code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
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def _notimplemented_flat(self):
"""Not implemented: Use :meth:`~jax.Array.flatten` instead."""
raise NotImplementedError("JAX Arrays do not implement the arr.flat property: "
"consider arr.flatten() instead.") | Not implemented: Use :meth:`~jax.Array.flatten` instead. | _notimplemented_flat | python | jax-ml/jax | jax/_src/numpy/array_methods.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/array_methods.py | Apache-2.0 |
def _multi_slice(self: Array,
start_indices: tuple[tuple[int, ...]],
limit_indices: tuple[tuple[int, ...]],
removed_dims: tuple[tuple[int, ...]]) -> list[Array]:
"""Extracts multiple slices from `arr`.
This is used to shard Array arguments to pmap. It's implemente... | Extracts multiple slices from `arr`.
This is used to shard Array arguments to pmap. It's implemented as a
Array method here to avoid circular imports.
| _multi_slice | python | jax-ml/jax | jax/_src/numpy/array_methods.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/array_methods.py | Apache-2.0 |
def get(self, *, indices_are_sorted: bool = False, unique_indices: bool = False,
mode: str | lax.GatherScatterMode | None = None,
fill_value: ArrayLike | None = None,
out_sharding: Sharding | PartitionSpec | None = None):
"""Equivalent to ``x[idx]``.
Returns the value of ``x`` tha... | Equivalent to ``x[idx]``.
Returns the value of ``x`` that would result from the NumPy-style
:mod:indexing <numpy.doc.indexing>` ``x[idx]``. This function differs from
the usual array indexing syntax in that it allows additional keyword
arguments ``indices_are_sorted`` and ``unique_indices`` to be passe... | get | python | jax-ml/jax | jax/_src/numpy/array_methods.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/array_methods.py | Apache-2.0 |
def set(self, values: ArrayLike, *, indices_are_sorted: bool = False,
unique_indices: bool = False,
mode: str | lax.GatherScatterMode | None = None) -> None:
"""Pure equivalent of ``x[idx] = y``.
Returns the value of ``x`` that would result from the NumPy-style
:mod:`indexed assignment ... | Pure equivalent of ``x[idx] = y``.
Returns the value of ``x`` that would result from the NumPy-style
:mod:`indexed assignment <numpy.doc.indexing>` ``x[idx] = y``.
See :mod:`jax.ops` for details.
| set | python | jax-ml/jax | jax/_src/numpy/array_methods.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/array_methods.py | Apache-2.0 |
def apply(self, func: Callable[[ArrayLike], Array], *,
indices_are_sorted: bool = False, unique_indices: bool = False,
mode: str | lax.GatherScatterMode | None = None) -> Array:
"""Pure equivalent of ``func.at(x, idx)`` for a unary ufunc ``func``.
Returns the value of ``x`` that would r... | Pure equivalent of ``func.at(x, idx)`` for a unary ufunc ``func``.
Returns the value of ``x`` that would result from applying the unary
function ``func`` to ``x`` at the given indices. This is similar to
``x.at[idx].set(func(x[idx]))``, but differs in the case of repeated indices:
in ``x.at[idx].apply(... | apply | python | jax-ml/jax | jax/_src/numpy/array_methods.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/array_methods.py | Apache-2.0 |
def add(self, values: ArrayLike, *,
indices_are_sorted: bool = False, unique_indices: bool = False,
mode: str | lax.GatherScatterMode | None = None) -> Array:
"""Pure equivalent of ``x[idx] += y``.
Returns the value of ``x`` that would result from the NumPy-style
:mod:indexed assignment... | Pure equivalent of ``x[idx] += y``.
Returns the value of ``x`` that would result from the NumPy-style
:mod:indexed assignment <numpy.doc.indexing>` ``x[idx] += y``.
See :mod:`jax.ops` for details.
| add | python | jax-ml/jax | jax/_src/numpy/array_methods.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/array_methods.py | Apache-2.0 |
def subtract(self, values: ArrayLike, *,
indices_are_sorted: bool = False, unique_indices: bool = False,
mode: str | lax.GatherScatterMode | None = None) -> Array:
"""Pure equivalent of ``x[idx] -= y``.
Returns the value of ``x`` that would result from the NumPy-style
:mod:ind... | Pure equivalent of ``x[idx] -= y``.
Returns the value of ``x`` that would result from the NumPy-style
:mod:indexed assignment <numpy.doc.indexing>` ``x[idx] -= y``.
See :mod:`jax.ops` for details.
| subtract | python | jax-ml/jax | jax/_src/numpy/array_methods.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/array_methods.py | Apache-2.0 |
def multiply(self, values: ArrayLike, *,
indices_are_sorted: bool = False, unique_indices: bool = False,
mode: str | lax.GatherScatterMode | None = None) -> Array:
"""Pure equivalent of ``x[idx] *= y``.
Returns the value of ``x`` that would result from the NumPy-style
:mod:ind... | Pure equivalent of ``x[idx] *= y``.
Returns the value of ``x`` that would result from the NumPy-style
:mod:indexed assignment <numpy.doc.indexing>` ``x[idx] *= y``.
See :mod:`jax.ops` for details.
| multiply | python | jax-ml/jax | jax/_src/numpy/array_methods.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/array_methods.py | Apache-2.0 |
def divide(self, values: ArrayLike, *,
indices_are_sorted: bool = False, unique_indices: bool = False,
mode: str | lax.GatherScatterMode | None = None) -> Array:
"""Pure equivalent of ``x[idx] /= y``.
Returns the value of ``x`` that would result from the NumPy-style
:mod:indexed a... | Pure equivalent of ``x[idx] /= y``.
Returns the value of ``x`` that would result from the NumPy-style
:mod:indexed assignment <numpy.doc.indexing>` ``x[idx] /= y``.
See :mod:`jax.ops` for details.
| divide | python | jax-ml/jax | jax/_src/numpy/array_methods.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/array_methods.py | Apache-2.0 |
def power(self, values: ArrayLike, *,
indices_are_sorted: bool = False, unique_indices: bool = False,
mode: str | lax.GatherScatterMode | None = None) -> Array:
"""Pure equivalent of ``x[idx] **= y``.
Returns the value of ``x`` that would result from the NumPy-style
:mod:indexed ass... | Pure equivalent of ``x[idx] **= y``.
Returns the value of ``x`` that would result from the NumPy-style
:mod:indexed assignment <numpy.doc.indexing>` ``x[idx] **= y``.
See :mod:`jax.ops` for details.
| power | python | jax-ml/jax | jax/_src/numpy/array_methods.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/array_methods.py | Apache-2.0 |
def min(self, values: ArrayLike, *,
indices_are_sorted: bool = False, unique_indices: bool = False,
mode: str | lax.GatherScatterMode | None = None) -> Array:
"""Pure equivalent of ``x[idx] = minimum(x[idx], y)``.
Returns the value of ``x`` that would result from the NumPy-style
:mod:in... | Pure equivalent of ``x[idx] = minimum(x[idx], y)``.
Returns the value of ``x`` that would result from the NumPy-style
:mod:indexed assignment <numpy.doc.indexing>`
``x[idx] = minimum(x[idx], y)``.
See :mod:`jax.ops` for details.
| min | python | jax-ml/jax | jax/_src/numpy/array_methods.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/array_methods.py | Apache-2.0 |
def max(self, values: ArrayLike, *,
indices_are_sorted: bool = False, unique_indices: bool = False,
mode: str | lax.GatherScatterMode | None = None) -> Array:
"""Pure equivalent of ``x[idx] = maximum(x[idx], y)``.
Returns the value of ``x`` that would result from the NumPy-style
:mod:in... | Pure equivalent of ``x[idx] = maximum(x[idx], y)``.
Returns the value of ``x`` that would result from the NumPy-style
:mod:indexed assignment <numpy.doc.indexing>`
``x[idx] = maximum(x[idx], y)``.
See :mod:`jax.ops` for details.
| max | python | jax-ml/jax | jax/_src/numpy/array_methods.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/array_methods.py | Apache-2.0 |
def register_jax_array_methods():
"""Call this function once to register methods of JAX arrays"""
_set_shaped_array_attributes(core.ShapedArray)
_set_shaped_array_attributes(core.DShapedArray)
_set_array_base_attributes(ArrayImpl, exclude={'__getitem__'})
_set_tracer_aval_forwarding(core.Tracer, exclude={*_i... | Call this function once to register methods of JAX arrays | register_jax_array_methods | python | jax-ml/jax | jax/_src/numpy/array_methods.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/array_methods.py | Apache-2.0 |
def _is_category_disabled(
category: Category | None,
) -> bool:
"""Check if the error checking behavior for the given category is disabled."""
if category is None:
return False
if category == "nan":
raise ValueError("nan is deprecated. Use `_set_error_if_nan` instead.")
if category == "divide":
... | Check if the error checking behavior for the given category is disabled. | _is_category_disabled | python | jax-ml/jax | jax/_src/numpy/error.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/error.py | Apache-2.0 |
def _set_error_if_with_category(
pred: Array,
/,
msg: str,
category: Category | None = None,
) -> None:
"""Set the internal error state if any element of `pred` is `True`.
This function is similar to :func:`set_error_if`, but it also takes a category
argument. The category can be "nan", "divide",... | Set the internal error state if any element of `pred` is `True`.
This function is similar to :func:`set_error_if`, but it also takes a category
argument. The category can be "nan", "divide", or "oob". The error checking
behavior for each category can be configured using
:func:`set_error_checking_behavior`. If ... | _set_error_if_with_category | python | jax-ml/jax | jax/_src/numpy/error.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/error.py | Apache-2.0 |
def _set_error_if_nan(pred: Array, /):
"""Set the internal error state if any element of `pred` is `NaN`.
This function is disabled if the `jax_error_checking_behavior_nan` flag is
set to "ignore".
"""
if config.error_checking_behavior_nan.value == "ignore":
return
if not dtypes.issubdtype(pred.dtype,... | Set the internal error state if any element of `pred` is `NaN`.
This function is disabled if the `jax_error_checking_behavior_nan` flag is
set to "ignore".
| _set_error_if_nan | python | jax-ml/jax | jax/_src/numpy/error.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/error.py | Apache-2.0 |
def _set_error_if_divide_by_zero(pred: Array, /):
"""Set the internal error state if any element of `pred` is zero.
This function is intended for checking if the denominator of a division is
zero.
This function is disabled if the `jax_error_checking_behavior_divide` flag is
set to "ignore".
"""
if confi... | Set the internal error state if any element of `pred` is zero.
This function is intended for checking if the denominator of a division is
zero.
This function is disabled if the `jax_error_checking_behavior_divide` flag is
set to "ignore".
| _set_error_if_divide_by_zero | python | jax-ml/jax | jax/_src/numpy/error.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/error.py | Apache-2.0 |
def _check_precondition_oob_gather(
shape: tuple[int, ...], gather_indices: ArrayLike
) -> None:
"""Check for out of bounds errors before calling `lax.gather`."""
if config.error_checking_behavior_oob.value == "ignore":
return
# TODO(mattjj): fix the circular import issue.
from jax._src import error_ch... | Check for out of bounds errors before calling `lax.gather`. | _check_precondition_oob_gather | python | jax-ml/jax | jax/_src/numpy/error.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/error.py | Apache-2.0 |
def _check_precondition_oob_dynamic_slice(
shape: tuple[int, ...],
start_indices: Sequence[ArrayLike],
slice_sizes: list[int],
allow_negative_indices: list[bool],
) -> None:
"""Check for out of bounds errors before calling `lax.dynamic_slice`."""
if config.error_checking_behavior_oob.value == "ignor... | Check for out of bounds errors before calling `lax.dynamic_slice`. | _check_precondition_oob_dynamic_slice | python | jax-ml/jax | jax/_src/numpy/error.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/error.py | Apache-2.0 |
def fftn(a: ArrayLike, s: Shape | None = None,
axes: Sequence[int] | None = None,
norm: str | None = None) -> Array:
r"""Compute a multidimensional discrete Fourier transform along given axes.
JAX implementation of :func:`numpy.fft.fftn`.
Args:
a: input array
s: sequence of integers. S... | Compute a multidimensional discrete Fourier transform along given axes.
JAX implementation of :func:`numpy.fft.fftn`.
Args:
a: input array
s: sequence of integers. Specifies the shape of the result. If not specified,
it will default to the shape of ``a`` along the specified ``axes``.
axes: seque... | fftn | python | jax-ml/jax | jax/_src/numpy/fft.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/fft.py | Apache-2.0 |
def ifftn(a: ArrayLike, s: Shape | None = None,
axes: Sequence[int] | None = None,
norm: str | None = None) -> Array:
r"""Compute a multidimensional inverse discrete Fourier transform.
JAX implementation of :func:`numpy.fft.ifftn`.
Args:
a: input array
s: sequence of integers. Specif... | Compute a multidimensional inverse discrete Fourier transform.
JAX implementation of :func:`numpy.fft.ifftn`.
Args:
a: input array
s: sequence of integers. Specifies the shape of the result. If not specified,
it will default to the shape of ``a`` along the specified ``axes``.
axes: sequence of i... | ifftn | python | jax-ml/jax | jax/_src/numpy/fft.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/fft.py | Apache-2.0 |
def rfftn(a: ArrayLike, s: Shape | None = None,
axes: Sequence[int] | None = None,
norm: str | None = None) -> Array:
"""Compute a multidimensional discrete Fourier transform of a real-valued array.
JAX implementation of :func:`numpy.fft.rfftn`.
Args:
a: real-valued input array.
s: o... | Compute a multidimensional discrete Fourier transform of a real-valued array.
JAX implementation of :func:`numpy.fft.rfftn`.
Args:
a: real-valued input array.
s: optional sequence of integers. Controls the effective size of the input
along each specified axis. If not specified, it will default to th... | rfftn | python | jax-ml/jax | jax/_src/numpy/fft.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/fft.py | Apache-2.0 |
def irfftn(a: ArrayLike, s: Shape | None = None,
axes: Sequence[int] | None = None,
norm: str | None = None) -> Array:
"""Compute a real-valued multidimensional inverse discrete Fourier transform.
JAX implementation of :func:`numpy.fft.irfftn`.
Args:
a: input array.
s: optional seq... | Compute a real-valued multidimensional inverse discrete Fourier transform.
JAX implementation of :func:`numpy.fft.irfftn`.
Args:
a: input array.
s: optional sequence of integers. Specifies the size of the output in each
specified axis. If not specified, the dimension of output along axis
``axe... | irfftn | python | jax-ml/jax | jax/_src/numpy/fft.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/fft.py | Apache-2.0 |
def fft(a: ArrayLike, n: int | None = None,
axis: int = -1, norm: str | None = None) -> Array:
r"""Compute a one-dimensional discrete Fourier transform along a given axis.
JAX implementation of :func:`numpy.fft.fft`.
Args:
a: input array
n: int. Specifies the dimension of the result along ``axis... | Compute a one-dimensional discrete Fourier transform along a given axis.
JAX implementation of :func:`numpy.fft.fft`.
Args:
a: input array
n: int. Specifies the dimension of the result along ``axis``. If not specified,
it will default to the dimension of ``a`` along ``axis``.
axis: int, default=... | fft | python | jax-ml/jax | jax/_src/numpy/fft.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/fft.py | Apache-2.0 |
def ifft(a: ArrayLike, n: int | None = None,
axis: int = -1, norm: str | None = None) -> Array:
r"""Compute a one-dimensional inverse discrete Fourier transform.
JAX implementation of :func:`numpy.fft.ifft`.
Args:
a: input array
n: int. Specifies the dimension of the result along ``axis``. If n... | Compute a one-dimensional inverse discrete Fourier transform.
JAX implementation of :func:`numpy.fft.ifft`.
Args:
a: input array
n: int. Specifies the dimension of the result along ``axis``. If not specified,
it will default to the dimension of ``a`` along ``axis``.
axis: int, default=-1. Specif... | ifft | python | jax-ml/jax | jax/_src/numpy/fft.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/fft.py | Apache-2.0 |
def rfft(a: ArrayLike, n: int | None = None,
axis: int = -1, norm: str | None = None) -> Array:
r"""Compute a one-dimensional discrete Fourier transform of a real-valued array.
JAX implementation of :func:`numpy.fft.rfft`.
Args:
a: real-valued input array.
n: int. Specifies the effective dimens... | Compute a one-dimensional discrete Fourier transform of a real-valued array.
JAX implementation of :func:`numpy.fft.rfft`.
Args:
a: real-valued input array.
n: int. Specifies the effective dimension of the input along ``axis``. If not
specified, it will default to the dimension of input along ``axis... | rfft | python | jax-ml/jax | jax/_src/numpy/fft.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/fft.py | Apache-2.0 |
def irfft(a: ArrayLike, n: int | None = None,
axis: int = -1, norm: str | None = None) -> Array:
"""Compute a real-valued one-dimensional inverse discrete Fourier transform.
JAX implementation of :func:`numpy.fft.irfft`.
Args:
a: input array.
n: int. Specifies the dimension of the result along... | Compute a real-valued one-dimensional inverse discrete Fourier transform.
JAX implementation of :func:`numpy.fft.irfft`.
Args:
a: input array.
n: int. Specifies the dimension of the result along ``axis``. If not specified,
``n = 2*(m-1)``, where ``m`` is the dimension of ``a`` along ``axis``.
ax... | irfft | python | jax-ml/jax | jax/_src/numpy/fft.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/fft.py | Apache-2.0 |
def fft2(a: ArrayLike, s: Shape | None = None, axes: Sequence[int] = (-2,-1),
norm: str | None = None) -> Array:
"""Compute a two-dimensional discrete Fourier transform along given axes.
JAX implementation of :func:`numpy.fft.fft2`.
Args:
a: input array. Must have ``a.ndim >= 2``.
s: optional l... | Compute a two-dimensional discrete Fourier transform along given axes.
JAX implementation of :func:`numpy.fft.fft2`.
Args:
a: input array. Must have ``a.ndim >= 2``.
s: optional length-2 sequence of integers. Specifies the size of the output
along each specified axis. If not specified, it will defau... | fft2 | python | jax-ml/jax | jax/_src/numpy/fft.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/fft.py | Apache-2.0 |
def ifft2(a: ArrayLike, s: Shape | None = None, axes: Sequence[int] = (-2,-1),
norm: str | None = None) -> Array:
"""Compute a two-dimensional inverse discrete Fourier transform.
JAX implementation of :func:`numpy.fft.ifft2`.
Args:
a: input array. Must have ``a.ndim >= 2``.
s: optional length-... | Compute a two-dimensional inverse discrete Fourier transform.
JAX implementation of :func:`numpy.fft.ifft2`.
Args:
a: input array. Must have ``a.ndim >= 2``.
s: optional length-2 sequence of integers. Specifies the size of the output
in each specified axis. If not specified, it will default to the s... | ifft2 | python | jax-ml/jax | jax/_src/numpy/fft.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/fft.py | Apache-2.0 |
def rfft2(a: ArrayLike, s: Shape | None = None, axes: Sequence[int] = (-2,-1),
norm: str | None = None) -> Array:
"""Compute a two-dimensional discrete Fourier transform of a real-valued array.
JAX implementation of :func:`numpy.fft.rfft2`.
Args:
a: real-valued input array. Must have ``a.ndim >= 2... | Compute a two-dimensional discrete Fourier transform of a real-valued array.
JAX implementation of :func:`numpy.fft.rfft2`.
Args:
a: real-valued input array. Must have ``a.ndim >= 2``.
s: optional length-2 sequence of integers. Specifies the effective size of the
output along each specified axis. If... | rfft2 | python | jax-ml/jax | jax/_src/numpy/fft.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/fft.py | Apache-2.0 |
def irfft2(a: ArrayLike, s: Shape | None = None, axes: Sequence[int] = (-2,-1),
norm: str | None = None) -> Array:
"""Compute a real-valued two-dimensional inverse discrete Fourier transform.
JAX implementation of :func:`numpy.fft.irfft2`.
Args:
a: input array. Must have ``a.ndim >= 2``.
s: o... | Compute a real-valued two-dimensional inverse discrete Fourier transform.
JAX implementation of :func:`numpy.fft.irfft2`.
Args:
a: input array. Must have ``a.ndim >= 2``.
s: optional length-2 sequence of integers. Specifies the size of the output
in each specified axis. If not specified, the dimensi... | irfft2 | python | jax-ml/jax | jax/_src/numpy/fft.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/fft.py | Apache-2.0 |
def fftshift(x: ArrayLike, axes: None | int | Sequence[int] = None) -> Array:
"""Shift zero-frequency fft component to the center of the spectrum.
JAX implementation of :func:`numpy.fft.fftshift`.
Args:
x: N-dimensional array array of frequencies.
axes: optional integer or sequence of integers specifyin... | Shift zero-frequency fft component to the center of the spectrum.
JAX implementation of :func:`numpy.fft.fftshift`.
Args:
x: N-dimensional array array of frequencies.
axes: optional integer or sequence of integers specifying which axes to
shift. If None (default), then shift all axes.
Returns:
... | fftshift | python | jax-ml/jax | jax/_src/numpy/fft.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/fft.py | Apache-2.0 |
def ifftshift(x: ArrayLike, axes: None | int | Sequence[int] = None) -> Array:
"""The inverse of :func:`jax.numpy.fft.fftshift`.
JAX implementation of :func:`numpy.fft.ifftshift`.
Args:
x: N-dimensional array array of frequencies.
axes: optional integer or sequence of integers specifying which axes to
... | The inverse of :func:`jax.numpy.fft.fftshift`.
JAX implementation of :func:`numpy.fft.ifftshift`.
Args:
x: N-dimensional array array of frequencies.
axes: optional integer or sequence of integers specifying which axes to
shift. If None (default), then shift all axes.
Returns:
A shifted copy o... | ifftshift | python | jax-ml/jax | jax/_src/numpy/fft.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/fft.py | Apache-2.0 |
def take(
a: ArrayLike,
indices: ArrayLike,
axis: int | None = None,
out: None = None,
mode: str | None = None,
unique_indices: bool = False,
indices_are_sorted: bool = False,
fill_value: StaticScalar | None = None,
) -> Array:
"""Take elements from an array.
JAX implementation of :... | Take elements from an array.
JAX implementation of :func:`numpy.take`, implemented in terms of
:func:`jax.lax.gather`. JAX's behavior differs from NumPy in the case
of out-of-bound indices; see the ``mode`` parameter below.
Args:
a: array from which to take values.
indices: N-dimensional array of inte... | take | python | jax-ml/jax | jax/_src/numpy/indexing.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/indexing.py | Apache-2.0 |
def _normalize_index(index, axis_size):
"""Normalizes an index value in the range [-N, N) to the range [0, N)."""
if dtypes.issubdtype(dtypes.dtype(index, canonicalize=True), np.unsignedinteger):
return index
if core.is_constant_dim(axis_size):
axis_size_val = lax_internal._const(index, axis_size)
else:... | Normalizes an index value in the range [-N, N) to the range [0, N). | _normalize_index | python | jax-ml/jax | jax/_src/numpy/indexing.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/indexing.py | Apache-2.0 |
def take_along_axis(
arr: ArrayLike,
indices: ArrayLike,
axis: int | None,
mode: str | lax.GatherScatterMode | None = None,
fill_value: StaticScalar | None = None,
) -> Array:
"""Take elements from an array.
JAX implementation of :func:`numpy.take_along_axis`, implemented in
terms of :func:`j... | Take elements from an array.
JAX implementation of :func:`numpy.take_along_axis`, implemented in
terms of :func:`jax.lax.gather`. JAX's behavior differs from NumPy
in the case of out-of-bound indices; see the ``mode`` parameter below.
Args:
a: array from which to take values.
indices: array of integer... | take_along_axis | python | jax-ml/jax | jax/_src/numpy/indexing.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/indexing.py | Apache-2.0 |
def put_along_axis(
arr: ArrayLike,
indices: ArrayLike,
values: ArrayLike,
axis: int | None,
inplace: bool = True,
*,
mode: str | None = None,
) -> Array:
"""Put values into the destination array by matching 1d index and data slices.
JAX implementation of :func:`numpy.put_along_axis`.
The semantic... | Put values into the destination array by matching 1d index and data slices.
JAX implementation of :func:`numpy.put_along_axis`.
The semantics of :func:`numpy.put_along_axis` are to modify arrays in-place, which
is not possible for JAX's immutable arrays. The JAX version returns a modified
copy of the input, a... | put_along_axis | python | jax-ml/jax | jax/_src/numpy/indexing.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/indexing.py | Apache-2.0 |
def split_index_for_jit(idx, shape):
"""Splits indices into necessarily-static and dynamic parts.
Used to pass indices into `jit`-ted function.
"""
# Convert list indices to tuples in cases (deprecated by NumPy.)
idx = eliminate_deprecated_list_indexing(idx)
if any(isinstance(i, str) for i in idx):
rai... | Splits indices into necessarily-static and dynamic parts.
Used to pass indices into `jit`-ted function.
| split_index_for_jit | python | jax-ml/jax | jax/_src/numpy/indexing.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/indexing.py | Apache-2.0 |
def merge_static_and_dynamic_indices(treedef, static_idx, dynamic_idx):
"""Recombines indices that were split by split_index_for_jit."""
idx = []
for s, d in zip(static_idx, dynamic_idx):
if d is not None:
idx.append(d)
elif isinstance(s, tuple):
idx.append(slice(s[0], s[1], s[2]))
else:
... | Recombines indices that were split by split_index_for_jit. | merge_static_and_dynamic_indices | python | jax-ml/jax | jax/_src/numpy/indexing.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/indexing.py | Apache-2.0 |
def _expand_bool_indices(idx, shape):
"""Converts concrete bool indexes into advanced integer indexes."""
out = []
total_dims = len(shape)
num_ellipsis = sum(e is Ellipsis for e in idx)
if num_ellipsis > 1:
raise IndexError("an index can only have a single ellipsis ('...')")
elif num_ellipsis == 1:
... | Converts concrete bool indexes into advanced integer indexes. | _expand_bool_indices | python | jax-ml/jax | jax/_src/numpy/indexing.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/indexing.py | Apache-2.0 |
def _is_slice_element_none_or_constant_or_symbolic(elt):
"""Return True if elt is a constant or None."""
if elt is None: return True
if core.is_symbolic_dim(elt): return True
try:
return core.is_concrete(elt)
except TypeError:
return False | Return True if elt is a constant or None. | _is_slice_element_none_or_constant_or_symbolic | python | jax-ml/jax | jax/_src/numpy/indexing.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/indexing.py | Apache-2.0 |
def _is_advanced_int_indexer(idx):
"""Returns True if idx should trigger int array indexing, False otherwise."""
# https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#advanced-indexing
assert isinstance(idx, tuple)
if all(e is None or e is Ellipsis or isinstance(e, slice)
or _is_scalar(e) a... | Returns True if idx should trigger int array indexing, False otherwise. | _is_advanced_int_indexer | python | jax-ml/jax | jax/_src/numpy/indexing.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/indexing.py | Apache-2.0 |
def _is_int_arraylike(x):
"""Returns True if x is array-like with integer dtype, False otherwise."""
return (isinstance(x, int) and not isinstance(x, bool)
or dtypes.issubdtype(getattr(x, "dtype", None), np.integer)
or isinstance(x, (list, tuple)) and all(_is_int_arraylike(e) for e in x)) | Returns True if x is array-like with integer dtype, False otherwise. | _is_int_arraylike | python | jax-ml/jax | jax/_src/numpy/indexing.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/indexing.py | Apache-2.0 |
def _is_scalar(x):
"""Checks if a Python or NumPy scalar."""
return np.isscalar(x) or (isinstance(x, (np.ndarray, Array))
and np.ndim(x) == 0) | Checks if a Python or NumPy scalar. | _is_scalar | python | jax-ml/jax | jax/_src/numpy/indexing.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/indexing.py | Apache-2.0 |
def _canonicalize_tuple_index(arr_ndim, idx):
"""Helper to remove Ellipsis and add in the implicit trailing slice(None)."""
num_dimensions_consumed = sum(not (e is None or e is Ellipsis or isinstance(e, bool)) for e in idx)
if num_dimensions_consumed > arr_ndim:
index_or_indices = "index" if num_dimensions_co... | Helper to remove Ellipsis and add in the implicit trailing slice(None). | _canonicalize_tuple_index | python | jax-ml/jax | jax/_src/numpy/indexing.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/indexing.py | Apache-2.0 |
def place(arr: ArrayLike, mask: ArrayLike, vals: ArrayLike, *,
inplace: bool = True) -> Array:
"""Update array elements based on a mask.
JAX implementation of :func:`numpy.place`.
The semantics of :func:`numpy.place` are to modify arrays in-place, which
is not possible for JAX's immutable arrays. Th... | Update array elements based on a mask.
JAX implementation of :func:`numpy.place`.
The semantics of :func:`numpy.place` are to modify arrays in-place, which
is not possible for JAX's immutable arrays. The JAX version returns a modified
copy of the input, and adds the ``inplace`` parameter which must be set to
... | place | python | jax-ml/jax | jax/_src/numpy/indexing.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/indexing.py | Apache-2.0 |
def put(a: ArrayLike, ind: ArrayLike, v: ArrayLike,
mode: str | None = None, *, inplace: bool = True) -> Array:
"""Put elements into an array at given indices.
JAX implementation of :func:`numpy.put`.
The semantics of :func:`numpy.put` are to modify arrays in-place, which
is not possible for JAX's imm... | Put elements into an array at given indices.
JAX implementation of :func:`numpy.put`.
The semantics of :func:`numpy.put` are to modify arrays in-place, which
is not possible for JAX's immutable arrays. The JAX version returns a modified
copy of the input, and adds the ``inplace`` parameter which must be set t... | put | python | jax-ml/jax | jax/_src/numpy/indexing.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/indexing.py | Apache-2.0 |
def iscomplexobj(x: Any) -> bool:
"""Check if the input is a complex number or an array containing complex elements.
JAX implementation of :func:`numpy.iscomplexobj`.
The function evaluates based on input type rather than value.
Inputs with zero imaginary parts are still considered complex.
Args:
x: in... | Check if the input is a complex number or an array containing complex elements.
JAX implementation of :func:`numpy.iscomplexobj`.
The function evaluates based on input type rather than value.
Inputs with zero imaginary parts are still considered complex.
Args:
x: input object to check.
Returns:
Tr... | iscomplexobj | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def _convert_and_clip_integer(val: ArrayLike, dtype: DType) -> Array:
"""
Convert integer-typed val to specified integer dtype, clipping to dtype
range rather than wrapping.
Args:
val: value to be converted
dtype: dtype of output
Returns:
equivalent of val in new dtype
Examples
--------
N... |
Convert integer-typed val to specified integer dtype, clipping to dtype
range rather than wrapping.
Args:
val: value to be converted
dtype: dtype of output
Returns:
equivalent of val in new dtype
Examples
--------
Normal integer type conversion will wrap:
>>> val = jnp.uint32(0xFFFFFFFF... | _convert_and_clip_integer | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def load(file: IO[bytes] | str | os.PathLike[Any], *args: Any, **kwargs: Any) -> Array:
"""Load JAX arrays from npy files.
JAX wrapper of :func:`numpy.load`.
This function is a simple wrapper of :func:`numpy.load`, but in the case of
``.npy`` files created with :func:`numpy.save` or :func:`jax.numpy.save`,
... | Load JAX arrays from npy files.
JAX wrapper of :func:`numpy.load`.
This function is a simple wrapper of :func:`numpy.load`, but in the case of
``.npy`` files created with :func:`numpy.save` or :func:`jax.numpy.save`,
the output will be returned as a :class:`jax.Array`, and ``bfloat16`` data
types will be re... | load | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def isscalar(element: Any) -> bool:
"""Return True if the input is a scalar.
JAX implementation of :func:`numpy.isscalar`. JAX's implementation differs
from NumPy's in that it considers zero-dimensional arrays to be scalars; see
the *Note* below for more details.
Args:
element: input object to check; an... | Return True if the input is a scalar.
JAX implementation of :func:`numpy.isscalar`. JAX's implementation differs
from NumPy's in that it considers zero-dimensional arrays to be scalars; see
the *Note* below for more details.
Args:
element: input object to check; any type is valid input.
Returns:
Tr... | isscalar | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def trunc(x: ArrayLike) -> Array:
"""Round input to the nearest integer towards zero.
JAX implementation of :func:`numpy.trunc`.
Args:
x: input array or scalar.
Returns:
An array with same shape and dtype as ``x`` containing the rounded values.
See also:
- :func:`jax.numpy.fix`: Rounds the inp... | Round input to the nearest integer towards zero.
JAX implementation of :func:`numpy.trunc`.
Args:
x: input array or scalar.
Returns:
An array with same shape and dtype as ``x`` containing the rounded values.
See also:
- :func:`jax.numpy.fix`: Rounds the input to the nearest integer towards zero.... | trunc | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def convolve(a: ArrayLike, v: ArrayLike, mode: str = 'full', *,
precision: PrecisionLike = None,
preferred_element_type: DTypeLike | None = None) -> Array:
r"""Convolution of two one dimensional arrays.
JAX implementation of :func:`numpy.convolve`.
Convolution of one dimensional arrays... | Convolution of two one dimensional arrays.
JAX implementation of :func:`numpy.convolve`.
Convolution of one dimensional arrays is defined as:
.. math::
c_k = \sum_j a_{k - j} v_j
Args:
a: left-hand input to the convolution. Must have ``a.ndim == 1``.
v: right-hand input to the convolution. Mus... | convolve | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def correlate(a: ArrayLike, v: ArrayLike, mode: str = 'valid', *,
precision: PrecisionLike = None,
preferred_element_type: DTypeLike | None = None) -> Array:
r"""Correlation of two one dimensional arrays.
JAX implementation of :func:`numpy.correlate`.
Correlation of one dimensional a... | Correlation of two one dimensional arrays.
JAX implementation of :func:`numpy.correlate`.
Correlation of one dimensional arrays is defined as:
.. math::
c_k = \sum_j a_{k + j} \overline{v_j}
where :math:`\overline{v_j}` is the complex conjugate of :math:`v_j`.
Args:
a: left-hand input to the co... | correlate | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def histogram_bin_edges(a: ArrayLike, bins: ArrayLike = 10,
range: None | Array | Sequence[ArrayLike] = None,
weights: ArrayLike | None = None) -> Array:
"""Compute the bin edges for a histogram.
JAX implementation of :func:`numpy.histogram_bin_edges`.
Args:
a... | Compute the bin edges for a histogram.
JAX implementation of :func:`numpy.histogram_bin_edges`.
Args:
a: array of values to be binned
bins: Specify the number of bins in the histogram (default: 10).
range: tuple of scalars. Specifies the range of the data. If not specified,
the range is inferred... | histogram_bin_edges | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def histogram(a: ArrayLike, bins: ArrayLike = 10,
range: Sequence[ArrayLike] | None = None,
weights: ArrayLike | None = None,
density: bool | None = None) -> tuple[Array, Array]:
"""Compute a 1-dimensional histogram.
JAX implementation of :func:`numpy.histogram`.
Args:
... | Compute a 1-dimensional histogram.
JAX implementation of :func:`numpy.histogram`.
Args:
a: array of values to be binned. May be any size or dimension.
bins: Specify the number of bins in the histogram (default: 10). ``bins``
may also be an array specifying the locations of the bin edges.
range: ... | histogram | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def histogram2d(x: ArrayLike, y: ArrayLike, bins: ArrayLike | list[ArrayLike] = 10,
range: Sequence[None | Array | Sequence[ArrayLike]] | None = None,
weights: ArrayLike | None = None,
density: bool | None = None) -> tuple[Array, Array, Array]:
"""Compute a 2-dimensiona... | Compute a 2-dimensional histogram.
JAX implementation of :func:`numpy.histogram2d`.
Args:
x: one-dimensional array of x-values for points to be binned.
y: one-dimensional array of y-values for points to be binned.
bins: Specify the number of bins in the histogram (default: 10). ``bins``
may also... | histogram2d | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def histogramdd(sample: ArrayLike, bins: ArrayLike | list[ArrayLike] = 10,
range: Sequence[None | Array | Sequence[ArrayLike]] | None = None,
weights: ArrayLike | None = None,
density: bool | None = None) -> tuple[Array, list[Array]]:
"""Compute an N-dimensional histogr... | Compute an N-dimensional histogram.
JAX implementation of :func:`numpy.histogramdd`.
Args:
sample: input array of shape ``(N, D)`` representing ``N`` points in
``D`` dimensions.
bins: Specify the number of bins in each dimension of the histogram.
(default: 10). May also be a length-D sequence ... | histogramdd | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def transpose(a: ArrayLike, axes: Sequence[int] | None = None) -> Array:
"""Return a transposed version of an N-dimensional array.
JAX implementation of :func:`numpy.transpose`, implemented in terms of
:func:`jax.lax.transpose`.
Args:
a: input array
axes: optionally specify the permutation using a len... | Return a transposed version of an N-dimensional array.
JAX implementation of :func:`numpy.transpose`, implemented in terms of
:func:`jax.lax.transpose`.
Args:
a: input array
axes: optionally specify the permutation using a length-`a.ndim` sequence of integers
``i`` satisfying ``0 <= i < a.ndim``. ... | transpose | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def permute_dims(a: ArrayLike, /, axes: tuple[int, ...]) -> Array:
"""Permute the axes/dimensions of an array.
JAX implementation of :func:`array_api.permute_dims`.
Args:
a: input array
axes: tuple of integers in range ``[0, a.ndim)`` specifying the
axes permutation.
Returns:
a copy of ``a`... | Permute the axes/dimensions of an array.
JAX implementation of :func:`array_api.permute_dims`.
Args:
a: input array
axes: tuple of integers in range ``[0, a.ndim)`` specifying the
axes permutation.
Returns:
a copy of ``a`` with axes permuted.
See also:
- :func:`jax.numpy.transpose`
... | permute_dims | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def matrix_transpose(x: ArrayLike, /) -> Array:
"""Transpose the last two dimensions of an array.
JAX implementation of :func:`numpy.matrix_transpose`, implemented in terms of
:func:`jax.lax.transpose`.
Args:
x: input array, Must have ``x.ndim >= 2``
Returns:
matrix-transposed copy of the array.
... | Transpose the last two dimensions of an array.
JAX implementation of :func:`numpy.matrix_transpose`, implemented in terms of
:func:`jax.lax.transpose`.
Args:
x: input array, Must have ``x.ndim >= 2``
Returns:
matrix-transposed copy of the array.
See Also:
- :attr:`jax.Array.mT`: same operation... | matrix_transpose | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def rot90(m: ArrayLike, k: int = 1, axes: tuple[int, int] = (0, 1)) -> Array:
"""Rotate an array by 90 degrees counterclockwise in the plane specified by axes.
JAX implementation of :func:`numpy.rot90`.
Args:
m: input array. Must have ``m.ndim >= 2``.
k: int, optional, default=1. Specifies the number of... | Rotate an array by 90 degrees counterclockwise in the plane specified by axes.
JAX implementation of :func:`numpy.rot90`.
Args:
m: input array. Must have ``m.ndim >= 2``.
k: int, optional, default=1. Specifies the number of times the array is rotated.
For negative values of ``k``, the array is rotat... | rot90 | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def flip(m: ArrayLike, axis: int | Sequence[int] | None = None) -> Array:
"""Reverse the order of elements of an array along the given axis.
JAX implementation of :func:`numpy.flip`.
Args:
m: Array.
axis: integer or sequence of integers. Specifies along which axis or axes
should the array elements... | Reverse the order of elements of an array along the given axis.
JAX implementation of :func:`numpy.flip`.
Args:
m: Array.
axis: integer or sequence of integers. Specifies along which axis or axes
should the array elements be reversed. Default is ``None``, which flips
along all axes.
Returns... | flip | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def fliplr(m: ArrayLike) -> Array:
"""Reverse the order of elements of an array along axis 1.
JAX implementation of :func:`numpy.fliplr`.
Args:
m: Array with at least two dimensions.
Returns:
An array with the elements in reverse order along axis 1.
See Also:
- :func:`jax.numpy.flip`: reverse ... | Reverse the order of elements of an array along axis 1.
JAX implementation of :func:`numpy.fliplr`.
Args:
m: Array with at least two dimensions.
Returns:
An array with the elements in reverse order along axis 1.
See Also:
- :func:`jax.numpy.flip`: reverse the order along the given axis
- :fu... | fliplr | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def flipud(m: ArrayLike) -> Array:
"""Reverse the order of elements of an array along axis 0.
JAX implementation of :func:`numpy.flipud`.
Args:
m: Array with at least one dimension.
Returns:
An array with the elements in reverse order along axis 0.
See Also:
- :func:`jax.numpy.flip`: reverse t... | Reverse the order of elements of an array along axis 0.
JAX implementation of :func:`numpy.flipud`.
Args:
m: Array with at least one dimension.
Returns:
An array with the elements in reverse order along axis 0.
See Also:
- :func:`jax.numpy.flip`: reverse the order along the given axis
- :fun... | flipud | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def iscomplex(x: ArrayLike) -> Array:
"""Return boolean array showing where the input is complex.
JAX implementation of :func:`numpy.iscomplex`.
Args:
x: Input array to check.
Returns:
A new array containing boolean values indicating complex elements.
See Also:
- :func:`jax.numpy.iscomplexobj`... | Return boolean array showing where the input is complex.
JAX implementation of :func:`numpy.iscomplex`.
Args:
x: Input array to check.
Returns:
A new array containing boolean values indicating complex elements.
See Also:
- :func:`jax.numpy.iscomplexobj`
- :func:`jax.numpy.isrealobj`
Examp... | iscomplex | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def isreal(x: ArrayLike) -> Array:
"""Return boolean array showing where the input is real.
JAX implementation of :func:`numpy.isreal`.
Args:
x: input array to check.
Returns:
A new array containing boolean values indicating real elements.
See Also:
- :func:`jax.numpy.iscomplex`
- :func:`j... | Return boolean array showing where the input is real.
JAX implementation of :func:`numpy.isreal`.
Args:
x: input array to check.
Returns:
A new array containing boolean values indicating real elements.
See Also:
- :func:`jax.numpy.iscomplex`
- :func:`jax.numpy.isrealobj`
Examples:
>>>... | isreal | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def angle(z: ArrayLike, deg: bool = False) -> Array:
"""Return the angle of a complex valued number or array.
JAX implementation of :func:`numpy.angle`.
Args:
z: A complex number or an array of complex numbers.
deg: Boolean. If ``True``, returns the result in degrees else returns
in radians. Defau... | Return the angle of a complex valued number or array.
JAX implementation of :func:`numpy.angle`.
Args:
z: A complex number or an array of complex numbers.
deg: Boolean. If ``True``, returns the result in degrees else returns
in radians. Default is ``False``.
Returns:
An array of counterclockw... | angle | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def diff(a: ArrayLike, n: int = 1, axis: int = -1,
prepend: ArrayLike | None = None,
append: ArrayLike | None = None) -> Array:
"""Calculate n-th order difference between array elements along a given axis.
JAX implementation of :func:`numpy.diff`.
The first order difference is computed by ``a[... | Calculate n-th order difference between array elements along a given axis.
JAX implementation of :func:`numpy.diff`.
The first order difference is computed by ``a[i+1] - a[i]``, and the n-th order
difference is computed ``n`` times recursively.
Args:
a: input array. Must have ``a.ndim >= 1``.
n: int,... | diff | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def ediff1d(ary: ArrayLike, to_end: ArrayLike | None = None,
to_begin: ArrayLike | None = None) -> Array:
"""Compute the differences of the elements of the flattened array.
JAX implementation of :func:`numpy.ediff1d`.
Args:
ary: input array or scalar.
to_end: scalar or array, optional, defau... | Compute the differences of the elements of the flattened array.
JAX implementation of :func:`numpy.ediff1d`.
Args:
ary: input array or scalar.
to_end: scalar or array, optional, default=None. Specifies the numbers to
append to the resulting array.
to_begin: scalar or array, optional, default=Non... | ediff1d | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def gradient(
f: ArrayLike,
*varargs: ArrayLike,
axis: int | Sequence[int] | None = None,
edge_order: int | None = None,
) -> Array | list[Array]:
"""Compute the numerical gradient of a sampled function.
JAX implementation of :func:`numpy.gradient`.
The gradient in ``jnp.gradient`` is computed u... | Compute the numerical gradient of a sampled function.
JAX implementation of :func:`numpy.gradient`.
The gradient in ``jnp.gradient`` is computed using second-order finite
differences across the array of sampled function values. This should not
be confused with :func:`jax.grad`, which computes a precise gradie... | gradient | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def reshape(
a: ArrayLike, shape: DimSize | Shape, order: str = "C", *,
copy: bool | None = None, out_sharding=None) -> Array:
"""Return a reshaped copy of an array.
JAX implementation of :func:`numpy.reshape`, implemented in terms of
:func:`jax.lax.reshape`.
Args:
a: input array to reshape
sh... | Return a reshaped copy of an array.
JAX implementation of :func:`numpy.reshape`, implemented in terms of
:func:`jax.lax.reshape`.
Args:
a: input array to reshape
shape: integer or sequence of integers giving the new shape, which must match the
size of the input array. If any single dimension is gi... | reshape | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def ravel(a: ArrayLike, order: str = "C", *, out_sharding=None) -> Array:
"""Flatten array into a 1-dimensional shape.
JAX implementation of :func:`numpy.ravel`, implemented in terms of
:func:`jax.lax.reshape`.
``ravel(arr, order=order)`` is equivalent to ``reshape(arr, -1, order=order)``.
Args:
a: arr... | Flatten array into a 1-dimensional shape.
JAX implementation of :func:`numpy.ravel`, implemented in terms of
:func:`jax.lax.reshape`.
``ravel(arr, order=order)`` is equivalent to ``reshape(arr, -1, order=order)``.
Args:
a: array to be flattened.
order: ``'F'`` or ``'C'``, specifies whether the reshap... | ravel | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def ravel_multi_index(multi_index: Sequence[ArrayLike], dims: Sequence[int],
mode: str = 'raise', order: str = 'C') -> Array:
"""Convert multi-dimensional indices into flat indices.
JAX implementation of :func:`numpy.ravel_multi_index`
Args:
multi_index: sequence of integer arrays cont... | Convert multi-dimensional indices into flat indices.
JAX implementation of :func:`numpy.ravel_multi_index`
Args:
multi_index: sequence of integer arrays containing indices in each dimension.
dims: sequence of integer sizes; must have ``len(dims) == len(multi_index)``
mode: how to handle out-of bound i... | ravel_multi_index | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def unravel_index(indices: ArrayLike, shape: Shape) -> tuple[Array, ...]:
"""Convert flat indices into multi-dimensional indices.
JAX implementation of :func:`numpy.unravel_index`. The JAX version differs in
its treatment of out-of-bound indices: unlike NumPy, negative indices are
supported, and out-of-bound i... | Convert flat indices into multi-dimensional indices.
JAX implementation of :func:`numpy.unravel_index`. The JAX version differs in
its treatment of out-of-bound indices: unlike NumPy, negative indices are
supported, and out-of-bound indices are clipped to the nearest valid value.
Args:
indices: integer ar... | unravel_index | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def resize(a: ArrayLike, new_shape: Shape) -> Array:
"""Return a new array with specified shape.
JAX implementation of :func:`numpy.resize`.
Args:
a: input array or scalar.
new_shape: int or tuple of ints. Specifies the shape of the resized array.
Returns:
A resized array with specified shape. Th... | Return a new array with specified shape.
JAX implementation of :func:`numpy.resize`.
Args:
a: input array or scalar.
new_shape: int or tuple of ints. Specifies the shape of the resized array.
Returns:
A resized array with specified shape. The elements of ``a`` are repeated in
the resized array,... | resize | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def squeeze(a: ArrayLike, axis: int | Sequence[int] | None = None) -> Array:
"""Remove one or more length-1 axes from array
JAX implementation of :func:`numpy.sqeeze`, implemented via :func:`jax.lax.squeeze`.
Args:
a: input array
axis: integer or sequence of integers specifying axes to remove. If any sp... | Remove one or more length-1 axes from array
JAX implementation of :func:`numpy.sqeeze`, implemented via :func:`jax.lax.squeeze`.
Args:
a: input array
axis: integer or sequence of integers specifying axes to remove. If any specified
axis does not have a length of 1, an error is raised. If not specifi... | squeeze | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def expand_dims(a: ArrayLike, axis: int | Sequence[int]) -> Array:
"""Insert dimensions of length 1 into array
JAX implementation of :func:`numpy.expand_dims`, implemented via
:func:`jax.lax.expand_dims`.
Args:
a: input array
axis: integer or sequence of integers specifying positions of axes to add.
... | Insert dimensions of length 1 into array
JAX implementation of :func:`numpy.expand_dims`, implemented via
:func:`jax.lax.expand_dims`.
Args:
a: input array
axis: integer or sequence of integers specifying positions of axes to add.
Returns:
Copy of ``a`` with added dimensions.
Notes:
Unlike... | expand_dims | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def swapaxes(a: ArrayLike, axis1: int, axis2: int) -> Array:
"""Swap two axes of an array.
JAX implementation of :func:`numpy.swapaxes`, implemented in terms of
:func:`jax.lax.transpose`.
Args:
a: input array
axis1: index of first axis
axis2: index of second axis
Returns:
Copy of ``a`` with... | Swap two axes of an array.
JAX implementation of :func:`numpy.swapaxes`, implemented in terms of
:func:`jax.lax.transpose`.
Args:
a: input array
axis1: index of first axis
axis2: index of second axis
Returns:
Copy of ``a`` with specified axes swapped.
Notes:
Unlike :func:`numpy.swapaxe... | swapaxes | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def moveaxis(a: ArrayLike, source: int | Sequence[int],
destination: int | Sequence[int]) -> Array:
"""Move an array axis to a new position
JAX implementation of :func:`numpy.moveaxis`, implemented in terms of
:func:`jax.lax.transpose`.
Args:
a: input array
source: index or indices of the... | Move an array axis to a new position
JAX implementation of :func:`numpy.moveaxis`, implemented in terms of
:func:`jax.lax.transpose`.
Args:
a: input array
source: index or indices of the axes to move.
destination: index or indices of the axes destinations
Returns:
Copy of ``a`` with axes move... | moveaxis | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def isclose(a: ArrayLike, b: ArrayLike, rtol: ArrayLike = 1e-05, atol: ArrayLike = 1e-08,
equal_nan: bool = False) -> Array:
r"""Check if the elements of two arrays are approximately equal within a tolerance.
JAX implementation of :func:`numpy.allclose`.
Essentially this function evaluates the follo... | Check if the elements of two arrays are approximately equal within a tolerance.
JAX implementation of :func:`numpy.allclose`.
Essentially this function evaluates the following condition:
.. math::
|a - b| \le \mathtt{atol} + \mathtt{rtol} * |b|
``jnp.inf`` in ``a`` will be considered equal to ``jnp.in... | isclose | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def interp(x: ArrayLike, xp: ArrayLike, fp: ArrayLike,
left: ArrayLike | str | None = None,
right: ArrayLike | str | None = None,
period: ArrayLike | None = None) -> Array:
"""One-dimensional linear interpolation.
JAX implementation of :func:`numpy.interp`.
Args:
x: N-dimens... | One-dimensional linear interpolation.
JAX implementation of :func:`numpy.interp`.
Args:
x: N-dimensional array of x coordinates at which to evaluate the interpolation.
xp: one-dimensional sorted array of points to be interpolated.
fp: array of shape ``xp.shape`` containing the function values associat... | interp | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def where(condition, x=None, y=None, /, *, size=None, fill_value=None):
"""Select elements from two arrays based on a condition.
JAX implementation of :func:`numpy.where`.
.. note::
when only ``condition`` is provided, ``jnp.where(condition)`` is equivalent
to ``jnp.nonzero(condition)``. For that case... | Select elements from two arrays based on a condition.
JAX implementation of :func:`numpy.where`.
.. note::
when only ``condition`` is provided, ``jnp.where(condition)`` is equivalent
to ``jnp.nonzero(condition)``. For that case, refer to the documentation of
:func:`jax.numpy.nonzero`. The docstring... | where | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def select(
condlist: Sequence[ArrayLike],
choicelist: Sequence[ArrayLike],
default: ArrayLike = 0,
) -> Array:
"""Select values based on a series of conditions.
JAX implementation of :func:`numpy.select`, implemented in terms
of :func:`jax.lax.select_n`
Args:
condlist: sequence of array-like ... | Select values based on a series of conditions.
JAX implementation of :func:`numpy.select`, implemented in terms
of :func:`jax.lax.select_n`
Args:
condlist: sequence of array-like conditions. All entries must be mutually
broadcast-compatible.
choicelist: sequence of array-like values to choose. Mus... | select | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def bincount(x: ArrayLike, weights: ArrayLike | None = None,
minlength: int = 0, *, length: int | None = None
) -> Array:
"""Count the number of occurrences of each value in an integer array.
JAX implementation of :func:`numpy.bincount`.
For an array of positive integers ``x``, this fu... | Count the number of occurrences of each value in an integer array.
JAX implementation of :func:`numpy.bincount`.
For an array of positive integers ``x``, this function returns an array ``counts``
of size ``x.max() + 1``, such that ``counts[i]`` contains the number of occurrences
of the value ``i`` in ``x``.
... | bincount | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def broadcast_shapes(*shapes):
"""Broadcast input shapes to a common output shape.
JAX implementation of :func:`numpy.broadcast_shapes`. JAX uses NumPy-style
broadcasting rules, which you can read more about at `NumPy broadcasting`_.
Args:
shapes: 0 or more shapes specified as sequences of integers
Ret... | Broadcast input shapes to a common output shape.
JAX implementation of :func:`numpy.broadcast_shapes`. JAX uses NumPy-style
broadcasting rules, which you can read more about at `NumPy broadcasting`_.
Args:
shapes: 0 or more shapes specified as sequences of integers
Returns:
The broadcasted shape as a... | broadcast_shapes | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def broadcast_to(array: ArrayLike, shape: DimSize | Shape,
*, out_sharding: NamedSharding | P | None = None) -> Array:
"""Broadcast an array to a specified shape.
JAX implementation of :func:`numpy.broadcast_to`. JAX uses NumPy-style
broadcasting rules, which you can read more about at `NumPy br... | Broadcast an array to a specified shape.
JAX implementation of :func:`numpy.broadcast_to`. JAX uses NumPy-style
broadcasting rules, which you can read more about at `NumPy broadcasting`_.
Args:
array: array to be broadcast.
shape: shape to which the array will be broadcast.
Returns:
a copy of arr... | broadcast_to | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def split(ary: ArrayLike, indices_or_sections: int | Sequence[int] | ArrayLike,
axis: int = 0) -> list[Array]:
"""Split an array into sub-arrays.
JAX implementation of :func:`numpy.split`.
Args:
ary: N-dimensional array-like object to split
indices_or_sections: either a single integer or a seq... | Split an array into sub-arrays.
JAX implementation of :func:`numpy.split`.
Args:
ary: N-dimensional array-like object to split
indices_or_sections: either a single integer or a sequence of indices.
- if ``indices_or_sections`` is an integer *N*, then *N* must evenly divide
``ary.shape[axis]... | split | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def hsplit(ary: ArrayLike, indices_or_sections: int | Sequence[int] | ArrayLike) -> list[Array]:
"""Split an array into sub-arrays horizontally.
JAX implementation of :func:`numpy.hsplit`.
Refer to the documentation of :func:`jax.numpy.split` for details. ``hsplit`` is
equivalent to ``split`` with ``axis=1``,... | Split an array into sub-arrays horizontally.
JAX implementation of :func:`numpy.hsplit`.
Refer to the documentation of :func:`jax.numpy.split` for details. ``hsplit`` is
equivalent to ``split`` with ``axis=1``, or ``axis=0`` for one-dimensional arrays.
Examples:
1D array:
>>> x = jnp.array([1, 2, 3,... | hsplit | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def array_split(ary: ArrayLike, indices_or_sections: int | Sequence[int] | ArrayLike,
axis: int = 0) -> list[Array]:
"""Split an array into sub-arrays.
JAX implementation of :func:`numpy.array_split`.
Refer to the documentation of :func:`jax.numpy.split` for details; ``array_split``
is equival... | Split an array into sub-arrays.
JAX implementation of :func:`numpy.array_split`.
Refer to the documentation of :func:`jax.numpy.split` for details; ``array_split``
is equivalent to ``split``, but allows integer ``indices_or_sections`` which does
not evenly divide the split axis.
Examples:
>>> x = jnp.a... | array_split | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def clip(
arr: ArrayLike | None = None,
/,
min: ArrayLike | None = None,
max: ArrayLike | None = None,
*,
a: ArrayLike | DeprecatedArg = DeprecatedArg(),
a_min: ArrayLike | None | DeprecatedArg = DeprecatedArg(),
a_max: ArrayLike | None | DeprecatedArg = DeprecatedArg()
) -> Array:
"""Clip array value... | Clip array values to a specified range.
JAX implementation of :func:`numpy.clip`.
Args:
arr: N-dimensional array to be clipped.
min: optional minimum value of the clipped range; if ``None`` (default) then
result will not be clipped to any minimum value. If specified, it should be
broadcast-com... | clip | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def round(a: ArrayLike, decimals: int = 0, out: None = None) -> Array:
"""Round input evenly to the given number of decimals.
JAX implementation of :func:`numpy.round`.
Args:
a: input array or scalar.
decimals: int, default=0. Number of decimal points to which the input needs
to be rounded. It mus... | Round input evenly to the given number of decimals.
JAX implementation of :func:`numpy.round`.
Args:
a: input array or scalar.
decimals: int, default=0. Number of decimal points to which the input needs
to be rounded. It must be specified statically. Not implemented for
``decimals < 0``.
o... | round | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def fix(x: ArrayLike, out: None = None) -> Array:
"""Round input to the nearest integer towards zero.
JAX implementation of :func:`numpy.fix`.
Args:
x: input array.
out: unused by JAX.
Returns:
An array with same shape and dtype as ``x`` containing the rounded values.
See also:
- :func:`ja... | Round input to the nearest integer towards zero.
JAX implementation of :func:`numpy.fix`.
Args:
x: input array.
out: unused by JAX.
Returns:
An array with same shape and dtype as ``x`` containing the rounded values.
See also:
- :func:`jax.numpy.trunc`: Rounds the input to nearest integer tow... | fix | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def nan_to_num(x: ArrayLike, copy: bool = True, nan: ArrayLike = 0.0,
posinf: ArrayLike | None = None,
neginf: ArrayLike | None = None) -> Array:
"""Replace NaN and infinite entries in an array.
JAX implementation of :func:`numpy.nan_to_num`.
Args:
x: array of values to be repl... | Replace NaN and infinite entries in an array.
JAX implementation of :func:`numpy.nan_to_num`.
Args:
x: array of values to be replaced. If it does not have an inexact
dtype it will be returned unmodified.
copy: unused by JAX
nan: value to substitute for NaN entries. Defaults to 0.0.
posinf: ... | nan_to_num | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def allclose(a: ArrayLike, b: ArrayLike, rtol: ArrayLike = 1e-05,
atol: ArrayLike = 1e-08, equal_nan: bool = False) -> Array:
r"""Check if two arrays are element-wise approximately equal within a tolerance.
JAX implementation of :func:`numpy.allclose`.
Essentially this function evaluates the follow... | Check if two arrays are element-wise approximately equal within a tolerance.
JAX implementation of :func:`numpy.allclose`.
Essentially this function evaluates the following condition:
.. math::
|a - b| \le \mathtt{atol} + \mathtt{rtol} * |b|
``jnp.inf`` in ``a`` will be considered equal to ``jnp.inf``... | allclose | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def nonzero(a: ArrayLike, *, size: int | None = None,
fill_value: None | ArrayLike | tuple[ArrayLike, ...] = None
) -> tuple[Array, ...]:
"""Return indices of nonzero elements of an array.
JAX implementation of :func:`numpy.nonzero`.
Because the size of the output of ``nonzero`` is data-dependen... | Return indices of nonzero elements of an array.
JAX implementation of :func:`numpy.nonzero`.
Because the size of the output of ``nonzero`` is data-dependent, the function
is not compatible with JIT and other transformations. The JAX version adds
the optional ``size`` argument which must be specified staticall... | nonzero | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def flatnonzero(a: ArrayLike, *, size: int | None = None,
fill_value: None | ArrayLike | tuple[ArrayLike, ...] = None) -> Array:
"""Return indices of nonzero elements in a flattened array
JAX implementation of :func:`numpy.flatnonzero`.
``jnp.flatnonzero(x)`` is equivalent to ``nonzero(ravel(a))... | Return indices of nonzero elements in a flattened array
JAX implementation of :func:`numpy.flatnonzero`.
``jnp.flatnonzero(x)`` is equivalent to ``nonzero(ravel(a))[0]``. For a full
discussion of the parameters to this function, refer to :func:`jax.numpy.nonzero`.
Args:
a: N-dimensional array.
size: ... | flatnonzero | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def unwrap(p: ArrayLike, discont: ArrayLike | None = None,
axis: int = -1, period: ArrayLike = 2 * np.pi) -> Array:
"""Unwrap a periodic signal.
JAX implementation of :func:`numpy.unwrap`.
Args:
p: input array
discont: the maximum allowable discontinuity in the sequence. The
default is ... | Unwrap a periodic signal.
JAX implementation of :func:`numpy.unwrap`.
Args:
p: input array
discont: the maximum allowable discontinuity in the sequence. The
default is ``period / 2``
axis: the axis along which to unwrap; defaults to -1
period: the period of the signal, which defaults to :mat... | unwrap | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def pad(array: ArrayLike, pad_width: PadValueLike[int | Array | np.ndarray],
mode: str | Callable[..., Any] = "constant", **kwargs) -> Array:
"""Add padding to an array.
JAX implementation of :func:`numpy.pad`.
Args:
array: array to pad.
pad_width: specify the pad width for each dimension of an ... | Add padding to an array.
JAX implementation of :func:`numpy.pad`.
Args:
array: array to pad.
pad_width: specify the pad width for each dimension of an array. Padding widths
may be separately specified for *before* and *after* the array. Options are:
- ``int`` or ``(int,)``: pad each array dim... | pad | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
def stack(arrays: np.ndarray | Array | Sequence[ArrayLike],
axis: int = 0, out: None = None, dtype: DTypeLike | None = None) -> Array:
"""Join arrays along a new axis.
JAX implementation of :func:`numpy.stack`.
Args:
arrays: a sequence of arrays to stack; each must have the same shape. If a
... | Join arrays along a new axis.
JAX implementation of :func:`numpy.stack`.
Args:
arrays: a sequence of arrays to stack; each must have the same shape. If a
single array is given it will be treated equivalently to
`arrays = unstack(arrays)`, but the implementation will avoid explicit
unstacking... | stack | python | jax-ml/jax | jax/_src/numpy/lax_numpy.py | https://github.com/jax-ml/jax/blob/master/jax/_src/numpy/lax_numpy.py | Apache-2.0 |
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