arrayfire.data module

Functions to create and manipulate arrays.

arrayfire.data.constant(val,d0,d1=None,d2=None,d3=None,dtype=<Dtype.f32:0>)[source]

Create a multi dimensional array whose elements contain the same value.

Parameters
valscalar.

Value of each element of the constant array.

d0int.

Length of first dimension.

d1optional: int. default: None.

Length of second dimension.

d2optional: int. default: None.

Length of third dimension.

d3optional: int. default: None.

Length of fourth dimension.

dtypeoptional: af.Dtype. default: af.Dtype.f32.

Data type of the array.

Returns
outaf.Array

Multi dimensional array whose elements are of valueval.- If d1 is None,out is 1D of size (d0,).- If d1 is not None and d2 is None,out is 2D of size (d0, d1).- If d1 and d2 are not None and d3 is None,out is 3D of size (d0, d1, d2).- If d1, d2, d3 are all not None,out is 4D of size (d0, d1, d2, d3).

arrayfire.data.diag(a,num=0,extract=True)[source]

Create a diagonal matrix or Extract the diagonal from a matrix.

Parameters
aaf.Array.

1 dimensional or 2 dimensional arrayfire array.

numoptional: int. default: 0.

The index of the diagonal.- num == 0 signifies the diagonal.- num > 0 signifies super diagonals.- num < 0 signifies sub diagonals.

extractoptional: bool. default: True.

  • If True , diagonal is extracted.a has to be 2D.

  • If False, diagonal matrix is created.a has to be 1D.

Returns
outaf.Array

  • if extract is True,out contains the num’th diagonal froma.

  • if extract is False,out containsa as the num’th diagonal.

arrayfire.data.flat(a)[source]

Flatten the input array.

Parameters
aaf.Array.

Multi dimensional array.

Returns
outaf.Array

  • 1 dimensional array containing all the elements froma.

arrayfire.data.flip(a,dim=0)[source]

Flip an array along a dimension.

Parameters
aaf.Array.

Multi dimensional array.

dimoptional: int. default: 0.

The dimension along which the flip is performed.

Returns
outaf.Array

The output after flippinga alongdim.

Examples

>>>importarrayfireasaf>>>a=af.randu(3,3)>>>af.display(a)[3 3 1 1]    0.7269     0.3569     0.3341    0.7104     0.1437     0.0899    0.5201     0.4563     0.5363
>>>af.display(b)[3 3 1 1]    0.5201     0.4563     0.5363    0.7104     0.1437     0.0899    0.7269     0.3569     0.3341
>>>af.display(c)[3 3 1 1]    0.3341     0.3569     0.7269    0.0899     0.1437     0.7104    0.5363     0.4563     0.5201
arrayfire.data.identity(d0,d1,d2=None,d3=None,dtype=<Dtype.f32:0>)[source]

Create an identity matrix or batch of identity matrices.

Parameters
d0int.

Length of first dimension.

d1int.

Length of second dimension.

d2optional: int. default: None.

Length of third dimension.

d3optional: int. default: None.

Length of fourth dimension.

dtypeoptional: af.Dtype. default: af.Dtype.f32.

Data type of the array.

Returns
outaf.Array

Multi dimensional array whose first two dimensions form a identity matrix.- If d2 is None,out is 2D of size (d0, d1).- If d2 is not None and d3 is None,out is 3D of size (d0, d1, d2).- If d2, d3 are not None,out is 4D of size (d0, d1, d2, d3).

arrayfire.data.iota(d0,d1=None,d2=None,d3=None,dim=-1,tile_dims=None,dtype=<Dtype.f32:0>)[source]

Create a multi dimensional array using the number of elements in the array as the range.

Parameters
valscalar.

Value of each element of the constant array.

d0int.

Length of first dimension.

d1optional: int. default: None.

Length of second dimension.

d2optional: int. default: None.

Length of third dimension.

d3optional: int. default: None.

Length of fourth dimension.

tile_dimsoptional: tuple of ints. default: None.

The number of times the data is tiled.

dtypeoptional: af.Dtype. default: af.Dtype.f32.

Data type of the array.

Returns
outaf.Array

Multi dimensional array whose elements are alongdim fall between [0 - self.elements() - 1].

Examples

>>>importarrayfireasaf>>>importarrayfireasaf>>>a=af.iota(3,3)# tile_dim is not specified, data is not tiled>>>af.display(a)# the elements range from [0 - 8] (9 elements)[3 3 1 1]    0.0000     3.0000     6.0000    1.0000     4.0000     7.0000    2.0000     5.0000     8.0000
>>>b=af.iota(3,3,tile_dims(1,2))# Asking to tile along second dimension.>>>af.display(b)[3 6 1 1]    0.0000     3.0000     6.0000     0.0000     3.0000     6.0000    1.0000     4.0000     7.0000     1.0000     4.0000     7.0000    2.0000     5.0000     8.0000     2.0000     5.0000     8.0000
arrayfire.data.join(dim,first,second,third=None,fourth=None)[source]

Join two or more arrayfire arrays along a specified dimension.

Parameters
dim: int.

Dimension along which the join occurs.

firstaf.Array.

Multi dimensional arrayfire array.

secondaf.Array.

Multi dimensional arrayfire array.

thirdoptional: af.Array. default: None.

Multi dimensional arrayfire array.

fourthoptional: af.Array. default: None.

Multi dimensional arrayfire array.

Returns
outaf.Array

An array containing the input arrays joined along the specified dimension.

Examples

>>>importarrayfireasaf>>>a=af.randu(2,3)>>>b=af.randu(2,3)>>>c=af.join(0,a,b)>>>d=af.join(1,a,b)>>>af.display(a)[2 3 1 1]    0.9508     0.2591     0.7928    0.5367     0.8359     0.8719
>>>af.display(b)[2 3 1 1]    0.3266     0.6009     0.2442    0.6275     0.0495     0.6591
>>>af.display(c)[4 3 1 1]    0.9508     0.2591     0.7928    0.5367     0.8359     0.8719    0.3266     0.6009     0.2442    0.6275     0.0495     0.6591
>>>af.display(d)[2 6 1 1]    0.9508     0.2591     0.7928     0.3266     0.6009     0.2442    0.5367     0.8359     0.8719     0.6275     0.0495     0.6591
arrayfire.data.lookup(a,idx,dim=0)[source]

Lookup the values of input array based on index.

Parameters
aaf.Array.

Multi dimensional array.

idxis lookup indices
dimoptional: int. default: 0.

Specifies the dimension for indexing

Returns
outaf.Array

An array containing values at locations specified by ‘idx’

Examples

>>>importarrayfireasaf>>>arr=af.Array([1,0,3,4,5,6],(2,3))>>>af.display(arr)[2 3 1 1]    1.0000     3.0000     5.0000    0.0000     4.0000     6.0000
>>>idx=af.array([0,2])>>>af.lookup(arr,idx,1)[2 2 1 1]    1.0000     5.0000    0.0000     6.0000
>>>idx=af.array([0])>>>af.lookup(arr,idx,0)[2 1 1 1]    0.0000    2.0000
arrayfire.data.lower(a,is_unit_diag=False)[source]

Extract the lower triangular matrix from the input.

Parameters
aaf.Array.

Multi dimensional array.

is_unit_diag: optional: bool. default: False.

Flag specifying if the diagonal elements are 1.

Returns
outaf.Array

An array containing the lower triangular elements froma.

arrayfire.data.moddims(a,d0,d1=1,d2=1,d3=1)[source]

Modify the shape of the array without changing the data layout.

Parameters
aaf.Array.

Multi dimensional array.

d0: int.

The first dimension of output.

d1: optional: int. default: 1.

The second dimension of output.

d2: optional: int. default: 1.

The third dimension of output.

d3: optional: int. default: 1.

The fourth dimension of output.

Returns
outaf.Array

  • An containing the same data asa with the specified shape.

  • The number of elements ina must matchd0 x d1 x d2 x d3.

arrayfire.data.pad(a,beginPadding,endPadding,padFillType=<PAD.ZERO:0>)[source]

Pad an array

This function will pad an array with the specified border size.Newly padded values can be filled in several different ways.

Parameters
a: af.Array

A multi dimensional input arrayfire array.

beginPadding: tuple of ints. default: (0, 0, 0, 0).
endPadding: tuple of ints. default: (0, 0, 0, 0).
padFillType: optional af.PAD default: af.PAD.ZERO

specifies type of values to fill padded border with

Returns
output: af.Array

A padded array

Examples

>>>importarrayfireasaf>>>a=af.randu(3,3)>>>af.display(a)[3 3 1 1]    0.4107     0.1794     0.3775    0.8224     0.4198     0.3027    0.9518     0.0081     0.6456
>>>padded=af.pad(a,(1,1),(1,1),af.ZERO)>>>af.display(padded)[5 5 1 1]    0.0000     0.0000     0.0000     0.0000     0.0000    0.0000     0.4107     0.1794     0.3775     0.0000    0.0000     0.8224     0.4198     0.3027     0.0000    0.0000     0.9518     0.0081     0.6456     0.0000    0.0000     0.0000     0.0000     0.0000     0.0000
arrayfire.data.range(d0,d1=None,d2=None,d3=None,dim=0,dtype=<Dtype.f32:0>)[source]

Create a multi dimensional array using length of a dimension as range.

Parameters
valscalar.

Value of each element of the constant array.

d0int.

Length of first dimension.

d1optional: int. default: None.

Length of second dimension.

d2optional: int. default: None.

Length of third dimension.

d3optional: int. default: None.

Length of fourth dimension.

dimoptional: int. default: 0.

The dimension along which the range is calculated.

dtypeoptional: af.Dtype. default: af.Dtype.f32.

Data type of the array.

Returns
outaf.Array

Multi dimensional array whose elements are alongdim fall between [0 - self.dims[dim]-1]- If d1 is None,out is 1D of size (d0,).- If d1 is not None and d2 is None,out is 2D of size (d0, d1).- If d1 and d2 are not None and d3 is None,out is 3D of size (d0, d1, d2).- If d1, d2, d3 are all not None,out is 4D of size (d0, d1, d2, d3).

Examples

>>>importarrayfireasaf>>>a=af.range(3,2)# dim is not specified, range is along first dimension.>>>af.display(a)# The data ranges from [0 - 2] (3 elements along first dimension)[3 2 1 1]    0.0000     0.0000    1.0000     1.0000    2.0000     2.0000
>>>a=af.range(3,2,dim=1)# dim is 1, range is along second dimension.>>>af.display(a)# The data ranges from [0 - 1] (2 elements along second dimension)[3 2 1 1]    0.0000     1.0000    0.0000     1.0000    0.0000     1.0000
arrayfire.data.reorder(a,d0=1,d1=0,d2=2,d3=3)[source]

Reorder the dimensions of the input.

Parameters
aaf.Array.

Multi dimensional array.

d0: optional: int. default: 1.

The location of the first dimension in the output.

d1: optional: int. default: 0.

The location of the second dimension in the output.

d2: optional: int. default: 2.

The location of the third dimension in the output.

d3: optional: int. default: 3.

The location of the fourth dimension in the output.

Returns
outaf.Array

  • An array containing the input aftern reordering its dimensions.

Examples

>>>importarrayfireasaf>>>a=af.randu(5,5,3)>>>af.display(a)[5 5 3 1]    0.4107     0.0081     0.6600     0.1046     0.8395    0.8224     0.3775     0.0764     0.8827     0.1933    0.9518     0.3027     0.0901     0.1647     0.7270    0.1794     0.6456     0.5933     0.8060     0.0322    0.4198     0.5591     0.1098     0.5938     0.0012

0.8703 0.9250 0.4387 0.6530 0.42240.5259 0.3063 0.3784 0.5476 0.52930.1443 0.9313 0.4002 0.8577 0.02120.3253 0.8684 0.4390 0.8370 0.11030.5081 0.6592 0.4718 0.0618 0.4420

0.8355 0.6767 0.1033 0.9426 0.92760.4878 0.6742 0.2119 0.4817 0.86620.2055 0.4523 0.5955 0.9097 0.35780.1794 0.1236 0.3745 0.6821 0.62630.5606 0.7924 0.9165 0.6056 0.9747

>>>b=af.reorder(a,2,0,1)>>>af.display(b)[3 5 5 1]    0.4107     0.8224     0.9518     0.1794     0.4198    0.8703     0.5259     0.1443     0.3253     0.5081    0.8355     0.4878     0.2055     0.1794     0.5606

0.0081 0.3775 0.3027 0.6456 0.55910.9250 0.3063 0.9313 0.8684 0.65920.6767 0.6742 0.4523 0.1236 0.7924

0.6600 0.0764 0.0901 0.5933 0.10980.4387 0.3784 0.4002 0.4390 0.47180.1033 0.2119 0.5955 0.3745 0.9165

0.1046 0.8827 0.1647 0.8060 0.59380.6530 0.5476 0.8577 0.8370 0.06180.9426 0.4817 0.9097 0.6821 0.6056

0.8395 0.1933 0.7270 0.0322 0.00120.4224 0.5293 0.0212 0.1103 0.44200.9276 0.8662 0.3578 0.6263 0.9747

arrayfire.data.replace(lhs,cond,rhs)[source]

Select elements from one of two arrays based on condition.

Parameters
lhsaf.Array or scalar

numerical array whose elements are replaced withrhs when conditional element is False

condaf.Array

Conditional array

rhsaf.Array or scalar

numerical array whose elements are picked when conditional element is False

Examples

>>>importarrayfireasaf>>>a=af.randu(3,3)>>>af.display(a)[3 3 1 1]    0.4107     0.1794     0.3775    0.8224     0.4198     0.3027    0.9518     0.0081     0.6456
>>>cond=(a>=0.25)&(a<=0.75)>>>af.display(cond)[3 3 1 1]         1          0          1         0          1          1         0          0          1
>>>af.replace(a,cond,0.3333)>>>af.display(a)[3 3 1 1]    0.3333     0.1794     0.3333    0.8224     0.3333     0.3333    0.9518     0.0081     0.3333
arrayfire.data.select(cond,lhs,rhs)[source]

Select elements from one of two arrays based on condition.

Parameters
condaf.Array

Conditional array

lhsaf.Array or scalar

numerical array whose elements are picked when conditional element is True

rhsaf.Array or scalar

numerical array whose elements are picked when conditional element is False

Returns
out: af.Array

An array containing elements fromlhs whencond is True andrhs when False.

Examples

>>>importarrayfireasaf>>>a=af.randu(3,3)>>>b=af.randu(3,3)>>>cond=a>b>>>res=af.select(cond,a,b)
>>>af.display(a)[3 3 1 1]    0.4107     0.1794     0.3775    0.8224     0.4198     0.3027    0.9518     0.0081     0.6456
>>>af.display(b)[3 3 1 1]    0.7269     0.3569     0.3341    0.7104     0.1437     0.0899    0.5201     0.4563     0.5363
>>>af.display(res)[3 3 1 1]    0.7269     0.3569     0.3775    0.8224     0.4198     0.3027    0.9518     0.4563     0.6456
arrayfire.data.shift(a,d0,d1=0,d2=0,d3=0)[source]

Shift the input along each dimension.

Parameters
aaf.Array.

Multi dimensional array.

d0: int.

The amount of shift along first dimension.

d1: optional: int. default: 0.

The amount of shift along second dimension.

d2: optional: int. default: 0.

The amount of shift along third dimension.

d3: optional: int. default: 0.

The amount of shift along fourth dimension.

Returns
outaf.Array

  • An array the same shape asa after shifting it by the specified amounts.

Examples

>>>importarrayfireasaf>>>a=af.randu(3,3)>>>b=af.shift(a,2)>>>c=af.shift(a,1,-1)>>>af.display(a)[3 3 1 1]    0.7269     0.3569     0.3341    0.7104     0.1437     0.0899    0.5201     0.4563     0.5363
>>>af.display(b)[3 3 1 1]    0.7104     0.1437     0.0899    0.5201     0.4563     0.5363    0.7269     0.3569     0.3341
>>>af.display(c)[3 3 1 1]    0.4563     0.5363     0.5201    0.3569     0.3341     0.7269    0.1437     0.0899     0.7104
arrayfire.data.tile(a,d0,d1=1,d2=1,d3=1)[source]

Tile an array along specified dimensions.

Parameters
aaf.Array.

Multi dimensional array.

d0: int.

The number of timesa has to be tiled along first dimension.

d1: optional: int. default: 1.

The number of timesa has to be tiled along second dimension.

d2: optional: int. default: 1.

The number of timesa has to be tiled along third dimension.

d3: optional: int. default: 1.

The number of timesa has to be tiled along fourth dimension.

Returns
outaf.Array

An array containing the input after tiling the the specified number of times.

Examples

>>>importarrayfireasaf>>>a=af.randu(2,3)>>>b=af.tile(a,2)>>>c=af.tile(a,1,2)>>>d=af.tile(a,2,2)>>>af.display(a)[2 3 1 1]    0.9508     0.2591     0.7928    0.5367     0.8359     0.8719
>>>af.display(b)[4 3 1 1]    0.4107     0.9518     0.4198    0.8224     0.1794     0.0081    0.4107     0.9518     0.4198    0.8224     0.1794     0.0081
>>>af.display(c)[2 6 1 1]    0.4107     0.9518     0.4198     0.4107     0.9518     0.4198    0.8224     0.1794     0.0081     0.8224     0.1794     0.0081
>>>af.display(d)[4 6 1 1]    0.4107     0.9518     0.4198     0.4107     0.9518     0.4198    0.8224     0.1794     0.0081     0.8224     0.1794     0.0081    0.4107     0.9518     0.4198     0.4107     0.9518     0.4198    0.8224     0.1794     0.0081     0.8224     0.1794     0.0081
arrayfire.data.upper(a,is_unit_diag=False)[source]

Extract the upper triangular matrix from the input.

Parameters
aaf.Array.

Multi dimensional array.

is_unit_diag: optional: bool. default: False.

Flag specifying if the diagonal elements are 1.

Returns
outaf.Array

An array containing the upper triangular elements froma.