arrayfire.array module

Array class and helper functions.

class arrayfire.array.Array(src=None, dims=None, dtype=None, is_device=False, offset=None, strides=None)

Bases: arrayfire.base.BaseArray

A multi dimensional array container.

Parameters:

src : optional: array.array, list or C buffer. default: None.

• When src is array.array or list, the data is copied to create the Array()
• When src is None, an empty buffer is created.

dims : optional: tuple of ints. default: (0,)

• When using the default values of dims, the dims are caclulated as len(src)

dtype: optional: str or arrayfire.Dtype. default: None.

• if str, must be one of the following:

  • ‘f’ for float
  • ‘d’ for double
  • ‘b’ for bool
  • ‘B’ for unsigned char
  • ‘h’ for signed 16 bit integer
  • ‘H’ for unsigned 16 bit integer
  • ‘i’ for signed 32 bit integer
  • ‘I’ for unsigned 32 bit integer
  • ‘l’ for signed 64 bit integer
  • ‘L’ for unsigned 64 bit integer
  • ‘F’ for 32 bit complex number
  • ‘D’ for 64 bit complex number

• if arrayfire.Dtype, must be one of the following:

  • Dtype.f32 for float
  • Dtype.f64 for double
  • Dtype.b8 for bool
  • Dtype.u8 for unsigned char
  • Dtype.s16 for signed 16 bit integer
  • Dtype.u16 for unsigned 16 bit integer
  • Dtype.s32 for signed 32 bit integer
  • Dtype.u32 for unsigned 32 bit integer
  • Dtype.s64 for signed 64 bit integer
  • Dtype.u64 for unsigned 64 bit integer
  • Dtype.c32 for 32 bit complex number
  • Dtype.c64 for 64 bit complex number

• if None, Dtype.f32 is assumed

Examples

Creating an af.Array() from array.array()

>>> import arrayfire as af
>>> import array
>>> a = array.array('f', (1, 2, 3, 4))
>>> b = af.Array(a, (2,2))
>>> af.display(b)
[2 2 1 1]
    1.0000     3.0000
    2.0000     4.0000

Creating an af.Array() from a list

>>> import arrayfire as af
>>> import array
>>> a = [1, 2, 3, 4]
>>> b = af.Array(a)
>>> af.display(b)
[4 1 1 1]
    1.0000
    2.0000
    3.0000
    4.0000

Creating an af.Array() from numpy.array()

>>> import numpy as np
>>> import arrayfire as af
>>> a = np.random.random((2,2))
>>> a
array([[ 0.33042524,  0.36135449],
       [ 0.86748649,  0.42199135]])
>>> b = af.Array(a.ctypes.data, a.shape, a.dtype.char)
>>> af.display(b)
[2 2 1 1]
    0.3304     0.8675
    0.3614     0.4220

Attributes

arr: ctypes.c_void_p

ctypes variable containing af_array from arrayfire library.

Methods

allocated()	Returns the number of bytes allocated by the memory manager for the array.
as_type(ty)	Cast current array to a specified data type
copy()	Performs a deep copy of the array.
device_ptr()	Return the device pointer exclusively held by the array.
dims()	Return the shape of the array as a tuple.
dtype()	Return the data type as a arrayfire.Dtype enum value.
elements()	Return the number of elements in the array.
is_bool()	Check if the array is of type b8.
is_column()	Check if the array is a column i.e.
is_complex()	Check if the array is of complex type.
is_double()	Check if the array is of double precision floating point type.
is_empty()	Check if the array is empty i.e.
is_floating()	Check if the array is of floating point type.
is_integer()	Check if the array is of integer type.
is_linear()	Check if all elements of the array are contiguous.
is_owner()	Check if the array owns the raw pointer or is a derived array.
is_real()	Check if the array is not of complex type.
is_real_floating()	Check if the array is real and of floating point type.
is_row()	Check if the array is a row i.e.
is_scalar()	Check if the array is scalar i.e.
is_single()	Check if the array is of single precision floating point type.
is_sparse()	Check if the array is a sparse matrix.
is_vector()	Check if the array is a vector i.e.
numdims()	Return the number of dimensions of the array.
offset()	Return the offset, of the first element relative to the raw pointer.
raw_ptr()	Return the device pointer held by the array.
scalar()	Return the first element of the array
strides()	Return the distance in bytes between consecutive elements for each dimension.
to_array([row_major, return_shape])	Return the data as array.array
to_ctype([row_major, return_shape])	Return the data as a ctype C array after copying to host memory
to_list([row_major])	Return the data as list
type()	Return the data type as an int.

H

Return the hermitian transpose of the array

T

Return the transpose of the array

allocated()

Returns the number of bytes allocated by the memory manager for the array.

as_type(ty)

Cast current array to a specified data type

Parameters:ty : Return data type

copy()

Performs a deep copy of the array.

Returns:

out: af.Array()

An identical copy of self.

device_ptr()

Return the device pointer exclusively held by the array.

Returns:

ptr : int

Contains location of the device pointer

dims()

Return the shape of the array as a tuple.

dtype()

Return the data type as a arrayfire.Dtype enum value.

elements()

Return the number of elements in the array.

is_bool()

Check if the array is of type b8.

is_column()

Check if the array is a column i.e. it has a shape of (rows, 1).

is_complex()

Check if the array is of complex type.

is_double()

Check if the array is of double precision floating point type.

is_empty()

Check if the array is empty i.e. it has no elements.

is_floating()

Check if the array is of floating point type.

is_integer()

Check if the array is of integer type.

is_linear()

Check if all elements of the array are contiguous.

is_owner()

Check if the array owns the raw pointer or is a derived array.

is_real()

Check if the array is not of complex type.

is_real_floating()

Check if the array is real and of floating point type.

is_row()

Check if the array is a row i.e. it has a shape of (1, cols).

is_scalar()

Check if the array is scalar i.e. it has only one element.

is_single()

Check if the array is of single precision floating point type.

is_sparse()

Check if the array is a sparse matrix.

is_vector()

Check if the array is a vector i.e. it has a shape of one of the following: - (rows, 1) - (1, cols) - (1, 1, vols) - (1, 1, 1, batch)

numdims()

Return the number of dimensions of the array.

offset()

Return the offset, of the first element relative to the raw pointer.

Returns:

offset : int

The offset in number of elements

raw_ptr()

Return the device pointer held by the array.

Returns:

ptr : int

Contains location of the device pointer

scalar()

Return the first element of the array

shape

The shape of the array

strides()

Return the distance in bytes between consecutive elements for each dimension.

Returns:

strides : tuple

The strides for each dimension

to_array(row_major=False, return_shape=False)

Return the data as array.array

Parameters:

row_major: optional: bool. default: False.

Specifies if a transpose needs to occur before copying to host memory.

return_shape: optional: bool. default: False.

Specifies if the shape of the array needs to be returned.

Returns:

If return_shape is False:

res: array.array of the appropriate type and length.

else :

(res, dims): array.array and the shape of the array

to_ctype(row_major=False, return_shape=False)

Return the data as a ctype C array after copying to host memory

Parameters:

row_major: optional: bool. default: False.

Specifies if a transpose needs to occur before copying to host memory.

return_shape: optional: bool. default: False.

Specifies if the shape of the array needs to be returned.

Returns:

If return_shape is False:

res: The ctypes array of the appropriate type and length.

else :

(res, dims): tuple of the ctypes array and the shape of the array

to_list(row_major=False)

Return the data as list

Parameters:

row_major: optional: bool. default: False.

Specifies if a transpose needs to occur before copying to host memory.

return_shape: optional: bool. default: False.

Specifies if the shape of the array needs to be returned.

Returns:

If return_shape is False:

res: list of the appropriate type and length.

else :

(res, dims): list and the shape of the array

type()

Return the data type as an int.

arrayfire.array.constant_array(val, d0, d1=None, d2=None, d3=None, dtype=<Dtype.f32: 0>)

Internal function to create a C array. Should not be used externall.

arrayfire.array.display(a, precision=4)

Displays the contents of an array.

Parameters:

a : af.Array

Multi dimensional arrayfire array

precision: int. optional.

Specifies the number of precision bits to display

arrayfire.array.get_display_dims_limit()

Gets the dimension limit after which array’s data won’t get presented to the result of str(arr).

Default is None, which means there is no limit.

Returns:

• tuple of the current limit

  • None is there is no limit

arrayfire.array.read_array(filename, index=None, key=None)

Read an array from disk.

Parameters:

filename : str

Location of the data file.

index : int. Optional. Default: None.

• The index of the array stored in the file.
• If None, key is used.

key : str. Optional. Default: None.

• A name / key associated with the array
• If None, index is used.

arrayfire.array.save_array(key, a, filename, append=False)

Save an array to disk.

Parameters:

key : str

A name / key associated with the array

a : af.Array

The array to be stored to disk

filename : str

Location of the data file.

append : Boolean. optional. default: False.

If the file already exists, specifies if the data should be appended or overwritten.

Returns:

index : int

The index of the array stored in the file.

arrayfire.array.set_display_dims_limit(*dims)

Sets the dimension limit after which array’s data won’t get presented to the result of str(arr).

Default is None, which means there is no limit.

Parameters:*dims : dimension limit args

arrayfire.array.transpose(a, conj=False)

Perform the transpose on an input.

Parameters:

a : af.Array

Multi dimensional arrayfire array.

conj : optional: bool. default: False.

Flag to specify if a complex conjugate needs to applied for complex inputs.

Returns:

out : af.Array

Containing the tranpose of a for all batches.

arrayfire.array.transpose_inplace(a, conj=False)

Perform inplace transpose on an input.

Parameters:

a : af.Array

• Multi dimensional arrayfire array.
• Contains transposed values on exit.

conj : optional: bool. default: False.

Flag to specify if a complex conjugate needs to applied for complex inputs.