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|
"""
pint.facets.numpy.numpy_func
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
:copyright: 2022 by Pint Authors, see AUTHORS for more details.
:license: BSD, see LICENSE for more details.
"""
from __future__ import annotations
import warnings
from inspect import signature
from itertools import chain
from ...compat import is_upcast_type, np, zero_or_nan
from ...errors import DimensionalityError, UnitStrippedWarning
from ...util import iterable, sized
HANDLED_UFUNCS = {}
HANDLED_FUNCTIONS = {}
# Shared Implementation Utilities
def _is_quantity(obj):
"""Test for _units and _magnitude attrs.
This is done in place of isinstance(Quantity, arg), which would cause a circular import.
Parameters
----------
obj : Object
Returns
-------
bool
"""
return hasattr(obj, "_units") and hasattr(obj, "_magnitude")
def _is_sequence_with_quantity_elements(obj):
"""Test for sequences of quantities.
Parameters
----------
obj : object
Returns
-------
True if obj is a sequence and at least one element is a Quantity; False otherwise
"""
return (
iterable(obj)
and sized(obj)
and not isinstance(obj, str)
and any(_is_quantity(item) for item in obj)
)
def _get_first_input_units(args, kwargs=None):
"""Obtain the first valid unit from a collection of args and kwargs."""
kwargs = kwargs or {}
for arg in chain(args, kwargs.values()):
if _is_quantity(arg):
return arg.units
elif _is_sequence_with_quantity_elements(arg):
return next(arg_i.units for arg_i in arg if _is_quantity(arg_i))
raise TypeError("Expected at least one Quantity; found none")
def convert_arg(arg, pre_calc_units):
"""Convert quantities and sequences of quantities to pre_calc_units and strip units.
Helper function for convert_to_consistent_units. pre_calc_units must be given as a
pint Unit or None.
"""
if pre_calc_units is not None:
if _is_quantity(arg):
return arg.m_as(pre_calc_units)
elif _is_sequence_with_quantity_elements(arg):
return [convert_arg(item, pre_calc_units) for item in arg]
elif arg is not None:
if pre_calc_units.dimensionless:
return pre_calc_units._REGISTRY.Quantity(arg).m_as(pre_calc_units)
elif not _is_quantity(arg) and zero_or_nan(arg, True):
return arg
else:
raise DimensionalityError("dimensionless", pre_calc_units)
elif _is_quantity(arg):
return arg.m
elif _is_sequence_with_quantity_elements(arg):
return [convert_arg(item, pre_calc_units) for item in arg]
return arg
def convert_to_consistent_units(*args, pre_calc_units=None, **kwargs):
"""Prepare args and kwargs for wrapping by unit conversion and stripping.
If pre_calc_units is not None, takes the args and kwargs for a NumPy function and
converts any Quantity or Sequence of Quantities into the units of the first
Quantity/Sequence of Quantities and returns the magnitudes. Other args/kwargs are
treated as dimensionless Quantities. If pre_calc_units is None, units are simply
stripped.
"""
return (
tuple(convert_arg(arg, pre_calc_units=pre_calc_units) for arg in args),
{
key: convert_arg(arg, pre_calc_units=pre_calc_units)
for key, arg in kwargs.items()
},
)
def unwrap_and_wrap_consistent_units(*args):
"""Strip units from args while providing a rewrapping function.
Returns the given args as parsed by convert_to_consistent_units assuming units of
first arg with units, along with a wrapper to restore that unit to the output.
"""
if all(not _is_quantity(arg) for arg in args):
return args, lambda x: x
first_input_units = _get_first_input_units(args)
args, _ = convert_to_consistent_units(*args, pre_calc_units=first_input_units)
return (
args,
lambda value: first_input_units._REGISTRY.Quantity(value, first_input_units),
)
def get_op_output_unit(unit_op, first_input_units, all_args=None, size=None):
"""Determine resulting unit from given operation.
Options for `unit_op`:
- "sum": `first_input_units`, unless non-multiplicative, which raises
OffsetUnitCalculusError
- "mul": product of all units in `all_args`
- "delta": `first_input_units`, unless non-multiplicative, which uses delta version
- "delta,div": like "delta", but divided by all units in `all_args` except the first
- "div": unit of first argument in `all_args` (or dimensionless if not a Quantity) divided
by all following units
- "variance": square of `first_input_units`, unless non-multiplicative, which raises
OffsetUnitCalculusError
- "square": square of `first_input_units`
- "sqrt": square root of `first_input_units`
- "reciprocal": reciprocal of `first_input_units`
- "size": `first_input_units` raised to the power of `size`
- "invdiv": inverse of `div`, product of all following units divided by first argument unit
Parameters
----------
unit_op :
first_input_units :
all_args :
(Default value = None)
size :
(Default value = None)
Returns
-------
"""
all_args = all_args or []
if unit_op == "sum":
result_unit = (1 * first_input_units + 1 * first_input_units).units
elif unit_op == "mul":
product = first_input_units._REGISTRY.parse_units("")
for x in all_args:
if hasattr(x, "units"):
product *= x.units
result_unit = product
elif unit_op == "delta":
result_unit = (1 * first_input_units - 1 * first_input_units).units
elif unit_op == "delta,div":
product = (1 * first_input_units - 1 * first_input_units).units
for x in all_args[1:]:
if hasattr(x, "units"):
product /= x.units
result_unit = product
elif unit_op == "div":
# Start with first arg in numerator, all others in denominator
product = getattr(
all_args[0], "units", first_input_units._REGISTRY.parse_units("")
)
for x in all_args[1:]:
if hasattr(x, "units"):
product /= x.units
result_unit = product
elif unit_op == "variance":
result_unit = ((1 * first_input_units + 1 * first_input_units) ** 2).units
elif unit_op == "square":
result_unit = first_input_units**2
elif unit_op == "sqrt":
result_unit = first_input_units**0.5
elif unit_op == "cbrt":
result_unit = first_input_units ** (1 / 3)
elif unit_op == "reciprocal":
result_unit = first_input_units**-1
elif unit_op == "size":
if size is None:
raise ValueError('size argument must be given when unit_op=="size"')
result_unit = first_input_units**size
elif unit_op == "invdiv":
# Start with first arg in numerator, all others in denominator
product = getattr(
all_args[0], "units", first_input_units._REGISTRY.parse_units("")
)
for x in all_args[1:]:
if hasattr(x, "units"):
product /= x.units
result_unit = product**-1
else:
raise ValueError("Output unit method {} not understood".format(unit_op))
return result_unit
def implements(numpy_func_string, func_type):
"""Register an __array_function__/__array_ufunc__ implementation for Quantity
objects.
"""
def decorator(func):
if func_type == "function":
HANDLED_FUNCTIONS[numpy_func_string] = func
elif func_type == "ufunc":
HANDLED_UFUNCS[numpy_func_string] = func
else:
raise ValueError("Invalid func_type {}".format(func_type))
return func
return decorator
def implement_func(func_type, func_str, input_units=None, output_unit=None):
"""Add default-behavior NumPy function/ufunc to the handled list.
Parameters
----------
func_type : str
"function" for NumPy functions, "ufunc" for NumPy ufuncs
func_str : str
String representing the name of the NumPy function/ufunc to add
input_units : pint.Unit or str or None
Parameter to control how the function downcasts to magnitudes of arguments. If
`pint.Unit`, converts all args and kwargs to this unit before downcasting to
magnitude. If "all_consistent", converts all args and kwargs to the unit of the
first Quantity in args and kwargs before downcasting to magnitude. If some
other string, the string is parsed as a unit, and all args and kwargs are
converted to that unit. If None, units are stripped without conversion.
output_unit : pint.Unit or str or None
Parameter to control the unit of the output. If `pint.Unit`, output is wrapped
with that unit. If "match_input", output is wrapped with the unit of the first
Quantity in args and kwargs. If a string representing a unit operation defined
in `get_op_output_unit`, output is wrapped by the unit determined by
`get_op_output_unit`. If some other string, the string is parsed as a unit,
which becomes the unit of the output. If None, the bare magnitude is returned.
"""
# If NumPy is not available, do not attempt implement that which does not exist
if np is None:
return
# Handle functions in submodules
func_str_split = func_str.split(".")
func = getattr(np, func_str_split[0], None)
# If the function is not available, do not attempt to implement it
if func is None:
return
for func_str_piece in func_str_split[1:]:
func = getattr(func, func_str_piece)
@implements(func_str, func_type)
def implementation(*args, **kwargs):
first_input_units = _get_first_input_units(args, kwargs)
if input_units == "all_consistent":
# Match all input args/kwargs to same units
stripped_args, stripped_kwargs = convert_to_consistent_units(
*args, pre_calc_units=first_input_units, **kwargs
)
else:
if isinstance(input_units, str):
# Conversion requires Unit, not str
pre_calc_units = first_input_units._REGISTRY.parse_units(input_units)
else:
pre_calc_units = input_units
# Match all input args/kwargs to input_units, or if input_units is None,
# simply strip units
stripped_args, stripped_kwargs = convert_to_consistent_units(
*args, pre_calc_units=pre_calc_units, **kwargs
)
# Determine result through plain numpy function on stripped arguments
result_magnitude = func(*stripped_args, **stripped_kwargs)
if output_unit is None:
# Short circuit and return magnitude alone
return result_magnitude
elif output_unit == "match_input":
result_unit = first_input_units
elif output_unit in [
"sum",
"mul",
"delta",
"delta,div",
"div",
"invdiv",
"variance",
"square",
"sqrt",
"cbrt",
"reciprocal",
"size",
]:
result_unit = get_op_output_unit(
output_unit, first_input_units, tuple(chain(args, kwargs.values()))
)
else:
result_unit = output_unit
return first_input_units._REGISTRY.Quantity(result_magnitude, result_unit)
"""
Define ufunc behavior collections.
- `strip_unit_input_output_ufuncs`: units should be ignored on both input and output
- `matching_input_bare_output_ufuncs`: inputs are converted to matching units, but
outputs are returned as-is
- `matching_input_set_units_output_ufuncs`: inputs are converted to matching units, and
the output units are as set by the dict value
- `set_units_ufuncs`: dict values are specified as (in_unit, out_unit), so that inputs
are converted to in_unit before having magnitude passed to NumPy ufunc, and outputs
are set to have out_unit
- `matching_input_copy_units_output_ufuncs`: inputs are converted to matching units, and
outputs are set to that unit
- `copy_units_output_ufuncs`: input units (except the first) are ignored, and output is
set to that of the first input unit
- `op_units_output_ufuncs`: determine output unit from input unit as determined by
operation (see `get_op_output_unit`)
"""
strip_unit_input_output_ufuncs = ["isnan", "isinf", "isfinite", "signbit", "sign"]
matching_input_bare_output_ufuncs = [
"equal",
"greater",
"greater_equal",
"less",
"less_equal",
"not_equal",
]
matching_input_set_units_output_ufuncs = {"arctan2": "radian"}
set_units_ufuncs = {
"cumprod": ("", ""),
"arccos": ("", "radian"),
"arcsin": ("", "radian"),
"arctan": ("", "radian"),
"arccosh": ("", "radian"),
"arcsinh": ("", "radian"),
"arctanh": ("", "radian"),
"exp": ("", ""),
"expm1": ("", ""),
"exp2": ("", ""),
"log": ("", ""),
"log10": ("", ""),
"log1p": ("", ""),
"log2": ("", ""),
"sin": ("radian", ""),
"cos": ("radian", ""),
"tan": ("radian", ""),
"sinh": ("radian", ""),
"cosh": ("radian", ""),
"tanh": ("radian", ""),
"radians": ("degree", "radian"),
"degrees": ("radian", "degree"),
"deg2rad": ("degree", "radian"),
"rad2deg": ("radian", "degree"),
"logaddexp": ("", ""),
"logaddexp2": ("", ""),
}
# TODO (#905 follow-up):
# while this matches previous behavior, some of these have optional arguments that
# should not be Quantities. This should be fixed, and tests using these optional
# arguments should be added.
matching_input_copy_units_output_ufuncs = [
"compress",
"conj",
"conjugate",
"copy",
"diagonal",
"max",
"mean",
"min",
"ptp",
"ravel",
"repeat",
"reshape",
"round",
"squeeze",
"swapaxes",
"take",
"trace",
"transpose",
"ceil",
"floor",
"hypot",
"rint",
"copysign",
"nextafter",
"trunc",
"absolute",
"negative",
"maximum",
"minimum",
"fabs",
]
copy_units_output_ufuncs = ["ldexp", "fmod", "mod", "remainder"]
op_units_output_ufuncs = {
"var": "square",
"multiply": "mul",
"true_divide": "div",
"divide": "div",
"floor_divide": "div",
"sqrt": "sqrt",
"cbrt": "cbrt",
"square": "square",
"reciprocal": "reciprocal",
"std": "sum",
"sum": "sum",
"cumsum": "sum",
"matmul": "mul",
}
# Perform the standard ufunc implementations based on behavior collections
for ufunc_str in strip_unit_input_output_ufuncs:
# Ignore units
implement_func("ufunc", ufunc_str, input_units=None, output_unit=None)
for ufunc_str in matching_input_bare_output_ufuncs:
# Require all inputs to match units, but output plain ndarray/duck array
implement_func("ufunc", ufunc_str, input_units="all_consistent", output_unit=None)
for ufunc_str, out_unit in matching_input_set_units_output_ufuncs.items():
# Require all inputs to match units, but output in specified unit
implement_func(
"ufunc", ufunc_str, input_units="all_consistent", output_unit=out_unit
)
for ufunc_str, (in_unit, out_unit) in set_units_ufuncs.items():
# Require inputs in specified unit, and output in specified unit
implement_func("ufunc", ufunc_str, input_units=in_unit, output_unit=out_unit)
for ufunc_str in matching_input_copy_units_output_ufuncs:
# Require all inputs to match units, and output as first unit in arguments
implement_func(
"ufunc", ufunc_str, input_units="all_consistent", output_unit="match_input"
)
for ufunc_str in copy_units_output_ufuncs:
# Output as first unit in arguments, but do not convert inputs
implement_func("ufunc", ufunc_str, input_units=None, output_unit="match_input")
for ufunc_str, unit_op in op_units_output_ufuncs.items():
implement_func("ufunc", ufunc_str, input_units=None, output_unit=unit_op)
# Define custom ufunc implementations for atypical cases
@implements("modf", "ufunc")
def _modf(x, *args, **kwargs):
(x,), output_wrap = unwrap_and_wrap_consistent_units(x)
return tuple(output_wrap(y) for y in np.modf(x, *args, **kwargs))
@implements("frexp", "ufunc")
def _frexp(x, *args, **kwargs):
(x,), output_wrap = unwrap_and_wrap_consistent_units(x)
mantissa, exponent = np.frexp(x, *args, **kwargs)
return output_wrap(mantissa), exponent
@implements("power", "ufunc")
def _power(x1, x2):
if _is_quantity(x1):
return x1**x2
else:
return x2.__rpow__(x1)
@implements("add", "ufunc")
def _add(x1, x2, *args, **kwargs):
(x1, x2), output_wrap = unwrap_and_wrap_consistent_units(x1, x2)
return output_wrap(np.add(x1, x2, *args, **kwargs))
@implements("subtract", "ufunc")
def _subtract(x1, x2, *args, **kwargs):
(x1, x2), output_wrap = unwrap_and_wrap_consistent_units(x1, x2)
return output_wrap(np.subtract(x1, x2, *args, **kwargs))
# Define custom function implementations
@implements("meshgrid", "function")
def _meshgrid(*xi, **kwargs):
# Simply need to map input units to onto list of outputs
input_units = (x.units for x in xi)
res = np.meshgrid(*(x.m for x in xi), **kwargs)
return [out * unit for out, unit in zip(res, input_units)]
@implements("full_like", "function")
def _full_like(a, fill_value, dtype=None, order="K", subok=True, shape=None):
# Make full_like by multiplying with array from ones_like in a
# non-multiplicative-unit-safe way
if hasattr(fill_value, "_REGISTRY"):
return fill_value._REGISTRY.Quantity(
(
np.ones_like(a, dtype=dtype, order=order, subok=subok, shape=shape)
* fill_value.m
),
fill_value.units,
)
else:
return (
np.ones_like(a, dtype=dtype, order=order, subok=subok, shape=shape)
* fill_value
)
@implements("interp", "function")
def _interp(x, xp, fp, left=None, right=None, period=None):
# Need to handle x and y units separately
(x, xp, period), _ = unwrap_and_wrap_consistent_units(x, xp, period)
(fp, right, left), output_wrap = unwrap_and_wrap_consistent_units(fp, left, right)
return output_wrap(np.interp(x, xp, fp, left=left, right=right, period=period))
@implements("where", "function")
def _where(condition, *args):
if not getattr(condition, "_is_multiplicative", True):
raise ValueError(
"Invalid units of the condition: Boolean value of Quantity with offset unit is ambiguous."
)
condition = getattr(condition, "magnitude", condition)
args, output_wrap = unwrap_and_wrap_consistent_units(*args)
return output_wrap(np.where(condition, *args))
@implements("concatenate", "function")
def _concatenate(sequence, *args, **kwargs):
sequence, output_wrap = unwrap_and_wrap_consistent_units(*sequence)
return output_wrap(np.concatenate(sequence, *args, **kwargs))
@implements("stack", "function")
def _stack(arrays, *args, **kwargs):
arrays, output_wrap = unwrap_and_wrap_consistent_units(*arrays)
return output_wrap(np.stack(arrays, *args, **kwargs))
@implements("unwrap", "function")
def _unwrap(p, discont=None, axis=-1):
# np.unwrap only dispatches over p argument, so assume it is a Quantity
discont = np.pi if discont is None else discont
return p._REGISTRY.Quantity(np.unwrap(p.m_as("rad"), discont, axis=axis), "rad").to(
p.units
)
@implements("copyto", "function")
def _copyto(dst, src, casting="same_kind", where=True):
if _is_quantity(dst):
if _is_quantity(src):
src = src.m_as(dst.units)
np.copyto(dst._magnitude, src, casting=casting, where=where)
else:
warnings.warn(
"The unit of the quantity is stripped when copying to non-quantity",
UnitStrippedWarning,
stacklevel=2,
)
np.copyto(dst, src.m, casting=casting, where=where)
@implements("einsum", "function")
def _einsum(subscripts, *operands, **kwargs):
operand_magnitudes, _ = convert_to_consistent_units(*operands, pre_calc_units=None)
output_unit = get_op_output_unit("mul", _get_first_input_units(operands), operands)
return np.einsum(subscripts, *operand_magnitudes, **kwargs) * output_unit
@implements("isin", "function")
def _isin(element, test_elements, assume_unique=False, invert=False):
if not _is_quantity(element):
raise ValueError(
"Cannot test if unit-aware elements are in not-unit-aware array"
)
if _is_quantity(test_elements):
try:
test_elements = test_elements.m_as(element.units)
except DimensionalityError:
# Incompatible unit test elements cannot be in element
return np.full(element.shape, False)
elif _is_sequence_with_quantity_elements(test_elements):
compatible_test_elements = []
for test_element in test_elements:
if not _is_quantity(test_element):
pass
try:
compatible_test_elements.append(test_element.m_as(element.units))
except DimensionalityError:
# Incompatible unit test elements cannot be in element, but others in
# sequence may
pass
test_elements = compatible_test_elements
else:
# Consider non-quantity like dimensionless quantity
if not element.dimensionless:
# Unit do not match, so all false
return np.full(element.shape, False)
else:
# Convert to units of element
element._REGISTRY.Quantity(test_elements).m_as(element.units)
return np.isin(element.m, test_elements, assume_unique=assume_unique, invert=invert)
@implements("pad", "function")
def _pad(array, pad_width, mode="constant", **kwargs):
def _recursive_convert(arg, unit):
if iterable(arg):
return tuple(_recursive_convert(a, unit=unit) for a in arg)
elif not _is_quantity(arg):
if arg == 0 or np.isnan(arg):
arg = unit._REGISTRY.Quantity(arg, unit)
else:
arg = unit._REGISTRY.Quantity(arg, "dimensionless")
return arg.m_as(unit)
# pad only dispatches on array argument, so we know it is a Quantity
units = array.units
# Handle flexible constant_values and end_values, converting to units if Quantity
# and ignoring if not
for key in ("constant_values", "end_values"):
if key in kwargs:
kwargs[key] = _recursive_convert(kwargs[key], units)
return units._REGISTRY.Quantity(
np.pad(array._magnitude, pad_width, mode=mode, **kwargs), units
)
@implements("any", "function")
def _any(a, *args, **kwargs):
# Only valid when multiplicative unit/no offset
if a._is_multiplicative:
return np.any(a._magnitude, *args, **kwargs)
else:
raise ValueError("Boolean value of Quantity with offset unit is ambiguous.")
@implements("all", "function")
def _all(a, *args, **kwargs):
# Only valid when multiplicative unit/no offset
if a._is_multiplicative:
return np.all(a._magnitude, *args, **kwargs)
else:
raise ValueError("Boolean value of Quantity with offset unit is ambiguous.")
def implement_prod_func(name):
if np is None:
return
func = getattr(np, name, None)
if func is None:
return
@implements(name, "function")
def _prod(a, *args, **kwargs):
arg_names = ("axis", "dtype", "out", "keepdims", "initial", "where")
all_kwargs = dict(**dict(zip(arg_names, args)), **kwargs)
axis = all_kwargs.get("axis", None)
where = all_kwargs.get("where", None)
registry = a.units._REGISTRY
if axis is not None and where is not None:
_, where_ = np.broadcast_arrays(a._magnitude, where)
exponents = np.unique(np.sum(where_, axis=axis))
if len(exponents) == 1 or (len(exponents) == 2 and 0 in exponents):
units = a.units ** np.max(exponents)
else:
units = registry.dimensionless
a = a.to(units)
elif axis is not None:
units = a.units ** a.shape[axis]
elif where is not None:
exponent = np.sum(where)
units = a.units**exponent
else:
exponent = (
np.sum(np.logical_not(np.isnan(a))) if name == "nanprod" else a.size
)
units = a.units**exponent
result = func(a._magnitude, *args, **kwargs)
return registry.Quantity(result, units)
for name in ["prod", "nanprod"]:
implement_prod_func(name)
# Implement simple matching-unit or stripped-unit functions based on signature
def implement_consistent_units_by_argument(func_str, unit_arguments, wrap_output=True):
# If NumPy is not available, do not attempt implement that which does not exist
if np is None:
return
if "." not in func_str:
func = getattr(np, func_str, None)
else:
parts = func_str.split(".")
module = np
for part in parts[:-1]:
module = getattr(module, part, None)
func = getattr(module, parts[-1], None)
# if NumPy does not implement it, do not implement it either
if func is None:
return
@implements(func_str, "function")
def implementation(*args, **kwargs):
# Bind given arguments to the NumPy function signature
bound_args = signature(func).bind(*args, **kwargs)
# Skip unit arguments that are supplied as None
valid_unit_arguments = [
label
for label in unit_arguments
if label in bound_args.arguments and bound_args.arguments[label] is not None
]
# Unwrap valid unit arguments, ensure consistency, and obtain output wrapper
unwrapped_unit_args, output_wrap = unwrap_and_wrap_consistent_units(
*(bound_args.arguments[label] for label in valid_unit_arguments)
)
# Call NumPy function with updated arguments
for i, unwrapped_unit_arg in enumerate(unwrapped_unit_args):
bound_args.arguments[valid_unit_arguments[i]] = unwrapped_unit_arg
ret = func(*bound_args.args, **bound_args.kwargs)
# Conditionally wrap output
if wrap_output:
return output_wrap(ret)
else:
return ret
for func_str, unit_arguments, wrap_output in [
("expand_dims", "a", True),
("squeeze", "a", True),
("rollaxis", "a", True),
("moveaxis", "a", True),
("around", "a", True),
("diagonal", "a", True),
("mean", "a", True),
("ptp", "a", True),
("ravel", "a", True),
("round_", "a", True),
("sort", "a", True),
("median", "a", True),
("nanmedian", "a", True),
("transpose", "a", True),
("copy", "a", True),
("average", "a", True),
("nanmean", "a", True),
("swapaxes", "a", True),
("nanmin", "a", True),
("nanmax", "a", True),
("percentile", "a", True),
("nanpercentile", "a", True),
("quantile", "a", True),
("nanquantile", "a", True),
("flip", "m", True),
("fix", "x", True),
("trim_zeros", ["filt"], True),
("broadcast_to", ["array"], True),
("amax", ["a", "initial"], True),
("amin", ["a", "initial"], True),
("searchsorted", ["a", "v"], False),
("isclose", ["a", "b"], False),
("nan_to_num", ["x", "nan", "posinf", "neginf"], True),
("clip", ["a", "a_min", "a_max"], True),
("append", ["arr", "values"], True),
("compress", "a", True),
("linspace", ["start", "stop"], True),
("tile", "A", True),
("lib.stride_tricks.sliding_window_view", "x", True),
("rot90", "m", True),
("insert", ["arr", "values"], True),
("resize", "a", True),
("reshape", "a", True),
("allclose", ["a", "b"], False),
("intersect1d", ["ar1", "ar2"], True),
]:
implement_consistent_units_by_argument(func_str, unit_arguments, wrap_output)
# Handle atleast_nd functions
def implement_atleast_nd(func_str):
# If NumPy is not available, do not attempt implement that which does not exist
if np is None:
return
func = getattr(np, func_str)
@implements(func_str, "function")
def implementation(*arrays):
stripped_arrays, _ = convert_to_consistent_units(*arrays)
arrays_magnitude = func(*stripped_arrays)
if len(arrays) > 1:
return [
array_magnitude
if not hasattr(original, "_REGISTRY")
else original._REGISTRY.Quantity(array_magnitude, original.units)
for array_magnitude, original in zip(arrays_magnitude, arrays)
]
else:
output_unit = arrays[0].units
return output_unit._REGISTRY.Quantity(arrays_magnitude, output_unit)
for func_str in ["atleast_1d", "atleast_2d", "atleast_3d"]:
implement_atleast_nd(func_str)
# Handle cumulative products (which must be dimensionless for consistent units across
# output array)
def implement_single_dimensionless_argument_func(func_str):
# If NumPy is not available, do not attempt implement that which does not exist
if np is None:
return
func = getattr(np, func_str)
@implements(func_str, "function")
def implementation(a, *args, **kwargs):
(a_stripped,), _ = convert_to_consistent_units(
a, pre_calc_units=a._REGISTRY.parse_units("dimensionless")
)
return a._REGISTRY.Quantity(func(a_stripped, *args, **kwargs))
for func_str in ["cumprod", "cumproduct", "nancumprod"]:
implement_single_dimensionless_argument_func(func_str)
# Handle single-argument consistent unit functions
for func_str in [
"block",
"hstack",
"vstack",
"dstack",
"column_stack",
"broadcast_arrays",
]:
implement_func(
"function", func_str, input_units="all_consistent", output_unit="match_input"
)
# Handle functions that ignore units on input and output
for func_str in [
"size",
"isreal",
"iscomplex",
"shape",
"ones_like",
"zeros_like",
"empty_like",
"argsort",
"argmin",
"argmax",
"alen",
"ndim",
"nanargmax",
"nanargmin",
"count_nonzero",
"nonzero",
"result_type",
]:
implement_func("function", func_str, input_units=None, output_unit=None)
# Handle functions with output unit defined by operation
for func_str in ["std", "nanstd", "sum", "nansum", "cumsum", "nancumsum"]:
implement_func("function", func_str, input_units=None, output_unit="sum")
for func_str in ["cross", "trapz", "dot"]:
implement_func("function", func_str, input_units=None, output_unit="mul")
for func_str in ["diff", "ediff1d"]:
implement_func("function", func_str, input_units=None, output_unit="delta")
for func_str in ["gradient"]:
implement_func("function", func_str, input_units=None, output_unit="delta,div")
for func_str in ["linalg.solve"]:
implement_func("function", func_str, input_units=None, output_unit="invdiv")
for func_str in ["var", "nanvar"]:
implement_func("function", func_str, input_units=None, output_unit="variance")
def numpy_wrap(func_type, func, args, kwargs, types):
"""Return the result from a NumPy function/ufunc as wrapped by Pint."""
if func_type == "function":
handled = HANDLED_FUNCTIONS
# Need to handle functions in submodules
name = ".".join(func.__module__.split(".")[1:] + [func.__name__])
elif func_type == "ufunc":
handled = HANDLED_UFUNCS
# ufuncs do not have func.__module__
name = func.__name__
else:
raise ValueError("Invalid func_type {}".format(func_type))
if name not in handled or any(is_upcast_type(t) for t in types):
return NotImplemented
return handled[name](*args, **kwargs)
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