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NPL
Neurological Programs and Libraries
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NPL
Neurological Programs and Libraries
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| Cnpl::Armijo | Implementation of Armijo approximate line search algorithm |
| Cnpl::Bytes< T > | |
| ▼Cnpl::ChunkSlicer | This class is used to step through an ND array in order of dimensions, but unlike Slicer it breaks the NDArray into chunks. Iteration stops at the end of each chunk until nextChunk is called. By default only one chunk is used, equal to the entire image. Use setBreaks() to set the frequency of chunks. The input to setBreaks() is an array of integers, where the next chunk occurs when distbreak == 0, with the special property that break=0 indicates no breaks for the dimension. So for a 3D image, setBreaks({1,0,0}) will stop every time a new x-values is reached. Note that the affects the order of iteration, so x cannot be the fastest iterator. setChunkSize() is an alias for setBreaks. Note that in cases where breaksize != 0, for example image size = 5 and break = 3, chunk sizes will differ during the course of iteration! |
| Cnpl::ChunkConstIter< T > | Constant iterator for NDArray. This is slightly different from order iterator in that the ROI may be broken down into chunks. When the end of a chunk is reached, no more iteration can be performed until nextChunk() is called. isEnd() will return false until nextChunk() is called while the current chunk is at the last available. Note that if setBreaks uses an array that is smaller than input dimension, then the whole of the dimension will be used |
| Cnpl::ChunkIter< T > | This class is used to iterate through an N-Dimensional array |
| ▼Cnpl::Classifier | Base class for all ND classifiers |
| Cnpl::ExpMax | Expectation Maximization With Gaussian Mixture Model |
| Cnpl::KMeans | K-means classifier |
| ▼Cstd::complex | STL class |
| Cnpl::cdouble_t | |
| Cnpl::cfloat_t | |
| Cnpl::cquad_t | |
| Cnpl::Counter< T, MAXDIM > | Very basic counter that iterates over an ND region |
| CDftHead | |
| Cnpl::DistCorrInfoComp | The distortion correction MI Computer is used to compute the mutual information and gradient of mutual information between two images using nonrigid, unidirectional, B-spline transform |
| ▼Cenable_shared_from_this | |
| ▼Cnpl::NDArray | Pure virtual interface to interact with an ND array |
| Cnpl::NDArrayStore< 1, double > | |
| Cnpl::NDArrayStore< 2, double > | |
| Cnpl::NDArrayStore< 3, double > | |
| ▼Cnpl::NDArrayStore< D, double > | |
| Cnpl::MRImageStore< 3, double > | |
| Cnpl::MRImageStore< 4, double > | |
| Cnpl::MRImageStore< 5, double > | |
| ▼Cnpl::MRImage | MRImage can basically be used like an NDArray, with the addition of orientation related additions |
| Cnpl::MRImageStore< 3, double > | |
| Cnpl::MRImageStore< 4, double > | |
| Cnpl::MRImageStore< 5, double > | |
| Cnpl::MRImageStore< D, T > | MRImageStore is a version of NDArray that has an orientation matrix. Right now it also has additional data that is unique to nifti. Eventually this class will be forked into a subclass, and this will only have the orientation |
| ▼Cnpl::NDArrayStore< D, T > | Basic storage unity for ND array. Creates a big chunk of memory |
| Cnpl::MRImageStore< D, T > | MRImageStore is a version of NDArray that has an orientation matrix. Right now it also has additional data that is unique to nifti. Eventually this class will be forked into a subclass, and this will only have the orientation |
| Cnpl::FlatConstIter< T > | Flat iterator iterator for NDArray. No information is kept about the current index. Just goes through all data. This casts the output to the type specified using T |
| Cnpl::FlatIter< T > | Flat iterator for NDArray. No information is kept about the current ND index. Just goes through all data. This casts the output to the type specified using T |
| Cnpl::Graph< T > | |
| Cnpl::KDTree< K, E, T, D > | |
| Cnpl::KDTreeNode< K, E, T, D > | |
| ▼Cnpl::KSlicer | This class is used to slice an image in along a dimension, and to step an arbitrary direction in an image. Order may be any size from 0 to the number of dimensions. The first member of order will be the fastest moving, and the last will be the slowest. Any not dimensions not included in the order vector will be slower than the last member of order |
| Cnpl::KernelIter< T > | Iterator for an image, that allows for easy access to the neighbors of the current element/pixel. Neighbors can be accessed through offset(i) which simply provides the value of the i'th neighbor. To get that neighbors index you can use it.offset_index(i). For the center use it.center()/it.center_index(). [] may also be used in place of offset. To get the number of neighbors in the kernel use ksize(), so it.offset(0), it.offset(1), ..., it.offset(ksize()-1) are valid calls |
| Cnpl::MatMap | |
| ▼Cnpl::MatrixP | |
| Cnpl::Matrix< D1, D2 > | |
| Cnpl::MatrixReorg | Reorganizes input images into tall and wide matrices (matrices that span the total rows and cols, respectively) |
| Cnpl::MemMap | Memory map class. The basic gyst is that this works like a malloc except that data may be initialized by file contents or left empty. This is useful for matrix manipulation where files need to be opened and closed a good bit |
| ▼Cnpl::NDConstView< T > | This is a basic accessor class, which allows for accessing array data in the type specified by the template |
| Cnpl::LanczosInterpNDView< T > | The purpose of this class is to view an image as a continuous 3D+vector dimension image rather than a 4+D image. Therefore all dimensions above the third are cast as a vector and interpolation is only performed between 3D points, with the 4th dimension assumed to be non-spatial. The would be applicable if the upper dimensions are of a different type than the first 3 |
| Cnpl::LinInterpNDView< T > | The purpose of this class is to view an image as a continuous ND image and to sample at a continuous ND-position within |
| Cnpl::NNInterpNDView< T > | General purpose Nearest-Neighbor interpolator |
| ▼Cnpl::Vector3DConstView< T > | The purpose of this class is to view an image as a 3D+vector dimension image rather than a 4+D image. Therefore all dimensions above the third are cast as a vector. If there is demand I may create a matrixx verion as well |
| Cnpl::LanczosInterp3DView< T > | The purpose of this class is to view an image as a continuous 3D+vector dimension image rather than a 4+D image. Therefore all dimensions above the third are cast as a vector and interpolation is only performed between 3D points, with the 4th dimension assumed to be non-spatial. The would be applicable if the upper dimensions are of a different type than the first 3 |
| Cnpl::LinInterp3DView< T > | The purpose of this class is to view an image as a continuous 3D+vector dimension image rather than a 4+D image. Therefore all dimensions above the third are cast as a vector and interpolation is only performed between 3D points, with the 4th dimension assumed to be non-spatial. The would be applicable if the upper dimensions are of a different type than the first 3 |
| Cnpl::NNInterp3DView< T > | The purpose of this class is to view an image as a continuous 3D+vector dimension image rather than a 4+D image. Therefore all dimensions above the third are cast as a vector and interpolation is only performed between 3D points, with the 4th dimension assumed to be non-spatial. The would be applicable if the upper dimensions are of a different type than the first 3 |
| ▼Cnpl::NDConstView< double > | |
| ▼Cnpl::Vector3DConstView< double > | |
| Cnpl::LinInterp3DView< double > | |
| ▼Cnpl::NDView< T > | This is a basic accessor class, which allows for accessing array data in the type specified by the template |
| Cnpl::BSplineView< T > | This is a specialized viewer for computing the value of a cubic B-Spline interpolation from the parameters. Thus the input to the constructor or setArray must be a parameter image |
| Cnpl::Pixel3DView< T > | The purpose of this class is to view an image as a 3D image rather than a ND image. Therefore all dimensions above the third will be ignored and index 0 will be used |
| Cnpl::Vector3DView< T > | The purpose of this class is to view an image as a 3D+vector dimension image rather than a 4+D image. Therefore all dimensions above the third are cast as a vector. If there is demand I may create a matrixx verion as well |
| ▼Cnpl::Optimizer | |
| Cnpl::BFGSOpt | |
| Cnpl::GradientOpt | |
| Cnpl::LBFGSOpt | Limited-Memory Broyden–Fletcher–Goldfarb–Shanno Algorithm based on "A Limited Memory Algorithm for Bound Constrained Optimization" Richard H. Byrd, Peihuang Lu, Jorge Nocedal, and Ciyou Zhu http://dx.doi.org/10.1137/0916069 |
| Cnpl::Plotter | Class for creating basic plots of arrays or functions. An example might be: |
| Cnpl::ProbDistCorrInfoComp | The distortion correction MI Computer is used to compute the mutual information and gradient of mutual information between two images using nonrigid, unidirectional, B-spline transform. In this variant the moving image should be a probability map |
| Cnpl::RegrResult | |
| Cnpl::rgb_t | |
| Cnpl::rgba_t | |
| Cnpl::Rigid3DTrans | Struct for holding information about a rigid transform. Note that rotation R = Rx*Ry*Rz, where Rx, Ry, and Rz are the rotations about x, y and z aaxes, and the angles are stored (in radians) in the rotation member |
| Cnpl::RigidCorrComp | The Rigid Corr Computer is used to compute the correlation and gradient of correlation between two images. As the name implies, it is designed for 6 parameter rigid transforms |
| Cnpl::RigidInfoComp | The Rigid MI Computer is used to compute the mutual information and gradient of mutual information between two images. As the name implies, it is designed for 6 parameter rigid transforms |
| ▼Cnpl::Slicer | This class is used to step through an ND array in order of dimensions. Order may be any size from 0 to the number of dimensions. The first member of order will be the fastest moving, and the last will be the slowest. Any not dimensions not included in the order vector will be slower than the last member of order |
| Cnpl::NDConstIter< double > | |
| Cnpl::NDConstIter< T > | Constant iterator for NDArray. Typical usage calls for NDConstIter it(array); it++; *it |
| Cnpl::NDIter< T > | This class is used to iterate through an N-Dimensional array |
| Cnpl::Vector3DConstIter< T > | This class is used to iterate through an 3D array, where each point then has has multiple higher dimensional variable. This is analogous to Vector3DView, where even if there are multiple higher dimensions they are all alligned into a single vector at each 3D point. This makes them easier to than simple iteration in N-dimensions. This is the constant version |
| Cnpl::Vector3DIter< T > | This class is used to iterate through an 3D array, where each point then has has multiple higher dimensional variable. This is analogous to Vector3DView, where even if there are multiple higher dimensions they are all alligned into a single vector at each 3D point. This makes them easier to than simple iteration in N-dimensions |
| Cnpl::StudentsT | Student's T-distribution. A cache of the Probability Density Function and cumulative density function is created using the analytical PDF |
| Cnpl::Plotter::StyleT | |
| CTrkHead | |
| Cnpl::Wolfe | Implementation of Armijo approximate line search algorithm |
1.8.9.1 
