately.

##### §Precision

The result of this operation is guaranteed to be the rounded infinite-precision result.

##### §Examples

```
let a: f32 = 7.0;
let b = 4.0;
assert_eq!(a.rem_euclid(b), 3.0);
assert_eq!((-a).rem_euclid(b), 1.0);
assert_eq!(a.rem_euclid(-b), 3.0);
assert_eq!((-a).rem_euclid(-b), 1.0);
// limitation due to round-off error
assert!((-f32::EPSILON).rem_euclid(3.0) != 0.0);
```

Run1.0.0 · source#### pub fn powi(self, n: i32) -> f32

#### pub fn powi(self, n: i32) -> f32

Raises a number to an integer power.

Using this function is generally faster than using `powf`

.
It might have a different sequence of rounding operations than `powf`

,
so the results are not guaranteed to agree.

##### §Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and can even differ within the same execution from one invocation to the next.

##### §Examples

```
let x = 2.0_f32;
let abs_difference = (x.powi(2) - (x * x)).abs();
assert!(abs_difference <= f32::EPSILON);
```

Run1.0.0 · source#### pub fn powf(self, n: f32) -> f32

#### pub fn powf(self, n: f32) -> f32

Raises a number to a floating point power.

##### §Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and can even differ within the same execution from one invocation to the next.

##### §Examples

```
let x = 2.0_f32;
let abs_difference = (x.powf(2.0) - (x * x)).abs();
assert!(abs_difference <= f32::EPSILON);
```

Run1.0.0 · source#### pub fn sqrt(self) -> f32

#### pub fn sqrt(self) -> f32

Returns the square root of a number.

Returns NaN if `self`

is a negative number other than `-0.0`

.

##### §Precision

The result of this operation is guaranteed to be the rounded
infinite-precision result. It is specified by IEEE 754 as `squareRoot`

and guaranteed not to change.

##### §Examples

```
let positive = 4.0_f32;
let negative = -4.0_f32;
let negative_zero = -0.0_f32;
assert_eq!(positive.sqrt(), 2.0);
assert!(negative.sqrt().is_nan());
assert!(negative_zero.sqrt() == negative_zero);
```

Run1.0.0 · source#### pub fn exp(self) -> f32

#### pub fn exp(self) -> f32

Returns `e^(self)`

, (the exponential function).

##### §Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and can even differ within the same execution from one invocation to the next.

##### §Examples

```
let one = 1.0f32;
// e^1
let e = one.exp();
// ln(e) - 1 == 0
let abs_difference = (e.ln() - 1.0).abs();
assert!(abs_difference <= f32::EPSILON);
```

Run1.0.0 · source#### pub fn exp2(self) -> f32

#### pub fn exp2(self) -> f32

Returns `2^(self)`

.

##### §Unspecified precision

##### §Examples

```
let f = 2.0f32;
// 2^2 - 4 == 0
let abs_difference = (f.exp2() - 4.0).abs();
assert!(abs_difference <= f32::EPSILON);
```

Run1.0.0 · source#### pub fn ln(self) -> f32

#### pub fn ln(self) -> f32

Returns the natural logarithm of the number.

##### §Unspecified precision

##### §Examples

```
let one = 1.0f32;
// e^1
let e = one.exp();
// ln(e) - 1 == 0
let abs_difference = (e.ln() - 1.0).abs();
assert!(abs_difference <= f32::EPSILON);
```

Run1.0.0 · source#### pub fn log(self, base: f32) -> f32

#### pub fn log(self, base: f32) -> f32

Returns the logarithm of the number with respect to an arbitrary base.

The result might not be correctly rounded owing to implementation details;
`self.log2()`

can produce more accurate results for base 2, and
`self.log10()`

can produce more accurate results for base 10.

##### §Unspecified precision

##### §Examples

```
let five = 5.0f32;
// log5(5) - 1 == 0
let abs_difference = (five.log(5.0) - 1.0).abs();
assert!(abs_difference <= f32::EPSILON);
```

Run1.0.0 · source#### pub fn log2(self) -> f32

#### pub fn log2(self) -> f32

Returns the base 2 logarithm of the number.

##### §Unspecified precision

##### §Examples

```
let two = 2.0f32;
// log2(2) - 1 == 0
let abs_difference = (two.log2() - 1.0).abs();
assert!(abs_difference <= f32::EPSILON);
```

Run1.0.0 · source#### pub fn log10(self) -> f32

#### pub fn log10(self) -> f32

Returns the base 10 logarithm of the number.

##### §Unspecified precision

##### §Examples

```
let ten = 10.0f32;
// log10(10) - 1 == 0
let abs_difference = (ten.log10() - 1.0).abs();
assert!(abs_difference <= f32::EPSILON);
```

Run1.0.0 · source#### pub fn abs_sub(self, other: f32) -> f32

👎Deprecated since 1.10.0: you probably meant `(self - other).abs()`

: this operation is `(self - other).max(0.0)`

except that `abs_sub`

also propagates NaNs (also known as `fdimf`

in C). If you truly need the positive difference, consider using that expression or the C function `fdimf`

, depending on how you wish to handle NaN (please consider filing an issue describing your use-case too).

#### pub fn abs_sub(self, other: f32) -> f32

`(self - other).abs()`

: this operation is `(self - other).max(0.0)`

except that `abs_sub`

also propagates NaNs (also known as `fdimf`

in C). If you truly need the positive difference, consider using that expression or the C function `fdimf`

, depending on how you wish to handle NaN (please consider filing an issue describing your use-case too).The positive difference of two numbers.

- If
`self <= other`

:`0.0`

- Else:
`self - other`

##### §Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and
can even differ within the same execution from one invocation to the next.
This function currently corresponds to the `fdimf`

from libc on Unix
and Windows. Note that this might change in the future.

##### §Examples

```
let x = 3.0f32;
let y = -3.0f32;
let abs_difference_x = (x.abs_sub(1.0) - 2.0).abs();
let abs_difference_y = (y.abs_sub(1.0) - 0.0).abs();
assert!(abs_difference_x <= f32::EPSILON);
assert!(abs_difference_y <= f32::EPSILON);
```

Run1.0.0 · source#### pub fn cbrt(self) -> f32

#### pub fn cbrt(self) -> f32

Returns the cube root of a number.

##### §Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and
can even differ within the same execution from one invocation to the next.
This function currently corresponds to the `cbrtf`

from libc on Unix
and Windows. Note that this might change in the future.

##### §Examples

```
let x = 8.0f32;
// x^(1/3) - 2 == 0
let abs_difference = (x.cbrt() - 2.0).abs();
assert!(abs_difference <= f32::EPSILON);
```

Run1.0.0 · source#### pub fn hypot(self, other: f32) -> f32

#### pub fn hypot(self, other: f32) -> f32

Compute the distance between the origin and a point (`x`

, `y`

) on the
Euclidean plane. Equivalently, compute the length of the hypotenuse of a
right-angle triangle with other sides having length `x.abs()`

and
`y.abs()`

.

##### §Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and
can even differ within the same execution from one invocation to the next.
This function currently corresponds to the `hypotf`

from libc on Unix
and Windows. Note that this might change in the future.

##### §Examples

```
let x = 2.0f32;
let y = 3.0f32;
// sqrt(x^2 + y^2)
let abs_difference = (x.hypot(y) - (x.powi(2) + y.powi(2)).sqrt()).abs();
assert!(abs_difference <= f32::EPSILON);
```

Run1.0.0 · source#### pub fn sin(self) -> f32

#### pub fn sin(self) -> f32

Computes the sine of a number (in radians).

##### §Unspecified precision

##### §Examples

```
let x = std::f32::consts::FRAC_PI_2;
let abs_difference = (x.sin() - 1.0).abs();
assert!(abs_difference <= f32::EPSILON);
```

Run1.0.0 · source#### pub fn cos(self) -> f32

#### pub fn cos(self) -> f32

Computes the cosine of a number (in radians).

##### §Unspecified precision

##### §Examples

```
let x = 2.0 * std::f32::consts::PI;
let abs_difference = (x.cos() - 1.0).abs();
assert!(abs_difference <= f32::EPSILON);
```

Run1.0.0 · source#### pub fn tan(self) -> f32

#### pub fn tan(self) -> f32

Computes the tangent of a number (in radians).

##### §Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and
can even differ within the same execution from one invocation to the next.
This function currently corresponds to the `tanf`

from libc on Unix and
Windows. Note that this might change in the future.

##### §Examples

```
let x = std::f32::consts::FRAC_PI_4;
let abs_difference = (x.tan() - 1.0).abs();
assert!(abs_difference <= f32::EPSILON);
```

Run1.0.0 · source#### pub fn asin(self) -> f32

#### pub fn asin(self) -> f32

Computes the arcsine of a number. Return value is in radians in the range [-pi/2, pi/2] or NaN if the number is outside the range [-1, 1].

##### §Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and
can even differ within the same execution from one invocation to the next.
This function currently corresponds to the `asinf`

from libc on Unix
and Windows. Note that this might change in the future.

##### §Examples

```
let f = std::f32::consts::FRAC_PI_2;
// asin(sin(pi/2))
let abs_difference = (f.sin().asin() - std::f32::consts::FRAC_PI_2).abs();
assert!(abs_difference <= f32::EPSILON);
```

Run1.0.0 · source#### pub fn acos(self) -> f32

#### pub fn acos(self) -> f32

Computes the arccosine of a number. Return value is in radians in the range [0, pi] or NaN if the number is outside the range [-1, 1].

##### §Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and
can even differ within the same execution from one invocation to the next.
This function currently corresponds to the `acosf`

from libc on Unix
and Windows. Note that this might change in the future.

##### §Examples

```
let f = std::f32::consts::FRAC_PI_4;
// acos(cos(pi/4))
let abs_difference = (f.cos().acos() - std::f32::consts::FRAC_PI_4).abs();
assert!(abs_difference <= f32::EPSILON);
```

Run1.0.0 · source#### pub fn atan(self) -> f32

#### pub fn atan(self) -> f32

Computes the arctangent of a number. Return value is in radians in the range [-pi/2, pi/2];

##### §Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and
can even differ within the same execution from one invocation to the next.
This function currently corresponds to the `atanf`

from libc on Unix
and Windows. Note that this might change in the future.

##### §Examples

```
let f = 1.0f32;
// atan(tan(1))
let abs_difference = (f.tan().atan() - 1.0).abs();
assert!(abs_difference <= f32::EPSILON);
```

Run1.0.0 · source#### pub fn atan2(self, other: f32) -> f32

#### pub fn atan2(self, other: f32) -> f32

Computes the four quadrant arctangent of `self`

(`y`

) and `other`

(`x`

) in radians.

`x = 0`

,`y = 0`

:`0`

`x >= 0`

:`arctan(y/x)`

->`[-pi/2, pi/2]`

`y >= 0`

:`arctan(y/x) + pi`

->`(pi/2, pi]`

`y < 0`

:`arctan(y/x) - pi`

->`(-pi, -pi/2)`

##### §Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and
can even differ within the same execution from one invocation to the next.
This function currently corresponds to the `atan2f`

from libc on Unix
and Windows. Note that this might change in the future.

##### §Examples

```
// Positive angles measured counter-clockwise
// from positive x axis
// -pi/4 radians (45 deg clockwise)
let x1 = 3.0f32;
let y1 = -3.0f32;
// 3pi/4 radians (135 deg counter-clockwise)
let x2 = -3.0f32;
let y2 = 3.0f32;
let abs_difference_1 = (y1.atan2(x1) - (-std::f32::consts::FRAC_PI_4)).abs();
let abs_difference_2 = (y2.atan2(x2) - (3.0 * std::f32::consts::FRAC_PI_4)).abs();
assert!(abs_difference_1 <= f32::EPSILON);
assert!(abs_difference_2 <= f32::EPSILON);
```

Run1.0.0 · source#### pub fn sin_cos(self) -> (f32, f32)

#### pub fn sin_cos(self) -> (f32, f32)

Simultaneously computes the sine and cosine of the number, `x`

. Returns
`(sin(x), cos(x))`

.

##### §Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and
can even differ within the same execution from one invocation to the next.
This function currently corresponds to the `(f32::sin(x), f32::cos(x))`

. Note that this might change in the future.

##### §Examples

```
let x = std::f32::consts::FRAC_PI_4;
let f = x.sin_cos();
let abs_difference_0 = (f.0 - x.sin()).abs();
let abs_difference_1 = (f.1 - x.cos()).abs();
assert!(abs_difference_0 <= f32::EPSILON);
assert!(abs_difference_1 <= f32::EPSILON);
```

Run1.0.0 · source#### pub fn exp_m1(self) -> f32

#### pub fn exp_m1(self) -> f32

Returns `e^(self) - 1`

in a way that is accurate even if the
number is close to zero.

##### §Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and
can even differ within the same execution from one invocation to the next.
This function currently corresponds to the `expm1f`

from libc on Unix
and Windows. Note that this might change in the future.

##### §Examples

```
let x = 1e-8_f32;
// for very small x, e^x is approximately 1 + x + x^2 / 2
let approx = x + x * x / 2.0;
let abs_difference = (x.exp_m1() - approx).abs();
assert!(abs_difference < 1e-10);
```

Run1.0.0 · source#### pub fn ln_1p(self) -> f32

#### pub fn ln_1p(self) -> f32

Returns `ln(1+n)`

(natural logarithm) more accurately than if
the operations were performed separately.

##### §Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and
can even differ within the same execution from one invocation to the next.
This function currently corresponds to the `log1pf`

from libc on Unix
and Windows. Note that this might change in the future.

##### §Examples

```
let x = 1e-8_f32;
// for very small x, ln(1 + x) is approximately x - x^2 / 2
let approx = x - x * x / 2.0;
let abs_difference = (x.ln_1p() - approx).abs();
assert!(abs_difference < 1e-10);
```

Run1.0.0 · source#### pub fn sinh(self) -> f32

#### pub fn sinh(self) -> f32

Hyperbolic sine function.

##### §Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and
can even differ within the same execution from one invocation to the next.
This function currently corresponds to the `sinhf`

from libc on Unix
and Windows. Note that this might change in the future.

##### §Examples

```
let e = std::f32::consts::E;
let x = 1.0f32;
let f = x.sinh();
// Solving sinh() at 1 gives `(e^2-1)/(2e)`
let g = ((e * e) - 1.0) / (2.0 * e);
let abs_difference = (f - g).abs();
assert!(abs_difference <= f32::EPSILON);
```

Run1.0.0 · source#### pub fn cosh(self) -> f32

#### pub fn cosh(self) -> f32

Hyperbolic cosine function.

##### §Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and
can even differ within the same execution from one invocation to the next.
This function currently corresponds to the `coshf`

from libc on Unix
and Windows. Note that this might change in the future.

##### §Examples

```
let e = std::f32::consts::E;
let x = 1.0f32;
let f = x.cosh();
// Solving cosh() at 1 gives this result
let g = ((e * e) + 1.0) / (2.0 * e);
let abs_difference = (f - g).abs();
// Same result
assert!(abs_difference <= f32::EPSILON);
```

Run1.0.0 · source#### pub fn tanh(self) -> f32

#### pub fn tanh(self) -> f32

Hyperbolic tangent function.

##### §Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and
can even differ within the same execution from one invocation to the next.
This function currently corresponds to the `tanhf`

from libc on Unix
and Windows. Note that this might change in the future.

##### §Examples

```
let e = std::f32::consts::E;
let x = 1.0f32;
let f = x.tanh();
// Solving tanh() at 1 gives `(1 - e^(-2))/(1 + e^(-2))`
let g = (1.0 - e.powi(-2)) / (1.0 + e.powi(-2));
let abs_difference = (f - g).abs();
assert!(abs_difference <= f32::EPSILON);
```

Run1.0.0 · source#### pub fn asinh(self) -> f32

#### pub fn asinh(self) -> f32

Inverse hyperbolic sine function.

##### §Unspecified precision

##### §Examples

```
let x = 1.0f32;
let f = x.sinh().asinh();
let abs_difference = (f - x).abs();
assert!(abs_difference <= f32::EPSILON);
```

Run1.0.0 · source#### pub fn acosh(self) -> f32

#### pub fn acosh(self) -> f32

Inverse hyperbolic cosine function.

##### §Unspecified precision

##### §Examples

```
let x = 1.0f32;
let f = x.cosh().acosh();
let abs_difference = (f - x).abs();
assert!(abs_difference <= f32::EPSILON);
```

Run1.0.0 · source#### pub fn atanh(self) -> f32

#### pub fn atanh(self) -> f32

Inverse hyperbolic tangent function.

##### §Unspecified precision

##### §Examples

```
let e = std::f32::consts::E;
let f = e.tanh().atanh();
let abs_difference = (f - e).abs();
assert!(abs_difference <= 1e-5);
```

Runsource#### pub fn gamma(self) -> f32

🔬This is a nightly-only experimental API. (`float_gamma`

#99842)

#### pub fn gamma(self) -> f32

`float_gamma`

#99842)Gamma function.

##### §Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and
can even differ within the same execution from one invocation to the next.
This function currently corresponds to the `tgammaf`

from libc on Unix
and Windows. Note that this might change in the future.

##### §Examples

```
#![feature(float_gamma)]
let x = 5.0f32;
let abs_difference = (x.gamma() - 24.0).abs();
assert!(abs_difference <= f32::EPSILON);
```

Runsource#### pub fn ln_gamma(self) -> (f32, i32)

🔬This is a nightly-only experimental API. (`float_gamma`

#99842)

#### pub fn ln_gamma(self) -> (f32, i32)

`float_gamma`

#99842)Natural logarithm of the absolute value of the gamma function

The integer part of the tuple indicates the sign of the gamma function.

##### §Unspecified precision

The precision of this function is non-deterministic. This means it varies by platform, Rust version, and
can even differ within the same execution from one invocation to the next.
This function currently corresponds to the `lgamma_r`

from libc on Unix
and Windows. Note that this might change in the future.

##### §Examples

```
#![feature(float_gamma)]
let x = 2.0f32;
let abs_difference = (x.ln_gamma().0 - 0.0).abs();
assert!(abs_difference <= f32::EPSILON);
```

Runsource§### impl f32

### impl f32

1.43.0 · source#### pub const MANTISSA_DIGITS: u32 = 24u32

#### pub const MANTISSA_DIGITS: u32 = 24u32

Number of significant digits in base 2.

1.43.0 · source#### pub const DIGITS: u32 = 6u32

#### pub const DIGITS: u32 = 6u32

Approximate number of significant digits in base 10.

This is the maximum *x* such that any decimal number with *x*
significant digits can be converted to `f32`

and back without loss.

Equal to floor(log_{10} 2^{MANTISSA_DIGITS − 1}).

1.43.0 · source#### pub const EPSILON: f32 = 1.1920929E-7f32

#### pub const EPSILON: f32 = 1.1920929E-7f32

Machine epsilon value for `f32`

.

This is the difference between `1.0`

and the next larger representable number.

Equal to 2^{1 − MANTISSA_DIGITS}.

1.43.0 · source#### pub const MIN_POSITIVE: f32 = 1.17549435E-38f32

#### pub const MIN_POSITIVE: f32 = 1.17549435E-38f32

Smallest positive normal `f32`

value.

Equal to 2^{MIN_EXP − 1}.

1.43.0 · source#### pub const MAX: f32 = 3.40282347E+38f32

#### pub const MAX: f32 = 3.40282347E+38f32

Largest finite `f32`

value.

Equal to
(1 − 2^{−MANTISSA_DIGITS}) 2^{MAX_EXP}.

1.43.0 · source#### pub const MIN_EXP: i32 = -125i32

#### pub const MIN_EXP: i32 = -125i32

One greater than the minimum possible normal power of 2 exponent.

If *x* = `MIN_EXP`

, then normal numbers
≥ 0.5 × 2^{x}.

1.43.0 · source#### pub const MAX_EXP: i32 = 128i32

#### pub const MAX_EXP: i32 = 128i32

Maximum possible power of 2 exponent.

If *x* = `MAX_EXP`

, then normal numbers
< 1 × 2^{x}.

1.43.0 · source#### pub const MIN_10_EXP: i32 = -37i32

#### pub const MIN_10_EXP: i32 = -37i32

Minimum *x* for which 10^{x} is normal.

Equal to ceil(log_{10} `MIN_POSITIVE`

).

1.43.0 · source#### pub const MAX_10_EXP: i32 = 38i32

#### pub const MAX_10_EXP: i32 = 38i32

Maximum *x* for which 10^{x} is normal.

Equal to floor(log_{10} `MAX`

).

1.43.0 · source#### pub const NAN: f32 = NaN_f32

#### pub const NAN: f32 = NaN_f32

Not a Number (NaN).

Note that IEEE 754 doesn’t define just a single NaN value; a plethora of bit patterns are considered to be NaN. Furthermore, the standard makes a difference between a “signaling” and a “quiet” NaN, and allows inspecting its “payload” (the unspecified bits in the bit pattern). This constant isn’t guaranteed to equal to any specific NaN bitpattern, and the stability of its representation over Rust versions and target platforms isn’t guaranteed.

1.43.0 · source#### pub const NEG_INFINITY: f32 = -Inf_f32

#### pub const NEG_INFINITY: f32 = -Inf_f32

Negative infinity (−∞).

1.0.0 (const: unstable) · source#### pub fn is_nan(self) -> bool

#### pub fn is_nan(self) -> bool

Returns `true`

if this value is NaN.

```
let nan = f32::NAN;
let f = 7.0_f32;
assert!(nan.is_nan());
assert!(!f.is_nan());
```

Run1.0.0 (const: unstable) · source#### pub fn is_infinite(self) -> bool

#### pub fn is_infinite(self) -> bool

Returns `true`

if this value is positive infinity or negative infinity, and
`false`

otherwise.

```
let f = 7.0f32;
let inf = f32::INFINITY;
let neg_inf = f32::NEG_INFINITY;
let nan = f32::NAN;
assert!(!f.is_infinite());
assert!(!nan.is_infinite());
assert!(inf.is_infinite());
assert!(neg_inf.is_infinite());
```

Run1.0.0 (const: unstable) · source#### pub fn is_finite(self) -> bool

#### pub fn is_finite(self) -> bool

Returns `true`

if this number is neither infinite nor NaN.

```
let f = 7.0f32;
let inf = f32::INFINITY;
let neg_inf = f32::NEG_INFINITY;
let nan = f32::NAN;
assert!(f.is_finite());
assert!(!nan.is_finite());
assert!(!inf.is_finite());
assert!(!neg_inf.is_finite());
```

Run1.53.0 (const: unstable) · source#### pub fn is_subnormal(self) -> bool

#### pub fn is_subnormal(self) -> bool

Returns `true`

if the number is subnormal.

```
let min = f32::MIN_POSITIVE; // 1.17549435e-38f32
let max = f32::MAX;
let lower_than_min = 1.0e-40_f32;
let zero = 0.0_f32;
assert!(!min.is_subnormal());
assert!(!max.is_subnormal());
assert!(!zero.is_subnormal());
assert!(!f32::NAN.is_subnormal());
assert!(!f32::INFINITY.is_subnormal());
// Values between `0` and `min` are Subnormal.
assert!(lower_than_min.is_subnormal());
```

Run1.0.0 (const: unstable) · source#### pub fn is_normal(self) -> bool

#### pub fn is_normal(self) -> bool

Returns `true`

if the number is neither zero, infinite,
subnormal, or NaN.

```
let min = f32::MIN_POSITIVE; // 1.17549435e-38f32
let max = f32::MAX;
let lower_than_min = 1.0e-40_f32;
let zero = 0.0_f32;
assert!(min.is_normal());
assert!(max.is_normal());
assert!(!zero.is_normal());
assert!(!f32::NAN.is_normal());
assert!(!f32::INFINITY.is_normal());
// Values between `0` and `min` are Subnormal.
assert!(!lower_than_min.is_normal());
```

Run1.0.0 (const: unstable) · source#### pub fn classify(self) -> FpCategory

#### pub fn classify(self) -> FpCategory

Returns the floating point category of the number. If only one property is going to be tested, it is generally faster to use the specific predicate instead.

```
use std::num::FpCategory;
let num = 12.4_f32;
let inf = f32::INFINITY;
assert_eq!(num.classify(), FpCategory::Normal);
assert_eq!(inf.classify(), FpCategory::Infinite);
```

Run1.0.0 (const: unstable) · source#### pub fn is_sign_positive(self) -> bool

#### pub fn is_sign_positive(self) -> bool

Returns `true`

if `self`

has a positive sign, including `+0.0`

, NaNs with
positive sign bit and positive infinity. Note that IEEE 754 doesn’t assign any
meaning to the sign bit in case of a NaN, and as Rust doesn’t guarantee that
the bit pattern of NaNs are conserved over arithmetic operations, the result of
`is_sign_positive`

on a NaN might produce an unexpected result in some cases.
See explanation of NaN as a special value for more info.

```
let f = 7.0_f32;
let g = -7.0_f32;
assert!(f.is_sign_positive());
assert!(!g.is_sign_positive());
```

Run1.0.0 (const: unstable) · source#### pub fn is_sign_negative(self) -> bool

#### pub fn is_sign_negative(self) -> bool

Returns `true`

if `self`

has a negative sign, including `-0.0`

, NaNs with
negative sign bit and negative infinity. Note that IEEE 754 doesn’t assign any
meaning to the sign bit in case of a NaN, and as Rust doesn’t guarantee that
the bit pattern of NaNs are conserved over arithmetic operations, the result of
`is_sign_negative`

on a NaN might produce an unexpected result in some cases.
See explanation of NaN as a special value for more info.

```
let f = 7.0f32;
let g = -7.0f32;
assert!(!f.is_sign_negative());
assert!(g.is_sign_negative());
```

Runsource#### pub const fn next_up(self) -> f32

🔬This is a nightly-only experimental API. (`float_next_up_down`

#91399)

#### pub const fn next_up(self) -> f32

`float_next_up_down`

#91399)Returns the least number greater than `self`

.

Let `TINY`

be the smallest representable positive `f32`

. Then,

- if
`self.is_nan()`

, this returns`self`

; - if
`self`

is`NEG_INFINITY`

, this returns`MIN`

; - if
`self`

is`-TINY`

, this returns -0.0; - if
`self`

is -0.0 or +0.0, this returns`TINY`

; - if
`self`

is`MAX`

or`INFINITY`

, this returns`INFINITY`

; - otherwise the unique least value greater than
`self`

is returned.

The identity `x.next_up() == -(-x).next_down()`

holds for all non-NaN `x`

. When `x`

is finite `x == x.next_up().next_down()`

also holds.

```
#![feature(float_next_up_down)]
// f32::EPSILON is the difference between 1.0 and the next number up.
assert_eq!(1.0f32.next_up(), 1.0 + f32::EPSILON);
// But not for most numbers.
assert!(0.1f32.next_up() < 0.1 + f32::EPSILON);
assert_eq!(16777216f32.next_up(), 16777218.0);
```

Runsource#### pub const fn next_down(self) -> f32

🔬This is a nightly-only experimental API. (`float_next_up_down`

#91399)

#### pub const fn next_down(self) -> f32

`float_next_up_down`

#91399)Returns the greatest number less than `self`

.

Let `TINY`

be the smallest representable positive `f32`

. Then,

- if
`self.is_nan()`

, this returns`self`

; - if
`self`

is`INFINITY`

, this returns`MAX`

; - if
`self`

is`TINY`

, this returns 0.0; - if
`self`

is -0.0 or +0.0, this returns`-TINY`

; - if
`self`

is`MIN`

or`NEG_INFINITY`

, this returns`NEG_INFINITY`

; - otherwise the unique greatest value less than
`self`

is returned.

The identity `x.next_down() == -(-x).next_up()`

holds for all non-NaN `x`

. When `x`

is finite `x == x.next_down().next_up()`

also holds.

```
#![feature(float_next_up_down)]
let x = 1.0f32;
// Clamp value into range [0, 1).
let clamped = x.clamp(0.0, 1.0f32.next_down());
assert!(clamped < 1.0);
assert_eq!(clamped.next_up(), 1.0);
```

Run1.0.0 · source#### pub fn recip(self) -> f32

#### pub fn recip(self) -> f32

Takes the reciprocal (inverse) of a number, `1/x`

.

```
let x = 2.0_f32;
let abs_difference = (x.recip() - (1.0 / x)).abs();
assert!(abs_difference <= f32::EPSILON);
```

Run1.7.0 · source#### pub fn to_degrees(self) -> f32

#### pub fn to_degrees(self) -> f32

Converts radians to degrees.

```
let angle = std::f32::consts::PI;
let abs_difference = (angle.to_degrees() - 180.0).abs();
assert!(abs_difference <= f32::EPSILON);
```

Run1.7.0 · source#### pub fn to_radians(self) -> f32

#### pub fn to_radians(self) -> f32

Converts degrees to radians.

```
let angle = 180.0f32;
let abs_difference = (angle.to_radians() - std::f32::consts::PI).abs();
assert!(abs_difference <= f32::EPSILON);
```

Run1.0.0 · source#### pub fn max(self, other: f32) -> f32

#### pub fn max(self, other: f32) -> f32

Returns the maximum of the two numbers, ignoring NaN.

If one of the arguments is NaN, then the other argument is returned. This follows the IEEE 754-2008 semantics for maxNum, except for handling of signaling NaNs; this function handles all NaNs the same way and avoids maxNum’s problems with associativity. This also matches the behavior of libm’s fmax.

```
let x = 1.0f32;
let y = 2.0f32;
assert_eq!(x.max(y), y);
```

Run1.0.0 · source#### pub fn min(self, other: f32) -> f32

#### pub fn min(self, other: f32) -> f32

Returns the minimum of the two numbers, ignoring NaN.

If one of the arguments is NaN, then the other argument is returned. This follows the IEEE 754-2008 semantics for minNum, except for handling of signaling NaNs; this function handles all NaNs the same way and avoids minNum’s problems with associativity. This also matches the behavior of libm’s fmin.

```
let x = 1.0f32;
let y = 2.0f32;
assert_eq!(x.min(y), x);
```

Runsource#### pub fn maximum(self, other: f32) -> f32

🔬This is a nightly-only experimental API. (`float_minimum_maximum`

#91079)

#### pub fn maximum(self, other: f32) -> f32

`float_minimum_maximum`

#91079)Returns the maximum of the two numbers, propagating NaN.

This returns NaN when *either* argument is NaN, as opposed to
`f32::max`

which only returns NaN when *both* arguments are NaN.

```
#![feature(float_minimum_maximum)]
let x = 1.0f32;
let y = 2.0f32;
assert_eq!(x.maximum(y), y);
assert!(x.maximum(f32::NAN).is_nan());
```

RunIf one of the arguments is NaN, then NaN is returned. Otherwise this returns the greater of the two numbers. For this operation, -0.0 is considered to be less than +0.0. Note that this follows the semantics specified in IEEE 754-2019.

Also note that “propagation” of NaNs here doesn’t necessarily mean that the bitpattern of a NaN operand is conserved; see explanation of NaN as a special value for more info.

source#### pub fn minimum(self, other: f32) -> f32

🔬This is a nightly-only experimental API. (`float_minimum_maximum`

#91079)

#### pub fn minimum(self, other: f32) -> f32

`float_minimum_maximum`

#91079)Returns the minimum of the two numbers, propagating NaN.

This returns NaN when *either* argument is NaN, as opposed to
`f32::min`

which only returns NaN when *both* arguments are NaN.

```
#![feature(float_minimum_maximum)]
let x = 1.0f32;
let y = 2.0f32;
assert_eq!(x.minimum(y), x);
assert!(x.minimum(f32::NAN).is_nan());
```

RunIf one of the arguments is NaN, then NaN is returned. Otherwise this returns the lesser of the two numbers. For this operation, -0.0 is considered to be less than +0.0. Note that this follows the semantics specified in IEEE 754-2019.

Also note that “propagation” of NaNs here doesn’t necessarily mean that the bitpattern of a NaN operand is conserved; see explanation of NaN as a special value for more info.

source#### pub fn midpoint(self, other: f32) -> f32

🔬This is a nightly-only experimental API. (`num_midpoint`

#110840)

#### pub fn midpoint(self, other: f32) -> f32

`num_midpoint`

#110840)1.44.0 · source#### pub unsafe fn to_int_unchecked<Int>(self) -> Intwhere
f32: FloatToInt<Int>,

#### pub unsafe fn to_int_unchecked<Int>(self) -> Intwhere
f32: FloatToInt<Int>,

Rounds toward zero and converts to any primitive integer type, assuming that the value is finite and fits in that type.

```
let value = 4.6_f32;
let rounded = unsafe { value.to_int_unchecked::<u16>() };
assert_eq!(rounded, 4);
let value = -128.9_f32;
let rounded = unsafe { value.to_int_unchecked::<i8>() };
assert_eq!(rounded, i8::MIN);
```

Run##### §Safety

The value must:

- Not be
`NaN`

- Not be infinite
- Be representable in the return type
`Int`

, after truncating off its fractional part

1.20.0 (const: unstable) · source#### pub fn to_bits(self) -> u32

#### pub fn to_bits(self) -> u32

Raw transmutation to `u32`

.

This is currently identical to `transmute::<f32, u32>(self)`

on all platforms.

See `from_bits`

for some discussion of the
portability of this operation (there are almost no issues).

Note that this function is distinct from `as`

casting, which attempts to
preserve the *numeric* value, and not the bitwise value.

##### §Examples

```
assert_ne!((1f32).to_bits(), 1f32 as u32); // to_bits() is not casting!
assert_eq!((12.5f32).to_bits(), 0x41480000);
```

Run1.20.0 (const: unstable) · source#### pub fn from_bits(v: u32) -> f32

#### pub fn from_bits(v: u32) -> f32

Raw transmutation from `u32`

.

This is currently identical to `transmute::<u32, f32>(v)`

on all platforms.
It turns out this is incredibly portable, for two reasons:

- Floats and Ints have the same endianness on all supported platforms.
- IEEE 754 very precisely specifies the bit layout of floats.

However there is one caveat: prior to the 2008 version of IEEE 754, how to interpret the NaN signaling bit wasn’t actually specified. Most platforms (notably x86 and ARM) picked the interpretation that was ultimately standardized in 2008, but some didn’t (notably MIPS). As a result, all signaling NaNs on MIPS are quiet NaNs on x86, and vice-versa.

Rather than trying to preserve signaling-ness cross-platform, this implementation favors preserving the exact bits. This means that any payloads encoded in NaNs will be preserved even if the result of this method is sent over the network from an x86 machine to a MIPS one.

If the results of this method are only manipulated by the same architecture that produced them, then there is no portability concern.

If the input isn’t NaN, then there is no portability concern.

If you don’t care about signalingness (very likely), then there is no portability concern.

Note that this function is distinct from `as`

casting, which attempts to
preserve the *numeric* value, and not the bitwise value.

##### §Examples

```
let v = f32::from_bits(0x41480000);
assert_eq!(v, 12.5);
```

Run1.40.0 (const: unstable) · source#### pub fn to_be_bytes(self) -> [u8; 4]

#### pub fn to_be_bytes(self) -> [u8; 4]

Return the memory representation of this floating point number as a byte array in big-endian (network) byte order.

See `from_bits`

for some discussion of the
portability of this operation (there are almost no issues).

##### §Examples

```
let bytes = 12.5f32.to_be_bytes();
assert_eq!(bytes, [0x41, 0x48, 0x00, 0x00]);
```

Run1.40.0 (const: unstable) · source#### pub fn to_le_bytes(self) -> [u8; 4]

#### pub fn to_le_bytes(self) -> [u8; 4]

Return the memory representation of this floating point number as a byte array in little-endian byte order.

See `from_bits`

for some discussion of the
portability of this operation (there are almost no issues).

##### §Examples

```
let bytes = 12.5f32.to_le_bytes();
assert_eq!(bytes, [0x00, 0x00, 0x48, 0x41]);
```

Run1.40.0 (const: unstable) · source#### pub fn to_ne_bytes(self) -> [u8; 4]

#### pub fn to_ne_bytes(self) -> [u8; 4]

Return the memory representation of this floating point number as a byte array in native byte order.

As the target platform’s native endianness is used, portable code
should use `to_be_bytes`

or `to_le_bytes`

, as appropriate, instead.

See `from_bits`

for some discussion of the
portability of this operation (there are almost no issues).

##### §Examples

```
let bytes = 12.5f32.to_ne_bytes();
assert_eq!(
bytes,
if cfg!(target_endian = "big") {
[0x41, 0x48, 0x00, 0x00]
} else {
[0x00, 0x00, 0x48, 0x41]
}
);
```

Run1.40.0 (const: unstable) · source#### pub fn from_ne_bytes(bytes: [u8; 4]) -> f32

#### pub fn from_ne_bytes(bytes: [u8; 4]) -> f32

Create a floating point value from its representation as a byte array in native endian.

As the target platform’s native endianness is used, portable code
likely wants to use `from_be_bytes`

or `from_le_bytes`

, as
appropriate instead.

See `from_bits`

for some discussion of the
portability of this operation (there are almost no issues).

##### §Examples

```
let value = f32::from_ne_bytes(if cfg!(target_endian = "big") {
[0x41, 0x48, 0x00, 0x00]
} else {
[0x00, 0x00, 0x48, 0x41]
});
assert_eq!(value, 12.5);
```

Run1.62.0 · source#### pub fn total_cmp(&self, other: &f32) -> Ordering

#### pub fn total_cmp(&self, other: &f32) -> Ordering

Return the ordering between `self`

and `other`

.

Unlike the standard partial comparison between floating point numbers,
this comparison always produces an ordering in accordance to
the `totalOrder`

predicate as defined in the IEEE 754 (2008 revision)
floating point standard. The values are ordered in the following sequence:

- negative quiet NaN
- negative signaling NaN
- negative infinity
- negative numbers
- negative subnormal numbers
- negative zero
- positive zero
- positive subnormal numbers
- positive numbers
- positive infinity
- positive signaling NaN
- positive quiet NaN.

The ordering established by this function does not always agree with the
`PartialOrd`

and `PartialEq`

implementations of `f32`

. For example,
they consider negative and positive zero equal, while `total_cmp`

doesn’t.

The interpretation of the signaling NaN bit follows the definition in the IEEE 754 standard, which may not match the interpretation by some of the older, non-conformant (e.g. MIPS) hardware implementations.

##### §Example

```
struct GoodBoy {
name: String,
weight: f32,
}
let mut bois = vec![
GoodBoy { name: "Pucci".to_owned(), weight: 0.1 },
GoodBoy { name: "Woofer".to_owned(), weight: 99.0 },
GoodBoy { name: "Yapper".to_owned(), weight: 10.0 },
GoodBoy { name: "Chonk".to_owned(), weight: f32::INFINITY },
GoodBoy { name: "Abs. Unit".to_owned(), weight: f32::NAN },
GoodBoy { name: "Floaty".to_owned(), weight: -5.0 },
];
bois.sort_by(|a, b| a.weight.total_cmp(&b.weight));
// `f32::NAN` could be positive or negative, which will affect the sort order.
if f32::NAN.is_sign_negative() {
assert!(bois.into_iter().map(|b| b.weight)
.zip([f32::NAN, -5.0, 0.1, 10.0, 99.0, f32::INFINITY].iter())
.all(|(a, b)| a.to_bits() == b.to_bits()))
} else {
assert!(bois.into_iter().map(|b| b.weight)
.zip([-5.0, 0.1, 10.0, 99.0, f32::INFINITY, f32::NAN].iter())
.all(|(a, b)| a.to_bits() == b.to_bits()))
}
```

Run1.50.0 · source#### pub fn clamp(self, min: f32, max: f32) -> f32

#### pub fn clamp(self, min: f32, max: f32) -> f32

Restrict a value to a certain interval unless it is NaN.

Returns `max`

if `self`

is greater than `max`

, and `min`

if `self`

is
less than `min`

. Otherwise this returns `self`

.

Note that this function returns NaN if the initial value was NaN as well.

##### §Panics

Panics if `min > max`

, `min`

is NaN, or `max`

is NaN.

##### §Examples

```
assert!((-3.0f32).clamp(-2.0, 1.0) == -2.0);
assert!((0.0f32).clamp(-2.0, 1.0) == 0.0);
assert!((2.0f32).clamp(-2.0, 1.0) == 1.0);
assert!((f32::NAN).clamp(-2.0, 1.0).is_nan());
```

Run## Trait Implementations§

1.22.0 · source§### impl AddAssign<&f32> for f32

### impl AddAssign<&f32> for f32

source§#### fn add_assign(&mut self, other: &f32)

#### fn add_assign(&mut self, other: &f32)

`+=`

operation. Read more1.8.0 · source§### impl AddAssign for f32

### impl AddAssign for f32

source§#### fn add_assign(&mut self, other: f32)

#### fn add_assign(&mut self, other: f32)

`+=`

operation. Read more1.22.0 · source§### impl DivAssign<&f32> for f32

### impl DivAssign<&f32> for f32

source§#### fn div_assign(&mut self, other: &f32)

#### fn div_assign(&mut self, other: &f32)

`/=`

operation. Read more1.8.0 · source§### impl DivAssign for f32

### impl DivAssign for f32

source§#### fn div_assign(&mut self, other: f32)

#### fn div_assign(&mut self, other: f32)

`/=`

operation. Read more1.0.0 · source§### impl FromStr for f32

### impl FromStr for f32

source§#### fn from_str(src: &str) -> Result<f32, ParseFloatError>

#### fn from_str(src: &str) -> Result<f32, ParseFloatError>

Converts a string in base 10 to a float. Accepts an optional decimal exponent.

This function accepts strings such as

- ‘3.14’
- ‘-3.14’
- ‘2.5E10’, or equivalently, ‘2.5e10’
- ‘2.5E-10’
- ‘5.’
- ‘.5’, or, equivalently, ‘0.5’
- ‘inf’, ‘-inf’, ‘+infinity’, ‘NaN’

Note that alphabetical characters are not case-sensitive.

Leading and trailing whitespace represent an error.

##### §Grammar

All strings that adhere to the following EBNF grammar when
lowercased will result in an `Ok`

being returned:

```
Float ::= Sign? ( 'inf' | 'infinity' | 'nan' | Number )
Number ::= ( Digit+ |
Digit+ '.' Digit* |
Digit* '.' Digit+ ) Exp?
Exp ::= 'e' Sign? Digit+
Sign ::= [+-]
Digit ::= [0-9]
```

##### §Arguments

- src - A string

##### §Return value

`Err(ParseFloatError)`

if the string did not represent a valid
number. Otherwise, `Ok(n)`

where `n`

is the closest
representable floating-point number to the number represented
by `src`

(following the same rules for rounding as for the
results of primitive operations).

§#### type Err = ParseFloatError

#### type Err = ParseFloatError

1.22.0 · source§### impl MulAssign<&f32> for f32

### impl MulAssign<&f32> for f32

source§#### fn mul_assign(&mut self, other: &f32)

#### fn mul_assign(&mut self, other: &f32)

`*=`

operation. Read more1.8.0 · source§### impl MulAssign for f32

### impl MulAssign for f32

source§#### fn mul_assign(&mut self, other: f32)

#### fn mul_assign(&mut self, other: f32)

`*=`

operation. Read more1.0.0 · source§### impl PartialOrd for f32

### impl PartialOrd for f32

source§#### fn le(&self, other: &f32) -> bool

#### fn le(&self, other: &f32) -> bool

`self`

and `other`

) and is used by the `<=`

operator. Read more1.0.0 · source§### impl Rem for f32

### impl Rem for f32

The remainder from the division of two floats.

The remainder has the same sign as the dividend and is computed as:
`x - (x / y).trunc() * y`

.

#### §Examples

```
let x: f32 = 50.50;
let y: f32 = 8.125;
let remainder = x - (x / y).trunc() * y;
// The answer to both operations is 1.75
assert_eq!(x % y, remainder);
```

Run1.22.0 · source§### impl RemAssign<&f32> for f32

### impl RemAssign<&f32> for f32

source§#### fn rem_assign(&mut self, other: &f32)

#### fn rem_assign(&mut self, other: &f32)

`%=`

operation. Read more1.8.0 · source§### impl RemAssign for f32

### impl RemAssign for f32

source§#### fn rem_assign(&mut self, other: f32)

#### fn rem_assign(&mut self, other: f32)

`%=`

operation. Read moresource§### impl SimdElement for f32

### impl SimdElement for f32

1.22.0 · source§### impl SubAssign<&f32> for f32

### impl SubAssign<&f32> for f32

source§#### fn sub_assign(&mut self, other: &f32)

#### fn sub_assign(&mut self, other: &f32)

`-=`

operation. Read more1.8.0 · source§### impl SubAssign for f32

### impl SubAssign for f32

source§#### fn sub_assign(&mut self, other: f32)

#### fn sub_assign(&mut self, other: f32)

`-=`

operation. Read more