#######################################################
# Copyright (c) 2015, ArrayFire
# All rights reserved.
#
# This file is distributed under 3-clause BSD license.
# The complete license agreement can be obtained at:
# http://arrayfire.com/licenses/BSD-3-Clause
########################################################
"""
Array class and helper functions.
"""
import inspect
import os
from .library import *
from .util import *
from .util import _is_number
from .bcast import _bcast_var
from .base import *
from .index import *
from .index import _Index4
_is_running_in_py_charm = "PYCHARM_HOSTED" in os.environ
_display_dims_limit = None
[docs]def 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
Example
-------
set_display_dims_limit(10, 10, 10, 10)
"""
global _display_dims_limit
_display_dims_limit = dims
[docs]def 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
Example
-------
get_display_dims_limit()
# None
set_display_dims_limit(10, 10, 10, 10)
get_display_dims_limit()
# (10, 10, 10, 10)
"""
return _display_dims_limit
def _in_display_dims_limit(dims):
if _is_running_in_py_charm:
return False
if _display_dims_limit is not None:
limit_len = len(_display_dims_limit)
dim_len = len(dims)
if dim_len > limit_len:
return False
for i in range(dim_len):
if dims[i] > _display_dims_limit[i]:
return False
return True
def _create_array(buf, numdims, idims, dtype, is_device):
out_arr = c_void_ptr_t(0)
c_dims = dim4(idims[0], idims[1], idims[2], idims[3])
if (not is_device):
safe_call(backend.get().af_create_array(c_pointer(out_arr), c_void_ptr_t(buf),
numdims, c_pointer(c_dims), dtype.value))
else:
safe_call(backend.get().af_device_array(c_pointer(out_arr), c_void_ptr_t(buf),
numdims, c_pointer(c_dims), dtype.value))
return out_arr
def _create_strided_array(buf, numdims, idims, dtype, is_device, offset, strides):
out_arr = c_void_ptr_t(0)
c_dims = dim4(idims[0], idims[1], idims[2], idims[3])
if offset is None:
offset = 0
offset = c_dim_t(offset)
if strides is None:
strides = (1, idims[0], idims[0]*idims[1], idims[0]*idims[1]*idims[2])
while len(strides) < 4:
strides = strides + (strides[-1],)
strides = dim4(strides[0], strides[1], strides[2], strides[3])
if is_device:
location = Source.device
else:
location = Source.host
safe_call(backend.get().af_create_strided_array(c_pointer(out_arr), c_void_ptr_t(buf),
offset, numdims, c_pointer(c_dims),
c_pointer(strides), dtype.value,
location.value))
return out_arr
def _create_empty_array(numdims, idims, dtype):
out_arr = c_void_ptr_t(0)
if numdims == 0: return out_arr
c_dims = dim4(idims[0], idims[1], idims[2], idims[3])
safe_call(backend.get().af_create_handle(c_pointer(out_arr),
numdims, c_pointer(c_dims), dtype.value))
return out_arr
[docs]def constant_array(val, d0, d1=None, d2=None, d3=None, dtype=Dtype.f32):
"""
Internal function to create a C array. Should not be used externall.
"""
if not isinstance(dtype, c_int_t):
if isinstance(dtype, int):
dtype = c_int_t(dtype)
elif isinstance(dtype, Dtype):
dtype = c_int_t(dtype.value)
else:
raise TypeError("Invalid dtype")
out = c_void_ptr_t(0)
dims = dim4(d0, d1, d2, d3)
if isinstance(val, complex):
c_real = c_double_t(val.real)
c_imag = c_double_t(val.imag)
if (dtype.value != Dtype.c32.value and dtype.value != Dtype.c64.value):
dtype = Dtype.c32.value
safe_call(backend.get().af_constant_complex(c_pointer(out), c_real, c_imag,
4, c_pointer(dims), dtype))
elif dtype.value == Dtype.s64.value:
c_val = c_longlong_t(val.real)
safe_call(backend.get().af_constant_long(c_pointer(out), c_val, 4, c_pointer(dims)))
elif dtype.value == Dtype.u64.value:
c_val = c_ulonglong_t(val.real)
safe_call(backend.get().af_constant_ulong(c_pointer(out), c_val, 4, c_pointer(dims)))
else:
c_val = c_double_t(val)
safe_call(backend.get().af_constant(c_pointer(out), c_val, 4, c_pointer(dims), dtype))
return out
def _binary_func(lhs, rhs, c_func):
out = Array()
other = rhs
if (_is_number(rhs)):
ldims = dim4_to_tuple(lhs.dims())
rty = implicit_dtype(rhs, lhs.type())
other = Array()
other.arr = constant_array(rhs, ldims[0], ldims[1], ldims[2], ldims[3], rty.value)
elif not isinstance(rhs, Array):
raise TypeError("Invalid parameter to binary function")
safe_call(c_func(c_pointer(out.arr), lhs.arr, other.arr, _bcast_var.get()))
return out
def _binary_funcr(lhs, rhs, c_func):
out = Array()
other = lhs
if (_is_number(lhs)):
rdims = dim4_to_tuple(rhs.dims())
lty = implicit_dtype(lhs, rhs.type())
other = Array()
other.arr = constant_array(lhs, rdims[0], rdims[1], rdims[2], rdims[3], lty.value)
elif not isinstance(lhs, Array):
raise TypeError("Invalid parameter to binary function")
c_func(c_pointer(out.arr), other.arr, rhs.arr, _bcast_var.get())
return out
def _ctype_to_lists(ctype_arr, dim, shape, offset=0):
if (dim == 0):
return list(ctype_arr[offset : offset + shape[0]])
else:
dim_len = shape[dim]
res = [[]] * dim_len
for n in range(dim_len):
res[n] = _ctype_to_lists(ctype_arr, dim - 1, shape, offset)
offset += shape[0]
return res
def _slice_to_length(key, dim):
tkey = [key.start, key.stop, key.step]
if tkey[0] is None:
tkey[0] = 0
elif tkey[0] < 0:
tkey[0] = dim - tkey[0]
if tkey[1] is None:
tkey[1] = dim
elif tkey[1] < 0:
tkey[1] = dim - tkey[1]
if tkey[2] is None:
tkey[2] = 1
return int(((tkey[1] - tkey[0] - 1) / tkey[2]) + 1)
def _get_info(dims, buf_len):
elements = 1
numdims = 0
if dims:
numdims = len(dims)
idims = [1]*4
for i in range(numdims):
elements *= dims[i]
idims[i] = dims[i]
elif (buf_len != 0):
idims = [buf_len, 1, 1, 1]
numdims = 1
else:
raise RuntimeError("Invalid size")
return numdims, idims
def _get_indices(key):
inds = _Index4()
if isinstance(key, tuple):
n_idx = len(key)
for n in range(n_idx):
inds[n] = Index(key[n])
else:
inds[0] = Index(key)
return inds
def _get_assign_dims(key, idims):
dims = [1]*4
for n in range(len(idims)):
dims[n] = idims[n]
if _is_number(key):
dims[0] = 1
return dims
elif isinstance(key, slice):
dims[0] = _slice_to_length(key, idims[0])
return dims
elif isinstance(key, ParallelRange):
dims[0] = _slice_to_length(key.S, idims[0])
return dims
elif isinstance(key, BaseArray):
# If the array is boolean take only the number of nonzeros
if(key.dtype() is Dtype.b8):
dims[0] = int(sum(key))
else:
dims[0] = key.elements()
return dims
elif isinstance(key, tuple):
n_inds = len(key)
for n in range(n_inds):
if (_is_number(key[n])):
dims[n] = 1
elif (isinstance(key[n], BaseArray)):
# If the array is boolean take only the number of nonzeros
if(key[n].dtype() is Dtype.b8):
dims[n] = int(sum(key[n]))
else:
dims[n] = key[n].elements()
elif (isinstance(key[n], slice)):
dims[n] = _slice_to_length(key[n], idims[n])
elif (isinstance(key[n], ParallelRange)):
dims[n] = _slice_to_length(key[n].S, idims[n])
else:
raise IndexError("Invalid type while assigning to arrayfire.array")
return dims
else:
raise IndexError("Invalid type while assigning to arrayfire.array")
[docs]def 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.
"""
out = Array()
safe_call(backend.get().af_transpose(c_pointer(out.arr), a.arr, conj))
return out
[docs]def 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.
Note
-------
Input `a` needs to be a square matrix or a batch of square matrices.
"""
safe_call(backend.get().af_transpose_inplace(a.arr, conj))
[docs]class Array(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
Attributes
-----------
arr: ctypes.c_void_p
ctypes variable containing af_array from arrayfire library.
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
Note
-----
- The class is currently limited to 4 dimensions.
- arrayfire.Array() uses column major format.
- numpy uses row major format by default which can cause issues during conversion
"""
# Numpy checks this attribute to know which class handles binary builtin operations, such as __add__.
# Setting to such a high value should make sure that arrayfire has priority over
# other classes, ensuring that e.g. numpy.float32(1)*arrayfire.randu(3) is handled by
# arrayfire's __radd__() instead of numpy's __add__()
__array_priority__ = 30
def __init__(self, src=None, dims=None, dtype=None, is_device=False, offset=None, strides=None):
super(Array, self).__init__()
buf=None
buf_len=0
if dtype is not None:
if isinstance(dtype, str):
type_char = dtype
else:
type_char = to_typecode[dtype.value]
else:
type_char = None
_type_char='f'
if src is not None:
if (isinstance(src, Array)):
safe_call(backend.get().af_retain_array(c_pointer(self.arr), src.arr))
return
host = __import__("array")
if isinstance(src, host.array):
buf,buf_len = src.buffer_info()
_type_char = src.typecode
numdims, idims = _get_info(dims, buf_len)
elif isinstance(src, list):
tmp = host.array('f', src)
buf,buf_len = tmp.buffer_info()
_type_char = tmp.typecode
numdims, idims = _get_info(dims, buf_len)
elif isinstance(src, int) or isinstance(src, c_void_ptr_t):
buf = src if not isinstance(src, c_void_ptr_t) else src.value
numdims, idims = _get_info(dims, buf_len)
elements = 1
for dim in idims:
elements *= dim
if (elements == 0):
raise RuntimeError("Expected dims when src is data pointer")
if (type_char is None):
raise TypeError("Expected type_char when src is data pointer")
_type_char = type_char
else:
raise TypeError("src is an object of unsupported class")
if (type_char is not None and
type_char != _type_char):
raise TypeError("Can not create array of requested type from input data type")
if(offset is None and strides is None):
self.arr = _create_array(buf, numdims, idims, to_dtype[_type_char], is_device)
else:
self.arr = _create_strided_array(buf, numdims, idims,
to_dtype[_type_char],
is_device, offset, strides)
else:
if type_char is None:
type_char = 'f'
numdims = len(dims) if dims else 0
idims = [1] * 4
for n in range(numdims):
idims[n] = dims[n]
self.arr = _create_empty_array(numdims, idims, to_dtype[type_char])
[docs] def as_type(self, ty):
"""
Cast current array to a specified data type
Parameters
----------
ty : Return data type
"""
return cast(self, ty)
[docs] def copy(self):
"""
Performs a deep copy of the array.
Returns
-------
out: af.Array()
An identical copy of self.
"""
out = Array()
safe_call(backend.get().af_copy_array(c_pointer(out.arr), self.arr))
return out
def __del__(self):
"""
Release the C array when going out of scope
"""
if self.arr.value:
backend.get().af_release_array(self.arr)
self.arr.value = 0
[docs] def device_ptr(self):
"""
Return the device pointer exclusively held by the array.
Returns
--------
ptr : int
Contains location of the device pointer
Note
----
- This can be used to integrate with custom C code and / or PyCUDA or PyOpenCL.
- Implies `af.device.lock_array()`. The device pointer of `a` is not freed by memory manager until `unlock_device_ptr()` is called.
- No other arrays will share the same device pointer.
- A copy of the memory is done if multiple arrays share the same memory or the array is not the owner of the memory.
- In case of a copy the return value points to the newly allocated memory which is now exclusively owned by the array.
"""
ptr = c_void_ptr_t(0)
backend.get().af_get_device_ptr(c_pointer(ptr), self.arr)
return ptr.value
[docs] def raw_ptr(self):
"""
Return the device pointer held by the array.
Returns
--------
ptr : int
Contains location of the device pointer
Note
----
- This can be used to integrate with custom C code and / or PyCUDA or PyOpenCL.
- No mem copy is peformed, this function returns the raw device pointer.
- This pointer may be shared with other arrays. Use this function with caution.
- In particular the JIT compiler will not be aware of the shared arrays.
- This results in JITed operations not being immediately visible through the other array.
"""
ptr = c_void_ptr_t(0)
backend.get().af_get_raw_ptr(c_pointer(ptr), self.arr)
return ptr.value
[docs] def offset(self):
"""
Return the offset, of the first element relative to the raw pointer.
Returns
--------
offset : int
The offset in number of elements
"""
offset = c_dim_t(0)
safe_call(backend.get().af_get_offset(c_pointer(offset), self.arr))
return offset.value
[docs] def strides(self):
"""
Return the distance in bytes between consecutive elements for each dimension.
Returns
--------
strides : tuple
The strides for each dimension
"""
s0 = c_dim_t(0)
s1 = c_dim_t(0)
s2 = c_dim_t(0)
s3 = c_dim_t(0)
safe_call(backend.get().af_get_strides(c_pointer(s0), c_pointer(s1),
c_pointer(s2), c_pointer(s3), self.arr))
strides = (s0.value,s1.value,s2.value,s3.value)
return strides[:self.numdims()]
[docs] def elements(self):
"""
Return the number of elements in the array.
"""
num = c_dim_t(0)
safe_call(backend.get().af_get_elements(c_pointer(num), self.arr))
return num.value
def __len__(self):
return(self.elements())
[docs] def allocated(self):
"""
Returns the number of bytes allocated by the memory manager for the array.
"""
num = c_size_t(0)
safe_call(backend.get().af_get_allocated_bytes(c_pointer(num), self.arr))
return num.value
[docs] def dtype(self):
"""
Return the data type as a arrayfire.Dtype enum value.
"""
dty = c_int_t(Dtype.f32.value)
safe_call(backend.get().af_get_type(c_pointer(dty), self.arr))
return to_dtype[to_typecode[dty.value]]
[docs] def type(self):
"""
Return the data type as an int.
"""
return self.dtype().value
@property
def T(self):
"""
Return the transpose of the array
"""
return transpose(self, False)
@property
def H(self):
"""
Return the hermitian transpose of the array
"""
return transpose(self, True)
[docs] def dims(self):
"""
Return the shape of the array as a tuple.
"""
d0 = c_dim_t(0)
d1 = c_dim_t(0)
d2 = c_dim_t(0)
d3 = c_dim_t(0)
safe_call(backend.get().af_get_dims(c_pointer(d0), c_pointer(d1),
c_pointer(d2), c_pointer(d3), self.arr))
dims = (d0.value,d1.value,d2.value,d3.value)
return dims[:self.numdims()]
@property
def shape(self):
"""
The shape of the array
"""
return self.dims()
[docs] def numdims(self):
"""
Return the number of dimensions of the array.
"""
nd = c_uint_t(0)
safe_call(backend.get().af_get_numdims(c_pointer(nd), self.arr))
return nd.value
[docs] def is_empty(self):
"""
Check if the array is empty i.e. it has no elements.
"""
res = c_bool_t(False)
safe_call(backend.get().af_is_empty(c_pointer(res), self.arr))
return res.value
[docs] def is_scalar(self):
"""
Check if the array is scalar i.e. it has only one element.
"""
res = c_bool_t(False)
safe_call(backend.get().af_is_scalar(c_pointer(res), self.arr))
return res.value
[docs] def is_row(self):
"""
Check if the array is a row i.e. it has a shape of (1, cols).
"""
res = c_bool_t(False)
safe_call(backend.get().af_is_row(c_pointer(res), self.arr))
return res.value
[docs] def is_column(self):
"""
Check if the array is a column i.e. it has a shape of (rows, 1).
"""
res = c_bool_t(False)
safe_call(backend.get().af_is_column(c_pointer(res), self.arr))
return res.value
[docs] def is_vector(self):
"""
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)
"""
res = c_bool_t(False)
safe_call(backend.get().af_is_vector(c_pointer(res), self.arr))
return res.value
[docs] def is_sparse(self):
"""
Check if the array is a sparse matrix.
"""
res = c_bool_t(False)
safe_call(backend.get().af_is_sparse(c_pointer(res), self.arr))
return res.value
[docs] def is_complex(self):
"""
Check if the array is of complex type.
"""
res = c_bool_t(False)
safe_call(backend.get().af_is_complex(c_pointer(res), self.arr))
return res.value
[docs] def is_real(self):
"""
Check if the array is not of complex type.
"""
res = c_bool_t(False)
safe_call(backend.get().af_is_real(c_pointer(res), self.arr))
return res.value
[docs] def is_double(self):
"""
Check if the array is of double precision floating point type.
"""
res = c_bool_t(False)
safe_call(backend.get().af_is_double(c_pointer(res), self.arr))
return res.value
[docs] def is_single(self):
"""
Check if the array is of single precision floating point type.
"""
res = c_bool_t(False)
safe_call(backend.get().af_is_single(c_pointer(res), self.arr))
return res.value
[docs] def is_half(self):
"""
Check if the array is of half floating point type (fp16).
"""
res = c_bool_t(False)
safe_call(backend.get().af_is_half(c_pointer(res), self.arr))
return res.value
[docs] def is_real_floating(self):
"""
Check if the array is real and of floating point type.
"""
res = c_bool_t(False)
safe_call(backend.get().af_is_realfloating(c_pointer(res), self.arr))
return res.value
[docs] def is_floating(self):
"""
Check if the array is of floating point type.
"""
res = c_bool_t(False)
safe_call(backend.get().af_is_floating(c_pointer(res), self.arr))
return res.value
[docs] def is_integer(self):
"""
Check if the array is of integer type.
"""
res = c_bool_t(False)
safe_call(backend.get().af_is_integer(c_pointer(res), self.arr))
return res.value
[docs] def is_bool(self):
"""
Check if the array is of type b8.
"""
res = c_bool_t(False)
safe_call(backend.get().af_is_bool(c_pointer(res), self.arr))
return res.value
[docs] def is_linear(self):
"""
Check if all elements of the array are contiguous.
"""
res = c_bool_t(False)
safe_call(backend.get().af_is_linear(c_pointer(res), self.arr))
return res.value
[docs] def is_owner(self):
"""
Check if the array owns the raw pointer or is a derived array.
"""
res = c_bool_t(False)
safe_call(backend.get().af_is_owner(c_pointer(res), self.arr))
return res.value
def __add__(self, other):
"""
Return self + other.
"""
return _binary_func(self, other, backend.get().af_add)
def __iadd__(self, other):
"""
Perform self += other.
"""
self = _binary_func(self, other, backend.get().af_add)
return self
def __radd__(self, other):
"""
Return other + self.
"""
return _binary_funcr(other, self, backend.get().af_add)
def __sub__(self, other):
"""
Return self - other.
"""
return _binary_func(self, other, backend.get().af_sub)
def __isub__(self, other):
"""
Perform self -= other.
"""
self = _binary_func(self, other, backend.get().af_sub)
return self
def __rsub__(self, other):
"""
Return other - self.
"""
return _binary_funcr(other, self, backend.get().af_sub)
def __mul__(self, other):
"""
Return self * other.
"""
return _binary_func(self, other, backend.get().af_mul)
def __imul__(self, other):
"""
Perform self *= other.
"""
self = _binary_func(self, other, backend.get().af_mul)
return self
def __rmul__(self, other):
"""
Return other * self.
"""
return _binary_funcr(other, self, backend.get().af_mul)
def __truediv__(self, other):
"""
Return self / other.
"""
return _binary_func(self, other, backend.get().af_div)
def __itruediv__(self, other):
"""
Perform self /= other.
"""
self = _binary_func(self, other, backend.get().af_div)
return self
def __rtruediv__(self, other):
"""
Return other / self.
"""
return _binary_funcr(other, self, backend.get().af_div)
def __div__(self, other):
"""
Return self / other.
"""
return _binary_func(self, other, backend.get().af_div)
def __idiv__(self, other):
"""
Perform other / self.
"""
self = _binary_func(self, other, backend.get().af_div)
return self
def __rdiv__(self, other):
"""
Return other / self.
"""
return _binary_funcr(other, self, backend.get().af_div)
def __mod__(self, other):
"""
Return self % other.
"""
return _binary_func(self, other, backend.get().af_mod)
def __imod__(self, other):
"""
Perform self %= other.
"""
self = _binary_func(self, other, backend.get().af_mod)
return self
def __rmod__(self, other):
"""
Return other % self.
"""
return _binary_funcr(other, self, backend.get().af_mod)
def __pow__(self, other):
"""
Return self ** other.
"""
return _binary_func(self, other, backend.get().af_pow)
def __ipow__(self, other):
"""
Perform self **= other.
"""
self = _binary_func(self, other, backend.get().af_pow)
return self
def __rpow__(self, other):
"""
Return other ** self.
"""
return _binary_funcr(other, self, backend.get().af_pow)
def __lt__(self, other):
"""
Return self < other.
"""
return _binary_func(self, other, backend.get().af_lt)
def __gt__(self, other):
"""
Return self > other.
"""
return _binary_func(self, other, backend.get().af_gt)
def __le__(self, other):
"""
Return self <= other.
"""
return _binary_func(self, other, backend.get().af_le)
def __ge__(self, other):
"""
Return self >= other.
"""
return _binary_func(self, other, backend.get().af_ge)
def __eq__(self, other):
"""
Return self == other.
"""
return _binary_func(self, other, backend.get().af_eq)
def __ne__(self, other):
"""
Return self != other.
"""
return _binary_func(self, other, backend.get().af_neq)
def __and__(self, other):
"""
Return self & other.
"""
return _binary_func(self, other, backend.get().af_bitand)
def __iand__(self, other):
"""
Perform self &= other.
"""
self = _binary_func(self, other, backend.get().af_bitand)
return self
def __or__(self, other):
"""
Return self | other.
"""
return _binary_func(self, other, backend.get().af_bitor)
def __ior__(self, other):
"""
Perform self |= other.
"""
self = _binary_func(self, other, backend.get().af_bitor)
return self
def __xor__(self, other):
"""
Return self ^ other.
"""
return _binary_func(self, other, backend.get().af_bitxor)
def __ixor__(self, other):
"""
Perform self ^= other.
"""
self = _binary_func(self, other, backend.get().af_bitxor)
return self
def __lshift__(self, other):
"""
Return self << other.
"""
return _binary_func(self, other, backend.get().af_bitshiftl)
def __ilshift__(self, other):
"""
Perform self <<= other.
"""
self = _binary_func(self, other, backend.get().af_bitshiftl)
return self
def __rshift__(self, other):
"""
Return self >> other.
"""
return _binary_func(self, other, backend.get().af_bitshiftr)
def __irshift__(self, other):
"""
Perform self >>= other.
"""
self = _binary_func(self, other, backend.get().af_bitshiftr)
return self
def __neg__(self):
"""
Return -self
"""
return 0 - self
def __pos__(self):
"""
Return +self
"""
return self
def __invert__(self):
"""
Return ~self
"""
out = Array()
safe_call(backend.get().af_bitnot(c_pointer(out.arr), self.arr))
return out
[docs] def logical_not(self):
"""
Return ~self
"""
out = Array()
safe_call(backend.get().af_not(c_pointer(out.arr), self.arr))
return out
[docs] def logical_and(self, other):
"""
Return self && other.
"""
out = Array()
safe_call(backend.get().af_and(c_pointer(out.arr), self.arr, other.arr)) #TODO: bcast var?
return out
[docs] def logical_or(self, other):
"""
Return self || other.
"""
out = Array()
safe_call(backend.get().af_or(c_pointer(out.arr), self.arr, other.arr)) #TODO: bcast var?
return out
def __nonzero__(self):
return self != 0
# TODO:
# def __abs__(self):
# return self
def __getitem__(self, key):
"""
Return self[key]
Note
----
Ellipsis not supported as key
"""
try:
out = Array()
n_dims = self.numdims()
if (isinstance(key, Array) and key.type() == Dtype.b8.value):
n_dims = 1
if (count(key) == 0):
return out
inds = _get_indices(key)
safe_call(backend.get().af_index_gen(c_pointer(out.arr),
self.arr, c_dim_t(n_dims), inds.pointer))
return out
except RuntimeError as e:
raise IndexError(str(e))
def __setitem__(self, key, val):
"""
Perform self[key] = val
Note
----
Ellipsis not supported as key
"""
try:
n_dims = self.numdims()
is_boolean_idx = isinstance(key, Array) and key.type() == Dtype.b8.value
if (is_boolean_idx):
n_dims = 1
num = count(key)
if (num == 0):
return
if (_is_number(val)):
tdims = _get_assign_dims(key, self.dims())
if (is_boolean_idx):
n_dims = 1
other_arr = constant_array(val, int(num), dtype=self.type())
else:
other_arr = constant_array(val, tdims[0] , tdims[1], tdims[2], tdims[3], self.type())
del_other = True
else:
other_arr = val.arr
del_other = False
out_arr = c_void_ptr_t(0)
inds = _get_indices(key)
safe_call(backend.get().af_assign_gen(c_pointer(out_arr),
self.arr, c_dim_t(n_dims), inds.pointer,
other_arr))
safe_call(backend.get().af_release_array(self.arr))
if del_other:
safe_call(backend.get().af_release_array(other_arr))
self.arr = out_arr
except RuntimeError as e:
raise IndexError(str(e))
def _reorder(self):
"""
Returns a reordered array to help interoperate with row major formats.
"""
ndims = self.numdims()
if (ndims == 1):
return self
rdims = tuple(reversed(range(ndims))) + tuple(range(ndims, 4))
out = Array()
safe_call(backend.get().af_reorder(c_pointer(out.arr), self.arr, *rdims))
return out
[docs] def to_ctype(self, 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
"""
if (self.arr.value == 0):
raise RuntimeError("Can not call to_ctype on empty array")
tmp = self._reorder() if (row_major) else self
ctype_type = to_c_type[self.type()] * self.elements()
res = ctype_type()
safe_call(backend.get().af_get_data_ptr(c_pointer(res), self.arr))
if (return_shape):
return res, self.dims()
else:
return res
[docs] def to_array(self, 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
"""
if (self.arr.value == 0):
raise RuntimeError("Can not call to_array on empty array")
res = self.to_ctype(row_major, return_shape)
host = __import__("array")
h_type = to_typecode[self.type()]
if (return_shape):
return host.array(h_type, res[0]), res[1]
else:
return host.array(h_type, res)
[docs] def to_list(self, 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
"""
ct_array, shape = self.to_ctype(row_major, True)
return _ctype_to_lists(ct_array, len(shape) - 1, shape)
[docs] def scalar(self):
"""
Return the first element of the array
"""
if (self.arr.value == 0):
raise RuntimeError("Can not call to_ctype on empty array")
ctype_type = to_c_type[self.type()]
res = ctype_type()
safe_call(backend.get().af_get_scalar(c_pointer(res), self.arr))
return res.value
def __str__(self):
"""
Converts the arrayfire array to string showing its meta data and contents.
Note
----
You can also use af.display(a, pres) to display the contents of the array with better precision.
"""
if not _in_display_dims_limit(self.dims()):
return self._get_metadata_str()
return self._get_metadata_str(dims=False) + self._as_str()
def __repr__(self):
"""
Displays the meta data of the arrayfire array.
Note
----
You can use af.display(a, pres) to display the contents of the array.
"""
return self._get_metadata_str()
def _get_metadata_str(self, dims=True):
return 'arrayfire.Array()\nType: {}\n{}' \
.format(to_typename[self.type()], 'Dims: {}'.format(str(self.dims())) if dims else '')
def _as_str(self):
arr_str = c_char_ptr_t(0)
be = backend.get()
safe_call(be.af_array_to_string(c_pointer(arr_str), "", self.arr, 4, True))
py_str = to_str(arr_str)
safe_call(be.af_free_host(arr_str))
return py_str
def __array__(self):
"""
Constructs a numpy.array from arrayfire.Array
"""
import numpy as np
res = np.empty(self.dims(), dtype=np.dtype(to_typecode[self.type()]), order='F')
safe_call(backend.get().af_get_data_ptr(c_void_ptr_t(res.ctypes.data), self.arr))
return res
[docs] def to_ndarray(self, output=None):
"""
Parameters
-----------
output: optional: numpy. default: None
Returns
----------
If output is None: Constructs a numpy.array from arrayfire.Array
If output is not None: copies content of af.array into numpy array.
Note
------
- An exception is thrown when output is not None and it is not contiguous.
- When output is None, The returned array is in fortran contiguous order.
"""
if output is None:
return self.__array__()
if (output.dtype != to_typecode[self.type()]):
raise TypeError("Output is not the same type as the array")
if (output.size != self.elements()):
raise RuntimeError("Output size does not match that of input")
flags = output.flags
tmp = None
if flags['F_CONTIGUOUS']:
tmp = self
elif flags['C_CONTIGUOUS']:
tmp = self._reorder()
else:
raise RuntimeError("When output is not None, it must be contiguous")
safe_call(backend.get().af_get_data_ptr(c_void_ptr_t(output.ctypes.data), tmp.arr))
return output
[docs]def 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
"""
expr = inspect.stack()[1][-2]
name = ""
try:
if (expr is not None):
st = expr[0].find('(') + 1
en = expr[0].rfind(')')
name = expr[0][st:en]
except IndexError:
pass
safe_call(backend.get().af_print_array_gen(name.encode('utf-8'),
a.arr, c_int_t(precision)))
[docs]def 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.
"""
index = c_int_t(-1)
safe_call(backend.get().af_save_array(c_pointer(index),
key.encode('utf-8'),
a.arr,
filename.encode('utf-8'),
append))
return index.value
[docs]def 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.
Returns
---------
"""
assert((index is not None) or (key is not None))
out = Array()
if (index is not None):
safe_call(backend.get().af_read_array_index(c_pointer(out.arr),
filename.encode('utf-8'),
index))
elif (key is not None):
safe_call(backend.get().af_read_array_key(c_pointer(out.arr),
filename.encode('utf-8'),
key.encode('utf-8')))
return out
from .algorithm import (sum, count)
from .arith import cast
