The default tensor class. More...

#include <npy.h>

Public Types
typedef T	value_type
	The value type of the tensor.

typedef value_type &	reference
	The reference type of the tensor.

typedef const value_type &	const_reference
	The const reference type of the tensor.

typedef value_type *	pointer
	The pointer type of the tensor.

typedef const value_type *	const_pointer
	The const pointer type of the tensor.

Public Member Functions
	tensor (const std::vector< size_t > &shape)
	Constructor.

	tensor (const std::vector< size_t > &shape, bool fortran_order)
	Constructor.

	tensor (const tensor< T > &other)
	Copy constructor.

	tensor (tensor< T > &&other)
	Move constructor.

void	save (std::basic_ostream< char > &output, endian_t endianness) const
	Save the tensor to the provided stream.

template<typename... Indices>
const T &	operator() (Indices... index) const
	Variable parameter index function.

const T &	operator() (const std::vector< std::size_t > &multi_index) const
	Index function.

template<typename... Indices>
T &	operator() (Indices... index)
	Variable parameter index function.

T &	operator() (const std::vector< std::size_t > &multi_index)
	Index function.

std::vector< T >::iterator	begin ()
	Iterator pointing at the beginning of the tensor in memory.

std::vector< T >::const_iterator	begin () const
	Iterator pointing at the beginning of the tensor in memory.

std::vector< T >::iterator	end ()
	Iterator pointing at the end of the tensor in memory.

std::vector< T >::const_iterator	end () const
	Iterator pointing at the end of the tensor in memory.

void	set (const std::vector< std::int32_t > &multi_index, const T &value)
	Sets the value at the provided index.

const T &	get (const std::vector< std::int32_t > &multi_index) const
	Gets the value at the provided index.

std::string	dtype (endian_t endianness) const
	The data type of the tensor.

data_type_t	dtype () const
	The data type of the tensor.

const std::vector< T > &	values () const
	The underlying values buffer.

void	copy_from (const T *source, size_t nitems)
	Copy values from the source to this tensor.

void	copy_from (const std::vector< T > &source)
	Copy values from the provided vector.

void	move_from (std::vector< T > &&source)
	Move values from the provided vector.

T *	data ()
	A pointer to the start of the underlying values buffer.

const T *	data () const
	A pointer to the start of the underlying values buffer.

size_t	size () const
	The number of elements in the tensor.

const std::vector< size_t > &	shape () const
	The shape of the vector. Each element is the size of the corresponding dimension.

size_t	shape (int index) const
	Returns the dimensionality of the tensor at the specified index.

size_t	ndim () const
	The number of dimensions of the tensor.

bool	fortran_order () const
	Whether the tensor data is stored in FORTRAN, or column-major, order.

tensor< T > &	operator= (const tensor< T > &other)
	Copy assignment operator.

tensor< T > &	operator= (tensor< T > &&other)
	Move assignment operator.

void	save (const std::string &path, endian_t endianness=npy::endian_t::NATIVE)
	Save this tensor to the provided location on disk.

template<class INDEX_IT , class SHAPE_IT >
size_t	ravel (INDEX_IT index, SHAPE_IT shape) const
	Ravels a multi-index into a single value indexing the buffer.

size_t	ravel (const std::vector< std::int32_t > &multi_index) const
	Ravels a multi-index into a single value indexing the buffer.

size_t	ravel (const std::vector< std::size_t > &abs_multi_index) const
	Ravels a multi-index into a single value indexing the buffer.

std::string	dtype (endian_t endianness) const
	Specialization of dtype for std::wstring tensors.

Static Public Member Functions
static tensor< T >	from_file (const std::string &path)
	Load a tensor from the specified location on disk.

static tensor< T >	load (std::basic_istream< char > &input, const header_info &info)
	Load a tensor from the provided stream.

Detailed Description

template<typename T>
class npy::tensor< T >

The default tensor class.

This class can be used as a data exchange format for the library, but the methods and classes will also work with your own tensor implementation. The library methods require the following methods to be present in a tensor type:

As long as these are present and have the same semantics, the library should handle them in the same was as this implementation. Only certain type of tensor objects are natively supported (see npy::data_type_t).

Note: This class is not optimized for access speed. It is intended as a simple data exchange format. Once the raw data has been extracted from the NPY or NPZ, it is recommended to convert it to a more efficient format for processing using the data() method.

Member Typedef Documentation

◆ const_pointer

template<typename T >

typedef const value_type* npy::tensor< T >::const_pointer

The const pointer type of the tensor.

◆ const_reference

template<typename T >

typedef const value_type& npy::tensor< T >::const_reference

The const reference type of the tensor.

◆ pointer

template<typename T >

typedef value_type* npy::tensor< T >::pointer

The pointer type of the tensor.

◆ reference

template<typename T >

typedef value_type& npy::tensor< T >::reference

The reference type of the tensor.

◆ value_type

template<typename T >

typedef T npy::tensor< T >::value_type

The value type of the tensor.

Constructor & Destructor Documentation

◆ tensor() [1/4]

template<typename T >

npy::tensor< T >::tensor ( const std::vector< size_t > & shape )

inline

Constructor.

This will allocate a data buffer of the appropriate size in row-major order.

Parameters

shape the shape of the tensor

◆ tensor() [2/4]

template<typename T >

npy::tensor< T >::tensor	(	const std::vector< size_t > &	shape,
		bool	fortran_order
	)

inline

Constructor.

This will allocate a data buffer of the appropriate size.

Parameters

shape	the shape of the tensor
fortran_order	whether the data is stored in FORTRAN, or column major, order

◆ tensor() [3/4]

template<typename T >

npy::tensor< T >::tensor ( const tensor< T > & other )

inline

Copy constructor.

◆ tensor() [4/4]

template<typename T >

npy::tensor< T >::tensor ( tensor< T > && other )

inline

Move constructor.

Member Function Documentation

◆ begin() [1/2]

template<typename T >

std::vector< T >::iterator npy::tensor< T >::begin ( )

inline

Iterator pointing at the beginning of the tensor in memory.

◆ begin() [2/2]

template<typename T >

std::vector< T >::const_iterator npy::tensor< T >::begin ( ) const

inline

Iterator pointing at the beginning of the tensor in memory.

◆ copy_from() [1/2]

template<typename T >

void npy::tensor< T >::copy_from ( const std::vector< T > & source )

inline

Copy values from the provided vector.

Parameters

source the source vector. Should have the same size as values.

◆ copy_from() [2/2]

template<typename T >

void npy::tensor< T >::copy_from	(	const T *	source,
		size_t	nitems
	)

inline

Copy values from the source to this tensor.

Parameters

source	pointer to the start of the source buffer
nitems	the number of items to copy. Should be equal to size.

◆ data() [1/2]

template<typename T >

T * npy::tensor< T >::data ( )

inline

A pointer to the start of the underlying values buffer.

◆ data() [2/2]

template<typename T >

const T * npy::tensor< T >::data ( ) const

inline

A pointer to the start of the underlying values buffer.

◆ dtype() [1/3]

template<typename T >

data_type_t npy::tensor< T >::dtype ( ) const

inline

The data type of the tensor.

This is one of the methods required by the library to read and write NPY files.

Returns: the data type of the tensor

◆ dtype() [2/3]

template<typename T >

std::string npy::tensor< T >::dtype ( endian_t endianness ) const

inline

The data type of the tensor.

◆ dtype() [3/3]

std::string npy::tensor< std::wstring >::dtype ( endian_t endianness ) const

inline

Specialization of dtype for std::wstring tensors.

◆ end() [1/2]

template<typename T >

std::vector< T >::iterator npy::tensor< T >::end ( )

inline

Iterator pointing at the end of the tensor in memory.

◆ end() [2/2]

template<typename T >

std::vector< T >::const_iterator npy::tensor< T >::end ( ) const

inline

Iterator pointing at the end of the tensor in memory.

◆ fortran_order()

template<typename T >

bool npy::tensor< T >::fortran_order ( ) const

inline

Whether the tensor data is stored in FORTRAN, or column-major, order.

This is one of the methods required by the library to read and write NPY files.

Returns: whether the tensor is stored in FORTRAN order

◆ from_file()

template<typename T >

static tensor< T > npy::tensor< T >::from_file ( const std::string & path )

inlinestatic

Load a tensor from the specified location on disk.

◆ get()

template<typename T >

const T & npy::tensor< T >::get ( const std::vector< std::int32_t > & multi_index ) const

inline

Gets the value at the provided index.

Parameters

multi_index the index into the tensor

Returns: the value at the provided index

◆ load()

template<typename T >

static tensor< T > npy::tensor< T >::load	(	std::basic_istream< char > &	input,
		const header_info &	info
	)

inlinestatic

Load a tensor from the provided stream.

This is one of the methods required by the library to read NPY files. If you implement this in a custom tensor, you will need to populate your internal data structure using the provided stream and header information. The npy::read_values method can be used to read raw data from the stream.

Parameters

input	the input stream
info	the header information

Returns: an instance of the tensor read from the stream

See also: npy::read_values

◆ move_from()

template<typename T >

void npy::tensor< T >::move_from ( std::vector< T > && source )

inline

Move values from the provided vector.

Parameters

source the source vector. Should have the same size as values.

◆ ndim()

template<typename T >

size_t npy::tensor< T >::ndim ( ) const

inline

The number of dimensions of the tensor.

This is one of the methods required by the library to read and write NPY files.

Returns: the number of dimensions

◆ operator()() [1/4]

template<typename T >

T & npy::tensor< T >::operator() ( const std::vector< std::size_t > & multi_index )

inline

Index function.

Parameters

multi_index the index into the tensor

Returns: the value at the provided index

◆ operator()() [2/4]

template<typename T >

const T & npy::tensor< T >::operator() ( const std::vector< std::size_t > & multi_index ) const

inline

Index function.

Parameters

multi_index the index into the tensor

Returns: the value at the provided index

◆ operator()() [3/4]

template<typename T >

template<typename... Indices>

T & npy::tensor< T >::operator() ( Indices... index )

inline

Variable parameter index function.

Parameters

index an index into the tensor. Can be negative (in which case it will work as in numpy)

Returns: the value at the provided index

◆ operator()() [4/4]

template<typename T >

template<typename... Indices>

const T & npy::tensor< T >::operator() ( Indices... index ) const

inline

Variable parameter index function.

Parameters

index an index into the tensor. Can be negative (in which case it will work as in numpy)

Returns: the value at the provided index

◆ operator=() [1/2]

template<typename T >

tensor< T > & npy::tensor< T >::operator= ( const tensor< T > & other )

inline

Copy assignment operator.

◆ operator=() [2/2]

template<typename T >

tensor< T > & npy::tensor< T >::operator= ( tensor< T > && other )

inline

Move assignment operator.

◆ ravel() [1/3]

template<typename T >

size_t npy::tensor< T >::ravel ( const std::vector< std::int32_t > & multi_index ) const

inline

Ravels a multi-index into a single value indexing the buffer.

Parameters

multi_index the multi-index value

Returns: the single value in the buffer corresponding to the multi-index

◆ ravel() [2/3]

template<typename T >

size_t npy::tensor< T >::ravel ( const std::vector< std::size_t > & abs_multi_index ) const

inline

Ravels a multi-index into a single value indexing the buffer.

Parameters

abs_multi_index the multi-index value

Returns: the single value in the buffer corresponding to the multi-index

◆ ravel() [3/3]

template<typename T >

template<class INDEX_IT , class SHAPE_IT >

size_t npy::tensor< T >::ravel	(	INDEX_IT	index,
		SHAPE_IT	shape
	)		const

inline

Ravels a multi-index into a single value indexing the buffer.

Template Parameters

INDEX_IT	the index iterator class
SHAPE_IT	the shape iterator class

Parameters

index	the multi-index iterator
shape	the shape iterator

Returns: the single value in the buffer corresponding to the multi-index

◆ save() [1/2]

template<typename T >

void npy::tensor< T >::save	(	const std::string &	path,
		endian_t	endianness = `npy::endian_t::NATIVE`
	)

inline

Save this tensor to the provided location on disk.

Parameters

path	a valid location on disk
endianness	the endianness to use in writing the tensor

◆ save() [2/2]

template<typename T >

void npy::tensor< T >::save	(	std::basic_ostream< char > &	output,
		endian_t	endianness
	)		const

inline

Save the tensor to the provided stream.

This is one of the methods required by the library to write NPY files. If you implement this in a custom tensor, you will need to write your internal data structure to the provided stream. The npy::write_values method can be used to write raw data to the stream.

Parameters

output	the output stream
endianness	the endianness to use in writing the data

See also: npy::write_values

◆ set()

template<typename T >

void npy::tensor< T >::set	(	const std::vector< std::int32_t > &	multi_index,
		const T &	value
	)

inline

Sets the value at the provided index.

Parameters

multi_index	an index into the tensor
value	the value to set

◆ shape() [1/2]

template<typename T >

const std::vector< size_t > & npy::tensor< T >::shape ( ) const

inline

The shape of the vector. Each element is the size of the corresponding dimension.

◆ shape() [2/2]

template<typename T >

size_t npy::tensor< T >::shape ( int index ) const

inline

Returns the dimensionality of the tensor at the specified index.

This is one of the methods required by the library to read and write NPY files.

Parameters

index index into the shape

Returns: the dimensionality at the index

◆ size()

template<typename T >

size_t npy::tensor< T >::size ( ) const

inline

The number of elements in the tensor.

◆ values()

template<typename T >

const std::vector< T > & npy::tensor< T >::values ( ) const

inline

The underlying values buffer.

The documentation for this class was generated from the following file:

include/npy/npy.h

Public Types

Public Member Functions

Static Public Member Functions

Detailed Description

Member Typedef Documentation

◆ const_pointer

◆ const_reference

◆ pointer

◆ reference

◆ value_type

Constructor & Destructor Documentation

◆ tensor() [1/4]

◆ tensor() [2/4]

◆ tensor() [3/4]

◆ tensor() [4/4]

Member Function Documentation

◆ begin() [1/2]

◆ begin() [2/2]

◆ copy_from() [1/2]

◆ copy_from() [2/2]

◆ data() [1/2]

◆ data() [2/2]

◆ dtype() [1/3]

◆ dtype() [2/3]

◆ dtype() [3/3]

◆ end() [1/2]

◆ end() [2/2]

◆ fortran_order()

◆ from_file()

◆ get()

◆ load()

◆ move_from()

◆ ndim()

◆ operator()() [1/4]

◆ operator()() [2/4]

◆ operator()() [3/4]

◆ operator()() [4/4]

◆ operator=() [1/2]

◆ operator=() [2/2]

◆ ravel() [1/3]

◆ ravel() [2/3]

◆ ravel() [3/3]

◆ save() [1/2]

◆ save() [2/2]

◆ set()

◆ shape() [1/2]

◆ shape() [2/2]

◆ size()

◆ values()