pruned venvs
This commit is contained in:
d3m1g0d
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"""
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========================================
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Special functions (:mod:`scipy.special`)
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========================================
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.. module:: scipy.special
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Nearly all of the functions below are universal functions and follow
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broadcasting and automatic array-looping rules. Exceptions are
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noted.
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.. seealso::
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`scipy.special.cython_special` -- Typed Cython versions of special functions
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Error handling
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==============
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Errors are handled by returning NaNs or other appropriate values.
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Some of the special function routines can emit warnings or raise
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exceptions when an error occurs. By default this is disabled; to
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query and control the current error handling state the following
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functions are provided.
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.. autosummary::
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:toctree: generated/
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geterr -- Get the current way of handling special-function errors.
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seterr -- Set how special-function errors are handled.
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errstate -- Context manager for special-function error handling.
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SpecialFunctionWarning -- Warning that can be emitted by special functions.
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SpecialFunctionError -- Exception that can be raised by special functions.
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Available functions
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===================
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Airy functions
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--------------
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.. autosummary::
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:toctree: generated/
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airy -- Airy functions and their derivatives.
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airye -- Exponentially scaled Airy functions and their derivatives.
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ai_zeros -- [+]Compute `nt` zeros and values of the Airy function Ai and its derivative.
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bi_zeros -- [+]Compute `nt` zeros and values of the Airy function Bi and its derivative.
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itairy -- Integrals of Airy functions
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Elliptic Functions and Integrals
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--------------------------------
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.. autosummary::
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:toctree: generated/
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ellipj -- Jacobian elliptic functions
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ellipk -- Complete elliptic integral of the first kind.
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ellipkm1 -- Complete elliptic integral of the first kind around `m` = 1
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ellipkinc -- Incomplete elliptic integral of the first kind
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ellipe -- Complete elliptic integral of the second kind
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ellipeinc -- Incomplete elliptic integral of the second kind
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Bessel Functions
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----------------
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.. autosummary::
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:toctree: generated/
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jv -- Bessel function of the first kind of real order and complex argument.
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jve -- Exponentially scaled Bessel function of order `v`.
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yn -- Bessel function of the second kind of integer order and real argument.
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yv -- Bessel function of the second kind of real order and complex argument.
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yve -- Exponentially scaled Bessel function of the second kind of real order.
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kn -- Modified Bessel function of the second kind of integer order `n`
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kv -- Modified Bessel function of the second kind of real order `v`
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kve -- Exponentially scaled modified Bessel function of the second kind.
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iv -- Modified Bessel function of the first kind of real order.
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ive -- Exponentially scaled modified Bessel function of the first kind
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hankel1 -- Hankel function of the first kind
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hankel1e -- Exponentially scaled Hankel function of the first kind
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hankel2 -- Hankel function of the second kind
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hankel2e -- Exponentially scaled Hankel function of the second kind
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The following is not an universal function:
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.. autosummary::
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:toctree: generated/
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lmbda -- [+]Jahnke-Emden Lambda function, Lambdav(x).
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Zeros of Bessel Functions
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^^^^^^^^^^^^^^^^^^^^^^^^^
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These are not universal functions:
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.. autosummary::
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:toctree: generated/
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jnjnp_zeros -- [+]Compute zeros of integer-order Bessel functions Jn and Jn'.
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jnyn_zeros -- [+]Compute nt zeros of Bessel functions Jn(x), Jn'(x), Yn(x), and Yn'(x).
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jn_zeros -- [+]Compute zeros of integer-order Bessel function Jn(x).
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jnp_zeros -- [+]Compute zeros of integer-order Bessel function derivative Jn'(x).
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yn_zeros -- [+]Compute zeros of integer-order Bessel function Yn(x).
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ynp_zeros -- [+]Compute zeros of integer-order Bessel function derivative Yn'(x).
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y0_zeros -- [+]Compute nt zeros of Bessel function Y0(z), and derivative at each zero.
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y1_zeros -- [+]Compute nt zeros of Bessel function Y1(z), and derivative at each zero.
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y1p_zeros -- [+]Compute nt zeros of Bessel derivative Y1'(z), and value at each zero.
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Faster versions of common Bessel Functions
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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.. autosummary::
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:toctree: generated/
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j0 -- Bessel function of the first kind of order 0.
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j1 -- Bessel function of the first kind of order 1.
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y0 -- Bessel function of the second kind of order 0.
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y1 -- Bessel function of the second kind of order 1.
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i0 -- Modified Bessel function of order 0.
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i0e -- Exponentially scaled modified Bessel function of order 0.
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i1 -- Modified Bessel function of order 1.
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i1e -- Exponentially scaled modified Bessel function of order 1.
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k0 -- Modified Bessel function of the second kind of order 0, :math:`K_0`.
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k0e -- Exponentially scaled modified Bessel function K of order 0
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k1 -- Modified Bessel function of the second kind of order 1, :math:`K_1(x)`.
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k1e -- Exponentially scaled modified Bessel function K of order 1
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Integrals of Bessel Functions
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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.. autosummary::
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:toctree: generated/
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itj0y0 -- Integrals of Bessel functions of order 0
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it2j0y0 -- Integrals related to Bessel functions of order 0
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iti0k0 -- Integrals of modified Bessel functions of order 0
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it2i0k0 -- Integrals related to modified Bessel functions of order 0
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besselpoly -- [+]Weighted integral of a Bessel function.
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Derivatives of Bessel Functions
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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.. autosummary::
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:toctree: generated/
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jvp -- Compute nth derivative of Bessel function Jv(z) with respect to `z`.
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yvp -- Compute nth derivative of Bessel function Yv(z) with respect to `z`.
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kvp -- Compute nth derivative of real-order modified Bessel function Kv(z)
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ivp -- Compute nth derivative of modified Bessel function Iv(z) with respect to `z`.
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h1vp -- Compute nth derivative of Hankel function H1v(z) with respect to `z`.
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h2vp -- Compute nth derivative of Hankel function H2v(z) with respect to `z`.
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Spherical Bessel Functions
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^^^^^^^^^^^^^^^^^^^^^^^^^^
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.. autosummary::
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:toctree: generated/
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spherical_jn -- Spherical Bessel function of the first kind or its derivative.
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spherical_yn -- Spherical Bessel function of the second kind or its derivative.
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spherical_in -- Modified spherical Bessel function of the first kind or its derivative.
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spherical_kn -- Modified spherical Bessel function of the second kind or its derivative.
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Riccati-Bessel Functions
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^^^^^^^^^^^^^^^^^^^^^^^^
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These are not universal functions:
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.. autosummary::
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:toctree: generated/
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riccati_jn -- [+]Compute Ricatti-Bessel function of the first kind and its derivative.
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riccati_yn -- [+]Compute Ricatti-Bessel function of the second kind and its derivative.
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Struve Functions
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----------------
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.. autosummary::
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:toctree: generated/
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struve -- Struve function.
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modstruve -- Modified Struve function.
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itstruve0 -- Integral of the Struve function of order 0.
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it2struve0 -- Integral related to the Struve function of order 0.
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itmodstruve0 -- Integral of the modified Struve function of order 0.
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Raw Statistical Functions
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-------------------------
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.. seealso:: :mod:`scipy.stats`: Friendly versions of these functions.
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.. autosummary::
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:toctree: generated/
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bdtr -- Binomial distribution cumulative distribution function.
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bdtrc -- Binomial distribution survival function.
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bdtri -- Inverse function to `bdtr` with respect to `p`.
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bdtrik -- Inverse function to `bdtr` with respect to `k`.
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bdtrin -- Inverse function to `bdtr` with respect to `n`.
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btdtr -- Cumulative density function of the beta distribution.
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btdtri -- The `p`-th quantile of the beta distribution.
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btdtria -- Inverse of `btdtr` with respect to `a`.
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btdtrib -- btdtria(a, p, x)
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fdtr -- F cumulative distribution function.
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fdtrc -- F survival function.
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fdtri -- The `p`-th quantile of the F-distribution.
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fdtridfd -- Inverse to `fdtr` vs dfd
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gdtr -- Gamma distribution cumulative density function.
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gdtrc -- Gamma distribution survival function.
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gdtria -- Inverse of `gdtr` vs a.
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gdtrib -- Inverse of `gdtr` vs b.
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gdtrix -- Inverse of `gdtr` vs x.
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nbdtr -- Negative binomial cumulative distribution function.
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nbdtrc -- Negative binomial survival function.
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nbdtri -- Inverse of `nbdtr` vs `p`.
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nbdtrik -- Inverse of `nbdtr` vs `k`.
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nbdtrin -- Inverse of `nbdtr` vs `n`.
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ncfdtr -- Cumulative distribution function of the non-central F distribution.
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ncfdtridfd -- Calculate degrees of freedom (denominator) for the noncentral F-distribution.
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ncfdtridfn -- Calculate degrees of freedom (numerator) for the noncentral F-distribution.
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ncfdtri -- Inverse cumulative distribution function of the non-central F distribution.
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ncfdtrinc -- Calculate non-centrality parameter for non-central F distribution.
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nctdtr -- Cumulative distribution function of the non-central `t` distribution.
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nctdtridf -- Calculate degrees of freedom for non-central t distribution.
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nctdtrit -- Inverse cumulative distribution function of the non-central t distribution.
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nctdtrinc -- Calculate non-centrality parameter for non-central t distribution.
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nrdtrimn -- Calculate mean of normal distribution given other params.
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nrdtrisd -- Calculate standard deviation of normal distribution given other params.
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pdtr -- Poisson cumulative distribution function
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pdtrc -- Poisson survival function
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pdtri -- Inverse to `pdtr` vs m
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pdtrik -- Inverse to `pdtr` vs k
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stdtr -- Student t distribution cumulative density function
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stdtridf -- Inverse of `stdtr` vs df
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stdtrit -- Inverse of `stdtr` vs `t`
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chdtr -- Chi square cumulative distribution function
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chdtrc -- Chi square survival function
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chdtri -- Inverse to `chdtrc`
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chdtriv -- Inverse to `chdtr` vs `v`
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ndtr -- Gaussian cumulative distribution function.
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log_ndtr -- Logarithm of Gaussian cumulative distribution function.
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ndtri -- Inverse of `ndtr` vs x
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chndtr -- Non-central chi square cumulative distribution function
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chndtridf -- Inverse to `chndtr` vs `df`
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chndtrinc -- Inverse to `chndtr` vs `nc`
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chndtrix -- Inverse to `chndtr` vs `x`
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smirnov -- Kolmogorov-Smirnov complementary cumulative distribution function
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smirnovi -- Inverse to `smirnov`
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kolmogorov -- Complementary cumulative distribution function of Kolmogorov distribution
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kolmogi -- Inverse function to `kolmogorov`
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tklmbda -- Tukey-Lambda cumulative distribution function
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logit -- Logit ufunc for ndarrays.
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expit -- Expit ufunc for ndarrays.
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boxcox -- Compute the Box-Cox transformation.
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boxcox1p -- Compute the Box-Cox transformation of 1 + `x`.
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inv_boxcox -- Compute the inverse of the Box-Cox transformation.
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inv_boxcox1p -- Compute the inverse of the Box-Cox transformation.
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owens_t -- Owen's T Function.
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Information Theory Functions
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----------------------------
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.. autosummary::
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:toctree: generated/
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entr -- Elementwise function for computing entropy.
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rel_entr -- Elementwise function for computing relative entropy.
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kl_div -- Elementwise function for computing Kullback-Leibler divergence.
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huber -- Huber loss function.
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pseudo_huber -- Pseudo-Huber loss function.
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Gamma and Related Functions
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---------------------------
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.. autosummary::
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:toctree: generated/
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gamma -- Gamma function.
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gammaln -- Logarithm of the absolute value of the Gamma function for real inputs.
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loggamma -- Principal branch of the logarithm of the Gamma function.
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gammasgn -- Sign of the gamma function.
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gammainc -- Regularized lower incomplete gamma function.
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gammaincinv -- Inverse to `gammainc`
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gammaincc -- Regularized upper incomplete gamma function.
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gammainccinv -- Inverse to `gammaincc`
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beta -- Beta function.
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betaln -- Natural logarithm of absolute value of beta function.
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betainc -- Incomplete beta integral.
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betaincinv -- Inverse function to beta integral.
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psi -- The digamma function.
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rgamma -- Gamma function inverted
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polygamma -- Polygamma function n.
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multigammaln -- Returns the log of multivariate gamma, also sometimes called the generalized gamma.
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digamma -- psi(x[, out])
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poch -- Rising factorial (z)_m
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Error Function and Fresnel Integrals
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------------------------------------
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.. autosummary::
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||||
:toctree: generated/
|
||||
|
||||
erf -- Returns the error function of complex argument.
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erfc -- Complementary error function, ``1 - erf(x)``.
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erfcx -- Scaled complementary error function, ``exp(x**2) * erfc(x)``.
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erfi -- Imaginary error function, ``-i erf(i z)``.
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erfinv -- Inverse function for erf.
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erfcinv -- Inverse function for erfc.
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wofz -- Faddeeva function
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dawsn -- Dawson's integral.
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fresnel -- Fresnel sin and cos integrals
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fresnel_zeros -- Compute nt complex zeros of sine and cosine Fresnel integrals S(z) and C(z).
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||||
modfresnelp -- Modified Fresnel positive integrals
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||||
modfresnelm -- Modified Fresnel negative integrals
|
||||
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||||
These are not universal functions:
|
||||
|
||||
.. autosummary::
|
||||
:toctree: generated/
|
||||
|
||||
erf_zeros -- [+]Compute nt complex zeros of error function erf(z).
|
||||
fresnelc_zeros -- [+]Compute nt complex zeros of cosine Fresnel integral C(z).
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||||
fresnels_zeros -- [+]Compute nt complex zeros of sine Fresnel integral S(z).
|
||||
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||||
Legendre Functions
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||||
------------------
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||||
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||||
.. autosummary::
|
||||
:toctree: generated/
|
||||
|
||||
lpmv -- Associated Legendre function of integer order and real degree.
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||||
sph_harm -- Compute spherical harmonics.
|
||||
|
||||
These are not universal functions:
|
||||
|
||||
.. autosummary::
|
||||
:toctree: generated/
|
||||
|
||||
clpmn -- [+]Associated Legendre function of the first kind for complex arguments.
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||||
lpn -- [+]Legendre function of the first kind.
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||||
lqn -- [+]Legendre function of the second kind.
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||||
lpmn -- [+]Sequence of associated Legendre functions of the first kind.
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||||
lqmn -- [+]Sequence of associated Legendre functions of the second kind.
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||||
|
||||
Ellipsoidal Harmonics
|
||||
---------------------
|
||||
|
||||
.. autosummary::
|
||||
:toctree: generated/
|
||||
|
||||
ellip_harm -- Ellipsoidal harmonic functions E^p_n(l)
|
||||
ellip_harm_2 -- Ellipsoidal harmonic functions F^p_n(l)
|
||||
ellip_normal -- Ellipsoidal harmonic normalization constants gamma^p_n
|
||||
|
||||
Orthogonal polynomials
|
||||
----------------------
|
||||
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||||
The following functions evaluate values of orthogonal polynomials:
|
||||
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||||
.. autosummary::
|
||||
:toctree: generated/
|
||||
|
||||
assoc_laguerre -- Compute the generalized (associated) Laguerre polynomial of degree n and order k.
|
||||
eval_legendre -- Evaluate Legendre polynomial at a point.
|
||||
eval_chebyt -- Evaluate Chebyshev polynomial of the first kind at a point.
|
||||
eval_chebyu -- Evaluate Chebyshev polynomial of the second kind at a point.
|
||||
eval_chebyc -- Evaluate Chebyshev polynomial of the first kind on [-2, 2] at a point.
|
||||
eval_chebys -- Evaluate Chebyshev polynomial of the second kind on [-2, 2] at a point.
|
||||
eval_jacobi -- Evaluate Jacobi polynomial at a point.
|
||||
eval_laguerre -- Evaluate Laguerre polynomial at a point.
|
||||
eval_genlaguerre -- Evaluate generalized Laguerre polynomial at a point.
|
||||
eval_hermite -- Evaluate physicist's Hermite polynomial at a point.
|
||||
eval_hermitenorm -- Evaluate probabilist's (normalized) Hermite polynomial at a point.
|
||||
eval_gegenbauer -- Evaluate Gegenbauer polynomial at a point.
|
||||
eval_sh_legendre -- Evaluate shifted Legendre polynomial at a point.
|
||||
eval_sh_chebyt -- Evaluate shifted Chebyshev polynomial of the first kind at a point.
|
||||
eval_sh_chebyu -- Evaluate shifted Chebyshev polynomial of the second kind at a point.
|
||||
eval_sh_jacobi -- Evaluate shifted Jacobi polynomial at a point.
|
||||
|
||||
The following functions compute roots and quadrature weights for
|
||||
orthogonal polynomials:
|
||||
|
||||
.. autosummary::
|
||||
:toctree: generated/
|
||||
|
||||
roots_legendre -- Gauss-Legendre quadrature.
|
||||
roots_chebyt -- Gauss-Chebyshev (first kind) quadrature.
|
||||
roots_chebyu -- Gauss-Chebyshev (second kind) quadrature.
|
||||
roots_chebyc -- Gauss-Chebyshev (first kind) quadrature.
|
||||
roots_chebys -- Gauss-Chebyshev (second kind) quadrature.
|
||||
roots_jacobi -- Gauss-Jacobi quadrature.
|
||||
roots_laguerre -- Gauss-Laguerre quadrature.
|
||||
roots_genlaguerre -- Gauss-generalized Laguerre quadrature.
|
||||
roots_hermite -- Gauss-Hermite (physicst's) quadrature.
|
||||
roots_hermitenorm -- Gauss-Hermite (statistician's) quadrature.
|
||||
roots_gegenbauer -- Gauss-Gegenbauer quadrature.
|
||||
roots_sh_legendre -- Gauss-Legendre (shifted) quadrature.
|
||||
roots_sh_chebyt -- Gauss-Chebyshev (first kind, shifted) quadrature.
|
||||
roots_sh_chebyu -- Gauss-Chebyshev (second kind, shifted) quadrature.
|
||||
roots_sh_jacobi -- Gauss-Jacobi (shifted) quadrature.
|
||||
|
||||
The functions below, in turn, return the polynomial coefficients in
|
||||
:class:`~.orthopoly1d` objects, which function similarly as :ref:`numpy.poly1d`.
|
||||
The :class:`~.orthopoly1d` class also has an attribute ``weights`` which returns
|
||||
the roots, weights, and total weights for the appropriate form of Gaussian
|
||||
quadrature. These are returned in an ``n x 3`` array with roots in the first
|
||||
column, weights in the second column, and total weights in the final column.
|
||||
Note that :class:`~.orthopoly1d` objects are converted to ``poly1d`` when doing
|
||||
arithmetic, and lose information of the original orthogonal polynomial.
|
||||
|
||||
.. autosummary::
|
||||
:toctree: generated/
|
||||
|
||||
legendre -- [+]Legendre polynomial.
|
||||
chebyt -- [+]Chebyshev polynomial of the first kind.
|
||||
chebyu -- [+]Chebyshev polynomial of the second kind.
|
||||
chebyc -- [+]Chebyshev polynomial of the first kind on :math:`[-2, 2]`.
|
||||
chebys -- [+]Chebyshev polynomial of the second kind on :math:`[-2, 2]`.
|
||||
jacobi -- [+]Jacobi polynomial.
|
||||
laguerre -- [+]Laguerre polynomial.
|
||||
genlaguerre -- [+]Generalized (associated) Laguerre polynomial.
|
||||
hermite -- [+]Physicist's Hermite polynomial.
|
||||
hermitenorm -- [+]Normalized (probabilist's) Hermite polynomial.
|
||||
gegenbauer -- [+]Gegenbauer (ultraspherical) polynomial.
|
||||
sh_legendre -- [+]Shifted Legendre polynomial.
|
||||
sh_chebyt -- [+]Shifted Chebyshev polynomial of the first kind.
|
||||
sh_chebyu -- [+]Shifted Chebyshev polynomial of the second kind.
|
||||
sh_jacobi -- [+]Shifted Jacobi polynomial.
|
||||
|
||||
.. warning::
|
||||
|
||||
Computing values of high-order polynomials (around ``order > 20``) using
|
||||
polynomial coefficients is numerically unstable. To evaluate polynomial
|
||||
values, the ``eval_*`` functions should be used instead.
|
||||
|
||||
|
||||
Hypergeometric Functions
|
||||
------------------------
|
||||
|
||||
.. autosummary::
|
||||
:toctree: generated/
|
||||
|
||||
hyp2f1 -- Gauss hypergeometric function 2F1(a, b; c; z).
|
||||
hyp1f1 -- Confluent hypergeometric function 1F1(a, b; x)
|
||||
hyperu -- Confluent hypergeometric function U(a, b, x) of the second kind
|
||||
hyp0f1 -- Confluent hypergeometric limit function 0F1.
|
||||
hyp2f0 -- Hypergeometric function 2F0 in y and an error estimate
|
||||
hyp1f2 -- Hypergeometric function 1F2 and error estimate
|
||||
hyp3f0 -- Hypergeometric function 3F0 in y and an error estimate
|
||||
|
||||
|
||||
Parabolic Cylinder Functions
|
||||
----------------------------
|
||||
|
||||
.. autosummary::
|
||||
:toctree: generated/
|
||||
|
||||
pbdv -- Parabolic cylinder function D
|
||||
pbvv -- Parabolic cylinder function V
|
||||
pbwa -- Parabolic cylinder function W
|
||||
|
||||
These are not universal functions:
|
||||
|
||||
.. autosummary::
|
||||
:toctree: generated/
|
||||
|
||||
pbdv_seq -- [+]Parabolic cylinder functions Dv(x) and derivatives.
|
||||
pbvv_seq -- [+]Parabolic cylinder functions Vv(x) and derivatives.
|
||||
pbdn_seq -- [+]Parabolic cylinder functions Dn(z) and derivatives.
|
||||
|
||||
Mathieu and Related Functions
|
||||
-----------------------------
|
||||
|
||||
.. autosummary::
|
||||
:toctree: generated/
|
||||
|
||||
mathieu_a -- Characteristic value of even Mathieu functions
|
||||
mathieu_b -- Characteristic value of odd Mathieu functions
|
||||
|
||||
These are not universal functions:
|
||||
|
||||
.. autosummary::
|
||||
:toctree: generated/
|
||||
|
||||
mathieu_even_coef -- [+]Fourier coefficients for even Mathieu and modified Mathieu functions.
|
||||
mathieu_odd_coef -- [+]Fourier coefficients for even Mathieu and modified Mathieu functions.
|
||||
|
||||
The following return both function and first derivative:
|
||||
|
||||
.. autosummary::
|
||||
:toctree: generated/
|
||||
|
||||
mathieu_cem -- Even Mathieu function and its derivative
|
||||
mathieu_sem -- Odd Mathieu function and its derivative
|
||||
mathieu_modcem1 -- Even modified Mathieu function of the first kind and its derivative
|
||||
mathieu_modcem2 -- Even modified Mathieu function of the second kind and its derivative
|
||||
mathieu_modsem1 -- Odd modified Mathieu function of the first kind and its derivative
|
||||
mathieu_modsem2 -- Odd modified Mathieu function of the second kind and its derivative
|
||||
|
||||
Spheroidal Wave Functions
|
||||
-------------------------
|
||||
|
||||
.. autosummary::
|
||||
:toctree: generated/
|
||||
|
||||
pro_ang1 -- Prolate spheroidal angular function of the first kind and its derivative
|
||||
pro_rad1 -- Prolate spheroidal radial function of the first kind and its derivative
|
||||
pro_rad2 -- Prolate spheroidal radial function of the secon kind and its derivative
|
||||
obl_ang1 -- Oblate spheroidal angular function of the first kind and its derivative
|
||||
obl_rad1 -- Oblate spheroidal radial function of the first kind and its derivative
|
||||
obl_rad2 -- Oblate spheroidal radial function of the second kind and its derivative.
|
||||
pro_cv -- Characteristic value of prolate spheroidal function
|
||||
obl_cv -- Characteristic value of oblate spheroidal function
|
||||
pro_cv_seq -- Characteristic values for prolate spheroidal wave functions.
|
||||
obl_cv_seq -- Characteristic values for oblate spheroidal wave functions.
|
||||
|
||||
The following functions require pre-computed characteristic value:
|
||||
|
||||
.. autosummary::
|
||||
:toctree: generated/
|
||||
|
||||
pro_ang1_cv -- Prolate spheroidal angular function pro_ang1 for precomputed characteristic value
|
||||
pro_rad1_cv -- Prolate spheroidal radial function pro_rad1 for precomputed characteristic value
|
||||
pro_rad2_cv -- Prolate spheroidal radial function pro_rad2 for precomputed characteristic value
|
||||
obl_ang1_cv -- Oblate spheroidal angular function obl_ang1 for precomputed characteristic value
|
||||
obl_rad1_cv -- Oblate spheroidal radial function obl_rad1 for precomputed characteristic value
|
||||
obl_rad2_cv -- Oblate spheroidal radial function obl_rad2 for precomputed characteristic value
|
||||
|
||||
Kelvin Functions
|
||||
----------------
|
||||
|
||||
.. autosummary::
|
||||
:toctree: generated/
|
||||
|
||||
kelvin -- Kelvin functions as complex numbers
|
||||
kelvin_zeros -- [+]Compute nt zeros of all Kelvin functions.
|
||||
ber -- Kelvin function ber.
|
||||
bei -- Kelvin function bei
|
||||
berp -- Derivative of the Kelvin function `ber`
|
||||
beip -- Derivative of the Kelvin function `bei`
|
||||
ker -- Kelvin function ker
|
||||
kei -- Kelvin function ker
|
||||
kerp -- Derivative of the Kelvin function ker
|
||||
keip -- Derivative of the Kelvin function kei
|
||||
|
||||
These are not universal functions:
|
||||
|
||||
.. autosummary::
|
||||
:toctree: generated/
|
||||
|
||||
ber_zeros -- [+]Compute nt zeros of the Kelvin function ber(x).
|
||||
bei_zeros -- [+]Compute nt zeros of the Kelvin function bei(x).
|
||||
berp_zeros -- [+]Compute nt zeros of the Kelvin function ber'(x).
|
||||
beip_zeros -- [+]Compute nt zeros of the Kelvin function bei'(x).
|
||||
ker_zeros -- [+]Compute nt zeros of the Kelvin function ker(x).
|
||||
kei_zeros -- [+]Compute nt zeros of the Kelvin function kei(x).
|
||||
kerp_zeros -- [+]Compute nt zeros of the Kelvin function ker'(x).
|
||||
keip_zeros -- [+]Compute nt zeros of the Kelvin function kei'(x).
|
||||
|
||||
Combinatorics
|
||||
-------------
|
||||
|
||||
.. autosummary::
|
||||
:toctree: generated/
|
||||
|
||||
comb -- [+]The number of combinations of N things taken k at a time.
|
||||
perm -- [+]Permutations of N things taken k at a time, i.e., k-permutations of N.
|
||||
|
||||
Lambert W and Related Functions
|
||||
-------------------------------
|
||||
|
||||
.. autosummary::
|
||||
:toctree: generated/
|
||||
|
||||
lambertw -- Lambert W function.
|
||||
wrightomega -- Wright Omega function.
|
||||
|
||||
Other Special Functions
|
||||
-----------------------
|
||||
|
||||
.. autosummary::
|
||||
:toctree: generated/
|
||||
|
||||
agm -- Arithmetic, Geometric Mean.
|
||||
bernoulli -- Bernoulli numbers B0..Bn (inclusive).
|
||||
binom -- Binomial coefficient
|
||||
diric -- Periodic sinc function, also called the Dirichlet function.
|
||||
euler -- Euler numbers E0..En (inclusive).
|
||||
expn -- Exponential integral E_n
|
||||
exp1 -- Exponential integral E_1 of complex argument z
|
||||
expi -- Exponential integral Ei
|
||||
factorial -- The factorial of a number or array of numbers.
|
||||
factorial2 -- Double factorial.
|
||||
factorialk -- [+]Multifactorial of n of order k, n(!!...!).
|
||||
shichi -- Hyperbolic sine and cosine integrals.
|
||||
sici -- Sine and cosine integrals.
|
||||
softmax -- Softmax function.
|
||||
spence -- Spence's function, also known as the dilogarithm.
|
||||
zeta -- Riemann zeta function.
|
||||
zetac -- Riemann zeta function minus 1.
|
||||
|
||||
Convenience Functions
|
||||
---------------------
|
||||
|
||||
.. autosummary::
|
||||
:toctree: generated/
|
||||
|
||||
cbrt -- Cube root of `x`
|
||||
exp10 -- 10**x
|
||||
exp2 -- 2**x
|
||||
radian -- Convert from degrees to radians
|
||||
cosdg -- Cosine of the angle `x` given in degrees.
|
||||
sindg -- Sine of angle given in degrees
|
||||
tandg -- Tangent of angle x given in degrees.
|
||||
cotdg -- Cotangent of the angle `x` given in degrees.
|
||||
log1p -- Calculates log(1+x) for use when `x` is near zero
|
||||
expm1 -- exp(x) - 1 for use when `x` is near zero.
|
||||
cosm1 -- cos(x) - 1 for use when `x` is near zero.
|
||||
round -- Round to nearest integer
|
||||
xlogy -- Compute ``x*log(y)`` so that the result is 0 if ``x = 0``.
|
||||
xlog1py -- Compute ``x*log1p(y)`` so that the result is 0 if ``x = 0``.
|
||||
logsumexp -- Compute the log of the sum of exponentials of input elements.
|
||||
exprel -- Relative error exponential, (exp(x)-1)/x, for use when `x` is near zero.
|
||||
sinc -- Return the sinc function.
|
||||
|
||||
.. [+] in the description indicates a function which is not a universal
|
||||
.. function and does not follow broadcasting and automatic
|
||||
.. array-looping rules.
|
||||
|
||||
"""
|
||||
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
from .sf_error import SpecialFunctionWarning, SpecialFunctionError
|
||||
|
||||
from ._ufuncs import *
|
||||
|
||||
from .basic import *
|
||||
from ._logsumexp import logsumexp, softmax
|
||||
from . import specfun
|
||||
from . import orthogonal
|
||||
from .orthogonal import *
|
||||
from .spfun_stats import multigammaln
|
||||
from ._ellip_harm import ellip_harm, ellip_harm_2, ellip_normal
|
||||
from .lambertw import lambertw
|
||||
from ._spherical_bessel import (spherical_jn, spherical_yn, spherical_in,
|
||||
spherical_kn)
|
||||
|
||||
from numpy import deprecate
|
||||
hyp2f0 = deprecate(hyp2f0, message="hyp2f0 is deprecated in SciPy 1.2")
|
||||
hyp1f2 = deprecate(hyp1f2, message="hyp1f2 is deprecated in SciPy 1.2")
|
||||
hyp3f0 = deprecate(hyp3f0, message="hyp3f0 is deprecated in SciPy 1.2")
|
||||
del deprecate
|
||||
|
||||
__all__ = [s for s in dir() if not s.startswith('_')]
|
||||
|
||||
from numpy.dual import register_func
|
||||
register_func('i0',i0)
|
||||
del register_func
|
||||
|
||||
from scipy._lib._testutils import PytestTester
|
||||
test = PytestTester(__name__)
|
||||
del PytestTester
|
||||
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@@ -1,209 +0,0 @@
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
import numpy as np
|
||||
|
||||
from ._ufuncs import _ellip_harm
|
||||
from ._ellip_harm_2 import _ellipsoid, _ellipsoid_norm
|
||||
|
||||
|
||||
def ellip_harm(h2, k2, n, p, s, signm=1, signn=1):
|
||||
r"""
|
||||
Ellipsoidal harmonic functions E^p_n(l)
|
||||
|
||||
These are also known as Lame functions of the first kind, and are
|
||||
solutions to the Lame equation:
|
||||
|
||||
.. math:: (s^2 - h^2)(s^2 - k^2)E''(s) + s(2s^2 - h^2 - k^2)E'(s) + (a - q s^2)E(s) = 0
|
||||
|
||||
where :math:`q = (n+1)n` and :math:`a` is the eigenvalue (not
|
||||
returned) corresponding to the solutions.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
h2 : float
|
||||
``h**2``
|
||||
k2 : float
|
||||
``k**2``; should be larger than ``h**2``
|
||||
n : int
|
||||
Degree
|
||||
s : float
|
||||
Coordinate
|
||||
p : int
|
||||
Order, can range between [1,2n+1]
|
||||
signm : {1, -1}, optional
|
||||
Sign of prefactor of functions. Can be +/-1. See Notes.
|
||||
signn : {1, -1}, optional
|
||||
Sign of prefactor of functions. Can be +/-1. See Notes.
|
||||
|
||||
Returns
|
||||
-------
|
||||
E : float
|
||||
the harmonic :math:`E^p_n(s)`
|
||||
|
||||
See Also
|
||||
--------
|
||||
ellip_harm_2, ellip_normal
|
||||
|
||||
Notes
|
||||
-----
|
||||
The geometric interpretation of the ellipsoidal functions is
|
||||
explained in [2]_, [3]_, [4]_. The `signm` and `signn` arguments control the
|
||||
sign of prefactors for functions according to their type::
|
||||
|
||||
K : +1
|
||||
L : signm
|
||||
M : signn
|
||||
N : signm*signn
|
||||
|
||||
.. versionadded:: 0.15.0
|
||||
|
||||
References
|
||||
----------
|
||||
.. [1] Digital Library of Mathematical Functions 29.12
|
||||
https://dlmf.nist.gov/29.12
|
||||
.. [2] Bardhan and Knepley, "Computational science and
|
||||
re-discovery: open-source implementations of
|
||||
ellipsoidal harmonics for problems in potential theory",
|
||||
Comput. Sci. Disc. 5, 014006 (2012)
|
||||
:doi:`10.1088/1749-4699/5/1/014006`.
|
||||
.. [3] David J.and Dechambre P, "Computation of Ellipsoidal
|
||||
Gravity Field Harmonics for small solar system bodies"
|
||||
pp. 30-36, 2000
|
||||
.. [4] George Dassios, "Ellipsoidal Harmonics: Theory and Applications"
|
||||
pp. 418, 2012
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> from scipy.special import ellip_harm
|
||||
>>> w = ellip_harm(5,8,1,1,2.5)
|
||||
>>> w
|
||||
2.5
|
||||
|
||||
Check that the functions indeed are solutions to the Lame equation:
|
||||
|
||||
>>> from scipy.interpolate import UnivariateSpline
|
||||
>>> def eigenvalue(f, df, ddf):
|
||||
... r = ((s**2 - h**2)*(s**2 - k**2)*ddf + s*(2*s**2 - h**2 - k**2)*df - n*(n+1)*s**2*f)/f
|
||||
... return -r.mean(), r.std()
|
||||
>>> s = np.linspace(0.1, 10, 200)
|
||||
>>> k, h, n, p = 8.0, 2.2, 3, 2
|
||||
>>> E = ellip_harm(h**2, k**2, n, p, s)
|
||||
>>> E_spl = UnivariateSpline(s, E)
|
||||
>>> a, a_err = eigenvalue(E_spl(s), E_spl(s,1), E_spl(s,2))
|
||||
>>> a, a_err
|
||||
(583.44366156701483, 6.4580890640310646e-11)
|
||||
|
||||
"""
|
||||
return _ellip_harm(h2, k2, n, p, s, signm, signn)
|
||||
|
||||
|
||||
_ellip_harm_2_vec = np.vectorize(_ellipsoid, otypes='d')
|
||||
|
||||
|
||||
def ellip_harm_2(h2, k2, n, p, s):
|
||||
r"""
|
||||
Ellipsoidal harmonic functions F^p_n(l)
|
||||
|
||||
These are also known as Lame functions of the second kind, and are
|
||||
solutions to the Lame equation:
|
||||
|
||||
.. math:: (s^2 - h^2)(s^2 - k^2)F''(s) + s(2s^2 - h^2 - k^2)F'(s) + (a - q s^2)F(s) = 0
|
||||
|
||||
where :math:`q = (n+1)n` and :math:`a` is the eigenvalue (not
|
||||
returned) corresponding to the solutions.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
h2 : float
|
||||
``h**2``
|
||||
k2 : float
|
||||
``k**2``; should be larger than ``h**2``
|
||||
n : int
|
||||
Degree.
|
||||
p : int
|
||||
Order, can range between [1,2n+1].
|
||||
s : float
|
||||
Coordinate
|
||||
|
||||
Returns
|
||||
-------
|
||||
F : float
|
||||
The harmonic :math:`F^p_n(s)`
|
||||
|
||||
Notes
|
||||
-----
|
||||
Lame functions of the second kind are related to the functions of the first kind:
|
||||
|
||||
.. math::
|
||||
|
||||
F^p_n(s)=(2n + 1)E^p_n(s)\int_{0}^{1/s}\frac{du}{(E^p_n(1/u))^2\sqrt{(1-u^2k^2)(1-u^2h^2)}}
|
||||
|
||||
.. versionadded:: 0.15.0
|
||||
|
||||
See Also
|
||||
--------
|
||||
ellip_harm, ellip_normal
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> from scipy.special import ellip_harm_2
|
||||
>>> w = ellip_harm_2(5,8,2,1,10)
|
||||
>>> w
|
||||
0.00108056853382
|
||||
|
||||
"""
|
||||
with np.errstate(all='ignore'):
|
||||
return _ellip_harm_2_vec(h2, k2, n, p, s)
|
||||
|
||||
|
||||
def _ellip_normal_vec(h2, k2, n, p):
|
||||
return _ellipsoid_norm(h2, k2, n, p)
|
||||
|
||||
|
||||
_ellip_normal_vec = np.vectorize(_ellip_normal_vec, otypes='d')
|
||||
|
||||
|
||||
def ellip_normal(h2, k2, n, p):
|
||||
r"""
|
||||
Ellipsoidal harmonic normalization constants gamma^p_n
|
||||
|
||||
The normalization constant is defined as
|
||||
|
||||
.. math::
|
||||
|
||||
\gamma^p_n=8\int_{0}^{h}dx\int_{h}^{k}dy\frac{(y^2-x^2)(E^p_n(y)E^p_n(x))^2}{\sqrt((k^2-y^2)(y^2-h^2)(h^2-x^2)(k^2-x^2)}
|
||||
|
||||
Parameters
|
||||
----------
|
||||
h2 : float
|
||||
``h**2``
|
||||
k2 : float
|
||||
``k**2``; should be larger than ``h**2``
|
||||
n : int
|
||||
Degree.
|
||||
p : int
|
||||
Order, can range between [1,2n+1].
|
||||
|
||||
Returns
|
||||
-------
|
||||
gamma : float
|
||||
The normalization constant :math:`\gamma^p_n`
|
||||
|
||||
See Also
|
||||
--------
|
||||
ellip_harm, ellip_harm_2
|
||||
|
||||
Notes
|
||||
-----
|
||||
.. versionadded:: 0.15.0
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> from scipy.special import ellip_normal
|
||||
>>> w = ellip_normal(5,8,3,7)
|
||||
>>> w
|
||||
1723.38796997
|
||||
|
||||
"""
|
||||
with np.errstate(all='ignore'):
|
||||
return _ellip_normal_vec(h2, k2, n, p)
|
||||
BIN
Binary file not shown.
File diff suppressed because it is too large
Load Diff
@@ -1,215 +0,0 @@
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
import numpy as np
|
||||
from scipy._lib._util import _asarray_validated
|
||||
|
||||
__all__ = ["logsumexp", "softmax"]
|
||||
|
||||
|
||||
def logsumexp(a, axis=None, b=None, keepdims=False, return_sign=False):
|
||||
"""Compute the log of the sum of exponentials of input elements.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
a : array_like
|
||||
Input array.
|
||||
axis : None or int or tuple of ints, optional
|
||||
Axis or axes over which the sum is taken. By default `axis` is None,
|
||||
and all elements are summed.
|
||||
|
||||
.. versionadded:: 0.11.0
|
||||
keepdims : bool, optional
|
||||
If this is set to True, the axes which are reduced are left in the
|
||||
result as dimensions with size one. With this option, the result
|
||||
will broadcast correctly against the original array.
|
||||
|
||||
.. versionadded:: 0.15.0
|
||||
b : array-like, optional
|
||||
Scaling factor for exp(`a`) must be of the same shape as `a` or
|
||||
broadcastable to `a`. These values may be negative in order to
|
||||
implement subtraction.
|
||||
|
||||
.. versionadded:: 0.12.0
|
||||
return_sign : bool, optional
|
||||
If this is set to True, the result will be a pair containing sign
|
||||
information; if False, results that are negative will be returned
|
||||
as NaN. Default is False (no sign information).
|
||||
|
||||
.. versionadded:: 0.16.0
|
||||
|
||||
Returns
|
||||
-------
|
||||
res : ndarray
|
||||
The result, ``np.log(np.sum(np.exp(a)))`` calculated in a numerically
|
||||
more stable way. If `b` is given then ``np.log(np.sum(b*np.exp(a)))``
|
||||
is returned.
|
||||
sgn : ndarray
|
||||
If return_sign is True, this will be an array of floating-point
|
||||
numbers matching res and +1, 0, or -1 depending on the sign
|
||||
of the result. If False, only one result is returned.
|
||||
|
||||
See Also
|
||||
--------
|
||||
numpy.logaddexp, numpy.logaddexp2
|
||||
|
||||
Notes
|
||||
-----
|
||||
Numpy has a logaddexp function which is very similar to `logsumexp`, but
|
||||
only handles two arguments. `logaddexp.reduce` is similar to this
|
||||
function, but may be less stable.
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> from scipy.special import logsumexp
|
||||
>>> a = np.arange(10)
|
||||
>>> np.log(np.sum(np.exp(a)))
|
||||
9.4586297444267107
|
||||
>>> logsumexp(a)
|
||||
9.4586297444267107
|
||||
|
||||
With weights
|
||||
|
||||
>>> a = np.arange(10)
|
||||
>>> b = np.arange(10, 0, -1)
|
||||
>>> logsumexp(a, b=b)
|
||||
9.9170178533034665
|
||||
>>> np.log(np.sum(b*np.exp(a)))
|
||||
9.9170178533034647
|
||||
|
||||
Returning a sign flag
|
||||
|
||||
>>> logsumexp([1,2],b=[1,-1],return_sign=True)
|
||||
(1.5413248546129181, -1.0)
|
||||
|
||||
Notice that `logsumexp` does not directly support masked arrays. To use it
|
||||
on a masked array, convert the mask into zero weights:
|
||||
|
||||
>>> a = np.ma.array([np.log(2), 2, np.log(3)],
|
||||
... mask=[False, True, False])
|
||||
>>> b = (~a.mask).astype(int)
|
||||
>>> logsumexp(a.data, b=b), np.log(5)
|
||||
1.6094379124341005, 1.6094379124341005
|
||||
|
||||
"""
|
||||
a = _asarray_validated(a, check_finite=False)
|
||||
if b is not None:
|
||||
a, b = np.broadcast_arrays(a, b)
|
||||
if np.any(b == 0):
|
||||
a = a + 0. # promote to at least float
|
||||
a[b == 0] = -np.inf
|
||||
|
||||
a_max = np.amax(a, axis=axis, keepdims=True)
|
||||
|
||||
if a_max.ndim > 0:
|
||||
a_max[~np.isfinite(a_max)] = 0
|
||||
elif not np.isfinite(a_max):
|
||||
a_max = 0
|
||||
|
||||
if b is not None:
|
||||
b = np.asarray(b)
|
||||
tmp = b * np.exp(a - a_max)
|
||||
else:
|
||||
tmp = np.exp(a - a_max)
|
||||
|
||||
# suppress warnings about log of zero
|
||||
with np.errstate(divide='ignore'):
|
||||
s = np.sum(tmp, axis=axis, keepdims=keepdims)
|
||||
if return_sign:
|
||||
sgn = np.sign(s)
|
||||
s *= sgn # /= makes more sense but we need zero -> zero
|
||||
out = np.log(s)
|
||||
|
||||
if not keepdims:
|
||||
a_max = np.squeeze(a_max, axis=axis)
|
||||
out += a_max
|
||||
|
||||
if return_sign:
|
||||
return out, sgn
|
||||
else:
|
||||
return out
|
||||
|
||||
|
||||
def softmax(x, axis=None):
|
||||
r"""
|
||||
Softmax function
|
||||
|
||||
The softmax function transforms each element of a collection by
|
||||
computing the exponential of each element divided by the sum of the
|
||||
exponentials of all the elements. That is, if `x` is a one-dimensional
|
||||
numpy array::
|
||||
|
||||
softmax(x) = np.exp(x)/sum(np.exp(x))
|
||||
|
||||
Parameters
|
||||
----------
|
||||
x : array_like
|
||||
Input array.
|
||||
axis : int or tuple of ints, optional
|
||||
Axis to compute values along. Default is None and softmax will be
|
||||
computed over the entire array `x`.
|
||||
|
||||
Returns
|
||||
-------
|
||||
s : ndarray
|
||||
An array the same shape as `x`. The result will sum to 1 along the
|
||||
specified axis.
|
||||
|
||||
Notes
|
||||
-----
|
||||
The formula for the softmax function :math:`\sigma(x)` for a vector
|
||||
:math:`x = \{x_0, x_1, ..., x_{n-1}\}` is
|
||||
|
||||
.. math:: \sigma(x)_j = \frac{e^{x_j}}{\sum_k e^{x_k}}
|
||||
|
||||
The `softmax` function is the gradient of `logsumexp`.
|
||||
|
||||
.. versionadded:: 1.2.0
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> from scipy.special import softmax
|
||||
>>> np.set_printoptions(precision=5)
|
||||
|
||||
>>> x = np.array([[1, 0.5, 0.2, 3],
|
||||
... [1, -1, 7, 3],
|
||||
... [2, 12, 13, 3]])
|
||||
...
|
||||
|
||||
Compute the softmax transformation over the entire array.
|
||||
|
||||
>>> m = softmax(x)
|
||||
>>> m
|
||||
array([[ 4.48309e-06, 2.71913e-06, 2.01438e-06, 3.31258e-05],
|
||||
[ 4.48309e-06, 6.06720e-07, 1.80861e-03, 3.31258e-05],
|
||||
[ 1.21863e-05, 2.68421e-01, 7.29644e-01, 3.31258e-05]])
|
||||
|
||||
>>> m.sum()
|
||||
1.0000000000000002
|
||||
|
||||
Compute the softmax transformation along the first axis (i.e. the columns).
|
||||
|
||||
>>> m = softmax(x, axis=0)
|
||||
|
||||
>>> m
|
||||
array([[ 2.11942e-01, 1.01300e-05, 2.75394e-06, 3.33333e-01],
|
||||
[ 2.11942e-01, 2.26030e-06, 2.47262e-03, 3.33333e-01],
|
||||
[ 5.76117e-01, 9.99988e-01, 9.97525e-01, 3.33333e-01]])
|
||||
|
||||
>>> m.sum(axis=0)
|
||||
array([ 1., 1., 1., 1.])
|
||||
|
||||
Compute the softmax transformation along the second axis (i.e. the rows).
|
||||
|
||||
>>> m = softmax(x, axis=1)
|
||||
>>> m
|
||||
array([[ 1.05877e-01, 6.42177e-02, 4.75736e-02, 7.82332e-01],
|
||||
[ 2.42746e-03, 3.28521e-04, 9.79307e-01, 1.79366e-02],
|
||||
[ 1.22094e-05, 2.68929e-01, 7.31025e-01, 3.31885e-05]])
|
||||
|
||||
>>> m.sum(axis=1)
|
||||
array([ 1., 1., 1.])
|
||||
|
||||
"""
|
||||
|
||||
# compute in log space for numerical stability
|
||||
return np.exp(x - logsumexp(x, axis=axis, keepdims=True))
|
||||
@@ -1,455 +0,0 @@
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
import os
|
||||
import sys
|
||||
import time
|
||||
|
||||
import numpy as np
|
||||
from numpy.testing import assert_
|
||||
import pytest
|
||||
|
||||
from scipy._lib.six import reraise
|
||||
from scipy.special._testutils import assert_func_equal
|
||||
|
||||
try:
|
||||
import mpmath
|
||||
except ImportError:
|
||||
pass
|
||||
|
||||
|
||||
# ------------------------------------------------------------------------------
|
||||
# Machinery for systematic tests with mpmath
|
||||
# ------------------------------------------------------------------------------
|
||||
|
||||
class Arg(object):
|
||||
"""Generate a set of numbers on the real axis, concentrating on
|
||||
'interesting' regions and covering all orders of magnitude.
|
||||
|
||||
"""
|
||||
|
||||
def __init__(self, a=-np.inf, b=np.inf, inclusive_a=True, inclusive_b=True):
|
||||
if a > b:
|
||||
raise ValueError("a should be less than or equal to b")
|
||||
if a == -np.inf:
|
||||
a = -0.5*np.finfo(float).max
|
||||
if b == np.inf:
|
||||
b = 0.5*np.finfo(float).max
|
||||
self.a, self.b = a, b
|
||||
|
||||
self.inclusive_a, self.inclusive_b = inclusive_a, inclusive_b
|
||||
|
||||
def _positive_values(self, a, b, n):
|
||||
if a < 0:
|
||||
raise ValueError("a should be positive")
|
||||
|
||||
# Try to put half of the points into a linspace between a and
|
||||
# 10 the other half in a logspace.
|
||||
if n % 2 == 0:
|
||||
nlogpts = n//2
|
||||
nlinpts = nlogpts
|
||||
else:
|
||||
nlogpts = n//2
|
||||
nlinpts = nlogpts + 1
|
||||
|
||||
if a >= 10:
|
||||
# Outside of linspace range; just return a logspace.
|
||||
pts = np.logspace(np.log10(a), np.log10(b), n)
|
||||
elif a > 0 and b < 10:
|
||||
# Outside of logspace range; just return a linspace
|
||||
pts = np.linspace(a, b, n)
|
||||
elif a > 0:
|
||||
# Linspace between a and 10 and a logspace between 10 and
|
||||
# b.
|
||||
linpts = np.linspace(a, 10, nlinpts, endpoint=False)
|
||||
logpts = np.logspace(1, np.log10(b), nlogpts)
|
||||
pts = np.hstack((linpts, logpts))
|
||||
elif a == 0 and b <= 10:
|
||||
# Linspace between 0 and b and a logspace between 0 and
|
||||
# the smallest positive point of the linspace
|
||||
linpts = np.linspace(0, b, nlinpts)
|
||||
if linpts.size > 1:
|
||||
right = np.log10(linpts[1])
|
||||
else:
|
||||
right = -30
|
||||
logpts = np.logspace(-30, right, nlogpts, endpoint=False)
|
||||
pts = np.hstack((logpts, linpts))
|
||||
else:
|
||||
# Linspace between 0 and 10, logspace between 0 and the
|
||||
# smallest positive point of the linspace, and a logspace
|
||||
# between 10 and b.
|
||||
if nlogpts % 2 == 0:
|
||||
nlogpts1 = nlogpts//2
|
||||
nlogpts2 = nlogpts1
|
||||
else:
|
||||
nlogpts1 = nlogpts//2
|
||||
nlogpts2 = nlogpts1 + 1
|
||||
linpts = np.linspace(0, 10, nlinpts, endpoint=False)
|
||||
if linpts.size > 1:
|
||||
right = np.log10(linpts[1])
|
||||
else:
|
||||
right = -30
|
||||
logpts1 = np.logspace(-30, right, nlogpts1, endpoint=False)
|
||||
logpts2 = np.logspace(1, np.log10(b), nlogpts2)
|
||||
pts = np.hstack((logpts1, linpts, logpts2))
|
||||
|
||||
return np.sort(pts)
|
||||
|
||||
def values(self, n):
|
||||
"""Return an array containing n numbers."""
|
||||
a, b = self.a, self.b
|
||||
if a == b:
|
||||
return np.zeros(n)
|
||||
|
||||
if not self.inclusive_a:
|
||||
n += 1
|
||||
if not self.inclusive_b:
|
||||
n += 1
|
||||
|
||||
if n % 2 == 0:
|
||||
n1 = n//2
|
||||
n2 = n1
|
||||
else:
|
||||
n1 = n//2
|
||||
n2 = n1 + 1
|
||||
|
||||
if a >= 0:
|
||||
pospts = self._positive_values(a, b, n)
|
||||
negpts = []
|
||||
elif b <= 0:
|
||||
pospts = []
|
||||
negpts = -self._positive_values(-b, -a, n)
|
||||
else:
|
||||
pospts = self._positive_values(0, b, n1)
|
||||
negpts = -self._positive_values(0, -a, n2 + 1)
|
||||
# Don't want to get zero twice
|
||||
negpts = negpts[1:]
|
||||
pts = np.hstack((negpts[::-1], pospts))
|
||||
|
||||
if not self.inclusive_a:
|
||||
pts = pts[1:]
|
||||
if not self.inclusive_b:
|
||||
pts = pts[:-1]
|
||||
return pts
|
||||
|
||||
|
||||
class FixedArg(object):
|
||||
def __init__(self, values):
|
||||
self._values = np.asarray(values)
|
||||
|
||||
def values(self, n):
|
||||
return self._values
|
||||
|
||||
|
||||
class ComplexArg(object):
|
||||
def __init__(self, a=complex(-np.inf, -np.inf), b=complex(np.inf, np.inf)):
|
||||
self.real = Arg(a.real, b.real)
|
||||
self.imag = Arg(a.imag, b.imag)
|
||||
|
||||
def values(self, n):
|
||||
m = int(np.floor(np.sqrt(n)))
|
||||
x = self.real.values(m)
|
||||
y = self.imag.values(m + 1)
|
||||
return (x[:,None] + 1j*y[None,:]).ravel()
|
||||
|
||||
|
||||
class IntArg(object):
|
||||
def __init__(self, a=-1000, b=1000):
|
||||
self.a = a
|
||||
self.b = b
|
||||
|
||||
def values(self, n):
|
||||
v1 = Arg(self.a, self.b).values(max(1 + n//2, n-5)).astype(int)
|
||||
v2 = np.arange(-5, 5)
|
||||
v = np.unique(np.r_[v1, v2])
|
||||
v = v[(v >= self.a) & (v < self.b)]
|
||||
return v
|
||||
|
||||
|
||||
def get_args(argspec, n):
|
||||
if isinstance(argspec, np.ndarray):
|
||||
args = argspec.copy()
|
||||
else:
|
||||
nargs = len(argspec)
|
||||
ms = np.asarray([1.5 if isinstance(spec, ComplexArg) else 1.0 for spec in argspec])
|
||||
ms = (n**(ms/sum(ms))).astype(int) + 1
|
||||
|
||||
args = []
|
||||
for spec, m in zip(argspec, ms):
|
||||
args.append(spec.values(m))
|
||||
args = np.array(np.broadcast_arrays(*np.ix_(*args))).reshape(nargs, -1).T
|
||||
|
||||
return args
|
||||
|
||||
|
||||
class MpmathData(object):
|
||||
def __init__(self, scipy_func, mpmath_func, arg_spec, name=None,
|
||||
dps=None, prec=None, n=None, rtol=1e-7, atol=1e-300,
|
||||
ignore_inf_sign=False, distinguish_nan_and_inf=True,
|
||||
nan_ok=True, param_filter=None):
|
||||
|
||||
# mpmath tests are really slow (see gh-6989). Use a small number of
|
||||
# points by default, increase back to 5000 (old default) if XSLOW is
|
||||
# set
|
||||
if n is None:
|
||||
try:
|
||||
is_xslow = int(os.environ.get('SCIPY_XSLOW', '0'))
|
||||
except ValueError:
|
||||
is_xslow = False
|
||||
|
||||
n = 5000 if is_xslow else 500
|
||||
|
||||
self.scipy_func = scipy_func
|
||||
self.mpmath_func = mpmath_func
|
||||
self.arg_spec = arg_spec
|
||||
self.dps = dps
|
||||
self.prec = prec
|
||||
self.n = n
|
||||
self.rtol = rtol
|
||||
self.atol = atol
|
||||
self.ignore_inf_sign = ignore_inf_sign
|
||||
self.nan_ok = nan_ok
|
||||
if isinstance(self.arg_spec, np.ndarray):
|
||||
self.is_complex = np.issubdtype(self.arg_spec.dtype, np.complexfloating)
|
||||
else:
|
||||
self.is_complex = any([isinstance(arg, ComplexArg) for arg in self.arg_spec])
|
||||
self.ignore_inf_sign = ignore_inf_sign
|
||||
self.distinguish_nan_and_inf = distinguish_nan_and_inf
|
||||
if not name or name == '<lambda>':
|
||||
name = getattr(scipy_func, '__name__', None)
|
||||
if not name or name == '<lambda>':
|
||||
name = getattr(mpmath_func, '__name__', None)
|
||||
self.name = name
|
||||
self.param_filter = param_filter
|
||||
|
||||
def check(self):
|
||||
np.random.seed(1234)
|
||||
|
||||
# Generate values for the arguments
|
||||
argarr = get_args(self.arg_spec, self.n)
|
||||
|
||||
# Check
|
||||
old_dps, old_prec = mpmath.mp.dps, mpmath.mp.prec
|
||||
try:
|
||||
if self.dps is not None:
|
||||
dps_list = [self.dps]
|
||||
else:
|
||||
dps_list = [20]
|
||||
if self.prec is not None:
|
||||
mpmath.mp.prec = self.prec
|
||||
|
||||
# Proper casting of mpmath input and output types. Using
|
||||
# native mpmath types as inputs gives improved precision
|
||||
# in some cases.
|
||||
if np.issubdtype(argarr.dtype, np.complexfloating):
|
||||
pytype = mpc2complex
|
||||
|
||||
def mptype(x):
|
||||
return mpmath.mpc(complex(x))
|
||||
else:
|
||||
def mptype(x):
|
||||
return mpmath.mpf(float(x))
|
||||
|
||||
def pytype(x):
|
||||
if abs(x.imag) > 1e-16*(1 + abs(x.real)):
|
||||
return np.nan
|
||||
else:
|
||||
return mpf2float(x.real)
|
||||
|
||||
# Try out different dps until one (or none) works
|
||||
for j, dps in enumerate(dps_list):
|
||||
mpmath.mp.dps = dps
|
||||
|
||||
try:
|
||||
assert_func_equal(self.scipy_func,
|
||||
lambda *a: pytype(self.mpmath_func(*map(mptype, a))),
|
||||
argarr,
|
||||
vectorized=False,
|
||||
rtol=self.rtol, atol=self.atol,
|
||||
ignore_inf_sign=self.ignore_inf_sign,
|
||||
distinguish_nan_and_inf=self.distinguish_nan_and_inf,
|
||||
nan_ok=self.nan_ok,
|
||||
param_filter=self.param_filter)
|
||||
break
|
||||
except AssertionError:
|
||||
if j >= len(dps_list)-1:
|
||||
reraise(*sys.exc_info())
|
||||
finally:
|
||||
mpmath.mp.dps, mpmath.mp.prec = old_dps, old_prec
|
||||
|
||||
def __repr__(self):
|
||||
if self.is_complex:
|
||||
return "<MpmathData: %s (complex)>" % (self.name,)
|
||||
else:
|
||||
return "<MpmathData: %s>" % (self.name,)
|
||||
|
||||
|
||||
def assert_mpmath_equal(*a, **kw):
|
||||
d = MpmathData(*a, **kw)
|
||||
d.check()
|
||||
|
||||
|
||||
def nonfunctional_tooslow(func):
|
||||
return pytest.mark.skip(reason=" Test not yet functional (too slow), needs more work.")(func)
|
||||
|
||||
|
||||
# ------------------------------------------------------------------------------
|
||||
# Tools for dealing with mpmath quirks
|
||||
# ------------------------------------------------------------------------------
|
||||
|
||||
def mpf2float(x):
|
||||
"""
|
||||
Convert an mpf to the nearest floating point number. Just using
|
||||
float directly doesn't work because of results like this:
|
||||
|
||||
with mp.workdps(50):
|
||||
float(mpf("0.99999999999999999")) = 0.9999999999999999
|
||||
|
||||
"""
|
||||
return float(mpmath.nstr(x, 17, min_fixed=0, max_fixed=0))
|
||||
|
||||
|
||||
def mpc2complex(x):
|
||||
return complex(mpf2float(x.real), mpf2float(x.imag))
|
||||
|
||||
|
||||
def trace_args(func):
|
||||
def tofloat(x):
|
||||
if isinstance(x, mpmath.mpc):
|
||||
return complex(x)
|
||||
else:
|
||||
return float(x)
|
||||
|
||||
def wrap(*a, **kw):
|
||||
sys.stderr.write("%r: " % (tuple(map(tofloat, a)),))
|
||||
sys.stderr.flush()
|
||||
try:
|
||||
r = func(*a, **kw)
|
||||
sys.stderr.write("-> %r" % r)
|
||||
finally:
|
||||
sys.stderr.write("\n")
|
||||
sys.stderr.flush()
|
||||
return r
|
||||
return wrap
|
||||
|
||||
|
||||
try:
|
||||
import posix
|
||||
import signal
|
||||
POSIX = ('setitimer' in dir(signal))
|
||||
except ImportError:
|
||||
POSIX = False
|
||||
|
||||
|
||||
class TimeoutError(Exception):
|
||||
pass
|
||||
|
||||
|
||||
def time_limited(timeout=0.5, return_val=np.nan, use_sigalrm=True):
|
||||
"""
|
||||
Decorator for setting a timeout for pure-Python functions.
|
||||
|
||||
If the function does not return within `timeout` seconds, the
|
||||
value `return_val` is returned instead.
|
||||
|
||||
On POSIX this uses SIGALRM by default. On non-POSIX, settrace is
|
||||
used. Do not use this with threads: the SIGALRM implementation
|
||||
does probably not work well. The settrace implementation only
|
||||
traces the current thread.
|
||||
|
||||
The settrace implementation slows down execution speed. Slowdown
|
||||
by a factor around 10 is probably typical.
|
||||
"""
|
||||
if POSIX and use_sigalrm:
|
||||
def sigalrm_handler(signum, frame):
|
||||
raise TimeoutError()
|
||||
|
||||
def deco(func):
|
||||
def wrap(*a, **kw):
|
||||
old_handler = signal.signal(signal.SIGALRM, sigalrm_handler)
|
||||
signal.setitimer(signal.ITIMER_REAL, timeout)
|
||||
try:
|
||||
return func(*a, **kw)
|
||||
except TimeoutError:
|
||||
return return_val
|
||||
finally:
|
||||
signal.setitimer(signal.ITIMER_REAL, 0)
|
||||
signal.signal(signal.SIGALRM, old_handler)
|
||||
return wrap
|
||||
else:
|
||||
def deco(func):
|
||||
def wrap(*a, **kw):
|
||||
start_time = time.time()
|
||||
|
||||
def trace(frame, event, arg):
|
||||
if time.time() - start_time > timeout:
|
||||
raise TimeoutError()
|
||||
return trace
|
||||
sys.settrace(trace)
|
||||
try:
|
||||
return func(*a, **kw)
|
||||
except TimeoutError:
|
||||
sys.settrace(None)
|
||||
return return_val
|
||||
finally:
|
||||
sys.settrace(None)
|
||||
return wrap
|
||||
return deco
|
||||
|
||||
|
||||
def exception_to_nan(func):
|
||||
"""Decorate function to return nan if it raises an exception"""
|
||||
def wrap(*a, **kw):
|
||||
try:
|
||||
return func(*a, **kw)
|
||||
except Exception:
|
||||
return np.nan
|
||||
return wrap
|
||||
|
||||
|
||||
def inf_to_nan(func):
|
||||
"""Decorate function to return nan if it returns inf"""
|
||||
def wrap(*a, **kw):
|
||||
v = func(*a, **kw)
|
||||
if not np.isfinite(v):
|
||||
return np.nan
|
||||
return v
|
||||
return wrap
|
||||
|
||||
|
||||
def mp_assert_allclose(res, std, atol=0, rtol=1e-17):
|
||||
"""
|
||||
Compare lists of mpmath.mpf's or mpmath.mpc's directly so that it
|
||||
can be done to higher precision than double.
|
||||
|
||||
"""
|
||||
try:
|
||||
len(res)
|
||||
except TypeError:
|
||||
res = list(res)
|
||||
|
||||
n = len(std)
|
||||
if len(res) != n:
|
||||
raise AssertionError("Lengths of inputs not equal.")
|
||||
|
||||
failures = []
|
||||
for k in range(n):
|
||||
try:
|
||||
assert_(mpmath.fabs(res[k] - std[k]) <= atol + rtol*mpmath.fabs(std[k]))
|
||||
except AssertionError:
|
||||
failures.append(k)
|
||||
|
||||
ndigits = int(abs(np.log10(rtol)))
|
||||
msg = [""]
|
||||
msg.append("Bad results ({} out of {}) for the following points:"
|
||||
.format(len(failures), n))
|
||||
for k in failures:
|
||||
resrep = mpmath.nstr(res[k], ndigits, min_fixed=0, max_fixed=0)
|
||||
stdrep = mpmath.nstr(std[k], ndigits, min_fixed=0, max_fixed=0)
|
||||
if std[k] == 0:
|
||||
rdiff = "inf"
|
||||
else:
|
||||
rdiff = mpmath.fabs((res[k] - std[k])/std[k])
|
||||
rdiff = mpmath.nstr(rdiff, 3)
|
||||
msg.append("{}: {} != {} (rdiff {})".format(k, resrep, stdrep, rdiff))
|
||||
if failures:
|
||||
assert_(False, "\n".join(msg))
|
||||
BIN
Binary file not shown.
BIN
Binary file not shown.
BIN
Binary file not shown.
BIN
Binary file not shown.
BIN
Binary file not shown.
BIN
Binary file not shown.
BIN
Binary file not shown.
BIN
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BIN
Binary file not shown.
BIN
Binary file not shown.
@@ -1,61 +0,0 @@
|
||||
"""Precompute the polynomials for the asymptotic expansion of the
|
||||
generalized exponential integral.
|
||||
|
||||
Sources
|
||||
-------
|
||||
[1] NIST, Digital Library of Mathematical Functions,
|
||||
https://dlmf.nist.gov/8.20#ii
|
||||
|
||||
"""
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
import os
|
||||
from scipy._lib._numpy_compat import suppress_warnings
|
||||
|
||||
try:
|
||||
# Can remove when sympy #11255 is resolved; see
|
||||
# https://github.com/sympy/sympy/issues/11255
|
||||
with suppress_warnings() as sup:
|
||||
sup.filter(DeprecationWarning, "inspect.getargspec.. is deprecated")
|
||||
import sympy
|
||||
from sympy import Poly
|
||||
x = sympy.symbols('x')
|
||||
except ImportError:
|
||||
pass
|
||||
|
||||
|
||||
def generate_A(K):
|
||||
A = [Poly(1, x)]
|
||||
for k in range(K):
|
||||
A.append(Poly(1 - 2*k*x, x)*A[k] + Poly(x*(x + 1))*A[k].diff())
|
||||
return A
|
||||
|
||||
|
||||
WARNING = """\
|
||||
/* This file was automatically generated by _precompute/expn_asy.py.
|
||||
* Do not edit it manually!
|
||||
*/
|
||||
"""
|
||||
|
||||
|
||||
def main():
|
||||
print(__doc__)
|
||||
fn = os.path.join('..', 'cephes', 'expn.h')
|
||||
|
||||
K = 12
|
||||
A = generate_A(K)
|
||||
with open(fn + '.new', 'w') as f:
|
||||
f.write(WARNING)
|
||||
f.write("#define nA {}\n".format(len(A)))
|
||||
for k, Ak in enumerate(A):
|
||||
tmp = ', '.join([str(x.evalf(18)) for x in Ak.coeffs()])
|
||||
f.write("static const double A{}[] = {{{}}};\n".format(k, tmp))
|
||||
tmp = ", ".join(["A{}".format(k) for k in range(K + 1)])
|
||||
f.write("static const double *A[] = {{{}}};\n".format(tmp))
|
||||
tmp = ", ".join([str(Ak.degree()) for Ak in A])
|
||||
f.write("static const int Adegs[] = {{{}}};\n".format(tmp))
|
||||
os.rename(fn + '.new', fn)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,119 +0,0 @@
|
||||
"""
|
||||
Precompute coefficients of Temme's asymptotic expansion for gammainc.
|
||||
|
||||
This takes about 8 hours to run on a 2.3 GHz Macbook Pro with 4GB ram.
|
||||
|
||||
Sources:
|
||||
[1] NIST, "Digital Library of Mathematical Functions",
|
||||
https://dlmf.nist.gov/
|
||||
|
||||
"""
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
import os
|
||||
from scipy.special._precompute.utils import lagrange_inversion
|
||||
|
||||
try:
|
||||
import mpmath as mp
|
||||
except ImportError:
|
||||
pass
|
||||
|
||||
|
||||
def compute_a(n):
|
||||
"""a_k from DLMF 5.11.6"""
|
||||
a = [mp.sqrt(2)/2]
|
||||
for k in range(1, n):
|
||||
ak = a[-1]/k
|
||||
for j in range(1, len(a)):
|
||||
ak -= a[j]*a[-j]/(j + 1)
|
||||
ak /= a[0]*(1 + mp.mpf(1)/(k + 1))
|
||||
a.append(ak)
|
||||
return a
|
||||
|
||||
|
||||
def compute_g(n):
|
||||
"""g_k from DLMF 5.11.3/5.11.5"""
|
||||
a = compute_a(2*n)
|
||||
g = []
|
||||
for k in range(n):
|
||||
g.append(mp.sqrt(2)*mp.rf(0.5, k)*a[2*k])
|
||||
return g
|
||||
|
||||
|
||||
def eta(lam):
|
||||
"""Function from DLMF 8.12.1 shifted to be centered at 0."""
|
||||
if lam > 0:
|
||||
return mp.sqrt(2*(lam - mp.log(lam + 1)))
|
||||
elif lam < 0:
|
||||
return -mp.sqrt(2*(lam - mp.log(lam + 1)))
|
||||
else:
|
||||
return 0
|
||||
|
||||
|
||||
def compute_alpha(n):
|
||||
"""alpha_n from DLMF 8.12.13"""
|
||||
coeffs = mp.taylor(eta, 0, n - 1)
|
||||
return lagrange_inversion(coeffs)
|
||||
|
||||
|
||||
def compute_d(K, N):
|
||||
"""d_{k, n} from DLMF 8.12.12"""
|
||||
M = N + 2*K
|
||||
d0 = [-mp.mpf(1)/3]
|
||||
alpha = compute_alpha(M + 2)
|
||||
for n in range(1, M):
|
||||
d0.append((n + 2)*alpha[n+2])
|
||||
d = [d0]
|
||||
g = compute_g(K)
|
||||
for k in range(1, K):
|
||||
dk = []
|
||||
for n in range(M - 2*k):
|
||||
dk.append((-1)**k*g[k]*d[0][n] + (n + 2)*d[k-1][n+2])
|
||||
d.append(dk)
|
||||
for k in range(K):
|
||||
d[k] = d[k][:N]
|
||||
return d
|
||||
|
||||
|
||||
header = \
|
||||
r"""/* This file was automatically generated by _precomp/gammainc.py.
|
||||
* Do not edit it manually!
|
||||
*/
|
||||
|
||||
#ifndef IGAM_H
|
||||
#define IGAM_H
|
||||
|
||||
#define K {}
|
||||
#define N {}
|
||||
|
||||
static const double d[K][N] =
|
||||
{{"""
|
||||
|
||||
footer = \
|
||||
r"""
|
||||
#endif
|
||||
"""
|
||||
|
||||
def main():
|
||||
print(__doc__)
|
||||
K = 25
|
||||
N = 25
|
||||
with mp.workdps(50):
|
||||
d = compute_d(K, N)
|
||||
fn = os.path.join(os.path.dirname(__file__), '..', 'cephes', 'igam.h')
|
||||
with open(fn + '.new', 'w') as f:
|
||||
f.write(header.format(K, N))
|
||||
for k, row in enumerate(d):
|
||||
row = map(lambda x: mp.nstr(x, 17, min_fixed=0, max_fixed=0), row)
|
||||
f.write('{')
|
||||
f.write(", ".join(row))
|
||||
if k < K - 1:
|
||||
f.write('},\n')
|
||||
else:
|
||||
f.write('}};\n')
|
||||
f.write(footer)
|
||||
os.rename(fn + '.new', fn)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,126 +0,0 @@
|
||||
"""Compute gammainc and gammaincc for large arguments and parameters
|
||||
and save the values to data files for use in tests. We can't just
|
||||
compare to mpmath's gammainc in test_mpmath.TestSystematic because it
|
||||
would take too long.
|
||||
|
||||
Note that mpmath's gammainc is computed using hypercomb, but since it
|
||||
doesn't allow the user to increase the maximum number of terms used in
|
||||
the series it doesn't converge for many arguments. To get around this
|
||||
we copy the mpmath implementation but use more terms.
|
||||
|
||||
This takes about 17 minutes to run on a 2.3 GHz Macbook Pro with 4GB
|
||||
ram.
|
||||
|
||||
Sources:
|
||||
[1] Fredrik Johansson and others. mpmath: a Python library for
|
||||
arbitrary-precision floating-point arithmetic (version 0.19),
|
||||
December 2013. http://mpmath.org/.
|
||||
|
||||
"""
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
import os
|
||||
from time import time
|
||||
import numpy as np
|
||||
from numpy import pi
|
||||
|
||||
from scipy.special._mptestutils import mpf2float
|
||||
|
||||
try:
|
||||
import mpmath as mp
|
||||
except ImportError:
|
||||
pass
|
||||
|
||||
|
||||
def gammainc(a, x, dps=50, maxterms=10**8):
|
||||
"""Compute gammainc exactly like mpmath does but allow for more
|
||||
summands in hypercomb. See
|
||||
|
||||
mpmath/functions/expintegrals.py#L134
|
||||
|
||||
in the mpmath github repository.
|
||||
|
||||
"""
|
||||
with mp.workdps(dps):
|
||||
z, a, b = mp.mpf(a), mp.mpf(x), mp.mpf(x)
|
||||
G = [z]
|
||||
negb = mp.fneg(b, exact=True)
|
||||
|
||||
def h(z):
|
||||
T1 = [mp.exp(negb), b, z], [1, z, -1], [], G, [1], [1+z], b
|
||||
return (T1,)
|
||||
|
||||
res = mp.hypercomb(h, [z], maxterms=maxterms)
|
||||
return mpf2float(res)
|
||||
|
||||
|
||||
def gammaincc(a, x, dps=50, maxterms=10**8):
|
||||
"""Compute gammaincc exactly like mpmath does but allow for more
|
||||
terms in hypercomb. See
|
||||
|
||||
mpmath/functions/expintegrals.py#L187
|
||||
|
||||
in the mpmath github repository.
|
||||
|
||||
"""
|
||||
with mp.workdps(dps):
|
||||
z, a = a, x
|
||||
|
||||
if mp.isint(z):
|
||||
try:
|
||||
# mpmath has a fast integer path
|
||||
return mpf2float(mp.gammainc(z, a=a, regularized=True))
|
||||
except mp.libmp.NoConvergence:
|
||||
pass
|
||||
nega = mp.fneg(a, exact=True)
|
||||
G = [z]
|
||||
# Use 2F0 series when possible; fall back to lower gamma representation
|
||||
try:
|
||||
def h(z):
|
||||
r = z-1
|
||||
return [([mp.exp(nega), a], [1, r], [], G, [1, -r], [], 1/nega)]
|
||||
return mpf2float(mp.hypercomb(h, [z], force_series=True))
|
||||
except mp.libmp.NoConvergence:
|
||||
def h(z):
|
||||
T1 = [], [1, z-1], [z], G, [], [], 0
|
||||
T2 = [-mp.exp(nega), a, z], [1, z, -1], [], G, [1], [1+z], a
|
||||
return T1, T2
|
||||
return mpf2float(mp.hypercomb(h, [z], maxterms=maxterms))
|
||||
|
||||
|
||||
def main():
|
||||
t0 = time()
|
||||
# It would be nice to have data for larger values, but either this
|
||||
# requires prohibitively large precision (dps > 800) or mpmath has
|
||||
# a bug. For example, gammainc(1e20, 1e20, dps=800) returns a
|
||||
# value around 0.03, while the true value should be close to 0.5
|
||||
# (DLMF 8.12.15).
|
||||
print(__doc__)
|
||||
pwd = os.path.dirname(__file__)
|
||||
r = np.logspace(4, 14, 30)
|
||||
ltheta = np.logspace(np.log10(pi/4), np.log10(np.arctan(0.6)), 30)
|
||||
utheta = np.logspace(np.log10(pi/4), np.log10(np.arctan(1.4)), 30)
|
||||
|
||||
regimes = [(gammainc, ltheta), (gammaincc, utheta)]
|
||||
for func, theta in regimes:
|
||||
rg, thetag = np.meshgrid(r, theta)
|
||||
a, x = rg*np.cos(thetag), rg*np.sin(thetag)
|
||||
a, x = a.flatten(), x.flatten()
|
||||
dataset = []
|
||||
for i, (a0, x0) in enumerate(zip(a, x)):
|
||||
if func == gammaincc:
|
||||
# Exploit the fast integer path in gammaincc whenever
|
||||
# possible so that the computation doesn't take too
|
||||
# long
|
||||
a0, x0 = np.floor(a0), np.floor(x0)
|
||||
dataset.append((a0, x0, func(a0, x0)))
|
||||
dataset = np.array(dataset)
|
||||
filename = os.path.join(pwd, '..', 'tests', 'data', 'local',
|
||||
'{}.txt'.format(func.__name__))
|
||||
np.savetxt(filename, dataset)
|
||||
|
||||
print("{} minutes elapsed".format((time() - t0)/60))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,72 +0,0 @@
|
||||
"""Compute a Pade approximation for the principle branch of the
|
||||
Lambert W function around 0 and compare it to various other
|
||||
approximations.
|
||||
|
||||
"""
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
import numpy as np
|
||||
|
||||
try:
|
||||
import mpmath
|
||||
import matplotlib.pyplot as plt
|
||||
except ImportError:
|
||||
pass
|
||||
|
||||
|
||||
def lambertw_pade():
|
||||
derivs = []
|
||||
for n in range(6):
|
||||
derivs.append(mpmath.diff(mpmath.lambertw, 0, n=n))
|
||||
p, q = mpmath.pade(derivs, 3, 2)
|
||||
return p, q
|
||||
|
||||
|
||||
def main():
|
||||
print(__doc__)
|
||||
with mpmath.workdps(50):
|
||||
p, q = lambertw_pade()
|
||||
p, q = p[::-1], q[::-1]
|
||||
print("p = {}".format(p))
|
||||
print("q = {}".format(q))
|
||||
|
||||
x, y = np.linspace(-1.5, 1.5, 75), np.linspace(-1.5, 1.5, 75)
|
||||
x, y = np.meshgrid(x, y)
|
||||
z = x + 1j*y
|
||||
lambertw_std = []
|
||||
for z0 in z.flatten():
|
||||
lambertw_std.append(complex(mpmath.lambertw(z0)))
|
||||
lambertw_std = np.array(lambertw_std).reshape(x.shape)
|
||||
|
||||
fig, axes = plt.subplots(nrows=3, ncols=1)
|
||||
# Compare Pade approximation to true result
|
||||
p = np.array([float(p0) for p0 in p])
|
||||
q = np.array([float(q0) for q0 in q])
|
||||
pade_approx = np.polyval(p, z)/np.polyval(q, z)
|
||||
pade_err = abs(pade_approx - lambertw_std)
|
||||
axes[0].pcolormesh(x, y, pade_err)
|
||||
# Compare two terms of asymptotic series to true result
|
||||
asy_approx = np.log(z) - np.log(np.log(z))
|
||||
asy_err = abs(asy_approx - lambertw_std)
|
||||
axes[1].pcolormesh(x, y, asy_err)
|
||||
# Compare two terms of the series around the branch point to the
|
||||
# true result
|
||||
p = np.sqrt(2*(np.exp(1)*z + 1))
|
||||
series_approx = -1 + p - p**2/3
|
||||
series_err = abs(series_approx - lambertw_std)
|
||||
im = axes[2].pcolormesh(x, y, series_err)
|
||||
|
||||
fig.colorbar(im, ax=axes.ravel().tolist())
|
||||
plt.show()
|
||||
|
||||
fig, ax = plt.subplots(nrows=1, ncols=1)
|
||||
pade_better = pade_err < asy_err
|
||||
im = ax.pcolormesh(x, y, pade_better)
|
||||
t = np.linspace(-0.3, 0.3)
|
||||
ax.plot(-2.5*abs(t) - 0.2, t, 'r')
|
||||
fig.colorbar(im, ax=ax)
|
||||
plt.show()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
@@ -1,46 +0,0 @@
|
||||
"""Precompute series coefficients for log-Gamma."""
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
try:
|
||||
import mpmath
|
||||
except ImportError:
|
||||
pass
|
||||
|
||||
|
||||
def stirling_series(N):
|
||||
coeffs = []
|
||||
with mpmath.workdps(100):
|
||||
for n in range(1, N + 1):
|
||||
coeffs.append(mpmath.bernoulli(2*n)/(2*n*(2*n - 1)))
|
||||
return coeffs
|
||||
|
||||
|
||||
def taylor_series_at_1(N):
|
||||
coeffs = []
|
||||
with mpmath.workdps(100):
|
||||
coeffs.append(-mpmath.euler)
|
||||
for n in range(2, N + 1):
|
||||
coeffs.append((-1)**n*mpmath.zeta(n)/n)
|
||||
return coeffs
|
||||
|
||||
|
||||
def main():
|
||||
print(__doc__)
|
||||
print()
|
||||
stirling_coeffs = [mpmath.nstr(x, 20, min_fixed=0, max_fixed=0)
|
||||
for x in stirling_series(8)[::-1]]
|
||||
taylor_coeffs = [mpmath.nstr(x, 20, min_fixed=0, max_fixed=0)
|
||||
for x in taylor_series_at_1(23)[::-1]]
|
||||
print("Stirling series coefficients")
|
||||
print("----------------------------")
|
||||
print("\n".join(stirling_coeffs))
|
||||
print()
|
||||
print("Taylor series coefficients")
|
||||
print("--------------------------")
|
||||
print("\n".join(taylor_coeffs))
|
||||
print()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
|
||||
@@ -1,13 +0,0 @@
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
|
||||
def configuration(parent_name='special', top_path=None):
|
||||
from numpy.distutils.misc_util import Configuration
|
||||
config = Configuration('_precompute', parent_name, top_path)
|
||||
return config
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
from numpy.distutils.core import setup
|
||||
setup(**configuration().todict())
|
||||
|
||||
-122
@@ -1,122 +0,0 @@
|
||||
"""
|
||||
Convergence regions of the expansions used in ``struve.c``
|
||||
|
||||
Note that for v >> z both functions tend rapidly to 0,
|
||||
and for v << -z, they tend to infinity.
|
||||
|
||||
The floating-point functions over/underflow in the lower left and right
|
||||
corners of the figure.
|
||||
|
||||
|
||||
Figure legend
|
||||
=============
|
||||
|
||||
Red region
|
||||
Power series is close (1e-12) to the mpmath result
|
||||
|
||||
Blue region
|
||||
Asymptotic series is close to the mpmath result
|
||||
|
||||
Green region
|
||||
Bessel series is close to the mpmath result
|
||||
|
||||
Dotted colored lines
|
||||
Boundaries of the regions
|
||||
|
||||
Solid colored lines
|
||||
Boundaries estimated by the routine itself. These will be used
|
||||
for determining which of the results to use.
|
||||
|
||||
Black dashed line
|
||||
The line z = 0.7*|v| + 12
|
||||
|
||||
"""
|
||||
from __future__ import absolute_import, division, print_function
|
||||
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
import mpmath
|
||||
|
||||
|
||||
def err_metric(a, b, atol=1e-290):
|
||||
m = abs(a - b) / (atol + abs(b))
|
||||
m[np.isinf(b) & (a == b)] = 0
|
||||
return m
|
||||
|
||||
|
||||
def do_plot(is_h=True):
|
||||
from scipy.special._ufuncs import (_struve_power_series,
|
||||
_struve_asymp_large_z,
|
||||
_struve_bessel_series)
|
||||
|
||||
vs = np.linspace(-1000, 1000, 91)
|
||||
zs = np.sort(np.r_[1e-5, 1.0, np.linspace(0, 700, 91)[1:]])
|
||||
|
||||
rp = _struve_power_series(vs[:,None], zs[None,:], is_h)
|
||||
ra = _struve_asymp_large_z(vs[:,None], zs[None,:], is_h)
|
||||
rb = _struve_bessel_series(vs[:,None], zs[None,:], is_h)
|
||||
|
||||
mpmath.mp.dps = 50
|
||||
if is_h:
|
||||
sh = lambda v, z: float(mpmath.struveh(mpmath.mpf(v), mpmath.mpf(z)))
|
||||
else:
|
||||
sh = lambda v, z: float(mpmath.struvel(mpmath.mpf(v), mpmath.mpf(z)))
|
||||
ex = np.vectorize(sh, otypes='d')(vs[:,None], zs[None,:])
|
||||
|
||||
err_a = err_metric(ra[0], ex) + 1e-300
|
||||
err_p = err_metric(rp[0], ex) + 1e-300
|
||||
err_b = err_metric(rb[0], ex) + 1e-300
|
||||
|
||||
err_est_a = abs(ra[1]/ra[0])
|
||||
err_est_p = abs(rp[1]/rp[0])
|
||||
err_est_b = abs(rb[1]/rb[0])
|
||||
|
||||
z_cutoff = 0.7*abs(vs) + 12
|
||||
|
||||
levels = [-1000, -12]
|
||||
|
||||
plt.cla()
|
||||
|
||||
plt.hold(1)
|
||||
plt.contourf(vs, zs, np.log10(err_p).T, levels=levels, colors=['r', 'r'], alpha=0.1)
|
||||
plt.contourf(vs, zs, np.log10(err_a).T, levels=levels, colors=['b', 'b'], alpha=0.1)
|
||||
plt.contourf(vs, zs, np.log10(err_b).T, levels=levels, colors=['g', 'g'], alpha=0.1)
|
||||
|
||||
plt.contour(vs, zs, np.log10(err_p).T, levels=levels, colors=['r', 'r'], linestyles=[':', ':'])
|
||||
plt.contour(vs, zs, np.log10(err_a).T, levels=levels, colors=['b', 'b'], linestyles=[':', ':'])
|
||||
plt.contour(vs, zs, np.log10(err_b).T, levels=levels, colors=['g', 'g'], linestyles=[':', ':'])
|
||||
|
||||
lp = plt.contour(vs, zs, np.log10(err_est_p).T, levels=levels, colors=['r', 'r'], linestyles=['-', '-'])
|
||||
la = plt.contour(vs, zs, np.log10(err_est_a).T, levels=levels, colors=['b', 'b'], linestyles=['-', '-'])
|
||||
lb = plt.contour(vs, zs, np.log10(err_est_b).T, levels=levels, colors=['g', 'g'], linestyles=['-', '-'])
|
||||
|
||||
plt.clabel(lp, fmt={-1000: 'P', -12: 'P'})
|
||||
plt.clabel(la, fmt={-1000: 'A', -12: 'A'})
|
||||
plt.clabel(lb, fmt={-1000: 'B', -12: 'B'})
|
||||
|
||||
plt.plot(vs, z_cutoff, 'k--')
|
||||
|
||||
plt.xlim(vs.min(), vs.max())
|
||||
plt.ylim(zs.min(), zs.max())
|
||||
|
||||
plt.xlabel('v')
|
||||
plt.ylabel('z')
|
||||
|
||||
|
||||
def main():
|
||||
plt.clf()
|
||||
plt.subplot(121)
|
||||
do_plot(True)
|
||||
plt.title('Struve H')
|
||||
|
||||
plt.subplot(122)
|
||||
do_plot(False)
|
||||
plt.title('Struve L')
|
||||
|
||||
plt.savefig('struve_convergence.png')
|
||||
plt.show()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -1,46 +0,0 @@
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
from scipy._lib._numpy_compat import suppress_warnings
|
||||
|
||||
try:
|
||||
import mpmath as mp
|
||||
except ImportError:
|
||||
pass
|
||||
|
||||
try:
|
||||
# Can remove when sympy #11255 is resolved; see
|
||||
# https://github.com/sympy/sympy/issues/11255
|
||||
with suppress_warnings() as sup:
|
||||
sup.filter(DeprecationWarning, "inspect.getargspec.. is deprecated")
|
||||
from sympy.abc import x
|
||||
except ImportError:
|
||||
pass
|
||||
|
||||
|
||||
def lagrange_inversion(a):
|
||||
"""Given a series
|
||||
|
||||
f(x) = a[1]*x + a[2]*x**2 + ... + a[n-1]*x**(n - 1),
|
||||
|
||||
use the Lagrange inversion formula to compute a series
|
||||
|
||||
g(x) = b[1]*x + b[2]*x**2 + ... + b[n-1]*x**(n - 1)
|
||||
|
||||
so that f(g(x)) = g(f(x)) = x mod x**n. We must have a[0] = 0, so
|
||||
necessarily b[0] = 0 too.
|
||||
|
||||
The algorithm is naive and could be improved, but speed isn't an
|
||||
issue here and it's easy to read.
|
||||
|
||||
"""
|
||||
n = len(a)
|
||||
f = sum(a[i]*x**i for i in range(len(a)))
|
||||
h = (x/f).series(x, 0, n).removeO()
|
||||
hpower = [h**0]
|
||||
for k in range(n):
|
||||
hpower.append((hpower[-1]*h).expand())
|
||||
b = [mp.mpf(0)]
|
||||
for k in range(1, n):
|
||||
b.append(hpower[k].coeff(x, k - 1)/k)
|
||||
b = map(lambda x: mp.mpf(x), b)
|
||||
return b
|
||||
@@ -1,29 +0,0 @@
|
||||
"""Compute the Taylor series for zeta(x) - 1 around x = 0."""
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
try:
|
||||
import mpmath
|
||||
except ImportError:
|
||||
pass
|
||||
|
||||
|
||||
def zetac_series(N):
|
||||
coeffs = []
|
||||
with mpmath.workdps(100):
|
||||
coeffs.append(-1.5)
|
||||
for n in range(1, N):
|
||||
coeff = mpmath.diff(mpmath.zeta, 0, n)/mpmath.factorial(n)
|
||||
coeffs.append(coeff)
|
||||
return coeffs
|
||||
|
||||
|
||||
def main():
|
||||
print(__doc__)
|
||||
coeffs = zetac_series(10)
|
||||
coeffs = [mpmath.nstr(x, 20, min_fixed=0, max_fixed=0)
|
||||
for x in coeffs]
|
||||
print("\n".join(coeffs[::-1]))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
@@ -1,205 +0,0 @@
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
from ._ufuncs import (_spherical_jn, _spherical_yn, _spherical_in,
|
||||
_spherical_kn, _spherical_jn_d, _spherical_yn_d,
|
||||
_spherical_in_d, _spherical_kn_d)
|
||||
|
||||
def spherical_jn(n, z, derivative=False):
|
||||
r"""Spherical Bessel function of the first kind or its derivative.
|
||||
|
||||
Defined as [1]_,
|
||||
|
||||
.. math:: j_n(z) = \sqrt{\frac{\pi}{2z}} J_{n + 1/2}(z),
|
||||
|
||||
where :math:`J_n` is the Bessel function of the first kind.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
n : int, array_like
|
||||
Order of the Bessel function (n >= 0).
|
||||
z : complex or float, array_like
|
||||
Argument of the Bessel function.
|
||||
derivative : bool, optional
|
||||
If True, the value of the derivative (rather than the function
|
||||
itself) is returned.
|
||||
|
||||
Returns
|
||||
-------
|
||||
jn : ndarray
|
||||
|
||||
Notes
|
||||
-----
|
||||
For real arguments greater than the order, the function is computed
|
||||
using the ascending recurrence [2]_. For small real or complex
|
||||
arguments, the definitional relation to the cylindrical Bessel function
|
||||
of the first kind is used.
|
||||
|
||||
The derivative is computed using the relations [3]_,
|
||||
|
||||
.. math::
|
||||
j_n'(z) = j_{n-1}(z) - \frac{n + 1}{z} j_n(z).
|
||||
|
||||
j_0'(z) = -j_1(z)
|
||||
|
||||
|
||||
.. versionadded:: 0.18.0
|
||||
|
||||
References
|
||||
----------
|
||||
.. [1] https://dlmf.nist.gov/10.47.E3
|
||||
.. [2] https://dlmf.nist.gov/10.51.E1
|
||||
.. [3] https://dlmf.nist.gov/10.51.E2
|
||||
"""
|
||||
if derivative:
|
||||
return _spherical_jn_d(n, z)
|
||||
else:
|
||||
return _spherical_jn(n, z)
|
||||
|
||||
|
||||
def spherical_yn(n, z, derivative=False):
|
||||
r"""Spherical Bessel function of the second kind or its derivative.
|
||||
|
||||
Defined as [1]_,
|
||||
|
||||
.. math:: y_n(z) = \sqrt{\frac{\pi}{2z}} Y_{n + 1/2}(z),
|
||||
|
||||
where :math:`Y_n` is the Bessel function of the second kind.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
n : int, array_like
|
||||
Order of the Bessel function (n >= 0).
|
||||
z : complex or float, array_like
|
||||
Argument of the Bessel function.
|
||||
derivative : bool, optional
|
||||
If True, the value of the derivative (rather than the function
|
||||
itself) is returned.
|
||||
|
||||
Returns
|
||||
-------
|
||||
yn : ndarray
|
||||
|
||||
Notes
|
||||
-----
|
||||
For real arguments, the function is computed using the ascending
|
||||
recurrence [2]_. For complex arguments, the definitional relation to
|
||||
the cylindrical Bessel function of the second kind is used.
|
||||
|
||||
The derivative is computed using the relations [3]_,
|
||||
|
||||
.. math::
|
||||
y_n' = y_{n-1} - \frac{n + 1}{z} y_n.
|
||||
|
||||
y_0' = -y_1
|
||||
|
||||
|
||||
.. versionadded:: 0.18.0
|
||||
|
||||
References
|
||||
----------
|
||||
.. [1] https://dlmf.nist.gov/10.47.E4
|
||||
.. [2] https://dlmf.nist.gov/10.51.E1
|
||||
.. [3] https://dlmf.nist.gov/10.51.E2
|
||||
"""
|
||||
if derivative:
|
||||
return _spherical_yn_d(n, z)
|
||||
else:
|
||||
return _spherical_yn(n, z)
|
||||
|
||||
|
||||
def spherical_in(n, z, derivative=False):
|
||||
r"""Modified spherical Bessel function of the first kind or its derivative.
|
||||
|
||||
Defined as [1]_,
|
||||
|
||||
.. math:: i_n(z) = \sqrt{\frac{\pi}{2z}} I_{n + 1/2}(z),
|
||||
|
||||
where :math:`I_n` is the modified Bessel function of the first kind.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
n : int, array_like
|
||||
Order of the Bessel function (n >= 0).
|
||||
z : complex or float, array_like
|
||||
Argument of the Bessel function.
|
||||
derivative : bool, optional
|
||||
If True, the value of the derivative (rather than the function
|
||||
itself) is returned.
|
||||
|
||||
Returns
|
||||
-------
|
||||
in : ndarray
|
||||
|
||||
Notes
|
||||
-----
|
||||
The function is computed using its definitional relation to the
|
||||
modified cylindrical Bessel function of the first kind.
|
||||
|
||||
The derivative is computed using the relations [2]_,
|
||||
|
||||
.. math::
|
||||
i_n' = i_{n-1} - \frac{n + 1}{z} i_n.
|
||||
|
||||
i_1' = i_0
|
||||
|
||||
|
||||
.. versionadded:: 0.18.0
|
||||
|
||||
References
|
||||
----------
|
||||
.. [1] https://dlmf.nist.gov/10.47.E7
|
||||
.. [2] https://dlmf.nist.gov/10.51.E5
|
||||
"""
|
||||
if derivative:
|
||||
return _spherical_in_d(n, z)
|
||||
else:
|
||||
return _spherical_in(n, z)
|
||||
|
||||
|
||||
def spherical_kn(n, z, derivative=False):
|
||||
r"""Modified spherical Bessel function of the second kind or its derivative.
|
||||
|
||||
Defined as [1]_,
|
||||
|
||||
.. math:: k_n(z) = \sqrt{\frac{\pi}{2z}} K_{n + 1/2}(z),
|
||||
|
||||
where :math:`K_n` is the modified Bessel function of the second kind.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
n : int, array_like
|
||||
Order of the Bessel function (n >= 0).
|
||||
z : complex or float, array_like
|
||||
Argument of the Bessel function.
|
||||
derivative : bool, optional
|
||||
If True, the value of the derivative (rather than the function
|
||||
itself) is returned.
|
||||
|
||||
Returns
|
||||
-------
|
||||
kn : ndarray
|
||||
|
||||
Notes
|
||||
-----
|
||||
The function is computed using its definitional relation to the
|
||||
modified cylindrical Bessel function of the second kind.
|
||||
|
||||
The derivative is computed using the relations [2]_,
|
||||
|
||||
.. math::
|
||||
k_n' = -k_{n-1} - \frac{n + 1}{z} k_n.
|
||||
|
||||
k_0' = -k_1
|
||||
|
||||
|
||||
.. versionadded:: 0.18.0
|
||||
|
||||
References
|
||||
----------
|
||||
.. [1] https://dlmf.nist.gov/10.47.E9
|
||||
.. [2] https://dlmf.nist.gov/10.51.E5
|
||||
"""
|
||||
if derivative:
|
||||
return _spherical_kn_d(n, z)
|
||||
else:
|
||||
return _spherical_kn(n, z)
|
||||
BIN
Binary file not shown.
@@ -1,319 +0,0 @@
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
import os
|
||||
|
||||
from distutils.version import LooseVersion
|
||||
|
||||
import functools
|
||||
|
||||
import numpy as np
|
||||
from numpy.testing import assert_
|
||||
import pytest
|
||||
|
||||
import scipy.special as sc
|
||||
|
||||
__all__ = ['with_special_errors', 'assert_func_equal', 'FuncData']
|
||||
|
||||
|
||||
#------------------------------------------------------------------------------
|
||||
# Check if a module is present to be used in tests
|
||||
#------------------------------------------------------------------------------
|
||||
|
||||
class MissingModule(object):
|
||||
def __init__(self, name):
|
||||
self.name = name
|
||||
|
||||
|
||||
def check_version(module, min_ver):
|
||||
if type(module) == MissingModule:
|
||||
return pytest.mark.skip(reason="{} is not installed".format(module.name))
|
||||
return pytest.mark.skipif(LooseVersion(module.__version__) < LooseVersion(min_ver),
|
||||
reason="{} version >= {} required".format(module.__name__, min_ver))
|
||||
|
||||
|
||||
#------------------------------------------------------------------------------
|
||||
# Enable convergence and loss of precision warnings -- turn off one by one
|
||||
#------------------------------------------------------------------------------
|
||||
|
||||
def with_special_errors(func):
|
||||
"""
|
||||
Enable special function errors (such as underflow, overflow,
|
||||
loss of precision, etc.)
|
||||
"""
|
||||
@functools.wraps(func)
|
||||
def wrapper(*a, **kw):
|
||||
with sc.errstate(all='raise'):
|
||||
res = func(*a, **kw)
|
||||
return res
|
||||
return wrapper
|
||||
|
||||
|
||||
#------------------------------------------------------------------------------
|
||||
# Comparing function values at many data points at once, with helpful
|
||||
# error reports
|
||||
#------------------------------------------------------------------------------
|
||||
|
||||
def assert_func_equal(func, results, points, rtol=None, atol=None,
|
||||
param_filter=None, knownfailure=None,
|
||||
vectorized=True, dtype=None, nan_ok=False,
|
||||
ignore_inf_sign=False, distinguish_nan_and_inf=True):
|
||||
if hasattr(points, 'next'):
|
||||
# it's a generator
|
||||
points = list(points)
|
||||
|
||||
points = np.asarray(points)
|
||||
if points.ndim == 1:
|
||||
points = points[:,None]
|
||||
nparams = points.shape[1]
|
||||
|
||||
if hasattr(results, '__name__'):
|
||||
# function
|
||||
data = points
|
||||
result_columns = None
|
||||
result_func = results
|
||||
else:
|
||||
# dataset
|
||||
data = np.c_[points, results]
|
||||
result_columns = list(range(nparams, data.shape[1]))
|
||||
result_func = None
|
||||
|
||||
fdata = FuncData(func, data, list(range(nparams)),
|
||||
result_columns=result_columns, result_func=result_func,
|
||||
rtol=rtol, atol=atol, param_filter=param_filter,
|
||||
knownfailure=knownfailure, nan_ok=nan_ok, vectorized=vectorized,
|
||||
ignore_inf_sign=ignore_inf_sign,
|
||||
distinguish_nan_and_inf=distinguish_nan_and_inf)
|
||||
fdata.check()
|
||||
|
||||
|
||||
class FuncData(object):
|
||||
"""
|
||||
Data set for checking a special function.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
func : function
|
||||
Function to test
|
||||
data : numpy array
|
||||
columnar data to use for testing
|
||||
param_columns : int or tuple of ints
|
||||
Columns indices in which the parameters to `func` lie.
|
||||
Can be imaginary integers to indicate that the parameter
|
||||
should be cast to complex.
|
||||
result_columns : int or tuple of ints, optional
|
||||
Column indices for expected results from `func`.
|
||||
result_func : callable, optional
|
||||
Function to call to obtain results.
|
||||
rtol : float, optional
|
||||
Required relative tolerance. Default is 5*eps.
|
||||
atol : float, optional
|
||||
Required absolute tolerance. Default is 5*tiny.
|
||||
param_filter : function, or tuple of functions/Nones, optional
|
||||
Filter functions to exclude some parameter ranges.
|
||||
If omitted, no filtering is done.
|
||||
knownfailure : str, optional
|
||||
Known failure error message to raise when the test is run.
|
||||
If omitted, no exception is raised.
|
||||
nan_ok : bool, optional
|
||||
If nan is always an accepted result.
|
||||
vectorized : bool, optional
|
||||
Whether all functions passed in are vectorized.
|
||||
ignore_inf_sign : bool, optional
|
||||
Whether to ignore signs of infinities.
|
||||
(Doesn't matter for complex-valued functions.)
|
||||
distinguish_nan_and_inf : bool, optional
|
||||
If True, treat numbers which contain nans or infs as as
|
||||
equal. Sets ignore_inf_sign to be True.
|
||||
|
||||
"""
|
||||
|
||||
def __init__(self, func, data, param_columns, result_columns=None,
|
||||
result_func=None, rtol=None, atol=None, param_filter=None,
|
||||
knownfailure=None, dataname=None, nan_ok=False, vectorized=True,
|
||||
ignore_inf_sign=False, distinguish_nan_and_inf=True):
|
||||
self.func = func
|
||||
self.data = data
|
||||
self.dataname = dataname
|
||||
if not hasattr(param_columns, '__len__'):
|
||||
param_columns = (param_columns,)
|
||||
self.param_columns = tuple(param_columns)
|
||||
if result_columns is not None:
|
||||
if not hasattr(result_columns, '__len__'):
|
||||
result_columns = (result_columns,)
|
||||
self.result_columns = tuple(result_columns)
|
||||
if result_func is not None:
|
||||
raise ValueError("Only result_func or result_columns should be provided")
|
||||
elif result_func is not None:
|
||||
self.result_columns = None
|
||||
else:
|
||||
raise ValueError("Either result_func or result_columns should be provided")
|
||||
self.result_func = result_func
|
||||
self.rtol = rtol
|
||||
self.atol = atol
|
||||
if not hasattr(param_filter, '__len__'):
|
||||
param_filter = (param_filter,)
|
||||
self.param_filter = param_filter
|
||||
self.knownfailure = knownfailure
|
||||
self.nan_ok = nan_ok
|
||||
self.vectorized = vectorized
|
||||
self.ignore_inf_sign = ignore_inf_sign
|
||||
self.distinguish_nan_and_inf = distinguish_nan_and_inf
|
||||
if not self.distinguish_nan_and_inf:
|
||||
self.ignore_inf_sign = True
|
||||
|
||||
def get_tolerances(self, dtype):
|
||||
if not np.issubdtype(dtype, np.inexact):
|
||||
dtype = np.dtype(float)
|
||||
info = np.finfo(dtype)
|
||||
rtol, atol = self.rtol, self.atol
|
||||
if rtol is None:
|
||||
rtol = 5*info.eps
|
||||
if atol is None:
|
||||
atol = 5*info.tiny
|
||||
return rtol, atol
|
||||
|
||||
def check(self, data=None, dtype=None, dtypes=None):
|
||||
"""Check the special function against the data."""
|
||||
|
||||
if self.knownfailure:
|
||||
pytest.xfail(reason=self.knownfailure)
|
||||
|
||||
if data is None:
|
||||
data = self.data
|
||||
|
||||
if dtype is None:
|
||||
dtype = data.dtype
|
||||
else:
|
||||
data = data.astype(dtype)
|
||||
|
||||
rtol, atol = self.get_tolerances(dtype)
|
||||
|
||||
# Apply given filter functions
|
||||
if self.param_filter:
|
||||
param_mask = np.ones((data.shape[0],), np.bool_)
|
||||
for j, filter in zip(self.param_columns, self.param_filter):
|
||||
if filter:
|
||||
param_mask &= list(filter(data[:,j]))
|
||||
data = data[param_mask]
|
||||
|
||||
# Pick parameters from the correct columns
|
||||
params = []
|
||||
for idx, j in enumerate(self.param_columns):
|
||||
if np.iscomplexobj(j):
|
||||
j = int(j.imag)
|
||||
params.append(data[:,j].astype(complex))
|
||||
elif dtypes and idx < len(dtypes):
|
||||
params.append(data[:, j].astype(dtypes[idx]))
|
||||
else:
|
||||
params.append(data[:,j])
|
||||
|
||||
# Helper for evaluating results
|
||||
def eval_func_at_params(func, skip_mask=None):
|
||||
if self.vectorized:
|
||||
got = func(*params)
|
||||
else:
|
||||
got = []
|
||||
for j in range(len(params[0])):
|
||||
if skip_mask is not None and skip_mask[j]:
|
||||
got.append(np.nan)
|
||||
continue
|
||||
got.append(func(*tuple([params[i][j] for i in range(len(params))])))
|
||||
got = np.asarray(got)
|
||||
if not isinstance(got, tuple):
|
||||
got = (got,)
|
||||
return got
|
||||
|
||||
# Evaluate function to be tested
|
||||
got = eval_func_at_params(self.func)
|
||||
|
||||
# Grab the correct results
|
||||
if self.result_columns is not None:
|
||||
# Correct results passed in with the data
|
||||
wanted = tuple([data[:,icol] for icol in self.result_columns])
|
||||
else:
|
||||
# Function producing correct results passed in
|
||||
skip_mask = None
|
||||
if self.nan_ok and len(got) == 1:
|
||||
# Don't spend time evaluating what doesn't need to be evaluated
|
||||
skip_mask = np.isnan(got[0])
|
||||
wanted = eval_func_at_params(self.result_func, skip_mask=skip_mask)
|
||||
|
||||
# Check the validity of each output returned
|
||||
assert_(len(got) == len(wanted))
|
||||
|
||||
for output_num, (x, y) in enumerate(zip(got, wanted)):
|
||||
if np.issubdtype(x.dtype, np.complexfloating) or self.ignore_inf_sign:
|
||||
pinf_x = np.isinf(x)
|
||||
pinf_y = np.isinf(y)
|
||||
minf_x = np.isinf(x)
|
||||
minf_y = np.isinf(y)
|
||||
else:
|
||||
pinf_x = np.isposinf(x)
|
||||
pinf_y = np.isposinf(y)
|
||||
minf_x = np.isneginf(x)
|
||||
minf_y = np.isneginf(y)
|
||||
nan_x = np.isnan(x)
|
||||
nan_y = np.isnan(y)
|
||||
|
||||
olderr = np.seterr(all='ignore')
|
||||
try:
|
||||
abs_y = np.absolute(y)
|
||||
abs_y[~np.isfinite(abs_y)] = 0
|
||||
diff = np.absolute(x - y)
|
||||
diff[~np.isfinite(diff)] = 0
|
||||
|
||||
rdiff = diff / np.absolute(y)
|
||||
rdiff[~np.isfinite(rdiff)] = 0
|
||||
finally:
|
||||
np.seterr(**olderr)
|
||||
|
||||
tol_mask = (diff <= atol + rtol*abs_y)
|
||||
pinf_mask = (pinf_x == pinf_y)
|
||||
minf_mask = (minf_x == minf_y)
|
||||
|
||||
nan_mask = (nan_x == nan_y)
|
||||
|
||||
bad_j = ~(tol_mask & pinf_mask & minf_mask & nan_mask)
|
||||
|
||||
point_count = bad_j.size
|
||||
if self.nan_ok:
|
||||
bad_j &= ~nan_x
|
||||
bad_j &= ~nan_y
|
||||
point_count -= (nan_x | nan_y).sum()
|
||||
|
||||
if not self.distinguish_nan_and_inf and not self.nan_ok:
|
||||
# If nan's are okay we've already covered all these cases
|
||||
inf_x = np.isinf(x)
|
||||
inf_y = np.isinf(y)
|
||||
both_nonfinite = (inf_x & nan_y) | (nan_x & inf_y)
|
||||
bad_j &= ~both_nonfinite
|
||||
point_count -= both_nonfinite.sum()
|
||||
|
||||
if np.any(bad_j):
|
||||
# Some bad results: inform what, where, and how bad
|
||||
msg = [""]
|
||||
msg.append("Max |adiff|: %g" % diff.max())
|
||||
msg.append("Max |rdiff|: %g" % rdiff.max())
|
||||
msg.append("Bad results (%d out of %d) for the following points (in output %d):"
|
||||
% (np.sum(bad_j), point_count, output_num,))
|
||||
for j in np.nonzero(bad_j)[0]:
|
||||
j = int(j)
|
||||
fmt = lambda x: "%30s" % np.array2string(x[j], precision=18)
|
||||
a = " ".join(map(fmt, params))
|
||||
b = " ".join(map(fmt, got))
|
||||
c = " ".join(map(fmt, wanted))
|
||||
d = fmt(rdiff)
|
||||
msg.append("%s => %s != %s (rdiff %s)" % (a, b, c, d))
|
||||
assert_(False, "\n".join(msg))
|
||||
|
||||
def __repr__(self):
|
||||
"""Pretty-printing, esp. for Nose output"""
|
||||
if np.any(list(map(np.iscomplexobj, self.param_columns))):
|
||||
is_complex = " (complex)"
|
||||
else:
|
||||
is_complex = ""
|
||||
if self.dataname:
|
||||
return "<Data for %s%s: %s>" % (self.func.__name__, is_complex,
|
||||
os.path.basename(self.dataname))
|
||||
else:
|
||||
return "<Data for %s%s>" % (self.func.__name__, is_complex)
|
||||
BIN
Binary file not shown.
BIN
Binary file not shown.
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
BIN
Binary file not shown.
@@ -1,236 +0,0 @@
|
||||
# This file is automatically generated by _generate_pyx.py.
|
||||
# Do not edit manually!
|
||||
|
||||
ctypedef fused Dd_number_t:
|
||||
double complex
|
||||
double
|
||||
|
||||
ctypedef fused dfg_number_t:
|
||||
double
|
||||
float
|
||||
long double
|
||||
|
||||
ctypedef fused dl_number_t:
|
||||
double
|
||||
long
|
||||
|
||||
cpdef double agm(double x0, double x1) nogil
|
||||
cdef void airy(Dd_number_t x0, Dd_number_t *y0, Dd_number_t *y1, Dd_number_t *y2, Dd_number_t *y3) nogil
|
||||
cdef void airye(Dd_number_t x0, Dd_number_t *y0, Dd_number_t *y1, Dd_number_t *y2, Dd_number_t *y3) nogil
|
||||
cpdef double bdtr(dl_number_t x0, dl_number_t x1, double x2) nogil
|
||||
cpdef double bdtrc(dl_number_t x0, dl_number_t x1, double x2) nogil
|
||||
cpdef double bdtri(dl_number_t x0, dl_number_t x1, double x2) nogil
|
||||
cpdef double bdtrik(double x0, double x1, double x2) nogil
|
||||
cpdef double bdtrin(double x0, double x1, double x2) nogil
|
||||
cpdef double bei(double x0) nogil
|
||||
cpdef double beip(double x0) nogil
|
||||
cpdef double ber(double x0) nogil
|
||||
cpdef double berp(double x0) nogil
|
||||
cpdef double besselpoly(double x0, double x1, double x2) nogil
|
||||
cpdef double beta(double x0, double x1) nogil
|
||||
cpdef double betainc(double x0, double x1, double x2) nogil
|
||||
cpdef double betaincinv(double x0, double x1, double x2) nogil
|
||||
cpdef double betaln(double x0, double x1) nogil
|
||||
cpdef double binom(double x0, double x1) nogil
|
||||
cpdef double boxcox(double x0, double x1) nogil
|
||||
cpdef double boxcox1p(double x0, double x1) nogil
|
||||
cpdef double btdtr(double x0, double x1, double x2) nogil
|
||||
cpdef double btdtri(double x0, double x1, double x2) nogil
|
||||
cpdef double btdtria(double x0, double x1, double x2) nogil
|
||||
cpdef double btdtrib(double x0, double x1, double x2) nogil
|
||||
cpdef double cbrt(double x0) nogil
|
||||
cpdef double chdtr(double x0, double x1) nogil
|
||||
cpdef double chdtrc(double x0, double x1) nogil
|
||||
cpdef double chdtri(double x0, double x1) nogil
|
||||
cpdef double chdtriv(double x0, double x1) nogil
|
||||
cpdef double chndtr(double x0, double x1, double x2) nogil
|
||||
cpdef double chndtridf(double x0, double x1, double x2) nogil
|
||||
cpdef double chndtrinc(double x0, double x1, double x2) nogil
|
||||
cpdef double chndtrix(double x0, double x1, double x2) nogil
|
||||
cpdef double cosdg(double x0) nogil
|
||||
cpdef double cosm1(double x0) nogil
|
||||
cpdef double cotdg(double x0) nogil
|
||||
cpdef Dd_number_t dawsn(Dd_number_t x0) nogil
|
||||
cpdef double ellipe(double x0) nogil
|
||||
cpdef double ellipeinc(double x0, double x1) nogil
|
||||
cdef void ellipj(double x0, double x1, double *y0, double *y1, double *y2, double *y3) nogil
|
||||
cpdef double ellipkinc(double x0, double x1) nogil
|
||||
cpdef double ellipkm1(double x0) nogil
|
||||
cpdef double entr(double x0) nogil
|
||||
cpdef Dd_number_t erf(Dd_number_t x0) nogil
|
||||
cpdef Dd_number_t erfc(Dd_number_t x0) nogil
|
||||
cpdef Dd_number_t erfcx(Dd_number_t x0) nogil
|
||||
cpdef Dd_number_t erfi(Dd_number_t x0) nogil
|
||||
cpdef Dd_number_t eval_chebyc(dl_number_t x0, Dd_number_t x1) nogil
|
||||
cpdef Dd_number_t eval_chebys(dl_number_t x0, Dd_number_t x1) nogil
|
||||
cpdef Dd_number_t eval_chebyt(dl_number_t x0, Dd_number_t x1) nogil
|
||||
cpdef Dd_number_t eval_chebyu(dl_number_t x0, Dd_number_t x1) nogil
|
||||
cpdef Dd_number_t eval_gegenbauer(dl_number_t x0, double x1, Dd_number_t x2) nogil
|
||||
cpdef Dd_number_t eval_genlaguerre(dl_number_t x0, double x1, Dd_number_t x2) nogil
|
||||
cpdef double eval_hermite(long x0, double x1) nogil
|
||||
cpdef double eval_hermitenorm(long x0, double x1) nogil
|
||||
cpdef Dd_number_t eval_jacobi(dl_number_t x0, double x1, double x2, Dd_number_t x3) nogil
|
||||
cpdef Dd_number_t eval_laguerre(dl_number_t x0, Dd_number_t x1) nogil
|
||||
cpdef Dd_number_t eval_legendre(dl_number_t x0, Dd_number_t x1) nogil
|
||||
cpdef Dd_number_t eval_sh_chebyt(dl_number_t x0, Dd_number_t x1) nogil
|
||||
cpdef Dd_number_t eval_sh_chebyu(dl_number_t x0, Dd_number_t x1) nogil
|
||||
cpdef Dd_number_t eval_sh_jacobi(dl_number_t x0, double x1, double x2, Dd_number_t x3) nogil
|
||||
cpdef Dd_number_t eval_sh_legendre(dl_number_t x0, Dd_number_t x1) nogil
|
||||
cpdef Dd_number_t exp1(Dd_number_t x0) nogil
|
||||
cpdef double exp10(double x0) nogil
|
||||
cpdef double exp2(double x0) nogil
|
||||
cpdef Dd_number_t expi(Dd_number_t x0) nogil
|
||||
cpdef dfg_number_t expit(dfg_number_t x0) nogil
|
||||
cpdef Dd_number_t expm1(Dd_number_t x0) nogil
|
||||
cpdef double expn(dl_number_t x0, double x1) nogil
|
||||
cpdef double exprel(double x0) nogil
|
||||
cpdef double fdtr(double x0, double x1, double x2) nogil
|
||||
cpdef double fdtrc(double x0, double x1, double x2) nogil
|
||||
cpdef double fdtri(double x0, double x1, double x2) nogil
|
||||
cpdef double fdtridfd(double x0, double x1, double x2) nogil
|
||||
cdef void fresnel(Dd_number_t x0, Dd_number_t *y0, Dd_number_t *y1) nogil
|
||||
cpdef Dd_number_t gamma(Dd_number_t x0) nogil
|
||||
cpdef double gammainc(double x0, double x1) nogil
|
||||
cpdef double gammaincc(double x0, double x1) nogil
|
||||
cpdef double gammainccinv(double x0, double x1) nogil
|
||||
cpdef double gammaincinv(double x0, double x1) nogil
|
||||
cpdef double gammaln(double x0) nogil
|
||||
cpdef double gammasgn(double x0) nogil
|
||||
cpdef double gdtr(double x0, double x1, double x2) nogil
|
||||
cpdef double gdtrc(double x0, double x1, double x2) nogil
|
||||
cpdef double gdtria(double x0, double x1, double x2) nogil
|
||||
cpdef double gdtrib(double x0, double x1, double x2) nogil
|
||||
cpdef double gdtrix(double x0, double x1, double x2) nogil
|
||||
cpdef double complex hankel1(double x0, double complex x1) nogil
|
||||
cpdef double complex hankel1e(double x0, double complex x1) nogil
|
||||
cpdef double complex hankel2(double x0, double complex x1) nogil
|
||||
cpdef double complex hankel2e(double x0, double complex x1) nogil
|
||||
cpdef double huber(double x0, double x1) nogil
|
||||
cpdef Dd_number_t hyp0f1(double x0, Dd_number_t x1) nogil
|
||||
cpdef Dd_number_t hyp1f1(double x0, double x1, Dd_number_t x2) nogil
|
||||
cdef void hyp1f2(double x0, double x1, double x2, double x3, double *y0, double *y1) nogil
|
||||
cdef void hyp2f0(double x0, double x1, double x2, dl_number_t x3, double *y0, double *y1) nogil
|
||||
cpdef Dd_number_t hyp2f1(double x0, double x1, double x2, Dd_number_t x3) nogil
|
||||
cdef void hyp3f0(double x0, double x1, double x2, double x3, double *y0, double *y1) nogil
|
||||
cpdef double hyperu(double x0, double x1, double x2) nogil
|
||||
cpdef double i0(double x0) nogil
|
||||
cpdef double i0e(double x0) nogil
|
||||
cpdef double i1(double x0) nogil
|
||||
cpdef double i1e(double x0) nogil
|
||||
cpdef double inv_boxcox(double x0, double x1) nogil
|
||||
cpdef double inv_boxcox1p(double x0, double x1) nogil
|
||||
cdef void it2i0k0(double x0, double *y0, double *y1) nogil
|
||||
cdef void it2j0y0(double x0, double *y0, double *y1) nogil
|
||||
cpdef double it2struve0(double x0) nogil
|
||||
cdef void itairy(double x0, double *y0, double *y1, double *y2, double *y3) nogil
|
||||
cdef void iti0k0(double x0, double *y0, double *y1) nogil
|
||||
cdef void itj0y0(double x0, double *y0, double *y1) nogil
|
||||
cpdef double itmodstruve0(double x0) nogil
|
||||
cpdef double itstruve0(double x0) nogil
|
||||
cpdef Dd_number_t iv(double x0, Dd_number_t x1) nogil
|
||||
cpdef Dd_number_t ive(double x0, Dd_number_t x1) nogil
|
||||
cpdef double j0(double x0) nogil
|
||||
cpdef double j1(double x0) nogil
|
||||
cpdef Dd_number_t jv(double x0, Dd_number_t x1) nogil
|
||||
cpdef Dd_number_t jve(double x0, Dd_number_t x1) nogil
|
||||
cpdef double k0(double x0) nogil
|
||||
cpdef double k0e(double x0) nogil
|
||||
cpdef double k1(double x0) nogil
|
||||
cpdef double k1e(double x0) nogil
|
||||
cpdef double kei(double x0) nogil
|
||||
cpdef double keip(double x0) nogil
|
||||
cdef void kelvin(double x0, double complex *y0, double complex *y1, double complex *y2, double complex *y3) nogil
|
||||
cpdef double ker(double x0) nogil
|
||||
cpdef double kerp(double x0) nogil
|
||||
cpdef double kl_div(double x0, double x1) nogil
|
||||
cpdef double kn(dl_number_t x0, double x1) nogil
|
||||
cpdef double kolmogi(double x0) nogil
|
||||
cpdef double kolmogorov(double x0) nogil
|
||||
cpdef Dd_number_t kv(double x0, Dd_number_t x1) nogil
|
||||
cpdef Dd_number_t kve(double x0, Dd_number_t x1) nogil
|
||||
cpdef Dd_number_t log1p(Dd_number_t x0) nogil
|
||||
cpdef Dd_number_t log_ndtr(Dd_number_t x0) nogil
|
||||
cpdef Dd_number_t loggamma(Dd_number_t x0) nogil
|
||||
cpdef dfg_number_t logit(dfg_number_t x0) nogil
|
||||
cpdef double lpmv(double x0, double x1, double x2) nogil
|
||||
cpdef double mathieu_a(double x0, double x1) nogil
|
||||
cpdef double mathieu_b(double x0, double x1) nogil
|
||||
cdef void mathieu_cem(double x0, double x1, double x2, double *y0, double *y1) nogil
|
||||
cdef void mathieu_modcem1(double x0, double x1, double x2, double *y0, double *y1) nogil
|
||||
cdef void mathieu_modcem2(double x0, double x1, double x2, double *y0, double *y1) nogil
|
||||
cdef void mathieu_modsem1(double x0, double x1, double x2, double *y0, double *y1) nogil
|
||||
cdef void mathieu_modsem2(double x0, double x1, double x2, double *y0, double *y1) nogil
|
||||
cdef void mathieu_sem(double x0, double x1, double x2, double *y0, double *y1) nogil
|
||||
cdef void modfresnelm(double x0, double complex *y0, double complex *y1) nogil
|
||||
cdef void modfresnelp(double x0, double complex *y0, double complex *y1) nogil
|
||||
cpdef double modstruve(double x0, double x1) nogil
|
||||
cpdef double nbdtr(dl_number_t x0, dl_number_t x1, double x2) nogil
|
||||
cpdef double nbdtrc(dl_number_t x0, dl_number_t x1, double x2) nogil
|
||||
cpdef double nbdtri(dl_number_t x0, dl_number_t x1, double x2) nogil
|
||||
cpdef double nbdtrik(double x0, double x1, double x2) nogil
|
||||
cpdef double nbdtrin(double x0, double x1, double x2) nogil
|
||||
cpdef double ncfdtr(double x0, double x1, double x2, double x3) nogil
|
||||
cpdef double ncfdtri(double x0, double x1, double x2, double x3) nogil
|
||||
cpdef double ncfdtridfd(double x0, double x1, double x2, double x3) nogil
|
||||
cpdef double ncfdtridfn(double x0, double x1, double x2, double x3) nogil
|
||||
cpdef double ncfdtrinc(double x0, double x1, double x2, double x3) nogil
|
||||
cpdef double nctdtr(double x0, double x1, double x2) nogil
|
||||
cpdef double nctdtridf(double x0, double x1, double x2) nogil
|
||||
cpdef double nctdtrinc(double x0, double x1, double x2) nogil
|
||||
cpdef double nctdtrit(double x0, double x1, double x2) nogil
|
||||
cpdef Dd_number_t ndtr(Dd_number_t x0) nogil
|
||||
cpdef double ndtri(double x0) nogil
|
||||
cpdef double nrdtrimn(double x0, double x1, double x2) nogil
|
||||
cpdef double nrdtrisd(double x0, double x1, double x2) nogil
|
||||
cdef void obl_ang1(double x0, double x1, double x2, double x3, double *y0, double *y1) nogil
|
||||
cdef void obl_ang1_cv(double x0, double x1, double x2, double x3, double x4, double *y0, double *y1) nogil
|
||||
cpdef double obl_cv(double x0, double x1, double x2) nogil
|
||||
cdef void obl_rad1(double x0, double x1, double x2, double x3, double *y0, double *y1) nogil
|
||||
cdef void obl_rad1_cv(double x0, double x1, double x2, double x3, double x4, double *y0, double *y1) nogil
|
||||
cdef void obl_rad2(double x0, double x1, double x2, double x3, double *y0, double *y1) nogil
|
||||
cdef void obl_rad2_cv(double x0, double x1, double x2, double x3, double x4, double *y0, double *y1) nogil
|
||||
cpdef double owens_t(double x0, double x1) nogil
|
||||
cdef void pbdv(double x0, double x1, double *y0, double *y1) nogil
|
||||
cdef void pbvv(double x0, double x1, double *y0, double *y1) nogil
|
||||
cdef void pbwa(double x0, double x1, double *y0, double *y1) nogil
|
||||
cpdef double pdtr(dl_number_t x0, double x1) nogil
|
||||
cpdef double pdtrc(dl_number_t x0, double x1) nogil
|
||||
cpdef double pdtri(dl_number_t x0, double x1) nogil
|
||||
cpdef double pdtrik(double x0, double x1) nogil
|
||||
cpdef double poch(double x0, double x1) nogil
|
||||
cdef void pro_ang1(double x0, double x1, double x2, double x3, double *y0, double *y1) nogil
|
||||
cdef void pro_ang1_cv(double x0, double x1, double x2, double x3, double x4, double *y0, double *y1) nogil
|
||||
cpdef double pro_cv(double x0, double x1, double x2) nogil
|
||||
cdef void pro_rad1(double x0, double x1, double x2, double x3, double *y0, double *y1) nogil
|
||||
cdef void pro_rad1_cv(double x0, double x1, double x2, double x3, double x4, double *y0, double *y1) nogil
|
||||
cdef void pro_rad2(double x0, double x1, double x2, double x3, double *y0, double *y1) nogil
|
||||
cdef void pro_rad2_cv(double x0, double x1, double x2, double x3, double x4, double *y0, double *y1) nogil
|
||||
cpdef double pseudo_huber(double x0, double x1) nogil
|
||||
cpdef Dd_number_t psi(Dd_number_t x0) nogil
|
||||
cpdef double radian(double x0, double x1, double x2) nogil
|
||||
cpdef double rel_entr(double x0, double x1) nogil
|
||||
cpdef Dd_number_t rgamma(Dd_number_t x0) nogil
|
||||
cpdef double round(double x0) nogil
|
||||
cdef void shichi(Dd_number_t x0, Dd_number_t *y0, Dd_number_t *y1) nogil
|
||||
cdef void sici(Dd_number_t x0, Dd_number_t *y0, Dd_number_t *y1) nogil
|
||||
cpdef double sindg(double x0) nogil
|
||||
cpdef double smirnov(dl_number_t x0, double x1) nogil
|
||||
cpdef double smirnovi(dl_number_t x0, double x1) nogil
|
||||
cpdef Dd_number_t spence(Dd_number_t x0) nogil
|
||||
cpdef double complex sph_harm(dl_number_t x0, dl_number_t x1, double x2, double x3) nogil
|
||||
cpdef double stdtr(double x0, double x1) nogil
|
||||
cpdef double stdtridf(double x0, double x1) nogil
|
||||
cpdef double stdtrit(double x0, double x1) nogil
|
||||
cpdef double struve(double x0, double x1) nogil
|
||||
cpdef double tandg(double x0) nogil
|
||||
cpdef double tklmbda(double x0, double x1) nogil
|
||||
cpdef double complex wofz(double complex x0) nogil
|
||||
cpdef double complex wrightomega(double complex x0) nogil
|
||||
cpdef Dd_number_t xlog1py(Dd_number_t x0, Dd_number_t x1) nogil
|
||||
cpdef Dd_number_t xlogy(Dd_number_t x0, Dd_number_t x1) nogil
|
||||
cpdef double y0(double x0) nogil
|
||||
cpdef double y1(double x0) nogil
|
||||
cpdef double yn(dl_number_t x0, double x1) nogil
|
||||
cpdef Dd_number_t yv(double x0, Dd_number_t x1) nogil
|
||||
cpdef Dd_number_t yve(double x0, Dd_number_t x1) nogil
|
||||
cpdef double zetac(double x0) nogil
|
||||
@@ -1,107 +0,0 @@
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
from ._ufuncs import _lambertw
|
||||
|
||||
|
||||
def lambertw(z, k=0, tol=1e-8):
|
||||
r"""
|
||||
lambertw(z, k=0, tol=1e-8)
|
||||
|
||||
Lambert W function.
|
||||
|
||||
The Lambert W function `W(z)` is defined as the inverse function
|
||||
of ``w * exp(w)``. In other words, the value of ``W(z)`` is
|
||||
such that ``z = W(z) * exp(W(z))`` for any complex number
|
||||
``z``.
|
||||
|
||||
The Lambert W function is a multivalued function with infinitely
|
||||
many branches. Each branch gives a separate solution of the
|
||||
equation ``z = w exp(w)``. Here, the branches are indexed by the
|
||||
integer `k`.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
z : array_like
|
||||
Input argument.
|
||||
k : int, optional
|
||||
Branch index.
|
||||
tol : float, optional
|
||||
Evaluation tolerance.
|
||||
|
||||
Returns
|
||||
-------
|
||||
w : array
|
||||
`w` will have the same shape as `z`.
|
||||
|
||||
Notes
|
||||
-----
|
||||
All branches are supported by `lambertw`:
|
||||
|
||||
* ``lambertw(z)`` gives the principal solution (branch 0)
|
||||
* ``lambertw(z, k)`` gives the solution on branch `k`
|
||||
|
||||
The Lambert W function has two partially real branches: the
|
||||
principal branch (`k = 0`) is real for real ``z > -1/e``, and the
|
||||
``k = -1`` branch is real for ``-1/e < z < 0``. All branches except
|
||||
``k = 0`` have a logarithmic singularity at ``z = 0``.
|
||||
|
||||
**Possible issues**
|
||||
|
||||
The evaluation can become inaccurate very close to the branch point
|
||||
at ``-1/e``. In some corner cases, `lambertw` might currently
|
||||
fail to converge, or can end up on the wrong branch.
|
||||
|
||||
**Algorithm**
|
||||
|
||||
Halley's iteration is used to invert ``w * exp(w)``, using a first-order
|
||||
asymptotic approximation (O(log(w)) or `O(w)`) as the initial estimate.
|
||||
|
||||
The definition, implementation and choice of branches is based on [2]_.
|
||||
|
||||
See Also
|
||||
--------
|
||||
wrightomega : the Wright Omega function
|
||||
|
||||
References
|
||||
----------
|
||||
.. [1] https://en.wikipedia.org/wiki/Lambert_W_function
|
||||
.. [2] Corless et al, "On the Lambert W function", Adv. Comp. Math. 5
|
||||
(1996) 329-359.
|
||||
http://www.apmaths.uwo.ca/~djeffrey/Offprints/W-adv-cm.pdf
|
||||
|
||||
Examples
|
||||
--------
|
||||
The Lambert W function is the inverse of ``w exp(w)``:
|
||||
|
||||
>>> from scipy.special import lambertw
|
||||
>>> w = lambertw(1)
|
||||
>>> w
|
||||
(0.56714329040978384+0j)
|
||||
>>> w * np.exp(w)
|
||||
(1.0+0j)
|
||||
|
||||
Any branch gives a valid inverse:
|
||||
|
||||
>>> w = lambertw(1, k=3)
|
||||
>>> w
|
||||
(-2.8535817554090377+17.113535539412148j)
|
||||
>>> w*np.exp(w)
|
||||
(1.0000000000000002+1.609823385706477e-15j)
|
||||
|
||||
**Applications to equation-solving**
|
||||
|
||||
The Lambert W function may be used to solve various kinds of
|
||||
equations, such as finding the value of the infinite power
|
||||
tower :math:`z^{z^{z^{\ldots}}}`:
|
||||
|
||||
>>> def tower(z, n):
|
||||
... if n == 0:
|
||||
... return z
|
||||
... return z ** tower(z, n-1)
|
||||
...
|
||||
>>> tower(0.5, 100)
|
||||
0.641185744504986
|
||||
>>> -lambertw(-np.log(0.5)) / np.log(0.5)
|
||||
(0.64118574450498589+0j)
|
||||
"""
|
||||
return _lambertw(z, k, tol)
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,160 +0,0 @@
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
import os
|
||||
import sys
|
||||
from os.path import join, dirname
|
||||
from distutils.sysconfig import get_python_inc
|
||||
import subprocess
|
||||
import numpy
|
||||
from numpy.distutils.misc_util import get_numpy_include_dirs
|
||||
|
||||
try:
|
||||
from numpy.distutils.misc_util import get_info
|
||||
except ImportError:
|
||||
raise ValueError("numpy >= 1.4 is required (detected %s from %s)" %
|
||||
(numpy.__version__, numpy.__file__))
|
||||
|
||||
|
||||
def configuration(parent_package='',top_path=None):
|
||||
from numpy.distutils.misc_util import Configuration
|
||||
from scipy._build_utils.system_info import get_info as get_system_info
|
||||
|
||||
config = Configuration('special', parent_package, top_path)
|
||||
|
||||
define_macros = []
|
||||
if sys.platform == 'win32':
|
||||
# define_macros.append(('NOINFINITIES',None))
|
||||
# define_macros.append(('NONANS',None))
|
||||
define_macros.append(('_USE_MATH_DEFINES',None))
|
||||
|
||||
curdir = os.path.abspath(os.path.dirname(__file__))
|
||||
inc_dirs = [get_python_inc(), os.path.join(curdir, "c_misc")]
|
||||
if inc_dirs[0] != get_python_inc(plat_specific=1):
|
||||
inc_dirs.append(get_python_inc(plat_specific=1))
|
||||
inc_dirs.insert(0, get_numpy_include_dirs())
|
||||
inc_dirs.append(join(dirname(dirname(__file__)), '_lib'))
|
||||
|
||||
# C libraries
|
||||
c_misc_src = [join('c_misc','*.c')]
|
||||
c_misc_hdr = [join('c_misc','*.h')]
|
||||
cephes_src = [join('cephes','*.c')]
|
||||
cephes_hdr = [join('cephes', '*.h')]
|
||||
config.add_library('sc_c_misc',sources=c_misc_src,
|
||||
include_dirs=[curdir] + inc_dirs,
|
||||
depends=(cephes_hdr + cephes_src
|
||||
+ c_misc_hdr + cephes_hdr
|
||||
+ ['*.h']),
|
||||
macros=define_macros)
|
||||
config.add_library('sc_cephes',sources=cephes_src,
|
||||
include_dirs=[curdir] + inc_dirs,
|
||||
depends=(cephes_hdr + ['*.h']),
|
||||
macros=define_macros)
|
||||
|
||||
# Fortran/C++ libraries
|
||||
mach_src = [join('mach','*.f')]
|
||||
amos_src = [join('amos','*.f')]
|
||||
cdf_src = [join('cdflib','*.f')]
|
||||
specfun_src = [join('specfun','*.f')]
|
||||
config.add_library('sc_mach',sources=mach_src,
|
||||
config_fc={'noopt':(__file__,1)})
|
||||
config.add_library('sc_amos',sources=amos_src)
|
||||
config.add_library('sc_cdf',sources=cdf_src)
|
||||
config.add_library('sc_specfun',sources=specfun_src)
|
||||
|
||||
# Extension specfun
|
||||
config.add_extension('specfun',
|
||||
sources=['specfun.pyf'],
|
||||
f2py_options=['--no-wrap-functions'],
|
||||
depends=specfun_src,
|
||||
define_macros=[],
|
||||
libraries=['sc_specfun'])
|
||||
|
||||
# Extension _ufuncs
|
||||
headers = ['*.h', join('c_misc', '*.h'), join('cephes', '*.h')]
|
||||
ufuncs_src = ['_ufuncs.c', 'sf_error.c', '_logit.c.src',
|
||||
"amos_wrappers.c", "cdf_wrappers.c", "specfun_wrappers.c"]
|
||||
ufuncs_dep = (headers + ufuncs_src + amos_src + c_misc_src + cephes_src
|
||||
+ mach_src + cdf_src + specfun_src)
|
||||
cfg = dict(get_system_info('lapack_opt'))
|
||||
cfg.setdefault('include_dirs', []).extend([curdir] + inc_dirs + [numpy.get_include()])
|
||||
cfg.setdefault('libraries', []).extend(['sc_amos','sc_c_misc','sc_cephes','sc_mach',
|
||||
'sc_cdf', 'sc_specfun'])
|
||||
cfg.setdefault('define_macros', []).extend(define_macros)
|
||||
config.add_extension('_ufuncs',
|
||||
depends=ufuncs_dep,
|
||||
sources=ufuncs_src,
|
||||
extra_info=get_info("npymath"),
|
||||
**cfg)
|
||||
|
||||
# Extension _ufuncs_cxx
|
||||
ufuncs_cxx_src = ['_ufuncs_cxx.cxx', 'sf_error.c',
|
||||
'_faddeeva.cxx', 'Faddeeva.cc',
|
||||
'_wright.cxx', 'wright.cc']
|
||||
ufuncs_cxx_dep = (headers + ufuncs_cxx_src + cephes_src
|
||||
+ ['*.hh'])
|
||||
config.add_extension('_ufuncs_cxx',
|
||||
sources=ufuncs_cxx_src,
|
||||
depends=ufuncs_cxx_dep,
|
||||
include_dirs=[curdir] + inc_dirs,
|
||||
define_macros=define_macros,
|
||||
extra_info=get_info("npymath"))
|
||||
|
||||
cfg = dict(get_system_info('lapack_opt'))
|
||||
config.add_extension('_ellip_harm_2',
|
||||
sources=['_ellip_harm_2.c', 'sf_error.c',],
|
||||
**cfg
|
||||
)
|
||||
|
||||
# Cython API
|
||||
config.add_data_files('cython_special.pxd')
|
||||
|
||||
cython_special_src = ['cython_special.c', 'sf_error.c', '_logit.c.src',
|
||||
"amos_wrappers.c", "cdf_wrappers.c", "specfun_wrappers.c"]
|
||||
cython_special_dep = (headers + ufuncs_src + ufuncs_cxx_src + amos_src
|
||||
+ c_misc_src + cephes_src + mach_src + cdf_src
|
||||
+ specfun_src)
|
||||
cfg = dict(get_system_info('lapack_opt'))
|
||||
cfg.setdefault('include_dirs', []).extend([curdir] + inc_dirs + [numpy.get_include()])
|
||||
cfg.setdefault('libraries', []).extend(['sc_amos','sc_c_misc','sc_cephes','sc_mach',
|
||||
'sc_cdf', 'sc_specfun'])
|
||||
cfg.setdefault('define_macros', []).extend(define_macros)
|
||||
config.add_extension('cython_special',
|
||||
depends=cython_special_dep,
|
||||
sources=cython_special_src,
|
||||
extra_info=get_info("npymath"),
|
||||
**cfg)
|
||||
|
||||
# combinatorics
|
||||
config.add_extension('_comb',
|
||||
sources=['_comb.c'])
|
||||
|
||||
# testing for _round.h
|
||||
config.add_extension('_test_round',
|
||||
sources=['_test_round.c'],
|
||||
depends=['_round.h', 'cephes/dd_idefs.h'],
|
||||
include_dirs=[numpy.get_include()] + inc_dirs,
|
||||
extra_info=get_info('npymath'))
|
||||
|
||||
config.add_data_files('tests/*.py')
|
||||
config.add_data_files('tests/data/README')
|
||||
|
||||
# regenerate npz data files
|
||||
makenpz = os.path.join(os.path.dirname(__file__),
|
||||
'utils', 'makenpz.py')
|
||||
data_dir = os.path.join(os.path.dirname(__file__),
|
||||
'tests', 'data')
|
||||
for name in ['boost', 'gsl', 'local']:
|
||||
subprocess.check_call([sys.executable, makenpz,
|
||||
'--use-timestamp',
|
||||
os.path.join(data_dir, name)])
|
||||
|
||||
config.add_data_files('tests/data/*.npz')
|
||||
|
||||
config.add_subpackage('_precompute')
|
||||
|
||||
return config
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
from numpy.distutils.core import setup
|
||||
setup(**configuration(top_path='').todict())
|
||||
@@ -1,15 +0,0 @@
|
||||
"""Warnings and Exceptions that can be raised by special functions."""
|
||||
import warnings
|
||||
|
||||
|
||||
class SpecialFunctionWarning(Warning):
|
||||
"""Warning that can be emitted by special functions."""
|
||||
pass
|
||||
|
||||
|
||||
warnings.simplefilter("always", category=SpecialFunctionWarning)
|
||||
|
||||
|
||||
class SpecialFunctionError(Exception):
|
||||
"""Exception that can be raised by special functions."""
|
||||
pass
|
||||
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@@ -1,95 +0,0 @@
|
||||
# Last Change: Sat Mar 21 02:00 PM 2009 J
|
||||
|
||||
# Copyright (c) 2001, 2002 Enthought, Inc.
|
||||
#
|
||||
# All rights reserved.
|
||||
#
|
||||
# Redistribution and use in source and binary forms, with or without
|
||||
# modification, are permitted provided that the following conditions are met:
|
||||
#
|
||||
# a. Redistributions of source code must retain the above copyright notice,
|
||||
# this list of conditions and the following disclaimer.
|
||||
# b. Redistributions in binary form must reproduce the above copyright
|
||||
# notice, this list of conditions and the following disclaimer in the
|
||||
# documentation and/or other materials provided with the distribution.
|
||||
# c. Neither the name of the Enthought nor the names of its contributors
|
||||
# may be used to endorse or promote products derived from this software
|
||||
# without specific prior written permission.
|
||||
#
|
||||
#
|
||||
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
||||
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
||||
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
|
||||
# ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR
|
||||
# ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
||||
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
||||
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
|
||||
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
|
||||
# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY
|
||||
# OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH
|
||||
# DAMAGE.
|
||||
|
||||
"""Some more special functions which may be useful for multivariate statistical
|
||||
analysis."""
|
||||
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
import numpy as np
|
||||
from scipy.special import gammaln as loggam
|
||||
|
||||
|
||||
__all__ = ['multigammaln']
|
||||
|
||||
|
||||
def multigammaln(a, d):
|
||||
r"""Returns the log of multivariate gamma, also sometimes called the
|
||||
generalized gamma.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
a : ndarray
|
||||
The multivariate gamma is computed for each item of `a`.
|
||||
d : int
|
||||
The dimension of the space of integration.
|
||||
|
||||
Returns
|
||||
-------
|
||||
res : ndarray
|
||||
The values of the log multivariate gamma at the given points `a`.
|
||||
|
||||
Notes
|
||||
-----
|
||||
The formal definition of the multivariate gamma of dimension d for a real
|
||||
`a` is
|
||||
|
||||
.. math::
|
||||
|
||||
\Gamma_d(a) = \int_{A>0} e^{-tr(A)} |A|^{a - (d+1)/2} dA
|
||||
|
||||
with the condition :math:`a > (d-1)/2`, and :math:`A > 0` being the set of
|
||||
all the positive definite matrices of dimension `d`. Note that `a` is a
|
||||
scalar: the integrand only is multivariate, the argument is not (the
|
||||
function is defined over a subset of the real set).
|
||||
|
||||
This can be proven to be equal to the much friendlier equation
|
||||
|
||||
.. math::
|
||||
|
||||
\Gamma_d(a) = \pi^{d(d-1)/4} \prod_{i=1}^{d} \Gamma(a - (i-1)/2).
|
||||
|
||||
References
|
||||
----------
|
||||
R. J. Muirhead, Aspects of multivariate statistical theory (Wiley Series in
|
||||
probability and mathematical statistics).
|
||||
|
||||
"""
|
||||
a = np.asarray(a)
|
||||
if not np.isscalar(d) or (np.floor(d) != d):
|
||||
raise ValueError("d should be a positive integer (dimension)")
|
||||
if np.any(a <= 0.5 * (d - 1)):
|
||||
raise ValueError("condition a (%f) > 0.5 * (d-1) (%f) not met"
|
||||
% (a, 0.5 * (d-1)))
|
||||
|
||||
res = (d * (d-1) * 0.25) * np.log(np.pi)
|
||||
res += np.sum(loggam([(a - (j - 1.)/2) for j in range(1, d+1)]), axis=0)
|
||||
return res
|
||||
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@@ -1,578 +0,0 @@
|
||||
This directory contains numerical data for testing special functions.
|
||||
The data is in version control as text files.
|
||||
|
||||
The data is automatically packed into npz files by setup.py.
|
||||
The npz files should not be checked in version control.
|
||||
|
||||
The data in gsl is computed using the GNU scientific library, the data
|
||||
in local is computed using mpmath, and the data in boost is a copy of
|
||||
data distributed with the boost library and comes with the following
|
||||
license:
|
||||
|
||||
Boost Software License - Version 1.0 - August 17th, 2003
|
||||
|
||||
Permission is hereby granted, free of charge, to any person or organization
|
||||
obtaining a copy of the software and accompanying documentation covered by
|
||||
this license (the "Software") to use, reproduce, display, distribute,
|
||||
execute, and transmit the Software, and to prepare derivative works of the
|
||||
Software, and to permit third-parties to whom the Software is furnished to
|
||||
do so, all subject to the following:
|
||||
|
||||
The copyright notices in the Software and this entire statement, including
|
||||
the above license grant, this restriction and the following disclaimer,
|
||||
must be included in all copies of the Software, in whole or in part, and
|
||||
all derivative works of the Software, unless such copies or derivative
|
||||
works are solely in the form of machine-executable object code generated by
|
||||
a source language processor.
|
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT. IN NO EVENT
|
||||
SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE
|
||||
FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE,
|
||||
ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
|
||||
DEALINGS IN THE SOFTWARE.
|
||||
|
||||
=========
|
||||
|
||||
Copyright holders of each file are listed here:
|
||||
|
||||
Jamfile.v2:# Copyright Daryle Walker, Hubert Holin, John Maddock 2006 - 2007
|
||||
acosh_data.ipp:// Copyright John Maddock 2008.
|
||||
acosh_test.hpp:// (C) Copyright Hubert Holin 2003.
|
||||
almost_equal.ipp:// Copyright (c) 2006 Johan Rade
|
||||
asinh_data.ipp:// Copyright John Maddock 2008.
|
||||
asinh_test.hpp:// (C) Copyright Hubert Holin 2003.
|
||||
assoc_legendre_p.ipp:// (C) Copyright John Maddock 2006-7.
|
||||
atanh_data.ipp:// Copyright John Maddock 2008.
|
||||
atanh_test.hpp:// (C) Copyright Hubert Holin 2003.
|
||||
bessel_i_data.ipp:// Copyright (c) 2007 John Maddock
|
||||
bessel_i_int_data.ipp:// Copyright (c) 2007 John Maddock
|
||||
bessel_j_data.ipp:// Copyright (c) 2007 John Maddock
|
||||
bessel_j_int_data.ipp:// Copyright (c) 2007 John Maddock
|
||||
bessel_j_large_data.ipp:// Copyright (c) 2007 John Maddock
|
||||
bessel_k_data.ipp:// Copyright (c) 2007 John Maddock
|
||||
bessel_k_int_data.ipp:// Copyright (c) 2007 John Maddock
|
||||
bessel_y01_data.ipp:// Copyright (c) 2007 John Maddock
|
||||
bessel_yn_data.ipp:// Copyright (c) 2007 John Maddock
|
||||
bessel_yv_data.ipp:// Copyright (c) 2007 John Maddock
|
||||
beta_exp_data.ipp:// (C) Copyright John Maddock 2006.
|
||||
beta_med_data.ipp:// (C) Copyright John Maddock 2006.
|
||||
beta_small_data.ipp:// (C) Copyright John Maddock 2006.
|
||||
binomial_data.ipp:// (C) Copyright John Maddock 2006-7.
|
||||
binomial_large_data.ipp:// (C) Copyright John Maddock 2006-7.
|
||||
binomial_quantile.ipp:// (C) Copyright John Maddock 2006-7.
|
||||
cbrt_data.ipp:// (C) Copyright John Maddock 2006-7.
|
||||
common_factor_test.cpp:// (C) Copyright Daryle Walker 2001, 2006.
|
||||
compile_test/tools_rational_inc_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/tools_real_cast_inc_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/tools_remez_inc_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/dist_chi_squared_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/dist_complement_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/sf_sign_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/sf_digamma_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/sf_trunc_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/constants_incl_test.cpp:// Copyright John Maddock 2012.
|
||||
compile_test/sf_sinc_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/sf_binomial_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/dist_binomial_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/tools_test_inc_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/dist_normal_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/sf_sinhc_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/sf_ellint_rc_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/sf_sin_pi_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/sf_sph_harm_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/dist_poisson_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/test_traits.cpp:// Copyright John Maddock 2007.
|
||||
compile_test/dist_gamma_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/sf_cos_pi_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/dist_logistic_incl_test.cpp:// Copyright John Maddock 2008.
|
||||
compile_test/sf_fpclassify_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/compl_atanh_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/tools_precision_inc_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/sf_hankel_incl_test.cpp:// Copyright John Maddock 2012.
|
||||
compile_test/sf_cbrt_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/dist_nc_beta_incl_test.cpp:// Copyright John Maddock 2008.
|
||||
compile_test/sf_legendre_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/tools_stats_inc_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/tools_polynomial_inc_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/tools_config_inc_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/dist_exponential_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/dist_students_t_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/dist_inv_gamma_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/compl_acosh_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/dist_beta_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/dist_fisher_f_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/dist_triangular_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/instantiate.hpp:// Copyright John Maddock 2006.
|
||||
compile_test/instantiate.hpp:// Copyright Paul A. Bristow 2007, 2010.
|
||||
compile_test/tools_solve_inc_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/sf_next_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/generate.sh:// Copyright John Maddock 2006.
|
||||
compile_test/generate.sh:// Copyright John Maddock 2006.
|
||||
compile_test/generate.sh:// Copyright John Maddock 2006.
|
||||
compile_test/distribution_concept_check.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/sf_laguerre_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/tr1_incl_test.cpp:// Copyright John Maddock 2008.
|
||||
compile_test/sf_ellint_rj_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/dist_nc_chi_squ_incl_test.cpp:// Copyright John Maddock 2008.
|
||||
compile_test/dist_skew_norm_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/sf_modf_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/dist_find_location_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/compl_acos_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/sf_ellint_rd_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/tools_roots_inc_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/tools_test_data_inc_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/compl_abs_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/dist_nc_t_incl_test.cpp:// Copyright John Maddock 2008.
|
||||
compile_test/sf_factorials_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/sf_gamma_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/compl_atan_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/sf_powm1_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/sf_hypot_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/dist_pareto_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/sf_round_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/dist_weibull_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/std_real_concept_check.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/dist_hypergeo_incl_test.cpp:// Copyright John Maddock 2008.
|
||||
compile_test/dist_inv_chi_sq_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/sf_sqrt1pm1_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/sf_log1p_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/sf_jacobi_incl_test.cpp:// Copyright John Maddock 2012.
|
||||
compile_test/dist_neg_binom_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/dist_nc_f_incl_test.cpp:// Copyright John Maddock 2008.
|
||||
compile_test/dist_find_scale_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/sf_bessel_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/tools_minima_inc_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/compl_asin_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/dist_extreme_val_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/sf_lanczos_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/dist_uniform_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/test_compile_result.hpp:// Copyright John Maddock 2007.
|
||||
compile_test/tools_series_inc_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/sf_ellint_3_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/sf_ellint_rf_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/sf_ellint_2_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/sf_hermite_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/poison.hpp:// Copyright John Maddock 2013.
|
||||
compile_test/sf_zeta_incl_test.cpp:// Copyright John Maddock 2007.
|
||||
compile_test/dist_laplace_incl_test.cpp:// Copyright John Maddock 2008.
|
||||
compile_test/sf_expint_incl_test.cpp:// Copyright John Maddock 2007.
|
||||
compile_test/sf_expm1_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/dist_bernoulli_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/compl_asinh_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/sf_beta_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/tools_fraction_inc_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/sf_owens_t_incl_test.cpp:// Copyright John Maddock 2012.
|
||||
compile_test/tools_toms748_inc_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/sf_ellint_1_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/sf_erf_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/main.cpp:// Copyright John Maddock 2009.
|
||||
compile_test/sf_math_fwd_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/sf_airy_incl_test.cpp:// Copyright John Maddock 2012.
|
||||
compile_test/dist_lognormal_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
compile_test/dist_cauchy_incl_test.cpp:// Copyright John Maddock 2006.
|
||||
complex_test.cpp:// (C) Copyright John Maddock 2005.
|
||||
digamma_data.ipp:// (C) Copyright John Maddock 2006-7.
|
||||
digamma_neg_data.ipp:// (C) Copyright John Maddock 2006-7.
|
||||
digamma_root_data.ipp:// (C) Copyright John Maddock 2006-7.
|
||||
digamma_small_data.ipp:// (C) Copyright John Maddock 2006-7.
|
||||
e_float_concept_check.cpp:// Copyright John Maddock 2011.
|
||||
ellint_e2_data.ipp:// Copyright (c) 2006 John Maddock
|
||||
ellint_e_data.ipp:// Copyright (c) 2006 John Maddock
|
||||
ellint_f_data.ipp:// Copyright (c) 2006 John Maddock
|
||||
ellint_k_data.ipp:// (C) Copyright John Maddock 2006-7.
|
||||
ellint_pi2_data.ipp:// Copyright (c) 2006 John Maddock
|
||||
ellint_pi3_data.ipp:// Copyright (c) 2006 John Maddock
|
||||
ellint_pi3_large_data.ipp:// Copyright (c) 2006 John Maddock
|
||||
ellint_rc_data.ipp:// Copyright (c) 2006 John Maddock
|
||||
ellint_rd_data.ipp:// Copyright (c) 2006 John Maddock
|
||||
ellint_rf_data.ipp:// Copyright (c) 2006 John Maddock
|
||||
ellint_rj_data.ipp:// Copyright (c) 2006 John Maddock
|
||||
erf_data.ipp:// (C) Copyright John Maddock 2006-7.
|
||||
erf_inv_data.ipp:// (C) Copyright John Maddock 2006-7.
|
||||
erf_large_data.ipp:// (C) Copyright John Maddock 2006-7.
|
||||
erf_small_data.ipp:// (C) Copyright John Maddock 2006.
|
||||
erfc_inv_big_data.ipp:// (C) Copyright John Maddock 2006-7.
|
||||
erfc_inv_data.ipp:// (C) Copyright John Maddock 2006-7.
|
||||
expint_1_data.ipp:// Copyright John Maddock 2008.
|
||||
expint_data.ipp:// Copyright John Maddock 2008.
|
||||
expint_small_data.ipp:// Copyright John Maddock 2008.
|
||||
expinti_data.ipp:// Copyright John Maddock 2008.
|
||||
expinti_data_double.ipp:// Copyright John Maddock 2008.
|
||||
expinti_data_long.ipp:// Copyright John Maddock 2008.
|
||||
functor.hpp:// (C) Copyright John Maddock 2007.
|
||||
gamma_inv_big_data.ipp:// (C) Copyright John Maddock 2006-7.
|
||||
gamma_inv_data.ipp:// (C) Copyright John Maddock 2006-7.
|
||||
gamma_inv_small_data.ipp:// (C) Copyright John Maddock 2006-7.
|
||||
handle_test_result.hpp:// (C) Copyright John Maddock 2006-7.
|
||||
hermite.ipp:// (C) Copyright John Maddock 2006-7.
|
||||
hypergeometric_dist_data2.ipp:// Copyright John Maddock 2008
|
||||
hypergeometric_test_data.ipp:// Copyright Gautam Sewani 2008
|
||||
hypot_test.cpp:// (C) Copyright John Maddock 2005.
|
||||
ibeta_data.ipp:// (C) Copyright John Maddock 2006.
|
||||
ibeta_int_data.ipp:// (C) Copyright John Maddock 2006-7.
|
||||
ibeta_inv_data.ipp:// (C) Copyright John Maddock 2006-7.
|
||||
ibeta_inva_data.ipp:// (C) Copyright John Maddock 2006-7.
|
||||
ibeta_large_data.ipp:// (C) Copyright John Maddock 2006.
|
||||
ibeta_small_data.ipp:// (C) Copyright John Maddock 2006.
|
||||
igamma_big_data.ipp:// (C) Copyright John Maddock 2006.
|
||||
igamma_int_data.ipp:// (C) Copyright John Maddock 2006-7.
|
||||
igamma_inva_data.ipp:// (C) Copyright John Maddock 2006-7.
|
||||
igamma_med_data.ipp:// (C) Copyright John Maddock 2006.
|
||||
igamma_small_data.ipp:// (C) Copyright John Maddock 2006.
|
||||
jacobi_elliptic.ipp:// Copyright John Maddock 2012.
|
||||
jacobi_elliptic_small.ipp:// Copyright John Maddock 2012.
|
||||
jacobi_large_phi.ipp:// Copyright John Maddock 2012.
|
||||
jacobi_near_1.ipp:// Copyright John Maddock 2012.
|
||||
laguerre2.ipp:// (C) Copyright John Maddock 2006-7.
|
||||
laguerre3.ipp:// (C) Copyright John Maddock 2006-7.
|
||||
legendre_p.ipp:// (C) Copyright John Maddock 2006-7.
|
||||
legendre_p_large.ipp:// (C) Copyright John Maddock 2006-7.
|
||||
log1p_expm1_data.ipp:// (C) Copyright John Maddock 2006-7.
|
||||
log1p_expm1_test.cpp:// Copyright John Maddock 2005.
|
||||
log1p_expm1_test.cpp:// Copyright Paul A. Bristow 2010
|
||||
log1p_expm1_test.hpp:// Copyright John Maddock 2005.
|
||||
log1p_expm1_test.hpp:// Copyright Paul A. Bristow 2010
|
||||
mpfr_concept_check.cpp:// Copyright John Maddock 2007-8.
|
||||
mpreal_concept_check.cpp:// Copyright John Maddock 2007-8.
|
||||
multiprc_concept_check_1.cpp:// Copyright John Maddock 2013.
|
||||
multiprc_concept_check_2.cpp:// Copyright John Maddock 2013.
|
||||
multiprc_concept_check_3.cpp:// Copyright John Maddock 2013.
|
||||
multiprc_concept_check_4.cpp:// Copyright John Maddock 2013.
|
||||
ncbeta.ipp:// Copyright John Maddock 2008.
|
||||
ncbeta_big.ipp:// Copyright John Maddock 2008.
|
||||
nccs.ipp:// Copyright John Maddock 2008.
|
||||
nccs_big.ipp:// Copyright John Maddock 2008.
|
||||
nct.ipp:// Copyright John Maddock 2008.
|
||||
nct_asym.ipp:// Copyright John Maddock 2012.
|
||||
nct_small_delta.ipp:// Copyright John Maddock 2012.
|
||||
negative_binomial_quantile.ipp:// (C) Copyright John Maddock 2006-7.
|
||||
ntl_concept_check.cpp:// Copyright John Maddock 2007-8.
|
||||
ntl_concept_check.cpp:// Copyright Paul A. Bristow 2009, 2011
|
||||
owens_t.ipp:// Copyright John Maddock 2012.
|
||||
owens_t_T7.hpp:// Copyright (C) Benjamin Sobotta 2012
|
||||
owens_t_large_data.ipp:// Copyright John Maddock 2012.
|
||||
pch.hpp:// Copyright John Maddock 2008.
|
||||
pch_light.hpp:// Copyright John Maddock 2008.
|
||||
poisson_quantile.ipp:// (C) Copyright John Maddock 2006-7.
|
||||
pow_test.cpp:// (C) Copyright Bruno Lalande 2008.
|
||||
powm1_sqrtp1m1_test.cpp:// (C) Copyright John Maddock 2006.
|
||||
powm1_sqrtp1m1_test.hpp:// Copyright John Maddock 2006.
|
||||
s_.ipp:// Copyright (c) 2006 Johan Rade
|
||||
s_.ipp:// Copyright (c) 2012 Paul A. Bristow
|
||||
sinc_test.hpp:// (C) Copyright Hubert Holin 2003.
|
||||
sinhc_test.hpp:// (C) Copyright Hubert Holin 2003.
|
||||
special_functions_test.cpp:// (C) Copyright Hubert Holin 2003.
|
||||
special_functions_test.cpp: BOOST_TEST_MESSAGE("(C) Copyright Hubert Holin 2003-2005.");
|
||||
sph_bessel_data.ipp:// Copyright (c) 2007 John Maddock
|
||||
sph_neumann_data.ipp:// Copyright (c) 2007 John Maddock
|
||||
spherical_harmonic.ipp:// (C) Copyright John Maddock 2006-7.
|
||||
std_real_concept_check.cpp:// Copyright John Maddock 2006.
|
||||
table_type.hpp:// Copyright John Maddock 2012.
|
||||
test_airy.cpp:// Copyright John Maddock 2012
|
||||
test_archive.cpp:// Copyright (c) 2006 Johan Rade
|
||||
test_archive.cpp:// Copyright (c) 2011 Paul A. Bristow - filename changes for boost-trunk.
|
||||
test_basic_nonfinite.cpp:// Copyright (c) 2006 Johan Rade
|
||||
test_basic_nonfinite.cpp:// Copyright (c) 2011 Paul A. Bristow comments
|
||||
test_basic_nonfinite.cpp:// Copyright (c) 2011 John Maddock
|
||||
test_bernoulli.cpp:// Copyright John Maddock 2006.
|
||||
test_bernoulli.cpp:// Copyright Paul A. Bristow 2007, 2012.
|
||||
test_bessel_airy_zeros.cpp:// Copyright John Maddock 2013
|
||||
test_bessel_airy_zeros.cpp:// Copyright Christopher Kormanyos 2013.
|
||||
test_bessel_airy_zeros.cpp:// Copyright Paul A. Bristow 2013.
|
||||
test_bessel_hooks.hpp:// (C) Copyright John Maddock 2007.
|
||||
test_bessel_i.cpp:// (C) Copyright John Maddock 2007.
|
||||
test_bessel_i.hpp:// (C) Copyright John Maddock 2007.
|
||||
test_bessel_j.cpp:// (C) Copyright John Maddock 2007.
|
||||
test_bessel_j.hpp:// (C) Copyright John Maddock 2007.
|
||||
test_bessel_k.cpp:// Copyright John Maddock 2006, 2007
|
||||
test_bessel_k.cpp:// Copyright Paul A. Bristow 2007
|
||||
test_bessel_k.hpp:// (C) Copyright John Maddock 2007.
|
||||
test_bessel_y.cpp:// (C) Copyright John Maddock 2007.
|
||||
test_bessel_y.hpp:// (C) Copyright John Maddock 2007.
|
||||
test_beta.cpp:// Copyright John Maddock 2006.
|
||||
test_beta.cpp:// Copyright Paul A. Bristow 2007, 2009
|
||||
test_beta.hpp:// Copyright John Maddock 2006.
|
||||
test_beta.hpp:// Copyright Paul A. Bristow 2007, 2009
|
||||
test_beta_dist.cpp:// Copyright John Maddock 2006.
|
||||
test_beta_dist.cpp:// Copyright Paul A. Bristow 2007, 2009, 2010, 2012.
|
||||
test_beta_hooks.hpp:// (C) Copyright John Maddock 2006.
|
||||
test_binomial.cpp:// Copyright John Maddock 2006.
|
||||
test_binomial.cpp:// Copyright Paul A. Bristow 2007.
|
||||
test_binomial_coeff.cpp:// (C) Copyright John Maddock 2006.
|
||||
test_binomial_coeff.hpp:// Copyright John Maddock 2006.
|
||||
test_binomial_coeff.hpp:// Copyright Paul A. Bristow 2007, 2009
|
||||
test_carlson.cpp:// Copyright 2006 John Maddock
|
||||
test_carlson.cpp:// Copyright Paul A. Bristow 2007.
|
||||
test_carlson.hpp:// Copyright John Maddock 2006.
|
||||
test_carlson.hpp:// Copyright Paul A. Bristow 2007, 2009
|
||||
test_cauchy.cpp:// Copyright John Maddock 2006, 2007.
|
||||
test_cauchy.cpp:// Copyright Paul A. Bristow 2007
|
||||
test_cbrt.cpp:// Copyright John Maddock 2006.
|
||||
test_cbrt.cpp:// Copyright Paul A. Bristow 2010
|
||||
test_cbrt.hpp:// Copyright John Maddock 2006.
|
||||
test_cbrt.hpp:// Copyright Paul A. Bristow 2007, 2009
|
||||
test_chi_squared.cpp:// Copyright Paul A. Bristow 2006.
|
||||
test_chi_squared.cpp:// Copyright John Maddock 2007.
|
||||
test_classify.cpp:// Copyright John Maddock 2006.
|
||||
test_classify.cpp:// Copyright Paul A. Bristow 2007
|
||||
test_common_factor_gmpxx.cpp:// (C) Copyright John Maddock 2010.
|
||||
test_constant_generate.cpp:// Copyright John Maddock 2010.
|
||||
test_constants.cpp:// Copyright Paul Bristow 2007, 2011.
|
||||
test_constants.cpp:// Copyright John Maddock 2006, 2011.
|
||||
test_digamma.cpp:// (C) Copyright John Maddock 2006.
|
||||
test_digamma.hpp:// Copyright John Maddock 2006.
|
||||
test_digamma.hpp:// Copyright Paul A. Bristow 2007, 2009
|
||||
test_dist_overloads.cpp:// Copyright John Maddock 2006.
|
||||
test_dist_overloads.cpp:// Copyright Paul A. Bristow 2007.
|
||||
test_ellint_1.cpp:// Copyright Xiaogang Zhang 2006
|
||||
test_ellint_1.cpp:// Copyright John Maddock 2006, 2007
|
||||
test_ellint_1.cpp:// Copyright Paul A. Bristow 2007
|
||||
test_ellint_1.hpp:// Copyright John Maddock 2006.
|
||||
test_ellint_1.hpp:// Copyright Paul A. Bristow 2007, 2009
|
||||
test_ellint_2.cpp:// Copyright Xiaogang Zhang 2006
|
||||
test_ellint_2.cpp:// Copyright John Maddock 2006, 2007
|
||||
test_ellint_2.cpp:// Copyright Paul A. Bristow 2007
|
||||
test_ellint_2.hpp:// Copyright John Maddock 2006.
|
||||
test_ellint_2.hpp:// Copyright Paul A. Bristow 2007, 2009
|
||||
test_ellint_3.cpp:// Copyright Xiaogang Zhang 2006
|
||||
test_ellint_3.cpp:// Copyright John Maddock 2006, 2007
|
||||
test_ellint_3.cpp:// Copyright Paul A. Bristow 2007
|
||||
test_ellint_3.hpp:// Copyright John Maddock 2006.
|
||||
test_ellint_3.hpp:// Copyright Paul A. Bristow 2007, 2009
|
||||
test_erf.cpp:// Copyright John Maddock 2006.
|
||||
test_erf.cpp:// Copyright Paul A. Bristow 2007
|
||||
test_erf.hpp:// Copyright John Maddock 2006.
|
||||
test_erf.hpp:// Copyright Paul A. Bristow 2007, 2009
|
||||
test_erf_hooks.hpp:// (C) Copyright John Maddock 2006.
|
||||
test_error_handling.cpp:// Copyright Paul A. Bristow 2006-7.
|
||||
test_error_handling.cpp:// Copyright John Maddock 2006-7.
|
||||
test_expint.cpp:// (C) Copyright John Maddock 2007.
|
||||
test_expint.hpp:// Copyright John Maddock 2006.
|
||||
test_expint.hpp:// Copyright Paul A. Bristow 2007, 2009
|
||||
test_expint_hooks.hpp:// (C) Copyright John Maddock 2006.
|
||||
test_exponential_dist.cpp:// Copyright John Maddock 2006.
|
||||
test_exponential_dist.cpp:// Copyright Paul A. Bristow 2007.
|
||||
test_extreme_value.cpp:// Copyright John Maddock 2006.
|
||||
test_factorials.cpp:// Copyright John Maddock 2006.
|
||||
test_find_location.cpp:// Copyright John Maddock 2007.
|
||||
test_find_location.cpp:// Copyright Paul A. Bristow 2007.
|
||||
test_find_scale.cpp:// Copyright John Maddock 2007.
|
||||
test_find_scale.cpp:// Copyright Paul A. Bristow 2007.
|
||||
test_fisher_f.cpp:// Copyright Paul A. Bristow 2006.
|
||||
test_fisher_f.cpp:// Copyright John Maddock 2007.
|
||||
test_fisher_f.cpp: // Distcalc version 1.2 Copyright 2002 H Lohninger, TU Wein
|
||||
test_gamma.cpp:// (C) Copyright John Maddock 2006.
|
||||
test_gamma.hpp:// Copyright John Maddock 2006.
|
||||
test_gamma.hpp:// Copyright Paul A. Bristow 2007, 2009
|
||||
test_gamma_data.ipp:// (C) Copyright John Maddock 2006.
|
||||
test_gamma_dist.cpp:// Copyright John Maddock 2006.
|
||||
test_gamma_dist.cpp:// Copyright Paul A. Bristow 2007, 2010.
|
||||
test_gamma_hooks.hpp:// (C) Copyright John Maddock 2006.
|
||||
test_geometric.cpp:// Copyright Paul A. Bristow 2010.
|
||||
test_geometric.cpp:// Copyright John Maddock 2010.
|
||||
test_hankel.cpp:// Copyright John Maddock 2012
|
||||
test_hermite.cpp:// Copyright John Maddock 2006, 2007
|
||||
test_hermite.cpp:// Copyright Paul A. Bristow 2007
|
||||
test_hermite.hpp:// Copyright John Maddock 2006.
|
||||
test_hermite.hpp:// Copyright Paul A. Bristow 2007, 2009
|
||||
test_hypergeometric_dist.cpp:// Copyright John Maddock 2008
|
||||
test_hypergeometric_dist.cpp:// Copyright Paul A. Bristow
|
||||
test_hypergeometric_dist.cpp:// Copyright Gautam Sewani
|
||||
test_ibeta.cpp:// (C) Copyright John Maddock 2006.
|
||||
test_ibeta.hpp:// Copyright John Maddock 2006.
|
||||
test_ibeta.hpp:// Copyright Paul A. Bristow 2007, 2009
|
||||
test_ibeta_inv.cpp:// (C) Copyright John Maddock 2006.
|
||||
test_ibeta_inv.hpp:// Copyright John Maddock 2006.
|
||||
test_ibeta_inv.hpp:// Copyright Paul A. Bristow 2007, 2009
|
||||
test_ibeta_inv_ab.cpp:// (C) Copyright John Maddock 2006.
|
||||
test_ibeta_inv_ab.hpp:// Copyright John Maddock 2006.
|
||||
test_ibeta_inv_ab.hpp:// Copyright Paul A. Bristow 2007, 2009
|
||||
test_igamma.cpp:// (C) Copyright John Maddock 2006.
|
||||
test_igamma.hpp:// Copyright John Maddock 2006.
|
||||
test_igamma.hpp:// Copyright Paul A. Bristow 2007, 2009
|
||||
test_igamma_inv.cpp:// (C) Copyright John Maddock 2006.
|
||||
test_igamma_inv.hpp:// Copyright John Maddock 2006.
|
||||
test_igamma_inv.hpp:// Copyright Paul A. Bristow 2007, 2009
|
||||
test_igamma_inva.cpp:// (C) Copyright John Maddock 2006.
|
||||
test_igamma_inva.hpp:// Copyright John Maddock 2006.
|
||||
test_igamma_inva.hpp:// Copyright Paul A. Bristow 2007, 2009
|
||||
test_instances/double_test_instances_4.cpp:// Copyright John Maddock 2011.
|
||||
test_instances/ldouble_test_instances_4.cpp:// Copyright John Maddock 2011.
|
||||
test_instances/float_test_instances_8.cpp:// Copyright John Maddock 2011.
|
||||
test_instances/double_test_instances_9.cpp:// Copyright John Maddock 2011.
|
||||
test_instances/Jamfile.v2:# Copyright ohn Maddock 2012
|
||||
test_instances/real_concept_test_instances_5.cpp:// Copyright John Maddock 2011.
|
||||
test_instances/ldouble_test_instances_6.cpp:// Copyright John Maddock 2011.
|
||||
test_instances/real_concept_test_instances_4.cpp:// Copyright John Maddock 2011.
|
||||
test_instances/double_test_instances_7.cpp:// Copyright John Maddock 2011.
|
||||
test_instances/real_concept_test_instances_2.cpp:// Copyright John Maddock 2011.
|
||||
test_instances/double_test_instances_5.cpp:// Copyright John Maddock 2011.
|
||||
test_instances/ldouble_test_instances_9.cpp:// Copyright John Maddock 2011.
|
||||
test_instances/real_concept_test_instances_1.cpp:// Copyright John Maddock 2011.
|
||||
test_instances/float_test_instances_6.cpp:// Copyright John Maddock 2011.
|
||||
test_instances/real_concept_test_instances_6.cpp:// Copyright John Maddock 2011.
|
||||
test_instances/ldouble_test_instances_7.cpp:// Copyright John Maddock 2011.
|
||||
test_instances/real_concept_test_instances_7.cpp:// Copyright John Maddock 2011.
|
||||
test_instances/float_test_instances_3.cpp:// Copyright John Maddock 2011.
|
||||
test_instances/double_test_instances_6.cpp:// Copyright John Maddock 2011.
|
||||
test_instances/real_concept_test_instances_9.cpp:// Copyright John Maddock 2011.
|
||||
test_instances/double_test_instances_2.cpp:// Copyright John Maddock 2011.
|
||||
test_instances/pch.hpp:// Copyright John Maddock 2012.
|
||||
test_instances/ldouble_test_instances_2.cpp:// Copyright John Maddock 2011.
|
||||
test_instances/long_double_test_instances_1.cpp:// Copyright John Maddock 2011.
|
||||
test_instances/float_test_instances_7.cpp:// Copyright John Maddock 2011.
|
||||
test_instances/test_instances.hpp:// Copyright John Maddock 2011.
|
||||
test_instances/double_test_instances_10.cpp:// Copyright John Maddock 2011.
|
||||
test_instances/double_test_instances_3.cpp:// Copyright John Maddock 2011.
|
||||
test_instances/ldouble_test_instances_3.cpp:// Copyright John Maddock 2011.
|
||||
test_instances/real_concept_test_instances_10.cpp:// Copyright John Maddock 2011.
|
||||
test_instances/float_test_instances_5.cpp:// Copyright John Maddock 2011.
|
||||
test_instances/real_concept_test_instances_8.cpp:// Copyright John Maddock 2011.
|
||||
test_instances/ldouble_test_instances_8.cpp:// Copyright John Maddock 2011.
|
||||
test_instances/double_test_instances_1.cpp:// Copyright John Maddock 2011.
|
||||
test_instances/float_test_instances_10.cpp:// Copyright John Maddock 2011.
|
||||
test_instances/ldouble_test_instances_10.cpp:// Copyright John Maddock 2011.
|
||||
test_instances/float_test_instances_9.cpp:// Copyright John Maddock 2011.
|
||||
test_instances/float_test_instances_4.cpp:// Copyright John Maddock 2011.
|
||||
test_instances/real_concept_test_instances_3.cpp:// Copyright John Maddock 2011.
|
||||
test_instances/float_test_instances_2.cpp:// Copyright John Maddock 2011.
|
||||
test_instances/float_test_instances_1.cpp:// Copyright John Maddock 2011.
|
||||
test_instances/double_test_instances_8.cpp:// Copyright John Maddock 2011.
|
||||
test_instances/ldouble_test_instances_5.cpp:// Copyright John Maddock 2011.
|
||||
test_instantiate1.cpp:// Copyright John Maddock 2006.
|
||||
test_instantiate2.cpp:// Copyright John Maddock 2006.
|
||||
test_inv_hyp.cpp:// (C) Copyright John Maddock 2006.
|
||||
test_inverse_chi_squared.cpp:// Copyright Paul A. Bristow 2010.
|
||||
test_inverse_chi_squared.cpp:// Copyright John Maddock 2010.
|
||||
test_inverse_chi_squared_distribution.cpp:// Copyright Paul A. Bristow 2010.
|
||||
test_inverse_chi_squared_distribution.cpp:// Copyright John Maddock 2010.
|
||||
test_inverse_gamma_distribution.cpp:// Copyright Paul A. Bristow 2010.
|
||||
test_inverse_gamma_distribution.cpp:// Copyright John Maddock 2010.
|
||||
test_inverse_gaussian.cpp:// Copyright Paul A. Bristow 2010.
|
||||
test_inverse_gaussian.cpp:// Copyright John Maddock 2010.
|
||||
test_jacobi.cpp:// Copyright John Maddock 2012
|
||||
test_jacobi.hpp:// Copyright John Maddock 2006.
|
||||
test_jacobi.hpp:// Copyright Paul A. Bristow 2007, 2009
|
||||
test_laguerre.cpp:// (C) Copyright John Maddock 2006.
|
||||
test_laguerre.hpp:// Copyright John Maddock 2006.
|
||||
test_laguerre.hpp:// Copyright Paul A. Bristow 2007, 2009
|
||||
test_laplace.cpp:// Copyright Thijs van den Berg, 2008.
|
||||
test_laplace.cpp:// Copyright John Maddock 2008.
|
||||
test_laplace.cpp:// Copyright Paul A. Bristow 2008, 2009.
|
||||
test_ldouble_simple.cpp:// Copyright John Maddock 2013.
|
||||
test_legacy_nonfinite.cpp:// Copyright (c) 2006 Johan Rade
|
||||
test_legacy_nonfinite.cpp:// Copyright (c) 2011 Paul A. Bristow comments
|
||||
test_legendre.cpp:// (C) Copyright John Maddock 2006.
|
||||
test_legendre.hpp:// Copyright John Maddock 2006.
|
||||
test_legendre.hpp:// Copyright Paul A. Bristow 2007, 2009
|
||||
test_legendre_hooks.hpp:// (C) Copyright John Maddock 2006.
|
||||
test_lexical_cast.cpp:// Copyright (c) 2006 Johan Rade
|
||||
test_lexical_cast.cpp:// Copyright (c) 2011 Paul A. Bristow incorporated Boost.Math
|
||||
test_logistic_dist.cpp:// Copyright 2008 Gautam Sewani
|
||||
test_lognormal.cpp:// Copyright John Maddock 2006.
|
||||
test_lognormal.cpp:// Copyright Paul A. Bristow 2007
|
||||
test_long_double_support.cpp:// Copyright John Maddock 2009
|
||||
test_math_fwd.cpp:// Copyright John Maddock 2010.
|
||||
test_math_fwd.cpp:// Copyright Paul A. Bristow 2010.
|
||||
test_minima.cpp:// Copyright John Maddock 2006.
|
||||
test_minima.cpp:// Copyright Paul A. Bristow 2007.
|
||||
test_nc_beta.cpp:// Copyright John Maddock 2008.
|
||||
test_nc_chi_squared.cpp:// Copyright John Maddock 2008.
|
||||
test_nc_f.cpp:// Copyright John Maddock 2008.
|
||||
test_nc_t.cpp:// Copyright John Maddock 2008, 2012.
|
||||
test_nc_t.cpp:// Copyright Paul A. Bristow 2012.
|
||||
test_ncbeta_hooks.hpp:// (C) Copyright John Maddock 2008.
|
||||
test_nccs_hooks.hpp:// (C) Copyright John Maddock 2008.
|
||||
test_negative_binomial.cpp:// Copyright Paul A. Bristow 2007.
|
||||
test_negative_binomial.cpp:// Copyright John Maddock 2006.
|
||||
test_next.cpp:// (C) Copyright John Maddock 2008.
|
||||
test_nonfinite_io.cpp:// Copyright 2011 Paul A. Bristow
|
||||
test_nonfinite_trap.cpp:// Copyright (c) 2006 Johan Rade
|
||||
test_nonfinite_trap.cpp:// Copyright (c) 2011 Paul A. Bristow To incorporate into Boost.Math
|
||||
test_normal.cpp:// Copyright Paul A. Bristow 2010.
|
||||
test_normal.cpp:// Copyright John Maddock 2007.
|
||||
test_out_of_range.hpp:// Copyright John Maddock 2012.
|
||||
test_owens_t.cpp:// Copyright Paul A. Bristow 2012.
|
||||
test_owens_t.cpp:// Copyright Benjamin Sobotta 2012.
|
||||
test_pareto.cpp:// Copyright Paul A. Bristow 2007, 2009.
|
||||
test_pareto.cpp:// Copyright John Maddock 2006.
|
||||
test_poisson.cpp:// Copyright Paul A. Bristow 2007.
|
||||
test_poisson.cpp:// Copyright John Maddock 2006.
|
||||
test_policy.cpp:// Copyright John Maddock 2007.
|
||||
test_policy_2.cpp:// Copyright John Maddock 2007.
|
||||
test_policy_3.cpp:// Copyright John Maddock 2007.
|
||||
test_policy_4.cpp:// Copyright John Maddock 2007.
|
||||
test_policy_5.cpp:// Copyright John Maddock 2007.
|
||||
test_policy_6.cpp:// Copyright John Maddock 2007.
|
||||
test_policy_7.cpp:// Copyright John Maddock 2007.
|
||||
test_policy_8.cpp:// Copyright John Maddock 2007.
|
||||
test_policy_sf.cpp:// (C) Copyright John Maddock 2007.
|
||||
test_print_info_on_type.cpp:// Copyright John Maddock 2010.
|
||||
test_rational_instances/test_rational_ldouble2.cpp:// (C) Copyright John Maddock 2006-7.
|
||||
test_rational_instances/test_rational_float2.cpp:// (C) Copyright John Maddock 2006-7.
|
||||
test_rational_instances/test_rational_double2.cpp:// (C) Copyright John Maddock 2006-7.
|
||||
test_rational_instances/test_rational_double3.cpp:// (C) Copyright John Maddock 2006-7.
|
||||
test_rational_instances/test_rational_ldouble1.cpp:// (C) Copyright John Maddock 2006-7.
|
||||
test_rational_instances/test_rational_float4.cpp:// (C) Copyright John Maddock 2006-7.
|
||||
test_rational_instances/test_rational_double5.cpp:// (C) Copyright John Maddock 2006-7.
|
||||
test_rational_instances/test_rational_double4.cpp:// (C) Copyright John Maddock 2006-7.
|
||||
test_rational_instances/test_rational_real_concept1.cpp:// (C) Copyright John Maddock 2006-7.
|
||||
test_rational_instances/test_rational_real_concept3.cpp:// (C) Copyright John Maddock 2006-7.
|
||||
test_rational_instances/test_rational.hpp:// (C) Copyright John Maddock 2006-7.
|
||||
test_rational_instances/test_rational_ldouble3.cpp:// (C) Copyright John Maddock 2006-7.
|
||||
test_rational_instances/test_rational_float3.cpp:// (C) Copyright John Maddock 2006-7.
|
||||
test_rational_instances/test_rational_real_concept5.cpp:// (C) Copyright John Maddock 2006-7.
|
||||
test_rational_instances/test_rational_ldouble5.cpp:// (C) Copyright John Maddock 2006-7.
|
||||
test_rational_instances/test_rational_ldouble4.cpp:// (C) Copyright John Maddock 2006-7.
|
||||
test_rational_instances/test_rational_double1.cpp:// (C) Copyright John Maddock 2006-7.
|
||||
test_rational_instances/test_rational_real_concept4.cpp:// (C) Copyright John Maddock 2006-7.
|
||||
test_rational_instances/test_rational_real_concept2.cpp:// (C) Copyright John Maddock 2006-7.
|
||||
test_rational_instances/test_rational_float1.cpp:// (C) Copyright John Maddock 2006-7.
|
||||
test_rationals.cpp:// (C) Copyright John Maddock 2006.
|
||||
test_rayleigh.cpp:// Copyright John Maddock 2006.
|
||||
test_real_concept.cpp:// Copyright John Maddock 2010
|
||||
test_real_concept_neg_bin.cpp:// Copyright Paul A. Bristow 2010.
|
||||
test_real_concept_neg_bin.cpp:// Copyright John Maddock 2010.
|
||||
test_remez.cpp:// Copyright John Maddock 2006
|
||||
test_remez.cpp:// Copyright Paul A. Bristow 2007
|
||||
test_roots.cpp:// (C) Copyright John Maddock 2006.
|
||||
test_round.cpp:// (C) Copyright John Maddock 2007.
|
||||
test_sign.cpp:#define BOOST_TEST_MAIN// Copyright John Maddock 2008
|
||||
test_sign.cpp:// (C) Copyright Paul A. Bristow 2011 (added tests for changesign)
|
||||
test_signed_zero.cpp:// Copyright 2006 Johan Rade
|
||||
test_signed_zero.cpp:// Copyright 2011 Paul A. Bristow To incorporate into Boost.Math
|
||||
test_signed_zero.cpp:// Copyright 2012 Paul A. Bristow with new tests.
|
||||
test_skew_normal.cpp:// Copyright Paul A. Bristow 2012.
|
||||
test_skew_normal.cpp:// Copyright John Maddock 2012.
|
||||
test_skew_normal.cpp:// Copyright Benjamin Sobotta 2012
|
||||
test_spherical_harmonic.cpp:// (C) Copyright John Maddock 2006.
|
||||
test_students_t.cpp:// Copyright Paul A. Bristow 2006.
|
||||
test_students_t.cpp:// Copyright John Maddock 2006.
|
||||
test_tgamma_ratio.cpp:// (C) Copyright John Maddock 2006.
|
||||
test_tgamma_ratio.hpp:// Copyright John Maddock 2006.
|
||||
test_tgamma_ratio.hpp:// Copyright Paul A. Bristow 2007, 2009
|
||||
test_toms748_solve.cpp:// (C) Copyright John Maddock 2006.
|
||||
test_tr1.c:/* (C) Copyright John Maddock 2008.
|
||||
test_tr1.cpp:// (C) Copyright John Maddock 2008.
|
||||
test_triangular.cpp:// Copyright Paul Bristow 2006, 2007.
|
||||
test_triangular.cpp:// Copyright John Maddock 2006, 2007.
|
||||
test_uniform.cpp:// Copyright Paul Bristow 2007.
|
||||
test_uniform.cpp:// Copyright John Maddock 2006.
|
||||
test_weibull.cpp:// Copyright John Maddock 2006, 2012.
|
||||
test_weibull.cpp:// Copyright Paul A. Bristow 2007, 2012.
|
||||
test_zeta.cpp:// (C) Copyright John Maddock 2006.
|
||||
test_zeta.hpp:// Copyright John Maddock 2006.
|
||||
test_zeta.hpp:// Copyright Paul A. Bristow 2007, 2009
|
||||
test_zeta_hooks.hpp:// (C) Copyright John Maddock 2006.
|
||||
tgamma_delta_ratio_data.ipp:// (C) Copyright John Maddock 2006-7.
|
||||
tgamma_delta_ratio_int.ipp:// (C) Copyright John Maddock 2006-7.
|
||||
tgamma_delta_ratio_int2.ipp:// (C) Copyright John Maddock 2006-7.
|
||||
tgamma_ratio_data.ipp:// (C) Copyright John Maddock 2006-7.
|
||||
zeta_1_below_data.ipp:// Copyright John Maddock 2008.
|
||||
zeta_1_up_data.ipp:// Copyright John Maddock 2008.
|
||||
zeta_data.ipp:// Copyright John Maddock 2008.
|
||||
zeta_neg_data.ipp:// Copyright John Maddock 2008.
|
||||
ztest_max_digits10.cpp: // Copyright 2010 Paul A. Bristow
|
||||
zztest_max_digits10.cpp:// Copyright 2010 Paul A. Bristow
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
File diff suppressed because it is too large
Load Diff
@@ -1,108 +0,0 @@
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
import numpy as np
|
||||
from numpy.testing import assert_equal, assert_almost_equal, assert_allclose
|
||||
from scipy.special import boxcox, boxcox1p, inv_boxcox, inv_boxcox1p
|
||||
|
||||
|
||||
# There are more tests of boxcox and boxcox1p in test_mpmath.py.
|
||||
|
||||
def test_boxcox_basic():
|
||||
x = np.array([0.5, 1, 2, 4])
|
||||
|
||||
# lambda = 0 => y = log(x)
|
||||
y = boxcox(x, 0)
|
||||
assert_almost_equal(y, np.log(x))
|
||||
|
||||
# lambda = 1 => y = x - 1
|
||||
y = boxcox(x, 1)
|
||||
assert_almost_equal(y, x - 1)
|
||||
|
||||
# lambda = 2 => y = 0.5*(x**2 - 1)
|
||||
y = boxcox(x, 2)
|
||||
assert_almost_equal(y, 0.5*(x**2 - 1))
|
||||
|
||||
# x = 0 and lambda > 0 => y = -1 / lambda
|
||||
lam = np.array([0.5, 1, 2])
|
||||
y = boxcox(0, lam)
|
||||
assert_almost_equal(y, -1.0 / lam)
|
||||
|
||||
def test_boxcox_underflow():
|
||||
x = 1 + 1e-15
|
||||
lmbda = 1e-306
|
||||
y = boxcox(x, lmbda)
|
||||
assert_allclose(y, np.log(x), rtol=1e-14)
|
||||
|
||||
|
||||
def test_boxcox_nonfinite():
|
||||
# x < 0 => y = nan
|
||||
x = np.array([-1, -1, -0.5])
|
||||
y = boxcox(x, [0.5, 2.0, -1.5])
|
||||
assert_equal(y, np.array([np.nan, np.nan, np.nan]))
|
||||
|
||||
# x = 0 and lambda <= 0 => y = -inf
|
||||
x = 0
|
||||
y = boxcox(x, [-2.5, 0])
|
||||
assert_equal(y, np.array([-np.inf, -np.inf]))
|
||||
|
||||
|
||||
def test_boxcox1p_basic():
|
||||
x = np.array([-0.25, -1e-20, 0, 1e-20, 0.25, 1, 3])
|
||||
|
||||
# lambda = 0 => y = log(1+x)
|
||||
y = boxcox1p(x, 0)
|
||||
assert_almost_equal(y, np.log1p(x))
|
||||
|
||||
# lambda = 1 => y = x
|
||||
y = boxcox1p(x, 1)
|
||||
assert_almost_equal(y, x)
|
||||
|
||||
# lambda = 2 => y = 0.5*((1+x)**2 - 1) = 0.5*x*(2 + x)
|
||||
y = boxcox1p(x, 2)
|
||||
assert_almost_equal(y, 0.5*x*(2 + x))
|
||||
|
||||
# x = -1 and lambda > 0 => y = -1 / lambda
|
||||
lam = np.array([0.5, 1, 2])
|
||||
y = boxcox1p(-1, lam)
|
||||
assert_almost_equal(y, -1.0 / lam)
|
||||
|
||||
|
||||
def test_boxcox1p_underflow():
|
||||
x = np.array([1e-15, 1e-306])
|
||||
lmbda = np.array([1e-306, 1e-18])
|
||||
y = boxcox1p(x, lmbda)
|
||||
assert_allclose(y, np.log1p(x), rtol=1e-14)
|
||||
|
||||
|
||||
def test_boxcox1p_nonfinite():
|
||||
# x < -1 => y = nan
|
||||
x = np.array([-2, -2, -1.5])
|
||||
y = boxcox1p(x, [0.5, 2.0, -1.5])
|
||||
assert_equal(y, np.array([np.nan, np.nan, np.nan]))
|
||||
|
||||
# x = -1 and lambda <= 0 => y = -inf
|
||||
x = -1
|
||||
y = boxcox1p(x, [-2.5, 0])
|
||||
assert_equal(y, np.array([-np.inf, -np.inf]))
|
||||
|
||||
|
||||
def test_inv_boxcox():
|
||||
x = np.array([0., 1., 2.])
|
||||
lam = np.array([0., 1., 2.])
|
||||
y = boxcox(x, lam)
|
||||
x2 = inv_boxcox(y, lam)
|
||||
assert_almost_equal(x, x2)
|
||||
|
||||
x = np.array([0., 1., 2.])
|
||||
lam = np.array([0., 1., 2.])
|
||||
y = boxcox1p(x, lam)
|
||||
x2 = inv_boxcox1p(y, lam)
|
||||
assert_almost_equal(x, x2)
|
||||
|
||||
|
||||
def test_inv_boxcox1p_underflow():
|
||||
x = 1e-15
|
||||
lam = 1e-306
|
||||
y = inv_boxcox1p(x, lam)
|
||||
assert_allclose(y, x, rtol=1e-14)
|
||||
|
||||
@@ -1,409 +0,0 @@
|
||||
"""
|
||||
Test cdflib functions versus mpmath, if available.
|
||||
|
||||
The following functions still need tests:
|
||||
|
||||
- ncfdtr
|
||||
- ncfdtri
|
||||
- ncfdtridfn
|
||||
- ncfdtridfd
|
||||
- ncfdtrinc
|
||||
- nbdtrik
|
||||
- nbdtrin
|
||||
- nrdtrimn
|
||||
- nrdtrisd
|
||||
- pdtrik
|
||||
- nctdtr
|
||||
- nctdtrit
|
||||
- nctdtridf
|
||||
- nctdtrinc
|
||||
|
||||
"""
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
import itertools
|
||||
|
||||
import numpy as np
|
||||
from numpy.testing import assert_equal
|
||||
import pytest
|
||||
|
||||
import scipy.special as sp
|
||||
from scipy._lib.six import with_metaclass
|
||||
from scipy.special._testutils import (
|
||||
MissingModule, check_version, FuncData)
|
||||
from scipy.special._mptestutils import (
|
||||
Arg, IntArg, get_args, mpf2float, assert_mpmath_equal)
|
||||
|
||||
try:
|
||||
import mpmath
|
||||
except ImportError:
|
||||
mpmath = MissingModule('mpmath')
|
||||
|
||||
|
||||
class ProbArg(object):
|
||||
"""Generate a set of probabilities on [0, 1]."""
|
||||
def __init__(self):
|
||||
# Include the endpoints for compatibility with Arg et. al.
|
||||
self.a = 0
|
||||
self.b = 1
|
||||
|
||||
def values(self, n):
|
||||
"""Return an array containing approximatively n numbers."""
|
||||
m = max(1, n//3)
|
||||
v1 = np.logspace(-30, np.log10(0.3), m)
|
||||
v2 = np.linspace(0.3, 0.7, m + 1, endpoint=False)[1:]
|
||||
v3 = 1 - np.logspace(np.log10(0.3), -15, m)
|
||||
v = np.r_[v1, v2, v3]
|
||||
return np.unique(v)
|
||||
|
||||
|
||||
class EndpointFilter(object):
|
||||
def __init__(self, a, b, rtol, atol):
|
||||
self.a = a
|
||||
self.b = b
|
||||
self.rtol = rtol
|
||||
self.atol = atol
|
||||
|
||||
def __call__(self, x):
|
||||
mask1 = np.abs(x - self.a) < self.rtol*np.abs(self.a) + self.atol
|
||||
mask2 = np.abs(x - self.b) < self.rtol*np.abs(self.b) + self.atol
|
||||
return np.where(mask1 | mask2, False, True)
|
||||
|
||||
|
||||
class _CDFData(object):
|
||||
def __init__(self, spfunc, mpfunc, index, argspec, spfunc_first=True,
|
||||
dps=20, n=5000, rtol=None, atol=None,
|
||||
endpt_rtol=None, endpt_atol=None):
|
||||
self.spfunc = spfunc
|
||||
self.mpfunc = mpfunc
|
||||
self.index = index
|
||||
self.argspec = argspec
|
||||
self.spfunc_first = spfunc_first
|
||||
self.dps = dps
|
||||
self.n = n
|
||||
self.rtol = rtol
|
||||
self.atol = atol
|
||||
|
||||
if not isinstance(argspec, list):
|
||||
self.endpt_rtol = None
|
||||
self.endpt_atol = None
|
||||
elif endpt_rtol is not None or endpt_atol is not None:
|
||||
if isinstance(endpt_rtol, list):
|
||||
self.endpt_rtol = endpt_rtol
|
||||
else:
|
||||
self.endpt_rtol = [endpt_rtol]*len(self.argspec)
|
||||
if isinstance(endpt_atol, list):
|
||||
self.endpt_atol = endpt_atol
|
||||
else:
|
||||
self.endpt_atol = [endpt_atol]*len(self.argspec)
|
||||
else:
|
||||
self.endpt_rtol = None
|
||||
self.endpt_atol = None
|
||||
|
||||
def idmap(self, *args):
|
||||
if self.spfunc_first:
|
||||
res = self.spfunc(*args)
|
||||
if np.isnan(res):
|
||||
return np.nan
|
||||
args = list(args)
|
||||
args[self.index] = res
|
||||
with mpmath.workdps(self.dps):
|
||||
res = self.mpfunc(*tuple(args))
|
||||
# Imaginary parts are spurious
|
||||
res = mpf2float(res.real)
|
||||
else:
|
||||
with mpmath.workdps(self.dps):
|
||||
res = self.mpfunc(*args)
|
||||
res = mpf2float(res.real)
|
||||
args = list(args)
|
||||
args[self.index] = res
|
||||
res = self.spfunc(*tuple(args))
|
||||
return res
|
||||
|
||||
def get_param_filter(self):
|
||||
if self.endpt_rtol is None and self.endpt_atol is None:
|
||||
return None
|
||||
|
||||
filters = []
|
||||
for rtol, atol, spec in zip(self.endpt_rtol, self.endpt_atol, self.argspec):
|
||||
if rtol is None and atol is None:
|
||||
filters.append(None)
|
||||
continue
|
||||
elif rtol is None:
|
||||
rtol = 0.0
|
||||
elif atol is None:
|
||||
atol = 0.0
|
||||
|
||||
filters.append(EndpointFilter(spec.a, spec.b, rtol, atol))
|
||||
return filters
|
||||
|
||||
def check(self):
|
||||
# Generate values for the arguments
|
||||
args = get_args(self.argspec, self.n)
|
||||
param_filter = self.get_param_filter()
|
||||
param_columns = tuple(range(args.shape[1]))
|
||||
result_columns = args.shape[1]
|
||||
args = np.hstack((args, args[:,self.index].reshape(args.shape[0], 1)))
|
||||
FuncData(self.idmap, args,
|
||||
param_columns=param_columns, result_columns=result_columns,
|
||||
rtol=self.rtol, atol=self.atol, vectorized=False,
|
||||
param_filter=param_filter).check()
|
||||
|
||||
|
||||
def _assert_inverts(*a, **kw):
|
||||
d = _CDFData(*a, **kw)
|
||||
d.check()
|
||||
|
||||
|
||||
def _binomial_cdf(k, n, p):
|
||||
k, n, p = mpmath.mpf(k), mpmath.mpf(n), mpmath.mpf(p)
|
||||
if k <= 0:
|
||||
return mpmath.mpf(0)
|
||||
elif k >= n:
|
||||
return mpmath.mpf(1)
|
||||
|
||||
onemp = mpmath.fsub(1, p, exact=True)
|
||||
return mpmath.betainc(n - k, k + 1, x2=onemp, regularized=True)
|
||||
|
||||
|
||||
def _f_cdf(dfn, dfd, x):
|
||||
if x < 0:
|
||||
return mpmath.mpf(0)
|
||||
dfn, dfd, x = mpmath.mpf(dfn), mpmath.mpf(dfd), mpmath.mpf(x)
|
||||
ub = dfn*x/(dfn*x + dfd)
|
||||
res = mpmath.betainc(dfn/2, dfd/2, x2=ub, regularized=True)
|
||||
return res
|
||||
|
||||
|
||||
def _student_t_cdf(df, t, dps=None):
|
||||
if dps is None:
|
||||
dps = mpmath.mp.dps
|
||||
with mpmath.workdps(dps):
|
||||
df, t = mpmath.mpf(df), mpmath.mpf(t)
|
||||
fac = mpmath.hyp2f1(0.5, 0.5*(df + 1), 1.5, -t**2/df)
|
||||
fac *= t*mpmath.gamma(0.5*(df + 1))
|
||||
fac /= mpmath.sqrt(mpmath.pi*df)*mpmath.gamma(0.5*df)
|
||||
return 0.5 + fac
|
||||
|
||||
|
||||
def _noncentral_chi_pdf(t, df, nc):
|
||||
res = mpmath.besseli(df/2 - 1, mpmath.sqrt(nc*t))
|
||||
res *= mpmath.exp(-(t + nc)/2)*(t/nc)**(df/4 - 1/2)/2
|
||||
return res
|
||||
|
||||
|
||||
def _noncentral_chi_cdf(x, df, nc, dps=None):
|
||||
if dps is None:
|
||||
dps = mpmath.mp.dps
|
||||
x, df, nc = mpmath.mpf(x), mpmath.mpf(df), mpmath.mpf(nc)
|
||||
with mpmath.workdps(dps):
|
||||
res = mpmath.quad(lambda t: _noncentral_chi_pdf(t, df, nc), [0, x])
|
||||
return res
|
||||
|
||||
|
||||
def _tukey_lmbda_quantile(p, lmbda):
|
||||
# For lmbda != 0
|
||||
return (p**lmbda - (1 - p)**lmbda)/lmbda
|
||||
|
||||
|
||||
@pytest.mark.slow
|
||||
@check_version(mpmath, '0.19')
|
||||
class TestCDFlib(object):
|
||||
|
||||
@pytest.mark.xfail(run=False)
|
||||
def test_bdtrik(self):
|
||||
_assert_inverts(
|
||||
sp.bdtrik,
|
||||
_binomial_cdf,
|
||||
0, [ProbArg(), IntArg(1, 1000), ProbArg()],
|
||||
rtol=1e-4)
|
||||
|
||||
def test_bdtrin(self):
|
||||
_assert_inverts(
|
||||
sp.bdtrin,
|
||||
_binomial_cdf,
|
||||
1, [IntArg(1, 1000), ProbArg(), ProbArg()],
|
||||
rtol=1e-4, endpt_atol=[None, None, 1e-6])
|
||||
|
||||
def test_btdtria(self):
|
||||
_assert_inverts(
|
||||
sp.btdtria,
|
||||
lambda a, b, x: mpmath.betainc(a, b, x2=x, regularized=True),
|
||||
0, [ProbArg(), Arg(0, 1e2, inclusive_a=False),
|
||||
Arg(0, 1, inclusive_a=False, inclusive_b=False)],
|
||||
rtol=1e-6)
|
||||
|
||||
def test_btdtrib(self):
|
||||
# Use small values of a or mpmath doesn't converge
|
||||
_assert_inverts(
|
||||
sp.btdtrib,
|
||||
lambda a, b, x: mpmath.betainc(a, b, x2=x, regularized=True),
|
||||
1, [Arg(0, 1e2, inclusive_a=False), ProbArg(),
|
||||
Arg(0, 1, inclusive_a=False, inclusive_b=False)],
|
||||
rtol=1e-7, endpt_atol=[None, 1e-18, 1e-15])
|
||||
|
||||
@pytest.mark.xfail(run=False)
|
||||
def test_fdtridfd(self):
|
||||
_assert_inverts(
|
||||
sp.fdtridfd,
|
||||
_f_cdf,
|
||||
1, [IntArg(1, 100), ProbArg(), Arg(0, 100, inclusive_a=False)],
|
||||
rtol=1e-7)
|
||||
|
||||
def test_gdtria(self):
|
||||
_assert_inverts(
|
||||
sp.gdtria,
|
||||
lambda a, b, x: mpmath.gammainc(b, b=a*x, regularized=True),
|
||||
0, [ProbArg(), Arg(0, 1e3, inclusive_a=False),
|
||||
Arg(0, 1e4, inclusive_a=False)], rtol=1e-7,
|
||||
endpt_atol=[None, 1e-7, 1e-10])
|
||||
|
||||
def test_gdtrib(self):
|
||||
# Use small values of a and x or mpmath doesn't converge
|
||||
_assert_inverts(
|
||||
sp.gdtrib,
|
||||
lambda a, b, x: mpmath.gammainc(b, b=a*x, regularized=True),
|
||||
1, [Arg(0, 1e2, inclusive_a=False), ProbArg(),
|
||||
Arg(0, 1e3, inclusive_a=False)], rtol=1e-5)
|
||||
|
||||
def test_gdtrix(self):
|
||||
_assert_inverts(
|
||||
sp.gdtrix,
|
||||
lambda a, b, x: mpmath.gammainc(b, b=a*x, regularized=True),
|
||||
2, [Arg(0, 1e3, inclusive_a=False), Arg(0, 1e3, inclusive_a=False),
|
||||
ProbArg()], rtol=1e-7,
|
||||
endpt_atol=[None, 1e-7, 1e-10])
|
||||
|
||||
def test_stdtr(self):
|
||||
# Ideally the left endpoint for Arg() should be 0.
|
||||
assert_mpmath_equal(
|
||||
sp.stdtr,
|
||||
_student_t_cdf,
|
||||
[IntArg(1, 100), Arg(1e-10, np.inf)], rtol=1e-7)
|
||||
|
||||
@pytest.mark.xfail(run=False)
|
||||
def test_stdtridf(self):
|
||||
_assert_inverts(
|
||||
sp.stdtridf,
|
||||
_student_t_cdf,
|
||||
0, [ProbArg(), Arg()], rtol=1e-7)
|
||||
|
||||
def test_stdtrit(self):
|
||||
_assert_inverts(
|
||||
sp.stdtrit,
|
||||
_student_t_cdf,
|
||||
1, [IntArg(1, 100), ProbArg()], rtol=1e-7,
|
||||
endpt_atol=[None, 1e-10])
|
||||
|
||||
def test_chdtriv(self):
|
||||
_assert_inverts(
|
||||
sp.chdtriv,
|
||||
lambda v, x: mpmath.gammainc(v/2, b=x/2, regularized=True),
|
||||
0, [ProbArg(), IntArg(1, 100)], rtol=1e-4)
|
||||
|
||||
@pytest.mark.xfail(run=False)
|
||||
def test_chndtridf(self):
|
||||
# Use a larger atol since mpmath is doing numerical integration
|
||||
_assert_inverts(
|
||||
sp.chndtridf,
|
||||
_noncentral_chi_cdf,
|
||||
1, [Arg(0, 100, inclusive_a=False), ProbArg(),
|
||||
Arg(0, 100, inclusive_a=False)],
|
||||
n=1000, rtol=1e-4, atol=1e-15)
|
||||
|
||||
@pytest.mark.xfail(run=False)
|
||||
def test_chndtrinc(self):
|
||||
# Use a larger atol since mpmath is doing numerical integration
|
||||
_assert_inverts(
|
||||
sp.chndtrinc,
|
||||
_noncentral_chi_cdf,
|
||||
2, [Arg(0, 100, inclusive_a=False), IntArg(1, 100), ProbArg()],
|
||||
n=1000, rtol=1e-4, atol=1e-15)
|
||||
|
||||
def test_chndtrix(self):
|
||||
# Use a larger atol since mpmath is doing numerical integration
|
||||
_assert_inverts(
|
||||
sp.chndtrix,
|
||||
_noncentral_chi_cdf,
|
||||
0, [ProbArg(), IntArg(1, 100), Arg(0, 100, inclusive_a=False)],
|
||||
n=1000, rtol=1e-4, atol=1e-15,
|
||||
endpt_atol=[1e-6, None, None])
|
||||
|
||||
def test_tklmbda_zero_shape(self):
|
||||
# When lmbda = 0 the CDF has a simple closed form
|
||||
one = mpmath.mpf(1)
|
||||
assert_mpmath_equal(
|
||||
lambda x: sp.tklmbda(x, 0),
|
||||
lambda x: one/(mpmath.exp(-x) + one),
|
||||
[Arg()], rtol=1e-7)
|
||||
|
||||
def test_tklmbda_neg_shape(self):
|
||||
_assert_inverts(
|
||||
sp.tklmbda,
|
||||
_tukey_lmbda_quantile,
|
||||
0, [ProbArg(), Arg(-25, 0, inclusive_b=False)],
|
||||
spfunc_first=False, rtol=1e-5,
|
||||
endpt_atol=[1e-9, 1e-5])
|
||||
|
||||
@pytest.mark.xfail(run=False)
|
||||
def test_tklmbda_pos_shape(self):
|
||||
_assert_inverts(
|
||||
sp.tklmbda,
|
||||
_tukey_lmbda_quantile,
|
||||
0, [ProbArg(), Arg(0, 100, inclusive_a=False)],
|
||||
spfunc_first=False, rtol=1e-5)
|
||||
|
||||
|
||||
def test_nonfinite():
|
||||
funcs = [
|
||||
("btdtria", 3),
|
||||
("btdtrib", 3),
|
||||
("bdtrik", 3),
|
||||
("bdtrin", 3),
|
||||
("chdtriv", 2),
|
||||
("chndtr", 3),
|
||||
("chndtrix", 3),
|
||||
("chndtridf", 3),
|
||||
("chndtrinc", 3),
|
||||
("fdtridfd", 3),
|
||||
("ncfdtr", 4),
|
||||
("ncfdtri", 4),
|
||||
("ncfdtridfn", 4),
|
||||
("ncfdtridfd", 4),
|
||||
("ncfdtrinc", 4),
|
||||
("gdtrix", 3),
|
||||
("gdtrib", 3),
|
||||
("gdtria", 3),
|
||||
("nbdtrik", 3),
|
||||
("nbdtrin", 3),
|
||||
("nrdtrimn", 3),
|
||||
("nrdtrisd", 3),
|
||||
("pdtrik", 2),
|
||||
("stdtr", 2),
|
||||
("stdtrit", 2),
|
||||
("stdtridf", 2),
|
||||
("nctdtr", 3),
|
||||
("nctdtrit", 3),
|
||||
("nctdtridf", 3),
|
||||
("nctdtrinc", 3),
|
||||
("tklmbda", 2),
|
||||
]
|
||||
|
||||
np.random.seed(1)
|
||||
|
||||
for func, numargs in funcs:
|
||||
func = getattr(sp, func)
|
||||
|
||||
args_choices = [(float(x), np.nan, np.inf, -np.inf) for x in
|
||||
np.random.rand(numargs)]
|
||||
|
||||
for args in itertools.product(*args_choices):
|
||||
res = func(*args)
|
||||
|
||||
if any(np.isnan(x) for x in args):
|
||||
# Nan inputs should result to nan output
|
||||
assert_equal(res, np.nan)
|
||||
else:
|
||||
# All other inputs should return something (but not
|
||||
# raise exceptions or cause hangs)
|
||||
pass
|
||||
@@ -1,332 +0,0 @@
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
from itertools import product
|
||||
|
||||
from numpy.testing import assert_allclose
|
||||
import pytest
|
||||
|
||||
from scipy import special
|
||||
from scipy.special import cython_special
|
||||
|
||||
|
||||
int_points = [-10, -1, 1, 10]
|
||||
real_points = [-10.0, -1.0, 1.0, 10.0]
|
||||
complex_points = [complex(*tup) for tup in product(real_points, repeat=2)]
|
||||
|
||||
|
||||
CYTHON_SIGNATURE_MAP = {
|
||||
'f': 'float',
|
||||
'd': 'double',
|
||||
'g': 'long double',
|
||||
'F': 'float complex',
|
||||
'D': 'double complex',
|
||||
'G': 'long double complex',
|
||||
'i':'int',
|
||||
'l': 'long'
|
||||
}
|
||||
|
||||
|
||||
TEST_POINTS = {
|
||||
'f': real_points,
|
||||
'd': real_points,
|
||||
'g': real_points,
|
||||
'F': complex_points,
|
||||
'D': complex_points,
|
||||
'G': complex_points,
|
||||
'i': int_points,
|
||||
'l': int_points,
|
||||
}
|
||||
|
||||
|
||||
PARAMS = [
|
||||
(special.agm, cython_special.agm, ('dd',), None),
|
||||
(special.airy, cython_special._airy_pywrap, ('d', 'D'), None),
|
||||
(special.airye, cython_special._airye_pywrap, ('d', 'D'), None),
|
||||
(special.bdtr, cython_special.bdtr, ('lld', 'ddd'), None),
|
||||
(special.bdtrc, cython_special.bdtrc, ('lld', 'ddd'), None),
|
||||
(special.bdtri, cython_special.bdtri, ('lld', 'ddd'), None),
|
||||
(special.bdtrik, cython_special.bdtrik, ('ddd',), None),
|
||||
(special.bdtrin, cython_special.bdtrin, ('ddd',), None),
|
||||
(special.bei, cython_special.bei, ('d',), None),
|
||||
(special.beip, cython_special.beip, ('d',), None),
|
||||
(special.ber, cython_special.ber, ('d',), None),
|
||||
(special.berp, cython_special.berp, ('d',), None),
|
||||
(special.besselpoly, cython_special.besselpoly, ('ddd',), None),
|
||||
(special.beta, cython_special.beta, ('dd',), None),
|
||||
(special.betainc, cython_special.betainc, ('ddd',), None),
|
||||
(special.betaincinv, cython_special.betaincinv, ('ddd',), None),
|
||||
(special.betaln, cython_special.betaln, ('dd',), None),
|
||||
(special.binom, cython_special.binom, ('dd',), None),
|
||||
(special.boxcox, cython_special.boxcox, ('dd',), None),
|
||||
(special.boxcox1p, cython_special.boxcox1p, ('dd',), None),
|
||||
(special.btdtr, cython_special.btdtr, ('ddd',), None),
|
||||
(special.btdtri, cython_special.btdtri, ('ddd',), None),
|
||||
(special.btdtria, cython_special.btdtria, ('ddd',), None),
|
||||
(special.btdtrib, cython_special.btdtrib, ('ddd',), None),
|
||||
(special.cbrt, cython_special.cbrt, ('d',), None),
|
||||
(special.chdtr, cython_special.chdtr, ('dd',), None),
|
||||
(special.chdtrc, cython_special.chdtrc, ('dd',), None),
|
||||
(special.chdtri, cython_special.chdtri, ('dd',), None),
|
||||
(special.chdtriv, cython_special.chdtriv, ('dd',), None),
|
||||
(special.chndtr, cython_special.chndtr, ('ddd',), None),
|
||||
(special.chndtridf, cython_special.chndtridf, ('ddd',), None),
|
||||
(special.chndtrinc, cython_special.chndtrinc, ('ddd',), None),
|
||||
(special.chndtrix, cython_special.chndtrix, ('ddd',), None),
|
||||
(special.cosdg, cython_special.cosdg, ('d',), None),
|
||||
(special.cosm1, cython_special.cosm1, ('d',), None),
|
||||
(special.cotdg, cython_special.cotdg, ('d',), None),
|
||||
(special.dawsn, cython_special.dawsn, ('d', 'D'), None),
|
||||
(special.ellipe, cython_special.ellipe, ('d',), None),
|
||||
(special.ellipeinc, cython_special.ellipeinc, ('dd',), None),
|
||||
(special.ellipj, cython_special._ellipj_pywrap, ('dd',), None),
|
||||
(special.ellipkinc, cython_special.ellipkinc, ('dd',), None),
|
||||
(special.ellipkm1, cython_special.ellipkm1, ('d',), None),
|
||||
(special.entr, cython_special.entr, ('d',), None),
|
||||
(special.erf, cython_special.erf, ('d', 'D'), None),
|
||||
(special.erfc, cython_special.erfc, ('d', 'D'), None),
|
||||
(special.erfcx, cython_special.erfcx, ('d', 'D'), None),
|
||||
(special.erfi, cython_special.erfi, ('d', 'D'), None),
|
||||
(special.eval_chebyc, cython_special.eval_chebyc, ('dd', 'dD', 'ld'), None),
|
||||
(special.eval_chebys, cython_special.eval_chebys, ('dd', 'dD', 'ld'),
|
||||
'd and l differ for negative int'),
|
||||
(special.eval_chebyt, cython_special.eval_chebyt, ('dd', 'dD', 'ld'),
|
||||
'd and l differ for negative int'),
|
||||
(special.eval_chebyu, cython_special.eval_chebyu, ('dd', 'dD', 'ld'),
|
||||
'd and l differ for negative int'),
|
||||
(special.eval_gegenbauer, cython_special.eval_gegenbauer, ('ddd', 'ddD', 'ldd'),
|
||||
'd and l differ for negative int'),
|
||||
(special.eval_genlaguerre, cython_special.eval_genlaguerre, ('ddd', 'ddD', 'ldd'),
|
||||
'd and l differ for negative int'),
|
||||
(special.eval_hermite, cython_special.eval_hermite, ('ld',), None),
|
||||
(special.eval_hermitenorm, cython_special.eval_hermitenorm, ('ld',), None),
|
||||
(special.eval_jacobi, cython_special.eval_jacobi, ('dddd', 'dddD', 'lddd'),
|
||||
'd and l differ for negative int'),
|
||||
(special.eval_laguerre, cython_special.eval_laguerre, ('dd', 'dD', 'ld'),
|
||||
'd and l differ for negative int'),
|
||||
(special.eval_legendre, cython_special.eval_legendre, ('dd', 'dD', 'ld'), None),
|
||||
(special.eval_sh_chebyt, cython_special.eval_sh_chebyt, ('dd', 'dD', 'ld'), None),
|
||||
(special.eval_sh_chebyu, cython_special.eval_sh_chebyu, ('dd', 'dD', 'ld'),
|
||||
'd and l differ for negative int'),
|
||||
(special.eval_sh_jacobi, cython_special.eval_sh_jacobi, ('dddd', 'dddD', 'lddd'),
|
||||
'd and l differ for negative int'),
|
||||
(special.eval_sh_legendre, cython_special.eval_sh_legendre, ('dd', 'dD', 'ld'), None),
|
||||
(special.exp1, cython_special.exp1, ('d', 'D'), None),
|
||||
(special.exp10, cython_special.exp10, ('d',), None),
|
||||
(special.exp2, cython_special.exp2, ('d',), None),
|
||||
(special.expi, cython_special.expi, ('d', 'D'), None),
|
||||
(special.expit, cython_special.expit, ('f', 'd', 'g'), None),
|
||||
(special.expm1, cython_special.expm1, ('d', 'D'), None),
|
||||
(special.expn, cython_special.expn, ('ld', 'dd'), None),
|
||||
(special.exprel, cython_special.exprel, ('d',), None),
|
||||
(special.fdtr, cython_special.fdtr, ('ddd',), None),
|
||||
(special.fdtrc, cython_special.fdtrc, ('ddd',), None),
|
||||
(special.fdtri, cython_special.fdtri, ('ddd',), None),
|
||||
(special.fdtridfd, cython_special.fdtridfd, ('ddd',), None),
|
||||
(special.fresnel, cython_special._fresnel_pywrap, ('d', 'D'), None),
|
||||
(special.gamma, cython_special.gamma, ('d', 'D'), None),
|
||||
(special.gammainc, cython_special.gammainc, ('dd',), None),
|
||||
(special.gammaincc, cython_special.gammaincc, ('dd',), None),
|
||||
(special.gammainccinv, cython_special.gammainccinv, ('dd',), None),
|
||||
(special.gammaincinv, cython_special.gammaincinv, ('dd',), None),
|
||||
(special.gammaln, cython_special.gammaln, ('d',), None),
|
||||
(special.gammasgn, cython_special.gammasgn, ('d',), None),
|
||||
(special.gdtr, cython_special.gdtr, ('ddd',), None),
|
||||
(special.gdtrc, cython_special.gdtrc, ('ddd',), None),
|
||||
(special.gdtria, cython_special.gdtria, ('ddd',), None),
|
||||
(special.gdtrib, cython_special.gdtrib, ('ddd',), None),
|
||||
(special.gdtrix, cython_special.gdtrix, ('ddd',), None),
|
||||
(special.hankel1, cython_special.hankel1, ('dD',), None),
|
||||
(special.hankel1e, cython_special.hankel1e, ('dD',), None),
|
||||
(special.hankel2, cython_special.hankel2, ('dD',), None),
|
||||
(special.hankel2e, cython_special.hankel2e, ('dD',), None),
|
||||
(special.huber, cython_special.huber, ('dd',), None),
|
||||
(special.hyp0f1, cython_special.hyp0f1, ('dd', 'dD'), None),
|
||||
(special.hyp1f1, cython_special.hyp1f1, ('ddd', 'ddD'), None),
|
||||
(special.hyp2f1, cython_special.hyp2f1, ('dddd', 'dddD'), None),
|
||||
(special.hyperu, cython_special.hyperu, ('ddd',), None),
|
||||
(special.i0, cython_special.i0, ('d',), None),
|
||||
(special.i0e, cython_special.i0e, ('d',), None),
|
||||
(special.i1, cython_special.i1, ('d',), None),
|
||||
(special.i1e, cython_special.i1e, ('d',), None),
|
||||
(special.inv_boxcox, cython_special.inv_boxcox, ('dd',), None),
|
||||
(special.inv_boxcox1p, cython_special.inv_boxcox1p, ('dd',), None),
|
||||
(special.it2i0k0, cython_special._it2i0k0_pywrap, ('d',), None),
|
||||
(special.it2j0y0, cython_special._it2j0y0_pywrap, ('d',), None),
|
||||
(special.it2struve0, cython_special.it2struve0, ('d',), None),
|
||||
(special.itairy, cython_special._itairy_pywrap, ('d',), None),
|
||||
(special.iti0k0, cython_special._iti0k0_pywrap, ('d',), None),
|
||||
(special.itj0y0, cython_special._itj0y0_pywrap, ('d',), None),
|
||||
(special.itmodstruve0, cython_special.itmodstruve0, ('d',), None),
|
||||
(special.itstruve0, cython_special.itstruve0, ('d',), None),
|
||||
(special.iv, cython_special.iv, ('dd', 'dD'), None),
|
||||
(special.ive, cython_special.ive, ('dd', 'dD'), None),
|
||||
(special.j0, cython_special.j0, ('d',), None),
|
||||
(special.j1, cython_special.j1, ('d',), None),
|
||||
(special.jv, cython_special.jv, ('dd', 'dD'), None),
|
||||
(special.jve, cython_special.jve, ('dd', 'dD'), None),
|
||||
(special.k0, cython_special.k0, ('d',), None),
|
||||
(special.k0e, cython_special.k0e, ('d',), None),
|
||||
(special.k1, cython_special.k1, ('d',), None),
|
||||
(special.k1e, cython_special.k1e, ('d',), None),
|
||||
(special.kei, cython_special.kei, ('d',), None),
|
||||
(special.keip, cython_special.keip, ('d',), None),
|
||||
(special.kelvin, cython_special._kelvin_pywrap, ('d',), None),
|
||||
(special.ker, cython_special.ker, ('d',), None),
|
||||
(special.kerp, cython_special.kerp, ('d',), None),
|
||||
(special.kl_div, cython_special.kl_div, ('dd',), None),
|
||||
(special.kn, cython_special.kn, ('ld', 'dd'), None),
|
||||
(special.kolmogi, cython_special.kolmogi, ('d',), None),
|
||||
(special.kolmogorov, cython_special.kolmogorov, ('d',), None),
|
||||
(special.kv, cython_special.kv, ('dd', 'dD'), None),
|
||||
(special.kve, cython_special.kve, ('dd', 'dD'), None),
|
||||
(special.log1p, cython_special.log1p, ('d', 'D'), None),
|
||||
(special.log_ndtr, cython_special.log_ndtr, ('d', 'D'), None),
|
||||
(special.loggamma, cython_special.loggamma, ('D',), None),
|
||||
(special.logit, cython_special.logit, ('f', 'd', 'g'), None),
|
||||
(special.lpmv, cython_special.lpmv, ('ddd',), None),
|
||||
(special.mathieu_a, cython_special.mathieu_a, ('dd',), None),
|
||||
(special.mathieu_b, cython_special.mathieu_b, ('dd',), None),
|
||||
(special.mathieu_cem, cython_special._mathieu_cem_pywrap, ('ddd',), None),
|
||||
(special.mathieu_modcem1, cython_special._mathieu_modcem1_pywrap, ('ddd',), None),
|
||||
(special.mathieu_modcem2, cython_special._mathieu_modcem2_pywrap, ('ddd',), None),
|
||||
(special.mathieu_modsem1, cython_special._mathieu_modsem1_pywrap, ('ddd',), None),
|
||||
(special.mathieu_modsem2, cython_special._mathieu_modsem2_pywrap, ('ddd',), None),
|
||||
(special.mathieu_sem, cython_special._mathieu_sem_pywrap, ('ddd',), None),
|
||||
(special.modfresnelm, cython_special._modfresnelm_pywrap, ('d',), None),
|
||||
(special.modfresnelp, cython_special._modfresnelp_pywrap, ('d',), None),
|
||||
(special.modstruve, cython_special.modstruve, ('dd',), None),
|
||||
(special.nbdtr, cython_special.nbdtr, ('lld', 'ddd'), None),
|
||||
(special.nbdtrc, cython_special.nbdtrc, ('lld', 'ddd'), None),
|
||||
(special.nbdtri, cython_special.nbdtri, ('lld', 'ddd'), None),
|
||||
(special.nbdtrik, cython_special.nbdtrik, ('ddd',), None),
|
||||
(special.nbdtrin, cython_special.nbdtrin, ('ddd',), None),
|
||||
(special.ncfdtr, cython_special.ncfdtr, ('dddd',), None),
|
||||
(special.ncfdtri, cython_special.ncfdtri, ('dddd',), None),
|
||||
(special.ncfdtridfd, cython_special.ncfdtridfd, ('dddd',), None),
|
||||
(special.ncfdtridfn, cython_special.ncfdtridfn, ('dddd',), None),
|
||||
(special.ncfdtrinc, cython_special.ncfdtrinc, ('dddd',), None),
|
||||
(special.nctdtr, cython_special.nctdtr, ('ddd',), None),
|
||||
(special.nctdtridf, cython_special.nctdtridf, ('ddd',), None),
|
||||
(special.nctdtrinc, cython_special.nctdtrinc, ('ddd',), None),
|
||||
(special.nctdtrit, cython_special.nctdtrit, ('ddd',), None),
|
||||
(special.ndtr, cython_special.ndtr, ('d', 'D'), None),
|
||||
(special.ndtri, cython_special.ndtri, ('d',), None),
|
||||
(special.nrdtrimn, cython_special.nrdtrimn, ('ddd',), None),
|
||||
(special.nrdtrisd, cython_special.nrdtrisd, ('ddd',), None),
|
||||
(special.obl_ang1, cython_special._obl_ang1_pywrap, ('dddd',), None),
|
||||
(special.obl_ang1_cv, cython_special._obl_ang1_cv_pywrap, ('ddddd',), None),
|
||||
(special.obl_cv, cython_special.obl_cv, ('ddd',), None),
|
||||
(special.obl_rad1, cython_special._obl_rad1_pywrap, ('dddd',), "see gh-6211"),
|
||||
(special.obl_rad1_cv, cython_special._obl_rad1_cv_pywrap, ('ddddd',), "see gh-6211"),
|
||||
(special.obl_rad2, cython_special._obl_rad2_pywrap, ('dddd',), "see gh-6211"),
|
||||
(special.obl_rad2_cv, cython_special._obl_rad2_cv_pywrap, ('ddddd',), "see gh-6211"),
|
||||
(special.pbdv, cython_special._pbdv_pywrap, ('dd',), None),
|
||||
(special.pbvv, cython_special._pbvv_pywrap, ('dd',), None),
|
||||
(special.pbwa, cython_special._pbwa_pywrap, ('dd',), None),
|
||||
(special.pdtr, cython_special.pdtr, ('ld', 'dd'), None),
|
||||
(special.pdtrc, cython_special.pdtrc, ('ld', 'dd'), None),
|
||||
(special.pdtri, cython_special.pdtri, ('ld', 'dd'), None),
|
||||
(special.pdtrik, cython_special.pdtrik, ('dd',), None),
|
||||
(special.poch, cython_special.poch, ('dd',), None),
|
||||
(special.pro_ang1, cython_special._pro_ang1_pywrap, ('dddd',), None),
|
||||
(special.pro_ang1_cv, cython_special._pro_ang1_cv_pywrap, ('ddddd',), None),
|
||||
(special.pro_cv, cython_special.pro_cv, ('ddd',), None),
|
||||
(special.pro_rad1, cython_special._pro_rad1_pywrap, ('dddd',), "see gh-6211"),
|
||||
(special.pro_rad1_cv, cython_special._pro_rad1_cv_pywrap, ('ddddd',), "see gh-6211"),
|
||||
(special.pro_rad2, cython_special._pro_rad2_pywrap, ('dddd',), "see gh-6211"),
|
||||
(special.pro_rad2_cv, cython_special._pro_rad2_cv_pywrap, ('ddddd',), "see gh-6211"),
|
||||
(special.pseudo_huber, cython_special.pseudo_huber, ('dd',), None),
|
||||
(special.psi, cython_special.psi, ('d', 'D'), None),
|
||||
(special.radian, cython_special.radian, ('ddd',), None),
|
||||
(special.rel_entr, cython_special.rel_entr, ('dd',), None),
|
||||
(special.rgamma, cython_special.rgamma, ('d', 'D'), None),
|
||||
(special.round, cython_special.round, ('d',), None),
|
||||
(special.shichi, cython_special._shichi_pywrap, ('d', 'D'), None),
|
||||
(special.sici, cython_special._sici_pywrap, ('d', 'D'), None),
|
||||
(special.sindg, cython_special.sindg, ('d',), None),
|
||||
(special.smirnov, cython_special.smirnov, ('ld', 'dd'), None),
|
||||
(special.smirnovi, cython_special.smirnovi, ('ld', 'dd'), None),
|
||||
(special.spence, cython_special.spence, ('d', 'D'), None),
|
||||
(special.sph_harm, cython_special.sph_harm, ('lldd', 'dddd'), None),
|
||||
(special.stdtr, cython_special.stdtr, ('dd',), None),
|
||||
(special.stdtridf, cython_special.stdtridf, ('dd',), None),
|
||||
(special.stdtrit, cython_special.stdtrit, ('dd',), None),
|
||||
(special.struve, cython_special.struve, ('dd',), None),
|
||||
(special.tandg, cython_special.tandg, ('d',), None),
|
||||
(special.tklmbda, cython_special.tklmbda, ('dd',), None),
|
||||
(special.wofz, cython_special.wofz, ('D',), None),
|
||||
(special.wrightomega, cython_special.wrightomega, ('D',), None),
|
||||
(special.xlog1py, cython_special.xlog1py, ('dd', 'DD'), None),
|
||||
(special.xlogy, cython_special.xlogy, ('dd', 'DD'), None),
|
||||
(special.y0, cython_special.y0, ('d',), None),
|
||||
(special.y1, cython_special.y1, ('d',), None),
|
||||
(special.yn, cython_special.yn, ('ld', 'dd'), None),
|
||||
(special.yv, cython_special.yv, ('dd', 'dD'), None),
|
||||
(special.yve, cython_special.yve, ('dd', 'dD'), None),
|
||||
(special.zetac, cython_special.zetac, ('d',), None),
|
||||
(special.owens_t, cython_special.owens_t, ('dd',), None)
|
||||
]
|
||||
|
||||
|
||||
IDS = [x[0].__name__ for x in PARAMS]
|
||||
|
||||
|
||||
def _generate_test_points(typecodes):
|
||||
axes = tuple(map(lambda x: TEST_POINTS[x], typecodes))
|
||||
pts = list(product(*axes))
|
||||
return pts
|
||||
|
||||
|
||||
def test_cython_api_completeness():
|
||||
# Check that everything is tested
|
||||
skip = {'hyp2f0', 'hyp1f2', 'hyp3f0'}
|
||||
for name in dir(cython_special):
|
||||
func = getattr(cython_special, name)
|
||||
if callable(func) and not (name.startswith('_') or name in skip):
|
||||
for _, cyfun, _, _ in PARAMS:
|
||||
if cyfun is func:
|
||||
break
|
||||
else:
|
||||
raise RuntimeError("{} missing from tests!".format(name))
|
||||
|
||||
|
||||
@pytest.mark.parametrize("param", PARAMS, ids=IDS)
|
||||
def test_cython_api(param):
|
||||
pyfunc, cyfunc, specializations, knownfailure = param
|
||||
if knownfailure:
|
||||
pytest.xfail(reason=knownfailure)
|
||||
|
||||
# Check which parameters are expected to be fused types
|
||||
values = [set() for code in specializations[0]]
|
||||
for typecodes in specializations:
|
||||
for j, v in enumerate(typecodes):
|
||||
values[j].add(v)
|
||||
seen = set()
|
||||
is_fused_code = [False] * len(values)
|
||||
for j, v in enumerate(values):
|
||||
vv = tuple(sorted(v))
|
||||
if vv in seen:
|
||||
continue
|
||||
is_fused_code[j] = (len(v) > 1)
|
||||
seen.add(vv)
|
||||
|
||||
# Check results
|
||||
for typecodes in specializations:
|
||||
# Pick the correct specialized function
|
||||
signature = []
|
||||
for j, code in enumerate(typecodes):
|
||||
if is_fused_code[j]:
|
||||
signature.append(CYTHON_SIGNATURE_MAP[code])
|
||||
|
||||
if signature:
|
||||
cy_spec_func = cyfunc[tuple(signature)]
|
||||
else:
|
||||
signature = None
|
||||
cy_spec_func = cyfunc
|
||||
|
||||
# Test it
|
||||
pts = _generate_test_points(typecodes)
|
||||
for pt in pts:
|
||||
pyval = pyfunc(*pt)
|
||||
cyval = cy_spec_func(*pt)
|
||||
assert_allclose(cyval, pyval, err_msg="{} {} {}".format(pt, typecodes, signature))
|
||||
@@ -1,500 +0,0 @@
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
import os
|
||||
|
||||
import numpy as np
|
||||
from numpy import arccosh, arcsinh, arctanh
|
||||
from scipy._lib._numpy_compat import suppress_warnings
|
||||
import pytest
|
||||
|
||||
from scipy.special import (
|
||||
lpn, lpmn, lpmv, lqn, lqmn, sph_harm, eval_legendre, eval_hermite,
|
||||
eval_laguerre, eval_genlaguerre, binom, cbrt, expm1, log1p, zeta,
|
||||
jn, jv, yn, yv, iv, kv, kn,
|
||||
gamma, gammaln, gammainc, gammaincc, gammaincinv, gammainccinv, digamma,
|
||||
beta, betainc, betaincinv, poch,
|
||||
ellipe, ellipeinc, ellipk, ellipkm1, ellipkinc, ellipj,
|
||||
erf, erfc, erfinv, erfcinv, exp1, expi, expn,
|
||||
bdtrik, btdtr, btdtri, btdtria, btdtrib, chndtr, gdtr, gdtrc, gdtrix, gdtrib,
|
||||
nbdtrik, pdtrik, owens_t,
|
||||
mathieu_a, mathieu_b, mathieu_cem, mathieu_sem, mathieu_modcem1,
|
||||
mathieu_modsem1, mathieu_modcem2, mathieu_modsem2,
|
||||
ellip_harm, ellip_harm_2, spherical_jn, spherical_yn,
|
||||
)
|
||||
from scipy.integrate import IntegrationWarning
|
||||
|
||||
from scipy.special._testutils import FuncData
|
||||
|
||||
DATASETS_BOOST = np.load(os.path.join(os.path.dirname(__file__),
|
||||
"data", "boost.npz"))
|
||||
|
||||
DATASETS_GSL = np.load(os.path.join(os.path.dirname(__file__),
|
||||
"data", "gsl.npz"))
|
||||
|
||||
DATASETS_LOCAL = np.load(os.path.join(os.path.dirname(__file__),
|
||||
"data", "local.npz"))
|
||||
|
||||
|
||||
def data(func, dataname, *a, **kw):
|
||||
kw.setdefault('dataname', dataname)
|
||||
return FuncData(func, DATASETS_BOOST[dataname], *a, **kw)
|
||||
|
||||
|
||||
def data_gsl(func, dataname, *a, **kw):
|
||||
kw.setdefault('dataname', dataname)
|
||||
return FuncData(func, DATASETS_GSL[dataname], *a, **kw)
|
||||
|
||||
|
||||
def data_local(func, dataname, *a, **kw):
|
||||
kw.setdefault('dataname', dataname)
|
||||
return FuncData(func, DATASETS_LOCAL[dataname], *a, **kw)
|
||||
|
||||
|
||||
def ellipk_(k):
|
||||
return ellipk(k*k)
|
||||
|
||||
|
||||
def ellipkinc_(f, k):
|
||||
return ellipkinc(f, k*k)
|
||||
|
||||
|
||||
def ellipe_(k):
|
||||
return ellipe(k*k)
|
||||
|
||||
|
||||
def ellipeinc_(f, k):
|
||||
return ellipeinc(f, k*k)
|
||||
|
||||
|
||||
def ellipj_(k):
|
||||
return ellipj(k*k)
|
||||
|
||||
|
||||
def zeta_(x):
|
||||
return zeta(x, 1.)
|
||||
|
||||
|
||||
def assoc_legendre_p_boost_(nu, mu, x):
|
||||
# the boost test data is for integer orders only
|
||||
return lpmv(mu, nu.astype(int), x)
|
||||
|
||||
def legendre_p_via_assoc_(nu, x):
|
||||
return lpmv(0, nu, x)
|
||||
|
||||
def lpn_(n, x):
|
||||
return lpn(n.astype('l'), x)[0][-1]
|
||||
|
||||
def lqn_(n, x):
|
||||
return lqn(n.astype('l'), x)[0][-1]
|
||||
|
||||
def legendre_p_via_lpmn(n, x):
|
||||
return lpmn(0, n, x)[0][0,-1]
|
||||
|
||||
def legendre_q_via_lqmn(n, x):
|
||||
return lqmn(0, n, x)[0][0,-1]
|
||||
|
||||
def mathieu_ce_rad(m, q, x):
|
||||
return mathieu_cem(m, q, x*180/np.pi)[0]
|
||||
|
||||
|
||||
def mathieu_se_rad(m, q, x):
|
||||
return mathieu_sem(m, q, x*180/np.pi)[0]
|
||||
|
||||
|
||||
def mathieu_mc1_scaled(m, q, x):
|
||||
# GSL follows a different normalization.
|
||||
# We follow Abramowitz & Stegun, they apparently something else.
|
||||
return mathieu_modcem1(m, q, x)[0] * np.sqrt(np.pi/2)
|
||||
|
||||
|
||||
def mathieu_ms1_scaled(m, q, x):
|
||||
return mathieu_modsem1(m, q, x)[0] * np.sqrt(np.pi/2)
|
||||
|
||||
|
||||
def mathieu_mc2_scaled(m, q, x):
|
||||
return mathieu_modcem2(m, q, x)[0] * np.sqrt(np.pi/2)
|
||||
|
||||
|
||||
def mathieu_ms2_scaled(m, q, x):
|
||||
return mathieu_modsem2(m, q, x)[0] * np.sqrt(np.pi/2)
|
||||
|
||||
def eval_legendre_ld(n, x):
|
||||
return eval_legendre(n.astype('l'), x)
|
||||
|
||||
def eval_legendre_dd(n, x):
|
||||
return eval_legendre(n.astype('d'), x)
|
||||
|
||||
def eval_hermite_ld(n, x):
|
||||
return eval_hermite(n.astype('l'), x)
|
||||
|
||||
def eval_laguerre_ld(n, x):
|
||||
return eval_laguerre(n.astype('l'), x)
|
||||
|
||||
def eval_laguerre_dd(n, x):
|
||||
return eval_laguerre(n.astype('d'), x)
|
||||
|
||||
def eval_genlaguerre_ldd(n, a, x):
|
||||
return eval_genlaguerre(n.astype('l'), a, x)
|
||||
|
||||
def eval_genlaguerre_ddd(n, a, x):
|
||||
return eval_genlaguerre(n.astype('d'), a, x)
|
||||
|
||||
def bdtrik_comp(y, n, p):
|
||||
return bdtrik(1-y, n, p)
|
||||
|
||||
def btdtri_comp(a, b, p):
|
||||
return btdtri(a, b, 1-p)
|
||||
|
||||
def btdtria_comp(p, b, x):
|
||||
return btdtria(1-p, b, x)
|
||||
|
||||
def btdtrib_comp(a, p, x):
|
||||
return btdtrib(a, 1-p, x)
|
||||
|
||||
def gdtr_(p, x):
|
||||
return gdtr(1.0, p, x)
|
||||
|
||||
def gdtrc_(p, x):
|
||||
return gdtrc(1.0, p, x)
|
||||
|
||||
def gdtrix_(b, p):
|
||||
return gdtrix(1.0, b, p)
|
||||
|
||||
def gdtrix_comp(b, p):
|
||||
return gdtrix(1.0, b, 1-p)
|
||||
|
||||
def gdtrib_(p, x):
|
||||
return gdtrib(1.0, p, x)
|
||||
|
||||
def gdtrib_comp(p, x):
|
||||
return gdtrib(1.0, 1-p, x)
|
||||
|
||||
def nbdtrik_comp(y, n, p):
|
||||
return nbdtrik(1-y, n, p)
|
||||
|
||||
def pdtrik_comp(p, m):
|
||||
return pdtrik(1-p, m)
|
||||
|
||||
def poch_(z, m):
|
||||
return 1.0 / poch(z, m)
|
||||
|
||||
def poch_minus(z, m):
|
||||
return 1.0 / poch(z, -m)
|
||||
|
||||
def spherical_jn_(n, x):
|
||||
return spherical_jn(n.astype('l'), x)
|
||||
|
||||
def spherical_yn_(n, x):
|
||||
return spherical_yn(n.astype('l'), x)
|
||||
|
||||
def sph_harm_(m, n, theta, phi):
|
||||
y = sph_harm(m, n, theta, phi)
|
||||
return (y.real, y.imag)
|
||||
|
||||
def cexpm1(x, y):
|
||||
z = expm1(x + 1j*y)
|
||||
return z.real, z.imag
|
||||
|
||||
def clog1p(x, y):
|
||||
z = log1p(x + 1j*y)
|
||||
return z.real, z.imag
|
||||
|
||||
|
||||
BOOST_TESTS = [
|
||||
data(arccosh, 'acosh_data_ipp-acosh_data', 0, 1, rtol=5e-13),
|
||||
data(arccosh, 'acosh_data_ipp-acosh_data', 0j, 1, rtol=5e-13),
|
||||
|
||||
data(arcsinh, 'asinh_data_ipp-asinh_data', 0, 1, rtol=1e-11),
|
||||
data(arcsinh, 'asinh_data_ipp-asinh_data', 0j, 1, rtol=1e-11),
|
||||
|
||||
data(arctanh, 'atanh_data_ipp-atanh_data', 0, 1, rtol=1e-11),
|
||||
data(arctanh, 'atanh_data_ipp-atanh_data', 0j, 1, rtol=1e-11),
|
||||
|
||||
data(assoc_legendre_p_boost_, 'assoc_legendre_p_ipp-assoc_legendre_p', (0,1,2), 3, rtol=1e-11),
|
||||
|
||||
data(legendre_p_via_assoc_, 'legendre_p_ipp-legendre_p', (0,1), 2, rtol=1e-11),
|
||||
data(legendre_p_via_assoc_, 'legendre_p_large_ipp-legendre_p_large', (0,1), 2, rtol=7e-14),
|
||||
data(legendre_p_via_lpmn, 'legendre_p_ipp-legendre_p', (0,1), 2, rtol=5e-14, vectorized=False),
|
||||
data(legendre_p_via_lpmn, 'legendre_p_large_ipp-legendre_p_large', (0,1), 2, rtol=7e-14, vectorized=False),
|
||||
data(lpn_, 'legendre_p_ipp-legendre_p', (0,1), 2, rtol=5e-14, vectorized=False),
|
||||
data(lpn_, 'legendre_p_large_ipp-legendre_p_large', (0,1), 2, rtol=3e-13, vectorized=False),
|
||||
data(eval_legendre_ld, 'legendre_p_ipp-legendre_p', (0,1), 2, rtol=6e-14),
|
||||
data(eval_legendre_ld, 'legendre_p_large_ipp-legendre_p_large', (0,1), 2, rtol=2e-13),
|
||||
data(eval_legendre_dd, 'legendre_p_ipp-legendre_p', (0,1), 2, rtol=2e-14),
|
||||
data(eval_legendre_dd, 'legendre_p_large_ipp-legendre_p_large', (0,1), 2, rtol=2e-13),
|
||||
|
||||
data(lqn_, 'legendre_p_ipp-legendre_p', (0,1), 3, rtol=2e-14, vectorized=False),
|
||||
data(lqn_, 'legendre_p_large_ipp-legendre_p_large', (0,1), 3, rtol=2e-12, vectorized=False),
|
||||
data(legendre_q_via_lqmn, 'legendre_p_ipp-legendre_p', (0,1), 3, rtol=2e-14, vectorized=False),
|
||||
data(legendre_q_via_lqmn, 'legendre_p_large_ipp-legendre_p_large', (0,1), 3, rtol=2e-12, vectorized=False),
|
||||
|
||||
data(beta, 'beta_exp_data_ipp-beta_exp_data', (0,1), 2, rtol=1e-13),
|
||||
data(beta, 'beta_exp_data_ipp-beta_exp_data', (0,1), 2, rtol=1e-13),
|
||||
data(beta, 'beta_small_data_ipp-beta_small_data', (0,1), 2),
|
||||
data(beta, 'beta_med_data_ipp-beta_med_data', (0,1), 2, rtol=5e-13),
|
||||
|
||||
data(betainc, 'ibeta_small_data_ipp-ibeta_small_data', (0,1,2), 5, rtol=6e-15),
|
||||
data(betainc, 'ibeta_data_ipp-ibeta_data', (0,1,2), 5, rtol=5e-13),
|
||||
data(betainc, 'ibeta_int_data_ipp-ibeta_int_data', (0,1,2), 5, rtol=2e-14),
|
||||
data(betainc, 'ibeta_large_data_ipp-ibeta_large_data', (0,1,2), 5, rtol=4e-10),
|
||||
|
||||
data(betaincinv, 'ibeta_inv_data_ipp-ibeta_inv_data', (0,1,2), 3, rtol=1e-5),
|
||||
|
||||
data(btdtr, 'ibeta_small_data_ipp-ibeta_small_data', (0,1,2), 5, rtol=6e-15),
|
||||
data(btdtr, 'ibeta_data_ipp-ibeta_data', (0,1,2), 5, rtol=4e-13),
|
||||
data(btdtr, 'ibeta_int_data_ipp-ibeta_int_data', (0,1,2), 5, rtol=2e-14),
|
||||
data(btdtr, 'ibeta_large_data_ipp-ibeta_large_data', (0,1,2), 5, rtol=4e-10),
|
||||
|
||||
data(btdtri, 'ibeta_inv_data_ipp-ibeta_inv_data', (0,1,2), 3, rtol=1e-5),
|
||||
data(btdtri_comp, 'ibeta_inv_data_ipp-ibeta_inv_data', (0,1,2), 4, rtol=8e-7),
|
||||
|
||||
data(btdtria, 'ibeta_inva_data_ipp-ibeta_inva_data', (2,0,1), 3, rtol=5e-9),
|
||||
data(btdtria_comp, 'ibeta_inva_data_ipp-ibeta_inva_data', (2,0,1), 4, rtol=5e-9),
|
||||
|
||||
data(btdtrib, 'ibeta_inva_data_ipp-ibeta_inva_data', (0,2,1), 5, rtol=5e-9),
|
||||
data(btdtrib_comp, 'ibeta_inva_data_ipp-ibeta_inva_data', (0,2,1), 6, rtol=5e-9),
|
||||
|
||||
data(binom, 'binomial_data_ipp-binomial_data', (0,1), 2, rtol=1e-13),
|
||||
data(binom, 'binomial_large_data_ipp-binomial_large_data', (0,1), 2, rtol=5e-13),
|
||||
|
||||
data(bdtrik, 'binomial_quantile_ipp-binomial_quantile_data', (2,0,1), 3, rtol=5e-9),
|
||||
data(bdtrik_comp, 'binomial_quantile_ipp-binomial_quantile_data', (2,0,1), 4, rtol=5e-9),
|
||||
|
||||
data(nbdtrik, 'negative_binomial_quantile_ipp-negative_binomial_quantile_data', (2,0,1), 3, rtol=4e-9),
|
||||
data(nbdtrik_comp, 'negative_binomial_quantile_ipp-negative_binomial_quantile_data', (2,0,1), 4, rtol=4e-9),
|
||||
|
||||
data(pdtrik, 'poisson_quantile_ipp-poisson_quantile_data', (1,0), 2, rtol=3e-9),
|
||||
data(pdtrik_comp, 'poisson_quantile_ipp-poisson_quantile_data', (1,0), 3, rtol=4e-9),
|
||||
|
||||
data(cbrt, 'cbrt_data_ipp-cbrt_data', 1, 0),
|
||||
|
||||
data(digamma, 'digamma_data_ipp-digamma_data', 0, 1),
|
||||
data(digamma, 'digamma_data_ipp-digamma_data', 0j, 1),
|
||||
data(digamma, 'digamma_neg_data_ipp-digamma_neg_data', 0, 1, rtol=2e-13),
|
||||
data(digamma, 'digamma_neg_data_ipp-digamma_neg_data', 0j, 1, rtol=1e-13),
|
||||
data(digamma, 'digamma_root_data_ipp-digamma_root_data', 0, 1, rtol=1e-15),
|
||||
data(digamma, 'digamma_root_data_ipp-digamma_root_data', 0j, 1, rtol=1e-15),
|
||||
data(digamma, 'digamma_small_data_ipp-digamma_small_data', 0, 1, rtol=1e-15),
|
||||
data(digamma, 'digamma_small_data_ipp-digamma_small_data', 0j, 1, rtol=1e-14),
|
||||
|
||||
data(ellipk_, 'ellint_k_data_ipp-ellint_k_data', 0, 1),
|
||||
data(ellipkinc_, 'ellint_f_data_ipp-ellint_f_data', (0,1), 2, rtol=1e-14),
|
||||
data(ellipe_, 'ellint_e_data_ipp-ellint_e_data', 0, 1),
|
||||
data(ellipeinc_, 'ellint_e2_data_ipp-ellint_e2_data', (0,1), 2, rtol=1e-14),
|
||||
|
||||
data(erf, 'erf_data_ipp-erf_data', 0, 1),
|
||||
data(erf, 'erf_data_ipp-erf_data', 0j, 1, rtol=1e-13),
|
||||
data(erfc, 'erf_data_ipp-erf_data', 0, 2, rtol=6e-15),
|
||||
data(erf, 'erf_large_data_ipp-erf_large_data', 0, 1),
|
||||
data(erf, 'erf_large_data_ipp-erf_large_data', 0j, 1),
|
||||
data(erfc, 'erf_large_data_ipp-erf_large_data', 0, 2, rtol=4e-14),
|
||||
data(erf, 'erf_small_data_ipp-erf_small_data', 0, 1),
|
||||
data(erf, 'erf_small_data_ipp-erf_small_data', 0j, 1, rtol=1e-13),
|
||||
data(erfc, 'erf_small_data_ipp-erf_small_data', 0, 2),
|
||||
|
||||
data(erfinv, 'erf_inv_data_ipp-erf_inv_data', 0, 1),
|
||||
data(erfcinv, 'erfc_inv_data_ipp-erfc_inv_data', 0, 1),
|
||||
data(erfcinv, 'erfc_inv_big_data_ipp-erfc_inv_big_data2', 0, 1),
|
||||
|
||||
data(exp1, 'expint_1_data_ipp-expint_1_data', 1, 2, rtol=1e-13),
|
||||
data(exp1, 'expint_1_data_ipp-expint_1_data', 1j, 2, rtol=5e-9),
|
||||
data(expi, 'expinti_data_ipp-expinti_data', 0, 1, rtol=1e-13),
|
||||
data(expi, 'expinti_data_double_ipp-expinti_data_double', 0, 1, rtol=1e-13),
|
||||
|
||||
data(expn, 'expint_small_data_ipp-expint_small_data', (0,1), 2),
|
||||
data(expn, 'expint_data_ipp-expint_data', (0,1), 2, rtol=1e-14),
|
||||
|
||||
data(gamma, 'test_gamma_data_ipp-near_0', 0, 1),
|
||||
data(gamma, 'test_gamma_data_ipp-near_1', 0, 1),
|
||||
data(gamma, 'test_gamma_data_ipp-near_2', 0, 1),
|
||||
data(gamma, 'test_gamma_data_ipp-near_m10', 0, 1),
|
||||
data(gamma, 'test_gamma_data_ipp-near_m55', 0, 1, rtol=7e-12),
|
||||
data(gamma, 'test_gamma_data_ipp-factorials', 0, 1, rtol=4e-14),
|
||||
data(gamma, 'test_gamma_data_ipp-near_0', 0j, 1, rtol=2e-9),
|
||||
data(gamma, 'test_gamma_data_ipp-near_1', 0j, 1, rtol=2e-9),
|
||||
data(gamma, 'test_gamma_data_ipp-near_2', 0j, 1, rtol=2e-9),
|
||||
data(gamma, 'test_gamma_data_ipp-near_m10', 0j, 1, rtol=2e-9),
|
||||
data(gamma, 'test_gamma_data_ipp-near_m55', 0j, 1, rtol=2e-9),
|
||||
data(gamma, 'test_gamma_data_ipp-factorials', 0j, 1, rtol=2e-13),
|
||||
data(gammaln, 'test_gamma_data_ipp-near_0', 0, 2, rtol=5e-11),
|
||||
data(gammaln, 'test_gamma_data_ipp-near_1', 0, 2, rtol=5e-11),
|
||||
data(gammaln, 'test_gamma_data_ipp-near_2', 0, 2, rtol=2e-10),
|
||||
data(gammaln, 'test_gamma_data_ipp-near_m10', 0, 2, rtol=5e-11),
|
||||
data(gammaln, 'test_gamma_data_ipp-near_m55', 0, 2, rtol=5e-11),
|
||||
data(gammaln, 'test_gamma_data_ipp-factorials', 0, 2),
|
||||
|
||||
data(gammainc, 'igamma_small_data_ipp-igamma_small_data', (0,1), 5, rtol=5e-15),
|
||||
data(gammainc, 'igamma_med_data_ipp-igamma_med_data', (0,1), 5, rtol=2e-13),
|
||||
data(gammainc, 'igamma_int_data_ipp-igamma_int_data', (0,1), 5, rtol=2e-13),
|
||||
data(gammainc, 'igamma_big_data_ipp-igamma_big_data', (0,1), 5, rtol=1e-12),
|
||||
|
||||
data(gdtr_, 'igamma_small_data_ipp-igamma_small_data', (0,1), 5, rtol=1e-13),
|
||||
data(gdtr_, 'igamma_med_data_ipp-igamma_med_data', (0,1), 5, rtol=2e-13),
|
||||
data(gdtr_, 'igamma_int_data_ipp-igamma_int_data', (0,1), 5, rtol=2e-13),
|
||||
data(gdtr_, 'igamma_big_data_ipp-igamma_big_data', (0,1), 5, rtol=2e-9),
|
||||
|
||||
data(gammaincc, 'igamma_small_data_ipp-igamma_small_data', (0,1), 3, rtol=1e-13),
|
||||
data(gammaincc, 'igamma_med_data_ipp-igamma_med_data', (0,1), 3, rtol=2e-13),
|
||||
data(gammaincc, 'igamma_int_data_ipp-igamma_int_data', (0,1), 3, rtol=4e-14),
|
||||
data(gammaincc, 'igamma_big_data_ipp-igamma_big_data', (0,1), 3, rtol=1e-11),
|
||||
|
||||
data(gdtrc_, 'igamma_small_data_ipp-igamma_small_data', (0,1), 3, rtol=1e-13),
|
||||
data(gdtrc_, 'igamma_med_data_ipp-igamma_med_data', (0,1), 3, rtol=2e-13),
|
||||
data(gdtrc_, 'igamma_int_data_ipp-igamma_int_data', (0,1), 3, rtol=4e-14),
|
||||
data(gdtrc_, 'igamma_big_data_ipp-igamma_big_data', (0,1), 3, rtol=1e-11),
|
||||
|
||||
data(gdtrib_, 'igamma_inva_data_ipp-igamma_inva_data', (1,0), 2, rtol=5e-9),
|
||||
data(gdtrib_comp, 'igamma_inva_data_ipp-igamma_inva_data', (1,0), 3, rtol=5e-9),
|
||||
|
||||
data(poch_, 'tgamma_delta_ratio_data_ipp-tgamma_delta_ratio_data', (0,1), 2, rtol=2e-13),
|
||||
data(poch_, 'tgamma_delta_ratio_int_ipp-tgamma_delta_ratio_int', (0,1), 2,),
|
||||
data(poch_, 'tgamma_delta_ratio_int2_ipp-tgamma_delta_ratio_int2', (0,1), 2,),
|
||||
data(poch_minus, 'tgamma_delta_ratio_data_ipp-tgamma_delta_ratio_data', (0,1), 3, rtol=2e-13),
|
||||
data(poch_minus, 'tgamma_delta_ratio_int_ipp-tgamma_delta_ratio_int', (0,1), 3),
|
||||
data(poch_minus, 'tgamma_delta_ratio_int2_ipp-tgamma_delta_ratio_int2', (0,1), 3),
|
||||
|
||||
|
||||
data(eval_hermite_ld, 'hermite_ipp-hermite', (0,1), 2, rtol=2e-14),
|
||||
data(eval_laguerre_ld, 'laguerre2_ipp-laguerre2', (0,1), 2, rtol=7e-12),
|
||||
data(eval_laguerre_dd, 'laguerre2_ipp-laguerre2', (0,1), 2, knownfailure='hyp2f1 insufficiently accurate.'),
|
||||
data(eval_genlaguerre_ldd, 'laguerre3_ipp-laguerre3', (0,1,2), 3, rtol=2e-13),
|
||||
data(eval_genlaguerre_ddd, 'laguerre3_ipp-laguerre3', (0,1,2), 3, knownfailure='hyp2f1 insufficiently accurate.'),
|
||||
|
||||
data(log1p, 'log1p_expm1_data_ipp-log1p_expm1_data', 0, 1),
|
||||
data(expm1, 'log1p_expm1_data_ipp-log1p_expm1_data', 0, 2),
|
||||
|
||||
data(iv, 'bessel_i_data_ipp-bessel_i_data', (0,1), 2, rtol=1e-12),
|
||||
data(iv, 'bessel_i_data_ipp-bessel_i_data', (0,1j), 2, rtol=2e-10, atol=1e-306),
|
||||
data(iv, 'bessel_i_int_data_ipp-bessel_i_int_data', (0,1), 2, rtol=1e-9),
|
||||
data(iv, 'bessel_i_int_data_ipp-bessel_i_int_data', (0,1j), 2, rtol=2e-10),
|
||||
|
||||
data(jn, 'bessel_j_int_data_ipp-bessel_j_int_data', (0,1), 2, rtol=1e-12),
|
||||
data(jn, 'bessel_j_int_data_ipp-bessel_j_int_data', (0,1j), 2, rtol=1e-12),
|
||||
data(jn, 'bessel_j_large_data_ipp-bessel_j_large_data', (0,1), 2, rtol=6e-11),
|
||||
data(jn, 'bessel_j_large_data_ipp-bessel_j_large_data', (0,1j), 2, rtol=6e-11),
|
||||
|
||||
data(jv, 'bessel_j_int_data_ipp-bessel_j_int_data', (0,1), 2, rtol=1e-12),
|
||||
data(jv, 'bessel_j_int_data_ipp-bessel_j_int_data', (0,1j), 2, rtol=1e-12),
|
||||
data(jv, 'bessel_j_data_ipp-bessel_j_data', (0,1), 2, rtol=1e-12),
|
||||
data(jv, 'bessel_j_data_ipp-bessel_j_data', (0,1j), 2, rtol=1e-12),
|
||||
|
||||
data(kn, 'bessel_k_int_data_ipp-bessel_k_int_data', (0,1), 2, rtol=1e-12),
|
||||
|
||||
data(kv, 'bessel_k_int_data_ipp-bessel_k_int_data', (0,1), 2, rtol=1e-12),
|
||||
data(kv, 'bessel_k_int_data_ipp-bessel_k_int_data', (0,1j), 2, rtol=1e-12),
|
||||
data(kv, 'bessel_k_data_ipp-bessel_k_data', (0,1), 2, rtol=1e-12),
|
||||
data(kv, 'bessel_k_data_ipp-bessel_k_data', (0,1j), 2, rtol=1e-12),
|
||||
|
||||
data(yn, 'bessel_y01_data_ipp-bessel_y01_data', (0,1), 2, rtol=1e-12),
|
||||
data(yn, 'bessel_yn_data_ipp-bessel_yn_data', (0,1), 2, rtol=1e-12),
|
||||
|
||||
data(yv, 'bessel_yn_data_ipp-bessel_yn_data', (0,1), 2, rtol=1e-12),
|
||||
data(yv, 'bessel_yn_data_ipp-bessel_yn_data', (0,1j), 2, rtol=1e-12),
|
||||
data(yv, 'bessel_yv_data_ipp-bessel_yv_data', (0,1), 2, rtol=1e-10),
|
||||
data(yv, 'bessel_yv_data_ipp-bessel_yv_data', (0,1j), 2, rtol=1e-10),
|
||||
|
||||
data(zeta_, 'zeta_data_ipp-zeta_data', 0, 1, param_filter=(lambda s: s > 1)),
|
||||
data(zeta_, 'zeta_neg_data_ipp-zeta_neg_data', 0, 1, param_filter=(lambda s: s > 1)),
|
||||
data(zeta_, 'zeta_1_up_data_ipp-zeta_1_up_data', 0, 1, param_filter=(lambda s: s > 1)),
|
||||
data(zeta_, 'zeta_1_below_data_ipp-zeta_1_below_data', 0, 1, param_filter=(lambda s: s > 1)),
|
||||
|
||||
data(gammaincinv, 'gamma_inv_small_data_ipp-gamma_inv_small_data', (0,1), 2, rtol=1e-11),
|
||||
data(gammaincinv, 'gamma_inv_data_ipp-gamma_inv_data', (0,1), 2, rtol=1e-14),
|
||||
data(gammaincinv, 'gamma_inv_big_data_ipp-gamma_inv_big_data', (0,1), 2, rtol=1e-11),
|
||||
|
||||
data(gammainccinv, 'gamma_inv_small_data_ipp-gamma_inv_small_data', (0,1), 3, rtol=1e-12),
|
||||
data(gammainccinv, 'gamma_inv_data_ipp-gamma_inv_data', (0,1), 3, rtol=1e-14),
|
||||
data(gammainccinv, 'gamma_inv_big_data_ipp-gamma_inv_big_data', (0,1), 3, rtol=1e-14),
|
||||
|
||||
data(gdtrix_, 'gamma_inv_small_data_ipp-gamma_inv_small_data', (0,1), 2, rtol=3e-13, knownfailure='gdtrix unflow some points'),
|
||||
data(gdtrix_, 'gamma_inv_data_ipp-gamma_inv_data', (0,1), 2, rtol=3e-15),
|
||||
data(gdtrix_, 'gamma_inv_big_data_ipp-gamma_inv_big_data', (0,1), 2),
|
||||
data(gdtrix_comp, 'gamma_inv_small_data_ipp-gamma_inv_small_data', (0,1), 2, knownfailure='gdtrix bad some points'),
|
||||
data(gdtrix_comp, 'gamma_inv_data_ipp-gamma_inv_data', (0,1), 3, rtol=6e-15),
|
||||
data(gdtrix_comp, 'gamma_inv_big_data_ipp-gamma_inv_big_data', (0,1), 3),
|
||||
|
||||
data(chndtr, 'nccs_ipp-nccs', (2,0,1), 3, rtol=3e-5),
|
||||
data(chndtr, 'nccs_big_ipp-nccs_big', (2,0,1), 3, rtol=5e-4, knownfailure='chndtr inaccurate some points'),
|
||||
|
||||
data(sph_harm_, 'spherical_harmonic_ipp-spherical_harmonic', (1,0,3,2), (4,5), rtol=5e-11,
|
||||
param_filter=(lambda p: np.ones(p.shape, '?'),
|
||||
lambda p: np.ones(p.shape, '?'),
|
||||
lambda p: np.logical_and(p < 2*np.pi, p >= 0),
|
||||
lambda p: np.logical_and(p < np.pi, p >= 0))),
|
||||
|
||||
data(spherical_jn_, 'sph_bessel_data_ipp-sph_bessel_data', (0,1), 2, rtol=1e-13),
|
||||
data(spherical_yn_, 'sph_neumann_data_ipp-sph_neumann_data', (0,1), 2, rtol=8e-15),
|
||||
|
||||
data(owens_t, 'owenst_data_ipp-owens_t', (0, 1), 2, rtol=5e-14),
|
||||
data(owens_t, 'owenst_data_ipp-owens_t_alarge', (0, 1), 2, rtol=5e-15),
|
||||
|
||||
# -- not used yet (function does not exist in scipy):
|
||||
# 'ellint_pi2_data_ipp-ellint_pi2_data',
|
||||
# 'ellint_pi3_data_ipp-ellint_pi3_data',
|
||||
# 'ellint_pi3_large_data_ipp-ellint_pi3_large_data',
|
||||
# 'ellint_rc_data_ipp-ellint_rc_data',
|
||||
# 'ellint_rd_data_ipp-ellint_rd_data',
|
||||
# 'ellint_rf_data_ipp-ellint_rf_data',
|
||||
# 'ellint_rj_data_ipp-ellint_rj_data',
|
||||
# 'ncbeta_big_ipp-ncbeta_big',
|
||||
# 'ncbeta_ipp-ncbeta',
|
||||
# 'powm1_sqrtp1m1_test_cpp-powm1_data',
|
||||
# 'powm1_sqrtp1m1_test_cpp-sqrtp1m1_data',
|
||||
# 'test_gamma_data_ipp-gammap1m1_data',
|
||||
# 'tgamma_ratio_data_ipp-tgamma_ratio_data',
|
||||
]
|
||||
|
||||
|
||||
@pytest.mark.parametrize('test', BOOST_TESTS, ids=repr)
|
||||
def test_boost(test):
|
||||
_test_factory(test)
|
||||
|
||||
|
||||
GSL_TESTS = [
|
||||
data_gsl(mathieu_a, 'mathieu_ab', (0, 1), 2, rtol=1e-13, atol=1e-13),
|
||||
data_gsl(mathieu_b, 'mathieu_ab', (0, 1), 3, rtol=1e-13, atol=1e-13),
|
||||
|
||||
# Also the GSL output has limited accuracy...
|
||||
data_gsl(mathieu_ce_rad, 'mathieu_ce_se', (0, 1, 2), 3, rtol=1e-7, atol=1e-13),
|
||||
data_gsl(mathieu_se_rad, 'mathieu_ce_se', (0, 1, 2), 4, rtol=1e-7, atol=1e-13),
|
||||
|
||||
data_gsl(mathieu_mc1_scaled, 'mathieu_mc_ms', (0, 1, 2), 3, rtol=1e-7, atol=1e-13),
|
||||
data_gsl(mathieu_ms1_scaled, 'mathieu_mc_ms', (0, 1, 2), 4, rtol=1e-7, atol=1e-13),
|
||||
|
||||
data_gsl(mathieu_mc2_scaled, 'mathieu_mc_ms', (0, 1, 2), 5, rtol=1e-7, atol=1e-13),
|
||||
data_gsl(mathieu_ms2_scaled, 'mathieu_mc_ms', (0, 1, 2), 6, rtol=1e-7, atol=1e-13),
|
||||
]
|
||||
|
||||
|
||||
@pytest.mark.parametrize('test', GSL_TESTS, ids=repr)
|
||||
def test_gsl(test):
|
||||
_test_factory(test)
|
||||
|
||||
|
||||
LOCAL_TESTS = [
|
||||
data_local(ellipkinc, 'ellipkinc_neg_m', (0, 1), 2),
|
||||
data_local(ellipkm1, 'ellipkm1', 0, 1),
|
||||
data_local(ellipeinc, 'ellipeinc_neg_m', (0, 1), 2),
|
||||
data_local(clog1p, 'log1p_expm1_complex', (0,1), (2,3), rtol=1e-14),
|
||||
data_local(cexpm1, 'log1p_expm1_complex', (0,1), (4,5), rtol=1e-14),
|
||||
data_local(gammainc, 'gammainc', (0, 1), 2, rtol=1e-12),
|
||||
data_local(gammaincc, 'gammaincc', (0, 1), 2, rtol=1e-11),
|
||||
data_local(ellip_harm_2, 'ellip',(0, 1, 2, 3, 4), 6, rtol=1e-10, atol=1e-13),
|
||||
data_local(ellip_harm, 'ellip',(0, 1, 2, 3, 4), 5, rtol=1e-10, atol=1e-13),
|
||||
]
|
||||
|
||||
|
||||
@pytest.mark.parametrize('test', LOCAL_TESTS, ids=repr)
|
||||
def test_local(test):
|
||||
_test_factory(test)
|
||||
|
||||
|
||||
def _test_factory(test, dtype=np.double):
|
||||
"""Boost test"""
|
||||
with suppress_warnings() as sup:
|
||||
sup.filter(IntegrationWarning, "The occurrence of roundoff error is detected")
|
||||
olderr = np.seterr(all='ignore')
|
||||
try:
|
||||
test.check(dtype=dtype)
|
||||
finally:
|
||||
np.seterr(**olderr)
|
||||
@@ -1,44 +0,0 @@
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
import numpy as np
|
||||
from numpy import pi, log, sqrt
|
||||
from numpy.testing import assert_, assert_equal
|
||||
|
||||
from scipy.special._testutils import FuncData
|
||||
import scipy.special as sc
|
||||
|
||||
# Euler-Mascheroni constant
|
||||
euler = 0.57721566490153286
|
||||
|
||||
|
||||
def test_consistency():
|
||||
# Make sure the implementation of digamma for real arguments
|
||||
# agrees with the implementation of digamma for complex arguments.
|
||||
|
||||
# It's all poles after -1e16
|
||||
x = np.r_[-np.logspace(15, -30, 200), np.logspace(-30, 300, 200)]
|
||||
dataset = np.vstack((x + 0j, sc.digamma(x))).T
|
||||
FuncData(sc.digamma, dataset, 0, 1, rtol=5e-14, nan_ok=True).check()
|
||||
|
||||
|
||||
def test_special_values():
|
||||
# Test special values from Gauss's digamma theorem. See
|
||||
#
|
||||
# https://en.wikipedia.org/wiki/Digamma_function
|
||||
|
||||
dataset = [(1, -euler),
|
||||
(0.5, -2*log(2) - euler),
|
||||
(1/3, -pi/(2*sqrt(3)) - 3*log(3)/2 - euler),
|
||||
(1/4, -pi/2 - 3*log(2) - euler),
|
||||
(1/6, -pi*sqrt(3)/2 - 2*log(2) - 3*log(3)/2 - euler),
|
||||
(1/8, -pi/2 - 4*log(2) - (pi + log(2 + sqrt(2)) - log(2 - sqrt(2)))/sqrt(2) - euler)]
|
||||
|
||||
dataset = np.asarray(dataset)
|
||||
FuncData(sc.digamma, dataset, 0, 1, rtol=1e-14).check()
|
||||
|
||||
|
||||
def test_nonfinite():
|
||||
pts = [0.0, -0.0, np.inf]
|
||||
std = [-np.inf, np.inf, np.inf]
|
||||
assert_equal(sc.digamma(pts), std)
|
||||
assert_(all(np.isnan(sc.digamma([-np.inf, -1]))))
|
||||
@@ -1,273 +0,0 @@
|
||||
#
|
||||
# Tests for the Ellipsoidal Harmonic Function,
|
||||
# Distributed under the same license as SciPy itself.
|
||||
#
|
||||
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
import numpy as np
|
||||
from numpy.testing import (assert_equal, assert_almost_equal, assert_allclose,
|
||||
assert_)
|
||||
from scipy._lib._numpy_compat import suppress_warnings
|
||||
from scipy.special._testutils import assert_func_equal
|
||||
from scipy.special import ellip_harm, ellip_harm_2, ellip_normal
|
||||
from scipy.integrate import IntegrationWarning
|
||||
from numpy import sqrt, pi
|
||||
|
||||
|
||||
def test_ellip_potential():
|
||||
def change_coefficient(lambda1, mu, nu, h2, k2):
|
||||
x = sqrt(lambda1**2*mu**2*nu**2/(h2*k2))
|
||||
y = sqrt((lambda1**2 - h2)*(mu**2 - h2)*(h2 - nu**2)/(h2*(k2 - h2)))
|
||||
z = sqrt((lambda1**2 - k2)*(k2 - mu**2)*(k2 - nu**2)/(k2*(k2 - h2)))
|
||||
return x, y, z
|
||||
|
||||
def solid_int_ellip(lambda1, mu, nu, n, p, h2, k2):
|
||||
return (ellip_harm(h2, k2, n, p, lambda1)*ellip_harm(h2, k2, n, p, mu)
|
||||
* ellip_harm(h2, k2, n, p, nu))
|
||||
|
||||
def solid_int_ellip2(lambda1, mu, nu, n, p, h2, k2):
|
||||
return (ellip_harm_2(h2, k2, n, p, lambda1)
|
||||
* ellip_harm(h2, k2, n, p, mu)*ellip_harm(h2, k2, n, p, nu))
|
||||
|
||||
def summation(lambda1, mu1, nu1, lambda2, mu2, nu2, h2, k2):
|
||||
tol = 1e-8
|
||||
sum1 = 0
|
||||
for n in range(20):
|
||||
xsum = 0
|
||||
for p in range(1, 2*n+2):
|
||||
xsum += (4*pi*(solid_int_ellip(lambda2, mu2, nu2, n, p, h2, k2)
|
||||
* solid_int_ellip2(lambda1, mu1, nu1, n, p, h2, k2)) /
|
||||
(ellip_normal(h2, k2, n, p)*(2*n + 1)))
|
||||
if abs(xsum) < 0.1*tol*abs(sum1):
|
||||
break
|
||||
sum1 += xsum
|
||||
return sum1, xsum
|
||||
|
||||
def potential(lambda1, mu1, nu1, lambda2, mu2, nu2, h2, k2):
|
||||
x1, y1, z1 = change_coefficient(lambda1, mu1, nu1, h2, k2)
|
||||
x2, y2, z2 = change_coefficient(lambda2, mu2, nu2, h2, k2)
|
||||
res = sqrt((x2 - x1)**2 + (y2 - y1)**2 + (z2 - z1)**2)
|
||||
return 1/res
|
||||
|
||||
pts = [
|
||||
(120, sqrt(19), 2, 41, sqrt(17), 2, 15, 25),
|
||||
(120, sqrt(16), 3.2, 21, sqrt(11), 2.9, 11, 20),
|
||||
]
|
||||
|
||||
with suppress_warnings() as sup:
|
||||
sup.filter(IntegrationWarning, "The occurrence of roundoff error")
|
||||
sup.filter(IntegrationWarning, "The maximum number of subdivisions")
|
||||
|
||||
for p in pts:
|
||||
err_msg = repr(p)
|
||||
exact = potential(*p)
|
||||
result, last_term = summation(*p)
|
||||
assert_allclose(exact, result, atol=0, rtol=1e-8, err_msg=err_msg)
|
||||
assert_(abs(result - exact) < 10*abs(last_term), err_msg)
|
||||
|
||||
|
||||
def test_ellip_norm():
|
||||
|
||||
def G01(h2, k2):
|
||||
return 4*pi
|
||||
|
||||
def G11(h2, k2):
|
||||
return 4*pi*h2*k2/3
|
||||
|
||||
def G12(h2, k2):
|
||||
return 4*pi*h2*(k2 - h2)/3
|
||||
|
||||
def G13(h2, k2):
|
||||
return 4*pi*k2*(k2 - h2)/3
|
||||
|
||||
def G22(h2, k2):
|
||||
res = (2*(h2**4 + k2**4) - 4*h2*k2*(h2**2 + k2**2) + 6*h2**2*k2**2 +
|
||||
sqrt(h2**2 + k2**2 - h2*k2)*(-2*(h2**3 + k2**3) + 3*h2*k2*(h2 + k2)))
|
||||
return 16*pi/405*res
|
||||
|
||||
def G21(h2, k2):
|
||||
res = (2*(h2**4 + k2**4) - 4*h2*k2*(h2**2 + k2**2) + 6*h2**2*k2**2
|
||||
+ sqrt(h2**2 + k2**2 - h2*k2)*(2*(h2**3 + k2**3) - 3*h2*k2*(h2 + k2)))
|
||||
return 16*pi/405*res
|
||||
|
||||
def G23(h2, k2):
|
||||
return 4*pi*h2**2*k2*(k2 - h2)/15
|
||||
|
||||
def G24(h2, k2):
|
||||
return 4*pi*h2*k2**2*(k2 - h2)/15
|
||||
|
||||
def G25(h2, k2):
|
||||
return 4*pi*h2*k2*(k2 - h2)**2/15
|
||||
|
||||
def G32(h2, k2):
|
||||
res = (16*(h2**4 + k2**4) - 36*h2*k2*(h2**2 + k2**2) + 46*h2**2*k2**2
|
||||
+ sqrt(4*(h2**2 + k2**2) - 7*h2*k2)*(-8*(h2**3 + k2**3) +
|
||||
11*h2*k2*(h2 + k2)))
|
||||
return 16*pi/13125*k2*h2*res
|
||||
|
||||
def G31(h2, k2):
|
||||
res = (16*(h2**4 + k2**4) - 36*h2*k2*(h2**2 + k2**2) + 46*h2**2*k2**2
|
||||
+ sqrt(4*(h2**2 + k2**2) - 7*h2*k2)*(8*(h2**3 + k2**3) -
|
||||
11*h2*k2*(h2 + k2)))
|
||||
return 16*pi/13125*h2*k2*res
|
||||
|
||||
def G34(h2, k2):
|
||||
res = (6*h2**4 + 16*k2**4 - 12*h2**3*k2 - 28*h2*k2**3 + 34*h2**2*k2**2
|
||||
+ sqrt(h2**2 + 4*k2**2 - h2*k2)*(-6*h2**3 - 8*k2**3 + 9*h2**2*k2 +
|
||||
13*h2*k2**2))
|
||||
return 16*pi/13125*h2*(k2 - h2)*res
|
||||
|
||||
def G33(h2, k2):
|
||||
res = (6*h2**4 + 16*k2**4 - 12*h2**3*k2 - 28*h2*k2**3 + 34*h2**2*k2**2
|
||||
+ sqrt(h2**2 + 4*k2**2 - h2*k2)*(6*h2**3 + 8*k2**3 - 9*h2**2*k2 -
|
||||
13*h2*k2**2))
|
||||
return 16*pi/13125*h2*(k2 - h2)*res
|
||||
|
||||
def G36(h2, k2):
|
||||
res = (16*h2**4 + 6*k2**4 - 28*h2**3*k2 - 12*h2*k2**3 + 34*h2**2*k2**2
|
||||
+ sqrt(4*h2**2 + k2**2 - h2*k2)*(-8*h2**3 - 6*k2**3 + 13*h2**2*k2 +
|
||||
9*h2*k2**2))
|
||||
return 16*pi/13125*k2*(k2 - h2)*res
|
||||
|
||||
def G35(h2, k2):
|
||||
res = (16*h2**4 + 6*k2**4 - 28*h2**3*k2 - 12*h2*k2**3 + 34*h2**2*k2**2
|
||||
+ sqrt(4*h2**2 + k2**2 - h2*k2)*(8*h2**3 + 6*k2**3 - 13*h2**2*k2 -
|
||||
9*h2*k2**2))
|
||||
return 16*pi/13125*k2*(k2 - h2)*res
|
||||
|
||||
def G37(h2, k2):
|
||||
return 4*pi*h2**2*k2**2*(k2 - h2)**2/105
|
||||
|
||||
known_funcs = {(0, 1): G01, (1, 1): G11, (1, 2): G12, (1, 3): G13,
|
||||
(2, 1): G21, (2, 2): G22, (2, 3): G23, (2, 4): G24,
|
||||
(2, 5): G25, (3, 1): G31, (3, 2): G32, (3, 3): G33,
|
||||
(3, 4): G34, (3, 5): G35, (3, 6): G36, (3, 7): G37}
|
||||
|
||||
def _ellip_norm(n, p, h2, k2):
|
||||
func = known_funcs[n, p]
|
||||
return func(h2, k2)
|
||||
_ellip_norm = np.vectorize(_ellip_norm)
|
||||
|
||||
def ellip_normal_known(h2, k2, n, p):
|
||||
return _ellip_norm(n, p, h2, k2)
|
||||
|
||||
# generate both large and small h2 < k2 pairs
|
||||
np.random.seed(1234)
|
||||
h2 = np.random.pareto(0.5, size=1)
|
||||
k2 = h2 * (1 + np.random.pareto(0.5, size=h2.size))
|
||||
|
||||
points = []
|
||||
for n in range(4):
|
||||
for p in range(1, 2*n+2):
|
||||
points.append((h2, k2, n*np.ones(h2.size), p*np.ones(h2.size)))
|
||||
points = np.array(points)
|
||||
with suppress_warnings() as sup:
|
||||
sup.filter(IntegrationWarning, "The occurrence of roundoff error")
|
||||
assert_func_equal(ellip_normal, ellip_normal_known, points, rtol=1e-12)
|
||||
|
||||
|
||||
def test_ellip_harm_2():
|
||||
|
||||
def I1(h2, k2, s):
|
||||
res = (ellip_harm_2(h2, k2, 1, 1, s)/(3 * ellip_harm(h2, k2, 1, 1, s))
|
||||
+ ellip_harm_2(h2, k2, 1, 2, s)/(3 * ellip_harm(h2, k2, 1, 2, s)) +
|
||||
ellip_harm_2(h2, k2, 1, 3, s)/(3 * ellip_harm(h2, k2, 1, 3, s)))
|
||||
return res
|
||||
|
||||
with suppress_warnings() as sup:
|
||||
sup.filter(IntegrationWarning, "The occurrence of roundoff error")
|
||||
assert_almost_equal(I1(5, 8, 10), 1/(10*sqrt((100-5)*(100-8))))
|
||||
|
||||
# Values produced by code from arXiv:1204.0267
|
||||
assert_almost_equal(ellip_harm_2(5, 8, 2, 1, 10), 0.00108056853382)
|
||||
assert_almost_equal(ellip_harm_2(5, 8, 2, 2, 10), 0.00105820513809)
|
||||
assert_almost_equal(ellip_harm_2(5, 8, 2, 3, 10), 0.00106058384743)
|
||||
assert_almost_equal(ellip_harm_2(5, 8, 2, 4, 10), 0.00106774492306)
|
||||
assert_almost_equal(ellip_harm_2(5, 8, 2, 5, 10), 0.00107976356454)
|
||||
|
||||
|
||||
def test_ellip_harm():
|
||||
|
||||
def E01(h2, k2, s):
|
||||
return 1
|
||||
|
||||
def E11(h2, k2, s):
|
||||
return s
|
||||
|
||||
def E12(h2, k2, s):
|
||||
return sqrt(abs(s*s - h2))
|
||||
|
||||
def E13(h2, k2, s):
|
||||
return sqrt(abs(s*s - k2))
|
||||
|
||||
def E21(h2, k2, s):
|
||||
return s*s - 1/3*((h2 + k2) + sqrt(abs((h2 + k2)*(h2 + k2)-3*h2*k2)))
|
||||
|
||||
def E22(h2, k2, s):
|
||||
return s*s - 1/3*((h2 + k2) - sqrt(abs((h2 + k2)*(h2 + k2)-3*h2*k2)))
|
||||
|
||||
def E23(h2, k2, s):
|
||||
return s * sqrt(abs(s*s - h2))
|
||||
|
||||
def E24(h2, k2, s):
|
||||
return s * sqrt(abs(s*s - k2))
|
||||
|
||||
def E25(h2, k2, s):
|
||||
return sqrt(abs((s*s - h2)*(s*s - k2)))
|
||||
|
||||
def E31(h2, k2, s):
|
||||
return s*s*s - (s/5)*(2*(h2 + k2) + sqrt(4*(h2 + k2)*(h2 + k2) -
|
||||
15*h2*k2))
|
||||
|
||||
def E32(h2, k2, s):
|
||||
return s*s*s - (s/5)*(2*(h2 + k2) - sqrt(4*(h2 + k2)*(h2 + k2) -
|
||||
15*h2*k2))
|
||||
|
||||
def E33(h2, k2, s):
|
||||
return sqrt(abs(s*s - h2))*(s*s - 1/5*((h2 + 2*k2) + sqrt(abs((h2 +
|
||||
2*k2)*(h2 + 2*k2) - 5*h2*k2))))
|
||||
|
||||
def E34(h2, k2, s):
|
||||
return sqrt(abs(s*s - h2))*(s*s - 1/5*((h2 + 2*k2) - sqrt(abs((h2 +
|
||||
2*k2)*(h2 + 2*k2) - 5*h2*k2))))
|
||||
|
||||
def E35(h2, k2, s):
|
||||
return sqrt(abs(s*s - k2))*(s*s - 1/5*((2*h2 + k2) + sqrt(abs((2*h2
|
||||
+ k2)*(2*h2 + k2) - 5*h2*k2))))
|
||||
|
||||
def E36(h2, k2, s):
|
||||
return sqrt(abs(s*s - k2))*(s*s - 1/5*((2*h2 + k2) - sqrt(abs((2*h2
|
||||
+ k2)*(2*h2 + k2) - 5*h2*k2))))
|
||||
|
||||
def E37(h2, k2, s):
|
||||
return s * sqrt(abs((s*s - h2)*(s*s - k2)))
|
||||
|
||||
assert_equal(ellip_harm(5, 8, 1, 2, 2.5, 1, 1),
|
||||
ellip_harm(5, 8, 1, 2, 2.5))
|
||||
|
||||
known_funcs = {(0, 1): E01, (1, 1): E11, (1, 2): E12, (1, 3): E13,
|
||||
(2, 1): E21, (2, 2): E22, (2, 3): E23, (2, 4): E24,
|
||||
(2, 5): E25, (3, 1): E31, (3, 2): E32, (3, 3): E33,
|
||||
(3, 4): E34, (3, 5): E35, (3, 6): E36, (3, 7): E37}
|
||||
|
||||
point_ref = []
|
||||
|
||||
def ellip_harm_known(h2, k2, n, p, s):
|
||||
for i in range(h2.size):
|
||||
func = known_funcs[(int(n[i]), int(p[i]))]
|
||||
point_ref.append(func(h2[i], k2[i], s[i]))
|
||||
return point_ref
|
||||
|
||||
np.random.seed(1234)
|
||||
h2 = np.random.pareto(0.5, size=30)
|
||||
k2 = h2*(1 + np.random.pareto(0.5, size=h2.size))
|
||||
s = np.random.pareto(0.5, size=h2.size)
|
||||
points = []
|
||||
for i in range(h2.size):
|
||||
for n in range(4):
|
||||
for p in range(1, 2*n+2):
|
||||
points.append((h2[i], k2[i], n, p, s[i]))
|
||||
points = np.array(points)
|
||||
assert_func_equal(ellip_harm, ellip_harm_known, points, rtol=1e-12)
|
||||
|
||||
@@ -1,46 +0,0 @@
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
import numpy as np
|
||||
from numpy.testing import assert_allclose
|
||||
|
||||
import scipy.special as sc
|
||||
from scipy.special._testutils import FuncData
|
||||
|
||||
|
||||
def test_line():
|
||||
# Test on the line a = x where a simpler asymptotic expansion
|
||||
# (analog of DLMF 8.12.15) is available.
|
||||
|
||||
def gammainc_line(x):
|
||||
c = np.array([-1/3, -1/540, 25/6048, 101/155520,
|
||||
-3184811/3695155200, -2745493/8151736420])
|
||||
res = 0
|
||||
xfac = 1
|
||||
for ck in c:
|
||||
res -= ck*xfac
|
||||
xfac /= x
|
||||
res /= np.sqrt(2*np.pi*x)
|
||||
res += 0.5
|
||||
return res
|
||||
|
||||
x = np.logspace(np.log10(25), 300, 500)
|
||||
a = x.copy()
|
||||
dataset = np.vstack((a, x, gammainc_line(x))).T
|
||||
|
||||
FuncData(sc.gammainc, dataset, (0, 1), 2, rtol=1e-11).check()
|
||||
|
||||
|
||||
def test_gammainc_roundtrip():
|
||||
a = np.logspace(-5, 10, 100)
|
||||
x = np.logspace(-5, 10, 100)
|
||||
|
||||
y = sc.gammaincinv(a, sc.gammainc(a, x))
|
||||
assert_allclose(x, y, rtol=1e-10)
|
||||
|
||||
|
||||
def test_gammaincc_roundtrip():
|
||||
a = np.logspace(-5, 10, 100)
|
||||
x = np.logspace(-5, 10, 100)
|
||||
|
||||
y = sc.gammainccinv(a, sc.gammaincc(a, x))
|
||||
assert_allclose(x, y, rtol=1e-14)
|
||||
@@ -1,414 +0,0 @@
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
import itertools
|
||||
import sys
|
||||
import pytest
|
||||
|
||||
import numpy as np
|
||||
from numpy.testing import assert_
|
||||
from scipy.special._testutils import FuncData
|
||||
|
||||
from scipy.special import kolmogorov, kolmogi, smirnov, smirnovi
|
||||
from scipy.special._ufuncs import (_kolmogc, _kolmogci, _kolmogp,
|
||||
_smirnovc, _smirnovci, _smirnovp)
|
||||
|
||||
_rtol = 1e-10
|
||||
|
||||
class TestSmirnov(object):
|
||||
def test_nan(self):
|
||||
assert_(np.isnan(smirnov(1, np.nan)))
|
||||
|
||||
def test_basic(self):
|
||||
dataset = [(1, 0.1, 0.9),
|
||||
(1, 0.875, 0.125),
|
||||
(2, 0.875, 0.125 * 0.125),
|
||||
(3, 0.875, 0.125 * 0.125 * 0.125)]
|
||||
|
||||
dataset = np.asarray(dataset)
|
||||
FuncData(smirnov, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
|
||||
dataset[:, -1] = 1 - dataset[:, -1]
|
||||
FuncData(_smirnovc, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
|
||||
|
||||
def test_x_equals_0(self):
|
||||
dataset = [(n, 0, 1) for n in itertools.chain(range(2, 20), range(1010, 1020))]
|
||||
dataset = np.asarray(dataset)
|
||||
FuncData(smirnov, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
|
||||
dataset[:, -1] = 1 - dataset[:, -1]
|
||||
FuncData(_smirnovc, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
|
||||
|
||||
def test_x_equals_1(self):
|
||||
dataset = [(n, 1, 0) for n in itertools.chain(range(2, 20), range(1010, 1020))]
|
||||
dataset = np.asarray(dataset)
|
||||
FuncData(smirnov, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
|
||||
dataset[:, -1] = 1 - dataset[:, -1]
|
||||
FuncData(_smirnovc, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
|
||||
|
||||
def test_x_equals_0point5(self):
|
||||
dataset = [(1, 0.5, 0.5),
|
||||
(2, 0.5, 0.25),
|
||||
(3, 0.5, 0.166666666667),
|
||||
(4, 0.5, 0.09375),
|
||||
(5, 0.5, 0.056),
|
||||
(6, 0.5, 0.0327932098765),
|
||||
(7, 0.5, 0.0191958707681),
|
||||
(8, 0.5, 0.0112953186035),
|
||||
(9, 0.5, 0.00661933257355),
|
||||
(10, 0.5, 0.003888705)]
|
||||
|
||||
dataset = np.asarray(dataset)
|
||||
FuncData(smirnov, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
|
||||
dataset[:, -1] = 1 - dataset[:, -1]
|
||||
FuncData(_smirnovc, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
|
||||
|
||||
def test_n_equals_1(self):
|
||||
x = np.linspace(0, 1, 101, endpoint=True)
|
||||
dataset = np.column_stack([[1]*len(x), x, 1-x])
|
||||
FuncData(smirnov, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
|
||||
dataset[:, -1] = 1 - dataset[:, -1]
|
||||
FuncData(_smirnovc, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
|
||||
|
||||
def test_n_equals_2(self):
|
||||
x = np.linspace(0.5, 1, 101, endpoint=True)
|
||||
p = np.power(1-x, 2)
|
||||
n = np.array([2] * len(x))
|
||||
dataset = np.column_stack([n, x, p])
|
||||
FuncData(smirnov, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
|
||||
dataset[:, -1] = 1 - dataset[:, -1]
|
||||
FuncData(_smirnovc, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
|
||||
|
||||
def test_n_equals_3(self):
|
||||
x = np.linspace(0.7, 1, 31, endpoint=True)
|
||||
p = np.power(1-x, 3)
|
||||
n = np.array([3] * len(x))
|
||||
dataset = np.column_stack([n, x, p])
|
||||
FuncData(smirnov, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
|
||||
dataset[:, -1] = 1 - dataset[:, -1]
|
||||
FuncData(_smirnovc, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
|
||||
|
||||
def test_n_large(self):
|
||||
# test for large values of n
|
||||
# Probabilities should go down as n goes up
|
||||
x = 0.4
|
||||
pvals = np.array([smirnov(n, x) for n in range(400, 1100, 20)])
|
||||
dfs = np.diff(pvals)
|
||||
assert_(np.all(dfs <= 0), msg='Not all diffs negative %s' % dfs)
|
||||
|
||||
|
||||
class TestSmirnovi(object):
|
||||
def test_nan(self):
|
||||
assert_(np.isnan(smirnovi(1, np.nan)))
|
||||
|
||||
def test_basic(self):
|
||||
dataset = [(1, 0.4, 0.6),
|
||||
(1, 0.6, 0.4),
|
||||
(1, 0.99, 0.01),
|
||||
(1, 0.01, 0.99),
|
||||
(2, 0.125 * 0.125, 0.875),
|
||||
(3, 0.125 * 0.125 * 0.125, 0.875),
|
||||
(10, 1.0 / 16 ** 10, 1 - 1.0 / 16)]
|
||||
|
||||
dataset = np.asarray(dataset)
|
||||
FuncData(smirnovi, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
|
||||
dataset[:, 1] = 1 - dataset[:, 1]
|
||||
FuncData(_smirnovci, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
|
||||
|
||||
def test_x_equals_0(self):
|
||||
dataset = [(n, 0, 1) for n in itertools.chain(range(2, 20), range(1010, 1020))]
|
||||
dataset = np.asarray(dataset)
|
||||
FuncData(smirnovi, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
|
||||
dataset[:, 1] = 1 - dataset[:, 1]
|
||||
FuncData(_smirnovci, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
|
||||
|
||||
def test_x_equals_1(self):
|
||||
dataset = [(n, 1, 0) for n in itertools.chain(range(2, 20), range(1010, 1020))]
|
||||
dataset = np.asarray(dataset)
|
||||
FuncData(smirnovi, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
|
||||
dataset[:, 1] = 1 - dataset[:, 1]
|
||||
FuncData(_smirnovci, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
|
||||
|
||||
def test_n_equals_1(self):
|
||||
pp = np.linspace(0, 1, 101, endpoint=True)
|
||||
# dataset = np.array([(1, p, 1-p) for p in pp])
|
||||
dataset = np.column_stack([[1]*len(pp), pp, 1-pp])
|
||||
FuncData(smirnovi, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
|
||||
dataset[:, 1] = 1 - dataset[:, 1]
|
||||
FuncData(_smirnovci, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
|
||||
|
||||
def test_n_equals_2(self):
|
||||
x = np.linspace(0.5, 1, 101, endpoint=True)
|
||||
p = np.power(1-x, 2)
|
||||
n = np.array([2] * len(x))
|
||||
dataset = np.column_stack([n, p, x])
|
||||
FuncData(smirnovi, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
|
||||
dataset[:, 1] = 1 - dataset[:, 1]
|
||||
FuncData(_smirnovci, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
|
||||
|
||||
def test_n_equals_3(self):
|
||||
x = np.linspace(0.7, 1, 31, endpoint=True)
|
||||
p = np.power(1-x, 3)
|
||||
n = np.array([3] * len(x))
|
||||
dataset = np.column_stack([n, p, x])
|
||||
FuncData(smirnovi, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
|
||||
dataset[:, 1] = 1 - dataset[:, 1]
|
||||
FuncData(_smirnovci, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
|
||||
|
||||
def test_round_trip(self):
|
||||
def _sm_smi(n, p):
|
||||
return smirnov(n, smirnovi(n, p))
|
||||
|
||||
def _smc_smci(n, p):
|
||||
return _smirnovc(n, _smirnovci(n, p))
|
||||
|
||||
dataset = [(1, 0.4, 0.4),
|
||||
(1, 0.6, 0.6),
|
||||
(2, 0.875, 0.875),
|
||||
(3, 0.875, 0.875),
|
||||
(3, 0.125, 0.125),
|
||||
(10, 0.999, 0.999),
|
||||
(10, 0.0001, 0.0001)]
|
||||
|
||||
dataset = np.asarray(dataset)
|
||||
FuncData(_sm_smi, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
|
||||
FuncData(_smc_smci, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
|
||||
|
||||
def test_x_equals_0point5(self):
|
||||
dataset = [(1, 0.5, 0.5),
|
||||
(2, 0.5, 0.366025403784),
|
||||
(2, 0.25, 0.5),
|
||||
(3, 0.5, 0.297156508177),
|
||||
(4, 0.5, 0.255520481121),
|
||||
(5, 0.5, 0.234559536069),
|
||||
(6, 0.5, 0.21715965898),
|
||||
(7, 0.5, 0.202722580034),
|
||||
(8, 0.5, 0.190621765256),
|
||||
(9, 0.5, 0.180363501362),
|
||||
(10, 0.5, 0.17157867006)]
|
||||
|
||||
dataset = np.asarray(dataset)
|
||||
FuncData(smirnovi, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
|
||||
dataset[:, 1] = 1 - dataset[:, 1]
|
||||
FuncData(_smirnovci, dataset, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
|
||||
|
||||
|
||||
class TestSmirnovp(object):
|
||||
def test_nan(self):
|
||||
assert_(np.isnan(_smirnovp(1, np.nan)))
|
||||
|
||||
def test_basic(self):
|
||||
# Check derivative at endpoints
|
||||
n1_10 = np.arange(1, 10)
|
||||
dataset0 = np.column_stack([n1_10, np.full_like(n1_10, 0), np.full_like(n1_10, -1)])
|
||||
FuncData(_smirnovp, dataset0, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
|
||||
|
||||
n2_10 = np.arange(2, 10)
|
||||
dataset1 = np.column_stack([n2_10, np.full_like(n2_10, 1.0), np.full_like(n2_10, 0)])
|
||||
FuncData(_smirnovp, dataset1, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
|
||||
|
||||
def test_oneminusoneovern(self):
|
||||
# Check derivative at x=1-1/n
|
||||
n = np.arange(1, 20)
|
||||
x = 1.0/n
|
||||
xm1 = 1-1.0/n
|
||||
pp1 = -n * x**(n-1)
|
||||
pp1 -= (1-np.sign(n-2)**2) * 0.5 # n=2, x=0.5, 1-1/n = 0.5, need to adjust
|
||||
dataset1 = np.column_stack([n, xm1, pp1])
|
||||
FuncData(_smirnovp, dataset1, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
|
||||
|
||||
def test_oneovertwon(self):
|
||||
# Check derivative at x=1/2n (Discontinuous at x=1/n, so check at x=1/2n)
|
||||
n = np.arange(1, 20)
|
||||
x = 1.0/2/n
|
||||
pp = -(n*x+1) * (1+x)**(n-2)
|
||||
dataset0 = np.column_stack([n, x, pp])
|
||||
FuncData(_smirnovp, dataset0, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
|
||||
|
||||
def test_oneovern(self):
|
||||
# Check derivative at x=1/n (Discontinuous at x=1/n, hard to tell if x==1/n, only use n=power of 2)
|
||||
n = 2**np.arange(1, 10)
|
||||
x = 1.0/n
|
||||
pp = -(n*x+1) * (1+x)**(n-2) + 0.5
|
||||
dataset0 = np.column_stack([n, x, pp])
|
||||
FuncData(_smirnovp, dataset0, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
|
||||
|
||||
@pytest.mark.xfail(sys.maxsize <= 2**32,
|
||||
reason="requires 64-bit platform")
|
||||
def test_oneovernclose(self):
|
||||
# Check derivative at x=1/n (Discontinuous at x=1/n, test on either side: x=1/n +/- 2epsilon)
|
||||
n = np.arange(3, 20)
|
||||
|
||||
x = 1.0/n - 2*np.finfo(float).eps
|
||||
pp = -(n*x+1) * (1+x)**(n-2)
|
||||
dataset0 = np.column_stack([n, x, pp])
|
||||
FuncData(_smirnovp, dataset0, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
|
||||
|
||||
x = 1.0/n + 2*np.finfo(float).eps
|
||||
pp = -(n*x+1) * (1+x)**(n-2) + 1
|
||||
dataset1 = np.column_stack([n, x, pp])
|
||||
FuncData(_smirnovp, dataset1, (0, 1), 2, rtol=_rtol).check(dtypes=[int, float, float])
|
||||
|
||||
|
||||
class TestKolmogorov(object):
|
||||
def test_nan(self):
|
||||
assert_(np.isnan(kolmogorov(np.nan)))
|
||||
|
||||
def test_basic(self):
|
||||
dataset = [(0, 1.0),
|
||||
(0.5, 0.96394524366487511),
|
||||
(0.8275735551899077, 0.5000000000000000),
|
||||
(1, 0.26999967167735456),
|
||||
(2, 0.00067092525577969533)]
|
||||
|
||||
dataset = np.asarray(dataset)
|
||||
FuncData(kolmogorov, dataset, (0,), 1, rtol=_rtol).check()
|
||||
|
||||
def test_linspace(self):
|
||||
x = np.linspace(0, 2.0, 21)
|
||||
dataset = [1.0000000000000000, 1.0000000000000000, 0.9999999999994950,
|
||||
0.9999906941986655, 0.9971923267772983, 0.9639452436648751,
|
||||
0.8642827790506042, 0.7112351950296890, 0.5441424115741981,
|
||||
0.3927307079406543, 0.2699996716773546, 0.1777181926064012,
|
||||
0.1122496666707249, 0.0680922218447664, 0.0396818795381144,
|
||||
0.0222179626165251, 0.0119520432391966, 0.0061774306344441,
|
||||
0.0030676213475797, 0.0014636048371873, 0.0006709252557797]
|
||||
|
||||
dataset_c = [0.0000000000000000, 6.609305242245699e-53, 5.050407338670114e-13,
|
||||
9.305801334566668e-06, 0.0028076732227017, 0.0360547563351249,
|
||||
0.1357172209493958, 0.2887648049703110, 0.4558575884258019,
|
||||
0.6072692920593457, 0.7300003283226455, 0.8222818073935988,
|
||||
0.8877503333292751, 0.9319077781552336, 0.9603181204618857,
|
||||
0.9777820373834749, 0.9880479567608034, 0.9938225693655559,
|
||||
0.9969323786524203, 0.9985363951628127, 0.9993290747442203]
|
||||
|
||||
dataset = np.column_stack([x, dataset])
|
||||
FuncData(kolmogorov, dataset, (0,), 1, rtol=_rtol).check()
|
||||
dataset_c = np.column_stack([x, dataset_c])
|
||||
FuncData(_kolmogc, dataset_c, (0,), 1, rtol=_rtol).check()
|
||||
|
||||
def test_linspacei(self):
|
||||
p = np.linspace(0, 1.0, 21, endpoint=True)
|
||||
dataset = [np.inf, 1.3580986393225507, 1.2238478702170823,
|
||||
1.1379465424937751, 1.0727491749396481, 1.0191847202536859,
|
||||
0.9730633753323726, 0.9320695842357622, 0.8947644549851197,
|
||||
0.8601710725555463, 0.8275735551899077, 0.7964065373291559,
|
||||
0.7661855555617682, 0.7364542888171910, 0.7067326523068980,
|
||||
0.6764476915028201, 0.6448126061663567, 0.6105590999244391,
|
||||
0.5711732651063401, 0.5196103791686224, 0.0000000000000000]
|
||||
|
||||
dataset_c = [0.0000000000000000, 0.5196103791686225, 0.5711732651063401,
|
||||
0.6105590999244391, 0.6448126061663567, 0.6764476915028201,
|
||||
0.7067326523068980, 0.7364542888171910, 0.7661855555617682,
|
||||
0.7964065373291559, 0.8275735551899077, 0.8601710725555463,
|
||||
0.8947644549851196, 0.9320695842357622, 0.9730633753323727,
|
||||
1.0191847202536859, 1.0727491749396481, 1.1379465424937754,
|
||||
1.2238478702170825, 1.3580986393225509, np.inf]
|
||||
|
||||
dataset = np.column_stack([p[1:], dataset[1:]])
|
||||
FuncData(kolmogi, dataset, (0,), 1, rtol=_rtol).check()
|
||||
dataset_c = np.column_stack([p[:-1], dataset_c[:-1]])
|
||||
FuncData(_kolmogci, dataset_c, (0,), 1, rtol=_rtol).check()
|
||||
|
||||
def test_smallx(self):
|
||||
epsilon = 0.1 ** np.arange(1, 14)
|
||||
x = np.array([0.571173265106, 0.441027698518, 0.374219690278, 0.331392659217,
|
||||
0.300820537459, 0.277539353999, 0.259023494805, 0.243829561254,
|
||||
0.231063086389, 0.220135543236, 0.210641372041, 0.202290283658,
|
||||
0.19487060742])
|
||||
|
||||
dataset = np.column_stack([x, 1-epsilon])
|
||||
FuncData(kolmogorov, dataset, (0,), 1, rtol=_rtol).check()
|
||||
|
||||
def test_round_trip(self):
|
||||
def _ki_k(_x):
|
||||
return kolmogi(kolmogorov(_x))
|
||||
|
||||
def _kci_kc(_x):
|
||||
return _kolmogci(_kolmogc(_x))
|
||||
|
||||
x = np.linspace(0.0, 2.0, 21, endpoint=True)
|
||||
x02 = x[(x == 0) | (x > 0.21)] # Exclude 0.1, 0.2. 0.2 almost makes succeeds, but 0.1 has no chance.
|
||||
dataset02 = np.column_stack([x02, x02])
|
||||
FuncData(_ki_k, dataset02, (0,), 1, rtol=_rtol).check()
|
||||
|
||||
dataset = np.column_stack([x, x])
|
||||
FuncData(_kci_kc, dataset, (0,), 1, rtol=_rtol).check()
|
||||
|
||||
|
||||
class TestKolmogi(object):
|
||||
def test_nan(self):
|
||||
assert_(np.isnan(kolmogi(np.nan)))
|
||||
|
||||
def test_basic(self):
|
||||
dataset = [(1.0, 0),
|
||||
(0.96394524366487511, 0.5),
|
||||
(0.9, 0.571173265106),
|
||||
(0.5000000000000000, 0.8275735551899077),
|
||||
(0.26999967167735456, 1),
|
||||
(0.00067092525577969533, 2)]
|
||||
|
||||
dataset = np.asarray(dataset)
|
||||
FuncData(kolmogi, dataset, (0,), 1, rtol=_rtol).check()
|
||||
|
||||
def test_smallpcdf(self):
|
||||
epsilon = 0.5 ** np.arange(1, 55, 3)
|
||||
# kolmogi(1-p) == _kolmogci(p) if 1-(1-p) == p, but not necessarily otherwise
|
||||
# Use epsilon s.t. 1-(1-epsilon)) == epsilon, so can use same x-array for both results
|
||||
|
||||
x = np.array([0.8275735551899077, 0.5345255069097583, 0.4320114038786941,
|
||||
0.3736868442620478, 0.3345161714909591, 0.3057833329315859,
|
||||
0.2835052890528936, 0.2655578150208676, 0.2506869966107999,
|
||||
0.2380971058736669, 0.2272549289962079, 0.2177876361600040,
|
||||
0.2094254686862041, 0.2019676748836232, 0.1952612948137504,
|
||||
0.1891874239646641, 0.1836520225050326, 0.1785795904846466])
|
||||
|
||||
dataset = np.column_stack([1-epsilon, x])
|
||||
FuncData(kolmogi, dataset, (0,), 1, rtol=_rtol).check()
|
||||
|
||||
dataset = np.column_stack([epsilon, x])
|
||||
FuncData(_kolmogci, dataset, (0,), 1, rtol=_rtol).check()
|
||||
|
||||
def test_smallpsf(self):
|
||||
epsilon = 0.5 ** np.arange(1, 55, 3)
|
||||
# kolmogi(p) == _kolmogci(1-p) if 1-(1-p) == p, but not necessarily otherwise
|
||||
# Use epsilon s.t. 1-(1-epsilon)) == epsilon, so can use same x-array for both results
|
||||
|
||||
x = np.array([0.8275735551899077, 1.3163786275161036, 1.6651092133663343,
|
||||
1.9525136345289607, 2.2027324540033235, 2.4272929437460848,
|
||||
2.6327688477341593, 2.8233300509220260, 3.0018183401530627,
|
||||
3.1702735084088891, 3.3302184446307912, 3.4828258153113318,
|
||||
3.6290214150152051, 3.7695513262825959, 3.9050272690877326,
|
||||
4.0359582187082550, 4.1627730557884890, 4.2858371743264527])
|
||||
|
||||
dataset = np.column_stack([epsilon, x])
|
||||
FuncData(kolmogi, dataset, (0,), 1, rtol=_rtol).check()
|
||||
|
||||
dataset = np.column_stack([1-epsilon, x])
|
||||
FuncData(_kolmogci, dataset, (0,), 1, rtol=_rtol).check()
|
||||
|
||||
def test_round_trip(self):
|
||||
def _k_ki(_p):
|
||||
return kolmogorov(kolmogi(_p))
|
||||
|
||||
p = np.linspace(0.1, 1.0, 10, endpoint=True)
|
||||
dataset = np.column_stack([p, p])
|
||||
FuncData(_k_ki, dataset, (0,), 1, rtol=_rtol).check()
|
||||
|
||||
|
||||
class TestKolmogp(object):
|
||||
def test_nan(self):
|
||||
assert_(np.isnan(_kolmogp(np.nan)))
|
||||
|
||||
def test_basic(self):
|
||||
dataset = [(0.000000, -0.0),
|
||||
(0.200000, -1.532420541338916e-10),
|
||||
(0.400000, -0.1012254419260496),
|
||||
(0.600000, -1.324123244249925),
|
||||
(0.800000, -1.627024345636592),
|
||||
(1.000000, -1.071948558356941),
|
||||
(1.200000, -0.538512430720529),
|
||||
(1.400000, -0.2222133182429472),
|
||||
(1.600000, -0.07649302775520538),
|
||||
(1.800000, -0.02208687346347873),
|
||||
(2.000000, -0.005367402045629683)]
|
||||
|
||||
dataset = np.asarray(dataset)
|
||||
FuncData(_kolmogp, dataset, (0,), 1, rtol=_rtol).check()
|
||||
@@ -1,103 +0,0 @@
|
||||
#
|
||||
# Tests for the lambertw function,
|
||||
# Adapted from the MPMath tests [1] by Yosef Meller, mellerf@netvision.net.il
|
||||
# Distributed under the same license as SciPy itself.
|
||||
#
|
||||
# [1] mpmath source code, Subversion revision 992
|
||||
# http://code.google.com/p/mpmath/source/browse/trunk/mpmath/tests/test_functions2.py?spec=svn994&r=992
|
||||
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
import numpy as np
|
||||
from numpy.testing import assert_, assert_equal, assert_array_almost_equal
|
||||
from scipy.special import lambertw
|
||||
from numpy import nan, inf, pi, e, isnan, log, r_, array, complex_
|
||||
|
||||
from scipy.special._testutils import FuncData
|
||||
|
||||
|
||||
def test_values():
|
||||
assert_(isnan(lambertw(nan)))
|
||||
assert_equal(lambertw(inf,1).real, inf)
|
||||
assert_equal(lambertw(inf,1).imag, 2*pi)
|
||||
assert_equal(lambertw(-inf,1).real, inf)
|
||||
assert_equal(lambertw(-inf,1).imag, 3*pi)
|
||||
|
||||
assert_equal(lambertw(1.), lambertw(1., 0))
|
||||
|
||||
data = [
|
||||
(0,0, 0),
|
||||
(0+0j,0, 0),
|
||||
(inf,0, inf),
|
||||
(0,-1, -inf),
|
||||
(0,1, -inf),
|
||||
(0,3, -inf),
|
||||
(e,0, 1),
|
||||
(1,0, 0.567143290409783873),
|
||||
(-pi/2,0, 1j*pi/2),
|
||||
(-log(2)/2,0, -log(2)),
|
||||
(0.25,0, 0.203888354702240164),
|
||||
(-0.25,0, -0.357402956181388903),
|
||||
(-1./10000,0, -0.000100010001500266719),
|
||||
(-0.25,-1, -2.15329236411034965),
|
||||
(0.25,-1, -3.00899800997004620-4.07652978899159763j),
|
||||
(-0.25,-1, -2.15329236411034965),
|
||||
(0.25,1, -3.00899800997004620+4.07652978899159763j),
|
||||
(-0.25,1, -3.48973228422959210+7.41405453009603664j),
|
||||
(-4,0, 0.67881197132094523+1.91195078174339937j),
|
||||
(-4,1, -0.66743107129800988+7.76827456802783084j),
|
||||
(-4,-1, 0.67881197132094523-1.91195078174339937j),
|
||||
(1000,0, 5.24960285240159623),
|
||||
(1000,1, 4.91492239981054535+5.44652615979447070j),
|
||||
(1000,-1, 4.91492239981054535-5.44652615979447070j),
|
||||
(1000,5, 3.5010625305312892+29.9614548941181328j),
|
||||
(3+4j,0, 1.281561806123775878+0.533095222020971071j),
|
||||
(-0.4+0.4j,0, -0.10396515323290657+0.61899273315171632j),
|
||||
(3+4j,1, -0.11691092896595324+5.61888039871282334j),
|
||||
(3+4j,-1, 0.25856740686699742-3.85211668616143559j),
|
||||
(-0.5,-1, -0.794023632344689368-0.770111750510379110j),
|
||||
(-1./10000,1, -11.82350837248724344+6.80546081842002101j),
|
||||
(-1./10000,-1, -11.6671145325663544),
|
||||
(-1./10000,-2, -11.82350837248724344-6.80546081842002101j),
|
||||
(-1./100000,4, -14.9186890769540539+26.1856750178782046j),
|
||||
(-1./100000,5, -15.0931437726379218666+32.5525721210262290086j),
|
||||
((2+1j)/10,0, 0.173704503762911669+0.071781336752835511j),
|
||||
((2+1j)/10,1, -3.21746028349820063+4.56175438896292539j),
|
||||
((2+1j)/10,-1, -3.03781405002993088-3.53946629633505737j),
|
||||
((2+1j)/10,4, -4.6878509692773249+23.8313630697683291j),
|
||||
(-(2+1j)/10,0, -0.226933772515757933-0.164986470020154580j),
|
||||
(-(2+1j)/10,1, -2.43569517046110001+0.76974067544756289j),
|
||||
(-(2+1j)/10,-1, -3.54858738151989450-6.91627921869943589j),
|
||||
(-(2+1j)/10,4, -4.5500846928118151+20.6672982215434637j),
|
||||
(pi,0, 1.073658194796149172092178407024821347547745350410314531),
|
||||
|
||||
# Former bug in generated branch,
|
||||
(-0.5+0.002j,0, -0.78917138132659918344 + 0.76743539379990327749j),
|
||||
(-0.5-0.002j,0, -0.78917138132659918344 - 0.76743539379990327749j),
|
||||
(-0.448+0.4j,0, -0.11855133765652382241 + 0.66570534313583423116j),
|
||||
(-0.448-0.4j,0, -0.11855133765652382241 - 0.66570534313583423116j),
|
||||
]
|
||||
data = array(data, dtype=complex_)
|
||||
|
||||
def w(x, y):
|
||||
return lambertw(x, y.real.astype(int))
|
||||
olderr = np.seterr(all='ignore')
|
||||
try:
|
||||
FuncData(w, data, (0,1), 2, rtol=1e-10, atol=1e-13).check()
|
||||
finally:
|
||||
np.seterr(**olderr)
|
||||
|
||||
|
||||
def test_ufunc():
|
||||
assert_array_almost_equal(
|
||||
lambertw(r_[0., e, 1.]), r_[0., 1., 0.567143290409783873])
|
||||
|
||||
|
||||
def test_lambertw_ufunc_loop_selection():
|
||||
# see https://github.com/scipy/scipy/issues/4895
|
||||
dt = np.dtype(np.complex128)
|
||||
assert_equal(lambertw(0, 0, 0).dtype, dt)
|
||||
assert_equal(lambertw([0], 0, 0).dtype, dt)
|
||||
assert_equal(lambertw(0, [0], 0).dtype, dt)
|
||||
assert_equal(lambertw(0, 0, [0]).dtype, dt)
|
||||
assert_equal(lambertw([0], [0], [0]).dtype, dt)
|
||||
@@ -1,72 +0,0 @@
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
import numpy as np
|
||||
from numpy.testing import assert_allclose, assert_
|
||||
|
||||
from scipy.special._testutils import FuncData
|
||||
from scipy.special import gamma, gammaln, loggamma
|
||||
|
||||
|
||||
def test_identities1():
|
||||
# test the identity exp(loggamma(z)) = gamma(z)
|
||||
x = np.array([-99.5, -9.5, -0.5, 0.5, 9.5, 99.5])
|
||||
y = x.copy()
|
||||
x, y = np.meshgrid(x, y)
|
||||
z = (x + 1J*y).flatten()
|
||||
dataset = np.vstack((z, gamma(z))).T
|
||||
|
||||
def f(z):
|
||||
return np.exp(loggamma(z))
|
||||
|
||||
FuncData(f, dataset, 0, 1, rtol=1e-14, atol=1e-14).check()
|
||||
|
||||
|
||||
def test_identities2():
|
||||
# test the identity loggamma(z + 1) = log(z) + loggamma(z)
|
||||
x = np.array([-99.5, -9.5, -0.5, 0.5, 9.5, 99.5])
|
||||
y = x.copy()
|
||||
x, y = np.meshgrid(x, y)
|
||||
z = (x + 1J*y).flatten()
|
||||
dataset = np.vstack((z, np.log(z) + loggamma(z))).T
|
||||
|
||||
def f(z):
|
||||
return loggamma(z + 1)
|
||||
|
||||
FuncData(f, dataset, 0, 1, rtol=1e-14, atol=1e-14).check()
|
||||
|
||||
|
||||
def test_complex_dispatch_realpart():
|
||||
# Test that the real parts of loggamma and gammaln agree on the
|
||||
# real axis.
|
||||
x = np.r_[-np.logspace(10, -10), np.logspace(-10, 10)] + 0.5
|
||||
|
||||
dataset = np.vstack((x, gammaln(x))).T
|
||||
|
||||
def f(z):
|
||||
z = np.array(z, dtype='complex128')
|
||||
return loggamma(z).real
|
||||
|
||||
FuncData(f, dataset, 0, 1, rtol=1e-14, atol=1e-14).check()
|
||||
|
||||
|
||||
def test_real_dispatch():
|
||||
x = np.logspace(-10, 10) + 0.5
|
||||
dataset = np.vstack((x, gammaln(x))).T
|
||||
|
||||
FuncData(loggamma, dataset, 0, 1, rtol=1e-14, atol=1e-14).check()
|
||||
assert_(loggamma(0) == np.inf)
|
||||
assert_(np.isnan(loggamma(-1)))
|
||||
|
||||
|
||||
def test_gh_6536():
|
||||
z = loggamma(complex(-3.4, +0.0))
|
||||
zbar = loggamma(complex(-3.4, -0.0))
|
||||
assert_allclose(z, zbar.conjugate(), rtol=1e-15, atol=0)
|
||||
|
||||
|
||||
def test_branch_cut():
|
||||
# Make sure negative zero is treated correctly
|
||||
x = -np.logspace(300, -30, 100)
|
||||
z = np.asarray([complex(x0, 0.0) for x0 in x])
|
||||
zbar = np.asarray([complex(x0, -0.0) for x0 in x])
|
||||
assert_allclose(z, zbar.conjugate(), rtol=1e-15, atol=0)
|
||||
@@ -1,81 +0,0 @@
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
import numpy as np
|
||||
from numpy.testing import (assert_equal, assert_almost_equal,
|
||||
assert_allclose)
|
||||
from scipy.special import logit, expit
|
||||
|
||||
|
||||
class TestLogit(object):
|
||||
def check_logit_out(self, dtype, expected):
|
||||
a = np.linspace(0,1,10)
|
||||
a = np.array(a, dtype=dtype)
|
||||
olderr = np.seterr(divide='ignore')
|
||||
try:
|
||||
actual = logit(a)
|
||||
finally:
|
||||
np.seterr(**olderr)
|
||||
|
||||
assert_almost_equal(actual, expected)
|
||||
|
||||
assert_equal(actual.dtype, np.dtype(dtype))
|
||||
|
||||
def test_float32(self):
|
||||
expected = np.array([-np.inf, -2.07944155,
|
||||
-1.25276291, -0.69314718,
|
||||
-0.22314353, 0.22314365,
|
||||
0.6931473, 1.25276303,
|
||||
2.07944155, np.inf], dtype=np.float32)
|
||||
self.check_logit_out('f4', expected)
|
||||
|
||||
def test_float64(self):
|
||||
expected = np.array([-np.inf, -2.07944154,
|
||||
-1.25276297, -0.69314718,
|
||||
-0.22314355, 0.22314355,
|
||||
0.69314718, 1.25276297,
|
||||
2.07944154, np.inf])
|
||||
self.check_logit_out('f8', expected)
|
||||
|
||||
def test_nan(self):
|
||||
expected = np.array([np.nan]*4)
|
||||
olderr = np.seterr(invalid='ignore')
|
||||
try:
|
||||
actual = logit(np.array([-3., -2., 2., 3.]))
|
||||
finally:
|
||||
np.seterr(**olderr)
|
||||
|
||||
assert_equal(expected, actual)
|
||||
|
||||
|
||||
class TestExpit(object):
|
||||
def check_expit_out(self, dtype, expected):
|
||||
a = np.linspace(-4,4,10)
|
||||
a = np.array(a, dtype=dtype)
|
||||
actual = expit(a)
|
||||
assert_almost_equal(actual, expected)
|
||||
assert_equal(actual.dtype, np.dtype(dtype))
|
||||
|
||||
def test_float32(self):
|
||||
expected = np.array([0.01798621, 0.04265125,
|
||||
0.09777259, 0.20860852,
|
||||
0.39068246, 0.60931754,
|
||||
0.79139149, 0.9022274,
|
||||
0.95734876, 0.98201376], dtype=np.float32)
|
||||
self.check_expit_out('f4',expected)
|
||||
|
||||
def test_float64(self):
|
||||
expected = np.array([0.01798621, 0.04265125,
|
||||
0.0977726, 0.20860853,
|
||||
0.39068246, 0.60931754,
|
||||
0.79139147, 0.9022274,
|
||||
0.95734875, 0.98201379])
|
||||
self.check_expit_out('f8', expected)
|
||||
|
||||
def test_large(self):
|
||||
for dtype in (np.float32, np.float64, np.longdouble):
|
||||
for n in (88, 89, 709, 710, 11356, 11357):
|
||||
n = np.array(n, dtype=dtype)
|
||||
assert_allclose(expit(n), 1.0, atol=1e-20)
|
||||
assert_allclose(expit(-n), 0.0, atol=1e-20)
|
||||
assert_equal(expit(n).dtype, dtype)
|
||||
assert_equal(expit(-n).dtype, dtype)
|
||||
@@ -1,196 +0,0 @@
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
import numpy as np
|
||||
from numpy.testing import (assert_almost_equal, assert_equal, assert_allclose,
|
||||
assert_array_almost_equal, assert_)
|
||||
|
||||
from scipy.special import logsumexp, softmax
|
||||
|
||||
|
||||
def test_logsumexp():
|
||||
# Test whether logsumexp() function correctly handles large inputs.
|
||||
a = np.arange(200)
|
||||
desired = np.log(np.sum(np.exp(a)))
|
||||
assert_almost_equal(logsumexp(a), desired)
|
||||
|
||||
# Now test with large numbers
|
||||
b = [1000, 1000]
|
||||
desired = 1000.0 + np.log(2.0)
|
||||
assert_almost_equal(logsumexp(b), desired)
|
||||
|
||||
n = 1000
|
||||
b = np.full(n, 10000, dtype='float64')
|
||||
desired = 10000.0 + np.log(n)
|
||||
assert_almost_equal(logsumexp(b), desired)
|
||||
|
||||
x = np.array([1e-40] * 1000000)
|
||||
logx = np.log(x)
|
||||
|
||||
X = np.vstack([x, x])
|
||||
logX = np.vstack([logx, logx])
|
||||
assert_array_almost_equal(np.exp(logsumexp(logX)), X.sum())
|
||||
assert_array_almost_equal(np.exp(logsumexp(logX, axis=0)), X.sum(axis=0))
|
||||
assert_array_almost_equal(np.exp(logsumexp(logX, axis=1)), X.sum(axis=1))
|
||||
|
||||
# Handling special values properly
|
||||
assert_equal(logsumexp(np.inf), np.inf)
|
||||
assert_equal(logsumexp(-np.inf), -np.inf)
|
||||
assert_equal(logsumexp(np.nan), np.nan)
|
||||
assert_equal(logsumexp([-np.inf, -np.inf]), -np.inf)
|
||||
|
||||
# Handling an array with different magnitudes on the axes
|
||||
assert_array_almost_equal(logsumexp([[1e10, 1e-10],
|
||||
[-1e10, -np.inf]], axis=-1),
|
||||
[1e10, -1e10])
|
||||
|
||||
# Test keeping dimensions
|
||||
assert_array_almost_equal(logsumexp([[1e10, 1e-10],
|
||||
[-1e10, -np.inf]],
|
||||
axis=-1,
|
||||
keepdims=True),
|
||||
[[1e10], [-1e10]])
|
||||
|
||||
# Test multiple axes
|
||||
assert_array_almost_equal(logsumexp([[1e10, 1e-10],
|
||||
[-1e10, -np.inf]],
|
||||
axis=(-1,-2)),
|
||||
1e10)
|
||||
|
||||
|
||||
def test_logsumexp_b():
|
||||
a = np.arange(200)
|
||||
b = np.arange(200, 0, -1)
|
||||
desired = np.log(np.sum(b*np.exp(a)))
|
||||
assert_almost_equal(logsumexp(a, b=b), desired)
|
||||
|
||||
a = [1000, 1000]
|
||||
b = [1.2, 1.2]
|
||||
desired = 1000 + np.log(2 * 1.2)
|
||||
assert_almost_equal(logsumexp(a, b=b), desired)
|
||||
|
||||
x = np.array([1e-40] * 100000)
|
||||
b = np.linspace(1, 1000, 100000)
|
||||
logx = np.log(x)
|
||||
|
||||
X = np.vstack((x, x))
|
||||
logX = np.vstack((logx, logx))
|
||||
B = np.vstack((b, b))
|
||||
assert_array_almost_equal(np.exp(logsumexp(logX, b=B)), (B * X).sum())
|
||||
assert_array_almost_equal(np.exp(logsumexp(logX, b=B, axis=0)),
|
||||
(B * X).sum(axis=0))
|
||||
assert_array_almost_equal(np.exp(logsumexp(logX, b=B, axis=1)),
|
||||
(B * X).sum(axis=1))
|
||||
|
||||
|
||||
def test_logsumexp_sign():
|
||||
a = [1,1,1]
|
||||
b = [1,-1,-1]
|
||||
|
||||
r, s = logsumexp(a, b=b, return_sign=True)
|
||||
assert_almost_equal(r,1)
|
||||
assert_equal(s,-1)
|
||||
|
||||
|
||||
def test_logsumexp_sign_zero():
|
||||
a = [1,1]
|
||||
b = [1,-1]
|
||||
|
||||
r, s = logsumexp(a, b=b, return_sign=True)
|
||||
assert_(not np.isfinite(r))
|
||||
assert_(not np.isnan(r))
|
||||
assert_(r < 0)
|
||||
assert_equal(s,0)
|
||||
|
||||
|
||||
def test_logsumexp_sign_shape():
|
||||
a = np.ones((1,2,3,4))
|
||||
b = np.ones_like(a)
|
||||
|
||||
r, s = logsumexp(a, axis=2, b=b, return_sign=True)
|
||||
|
||||
assert_equal(r.shape, s.shape)
|
||||
assert_equal(r.shape, (1,2,4))
|
||||
|
||||
r, s = logsumexp(a, axis=(1,3), b=b, return_sign=True)
|
||||
|
||||
assert_equal(r.shape, s.shape)
|
||||
assert_equal(r.shape, (1,3))
|
||||
|
||||
|
||||
def test_logsumexp_shape():
|
||||
a = np.ones((1, 2, 3, 4))
|
||||
b = np.ones_like(a)
|
||||
|
||||
r = logsumexp(a, axis=2, b=b)
|
||||
assert_equal(r.shape, (1, 2, 4))
|
||||
|
||||
r = logsumexp(a, axis=(1, 3), b=b)
|
||||
assert_equal(r.shape, (1, 3))
|
||||
|
||||
|
||||
def test_logsumexp_b_zero():
|
||||
a = [1,10000]
|
||||
b = [1,0]
|
||||
|
||||
assert_almost_equal(logsumexp(a, b=b), 1)
|
||||
|
||||
|
||||
def test_logsumexp_b_shape():
|
||||
a = np.zeros((4,1,2,1))
|
||||
b = np.ones((3,1,5))
|
||||
|
||||
logsumexp(a, b=b)
|
||||
|
||||
|
||||
def test_softmax_fixtures():
|
||||
assert_allclose(softmax([1000, 0, 0, 0]), np.array([1, 0, 0, 0]),
|
||||
rtol=1e-13)
|
||||
assert_allclose(softmax([1, 1]), np.array([.5, .5]), rtol=1e-13)
|
||||
assert_allclose(softmax([0, 1]), np.array([1, np.e])/(1 + np.e),
|
||||
rtol=1e-13)
|
||||
|
||||
# Expected value computed using mpmath (with mpmath.mp.dps = 200) and then
|
||||
# converted to float.
|
||||
x = np.arange(4)
|
||||
expected = np.array([0.03205860328008499,
|
||||
0.08714431874203256,
|
||||
0.23688281808991013,
|
||||
0.6439142598879722])
|
||||
|
||||
assert_allclose(softmax(x), expected, rtol=1e-13)
|
||||
|
||||
# Translation property. If all the values are changed by the same amount,
|
||||
# the softmax result does not change.
|
||||
assert_allclose(softmax(x + 100), expected, rtol=1e-13)
|
||||
|
||||
# When axis=None, softmax operates on the entire array, and preserves
|
||||
# the shape.
|
||||
assert_allclose(softmax(x.reshape(2, 2)), expected.reshape(2, 2),
|
||||
rtol=1e-13)
|
||||
|
||||
|
||||
def test_softmax_multi_axes():
|
||||
assert_allclose(softmax([[1000, 0], [1000, 0]], axis=0),
|
||||
np.array([[.5, .5], [.5, .5]]), rtol=1e-13)
|
||||
assert_allclose(softmax([[1000, 0], [1000, 0]], axis=1),
|
||||
np.array([[1, 0], [1, 0]]), rtol=1e-13)
|
||||
|
||||
# Expected value computed using mpmath (with mpmath.mp.dps = 200) and then
|
||||
# converted to float.
|
||||
x = np.array([[-25, 0, 25, 50],
|
||||
[1, 325, 749, 750]])
|
||||
expected = np.array([[2.678636961770877e-33,
|
||||
1.9287498479371314e-22,
|
||||
1.3887943864771144e-11,
|
||||
0.999999999986112],
|
||||
[0.0,
|
||||
1.9444526359919372e-185,
|
||||
0.2689414213699951,
|
||||
0.7310585786300048]])
|
||||
assert_allclose(softmax(x, axis=1), expected, rtol=1e-13)
|
||||
assert_allclose(softmax(x.T, axis=0), expected.T, rtol=1e-13)
|
||||
|
||||
# 3-d input, with a tuple for the axis.
|
||||
x3d = x.reshape(2, 2, 2)
|
||||
assert_allclose(softmax(x3d, axis=(1, 2)), expected.reshape(2, 2, 2),
|
||||
rtol=1e-13)
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,64 +0,0 @@
|
||||
"""Test how the ufuncs in special handle nan inputs.
|
||||
|
||||
"""
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
import numpy as np
|
||||
from numpy.testing import assert_array_equal, assert_
|
||||
import pytest
|
||||
|
||||
import scipy.special as sc
|
||||
from scipy._lib._numpy_compat import suppress_warnings
|
||||
|
||||
|
||||
KNOWNFAILURES = {}
|
||||
|
||||
POSTPROCESSING = {}
|
||||
|
||||
|
||||
def _get_ufuncs():
|
||||
ufuncs = []
|
||||
ufunc_names = []
|
||||
for name in sorted(sc.__dict__):
|
||||
obj = sc.__dict__[name]
|
||||
if not isinstance(obj, np.ufunc):
|
||||
continue
|
||||
msg = KNOWNFAILURES.get(obj)
|
||||
if msg is None:
|
||||
ufuncs.append(obj)
|
||||
ufunc_names.append(name)
|
||||
else:
|
||||
fail = pytest.mark.xfail(run=False, reason=msg)
|
||||
ufuncs.append(pytest.param(obj, marks=fail))
|
||||
ufunc_names.append(name)
|
||||
return ufuncs, ufunc_names
|
||||
|
||||
|
||||
UFUNCS, UFUNC_NAMES = _get_ufuncs()
|
||||
|
||||
|
||||
@pytest.mark.parametrize("func", UFUNCS, ids=UFUNC_NAMES)
|
||||
def test_nan_inputs(func):
|
||||
args = (np.nan,)*func.nin
|
||||
with suppress_warnings() as sup:
|
||||
# Ignore warnings about unsafe casts from legacy wrappers
|
||||
sup.filter(RuntimeWarning,
|
||||
"floating point number truncated to an integer")
|
||||
try:
|
||||
res = func(*args)
|
||||
except TypeError:
|
||||
# One of the arguments doesn't take real inputs
|
||||
return
|
||||
if func in POSTPROCESSING:
|
||||
res = POSTPROCESSING[func](*res)
|
||||
|
||||
msg = "got {} instead of nan".format(res)
|
||||
assert_array_equal(np.isnan(res), True, err_msg=msg)
|
||||
|
||||
|
||||
def test_legacy_cast():
|
||||
with suppress_warnings() as sup:
|
||||
sup.filter(RuntimeWarning,
|
||||
"floating point number truncated to an integer")
|
||||
res = sc.bdtrc(np.nan, 1, 0.5)
|
||||
assert_(np.isnan(res))
|
||||
@@ -1,756 +0,0 @@
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
import numpy as np
|
||||
from numpy import array, sqrt
|
||||
from numpy.testing import (assert_array_almost_equal, assert_equal,
|
||||
assert_almost_equal, assert_allclose)
|
||||
from pytest import raises as assert_raises
|
||||
|
||||
from scipy._lib.six import xrange
|
||||
from scipy import integrate
|
||||
import scipy.special as sc
|
||||
from scipy.special import gamma
|
||||
import scipy.special.orthogonal as orth
|
||||
|
||||
|
||||
class TestCheby(object):
|
||||
def test_chebyc(self):
|
||||
C0 = orth.chebyc(0)
|
||||
C1 = orth.chebyc(1)
|
||||
olderr = np.seterr(all='ignore')
|
||||
try:
|
||||
C2 = orth.chebyc(2)
|
||||
C3 = orth.chebyc(3)
|
||||
C4 = orth.chebyc(4)
|
||||
C5 = orth.chebyc(5)
|
||||
finally:
|
||||
np.seterr(**olderr)
|
||||
|
||||
assert_array_almost_equal(C0.c,[2],13)
|
||||
assert_array_almost_equal(C1.c,[1,0],13)
|
||||
assert_array_almost_equal(C2.c,[1,0,-2],13)
|
||||
assert_array_almost_equal(C3.c,[1,0,-3,0],13)
|
||||
assert_array_almost_equal(C4.c,[1,0,-4,0,2],13)
|
||||
assert_array_almost_equal(C5.c,[1,0,-5,0,5,0],13)
|
||||
|
||||
def test_chebys(self):
|
||||
S0 = orth.chebys(0)
|
||||
S1 = orth.chebys(1)
|
||||
S2 = orth.chebys(2)
|
||||
S3 = orth.chebys(3)
|
||||
S4 = orth.chebys(4)
|
||||
S5 = orth.chebys(5)
|
||||
assert_array_almost_equal(S0.c,[1],13)
|
||||
assert_array_almost_equal(S1.c,[1,0],13)
|
||||
assert_array_almost_equal(S2.c,[1,0,-1],13)
|
||||
assert_array_almost_equal(S3.c,[1,0,-2,0],13)
|
||||
assert_array_almost_equal(S4.c,[1,0,-3,0,1],13)
|
||||
assert_array_almost_equal(S5.c,[1,0,-4,0,3,0],13)
|
||||
|
||||
def test_chebyt(self):
|
||||
T0 = orth.chebyt(0)
|
||||
T1 = orth.chebyt(1)
|
||||
T2 = orth.chebyt(2)
|
||||
T3 = orth.chebyt(3)
|
||||
T4 = orth.chebyt(4)
|
||||
T5 = orth.chebyt(5)
|
||||
assert_array_almost_equal(T0.c,[1],13)
|
||||
assert_array_almost_equal(T1.c,[1,0],13)
|
||||
assert_array_almost_equal(T2.c,[2,0,-1],13)
|
||||
assert_array_almost_equal(T3.c,[4,0,-3,0],13)
|
||||
assert_array_almost_equal(T4.c,[8,0,-8,0,1],13)
|
||||
assert_array_almost_equal(T5.c,[16,0,-20,0,5,0],13)
|
||||
|
||||
def test_chebyu(self):
|
||||
U0 = orth.chebyu(0)
|
||||
U1 = orth.chebyu(1)
|
||||
U2 = orth.chebyu(2)
|
||||
U3 = orth.chebyu(3)
|
||||
U4 = orth.chebyu(4)
|
||||
U5 = orth.chebyu(5)
|
||||
assert_array_almost_equal(U0.c,[1],13)
|
||||
assert_array_almost_equal(U1.c,[2,0],13)
|
||||
assert_array_almost_equal(U2.c,[4,0,-1],13)
|
||||
assert_array_almost_equal(U3.c,[8,0,-4,0],13)
|
||||
assert_array_almost_equal(U4.c,[16,0,-12,0,1],13)
|
||||
assert_array_almost_equal(U5.c,[32,0,-32,0,6,0],13)
|
||||
|
||||
|
||||
class TestGegenbauer(object):
|
||||
|
||||
def test_gegenbauer(self):
|
||||
a = 5*np.random.random() - 0.5
|
||||
if np.any(a == 0):
|
||||
a = -0.2
|
||||
Ca0 = orth.gegenbauer(0,a)
|
||||
Ca1 = orth.gegenbauer(1,a)
|
||||
Ca2 = orth.gegenbauer(2,a)
|
||||
Ca3 = orth.gegenbauer(3,a)
|
||||
Ca4 = orth.gegenbauer(4,a)
|
||||
Ca5 = orth.gegenbauer(5,a)
|
||||
|
||||
assert_array_almost_equal(Ca0.c,array([1]),13)
|
||||
assert_array_almost_equal(Ca1.c,array([2*a,0]),13)
|
||||
assert_array_almost_equal(Ca2.c,array([2*a*(a+1),0,-a]),13)
|
||||
assert_array_almost_equal(Ca3.c,array([4*orth.poch(a,3),0,-6*a*(a+1),
|
||||
0])/3.0,11)
|
||||
assert_array_almost_equal(Ca4.c,array([4*orth.poch(a,4),0,-12*orth.poch(a,3),
|
||||
0,3*a*(a+1)])/6.0,11)
|
||||
assert_array_almost_equal(Ca5.c,array([4*orth.poch(a,5),0,-20*orth.poch(a,4),
|
||||
0,15*orth.poch(a,3),0])/15.0,11)
|
||||
|
||||
|
||||
class TestHermite(object):
|
||||
def test_hermite(self):
|
||||
H0 = orth.hermite(0)
|
||||
H1 = orth.hermite(1)
|
||||
H2 = orth.hermite(2)
|
||||
H3 = orth.hermite(3)
|
||||
H4 = orth.hermite(4)
|
||||
H5 = orth.hermite(5)
|
||||
assert_array_almost_equal(H0.c,[1],13)
|
||||
assert_array_almost_equal(H1.c,[2,0],13)
|
||||
assert_array_almost_equal(H2.c,[4,0,-2],13)
|
||||
assert_array_almost_equal(H3.c,[8,0,-12,0],13)
|
||||
assert_array_almost_equal(H4.c,[16,0,-48,0,12],12)
|
||||
assert_array_almost_equal(H5.c,[32,0,-160,0,120,0],12)
|
||||
|
||||
def test_hermitenorm(self):
|
||||
# He_n(x) = 2**(-n/2) H_n(x/sqrt(2))
|
||||
psub = np.poly1d([1.0/sqrt(2),0])
|
||||
H0 = orth.hermitenorm(0)
|
||||
H1 = orth.hermitenorm(1)
|
||||
H2 = orth.hermitenorm(2)
|
||||
H3 = orth.hermitenorm(3)
|
||||
H4 = orth.hermitenorm(4)
|
||||
H5 = orth.hermitenorm(5)
|
||||
he0 = orth.hermite(0)(psub)
|
||||
he1 = orth.hermite(1)(psub) / sqrt(2)
|
||||
he2 = orth.hermite(2)(psub) / 2.0
|
||||
he3 = orth.hermite(3)(psub) / (2*sqrt(2))
|
||||
he4 = orth.hermite(4)(psub) / 4.0
|
||||
he5 = orth.hermite(5)(psub) / (4.0*sqrt(2))
|
||||
|
||||
assert_array_almost_equal(H0.c,he0.c,13)
|
||||
assert_array_almost_equal(H1.c,he1.c,13)
|
||||
assert_array_almost_equal(H2.c,he2.c,13)
|
||||
assert_array_almost_equal(H3.c,he3.c,13)
|
||||
assert_array_almost_equal(H4.c,he4.c,13)
|
||||
assert_array_almost_equal(H5.c,he5.c,13)
|
||||
|
||||
|
||||
class _test_sh_legendre(object):
|
||||
|
||||
def test_sh_legendre(self):
|
||||
# P*_n(x) = P_n(2x-1)
|
||||
psub = np.poly1d([2,-1])
|
||||
Ps0 = orth.sh_legendre(0)
|
||||
Ps1 = orth.sh_legendre(1)
|
||||
Ps2 = orth.sh_legendre(2)
|
||||
Ps3 = orth.sh_legendre(3)
|
||||
Ps4 = orth.sh_legendre(4)
|
||||
Ps5 = orth.sh_legendre(5)
|
||||
pse0 = orth.legendre(0)(psub)
|
||||
pse1 = orth.legendre(1)(psub)
|
||||
pse2 = orth.legendre(2)(psub)
|
||||
pse3 = orth.legendre(3)(psub)
|
||||
pse4 = orth.legendre(4)(psub)
|
||||
pse5 = orth.legendre(5)(psub)
|
||||
assert_array_almost_equal(Ps0.c,pse0.c,13)
|
||||
assert_array_almost_equal(Ps1.c,pse1.c,13)
|
||||
assert_array_almost_equal(Ps2.c,pse2.c,13)
|
||||
assert_array_almost_equal(Ps3.c,pse3.c,13)
|
||||
assert_array_almost_equal(Ps4.c,pse4.c,12)
|
||||
assert_array_almost_equal(Ps5.c,pse5.c,12)
|
||||
|
||||
|
||||
class _test_sh_chebyt(object):
|
||||
|
||||
def test_sh_chebyt(self):
|
||||
# T*_n(x) = T_n(2x-1)
|
||||
psub = np.poly1d([2,-1])
|
||||
Ts0 = orth.sh_chebyt(0)
|
||||
Ts1 = orth.sh_chebyt(1)
|
||||
Ts2 = orth.sh_chebyt(2)
|
||||
Ts3 = orth.sh_chebyt(3)
|
||||
Ts4 = orth.sh_chebyt(4)
|
||||
Ts5 = orth.sh_chebyt(5)
|
||||
tse0 = orth.chebyt(0)(psub)
|
||||
tse1 = orth.chebyt(1)(psub)
|
||||
tse2 = orth.chebyt(2)(psub)
|
||||
tse3 = orth.chebyt(3)(psub)
|
||||
tse4 = orth.chebyt(4)(psub)
|
||||
tse5 = orth.chebyt(5)(psub)
|
||||
assert_array_almost_equal(Ts0.c,tse0.c,13)
|
||||
assert_array_almost_equal(Ts1.c,tse1.c,13)
|
||||
assert_array_almost_equal(Ts2.c,tse2.c,13)
|
||||
assert_array_almost_equal(Ts3.c,tse3.c,13)
|
||||
assert_array_almost_equal(Ts4.c,tse4.c,12)
|
||||
assert_array_almost_equal(Ts5.c,tse5.c,12)
|
||||
|
||||
|
||||
class _test_sh_chebyu(object):
|
||||
|
||||
def test_sh_chebyu(self):
|
||||
# U*_n(x) = U_n(2x-1)
|
||||
psub = np.poly1d([2,-1])
|
||||
Us0 = orth.sh_chebyu(0)
|
||||
Us1 = orth.sh_chebyu(1)
|
||||
Us2 = orth.sh_chebyu(2)
|
||||
Us3 = orth.sh_chebyu(3)
|
||||
Us4 = orth.sh_chebyu(4)
|
||||
Us5 = orth.sh_chebyu(5)
|
||||
use0 = orth.chebyu(0)(psub)
|
||||
use1 = orth.chebyu(1)(psub)
|
||||
use2 = orth.chebyu(2)(psub)
|
||||
use3 = orth.chebyu(3)(psub)
|
||||
use4 = orth.chebyu(4)(psub)
|
||||
use5 = orth.chebyu(5)(psub)
|
||||
assert_array_almost_equal(Us0.c,use0.c,13)
|
||||
assert_array_almost_equal(Us1.c,use1.c,13)
|
||||
assert_array_almost_equal(Us2.c,use2.c,13)
|
||||
assert_array_almost_equal(Us3.c,use3.c,13)
|
||||
assert_array_almost_equal(Us4.c,use4.c,12)
|
||||
assert_array_almost_equal(Us5.c,use5.c,11)
|
||||
|
||||
|
||||
class _test_sh_jacobi(object):
|
||||
def test_sh_jacobi(self):
|
||||
# G^(p,q)_n(x) = n! gamma(n+p)/gamma(2*n+p) * P^(p-q,q-1)_n(2*x-1)
|
||||
conv = lambda n,p: gamma(n+1)*gamma(n+p)/gamma(2*n+p)
|
||||
psub = np.poly1d([2,-1])
|
||||
q = 4 * np.random.random()
|
||||
p = q-1 + 2*np.random.random()
|
||||
# print("shifted jacobi p,q = ", p, q)
|
||||
G0 = orth.sh_jacobi(0,p,q)
|
||||
G1 = orth.sh_jacobi(1,p,q)
|
||||
G2 = orth.sh_jacobi(2,p,q)
|
||||
G3 = orth.sh_jacobi(3,p,q)
|
||||
G4 = orth.sh_jacobi(4,p,q)
|
||||
G5 = orth.sh_jacobi(5,p,q)
|
||||
ge0 = orth.jacobi(0,p-q,q-1)(psub) * conv(0,p)
|
||||
ge1 = orth.jacobi(1,p-q,q-1)(psub) * conv(1,p)
|
||||
ge2 = orth.jacobi(2,p-q,q-1)(psub) * conv(2,p)
|
||||
ge3 = orth.jacobi(3,p-q,q-1)(psub) * conv(3,p)
|
||||
ge4 = orth.jacobi(4,p-q,q-1)(psub) * conv(4,p)
|
||||
ge5 = orth.jacobi(5,p-q,q-1)(psub) * conv(5,p)
|
||||
|
||||
assert_array_almost_equal(G0.c,ge0.c,13)
|
||||
assert_array_almost_equal(G1.c,ge1.c,13)
|
||||
assert_array_almost_equal(G2.c,ge2.c,13)
|
||||
assert_array_almost_equal(G3.c,ge3.c,13)
|
||||
assert_array_almost_equal(G4.c,ge4.c,13)
|
||||
assert_array_almost_equal(G5.c,ge5.c,13)
|
||||
|
||||
|
||||
class TestCall(object):
|
||||
def test_call(self):
|
||||
poly = []
|
||||
for n in xrange(5):
|
||||
poly.extend([x.strip() for x in
|
||||
("""
|
||||
orth.jacobi(%(n)d,0.3,0.9)
|
||||
orth.sh_jacobi(%(n)d,0.3,0.9)
|
||||
orth.genlaguerre(%(n)d,0.3)
|
||||
orth.laguerre(%(n)d)
|
||||
orth.hermite(%(n)d)
|
||||
orth.hermitenorm(%(n)d)
|
||||
orth.gegenbauer(%(n)d,0.3)
|
||||
orth.chebyt(%(n)d)
|
||||
orth.chebyu(%(n)d)
|
||||
orth.chebyc(%(n)d)
|
||||
orth.chebys(%(n)d)
|
||||
orth.sh_chebyt(%(n)d)
|
||||
orth.sh_chebyu(%(n)d)
|
||||
orth.legendre(%(n)d)
|
||||
orth.sh_legendre(%(n)d)
|
||||
""" % dict(n=n)).split()
|
||||
])
|
||||
olderr = np.seterr(all='ignore')
|
||||
try:
|
||||
for pstr in poly:
|
||||
p = eval(pstr)
|
||||
assert_almost_equal(p(0.315), np.poly1d(p.coef)(0.315),
|
||||
err_msg=pstr)
|
||||
finally:
|
||||
np.seterr(**olderr)
|
||||
|
||||
|
||||
class TestGenlaguerre(object):
|
||||
def test_regression(self):
|
||||
assert_equal(orth.genlaguerre(1, 1, monic=False)(0), 2.)
|
||||
assert_equal(orth.genlaguerre(1, 1, monic=True)(0), -2.)
|
||||
assert_equal(orth.genlaguerre(1, 1, monic=False), np.poly1d([-1, 2]))
|
||||
assert_equal(orth.genlaguerre(1, 1, monic=True), np.poly1d([1, -2]))
|
||||
|
||||
|
||||
def verify_gauss_quad(root_func, eval_func, weight_func, a, b, N,
|
||||
rtol=1e-15, atol=1e-14):
|
||||
# this test is copied from numpy's TestGauss in test_hermite.py
|
||||
x, w, mu = root_func(N, True)
|
||||
|
||||
n = np.arange(N)
|
||||
v = eval_func(n[:,np.newaxis], x)
|
||||
vv = np.dot(v*w, v.T)
|
||||
vd = 1 / np.sqrt(vv.diagonal())
|
||||
vv = vd[:, np.newaxis] * vv * vd
|
||||
assert_allclose(vv, np.eye(N), rtol, atol)
|
||||
|
||||
# check that the integral of 1 is correct
|
||||
assert_allclose(w.sum(), mu, rtol, atol)
|
||||
|
||||
# compare the results of integrating a function with quad.
|
||||
f = lambda x: x**3 - 3*x**2 + x - 2
|
||||
resI = integrate.quad(lambda x: f(x)*weight_func(x), a, b)
|
||||
resG = np.vdot(f(x), w)
|
||||
rtol = 1e-6 if 1e-6 < resI[1] else resI[1] * 10
|
||||
assert_allclose(resI[0], resG, rtol=rtol)
|
||||
|
||||
def test_roots_jacobi():
|
||||
rf = lambda a, b: lambda n, mu: sc.roots_jacobi(n, a, b, mu)
|
||||
ef = lambda a, b: lambda n, x: orth.eval_jacobi(n, a, b, x)
|
||||
wf = lambda a, b: lambda x: (1 - x)**a * (1 + x)**b
|
||||
|
||||
vgq = verify_gauss_quad
|
||||
vgq(rf(-0.5, -0.75), ef(-0.5, -0.75), wf(-0.5, -0.75), -1., 1., 5)
|
||||
vgq(rf(-0.5, -0.75), ef(-0.5, -0.75), wf(-0.5, -0.75), -1., 1.,
|
||||
25, atol=1e-12)
|
||||
vgq(rf(-0.5, -0.75), ef(-0.5, -0.75), wf(-0.5, -0.75), -1., 1.,
|
||||
100, atol=1e-11)
|
||||
|
||||
vgq(rf(0.5, -0.5), ef(0.5, -0.5), wf(0.5, -0.5), -1., 1., 5)
|
||||
vgq(rf(0.5, -0.5), ef(0.5, -0.5), wf(0.5, -0.5), -1., 1., 25, atol=1.5e-13)
|
||||
vgq(rf(0.5, -0.5), ef(0.5, -0.5), wf(0.5, -0.5), -1., 1., 100, atol=1e-12)
|
||||
|
||||
vgq(rf(1, 0.5), ef(1, 0.5), wf(1, 0.5), -1., 1., 5, atol=2e-13)
|
||||
vgq(rf(1, 0.5), ef(1, 0.5), wf(1, 0.5), -1., 1., 25, atol=2e-13)
|
||||
vgq(rf(1, 0.5), ef(1, 0.5), wf(1, 0.5), -1., 1., 100, atol=1e-12)
|
||||
|
||||
vgq(rf(0.9, 2), ef(0.9, 2), wf(0.9, 2), -1., 1., 5)
|
||||
vgq(rf(0.9, 2), ef(0.9, 2), wf(0.9, 2), -1., 1., 25, atol=1e-13)
|
||||
vgq(rf(0.9, 2), ef(0.9, 2), wf(0.9, 2), -1., 1., 100, atol=3e-13)
|
||||
|
||||
vgq(rf(18.24, 27.3), ef(18.24, 27.3), wf(18.24, 27.3), -1., 1., 5)
|
||||
vgq(rf(18.24, 27.3), ef(18.24, 27.3), wf(18.24, 27.3), -1., 1., 25)
|
||||
vgq(rf(18.24, 27.3), ef(18.24, 27.3), wf(18.24, 27.3), -1., 1.,
|
||||
100, atol=1e-13)
|
||||
|
||||
vgq(rf(47.1, -0.2), ef(47.1, -0.2), wf(47.1, -0.2), -1., 1., 5, atol=1e-13)
|
||||
vgq(rf(47.1, -0.2), ef(47.1, -0.2), wf(47.1, -0.2), -1., 1., 25, atol=2e-13)
|
||||
vgq(rf(47.1, -0.2), ef(47.1, -0.2), wf(47.1, -0.2), -1., 1.,
|
||||
100, atol=1e-11)
|
||||
|
||||
vgq(rf(2.25, 68.9), ef(2.25, 68.9), wf(2.25, 68.9), -1., 1., 5)
|
||||
vgq(rf(2.25, 68.9), ef(2.25, 68.9), wf(2.25, 68.9), -1., 1., 25, atol=1e-13)
|
||||
vgq(rf(2.25, 68.9), ef(2.25, 68.9), wf(2.25, 68.9), -1., 1.,
|
||||
100, atol=1e-13)
|
||||
|
||||
# when alpha == beta == 0, P_n^{a,b}(x) == P_n(x)
|
||||
xj, wj = sc.roots_jacobi(6, 0.0, 0.0)
|
||||
xl, wl = sc.roots_legendre(6)
|
||||
assert_allclose(xj, xl, 1e-14, 1e-14)
|
||||
assert_allclose(wj, wl, 1e-14, 1e-14)
|
||||
|
||||
# when alpha == beta != 0, P_n^{a,b}(x) == C_n^{alpha+0.5}(x)
|
||||
xj, wj = sc.roots_jacobi(6, 4.0, 4.0)
|
||||
xc, wc = sc.roots_gegenbauer(6, 4.5)
|
||||
assert_allclose(xj, xc, 1e-14, 1e-14)
|
||||
assert_allclose(wj, wc, 1e-14, 1e-14)
|
||||
|
||||
x, w = sc.roots_jacobi(5, 2, 3, False)
|
||||
y, v, m = sc.roots_jacobi(5, 2, 3, True)
|
||||
assert_allclose(x, y, 1e-14, 1e-14)
|
||||
assert_allclose(w, v, 1e-14, 1e-14)
|
||||
|
||||
muI, muI_err = integrate.quad(wf(2,3), -1, 1)
|
||||
assert_allclose(m, muI, rtol=muI_err)
|
||||
|
||||
assert_raises(ValueError, sc.roots_jacobi, 0, 1, 1)
|
||||
assert_raises(ValueError, sc.roots_jacobi, 3.3, 1, 1)
|
||||
assert_raises(ValueError, sc.roots_jacobi, 3, -2, 1)
|
||||
assert_raises(ValueError, sc.roots_jacobi, 3, 1, -2)
|
||||
assert_raises(ValueError, sc.roots_jacobi, 3, -2, -2)
|
||||
|
||||
def test_roots_sh_jacobi():
|
||||
rf = lambda a, b: lambda n, mu: sc.roots_sh_jacobi(n, a, b, mu)
|
||||
ef = lambda a, b: lambda n, x: orth.eval_sh_jacobi(n, a, b, x)
|
||||
wf = lambda a, b: lambda x: (1. - x)**(a - b) * (x)**(b - 1.)
|
||||
|
||||
vgq = verify_gauss_quad
|
||||
vgq(rf(-0.5, 0.25), ef(-0.5, 0.25), wf(-0.5, 0.25), 0., 1., 5)
|
||||
vgq(rf(-0.5, 0.25), ef(-0.5, 0.25), wf(-0.5, 0.25), 0., 1.,
|
||||
25, atol=1e-12)
|
||||
vgq(rf(-0.5, 0.25), ef(-0.5, 0.25), wf(-0.5, 0.25), 0., 1.,
|
||||
100, atol=1e-11)
|
||||
|
||||
vgq(rf(0.5, 0.5), ef(0.5, 0.5), wf(0.5, 0.5), 0., 1., 5)
|
||||
vgq(rf(0.5, 0.5), ef(0.5, 0.5), wf(0.5, 0.5), 0., 1., 25, atol=1e-13)
|
||||
vgq(rf(0.5, 0.5), ef(0.5, 0.5), wf(0.5, 0.5), 0., 1., 100, atol=1e-12)
|
||||
|
||||
vgq(rf(1, 0.5), ef(1, 0.5), wf(1, 0.5), 0., 1., 5)
|
||||
vgq(rf(1, 0.5), ef(1, 0.5), wf(1, 0.5), 0., 1., 25, atol=1.5e-13)
|
||||
vgq(rf(1, 0.5), ef(1, 0.5), wf(1, 0.5), 0., 1., 100, atol=1e-12)
|
||||
|
||||
vgq(rf(2, 0.9), ef(2, 0.9), wf(2, 0.9), 0., 1., 5)
|
||||
vgq(rf(2, 0.9), ef(2, 0.9), wf(2, 0.9), 0., 1., 25, atol=1e-13)
|
||||
vgq(rf(2, 0.9), ef(2, 0.9), wf(2, 0.9), 0., 1., 100, atol=1e-12)
|
||||
|
||||
vgq(rf(27.3, 18.24), ef(27.3, 18.24), wf(27.3, 18.24), 0., 1., 5)
|
||||
vgq(rf(27.3, 18.24), ef(27.3, 18.24), wf(27.3, 18.24), 0., 1., 25)
|
||||
vgq(rf(27.3, 18.24), ef(27.3, 18.24), wf(27.3, 18.24), 0., 1.,
|
||||
100, atol=1e-13)
|
||||
|
||||
vgq(rf(47.1, 0.2), ef(47.1, 0.2), wf(47.1, 0.2), 0., 1., 5, atol=1e-12)
|
||||
vgq(rf(47.1, 0.2), ef(47.1, 0.2), wf(47.1, 0.2), 0., 1., 25, atol=1e-11)
|
||||
vgq(rf(47.1, 0.2), ef(47.1, 0.2), wf(47.1, 0.2), 0., 1., 100, atol=1e-10)
|
||||
|
||||
vgq(rf(68.9, 2.25), ef(68.9, 2.25), wf(68.9, 2.25), 0., 1., 5, atol=3.5e-14)
|
||||
vgq(rf(68.9, 2.25), ef(68.9, 2.25), wf(68.9, 2.25), 0., 1., 25, atol=2e-13)
|
||||
vgq(rf(68.9, 2.25), ef(68.9, 2.25), wf(68.9, 2.25), 0., 1.,
|
||||
100, atol=1e-12)
|
||||
|
||||
x, w = sc.roots_sh_jacobi(5, 3, 2, False)
|
||||
y, v, m = sc.roots_sh_jacobi(5, 3, 2, True)
|
||||
assert_allclose(x, y, 1e-14, 1e-14)
|
||||
assert_allclose(w, v, 1e-14, 1e-14)
|
||||
|
||||
muI, muI_err = integrate.quad(wf(3,2), 0, 1)
|
||||
assert_allclose(m, muI, rtol=muI_err)
|
||||
|
||||
assert_raises(ValueError, sc.roots_sh_jacobi, 0, 1, 1)
|
||||
assert_raises(ValueError, sc.roots_sh_jacobi, 3.3, 1, 1)
|
||||
assert_raises(ValueError, sc.roots_sh_jacobi, 3, 1, 2) # p - q <= -1
|
||||
assert_raises(ValueError, sc.roots_sh_jacobi, 3, 2, -1) # q <= 0
|
||||
assert_raises(ValueError, sc.roots_sh_jacobi, 3, -2, -1) # both
|
||||
|
||||
def test_roots_hermite():
|
||||
rootf = sc.roots_hermite
|
||||
evalf = orth.eval_hermite
|
||||
weightf = orth.hermite(5).weight_func
|
||||
|
||||
verify_gauss_quad(rootf, evalf, weightf, -np.inf, np.inf, 5)
|
||||
verify_gauss_quad(rootf, evalf, weightf, -np.inf, np.inf, 25, atol=1e-13)
|
||||
verify_gauss_quad(rootf, evalf, weightf, -np.inf, np.inf, 100, atol=1e-12)
|
||||
|
||||
# Golub-Welsch branch
|
||||
x, w = sc.roots_hermite(5, False)
|
||||
y, v, m = sc.roots_hermite(5, True)
|
||||
assert_allclose(x, y, 1e-14, 1e-14)
|
||||
assert_allclose(w, v, 1e-14, 1e-14)
|
||||
|
||||
muI, muI_err = integrate.quad(weightf, -np.inf, np.inf)
|
||||
assert_allclose(m, muI, rtol=muI_err)
|
||||
|
||||
# Asymptotic branch (switch over at n >= 150)
|
||||
x, w = sc.roots_hermite(200, False)
|
||||
y, v, m = sc.roots_hermite(200, True)
|
||||
assert_allclose(x, y, 1e-14, 1e-14)
|
||||
assert_allclose(w, v, 1e-14, 1e-14)
|
||||
assert_allclose(sum(v), m, 1e-14, 1e-14)
|
||||
|
||||
assert_raises(ValueError, sc.roots_hermite, 0)
|
||||
assert_raises(ValueError, sc.roots_hermite, 3.3)
|
||||
|
||||
def test_roots_hermite_asy():
|
||||
# Recursion for Hermite functions
|
||||
def hermite_recursion(n, nodes):
|
||||
H = np.zeros((n, nodes.size))
|
||||
H[0,:] = np.pi**(-0.25) * np.exp(-0.5*nodes**2)
|
||||
if n > 1:
|
||||
H[1,:] = sqrt(2.0) * nodes * H[0,:]
|
||||
for k in xrange(2, n):
|
||||
H[k,:] = sqrt(2.0/k) * nodes * H[k-1,:] - sqrt((k-1.0)/k) * H[k-2,:]
|
||||
return H
|
||||
|
||||
# This tests only the nodes
|
||||
def test(N, rtol=1e-15, atol=1e-14):
|
||||
x, w = orth._roots_hermite_asy(N)
|
||||
H = hermite_recursion(N+1, x)
|
||||
assert_allclose(H[-1,:], np.zeros(N), rtol, atol)
|
||||
assert_allclose(sum(w), sqrt(np.pi), rtol, atol)
|
||||
|
||||
test(150, atol=1e-12)
|
||||
test(151, atol=1e-12)
|
||||
test(300, atol=1e-12)
|
||||
test(301, atol=1e-12)
|
||||
test(500, atol=1e-12)
|
||||
test(501, atol=1e-12)
|
||||
test(999, atol=1e-12)
|
||||
test(1000, atol=1e-12)
|
||||
test(2000, atol=1e-12)
|
||||
test(5000, atol=1e-12)
|
||||
|
||||
def test_roots_hermitenorm():
|
||||
rootf = sc.roots_hermitenorm
|
||||
evalf = orth.eval_hermitenorm
|
||||
weightf = orth.hermitenorm(5).weight_func
|
||||
|
||||
verify_gauss_quad(rootf, evalf, weightf, -np.inf, np.inf, 5)
|
||||
verify_gauss_quad(rootf, evalf, weightf, -np.inf, np.inf, 25, atol=1e-13)
|
||||
verify_gauss_quad(rootf, evalf, weightf, -np.inf, np.inf, 100, atol=1e-12)
|
||||
|
||||
x, w = sc.roots_hermitenorm(5, False)
|
||||
y, v, m = sc.roots_hermitenorm(5, True)
|
||||
assert_allclose(x, y, 1e-14, 1e-14)
|
||||
assert_allclose(w, v, 1e-14, 1e-14)
|
||||
|
||||
muI, muI_err = integrate.quad(weightf, -np.inf, np.inf)
|
||||
assert_allclose(m, muI, rtol=muI_err)
|
||||
|
||||
assert_raises(ValueError, sc.roots_hermitenorm, 0)
|
||||
assert_raises(ValueError, sc.roots_hermitenorm, 3.3)
|
||||
|
||||
def test_roots_gegenbauer():
|
||||
rootf = lambda a: lambda n, mu: sc.roots_gegenbauer(n, a, mu)
|
||||
evalf = lambda a: lambda n, x: orth.eval_gegenbauer(n, a, x)
|
||||
weightf = lambda a: lambda x: (1 - x**2)**(a - 0.5)
|
||||
|
||||
vgq = verify_gauss_quad
|
||||
vgq(rootf(-0.25), evalf(-0.25), weightf(-0.25), -1., 1., 5)
|
||||
vgq(rootf(-0.25), evalf(-0.25), weightf(-0.25), -1., 1., 25, atol=1e-12)
|
||||
vgq(rootf(-0.25), evalf(-0.25), weightf(-0.25), -1., 1., 100, atol=1e-11)
|
||||
|
||||
vgq(rootf(0.1), evalf(0.1), weightf(0.1), -1., 1., 5)
|
||||
vgq(rootf(0.1), evalf(0.1), weightf(0.1), -1., 1., 25, atol=1e-13)
|
||||
vgq(rootf(0.1), evalf(0.1), weightf(0.1), -1., 1., 100, atol=1e-12)
|
||||
|
||||
vgq(rootf(1), evalf(1), weightf(1), -1., 1., 5)
|
||||
vgq(rootf(1), evalf(1), weightf(1), -1., 1., 25, atol=1e-13)
|
||||
vgq(rootf(1), evalf(1), weightf(1), -1., 1., 100, atol=1e-12)
|
||||
|
||||
vgq(rootf(10), evalf(10), weightf(10), -1., 1., 5)
|
||||
vgq(rootf(10), evalf(10), weightf(10), -1., 1., 25, atol=1e-13)
|
||||
vgq(rootf(10), evalf(10), weightf(10), -1., 1., 100, atol=1e-12)
|
||||
|
||||
vgq(rootf(50), evalf(50), weightf(50), -1., 1., 5, atol=1e-13)
|
||||
vgq(rootf(50), evalf(50), weightf(50), -1., 1., 25, atol=1e-12)
|
||||
vgq(rootf(50), evalf(50), weightf(50), -1., 1., 100, atol=1e-11)
|
||||
|
||||
# this is a special case that the old code supported.
|
||||
# when alpha = 0, the gegenbauer polynomial is uniformly 0. but it goes
|
||||
# to a scaled down copy of T_n(x) there.
|
||||
vgq(rootf(0), orth.eval_chebyt, weightf(0), -1., 1., 5)
|
||||
vgq(rootf(0), orth.eval_chebyt, weightf(0), -1., 1., 25)
|
||||
vgq(rootf(0), orth.eval_chebyt, weightf(0), -1., 1., 100, atol=1e-12)
|
||||
|
||||
x, w = sc.roots_gegenbauer(5, 2, False)
|
||||
y, v, m = sc.roots_gegenbauer(5, 2, True)
|
||||
assert_allclose(x, y, 1e-14, 1e-14)
|
||||
assert_allclose(w, v, 1e-14, 1e-14)
|
||||
|
||||
muI, muI_err = integrate.quad(weightf(2), -1, 1)
|
||||
assert_allclose(m, muI, rtol=muI_err)
|
||||
|
||||
assert_raises(ValueError, sc.roots_gegenbauer, 0, 2)
|
||||
assert_raises(ValueError, sc.roots_gegenbauer, 3.3, 2)
|
||||
assert_raises(ValueError, sc.roots_gegenbauer, 3, -.75)
|
||||
|
||||
def test_roots_chebyt():
|
||||
weightf = orth.chebyt(5).weight_func
|
||||
verify_gauss_quad(sc.roots_chebyt, orth.eval_chebyt, weightf, -1., 1., 5)
|
||||
verify_gauss_quad(sc.roots_chebyt, orth.eval_chebyt, weightf, -1., 1., 25)
|
||||
verify_gauss_quad(sc.roots_chebyt, orth.eval_chebyt, weightf, -1., 1., 100, atol=1e-12)
|
||||
|
||||
x, w = sc.roots_chebyt(5, False)
|
||||
y, v, m = sc.roots_chebyt(5, True)
|
||||
assert_allclose(x, y, 1e-14, 1e-14)
|
||||
assert_allclose(w, v, 1e-14, 1e-14)
|
||||
|
||||
muI, muI_err = integrate.quad(weightf, -1, 1)
|
||||
assert_allclose(m, muI, rtol=muI_err)
|
||||
|
||||
assert_raises(ValueError, sc.roots_chebyt, 0)
|
||||
assert_raises(ValueError, sc.roots_chebyt, 3.3)
|
||||
|
||||
def test_chebyt_symmetry():
|
||||
x, w = sc.roots_chebyt(21)
|
||||
pos, neg = x[:10], x[11:]
|
||||
assert_equal(neg, -pos[::-1])
|
||||
assert_equal(x[10], 0)
|
||||
|
||||
def test_roots_chebyu():
|
||||
weightf = orth.chebyu(5).weight_func
|
||||
verify_gauss_quad(sc.roots_chebyu, orth.eval_chebyu, weightf, -1., 1., 5)
|
||||
verify_gauss_quad(sc.roots_chebyu, orth.eval_chebyu, weightf, -1., 1., 25)
|
||||
verify_gauss_quad(sc.roots_chebyu, orth.eval_chebyu, weightf, -1., 1., 100)
|
||||
|
||||
x, w = sc.roots_chebyu(5, False)
|
||||
y, v, m = sc.roots_chebyu(5, True)
|
||||
assert_allclose(x, y, 1e-14, 1e-14)
|
||||
assert_allclose(w, v, 1e-14, 1e-14)
|
||||
|
||||
muI, muI_err = integrate.quad(weightf, -1, 1)
|
||||
assert_allclose(m, muI, rtol=muI_err)
|
||||
|
||||
assert_raises(ValueError, sc.roots_chebyu, 0)
|
||||
assert_raises(ValueError, sc.roots_chebyu, 3.3)
|
||||
|
||||
def test_roots_chebyc():
|
||||
weightf = orth.chebyc(5).weight_func
|
||||
verify_gauss_quad(sc.roots_chebyc, orth.eval_chebyc, weightf, -2., 2., 5)
|
||||
verify_gauss_quad(sc.roots_chebyc, orth.eval_chebyc, weightf, -2., 2., 25)
|
||||
verify_gauss_quad(sc.roots_chebyc, orth.eval_chebyc, weightf, -2., 2., 100, atol=1e-12)
|
||||
|
||||
x, w = sc.roots_chebyc(5, False)
|
||||
y, v, m = sc.roots_chebyc(5, True)
|
||||
assert_allclose(x, y, 1e-14, 1e-14)
|
||||
assert_allclose(w, v, 1e-14, 1e-14)
|
||||
|
||||
muI, muI_err = integrate.quad(weightf, -2, 2)
|
||||
assert_allclose(m, muI, rtol=muI_err)
|
||||
|
||||
assert_raises(ValueError, sc.roots_chebyc, 0)
|
||||
assert_raises(ValueError, sc.roots_chebyc, 3.3)
|
||||
|
||||
def test_roots_chebys():
|
||||
weightf = orth.chebys(5).weight_func
|
||||
verify_gauss_quad(sc.roots_chebys, orth.eval_chebys, weightf, -2., 2., 5)
|
||||
verify_gauss_quad(sc.roots_chebys, orth.eval_chebys, weightf, -2., 2., 25)
|
||||
verify_gauss_quad(sc.roots_chebys, orth.eval_chebys, weightf, -2., 2., 100)
|
||||
|
||||
x, w = sc.roots_chebys(5, False)
|
||||
y, v, m = sc.roots_chebys(5, True)
|
||||
assert_allclose(x, y, 1e-14, 1e-14)
|
||||
assert_allclose(w, v, 1e-14, 1e-14)
|
||||
|
||||
muI, muI_err = integrate.quad(weightf, -2, 2)
|
||||
assert_allclose(m, muI, rtol=muI_err)
|
||||
|
||||
assert_raises(ValueError, sc.roots_chebys, 0)
|
||||
assert_raises(ValueError, sc.roots_chebys, 3.3)
|
||||
|
||||
def test_roots_sh_chebyt():
|
||||
weightf = orth.sh_chebyt(5).weight_func
|
||||
verify_gauss_quad(sc.roots_sh_chebyt, orth.eval_sh_chebyt, weightf, 0., 1., 5)
|
||||
verify_gauss_quad(sc.roots_sh_chebyt, orth.eval_sh_chebyt, weightf, 0., 1., 25)
|
||||
verify_gauss_quad(sc.roots_sh_chebyt, orth.eval_sh_chebyt, weightf, 0., 1.,
|
||||
100, atol=1e-13)
|
||||
|
||||
x, w = sc.roots_sh_chebyt(5, False)
|
||||
y, v, m = sc.roots_sh_chebyt(5, True)
|
||||
assert_allclose(x, y, 1e-14, 1e-14)
|
||||
assert_allclose(w, v, 1e-14, 1e-14)
|
||||
|
||||
muI, muI_err = integrate.quad(weightf, 0, 1)
|
||||
assert_allclose(m, muI, rtol=muI_err)
|
||||
|
||||
assert_raises(ValueError, sc.roots_sh_chebyt, 0)
|
||||
assert_raises(ValueError, sc.roots_sh_chebyt, 3.3)
|
||||
|
||||
def test_roots_sh_chebyu():
|
||||
weightf = orth.sh_chebyu(5).weight_func
|
||||
verify_gauss_quad(sc.roots_sh_chebyu, orth.eval_sh_chebyu, weightf, 0., 1., 5)
|
||||
verify_gauss_quad(sc.roots_sh_chebyu, orth.eval_sh_chebyu, weightf, 0., 1., 25)
|
||||
verify_gauss_quad(sc.roots_sh_chebyu, orth.eval_sh_chebyu, weightf, 0., 1.,
|
||||
100, atol=1e-13)
|
||||
|
||||
x, w = sc.roots_sh_chebyu(5, False)
|
||||
y, v, m = sc.roots_sh_chebyu(5, True)
|
||||
assert_allclose(x, y, 1e-14, 1e-14)
|
||||
assert_allclose(w, v, 1e-14, 1e-14)
|
||||
|
||||
muI, muI_err = integrate.quad(weightf, 0, 1)
|
||||
assert_allclose(m, muI, rtol=muI_err)
|
||||
|
||||
assert_raises(ValueError, sc.roots_sh_chebyu, 0)
|
||||
assert_raises(ValueError, sc.roots_sh_chebyu, 3.3)
|
||||
|
||||
def test_roots_legendre():
|
||||
weightf = orth.legendre(5).weight_func
|
||||
verify_gauss_quad(sc.roots_legendre, orth.eval_legendre, weightf, -1., 1., 5)
|
||||
verify_gauss_quad(sc.roots_legendre, orth.eval_legendre, weightf, -1., 1.,
|
||||
25, atol=1e-13)
|
||||
verify_gauss_quad(sc.roots_legendre, orth.eval_legendre, weightf, -1., 1.,
|
||||
100, atol=1e-12)
|
||||
|
||||
x, w = sc.roots_legendre(5, False)
|
||||
y, v, m = sc.roots_legendre(5, True)
|
||||
assert_allclose(x, y, 1e-14, 1e-14)
|
||||
assert_allclose(w, v, 1e-14, 1e-14)
|
||||
|
||||
muI, muI_err = integrate.quad(weightf, -1, 1)
|
||||
assert_allclose(m, muI, rtol=muI_err)
|
||||
|
||||
assert_raises(ValueError, sc.roots_legendre, 0)
|
||||
assert_raises(ValueError, sc.roots_legendre, 3.3)
|
||||
|
||||
def test_roots_sh_legendre():
|
||||
weightf = orth.sh_legendre(5).weight_func
|
||||
verify_gauss_quad(sc.roots_sh_legendre, orth.eval_sh_legendre, weightf, 0., 1., 5)
|
||||
verify_gauss_quad(sc.roots_sh_legendre, orth.eval_sh_legendre, weightf, 0., 1.,
|
||||
25, atol=1e-13)
|
||||
verify_gauss_quad(sc.roots_sh_legendre, orth.eval_sh_legendre, weightf, 0., 1.,
|
||||
100, atol=1e-12)
|
||||
|
||||
x, w = sc.roots_sh_legendre(5, False)
|
||||
y, v, m = sc.roots_sh_legendre(5, True)
|
||||
assert_allclose(x, y, 1e-14, 1e-14)
|
||||
assert_allclose(w, v, 1e-14, 1e-14)
|
||||
|
||||
muI, muI_err = integrate.quad(weightf, 0, 1)
|
||||
assert_allclose(m, muI, rtol=muI_err)
|
||||
|
||||
assert_raises(ValueError, sc.roots_sh_legendre, 0)
|
||||
assert_raises(ValueError, sc.roots_sh_legendre, 3.3)
|
||||
|
||||
def test_roots_laguerre():
|
||||
weightf = orth.laguerre(5).weight_func
|
||||
verify_gauss_quad(sc.roots_laguerre, orth.eval_laguerre, weightf, 0., np.inf, 5)
|
||||
verify_gauss_quad(sc.roots_laguerre, orth.eval_laguerre, weightf, 0., np.inf,
|
||||
25, atol=1e-13)
|
||||
verify_gauss_quad(sc.roots_laguerre, orth.eval_laguerre, weightf, 0., np.inf,
|
||||
100, atol=1e-12)
|
||||
|
||||
x, w = sc.roots_laguerre(5, False)
|
||||
y, v, m = sc.roots_laguerre(5, True)
|
||||
assert_allclose(x, y, 1e-14, 1e-14)
|
||||
assert_allclose(w, v, 1e-14, 1e-14)
|
||||
|
||||
muI, muI_err = integrate.quad(weightf, 0, np.inf)
|
||||
assert_allclose(m, muI, rtol=muI_err)
|
||||
|
||||
assert_raises(ValueError, sc.roots_laguerre, 0)
|
||||
assert_raises(ValueError, sc.roots_laguerre, 3.3)
|
||||
|
||||
def test_roots_genlaguerre():
|
||||
rootf = lambda a: lambda n, mu: sc.roots_genlaguerre(n, a, mu)
|
||||
evalf = lambda a: lambda n, x: orth.eval_genlaguerre(n, a, x)
|
||||
weightf = lambda a: lambda x: x**a * np.exp(-x)
|
||||
|
||||
vgq = verify_gauss_quad
|
||||
vgq(rootf(-0.5), evalf(-0.5), weightf(-0.5), 0., np.inf, 5)
|
||||
vgq(rootf(-0.5), evalf(-0.5), weightf(-0.5), 0., np.inf, 25, atol=1e-13)
|
||||
vgq(rootf(-0.5), evalf(-0.5), weightf(-0.5), 0., np.inf, 100, atol=1e-12)
|
||||
|
||||
vgq(rootf(0.1), evalf(0.1), weightf(0.1), 0., np.inf, 5)
|
||||
vgq(rootf(0.1), evalf(0.1), weightf(0.1), 0., np.inf, 25, atol=1e-13)
|
||||
vgq(rootf(0.1), evalf(0.1), weightf(0.1), 0., np.inf, 100, atol=1e-13)
|
||||
|
||||
vgq(rootf(1), evalf(1), weightf(1), 0., np.inf, 5)
|
||||
vgq(rootf(1), evalf(1), weightf(1), 0., np.inf, 25, atol=1e-13)
|
||||
vgq(rootf(1), evalf(1), weightf(1), 0., np.inf, 100, atol=1e-13)
|
||||
|
||||
vgq(rootf(10), evalf(10), weightf(10), 0., np.inf, 5)
|
||||
vgq(rootf(10), evalf(10), weightf(10), 0., np.inf, 25, atol=1e-13)
|
||||
vgq(rootf(10), evalf(10), weightf(10), 0., np.inf, 100, atol=1e-12)
|
||||
|
||||
vgq(rootf(50), evalf(50), weightf(50), 0., np.inf, 5)
|
||||
vgq(rootf(50), evalf(50), weightf(50), 0., np.inf, 25, atol=1e-13)
|
||||
vgq(rootf(50), evalf(50), weightf(50), 0., np.inf, 100, rtol=1e-14, atol=2e-13)
|
||||
|
||||
x, w = sc.roots_genlaguerre(5, 2, False)
|
||||
y, v, m = sc.roots_genlaguerre(5, 2, True)
|
||||
assert_allclose(x, y, 1e-14, 1e-14)
|
||||
assert_allclose(w, v, 1e-14, 1e-14)
|
||||
|
||||
muI, muI_err = integrate.quad(weightf(2.), 0., np.inf)
|
||||
assert_allclose(m, muI, rtol=muI_err)
|
||||
|
||||
assert_raises(ValueError, sc.roots_genlaguerre, 0, 2)
|
||||
assert_raises(ValueError, sc.roots_genlaguerre, 3.3, 2)
|
||||
assert_raises(ValueError, sc.roots_genlaguerre, 3, -1.1)
|
||||
|
||||
|
||||
def test_gh_6721():
|
||||
# Regresssion test for gh_6721. This should not raise.
|
||||
sc.chebyt(65)(0.2)
|
||||
@@ -1,248 +0,0 @@
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
import numpy as np
|
||||
from numpy.testing import assert_, assert_allclose
|
||||
import scipy.special.orthogonal as orth
|
||||
|
||||
from scipy.special._testutils import FuncData
|
||||
|
||||
|
||||
def test_eval_chebyt():
|
||||
n = np.arange(0, 10000, 7)
|
||||
x = 2*np.random.rand() - 1
|
||||
v1 = np.cos(n*np.arccos(x))
|
||||
v2 = orth.eval_chebyt(n, x)
|
||||
assert_(np.allclose(v1, v2, rtol=1e-15))
|
||||
|
||||
|
||||
def test_eval_genlaguerre_restriction():
|
||||
# check it returns nan for alpha <= -1
|
||||
assert_(np.isnan(orth.eval_genlaguerre(0, -1, 0)))
|
||||
assert_(np.isnan(orth.eval_genlaguerre(0.1, -1, 0)))
|
||||
|
||||
|
||||
def test_warnings():
|
||||
# ticket 1334
|
||||
olderr = np.seterr(all='raise')
|
||||
try:
|
||||
# these should raise no fp warnings
|
||||
orth.eval_legendre(1, 0)
|
||||
orth.eval_laguerre(1, 1)
|
||||
orth.eval_gegenbauer(1, 1, 0)
|
||||
finally:
|
||||
np.seterr(**olderr)
|
||||
|
||||
|
||||
class TestPolys(object):
|
||||
"""
|
||||
Check that the eval_* functions agree with the constructed polynomials
|
||||
|
||||
"""
|
||||
|
||||
def check_poly(self, func, cls, param_ranges=[], x_range=[], nn=10,
|
||||
nparam=10, nx=10, rtol=1e-8):
|
||||
np.random.seed(1234)
|
||||
|
||||
dataset = []
|
||||
for n in np.arange(nn):
|
||||
params = [a + (b-a)*np.random.rand(nparam) for a,b in param_ranges]
|
||||
params = np.asarray(params).T
|
||||
if not param_ranges:
|
||||
params = [0]
|
||||
for p in params:
|
||||
if param_ranges:
|
||||
p = (n,) + tuple(p)
|
||||
else:
|
||||
p = (n,)
|
||||
x = x_range[0] + (x_range[1] - x_range[0])*np.random.rand(nx)
|
||||
x[0] = x_range[0] # always include domain start point
|
||||
x[1] = x_range[1] # always include domain end point
|
||||
poly = np.poly1d(cls(*p).coef)
|
||||
z = np.c_[np.tile(p, (nx,1)), x, poly(x)]
|
||||
dataset.append(z)
|
||||
|
||||
dataset = np.concatenate(dataset, axis=0)
|
||||
|
||||
def polyfunc(*p):
|
||||
p = (p[0].astype(int),) + p[1:]
|
||||
return func(*p)
|
||||
|
||||
olderr = np.seterr(all='raise')
|
||||
try:
|
||||
ds = FuncData(polyfunc, dataset, list(range(len(param_ranges)+2)), -1,
|
||||
rtol=rtol)
|
||||
ds.check()
|
||||
finally:
|
||||
np.seterr(**olderr)
|
||||
|
||||
def test_jacobi(self):
|
||||
self.check_poly(orth.eval_jacobi, orth.jacobi,
|
||||
param_ranges=[(-0.99, 10), (-0.99, 10)], x_range=[-1, 1],
|
||||
rtol=1e-5)
|
||||
|
||||
def test_sh_jacobi(self):
|
||||
self.check_poly(orth.eval_sh_jacobi, orth.sh_jacobi,
|
||||
param_ranges=[(1, 10), (0, 1)], x_range=[0, 1],
|
||||
rtol=1e-5)
|
||||
|
||||
def test_gegenbauer(self):
|
||||
self.check_poly(orth.eval_gegenbauer, orth.gegenbauer,
|
||||
param_ranges=[(-0.499, 10)], x_range=[-1, 1],
|
||||
rtol=1e-7)
|
||||
|
||||
def test_chebyt(self):
|
||||
self.check_poly(orth.eval_chebyt, orth.chebyt,
|
||||
param_ranges=[], x_range=[-1, 1])
|
||||
|
||||
def test_chebyu(self):
|
||||
self.check_poly(orth.eval_chebyu, orth.chebyu,
|
||||
param_ranges=[], x_range=[-1, 1])
|
||||
|
||||
def test_chebys(self):
|
||||
self.check_poly(orth.eval_chebys, orth.chebys,
|
||||
param_ranges=[], x_range=[-2, 2])
|
||||
|
||||
def test_chebyc(self):
|
||||
self.check_poly(orth.eval_chebyc, orth.chebyc,
|
||||
param_ranges=[], x_range=[-2, 2])
|
||||
|
||||
def test_sh_chebyt(self):
|
||||
olderr = np.seterr(all='ignore')
|
||||
try:
|
||||
self.check_poly(orth.eval_sh_chebyt, orth.sh_chebyt,
|
||||
param_ranges=[], x_range=[0, 1])
|
||||
finally:
|
||||
np.seterr(**olderr)
|
||||
|
||||
def test_sh_chebyu(self):
|
||||
self.check_poly(orth.eval_sh_chebyu, orth.sh_chebyu,
|
||||
param_ranges=[], x_range=[0, 1])
|
||||
|
||||
def test_legendre(self):
|
||||
self.check_poly(orth.eval_legendre, orth.legendre,
|
||||
param_ranges=[], x_range=[-1, 1])
|
||||
|
||||
def test_sh_legendre(self):
|
||||
olderr = np.seterr(all='ignore')
|
||||
try:
|
||||
self.check_poly(orth.eval_sh_legendre, orth.sh_legendre,
|
||||
param_ranges=[], x_range=[0, 1])
|
||||
finally:
|
||||
np.seterr(**olderr)
|
||||
|
||||
def test_genlaguerre(self):
|
||||
self.check_poly(orth.eval_genlaguerre, orth.genlaguerre,
|
||||
param_ranges=[(-0.99, 10)], x_range=[0, 100])
|
||||
|
||||
def test_laguerre(self):
|
||||
self.check_poly(orth.eval_laguerre, orth.laguerre,
|
||||
param_ranges=[], x_range=[0, 100])
|
||||
|
||||
def test_hermite(self):
|
||||
self.check_poly(orth.eval_hermite, orth.hermite,
|
||||
param_ranges=[], x_range=[-100, 100])
|
||||
|
||||
def test_hermitenorm(self):
|
||||
self.check_poly(orth.eval_hermitenorm, orth.hermitenorm,
|
||||
param_ranges=[], x_range=[-100, 100])
|
||||
|
||||
|
||||
class TestRecurrence(object):
|
||||
"""
|
||||
Check that the eval_* functions sig='ld->d' and 'dd->d' agree.
|
||||
|
||||
"""
|
||||
|
||||
def check_poly(self, func, param_ranges=[], x_range=[], nn=10,
|
||||
nparam=10, nx=10, rtol=1e-8):
|
||||
np.random.seed(1234)
|
||||
|
||||
dataset = []
|
||||
for n in np.arange(nn):
|
||||
params = [a + (b-a)*np.random.rand(nparam) for a,b in param_ranges]
|
||||
params = np.asarray(params).T
|
||||
if not param_ranges:
|
||||
params = [0]
|
||||
for p in params:
|
||||
if param_ranges:
|
||||
p = (n,) + tuple(p)
|
||||
else:
|
||||
p = (n,)
|
||||
x = x_range[0] + (x_range[1] - x_range[0])*np.random.rand(nx)
|
||||
x[0] = x_range[0] # always include domain start point
|
||||
x[1] = x_range[1] # always include domain end point
|
||||
kw = dict(sig=(len(p)+1)*'d'+'->d')
|
||||
z = np.c_[np.tile(p, (nx,1)), x, func(*(p + (x,)), **kw)]
|
||||
dataset.append(z)
|
||||
|
||||
dataset = np.concatenate(dataset, axis=0)
|
||||
|
||||
def polyfunc(*p):
|
||||
p = (p[0].astype(int),) + p[1:]
|
||||
kw = dict(sig='l'+(len(p)-1)*'d'+'->d')
|
||||
return func(*p, **kw)
|
||||
|
||||
olderr = np.seterr(all='raise')
|
||||
try:
|
||||
ds = FuncData(polyfunc, dataset, list(range(len(param_ranges)+2)), -1,
|
||||
rtol=rtol)
|
||||
ds.check()
|
||||
finally:
|
||||
np.seterr(**olderr)
|
||||
|
||||
def test_jacobi(self):
|
||||
self.check_poly(orth.eval_jacobi,
|
||||
param_ranges=[(-0.99, 10), (-0.99, 10)], x_range=[-1, 1])
|
||||
|
||||
def test_sh_jacobi(self):
|
||||
self.check_poly(orth.eval_sh_jacobi,
|
||||
param_ranges=[(1, 10), (0, 1)], x_range=[0, 1])
|
||||
|
||||
def test_gegenbauer(self):
|
||||
self.check_poly(orth.eval_gegenbauer,
|
||||
param_ranges=[(-0.499, 10)], x_range=[-1, 1])
|
||||
|
||||
def test_chebyt(self):
|
||||
self.check_poly(orth.eval_chebyt,
|
||||
param_ranges=[], x_range=[-1, 1])
|
||||
|
||||
def test_chebyu(self):
|
||||
self.check_poly(orth.eval_chebyu,
|
||||
param_ranges=[], x_range=[-1, 1])
|
||||
|
||||
def test_chebys(self):
|
||||
self.check_poly(orth.eval_chebys,
|
||||
param_ranges=[], x_range=[-2, 2])
|
||||
|
||||
def test_chebyc(self):
|
||||
self.check_poly(orth.eval_chebyc,
|
||||
param_ranges=[], x_range=[-2, 2])
|
||||
|
||||
def test_sh_chebyt(self):
|
||||
self.check_poly(orth.eval_sh_chebyt,
|
||||
param_ranges=[], x_range=[0, 1])
|
||||
|
||||
def test_sh_chebyu(self):
|
||||
self.check_poly(orth.eval_sh_chebyu,
|
||||
param_ranges=[], x_range=[0, 1])
|
||||
|
||||
def test_legendre(self):
|
||||
self.check_poly(orth.eval_legendre,
|
||||
param_ranges=[], x_range=[-1, 1])
|
||||
|
||||
def test_sh_legendre(self):
|
||||
self.check_poly(orth.eval_sh_legendre,
|
||||
param_ranges=[], x_range=[0, 1])
|
||||
|
||||
def test_genlaguerre(self):
|
||||
self.check_poly(orth.eval_genlaguerre,
|
||||
param_ranges=[(-0.99, 10)], x_range=[0, 100])
|
||||
|
||||
def test_laguerre(self):
|
||||
self.check_poly(orth.eval_laguerre,
|
||||
param_ranges=[], x_range=[0, 100])
|
||||
|
||||
def test_hermite(self):
|
||||
v = orth.eval_hermite(70, 1.0)
|
||||
a = -1.457076485701412e60
|
||||
assert_allclose(v,a)
|
||||
@@ -1,44 +0,0 @@
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
import numpy as np
|
||||
from numpy.testing import assert_equal, assert_allclose
|
||||
|
||||
import scipy.special as sc
|
||||
|
||||
|
||||
def test_symmetries():
|
||||
np.random.seed(1234)
|
||||
a, h = np.random.rand(100), np.random.rand(100)
|
||||
assert_equal(sc.owens_t(h, a), sc.owens_t(-h, a))
|
||||
assert_equal(sc.owens_t(h, a), -sc.owens_t(h, -a))
|
||||
|
||||
|
||||
def test_special_cases():
|
||||
assert_equal(sc.owens_t(5, 0), 0)
|
||||
assert_allclose(sc.owens_t(0, 5), 0.5*np.arctan(5)/np.pi,
|
||||
rtol=5e-14)
|
||||
# Target value is 0.5*Phi(5)*(1 - Phi(5)) for Phi the CDF of the
|
||||
# standard normal distribution
|
||||
assert_allclose(sc.owens_t(5, 1), 1.4332574485503512543e-07,
|
||||
rtol=5e-14)
|
||||
|
||||
|
||||
def test_nans():
|
||||
assert_equal(sc.owens_t(20, np.nan), np.nan)
|
||||
assert_equal(sc.owens_t(np.nan, 20), np.nan)
|
||||
assert_equal(sc.owens_t(np.nan, np.nan), np.nan)
|
||||
|
||||
|
||||
def test_infs():
|
||||
h = 1
|
||||
res = 0.5*sc.erfc(h/np.sqrt(2))
|
||||
assert_allclose(sc.owens_t(h, np.inf), res, rtol=5e-14)
|
||||
assert_allclose(sc.owens_t(h, -np.inf), -res, rtol=5e-14)
|
||||
|
||||
assert_equal(sc.owens_t(np.inf, 1), 0)
|
||||
assert_equal(sc.owens_t(-np.inf, 1), 0)
|
||||
|
||||
assert_equal(sc.owens_t(np.inf, np.inf), 0)
|
||||
assert_equal(sc.owens_t(-np.inf, np.inf), 0)
|
||||
assert_equal(sc.owens_t(np.inf, -np.inf), -0.0)
|
||||
assert_equal(sc.owens_t(-np.inf, -np.inf), -0.0)
|
||||
@@ -1,24 +0,0 @@
|
||||
"""Tests for parabolic cylinder functions.
|
||||
|
||||
"""
|
||||
import numpy as np
|
||||
from numpy.testing import assert_allclose, assert_equal
|
||||
import scipy.special as sc
|
||||
|
||||
|
||||
def test_pbwa_segfault():
|
||||
# Regression test for https://github.com/scipy/scipy/issues/6208.
|
||||
#
|
||||
# Data generated by mpmath.
|
||||
#
|
||||
w = 1.02276567211316867161
|
||||
wp = -0.48887053372346189882
|
||||
assert_allclose(sc.pbwa(0, 0), (w, wp), rtol=1e-13, atol=0)
|
||||
|
||||
|
||||
def test_pbwa_nan():
|
||||
# Check that NaN's are returned outside of the range in which the
|
||||
# implementation is accurate.
|
||||
pts = [(-6, -6), (-6, 6), (6, -6), (6, 6)]
|
||||
for p in pts:
|
||||
assert_equal(sc.pbwa(*p), (np.nan, np.nan))
|
||||
-26
@@ -1,26 +0,0 @@
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
from numpy.testing import assert_equal
|
||||
|
||||
from scipy.special._testutils import check_version, MissingModule
|
||||
from scipy.special._precompute.expn_asy import generate_A
|
||||
|
||||
try:
|
||||
import sympy
|
||||
from sympy import Poly
|
||||
except ImportError:
|
||||
sympy = MissingModule("sympy")
|
||||
|
||||
|
||||
@check_version(sympy, "1.0")
|
||||
def test_generate_A():
|
||||
# Data from DLMF 8.20.5
|
||||
x = sympy.symbols('x')
|
||||
Astd = [Poly(1, x),
|
||||
Poly(1, x),
|
||||
Poly(1 - 2*x),
|
||||
Poly(1 - 8*x + 6*x**2)]
|
||||
Ares = generate_A(len(Astd))
|
||||
|
||||
for p, q in zip(Astd, Ares):
|
||||
assert_equal(p, q)
|
||||
-116
@@ -1,116 +0,0 @@
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from scipy.special._testutils import MissingModule, check_version
|
||||
from scipy.special._mptestutils import (
|
||||
Arg, IntArg, mp_assert_allclose, assert_mpmath_equal)
|
||||
from scipy.special._precompute.gammainc_asy import (
|
||||
compute_g, compute_alpha, compute_d)
|
||||
from scipy.special._precompute.gammainc_data import gammainc, gammaincc
|
||||
|
||||
try:
|
||||
import sympy
|
||||
except ImportError:
|
||||
sympy = MissingModule('sympy')
|
||||
|
||||
try:
|
||||
import mpmath as mp
|
||||
except ImportError:
|
||||
mp = MissingModule('mpmath')
|
||||
|
||||
|
||||
_is_32bit_platform = np.intp(0).itemsize < 8
|
||||
|
||||
|
||||
@check_version(mp, '0.19')
|
||||
def test_g():
|
||||
# Test data for the g_k. See DLMF 5.11.4.
|
||||
with mp.workdps(30):
|
||||
g = [mp.mpf(1), mp.mpf(1)/12, mp.mpf(1)/288,
|
||||
-mp.mpf(139)/51840, -mp.mpf(571)/2488320,
|
||||
mp.mpf(163879)/209018880, mp.mpf(5246819)/75246796800]
|
||||
mp_assert_allclose(compute_g(7), g)
|
||||
|
||||
|
||||
@pytest.mark.slow
|
||||
@check_version(mp, '0.19')
|
||||
@check_version(sympy, '0.7')
|
||||
@pytest.mark.xfail(condition=_is_32bit_platform, reason="rtol only 2e-11, see gh-6938")
|
||||
def test_alpha():
|
||||
# Test data for the alpha_k. See DLMF 8.12.14.
|
||||
with mp.workdps(30):
|
||||
alpha = [mp.mpf(0), mp.mpf(1), mp.mpf(1)/3, mp.mpf(1)/36,
|
||||
-mp.mpf(1)/270, mp.mpf(1)/4320, mp.mpf(1)/17010,
|
||||
-mp.mpf(139)/5443200, mp.mpf(1)/204120]
|
||||
mp_assert_allclose(compute_alpha(9), alpha)
|
||||
|
||||
|
||||
@pytest.mark.xslow
|
||||
@check_version(mp, '0.19')
|
||||
@check_version(sympy, '0.7')
|
||||
def test_d():
|
||||
# Compare the d_{k, n} to the results in appendix F of [1].
|
||||
#
|
||||
# Sources
|
||||
# -------
|
||||
# [1] DiDonato and Morris, Computation of the Incomplete Gamma
|
||||
# Function Ratios and their Inverse, ACM Transactions on
|
||||
# Mathematical Software, 1986.
|
||||
|
||||
with mp.workdps(50):
|
||||
dataset = [(0, 0, -mp.mpf('0.333333333333333333333333333333')),
|
||||
(0, 12, mp.mpf('0.102618097842403080425739573227e-7')),
|
||||
(1, 0, -mp.mpf('0.185185185185185185185185185185e-2')),
|
||||
(1, 12, mp.mpf('0.119516285997781473243076536700e-7')),
|
||||
(2, 0, mp.mpf('0.413359788359788359788359788360e-2')),
|
||||
(2, 12, -mp.mpf('0.140925299108675210532930244154e-7')),
|
||||
(3, 0, mp.mpf('0.649434156378600823045267489712e-3')),
|
||||
(3, 12, -mp.mpf('0.191111684859736540606728140873e-7')),
|
||||
(4, 0, -mp.mpf('0.861888290916711698604702719929e-3')),
|
||||
(4, 12, mp.mpf('0.288658297427087836297341274604e-7')),
|
||||
(5, 0, -mp.mpf('0.336798553366358150308767592718e-3')),
|
||||
(5, 12, mp.mpf('0.482409670378941807563762631739e-7')),
|
||||
(6, 0, mp.mpf('0.531307936463992223165748542978e-3')),
|
||||
(6, 12, -mp.mpf('0.882860074633048352505085243179e-7')),
|
||||
(7, 0, mp.mpf('0.344367606892377671254279625109e-3')),
|
||||
(7, 12, -mp.mpf('0.175629733590604619378669693914e-6')),
|
||||
(8, 0, -mp.mpf('0.652623918595309418922034919727e-3')),
|
||||
(8, 12, mp.mpf('0.377358774161109793380344937299e-6')),
|
||||
(9, 0, -mp.mpf('0.596761290192746250124390067179e-3')),
|
||||
(9, 12, mp.mpf('0.870823417786464116761231237189e-6'))]
|
||||
d = compute_d(10, 13)
|
||||
res = []
|
||||
for k, n, std in dataset:
|
||||
res.append(d[k][n])
|
||||
std = map(lambda x: x[2], dataset)
|
||||
mp_assert_allclose(res, std)
|
||||
|
||||
|
||||
@check_version(mp, '0.19')
|
||||
def test_gammainc():
|
||||
# Quick check that the gammainc in
|
||||
# special._precompute.gammainc_data agrees with mpmath's
|
||||
# gammainc.
|
||||
assert_mpmath_equal(gammainc,
|
||||
lambda a, x: mp.gammainc(a, b=x, regularized=True),
|
||||
[Arg(0, 100, inclusive_a=False), Arg(0, 100)],
|
||||
nan_ok=False, rtol=1e-17, n=50, dps=50)
|
||||
|
||||
|
||||
@pytest.mark.xslow
|
||||
@check_version(mp, '0.19')
|
||||
def test_gammaincc():
|
||||
# Check that the gammaincc in special._precompute.gammainc_data
|
||||
# agrees with mpmath's gammainc.
|
||||
assert_mpmath_equal(lambda a, x: gammaincc(a, x, dps=1000),
|
||||
lambda a, x: mp.gammainc(a, a=x, regularized=True),
|
||||
[Arg(20, 100), Arg(20, 100)],
|
||||
nan_ok=False, rtol=1e-17, n=50, dps=1000)
|
||||
|
||||
# Test the fast integer path
|
||||
assert_mpmath_equal(gammaincc,
|
||||
lambda a, x: mp.gammainc(a, a=x, regularized=True),
|
||||
[IntArg(1, 100), Arg(0, 100)],
|
||||
nan_ok=False, rtol=1e-17, n=50, dps=50)
|
||||
@@ -1,42 +0,0 @@
|
||||
from __future__ import division, print_function, absolute_import
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from scipy.special._testutils import MissingModule, check_version
|
||||
from scipy.special._mptestutils import mp_assert_allclose
|
||||
from scipy.special._precompute.utils import lagrange_inversion
|
||||
|
||||
try:
|
||||
import sympy
|
||||
except ImportError:
|
||||
sympy = MissingModule('sympy')
|
||||
|
||||
try:
|
||||
import mpmath as mp
|
||||
except ImportError:
|
||||
mp = MissingModule('mpmath')
|
||||
|
||||
|
||||
_is_32bit_platform = np.intp(0).itemsize < 8
|
||||
|
||||
|
||||
@pytest.mark.slow
|
||||
@check_version(sympy, '0.7')
|
||||
@check_version(mp, '0.19')
|
||||
class TestInversion(object):
|
||||
@pytest.mark.xfail(condition=_is_32bit_platform, reason="rtol only 2e-9, see gh-6938")
|
||||
def test_log(self):
|
||||
with mp.workdps(30):
|
||||
logcoeffs = mp.taylor(lambda x: mp.log(1 + x), 0, 10)
|
||||
expcoeffs = mp.taylor(lambda x: mp.exp(x) - 1, 0, 10)
|
||||
invlogcoeffs = lagrange_inversion(logcoeffs)
|
||||
mp_assert_allclose(invlogcoeffs, expcoeffs)
|
||||
|
||||
@pytest.mark.xfail(condition=_is_32bit_platform, reason="rtol only 1e-15, see gh-6938")
|
||||
def test_sin(self):
|
||||
with mp.workdps(30):
|
||||
sincoeffs = mp.taylor(mp.sin, 0, 10)
|
||||
asincoeffs = mp.taylor(mp.asin, 0, 10)
|
||||
invsincoeffs = lagrange_inversion(sincoeffs)
|
||||
mp_assert_allclose(invsincoeffs, asincoeffs, atol=1e-30)
|
||||
|
||||
@@ -1,18 +0,0 @@
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from scipy.special import _test_round
|
||||
|
||||
|
||||
@pytest.mark.skipif(not _test_round.have_fenv(), reason="no fenv()")
|
||||
def test_add_round_up():
|
||||
np.random.seed(1234)
|
||||
_test_round.test_add_round(10**5, 'up')
|
||||
|
||||
|
||||
@pytest.mark.skipif(not _test_round.have_fenv(), reason="no fenv()")
|
||||
def test_add_round_down():
|
||||
np.random.seed(1234)
|
||||
_test_round.test_add_round(10**5, 'down')
|
||||
@@ -1,115 +0,0 @@
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
import warnings
|
||||
|
||||
from numpy.testing import assert_, assert_equal
|
||||
from scipy._lib._numpy_compat import suppress_warnings
|
||||
import pytest
|
||||
from pytest import raises as assert_raises
|
||||
|
||||
import scipy.special as sc
|
||||
from scipy.special._ufuncs import _sf_error_test_function
|
||||
|
||||
_sf_error_code_map = {
|
||||
# skip 'ok'
|
||||
'singular': 1,
|
||||
'underflow': 2,
|
||||
'overflow': 3,
|
||||
'slow': 4,
|
||||
'loss': 5,
|
||||
'no_result': 6,
|
||||
'domain': 7,
|
||||
'arg': 8,
|
||||
'other': 9
|
||||
}
|
||||
|
||||
_sf_error_actions = [
|
||||
'ignore',
|
||||
'warn',
|
||||
'raise'
|
||||
]
|
||||
|
||||
|
||||
def _check_action(fun, args, action):
|
||||
if action == 'warn':
|
||||
with pytest.warns(sc.SpecialFunctionWarning):
|
||||
fun(*args)
|
||||
elif action == 'raise':
|
||||
with assert_raises(sc.SpecialFunctionError):
|
||||
fun(*args)
|
||||
else:
|
||||
# action == 'ignore', make sure there are no warnings/exceptions
|
||||
with warnings.catch_warnings():
|
||||
warnings.simplefilter("error")
|
||||
fun(*args)
|
||||
|
||||
|
||||
def test_geterr():
|
||||
err = sc.geterr()
|
||||
for key, value in err.items():
|
||||
assert_(key in _sf_error_code_map.keys())
|
||||
assert_(value in _sf_error_actions)
|
||||
|
||||
|
||||
def test_seterr():
|
||||
entry_err = sc.geterr()
|
||||
try:
|
||||
for category in _sf_error_code_map.keys():
|
||||
for action in _sf_error_actions:
|
||||
geterr_olderr = sc.geterr()
|
||||
seterr_olderr = sc.seterr(**{category: action})
|
||||
assert_(geterr_olderr == seterr_olderr)
|
||||
newerr = sc.geterr()
|
||||
assert_(newerr[category] == action)
|
||||
geterr_olderr.pop(category)
|
||||
newerr.pop(category)
|
||||
assert_(geterr_olderr == newerr)
|
||||
_check_action(_sf_error_test_function,
|
||||
(_sf_error_code_map[category],),
|
||||
action)
|
||||
finally:
|
||||
sc.seterr(**entry_err)
|
||||
|
||||
|
||||
def test_errstate_pyx_basic():
|
||||
olderr = sc.geterr()
|
||||
with sc.errstate(singular='raise'):
|
||||
with assert_raises(sc.SpecialFunctionError):
|
||||
sc.loggamma(0)
|
||||
assert_equal(olderr, sc.geterr())
|
||||
|
||||
|
||||
def test_errstate_c_basic():
|
||||
olderr = sc.geterr()
|
||||
with sc.errstate(domain='raise'):
|
||||
with assert_raises(sc.SpecialFunctionError):
|
||||
sc.spence(-1)
|
||||
assert_equal(olderr, sc.geterr())
|
||||
|
||||
|
||||
def test_errstate_cpp_basic():
|
||||
olderr = sc.geterr()
|
||||
with sc.errstate(underflow='raise'):
|
||||
with assert_raises(sc.SpecialFunctionError):
|
||||
sc.wrightomega(-1000)
|
||||
assert_equal(olderr, sc.geterr())
|
||||
|
||||
|
||||
def test_errstate():
|
||||
for category in _sf_error_code_map.keys():
|
||||
for action in _sf_error_actions:
|
||||
olderr = sc.geterr()
|
||||
with sc.errstate(**{category: action}):
|
||||
_check_action(_sf_error_test_function,
|
||||
(_sf_error_code_map[category],),
|
||||
action)
|
||||
assert_equal(olderr, sc.geterr())
|
||||
|
||||
|
||||
def test_errstate_all_but_one():
|
||||
olderr = sc.geterr()
|
||||
with sc.errstate(all='raise', singular='ignore'):
|
||||
sc.gammaln(0)
|
||||
with assert_raises(sc.SpecialFunctionError):
|
||||
sc.spence(-1.0)
|
||||
assert_equal(olderr, sc.geterr())
|
||||
@@ -1,38 +0,0 @@
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
import numpy as np
|
||||
|
||||
import scipy.special as sc
|
||||
from scipy.special._testutils import FuncData
|
||||
|
||||
|
||||
def test_sici_consistency():
|
||||
# Make sure the implementation of sici for real arguments agrees
|
||||
# with the implementation of sici for complex arguments.
|
||||
|
||||
# On the negative real axis Cephes drops the imaginary part in ci
|
||||
def sici(x):
|
||||
si, ci = sc.sici(x + 0j)
|
||||
return si.real, ci.real
|
||||
|
||||
x = np.r_[-np.logspace(8, -30, 200), 0, np.logspace(-30, 8, 200)]
|
||||
si, ci = sc.sici(x)
|
||||
dataset = np.column_stack((x, si, ci))
|
||||
FuncData(sici, dataset, 0, (1, 2), rtol=1e-12).check()
|
||||
|
||||
|
||||
def test_shichi_consistency():
|
||||
# Make sure the implementation of shichi for real arguments agrees
|
||||
# with the implementation of shichi for complex arguments.
|
||||
|
||||
# On the negative real axis Cephes drops the imaginary part in chi
|
||||
def shichi(x):
|
||||
shi, chi = sc.shichi(x + 0j)
|
||||
return shi.real, chi.real
|
||||
|
||||
# Overflow happens quickly, so limit range
|
||||
x = np.r_[-np.logspace(np.log10(700), -30, 200), 0,
|
||||
np.logspace(-30, np.log10(700), 200)]
|
||||
shi, chi = sc.shichi(x)
|
||||
dataset = np.column_stack((x, shi, chi))
|
||||
FuncData(shichi, dataset, 0, (1, 2), rtol=1e-14).check()
|
||||
@@ -1,34 +0,0 @@
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
import numpy as np
|
||||
from numpy import sqrt, log, pi
|
||||
from scipy.special._testutils import FuncData
|
||||
from scipy.special import spence
|
||||
|
||||
|
||||
def test_consistency():
|
||||
# Make sure the implementation of spence for real arguments
|
||||
# agrees with the implementation of spence for imaginary arguments.
|
||||
|
||||
x = np.logspace(-30, 300, 200)
|
||||
dataset = np.vstack((x + 0j, spence(x))).T
|
||||
FuncData(spence, dataset, 0, 1, rtol=1e-14).check()
|
||||
|
||||
|
||||
def test_special_points():
|
||||
# Check against known values of Spence's function.
|
||||
|
||||
phi = (1 + sqrt(5))/2
|
||||
dataset = [(1, 0),
|
||||
(2, -pi**2/12),
|
||||
(0.5, pi**2/12 - log(2)**2/2),
|
||||
(0, pi**2/6),
|
||||
(-1, pi**2/4 - 1j*pi*log(2)),
|
||||
((-1 + sqrt(5))/2, pi**2/15 - log(phi)**2),
|
||||
((3 - sqrt(5))/2, pi**2/10 - log(phi)**2),
|
||||
(phi, -pi**2/15 + log(phi)**2/2),
|
||||
# Corrected from Zagier, "The Dilogarithm Function"
|
||||
((3 + sqrt(5))/2, -pi**2/10 - log(phi)**2)]
|
||||
|
||||
dataset = np.asarray(dataset)
|
||||
FuncData(spence, dataset, 0, 1, rtol=1e-14).check()
|
||||
@@ -1,63 +0,0 @@
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
import numpy as np
|
||||
from numpy.testing import (assert_array_equal,
|
||||
assert_array_almost_equal_nulp, assert_almost_equal)
|
||||
from pytest import raises as assert_raises
|
||||
|
||||
from scipy.special import gammaln, multigammaln
|
||||
|
||||
|
||||
class TestMultiGammaLn(object):
|
||||
|
||||
def test1(self):
|
||||
# A test of the identity
|
||||
# Gamma_1(a) = Gamma(a)
|
||||
np.random.seed(1234)
|
||||
a = np.abs(np.random.randn())
|
||||
assert_array_equal(multigammaln(a, 1), gammaln(a))
|
||||
|
||||
def test2(self):
|
||||
# A test of the identity
|
||||
# Gamma_2(a) = sqrt(pi) * Gamma(a) * Gamma(a - 0.5)
|
||||
a = np.array([2.5, 10.0])
|
||||
result = multigammaln(a, 2)
|
||||
expected = np.log(np.sqrt(np.pi)) + gammaln(a) + gammaln(a - 0.5)
|
||||
assert_almost_equal(result, expected)
|
||||
|
||||
def test_bararg(self):
|
||||
assert_raises(ValueError, multigammaln, 0.5, 1.2)
|
||||
|
||||
|
||||
def _check_multigammaln_array_result(a, d):
|
||||
# Test that the shape of the array returned by multigammaln
|
||||
# matches the input shape, and that all the values match
|
||||
# the value computed when multigammaln is called with a scalar.
|
||||
result = multigammaln(a, d)
|
||||
assert_array_equal(a.shape, result.shape)
|
||||
a1 = a.ravel()
|
||||
result1 = result.ravel()
|
||||
for i in range(a.size):
|
||||
assert_array_almost_equal_nulp(result1[i], multigammaln(a1[i], d))
|
||||
|
||||
|
||||
def test_multigammaln_array_arg():
|
||||
# Check that the array returned by multigammaln has the correct
|
||||
# shape and contains the correct values. The cases have arrays
|
||||
# with several differnent shapes.
|
||||
# The cases include a regression test for ticket #1849
|
||||
# (a = np.array([2.0]), an array with a single element).
|
||||
np.random.seed(1234)
|
||||
|
||||
cases = [
|
||||
# a, d
|
||||
(np.abs(np.random.randn(3, 2)) + 5, 5),
|
||||
(np.abs(np.random.randn(1, 2)) + 5, 5),
|
||||
(np.arange(10.0, 18.0).reshape(2, 2, 2), 3),
|
||||
(np.array([2.0]), 3),
|
||||
(np.float64(2.0), 3),
|
||||
]
|
||||
|
||||
for a, d in cases:
|
||||
_check_multigammaln_array_result(a, d)
|
||||
|
||||
@@ -1,39 +0,0 @@
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
import numpy as np
|
||||
from numpy.testing import assert_allclose
|
||||
import scipy.special as sc
|
||||
|
||||
|
||||
def test_first_harmonics():
|
||||
# Test against explicit representations of the first four
|
||||
# spherical harmonics which use `theta` as the azimuthal angle,
|
||||
# `phi` as the polar angle, and include the Condon-Shortley
|
||||
# phase.
|
||||
|
||||
# Notation is Ymn
|
||||
def Y00(theta, phi):
|
||||
return 0.5*np.sqrt(1/np.pi)
|
||||
|
||||
def Yn11(theta, phi):
|
||||
return 0.5*np.sqrt(3/(2*np.pi))*np.exp(-1j*theta)*np.sin(phi)
|
||||
|
||||
def Y01(theta, phi):
|
||||
return 0.5*np.sqrt(3/np.pi)*np.cos(phi)
|
||||
|
||||
def Y11(theta, phi):
|
||||
return -0.5*np.sqrt(3/(2*np.pi))*np.exp(1j*theta)*np.sin(phi)
|
||||
|
||||
harms = [Y00, Yn11, Y01, Y11]
|
||||
m = [0, -1, 0, 1]
|
||||
n = [0, 1, 1, 1]
|
||||
|
||||
theta = np.linspace(0, 2*np.pi)
|
||||
phi = np.linspace(0, np.pi)
|
||||
theta, phi = np.meshgrid(theta, phi)
|
||||
|
||||
for harm, m, n in zip(harms, m, n):
|
||||
assert_allclose(sc.sph_harm(m, n, theta, phi),
|
||||
harm(theta, phi),
|
||||
rtol=1e-15, atol=1e-15,
|
||||
err_msg="Y^{}_{} incorrect".format(m, n))
|
||||
@@ -1,383 +0,0 @@
|
||||
#
|
||||
# Tests of spherical Bessel functions.
|
||||
#
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
import numpy as np
|
||||
from numpy.testing import (assert_almost_equal, assert_allclose,
|
||||
assert_array_almost_equal)
|
||||
import pytest
|
||||
from numpy import sin, cos, sinh, cosh, exp, inf, nan, r_, pi
|
||||
|
||||
from scipy.special import spherical_jn, spherical_yn, spherical_in, spherical_kn
|
||||
from scipy.integrate import quad
|
||||
|
||||
from scipy._lib._numpy_compat import suppress_warnings
|
||||
|
||||
|
||||
class TestSphericalJn:
|
||||
def test_spherical_jn_exact(self):
|
||||
# https://dlmf.nist.gov/10.49.E3
|
||||
# Note: exact expression is numerically stable only for small
|
||||
# n or z >> n.
|
||||
x = np.array([0.12, 1.23, 12.34, 123.45, 1234.5])
|
||||
assert_allclose(spherical_jn(2, x),
|
||||
(-1/x + 3/x**3)*sin(x) - 3/x**2*cos(x))
|
||||
|
||||
def test_spherical_jn_recurrence_complex(self):
|
||||
# https://dlmf.nist.gov/10.51.E1
|
||||
n = np.array([1, 2, 3, 7, 12])
|
||||
x = 1.1 + 1.5j
|
||||
assert_allclose(spherical_jn(n - 1, x) + spherical_jn(n + 1, x),
|
||||
(2*n + 1)/x*spherical_jn(n, x))
|
||||
|
||||
def test_spherical_jn_recurrence_real(self):
|
||||
# https://dlmf.nist.gov/10.51.E1
|
||||
n = np.array([1, 2, 3, 7, 12])
|
||||
x = 0.12
|
||||
assert_allclose(spherical_jn(n - 1, x) + spherical_jn(n + 1,x),
|
||||
(2*n + 1)/x*spherical_jn(n, x))
|
||||
|
||||
def test_spherical_jn_inf_real(self):
|
||||
# https://dlmf.nist.gov/10.52.E3
|
||||
n = 6
|
||||
x = np.array([-inf, inf])
|
||||
assert_allclose(spherical_jn(n, x), np.array([0, 0]))
|
||||
|
||||
def test_spherical_jn_inf_complex(self):
|
||||
# https://dlmf.nist.gov/10.52.E3
|
||||
n = 7
|
||||
x = np.array([-inf + 0j, inf + 0j, inf*(1+1j)])
|
||||
with suppress_warnings() as sup:
|
||||
sup.filter(RuntimeWarning, "invalid value encountered in multiply")
|
||||
assert_allclose(spherical_jn(n, x), np.array([0, 0, inf*(1+1j)]))
|
||||
|
||||
def test_spherical_jn_large_arg_1(self):
|
||||
# https://github.com/scipy/scipy/issues/2165
|
||||
# Reference value computed using mpmath, via
|
||||
# besselj(n + mpf(1)/2, z)*sqrt(pi/(2*z))
|
||||
assert_allclose(spherical_jn(2, 3350.507), -0.00029846226538040747)
|
||||
|
||||
def test_spherical_jn_large_arg_2(self):
|
||||
# https://github.com/scipy/scipy/issues/1641
|
||||
# Reference value computed using mpmath, via
|
||||
# besselj(n + mpf(1)/2, z)*sqrt(pi/(2*z))
|
||||
assert_allclose(spherical_jn(2, 10000), 3.0590002633029811e-05)
|
||||
|
||||
def test_spherical_jn_at_zero(self):
|
||||
# https://dlmf.nist.gov/10.52.E1
|
||||
# But note that n = 0 is a special case: j0 = sin(x)/x -> 1
|
||||
n = np.array([0, 1, 2, 5, 10, 100])
|
||||
x = 0
|
||||
assert_allclose(spherical_jn(n, x), np.array([1, 0, 0, 0, 0, 0]))
|
||||
|
||||
|
||||
class TestSphericalYn:
|
||||
def test_spherical_yn_exact(self):
|
||||
# https://dlmf.nist.gov/10.49.E5
|
||||
# Note: exact expression is numerically stable only for small
|
||||
# n or z >> n.
|
||||
x = np.array([0.12, 1.23, 12.34, 123.45, 1234.5])
|
||||
assert_allclose(spherical_yn(2, x),
|
||||
(1/x - 3/x**3)*cos(x) - 3/x**2*sin(x))
|
||||
|
||||
def test_spherical_yn_recurrence_real(self):
|
||||
# https://dlmf.nist.gov/10.51.E1
|
||||
n = np.array([1, 2, 3, 7, 12])
|
||||
x = 0.12
|
||||
assert_allclose(spherical_yn(n - 1, x) + spherical_yn(n + 1,x),
|
||||
(2*n + 1)/x*spherical_yn(n, x))
|
||||
|
||||
def test_spherical_yn_recurrence_complex(self):
|
||||
# https://dlmf.nist.gov/10.51.E1
|
||||
n = np.array([1, 2, 3, 7, 12])
|
||||
x = 1.1 + 1.5j
|
||||
assert_allclose(spherical_yn(n - 1, x) + spherical_yn(n + 1, x),
|
||||
(2*n + 1)/x*spherical_yn(n, x))
|
||||
|
||||
def test_spherical_yn_inf_real(self):
|
||||
# https://dlmf.nist.gov/10.52.E3
|
||||
n = 6
|
||||
x = np.array([-inf, inf])
|
||||
assert_allclose(spherical_yn(n, x), np.array([0, 0]))
|
||||
|
||||
def test_spherical_yn_inf_complex(self):
|
||||
# https://dlmf.nist.gov/10.52.E3
|
||||
n = 7
|
||||
x = np.array([-inf + 0j, inf + 0j, inf*(1+1j)])
|
||||
with suppress_warnings() as sup:
|
||||
sup.filter(RuntimeWarning, "invalid value encountered in multiply")
|
||||
assert_allclose(spherical_yn(n, x), np.array([0, 0, inf*(1+1j)]))
|
||||
|
||||
def test_spherical_yn_at_zero(self):
|
||||
# https://dlmf.nist.gov/10.52.E2
|
||||
n = np.array([0, 1, 2, 5, 10, 100])
|
||||
x = 0
|
||||
assert_allclose(spherical_yn(n, x), -inf*np.ones(shape=n.shape))
|
||||
|
||||
def test_spherical_yn_at_zero_complex(self):
|
||||
# Consistently with numpy:
|
||||
# >>> -np.cos(0)/0
|
||||
# -inf
|
||||
# >>> -np.cos(0+0j)/(0+0j)
|
||||
# (-inf + nan*j)
|
||||
n = np.array([0, 1, 2, 5, 10, 100])
|
||||
x = 0 + 0j
|
||||
assert_allclose(spherical_yn(n, x), nan*np.ones(shape=n.shape))
|
||||
|
||||
|
||||
class TestSphericalJnYnCrossProduct:
|
||||
def test_spherical_jn_yn_cross_product_1(self):
|
||||
# https://dlmf.nist.gov/10.50.E3
|
||||
n = np.array([1, 5, 8])
|
||||
x = np.array([0.1, 1, 10])
|
||||
left = (spherical_jn(n + 1, x) * spherical_yn(n, x) -
|
||||
spherical_jn(n, x) * spherical_yn(n + 1, x))
|
||||
right = 1/x**2
|
||||
assert_allclose(left, right)
|
||||
|
||||
def test_spherical_jn_yn_cross_product_2(self):
|
||||
# https://dlmf.nist.gov/10.50.E3
|
||||
n = np.array([1, 5, 8])
|
||||
x = np.array([0.1, 1, 10])
|
||||
left = (spherical_jn(n + 2, x) * spherical_yn(n, x) -
|
||||
spherical_jn(n, x) * spherical_yn(n + 2, x))
|
||||
right = (2*n + 3)/x**3
|
||||
assert_allclose(left, right)
|
||||
|
||||
|
||||
class TestSphericalIn:
|
||||
def test_spherical_in_exact(self):
|
||||
# https://dlmf.nist.gov/10.49.E9
|
||||
x = np.array([0.12, 1.23, 12.34, 123.45])
|
||||
assert_allclose(spherical_in(2, x),
|
||||
(1/x + 3/x**3)*sinh(x) - 3/x**2*cosh(x))
|
||||
|
||||
def test_spherical_in_recurrence_real(self):
|
||||
# https://dlmf.nist.gov/10.51.E4
|
||||
n = np.array([1, 2, 3, 7, 12])
|
||||
x = 0.12
|
||||
assert_allclose(spherical_in(n - 1, x) - spherical_in(n + 1,x),
|
||||
(2*n + 1)/x*spherical_in(n, x))
|
||||
|
||||
def test_spherical_in_recurrence_complex(self):
|
||||
# https://dlmf.nist.gov/10.51.E1
|
||||
n = np.array([1, 2, 3, 7, 12])
|
||||
x = 1.1 + 1.5j
|
||||
assert_allclose(spherical_in(n - 1, x) - spherical_in(n + 1,x),
|
||||
(2*n + 1)/x*spherical_in(n, x))
|
||||
|
||||
def test_spherical_in_inf_real(self):
|
||||
# https://dlmf.nist.gov/10.52.E3
|
||||
n = 5
|
||||
x = np.array([-inf, inf])
|
||||
assert_allclose(spherical_in(n, x), np.array([-inf, inf]))
|
||||
|
||||
def test_spherical_in_inf_complex(self):
|
||||
# https://dlmf.nist.gov/10.52.E5
|
||||
# Ideally, i1n(n, 1j*inf) = 0 and i1n(n, (1+1j)*inf) = (1+1j)*inf, but
|
||||
# this appears impossible to achieve because C99 regards any complex
|
||||
# value with at least one infinite part as a complex infinity, so
|
||||
# 1j*inf cannot be distinguished from (1+1j)*inf. Therefore, nan is
|
||||
# the correct return value.
|
||||
n = 7
|
||||
x = np.array([-inf + 0j, inf + 0j, inf*(1+1j)])
|
||||
assert_allclose(spherical_in(n, x), np.array([-inf, inf, nan]))
|
||||
|
||||
def test_spherical_in_at_zero(self):
|
||||
# https://dlmf.nist.gov/10.52.E1
|
||||
# But note that n = 0 is a special case: i0 = sinh(x)/x -> 1
|
||||
n = np.array([0, 1, 2, 5, 10, 100])
|
||||
x = 0
|
||||
assert_allclose(spherical_in(n, x), np.array([1, 0, 0, 0, 0, 0]))
|
||||
|
||||
|
||||
class TestSphericalKn:
|
||||
def test_spherical_kn_exact(self):
|
||||
# https://dlmf.nist.gov/10.49.E13
|
||||
x = np.array([0.12, 1.23, 12.34, 123.45])
|
||||
assert_allclose(spherical_kn(2, x),
|
||||
pi/2*exp(-x)*(1/x + 3/x**2 + 3/x**3))
|
||||
|
||||
def test_spherical_kn_recurrence_real(self):
|
||||
# https://dlmf.nist.gov/10.51.E4
|
||||
n = np.array([1, 2, 3, 7, 12])
|
||||
x = 0.12
|
||||
assert_allclose((-1)**(n - 1)*spherical_kn(n - 1, x) - (-1)**(n + 1)*spherical_kn(n + 1,x),
|
||||
(-1)**n*(2*n + 1)/x*spherical_kn(n, x))
|
||||
|
||||
def test_spherical_kn_recurrence_complex(self):
|
||||
# https://dlmf.nist.gov/10.51.E4
|
||||
n = np.array([1, 2, 3, 7, 12])
|
||||
x = 1.1 + 1.5j
|
||||
assert_allclose((-1)**(n - 1)*spherical_kn(n - 1, x) - (-1)**(n + 1)*spherical_kn(n + 1,x),
|
||||
(-1)**n*(2*n + 1)/x*spherical_kn(n, x))
|
||||
|
||||
def test_spherical_kn_inf_real(self):
|
||||
# https://dlmf.nist.gov/10.52.E6
|
||||
n = 5
|
||||
x = np.array([-inf, inf])
|
||||
assert_allclose(spherical_kn(n, x), np.array([-inf, 0]))
|
||||
|
||||
def test_spherical_kn_inf_complex(self):
|
||||
# https://dlmf.nist.gov/10.52.E6
|
||||
# The behavior at complex infinity depends on the sign of the real
|
||||
# part: if Re(z) >= 0, then the limit is 0; if Re(z) < 0, then it's
|
||||
# z*inf. This distinction cannot be captured, so we return nan.
|
||||
n = 7
|
||||
x = np.array([-inf + 0j, inf + 0j, inf*(1+1j)])
|
||||
assert_allclose(spherical_kn(n, x), np.array([-inf, 0, nan]))
|
||||
|
||||
def test_spherical_kn_at_zero(self):
|
||||
# https://dlmf.nist.gov/10.52.E2
|
||||
n = np.array([0, 1, 2, 5, 10, 100])
|
||||
x = 0
|
||||
assert_allclose(spherical_kn(n, x), inf*np.ones(shape=n.shape))
|
||||
|
||||
def test_spherical_kn_at_zero_complex(self):
|
||||
# https://dlmf.nist.gov/10.52.E2
|
||||
n = np.array([0, 1, 2, 5, 10, 100])
|
||||
x = 0 + 0j
|
||||
assert_allclose(spherical_kn(n, x), nan*np.ones(shape=n.shape))
|
||||
|
||||
|
||||
class SphericalDerivativesTestCase:
|
||||
def fundamental_theorem(self, n, a, b):
|
||||
integral, tolerance = quad(lambda z: self.df(n, z), a, b)
|
||||
assert_allclose(integral,
|
||||
self.f(n, b) - self.f(n, a),
|
||||
atol=tolerance)
|
||||
|
||||
@pytest.mark.slow
|
||||
def test_fundamental_theorem_0(self):
|
||||
self.fundamental_theorem(0, 3.0, 15.0)
|
||||
|
||||
@pytest.mark.slow
|
||||
def test_fundamental_theorem_7(self):
|
||||
self.fundamental_theorem(7, 0.5, 1.2)
|
||||
|
||||
|
||||
class TestSphericalJnDerivatives(SphericalDerivativesTestCase):
|
||||
def f(self, n, z):
|
||||
return spherical_jn(n, z)
|
||||
|
||||
def df(self, n, z):
|
||||
return spherical_jn(n, z, derivative=True)
|
||||
|
||||
def test_spherical_jn_d_zero(self):
|
||||
n = np.array([0, 1, 2, 3, 7, 15])
|
||||
assert_allclose(spherical_jn(n, 0, derivative=True),
|
||||
np.array([0, 1/3, 0, 0, 0, 0]))
|
||||
|
||||
|
||||
class TestSphericalYnDerivatives(SphericalDerivativesTestCase):
|
||||
def f(self, n, z):
|
||||
return spherical_yn(n, z)
|
||||
|
||||
def df(self, n, z):
|
||||
return spherical_yn(n, z, derivative=True)
|
||||
|
||||
|
||||
class TestSphericalInDerivatives(SphericalDerivativesTestCase):
|
||||
def f(self, n, z):
|
||||
return spherical_in(n, z)
|
||||
|
||||
def df(self, n, z):
|
||||
return spherical_in(n, z, derivative=True)
|
||||
|
||||
def test_spherical_in_d_zero(self):
|
||||
n = np.array([1, 2, 3, 7, 15])
|
||||
assert_allclose(spherical_in(n, 0, derivative=True),
|
||||
np.zeros(5))
|
||||
|
||||
|
||||
class TestSphericalKnDerivatives(SphericalDerivativesTestCase):
|
||||
def f(self, n, z):
|
||||
return spherical_kn(n, z)
|
||||
|
||||
def df(self, n, z):
|
||||
return spherical_kn(n, z, derivative=True)
|
||||
|
||||
|
||||
class TestSphericalOld:
|
||||
# These are tests from the TestSpherical class of test_basic.py,
|
||||
# rewritten to use spherical_* instead of sph_* but otherwise unchanged.
|
||||
|
||||
def test_sph_in(self):
|
||||
# This test reproduces test_basic.TestSpherical.test_sph_in.
|
||||
i1n = np.empty((2,2))
|
||||
x = 0.2
|
||||
|
||||
i1n[0][0] = spherical_in(0, x)
|
||||
i1n[0][1] = spherical_in(1, x)
|
||||
i1n[1][0] = spherical_in(0, x, derivative=True)
|
||||
i1n[1][1] = spherical_in(1, x, derivative=True)
|
||||
|
||||
inp0 = (i1n[0][1])
|
||||
inp1 = (i1n[0][0] - 2.0/0.2 * i1n[0][1])
|
||||
assert_array_almost_equal(i1n[0],np.array([1.0066800127054699381,
|
||||
0.066933714568029540839]),12)
|
||||
assert_array_almost_equal(i1n[1],[inp0,inp1],12)
|
||||
|
||||
def test_sph_in_kn_order0(self):
|
||||
x = 1.
|
||||
sph_i0 = np.empty((2,))
|
||||
sph_i0[0] = spherical_in(0, x)
|
||||
sph_i0[1] = spherical_in(0, x, derivative=True)
|
||||
sph_i0_expected = np.array([np.sinh(x)/x,
|
||||
np.cosh(x)/x-np.sinh(x)/x**2])
|
||||
assert_array_almost_equal(r_[sph_i0], sph_i0_expected)
|
||||
|
||||
sph_k0 = np.empty((2,))
|
||||
sph_k0[0] = spherical_kn(0, x)
|
||||
sph_k0[1] = spherical_kn(0, x, derivative=True)
|
||||
sph_k0_expected = np.array([0.5*pi*exp(-x)/x,
|
||||
-0.5*pi*exp(-x)*(1/x+1/x**2)])
|
||||
assert_array_almost_equal(r_[sph_k0], sph_k0_expected)
|
||||
|
||||
def test_sph_jn(self):
|
||||
s1 = np.empty((2,3))
|
||||
x = 0.2
|
||||
|
||||
s1[0][0] = spherical_jn(0, x)
|
||||
s1[0][1] = spherical_jn(1, x)
|
||||
s1[0][2] = spherical_jn(2, x)
|
||||
s1[1][0] = spherical_jn(0, x, derivative=True)
|
||||
s1[1][1] = spherical_jn(1, x, derivative=True)
|
||||
s1[1][2] = spherical_jn(2, x, derivative=True)
|
||||
|
||||
s10 = -s1[0][1]
|
||||
s11 = s1[0][0]-2.0/0.2*s1[0][1]
|
||||
s12 = s1[0][1]-3.0/0.2*s1[0][2]
|
||||
assert_array_almost_equal(s1[0],[0.99334665397530607731,
|
||||
0.066400380670322230863,
|
||||
0.0026590560795273856680],12)
|
||||
assert_array_almost_equal(s1[1],[s10,s11,s12],12)
|
||||
|
||||
def test_sph_kn(self):
|
||||
kn = np.empty((2,3))
|
||||
x = 0.2
|
||||
|
||||
kn[0][0] = spherical_kn(0, x)
|
||||
kn[0][1] = spherical_kn(1, x)
|
||||
kn[0][2] = spherical_kn(2, x)
|
||||
kn[1][0] = spherical_kn(0, x, derivative=True)
|
||||
kn[1][1] = spherical_kn(1, x, derivative=True)
|
||||
kn[1][2] = spherical_kn(2, x, derivative=True)
|
||||
|
||||
kn0 = -kn[0][1]
|
||||
kn1 = -kn[0][0]-2.0/0.2*kn[0][1]
|
||||
kn2 = -kn[0][1]-3.0/0.2*kn[0][2]
|
||||
assert_array_almost_equal(kn[0],[6.4302962978445670140,
|
||||
38.581777787067402086,
|
||||
585.15696310385559829],12)
|
||||
assert_array_almost_equal(kn[1],[kn0,kn1,kn2],9)
|
||||
|
||||
def test_sph_yn(self):
|
||||
sy1 = spherical_yn(2, 0.2)
|
||||
sy2 = spherical_yn(0, 0.2)
|
||||
assert_almost_equal(sy1,-377.52483,5) # previous values in the system
|
||||
assert_almost_equal(sy2,-4.9003329,5)
|
||||
sphpy = (spherical_yn(0, 0.2) - 2*spherical_yn(2, 0.2))/3
|
||||
sy3 = spherical_yn(1, 0.2, derivative=True)
|
||||
assert_almost_equal(sy3,sphpy,4) # compare correct derivative val. (correct =-system val).
|
||||
@@ -1,77 +0,0 @@
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
import sys
|
||||
|
||||
import numpy as np
|
||||
from numpy.testing import assert_equal, assert_allclose
|
||||
import pytest
|
||||
|
||||
from scipy.special._ufuncs import _sinpi as sinpi
|
||||
from scipy.special._ufuncs import _cospi as cospi
|
||||
|
||||
from scipy._lib._numpy_compat import suppress_warnings
|
||||
|
||||
|
||||
def test_integer_real_part():
|
||||
x = np.arange(-100, 101)
|
||||
y = np.hstack((-np.linspace(310, -30, 10), np.linspace(-30, 310, 10)))
|
||||
x, y = np.meshgrid(x, y)
|
||||
z = x + 1j*y
|
||||
# In the following we should be *exactly* right
|
||||
res = sinpi(z)
|
||||
assert_equal(res.real, 0.0)
|
||||
res = cospi(z)
|
||||
assert_equal(res.imag, 0.0)
|
||||
|
||||
|
||||
def test_half_integer_real_part():
|
||||
x = np.arange(-100, 101) + 0.5
|
||||
y = np.hstack((-np.linspace(310, -30, 10), np.linspace(-30, 310, 10)))
|
||||
x, y = np.meshgrid(x, y)
|
||||
z = x + 1j*y
|
||||
# In the following we should be *exactly* right
|
||||
res = sinpi(z)
|
||||
assert_equal(res.imag, 0.0)
|
||||
res = cospi(z)
|
||||
assert_equal(res.real, 0.0)
|
||||
|
||||
|
||||
def test_intermediate_overlow():
|
||||
# Make sure we avoid overflow in situations where cosh/sinh would
|
||||
# overflow but the product with sin/cos would not
|
||||
sinpi_pts = [complex(1 + 1e-14, 227),
|
||||
complex(1e-35, 250),
|
||||
complex(1e-301, 445)]
|
||||
# Data generated with mpmath
|
||||
sinpi_std = [complex(-8.113438309924894e+295, -np.inf),
|
||||
complex(1.9507801934611995e+306, np.inf),
|
||||
complex(2.205958493464539e+306, np.inf)]
|
||||
with suppress_warnings() as sup:
|
||||
sup.filter(RuntimeWarning, "invalid value encountered in multiply")
|
||||
for p, std in zip(sinpi_pts, sinpi_std):
|
||||
assert_allclose(sinpi(p), std)
|
||||
|
||||
# Test for cosine, less interesting because cos(0) = 1.
|
||||
p = complex(0.5 + 1e-14, 227)
|
||||
std = complex(-8.113438309924894e+295, -np.inf)
|
||||
with suppress_warnings() as sup:
|
||||
sup.filter(RuntimeWarning, "invalid value encountered in multiply")
|
||||
assert_allclose(cospi(p), std)
|
||||
|
||||
|
||||
@pytest.mark.xfail('win32' in sys.platform
|
||||
and np.intp(0).itemsize < 8
|
||||
and sys.version_info < (3, 5),
|
||||
reason="fails on 32-bit Windows with old MSVC")
|
||||
def test_zero_sign():
|
||||
y = sinpi(-0.0)
|
||||
assert y == 0.0
|
||||
assert np.signbit(y)
|
||||
|
||||
y = sinpi(0.0)
|
||||
assert y == 0.0
|
||||
assert not np.signbit(y)
|
||||
|
||||
y = cospi(0.5)
|
||||
assert y == 0.0
|
||||
assert not np.signbit(y)
|
||||
@@ -1,55 +0,0 @@
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
import numpy as np
|
||||
from numpy.testing import assert_, assert_equal
|
||||
|
||||
import scipy.special as sc
|
||||
|
||||
|
||||
def test_wrightomega_nan():
|
||||
pts = [complex(np.nan, 0),
|
||||
complex(0, np.nan),
|
||||
complex(np.nan, np.nan),
|
||||
complex(np.nan, 1),
|
||||
complex(1, np.nan)]
|
||||
for p in pts:
|
||||
res = sc.wrightomega(p)
|
||||
assert_(np.isnan(res.real))
|
||||
assert_(np.isnan(res.imag))
|
||||
|
||||
|
||||
def test_wrightomega_inf_branch():
|
||||
pts = [complex(-np.inf, np.pi/4),
|
||||
complex(-np.inf, -np.pi/4),
|
||||
complex(-np.inf, 3*np.pi/4),
|
||||
complex(-np.inf, -3*np.pi/4)]
|
||||
expected_results = [complex(0.0, 0.0),
|
||||
complex(0.0, -0.0),
|
||||
complex(-0.0, 0.0),
|
||||
complex(-0.0, -0.0)]
|
||||
for p, expected in zip(pts, expected_results):
|
||||
res = sc.wrightomega(p)
|
||||
# We can't use assert_equal(res, expected) because in older versions of
|
||||
# numpy, assert_equal doesn't check the sign of the real and imaginary
|
||||
# parts when comparing complex zeros. It does check the sign when the
|
||||
# arguments are *real* scalars.
|
||||
assert_equal(res.real, expected.real)
|
||||
assert_equal(res.imag, expected.imag)
|
||||
|
||||
|
||||
def test_wrightomega_inf():
|
||||
pts = [complex(np.inf, 10),
|
||||
complex(-np.inf, 10),
|
||||
complex(10, np.inf),
|
||||
complex(10, -np.inf)]
|
||||
for p in pts:
|
||||
assert_equal(sc.wrightomega(p), p)
|
||||
|
||||
|
||||
def test_wrightomega_singular():
|
||||
pts = [complex(-1.0, np.pi),
|
||||
complex(-1.0, -np.pi)]
|
||||
for p in pts:
|
||||
res = sc.wrightomega(p)
|
||||
assert_equal(res, -1.0)
|
||||
assert_(np.signbit(res.imag) == False)
|
||||
@@ -1,39 +0,0 @@
|
||||
from __future__ import division, print_function, absolute_import
|
||||
|
||||
import scipy.special as sc
|
||||
import numpy as np
|
||||
from numpy.testing import assert_, assert_equal, assert_allclose
|
||||
|
||||
|
||||
def test_zeta():
|
||||
assert_allclose(sc.zeta(2,2), np.pi**2/6 - 1, rtol=1e-12)
|
||||
|
||||
|
||||
def test_zeta_1arg():
|
||||
assert_allclose(sc.zeta(2), np.pi**2/6, rtol=1e-12)
|
||||
assert_allclose(sc.zeta(4), np.pi**4/90, rtol=1e-12)
|
||||
|
||||
|
||||
def test_zetac():
|
||||
assert_equal(sc.zetac(0), -1.5)
|
||||
assert_equal(sc.zetac(1.0), np.inf)
|
||||
# Expected values in the following were computed using
|
||||
# Wolfram Alpha `Zeta[x] - 1`:
|
||||
rtol = 1e-12
|
||||
assert_allclose(sc.zetac(-2.1), -0.9972705002153750, rtol=rtol)
|
||||
assert_allclose(sc.zetac(0.8), -5.437538415895550, rtol=rtol)
|
||||
assert_allclose(sc.zetac(0.9999), -10000.42279161673, rtol=rtol)
|
||||
assert_allclose(sc.zetac(9), 0.002008392826082214, rtol=rtol)
|
||||
assert_allclose(sc.zetac(50), 8.881784210930816e-16, rtol=rtol)
|
||||
assert_allclose(sc.zetac(75), 2.646977960169853e-23, rtol=rtol)
|
||||
|
||||
|
||||
def test_zetac_negative_even():
|
||||
pts = [-2, -50, -100]
|
||||
for p in pts:
|
||||
assert_equal(sc.zetac(p), -1)
|
||||
|
||||
|
||||
def test_zetac_inf():
|
||||
assert_equal(sc.zetac(np.inf), 0.0)
|
||||
assert_(np.isnan(sc.zetac(-np.inf)))
|
||||
Reference in New Issue
Block a user