1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071
|
"""
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, OffsetUnitCalculusError, 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
"""
if np is not None and isinstance(obj, np.ndarray) and not obj.dtype.hasobject:
# If obj is a numpy array, avoid looping on all elements
# if dtype does not have objects
return False
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 isinstance(arg, bool):
return arg
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 (except booleans) 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(f"Output unit method {unit_op} not understood")
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(f"Invalid func_type {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):
if func_str in ["multiply", "true_divide", "divide", "floor_divide"] and any(
[
not _is_quantity(arg) and _is_sequence_with_quantity_elements(arg)
for arg in args
]
):
# the sequence may contain different units, so fall back to element-wise
return np.array(
[func(*func_args) for func_args in zip(*args)], dtype=object
)
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",
"roll",
"ceil",
"floor",
"hypot",
"rint",
"copysign",
"nextafter",
"trunc",
"absolute",
"positive",
"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
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, **kwargs):
# 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, **kwargs) * fill_value.m,
fill_value.units,
)
return np.ones_like(a, **kwargs) * 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)
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)
# Handle mutliplicative functions separately to deal with non-multiplicative units
def _base_unit_if_needed(a):
if a._is_multiplicative:
return a
else:
if a.units._REGISTRY.autoconvert_offset_to_baseunit:
return a.to_base_units()
else:
raise OffsetUnitCalculusError(a.units)
# NP2 Can remove trapz wrapping when we only support numpy>=2
@implements("trapz", "function")
@implements("trapezoid", "function")
def _trapz(y, x=None, dx=1.0, **kwargs):
trapezoid = np.trapezoid if hasattr(np, "trapezoid") else np.trapz
y = _base_unit_if_needed(y)
units = y.units
if x is not None:
if hasattr(x, "units"):
x = _base_unit_if_needed(x)
units *= x.units
x = x._magnitude
ret = trapezoid(y._magnitude, x, **kwargs)
else:
if hasattr(dx, "units"):
dx = _base_unit_if_needed(dx)
units *= dx.units
dx = dx._magnitude
ret = trapezoid(y._magnitude, dx=dx, **kwargs)
return y.units._REGISTRY.Quantity(ret, units)
@implements("correlate", "function")
def _correlate(a, v, mode="valid", **kwargs):
a = _base_unit_if_needed(a)
v = _base_unit_if_needed(v)
units = a.units * v.units
ret = np.correlate(a._magnitude, v._magnitude, mode=mode, **kwargs)
return a.units._REGISTRY.Quantity(ret, units)
def implement_mul_func(func):
# 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, b, **kwargs):
a = _base_unit_if_needed(a)
units = a.units
if hasattr(b, "units"):
b = _base_unit_if_needed(b)
units *= b.units
b = b._magnitude
mag = func(a._magnitude, b, **kwargs)
return a.units._REGISTRY.Quantity(mag, units)
for func_str in ("cross", "dot"):
implement_mul_func(func_str)
# 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)
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),
("round", "a", True),
("sort", "a", True),
("median", "a", True),
("nanmedian", "a", True),
("transpose", "a", True),
("roll", "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),
("max", ["a", "initial"], True),
("min", ["a", "initial"], True),
("searchsorted", ["a", "v"], 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),
("delete", ["arr"], True),
("resize", "a", True),
("reshape", "a", True),
("intersect1d", ["ar1", "ar2"], True),
):
implement_consistent_units_by_argument(func_str, unit_arguments, wrap_output)
# implement isclose and allclose
def implement_close(func_str):
if np is None:
return
func = getattr(np, func_str)
@implements(func_str, "function")
def implementation(*args, **kwargs):
bound_args = signature(func).bind(*args, **kwargs)
labels = ["a", "b"]
arrays = {label: bound_args.arguments[label] for label in labels}
if "atol" in bound_args.arguments:
atol = bound_args.arguments["atol"]
a = arrays["a"]
if not hasattr(atol, "_REGISTRY") and hasattr(a, "_REGISTRY"):
# always use the units of `a`
atol_ = a._REGISTRY.Quantity(atol, a.units)
else:
atol_ = atol
arrays["atol"] = atol_
args, _ = unwrap_and_wrap_consistent_units(*arrays.values())
for label, value in zip(arrays.keys(), args):
bound_args.arguments[label] = value
return func(*bound_args.args, **bound_args.kwargs)
for func_str in ("isclose", "allclose"):
implement_close(func_str)
# 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", "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",
"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",
"linalg.norm",
):
implement_func("function", func_str, input_units=None, output_unit="sum")
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(f"Invalid func_type {func_type}")
if name not in handled or any(is_upcast_type(t) for t in types):
return NotImplemented
return handled[name](*args, **kwargs)
|