forked from M-Labs/nalgebra
Merge pull request #53 from aepsil0n/update_nightlies
Update to latest Rust
This commit is contained in:
commit
6c431ff666
@ -18,7 +18,7 @@ use structs::rot::{Rot2, Rot3, Rot4};
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///
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/// This is the composition of a rotation followed by a translation.
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/// Isometries conserve angles and distances, hence do not allow shearing nor scaling.
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Show)]
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Show, Copy)]
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pub struct Iso2<N> {
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/// The rotation applicable by this isometry.
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pub rotation: Rot2<N>,
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@ -30,7 +30,7 @@ pub struct Iso2<N> {
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///
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/// This is the composition of a rotation followed by a translation.
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/// Isometries conserve angles and distances, hence do not allow shearing nor scaling.
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Show)]
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Show, Copy)]
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pub struct Iso3<N> {
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/// The rotation applicable by this isometry.
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pub rotation: Rot3<N>,
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@ -41,7 +41,7 @@ pub struct Iso3<N> {
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/// Four dimensional isometry.
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///
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/// Isometries conserve angles and distances, hence do not allow shearing nor scaling.
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Show)]
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Show, Copy)]
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pub struct Iso4<N> {
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/// The rotation applicable by this isometry.
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pub rotation: Rot4<N>,
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@ -17,7 +17,7 @@ use linalg;
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/// Special identity matrix. All its operation are no-ops.
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#[deriving(Eq, PartialEq, Decodable, Clone, Rand, Show)]
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#[deriving(Eq, PartialEq, Decodable, Clone, Rand, Show, Copy)]
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pub struct Identity;
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impl Identity {
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@ -29,7 +29,7 @@ impl Identity {
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}
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/// Square matrix of dimension 1.
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Hash, Rand, Show)]
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Hash, Rand, Show, Copy)]
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pub struct Mat1<N> {
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pub m11: N
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}
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@ -73,7 +73,7 @@ outer_impl!(Vec1, Mat1)
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eigen_qr_impl!(Mat1, Vec1)
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/// Square matrix of dimension 2.
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Hash, Rand, Show)]
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Hash, Rand, Show, Copy)]
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pub struct Mat2<N> {
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pub m11: N, pub m21: N,
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pub m12: N, pub m22: N
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@ -121,7 +121,7 @@ outer_impl!(Vec2, Mat2)
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eigen_qr_impl!(Mat2, Vec2)
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/// Square matrix of dimension 3.
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Hash, Rand, Show)]
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Hash, Rand, Show, Copy)]
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pub struct Mat3<N> {
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pub m11: N, pub m21: N, pub m31: N,
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pub m12: N, pub m22: N, pub m32: N,
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@ -203,7 +203,7 @@ outer_impl!(Vec3, Mat3)
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eigen_qr_impl!(Mat3, Vec3)
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/// Square matrix of dimension 4.
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Hash, Rand, Show)]
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Hash, Rand, Show, Copy)]
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pub struct Mat4<N> {
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pub m11: N, pub m21: N, pub m31: N, pub m41: N,
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pub m12: N, pub m22: N, pub m32: N, pub m42: N,
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@ -303,7 +303,7 @@ outer_impl!(Vec4, Mat4)
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eigen_qr_impl!(Mat4, Vec4)
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/// Square matrix of dimension 5.
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Hash, Rand, Show)]
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Hash, Rand, Show, Copy)]
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pub struct Mat5<N> {
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pub m11: N, pub m21: N, pub m31: N, pub m41: N, pub m51: N,
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pub m12: N, pub m22: N, pub m32: N, pub m42: N, pub m52: N,
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@ -417,7 +417,7 @@ outer_impl!(Vec5, Mat5)
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eigen_qr_impl!(Mat5, Vec5)
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/// Square matrix of dimension 6.
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Hash, Rand, Show)]
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Hash, Rand, Show, Copy)]
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pub struct Mat6<N> {
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pub m11: N, pub m21: N, pub m31: N, pub m41: N, pub m51: N, pub m61: N,
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pub m12: N, pub m22: N, pub m32: N, pub m42: N, pub m52: N, pub m62: N,
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@ -5,7 +5,7 @@ use structs::{Pnt3, Vec3, Mat4};
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/// A 3D orthographic projection stored without any matrix.
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///
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/// Reading or modifying its individual properties is cheap but applying the transformation is costly.
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Show)]
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Show, Copy)]
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pub struct Ortho3<N> {
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width: N,
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height: N,
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@ -16,7 +16,7 @@ pub struct Ortho3<N> {
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/// A 3D orthographic projection stored as a 4D matrix.
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///
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/// Reading or modifying its individual properties is costly but applying the transformation is cheap.
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Show)]
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Show, Copy)]
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pub struct OrthoMat3<N> {
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mat: Mat4<N>
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}
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@ -4,7 +4,7 @@ use structs::{Pnt3, Vec3, Mat4};
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/// A 3D perspective projection stored without any matrix.
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///
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/// Reading or modifying its individual properties is cheap but applying the transformation is costly.
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Show)]
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Show, Copy)]
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pub struct Persp3<N> {
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aspect: N,
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fov: N,
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@ -15,7 +15,7 @@ pub struct Persp3<N> {
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/// A 3D perspective projection stored as a 4D matrix.
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///
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/// Reading or modifying its individual properties is costly but applying the transformation is cheap.
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Show)]
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Show, Copy)]
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pub struct PerspMat3<N> {
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mat: Mat4<N>
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}
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@ -14,7 +14,7 @@ use structs::vec::{Vec1, Vec2, Vec3, Vec4, Vec5, Vec6};
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/// Point of dimension 0.
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#[deriving(Eq, PartialEq, Decodable, Clone, Rand, Show)]
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#[deriving(Eq, PartialEq, Decodable, Clone, Rand, Show, Copy)]
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pub struct Pnt0<N>;
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impl<N> Pnt0<N> {
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@ -32,7 +32,7 @@ impl<N> Pnt0<N> {
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}
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/// Point of dimension 1.
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Hash, Rand, Show)]
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Hash, Rand, Show, Copy)]
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pub struct Pnt1<N> {
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/// First component of the point.
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pub x: N
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@ -70,7 +70,7 @@ pnt_from_homogeneous_impl!(Pnt1, Pnt2, y, x)
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num_float_pnt_impl!(Pnt1, Vec1)
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/// Point of dimension 2.
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Hash, Rand, Show)]
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Hash, Rand, Show, Copy)]
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pub struct Pnt2<N> {
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/// First component of the point.
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pub x: N,
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@ -110,7 +110,7 @@ pnt_from_homogeneous_impl!(Pnt2, Pnt3, z, x, y)
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num_float_pnt_impl!(Pnt2, Vec2)
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/// Point of dimension 3.
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Hash, Rand, Show)]
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Hash, Rand, Show, Copy)]
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pub struct Pnt3<N> {
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/// First component of the point.
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pub x: N,
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@ -152,7 +152,7 @@ pnt_from_homogeneous_impl!(Pnt3, Pnt4, w, x, y, z)
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num_float_pnt_impl!(Pnt3, Vec3)
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/// Point of dimension 4.
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Hash, Rand, Show)]
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Hash, Rand, Show, Copy)]
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pub struct Pnt4<N> {
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/// First component of the point.
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pub x: N,
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@ -196,7 +196,7 @@ pnt_from_homogeneous_impl!(Pnt4, Pnt5, a, x, y, z, w)
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num_float_pnt_impl!(Pnt4, Vec4)
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/// Point of dimension 5.
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Hash, Rand, Show)]
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Hash, Rand, Show, Copy)]
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pub struct Pnt5<N> {
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/// First component of the point.
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pub x: N,
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@ -242,7 +242,7 @@ pnt_from_homogeneous_impl!(Pnt5, Pnt6, b, x, y, z, w, a)
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num_float_pnt_impl!(Pnt5, Vec5)
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/// Point of dimension 6.
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Hash, Rand, Show)]
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Hash, Rand, Show, Copy)]
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pub struct Pnt6<N> {
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/// First component of the point.
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pub x: N,
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@ -14,7 +14,7 @@ use traits::structure::{Cast, Indexable, Iterable, IterableMut, Dim, Shape, Base
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use traits::geometry::{Norm, Cross, Rotation, Rotate, Transform};
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/// A quaternion.
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Hash, Rand, Show)]
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Hash, Rand, Show, Copy)]
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pub struct Quat<N> {
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/// The scalar component of the quaternion.
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pub w: N,
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@ -139,7 +139,7 @@ impl<N: ApproxEq<N> + BaseFloat + Clone> Div<Quat<N>, Quat<N>> for Quat<N> {
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}
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/// A unit quaternion that can represent a 3D rotation.
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Hash, Show)]
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Hash, Show, Copy)]
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pub struct UnitQuat<N> {
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q: Quat<N>
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}
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@ -13,7 +13,7 @@ use structs::mat::{Mat2, Mat3, Mat4, Mat5};
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/// Two dimensional rotation matrix.
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Show, Hash)]
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Show, Hash, Copy)]
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pub struct Rot2<N> {
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submat: Mat2<N>
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}
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@ -90,7 +90,7 @@ impl<N: BaseFloat> AbsoluteRotate<Vec2<N>> for Rot2<N> {
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* 3d rotation
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*/
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/// Three dimensional rotation matrix.
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Show, Hash)]
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Show, Hash, Copy)]
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pub struct Rot3<N> {
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submat: Mat3<N>
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}
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@ -288,7 +288,7 @@ impl<N: BaseFloat> AbsoluteRotate<Vec3<N>> for Rot3<N> {
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}
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/// Four dimensional rotation matrix.
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Show, Hash)]
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Show, Hash, Copy)]
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pub struct Rot4<N> {
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submat: Mat4<N>
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}
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@ -234,7 +234,7 @@ impl<N: Cast<f64> + Clone> UniformSphereSample for Vec2<N> {
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#[inline(always)]
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fn sample(f: |Vec2<N>| -> ()) {
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for sample in SAMPLES_2_F64.iter() {
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f(Cast::from(*sample))
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f(Cast::from(sample.clone()))
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}
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}
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}
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@ -243,7 +243,7 @@ impl<N: Cast<f64> + Clone> UniformSphereSample for Vec3<N> {
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#[inline(always)]
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fn sample(f: |Vec3<N>| -> ()) {
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for sample in SAMPLES_3_F64.iter() {
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f(Cast::from(*sample))
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f(Cast::from(sample.clone()))
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}
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}
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}
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@ -15,7 +15,7 @@ use structs::pnt::{Pnt1, Pnt2, Pnt3, Pnt4, Pnt5, Pnt6};
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/// Vector of dimension 0.
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#[deriving(Eq, PartialEq, Decodable, Clone, Rand, Zero, Show)]
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#[deriving(Eq, PartialEq, Decodable, Clone, Rand, Zero, Show, Copy)]
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pub struct Vec0<N>;
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impl<N> Vec0<N> {
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@ -33,7 +33,7 @@ impl<N> Vec0<N> {
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}
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/// Vector of dimension 1.
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Hash, Rand, Show)]
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Hash, Rand, Show, Copy)]
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pub struct Vec1<N> {
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/// First component of the vector.
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pub x: N
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@ -82,7 +82,7 @@ num_float_vec_impl!(Vec1)
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absolute_vec_impl!(Vec1, x)
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/// Vector of dimension 2.
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Hash, Rand, Zero, Show)]
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Hash, Rand, Zero, Show, Copy)]
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pub struct Vec2<N> {
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/// First component of the vector.
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pub x: N,
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@ -133,7 +133,7 @@ num_float_vec_impl!(Vec2)
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absolute_vec_impl!(Vec2, x, y)
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/// Vector of dimension 3.
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Hash, Rand, Zero, Show)]
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Hash, Rand, Zero, Show, Copy)]
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pub struct Vec3<N> {
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/// First component of the vector.
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pub x: N,
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@ -187,7 +187,7 @@ absolute_vec_impl!(Vec3, x, y, z)
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/// Vector of dimension 4.
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Hash, Rand, Zero, Show)]
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Hash, Rand, Zero, Show, Copy)]
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pub struct Vec4<N> {
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/// First component of the vector.
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pub x: N,
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@ -242,7 +242,7 @@ num_float_vec_impl!(Vec4)
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absolute_vec_impl!(Vec4, x, y, z, w)
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/// Vector of dimension 5.
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Hash, Rand, Zero, Show)]
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Hash, Rand, Zero, Show, Copy)]
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pub struct Vec5<N> {
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/// First component of the vector.
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pub x: N,
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@ -299,7 +299,7 @@ num_float_vec_impl!(Vec5)
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absolute_vec_impl!(Vec5, x, y, z, w, a)
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/// Vector of dimension 6.
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Hash, Rand, Zero, Show)]
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Hash, Rand, Zero, Show, Copy)]
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pub struct Vec6<N> {
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/// First component of the vector.
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pub x: N,
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@ -4,7 +4,7 @@ use std::num::{Float, SignedInt};
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use traits::structure::SquareMat;
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/// Result of a partial ordering.
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Show)]
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#[deriving(Eq, PartialEq, Encodable, Decodable, Clone, Show, Copy)]
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pub enum POrdering {
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/// Result of a strict comparison.
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PartialLess,
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