def sortable_proxy(
poly: PolyLike,
graded: bool = False,
reverse: bool = False,
) -> numpy.ndarray:
"""
Create a numerical proxy for a polynomial to allow compare.
As polynomials are not inherently sortable, values are sorted using the
highest `lexicographical` ordering. Between the values that have the same
highest ordering, the elements are sorted using the coefficients. This also
ensures that the method behaves as expected with ``numpy.ndarray``.
Args:
poly:
Polynomial to convert into something sortable.
graded:
Graded sorting, meaning the indices are always sorted by the index
sum. E.g. ``q0**2*q1**2*q2**2`` has an exponent sum of 6, and will
therefore be consider larger than both ``q0**3*q1*q2``,
``q0*q1**3*q2`` and ``q0*q1*z**3``.
reverse:
Reverses lexicographical sorting meaning that ``q0*q1**3`` is
considered bigger than ``q0**3*q1``, instead of the opposite.
Returns:
Integer array where ``a > b`` is retained for the giving rule of
``ordering``.
Examples:
>>> q0, q1 = numpoly.variable(2)
>>> poly = numpoly.polynomial(
... [q0**2, 2*q0, 3*q1, 4*q0, 5])
>>> numpoly.sortable_proxy(poly)
array([3, 1, 4, 2, 0])
>>> numpoly.sortable_proxy(poly, reverse=True)
array([4, 2, 1, 3, 0])
>>> numpoly.sortable_proxy([8, 4, 10, -100])
array([2, 1, 3, 0])
>>> numpoly.sortable_proxy([[8, 4], [10, -100]])
array([[2, 1],
[3, 0]])
"""
poly = numpoly.aspolynomial(poly)
coefficients = poly.coefficients
proxy = numpy.tile(-1, poly.shape)
largest = numpoly.lead_exponent(poly, graded=graded, reverse=reverse)
for idx in numpoly.glexsort(
poly.exponents.T, graded=graded, reverse=reverse):
indices = numpy.all(largest == poly.exponents[idx], axis=-1)
values = numpy.argsort(coefficients[idx][indices])
proxy[indices] = numpy.argsort(values)+numpy.max(proxy)+1
proxy = numpy.argsort(numpy.argsort(proxy.ravel())).reshape(proxy.shape)
return proxy
def lead_exponent(
poly: PolyLike,
graded: bool = False,
reverse: bool = False,
) -> numpy.ndarray:
"""
Find the lead exponents for each polynomial.
As polynomials are not inherently sortable, values are sorted using the
highest `lexicographical` ordering. Between the values that have the same
highest ordering, the elements are sorted using the coefficients.
Args:
poly:
Polynomial to locate exponents on.
graded:
Graded sorting, meaning the indices are always sorted by the index
sum. E.g. ``q0**2*q1**2*q2**2`` has an exponent sum of 6, and will
therefore be consider larger than both ``q0**3*q1*q2``,
``q0*q1**3*q2`` and ``q0*q1*z**3``.
reverse:
Reverses lexicographical sorting meaning that ``q0*q1**3`` is
considered bigger than ``q0**3*q1``, instead of the opposite.
Returns:
Integer array with the largest exponents in the polynomials. The
shape is ``poly.shape + (len(poly.names),)``. The extra dimension
is used to indicate the exponent for the different indeterminants.
Examples:
>>> q0 = numpoly.variable()
>>> numpoly.lead_exponent([1, q0+1, q0**2+q0+1]).T
array([[0, 1, 2]])
>>> q0, q1 = numpoly.variable(2)
>>> numpoly.lead_exponent(
... [1, q0, q1, q0*q1, q0**3-1]).T
array([[0, 1, 0, 1, 3],
[0, 0, 1, 1, 0]])
"""
poly_ = numpoly.aspolynomial(poly)
shape = poly_.shape
poly = poly_.ravel()
out = numpy.zeros(poly_.shape + (len(poly_.names), ), dtype=int)
if not poly_.size:
return out
for idx in numpoly.glexsort(poly_.exponents.T,
graded=graded,
reverse=reverse):
out[poly_.coefficients[idx] != 0] = poly_.exponents[idx]
return out.reshape(shape + (len(poly_.names), ))
def lead_coefficient(
poly: PolyLike,
graded: bool = False,
reverse: bool = False,
) -> numpy.ndarray:
"""
Find the lead coefficients for each polynomial.
As polynomials are not inherently sortable, values are sorted using the
highest `lexicographical` ordering. Between the values that have the same
highest ordering, the elements are sorted using the coefficients.
Args:
poly:
Polynomial to locate coefficients on.
graded:
Graded sorting, meaning the indices are always sorted by the index
sum. E.g. ``q0**2*q1**2*q2**2`` has an exponent sum of 6, and will
therefore be consider larger than both ``q0**3*q1*q2``,
``q0*q1**3*q2`` and ``q0*q1*z**3``.
reverse:
Reverses lexicographical sorting meaning that ``q0*q1**3`` is
considered bigger than ``q0**3*q1``, instead of the opposite.
Returns:
Array of same shape and type as `poly`, containing all the lead
coefficients.
Examples:
>>> q0, q1 = numpoly.variable(2)
>>> numpoly.lead_coefficient(q0+2*q0**2+3*q0**3)
3
>>> numpoly.lead_coefficient([1-4*q1+q0, 2*q0**2-q1, 4])
array([-4, -1, 4])
"""
poly = numpoly.aspolynomial(poly)
out = numpy.zeros(poly.shape, dtype=poly.dtype)
if not out.size:
return out
for idx in numpoly.glexsort(
poly.exponents.T, graded=graded, reverse=reverse):
values = poly.coefficients[idx]
indices = values != 0
out[indices] = values[indices]
if not poly.shape:
out = out.item()
return out
def _to_string(
poly: ndpoly,
precision: float,
suppress_small: bool,
) -> List[str]:
"""Backend for to_string."""
exponents = poly.exponents.copy()
coefficients = poly.coefficients
options = numpoly.get_options()
output: List[str] = []
indices = numpoly.glexsort(
exponents.T,
graded=options["display_graded"],
reverse=options["display_reverse"],
)
if options["display_inverse"]:
indices = indices[::-1]
for idx in indices:
if not coefficients[idx]:
continue
if suppress_small and abs(coefficients[idx]) < 10**-precision:
continue
if coefficients[idx] == 1 and any(exponents[idx]):
out = ""
elif coefficients[idx] == -1 and any(exponents[idx]):
out = "-"
else:
out = str(coefficients[idx])
exps_and_names = list(zip(exponents[idx], poly.names))
for exponent, indeterminant in exps_and_names:
if exponent:
if out not in ("", "-"):
out += options["display_multiply"]
out += indeterminant
if exponent > 1:
out += options["display_exponent"] + str(exponent)
if output and float(coefficients[idx]) >= 0:
out = "+" + out
output.append(out)
return output
def test_glexsort():
indices = numpy.array([[0, 0, 0, 1, 2, 1],
[1, 2, 0, 0, 0, 1]])
assert numpy.all(indices.T[numpoly.glexsort(indices)].T ==
[[0, 1, 2, 0, 1, 0],
[0, 0, 0, 1, 1, 2]])
assert numpy.all(indices.T[numpoly.glexsort(indices, graded=True)].T ==
[[0, 1, 0, 2, 1, 0],
[0, 0, 1, 0, 1, 2]])
assert numpy.all(indices.T[numpoly.glexsort(indices, reverse=True)].T ==
[[0, 0, 0, 1, 1, 2],
[0, 1, 2, 0, 1, 0]])
assert numpy.all(indices.T[numpoly.glexsort(indices, graded=True, reverse=True)].T ==
[[0, 0, 1, 0, 1, 2],
[0, 1, 0, 2, 1, 0]])
indices = numpy.array([4, 5, 6, 3, 2, 1])
assert numpy.all(numpoly.glexsort(indices) == [5, 4, 3, 0, 1, 2])
indices = numpy.array([[4, 5, 6, 3, 2, 1]])
assert numpy.all(numpoly.glexsort(indices) == [5, 4, 3, 0, 1, 2])
def greater(
x1: PolyLike,
x2: PolyLike,
out: Optional[numpy.ndarray] = None,
**kwargs: Any,
) -> numpy.ndarray:
"""
Return the truth value of (x1 > x2) element-wise.
Args:
x1, x2:
Input arrays. If ``x1.shape != x2.shape``, they must be
broadcastable to a common shape (which becomes the shape of the
output).
out:
A location into which the result is stored. If provided, it must
have a shape that the inputs broadcast to. If not provided or
`None`, a freshly-allocated array is returned. A tuple (possible
only as a keyword argument) must have length equal to the number of
outputs.
where:
This condition is broadcast over the input. At locations where the
condition is True, the `out` array will be set to the ufunc result.
Elsewhere, the `out` array will retain its original value. Note
that if an uninitialized `out` array is created via the default
``out=None``, locations within it where the condition is False will
remain uninitialized.
kwargs:
Keyword args passed to numpy.ufunc.
Returns:
Output array, element-wise comparison of `x1` and `x2`. Typically of
type bool, unless ``dtype=object`` is passed. This is a scalar if both
`x1` and `x2` are scalars.
Examples:
>>> q0, q1 = numpoly.variable(2)
>>> numpoly.greater(3, 4)
False
>>> numpoly.greater(4*q0, 3*q0)
True
>>> numpoly.greater(q0, q1)
False
>>> numpoly.greater(q0**2, q0)
True
>>> numpoly.greater([1, q0, q0**2, q0**3], q1)
array([False, False, True, True])
>>> numpoly.greater(q0+1, q0-1)
True
>>> numpoly.greater(q0, q0)
False
"""
x1, x2 = numpoly.align_polynomials(x1, x2)
coefficients1 = x1.coefficients
coefficients2 = x2.coefficients
if out is None:
out = numpy.greater(coefficients1[0], coefficients2[0], **kwargs)
if not out.shape:
return greater(x1.ravel(), x2.ravel(), out=out.ravel()).item()
options = numpoly.get_options()
for idx in numpoly.glexsort(x1.exponents.T,
graded=options["sort_graded"],
reverse=options["sort_reverse"]):
indices = (coefficients1[idx] != 0) | (coefficients2[idx] != 0)
indices &= coefficients1[idx] != coefficients2[idx]
out[indices] = numpy.greater(coefficients1[idx], coefficients2[idx],
**kwargs)[indices]
return out
def minimum(
x1: PolyLike,
x2: PolyLike,
out: Optional[ndpoly] = None,
**kwargs: Any,
) -> ndpoly:
"""
Element-wise minimum of array elements.
Compare two arrays and returns a new array containing the element-wise
minima. If one of the elements being compared is a NaN, then that
element is returned. If both elements are NaNs then the first is
returned. The latter distinction is important for complex NaNs, which
are defined as at least one of the real or imaginary parts being a NaN.
The net effect is that NaNs are propagated.
Args:
x1, x2 :
The arrays holding the elements to be compared. If ``x1.shape !=
x2.shape``, they must be broadcastable to a common shape (which
becomes the shape of the output).
out:
A location into which the result is stored. If provided, it must
have a shape that the inputs broadcast to. If not provided or
`None`, a freshly-allocated array is returned. A tuple (possible
only as a keyword argument) must have length equal to the number of
outputs.
where:
This condition is broadcast over the input. At locations where the
condition is True, the `out` array will be set to the ufunc result.
Elsewhere, the `out` array will retain its original value. Note
that if an uninitialized `out` array is created via the default
``out=None``, locations within it where the condition is False will
remain uninitialized.
kwargs:
Keyword args passed to numpy.ufunc.
Returns:
The minimum of `x1` and `x2`, element-wise. This is a scalar if
both `x1` and `x2` are scalars.
Examples:
>>> q0, q1 = numpoly.variable(2)
>>> numpoly.minimum(3, 4)
polynomial(3)
>>> numpoly.minimum(4*q0, 3*q0)
polynomial(3*q0)
>>> numpoly.minimum(q0, q1)
polynomial(q0)
>>> numpoly.minimum(q0**2, q0)
polynomial(q0)
>>> numpoly.minimum([1, q0, q0**2, q0**3], q1)
polynomial([1, q0, q1, q1])
>>> numpoly.minimum(q0+1, q0-1)
polynomial(q0-1)
"""
del out
x1, x2 = numpoly.align_polynomials(x1, x2)
coefficients1 = x1.coefficients
coefficients2 = x2.coefficients
out_ = numpy.zeros(x1.shape, dtype=bool)
options = numpoly.get_options()
for idx in numpoly.glexsort(x1.exponents.T,
graded=options["sort_graded"],
reverse=options["sort_reverse"]):
indices = (coefficients1[idx] != 0) | (coefficients2[idx] != 0)
indices = coefficients1[idx] != coefficients2[idx]
out_[indices] = (coefficients1[idx] < coefficients2[idx])[indices]
return numpoly.where(out_, x1, x2)