MathieuSPrime[a,q,z]
gives the derivative with respect to z of the odd Mathieu function with characteristic value a and parameter q.
MathieuSPrime
MathieuSPrime[a,q,z]
gives the derivative with respect to z of the odd Mathieu function with characteristic value a and parameter q.
Details
- Mathematical function, suitable for both symbolic and numerical manipulation.
- For certain special arguments, MathieuSPrime automatically evaluates to exact values.
- MathieuSPrime can be evaluated to arbitrary numerical precision.
- MathieuSPrime automatically threads over lists.
Examples
open all close allBasic Examples (4)
MathieuSPrime[2, 1, 3.2]Plot over a subset of the reals:
Plot[MathieuSPrime[3, 2, x], {x, -4, 4}]Plot over a subset of the complexes:
ComplexPlot3D[MathieuSPrime[3, 1, z], {z, -2 - 2I, 2 + 2I}, PlotLegends -> Automatic]Series expansion at the origin:
Series[MathieuSPrime[3, 2, z], {z, 0, 3}]Scope (19)
Numerical Evaluation (5)
Evaluate numerically to high precision:
N[MathieuSPrime[2, 1, 37 / 10], 50]The precision of the output tracks the precision of the input:
MathieuSPrime[2, 1, 3.70000000000000000000000000000000]Evaluate for complex arguments and parameters:
MathieuSPrime[I, -2, 2.5 + I]Evaluate MathieuSPrime efficiently at high precision:
MathieuSPrime[2, 1, 3.7`500]//TimingMathieuSPrime[2, 1, 3.7`10000];//TimingMathieuSPrime threads elementwise over lists:
MathieuSPrime[a, q, {z1, z2, z3}]Compute the elementwise values of an array:
MathieuSPrime[1, 0, {{-π / 2, 0}, {0, π / 4}}]Or compute the matrix MathieuSPrime function using MatrixFunction:
MatrixFunction[MathieuSPrime[1, 0, #]&, {{-π / 2, 0}, {0, π / 4}}]Specific Values (3)
Simple exact values are generated automatically:
MathieuSPrime[a, 0, z]Find a zero of MathieuSPrime:
xzero = FindRoot[MathieuSPrime[3, 2, x] == 0, {x, 1}][[1, 2]]Plot[MathieuSPrime[3, 2, x], {x, -2, 2}, Epilog -> Style[Point[{xzero, MathieuSPrime[3, 2, xzero]}], PointSize[Large], Red]]MathieuSPrime is an even function:
MathieuSPrime[a, q, -x]Visualization (2)
Plot the MathieuSPrime function:
Plot[{MathieuSPrime[3, -2, x], MathieuSPrime[2, 1, x], MathieuSPrime[1, 0, x]}, {x, -7, 7}]Plot the real part of MathieuSPrime for
and
:
ComplexContourPlot[Re[MathieuSPrime[2, 1, z]], {z, -π - 2I, π + 2I}, IconizedObject[«PlotOptions»]]Plot the imaginary part of MathieuSPrime for
and
:
ComplexContourPlot[Im[MathieuSPrime[2, 1, z]], {z, -π - 2I, π + 2I}, IconizedObject[«PlotOptions»]]Function Properties (4)
MathieuSPrime has singularities and discontinuities when the characteristic exponent is an integer:
FunctionSingularities[MathieuSPrime[a, q, z], {a, q, z}]//QuietFunctionDiscontinuities[MathieuSPrime[a, q, z], {a, q, z}]//Quiet
is neither nondecreasing nor nonincreasing:
FunctionMonotonicity[MathieuSPrime[3, 2, x], x]MathieuSPrime is neither non-negative nor non-positive:
FunctionSign[MathieuSPrime[a, q, x], {a, q, x}]MathieuSPrime is neither convex nor concave:
FunctionConvexity[MathieuSPrime[a, q, x], {a, q, x}]Differentiation (3)
D[MathieuSPrime[a, q, x], x]derivs = Table[D[MathieuSPrime[a, q, x], {x, n}], {n, 1, 4}]Plot higher derivatives for
and
:
Plot[Evaluate[derivs /. {a -> 3, q -> 2}], {x, -2, 2}, PlotLegends -> {"First Derivative", "Second Derivative", "Third Derivative", "Fourth Derivative"}]Plot higher derivatives for
and
:
Plot[Evaluate[derivs /. {a -> 3, q -> -2}], {x, -2, 2}, PlotLegends -> {"First Derivative", "Second Derivative", "Third Derivative", "Fourth Derivative"}]MathieuSPrime is the derivative of MathieuS:
D[MathieuS[a, q, x], x]Series Expansions (2)
Series[MathieuSPrime[3, 2, x], {x, 0, 7}]Plot the first three approximations for MathieuSPrime around
:
terms = Normal@Table[Series[MathieuSPrime[3, 2, x], {x, 0, m}], {m, 2, 6, 2}];
Plot[{MathieuSPrime[3, 2, x], terms}, {x, -0.9, 0.9}]Taylor expansion of MathieuSPrime at a generic point:
Series[MathieuSPrime[a, q, x], {x, x0, 2}]Applications (1)
Mathieu functions arise as solutions of the Laplace equation in an ellipse:
ellipseψ[n_, z0_][r_, φ_] := I MathieuS[MathieuCharacteristicB[n, z0], z0, I r] MathieuS[MathieuCharacteristicB[n, z0], z0, φ]This defines the square of the gradient (the local kinetic energy of a vibrating membrane):
gradientSquareEllipseψ[n_, z0_][r_, φ_] = -2 / (Cos[2φ] - Cosh[2r])(D[ellipseψ[n, z0][r, φ], φ] ^ 2 + D[ellipseψ[n, z0][r, φ], r] ^ 2);FindRoot[MathieuS[MathieuCharacteristicB[3, q], q, I / 2], {q, 15}]This plots the absolute value of the gradient of an eigenfunction:
ParametricPlot3D[{Cosh[r] Cos[φ], Sinh[r] Sin[φ], 5Re[gradientSquareEllipseψ[3, q /. %][r, φ]]}, {r, 0, 1 / 2}, {φ, 0, 2Pi}, Mesh -> False]//QuietSee Also
Tech Notes
Related Guides
Related Links
History
Introduced in 1996 (3.0)
Text
Wolfram Research (1996), MathieuSPrime, Wolfram Language function, https://reference.wolfram.com/language/ref/MathieuSPrime.html.
CMS
Wolfram Language. 1996. "MathieuSPrime." Wolfram Language & System Documentation Center. Wolfram Research. https://reference.wolfram.com/language/ref/MathieuSPrime.html.
APA
Wolfram Language. (1996). MathieuSPrime. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/MathieuSPrime.html
BibTeX
@misc{reference.wolfram_2026_mathieusprime, author="Wolfram Research", title="{MathieuSPrime}", year="1996", howpublished="\url{https://reference.wolfram.com/language/ref/MathieuSPrime.html}", note=[Accessed: 13-June-2026]}
BibLaTeX
@online{reference.wolfram_2026_mathieusprime, organization={Wolfram Research}, title={MathieuSPrime}, year={1996}, url={https://reference.wolfram.com/language/ref/MathieuSPrime.html}, note=[Accessed: 13-June-2026]}