Struct ShadowRadius 

pub struct ShadowRadius(pub f32);

A metadata field for AbstractDisplay.

Tuple Fields

0: f32

Methods from Deref<Target = f32>

pub fn floor(self) -> f32

Returns the largest integer that is less than or equal to self.

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.floor(), 3.0);
assert_eq!(g.floor(), 3.0);
assert_eq!(h.floor(), -4.0);

pub fn ceil(self) -> f32

Returns the smallest integer that is greater than or equal to self.

This function always returns the precise result.

Examples

let f = 3.01_f32;
let g = 4.0_f32;
let h = -3.01_f32;

assert_eq!(f.ceil(), 4.0);
assert_eq!(g.ceil(), 4.0);
assert_eq!(h.ceil(), -3.0);

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.

On most hardware platforms, round_ties_even may execute faster than round. If both rounding methods fit the use case, consider using round_ties_even. Note that the two methods apply different rounding rules to values exactly halfway between two integers.

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);

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);

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);

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);

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);

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

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

Calculates the least nonnegative remainder of self when divided by 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);

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.

Note that this function is special in that it can return non-NaN results for NaN inputs. For example, f32::powi(f32::NAN, 0) returns 1.0. However, if an input is a signaling NaN, then the result is non-deterministically either a NaN or the result that the corresponding quiet NaN would produce.

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 <= 1e-5);

assert_eq!(f32::powi(f32::NAN, 0), 1.0);
assert_eq!(f32::powi(0.0, 0), 1.0);

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

Raises a number to a floating point power.

Note that this function is special in that it can return non-NaN results for NaN inputs. For example, f32::powf(f32::NAN, 0.0) returns 1.0. However, if an input is a signaling NaN, then the result is non-deterministically either a NaN or the result that the corresponding quiet NaN would produce.

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 <= 1e-5);

assert_eq!(f32::powf(1.0, f32::NAN), 1.0);
assert_eq!(f32::powf(f32::NAN, 0.0), 1.0);
assert_eq!(f32::powf(0.0, 0.0), 1.0);

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);

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 <= 1e-6);

pub fn exp2(self) -> f32

Returns 2^(self).

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 f = 2.0f32;

// 2^2 - 4 == 0
let abs_difference = (f.exp2() - 4.0).abs();

assert!(abs_difference <= 1e-5);

pub fn ln(self) -> f32

Returns the natural logarithm of the number.

This returns NaN when the number is negative, and negative infinity when number is 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.

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 <= 1e-6);

Non-positive values:

assert_eq!(0_f32.ln(), f32::NEG_INFINITY);
assert!((-42_f32).ln().is_nan());

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

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

This returns NaN when the number is negative, and negative infinity when number is zero.

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

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 five = 5.0f32;

// log5(5) - 1 == 0
let abs_difference = (five.log(5.0) - 1.0).abs();

assert!(abs_difference <= 1e-6);

Non-positive values:

assert_eq!(0_f32.log(10.0), f32::NEG_INFINITY);
assert!((-42_f32).log(10.0).is_nan());

pub fn log2(self) -> f32

Returns the base 2 logarithm of the number.

This returns NaN when the number is negative, and negative infinity when number is 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.

Examples

let two = 2.0f32;

// log2(2) - 1 == 0
let abs_difference = (two.log2() - 1.0).abs();

assert!(abs_difference <= 1e-6);

Non-positive values:

assert_eq!(0_f32.log2(), f32::NEG_INFINITY);
assert!((-42_f32).log2().is_nan());

pub fn log10(self) -> f32

Returns the base 10 logarithm of the number.

This returns NaN when the number is negative, and negative infinity when number is 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.

Examples

let ten = 10.0f32;

// log10(10) - 1 == 0
let abs_difference = (ten.log10() - 1.0).abs();

assert!(abs_difference <= 1e-6);

Non-positive values:

assert_eq!(0_f32.log10(), f32::NEG_INFINITY);
assert!((-42_f32).log10().is_nan());

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).

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 <= 1e-6);
assert!(abs_difference_y <= 1e-6);

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 <= 1e-6);

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 <= 1e-5);

pub fn sin(self) -> f32

Computes the sine 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.

Examples

let x = std::f32::consts::FRAC_PI_2;

let abs_difference = (x.sin() - 1.0).abs();

assert!(abs_difference <= 1e-6);

pub fn cos(self) -> f32

Computes the cosine 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.

Examples

let x = 2.0 * std::f32::consts::PI;

let abs_difference = (x.cos() - 1.0).abs();

assert!(abs_difference <= 1e-6);

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 <= 1e-6);

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_4;

// asin(sin(pi/2))
let abs_difference = (f.sin().asin() - f).abs();

assert!(abs_difference <= 1e-6);

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 <= 1e-6);

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 <= 1e-6);

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

Computes the four quadrant arctangent of self (y) and other (x) in radians.

x	y	Piecewise Definition	Range
>= +0	>= +0	arctan(y/x)	[+0, +pi/2]
>= +0	<= -0	arctan(y/x)	[-pi/2, -0]
<= -0	>= +0	arctan(y/x) + pi	[+pi/2, +pi]
<= -0	<= -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 <= 1e-5);
assert!(abs_difference_2 <= 1e-5);

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 <= 1e-4);
assert!(abs_difference_1 <= 1e-4);

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);

pub fn ln_1p(self) -> f32

Returns ln(1+n) (natural logarithm) more accurately than if the operations were performed separately.

This returns NaN when n < -1.0, and negative infinity when n == -1.0.

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);

Out-of-range values:

assert_eq!((-1.0_f32).ln_1p(), f32::NEG_INFINITY);
assert!((-2.0_f32).ln_1p().is_nan());

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 <= 1e-6);

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 <= 1e-6);

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 <= 1e-6);

pub fn asinh(self) -> f32

Inverse 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.

Examples

let x = 1.0f32;
let f = x.sinh().asinh();

let abs_difference = (f - x).abs();

assert!(abs_difference <= 1e-6);

pub fn acosh(self) -> f32

Inverse 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.

Examples

let x = 1.0f32;
let f = x.cosh().acosh();

let abs_difference = (f - x).abs();

assert!(abs_difference <= 1e-6);

pub fn atanh(self) -> f32

Inverse 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.

Examples

let x = std::f32::consts::FRAC_PI_6;
let f = x.tanh().atanh();

let abs_difference = (f - x).abs();

assert!(abs_difference <= 1e-5);

pub fn gamma(self) -> f32

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

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 <= 1e-5);

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

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

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);

pub fn erf(self) -> f32

🔬This is a nightly-only experimental API. (float_erf)

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

Examples

#![feature(float_erf)]
/// The error function relates what percent of a normal distribution lies
/// within `x` standard deviations (scaled by `1/sqrt(2)`).
fn within_standard_deviations(x: f32) -> f32 {
    (x * std::f32::consts::FRAC_1_SQRT_2).erf() * 100.0
}

// 68% of a normal distribution is within one standard deviation
assert!((within_standard_deviations(1.0) - 68.269).abs() < 0.01);
// 95% of a normal distribution is within two standard deviations
assert!((within_standard_deviations(2.0) - 95.450).abs() < 0.01);
// 99.7% of a normal distribution is within three standard deviations
assert!((within_standard_deviations(3.0) - 99.730).abs() < 0.01);

pub fn erfc(self) -> f32

🔬This is a nightly-only experimental API. (float_erf)

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

Examples

#![feature(float_erf)]
let x: f32 = 0.123;

let one = x.erf() + x.erfc();
let abs_difference = (one - 1.0).abs();

assert!(abs_difference <= 1e-6);

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());

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());

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());

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());

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());

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);

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 or non-portable result. See the specification of NaN bit patterns for more info. Use self.signum() == 1.0 if you need fully portable behavior (evaluates to false for all NaNs).

let f = 7.0_f32;
let g = -7.0_f32;

assert!(f.is_sign_positive());
assert!(!g.is_sign_positive());

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 or non-portable result. See the specification of NaN bit patterns for more info. Use self.signum() == -1.0 if you need fully portable behavior (evaluates to false for all NaNs).

let f = 7.0f32;
let g = -7.0f32;

assert!(!f.is_sign_negative());
assert!(g.is_sign_negative());

pub fn next_up(self) -> f32

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.

// 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);

This operation corresponds to IEEE-754 nextUp.

pub fn next_down(self) -> f32

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.

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);

This operation corresponds to IEEE-754 nextDown.

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);

pub fn to_degrees(self) -> f32

Converts radians to degrees.

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 angle = std::f32::consts::PI;

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

pub fn to_radians(self) -> f32

Converts degrees to 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.

Examples

let angle = 180.0f32;

let abs_difference = (angle.to_radians() - std::f32::consts::PI).abs();

assert!(abs_difference <= f32::EPSILON);

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

Returns the maximum of the two numbers, ignoring NaN.

If exactly one of the arguments is NaN (quiet or signaling), then the other argument is returned. If both arguments are NaN, the return value is NaN, with the bit pattern picked using the usual rules for arithmetic operations. If the inputs compare equal (such as for the case of +0.0 and -0.0), either input may be returned non-deterministically.

The handling of NaNs follows the IEEE 754-2019 semantics for maximumNumber, treating all NaNs the same way to ensure the operation is associative. The handling of signed zeros follows the IEEE 754-2008 semantics for maxNum.

let x = 1.0f32;
let y = 2.0f32;

assert_eq!(x.max(y), y);
assert_eq!(x.max(f32::NAN), x);

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

Returns the minimum of the two numbers, ignoring NaN.

If exactly one of the arguments is NaN (quiet or signaling), then the other argument is returned. If both arguments are NaN, the return value is NaN, with the bit pattern picked using the usual rules for arithmetic operations. If the inputs compare equal (such as for the case of +0.0 and -0.0), either input may be returned non-deterministically.

The handling of NaNs follows the IEEE 754-2019 semantics for minimumNumber, treating all NaNs the same way to ensure the operation is associative. The handling of signed zeros follows the IEEE 754-2008 semantics for minNum.

let x = 1.0f32;
let y = 2.0f32;

assert_eq!(x.min(y), x);
assert_eq!(x.min(f32::NAN), x);

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

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

Returns the maximum of the two numbers, propagating NaN.

If at least one of the arguments is NaN, the return value is NaN, with the bit pattern picked using the usual rules for arithmetic operations. Furthermore, -0.0 is considered to be less than +0.0, making this function fully deterministic for non-NaN inputs.

This is in contrast to f32::max which only returns NaN when both arguments are NaN, and which does not reliably order -0.0 and +0.0.

This follows the IEEE 754-2019 semantics for maximum.

#![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());

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

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

Returns the minimum of the two numbers, propagating NaN.

If at least one of the arguments is NaN, the return value is NaN, with the bit pattern picked using the usual rules for arithmetic operations. Furthermore, -0.0 is considered to be less than +0.0, making this function fully deterministic for non-NaN inputs.

This is in contrast to f32::min which only returns NaN when both arguments are NaN, and which does not reliably order -0.0 and +0.0.

This follows the IEEE 754-2019 semantics for minimum.

#![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());

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

Calculates the midpoint (average) between self and rhs.

This returns NaN when either argument is NaN or if a combination of +inf and -inf is provided as arguments.

Examples

assert_eq!(1f32.midpoint(4.0), 2.5);
assert_eq!((-5.5f32).midpoint(8.0), 1.25);

pub unsafe fn to_int_unchecked<Int>(self) -> Int

where 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);

Safety

The value must:

• Not be NaN
• Not be infinite
• Be representable in the return type Int, after truncating off its fractional part

pub fn cast<Flt>(self) -> Flt

where f32: FloatToFloat<Flt>,

🔬This is a nightly-only experimental API. (float_conversions)

Converts to the target float type, rounding as defined in IEEE 754.

This is equivalent to self as Flt. Narrowing to a smaller type can produce an infinity.

#![feature(float_conversions)]

let x = 1.5_f32;
assert_eq!(x.cast::<f64>(), 1.5_f64);

pub fn to_int_saturating<Int>(self) -> Int

where f32: FloatToInt<Int>,

🔬This is a nightly-only experimental API. (float_conversions)

Rounds toward zero and converts to any primitive integer type, saturating at the type’s boundaries and mapping NaN to zero.

This is equivalent to self as Int.

#![feature(float_conversions)]

assert_eq!(255.5_f32.to_int_saturating::<u8>(), 255);
assert_eq!(300.0_f32.to_int_saturating::<u8>(), 255);
assert_eq!((-1.0_f32).to_int_saturating::<u8>(), 0);
assert_eq!(f32::NAN.to_int_saturating::<u8>(), 0);

pub fn to_int_checked<Int>(self) -> Option<Int>

where f32: FloatToInt<Int>,

🔬This is a nightly-only experimental API. (float_conversions)

Rounds toward zero and converts to any primitive integer type, returning None if the value is NaN, infinite, or does not fit in the target type.

#![feature(float_conversions)]

assert_eq!(255.5_f32.to_int_checked::<u8>(), Some(255));
assert_eq!(256.0_f32.to_int_checked::<u8>(), None);
assert_eq!(f32::NAN.to_int_checked::<u8>(), None);

pub fn to_int_strict<Int>(self) -> Int

where f32: FloatToInt<Int>,

🔬This is a nightly-only experimental API. (float_conversions)

Rounds toward zero and converts to any primitive integer type.

This is equivalent to self.to_int_checked().unwrap().

Panics

Panics if the value is NaN, infinite, or does not fit in the target type.

#![feature(float_conversions)]

assert_eq!(255.5_f32.to_int_strict::<u8>(), 255);

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);

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

Returns 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]);

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

Returns 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]);

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

Returns 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]
    }
);

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

Returns 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()))
}

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. If the result is zero and among the three inputs self, min, and max there are zeros with different sign, either 0.0 or -0.0 is returned non-deterministically.

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());

// These always returns zero, but the sign (which is ignored by `==`) is non-deterministic.
assert!((0.0f32).clamp(-0.0, -0.0) == 0.0);
assert!((1.0f32).clamp(-0.0, 0.0) == 0.0);
// This is definitely a negative zero.
assert!((-1.0f32).clamp(-0.0, 1.0).is_sign_negative());

pub fn clamp_magnitude(self, limit: f32) -> f32

🔬This is a nightly-only experimental API. (clamp_magnitude)

Clamps this number to a symmetric range centered around zero.

The method clamps the number’s magnitude (absolute value) to be at most limit.

This is functionally equivalent to self.clamp(-limit, limit), but is more explicit about the intent.

Panics

Panics if limit is negative or NaN, as this indicates a logic error.

Examples

#![feature(clamp_magnitude)]
assert_eq!(5.0f32.clamp_magnitude(3.0), 3.0);
assert_eq!((-5.0f32).clamp_magnitude(3.0), -3.0);
assert_eq!(2.0f32.clamp_magnitude(3.0), 2.0);
assert_eq!((-2.0f32).clamp_magnitude(3.0), -2.0);

pub fn clamp_to<R>(self, range: R) -> f32

where R: ClampBounds<f32>,

🔬This is a nightly-only experimental API. (clamp_to)

Restrict a value to a certain range, unless it is NaN.

This is largely equal to max, min, or clamp, depending on whether the range is min.., ..=max, or min..=max, respectively. However, unlike max and min, it will panic if any bound is NaN.

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

Exclusive ranges are not permitted.

Panics

Panics on min..=max if min > max, or if any bound is NaN.

Examples

#![feature(clamp_to)]
assert_eq!((-3.0f32).clamp_to(-2.0..=1.0), -2.0);
assert_eq!(0.0f32.clamp_to(-2.0..=1.0), 0.0);
assert_eq!(2.0f32.clamp_to(..=1.0), 1.0);
assert_eq!(5.0f32.clamp_to(7.0..), 7.0);
assert!(f32::NAN.clamp_to(1.0..=2.0).is_nan());

pub fn abs(self) -> f32

Computes the absolute value of self.

This function always returns the precise result.

Examples

let x = 3.5_f32;
let y = -3.5_f32;

assert_eq!(x.abs(), x);
assert_eq!(y.abs(), -y);

assert!(f32::NAN.abs().is_nan());

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());

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 same payload as self and the sign bit of sign is returned.

If sign is a NaN, then this operation will still carry over its sign into the result. 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 copysign with sign being a NaN might produce an unexpected or non-portable result. See the specification of NaN bit patterns 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());

pub fn algebraic_add(self, rhs: f32) -> f32

Float addition that allows optimizations based on algebraic rules.

See algebraic operators for more info.

pub fn algebraic_sub(self, rhs: f32) -> f32

Float subtraction that allows optimizations based on algebraic rules.

See algebraic operators for more info.

pub fn algebraic_mul(self, rhs: f32) -> f32

Float multiplication that allows optimizations based on algebraic rules.

See algebraic operators for more info.

pub fn algebraic_div(self, rhs: f32) -> f32

Float division that allows optimizations based on algebraic rules.

See algebraic operators for more info.

pub fn algebraic_rem(self, rhs: f32) -> f32

Float remainder that allows optimizations based on algebraic rules.

See algebraic operators for more info.

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

🔬This is a nightly-only experimental API. (float_mul_add_relaxed)

Computes (self * a) + b with nondeterministic rounding.

This is similar to mul_add, but the intermediate result may be rounded differently depending on the implementation. The operation is either executed as a single fused multiply-add instruction, or as separate multiply and add instructions.

The choice of which one is used is unspecified and non-deterministic: it may vary by target, optimization level, and surrounding code, and even two invocations of this operation with the same inputs may produce different results.

Precision

The result of this operation is not guaranteed: it is either the result of mul_add (one rounding of the infinite-precision result) or of self * a + b (two roundings, with an intermediate rounding of the product).

Examples

#![feature(float_mul_add_relaxed)]

// When the fused and unfused operations round differently, either
// result may be returned:
// - 5.2154064e-10 is the fused result (one rounding)
// - 9.313226e-10 is the unfused result (two roundings)
let r = 0.1_f32.mul_add_relaxed(0.1_f32, -0.01_f32);
assert!(r == 5.2154064e-10 || r == 9.313226e-10);

pub fn nan_to(self, replacement: f32) -> f32

🔬This is a nightly-only experimental API. (float_nan_to)

Returns self if the value is not NaN, otherwise returns replacement if self is NaN.

Examples

#![feature(float_nan_to)]

let n = f32::NAN;
let x = 2.0f32;
let y = f32::INFINITY;

assert_eq!(n.nan_to(0.0f32), 0.0f32);
assert_eq!(x.nan_to(0.0f32), 2.0f32);
assert_eq!(y.nan_to(0.0f32), f32::INFINITY);

Trait Implementations

impl Clone for ShadowRadius

fn clone(&self) -> Self

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Component for ShadowRadius

where Self: Send + Sync + 'static,

const STORAGE_TYPE: StorageType = bevy_ecs::component::StorageType::Table

A constant indicating the storage type used for this component.

type Mutability = Mutable

A marker type to assist Bevy with determining if this component is mutable, or immutable. Mutable components will have [Component<Mutability = Mutable>], while immutable components will instead have [Component<Mutability = Immutable>].

fn register_required_components( _requiree: ComponentId, required_components: &mut RequiredComponentsRegistrator<'_, '_>, )

Registers required components.

fn clone_behavior() -> ComponentCloneBehavior

Called when registering this component, allowing to override clone function (or disable cloning altogether) for this component.

fn relationship_accessor() -> Option<ComponentRelationshipAccessor<Self>>

Returns [ComponentRelationshipAccessor] required for working with relationships in dynamic contexts.

fn on_add() -> Option<for<'w> fn(DeferredWorld<'w>, HookContext)>

Gets the on_add [ComponentHook] for this [Component] if one is defined.

fn on_insert() -> Option<for<'w> fn(DeferredWorld<'w>, HookContext)>

Gets the on_insert [ComponentHook] for this [Component] if one is defined.

fn on_discard() -> Option<for<'w> fn(DeferredWorld<'w>, HookContext)>

Gets the on_discard [ComponentHook] for this [Component] if one is defined.

fn on_remove() -> Option<for<'w> fn(DeferredWorld<'w>, HookContext)>

Gets the on_remove [ComponentHook] for this [Component] if one is defined.

fn on_despawn() -> Option<for<'w> fn(DeferredWorld<'w>, HookContext)>

Gets the on_despawn [ComponentHook] for this [Component] if one is defined.

fn map_entities<E>(_this: &mut Self, _mapper: &mut E)

where E: EntityMapper,

Maps the entities on this component using the given [EntityMapper]. This is used to remap entities in contexts like scenes and entity cloning. When deriving [Component], this is populated by annotating fields containing entities with #[entities]

const HAS_SUMMARY_TICK: bool = false

Set this constant to true if the component should track a summary tick.

impl Deref for ShadowRadius

type Target = f32

The resulting type after dereferencing.

fn deref(&self) -> &Self::Target

Dereferences the value.

impl DerefMut for ShadowRadius

fn deref_mut(&mut self) -> &mut Self::Target

Mutably dereferences the value.

impl PartialEq for ShadowRadius

fn eq(&self, other: &Self) -> bool

Equality operator ==.

fn ne(&self, other: &Rhs) -> bool

Inequality operator !=.

impl StructuralPartialEq for ShadowRadius