def paintCSNew(width,
height,
xMax,
yMax,
xfactor,
yfactor,
ticlength,
xMin=5,
yMin=1,
xoffset=1,
dashedx=5,
dashedy=5):
# x-axis
xmlText = ("<line x1='0' y1='" + str(height) + "' x2='" + str(width) +
"' y2='" + str(height) + "' style='stroke:rgb(0,0,0);'/>\n")
xsign = 1 if xMax >= 0 else -1
ysign = 1 if yMax >= 0 else -1
for i in srange(xMin, xMax, xsign * dashedx):
xmlText = xmlText + ("<text x='" + str(i * xsign * xfactor - 6) +
"' y='" + str(xsign * height - 2 * ticlength) +
"' style='fill:rgb(102,102,102);font-size:11px;'>"
+ "{:.5g}".format(i + xoffset) + "</text>\n")
xmlText = xmlText + (
"<line y1='0' x1='" + str(i * xsign * xfactor) + "' y2='" +
str(ysign * height) + "' x2='" + str(i * xsign * xfactor) +
"' style='stroke:rgb(204,204,204);stroke-dasharray:3,3;'/>\n")
for i in srange(xMin, xMax, xsign * dashedx):
xmlText = xmlText + ("<line x1='" + str(i * xsign * xfactor) +
"' y1='" + str(ysign * height - ticlength) +
"' x2='" + str(i * xfactor) + "' y2='" +
str(ysign * height) +
"' style='stroke:rgb(0,0,0);'/>\n")
# y-axis
xmlText += "<line x1='0' y1='%d' x2='0' y2='0' style='stroke:rgb(0,0,0);'/>\n" % height
for i in srange(yMin, yMax + 1, ysign * dashedy):
xmlText = xmlText + ("<text x='-10' y='" +
str(ysign * height - i * ysign * yfactor + 3) +
"' style='fill:rgb(102,102,102);font-size:11px;'>"
+ "{:.5g}".format(float(i)) + "</text>\n")
xmlText = xmlText + (
"<line x1='0' y1='" + str(height - i * ysign * yfactor) +
"' x2='" + str(xsign * width) + "' y2='" +
str(height - i * ysign * yfactor) +
"' style='stroke:rgb(204,204,204);stroke-dasharray:3,3;'/>\n")
for i in srange(yMin, yMax + 1, ysign * dashedy):
xmlText = xmlText + ("<line x1='0' y1='" +
str(height - i * ysign * yfactor) + "' x2='" +
str(ticlength) + "' y2='" +
str(height - i * ysign * yfactor) +
"' style='stroke:rgb(0,0,0);'/>\n")
return (xmlText)
def eisenstein_series_at_inf(phi, psi, k, prec=10, t=1, base_ring=None):
r"""
Return Fourier expansion of Eistenstein series at a cusp.
INPUT:
- ``phi`` -- Dirichlet character.
- ``psi`` -- Dirichlet character.
- ``k`` -- integer, the weight of the Eistenstein series.
- ``prec`` -- integer (default: 10).
- ``t`` -- integer (default: 1).
OUTPUT:
The Fourier expansion of the Eisenstein series $E_k^{\phi,\psi, t}$ (as
defined by [Diamond-Shurman]) at the specific cusp.
EXAMPLES:
sage: phi = DirichletGroup(3)[1]
sage: psi = DirichletGroup(5)[1]
sage: E = eisenstein_series_at_inf(phi, psi, 4)
"""
N1, N2 = phi.level(), psi.level()
N = N1 * N2
#The Fourier expansion of the Eisenstein series at infinity is in the field Q(zeta_Ncyc)
Ncyc = lcm([euler_phi(N1), euler_phi(N2)])
if base_ring == None:
base_ring = CyclotomicField(Ncyc)
Q = PowerSeriesRing(base_ring, 'q')
q = Q.gen()
s = O(q**prec)
#Weight 2 with trivial characters is calculated separately
if k == 2 and phi.conductor() == 1 and psi.conductor() == 1:
if t == 1:
raise TypeError('E_2 is not a modular form.')
s = 1 / 24 * (t - 1)
for m in srange(1, prec):
for n in srange(1, prec / m):
s += n * (q**(m * n) - t * q**(m * n * t))
return s + O(q**prec)
if psi.level() == 1 and k == 1:
s -= phi.bernoulli(k) / k
elif phi.level() == 1:
s -= psi.bernoulli(k) / k
for m in srange(1, prec / t):
for n in srange(1, prec / t / m + 1):
s += 2 * base_ring(phi(m)) * base_ring(
psi(n)) * n**(k - 1) * q**(m * n * t)
return s + O(q**prec)
def QuadraticResidueCodeOddPair(n, F):
"""
Quadratic residue codes of a given odd prime length and base ring
either don't exist at all or occur as 4-tuples - a pair of
"odd-like" codes and a pair of "even-like" codes. If n 2 is prime
then (Theorem 6.6.2 in [HP2003]_) a QR code exists over GF(q) iff q is a
quadratic residue mod n.
They are constructed as "odd-like" duadic codes associated the
splitting (Q,N) mod n, where Q is the set of non-zero quadratic
residues and N is the non-residues.
EXAMPLES::
sage: codes.QuadraticResidueCodeOddPair(17, GF(13)) # known bug (#25896)
([17, 9] Cyclic Code over GF(13),
[17, 9] Cyclic Code over GF(13))
sage: codes.QuadraticResidueCodeOddPair(17, GF(2))
([17, 9] Cyclic Code over GF(2),
[17, 9] Cyclic Code over GF(2))
sage: codes.QuadraticResidueCodeOddPair(13, GF(9,"z")) # known bug (#25896)
([13, 7] Cyclic Code over GF(9),
[13, 7] Cyclic Code over GF(9))
sage: C1 = codes.QuadraticResidueCodeOddPair(17, GF(2))[1]
sage: C1x = C1.extended_code()
sage: C2 = codes.QuadraticResidueCodeOddPair(17, GF(2))[0]
sage: C2x = C2.extended_code()
sage: C2x.spectrum(); C1x.spectrum()
[1, 0, 0, 0, 0, 0, 102, 0, 153, 0, 153, 0, 102, 0, 0, 0, 0, 0, 1]
[1, 0, 0, 0, 0, 0, 102, 0, 153, 0, 153, 0, 102, 0, 0, 0, 0, 0, 1]
sage: C3 = codes.QuadraticResidueCodeOddPair(7, GF(2))[0]
sage: C3x = C3.extended_code()
sage: C3x.spectrum()
[1, 0, 0, 0, 14, 0, 0, 0, 1]
This is consistent with Theorem 6.6.14 in [HP2003]_.
TESTS::
sage: codes.QuadraticResidueCodeOddPair(9,GF(2))
Traceback (most recent call last):
...
ValueError: the argument n must be an odd prime
"""
from sage.arith.srange import srange
from sage.categories.finite_fields import FiniteFields
if F not in FiniteFields():
raise ValueError("the argument F must be a finite field")
q = F.order()
n = Integer(n)
if n <= 2 or not n.is_prime():
raise ValueError("the argument n must be an odd prime")
Q = quadratic_residues(n)
Q.remove(0) # non-zero quad residues
N = [x for x in srange(1, n) if x not in Q] # non-zero quad non-residues
if q not in Q:
raise ValueError(
"the order of the finite field must be a quadratic residue modulo n"
)
return DuadicCodeOddPair(F, Q, N)
def QuadraticResidueCodeOddPair(n, F):
"""
Quadratic residue codes of a given odd prime length and base ring
either don't exist at all or occur as 4-tuples - a pair of
"odd-like" codes and a pair of "even-like" codes. If n 2 is prime
then (Theorem 6.6.2 in [HP]_) a QR code exists over GF(q) iff q is a
quadratic residue mod n.
They are constructed as "odd-like" duadic codes associated the
splitting (Q,N) mod n, where Q is the set of non-zero quadratic
residues and N is the non-residues.
EXAMPLES::
sage: codes.QuadraticResidueCodeOddPair(17,GF(13))
(Linear code of length 17, dimension 9 over Finite Field of size 13,
Linear code of length 17, dimension 9 over Finite Field of size 13)
sage: codes.QuadraticResidueCodeOddPair(17,GF(2))
(Linear code of length 17, dimension 9 over Finite Field of size 2,
Linear code of length 17, dimension 9 over Finite Field of size 2)
sage: codes.QuadraticResidueCodeOddPair(13,GF(9,"z"))
(Linear code of length 13, dimension 7 over Finite Field in z of size 3^2,
Linear code of length 13, dimension 7 over Finite Field in z of size 3^2)
sage: C1 = codes.QuadraticResidueCodeOddPair(17,GF(2))[1]
sage: C1x = C1.extended_code()
sage: C2 = codes.QuadraticResidueCodeOddPair(17,GF(2))[0]
sage: C2x = C2.extended_code()
sage: C2x.spectrum(); C1x.spectrum()
[1, 0, 0, 0, 0, 0, 102, 0, 153, 0, 153, 0, 102, 0, 0, 0, 0, 0, 1]
[1, 0, 0, 0, 0, 0, 102, 0, 153, 0, 153, 0, 102, 0, 0, 0, 0, 0, 1]
sage: C3 = codes.QuadraticResidueCodeOddPair(7,GF(2))[0]
sage: C3x = C3.extended_code()
sage: C3x.spectrum()
[1, 0, 0, 0, 14, 0, 0, 0, 1]
This is consistent with Theorem 6.6.14 in [HP]_.
TESTS::
sage: codes.QuadraticResidueCodeOddPair(9,GF(2))
Traceback (most recent call last):
...
ValueError: the argument n must be an odd prime
"""
from sage.arith.srange import srange
from sage.categories.finite_fields import FiniteFields
if F not in FiniteFields():
raise ValueError("the argument F must be a finite field")
q = F.order()
n = Integer(n)
if n <= 2 or not n.is_prime():
raise ValueError("the argument n must be an odd prime")
Q = quadratic_residues(n)
Q.remove(0) # non-zero quad residues
N = [x for x in srange(1, n) if x not in Q] # non-zero quad non-residues
if q not in Q:
raise ValueError("the order of the finite field must be a quadratic residue modulo n")
return DuadicCodeOddPair(F, Q, N)
def plot_fft(self, npoints=None, channel=0, half=True, **kwds):
v = self.vector(npoints=npoints)
w = v.fft()
if half:
w = w[:len(w) // 2]
z = [abs(x) for x in w]
if half:
r = math.pi
else:
r = 2 * math.pi
data = zip(srange(0, r, r / len(z)), z)
L = list_plot(data, plotjoined=True, **kwds)
L.xmin(0)
L.xmax(r)
return L
def plot_fft(self, npoints=None, channel=0, half=True, **kwds):
v = self.vector(npoints=npoints)
w = v.fft()
if half:
w = w[:len(w)//2]
z = [abs(x) for x in w]
if half:
r = math.pi
else:
r = 2*math.pi
data = zip(srange(0, r, r/len(z)), z)
L = list_plot(data, plotjoined=True, **kwds)
L.xmin(0)
L.xmax(r)
return L
def _log_StirlingNegativePowers_(var, precision):
r"""
Helper function to calculate the logarithm of Stirling's approximation
formula from the negative powers of ``var`` on, i.e., it skips the
summands `n \log n - n + (\log n)/2 + \log(2\pi)/2`.
INPUT:
- ``var`` -- a string for the variable name.
- ``precision`` -- an integer specifying the number of exact summands.
If this is negative, then the result is `0`.
OUTPUT:
An asymptotic expansion.
TESTS::
sage: asymptotic_expansions._log_StirlingNegativePowers_(
....: 'm', precision=-1)
0
sage: asymptotic_expansions._log_StirlingNegativePowers_(
....: 'm', precision=0)
O(m^(-1))
sage: asymptotic_expansions._log_StirlingNegativePowers_(
....: 'm', precision=3)
1/12*m^(-1) - 1/360*m^(-3) + 1/1260*m^(-5) + O(m^(-7))
sage: _.parent()
Asymptotic Ring <m^ZZ> over Rational Field
"""
from asymptotic_ring import AsymptoticRing
from sage.rings.rational_field import QQ
A = AsymptoticRing(growth_group='{n}^ZZ'.format(n=var),
coefficient_ring=QQ)
if precision < 0:
return A.zero()
n = A.gen()
from sage.arith.all import bernoulli
from sage.arith.srange import srange
result = sum((bernoulli(k) / k / (k-1) / n**(k-1)
for k in srange(2, 2*precision + 2, 2)),
A.zero())
return result + (1 / n**(2*precision + 1)).O()
def _log_StirlingNegativePowers_(var, precision):
r"""
Helper function to calculate the logarithm of Stirling's approximation
formula from the negative powers of ``var`` on, i.e., it skips the
summands `n \log n - n + (\log n)/2 + \log(2\pi)/2`.
INPUT:
- ``var`` -- a string for the variable name.
- ``precision`` -- an integer specifying the number of exact summands.
If this is negative, then the result is `0`.
OUTPUT:
An asymptotic expansion.
TESTS::
sage: asymptotic_expansions._log_StirlingNegativePowers_(
....: 'm', precision=-1)
0
sage: asymptotic_expansions._log_StirlingNegativePowers_(
....: 'm', precision=0)
O(m^(-1))
sage: asymptotic_expansions._log_StirlingNegativePowers_(
....: 'm', precision=3)
1/12*m^(-1) - 1/360*m^(-3) + 1/1260*m^(-5) + O(m^(-7))
sage: _.parent()
Asymptotic Ring <m^ZZ> over Rational Field
"""
from asymptotic_ring import AsymptoticRing
from sage.rings.rational_field import QQ
A = AsymptoticRing(growth_group='{n}^ZZ'.format(n=var),
coefficient_ring=QQ)
if precision < 0:
return A.zero()
n = A.gen()
from sage.arith.all import bernoulli
from sage.arith.srange import srange
result = sum((bernoulli(k) / k / (k - 1) / n**(k - 1)
for k in srange(2, 2 * precision + 2, 2)), A.zero())
return result + (1 / n**(2 * precision + 1)).O()
def __old_make_values_list(vmin, vmax, step_size):
"""
This code is from slider_generic.__init__.
This code requires sage mode to be checked.
"""
from sage.arith.srange import srange
if isinstance(vmin, list):
vals = vmin
else:
if vmax is None:
vmax = vmin
vmin = 0
#Compute step size; vmin and vmax are both defined here
#500 is the length of the slider (in px)
if step_size is None:
step_size = (vmax - vmin) / 499.0
elif step_size <= 0:
raise ValueError(
"invalid negative step size -- step size must be positive")
#Compute list of values
num_steps = int(math.ceil((vmax - vmin) / float(step_size)))
if num_steps <= 1:
vals = [vmin, vmax]
else:
vals = srange(vmin, vmax, step_size, include_endpoint=True)
if vals[-1] != vmax:
try:
if vals[-1] > vmax:
vals[-1] = vmax
else:
vals.append(vmax)
except (ValueError, TypeError):
pass
#If the list of values is small, use the whole list.
#Otherwise, use evenly spaced values in the list.
if len(vals) == 0:
return_values = [0]
elif (len(vals) <= 500):
return_values = vals
else:
vlen = (len(vals) - 1) / 499.0
return_values = [vals[(int)(i * vlen)] for i in range(500)]
return return_values
def __old_make_values_list(vmin, vmax, step_size):
"""
This code is from slider_generic.__init__.
This code requires sage mode to be checked.
"""
from sage.arith.srange import srange
if isinstance(vmin, list):
vals=vmin
else:
if vmax is None:
vmax=vmin
vmin=0
#Compute step size; vmin and vmax are both defined here
#500 is the length of the slider (in px)
if step_size is None:
step_size = (vmax-vmin)/499.0
elif step_size <= 0:
raise ValueError, "invalid negative step size -- step size must be positive"
#Compute list of values
num_steps = int(math.ceil((vmax-vmin)/float(step_size)))
if num_steps <= 1:
vals = [vmin, vmax]
else:
vals = srange(vmin, vmax, step_size, include_endpoint=True)
if vals[-1] != vmax:
try:
if vals[-1] > vmax:
vals[-1] = vmax
else:
vals.append(vmax)
except (ValueError, TypeError):
pass
#If the list of values is small, use the whole list.
#Otherwise, use evenly spaced values in the list.
if len(vals) == 0:
return_values = [0]
elif(len(vals)<=500):
return_values = vals
else:
vlen = (len(vals)-1)/499.0
return_values = [vals[(int)(i*vlen)] for i in range(500)]
return return_values
def boolean_cayley_graph(dim, f):
r"""
Construct the Cayley graph of a Boolean function.
Given the non-negative integer ``dim`` and the function ``f``,
a Boolean function that takes a non-negative integer argument,
the function ``Boolean_Cayley_graph`` constructs the Cayley graph of ``f``
as a Boolean function on :math:`\mathbb{F}_2^{dim}`,
with the lexicographica ordering.
The value ``f(0)`` is assumed to be ``0``, so the graph is always simple.
INPUT:
- ``dim`` -- integer. The Boolean dimension of the given function.
- ``f`` -- function. A Boolean function expressed as a Python function
taking non-negative integer arguments.
OUTPUT:
A ``Graph`` object representing the Cayley graph of ``f``.
.. SEEALSO:
:module:`sage.graphs.graph`
EXAMPLES:
The Cayley graph of the function ``f`` where :math:`f(n) = n \mod 2`.
::
sage: from boolean_cayley_graphs.boolean_cayley_graph import boolean_cayley_graph
sage: f = lambda n: n % 2
sage: g = boolean_cayley_graph(2, f)
sage: g.adjacency_matrix()
[0 1 0 1]
[1 0 1 0]
[0 1 0 1]
[1 0 1 0]
"""
return Graph([srange(2 ** dim), lambda i, j: f(i ^ j)],
format="rule",
immutable=True)
def slider(vmin, vmax=None, step_size=None, default=None, label=None, display_value=True, _range=False):
"""
A slider widget.
INPUT:
For a numeric slider (select a value from a range):
- ``vmin``, ``vmax`` -- minimum and maximum value
- ``step_size`` -- the step size
For a selection slider (select a value from a list of values):
- ``vmin`` -- a list of possible values for the slider
For all sliders:
- ``default`` -- initial value
- ``label`` -- optional label
- ``display_value`` -- (boolean) if ``True``, display the current
value.
EXAMPLES::
sage: from sage.repl.ipython_kernel.all_jupyter import slider
sage: slider(5, label="slide me")
TransformIntSlider(value=5, description=u'slide me', min=5)
sage: slider(5, 20)
TransformIntSlider(value=5, max=20, min=5)
sage: slider(5, 20, 0.5)
TransformFloatSlider(value=5.0, max=20.0, min=5.0, step=0.5)
sage: slider(5, 20, default=12)
TransformIntSlider(value=12, max=20, min=5)
The parent of the inputs determines the parent of the value::
sage: w = slider(5); w
TransformIntSlider(value=5, min=5)
sage: parent(w.get_interact_value())
Integer Ring
sage: w = slider(int(5)); w
IntSlider(value=5, min=5)
sage: parent(w.get_interact_value())
<... 'int'>
sage: w = slider(5, 20, step_size=RDF("0.1")); w
TransformFloatSlider(value=5.0, max=20.0, min=5.0)
sage: parent(w.get_interact_value())
Real Double Field
sage: w = slider(5, 20, step_size=10/3); w
SelectionSlider(index=2, options=(5, 25/3, 35/3, 15, 55/3), value=35/3)
sage: parent(w.get_interact_value())
Rational Field
Symbolic input is evaluated numerically::
sage: w = slider(e, pi); w
TransformFloatSlider(value=2.718281828459045, max=3.141592653589793, min=2.718281828459045)
sage: parent(w.get_interact_value())
Real Field with 53 bits of precision
For a selection slider, the default is adjusted to one of the
possible values::
sage: slider(range(10), default=17/10)
SelectionSlider(index=2, options=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9), value=2)
TESTS::
sage: slider(range(5), range(5))
Traceback (most recent call last):
...
TypeError: unexpected argument 'vmax' for a selection slider
sage: slider(range(5), step_size=2)
Traceback (most recent call last):
...
TypeError: unexpected argument 'step_size' for a selection slider
sage: slider(5).readout
True
sage: slider(5, display_value=False).readout
False
"""
kwds = {"readout": display_value}
if label:
kwds["description"] = u(label)
# If vmin is iterable, return a SelectionSlider
if isinstance(vmin, Iterable):
if vmax is not None:
raise TypeError("unexpected argument 'vmax' for a selection slider")
if step_size is not None:
raise TypeError("unexpected argument 'step_size' for a selection slider")
if _range:
# https://github.com/ipython/ipywidgets/issues/760
raise NotImplementedError("range_slider does not support a list of values")
options = list(vmin)
# Find default in options
def err(v):
if v is default:
return (-1, 0)
try:
if v == default:
return (0, 0)
return (0, abs(v - default))
except Exception:
return (1, 0)
kwds["options"] = options
if default is not None:
kwds["value"] = min(options, key=err)
return SelectionSlider(**kwds)
if default is not None:
kwds["value"] = default
# Sum all input numbers to figure out type/parent
p = parent(sum(x for x in (vmin, vmax, step_size) if x is not None))
# Change SR to RR
if p is SR:
p = RR
# Convert all inputs to the common parent
if vmin is not None:
vmin = p(vmin)
if vmax is not None:
vmax = p(vmax)
if step_size is not None:
step_size = p(step_size)
def tuple_elements_p(t):
"Convert all entries of the tuple `t` to `p`"
return tuple(p(x) for x in t)
zero = p()
if isinstance(zero, Integral):
if p is int:
if _range:
cls = IntRangeSlider
else:
cls = IntSlider
else:
if _range:
kwds["transform"] = tuple_elements_p
cls = TransformIntRangeSlider
else:
kwds["transform"] = p
cls = TransformIntSlider
elif isinstance(zero, Rational):
# Rational => implement as SelectionSlider
if _range:
# https://github.com/ipython/ipywidgets/issues/760
raise NotImplementedError("range_slider does not support rational numbers")
vmin, vmax, value = _get_min_max_value(vmin, vmax, default, step_size)
kwds["value"] = value
kwds["options"] = srange(vmin, vmax, step_size, include_endpoint=True)
return SelectionSlider(**kwds)
elif isinstance(zero, Real):
if p is float:
if _range:
cls = FloatRangeSlider
else:
cls = FloatSlider
else:
if _range:
kwds["transform"] = tuple_elements_p
cls = TransformFloatRangeSlider
else:
kwds["transform"] = p
cls = TransformFloatSlider
else:
raise TypeError("unknown parent {!r} for slider".format(p))
kwds["min"] = vmin
if vmax is not None:
kwds["max"] = vmax
if step_size is not None:
kwds["step"] = step_size
return cls(**kwds)
def ruler(start,
end,
ticks=4,
sub_ticks=4,
absolute=False,
snap=False,
**kwds):
"""
Draw a ruler in 3-D, with major and minor ticks.
INPUT:
- ``start`` -- the beginning of the ruler, as a list,
tuple, or vector.
- ``end`` -- the end of the ruler, as a list, tuple,
or vector.
- ``ticks`` -- (default: 4) the number of major ticks
shown on the ruler.
- ``sub_ticks`` -- (default: 4) the number of shown
subdivisions between each major tick.
- ``absolute`` -- (default: ``False``) if ``True``, makes a huge ruler
in the direction of an axis.
- ``snap`` -- (default: ``False``) if ``True``, snaps to an implied
grid.
Type ``line3d.options`` for a dictionary of the default
options for lines, which are also available.
EXAMPLES:
A ruler::
sage: from sage.plot.plot3d.shapes2 import ruler
sage: R = ruler([1,2,3],vector([2,3,4])); R
Graphics3d Object
A ruler with some options::
sage: R = ruler([1,2,3],vector([2,3,4]),ticks=6, sub_ticks=2, color='red'); R
Graphics3d Object
The keyword ``snap`` makes the ticks not necessarily coincide
with the ruler::
sage: ruler([1,2,3],vector([1,2,4]),snap=True)
Graphics3d Object
The keyword ``absolute`` makes a huge ruler in one of the axis
directions::
sage: ruler([1,2,3],vector([1,2,4]),absolute=True)
Graphics3d Object
TESTS::
sage: ruler([1,2,3],vector([1,3,4]),absolute=True)
Traceback (most recent call last):
...
ValueError: Absolute rulers only valid for axis-aligned paths
"""
start = vector(RDF, start)
end = vector(RDF, end)
dir = end - start
dist = math.sqrt(dir.dot_product(dir))
dir /= dist
one_tick = dist / ticks * 1.414
unit = 10**math.floor(math.log(dist / ticks, 10))
if unit * 5 < one_tick:
unit *= 5
elif unit * 2 < one_tick:
unit *= 2
if dir[0]:
tick = dir.cross_product(vector(RDF, (0, 0, -dist / 30)))
elif dir[1]:
tick = dir.cross_product(vector(RDF, (0, 0, dist / 30)))
else:
tick = vector(RDF, (dist / 30, 0, 0))
if snap:
for i in range(3):
start[i] = unit * math.floor(start[i] / unit + 1e-5)
end[i] = unit * math.ceil(end[i] / unit - 1e-5)
if absolute:
if dir[0] * dir[1] or dir[1] * dir[2] or dir[0] * dir[2]:
raise ValueError(
"Absolute rulers only valid for axis-aligned paths")
m = max(dir[0], dir[1], dir[2])
if dir[0] == m:
off = start[0]
elif dir[1] == m:
off = start[1]
else:
off = start[2]
first_tick = unit * math.ceil(off / unit - 1e-5) - off
else:
off = 0
first_tick = 0
ruler = shapes.LineSegment(start, end, **kwds)
for k in range(1, int(sub_ticks * first_tick / unit)):
P = start + dir * (k * unit / sub_ticks)
ruler += shapes.LineSegment(P, P + tick / 2, **kwds)
for d in srange(first_tick, dist + unit / (sub_ticks + 1), unit):
P = start + dir * d
ruler += shapes.LineSegment(P, P + tick, **kwds)
ruler += shapes.Text(str(d + off), **kwds).translate(P - tick)
if dist - d < unit:
sub_ticks = int(sub_ticks * (dist - d) / unit)
for k in range(1, sub_ticks):
P += dir * (unit / sub_ticks)
ruler += shapes.LineSegment(P, P + tick / 2, **kwds)
return ruler
def _parametric_plot3d_surface(f, urange, vrange, plot_points, boundary_style, **kwds):
r"""
Return a parametric three-dimensional space surface.
This function is used internally by the
:func:`parametric_plot3d` command.
There are two ways this function is invoked by
:func:`parametric_plot3d`.
- ``parametric_plot3d([f_x, f_y, f_z], (u_min, u_max),
(v_min, v_max))``:
`f_x, f_y, f_z` are each functions of two variables
- ``parametric_plot3d([f_x, f_y, f_z], (u, u_min,
u_max), (v, v_min, v_max))``:
`f_x, f_y, f_z` can be viewed as functions of
`u` and `v`
INPUT:
- ``f`` - a 3-tuple of functions or expressions, or vector of size 3
- ``urange`` - a 2-tuple (u_min, u_max) or a 3-tuple
(u, u_min, u_max)
- ``vrange`` - a 2-tuple (v_min, v_max) or a 3-tuple
(v, v_min, v_max)
- ``plot_points`` - (default: "automatic", which is [40,40]
for surfaces) initial number of sample points in each parameter;
a pair of integers.
- ``boundary_style`` - (default: None, no boundary) a dict that describes
how to draw the boundaries of regions by giving options that are passed
to the line3d command.
EXAMPLES:
We demonstrate each of the two ways of calling this. See
:func:`parametric_plot3d` for many more examples.
We do the first one with lambda functions::
sage: f = (lambda u,v: cos(u), lambda u,v: sin(u)+cos(v), lambda u,v: sin(v))
sage: parametric_plot3d(f, (0, 2*pi), (-pi, pi)) # indirect doctest
Graphics3d Object
Now we do the same thing with symbolic expressions::
sage: u, v = var('u,v')
sage: parametric_plot3d((cos(u), sin(u) + cos(v), sin(v)), (u, 0, 2*pi), (v, -pi, pi), mesh=True)
Graphics3d Object
"""
from sage.plot.misc import setup_for_eval_on_grid
g, ranges = setup_for_eval_on_grid(f, [urange, vrange], plot_points)
urange = srange(*ranges[0], include_endpoint=True)
vrange = srange(*ranges[1], include_endpoint=True)
G = ParametricSurface(g, (urange, vrange), **kwds)
if boundary_style is not None:
for u in (urange[0], urange[-1]):
G += line3d([(g[0](u,v), g[1](u,v), g[2](u,v)) for v in vrange], **boundary_style)
for v in (vrange[0], vrange[-1]):
G += line3d([(g[0](u,v), g[1](u,v), g[2](u,v)) for u in urange], **boundary_style)
return G
def SingularityAnalysis(var, zeta=1, alpha=0, beta=0, delta=0,
precision=None, normalized=True):
r"""
Return the asymptotic expansion of the coefficients of
an power series with specified pole and logarithmic singularity.
More precisely, this extracts the `n`-th coefficient
.. MATH::
[z^n] \left(\frac{1}{1-z/\zeta}\right)^\alpha
\left(\frac{1}{z/\zeta} \log \frac{1}{1-z/\zeta}\right)^\beta
\left(\frac{1}{z/\zeta} \log
\left(\frac{1}{z/\zeta} \log \frac{1}{1-z/\zeta}\right)\right)^\delta
(if ``normalized=True``, the default) or
.. MATH::
[z^n] \left(\frac{1}{1-z/\zeta}\right)^\alpha
\left(\log \frac{1}{1-z/\zeta}\right)^\beta
\left(\log
\left(\frac{1}{z/\zeta} \log \frac{1}{1-z/\zeta}\right)\right)^\delta
(if ``normalized=False``).
INPUT:
- ``var`` -- a string for the variable name.
- ``zeta`` -- (default: `1`) the location of the singularity.
- ``alpha`` -- (default: `0`) the pole order of the singularty.
- ``beta`` -- (default: `0`) the order of the logarithmic singularity.
- ``delta`` -- (default: `0`) the order of the log-log singularity.
Not yet implemented for ``delta != 0``.
- ``precision`` -- (default: ``None``) an integer. If ``None``, then
the default precision of the asymptotic ring is used.
- ``normalized`` -- (default: ``True``) a boolean, see above.
OUTPUT:
An asymptotic expansion.
EXAMPLES::
sage: asymptotic_expansions.SingularityAnalysis('n', alpha=1)
1
sage: asymptotic_expansions.SingularityAnalysis('n', alpha=2)
n + 1
sage: asymptotic_expansions.SingularityAnalysis('n', alpha=3)
1/2*n^2 + 3/2*n + 1
sage: _.parent()
Asymptotic Ring <n^ZZ> over Rational Field
::
sage: asymptotic_expansions.SingularityAnalysis('n', alpha=-3/2,
....: precision=3)
3/4/sqrt(pi)*n^(-5/2)
+ 45/32/sqrt(pi)*n^(-7/2)
+ 1155/512/sqrt(pi)*n^(-9/2)
+ O(n^(-11/2))
sage: asymptotic_expansions.SingularityAnalysis('n', alpha=-1/2,
....: precision=3)
-1/2/sqrt(pi)*n^(-3/2)
- 3/16/sqrt(pi)*n^(-5/2)
- 25/256/sqrt(pi)*n^(-7/2)
+ O(n^(-9/2))
sage: asymptotic_expansions.SingularityAnalysis('n', alpha=1/2,
....: precision=4)
1/sqrt(pi)*n^(-1/2)
- 1/8/sqrt(pi)*n^(-3/2)
+ 1/128/sqrt(pi)*n^(-5/2)
+ 5/1024/sqrt(pi)*n^(-7/2)
+ O(n^(-9/2))
sage: _.parent()
Asymptotic Ring <n^QQ> over Symbolic Constants Subring
::
sage: S = SR.subring(rejecting_variables=('n',))
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', alpha=S.var('a'),
....: precision=4).map_coefficients(lambda c: c.factor())
1/gamma(a)*n^(a - 1)
+ (1/2*(a - 1)*a/gamma(a))*n^(a - 2)
+ (1/24*(3*a - 1)*(a - 1)*(a - 2)*a/gamma(a))*n^(a - 3)
+ (1/48*(a - 1)^2*(a - 2)*(a - 3)*a^2/gamma(a))*n^(a - 4)
+ O(n^(a - 5))
sage: _.parent()
Asymptotic Ring <n^(Symbolic Subring rejecting the variable n)>
over Symbolic Subring rejecting the variable n
::
sage: ae = asymptotic_expansions.SingularityAnalysis('n',
....: alpha=1/2, beta=1, precision=4); ae
1/sqrt(pi)*n^(-1/2)*log(n) + ((euler_gamma + 2*log(2))/sqrt(pi))*n^(-1/2)
- 5/8/sqrt(pi)*n^(-3/2)*log(n) + (1/8*(3*euler_gamma + 6*log(2) - 8)/sqrt(pi)
- (euler_gamma + 2*log(2) - 2)/sqrt(pi))*n^(-3/2) + O(n^(-5/2)*log(n))
sage: n = ae.parent().gen()
sage: ae.subs(n=n-1).map_coefficients(lambda x: x.canonicalize_radical())
1/sqrt(pi)*n^(-1/2)*log(n)
+ ((euler_gamma + 2*log(2))/sqrt(pi))*n^(-1/2)
- 1/8/sqrt(pi)*n^(-3/2)*log(n)
+ (-1/8*(euler_gamma + 2*log(2))/sqrt(pi))*n^(-3/2)
+ O(n^(-5/2)*log(n))
::
sage: asymptotic_expansions.SingularityAnalysis('n',
....: alpha=1, beta=1/2, precision=4)
log(n)^(1/2) + 1/2*euler_gamma*log(n)^(-1/2)
+ (-1/8*euler_gamma^2 + 1/48*pi^2)*log(n)^(-3/2)
+ (1/16*euler_gamma^3 - 1/32*euler_gamma*pi^2 + 1/8*zeta(3))*log(n)^(-5/2)
+ O(log(n)^(-7/2))
::
sage: ae = asymptotic_expansions.SingularityAnalysis('n',
....: alpha=0, beta=2, precision=14)
sage: n = ae.parent().gen()
sage: ae.subs(n=n-2)
2*n^(-1)*log(n) + 2*euler_gamma*n^(-1) - n^(-2) - 1/6*n^(-3) + O(n^(-5))
::
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', 1, alpha=-1/2, beta=1, precision=2, normalized=False)
-1/2/sqrt(pi)*n^(-3/2)*log(n)
+ (-1/2*(euler_gamma + 2*log(2) - 2)/sqrt(pi))*n^(-3/2)
+ O(n^(-5/2)*log(n))
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', 1/2, alpha=0, beta=1, precision=3, normalized=False)
2^n*n^(-1) + O(2^n*n^(-2))
ALGORITHM:
See [FS2009]_ together with the
`errata list <http://algo.inria.fr/flajolet/Publications/AnaCombi/errata.pdf>`_.
REFERENCES:
.. [FS2009] Philippe Flajolet and Robert Sedgewick,
`Analytic combinatorics <http://algo.inria.fr/flajolet/Publications/AnaCombi/book.pdf>`_.
Cambridge University Press, Cambridge, 2009.
TESTS::
sage: ex = asymptotic_expansions.SingularityAnalysis('n', alpha=-1/2,
....: precision=4)
sage: n = ex.parent().gen()
sage: coefficients = ((1-x)^(1/2)).series(
....: x, 21).truncate().coefficients(x, sparse=False)
sage: ex.compare_with_values(n, # rel tol 1e-6
....: lambda k: coefficients[k], [5, 10, 20])
[(5, 0.015778873294?), (10, 0.01498952777?), (20, 0.0146264622?)]
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', alpha=3, precision=2)
1/2*n^2 + 3/2*n + O(1)
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', alpha=3, precision=3)
1/2*n^2 + 3/2*n + 1
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', alpha=3, precision=4)
1/2*n^2 + 3/2*n + 1
::
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', alpha=0)
Traceback (most recent call last):
...
NotImplementedOZero: The error term in the result is O(0)
which means 0 for sufficiently large n.
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', alpha=-1)
Traceback (most recent call last):
...
NotImplementedOZero: The error term in the result is O(0)
which means 0 for sufficiently large n.
::
sage: asymptotic_expansions.SingularityAnalysis(
....: 'm', alpha=-1/2, precision=3)
-1/2/sqrt(pi)*m^(-3/2)
- 3/16/sqrt(pi)*m^(-5/2)
- 25/256/sqrt(pi)*m^(-7/2)
+ O(m^(-9/2))
sage: _.parent()
Asymptotic Ring <m^QQ> over Symbolic Constants Subring
Location of the singularity::
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', alpha=1, zeta=2, precision=3)
(1/2)^n
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', alpha=1, zeta=1/2, precision=3)
2^n
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', alpha=1, zeta=CyclotomicField(3).gen(),
....: precision=3)
(-zeta3 - 1)^n
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', alpha=4, zeta=2, precision=3)
1/6*(1/2)^n*n^3 + (1/2)^n*n^2 + 11/6*(1/2)^n*n + O((1/2)^n)
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', alpha=-1, zeta=2, precision=3)
Traceback (most recent call last):
...
NotImplementedOZero: The error term in the result is O(0)
which means 0 for sufficiently large n.
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', alpha=1/2, zeta=2, precision=3)
1/sqrt(pi)*(1/2)^n*n^(-1/2) - 1/8/sqrt(pi)*(1/2)^n*n^(-3/2)
+ 1/128/sqrt(pi)*(1/2)^n*n^(-5/2) + O((1/2)^n*n^(-7/2))
The following tests correspond to Table VI.5 in [FS2009]_. ::
sage: A.<n> = AsymptoticRing('n^QQ * log(n)^QQ', QQ)
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', 1, alpha=-1/2, beta=1, precision=2,
....: normalized=False) * (- sqrt(pi*n^3))
1/2*log(n) + 1/2*euler_gamma + log(2) - 1 + O(n^(-1)*log(n))
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', 1, alpha=0, beta=1, precision=3,
....: normalized=False)
n^(-1) + O(n^(-2))
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', 1, alpha=0, beta=2, precision=14,
....: normalized=False) * n
2*log(n) + 2*euler_gamma - n^(-1) - 1/6*n^(-2) + O(n^(-4))
sage: (asymptotic_expansions.SingularityAnalysis(
....: 'n', 1, alpha=1/2, beta=1, precision=4,
....: normalized=False) * sqrt(pi*n)).\
....: map_coefficients(lambda x: x.expand())
log(n) + euler_gamma + 2*log(2) - 1/8*n^(-1)*log(n) +
(-1/8*euler_gamma - 1/4*log(2))*n^(-1) + O(n^(-2)*log(n))
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', 1, alpha=1, beta=1, precision=13,
....: normalized=False)
log(n) + euler_gamma + 1/2*n^(-1) - 1/12*n^(-2) + 1/120*n^(-4)
+ O(n^(-6))
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', 1, alpha=1, beta=2, precision=4,
....: normalized=False)
log(n)^2 + 2*euler_gamma*log(n) + euler_gamma^2 - 1/6*pi^2
+ O(n^(-1)*log(n))
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', 1, alpha=3/2, beta=1, precision=3,
....: normalized=False) * sqrt(pi/n)
2*log(n) + 2*euler_gamma + 4*log(2) - 4 + 3/4*n^(-1)*log(n)
+ O(n^(-1))
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', 1, alpha=2, beta=1, precision=5,
....: normalized=False)
n*log(n) + (euler_gamma - 1)*n + log(n) + euler_gamma + 1/2
+ O(n^(-1))
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', 1, alpha=2, beta=2, precision=4,
....: normalized=False) / n
log(n)^2 + (2*euler_gamma - 2)*log(n)
- 2*euler_gamma + euler_gamma^2 - 1/6*pi^2 + 2
+ n^(-1)*log(n)^2 + O(n^(-1)*log(n))
Be aware that the last result does *not* coincide with [FS2009]_,
they do have a different error term.
Checking parameters::
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', 1, 1, 1/2, precision=0, normalized=False)
Traceback (most recent call last):
...
ValueError: beta and delta must be integers
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', 1, 1, 1, 1/2, normalized=False)
Traceback (most recent call last):
...
ValueError: beta and delta must be integers
::
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', alpha=0, beta=0, delta=1, precision=3)
Traceback (most recent call last):
...
NotImplementedError: not implemented for delta!=0
"""
from itertools import islice, count
from asymptotic_ring import AsymptoticRing
from growth_group import ExponentialGrowthGroup, \
MonomialGrowthGroup
from sage.arith.all import falling_factorial
from sage.categories.cartesian_product import cartesian_product
from sage.functions.other import binomial, gamma
from sage.calculus.calculus import limit
from sage.misc.cachefunc import cached_function
from sage.arith.srange import srange
from sage.rings.rational_field import QQ
from sage.rings.integer_ring import ZZ
from sage.symbolic.ring import SR
SCR = SR.subring(no_variables=True)
s = SR('s')
iga = 1/gamma(alpha)
if iga.parent() is SR:
try:
iga = SCR(iga)
except TypeError:
pass
coefficient_ring = iga.parent()
if beta != 0:
coefficient_ring = SCR
@cached_function
def inverse_gamma_derivative(shift, r):
"""
Return value of `r`-th derivative of 1/Gamma
at alpha-shift.
"""
if r == 0:
result = iga*falling_factorial(alpha-1, shift)
else:
result = limit((1/gamma(s)).diff(s, r), s=alpha-shift)
try:
return coefficient_ring(result)
except TypeError:
return result
if isinstance(alpha, int):
alpha = ZZ(alpha)
if isinstance(beta, int):
beta = ZZ(beta)
if isinstance(delta, int):
delta = ZZ(delta)
if precision is None:
precision = AsymptoticRing.__default_prec__
if not normalized and not (beta in ZZ and delta in ZZ):
raise ValueError("beta and delta must be integers")
if delta != 0:
raise NotImplementedError("not implemented for delta!=0")
groups = []
if zeta != 1:
groups.append(ExponentialGrowthGroup((1/zeta).parent(), var))
groups.append(MonomialGrowthGroup(alpha.parent(), var))
if beta != 0:
groups.append(MonomialGrowthGroup(beta.parent(), 'log({})'.format(var)))
group = cartesian_product(groups)
A = AsymptoticRing(growth_group=group, coefficient_ring=coefficient_ring,
default_prec=precision)
n = A.gen()
if zeta == 1:
exponential_factor = 1
else:
exponential_factor = n.rpow(1/zeta)
if beta in ZZ and beta >= 0:
it = ((k, r)
for k in count()
for r in srange(beta+1))
k_max = precision
else:
it = ((0, r)
for r in count())
k_max = 0
if beta != 0:
log_n = n.log()
else:
# avoid construction of log(n)
# because it does not exist in growth group.
log_n = 1
it = reversed(list(islice(it, precision+1)))
if normalized:
beta_denominator = beta
else:
beta_denominator = 0
L = _sa_coefficients_lambda_(max(1, k_max), beta=beta_denominator)
(k, r) = next(it)
result = (n**(-k) * log_n**(-r)).O()
if alpha in ZZ and beta == 0:
if alpha > 0 and alpha <= precision:
result = A(0)
elif alpha <= 0 and precision > 0:
from misc import NotImplementedOZero
raise NotImplementedOZero(A)
for (k, r) in it:
result += binomial(beta, r) * \
sum(L[(k, ell)] * (-1)**ell *
inverse_gamma_derivative(ell, r)
for ell in srange(k, 2*k+1)
if (k, ell) in L) * \
n**(-k) * log_n**(-r)
result *= exponential_factor * n**(alpha-1) * log_n**beta
return result
def SingularityAnalysis(var,
zeta=1,
alpha=0,
beta=0,
delta=0,
precision=None,
normalized=True):
r"""
Return the asymptotic expansion of the coefficients of
an power series with specified pole and logarithmic singularity.
More precisely, this extracts the `n`-th coefficient
.. MATH::
[z^n] \left(\frac{1}{1-z/\zeta}\right)^\alpha
\left(\frac{1}{z/\zeta} \log \frac{1}{1-z/\zeta}\right)^\beta
\left(\frac{1}{z/\zeta} \log
\left(\frac{1}{z/\zeta} \log \frac{1}{1-z/\zeta}\right)\right)^\delta
(if ``normalized=True``, the default) or
.. MATH::
[z^n] \left(\frac{1}{1-z/\zeta}\right)^\alpha
\left(\log \frac{1}{1-z/\zeta}\right)^\beta
\left(\log
\left(\frac{1}{z/\zeta} \log \frac{1}{1-z/\zeta}\right)\right)^\delta
(if ``normalized=False``).
INPUT:
- ``var`` -- a string for the variable name.
- ``zeta`` -- (default: `1`) the location of the singularity.
- ``alpha`` -- (default: `0`) the pole order of the singularty.
- ``beta`` -- (default: `0`) the order of the logarithmic singularity.
- ``delta`` -- (default: `0`) the order of the log-log singularity.
Not yet implemented for ``delta != 0``.
- ``precision`` -- (default: ``None``) an integer. If ``None``, then
the default precision of the asymptotic ring is used.
- ``normalized`` -- (default: ``True``) a boolean, see above.
OUTPUT:
An asymptotic expansion.
EXAMPLES::
sage: asymptotic_expansions.SingularityAnalysis('n', alpha=1)
1
sage: asymptotic_expansions.SingularityAnalysis('n', alpha=2)
n + 1
sage: asymptotic_expansions.SingularityAnalysis('n', alpha=3)
1/2*n^2 + 3/2*n + 1
sage: _.parent()
Asymptotic Ring <n^ZZ> over Rational Field
::
sage: asymptotic_expansions.SingularityAnalysis('n', alpha=-3/2,
....: precision=3)
3/4/sqrt(pi)*n^(-5/2)
+ 45/32/sqrt(pi)*n^(-7/2)
+ 1155/512/sqrt(pi)*n^(-9/2)
+ O(n^(-11/2))
sage: asymptotic_expansions.SingularityAnalysis('n', alpha=-1/2,
....: precision=3)
-1/2/sqrt(pi)*n^(-3/2)
- 3/16/sqrt(pi)*n^(-5/2)
- 25/256/sqrt(pi)*n^(-7/2)
+ O(n^(-9/2))
sage: asymptotic_expansions.SingularityAnalysis('n', alpha=1/2,
....: precision=4)
1/sqrt(pi)*n^(-1/2)
- 1/8/sqrt(pi)*n^(-3/2)
+ 1/128/sqrt(pi)*n^(-5/2)
+ 5/1024/sqrt(pi)*n^(-7/2)
+ O(n^(-9/2))
sage: _.parent()
Asymptotic Ring <n^QQ> over Symbolic Constants Subring
::
sage: S = SR.subring(rejecting_variables=('n',))
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', alpha=S.var('a'),
....: precision=4).map_coefficients(lambda c: c.factor())
1/gamma(a)*n^(a - 1)
+ (1/2*(a - 1)*a/gamma(a))*n^(a - 2)
+ (1/24*(3*a - 1)*(a - 1)*(a - 2)*a/gamma(a))*n^(a - 3)
+ (1/48*(a - 1)^2*(a - 2)*(a - 3)*a^2/gamma(a))*n^(a - 4)
+ O(n^(a - 5))
sage: _.parent()
Asymptotic Ring <n^(Symbolic Subring rejecting the variable n)>
over Symbolic Subring rejecting the variable n
::
sage: ae = asymptotic_expansions.SingularityAnalysis('n',
....: alpha=1/2, beta=1, precision=4); ae
1/sqrt(pi)*n^(-1/2)*log(n) + ((euler_gamma + 2*log(2))/sqrt(pi))*n^(-1/2)
- 5/8/sqrt(pi)*n^(-3/2)*log(n) + (1/8*(3*euler_gamma + 6*log(2) - 8)/sqrt(pi)
- (euler_gamma + 2*log(2) - 2)/sqrt(pi))*n^(-3/2) + O(n^(-5/2)*log(n))
sage: n = ae.parent().gen()
sage: ae.subs(n=n-1).map_coefficients(lambda x: x.canonicalize_radical())
1/sqrt(pi)*n^(-1/2)*log(n)
+ ((euler_gamma + 2*log(2))/sqrt(pi))*n^(-1/2)
- 1/8/sqrt(pi)*n^(-3/2)*log(n)
+ (-1/8*(euler_gamma + 2*log(2))/sqrt(pi))*n^(-3/2)
+ O(n^(-5/2)*log(n))
::
sage: asymptotic_expansions.SingularityAnalysis('n',
....: alpha=1, beta=1/2, precision=4)
log(n)^(1/2) + 1/2*euler_gamma*log(n)^(-1/2)
+ (-1/8*euler_gamma^2 + 1/48*pi^2)*log(n)^(-3/2)
+ (1/16*euler_gamma^3 - 1/32*euler_gamma*pi^2 + 1/8*zeta(3))*log(n)^(-5/2)
+ O(log(n)^(-7/2))
::
sage: ae = asymptotic_expansions.SingularityAnalysis('n',
....: alpha=0, beta=2, precision=14)
sage: n = ae.parent().gen()
sage: ae.subs(n=n-2)
2*n^(-1)*log(n) + 2*euler_gamma*n^(-1) - n^(-2) - 1/6*n^(-3) + O(n^(-5))
::
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', 1, alpha=-1/2, beta=1, precision=2, normalized=False)
-1/2/sqrt(pi)*n^(-3/2)*log(n)
+ (-1/2*(euler_gamma + 2*log(2) - 2)/sqrt(pi))*n^(-3/2)
+ O(n^(-5/2)*log(n))
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', 1/2, alpha=0, beta=1, precision=3, normalized=False)
2^n*n^(-1) + O(2^n*n^(-2))
ALGORITHM:
See [FS2009]_ together with the
`errata list <http://algo.inria.fr/flajolet/Publications/AnaCombi/errata.pdf>`_.
REFERENCES:
.. [FS2009] Philippe Flajolet and Robert Sedgewick,
`Analytic combinatorics <http://algo.inria.fr/flajolet/Publications/AnaCombi/book.pdf>`_.
Cambridge University Press, Cambridge, 2009.
TESTS::
sage: ex = asymptotic_expansions.SingularityAnalysis('n', alpha=-1/2,
....: precision=4)
sage: n = ex.parent().gen()
sage: coefficients = ((1-x)^(1/2)).series(
....: x, 21).truncate().coefficients(x, sparse=False)
sage: ex.compare_with_values(n, # rel tol 1e-6
....: lambda k: coefficients[k], [5, 10, 20])
[(5, 0.015778873294?), (10, 0.01498952777?), (20, 0.0146264622?)]
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', alpha=3, precision=2)
1/2*n^2 + 3/2*n + O(1)
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', alpha=3, precision=3)
1/2*n^2 + 3/2*n + 1
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', alpha=3, precision=4)
1/2*n^2 + 3/2*n + 1
::
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', alpha=0)
Traceback (most recent call last):
...
NotImplementedOZero: The error term in the result is O(0)
which means 0 for sufficiently large n.
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', alpha=-1)
Traceback (most recent call last):
...
NotImplementedOZero: The error term in the result is O(0)
which means 0 for sufficiently large n.
::
sage: asymptotic_expansions.SingularityAnalysis(
....: 'm', alpha=-1/2, precision=3)
-1/2/sqrt(pi)*m^(-3/2)
- 3/16/sqrt(pi)*m^(-5/2)
- 25/256/sqrt(pi)*m^(-7/2)
+ O(m^(-9/2))
sage: _.parent()
Asymptotic Ring <m^QQ> over Symbolic Constants Subring
Location of the singularity::
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', alpha=1, zeta=2, precision=3)
(1/2)^n
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', alpha=1, zeta=1/2, precision=3)
2^n
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', alpha=1, zeta=CyclotomicField(3).gen(),
....: precision=3)
(-zeta3 - 1)^n
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', alpha=4, zeta=2, precision=3)
1/6*(1/2)^n*n^3 + (1/2)^n*n^2 + 11/6*(1/2)^n*n + O((1/2)^n)
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', alpha=-1, zeta=2, precision=3)
Traceback (most recent call last):
...
NotImplementedOZero: The error term in the result is O(0)
which means 0 for sufficiently large n.
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', alpha=1/2, zeta=2, precision=3)
1/sqrt(pi)*(1/2)^n*n^(-1/2) - 1/8/sqrt(pi)*(1/2)^n*n^(-3/2)
+ 1/128/sqrt(pi)*(1/2)^n*n^(-5/2) + O((1/2)^n*n^(-7/2))
The following tests correspond to Table VI.5 in [FS2009]_. ::
sage: A.<n> = AsymptoticRing('n^QQ * log(n)^QQ', QQ)
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', 1, alpha=-1/2, beta=1, precision=2,
....: normalized=False) * (- sqrt(pi*n^3))
1/2*log(n) + 1/2*euler_gamma + log(2) - 1 + O(n^(-1)*log(n))
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', 1, alpha=0, beta=1, precision=3,
....: normalized=False)
n^(-1) + O(n^(-2))
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', 1, alpha=0, beta=2, precision=14,
....: normalized=False) * n
2*log(n) + 2*euler_gamma - n^(-1) - 1/6*n^(-2) + O(n^(-4))
sage: (asymptotic_expansions.SingularityAnalysis(
....: 'n', 1, alpha=1/2, beta=1, precision=4,
....: normalized=False) * sqrt(pi*n)).\
....: map_coefficients(lambda x: x.expand())
log(n) + euler_gamma + 2*log(2) - 1/8*n^(-1)*log(n) +
(-1/8*euler_gamma - 1/4*log(2))*n^(-1) + O(n^(-2)*log(n))
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', 1, alpha=1, beta=1, precision=13,
....: normalized=False)
log(n) + euler_gamma + 1/2*n^(-1) - 1/12*n^(-2) + 1/120*n^(-4)
+ O(n^(-6))
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', 1, alpha=1, beta=2, precision=4,
....: normalized=False)
log(n)^2 + 2*euler_gamma*log(n) + euler_gamma^2 - 1/6*pi^2
+ O(n^(-1)*log(n))
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', 1, alpha=3/2, beta=1, precision=3,
....: normalized=False) * sqrt(pi/n)
2*log(n) + 2*euler_gamma + 4*log(2) - 4 + 3/4*n^(-1)*log(n)
+ O(n^(-1))
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', 1, alpha=2, beta=1, precision=5,
....: normalized=False)
n*log(n) + (euler_gamma - 1)*n + log(n) + euler_gamma + 1/2
+ O(n^(-1))
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', 1, alpha=2, beta=2, precision=4,
....: normalized=False) / n
log(n)^2 + (2*euler_gamma - 2)*log(n)
- 2*euler_gamma + euler_gamma^2 - 1/6*pi^2 + 2
+ n^(-1)*log(n)^2 + O(n^(-1)*log(n))
Be aware that the last result does *not* coincide with [FS2009]_,
they do have a different error term.
Checking parameters::
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', 1, 1, 1/2, precision=0, normalized=False)
Traceback (most recent call last):
...
ValueError: beta and delta must be integers
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', 1, 1, 1, 1/2, normalized=False)
Traceback (most recent call last):
...
ValueError: beta and delta must be integers
::
sage: asymptotic_expansions.SingularityAnalysis(
....: 'n', alpha=0, beta=0, delta=1, precision=3)
Traceback (most recent call last):
...
NotImplementedError: not implemented for delta!=0
"""
from itertools import islice, count
from asymptotic_ring import AsymptoticRing
from growth_group import ExponentialGrowthGroup, \
MonomialGrowthGroup
from sage.arith.all import falling_factorial
from sage.categories.cartesian_product import cartesian_product
from sage.functions.other import binomial, gamma
from sage.calculus.calculus import limit
from sage.misc.cachefunc import cached_function
from sage.arith.srange import srange
from sage.rings.rational_field import QQ
from sage.rings.integer_ring import ZZ
from sage.symbolic.ring import SR
SCR = SR.subring(no_variables=True)
s = SR('s')
iga = 1 / gamma(alpha)
if iga.parent() is SR:
try:
iga = SCR(iga)
except TypeError:
pass
coefficient_ring = iga.parent()
if beta != 0:
coefficient_ring = SCR
@cached_function
def inverse_gamma_derivative(shift, r):
"""
Return value of `r`-th derivative of 1/Gamma
at alpha-shift.
"""
if r == 0:
result = iga * falling_factorial(alpha - 1, shift)
else:
result = limit((1 / gamma(s)).diff(s, r), s=alpha - shift)
try:
return coefficient_ring(result)
except TypeError:
return result
if isinstance(alpha, int):
alpha = ZZ(alpha)
if isinstance(beta, int):
beta = ZZ(beta)
if isinstance(delta, int):
delta = ZZ(delta)
if precision is None:
precision = AsymptoticRing.__default_prec__
if not normalized and not (beta in ZZ and delta in ZZ):
raise ValueError("beta and delta must be integers")
if delta != 0:
raise NotImplementedError("not implemented for delta!=0")
groups = []
if zeta != 1:
groups.append(ExponentialGrowthGroup((1 / zeta).parent(), var))
groups.append(MonomialGrowthGroup(alpha.parent(), var))
if beta != 0:
groups.append(
MonomialGrowthGroup(beta.parent(), 'log({})'.format(var)))
group = cartesian_product(groups)
A = AsymptoticRing(growth_group=group,
coefficient_ring=coefficient_ring,
default_prec=precision)
n = A.gen()
if zeta == 1:
exponential_factor = 1
else:
exponential_factor = n.rpow(1 / zeta)
if beta in ZZ and beta >= 0:
it = ((k, r) for k in count() for r in srange(beta + 1))
k_max = precision
else:
it = ((0, r) for r in count())
k_max = 0
if beta != 0:
log_n = n.log()
else:
# avoid construction of log(n)
# because it does not exist in growth group.
log_n = 1
it = reversed(list(islice(it, precision + 1)))
if normalized:
beta_denominator = beta
else:
beta_denominator = 0
L = _sa_coefficients_lambda_(max(1, k_max), beta=beta_denominator)
(k, r) = next(it)
result = (n**(-k) * log_n**(-r)).O()
if alpha in ZZ and beta == 0:
if alpha > 0 and alpha <= precision:
result = A(0)
elif alpha <= 0 and precision > 0:
from misc import NotImplementedOZero
raise NotImplementedOZero(A)
for (k, r) in it:
result += binomial(beta, r) * \
sum(L[(k, ell)] * (-1)**ell *
inverse_gamma_derivative(ell, r)
for ell in srange(k, 2*k+1)
if (k, ell) in L) * \
n**(-k) * log_n**(-r)
result *= exponential_factor * n**(alpha - 1) * log_n**beta
return result
def _Omega_numerator_(a, x, y, t):
r"""
Return the numerator of `\Omega_{\ge}` of the expression
specified by the input.
To be more precise, calculate
.. MATH::
\Omega_{\ge} \frac{\mu^a}{
(1 - x_1 \mu) \dots (1 - x_n \mu)
(1 - y_1 / \mu) \dots (1 - y_m / \mu)}
and return its numerator.
This function is meant to be a helper function of :func:`MacMahonOmega`.
INPUT:
- ``a`` -- an integer
- ``x`` and ``y`` -- a tuple of tuples of Laurent polynomials
The
flattened ``x`` contains `x_1,...,x_n`, the flattened ``y`` the
`y_1,...,y_m`.
The non-flatness of these parameters is to be interface-consistent
with :func:`_Omega_factors_denominator_`.
- ``t`` -- a temporary Laurent polynomial variable used for substituting
OUTPUT:
A Laurent polynomial
The output is normalized such that the corresponding denominator
(:func:`_Omega_factors_denominator_`) has constant term `1`.
EXAMPLES::
sage: from sage.rings.polynomial.omega import _Omega_numerator_, _Omega_factors_denominator_
sage: L.<x0, x1, x2, x3, y0, y1, t> = LaurentPolynomialRing(ZZ)
sage: _Omega_numerator_(0, ((x0,),), ((y0,),), t)
1
sage: _Omega_numerator_(0, ((x0,), (x1,)), ((y0,),), t)
-x0*x1*y0 + 1
sage: _Omega_numerator_(0, ((x0,),), ((y0,), (y1,)), t)
1
sage: _Omega_numerator_(0, ((x0,), (x1,), (x2,)), ((y0,),), t)
x0*x1*x2*y0^2 + x0*x1*x2*y0 - x0*x1*y0 - x0*x2*y0 - x1*x2*y0 + 1
sage: _Omega_numerator_(0, ((x0,), (x1,)), ((y0,), (y1,)), t)
x0^2*x1*y0*y1 + x0*x1^2*y0*y1 - x0*x1*y0*y1 - x0*x1*y0 - x0*x1*y1 + 1
sage: _Omega_numerator_(-2, ((x0,),), ((y0,),), t)
x0^2
sage: _Omega_numerator_(-1, ((x0,),), ((y0,),), t)
x0
sage: _Omega_numerator_(1, ((x0,),), ((y0,),), t)
-x0*y0 + y0 + 1
sage: _Omega_numerator_(2, ((x0,),), ((y0,),), t)
-x0*y0^2 - x0*y0 + y0^2 + y0 + 1
TESTS::
sage: _Omega_factors_denominator_((), ())
()
sage: _Omega_numerator_(0, (), (), t)
1
sage: _Omega_numerator_(+2, (), (), t)
1
sage: _Omega_numerator_(-2, (), (), t)
0
sage: _Omega_factors_denominator_(((x0,),), ())
(-x0 + 1,)
sage: _Omega_numerator_(0, ((x0,),), (), t)
1
sage: _Omega_numerator_(+2, ((x0,),), (), t)
1
sage: _Omega_numerator_(-2, ((x0,),), (), t)
x0^2
sage: _Omega_factors_denominator_((), ((y0,),))
()
sage: _Omega_numerator_(0, (), ((y0,),), t)
1
sage: _Omega_numerator_(+2, (), ((y0,),), t)
y0^2 + y0 + 1
sage: _Omega_numerator_(-2, (), ((y0,),), t)
0
::
sage: L.<X, Y, t> = LaurentPolynomialRing(ZZ)
sage: _Omega_numerator_(2, ((X,),), ((Y,),), t)
-X*Y^2 - X*Y + Y^2 + Y + 1
"""
from sage.arith.srange import srange
from sage.misc.misc_c import prod
x_flat = sum(x, tuple())
y_flat = sum(y, tuple())
n = len(x_flat)
m = len(y_flat)
xy = x_flat + y_flat
import logging
logger = logging.getLogger(__name__)
logger.info('Omega_numerator: a=%s, n=%s, m=%s', a, n, m)
if m == 0:
result = 1 - (prod(_Omega_factors_denominator_(x, y)) * sum(
homogenous_symmetric_function(j, xy)
for j in srange(-a)) if a < 0 else 0)
elif n == 0:
result = sum(
homogenous_symmetric_function(j, xy) for j in srange(a + 1))
else:
result = _Omega_numerator_P_(a, x_flat[:-1], y_flat,
t).subs({t: x_flat[-1]})
L = t.parent()
result = L(result)
logger.info('_Omega_numerator_: %s terms', result.number_of_terms())
return result
def plot_vector_field3d(functions, xrange, yrange, zrange,
plot_points=5, colors='jet', center_arrows=False, **kwds):
r"""
Plot a 3d vector field
INPUT:
- ``functions`` - a list of three functions, representing the x-,
y-, and z-coordinates of a vector
- ``xrange``, ``yrange``, and ``zrange`` - three tuples of the
form (var, start, stop), giving the variables and ranges for each axis
- ``plot_points`` (default 5) - either a number or list of three
numbers, specifying how many points to plot for each axis
- ``colors`` (default 'jet') - a color, list of colors (which are
interpolated between), or matplotlib colormap name, giving the coloring
of the arrows. If a list of colors or a colormap is given,
coloring is done as a function of length of the vector
- ``center_arrows`` (default False) - If True, draw the arrows
centered on the points; otherwise, draw the arrows with the tail
at the point
- any other keywords are passed on to the plot command for each arrow
EXAMPLES::
sage: x,y,z=var('x y z')
sage: plot_vector_field3d((x*cos(z),-y*cos(z),sin(z)), (x,0,pi), (y,0,pi), (z,0,pi))
Graphics3d Object
sage: plot_vector_field3d((x*cos(z),-y*cos(z),sin(z)), (x,0,pi), (y,0,pi), (z,0,pi),colors=['red','green','blue'])
Graphics3d Object
sage: plot_vector_field3d((x*cos(z),-y*cos(z),sin(z)), (x,0,pi), (y,0,pi), (z,0,pi),colors='red')
Graphics3d Object
sage: plot_vector_field3d((x*cos(z),-y*cos(z),sin(z)), (x,0,pi), (y,0,pi), (z,0,pi),plot_points=4)
Graphics3d Object
sage: plot_vector_field3d((x*cos(z),-y*cos(z),sin(z)), (x,0,pi), (y,0,pi), (z,0,pi),plot_points=[3,5,7])
Graphics3d Object
sage: plot_vector_field3d((x*cos(z),-y*cos(z),sin(z)), (x,0,pi), (y,0,pi), (z,0,pi),center_arrows=True)
Graphics3d Object
TESTS:
This tests that :trac:`2100` is fixed in a way compatible with this command::
sage: plot_vector_field3d((x*cos(z),-y*cos(z),sin(z)), (x,0,pi), (y,0,pi), (z,0,pi),center_arrows=True,aspect_ratio=(1,2,1))
Graphics3d Object
"""
(ff,gg,hh), ranges = setup_for_eval_on_grid(functions, [xrange, yrange, zrange], plot_points)
xpoints, ypoints, zpoints = [srange(*r, include_endpoint=True) for r in ranges]
points = [vector((i,j,k)) for i in xpoints for j in ypoints for k in zpoints]
vectors = [vector((ff(*point), gg(*point), hh(*point))) for point in points]
try:
from matplotlib.cm import get_cmap
cm = get_cmap(colors)
except (TypeError, ValueError):
cm = None
if cm is None:
if isinstance(colors, (list, tuple)):
from matplotlib.colors import LinearSegmentedColormap
cm = LinearSegmentedColormap.from_list('mymap',colors)
else:
cm = lambda x: colors
max_len = max(v.norm() for v in vectors)
scaled_vectors = [v/max_len for v in vectors]
if center_arrows:
G = sum([plot(v,color=cm(v.norm()),**kwds).translate(p-v/2) for v,p in zip(scaled_vectors, points)])
G._set_extra_kwds(kwds)
return G
else:
G = sum([plot(v,color=cm(v.norm()),**kwds).translate(p) for v,p in zip(scaled_vectors, points)])
G._set_extra_kwds(kwds)
return G
def product_space(chi, k, weights=False, base_ring=None, verbose=False):
r"""
Computes all eisenstein series, and products of pairs of eisenstein series
of lower weight, lying in the space of modular forms of weight $k$ and
nebentypus $\chi$.
INPUT:
- chi - Dirichlet character, the nebentypus of the target space
- k - an integer, the weight of the target space
OUTPUT:
- a matrix of coefficients of q-expansions, which are the products of
Eisenstein series in M_k(chi).
WARNING: It is only for principal chi that we know that the resulting
space is the whole space of modular forms.
"""
if weights == False:
weights = srange(1, k / 2 + 1)
weight_dict = {}
weight_dict[-1] = [w for w in weights if w % 2] # Odd weights
weight_dict[1] = [w for w in weights if not w % 2] # Even weights
try:
N = chi.modulus()
except AttributeError:
if chi.parent() == ZZ:
N = chi
chi = DirichletGroup(N)[0]
Id = DirichletGroup(1)[0]
if chi(-1) != (-1)**k:
raise ValueError('chi(-1)!=(-1)^k')
sturm = ModularForms(N, k).sturm_bound() + 1
if N > 1:
target_dim = dimension_modular_forms(chi, k)
else:
target_dim = dimension_modular_forms(1, k)
D = DirichletGroup(N)
# product_space should ideally be called over number fields. Over complex
# numbers the exact linear algebra solutions might not exist.
if base_ring == None:
base_ring = CyclotomicField(euler_phi(N))
Q = PowerSeriesRing(base_ring, 'q')
q = Q.gen()
d = len(D)
prim_chars = [phi.primitive_character() for phi in D]
divs = divisors(N)
products = Matrix(base_ring, [])
indexlist = []
rank = 0
if verbose:
print(D)
print('Sturm bound', sturm)
#TODO: target_dim needs refinment in the case of weight 2.
print('Target dimension', target_dim)
for i in srange(0, d): # First character
phi = prim_chars[i]
M1 = phi.conductor()
for j in srange(0, d): # Second character
psi = prim_chars[j]
M2 = psi.conductor()
if not M1 * M2 in divs:
continue
parity = psi(-1) * phi(-1)
for t1 in divs:
if not M1 * M2 * t1 in divs:
continue
#TODO: THE NEXT CONDITION NEEDS TO BE CORRECTED. THIS IS JUST A TEST
if phi.bar() == psi and not (
k == 2): #and i==0 and j==0 and t1==1):
E = eisenstein_series_at_inf(phi, psi, k, sturm, t1,
base_ring).padded_list()
try:
products.T.solve_right(vector(base_ring, E))
except ValueError:
products = Matrix(products.rows() + [E])
indexlist.append([k, i, j, t1])
rank += 1
if verbose:
print('Added ', [k, i, j, t1])
print('Rank is now', rank)
if rank == target_dim:
return products, indexlist
for t in divs:
if not M1 * M2 * t1 * t in divs:
continue
for t2 in divs:
if not M1 * M2 * t1 * t2 * t in divs:
continue
for l in weight_dict[parity]:
if l == 1 and phi.is_odd():
continue
if i == 0 and j == 0 and (l == 2 or l == k - 2):
continue
#TODO: THE NEXT CONDITION NEEDS TO BE REMOVED. THIS IS JUST A TEST
if l == 2 or l == k - 2:
continue
E1 = eisenstein_series_at_inf(
phi, psi, l, sturm, t1 * t, base_ring)
E2 = eisenstein_series_at_inf(
phi**(-1), psi**(-1), k - l, sturm, t2 * t,
base_ring)
#If chi is non-principal this needs to be changed to be something like chi*phi^(-1) instead of phi^(-1)
E = (E1 * E2 + O(q**sturm)).padded_list()
try:
products.T.solve_right(vector(base_ring, E))
except ValueError:
products = Matrix(products.rows() + [E])
indexlist.append([l, k - l, i, j, t1, t2, t])
rank += 1
if verbose:
print('Added ',
[l, k - l, i, j, t1, t2, t])
print('Rank', rank)
if rank == target_dim:
return products, indexlist
return products, indexlist
def slider(vmin,
vmax=None,
step_size=None,
default=None,
label=None,
display_value=True,
_range=False):
"""
A slider widget.
INPUT:
For a numeric slider (select a value from a range):
- ``vmin``, ``vmax`` -- minimum and maximum value
- ``step_size`` -- the step size
For a selection slider (select a value from a list of values):
- ``vmin`` -- a list of possible values for the slider
For all sliders:
- ``default`` -- initial value
- ``label`` -- optional label
- ``display_value`` -- (boolean) if ``True``, display the current
value.
EXAMPLES::
sage: from sage.repl.ipython_kernel.all_jupyter import slider
sage: slider(5, label="slide me")
TransformIntSlider(value=5, description=u'slide me', min=5)
sage: slider(5, 20)
TransformIntSlider(value=5, max=20, min=5)
sage: slider(5, 20, 0.5)
TransformFloatSlider(value=5.0, max=20.0, min=5.0, step=0.5)
sage: slider(5, 20, default=12)
TransformIntSlider(value=12, max=20, min=5)
The parent of the inputs determines the parent of the value::
sage: w = slider(5); w
TransformIntSlider(value=5, min=5)
sage: parent(w.get_interact_value())
Integer Ring
sage: w = slider(int(5)); w
IntSlider(value=5, min=5)
sage: parent(w.get_interact_value())
<... 'int'>
sage: w = slider(5, 20, step_size=RDF("0.1")); w
TransformFloatSlider(value=5.0, max=20.0, min=5.0)
sage: parent(w.get_interact_value())
Real Double Field
sage: w = slider(5, 20, step_size=10/3); w
SelectionSlider(index=2, options=(5, 25/3, 35/3, 15, 55/3), value=35/3)
sage: parent(w.get_interact_value())
Rational Field
Symbolic input is evaluated numerically::
sage: w = slider(e, pi); w
TransformFloatSlider(value=2.718281828459045, max=3.141592653589793, min=2.718281828459045)
sage: parent(w.get_interact_value())
Real Field with 53 bits of precision
For a selection slider, the default is adjusted to one of the
possible values::
sage: slider(range(10), default=17/10)
SelectionSlider(index=2, options=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9), value=2)
TESTS::
sage: slider(range(5), range(5))
Traceback (most recent call last):
...
TypeError: unexpected argument 'vmax' for a selection slider
sage: slider(range(5), step_size=2)
Traceback (most recent call last):
...
TypeError: unexpected argument 'step_size' for a selection slider
sage: slider(5).readout
True
sage: slider(5, display_value=False).readout
False
"""
kwds = {"readout": display_value}
if label:
kwds["description"] = u(label)
# If vmin is iterable, return a SelectionSlider
if isinstance(vmin, Iterable):
if vmax is not None:
raise TypeError(
"unexpected argument 'vmax' for a selection slider")
if step_size is not None:
raise TypeError(
"unexpected argument 'step_size' for a selection slider")
if _range:
# https://github.com/ipython/ipywidgets/issues/760
raise NotImplementedError(
"range_slider does not support a list of values")
options = list(vmin)
# Find default in options
def err(v):
if v is default:
return (-1, 0)
try:
if v == default:
return (0, 0)
return (0, abs(v - default))
except Exception:
return (1, 0)
kwds["options"] = options
if default is not None:
kwds["value"] = min(options, key=err)
return SelectionSlider(**kwds)
if default is not None:
kwds["value"] = default
# Sum all input numbers to figure out type/parent
p = parent(sum(x for x in (vmin, vmax, step_size) if x is not None))
# Change SR to RR
if p is SR:
p = RR
# Convert all inputs to the common parent
if vmin is not None:
vmin = p(vmin)
if vmax is not None:
vmax = p(vmax)
if step_size is not None:
step_size = p(step_size)
def tuple_elements_p(t):
"Convert all entries of the tuple `t` to `p`"
return tuple(p(x) for x in t)
zero = p()
if isinstance(zero, Integral):
if p is int:
if _range:
cls = IntRangeSlider
else:
cls = IntSlider
else:
if _range:
kwds["transform"] = tuple_elements_p
cls = TransformIntRangeSlider
else:
kwds["transform"] = p
cls = TransformIntSlider
elif isinstance(zero, Rational):
# Rational => implement as SelectionSlider
if _range:
# https://github.com/ipython/ipywidgets/issues/760
raise NotImplementedError(
"range_slider does not support rational numbers")
vmin, vmax, value = _get_min_max_value(vmin, vmax, default, step_size)
kwds["value"] = value
kwds["options"] = srange(vmin, vmax, step_size, include_endpoint=True)
return SelectionSlider(**kwds)
elif isinstance(zero, Real):
if p is float:
if _range:
cls = FloatRangeSlider
else:
cls = FloatSlider
else:
if _range:
kwds["transform"] = tuple_elements_p
cls = TransformFloatRangeSlider
else:
kwds["transform"] = p
cls = TransformFloatSlider
else:
raise TypeError("unknown parent {!r} for slider".format(p))
kwds["min"] = vmin
if vmax is not None:
kwds["max"] = vmax
if step_size is not None:
kwds["step"] = step_size
return cls(**kwds)
def _Omega_numerator_P_(a, x, y, t):
r"""
Helper function for :func:`_Omega_numerator_`.
This is an implementation of the function `P` of [APR2001]_.
INPUT:
- ``a`` -- an integer
- ``x`` and ``y`` -- a tuple of Laurent polynomials
The tuple ``x`` here is the flattened ``x`` of :func:`_Omega_numerator_`
but without its last entry.
- ``t`` -- a temporary Laurent polynomial variable
In the (final) result, ``t`` has to be substituted by the last
entry of the flattened ``x`` of :func:`_Omega_numerator_`.
OUTPUT:
A Laurent polynomial
TESTS::
sage: from sage.rings.polynomial.omega import _Omega_numerator_P_
sage: L.<x0, x1, y0, y1, t> = LaurentPolynomialRing(ZZ)
sage: _Omega_numerator_P_(0, (x0,), (y0,), t).subs({t: x1})
-x0*x1*y0 + 1
"""
# This function takes Laurent polynomials as inputs. It would
# be possible to input only the sizes of ``x`` and ``y`` and
# perform a substitution afterwards; in this way caching of this
# function would make sense. However, the way it is now allows
# automatic collection and simplification of the summands, which
# makes it more efficient for higher powers at the input of
# :func:`Omega_ge`.
# Caching occurs in :func:`Omega_ge`.
import logging
logger = logging.getLogger(__name__)
from sage.arith.srange import srange
from sage.misc.misc_c import prod
n = len(x)
if n == 0:
x0 = t
result = x0**(-a) + \
(prod(1 - x0*yy for yy in y) *
sum(homogenous_symmetric_function(j, y) * (1-x0**(j-a))
for j in srange(a))
if a > 0 else 0)
else:
Pprev = _Omega_numerator_P_(a, x[:n - 1], y, t)
x2 = x[n - 1]
logger.debug('Omega_numerator: P(%s): substituting...', n)
x1 = t
p1 = Pprev
p2 = Pprev.subs({t: x2})
logger.debug('Omega_numerator: P(%s): preparing...', n)
dividend = x1 * (1-x2) * prod(1 - x2*yy for yy in y) * p1 - \
x2 * (1-x1) * prod(1 - x1*yy for yy in y) * p2
logger.debug('Omega_numerator: P(%s): dividing...', n)
q, r = dividend.quo_rem(x1 - x2)
assert r == 0
result = q
logger.debug('Omega_numerator: P(%s) has %s terms', n,
result.number_of_terms())
return result
def _parametric_plot3d_surface(f, urange, vrange, plot_points, boundary_style,
**kwds):
r"""
Return a parametric three-dimensional space surface.
This function is used internally by the
:func:`parametric_plot3d` command.
There are two ways this function is invoked by
:func:`parametric_plot3d`.
- ``parametric_plot3d([f_x, f_y, f_z], (u_min, u_max),
(v_min, v_max))``:
`f_x, f_y, f_z` are each functions of two variables
- ``parametric_plot3d([f_x, f_y, f_z], (u, u_min,
u_max), (v, v_min, v_max))``:
`f_x, f_y, f_z` can be viewed as functions of
`u` and `v`
INPUT:
- ``f`` - a 3-tuple of functions or expressions, or vector of size 3
- ``urange`` - a 2-tuple (u_min, u_max) or a 3-tuple
(u, u_min, u_max)
- ``vrange`` - a 2-tuple (v_min, v_max) or a 3-tuple
(v, v_min, v_max)
- ``plot_points`` - (default: "automatic", which is [40,40]
for surfaces) initial number of sample points in each parameter;
a pair of integers.
- ``boundary_style`` - (default: None, no boundary) a dict that describes
how to draw the boundaries of regions by giving options that are passed
to the line3d command.
EXAMPLES:
We demonstrate each of the two ways of calling this. See
:func:`parametric_plot3d` for many more examples.
We do the first one with lambda functions::
sage: f = (lambda u,v: cos(u), lambda u,v: sin(u)+cos(v), lambda u,v: sin(v))
sage: parametric_plot3d(f, (0, 2*pi), (-pi, pi)) # indirect doctest
Graphics3d Object
Now we do the same thing with symbolic expressions::
sage: u, v = var('u,v')
sage: parametric_plot3d((cos(u), sin(u) + cos(v), sin(v)), (u, 0, 2*pi), (v, -pi, pi), mesh=True)
Graphics3d Object
"""
from sage.plot.misc import setup_for_eval_on_grid
g, ranges = setup_for_eval_on_grid(f, [urange, vrange], plot_points)
urange = srange(*ranges[0], include_endpoint=True)
vrange = srange(*ranges[1], include_endpoint=True)
G = ParametricSurface(g, (urange, vrange), **kwds)
if boundary_style is not None:
for u in (urange[0], urange[-1]):
G += line3d([(g[0](u, v), g[1](u, v), g[2](u, v)) for v in vrange],
**boundary_style)
for v in (vrange[0], vrange[-1]):
G += line3d([(g[0](u, v), g[1](u, v), g[2](u, v)) for u in urange],
**boundary_style)
return G
def QuadraticResidueCodeEvenPair(n, F):
"""
Quadratic residue codes of a given odd prime length and base ring
either don't exist at all or occur as 4-tuples - a pair of
"odd-like" codes and a pair of "even-like" codes. If `n > 2` is prime
then (Theorem 6.6.2 in [HP2003]_) a QR code exists over `GF(q)` iff q is a
quadratic residue mod `n`.
They are constructed as "even-like" duadic codes associated the
splitting (Q,N) mod n, where Q is the set of non-zero quadratic
residues and N is the non-residues.
EXAMPLES::
sage: codes.QuadraticResidueCodeEvenPair(17, GF(13)) # known bug (#25896)
([17, 8] Cyclic Code over GF(13),
[17, 8] Cyclic Code over GF(13))
sage: codes.QuadraticResidueCodeEvenPair(17, GF(2))
([17, 8] Cyclic Code over GF(2),
[17, 8] Cyclic Code over GF(2))
sage: codes.QuadraticResidueCodeEvenPair(13,GF(9,"z")) # known bug (#25896)
([13, 6] Cyclic Code over GF(9),
[13, 6] Cyclic Code over GF(9))
sage: C1,C2 = codes.QuadraticResidueCodeEvenPair(7,GF(2))
sage: C1.is_self_orthogonal()
True
sage: C2.is_self_orthogonal()
True
sage: C3 = codes.QuadraticResidueCodeOddPair(17,GF(2))[0]
sage: C4 = codes.QuadraticResidueCodeEvenPair(17,GF(2))[1]
sage: C3.systematic_generator_matrix() == C4.dual_code().systematic_generator_matrix()
True
This is consistent with Theorem 6.6.9 and Exercise 365 in [HP2003]_.
TESTS::
sage: codes.QuadraticResidueCodeEvenPair(14,Zmod(4))
Traceback (most recent call last):
...
ValueError: the argument F must be a finite field
sage: codes.QuadraticResidueCodeEvenPair(14,GF(2))
Traceback (most recent call last):
...
ValueError: the argument n must be an odd prime
sage: codes.QuadraticResidueCodeEvenPair(5,GF(2))
Traceback (most recent call last):
...
ValueError: the order of the finite field must be a quadratic residue modulo n
"""
from sage.arith.srange import srange
from sage.categories.finite_fields import FiniteFields
if F not in FiniteFields():
raise ValueError("the argument F must be a finite field")
q = F.order()
n = Integer(n)
if n <= 2 or not n.is_prime():
raise ValueError("the argument n must be an odd prime")
Q = quadratic_residues(n)
Q.remove(0) # non-zero quad residues
N = [x for x in srange(1, n) if x not in Q] # non-zero quad non-residues
if q not in Q:
raise ValueError(
"the order of the finite field must be a quadratic residue modulo n"
)
return DuadicCodeEvenPair(F, Q, N)
def Krawtchouk(n, q, l, x, check=True):
"""
Compute ``K^{n,q}_l(x)``, the Krawtchouk polynomial.
See :wikipedia:`Kravchuk_polynomials`; It is defined by the generating function
`(1+(q-1)z)^{n-x}(1-z)^x=\sum_{l} K^{n,q}_l(x)z^l` and is equal to
.. math::
K^{n,q}_l(x)=\sum_{j=0}^l (-1)^j(q-1)^{(l-j)}{x \choose j}{n-x \choose l-j},
INPUT:
- ``n, q, x`` -- arbitrary numbers
- ``l`` -- a nonnegative integer
- ``check`` -- check the input for correctness. ``True`` by default. Otherwise, pass it
as it is. Use ``check=False`` at your own risk.
EXAMPLES::
sage: Krawtchouk(24,2,5,4)
2224
sage: Krawtchouk(12300,4,5,6)
567785569973042442072
TESTS:
check that the bug reported on :trac:`19561` is fixed::
sage: Krawtchouk(3,2,3,3)
-1
sage: Krawtchouk(int(3),int(2),int(3),int(3))
-1
sage: Krawtchouk(int(3),int(2),int(3),int(3),check=False)
-5
other unusual inputs ::
sage: Krawtchouk(sqrt(5),1-I*sqrt(3),3,55.3).n()
211295.892797... + 1186.42763...*I
sage: Krawtchouk(-5/2,7*I,3,-1/10)
480053/250*I - 357231/400
sage: Krawtchouk(1,1,-1,1)
Traceback (most recent call last):
...
ValueError: l must be a nonnegative integer
sage: Krawtchouk(1,1,3/2,1)
Traceback (most recent call last):
...
TypeError: no conversion of this rational to integer
"""
from sage.arith.all import binomial
from sage.arith.srange import srange
# Use the expression in equation (55) of MacWilliams & Sloane, pg 151
# We write jth term = some_factor * (j-1)th term
if check:
from sage.rings.integer_ring import ZZ
l0 = ZZ(l)
if l0 != l or l0 < 0:
raise ValueError('l must be a nonnegative integer')
l = l0
kraw = jth_term = (q - 1)**l * binomial(n, l) # j=0
for j in srange(1, l + 1):
jth_term *= -q * (l - j + 1) * (x - j + 1) / ((q - 1) * j *
(n - j + 1))
kraw += jth_term
return kraw
def QuadraticResidueCodeEvenPair(n,F):
"""
Quadratic residue codes of a given odd prime length and base ring
either don't exist at all or occur as 4-tuples - a pair of
"odd-like" codes and a pair of "even-like" codes. If `n > 2` is prime
then (Theorem 6.6.2 in [HP2003]_) a QR code exists over `GF(q)` iff q is a
quadratic residue mod `n`.
They are constructed as "even-like" duadic codes associated the
splitting (Q,N) mod n, where Q is the set of non-zero quadratic
residues and N is the non-residues.
EXAMPLES::
sage: codes.QuadraticResidueCodeEvenPair(17, GF(13))
([17, 8] Cyclic Code over GF(13),
[17, 8] Cyclic Code over GF(13))
sage: codes.QuadraticResidueCodeEvenPair(17, GF(2))
([17, 8] Cyclic Code over GF(2),
[17, 8] Cyclic Code over GF(2))
sage: codes.QuadraticResidueCodeEvenPair(13,GF(9,"z"))
([13, 6] Cyclic Code over GF(9),
[13, 6] Cyclic Code over GF(9))
sage: C1,C2 = codes.QuadraticResidueCodeEvenPair(7,GF(2))
sage: C1.is_self_orthogonal()
True
sage: C2.is_self_orthogonal()
True
sage: C3 = codes.QuadraticResidueCodeOddPair(17,GF(2))[0]
sage: C4 = codes.QuadraticResidueCodeEvenPair(17,GF(2))[1]
sage: C3.systematic_generator_matrix() == C4.dual_code().systematic_generator_matrix()
True
This is consistent with Theorem 6.6.9 and Exercise 365 in [HP2003]_.
TESTS::
sage: codes.QuadraticResidueCodeEvenPair(14,Zmod(4))
Traceback (most recent call last):
...
ValueError: the argument F must be a finite field
sage: codes.QuadraticResidueCodeEvenPair(14,GF(2))
Traceback (most recent call last):
...
ValueError: the argument n must be an odd prime
sage: codes.QuadraticResidueCodeEvenPair(5,GF(2))
Traceback (most recent call last):
...
ValueError: the order of the finite field must be a quadratic residue modulo n
"""
from sage.arith.srange import srange
from sage.categories.finite_fields import FiniteFields
if F not in FiniteFields():
raise ValueError("the argument F must be a finite field")
q = F.order()
n = Integer(n)
if n <= 2 or not n.is_prime():
raise ValueError("the argument n must be an odd prime")
Q = quadratic_residues(n); Q.remove(0) # non-zero quad residues
N = [x for x in srange(1,n) if x not in Q] # non-zero quad non-residues
if q not in Q:
raise ValueError("the order of the finite field must be a quadratic residue modulo n")
return DuadicCodeEvenPair(F,Q,N)
def ruler(start, end, ticks=4, sub_ticks=4, absolute=False, snap=False, **kwds):
"""
Draw a ruler in 3-D, with major and minor ticks.
INPUT:
- ``start`` -- the beginning of the ruler, as a list,
tuple, or vector.
- ``end`` -- the end of the ruler, as a list, tuple,
or vector.
- ``ticks`` -- (default: 4) the number of major ticks
shown on the ruler.
- ``sub_ticks`` -- (default: 4) the number of shown
subdivisions between each major tick.
- ``absolute`` -- (default: ``False``) if ``True``, makes a huge ruler
in the direction of an axis.
- ``snap`` -- (default: ``False``) if ``True``, snaps to an implied
grid.
Type ``line3d.options`` for a dictionary of the default
options for lines, which are also available.
EXAMPLES:
A ruler::
sage: from sage.plot.plot3d.shapes2 import ruler
sage: R = ruler([1,2,3],vector([2,3,4])); R
Graphics3d Object
A ruler with some options::
sage: R = ruler([1,2,3],vector([2,3,4]),ticks=6, sub_ticks=2, color='red'); R
Graphics3d Object
The keyword ``snap`` makes the ticks not necessarily coincide
with the ruler::
sage: ruler([1,2,3],vector([1,2,4]),snap=True)
Graphics3d Object
The keyword ``absolute`` makes a huge ruler in one of the axis
directions::
sage: ruler([1,2,3],vector([1,2,4]),absolute=True)
Graphics3d Object
TESTS::
sage: ruler([1,2,3],vector([1,3,4]),absolute=True)
Traceback (most recent call last):
...
ValueError: Absolute rulers only valid for axis-aligned paths
"""
start = vector(RDF, start)
end = vector(RDF, end)
dir = end - start
dist = math.sqrt(dir.dot_product(dir))
dir /= dist
one_tick = dist/ticks * 1.414
unit = 10 ** math.floor(math.log(dist/ticks, 10))
if unit * 5 < one_tick:
unit *= 5
elif unit * 2 < one_tick:
unit *= 2
if dir[0]:
tick = dir.cross_product(vector(RDF, (0,0,-dist/30)))
elif dir[1]:
tick = dir.cross_product(vector(RDF, (0,0,dist/30)))
else:
tick = vector(RDF, (dist/30,0,0))
if snap:
for i in range(3):
start[i] = unit * math.floor(start[i]/unit + 1e-5)
end[i] = unit * math.ceil(end[i]/unit - 1e-5)
if absolute:
if dir[0]*dir[1] or dir[1]*dir[2] or dir[0]*dir[2]:
raise ValueError("Absolute rulers only valid for axis-aligned paths")
m = max(dir[0], dir[1], dir[2])
if dir[0] == m:
off = start[0]
elif dir[1] == m:
off = start[1]
else:
off = start[2]
first_tick = unit * math.ceil(off/unit - 1e-5) - off
else:
off = 0
first_tick = 0
ruler = shapes.LineSegment(start, end, **kwds)
for k in range(1, int(sub_ticks * first_tick/unit)):
P = start + dir*(k*unit/sub_ticks)
ruler += shapes.LineSegment(P, P + tick/2, **kwds)
for d in srange(first_tick, dist + unit/(sub_ticks+1), unit):
P = start + dir*d
ruler += shapes.LineSegment(P, P + tick, **kwds)
ruler += shapes.Text(str(d+off), **kwds).translate(P - tick)
if dist - d < unit:
sub_ticks = int(sub_ticks * (dist - d)/unit)
for k in range(1, sub_ticks):
P += dir * (unit/sub_ticks)
ruler += shapes.LineSegment(P, P + tick/2, **kwds)
return ruler
def Omega_ge(a, exponents):
r"""
Return `\Omega_{\ge}` of the expression specified by the input.
To be more precise, calculate
.. MATH::
\Omega_{\ge} \frac{\mu^a}{
(1 - z_0 \mu^{e_0}) \dots (1 - z_{n-1} \mu^{e_{n-1}})}
and return its numerator and a factorization of its denominator.
Note that `z_0`, ..., `z_{n-1}` only appear in the output, but not in the
input.
INPUT:
- ``a`` -- an integer
- ``exponents`` -- a tuple of integers
OUTPUT:
A pair representing a quotient as follows: Its first component is the
numerator as a Laurent polynomial, its second component a factorization
of the denominator as a tuple of Laurent polynomials, where each
Laurent polynomial `z` represents a factor `1 - z`.
The parents of these Laurent polynomials is always a
Laurent polynomial ring in `z_0`, ..., `z_{n-1}` over `\ZZ`, where
`n` is the length of ``exponents``.
EXAMPLES::
sage: from sage.rings.polynomial.omega import Omega_ge
sage: Omega_ge(0, (1, -2))
(1, (z0, z0^2*z1))
sage: Omega_ge(0, (1, -3))
(1, (z0, z0^3*z1))
sage: Omega_ge(0, (1, -4))
(1, (z0, z0^4*z1))
sage: Omega_ge(0, (2, -1))
(z0*z1 + 1, (z0, z0*z1^2))
sage: Omega_ge(0, (3, -1))
(z0*z1^2 + z0*z1 + 1, (z0, z0*z1^3))
sage: Omega_ge(0, (4, -1))
(z0*z1^3 + z0*z1^2 + z0*z1 + 1, (z0, z0*z1^4))
sage: Omega_ge(0, (1, 1, -2))
(-z0^2*z1*z2 - z0*z1^2*z2 + z0*z1*z2 + 1, (z0, z1, z0^2*z2, z1^2*z2))
sage: Omega_ge(0, (2, -1, -1))
(z0*z1*z2 + z0*z1 + z0*z2 + 1, (z0, z0*z1^2, z0*z2^2))
sage: Omega_ge(0, (2, 1, -1))
(-z0*z1*z2^2 - z0*z1*z2 + z0*z2 + 1, (z0, z1, z0*z2^2, z1*z2))
::
sage: Omega_ge(0, (2, -2))
(-z0*z1 + 1, (z0, z0*z1, z0*z1))
sage: Omega_ge(0, (2, -3))
(z0^2*z1 + 1, (z0, z0^3*z1^2))
sage: Omega_ge(0, (3, 1, -3))
(-z0^3*z1^3*z2^3 + 2*z0^2*z1^3*z2^2 - z0*z1^3*z2
+ z0^2*z2^2 - 2*z0*z2 + 1,
(z0, z1, z0*z2, z0*z2, z0*z2, z1^3*z2))
::
sage: Omega_ge(0, (3, 6, -1))
(-z0*z1*z2^8 - z0*z1*z2^7 - z0*z1*z2^6 - z0*z1*z2^5 - z0*z1*z2^4 +
z1*z2^5 - z0*z1*z2^3 + z1*z2^4 - z0*z1*z2^2 + z1*z2^3 -
z0*z1*z2 + z0*z2^2 + z1*z2^2 + z0*z2 + z1*z2 + 1,
(z0, z1, z0*z2^3, z1*z2^6))
TESTS::
sage: Omega_ge(0, (2, 2, 1, 1, 1, 1, 1, -1, -1))[0].number_of_terms() # long time
27837
::
sage: Omega_ge(1, (2,))
(1, (z0,))
"""
import logging
logger = logging.getLogger(__name__)
logger.info('Omega_ge: a=%s, exponents=%s', a, exponents)
from sage.arith.misc import lcm
from sage.arith.srange import srange
from sage.misc.misc_c import prod
from sage.rings.integer_ring import ZZ
from sage.rings.polynomial.laurent_polynomial_ring import LaurentPolynomialRing
from sage.rings.number_field.number_field import CyclotomicField
if not exponents or any(e == 0 for e in exponents):
raise NotImplementedError
rou = sorted(set(abs(e) for e in exponents) - set([1]))
ellcm = lcm(rou)
B = CyclotomicField(ellcm, 'zeta')
zeta = B.gen()
z_names = tuple('z{}'.format(i) for i in range(len(exponents)))
L = LaurentPolynomialRing(B, ('t', ) + z_names, len(z_names) + 1)
t = L.gens()[0]
Z = LaurentPolynomialRing(ZZ, z_names, len(z_names))
powers = {i: L(zeta**(ellcm // i)) for i in rou}
powers[2] = L(-1)
powers[1] = L(1)
exponents_and_values = tuple(
(e, tuple(powers[abs(e)]**j * z for j in srange(abs(e))))
for z, e in zip(L.gens()[1:], exponents))
x = tuple(v for e, v in exponents_and_values if e > 0)
y = tuple(v for e, v in exponents_and_values if e < 0)
def subs_power(expression, var, exponent):
r"""
Substitute ``var^exponent`` by ``var`` in ``expression``.
It is assumed that ``var`` only occurs with exponents
divisible by ``exponent``.
"""
p = tuple(var.dict().popitem()[0]).index(
1) # var is the p-th generator
def subs_e(e):
e = list(e)
assert e[p] % exponent == 0
e[p] = e[p] // exponent
return tuple(e)
parent = expression.parent()
result = parent(
{subs_e(e): c
for e, c in iteritems(expression.dict())})
return result
def de_power(expression):
expression = Z(expression)
for e, var in zip(exponents, Z.gens()):
if abs(e) == 1:
continue
expression = subs_power(expression, var, abs(e))
return expression
logger.debug('Omega_ge: preparing denominator')
factors_denominator = tuple(
de_power(1 - factor) for factor in _Omega_factors_denominator_(x, y))
logger.debug('Omega_ge: preparing numerator')
numerator = de_power(_Omega_numerator_(a, x, y, t))
logger.info('Omega_ge: completed')
return numerator, factors_denominator
def plot_vector_field3d(functions, xrange, yrange, zrange,
plot_points=5, colors='jet', center_arrows=False,**kwds):
r"""
Plot a 3d vector field
INPUT:
- ``functions`` - a list of three functions, representing the x-,
y-, and z-coordinates of a vector
- ``xrange``, ``yrange``, and ``zrange`` - three tuples of the
form (var, start, stop), giving the variables and ranges for each axis
- ``plot_points`` (default 5) - either a number or list of three
numbers, specifying how many points to plot for each axis
- ``colors`` (default 'jet') - a color, list of colors (which are
interpolated between), or matplotlib colormap name, giving the coloring
of the arrows. If a list of colors or a colormap is given,
coloring is done as a function of length of the vector
- ``center_arrows`` (default False) - If True, draw the arrows
centered on the points; otherwise, draw the arrows with the tail
at the point
- any other keywords are passed on to the plot command for each arrow
EXAMPLES::
sage: x,y,z=var('x y z')
sage: plot_vector_field3d((x*cos(z),-y*cos(z),sin(z)), (x,0,pi), (y,0,pi), (z,0,pi))
Graphics3d Object
sage: plot_vector_field3d((x*cos(z),-y*cos(z),sin(z)), (x,0,pi), (y,0,pi), (z,0,pi),colors=['red','green','blue'])
Graphics3d Object
sage: plot_vector_field3d((x*cos(z),-y*cos(z),sin(z)), (x,0,pi), (y,0,pi), (z,0,pi),colors='red')
Graphics3d Object
sage: plot_vector_field3d((x*cos(z),-y*cos(z),sin(z)), (x,0,pi), (y,0,pi), (z,0,pi),plot_points=4)
Graphics3d Object
sage: plot_vector_field3d((x*cos(z),-y*cos(z),sin(z)), (x,0,pi), (y,0,pi), (z,0,pi),plot_points=[3,5,7])
Graphics3d Object
sage: plot_vector_field3d((x*cos(z),-y*cos(z),sin(z)), (x,0,pi), (y,0,pi), (z,0,pi),center_arrows=True)
Graphics3d Object
TESTS:
This tests that :trac:`2100` is fixed in a way compatible with this command::
sage: plot_vector_field3d((x*cos(z),-y*cos(z),sin(z)), (x,0,pi), (y,0,pi), (z,0,pi),center_arrows=True,aspect_ratio=(1,2,1))
Graphics3d Object
"""
(ff,gg,hh), ranges = setup_for_eval_on_grid(functions, [xrange, yrange, zrange], plot_points)
xpoints, ypoints, zpoints = [srange(*r, include_endpoint=True) for r in ranges]
points = [vector((i,j,k)) for i in xpoints for j in ypoints for k in zpoints]
vectors = [vector((ff(*point), gg(*point), hh(*point))) for point in points]
try:
from matplotlib.cm import get_cmap
cm = get_cmap(colors)
except (TypeError, ValueError):
cm = None
if cm is None:
if isinstance(colors, (list, tuple)):
from matplotlib.colors import LinearSegmentedColormap
cm = LinearSegmentedColormap.from_list('mymap',colors)
else:
cm = lambda x: colors
max_len = max(v.norm() for v in vectors)
scaled_vectors = [v/max_len for v in vectors]
if center_arrows:
return sum([plot(v,color=cm(v.norm()),**kwds).translate(p-v/2) for v,p in zip(scaled_vectors, points)])
else:
return sum([plot(v,color=cm(v.norm()),**kwds).translate(p) for v,p in zip(scaled_vectors, points)])
def HarmonicNumber(var, precision=None, skip_constant_summand=False):
r"""
Return the asymptotic expansion of a harmonic number.
INPUT:
- ``var`` -- a string for the variable name.
- ``precision`` -- (default: ``None``) an integer. If ``None``, then
the default precision of the asymptotic ring is used.
- ``skip_constant_summand`` -- (default: ``False``) a
boolean. If set, then the constant summand ``euler_gamma`` is left out.
As a consequence, the coefficient ring of the output changes
from ``Symbolic Constants Subring`` (if ``False``) to
``Rational Field`` (if ``True``).
OUTPUT:
An asymptotic expansion.
EXAMPLES::
sage: asymptotic_expansions.HarmonicNumber('n', precision=5)
log(n) + euler_gamma + 1/2*n^(-1) - 1/12*n^(-2) + 1/120*n^(-4) + O(n^(-6))
TESTS::
sage: ex = asymptotic_expansions.HarmonicNumber('n', precision=5)
sage: n = ex.parent().gen()
sage: ex.compare_with_values(n, # rel tol 1e-6
....: lambda x: sum(1/k for k in srange(1, x+1)), [5, 10, 20])
[(5, 0.0038125360?), (10, 0.00392733?), (20, 0.0039579?)]
sage: asymptotic_expansions.HarmonicNumber('n')
log(n) + euler_gamma + 1/2*n^(-1) - 1/12*n^(-2) + 1/120*n^(-4)
- 1/252*n^(-6) + 1/240*n^(-8) - 1/132*n^(-10)
+ 691/32760*n^(-12) - 1/12*n^(-14) + 3617/8160*n^(-16)
- 43867/14364*n^(-18) + 174611/6600*n^(-20) - 77683/276*n^(-22)
+ 236364091/65520*n^(-24) - 657931/12*n^(-26)
+ 3392780147/3480*n^(-28) - 1723168255201/85932*n^(-30)
+ 7709321041217/16320*n^(-32)
- 151628697551/12*n^(-34) + O(n^(-36))
sage: _.parent()
Asymptotic Ring <n^ZZ * log(n)^ZZ> over Symbolic Constants Subring
::
sage: asymptotic_expansions.HarmonicNumber(
....: 'n', precision=5, skip_constant_summand=True)
log(n) + 1/2*n^(-1) - 1/12*n^(-2) + 1/120*n^(-4) + O(n^(-6))
sage: _.parent()
Asymptotic Ring <n^ZZ * log(n)^ZZ> over Rational Field
sage: for p in range(5):
....: print asymptotic_expansions.HarmonicNumber(
....: 'n', precision=p)
O(log(n))
log(n) + O(1)
log(n) + euler_gamma + O(n^(-1))
log(n) + euler_gamma + 1/2*n^(-1) + O(n^(-2))
log(n) + euler_gamma + 1/2*n^(-1) - 1/12*n^(-2) + O(n^(-4))
sage: asymptotic_expansions.HarmonicNumber('m', precision=5)
log(m) + euler_gamma + 1/2*m^(-1) - 1/12*m^(-2) + 1/120*m^(-4) + O(m^(-6))
"""
if not skip_constant_summand:
from sage.symbolic.ring import SR
coefficient_ring = SR.subring(no_variables=True)
else:
from sage.rings.rational_field import QQ
coefficient_ring = QQ
from asymptotic_ring import AsymptoticRing
A = AsymptoticRing(growth_group='{n}^ZZ * log({n})^ZZ'.format(n=var),
coefficient_ring=coefficient_ring)
n = A.gen()
if precision is None:
precision = A.default_prec
from sage.functions.log import log
result = A.zero()
if precision >= 1:
result += log(n)
if precision >= 2 and not skip_constant_summand:
from sage.symbolic.constants import euler_gamma
result += coefficient_ring(euler_gamma)
if precision >= 3:
result += 1 / (2 * n)
from sage.arith.srange import srange
from sage.arith.all import bernoulli
for k in srange(2, 2*precision - 4, 2):
result += -bernoulli(k) / k / n**k
if precision < 1:
result += (log(n)).O()
elif precision == 1:
result += A(1).O()
elif precision == 2:
result += (1 / n).O()
else:
result += (1 / n**(2*precision - 4)).O()
return result
def Krawtchouk(n,q,l,x,check=True):
"""
Compute ``K^{n,q}_l(x)``, the Krawtchouk (a.k.a. Kravchuk) polynomial.
See :wikipedia:`Kravchuk_polynomials`; It is defined by the generating function
`(1+(q-1)z)^{n-x}(1-z)^x=\sum_{l} K^{n,q}_l(x)z^l` and is equal to
.. math::
K^{n,q}_l(x)=\sum_{j=0}^l (-1)^j(q-1)^{(l-j)}{x \choose j}{n-x \choose l-j},
INPUT:
- ``n, q, x`` -- arbitrary numbers
- ``l`` -- a nonnegative integer
- ``check`` -- check the input for correctness. ``True`` by default. Otherwise, pass it
as it is. Use ``check=False`` at your own risk.
EXAMPLES::
sage: Krawtchouk(24,2,5,4)
2224
sage: Krawtchouk(12300,4,5,6)
567785569973042442072
TESTS:
check that the bug reported on :trac:`19561` is fixed::
sage: Krawtchouk(3,2,3,3)
-1
sage: Krawtchouk(int(3),int(2),int(3),int(3))
-1
sage: Krawtchouk(int(3),int(2),int(3),int(3),check=False)
-5
sage: Kravchuk(24,2,5,4)
2224
other unusual inputs ::
sage: Krawtchouk(sqrt(5),1-I*sqrt(3),3,55.3).n()
211295.892797... + 1186.42763...*I
sage: Krawtchouk(-5/2,7*I,3,-1/10)
480053/250*I - 357231/400
sage: Krawtchouk(1,1,-1,1)
Traceback (most recent call last):
...
ValueError: l must be a nonnegative integer
sage: Krawtchouk(1,1,3/2,1)
Traceback (most recent call last):
...
TypeError: no conversion of this rational to integer
"""
from sage.arith.all import binomial
from sage.arith.srange import srange
# Use the expression in equation (55) of MacWilliams & Sloane, pg 151
# We write jth term = some_factor * (j-1)th term
if check:
from sage.rings.integer_ring import ZZ
l0 = ZZ(l)
if l0 != l or l0<0:
raise ValueError('l must be a nonnegative integer')
l = l0
kraw = jth_term = (q-1)**l * binomial(n, l) # j=0
for j in srange(1,l+1):
jth_term *= -q*(l-j+1)*(x-j+1)/((q-1)*j*(n-j+1))
kraw += jth_term
return kraw
def HarmonicNumber(var, precision=None, skip_constant_summand=False):
r"""
Return the asymptotic expansion of a harmonic number.
INPUT:
- ``var`` -- a string for the variable name.
- ``precision`` -- (default: ``None``) an integer. If ``None``, then
the default precision of the asymptotic ring is used.
- ``skip_constant_summand`` -- (default: ``False``) a
boolean. If set, then the constant summand ``euler_gamma`` is left out.
As a consequence, the coefficient ring of the output changes
from ``Symbolic Constants Subring`` (if ``False``) to
``Rational Field`` (if ``True``).
OUTPUT:
An asymptotic expansion.
EXAMPLES::
sage: asymptotic_expansions.HarmonicNumber('n', precision=5)
log(n) + euler_gamma + 1/2*n^(-1) - 1/12*n^(-2) + 1/120*n^(-4) + O(n^(-6))
TESTS::
sage: ex = asymptotic_expansions.HarmonicNumber('n', precision=5)
sage: n = ex.parent().gen()
sage: ex.compare_with_values(n, # rel tol 1e-6
....: lambda x: sum(1/k for k in srange(1, x+1)), [5, 10, 20])
[(5, 0.0038125360?), (10, 0.00392733?), (20, 0.0039579?)]
sage: asymptotic_expansions.HarmonicNumber('n')
log(n) + euler_gamma + 1/2*n^(-1) - 1/12*n^(-2) + 1/120*n^(-4)
- 1/252*n^(-6) + 1/240*n^(-8) - 1/132*n^(-10)
+ 691/32760*n^(-12) - 1/12*n^(-14) + 3617/8160*n^(-16)
- 43867/14364*n^(-18) + 174611/6600*n^(-20) - 77683/276*n^(-22)
+ 236364091/65520*n^(-24) - 657931/12*n^(-26)
+ 3392780147/3480*n^(-28) - 1723168255201/85932*n^(-30)
+ 7709321041217/16320*n^(-32)
- 151628697551/12*n^(-34) + O(n^(-36))
sage: _.parent()
Asymptotic Ring <n^ZZ * log(n)^ZZ> over Symbolic Constants Subring
::
sage: asymptotic_expansions.HarmonicNumber(
....: 'n', precision=5, skip_constant_summand=True)
log(n) + 1/2*n^(-1) - 1/12*n^(-2) + 1/120*n^(-4) + O(n^(-6))
sage: _.parent()
Asymptotic Ring <n^ZZ * log(n)^ZZ> over Rational Field
sage: for p in range(5):
....: print asymptotic_expansions.HarmonicNumber(
....: 'n', precision=p)
O(log(n))
log(n) + O(1)
log(n) + euler_gamma + O(n^(-1))
log(n) + euler_gamma + 1/2*n^(-1) + O(n^(-2))
log(n) + euler_gamma + 1/2*n^(-1) - 1/12*n^(-2) + O(n^(-4))
sage: asymptotic_expansions.HarmonicNumber('m', precision=5)
log(m) + euler_gamma + 1/2*m^(-1) - 1/12*m^(-2) + 1/120*m^(-4) + O(m^(-6))
"""
if not skip_constant_summand:
from sage.symbolic.ring import SR
coefficient_ring = SR.subring(no_variables=True)
else:
from sage.rings.rational_field import QQ
coefficient_ring = QQ
from asymptotic_ring import AsymptoticRing
A = AsymptoticRing(growth_group='{n}^ZZ * log({n})^ZZ'.format(n=var),
coefficient_ring=coefficient_ring)
n = A.gen()
if precision is None:
precision = A.default_prec
from sage.functions.log import log
result = A.zero()
if precision >= 1:
result += log(n)
if precision >= 2 and not skip_constant_summand:
from sage.symbolic.constants import euler_gamma
result += coefficient_ring(euler_gamma)
if precision >= 3:
result += 1 / (2 * n)
from sage.arith.srange import srange
from sage.arith.all import bernoulli
for k in srange(2, 2 * precision - 4, 2):
result += -bernoulli(k) / k / n**k
if precision < 1:
result += (log(n)).O()
elif precision == 1:
result += A(1).O()
elif precision == 2:
result += (1 / n).O()
else:
result += (1 / n**(2 * precision - 4)).O()
return result
