# Special#

## DiracDelta#

class sympy.functions.special.delta_functions.DiracDelta(arg, k=0)[source]#

The DiracDelta function and its derivatives.

Explanation

DiracDelta is not an ordinary function. It can be rigorously defined either as a distribution or as a measure.

DiracDelta only makes sense in definite integrals, and in particular, integrals of the form Integral(f(x)*DiracDelta(x - x0), (x, a, b)), where it equals f(x0) if a <= x0 <= b and 0 otherwise. Formally, DiracDelta acts in some ways like a function that is 0 everywhere except at 0, but in many ways it also does not. It can often be useful to treat DiracDelta in formal ways, building up and manipulating expressions with delta functions (which may eventually be integrated), but care must be taken to not treat it as a real function. SymPy’s oo is similar. It only truly makes sense formally in certain contexts (such as integration limits), but SymPy allows its use everywhere, and it tries to be consistent with operations on it (like 1/oo), but it is easy to get into trouble and get wrong results if oo is treated too much like a number. Similarly, if DiracDelta is treated too much like a function, it is easy to get wrong or nonsensical results.

DiracDelta function has the following properties:

1. $$\frac{d}{d x} \theta(x) = \delta(x)$$

2. $$\int_{-\infty}^\infty \delta(x - a)f(x)\, dx = f(a)$$ and $$\int_{a- \epsilon}^{a+\epsilon} \delta(x - a)f(x)\, dx = f(a)$$

3. $$\delta(x) = 0$$ for all $$x \neq 0$$

4. $$\delta(g(x)) = \sum_i \frac{\delta(x - x_i)}{\|g'(x_i)\|}$$ where $$x_i$$ are the roots of $$g$$

5. $$\delta(-x) = \delta(x)$$

Derivatives of k-th order of DiracDelta have the following properties:

1. $$\delta(x, k) = 0$$ for all $$x \neq 0$$

2. $$\delta(-x, k) = -\delta(x, k)$$ for odd $$k$$

3. $$\delta(-x, k) = \delta(x, k)$$ for even $$k$$

Examples

>>> from sympy import DiracDelta, diff, pi
>>> from sympy.abc import x, y

>>> DiracDelta(x)
DiracDelta(x)
>>> DiracDelta(1)
0
>>> DiracDelta(-1)
0
>>> DiracDelta(pi)
0
>>> DiracDelta(x - 4).subs(x, 4)
DiracDelta(0)
>>> diff(DiracDelta(x))
DiracDelta(x, 1)
>>> diff(DiracDelta(x - 1), x, 2)
DiracDelta(x - 1, 2)
>>> diff(DiracDelta(x**2 - 1), x, 2)
2*(2*x**2*DiracDelta(x**2 - 1, 2) + DiracDelta(x**2 - 1, 1))
>>> DiracDelta(3*x).is_simple(x)
True
>>> DiracDelta(x**2).is_simple(x)
False
>>> DiracDelta((x**2 - 1)*y).expand(diracdelta=True, wrt=x)
DiracDelta(x - 1)/(2*Abs(y)) + DiracDelta(x + 1)/(2*Abs(y))


References

classmethod eval(arg, k=0)[source]#

Returns a simplified form or a value of DiracDelta depending on the argument passed by the DiracDelta object.

Parameters:

k : integer

order of derivative

arg : argument passed to DiracDelta

Explanation

The eval() method is automatically called when the DiracDelta class is about to be instantiated and it returns either some simplified instance or the unevaluated instance depending on the argument passed. In other words, eval() method is not needed to be called explicitly, it is being called and evaluated once the object is called.

Examples

>>> from sympy import DiracDelta, S
>>> from sympy.abc import x

>>> DiracDelta(x)
DiracDelta(x)

>>> DiracDelta(-x, 1)
-DiracDelta(x, 1)

>>> DiracDelta(1)
0

>>> DiracDelta(5, 1)
0

>>> DiracDelta(0)
DiracDelta(0)

>>> DiracDelta(-1)
0

>>> DiracDelta(S.NaN)
nan

>>> DiracDelta(x - 100).subs(x, 5)
0

>>> DiracDelta(x - 100).subs(x, 100)
DiracDelta(0)

fdiff(argindex=1)[source]#

Returns the first derivative of a DiracDelta Function.

Parameters:

argindex : integer

degree of derivative

Explanation

The difference between diff() and fdiff() is: diff() is the user-level function and fdiff() is an object method. fdiff() is a convenience method available in the Function class. It returns the derivative of the function without considering the chain rule. diff(function, x) calls Function._eval_derivative which in turn calls fdiff() internally to compute the derivative of the function.

Examples

>>> from sympy import DiracDelta, diff
>>> from sympy.abc import x

>>> DiracDelta(x).fdiff()
DiracDelta(x, 1)

>>> DiracDelta(x, 1).fdiff()
DiracDelta(x, 2)

>>> DiracDelta(x**2 - 1).fdiff()
DiracDelta(x**2 - 1, 1)

>>> diff(DiracDelta(x, 1)).fdiff()
DiracDelta(x, 3)

is_simple(x)[source]#

Tells whether the argument(args[0]) of DiracDelta is a linear expression in x.

Parameters:

x : can be a symbol

Examples

>>> from sympy import DiracDelta, cos
>>> from sympy.abc import x, y

>>> DiracDelta(x*y).is_simple(x)
True
>>> DiracDelta(x*y).is_simple(y)
True

>>> DiracDelta(x**2 + x - 2).is_simple(x)
False

>>> DiracDelta(cos(x)).is_simple(x)
False


## Heaviside#

class sympy.functions.special.delta_functions.Heaviside(arg, H0=1 / 2)[source]#

Heaviside step function.

Explanation

The Heaviside step function has the following properties:

1. $$\frac{d}{d x} \theta(x) = \delta(x)$$

2. $$\theta(x) = \begin{cases} 0 & \text{for}\: x < 0 \\ \frac{1}{2} & \text{for}\: x = 0 \\1 & \text{for}\: x > 0 \end{cases}$$

3. $$\frac{d}{d x} \max(x, 0) = \theta(x)$$

Heaviside(x) is printed as $$\theta(x)$$ with the SymPy LaTeX printer.

The value at 0 is set differently in different fields. SymPy uses 1/2, which is a convention from electronics and signal processing, and is consistent with solving improper integrals by Fourier transform and convolution.

To specify a different value of Heaviside at x=0, a second argument can be given. Using Heaviside(x, nan) gives an expression that will evaluate to nan for x=0.

Changed in version 1.9: Heaviside(0) now returns 1/2 (before: undefined)

Examples

>>> from sympy import Heaviside, nan
>>> from sympy.abc import x
>>> Heaviside(9)
1
>>> Heaviside(-9)
0
>>> Heaviside(0)
1/2
>>> Heaviside(0, nan)
nan
>>> (Heaviside(x) + 1).replace(Heaviside(x), Heaviside(x, 1))
Heaviside(x, 1) + 1


References

classmethod eval(arg, H0=1 / 2)[source]#

Returns a simplified form or a value of Heaviside depending on the argument passed by the Heaviside object.

Parameters:

arg : argument passed by Heaviside object

H0 : value of Heaviside(0)

Explanation

The eval() method is automatically called when the Heaviside class is about to be instantiated and it returns either some simplified instance or the unevaluated instance depending on the argument passed. In other words, eval() method is not needed to be called explicitly, it is being called and evaluated once the object is called.

Examples

>>> from sympy import Heaviside, S
>>> from sympy.abc import x

>>> Heaviside(x)
Heaviside(x)

>>> Heaviside(19)
1

>>> Heaviside(0)
1/2

>>> Heaviside(0, 1)
1

>>> Heaviside(-5)
0

>>> Heaviside(S.NaN)
nan

>>> Heaviside(x - 100).subs(x, 5)
0

>>> Heaviside(x - 100).subs(x, 105)
1

fdiff(argindex=1)[source]#

Returns the first derivative of a Heaviside Function.

Parameters:

argindex : integer

order of derivative

Examples

>>> from sympy import Heaviside, diff
>>> from sympy.abc import x

>>> Heaviside(x).fdiff()
DiracDelta(x)

>>> Heaviside(x**2 - 1).fdiff()
DiracDelta(x**2 - 1)

>>> diff(Heaviside(x)).fdiff()
DiracDelta(x, 1)

property pargs#

Args without default S.Half

## Singularity Function#

class sympy.functions.special.singularity_functions.SingularityFunction(variable, offset, exponent)[source]#

Singularity functions are a class of discontinuous functions.

Explanation

Singularity functions take a variable, an offset, and an exponent as arguments. These functions are represented using Macaulay brackets as:

SingularityFunction(x, a, n) := <x - a>^n

The singularity function will automatically evaluate to Derivative(DiracDelta(x - a), x, -n - 1) if n < 0 and (x - a)**n*Heaviside(x - a) if n >= 0.

Examples

>>> from sympy import SingularityFunction, diff, Piecewise, DiracDelta, Heaviside, Symbol
>>> from sympy.abc import x, a, n
>>> SingularityFunction(x, a, n)
SingularityFunction(x, a, n)
>>> y = Symbol('y', positive=True)
>>> n = Symbol('n', nonnegative=True)
>>> SingularityFunction(y, -10, n)
(y + 10)**n
>>> y = Symbol('y', negative=True)
>>> SingularityFunction(y, 10, n)
0
>>> SingularityFunction(x, 4, -1).subs(x, 4)
oo
>>> SingularityFunction(x, 10, -2).subs(x, 10)
oo
>>> SingularityFunction(4, 1, 5)
243
>>> diff(SingularityFunction(x, 1, 5) + SingularityFunction(x, 1, 4), x)
4*SingularityFunction(x, 1, 3) + 5*SingularityFunction(x, 1, 4)
>>> diff(SingularityFunction(x, 4, 0), x, 2)
SingularityFunction(x, 4, -2)
>>> SingularityFunction(x, 4, 5).rewrite(Piecewise)
Piecewise(((x - 4)**5, x > 4), (0, True))
>>> expr = SingularityFunction(x, a, n)
>>> y = Symbol('y', positive=True)
>>> n = Symbol('n', nonnegative=True)
>>> expr.subs({x: y, a: -10, n: n})
(y + 10)**n


The methods rewrite(DiracDelta), rewrite(Heaviside), and rewrite('HeavisideDiracDelta') returns the same output. One can use any of these methods according to their choice.

>>> expr = SingularityFunction(x, 4, 5) + SingularityFunction(x, -3, -1) - SingularityFunction(x, 0, -2)
>>> expr.rewrite(Heaviside)
(x - 4)**5*Heaviside(x - 4) + DiracDelta(x + 3) - DiracDelta(x, 1)
>>> expr.rewrite(DiracDelta)
(x - 4)**5*Heaviside(x - 4) + DiracDelta(x + 3) - DiracDelta(x, 1)
>>> expr.rewrite('HeavisideDiracDelta')
(x - 4)**5*Heaviside(x - 4) + DiracDelta(x + 3) - DiracDelta(x, 1)


References

classmethod eval(variable, offset, exponent)[source]#

Returns a simplified form or a value of Singularity Function depending on the argument passed by the object.

Explanation

The eval() method is automatically called when the SingularityFunction class is about to be instantiated and it returns either some simplified instance or the unevaluated instance depending on the argument passed. In other words, eval() method is not needed to be called explicitly, it is being called and evaluated once the object is called.

Examples

>>> from sympy import SingularityFunction, Symbol, nan
>>> from sympy.abc import x, a, n
>>> SingularityFunction(x, a, n)
SingularityFunction(x, a, n)
>>> SingularityFunction(5, 3, 2)
4
>>> SingularityFunction(x, a, nan)
nan
>>> SingularityFunction(x, 3, 0).subs(x, 3)
1
>>> SingularityFunction(4, 1, 5)
243
>>> x = Symbol('x', positive = True)
>>> a = Symbol('a', negative = True)
>>> n = Symbol('n', nonnegative = True)
>>> SingularityFunction(x, a, n)
(-a + x)**n
>>> x = Symbol('x', negative = True)
>>> a = Symbol('a', positive = True)
>>> SingularityFunction(x, a, n)
0

fdiff(argindex=1)[source]#

Returns the first derivative of a DiracDelta Function.

Explanation

The difference between diff() and fdiff() is: diff() is the user-level function and fdiff() is an object method. fdiff() is a convenience method available in the Function class. It returns the derivative of the function without considering the chain rule. diff(function, x) calls Function._eval_derivative which in turn calls fdiff() internally to compute the derivative of the function.

## Gamma, Beta and related Functions#

class sympy.functions.special.gamma_functions.gamma(arg)[source]#

The gamma function

$\Gamma(x) := \int^{\infty}_{0} t^{x-1} e^{-t} \mathrm{d}t.$

Explanation

The gamma function implements the function which passes through the values of the factorial function (i.e., $$\Gamma(n) = (n - 1)!$$ when n is an integer). More generally, $$\Gamma(z)$$ is defined in the whole complex plane except at the negative integers where there are simple poles.

Examples

>>> from sympy import S, I, pi, gamma
>>> from sympy.abc import x


Several special values are known:

>>> gamma(1)
1
>>> gamma(4)
6
>>> gamma(S(3)/2)
sqrt(pi)/2


The gamma function obeys the mirror symmetry:

>>> from sympy import conjugate
>>> conjugate(gamma(x))
gamma(conjugate(x))


Differentiation with respect to $$x$$ is supported:

>>> from sympy import diff
>>> diff(gamma(x), x)
gamma(x)*polygamma(0, x)


Series expansion is also supported:

>>> from sympy import series
>>> series(gamma(x), x, 0, 3)
1/x - EulerGamma + x*(EulerGamma**2/2 + pi**2/12) + x**2*(-EulerGamma*pi**2/12 + polygamma(2, 1)/6 - EulerGamma**3/6) + O(x**3)


We can numerically evaluate the gamma function to arbitrary precision on the whole complex plane:

>>> gamma(pi).evalf(40)
2.288037795340032417959588909060233922890
>>> gamma(1+I).evalf(20)
0.49801566811835604271 - 0.15494982830181068512*I


lowergamma

Lower incomplete gamma function.

uppergamma

Upper incomplete gamma function.

polygamma

Polygamma function.

loggamma

Log Gamma function.

digamma

Digamma function.

trigamma

Trigamma function.

beta

Euler Beta function.

References

class sympy.functions.special.gamma_functions.loggamma(z)[source]#

The loggamma function implements the logarithm of the gamma function (i.e., $$\log\Gamma(x)$$).

Examples

Several special values are known. For numerical integral arguments we have:

>>> from sympy import loggamma
>>> loggamma(-2)
oo
>>> loggamma(0)
oo
>>> loggamma(1)
0
>>> loggamma(2)
0
>>> loggamma(3)
log(2)


And for symbolic values:

>>> from sympy import Symbol
>>> n = Symbol("n", integer=True, positive=True)
>>> loggamma(n)
log(gamma(n))
>>> loggamma(-n)
oo


For half-integral values:

>>> from sympy import S
>>> loggamma(S(5)/2)
log(3*sqrt(pi)/4)
>>> loggamma(n/2)
log(2**(1 - n)*sqrt(pi)*gamma(n)/gamma(n/2 + 1/2))


And general rational arguments:

>>> from sympy import expand_func
>>> L = loggamma(S(16)/3)
>>> expand_func(L).doit()
-5*log(3) + loggamma(1/3) + log(4) + log(7) + log(10) + log(13)
>>> L = loggamma(S(19)/4)
>>> expand_func(L).doit()
-4*log(4) + loggamma(3/4) + log(3) + log(7) + log(11) + log(15)
>>> L = loggamma(S(23)/7)
>>> expand_func(L).doit()
-3*log(7) + log(2) + loggamma(2/7) + log(9) + log(16)


The loggamma function has the following limits towards infinity:

>>> from sympy import oo
>>> loggamma(oo)
oo
>>> loggamma(-oo)
zoo


The loggamma function obeys the mirror symmetry if $$x \in \mathbb{C} \setminus \{-\infty, 0\}$$:

>>> from sympy.abc import x
>>> from sympy import conjugate
>>> conjugate(loggamma(x))
loggamma(conjugate(x))


Differentiation with respect to $$x$$ is supported:

>>> from sympy import diff
>>> diff(loggamma(x), x)
polygamma(0, x)


Series expansion is also supported:

>>> from sympy import series
>>> series(loggamma(x), x, 0, 4).cancel()
-log(x) - EulerGamma*x + pi**2*x**2/12 + x**3*polygamma(2, 1)/6 + O(x**4)


We can numerically evaluate the gamma function to arbitrary precision on the whole complex plane:

>>> from sympy import I
>>> loggamma(5).evalf(30)
3.17805383034794561964694160130
>>> loggamma(I).evalf(20)
-0.65092319930185633889 - 1.8724366472624298171*I


gamma

Gamma function.

lowergamma

Lower incomplete gamma function.

uppergamma

Upper incomplete gamma function.

polygamma

Polygamma function.

digamma

Digamma function.

trigamma

Trigamma function.

beta

Euler Beta function.

References

class sympy.functions.special.gamma_functions.polygamma(n, z)[source]#

The function polygamma(n, z) returns log(gamma(z)).diff(n + 1).

Explanation

It is a meromorphic function on $$\mathbb{C}$$ and defined as the $$(n+1)$$-th derivative of the logarithm of the gamma function:

$\psi^{(n)} (z) := \frac{\mathrm{d}^{n+1}}{\mathrm{d} z^{n+1}} \log\Gamma(z).$

Examples

Several special values are known:

>>> from sympy import S, polygamma
>>> polygamma(0, 1)
-EulerGamma
>>> polygamma(0, 1/S(2))
-2*log(2) - EulerGamma
>>> polygamma(0, 1/S(3))
-log(3) - sqrt(3)*pi/6 - EulerGamma - log(sqrt(3))
>>> polygamma(0, 1/S(4))
-pi/2 - log(4) - log(2) - EulerGamma
>>> polygamma(0, 2)
1 - EulerGamma
>>> polygamma(0, 23)
19093197/5173168 - EulerGamma

>>> from sympy import oo, I
>>> polygamma(0, oo)
oo
>>> polygamma(0, -oo)
oo
>>> polygamma(0, I*oo)
oo
>>> polygamma(0, -I*oo)
oo


Differentiation with respect to $$x$$ is supported:

>>> from sympy import Symbol, diff
>>> x = Symbol("x")
>>> diff(polygamma(0, x), x)
polygamma(1, x)
>>> diff(polygamma(0, x), x, 2)
polygamma(2, x)
>>> diff(polygamma(0, x), x, 3)
polygamma(3, x)
>>> diff(polygamma(1, x), x)
polygamma(2, x)
>>> diff(polygamma(1, x), x, 2)
polygamma(3, x)
>>> diff(polygamma(2, x), x)
polygamma(3, x)
>>> diff(polygamma(2, x), x, 2)
polygamma(4, x)

>>> n = Symbol("n")
>>> diff(polygamma(n, x), x)
polygamma(n + 1, x)
>>> diff(polygamma(n, x), x, 2)
polygamma(n + 2, x)


We can rewrite polygamma functions in terms of harmonic numbers:

>>> from sympy import harmonic
>>> polygamma(0, x).rewrite(harmonic)
harmonic(x - 1) - EulerGamma
>>> polygamma(2, x).rewrite(harmonic)
2*harmonic(x - 1, 3) - 2*zeta(3)
>>> ni = Symbol("n", integer=True)
>>> polygamma(ni, x).rewrite(harmonic)
(-1)**(n + 1)*(-harmonic(x - 1, n + 1) + zeta(n + 1))*factorial(n)


gamma

Gamma function.

lowergamma

Lower incomplete gamma function.

uppergamma

Upper incomplete gamma function.

loggamma

Log Gamma function.

digamma

Digamma function.

trigamma

Trigamma function.

beta

Euler Beta function.

References

class sympy.functions.special.gamma_functions.digamma(z)[source]#

The digamma function is the first derivative of the loggamma function

$\psi(x) := \frac{\mathrm{d}}{\mathrm{d} z} \log\Gamma(z) = \frac{\Gamma'(z)}{\Gamma(z) }.$

In this case, digamma(z) = polygamma(0, z).

Examples

>>> from sympy import digamma
>>> digamma(0)
zoo
>>> from sympy import Symbol
>>> z = Symbol('z')
>>> digamma(z)
polygamma(0, z)


To retain digamma as it is:

>>> digamma(0, evaluate=False)
digamma(0)
>>> digamma(z, evaluate=False)
digamma(z)


gamma

Gamma function.

lowergamma

Lower incomplete gamma function.

uppergamma

Upper incomplete gamma function.

polygamma

Polygamma function.

loggamma

Log Gamma function.

trigamma

Trigamma function.

beta

Euler Beta function.

References

class sympy.functions.special.gamma_functions.trigamma(z)[source]#

The trigamma function is the second derivative of the loggamma function

$\psi^{(1)}(z) := \frac{\mathrm{d}^{2}}{\mathrm{d} z^{2}} \log\Gamma(z).$

In this case, trigamma(z) = polygamma(1, z).

Examples

>>> from sympy import trigamma
>>> trigamma(0)
zoo
>>> from sympy import Symbol
>>> z = Symbol('z')
>>> trigamma(z)
polygamma(1, z)


To retain trigamma as it is:

>>> trigamma(0, evaluate=False)
trigamma(0)
>>> trigamma(z, evaluate=False)
trigamma(z)


gamma

Gamma function.

lowergamma

Lower incomplete gamma function.

uppergamma

Upper incomplete gamma function.

polygamma

Polygamma function.

loggamma

Log Gamma function.

digamma

Digamma function.

beta

Euler Beta function.

References

class sympy.functions.special.gamma_functions.uppergamma(a, z)[source]#

The upper incomplete gamma function.

Explanation

It can be defined as the meromorphic continuation of

$\Gamma(s, x) := \int_x^\infty t^{s-1} e^{-t} \mathrm{d}t = \Gamma(s) - \gamma(s, x).$

where $$\gamma(s, x)$$ is the lower incomplete gamma function, lowergamma. This can be shown to be the same as

$\Gamma(s, x) = \Gamma(s) - \frac{x^s}{s} {}_1F_1\left({s \atop s+1} \middle| -x\right),$

where $${}_1F_1$$ is the (confluent) hypergeometric function.

The upper incomplete gamma function is also essentially equivalent to the generalized exponential integral:

$\operatorname{E}_{n}(x) = \int_{1}^{\infty}{\frac{e^{-xt}}{t^n} \, dt} = x^{n-1}\Gamma(1-n,x).$

Examples

>>> from sympy import uppergamma, S
>>> from sympy.abc import s, x
>>> uppergamma(s, x)
uppergamma(s, x)
>>> uppergamma(3, x)
2*(x**2/2 + x + 1)*exp(-x)
>>> uppergamma(-S(1)/2, x)
-2*sqrt(pi)*erfc(sqrt(x)) + 2*exp(-x)/sqrt(x)
>>> uppergamma(-2, x)
expint(3, x)/x**2


gamma

Gamma function.

lowergamma

Lower incomplete gamma function.

polygamma

Polygamma function.

loggamma

Log Gamma function.

digamma

Digamma function.

trigamma

Trigamma function.

beta

Euler Beta function.

References

[R327]

Abramowitz, Milton; Stegun, Irene A., eds. (1965), Chapter 6, Section 5, Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables

class sympy.functions.special.gamma_functions.lowergamma(a, x)[source]#

The lower incomplete gamma function.

Explanation

It can be defined as the meromorphic continuation of

$\gamma(s, x) := \int_0^x t^{s-1} e^{-t} \mathrm{d}t = \Gamma(s) - \Gamma(s, x).$

This can be shown to be the same as

$\gamma(s, x) = \frac{x^s}{s} {}_1F_1\left({s \atop s+1} \middle| -x\right),$

where $${}_1F_1$$ is the (confluent) hypergeometric function.

Examples

>>> from sympy import lowergamma, S
>>> from sympy.abc import s, x
>>> lowergamma(s, x)
lowergamma(s, x)
>>> lowergamma(3, x)
-2*(x**2/2 + x + 1)*exp(-x) + 2
>>> lowergamma(-S(1)/2, x)
-2*sqrt(pi)*erf(sqrt(x)) - 2*exp(-x)/sqrt(x)


gamma

Gamma function.

uppergamma

Upper incomplete gamma function.

polygamma

Polygamma function.

loggamma

Log Gamma function.

digamma

Digamma function.

trigamma

Trigamma function.

beta

Euler Beta function.

References

[R333]

Abramowitz, Milton; Stegun, Irene A., eds. (1965), Chapter 6, Section 5, Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables

class sympy.functions.special.gamma_functions.multigamma(x, p)[source]#

The multivariate gamma function is a generalization of the gamma function

$\Gamma_p(z) = \pi^{p(p-1)/4}\prod_{k=1}^p \Gamma[z + (1 - k)/2].$

In a special case, multigamma(x, 1) = gamma(x).

Parameters:

p : order or dimension of the multivariate gamma function

Examples

>>> from sympy import S, multigamma
>>> from sympy import Symbol
>>> x = Symbol('x')
>>> p = Symbol('p', positive=True, integer=True)

>>> multigamma(x, p)
pi**(p*(p - 1)/4)*Product(gamma(-_k/2 + x + 1/2), (_k, 1, p))


Several special values are known:

>>> multigamma(1, 1)
1
>>> multigamma(4, 1)
6
>>> multigamma(S(3)/2, 1)
sqrt(pi)/2


Writing multigamma in terms of the gamma function:

>>> multigamma(x, 1)
gamma(x)

>>> multigamma(x, 2)
sqrt(pi)*gamma(x)*gamma(x - 1/2)

>>> multigamma(x, 3)
pi**(3/2)*gamma(x)*gamma(x - 1)*gamma(x - 1/2)


References

class sympy.functions.special.beta_functions.beta(x, y=None)[source]#

The beta integral is called the Eulerian integral of the first kind by Legendre:

$\mathrm{B}(x,y) \int^{1}_{0} t^{x-1} (1-t)^{y-1} \mathrm{d}t.$

Explanation

The Beta function or Euler’s first integral is closely associated with the gamma function. The Beta function is often used in probability theory and mathematical statistics. It satisfies properties like:

$\begin{split}\mathrm{B}(a,1) = \frac{1}{a} \\ \mathrm{B}(a,b) = \mathrm{B}(b,a) \\ \mathrm{B}(a,b) = \frac{\Gamma(a) \Gamma(b)}{\Gamma(a+b)}\end{split}$

Therefore for integral values of $$a$$ and $$b$$:

$\mathrm{B} = \frac{(a-1)! (b-1)!}{(a+b-1)!}$

A special case of the Beta function when $$x = y$$ is the Central Beta function. It satisfies properties like:

$\mathrm{B}(x) = 2^{1 - 2x}\mathrm{B}(x, \frac{1}{2}) \mathrm{B}(x) = 2^{1 - 2x} cos(\pi x) \mathrm{B}(\frac{1}{2} - x, x) \mathrm{B}(x) = \int_{0}^{1} \frac{t^x}{(1 + t)^{2x}} dt \mathrm{B}(x) = \frac{2}{x} \prod_{n = 1}^{\infty} \frac{n(n + 2x)}{(n + x)^2}$

Examples

>>> from sympy import I, pi
>>> from sympy.abc import x, y


The Beta function obeys the mirror symmetry:

>>> from sympy import beta, conjugate
>>> conjugate(beta(x, y))
beta(conjugate(x), conjugate(y))


Differentiation with respect to both $$x$$ and $$y$$ is supported:

>>> from sympy import beta, diff
>>> diff(beta(x, y), x)
(polygamma(0, x) - polygamma(0, x + y))*beta(x, y)

>>> diff(beta(x, y), y)
(polygamma(0, y) - polygamma(0, x + y))*beta(x, y)

>>> diff(beta(x), x)
2*(polygamma(0, x) - polygamma(0, 2*x))*beta(x, x)


We can numerically evaluate the Beta function to arbitrary precision for any complex numbers x and y:

>>> from sympy import beta
>>> beta(pi).evalf(40)
0.02671848900111377452242355235388489324562

>>> beta(1 + I).evalf(20)
-0.2112723729365330143 - 0.7655283165378005676*I


gamma

Gamma function.

uppergamma

Upper incomplete gamma function.

lowergamma

Lower incomplete gamma function.

polygamma

Polygamma function.

loggamma

Log Gamma function.

digamma

Digamma function.

trigamma

Trigamma function.

References

## Error Functions and Fresnel Integrals#

class sympy.functions.special.error_functions.erf(arg)[source]#

The Gauss error function.

Explanation

This function is defined as:

$\mathrm{erf}(x) = \frac{2}{\sqrt{\pi}} \int_0^x e^{-t^2} \mathrm{d}t.$

Examples

>>> from sympy import I, oo, erf
>>> from sympy.abc import z


Several special values are known:

>>> erf(0)
0
>>> erf(oo)
1
>>> erf(-oo)
-1
>>> erf(I*oo)
oo*I
>>> erf(-I*oo)
-oo*I


In general one can pull out factors of -1 and $$I$$ from the argument:

>>> erf(-z)
-erf(z)


The error function obeys the mirror symmetry:

>>> from sympy import conjugate
>>> conjugate(erf(z))
erf(conjugate(z))


Differentiation with respect to $$z$$ is supported:

>>> from sympy import diff
>>> diff(erf(z), z)
2*exp(-z**2)/sqrt(pi)


We can numerically evaluate the error function to arbitrary precision on the whole complex plane:

>>> erf(4).evalf(30)
0.999999984582742099719981147840

>>> erf(-4*I).evalf(30)
-1296959.73071763923152794095062*I


erfc

Complementary error function.

erfi

Imaginary error function.

erf2

Two-argument error function.

erfinv

Inverse error function.

erfcinv

Inverse Complementary error function.

erf2inv

Inverse two-argument error function.

References

inverse(argindex=1)[source]#

Returns the inverse of this function.

class sympy.functions.special.error_functions.erfc(arg)[source]#

Complementary Error Function.

Explanation

The function is defined as:

$\mathrm{erfc}(x) = \frac{2}{\sqrt{\pi}} \int_x^\infty e^{-t^2} \mathrm{d}t$

Examples

>>> from sympy import I, oo, erfc
>>> from sympy.abc import z


Several special values are known:

>>> erfc(0)
1
>>> erfc(oo)
0
>>> erfc(-oo)
2
>>> erfc(I*oo)
-oo*I
>>> erfc(-I*oo)
oo*I


The error function obeys the mirror symmetry:

>>> from sympy import conjugate
>>> conjugate(erfc(z))
erfc(conjugate(z))


Differentiation with respect to $$z$$ is supported:

>>> from sympy import diff
>>> diff(erfc(z), z)
-2*exp(-z**2)/sqrt(pi)


It also follows

>>> erfc(-z)
2 - erfc(z)


We can numerically evaluate the complementary error function to arbitrary precision on the whole complex plane:

>>> erfc(4).evalf(30)
0.0000000154172579002800188521596734869

>>> erfc(4*I).evalf(30)
1.0 - 1296959.73071763923152794095062*I


erf

Gaussian error function.

erfi

Imaginary error function.

erf2

Two-argument error function.

erfinv

Inverse error function.

erfcinv

Inverse Complementary error function.

erf2inv

Inverse two-argument error function.

References

inverse(argindex=1)[source]#

Returns the inverse of this function.

class sympy.functions.special.error_functions.erfi(z)[source]#

Imaginary error function.

Explanation

The function erfi is defined as:

$\mathrm{erfi}(x) = \frac{2}{\sqrt{\pi}} \int_0^x e^{t^2} \mathrm{d}t$

Examples

>>> from sympy import I, oo, erfi
>>> from sympy.abc import z


Several special values are known:

>>> erfi(0)
0
>>> erfi(oo)
oo
>>> erfi(-oo)
-oo
>>> erfi(I*oo)
I
>>> erfi(-I*oo)
-I


In general one can pull out factors of -1 and $$I$$ from the argument:

>>> erfi(-z)
-erfi(z)

>>> from sympy import conjugate
>>> conjugate(erfi(z))
erfi(conjugate(z))


Differentiation with respect to $$z$$ is supported:

>>> from sympy import diff
>>> diff(erfi(z), z)
2*exp(z**2)/sqrt(pi)


We can numerically evaluate the imaginary error function to arbitrary precision on the whole complex plane:

>>> erfi(2).evalf(30)
18.5648024145755525987042919132

>>> erfi(-2*I).evalf(30)
-0.995322265018952734162069256367*I


erf

Gaussian error function.

erfc

Complementary error function.

erf2

Two-argument error function.

erfinv

Inverse error function.

erfcinv

Inverse Complementary error function.

erf2inv

Inverse two-argument error function.

References

class sympy.functions.special.error_functions.erf2(x, y)[source]#

Two-argument error function.

Explanation

This function is defined as:

$\mathrm{erf2}(x, y) = \frac{2}{\sqrt{\pi}} \int_x^y e^{-t^2} \mathrm{d}t$

Examples

>>> from sympy import oo, erf2
>>> from sympy.abc import x, y


Several special values are known:

>>> erf2(0, 0)
0
>>> erf2(x, x)
0
>>> erf2(x, oo)
1 - erf(x)
>>> erf2(x, -oo)
-erf(x) - 1
>>> erf2(oo, y)
erf(y) - 1
>>> erf2(-oo, y)
erf(y) + 1


In general one can pull out factors of -1:

>>> erf2(-x, -y)
-erf2(x, y)


The error function obeys the mirror symmetry:

>>> from sympy import conjugate
>>> conjugate(erf2(x, y))
erf2(conjugate(x), conjugate(y))


Differentiation with respect to $$x$$, $$y$$ is supported:

>>> from sympy import diff
>>> diff(erf2(x, y), x)
-2*exp(-x**2)/sqrt(pi)
>>> diff(erf2(x, y), y)
2*exp(-y**2)/sqrt(pi)


erf

Gaussian error function.

erfc

Complementary error function.

erfi

Imaginary error function.

erfinv

Inverse error function.

erfcinv

Inverse Complementary error function.

erf2inv

Inverse two-argument error function.

References

class sympy.functions.special.error_functions.erfinv(z)[source]#

Inverse Error Function. The erfinv function is defined as:

$\mathrm{erf}(x) = y \quad \Rightarrow \quad \mathrm{erfinv}(y) = x$

Examples

>>> from sympy import erfinv
>>> from sympy.abc import x


Several special values are known:

>>> erfinv(0)
0
>>> erfinv(1)
oo


Differentiation with respect to $$x$$ is supported:

>>> from sympy import diff
>>> diff(erfinv(x), x)
sqrt(pi)*exp(erfinv(x)**2)/2


We can numerically evaluate the inverse error function to arbitrary precision on [-1, 1]:

>>> erfinv(0.2).evalf(30)
0.179143454621291692285822705344


erf

Gaussian error function.

erfc

Complementary error function.

erfi

Imaginary error function.

erf2

Two-argument error function.

erfcinv

Inverse Complementary error function.

erf2inv

Inverse two-argument error function.

References

inverse(argindex=1)[source]#

Returns the inverse of this function.

class sympy.functions.special.error_functions.erfcinv(z)[source]#

Inverse Complementary Error Function. The erfcinv function is defined as:

$\mathrm{erfc}(x) = y \quad \Rightarrow \quad \mathrm{erfcinv}(y) = x$

Examples

>>> from sympy import erfcinv
>>> from sympy.abc import x


Several special values are known:

>>> erfcinv(1)
0
>>> erfcinv(0)
oo


Differentiation with respect to $$x$$ is supported:

>>> from sympy import diff
>>> diff(erfcinv(x), x)
-sqrt(pi)*exp(erfcinv(x)**2)/2


erf

Gaussian error function.

erfc

Complementary error function.

erfi

Imaginary error function.

erf2

Two-argument error function.

erfinv

Inverse error function.

erf2inv

Inverse two-argument error function.

References

inverse(argindex=1)[source]#

Returns the inverse of this function.

class sympy.functions.special.error_functions.erf2inv(x, y)[source]#

Two-argument Inverse error function. The erf2inv function is defined as:

$\mathrm{erf2}(x, w) = y \quad \Rightarrow \quad \mathrm{erf2inv}(x, y) = w$

Examples

>>> from sympy import erf2inv, oo
>>> from sympy.abc import x, y


Several special values are known:

>>> erf2inv(0, 0)
0
>>> erf2inv(1, 0)
1
>>> erf2inv(0, 1)
oo
>>> erf2inv(0, y)
erfinv(y)
>>> erf2inv(oo, y)
erfcinv(-y)


Differentiation with respect to $$x$$ and $$y$$ is supported:

>>> from sympy import diff
>>> diff(erf2inv(x, y), x)
exp(-x**2 + erf2inv(x, y)**2)
>>> diff(erf2inv(x, y), y)
sqrt(pi)*exp(erf2inv(x, y)**2)/2


erf

Gaussian error function.

erfc

Complementary error function.

erfi

Imaginary error function.

erf2

Two-argument error function.

erfinv

Inverse error function.

erfcinv

Inverse complementary error function.

References

class sympy.functions.special.error_functions.FresnelIntegral(z)[source]#

Base class for the Fresnel integrals.

class sympy.functions.special.error_functions.fresnels(z)[source]#

Fresnel integral S.

Explanation

This function is defined by

$\operatorname{S}(z) = \int_0^z \sin{\frac{\pi}{2} t^2} \mathrm{d}t.$

It is an entire function.

Examples

>>> from sympy import I, oo, fresnels
>>> from sympy.abc import z


Several special values are known:

>>> fresnels(0)
0
>>> fresnels(oo)
1/2
>>> fresnels(-oo)
-1/2
>>> fresnels(I*oo)
-I/2
>>> fresnels(-I*oo)
I/2


In general one can pull out factors of -1 and $$i$$ from the argument:

>>> fresnels(-z)
-fresnels(z)
>>> fresnels(I*z)
-I*fresnels(z)


The Fresnel S integral obeys the mirror symmetry $$\overline{S(z)} = S(\bar{z})$$:

>>> from sympy import conjugate
>>> conjugate(fresnels(z))
fresnels(conjugate(z))


Differentiation with respect to $$z$$ is supported:

>>> from sympy import diff
>>> diff(fresnels(z), z)
sin(pi*z**2/2)


Defining the Fresnel functions via an integral:

>>> from sympy import integrate, pi, sin, expand_func
>>> integrate(sin(pi*z**2/2), z)
3*fresnels(z)*gamma(3/4)/(4*gamma(7/4))
>>> expand_func(integrate(sin(pi*z**2/2), z))
fresnels(z)


We can numerically evaluate the Fresnel integral to arbitrary precision on the whole complex plane:

>>> fresnels(2).evalf(30)
0.343415678363698242195300815958

>>> fresnels(-2*I).evalf(30)
0.343415678363698242195300815958*I


fresnelc

Fresnel cosine integral.

References

[R362]

The converging factors for the fresnel integrals by John W. Wrench Jr. and Vicki Alley

class sympy.functions.special.error_functions.fresnelc(z)[source]#

Fresnel integral C.

Explanation

This function is defined by

$\operatorname{C}(z) = \int_0^z \cos{\frac{\pi}{2} t^2} \mathrm{d}t.$

It is an entire function.

Examples

>>> from sympy import I, oo, fresnelc
>>> from sympy.abc import z


Several special values are known:

>>> fresnelc(0)
0
>>> fresnelc(oo)
1/2
>>> fresnelc(-oo)
-1/2
>>> fresnelc(I*oo)
I/2
>>> fresnelc(-I*oo)
-I/2


In general one can pull out factors of -1 and $$i$$ from the argument:

>>> fresnelc(-z)
-fresnelc(z)
>>> fresnelc(I*z)
I*fresnelc(z)


The Fresnel C integral obeys the mirror symmetry $$\overline{C(z)} = C(\bar{z})$$:

>>> from sympy import conjugate
>>> conjugate(fresnelc(z))
fresnelc(conjugate(z))


Differentiation with respect to $$z$$ is supported:

>>> from sympy import diff
>>> diff(fresnelc(z), z)
cos(pi*z**2/2)


Defining the Fresnel functions via an integral:

>>> from sympy import integrate, pi, cos, expand_func
>>> integrate(cos(pi*z**2/2), z)
fresnelc(z)*gamma(1/4)/(4*gamma(5/4))
>>> expand_func(integrate(cos(pi*z**2/2), z))
fresnelc(z)


We can numerically evaluate the Fresnel integral to arbitrary precision on the whole complex plane:

>>> fresnelc(2).evalf(30)
0.488253406075340754500223503357

>>> fresnelc(-2*I).evalf(30)
-0.488253406075340754500223503357*I


fresnels

Fresnel sine integral.

References

[R367]

The converging factors for the fresnel integrals by John W. Wrench Jr. and Vicki Alley

## Exponential, Logarithmic and Trigonometric Integrals#

class sympy.functions.special.error_functions.Ei(z)[source]#

The classical exponential integral.

Explanation

For use in SymPy, this function is defined as

$\operatorname{Ei}(x) = \sum_{n=1}^\infty \frac{x^n}{n\, n!} + \log(x) + \gamma,$

where $$\gamma$$ is the Euler-Mascheroni constant.

If $$x$$ is a polar number, this defines an analytic function on the Riemann surface of the logarithm. Otherwise this defines an analytic function in the cut plane $$\mathbb{C} \setminus (-\infty, 0]$$.

Background

The name exponential integral comes from the following statement:

$\operatorname{Ei}(x) = \int_{-\infty}^x \frac{e^t}{t} \mathrm{d}t$

If the integral is interpreted as a Cauchy principal value, this statement holds for $$x > 0$$ and $$\operatorname{Ei}(x)$$ as defined above.

Examples

>>> from sympy import Ei, polar_lift, exp_polar, I, pi
>>> from sympy.abc import x

>>> Ei(-1)
Ei(-1)


This yields a real value:

>>> Ei(-1).n(chop=True)
-0.219383934395520


On the other hand the analytic continuation is not real:

>>> Ei(polar_lift(-1)).n(chop=True)
-0.21938393439552 + 3.14159265358979*I


The exponential integral has a logarithmic branch point at the origin:

>>> Ei(x*exp_polar(2*I*pi))
Ei(x) + 2*I*pi


Differentiation is supported:

>>> Ei(x).diff(x)
exp(x)/x


The exponential integral is related to many other special functions. For example:

>>> from sympy import expint, Shi
>>> Ei(x).rewrite(expint)
-expint(1, x*exp_polar(I*pi)) - I*pi
>>> Ei(x).rewrite(Shi)
Chi(x) + Shi(x)


expint

Generalised exponential integral.

E1

Special case of the generalised exponential integral.

li

Logarithmic integral.

Li

Offset logarithmic integral.

Si

Sine integral.

Ci

Cosine integral.

Shi

Hyperbolic sine integral.

Chi

Hyperbolic cosine integral.

uppergamma

Upper incomplete gamma function.

References

[R370]

Abramowitz & Stegun, section 5: http://people.math.sfu.ca/~cbm/aands/page_228.htm

class sympy.functions.special.error_functions.expint(nu, z)[source]#

Generalized exponential integral.

Explanation

This function is defined as

$\operatorname{E}_\nu(z) = z^{\nu - 1} \Gamma(1 - \nu, z),$

where $$\Gamma(1 - \nu, z)$$ is the upper incomplete gamma function (uppergamma).

Hence for $$z$$ with positive real part we have

$\operatorname{E}_\nu(z) = \int_1^\infty \frac{e^{-zt}}{t^\nu} \mathrm{d}t,$

which explains the name.

The representation as an incomplete gamma function provides an analytic continuation for $$\operatorname{E}_\nu(z)$$. If $$\nu$$ is a non-positive integer, the exponential integral is thus an unbranched function of $$z$$, otherwise there is a branch point at the origin. Refer to the incomplete gamma function documentation for details of the branching behavior.

Examples

>>> from sympy import expint, S
>>> from sympy.abc import nu, z


Differentiation is supported. Differentiation with respect to $$z$$ further explains the name: for integral orders, the exponential integral is an iterated integral of the exponential function.

>>> expint(nu, z).diff(z)
-expint(nu - 1, z)


Differentiation with respect to $$\nu$$ has no classical expression:

>>> expint(nu, z).diff(nu)
-z**(nu - 1)*meijerg(((), (1, 1)), ((0, 0, 1 - nu), ()), z)


At non-postive integer orders, the exponential integral reduces to the exponential function:

>>> expint(0, z)
exp(-z)/z
>>> expint(-1, z)
exp(-z)/z + exp(-z)/z**2


At half-integers it reduces to error functions:

>>> expint(S(1)/2, z)
sqrt(pi)*erfc(sqrt(z))/sqrt(z)


At positive integer orders it can be rewritten in terms of exponentials and expint(1, z). Use expand_func() to do this:

>>> from sympy import expand_func
>>> expand_func(expint(5, z))
z**4*expint(1, z)/24 + (-z**3 + z**2 - 2*z + 6)*exp(-z)/24


The generalised exponential integral is essentially equivalent to the incomplete gamma function:

>>> from sympy import uppergamma
>>> expint(nu, z).rewrite(uppergamma)
z**(nu - 1)*uppergamma(1 - nu, z)


As such it is branched at the origin:

>>> from sympy import exp_polar, pi, I
>>> expint(4, z*exp_polar(2*pi*I))
I*pi*z**3/3 + expint(4, z)
>>> expint(nu, z*exp_polar(2*pi*I))
z**(nu - 1)*(exp(2*I*pi*nu) - 1)*gamma(1 - nu) + expint(nu, z)


Ei

Another related function called exponential integral.

E1

The classical case, returns expint(1, z).

li

Logarithmic integral.

Li

Offset logarithmic integral.

Si

Sine integral.

Ci

Cosine integral.

Shi

Hyperbolic sine integral.

Chi

Hyperbolic cosine integral.

uppergamma

References

sympy.functions.special.error_functions.E1(z)[source]#

Classical case of the generalized exponential integral.

Explanation

This is equivalent to expint(1, z).

Examples

>>> from sympy import E1
>>> E1(0)
expint(1, 0)

>>> E1(5)
expint(1, 5)


Ei

Exponential integral.

expint

Generalised exponential integral.

li

Logarithmic integral.

Li

Offset logarithmic integral.

Si

Sine integral.

Ci

Cosine integral.

Shi

Hyperbolic sine integral.

Chi

Hyperbolic cosine integral.

class sympy.functions.special.error_functions.li(z)[source]#

The classical logarithmic integral.

Explanation

For use in SymPy, this function is defined as

$\operatorname{li}(x) = \int_0^x \frac{1}{\log(t)} \mathrm{d}t \,.$

Examples

>>> from sympy import I, oo, li
>>> from sympy.abc import z


Several special values are known:

>>> li(0)
0
>>> li(1)
-oo
>>> li(oo)
oo


Differentiation with respect to $$z$$ is supported:

>>> from sympy import diff
>>> diff(li(z), z)
1/log(z)


Defining the li function via an integral: >>> from sympy import integrate >>> integrate(li(z)) z*li(z) - Ei(2*log(z))

>>> integrate(li(z),z)
z*li(z) - Ei(2*log(z))


The logarithmic integral can also be defined in terms of Ei:

>>> from sympy import Ei
>>> li(z).rewrite(Ei)
Ei(log(z))
>>> diff(li(z).rewrite(Ei), z)
1/log(z)


We can numerically evaluate the logarithmic integral to arbitrary precision on the whole complex plane (except the singular points):

>>> li(2).evalf(30)
1.04516378011749278484458888919

>>> li(2*I).evalf(30)
1.0652795784357498247001125598 + 3.08346052231061726610939702133*I


We can even compute Soldner’s constant by the help of mpmath:

>>> from mpmath import findroot
>>> findroot(li, 2)
1.45136923488338


Further transformations include rewriting li in terms of the trigonometric integrals Si, Ci, Shi and Chi:

>>> from sympy import Si, Ci, Shi, Chi
>>> li(z).rewrite(Si)
-log(I*log(z)) - log(1/log(z))/2 + log(log(z))/2 + Ci(I*log(z)) + Shi(log(z))
>>> li(z).rewrite(Ci)
-log(I*log(z)) - log(1/log(z))/2 + log(log(z))/2 + Ci(I*log(z)) + Shi(log(z))
>>> li(z).rewrite(Shi)
-log(1/log(z))/2 + log(log(z))/2 + Chi(log(z)) - Shi(log(z))
>>> li(z).rewrite(Chi)
-log(1/log(z))/2 + log(log(z))/2 + Chi(log(z)) - Shi(log(z))


Li

Offset logarithmic integral.

Ei

Exponential integral.

expint

Generalised exponential integral.

E1

Special case of the generalised exponential integral.

Si

Sine integral.

Ci

Cosine integral.

Shi

Hyperbolic sine integral.

Chi

Hyperbolic cosine integral.

References

class sympy.functions.special.error_functions.Li(z)[source]#

The offset logarithmic integral.

Explanation

For use in SymPy, this function is defined as

$\operatorname{Li}(x) = \operatorname{li}(x) - \operatorname{li}(2)$

Examples

>>> from sympy import Li
>>> from sympy.abc import z


The following special value is known:

>>> Li(2)
0


Differentiation with respect to $$z$$ is supported:

>>> from sympy import diff
>>> diff(Li(z), z)
1/log(z)


The shifted logarithmic integral can be written in terms of $$li(z)$$:

>>> from sympy import li
>>> Li(z).rewrite(li)
li(z) - li(2)


We can numerically evaluate the logarithmic integral to arbitrary precision on the whole complex plane (except the singular points):

>>> Li(2).evalf(30)
0

>>> Li(4).evalf(30)
1.92242131492155809316615998938


li

Logarithmic integral.

Ei

Exponential integral.

expint

Generalised exponential integral.

E1

Special case of the generalised exponential integral.

Si

Sine integral.

Ci

Cosine integral.

Shi

Hyperbolic sine integral.

Chi

Hyperbolic cosine integral.

References

class sympy.functions.special.error_functions.Si(z)[source]#

Sine integral.

Explanation

This function is defined by

$\operatorname{Si}(z) = \int_0^z \frac{\sin{t}}{t} \mathrm{d}t.$

It is an entire function.

Examples

>>> from sympy import Si
>>> from sympy.abc import z


The sine integral is an antiderivative of $$sin(z)/z$$:

>>> Si(z).diff(z)
sin(z)/z


It is unbranched:

>>> from sympy import exp_polar, I, pi
>>> Si(z*exp_polar(2*I*pi))
Si(z)


Sine integral behaves much like ordinary sine under multiplication by I:

>>> Si(I*z)
I*Shi(z)
>>> Si(-z)
-Si(z)


It can also be expressed in terms of exponential integrals, but beware that the latter is branched:

>>> from sympy import expint
>>> Si(z).rewrite(expint)
-I*(-expint(1, z*exp_polar(-I*pi/2))/2 +
expint(1, z*exp_polar(I*pi/2))/2) + pi/2


It can be rewritten in the form of sinc function (by definition):

>>> from sympy import sinc
>>> Si(z).rewrite(sinc)
Integral(sinc(t), (t, 0, z))


Ci

Cosine integral.

Shi

Hyperbolic sine integral.

Chi

Hyperbolic cosine integral.

Ei

Exponential integral.

expint

Generalised exponential integral.

sinc

unnormalized sinc function

E1

Special case of the generalised exponential integral.

li

Logarithmic integral.

Li

Offset logarithmic integral.

References

class sympy.functions.special.error_functions.Ci(z)[source]#

Cosine integral.

Explanation

This function is defined for positive $$x$$ by

$\operatorname{Ci}(x) = \gamma + \log{x} + \int_0^x \frac{\cos{t} - 1}{t} \mathrm{d}t = -\int_x^\infty \frac{\cos{t}}{t} \mathrm{d}t,$

where $$\gamma$$ is the Euler-Mascheroni constant.

We have

$\operatorname{Ci}(z) = -\frac{\operatorname{E}_1\left(e^{i\pi/2} z\right) + \operatorname{E}_1\left(e^{-i \pi/2} z\right)}{2}$

which holds for all polar $$z$$ and thus provides an analytic continuation to the Riemann surface of the logarithm.

The formula also holds as stated for $$z \in \mathbb{C}$$ with $$\Re(z) > 0$$. By lifting to the principal branch, we obtain an analytic function on the cut complex plane.

Examples

>>> from sympy import Ci
>>> from sympy.abc import z


The cosine integral is a primitive of $$\cos(z)/z$$:

>>> Ci(z).diff(z)
cos(z)/z


It has a logarithmic branch point at the origin:

>>> from sympy import exp_polar, I, pi
>>> Ci(z*exp_polar(2*I*pi))
Ci(z) + 2*I*pi


The cosine integral behaves somewhat like ordinary $$\cos$$ under multiplication by $$i$$:

>>> from sympy import polar_lift
>>> Ci(polar_lift(I)*z)
Chi(z) + I*pi/2
>>> Ci(polar_lift(-1)*z)
Ci(z) + I*pi


It can also be expressed in terms of exponential integrals:

>>> from sympy import expint
>>> Ci(z).rewrite(expint)
-expint(1, z*exp_polar(-I*pi/2))/2 - expint(1, z*exp_polar(I*pi/2))/2


Si

Sine integral.

Shi

Hyperbolic sine integral.

Chi

Hyperbolic cosine integral.

Ei

Exponential integral.

expint

Generalised exponential integral.

E1

Special case of the generalised exponential integral.

li

Logarithmic integral.

Li

Offset logarithmic integral.

References

class sympy.functions.special.error_functions.Shi(z)[source]#

Sinh integral.

Explanation

This function is defined by

$\operatorname{Shi}(z) = \int_0^z \frac{\sinh{t}}{t} \mathrm{d}t.$

It is an entire function.

Examples

>>> from sympy import Shi
>>> from sympy.abc import z


The Sinh integral is a primitive of $$\sinh(z)/z$$:

>>> Shi(z).diff(z)
sinh(z)/z


It is unbranched:

>>> from sympy import exp_polar, I, pi
>>> Shi(z*exp_polar(2*I*pi))
Shi(z)


The $$\sinh$$ integral behaves much like ordinary $$\sinh$$ under multiplication by $$i$$:

>>> Shi(I*z)
I*Si(z)
>>> Shi(-z)
-Shi(z)


It can also be expressed in terms of exponential integrals, but beware that the latter is branched:

>>> from sympy import expint
>>> Shi(z).rewrite(expint)
expint(1, z)/2 - expint(1, z*exp_polar(I*pi))/2 - I*pi/2


Si

Sine integral.

Ci

Cosine integral.

Chi

Hyperbolic cosine integral.

Ei

Exponential integral.

expint

Generalised exponential integral.

E1

Special case of the generalised exponential integral.

li

Logarithmic integral.

Li

Offset logarithmic integral.

References

class sympy.functions.special.error_functions.Chi(z)[source]#

Cosh integral.

Explanation

This function is defined for positive $$x$$ by

$\operatorname{Chi}(x) = \gamma + \log{x} + \int_0^x \frac{\cosh{t} - 1}{t} \mathrm{d}t,$

where $$\gamma$$ is the Euler-Mascheroni constant.

We have

$\operatorname{Chi}(z) = \operatorname{Ci}\left(e^{i \pi/2}z\right) - i\frac{\pi}{2},$

which holds for all polar $$z$$ and thus provides an analytic continuation to the Riemann surface of the logarithm. By lifting to the principal branch we obtain an analytic function on the cut complex plane.

Examples

>>> from sympy import Chi
>>> from sympy.abc import z


The $$\cosh$$ integral is a primitive of $$\cosh(z)/z$$:

>>> Chi(z).diff(z)
cosh(z)/z


It has a logarithmic branch point at the origin:

>>> from sympy import exp_polar, I, pi
>>> Chi(z*exp_polar(2*I*pi))
Chi(z) + 2*I*pi


The $$\cosh$$ integral behaves somewhat like ordinary $$\cosh$$ under multiplication by $$i$$:

>>> from sympy import polar_lift
>>> Chi(polar_lift(I)*z)
Ci(z) + I*pi/2
>>> Chi(polar_lift(-1)*z)
Chi(z) + I*pi


It can also be expressed in terms of exponential integrals:

>>> from sympy import expint
>>> Chi(z).rewrite(expint)
-expint(1, z)/2 - expint(1, z*exp_polar(I*pi))/2 - I*pi/2


Si

Sine integral.

Ci

Cosine integral.

Shi

Hyperbolic sine integral.

Ei

Exponential integral.

expint

Generalised exponential integral.

E1

Special case of the generalised exponential integral.

li

Logarithmic integral.

Li

Offset logarithmic integral.

References

## Bessel Type Functions#

class sympy.functions.special.bessel.BesselBase(nu, z)[source]#

Abstract base class for Bessel-type functions.

This class is meant to reduce code duplication. All Bessel-type functions can 1) be differentiated, with the derivatives expressed in terms of similar functions, and 2) be rewritten in terms of other Bessel-type functions.

Here, Bessel-type functions are assumed to have one complex parameter.

To use this base class, define class attributes _a and _b such that 2*F_n' = -_a*F_{n+1} + b*F_{n-1}.

property argument#

The argument of the Bessel-type function.

property order#

The order of the Bessel-type function.

class sympy.functions.special.bessel.besselj(nu, z)[source]#

Bessel function of the first kind.

Explanation

The Bessel $$J$$ function of order $$\nu$$ is defined to be the function satisfying Bessel’s differential equation

$z^2 \frac{\mathrm{d}^2 w}{\mathrm{d}z^2} + z \frac{\mathrm{d}w}{\mathrm{d}z} + (z^2 - \nu^2) w = 0,$

with Laurent expansion

$J_\nu(z) = z^\nu \left(\frac{1}{\Gamma(\nu + 1) 2^\nu} + O(z^2) \right),$

if $$\nu$$ is not a negative integer. If $$\nu=-n \in \mathbb{Z}_{<0}$$ is a negative integer, then the definition is

$J_{-n}(z) = (-1)^n J_n(z).$

Examples

Create a Bessel function object:

>>> from sympy import besselj, jn
>>> from sympy.abc import z, n
>>> b = besselj(n, z)


Differentiate it:

>>> b.diff(z)
besselj(n - 1, z)/2 - besselj(n + 1, z)/2


Rewrite in terms of spherical Bessel functions:

>>> b.rewrite(jn)
sqrt(2)*sqrt(z)*jn(n - 1/2, z)/sqrt(pi)


Access the parameter and argument:

>>> b.order
n
>>> b.argument
z


References

[R385]

Abramowitz, Milton; Stegun, Irene A., eds. (1965), “Chapter 9”, Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables

[R386]

Luke, Y. L. (1969), The Special Functions and Their Approximations, Volume 1

class sympy.functions.special.bessel.bessely(nu, z)[source]#

Bessel function of the second kind.

Explanation

The Bessel $$Y$$ function of order $$\nu$$ is defined as

$Y_\nu(z) = \lim_{\mu \to \nu} \frac{J_\mu(z) \cos(\pi \mu) - J_{-\mu}(z)}{\sin(\pi \mu)},$

where $$J_\mu(z)$$ is the Bessel function of the first kind.

It is a solution to Bessel’s equation, and linearly independent from $$J_\nu$$.

Examples

>>> from sympy import bessely, yn
>>> from sympy.abc import z, n
>>> b = bessely(n, z)
>>> b.diff(z)
bessely(n - 1, z)/2 - bessely(n + 1, z)/2
>>> b.rewrite(yn)
sqrt(2)*sqrt(z)*yn(n - 1/2, z)/sqrt(pi)


References

class sympy.functions.special.bessel.besseli(nu, z)[source]#

Modified Bessel function of the first kind.

Explanation

The Bessel $$I$$ function is a solution to the modified Bessel equation

$z^2 \frac{\mathrm{d}^2 w}{\mathrm{d}z^2} + z \frac{\mathrm{d}w}{\mathrm{d}z} + (z^2 + \nu^2)^2 w = 0.$

It can be defined as

$I_\nu(z) = i^{-\nu} J_\nu(iz),$

where $$J_\nu(z)$$ is the Bessel function of the first kind.

Examples

>>> from sympy import besseli
>>> from sympy.abc import z, n
>>> besseli(n, z).diff(z)
besseli(n - 1, z)/2 + besseli(n + 1, z)/2


References

class sympy.functions.special.bessel.besselk(nu, z)[source]#

Modified Bessel function of the second kind.

Explanation

The Bessel $$K$$ function of order $$\nu$$ is defined as

$K_\nu(z) = \lim_{\mu \to \nu} \frac{\pi}{2} \frac{I_{-\mu}(z) -I_\mu(z)}{\sin(\pi \mu)},$

where $$I_\mu(z)$$ is the modified Bessel function of the first kind.

It is a solution of the modified Bessel equation, and linearly independent from $$Y_\nu$$.

Examples

>>> from sympy import besselk
>>> from sympy.abc import z, n
>>> besselk(n, z).diff(z)
-besselk(n - 1, z)/2 - besselk(n + 1, z)/2


References

class sympy.functions.special.bessel.hankel1(nu, z)[source]#

Hankel function of the first kind.

Explanation

This function is defined as

$H_\nu^{(1)} = J_\nu(z) + iY_\nu(z),$

where $$J_\nu(z)$$ is the Bessel function of the first kind, and $$Y_\nu(z)$$ is the Bessel function of the second kind.

It is a solution to Bessel’s equation.

Examples

>>> from sympy import hankel1
>>> from sympy.abc import z, n
>>> hankel1(n, z).diff(z)
hankel1(n - 1, z)/2 - hankel1(n + 1, z)/2


References

class sympy.functions.special.bessel.hankel2(nu, z)[source]#

Hankel function of the second kind.

Explanation

This function is defined as

$H_\nu^{(2)} = J_\nu(z) - iY_\nu(z),$

where $$J_\nu(z)$$ is the Bessel function of the first kind, and $$Y_\nu(z)$$ is the Bessel function of the second kind.

It is a solution to Bessel’s equation, and linearly independent from $$H_\nu^{(1)}$$.

Examples

>>> from sympy import hankel2
>>> from sympy.abc import z, n
>>> hankel2(n, z).diff(z)
hankel2(n - 1, z)/2 - hankel2(n + 1, z)/2


References

class sympy.functions.special.bessel.jn(nu, z)[source]#

Spherical Bessel function of the first kind.

Explanation

This function is a solution to the spherical Bessel equation

$z^2 \frac{\mathrm{d}^2 w}{\mathrm{d}z^2} + 2z \frac{\mathrm{d}w}{\mathrm{d}z} + (z^2 - \nu(\nu + 1)) w = 0.$

It can be defined as

$j_\nu(z) = \sqrt{\frac{\pi}{2z}} J_{\nu + \frac{1}{2}}(z),$

where $$J_\nu(z)$$ is the Bessel function of the first kind.

The spherical Bessel functions of integral order are calculated using the formula:

$j_n(z) = f_n(z) \sin{z} + (-1)^{n+1} f_{-n-1}(z) \cos{z},$

where the coefficients $$f_n(z)$$ are available as sympy.polys.orthopolys.spherical_bessel_fn().

Examples

>>> from sympy import Symbol, jn, sin, cos, expand_func, besselj, bessely
>>> z = Symbol("z")
>>> nu = Symbol("nu", integer=True)
>>> print(expand_func(jn(0, z)))
sin(z)/z
>>> expand_func(jn(1, z)) == sin(z)/z**2 - cos(z)/z
True
>>> expand_func(jn(3, z))
(-6/z**2 + 15/z**4)*sin(z) + (1/z - 15/z**3)*cos(z)
>>> jn(nu, z).rewrite(besselj)
sqrt(2)*sqrt(pi)*sqrt(1/z)*besselj(nu + 1/2, z)/2
>>> jn(nu, z).rewrite(bessely)
(-1)**nu*sqrt(2)*sqrt(pi)*sqrt(1/z)*bessely(-nu - 1/2, z)/2
>>> jn(2, 5.2+0.3j).evalf(20)
0.099419756723640344491 - 0.054525080242173562897*I


References

class sympy.functions.special.bessel.yn(nu, z)[source]#

Spherical Bessel function of the second kind.

Explanation

This function is another solution to the spherical Bessel equation, and linearly independent from $$j_n$$. It can be defined as

$y_\nu(z) = \sqrt{\frac{\pi}{2z}} Y_{\nu + \frac{1}{2}}(z),$

where $$Y_\nu(z)$$ is the Bessel function of the second kind.

For integral orders $$n$$, $$y_n$$ is calculated using the formula:

$y_n(z) = (-1)^{n+1} j_{-n-1}(z)$

Examples

>>> from sympy import Symbol, yn, sin, cos, expand_func, besselj, bessely
>>> z = Symbol("z")
>>> nu = Symbol("nu", integer=True)
>>> print(expand_func(yn(0, z)))
-cos(z)/z
>>> expand_func(yn(1, z)) == -cos(z)/z**2-sin(z)/z
True
>>> yn(nu, z).rewrite(besselj)
(-1)**(nu + 1)*sqrt(2)*sqrt(pi)*sqrt(1/z)*besselj(-nu - 1/2, z)/2
>>> yn(nu, z).rewrite(bessely)
sqrt(2)*sqrt(pi)*sqrt(1/z)*bessely(nu + 1/2, z)/2
>>> yn(2, 5.2+0.3j).evalf(20)
0.18525034196069722536 + 0.014895573969924817587*I


References

sympy.functions.special.bessel.jn_zeros(n, k, method='sympy', dps=15)[source]#

Zeros of the spherical Bessel function of the first kind.

Parameters:

n : integer

order of Bessel function

k : integer

number of zeros to return

Explanation

This returns an array of zeros of $$jn$$ up to the $$k$$-th zero.

• method = “sympy”: uses mpmath.besseljzero

• method = “scipy”: uses the SciPy’s sph_jn and newton to find all roots, which is faster than computing the zeros using a general numerical solver, but it requires SciPy and only works with low precision floating point numbers. (The function used with method=”sympy” is a recent addition to mpmath; before that a general solver was used.)

Examples

>>> from sympy import jn_zeros
>>> jn_zeros(2, 4, dps=5)
[5.7635, 9.095, 12.323, 15.515]

class sympy.functions.special.bessel.marcumq(m, a, b)[source]#

The Marcum Q-function.

Explanation

The Marcum Q-function is defined by the meromorphic continuation of

$Q_m(a, b) = a^{- m + 1} \int_{b}^{\infty} x^{m} e^{- \frac{a^{2}}{2} - \frac{x^{2}}{2}} I_{m - 1}\left(a x\right)\, dx$

Examples

>>> from sympy import marcumq
>>> from sympy.abc import m, a, b
>>> marcumq(m, a, b)
marcumq(m, a, b)


Special values:

>>> marcumq(m, 0, b)
uppergamma(m, b**2/2)/gamma(m)
>>> marcumq(0, 0, 0)
0
>>> marcumq(0, a, 0)
1 - exp(-a**2/2)
>>> marcumq(1, a, a)
1/2 + exp(-a**2)*besseli(0, a**2)/2
>>> marcumq(2, a, a)
1/2 + exp(-a**2)*besseli(0, a**2)/2 + exp(-a**2)*besseli(1, a**2)


Differentiation with respect to $$a$$ and $$b$$ is supported:

>>> from sympy import diff
>>> diff(marcumq(m, a, b), a)
a*(-marcumq(m, a, b) + marcumq(m + 1, a, b))
>>> diff(marcumq(m, a, b), b)
-a**(1 - m)*b**m*exp(-a**2/2 - b**2/2)*besseli(m - 1, a*b)


References

## Airy Functions#

class sympy.functions.special.bessel.AiryBase(*args)[source]#

Abstract base class for Airy functions.

This class is meant to reduce code duplication.

class sympy.functions.special.bessel.airyai(arg)[source]#

The Airy function $$\operatorname{Ai}$$ of the first kind.

Explanation

The Airy function $$\operatorname{Ai}(z)$$ is defined to be the function satisfying Airy’s differential equation

$\frac{\mathrm{d}^2 w(z)}{\mathrm{d}z^2} - z w(z) = 0.$

Equivalently, for real $$z$$

$\operatorname{Ai}(z) := \frac{1}{\pi} \int_0^\infty \cos\left(\frac{t^3}{3} + z t\right) \mathrm{d}t.$

Examples

Create an Airy function object:

>>> from sympy import airyai
>>> from sympy.abc import z

>>> airyai(z)
airyai(z)


Several special values are known:

>>> airyai(0)
3**(1/3)/(3*gamma(2/3))
>>> from sympy import oo
>>> airyai(oo)
0
>>> airyai(-oo)
0


The Airy function obeys the mirror symmetry:

>>> from sympy import conjugate
>>> conjugate(airyai(z))
airyai(conjugate(z))


Differentiation with respect to $$z$$ is supported:

>>> from sympy import diff
>>> diff(airyai(z), z)
airyaiprime(z)
>>> diff(airyai(z), z, 2)
z*airyai(z)


Series expansion is also supported:

>>> from sympy import series
>>> series(airyai(z), z, 0, 3)
3**(5/6)*gamma(1/3)/(6*pi) - 3**(1/6)*z*gamma(2/3)/(2*pi) + O(z**3)


We can numerically evaluate the Airy function to arbitrary precision on the whole complex plane:

>>> airyai(-2).evalf(50)
0.22740742820168557599192443603787379946077222541710


Rewrite $$\operatorname{Ai}(z)$$ in terms of hypergeometric functions:

>>> from sympy import hyper
>>> airyai(z).rewrite(hyper)
-3**(2/3)*z*hyper((), (4/3,), z**3/9)/(3*gamma(1/3)) + 3**(1/3)*hyper((), (2/3,), z**3/9)/(3*gamma(2/3))


airybi

Airy function of the second kind.

airyaiprime

Derivative of the Airy function of the first kind.

airybiprime

Derivative of the Airy function of the second kind.

References

class sympy.functions.special.bessel.airybi(arg)[source]#

The Airy function $$\operatorname{Bi}$$ of the second kind.

Explanation

The Airy function $$\operatorname{Bi}(z)$$ is defined to be the function satisfying Airy’s differential equation

$\frac{\mathrm{d}^2 w(z)}{\mathrm{d}z^2} - z w(z) = 0.$

Equivalently, for real $$z$$

$\operatorname{Bi}(z) := \frac{1}{\pi} \int_0^\infty \exp\left(-\frac{t^3}{3} + z t\right) + \sin\left(\frac{t^3}{3} + z t\right) \mathrm{d}t.$

Examples

Create an Airy function object:

>>> from sympy import airybi
>>> from sympy.abc import z

>>> airybi(z)
airybi(z)


Several special values are known:

>>> airybi(0)
3**(5/6)/(3*gamma(2/3))
>>> from sympy import oo
>>> airybi(oo)
oo
>>> airybi(-oo)
0


The Airy function obeys the mirror symmetry:

>>> from sympy import conjugate
>>> conjugate(airybi(z))
airybi(conjugate(z))


Differentiation with respect to $$z$$ is supported:

>>> from sympy import diff
>>> diff(airybi(z), z)
airybiprime(z)
>>> diff(airybi(z), z, 2)
z*airybi(z)


Series expansion is also supported:

>>> from sympy import series
>>> series(airybi(z), z, 0, 3)
3**(1/3)*gamma(1/3)/(2*pi) + 3**(2/3)*z*gamma(2/3)/(2*pi) + O(z**3)


We can numerically evaluate the Airy function to arbitrary precision on the whole complex plane:

>>> airybi(-2).evalf(50)
-0.41230258795639848808323405461146104203453483447240


Rewrite $$\operatorname{Bi}(z)$$ in terms of hypergeometric functions:

>>> from sympy import hyper
>>> airybi(z).rewrite(hyper)
3**(1/6)*z*hyper((), (4/3,), z**3/9)/gamma(1/3) + 3**(5/6)*hyper((), (2/3,), z**3/9)/(3*gamma(2/3))


airyai

Airy function of the first kind.

airyaiprime

Derivative of the Airy function of the first kind.

airybiprime

Derivative of the Airy function of the second kind.

References

class sympy.functions.special.bessel.airyaiprime(arg)[source]#

The derivative $$\operatorname{Ai}^\prime$$ of the Airy function of the first kind.

Explanation

The Airy function $$\operatorname{Ai}^\prime(z)$$ is defined to be the function

$\operatorname{Ai}^\prime(z) := \frac{\mathrm{d} \operatorname{Ai}(z)}{\mathrm{d} z}.$

Examples

Create an Airy function object:

>>> from sympy import airyaiprime
>>> from sympy.abc import z

>>> airyaiprime(z)
airyaiprime(z)


Several special values are known:

>>> airyaiprime(0)
-3**(2/3)/(3*gamma(1/3))
>>> from sympy import oo
>>> airyaiprime(oo)
0


The Airy function obeys the mirror symmetry:

>>> from sympy import conjugate
>>> conjugate(airyaiprime(z))
airyaiprime(conjugate(z))


Differentiation with respect to $$z$$ is supported:

>>> from sympy import diff
>>> diff(airyaiprime(z), z)
z*airyai(z)
>>> diff(airyaiprime(z), z, 2)
z*airyaiprime(z) + airyai(z)


Series expansion is also supported:

>>> from sympy import series
>>> series(airyaiprime(z), z, 0, 3)
-3**(2/3)/(3*gamma(1/3)) + 3**(1/3)*z**2/(6*gamma(2/3)) + O(z**3)


We can numerically evaluate the Airy function to arbitrary precision on the whole complex plane:

>>> airyaiprime(-2).evalf(50)
0.61825902074169104140626429133247528291577794512415


Rewrite $$\operatorname{Ai}^\prime(z)$$ in terms of hypergeometric functions:

>>> from sympy import hyper
>>> airyaiprime(z).rewrite(hyper)
3**(1/3)*z**2*hyper((), (5/3,), z**3/9)/(6*gamma(2/3)) - 3**(2/3)*hyper((), (1/3,), z**3/9)/(3*gamma(1/3))


airyai

Airy function of the first kind.

airybi

Airy function of the second kind.

airybiprime

Derivative of the Airy function of the second kind.

References

class sympy.functions.special.bessel.airybiprime(arg)[source]#

The derivative $$\operatorname{Bi}^\prime$$ of the Airy function of the first kind.

Explanation

The Airy function $$\operatorname{Bi}^\prime(z)$$ is defined to be the function

$\operatorname{Bi}^\prime(z) := \frac{\mathrm{d} \operatorname{Bi}(z)}{\mathrm{d} z}.$

Examples

Create an Airy function object:

>>> from sympy import airybiprime
>>> from sympy.abc import z

>>> airybiprime(z)
airybiprime(z)


Several special values are known:

>>> airybiprime(0)
3**(1/6)/gamma(1/3)
>>> from sympy import oo
>>> airybiprime(oo)
oo
>>> airybiprime(-oo)
0


The Airy function obeys the mirror symmetry:

>>> from sympy import conjugate
>>> conjugate(airybiprime(z))
airybiprime(conjugate(z))


Differentiation with respect to $$z$$ is supported:

>>> from sympy import diff
>>> diff(airybiprime(z), z)
z*airybi(z)
>>> diff(airybiprime(z), z, 2)
z*airybiprime(z) + airybi(z)


Series expansion is also supported:

>>> from sympy import series
>>> series(airybiprime(z), z, 0, 3)
3**(1/6)/gamma(1/3) + 3**(5/6)*z**2/(6*gamma(2/3)) + O(z**3)


We can numerically evaluate the Airy function to arbitrary precision on the whole complex plane:

>>> airybiprime(-2).evalf(50)
0.27879516692116952268509756941098324140300059345163


Rewrite $$\operatorname{Bi}^\prime(z)$$ in terms of hypergeometric functions:

>>> from sympy import hyper
>>> airybiprime(z).rewrite(hyper)
3**(5/6)*z**2*hyper((), (5/3,), z**3/9)/(6*gamma(2/3)) + 3**(1/6)*hyper((), (1/3,), z**3/9)/gamma(1/3)


airyai

Airy function of the first kind.

airybi

Airy function of the second kind.

airyaiprime

Derivative of the Airy function of the first kind.

References

## B-Splines#

sympy.functions.special.bsplines.bspline_basis(d, knots, n, x)[source]#

The $$n$$-th B-spline at $$x$$ of degree $$d$$ with knots.

Parameters:

d : integer

degree of bspline

knots : list of integer values

list of knots points of bspline

n : integer

$$n$$-th B-spline

x : symbol

Explanation

B-Splines are piecewise polynomials of degree $$d$$. They are defined on a set of knots, which is a sequence of integers or floats.

Examples

The 0th degree splines have a value of 1 on a single interval:

>>> from sympy import bspline_basis
>>> from sympy.abc import x
>>> d = 0
>>> knots = tuple(range(5))
>>> bspline_basis(d, knots, 0, x)
Piecewise((1, (x >= 0) & (x <= 1)), (0, True))


For a given (d, knots) there are len(knots)-d-1 B-splines defined, that are indexed by n (starting at 0).

Here is an example of a cubic B-spline:

>>> bspline_basis(3, tuple(range(5)), 0, x)
Piecewise((x**3/6, (x >= 0) & (x <= 1)),
(-x**3/2 + 2*x**2 - 2*x + 2/3,
(x >= 1) & (x <= 2)),
(x**3/2 - 4*x**2 + 10*x - 22/3,
(x >= 2) & (x <= 3)),
(-x**3/6 + 2*x**2 - 8*x + 32/3,
(x >= 3) & (x <= 4)),
(0, True))


By repeating knot points, you can introduce discontinuities in the B-splines and their derivatives:

>>> d = 1
>>> knots = (0, 0, 2, 3, 4)
>>> bspline_basis(d, knots, 0, x)
Piecewise((1 - x/2, (x >= 0) & (x <= 2)), (0, True))


It is quite time consuming to construct and evaluate B-splines. If you need to evaluate a B-spline many times, it is best to lambdify them first:

>>> from sympy import lambdify
>>> d = 3
>>> knots = tuple(range(10))
>>> b0 = bspline_basis(d, knots, 0, x)
>>> f = lambdify(x, b0)
>>> y = f(0.5)


References

sympy.functions.special.bsplines.bspline_basis_set(d, knots, x)[source]#

Return the len(knots)-d-1 B-splines at x of degree d with knots.

Parameters:

d : integer

degree of bspline

knots : list of integers

list of knots points of bspline

x : symbol

Explanation

This function returns a list of piecewise polynomials that are the len(knots)-d-1 B-splines of degree d for the given knots. This function calls bspline_basis(d, knots, n, x) for different values of n.

Examples

>>> from sympy import bspline_basis_set
>>> from sympy.abc import x
>>> d = 2
>>> knots = range(5)
>>> splines = bspline_basis_set(d, knots, x)
>>> splines
[Piecewise((x**2/2, (x >= 0) & (x <= 1)),
(-x**2 + 3*x - 3/2, (x >= 1) & (x <= 2)),
(x**2/2 - 3*x + 9/2, (x >= 2) & (x <= 3)),
(0, True)),
Piecewise((x**2/2 - x + 1/2, (x >= 1) & (x <= 2)),
(-x**2 + 5*x - 11/2, (x >= 2) & (x <= 3)),
(x**2/2 - 4*x + 8, (x >= 3) & (x <= 4)),
(0, True))]

sympy.functions.special.bsplines.interpolating_spline(d, x, X, Y)[source]#

Return spline of degree d, passing through the given X and Y values.

Parameters:

d : integer

Degree of Bspline strictly greater than equal to one

x : symbol

X : list of strictly increasing integer values

list of X coordinates through which the spline passes

Y : list of strictly increasing integer values

list of Y coordinates through which the spline passes

Explanation

This function returns a piecewise function such that each part is a polynomial of degree not greater than d. The value of d must be 1 or greater and the values of X must be strictly increasing.

Examples

>>> from sympy import interpolating_spline
>>> from sympy.abc import x
>>> interpolating_spline(1, x, [1, 2, 4, 7], [3, 6, 5, 7])
Piecewise((3*x, (x >= 1) & (x <= 2)),
(7 - x/2, (x >= 2) & (x <= 4)),
(2*x/3 + 7/3, (x >= 4) & (x <= 7)))
>>> interpolating_spline(3, x, [-2, 0, 1, 3, 4], [4, 2, 1, 1, 3])
Piecewise((7*x**3/117 + 7*x**2/117 - 131*x/117 + 2, (x >= -2) & (x <= 1)),
(10*x**3/117 - 2*x**2/117 - 122*x/117 + 77/39, (x >= 1) & (x <= 4)))


## Riemann Zeta and Related Functions#

class sympy.functions.special.zeta_functions.zeta(z, a_=None)[source]#

Hurwitz zeta function (or Riemann zeta function).

Explanation

For $$\operatorname{Re}(a) > 0$$ and $$\operatorname{Re}(s) > 1$$, this function is defined as

$\zeta(s, a) = \sum_{n=0}^\infty \frac{1}{(n + a)^s},$

where the standard choice of argument for $$n + a$$ is used. For fixed $$a$$ with $$\operatorname{Re}(a) > 0$$ the Hurwitz zeta function admits a meromorphic continuation to all of $$\mathbb{C}$$, it is an unbranched function with a simple pole at $$s = 1$$.

Analytic continuation to other $$a$$ is possible under some circumstances, but this is not typically done.

The Hurwitz zeta function is a special case of the Lerch transcendent:

$\zeta(s, a) = \Phi(1, s, a).$

This formula defines an analytic continuation for all possible values of $$s$$ and $$a$$ (also $$\operatorname{Re}(a) < 0$$), see the documentation of lerchphi for a description of the branching behavior.

If no value is passed for $$a$$, by this function assumes a default value of $$a = 1$$, yielding the Riemann zeta function.

Examples

For $$a = 1$$ the Hurwitz zeta function reduces to the famous Riemann zeta function:

$\zeta(s, 1) = \zeta(s) = \sum_{n=1}^\infty \frac{1}{n^s}.$
>>> from sympy import zeta
>>> from sympy.abc import s
>>> zeta(s, 1)
zeta(s)
>>> zeta(s)
zeta(s)


The Riemann zeta function can also be expressed using the Dirichlet eta function:

>>> from sympy import dirichlet_eta
>>> zeta(s).rewrite(dirichlet_eta)
dirichlet_eta(s)/(1 - 2**(1 - s))


The Riemann zeta function at positive even integer and negative odd integer values is related to the Bernoulli numbers:

>>> zeta(2)
pi**2/6
>>> zeta(4)
pi**4/90
>>> zeta(-1)
-1/12


The specific formulae are:

$\zeta(2n) = (-1)^{n+1} \frac{B_{2n} (2\pi)^{2n}}{2(2n)!}$
$\zeta(-n) = -\frac{B_{n+1}}{n+1}$

At negative even integers the Riemann zeta function is zero:

>>> zeta(-4)
0


No closed-form expressions are known at positive odd integers, but numerical evaluation is possible:

>>> zeta(3).n()
1.20205690315959


The derivative of $$\zeta(s, a)$$ with respect to $$a$$ can be computed:

>>> from sympy.abc import a
>>> zeta(s, a).diff(a)
-s*zeta(s + 1, a)


However the derivative with respect to $$s$$ has no useful closed form expression:

>>> zeta(s, a).diff(s)
Derivative(zeta(s, a), s)


The Hurwitz zeta function can be expressed in terms of the Lerch transcendent, lerchphi:

>>> from sympy import lerchphi
>>> zeta(s, a).rewrite(lerchphi)
lerchphi(1, s, a)


References

class sympy.functions.special.zeta_functions.dirichlet_eta(s)[source]#

Dirichlet eta function.

Explanation

For $$\operatorname{Re}(s) > 0$$, this function is defined as

$\eta(s) = \sum_{n=1}^\infty \frac{(-1)^{n-1}}{n^s}.$

It admits a unique analytic continuation to all of $$\mathbb{C}$$. It is an entire, unbranched function.

Examples

The Dirichlet eta function is closely related to the Riemann zeta function:

>>> from sympy import dirichlet_eta, zeta
>>> from sympy.abc import s
>>> dirichlet_eta(s).rewrite(zeta)
(1 - 2**(1 - s))*zeta(s)


References

class sympy.functions.special.zeta_functions.polylog(s, z)[source]#

Polylogarithm function.

Explanation

For $$|z| < 1$$ and $$s \in \mathbb{C}$$, the polylogarithm is defined by

$\operatorname{Li}_s(z) = \sum_{n=1}^\infty \frac{z^n}{n^s},$

where the standard branch of the argument is used for $$n$$. It admits an analytic continuation which is branched at $$z=1$$ (notably not on the sheet of initial definition), $$z=0$$ and $$z=\infty$$.

The name polylogarithm comes from the fact that for $$s=1$$, the polylogarithm is related to the ordinary logarithm (see examples), and that

$\operatorname{Li}_{s+1}(z) = \int_0^z \frac{\operatorname{Li}_s(t)}{t} \mathrm{d}t.$

The polylogarithm is a special case of the Lerch transcendent:

$\operatorname{Li}_{s}(z) = z \Phi(z, s, 1).$

Examples

For $$z \in \{0, 1, -1\}$$, the polylogarithm is automatically expressed using other functions:

>>> from sympy import polylog
>>> from sympy.abc import s
>>> polylog(s, 0)
0
>>> polylog(s, 1)
zeta(s)
>>> polylog(s, -1)
-dirichlet_eta(s)


If $$s$$ is a negative integer, $$0$$ or $$1$$, the polylogarithm can be expressed using elementary functions. This can be done using expand_func():

>>> from sympy import expand_func
>>> from sympy.abc import z
>>> expand_func(polylog(1, z))
-log(1 - z)
>>> expand_func(polylog(0, z))
z/(1 - z)


The derivative with respect to $$z$$ can be computed in closed form:

>>> polylog(s, z).diff(z)
polylog(s - 1, z)/z


The polylogarithm can be expressed in terms of the lerch transcendent:

>>> from sympy import lerchphi
>>> polylog(s, z).rewrite(lerchphi)
z*lerchphi(z, s, 1)


class sympy.functions.special.zeta_functions.lerchphi(*args)[source]#

Lerch transcendent (Lerch phi function).

Explanation

For $$\operatorname{Re}(a) > 0$$, $$|z| < 1$$ and $$s \in \mathbb{C}$$, the Lerch transcendent is defined as

$\Phi(z, s, a) = \sum_{n=0}^\infty \frac{z^n}{(n + a)^s},$

where the standard branch of the argument is used for $$n + a$$, and by analytic continuation for other values of the parameters.

A commonly used related function is the Lerch zeta function, defined by

$L(q, s, a) = \Phi(e^{2\pi i q}, s, a).$

Analytic Continuation and Branching Behavior

It can be shown that

$\Phi(z, s, a) = z\Phi(z, s, a+1) + a^{-s}.$

This provides the analytic continuation to $$\operatorname{Re}(a) \le 0$$.

Assume now $$\operatorname{Re}(a) > 0$$. The integral representation

$\Phi_0(z, s, a) = \int_0^\infty \frac{t^{s-1} e^{-at}}{1 - ze^{-t}} \frac{\mathrm{d}t}{\Gamma(s)}$

provides an analytic continuation to $$\mathbb{C} - [1, \infty)$$. Finally, for $$x \in (1, \infty)$$ we find

$\lim_{\epsilon \to 0^+} \Phi_0(x + i\epsilon, s, a) -\lim_{\epsilon \to 0^+} \Phi_0(x - i\epsilon, s, a) = \frac{2\pi i \log^{s-1}{x}}{x^a \Gamma(s)},$

using the standard branch for both $$\log{x}$$ and $$\log{\log{x}}$$ (a branch of $$\log{\log{x}}$$ is needed to evaluate $$\log{x}^{s-1}$$). This concludes the analytic continuation. The Lerch transcendent is thus branched at $$z \in \{0, 1, \infty\}$$ and $$a \in \mathbb{Z}_{\le 0}$$. For fixed $$z, a$$ outside these branch points, it is an entire function of $$s$$.

Examples

The Lerch transcendent is a fairly general function, for this reason it does not automatically evaluate to simpler functions. Use expand_func() to achieve this.

If $$z=1$$, the Lerch transcendent reduces to the Hurwitz zeta function:

>>> from sympy import lerchphi, expand_func
>>> from sympy.abc import z, s, a
>>> expand_func(lerchphi(1, s, a))
zeta(s, a)


More generally, if $$z$$ is a root of unity, the Lerch transcendent reduces to a sum of Hurwitz zeta functions:

>>> expand_func(lerchphi(-1, s, a))
zeta(s, a/2)/2**s - zeta(s, a/2 + 1/2)/2**s


If $$a=1$$, the Lerch transcendent reduces to the polylogarithm:

>>> expand_func(lerchphi(z, s, 1))
polylog(s, z)/z


More generally, if $$a$$ is rational, the Lerch transcendent reduces to a sum of polylogarithms:

>>> from sympy import S
>>> expand_func(lerchphi(z, s, S(1)/2))
2**(s - 1)*(polylog(s, sqrt(z))/sqrt(z) -
polylog(s, sqrt(z)*exp_polar(I*pi))/sqrt(z))
>>> expand_func(lerchphi(z, s, S(3)/2))
-2**s/z + 2**(s - 1)*(polylog(s, sqrt(z))/sqrt(z) -
polylog(s, sqrt(z)*exp_polar(I*pi))/sqrt(z))/z


The derivatives with respect to $$z$$ and $$a$$ can be computed in closed form:

>>> lerchphi(z, s, a).diff(z)
(-a*lerchphi(z, s, a) + lerchphi(z, s - 1, a))/z
>>> lerchphi(z, s, a).diff(a)
-s*lerchphi(z, s + 1, a)


References

[R418]

Bateman, H.; Erdelyi, A. (1953), Higher Transcendental Functions, Vol. I, New York: McGraw-Hill. Section 1.11.

class sympy.functions.special.zeta_functions.stieltjes(n, a=None)[source]#

Represents Stieltjes constants, $$\gamma_{k}$$ that occur in Laurent Series expansion of the Riemann zeta function.

Examples

>>> from sympy import stieltjes
>>> from sympy.abc import n, m
>>> stieltjes(n)
stieltjes(n)


The zero’th stieltjes constant:

>>> stieltjes(0)
EulerGamma
>>> stieltjes(0, 1)
EulerGamma


For generalized stieltjes constants:

>>> stieltjes(n, m)
stieltjes(n, m)


Constants are only defined for integers >= 0:

>>> stieltjes(-1)
zoo


References

## Hypergeometric Functions#

class sympy.functions.special.hyper.hyper(ap, bq, z)[source]#

The generalized hypergeometric function is defined by a series where the ratios of successive terms are a rational function of the summation index. When convergent, it is continued analytically to the largest possible domain.

Explanation

The hypergeometric function depends on two vectors of parameters, called the numerator parameters $$a_p$$, and the denominator parameters $$b_q$$. It also has an argument $$z$$. The series definition is

$\begin{split}{}_pF_q\left(\begin{matrix} a_1, \cdots, a_p \\ b_1, \cdots, b_q \end{matrix} \middle| z \right) = \sum_{n=0}^\infty \frac{(a_1)_n \cdots (a_p)_n}{(b_1)_n \cdots (b_q)_n} \frac{z^n}{n!},\end{split}$

where $$(a)_n = (a)(a+1)\cdots(a+n-1)$$ denotes the rising factorial.

If one of the $$b_q$$ is a non-positive integer then the series is undefined unless one of the $$a_p$$ is a larger (i.e., smaller in magnitude) non-positive integer. If none of the $$b_q$$ is a non-positive integer and one of the $$a_p$$ is a non-positive integer, then the series reduces to a polynomial. To simplify the following discussion, we assume that none of the $$a_p$$ or $$b_q$$ is a non-positive integer. For more details, see the references.

The series converges for all $$z$$ if $$p \le q$$, and thus defines an entire single-valued function in this case. If $$p = q+1$$ the series converges for $$|z| < 1$$, and can be continued analytically into a half-plane. If $$p > q+1$$ the series is divergent for all $$z$$.

Please note the hypergeometric function constructor currently does not check if the parameters actually yield a well-defined function.

Examples

The parameters $$a_p$$ and $$b_q$$ can be passed as arbitrary iterables, for example:

>>> from sympy import hyper
>>> from sympy.abc import x, n, a
>>> hyper((1, 2, 3), [3, 4], x)
hyper((1, 2, 3), (3, 4), x)


There is also pretty printing (it looks better using Unicode):

>>> from sympy import pprint
>>> pprint(hyper((1, 2, 3), [3, 4], x), use_unicode=False)
_
|_  /1, 2, 3 |  \
|   |        | x|
3  2 \  3, 4  |  /


The parameters must always be iterables, even if they are vectors of length one or zero:

>>> hyper((1, ), [], x)
hyper((1,), (), x)


But of course they may be variables (but if they depend on $$x$$ then you should not expect much implemented functionality):

>>> hyper((n, a), (n**2,), x)
hyper((n, a), (n**2,), x)


The hypergeometric function generalizes many named special functions. The function hyperexpand() tries to express a hypergeometric function using named special functions. For example:

>>> from sympy import hyperexpand
>>> hyperexpand(hyper([], [], x))
exp(x)


You can also use expand_func():

>>> from sympy import expand_func
>>> expand_func(x*hyper([1, 1], [2], -x))
log(x + 1)


More examples:

>>> from sympy import S
>>> hyperexpand(hyper([], [S(1)/2], -x**2/4))
cos(x)
>>> hyperexpand(x*hyper([S(1)/2, S(1)/2], [S(3)/2], x**2))
asin(x)


We can also sometimes hyperexpand() parametric functions:

>>> from sympy.abc import a
>>> hyperexpand(hyper([-a], [], x))
(1 - x)**a


References

[R422]

Luke, Y. L. (1969), The Special Functions and Their Approximations, Volume 1

property ap#

Numerator parameters of the hypergeometric function.

property argument#

Argument of the hypergeometric function.

property bq#

Denominator parameters of the hypergeometric function.

property convergence_statement#

Return a condition on z under which the series converges.

property eta#

A quantity related to the convergence of the series.

Compute the radius of convergence of the defining series.

Explanation

Note that even if this is not oo, the function may still be evaluated outside of the radius of convergence by analytic continuation. But if this is zero, then the function is not actually defined anywhere else.

Examples

>>> from sympy import hyper
>>> from sympy.abc import z
1
>>> hyper((1, 2, 3), [4], z).radius_of_convergence
0
>>> hyper((1, 2), (3, 4), z).radius_of_convergence
oo

class sympy.functions.special.hyper.meijerg(*args)[source]#

The Meijer G-function is defined by a Mellin-Barnes type integral that resembles an inverse Mellin transform. It generalizes the hypergeometric functions.

Explanation

The Meijer G-function depends on four sets of parameters. There are “numerator parameters$$a_1, \ldots, a_n$$ and $$a_{n+1}, \ldots, a_p$$, and there are “denominator parameters$$b_1, \ldots, b_m$$ and $$b_{m+1}, \ldots, b_q$$. Confusingly, it is traditionally denoted as follows (note the position of $$m$$, $$n$$, $$p$$, $$q$$, and how they relate to the lengths of the four parameter vectors):

$\begin{split}G_{p,q}^{m,n} \left(\begin{matrix}a_1, \cdots, a_n & a_{n+1}, \cdots, a_p \\ b_1, \cdots, b_m & b_{m+1}, \cdots, b_q \end{matrix} \middle| z \right).\end{split}$

However, in SymPy the four parameter vectors are always available separately (see examples), so that there is no need to keep track of the decorating sub- and super-scripts on the G symbol.

The G function is defined as the following integral:

$\frac{1}{2 \pi i} \int_L \frac{\prod_{j=1}^m \Gamma(b_j - s) \prod_{j=1}^n \Gamma(1 - a_j + s)}{\prod_{j=m+1}^q \Gamma(1- b_j +s) \prod_{j=n+1}^p \Gamma(a_j - s)} z^s \mathrm{d}s,$

where $$\Gamma(z)$$ is the gamma function. There are three possible contours which we will not describe in detail here (see the references). If the integral converges along more than one of them, the definitions agree. The contours all separate the poles of $$\Gamma(1-a_j+s)$$ from the poles of $$\Gamma(b_k-s)$$, so in particular the G function is undefined if $$a_j - b_k \in \mathbb{Z}_{>0}$$ for some $$j \le n$$ and $$k \le m$$.

The conditions under which one of the contours yields a convergent integral are complicated and we do not state them here, see the references.

Please note currently the Meijer G-function constructor does not check any convergence conditions.

Examples

You can pass the parameters either as four separate vectors:

>>> from sympy import meijerg, Tuple, pprint
>>> from sympy.abc import x, a
>>> pprint(meijerg((1, 2), (a, 4), (5,), [], x), use_unicode=False)
__1, 2 /1, 2  a, 4 |  \
/__     |           | x|
\_|4, 1 \ 5         |  /


Or as two nested vectors:

>>> pprint(meijerg([(1, 2), (3, 4)], ([5], Tuple()), x), use_unicode=False)
__1, 2 /1, 2  3, 4 |  \
/__     |           | x|
\_|4, 1 \ 5         |  /


As with the hypergeometric function, the parameters may be passed as arbitrary iterables. Vectors of length zero and one also have to be passed as iterables. The parameters need not be constants, but if they depend on the argument then not much implemented functionality should be expected.

All the subvectors of parameters are available:

>>> from sympy import pprint
>>> g = meijerg([1], [2], [3], [4], x)
>>> pprint(g, use_unicode=False)
__1, 1 /1  2 |  \
/__     |     | x|
\_|2, 2 \3  4 |  /
>>> g.an
(1,)
>>> g.ap
(1, 2)
>>> g.aother
(2,)
>>> g.bm
(3,)
>>> g.bq
(3, 4)
>>> g.bother
(4,)


The Meijer G-function generalizes the hypergeometric functions. In some cases it can be expressed in terms of hypergeometric functions, using Slater’s theorem. For example:

>>> from sympy import hyperexpand
>>> from sympy.abc import a, b, c
>>> hyperexpand(meijerg([a], [], [c], [b], x), allow_hyper=True)
x**c*gamma(-a + c + 1)*hyper((-a + c + 1,),
(-b + c + 1,), -x)/gamma(-b + c + 1)


Thus the Meijer G-function also subsumes many named functions as special cases. You can use expand_func() or hyperexpand() to (try to) rewrite a Meijer G-function in terms of named special functions. For example:

>>> from sympy import expand_func, S
>>> expand_func(meijerg([[],[]], [[0],[]], -x))
exp(x)
>>> hyperexpand(meijerg([[],[]], [[S(1)/2],[0]], (x/2)**2))
sin(x)/sqrt(pi)


References

[R424]

Luke, Y. L. (1969), The Special Functions and Their Approximations, Volume 1

property an#

First set of numerator parameters.

property aother#

Second set of numerator parameters.

property ap#

Combined numerator parameters.

property argument#

Argument of the Meijer G-function.

property bm#

First set of denominator parameters.

property bother#

Second set of denominator parameters.

property bq#

Combined denominator parameters.

property delta#

A quantity related to the convergence region of the integral, c.f. references.

get_period()[source]#

Return a number $$P$$ such that $$G(x*exp(I*P)) == G(x)$$.

Examples

>>> from sympy import meijerg, pi, S
>>> from sympy.abc import z

>>> meijerg([1], [], [], [], z).get_period()
2*pi
>>> meijerg([pi], [], [], [], z).get_period()
oo
>>> meijerg([1, 2], [], [], [], z).get_period()
oo
>>> meijerg([1,1], [2], [1, S(1)/2, S(1)/3], [1], z).get_period()
12*pi

integrand(s)[source]#

Get the defining integrand D(s).

property is_number#

Returns true if expression has numeric data only.

property nu#

A quantity related to the convergence region of the integral, c.f. references.

class sympy.functions.special.hyper.appellf1(a, b1, b2, c, x, y)[source]#

This is the Appell hypergeometric function of two variables as:

$F_1(a,b_1,b_2,c,x,y) = \sum_{m=0}^{\infty} \sum_{n=0}^{\infty} \frac{(a)_{m+n} (b_1)_m (b_2)_n}{(c)_{m+n}} \frac{x^m y^n}{m! n!}.$

Examples

>>> from sympy import appellf1, symbols
>>> x, y, a, b1, b2, c = symbols('x y a b1 b2 c')
>>> appellf1(2., 1., 6., 4., 5., 6.)
0.0063339426292673
>>> appellf1(12., 12., 6., 4., 0.5, 0.12)
172870711.659936
>>> appellf1(40, 2, 6, 4, 15, 60)
appellf1(40, 2, 6, 4, 15, 60)
>>> appellf1(20., 12., 10., 3., 0.5, 0.12)
15605338197184.4
>>> appellf1(40, 2, 6, 4, x, y)
appellf1(40, 2, 6, 4, x, y)
>>> appellf1(a, b1, b2, c, x, y)
appellf1(a, b1, b2, c, x, y)


References

## Elliptic integrals#

class sympy.functions.special.elliptic_integrals.elliptic_k(m)[source]#

The complete elliptic integral of the first kind, defined by

$K(m) = F\left(\tfrac{\pi}{2}\middle| m\right)$

where $$F\left(z\middle| m\right)$$ is the Legendre incomplete elliptic integral of the first kind.

Explanation

The function $$K(m)$$ is a single-valued function on the complex plane with branch cut along the interval $$(1, \infty)$$.

Note that our notation defines the incomplete elliptic integral in terms of the parameter $$m$$ instead of the elliptic modulus (eccentricity) $$k$$. In this case, the parameter $$m$$ is defined as $$m=k^2$$.

Examples

>>> from sympy import elliptic_k, I
>>> from sympy.abc import m
>>> elliptic_k(0)
pi/2
>>> elliptic_k(1.0 + I)
1.50923695405127 + 0.625146415202697*I
>>> elliptic_k(m).series(n=3)
pi/2 + pi*m/8 + 9*pi*m**2/128 + O(m**3)


References

class sympy.functions.special.elliptic_integrals.elliptic_f(z, m)[source]#

The Legendre incomplete elliptic integral of the first kind, defined by

$F\left(z\middle| m\right) = \int_0^z \frac{dt}{\sqrt{1 - m \sin^2 t}}$

Explanation

This function reduces to a complete elliptic integral of the first kind, $$K(m)$$, when $$z = \pi/2$$.

Note that our notation defines the incomplete elliptic integral in terms of the parameter $$m$$ instead of the elliptic modulus (eccentricity) $$k$$. In this case, the parameter $$m$$ is defined as $$m=k^2$$.

Examples

>>> from sympy import elliptic_f, I
>>> from sympy.abc import z, m
>>> elliptic_f(z, m).series(z)
z + z**5*(3*m**2/40 - m/30) + m*z**3/6 + O(z**6)
>>> elliptic_f(3.0 + I/2, 1.0 + I)
2.909449841483 + 1.74720545502474*I


References

class sympy.functions.special.elliptic_integrals.elliptic_e(m, z=None)[source]#

Called with two arguments $$z$$ and $$m$$, evaluates the incomplete elliptic integral of the second kind, defined by

$E\left(z\middle| m\right) = \int_0^z \sqrt{1 - m \sin^2 t} dt$

Called with a single argument $$m$$, evaluates the Legendre complete elliptic integral of the second kind

$E(m) = E\left(\tfrac{\pi}{2}\middle| m\right)$

Explanation

The function $$E(m)$$ is a single-valued function on the complex plane with branch cut along the interval $$(1, \infty)$$.

Note that our notation defines the incomplete elliptic integral in terms of the parameter $$m$$ instead of the elliptic modulus (eccentricity) $$k$$. In this case, the parameter $$m$$ is defined as $$m=k^2$$.

Examples

>>> from sympy import elliptic_e, I
>>> from sympy.abc import z, m
>>> elliptic_e(z, m).series(z)
z + z**5*(-m**2/40 + m/30) - m*z**3/6 + O(z**6)
>>> elliptic_e(m).series(n=4)
pi/2 - pi*m/8 - 3*pi*m**2/128 - 5*pi*m**3/512 + O(m**4)
>>> elliptic_e(1 + I, 2 - I/2).n()
1.55203744279187 + 0.290764986058437*I
>>> elliptic_e(0)
pi/2
>>> elliptic_e(2.0 - I)
0.991052601328069 + 0.81879421395609*I


References

class sympy.functions.special.elliptic_integrals.elliptic_pi(n, m, z=None)[source]#

Called with three arguments $$n$$, $$z$$ and $$m$$, evaluates the Legendre incomplete elliptic integral of the third kind, defined by

$\Pi\left(n; z\middle| m\right) = \int_0^z \frac{dt} {\left(1 - n \sin^2 t\right) \sqrt{1 - m \sin^2 t}}$

Called with two arguments $$n$$ and $$m$$, evaluates the complete elliptic integral of the third kind:

$\Pi\left(n\middle| m\right) = \Pi\left(n; \tfrac{\pi}{2}\middle| m\right)$

Explanation

Note that our notation defines the incomplete elliptic integral in terms of the parameter $$m$$ instead of the elliptic modulus (eccentricity) $$k$$. In this case, the parameter $$m$$ is defined as $$m=k^2$$.

Examples

>>> from sympy import elliptic_pi, I
>>> from sympy.abc import z, n, m
>>> elliptic_pi(n, z, m).series(z, n=4)
z + z**3*(m/6 + n/3) + O(z**4)
>>> elliptic_pi(0.5 + I, 1.0 - I, 1.2)
2.50232379629182 - 0.760939574180767*I
>>> elliptic_pi(0, 0)
pi/2
>>> elliptic_pi(1.0 - I/3, 2.0 + I)
3.29136443417283 + 0.32555634906645*I


References

## Mathieu Functions#

class sympy.functions.special.mathieu_functions.MathieuBase(*args)[source]#

Abstract base class for Mathieu functions.

This class is meant to reduce code duplication.

class sympy.functions.special.mathieu_functions.mathieus(a, q, z)[source]#

The Mathieu Sine function $$S(a,q,z)$$.

Explanation

This function is one solution of the Mathieu differential equation:

$y(x)^{\prime\prime} + (a - 2 q \cos(2 x)) y(x) = 0$

The other solution is the Mathieu Cosine function.

Examples

>>> from sympy import diff, mathieus
>>> from sympy.abc import a, q, z

>>> mathieus(a, q, z)
mathieus(a, q, z)

>>> mathieus(a, 0, z)
sin(sqrt(a)*z)

>>> diff(mathieus(a, q, z), z)
mathieusprime(a, q, z)


mathieuc

Mathieu cosine function.

mathieusprime

Derivative of Mathieu sine function.

mathieucprime

Derivative of Mathieu cosine function.

References

class sympy.functions.special.mathieu_functions.mathieuc(a, q, z)[source]#

The Mathieu Cosine function $$C(a,q,z)$$.

Explanation

This function is one solution of the Mathieu differential equation:

$y(x)^{\prime\prime} + (a - 2 q \cos(2 x)) y(x) = 0$

The other solution is the Mathieu Sine function.

Examples

>>> from sympy import diff, mathieuc
>>> from sympy.abc import a, q, z

>>> mathieuc(a, q, z)
mathieuc(a, q, z)

>>> mathieuc(a, 0, z)
cos(sqrt(a)*z)

>>> diff(mathieuc(a, q, z), z)
mathieucprime(a, q, z)


mathieus

Mathieu sine function

mathieusprime

Derivative of Mathieu sine function

mathieucprime

Derivative of Mathieu cosine function

References

class sympy.functions.special.mathieu_functions.mathieusprime(a, q, z)[source]#

The derivative $$S^{\prime}(a,q,z)$$ of the Mathieu Sine function.

Explanation

This function is one solution of the Mathieu differential equation:

$y(x)^{\prime\prime} + (a - 2 q \cos(2 x)) y(x) = 0$

The other solution is the Mathieu Cosine function.

Examples

>>> from sympy import diff, mathieusprime
>>> from sympy.abc import a, q, z

>>> mathieusprime(a, q, z)
mathieusprime(a, q, z)

>>> mathieusprime(a, 0, z)
sqrt(a)*cos(sqrt(a)*z)

>>> diff(mathieusprime(a, q, z), z)
(-a + 2*q*cos(2*z))*mathieus(a, q, z)


mathieus

Mathieu sine function

mathieuc

Mathieu cosine function

mathieucprime

Derivative of Mathieu cosine function

References

class sympy.functions.special.mathieu_functions.mathieucprime(a, q, z)[source]#

The derivative $$C^{\prime}(a,q,z)$$ of the Mathieu Cosine function.

Explanation

This function is one solution of the Mathieu differential equation:

$y(x)^{\prime\prime} + (a - 2 q \cos(2 x)) y(x) = 0$

The other solution is the Mathieu Sine function.

Examples

>>> from sympy import diff, mathieucprime
>>> from sympy.abc import a, q, z

>>> mathieucprime(a, q, z)
mathieucprime(a, q, z)

>>> mathieucprime(a, 0, z)
-sqrt(a)*sin(sqrt(a)*z)

>>> diff(mathieucprime(a, q, z), z)
(-a + 2*q*cos(2*z))*mathieuc(a, q, z)


mathieus

Mathieu sine function

mathieuc

Mathieu cosine function

mathieusprime

Derivative of Mathieu sine function

References

## Orthogonal Polynomials#

This module mainly implements special orthogonal polynomials.

### Jacobi Polynomials#

class sympy.functions.special.polynomials.jacobi(n, a, b, x)[source]#

Jacobi polynomial $$P_n^{\left(\alpha, \beta\right)}(x)$$.

Explanation

jacobi(n, alpha, beta, x) gives the $$n$$th Jacobi polynomial in $$x$$, $$P_n^{\left(\alpha, \beta\right)}(x)$$.

The Jacobi polynomials are orthogonal on $$[-1, 1]$$ with respect to the weight $$\left(1-x\right)^\alpha \left(1+x\right)^\beta$$.

Examples

>>> from sympy import jacobi, S, conjugate, diff
>>> from sympy.abc import a, b, n, x

>>> jacobi(0, a, b, x)
1
>>> jacobi(1, a, b, x)
a/2 - b/2 + x*(a/2 + b/2 + 1)
>>> jacobi(2, a, b, x)
a**2/8 - a*b/4 - a/8 + b**2/8 - b/8 + x**2*(a**2/8 + a*b/4 + 7*a/8 + b**2/8 + 7*b/8 + 3/2) + x*(a**2/4 + 3*a/4 - b**2/4 - 3*b/4) - 1/2

>>> jacobi(n, a, b, x)
jacobi(n, a, b, x)

>>> jacobi(n, a, a, x)
RisingFactorial(a + 1, n)*gegenbauer(n,
a + 1/2, x)/RisingFactorial(2*a + 1, n)

>>> jacobi(n, 0, 0, x)
legendre(n, x)

>>> jacobi(n, S(1)/2, S(1)/2, x)
RisingFactorial(3/2, n)*chebyshevu(n, x)/factorial(n + 1)

>>> jacobi(n, -S(1)/2, -S(1)/2, x)
RisingFactorial(1/2, n)*chebyshevt(n, x)/factorial(n)

>>> jacobi(n, a, b, -x)
(-1)**n*jacobi(n, b, a, x)

>>> jacobi(n, a, b, 0)
gamma(a + n + 1)*hyper((-b - n, -n), (a + 1,), -1)/(2**n*factorial(n)*gamma(a + 1))
>>> jacobi(n, a, b, 1)
RisingFactorial(a + 1, n)/factorial(n)

>>> conjugate(jacobi(n, a, b, x))
jacobi(n, conjugate(a), conjugate(b), conjugate(x))

>>> diff(jacobi(n,a,b,x), x)
(a/2 + b/2 + n/2 + 1/2)*jacobi(n - 1, a + 1, b + 1, x)


References

sympy.functions.special.polynomials.jacobi_normalized(n, a, b, x)[source]#

Jacobi polynomial $$P_n^{\left(\alpha, \beta\right)}(x)$$.

Parameters:

n : integer degree of polynomial

a : alpha value

b : beta value

x : symbol

Explanation

jacobi_normalized(n, alpha, beta, x) gives the $$n$$th Jacobi polynomial in $$x$$, $$P_n^{\left(\alpha, \beta\right)}(x)$$.

The Jacobi polynomials are orthogonal on $$[-1, 1]$$ with respect to the weight $$\left(1-x\right)^\alpha \left(1+x\right)^\beta$$.

This functions returns the polynomials normilzed:

$\int_{-1}^{1} P_m^{\left(\alpha, \beta\right)}(x) P_n^{\left(\alpha, \beta\right)}(x) (1-x)^{\alpha} (1+x)^{\beta} \mathrm{d}x = \delta_{m,n}$

Examples

>>> from sympy import jacobi_normalized
>>> from sympy.abc import n,a,b,x

>>> jacobi_normalized(n, a, b, x)
jacobi(n, a, b, x)/sqrt(2**(a + b + 1)*gamma(a + n + 1)*gamma(b + n + 1)/((a + b + 2*n + 1)*factorial(n)*gamma(a + b + n + 1)))


References

### Gegenbauer Polynomials#

class sympy.functions.special.polynomials.gegenbauer(n, a, x)[source]#

Gegenbauer polynomial $$C_n^{\left(\alpha\right)}(x)$$.

Explanation

gegenbauer(n, alpha, x) gives the $$n$$th Gegenbauer polynomial in $$x$$, $$C_n^{\left(\alpha\right)}(x)$$.

The Gegenbauer polynomials are orthogonal on $$[-1, 1]$$ with respect to the weight $$\left(1-x^2\right)^{\alpha-\frac{1}{2}}$$.

Examples

>>> from sympy import gegenbauer, conjugate, diff
>>> from sympy.abc import n,a,x
>>> gegenbauer(0, a, x)
1
>>> gegenbauer(1, a, x)
2*a*x
>>> gegenbauer(2, a, x)
-a + x**2*(2*a**2 + 2*a)
>>> gegenbauer(3, a, x)
x**3*(4*a**3/3 + 4*a**2 + 8*a/3) + x*(-2*a**2 - 2*a)

>>> gegenbauer(n, a, x)
gegenbauer(n, a, x)
>>> gegenbauer(n, a, -x)
(-1)**n*gegenbauer(n, a, x)

>>> gegenbauer(n, a, 0)
2**n*sqrt(pi)*gamma(a + n/2)/(gamma(a)*gamma(1/2 - n/2)*gamma(n + 1))
>>> gegenbauer(n, a, 1)
gamma(2*a + n)/(gamma(2*a)*gamma(n + 1))

>>> conjugate(gegenbauer(n, a, x))
gegenbauer(n, conjugate(a), conjugate(x))

>>> diff(gegenbauer(n, a, x), x)
2*a*gegenbauer(n - 1, a + 1, x)


References

### Chebyshev Polynomials#

class sympy.functions.special.polynomials.chebyshevt(n, x)[source]#

Chebyshev polynomial of the first kind, $$T_n(x)$$.

Explanation

chebyshevt(n, x) gives the $$n$$th Chebyshev polynomial (of the first kind) in $$x$$, $$T_n(x)$$.

The Chebyshev polynomials of the first kind are orthogonal on $$[-1, 1]$$ with respect to the weight $$\frac{1}{\sqrt{1-x^2}}$$.

Examples

>>> from sympy import chebyshevt, diff
>>> from sympy.abc import n,x
>>> chebyshevt(0, x)
1
>>> chebyshevt(1, x)
x
>>> chebyshevt(2, x)
2*x**2 - 1

>>> chebyshevt(n, x)
chebyshevt(n, x)
>>> chebyshevt(n, -x)
(-1)**n*chebyshevt(n, x)
>>> chebyshevt(-n, x)
chebyshevt(n, x)

>>> chebyshevt(n, 0)
cos(pi*n/2)
>>> chebyshevt(n, -1)
(-1)**n

>>> diff(chebyshevt(n, x), x)
n*chebyshevu(n - 1, x)


References

class sympy.functions.special.polynomials.chebyshevu(n, x)[source]#

Chebyshev polynomial of the second kind, $$U_n(x)$$.

Explanation

chebyshevu(n, x) gives the $$n$$th Chebyshev polynomial of the second kind in x, $$U_n(x)$$.

The Chebyshev polynomials of the second kind are orthogonal on $$[-1, 1]$$ with respect to the weight $$\sqrt{1-x^2}$$.

Examples

>>> from sympy import chebyshevu, diff
>>> from sympy.abc import n,x
>>> chebyshevu(0, x)
1
>>> chebyshevu(1, x)
2*x
>>> chebyshevu(2, x)
4*x**2 - 1

>>> chebyshevu(n, x)
chebyshevu(n, x)
>>> chebyshevu(n, -x)
(-1)**n*chebyshevu(n, x)
>>> chebyshevu(-n, x)
-chebyshevu(n - 2, x)

>>> chebyshevu(n, 0)
cos(pi*n/2)
>>> chebyshevu(n, 1)
n + 1

>>> diff(chebyshevu(n, x), x)
(-x*chebyshevu(n, x) + (n + 1)*chebyshevt(n + 1, x))/(x**2 - 1)


References

class sympy.functions.special.polynomials.chebyshevt_root(n, k)[source]#

chebyshev_root(n, k) returns the $$k$$th root (indexed from zero) of the $$n$$th Chebyshev polynomial of the first kind; that is, if $$0 \le k < n$$, chebyshevt(n, chebyshevt_root(n, k)) == 0.

Examples

>>> from sympy import chebyshevt, chebyshevt_root
>>> chebyshevt_root(3, 2)
-sqrt(3)/2
>>> chebyshevt(3, chebyshevt_root(3, 2))
0

class sympy.functions.special.polynomials.chebyshevu_root(n, k)[source]#

chebyshevu_root(n, k) returns the $$k$$th root (indexed from zero) of the $$n$$th Chebyshev polynomial of the second kind; that is, if $$0 \le k < n$$, chebyshevu(n, chebyshevu_root(n, k)) == 0.

Examples

>>> from sympy import chebyshevu, chebyshevu_root
>>> chebyshevu_root(3, 2)
-sqrt(2)/2
>>> chebyshevu(3, chebyshevu_root(3, 2))
0


### Legendre Polynomials#

class sympy.functions.special.polynomials.legendre(n, x)[source]#

legendre(n, x) gives the $$n$$th Legendre polynomial of $$x$$, $$P_n(x)$$

Explanation

The Legendre polynomials are orthogonal on $$[-1, 1]$$ with respect to the constant weight 1. They satisfy $$P_n(1) = 1$$ for all $$n$$; further, $$P_n$$ is odd for odd $$n$$ and even for even $$n$$.

Examples

>>> from sympy import legendre, diff
>>> from sympy.abc import x, n
>>> legendre(0, x)
1
>>> legendre(1, x)
x
>>> legendre(2, x)
3*x**2/2 - 1/2
>>> legendre(n, x)
legendre(n, x)
>>> diff(legendre(n,x), x)
n*(x*legendre(n, x) - legendre(n - 1, x))/(x**2 - 1)


References

class sympy.functions.special.polynomials.assoc_legendre(n, m, x)[source]#

assoc_legendre(n, m, x) gives $$P_n^m(x)$$, where $$n$$ and $$m$$ are the degree and order or an expression which is related to the nth order Legendre polynomial, $$P_n(x)$$ in the following manner:

$P_n^m(x) = (-1)^m (1 - x^2)^{\frac{m}{2}} \frac{\mathrm{d}^m P_n(x)}{\mathrm{d} x^m}$

Explanation

Associated Legendre polynomials are orthogonal on $$[-1, 1]$$ with:

• weight $$= 1$$ for the same $$m$$ and different $$n$$.

• weight $$= \frac{1}{1-x^2}$$ for the same $$n$$ and different $$m$$.

Examples

>>> from sympy import assoc_legendre
>>> from sympy.abc import x, m, n
>>> assoc_legendre(0,0, x)
1
>>> assoc_legendre(1,0, x)
x
>>> assoc_legendre(1,1, x)
-sqrt(1 - x**2)
>>> assoc_legendre(n,m,x)
assoc_legendre(n, m, x)


References

### Hermite Polynomials#

class sympy.functions.special.polynomials.hermite(n, x)[source]#

hermite(n, x) gives the $$n$$th Hermite polynomial in $$x$$, $$H_n(x)$$

Explanation

The Hermite polynomials are orthogonal on $$(-\infty, \infty)$$ with respect to the weight $$\exp\left(-x^2\right)$$.

Examples

>>> from sympy import hermite, diff
>>> from sympy.abc import x, n
>>> hermite(0, x)
1
>>> hermite(1, x)
2*x
>>> hermite(2, x)
4*x**2 - 2
>>> hermite(n, x)
hermite(n, x)
>>> diff(hermite(n,x), x)
2*n*hermite(n - 1, x)
>>> hermite(n, -x)
(-1)**n*hermite(n, x)


References

### Laguerre Polynomials#

class sympy.functions.special.polynomials.laguerre(n, x)[source]#

Returns the $$n$$th Laguerre polynomial in $$x$$, $$L_n(x)$$.

Parameters:

n : int

Degree of Laguerre polynomial. Must be $$n \ge 0$$.

Examples

>>> from sympy import laguerre, diff
>>> from sympy.abc import x, n
>>> laguerre(0, x)
1
>>> laguerre(1, x)
1 - x
>>> laguerre(2, x)
x**2/2 - 2*x + 1
>>> laguerre(3, x)
-x**3/6 + 3*x**2/2 - 3*x + 1

>>> laguerre(n, x)
laguerre(n, x)

>>> diff(laguerre(n, x), x)
-assoc_laguerre(n - 1, 1, x)


References

class sympy.functions.special.polynomials.assoc_laguerre(n, alpha, x)[source]#

Returns the $$n$$th generalized Laguerre polynomial in $$x$$, $$L_n(x)$$.

Parameters:

n : int

Degree of Laguerre polynomial. Must be $$n \ge 0$$.

alpha : Expr

Arbitrary expression. For alpha=0 regular Laguerre polynomials will be generated.

Examples

>>> from sympy import assoc_laguerre, diff
>>> from sympy.abc import x, n, a
>>> assoc_laguerre(0, a, x)
1
>>> assoc_laguerre(1, a, x)
a - x + 1
>>> assoc_laguerre(2, a, x)
a**2/2 + 3*a/2 + x**2/2 + x*(-a - 2) + 1
>>> assoc_laguerre(3, a, x)
a**3/6 + a**2 + 11*a/6 - x**3/6 + x**2*(a/2 + 3/2) +
x*(-a**2/2 - 5*a/2 - 3) + 1

>>> assoc_laguerre(n, a, 0)
binomial(a + n, a)

>>> assoc_laguerre(n, a, x)
assoc_laguerre(n, a, x)

>>> assoc_laguerre(n, 0, x)
laguerre(n, x)

>>> diff(assoc_laguerre(n, a, x), x)
-assoc_laguerre(n - 1, a + 1, x)

>>> diff(assoc_laguerre(n, a, x), a)
Sum(assoc_laguerre(_k, a, x)/(-a + n), (_k, 0, n - 1))


References

## Spherical Harmonics#

class sympy.functions.special.spherical_harmonics.Ynm(n, m, theta, phi)[source]#

Spherical harmonics defined as

$Y_n^m(\theta, \varphi) := \sqrt{\frac{(2n+1)(n-m)!}{4\pi(n+m)!}} \exp(i m \varphi) \mathrm{P}_n^m\left(\cos(\theta)\right)$

Explanation

Ynm() gives the spherical harmonic function of order $$n$$ and $$m$$ in $$\theta$$ and $$\varphi$$, $$Y_n^m(\theta, \varphi)$$. The four parameters are as follows: $$n \geq 0$$ an integer and $$m$$ an integer such that $$-n \leq m \leq n$$ holds. The two angles are real-valued with $$\theta \in [0, \pi]$$ and $$\varphi \in [0, 2\pi]$$.

Examples

>>> from sympy import Ynm, Symbol, simplify
>>> from sympy.abc import n,m
>>> theta = Symbol("theta")
>>> phi = Symbol("phi")

>>> Ynm(n, m, theta, phi)
Ynm(n, m, theta, phi)


Several symmetries are known, for the order:

>>> Ynm(n, -m, theta, phi)
(-1)**m*exp(-2*I*m*phi)*Ynm(n, m, theta, phi)


As well as for the angles:

>>> Ynm(n, m, -theta, phi)
Ynm(n, m, theta, phi)

>>> Ynm(n, m, theta, -phi)
exp(-2*I*m*phi)*Ynm(n, m, theta, phi)


For specific integers $$n$$ and $$m$$ we can evaluate the harmonics to more useful expressions:

>>> simplify(Ynm(0, 0, theta, phi).expand(func=True))
1/(2*sqrt(pi))

>>> simplify(Ynm(1, -1, theta, phi).expand(func=True))
sqrt(6)*exp(-I*phi)*sin(theta)/(4*sqrt(pi))

>>> simplify(Ynm(1, 0, theta, phi).expand(func=True))
sqrt(3)*cos(theta)/(2*sqrt(pi))

>>> simplify(Ynm(1, 1, theta, phi).expand(func=True))
-sqrt(6)*exp(I*phi)*sin(theta)/(4*sqrt(pi))

>>> simplify(Ynm(2, -2, theta, phi).expand(func=True))
sqrt(30)*exp(-2*I*phi)*sin(theta)**2/(8*sqrt(pi))

>>> simplify(Ynm(2, -1, theta, phi).expand(func=True))
sqrt(30)*exp(-I*phi)*sin(2*theta)/(8*sqrt(pi))

>>> simplify(Ynm(2, 0, theta, phi).expand(func=True))
sqrt(5)*(3*cos(theta)**2 - 1)/(4*sqrt(pi))

>>> simplify(Ynm(2, 1, theta, phi).expand(func=True))
-sqrt(30)*exp(I*phi)*sin(2*theta)/(8*sqrt(pi))

>>> simplify(Ynm(2, 2, theta, phi).expand(func=True))
sqrt(30)*exp(2*I*phi)*sin(theta)**2/(8*sqrt(pi))


We can differentiate the functions with respect to both angles:

>>> from sympy import Ynm, Symbol, diff
>>> from sympy.abc import n,m
>>> theta = Symbol("theta")
>>> phi = Symbol("phi")

>>> diff(Ynm(n, m, theta, phi), theta)
m*cot(theta)*Ynm(n, m, theta, phi) + sqrt((-m + n)*(m + n + 1))*exp(-I*phi)*Ynm(n, m + 1, theta, phi)

>>> diff(Ynm(n, m, theta, phi), phi)
I*m*Ynm(n, m, theta, phi)


Further we can compute the complex conjugation:

>>> from sympy import Ynm, Symbol, conjugate
>>> from sympy.abc import n,m
>>> theta = Symbol("theta")
>>> phi = Symbol("phi")

>>> conjugate(Ynm(n, m, theta, phi))
(-1)**(2*m)*exp(-2*I*m*phi)*Ynm(n, m, theta, phi)


To get back the well known expressions in spherical coordinates, we use full expansion:

>>> from sympy import Ynm, Symbol, expand_func
>>> from sympy.abc import n,m
>>> theta = Symbol("theta")
>>> phi = Symbol("phi")

>>> expand_func(Ynm(n, m, theta, phi))
sqrt((2*n + 1)*factorial(-m + n)/factorial(m + n))*exp(I*m*phi)*assoc_legendre(n, m, cos(theta))/(2*sqrt(pi))


References

sympy.functions.special.spherical_harmonics.Ynm_c(n, m, theta, phi)[source]#

Conjugate spherical harmonics defined as

$\overline{Y_n^m(\theta, \varphi)} := (-1)^m Y_n^{-m}(\theta, \varphi).$

Examples

>>> from sympy import Ynm_c, Symbol, simplify
>>> from sympy.abc import n,m
>>> theta = Symbol("theta")
>>> phi = Symbol("phi")
>>> Ynm_c(n, m, theta, phi)
(-1)**(2*m)*exp(-2*I*m*phi)*Ynm(n, m, theta, phi)
>>> Ynm_c(n, m, -theta, phi)
(-1)**(2*m)*exp(-2*I*m*phi)*Ynm(n, m, theta, phi)


For specific integers $$n$$ and $$m$$ we can evaluate the harmonics to more useful expressions:

>>> simplify(Ynm_c(0, 0, theta, phi).expand(func=True))
1/(2*sqrt(pi))
>>> simplify(Ynm_c(1, -1, theta, phi).expand(func=True))
sqrt(6)*exp(I*(-phi + 2*conjugate(phi)))*sin(theta)/(4*sqrt(pi))


References

class sympy.functions.special.spherical_harmonics.Znm(n, m, theta, phi)[source]#

Real spherical harmonics defined as

$\begin{split}Z_n^m(\theta, \varphi) := \begin{cases} \frac{Y_n^m(\theta, \varphi) + \overline{Y_n^m(\theta, \varphi)}}{\sqrt{2}} &\quad m > 0 \\ Y_n^m(\theta, \varphi) &\quad m = 0 \\ \frac{Y_n^m(\theta, \varphi) - \overline{Y_n^m(\theta, \varphi)}}{i \sqrt{2}} &\quad m < 0 \\ \end{cases}\end{split}$

which gives in simplified form

$\begin{split}Z_n^m(\theta, \varphi) = \begin{cases} \frac{Y_n^m(\theta, \varphi) + (-1)^m Y_n^{-m}(\theta, \varphi)}{\sqrt{2}} &\quad m > 0 \\ Y_n^m(\theta, \varphi) &\quad m = 0 \\ \frac{Y_n^m(\theta, \varphi) - (-1)^m Y_n^{-m}(\theta, \varphi)}{i \sqrt{2}} &\quad m < 0 \\ \end{cases}\end{split}$

Examples

>>> from sympy import Znm, Symbol, simplify
>>> from sympy.abc import n, m
>>> theta = Symbol("theta")
>>> phi = Symbol("phi")
>>> Znm(n, m, theta, phi)
Znm(n, m, theta, phi)


For specific integers n and m we can evaluate the harmonics to more useful expressions:

>>> simplify(Znm(0, 0, theta, phi).expand(func=True))
1/(2*sqrt(pi))
>>> simplify(Znm(1, 1, theta, phi).expand(func=True))
-sqrt(3)*sin(theta)*cos(phi)/(2*sqrt(pi))
>>> simplify(Znm(2, 1, theta, phi).expand(func=True))
-sqrt(15)*sin(2*theta)*cos(phi)/(4*sqrt(pi))


References

## Tensor Functions#

sympy.functions.special.tensor_functions.Eijk(*args, **kwargs)[source]#

Represent the Levi-Civita symbol.

This is a compatibility wrapper to LeviCivita().

sympy.functions.special.tensor_functions.eval_levicivita(*args)[source]#

Evaluate Levi-Civita symbol.

class sympy.functions.special.tensor_functions.LeviCivita(*args)[source]#

Represent the Levi-Civita symbol.

Explanation

For even permutations of indices it returns 1, for odd permutations -1, and for everything else (a repeated index) it returns 0.

Thus it represents an alternating pseudotensor.

Examples

>>> from sympy import LeviCivita
>>> from sympy.abc import i, j, k
>>> LeviCivita(1, 2, 3)
1
>>> LeviCivita(1, 3, 2)
-1
>>> LeviCivita(1, 2, 2)
0
>>> LeviCivita(i, j, k)
LeviCivita(i, j, k)
>>> LeviCivita(i, j, i)
0

class sympy.functions.special.tensor_functions.KroneckerDelta(i, j, delta_range=None)[source]#

The discrete, or Kronecker, delta function.

Parameters:

i : Number, Symbol

The first index of the delta function.

j : Number, Symbol

The second index of the delta function.

Explanation

A function that takes in two integers $$i$$ and $$j$$. It returns $$0$$ if $$i$$ and $$j$$ are not equal, or it returns $$1$$ if $$i$$ and $$j$$ are equal.

Examples

An example with integer indices:

>>> from sympy import KroneckerDelta
>>> KroneckerDelta(1, 2)
0
>>> KroneckerDelta(3, 3)
1


Symbolic indices:

>>> from sympy.abc import i, j, k
>>> KroneckerDelta(i, j)
KroneckerDelta(i, j)
>>> KroneckerDelta(i, i)
1
>>> KroneckerDelta(i, i + 1)
0
>>> KroneckerDelta(i, i + 1 + k)
KroneckerDelta(i, i + k + 1)


References

classmethod eval(i, j, delta_range=None)[source]#

Evaluates the discrete delta function.

Examples

>>> from sympy import KroneckerDelta
>>> from sympy.abc import i, j, k

>>> KroneckerDelta(i, j)
KroneckerDelta(i, j)
>>> KroneckerDelta(i, i)
1
>>> KroneckerDelta(i, i + 1)
0
>>> KroneckerDelta(i, i + 1 + k)
KroneckerDelta(i, i + k + 1)


# indirect doctest

property indices_contain_equal_information#

Returns True if indices are either both above or below fermi.

Examples

>>> from sympy import KroneckerDelta, Symbol
>>> a = Symbol('a', above_fermi=True)
>>> i = Symbol('i', below_fermi=True)
>>> p = Symbol('p')
>>> q = Symbol('q')
>>> KroneckerDelta(p, q).indices_contain_equal_information
True
>>> KroneckerDelta(p, q+1).indices_contain_equal_information
True
>>> KroneckerDelta(i, p).indices_contain_equal_information
False

property is_above_fermi#

True if Delta can be non-zero above fermi.

Examples

>>> from sympy import KroneckerDelta, Symbol
>>> a = Symbol('a', above_fermi=True)
>>> i = Symbol('i', below_fermi=True)
>>> p = Symbol('p')
>>> q = Symbol('q')
>>> KroneckerDelta(p, a).is_above_fermi
True
>>> KroneckerDelta(p, i).is_above_fermi
False
>>> KroneckerDelta(p, q).is_above_fermi
True

property is_below_fermi#

True if Delta can be non-zero below fermi.

Examples

>>> from sympy import KroneckerDelta, Symbol
>>> a = Symbol('a', above_fermi=True)
>>> i = Symbol('i', below_fermi=True)
>>> p = Symbol('p')
>>> q = Symbol('q')
>>> KroneckerDelta(p, a).is_below_fermi
False
>>> KroneckerDelta(p, i).is_below_fermi
True
>>> KroneckerDelta(p, q).is_below_fermi
True

property is_only_above_fermi#

True if Delta is restricted to above fermi.

Examples

>>> from sympy import KroneckerDelta, Symbol
>>> a = Symbol('a', above_fermi=True)
>>> i = Symbol('i', below_fermi=True)
>>> p = Symbol('p')
>>> q = Symbol('q')
>>> KroneckerDelta(p, a).is_only_above_fermi
True
>>> KroneckerDelta(p, q).is_only_above_fermi
False
>>> KroneckerDelta(p, i).is_only_above_fermi
False

property is_only_below_fermi#

True if Delta is restricted to below fermi.

Examples

>>> from sympy import KroneckerDelta, Symbol
>>> a = Symbol('a', above_fermi=True)
>>> i = Symbol('i', below_fermi=True)
>>> p = Symbol('p')
>>> q = Symbol('q')
>>> KroneckerDelta(p, i).is_only_below_fermi
True
>>> KroneckerDelta(p, q).is_only_below_fermi
False
>>> KroneckerDelta(p, a).is_only_below_fermi
False

property killable_index#

Returns the index which is preferred to substitute in the final expression.

Explanation

The index to substitute is the index with less information regarding fermi level. If indices contain the same information, ‘a’ is preferred before ‘b’.

Examples

>>> from sympy import KroneckerDelta, Symbol
>>> a = Symbol('a', above_fermi=True)
>>> i = Symbol('i', below_fermi=True)
>>> j = Symbol('j', below_fermi=True)
>>> p = Symbol('p')
>>> KroneckerDelta(p, i).killable_index
p
>>> KroneckerDelta(p, a).killable_index
p
>>> KroneckerDelta(i, j).killable_index
j

property preferred_index#

Returns the index which is preferred to keep in the final expression.

Explanation

The preferred index is the index with more information regarding fermi level. If indices contain the same information, ‘a’ is preferred before ‘b’.

Examples

>>> from sympy import KroneckerDelta, Symbol
>>> a = Symbol('a', above_fermi=True)
>>> i = Symbol('i', below_fermi=True)
>>> j = Symbol('j', below_fermi=True)
>>> p = Symbol('p')
>>> KroneckerDelta(p, i).preferred_index
i
>>> KroneckerDelta(p, a).preferred_index
a
>>> KroneckerDelta(i, j).preferred_index
i