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#######################################################

# This file is distributed under 3-clause BSD license.

# The complete license agreement can be obtained at:

# http://arrayfire.com/licenses/BSD-3-Clause

########################################################

"""

ArrayFire is a high performance scientific computing library with an easy to use API.

>>> # Monte Carlo estimation of pi

>>> def calc_pi_device(samples):

# Simple, array based API

# Generate uniformly distributed random numers

x = af.randu(samples)

y = af.randu(samples)

# Supports Just In Time Compilation

# The following line generates a single kernel

within_unit_circle = (x * x + y * y) < 1

# Intuitive function names

return 4 * af.count(within_unit_circle) / samples

Programs written using ArrayFire are portable across CUDA, OpenCL and CPU devices.

The default backend is chosen in the following order of preference based on the available libraries:

1. CUDA

2. OpenCL

3. CPU

The backend can be chosen at the beginning of the program by using the following function

>>> af.set_backend(name)

where name is one of 'cuda', 'opencl' or 'cpu'.

The functionality provided by ArrayFire spans the following domains:

1. Vector Algorithms

2. Image Processing

3. Signal Processing

4. Computer Vision

5. Linear Algebra

6. Statistics

"""

try:

import pycuda.autoinit

except ImportError:

pass

from .library import *

from .array import *

from .data import *

from .util import *

from .algorithm import *

from .device import *

from .blas import *

from .arith import *

from .statistics import *

from .lapack import *

from .signal import *

from .image import *

from .features import *

from .vision import *

from .graphics import *

from .bcast import *

from .index import *

from .interop import *

from .timer import *

from .random import *

from .sparse import *

from .ml import *

# do not export default modules as part of arrayfire

del ct

del inspect

del numbers

del os

if (AF_NUMPY_FOUND):

del np