# Special¶

## DiracDelta¶

class sympy.functions.special.delta_functions.DiracDelta[source]

The DiracDelta function and its derivatives.

DiracDelta function has the following properties:

1. diff(Heaviside(x),x) = DiracDelta(x)
2. integrate(DiracDelta(x-a)*f(x),(x,-oo,oo)) = f(a) and integrate(DiracDelta(x-a)*f(x),(x,a-e,a+e)) = f(a)
3. DiracDelta(x) = 0 for all x != 0
4. DiracDelta(g(x)) = Sum_i(DiracDelta(x-x_i)/abs(g'(x_i))) Where x_i-s are the roots of g

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

1. DiracDelta(x,k) = 0, for all x != 0

References

http://mathworld.wolfram.com/DeltaFunction.html

is_simple(self, x)[source]

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

x can be:

• a symbol

simplify, Directdelta

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

simplify(self, x)[source]

Compute a simplified representation of the function using property number 4.

x can be:

• a symbol

is_simple, Directdelta

Examples

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

>>> DiracDelta(x*y).simplify(x)
DiracDelta(x)/Abs(y)
>>> DiracDelta(x*y).simplify(y)
DiracDelta(y)/Abs(x)

>>> DiracDelta(x**2 + x - 2).simplify(x)
DiracDelta(x - 1)/3 + DiracDelta(x + 2)/3


## Heaviside¶

class sympy.functions.special.delta_functions.Heaviside[source]

Heaviside Piecewise function

Heaviside function has the following properties [*]:

1. diff(Heaviside(x),x) = DiracDelta(x)

( 0, if x < 0

2. Heaviside(x) = < ( 1/2 if x==0 [*]

( 1, if x > 0

 [*] Regarding to the value at 0, Mathematica defines H(0) = 1, but Maple uses H(0) = undefined

I think is better to have H(0) = 1/2, due to the following:

integrate(DiracDelta(x), x) = Heaviside(x)
integrate(DiracDelta(x), (x, -oo, oo)) = 1


and since DiracDelta is a symmetric function, integrate(DiracDelta(x), (x, 0, oo)) should be 1/2 (which is what Maple returns).

If we take Heaviside(0) = 1/2, we would have integrate(DiracDelta(x), (x, 0, oo)) =  Heaviside(oo) - Heaviside(0) = 1 - 1/2 = 1/2 and integrate(DiracDelta(x), (x, -oo, 0)) =  Heaviside(0) - Heaviside(-oo) = 1/2 - 0 = 1/2

If we consider, instead Heaviside(0) = 1, we would have integrate(DiracDelta(x), (x, 0, oo)) = Heaviside(oo) - Heaviside(0) = 0 and integrate(DiracDelta(x), (x, -oo, 0)) = Heaviside(0) - Heaviside(-oo) = 1

DiracDelta

References

http://mathworld.wolfram.com/HeavisideStepFunction.html

## beta¶

sympy.functions.special.gamma_functions.beta(x, y)[source]

Euler Beta function

beta(x, y) == gamma(x)*gamma(y) / gamma(x+y)

## erf¶

class sympy.functions.special.error_functions.erf[source]

The Gauss error function.

This function is defined as:

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

Or, in ASCII:

        x
/
|
|     2
|   -t
2* |  e    dt
|
/
0
-------------
____
\/ pi

References

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


## Special Cases of the Incomplete Gamma Functions¶

class sympy.functions.special.error_functions.erf[source]

The Gauss error function.

This function is defined as:

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

Or, in ASCII:

        x
/
|
|     2
|   -t
2* |  e    dt
|
/
0
-------------
____
\/ pi

References

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

class sympy.functions.special.error_functions.Ei[source]

The classical exponential integral.

For the 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.

Note that we carefully avoided defining $$\operatorname{Ei}(x)$$ for negative real x. This is because above integral formula does not hold for any polar lift of such $$x$$, indeed all branches of $$\operatorname{Ei}(x)$$ above the negative reals are imaginary.

However, the following statement holds for all $$x \in \mathbb{R}^*$$:

$\int_{-\infty}^x \frac{e^t}{t} \mathrm{d}t = \frac{\operatorname{Ei}\left(|x|e^{i \arg(x)}\right) + \operatorname{Ei}\left(|x|e^{- i \arg(x)}\right)}{2},$

where the integral is again understood to be a principal value if $$x > 0$$, and $$|x|e^{i \arg(x)}$$, $$|x|e^{- i \arg(x)}$$ denote two conjugate polar lifts of $$x$$.

References

Examples

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


The exponential integral in SymPy is strictly undefined for negative values of the argument. For convenience, exponential integrals with negative arguments are immediately converted into an expression that agrees with the classical integral definition:

>>> Ei(-1)
-I*pi + Ei(exp_polar(I*pi))


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

class sympy.functions.special.error_functions.expint[source]

Generalized exponential integral.

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

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

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.

E1
The classical case, returns expint(1, z).
Ei
Another related function called exponential integral.

sympy.functions.special.gamma_functions.uppergamma

References

Examples

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


Differentiation is supported. Differentiation with respect to z explains further 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, -nu + 1), ()), 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)*erf(sqrt(z))/sqrt(z) + sqrt(pi)/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(-nu + 1, 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(-nu + 1) + expint(nu, z)

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

Classical case of the generalized exponential integral.

This is equivalent to expint(1, z).

class sympy.functions.special.error_functions.Si[source]

Sine integral.

This function is defined by

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

It is an entire function.

Ci
Cosine integral.
Shi
Sinh integral.
Chi
Cosh integral.
expint
The generalised exponential integral.

References

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

class sympy.functions.special.error_functions.Ci[source]

Cosine integral.

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.

Si
Sine integral.
Shi
Sinh integral.
Chi
Cosh integral.
expint
The generalised exponential integral.

References

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


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

class sympy.functions.special.error_functions.Shi[source]

Sinh integral.

This function is defined by

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

It is an entire function.

Si
Sine integral.
Ci
Cosine integral.
Chi
Cosh integral.
expint
The generalised exponential integral.

References

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)


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

class sympy.functions.special.error_functions.Chi[source]

Cosh integral.

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.

Si
Sine integral.
Ci
Cosine integral.
Shi
Sinh integral.
expint
The generalised exponential integral.

References

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


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


## Bessel Type Functions¶

class sympy.functions.special.bessel.BesselBase[source]

Abstract base class for bessel-type functions.

This class is meant to reduce code duplication. All bessel type functions can 1) be differentiated, and 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}.

argument[source]

The argument of the bessel-type function.

order[source]

The order of the bessel-type function.

class sympy.functions.special.bessel.besselj[source]

Bessel function of the first kind.

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

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

bessely, besseli, besselk

References

• Abramowitz, Milton; Stegun, Irene A., eds. (1965), “Chapter 9”, Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables
• Luke, Y. L. (1969), The Special Functions and Their Approximations, Volume 1
• http://en.wikipedia.org/wiki/Bessel_function

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

class sympy.functions.special.bessel.bessely[source]

Bessel function of the second kind.

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 $$J_\nu$$.

besselj, besseli, besselk

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)

class sympy.functions.special.bessel.besseli[source]

Modified Bessel function of the first kind.

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

besselj, bessely, besselk

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

class sympy.functions.special.bessel.besselk[source]

Modified Bessel function of the second kind.

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 $$Y_\nu$$.

besselj, besseli, bessely

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

class sympy.functions.special.bessel.hankel1[source]

Hankel function of the first kind.

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.

hankel2, besselj, bessely

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

class sympy.functions.special.bessel.hankel2[source]

Hankel function of the second kind.

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 $$H_\nu^{(1)}$$.

hankel1, besselj, bessely

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

class sympy.functions.special.bessel.jn[source]

Spherical Bessel function of the first kind.

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.

besselj, bessely, besselk, yn

Examples

>>> from sympy import Symbol, jn, sin, cos, expand_func
>>> z = Symbol("z")
>>> print(jn(0, z).expand(func=True))
sin(z)/z
>>> jn(1, z).expand(func=True) == 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)


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 polys.orthopolys.spherical_bessel_fn().

class sympy.functions.special.bessel.yn[source]

Spherical Bessel function of the second kind.

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

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

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

besselj, bessely, besselk, jn

Examples

>>> from sympy import Symbol, yn, sin, cos, expand_func
>>> z = Symbol("z")
>>> print(expand_func(yn(0, z)))
-cos(z)/z
>>> expand_func(yn(1, z)) == -cos(z)/z**2-sin(z)/z
True


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

$y_n(z) = (-1)^{n+1} j_{-n-1}(z)$
sympy.functions.special.bessel.jn_zeros(n, k, method='sympy', dps=15)[source]

Zeros of the spherical Bessel function of the first kind.

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]

jn, yn, besselj, besselk, bessely

Examples

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


## B-Splines¶

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

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

B-Splines are piecewise polynomials of degree d [1]. They are defined on a set of knots, which is a sequence of integers or floats.

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

>>> from sympy import bspline_basis
>>> from sympy.abc import x
>>> d = 0
>>> knots = list(range(5))
>>> bspline_basis(d, knots, 0, x)
Piecewise((1, And(x <= 1, x >= 0)), (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, list(range(5)), 0, x)
Piecewise((x**3/6, And(x < 1, x >= 0)),
(-x**3/2 + 2*x**2 - 2*x + 2/3, And(x < 2, x >= 1)),
(x**3/2 - 4*x**2 + 10*x - 22/3, And(x < 3, x >= 2)),
(-x**3/6 + 2*x**2 - 8*x + 32/3, And(x <= 4, x >= 3)),
(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((-x/2 + 1, And(x <= 2, x >= 0)), (0, True))


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

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


bsplines_basis_set

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.

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.

bsplines_basis

Examples

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


## Riemann Zeta and Related Functions¶

class sympy.functions.special.zeta_functions.zeta[source]

Hurwitz zeta function (or Riemann zeta function).

For $$Re(a) > 0$$ and $$Re(s) > 1$$, 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 $$Re(a) > 0$$ 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 $$Re(a) < 0$$), 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.

References

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)


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)/(-2**(-s + 1) + 1)


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 $$a$$ is easily 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, sympy.functions.special.lerchphi:

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

class sympy.functions.special.zeta_functions.dirichlet_eta[source]

Dirichlet eta function.

For $$Re(s) > 0$$, this function is defined as

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

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

zeta

References

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)
(-2**(-s + 1) + 1)*zeta(s)

class sympy.functions.special.zeta_functions.polylog[source]

Polylogarithm function.

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(z*exp_polar(-I*pi) + 1)
>>> expand_func(polylog(0, z))
z/(-z + 1)


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[source]

Lerch transcendent (Lerch phi function).

For $$Re(a) > 0$$, $$|z| < 1$$ 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 $$Re(a) \le 0$$.

Assume now $$Re(a) > 0$$. 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 $$s$$.

References

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


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)


## Hypergeometric Functions¶

class sympy.functions.special.hyper.hyper[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.

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

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

where $$(a)_n = (a)(a+1)\dots(a+n-1)$$ 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 $$z$$.

Note: The hypergeometric function constructor currently does not check if the parameters actually yield a well-defined function.

References

Examples

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

>>> from sympy.functions 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))
(-x + 1)**a

ap[source]

Numerator parameters of the hypergeometric function.

argument[source]

Argument of the hypergeometric function.

bq[source]

Denominator parameters of the hypergeometric function.

convergence_statement[source]

Return a condition on z under which the series converges.

eta[source]

A quantity related to the convergence of the series.

Compute the radius of convergence of the defining series.

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.

>>> from sympy.functions 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[source]

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

The Meijer G-function depends on four sets of parameters. There are “numerator parameters$$a_1, \dots, a_n$$ and $$a_{n+1}, \dots, a_p$$, and there are “denominator parameters$$b_1, \dots, b_m$$ and $$b_{m+1}, \dots, b_q$$. 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):

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

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.

Note: Currently the Meijer G-function constructor does not check any convergence conditions.

References

Examples

You can pass the parameters either as four separate vectors:

>>> from sympy.functions import meijerg
>>> from sympy.abc import x, a
>>> from sympy.core.containers import Tuple
>>> from sympy import pprint
>>> 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)

an[source]

First set of numerator parameters.

aother[source]

Second set of numerator parameters.

ap[source]

Combined numerator parameters.

argument[source]

Argument of the Meijer G-function.

bm[source]

First set of denominator parameters.

bother[source]

Second set of denominator parameters.

bq[source]

Combined denominator parameters.

delta[source]

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).

>>> from sympy.functions.special.hyper import meijerg
>>> from sympy.abc import z
>>> from sympy import pi, S

>>> 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).

nu[source]

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

## Orthogonal Polynomials¶

This module mainly implements special orthogonal polynomials.

### Jacobi Polynomials¶

class sympy.functions.special.polynomials.jacobi[source]

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

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

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

References

Examples

>>> from sympy import jacobi, S, conjugate, diff
>>> from sympy.abc import n,a,b,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)/(n + 1)!

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

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

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


### Gegenbauer Polynomials¶

class sympy.functions.special.polynomials.gegenbauer[source]

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

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

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

References

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(-n/2 + 1/2)*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)


### Chebyshev Polynomials¶

class sympy.functions.special.polynomials.chebyshevt[source]

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

chebyshevt(n, x) gives the nth 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 $$\frac{1}{\sqrt{1-x^2}}$$.

References

Examples

>>> from sympy import chebyshevt, chebyshevu, 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)

class sympy.functions.special.polynomials.chebyshevu[source]

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

chebyshevu(n, x) gives the nth 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 $$\sqrt{1-x^2}$$.

References

Examples

>>> from sympy import chebyshevt, 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)

class sympy.functions.special.polynomials.chebyshevt_root[source]

chebyshev_root(n, k) returns the kth root (indexed from zero) of the nth Chebyshev polynomial of the first kind; that is, if 0 <= 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[source]

chebyshevu_root(n, k) returns the kth root (indexed from zero) of the nth Chebyshev polynomial of the second kind; that is, if 0 <= 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[source]

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

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.

References

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)

class sympy.functions.special.polynomials.assoc_legendre[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:

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

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

• weight = 1 for the same m, and different n.
• weight = 1/(1-x**2) for the same n, and different m.

References

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(-x**2 + 1)
>>> assoc_legendre(n,m,x)
assoc_legendre(n, m, x)


### Hermite Polynomials¶

class sympy.functions.special.polynomials.hermite[source]

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

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

References

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)
>>> diff(hermite(n,x), x)
2*n*hermite(n - 1, x)
>>> hermite(n, -x)
(-1)**n*hermite(n, x)


### Laguerre Polynomials¶

class sympy.functions.special.polynomials.laguerre[source]

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

Parameters : n : int Degree of Laguerre polynomial. Must be n >= 0.

References

Examples

>>> from sympy import laguerre, diff
>>> from sympy.abc import x, n
>>> laguerre(0, x)
1
>>> laguerre(1, x)
-x + 1
>>> 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)

class sympy.functions.special.polynomials.assoc_laguerre[source]

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

Parameters : n : int Degree of Laguerre polynomial. Must be n >= 0. alpha : Expr Arbitrary expression. For alpha=0 regular Laguerre polynomials will be generated.

References

Examples

>>> from sympy import laguerre, 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))


## Spherical Harmonics¶

sympy.functions.special.spherical_harmonics.Plmcos(l, m, th)[source]

Plm(cos(th)).

sympy.functions.special.spherical_harmonics.Ylm(l, m, theta, phi)[source]

Spherical harmonics Ylm.

Examples

>>> from sympy import symbols, Ylm
>>> theta, phi = symbols("theta phi")
>>> Ylm(0, 0, theta, phi)
1/(2*sqrt(pi))
>>> Ylm(1, -1, theta, phi)
sqrt(6)*exp(-I*phi)*sin(theta)/(4*sqrt(pi))
>>> Ylm(1, 0, theta, phi)
sqrt(3)*cos(theta)/(2*sqrt(pi))

sympy.functions.special.spherical_harmonics.Ylm_c(l, m, theta, phi)[source]

Conjugate spherical harmonics.

sympy.functions.special.spherical_harmonics.Zlm(l, m, th, ph)[source]

Real spherical harmonics.

## Tensor Functions¶

sympy.functions.special.tensor_functions.Eijk(*args, **kwargs)[source]

Represent the Levi-Civita symbol.

This is just compatibility wrapper to LeviCivita().

LeviCivita

sympy.functions.special.tensor_functions.eval_levicivita(*args)[source]

Evaluate Levi-Civita symbol.

class sympy.functions.special.tensor_functions.LeviCivita[source]

Represent the Levi-Civita symbol.

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.

Eijk

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


Attributes

 nargs
class sympy.functions.special.tensor_functions.KroneckerDelta[source]

The discrete, or Kronecker, delta function.

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.

Parameters : i : Number, Symbol The first index of the delta function. j : Number, Symbol The second index of the delta function.

References

http://en.wikipedia.org/wiki/Kronecker_delta

Examples

A simple example with integer indices:

>>> from sympy.functions.special.tensor_functions 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)

classmethod eval(i, j)[source]

Evaluates the discrete delta function.

Examples

>>> from sympy.functions.special.tensor_functions 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

indices_contain_equal_information[source]

Returns True if indices are either both above or below fermi.

Examples

>>> from sympy.functions.special.tensor_functions import KroneckerDelta
>>> from sympy import 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

is_above_fermi[source]

True if Delta can be non-zero above fermi

is_below_fermi, is_only_below_fermi, is_only_above_fermi

Examples

>>> from sympy.functions.special.tensor_functions import KroneckerDelta
>>> from sympy import 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

is_below_fermi[source]

True if Delta can be non-zero below fermi

is_above_fermi, is_only_above_fermi, is_only_below_fermi

Examples

>>> from sympy.functions.special.tensor_functions import KroneckerDelta
>>> from sympy import 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

is_only_above_fermi[source]

True if Delta is restricted to above fermi

is_above_fermi, is_below_fermi, is_only_below_fermi

Examples

>>> from sympy.functions.special.tensor_functions import KroneckerDelta
>>> from sympy import 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

is_only_below_fermi[source]

True if Delta is restricted to below fermi

is_above_fermi, is_below_fermi, is_only_above_fermi

Examples

>>> from sympy.functions.special.tensor_functions import KroneckerDelta
>>> from sympy import 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

killable_index[source]

Returns the index which is preferred to substitute in the final expression.

The index to substitute is the index with less information regarding fermi level. If indices contain same information, ‘a’ is preferred before ‘b’.

preferred_index

Examples

>>> from sympy.functions.special.tensor_functions import KroneckerDelta
>>> from sympy import 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

preferred_index[source]

Returns the index which is preferred to keep in the final expression.

The preferred index is the index with more information regarding fermi level. If indices contain same information, ‘a’ is preferred before ‘b’.

killable_index

Examples

>>> from sympy.functions.special.tensor_functions import KroneckerDelta
>>> from sympy import 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