[−][src]Enum nifti::typedef::Intent
An enum type for representing a NIFTI intent code.
Variants
default: no intention is indicated in the header.
nifti1 intent codes, to describe intended meaning of dataset contents
[C2, chap 28] Student t statistic (1 param): p1 = DOF.
[C2, chap 27] Fisher F statistic (2 params): p1 = numerator DOF, p2 = denominator DOF.
[C1, chap 13] Standard normal (0 params): Density = N(0,1).
[C1, chap 18] Chi-squared (1 param): p1 = DOF. Density(x) proportional to exp(-x/2) * x^(p1/2-1).
[C2, chap 25] Beta distribution (2 params): p1=a, p2=b. Density(x) proportional to x^(a-1) * (1-x)^(b-1).
[U, chap 3] Binomial distribution (2 params): p1 = number of trials, p2 = probability per trial. Prob(x) = (p1 choose x) * p2^x * (1-p2)^(p1-x), for x=0,1,...,p1.
[C1, chap 17] Gamma distribution (2 params): p1 = shape, p2 = scale. Density(x) proportional to x^(p1-1) * exp(-p2*x).
[U, chap 4] Poisson distribution (1 param): p1 = mean. Prob(x) = exp(-p1) * p1^x / x! , for x=0,1,2,....
[C1, chap 13] Normal distribution (2 params): p1 = mean, p2 = standard deviation.
[C2, chap 30] Noncentral F statistic (3 params): p1 = numerator DOF, p2 = denominator DOF, p3 = numerator noncentrality parameter.
[C2, chap 29] Noncentral chi-squared statistic (2 params): p1 = DOF, p2 = noncentrality parameter.
[C2, chap 23] Logistic distribution (2 params): p1 = location, p2 = scale. Density(x) proportional to sech^2((x-p1)/(2*p2)).
[C2, chap 24] Laplace distribution (2 params): p1 = location, p2 = scale. Density(x) proportional to exp(-abs(x-p1)/p2).
[C2, chap 26] Uniform distribution: p1 = lower end, p2 = upper end.
[C2, chap 31] Noncentral t statistic (2 params): p1 = DOF, p2 = noncentrality parameter.
[C1, chap 21] Weibull distribution (3 params): p1 = location, p2 = scale, p3 = power. Density(x) proportional to ((x-p1)/p2)^(p3-1) * exp(-((x-p1)/p2)^p3) for x > p1.
[C1, chap 18] Chi distribution (1 param): p1 = DOF. Density(x) proportional to x^(p1-1) * exp(-x^2/2) for x > 0. p1 = 1 = 'half normal' distribution p1 = 2 = Rayleigh distribution p1 = 3 = Maxwell-Boltzmann distribution.
[C1, chap 15] Inverse Gaussian (2 params): p1 = mu, p2 = lambda Density(x) proportional to exp(-p2*(x-p1)^2/(2p1^2x)) / x^3 for x > 0.
[C2, chap 22] Extreme value type I (2 params): p1 = location, p2 = scale cdf(x) = exp(-exp(-(x-p1)/p2)).
Data is a 'p-value' (no params).
Data is ln(p-value) (no params). To be safe, a program should compute p = exp(-abs(this_value)). The nifti_stats.c library returns this_value as positive, so that this_value = -log(p).
Data is log10(p-value) (no params). To be safe, a program should compute p = pow(10.,-abs(this_value)). The nifti_stats.c library returns this_value as positive, so that this_value = -log10(p).
To signify that the value at each voxel is an estimate
of some parameter, set intent_code = NIFTI_INTENT_ESTIMATE
.
The name of the parameter may be stored in intent_name.
To signify that the value at each voxel is an index into
some set of labels, set intent_code = NIFTI_INTENT_LABEL
.
The filename with the labels may stored in aux_file.
To signify that the value at each voxel is an index into the
NeuroNames labels set, set intent_code = NIFTI_INTENT_NEURONAME
.
To store an M x N matrix at each voxel:
- dataset must have a 5th dimension (dim[0]=5 and dim[5]>1)
- intent_code must be
NIFTI_INTENT_GENMATRIX
- dim[5] must be M*N
- intent_p1 must be M (in float format)
- intent_p2 must be N (ditto)
- the matrix values A[i][[j] are stored in row-order:
- A[0][0] A[0][1] ... A[0][N-1]
- A[1][0] A[1][1] ... A[1][N-1]
- etc., until
- A[M-1][0] A[M-1][1] ... A[M-1][N-1]
To store an NxN symmetric matrix at each voxel:
- dataset must have a 5th dimension
- intent_code must be
NIFTI_INTENT_SYMMATRIX
- dim[5] must be N*(N+1)/2
- intent_p1 must be N (in float format)
- the matrix values A[i][[j] are stored in row-order:
- A[0][0]
- A[1][0] A[1][1]
- A[2][0] A[2][1] A[2][2]
- etc.: row-by-row
To signify that the vector value at each voxel is to be taken as a displacement field or vector:
- dataset must have a 5th dimension
- intent_code must be
NIFTI_INTENT_DISPVECT
- dim[5] must be the dimensionality of the displacment vector (e.g., 3 for spatial displacement, 2 for in-plane)
(specifically for displacements)
(for any other type of vector)
To signify that the vector value at each voxel is really a spatial coordinate (e.g., the vertices or nodes of a surface mesh):
- dataset must have a 5th dimension
- intent_code must be
NIFTI_INTENT_POINTSET
- dim[0] = 5
- dim[1] = number of points
- dim[2] = dim[3] = dim[4] = 1
- dim[5] must be the dimensionality of space (e.g., 3 => 3D space).
- intent_name may describe the object these points come from (e.g., "pial", "gray/white" , "EEG", "MEG").
To signify that the vector value at each voxel is really a triple of indexes (e.g., forming a triangle) from a pointset dataset:
- dataset must have a 5th dimension
- intent_code must be
NIFTI_INTENT_TRIANGLE
- dim[0] = 5
- dim[1] = number of triangles
- dim[2] = dim[3] = dim[4] = 1
- dim[5] = 3
- datatype should be an integer type (preferably
NiftiType::Int32
) - the data values are indexes (0,1,...) into a pointset dataset.
To signify that the vector value at each voxel is a quaternion:
- dataset must have a 5th dimension
- intent_code must be
NIFTI_INTENT_QUATERNION
- dim[0] = 5
- dim[5] = 4
- datatype should be a floating point type
Dimensionless value - no params - although, as in _ESTIMATE
the name of the parameter may be stored in intent_name.
To signify that the value at each location is from a time series.
To signify that the value at each location is a node index, from a complete surface dataset.
To signify that the vector value at each location is an RGB triplet, of whatever type.
- dataset must have a 5th dimension
- dim[0] = 5
- dim[1] = number of nodes
- dim[2] = dim[3] = dim[4] = 1
- dim[5] = 3
To signify that the vector value at each location is a 4 valued RGBA vector, of whatever type.
- dataset must have a 5th dimension
- dim[0] = 5
- dim[1] = number of nodes
- dim[2] = dim[3] = dim[4] = 1
- dim[5] = 4
To signify that the value at each location is a shape value, such as the curvature.
FSL FNIRT Displacement field.
FSL Cubic spline coefficients.
FSL Discrete cosine transform coefficients.
FSL Quadratic spline coefficients.
FSL Topup cubic spline coefficients.
FSL Topup quadratic spline coefficients.
FSL Topup field.
Implementations
impl Intent
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pub fn is_statcode(self) -> bool
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Check whether this intent code is used for statistics.
Trait Implementations
impl Clone for Intent
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impl Copy for Intent
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impl Debug for Intent
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impl Eq for Intent
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impl FromPrimitive for Intent
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pub fn from_i64(n: i64) -> Option<Self>
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pub fn from_u64(n: u64) -> Option<Self>
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pub fn from_isize(n: isize) -> Option<Self>
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pub fn from_i8(n: i8) -> Option<Self>
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pub fn from_i16(n: i16) -> Option<Self>
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pub fn from_i32(n: i32) -> Option<Self>
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pub fn from_i128(n: i128) -> Option<Self>
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pub fn from_usize(n: usize) -> Option<Self>
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pub fn from_u8(n: u8) -> Option<Self>
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pub fn from_u16(n: u16) -> Option<Self>
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pub fn from_u32(n: u32) -> Option<Self>
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pub fn from_u128(n: u128) -> Option<Self>
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pub fn from_f32(n: f32) -> Option<Self>
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pub fn from_f64(n: f64) -> Option<Self>
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impl Hash for Intent
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pub fn hash<__H: Hasher>(&self, state: &mut __H)
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pub fn hash_slice<H>(data: &[Self], state: &mut H) where
H: Hasher,
1.3.0[src]
H: Hasher,
impl PartialEq<Intent> for Intent
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pub fn eq(&self, other: &Intent) -> bool
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#[must_use]pub fn ne(&self, other: &Rhs) -> bool
1.0.0[src]
impl StructuralEq for Intent
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impl StructuralPartialEq for Intent
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Auto Trait Implementations
impl RefUnwindSafe for Intent
impl Send for Intent
impl Sync for Intent
impl Unpin for Intent
impl UnwindSafe for Intent
Blanket Implementations
impl<T> Any for T where
T: 'static + ?Sized,
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T: 'static + ?Sized,
impl<T> Borrow<T> for T where
T: ?Sized,
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T: ?Sized,
impl<T> BorrowMut<T> for T where
T: ?Sized,
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T: ?Sized,
pub fn borrow_mut(&mut self) -> &mut T
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impl<T> From<T> for T
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impl<T, U> Into<U> for T where
U: From<T>,
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U: From<T>,
impl<T> Same<T> for T
type Output = T
Should always be Self
impl<T> Scalar for T where
T: PartialEq<T> + Copy + Any + Debug,
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T: PartialEq<T> + Copy + Any + Debug,
impl<SS, SP> SupersetOf<SS> for SP where
SS: SubsetOf<SP>,
SS: SubsetOf<SP>,
pub fn to_subset(&self) -> Option<SS>
pub fn is_in_subset(&self) -> bool
pub fn to_subset_unchecked(&self) -> SS
pub fn from_subset(element: &SS) -> SP
impl<T> ToOwned for T where
T: Clone,
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T: Clone,
type Owned = T
The resulting type after obtaining ownership.
pub fn to_owned(&self) -> T
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pub fn clone_into(&self, target: &mut T)
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impl<T, U> TryFrom<U> for T where
U: Into<T>,
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U: Into<T>,
type Error = Infallible
The type returned in the event of a conversion error.
pub fn try_from(value: U) -> Result<T, <T as TryFrom<U>>::Error>
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impl<T, U> TryInto<U> for T where
U: TryFrom<T>,
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U: TryFrom<T>,
type Error = <U as TryFrom<T>>::Error
The type returned in the event of a conversion error.
pub fn try_into(self) -> Result<U, <U as TryFrom<T>>::Error>
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impl<V, T> VZip<V> for T where
V: MultiLane<T>,
V: MultiLane<T>,