def cast(pda: Union[pdarray, Strings],
dt: Union[np.dtype, str]) -> Union[pdarray, Strings]:
"""
Cast an array to another dtype.
Parameters
----------
pda : pdarray or Strings
The array of values to cast
dtype : np.dtype or str
The target dtype to cast values to
Returns
-------
pdarray or Strings
Array of values cast to desired dtype
Notes
-----
The cast is performed according to Chapel's casting rules and is NOT safe
from overflows or underflows. The user must ensure that the target dtype
has the precision and capacity to hold the desired result.
Examples
--------
>>> ak.cast(ak.linspace(1.0,5.0,5), dt=ak.int64)
array([1, 2, 3, 4, 5])
>>> ak.cast(ak.arange(0,5), dt=ak.float64).dtype
dtype('float64')
>>> ak.cast(ak.arange(0,5), dt=ak.bool)
array([False, True, True, True, True])
>>> ak.cast(ak.linspace(0,4,5), dt=ak.bool)
array([False, True, True, True, True])
"""
if isinstance(pda, pdarray):
name = pda.name
objtype = "pdarray"
elif isinstance(pda, Strings):
name = '+'.join((pda.offsets.name, pda.bytes.name))
objtype = "str"
# typechecked decorator guarantees no other case
dt = _as_dtype(dt)
opt = ""
cmd = "cast"
args = "{} {} {} {}".format(name, objtype, dt.name, opt)
repMsg = generic_msg(cmd=cmd, args=args)
if dt.name.startswith("str"):
return Strings(*(type_cast(str, repMsg).split("+")))
else:
return create_pdarray(type_cast(str, repMsg))
def hash(pda: pdarray,
full: bool = True) -> Union[Tuple[pdarray, pdarray], pdarray]:
"""
Return an element-wise hash of the array.
Parameters
----------
pda : pdarray
full : bool
By default, a 128-bit hash is computed and returned as
two int64 arrays. If full=False, then a 64-bit hash
is computed and returned as a single int64 array.
Returns
-------
hashes
If full=True, a 2-tuple of pdarrays containing the high
and low 64 bits of each hash, respectively.
If full=False, a single pdarray containing a 64-bit hash
Raises
------
TypeError
Raised if the parameter is not a pdarray
Notes
-----
This function uses the SIPhash algorithm, which can output
either a 64-bit or 128-bit hash. However, the 64-bit hash
runs a significant risk of collisions when applied to more
than a few million unique values. Unless the number of unique
values is known to be small, the 128-bit hash is strongly
recommended.
Note that this hash should not be used for security, or for
any cryptographic application. Not only is SIPhash not
intended for such uses, but this implementation employs a
fixed key for the hash, which makes it possible for an
adversary with control over input to engineer collisions.
"""
if full:
subcmd = "hash128"
else:
subcmd = "hash64"
repMsg = type_cast(
str, generic_msg(cmd="efunc", args="{} {}".format(subcmd, pda.name)))
if full:
a, b = type_cast(str, repMsg).split('+')
return create_pdarray(type_cast(str,
a)), create_pdarray(type_cast(str, b))
else:
return create_pdarray(type_cast(str, repMsg))
def abs(pda: pdarray) -> pdarray:
"""
Return the element-wise absolute value of the array.
Parameters
----------
pda : pdarray
Returns
-------
pdarray
A pdarray containing absolute values of the input array elements
Raises
------
TypeError
Raised if the parameter is not a pdarray
Examples
--------
>>> ak.abs(ak.arange(-5,-1))
array([5, 4, 3, 2])
>>> ak.abs(ak.linspace(-5,-1,5))
array([5, 4, 3, 2, 1])
"""
repMsg = generic_msg(cmd="efunc", args="{} {}".format("abs", pda.name))
return create_pdarray(type_cast(str, repMsg))
def exp(pda: pdarray) -> pdarray:
"""
Return the element-wise exponential of the array.
Parameters
----------
pda : pdarray
Returns
-------
pdarray
A pdarray containing exponential values of the input
array elements
Raises
------
TypeError
Raised if the parameter is not a pdarray
Examples
--------
>>> ak.exp(ak.arange(1,5))
array([2.7182818284590451, 7.3890560989306504, 20.085536923187668, 54.598150033144236])
>>> ak.exp(ak.uniform(5,1.0,5.0))
array([11.84010843172504, 46.454368507659211, 5.5571769623557188,
33.494295836924771, 13.478894913238722])
"""
repMsg = generic_msg(cmd="efunc", args="{} {}".format("exp", pda.name))
return create_pdarray(type_cast(str, repMsg))
def cumprod(pda: pdarray) -> pdarray:
"""
Return the cumulative product over the array.
The product is inclusive, such that the ``i`` th element of the
result is the product of elements up to and including ``i``.
Parameters
----------
pda : pdarray
Returns
-------
pdarray
A pdarray containing cumulative products for each element
of the original pdarray
Raises
------
TypeError
Raised if the parameter is not a pdarray
Examples
--------
>>> ak.cumprod(ak.arange(1,5))
array([1, 2, 6, 24]))
>>> ak.cumprod(ak.uniform(5,1.0,5.0))
array([1.5728783400481925, 7.0472855509390593, 33.78523998586553,
134.05309592737584, 450.21589865655358])
"""
repMsg = generic_msg(cmd="efunc", args="{} {}".format("cumprod", pda.name))
return create_pdarray(type_cast(str, repMsg))
def get_alt_field(self, var_info: Union[Variable, FieldInfo],
alt_prefix: str) -> Variable:
"""Get the alternate input/output field for a given element of `ScanArgs`.
For example, if `var_info` is in ``ScanArgs.outer_out_sit_sot``, then
``get_alt_field(var_info, "inner_out")`` returns the element corresponding
`var_info` in ``ScanArgs.inner_out_sit_sot``.
Parameters
----------
var_info:
The element for which we want the alternate
alt_prefix:
The string prefix for the alternate field type. It can be one of
the following: ``"inner_out"``, ``"inner_in"``, ``"outer_in"``, and
``"outer_out"``.
Outputs
-------
The alternate variable.
"""
_var_info: FieldInfo
if not isinstance(var_info, FieldInfo):
find_var_info = self.find_among_fields(var_info)
if find_var_info is None:
raise ValueError(f"Couldn't find {var_info} among fields")
_var_info = find_var_info
else:
_var_info = var_info
alt_type = _var_info.name[(_var_info.name.index("_", 6) + 1):]
alt_var = getattr(self, f"{alt_prefix}_{alt_type}")[_var_info.index]
return type_cast(Variable, alt_var)
def _cast_tile_layer(raw_layer: RawLayer) -> TileLayer:
"""Cast the raw_layer to a TileLayer.
Args:
raw_layer: RawLayer to be casted to a TileLayer
Returns:
TileLayer: The TileLayer created from raw_layer
"""
tile_layer = TileLayer(**_get_common_attributes(raw_layer).__dict__)
if raw_layer.get("chunks") is not None:
tile_layer.chunks = []
for chunk in raw_layer["chunks"]:
if raw_layer.get("encoding") is not None:
tile_layer.chunks.append(
_cast_chunk(chunk, raw_layer["encoding"],
raw_layer["compression"]))
else:
tile_layer.chunks.append(_cast_chunk(chunk))
if raw_layer.get("data") is not None:
if raw_layer.get("encoding") is not None:
tile_layer.data = _decode_tile_layer_data(
data=type_cast(str, raw_layer["data"]),
compression=raw_layer["compression"],
layer_width=raw_layer["width"],
)
else:
tile_layer.data = _convert_raw_tile_layer_data(
raw_layer["data"],
raw_layer["width"] # type: ignore
)
return tile_layer
def histogram(pda: pdarray, bins: int_scalars = 10) -> pdarray:
"""
Compute a histogram of evenly spaced bins over the range of an array.
Parameters
----------
pda : pdarray
The values to histogram
bins : int_scalars
The number of equal-size bins to use (default: 10)
Returns
-------
pdarray, int64 or float64
The number of values present in each bin
Raises
------
TypeError
Raised if the parameter is not a pdarray or if bins is
not an int.
ValueError
Raised if bins < 1
NotImplementedError
Raised if pdarray dtype is bool or uint8
See Also
--------
value_counts
Notes
-----
The bins are evenly spaced in the interval [pda.min(), pda.max()].
Currently, the user must re-compute the bin edges, e.g. with np.linspace
(see below) in order to plot the histogram.
Examples
--------
>>> import matplotlib.pyplot as plt
>>> A = ak.arange(0, 10, 1)
>>> nbins = 3
>>> h = ak.histogram(A, bins=nbins)
>>> h
array([3, 3, 4])
# Recreate the bin edges in NumPy
>>> binEdges = np.linspace(A.min(), A.max(), nbins+1)
>>> binEdges
array([0., 3., 6., 9.])
# To plot, use only the left edges, and export the histogram to NumPy
>>> plt.plot(binEdges[:-1], h.to_ndarray())
"""
if bins < 1:
raise ValueError('bins must be 1 or greater')
repMsg = generic_msg(cmd="histogram", args="{} {}".format(pda.name, bins))
return create_pdarray(type_cast(str, repMsg))
def cos(pda: pdarray) -> pdarray:
"""
Return the element-wise cosine of the array.
Parameters
----------
pda : pdarray
Returns
-------
pdarray
A pdarray containing cosine for each element
of the original pdarray
Raises
------
TypeError
Raised if the parameter is not a pdarray
"""
repMsg = type_cast(str, generic_msg("efunc {} {}".format("cos", pda.name)))
return create_pdarray(type_cast(str, repMsg))
def cast(pda: Union[pdarray, Strings], dt) -> Union[pdarray, Strings]:
"""
Cast an array to another dtype.
Parameters
----------
pda : pdarray or Strings
The array of values to cast
dtype : np.dtype or str
The target dtype to cast values to
Returns
-------
pdarray or Strings
Array of values cast to desired dtype
Notes
-----
The cast is performed according to Chapel's casting rules and is NOT safe
from overflows or underflows. The user must ensure that the target dtype
has the precision and capacity to hold the desired result.
"""
if isinstance(pda, pdarray):
name = pda.name
objtype = "pdarray"
elif isinstance(pda, Strings):
name = '+'.join((pda.offsets.name, pda.bytes.name))
objtype = "str"
# typechecked decorator guarantees no other case
dt = _as_dtype(dt)
opt = ""
msg = "cast {} {} {} {}".format(name, objtype, dt.name, opt)
repMsg = generic_msg(msg)
if dt.name.startswith("str"):
return Strings(*(type_cast(str, repMsg).split("+")))
else:
return create_pdarray(type_cast(str, repMsg))
def cast(raw_properties: List[RawProperty]) -> Properties:
"""Cast a list of `RawProperty`s into `Properties`
Args:
raw_properties: The list of `RawProperty`s to cast.
Returns:
Properties: The casted `Properties`.
"""
final: Properties = {}
value: Property
for property_ in raw_properties:
if property_["type"] == "file":
value = Path(type_cast(str, property_["value"]))
elif property_["type"] == "color":
value = parse_color(type_cast(str, property_["value"]))
else:
value = property_["value"]
final[property_["name"]] = value
return final
def abs(pda: pdarray) -> pdarray:
"""
Return the element-wise absolute value of the array.
Parameters
----------
pda : pdarray
Returns
-------
pdarray
A pdarray containing absolute values of the input array elements
Raises
------
TypeError
Raised if the parameter is not a pdarray
"""
repMsg = generic_msg("efunc {} {}".format("abs", pda.name))
return create_pdarray(type_cast(str, repMsg))
def sin(pda: pdarray) -> pdarray:
"""
Return the element-wise sine of the array.
Parameters
----------
pda : pdarray
Returns
-------
pdarray
A pdarray containing sin for each element
of the original pdarray
Raises
------
TypeError
Raised if the parameter is not a pdarray
"""
repMsg = generic_msg(cmd="efunc", args="{} {}".format("sin", pda.name))
return create_pdarray(type_cast(str, repMsg))
def isnan(pda: pdarray) -> pdarray:
"""
Test a pdarray for Not a number / NaN values
Currently only supports float-value-based arrays
Parameters
----------
pda : pdarray to test
Returns
-------
pdarray consisting of True / False values; True where NaN, False otherwise
Raises
------
TypeError
Raised if the parameter is not a pdarray
RuntimeError
if the underlying pdarray is not float-based
"""
rep_msg = generic_msg(cmd="efunc", args=f"isnan {pda.name}")
return create_pdarray(type_cast(str, rep_msg))
def log(pda: pdarray) -> pdarray:
"""
Return the element-wise natural log of the array.
Parameters
----------
pda : pdarray
Returns
-------
pdarray
A pdarray containing natural log values of the input
array elements
Raises
------
TypeError
Raised if the parameter is not a pdarray
Notes
-----
Logarithms with other bases can be computed as follows:
Examples
--------
>>> A = ak.array([1, 10, 100])
# Natural log
>>> ak.log(A)
array([0, 2.3025850929940459, 4.6051701859880918])
# Log base 10
>>> ak.log(A) / np.log(10)
array([0, 1, 2])
# Log base 2
>>> ak.log(A) / np.log(2)
array([0, 3.3219280948873626, 6.6438561897747253])
"""
repMsg = generic_msg(cmd="efunc", args="{} {}".format("log", pda.name))
return create_pdarray(type_cast(str, repMsg))
def cumsum(pda: pdarray) -> pdarray:
"""
Return the cumulative sum over the array.
The sum is inclusive, such that the ``i`` th element of the
result is the sum of elements up to and including ``i``.
Parameters
----------
pda : pdarray
Returns
-------
pdarray
A pdarray containing cumulative sums for each element
of the original pdarray
Raises
------
TypeError
Raised if the parameter is not a pdarray
Examples
--------
>>> ak.cumsum(ak.arange([1,5]))
array([1, 3, 6])
>>> ak.cumsum(ak.uniform(5,1.0,5.0))
array([3.1598310770203937, 5.4110385860243131, 9.1622479306453748,
12.710615785506533, 13.945880905466208])
>>> ak.cumsum(ak.randint(0, 1, 5, dtype=ak.bool))
array([0, 1, 1, 2, 3])
"""
repMsg = generic_msg(cmd="efunc", args="{} {}".format("cumsum", pda.name))
return create_pdarray(type_cast(str, repMsg))
def cumprod(pda: pdarray) -> pdarray:
"""
Return the cumulative product over the array.
The product is inclusive, such that the ``i`` th element of the
result is the product of elements up to and including ``i``.
Parameters
----------
pda : pdarray
Returns
-------
pdarray
A pdarray containing cumulative products for each element
of the original pdarray
Raises
------
TypeError
Raised if the parameter is not a pdarray
"""
repMsg = generic_msg("efunc {} {}".format("cumprod", pda.name))
return create_pdarray(type_cast(str, repMsg))
def where(condition: pdarray, A: Union[numeric_scalars, pdarray],
B: Union[numeric_scalars, pdarray]) -> pdarray:
"""
Returns an array with elements chosen from A and B based upon a
conditioning array. As is the case with numpy.where, the return array
consists of values from the first array (A) where the conditioning array
elements are True and from the second array (B) where the conditioning
array elements are False.
Parameters
----------
condition : pdarray
Used to choose values from A or B
A : Union[numeric_scalars, pdarray]
Value(s) used when condition is True
B : Union[numeric_scalars, pdarray]
Value(s) used when condition is False
Returns
-------
pdarray
Values chosen from A where the condition is True and B where
the condition is False
Raises
------
TypeError
Raised if the condition object is not a pdarray, if A or B is not
an int, np.int64, float, np.float64, or pdarray, if pdarray dtypes
are not supported or do not match, or multiple condition clauses (see
Notes section) are applied
ValueError
Raised if the shapes of the condition, A, and B pdarrays are unequal
Examples
--------
>>> a1 = ak.arange(1,10)
>>> a2 = ak.ones(9, dtype=np.int64)
>>> cond = a1 < 5
>>> ak.where(cond,a1,a2)
array([1, 2, 3, 4, 1, 1, 1, 1, 1])
>>> a1 = ak.arange(1,10)
>>> a2 = ak.ones(9, dtype=np.int64)
>>> cond = a1 == 5
>>> ak.where(cond,a1,a2)
array([1, 1, 1, 1, 5, 1, 1, 1, 1])
>>> a1 = ak.arange(1,10)
>>> a2 = 10
>>> cond = a1 < 5
>>> ak.where(cond,a1,a2)
array([1, 2, 3, 4, 10, 10, 10, 10, 10])
Notes
-----
A and B must have the same dtype and only one conditional clause
is supported e.g., n < 5, n > 1, which is supported in numpy
is not currently supported in Arkouda
"""
if (not isSupportedNumber(A) and not isinstance(A,pdarray)) or \
(not isSupportedNumber(B) and not isinstance(B,pdarray)):
raise TypeError(
'both A and B must be an int, np.int64, float, np.float64, or pdarray'
)
if isinstance(A, pdarray) and isinstance(B, pdarray):
repMsg = generic_msg(cmd="efunc3vv", args="{} {} {} {}".\
format("where",
condition.name,
A.name,
B.name))
# For scalars, try to convert it to the array's dtype
elif isinstance(A, pdarray) and np.isscalar(B):
repMsg = generic_msg(cmd="efunc3vs", args="{} {} {} {} {}".\
format("where",
condition.name,
A.name,
A.dtype.name,
A.format_other(B)))
elif isinstance(B, pdarray) and np.isscalar(A):
repMsg = generic_msg(cmd="efunc3sv", args="{} {} {} {} {}".\
format("where",
condition.name,
B.dtype.name,
B.format_other(A),
B.name))
elif np.isscalar(A) and np.isscalar(B):
# Scalars must share a common dtype (or be cast)
dtA = resolve_scalar_dtype(A)
dtB = resolve_scalar_dtype(B)
# Make sure at least one of the dtypes is supported
if not (dtA in DTypes or dtB in DTypes):
raise TypeError(
("Not implemented for scalar types {} " + "and {}").format(
dtA, dtB))
# If the dtypes are the same, do not cast
if dtA == dtB: # type: ignore
dt = dtA
# If the dtypes are different, try casting one direction then the other
elif dtB in DTypes and np.can_cast(A, dtB):
A = np.dtype(dtB).type(A)
dt = dtB
elif dtA in DTypes and np.can_cast(B, dtA):
B = np.dtype(dtA).type(B)
dt = dtA
# Cannot safely cast
else:
raise TypeError(("Cannot cast between scalars {} and {} to " +
"supported dtype").format(A, B))
repMsg = generic_msg(cmd="efunc3ss", args="{} {} {} {} {} {}".\
format("where",
condition.name,
dt,
A,
dt,
B))
return create_pdarray(type_cast(str, repMsg))
def conv_general_dilated_local(
lhs: jnp.ndarray,
rhs: jnp.ndarray,
window_strides: Sequence[int],
padding: Union[str, Sequence[Tuple[int, int]]],
filter_shape: Sequence[int],
lhs_dilation: Optional[Sequence[int]] = None,
rhs_dilation: Optional[Sequence[int]] = None,
dimension_numbers: Optional[
convolution.ConvGeneralDilatedDimensionNumbers] = None,
precision: lax.PrecisionLike = None) -> jnp.ndarray:
"""General n-dimensional unshared convolution operator with optional dilation.
Also known as locally connected layer, the operation is equivalent to
convolution with a separate (unshared) `rhs` kernel used at each output
spatial location. Docstring below adapted from `jax.lax.conv_general_dilated`.
See Also:
https://www.tensorflow.org/xla/operation_semantics#conv_convolution
Args:
lhs: a rank `n+2` dimensional input array.
rhs: a rank `n+2` dimensional array of kernel weights. Unlike in regular
CNNs, its spatial coordinates (`H`, `W`, ...) correspond to output spatial
locations, while input spatial locations are fused with the input channel
locations in the single `I` dimension, in the order of
`"C" + ''.join(c for c in rhs_spec if c not in 'OI')`, where
`rhs_spec = dimension_numbers[1]`. For example, if `rhs_spec == "WHIO",
the unfolded kernel shape is
`"[output W][output H]{I[receptive window W][receptive window H]}O"`.
window_strides: a sequence of `n` integers, representing the inter-window
strides.
padding: either the string `'SAME'`, the string `'VALID'`, or a sequence of
`n` `(low, high)` integer pairs that give the padding to apply before and
after each spatial dimension.
filter_shape: a sequence of `n` integers, representing the receptive window
spatial shape in the order as specified in
`rhs_spec = dimension_numbers[1]`.
lhs_dilation: `None`, or a sequence of `n` integers, giving the
dilation factor to apply in each spatial dimension of `lhs`. LHS dilation
is also known as transposed convolution.
rhs_dilation: `None`, or a sequence of `n` integers, giving the
dilation factor to apply in each input spatial dimension of `rhs`.
RHS dilation is also known as atrous convolution.
dimension_numbers: either `None`, a `ConvDimensionNumbers` object, or
a 3-tuple `(lhs_spec, rhs_spec, out_spec)`, where each element is a string
of length `n+2`.
precision: Optional. Either ``None``, which means the default precision for
the backend, a ``lax.Precision`` enum value (``Precision.DEFAULT``,
``Precision.HIGH`` or ``Precision.HIGHEST``) or a tuple of two
``lax.Precision`` enums indicating precision of ``lhs``` and ``rhs``.
Returns:
An array containing the unshared convolution result.
In the string case of `dimension_numbers`, each character identifies by
position:
- the batch dimensions in `lhs`, `rhs`, and the output with the character
'N',
- the feature dimensions in `lhs` and the output with the character 'C',
- the input and output feature dimensions in rhs with the characters 'I'
and 'O' respectively, and
- spatial dimension correspondences between `lhs`, `rhs`, and the output using
any distinct characters.
For example, to indicate dimension numbers consistent with the `conv` function
with two spatial dimensions, one could use `('NCHW', 'OIHW', 'NCHW')`. As
another example, to indicate dimension numbers consistent with the TensorFlow
Conv2D operation, one could use `('NHWC', 'HWIO', 'NHWC')`. When using the
latter form of convolution dimension specification, window strides are
associated with spatial dimension character labels according to the order in
which the labels appear in the `rhs_spec` string, so that `window_strides[0]`
is matched with the dimension corresponding to the first character
appearing in rhs_spec that is not `'I'` or `'O'`.
If `dimension_numbers` is `None`, the default is `('NCHW', 'OIHW', 'NCHW')`
(for a 2D convolution).
"""
c_precision = lax.canonicalize_precision(precision)
lhs_precision = type_cast(Optional[lax.PrecisionType],
(c_precision[0] if
(isinstance(c_precision, tuple)
and len(c_precision) == 2) else c_precision))
patches = conv_general_dilated_patches(lhs=lhs,
filter_shape=filter_shape,
window_strides=window_strides,
padding=padding,
lhs_dilation=lhs_dilation,
rhs_dilation=rhs_dilation,
dimension_numbers=dimension_numbers,
precision=lhs_precision)
lhs_spec, rhs_spec, out_spec = convolution.conv_dimension_numbers(
lhs.shape, (1, 1) + tuple(filter_shape), dimension_numbers)
lhs_c_dims, rhs_c_dims = [out_spec[1]], [rhs_spec[1]]
lhs_b_dims = out_spec[2:]
rhs_b_dims = rhs_spec[2:]
rhs_b_dims = [
rhs_b_dims[i]
for i in sorted(range(len(rhs_b_dims)), key=lambda k: lhs_b_dims[k])
]
lhs_b_dims = sorted(lhs_b_dims)
dn = ((lhs_c_dims, rhs_c_dims), (lhs_b_dims, rhs_b_dims))
out = lax.dot_general(patches,
rhs,
dimension_numbers=dn,
precision=precision)
out = jnp.moveaxis(out, (-2, -1), (out_spec[0], out_spec[1]))
return out