Enum nifti::typedef::Intent

#[repr(u16)]
pub enum Intent {
    None,
    Correl,
    Ttest,
    Ftest,
    Zscore,
    Chisq,
    Beta,
    Binom,
    Gamma,
    Poisson,
    Normal,
    FtestNonc,
    ChisqNonc,
    Logistic,
    Laplace,
    Uniform,
    TtestNonc,
    Weibull,
    Chi,
    Invgauss,
    Extval,
    Pval,
    Logpval,
    Log10pval,
    Estimate,
    Label,
    Neuroname,
    Genmatrix,
    Symmatrix,
    Dispvect,
    Vector,
    Pointset,
    Triangle,
    Quaternion,
    Dimless,
    TimeSeries,
    NodeIndex,
    RgbVector,
    RgbaVector,
    Shape,
}

An enum type for representing a NIFTI intent code.

Variants

None

default: no intention is indicated in the header.

Correl

nifti1 intent codes, to describe intended meaning of dataset contents

Ttest

[C2, chap 28] Student t statistic (1 param): p1 = DOF.

Ftest

[C2, chap 27] Fisher F statistic (2 params): p1 = numerator DOF, p2 = denominator DOF.

Zscore

[C1, chap 13] Standard normal (0 params): Density = N(0,1).

Chisq

[C1, chap 18] Chi-squared (1 param): p1 = DOF. Density(x) proportional to exp(-x/2) * x^(p1/2-1).

Beta

[C2, chap 25] Beta distribution (2 params): p1=a, p2=b. Density(x) proportional to x^(a-1) * (1-x)^(b-1).

Binom

[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.

Gamma

[C1, chap 17] Gamma distribution (2 params): p1 = shape, p2 = scale. Density(x) proportional to x^(p1-1) * exp(-p2*x).

Poisson

[U, chap 4] Poisson distribution (1 param): p1 = mean. Prob(x) = exp(-p1) * p1^x / x! , for x=0,1,2,....

Normal

[C1, chap 13] Normal distribution (2 params): p1 = mean, p2 = standard deviation.

FtestNonc

[C2, chap 30] Noncentral F statistic (3 params): p1 = numerator DOF, p2 = denominator DOF, p3 = numerator noncentrality parameter.

ChisqNonc

[C2, chap 29] Noncentral chi-squared statistic (2 params): p1 = DOF, p2 = noncentrality parameter.

Logistic

[C2, chap 23] Logistic distribution (2 params): p1 = location, p2 = scale. Density(x) proportional to sech^2((x-p1)/(2*p2)).

Laplace

[C2, chap 24] Laplace distribution (2 params): p1 = location, p2 = scale. Density(x) proportional to exp(-abs(x-p1)/p2).

Uniform

[C2, chap 26] Uniform distribution: p1 = lower end, p2 = upper end.

TtestNonc

[C2, chap 31] Noncentral t statistic (2 params): p1 = DOF, p2 = noncentrality parameter.

Weibull

[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.

Chi

[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.

Invgauss

[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.

Extval

[C2, chap 22] Extreme value type I (2 params): p1 = location, p2 = scale cdf(x) = exp(-exp(-(x-p1)/p2)).

Pval

Data is a 'p-value' (no params).

Logpval

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).

Log10pval

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).

Estimate

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.

Label

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.

Neuroname

To signify that the value at each voxel is an index into the NeuroNames labels set, set intent_code = NIFTI_INTENT_NEURONAME.

Genmatrix

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]

Symmatrix

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

Dispvect

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)

Vector

(for any other type of vector)

Pointset

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").

Triangle

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.

Quaternion

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

Dimless

Dimensionless value - no params - although, as in _ESTIMATE the name of the parameter may be stored in intent_name.

TimeSeries

To signify that the value at each location is from a time series.

NodeIndex

To signify that the value at each location is a node index, from a complete surface dataset.

RgbVector

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

RgbaVector

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

Shape

To signify that the value at each location is a shape value, such as the curvature.

Methods

impl Intent

pub fn is_statcode(self) -> bool

Check whether this intent code is used for statistics.

Trait Implementations

impl Debug for Intent

fn fmt(&self, f: &mut Formatter) -> Result

Formats the value using the given formatter.

impl PartialEq for Intent

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

This method tests for self and other values to be equal, and is used by ==.

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

This method tests for !=.

impl Eq for Intent

impl Hash for Intent

fn hash<__H: Hasher>(&self, state: &mut __H)

Feeds this value into the given [Hasher].

fn hash_slice<H>(data: &[Self], state: &mut H) where     H: Hasher,

Feeds a slice of this type into the given [Hasher].

impl Clone for Intent

fn clone(&self) -> Intent

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Copy for Intent