# Crate basic_dsp_matrix

source ·## Expand description

In this lib a matrix is simply a collection of vectors. The idea is that the matrix types can be used to reduce the size of a large matrix and that the return types are basic enough so that other specialized matrix libs can do the rest of the work, e.g. inverting the resulting matrix.

## Structs§

- A matrix which can hold exactly 2 vectors.
- A matrix which can hold exactly 3 vectors.
- A matrix which can hold exactly 4 vectors.
- A matrix which can hold 1 to N vectors.

## Traits§

- Retrieves the underlying storage from a matrix.
- A trait for matrix types. In this lib a matrix is simply a collection of vectors. The idea is that the matrix types can be used to reduce the size of a large matrix and that the return types are basic enough so that other specialized matrix libs can do the rest of the work, e.g. inverting the resulting matrix.
- Conversion from a generic data type into a dsp vector with complex data.
- Conversion from a generic data type into a dsp vector with complex data.
- Conversion from a generic data type into a dsp matrix which tracks its meta information (domain and number space) only at runtime. See
`ToRealMatrix`

and`ToComplexMatrix`

for alternatives which track most of the meta data with the type system and therefore avoid runtime errors. - Conversion from a collection of vectors to a matrix.
- Conversion from a generic data type into a dsp matrix with real data.
- Conversion from a generic data type into a dsp matrix with real data.

## Type Aliases§

- A matrix which can hold exactly 2 vectors of 32 bit floating point numbers in complex number space and frequency domain.
- A matrix which can hold exactly 4 vectors of 32 bit floating point numbers in complex number space and frequency domain.
- A matrix which can hold exactly 4 vectors of 32 bit floating point numbers in complex number space and frequency domain.
- A matrix which can hold 1 to N vectors of 32 bit floating point numbers in complex number space and frequency domain.
- A matrix which can hold exactly 2 vectors of 64 bit floating point numbers in complex number space and frequency domain.
- A matrix which can hold exactly 4 vectors of 64 bit floating point numbers in complex number space and frequency domain.
- A matrix which can hold exactly 4 vectors of 64 bit floating point numbers in complex number space and frequency domain.
- A matrix which can hold 1 to N vectors of 64 bit floating point numbers in complex number space and frequency domain.
- A matrix which can hold exactly 2 vectors of 32 bit floating point numbers in complex number space and time domain.
- A matrix which can hold exactly 3 vectors of 32 bit floating point numbers in complex number space and time domain.
- A matrix which can hold exactly 4 vectors of 32 bit floating point numbers in complex number space and time domain.
- A matrix which can hold 1 to N vectors of 32 bit floating point numbers in complex number space and time domain.
- A matrix which can hold exactly 2 vectors of 64 bit floating point numbers in complex number space and time domain.
- A matrix which can hold exactly 3 vectors of 64 bit floating point numbers in complex number space and time domain.
- A matrix which can hold exactly 4 vectors of 64 bit floating point numbers in complex number space and time domain.
- A matrix which can hold 1 to N vectors of 64 bit floating point numbers in complex number space and time domain.
- A matrix which can hold exactly 2 vectors of 32 bit floating point numbers in any number space or domain.
- A matrix which can hold exactly 3 vectors of 32 bit floating point numbers in any number space or domain.
- A matrix which can hold exactly 4 vectors of 32 bit floating point numbers in any number space or domain.
- A matrix which can hold 1 to N vectors of 32 bit floating point numbers in any number space or domain.
- A matrix which can hold exactly 2 vectors of 64 bit floating point numbers in any number space or domain.
- A matrix which can hold exactly 3 vectors of 64 bit floating point numbers in any number space or domain.
- A matrix which can hold exactly 4 vectors of 64 bit floating point numbers in any number space or domain.
- A matrix which can hold 1 to N vectors of 64 bit floating point numbers in any number space or domain.
- A matrix which can hold exactly 2 vectors of 32 bit floating point numbers in real number space and frequency domain.
- A matrix which can hold exactly 3 vectors of 32 bit floating point numbers in real number space and frequency domain.
- A matrix which can hold exactly 4 vectors of 32 bit floating point numbers in real number space and frequency domain.
- A matrix which can hold 1 to N vectors of 32 bit floating point numbers in real number space and frequency domain.
- A matrix which can hold exactly 2 vectors of 64 bit floating point numbers in real number space and frequency domain.
- A matrix which can hold exactly 4 vectors of 64 bit floating point numbers in real number space and frequency domain.
- A matrix which can hold exactly 4 vectors of 64 bit floating point numbers in real number space and frequency domain.
- A matrix which can hold 1 to N vectors of 64 bit floating point numbers in real number space and frequency domain.
- A matrix which can hold exactly 2 vectors of 32 bit floating point numbers in real number space and time domain.
- A matrix which can hold exactly 3 vectors of 32 bit floating point numbers in real number space and time domain.
- A matrix which can hold exactly 4 vectors of 32 bit floating point numbers in real number space and time domain.
- A matrix which can hold 1 to N vectors of 32 bit floating point numbers in real number space and time domain.
- A matrix which can hold exactly 2 vectors of 64 bit floating point numbers in real number space and time domain.
- A matrix which can hold exactly 3 vectors of 64 bit floating point numbers in real number space and time domain.
- A matrix which can hold exactly 4 vectors of 64 bit floating point numbers in real number space and time domain.
- A matrix which can hold 1 to N vectors of 64 bit floating point numbers in real number space and time domain.